From ca228efbb023a9fc6f7645b53c615cc7c2fbc57b Mon Sep 17 00:00:00 2001
From: jejon <jejon@users.noreply.github.com>
Date: Mon, 30 Sep 2024 13:34:20 +0000
Subject: [PATCH] deploy: d79ea5ef4790ff8850e608afff4b694cb83ce15b

---
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diff --git a/.doctrees/environment.pickle b/.doctrees/environment.pickle
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+</p>
+<section id="training-and-evaluating-adaptive-heatmap-regression-model-for-landmark-detection-with-isbi-2015-cephalometric-x-ray-dataset">
+<h1>Training and Evaluating Adaptive Heatmap Regression Model for Landmark Detection with ISBI 2015 Cephalometric X-ray Dataset<a class="headerlink" href="#training-and-evaluating-adaptive-heatmap-regression-model-for-landmark-detection-with-isbi-2015-cephalometric-x-ray-dataset" title="Link to this heading">#</a></h1>
+<p>In this tutorial, we will train and evaluate an adaptive heatmap regression model for landmark
+detection with the ISBI 2015 Cephalometric X-ray dataset. The ISBI 2015 Cephalometric X-ray dataset
+is a dataset of 2D cephalometric X-rays. The dataset contains 400 images, each with 19 landmarks
+annotated. The dataset is split into a training set of 150 images and two test sets of 150 and
+100 images respectively.</p>
+<p>We will go through the following steps:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#Loading-the-dataset"><span class="xref myst">Loading the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Constructing-a-heatmap-generator"><span class="xref myst">Constructing a heatmap generator</span></a></p></li>
+<li><p><a class="reference internal" href="#Inspecting-the-dataset"><span class="xref myst">Inspecting the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Training-the-model"><span class="xref myst">Training and initializing the SpatialConfiguration model</span></a></p></li>
+<li><p><a class="reference internal" href="#Evaluating-the-model"><span class="xref myst">Evaluating the model</span></a></p></li>
+</ul>
+<a target="_blank" href="https://colab.research.google.com/github/predict-idlab/landmarker/examples/adaptive_scn_isbi2015.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a><section id="setup-environment">
+<h2>Setup environment<a class="headerlink" href="#setup-environment" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># !python -c &quot;import landmarker&quot; || pip install landmarker</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../src/&quot;</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">landmarker</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setup-imports-and-variables">
+<h2>Setup imports and variables<a class="headerlink" href="#setup-imports-and-variables" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">monai.transforms</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Compose</span><span class="p">,</span> <span class="n">RandAffined</span><span class="p">,</span> <span class="n">RandGaussianNoised</span><span class="p">,</span> <span class="n">RandStdShiftIntensityd</span><span class="p">,</span>
+                              <span class="n">RandScaleIntensityd</span><span class="p">,</span> <span class="n">RandAdjustContrastd</span><span class="p">,</span> <span class="n">RandHistogramShiftd</span><span class="p">,</span>
+                              <span class="n">ScaleIntensityd</span><span class="p">,</span> <span class="n">Lambdad</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">tqdm.notebook</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-the-dataset">
+<h2>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h2>
+<section id="short-description-of-the-data-and-dataset-module">
+<h3>Short description of the data and dataset module<a class="headerlink" href="#short-description-of-the-data-and-dataset-module" title="Link to this heading">#</a></h3>
+<p>The <a class="reference external" href="https://github.com/predict-idlab/landmarker">landmarker</a> package has several built-in
+datasets in the <code class="docutils literal notranslate"><span class="pre">landmarker.datasets</span></code> module, as well as utility classes for building your own
+datasets in the <code class="docutils literal notranslate"><span class="pre">landmarker.data</span></code> module. There are three types of datasets: ‘LandmarkDataset’,
+‘HeatmapDataset’, and ‘MaskDataset’. The ‘LandmarkDataset’ is a dataset of images with landmarks,
+the ‘HeatmapDataset’ is a dataset of images with heatmaps, and the ‘MaskDataset’ is a dataset of
+images with masks (i.e., binary segmentation masks indiciating the location of the landmarks). The
+‘HeatmapDataset’ and ‘MaskDataset’ both inherit from the ‘LandmarkDataset’ class, and thus also
+contain information about the landmarks. The ‘MaskDataset’ can be constructed from specified image
+and landmarks pairs, or from images and masks pairs, because often that is how the data is
+distributed. The ‘HeatmapDataset’ can be constructed from images and landmarks pairs.</p>
+<p>Images can be provided as a list of paths to stored images, or as a a numpy arary, torch tensor,
+list of numpy  arrays or list of torch tensors. Landmarks can be as numpy arrays or torch tensors.
+These landmarks can be provided in three different shapes: (1) (N, D) where N is the number of
+samples and D is the number of dimensions, (2) (N, C, D) where C is the number of landmark
+classes, (3) (N, C, I, D) where I is the number of instances per landmark class, if less than I
+instances are provided, the remaining instances are filled with NaNs.</p>
+<p>For built-in datasets, the <code class="docutils literal notranslate"><span class="pre">landmarker.datasets</span></code> module provides a function for
+downloading and loading the dataset, e.g. <code class="docutils literal notranslate"><span class="pre">get_cepha_landmark_datasets</span></code> for the ISBI 2015
+Cephalometric X-ray dataset. Most of these functions take the same arguments, namely <code class="docutils literal notranslate"><span class="pre">path_dir</span></code>,
+some are dataset specific. The <code class="docutils literal notranslate"><span class="pre">path_dir</span></code> argument specifies the directory where the dataset is
+downloaded to, or loaded from if it is already downloaded. For most datasets multiple functions
+are provided for getting different types of datasets. For example, the ISBI 2015 Cephalometric
+X-ray dataset has the following functions: <code class="docutils literal notranslate"><span class="pre">get_cepha_landmark_datasets</span></code> and
+<code class="docutils literal notranslate"><span class="pre">get_cepha_heatmap_datasets</span></code>.</p>
+</section>
+<section id="download-and-load-isbi-2015-landmark-dataset">
+<h3>Download and load ISBI 2015 landmark dataset<a class="headerlink" href="#download-and-load-isbi-2015-landmark-dataset" title="Link to this heading">#</a></h3>
+<p>The ISBI 2015 Cephalometric X-ray dataset is a dataset of 2D cephalometric X-ray images with 19
+landmarks. The dataset is split into a training set of 150 images and two test sets, where test
+set  1 contains 150 images and test set 2 contains 100 images. When loading the dataset, you can
+also specify a transform function, which is applied to the images and landmarks of the training
+set. Currently, we only support the <code class="docutils literal notranslate"><span class="pre">monai.transforms.ComposeD</span> <span class="pre">transform,</span> <span class="pre">which</span> <span class="pre">allows</span> <span class="pre">you</span> <span class="pre">to</span> <span class="pre">compose</span> <span class="pre">multiple</span> <span class="pre">transforms.</span> <span class="pre">The</span> </code>monai.transforms<code class="docutils literal notranslate"><span class="pre">module</span> <span class="pre">contains</span> <span class="pre">many</span> <span class="pre">useful</span> <span class="pre">transforms,</span> <span class="pre">such</span> <span class="pre">as</span></code>RandomAffine<code class="docutils literal notranslate"><span class="pre">and</span></code>NormalizeIntensity`. The transforms must be dictionary transforms, i.e.,
+they must return a dictionary with the keys ‘image’ and (‘seg’), in the case of heatmap and mask
+regression.</p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.transforms.images</span> <span class="kn">import</span> <span class="n">UseOnlyFirstChannel</span>
+<span class="n">fn_keys</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;image&#39;</span><span class="p">,)</span>
+<span class="n">spatial_transformd</span> <span class="o">=</span> <span class="p">[</span><span class="n">RandAffined</span><span class="p">(</span><span class="n">fn_keys</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                        <span class="n">rotate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">),</span>
+                        <span class="n">translate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
+                        <span class="n">scale_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
+                        <span class="n">shear_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+                        <span class="p">)]</span>
+
+<span class="n">train_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+                            <span class="n">UseOnlyFirstChannel</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>
+                            <span class="n">RandGaussianNoised</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>  <span class="c1"># Add gaussian noise</span>
+                            <span class="n">RandScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">factors</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">),</span>  <span class="c1"># Add random intensity scaling</span>
+                            <span class="n">RandAdjustContrastd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">)),</span>  <span class="c1"># Randomly adjust contrast</span>
+                            <span class="n">RandHistogramShiftd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">),</span>  <span class="c1"># Randomly shift histogram</span>
+                            <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>  <span class="c1"># Scale intensity</span>
+                        <span class="p">]</span> <span class="o">+</span> <span class="n">spatial_transformd</span><span class="p">)</span>
+
+<span class="n">inference_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+    <span class="n">UseOnlyFirstChannel</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>
+    <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>
+<span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.datasets</span> <span class="kn">import</span> <span class="n">get_cepha_landmark_datasets</span>
+
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;/Users/jefjonkers/Data/landmark-datasets&quot;</span>
+<span class="n">ds_train</span><span class="p">,</span> <span class="n">ds_test1</span><span class="p">,</span> <span class="n">ds_test2</span> <span class="o">=</span> <span class="n">get_cepha_landmark_datasets</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train_transform</span><span class="o">=</span><span class="n">train_transformd</span><span class="p">,</span>
+                                                           <span class="n">inference_transform</span><span class="o">=</span><span class="n">inference_transformd</span><span class="p">,</span>
+                                                           <span class="n">store_imgs</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">dim_img</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+                                                           <span class="n">junior</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">single_dataset</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading 150 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 150/150 [00:06&lt;00:00, 23.63it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Resizing 150 images and landmarks...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 150/150 [00:03&lt;00:00, 42.01it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading 150 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 150/150 [00:06&lt;00:00, 22.50it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Resizing 150 images and landmarks...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 150/150 [00:03&lt;00:00, 38.48it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading 100 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:04&lt;00:00, 24.65it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Resizing 100 images and landmarks...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:01&lt;00:00, 61.08it/s]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="constructing-a-heatmap-generator">
+<h2>Constructing a heatmap generator<a class="headerlink" href="#constructing-a-heatmap-generator" title="Link to this heading">#</a></h2>
+<p>The heatmap generator is a class that generates heatmaps from landmarks. It is used to generate
+heatmaps from the landmarks of the training set, which are then used to train the model. The
+<code class="docutils literal notranslate"><span class="pre">landmarker.heatmap_generator</span></code> module contains several heatmap generators, such as the
+<code class="docutils literal notranslate"><span class="pre">GaussianHeatmapGenerator</span></code> and <code class="docutils literal notranslate"><span class="pre">LaplaceHeatmapGenerator</span></code> which generate a multivariate
+Gaussian and Laplace distribution respectively. The HeatmapGenerator subclasses take the following
+arguments:</p>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">sigmas</span></code>: the standard deviation of the Gaussian distribution, or the scale of the Laplace. This
+could be a scalar, or a list of scalars, one for each landmark class. Additionally, it could be a
+covariance matrix, or a list of covariance matrices, one for each landmark class.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">gamma</span></code>: If provided, the heatmaps are scaled by <code class="docutils literal notranslate"><span class="pre">gamma</span></code> before being returned.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">rotation</span></code>: If provided, the heatmaps are rotated by <code class="docutils literal notranslate"><span class="pre">rotation</span></code> before being returned.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">heatmap_size</span></code>: The size of the returned heatmaps.</p></li>
+<li><p>learnable: If True, the <code class="docutils literal notranslate"><span class="pre">sigma</span></code> and <code class="docutils literal notranslate"><span class="pre">rotation</span></code> parameters are learnable parameters, and thus
+will be optimized during training.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">background</span></code>: A boolean indicating whether to add a background class to the heatmaps. If True,
+the heatmaps will have an additional channel, which is 1 everywhere except at the location of the
+landmarks, where it is 0. The background class is the first class, i.e., the first channel.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">all_points</span></code>: A boolean indicating whether to add a channel with all the landmarks. If True, the
+heatmaps will have an additional channel, which is 1 at the location of the landmarks, and 0.
+everywhere else.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">continuous</span></code>: A boolean indicating whether to use continuous or discrete landmarks.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">device</span></code>: The device on which the heatmaps are generated.</p></li>
+</ul>
+<p>The landmarks provide to the heatmap generator must be a torch.Tensor and can be in three different
+shapes: (1) (N, D) where N is the number of samples and D is the number of dimensions, (2) (N, C, D)
+where C is the number of landmark classes, (3) (N, C, I, D) where I is the number of instances per
+landmark class, if less than I instances are provided, the remaining instances are filled with NaNs.
+The heatmap generator will return a torch.Tensor of shape (N, C, H, W), where H and W are the height
+and width of the heatmaps respectively.</p>
+<p><strong>Note that with 2D landmarks the y coordinates are the first dimension, and the x coordinates are the
+second dimension.</strong></p>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="n">GaussianHeatmapGenerator</span>
+
+<span class="n">heatmap_generator</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+    <span class="n">nb_landmarks</span><span class="o">=</span><span class="mi">19</span><span class="p">,</span>
+    <span class="n">sigmas</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">gamma</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span>
+    <span class="n">heatmap_size</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+    <span class="n">learnable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="c1"># If True, the heatmap generator will be trainable</span>
+<span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="inspecting-the-dataset">
+<h2>Inspecting the dataset<a class="headerlink" href="#inspecting-the-dataset" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">inspection_plot</span>
+
+<span class="c1"># Plot the first 3 images from the training set</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span> <span class="n">heatmap_generator</span><span class="o">=</span><span class="n">heatmap_generator</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/09a57b5f8a34cb3e15ee833a28bbce4d49a718356bbb6d638fadcb23565d1134.png" src="../../_images/09a57b5f8a34cb3e15ee833a28bbce4d49a718356bbb6d638fadcb23565d1134.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot the first 3 images from dataset without transforms</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span> <span class="c1"># because dataset tensors are still on cpu</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_test1</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">heatmap_generator</span><span class="o">=</span><span class="n">heatmap_generator</span><span class="p">)</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> <span class="c1"># set the desired device back</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/a2ba941f1088c27a10bff0ef42f1580902fb004818f6f5536ad1660a51143fd0.png" src="../../_images/a2ba941f1088c27a10bff0ef42f1580902fb004818f6f5536ad1660a51143fd0.png" />
+</div>
+</div>
+</section>
+<section id="training-and-initializing-the-spatialconfiguration-model">
+<h2>Training and initializing the SpatialConfiguration model<a class="headerlink" href="#training-and-initializing-the-spatialconfiguration-model" title="Link to this heading">#</a></h2>
+<section id="initializing-the-model-optimizer-and-loss-function">
+<h3>Initializing the model, optimizer and loss function<a class="headerlink" href="#initializing-the-model-optimizer-and-loss-function" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.models.spatial_configuration_net</span> <span class="kn">import</span> <span class="n">OriginalSpatialConfigurationNet</span>
+<span class="kn">from</span> <span class="nn">landmarker.losses</span> <span class="kn">import</span> <span class="n">GaussianHeatmapL2Loss</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">OriginalSpatialConfigurationNet</span><span class="p">(</span><span class="n">in_channels</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="mi">19</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Number of learnable parameters: </span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
+    <span class="nb">sum</span><span class="p">(</span><span class="n">p</span><span class="o">.</span><span class="n">numel</span><span class="p">()</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">()</span> <span class="k">if</span> <span class="n">p</span><span class="o">.</span><span class="n">requires_grad</span><span class="p">)))</span>
+<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-6</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">1</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">200</span>
+
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">SGD</span><span class="p">([</span>
+    <span class="p">{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="s2">&quot;weight_decay&quot;</span><span class="p">:</span><span class="mf">1e-3</span><span class="p">},</span>
+    <span class="p">{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">heatmap_generator</span><span class="o">.</span><span class="n">sigmas</span><span class="p">},</span>
+    <span class="p">{</span><span class="s1">&#39;params&#39;</span><span class="p">:</span> <span class="n">heatmap_generator</span><span class="o">.</span><span class="n">rotation</span><span class="p">}]</span>
+    <span class="p">,</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">momentum</span><span class="o">=</span><span class="mf">0.99</span><span class="p">,</span> <span class="n">nesterov</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+
+<span class="n">criterion</span> <span class="o">=</span> <span class="n">GaussianHeatmapL2Loss</span><span class="p">(</span>
+    <span class="n">alpha</span><span class="o">=</span><span class="mi">5</span>
+<span class="p">)</span>
+
+<span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                                                          <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cooldown</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Number of learnable parameters: 6181030
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
+  warnings.warn(&quot;The verbose parameter is deprecated. Please use get_last_lr() &quot;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setting-the-data-loaders">
+<h3>Setting the data loaders<a class="headerlink" href="#setting-the-data-loaders" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">val_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test1</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">test_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test2</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="training-the-model">
+<h3>Training the model<a class="headerlink" href="#training-the-model" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.heatmap.decoder</span> <span class="kn">import</span> <span class="n">heatmap_to_coord</span>
+<span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">point_error</span>
+
+<span class="k">def</span> <span class="nf">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
+    <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="o">.</span><span class="n">sigmas</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+        <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;local_soft_argmax&quot;</span><span class="p">):</span>
+    <span class="n">eval_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">eval_mpe</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)):</span>
+            <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+            <span class="n">dim_orig</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;pixel_spacing&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="o">.</span><span class="n">sigmas</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+            <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="n">heatmap_to_coord</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>
+            <span class="n">eval_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+            <span class="n">eval_mpe</span> <span class="o">+=</span> <span class="n">point_error</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span> <span class="n">dim_orig</span><span class="p">,</span>
+                                    <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">eval_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_loader</span><span class="p">),</span> <span class="n">eval_mpe</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">)):</span>
+        <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_mpe</span> <span class="o">=</span> <span class="n">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">epochs</span><span class="si">}</span><span class="s2"> - Train loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val loss: </span><span class="si">{</span><span class="n">val_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val mpe: </span><span class="si">{</span><span class="n">val_mpe</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lr_scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span>
+      <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "1cb48970e6e245c2b2d9f619e42ac71b", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "0460a93fc99642a6a1af5f7a0f71ef0c", "version_major": 2, "version_minor": 0}</script><div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">12</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">2</span>       <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
+
+<span class="nn">Cell In[11], line 41,</span> in <span class="ni">train</span><span class="nt">(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs)</span>
+<span class="g g-Whitespace">     </span><span class="mi">39</span> <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+<span class="g g-Whitespace">     </span><span class="mi">40</span>     <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">)):</span>
+<span class="ne">---&gt; </span><span class="mi">41</span>         <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">42</span>         <span class="n">val_loss</span><span class="p">,</span> <span class="n">val_mpe</span> <span class="o">=</span> <span class="n">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">43</span>         <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">epochs</span><span class="si">}</span><span class="s2"> - Train loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val loss: </span><span class="si">{</span><span class="n">val_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val mpe: </span><span class="si">{</span><span class="n">val_mpe</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+<span class="nn">Cell In[11], line 11,</span> in <span class="ni">train_epoch</span><span class="nt">(model, heatmap_generator, train_loader, criterion, optimizer, device)</span>
+<span class="g g-Whitespace">      </span><span class="mi">9</span> <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">10</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+<span class="ne">---&gt; </span><span class="mi">11</span> <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">12</span> <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">13</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="o">.</span><span class="n">sigmas</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1530</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="g g-Whitespace">   </span><span class="mi">1531</span> <span class="k">else</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1532</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1536</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
+<span class="g g-Whitespace">   </span><span class="mi">1537</span> <span class="c1"># this function, and just call forward.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1538</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1539</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1540</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1541</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1543</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1544</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="nn">File ~/Code/landmarker/examples/../src/landmarker/models/spatial_configuration_net.py:280,</span> in <span class="ni">OriginalSpatialConfigurationNet.forward</span><span class="nt">(self, x)</span>
+<span class="g g-Whitespace">    </span><span class="mi">278</span> <span class="n">skips</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="g g-Whitespace">    </span><span class="mi">279</span> <span class="k">for</span> <span class="n">down_layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">la_downlayers</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">280</span>     <span class="n">out_la</span><span class="p">,</span> <span class="n">skip</span> <span class="o">=</span> <span class="n">down_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">281</span>     <span class="n">skips</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">skip</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">282</span> <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1530</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="g g-Whitespace">   </span><span class="mi">1531</span> <span class="k">else</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1532</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1536</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
+<span class="g g-Whitespace">   </span><span class="mi">1537</span> <span class="c1"># this function, and just call forward.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1538</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1539</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1540</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1541</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1543</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1544</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="nn">File ~/Code/landmarker/examples/../src/landmarker/models/spatial_configuration_net.py:352,</span> in <span class="ni">DownLayer.forward</span><span class="nt">(self, x)</span>
+<span class="g g-Whitespace">    </span><span class="mi">350</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">351</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">352</span> <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">353</span> <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">out</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1530</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="g g-Whitespace">   </span><span class="mi">1531</span> <span class="k">else</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1532</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1536</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
+<span class="g g-Whitespace">   </span><span class="mi">1537</span> <span class="c1"># this function, and just call forward.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1538</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1539</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1540</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1541</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1543</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1544</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/container.py:217,</span> in <span class="ni">Sequential.forward</span><span class="nt">(self, input)</span>
+<span class="g g-Whitespace">    </span><span class="mi">215</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">216</span>     <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="bp">self</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">217</span>         <span class="nb">input</span> <span class="o">=</span> <span class="n">module</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">218</span>     <span class="k">return</span> <span class="nb">input</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532,</span> in <span class="ni">Module._wrapped_call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1530</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_compiled_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>  <span class="c1"># type: ignore[misc]</span>
+<span class="g g-Whitespace">   </span><span class="mi">1531</span> <span class="k">else</span><span class="p">:</span>
+<span class="ne">-&gt; </span><span class="mi">1532</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_call_impl</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541,</span> in <span class="ni">Module._call_impl</span><span class="nt">(self, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1536</span> <span class="c1"># If we don&#39;t have any hooks, we want to skip the rest of the logic in</span>
+<span class="g g-Whitespace">   </span><span class="mi">1537</span> <span class="c1"># this function, and just call forward.</span>
+<span class="g g-Whitespace">   </span><span class="mi">1538</span> <span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_backward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_backward_pre_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_hooks</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">_forward_pre_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1539</span>         <span class="ow">or</span> <span class="n">_global_backward_pre_hooks</span> <span class="ow">or</span> <span class="n">_global_backward_hooks</span>
+<span class="g g-Whitespace">   </span><span class="mi">1540</span>         <span class="ow">or</span> <span class="n">_global_forward_hooks</span> <span class="ow">or</span> <span class="n">_global_forward_pre_hooks</span><span class="p">):</span>
+<span class="ne">-&gt; </span><span class="mi">1541</span>     <span class="k">return</span> <span class="n">forward_call</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1543</span> <span class="k">try</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1544</span>     <span class="n">result</span> <span class="o">=</span> <span class="kc">None</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/conv.py:460,</span> in <span class="ni">Conv2d.forward</span><span class="nt">(self, input)</span>
+<span class="g g-Whitespace">    </span><span class="mi">459</span> <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">input</span><span class="p">:</span> <span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">460</span>     <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_conv_forward</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">bias</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/conv.py:456,</span> in <span class="ni">Conv2d._conv_forward</span><span class="nt">(self, input, weight, bias)</span>
+<span class="g g-Whitespace">    </span><span class="mi">452</span> <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">padding_mode</span> <span class="o">!=</span> <span class="s1">&#39;zeros&#39;</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">453</span>     <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_reversed_padding_repeated_twice</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">padding_mode</span><span class="p">),</span>
+<span class="g g-Whitespace">    </span><span class="mi">454</span>                     <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">455</span>                     <span class="n">_pair</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">456</span> <span class="k">return</span> <span class="n">F</span><span class="o">.</span><span class="n">conv2d</span><span class="p">(</span><span class="nb">input</span><span class="p">,</span> <span class="n">weight</span><span class="p">,</span> <span class="n">bias</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">stride</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">457</span>                 <span class="bp">self</span><span class="o">.</span><span class="n">padding</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dilation</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">groups</span><span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/monai/data/meta_tensor.py:282,</span> in <span class="ni">MetaTensor.__torch_function__</span><span class="nt">(cls, func, types, args, kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">280</span> <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">281</span>     <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="ne">--&gt; </span><span class="mi">282</span> <span class="n">ret</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__torch_function__</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">283</span> <span class="c1"># if `out` has been used as argument, metadata is not copied, nothing to do.</span>
+<span class="g g-Whitespace">    </span><span class="mi">284</span> <span class="c1"># if &quot;out&quot; in kwargs:</span>
+<span class="g g-Whitespace">    </span><span class="mi">285</span> <span class="c1">#     return ret</span>
+<span class="g g-Whitespace">    </span><span class="mi">286</span> <span class="k">if</span> <span class="n">_not_requiring_metadata</span><span class="p">(</span><span class="n">ret</span><span class="p">):</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:1443,</span> in <span class="ni">Tensor.__torch_function__</span><span class="nt">(cls, func, types, args, kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1440</span>     <span class="k">return</span> <span class="bp">NotImplemented</span>
+<span class="g g-Whitespace">   </span><span class="mi">1442</span> <span class="k">with</span> <span class="n">_C</span><span class="o">.</span><span class="n">DisableTorchFunctionSubclass</span><span class="p">():</span>
+<span class="ne">-&gt; </span><span class="mi">1443</span>     <span class="n">ret</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1444</span>     <span class="k">if</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">get_default_nowrap_functions</span><span class="p">():</span>
+<span class="g g-Whitespace">   </span><span class="mi">1445</span>         <span class="k">return</span> <span class="n">ret</span>
+
+<span class="ne">KeyboardInterrupt</span>: 
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="evaluating-the-model">
+<h2>Evaluating the model<a class="headerlink" href="#evaluating-the-model" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">true_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">dim_origs</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">paddings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">test_mpe</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="p">(</span><span class="n">images</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">,</span> <span class="n">affine_matrixs</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="p">,</span>
+        <span class="n">dim_orig</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">dim_orig</span> <span class="o">=</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">padding</span> <span class="o">=</span> <span class="n">padding</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+        <span class="c1"># heatmap = heatmap_generator(landmarks)</span>
+        <span class="n">offset_coords</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_landmark</span> <span class="o">=</span> <span class="n">heatmap_to_coord</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">offset_coords</span><span class="o">=</span><span class="n">offset_coords</span><span class="p">,</span>
+                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;local_soft_argmax&quot;</span><span class="p">)</span>
+        <span class="n">test_mpe</span> <span class="o">+=</span> <span class="n">point_error</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">pred_landmark</span><span class="p">,</span> <span class="n">images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span> <span class="n">dim_orig</span><span class="p">,</span>
+                                <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">)</span>
+        <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_landmark</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+        <span class="n">true_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+        <span class="n">dim_origs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dim_orig</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+        <span class="n">pixel_spacings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+        <span class="n">paddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">padding</span><span class="o">.</span><span class="n">cpu</span><span class="p">())</span>
+
+<span class="n">pred_landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">)</span>
+<span class="n">true_landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>
+<span class="n">dim_origs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">)</span>
+<span class="n">pixel_spacings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">)</span>
+<span class="n">paddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">)</span>
+
+<span class="n">test_mpe</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test Mean PE: </span><span class="si">{</span><span class="n">test_mpe</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:06&lt;00:00, 15.84it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Test Mean PE: 1.0885
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">sdr</span>
+
+<span class="n">sdr_test</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">([</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">true_landmarks</span><span class="o">=</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks</span><span class="p">,</span>
+               <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_origs</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">paddings</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sdr_test</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SDR for </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">mm: </span><span class="si">{</span><span class="n">sdr_test</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SDR for 2.0mm: 88.4211
+SDR for 2.5mm: 92.1579
+SDR for 3.0mm: 94.5789
+SDR for 4.0mm: 97.6316
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">sdr</span>
+
+<span class="n">sdr_test</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">([</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">true_landmarks</span><span class="o">=</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks</span><span class="p">,</span>
+               <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_origs</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">paddings</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sdr_test</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SDR for </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">mm: </span><span class="si">{</span><span class="n">sdr_test</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>SDR for 2.0mm: 88.4211
+SDR for 2.5mm: 92.1579
+SDR for 3.0mm: 94.5789
+SDR for 4.0mm: 97.6316
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize.utils</span> <span class="kn">import</span> <span class="n">prediction_inspect_plot</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="n">prediction_inspect_plot</span><span class="p">(</span><span class="n">ds_test2</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">ds_test2</span><span class="o">.</span><span class="n">indices</span><span class="p">[:</span><span class="mi">3</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/e64bd8d30b04d73a9bd7e1454d9baebb229c7044d98fff5d251dd10b3000f37f.png" src="../../_images/e64bd8d30b04d73a9bd7e1454d9baebb229c7044d98fff5d251dd10b3000f37f.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">detection_report</span>
+
+<span class="n">detection_report</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_origs</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span>
+                    <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">paddings</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds</span><span class="o">.</span><span class="n">class_names</span><span class="p">,</span>
+                    <span class="n">radius</span><span class="o">=</span><span class="p">[</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="n">digits</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Detection report:
+1# Point-to-point error (PE) statistics:
+======================================================================
+Class               Mean PE   Median PE Std PE    Min       Max       
+----------------------------------------------------------------------
+landmark_0          0.71      0.61      0.43      0.01      2.33      
+landmark_1          1.09      0.68      1.33      0.06      8.42      
+landmark_2          1.22      0.86      1.17      0.08      5.13      
+landmark_3          1.50      1.02      1.58      0.18      8.13      
+landmark_4          1.36      1.08      0.96      0.14      4.64      
+landmark_5          1.04      0.80      0.76      0.05      3.28      
+landmark_6          0.89      0.86      0.58      0.05      2.77      
+landmark_7          0.78      0.60      0.56      0.03      2.69      
+landmark_8          0.76      0.61      0.51      0.02      2.24      
+landmark_9          2.16      1.82      1.47      0.10      6.26      
+landmark_10         0.80      0.63      0.68      0.08      4.46      
+landmark_11         0.59      0.35      1.00      0.03      8.29      
+landmark_12         1.11      0.94      0.70      0.05      3.34      
+landmark_13         0.91      0.79      0.63      0.04      3.11      
+landmark_14         1.13      0.90      1.02      0.09      6.60      
+landmark_15         1.13      0.81      1.04      0.10      6.36      
+landmark_16         1.00      0.81      0.89      0.12      7.53      
+landmark_17         1.27      1.00      1.08      0.05      6.18      
+landmark_18         1.23      0.87      1.23      0.09      7.43      
+======================================================================
+
+2# Success detection rate (SDR):
+================================================================================
+Class               SDR (PE≤2.0mm) SDR (PE≤2.5mm) SDR (PE≤3.0mm) SDR (PE≤4.0mm) 
+--------------------------------------------------------------------------------
+landmark_0          98.00          100.00         100.00         100.00         
+landmark_1          86.00          88.00          91.00          96.00          
+landmark_2          84.00          89.00          91.00          94.00          
+landmark_3          83.00          87.00          90.00          93.00          
+landmark_4          82.00          88.00          90.00          98.00          
+landmark_5          91.00          95.00          98.00          100.00         
+landmark_6          93.00          97.00          100.00         100.00         
+landmark_7          96.00          99.00          100.00         100.00         
+landmark_8          97.00          100.00         100.00         100.00         
+landmark_9          54.00          63.00          73.00          87.00          
+landmark_10         95.00          96.00          98.00          99.00          
+landmark_11         94.00          95.00          97.00          99.00          
+landmark_12         89.00          95.00          98.00          100.00         
+landmark_13         94.00          97.00          99.00          100.00         
+landmark_14         89.00          92.00          96.00          98.00          
+landmark_15         91.00          93.00          96.00          97.00          
+landmark_16         94.00          97.00          97.00          99.00          
+landmark_17         85.00          90.00          91.00          98.00          
+landmark_18         85.00          90.00          92.00          97.00          
+================================================================================
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">plot_cpe</span>
+
+<span class="n">plot_cpe</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_origs</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span>
+                    <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">paddings</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds</span><span class="o">.</span><span class="n">class_names</span><span class="p">,</span>
+                    <span class="n">group</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;CPE curve&quot;</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                    <span class="n">stat</span><span class="o">=</span><span class="s1">&#39;proportion&#39;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;mm&#39;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;ecdf&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/6ac8ba80b7e64132fc221f47a84f6706a12ee4cf935235615dbc6821e8d7e22d.png" src="../../_images/6ac8ba80b7e64132fc221f47a84f6706a12ee4cf935235615dbc6821e8d7e22d.png" />
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+            <ul>
+<li><a class="reference internal" href="#">Training and Evaluating Adaptive Heatmap Regression Model for Landmark Detection with ISBI 2015 Cephalometric X-ray Dataset</a><ul>
+<li><a class="reference internal" href="#setup-environment">Setup environment</a></li>
+<li><a class="reference internal" href="#setup-imports-and-variables">Setup imports and variables</a></li>
+<li><a class="reference internal" href="#loading-the-dataset">Loading the dataset</a><ul>
+<li><a class="reference internal" href="#short-description-of-the-data-and-dataset-module">Short description of the data and dataset module</a></li>
+<li><a class="reference internal" href="#download-and-load-isbi-2015-landmark-dataset">Download and load ISBI 2015 landmark dataset</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#constructing-a-heatmap-generator">Constructing a heatmap generator</a></li>
+<li><a class="reference internal" href="#inspecting-the-dataset">Inspecting the dataset</a></li>
+<li><a class="reference internal" href="#training-and-initializing-the-spatialconfiguration-model">Training and initializing the SpatialConfiguration model</a><ul>
+<li><a class="reference internal" href="#initializing-the-model-optimizer-and-loss-function">Initializing the model, optimizer and loss function</a></li>
+<li><a class="reference internal" href="#setting-the-data-loaders">Setting the data loaders</a></li>
+<li><a class="reference internal" href="#training-the-model">Training the model</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#evaluating-the-model">Evaluating the model</a></li>
+</ul>
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diff --git a/_collections/examples/endovis2015.html b/_collections/examples/endovis2015.html
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+        <article role="main">
+          <p align="center">
+    <a href="https://predict-idlab.github.io/landmarker">
+        <img alt="landmarker" src="https://raw.githubusercontent.com/predict-idlab/landmarker/main/docs/_static/images/logo.svg" width="66%">
+    </a>
+</p>
+<section id="training-and-evaluating-static-heatmap-regression-model-for-multi-instance-and-multi-class-landmark-detetection-endovis-2015-challenge">
+<h1>Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Multi-Class Landmark Detetection (EndoVis 2015 Challenge)<a class="headerlink" href="#training-and-evaluating-static-heatmap-regression-model-for-multi-instance-and-multi-class-landmark-detetection-endovis-2015-challenge" title="Link to this heading">#</a></h1>
+<p>In this tutorial, we will train and evaluate an direct static heatmap regression model for landmark
+detection with EndoVis 2015 Challenge. We will use part of the EndoVis 2015 challenge dataset to
+construct a multi-instance and multi-class landmark detection task. The dataset contains 4 training
+and 6 testing videos of robotic surgery. The goal is to predict the location of instruments in the video,
+more specifically the tip of the clasper. We only consider the clasper points and ignore the other points,
+since they are way more ambiguous. One of the difficulties</p>
+<p>The videos are transformed into images and the annotations are
+given as 2D points. The dataset is split into a training and testing set. The training set contains 4 videos and
+the testing set contains 6 videos, such as specified in the challenge.</p>
+<p>We will go through the following steps:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#Loading-the-dataset"><span class="xref myst">Loading the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Inspecting-the-dataset"><span class="xref myst">Inspecting the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Training-the-model"><span class="xref myst">Training and initializing the UNet model</span></a></p></li>
+<li><p><a class="reference internal" href="#Evaluating-the-model"><span class="xref myst">Evaluating the model</span></a></p></li>
+</ul>
+<a target="_blank" href="https://colab.research.google.com/github/predict-idlab/landmarker/examples/static_unet_endovis2015.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a><section id="setup-environment">
+<h2>Setup environment<a class="headerlink" href="#setup-environment" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># !python -c &quot;import landmarker&quot; || pip install landmarker</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../src/&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setup-imports-and-variables">
+<h2>Setup imports and variables<a class="headerlink" href="#setup-imports-and-variables" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">monai.transforms</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Compose</span><span class="p">,</span> <span class="n">RandAffined</span><span class="p">,</span> <span class="n">RandGaussianNoised</span><span class="p">,</span> <span class="n">ScaleIntensityd</span><span class="p">,</span>
+                              <span class="n">RandScaleIntensityd</span><span class="p">,</span> <span class="n">RandAdjustContrastd</span><span class="p">,</span> <span class="n">RandHistogramShiftd</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">tqdm.notebook</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-the-dataset">
+<h2>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fn_keys</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="s1">&#39;mask&#39;</span><span class="p">)</span>
+<span class="n">spatial_transformd</span> <span class="o">=</span> <span class="p">[</span><span class="n">RandAffined</span><span class="p">(</span><span class="n">fn_keys</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                        <span class="n">rotate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">),</span>
+                        <span class="n">translate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
+                        <span class="n">scale_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
+                        <span class="n">shear_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+                        <span class="p">)]</span>
+
+<span class="n">train_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+                            <span class="n">RandGaussianNoised</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>  <span class="c1"># Add gaussian noise</span>
+                            <span class="n">RandScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">factors</span><span class="o">=</span><span class="mf">0.25</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">),</span>  <span class="c1"># Add random intensity scaling</span>
+                            <span class="n">RandAdjustContrastd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">4.5</span><span class="p">)),</span>  <span class="c1"># Randomly adjust contrast</span>
+                            <span class="n">RandHistogramShiftd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">),</span> <span class="n">prob</span><span class="o">=</span><span class="mf">0.2</span><span class="p">),</span>  <span class="c1"># Randomly shift histogram</span>
+                            <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>  <span class="c1"># Scale intensity</span>
+                        <span class="p">]</span> <span class="o">+</span> <span class="n">spatial_transformd</span><span class="p">)</span>
+
+<span class="n">inference_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+    <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>
+<span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.datasets</span> <span class="kn">import</span> <span class="n">get_endovis2015_heatmap_datasets</span>
+
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;/Users/jefjonkers/Data/landmark-datasets&quot;</span>
+<span class="n">ds_train</span><span class="p">,</span> <span class="n">ds_test</span> <span class="o">=</span> <span class="n">get_endovis2015_heatmap_datasets</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train_transform</span> <span class="o">=</span> <span class="n">train_transformd</span><span class="p">,</span>
+                                                     <span class="n">inference_transform</span><span class="o">=</span> <span class="n">inference_transformd</span><span class="p">,</span>
+                                                     <span class="n">dim_img</span> <span class="o">=</span> <span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">sigma</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="inspecting-the-dataset">
+<h2>Inspecting the dataset<a class="headerlink" href="#inspecting-the-dataset" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">inspection_plot</span>
+
+<span class="c1"># Plot the first 3 images from the training set</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds_train</span><span class="p">),</span> <span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot the first 3 images from the test1 set</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_test</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="training-and-initializing-the-spatialconfiguration-model">
+<h2>Training and initializing the SpatialConfiguration model<a class="headerlink" href="#training-and-initializing-the-spatialconfiguration-model" title="Link to this heading">#</a></h2>
+<section id="initializing-the-model-optimizer-and-loss-function">
+<h3>Initializing the model, optimizer and loss function<a class="headerlink" href="#initializing-the-model-optimizer-and-loss-function" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">monai.networks.nets</span> <span class="kn">import</span> <span class="n">UNet</span>
+
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">UNet</span><span class="p">(</span>
+    <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">in_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">out_channels</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="n">channels</span><span class="o">=</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
+    <span class="n">strides</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+    <span class="n">num_res_units</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+
+<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-4</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">5</span>
+
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+
+<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
+
+<span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                                                          <span class="n">patience</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cooldown</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setting-the-data-loaders-and-split-training-set">
+<h3>Setting the data loaders and split training set<a class="headerlink" href="#setting-the-data-loaders-and-split-training-set" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">split_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
+<span class="n">ds_train_train</span><span class="p">,</span> <span class="n">ds_train_val</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">random_split</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="n">split_lengths</span><span class="p">)</span>
+<span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_train_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">val_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_train_val</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">test_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="training-the-model">
+<h3>Training the model<a class="headerlink" href="#training-the-model" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.heatmap.decoder</span> <span class="kn">import</span> <span class="n">heatmap_to_coord</span><span class="p">,</span> <span class="n">heatmap_to_multiple_coord</span>
+<span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">point_error</span>
+
+<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">sigmoid</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.metrics.metrics</span> <span class="kn">import</span> <span class="n">multi_instance_point_error</span>
+
+<span class="k">def</span> <span class="nf">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
+    <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;mask&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+        <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
+    <span class="n">eval_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)):</span>
+            <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;mask&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+            <span class="n">eval_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">eval_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">)):</span>
+        <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="n">ds_train</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">val_loss</span> <span class="o">=</span> <span class="n">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">epochs</span><span class="si">}</span><span class="s2"> - Train loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val loss: </span><span class="si">{</span><span class="n">val_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lr_scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span>
+      <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
+<span class="c1"># model.load_state_dict(torch.load(&quot;best_weights_unet_endovis_static.pt&quot;))</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="evaluating-the-model">
+<h2>Evaluating the model<a class="headerlink" href="#evaluating-the-model" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">true_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">dim_origs</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">paddings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">tp</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">fp</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">fn</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">test_mpe</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_tp</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_fp</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_fn</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;mask&quot;</span><span class="p">]</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">))</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+        <span class="n">offset_coords</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_landmarks_list</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heatmap_to_multiple_coord</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> <span class="n">window</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+                                                           <span class="n">threshold</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                                                           <span class="n">method</span><span class="o">=</span><span class="s2">&quot;argmax&quot;</span><span class="p">)</span>
+        <span class="n">pe_batch</span><span class="p">,</span> <span class="n">tp_batch</span><span class="p">,</span> <span class="n">fp_batch</span><span class="p">,</span> <span class="n">fn_batch</span><span class="p">,</span> <span class="n">pred_landmarks_torch</span> <span class="o">=</span> <span class="n">multi_instance_point_error</span><span class="p">(</span>
+            <span class="n">true_landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks_list</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">],</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;spacing&quot;</span><span class="p">],</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">],</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+        <span class="n">test_mpe</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">pe_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_tp</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">tp_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_fp</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">fp_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_fn</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">fn_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_landmarks_torch</span><span class="p">)</span>
+        <span class="n">true_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+        <span class="n">dim_origs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">])</span>
+        <span class="n">pixel_spacings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;spacing&quot;</span><span class="p">])</span>
+        <span class="n">paddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">])</span>
+        <span class="n">tp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tp_batch</span><span class="p">)</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fp_batch</span><span class="p">)</span>
+        <span class="n">fn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fn_batch</span><span class="p">)</span>
+
+
+<span class="n">test_mpe</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_loader</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test Mean PE: </span><span class="si">{</span><span class="n">test_mpe</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test TP: </span><span class="si">{</span><span class="n">test_tp</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test FP: </span><span class="si">{</span><span class="n">test_fp</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test FN: </span><span class="si">{</span><span class="n">test_fn</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">sdr</span>
+
+<span class="n">sdr_test</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="n">true_landmarks</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+               <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+               <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sdr_test</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SDR for </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">mm: </span><span class="si">{</span><span class="n">sdr_test</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize.utils</span> <span class="kn">import</span> <span class="n">prediction_inspect_plot_multi_instance</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="n">prediction_inspect_plot_multi_instance</span><span class="p">(</span><span class="n">ds_test</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">))</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">plot_cpe</span>
+
+<span class="n">plot_cpe</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds_test</span><span class="o">.</span><span class="n">class_names</span><span class="p">,</span>
+            <span class="n">group</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;CPE curve&quot;</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">stat</span><span class="o">=</span><span class="s1">&#39;proportion&#39;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;pixels&#39;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;ecdf&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize.evaluation</span> <span class="kn">import</span> <span class="n">multi_instance_detection_report</span>
+
+<span class="n">multi_instance_detection_report</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+                                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+                                <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+                                <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds_test</span><span class="o">.</span><span class="n">class_names</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
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+            <ul>
+<li><a class="reference internal" href="#">Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Multi-Class Landmark Detetection (EndoVis 2015 Challenge)</a><ul>
+<li><a class="reference internal" href="#setup-environment">Setup environment</a></li>
+<li><a class="reference internal" href="#setup-imports-and-variables">Setup imports and variables</a></li>
+<li><a class="reference internal" href="#loading-the-dataset">Loading the dataset</a></li>
+<li><a class="reference internal" href="#inspecting-the-dataset">Inspecting the dataset</a></li>
+<li><a class="reference internal" href="#training-and-initializing-the-spatialconfiguration-model">Training and initializing the SpatialConfiguration model</a><ul>
+<li><a class="reference internal" href="#initializing-the-model-optimizer-and-loss-function">Initializing the model, optimizer and loss function</a></li>
+<li><a class="reference internal" href="#setting-the-data-loaders-and-split-training-set">Setting the data loaders and split training set</a></li>
+<li><a class="reference internal" href="#training-the-model">Training the model</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+</ul>
+
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+<li class="toctree-l2"><a class="reference internal" href="../../reference/data.html">Data Module</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../dev/contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
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+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../../reference/data.html">Data Module</a></li>
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+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <p align="center">
+    <a href="https://predict-idlab.github.io/landmarker">
+        <img alt="landmarker" src="https://raw.githubusercontent.com/predict-idlab/landmarker/main/docs/_static/images/logo.svg" width="66%">
+    </a>
+</p>
+<section id="training-and-evaluating-static-heatmap-regression-model-for-multi-instance-and-single-class-landmark-detetection-plant-centroids">
+<h1>Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Single-Class Landmark Detetection (Plant Centroids)<a class="headerlink" href="#training-and-evaluating-static-heatmap-regression-model-for-multi-instance-and-single-class-landmark-detetection-plant-centroids" title="Link to this heading">#</a></h1>
+<p>We will go through the following steps:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#Loading-the-dataset"><span class="xref myst">Loading the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Inspecting-the-dataset"><span class="xref myst">Inspecting the dataset</span></a></p></li>
+<li><p><a class="reference internal" href="#Training-the-model"><span class="xref myst">Training and initializing the UNet model</span></a></p></li>
+<li><p><a class="reference internal" href="#Evaluating-the-model"><span class="xref myst">Evaluating the model</span></a></p></li>
+</ul>
+<a target="_blank" href="https://colab.research.google.com/github/predict-idlab/landmarker/examples/static_unet_plant_centroids.ipynb">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a><section id="setup-environment">
+<h2>Setup environment<a class="headerlink" href="#setup-environment" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># !python -c &quot;import landmarker&quot; || pip install landmarker</span>
+
+<span class="kn">import</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">os</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">&quot;../src/&quot;</span><span class="p">)</span>
+<span class="kn">import</span> <span class="nn">landmarker</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setup-imports-and-variables">
+<h2>Setup imports and variables<a class="headerlink" href="#setup-imports-and-variables" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
+<span class="kn">from</span> <span class="nn">monai.transforms</span> <span class="kn">import</span> <span class="p">(</span><span class="n">Compose</span><span class="p">,</span> <span class="n">RandAffined</span><span class="p">,</span> <span class="n">RandGaussianNoised</span><span class="p">,</span> <span class="n">RandStdShiftIntensityd</span><span class="p">,</span>
+                              <span class="n">RandScaleIntensityd</span><span class="p">,</span> <span class="n">RandAdjustContrastd</span><span class="p">,</span> <span class="n">RandHistogramShiftd</span><span class="p">,</span>
+                              <span class="n">ScaleIntensityd</span><span class="p">,</span> <span class="n">Lambdad</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">tqdm.notebook</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span> <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">cuda</span><span class="o">.</span><span class="n">is_available</span><span class="p">()</span> <span class="k">else</span> <span class="s1">&#39;cpu&#39;</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="loading-the-dataset">
+<h2>Loading the dataset<a class="headerlink" href="#loading-the-dataset" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.transforms.images</span> <span class="kn">import</span> <span class="n">UseOnlyFirstChannel</span>
+<span class="n">fn_keys</span> <span class="o">=</span> <span class="p">(</span><span class="s1">&#39;image&#39;</span><span class="p">,)</span>
+<span class="n">spatial_transformd</span> <span class="o">=</span> <span class="p">[</span><span class="n">RandAffined</span><span class="p">(</span><span class="n">fn_keys</span><span class="p">,</span> <span class="n">prob</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                        <span class="n">rotate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="mi">12</span><span class="p">),</span>
+                        <span class="n">translate_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span>
+                        <span class="n">scale_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span>
+                        <span class="n">shear_range</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">)</span>
+                        <span class="p">)]</span>
+
+<span class="n">train_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+                            <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>  <span class="c1"># Scale intensity</span>
+                        <span class="p">]</span> <span class="o">+</span> <span class="n">spatial_transformd</span><span class="p">)</span>
+
+<span class="n">inference_transformd</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">([</span>
+    <span class="n">ScaleIntensityd</span><span class="p">((</span><span class="s1">&#39;image&#39;</span><span class="p">,</span> <span class="p">)),</span>
+<span class="p">])</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.datasets</span> <span class="kn">import</span> <span class="n">get_plant_centroids_landmark_datasets</span>
+
+<span class="n">data_dir</span> <span class="o">=</span> <span class="s2">&quot;/Users/jefjonkers/Data/landmark-datasets&quot;</span>
+<span class="n">ds_train</span><span class="p">,</span> <span class="n">ds_test_A</span><span class="p">,</span> <span class="n">ds_test_B</span><span class="p">,</span> <span class="n">ds_test_C</span> <span class="o">=</span> <span class="n">get_plant_centroids_landmark_datasets</span><span class="p">(</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train_transform</span> <span class="o">=</span> <span class="n">train_transformd</span><span class="p">,</span>
+                                                                                  <span class="n">inference_transform</span><span class="o">=</span><span class="n">inference_transformd</span><span class="p">,</span>
+                                                                                  <span class="n">dim_img</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span><span class="mi">512</span><span class="p">),</span>
+                                                                                  <span class="n">store_imgs</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1398/1398 [00:00&lt;00:00, 62890.94it/s]
+100%|██████████| 100/100 [00:00&lt;00:00, 32668.46it/s]
+100%|██████████| 275/275 [00:00&lt;00:00, 58336.72it/s]
+100%|██████████| 234/234 [00:00&lt;00:00, 57365.55it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading and extracting masks from 1398 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 1398/1398 [00:05&lt;00:00, 253.85it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading and extracting masks from 100 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 100/100 [00:00&lt;00:00, 246.31it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading and extracting masks from 275 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 275/275 [00:01&lt;00:00, 263.84it/s]
+</pre></div>
+</div>
+<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Reading and extracting masks from 234 images...
+</pre></div>
+</div>
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>100%|██████████| 234/234 [00:00&lt;00:00, 267.46it/s]
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="constructing-a-heatmap-generator">
+<h2>Constructing a heatmap generator<a class="headerlink" href="#constructing-a-heatmap-generator" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="n">GaussianHeatmapGenerator</span>
+
+<span class="n">heatmap_generator</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+    <span class="n">nb_landmarks</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+    <span class="n">sigmas</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+    <span class="n">gamma</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">heatmap_size</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+    <span class="n">learnable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="c1"># If True, the heatmap generator will be trainable</span>
+<span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="inspecting-the-dataset">
+<h2>Inspecting the dataset<a class="headerlink" href="#inspecting-the-dataset" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">inspection_plot</span>
+
+<span class="c1"># Plot the first 3 images from the training set</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span> <span class="c1"># because dataset tensors are still on cpu</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds_train</span><span class="p">),</span> <span class="mi">3</span><span class="p">),</span>
+               <span class="n">heatmap_generator</span><span class="p">)</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> <span class="c1"># set the desired device back</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/2066954125f6be263e8abc9451ac1585cbb0c9f715578d6143690c14242543f1.png" src="../../_images/2066954125f6be263e8abc9451ac1585cbb0c9f715578d6143690c14242543f1.png" />
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Plot the first 3 images from the test1 set</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s2">&quot;cpu&quot;</span> <span class="c1"># because dataset tensors are still on cpu</span>
+<span class="n">inspection_plot</span><span class="p">(</span><span class="n">ds_test_A</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">heatmap_generator</span><span class="p">)</span>
+<span class="n">heatmap_generator</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span> <span class="c1"># set the desired device back</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<img alt="../../_images/1db41bbda4507364e52958aa5f854ef8a47b0d07e8440ca60feaa907c5cfa0d9.png" src="../../_images/1db41bbda4507364e52958aa5f854ef8a47b0d07e8440ca60feaa907c5cfa0d9.png" />
+</div>
+</div>
+</section>
+<section id="training-and-initializing-the-spatialconfiguration-model">
+<h2>Training and initializing the SpatialConfiguration model<a class="headerlink" href="#training-and-initializing-the-spatialconfiguration-model" title="Link to this heading">#</a></h2>
+<section id="initializing-the-model-optimizer-and-loss-function">
+<h3>Initializing the model, optimizer and loss function<a class="headerlink" href="#initializing-the-model-optimizer-and-loss-function" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">import</span> <span class="nn">segmentation_models_pytorch</span> <span class="k">as</span> <span class="nn">smp</span>
+
+<span class="n">model</span> <span class="o">=</span> <span class="n">smp</span><span class="o">.</span><span class="n">Unet</span><span class="p">(</span>
+        <span class="n">encoder_name</span><span class="o">=</span><span class="s2">&quot;resnet34&quot;</span><span class="p">,</span>
+        <span class="n">encoder_weights</span><span class="o">=</span><span class="s2">&quot;imagenet&quot;</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+        <span class="n">classes</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="n">decoder_channels</span><span class="o">=</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">64</span><span class="p">,</span> <span class="mi">32</span><span class="p">,</span> <span class="mi">32</span><span class="p">),</span>
+<span class="p">)</span>
+
+<span class="n">lr</span> <span class="o">=</span> <span class="mf">1e-4</span>
+<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">4</span>
+<span class="n">epochs</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="n">optimizer</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">AdamW</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">(),</span> <span class="n">lr</span><span class="o">=</span><span class="n">lr</span><span class="p">,</span> <span class="n">weight_decay</span><span class="o">=</span><span class="mf">1e-3</span><span class="p">)</span>
+
+<span class="n">criterion</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">BCEWithLogitsLoss</span><span class="p">()</span>
+
+<span class="n">lr_scheduler</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">optim</span><span class="o">.</span><span class="n">lr_scheduler</span><span class="o">.</span><span class="n">ReduceLROnPlateau</span><span class="p">(</span><span class="n">optimizer</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;min&#39;</span><span class="p">,</span> <span class="n">factor</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
+                                                          <span class="n">patience</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">cooldown</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<div class="output stderr highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.
+  warnings.warn(&quot;The verbose parameter is deprecated. Please use get_last_lr() &quot;
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="setting-the-data-loaders-and-split-training-set">
+<h3>Setting the data loaders and split training set<a class="headerlink" href="#setting-the-data-loaders-and-split-training-set" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">split_lengths</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.2</span><span class="p">]</span>
+<span class="n">ds_train_train</span><span class="p">,</span> <span class="n">ds_train_val</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">utils</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">random_split</span><span class="p">(</span><span class="n">ds_train</span><span class="p">,</span> <span class="n">split_lengths</span><span class="p">)</span>
+<span class="n">train_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_train_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">val_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_train_val</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">test_A_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test_A</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">test_B_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test_B</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">test_C_loader</span> <span class="o">=</span> <span class="n">DataLoader</span><span class="p">(</span><span class="n">ds_test_C</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">shuffle</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">num_workers</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="training-the-model">
+<h3>Training the model<a class="headerlink" href="#training-the-model" title="Link to this heading">#</a></h3>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.heatmap.decoder</span> <span class="kn">import</span> <span class="n">heatmap_to_coord</span><span class="p">,</span> <span class="n">heatmap_to_multiple_coord</span>
+<span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">point_error</span>
+
+<span class="kn">from</span> <span class="nn">torch.nn.functional</span> <span class="kn">import</span> <span class="n">sigmoid</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.metrics.metrics</span> <span class="kn">import</span> <span class="n">multi_instance_point_error</span>
+
+<span class="k">def</span> <span class="nf">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
+    <span class="n">running_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">zero_grad</span><span class="p">()</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+        <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+        <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+        <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">running_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">):</span>
+    <span class="n">eval_loss</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+    <span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+        <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)):</span>
+            <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+            <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="p">)</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+            <span class="n">eval_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+    <span class="k">return</span> <span class="n">eval_loss</span> <span class="o">/</span> <span class="nb">len</span><span class="p">(</span><span class="n">val_loader</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">1000</span><span class="p">):</span>
+    <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">)):</span>
+        <span class="n">ds_train</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">train_transformd</span>
+        <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="n">ds_train</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="n">val_loss</span> <span class="o">=</span> <span class="n">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Epoch </span><span class="si">{</span><span class="n">epoch</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">epochs</span><span class="si">}</span><span class="s2"> - Train loss: </span><span class="si">{</span><span class="n">train_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2"> - Val loss: </span><span class="si">{</span><span class="n">val_loss</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="n">lr_scheduler</span><span class="o">.</span><span class="n">step</span><span class="p">(</span><span class="n">val_loss</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span>
+      <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
+
+<span class="c1"># model.load_state_dict(torch.load(&quot;model_static_unet_plant_centroids.pt&quot;))</span>
+</pre></div>
+</div>
+</div>
+<div class="cell_output docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "ae0d4c5d8efe4b6ea72d7badab4adf4e", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "f1f0d40ba16e4e8cac1d58bcb03448e5", "version_major": 2, "version_minor": 0}</script><script type="application/vnd.jupyter.widget-view+json">{"model_id": "e4728585bcec469e9a491a5d787b7442", "version_major": 2, "version_minor": 0}</script><div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Epoch 1/100 - Train loss: 0.0938 - Val loss: 0.0283
+</pre></div>
+</div>
+<script type="application/vnd.jupyter.widget-view+json">{"model_id": "5648a8372c35425bb9d735856179eddf", "version_major": 2, "version_minor": 0}</script><div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
+<span class="ne">KeyboardInterrupt</span><span class="g g-Whitespace">                         </span>Traceback (most recent call last)
+<span class="n">Cell</span> <span class="n">In</span><span class="p">[</span><span class="mi">14</span><span class="p">],</span> <span class="n">line</span> <span class="mi">1</span>
+<span class="ne">----&gt; </span><span class="mi">1</span> <span class="n">train</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">,</span>
+<span class="g g-Whitespace">      </span><span class="mi">2</span>       <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">)</span>
+<span class="g g-Whitespace">      </span><span class="mi">4</span> <span class="c1"># model.load_state_dict(torch.load(&quot;model_static_unet_plant_centroids.pt&quot;))</span>
+
+<span class="nn">Cell In[13], line 39,</span> in <span class="ni">train</span><span class="nt">(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs)</span>
+<span class="g g-Whitespace">     </span><span class="mi">37</span> <span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">)):</span>
+<span class="g g-Whitespace">     </span><span class="mi">38</span>     <span class="n">ds_train</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">train_transformd</span>
+<span class="ne">---&gt; </span><span class="mi">39</span>     <span class="n">train_loss</span> <span class="o">=</span> <span class="n">train_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">train_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">optimizer</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">40</span>     <span class="n">ds_train</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="kc">None</span>
+<span class="g g-Whitespace">     </span><span class="mi">41</span>     <span class="n">val_loss</span> <span class="o">=</span> <span class="n">val_epoch</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">heatmap_generator</span><span class="p">,</span> <span class="n">val_loader</span><span class="p">,</span> <span class="n">criterion</span><span class="p">,</span> <span class="n">device</span><span class="p">)</span>
+
+<span class="nn">Cell In[13], line 18,</span> in <span class="ni">train_epoch</span><span class="nt">(model, heatmap_generator, train_loader, criterion, optimizer, device)</span>
+<span class="g g-Whitespace">     </span><span class="mi">16</span> <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+<span class="g g-Whitespace">     </span><span class="mi">17</span> <span class="n">loss</span> <span class="o">=</span> <span class="n">criterion</span><span class="p">(</span><span class="n">outputs</span><span class="p">,</span> <span class="n">heatmaps</span><span class="p">)</span>
+<span class="ne">---&gt; </span><span class="mi">18</span> <span class="n">loss</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">19</span> <span class="n">optimizer</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
+<span class="g g-Whitespace">     </span><span class="mi">20</span> <span class="n">running_loss</span> <span class="o">+=</span> <span class="n">loss</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:516,</span> in <span class="ni">Tensor.backward</span><span class="nt">(self, gradient, retain_graph, create_graph, inputs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">469</span><span class="w"> </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;Computes the gradient of current tensor wrt graph leaves.</span>
+<span class="g g-Whitespace">    </span><span class="mi">470</span><span class="sd"> </span>
+<span class="g g-Whitespace">    </span><span class="mi">471</span><span class="sd"> The graph is differentiated using the chain rule. If the tensor is</span>
+<span class="sd">   (...)</span>
+<span class="g g-Whitespace">    </span><span class="mi">513</span><span class="sd">         used to compute the attr::tensors.</span>
+<span class="g g-Whitespace">    </span><span class="mi">514</span><span class="sd"> &quot;&quot;&quot;</span>
+<span class="g g-Whitespace">    </span><span class="mi">515</span> <span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="ne">--&gt; </span><span class="mi">516</span>     <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">517</span>         <span class="n">Tensor</span><span class="o">.</span><span class="n">backward</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">518</span>         <span class="p">(</span><span class="bp">self</span><span class="p">,),</span>
+<span class="g g-Whitespace">    </span><span class="mi">519</span>         <span class="bp">self</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">520</span>         <span class="n">gradient</span><span class="o">=</span><span class="n">gradient</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">521</span>         <span class="n">retain_graph</span><span class="o">=</span><span class="n">retain_graph</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">522</span>         <span class="n">create_graph</span><span class="o">=</span><span class="n">create_graph</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">523</span>         <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">524</span>     <span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">525</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">526</span>     <span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span>
+<span class="g g-Whitespace">    </span><span class="mi">527</span> <span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/overrides.py:1636,</span> in <span class="ni">handle_torch_function</span><span class="nt">(public_api, relevant_args, *args, **kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1630</span>     <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">&quot;Defining your `__torch_function__ as a plain method is deprecated and &quot;</span>
+<span class="g g-Whitespace">   </span><span class="mi">1631</span>                   <span class="s2">&quot;will be an error in future, please define it as a classmethod.&quot;</span><span class="p">,</span>
+<span class="g g-Whitespace">   </span><span class="mi">1632</span>                   <span class="ne">DeprecationWarning</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1634</span> <span class="c1"># Use `public_api` instead of `implementation` so __torch_function__</span>
+<span class="g g-Whitespace">   </span><span class="mi">1635</span> <span class="c1"># implementations can do equality/identity comparisons.</span>
+<span class="ne">-&gt; </span><span class="mi">1636</span> <span class="n">result</span> <span class="o">=</span> <span class="n">torch_func_method</span><span class="p">(</span><span class="n">public_api</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1638</span> <span class="k">if</span> <span class="n">result</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">NotImplemented</span><span class="p">:</span>
+<span class="g g-Whitespace">   </span><span class="mi">1639</span>     <span class="k">return</span> <span class="n">result</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/monai/data/meta_tensor.py:282,</span> in <span class="ni">MetaTensor.__torch_function__</span><span class="nt">(cls, func, types, args, kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">280</span> <span class="k">if</span> <span class="n">kwargs</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">281</span>     <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="ne">--&gt; </span><span class="mi">282</span> <span class="n">ret</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__torch_function__</span><span class="p">(</span><span class="n">func</span><span class="p">,</span> <span class="n">types</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">283</span> <span class="c1"># if `out` has been used as argument, metadata is not copied, nothing to do.</span>
+<span class="g g-Whitespace">    </span><span class="mi">284</span> <span class="c1"># if &quot;out&quot; in kwargs:</span>
+<span class="g g-Whitespace">    </span><span class="mi">285</span> <span class="c1">#     return ret</span>
+<span class="g g-Whitespace">    </span><span class="mi">286</span> <span class="k">if</span> <span class="n">_not_requiring_metadata</span><span class="p">(</span><span class="n">ret</span><span class="p">):</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:1443,</span> in <span class="ni">Tensor.__torch_function__</span><span class="nt">(cls, func, types, args, kwargs)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1440</span>     <span class="k">return</span> <span class="bp">NotImplemented</span>
+<span class="g g-Whitespace">   </span><span class="mi">1442</span> <span class="k">with</span> <span class="n">_C</span><span class="o">.</span><span class="n">DisableTorchFunctionSubclass</span><span class="p">():</span>
+<span class="ne">-&gt; </span><span class="mi">1443</span>     <span class="n">ret</span> <span class="o">=</span> <span class="n">func</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+<span class="g g-Whitespace">   </span><span class="mi">1444</span>     <span class="k">if</span> <span class="n">func</span> <span class="ow">in</span> <span class="n">get_default_nowrap_functions</span><span class="p">():</span>
+<span class="g g-Whitespace">   </span><span class="mi">1445</span>         <span class="k">return</span> <span class="n">ret</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:525,</span> in <span class="ni">Tensor.backward</span><span class="nt">(self, gradient, retain_graph, create_graph, inputs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">515</span> <span class="k">if</span> <span class="n">has_torch_function_unary</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="g g-Whitespace">    </span><span class="mi">516</span>     <span class="k">return</span> <span class="n">handle_torch_function</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">517</span>         <span class="n">Tensor</span><span class="o">.</span><span class="n">backward</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">518</span>         <span class="p">(</span><span class="bp">self</span><span class="p">,),</span>
+   <span class="p">(</span><span class="o">...</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">523</span>         <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">524</span>     <span class="p">)</span>
+<span class="ne">--&gt; </span><span class="mi">525</span> <span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">backward</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">526</span>     <span class="bp">self</span><span class="p">,</span> <span class="n">gradient</span><span class="p">,</span> <span class="n">retain_graph</span><span class="p">,</span> <span class="n">create_graph</span><span class="p">,</span> <span class="n">inputs</span><span class="o">=</span><span class="n">inputs</span>
+<span class="g g-Whitespace">    </span><span class="mi">527</span> <span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/autograd/__init__.py:267,</span> in <span class="ni">backward</span><span class="nt">(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">262</span>     <span class="n">retain_graph</span> <span class="o">=</span> <span class="n">create_graph</span>
+<span class="g g-Whitespace">    </span><span class="mi">264</span> <span class="c1"># The reason we repeat the same comment below is that</span>
+<span class="g g-Whitespace">    </span><span class="mi">265</span> <span class="c1"># some Python versions print out the first line of a multi-line function</span>
+<span class="g g-Whitespace">    </span><span class="mi">266</span> <span class="c1"># calls in the traceback and some print out the last line</span>
+<span class="ne">--&gt; </span><span class="mi">267</span> <span class="n">_engine_run_backward</span><span class="p">(</span>
+<span class="g g-Whitespace">    </span><span class="mi">268</span>     <span class="n">tensors</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">269</span>     <span class="n">grad_tensors_</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">270</span>     <span class="n">retain_graph</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">271</span>     <span class="n">create_graph</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">272</span>     <span class="n">inputs</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">273</span>     <span class="n">allow_unreachable</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">274</span>     <span class="n">accumulate_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="g g-Whitespace">    </span><span class="mi">275</span> <span class="p">)</span>
+
+<span class="nn">File /opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/autograd/graph.py:744,</span> in <span class="ni">_engine_run_backward</span><span class="nt">(t_outputs, *args, **kwargs)</span>
+<span class="g g-Whitespace">    </span><span class="mi">742</span>     <span class="n">unregister_hooks</span> <span class="o">=</span> <span class="n">_register_logging_hooks_on_whole_graph</span><span class="p">(</span><span class="n">t_outputs</span><span class="p">)</span>
+<span class="g g-Whitespace">    </span><span class="mi">743</span> <span class="k">try</span><span class="p">:</span>
+<span class="ne">--&gt; </span><span class="mi">744</span>     <span class="k">return</span> <span class="n">Variable</span><span class="o">.</span><span class="n">_execution_engine</span><span class="o">.</span><span class="n">run_backward</span><span class="p">(</span>  <span class="c1"># Calls into the C++ engine to run the backward pass</span>
+<span class="g g-Whitespace">    </span><span class="mi">745</span>         <span class="n">t_outputs</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+<span class="g g-Whitespace">    </span><span class="mi">746</span>     <span class="p">)</span>  <span class="c1"># Calls into the C++ engine to run the backward pass</span>
+<span class="g g-Whitespace">    </span><span class="mi">747</span> <span class="k">finally</span><span class="p">:</span>
+<span class="g g-Whitespace">    </span><span class="mi">748</span>     <span class="k">if</span> <span class="n">attach_logging_hooks</span><span class="p">:</span>
+
+<span class="ne">KeyboardInterrupt</span>: 
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+<section id="evaluating-the-model">
+<h2>Evaluating the model<a class="headerlink" href="#evaluating-the-model" title="Link to this heading">#</a></h2>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">true_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">dim_origs</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">paddings</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">tp</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">fp</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">fn</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="n">test_mpe</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_tp</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_fp</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">test_fn</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">threshold</span> <span class="o">=</span> <span class="mf">0.05</span>
+<span class="n">window</span> <span class="o">=</span> <span class="mi">9</span>
+<span class="n">model</span><span class="o">.</span><span class="n">eval</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">)</span>
+<span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">test_A_loader</span><span class="p">)):</span>
+        <span class="n">images</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">images</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">device</span><span class="p">))</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+        <span class="n">offset_coords</span> <span class="o">=</span> <span class="n">outputs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">-</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">pred_landmarks_list</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heatmap_to_multiple_coord</span><span class="p">(</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">outputs</span><span class="p">),</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span>
+                                                           <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span>
+                                                           <span class="n">method</span><span class="o">=</span><span class="s2">&quot;argmax&quot;</span><span class="p">)</span>
+        <span class="n">pe_batch</span><span class="p">,</span> <span class="n">tp_batch</span><span class="p">,</span> <span class="n">fp_batch</span><span class="p">,</span> <span class="n">fn_batch</span><span class="p">,</span> <span class="n">pred_landmarks_torch</span> <span class="o">=</span> <span class="n">multi_instance_point_error</span><span class="p">(</span>
+            <span class="n">true_landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks_list</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">],</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;spacing&quot;</span><span class="p">],</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">],</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+        <span class="n">test_mpe</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">pe_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_tp</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">tp_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_fp</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">fp_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">test_fn</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">fn_batch</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_landmarks_torch</span><span class="p">)</span>
+        <span class="n">true_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+        <span class="n">dim_origs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">])</span>
+        <span class="n">pixel_spacings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;spacing&quot;</span><span class="p">])</span>
+        <span class="n">paddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">])</span>
+        <span class="n">tp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">tp_batch</span><span class="p">)</span>
+        <span class="n">fp</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fp_batch</span><span class="p">)</span>
+        <span class="n">fn</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fn_batch</span><span class="p">)</span>
+
+
+<span class="n">test_mpe</span> <span class="o">/=</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_A_loader</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test Mean PE: </span><span class="si">{</span><span class="n">test_mpe</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test TP: </span><span class="si">{</span><span class="n">test_tp</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test FP: </span><span class="si">{</span><span class="n">test_fp</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Test FN: </span><span class="si">{</span><span class="n">test_fn</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.metrics</span> <span class="kn">import</span> <span class="n">sdr</span>
+
+<span class="n">sdr_test</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">([</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">20</span><span class="p">],</span> <span class="n">true_landmarks</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+               <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+               <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
+<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">sdr_test</span><span class="p">:</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;SDR for </span><span class="si">{</span><span class="n">key</span><span class="si">}</span><span class="s2">mm: </span><span class="si">{</span><span class="n">sdr_test</span><span class="p">[</span><span class="n">key</span><span class="p">]</span><span class="si">:</span><span class="s2">.4f</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize.utils</span> <span class="kn">import</span> <span class="n">prediction_inspect_plot_multi_instance</span>
+<span class="n">model</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="n">prediction_inspect_plot_multi_instance</span><span class="p">(</span><span class="n">ds_test_A</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="nb">range</span><span class="p">(</span><span class="mi">3</span><span class="p">),</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">nn</span><span class="o">.</span><span class="n">Sigmoid</span><span class="p">())</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize</span> <span class="kn">import</span> <span class="n">plot_cpe</span>
+
+<span class="n">plot_cpe</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds_test_A</span><span class="o">.</span><span class="n">class_names</span><span class="p">,</span>
+            <span class="n">group</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="s2">&quot;CPE curve&quot;</span><span class="p">,</span> <span class="n">save_path</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">stat</span><span class="o">=</span><span class="s1">&#39;proportion&#39;</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;pixels&#39;</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;ecdf&#39;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+<div class="cell docutils container">
+<div class="cell_input docutils container">
+<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">landmarker.visualize.evaluation</span> <span class="kn">import</span> <span class="n">multi_instance_detection_report</span>
+
+<span class="n">multi_instance_detection_report</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+                                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">tp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">fn</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+                                <span class="n">dim_orig</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">dim_origs</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">pixel_spacings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+                                <span class="n">padding</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">paddings</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">class_names</span><span class="o">=</span><span class="n">ds_test_A</span><span class="o">.</span><span class="n">class_names</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
+          
+          
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023, Jef Jonkers
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
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+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
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+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Single-Class Landmark Detetection (Plant Centroids)</a><ul>
+<li><a class="reference internal" href="#setup-environment">Setup environment</a></li>
+<li><a class="reference internal" href="#setup-imports-and-variables">Setup imports and variables</a></li>
+<li><a class="reference internal" href="#loading-the-dataset">Loading the dataset</a></li>
+<li><a class="reference internal" href="#constructing-a-heatmap-generator">Constructing a heatmap generator</a></li>
+<li><a class="reference internal" href="#inspecting-the-dataset">Inspecting the dataset</a></li>
+<li><a class="reference internal" href="#training-and-initializing-the-spatialconfiguration-model">Training and initializing the SpatialConfiguration model</a><ul>
+<li><a class="reference internal" href="#initializing-the-model-optimizer-and-loss-function">Initializing the model, optimizer and loss function</a></li>
+<li><a class="reference internal" href="#setting-the-data-loaders-and-split-training-set">Setting the data loaders and split training set</a></li>
+<li><a class="reference internal" href="#training-the-model">Training the model</a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#evaluating-the-model">Evaluating the model</a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
+  </div>
+</div><script src="../../_static/documentation_options.js?v=5929fcd5"></script>
+    <script src="../../_static/doctools.js?v=888ff710"></script>
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\ No newline at end of file
diff --git a/_images/09a57b5f8a34cb3e15ee833a28bbce4d49a718356bbb6d638fadcb23565d1134.png b/_images/09a57b5f8a34cb3e15ee833a28bbce4d49a718356bbb6d638fadcb23565d1134.png
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diff --git a/_images/social_previews/summary__collections_examples_plant_centroids_5dd01138.png b/_images/social_previews/summary__collections_examples_plant_centroids_5dd01138.png
new file mode 100644
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+  <div class="visually-hidden">Hide navigation sidebar</div>
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+<label class="overlay toc-overlay" for="__toc">
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+    <img class="sidebar-logo only-dark" src="../_static//images/logo-dark-mode.svg" alt="Dark Logo"/>
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+  
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+<li class="toctree-l2"><a class="reference internal" href="../reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/losses.html">Losses Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/models.html">Models Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/train.html">Train Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/uncertainty.html">Uncertainty Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/utils.html">Utils Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/visualize.html">Visualize Module</a></li>
+</ul>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../dev/contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
+<li class="toctree-l1"><a class="reference internal" href="../dev/changelog.html">📈 Changelog</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../dev/code_of_conduct.html">🫶 Code of Conduct</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../dev/license.html">📝 License</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../reference/data.html">Data Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/losses.html">Losses Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/models.html">Models Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/train.html">Train Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/utils.html">Utils Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../reference/visualize.html">Visualize Module</a></li>
+</ul>
+
+</div>
+</div>
+
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
+          </svg>
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+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <h1>All modules for which code is available</h1>
+<ul><li><a href="landmarker/data/landmark_dataset.html">landmarker.data.landmark_dataset</a></li>
+<li><a href="landmarker/datasets/cepha400.html">landmarker.datasets.cepha400</a></li>
+<li><a href="landmarker/heatmap/decoder.html">landmarker.heatmap.decoder</a></li>
+<li><a href="landmarker/heatmap/generator.html">landmarker.heatmap.generator</a></li>
+<li><a href="landmarker/losses/losses.html">landmarker.losses.losses</a></li>
+<li><a href="landmarker/metrics/metrics.html">landmarker.metrics.metrics</a></li>
+<li><a href="landmarker/models/cholesky_hourglass.html">landmarker.models.cholesky_hourglass</a></li>
+<li><a href="landmarker/models/coord_conv.html">landmarker.models.coord_conv</a></li>
+<li><a href="landmarker/models/hourglass.html">landmarker.models.hourglass</a></li>
+<li><a href="landmarker/models/spatial_configuration_net.html">landmarker.models.spatial_configuration_net</a></li>
+<li><a href="landmarker/schedulers/adaloss.html">landmarker.schedulers.adaloss</a></li>
+<li><a href="landmarker/utils/preprocessing.html">landmarker.utils.preprocessing</a></li>
+<li><a href="landmarker/utils/utils.html">landmarker.utils.utils</a></li>
+<li><a href="landmarker/visualize/evaluation.html">landmarker.visualize.evaluation</a></li>
+<li><a href="landmarker/visualize/utils.html">landmarker.visualize.utils</a></li>
+</ul>
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
+          
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+        </div>
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+          <div class="right-details">
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+              <a class="muted-link " href="https://github.com/predict-idlab/landmarker" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
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diff --git a/_modules/landmarker/data/landmark_dataset.html b/_modules/landmarker/data/landmark_dataset.html
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+        <article role="main">
+          <h1>Source code for landmarker.data.landmark_dataset</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Module containing the ``LandmarkDataset`` class and its subclasses ``MaskDataset``</span>
+<span class="sd">and ``HeatmapDataset``. All three classe allow to create a dataset of images and landmarks.</span>
+<span class="sd">The three classes are subclasses of ``torch.utils.data.Dataset``.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="kn">from</span> <span class="nn">monai.transforms</span> <span class="kn">import</span> <span class="n">Compose</span><span class="p">,</span> <span class="n">LoadImage</span><span class="p">,</span> <span class="n">Resize</span><span class="p">,</span> <span class="n">Transpose</span>  <span class="c1"># type: ignore</span>
+<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">Dataset</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>  <span class="c1"># type: ignore</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">GaussianHeatmapGenerator</span><span class="p">,</span>
+    <span class="n">HeatmapGenerator</span><span class="p">,</span>
+    <span class="n">LaplacianHeatmapGenerator</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">landmarker.transforms.images</span> <span class="kn">import</span> <span class="n">resize_with_pad</span>
+<span class="kn">from</span> <span class="nn">landmarker.transforms.landmarks</span> <span class="kn">import</span> <span class="n">affine_landmarks</span><span class="p">,</span> <span class="n">resize_landmarks</span>
+<span class="kn">from</span> <span class="nn">landmarker.utils</span> <span class="kn">import</span> <span class="n">extract_roi</span>
+
+<span class="c1"># TODO: remove scale_intensity and grayscale options</span>
+
+
+<div class="viewcode-block" id="LandmarkDataset">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.LandmarkDataset">[docs]</a>
+<span class="k">class</span> <span class="nc">LandmarkDataset</span><span class="p">(</span><span class="n">Dataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ``LandmarkDataset`` is a subclass of ``torch.utils.data.Dataset``. It represents a dataset of</span>
+<span class="sd">    images and landmarks. The images can be provided as a list of paths to the images or as a list</span>
+<span class="sd">    of numpy arrays or as a numpy array/torch.Tensor.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        imgs (list[str] | list[np.array] | np.ndarray | torch.Tensor): list of paths to the images</span>
+<span class="sd">            or list of numpy arrays or numpy array/torch.Tensor.</span>
+<span class="sd">        landmarks (np.ndarray | torch.Tensor): landmarks of the images. If the landmarks are 2D: y, x</span>
+<span class="sd">            or 3D: z, y, x.</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the images. (defaults: 2)</span>
+<span class="sd">        pixel_spacing (Optional[np.ndarray | torch.Tensor]): pixel spacing of the images.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        class_names (Optional[list]): names of the landmarks. (defaults: None)</span>
+<span class="sd">        transform (Optional[Callable]): transformation to apply to the images and landmarks.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        store_imgs (bool): whether to store the images in memory or not. (defaults: True)</span>
+<span class="sd">        dim_img (Optional[tuple[int, int]]): dimension of the images. (defaults: None)</span>
+<span class="sd">        img_paths (Optional[list[str]]): list of paths to the images. (defaults: None)</span>
+<span class="sd">        resize_pad (bool): whether to resize the images and landmarks or not. (defaults: True)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LandmarkDataset.__init__">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.LandmarkDataset.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">store_imgs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">dim_img</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">img_paths</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">resize_pad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">spatial_dims</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;spatial_dims must be 2 or 3, got </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span> <span class="o">=</span> <span class="n">resize_pad</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="o">=</span> <span class="n">transform</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span> <span class="o">=</span> <span class="n">store_imgs</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="o">=</span> <span class="n">dim_img</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">LoadImage</span><span class="p">(</span><span class="n">image_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ensure_channel_first</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+                    <span class="n">Transpose</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">LoadImage</span><span class="p">(</span><span class="n">image_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ensure_channel_first</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+                    <span class="n">Transpose</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_init_landmarks</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_init_imgs</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">img_paths</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_init_pixel_spacing</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_init_landmarks</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span>
+                <span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="p">)</span>  <span class="c1"># (N, D) =&gt; (N, 1, D) only one class</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">copy</span><span class="p">())</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">landmarks</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;landmarks must be provided&quot;</span><span class="p">)</span>
+            <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;landmarks type not supported&quot;</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="n">class_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;landmark_</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">)</span> <span class="o">!=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;class_names must be of length landmarks.shape[1] or None&quot;</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">class_names</span> <span class="o">=</span> <span class="n">class_names</span>
+
+    <span class="k">def</span> <span class="nf">_init_pixel_spacing</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">,</span> <span class="p">(</span><span class="nb">tuple</span><span class="p">,</span> <span class="nb">list</span><span class="p">)):</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="nb">len</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span>
+                <span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;pixel_spacing must be of shape </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">elif</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">),):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span> <span class="o">=</span> <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+                    <span class="sa">f</span><span class="s2">&quot;Pixel_spacing must be of shape (N, </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">) or &quot;</span>
+                    <span class="s2">&quot;(</span><span class="si">{self.spatial_dims}</span><span class="s2">,) since spatial_dims is </span><span class="si">{self.spatial_dims}</span><span class="s2">.&quot;</span>
+                <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_init_imgs</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
+        <span class="n">img_paths</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="nb">list</span><span class="p">):</span>
+            <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span> <span class="o">=</span> <span class="n">imgs</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">))]</span>
+            <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span> <span class="o">=</span> <span class="n">imgs</span>  <span class="c1"># type: ignore</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;imgs type not supported, got </span><span class="si">{</span><span class="nb">type</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span> <span class="o">=</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">))]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span> <span class="o">=</span> <span class="n">img_paths</span> <span class="k">if</span> <span class="n">img_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="k">else</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="nb">str</span><span class="p">):</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span>
+
+        <span class="c1"># images need to be stored in memory and images are provided as paths</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span> <span class="ow">and</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;imgs&quot;</span><span class="p">):</span>
+            <span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read_all_imgs</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+            <span class="p">(</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">,</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">paddings</span><span class="p">,</span>
+            <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_resize_all_imgs_landmarks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">paddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">))</span>
+            <span class="k">if</span> <span class="ow">not</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;dim_original&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">))</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:]</span>
+                    <span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="fm">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">int</span><span class="p">:</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span><span class="p">:</span>
+                    <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+                    <span class="n">img</span><span class="p">,</span> <span class="n">pad</span> <span class="o">=</span> <span class="n">resize_with_pad</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pad</span><span class="p">)</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">,</span> <span class="n">padding</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">img</span> <span class="o">=</span> <span class="n">Resize</span><span class="p">(</span><span class="n">spatial_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)(</span><span class="n">img</span><span class="p">)</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+                    <span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+                <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">paddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">img</span><span class="p">,</span> <span class="n">landmark_t</span><span class="p">,</span> <span class="n">affine_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_img_landmark</span><span class="p">(</span>
+                <span class="n">img</span><span class="p">,</span> <span class="n">landmark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span>  <span class="c1"># type: ignore</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">landmark</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">img</span><span class="p">,</span>
+            <span class="s2">&quot;landmark&quot;</span><span class="p">:</span> <span class="n">landmark_t</span><span class="p">,</span>
+            <span class="s2">&quot;affine&quot;</span><span class="p">:</span> <span class="n">affine_matrix</span><span class="p">,</span>
+            <span class="s2">&quot;dim_original&quot;</span><span class="p">:</span> <span class="n">dim_original</span><span class="p">,</span>
+            <span class="s2">&quot;spacing&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="s2">&quot;padding&quot;</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_transform_img_landmark</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">landmark</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">transform</span><span class="p">:</span> <span class="n">Compose</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Transform the image and the landmark using the transformation arg transform.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            img (torch.Tensor): image to transform.</span>
+<span class="sd">            landmark (torch.Tensor): landmark to transform.</span>
+<span class="sd">            transform (Callable): transformation to apply to the image and the landmark.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            img_t (torch.Tensor): transformed image.</span>
+<span class="sd">            landmark_t (torch.Tensor): transformed landmark.</span>
+<span class="sd">            affine_matrix_push (torch.Tensor): push affine matrix of the transformation.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">transform</span><span class="p">({</span><span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">img</span><span class="p">})[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+        <span class="n">affine_matrix_pull</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;affine&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>  <span class="c1"># type: ignore</span>
+        <span class="n">affine_matrix_push</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">affine_matrix_pull</span><span class="p">)</span>
+        <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">affine_landmarks</span><span class="p">(</span><span class="n">landmark</span><span class="p">,</span> <span class="n">affine_matrix_push</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">img_t</span><span class="p">,</span> <span class="n">landmark_t</span><span class="p">,</span> <span class="n">affine_matrix_push</span>
+
+    <span class="k">def</span> <span class="nf">_read_all_imgs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Read all images in ``paths``.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            paths (list[str]): list of paths to the images.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            imgs (list[torch.Tensor]): list of images.</span>
+<span class="sd">            dim_original (torch.Tensor): original dimensions of the images.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reading </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">)</span><span class="si">}</span><span class="s2"> images...&quot;</span><span class="p">)</span>
+        <span class="n">imgs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tqdm</span><span class="p">(</span><span class="n">paths</span><span class="p">)):</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+            <span class="n">dim_original</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="n">imgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">dim_original</span>
+
+    <span class="k">def</span> <span class="nf">_resize_all_imgs_landmarks</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">dim_img</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Resize all images in ``imgs`` and resize all landmarks in ``landmarks``.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            imgs (torch.Tensor): images.</span>
+<span class="sd">            landmarks (torch.Tensor): landmarks of the images.</span>
+<span class="sd">            dim_img (tuple[int, int]): dimension of the images.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            imgs (list[torch.Tensor]): list of resized images.</span>
+<span class="sd">            landmarks (torch.Tensor): resized landmarks.</span>
+<span class="sd">            dim_original (torch.Tensor): original dimensions of the images.</span>
+<span class="sd">            paddings (torch.Tensor): paddings applied to the images.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Resizing </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">)</span><span class="si">}</span><span class="s2"> images and landmarks...&quot;</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
+        <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">))</span>
+        <span class="n">paddings</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">imgs</span><span class="p">))):</span>
+            <span class="n">dim_original</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span><span class="p">:</span>
+                <span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">padding</span> <span class="o">=</span> <span class="n">resize_with_pad</span><span class="p">(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dim_img</span><span class="p">)</span>
+                <span class="n">paddings</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">padding</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">Resize</span><span class="p">(</span><span class="n">dim_img</span><span class="p">)(</span><span class="n">imgs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+                <span class="n">paddings</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+            <span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">dim_img</span><span class="p">,</span> <span class="n">paddings</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        <span class="k">return</span> <span class="n">imgs</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">,</span> <span class="n">dim_original</span><span class="p">,</span> <span class="n">paddings</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">PatchDataset</span><span class="p">(</span><span class="n">LandmarkDataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ``PatchDataset`` is a subclass of ``LandmarkDataset``. It represents a dataset of images and</span>
+<span class="sd">    landmarks. The images can be provided as a list of paths to the images or as a list of numpy</span>
+<span class="sd">    arrays or as a numpy array/torch.Tensor. The landmarks can be provided as a numpy array or</span>
+<span class="sd">    torch.Tensor. The landmarks can be extracted from the images and stored as a torch.Tensor or</span>
+<span class="sd">    they can be genererated from provided landmarks. The images are cropped around the landmarks</span>
+<span class="sd">    and stored as torch.Tensor. The landmarks are the landmarks of the cropped images. The offset</span>
+<span class="sd">    is randomly generated in a range of [-range_aug_patch, range_aug_patch] and added to the</span>
+<span class="sd">    landmarks to create the cropped images. If the cropped images are not of the same size, they</span>
+<span class="sd">    are resized to the same size.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        imgs (list[str] | list[np.array] | np.ndarray | torch.Tensor): list of paths to the images</span>
+<span class="sd">            or list of numpy arrays or numpy array/torch.Tensor.</span>
+<span class="sd">        landmarks (np.ndarray | torch.Tensor): landmarks of the images. If the landmarks are 2D: y, x</span>
+<span class="sd">            or 3D: z, y, x.</span>
+<span class="sd">        index_landmark (int): index of the landmark to use to extract the patch. (defaults: 0)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the images. (defaults: 2)</span>
+<span class="sd">        pixel_spacing (Optional[np.ndarray | torch.Tensor]): pixel spacing of the images.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        class_names (Optional[list]): names of the landmarks. (defaults: None)</span>
+<span class="sd">        transform (Optional[Callable]): transformation to apply to the images and landmarks.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        store_imgs (bool): whether to store the images in memory or not. (defaults: True)</span>
+<span class="sd">        dim_patch (tuple[int, ...]): dimension of the patches. (defaults: (256, 256))</span>
+<span class="sd">        range_aug_patch (int): range of the random offset to apply to the landmarks to extract the</span>
+<span class="sd">            patches. (defaults: 64)</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">index_landmark</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">store_imgs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">dim_patch</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
+        <span class="n">range_aug_patch</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">range_aug_patch</span> <span class="o">=</span> <span class="n">range_aug_patch</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dim_patch</span> <span class="o">=</span> <span class="n">dim_patch</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">index_landmark</span> <span class="o">=</span> <span class="n">index_landmark</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">imgs</span><span class="o">=</span><span class="n">imgs</span><span class="p">,</span>
+            <span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+            <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">resize_pad</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="n">results_landmark_ds</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">landmark</span> <span class="o">=</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">][</span><span class="bp">self</span><span class="o">.</span><span class="n">index_landmark</span><span class="p">]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_aug_patch</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">offset</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">range_aug_patch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">range_aug_patch</span><span class="p">,</span> <span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">roi_middle</span> <span class="o">=</span> <span class="n">landmark</span> <span class="o">+</span> <span class="n">offset</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">offset</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+            <span class="n">roi_middle</span> <span class="o">=</span> <span class="n">landmark</span>
+        <span class="n">patch</span><span class="p">,</span> <span class="n">landmark_patch</span><span class="p">,</span> <span class="n">roi_corner_point</span> <span class="o">=</span> <span class="n">extract_roi</span><span class="p">(</span>
+            <span class="n">img</span><span class="p">,</span> <span class="n">roi_middle</span><span class="p">,</span> <span class="n">landmark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_patch</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">ensure_dim</span><span class="o">=</span><span class="kc">True</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">patch</span><span class="p">,</span>
+            <span class="s2">&quot;landmark&quot;</span><span class="p">:</span> <span class="n">landmark_patch</span><span class="p">,</span>
+            <span class="s2">&quot;offset&quot;</span><span class="p">:</span> <span class="n">offset</span><span class="p">,</span>
+            <span class="s2">&quot;landmark_original&quot;</span><span class="p">:</span> <span class="n">landmark</span><span class="p">,</span>
+            <span class="s2">&quot;roi_corner_point&quot;</span><span class="p">:</span> <span class="n">roi_corner_point</span><span class="p">,</span>
+            <span class="s2">&quot;affine&quot;</span><span class="p">:</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;affine&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;dim_original&quot;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_patch</span><span class="p">),</span>
+            <span class="s2">&quot;dim_original_full_image&quot;</span><span class="p">:</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;spacing&quot;</span><span class="p">:</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;spacing&quot;</span><span class="p">],</span>
+            <span class="s2">&quot;padding&quot;</span><span class="p">:</span> <span class="n">results_landmark_ds</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">],</span>
+        <span class="p">}</span>
+
+
+<div class="viewcode-block" id="MaskDataset">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.MaskDataset">[docs]</a>
+<span class="k">class</span> <span class="nc">MaskDataset</span><span class="p">(</span><span class="n">LandmarkDataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ``MaskDataset`` is a subclass of ``LandmarkDataset``. It represents a dataset of images</span>
+<span class="sd">    and landmarks. The images can be provided as a list of paths to the images or as a list of</span>
+<span class="sd">    numpy arrays or as a numpy array/torch.Tensor. The landmarks can be provided as a list of paths</span>
+<span class="sd">    to the masks or as a list of numpy arrays or as a numpy array/torch.Tensor. The masks must be</span>
+<span class="sd">    grayscale images with the landmarks represented by different values. The landmarks can be</span>
+<span class="sd">    extracted from the masks and stored as a torch.Tensor or they can be genererated from provided</span>
+<span class="sd">    landmarks. Note that if mask paths are provide this can be only be grayscale images and thus</span>
+<span class="sd">    the dataset can only contain one class of landmark per image.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        imgs (list[str] | list[np.array] | np.ndarray | torch.Tensor): list of paths to the images</span>
+<span class="sd">            or list of numpy arrays or numpy array/torch.Tensor.</span>
+<span class="sd">        landmarks (np.ndarray | torch.Tensor): landmarks of the images. If the landmarks are 2D: y, x</span>
+<span class="sd">            or 3D: z, y, x.</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the images. (defaults: 2)</span>
+<span class="sd">        pixel_spacing (Optional[np.ndarray | torch.Tensor]): pixel spacing of the images.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        class_names (Optional[list]): names of the landmarks. (defaults: None)</span>
+<span class="sd">        transform (Optional[Callable]): transformation to apply to the images and landmarks.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        store_imgs (bool): whether to store the images in memory or not. (defaults: True)</span>
+<span class="sd">        store_masks (bool): whether to store the masks in memory or not.</span>
+<span class="sd">            (defaults: False)</span>
+<span class="sd">        dim_img (Optional[tuple[int, int]]): dimension of the images. (defaults: None)</span>
+<span class="sd">        mask_paths (Optional[list[str]]): list of paths to the masks. (defaults: None)</span>
+<span class="sd">        nb_landmarks (int): number of landmarks in the masks. (defaults: 1)</span>
+<span class="sd">        img_paths (Optional[list[str]]): list of paths to the images. (defaults: None)</span>
+<span class="sd">        resize_pad (bool): whether to resize the images and landmarks or not. (defaults: True)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="MaskDataset.__init__">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.MaskDataset.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">store_imgs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">dim_img</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">mask_paths</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">img_paths</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">resize_pad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">=</span> <span class="n">nb_landmarks</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">mask_paths</span> <span class="o">=</span> <span class="n">mask_paths</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="o">=</span> <span class="n">dim_img</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span> <span class="o">=</span> <span class="n">resize_pad</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mask_loader</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">LoadImage</span><span class="p">(</span><span class="n">image_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ensure_channel_first</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+                    <span class="n">Transpose</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">mask_loader</span> <span class="o">=</span> <span class="n">Compose</span><span class="p">(</span>
+                <span class="p">[</span>
+                    <span class="n">LoadImage</span><span class="p">(</span><span class="n">image_only</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ensure_channel_first</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+                    <span class="n">Transpose</span><span class="p">(</span><span class="n">indices</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">landmarks</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+                <span class="n">imgs</span><span class="o">=</span><span class="n">imgs</span><span class="p">,</span>
+                <span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+                <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+                <span class="n">img_paths</span><span class="o">=</span><span class="n">img_paths</span><span class="p">,</span>
+                <span class="n">resize_pad</span><span class="o">=</span><span class="n">resize_pad</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_paths</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read_extract_landmarks</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">mask_paths</span><span class="p">)</span>
+            <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+                <span class="n">imgs</span><span class="o">=</span><span class="n">imgs</span><span class="p">,</span>
+                <span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+                <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+                <span class="n">img_paths</span><span class="o">=</span><span class="n">img_paths</span><span class="p">,</span>
+                <span class="n">resize_pad</span><span class="o">=</span><span class="n">resize_pad</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Either mask_paths or landmarks must be provided&quot;</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span><span class="p">:</span>
+                    <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+                    <span class="n">img</span><span class="p">,</span> <span class="n">pad</span> <span class="o">=</span> <span class="n">resize_with_pad</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">pad</span><span class="p">)</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">,</span> <span class="n">padding</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">img</span> <span class="o">=</span> <span class="n">Resize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)(</span><span class="n">img</span><span class="p">)</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+                    <span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+                <span class="n">dim_img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+                <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="n">dim_img</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span> <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dim_img</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                    <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">paddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">dim_img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="n">dim_img</span> <span class="o">=</span> <span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span> <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">dim_img</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                    <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">img</span><span class="p">,</span> <span class="n">landmark_t</span><span class="p">,</span> <span class="n">affine_matrix</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_img_landmark</span><span class="p">(</span>
+                <span class="n">img</span><span class="p">,</span> <span class="n">landmark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span>  <span class="c1"># type: ignore</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">landmark</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_mask</span><span class="p">(</span><span class="n">landmark_t</span><span class="p">,</span> <span class="n">dim_img</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">img</span><span class="p">,</span>
+            <span class="s2">&quot;mask&quot;</span><span class="p">:</span> <span class="n">mask</span><span class="p">,</span>
+            <span class="s2">&quot;landmark&quot;</span><span class="p">:</span> <span class="n">landmark_t</span><span class="p">,</span>
+            <span class="s2">&quot;affine&quot;</span><span class="p">:</span> <span class="n">affine_matrix</span><span class="p">,</span>
+            <span class="s2">&quot;dim_original&quot;</span><span class="p">:</span> <span class="n">dim_original</span><span class="p">,</span>
+            <span class="s2">&quot;spacing&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="s2">&quot;padding&quot;</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_create_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">landmark</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a mask from a landmark.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark (torch.Tensor): landmark of the image.</span>
+<span class="sd">            dim (tuple[int, ...]): dimension of the image.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            mask (torch.Tensor): mask.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">*</span><span class="n">dim</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                            <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="p">(</span>
+                            <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                            <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="p">):</span>
+                            <span class="n">mask</span><span class="p">[</span>
+                                <span class="n">i</span><span class="p">,</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                        <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="p">(</span>
+                        <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                        <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+                    <span class="p">):</span>
+                        <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">mask</span>
+
+    <span class="k">def</span> <span class="nf">_read_extract_landmarks</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Read masks and extract landmarks from these masks.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            paths (list[str]): list of paths to the masks.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            landmarks (torch.Tensor): landmarks.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reading and extracting masks from </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">)</span><span class="si">}</span><span class="s2"> images...&quot;</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span>
+            <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_read_extract_landmark</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+            <span class="n">landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmark</span><span class="p">)</span>
+        <span class="n">max_len</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="n">landmarks</span><span class="p">:</span>
+            <span class="n">max_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">max_len</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_len</span> <span class="o">!=</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">landmark</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">max_len</span> <span class="o">-</span> <span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">value</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="n">landmarks</span>
+            <span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># Remove sample channel if every class has only one landmark</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="p">[</span><span class="n">landmark</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="k">for</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="n">landmarks</span><span class="p">]</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">_read_extract_landmark</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Read a mask and extract the landmark from this mask.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            path (str): path to the mask.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            landmarks (torch.Tensor): landmark.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">mask_loader</span><span class="p">(</span><span class="n">path</span><span class="p">)</span>
+        <span class="n">landmarks</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_extract_landmark_from_mask</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">landmarks</span>
+
+    <span class="k">def</span> <span class="nf">_extract_landmark_from_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Extract the landmark from a mask.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            mask (torch.Tensor): mask.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            landmarks (torch.Tensor): landmark.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">landmarks_list</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">max_len</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">landmarks_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mask</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
+            <span class="n">max_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks_list</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">max_len</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">):</span>
+                <span class="n">landmarks_list</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">mask</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()</span><span class="o">.</span><span class="n">float</span><span class="p">())</span>
+                <span class="n">max_len</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_len</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;No landmark found in mask&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">max_len</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">landmarks_list</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">landmarks_list</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">landmark</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">)</span> <span class="k">for</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="n">landmarks</span>
+            <span class="p">]</span>
+            <span class="n">landmarks_list</span> <span class="o">=</span> <span class="p">[</span>
+                <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">landmark</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">max_len</span> <span class="o">-</span> <span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">value</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">landmark</span> <span class="ow">in</span> <span class="n">landmarks_list</span>
+            <span class="p">]</span>
+            <span class="n">landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span><span class="n">landmarks_list</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">landmarks</span></div>
+
+
+
+<span class="k">class</span> <span class="nc">PatchMaskDataset</span><span class="p">(</span><span class="n">PatchDataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ``PatchMaskDataset`` is a subclass of ``PatchDataset``. It represents a dataset of images and</span>
+<span class="sd">    landmarks. The images can be provided as a list of paths to the images or as a list of numpy</span>
+<span class="sd">    arrays or as a numpy array/torch.Tensor. The landmarks can be provided as a list of paths to</span>
+<span class="sd">    the masks or as a list of numpy arrays or as a numpy array/torch.Tensor. The masks must be</span>
+<span class="sd">    grayscale images with the landmarks represented by different values. The landmarks can be</span>
+<span class="sd">    extracted from the masks and stored as a torch.Tensor or they can be genererated from provided</span>
+<span class="sd">    landmarks. The images are cropped around the landmarks and stored as torch.Tensor. The landmarks</span>
+<span class="sd">    are the landmarks of the cropped images. The offset is randomly generated in a range of</span>
+<span class="sd">    [-range_aug_patch, range_aug_patch] and added to the landmarks to create the cropped images.</span>
+<span class="sd">    If the cropped images are not of the same size, they are resized to the same size.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">index_landmark</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">store_imgs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">dim_patch</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
+        <span class="n">range_aug_patch</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">imgs</span><span class="o">=</span><span class="n">imgs</span><span class="p">,</span>
+            <span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span>
+            <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+            <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_patch</span><span class="o">=</span><span class="n">dim_patch</span><span class="p">,</span>
+            <span class="n">range_aug_patch</span><span class="o">=</span><span class="n">range_aug_patch</span><span class="p">,</span>
+        <span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="n">batch</span> <span class="o">=</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__getitem__</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_mask</span><span class="p">(</span>
+            <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="p">:]</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="p">{</span><span class="o">**</span><span class="n">batch</span><span class="p">,</span> <span class="s2">&quot;mask&quot;</span><span class="p">:</span> <span class="n">mask</span><span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_create_mask</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">landmark</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a mask from a landmark.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark (torch.Tensor): landmark of the image.</span>
+<span class="sd">            dim (tuple[int, ...]): dimension of the image.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            mask (torch.Tensor): mask.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">*</span><span class="n">dim</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                    <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                            <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+                    <span class="k">else</span><span class="p">:</span>
+                        <span class="k">if</span> <span class="p">(</span>
+                            <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                            <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                            <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="p">):</span>
+                            <span class="n">mask</span><span class="p">[</span>
+                                <span class="n">i</span><span class="p">,</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                                <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">and</span> <span class="mi">0</span> <span class="o">&lt;=</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+                        <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="k">if</span> <span class="p">(</span>
+                        <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                        <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                        <span class="ow">and</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">dim</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span>
+                    <span class="p">):</span>
+                        <span class="n">mask</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">2</span><span class="p">])]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">mask</span>
+
+
+<div class="viewcode-block" id="HeatmapDataset">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.HeatmapDataset">[docs]</a>
+<span class="k">class</span> <span class="nc">HeatmapDataset</span><span class="p">(</span><span class="n">LandmarkDataset</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    ``HeatmapDataset`` is a subclass of ``LandmarkDataset``. It represents a dataset of images and</span>
+<span class="sd">    landmarks. The images can be provided as a list of paths to the images or as a list of numpy</span>
+<span class="sd">    arrays or as a numpy array/torch.Tensor. Th landmarks must be provided. The heatmaps are created</span>
+<span class="sd">    from the landmarks using a Gaussian or Laplacian function. When using the ``HeatmapDataset``,</span>
+<span class="sd">    images are always stored in memory.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        imgs (list[str] | list[np.array] | np.ndarray | torch.Tensor): list of paths to the images</span>
+<span class="sd">            or list of numpy arrays or numpy array/torch.Tensor.</span>
+<span class="sd">        landmarks (np.ndarray | torch.Tensor): landmarks of the images. If the landmarks are 2D: y, x</span>
+<span class="sd">            or 3D: z, y, x.</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the images. (defaults: 2)</span>
+<span class="sd">        pixel_spacing (Optional[np.ndarray | torch.Tensor]): pixel spacing of the images.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        class_names (Optional[list]): names of the landmarks. (defaults: None)</span>
+<span class="sd">        transform (Optional[Callable]): transformation to apply to the images and landmarks.</span>
+<span class="sd">            (defaults: None)</span>
+<span class="sd">        sigma (float | list[float]): sigma of the Gaussian or Laplacian function. (defaults: 5)</span>
+<span class="sd">        dim_img (Optional[tuple[int, int]]): dimension of the images. (defaults: None)</span>
+<span class="sd">        batch_size (int): batch size to use when creating the heatmaps. (defaults: 32)</span>
+<span class="sd">        background (bool): whether to add a background channel or not. (defaults: False)</span>
+<span class="sd">        all_points (bool): whether to add a channel for all points or not. (defaults: False)</span>
+<span class="sd">        gamma (Optional[float]): gamma of the Gaussian or Laplacian function. (defaults: None)</span>
+<span class="sd">        heatmap_fun (str): function to use to create the heatmaps. (defaults: &quot;gaussian&quot;)</span>
+<span class="sd">        heatmap_size (Optional[tuple[int, int]]): size of the heatmaps. (defaults: None)</span>
+<span class="sd">        img_paths (Optional[list[str]]): list of paths to the images. (defaults: None)</span>
+<span class="sd">        resize_pad (bool): whether to resize the images and landmarks or not. (defaults: True)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="HeatmapDataset.__init__">
+<a class="viewcode-back" href="../../../reference/data.html#landmarker.data.HeatmapDataset.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">imgs</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">transform</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Callable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">store_imgs</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">sigma</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
+        <span class="n">dim_img</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">32</span><span class="p">,</span>
+        <span class="n">background</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">all_points</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">heatmap_fun</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">,</span>
+        <span class="n">heatmap_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">img_paths</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">resize_pad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">imgs</span><span class="o">=</span><span class="n">imgs</span><span class="p">,</span>
+            <span class="n">landmarks</span><span class="o">=</span><span class="n">landmarks</span><span class="p">,</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+            <span class="n">class_names</span><span class="o">=</span><span class="n">class_names</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+            <span class="n">img_paths</span><span class="o">=</span><span class="n">img_paths</span><span class="p">,</span>
+            <span class="n">resize_pad</span><span class="o">=</span><span class="n">resize_pad</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="n">heatmap_size</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="n">heatmap_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">assert</span> <span class="p">(</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span>
+                <span class="p">),</span> <span class="s2">&quot;Heatmap size must be provided if imgs are not stored, and dim_img is None&quot;</span>
+                <span class="c1"># heatmap size becomes the size of the first original image</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                    <span class="n">heatmap_size</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">heatmap_size</span> <span class="o">=</span> <span class="p">(</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                        <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()),</span>
+                    <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">background</span> <span class="o">=</span> <span class="n">background</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">all_points</span> <span class="o">=</span> <span class="n">all_points</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="p">:</span> <span class="n">HeatmapGenerator</span>
+        <span class="k">if</span> <span class="n">heatmap_fun</span> <span class="o">==</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+                <span class="n">nb_landmarks</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">class_names</span><span class="p">),</span>
+                <span class="n">sigmas</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+                <span class="n">heatmap_size</span><span class="o">=</span><span class="n">heatmap_size</span><span class="p">,</span>
+                <span class="n">learnable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">background</span><span class="o">=</span><span class="n">background</span><span class="p">,</span>
+                <span class="n">all_points</span><span class="o">=</span><span class="n">all_points</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">heatmap_fun</span> <span class="o">==</span> <span class="s2">&quot;laplacian&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span> <span class="o">=</span> <span class="n">LaplacianHeatmapGenerator</span><span class="p">(</span>
+                <span class="n">nb_landmarks</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">class_names</span><span class="p">),</span>
+                <span class="n">sigmas</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+                <span class="n">heatmap_size</span><span class="o">=</span><span class="n">heatmap_size</span><span class="p">,</span>
+                <span class="n">learnable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                <span class="n">background</span><span class="o">=</span><span class="n">background</span><span class="p">,</span>
+                <span class="n">all_points</span><span class="o">=</span><span class="n">all_points</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Heatmap function not supported&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">batch_size</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">batch_size</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="mi">2</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">heatmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_heatmaps_batch</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="p">)</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_imgs</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">imgs</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmaps</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">landmark</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">landmarks</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">paddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_original</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+            <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">resize_pad</span><span class="p">:</span>
+                    <span class="n">dim_original</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:])</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+                    <span class="n">img</span><span class="p">,</span> <span class="p">(</span><span class="n">hp</span><span class="p">,</span> <span class="n">wp</span><span class="p">)</span> <span class="o">=</span> <span class="n">resize_with_pad</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">([</span><span class="n">hp</span><span class="p">,</span> <span class="n">wp</span><span class="p">])</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">,</span> <span class="n">padding</span>
+                    <span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">img</span> <span class="o">=</span> <span class="n">Resize</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span><span class="p">)(</span><span class="n">img</span><span class="p">)</span>
+                    <span class="n">landmark</span> <span class="o">=</span> <span class="n">resize_landmarks</span><span class="p">(</span>
+                        <span class="bp">self</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="n">dim_original</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">dim_img</span>
+                    <span class="p">)</span>
+                    <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_create_heatmap</span><span class="p">(</span><span class="n">landmark</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="p">(</span>
+                <span class="n">img</span><span class="p">,</span>
+                <span class="n">heatmap</span><span class="p">,</span>
+                <span class="n">landmark_t</span><span class="p">,</span>
+                <span class="n">affine_matrix</span><span class="p">,</span>
+            <span class="p">)</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_transform_img_heatmap_landmark</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">heatmap</span><span class="p">,</span> <span class="n">landmark</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">landmark</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">{</span>
+            <span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">img</span><span class="p">,</span>
+            <span class="s2">&quot;mask&quot;</span><span class="p">:</span> <span class="n">heatmap</span><span class="p">,</span>
+            <span class="s2">&quot;landmark&quot;</span><span class="p">:</span> <span class="n">landmark_t</span><span class="p">,</span>
+            <span class="s2">&quot;affine&quot;</span><span class="p">:</span> <span class="n">affine_matrix</span><span class="p">,</span>
+            <span class="s2">&quot;dim_original&quot;</span><span class="p">:</span> <span class="n">dim_original</span><span class="p">,</span>
+            <span class="s2">&quot;spacing&quot;</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">pixel_spacings</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span>
+            <span class="s2">&quot;padding&quot;</span><span class="p">:</span> <span class="n">padding</span><span class="p">,</span>
+        <span class="p">}</span>
+
+    <span class="k">def</span> <span class="nf">_transform_img_heatmap_landmark</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">img</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">landmark</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">transform</span><span class="p">:</span> <span class="n">Callable</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Transform the image, the heatmap and the landmark using the transformation</span>
+<span class="sd">        ``self.transform``.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            img (torch.Tensor): image to transform.</span>
+<span class="sd">            heatmap (torch.Tensor): heatmap to transform.</span>
+<span class="sd">            landmark (torch.Tensor): landmark to transform.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            img_t (torch.Tensor): transformed image.</span>
+<span class="sd">            heatmap_t (torch.Tensor): transformed heatmap.</span>
+<span class="sd">            landmark_t (torch.Tensor): transformed landmark.</span>
+<span class="sd">            affine_matrix_push (torch.Tensor): push affine matrix of the transformation.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">transform</span><span class="p">({</span><span class="s2">&quot;image&quot;</span><span class="p">:</span> <span class="n">img</span><span class="p">,</span> <span class="s2">&quot;mask&quot;</span><span class="p">:</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)})</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">heatmap_t</span> <span class="o">=</span> <span class="n">out</span><span class="p">[</span><span class="s2">&quot;mask&quot;</span><span class="p">]</span>
+        <span class="n">affine_matrix_pull</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">meta</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">&quot;affine&quot;</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="n">affine_matrix_push</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">affine_matrix_pull</span><span class="p">)</span>
+        <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">affine_landmarks</span><span class="p">(</span><span class="n">landmark</span><span class="p">,</span> <span class="n">affine_matrix_push</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">img_t</span><span class="p">,</span> <span class="n">heatmap_t</span><span class="p">,</span> <span class="n">landmark_t</span><span class="p">,</span> <span class="n">affine_matrix_push</span>
+
+    <span class="k">def</span> <span class="nf">_create_heatmaps</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create heatmaps from landmarks.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmarks (torch.Tensor): landmarks of the images.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            heatmaps (list[torch.Tensor]): list of heatmaps.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating heatmaps for </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span><span class="si">}</span><span class="s2"> images...&quot;</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+            <span class="n">heatmaps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_create_heatmap</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+        <span class="k">return</span> <span class="n">heatmaps</span>
+
+    <span class="k">def</span> <span class="nf">_create_heatmaps_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create heatmaps from landmarks using batches.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmarks (torch.Tensor): landmarks of the images.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            heatmaps (torch.Tensor): heatmaps.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Creating heatmaps for </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span><span class="si">}</span><span class="s2"> images... (batch size: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="si">}</span><span class="s2">)&quot;</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="p">),</span>
+                <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">class_names</span><span class="p">)</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">all_points</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">background</span><span class="p">),</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)):</span>
+            <span class="n">heatmaps</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="p">:</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="p">(</span>
+                <span class="n">landmarks</span><span class="p">[</span><span class="n">i</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="p">:</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span><span class="p">]</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">heatmaps</span><span class="p">[(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="p">:]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="p">(</span>
+                    <span class="n">landmarks</span><span class="p">[(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">batch_size</span> <span class="p">:]</span>
+                <span class="p">)</span>
+            <span class="k">except</span> <span class="ne">RuntimeError</span><span class="p">:</span>
+                <span class="n">heatmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">heatmaps</span>
+
+    <span class="k">def</span> <span class="nf">_create_heatmap</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">landmark</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Create a heatmap from a landmark.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark (torch.Tensor): landmark of the image.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            heatmap (torch.Tensor): heatmap.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span></div>
+
+</pre></div>
+        </article>
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diff --git a/_modules/landmarker/datasets/cepha400.html b/_modules/landmarker/datasets/cepha400.html
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+          <h1>Source code for landmarker.datasets.cepha400</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">This module contains the functions to load the ISBI 2015 cephalometric landmark detection challenge</span>
+<span class="sd">dataset.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">zipfile</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">opendatasets</span> <span class="k">as</span> <span class="nn">od</span>  <span class="c1"># type: ignore</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>  <span class="c1"># type: ignore</span>
+<span class="kn">import</span> <span class="nn">rarfile</span>  <span class="c1"># type: ignore</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.data.landmark_dataset</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">HeatmapDataset</span><span class="p">,</span>
+    <span class="n">LandmarkDataset</span><span class="p">,</span>
+    <span class="n">MaskDataset</span><span class="p">,</span>
+    <span class="n">PatchDataset</span><span class="p">,</span>
+<span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">junior</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">cv</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns the paths to the images and the landmarks of the CEPH dataset from the ISBI 2014 &amp;</span>
+<span class="sd">    2015 challenges. But not from the kaggle dataset but from this repository:</span>
+<span class="sd">        https://figshare.com/s/37ec464af8e81ae6ebbf?file=5466581</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        path_dir (str): The path to the directory where the dataset should be stored.</span>
+<span class="sd">        junior (bool, optional): Whether to use the junior or senior annotator. Defaults to False.</span>
+<span class="sd">        cv (bool, optional): Whether to use the cross validation splits from the paper. Defaults to</span>
+<span class="sd">            True.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015&quot;</span><span class="p">):</span>
+        <span class="n">od</span><span class="o">.</span><span class="n">download</span><span class="p">(</span>
+            <span class="s2">&quot;https://figshare.com/ndownloader/articles/3471833?private_link=37ec464af8e81ae6ebbf&quot;</span><span class="p">,</span>
+            <span class="n">path_dir</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="c1"># Change the name of the folder to ISBI2015</span>
+        <span class="k">with</span> <span class="n">zipfile</span><span class="o">.</span><span class="n">ZipFile</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/3471833.zip&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zip_ref</span><span class="p">:</span>
+            <span class="n">zip_ref</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/3471833.zip&quot;</span><span class="p">)</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="k">with</span> <span class="n">rarfile</span><span class="o">.</span><span class="n">RarFile</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/AnnotationsByMD.rar&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zip_ref</span><span class="p">:</span>
+                <span class="n">zip_ref</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015&quot;</span><span class="p">)</span>
+            <span class="k">with</span> <span class="n">rarfile</span><span class="o">.</span><span class="n">RarFile</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/RawImage.rar&quot;</span><span class="p">,</span> <span class="s2">&quot;r&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">zip_ref</span><span class="p">:</span>
+                <span class="n">zip_ref</span><span class="o">.</span><span class="n">extractall</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015&quot;</span><span class="p">)</span>
+        <span class="k">except</span> <span class="n">rarfile</span><span class="o">.</span><span class="n">BadRarFile</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="n">rarfile</span><span class="o">.</span><span class="n">BadRarFile</span><span class="p">(</span>
+                <span class="s2">&quot;Possibly you need to install unrar. On linux: sudo apt install unrar&quot;</span>
+            <span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/AnnotationsByMD.rar&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/RawImage.rar&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/EvaluationCode.rar&quot;</span><span class="p">)</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/cv_payer&quot;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">5</span><span class="p">):</span>
+            <span class="n">od</span><span class="o">.</span><span class="n">download</span><span class="p">(</span>
+                <span class="s2">&quot;https://raw.githubusercontent.com/christianpayer/MedicalDataAugmentationTool&quot;</span>
+                <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;-HeatmapUncertainty/main/setup_ann/all_landmarks/cv/</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span>
+                <span class="n">path_dir</span> <span class="o">+</span> <span class="s2">&quot;/ISBI2015/cv_payer&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">junior</span><span class="p">:</span>
+        <span class="n">annotator</span> <span class="o">=</span> <span class="s2">&quot;junior&quot;</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">annotator</span> <span class="o">=</span> <span class="s2">&quot;senior&quot;</span>
+    <span class="n">landmarks_list</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">400</span><span class="p">):</span>
+        <span class="n">landmarks_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span>
+                <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/400_</span><span class="si">{</span><span class="n">annotator</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span>
+                <span class="n">sep</span><span class="o">=</span><span class="s2">&quot;,&quot;</span><span class="p">,</span>
+                <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="p">)[:</span><span class="mi">19</span><span class="p">]</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+        <span class="p">)</span>
+    <span class="n">landmarks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span><span class="n">landmarks_list</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">19</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="n">landmarks</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="n">indices_fold_1</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/cv_payer/</span><span class="si">{</span><span class="mi">1</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">indices_fold_2</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/cv_payer/</span><span class="si">{</span><span class="mi">2</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">indices_fold_3</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/cv_payer/</span><span class="si">{</span><span class="mi">3</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">indices_fold_4</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">pd</span><span class="o">.</span><span class="n">read_table</span><span class="p">(</span><span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/cv_payer/</span><span class="si">{</span><span class="mi">4</span><span class="si">}</span><span class="s2">.txt&quot;</span><span class="p">,</span> <span class="n">header</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+            <span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
+        <span class="p">)</span>
+        <span class="n">image_paths_fold1</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/TrainingData/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices_fold_1</span>
+        <span class="p">]</span>
+        <span class="n">image_paths_fold2</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/TrainingData/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices_fold_2</span>
+        <span class="p">]</span>
+        <span class="n">image_paths_fold3</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/TrainingData/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices_fold_3</span>
+        <span class="p">]</span>
+        <span class="n">image_paths_fold4</span> <span class="o">=</span> <span class="p">[</span>
+            <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/TrainingData/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices_fold_4</span>
+        <span class="p">]</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">image_paths_fold1</span><span class="p">,</span>
+            <span class="n">image_paths_fold2</span><span class="p">,</span>
+            <span class="n">image_paths_fold3</span><span class="p">,</span>
+            <span class="n">image_paths_fold4</span><span class="p">,</span>
+            <span class="n">landmarks</span><span class="p">[</span><span class="n">indices_fold_1</span><span class="p">],</span>
+            <span class="n">landmarks</span><span class="p">[</span><span class="n">indices_fold_2</span><span class="p">],</span>
+            <span class="n">landmarks</span><span class="p">[</span><span class="n">indices_fold_3</span><span class="p">],</span>
+            <span class="n">landmarks</span><span class="p">[</span><span class="n">indices_fold_4</span><span class="p">],</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">image_paths_fold1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">image_paths_fold2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">image_paths_fold3</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">image_paths_fold4</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+        <span class="p">)</span>
+
+    <span class="n">image_paths_train</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/TrainingData/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">151</span><span class="p">)</span>
+    <span class="p">]</span>
+    <span class="n">landmarks_train</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[:</span><span class="mi">150</span><span class="p">]</span>
+    <span class="n">pixel_spacings_train</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks_train</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">image_paths_test1</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/Test1Data/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">151</span><span class="p">,</span> <span class="mi">301</span><span class="p">)</span>
+    <span class="p">]</span>
+    <span class="n">landmarks_test1</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="mi">150</span><span class="p">:</span><span class="mi">300</span><span class="p">]</span>
+    <span class="n">pixel_spacings_test1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks_test1</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">image_paths_test2</span> <span class="o">=</span> <span class="p">[</span>
+        <span class="n">path_dir</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/ISBI2015/RawImage/Test2Data/</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="si">}</span><span class="s2">.bmp&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">301</span><span class="p">,</span> <span class="mi">401</span><span class="p">)</span>
+    <span class="p">]</span>
+    <span class="n">landmarks_test2</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="mi">300</span><span class="p">:]</span>
+    <span class="n">pixel_spacings_test2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">]])</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks_test2</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">image_paths_train</span><span class="p">,</span>
+        <span class="n">image_paths_test1</span><span class="p">,</span>
+        <span class="n">image_paths_test2</span><span class="p">,</span>
+        <span class="n">landmarks_train</span><span class="p">,</span>
+        <span class="n">landmarks_test1</span><span class="p">,</span>
+        <span class="n">landmarks_test2</span><span class="p">,</span>
+        <span class="n">pixel_spacings_train</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test1</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test2</span><span class="p">,</span>
+    <span class="p">)</span>
+
+
+<div class="viewcode-block" id="get_cepha_landmark_datasets">
+<a class="viewcode-back" href="../../../reference/datasets.html#landmarker.datasets.get_cepha_landmark_datasets">[docs]</a>
+<span class="k">def</span> <span class="nf">get_cepha_landmark_datasets</span><span class="p">(</span>
+    <span class="n">path_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">train_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">inference_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">store_imgs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">dim_img</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">junior</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">single_dataset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
+    <span class="n">LandmarkDataset</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">]</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a LandmarkDataset objects with the CEPH dataset, a combination of the ISBI 2014 &amp;</span>
+<span class="sd">    2015 challenges. The dataset is split into train, test1 and test2. The same approach as in</span>
+<span class="sd">    &quot;CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection&quot; - JIAHOONG QIAN</span>
+<span class="sd">        et al. is used.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">single_dataset</span> <span class="ow">and</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot have single dataset and cross validation at the same time.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="p">(</span>
+            <span class="n">image_paths_fold1</span><span class="p">,</span>
+            <span class="n">image_paths_fold2</span><span class="p">,</span>
+            <span class="n">image_paths_fold3</span><span class="p">,</span>
+            <span class="n">image_paths_fold4</span><span class="p">,</span>
+            <span class="n">landmarks_fold1</span><span class="p">,</span>
+            <span class="n">landmarks_fold2</span><span class="p">,</span>
+            <span class="n">landmarks_fold3</span><span class="p">,</span>
+            <span class="n">landmarks_fold4</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+        <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">LandmarkDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold1</span><span class="p">,</span>
+                <span class="n">landmarks_fold1</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">LandmarkDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold2</span><span class="p">,</span>
+                <span class="n">landmarks_fold2</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">LandmarkDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold3</span><span class="p">,</span>
+                <span class="n">landmarks_fold3</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">LandmarkDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold4</span><span class="p">,</span>
+                <span class="n">landmarks_fold4</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">image_paths_train</span><span class="p">,</span>
+        <span class="n">image_paths_test1</span><span class="p">,</span>
+        <span class="n">image_paths_test2</span><span class="p">,</span>
+        <span class="n">landmarks_train</span><span class="p">,</span>
+        <span class="n">landmarks_test1</span><span class="p">,</span>
+        <span class="n">landmarks_test2</span><span class="p">,</span>
+        <span class="n">pixel_spacings_train</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test1</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">single_dataset</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">LandmarkDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span> <span class="o">+</span> <span class="n">image_paths_test1</span> <span class="o">+</span> <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">landmarks_train</span><span class="p">,</span> <span class="n">landmarks_test1</span><span class="p">,</span> <span class="n">landmarks_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">pixel_spacings_train</span><span class="p">,</span> <span class="n">pixel_spacings_test1</span><span class="p">,</span> <span class="n">pixel_spacings_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+            <span class="p">),</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">LandmarkDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span><span class="p">,</span>
+            <span class="n">landmarks_train</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_train</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">LandmarkDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test1</span><span class="p">,</span>
+            <span class="n">landmarks_test1</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test1</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">LandmarkDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">landmarks_test2</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test2</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="n">dim_img</span><span class="o">=</span><span class="n">dim_img</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="get_cepha_heatmap_datasets">
+<a class="viewcode-back" href="../../../reference/datasets.html#landmarker.datasets.get_cepha_heatmap_datasets">[docs]</a>
+<span class="k">def</span> <span class="nf">get_cepha_heatmap_datasets</span><span class="p">(</span>
+    <span class="n">path_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">train_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">inference_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">sigma</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">junior</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">single_dataset</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">cv</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
+    <span class="n">HeatmapDataset</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">HeatmapDataset</span><span class="p">]</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">HeatmapDataset</span><span class="p">]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a HeatmapDataset with the ISBI 2015 cephalogram challenge dataset. The dataset is</span>
+<span class="sd">    split into train, test1 and test2. The same approach as in &quot;CephaNN: A Multi-Head Attention</span>
+<span class="sd">    Network for Cephalometric Landmark Detection&quot; - JIAHOONG QIAN et al. is used.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        path_dir (str): The path to the directory where the dataset should be stored.</span>
+<span class="sd">        train_transform (Optional[Callable], optional): A transformation to apply to the images and</span>
+<span class="sd">            landmarks during training. Defaults to None.</span>
+<span class="sd">        inference_transform (Optional[Callable], optional): A transformation to apply to the images</span>
+<span class="sd">            and landmarks during inference. Defaults to None.</span>
+<span class="sd">        sigma (int, optional): The sigma value for the gaussian kernel. Defaults to 1.</span>
+<span class="sd">        junior (bool, optional): Whether to use the junior or senior annotator. Defaults to False.</span>
+<span class="sd">        single_dataset (bool, optional): Whether to return a single dataset with all images and</span>
+<span class="sd">            landmarks. Defaults to False.</span>
+<span class="sd">        **kwargs: Additional keyword arguments for the HeatmapDataset.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">single_dataset</span> <span class="ow">and</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot have single dataset and cross validation at the same time.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="p">(</span>
+            <span class="n">image_paths_fold1</span><span class="p">,</span>
+            <span class="n">image_paths_fold2</span><span class="p">,</span>
+            <span class="n">image_paths_fold3</span><span class="p">,</span>
+            <span class="n">image_paths_fold4</span><span class="p">,</span>
+            <span class="n">landmarks_fold1</span><span class="p">,</span>
+            <span class="n">landmarks_fold2</span><span class="p">,</span>
+            <span class="n">landmarks_fold3</span><span class="p">,</span>
+            <span class="n">landmarks_fold4</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+        <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">HeatmapDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold1</span><span class="p">,</span>
+                <span class="n">landmarks_fold1</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">HeatmapDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold2</span><span class="p">,</span>
+                <span class="n">landmarks_fold2</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">HeatmapDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold3</span><span class="p">,</span>
+                <span class="n">landmarks_fold3</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">HeatmapDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold4</span><span class="p">,</span>
+                <span class="n">landmarks_fold4</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">image_paths_train</span><span class="p">,</span>
+        <span class="n">image_paths_test1</span><span class="p">,</span>
+        <span class="n">image_paths_test2</span><span class="p">,</span>
+        <span class="n">landmarks_train</span><span class="p">,</span>
+        <span class="n">landmarks_test1</span><span class="p">,</span>
+        <span class="n">landmarks_test2</span><span class="p">,</span>
+        <span class="n">pixel_spacings_train</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test1</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">single_dataset</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">HeatmapDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span> <span class="o">+</span> <span class="n">image_paths_test1</span> <span class="o">+</span> <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">landmarks_train</span><span class="p">,</span> <span class="n">landmarks_test1</span><span class="p">,</span> <span class="n">landmarks_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">pixel_spacings_train</span><span class="p">,</span> <span class="n">pixel_spacings_test1</span><span class="p">,</span> <span class="n">pixel_spacings_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+            <span class="p">),</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">HeatmapDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span><span class="p">,</span>
+            <span class="n">landmarks_train</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_train</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">HeatmapDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test1</span><span class="p">,</span>
+            <span class="n">landmarks_test1</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test1</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">HeatmapDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">landmarks_test2</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test2</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">sigma</span><span class="o">=</span><span class="n">sigma</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="get_cepha_mask_datasets">
+<a class="viewcode-back" href="../../../reference/datasets.html#landmarker.datasets.get_cepha_mask_datasets">[docs]</a>
+<span class="k">def</span> <span class="nf">get_cepha_mask_datasets</span><span class="p">(</span>
+    <span class="n">path_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">train_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">inference_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">junior</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">single_dataset</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">cv</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
+    <span class="n">MaskDataset</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">MaskDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">]</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">MaskDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a MaskDataset with the ISBI 2015 cephalogram challenge dataset. The dataset is</span>
+<span class="sd">    split into train, test1 and test2. The same approach as in &quot;CephaNN: A Multi-Head Attention</span>
+<span class="sd">    Network for Cephalometric Landmark Detection&quot; - JIAHOONG QIAN et al. is used.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        path_dir (str): The path to the directory where the dataset should be stored.</span>
+<span class="sd">        train_transform (Optional[Callable], optional): A transformation to apply to the images and</span>
+<span class="sd">            masks during training. Defaults to None.</span>
+<span class="sd">        inference_transform (Optional[Callable], optional): A transformation to apply to the images</span>
+<span class="sd">            and masks during inference. Defaults to None.</span>
+<span class="sd">        junior (bool, optional): Whether to use the junior or senior annotator. Defaults to False.</span>
+<span class="sd">        single_dataset (bool, optional): Whether to return a single dataset with all images and</span>
+<span class="sd">            landmarks. Defaults to False.</span>
+<span class="sd">        **kwargs: Additional keyword arguments for the MaskDataset.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">single_dataset</span> <span class="ow">and</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot have single dataset and cross validation at the same time.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="p">(</span>
+            <span class="n">image_paths_fold1</span><span class="p">,</span>
+            <span class="n">image_paths_fold2</span><span class="p">,</span>
+            <span class="n">image_paths_fold3</span><span class="p">,</span>
+            <span class="n">image_paths_fold4</span><span class="p">,</span>
+            <span class="n">landmarks_fold1</span><span class="p">,</span>
+            <span class="n">landmarks_fold2</span><span class="p">,</span>
+            <span class="n">landmarks_fold3</span><span class="p">,</span>
+            <span class="n">landmarks_fold4</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+        <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">MaskDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold1</span><span class="p">,</span>
+                <span class="n">landmarks_fold1</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">MaskDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold2</span><span class="p">,</span>
+                <span class="n">landmarks_fold2</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">MaskDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold3</span><span class="p">,</span>
+                <span class="n">landmarks_fold3</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">MaskDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold4</span><span class="p">,</span>
+                <span class="n">landmarks_fold4</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">image_paths_train</span><span class="p">,</span>
+        <span class="n">image_paths_test1</span><span class="p">,</span>
+        <span class="n">image_paths_test2</span><span class="p">,</span>
+        <span class="n">landmarks_train</span><span class="p">,</span>
+        <span class="n">landmarks_test1</span><span class="p">,</span>
+        <span class="n">landmarks_test2</span><span class="p">,</span>
+        <span class="n">pixel_spacings_train</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test1</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">single_dataset</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">MaskDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span> <span class="o">+</span> <span class="n">image_paths_test1</span> <span class="o">+</span> <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">landmarks_train</span><span class="p">,</span> <span class="n">landmarks_test1</span><span class="p">,</span> <span class="n">landmarks_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">pixel_spacings_train</span><span class="p">,</span> <span class="n">pixel_spacings_test1</span><span class="p">,</span> <span class="n">pixel_spacings_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+            <span class="p">),</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">MaskDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span><span class="p">,</span>
+            <span class="n">landmarks_train</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_train</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">MaskDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test1</span><span class="p">,</span>
+            <span class="n">landmarks_test1</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test1</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">MaskDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">landmarks_test2</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test2</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="get_cepha_patch_datasets">
+<a class="viewcode-back" href="../../../reference/datasets.html#landmarker.datasets.get_cepha_patch_datasets">[docs]</a>
+<span class="k">def</span> <span class="nf">get_cepha_patch_datasets</span><span class="p">(</span>
+    <span class="n">path_dir</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span>
+    <span class="n">index_landmark</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">train_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">inference_transform</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+    <span class="n">store_imgs</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+    <span class="n">junior</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">single_dataset</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+    <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Returns a PatchDataset objects with the CEPH dataset, a combination of the ISBI 2014 &amp;</span>
+<span class="sd">    2015 challenges. The dataset is split into train, test1 and test2. The same approach as in</span>
+<span class="sd">    &quot;CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection&quot; - JIAHOONG QIAN</span>
+<span class="sd">        et al. is used.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">single_dataset</span> <span class="ow">and</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Cannot have single dataset and cross validation at the same time.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">cv</span><span class="p">:</span>
+        <span class="p">(</span>
+            <span class="n">image_paths_fold1</span><span class="p">,</span>
+            <span class="n">image_paths_fold2</span><span class="p">,</span>
+            <span class="n">image_paths_fold3</span><span class="p">,</span>
+            <span class="n">image_paths_fold4</span><span class="p">,</span>
+            <span class="n">landmarks_fold1</span><span class="p">,</span>
+            <span class="n">landmarks_fold2</span><span class="p">,</span>
+            <span class="n">landmarks_fold3</span><span class="p">,</span>
+            <span class="n">landmarks_fold4</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+            <span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+        <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">PatchDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold1</span><span class="p">,</span>
+                <span class="n">landmarks_fold1</span><span class="p">,</span>
+                <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold1</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">PatchDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold2</span><span class="p">,</span>
+                <span class="n">landmarks_fold2</span><span class="p">,</span>
+                <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold2</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">PatchDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold3</span><span class="p">,</span>
+                <span class="n">landmarks_fold3</span><span class="p">,</span>
+                <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold3</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">PatchDataset</span><span class="p">(</span>
+                <span class="n">image_paths_fold4</span><span class="p">,</span>
+                <span class="n">landmarks_fold4</span><span class="p">,</span>
+                <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+                <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_fold4</span><span class="p">,</span>
+                <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+                <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+                <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">)</span>
+    <span class="p">(</span>
+        <span class="n">image_paths_train</span><span class="p">,</span>
+        <span class="n">image_paths_test1</span><span class="p">,</span>
+        <span class="n">image_paths_test2</span><span class="p">,</span>
+        <span class="n">landmarks_train</span><span class="p">,</span>
+        <span class="n">landmarks_test1</span><span class="p">,</span>
+        <span class="n">landmarks_test2</span><span class="p">,</span>
+        <span class="n">pixel_spacings_train</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test1</span><span class="p">,</span>
+        <span class="n">pixel_spacings_test2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="n">get_cepha_dataset</span><span class="p">(</span><span class="n">path_dir</span><span class="p">,</span> <span class="n">junior</span><span class="o">=</span><span class="n">junior</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">single_dataset</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">PatchDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span> <span class="o">+</span> <span class="n">image_paths_test1</span> <span class="o">+</span> <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">([</span><span class="n">landmarks_train</span><span class="p">,</span> <span class="n">landmarks_test1</span><span class="p">,</span> <span class="n">landmarks_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span>
+            <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">(</span>
+                <span class="p">[</span><span class="n">pixel_spacings_train</span><span class="p">,</span> <span class="n">pixel_spacings_test1</span><span class="p">,</span> <span class="n">pixel_spacings_test2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span>
+            <span class="p">),</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">PatchDataset</span><span class="p">(</span>
+            <span class="n">image_paths_train</span><span class="p">,</span>
+            <span class="n">landmarks_train</span><span class="p">,</span>
+            <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_train</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">train_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">PatchDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test1</span><span class="p">,</span>
+            <span class="n">landmarks_test1</span><span class="p">,</span>
+            <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test1</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+        <span class="n">PatchDataset</span><span class="p">(</span>
+            <span class="n">image_paths_test2</span><span class="p">,</span>
+            <span class="n">landmarks_test2</span><span class="p">,</span>
+            <span class="n">index_landmark</span><span class="o">=</span><span class="n">index_landmark</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacings_test2</span><span class="p">,</span>
+            <span class="n">transform</span><span class="o">=</span><span class="n">inference_transform</span><span class="p">,</span>
+            <span class="n">store_imgs</span><span class="o">=</span><span class="n">store_imgs</span><span class="p">,</span>
+            <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+        <span class="p">),</span>
+    <span class="p">)</span></div>
+
+</pre></div>
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
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diff --git a/_modules/landmarker/heatmap/decoder.html b/_modules/landmarker/heatmap/decoder.html
new file mode 100644
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+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../../../">
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+
+    <link rel="shortcut icon" href="../../../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
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+          <h1>Source code for landmarker.heatmap.decoder</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Decoder module for retrieving coordinates and other statistics from heatmaps.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">import</span> <span class="nn">torch.nn.functional</span> <span class="k">as</span> <span class="nn">F</span>
+<span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">optimize</span>  <span class="c1"># type: ignore</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="n">GaussianHeatmapGenerator</span>
+
+
+<div class="viewcode-block" id="coord_argmax">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.coord_argmax">[docs]</a>
+<span class="k">def</span> <span class="nf">coord_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the coordinates of the maximum value of the heatmap</span>
+<span class="sd">    for each batch and channel (landmark).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Think about way to deal with ties (multiple maxima) in the output</span>
+    <span class="c1">#    now we just take the first one</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">idx_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">idx_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">idx_pred</span> <span class="o">//</span> <span class="n">w</span><span class="p">,</span> <span class="n">idx_pred</span> <span class="o">%</span> <span class="n">w</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:]</span>
+        <span class="n">idx_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">idx_pred</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">idx_pred</span> <span class="o">//</span> <span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">w</span><span class="p">),</span> <span class="p">(</span><span class="n">idx_pred</span> <span class="o">%</span> <span class="p">(</span><span class="n">h</span> <span class="o">*</span> <span class="n">w</span><span class="p">))</span> <span class="o">//</span> <span class="n">w</span><span class="p">,</span> <span class="n">idx_pred</span> <span class="o">%</span> <span class="n">w</span><span class="p">),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span>
+        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">idx_pred</span><span class="o">.</span><span class="n">int</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="coord_local_soft_argmax">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.coord_local_soft_argmax">[docs]</a>
+<span class="k">def</span> <span class="nf">coord_local_soft_argmax</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span> <span class="n">t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns coordiantes through applying the local soft-argmax function on the heatmaps.</span>
+<span class="sd">        Source: &quot;Subpixel Heatmap Regression for Facial Landmark Localization&quot; - Bulat et al. (2021)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        window (int): local window size</span>
+<span class="sd">        t (float ): temperature that controls the resulting probability map</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">coord_local_weighted</span><span class="p">(</span>
+        <span class="n">heatmap</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="n">t</span>
+    <span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">coord_local_weighted</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">9</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns coordiantes through applying the local soft-argmax function on the heatmaps.</span>
+<span class="sd">        Source: &quot;Subpixel Heatmap Regression for Facial Landmark Localization&quot; - Bulat et al. (2021)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        window (int): local window size</span>
+<span class="sd">        t (float ): temperature that controls the resulting probability map</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">padded_heatmap</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">padded_heatmap</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="n">argmax_coords</span> <span class="o">=</span> <span class="n">coord_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">)</span> <span class="o">+</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">b1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">c1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                        <span class="n">mask</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="n">argmax_coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span> <span class="n">argmax_coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">masked_output</span> <span class="o">=</span> <span class="n">padded_heatmap</span><span class="p">[</span><span class="n">mask</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">window</span><span class="p">)</span>
+        <span class="n">local_coords</span> <span class="o">=</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">masked_output</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="n">t</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">local_coords</span> <span class="o">+</span> <span class="p">(</span><span class="n">argmax_coords</span> <span class="o">-</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">padding</span> <span class="o">=</span> <span class="p">[</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">spatial_dims</span><span class="p">)</span>  <span class="c1"># Pad all spatial dimensions</span>
+        <span class="n">padded_heatmap</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">padding</span><span class="p">)</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">padded_heatmap</span><span class="o">.</span><span class="n">shape</span>
+
+        <span class="n">argmax_coords</span> <span class="o">=</span> <span class="n">coord_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span> <span class="o">+</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span>
+
+        <span class="n">mask</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">b1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">c1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
+                <span class="k">for</span> <span class="n">z</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">),</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">):</span>
+                            <span class="n">mask</span><span class="p">[</span>
+                                <span class="n">b1</span><span class="p">,</span>
+                                <span class="n">c1</span><span class="p">,</span>
+                                <span class="n">argmax_coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">z</span><span class="p">,</span>
+                                <span class="n">argmax_coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">i</span><span class="p">,</span>
+                                <span class="n">argmax_coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">j</span><span class="p">,</span>
+                            <span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="n">masked_output</span> <span class="o">=</span> <span class="n">padded_heatmap</span><span class="p">[</span><span class="n">mask</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">window</span><span class="p">,</span> <span class="n">window</span><span class="p">)</span>
+
+        <span class="n">local_coords</span> <span class="o">=</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">masked_output</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="n">t</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">local_coords</span> <span class="o">+</span> <span class="p">(</span><span class="n">argmax_coords</span> <span class="o">-</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">_activate_norm_heatmap</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;softmax&quot;</span><span class="p">,</span>
+    <span class="n">t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+    <span class="n">dim</span> <span class="o">=</span> <span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span> <span class="k">else</span> <span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">activation</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;softmax&quot;</span><span class="p">:</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="n">t</span> <span class="o">*</span> <span class="n">heatmap</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;sigmoid&quot;</span><span class="p">:</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sigmoid</span><span class="p">(</span><span class="n">heatmap</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">activation</span> <span class="o">==</span> <span class="s2">&quot;ReLU&quot;</span><span class="p">:</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">relu</span><span class="p">(</span><span class="n">heatmap</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Activation function </span><span class="si">{</span><span class="n">activation</span><span class="si">}</span><span class="s2"> not implemented.&quot;</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">heatmap</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="coord_weighted_spatial_mean">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.coord_weighted_spatial_mean">[docs]</a>
+<span class="k">def</span> <span class="nf">coord_weighted_spatial_mean</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">require_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the spatial weighted mean of the heatmap.</span>
+<span class="sd">    Source: &quot;UGLLI Face Alignment: Estimating Uncertainty with</span>
+<span class="sd">        Gaussian Log-Likelihood Loss&quot; - Kumar et al. (2019)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+<span class="sd">        activation (str): activation function to apply to the heatmap</span>
+<span class="sd">        require_grad (bool): whether to require gradient for the coordinates</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">heatmap</span> <span class="o">=</span> <span class="n">_activate_norm_heatmap</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span> <span class="n">t</span><span class="o">=</span><span class="n">t</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">xy</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;xy&quot;</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="n">xy</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">flip</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">zs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">xyz</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;ij&quot;</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">xyz</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">coord_soft_argmax_2d</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">logit_scale</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">require_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the spatial mean over the softmax distribution of the heatmap,</span>
+<span class="sd">    but for a 2D heatmap, without the batch and channel dimensions.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (H, W)</span>
+<span class="sd">        logit_scale (bool): whether the input is logit scaled</span>
+<span class="sd">        require_grad (bool): whether to require gradient for the coordinates</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">activation</span> <span class="o">=</span> <span class="s2">&quot;softmax&quot;</span> <span class="k">if</span> <span class="ow">not</span> <span class="n">logit_scale</span> <span class="k">else</span> <span class="kc">None</span>
+    <span class="k">return</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+        <span class="n">heatmap</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+        <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span>
+        <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">,</span>
+    <span class="p">)[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+
+
+<div class="viewcode-block" id="heatmap_to_coord">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.heatmap_to_coord">[docs]</a>
+<span class="k">def</span> <span class="nf">heatmap_to_coord</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">offset_coords</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;argmax&quot;</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">require_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the retrieved coordinates via specified method from a heatmap. The offset_coords is used</span>
+<span class="sd">    to remove the first offset_coords coordinates from the heatmap. This is used to remove the</span>
+<span class="sd">    background class (if present).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        offset_coords (int): number of coordinates to remove</span>
+<span class="sd">        method (str): method to retrieve the coordinates</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+<span class="sd">        require_grad (bool): whether to require gradient for the coordinates</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap</span><span class="p">[:,</span> <span class="n">offset_coords</span><span class="p">:]</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;argmax&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;local_soft_argmax&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_local_soft_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean_relu&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;ReLU&quot;</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;soft_argmax&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean_sigmoid&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;sigmoid&quot;</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span>
+        <span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Method not implemented.&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="heatmap_to_coord_enlarge">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.heatmap_to_coord_enlarge">[docs]</a>
+<span class="k">def</span> <span class="nf">heatmap_to_coord_enlarge</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">offset_coords</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+    <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;argmax&quot;</span><span class="p">,</span>
+    <span class="n">enlarge_factor</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+    <span class="n">enlarge_dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">enlarge_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;bilinear&quot;</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the retrieved coordinates via specified method from an enlarged heatmap.</span>
+<span class="sd">    The offset_coords is used to remove the first offset_coords coordinates from the heatmap.</span>
+<span class="sd">    This is used to remove the background class (if present).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        offset_coords (int): number of coordinates to remove</span>
+<span class="sd">        method (str): method to retrieve the coordinates</span>
+<span class="sd">        enlarge_factor (int): factor to enlarge the heatmap</span>
+<span class="sd">        enlarge_dim (tuple[int, ...] or None): dimensions to enlarge the heatmap to</span>
+<span class="sd">        enlarge_mode (str): interpolation mode to enlarge the heatmap</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap</span><span class="p">[:,</span> <span class="n">offset_coords</span><span class="p">:]</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">enlarge_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">enlarge_dim</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="s2">&quot;enlarge_dim must be a tuple of 2 elements&quot;</span>
+            <span class="n">heatmap_scaled</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">enlarge_dim</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">enlarge_mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">heatmap_scaled</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="n">enlarge_factor</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">enlarge_mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="n">coords</span> <span class="o">=</span> <span class="n">heatmap_to_coord</span><span class="p">(</span><span class="n">heatmap_scaled</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">enlarge_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="n">coords</span> <span class="o">/</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">enlarge_dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+                <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="n">coords</span> <span class="o">/</span> <span class="n">enlarge_factor</span>
+        <span class="k">return</span> <span class="n">coords</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">enlarge_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">enlarge_dim</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="s2">&quot;enlarge_dim must be a tuple of 3 elements&quot;</span>
+            <span class="n">heatmap_scaled</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">enlarge_dim</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">enlarge_mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">heatmap_scaled</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">interpolate</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">,</span> <span class="n">scale_factor</span><span class="o">=</span><span class="n">enlarge_factor</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">enlarge_mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span>
+            <span class="p">)</span>
+        <span class="n">coords</span> <span class="o">=</span> <span class="n">heatmap_to_coord</span><span class="p">(</span><span class="n">heatmap_scaled</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="n">method</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">enlarge_dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="n">coords</span> <span class="o">/</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">enlarge_dim</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+                <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">coords</span> <span class="o">=</span> <span class="n">coords</span> <span class="o">/</span> <span class="n">enlarge_factor</span>
+        <span class="k">return</span> <span class="n">coords</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="coord_weighted_spatial_mean_cov">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.coord_weighted_spatial_mean_cov">[docs]</a>
+<span class="k">def</span> <span class="nf">coord_weighted_spatial_mean_cov</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">require_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the spatial weighted mean and the weighted sample covariance of the possitive elements</span>
+<span class="sd">    of the heatmap by the heatmap values.</span>
+<span class="sd">        source: https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+<span class="sd">        require_grad (bool): whether to require gradient for the coordinates</span>
+<span class="sd">        activation (str): activation function to apply to the heatmap</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">heatmap</span> <span class="o">=</span> <span class="n">_activate_norm_heatmap</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;xy&quot;</span><span class="p">)</span>
+        <span class="n">yx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">mean_coords</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span> <span class="o">*</span> <span class="n">yx</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
+        <span class="n">dist_x</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">-</span> <span class="n">mean_coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_y</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">-</span> <span class="n">mean_coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">dist_y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span> <span class="n">dist_x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)),</span>
+            <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>  <span class="c1"># (B, C, H*W, 2)</span>
+
+        <span class="n">covariances</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dist</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+<div class="viewcode-block" id="heatmap_to_coord_cov">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.heatmap_to_coord_cov">[docs]</a>
+            <span class="o">@</span> <span class="n">dist</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="n">v2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">mean_coords</span><span class="p">,</span> <span class="n">covariances</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v2</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">zs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;ij&quot;</span><span class="p">)</span>
+        <span class="n">zyx</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="n">mean_coords</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="n">zyx</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
+
+        <span class="n">dist_x</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">-</span> <span class="n">mean_coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_y</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">-</span> <span class="n">mean_coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_z</span> <span class="o">=</span> <span class="n">zs</span> <span class="o">-</span> <span class="n">mean_coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">dist_z</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">dist_y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">dist_x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">covariances</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dist</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+            <span class="o">@</span> <span class="n">dist</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+
+        <span class="n">v2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">mean_coords</span><span class="p">,</span> <span class="n">covariances</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">heatmap_to_coord_cov</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;soft_argmax&quot;</span><span class="p">,</span>
+    <span class="n">require_grad</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the modal coordinates and covariance matrix from a heatmap.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        method (str): method to retrieve the coordinates</span>
+<span class="sd">        require_grad (bool): whether to require gradient for the coordinates</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;soft_argmax&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean_cov</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;softmax&quot;</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean_cov</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean_sigmoid&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean_cov</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;sigmoid&quot;</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;weighted_spatial_mean_relu&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_weighted_spatial_mean_cov</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">require_grad</span><span class="o">=</span><span class="n">require_grad</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s2">&quot;ReLU&quot;</span>
+        <span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Method not implemented.&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="coord_cov_from_gaussian_ls">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.coord_cov_from_gaussian_ls">[docs]</a>
+<span class="k">def</span> <span class="nf">coord_cov_from_gaussian_ls</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">ls_library</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;scipy&quot;</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the modal coordinates and covariance matrix from a heatmap through fitting the heatmap</span>
+<span class="sd">    on Gaussian distribution with a specicic scaling factor gamma with help of least squares</span>
+<span class="sd">    optimization.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        gamma (float): gamma parameter of the gaussian heatmap generator</span>
+<span class="sd">        ls_library (str): library to use for least squares optimization. (scipy or pytorch)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: see if we can use a pytorch implementation (e.g, pytorch-minimize seems to be broken)</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ls_library</span> <span class="o">==</span> <span class="s2">&quot;scipy&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">coord_cov_from_gaussian_ls_scipy</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Method not implemented.&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">coord_cov_from_gaussian_ls_scipy</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the modal coordinates and covariance matrix from a heatmap through fitting the heatmap</span>
+<span class="sd">    on Gaussian distribution with a specicic scaling factor gamma with help of least squares</span>
+<span class="sd">    optimization.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        gamma (float): gamma parameter of the gaussian heatmap generator</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">generate_gaussian</span><span class="p">(</span><span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the gaussian function for the given landmarks, sigma and rotation.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">gaussian_generator</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+            <span class="mi">1</span><span class="p">,</span>
+            <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+            <span class="n">heatmap_size</span><span class="o">=</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="n">learnable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">fun_to_minimize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">gaussian_generator</span><span class="o">.</span><span class="n">set_sigmas</span><span class="p">(</span><span class="n">x</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">gaussian_generator</span><span class="o">.</span><span class="n">set_rotation</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+            <span class="k">return</span> <span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">heatmap</span>
+                    <span class="o">-</span> <span class="n">gaussian_generator</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">:])</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
+                <span class="p">)</span>
+                <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">detach</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fun_to_minimize</span>
+
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+    <span class="n">coords</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="n">covs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">b1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">c1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
+            <span class="n">init_coord</span> <span class="o">=</span> <span class="n">coord_argmax</span><span class="p">(</span><span class="n">heatmap</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">))[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span>
+                <span class="n">generate_gaussian</span><span class="p">(</span><span class="n">heatmap</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">]),</span>
+                <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">init_coord</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">init_coord</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()]),</span>
+                <span class="n">method</span><span class="o">=</span><span class="s2">&quot;trf&quot;</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+            <span class="n">coords</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">x</span><span class="p">[</span><span class="mi">4</span><span class="p">]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">)</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+                <span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span>
+            <span class="p">)</span>
+            <span class="n">diagonal</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">))</span>
+            <span class="n">covs</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">rotation</span><span class="p">,</span> <span class="n">diagonal</span><span class="p">),</span> <span class="n">rotation</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">coords</span><span class="p">,</span> <span class="n">covs</span>
+
+
+<div class="viewcode-block" id="cov_from_gaussian_ls">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.cov_from_gaussian_ls">[docs]</a>
+<span class="k">def</span> <span class="nf">cov_from_gaussian_ls</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">ls_library</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;scipy&quot;</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns covariance matrix from a heatmap through fitting the heatmap on Gaussian distribution</span>
+<span class="sd">    with a specicic scaling factor gamma and specified coordinates</span>
+<span class="sd">    with help of least squares optimization.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        coords (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">        gamma (float): gamma parameter of the gaussian heatmap generator</span>
+<span class="sd">        ls_library (str): library to use for least squares optimization. (scipy or pytorch)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: see if we can use a pytorch implementation (e.g, pytorch-minimize seems to be broken)</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">ls_library</span> <span class="o">==</span> <span class="s2">&quot;scipy&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">cov_from_gaussian_ls_scipy</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">coords</span><span class="p">,</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Method not implemented.&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">cov_from_gaussian_ls_scipy</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the covariance matrix from a heatmap through fitting the heatmap on Gaussian</span>
+<span class="sd">    distribution with a specicic scaling factor gamma and retrieved coordinates with specified</span>
+<span class="sd">    decoding method with help of least squares optimization.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        coords (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">        gamma (float): gamma parameter of the gaussian heatmap generator</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">generate_gaussian</span><span class="p">(</span><span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Callable</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Returns the gaussian function for the given landmarks, sigma and rotation.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">gaussian_generator</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+            <span class="mi">1</span><span class="p">,</span>
+            <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+            <span class="n">heatmap_size</span><span class="o">=</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">],</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span>
+            <span class="n">learnable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+
+        <span class="k">def</span> <span class="nf">fun_to_minimize</span><span class="p">(</span><span class="n">x</span><span class="p">):</span>
+            <span class="n">gaussian_generator</span><span class="o">.</span><span class="n">set_sigmas</span><span class="p">(</span><span class="n">x</span><span class="p">[:</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">gaussian_generator</span><span class="o">.</span><span class="n">set_rotation</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+            <span class="k">return</span> <span class="p">(</span>
+                <span class="p">(</span><span class="n">heatmap</span> <span class="o">-</span> <span class="n">gaussian_generator</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">flatten</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">detach</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">cpu</span><span class="p">()</span>
+                <span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+            <span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fun_to_minimize</span>
+
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">covs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">least_squares</span><span class="p">(</span>
+                <span class="n">generate_gaussian</span><span class="p">(</span><span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">],</span> <span class="n">heatmap</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;trf&quot;</span>
+            <span class="p">)</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">x</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+                <span class="p">[[</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">])]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span>
+            <span class="p">)</span>
+            <span class="n">diagonal</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">x</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">))</span>
+            <span class="n">covs</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">mm</span><span class="p">(</span><span class="n">rotation</span><span class="p">,</span> <span class="n">diagonal</span><span class="p">),</span> <span class="n">rotation</span><span class="o">.</span><span class="n">t</span><span class="p">())</span>
+    <span class="k">return</span> <span class="n">covs</span>
+
+
+<div class="viewcode-block" id="weighted_sample_cov">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.weighted_sample_cov">[docs]</a>
+<span class="k">def</span> <span class="nf">weighted_sample_cov</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculate the covariance matrix from a heatmap by calculating the mean of the</span>
+<span class="sd">    heatmap values weighted by the heatmap values.</span>
+<span class="sd">    source: https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W) or (B, C, D, H, W)</span>
+<span class="sd">        coords (torch.Tensor): coordinates of shape (B, C, 2) or (B, C, 3)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+<span class="sd">        activation (str): activation function to apply to the heatmap</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">heatmap</span> <span class="o">=</span> <span class="n">_activate_norm_heatmap</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Coordinates must have 2 elements: </span><span class="si">{</span><span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;xy&quot;</span><span class="p">)</span>
+
+        <span class="n">dist_x</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_y</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">dist_y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span> <span class="n">dist_x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)),</span>
+            <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>  <span class="c1"># (B, C, H*W, 2)</span>
+
+        <span class="n">covariances</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dist</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+<div class="viewcode-block" id="windowed_weigthed_sample_cov">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.windowed_weigthed_sample_cov">[docs]</a>
+            <span class="o">@</span> <span class="n">dist</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+        <span class="n">v2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">covariances</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">covariances</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v2</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Coordinates must have 3 elements: </span><span class="si">{</span><span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">zs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">d</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;ij&quot;</span><span class="p">)</span>
+
+        <span class="n">dist_x</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_y</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist_z</span> <span class="o">=</span> <span class="n">zs</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">dist</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">dist_z</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">dist_y</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">dist_x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+
+        <span class="n">covariances</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">start_dim</span><span class="o">=-</span><span class="mi">3</span><span class="p">,</span> <span class="n">end_dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">dist</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+            <span class="o">@</span> <span class="n">dist</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+
+        <span class="n">v2</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">heatmap</span><span class="o">**</span><span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">covariances</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">covariances</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">v2</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2 or 3: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">windowed_weigthed_sample_cov</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculate the covariance matrix from a heatmap by calculating the mean of the</span>
+<span class="sd">    heatmap values weighted by the heatmap values. The window is determined by the</span>
+<span class="sd">    distance to the closest edge.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        coords (torch.Tensor): coordinates of shape (B, C, 2)</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions (2 or 3)</span>
+<span class="sd">        activation (str): activation function to apply to the heatmap</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (torch.Tensor): covariance matrix of shape (B, C, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span>
+    <span class="n">covs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">b</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
+            <span class="n">window</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span>
+                <span class="nb">min</span><span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">w</span> <span class="o">-</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
+            <span class="p">)</span>
+            <span class="n">covs</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">weighted_sample_cov</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">[</span>
+                    <span class="n">b</span><span class="p">,</span>
+                    <span class="n">c</span><span class="p">,</span>
+                    <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">-</span> <span class="n">window</span> <span class="p">:</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
+                    <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">-</span> <span class="n">window</span> <span class="p">:</span> <span class="n">coords</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">()</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
+                <span class="p">]</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[[</span><span class="n">window</span><span class="p">,</span> <span class="n">window</span><span class="p">]]],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">),</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">activation</span><span class="o">=</span><span class="n">activation</span><span class="p">,</span>
+            <span class="p">)</span>
+    <span class="k">return</span> <span class="n">covs</span></div>
+
+
+
+<div class="viewcode-block" id="heatmap_to_multiple_coord">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.heatmap_to_multiple_coord">[docs]</a>
+<span class="k">def</span> <span class="nf">heatmap_to_multiple_coord</span><span class="p">(</span>
+    <span class="n">heatmaps</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
+    <span class="n">threshold</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">method</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;argmax&quot;</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]],</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]]]]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the multiple coordinates of the maximum value of the heatmap</span>
+<span class="sd">    for each batch and channel. Additionally, the scores of the maxima are returned.</span>
+<span class="sd">    If threshold is not None, only the maxima with a score higher than the threshold are returned.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (B, C, H, W)</span>
+<span class="sd">        window (tuple): window size</span>
+<span class="sd">        threshold (float): threshold to remove points. If None, no threshold is applied. Default</span>
+<span class="sd">            None.</span>
+<span class="sd">        method (str): method to retrieve the coordinates</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list(list(list(torch.Tensor)))): list of coordinates of the maxima for each batch and</span>
+<span class="sd">            channel, with the first list the batch, the second list the channel and the third list</span>
+<span class="sd">            the coordinates</span>
+<span class="sd">        (list(list(list(float)))): list of scores of the maxima for each batch and channel, with</span>
+<span class="sd">            the first list the batch, the second list the channel and the third list the scores.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Spatial dimensions must be 2: </span><span class="si">{</span><span class="n">spatial_dims</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">argmax_fun</span><span class="p">:</span> <span class="n">Callable</span><span class="p">[[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span>
+    <span class="k">if</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;argmax&quot;</span><span class="p">:</span>
+        <span class="n">argmax_fun</span> <span class="o">=</span> <span class="n">non_maximum_surpression</span>
+    <span class="k">elif</span> <span class="n">method</span> <span class="o">==</span> <span class="s2">&quot;local_soft_argmax&quot;</span><span class="p">:</span>
+        <span class="n">argmax_fun</span> <span class="o">=</span> <span class="n">non_maximum_surpression_local_soft_argmax</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Method not implemented.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">shape</span>
+    <span class="n">out</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">scores</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">b1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">b</span><span class="p">):</span>
+        <span class="n">out_classes</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">scores_classes</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">c1</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">c</span><span class="p">):</span>
+            <span class="n">out_class</span> <span class="o">=</span> <span class="n">argmax_fun</span><span class="p">(</span><span class="n">heatmaps</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">],</span> <span class="n">window</span><span class="p">)</span>
+            <span class="n">scores_class</span> <span class="o">=</span> <span class="p">[</span><span class="n">heatmaps</span><span class="p">[</span><span class="n">b1</span><span class="p">,</span> <span class="n">c1</span><span class="p">,</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">(),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">int</span><span class="p">()]</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">out_class</span><span class="p">]</span>
+            <span class="n">sorted_idx</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span>
+                <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">scores_class</span><span class="p">)),</span> <span class="n">key</span><span class="o">=</span><span class="k">lambda</span> <span class="n">k</span><span class="p">:</span> <span class="n">scores_class</span><span class="p">[</span><span class="n">k</span><span class="p">],</span> <span class="n">reverse</span><span class="o">=</span><span class="kc">True</span>
+            <span class="p">)</span>
+            <span class="n">out_class</span> <span class="o">=</span> <span class="p">[</span><span class="n">out_class</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">sorted_idx</span><span class="p">]</span>
+            <span class="n">scores_class</span> <span class="o">=</span> <span class="p">[</span><span class="n">scores_class</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">sorted_idx</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">threshold</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">scores_class</span><span class="p">)):</span>
+                    <span class="k">if</span> <span class="n">scores_class</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">threshold</span><span class="p">:</span>
+                        <span class="k">break</span>
+                    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">scores_class</span> <span class="o">=</span> <span class="n">scores_class</span><span class="p">[:</span><span class="n">i</span><span class="p">]</span>
+                <span class="n">out_class</span> <span class="o">=</span> <span class="n">out_class</span><span class="p">[:</span><span class="n">i</span><span class="p">]</span>
+            <span class="n">out_classes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_class</span><span class="p">)</span>
+            <span class="n">scores_classes</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scores_class</span><span class="p">)</span>
+        <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">out_classes</span><span class="p">)</span>
+        <span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scores_classes</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">out</span><span class="p">,</span> <span class="n">scores</span></div>
+
+
+
+<div class="viewcode-block" id="non_maximum_surpression">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.non_maximum_surpression">[docs]</a>
+<span class="k">def</span> <span class="nf">non_maximum_surpression</span><span class="p">(</span><span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Non-Maximum Surpression (NMS)</span>
+
+<span class="sd">    source: Efficient Non-Maximum Suppression - Neubeck and Van Gool (2006)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (H, W)</span>
+<span class="sd">        window (int): window size</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list(tuple(int, int))): list of coordinates of the maxima</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># TODO: Think about way to deal with ties (in block) (multiple maxima) in the output</span>
+    <span class="c1">#    now we just take the first one</span>
+    <span class="k">assert</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">==</span> <span class="mi">2</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;Heatmap must be 2D, got </span><span class="si">{</span><span class="n">heatmap</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span><span class="si">}</span><span class="s2">&quot;</span>
+    <span class="n">set_local_argmax</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">window</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">window</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="p">(</span><span class="n">window</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">window</span><span class="p">):</span>
+            <span class="n">local_argmax</span> <span class="o">=</span> <span class="n">coord_argmax</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">[</span>
+                    <span class="n">i</span> <span class="p">:</span> <span class="nb">min</span><span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="n">window</span><span class="p">,</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">j</span> <span class="p">:</span> <span class="nb">min</span><span class="p">(</span><span class="n">j</span> <span class="o">+</span> <span class="n">window</span><span class="p">,</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+            <span class="n">local_argmax</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])</span>
+            <span class="n">local_argmax_around</span> <span class="o">=</span> <span class="n">coord_argmax</span><span class="p">(</span>
+                <span class="n">heatmap</span><span class="p">[</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span>
+                        <span class="n">local_argmax</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span>
+                        <span class="n">local_argmax</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="p">),</span>
+                <span class="p">]</span>
+            <span class="p">)</span>
+            <span class="n">local_argmax_around</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">local_argmax</span> <span class="o">==</span> <span class="n">local_argmax_around</span><span class="p">):</span>
+                <span class="n">set_local_argmax</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">local_argmax</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">set_local_argmax</span><span class="p">]</span></div>
+
+
+
+<div class="viewcode-block" id="non_maximum_surpression_local_soft_argmax">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.non_maximum_surpression_local_soft_argmax">[docs]</a>
+<span class="k">def</span> <span class="nf">non_maximum_surpression_local_soft_argmax</span><span class="p">(</span>
+    <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Returns the coordinates of the maximum value of the heatmap for each batch and channel</span>
+<span class="sd">    (landmark), with the soft-argmax function applied on the heatmaps.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        heatmap (torch.Tensor): heatmap of shape (H, W)</span>
+<span class="sd">        window (int): window size</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        (list(tuple(int, int))): list of coordinates of the maxima</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">set_local_argmax</span> <span class="o">=</span> <span class="n">non_maximum_surpression</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span><span class="p">)</span>
+    <span class="n">list_local_soft_argmax</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="n">heatmap_pad</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="p">(</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">local_argmax</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">set_local_argmax</span><span class="p">):</span>
+        <span class="n">local_argmax_tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">local_argmax</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="nb">int</span><span class="p">(</span><span class="n">local_argmax</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+        <span class="p">)</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">])</span>
+        <span class="n">local_argmax_around</span> <span class="o">=</span> <span class="n">coord_soft_argmax_2d</span><span class="p">(</span>
+            <span class="n">heatmap_pad</span><span class="p">[</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax_tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span>
+                    <span class="n">local_argmax_tensor</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">heatmap_pad</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+                <span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax_tensor</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span>
+                    <span class="n">local_argmax_tensor</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">window</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="nb">max</span><span class="o">=</span><span class="n">heatmap_pad</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+                <span class="p">),</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">local_argmax_around</span> <span class="o">+=</span> <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">local_argmax_tensor</span> <span class="o">-</span> <span class="n">window</span><span class="p">,</span> <span class="nb">min</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="p">[</span><span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span> <span class="n">window</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span>
+        <span class="p">)</span>
+        <span class="n">list_local_soft_argmax</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">local_argmax_around</span><span class="p">)</span>
+    <span class="k">return</span> <span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">((</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">item</span><span class="p">()))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">list_local_soft_argmax</span><span class="p">]</span></div>
+
+</pre></div>
+        </article>
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diff --git a/_modules/landmarker/heatmap/generator.html b/_modules/landmarker/heatmap/generator.html
new file mode 100644
index 0000000..8c9e221
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+++ b/_modules/landmarker/heatmap/generator.html
@@ -0,0 +1,996 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../../../">
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+    <link rel="shortcut icon" href="../../../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
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+          <h1>Source code for landmarker.heatmap.generator</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Heatmap generator&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+
+<span class="k">class</span> <span class="nc">HeatmapGenerator</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Heatmap generator abstract class for generating heatmaps from landmarks</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_landmarks (int): number of landmarks</span>
+<span class="sd">        sigmas (float or list[float] or torch.Tensor or np.ndarray): sigmas of the heatmap function</span>
+<span class="sd">        gamma (float or None): scaling factor of the heatmap function</span>
+<span class="sd">        rotation (float or list[float] or torch.Tensor or np.ndarray): rotation of the heatmap</span>
+<span class="sd">            function</span>
+<span class="sd">        heatmap_size (tuple[int, ...]): size of the heatmap</span>
+<span class="sd">        learnable (bool): whether the sigmas and rotation are learnable</span>
+<span class="sd">        background (bool): whether to add a background channel to the heatmap</span>
+<span class="sd">        all_points (bool): whether to add a channel with the sum of all the landmarks</span>
+<span class="sd">        continuous (bool): whether to use continuous or discrete landmarks</span>
+<span class="sd">        na_zero (bool): whether to set the value of the landmarks to zero if they are not available</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">sigmas</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">rotation</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+        <span class="n">heatmap_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+        <span class="n">learnable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">background</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">all_points</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">na_zero</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">HeatmapGenerator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">=</span> <span class="n">nb_landmarks</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">learnable</span> <span class="o">=</span> <span class="n">learnable</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span> <span class="o">=</span> <span class="n">heatmap_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">heatmap_size</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_sigmas</span><span class="p">(</span><span class="n">sigmas</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">set_rotation</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span> <span class="o">=</span> <span class="n">gamma</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">background</span> <span class="o">=</span> <span class="n">background</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">all_points</span> <span class="o">=</span> <span class="n">all_points</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">continuous</span> <span class="o">=</span> <span class="n">continuous</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">na_zero</span> <span class="o">=</span> <span class="n">na_zero</span>
+
+    <span class="k">def</span> <span class="nf">set_sigmas</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sigmas</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the sigmas of the heatmap function.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            sigmas (float or list[float] or torch.Tensor or np.ndarray): sigmas of the heatmap</span>
+<span class="sd">                function</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sigmas</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="n">device</span> <span class="o">=</span> <span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+        <span class="n">sigmas</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="n">sigmas</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learnable</span>
+        <span class="p">)</span>
+        <span class="n">sigmas</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
+                <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">),</span>
+                <span class="n">requires_grad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learnable</span><span class="p">,</span>
+                <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
+                <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="o">*</span> <span class="n">sigmas</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learnable</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">sigmas</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;sigmas&quot;</span><span class="p">,</span> <span class="n">sigmas</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">set_rotation</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rotation</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Set the rotation of the heatmap function.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            rotation (float or list[float] or torch.Tensor or np.ndarray): rotation of the heatmap</span>
+<span class="sd">                function</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="s2">&quot;sigmas&quot;</span><span class="p">):</span>
+            <span class="n">device</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+        <span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">rotation</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+            <span class="n">device</span> <span class="o">=</span> <span class="n">rotation</span><span class="o">.</span><span class="n">device</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">device</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+        <span class="n">rotation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span>
+            <span class="n">rotation</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learnable</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
+                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">,),</span>
+                    <span class="n">requires_grad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learnable</span><span class="p">,</span>
+                    <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="o">*</span> <span class="n">rotation</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
+                    <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">),</span>
+                    <span class="n">requires_grad</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">learnable</span><span class="p">,</span>
+                    <span class="n">device</span><span class="o">=</span><span class="n">device</span><span class="p">,</span>
+                    <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="o">*</span> <span class="n">rotation</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">learnable</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">rotation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">Parameter</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s2">&quot;rotation&quot;</span><span class="p">,</span> <span class="n">rotation</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">affine_matrix</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+        <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="c1"># go from 2 by 2 affine matrix to 4 by 4</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">from_2by2_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="c1"># go from 3 by 3 affine matrix to 4 by 4</span>
+            <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">from_3by3_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="p">)</span>
+        <span class="n">affine_matrix</span> <span class="o">=</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_heatmap</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">1</span><span class="p">),</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="n">affine_matrix</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_heatmap</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="n">affine_matrix</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">create_heatmap</span><span class="p">(</span><span class="n">landmarks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">gamma</span><span class="p">,</span> <span class="n">affine_matrix</span><span class="p">)</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">all_points</span><span class="p">:</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">heatmaps</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span> <span class="n">heatmaps</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">background</span><span class="p">:</span>
+            <span class="n">heatmaps</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span>
+                        <span class="p">(</span><span class="n">heatmaps</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+                    <span class="p">)</span>
+                    <span class="o">-</span> <span class="n">heatmaps</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">),</span>
+                    <span class="n">heatmaps</span><span class="p">,</span>
+                <span class="p">),</span>
+                <span class="mi">1</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">na_zero</span><span class="p">:</span>
+            <span class="n">heatmaps</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">heatmaps</span><span class="p">)]</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">return</span> <span class="n">heatmaps</span>
+
+    <span class="nd">@abstractmethod</span>
+    <span class="k">def</span> <span class="nf">heatmap_fun</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">landmark_t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">covariance</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
+    <span class="p">):</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Abstract heatmap function</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark_t (torch.Tensor): coordinates of the landmark (y, x) or (y, x, z)</span>
+<span class="sd">            coords (torch.Tensor): coordinates of the pixel (y, x) or (y, x, z)</span>
+<span class="sd">            covariance (torch.Tensor): covariance matrix</span>
+<span class="sd">            gamma (float or None): scaling factor of the heatmap function</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="nf">get_covariance_matrix</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">return4by4</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Get the covariance matrix of the heatmap function.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            return4by4 (bool): whether to return a 4 by 4 covariance matrix or a spatial_dims by</span>
+<span class="sd">                spatial_dims covariance matrix.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: covariance matrix</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">),</span> <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="p">),</span>
+                <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+
+            <span class="n">diagonal</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag_embed</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">return4by4</span><span class="p">:</span>
+                <span class="n">rotation</span> <span class="o">=</span> <span class="n">from_2by2_to_4by4</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span>
+                <span class="n">diagonal</span> <span class="o">=</span> <span class="n">from_2by2_to_4by4</span><span class="p">(</span><span class="n">diagonal</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>  <span class="c1"># 3D case</span>
+            <span class="c1"># see: https://msl.cs.uiuc.edu/planning/node102.html</span>
+            <span class="n">rotation_yaw</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                <span class="p">),</span>
+                <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">rotation_pitch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                <span class="p">),</span>
+                <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">rotation_roll</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                <span class="p">(</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="o">-</span><span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span>
+                        <span class="p">(</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                            <span class="n">torch</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rotation</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]),</span>
+                        <span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="p">),</span>
+                <span class="p">),</span>
+                <span class="n">dim</span><span class="o">=-</span><span class="mi">2</span><span class="p">,</span>
+            <span class="p">)</span>
+            <span class="n">rotation</span> <span class="o">=</span> <span class="n">rotation_yaw</span> <span class="o">@</span> <span class="n">rotation_pitch</span> <span class="o">@</span> <span class="n">rotation_roll</span>
+
+            <span class="n">diagonal</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">diag_embed</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">return4by4</span><span class="p">:</span>
+                <span class="n">rotation</span> <span class="o">=</span> <span class="n">from_3by3_to_4by4</span><span class="p">(</span><span class="n">rotation</span><span class="p">)</span>
+                <span class="n">diagonal</span> <span class="o">=</span> <span class="n">from_3by3_to_4by4</span><span class="p">(</span><span class="n">diagonal</span><span class="p">)</span>
+        <span class="n">covariance</span> <span class="o">=</span> <span class="n">rotation</span> <span class="o">@</span> <span class="n">diagonal</span> <span class="o">@</span> <span class="n">rotation</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">covariance</span>
+
+    <span class="k">def</span> <span class="nf">create_heatmap</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">affine_matrix</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Create a heatmap for a given landmark in an image returns a heatmap with the same size as</span>
+<span class="sd">        the image. Works with batches and multiple landmarks.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmarks (torch.Tensor): landmarks of shape (B, C, M, S) or (B, C, S)</span>
+<span class="sd">            gamma (float or None): scaling factor of the heatmap function</span>
+<span class="sd">            affine_matrix (torch.Tensor): affine matrix of shape (B, 1, 4, 4)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: heatmap of shape (B, C, M, H, W,) or (B, C, H, W) if landmarks are 2D</span>
+<span class="sd">                else (B, C, M, D, H, W) or (B, C, D, H, W) if landmarks are 3D</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">covariance</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_covariance_matrix</span><span class="p">(</span><span class="n">return4by4</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">covariance</span> <span class="o">=</span> <span class="n">affine_matrix</span> <span class="o">@</span> <span class="n">covariance</span> <span class="o">@</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">x_round</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+            <span class="n">y_round</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+            <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+            <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;xy&quot;</span><span class="p">)</span>
+            <span class="n">pre_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="mi">1</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])))</span>
+            <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">pre_shape</span><span class="p">,</span> <span class="o">*</span><span class="n">xs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">pre_shape</span><span class="p">,</span> <span class="o">*</span><span class="n">ys</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous</span><span class="p">:</span>
+                <span class="n">x_t</span><span class="p">,</span> <span class="n">y_t</span> <span class="o">=</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">x_t</span><span class="p">,</span> <span class="n">y_t</span> <span class="o">=</span> <span class="n">x_round</span><span class="p">,</span> <span class="n">y_round</span>
+            <span class="n">x_t</span> <span class="o">=</span> <span class="n">x_t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">y_t</span> <span class="o">=</span> <span class="n">y_t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span><span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">y_t</span><span class="p">,</span> <span class="n">x_t</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">covariance</span><span class="p">,</span> <span class="n">gamma</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">z</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span>
+            <span class="n">y</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">landmarks</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span>
+            <span class="n">z_round</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">z</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+            <span class="n">y_round</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">y</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+            <span class="n">x_round</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">round</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+            <span class="n">zs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+            <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span>
+                <span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_size</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">sigmas</span><span class="o">.</span><span class="n">device</span>
+            <span class="p">)</span>
+            <span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s2">&quot;ij&quot;</span><span class="p">)</span>
+            <span class="n">pre_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="mi">1</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])))</span>
+            <span class="n">zs</span> <span class="o">=</span> <span class="n">zs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">pre_shape</span><span class="p">,</span> <span class="o">*</span><span class="n">zs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">pre_shape</span><span class="p">,</span> <span class="o">*</span><span class="n">ys</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">pre_shape</span><span class="p">,</span> <span class="o">*</span><span class="n">xs</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">continuous</span><span class="p">:</span>
+                <span class="n">z_t</span><span class="p">,</span> <span class="n">y_t</span><span class="p">,</span> <span class="n">x_t</span> <span class="o">=</span> <span class="n">z</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">x</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">z_t</span><span class="p">,</span> <span class="n">y_t</span><span class="p">,</span> <span class="n">x_t</span> <span class="o">=</span> <span class="n">z_round</span><span class="p">,</span> <span class="n">y_round</span><span class="p">,</span> <span class="n">x_round</span>
+            <span class="n">z_t</span> <span class="o">=</span> <span class="n">z_t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">y_t</span> <span class="o">=</span> <span class="n">y_t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">x_t</span> <span class="o">=</span> <span class="n">x_t</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span><span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">z_t</span><span class="p">,</span> <span class="n">y_t</span><span class="p">,</span> <span class="n">x_t</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">zs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">covariance</span><span class="p">,</span> <span class="n">gamma</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="n">heatmap</span>
+
+
+<div class="viewcode-block" id="GaussianHeatmapGenerator">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator">[docs]</a>
+<span class="k">class</span> <span class="nc">GaussianHeatmapGenerator</span><span class="p">(</span><span class="n">HeatmapGenerator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Gaussian heatmap generator for generating heatmaps from landmarks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_landmarks (int): number of landmarks</span>
+<span class="sd">        sigmas (float or list[float] or torch.Tensor or np.ndarray): sigmas of the gaussian heatmap</span>
+<span class="sd">            function</span>
+<span class="sd">        gamma (float or None): scaling factor of the gaussian heatmap function</span>
+<span class="sd">        rotation (float or list[float] or torch.Tensor or np.ndarray): rotation of the gaussian</span>
+<span class="sd">            heatmap function</span>
+<span class="sd">        heatmap_size (tuple[int, int]): size of the heatmap</span>
+<span class="sd">        learnable (bool): whether the sigmas and rotation are learnable</span>
+<span class="sd">        background (bool): whether to add a background channel to the heatmap</span>
+<span class="sd">        all_points (bool): whether to add a channel with the sum of all the landmarks</span>
+<span class="sd">        continuous (bool): whether to use continuous or discrete landmarks</span>
+<span class="sd">        na_zero (bool): whether to set the value of the landmarks to zero if they are not available</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="GaussianHeatmapGenerator.__init__">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">sigmas</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">rotation</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+        <span class="n">heatmap_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+        <span class="n">learnable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">background</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">all_points</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">na_zero</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GaussianHeatmapGenerator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">nb_landmarks</span><span class="p">,</span>
+            <span class="n">sigmas</span><span class="p">,</span>
+            <span class="n">gamma</span><span class="p">,</span>
+            <span class="n">rotation</span><span class="p">,</span>
+            <span class="n">heatmap_size</span><span class="p">,</span>
+            <span class="n">learnable</span><span class="p">,</span>
+            <span class="n">background</span><span class="p">,</span>
+            <span class="n">all_points</span><span class="p">,</span>
+            <span class="n">continuous</span><span class="p">,</span>
+            <span class="n">na_zero</span><span class="o">=</span><span class="n">na_zero</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GaussianHeatmapGenerator.heatmap_fun">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator.heatmap_fun">[docs]</a>
+    <span class="k">def</span> <span class="nf">heatmap_fun</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">landmark_t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">covariance</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Gaussian heatmap function</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark_t (torch.Tensor): coordinates of the landmark (y, x) or (z, y, x)</span>
+<span class="sd">            coords (torch.Tensor): coordinates of the pixel (y, x) or (z, y, x)</span>
+<span class="sd">            covariance (torch.Tensor): covariance matrix (y, x) or (z, y, x)</span>
+<span class="sd">            gamma (float or None): scaling factor of the heatmap function</span>
+
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: value of the gaussian heatmap function for the given pixel</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">covariance</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span> <span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]):</span>
+            <span class="n">inverse_covariance</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># multiple of the same landmarks</span>
+            <span class="n">inverse_covariance</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+            <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">inverse_covariance</span> <span class="o">=</span> <span class="n">inverse_covariance</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">diff</span> <span class="o">=</span> <span class="p">(</span><span class="n">landmark_t</span> <span class="o">-</span> <span class="n">coords</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+        <span class="c1"># if self.spatial_dims == 3:</span>
+        <span class="c1">#     assert False, f&quot;diff shape: {diff.shape}&quot; + f&quot;inv shape: {inverse_covariance.shape}&quot;</span>
+        <span class="k">if</span> <span class="n">gamma</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="n">gamma</span> <span class="o">/</span> <span class="p">(</span>
+                    <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+                    <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                    <span class="p">)</span>
+                <span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">scale</span> <span class="o">=</span> <span class="n">gamma</span> <span class="o">/</span> <span class="p">(</span>
+                    <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="o">**</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+                    <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                    <span class="p">)</span>
+                <span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">scale</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="k">return</span> <span class="n">scale</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span><span class="n">diff</span> <span class="o">@</span> <span class="n">inverse_covariance</span> <span class="o">@</span> <span class="n">diff</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
+            <span class="o">*</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span> <span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)],</span>
+            <span class="o">*</span><span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="p">:</span> <span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="LaplacianHeatmapGenerator">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator">[docs]</a>
+<span class="k">class</span> <span class="nc">LaplacianHeatmapGenerator</span><span class="p">(</span><span class="n">HeatmapGenerator</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Laplacian heatmap generator for generating heatmaps from landmarks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_landmarks (int): number of landmarks</span>
+<span class="sd">        sigmas (float or list[float] or torch.Tensor or np.ndarray): sigmas of the Laplacian heatmap</span>
+<span class="sd">            function</span>
+<span class="sd">        gamma (float or None): scaling factor of the Laplacian heatmap function</span>
+<span class="sd">        rotation (float or list[float] or torch.Tensor or np.ndarray): rotation of the laplacian</span>
+<span class="sd">            heatmap function</span>
+<span class="sd">        heatmap_size (tuple[int, int]): size of the heatmap</span>
+<span class="sd">        learnable (bool): whether the sigmas and rotation are learnable</span>
+<span class="sd">        background (bool): whether to add a background channel to the heatmap</span>
+<span class="sd">        all_points (bool): whether to add a channel with the sum of all the landmarks</span>
+<span class="sd">        continuous (bool): whether to use continuous or discrete landmarks</span>
+<span class="sd">        na_zero (bool): whether to set the value of the landmarks to zero if they are not available</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="LaplacianHeatmapGenerator.__init__">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">sigmas</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+        <span class="n">rotation</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span>
+        <span class="n">heatmap_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+        <span class="n">learnable</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">background</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">all_points</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">continuous</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">na_zero</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">LaplacianHeatmapGenerator</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">nb_landmarks</span><span class="p">,</span>
+            <span class="n">sigmas</span><span class="p">,</span>
+            <span class="n">gamma</span><span class="p">,</span>
+            <span class="n">rotation</span><span class="p">,</span>
+            <span class="n">heatmap_size</span><span class="p">,</span>
+            <span class="n">learnable</span><span class="p">,</span>
+            <span class="n">background</span><span class="p">,</span>
+            <span class="n">all_points</span><span class="p">,</span>
+            <span class="n">continuous</span><span class="p">,</span>
+            <span class="n">na_zero</span><span class="o">=</span><span class="n">na_zero</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Laplacian heatmap generator only works in 2D&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="LaplacianHeatmapGenerator.heatmap_fun">
+<a class="viewcode-back" href="../../../reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator.heatmap_fun">[docs]</a>
+    <span class="k">def</span> <span class="nf">heatmap_fun</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">landmark_t</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">coords</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">covariance</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;Laplacian heatmap function</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            landmark_t (torch.Tensor): coordinates of the landmark (y, x)</span>
+<span class="sd">            coords (torch.Tensor): coordinates of the pixel (y, x)</span>
+<span class="sd">            covariance (torch.Tensor): covariance matrix (y, x)</span>
+<span class="sd">            gamma (float or None): scaling factor of the heatmap function</span>
+
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: value of the gaussian heatmap function for the given pixel</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">covariance</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">inverse_covariance</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># multiple of the same landmarks</span>
+            <span class="n">inverse_covariance</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">inverse</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">])</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+            <span class="p">)</span>
+        <span class="n">diff</span> <span class="o">=</span> <span class="p">(</span><span class="n">landmark_t</span> <span class="o">-</span> <span class="n">coords</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">gamma</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="k">return</span> <span class="p">(</span>
+                <span class="n">gamma</span>
+                <span class="o">/</span> <span class="p">((</span><span class="mi">2</span> <span class="o">/</span> <span class="mi">3</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">covariance</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">,</span> <span class="p">:</span><span class="mi">2</span><span class="p">])))</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+                <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+                    <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="p">(</span><span class="n">diff</span> <span class="o">@</span> <span class="n">inverse_covariance</span> <span class="o">@</span> <span class="n">diff</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
+                <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="o">*</span><span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+            <span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span>
+            <span class="o">-</span><span class="mi">1</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">3</span> <span class="o">*</span> <span class="p">(</span><span class="n">diff</span> <span class="o">@</span> <span class="n">inverse_covariance</span> <span class="o">@</span> <span class="n">diff</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">*</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">],</span> <span class="o">*</span><span class="n">coords</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span></div>
+</div>
+
+
+
+<span class="k">def</span> <span class="nf">from_2by2_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Converts a 2 by 2 affine matrix to a 4 by 4 affine matrix.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        affine_matrix (torch.Tensor): 2 by 2 affine matrix</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: 4 by 4 affine matrix</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">assert</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span>
+    <span class="k">assert</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="mi">2</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">affine_matrix</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">)),</span>
+                    <span class="p">),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span><span class="n">affine_matrix</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">)),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">))</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span>
+                            <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
+                        <span class="p">),</span>
+                    <span class="p">),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">from_2by2_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
+            <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span>
+        <span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Affine matrix should be of shape (2,2) or (B,2,2) or (B,C,2,2))&quot;</span><span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">from_3by3_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Converts a 3 by 3 affine matrix to a 4 by 4 affine matrix.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        affine_matrix (torch.Tensor): 3 by 3 affine matrix</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: 4 by 4 affine matrix</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">assert</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span>
+    <span class="k">assert</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">==</span> <span class="mi">3</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">affine_matrix</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">)),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">)),</span>
+                    <span class="p">),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+            <span class="p">(</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span>
+                        <span class="n">affine_matrix</span><span class="p">,</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                    <span class="p">),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">),</span>
+                <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">(</span>
+                    <span class="p">(</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">),</span>
+                        <span class="n">torch</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">device</span><span class="p">))</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span>
+                            <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
+                        <span class="p">),</span>
+                    <span class="p">),</span>
+                    <span class="n">dim</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+                <span class="p">),</span>
+            <span class="p">),</span>
+            <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">from_3by3_to_4by4</span><span class="p">(</span><span class="n">affine_matrix</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span><span class="o">.</span><span class="n">view</span><span class="p">(</span>
+            <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">affine_matrix</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">4</span>
+        <span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Affine matrix should be of shape (3,3) or (B,3,3), (B,C,3,3)&quot;</span><span class="p">)</span>
+</pre></div>
+        </article>
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+        <article role="main">
+          <h1>Source code for landmarker.losses.losses</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Heatmap loss functions.&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">kornia.losses</span> <span class="kn">import</span> <span class="n">js_div_loss_2d</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">torch.nn</span> <span class="kn">import</span> <span class="n">functional</span> <span class="k">as</span> <span class="n">F</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="p">(</span>
+    <span class="n">GaussianHeatmapGenerator</span><span class="p">,</span>
+    <span class="n">HeatmapGenerator</span><span class="p">,</span>
+    <span class="n">LaplacianHeatmapGenerator</span><span class="p">,</span>
+<span class="p">)</span>
+<span class="kn">from</span> <span class="nn">landmarker.models.utils</span> <span class="kn">import</span> <span class="n">SoftmaxND</span>
+
+
+<div class="viewcode-block" id="GeneralizedNormalHeatmapLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">GeneralizedNormalHeatmapLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Loss function for adaptive generalized normal direct heatmap regression. The loss function is an</span>
+<span class="sd">    extension of the loss function proposed by Thaler et al. (2021) for adaptive heatmap regression,</span>
+<span class="sd">    where they used a anistropic Gaussian distribution for adaptive heatmap regression. The loss</span>
+<span class="sd">    function is defined as the sum of a specified distance function between the predicted heatmap</span>
+<span class="sd">    and the target heatmap, additionaly a determinant of the supplied covariance matrix is added as</span>
+<span class="sd">    regularization term to penalize the loss of the fitted covariance matrix.</span>
+
+<span class="sd">    # TODO: add formula.</span>
+<span class="sd">        source: Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        alpha (float, optional): Weight of the regularization term. Defaults to 5.0.</span>
+<span class="sd">        distance (str, optional): Distance function to use for the loss calculation. Defaults to</span>
+<span class="sd">            &#39;l2&#39;. Possible values are &#39;l2&#39;, &#39;l1&#39;, &#39;smooth-l1&#39;,</span>
+<span class="sd">            &#39;bce-with-logits&#39; and &#39;bce&#39;.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">        **kwargs: Additional keyword arguments for the distance function.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="GeneralizedNormalHeatmapLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">5.0</span><span class="p">,</span> <span class="n">distance</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;l2&quot;</span><span class="p">,</span> <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">=</span> <span class="n">distance</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;l2&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;l1&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">l1_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;smooth-l1&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">smooth_l1_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;bce-with-logits&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy_with_logits</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;bce&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">binary_cross_entropy</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid distance function: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">distance</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GeneralizedNormalHeatmapLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">cov</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">heatmap_target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            heatmap (torch.Tensor): Predicted heatmap.</span>
+<span class="sd">            cov (torch.Tensor): Covariance matrix of the fitted heatmap.</span>
+<span class="sd">            heatmap_target (torch.Tensor): Target heatmap.</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: Loss value.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">det_cov</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span>
+        <span class="k">assert</span> <span class="n">torch</span><span class="o">.</span><span class="n">all</span><span class="p">(</span><span class="n">det_cov</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">),</span> <span class="sa">f</span><span class="s2">&quot;Determinant of covariance matrix is negative: </span><span class="si">{</span><span class="n">det_cov</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">heatmap_target</span><span class="p">)</span> <span class="o">/</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">det_cov</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="GaussianHeatmapL2Loss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss">[docs]</a>
+<span class="k">class</span> <span class="nc">GaussianHeatmapL2Loss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Loss function for Gaussian heatmap regression.</span>
+<span class="sd">    source: http://arxiv.org/abs/2109.09533&quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="GaussianHeatmapL2Loss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">(</span><span class="n">GaussianHeatmapL2Loss</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="n">spatial_dims</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;spatial_dims must be 2 or 3&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="GaussianHeatmapL2Loss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">heatmap</span><span class="p">,</span> <span class="n">sigmas</span><span class="p">,</span> <span class="n">heatmap_target</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">heatmap_target</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span>
+                <span class="mi">0</span>
+            <span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">sigmas</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">sigmas</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">heatmap_target</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span> <span class="o">/</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span>
+                <span class="mi">0</span>
+            <span class="p">]</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">sigmas</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">sigmas</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">*</span> <span class="n">sigmas</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="EuclideanDistanceVarianceReg">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg">[docs]</a>
+<span class="k">class</span> <span class="nc">EuclideanDistanceVarianceReg</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Euclidean distance loss with variance regularization. The regularization term is defined as the</span>
+<span class="sd">    squared difference between the fitted/predicted variance and a predefined target variance, as</span>
+<span class="sd">    proposed by Nibali et al. (2018). The authors point out that this regularization term does not</span>
+<span class="sd">    directly constrain the specif shape of the learned heatmaps.</span>
+<span class="sd">        source: Numerical Coordinate Regression with Convolutional Neural Networks - Nibali et al.</span>
+<span class="sd">            (2018)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        alpha (float, optional): Weight of the regularization term. Defaults to 1.0.</span>
+<span class="sd">        var_t (float, optional): Target variance. Defaults to 1.0.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">        eps (float, optional): Epsilon value to avoid division by zero. Defaults to 1e-6.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="EuclideanDistanceVarianceReg.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">var_t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+        <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-6</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span> <span class="o">=</span> <span class="n">var_t</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="n">spatial_dims</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;spatial_dims must be 2 or 3&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="EuclideanDistanceVarianceReg.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">cov</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            cov (torch.Tensor): Related covariance matrix of the predicted coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">reg</span> <span class="o">=</span> <span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">+</span> <span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">reg</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+                <span class="o">+</span> <span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+                <span class="o">+</span> <span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">var_t</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span>
+            <span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">_euclidean_distance</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">reg</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="EuclideanDistanceJSDivergenceReg">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg">[docs]</a>
+<span class="k">class</span> <span class="nc">EuclideanDistanceJSDivergenceReg</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Euclidean distance loss with Jensen-Shannon divergence regularization. The regularization term</span>
+<span class="sd">    imposes a Gaussian distribution on the predicted heatmaps and calculates the Jensen-Shannon</span>
+<span class="sd">    divergence between the predicted and the target heatmap. The Jensen-Shannon divergence is a</span>
+<span class="sd">    method to measure the similarity between two probability distributions. It is a symmetrized</span>
+<span class="sd">    and smoothed version of the Kullback-Leibler divergence, and is defined as the average of the</span>
+<span class="sd">    Kullback-Leibler divergence between the two distributions and a mixture M of the two</span>
+<span class="sd">    distributions:</span>
+<span class="sd">        :math:`JSD(P||Q) = 0.5 * KL(P||M) + 0.5 * KL(Q||M)` where :math:`M = 0.5 * (P + Q)`</span>
+<span class="sd">    Generalization of regularization term proposed by Nibali et al. (2018), which only considers</span>
+<span class="sd">    Multivariate Gaussian distributions, to a generalized Gaussian distribution. (However, now</span>
+<span class="sd">    we only consider the Gaussian and the Laplace distribution.)</span>
+
+<span class="sd">        source: Numerical Coordinate Regression with Convolutional Neural Networks - Nibali et al.</span>
+<span class="sd">            (2018)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        alpha (float, optional): Weight of the regularization term. Defaults to 1.0.</span>
+<span class="sd">        sigma_t (float, optional): Target sigma value. Defaults to 1.0.</span>
+<span class="sd">        rotation_t (float, optional): Target rotation value. Defaults to 0.0.</span>
+<span class="sd">        nb_landmarks (int, optional): Number of landmarks. Defaults to 1.</span>
+<span class="sd">        heatmap_fun (str, optional): Specifies the type of heatmap function to use. Defaults to</span>
+<span class="sd">            &#39;gaussian&#39;. Possible values are &#39;gaussian&#39; and &#39;laplacian&#39;.</span>
+<span class="sd">        heatmap_size (tuple[int, int], optional): Size of the heatmap. Defaults to (512, 512).</span>
+<span class="sd">        gamma (Optional[float], optional): Gamma value for the Laplace distribution. Defaults to</span>
+<span class="sd">            1.0.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">        eps (float, optional): Epsilon value to avoid division by zero. Defaults to 1e-6.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># TODO: Implement generalized Gaussian distribution. (Currently only Gaussian and Laplace)</span>
+
+<div class="viewcode-block" id="EuclideanDistanceJSDivergenceReg.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">sigma_t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">rotation_t</span><span class="p">:</span> <span class="nb">float</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>
+        <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">heatmap_fun</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">,</span>
+        <span class="n">heatmap_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="mi">512</span><span class="p">),</span>
+        <span class="n">gamma</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+        <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-6</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span><span class="p">:</span> <span class="n">HeatmapGenerator</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">heatmap_size</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only 2D heatmaps are supported.&quot;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">heatmap_fun</span> <span class="o">==</span> <span class="s2">&quot;gaussian&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span> <span class="o">=</span> <span class="n">GaussianHeatmapGenerator</span><span class="p">(</span>
+                <span class="n">nb_landmarks</span><span class="o">=</span><span class="n">nb_landmarks</span><span class="p">,</span>
+                <span class="n">sigmas</span><span class="o">=</span><span class="n">sigma_t</span><span class="p">,</span>
+                <span class="n">rotation</span><span class="o">=</span><span class="n">rotation_t</span><span class="p">,</span>
+                <span class="n">heatmap_size</span><span class="o">=</span><span class="n">heatmap_size</span><span class="p">,</span>
+                <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">elif</span> <span class="n">heatmap_fun</span> <span class="o">==</span> <span class="s2">&quot;laplacian&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span> <span class="o">=</span> <span class="n">LaplacianHeatmapGenerator</span><span class="p">(</span>
+                <span class="n">nb_landmarks</span><span class="o">=</span><span class="n">nb_landmarks</span><span class="p">,</span>
+                <span class="n">sigmas</span><span class="o">=</span><span class="n">sigma_t</span><span class="p">,</span>
+                <span class="n">rotation</span><span class="o">=</span><span class="n">rotation_t</span><span class="p">,</span>
+                <span class="n">heatmap_size</span><span class="o">=</span><span class="n">heatmap_size</span><span class="p">,</span>
+                <span class="n">gamma</span><span class="o">=</span><span class="n">gamma</span><span class="p">,</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid heatmap function: </span><span class="si">{</span><span class="n">heatmap_fun</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="EuclideanDistanceJSDivergenceReg.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">pred_heatmap</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            pred_heatmap (torch.Tensor): Predicted heatmap.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">heatmap_t</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">heatmap_fun</span><span class="p">(</span><span class="n">target</span><span class="p">)</span>
+        <span class="c1"># Normalize heatmaps to sum to 1</span>
+        <span class="n">heatmap_t</span> <span class="o">=</span> <span class="p">(</span><span class="n">heatmap_t</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">heatmap_t</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+        <span class="n">pred_heatmap</span> <span class="o">=</span> <span class="p">(</span><span class="n">pred_heatmap</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span>
+            <span class="n">pred_heatmap</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">),</span> <span class="n">keepdim</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span>
+        <span class="p">)</span>
+        <span class="n">reg</span> <span class="o">=</span> <span class="n">js_div_loss_2d</span><span class="p">(</span><span class="n">pred_heatmap</span><span class="p">,</span> <span class="n">heatmap_t</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">_euclidean_distance</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">target</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">*</span> <span class="n">reg</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="MultivariateGaussianNLLLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">MultivariateGaussianNLLLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Negative log-likelihood loss for multivariate Gaussian distributions. The loss function is</span>
+<span class="sd">    defined as the negative log-likelihood of the predicted coordinates given the predicted</span>
+<span class="sd">    covariance matrix. The loss function is defined as:</span>
+<span class="sd">        :math:`NLL = 0.5 * (log(det(Cov)) + (x - mu)^T * Cov^{-1} * (x - mu))`</span>
+
+<span class="sd">        As proposed in: &quot;UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood</span>
+<span class="sd">            Loss&quot; - Kumar et al. (2019)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">        eps (float, optional): Epsilon value to avoid division by zero. Defaults to 1e-6.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="MultivariateGaussianNLLLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">eps</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-6</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">eps</span> <span class="o">=</span> <span class="n">eps</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span></div>
+
+
+<div class="viewcode-block" id="MultivariateGaussianNLLLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">cov</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            cov (torch.Tensor): Related covariance matrix of the predicted coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="mf">0.5</span> <span class="o">*</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">eps</span><span class="p">)</span>
+            <span class="o">+</span> <span class="p">(</span>
+                <span class="p">(</span><span class="n">target</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span>
+<div class="viewcode-block" id="WingLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.WingLoss">[docs]</a>
+                <span class="o">@</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span>
+                <span class="o">@</span> <span class="p">(</span><span class="n">target</span> <span class="o">-</span> <span class="n">pred</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="p">)</span>
+            <span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+            <span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">WingLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Wing loss, proposed for facial landmark detection by Feng et al. (2018), is a piece-wise loss</span>
+<span class="sd">    function that focusses more attention on small and medium range erros compared to L2, L1, and</span>
+<span class="sd">    smooth L1. It has large gradient when the error is small and a constant gradient when the error</span>
+<span class="sd">    is large.</span>
+<span class="sd">    The loss function is defined as:</span>
+
+<span class="sd">    .. math::</span>
+<span class="sd">        Wing(x, y) =</span>
+<span class="sd">        \begin{cases}</span>
+<span class="sd">            \omega * log(1 + \frac{|x - y|}{\epsilon}) &amp; \text{if } |x - y| &lt; \omega \\</span>
+<span class="sd">            |x - y| - C &amp; \text{otherwise}</span>
+<span class="sd">        \end{cases}</span>
+
+<span class="sd">    where :math:`C = \omega - \omega * log(1 + \frac{\omega}{\epsilon})`</span>
+<span class="sd">        source: &quot;Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural</span>
+<span class="sd">            Networks&quot; - Feng et al. (2018)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        omega (float, optional): Wing loss parameter. Defaults to 5.0.</span>
+<span class="sd">        epsilon (float, optional): Wing loss parameter. Defaults to 0.5.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="WingLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.WingLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">omega</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">5.0</span><span class="p">,</span> <span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">c</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only 2D heatmaps are supported.&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="WingLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.WingLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">diff_abs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
+            <span class="n">diff_abs</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">diff_abs</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">),</span>
+            <span class="n">diff_abs</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">c</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="AdaptiveWingLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.AdaptiveWingLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">AdaptiveWingLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adaptive wing loss is a loss function that behaves like a smoothed Wing loss when the target is</span>
+<span class="sd">    close to 1 and like the MSE loss when the target is close to 0.</span>
+
+<span class="sd">    The loss function is defined as:</span>
+
+<span class="sd">    .. math::</span>
+<span class="sd">        AWing(x, y) =</span>
+<span class="sd">        \begin{cases}</span>
+<span class="sd">            \omega \log(1 + |\frac{x-y}{\epsilon}|^{\alpha-y} &amp;</span>
+<span class="sd">                \text{if } |x - y| &lt; \theta \\</span>
+<span class="sd">            A|x - y| - C &amp; \text{otherwise}</span>
+<span class="sd">        \end{cases}})</span>
+<span class="sd">    where :math:`A = \omega (1/(1+(\theta/\epsilon)^{\alpha-y}))(\alpha - y)</span>
+<span class="sd">                    ((\theta/\epsilon)^(\alpha-y-1))(1/\epsilon)` and</span>
+<span class="sd">                :math:`C = (\theta * A - \omega * log(1 + (\theta/\epsilon)^{\alpha-y})))`</span>
+<span class="sd">        source: &quot;Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression&quot; - Wang et al.</span>
+<span class="sd">            (2019)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        omega (float, optional): Adaptive wing loss parameter. Defaults to 5.0.</span>
+<span class="sd">        epsilon (float, optional): Adaptive wing loss parameter. Defaults to 0.5.</span>
+<span class="sd">        alpha (float, optional): Adaptive wing loss parameter. Defaults to 2.1.</span>
+<span class="sd">        theta (float, optional): Adaptive wing loss parameter. Defaults to 0.5.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="AdaptiveWingLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.AdaptiveWingLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">omega</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
+        <span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">2.1</span><span class="p">,</span>
+        <span class="n">theta</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">=</span> <span class="n">theta</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Only 2D heatmaps are supported.&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="AdaptiveWingLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.AdaptiveWingLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">diff_abs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">pred</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">omega</span>
+            <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">target</span><span class="p">)))</span>
+            <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span>
+            <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">target</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">c</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">*</span> <span class="n">a</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span>
+            <span class="mi">1</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">target</span><span class="p">)</span>
+        <span class="p">)</span>
+        <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">where</span><span class="p">(</span>
+            <span class="n">diff_abs</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta</span><span class="p">,</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">diff_abs</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">-</span> <span class="n">target</span><span class="p">)),</span>
+            <span class="n">a</span> <span class="o">*</span> <span class="n">diff_abs</span> <span class="o">-</span> <span class="n">c</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="StarLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StarLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">StarLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Self-adapTive Ambiguity Reduction (STAR) loss. Star loss takes into account the ambuigity</span>
+<span class="sd">    (uncertainty) of the intermediate heatmap predictions by using the covariance matrix of the</span>
+<span class="sd">    heatmap predictions, and extracting the eigenvectors and eigenvalues of the covariance matrix.</span>
+
+<span class="sd">        source: &quot;STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection&quot; - Zhou et al.</span>
+<span class="sd">            (2023)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        omega (float, optional): Weight of the regularization term. Defaults to 1.0.</span>
+<span class="sd">        distance (str, optional): Distance function to use for the loss calculation. Defaults to</span>
+<span class="sd">            &#39;l2&#39;. Possible values are &#39;l2&#39;, &#39;l1&#39;, &#39;smooth-l1&#39;.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">        **kwargs: Additional keyword arguments for the distance function.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="StarLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StarLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">omega</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+        <span class="n">distance</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;l2&quot;</span><span class="p">,</span>
+        <span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1e-5</span><span class="p">,</span>
+        <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span>
+        <span class="o">**</span><span class="n">kwargs</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">=</span> <span class="n">omega</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">=</span> <span class="n">distance</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;l2&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">mse_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;l1&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">l1_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">distance</span> <span class="o">==</span> <span class="s2">&quot;smooth-l1&quot;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">F</span><span class="o">.</span><span class="n">smooth_l1_loss</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid distance function: </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">distance</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span></div>
+
+
+<div class="viewcode-block" id="StarLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StarLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">pred</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">cov</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            pred (torch.Tensor): Predicted coordinates.</span>
+<span class="sd">            cov (torch.Tensor): Related covariance matrix of the predicted coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target coordinates.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">eig_values</span><span class="p">,</span> <span class="n">eig_vecs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">eig</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span>
+        <span class="n">eig_values</span> <span class="o">=</span> <span class="n">eig_values</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="n">eig_vecs</span> <span class="o">=</span> <span class="n">eig_vecs</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span>
+                <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+            <span class="p">)</span> <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span>
+                <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+            <span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="p">(</span>
+                <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span>
+                <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="p">)</span>
+                <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span>
+                <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="p">)</span>
+                <span class="o">+</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">eig_values</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span>
+                <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">dist_func</span><span class="p">(</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">pred</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                    <span class="n">eig_vecs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">)</span> <span class="o">@</span> <span class="n">target</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+        <span class="n">loss</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">omega</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">eig_values</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="n">loss</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">loss</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">loss</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="NLLLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.NLLLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">NLLLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Negative log-likelihood loss for 2D/3D heatmaps. Assumes that the input is a probability</span>
+<span class="sd">    distribution and calculates the negative log-likelihood of the predicted heatmap given the</span>
+<span class="sd">    target heatmap.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        spatial_dims (int, optional): Spatial dimension of the heatmaps. Defaults to 2.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="NLLLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.NLLLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> <span class="n">apply_softmax</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">apply_softmax</span> <span class="o">=</span> <span class="n">apply_softmax</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_softmax</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span> <span class="o">=</span> <span class="n">SoftmaxND</span><span class="p">(</span><span class="n">spatial_dims</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">spatial_dims</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;spatial_dims must be 2 or 3&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="NLLLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.NLLLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output</span><span class="p">,</span> <span class="n">target</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply_softmax</span><span class="p">:</span>
+            <span class="n">output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">output</span><span class="p">)</span>
+        <span class="n">nll</span> <span class="o">=</span> <span class="o">-</span><span class="n">target</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">output</span><span class="o">.</span><span class="n">double</span><span class="p">())</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">nll</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">nll</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">nll</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="StackedLoss">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StackedLoss">[docs]</a>
+<span class="k">class</span> <span class="nc">StackedLoss</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Stacked loss function. Applies a specified loss function to each list of predictions. This</span>
+<span class="sd">    loss function is used to calculate the loss for each heatmap in the stacked heatmap regression,</span>
+<span class="sd">    suchs as stacked hourglass and U-Net networks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        loss_fn (nn.Module): Loss function to use.</span>
+<span class="sd">        reduction (str, optional): Specifies the reduction to apply to the output. Defaults to</span>
+<span class="sd">            &#39;mean&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="StackedLoss.__init__">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StackedLoss.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">loss_fn</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span> <span class="o">=</span> <span class="n">loss_fn</span><span class="p">(</span><span class="n">reduction</span><span class="o">=</span><span class="n">reduction</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">=</span> <span class="n">reduction</span>
+        <span class="k">if</span> <span class="n">reduction</span> <span class="ow">not</span> <span class="ow">in</span> <span class="p">[</span><span class="s2">&quot;mean&quot;</span><span class="p">,</span> <span class="s2">&quot;sum&quot;</span><span class="p">,</span> <span class="s2">&quot;none&quot;</span><span class="p">]:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid reduction: </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="StackedLoss.forward">
+<a class="viewcode-back" href="../../../reference/losses.html#landmarker.losses.StackedLoss.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">preds</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">target</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            preds(list[torch.Tensor]): List of predicted heatmaps or coordinates.</span>
+<span class="sd">            target (torch.Tensor): Target heatmap.</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">losses</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">pred</span> <span class="ow">in</span> <span class="n">preds</span><span class="p">:</span>
+            <span class="n">losses</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">pred</span><span class="p">,</span> <span class="n">target</span><span class="p">))</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;sum&quot;</span><span class="p">:</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">losses</span><span class="p">)</span></div>
+</div>
+
+
+
+<span class="k">def</span> <span class="nf">_euclidean_distance</span><span class="p">(</span><span class="n">x1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">x2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Computes the euclidean distance between two tensors.&quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">x1</span> <span class="o">-</span> <span class="n">x2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="n">dim</span><span class="o">=-</span><span class="mi">1</span><span class="p">))</span>
+</pre></div>
+        </article>
+      </div>
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diff --git a/_modules/landmarker/metrics/metrics.html b/_modules/landmarker/metrics/metrics.html
new file mode 100644
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+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../../../">
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+
+    <link rel="shortcut icon" href="../../../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
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+          <h1>Source code for landmarker.metrics.metrics</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Metrics for evaluating the performance of landmark detection models.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">collections.abc</span> <span class="kn">import</span> <span class="n">Iterable</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Callable</span><span class="p">,</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.utils.utils</span> <span class="kn">import</span> <span class="n">pixel_to_unit</span>
+
+
+<div class="viewcode-block" id="point_error">
+<a class="viewcode-back" href="../../../reference/metrics.html#landmarker.metrics.point_error">[docs]</a>
+<span class="k">def</span> <span class="nf">point_error</span><span class="p">(</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the point error between true and predicted landmarks. The point error is the mean</span>
+<span class="sd">    Euclidean distance between the true and predicted landmarks. If the pixel spacing is given, the</span>
+<span class="sd">    point error is calculated in mm. If the pixel spacing is not given, the point error is</span>
+<span class="sd">    calculated in pixels. If the dim and dim_orig are given, the landmarks are rescaled to the</span>
+<span class="sd">    original image size.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        true_landmarks (torch.Tensor): true landmarks</span>
+<span class="sd">        pred_landmarks (torch.Tensor): predicted landmarks</span>
+<span class="sd">        dim (Optional[tuple[int, ...] | torch.Tensor], optional): image size. Defaults to None.</span>
+<span class="sd">        dim_orig (Optional[torch.Tensor], optional): original image size. Defaults to None.</span>
+<span class="sd">        pixel_spacing (Optional[torch.Tensor], optional): pixel spacing. Defaults to None.</span>
+<span class="sd">        padding (Optional[torch.Tensor], optional): padding. Defaults to None.</span>
+<span class="sd">        reduction (str, optional): reduction method. Defaults to &quot;mean&quot;. Can be &quot;mean&quot;, &quot;nanmean&quot;,</span>
+<span class="sd">            or &quot;none&quot;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="n">true_landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+    <span class="n">true_landmarks</span> <span class="o">=</span> <span class="n">pixel_to_unit</span><span class="p">(</span>
+        <span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span>
+    <span class="p">)</span>
+    <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="n">pixel_to_unit</span><span class="p">(</span>
+        <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span> <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span>
+    <span class="p">)</span>
+
+    <span class="c1"># Calculate MRE</span>
+    <span class="k">if</span> <span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">true_landmarks</span> <span class="o">-</span> <span class="n">pred_landmarks</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
+    <span class="k">if</span> <span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;none&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">true_landmarks</span> <span class="o">-</span> <span class="n">pred_landmarks</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;nanmean&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">true_landmarks</span> <span class="o">-</span> <span class="n">pred_landmarks</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)))</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Reduction method </span><span class="si">{</span><span class="n">reduction</span><span class="si">}</span><span class="s2"> not supported.&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">multi_instance_point_error</span><span class="p">(</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="nb">list</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">reduction</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mean&quot;</span><span class="p">,</span>
+    <span class="n">threshold</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="p">(</span>
+    <span class="nb">tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
+    <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the multi-instance point error between true and predicted landmarks. The multi-</span>
+<span class="sd">    instance point error is the mean Euclidean distance between the true and predicted landmarks</span>
+<span class="sd">    for each instance. If the pixel spacing is given, the point error is calculated in mm. If the</span>
+<span class="sd">    pixel spacing is not given, the point error is calculated in pixels. If the dim and dim_orig</span>
+<span class="sd">    are given, the landmarks are rescaled to the original image size. The multi-instance point</span>
+<span class="sd">    error is the mean of the instance point errors. Next to the multi-instance point error, the</span>
+<span class="sd">    true positive and false positive rates are returned.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        true_landmarks (list | torch.Tensor): true landmarks</span>
+<span class="sd">        pred_landmarks (list | torch.Tensor): predicted landmarks</span>
+<span class="sd">        dim (Optional[tuple[int, ...] | torch.Tensor | list[tuple[int, ...]]], optional): image</span>
+<span class="sd">            size. Defaults to None.</span>
+<span class="sd">        dim_orig (Optional[torch.Tensor], optional): original image size. Defaults to None.</span>
+<span class="sd">        pixel_spacing (Optional[torch.Tensor], optional): pixel spacing. Defaults to None.</span>
+<span class="sd">        padding (Optional[torch.Tensor], optional): padding. Defaults to None.</span>
+<span class="sd">        reduction (str, optional): reduction method. Defaults to &quot;mean&quot;. Can be &quot;mean&quot; or &quot;none&quot;.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        tuple[float, float, float, float, torch.Tensor] | tuple[torch.Tensor, torch.Tensor,</span>
+<span class="sd">            torch.Tensor, torch.Tensor, torch.Tensor]: multi-instance point error, true positive,</span>
+<span class="sd">            false positive, false negative, predicted landmarks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="n">transform_multi_instance_tensor</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[[</span><span class="n">pred_landmarks</span><span class="p">]]</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
+        <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[</span><span class="n">pred_landmarks</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">)</span>
+    <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">dim</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+    <span class="k">if</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">dim_orig</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+    <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+    <span class="k">if</span> <span class="n">padding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">padding</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">):</span>
+        <span class="n">dim</span> <span class="o">=</span> <span class="p">[</span><span class="n">dim</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">dim_orig</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">dim_orig</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">dim_orig</span> <span class="o">=</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">pixel_spacing</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">padding</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="n">padding</span> <span class="o">=</span> <span class="n">padding</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
+    <span class="n">pe</span><span class="p">[</span><span class="n">pe</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
+    <span class="n">tp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
+    <span class="n">fp</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
+    <span class="n">fn</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
+    <span class="n">pred_landmarks_torch</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">)</span>
+    <span class="n">pred_landmarks_torch</span><span class="p">[</span><span class="n">pred_landmarks_torch</span> <span class="o">==</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;nan&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">b</span><span class="p">,</span> <span class="n">landmark_batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">landmark_class</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">landmark_batch</span><span class="p">):</span>
+            <span class="n">picked_true</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">]))</span>
+            <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">pred_landmark_instance</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">]):</span>
+                <span class="n">best_pe</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="s2">&quot;inf&quot;</span><span class="p">)</span>
+                <span class="n">best_j</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                <span class="k">for</span> <span class="n">j</span><span class="p">,</span> <span class="n">true_landmark_instance</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">landmark_class</span><span class="p">):</span>
+                    <span class="k">if</span> <span class="n">picked_true</span><span class="p">[</span><span class="n">j</span><span class="p">]:</span>
+                        <span class="k">continue</span>
+                    <span class="k">assert</span> <span class="n">pred_landmark_instance</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">true_landmark_instance</span><span class="o">.</span><span class="n">shape</span>
+                    <span class="n">pe_inst</span> <span class="o">=</span> <span class="n">point_error</span><span class="p">(</span>
+                        <span class="n">true_landmark_instance</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+                        <span class="n">pred_landmark_instance</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+                        <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">[</span><span class="n">b</span><span class="p">],</span>
+                        <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">[</span><span class="n">b</span><span class="p">],</span>  <span class="c1"># type: ignore</span>
+                        <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">[</span><span class="n">b</span><span class="p">],</span>  <span class="c1"># type: ignore</span>
+                        <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">[</span><span class="n">b</span><span class="p">],</span>  <span class="c1"># type: ignore</span>
+                    <span class="p">)</span>  <span class="c1"># type: ignore</span>
+                    <span class="k">if</span> <span class="n">pe_inst</span> <span class="o">&lt;</span> <span class="n">best_pe</span><span class="p">:</span>
+                        <span class="n">best_pe</span> <span class="o">=</span> <span class="n">pe_inst</span>
+                        <span class="n">best_j</span> <span class="o">=</span> <span class="n">j</span>
+                <span class="k">if</span> <span class="n">best_j</span> <span class="o">!=</span> <span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="n">picked_true</span><span class="p">[</span><span class="n">best_j</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span>
+                    <span class="k">if</span> <span class="n">pe</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span><span class="o">.</span><span class="n">isnan</span><span class="p">():</span>
+                        <span class="n">pe</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
+                    <span class="n">pe</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">+=</span> <span class="n">best_pe</span>
+                    <span class="n">pred_landmarks_torch</span><span class="p">[</span>
+                        <span class="n">b</span><span class="p">,</span>
+                        <span class="n">c</span><span class="p">,</span>
+                        <span class="n">best_j</span><span class="p">,</span>
+                    <span class="p">]</span> <span class="o">=</span> <span class="n">pred_landmark_instance</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">pe</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">/=</span> <span class="nb">min</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark_class</span><span class="p">))</span>
+            <span class="n">tp</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">picked_true</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+            <span class="n">fn</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">])</span> <span class="o">-</span> <span class="n">tp</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span>
+            <span class="n">fp</span><span class="p">[</span><span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">]</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="n">b</span><span class="p">][</span><span class="n">c</span><span class="p">])</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">picked_true</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span> <span class="mi">0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">reduction</span> <span class="o">==</span> <span class="s2">&quot;mean&quot;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="p">(</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">pe</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">tp</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fp</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+            <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">fn</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">(),</span>
+            <span class="n">pred_landmarks_torch</span><span class="p">,</span>
+        <span class="p">)</span>
+    <span class="k">return</span> <span class="n">pe</span><span class="p">,</span> <span class="n">tp</span><span class="p">,</span> <span class="n">fp</span><span class="p">,</span> <span class="n">fn</span><span class="p">,</span> <span class="n">pred_landmarks_torch</span>
+
+
+<span class="k">def</span> <span class="nf">transform_multi_instance_tensor</span><span class="p">(</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Transforms a multi-instance landmarks into a lists of landmarks. Where nan values are not</span>
+<span class="sd">        included, since they are not landmarks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        tensor (torch.Tensor): multi-instance tensor</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        list[torch.Tensor] | list[list[torch.Tensor]] | list[list[list[torch.Tensor]]]: list of</span>
+<span class="sd">            tensors</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span>
+            <span class="s2">&quot;If true_landmarks is a torch.Tensor, it must have at most 4 &quot;</span>
+            <span class="o">+</span> <span class="s2">&quot;dimensions. and at least 2 dimensions.&quot;</span>
+        <span class="p">)</span>
+    <span class="n">landmarks_list</span><span class="p">:</span> <span class="p">(</span>
+        <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]]</span> <span class="o">|</span> <span class="nb">list</span>
+    <span class="p">)</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">landmarks_list</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+            <span class="k">continue</span>
+        <span class="n">landmarks_list</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>  <span class="c1"># type: ignore</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">isnan</span><span class="p">()):</span>
+                    <span class="n">landmarks_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+                <span class="k">continue</span>
+            <span class="n">landmarks_list</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">([])</span>  <span class="c1"># type: ignore</span>
+            <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">torch</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">isnan</span><span class="p">()):</span>
+                    <span class="n">landmarks_list</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">,</span> <span class="n">k</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+    <span class="k">return</span> <span class="n">landmarks_list</span>
+
+
+<div class="viewcode-block" id="sdr">
+<a class="viewcode-back" href="../../../reference/metrics.html#landmarker.metrics.sdr">[docs]</a>
+<span class="k">def</span> <span class="nf">sdr</span><span class="p">(</span>
+    <span class="n">radius</span><span class="p">:</span> <span class="n">Iterable</span> <span class="o">|</span> <span class="nb">float</span><span class="p">,</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">nanmean</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculates the success detection rate (SDR), which is the percentage of</span>
+<span class="sd">    predicted landmarks with a point error smaller or equal than the radius.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        radius (Iterable | float): radius or list of radii</span>
+<span class="sd">        true_landmarks (torch.Tensor): true landmarks</span>
+<span class="sd">        pred_landmarks (torch.Tensor): predicted landmarks</span>
+<span class="sd">        dim (Optional[tuple[int, ...] | torch.Tensor], optional): image size. Defaults to None.</span>
+<span class="sd">        dim_orig (Optional[torch.Tensor], optional): original image size. Defaults to None.</span>
+<span class="sd">        pixel_spacing (Optional[torch.Tensor], optional): pixel spacing. Defaults to None.</span>
+<span class="sd">        padding (Optional[torch.Tensor], optional): padding. Defaults to None.</span>
+<span class="sd">        nanmean (bool, optional): use nanmean instead of mean. Defaults to False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        float | dict[float, float]: SDR or dictionary of SDRs for each radius</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">point_error_</span> <span class="o">=</span> <span class="n">point_error</span><span class="p">(</span>
+        <span class="n">true_landmarks</span><span class="p">,</span>
+        <span class="n">pred_landmarks</span><span class="p">,</span>
+        <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span>
+        <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span>
+        <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+        <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
+        <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span><span class="p">,</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="n">nanmean</span><span class="p">:</span>
+        <span class="n">reduce_func</span><span class="p">:</span> <span class="n">Callable</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">nanmean</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">reduce_func</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">mean</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">radius</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">):</span>
+        <span class="k">return</span> <span class="p">{</span><span class="n">r</span><span class="p">:</span> <span class="n">reduce_func</span><span class="p">((</span><span class="n">point_error_</span> <span class="o">&lt;=</span> <span class="n">r</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">())</span><span class="o">.</span><span class="n">item</span><span class="p">()</span> <span class="o">*</span> <span class="mf">100.0</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">radius</span><span class="p">}</span>
+    <span class="k">return</span> <span class="p">(</span><span class="n">reduce_func</span><span class="p">((</span><span class="n">point_error_</span> <span class="o">&lt;=</span> <span class="n">radius</span><span class="p">)</span><span class="o">.</span><span class="n">float</span><span class="p">())</span> <span class="o">*</span> <span class="mi">100</span><span class="p">)</span><span class="o">.</span><span class="n">item</span><span class="p">()</span></div>
+
+</pre></div>
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+          <h1>Source code for landmarker.models.cholesky_hourglass</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Stacked Cholesky Hourglass Network as proposed in &quot;UGLLI Face Alignment: Estimating Uncertainty</span>
+<span class="sd">with with Gaussian Log-Likelihood Loss&quot; - Kumar et al. (2019)</span>
+
+<span class="sd">#TODO: Check if the implementation is correct. The LUVLi paper seems to provide some code.</span>
+<span class="sd"># see: https://github.com/abhi1kumar/LUVLi</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">monai.networks.blocks</span> <span class="kn">import</span> <span class="n">Convolution</span><span class="p">,</span> <span class="n">ResidualUnit</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.models.hourglass</span> <span class="kn">import</span> <span class="n">Hourglass</span>
+
+
+<div class="viewcode-block" id="StackedCholeskyHourglass">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedCholeskyHourglass">[docs]</a>
+<span class="k">class</span> <span class="nc">StackedCholeskyHourglass</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stacked Cholesky Hourglass Network as proposed in &quot;UGLLI Face Alignment: Estimating Uncertainty</span>
+<span class="sd">    with Gaussian Log-Likelihood Loss&quot; - Kumar et al. (2019).  It is a stack of hourglass networks</span>
+<span class="sd">    with a Cholesky Estimator Network at the bottleneck of each hourglass. The output of the</span>
+<span class="sd">    Cholesky Estimator Network is a lower triangular matrix that is used to estimate the covariance</span>
+<span class="sd">    matrix of the Gaussian distribution of the predicted heatmaps. The covariance matrix is then</span>
+<span class="sd">    used to compute the Gaussian Log-Likelihood Loss.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_stacks (int): number of hourglass networks to stack.</span>
+<span class="sd">        img_size (tuple[int, int]): size of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        channels (Sequence[int], optional): number of output channels for each convolutional layer.</span>
+<span class="sd">        conv_block (nn.Module, optional): convolutional block to use. Defaults to ResidualUnit.</span>
+<span class="sd">        up_sample_mode (str, optional): upsampling mode. Defaults to &#39;nearest&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="StackedCholeskyHourglass.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedCholeskyHourglass.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_stacks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">img_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
+        <span class="n">conv_block</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">ResidualUnit</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">up_sample_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_stacks</span> <span class="o">=</span> <span class="n">nb_stacks</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">stacks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">CholeskyHourglass</span><span class="p">(</span>
+                    <span class="n">img_size</span><span class="o">=</span><span class="n">img_size</span><span class="p">,</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                    <span class="n">channels</span><span class="o">=</span><span class="n">channels</span><span class="p">,</span>
+                    <span class="n">conv_block</span><span class="o">=</span><span class="n">conv_block</span><span class="p">,</span>
+                    <span class="n">up_sample_mode</span><span class="o">=</span><span class="n">up_sample_mode</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nb_stacks</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_to_feature_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">Convolution</span><span class="p">(</span>
+                    <span class="n">spatial_dims</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">img_size</span><span class="p">),</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                    <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">,</span>
+                    <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;batch&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nb_stacks</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="StackedCholeskyHourglass.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedCholeskyHourglass.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            heatmaps: list of predicted heatmaps of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">            cens: list of covariance matrices of the predicted heatmaps of shape (batch_size,</span>
+<span class="sd">                out_channels, 2, 2)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">heatmaps</span><span class="p">,</span> <span class="n">cens</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+        <span class="n">next_x</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">stack</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stacks</span><span class="p">):</span>
+            <span class="n">pred</span><span class="p">,</span> <span class="n">output_cen</span> <span class="o">=</span> <span class="n">stack</span><span class="p">(</span><span class="n">next_x</span><span class="p">)</span>
+            <span class="n">cens</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output_cen</span><span class="p">)</span>
+            <span class="n">heatmaps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_stacks</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">next_x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_feature_block</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">pred</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">heatmaps</span><span class="p">,</span> <span class="n">cens</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="CholeskyHourglass">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CholeskyHourglass">[docs]</a>
+<span class="k">class</span> <span class="nc">CholeskyHourglass</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Proposed in &quot;UGLLI Face Alignment: Estimating Uncertainty with</span>
+<span class="sd">    Gaussian Log-Likelihood Loss&quot; - Kumar et al. (2019)</span>
+<span class="sd">    # TODO: Note that the implementation of Kumar et al. use DU-Net as the backbone.</span>
+<span class="sd">    # We use the residual hourglass.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        img_size (tuple[int, int]): size of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        channels (Sequence[int], optional): number of output channels for each convolutional layer.</span>
+<span class="sd">        subunits (int, optional): number of subunits in each convolutional layer.</span>
+<span class="sd">        up_sample_mode (str, optional): upsampling mode. Defaults to &#39;nearest&#39;.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        pred: predicted heatmaps of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        cen: covariance matrices of the predicted heatmaps of shape (batch_size,</span>
+<span class="sd">            out_channels, 2, 2)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CholeskyHourglass.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CholeskyHourglass.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">img_size</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
+        <span class="n">conv_block</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">ResidualUnit</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">up_sample_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hg</span> <span class="o">=</span> <span class="n">Hourglass</span><span class="p">(</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">img_size</span><span class="p">),</span>
+            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+            <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+            <span class="n">channels</span><span class="o">=</span><span class="n">channels</span><span class="p">,</span>
+            <span class="n">conv_block</span><span class="o">=</span><span class="n">conv_block</span><span class="p">,</span>
+            <span class="n">up_sample_mode</span><span class="o">=</span><span class="n">up_sample_mode</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">img_size</span> <span class="o">=</span> <span class="n">img_size</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">cen</span> <span class="o">=</span> <span class="n">CholeskyBlock</span><span class="p">(</span>
+            <span class="n">nb_classes</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+            <span class="n">input_units</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">img_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)))</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span>
+            <span class="n">hidden_units</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span>
+            <span class="n">class_output_units</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CholeskyHourglass.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CholeskyHourglass.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            pred: predicted heatmaps of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">            cen: covariance matrices of the predicted heatmaps of shape (batch_size,</span>
+<span class="sd">                out_channels, 2, 2)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">down_outputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">down_block</span><span class="p">,</span> <span class="n">skip_block</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hg</span><span class="o">.</span><span class="n">down_blocks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">hg</span><span class="o">.</span><span class="n">skip_blocks</span><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">down_block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">down_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">skip_block</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hg</span><span class="o">.</span><span class="n">neck_block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">cen_output</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">cen</span><span class="p">(</span><span class="n">x</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">for</span> <span class="n">up_block</span><span class="p">,</span> <span class="n">skip_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hg</span><span class="o">.</span><span class="n">up_blocks</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">down_outputs</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">up_block</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">skip_output</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">hg</span><span class="o">.</span><span class="n">last_layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">x</span><span class="p">,</span> <span class="n">cen_output</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">CholeskyBlock</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Cholesky Estimator Network tries to estimate the Cholesky decomposition of the covariance matrix</span>
+<span class="sd">    out of the bottleneck of each hourglass of the stack.</span>
+
+<span class="sd">    Proposed in &quot;UGLLI Face Alignment: Estimating Uncertainty with</span>
+<span class="sd">    Gaussian Log-Likelihood Loss&quot; - Kumar et al. (2019)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_classes (int): number of output classes.</span>
+<span class="sd">        input_units (int): number of input units.</span>
+<span class="sd">        hidden_units (int): number of hidden units.</span>
+<span class="sd">        class_output_units (int): number of output units.</span>
+<span class="sd">        elu_alpha (float, optional): alpha parameter of the ELU activation function.</span>
+<span class="sd">            Defaults to 1.0.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_classes</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">input_units</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">hidden_units</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">class_output_units</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">elu_alpha</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">1.0</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_classes</span> <span class="o">=</span> <span class="n">nb_classes</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">input_units</span> <span class="o">=</span> <span class="n">input_units</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">hidden_units</span> <span class="o">=</span> <span class="n">hidden_units</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">class_output_units</span> <span class="o">=</span> <span class="n">class_output_units</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">seq</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">input_units</span><span class="p">,</span> <span class="n">hidden_units</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">ReLU</span><span class="p">(),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="n">hidden_units</span><span class="p">,</span> <span class="n">nb_classes</span> <span class="o">*</span> <span class="n">class_output_units</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">elu_alpha</span> <span class="o">=</span> <span class="n">elu_alpha</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">elu</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ELU</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">elu_alpha</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, input_units)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            cov: covariance matrices of the predicted heatmaps of shape (batch_size,</span>
+<span class="sd">                out_channels, 2, 2)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">cholesky_decom</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">seq</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">view</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_classes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">class_output_units</span><span class="p">)</span>
+        <span class="c1"># Transform the output of the network to a lower triangular matrix</span>
+        <span class="n">l_mat</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span>
+            <span class="p">(</span><span class="n">cholesky_decom</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_classes</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">cholesky_decom</span><span class="o">.</span><span class="n">device</span>
+        <span class="p">)</span>
+        <span class="n">l_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">elu</span><span class="p">(</span><span class="n">cholesky_decom</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">elu_alpha</span>
+        <span class="n">l_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">cholesky_decom</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">]</span>
+        <span class="n">l_mat</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">elu</span><span class="p">(</span><span class="n">cholesky_decom</span><span class="p">[:,</span> <span class="p">:,</span> <span class="mi">2</span><span class="p">])</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">elu_alpha</span>
+        <span class="n">cov</span> <span class="o">=</span> <span class="n">l_mat</span> <span class="o">@</span> <span class="n">l_mat</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">cov</span>
+</pre></div>
+        </article>
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+          <h1>Source code for landmarker.models.coord_conv</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;CoordConv implementation proposed in &quot;An intriguing failing of convolutional neural networks and</span>
+<span class="sd">    the CoordConv solution&quot; - Liu et al.&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+<span class="kn">from</span> <span class="nn">monai.networks.blocks</span> <span class="kn">import</span> <span class="n">ResidualUnit</span>
+
+
+<div class="viewcode-block" id="CoordConvLayer">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CoordConvLayer">[docs]</a>
+<span class="k">class</span> <span class="nc">CoordConvLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    CoordConv is a convolutional layer that adds the x and y coordinates of each pixel as additional</span>
+<span class="sd">    channels to the input tensor. Optionally, it can also add the radial distance of each pixel to</span>
+<span class="sd">    the center of the image as an additional channel. This is done to provide the network with</span>
+<span class="sd">    spatial information.</span>
+<span class="sd">        source: &quot;An intriguing failing of convolutional neural networks and the CoordConv</span>
+<span class="sd">            solution&quot; - Liu et al.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        radial_channel (bool, optional): whether to add the radial distance of each pixel to the</span>
+<span class="sd">            center of the image as an additional channel. Defaults to False.</span>
+<span class="sd">        conv_block (nn.Module, optional): convolutional block to use. Defaults to ResidualUnit.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="CoordConvLayer.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CoordConvLayer.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+                 <span class="n">radial_channel</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+                 <span class="n">conv_block</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">ResidualUnit</span>  <span class="c1"># type: ignore[assignment]</span>
+                 <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">radial_channel</span> <span class="o">=</span> <span class="n">radial_channel</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">add_coord_channels</span> <span class="o">=</span> <span class="n">AddCoordChannels</span><span class="p">(</span><span class="n">radial_channel</span><span class="o">=</span><span class="n">radial_channel</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">conv_block</span> <span class="o">=</span> <span class="n">conv_block</span><span class="p">(</span><span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                                     <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span> <span class="o">+</span> <span class="mi">2</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="n">radial_channel</span><span class="p">),</span>
+                                     <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="CoordConvLayer.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.CoordConvLayer.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *spatial_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            output tensor of shape (batch_size, out_channels, *spatial_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">add_coord_channels</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv_block</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="AddCoordChannels">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.AddCoordChannels">[docs]</a>
+<span class="k">class</span> <span class="nc">AddCoordChannels</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adds the x and y coordinates of each pixel as additional channels to the input tensor.</span>
+<span class="sd">    Optionally, it can also add the radial distance of each pixel to the center of the image as an</span>
+<span class="sd">    additional channel. This is done to provide the network with spatial information.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        radial_channel (bool, optional): whether to add the radial distance of each pixel to the</span>
+<span class="sd">            center of the image as an additional channel. Defaults to False.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="AddCoordChannels.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.AddCoordChannels.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">radial_channel</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">radial_channel</span> <span class="o">=</span> <span class="n">radial_channel</span></div>
+
+
+<div class="viewcode-block" id="AddCoordChannels.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.AddCoordChannels.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *spatial_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            output tensor of shape (batch_size, in_channels + 2 + radial_channel, *spatial_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">b</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">w</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">shape</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">h</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">xs</span><span class="p">,</span> <span class="n">ys</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s1">&#39;xy&#39;</span><span class="p">)</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">/</span> <span class="p">(</span><span class="n">w</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">/</span> <span class="p">(</span><span class="n">h</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">-</span> <span class="mi">1</span>
+        <span class="n">xs</span> <span class="o">=</span> <span class="n">xs</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">ys</span> <span class="o">=</span> <span class="n">ys</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">radial_channel</span><span class="p">:</span>
+            <span class="n">r</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span> <span class="o">+</span> <span class="n">torch</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="n">ys</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">,</span> <span class="n">r</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">xs</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span></div>
+</div>
+
+</pre></div>
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+        <article role="main">
+          <h1>Source code for landmarker.models.hourglass</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Hourglass network.</span>
+<span class="sd">Proposed in: https://github.com/princeton-vl/pytorch_stacked_hourglass/tree/master</span>
+<span class="sd">Partially inspired by: &quot;Stacked Hourglass Network for Robust Facial Landmark Localisation&quot;</span>
+<span class="sd">            - Yang et al. (2017)</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">monai.networks.blocks</span> <span class="kn">import</span> <span class="n">Convolution</span><span class="p">,</span> <span class="n">ResidualUnit</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+
+<div class="viewcode-block" id="Hourglass">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.Hourglass">[docs]</a>
+<span class="k">class</span> <span class="nc">Hourglass</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Hourglass network is a network with symmetrical encoder and decoder paths. The encoder path</span>
+<span class="sd">    downsamples the input image while the decoder path upsamples the image. Skip connections are</span>
+<span class="sd">    added between the encoder and decoder paths to preserve spatial information. This network is</span>
+<span class="sd">    used for pose estimation.</span>
+<span class="sd">        Proposed in: &quot;Stacked Hourglass Networks for Human Pose Estimation&quot; - Newell et al. (2016)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        channels (Sequence[int], optional): number of output channels for each convolutional layer.</span>
+<span class="sd">        conv_block (nn.Module, optional): convolutional block to use. Defaults to ResidualUnit.</span>
+<span class="sd">        pooling (nn.Module, optional): pooling layer to use. Defaults to nn.MaxPool2d.</span>
+<span class="sd">        up_sample_mode (str, optional): upsampling mode. Defaults to &#39;nearest&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># TODO: implment the out_channels</span>
+
+<div class="viewcode-block" id="Hourglass.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.Hourglass.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
+        <span class="n">conv_block</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">ResidualUnit</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">pooling</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">up_sample_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span> <span class="o">=</span> <span class="n">spatial_dims</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">in_channels</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">depth</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">channels</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">down_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+                    <span class="n">conv_block</span><span class="p">(</span>
+                        <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                        <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                        <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="p">),</span>
+                    <span class="n">pooling</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">up_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+                    <span class="n">conv_block</span><span class="p">(</span>
+                        <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                        <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                    <span class="p">),</span>
+                    <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">up_sample_mode</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">skip_blocks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">conv_block</span><span class="p">(</span>
+                    <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">channel</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">channels</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">down_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+                    <span class="n">conv_block</span><span class="p">(</span>
+                        <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                        <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="n">i</span><span class="p">],</span>
+                        <span class="n">out_channels</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span>
+                    <span class="p">),</span>
+                    <span class="n">pooling</span><span class="p">(</span><span class="mi">2</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">skip_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">conv_block</span><span class="p">(</span>
+                    <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channel</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">up_blocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+                    <span class="n">conv_block</span><span class="p">(</span>
+                        <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                        <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                    <span class="p">),</span>
+                    <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">up_sample_mode</span><span class="p">),</span>
+                <span class="p">)</span>
+            <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">neck_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv_block</span><span class="p">(</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span> <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+            <span class="p">),</span>
+            <span class="n">Convolution</span><span class="p">(</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">,</span>
+                <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;batch&quot;</span><span class="p">,</span>
+            <span class="p">),</span>  <span class="c1"># TODO: check if this is correct (if it makes sense)</span>
+            <span class="n">Convolution</span><span class="p">(</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">,</span>
+                <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;batch&quot;</span><span class="p">,</span>
+            <span class="p">),</span>  <span class="c1"># TODO: check if this is correct (if it makes sense)</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">Convolution</span><span class="p">(</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">,</span>
+                <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;batch&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">Convolution</span><span class="p">(</span>
+                <span class="n">spatial_dims</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">spatial_dims</span><span class="p">,</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">channels</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="Hourglass.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.Hourglass.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            output tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">down_outputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">down_block</span><span class="p">,</span> <span class="n">skip_block</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">down_blocks</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">skip_blocks</span><span class="p">):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">down_block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+            <span class="n">down_outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">skip_block</span><span class="p">(</span><span class="n">x</span><span class="p">))</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">neck_block</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">up_block</span><span class="p">,</span> <span class="n">skip_output</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">up_blocks</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">down_outputs</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]):</span>
+            <span class="n">x</span> <span class="o">=</span> <span class="n">up_block</span><span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">skip_output</span><span class="p">)</span>
+        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_layer</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">x</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="StackedHourglass">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedHourglass">[docs]</a>
+<span class="k">class</span> <span class="nc">StackedHourglass</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Stacked hourglass.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_stacks (int): number of hourglass modules to stack.</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        channels (Sequence[int], optional): number of output channels for each convolutional layer.</span>
+
+<span class="sd">        up_sample_mode (str, optional): upsampling mode. Defaults to &#39;nearest&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="StackedHourglass.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedHourglass.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">nb_stacks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">],</span>
+        <span class="n">conv_block</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">ResidualUnit</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">pooling</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">MaxPool2d</span><span class="p">,</span>  <span class="c1"># type: ignore[assignment]</span>
+        <span class="n">up_sample_mode</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;nearest&quot;</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_stacks</span> <span class="o">=</span> <span class="n">nb_stacks</span>
+        <span class="c1"># TODO: in the orignal paper they add a pre sequential block</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">stacks</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">Hourglass</span><span class="p">(</span>
+                    <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                    <span class="n">channels</span><span class="o">=</span><span class="n">channels</span><span class="p">,</span>
+                    <span class="n">conv_block</span><span class="o">=</span><span class="n">conv_block</span><span class="p">,</span>
+                    <span class="n">pooling</span><span class="o">=</span><span class="n">pooling</span><span class="p">,</span>
+                    <span class="n">up_sample_mode</span><span class="o">=</span><span class="n">up_sample_mode</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nb_stacks</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">output_to_feature_block</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">Convolution</span><span class="p">(</span>
+                    <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                    <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                    <span class="n">adn_ordering</span><span class="o">=</span><span class="s2">&quot;N&quot;</span><span class="p">,</span>
+                    <span class="n">norm</span><span class="o">=</span><span class="s2">&quot;batch&quot;</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nb_stacks</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="StackedHourglass.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.StackedHourglass.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            list of output tensors of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">outputs</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">next_x</span> <span class="o">=</span> <span class="n">x</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">stack</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">stacks</span><span class="p">):</span>
+            <span class="n">pred</span> <span class="o">=</span> <span class="n">stack</span><span class="p">(</span><span class="n">next_x</span><span class="p">)</span>
+            <span class="n">outputs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">nb_stacks</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                <span class="n">next_x</span> <span class="o">=</span> <span class="n">x</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_to_feature_block</span><span class="p">[</span><span class="n">i</span><span class="p">](</span><span class="n">pred</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">outputs</span></div>
+</div>
+
+</pre></div>
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+        <article role="main">
+          <h1>Source code for landmarker.models.spatial_configuration_net</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Spatial Configuration Network (SCN) from the paper</span>
+<span class="sd">&quot;Integrating spatial configuration into heatmap regression based CNNs for landmark localization&quot;</span>
+<span class="sd">by Payer et al. (2019). https://www.sciencedirect.com/science/article/pii/S1361841518305784&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">monai.networks.nets</span> <span class="kn">import</span> <span class="n">UNet</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+
+<div class="viewcode-block" id="SpatialConfigurationNet">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.SpatialConfigurationNet">[docs]</a>
+<span class="k">class</span> <span class="nc">SpatialConfigurationNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adapted implementation of the Spatial Configuration Network (SCN) from the paper</span>
+<span class="sd">    &quot;Integrating spatial configuration into heatmap regression based CNNs for landmark localization&quot;</span>
+<span class="sd">    by Payer et al. (2019).</span>
+<span class="sd">    https://www.sciencedirect.com/science/article/pii/S1361841518305784</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        spatial_dims (int): number of spatial dimensions of the input image.</span>
+<span class="sd">        in_channels (int): number of input channels.</span>
+<span class="sd">        out_channels (int): number of output channels.</span>
+<span class="sd">        la_channels (Sequence[int], optional): number of output channels for each convolutional</span>
+<span class="sd">            layer.</span>
+<span class="sd">        la_kernel_size (int, optional): kernel size for the convolutional layers.</span>
+<span class="sd">        la_strides (Sequence[int], optional): strides for the convolutional layers.</span>
+<span class="sd">        la_num_res_units (int, optional): number of residual units in the convolutional layers.</span>
+<span class="sd">        la_norm (str, optional): type of normalization to use. Defaults to &quot;INSTANCE&quot;.</span>
+<span class="sd">        la_activation (str, optional): type of activation to use. Defaults to &quot;PRELU&quot;.</span>
+<span class="sd">        la_adn_ordering (str, optional): ordering of the layers in the residual units. Defaults to</span>
+<span class="sd">            &quot;ADN&quot;.</span>
+<span class="sd">        la_dropout (float, optional): dropout probability. Defaults to 0.0.</span>
+<span class="sd">        sp_channels (int, optional): number of channels for the convolutional layers.</span>
+<span class="sd">        sp_kernel_size (int, optional): kernel size for the convolutional layers.</span>
+<span class="sd">        sp_downsample (int, optional): factor by which the image is downsampled.</span>
+<span class="sd">        sp_image_input (bool, optional): whether to use the input image as input for the spatial</span>
+<span class="sd">            configuration network.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="SpatialConfigurationNet.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.SpatialConfigurationNet.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
+        <span class="n">la_channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
+        <span class="n">la_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="c1"># We use strides instead of max_pooling as in the paper as it is superior.</span>
+        <span class="c1"># https://stats.stackexchange.com/questions/387482/pooling-vs-stride-for-downsampling</span>
+        <span class="n">la_strides</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+        <span class="n">la_num_res_units</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">la_norm</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;INSTANCE&quot;</span><span class="p">,</span>
+        <span class="n">la_activation</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;PRELU&quot;</span><span class="p">,</span>
+        <span class="n">la_adn_ordering</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;ADN&quot;</span><span class="p">,</span>
+        <span class="n">la_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>  <span class="c1"># In the paper, they use 0.5 dropout, however,</span>
+        <span class="c1"># this seems to be too much, since we use residual connections.</span>
+        <span class="n">sp_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
+        <span class="n">sp_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">11</span><span class="p">,</span>
+        <span class="n">sp_downsample</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
+        <span class="n">sp_image_input</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span> <span class="o">=</span> <span class="n">sp_image_input</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">la_net</span> <span class="o">=</span> <span class="n">UNet</span><span class="p">(</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+            <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+            <span class="n">channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+            <span class="n">strides</span><span class="o">=</span><span class="n">la_strides</span><span class="p">,</span>
+            <span class="n">num_res_units</span><span class="o">=</span><span class="n">la_num_res_units</span><span class="p">,</span>
+            <span class="n">norm</span><span class="o">=</span><span class="n">la_norm</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="n">la_dropout</span><span class="p">,</span>
+            <span class="n">act</span><span class="o">=</span><span class="n">la_activation</span><span class="p">,</span>
+            <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+            <span class="n">adn_ordering</span><span class="o">=</span><span class="n">la_adn_ordering</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span><span class="p">)</span> <span class="o">*</span> <span class="n">in_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;bicubic&quot;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">(),</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="SpatialConfigurationNet.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.SpatialConfigurationNet.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: output tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">la_net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span><span class="p">:</span>
+            <span class="n">out_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">out_la</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">out_la</span> <span class="o">*</span> <span class="n">out_sc</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="OriginalSpatialConfigurationNet">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.OriginalSpatialConfigurationNet">[docs]</a>
+<span class="k">class</span> <span class="nc">OriginalSpatialConfigurationNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Implementation of the Spatial Configuration Network (SCN) from the paper</span>
+<span class="sd">    &quot;Integrating spatial configuration into heatmap regression based CNNs for landmark localization&quot;</span>
+<span class="sd">    by Payer et al. (2019).</span>
+<span class="sd">    https://www.sciencedirect.com/science/article/pii/S1361841518305784</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        in_channels (int, optional): number of input channels. Defaults to 1.</span>
+<span class="sd">        out_channels (int, optional): number of output channels. Defaults to 4.</span>
+<span class="sd">        la_channels (int, optional): number of output channels for each convolutional layer.</span>
+<span class="sd">            Defaults to 128.</span>
+<span class="sd">        la_depth (int, optional): number of convolutional layers. Defaults to 3.</span>
+<span class="sd">        la_kernel_size (int, optional): kernel size for the convolutional layers. Defaults to 3.</span>
+<span class="sd">        la_dropout (float, optional): dropout probability. Defaults to 0.5.</span>
+<span class="sd">        sp_channels (int, optional): number of channels for the convolutional layers. Defaults to</span>
+<span class="sd">            128.</span>
+<span class="sd">        sp_kernel_size (int, optional): kernel size for the convolutional layers. Defaults to 11.</span>
+<span class="sd">        sp_downsample (int, optional): factor by which the image is downsampled. Defaults to 16.</span>
+<span class="sd">        init_weights (bool, optional): whether to initialize the weights of the convolutional</span>
+<span class="sd">            layers.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="OriginalSpatialConfigurationNet.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.OriginalSpatialConfigurationNet.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
+        <span class="n">la_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
+        <span class="n">la_depth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">la_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">la_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">sp_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
+        <span class="n">sp_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">11</span><span class="p">,</span>
+        <span class="n">sp_downsample</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
+        <span class="n">init_weigths</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+        <span class="n">spatial_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">init_weights</span> <span class="o">=</span> <span class="n">init_weigths</span>
+        <span class="k">if</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span>  <span class="c1"># type: ignore</span>
+            <span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span>  <span class="c1"># type: ignore</span>
+            <span class="n">mode</span> <span class="o">=</span> <span class="s2">&quot;bilinear&quot;</span>
+        <span class="k">elif</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv3d</span>  <span class="c1"># type: ignore</span>
+            <span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool3d</span>  <span class="c1"># type: ignore</span>
+            <span class="n">mode</span> <span class="o">=</span> <span class="s2">&quot;trilinear&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;spatial_dim must be 2 or 3, got </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">first_conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+                <span class="n">bias</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">la_downlayers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">DownLayer</span><span class="p">(</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                    <span class="n">dropout</span><span class="o">=</span><span class="n">la_dropout</span><span class="p">,</span>
+                    <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+                    <span class="n">spatial_dim</span><span class="o">=</span><span class="n">spatial_dim</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">la_depth</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">up_layers</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">ModuleList</span><span class="p">(</span>
+            <span class="p">[</span>
+                <span class="n">UpLayer</span><span class="p">(</span>
+                    <span class="n">in_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                    <span class="n">out_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                    <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+                    <span class="n">spatial_dim</span><span class="o">=</span><span class="n">spatial_dim</span><span class="p">,</span>
+                <span class="p">)</span>
+                <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">la_depth</span><span class="p">)</span>
+            <span class="p">]</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_layer</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">last_layer</span> <span class="o">=</span> <span class="n">conv</span><span class="p">(</span>
+            <span class="n">in_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span> <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span>
+        <span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">avg_pool</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">),</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Tanh</span><span class="p">(),</span>
+        <span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">init_weights</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">m</span><span class="p">:</span> <span class="bp">self</span><span class="o">.</span><span class="n">_init_weights</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="o">=</span><span class="mf">0.0001</span><span class="p">))</span></div>
+
+
+    <span class="k">def</span> <span class="nf">_init_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">module</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span> <span class="n">mean</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span> <span class="n">std</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.001</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">,</span> <span class="n">nn</span><span class="o">.</span><span class="n">ConvTranspose2d</span><span class="p">)):</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">init</span><span class="o">.</span><span class="n">trunc_normal_</span><span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">weight</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">)</span>
+
+<div class="viewcode-block" id="OriginalSpatialConfigurationNet.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.OriginalSpatialConfigurationNet.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: output tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">first_conv</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">skips</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">down_layer</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">la_downlayers</span><span class="p">:</span>
+            <span class="n">out_la</span><span class="p">,</span> <span class="n">skip</span> <span class="o">=</span> <span class="n">down_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+            <span class="n">skips</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">skip</span><span class="p">)</span>
+        <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">bottleneck_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">up_layer</span><span class="p">,</span> <span class="n">skip</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">up_layers</span><span class="p">,</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">skips</span><span class="p">)):</span>
+            <span class="n">out_la</span> <span class="o">=</span> <span class="n">up_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">,</span> <span class="n">skip</span><span class="p">)</span>
+        <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_layer</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+        <span class="n">out_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">out_la</span> <span class="o">*</span> <span class="n">out_sc</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+
+
+<span class="k">class</span> <span class="nc">DownLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Down layer of the Localisation Network (LA) (UNet) from the original SCN paper.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">dropout</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
+        <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">spatial_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span>
+            <span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span>
+            <span class="n">dropout_func</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout2d</span>
+        <span class="k">elif</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv3d</span>
+            <span class="n">avg_pool</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool3d</span>
+            <span class="n">dropout_func</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Dropout3d</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;spatial_dim must be 2 or 3, got </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
+                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+                <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span> <span class="o">=</span> <span class="n">dropout_func</span><span class="p">(</span><span class="n">p</span><span class="o">=</span><span class="n">dropout</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
+                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+                <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">pool</span> <span class="o">=</span> <span class="n">avg_pool</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">stride</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">        </span><span class="sa">r</span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            tuple[torch.Tensor, torch.Tensor]: output tensor of shape (batch_size, out_channels,</span>
+<span class="sd">                *img_dims) and skip connection tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dropout</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv2</span><span class="p">(</span><span class="n">out</span><span class="p">)</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pool</span><span class="p">(</span><span class="n">out</span><span class="p">),</span> <span class="n">out</span>
+
+
+<span class="k">class</span> <span class="nc">UpLayer</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Up layer of the Localisation Network (LA) (UNet) from the original SCN paper.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
+        <span class="n">kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">bias</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+        <span class="n">spatial_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span>
+            <span class="n">mode</span> <span class="o">=</span> <span class="s2">&quot;bilinear&quot;</span>
+        <span class="k">elif</span> <span class="n">spatial_dim</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+            <span class="n">conv</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Conv3d</span>
+            <span class="n">mode</span> <span class="o">=</span> <span class="s2">&quot;trilinear&quot;</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;spatial_dim must be 2 or 3, got </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2">.&quot;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">conv</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">kernel_size</span><span class="p">,</span>
+                <span class="n">stride</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+                <span class="n">bias</span><span class="o">=</span><span class="n">bias</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">LeakyReLU</span><span class="p">(</span><span class="n">negative_slope</span><span class="o">=</span><span class="mf">0.1</span><span class="p">),</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">upsample</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="n">mode</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">skip</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+<span class="sd">            skip: skip connection tensor of shape (batch_size, out_channels, *img_dims)</span>
+
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: output tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">upsample</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">conv1</span><span class="p">(</span><span class="n">skip</span><span class="p">)</span>
+
+
+<div class="viewcode-block" id="OriginalSpatialConfigurationNet3d">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.OriginalSpatialConfigurationNet3d">[docs]</a>
+<span class="k">class</span> <span class="nc">OriginalSpatialConfigurationNet3d</span><span class="p">(</span><span class="n">OriginalSpatialConfigurationNet</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Implementation of the Spatial Configuration Network (SCN) from the paper</span>
+<span class="sd">    &quot;Integrating spatial configuration into heatmap regression based CNNs for landmark localization&quot;</span>
+<span class="sd">    by Payer et al. (2019).</span>
+<span class="sd">    https://www.sciencedirect.com/science/article/pii/S1361841518305784</span>
+
+<span class="sd">    This is the 3D version of the original SCN.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="OriginalSpatialConfigurationNet3d.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.OriginalSpatialConfigurationNet3d.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
+        <span class="n">la_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>
+        <span class="n">la_depth</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">la_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="n">la_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+        <span class="n">sp_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span>
+        <span class="n">sp_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">7</span><span class="p">,</span>
+        <span class="n">sp_downsample</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
+    <span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span>
+            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+            <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+            <span class="n">la_channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+            <span class="n">la_depth</span><span class="o">=</span><span class="n">la_depth</span><span class="p">,</span>
+            <span class="n">la_kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+            <span class="n">la_dropout</span><span class="o">=</span><span class="n">la_dropout</span><span class="p">,</span>
+            <span class="n">sp_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+            <span class="n">sp_kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+            <span class="n">sp_downsample</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">,</span>
+            <span class="n">spatial_dim</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span>
+        <span class="p">)</span></div>
+</div>
+
+
+
+<div class="viewcode-block" id="ProbSpatialConfigurationNet">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.ProbSpatialConfigurationNet">[docs]</a>
+<span class="k">class</span> <span class="nc">ProbSpatialConfigurationNet</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Probabilistic Spatial Configuration Network (PSCN)</span>
+
+<span class="sd">    Adapted implementation of the Probabilistic Spatial Configuration Network (PSCN) from the paper</span>
+<span class="sd">    &quot;Integrating spatial configuration into heatmap regression based CNNs for landmark localization&quot;</span>
+<span class="sd">    by Payer et al. (2019). This is the same as the Spatial Configuration Network (SCN), but with</span>
+<span class="sd">    a different last layer. Instead of multiplying the output of the SCN with the output of the</span>
+<span class="sd">    spatial configuration network, we add them together, since the output of the spatial</span>
+<span class="sd">    configuration network is a probability distribution in the logit space.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        spatial_dims (int, optional): number of spatial dimensions of the input image. Defaults to</span>
+<span class="sd">            2.</span>
+<span class="sd">        in_channels (int, optional): number of input channels. Defaults to 1.</span>
+<span class="sd">        out_channels (int, optional): number of output channels. Defaults to 4.</span>
+<span class="sd">        la_channels (Sequence[int], optional): number of output channels for each convolutional</span>
+<span class="sd">            layer. Defaults to (128, 128, 128, 128).</span>
+<span class="sd">        la_kernel_size (int | tuple[int, int], optional): kernel size for the convolutional</span>
+<span class="sd">            layers. Defaults to 3.</span>
+<span class="sd">        la_strides (Sequence[int], optional): strides for the convolutional layers. Defaults to</span>
+<span class="sd">            (2, 2, 2).</span>
+<span class="sd">        la_num_res_units (int, optional): number of residual units in the convolutional layers.</span>
+<span class="sd">            Defaults to 2.</span>
+<span class="sd">        la_norm (str, optional): type of normalization to use. Defaults to &quot;instance&quot;.</span>
+<span class="sd">        la_activation (str, optional): type of activation to use. Defaults to &quot;PRELU&quot;.</span>
+<span class="sd">        la_adn_ordering (str, optional): ordering of the layers in the residual units. Defaults to</span>
+<span class="sd">            &quot;NDA&quot;.</span>
+<span class="sd">        la_dropout (float, optional): dropout probability. Defaults to 0.0.</span>
+<span class="sd">        sp_channels (int, optional): number of channels for the convolutional layers. Defaults to</span>
+<span class="sd">            128.</span>
+<span class="sd">        sp_kernel_size (int, optional): kernel size for the convolutional layers. Defaults to 11.</span>
+<span class="sd">        sp_downsample (int, optional): factor by which the image is downsampled. Defaults to 16.</span>
+<span class="sd">        sp_image_input (bool, optional): whether to use the input image as input for the spatial</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="ProbSpatialConfigurationNet.__init__">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.ProbSpatialConfigurationNet.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span>
+        <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">in_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span>
+        <span class="n">out_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span>
+        <span class="n">la_channels</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">128</span><span class="p">),</span>
+        <span class="n">la_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span>
+        <span class="c1"># We use strides instead of max_pooling as in the paper as it is superior.</span>
+        <span class="c1"># https://stats.stackexchange.com/questions/387482/pooling-vs-stride-for-downsampling</span>
+        <span class="n">la_strides</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span>
+        <span class="n">la_num_res_units</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+        <span class="n">la_norm</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;instance&quot;</span><span class="p">,</span>
+        <span class="n">la_activation</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;PRELU&quot;</span><span class="p">,</span>
+        <span class="n">la_adn_ordering</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;NDA&quot;</span><span class="p">,</span>
+        <span class="n">la_dropout</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.0</span><span class="p">,</span>  <span class="c1"># In the paper, they use 0.5 dropout, however,</span>
+        <span class="c1"># this seems to be too much, since we use residual connections.</span>
+        <span class="n">sp_channels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span>
+        <span class="n">sp_kernel_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">11</span><span class="p">,</span>
+        <span class="n">sp_downsample</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">16</span><span class="p">,</span>
+        <span class="n">sp_image_input</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span> <span class="o">=</span> <span class="n">sp_image_input</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">la_net</span> <span class="o">=</span> <span class="n">UNet</span><span class="p">(</span>
+            <span class="n">spatial_dims</span><span class="o">=</span><span class="n">spatial_dims</span><span class="p">,</span>
+            <span class="n">in_channels</span><span class="o">=</span><span class="n">in_channels</span><span class="p">,</span>
+            <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+            <span class="n">channels</span><span class="o">=</span><span class="n">la_channels</span><span class="p">,</span>
+            <span class="n">strides</span><span class="o">=</span><span class="n">la_strides</span><span class="p">,</span>
+            <span class="n">num_res_units</span><span class="o">=</span><span class="n">la_num_res_units</span><span class="p">,</span>
+            <span class="n">norm</span><span class="o">=</span><span class="n">la_norm</span><span class="p">,</span>
+            <span class="n">dropout</span><span class="o">=</span><span class="n">la_dropout</span><span class="p">,</span>
+            <span class="n">act</span><span class="o">=</span><span class="n">la_activation</span><span class="p">,</span>
+            <span class="n">kernel_size</span><span class="o">=</span><span class="n">la_kernel_size</span><span class="p">,</span>
+            <span class="n">adn_ordering</span><span class="o">=</span><span class="n">la_adn_ordering</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">AvgPool2d</span><span class="p">(</span><span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">out_channels</span> <span class="o">+</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span><span class="p">)</span> <span class="o">*</span> <span class="n">in_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">PReLU</span><span class="p">(),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">PReLU</span><span class="p">(),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">PReLU</span><span class="p">(),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Conv2d</span><span class="p">(</span>
+                <span class="n">in_channels</span><span class="o">=</span><span class="n">sp_channels</span><span class="p">,</span>
+                <span class="n">out_channels</span><span class="o">=</span><span class="n">out_channels</span><span class="p">,</span>
+                <span class="n">kernel_size</span><span class="o">=</span><span class="n">sp_kernel_size</span><span class="p">,</span>
+                <span class="n">padding</span><span class="o">=</span><span class="s2">&quot;same&quot;</span><span class="p">,</span>
+            <span class="p">),</span>
+            <span class="n">nn</span><span class="o">.</span><span class="n">Upsample</span><span class="p">(</span><span class="n">scale_factor</span><span class="o">=</span><span class="n">sp_downsample</span><span class="p">,</span> <span class="n">mode</span><span class="o">=</span><span class="s2">&quot;bicubic&quot;</span><span class="p">,</span> <span class="n">align_corners</span><span class="o">=</span><span class="kc">False</span><span class="p">),</span>
+        <span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ProbSpatialConfigurationNet.forward">
+<a class="viewcode-back" href="../../../reference/models.html#landmarker.models.ProbSpatialConfigurationNet.forward">[docs]</a>
+    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">        </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">        Args:</span>
+<span class="sd">            x: input tensor of shape (batch_size, in_channels, *img_dims)</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            torch.Tensor: output tensor of shape (batch_size, out_channels, *img_dims)</span>
+<span class="sd">        &quot;&quot;&quot;</span>
+        <span class="n">out_la</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">la_net</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">sp_image_input</span><span class="p">:</span>
+            <span class="n">out_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cat</span><span class="p">((</span><span class="n">x</span><span class="p">,</span> <span class="n">out_la</span><span class="p">),</span> <span class="n">dim</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">out_sc</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sc_net</span><span class="p">(</span><span class="n">out_la</span><span class="p">)</span>
+        <span class="n">out</span> <span class="o">=</span> <span class="n">out_la</span> <span class="o">+</span> <span class="n">out_sc</span>
+        <span class="k">return</span> <span class="n">out</span></div>
+</div>
+
+</pre></div>
+        </article>
+      </div>
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diff --git a/_modules/landmarker/schedulers/adaloss.html b/_modules/landmarker/schedulers/adaloss.html
new file mode 100644
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+          <h1>Source code for landmarker.schedulers.adaloss</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Adaloss scheduler for sigma parameter of gaussian heatmaps&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">deque</span>
+
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+
+<div class="viewcode-block" id="AdalossScheduler">
+<a class="viewcode-back" href="../../../reference/schedulers.html#landmarker.schedulers.AdalossScheduler">[docs]</a>
+<span class="k">class</span> <span class="nc">AdalossScheduler</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Scheduler for sigma parameter of gaussian heatmaps. In essence the scheduler increases the</span>
+<span class="sd">    problem difficulty by decreasing the sigma parameter. Implicitly by doing this, it creates a</span>
+<span class="sd">    training curriculum.</span>
+<span class="sd">        Proposed in &quot;Adaloss: Adaptive Loss Function for Landmark Localization&quot; - Teixeira et al.</span>
+<span class="sd">            (2019)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        nb_landmarks (int): number of landmarks</span>
+<span class="sd">        rho (float, optional): [description]. Defaults to 0.9.</span>
+<span class="sd">        window (int, optional): [description]. Defaults to 3.</span>
+<span class="sd">        non_increasing (bool, optional): [description]. Defaults to False.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+<div class="viewcode-block" id="AdalossScheduler.__init__">
+<a class="viewcode-back" href="../../../reference/schedulers.html#landmarker.schedulers.AdalossScheduler.__init__">[docs]</a>
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span>
+        <span class="bp">self</span><span class="p">,</span> <span class="n">nb_landmarks</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">rho</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.9</span><span class="p">,</span> <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> <span class="n">non_increasing</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span>
+    <span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span> <span class="o">=</span> <span class="n">nb_landmarks</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">=</span> <span class="n">rho</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="n">window</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">non_increasing</span> <span class="o">=</span> <span class="n">non_increasing</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">deque</span><span class="p">[</span><span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="p">[</span><span class="n">deque</span><span class="p">([])</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nb_landmarks</span><span class="p">)]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">prev_variances</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">=</span> <span class="mi">0</span></div>
+
+
+    <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">losses</span><span class="p">,</span> <span class="n">sigmas</span><span class="p">):</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">nb_landmarks</span><span class="p">):</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">==</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">prev_variances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">**</span> <span class="mi">2</span>
+                <span class="p">)</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">counter</span> <span class="o">&gt;</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">popleft</span><span class="p">()</span>
+                <span class="n">variance</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">window</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">losses</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">**</span> <span class="mi">2</span>
+                <span class="n">adjustment</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">rho</span> <span class="o">*</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">prev_variances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">/</span> <span class="n">variance</span><span class="p">)</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">non_increasing</span><span class="p">:</span>
+                    <span class="n">adjustment</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">adjustment</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">)</span>
+                <span class="n">sigmas</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+=</span> <span class="n">adjustment</span>
+                <span class="n">sigmas</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+=</span> <span class="n">adjustment</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">prev_variances</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">variance</span></div>
+
+</pre></div>
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+          <h1>Source code for landmarker.utils.preprocessing</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Preprocessing utils functions for images&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">os.path</span> <span class="kn">import</span> <span class="n">join</span>
+
+<span class="kn">import</span> <span class="nn">cv2</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>  <span class="c1"># type: ignore</span>
+
+
+<div class="viewcode-block" id="normalize">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.normalize">[docs]</a>
+<span class="k">def</span> <span class="nf">normalize</span><span class="p">(</span><span class="n">img</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Normalize image to 0-1 range</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        img (np.ndarray): Image to normalize</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: Normalized image</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">img</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">uint16</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">cv2</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">65535</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">NORM_MINMAX</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+    <span class="k">if</span> <span class="n">img</span><span class="o">.</span><span class="n">dtype</span> <span class="o">==</span> <span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">cv2</span><span class="o">.</span><span class="n">normalize</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="kc">None</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">255</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">NORM_MINMAX</span><span class="p">)</span>  <span class="c1"># type: ignore</span>
+    <span class="k">raise</span> <span class="ne">TypeError</span><span class="p">(</span><span class="s2">&quot;Image type not supported&quot;</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="preprocess_all">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.preprocess_all">[docs]</a>
+<span class="k">def</span> <span class="nf">preprocess_all</span><span class="p">(</span><span class="n">folder</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">output_folder</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">fun_name</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Apply a preprocessing function to all images in a folder.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        folder (str): Folder containing images to preprocess</span>
+<span class="sd">        output_folder (str): Folder to store the preprocessed images</span>
+<span class="sd">        fun_name (str): Name of the function to apply. (Only normalize supported for now)</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">fun</span> <span class="o">=</span> <span class="nb">globals</span><span class="p">()[</span><span class="n">fun_name</span><span class="p">]</span>
+    <span class="k">for</span> <span class="n">file</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">folder</span> <span class="o">+</span> <span class="sa">r</span><span class="s2">&quot;/**/*.png&quot;</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)):</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">join</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">file</span><span class="p">),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
+        <span class="n">adj_img</span> <span class="o">=</span> <span class="n">fun</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+        <span class="n">target_path</span> <span class="o">=</span> <span class="n">join</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
+        <span class="n">target_path</span> <span class="o">=</span> <span class="n">target_path</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="n">folder</span><span class="p">,</span> <span class="n">output_folder</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">target_path</span><span class="p">))</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">:</span>
+            <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">dirname</span><span class="p">(</span><span class="n">target_path</span><span class="p">))</span>
+        <span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</span><span class="p">(</span><span class="n">target_path</span><span class="p">,</span> <span class="n">adj_img</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="extract_roi">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.extract_roi">[docs]</a>
+<span class="k">def</span> <span class="nf">extract_roi</span><span class="p">(</span>
+    <span class="n">imgs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">roi_middle</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span>
+    <span class="n">spatial_dims</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">ensure_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract a region of interest around the landmarks in the image.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        img (torch.Tensor): Image to extract the region of interest from.</span>
+<span class="sd">        roi_middle (torch.Tensor): Middle of the region of interest.</span>
+<span class="sd">        landmark (torch.Tensor): Landmark to transform to the region of interest.</span>
+<span class="sd">        size (int | tuple[int, int]): Size of the region of interest.</span>
+<span class="sd">        spatial_dims (int): Number of spatial dimensions of the image.</span>
+<span class="sd">        ensure_dim (bool): Ensure the output has the same number of dimensions as the input.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: Region of interest around the landmarks.</span>
+<span class="sd">        torch.Tensor: Landmarks in the region of interest.</span>
+<span class="sd">        torch.Tensor: Upper left corner of the region of interest.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">extract_roi_2d</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">roi_middle</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">ensure_dim</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">spatial_dims</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">extract_roi_3d</span><span class="p">(</span><span class="n">imgs</span><span class="p">,</span> <span class="n">roi_middle</span><span class="p">,</span> <span class="n">landmarks</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">ensure_dim</span><span class="p">)</span>
+    <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;spatial_dims must be 2 or 3.&quot;</span><span class="p">)</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">extract_roi_2d</span><span class="p">(</span>
+    <span class="n">imgs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">roi_middle</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">ensure_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract a region of interest around the landmarks in the image.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        img (torch.Tensor): Image to extract the region of interest from. (C, H, W)</span>
+<span class="sd">        roi_middle (torch.Tensor): Middle of the region of interest. (2)</span>
+<span class="sd">        landmarks (torch.Tensor): Landmarks to transform to the region of interest. (2)</span>
+<span class="sd">        size (int): Size of the region of interest.</span>
+<span class="sd">        ensure_dim (bool): Ensure the output has the same number of dimensions as the input.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: Region of interest around the landmarks.</span>
+<span class="sd">        torch.Tensor: Landmarks in the region of interest.</span>
+<span class="sd">        torch.Tensor: Upper left corner of the region of interest.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># perform checks on the input</span>
+    <span class="k">if</span> <span class="n">imgs</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input image must have 3 dimensions.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Landmarks must have 2 dimensions.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">roi_middle</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;ROI middle must have 2 dimensions.&quot;</span><span class="p">)</span>
+    <span class="c1"># check if tuple is passed</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">or</span> <span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Size must be smaller than the image.&quot;</span><span class="p">)</span>
+    <span class="n">H</span><span class="p">,</span> <span class="n">W</span> <span class="o">=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:]</span>
+    <span class="n">roi_height_lower</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_middle</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+    <span class="n">roi_height_upper</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_middle</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+    <span class="n">roi_width_lower</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_middle</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+    <span class="n">roi_width_upper</span> <span class="o">=</span> <span class="p">(</span><span class="n">roi_middle</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">int</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">ensure_dim</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">roi_height_lower</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">roi_height_upper</span> <span class="o">-=</span> <span class="n">roi_height_lower</span>  <span class="c1"># type: ignore</span>
+            <span class="n">roi_height_lower</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># type: ignore</span>
+        <span class="k">if</span> <span class="n">roi_height_upper</span> <span class="o">&gt;</span> <span class="n">H</span><span class="p">:</span>
+            <span class="n">roi_height_lower</span> <span class="o">-=</span> <span class="n">roi_height_upper</span> <span class="o">-</span> <span class="n">H</span>  <span class="c1"># type: ignore</span>
+            <span class="n">roi_height_upper</span> <span class="o">=</span> <span class="n">H</span>  <span class="c1"># type: ignore</span>
+        <span class="k">if</span> <span class="n">roi_width_lower</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">roi_width_upper</span> <span class="o">-=</span> <span class="n">roi_width_lower</span>  <span class="c1"># type: ignore</span>
+            <span class="n">roi_width_lower</span> <span class="o">=</span> <span class="mi">0</span>  <span class="c1"># type: ignore</span>
+        <span class="k">if</span> <span class="n">roi_width_upper</span> <span class="o">&gt;</span> <span class="n">W</span><span class="p">:</span>
+            <span class="n">roi_width_lower</span> <span class="o">-=</span> <span class="n">roi_width_upper</span> <span class="o">-</span> <span class="n">W</span>  <span class="c1"># type: ignore</span>
+            <span class="n">roi_width_upper</span> <span class="o">=</span> <span class="n">W</span>  <span class="c1"># type: ignore</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">roi_height_lower</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">H</span><span class="p">)</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">roi_height_upper</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">H</span><span class="p">)</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">roi_width_lower</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">clamp</span><span class="p">(</span><span class="n">roi_width_upper</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">W</span><span class="p">)</span>
+    <span class="n">roi_upper_left_point</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">roi_height_lower</span><span class="p">,</span> <span class="n">roi_width_lower</span><span class="p">])</span>
+    <span class="n">landmarks_patch</span> <span class="o">=</span> <span class="n">landmarks</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="n">roi_height_upper</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">roi_width_lower</span><span class="p">])</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">imgs</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">roi_height_lower</span><span class="p">:</span><span class="n">roi_height_upper</span><span class="p">,</span> <span class="n">roi_width_lower</span><span class="p">:</span><span class="n">roi_width_upper</span><span class="p">],</span>
+        <span class="n">landmarks_patch</span><span class="p">,</span>
+        <span class="n">roi_upper_left_point</span><span class="p">,</span>
+    <span class="p">)</span>
+
+
+<span class="k">def</span> <span class="nf">extract_roi_3d</span><span class="p">(</span>
+    <span class="n">imgs</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">roi_middle</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
+    <span class="n">ensure_dim</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]:</span>
+    <span class="k">if</span> <span class="n">imgs</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">5</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Input image must have 5 dimensions.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Landmarks must have 3 dimensions.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Landmarks must have 3 columns.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">!=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Number of landmarks must match the batch size.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">roi_middle</span><span class="o">.</span><span class="n">dim</span><span class="p">()</span> <span class="o">!=</span> <span class="mi">2</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;ROI middle must have 2 dimensions.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">roi_middle</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;ROI middle must have 3 columns.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">roi_middle</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">!=</span> <span class="n">imgs</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="mi">0</span><span class="p">):</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Number of ROI middle must match the batch size.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">,</span> <span class="n">size</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">size</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;Size must be an odd number.&quot;</span><span class="p">)</span>
+    <span class="n">roi_front_upper_left_point</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">size</span><span class="p">)</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_height_lower</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_height_upper</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_width_lower</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_width_upper</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_depth_lower</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">-</span> <span class="n">size</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">roi_depth_upper</span> <span class="o">=</span> <span class="n">roi_middle</span><span class="p">[:,</span> <span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="n">size</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">//</span> <span class="mi">2</span>
+    <span class="n">landmarks</span> <span class="o">=</span> <span class="n">landmarks</span> <span class="o">-</span> <span class="n">roi_front_upper_left_point</span>
+    <span class="k">return</span> <span class="p">(</span>
+        <span class="n">imgs</span><span class="p">[</span>
+            <span class="p">:,</span>
+            <span class="p">:,</span>
+            <span class="n">roi_height_lower</span><span class="p">:</span><span class="n">roi_height_upper</span><span class="p">,</span>
+            <span class="n">roi_width_lower</span><span class="p">:</span><span class="n">roi_width_upper</span><span class="p">,</span>
+            <span class="n">roi_depth_lower</span><span class="p">:</span><span class="n">roi_depth_upper</span><span class="p">,</span>
+        <span class="p">],</span>
+        <span class="n">landmarks</span><span class="p">,</span>
+        <span class="n">roi_front_upper_left_point</span><span class="p">,</span>
+    <span class="p">)</span>
+</pre></div>
+        </article>
+      </div>
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diff --git a/_modules/landmarker/utils/utils.html b/_modules/landmarker/utils/utils.html
new file mode 100644
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+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../../../">
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+          <h1>Source code for landmarker.utils.utils</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Utility functions for the package.&quot;&quot;&quot;</span>
+
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">json</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">cv2</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>  <span class="c1"># type: ignore</span>
+
+
+<div class="viewcode-block" id="get_paths">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.get_paths">[docs]</a>
+<span class="k">def</span> <span class="nf">get_paths</span><span class="p">(</span><span class="n">folder_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">extension</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Retrieve all paths with a defined extension files recursively from the folder path.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        folder_path (str): path to the folder</span>
+<span class="sd">        extension (str): file extension</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        list[str]: list of paths</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># return all paths with a defined extension files recursively from the folder path</span>
+    <span class="k">return</span> <span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">folder_path</span> <span class="o">+</span> <span class="s2">&quot;/**/*.&quot;</span> <span class="o">+</span> <span class="n">extension</span><span class="p">,</span> <span class="n">recursive</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span></div>
+
+
+
+<div class="viewcode-block" id="annotation_to_landmark">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.annotation_to_landmark">[docs]</a>
+<span class="k">def</span> <span class="nf">annotation_to_landmark</span><span class="p">(</span><span class="n">json_obj</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">class_names</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert the annotation of LabelMe to landmarks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        json_obj (dict): annotation in json format</span>
+<span class="sd">        class_names (list[str]): class names</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: landmarks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">shapes_list</span> <span class="o">=</span> <span class="n">json_obj</span><span class="p">[</span><span class="s2">&quot;shapes&quot;</span><span class="p">]</span>
+    <span class="n">landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">nan</span>
+
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">class_names</span><span class="p">):</span>
+        <span class="k">for</span> <span class="n">shape</span> <span class="ow">in</span> <span class="n">shapes_list</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">shape</span><span class="p">[</span><span class="s2">&quot;label&quot;</span><span class="p">]</span> <span class="o">==</span> <span class="n">name</span><span class="p">:</span>
+                <span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">(</span><span class="n">shape</span><span class="p">[</span><span class="s2">&quot;points&quot;</span><span class="p">][</span><span class="mi">0</span><span class="p">][::</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">break</span>
+    <span class="k">return</span> <span class="n">landmarks</span></div>
+
+
+
+<div class="viewcode-block" id="annotation_to_landmark_numpy">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.annotation_to_landmark_numpy">[docs]</a>
+<span class="k">def</span> <span class="nf">annotation_to_landmark_numpy</span><span class="p">(</span><span class="n">json_obj</span><span class="p">:</span> <span class="nb">dict</span><span class="p">,</span> <span class="n">class_names</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert the annotation of LabelMe to landmarks (Numpy version).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        json_obj (dict): annotation in json format</span>
+<span class="sd">        class_names (list[str]): class names</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: landmarks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">annotation_to_landmark</span><span class="p">(</span><span class="n">json_obj</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="all_annotations_to_landmarks">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.all_annotations_to_landmarks">[docs]</a>
+<span class="k">def</span> <span class="nf">all_annotations_to_landmarks</span><span class="p">(</span><span class="n">paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">class_names</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert all annotations of LabelMe to landmarks.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        paths (list[str]): list of paths to the annotations</span>
+<span class="sd">        class_names (list[str]): class names</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: landmarks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">landmarks</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">paths</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">class_names</span><span class="p">),</span> <span class="mi">2</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">path</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">paths</span><span class="p">):</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s2">&quot;utf8&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
+            <span class="n">json_obj</span> <span class="o">=</span> <span class="n">json</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
+        <span class="n">landmarks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">annotation_to_landmark</span><span class="p">(</span><span class="n">json_obj</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">landmarks</span></div>
+
+
+
+<div class="viewcode-block" id="all_annotations_to_landmarks_numpy">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.all_annotations_to_landmarks_numpy">[docs]</a>
+<span class="k">def</span> <span class="nf">all_annotations_to_landmarks_numpy</span><span class="p">(</span><span class="n">paths</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span> <span class="n">class_names</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">])</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert all annotations of LabelMe to landmarks (Numpy version).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        paths (list[str]): list of paths to the annotations</span>
+<span class="sd">        class_names (list[str]): class names</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: landmarks</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">return</span> <span class="n">all_annotations_to_landmarks</span><span class="p">(</span><span class="n">paths</span><span class="p">,</span> <span class="n">class_names</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="get_angle">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.get_angle">[docs]</a>
+<span class="k">def</span> <span class="nf">get_angle</span><span class="p">(</span><span class="n">p1</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">p2</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">p3</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">radial</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculate the angle between three points. The angle is calculated in degrees or radians. The</span>
+<span class="sd">    angle is calculated in the direction of p2 -&gt; p3. If the angle is radial, the angle is between</span>
+<span class="sd">    0 and 2pi. If the angle is not radial, the angle is between 0 and 360.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        p1 (torch.Tensor): first point</span>
+<span class="sd">        p2 (torch.Tensor): second point</span>
+<span class="sd">        p3 (torch.Tensor): third point</span>
+<span class="sd">        radial (bool, optional): whether the angle is radial. Defaults to True.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: angle</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">p1</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">p2</span> <span class="o">=</span> <span class="n">p2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">p3</span> <span class="o">=</span> <span class="n">p3</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">angle</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">acos</span><span class="p">(</span>
+        <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span> <span class="o">-</span> <span class="n">p1</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="o">/</span> <span class="n">torch</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p3</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="n">radial</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">angle</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">angle</span> <span class="o">*</span> <span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">%</span> <span class="mi">360</span></div>
+
+
+
+<div class="viewcode-block" id="get_angle_numpy">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.get_angle_numpy">[docs]</a>
+<span class="k">def</span> <span class="nf">get_angle_numpy</span><span class="p">(</span><span class="n">p1</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">p2</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">p3</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">radial</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculate the angle between three points (Numpy version). See documentation of get_angle.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        p1 (np.ndarray): first point</span>
+<span class="sd">        p2 (np.ndarray): second point</span>
+<span class="sd">        p3 (np.ndarray): third point</span>
+<span class="sd">        radial (bool, optional): whether the angle is radial. Defaults to True.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: angle</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">p1</span> <span class="o">=</span> <span class="n">p1</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">p2</span> <span class="o">=</span> <span class="n">p2</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">p3</span> <span class="o">=</span> <span class="n">p3</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">p3</span> <span class="o">-</span> <span class="n">p1</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p2</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">p3</span> <span class="o">-</span> <span class="n">p1</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="p">)</span>
+    <span class="k">if</span> <span class="n">radial</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">angle</span> <span class="o">%</span> <span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">angle</span> <span class="o">*</span> <span class="mi">180</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">%</span> <span class="mi">360</span></div>
+
+
+
+<div class="viewcode-block" id="pixel_to_unit">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.pixel_to_unit">[docs]</a>
+<span class="k">def</span> <span class="nf">pixel_to_unit</span><span class="p">(</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert the landmarks from pixel to unit.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        landmarks (torch.Tensor): landmarks</span>
+<span class="sd">        pixel_spacing (Optional[torch.Tensor], optional): pixel spacing. Defaults to None.</span>
+<span class="sd">        dim (Optional[tuple[int, ...] | torch.Tensor], optional): image size. Defaults to None.</span>
+<span class="sd">        dim_orig (Optional[torch.Tensor], optional): original image size. Defaults to None.</span>
+<span class="sd">        padding (Optional[torch.Tensor], optional): padding. Defaults to None.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        torch.Tensor: landmarks in units</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">spatial_dim</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span> <span class="o">==</span> <span class="n">spatial_dim</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dim must have </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2"> elements.&quot;</span>
+    <span class="k">if</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">spatial_dim</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dim_orig must have </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2"> elements.&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">added_dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>  <span class="c1"># for multi-instance</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">added_dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">padding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">dim_orig</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">landmarks</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">padding</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">),</span> <span class="n">device</span><span class="o">=</span><span class="n">landmarks</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">dim</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">as_tensor</span><span class="p">(</span><span class="n">dim</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">landmarks</span><span class="o">.</span><span class="n">device</span><span class="p">)</span>
+        <span class="n">landmarks_unresize</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">landmarks</span>
+            <span class="o">*</span> <span class="p">(</span><span class="n">dim_orig</span> <span class="o">+</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">padding</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+            <span class="o">/</span> <span class="n">dim</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+        <span class="p">)</span>
+        <span class="n">landmarks_unpadded</span> <span class="o">=</span> <span class="n">landmarks_unresize</span> <span class="o">-</span> <span class="n">padding</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">landmarks_unpadded</span> <span class="o">*</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dim and dim_orig must be both None or both not None.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">landmarks</span>
+    <span class="k">return</span> <span class="n">landmarks</span> <span class="o">*</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="pixel_to_unit_numpy">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.pixel_to_unit_numpy">[docs]</a>
+<span class="k">def</span> <span class="nf">pixel_to_unit_numpy</span><span class="p">(</span>
+    <span class="n">landmarks</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+<span class="p">)</span> <span class="o">-&gt;</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
+    <span class="c1"># TODO: adjust to account for four dimensions (multi-instance)</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Convert the landmarks from pixel to unit (Numpy version).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        landmarks (np.ndarray): landmarks</span>
+<span class="sd">        pixel_spacing (Optional[np.ndarray], optional): pixel spacing. Defaults to None.</span>
+<span class="sd">        dim (Optional[tuple[int, ...] | np.ndarray], optional): image size. Defaults to None.</span>
+<span class="sd">        dim_orig (Optional[np.ndarray], optional): original image size. Defaults to None.</span>
+<span class="sd">        padding (Optional[np.ndarray], optional): padding. Defaults to None.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        np.ndarray: landmarks in units</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">spatial_dim</span> <span class="o">=</span> <span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span> <span class="o">==</span> <span class="n">spatial_dim</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dim must have </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2"> elements.&quot;</span>
+    <span class="k">if</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">assert</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">spatial_dim</span><span class="p">,</span> <span class="sa">f</span><span class="s2">&quot;dim_orig must have </span><span class="si">{</span><span class="n">spatial_dim</span><span class="si">}</span><span class="s2"> elements.&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
+        <span class="n">added_dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">added_dim</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,)</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">padding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">padding</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">dim_orig</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">assert</span> <span class="n">padding</span><span class="o">.</span><span class="n">shape</span> <span class="o">==</span> <span class="n">dim_orig</span><span class="o">.</span><span class="n">shape</span>
+        <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">pixel_spacing</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+        <span class="n">dim</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dim</span><span class="p">)</span>
+        <span class="n">landmarks_unresize</span> <span class="o">=</span> <span class="p">(</span>
+            <span class="n">landmarks</span>
+            <span class="o">*</span> <span class="p">(</span><span class="n">dim_orig</span> <span class="o">+</span> <span class="mf">2.0</span> <span class="o">*</span> <span class="n">padding</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+            <span class="o">/</span> <span class="n">dim</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+        <span class="p">)</span>
+        <span class="n">landmarks_unpadded</span> <span class="o">=</span> <span class="n">landmarks_unresize</span> <span class="o">-</span> <span class="n">padding</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+        <span class="k">return</span> <span class="n">landmarks_unpadded</span> <span class="o">*</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">dim</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">dim_orig</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;dim and dim_orig must be both None or both not None.&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">pixel_spacing</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">landmarks</span>
+    <span class="k">return</span> <span class="n">landmarks</span> <span class="o">*</span> <span class="n">pixel_spacing</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">*</span><span class="n">added_dim</span><span class="p">,</span> <span class="n">spatial_dim</span><span class="p">))</span></div>
+
+
+
+<div class="viewcode-block" id="covert_video_to_frames">
+<a class="viewcode-back" href="../../../reference/utils.html#landmarker.utils.covert_video_to_frames">[docs]</a>
+<span class="k">def</span> <span class="nf">covert_video_to_frames</span><span class="p">(</span><span class="n">video_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">frames_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="n">zero_fill</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">6</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Convert a video to frames.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        video_path (str): path to the video</span>
+<span class="sd">        frames_path (str): path to the folder where the frames are saved</span>
+<span class="sd">        zero_fill (int, optional): number of digits in the frame name. Defaults to 6.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        None</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">frames_path</span><span class="p">):</span>
+        <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">frames_path</span><span class="p">)</span>
+    <span class="n">vidcap</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">VideoCapture</span><span class="p">(</span><span class="n">video_path</span><span class="p">)</span>
+    <span class="n">count</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">frame_count</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">vidcap</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">cv2</span><span class="o">.</span><span class="n">CAP_PROP_FRAME_COUNT</span><span class="p">))</span>
+    <span class="n">tqdm</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Transform video to frames: </span><span class="si">{</span><span class="n">frame_count</span><span class="si">}</span><span class="s2"> frames&quot;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">frame_count</span><span class="p">)):</span>
+        <span class="n">count</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">success</span><span class="p">,</span> <span class="n">image</span> <span class="o">=</span> <span class="n">vidcap</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="n">success</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Error in frame </span><span class="si">{</span><span class="n">count</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+            <span class="k">continue</span>
+        <span class="c1"># save frame as PNG file</span>
+        <span class="n">cv2</span><span class="o">.</span><span class="n">imwrite</span><span class="p">(</span><span class="n">frames_path</span> <span class="o">+</span> <span class="sa">f</span><span class="s2">&quot;/img_</span><span class="si">{</span><span class="nb">str</span><span class="p">(</span><span class="n">count</span><span class="p">)</span><span class="o">.</span><span class="n">zfill</span><span class="p">(</span><span class="n">zero_fill</span><span class="p">)</span><span class="si">}</span><span class="s2">.png&quot;</span><span class="p">,</span> <span class="n">image</span><span class="p">)</span></div>
+
+</pre></div>
+        </article>
+      </div>
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diff --git a/_modules/landmarker/visualize/evaluation.html b/_modules/landmarker/visualize/evaluation.html
new file mode 100644
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+<!doctype html>
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+          <h1>Source code for landmarker.visualize.evaluation</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;Evaluation visualization functions.&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>  <span class="c1"># type: ignore</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>  <span class="c1"># type: ignore</span>
+<span class="kn">import</span> <span class="nn">torch</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.metrics.metrics</span> <span class="kn">import</span> <span class="n">point_error</span><span class="p">,</span> <span class="n">sdr</span>  <span class="c1"># type: ignore</span>
+
+
+<div class="viewcode-block" id="plot_cpe">
+<a class="viewcode-back" href="../../../reference/visualize.html#landmarker.visualize.plot_cpe">[docs]</a>
+<span class="k">def</span> <span class="nf">plot_cpe</span><span class="p">(</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">group</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span>
+    <span class="n">title</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">save_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">stat</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;proportion&quot;</span><span class="p">,</span>
+    <span class="n">unit</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mm&quot;</span><span class="p">,</span>
+    <span class="n">kind</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;ecdf&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the cumulative point-to-point error (CPE) and plot the CPE curve.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        y_true : numpy.ndarray</span>
+<span class="sd">            The true values of the target variable, with shape (n, p, 2).</span>
+<span class="sd">            n is the number of samples, p is the number of landmarks,</span>
+<span class="sd">            and 2 is respectively the y and x coordinates of the landmarks.</span>
+<span class="sd">        y_pred : numpy.ndarray</span>
+<span class="sd">            The predicted values of the target variable, with shape (n, p, 2).</span>
+<span class="sd">            n is the number of samples, p is the number of landmarks,</span>
+<span class="sd">            and 2 is respectively the y and x coordinates of the landmarks.</span>
+<span class="sd">        class_names: list</span>
+<span class="sd">            The names of the landmarks. The default is None.</span>
+<span class="sd">        group : bool, optional</span>
+<span class="sd">            Whether to group the CPE curves by landmarks. The default is True.</span>
+<span class="sd">        title : str, optional</span>
+<span class="sd">            The title of the plot. The default is None.</span>
+<span class="sd">        save_path : str, optional</span>
+<span class="sd">            The path to save the plot. The default is None.</span>
+<span class="sd">        stat : str, optional</span>
+<span class="sd">            The type of statistic to plot. The default is &#39;proportion&#39;.</span>
+<span class="sd">        unit : str, optional</span>
+<span class="sd">            The unit of distance. The default is &#39;mm&#39;.</span>
+<span class="sd">        kind : str, optional</span>
+<span class="sd">            The type of plot. The default is &#39;ecdf&#39;.</span>
+<span class="sd">            Possible values are &#39;ecdf&#39;, &#39;kde&#39;, and &#39;hist&#39;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Calculate the point-to-point error (PE)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">point_error</span><span class="p">(</span>
+        <span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dim_orig</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span>
+    <span class="p">)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">pe</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">pe</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+        <span class="n">pe</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">pe</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Plot the CPE curve</span>
+    <span class="k">if</span> <span class="n">group</span><span class="p">:</span>
+        <span class="c1"># Calculate the mean point-to-point error (MPE) for each sample</span>
+        <span class="n">pe</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">pe</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;ecdf&quot;</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">ecdfplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="n">stat</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sample MPE&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;kde&quot;</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sample MPE&quot;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;hist&quot;</span><span class="p">:</span>
+            <span class="n">ax</span> <span class="o">=</span> <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">,</span> <span class="n">stat</span><span class="o">=</span><span class="n">stat</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s2">&quot;Sample MPE&quot;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid kind: </span><span class="si">{</span><span class="n">kind</span><span class="si">!r}</span><span class="s2">.&quot;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">class_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Landmark </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">true_landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">class_names</span><span class="p">):</span>
+            <span class="k">if</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;ecdf&quot;</span><span class="p">:</span>
+                <span class="n">sns</span><span class="o">.</span><span class="n">ecdfplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">stat</span><span class="o">=</span><span class="n">stat</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">class_name</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;kde&quot;</span><span class="p">:</span>
+                <span class="n">sns</span><span class="o">.</span><span class="n">kdeplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">cumulative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">class_name</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">kind</span> <span class="o">==</span> <span class="s2">&quot;hist&quot;</span><span class="p">:</span>
+                <span class="n">sns</span><span class="o">.</span><span class="n">histplot</span><span class="p">(</span><span class="n">x</span><span class="o">=</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="n">stat</span><span class="o">=</span><span class="n">stat</span><span class="p">,</span> <span class="n">cumulative</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">class_name</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Invalid kind: </span><span class="si">{</span><span class="n">kind</span><span class="si">!r}</span><span class="s2">.&quot;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">set</span><span class="p">(</span><span class="n">xlabel</span><span class="o">=</span><span class="sa">f</span><span class="s2">&quot;IPE in </span><span class="si">{</span><span class="n">unit</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">,</span> <span class="n">ylabel</span><span class="o">=</span><span class="s2">&quot;Proportion of images&quot;</span><span class="p">,</span> <span class="n">title</span><span class="o">=</span><span class="n">title</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s2">&quot;center left&quot;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">))</span>
+    <span class="k">if</span> <span class="n">save_path</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="detection_report">
+<a class="viewcode-back" href="../../../reference/visualize.html#landmarker.visualize.detection_report">[docs]</a>
+<span class="k">def</span> <span class="nf">detection_report</span><span class="p">(</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">radius</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
+    <span class="n">digits</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">unit</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mm&quot;</span><span class="p">,</span>
+    <span class="n">output_dict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the detection report.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        true_landmarks : torch.Tensor</span>
+<span class="sd">            The true landmarks, with shape (n, p, 2).</span>
+<span class="sd">            n is the number of samples, p is the number of landmarks,</span>
+<span class="sd">            and 2 is respectively the y and x coordinates of the landmarks.</span>
+<span class="sd">        pred_landmarks : torch.Tensor</span>
+<span class="sd">            The predicted landmarks, with shape (n, p, 2).</span>
+<span class="sd">            n is the number of samples, p is the number of landmarks,</span>
+<span class="sd">            and 2 is respectively the y and x coordinates of the landmarks.</span>
+<span class="sd">        dim : tuple[int, ...] | torch.Tensor</span>
+<span class="sd">            The dimension of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        dim_orig : torch.Tensor</span>
+<span class="sd">            The original dimension of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        pixel_spacing : torch.Tensor</span>
+<span class="sd">            The pixel spacing of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        padding : torch.Tensor</span>
+<span class="sd">            The padding of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        class_names: list</span>
+<span class="sd">            The names of the landmarks. The default is None.</span>
+<span class="sd">        radius : list[float], optional</span>
+<span class="sd">            The radius of the success detection rate (SDR). The default is [2, 2.5, 3, 4].</span>
+<span class="sd">        digits : int, optional</span>
+<span class="sd">            The number of digits to round the statistics. The default is 2.</span>
+<span class="sd">        unit : str, optional</span>
+<span class="sd">            The unit of distance. The default is &#39;mm&#39;.</span>
+<span class="sd">        output_dict : bool, optional</span>
+<span class="sd">            Whether to output the detection report as a dictionary. The default is False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        report : dict</span>
+<span class="sd">            The detection report. Only output when output_dict is True.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Calculate the cumulative point-to-point error (CPE)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">point_error</span><span class="p">(</span>
+        <span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dim_orig</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span>
+    <span class="p">)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">pe</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">class_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
+        <span class="n">class_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;Landmark </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">true_landmarks</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])]</span>
+    <span class="c1"># Calculate the detection report</span>
+    <span class="n">report</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">class_names</span><span class="p">):</span>  <span class="c1"># type: ignore</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Median&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Min&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Max&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">sdr_class</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">(</span>
+            <span class="n">radius</span><span class="p">,</span>
+            <span class="n">true_landmarks</span><span class="o">=</span><span class="n">true_landmarks</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span>
+            <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span>
+            <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;SDR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sdr_class</span>
+
+    <span class="c1"># Print the detection report in a nice table</span>
+    <span class="n">sdr_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;SDR (PE≤</span><span class="si">{</span><span class="n">r</span><span class="si">}{</span><span class="n">unit</span><span class="si">}</span><span class="s2">)&quot;</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">radius</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detection report:&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;1# Point-to-point error (PE) statistics:&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;Class&#39;</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}{</span><span class="s1">&#39;Mean&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Median&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Std&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Min&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Max&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">report</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">class_name</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Mean&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Median&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Std&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Min&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Max&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">2# Success detection rate (SDR):&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;Class&#39;</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">sdr_name</span><span class="si">:</span><span class="s2">&lt;15</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">sdr_name</span> <span class="ow">in</span> <span class="n">sdr_names</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">report</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+                <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">class_name</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span>
+                <span class="o">+</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;SDR&#39;</span><span class="p">][</span><span class="n">r</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;15.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">radius</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+
+    <span class="k">if</span> <span class="n">output_dict</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">report</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">multi_instance_detection_report</span><span class="p">(</span>
+    <span class="n">true_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pred_landmarks</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">true_positives</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">false_positives</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">false_negatives</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim</span><span class="p">:</span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">dim_orig</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">pixel_spacing</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">padding</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span>
+    <span class="n">class_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">list</span><span class="p">[</span><span class="nb">str</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">radius</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span>
+    <span class="n">digits</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span>
+    <span class="n">unit</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;mm&quot;</span><span class="p">,</span>
+    <span class="n">output_dict</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;Calculate the multi instance detection report.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        true_landmarks : torch.Tensor</span>
+<span class="sd">            The true landmarks, with shape (n, p, i, 2). n is the number of samples, p is the number</span>
+<span class="sd">            of classes of landmarks, i is the number of instances, and 2 is respectively the y and</span>
+<span class="sd">            x coordinates of the landmarks.</span>
+<span class="sd">        pred_landmarks : torch.Tensor</span>
+<span class="sd">            The predicted landmarks, with shape (n, p, i, 2). n is the number of samples, p is the</span>
+<span class="sd">            number of classes of landmarks, i is the number of instances, and 2 is respectively the</span>
+<span class="sd">            y and x coordinates of the landmarks.</span>
+<span class="sd">        true_positives : torch.Tensor</span>
+<span class="sd">            The true positives, with shape (n, p, i). n is the number of samples, p is the number of</span>
+<span class="sd">            classes of landmarks, and i is the number of instances.</span>
+<span class="sd">        false_positives : torch.Tensor</span>
+<span class="sd">            The false positives, with shape (n, p, i). n is the number of samples, p is the number</span>
+<span class="sd">            of classes of landmarks, and i is the number of instances.</span>
+<span class="sd">        false_negatives : torch.Tensor</span>
+<span class="sd">            The false negatives, with shape (n, p, i). n is the number of samples, p is the number</span>
+<span class="sd">            of classes of landmarks, and i is the number of instances.</span>
+<span class="sd">        dim : tuple[int, ...] | torch.Tensor</span>
+<span class="sd">            The dimension of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        dim_orig : torch.Tensor</span>
+<span class="sd">            The original dimension of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        pixel_spacing : torch.Tensor</span>
+<span class="sd">            The pixel spacing of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        padding : torch.Tensor</span>
+<span class="sd">            The padding of the image, with shape (2,).</span>
+<span class="sd">            2 is respectively the height and width of the image.</span>
+<span class="sd">        class_names: list</span>
+<span class="sd">            The names of the landmarks. The default is None.</span>
+<span class="sd">        radius : list[float], optional</span>
+<span class="sd">            The radius of the success detection rate (SDR). The default is [2, 2.5, 3, 4].</span>
+<span class="sd">        digits : int, optional</span>
+<span class="sd">            The number of digits to round the statistics. The default is 2.</span>
+<span class="sd">        unit : str, optional</span>
+<span class="sd">            The unit of distance. The default is &#39;mm&#39;.</span>
+<span class="sd">        output_dict : bool, optional</span>
+<span class="sd">            Whether to output the detection report as a dictionary. The default is False.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        report : dict</span>
+<span class="sd">            The detection report. Only output when output_dict is True.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Calculate the cumulative point-to-point error (CPE)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">point_error</span><span class="p">(</span>
+        <span class="n">true_landmarks</span><span class="p">,</span> <span class="n">pred_landmarks</span><span class="p">,</span> <span class="n">dim</span><span class="p">,</span> <span class="n">dim_orig</span><span class="p">,</span> <span class="n">pixel_spacing</span><span class="p">,</span> <span class="n">padding</span><span class="p">,</span> <span class="n">reduction</span><span class="o">=</span><span class="s2">&quot;none&quot;</span>
+    <span class="p">)</span>
+    <span class="n">pe</span> <span class="o">=</span> <span class="n">pe</span><span class="o">.</span><span class="n">cpu</span><span class="p">()</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
+
+    <span class="c1"># Calculate the detection report</span>
+    <span class="n">report</span><span class="p">:</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">float</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]]</span> <span class="o">=</span> <span class="p">{}</span>
+    <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">class_names</span><span class="p">):</span>  <span class="c1"># type: ignore</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">]</span> <span class="o">=</span> <span class="p">{}</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Mean&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmean</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Median&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Std&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Min&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmin</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;Max&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">pe</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;TP&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">true_positives</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;FP&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">false_positives</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;FN&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">false_negatives</span><span class="p">[:,</span> <span class="n">i</span><span class="p">])</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;TPR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;TP&quot;</span><span class="p">]</span> <span class="o">/</span> <span class="p">(</span>  <span class="c1"># type: ignore</span>
+            <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;TP&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;FN&quot;</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+        <span class="p">)</span>
+        <span class="n">sdr_class</span> <span class="o">=</span> <span class="n">sdr</span><span class="p">(</span>
+            <span class="n">radius</span><span class="p">,</span>
+            <span class="n">true_landmarks</span><span class="o">=</span><span class="n">true_landmarks</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span>
+            <span class="n">pred_landmarks</span><span class="o">=</span><span class="n">pred_landmarks</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span>
+            <span class="n">dim</span><span class="o">=</span><span class="n">dim</span><span class="p">,</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="n">pixel_spacing</span><span class="p">,</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s2">&quot;SDR&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="n">sdr_class</span>
+
+    <span class="c1"># Print the detection report in a nice table</span>
+    <span class="n">sdr_names</span> <span class="o">=</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;SDR (PE≤</span><span class="si">{</span><span class="n">r</span><span class="si">}{</span><span class="n">unit</span><span class="si">}</span><span class="s2">)&quot;</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">radius</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Detection report:&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;1# Instance detection statistics:&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;Class&#39;</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}{</span><span class="s1">&#39;TP&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;FP&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;FN&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;TPR&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">4</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">report</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">class_name</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;TP&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;FP&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;FN&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;TPR&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">4</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">2# Point-to-point error (PE) statistics:&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;Class&#39;</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}{</span><span class="s1">&#39;Mean&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Median&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Std&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Min&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}{</span><span class="s1">&#39;Max&#39;</span><span class="si">:</span><span class="s2">&lt;10</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+    <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">report</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">class_name</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Mean&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Median&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Std&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Min&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+            <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;Max&#39;</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;10.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">10</span> <span class="o">*</span> <span class="mi">5</span><span class="p">))</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">3# Success detection rate (SDR):&quot;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="s1">&#39;Class&#39;</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span> <span class="o">+</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">sdr_name</span><span class="si">:</span><span class="s2">&lt;15</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">sdr_name</span> <span class="ow">in</span> <span class="n">sdr_names</span><span class="p">]))</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+    <span class="k">for</span> <span class="n">class_name</span> <span class="ow">in</span> <span class="n">report</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span>
+            <span class="s2">&quot;&quot;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span>
+                <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">class_name</span><span class="si">:</span><span class="s2">&lt;20</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">]</span>
+                <span class="o">+</span> <span class="p">[</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">report</span><span class="p">[</span><span class="n">class_name</span><span class="p">][</span><span class="s1">&#39;SDR&#39;</span><span class="p">][</span><span class="n">r</span><span class="p">]</span><span class="si">:</span><span class="s2">&lt;15.</span><span class="si">{</span><span class="n">digits</span><span class="si">}</span><span class="s2">f</span><span class="si">}</span><span class="s2">&quot;</span> <span class="k">for</span> <span class="n">r</span> <span class="ow">in</span> <span class="n">radius</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+            <span class="p">)</span>
+        <span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;=&quot;</span> <span class="o">*</span> <span class="p">(</span><span class="mi">20</span> <span class="o">+</span> <span class="mi">15</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">radius</span><span class="p">)))</span>
+
+    <span class="k">if</span> <span class="n">output_dict</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">report</span>
+</pre></div>
+        </article>
+      </div>
+      <footer>
+        
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+          
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diff --git a/_modules/landmarker/visualize/utils.html b/_modules/landmarker/visualize/utils.html
new file mode 100644
index 0000000..0cc032c
--- /dev/null
+++ b/_modules/landmarker/visualize/utils.html
@@ -0,0 +1,600 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../../../">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="../../../genindex.html" /><link rel="search" title="Search" href="../../../search.html" />
+
+    <link rel="shortcut icon" href="../../../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
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+    
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+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #272822;
+  --color-code-foreground: #f8f8f2;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
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+<li class="toctree-l2"><a class="reference internal" href="../../../reference/data.html">Data Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../reference/losses.html">Losses Module</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../../../reference/uncertainty.html">Uncertainty Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../reference/utils.html">Utils Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../../../reference/visualize.html">Visualize Module</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../dev/contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
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+</ul>
+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../reference/data.html">Data Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../reference/losses.html">Losses Module</a></li>
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+          <h1>Source code for landmarker.visualize.utils</h1><div class="highlight"><pre>
+<span></span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">Utility functions for visualizing data.</span>
+<span class="sd">&quot;&quot;&quot;</span>
+
+<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Sequence</span>
+
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">torch</span> <span class="kn">import</span> <span class="n">nn</span>
+
+<span class="kn">from</span> <span class="nn">landmarker.data</span> <span class="kn">import</span> <span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">LandmarkDataset</span><span class="p">,</span> <span class="n">MaskDataset</span>
+<span class="kn">from</span> <span class="nn">landmarker.heatmap.decoder</span> <span class="kn">import</span> <span class="n">heatmap_to_coord</span><span class="p">,</span> <span class="n">heatmap_to_multiple_coord</span>
+<span class="kn">from</span> <span class="nn">landmarker.heatmap.generator</span> <span class="kn">import</span> <span class="n">HeatmapGenerator</span>
+<span class="kn">from</span> <span class="nn">landmarker.utils.utils</span> <span class="kn">import</span> <span class="n">pixel_to_unit</span>
+
+
+<div class="viewcode-block" id="inspection_plot">
+<a class="viewcode-back" href="../../../reference/visualize.html#landmarker.visualize.inspection_plot">[docs]</a>
+<span class="k">def</span> <span class="nf">inspection_plot</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">:</span> <span class="n">LandmarkDataset</span><span class="p">,</span>
+    <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
+    <span class="n">heatmap_generator</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">HeatmapGenerator</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">save_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">fig_title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;Landmark Dataset Inspection Plot&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plots the transformed image, heatmap, and original image with landmarks for the given dataset</span>
+<span class="sd">    indices.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        ds (LandmarkDataset): Dataset to inspect.</span>
+<span class="sd">        idx (int | Iterable[int]): Indices of the dataset to inspect.</span>
+<span class="sd">        heatmap_generator (HeatmapGenerator, optional): Heatmap generator to use. Defaults to None.</span>
+<span class="sd">        save_path (str, optional): Path to save the plot to. If None, the plot is not saved.</span>
+<span class="sd">            Defaults to None.</span>
+<span class="sd">        fig_title (str, optional): Title of the figure. Defaults to &quot;Landmark Dataset Inspection</span>
+<span class="sd">            Plot&quot;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)))</span>
+    <span class="c1"># fig.suptitle(fig_title)</span>
+
+    <span class="c1"># Create len(idx)x1 subfigures</span>
+    <span class="n">subfigs</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">subfigures</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">subfig</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subfigs</span><span class="p">):</span>
+        <span class="n">ds_idx</span> <span class="o">=</span> <span class="n">idx</span><span class="p">[</span><span class="n">row</span><span class="p">]</span>
+        <span class="n">batch</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+        <span class="n">landmark</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span>
+        <span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">ds</span><span class="p">,</span> <span class="p">(</span><span class="n">HeatmapDataset</span><span class="p">,</span> <span class="n">MaskDataset</span><span class="p">)):</span>
+            <span class="n">batch</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">heatmap_generator</span><span class="p">:</span>
+                <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap_generator</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="k">assert</span> <span class="n">heatmap</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">,</span> <span class="s2">&quot;Heatmap generator must return a heatmap.&quot;</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">heatmap</span> <span class="o">=</span> <span class="kc">None</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">heatmap</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;mask&quot;</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># If more than 3 channels, remove other channels</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">heatmap</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
+            <span class="n">axs</span> <span class="o">=</span> <span class="n">subfig</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;jet&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># If multiple instances</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmarks_original</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                        <span class="n">landmarks_original</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmarks_original</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span>
+                    <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmark</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmark</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ landmarks&quot;</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ heatmap&quot;</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Original w/ landmarks&quot;</span><span class="p">)</span>
+
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">axs</span> <span class="o">=</span> <span class="n">subfig</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmark</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmarks_original</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                        <span class="n">landmarks_original</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmarks_original</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span>
+                    <span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmark</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmark</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ landmarks&quot;</span><span class="p">)</span>
+            <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Original w/ landmarks&quot;</span><span class="p">)</span>
+        <span class="n">subfig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Image </span><span class="si">{</span><span class="n">ds_idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">save_path</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+
+
+
+<div class="viewcode-block" id="prediction_inspect_plot">
+<a class="viewcode-back" href="../../../reference/visualize.html#landmarker.visualize.prediction_inspect_plot">[docs]</a>
+<span class="k">def</span> <span class="nf">prediction_inspect_plot</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">:</span> <span class="n">LandmarkDataset</span><span class="p">,</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span>
+    <span class="n">save_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">fig_title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;Landmark Prediction Model Inspection Plot&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Plots the transformed image, predicted heatmap, and original image with predicted and true</span>
+<span class="sd">    landmarks for the given dataset indices.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        ds (LandmarkDataset): Dataset to inspect.</span>
+<span class="sd">        model (nn.Module): Model to use for prediction.</span>
+<span class="sd">        idx (int | Sequence[int]): Indices of the dataset to inspect.</span>
+<span class="sd">        activation (nn.Module, optional): Activation function to use. Defaults to nn.Identity().</span>
+<span class="sd">        save_path (str, optional): Path to save the plot to. If None, the plot is not saved.</span>
+<span class="sd">            Defaults to None.</span>
+<span class="sd">        fig_title (str, optional): Title of the figure. Defaults to &quot;Landmark Prediction Model</span>
+<span class="sd">            Inspection Plot&quot;.</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)))</span>
+    <span class="c1"># fig.suptitle(fig_title)</span>
+
+    <span class="c1"># Create len(idx)x1 subfigures</span>
+    <span class="n">subfigs</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">subfigures</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">subfig</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subfigs</span><span class="p">):</span>
+        <span class="n">ds_idx</span> <span class="o">=</span> <span class="n">idx</span><span class="p">[</span><span class="n">row</span><span class="p">]</span>
+        <span class="n">batch</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">heatmap</span> <span class="o">=</span> <span class="n">activation</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">img_t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span>
+        <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+        <span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">landmarks_original</span><span class="o">.</span><span class="n">view</span><span class="p">((</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">landmarks_original</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+        <span class="n">pred_landmarks_t</span> <span class="o">=</span> <span class="n">heatmap_to_coord</span><span class="p">(</span><span class="n">heatmap</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;local_soft_argmax&quot;</span><span class="p">)</span>
+        <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="n">pixel_to_unit</span><span class="p">(</span>
+            <span class="n">pred_landmarks_t</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">dim</span><span class="o">=</span><span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">],</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">],</span>
+        <span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">pred_landmarks_t</span> <span class="o">=</span> <span class="n">pred_landmarks_t</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># If more than 3 channels, remove other channels</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">float</span><span class="p">()</span>
+        <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">axs</span> <span class="o">=</span> <span class="n">subfig</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmark_t</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmark_t</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">pred_landmarks_t</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[:,</span> <span class="mi">1</span><span class="p">],</span>
+            <span class="n">pred_landmarks_t</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[:,</span> <span class="mi">0</span><span class="p">],</span>
+            <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span>
+            <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;jet&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">landmarks_original</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+            <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[:,</span> <span class="mi">1</span><span class="p">],</span> <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()[:,</span> <span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span>
+        <span class="p">)</span>
+
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ landmarks&quot;</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ heatmap&quot;</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Original w/ landmarks&quot;</span><span class="p">)</span>
+        <span class="n">subfig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Image </span><span class="si">{</span><span class="n">ds_idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;True&quot;</span><span class="p">,</span> <span class="s2">&quot;Predicted&quot;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">save_path</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span></div>
+
+
+
+<span class="k">def</span> <span class="nf">prediction_inspect_plot_multi_instance</span><span class="p">(</span>
+    <span class="n">ds</span><span class="p">:</span> <span class="n">LandmarkDataset</span><span class="p">,</span>
+    <span class="n">model</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span><span class="p">,</span>
+    <span class="n">idx</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span>
+    <span class="n">threshold</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span>
+    <span class="n">window</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">5</span><span class="p">,</span>
+    <span class="n">activation</span><span class="p">:</span> <span class="n">nn</span><span class="o">.</span><span class="n">Module</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Identity</span><span class="p">(),</span>
+    <span class="n">save_path</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
+    <span class="n">fig_title</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="s2">&quot;Landmark Prediction Model Inspection Plot&quot;</span><span class="p">,</span>
+<span class="p">):</span>
+    <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="nb">int</span><span class="p">):</span>
+        <span class="n">idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">idx</span><span class="p">]</span>
+
+    <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">5</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">)))</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="n">fig_title</span><span class="p">)</span>
+
+    <span class="c1"># Create len(idx)x1 subfigures</span>
+    <span class="n">subfigs</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">subfigures</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">idx</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">row</span><span class="p">,</span> <span class="n">subfig</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">subfigs</span><span class="p">):</span>
+        <span class="n">ds_idx</span> <span class="o">=</span> <span class="n">idx</span><span class="p">[</span><span class="n">row</span><span class="p">]</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">image_loader</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">img_paths</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">])</span>  <span class="c1"># type: ignore</span>
+        <span class="n">batch</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="n">ds_idx</span><span class="p">]</span>
+        <span class="n">img_t</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;image&quot;</span><span class="p">]</span>
+        <span class="n">landmark_t</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;landmark&quot;</span><span class="p">]</span>
+        <span class="n">landmarks_original</span> <span class="o">=</span> <span class="n">pixel_to_unit</span><span class="p">(</span>
+            <span class="n">landmark_t</span><span class="p">,</span>
+            <span class="n">pixel_spacing</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+            <span class="n">dim</span><span class="o">=</span><span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span>
+            <span class="n">dim_orig</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">],</span>
+            <span class="n">padding</span><span class="o">=</span><span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">],</span>
+        <span class="p">)</span>
+        <span class="n">dim_orig</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;dim_original&quot;</span><span class="p">]</span>
+        <span class="n">padding</span> <span class="o">=</span> <span class="n">batch</span><span class="p">[</span><span class="s2">&quot;padding&quot;</span><span class="p">]</span>
+        <span class="n">heatmap</span> <span class="o">=</span> <span class="n">activation</span><span class="p">(</span><span class="n">model</span><span class="p">(</span><span class="n">img_t</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)))</span>
+        <span class="n">pred_landmarks_t_batch</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">heatmap_to_multiple_coord</span><span class="p">(</span>
+            <span class="n">heatmap</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;argmax&quot;</span><span class="p">,</span> <span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">,</span> <span class="n">window</span><span class="o">=</span><span class="n">window</span>
+        <span class="p">)</span>
+        <span class="n">pred_landmarks_t</span> <span class="o">=</span> <span class="n">pred_landmarks_t_batch</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>  <span class="c1"># type: ignore</span>
+        <span class="n">true_landmarks_t</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">true_landmarks_original</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">pred_landmarks</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
+            <span class="n">true_landmarks_t_class</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">true_landmarks_original_class</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">pred_landmarks_class</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">landmark_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]):</span>
+                <span class="k">if</span> <span class="ow">not</span> <span class="n">landmark_t</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">isnan</span><span class="p">()</span><span class="o">.</span><span class="n">any</span><span class="p">():</span>
+                    <span class="n">true_landmarks_t_class</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmark_t</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
+                    <span class="n">true_landmarks_original_class</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">landmarks_original</span><span class="p">[</span><span class="n">c</span><span class="p">,</span> <span class="n">i</span><span class="p">])</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">])):</span>
+                <span class="n">pred_landmarks_class</span><span class="o">.</span><span class="n">append</span><span class="p">(</span>
+                    <span class="n">pixel_to_unit</span><span class="p">(</span>
+                        <span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">unsqueeze</span><span class="p">(</span><span class="mi">0</span><span class="p">),</span>
+                        <span class="n">pixel_spacing</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                        <span class="n">dim</span><span class="o">=</span><span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">:],</span>
+                        <span class="n">dim_orig</span><span class="o">=</span><span class="n">dim_orig</span><span class="p">,</span>
+                        <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span>
+                    <span class="p">)</span>
+                    <span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                    <span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+                <span class="p">)</span>
+            <span class="n">true_landmarks_t</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">true_landmarks_t_class</span><span class="p">)</span>
+            <span class="n">true_landmarks_original</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">true_landmarks_original_class</span><span class="p">)</span>
+            <span class="n">pred_landmarks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pred_landmarks_class</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span>  <span class="c1"># If more than 3 channels, remove other channels</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">img_t</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">img_t</span> <span class="o">=</span> <span class="n">img_t</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+            <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">permute</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
+        <span class="n">img</span> <span class="o">=</span> <span class="n">img</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">uint8</span><span class="p">)</span>
+        <span class="n">heatmap</span> <span class="o">=</span> <span class="n">heatmap</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">axs</span> <span class="o">=</span> <span class="n">subfig</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">nrows</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">ncols</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img_t</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">heatmap</span><span class="o">.</span><span class="n">detach</span><span class="p">()</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">),</span> <span class="n">cmap</span><span class="o">=</span><span class="s2">&quot;jet&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">img</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">c</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks_t</span><span class="p">)):</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">]),</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">]))):</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">true_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">true_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">true_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span>
+                        <span class="n">true_landmarks_original</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span>
+                        <span class="n">true_landmarks_original</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span>
+                        <span class="n">c</span><span class="o">=</span><span class="s2">&quot;b&quot;</span><span class="p">,</span>
+                        <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span>
+                    <span class="p">)</span>
+                <span class="k">if</span> <span class="n">i</span> <span class="o">&lt;</span> <span class="nb">len</span><span class="p">(</span><span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">]):</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">pred_landmarks_t</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pred_landmarks</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">pred_landmarks</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s2">&quot;r&quot;</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
+
+        <span class="n">axs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ landmarks&quot;</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Transformed w/ heatmap&quot;</span><span class="p">)</span>
+        <span class="n">axs</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s2">&quot;Original w/ landmarks&quot;</span><span class="p">)</span>
+        <span class="n">subfig</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Image </span><span class="si">{</span><span class="n">ds_idx</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+    <span class="n">fig</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s2">&quot;True&quot;</span><span class="p">,</span> <span class="s2">&quot;Predicted&quot;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">save_path</span><span class="p">:</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="n">save_path</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+        </article>
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diff --git a/_sources/_collections/examples/ISBI2015.ipynb.txt b/_sources/_collections/examples/ISBI2015.ipynb.txt
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p align=\"center\">\n",
+    "    <a href=\"https://predict-idlab.github.io/landmarker\">\n",
+    "        <img alt=\"landmarker\" src=\"https://raw.githubusercontent.com/predict-idlab/landmarker/main/docs/_static/images/logo.svg\" width=\"66%\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "# Training and Evaluating Adaptive Heatmap Regression Model for Landmark Detection with ISBI 2015 Cephalometric X-ray Dataset\n",
+    "\n",
+    "In this tutorial, we will train and evaluate an adaptive heatmap regression model for landmark \n",
+    "detection with the ISBI 2015 Cephalometric X-ray dataset. The ISBI 2015 Cephalometric X-ray dataset\n",
+    "is a dataset of 2D cephalometric X-rays. The dataset contains 400 images, each with 19 landmarks\n",
+    "annotated. The dataset is split into a training set of 150 images and two test sets of 150 and\n",
+    "100 images respectively.\n",
+    "\n",
+    "We will go through the following steps:\n",
+    "* [Loading the dataset](#Loading-the-dataset)\n",
+    "* [Constructing a heatmap generator](#Constructing-a-heatmap-generator)\n",
+    "* [Inspecting the dataset](#Inspecting-the-dataset)\n",
+    "* [Training and initializing the SpatialConfiguration model](#Training-the-model)\n",
+    "* [Evaluating the model](#Evaluating-the-model)\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/predict-idlab/landmarker/examples/adaptive_scn_isbi2015.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !python -c \"import landmarker\" || pip install landmarker\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(\"../src/\")\n",
+    "import landmarker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup imports and variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from monai.transforms import (Compose, RandAffined, RandGaussianNoised, RandStdShiftIntensityd,\n",
+    "                              RandScaleIntensityd, RandAdjustContrastd, RandHistogramShiftd,\n",
+    "                              ScaleIntensityd, Lambdad)\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the dataset\n",
+    "\n",
+    "### Short description of the data and dataset module\n",
+    "The [landmarker](https://github.com/predict-idlab/landmarker) package has several built-in\n",
+    "datasets in the `landmarker.datasets` module, as well as utility classes for building your own\n",
+    "datasets in the `landmarker.data` module. There are three types of datasets: 'LandmarkDataset',\n",
+    "'HeatmapDataset', and 'MaskDataset'. The 'LandmarkDataset' is a dataset of images with landmarks,\n",
+    "the 'HeatmapDataset' is a dataset of images with heatmaps, and the 'MaskDataset' is a dataset of\n",
+    "images with masks (i.e., binary segmentation masks indiciating the location of the landmarks). The \n",
+    "'HeatmapDataset' and 'MaskDataset' both inherit from the 'LandmarkDataset' class, and thus also \n",
+    "contain information about the landmarks. The 'MaskDataset' can be constructed from specified image \n",
+    "and landmarks pairs, or from images and masks pairs, because often that is how the data is\n",
+    "distributed. The 'HeatmapDataset' can be constructed from images and landmarks pairs.\n",
+    "\n",
+    "Images can be provided as a list of paths to stored images, or as a a numpy arary, torch tensor, \n",
+    "list of numpy  arrays or list of torch tensors. Landmarks can be as numpy arrays or torch tensors.\n",
+    "These landmarks can be provided in three different shapes: (1) (N, D) where N is the number of\n",
+    "samples and D is the number of dimensions, (2) (N, C, D) where C is the number of landmark\n",
+    "classes, (3) (N, C, I, D) where I is the number of instances per landmark class, if less than I\n",
+    "instances are provided, the remaining instances are filled with NaNs.\n",
+    "\n",
+    "For built-in datasets, the `landmarker.datasets` module provides a function for\n",
+    "downloading and loading the dataset, e.g. `get_cepha_landmark_datasets` for the ISBI 2015 \n",
+    "Cephalometric X-ray dataset. Most of these functions take the same arguments, namely `path_dir`,\n",
+    "some are dataset specific. The `path_dir` argument specifies the directory where the dataset is\n",
+    "downloaded to, or loaded from if it is already downloaded. For most datasets multiple functions\n",
+    "are provided for getting different types of datasets. For example, the ISBI 2015 Cephalometric\n",
+    "X-ray dataset has the following functions: `get_cepha_landmark_datasets` and \n",
+    "`get_cepha_heatmap_datasets`.\n",
+    "\n",
+    "### Download and load ISBI 2015 landmark dataset\n",
+    "The ISBI 2015 Cephalometric X-ray dataset is a dataset of 2D cephalometric X-ray images with 19\n",
+    "landmarks. The dataset is split into a training set of 150 images and two test sets, where test\n",
+    "set  1 contains 150 images and test set 2 contains 100 images. When loading the dataset, you can\n",
+    "also specify a transform function, which is applied to the images and landmarks of the training\n",
+    "set. Currently, we only support the `monai.transforms.ComposeD transform, which allows you to\n",
+    "compose multiple transforms. The `monai.transforms` module contains many useful transforms, such\n",
+    "as `RandomAffine` and `NormalizeIntensity`. The transforms must be dictionary transforms, i.e.,\n",
+    "they must return a dictionary with the keys 'image' and ('seg'), in the case of heatmap and mask\n",
+    "regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.transforms.images import UseOnlyFirstChannel\n",
+    "fn_keys = ('image',)\n",
+    "spatial_transformd = [RandAffined(fn_keys, prob=1,\n",
+    "                        rotate_range=(-np.pi/12, np.pi/12),\n",
+    "                        translate_range=(-10, 10),\n",
+    "                        scale_range=(-0.1, 0.1),\n",
+    "                        shear_range=(-0.1, 0.1)\n",
+    "                        )]\n",
+    "\n",
+    "train_transformd = Compose([\n",
+    "                            UseOnlyFirstChannel(('image', )),\n",
+    "                            RandGaussianNoised(('image', ), prob=0.2, mean=0, std=0.1),  # Add gaussian noise\n",
+    "                            RandScaleIntensityd(('image', ), factors=0.25, prob=0.2),  # Add random intensity scaling\n",
+    "                            RandAdjustContrastd(('image', ), prob=0.2, gamma=(0.5,4.5)),  # Randomly adjust contrast\n",
+    "                            RandHistogramShiftd(('image', ), prob=0.2),  # Randomly shift histogram\n",
+    "                            ScaleIntensityd(('image', )),  # Scale intensity\n",
+    "                        ] + spatial_transformd)\n",
+    "\n",
+    "inference_transformd = Compose([\n",
+    "    UseOnlyFirstChannel(('image', )),\n",
+    "    ScaleIntensityd(('image', )),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading 150 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 150/150 [00:06<00:00, 23.63it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resizing 150 images and landmarks...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 150/150 [00:03<00:00, 42.01it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading 150 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 150/150 [00:06<00:00, 22.50it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resizing 150 images and landmarks...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 150/150 [00:03<00:00, 38.48it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading 100 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:04<00:00, 24.65it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Resizing 100 images and landmarks...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:01<00:00, 61.08it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.datasets import get_cepha_landmark_datasets\n",
+    "\n",
+    "data_dir = \"/Users/jefjonkers/Data/landmark-datasets\"\n",
+    "ds_train, ds_test1, ds_test2 = get_cepha_landmark_datasets(data_dir, train_transform=train_transformd,\n",
+    "                                                           inference_transform=inference_transformd,\n",
+    "                                                           store_imgs = True, dim_img=(512, 512),\n",
+    "                                                           junior = True, single_dataset = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Constructing a heatmap generator\n",
+    "The heatmap generator is a class that generates heatmaps from landmarks. It is used to generate\n",
+    "heatmaps from the landmarks of the training set, which are then used to train the model. The\n",
+    "`landmarker.heatmap_generator` module contains several heatmap generators, such as the\n",
+    "`GaussianHeatmapGenerator` and `LaplaceHeatmapGenerator` which generate a multivariate \n",
+    "Gaussian and Laplace distribution respectively. The HeatmapGenerator subclasses take the following\n",
+    "arguments:\n",
+    "* `sigmas`: the standard deviation of the Gaussian distribution, or the scale of the Laplace. This \n",
+    "could be a scalar, or a list of scalars, one for each landmark class. Additionally, it could be a\n",
+    "covariance matrix, or a list of covariance matrices, one for each landmark class.\n",
+    "* `gamma`: If provided, the heatmaps are scaled by `gamma` before being returned.\n",
+    "* `rotation`: If provided, the heatmaps are rotated by `rotation` before being returned.\n",
+    "* `heatmap_size`: The size of the returned heatmaps.\n",
+    "* learnable: If True, the `sigma` and `rotation` parameters are learnable parameters, and thus\n",
+    "will be optimized during training.\n",
+    "* `background`: A boolean indicating whether to add a background class to the heatmaps. If True,\n",
+    "the heatmaps will have an additional channel, which is 1 everywhere except at the location of the\n",
+    "landmarks, where it is 0. The background class is the first class, i.e., the first channel.\n",
+    "* `all_points`: A boolean indicating whether to add a channel with all the landmarks. If True, the\n",
+    "heatmaps will have an additional channel, which is 1 at the location of the landmarks, and 0.\n",
+    "everywhere else.\n",
+    "* `continuous`: A boolean indicating whether to use continuous or discrete landmarks.\n",
+    "* `device`: The device on which the heatmaps are generated.\n",
+    "\n",
+    "The landmarks provide to the heatmap generator must be a torch.Tensor and can be in three different \n",
+    "shapes: (1) (N, D) where N is the number of samples and D is the number of dimensions, (2) (N, C, D)\n",
+    "where C is the number of landmark classes, (3) (N, C, I, D) where I is the number of instances per\n",
+    "landmark class, if less than I instances are provided, the remaining instances are filled with NaNs.\n",
+    "The heatmap generator will return a torch.Tensor of shape (N, C, H, W), where H and W are the height\n",
+    "and width of the heatmaps respectively.\n",
+    "\n",
+    "**Note that with 2D landmarks the y coordinates are the first dimension, and the x coordinates are the\n",
+    "second dimension.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.heatmap.generator import GaussianHeatmapGenerator\n",
+    "\n",
+    "heatmap_generator = GaussianHeatmapGenerator(\n",
+    "    nb_landmarks=19,\n",
+    "    sigmas=3,\n",
+    "    gamma=100,\n",
+    "    heatmap_size=(512, 512),\n",
+    "    learnable=True, # If True, the heatmap generator will be trainable\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1000 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from landmarker.visualize import inspection_plot\n",
+    "\n",
+    "# Plot the first 3 images from the training set\n",
+    "inspection_plot(ds_train, range(2), heatmap_generator=heatmap_generator)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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23HJLgBUKwhaLxWKx/OTQFovFYrFYvjF33HGHBvRLL7200usEQaB939fHH3+83mKLLSq+A3SXLl30smXLkrI5c+ZoKaW+6qqrkrLttttOd+/eXTc1NSVly5Yt0+3bt9fNp3lAd+3aVdfX1ydl//znPzWgBw4cqJVSSfmNN96oAf3GG2+02nellPZ9X3/22Wca0P/617+S78aNG6cBffHFF7dY75hjjtG5XE5rrfXVV1+tHcfR48ePX+E4NW93/vz5rX7f1NSkAT1ixIhV7vO1116rAf3pp5+usA9xG0899ZQG9IwZM7TWWi9YsEAD+sYbb1zuup9//rl2XVefeuqpFeV1dXW6a9eu+uCDD07KTjnllBbHb0WUj2tMr169dCaT0Z999llS1tTUpNu3b69PPPHEpGzTTTfV++2339e2D+ibbrqpovw3v/mNBvSzzz6rtdb6hRde0IC+/vrrK+p98cUXuqqqSp9zzjnJPrdp00bvtNNOFeddc1Y0Dr169dLHHHNMsjx27FgN6P/9738V9U4++WQthNDvv/++1lrrTz/9VAN6s80200EQJPWmT5+uAX3PPfescCymTZumAT1p0iTt+75ubGzUTz/9tN5ggw204zjJORFv54477kjW3X777XXnzp11XV1dUhYEgd500011jx49krH4+9//rgE9bdq0FfbFYrFYLJafKtbN0GKxWCyW75C///3v7LjjjlRXV+O6Lp7ncfvtt1dkQYsZOnQoNTU1yXKXLl3o3Llz4sbU0NDASy+9xOjRo8lkMkm9mpoa9t5771a3P3ToUHK5XLLcr18/AEaMGFGR/TAuL3eZmjdvHieddBLrrrtu0vdevXoBtNr/Aw44oNU+aK058cQTGTduHHfffTfnnHNOq/VWFa11i7JV7XNrfPLJJxx++OF07doVx3HwPI/BgwdXtNG+fXvWX399rr32Wm644QZee+21Ctc2gMcff5wgCDj66KMJgiD5y2QyDB48OHFrXJMMHDiQnj17JsuZTIa+fftWHNdtt92WRx99lLFjx/Lkk0/S1NS03PaOOOKIiuXDDz8cgGnTpgHG8k8IwZFHHlmxj127dmXzzTdP9vH5559n2bJljBkzZo1l3Zw6dSr9+/dn2223rSg/9thj0VpXWNEBjBo1qsIScMCAAcDXuwnGHHLIIXieRzabZZdddiEMQ+6///6kneY0NDTwv//9jwMPPLAi86TjOBx11FF8+eWXvP/++yu1bYvFYrFYfupYN0OLxWKxWL4jHnzwQQ4++GAOOuggzj77bLp27Yrrutx6660t4hgBdOjQoUVZOp1OxIbFixejlKJr164t6rVWBkZ0KSeVSq2wPJ/PA6CUYvfdd2fWrFlcdNFFbLbZZuRyOZRSbL/99q0KIMtzBysWi0yaNIlNNtmEESNGtFpndYhFiO7du692n5tTX1/PzjvvTCaT4YorrqBv375ks1m++OILRo8enbQhhOCJJ57gsssu45prruHMM8+kffv2HHHEEfzmN7+hpqYmcf3cZpttWt2WlGv+HePXnUMAv/vd7+jRoweTJk1i/PjxZDIZ9thjD6699lo23HDDpJ7rui3ai8+z2CVx7ty5aK0TV8LmxO6x8+fPB6BHjx7fYO8qWbhwIb17925RHp8Pzd0mm+9LnDRgZc4LgPHjx7PrrrviOA4dO3Zk3XXXXWH9xYsXo7Vu9XexvD5aLBaLxWJpHStmWSwWi8XyHfG3v/2NPn36MGnSpAprlEKhsFrttWvXDiEEc+bMafFda2XfhLfeeosZM2YwceJEjjnmmKS8eZD4cpZncZNOp5k2bRp77LEHw4cP57HHHqNdu3bfuI///ve/ARgyZMhq97k5U6dOZdasWTz55JOJNRZQEQcrplevXtx+++0AfPDBB9x3331ccsklFItFbrvttiQD4/33359Yh30fyOVyXHrppVx66aXMnTs3sdLae++9ee+995J6QRCwcOHCChEoPs/iso4dOyKE4Jlnnmk1o2RcFseV+vLLL9fYfnTo0IHZs2e3KJ81a1bStzXJeuutx9Zbb73S9du1a4eU8jvto8VisVgsP1asm6HFYrFYLN8RQghSqVSFyDNnzpxWsxmuDLlcjm233ZYHH3wwsaACqKurqwjIvSaI+9xcoPjDH/6wWu1tscUWPPXUU3z55ZcMGTKEefPmfaP+zZgxgyuvvJLevXtz8MEHr3Kfl2eVs7r73bdvXy688EI222wzXn31VQD22GMPXNfl448/Zuutt2717+v6823TpUsXjj32WA477DDef//9FpkK77rrrorlu+++GygJiHvttRdaa7766qtW92+zzTYDYIcddqC2tpbbbrutVffQmFUZh2HDhvHOO+8k4x3z17/+FSEEQ4cO/do2vk1yuRzbbbcdDz74YMX+KKX429/+Ro8ePejbty+w9o6/xWKxWCw/FKxllsVisVgsa5CpU6cyc+bMFuUjR45kr7324sEHH2TMmDEceOCBfPHFF1x++eV069aNDz/8cLW2d/nll7Pnnnuy2267ceaZZxKGIePHjyeXy7Fo0aJvuDclNt54Y9Zff33Gjh2L1pr27dvz0EMPMXny5NVus1+/fjzzzDMMHz6cXXbZhSlTpqyU29krr7xCbW0tvu8za9YsnnjiCe688046d+7MQw89lLhIrkqfY5Hlpptu4phjjsHzPDbaaCN22GEH2rVrx0knncS4cePwPI+77rqLGTNmVKz/xhtv8Mtf/pKDDjqIDTfckFQqxdSpU3njjTcYO3YsAL179+ayyy7jggsu4JNPPmHPPfekXbt2zJ07l+nTpycWUuX9GT9+PCNGjMBxHAYMGJDs25pku+22Y6+99mLAgAG0a9eOd999lzvvvJNBgwaRzWaTeqlUiuuvv576+nq22WabJJvhiBEj2GmnnQDYcccd+cUvfsFxxx3Hyy+/zC677EIul2P27Nk8++yzbLbZZpx88slUV1dz/fXXc8IJJzB8+HB+/vOf06VLFz766CNmzJjBLbfcssrj8Otf/5q//vWvjBo1issuu4xevXrxyCOPMGHCBE4++eREKFqbXHXVVey2224MHTqUs846i1QqxYQJE3jrrbe45557EvF00003BeCPf/wjNTU1ZDIZ+vTp06rbqMVigRdffJHrr7+eZ599loULF9K+fXt22mknzjzzTAYNGrTS7VxyySVceumlKxTZl8eTTz7J0KFDmTZtWiLwfxvEbX8bcRbLGT16NGEYLveF23e1v8ujd+/eDBkyhIkTJ37n214RQghOOeWUZB6zfIusrcjzFovFYrH8mIizGS7vL86Sd/XVV+vevXvrdDqt+/Xrp//0pz8lWfrKAfQpp5zSYjvNM7hprfW///1vPWDAAJ1KpXTPnj311VdfvdJtxhnXrr322oryOGPb3//+96TsnXfe0bvttpuuqanR7dq10wcddJD+/PPPNaDHjRuX1FtR1sHWsu59+eWXeuONN9a9e/fWH3/8cYt1mrcb/6XTad2tWze9++6765tuuqki8+Oq9llrrc877zzdvXt3LaWsyCT3/PPP60GDBulsNqs7deqkTzjhBP3qq69WZKqbO3euPvbYY/XGG2+sc7mcrq6u1gMGDNC//e1vKzLmaW0ySA4dOlS3adNGp9Np3atXL33ggQfqKVOmJHUKhYI+4YQTdKdOnbQQ4mszLS4vm+GoUaNa1B08eLAePHhwsjx27Fi99dZb63bt2ul0Oq3XW289/etf/1ovWLCgRftvvPGGHjJkiK6qqtLt27fXJ598ckV2zJg///nPervtttO5XE5XVVXp9ddfXx999NH65Zdfrqj3n//8Rw8ePFjncjmdzWZ1//79K7JbrmgcWvstfPbZZ/rwww/XHTp00J7n6Y022khfe+21OgzDpM7yznmtdavnRXNa+220RmvZDLXW+plnntG77rprMjbbb7+9fuihh1qsf+ONN+o+ffpox3FabcdisRh+97vfaSml3n777fVf//pX/dRTT+k777xTb7/99lpKqW+++eaVbuuLL77QL7zwwmr1Y+nSpfqFF17QS5cuXa31V5bm1/Bvg/r6el1VVaX/8pe/LLdOfC1cW1lXW5sDvg8s7/7NsuYRWq+G7GyxWCwWi8Vi+clw7LHHcv/991NfX7+2u2KxWCwJzz33HLvssgsjR47kH//4B65bcjwKgoD999+f//znPzz99NPsuOOOy22nsbGxwhL1+8x3YZn197//nSOOOIK5c+cuN6altcyqpKmpiaqqKmuZ9R1iY2ZZLBaLxWKxWCwWi+UHx1VXXYUQgltvvbVCyAKTgXXChAkIIbj66quT8ksuuQQhBK+++ioHHngg7dq1Y/3116/4rpxCocCZZ55J165dyWaz7LLLLrzyyiv07t2bY489Nqn35JNPIoSoEJmOPfZYqqur+eijjxg5ciTV1dWsu+66nHnmmS2Sv1x66aVst912tG/fnjZt2rDlllty++23r5bL49lnn01tbS1hGCZlp556KkIIrr322qRs4cKFSCm5+eabK9Z/4IEH2HXXXVc5OcvLL7/MoYceSu/evamqqqJ3794cdthhSbbhmIkTJyKEYNq0aZx88sl07NiRDh06MHr06CQhRozv+5xzzjnJ+O+0005Mnz69xbbjNqdOncrPf/5zOnToQJs2bTj66KNpaGhgzpw5HHzwwbRt25Zu3bpx1lln4ft+RRsrewx69+6dhI7YYostyGQySZiA5mitOf/88/E8jz/96U+AiZV4xRVXsNFGG1FVVUXbtm0ZMGAAN9100yqN908dGzPLYrFYLBaLxWKxWCw/KMIwZNq0aWy99dbLjbe47rrrstVWWzF16lTCMMRxnOS70aNHc+ihh3LSSSfR0NCw3O0cd9xxTJo0iXPOOYddd92Vd955h/33359ly5atVD9932efffbh+OOP58wzz+Tpp5/m8ssvp7a2losvvjipN3PmTE488UR69uwJmDhgp556Kl999VVFvZVh+PDhXHfddUyfPj2JGTZlyhSqqqqYPHkyZ599NgBPPPEEWmuGDx+erJvP53nkkUe44YYbVmmb8T5stNFGHHroobRv357Zs2dz6623ss022/DOO++0yNh6wgknMGrUKO6++26++OILzj77bI488kimTp2a1Pn5z3/OX//6V8466yx222033nrrLUaPHk1dXV2rfTjhhBMYPXo09957L6+99hrnn38+QRDw/vvvM3r0aH7xi18wZcoUxo8fT/fu3TnjjDMq+r+yx+DVV1/l3Xff5cILL6RPnz7kcrkWfSkUChx77LE88sgjPPTQQ+y5554AXHPNNVxyySVceOGF7LLLLvi+z3vvvddqpmTLClibPo4Wi8VisVgsFovFYrGsKnPmzNGAPvTQQ1dY75BDDtGAnjt3rta6FH/x4osvblG3ebzJt99+WwP63HPPrah3zz33aKAiZlNrMaSOOeYYDej77ruvYv2RI0fqjTbaaLl9DsNQ+76vL7vsMt2hQwetlEq+W5mYWQ0NDTqVSunLLrtMa21iU8b7UVVVpfP5vNZa65///Oe6e/fuFev+85//1I7j6Hnz5q1wGysTMysIAl1fX69zuZy+6aabkvI4zuiYMWMq6l9zzTUa0LNnz9Zaa/3uu+9qQP/617+uqHfXXXe1GP+4zVNPPbWi7n777acBfcMNN1SUDxw4UG+55ZbL7fuKjkGvXr204zj6/fffb7EeUcyshQsX6p122kmvs846+vXXX6+os9dee+mBAwcud9uWlcO6GVosFovFYrFYLBaL5UeJjlzEmrsPHnDAAV+77lNPPQXAwQcfXFF+4IEHtnBrXB5CCPbee++KsgEDBrRwvZs6dSrDhw+ntrYWx3HwPI+LL76YhQsXMm/evJXaVkw2m2XQoEFMmTIFgMmTJ9O2bVvOPvtsisUizz77LGCstcqtssC4GO6888506tRplbYJUF9fz7nnnssGG2yA67q4rkt1dTUNDQ28++67Lervs88+FcsDBgwASMZm2rRpABxxxBEV9Q4++ODljv9ee+1VsdyvXz8ARo0a1aL8mxyDAQMGLDdL7qeffsqgQYNYtmwZL774IptvvnnF99tuuy0zZsxgzJgxPP744ytt5WepxIpZFovFYrFYLBaLxWL5QdGxY0ey2SyffvrpCuvNnDmTbDZL+/btK8q7dev2tdtYuHAhAF26dKkod12XDh06rFQ/s9ksmUymoiydTpPP55Pl6dOns/vuuwPwpz/9ieeee46XXnqJCy64ADDBxVeV4cOH8+KLL9LQ0MCUKVPYdddd6dChA1tttRVTpkzh008/5dNPP60Qs3zf56GHHlopoa81Dj/8cG655RZOOOEEHn/8caZPn85LL71Ep06dWt2H5mOYTqeB0v7G49+1a9eKeisa/+bHOZVKLbf8mxyDFZ0/06dP54MPPuCQQw5p1QX2vPPO47rrruPFF19kxIgRdOjQgWHDhvHyyy8vt01LS2zMLIvFYrFYLBaLxWKx/KBwHIehQ4fy2GOP8eWXX7YqGnz55Ze88sorjBgxoiJeFrS01GqNWDCZO3cu66yzTlIeBEEitKwJ7r33XjzP4+GHH64Qvv75z3+udpvDhg3joosu4umnn+aJJ55g3LhxSfl///tf+vTpkyzHTJkyhaVLl7L//vuv8vaWLl3Kww8/zLhx4xg7dmxSXigUWLRo0WrtQzz+c+bM+VbHH1b9GKzo/DnkkEPo2rUrF1xwAUopLrzwworvXdfljDPO4IwzzmDJkiVMmTKF888/nz322IMvvvjiB5NZc21jLbMsFovFYrFYLBaLxfKD47zzzkNrzZgxYyoy94EJEH/yySejtea8885brfZ32WUXACZNmlRRfv/99xMEwep1uhWEELiuWyG4NTU1ceedd652m9tuuy1t2rThxhtvZM6cOey2226Asdh67bXXuO++++jfvz/du3dP1nnggQfYfvvtK4SjVdkHrXViXRXzf//3fy2OzcoyZMgQAO66666K8vvuu2+Njj+s+WNw4YUXcuONN3LxxRev8Pxr27YtBx54IKeccgqLFi1i5syZq7W9nyLWMstisVgsFovFYrFYLD84dtxxR2688UZOP/10dtppJ375y1/Ss2dPPv/8c37/+9/zv//9jxtvvJEddthhtdrfZJNNOOyww7j++utxHIddd92Vt99+m+uvv57a2lqkXDO2IaNGjeKGG27g8MMP5xe/+AULFy7kuuuuayEMrQqO4zB48GAeeugh+vTpw/rrrw+YMUun0zzxxBOcdtppSf0wDPnXv/5VYVW1KrRp04ZddtmFa6+9lo4dO9K7d2+eeuopbr/9dtq2bbtabfbr148jjzySG2+8Ec/zGD58OG+99RbXXXcdbdq0Wa02l8e3cQx+9atfUV1dzS9+8Qvq6+v53e9+l8RQ23TTTdl6663p1KkTn332GTfeeCO9evViww03XIN79ePGilkWi8VisVgsFovFYvlBcuqpp7LNNttw/fXXc+aZZ7Jw4ULat2/PTjvtxLPPPsugQYO+Uft33HEH3bp14/bbb+e3v/0tAwcO5L777mPPPfdcbZGmObvuuit//vOfGT9+PHvvvTfrrLMOP//5z+ncuTPHH3/8arc7fPhwHnrooYq4WOl0mp122onJkydXlD/55JMsWLCA0aNHr/b27r77bn71q19xzjnnEAQBO+64I5MnT24RfH1VuP322+nSpQsTJ07kd7/7HQMHDuSBBx7g0EMPXe02W+PbOgbHH388uVyOo446ioaGBv7v//6PoUOH8sADD/B///d/LFu2jK5du7Lbbrtx0UUX4XneGtyrHzdCx+kdLBaLxWKxWCwWi8VisayQ559/nh133JG77rqLww8/fG13Z40wZswY/ve///HKK6+s7a5YLCuFFbMsFovFYrFYLBaLxWJphcmTJ/PCCy+w1VZbUVVVxYwZM7j66qupra3ljTfeaJGp0GKxfDdYN0OLxWKxWCwWi8VisVhaoU2bNvz3v//lxhtvpK6ujo4dOzJixAiuuuoqK2RZLGsRa5llsVgsFovFYrFYLBaLxWL5wbBm0i9YLBaLxWKxWCwWi2WVmDBhAn369CGTybDVVlvxzDPPrO0uWSwWyw8CK2ZZLBaLxWKxWCwWy3fMpEmTOP3007ngggt47bXX2HnnnRkxYgSff/752u6axWKxfO+xboYWi8VisVgsFovF8h2z3XbbseWWW3LrrbcmZf369WO//fbjqquualG/UChQKBSSZaUUixYtokOHDgghvpM+WywWy7eJ1pq6ujq6d++OlCu2vbIB4C0Wi8VisVgsFovlO6RYLPLKK68wduzYivLdd9+d559/vtV1rrrqKi699NLvonsWi8WyVvniiy/o0aPHCutYMctisVgsFovFYrFYvkMWLFhAGIZ06dKlorxLly7MmTOn1XXOO+88zjjjjGR56dKl9OzZk89e7U2b6pIFw5KwkalNXVFIujhL2SET8mpBUy2L9PXSLFJNzCi0Y3BVE3PCJv6X70FWFBiWXYaLwzN5lwVhGzZJzU7qP9PUjZQI2bVqMfPCIi8XeiBRSVmT9nmqqRsOmmHZ+VSJFHUqz/8K7di1qhFPON/OQFoslh8Vy+oVvbacSU1NzdfWtWLWD4iVNR+eNm0aQ4YM+XY7swo88cQTnHvuubz77rs0Njbyj3/8g/32229td+sb8+STTzJ06NBvfbz//e9/M3r0aGbPnk2nTp1ardO7d2+GDBnCxIkTv7V+LI9LLrmESy+9lO+bx/Kxxx7L/fffT319/druisXyg8fOP98vvk/zjxCCU045hVtuueVb68fyuPLKK+nfv/+P4phafro0v75qrZd7zU2n06TT6RblbaolbWpKYtbmD/2a6o881PZLaXdPNUde9jA337sv7T4M+cUVD/BM3UZ8eFl/Ot78J355y6+o6xvQ5VnJ3T+fSY/sEp69Z0vq+gZ0eMXhD+ffxFH/dy75LiHtZ0h+d8B8QiWpvqWWpWOW0SZTYOuN7mKvu85Bu1A9U3DF4HpO22wat/51b7o928hGf7uNASlvzQ6cxWL5UbMy955WzPoB8cILL1QsX3755UybNo2pU6dWlPfv3/+77NYK0Vpz8MEH07dvX/7973+Ty+XYaKON1na3flA88MAD7LLLLst9kLBYLJZvGzv//DT5vs8/V155JQceeKAVsyw/SDp27IjjOC2ssObNm9fCWmtVCLWizTsedRuGPLnNHxi87Fe0dRpx81Dz+Dtc1f9gNt/jPbz6gPlBG7o/U8fWR7/OYy/txFszu3PTkPs54pfP8+DirXlr4qZ8EbRnnaeb6HbNx7z+2aYs/qAjud5LSeck/x54O+1lioIWdHshJDhlAYsau8IHOQ7a4T0mj+hPfnJHlLbxvCw/DhaEDQB0dHJruScWsGLWD4rtt9++YrlTp05IKVuUN6exsZFsNvttdm25zJo1i0WLFrH//vszbNiwNdJmU1MTmUzmJxHo0vd9/v3vf3PFFVes7a78YFib57vF8mPFzj8GO/9YLJY1RSqVYquttmLy5Mnsv//+SfnkyZPZd999v1Hbg496icn/3oYRt53DdqPeY+PUHGQRZh+7GZ1fDXi5TV/WKzZxx6wdIVD896uNSTVo5PwUtyzYhefmrof/r07IfvDo4s0QoeLV2euSatCkFkvqstWs89EyRt54DsW2mkP2fZrZgxza3tOFLvN9PjtQc+W8wcxpqKFdqLlu9h50TNfjoJBC44kQKYxFvyMUoZY4QiHRKAQSTUevDk+E1IcZ8spjVqEtTaFHU+gldQAU5nrsCoXSgrQTINFJ+2kZkHMLbJX7lANyi3HEigNKryyzg3qealqXonZoVGmyskCjSuPr0uN1Pvq/oDwkOtnXjPSRQuGJEF87KG36JIUyY4ImRKC0JIz2z1cuvnaSMZNoPBmQEQG1TgN5nQIgJQIkCkdoMsInI4vkVSrpk1lXkRE+KRFSI/M40Xi1lwHdnOwaGyMAX4c06iIeDo4QhFrjRHNoqDUKhURGfSvNrRKJpHKuXZP9Wl2siPX9wopZPzKGDBnCggULmDBhAmPHjuX1119nn3324d5772XSpEncfvvtvPnmmyxZsoTevXuz7777ctFFF5HLlX6YsXvW66+/zmmnncbTTz9Nu3btOPjgg7nyyisrzJtvvfVWbrvtNj7++GOEEKyzzjqMHj2aK6+8MnE/Azj33HM599xz6dWrFzNnzgTg2WefZdy4cUyfPp0wDBk4cCAXXHABo0aNStqfOHEixx13HI8//jj33nsvDz30EAsWLKCpqYk999yTBQsW8Kc//YkzzzyT1157jS5dujBu3DiOO+44HnnkES666CLee+891l9/fa699lr23HPPivH68MMPGTduHFOmTGHp0qWst956/PKXv+SUU06pqPfee+9x+umn8/TTT5PNZjnwwAMr+rk83n77bTbddFPuu+8+DjroIABeeeUVtt56a/r378/bb7+d1N1nn3346quveOWVV5KyJ554gqVLl1bc5KwM+XyeCy64gCeeeIJPP/0Ux3HYaKONGDt2bIsbpNhFZLvttuPKK6/ks88+Y8MNN+Q3v/kNe+21V0XdRx55hAsuuIB3332X7t27txin5m1utdVWXH311Xz++edssskm3HLLLWy33XZcd911TJgwgfnz57Ptttvyxz/+kQ022CBZf/Lkydxyyy288sorLFiwgB49ejBs2DB+85vf0LFjx6RefI698sorXHnllTzxxBNkMhlmz57dar+ee+459t13X7bffnsmTZpELpdj6tSpXHbZZbz55ps0NjbSqVMnttlmG+68804rilksq4Cdf37a88+dd975tXPIyuzzys5fsaD4l7/8hb/85S8ADB48mCeffDI5dk888QT33HMPDz74IL7vs99++3HrrbdSV1fHaaedxn//+1+qqqo44ogjuOqqq/C8khvUpZdeyn/+8x8+/PBDgiBggw024JRTTuFnP/tZhZjZu3dvNt10U44//njGjRvHBx98QPfu3Tn99NM57bTTVmrsLD9dzjjjDI466ii23nprBg0axB//+Ec+//xzTjrppNVu0xGSDavm0Wb0M9z7xI4suLA3B+z7K7rPCqlfx2Hpei4b3rkYUQyYf3tvOuQX4NzVgeov8ngNKd745wBqhKCps6LNO4uYUdycdsuW0PbuWjILCoQzHYKsZFnfNrT5PKTNo4uY/M7OrP/eUhp6V+MUFOv9Dd77/UZkumYRTYuZdfH6LJxnLFpQlf0VrYSp0J5DvksW5UncphAnH+IuaoAghGb1hWo9zIWW0e9UCPBc/jNyB/Y6/UayItVq/VXl94sG8cQ1O5ZtD9Cl7QqtcYqmfzLQiDCuGHcclCdKy3EbwrShBQgNItQgQIQgffO/dqI6EkJPEFSZdrQDygMtBEgIUxCmQfok6yk3rqdRLgRtQkgrkJqado38brNJDKlqdpC+AR/5BX4zayRtvSakUFQ5PhnpszSoAjACpAxIywCArCxS4+SRQpGTBbKyQEqEZISPJwIywscpG7SMMOt5QuEJRRhZAaaEIpa+nGbvnzzAK7uOe0KiovMqRONQ+R0Ycc0RIhHZvqmwVtA+aVGac0Ktvhdi3Q8NK2b9CJk9ezZHHnkk55xzDldeeWWS0vLDDz9k5MiRnH766eRyOd577z3Gjx/P9OnTW7iK+L7PPvvsw/HHH8+ZZ57J008/zeWXX05tbS0XX3wxAPfeey9jxozh1FNP5brrrkNKyUcffcQ777wDwAknnMDmm2/O6NGjOfXUUzn88MOTB5GnnnqK3XbbjQEDBnD77beTTqeZMGECe++9N/fccw+HHHJIRX9+9rOfMWrUKO68804aGhqSG845c+Zw3HHHcc4559CjRw9uvvlmfvazn/HFF19w//33c/7551NbW8tll13GfvvtxyeffEL37t0BeOedd9hhhx3o2bMn119/PV27duXxxx/ntNNOY8GCBYwbNw6AuXPnMnjwYDzPY8KECXTp0oW77rqLX/7yl197LDbZZBO6devGlClTkoeJKVOmUFVVxTvvvMOsWbPo3r07QRDw1FNPtbh5eeCBBxg0aFDS55WlUCiwaNEizjrrLNZZZx2KxSJTpkxh9OjR3HHHHRx99NEV9R955BFeeuklLrvsMqqrq7nmmmvYf//9ef/991lvvfUA82Cz7777MmjQIO69917CMOSaa65h7ty5rfbh4Ycf5rXXXuPqq69GCMG5557LqFGjOOaYY/jkk0+45ZZbWLp0KWeccQYHHHAAr7/+evKA8PHHHzNo0CBOOOEEamtrmTlzJjfccAM77bQTb775ZsUDB8Do0aM59NBDOemkk2hoaGi1P/fddx9HH300P/vZz7j55ptxHIeZM2cyatQodt55Z/785z/Ttm1bvvrqKx577DGKxaIVsyyWVcTOPz/N+Wdl5pCV3eeVnb9eeOEFdt11V4YOHcpFF10EQJs2bSr6dcIJJzB69GjuvfdeXnvtNc4//3yCIOD9999n9OjR/OIXv2DKlCmMHz+e7t27VwTWnjlzJieeeCI9e/YE4MUXX+TUU0/lq6++Ss7DmNdff53TTz+dSy65hK5du3LXXXfxq1/9imKxyFlnnfW142f56XLIIYewcOFCLrvsMmbPns2mm27Kf/7zH3r16rXabc4LG/jjxFGklmnS7QVuvU/bd9N4DQGZhYKmTpIwl8LN+zi+JmiXRUvQkbiybL0qRKgRClR1BqeoCdpmUK4AAWFaEKRjoQhUNoX0NX7bDMoxIoxKS9Kzi0jfCBZOQSEaC0kfKwSoWJzS2vwJgZCStCMIatKIUKOlQOSL4ActxKwEpSCacxDCyBFCgBQQhFTPUixQRXrKNSNmLQsyVH+eNwuOMAJSNCZaiOjTCFLoMtFOgwgUMlCI6A+lWoh8MSIMQZWNUfJFtG9CoKUEV6IdAVKi3DJRJBLXtBRoR5hPV6AcI3gpTxBkXLQAP9eWa2r3ZMhG/1kjYwSwVKX53zP9cPIC7WiUB0KZsdFumTjnarTU4GpESiEcheOFpNMBKdcIVik3JOMGVLk+AK40g5Z1i1Q5PjVuPtlubP1X/pl1SudgRgTUOE3JcjGyonOiAxEiyYgiKRHiiZCMLOKgycoCbWWefp6HJ5w1JkItU3nqtCLUJfEtIwRFrenkpCuErzVFqCNLwB+wiGbFrB8hixYt4u9//zu77rprRfmFF16Y/K+1Zscdd6Rfv34MHjyYN954gwEDBiTfF4tFLr300uQGeNiwYbz88svcfffdyU3cc889R9u2bfnd736XrFfuytGjRw+CwFx8evbsWeGOMnbsWNq1a8eTTz5JdXU1AHvttRcDBw7krLPO4uCDD6548zls2DD+8Ic/tNjXhQsX8vjjj7PVVlsBsPXWW9O5c2euvvpqPvroo+QmvHv37gwcOJAHHniAU089FTBvw2pqanj22WeTG+DddtuNQqHA1VdfzWmnnUa7du347W9/y/z583nttdfYfPPNARgxYgS77747n3/++dcej2HDhjFlypRkecqUKRx55JHcf//9TJkyhaOPPprp06ezbNkyhg8fntQLw5B//vOfnHfeeV+7jebU1tZyxx13VLQ1bNgwFi9ezI033thCzGpqamLKlClJ1ogtt9yS7t27c9999yUpoy+44AK6dOnC5MmTyWQyAOyxxx707t271T4UCgX++9//JlYXQgj2228/pk2bxquvvpoc3/nz53P66afz1ltvsdlmmwFUPFRprdlhhx0YMmQIvXr14tFHH2Wfffap2NYxxxyzwlTV48eP54ILLuDKK6/knHPOScpfeeUV8vk81157bXJsAQ4//PDltmWxWJaPnX9+mvPPyswhK7vPKzt/bb/99kgp6dSp03LdXffaay+uu+66ZFsvvPAC99xzDzfccAO//vWvARg+fDiPP/44d911V4WYVd4HpRRDhgxBa81NN93ERRddVHGOzJo1q8UxmjdvHpdffjljxoyxL0YsK2TMmDGMGTNmjbT1drGJvR4/nTYBZOcrsvNg6YY53CZYsp6HDKHzyw00ds8gumTIzS7QsE6GzOKQQscUXn1IepFPUycPr1FR1ydHzYd11K9XjVevKLZxcQqazPwmGtbJmDrr19Dmg2WRVZYmzDjIQIMfIEJNQ9/2ZObnSwJWLFpp3VKkicQshMJZ3GAEHkcg8oERssKWllnEv0Upo3XLzHBk6X+3SROuwVxF/bOzeC/sj8wHiZBkrKWEuT60og8kIpcnjZjkOaC1Ebb8sCRuRaKdCJXZ55hQVe5ztI8i3m8h0I5ExsuOTMZLO1E2SScSvxJhSxJmXITWhCnJR3M7whoMLxkiyM4SuE3aCKISUGC0I0GYjizFHECWRC6VgjClafA09Sll1nM0wlVIVyGlwnE0Qmg8N8SRiup0EaUFnlNyY3WFwpHGvbVtqolASyTmf4AqWYz6KfFEiNICVyoCJRNrsbQMcISi2smTET41Tp5e7pdkSfF50MT6XvUqj0tzcapOKz7x2yRjBkaQc1CkRBOdncr6vjbnxepaifk6REXCXUGZ/Sx383Qx54tCrxFLtG+L72evLN+Idu3atXiQAPjkk084/PDD6dq1K47j4HkegwcPBuDdd9+tqCuEYO+9964oGzBgAJ999lmyvO2227JkyRIOO+ww/vWvf7FgwYKV6l9DQwP/+9//OPDAA5MHCQDHcTjqqKP48ssvef/99yvWOeCAA1ptq1u3bsmDBED79u3p3LkzAwcOrHib3K9fP4Ck//l8nieeeIL999+fbDZLEATJ38iRI8nn87z44ouAyc61ySabVIgdsPKCx7Bhw/jkk0/49NNPyefzPPvss+y5554MHTqUyZMnA+YBI51Os9NOOyXrPfXUUyxYsIDRo0ev1Haa8/e//50dd9yR6upqXNfF8zxuv/32FscaYOjQoRXpT7t06ULnzp2T8WpoaOCll15i9OjRiZAFUFNT0+I8KW+z3H0oPgYjRoyoeAhofmzABD896aSTWHfddZO+x28pW+v/8s4PrTUnnngi48aN4+67764QsgAGDhxIKpXiF7/4BX/5y1/45JNPWm3HYrGsHHb++WnOP183h6zKPsOqzV8rormbY3wsmrtp9uvXr+L8Apg6dSrDhw+ntrY2OWcvvvhiFi5cyLx58yrqLu8YLVu2jFdffXWV+myxrC4F7XPUG8eyzn8lnV9qJPd5A7mZ9bR9ZxltP2igzWcBHWbU4y6op80bC6h5ewHerKXUvj6f7IeLqH53EZmZi0nNWkrtGwvJfrSY2hkLkI0F2ry1kNzHi6n+YDFVny7GXdhA7VuLyH6yhNoZCxANearfXkDug4Xk3l9A1UcLEEqT+XQhuXfn485bFolXyrgKBqERZlT0F4YQRGJVGJqyoo8zfwnOnMXIhUsgCNCqTMwRAly37M8x4o2MLJacSNyKRK4o3NQao71bj9AaWQwQxaAkRmkiUaYkbinXWEtpaSyilCcJMg5hlYNKOwRZD782g9+uCr99lqBjNaptDtUmi06nwCnrfLkYqLUZx2RMQ0Rg/ghDI4pF34lCEVH0EXkfWfCRTT6yECCbApx8gCwq3MYQf0kmsdhZU7h5jfRBBhqnoHGKGrfJ/O82gZMHp2A+3Ty4jQK3QeDVC9wGibvERdY7iCYH3egS1nv49SkKjR75xhT19RnqGzLMW1rNwrocSxqrWNKUoa6QZnG+iqWFDIvzVXxZ35aF+RxLilXMy1dTH6RY5OdYFlTRFHosCzIsC6pY4ldRH6aZW2jD3EIbFvo55hVrWODXsCCoYX5QQ6MK8YRDT7dqjYxRXgvmhTUsUVmWhDmWhDnmB21oUGnqWnGl9XVIQfsoVk+h9XXIUlWkUfk0avOX1wGNynwGhBR0gEKh0Pg6xNchoVbJ+VH+/9rCWmb9COnWrVuLsvr6enbeeWcymQxXXHEFffv2JZvN8sUXXzB69Giampoq6mez2QrRAkw64Hy+ZL551FFHEQQBf/rTnzjggANQSrHNNttwxRVXsNtuuy23f4sXL0Zr3Wo/4weAhQsXfu0+gXl4aE4qlWpRnkoZk+K4/wsXLiQIAm6++WZuvvnmVtuOH44WLlxInz59WnzftWvXVtdrTvy2e8qUKfTp0wff99l1112ZO3cul19+efLdjjvuSFVV6YJ4//33s9VWWy3X8mlFPPjggxx88MEcdNBBnH322XTt2hXXdbn11lv585//3KJ+hw4dWpSl0+nkvFi8eDFKqVb3eXnjsLxj8HXHRinF7rvvzqxZs7jooovYbLPNyOVyKKXYfvvtW5yrsPzzo1gsMmnSJDbZZBNGjBjR4vv111+fKVOmcM0113DKKafQ0NDAeuutx2mnncavfvWrVtu0WCzLx84/P8355+vmkFXZ51Wdv1bEqsxD5efX9OnT2X333RkyZAh/+tOf6NGjB6lUin/+85/85je/aXHOrmhubH4+WSzfFr9bvDHVf64l+0U9ohAa97RIMHLqIbuoAZQy1j4xsZWTLNk3VMSaKrcAis2ahIhEFF1pJRWJK81dCEXYTHiJRa3oex23obQRoUIFUrWaaENIWWmBJcv6WF6/2bpaCry6kE+CWvp4IWsKEUYWaNqJ9guQAi0keCKJfaVdSZLQUZC4IgqtwY1ibKlyt0Rt4mUFGifrIX1lBLPGghGq4thhsSVbNJaEOhlHoWXyXXJMk7E2LpnGFVPjKIX2HJTnIPNr1tYlRJp4X9Gwa5ckBlgcZ0yo6HttYnrF3wspEL4GF5y8QAXCuCOmNFpodCBBaLQE5WhkKsRxQ+obMrheSCblo7TADxwTm8sLwPdwpIIM5EOPlAxIOSEpGRAoh6JyyDgBgZYoLUjJEAKQQhNoB1eEkII6LegGeGLNqaSNKk0+Ct4fuzv6OBS1rHBnDLWRmHytCClSTXqFllOtuUL6mANSF4lREiNwecLEX4u/Nz/nsEWQ/jjGmESs1XhfVsz6EdLaxX/q1KnMmjWLJ598MnkbDrBkyZJvtK3jjjuO4447joaGBp5++mnGjRvHXnvtxQcffLBcf/927dohpWw1SPesWbMAKoJ8Q+v79E1o165d8iZ+eUHM4weIDh06tEibDLRa1ho9evSgb9++TJkyhd69e7P11lvTtm1bhg0bxpgxY/jf//7Hiy++WOEmp5TiH//4x2oHj/3b3/5Gnz59mDRpUsXYFQqFFay1fNq1a4cQ4huNw8ry1ltvMWPGDCZOnMgxxxyTlH/00UfLXWd550c6nWbatGnsscceDB8+nMcee4x27dpV1Nl5553ZeeedCcOQl19+mZtvvpnTTz+dLl26cOihh66ZnbJYfiLY+efr+bHPP62xKvu8puev1eHee+/F8zwefvjhCmH1n//8Z6v1V3SMWhP6LJZvgzfr1iEzr2CErKJvRA+lSq5pcUyp2PUsFqXKv8PEs6oQP5qLRLGIEn9fhmjNbTAWsWIXwbLt6lBBuWWHkmZZSXQS/koYyyQpWxevWhGuWiAEIjRxk9YUHZx6wrSD1NpYPsViFpEnnRBoT0TufJh4VnEfnbifkdhULnTFmmEU/F27AhFoZOjipFwjSPohwjeWV4m4lTQe90Mlop8JIl95THUsPjoSlEj67eQFCs2akmhShCCi4PUIQpfk2GoZ/TlGvEJHbmOhCV4vIemr8qI6AiiIZH5I4m65GhUKtCeRqRClBPmih4ziarmOwg8cROR+2BR4aC3IesatcFmxipQToLSgqBwCJXGlyZBZUC5KC6ocH9cNWRpU0ahayiiNqkh2NWOyZYWmQaXJCB9fQ0b6JntlCI2OW3FMAkLyOsRBoLQmECHOChzumh/P2JoqrzV5LfAwY+gAvtb4lILhA2SEg0KZZW2yUipUhSsikaD1XQtbVsz6iRD/4MszQQGtxgFZHXK5HCNGjKBYLLLffvvx9ttvL/dhIpfLsd122/Hggw9y3XXXJW+DlVL87W9/S26+v02y2SxDhw7ltddeY8CAAcnb2tYYOnQo11xzDTNmzKhwI7j77rtXenvDhw/nvvvuY911103cG/r27UvPnj25+OKL8X2/Il7J888/z5w5c5br3vJ1CCFIpVIVDwJz5szhX//612q1l8vl2HbbbXnwwQe59tprk5v7uro6HnroodVqc3ms6XN1iy224KmnnmL48OEMGTKEyZMn07lz5xb1HMdhu+22Y+ONN+auu+7i1VdftWKWxbIGsPNPJT/2+ac1VmWfV2X+Krf+WpMIIXBdF6fMtaepqYk777yz1fpvv/12q8eopqaGLbfcco33z2JpjZ5Vi5kt10P4AaLol6x34phLSWwpEVnvUIpZVS60rEgwKndvi62jmsewKhfIYkusMERrXfG7biFkQcVyhYgVuwyupHDVWlmQc+gkG4E1E8Out7sUv8bDm1MaExEqhFJmX0Pz4K9wzPiaxVK2wjg+VBKgvRQsPha1pIQAiQw0KjSuiiLUxp1RRZ9+JG4VfYQfRK6bunSMm1vfxX3V5jzQSplx9pyovTUyPAnd3Sby7QVuoy6Jdrok4GkHo7upKG5WhFDRmAQY6zWtkSEoBLg6yfwo/CgOlzZDp7VESbPPQoSEoUQICAClJI6jEMInH7g4QlMIXaTQuEKRD01cqmLg4gpFrE7W+2mkKJ2bBemS1y1llNUVski2BL52CJEsKNZQ6zSSx6NBp1D4UCZJOQhCNL7WeDpc5QDxvlb40anhE42vAF+DJygJWrpkJQaghMLBSSy1AuLYXQ6+Dr9zSy0rZv1E2GGHHWjXrh0nnXQS48aNw/M87rrrLmbMmLHabf785z+nqqqKHXfckW7dujFnzhyuuuoqamtr2WabbVa47lVXXcVuu+3G0KFDOeuss0ilUkyYMIG33nqLe+65Z42/CW+Nm266iZ122omdd96Zk08+md69e1NXV8dHH33EQw89lGTYOv300/nzn//MqFGjuOKKK5JsUu+9995Kb2vYsGFMmDCBBQsWcOONN1aU33HHHbRr164i9sr999/PpptuutoPVXvttRcPPvggY8aM4cADD+SLL77g8ssvp1u3bnz44Yer1ebll1/OnnvuyW677caZZ55JGIaMHz+eXC7HokWLVqvN1th4441Zf/31GTt2LFpr2rdvz0MPPZTEd1kd+vXrxzPPPMPw4cPZZZddmDJlCj169OC2225j6tSpjBo1ip49e5LP5xM3lvKHO4vFsvrY+aclP+b5Z3ms7D6vyvy12Wab8eSTT/LQQw/RrVs3ampq2Gijbx65eNSoUdxwww0cfvjh/OIXv2DhwoVcd911LQTZmO7du7PPPvtwySWX0K1bN/72t78xefJkxo8fb4O/W74zHKGMgBK7F4YhOrbYkVF2vzAsWTiFqnVxqPk1MF5WqiSMNBOxhIoDusdWYDqJhaXjQO+xaKOaCVgV25LGUiiOgdVMwKpwgVxuG8uxzPq2Q/vEIp+vIysthRO5CwKolCR0Sq6HyhUlQUeafRNal9wPiay5dOnTEdFYpyMrusgVUSidiFyyGJig9IWicSktc+ls0V8i0cghciONTaPWHBkhCLIQpgQyLLPGivYzFq1UIvSVRD0ZQCiNRZZTEIRpHQl9wlhpRaKVViBiKzcPdCBRgHYUYWSNJaRASk0YSvLaCD9VnrHE8kMn0Ymk0CgtUELQGKQiUcuUFyO1TSo3aqPlSVWvjMt6tcy0+G5FxMMeIikoj2onTxiV5pWHr/MVglWIpjE6hnkdsrwQ9LFLYoUQJiROpJiGWuAIXfZds/WBvFY4QiCBuAcFXVI9y10tFTpZ/i5ELRsA/idChw4deOSRR8hmsxx55JH87Gc/o7q6mkmTJq12mzvvvDNvvfUWv/rVr9htt9349a9/Td++fXnmmWfo1KnTCtcdPHgwU6dOJZfLceyxx3LooYeydOlS/v3vf7dIi/5t0b9/f1599VU23XRTLrzwQnbffXeOP/547r///oqsWF27duWpp56if//+nHzyyRx55JFkMhluueWWld7WrrvuipSSXC7HoEGDkvJYMBk6dGiSwh5MzJBv8lb8uOOO4+qrr+bRRx9l5MiRjB8/nrFjx36jLH277bYb//znP1m2bBmHHHIIZ5xxBgcccAA/+9nPVrvN1vA8j4ceeoi+ffty4okncthhhzFv3ryKjFyrw3rrrcczzzyDEIKdd96ZTz75hIEDBxIEAePGjWPEiBEcddRRzJ8/n3//+9/svvvua2iPLJafNnb+acmPef5ZHiu7z6syf910001suOGGHHrooWyzzTaceOKJa6Svu+66K3/+859588032Xvvvbngggs48MADk8yMzRk4cCA33HAD119/Pfvuuy/PPfccN9xwQ4ukIxbLt0lWFqNYQ0bAaC4a6Tg+VeQWFxVWuu7FlItIcYD25VhjJRn34u3FAkorQpZpLyort8oSEhwHkU5BVQZSHkiJdmQSRD0R2ipiZjWPoVX2f/mfFAilKazpKPCtEbsd5n1EPkDm/STAuvR15MYZVXVKQpZ2IPRElNlPEHqCIC0IUwIV14uCx6uU+QvTEr/GIcg6+G1c/NoUxQ5VFLpUE3SpJezYBl2dRXvLsV9JskoqE2NNglo1A5+vRQJaarN/qcgaLcrqKELzpzxQaVAp86clOEUjZjm+sc4SoVk2geSjZV8YF8nIyk0EAhEI8AU6kPh513itKoEKHQLfRSmB1oIgdGjyXUIlKYQuDX4KpQWBkvjKoSnw8EMHKRT50KWumGFpsYr5+WqKyqmw1ConLbwVWkktL2B6fGYWlEeIwNcOBeXha4e89mjUJVdSE4zdZOf0NZEwtXzyOqjYbqgVPpqiligEee3gIyjEBptAUWuKWqMwgpax0IqtwcLos9RmyUJLJNuK3Q6/TYTWzWVai8WyNpk+fTrbbbcdb7zxBpttttna7o7FYrFYfiLY+WfV6d27N5tuuikPP/zw2u6K5SfIsmXLqK2tZfEH63Fp/da8dfImuLMXl8SkIDAVY+sIKRCeVxKmyjL+JWJRuWBVLoiVC15xoPfYlTB2XdTaZB2sCDKvStuPl8stvBzH9MlzTXykcuGr3M2xfPvN65S31xpCULdRW2674UY2Sa2Z7HMf+A38/JRfk/twUetxxGJxzXVQGReVdgkzrhGhMkasClMC7URB4iO3OV2+CxJkERzf+NA5xciaSpO4KRqrM+PCJ1TJqskpKiP4FBVOIYwyF/pQHk+t7HhrR6JzGd47qZqP9r5tjVnUfOrXs+ffzqbmEyNQKRe0ayy1hNKEGWHELMeUi8AEg5e+2Q/lkVhsaWGWjbumTtrS0vgtak8b4SytwNHmTwmEo0FoRGTJ5bgKx1WkvIC0FyCFxpEKgbHACrUg4wa4QuE5oYmbFRpBUApNjZfnnB6PsX1m1cTRUCsKOmjVHXF2UM+D9f1oVCmUlngiRApFQXl08ZYyMvcpnR2TJX6pamJuqAi1ICUUaQE93NZts0KtaNJFPOEkIluoFYtVE3PDKKA8AgeNJ5SJn0WUywDIROdjbJmVia2uyrIoOggkEk84SEzMNYlY7Rhay+oU7fp+wtKlS2nTps0K61o3Q4vle8a2226L1ZgtFovF8l1j5x+L5YfLv97bnA38YkmkABMLKbaCchyTDRAqhSwhWgpZUdbDCrfDsuDtFRn1wDz5xtZgYcmCpGTB1Yqrm3QQKSNiadcp9av5Z9yH1oLRx5uRsuSa15qg5Rjh6KpZI2jrNeHKkKwssk56Met4i1nXXURWBFHV8od0nViqxBS1pFG7fOx3Z8EAl4aunRAhuHlNqk7hNQTIfIgsBkkmx8QF0FeRMGPEQ6Uia6VYyCoLjC6iuFIyckkUSqM84y6ptU5EsDgzoo6sz4QyZWHaMeKWksjAQfoe0s/gNIU4TT6ysWiErfKg/VrjNEjOnLMtaWlc8OKg+V6UjtBXTvJ/WgR4MqBG5pFCkxIBngjJCB9HKDLC5+PiuqQWC5yixima/oWxACcFRFkctWsGOh7+MGUssOJYY/F+OcWSNRtodAjaNTHIiOJraS0qDprWmOQCQiGERmuB7xv3Qw2JYBUqQcoNTSytwMUXmqbAI+0GSVuFwEWS5o3CukjxGSkUaRGiEMhI4PGR0bmjo8yEmnQUjwrAU8Xo/Ir2FfgsyNKoUjSGxp09FMZqKi1bD2LmofGRFLUkuwIfWhVZUDXPumgsroxVloPGESG+NmPklQlVjRrSwlhmpYRIAs/HhOhIrFZITKD6cr5tN0MrZlksFovFYrFYLBbLD5iwKI2rGEBkkSWkRKMQouyRb3nWS1JGwcNVSWhozaUwCTJeyoBIGJYCujcXxFtxYRSuC5m0EbHKra5i8ap8vThrXZxtMYorZfpcalbTitAlhBF6PEnVPJ/Px/flo2pJsUZQrBXkOyvCmpBUbYF02sdzQjJekAhajlRJcPBAS/zQwVeSQuyetkkT+X6glUArAaFAFDxkYxrpGwHGbRQ4eeMW5zZp3Dy4TSpxLYxdDZVrXO8QJg6WjoKaKwccZVwPRRjHtBJJsPTyAPKx2FU2KIApk6FG+hqZc3CKLtLP4DYEyEbfBJAvFBF5nz7/yPP2PzY1Alw81gK0J5M4Vybul4i2KVEpgXJFsg8mBpYgTEOxVlA9WxnXQBUJkkUT5V65pq3YbVAoI2BpQeLyKH0gLAl8QkVuia7JvFgeWD9MRZZcWhorrdg6C4zlVuCgHVMuU+a3EgQOWkcCWIQjjeCVdgNcqSgELp4T4ocOoRY0+Cnu+XIb7tbbkvWKuFKRkkHyCVDlGBHKEZoqWURG55SxuDL/Z6SPJ8IoJpaD0iKJ2eWXiU955VHUusLKyUfga4kUGr+V2F3l+OgkYHvSptb42ohuISKxzqpTHp5QZESIROOV/azCSFBWZSJvWsTWXZqAsGI75S6G35aoZcUsi8VisVgsFotlNZg5c+ba7oLFAoCTDtGOUyksCRPsPckkKKURm2KrLEDH/4ehEavKXf3KhSw/MNZYYVhyoYuCwusgiNwSmwlZssyiSmtjjZVJG2us8gyF5ZZhEa0JVrB80apiPQnac/DbpGjs5NLYVZLvoAmzGpVR4ClkKsRLB2RTAW0yBao83zy8O2EiTAC4QuHKkEA5BFpSDF2CSERoqE4hhbHAEVFmPFcq6ovGjcyJLH/q82mqUr4RK6Ri/tIcQcE1AkveQTZK3CZwmkQSC0qGGLVAlFwLY5RLEki+3JoLEX9XcjeM1w21QAZR+6ERgdxqB7fRQ/oKJ5/BqS/gLWqstLqLrOa046C9OEo6yTHVnpPENlMpWWYxZsStqkVmW0lwe1kSrExgdxMkP0wR1TPilHIx8bAgcb+M3Q+dgrFSi8WryHgMR8fCHZELp0Z7UdB4QLu6LGi8IAjM4CklUErgOBpfOgQhuI5xpMsHLimnZG3oRG6IDcUUjlRACiE0kCbtmPPGFYpGmUr+X0YGT4a4QiGFosox58IinasQutIyoKDcRNSSQqNCgfQ0vjZWVmiVxKoKI2uwcAUG1SoSunwdtrDO8oRCaSNi5bVDLrJO9KJ1FCbgu69LgeFN7CxjTeYIga8VDgJPSEKtCaPYWU70m3RxVtvdcGVYq2LWhAkTuPbaa5k9ezabbLIJN954IzvvvPPa7JLFYrFYfkTYecZisVgsPwXSaWMJkghLADoEHJMhMHIfTFwPHYF2nSj2VZz1UCbuamAEk9gaS/s+KG3aglKA9zh7YjlxDK0ykUmkUpXWWOViFssRr5aTmTCmYh0h0K4krHLJd/Co7+6Q76gptlXobBEnG1CVMdZXrqPIpYqISDCo8Qq0STXhRFZYObdAtVOgoNxEaABwUBRU6fG5oDyaQg8pNFVOkbogQ63XRKCcCvewgvISN7ScW4CuoLSg2ikAoBDUB2m+aGrHx4s7sGRJDtXg4TRIUoskXr0RcJwiKClKmf/ioYhErdgVD4xlFBhrKCWNwKeCUjwv6RvBJ8gInIJEKBfZLoXXEODWFZENBXNc/cCcD06I8GWF8KidyJ3P0+A5yEBBAMqNzMqQxpossjhTron7pRyBDHTSf1nmmWosvMynU9BJYHxj6Ra5UWrALQWDN+sZ7UtE4cukD6IoUIEwMbVErL1KtFAkuo4WhIFEOgqlBPm8Z7IeuuZEdKQRr1RgjnugJH4okQIKvksQOriOcU30Q9No2g1IO6ZjSguybpGUI0nJAE8ImgAVq5CkcCM1blmQwYvE01CDRwjCZDf0y0RcBfhaGjdBlcKTy7fMii2lVCvWW/koIYIfuRoiICNCfCSFWEiUfvKTjMWtEEhFMbRijPSnCDGB4p3IiTIW4H50llmTJk3i9NNPZ8KECey444784Q9/YMSIEbzzzjv07NlzbXXLYrFYLD8S7DxjsVgslp8KxYKHCPNmIbaQil3QdGTPlMTSMkJSEvtKCBOAHeNKqB1phAM/gEKxFNA9bk+pKCth2Lo1VjmOscbSnmusuaJtx26DyRPxCoK3x7QQvKREuRKVcSi09WjsLGnqLPBzmrAmhFyAcBSpdIDnheTSRXKpImknICVDEzvLLVLl+FGMKEkbt4m0DMjGQlMUjDstfRwUIUZcAOMy5msHXzs0qhRt3Dw1Tp5GlaLayeMrF0eoJJh37NJVI/P42iErC3R26/DieF1tNQ1dU+RVill+Oz4vtOfVResyc04HnC8zZOZJnEIk6oQlay1dJhZpp+Tqh47iSUXf45XHkjKujGFaIKqEyR4YavxcCq/axW1M4y0tIOvyoIIoa2VZHDUhENqpOB4iNFZ9MooeHouiKsrCKAPjVuhEpkQi+pRFk0RACwgzJtK79HXiiikjt0Iw+1wuiiHi8Ygs8hyMwiFAFiNRFfOd9AVhWqOVKA2DACE1YRibuAm0Mm6GYShxHIWUKnFBBcx32ghbWmuUhpQbEiqJ55jPJOaVE5APPfKhh9KCNinzG804Pl7kE1pQLoFyUAgCNCpMGestYWKW5bVLXjsoFBKHMHIRTEQoWK7lk69DilqTbfbzMpkQHXztEhJvp9RejfRpiL4zJ5KxzvKJBC2tkUIkGQ7N9yI6HSR5HZApc2/+tiyz1lo2w+22244tt9ySW2+9NSnr168f++23H1ddddXa6JLFYrFYfkTYecZisVgsP2bibIYL3u/NVk+cwsY3LYCFi80DfCI6GGsaIaWxyooto+KMdq4TWWtFLoaONMJF0UcXfeOuCEaUEqLkwtjcGqt51kJHItJpSKeMNVa5iFXmptYiU2F5kxUZDClZBInIjbDGo6GLS2NXQbGtJmij0OkQmQlx3BApNUoJclVFpFR0zDaS8wrUpvIUQpecWyQtA6qcItVOAV871Dix0FQkHcU0Ko9llIksrvLKS/6P4x6FkapTUB5ZWUiWM6JISoR4IqRBpcjJIp4ISImQrCyQET5tZZGMiIWJyApGwMygmjlBLe82rcPT8zdg5uwOiLlpUkskbmMp1pT5NAJQnAFQqDI3RKfMeguSmF5aQqSl4fjalIeQalA4eYXbGOI2+Mj6PKJQsjZL3ERdB+1K44IYHR+VctCuQEVWfFpi6iSB242QZTIuauIg9sozolcc8F150bF2ovhcZe6VEAlcsaGgZ4SvMANBFShPI0PjVhm7W2oJYUYTZjSqOgRXRwZkGuGYbIfxaem4YZRsU6G1wPOCRMwKlcAPHYLABJGX0riZCiAd1Uk5JjZWdapAqCRBdCDapRvJhx7VXoGUNPHZ4kyJaSdIgsgDuJHJWq/MIvZqM4ONPAcXh9lhI3PDFEtUFTlRJCt9NvFSrYpFBe1Tp4p4QlIrq6LzSzEzaOSLoA1F7aCQZGUBpc0nQI3wiW2yPBSO0HhoHKMLkmr2mw21xhOCGpnC12GS3bCcOMPh1/G9z2ZYLBZ55ZVXGDt2bEX57rvvzvPPP9+ifqFQoFAoJMtKKRYtWkSHDh2M/7fFYrFYvhFaa+rq6ujevTtSrvk3J981qzrPgJ1rLBaL5dvmxzbXfF9whIRcUBKFymNexdNXbBUVqlIA95SxMMIPEpFLFHzw/Up3RWlcE7WKfbp0SeRqDddF5KoqrLEqRCz4ehfDmDIBC2ksf/w2KerWcWnsJsh3DtG5EJkOcd0oy146SLLWSaGpyRSocn1q001kHJ+UDKhx83TwGhIBKyuLyEjpUVpS6zREY6txUGSkTzGyRFJaknXN/UIuevjPCxMnq0GZWEo5WaCoHdo4eUItyUWiVUam8AjJSB+lJQ6KVGQn5AFZ6eDh4AiBRFIrm9jYa2D7zFcMr3mLOT3b8kmxE68t7cnLn/cknF1lhK08iCgmFnHw+PhJX0RiVtmtjIqslwB0Kgq0Lk0cKqE0YUriNQmCrMSpdnHbpHDrijgNURbE2EqrqBBhKa4WUiN8QEtkqE3g+GRDJnYWOrLaKo+lpSNrM2k+EeBEGRy1iiyutLESigU6oY3LpYkRZtI/ygBjvaajWF1RWyLKmqg8cJsEAY6JnxaLfZ4CV4Gj0aEgUC6uF+KHLkJoXFcQRNZbccB4ITRB4CClRmtIpQLyvovvu1RXFUi7AcsKmUQI9ZyQpcUqPBmyrJghVeZf6cqQQEvcsgj+6cg1taBdGpVHqMNmbq+64rM1JNLEtKK5sBSdB0g8EZBXHo5Q+NrBEyF12gSlzwljIaa0wBFhmSGmTtwOHSHMHyIRshwhKlwb3WbbX1OsFTFrwYIFhGFIly5dKsq7dOnCnDlzWtS/6qqruPTSS7+r7lksFstPli+++IIePXqs7W58Y1Z1ngE711gsFst3xY9lrvk+kc4VEUXfCEOhKllmgbHKkhKKfmR24oDrGussKIlaRd/ExgqCSlfFOEthLGI1c2NMkMLExqrKGCErtsZyRKWIRTOrK2gpdEUkIpYQ6JSkrkeaZX0kTd0DRHWAlw6QUuM45sHZkYqUG5qA7sJYzNSm8rRLN5JzjLWVKxUdvTpqZJ689mjrNNLJXUaDStOo0rR1GsmKAqkollFGGAutWDTIa5eMCBKRQgpNY+R6CESuWdCg0nR26gkRZESIJxR1yiNE4GsnsdaCKAaSCCloRUa60ZAIqkWarNAoFLWywIbebLbJzGJk9VvM7NKOGY29eHbh+nwwuzPMyuDVS2QhiiWlS66IcfZDosDricWWG1lBpaIwZ8pUchwj/DgFgZPRFGskbhsXryGNW+/j1BcQhSizZRgi8hrhKrQbWWwFCjwjggop4sSKxhIqtpRyS1kE4/PBBK7XyfciwAiTsfWZNu6F5pwywpfyBAQQykiUU0bQUm7JLTH+XwYC5WmcgkAGDmEkaGkh0KEDKVWWs6B0HpayHpasteLTNPCNhZbvR8dNKpSGYujgBw5pz4irHiaGltLCWGO5ZhmgWoYUQ5c8AleEuFIRBClq3AJhFB8rpqChQacoaof2TiNZES7X4skTDlnptShXQFE7pl1t+u1QEnPj7IbxuZmNg5NREsIyQpgMh0AxFs61whPga002EnjLRa04w+GacjlcqwHgm7/pTjJtNOO8887jjDPOSJaXLl1Kz5492YmRuLQ8ON8Fsn9fBvzxPcZ1fjspq1d5LpmzE4v9Kmq9Jk7r9CR/XjyIAdkvGF41i5sXbc3O1e/hCcWf5+5MRvqM7TaFOUEVf5w3mJQIGNttCvPCNLfNHYorQk7v8gT/qtucGUt7UO0VOKzji7SXTfx+3lAAxnb9L9VS8KfFW7FBZi4H5BZ+awHWLBbLt8O+7+9J6sSQYFbrIst3QYDPs/yHmpqatdaHb4OVnWdg+XNNT45CkvpW+7k8RMcOdN5nAUNz85Kyog6YVt+TptAl44Rsl/2U1/Lr0sVbynpuHf/Ld6eXtwBHaF6t74knFTtVf0K9cnm5oTeuMMsNyuGlhj44QrF97lPeK3ZlTr6GlAzZLPslWeHzv4Y+AOxc/TEpAa/ku9PebaC/14i01moWyw+KexZsgPOwRtXVr7U+KIp8zp0/urnm+0AYyDJLqlgUkiZgu5ToYtEIC1F2OoLACFyeawSJIDTWWEW/smEVqQ/lIlb5d/G2XBdRVYXOpIyVl5RJZsGVDvSe1Cntg47EsGK7FIs2cmnoFSLaNpHNFqM48gpHKqpSkcufVGRcn7bpJiQaV4a09ZromlqWxK/yREit00BKGAupjPDp5NThOCYgvIMmK0LaSokUxqrFE3HMorjjLR+j44xxihBfh/g6jydk5F7l0qh8akSREGiMRAwHjaQUzkoSZYwTAoVGIqI/h2rhkBWmLHBCeriL2Sy1gH3bvM5X67bhuYa+PPplf+Z/3o7UAgcnb7IilrsbokoCVpzxULlRdsFIcxA6qhMKwrTJfigCTZARBFUCN+cYS62GAKexiGwsGmHUDxBBdCw9JxJCTaDw2NoLUUowEMfMSiy2iESw2JVQgYyFrci6THklN0UhRKzDmLq+yYwohRHzlGdigunI2zYW05yCsdoKMxrpy8j9UKBTJp4WKTM4KhSJ56zvOyglItdViecFKFU6ibUW+EUXKVXiXhqfn34oUUom1lxpR+JHy3Gg+Ho/DUCmLHB8Ubm4QlEfpvG1i6KIr0tiaU4WqBEB7Z0VWz2lhZeISGaoNEUtUVE/4/ZMcHeJQ5iIt/HvwdcSHxMgXhGLYVFmQ63JRL9dP75WAEqY30v8m4k1inBFVp2ryFoRszp27IjjOC3ejs+bN6/FW3SAdDpNOp1uUe7i4Yq1JGY5adLVHm1qSifx9fO34eVJ27HFsW/y3H+2p3GPWj7468a8vEjx2cXP8tg/B/PkkAEsfaMDfqeANm95nLlvJxr9FE2+R/GJjvxijx7MfbMLYU1I9UcuJw3uReNLHcmv43PWzo/RNgNHP3I6AFWzHI4b1Iu+Hebz2e/7ct+eRY4a9gfSVsyyfMf4OkShSK+l3+MPHTeXNn7xa3P8kgQ3Pw6BYlXnGVj+XCNJrT0xS6ZxUynS6dK58XxDd2a91ZtuW8zliw964G+QZeGMTsxq0iwd/Bkfv9ubz/qsQ35OFpVTpOdJHt+4jYnvoBzCT7I8tEF76udWo1OK1CLJw7074n+VJWij2KHXh1S5Lg9+sD0AXp3gn+t2oENVI0und+DtDUI2X+9lXPHtmIxbLMsj1AqNtufeaiJT6chFpbi2u/KjmWu+TwTzqoDFpQIhksyDulAwwlYkZOkgQHieEZ1CZYSuOCth/KAZP09EyzpUlcetTDAj5SGyVeiU19KlsNzKymlmeVUuXpW3CUmgcb/GY2lvl7r1IOhUIFebx3NChNCESlKV8qlN50m7ASkZkHGCJBthGzePFwlWHd1lAOSkOf+7uktw0HgipLtTpFaamEOtxfopseJrT2k9p9V74rRTWRZqhUK3yDRneiEIiH0GaSakGYEiLTyqhUI5ml5uHQPT0zmg9hVeWq83D83bnDe/6I6Ym0b6xv1QBCKJr4UoS3pZJmRpWQooT5QVUIWRRVPKWHD5WYGbF7jVErfBxW1Km7haDQUjaBUCUEZw0q5EK41yJW5ZlkzzTyRiKUBrhCPQysTHQkZilxRJZkYouROqskMhQnAUyaUtdmWUgUCLUgB45ZQEPIgCxBMJewrCQKBSxgpMS9ApgXBMLDkhNaF20FIhpE6yHkabQocChBGhpFSJ+6FSIrHeK/hG7IJyqy83yarpCkVT4FHl+igNrjCuh0oLitrB14qsdKIzLHJNFbRwIWyNgBCnFT9eP1IDPREQ6vhck3gEJRM+oVBakBYhRS1xhKaoBClhrLAkxhKrPJaWJ2Qk7przWZWJXGuStSJmpVIpttpqKyZPnsz++++flE+ePJl99913bXRpjdDObUC7gq8aaxm574vsWvsO49TGVM0tcOezO9G2XtNY8KiaK3A2zIPwWNSU5Y5N/sp1c3bnjcYOKC2omi9oWqeIdl2WLsuR9sFb5PLc4g0Y2O0ztKtJLXTw6k3mhIvWeYSD1t8YXXCYHxbIiCJOmSmiFAIVZRwoJ64TolFa46MTU8Hl4ZS1IaM2qmV6BRf9laegfUJduqCHzfx/VSt9WxnLgPL1ytuM97+1NsrHr8U2E3VZJMtrYv+TPn6L6Uu/LRTKnDv23tTyPeHHOs8AJuCshGV+mg03/pI+mflM053w6gNmfN6LTBH8wMFrgLBDAJgXJvt1nsHz9esz18+iNXgN4NeEaCnJF1K4CmST5POm9nStWQpS4zRKZNGkph7c5gPua78DhJJGHeLqEFH2oy+P5VuOjOoozE2fiuIsrIiKF/aAQJASzhq5NgY6jLwcdNKvclqbBlfmubt8PV3WZjxGrbUhV3DRTNYrW16Tc0Nr9wXfd3TFyFoslpiC9snMK7s+mMjVJli7Mm5swpVJFkKRThkXwCBEF4rGSqv8QbNcyGpujQUVopPIpNFl8bESl8LWLLGkrJgoykWspJ4w/Q+qHJb1TrF0Awi6FMm2ydMuUyTjBkn8obQTkHWLdEg3knMLeCIk6xSpdZpo79YjUeRk0ViwyCYAcsJkkauVITkhyUoPl+xyr6+hVgSRpRWUnptCNHmtSQkRWVZJMsJdYWyg5ttwhCSWJlpFl+aoUGtCophgZVni4jY84VANdJCKXu6n7Jb7iPfWaceTdf2YOrsv8+bVQp2LzJuMiLIojAVW5HaovMhFj0jIiYSfUJasnOKg7CI0Fk9BWuNkJU7Rwc27uPUpUkuLJli8H4CSCN8cW1nmamqCwZtkA8kxj/Y3yYyoo2DvygRlRwhzLgbGuimOsyXDkgAXB8BPhk9rI2jFlmcaCMraF3G8LXAajUujCiCskqi0gqI0ll8StNBIVxEqSZwBUZXfzESbVYGxSBQStFPy59RSEwKizELLDyW+1Emw+IJ2caWiqBxcqZBoPCekKUzh45DXCpRPXjvUqSo8EVDQ4BOyIoyVYJgIrMZSMD6vZFQmyUjfLEcWVV7UrtICTygK2kEKja9NUPgyYz9CXRqDEI1El8XQ0mv0WbmcteZmeMYZZ3DUUUex9dZbM2jQIP74xz/y+eefc9JJJ62tLn1jRte8xfOHrs/rz/blqwW9+O+OG1PdpJm9Y5Ye/w3ILGhivltL2498Fqsa2n3os8TvxKHTziSzUNP28yLzpnah/bsBy+qztPk8oG5JhvQyhQhh9lMbcNKAfnSap/EaFJlFAQtFZw6ffCbtvwhRHzrs9eY55kIQC96t3acKY05arNUgwckLvHrw6nWSESNeL/6hx+slSr0w6rZfI9jhoNeYsM5z3+gm+8ugnqHP/hI1L2NEYAej6MfWIr4oy9Qhki9MdoyokbK3C3EftYAktp4yKVnNetoEQnQgrCoL/udGzuUSZCZASIyJqdBIRyMdRSoVkHIDOmYb8ZyQ9ukGDuk0nVHZ/Grvf8zsoJ7Jjb3JySKOUGSEH/nVuxQT3/oABx357wdkRIAnFB6KlFCJqXKMA1EQSUrpUzGTbqmOKXcQLcS65RHXkwhcHNLfIJDrD1HAs3z/+THOMwD90/P4YrN2zPmsI3WNbfm4Z0dSvqaup0ebjxVuY0CjTJNZpGjSKaoWKvIqx/2f7oDbBJmlIQ2fVlM1XyGLHumlikLexS2Y14v1MzvwcJdOZBtMZiO3SdFIjgc+3oGqpRq9UHDX3B0rO7Wcy4Ux3TffiwCT+rsYvQEum6sq/qflvKPSsO4msxlV88U3EmCWqSITP9sW3eCWtln2nCZCUYoxUv78JspcNKgsj/ubxGzVZf/H86YE5ZYJ/lIn85Rwlamn4uc/k1HJcYzrTNbzkVJR5fhsmvuKvl7AN6VOFfnYb0tKmJtMV4QoBEpLAgQuGinMjbSLQgqFG2UzMjeoJpxv2ctyot1MxMdYdpIVL8FKbjPNxbrlUapn5qdvcvx/iAKexbIyuDg0rRtEcapMnKLE0sqRJWELIz4hRSRkFSrjY0EpRlZZWQsXfWXibolcFl2VBs9tYY1VIWKVi1ux2FAuOggRXWcF2pM0dEuxcDMBfRro0KaRjBvgSEXaCWibMqKUK0OqHJ82bp5qp0DWKZCJ0vJ19xZTI5vISJ+cKNJWFs19sDBxfiRQK6taZFeLLaXACISNOqRBaZZGmQpDBPkoMPaSMIcjFKGWOCK2kgmS+FoZYQK8S2ECvCeuXNE9e04qaqVDVqSWm+WtJHZVvhhaEY6Q1IoqqoWiW1WebdIvcmz7F3hzg248sbQ/z33Vh2WLs8jFHk7RCDgyEChHo53IgiuyNlKeRhaNRRaR9ZIJxA5kQVYJE3g+BFl0cNo4+G1cUktNsHjZ6CPCaM/j57FQITy3hQuqFsIcIGEyGprMl8bFVOvSduPnVFcZQUsGGuUaYUtLkQS2N4HhTUD4kMgaDSp0QxkZIsaCliya+wATZ14at0MR3ShIYzVGFDdLRBkQdSgQjhkwFUhTpgU6AOV7iDi+llQ4jk6ssOKjqMtEZK0FuCYWmxQaRypcoQi0LIlOUTD2onbwRECDdpGhOe9aswaMXfpCdPK8pdCE2pzLcWwshTTLWpIiNNuTJl5c7GYYRgHp4+yGCjM0jiAJBl+x7fg+gDhuVunZcU09+601MeuQQw5h4cKFXHbZZcyePZtNN92U//znP/Tq1WttdekbEWrFHYu35a1HNyLoEtL1Q03j0lrSiwPybSVBRuJ9Np/qzj3w6nxycx28Op/qWZE5nwtOPqTtRyGpJUWqU8J8OinCtEQ54Nb7tHtfkF5YpNAxhSyEtH+vSHpuA0HbDLIQ0vaNvMlIAslr4tgvufy1sfZcwvY5VNpBBBqnvoBcUh8FdlyJ957GCRhVnWXaln0pdH8qCfK2OixSLtXPZWn/XsFctCIFPhGmiE1AtTE5jcxSRaBN+uDETFW3mBxFqEpjEPtmRxdPLYV5O+DIKIVstCwA6SYTrnZAucL8Rf/Pz3ZAeYKZVfDyNj0ZvP3/US0zqz0GAB8G1Vzy4r7oogTXBFIkEu/ii6bjKKTUZFI+nhuScQOyXpGsW8SViozjU+0WSUWB+uLYAPFEH0+scbmDStIDS6GMUCaLeITJJA0k+rtEJaJaJhLW0iKkRiq6Oct/s7Uiyk1fQ62o1wV8rVpYz1WLtBW9LCvNj22eAfMw/lrTOsz7sCMqp8ks0viFDE5e4WTMNcpZ0kAqV4sshqTqhfmsix8iQASKzCKJkw9JOcJ8SpLU17IYklkIbmNIkHUQgaJqAbgNRVTGM+vPjYK+QmmuSaaOsrlGSnRVygSEVRpZDBD5YmSTv5JzjRDolMen3ToS1nyGXAlz+uXRpCWpLzyqFoRl84soe/Gjk+CzJUFLG3EqjvER+xRUiG9RDJDEZyPuf2k/tBRRJq7owS2OCSJkhSim4zrS/N/gmf+XeDBrnVp693iVlPhmt2+LVIonv9gYQmGENRm/no/fgMepxsF1QqRUuFLhOSGeDBN3iJQMk4c4JxK/UjLAEyE6krTichkJZA7K3MxjXCgcVCR9ifgIREOno3gdRkiT0c1zWmiqhbdaopRx04mzkWmKBCitW1jPpXCt6GX5QeEIiagqy2YYW1o5jrnGBAFIx1hkQYWQlYhczcUqjIjVAilMfKzqHDpt4mOVCxFAC6EipnksLLNhU0+7gmKNx5L1XZb1C+jYYwmdcvVkHJ+s6+PJkJxTpEOqnlDLJHB6j9QikylQ+pElVqEiYHtWBNRIQTqynIpd+MrvJ2PLldjCxdcKX2satYkn1KDNY3qDSuMIRYNK42uHOpWhRuYTQasurGKhro7acPG1Q1YW8LWb3FN7ImBpmAOgxmmiq7sUgLaykVpZoIOjqZWpFsJE3N+VnQFjIaydk6VahvRyF7JDZgpfdEzzalNvHpu/Ke/O6UJxcQbZKJGBQIQa6UdTdDwvyTIhSBgLrURUciDImsoyAB8I05JCTQqv0SWzKMBbWijF1EoGPKg83xxp4l85AqRERsIUjoyMHIywolsTQAU4AdG6kXDiisR1EI151i2W7oNiCzAtjKCl3JIlGoE5H4NIoEFL8zuQGp1EzQcdGqsxtEDIMBKsNDrvmPYdbTIj+g5aK6QLQiiEgELRNS+sHEUxcAmVSVqgNIRa4EauiIXQzPW+cmhU6UQPNNZRLnVKkhNFMo45f11aWrArNPWqgI+mWsRlCp+SQGYELXPOeiKIDCjA145xZyw7VKF2QIATW21hLheeIImfFX/jIIz4RqwYmv36JvdxzVmrAeDHjBnDmDFj1mYX1hg3Ld6Avzy1M90+VMh3TZkIoNDOoWqhIr00pGnjrjhFRbHWw6sLKbbxcBtDlCdxCppiGw+vIcSv8XCaFH6Nh9ukkL6ZSIJqsxzkXGRBE+RcnKJCFAPCtGMyNwShEXfA3FTHDxvNRCqhFM4ioG3WLOf9UsDHuF7yRkWWlpMyYW72wxC/PtXC13tVyYgQBHiL8iaFqyOJ0+8mVlNJlgtduoA55iomzK8f4SvzBiBQSephEacgBlMW97/ZPuoo40qSeUXK0rYcs2w+jeClUg7aMQ+PczJtqNs2oPob6iyNKk3unTRufTRZQGIRoLxYVDOfjY5GuxCmtEktmzLpZIVjzGAdR+G6IUJgHkDckFyqmDyECKGNCaswDx9Z1ziae0KRcwukoweSOIih+S7E1050w6Ajc+4CNTJPB7eewZl5pIWLQq2SsFc+YSs0XwawRFUBJhBhKgpEuJ6bp52TTerGb9CW90ZrRRR0ZYDT8jgE5dYDVjz7YfNjmmcAXsy35/WZvahZqBHzTZlQJnCp26Rx8xq/Yw0i1IRpB1mMPn2TQlsEROWKMHqZYb7XRB4UqJRZVilzE2k+NYQK5QiklghV9pKAZiJO+VyjNTQVIRM/QKnSdbj5XFN+Y1v+kBPFWVBF5xs7mbnRXCWbgtIcEz1IJYJS3IX4/+hmOX45a15Xa0TkrmOEL91MzNKlRsr/iUUtiOah8oZFdHMtSw93Mr65NwJXvZumsI4i9Q11Fl87pOY7yGL0tjsiDrAbzztagh//72hjvezo6CHVvGSR0R8iErSkIuWYuUdGZm5SxJZcCi8yl3aExpMBbiR26bK7ZSkUSktcYd7oxy9g0iIgK4v0chtwo7VWRdgrj7Wl0SxTkI8eUjUCB9OXdjKgqnxe0jrqoVhlkSvQle4fFa65iYy36u1aLM3p0KHeXHvC6JxzHHN9CQIjCsRClh9lLPSDSrEqvjaVC1nx/XP8LCAFpNNQk2vVrbCFSyFllrbR+i1ELIGxxuqaYuHmAneDZazXdhkdMg24QqEQeDKk1muis1dHe9ckMMhIn6ws0MGpJyeKSKGoET45qZIg2ZlIOG8nq1q4OcUWKwUd4AiBTxi5RZlyH/OALrWmvcxTpz1ysmC27fikREhee4kXRV6Z7HKOMFYvGenjaGVCAyioU1VkhI+vXaRQFJRHUbssDKtZFFQTRtax1U6etk4DXd2ldHUaaC8hKzzSwl3te1Kz7w6dHYeOjqa/9zF75N5nRveuPFe3IS/M68PsBbWoZZ5xQyyKRMBSqUjgElEMqrgLEoKURiiB8CGMQo6GaYGTB7/aIaiSuO1dMovSpBY2IRoLyTklyi3/lHn2Ej5JQgIhyp+9oqDwjix5DzmxOBXNldE8LpSOAt5Hz43KuE/KQKO8SOiKvICkIpnsyy20tG88l4jsJBDR/YCjjZGBIpowzT6oSOAx8bUwdQJR9luSkTBmvH2E0KhQor0wycJZCEpBbnSU8dCL3A/zoRvFzDLzUaNK4WsHXzvkpUejCsjLgLTQLWQihUIKQbbVeFkORW1mPj8yVPBEgEISovAwAlf8LBZbcfnRy6bKkEAqvo0hcrDC+w7mtbUqZv1Y+MBvYMJju7Pukwqv3kf6CrQmvUhSbOuRXlDAXdqETrkQKIRS6JQxr5T5IkiJSkWpPAu+Ma/MRMtNzZYbjeCgo2DA8UUhPavePFwEoblIaF35wBCXAXFgExHmcQvFyP84qHxLLowynghY5al3yx881tBJmhGaIAOyGKCVRHsOynPK3prH6ntpEkze7kQPWuZBosxiyzfCnmwyQp0IQrOPUeDACoQy7iVSQmAuivEbg4rPOKilK5G+Mg99AlJLUjR+s2cswKRIdRvAzWtUULJ0MGKWSFLLxm8nlAMyDarooBwH7WpUWhFKCF1FMRK28hIcN6TeSSOlJuUGOFInGWAERvBKyRDPCan2UtHbdWPSHWqRWHQBKJ0jLQOkUFQ7BeqdDL52aEjPoVqa9+zfhIWqikWhebMVv+2qkU006KW0K6sXxzBIi5UJfVgi1IpGZcSsxL0yOs4OAk84hNr4d6voQSQ273aEtG6RlrXCwrDISx+uT+1MjSyq5JrnNinCjMRpDJF53wjysbDiRLEbAvPGXkcZb0RgXiDoKBiw8JsvRxa+cYYc38wRbl3RzB+JcKMpd0VJrrFg5gcFQgWIMIxEINVyrqmYU8r3uFzQWjNzjSvMNdT0x6g0JmaHNuNTtr3krTSYa64TxReJLLMEUcrxMNpnPzTpyVuz0KI0JiYWrQBVskJOCM2b23hchBRmTnSMBOLkzc3sNyXUMrq5pzIeqwAdxx+JfAbjmCLaEeZNtMS8oXY0WkAoNaEEITWBcJBSUZRu9G4oxMRw1lHgcSNoOUIb0Us6ybIjVKSFmpt9AK09nOjlS0qEFKVrXO+pJ4X4xvGzmrRHk4pThxv3iZTw8SlQVVZPYV6cOIkj5cqhtE4ejGPRqvSsbzKURQYCSczSWOxaXrxTi2V5rFOzlCK50v0/mP9d17gWghGyCibYewurq+bWWGVCloieBUQuh85mlu9WWLFc1na5NVaZiIUjCNMOS9b3WLxlQPd1F9KnzSJyboG2XhMF5RIoh5xboNZpoou3NLHCyooCbWSeTk4T2WhbIcaN0BECT0iqIq+R5d2zFbSZ6/zkXk9GVi86cZkybSpSokBRS3wkjcqjGAkBMcbLwYnWMZ/ZSPxyhKat04ivHUItyQifjGPChfjaSYSsRpUiRLA4yPFlsUNy3+2g6eItoae7iPW8PO1kZrViEFXE15LQxVnEDplnmdluOm/2Wpdnl27Iy7PXpX5+DlGQSYB0Hc1dSsRCUWl607LkgqqlNkHiXWOppVICWXQIMpJ0tUNmvoe7pCnyIoqfycyrjORFT9FHSGPUICKxVEiZWHiL+EWUJnkhJZQ2z2WuKCUZAPOdNlZZ8YsaGUTnefScFVueKa/kEST90jwoQ2HsIzJGlCopThjrZhEJXIBGkth3aJJyBOiCg0qFUTg6E1PLLwoCqXBd44roOCbQehCaZzQpNI2+hyfTSYyrvDYuq7GxQVE7FJHktSarw4oX8aFWkdWhogBUlQ2NCUqvUNozls8ofO2yJHQTd9k6VUVWFMgIE26AMovIvHbIisCIzaJ0zfGj4XAoi1stwFhqtS4of5NnKitmfUMK2ufot4+h52M+6fmNiGKQWAQBeHNlIqKIJnNBQ2tEvnTQtBTIQrHipl7GDxIRsjxNrtYIP6hwHxQNTaWHiFCVBKzoex274CUNRhlJyjcTC1jlb1+aC1bNb6q0RtY5NOqQ6lUauZYIjTE7VdK0q7URtYSD9iK3C1dWxC1J3qpHIlbpygpCewilcQrGJVMUQmS+iCj4ZmzCsocqbSy7YjdFoWWyr+UmrShlLrBKQqBw/BDtOXiNmrxeMyaTMtDI2FO0zD88/oy/U150yIrRALiYoIihNFZdocngoaSxmw1dB+EpXC+kWHBJpU38gZiUazLDpJyQQElSTmiywigTaDPQMnlDBhA4EommoFwaVQqJplGL5O0PrH4sLKUlS8Js4tKY114S5LC8zTB6UAgpUs3yXRCb90NFbxXy0fGXaBxt3iDE+2cCfJYyx8QxxJTWia+3KbeiluXbJ9AhD84fSO1HCrexGM0zJdFE1seprnXpuq4x14R4UcQiVumaJsofepovR/NJhfugH/kflItYZddR3WwZIeJ7mLKNNJtbYrGqNSGr1BlEwbwx/6ZWSUB0/RelfYvf6sauf7LZg1h0g6sTU62yr4wCg0wZl0wdakQQGAvh5AVK2ZjouL3IvS3+qvRKtjQ+keWxCM0baelDsIaEvcQFA0puFzG69B1xfJSw9F38ZtrMUQLCksV0KAVIE2cyROK4KnkgA3Ck2ZgjNcoRiZDlShUlCBAVllqeDE0QYiHxtYPABJ91yuI0rq7oo7WgSXuRS6MiwLy0CXRlmxoz3ygRIvTyraia90OjUejkJykiN9Z4SBFmPopkrqRO3DeBSB4GrKhl+TremdWVDcOl5r41jpnlpYyQpbURsXzzEluHlRaDiRVVfK0Kw+S5wLzMdYxbYVXauBWuQMiquG+G0vU1fkEQPV9oV9DU0WP+FpKq/kvYqsN8emUX0cFrwJMBvnLxvCAJi7Gut5CcLNBWNhn3QRmQFZoa6SQvIltzIWxOaxkEPeGQjtZVWpEWEh9FndJJHFpfG1HK15CVPo5SdJD10XcuGeEnYTgAFobV+Nolr8w9bE4Wk+/yyiMj/Wi9QtQvSVYWyGsPFXk/mLYdGpXHgqAHzwcb0tGrp29mNhun5tLL1d8oBEdWpsiSolb69Pdmsmv2I97v2IFn6/vy3Pz1+HJ+O4IGD9ngmOcNUXoBUu56qF1degGiQDsaFQjCNJEFsEB5Dn62inRtisz8JuSyJkT8fKtUWaxJgY4toFwzF2kPhK/NeeoIiEPCCCIrq8pzToQgnFi4ip6HELiBjqy4SF7WxBdkIcv+j563ZCAIo3hhANoVaE+bDInxJkOB0FGf44u91KY8iMqj2zPtR8+xsWWza5oJQ4kQmkLew3FDPC8EJWkqSlKucWFcEmbxEeSEOd/qwgwZac45E/PKvHgJMC8P49hYvlbktY6spUqWW0VkFJPZRWkBsmCyGIoQX7uJS21ee6BACRllPHTxRIjSkgJlz1FCm0dpzL6GaKS5VSCNiXkZap08T60p4wArZn1DQq2Zv7CGDZYVEcWgpVCi4x+erHwbrVRJNPo6ym+EIyriYJWLWGFYuhEuF7HK0+xqBcr8mER8w+w4iXvd8sSrFpNT/L1IPAFXGw/wc9FCqCoefoQQSAmhlJELRCwylcQsXfYwlGSr0OYzTEmcyM1GFlxk0bghCj804pkfGD/ueBxVtPHErVEk44kQidm1cB0zxkLgFFgjYlZOFowbT6CSNLRxs/EbgjjwofTNPiowqXaFCWovAyNu6VBEJrqYt+hKo5UgUAKZCgkCSSgkUpoYXMXASQIO5gMPXzlIzwhUMUoYs9eME9AUeiYGinJxhGZpWEWjqryklF80VxaJeVOQV170dsycCHVU0eC6SZuhVviEUUBDTSBaTznbGnH2xdjCIfbzNhd7YzargLSQgDJmtNqpCIofuyGCDWBv+fbRQGNjmvYFE9C3VaEkuX7ryhVX5SG4+VxTLmSVi1jxduO5Jl63mQUwYN7olvcvca+r2FKpC612V7RcZTWQlLlwKw0YsU67xrfA6EexoFXWn3KL4PIux7urhRGxnEh8CmQpXmN0T1DulmjGpvJYifJGNdHNcjReyoydDCBoNRL9quGJoGXmp4r9JRHdRBj9H/sNRPqbUFElBYmVRbyeNiKMcBRKGakodq0IFZGFkiJQjnnTLzW+KnvJF0k6rlD4yghYQkiEDCgoD7/ZGOhEIVx5TB80gTZX8jguZEFpfCmTNk3WMiM5aQ1KlOJufR06mkvicJ1OdA4HGNFKx7+d6D4qtv4SzdqIhS5rqWVZHqFWMDMLeknpvj8WspSqjI9VLmTFL7JpJmSVk/JK8bFiD4VEuIpfgNPyuaY1a6wofEiQcajr6bFwe5+t+n5Mn9xCOnr1pKVPRhj3wfJYrp4I6OzUUSOLZERIVpiXjNUihSNEq7GCWiO2wPJ1iBNZcMUCWExaeJHQFdBWlu4JFSaLoflW0EE2kI3S/zUqDykUWUxs2hBBThZoUMYlEeLg8GZ/crJAXqXIyCYTyU/mCRHUhVVJSI9YzFoaZlEIfOVQVC6zCm3xtcNnxY70Si1g49Rcujsh/8/enz3JkuX3ndjnd467x5KZN+9WS1d19Ybe0Gh0NxoLSQDcMBQ5HEmUjVGiSaJMD1rMRjLJTA961av+COlBZjKZaWRDmkEjUkPOgKQNQAIECYAAQQKNBhvdjequ7S51783MyAh3P+enh9/vHPeIm/dWdVU3hCVPo3AzI2Px8PA45/y+v+9yK6w+8F50IS0LaVlJ5qV4xhe7X+Gv3/htfvvV1/gn9z/Pv3vjIwwPl8hgTRXJPmmVJaz0ZcSAJ4KaZBBoRBgw5tO4FsalkFZHLB52tA83hPPtfu0cQ90zaGmK+bU2mYBkv77CtHaFwp7GmVpU1pUGiL26N6jW9SpHoSxlEiwIzdY9kyLmxpSF5f0m8bWmYb582+MRIxJEK0Il2+/muenfhazQqH9flJwE1WhSfTD5YRZbN8X+DQ7EZQ1ea1qTP4iSNPAkL12+al5vgfKmp8PLwE7x69rUJ1s1qeLOr93J182OtQ0mNyy+mMlQS8Dk+SdhS0/gyAHaNNvTzLHFgB1XYRIWkkCpnT5sDXUNZn3I0UqkWxpLSnbuO5WSRd/CZG5XuiRlzL54fsfpb6UImBcLs8JA9jbD2JffZYJlIarPNgeyoP4snmxCE6fnv4qJdHhsB0NDIK/yXjLeBxlLCYxH/n7y5HUlOSNJySkCDQQhB6OvVpPcOEPYD7rLhT6qMSBdIHSBMDTVSF68KJQhm8RzTB4le+DpMvu3gFsK0Eo9xvQ9bqSvGi/Hc/qbmFlzBekOigv8ZwftQiq/i2+3pyLDwC3fSGRB1RaBLEoWS2ccx2kSDTOm1rIZuRxbFs2MvqeGvvc5GooPNCFzPhoza6v7U0qZuAZN3xMdOnuhMmhko515d+VwJWCYgV6V5Sxy9un7PA2qJaifWdCpe1DqtaTKzsubiJDF6LF5r5cXyE7pvQa0rscPcgSE2NhGzzwB09NzfgiH+IijUTOW0VVrDVy91hTwqjxfYfyWAhyMEVv+NgfCynNUxu8cwLoCGDo8tsMhgjYfvpBv8FSmcozJuh7F80pzILSBXECtUrCVNaaex/3ntdNnsgHJlmwkKfiagUkjsgGBxQ5A9iSX6k8+/S7lZnDG8P65/zDjOPSkJdUQt76Pq+5ctil59rtLTEQ9tVCmv2v1EZEZMGjNlEm2P71SEwzUmjOF7WWM2VWu8LL2CMp40LiYSyq+l3lY/QPNiBnduuRivAJRVWzdaJ7zCVwFqnlvzV6n4L9MX1XrYhuDS5xBULy05p92Uq1MrWtA63ocjp2OLB7KtH9dLJCuMyCrt/pES8P7cOT89O8OTMlyiRR/rOcBWXNZ4bMM3t0DsD9tePezDZsvX/Izn/omnz9+ixfbJwAVxDkKPa+1D9jkBeuwYymJm2FkKcLaTdyjyDP3fYfj0GN1Dl5ldG8OCT7JtZjdycY9VltgGWwuyiin4ZJOhK0qRzJyli0ZzryCEi/HJxDhQjuys67OsvvJasNR2LkHUWbtKoQcAssw8GA8NmYr2Q3kCxBgiojH44qkgbflpskT2/u80my4HRoW0n4gCSLYXLqWjoU0vBAHvtB+jb+y/jr/8oVP8vff+irfeOcu/XmHbCPhUsyipQxhSot36XnYCakzdvHYWDMpR0srTF3HYhnpHnXEx5emYBrTJJMVy7C1mXV0EkGABvskfX3NrS1IoupSfWNkFQJETUSMvlWZNW4KQ1mjgU7qIFVu3QvMDyV6Y7DI7nM31ZiFPMHgj1OZbi8M5gTa+YKeHPTy06ZBTaoZbGWQAIObyQOkRhhSqacCF7lIY61GOso7LsKO22z9M5yS6QOWVl+a90m1LlElsXCrjYV+kc0byyWGW20NQKO17yADlKAwGWpC56BFmgstmU4ySY0osFWlE/sUBxKt11ETEHIw93yAcQ1mfZ+GjC4tTAktaQ3BEGXrxs6YWc9iP80LiTmQBftAVtkAF1BrBp7Z0xT2kD59kcSINI2BWMUTi+eAas9904ZS701kH2YcPk3OMFiREbJLXTJA9InJJ5RiVuuTFsB8P1knlGgb73QA1oXkwFZeIEMm7hKyG5DdaPTXw4X/oHiTMV/NWvsAo5XsWnOb/NRjZQ14A+18YzybJEvHXEYIKuSgFvGuZcesdc6QIaCS0RBIQQ2QUvHOeEY10IMnVwVEIozUIqMCWC45HDUwqtJ72kZ/Rbd60MRGe05l9dTfnjVKh6BsarIGBswgf6cb87LyDlnvDKvhGRNi0Yvva8hLt9zeT6GNt2KGn6glcSSfhGN9Lq1JNy2R79PHfj2ux/se82ZGnfcdXDep9QygkoKCM/t99vMhkFV+hiqdq2hKec3ZfFihlyvYw4RQPS/mx3ElqPaeb9pBsKspWx9+FJAOreBcKGatctAsKYdx1fGXju28CTE7/5JLQmL0Od2BrZQnT7G94/LnLKBifsbrfoARmQqOuT8WebZBL+Cdb9RVKfQhex+NXzqIbzWmtaak8NbNv9q5EH8yCQLZZBVZxBoL/nt56wXAssJRTDVVOrpXsHALW/f9MnTBQDVjUVjRqGpdejO8HYguePQaiaQeNX7Fx5ALIKX78kTbujiIh8kwSox5uU/CGimyd1vxyglXvdz1uB57o5XIuMLAgK41IGscrSbZ7faBrEN1yHzNKKwsEeT4yIzeXb1xKC2sX7UQrgay6j5cIAo5Bi5fbLn/ZeGFH3uLv/7Ct/jM6u3KwipMpGXoWcpARLkZLlmHkRNR1iHWNML3C9ak2d5wzqYvo3hIFSC8MEXKOc1k1s7FCr6HLC1NM9sWAoktSgojS5daDV7kbz0Nbhms+L8ZLrnwJm0BDzZ5wTIM5Bw4ap5YwlwDT9KSTKgA2Dr2nKcFbUj0uSGHnsdpZcb38ZJlGhl0YCEjN0PDSroP3GS18xJYxJZbIfPR5pv8+dU3+c1XPsq/ePIZfuWNT/Do/jGyjchg4TL2WTN5anWKjnbOx5XXMe73mxdiTK2FMK4C7XFL92BLONsYqSDNUKRgTOcaBDbizCix1WEE2mjrUVKaMVdGuJm6Wwph9l1LGKb6KospAmd8L/PJGgEMhMueKCxOEMid176iyGhPYIoZh96K8smHjLNNQTOTKA7BX9zYYvZeCy8ZcgqEmOn7hm1nNdGFdiwPQkXAmFIGKrEntU1qhJNWEhc5uDLF2vIXuqqAlYUdJHfcMeN5lBrGAjBgKY1tGGpwSpHXtiSWksgIvQaWkikGSQFYSmRb2JAV0KKsvPAhCAHXYNaHHBlLjXvKgwkmJlbZBapOksNCtT0Etg6N2sEAkzmIVZ57zsaaJ0KUHbF3cYuMUJrGEiJC2Adf5mDa/N95qsmzqvYQTId8VafnexymWw77hZGqsaWybeTj3uuEOrFl5+7XTfnsOYtJoKHrYSoYfKM+SRJtopWkhL4jDplwORK2Dmy5AfI8hUPHBK0wrj2RkffXIXrWGDSQXXcu1dpG63uzjga1iow7n3DFJubsIJa0k3bdzo1OAF8SNAt5sILKTqUVF9mj2PuxIYiBPYPEmqaRcrCf1Trju9RUgGvZDBbfelBItBKfCWTtdLiyqxYp5ob+xgA08iQvGdT8CWwizuxKt+E53fLJvHcattko1Sl0kqscJLEfMWuLg5JIB885dT8+7GR8Pa7H84biXbt5s6P+8YqfK+P2GeDLoSTQb9sDsfB/5mys2Wvp/LnKqCBW2O/Uz1/n8Pd5Bf+sqt3lk/q8+7zPUcND8uzY1cEizZMHls9oNEzBI7VQ2z8OI+y615PyFPxga83Bec4gKRKyJ/FWYMu7FA5O2obc1uHcQiPPnuve70g4s1mYGFUzAOtwLWX0+/m6pMFuwx8zyTO1YnrqsgxNTI0zmRpuEiHl4I+1ZnX02PKsUg3hs9j9iodWE7Jv9vfPcSm+rhqjpr0kwzK8FHLwaFoPdtqQtLf3gXksjvUr8ezzP7GppmOz7cv0e8nk9G1NlR7mejx+EHvPaVBg+d0uh2uI63pcMbrWGCxjssTCcTQg61A6WMZsX1tHjIQbJ+jxutYtV3pkwfOBrLIGRCG1gfOPdtz784m/8KO/z8/c+g+chEtuxC29RjpJnLgX1lIGWrHi+CQkTkOkJbLw5NL3u88qbCzgoKHpDVq0suvLHnE4AAoaoodXWAEeCOyKZMrvG31TfjtAaWhk4EFS1jKyltG8Af1YWjV2y+ByxBgM1Lodz+nIPNEFUbObwxtY8CiteTges4oDZ2NkyJHztKiG8nfiORdhQ1DlQo2pdztkjsPiPT3E3mtECRyz4NNt4nb4Nj/SvcF/fPPf8o8efYlffetjvPvghHwZDbQR0FQAHiV3StxN0v3UOqg1TGtq7gLDUct4FKv0UC4uDZh1OwCCeVCb7DAjar5tMttkhMNmWxBofaEbM0EF6U3dUwA3SvOGQkBx9U+GmDBWmdeRsS97APVyb3a9N36d9Xbf3FnifCUdqCDqFjDBmOYAVdAixl4mCTl7s8nlhsUU/nFaMWj0Jk+uaYZBMidha75VV6yB2Zv+S8m0s5pr0MZN3yMtsMkGsmZCDSUoRPbs9XMiMOTo0t/EUkqYlnChDR1mWr+sYJV9FwbNDvyGGnaSyTTy4ds112DWhxwP047Nd4+R3QNHEmSSFGp2L6o4gUqHANa8G35YWPjtkvLk6VRG0b1fxUbZKyyiLWqLbgKxrgKvrmJm1b9d/d5VBG1tc/hPL1/mZrwgopyELTdDz0kQ2oML9KrN16CZhxnSSeLs87eJu2z/bUZCP3oqofdpx0wY0iQtJLipoLOZfLK047N/s2BU0eTbwZLWEmbxsmIPEO84S7ZI+jC0xH5B6DNxMxiwddlXk2RJ2RIEG+E3th/jXnpQFyvT+ee6QY7Y4hUkO/BldMyywAXgt3ev0VwIcShsMTNPNFmlv6/C6k42CeZSfHhRIoN1DAr4JaIwWIJSbn33XHxPslgnoLDrFEYiOYt7mwhNzPTJAK3y6VkCxnSdZRW2Y8u9dIM3x7eeG8U6Tw7MqmxlpITR2iZh5CzfYpdbN830cxVGjzPef+5eZ2DSc0bRiU+/23GnsvFQoTgcm77bzu1kr6M1XQrwaya5r9b1uB4/2HGpieFJB2kzrSew3yiZd9or2HLFd/FZa82c8VtGzrZ5fK+GhVjaUImCvxJAm3Xqnzouefqm+R8tcRC+OR6zlJ4AToVPLORp8OiqKSircqmgXaa/u0ZGNWbu4LL2WUNKshqwJCbdDEB22WEBgQ6PVwNVYpdhSqP1++5ts7U0UoxxJF1AkjUYQgG2SiPHj8ekCPDGeMpF3tSlWURdlCblbGHQj1bgK9Tb7LDfHm9YmmGigmaVfSaz48Y342H6vbK1EtXDUSMTU1uwLvMME0Vxf5VypEJOoJ6DHsRkColIDOVBbgM/B1AVxmxMhrN8/lznqnlyoCqY+4fUv42a6XVJ0sDoBQEYoJYIT11DZQV8L6Hn4d8LPpx9rZmDwoI1UMJsq1W+3rV3WVIgr/lZ1+M5IyD0t7PZagyjeWT1AzqO0zwOE2PkEMAqo20JpzfM6P15QFYsP5cDmAFcBchyeVdaWlrhxc9e8Hc+/1t8af06LzeP2Grr+9/MUdhxErZkDZyEnrUkbobAwiVv8MECdwLC6NzLdFAzZTKNM0OKXUSZI0ZSTbW++nlDFUEvJJAYWEqkleiKhEQnmV4DRyFXttZSlBNGMrCWxIDQaq4+XFuN3JAdW5oq9SrG8EOcyvade9TusjWV3xhu0UpiGy9YysCGll53vMyOhTQsaD40oBUJvBgjp2Hk0+1DvrL4BX771l3+3v2f5F+/8TEu3l3ZZ99PjQHEw0GCVlk3bWnaW0p7WgipE1JrbK20inQPW8KjC/eiVsjJ5fZq+4ERpLCzQiCkZE0fkX2VDhkpUsNcPruJBBEGrYCSqqfEY8ecG1sjRe14cxbjXESpn31d+wZ7vuq55f7FYDWoBmN5GcjmhAOvOwm+Fvrapwo6RqTLiKtodoMdZCuJnUY2aumkc1uWTjKBZg+8jBKcmZUZ1Br0jZ+ehLDJBogexy2btCAGZRl2Zv5OposbEsFTOyM3wrZeE4OD0GDKmVZGEFgWXrG62sVJGQGXQ0tjaaMHBJAPatdyDWZ9yHGmQvs4UHyeUKWabhOnRaN2ySckmjCxkKoJ62wHIyWJZJx1CXI2aclVuvcDCYk0jYFYMdoXfw6ihYPjOhhTx+WKN10WsyjkLvLqPxX+z7/5dxmPhP4Edi8l4q0dd2+dcdz1LOJIF0YajxgNmMn4qIE+Rbap5XJsaW9u+c5/sjBgJUfCrqXZCM25fdnjFppLQ/TDiAM+BmKV/+am8DJnq81Q9/J+J3nibBNfHyIOapnvSRiV2DfE3ZK4TROwNVgn/e6/veT/+n/6T/c8RTTYRFg3+z4B2sTtAFWREkZIHaSFcvqG0mznxaNdEyY3dClCohoaei1QKb71UmnttUNvM4hGyCOkzmm0rfqGIxvFNXnUurpJbwrkHBgcSEyN0MbMmEP9LIv8cEiRh9s1/5fv/CX+73Fg3QwswkgTEguPX1yE0R5DZhHGPZCqIPxBMrvccpaWFLPLQczHhIx3IfY3I4Vd1evBCZjfZ2Y8CLaB2XrXISF1Or3QhpbMcpa8lcCKU9nv+pQNz572m6fp69fjenw/Rq8QtzKBV+pR1j4f783ih7K+A+ZVvW1PUpj3GL4T6/e9/QwkBPdfnLF+D1lXzIGsvUcf3O/wT1NBdPJN4Zfe+hK5s/lyPFbCcmS92tHFRAyZKDY32RnQyvJJKow5MuZAWI48+UzjTCxBUmPygB5QW19KwIZkk6KX7u1TYNa0X61zvSC2gS+nckKdps9k/jYd9JHSUEmBMDbevMnVY4usrN8e+Y1/+sPs4SXz9Uumz1fDtC7OgaocrRGyOKNGlGcURoE4gVUhTedgDt7J4SXha9D8nGi0okBh6iHUO03nMAhoDrbsl+JALZSkNE3E9wxgaV+XY8uvPfk4vxkSbcg0HhleTGobyfXzL38DZ31hjSUzfjczZfXnNXeW8jreuJh9VvOk22eNpPt+Vu70UtlfZQxeCM/9txRbbwI6C15gj5E1H3NG/DVL63oAxI01bHW7rUDWU6mFzxiqSlgskFunaNc+H8jy3w8ZWUDd/xLtvuNR5P4XWxY/e5//7JO/xqcXb3EjbCvrqJXRkv6kJ6LciZecBPPFWkj7PbGK5obS87TCrY4sxQCoSd40NULn+7gCcDVEprg+Kisrk6v8MGMBQS2RtZvGD2rBRGuJhJDqnrUVaCvz0n5PCktRWge3ks9BnWRazQwaOHK5ZZQ1yzCwyS9yGi8Zgu2N7w+WJf+Hu9tsc8sXVt81BlRdeHZOMMis+OCywzKKpxbASjK3l4/4/Ef+K/7lrY/zX7z1E3zj4R0uNwvSLlZfqEz2RN7ssjyrd1SM7YQKYyh1USC1Qlqs6VatsbQ22z2WlgBk36ME+yQ1mtRRm4AMLqMV96/KDiJFI0AwQsgmPZwrdLSsd0nI0evyBGTQ1tZLHYTQ2FEUnyyr4XTaCyQIGWspOcAlBc2xe9h+A5Msog5+hZKI6HuIJNaCCcqYApvUsc0tNxsDlAqQVYgkJ0EcKHpaIbMWk8KCAbWDZlqs7gqoAVliLC1LkE8kAhfZQLNUSQjm6baWnflteQVVghrKf1uNLMXoHBHzNo5AG4J7Z3X+nZpqs2uZ4f+fRovWZCTNs2LgKhP1w01+znssIfvBi4vRkvYOCwkdk6HTz3odMDbWooOu3QexDlhhzwWs5sc5v3+A3DUMpx2XdyObFwO7W0paK2mVYJlpliOLpWGxYw6AdQ2Wjs82IdNglMnyRVw1A6/dfUS6PXlmpBxIKmyHppLdtn1LcHR/GCL9WefglxAuA3EnxEsrRMIghGQ/iyoyToh50UrPTX21gFteFNhhiBc3wrjE/bUizbahuTApYrwYaB5fcnr/3KSmIpOUtI17Cz9AbqMxy9pAjt5BEO9GdJPZ4FyzjYqlcAjVnBAcxGvF49HtbzI62Lezgq9qvUc8nUPJKZBbJTdUVpZFx1oFk1UYx+gfv/plo4wp0jVWNCY1L6nSaUkq3N8c0cZEF1P9rBdxpJFMFw3M6oLRrUuRUcCurIFV7P2y9gLLfb+CKEnM12RQJemURlhkjs8quTP6VPGRnKVQWHSDi/yzsxPm8FR53qSTTLGVqdAJavLDKOKPe+9Y6OtxPb7XYZs1+3mPKfV+2E+q+ywhvw14WsJe/lxue9br+G3i7OM9EMtBqKeO41n19sH9aoMhBvIiMqyF4UhIK2OXaqPQKKHJxHbm55dNet4EZ1j699OYMfbcTcicHm3Ja6nwQlZBEcYUKsY3JkvfU4ScxDbmzrqSwUxvLdHJN6oFiQAHyRznKsCSTG/xEAirHB3fSEsjxqLVSBgDoXcpYp8I24HFZkd1Ei/nO4Q9jxrA13+cyVw20rZe5Ch714SUNipCLOvkrMGjYVojy+/FOBe8OXRg+hQyaBb3LGG6+Mp9oqcD5unasUvHEp5iUAcF7UQV5pkibIaWGJoKYJXmSsD+FajAZsAaNOW+qkLr18jscEHFict2PSQMMDKwa/oqPMu67dCwHWz9EQygyhioGv0aKyytvWPALqN6irD75pkp/HQ27H/XQNb1ALjUnvVb4k3w8Uogq0p8y75+DoiulsjpjWcDWfA0kHWF0XsFsoIZvd/7UsNH//Lr/N1Xf5XX2ge0kuh8siyphSYtTJyGxJEEjsPie/bEKqDUqGmWWDjtDAeSA0jGtir+SDstzj7JwbNQmVqN7/rs91CtMQZNFRRr630M2LL0U4XKiLHjsCnW9q1HQaqFRTlvS4ofn/I4m5SsFbPcWMpAr9FN5Hu22rBkYJM7juOOrMImd5ynBX+we5GPd/e5GS/oiTzOC6Js2aSRk5C48yESDw9HlMCxLA3UWr/OVz7+HX7phU/zD975Et9+9xaXlx1p0xho1BgzqbJ8W/P3zRiAI6MYsVt9jWqisbUWge5RS3i8MR/juewwzRiHpbNf6ubga2S04JHchInprGog0ZANQBPx+lDrOh8QGCzcq87RTlAIA/tr4ej8lOjr4uhqmKAUKX5hMxcbmbrmu1zf1hWf591HizEY2ywqQ98YCw/zYUsa2GQDt8BqqPYZtYdd06Y42enIWlpP6AyeDhzZ5YZFGFmGgV1uyRJYhIGNg1mtRNZhV59zqy2dJBIFxJoaR4XFnHwdbbH6KYrU7+SOoX7H5+nwH2Rcg1kfcrTi0i1V87CqI07gUZEfMgFJUk3i7aIrzCxJGYbRdO7YwoPIBJTNgaz5EAEJVVKo7o01B7CuBK+uKoR81EUrBHIbGI9btrcaLl4ObF9QhpsZPdrRrQeOFgNdk1i1A21MNJI56basm55WMqs4cKO53JPcBbHOa0noGLxLmlScDhkq0FFYPInAmE0OcBx3U9JH7OsX+7uXN/nmk9vcf3xMf94RHzUsHgTaM+CcKVmCg6ICZl13/5zUYlxTxqUOVsCMy0hcB+KghFst8TLTng3E8x2yGy3VEpAh2Gfon4NRYRVtg3UAgqCezIgKYZCpe+6abvPE8gm47EsEo+PWZBAHrQaPhff3GEbZA/ASWHT8qIQshJ2BYYiijZAXZg6vUSePGCxRY7s1IDHlUOWHfijGekjRrNxSyxCjsySUy6GljYlVY+ckiLKMA03IdGGsgGaQTE4LGkmMs9SWINk+/wDb3M7qxcyA0BPMvLBuSK4ewyztMPomZquNJ8nYpiGIMhAYNBhDi1yNesvXJs6+I1mVLGUDI7VbV+js14DW9fh+jSi4dOsKtlQBNfb8svzHPLuNaa9mqT95St71+1Sg7HnSwpmkcGqY+LNIfbZ98OqpfcrBWlNAH/fPGFeB4dj8CNNSoUvENjmYrjTB/g0oXRxpQ3KQ3Nigc8mdiDVAKrNGA0mNg1NAhgJ0FIDbDMdNbt1JKltM2pCsmaKRJ+OSR7sVm21H6iOyDTQb67jGXvb8veZgVvn3KamizE95iQUXgnuMyNJYwmGXCb0b5Faz5uz7hfI5OFLi1VK9PPz1Ypq9fvACogJa7DGDcjWKn2SWUjbnOgO2ZsBXfa+F/UZZd9W6z43W66a+Z7UXHoeIBJ9fvZlR9kqmhLX1c0hCEmdvCebjIZmmpDbXpokBWXVdF6XPzZ6xbblOol8Vo5YrwYCjwppQ5Cmp0uGYS96DA1AjZj5cmigifo35bY0XtnNwa756mBJU66a/PO91uuH1KOPdPLJ4V42RNVwNZJWxF+QhghytkRsnaEk4f6YlCdN6U57rGUDW9k7LW39e+PM//Tv8rbv/hk+09+nIdd+VCNwMlywkMRC4HRKnobuSWXI4DgEsC/XJMxZUYKuJrSrL2mhU0FRBqcNRGpFzptb89wKQFRBroyZDXIdYwSqTGypzj6DSTG19fTzy01VCH6LIzEOoHIsSvUYqyoUbYcuTvDQGjE5743XTs8kdQZR1sJ/fGk8BuBkvyAQeZbc3ySNwybG0H0q6eTiiBG7FNTdC5qM3/gM/vf4Gv3D3C/zjt7/AHz68xfa8g9ERm8Qkp2sgBatZgoBkYcDWPVOrBMZly3gcWaxa2vvnlnhoHSeXHQZjaWVbmMTrB238mlerfwMYoIX37bE1ISiuTBFUi0WLQFaCSwWL52Xyta56FTvpQMv3wi+ropQp6pxSZ5p9jYFV2ZYafw0msoL4hREwS7ZkToopBS7GRTVsf5TWbHPLLjc8SMcsQ8+ZXnLjitpjpwNnWbmXVizlkoVfb717bu1y42mEEXIxgrfnWATzQ24lsdWOKDt6jVXBMjiLwrywgsuEt3u1VbFtKRr6wAQ0J9TrqkDRgX6v1+Q1mPUhx04h7sRM6YArzdJLamAockS/T9j/sGQYnRY8pSEC+7LCQyCrPFeMSNehi6mjUju1BxKH/Re9YkEqXZcmkFYNu1stm7uBy5eE4UQZT0ZkPRIapVsMLNqRm+tLVs1AFxJdHDlqeo5izyIMNsHGnnXoKyhVJuHiKbWbeSMlhE1a1E1eGyaQ0B6j1Y+qlUSUzO14zlHY2e+nme1LLY/SEffGE752+RF+4/5rvP7GbbrvdCweWbERRp1YUKXgmBnJgwE51UNkojQYMLSw7nzsIawC41GkuWjpHvfEsx3s+gpaFlmpREfpiywjCpoD2ohpqsXArRzF/UjUu/XqaL/N+BoEVsEm0d4Bt1YchPKj1AKSTu+tMtB8gpUkHl9rnRBJwZIsBDsmN4QvK2z26y9nA7bamFi0I5YIZWwGxVh1XZMYgSYmQjaz+CDKIo5sxo6Ug10zztxaRTNw2c0+e4AmJLIEYPC0jqlYGNRMCiNKr+FKACnXrcc0klNge2JNTAxuyNnK5F0weKHQOqAVD57Dvi5avUxKx2/uv3ANaF2P78cY1b6vkmdG7FeysmbAVpFJHdxPPLBknoZoTzkDyg6BrPJ6ISBzEGueVngI2Oy/6vRUhxLE4DHdS2dhHRv1Pi8ytBkJEJtEEzLL1oDwKJkYMl2wBKxGTDrWeiJWYXcK6vIyK/1HT5YCAxMGjRWoKoAWOJDuAIdUAEJZS0/rZr6CMh4FttpxkTvuDye8ubnBk7M18Ukkbqm+UhXomV7Az8XBKZL5nex+KRrKEZIxtkIbCEMgbhPSjzNvLZ0kpMENbiXsd5xnbCscoLLNdjH/92PN82vHgS6ZRZ0fAHEzBSGuCq8elvV5sz1fkTGqLW/TpSxUBp26vF01ek/QwnYEZ3OpTIw79+cKkp39FPzzVIYcUTW23ujMrTZYB3lwieEEHmUUpSEzYmuhFqa4F98WBHI1gPQ0L8vWqtHTEo2VZR5ggqXoBn9M9vMQDwCt8rxQmipSr6NrQOt6zMc3hhvc+NYW3e6eMnuXK64NKfP5eoWcHB8oOZ6uH6Z5u+wl/Y9lzzlLLLx4peXtn83893/iN/gbN3+bV+NjYGJsHMlgey3JrEXNRyosaIjP3TMNmjjPu+pfOqC0GDtx0GzMfQDNRHBQSWsT0iwnvIDGHpPQ6nU1B8eKmVH5vez5trmnlcBCAkNhZ5WUQ/dLXMx+j4gdp6/LBWCzxwmD2t+2qvV0H4VsBb97Dw0aOAmXJIQzMdLAoOYvuNWGRRj2rDr+cHfHn3/kKOw8rU65oKFPGeJgDJ3Qgn7/LDGiBE5lxQ+3iVdOf5ufWP8B//D2V/iF73yWR4+OyOJ7h+wNFNGqDkkrq61CY/VH9GZ7WppqJUchdzdoH26Jjy8MmEtTTS2+kBTpoUC9biXaGhYGu09uQ22e0AQjdPn98c8hN7ZQRTeMV1VyFEKYZuja3Mm+tJb60de4uBW0tUbkIaM39uV7ZGutRgd8hOp7TOt/T4KOgV1u2OaWi2Ayz/NkrKk3+5tkFV6Nj3kp7if6Js08yiPfGk95lI54IV6yrtdVQ9LARSESSCAHM5wHqm/xQCQRzMMtt6bS0gBhV8W3Ji1sLawgrziS3lQ1KrRiCZ/LGT5SvZPJxBkD8v0yMufjGsz6kGOrkeZc9r5QT40CLhUT97JglB2ceielHwyVFGdUZUX1GSBWfW5BlktjYzXxaRDroLOiV2125hTiYCDWcKNj82LD+avC7raSjhMsE80isWyTS8+Uo2XPuh24tdhw3O6Iopw0W1pJHDe7WliAobvr0JsRumQsiaFhLTuOnLpYUhH6GP382he2k4mhZSknI5u84Gbc1Metw44TGTgJmU6EpI+JImyPf483bq1461On/NrFJ/nVB5/g2+/cJr21ons30FRZop2Omhboo8SX2y/YH/3jkGz+V3GAcSk0CyGtAvG0m5ha24E9TzWPMNKWWQpHQFthvzjwbWpWQp/N20TsEtEWYp8rCGXdAa0+XRpAx8kzBfGkqF15b2JdiwRkqd11qx8CKYC61p0mI7EkqQlpjGjMlrah1uWOLv0s8kSTJAZizKQs7IBFO9K6x9aYzey3z5FRA40XISHp1Kl2psVlahmdqbfLLUMBjiqg1fCUYdje5R2eCiKwxwvb3DqPStnSVur7PHkqeanhCh1Pu9FZN21KB4mzybl09a7H9fh+jJHi6aRcCWTBVIgUE/dQEAL8e68GYpXmyAz4qp3657GxmmaSFB6CWPVwnrPW7AE3zsJaRIajQH8DxhVol01CGDNNNGRFgLYZaWNm6U2TKlnG/i1+fGDyslbSHhiVNRBm61HDxPYFKht0YmiZF1Mk02tk5QtEgyVMdZI9lh0yOwIwdg84WzWc31ry3eEm393c5NHFGj1viJdCGCdwC2brCrPfp57J/B8DfyJIdlPaJhhra4wTU6vsMdR35p7MWOSGFScL+2tNfSHFg0e0NnA0OivMj09E3GSXWRqiVOAK/DgTNTCyAmg6gV6KTICXA1O4rFDKGmUGUg6cGSoWZAKxcrbbcgYJmeAFWaN5T6ouYmy8nD1tkuKOZSBmEPvPmF22Bo1arqapeZJL1fKMIc6YOhwKjGqvFzQzigOqOvt8y0fiQBe+hfNTUCVKpeicLpMC2l6vNX/WRyt58tq9Yujh3B4CcnKMHK0nz14RkxfOa4cCZLlSYy4vLIC3eoJBagNPPtnx+K9e8r/+4q/wc8e/w1LGqrYwydzIaRiIAicSWIeOpHplsnUZBcRKKA9zCU+CrYr7nMJWcUmfgcKDwlYDUZQ1StLEOkTbE4owaKKV4I0LB59mrc/ij7VVCykqSWy2D7Sfo0iVFm4104oFXxVwOaF1vzio7SNb7FQWxthSDGjqRHiUzUPrIofKfEkY+7QlcRK2XMRLBm0YNHKRF6ylNz9ZcDN9ZRkG7g8ntJJ4uXnEMg4kbUgYCy6nxFoSMLD2+fn72XhtJXI3HvEXQuITd/45P370TX7+3lf5rbde4fJsiW4t01WSmNy8AKINZLEGRXZASxuvTWMgty1pGVgsIvHhhbG0cp46KUV6CLYONt5qHqdrX5tAGPwh0WZXbQ3QYsy1oxCAFKwuMZDKrGt0tGaXjJCWUiWH2ddo3GsSL+9zABkc1PJFWLItjjIHv/DbgdzZGi6j1HVCk7AZWy7ygpvakAhkFS5Tx9mw5DvbW7zWPeDT+uip79LD3PD6cIdtbnnUdqyl3/t7UqER2w8lQpUuRslETzjcqaXZZxWiWh1/llechEuXPppJfDurxx7nBUv/PYpOvlkSiAhbTSwl1u+asbu+dxLANZj1IcdJSKS1f0nmQJZmRJopyXDwDzcEB73Kzxl2/eSFBXZRz5lYV1HaxQAyWa3QZefmu/KUUWM9nDnrCvYLoVmXJS8j5690nH0ssH0xk08H4nKki2bI2rYji8ajaEPmdLHldHHJabtlEUaOmh23mwuSBm435yzDQO/Rn4U5BdYtWMpAJlQqYiDXVISttnv/Jg0chR2BzDIMLGXgLC8ruyuibiXn3Q6E09jVBepuSHxO3+FHu7f4W6e/wVsfO+XfXn6Mf37/h/j9776IvLOgexQIPZP/CdgkEqieVHsSRV8AwmjdgwJspUWkWQbGdaygVnPeI5e9fa4pWWdWtaZBlo8mt8HxTJOoiE7ssae64Bnzg/FjUp06BOX4cuPdj2CdgRxkAu1m77N4psho0kM0kBfuTZODmcU3GbKQ41x0B2MMpErm8BTEFLymEnIINR2RBvrdghgybcjsaGiDdZhGtYm5CZllHOjHOBUkXryaAfz02uXniBlmXjUC4r4F+2h/RAtERa/RNiv+AQ9Ylx+1DSKUYsLArWIKX26vGyANnkp1zca6Ht/fsRBFy/5kPn8X/5M5G6vcp3Qey99Smrywypgzsa4Csvy5pW2tYVIAMpmDWFesNVfV1ntNE6E/ifSnwnik6CIjTSY6GzTERONSBBFlGUcWzcAyjEQxRtYq9GSEVehpJJPUCpPCnIICSiUHTwqEMfnljQdgVkboZKSYhzckdtpUJlaRiZUzFRAWEiqYsI7KnXDBi/Gczy/e5Px0yVvjKX+4ucXDJ8dwEYlb2QO17KTYOZvbSu3J9crffS3KUQjRmLWhDcgYibtE6D00ZuZ5Jlines74qUlKM0Clrnvzz24GQhkdSGsBi5aPX2uS4550PzmOVR7rz13Wr7pRb/x5nTmms58rK7h4qVSQz55Is/iWKhSrEzumkCsbuAhKAyYrzJicJIg1TVKevDoVNS9UFdLsRBRvxrnX4uEQhChPA1pSjhcrTO0r6UWLA2rGLCvH4K4S/hUul4nC7MoTP83XINb1sDFoqEnbh8DV4e8SowFZ69U+kDWXFxaVxqHcsD4Je0bvaRl58IWW9V97h//DJ36Vryy/ze24ZdDAhcfFdRgbKwO3Q+RYjFmyCFfvmZJmdjryOPckHKhiYsqX7+LGy6qHnuy39kbHJIUyjCKrsmOssr4yikWEvWaRFVqDMiLVJ2sgVSb+HvAlU3p7YY2VgIYiJwQDwdoQqq9Wi7DV7POGvR8zhZ+eu0WJMrqh9sCdeM5FXlRWTZRsjVkNxqKRWMOKztOSs7AiSmbpRvIZ29cOCA9zZqs7k3d+yLTDq0YrkY81x5yu3+Kzr/wjfunmZ/nH977AH9y/w+XZAt2VgsoBH5eipwDi9ilxJ1OIV4DUNYzLwHLZ0L1zPpnDj8nWkehSwiDWxS9S+xFT3xQmckWbgNEa9uq1l0YHoVQmP1Cs2RPV1hwNwM6UMSTvtziuVuxdcjP72hjaOKUdeu0WnHgQeqqXccGDZoKgui4nX8sG91A7Gxes4sCQzaz90CM4o9Vjq6wxpRm/VZMpjt7IqSFXfhkU5Yp9lqnaAvXa1Jo9e9ul1/0ay0zk7Y3sNNKSQZJ9jx1cnn+Xisz3g4xrMOtDjiu/9iIGYsVYi4caWZ7zJDscEwyD+WPNDd2L99ZhulT5uwQzeF8tzajxWWkjVx3oDOQq3RV1SeH2hY5Hn45cfDQhty9ZLIcqJWuiSTi6mLix2HpnPPPy6oybzYaFFxin8ZKTeEkriZNwyQ3XzbaSOJKekzDQYrKt7uAYy741QKUKF5B7o+KJH1Q67i7s6DWw0YZWJomYeVtoNVe0YqNhIQ3HQfmIJr7QPuQnF+/wn974N3zjtTv8u8vX+CfvfI5vfvcu8c0F7flk7luYTxXIKh0E7xiX1EBRA7VyZwyDMAhhCLQnkWbT0Z6PNBcDYdMbuDmMJkNcNEBjVNjRuss5mPTPCojZNaDYxrcUCWL/T5JWKUmuHlxaZOmk1ounUYnJGVoKuTNdeukYSIKoxiBIo5swLrLpyRsB9znRaFvznIW+b5yVboBnralzYBys4AhBGVJkTAZQhZBJMdOEDI3JEotvTZ9iLRwK42IztgTJbHLHoIHijdD7FHYadpyGq40PowRWdHu3WRdt8ssydtZ8Y5Jdzlo2I1I3TXsbE7+Gtz4pmxyxFD12LOkDasCvx/WYj2cu8cHYuIVdtQdsFeZUvgLIMu2W/Vz0XocvKG7w3jZojNP6MQOxnlpr5r/uNU0c6ArCuI5sbwv9DUVWI7FNtmxKdgq/sT0X0dYVEeW46VmGocoJlzI4MyuzkJGFWBpU9A72oqToyL48uBxi2RvOmqgoVvQ0jqNEYARWmkgIg3f5CxhWwIXyePHufZRAJ8oJygvxkleaC3548Sbv3ljz9niDb17c5d0na8JZYw0UXz8qEOQ/132sYCBPaarMmiy5zO1ZyJ0QhmhM3iGbdUHKlTku0TgIiFT2lfloFTP4/bWmJEFVySFWCBQAqxa7FGDON/aOwIj6xt2Lg/qfzN5zNYj3FKcA7txfPb4E2w6VrW9hhoMVFaiZ9IMiwZMIk903+/pjwJWBUjHbcyQVNDsjz2fuQU1eb4WCyfis8LT5eyHJUtGuYB4GEZqDDb0qe35ZhQ02XYtascF62nn657J6jJodNJvjg14Q+Hf7Wm74Z3O0ksmLuJe6fNWQGAmnN2C5mEkFZ4AW7Cs7ZmNeN8zrjeGk4d5XGj71H32Tv/Pyv+az3dvcjls22X2ZMHnuSbD86ZvBEvGety/a6cBWRx4mM27fqLD1ojjX2XsCe7caWEqiFWWroSaGxip1ymw11z1cK1JrjnMdWBBoibav830mah5YOx3qnq6VWBML54miRU5Y/LPash6gJknUTHBwLKMOkjWYiMseuxbDPJJjLCduFD8AN0PPViNbt1fZ5GhNfgeqCnlg0EiIJqMeNHJvPCGp8HLzmF4iR2LPA7AU8xU7ITOS+H4ztMo4DSt+uBt4pfkdvrr6Jv/1rR/lF978HG/dOyXvovlpRWtkmNjC1oMwGDBY/BrNEB5UAhpacnPC4l4kPL5wQGu0RaY08sG6AiXZMGHXjQavsQRt4+RbPWpN9LWQF3W5oUkUNYqtKYOn1QeQrNUaBrGLqzSJCmtrzsIuHloV9PJmjzZa/y7l+ZPt42QUcoDN2DFo5FFeV7/HPu2vOTvNe+ymPJMARsyzzgSstu4Vz6yM7KX/FgVNcpAUJnDLiAWrentZoNayY9CmqqwA+loLCUu17/IktVUW/ndjSsYPBGhdg1kfcmxUTGY4XzzETL91HOsGEjBWTtPYRm0YTVaYjalDmTwKE+sqWWHxxlou0dUC3OS9pOHtSQkPGVhzAMufyyRqwnjc8uTjDU8+CekjW1bHO5poNM1lO3LU9awa8yk5bnbc7C5ZhZ5FGLnVXnAaL1k6ZfFOc26Tqgy8EC+5Gah69pZIK0s+SNLboeHqoekjFJAh1pjcSfL1dAcm0LCKHXej8vHmIT+9vMf/4MZv8puvfZR/eP9L/Nrrr5HeXNNcSDXnE49nFZkKjznIpRgoZEkXBgiF0bxfhpVJadpNQ3veEi8G4oWBWrIbITMZw2eTeYS6YWDyznIfEeI0gUIpbmTWEZiSn4BqWFii1stjUpqqhdz65nkEEfdRyZAGY2nlTtFsC012nxIJQg4GTqkGu5T9ZbOLv8VNq/u+saIieGcjB1LMjDkQg/3bxlQnzyiZRqbWfJ8bhhzpCZ4kUz7PkaXkGhV81YhijzksapMGu24kEzQw4C0RDURJNd0w+L8l3aotX7EZMGDAq3U2Pojm+3pcj+eNQZlkhmWIG2sXCXOtiNU3b0CeyQprGInf5yoQC+xxISBNM8nX50AWs7XmcN9xVffe75e7yO5mYHcT8slI2yWbO7CUwS6aL1aREC7jWH0RV7FnISONb57WoafBvLKOZPDYdZOUGWm94YMkveWDQlBRVCbgCkrTJXhBMm29Dh2TjK8VaCSyFuVmuOS15oLPL97irRs3+PrmJd54fIqet4R+SrItm9v6X9n4zn7WOIFFEss6JEgrhE6IQyD0gdDvg1oTw8rQEImYbLCiRloBMwPVvEEhZS5nD8DSkhhVPu4CZM2WbFFnGvn1UKWDs/UUdV+SZgLtUJztZde2gbW27hRTebD7VOSngF5+3IFskvlgbCsLnokOklKfMxTKGFNAQPKis3yqQTINz5/fgxSD+P3rTp3pJVI8rmzzYAoTraCiXVslgdPAWH9bfk2V34tY8hq4uh42vthdcv+LK17+tedIYWMk3DiB5WK/PpixsipQBU956VYgy71bNQr9zYa3/lzkq3/pa/xPX/yX3IwbWkmc5ZYL7Wqj8CT0rIX3ZfK+yT3nOnCRlYe543borfGowoamNq83ueE0mHNVJ5b8N1lRGICcVOi80d2JeVSVb3A/868ykClPQBbWsCzhPnafprK6ltK42XuubJgFJidcuIxq0Mxp6NjpWL/Dxu5X1tISRVgyBR5lrAYMUmSS9rz2c2TpVitZAjFO602vsYIa62CAQhsnuen98QaZwAvNk+ozmVUYMLBuo4ljnLnzA2rALqTlxdhyGgZeib/KZ5dv8ffXX+X3H7zAxeMVehlNZqfUdEON9plLUHIH8bJYq1hQTG5b8uKYZdcQH5zB4PX4mExi6Anz9UwFsUUPI5moYveZ6eTVk35VTUoYtC4Nrmi0+ickqetxVck0Mql2Crg1FJWM9xg9vbCsWblcjNkAseJ9LL7Xquuswi417HLr4FTDk3HBqLGGnmxzS3+whwkEBr/J2FKpXsN9AbLUknbLenIxLhzcMuDqVrPhTJfcbi4wX6vR0+Wj+0E6Q9ATDot3ViLQkujILMSagqiykOm7N//OWdPn2jPrj3zcSyu6x/6LCMXvSgu7Kkb7QgC0rhHpB7Tvn9a1F8AmzzrsMMkXm8a07YvOOuXWyn4axHoPAKu0AYeTlrPXGp78EIwv96xvbDlZbT35R1k0IyftljsLY14twsCtdsOt5oKjsGMddixl4EbYOqAwctMXq3WIrGV55abPaMNDBaJKd2OwnrFp0UVYSvOUXKt80Uw61j5dSF0x5hG6ybXrwU0mrTsTOQZuhcyn27f4udW3+fZHVvzC+Y/w3977DN986y7pcUtzHs04fl504ICWujdVYKKouoxkXBqwNfQWL9+cBGLf0p4vaZ/0xCc7JCUkZ3QIngQzMe20CXs018q6iEZLrrHn4h1aVUgyNdiG0o0oj8cWi+AFSgt0VhSF+Rzo77HZCHkQ0kLNoBEvfhq17rmzyHKA4m+js8NUT5wyyaEXEA2EoIw50IRME7MZyOdA54DWQGTIsSZRZR3N/FBbBh3YOkV2m1ty3Hif4b2ugf3rscgMUViGwUx+teVopiePlIRDp7RLts6nTNT1uPecEz39g0zK1+N6XDUutCVuZzeUrniNoXYZmaqxgGHyxzpMP5y0WuzxP+paEZCuNTZW9MklTHd4CsR6FoDlP+cusjsN7G5BPk60y5F1Mzg2pg5kjazj4CC2SY1XHhrSykjrDCwDFLJ34HGPkqsLo6zKqKkCUdlBg1zYMA5+NUwywektSP33/W7qk07QRzmrE6AmRAKdwFKV2905n2wf8/ik4Rv9i3z74jbvnh+hWwOhJDHRxnwUQKvM9zq7zacmM19PQnZQS1Ik9g1hlwi7ETRb91kECaBjmD6+skeYfY5SgC9sXTH2mLp57wz0wgEs1aeXZcHCC3x9LMdaTlS1OxkwppbLS+qJLOCX713Ur9uy1tQfigzRn1MEu5ZFycmALHXGVw7igJadyCxmpy6iqEtWRyJJB0ZgxKTwKmbJ3D5jbp9fA/NRz4pix4N5cHWzxdkyq6RKD614t9Of/N+w95z2POU6ux7XI/beyLhiVCBrtaT6XM3ZWAXIOmyGB2rjfA/Iaiyx8M2fFn7uZ/4t/93bv8WrzSMiyhNd7EmUXnZ/23WIrMOzG4+DJnY68DCPJIWNRm6GvgJUC0ns1PeFKOswMiAmDWaynihsemPqTiE+SSeT9aV71xmwVAClwsBsa3rhQtoqg7LzMhlWl8e0fq422RgpZX4oRvCtBJImltI4A6Wt0sZSJy0IDCQWAjsduWCyuFgIJBJbjXRkBhmdLWxyL4jcjBs22eRemwxrSTxMR/76ibO0ImAhKigcSU8isZTEWc5c0HMShNNgpIMfVIDRQlo+1kRO1n/IZ157i1+4+UX+v2/8CG89vEG6aJGdNeK0NYDI6pyy+GHXaQSw4KschdysWCwi7TtnU9phypSwtclHy2fKLF67+YXhxAANUkkDQa1WqTaUrcn6xRsvCa/TI6UHTsaS4u0ysTpBRKx3lHHGGMgwNaXsha1ZWdjL4mBYDUtxdlbKoUr9CqB7MXRWO4XEWV7525m8p4qHb3bf4U1esA07MrDJJvPtc2Qzdhy3O0Ka1q9V05s3F7ZeltcuiiswttY2t+ZhLcaQxF6RI7HE+hASJSHaNwqT/zDZ93LFO2sCVN/vuAazPuQ4CT3DDdyzKtgH4BtFz5a2L5MDWdr30A/7QFa5yp2NtQdkldF2yNHKgKwm7rGxnlp8RPYArDK0LEBt4OIjHQ9/WEif2nLr9IJVO1TwqouppsvdbDecNpc1ifB2c87NeMFSBm6Gy6dkg8fS0XoqSBlJMxn1RWrkQjOPc6yyjUEjF9pVf6y5f5aZvafpdpm+ZC2ZTjJrgZPQsJD2StaXHUskaaaRp5laZRi4FfhI03I3Jn64+zf8ndNf5zdfe4VfPvsM/+LNT/HgwTHyoCP2ggxlIy/lu1mTKQJSTXCL7psVpJUwbm1C7I8DzWlk8aidzOLHDMk/fy84raCcgZb+szYBjaF2x6wrgG+WQTxqVpJWMMwmyuKRJkbUyGrdkINLLqSpYxCUakyYCGhrWpPiT6JioBXiRvHlmstS2hakJIAlOmoWQiySIqOflpcvEsMYMq0K0eWLJXksO+0bqAbuZ7llIQNrSVcCqCVWuZgimiln415Z6hPwdP1t/fmjjHVzVLxXDv1SytWW8ARDjJoQiYyubr/unl+PDzsWkkgLJpn6U/5Y/sV3IEvdn2+P5Suwz8aaAVllxLjvjzVjYz0XxJr9WO6nURiOI5cvCPnWyGrZ04RUwavoxtutJJZxYBHG6oO4Ci4rJLGU8SnZYOd09Pl8n1UraDVqZkDZqgm5kgoZMygdZ53wAho0bhhfb5+9n/K9bwXvuIcrWV+V1l/m7mesNcGB7hOJrCVzd/UmX1y8wZunJ7y+u8Pr57fYbDpkEz3Bkukzm4E1lUwFFFRDI9BaMyWM7uXYCWEpNNswmcVntbWmPCGABCeJTxt7P2CqabxP+BqKn6MYAz1Mx2CPZdqL+HaovNTcpwsglIYMmPeII1QZfPEp79Jkf+TZsZTTMkP21F+0Mq9CkSU6OdHPl8o0t6u/xYhdvwlrwJTPsBi49xpoNLsB9dOFXrn+9n7XYAEO2Hoxv/5GpV57Ze2Tp98ezH6vCYbeABRnlIj/73r82Rz3Uub4javDcA4ZWfW7XcCswr6d3XZo+n4IZF2+0PHGz2X+zp/7Vf7Syde4Ey5oJXOWJ7CqI3ESek6DsJTIcVg+8/gHTdxPl2wULrSpe63CSCoedguvCXo1k/Ri9g7szeFzH9VBzRw+zvbWkz1EKb4DC2kYsebnPNCn8f1cSawO/v1LlII+OagVaMEN36c9pzFO7H+rWeJ1OcLG54QKcBE4Ka+vbi6PgiS2RI4YarpcIpCzrW3BpSLLYKlzJf29MEHP8opv9Q13mnMGaViHHYmBrUbWMkLORHasQ2vgnRZA7furOIgSuBuPOAkDL9/8db6w+i5/78ZP8K9f/xj9poWdrX3ga1pUws6YuwnzldLooTgi5CaSmwW5iyzePkcuLt070o6+sthn172ArRlRUWfzGpjlTK02mM9AUFQCoZ/kjmBqF8lamWKpcwDK0+cryaCU+7VOnzVn1BU8BY1RzEsygIpZxZivltVYl0NbASVLrrQHphzIITvrqlyP4i8banJ7YRwPTNYpo0vqm5Dos11DreTqU9xKYshxSvMVY2ERjCFvMsTR0jU93K2MrbachC1br7WWMkmDTYprI6HgEkQ75viUIut54xrM+pDjZhgZjnyzpXlCgsF+98IAVfPG2u2eZl5Bve2pESOyXKBHK7RrJ8ZOSSyEiY01Z2IV8GLGxNJG2N1qefSphrPPDbz02rvcXV+wbnrrgseBO+1F9b9ahIGXmscchb7Gux5J78WGdURuB6PaHgJJBcDa6bCX9LFVGBAutKmsGoCNLniSlhyFnuhd0UFjNZQbtKlGcoM2NXFqm1uCKHfiOcvQV5Dtdui5HSNrB9dgKjTez3TcSuRUVhxL5uPNfX5u9Qbfuf0v+Pf9K/zD+1/it9/5COf3jwgXztYamHQAwiQ/LB10B6NT53RTp5OGUeiPW5ptw/JhR/fYmVrDWNkUxdCzULylSEtTRGI2UGsUv22/4KxFpReWxZ/EfLVswg1JiP2sS+CdcNTxWe8UMPqbE8jZ0f5YihKpLXItDC2nQOsokPy6jSZVLHt9Va2m8TFm+rExmVHIqJohYcS6BpKUXW640GmjlDHDw4vQstbRfQ32C9ykuXbLpk6F0WvNDLiMxqmzMtsUUPXjqPnxJF+BBrVuoD2vVveG6Oeo+lZIrhPztW/W9figYymZ3DEDpLTO+UVWKMU7K2f3joCnAKt628EIllaoe/5YTEUNs6e5AsTau08QxmVgeyvQ380cnW5Ytz1tsEZJGxKrmf9VI4mjsKNzIMvSAq3AD959XAk0VwBJBcCy8kPdo8hAgtKpL6wasPVjq019/uxAV/FEstTDXH8uIMiotjVch55GUgXZVpJYBXFpewFH6ol6z8+1NFG6oJyGDZ9qzniy+kPeSSd8ffMS71yc0G86pBfb3M+BLSp+M22Qy8cbbdoVBVpbc1IXCWOguYzErTO19gIBkrHB/LO3JoegGqzLXMFNnfxIpHCOZFoGArWJgcvfUXuMbfZnl1DBqTJzDZ0TAV1SQqEllY6RPXBia/nxZ3zxtdt0fvoFS8lyADW55NBYUoI4YpisaiepFQD1PCP02tDLSKuZJNnez+y7kNX8cuxQpuTFAoyl+n7tGsu49NCvvSqGUWhlgkJHneqgul6pA1rFyLge5ZSkdj3+bI0GJbdPf+4iMgFZYcb0L42Rubwwzn6eA1kx7EkLNy91vPnXR/7nP/4r/MzR13mtecyZtrX5Z2bjRV6YOAmL56YVPs6XnOXEzoGsYvGwlERScbaSNR7LvqsQagLOFHEAq3dvw4Kvl8K9fJuLV1b2nw3QihVQmjcgi6VJaUweppkaA980JUUuVTy15gltgVCBr9H/DUwpbmUUgGvODLNDGZ0RajPKvbzy92I+V+uwo09rAwwcyBq0YRGGCmSZN1LLOQYoLsNQ5WBLGdjKyEnoOUvwMj3Hzh4r4wexh11Iy6sxcrq6x6de+Uf8l8df4Rfe+jzfuXeLtI2wc0BJhdx6+mFTmh/2+7jCQaZAblo0HLN4RwhPNgZoJTWwVh1kmp1vbRvDuoLV4GHI5MaTDbMnIROIfTbvLK/laoJhxFQnjRJ7mRLkHaSyF7E1BgE58NAqazd4nTWVG2ZvgoFaAsggjMmSBk2hEunzFFyTVdjkzq/tXJUhmUx2eeEg0erp2SYiqaX3BlW6OLJNLcSBlsyokUUY3SOryFgteCdoZpdbFmEAbcgEUp59/x1ELUSBG7LjQhtOGLhQ4SRY+ueAsqB8j/BjvroR+KxxDWZ9yLEWYTzxwiLNJjlVkwUWIKsfYOifBq2CTLfN5CKAAVlHa3S9fEpWuCcpfJ6cECoba/NSx/0vCfEzZ3z29rvcWV7QhETWwI1mxwvdGXfbM2dDjdyMGwOJPFb3REai4EkbFid7IyyfmtyS2sRfJ2KoTJrom9oTGeiwRCqAqIbmFpAqaWesmWrcaMVNwuI/06wwQeFMltwbT9iqJXqcxg1HYccLzRPuhA2nYTDDydA+d0E9HKXQWMSWWyHz2fZN/uLq2/zOS3f4xbPP8asPPsG337lN/6gjXsTqSVXizFEDiypQ1BkLSkbqJj4thKEXxpXQ3owsH7YsHmwJTy5roSGFtRdtUZJRkMaZV6MDm43TwOvGBGROC5/5kIg764qCjBCzmmdWiU8qBYZPzAUg196JHyoggdzlqYWcqV0OEDSp/Sx+u3uOITbpqgohKhqydcDbRIzZJlYHtaBhNzY0rv2/TK1FrGOdtt4n2K22bLVnq4mF6B5gWYqKq0YBS03XPXU0o3ckEOh0otTWJEWx44wyLYvlNWuRPaOPl3ENaF2PDzpasLhmZQY++ChAFkyMrEPQqnynOWB0gQFZbWsbuwNZ4R4b63lywvJ7FIajyOYlIdzpubO6ZNX0xn5BLPU29qzjjgYzG12JSQobMlEsmdaXRhqBBjPKPfweZ5eqHzJh/C2Bmo9KVK3fO3E2VmXq6NRxzIiHP5gQcZwhRMVsdacNF7lj1Igidb08CjvWMrCQzFKs2998D53swtZqJLL0Bs3H28fcO17xrd1dvnt5k0fnK/I2IkPYr6lKvyPXw7VpXKnSBhTzO0xikedLobkMxM2I7IZ6TRQjeC0nUMTWCxFnREndtzC/NkLBV2f+JPVa9bWmAHGFojRbayphYs7QKmtH/Vxluo5nTZfy+tM6xLQvKrhv8ERdX6MkOIMql4QniyQvQOaQozOzbM9jPlrCqIFRAmOdy6drUtGnviJlFLBU1ZgSzYzxm3w9jc6viuRazEe/LieO2NNfR3WWx3xcA1p/9sZOg6sBXHLuTF05PqpAVh0y+y6X5nhxXTgAsgorszCyNi92vPk3Bv6zn/hFfmz1LV5uzsgIHZkLbVmWKDZJvBB23A5NBYquGpvcs8mTDyoY+ypjhu9HMrKrgK5WcKr1eqSdAVwBKqBVvj8tyrIsdbPXaClevhPLN2ne+/kwZa0ABFECWdPe+zJwze5X9n5zoGohzZVFelNBB/seN74ClecoZvP2GRem2ljBhaWMXGjHjbCl1+hSs8AiDHRibOeLvLAAJeCd/gab3HEct7zQWM23DjuOwo6UbR5q2RLiwLqcK2eT8QPYw0YJnMqKTzc9f+f01/ns8k3+3von+Pdvv8zlhSceJrFm/cjEdBJhFCW0YvWL67Bz25K7E5ZvRuKj86m29gA2IxrYgiMp2XWuioxGDhC1ppgMikYXvoVi8C7mjxWNFBBGm9MLWzk3FjAyZ15JgDAquDc94J7VTMCW12ICpIUzscTW7Oq1lWBwX7SkwpAtrXc32r9Dirw7HtXk0DwDcEuA1sPxqNY9GVuX+mx1loitTUGUPjdk//lRXnsjMrEMA9mB401amG9bxgEtKisrSfm+ZE/ZHLlwUGtwQsxO7RiXYt5y9n0txz6jeb+PcQ1mfcgR8QSeeXGR1dIG2xbG0RhZ/XA1YHUFkCUi0LXI8dHzZYVloSlj7nfhCw9RGI4aHv1Qy+Mv93zqE+/w6Rv3uNFsWceewVHdu+05d5sn3IwbljJwFHbcDDtuh8SydptjNVZvZl3o+SgxukBFWVsRsg5kMQYLAkeSWWq2DifCReooiXJJA8tgnkUlchamzeCNcFm/jMvYV8AriBLVFrqH6YiH6Yi3xlOys7zWoeckXvKZ7i0+3mx4IT6/U/TUZy2WvvKx0PFSPOcnFr/CG7f+FV/72Ev84uPP88tvfoJH7x7BeQujWPx6hly6xqV7HtSQD/+ocmvJiWkpDMfCuGoZTiLL+x3tu5fIZmcViepUVIClZBZ6+CwpUxzUKga/NFinu1BZxToJxesk9g6KZaZudgVC7WIqvlxhoE7qll1g5vAaZq3jVLfa+xInmOQhWdE+kBsli6dHEUlJCME8XJJ7DEQ/f5cqbEa7TnpVekf8t2oA11YjWx05oQBNofqz7TSX2pykJTggWCdBA1EGBm1qCkdPrJT26MmGEbVUjoO3lNRxOsE14MbOKtG38bqguB7fhyFI7UbWoWppg/79NyCraJ4PAKsrgSybP6Trni8rfB8gFkFIbWB7O7B7KXHr1gW3FxdVOlgaEOvYsw47VjLQiBnRGsNJaQrHR8xfypa6cGVRbglzuZ4bMNBrIFMMtINAh3qRoM6uiZREOVWphvIBrWB14RotXMIBMymYv1pAGREuteVSW87zEqWYBSe6MHInnnMzDKwlfiBg69STp16Jr3O2+i73To/49vYur5/fZHvZQR99bpen5iVg7mkLQHLpe24gjybNiF2g2UTidoDBaVOKN1BsN67V1EoQsTlQiySxXAvZjNlVXbYeZiEk5ZJLxcgdC5Saf6zl51mn2olU1vlWLwREp/vm2YMP37/O/k32msVHK7mfpYRZ4iG+RoTAkMXALIxNZbIMY/gpwqjCWK4tB43K9ZiKZ5gUsJUCn1oDR6yB2AONs9CzCGgyBo2ftVTe0+wczUOvA/b84gDfwV2vx5/BkfECuV4oAVmvkZnZe/XJKjXIM5rjcyCrWpQ0wsVHOt7+j3v+l1/5Zb64ep2bcWO+ohq40JaT0FcLkIXA7dA90yMraeZJ3rLVzDC7vUiRDIiyQxv8uxjR2kg0RhZsHNDC72tFurIWrfLC0kstO/6WSV44H2XfWMYcrJorT/bPu4FQpeYJSGVyHYJ4rUQDxUn1cfO/1aGHIJg5DA+qtGSWIbHVzKO8rGDBQKRzj8mlGDurxzy1OhnJYiDgOlp99Xg0Jtdp3LiZd8PSjfaXMnKWe7KvX+9P0/Lhxjp0fFIiJ/JdXn3lXf7ZjS/wX73xBd66b4mHmoMFllBqILMsyU5vSr7om/IuovGIVRPcGH60vU9W2/Oo7C0ZMoI2waX3VltNjSEDRiWD9LmyH2MuSYfWKNFoFjK2TgkhecrhgVquKHbCaI8PPfa+XAkTCtnBFgLKBC9RyVk8eXDyziohWkDdZ4Fdh/Nr9ThuuT8cm2eWsxftMdakWTiVrAm2hxo1cDF2ZISj2BOiskkdizCyo6GVRHDGX6sj2aWMpRZPOvhrGBGlc0ZikQ3bMe6Pwmj8XuumazDrQ463EqzejPusrK6dgKzeEuv00OwdJoZWofmLGDixWCDHa7Rrv3cgywuPuab9/peFox99wF984U0+unzE3fbcUFSo/iR3mnNeiE84Cb2nZSgnEmilqfTb56HxRVY4TwAp3YyMWlfdl5NUi6lJirWUgTbasRRJ4SC9ocmy4FFaTwuYZG6EoWp/i+fRkoEhNFzkRfVWMiq/ss0t98djfv/yRX43vsLHFg/4oe4dPtU+5KOxfa4h5VVjIW1la32qeZufXHyXv3nrLv/q4of45fuf4ltv3yE9XBB2ZtpXE54KrhVnG/nGWFGSIXfi2uvAsFqwOoosHrTEdzdI793zIj9U6xpU5pZG135jpoZBfHL2rvfMP02yFTXRO/CalBCmboIGqX5ZFlUrjrr73z0KvryH3OEFjnf387TBNidDNVN6TydhnLrUxuRyD65Cua7aGaBJjEPDsh3JKu6TZRu3bS5MreAdbJsMJxaKJ8e4DLBsHhKe/uJ09okZqFX3jRqAusXouVM3I1XTzDR7oflSn3wTdjiuWVnX44OOc4X27AAgvgrIutJnoEw8Oqt6xdJ+itH7BwGy6noD4zqyeVloX9rwsfV5bZjMwaKAsg4GZi0k0WA+hp13wktB8DxGSZEVFp6KbyUrd6WRYCCXzsQEdTpRGslE6SnyAVsjkskdNXKZW2NoYeytJaNR+Jk2Xk0BJNTirItUWlUYadjkjgfjEfflhNNmw+14wa1wyY0Qn2JrvtdoHAhbqnIrXPBqPOPeas13+1u8vrnFo/M1+bKpXePCWqrY0MGpLIElZQ3KUchtQ9MFms0I28G61QoFLbK5XpxZax1TkTxt8sv6kmdgVTn1hUVV1iHRyiCzS82un/JyjpW5BN4vZy2ApRUM/k6mNyhMb7pMycUwOO+DrhomWZ7meepvgJDJKdQ050FDOZ3GxHP/tfoS/v+LZiQzXXPF18qYXdY0sa+eGc2XlLXgn5mKFRQmZS/PYWEjjTxbdFEZawfjmpX1Z29stCHsfF4UQdYrA7JcTrhn+L73M/s1RvmOzYCs3EXOP9rx4G9s+d98+Rf56upb3Alm6l78rY5k4MitGsxIvHtmw7gAWY9yrnNr+e4sRbnIgc7TPgekJhLa36c1roBYGTiRwJmvf62zUjrf9yY1BsjyYP4t3latxbpeeayH+7aaOOjrVn1GnVjCpf6ZG3HvPZ9OUqq6Lz14/YkdpgQCa4FFVB7nVM/hzbBl0MCjvGIpAxd5YWmHIXCTjSfNTXPBSbx0v+LI4H5LZ3lpv4fIRbaGsakRHpHzSC8jt4OZ0y/F1DA/qL1sK5EX45p12PHC6a/zycU9/ovVT/C1t19kJwu0D7U0QITcmdRQg9ZE97I/0RCBFUuB+PDcGMhQG3qSLanYWMZqQJYmyFY7SQgwZrtfDp5UaOQDdU/ioA5CBWdpBczI3dMHRbR6f1mTyNbe7OtVeS/zQJQANQClsra8AaMqXKaOs7SilcRm7BBR88wS812Mfr2UUX7e5K7a9wwEOlejRH9cVuFiWNBn8zBtgjGFuzASRF2yKlymllW0GnwRJl9r8CAtJ5Bs1TyNWxlJpa6ifDftjU/BWlKPdfKqe9aq9/S4BrM+5HgjnbB8oNXQXRYdNA0MA7rrLdVwDmSVDcY8Xap8sWJE1iv0eG1yj+KNJKYFBmYyQ3/sgTdWYWONq4az1xoefDXx5S98mx+7+Tq3mws3dkvcjBfc9IWok8RJ2HI79CzF0kYi8kxD9fmYJtrpQiyYSd20q8ut1BcOzJg3qXISyuK0qYuZMWyiR8cGurDxqNnorC3rOCDUSTe5EeRc2niRF3Qyss0tbRjZJbvcHw0r4A6b3PHOeMJnu7f5aHPOC3HxnqDd4YgSOJYlxwFeik/4aver/K0b/4Zff+UT/PxbX+H333yR8Yn5nYRdmGQWBTSKOsW49r5FzkJaCGkFadkwHEWWRy3dg0vkYmugVspWOLhXjrqWQiqHNUATENd/WzrGlGooSSeTQZcg5ijVHB5RpIFcZIeebmFacDNhFJcVjoJN/q37qHixRDE0aIy5Jal4voiBbsFPhBqIpdnvFz2JUdRsf9SKo0J9NTaWhQdkjJ31JC9Zhx23tb8SSNoW3Ljy/+za2ZUVI7v3ACNtyNUvpYBaUJJDikF1mYjNjL5ssgpo1sq+EXydlK9lhtfjA46z3NFcsrdeEAKkNCUWHpq9M93ffi5/C0jbmA9jMf4ta0i5Pg+BrLIfmwNZQchNYHcauPxI5qUX3uUjyyesQm+UdZRl6FmJbSIjSuc+U43Y9yQgzzRUn48CYs1TBsu9J+P1AjgFl295giGw8F1j47lYCowOIKiYrDBKpo3m0aIIjeTqteX9Wmdz7Zt895iP4zhjFwNscwPjmkEj52HB3XjOjdCz9tSe7wVwCCJ0NHQRjsOOj8Tv8rnFW7xxcpOvnb/Mw7MjsssxJE2gVv3cgk5Aj2+uc4ulNbWQG4s5b7pI3AwwjAZq1UQm/PpwilNwdxcHo6p0o5yXbIzbQomYHm/3EylAmwOs1WvUD7NMk2U9UZzBhDVHkKnnoVSJSQGwDDArgKxWiTv4MYgV/OX85ASozewiWtMMR8Vk8Qgjga02vjdJzuja/wzHirFNYJb6HgXM/qWk4jbumzWlF2ZLLcRkIQUABtxbi7r2zOXtcyP4AvKaxdk1oPVnaWSdCn05WiOrlYdTHQBZdQ4vTQuvK+L+z3Mg69GnO/q/+Zj/xQ/9Oj+6/A43wyW9NxHLf+swchKEFrnSgqSMQRPneceZZjY6seDBWVk6XfMlfGMZtGxDOQriRuziz6d0ImzVJN4BaEOoLPngdUcrga0mN2mf/LGSOy6WlHNj7+enWFpllO/8nKkVJThIleu/5W/zf8vj5vc59Bt+GvjK7sMFg052GCchc5atsXsStmzyYpaOuqONI4M2JA2cZffJcn+s6KhJUb4sm8H3vVYnfXe4TUuik8QL8YJMb8b2Qb4nlvEHGVECxyxo48h69W1e/ugj/j/HP8Y/e/0znJ8vyeetA1jURkZZg3Jbbi91ckTDimUMNPfPkG1v9VMhAqRs3wFVJGfvTZhXphaGFkzrCsazVbUGVyEJhOTJsh5gUuo6RBBncM2tXgK+BmWtnsYohDxbqpqihvH3mSElqQzyjNCnSD8WQ3gDrC60qQCtrdIZiGzSgrF4bKnQEzhPSy7GicwxaqAfYk2WX0bbu52NC4Lkyt4K2bwnH49rTuKWGJStNjWUIGCqqizB06hTJQZcaEcr21mwQ0kZNVJD1sJyfP/r1zWY9SFHRCuiWoGscdwHslz+BewXFv4Fsidyf6yjFVwFZO11TvzxV3ljNYHxKPLw8y2br17yM5/8Jl++8TovNGcVPW1l5EbYcideAHASBk5EWYfIUpq9jsLzRppNxPa8ce/2p86VCLEuIMpCTKaZUJa+6AworSZCVmfJ+ASPcJa7yszqQmKrLUEGp+zHKgdbxqEujoXyaMeX6s+XqeU8LGkl8YfjQJD7ZHasRTh5TjfpeWPtdOq7MfHp9hv8+PJb/PKLn+G/ffgZvnbvJS7urZFdIAxSu8aVxuoRtIqQlkpIwhBMk51aIXUtaRloH3e0Dy4sejZlZ2IoosGke0yFgGCbeREgRAOEzPzDXlPEOw1l8rC/VS03NgmLGkurLBZh9I202uQZd1bAlAZ47bonce35bPPkDRDFi5nshZFP+hrMIF6Cp2OJkEYrisYUGJIBmpvc0GPd7p0bXW5zyxAnUHX/2rMOQFKlETsFvUa2ri0PM9Cq08m7YdCmFoDRPdsGjWwRloyVKttrqBNxAJaY7ARxryOmWOdr36zr8UHGXGFYgazCyCpA1p4p/OzBM4yBIMbGKv5Yh0BWWV8OgKynvLGCkLvA5d3A8JGR1269y8uLx6xDX41/g2SXENqGaCGJzkGsxgGs9wKxgJoOWOCrw9TAwyHlvr48Rqgs4YYpCS46RBXQumYomR2RmnQoZoIqYt5+SWxDm924PltXhSk7yID38vOg5kkRUB7JCmGDhmSFAt+b/LCMkhi8lszt+JBXmke8fnSbb13e4f7mmOGiRUepDYf5xWAbal8pXIKeZOou5xhomo64jYTLHhm8uNQCy5QGhPMppKw1/q9vygUHspiBU4UFwsQeKWtgWSvUGcK1IVKeWyap/KwxX+8gHiwyv05rauJ8r1UXqnJMWkGtnA3IytnSwdSbJsXAfXTG96hh9jT7127w9aUcY1YYHQgD60on7IQoubKwsp/L6MLEAp6OWHpiUxomFA+z6TpXpQJrc8D32jfrz9Z4mNc0F6OpOxZdnd+fSi58lk/WM4Csh59fEP7mff5nn/g3fHH1Oifhsgbx3JAdAC/FgSjCsTxb6VD8dN8YdxP5ywHbTnJNOEdytQ8pckLwNEKgn7GsitdOK4GMMqAMOk0e8+vf7B/iAWtFatFfjhEmVtShtLAGSV3hFTx/XBnPSwF81j7wucCXCClTPcCWkuu5i8wBBUNFBkw6mBBuxo0192dMmizBVS7BgcNczcW/NbzAy80jr796XoqXnBLY6ciC5ge6jy22LlGETp5wcvtXeHXxLv/wjR/ldW6RLxrIoTZuJJlvFlmJKqSFL33iLYCwtBCZe2dGCChsrLI2jOadRfR6O2dvzk9RAP4BODDs9VAqbFzz0qp7LV8QJSna2JwtI/Wx6izk0uhJwWSHU1PGgLHSzFGBMAj9GCsgBcb6zdnAt8zU8N/pxhVSdt+zvGSXG56MCzLB6yepHlnbsal2AkWymFXok/lxreLgDC5fxySZvDAkt3lpGHLD4CSURQh0MgLm13YUdmQNDnrvqidkr4G2+FS6B6pJgOF7kbZeg1kfcrQyGltl0UHKJivsh30gC/YBrfK7D+k65MYJulpYF2UGZD1NAeYpNtaerPBuy/0vBz7yU2/wP375d/lo94Ajn8iOws7NapOj84nTkDiSwDp0Vdv9fkGsMuadjMP71XQnhLJqLuQgvWPmK2TU+8RNTznIZC6ycqYNN8POz7l5Ii119OhQTz4k1ve3yQvamNjowtBh/5KuQ29md7nhfFww5OgJiBs6MmeSOcs7bsdxLwnxexklCfFLXeLjze/wF9df52svvsw/fveL/Obbr/Lo/jFyGafOOaDRUmgkW5dcW0VayAsDtLQJjAuhvRFZnJhBfHz3wroM2RduL24ZsSRB1Yku7ud53pmWaGUcQS01Y/RCItS6xQsTA2xzI25CaDOrFmAqli66T9JQ5yAZjJKbG9ORF2aaJNAk5rESDRUz7zk/HyoGdPkQP6ZdajjLS/OugkpbjWRPQlPiQcEbVapJaLnOBoRBm8nUObfV+Leku4AtFMGLjKxuEu8FR8JSOTtynYzLSH4cEamy2kRiIddT7vX4YCNItu9ZjKA6Gb3PgSzYB7TK7+WrFCOy6Jz5O1tjBGp1DLPOvT/F/HeZyQpfEo4/esYXj+9xI17SiQHBnaf9RVEaLMFwKVp9Sqq3yPsEsaZzYCDx4e3zon0OLkTv7JdhwFkBxuxxSw+MUJReoUeqgXEx327ItuF0gCERaBirLD4G+3eYvVrxCUsOZiUNNJJYyUDMyk6scbMKeS8J8XsZJaDkpZi5Ge7x8fYB946O+cbJi7x1foPtpoOhVKvlJLhHolLne1Hz06qAViPEhRAXkWYzItseGWehAmXDnrNdR1m8gx1Ko3y/qRow9m5JZSoSddFJu14eWFhapR41JNI+LymX5vTkNZOjSDmKf6NOh1pa3bXDXSKk3MR+LsVUEVSNkbfTpoKc4siYVBB0auSVay+oXZ9x9lXK2NpRCoXRnFZBrPxsag6uICokMXe34NVO4VoNXgiU67cer4NXds/ptsjzv1vX40/f+NruFdqHG2+sx/2a46rkwvLnWZ2hHhqkQRiPGh58saP5yw/42x//LT63fLPujQDuhEuWklgLtRn+vEbwTkfu554BYZNnRSyBI9NQEWXy1Wkl73lhdbVRUywljG2VNNXE9CjijfP96780zluPWWhnoNYh0wq4Esiqz3UwVz/r96RPhwC917gKJJvftpaOIWyJGBOtAHzWXR4gU/1dt7TcDBu22nIUdgZ20dZ98zJYeMlZXrLJnTWiPAClDYldbnl9uMNJsLU9yDssZcdJaMzzS58N1H2/xkJaboVA2+xYHv97Tl+75Ofbr/D1N14ibRqrI5IgvdUVuRMIRYXiypECSumCpWIMrWLbkpIxsoKv3UGsxgGvWbw7XxQuyZs4DkTVoJICnNW1z4ApmSZlr3+sLgulR+RrVRzUrF28PiryRWObTecj95HLZGDjWVoypOjp79ZwSSp0JGMjaiLMrp1iv2NJl5Fu9l0ugFYTcv19GCOrZmA7tgS0JlE3kti5RLX4ds1K+lpL9c5iNp5EIM48tqJMrONBYTH7nptn6D5O8F7jurL6kOMk9Ixr32EVo/e+N+lH6SaXCTg//cHIcoGcOJAVw1NA1l635Aogq1CHcxfYvNjyzp+DH/+Jr/PX7vwuH2sf7NF3b4aNo6IDJ2J04LV0LOSDIewjluRx2IUoHfC5BjyjDJpmXlqFJukaWolOMzS9fCl4BhK9JJYk09kKbug4SbsQPO7TTA0HDcSwtfh1TVyIdYkSgSFa1+IytSQCm9zx9nDKOuyIkk1zLonMjruxZ8nzF+fnjVYit8KKdTvyyfYNvrr8Dr9x66P8vXd+nH//1kfYPllYS7y0eINO9FiAxpMPMwwYfTYthNwKebFi0UUziL+4dNmhT8oxov6vqG9gBqxz7ZsVATQLIRtgpU610qZ4ZQkxqU+i7oGiVgTlxjsIGbQRY+u6vMRDmWrjn+zdC78mcON5FVxfbndUvx9uIGxFjpCzIFGRkNEc2KVp4Rzc82ooaWS+8Zl32aBosCshrcqDStTsIgzWYSjXUl5AmCKPE8KRdx+TFxSdGDMwVjaWetFioNmgzlTT+TFdvVG6Htfj/YyFJPs+Ch43XcCsuX/UtNl/ajQRWSwOjN4nIGsCrN4DyIrCcBS4eBU+8up9PrW6z824MYaSD6OZK61kOswIuJVI5HuT1pVRmI1zEGvyJHKmlf/NxAFaWSvVH8nPUSn6DVKw4kcQEsbsbTwsojBsGrQapbaCe004AKGCyOjyQiWIGbIrQgojSpFDC1kjF8nYwBKUhsxARnNiHWy/8EHlG1ECS1ramLkZz3ilecIbyxv8zsUr3Ds/YdxFW2cqoIXJBWU6N2beDrnzBkVhZ8SGJgphOyD9WFlaIgriEvfgDC2xeboGlcwuxxr8V5hivm5IxlhZYX7d6VRgewOk3FQvn1JAlO2HLxtU+Gdaa4wtrNPL10bg9F9Zs6wRJKSZ11ZyFnCRG4rYXiTMri8oYKn3l2S65hLGfDCvNamKypKYW+SFWaZrS33dNfmqA2D+LVNMchXFyG/BQcCrjumanfVnZ6xCj7YRcWkhTDXCUz5Z8FSdUf2GgjDcaLn35ZblT9/nv/exf1+BrMEL1Buy49TTyJcu23veXnnQxOPc23WLesPP0OYlY91/Db7nWkuq+7YMrMUYWUcSaMVUHOD+pBK8MRH2bpsSADMLP8YoAjqvQZ6WCj9LXfJef38vkOuDjPlzlGboUmJtmC6ZzsXSAa2tRqJqNYMPHqQVJbMMA0G9+TJLQ0wIu9wSQ94DzCOZt8fTyuZaLt6cqXyoYUs/yNFK5FZc08qWo/XXefGjT/jPu5/it954le3jBaRoCfLZmuJZBCnNc2A4wZVMxtBaqxLvn00eWmVxUZ0UL8UkvrH3JmMJkTIFCQloXL1SGMgFrHJ1S27s+xcsRcTAqWSNowJaUQAtqEziakcDtiZl/9dfp6QuZxWGHNgNLW1MtE1yz0Wp17qbARBRFmF0MMmuo4QYuDW25rnlQWrDzFD+cmxpQ6py+NP2kqQNOxpWcTAzehHzvC5AFcoA3IwbSlLvJi/IYWCpA9GZhK2/F2Nkqq+FVFVLumof+4xxDWZ9yHGWO+IWGBO63aLDeDWQdcWQ1Qo5PTGj92cBWXOPrGf4Y6VF5PEnWt796Z6//aXf4M8ff4M78bxOYGVCO5KBdRi5GWDtHZTvRU4IEyhlP+d5g7QuDmMlzj9N5Z2eZ06jFfc2sYJlSu6w+xxJoJNJLx9katiWDd2JG9llZswtSQw6sGZXX6tM3uvQM6gh3E/GJV/LrwDwYnNG6135RM/N0HMs+oEBv0KVBTiWzCtHb/Ll1/5Lfunup/kH73yJbz68zeZ8Qe7Nsi+XN4FvwBtlEIiNFRfmcSKkNjIulnQnLcv7HeHxBtk5S0vylNRRkpUc5q+phSE4m8MmSI1+7Y3e1PYJtihCrEMg5GwyxKBYzDtKCEJomDZPYsddAHtLALE/lMKidjJcYiiinhBSFiR7XZIVnGVx2g0Nm7SojKrioxYxIPJEwlO+ZybHEbZ+7ZYFuIyd066LYWHEAgOSFDrtSNbAOuxY+iTcYx2OiQ2mVf8dcHWNezoUD6/iyXA9rscHGTuNlnKT1Zi/zsh6Csi6arSNAVkxPBvIKkW+3QQcAFlByFHY3Yxcvpb4wstv8NHuXdZi/lhjZY5kOhKtZJZi8o/344kFPAVWFeBZK02nHI49z1WdO2OpTEPR2d8KLmIAWOMsmHKfFpO1lGctjVY7Nj91vh4qBpAU5pbFVY8VgEliE2jrMrUxR3a54f5wAq35XpUYa80WKGEg2wcD/EoCYkukC8pJd87Lze/x7fVtvn7xEo8uVwy9dbLB1pHqv6YOkIiZ2Hq9ZxL0GJypNWNppYmlZZh9mdQDItmlGH6SxOXmfv4pMsLsn4bvb6TcQ/x4gq014o1wzbZWVVCsvnGmrUYpEOoFPL+kvWnCtDZVPaN/D4jWpEGEMRvjW+u1NnlbNSQ6ng4rCL5BH2XysZqDvKOag2JWYXQp60iorxGwDnsnySWsBoSpmp+bLZuWotmUcw9F8FkL87nU8Hr82Rl34xlp3RIee93wPPCqNE69zjB5kzUrdnc63vlqw8mP3+dvfPR3+aHF2zVhHKxZsfDr8zTE91Qx7HTgXtox+GR6uFcaMIlc8YsDrbdFgSXG/BrU/HZbv7ZLuE9lXTlgtdORhE6eWX4cpZnevMf8+n6ZV2W8F/j1/Rhz2WNLrIqWYfbapUYq/q7reE72hkoXzKtozqyLZHppOAmXbHPLRhd7f2tlNMlhbhho+Ha+a6SA7k1eiT3roLToD1xyWMZKOj7aRNbyFuuXfomf736cX3z9h7h4tIKdraYyGtgUeyF16inswqgFWAqEF5askhLedVN4D9YSEUjj5CMaMJmgMCs48aRfKKx3Kd+nEgQXrF6TZAynup9IeCPJlC6lGSMK2ZOGA0pRK6byHOAJjUAynyyrg5yYoFTfrMEliKWpX8IGyl6jhFyd5RVttDC4ILk+PrmaZ0iTtxaNJRz2KXIZDLBehFTrJmAKzvKfW4wYYLL5kWVIBJewFluXjszABECXzpXV+PnK/d2zxjWY9SHHW+NNFo+zGb4PoxUZVwydbdJFxMwZT45N7iEy6dfL/fcWG54JZI3LyONPtZz/5Qv+V1/4VX5i/Qcsw1A9S0xaaN4+JyFxIoHjsHhf1NC5mXr5eXBqaRmHEZqZyYTdOvMlAW5ibJWR6mScKl3YcqXsPhZ17QuUTiBW2YB3IvSYRKRQkUti3YV7QAVROs3VNLWVxDIMLBmqdvw82QT+5nCLKMoL8QlbbTnLCgxc0PNCzKzoPtSEXcziP9tmXo7/gR9ffot/eueH+SfvfJ7X373J5dnS3o26ObtO0rvsHYcw2DVQro/cRLRZ0a0b2vsbY2mVzrmdRItKb+IkdQ0YuBWtqzClHFpHPeP668Y32BW4mqivGmVSNWVqioc/jcsl1OqEUWpxUnYVVZVXduNiHYE96nt5fhHUtY/92LDThh5jVe1yyyZ39DW98mpw1ijpRksvoGvvPltZhTYkFjJWYCtIZshNBTZNPhUYHMQq15LFp8cq/yjeWeV6HFBP7BxZSlOv/8NO4PW4Hu81zvOSuNUqLdQrmL5wCN4IdC3SdftG7xysNe8HyGqE7a1I//Ger774HV5t36WRjMzmVksnzCxE6RA6eX/yucOGCWDB5bPGnMh+eT4HuxxGqbfPjbDttrJv1Lru7IFlDuYEEYJOjy5HFTES7WwpdlAMep1OX9SJOh/dVB43kY9BayLqeVoSUI7CjpGIBZAlek0cBUtV/DBsmmIWfycox515an1zfZc/uLjLk8sVw66BslVxxpb5LKozsqaNd3lzGoDQENtA2PTG0sI385QfXXaYpCbalqJakgNXgBSj+JI4GECze0HNGVvV08Q/BJ261DBnXc3+5hfE5MVF/YylfFClGCl7qTK8I64BUo7VSya5X1ZhARfPx6s+I8VqnoTWZmZyny1VIYsBU2hwtphZJQS/rVw3xcPGDKW1gmLGBrS9jrik0Q7d1ttRMubQ9j5A7uvxp2q8EM9Iq4b2EMiasbLmtQUw/exhHtsXFrzz1cBLP/Umf+ml/8Cnl29zI25JGrgZzxm0YSlmBbL0Zu17AVkP046NCkspDKLEViPrwvBhalYXL6jBwWDb30/M22NpGUi0Otk5AE/JCrNLEY01ZiNpKZT32VjPMnp/v+OPimU/WbooA6ma2y8QNtkatS1KlsTg2+3BmS+BzBIzeS/72ifF/F0GkoQqDyv146YkwyPVKP7bu7umXlh+i9vacxJc2skP/hwUWf1pUL6yeMTRnV+mCyO/2PwQjx4ee/1kAFD1DY6g7rUoLQxHdqPkJcuciQ/OjOnuRID6vSjywmz1kypIzpb87J0tcY8rEUUKUos3XgRosKTj+bqE908KEFZANqgyeMlUyeF8hASMwja1dT1QoImZYYyWRjh2GGw00DpT0rzkYpUGLlxeWjx/gZrI3I8NTUz0Y0PXjHQxsYh2vWxTSxBl3fRkFR4Na4JkYsycpwVRMkEDbdyREM7TktO4oQS1RYevs384W20IkrnQhqNq7WDL8Fke2V0zs/7oxu9tP8LqfkJ3/X5qIdSNzB6QFSNydIQcr13uESCG/QXmCiBrXljMjd7vfanl5C++w//xk7/Mjyy+w5EM1U8I4ETMtG0tys3QsJJnAzIlmXAOYmUyWzfELoz/3pNDbKJM1Y+quL0WXXpBhg+ZWgXwSlgcaMJMG2v6hy9QZTGbg2etd25R3aMgTuwuG12dlJWBwEm4pNfIRV6QJVQUufVkhoIwf6e/DR3c4RwCpCwcycjbaeR2yKxDSyB8KJ14lMCtuOYrIfPp9t/xl4++xi/c/SL/zduf5zv3bjFuGgNvwD7vxiRrNkHbrl2DyQ3HpTCuhf4ksFxGFg9aY2n1gxsaeoGQMho9ajaGqsW2629WnQUh5DxFziZFG5PJmJROiAPG0DLOLblRM7Vnmow1MMknsdcK0Q3evWMv2bsKDnJpYZMF70L4ezd3YmNoDUPkfFywyQt614xfppb74w1uxC2v5Lc4ln0Jn3UFA4/ygqVsaMW6AVnFKLLl+g2wYDSfLE/9DKIkghWhGUJwPaRHUWcCyZmArbNRBoTSWilXSfbNR6D4wwVK6s0fRVfvevzJH/fHY9qNe2Xl/UX+UEoHIBIcyGotRGQOZFU8S/ZZLsJeU6WsN7kLbF4KdB+/4Kdvvs6LzRM60p6MvfP0tVasgGh4NiBTkgnnzQ1FGTVX4EmxFSX67wYnS7032CbNBLwmrTrs5JXXmDdTsgMw5e/FDN6XlTqCH0Vmzw6iroPlrqVREjBmU8dIEkvSmoMS0ZMzgmQSgSdpBRhLuEwZrWTOc2YlSuvv6sMUSkGElbQsRLkd3+ET7X2+sX6RPzi/y5OLFXkIDt7M5u9yfsTTmnw+z42tObETmmZB3ERkN8wSD41VSz4spKmyw5p6iH2oIoUcVUCt6fMt8oyawsgMZCuPZwKyyih2WNM1zgS4ldsq0kWdq7V8sOKfexL63FT2b58bxhzZ5AXnMnISzunYl/AlMhcqbLWh8cSwAvemmW+WvXb2zrpW1lXxY0Oho4SqBIJfS4UJGDXTlY6/7lljVjP4+dsvyYbPCky4Hn86xuO8Jl6OzwSygL06Q6NURYjGwO5ux72vRE6/eo+fffEbfGzxgJOwrelkS1d4tGTW74ORNWhiqyMb9b24To3n05Bsf+T79tbLnui1BTMv3SJBykwN9SCC7cadpc/UcG/LnDlrXoLVHq10V+65/iTYPkwm88ImZ6IYE3cg1RooAhsPrihgRVEPZJRlGCp7J4rSMRqDxm1WjDmj3AiXFcwC89c6S0vO04I2jPx+/xIvN495rXlCZKAJH8z38YOMdeiIKnymveR/dOtfcbc95/8dvsSDh8eoNLBz+XtRebRar/+S3i4aIa9ZjZnw+MKtG6wZY2tFYSsG+5s0lZ0lIjA6A1lCNWqHqTkYRpM7BlWXHAphcJKAQuwdsMLWx5LUa/7E9m/svcFfGzEgSRhyrODiSTcpj1K2FOdNXgDDXkjbkfQcxx27puFm3HAkvVkgaGAs7KoUSVmJIZcQY1IOXI4tXUjEkBlz5Em/4kZ3SUtm1IZLUY7irl4rOYkDZmbH0pIIOddzZHNJpifSKayD2VIUNiaqvvd7/42YazDrQ47v7G6xeLBFx/Ep9pUebBykaZDjI2S9MiBLxIGFsL/AHCxCTxm9R6E/bbj3lYZP/pVv8Xdf+VU+073lsjq7Wo4cdV2IyfQW0j5TKlcSRh7n3n83vWvLBBJ5ujZQGC7KUZgS24zKa8yqrEoWEwJOQJYtJEVOWACr8u/CNe+hvp5RictGsXRZ5stmK1K8Ye24oRp926IIlPciiaOwY+Mm8glLwUsI62i3DbnhLC15Y7hZKbYdiS3Rz+vAVs380F7/w0nGogROZcWPdYlP3PoNvrz+Nj9/48f5l298nIvHK3S7bxJfgCPbhHgHvXEvrVbQ0JC7NYsu0jy4QLaeeJjV9N9MwKq4jlCzwVTkbCytxjQgkoXcOjKV1ZhXWj5HiIkagWvsczXAqfE6IUDM88kaws4eI8ELFB8hCSQHLYPXUaX7PlpxRKMogZQCl7ljqy3Jjf93ueXN/pRtbnmtechLMe11iR7mkW8ML7LJC16Ilyw1kzUyaONgVqG52vcwoOxyC8HosiZJ9dRDbYmqNR1m0IaWkYFogCjBDYDTXgFc/Bv++G+Xrscf1/FkXBEvTV54yL6a/w4YaN11SNvY+gJ74EIFB/bkJwdsLG+mpGVg83Lg5ice8aWT73AnntN40hQYANNgKaEtJt17llQu+/y/q1L1SSI1B4nKuzGGC3QyFekFuFKMjq+OXsyBrCIcLGDW3MexYs0yY2Q50wbKbfsYX2mSzm/LDiIIVH8tu6+tHWV9sLAIawiU5klyc/GzvERQgne3Rw0O8NmavHCJwIctEoIIS1pejpmbyzd5uX3M1xYf4TtnN+m37cTS0okVlRs/X8EJVIFqEK8S0NgSY7DEw3Gk7n6F6lFlBtR+5nTCkYyBJTWl0Bq1tmeS2gTxrncpFGQGZMG0VjgOVpnByt5jDvCj6bH7uKgfs9QLMWdh0GgJhs5uGDVwnhaMGrgRLjmK+9LxS828m47oNXIkAw0e4T5jZtlL2r8G0toBJGyfUrrWxuqlyg2zg1pgzK4Ckh1++6OUpM73Xwhcjz8dwxKVZ/XDfMj+fF+BLLHv9OWLC+5/ObL48rv81Vd+n48tHnAjXFYWz514wUkwgPYkCEtp3xPIejdv6VUrgNWC78etjogy/VxGmnm8lTWhgFqLmSeWzYvTvB8xK5Oz3BOlSK1CrWF2OnIcTIXxLJP1P+5epuUYA4G1tOx0rOcWJvsVCzWZjPRNdlj8jDItlszdyshR2PEkLS0RXFtPB7d99VYbZ3LZc10mA1CCp9pd5AWP8prPte8QZMeprP7IzkUxhv9ce8nRyW+x+MTA3w8/xjv3bqC5Me9Gb3RotL2D7XnstnAkSI7Efs1iSMjZZmJj+fVmjRdTtEj2HYQoOmZbDwluFu/rWRCTIaq3yYp5oihx5+uZ11O2tHp96xuMkAzsqp5ZAiVxUdRrsCRcDi2XqeW42fHpk/tcpI4ujPzeo5dY+B5j0FQ9OEvT8KPdQz7aPeTV5l2WMlbiy1m/YDc0hKCoKJd9a/snFdbdADlAMOLKNplJ/GbsOGl3NN7M32VLPWxlUrkMNJzELTv3rOmcxLLBwteOpCe4x9tAcG/sVEMNvpc2/zWY9SFHzS46kHxcBWSFk2NYLfflHntd8EMte7mdyei9DVy+0PLWzyr/0U/9Fn/7zq/x8eZdBkIFspaSPKlQOAmd9y6e7vAWEOss9wyqbPyQ6+IisM0GIpXY1/nEeZGVo2CgN2JpbQWQmicRFFmHGboPxtjyoqaMlkiWSWJYRukklpSSCeQq538mBfHO4648hWEjFeQ7kp4Uz1mGwRhaKuzZVQZYYiaID9IxyzD4bbaAkyGFkZRHjiS/b7nme41WIi/GI/7a6ozPv/yP+W9OPscvPPhhfuftl9meL9Bt9MIAA31a23TnqOQFxK1H0kogtcK4XLJYNyzubaaOw5ggKqJ+xfq1IjLtKpRoso6sxuLy6kwGJUerCkQcjCp7IlWis7IkKTmX7gfkzkCv8lmos63AuhTzlI8yseeovoC4Eb3470VrnoVdalz+IWQVnowLLlLHo2HNg/URSS/2qs6LHHiU1mQCW40kksXSYtKRgGnIgypDNhlJvSR8A1AovVEVfHNXALVWRvfMsqupwxgqgUyPsRirr8EV9cUf583T9fjjM2pW2cHachWQJV0HbfMUWLX3bLMmycEL1cbJcBQ4/xh86tW3+ML6DU7D1gp7nwAaURqUpUAnkWK4fghkFRCr12QeKQX4KSCQYJ4WTHu4OdukV+jEUldtU6oVkJqDeRMpx4qcOfBVRiQ4w+XgPPqvo4POFeTy+/keGIAC0Yxl+XegpnHwrJOEhp5BM71GdjQEqB1QXEo2auRSO1qfHxpnt9n/GY+sRen4/pkJH0nHp2TH3eNv8I3FHf5g8wL3Lo4Zd401D4rcO2NNEzGgTxqb+jRSi+HcCLENNJse2Q5eDKh3t2Xy0iqHnq2rrcGQqGL8Xjy7imSwQj1zMEqVkIwtJtmOo4BjGmdAlwLzUzX/udQWBUFVe53CSBMwgniwJ07FvB/b2O+cqbVNLZdti7XKptGrcKmtfbbOZS5AZlZzs0p+TSWdvLIAk/aXq62Amo6+jUS0xKj772ia5K+qJCnEgYLOPf35XxvB/+keb6dTQp/2a4tQQKt5bSG1KGPdSwABAABJREFUxtAmsLtjQFb7lXf5i6/+AR/pHnESt+Z/KImTcFmBrLvxvVML50AW7O/TC6BldYXf5tvQ9Uzyuw7R1w3b85fawFQbNkzdYc3vpUQ2edi7HQz0MFeWZ1/7f1L2YFOglv07JQObwb15Eqn/DbbuHTw1nhJonGwztCGQOQo998YFmzR5ZiURWkm0MVWACwqQJZylZZUl/oGMRHngXmofLDDrg4xWIqdhSWx2/CfH/47244n/nJ/gHT019XwU4iZMjQ6wNaWBcWmg0fZOS9it6bY91ITDXL83tfuVvAo4qJckp7oeajF0VG8yZF+j/LqeQC78DyYNt7WQmixM+XuyY5Xs6hdv1CSVmjp4mVoe7I7owshmaDkbF95oz279YJ9bsTgYNPLd8RY0Li/1hcLSEJWc7WTFkE12mCIxZBaYWiczpT0POdI1I2O25NFdtmTnTe5c2VLUWkInY2UdW2Km2S6kvOQkbD0UQvz+3/u4BrP+CIY0DeHGCSwX+0DWobywPuBAXigCUcgxcPZax72fGfmf/OSv8ldOfpdXmydVMmWpa2ONyj0J3VNm2GUMmtjpwFkeGbBJbzLFLiZu9ltfmStCX4sQ7yaW1IGcaWeUYFuAJgqwsa1Sfe1AqHLEgcSgY6XBF4khs+5ioR6X35diZpBlc7bzBKqF+CLpx7kUWzjXLvHonWIbaS1pgUCbrUtx7klTg0bO05J35AY0WEqEmyYOGth4n3ShJdHj+2PqvZCWTzSR/+HJ1/nq6lv816c/yj95+3P84du3yZcNmjBjd9EqtzAzX/XgAGM+5TaQm5bcHrNsAvHdC5ukR8t7FaheWkXmav4oGR1BouB6GDuwMG1GxCNv1bsHYYQc7XOyJoQBX5Ixf69aMDB5l/ixzxPGSwET1AsLmZnnihc8o73fjDiQZJKMMUdGIMSBwa+nMsriX5hVBvgmp7xHdqkhI3TezQhRzfhXIlEz62Jw6JVsyoGlFGB3tFSOqp0xT60oykYbwP7e+3ekmJZej+vxgxoSArJYQBP3Gb5PyQsPH8gEbvm6szuNbF7LfPHV7/DJxT1Ows6KbQedCiOrdSDr0Ay7jKSZRGantgUffUMEVASpzNdz2WIN/PAJxL7Jaowov0920KckEto9JoDPIKPCVAnuw6WVlVX+BpNhtm9H6++NBJI/xo7L5lBXC1TFZ+NrT4vp6JLY+xYNLGVAvSGUEXptKmOrzw3nLDgOEKV36MTPhdout0Hh+8gaaCRyMwR+pHvAK80j/sPyJb55fofH52t0DI7O+T7EDdJVXf4jttmsLK0Q0LigCcbSosoOMbN2rZgRZSIVtdTDAiypTOcbFC1+Wzp1pcmgwQCtKmcvwFfx9yrDK+fqpTVfa/zf+rr1uf18R0yeogWwDA4yWfPEPBbts0tkWopRrl9zZb/kB2erqTh4NfnRtC4zTGKRBS3J48uzMeG1ofHWSsQKC/XrNaiQxPiG2Y3DAkrJQbz2ZPyzOXqXHj0PyNqzMYmB4aTlwY806BfO+AuvfItPre6xDj2RzM2wYRkGbobdjJHV1AL5qmF1RV9B1cH6wCxkv0g9CTIBL8BRsPVjk6c6ofV9v5m5K2uJbHRgUKUNweoBsUboRhO9KksRdpqrsqMoQsrpsOe9+vj/pIBaDZHE6IDFxO4szKza4Md8XIvJdixoCNaonQMcW21pwzgBFZ70ndWSXAFuNNvqMRwls0kLZ3C13AwbTmTD3fjhbFi+19FK5EZYAlv+O0e/S/uJxP8z/CRvvH0TvWjIC6tZpJcKamWFEA3QQiEMC8J4g+atRzB4uA6GLIlbtFTCSigRM6mSAqz+CsbE8o2LRl+3xkmRYf5aQGncRGozh6ROGNDKEs5gdi/R16oE4gEuF+OCS2n5vUcv8u7FinGMaBYeLy+ntEI1xjzARhd8ffsy3728yQ8d3YMlfKK9x5AbUg7ugW9HGjzRMvi6n3JgNza0cbIVCh76U9a7ICZBHCTSkrwWynVPt3W1C2LG8CFkjoIFDLSaiNLTYjYtWdXni/2G4/PGNZj1IcdPnXyD3/roVzj+nav/PgeyJoNd2dewz+WFM2CrRORqmBILz37ugv/9F/85X119i5OwZauRrZa41pG1JG6G55u8l8XmLCsbN2EcdGJ2QUFfzRMlznaCW420GK2w+FIVWWLpCs4T3Kbnyw5t+aQqQqQhqbKUxjop5LohXTiyXIqjBRNotSiSLZEqbyysLcgM1eRxWjyTCq1kjugZJNZqqDBrNnnBOuz24mo3ueOt4RSA2/GcKH2dvDZkjtQBkAq4fX8653fjEach8drNX+Mn1t/k/3XyU/zWO6/w6N0j9LKxycw3tqAu91Ny62BWI9XfJC+OWC4amvvnyOXONutj8thZRcsMUEx71SfoJlh3QsWMAQEpFFlvi2npHnQzCQ9Y+kfEYsxbr4Wy3T9gevBAbTrXgkRt126/jGLyRrXJPvrEn4bAxdgZk4rMZWrZJpcAijJoY526WeFXfEhKZGxhGg65qYaI5vWTuRhtoS7srKSBddxRIqQtzKSZ+cqNFeiKsy5E8SoY1Ddxfk3abYkajvDHnNZ+Pf74jFcX7/L2jY/Q3bv673Mga6+ynwNYDigcsrLmv5fEwt0ne37qpT/kleYRnYyMGhj9e9E4Y2gpzzd5H9VMzXdqRrStd9+qT50DQo3DT2G2eRkJVJ++2Wa9EGfKxn2Wk2fvxYGpAo4IuCG2/yt2nyxTpz/PwKrSMCmM4ox6/VdYZ9asaVx+LzIxyuwY7T6tuqeYn5rCrCnS5OzvD/B0IZt7VqFHSBPNTJWWTCM2Z8P3h2ETRFhLx0IyN5Zv8Gr7Lv9u8SpvXZywvexgmCi4hterSwxBYtmIF0BL0NjSxEDY7JAx2Vrja4Q9dnaNZEHcbLf2AgreGmZAVipolDgoZIBWlSIKk5ywGMTrDOTyC6U2Ugp4VYCwwGTWi32YMtpraBJ6Z+oWk/axMJrVQCt7i5MsSv3zLx6T5bpI88di13kpJHMWCCOqJkMNfvAGlBpghgSiZpIEB8O0vl5TG4sToCr1+6L1FBXfrOvxp3eIZJcPzmqL/Tt4XWE+WXkZefTpjs0Pb/mpV7/La8t3WYSBo7AjSGYZBl4IG05CZinCWtpnNsjBaoutjmzdu6mMVvbn7RZ872+A1uSNNXlCTfedQKmdjgyqLCWw08yxmGBuanKLBUmpVt8sq0kiOx2w5Nm4tz+c+2f9SdiLRQkMmiblC1rrooUEBowda+dQ3colcZbbuo5eaMegDdHXI7Aa52E69uc0Vlac1WRlrbLgo8Baejapq6DXt4YXOJI3WMru+6Za+V7OyY2wpJWen1v/HvFjmf8Hf4433r5JpiFsQ5UaWtiuWPCgwIiwOw3EfoHsTogPn1iNNGNoCcwALadPZV+RxKXw4mzxNLGRi0H8HMWVXNYomTYNzNatXBhazkQua5Wvh5KoLOGswpPtgu1lZw0gYJcs2CqIyW5z/dxGLlPL25cn3O4uaFdjZQ2HsoaMgdhkRGDMoTLARKye240mMRw10A8Ljtsdl6llEUYuU0f2PY4RIGwPtHOAPRqty8khBTA17+FtNk+u1t/8MiQDpa/BrD+6YXICaod5Pqq00IGsQ1PGveQ22JMXlgh1DUJaRd79TMfurz7hf/vD/5yfXP0BSxm50I7sYMxSRm6GnqMg3AjLZ3pj7XTkce7ZKmw1WHcQu/AKfbSboalLUbZuJjilNFkca5TJoDdgi04Fsg5ef556OJcgltuiCOhk1ViSOlpiBcIy1m2Z31Yfj0WBWpKHmdaXrk/AzImLdrxE01oX1Dd9Egghs8mW9HCelmQNbGl5MB7TykgWP9duMPIoJ5bScxpgIc33VW9v0sM1P7t8zMsv/QK/dPJZfv7NL/OH79xmPG+RIUCwyNgMU9iAS0Q0WmcgtRENSxZdpHv7DNlsHaSy9y1j8sREh6Ky+V7JiHepQ2UQSlKnmVITDjWYkaE09rmX4zCgaioyJVtBmMsmZcbWKvTaagw/4DOTo0e1+BAYA71P1idxC1jCBkAXExd58RSaX4rnwYHfrZrJZQUtxxYa6oTeBlvEsydPDbkhBgNni+ywSApbGWviVZRsPnNo3TSkgnRhjMVBEi1xj/J+bQB/Pd7PiOSnzK7LqNJCB7K0AFWFkVUoIvUB049awAPxxMI7kfGTO37y7h9aYiHZhNa+UWnILCXRic17z/LGStg8PKptGOMMdirMqHIbWCE+Ki7DsmFyXavN6mMxYKlKAQ9eX2aPn0sM9/A8ZAKtyAiWtDNPQjQWzXTb/DmDn+MGM60fZ88dUUYHMxp3O9cCICoG50lpAGV6bSiSw03uCC5BDJJpNIOYdKQhsRQ7piLn/36MKIEjWj7W7Dg++gO+3d3ha+3LPD5fkfsIM+m4vQFhmtmnuTs76zxGIV7skGHmo6ViniOVGeLoVQGy8GuifNBZzRQ+OqDla11IxpjK0bvY9gF6J60c0AQwyvyDhyrvKGpPx4nsvjNAzRZW60iPGlj45nucGSf3Or96y0tPZu8jgZHMyJSAO2SjORewKYjJ0dUZ10nN5yWqMbGSX5loIooVMSMRkXHGIJyArTLMMiwTDzha1wbwf7rHUdiR29n+4oomOdHmrtwEnnxsweMvJL70ie/yueO3OY5bIplI5k48t59F3YcpPtd391J7Nr7vbpm+loj9O2DNveWstl9i+8Z+1oAujfHoe6fCyiqvGmUWDsW0dwp+jKXRbbeVWiP+iWdjlX2i1XHD3vucs/5bB7vKu2pL3Tbbk5qkK7PVhdVDAVpNDLplUKulNnnBJnfcai44iVs22cCKdexpJbFJnSWJ54bv7m6xlJG34g1aecSpXvJCXPyRSg6jBFZ0vNLs+Lmjr9N+PPF/46eNoaUtWTCpnoAmJXfTnmhcwfZmRMYVyyERzi7cb3jG0CqhV6qgasEnGqa1RJUwJLSxhpk1RcoaV6T7WuXwZQ+UK4AlhNHWudQV2SK2no12/7Sw+w4psMvRpevsTf5DDt68z+5d7ecH5WxcMjr7aptbNnnBNreMOZCzGDNrxFjAGmia5D8LYw7EkNmODctmQFV40i9ZxhFaWMXBfLOS+akdNbvKSAZr2AXJnKUlyzCQJdDFDWDhAmCAWXRVzqD7IW/vNa7BrA85Xh9us3h3fOr2ZwJZIUysrBntd8+McQZkDccND3+45cZff4v/3cd/mc8s3uJIhsrGAmNkvRB7bgfTsT9LVniedzzKmTNHQwFnlKg/T3IpYWApmSgmP4QJlV9KMsDIH9tKAaJKB8A6I8WMsSwmBaYqi8+zbi+jPL7c1xBmqT/PreAn1ldBmIv5YwERSifTZpElhkiX5I7ShUgElmFwVDvXDehZXjL0d7jdnHMUdpYCIQODjGzFHJhuhmwT9/exyxMlcCxLfqRLfLT5HT63eIN/cPMr/LPXP1MN4iVJlV/IYCBWWhrjygzi7boaFx25O2XxTms+WoPPjsF8sGoOpJSEQTxhygqJwtKiCSYfbIrscPo3DsbcKskdMkIomvDynJ7mXmm3Zc+l1OdVYeb85zO+f11yEi7HtjKtsprEVl3+kVQ4hIYGB5uyBnoiybvqZu5rR9J7kbIIY5UvJgLJiw8yDGJm8G2w66jXSOvdrYDJDQcaWklcaENHZkEyyaxv6LIDWpGGAiNfS0Kux/sZT/KK5vJp4HMCssyUbg/ImmFZpbLXGSt4DmSlLnB5N7D49Dk/dfo6d5pzOlJlY4Exso5CYvUck/ek5o21VaV3Vguwx7BqyGQxaXyDFTLFf0pmr+X4NtlZJ6VbXwAm82cU5t5Yh8mOz7q9DDN/l3rfcsTizz1HQ+asrwrNyUwKqQW8UUaBRg3AEaybmcVBPTWga3QfpeKdtNOGlFasQk9HoiNhYsXMiKIkN/cNe4GWHxbYCiJ0NLwQMzfCPe42Z/ze8uX/H3v/GmNbltX3gr8x51p779gRcd4nz8lnPZMqqjIpQ2EwcLkUGHBXN2Aa+9IY+wpLfLCEhFQyCAnxpT6BhGRAF1pqtYUENhehvi2BZFtuA763sRENFI8CCgOmqCqozMr3eUXEjr3XWnPO/jDGmGvtOOdkVpHnFCeTmKnIiLPfj7XmnOM//g8+dfOiGcQLRmkbu9JBWbZuE0KAXoTcREpc0Bz1yKbTtcPnef/cnNXrzC2UuSVlZEqpHN2ALMQuUxArJCilVPNqpuwqHw6SOTNuctWWQbx/Zf5N+3pVhCFHPJGypu3a7+pvNRmpiAFaYmuOPs6Ujah2DpFGNMgh2zFX/dTssVvS2DHHA0RKPV5yCRTJ9CVMmMOlhhJoIz8bK3E8F07Hm3d85eJT/D/ev8Mjv3qszPuTdYb/bgKrh+fc+EJ47J0v8p4zzyloEda0MnAhHnIuHLOUgYUUzobZqwJZh2VDLgZKAWs/McQCo1APRG+AOzurp9QgJxgb4kANg2qNhdWXwhoF1lqTYDmTq2datGvt4Ox5rzW8MG7kznvzN4L5O1AN4GHbY3ja4IimWsmoDYD7CAcpzEqGsCGUlllJHJVZZYkuQk8s2mDxZq+GIG1ICPPQEg0g2YsbghSOUcn1tWGXV5o9ApmDsCZy/HmXHEYJ7DEnhzVfvfwL1k+0/Fz5Ml6Qs5SbrXrvIqQZBJP1UdA1ZYDubEPslsz6ATk63n7w4vI/q4fEFjCJ1C4/RX2Ho/MFRNlZUVc+Sbq+lWANlWysrgI0tgIFLHBLzeC90ZJbkKT11JAi85DY2BpXkrF+Y2a1mbHOLetS2EclqQNa2zeS6VPkaJizLjMLqjFxeg6UDJnA4JsxRp+0PhU2fcO8Hdikhp1mTMXssgJV/rPJDQwQG22Enm+PFCQtKj8MpVjNbdYLRVPhj0rLvnT1eP1cvLNOwax7Mbx48H/GOAJZIWwzsqYa9pMLzAkgK80DrzzVcvEbPsM/f+I3eHr+DK0xjBLCjEQIPQtJ7EtgR2Z3BbKu5zVHubCe+F9FSkVBXTLoAJCb70brgDD5d9Vjy0gHxhaXVqQyT0ZdvRbsJw13HYCq/7I/M5mopVJdkCrwZa9zCmApJOCgmz7PXNpRCibUToXSbgVNnFIAK5VAEk+Z0sdZSE8SBUBWecaqzFUXHlcchQ27YcNCenalY1MGNqXnSqQCeAD3SkLWSuRS3OV/WBzx1sv/Xx5fXOOXX3gPn3zhImnVaDSsyy4SJvvAZjnMLB5NO2x21fPpxqFpwwHJ6sVVvxjRjZC2mHVyBmsN2N8DhFzIrfptBffxytimXaUg2YsWDMhqdfM+BbgqoBWMBBDY7rJnTXGUDNKr9KMv0RZc/f6GHFgn7TQk82xzYNELaAe/fOQidEnTqhryOCGXwpBj9bQBCJa+uMoz+4xgJgNHeUYUZftFoxiE0jIj0Yn656zNB2VmHcX5iYLi5HlxOk7HXYdjK/WUnABZMgGy6m3tDg5kTdapLUZWFI4fCuy844C/c/bTXIm3iFIYihgIpUyRRjIzdG6/G5C1LgOdsbGAynVyu+sR0HJ2yvZb8zc3eZsEqdhDBbg0OCjYZeN5fcd0R6bcqhGemoJTfh6OwFe57TZTUMz/HSeAnc6lZetrUqtYBbVKMeGXUMGNRpJCFMYe9QS9RejpLFG3MWArlcwgmb0wyh7h3knIogSWMuMJ6Tm3/BRnm2P+4vAy1w+XlN6+wWLv3OlUMvYetC6wtMMwoxEheKqufSL1k3XmL0yKCgO2ZGoKr4hTMS/GLeqVscXEvRano3ZM7Pf2l6JrI0xkHXq5/+1Gu6mMcldvmgy2Bjmw6sDiCGrqbf1AK95oKYFghszjcTf++MvEGMDj96L+Ns72G+E0X6f0uZ0RqL45tycanjwvTsebazzRLIlf9wrlvzbIervJXkTRJE1Dn3H9XZG997zCVzz0SS61hyykZz8e6742bLSuCJn9cHdGlo9NydWvqi+FbCDWprjtgjOptDyeiWyZuk//9nmsL9nkinrfVkINggJqAjqwdZz3ZbQ08TrBpYav1jh8kIGsk0PBOpgCWs5gSybDVFP8caVLYaA3CVfKrSW163teSE9fGmZhQyoamOTm7odpAVHlhReaI26mHda5NYliZB4GSz9seGnYR19VYJl74JhLcedvRHIYZMM37P4pm8db/tfhS7m2PktJEekUSCpAaQrZ9kG9rUGSW8J6l7brYb1RdlaJulaFYGqWYHurgAT1eBSS1vZZ6b7aGLFGjsv128k+ZTQKVVZWX8iN/Z2KJd+WagERLHwldMKQRtk6OHAGZQhVhQJwkDv2g9YsfWlqGqXv6ZLVt12KZPPiKkWBLYASEikFmlbnkrn97lJkp+lrqu6QA7fSDsumY5DMPOrt3Aj+MC2Yy0BCWIZOfSdl4CjPmUmiK5GF9JwJa63LUNXU56JZOQWzXufoc7Md/dw0yN7unYEsOwF8UZn+rXREsc2UMrJeearl0Q/+Jf/zo7/Jk7PnWUhibQfpfugALZavRu4qLXR/rK6MB0a71cUYL3dJlCcWBsb9oMsNE1IlhgE3gJdKcx0ZWVJjZFuJlSJ7N+PIaXS681XgdnP1KYClC1W87TH9sfy+2RZARYCFGdmKicCMBEFp/UcZQE/8RegJZPPSgpeHPVb9jFWac6E5pI8N++GYXpqaCNFKx9mgpvauVb6XU/gyzHibRP7JmT/gPYtn+aW99/O7LzzGjRu7mngYBBlMU93YHBqgNEZZDWL01V0WbSBeO1QpyEQXjggl6XclBh6Wwf4m4wJuiaLbhN4yw4p26Sm6maYoAOWG7lUPnjQRUIFb/beDbwX9t5gKMmP3axTQKtbZ6NLIyMol0KfIkAPHQ8vNtNzKl8oGXvYm4XF5ICi41eWGIWuxMosDXW5qjPpxatmJvU6+yFanKpRcwa5stPy2jBHGCSGVyJpiaZqAbS5yKfSkCs5OC/HTcTruNtxA2oeEAHcDsoJX7jZRO5Dlt/E1yxlZDwX2n7zB+/af4WI8rKwhgLkbxlLYk7tLC90fyxuNfp/6eieX177fbUDWKDd0rMIxN/dcmTYuRh8rCw+ZFDh3O6+moNQUnDpZ6EwBrMoEO/GYfr3ftxiYMHqJK+hQbPOoOJZNarY6ODjR4/KOWQW1dkJHloG56HzbGEcrlsRcxFo+E5bYPRqtRM4F4en5C1xuDvjT2cN85uiMemkNwdiz1vSw4yuLNiScCaJs85YmCHK8QTzpsL5SoeRSNxnVFN68GZ0YXLtpaQS0SrDvzTYv7qPlnlhVnlGvn4BV/jF5c33ysU0DSyh+zil0VAzYUjCrsMnt1mbbWVbJWb+TY8Uvz0UQk2v4Y2eUldWISTrQvQmSq19XcMaXFE0QZjyHs509UrCYdH1PDbaOygjOnjKz3twjSuD9V57hU3tfgGzSFiuLgNmWNNx8R0v3nhVfdeUZLjRHJFTVECmmPui5EBL7oWFHdE+8Kf1tfllTj6x1ETbGzPL6IApQCqnoPN4CCwn0FNYlGytL5++WwEEezF5kZBatS67gTEKliT3jOdCXvAVmtRKqTYkCPANz0VLX64I7sbD8sgedoQVseWZFpPpj9UWB7R5NgPQGjcpEFehy/+C+RFoZ2AVulcUYZoSC5/tBbTwO8qL6HiWEdW7Z5Ia9uGE/rlXJQmEZO7PYyLQycJBnZuvyN+OhtWTG5bDh6/f+G+u3tPxC+hIOnt+HEpDB5vQ4rhG5gWFH6PpAvDgnHu8ShgTDQPXPytqAqZLDnM3/Udt1zloWJh2S4LU+tVlDsbUqjfsjrD6S6OuQ3i7YbXIz+kvmHNikhsbAJvK4Hjib14eTOpayYSf2dCnSWzMmipE6sqjnVhGkyeQsSBgfp0+Rechs+oYmZpqQOezmREtUHCTU5z0Y5mxyw26zoTFWQiqBNg5scqsJmZK4NuyxH9fMJGm6oWQ61Dw+o3X6dlbwq49TMOt1jk1p6skgMSLLJXInIMvZVyHUzdsWBbiaqAaGZeTl97U8/A2f5n9+9Dd5R/simr4W8MTCVrLRfyN7Mr/j5LvKHddyVzdczsByVpaPrQQpA6tAOyutjHYUUxDLL4sGZN2uUQ+3ATnTYsEXjTtdrzlPDmzdnswTzeRCg8vLbY+rH27eAtNaQ8iTJLLJwNaMbJ2Ixqnvhs7ELdpdcPbW2Xhci4xVnleZ20J6WkOWlzIQSOzKaGiZitzThbGVyMNxye78Glcf+j/4td1380vPvo/PvHKWdNDqJOlgUvRioFRGRonCcYnkZskSFNDa9GZiZbv7PPm8k03MfdEZNmh1oYVm1o5EyeRG1AbF6LXaXUCLFe+Cm49XySMr142Fq/GurQFAlZp4USJFbD2Rivh7ulQyTffJEexYjxT6HDnK88pOBJ2k9bsWMy+0hI4ktbtQTeKl1HSOXDS5MCh9gI7IgkDG0j0EZVIg9fx6taOgGmKfjtNxl5FKYCRJBmhbpIm3A1mVjTVucE4CWR4uklthdSWw/46bvG//GS7EIwWOjKnZWoRyIzA3KdqdgKy+JI7LKHoQRj+s6a3LXf5OhVEexTaItXUZMr7NCuL4M45jWrQ70HWn6xXGvh3Ymt6XErYAra3rGFlRuZQKsDW2NhUp9ATzAgs1ETBKZqYug/ZZmPxQSu10qxRa/bQSgaYkovkotUUBsJZc/SnvpY8WuGSj5fF4zN7up/jU7BJ/Gq9wsFpQOp03y3RzYV9WZWkF/V8JrYbBHG/UHNeAffyXo5RZkJAhSU19UglhqamIUh/XUCvfP1nno757O9YLbJm/+1X1wJysMX6geVS6hp9gwSEKSJpdyh3nawU7LVWwBLrSVHYiUJOilKGnqVXK4Ao1Zt2LSRE/Jg1EEz3yQoEkwY6ukf3lNr/Oe3u1o2DCjTsdb7KRSubPbjzE7E7ywhAoTeDgsTk33jvw/sef4bHFdbVOkIGLUW00LoYVZ0PPQkL1PNJ9dtjay/Ylsco9PQpWaX1SVOVhtQPo7zvtfVoR83fK9fyrRvAKDbA0xYezjnzMRdvaNSGdwtxe29REXtP+gv2dNCjqNfbkDzqQFSWYb7C+J3+//hmtC+wLBhiOQVypjPWf1naJVJra4PUkVdDrjpjrGpQbNgjz0Ne091yEwzTnfLOqbK1WUpWZab3acCPD2bBi/ioJkvdrtBJZhpZH6Pj6/Y9x/Ykl/75/ivVLO4QSkGQ1kvlneXN9WMJmCMRuyWK1QY7MO8sDTExi6IxeUh4DtRIGZImm+jbWzpOiwSaD1ViRcYNja4qvR8rQ0tsEz1HxcBPrFKYkasI+RF1XstR1NBUxT2CYCdX+B8Z1a50atc8pXttQF8jcB0Kj7yENkRIzISQGq5WGFJg3+lihCOuhZR4HstVOjR0DQ44cg7L6zAg+ecM/6JpVvyvS6K1FZCGJrjiX87Mbp2DW6xzr3BJSRmKEWVuBrDpOsLJq19JBLUsVcSCrO9fy4vsb3vW1f8F3XP1t3tq+vMX2UOqvOv3vyYylUQinw80Yb+SBgxwqfT1QWFjss4NWSvs90U2Bmg5YjSRLYRZka1FqoSaH+MkSTeeO5LpovFbH446LR3FvrTsAWf77DsbZqS5k4+005rOhJdPJQAb2zbJXjRAVgAhkdsOmdijWpaUrDbthUydq75iTlZp7VDSBSuVmmXPlmHOho6ejRVgGlcLcy+E02ne1HZfP/CGPtNf59/vv4/c+8xjHB3NYR6RX8Iigpuva0RUzK7TjjyU7baR56UC9TaxLLll7zIih84PJPvTJAQWWSgk6gyQsdbCQJUBSI8NsUnLf92eEuCm1w+C02WLduxKkAsMMBni11G4E2GKTIuvcshfXmsKZA0OKdKKpGBoFraw970w5EOmjs+/RzQ2P+hkbaZg3A7MwMAuJPkc20uj3niI7sYds51Ec6EtDIBvVNqj5vOh53VrK4cxeS2cwr1dkbmzt58rpOB2vNYYSNdFGAjQBMUbW1qiNE8ZiXcIdgay0CBw9HLj4tms8tfcs5+OqMqkyQiOFuRQaYCZ3NtDNpTCQWJfMZiJVFsaEQgeiJvu1LaDKcIw6S2Z0E3ayIA+iYEJNTcVL8lKBnJO/T447m9X7a74DkIUDVbfdbWSATW5XitCIgl/JZh8pJjsomd6BDAozGXD52lACnfntORcoi0nazGGvp6Ev0ElEKPQysJCE83e0aLy3BUMQYU7DpZjYDS+wf+6Y/z6/ynMHZxg2jXppebqu6Kfodp71oxYBWpoohKONGeeiG/xJ80W72nYXJvO+2M2DMrnEblujzysYxnjQ6ORaDzTx2/h6NAFOxabl4iGgfjxkKks3i8sExaLq9XxU25URDAVrkJ1oEKYJPbm3hMQYsnWkS5UyihSSJUYP+mnSmH+nSKnQa+fPIQPBjq9IppSiwJcbgeHS3jvZxJ+ON9s4Lh0v3NjnrSckhgRtJG4uzrnxLnj0bS/zjr2X2Y/rmlq4CB3nwoq5JPaDsBfmt+2/ffQlcTOvNdQG88AyEANgZcfwQpSN3rkc0OqKPQPJelI1bbeXqeBVKXT2XIsKaJk8cVIvtEQDcxW48pcbRFiXMeXQRyramDlZJ5z8+0EfAang3lyUr5uMgePJketSKiFBiQe61nlyfSsZGFjLQFdZOir96ktkN2w4SDvG4DK7l2K+wqhpuNdCzrbZD+ozdSsv2A0bujznWu6IdNV65vPJfptLSwiBx+Mx/6ezf8jRW+f8cv9u8rW5AkjWmAgdENVg3dMJN31Dc3mfth+g67cBrVIgZ8TZ8iJARkJQX65Q9LbJGwuZEuIonbfnnto+aNPeaq2s65uzs6Sgxur22tIQ6SzFPWepzReK1jOtpMp6zyaxjeav2A/RbFTKFpGlZDHQzWSTQdejkoWUdL3y1WPIth4VIVq6YYOGZiHQ5QbCQCiFLjfMw8BBWtBKqp7Ui9Ar8JZbTYy3Oqgj1tc1fA7L1SmY9TrHcWqRPsPOYkyTEuE2nyzvjkwovyeBrP5Mw3NfEfmir/7vfMvlj/JIex3AGEDKxtoP6o91N9pmKpnr+Zj1RFYYKLZhEvPvKQY03RnEcv16MAqw03t96pl2gqd0d9epB9l+PXd6jXcar5XqdnISfLXJcHo7lTyalh8slQ4WJFp0AT6ipRUFKBJiXai8hR67ee9BXgDUSd7T7T7VNVxtb9CXI45Kz+UwMDdJ2b3uSrg5fCs9X7Pzaa5euckvzb+E//qZt3Pt5X0KhhIlvLIkt1pODrvW8S4BZM5OKTQvHyLHG13tJEMwitWQ9MOK+m9d07QlUaSoD1b9GqTSZgMjRjOyQqzuMD14bW673DAXcjsCWk4BDsPI1goJ7UbYnYcc6QZNWks5mJRQzT+Dff/r0rDKM46SLtIb+76GHFkPuqkSKQwEJI2Gh2PCxrQbGWsqYvQKKMAClaUmkWqmCZp2qp+Y0Bv7Qjd22SjvQf3h5LTIOB2vPvqimyPaRpsnJiWsrKwpiFXnZb/sBJA1Dxw8Frjylld41+7znLFkUI0uzwQDsmYIM7lzFHsuhXXpsUwH7NlGgAapf4sV1SdBLJfIKY4wAgIVk3BGqYFYWfz6ky5YI7h08jXeabxWqttJQOzVGE/T27kwMdk7cXJEU7Ka1ZdCJ9reyAaSiDFCR98tIyyVwIZGO7EidfM5lIYbaZe9uCbT0ZNZSta+wn0oEtwcPpB4a3uL/bDhT+LD/NXBeY5X2swpSE0DlDDO19nejy9CTYGw2iBDGhlaYpt2B6NEEaXa+R4/EJNzGEOuuFdJmRyAk+9JP2ScubjljWVTt/hdDUubAmLq82vsJ7ToSTkYDicmJRTnSAEwGGO7y435bamE0H229Km0KNETV4uCxvYZ/lyIHR8Uhuq/opc3RcMTtOg3oLrkWrxM8b2kD0pBrRb8+Dwdb86xKYnyiV3C+mY9F5yVlXYarj/ZUN55yHsvPMeF5ohl2LAMG87FFRfDEa0kdkNmKTNOWoX48DQ9ZwFNd+2t6BK1kEQrutfuJnNtsrnysPTV8yraXJusud6VUn213B4l2WV6TJeaaJ6skYHoPNUX1YW0hSpfVKArmYfUm6PkzUY0MM4rKwfuBCilfubOjououMIJET6ipOoPvCpzUgnMJLECOquFQG1XXGWwDB2rNOM4z2CAM40mIOpaH7T5T6q11Cq3rGVQudukWfD5GgHhUtzhPdyECx/h5Sd2+Z3hLeSbLaET9amSQuj1A5MCw1LoBggPzYlHu4RXbinTKk+O9hi0JsrFKmR1KhSb24uMa5sUgT5RWpPLpG32rJKYzffR1ipvwBQYExAzlWm8NjArhFLXLLKw2TRcH3ZZF2G/Mh1H5lMq6jusktFZrXO82VIGUTVPKSBFmV9J6EWb/xIzx13LvB0oRdSYJ+iaNORYWcbjeyu1XvMmj9dRicBMjlmVuRIAbPS29n0uO5k3x5n9Nzg+cXiJeNQpkOWeWCeTC93wfUteaMmFIpQ2sHpoxot/N/D+r/oz/s8X/5Crzc36HPuhYymJ3SAspXlVo/ebec3NXOgMtPLRMh7wDmRVGvAd3tdCoumvjb11YjM/LQZaPD1w22Bxmr7h/54CViffw90SRqbXfbYb9elzRwkMJbGQhr4iJYWlKMi31ggHOpcVmv54XVpaY+B0KRo7S9/32ui5QYpJzTLr0vLp7iI34i774Zi2fZmFjCfo/aDZzqXlUgws5JD9C7/Bo/Pr/FL7Pp578RxFGu2ad0CyuNegDKjaRY+BIguWIjQvoQytUrTYCMUCCYwR4qkc7oMl1A05+nCEQUG/0aAEiM66KmCeJ5ILeaIr8sIi9KZFtw4EWIe8sUIkQdc3bHLDJresU0OwDdRJ6Uc1/0S74X2OdCXS2et1g8tNikQpNEGL+M3gBaweHy1CI0m9tUSLc6deL827LhGgNMTagcwsrPhUcFQpuYnxvPORSrnttZ+O03FyXO+WhH5QIEvuBmQZVOSFrV/nf0eh340cPSI8/MTLPLl8oXpjgIKvLYWZaNPi1YzeN2VgbRvkqTeWiakA3bxoM2V63fZoRA16ywQAmo4p7uQs16n5OUwlfyNbarpGnXwPd2Zo3c60+mzG9LmdxaWcTWdUa1EhFAYDXhKijE6bJgcic2PgJJO9i2TmDAwEZeWJlxYKvN9MO6ylZS4DIa5oyDTGWL4fXe9GIkuEJnbMdj7NmeaYP41XOThcAGqrIKkoYdrndhlV62oM3yiDzBlaBloh1Hlf2+XYpr5owAnjbQRjGhudb0JC0rRDkZFdVTAkVCoLq+JFaQTf0CV8lLYLkCGlqAExxtBy3O0kFuoA29QbK9naA9QQlWT+KsFu74EzEnS90URm7XC7f5Z7a7WTxgi22e/tuGrs8mxnRUaPx0ZOvk7nap0CWm/G8af9Lpc+OjlAre7ITeDWE3MOvmDgfQ+/wNX5LWXSxLV6ZIUNQTL7oTfFhe5VTzaYXfWxLokeWJfATHIFTtdFzd798HJ/LBhZV70BWH3JlZUVRDjIWcOJ7Lkc1HJgZibCuoym5hoQpftrn3tdKQK6fqUTx7mCaaM1ycn390ZhZ6mcXZUHfUkK7omysxTkglUZwQBfg/oCR6WpqfTX8oz90JHQAKXe6hpPM3QmVrI97zJuODTfo1kYOM4z+j6yFzccpjk30lITMcOxJuWhddSqdFzLA/uSmcsIYH4+pIdRlK98Ic55Fzf5tsu/x/XNko+nh8gHDaGD0mojoZRCToLMYFgI3X5gfXWXneMOuWXeWSIKbEEFtHSh0zqSxogAyedwA7QQC0IZd0jOxDKvAu3tFLvMDt0wmOyw6N+ShJKETWrYn21IQ9hClNMQWSX1K+sZm4YuJxUg5cBhWlTW8OC+W1mBLIAyBIYsNG0ihEJKgdls8J4KKQuxUQVWsHXsaFClWJBsIFbQv81jbVo/HbBgP6zpSiTlwEIC++HYJKqRNvRbMv3XGqdg1usc2V1KLengroysqbwwSk0uLE1gdWXO818B73v/x/m6C3/KxeawPv4y9OyHRAssJd4VyEolsyodq2JpfWbmiCiQ1Rpi3yPj4lMmqYUik4lP6eyhnlCldjmY3KY13brfH1xb/9kfgHdiY91L6q/f303il9LSo+HALToHLCjk0HNgJoetJKIxtpR+W+hCZFYS69IqWIMWJJvcMg89QdQ3IBF4oT/LjbBkJomZvMKlqF1o73Td69FK5ExY8M52w3Lvj9h/Ys0vtl/MX7xwSX20su/ONbnD482HJTaBKkNrmQvNywfIusNnUsWrZNSFE4wxlXWFNIYWDmwZVXbK0ipFwSjEfLS83hYDuWzSrkoM9OlDsjl6IjfRhESpnmVdbtgMSiafNaNRdSvNeGxSaIPKRNdlVplWQ1Hj+JQDMSpYFYOmc+ShrX5cgUIKwiBqyLxKM0IstbhwT5VFGBQoJrDOs/qcKun1YkNjqimlRlDDqWfW6XjtUcYTp7JNtpILHT2wX1VeCDVgpN+NHD4OVx55hbftvFzBWFCZyFzG5Ki7AVkeYNCDgU/GumJk+joeEe0636uBLYX+nqxZMgLjlhw4edpia9GYIjhe/rkYWt+JjTV9f6/Xb2rqGwku78i1sCyouX0hmx8ERGNkBQcFpdAod4fBRDtNoYIkUXKFIgrCYV6wdgPVcMzS2rlyj/2zfEQJzBEuxIFWXmR2duBPw8NcO1xSumgSSv1u/Bh1ubiCdgI0tFOGlq81bqfF5GAxiKg4wuSLg8+XpYxJvjam5vB1WvUiZLJUTYeMNQZO9BLDBDIGTBXd3AtUc1w97sdjU4FbTf4cih/9TAJLdG+m/ozmiWVsr4KCWs6satEYc2FsvKQSELM80FRglaHqcTFyrvzxppZm9fM5xbHetOPlYZ/F9WFsYESBENhcnHPzSXj0rS/z1NnPcMlqjIX01ff1XOi4EGBphu++l5/uWd2+ZGaPv5BcAa0WTUtXBtQYSO0glh+L4/5Hb7uxGqDFbBdsxMnv3q7blcC6pJq6PgXKkj2m+22lkrZqFG1v5skj2+O/QUzf7zRG1YkFCmlblYz6jQFVpZOK14JqtbIqsXoxRzfmkEwsmZmoWbcyq2Z1r3uYFjXBMBEYsibvbnKjNhzAK2mPKJkLQY+xXAKv5B3gmDYklTeWYE3oz9/nPpeWyxG+ZPEMNx7+PX5m8xW8kM5RUqOG8GJ1SjFW7o6Cf+vzkeZon5mnG/paom8OpFjNo/VM9dSSbGGG2i0pja1h7lmQoFq5ZIEhI8EbEsoUU78sqXVREfRyW/OGHEY2b1bGsI+uBLqiaaS5lKoSKWjt1ecx0CoXUcuAIkbrFUq0xpsEcg6EkBmGSIxGMAlZE5hzsD2e7l+OmKmHVgmcmR3bxxQ4THPmYSAX4WxzbM/dAJsaRtATq9fwukSG11BrTccpmPU6x5WdA55dXibceg3D96m8UJQRk+eRW2+Z8/KXZb70fR/nq89/nIeaW1V3vpBezd5RVPlkkoiPVDK38pob2Tc8o4TQFxQ/JKpPlh3zi4ms0CWFsJ0QolIoqUytiIw/InjC4N3Gq4FTd0oUuR+jMrsQKLF2hmDU5s9FDXbnRdMUNKWl4SjPNbrWTpck2q1wbbk+brb7tKxzyyrP+NPyMOvS8q72RS7bRD/n1SOOX8/722PO29vEYvfPuPrETX5u9vf4o888QndzbrtksYlXyHMtGAao7KowqOQwvnygpvBDggY1Zp9oR4VgSVOW35TQx0+atJQljBM2WlTrbYpKBr3+zlb2REtgtOJBF2MrFmzDH3rr8GdIKZiMI9jrUVPCPoVqAj89V4KMmRga+xqrPrvYfWNlZTXKyAq6yHap4Vgyc4SZLeB91i55G1Vi2gI9DauMUXa1uOxRALRHNw3+npKuOuZVV0ikcUE6HafjLmOv3XCr3SVuXsPw3YEtu43K2IXNuYbVo4VHrr7CWxbX2AubyqhqTHIdgB2JJhe/M5C1sfQqGJlXDrAw+W2El4pLNIyywqmkMJcRlAqiLEZnarkwKtgtbhcXbo9XA6dO/vu1pIZ/3TE1ho8Ea3TYZtduE83/KFo5NhCqRK0lq/deUYP4xgzhlbGlgEnBwHhLznupBIYYucgRu+Jyjjt/h/fi/c1Kw/mQadpX2D+74Q/iY7x4sE9aq2G9KhbME8t8FTNAUUBLcqQtM1iZKbz5kVTm1Ba4ZEeLNTXGBoiCWmX6Pcok7XCS8KR2XBPWlp8nSmSzG9kvU4yoLYqCiD4/C5BzIMvIpp2eK1J3XPo0yWSEPtQ0tyBiviNo8qWYhLGn0ATwyPNizZSZsbXUDz/Qo8mF+vzq+xlLIZk3W8BqE2Nn5QqrlfrX6XjzjZt5h9Db3sZqjrRsuPFkQ377ii+6+BnONitaGbjQHBIlq8wwrLkQYD/MtgKYTiYXHuSBTaHWKOuinrwnlSBgLCwsQQ8qAOaP2FOYU3OyK4skAusySuR2JVRfXweqXJbYTo7lo6IN6Haylpwcc2nv2FR+owJa7gmsDVxLgrO1xus5BQA1Gc7N+F3O2Uq2UKSWhfSmNsnqw2djGTpWeUYqgbPNCrICIJvckG1fvMktTchcH3aJTeFW0jikmWjed0R4Je/QyhHkwRh7qpbxRfHz5aH1SEx89fLjvPDoWf639RdzlJeEo6hpuBGzMrDGRxb6XTi+MiMe7hFfGih9b0wr2PLQErHaqNTLJJVRbpgKUhLEQIlh9M+qno+2NxI9d6Wyu9DaCfMMHYAu0KWJpC8JknS9K1mqb6MyITUEYRk26r0IbFJTvaKrl3Asumi4BxdAo5YwmOQwJQssaTIpB1IujAm86p/lTRtsrRpyZN4ogzKIrlXukwWwynOWQf/uS0MSZYfOyJMd02uPUzDrdY6d2CvDynfsJ1hZdYjULkmJQp5HDh+b8dLfS3z5F32cv3fuE1xpb9DKYJ2SgV0ZOBuEpbQ1VeTkSCVzWDbcyLnu/zyxAsZFJ5UxmbCvtxOqBr2mALIFbDmQBVSmluvQfdzJpP2vC0rdzwktlUyyYqyVsMU8mwsWV6uFhiZ+6CK6CL1uYAmVqunsLX/rUVRmqHHdOplcH3ZNS57pucZVA7S883Wvh9JpA480sJDn4epv8v+OX8rvyONsbi6U+jTI6GeSC9IIeQbDrnCcI5QFyz4Rrx1CPxgtlnGStnhaaSIlZ5CoOFkBSbYFSmXblzoUBaKSlbzeNfSCAzVELAGl0+pdbjMAxv2zjGLrCYQ+gQIc51nVZcMIjibbbLWSmJEsPUOqD8qQIk1MpByYxbHz1+VISI1R0wtHJdCGxCrPqq9WkAwlWspYYk1La52yZJTvdVFzZwpmvKmdxJXRwk/H6Xit0UjWjtn0cNkCsOqFk8aKdue7M5HVY5lHr1zjscV1ds34V/2xsqXjKsuzuQv1P5dChwJZjgfEyXZD6u1GlVmut3Nu6IQxVLaBLQey/G1RxPo/UyD9doDqrwtK3Q+gx0cuDjiZV5G9H1BwIaJMXv/8sgHbjYwpdUNllmYGxqQpDa0IVX6WizZRXmEXoZDjMXskZgL32hDeh35Xkf0AjRzC3jP8cXiEz8jZEdCazt0u5YvK0hp2AtDQ5qJejalUypDKCLGu93hfimwfZJ50OP0aJ2DXlLnn/vLVfFcYA0f8/LHut/+txvAGZkFNIHRwqi/TTOgRHHWpn8fU16TBoo/hYTO6djFKEIsy6pzo3Funuwek6LEhJCtC1ENtEG2USJEaojCYAAnby9jHYkXv6Xgzj1eGPUKXqxokt5HDR+ccvCPxJY8/w2Pz6+yHNftxzUJ6LsZD9kPHhTCYTYglFp4AdvqSuJY2HNji01MsaEf/7QwtP94cjOrKePydBLVSKfTkaiLvoxUhyigzBAe5tAF9UiHiY9eYXi5jXBLMJiXjfr76WkpFsN+oBvAwvg8N/9B2+kIa1gyV4aafbakyw6Mc2A2ZIwsG60vgXNhwkAu3PE2uNGQC89Azp68pdA5OpGJgROgqcHGcVKGybDoO0oJ56OlLZ6+zZTpRrsOac2FQX7MgBAnWwPr8gIl7YcFjzTHfdOajvPD4GX518y76NNfmTz82Q0oQ0hxkEDYp0Dy8ZLnaIDcHbaD4vqMUXb+s9peUx/0XWckAUawxHxCy2qy00UJNUPVJUU/ijDEqAcml+mTlZqyPiIXk6bjoawR9nNIHNrmhL4GF7Rly0XCsWif1bW38L0Kvl3sioq1/Wq8FpBkUFxiiMZIzKYW6FqYsxKB1VBMyTdA6qmPi65VKZWWBs8H0mFJpa6kWLaBNoKPS0nAiyOJVximY9TrHcWqVYjjtkp+UFxqANQWybr1lzstfmnn/U5/gy85+isvNAbthw0J69sOahSSWosj1/C6mha5fX2WXV40Sj1aKMUYYr8PkgGVMKaygsMkSggitTfzZ+Fy+YLjBoHtk+fV36mo8qAtDK1HN8Aywy4wLZl+UmjmzJMYghZZEZxRI/02YmL/nRum6IW8ZhbeSuN4v6XPk0+Gi6sabazzWHBNF7gpO3osxl5YLEb50/jznrvwXzrRfxn995u0cvrKE46hpG1mg8e9WI2q1CIiEfslOKoQbh8rOqoAW4ySdRYEso9KG3mi0rs2wY999VMQnSPcCCdr1CINRc/30MeNDY8Dq9Vkn92LxtLmPHKUZfY48vHNLddhFWPUzAoV1iVsblhmZC80Ri9Bztb2h3xuxmsfnIvZbi41ZTPTJTdkzXW4Y+shes2En9vQ50kgimwlwyIUsufoDtKQKeq6dQi+JWAohJANJk/rq42TN+8MSOR1vnjGUwBbF3YEsr8aFysSqQFYTlJH1SOHhh67z6OIGu2HDTBINiZkMNCYRaQwMv9Pw1MK+Ag4VS6gBIlOCi2MQTH47AFbGfR/ROFeVpWW/3ezdX49ff6ekwvsJSr2eEVAQy1kOacKJUUm6GGPTQLviQSvKdwtu6k6ocpAeTb2bMjmDFNZJC46bskNPoA/HnAkm6ruPviSNRHaAR5pDFrt/yTw8yl/dOk+3aimDMnTdYxFsr1xV7wFJrQJa646pue7U28oRJikqNxRLKbRm8XisG2BVjeEFaspimQBX/qvYj0n7psBWVTEmM8stgf12g6ff9lnzAYeiPDknIkcKO6GnkcxeXFfWVC4qZ9egEmUrF7t9yqKx7aKphjkFShhoQx6tVIpUuWMwNtdAIBT1T9PTP1YAq6DrbEI7/FPM+9QA/s07bg07I709BLpzM66/W3jsyRd49/4LXGgOWVqdcSasuRDW7AaVIrU2G6tkbZxf3Iv3oKj0pyXXmqC1vXJGAS1lWAlLKbW2cPNxrR1U9hZQVYivJ9P1Yz0paqNIZXf1jHVLT6kG8g5gOZi7HxpTXYxjU4aazpgnx/8bkY3lDdrpewm24lb/LLt8IYGDol5k6jeWWdtc0pdAR6AroXo4hqLg+7q0zCzZ8K6vg1Dlh/M4mMBRG/seiuV105iKqPKzyJHNjQO7osyhHZl9/gAtmfP2Zs23nv9dnn90n4/2j1P6dmz2ByitljlpR5vo6wuRZnWG+aaD4zWlFPN9RAOyRBgTD4UaaoI2/YkKWJUsFnqVVbViGtxpsmH1yUKZwsXlj0lrLYZtxm9pCtIHGIBQjAG1vUeKouqTfojMW/2+OmN8FwOzKrurscfOWm95yqEIlBwg6vve9E09m4KgkkPR2imSWafWGMV2zEpmJ/aaiGmAKMDGLH7OhFxrJ0RDCD7bcQpmvc5xnNrtAgMm7CxGUMv8I3IbOXh8zsvvz7zjPZ/h7577Sy40hyykJ1I4F47ZDz1LKeyHV/fIOiwbDgzImiYSItRU6ulwX6wqg2DseGx1uAWS0eVbIsmoq76QVCZJCdWLavoafUJ6EBeKTKYlmm/WyDJTyq3eolfs3DzHMguDBFVPXpiFDUd5zlGes8rmL3ACyFpnZWkFKbzYn9GUvdySeJGZHHMx3Fkyeq/GXFquxMBCDggXf5NZGPjl9G6O8w45a/KIDEarbczXZAb9rrC+EAnDksWmRw5W1nmYMLQCkPJWp6EEm1xz0m6DsxWtYBDvXPgEb2Bhts02mDbcJmiphfooOwmDTeoZOgOb9tu1Lsw5cmzJhMk2ZA4CJ4QLzSGXyMrIQrvtfQl0Q0NKAWKmM4nhZmiYNYMavodCn5Qm2+XIPA4ECpust13SQdBClACtGaOmIiov9NRDnL0XmZGNnWUdMziVGZ6O1xx9jtxeh25VqVsbohIDmzOR1cOFC5dv8ejiBjuhozFzzoUMzC11aibyqh5ZHUPtsk8TCTFA6+TdHK9wGMWBLPfD8qVGKf3eLDFvCb/x+AudeVw2PF46mq7fH4+o1zM0pTGorBjbEhSV/kdD9XQWUIBBk7gTSGRAfbQaSXQF1rRVDrCeRLZEcpUbipSa2DqUSOaIGBT+up+fTSORXYQmbnh6+QxRMn9RLjGsWpvXlIFbs0KcoTVDPahyS5MSshn9xYAR5MzYAmX+WWYspWsKo3G83zVTVfVkPzZv92ccO+vm8zWRNnqBQdG5XKQwCwO7jTKohm7simdrjOlTCzuhY2msLGd0JWMAqwfj+JjJfRqLGl870zhZ1LmgzzdlrmRUFlwQa6br4wdU/kjRtXmwx3TZi388pzLDN+9IBJtoAmkeufHOlvjem/ydi8/w2Owai9CzGzoNKJKBKIWlSE0vBPdh0uPbvXhv5sLKCs4sws3c0to6Amx5AEYKmwJL0WZ6RllBG6tJWnQOnBqUT5ML/XpfO6aMLq9X/GiuTWk7pN1Q3n1/Nd1QjEmme8JpSqO/xwe1Xrnb8AZJJiuAJbHWYqDWMM5i22fbCL+1z8ebKb15Z+1Kx1pa83lV9lWVoNlw/6xl6Lg+LJkH3SfPJqybYGDWjbQEYD8cs7bm/SvDHimqzLWVBLknhrHh2xC3wNT79X1ECZwJC75kfoN/evW3OOgXfHy4QhIIG0EGIUdNz5Ws6YYUOL7c0tzcJ246ymCZxUHUQxisoyJm7G6feSq1ViIUJFj8RwGyNls8Fb40zgxjbPTHURouxRr+vTCkqE33kEmzbOtgIc6TeZqpXQMiZhtUVEIaCm1UZdEqz9nkhmEIyKBgVmmtNpwAWraQUNQ7AYC+V/+sGDMxjMfXUTejjckCtDKLWOiy1mJtiGxyIZcdrd+KKgSWoSOSKyAKShhpJsfta41TMOt1jtUwQ4Y87uY90TB6d1wqQ6W0kaNH57z0JfDke5/lKy99gvMWj7sbNuyHY5YysJDChTBjLnf2V3IgyxlZfRk9sLSzS60CfNvrZtPTfoU/cpp0juumzDZ6K/rxPj751+JcKgPmTqysB2lhGLsZoQJ1oAuje4b1jEWG/jvUCXpGYlc69TJBiwif6OcWbRpRGdtBWuimNnb1/pvc8vJwhkxgVz7NvN1wVnbu63tuJXI2LHhPe8Ts/G8B8L83T3Lw8i75SKF+TcZQ36psBUa3J0hqiMd7tENSGchJQIsMA0gsOvM2UAY77KIBuGlS5KpJSJ3ssQ53cJP44IloYgbzVOkhQIkaZy4DkFSqsckNL673+cuD86z7hm5ouLGrHaCezaTTGHmhP8u1YZeD+Q5Pzp4HFIB0ECklBa6CLazd0KhRrxUaO1i3o0hNiKrMrFIIotG3muym1NkoSu0NIRPIHJU5Z8KaZI+zQeWt2R7rdJyOVxt9jrezgGXye8rOikK3H1k9DBcfusXjy+ssgsrXZ/bTSqYR9ciKdwE8HMjyDvq0SeKY09TYHbYBLB9SLxtlhVUCZjfsp1vuEWeo93cGzJ1YWQ8SkOVr55RxBu4XpoWIeaziWI36I+ntIpnZhMcllOoP6FJDT4jcFN3utcF890S9tFZ5TkGYcZMYBxZ3zCy+d0ON4Rsuh46nd54B4JPhAt3RTDfxWDy5M6EM0EozQYr6NYZckH64C6CVjQooSMjaHdYbWEfcCwb7nSefnh9DTp8S2aYROp7qh5ADbtZ0KSY1PBpm3Ox2GJKyrNazVo3hGXAT+IxwlOYc5xld03Axqgmy2vz73srls3ZO5YBYN70gNKh8ww11wUEzfQ5n83nDTROjlLkndt++RGsW6f0G884y4trpeJOOLjcWLiUcPbrg5hcOvP+hF3h8cU0ZWbYGLELPubBhX0oFsu7kX3tcOg5yYj0BNerfNiclhP3QT3y0Iq3kajzenlgftNmnfx+ZEkKlgeP64smFriLxZx89gKQax2djbrkP7jTV0OuZpbFTB9IW6PNGUJOcHJrOrmYx2oBSFY3ap7hqRlgXtbXwVVUtZZQ5B7CQZMmGQ01nB000XkjHQuBm2h19gcWawSVUv6O+RAg6hw0l8lx3lrPNMQ+1txScKGIm3+6NpOqFg7zDQnoIMCsbbfhkDSLQ9+Ab//sHMEYJnA0LvnLxGQ4e/S3+l6Ov5fpwRpv9nkI4FPJMJ04ZPN1wyfLwGG4daqphDjYjo6wsN4BPWdPgRbT5D7qeuHqlFK2lJpGz4tL6ZGoUb8gkXS/UN1jBts0Q2ZsXLp49Ipw7ZNGMkryz8VjZwOh+LAPLsOEdy5dZXZ5xcX7Eo3NVqWxyw9CrCT5FgbLSam0IUiWVJdrijYbTNG3St5aCgpwxkbKwaIeqbomNssEaS2oOjHJDBa+EPqtfWxSvq5UAsJCe4QSY+mrjFMy6B6PEMO7onYk1/Tuo4dvm/IxX3itcfNfL/J0Lz6j0STpmkjgXVlyIa5aihn2t3N3sfSot9Abiya/cuxs9mmoRrTsxHT7Rp9rBACi0BKJIpe9O2Vt+nyhiMbe3fx4Peocjk8llNOQGA/smzDQKddM40x53peL2pakU2xhGOmeQUWqok7LK2QA1hU8zEsJfxfMsw0u0dHcFLO/VcEDrXe2G7zj/W7SS+I/5PRzlJZlIzGyZHibbwHcFjh+aIcM+7fMJOusNqysu1UPL0jyquWEIlAQSrD0+OT78I9aOhcllxOSDNkJS0Eqv0y9DEiZLLAQzKBxK4CjNeW51hpdu7JF69e467Of0paGdaK0TwovdPn9xeJl4JvN4+4r6AliVMwyBGLWiUQ8tvbzPAYytNZRATm0tLuYBNcB0nwGJ7Md11YLHoKkwXnxmW5yVep1MMiLmnVNOwazT8VmNEiZJf1L/hxbtChSXIKRF5PghYefSEVd3bhkjK9HYJmFHBlqBFvWpuhuQNZUWjhyq7WG1f+2qG39m6zYVuKJUPymMuYQYwFO22VvVP4ttrOHka3yQgKyTo6BMs2lnfGuttjfn8F6k0BFUjicKVBRbdyZb5ip3A5UZBpPZgMpR+6yl5U1R090Y0l0By3s1HNC6GBJP7yhD68/LQ/RHrUKXHv7hksNCTXHsdyNtnhMPsxYDpUw+G3G7MbsjiGjBoN5c1sF2oNR9tPI2aApjcwSoHXDx7ji6mTf/ZPPM0sKwyw2H/Zyj45kCaaLsYJXMjA/qYNa1bolQOBuOSYSRiZjEVChumuufhaghvBiwmXU9A2isY12KNkYzMDNWcylRvUsINHhala1rJTKTYQury5PrT8ebbzSSIArd3pxr7xUuPXGDd++/wNl4XFlTClb0LCWxH5o77kE9VOqgqI9sQuhM7gzeuPWkZjNeF22sLyWZusEAFGOjthMMGWAhVE8fzRenpiB2pWzB7yqRs8R1EVZ2Qi2sDtHX7HOGShCn9UqwmqYvaVxQTrzfB7lmOTncpN8ZWoFovq7CYPPAQmIN8kqUWismb+AiLESByiDacG1LYl1mNfkuSGYpg91Wg0hWecYmt2xyQxtS3bvuhA4NSNJ5sQ2JvjT0JTETDUs6yAv1gw4bbuQlQTKvZLgaN1qfTkLG3IrFiQj3Kwn+Qpzzdxd/yTe/5WP8b90Xc/zSEgYNuaKBZKzgPIM0h+5sZHZpn/Z4o0yl6chGbPFAEwe0Sqn2Q8rUKrZfK1tyw1JAoik1bB0rUermSzI060J7K3B0uGA561k0A21M7DYdTUgq7UODrlZF7BhR8PKpnWeqz+889NxMS/77rYdIq4boyhdQI/hYxk2XgQyaohi2UkdLgRA08Z0cWPeNNTl1j+JSw0UcuDWobHUndgQprNK81tStqKVPsuTEVoYqT/xsximY9TrHsuk4noXaLa8+WZWppV3y/uyMa+9umD11gy976K+41B7UDolSfhML0Ql7L8wrwj4dfUmsDMhyxDUAc/Hrxy4IjJr03iZ5715MGVl3GtuU1LHQiUykHicYTtPF4EFeFPqifllTWaUbD1ePMNOW96iuHJS+7d3xQGbX0hc80VCZQi3LsGEZNxymBQDL2NnzRo6T0nb3w5ozYU3kFhfCwJmwuO+A1pmw4F3tmn96/jdpJfHvw3s5eHGPZNIiSbqpT3OpRfLmrHXMuz3iKwejf5ZPzmXsmEtSxKmQkWBeKX0mN6P7QolaeFSznTICXOoDr3p1ScbQstlJinYmAE1XzEKf9PNMWWNjS9aFocuxgoseiRBRY/i1JXjoAhvpcrS3oN0GESHnCI3GzrrBYhCVGmpxOWcRA227ZigNg50ru82m+mcRBu1GZSqTbxZXBLJRuBXIWtzl/Dsdp+NOow2JISrco0Mc/an/RCDPI8eXAvGhNY/u3mQZN+q/JImZ9KjRuC7+s1dpmvQGZJXxoSsrMU3YwIB5O7k8ZPRLqef+XY71KejlHlP17wpmbTOcpgDWgwxkebGx1Scx0KRMAMKGkUGjl40sHmGM1O5o7L7CYP4j6uloFH67XTYJdU5zZjIwlwFhw45k5jSfB0BLuBgGvmjxDOFc4b/LZbrDuQJ0Sap8jzgWt2muCYeS5oTVRteVXLYN1/QDYapD9X96ymEFtPyTFP2sdU9sn28oFSBTU3imB2otGlSKKOQctJlhLC2vmVMJ9fVXTzcwmaeCkLpvUGaXAmVCztoETNb0cBaWv4ycg4JdOdKIEKNe7/YrMysgExoKkEtgYASpdiTjDK1y4hM5HW/u0UoizSPXv6AlP3nEl1/5Sx6bXeOM1RiuANmXnv0gd22ab8rAS1nPs97mk4g2bmcTSCpSWNq8c2BGp2eDRlMvhC157MZO54WYvw63s3kTWsdoGMnYhF9M6pb1CSCrFalgl8vrNiWzNMlhtL19JN4mx5vWLfcTNLnXQ8OetJ7xMcpElanlNeS6pC35pq8zkbLFuAPYDRt6In3aqZIvZ2z1HnIkGoDUBge8Cmeb42qvEkWBCH88v39CWCcFM9R0PLCSOQQ4yj2EbDJUN/JP9T24Qby/93s5NOGw54P7f8gnH73Ib2zeQSot8ShWmXpptDYKvdAvYX1lTnNrD3nlBmUYRnaWL/amYtkKg5t6DwPSWOPf9wIxVEN4CbUoGl+ogV/NGpbPFQ7mC15YR5rlQNMmduYdsyax6Rs+ubjEFy8XLKRnIYlzYaAvgaM848Vun01u6Uvg47cu84lPXyYcRMIg5k9s6dRF9O8klBl4uV+wRlsWhj4S4tgiaRtlZx33DbMmqXeerW2roWURBza5oQmJpXQ1dVGtYUYv7lWeM5NEyp99WNqDf9Y+4OMD5/+Mm29bUMIIaLm8sFiXPM8jN9/WcPjeDV/28F/xlp2XORuP2Y9rzoXVxCdLOBsWrwpkbUquE77r0TVVRIuLKY6pl0n1uGpFJXWeXKjpIGUEvPx+RbvnbqIYkS0gSx87b/1+0BeAEWibdGtObOoD4wnRytjBAFhIXw36o+RqBr8bNsrAKtEm+8AydPoTO1pJzE0f3ppp+LVhjxfTPp8ezvB8gmNL/bjf7/9MWPCWJvGt536Xf/DEn7K8uKLsJKWPBpUY5nkht9qBGJbC5kxkc2mHvLecIE9GkfUOerJues5WqChTi5wtdt1vbwBWLoQ+EwYHxEAGNTYMqRCSAloOsAFVdigZSMo+CMakKlmsYwDHQ6upkmU7BSOZaa8DiqCLsANhOQeGQX9SDgw5MKRAN2inoLONWp/UOH6TG/UwQhlax6lVn7TS1LhbZ/C50aKzsxT81Al8ZV3L01LjdLzWeOvOy6zPNY6I6DDgubKymsD6fKB7KPHo/k3OtSvmBmjsSD/6ZMGryth7EsmArFRG/MFOYdwOz4dyLbeLkynjS5AKdqXp/XwKqEbAsgVk6WOXrd8PMoAFTN7zhF124iVPMcgoY4EB6ofUmkG/SGFADVRnon59nmZXrLBoJdGG0aMiiDIlShGO84zDMuNWnnNYqF37+/3+59JwNhS+cPEZ3nn2ZdplD03Bo/pK1C6wezbqmhNIy4Yya8cPzMGl8UCp64keDqVq5yRP/nY6kl2uYJavP7a+TJKi3DgeqL5alv+BpxMWRKllzujNgYHIcMJsWhlXCgD4HqIy6QwQy8l+DCTzVN1s/lqgoFYuykL2Rpp7PQ4T4143mDcIq6ZcApUVVtB94uk6c+/Ghz/8YfXUmfxcvXq1Xl9K4cMf/jCPPPIIOzs7fOADH+CP//iPtx5js9nwvd/7vVy6dInd3V2+5Vu+hWeeeeav/Zo2ueHgsZabT/W855HnefvOS1xuDmrTfD8cczkecy5k9sOMhnhb8vgqd7yQOla54UaecWTNP1B2B8BcEsHYWf1k7lKfKlibxKm3A84DL6ajL5PrGQGt3SAVyAoGWE1TEVtgXwJdUWsQr11WpbApaiY+N1BrWrf0JVVGSZRQf6YgyYNexwCWyq4/vr6cZEI3ysNVRhpmfYFKDD1N239PM1m10evNkYFIrk2Sk9+fz0l6Wz0e9uOas/GYIIWZDNU3qysNfW5Yxg37YW3S6MyLwz7PD2e5lhesi3AzFw7yUPcDDtbd73XrfFzyrnbDd17+Ld5x9SVkdyAv82hzEnS9yrOxLuoe2oX5TM3fSx5ptj68dvE6aSqfd4WL1z+5jGtcXbPKdt2D1klxU9h5JXP+T+HsH8yY/8GS8Pv7rD56geu/f5mjPznP77/4KH+2foTPDOd5ftjn+TTnU/0l/j+vPM1//OR7+I9/9h5++aNP8ZcffYT2+RnNatLuKJMfML8sf09+3biuhVAs2CSw6fXYiaHQhsyqn7FJTfXM8rCuIUduDjscprnVcYGVhQ0sRA3iuxLZlM/eHuHBP3Mf8PFNe3/Gy1+SKW0cWVkYQyuqT9bho3Ouvy/x5V/wSd6xfIll6DgTjjkXjzgXV5wLHRcC7IfRhPHk2JSeg6yTsaYSjouBL0UBnehnootBqJfJCEiZrty7G63dtpq7T56/LibTrviEveRRvm+kMZ3gffQlV9km6Oc3E2E3ZJZBi7+FpOqJ5Xpe/+2AVpBcKbjz0Fs8bWRji0MTVD+8KQ0vDWd4Je3xUl7yQho4zOv7/t6jBPbCnCebnu84/1t8zRN/wfzcmjIrlFA00TBCbouBWdCdEdYXG7qHdikLQ8lzVpaWAVoypNpJFwOyxIAtn6QVnMqEPiPDZFJPCmBJstsNfh8M1LKf4n8X7Y6kCeBrk2sxIKovTTX8zKbLHnK0wkDv15WoCWBpBMNKDlCEYQjG1hrVw32KNZp9KIHDfq5yHlvt3MNrndtqnKmyUy1O1qU1r4AFfWnosGIHqRu603E6Xm28a/YKq4eL0tadlYWtNUZH7/Yj6yuZRy9e53x7ZGC6bg68S7cjysiSu6w1iVzNeJUubsXIpBhWoMYSchnBGQeiXCqoUkUdfttQH2N8/nr7yVozgljbTK03yvDPYbqm5xONI/9MZqKFYZRCI2peHO13azB4Y4BWJFdfpFSCyketUVLZwx6bbZvEVZmxKi2HOdOVzz7u+q87gggziVwImacWz/KWs9eIO4OCWJZmW8SKhMjYQNkJpN0ZpXFfHt30O6AlJ4GsDFLyBMQqjiZNQCz/8BkBL1xGOAG4KqhFNYCXTJWd11FUbqEgVKgMvIJ5WZVguNvI6h5y1LfhhUDRMyZnodjtHL9L/uGYlFDljCPIVQzgGtzo39Ylxd6EHr3OGyvOBHYw+nTcu/He976X5557rv780R/9Ub3uR3/0R/mxH/sxfuqnfoqPfOQjXL16lW/4hm/g4OCg3uZDH/oQv/iLv8gv/MIv8Ou//uscHh7yTd/0TaT01yven979NK98cebxt7zMk/svsgybCkQpK6uzcKmGhngboJNK5mbuWJ1g7PgxlIvKDa/lBatJuA0o0BWlVPmg72u08S5Vighas8zldunh9Fl7Cqucam2ynoBaPVoHeambJ387M2vLW9HYPv5YDgbBGwfE8hFF5xwHeHzN9N+tuAQxG0PLFSWwlEJrt5uysvrJvhhUXrjOo62GN+jdRHwuQ7XQWEzqHDd/16CL8ZvNRapiZWXhWevcsi4zrg173MhLXko7rEtkVbjNEsdllcDWsXovx5mw4H2zV/inj/4mD12+RZnpouCNl9wUkkkN+yUcX2rJ5/aUjQVQMiVl9dGaAFO10QJaN2WTGPr6A7VRU2+XR8YWuSCD1k1itOAwQHuUWb6Y2Xsms/dMYf+TsP8JWD4r3Ly55CAtuJGWXEt7vJT2+fjmCr/37GMMH99n/mc7LJ5paVbmQyzjmge6JitNWD2+JAvShbrRK0nIG005TEnXMbClN4d63MSQGXJgMyigdatfaBJ9Gf2oD5MGqfmxcyvv1BRN4bP/jt84Z/ADOnYlEC9tKG0Y5YUGapUgrC9pNO5b3/EC79x9iWXc1G7qrnSV7utdEjUzHL/AVDKHec2qJFZFLB5Xr2ttMZh+iW707htmB6QA1iVP2FbjhOG3dUmhT37V6B3XXGczRk91opxu1E++7gdtRDNKrP+udFupmv3p0GKtVK8B7XhnS4HJ4w+qP14YtXZdGqVxTsANXwycqeWPA3Ajz7iRBzal536PViJ7Yc5bmp5vv/DbfMUTn0LOdeSZ7uQLJq8QyBHSQhMONxca0sU9aBvqrtxBrDrpKsglOZuHlgFbQyYMeZyoYew8lBHQqgVLsuLCipGQCmGwLkUGspixpHa+i0Y61ZfhBV1fPOklMY8DfYr0OZo8QwvFnIPSeQvWKR+lJCkrM6tPatV7PLRsUsNgTC393bAxppYDZs6y8DTLVZ7XbpdT9ZW5NWE6nnpmnY7XGC1CWA7cFicogAjDMnJ8STh34ZALs6PaLY1SmJGYSWZuQJY51o0JtijQ0pXBzhvoCjjuHLU3swUnTaUh+kpGmeAweWxh3Gj7bV1S6MXFKNOSCTBQSPVf295HJ1/3gzaC3G7+Di6ZvJ0hM4KBLjLUn2DAVaDU66JkGlutBkaWjoMbvr8IdltBk/VAC5h1yQzl/jO0ogRmEjkXEk/tPMvjZ68ji2QeiY4o6U8JytBKM2HYCZSdmZrpTteWMvnkJgzhuvGfglhbXXC9veBd7gmgNQGwKOO6RL3c2U4yNtcnX57P9ano8RrRPUEqI3vOv7dSQSzqGuPPW8yrMU/8Gkem1vjbWVr62ONLGYruZtzyAMwwGCqTz8cDeLq8YUfTNFy9erX+XL58GVBW1k/8xE/wQz/0Q3zbt30bTz31FD/7sz/LarXi53/+5wG4efMmP/3TP82/+lf/iq//+q/ni7/4i/m5n/s5/uiP/ohf/dVf/Wu9nvfOnuPhJ1/iiy58hkfn19mPa/bjMefCiv3QsS8DuxJqSvpUptaXxK285lqOHORZLUgB9g3IOCotqzyvErSj0pCKsDIJmkrXdK3w2sQZPZ3JcldlZGTl+tywLtQmfCvBfo8phyfXG691Eio5BLNSQRUo9X2R6EmmREm3sZj0fvmBrFlebZwkEvja6YytVEq9TSuhfkY+HIhsLb1wRpo0vTT1ciGdsUfVI3ge+ur5GijMvebJLX2OrNKcVAIHecG6zNiPa5IFVCxDxzz0vNCf4SArqOXz0pHtkdd2HCnrzsG6fBtg19+H9StK4FLc4at3PsW3PPZHLM6tSbuZEse6aGy8CP1uoLu0hMWcanBYsloI5BPH0hTcyhnph8rEquysPlvTv9RJWkqpIVkhFSUCFL+cqmShQOxLJQCULNwaFvbZ62sJkhn6xtIa9XahEwWqBrWaCQOEQfTyXggDSB+QXm5nbIVCTlHVLUWN4POkZjruW457s4Kx81Ebb5kh617FVS6ubvJ5xZUtw21u4Hcfp2DW6xwv5cJwa8Zk3q+m78PejOtf0NA+fZMvufBpM3zvOROOuRgPuRBXnAuZPWknhn7bGu7j0rEuqXYkQBeAdRFN/mBcNPxrbxGWId6WTKjpg6P80B8LTCIiQkvcArFgBLV6A8MiqrVPRbsDbwSvLB93YpL1tukDBfaOcql05NFfTL0C1LBb2T9Tmq0XC31WIMvN4kHlCJuiFNt5GCxRJpFROdqNvMPNHFnl/r5M0ifHaAp/i3986SO874ln4GyvHlVBJ+vcFDU8nEG/FDZnAutLC/KZpdJqvbjIpcoJxaWGdYLOSM6a9plzZWdJNpBr2omAcZIsKjms3fIKbtkEnDRtsMsN3RAVzEpSQS6PpB2P46IgVhGOhhlHeU6agE6lUGWKoH+nFGqalMs99LYqV1QpoiXk5FhZWs6WcBBzk0eabCqBVgZLPQw1HTHb456O0/FqY1WKdsMmw+WFeRZZXwzEK2se3rnFTuhpjJW1lI6d0LOQsgVkFUbvKTd7H0zGXh8fBbTcnN0BLT9aA8rsPZlMGBj3PXeyPRKBSKibU7//2H1VMMxlhwXd1L4RvLJ83IlJlicQlxodj2uw39qN4INBeC4h81FT8AzAKpPLHPRow0AjmcYlhzhDtGFdlJ39+SjeogTm0nAxbnjP8jNcOXcTFqkaqpU4FgnTQmFYNpS5yQ1r95q6tlSG1ogGISVvMbfq37lUQOu24QDWBNQ6ydBKWQGklJ1Ophv7wrifKvU4xhoi0OVIV5oq/6tPOV3yythAESYgF9Tv3m+DPbYnGSY7Lgbz6JruR7TYzBUWrcby9rin496MP//zP+eRRx7hbW97G9/xHd/BJz7xCQA++clP8vzzz/ON3/iN9bbz+Zyv+Zqv4Td+4zcA+N3f/V36vt+6zSOPPMJTTz1Vb3O3sdlsuHXr1tYPwMXY8/5Ln+btOy+xH9YmLVxrkqEkzgU9H4EJ6JErsHUtj56eK2OYu7fSUZmZUfPAjLTluZRsflH3UQWnNnYcOxPI2Vr18d2IvIw2KTDWHT47LUS2gJiMGsSPzWiYS2AhY3CV1zKuwhhl7Makn7B83kheWTB+b2FrTbj9nFY/tPFyVefo5xysqdpKNj+0TMf4XbolRpRCKwP78ZhItsCLwl5cswg9qzxT7z+MVTMsWOWZ+mYW34+PUuregIuVeWcFKSY9C9zKC9YGjuoxpDCWKyx83AmMvFejlchjzQ7/1zO/z//wxCeIZztyWyhNsYaLKljyXOui9cWWdPksEk8cO4rujGEmZVL7+PVTW5bJ0FqqjJI+Rmm8/q0N/9CXukZNGzQhFUoXrQ4NJnlvqly0hO31LgyjjNE2WtTOj11ffNOXRc3hsaU5K0ur5EBKqmoBGFIkZSGaGmmYsLWGMtY+jdkjrI384ay+VtJtstbXGm+Ms/cBHtfSgvkLDQx5i5WVZ5HDx2Ycvqvj6Yee46HZAfPQswwbzsUVy7BhV4aaJgI6IU3NCQeSMbIKm6K+Py2lfmnerbgTdumMrFbGBKNUSv3pij5O1ZAzSSqc3Nf9s3wxaEXputqJNACnpNs2xg/qwjA1SRzN7KmfRWK6qG6ztYIUFjIwk1TZWYFcjViDZDLCPPR2+8wi9CQCqzSryQ2g1NxArhr1G3luyTHjZ3k/iw1P8Xh69jLfcfW3ecdjL5F3E3lmEpAWSjT/rB3o94TNuUB3eZeyM9eueRrlhRXcUkrTCF4NDnqx3U2HbdmHA1t+GWwVGhTM50Tv1w2R9WBAkRcYSeWHzoI6OYakZoMOPAZKve/Udwsp5BTU4NCArG6IdCnWjvlgXXJ9GxOfk6Ksq1WeVR+1OlmXWM0w17nVzSLhc560T8ffznFcGuJRGJElY2WVKHRnIt3FxEO7B+zGDY2xc3Ysir0lM5cxeU3lb9seiH3J9Bh4hTjeAOgpOgWxpmPKsvK9c578JJydxeS55bb7TpuXoICVgz9lUnycZGI9qMDW1MB+/Hv8LAqjjFnYZmuJaIqdM4IruweVlIlFhze2QRUpKjXEWJ+5QWzz2IiKzHS1CqxLQ4cyifyzvJ/stiiBHYlciSue2nuWC2eOVNoeyyg13GJnwbAQlRu25hFX1xdGEGu6nvjfTG7jWBdMmFb+94nrp7cBpr5bydYBv42uV8akKnfe53gKpYOM4mdQQdeb+rxF1x4HtRjBMy8AHbzSW4/AVrHncTZWsrNKmXpSu9reaHEw63Tcm/HlX/7l/Jt/82/4T//pP/Gv//W/5vnnn+crv/IreeWVV3j++ecBuHLlytZ9rly5Uq97/vnnmc1mnD9//q63udv4kR/5Ec6ePVt/Hn/8cQD2QuQ9y89woTlkN2yqz+uMzL4U5jKmFzoo4DLDTRk4sObbQgY6lClxI+8YiyaYj5Lug2uD15QL2cBdlxKmCdjroxVTO1BYSCGVUWHSolLCnu16o5/ICyMjuNXj+3UxL+FSPYH7klmVno0xjU9apTiLyceDWq+cHKkoS2lTBm0+MXpKTdlL06aQyy5VVaO1zswsMGoDllDBqhZNu1wX3bOOnlmBuSXh7Qe1RWkl1Sb92eaYi+0RFxplhav8sKl74L40RKuJlnFDXxpWZvDtwUxHec7NPGdd1NQ/oeyyab0Jdwbv7tVoJfJ4E/ifLv42j1++TtlN2uhX6rR6PVrTv9sVNhcXsNxRdpatQx7wsuUpfHKDk82OpejvkQDglitmy5ILIeWttYyCJfFi1283/km2bhRRSxU7r0tBmVhGDKgbD9/g+choWEuxoJSs9aBkJQ3QB5UaDoGSrOGSItn9h1MghmJqGK2ZNkNTkw2HEmzecCB5ouoicGTzzfrUAP7zNz5y/HbO/bkyUEA75SUGjh+ac+098M63vcDbd19mGTrOxRUXm0P2wzG7MjAXavwobGu3N6VnlXvWpbC2zctMxtO5RfXno9eVjt4WA5cTJtQU0SWF0667+2Z5N8NHssXEOxw+pqmG/koUEnKw7P6DMPditBJr16KVQCuB2YSpNrKxJobwZHZlIEhhP6zZFQ1Qd3ZV9UmqHfJt1tYmN6oNTjNuJtUEOxgWyOanFDksPcelY1N6TkpO7/WYS8uVOOfvzp/lf3rkd7n8yA3yMlGaor5ZM8ht0QJjrnLD48stw6V9lRuCgVl5AmBN/u0yRJccWkddhkkHfSInVN8se3GTgmM6WYc0dss1jXAyAxc47loOLEkSMH+4xtKoNE79IC+IMsbcY5N/yTo5Z/PjSimQkgFWeVJcTAyA10OrRsA5msxQzeOrrCe32r1Ci411ntFP4Gd/rJTD7dTk03E6JuPZ/jyLV5SBAiMra9htOL4MF84fcn62smTcnmXomMlAK5nGmFA+gozy86Ek+pIZmMgKJwyigHdzdfgZp9LskcXqf/tGelo4D5PH2kompNg0ULZM0v3vwCjXm4JCnw8Q5l6M0dBe11mNi9cx3T9O/3afLBGYGQvCgazEKDWrmX2TOTCgnn5dUUn1Jjd0RYEtly4OJbApQleSsfESJyWn93o0EtmVyKPxgPfsP8dy/5jSFggGasUJS6uBPBP6ZSAvrXGib3QLwLqjKbxLDu32U1N4/+3G7tO1Zsr+mpruStG1XE3Vt8eQRj9MwJjqBhoVbT5uSlMZdvW5MmMDpXouTkCqcjuwq6a50zVoZGz5cTA4sGXf9DCRbfjH549/qjV8/eODH/wg/+gf/SOefvppvv7rv57/8B/+AwA/+7M/W28jJ4D2Usptl50cn81tfvAHf5CbN2/Wn09/+tOA7s3345oL8ZBl2FTgaBkGFhJuSy8cSBXIupZ1r+QNuRmpNuCCZJOfaZhRZ6B4EGVn+dmhMmahMwYPjKytnlFNAqPf79QIfmYyQwemsjWYYQyz0jVmVKOMoSNjfdJKqL9VYjfuuZww4J/FGwXIAq0RW4nMpakN+Uw2w3epP24DU2scNOxraqetx0SqMrRqpVL/nSsodZTV1ygXYSG93b+rqYStJGVoWVPfPbfWpa1gmKsWHHzvS1SgVQb6Enlp2Oda2uNWXrDKDasirHKqtjYj+UAqYHe/6qO9sODvzG/wf3v0d9i/eETeTdvsrEmTvzsTSRf2EA8uKRlSotxlPy8nmv8yZDMkLVtN/9Ezy+5XRkZVrZlcEm+BWS5JxNafRej1kzOGtgjVhxgZWVlhmEgO01hn1XUwg2yCrY8CwdfTsTEjsr2mdEPkuGuNkRfYpKauccdJZanHdkz5urVK80oCgG2Q67XGG+csfkDHx44eYecl26qb6Xtattx4R2T25C2+6NyznG1WzMOYhreQgQshsR/urAdVs/eO9dSDygGjSfejBetE6HWtWGKUAVHTRMKRgcXW76lLk/tieSejn3TB/d8+3FDxpCzuQQeyYET104kNXWsdn5nc7qEVpVQDTF+k+9LQFRXLAFUO0meVIPaW4DAPenwcp1k9SVd5xkHaIRNYBDWTP8gzXknCuiR6+7mflFqwWNpmzlfvfJz/y2N/zP5Dh+RFVlqpFxat/gw7QrcvrB+ak8/uWorHZGKuyYOTy5IBvcbUIpWJhMPALTd/LyjQ5RJEn5ynAFdWA3j3FlEQCPtReWAi1ONai8eyBTL6QjrkQB4EBvPNyqLdhuSJU6GmdPRGmy02MfdJmWHHQ8s6taxTU7vfPhlv7Pv3f/clcpRnlTnWEW1T+Nnrwk/H397xYr9Pu5qI0gRyG1ifF+LFDVcWmloVJTOzNLyGzI6UCtafHENJBmqM88yJBmAFsgKytX7UutwBqQpq1Ve49Xs6n1ZfrDL5Xfdh28CKTDav0/GgA1kwsrNOvtIINIyf4/T6YPBdKRNJIYGBEbhwaMqlZi4x881fn2MF630uUiaXCki6ElkVYbBOv38f93M0EtkPkbe01/iCMy8x29tQGt8JM8oNnaE1F4bdSFm0o3/WVHKIs622Qa4tEMvHFMhyaKkmSI0FwtYoWjBsg0DUH/fEqse7uOfZeHsHIDV1F11n6uPYZQVlBRtI5SmFzsJSZliscvbBvSIxU25kiyWc7Tm7iX9WMgbwcLrlv29jd3eXp59+mj//8z+vqYYnGVYvvvhiZWtdvXqVruu4fv36XW9ztzGfzzlz5szWDyhA82hznd2w4YyxZ/ZDz74UlqG9LSk9mBh1UwaObF9yZGE1HbGGHOUSKlMHYEZmV/panPaMYThb0kMDspy93tv54t5IbpPijXllEhXWRRvpPbp2OWjlPlqgjfsoUpvyQA2smtYt/j4DoZqjOxvtjeiVBaPUbgryTJlZfpn7ZrUSWIbIfmgqYNjavli/yzFRXcHPjdmhDDXkysEr32P0JVYf4Om+V9cbSxXPrfoIogBXa7WQShxT3Quvs4YjPT+c5aV0hhfTHqvcsC6wyqnWmlP/rOl7vh/f4fmw4Bt2/4x/8MSf0uz2tbFfGkhzDcnKM+j2hM2lHVWswBbzassMPk0WE79dLmPn7kRwlprEb695MpQ710gWAlRv10tNWPe9wSrPqB9TgdDrj2GN+nc2sCrj9o7IILXJQ0Flhs7QMnuX3IfaVJuCWp7iO5XTr5MSABzQyiVwOCjRwwNUdsPGGOmnYNbnbVzvlqpdFY1Hz7PIwRNzDt/d8cUPP8vV+U3OxmPOhGMW0nMuHHM5dpwNM/ZkXh9n6pN1kDsOcjFgShFW9wKajgTGwiq3gS/Tv1MptbjX+4ymiQsZde5T8Mu7HFNAzS/zidK9s/z13ynu9kEdAe3yBAItsSY+trbAukY/AwtxSrRYJ6qxyXioBvFBymSCHpFl1f4qgBJEGVpulOipDevc0pknwbW84GYurIp2JFx2eL8ZWo83gW8681G+4fE/oz2/VkArWCdiZumGO6YTPxdZP7xHWSoDqjg7a/JTGVqgRYF1HpxKG6bsLGdsnfQuSZ5uOO1E6KQ79JFNanQpS9ZRyM7aCNWHIZdSO0Mwdq+rB4oP14L75GyAVhoCw6AGh6oDH415c5FqBF9BMpucs3kFeKJULoGuNEQpWmTUc8hBtvv29Z6ON8k4Tq1tPKQygLuzDd2lxNX9W+w1a+YyMBf1y1rIwG5ILCQyY2SQTH2yupLYWN2vKl+5oxxJgStdZ06CL9O/p7KSEexS4MaZ+o4/eDPBAZ8poDaV6MHIcPLXP/150EEt55MJYi4uY8qhQP1mCtCI/mRUfjnYds5nDAdLil3mcwuoDL5g/kuWuuqd8E1p6Yyhqga7keOixYI2rwzUKvefoXU2CF8wf553nHmFsBi04y3FJLMKZNUGyiIw7M1VboiyVjjxUxla/ikWPUK32MAnmVnFQCe/m3efJ2CVyzZymjCzqmfWyKKakr+m/C2Xefhmvo6CFQNYZ1uLhewG8A6UVenhyPSqj3fisR3Y9H+nos54A6Nf1wiy3eMv9XQA6mP1J3/yJzz88MO87W1v4+rVq/zKr/xKvb7rOn7t136Nr/zKrwTg/e9/P23bbt3mueee42Mf+1i9zec6GiJX4iH7YU2QzOV4tJVe6MP3k87gWZfMKrcclRnr0lrjdtwfdSVq47aaSRc6Aq1kWrIlFaoc2qWxvbGz3Cx8Y6E3XRmtFRYysrOOJpugCGxONOJ9ONgFIztrapeSDQjzdW6qxHCJ4RvNJ+vk2LZLkTskG/r7G1U0AGsjH7gUdCbZDOA1cTtX4GNOsO8VMBJGxzJsaEVXpWXYcJAWBliOad7uGazPo5Ys3rDfeNp3bjhIC64Nuwq4WI20SnNupCU38pLn0xkOSsPKAC2VjCZOemjdr9qolciV2PDN536f9z76HGV/IC9ynUhdajjsCJuzkXx+D2kn3Dd/XVMLlomUUG9TxtCs6aEoojUUVCax/j16CTvoBVh9hXlpKctqNcxMvhlZWZ2pN95e5/R16NrrpZJkqayt2pT0ZoyDVQVlaAn1spK1Xkop0PWN/h60bgqmhtkMDU3INe09I9Xfy0dXGkthHjGS1xqf85n8X/7Lf+Gbv/mbeeSRRxARfumXfmnr+lIKH/7wh3nkkUfY2dnhAx/4AH/8x3+8dZvNZsP3fu/3cunSJXZ3d/mWb/kWnnnmmc/1pTwQ41a3IPRZUdQmsLkw58aTKi98997z7Ic1Z8IxF+IhF+Ih50LHUqRSXBVUGYGsw7LhZi61g6E/QjxB4fPTt5sCVaWYwZ91OCZAF3a5o/K+rJ08ALyT4b9b63T0tkCclBjW+71BPLNgnPAbtuWGqS4EJ+SGZaRBg/oJKD3WtOS2UVWjza6mSUWj2gJGvS0KZhnQpXJD3TyozHCHl9IZXko7vJKEg5xYlb7q4e8noLUjM97eDPzD87/HFz36GcJ+X/2zgCoFyS43vBgZLu+DT96+4ZjICytDyyWI5p+l5oa5yg3dKwuceTVO9m72q2mGJ84BLwS8Q4CCXKsTOutkG6ucA0f9rHYqRIoWJV5xF2VpVQ8Tj1/P28VImUg+mqAvvMuRddJkw978S9yM2ccoR23qZtFp/acG8NvjdJ25fWxSM3bsgpB2IuuLKi+8NDtkZkDWMnTsSMdCEi0GBJlJ+xaQxcC6TA5/+zkpqPJ/TRsmmWohYfPlCHTBWGQItzO0xsctW7+j/Z1wlsu2xNDHG8UzC8b35kWHyw3H964/lb1WdL3x0WAm7uLFp95yZnIfl3S4vBAwSSGVtQWwMRaxJx52lkq2Kirn6MrY+b7vkkMi50Pm3TvPceXMATLPysiaHCjFga1WGyh5OVcmMGx3vmvgCNvsLPPP2jaBL+MHbo9zMvlwvGz7NRdfHyb3zynUtbzeDt0PlKL+jVXK7mvNpHjwFF2tcUxamDlx9o2AlhvoJpOyD/b9Ovg8DQlwY3hni5eirCz/CE7H6x/f//3fz6/92q/xyU9+kt/6rd/iH//jf8ytW7f4ru/6LkSED33oQ/zwD/8wv/iLv8jHPvYx/vk//+csl0u+8zu/E4CzZ8/y3d/93Xzf930f//k//2d+//d/n3/2z/5ZlS3+dcdC1BLjXFjTSmYmUtMLp8MBkVXuuZFD3YMe5bkyswy8aiWpqsS8YN3ce4aah/cocOqA1fj4hc5A1SiFuSWszkzyFBlrk+qb5VYmVsNMgay1sbaiCC2jxcoyRBb23rQJrNYqlZ1VgS+X5U09pfIDXau82nAgEpxhl6tSZpTkKRvNjfAjYub5VIbcdL13CWFA15zdsGE/HAMwkzQ2X+3xl3HDJrdkhGXszAu4GLiZzEdYmaQKgmnCbkblsM7OOkwL5sb4OkwLXujPcSMtOcgzjrzpQmFdRlDL3//AtjroXo69sODpdsV3XP1t9s+vqs9jbqAEtWIZlsbOurig7O5QQ0tKoaSszX5naE1BrOlPylVuKDmD10bTpo2xseqS4jXSAGEoxN5qJdtAuBx9XRRArE396aZDRpmhM6+25IW+6fO1y9Yx6azu6lUZU4YwsrOqqgVCUPP3wQK1+hQRKaxN0aJKJiPEkLf8htd2XH2243M+i4+Ojnjf+97HT/3UT93x+h/90R/lx37sx/ipn/opPvKRj3D16lW+4Ru+gYODg3qbD33oQ/ziL/4iv/ALv8Cv//qvc3h4yDd90zeR0v1PcrvXYz20erBFIS0brr+rYfaUphdeag7Yj5okciasuRg2XAiBpflkTSV6nlx4LSWl4BapyR8+2fgC4MMLCy8onHXV2YQ/epno6MrESFE8McKAKhTAmhrBO6gTkbpYJIoZCSabJMeD7Y1C2Z1qrqejldEQ3oFB/2y7EsbOkp02bgKf73IaTc0T52EwDbqi0dEWdI2jVYDjRlry4nCGF9M+L+UlN3PkZi7czB2r0t1XQCtK4IxN3P/kym/zzkdeUuPDtmwVFj5593vC6uEF+eIZJATtmKcEwzBO2jlvFRPun7XF1PKOQh5jZ4HR7L1MuwXU+NiaACXFugNalUsYj/nRS0E3Uw46bsyUshSVFYr9jFW7UIagP1mTOlxuuBkiQwpm0qtdhk1qSDmY3LDVlEUDLR3QdPBqpGI3BrI1rEtzCmadGKfrzO1jyCbrFSht4PhSID6k6YXLsGFu4RRzGdiRxI6MzNnpvOHJhcdZmRsqAdkGVaYgFIynRmVWMa45PkdOb5cY/RmDXe6X6WMUph5Y4/5KaOr6Y74Y1k0/CWg96Iws0PfjyZHT4QCcMAKD/tm6NGfKxlZvxYn30omhBr7WpDFwK6AUf2dzZSs8E4Hj0nKY5+qFUloz3IWNMYLvJ6AVRJhLw0Oh5+m9Z7mwfwSzrPM4jBHotubkmdDvN5SdOSLGGnQWMGwzrib/1oNy0gTZArEm/54wsrZSnezfCgBZR3oq5fBuNNvnjXagdSRnoUykhUySc/3fpcoPrRgogSGPjZRS1KMx2Vo0yg3HHwU0YwWvVOZlrGH0+x8IE9TwdLye8cwzz/BP/sk/4V3vehff9m3fxmw24zd/8zd5y1veAsAP/MAP8KEPfYjv+Z7v4Uu/9Et59tln+eVf/mX29/frY/z4j/843/qt38q3f/u381Vf9VUsl0v+3b/7d8QY7/a0rzkWQmVM7UtheUJaOF0LegN/VrYPBTgT1saoGv2Ntu6PWIiENuNWVnR64Znq/O3AlPpqTX1KnQ00bRL3RRN7fbhHlj4m9bcHNbl3ltuiTH1/A2w136f/6fVSTfDfqHLDxni+t8nvJ6BWDeki1c9pJmpNsxRYSGImmYX5bLo0dDds2JVOvyPilrxwWoO2kmrg1dwaLG3Q37kEVnlmoIQyc/y6VhJrS3hfm9Rwk9Xv1hlbr6Q9nh/OcZBnHJSGg1xYGUnDQ8i8xnXvt/sxzoQFf3fxab7u8T/XJv+81HoozxXQ0oAsY2c1zQS00tdUyth4wRlX0/CS6SgFSUmBLa+PfE2z+0nyBr/dx5aOEYQSjvo569KYF5qCiKVIraFCz7jGhRHUCj3G+pIR3PIabHBamox/J1Hvy8lb8XpmKjFcD+rduUkNXdZQgGr/YmtVoFSZqgdnfbbjzrFfrzI++MEP8sEPfvCO15VS+Imf+Al+6Id+iG/7tm8D1AzxypUr/PzP/zz/4l/8C27evMlP//RP82//7b+t3Yef+7mf4/HHH+dXf/VX+Qf/4B98ri/pb3TkIiBCbiOHj8w5eE/H1z38Vzwxf0UTC+1nGXozfG+2QCDvCgwkbuSBA2NqKIsn1m4cRSd/Tw7xFJCT0kIvLNZlkhTCuBhExgXBRzzR2T65KHg6iL7fCZgmmXDHLMUHc3gSCOgkn0qpxZ5nG24YFNAzvX5EJ5MoI9Di8bTqxaS6XpeVeafWfUxaSSoHQb/LJqSKOoeYWZkRvNJ3E52BWrpoZBYyEOgIIbOhZy4tuaTbjp97MRzQ+srFZzh49Lf5vx9/gJeHs1AioRc1hkeTMIalsDkbaK8sWd5awdGKkjIE0YnYh3cpctYJUIRCMAAqQgBJqvIX/SIoIkaj9TJBi7Jqfpgg94E+B5qQiXsDeSEgsLdcMw+9bZTG4mMWBhbtwG7bESiq508N9AFJUpM6Sizj5Gy68Azm0aXdBom+QKl/lp+v0c6RTVLT31kYOBzmNCGzLBuQxHzSXU++gpyO28bpOnP7KAWVs8dAtx/pLifetn+Ts42avnvKqhu+NzJK2mAiL0RlJZ0fz6JskpMEpxEEUd+s6ZZrPDPV3N1v48AVk+vz1mOeeE+TR3XWkq9NW4QZKVtg1oM+3AsMqFCWz9liDaNBZ1MaFLgLop+jx6brfcWAKXAD92KXOy3fpYVBMmIpemIbxWQMndZMdqUUk8fnCmpJ0cdtbEs5p5AkE9EUqZPHz70YQYQ5DY83B2zOPMtHhreyyipbl6QHUsWkWvPP2mtpNz30vRYHIibPsKPKDzjsdyijLKKIdaELJegNHRgTdE4ZHcm2ZRjFGLpBCjLLuOf7rNVmlgO1XuRFyTQh00b9FpUNF0wOr4VDrTy0W1gZWiBW+AhIoBjzV2WF2HebESsWhhyQoM/ZZTXkbi1uwXdmbgh/v/03/7aNX/iFX3jV60WED3/4w3z4wx++620WiwU/+ZM/yU/+5E/es9cVRWiLNvDiHVhZvtvalIFE4SgXjspsy/8V2JKZ+VxzkBfVSoOiYMYZ2RgbXcxjK9FKrh5aEQ20cC8t9exzeomCWMrkodYsXSmaWFgK6+IpiHqetSIskBpq1UpgU3Jt1J/0h/Q0PLcVmQZWtRJJRdk9zRuolvFz2d9Lrp+FvQeXgxpjC6ZkBSclYJePPsBe1zjgpIFJO/V5ayAAqjTps9Y4y9DVECzdj4zeWH1pCKVUppA3+aeyw1YSN9MOx6m196HKlmzH1cV4CPGICzKQSmFDrt9nXxJzaZWhVbZronvBvIsSeKyZ863nfo8/uPoon1o9REyxekmVVhv8mzPC+vKC5Y1dbeonkw7mgsQTno+2bo3rgYJcIkKRQN2MFWVnlWylVPTLoTRiwJcvcTImHCa1VNHvcqjednZQGEgmGsAYx/XOcWsxhnDohTyzFlpycEvBPIrVS0F0bWu0ZkoEtUjoG0pJhFB0rQyZlANtTNWq5SjNaCQzD+NrXAb1b8ufY9PlnvIrP/nJT/L888/zjd/4jfWy+XzO13zN1/Abv/EbAPzu7/4ufd9v3eaRRx7hqaeeqrc5OTabDbdu3dr6eVBGLqLR6BdmXHuP8M63vsA7li9pYmHo1DxPOi6EgXOhmei3R2+pVe6MfTN2NvpJN8S/VKfndiXQI2zKtvwNrFtetv/tUhLvpFcT38mk7wytdcn1tp11PtYnTN6Dy/KKGke+USJup6/NJYZuoOhywztt2PtJN8knerBFwMyW9+O6mhwqONVVOu0ydixjNwKTwK1hQZ8bWhm2pGhqnDjjKM+5kZcc5AU3c8tBVortqnT0lkJ1P5haUQKX4g5ft/wE//CJP2T38oq8yJrgEaE05p+1UEBrfbFheOjMqBXPk8na9eHTWNpqCM9tUbT6oSpDS6YJVQZguSQxdFDWkW5oOLPY8Njl67z7ied5zxPP8bZz12yzpZ9pX3Rz9cV7f8Xfu/Ip3n/+r7jUHrDJLbfW87oguYeJJEOIB/1dkrK30hCr6XxvKR2bvqla8D7F6qMFWkB4AdRKos8NG5ecMnoUxEnhejo+u3G/1hl4sNeagsYkp53I8WXhwrlDzrdHmngnicYArR3JLCYS9qm3VF+Ssm/8NDUWUH0OuzwakOWBI0PZXlf09Yynu//byZX+4488XRWcoTVUYIYqURxOzGeaqSJ2m7wFfj3I8sKtFGAroJyN5nLDO738yrY2MGrsXCoTKJKZizsh6fWtJDVaRWiDFiHTh95YIEmwzqyPVIJJDqPJiho2JbAp+j309onfL+lhEGEpDW9vr/Ousy/Q7vaWGFVqAEluqB5aw04g786Rabqh/3YpxnZ7eFw/Tt4GtMjI1Ns4Eja9TBIwKCNq3iTO7B5z6ewhl88dcG5xTDSGCegx3Ejm6uwmj+3d4JHFTZZxo34yQxwZWfUEmaw99m9dEsNEtW/hIybTEIoGkTDpftuPiKaR1UYJCoDq+Rut0fngnjOn4/WPaMl1+0E4G4S5bDN3Rq8sVVe8kLL6EuU5M0lcjIcmLRzoidzIy9ESIbcmMQxVHhbJNdVw6pvVFzWE35TIwQm5kO+nvTHvyexuBu/Vxpi2Pnl/bDdH1DBeL5mJsCuBuSUZwrYVyp2Gfx6vdbsHaWhTPm+pYnw4+8wllc7QWkhDO90LMH6OHpC0kKGmVoIzeVVJMPXAWoYNB3mHfuKh1pdYZahAZfktQ8c8aGLhNEDAG/17ca3gVxjY5IZNbpiHgU1uWaUZ69JwkHbMiiVyM0fWZfze/b06YHeyJrpX9ehcWt4zO+CbH/4j2rMbkxlivllF7Vf2hGOvh+ZzBXkAStYGBWzLDX09OpFuiNmw6H3d8N3QJTd+d9nhNFCrjH+HXlj1raWpz+t3RKH6EMMIfNWEQ19MoCYejvUXIKUythTYGplaZR01QKu39UuKWbbofsY9il1+f6tbsB5a5mGgL1pHrfJMWXtmx/K5jHt6Bntyx8kkjitXrtTrnn/+eWazGefPn7/rbU6OH/mRH+Hs2bP15/HHH7+XL/t1jSZk0iJy/Qta5F2H/J3zz3CpPVB9sU0M+6FjIcJcbk8T0UhcNXz3BWC6FfUFYmGbJqWA5roAuJGfBcUR0Mm/xejGUBeK+pgn6LvBLnNj+GnyYfWNmnRBIoqIn2R06et4sCm7zorz7pRH2tbriTURZTocHHGjd32sREuq7KtxQU/1B6hUSaXkDuzEvnotAaMUrWgiyM20wyrP6UvDUZlxq8xZlUhv0pptGnG55593K5FLYcY37v8R/+Pjf0HY7y1GnQpoabdc/bPWlxaU/WWdvLdMel1umEZwqwJalsrhEzbun1ULEOpk7ckdkmB2qzB7qeHajV2ur3ZY9Zoq2KfIaphpUmSecSNrqmGgcLk54OrsFq0kXuzP8P+79nZuXN8lrGWczIcpoGW8XZOA5EEBrb4fQS2AlJU+m2unPNLlyHFqORgWY9rPJOWld3ND86YAtgCF0/Hq436tM/BgrzVBCqUJrC8GuNRxdeeWguYmQWvwFEOI3B45nsgcm+G7S9mms5ygdPRmAjLNpNQ1xYsKx5kFPeUDalrut5seyYFxDXFwy9nDhXFDLUwVXF7EOPBzZ06WSw0fVLlhlIDLDMXWTNla27WDeVKC6OuLTNhw0YCsKJ5jqPNa8MvtkxzqOqVeW615NfpQY/hYaf0qudaQio7IhoYeTfPVaXiU4zugdS8/7yiBpUTeOXuBt5y5pswnM1tzQKumG86EYdlQ5m1FAhWDcvCKbUDrxDpU5YbVsZ1xrYFtuaH9xA3Eo8Dx8Yx139An9avKWcM++goGaoEowG7o2IsbghSO0pxPH59nfTxDBnuO7IAZI6DFBNDKCmildGevRpeauidjnyNdHqXq2TwbU03uNb80OxMfzJ3Z6bhXYxnaCuYsTAkCozLB97ubos3ZlSXPARzkHa6lPVIJHKQdOrNE8OFeWr2xPSOFjliL0YCy99cl1qCkk815Hy2FuYz1iR+XW9JD9LpVkcrKcksUr1sO8mifMpfGgqlGQMsb1frY2435KaPpJKPnQR3qtawgpb/mgLPO/L/bV8w4+d59+PcTRevMmTfkrY7ZFfUAXohKDBfGmvGEQw8haSWRSiBKZpXm9tiBhXQKZBkQ6g0a94DU11VMVq1S1KM0V1CewGFacDMtuZF2q4eWr0+JbQ+tadrh/Uo3/LrdP+FLHn+GskzKShJMEl9IC+jOCqurc8qZXV2jvJ5JaQS07jZONvvt93RsmcG7t6PLDweqZ1YYrBFiKqB1UfabhFITgym2xnrTxp/DAa4CJRTCIOP1vl4F6vsHlLUlBQn2I3rbXIQQ1OLFa6UuRfqkNi8uTz5OLTf6HTa54YX+DEAFSj/b8TnLDD+bISdAjlLKbZedHK92mx/8wR/kX/7Lf1n/fevWrQemyBApHDzecOu9HV/12Kd5cucFzsWjqjm+Eg+5GAv7YYanaPgE1JfEtdxxLWsx7F5MI9XTuwbjSaAJIZZIgVF0T3QuQNlYLvuo9Fy8QNCJTWV0OlIpuDo1Te5DKdrl99So+lzKj4x38WV5UBlaYyfmzsdaFD0B+ztcp/I0jStWHmbc6sx6QaGFYlPvMx0eb5tKULNEKearEeoEH1GtcBd0o5xKML+tY3oyS4pJP5Mt0uG2Y+v1jmWY8a72mG+/8Nv81WPn+W/pYVJpCRszkW2KFhcLYX0+0l7ZZ75aU1Zrnbhj1MnYPgUwCq1TaosaHZaMCmlEJ6/cBJ2gnQZi2ptilV0YCvODzP4nA8dHOxyfXXA4K7y0yBALMs/stRuenL/Auqw4V465Veb8/uot/NYrb+XmesHNwx26awviYSQkp9bawpQFgkkOS6nG8u7nAtuMiiBjh9yNDBfALKjZ6bGZ0W+kYS9uKiNrX44JkumK0rhPPbM+93Gv1xl4wNcaCpuzgc3lxONnbnKhOWIROmaSmJHYDR1LKcwkIoypTuCejInjoudvBUwYQRH/t4+EVBaLYM0Smb4eHYNd7oCU7XVMGqeyulxL8FEODyNYUzBmCzIqk+01OcdEGNeVKaDyoDK0cv3s7vz69LO/s/xLgKFMHcVGlg24FNHYbPUT2n6c1th6GaER5UZ48p1Mvu+hBAbVMKicOxQiPVkKrR1DMvn+Th5br3e0ErkYe57aeZabZxe8VPbJJY4sWffQapSdFbo5TT9Q+gFypoQwMXHXI9BBxMlF1GVbZCI3tE17Ntmh626tgdJsoNwQhr6hnzd0sbBqvGtYmMWBi/GIQXqS9GxoeK4/y7Orc6yHhk3Xko4bpAvW2Z6Al47uFjvgi1DToSbvpv4t45tR/ysNCIj23fYlQoYkhRmJLGr6PmNAbJ/R0XDqmfXmH62BxFPp3HSe6UviIBeOSsMreZeupm9ruNGqzBXECF2VHu4aI+dGXlYwI0hm4awYk/K6nHAZ+i0QK95hnuuLglkHObAbsilBlGmTEBZSqgzR2USt1S6e0h6FGmwFCnK0eMq6JxjqfvkkmeBkAJfeJ9wGbD1o9cxUKjm9zM3QNyVVxpJj+NVbzCSXPqLVNRXYstqzL4FWBs7FI45OpBvuhs3YiEWYh/42f7WI2qj43iJIVgZWamqj3+/Tl8g89Bymeb330RAYQqgyRK+vFup4DoyBZ/o6CpRkBI8A9/h7ayXytrbjmy/9Af/t8hUON2eQQQhV5qeKlfW5wPzhMyxuHVHWa/cO2K6LJibxW3LDbFYtRT2oxt8CZsdS1wz7CkeAS+sVCQpG9UNkkxsWTW9N9KjXeTNFFKhC7LmF0T/L+j2hl9pUck+tIgqcaW2GJhE7Wbq319ekuscYBt+fQM6F5byr14GmdAcKnRR2Yq8haUEb/7kMn/X3c0/BrKtXrwLaFX/44Yfr5S+++GLtol+9epWu67h+/fpW1/zFF1+8axztfD5nPv/sIxo/n+OdZ17mPz99lXe+7QWe3H3R0OrCrnScC8fshszcFpWTE+TNvOYgj2ysbFIAlx51pTFjvkRvkwySq7bcJ3efrLRjocP9tNyS4WR6oevNHbBKUDXqQO10OALu65CzskAprZF4G4j1IHc1YPQMmC7u2tmIuAOG6/BBO0jJOk7aiTJTVet6Tocu7noCru30cpCqtcLCNeNBxo46aBxpNfw187u+NNxgl0ywTskAIbHP9qbX39O9XHj3ZM5TswO+/epH+H+u/0ee3VyEXigi5AZkrqzZLgnHl1vaa7uETUcxlpV6gtjEjShDi6CTX8rQRNPPWAGR1QukTLrjFCguPbeHkQSLm5nZkZBmQm7Usy5HYXMBPnnpIs9eOM86thzFGc/35/iPn/5Crn/iAs2Rdhlm9tnVtTdQbUy0QW5AlgCxaFpHKYSYSEm9vwZR6ce8HSqNNkihy5E8zBmsW9mIJsZsTGoYgxr/x1KYSVIxjxc0p+M1x/1aZ+DBXmsuzFd84qF9Lpw/5KLJCwMwI1m66sQXZLJZzSYH35SRjeXlvnfSUxEaUXaXM0w0OGGbyOIYs+MDMPppeW0+gh72/JXZM8oJG7ZNff36UtuFVFYWdrmDKD5qg+UBHoUTwArgXDPnv4WJT9iUteaeV+6JdTJoRKTQ2Bo1SCCUsXkS7dGTrWr+XftIZfyXAlpaVKzt+WJQA2mkMPMv9sR7upeA1oyGh+KG9+59ht8d3sLBsLQNulRD+NKgASS7kXg8Q4Y0soC3PxhtlmAb8Wxoa8EWFbu+YBCd+2fpsTcFuiiFZg2xgxKFEtRWoggMO3Bjd8mtnQW9BLoQOUwLPn7zMsfXdwid+jz63qucODGkHurj/gpQNnABaWpto99ghhj0u1TJobLrShYyiUUoJBGkCAOFxkzhs7E4ihlwF07XmjfzSKVUfs60ce5gTkZDhboSuGasl92wqT5HPqJk3adI4Uaecy6sqjm4AiAtKQXOhDUzu+9qsh/uS2ApA5sS1ZBexv20nwMZBbRmkm+zTNH3Ys36on+vbS/euiXIZN4ENTpvUSZWX2qnktFDyhrPVqdMf99t3/ygAVnTMfXO0hogVPBOLw/0JK3brL5rTWboIyEsJbEqemwEKRWEbCWRCLZPFTpbJ9TAvdXGPIGbwxLb3VevK/+ON7mtIGkMmZvDsj63g5QBBTISQcOTcqQNieM8Yz+sWeUZr6Q9OiMQwArCQEthGZxMMLLTfPQTf2F4/d/lUmZ8yfzT/I+PfoL/cO1pWAWww6yY73u/J6yutMxeOYt8pods55UztJgsp/EOr6fWTgKMNZP4vG1eQiWKKltgsl5RmVbdEKt0b27nZ62tBiu5sLXO6iriuC45e0tAbVdGEGDi/1iQKJRpE8aXZKECmW4CH8RY5aLpyzuThW8WPKG5VF++7kQy/auNe3qWvu1tb+Pq1av8yq/8Sr2s6zp+7dd+rRYQ73//+2nbdus2zz33HB/72Mdetch4UMffP/ffeOLdL/BF557lSnvTZIXH7Ic1+6FnXwJ7YrTLiQTvsGx4KQkrSxsAqine2uRlilpLTTe8m7eOI+oOXm3Kdvd7bcdLK2NSob6e8UCqwJdINVCciWxp1H3jql4aDzZgdbdxsgs+nejcH8RjbFuEhYhJbEZ5oYONarask3Rr5stA9RxwDbr7mkTJ9DlWo8SN6ZnjJHp9P6zpc8OL/RkO8k41lT/Kcw7ygnWJ9GUCRm59h/p3X+5NskeUwPmwwweWn+LbHvsoy4sr8qJQrDOdY1E/kzl0+4Hu0i4s5qirYFZD+Ik/SR0n/bMmPiYyaESt6sip3fSa3pFGGmzoC3FTiF2hWUFzXIgbWPcNqzyrFPl1aTlcLWhvCe2BEDdC6D3FEEKCMEil08og9Udf7/jScw6UHGrK1JA0dnY9NGyMPuva8CBadGxyw2CSFHBPPD0OnHl3Wlt89uNv4zoD8LbFS5y9fMiVxQG7ca3GmdKbZ1ZmhjAzAH0qwesYOCoq9Qh186ubigH1xUjoMejphnc7HMuJH/fImrK0QPc7W+mDd3gMZw2Ptx9PNcdITvpkvZHGydc9/Tw84dA9tFTy7rLNMbWwbmdFIYnWpIZOwXefpIaMTDaKzsQZJpKzvmgstndFZ+bXeJTndKWplP+uRDalsbS8bTDy5HtLJd8TQDGIsJCWt7U3+MIzz9Mue4rFn3vn2AuGNBPScgZNU4GrLVBr64WW8ffUPwsmsnbGIsB/zE9r/DfVoyT02qEOCYYUzFMm1LTArm+IGyF2jP4kW48tW0lRWNDIydfuMefOpstmmquphmLrxnicFMSkhWOyXLa94VAig4Gib9DT6XR8lsNtRKbsnUyuLCVVhEReSHu8mPbr/rIvDWsDDXROiGMaJyoxPDL/na5EWpKtHcJRaWvd4sweB698eELrytQN6xKqTUo7KW0Wdr/lhL0xlbnD6PEbRSYNfAPuXLkyYWG5iuHk/l+laffequN+DwdlTibKgwJYLq2MIiykqWw19VBTdpta0ejnsa7gla4xysyKmnaosxu9SdJBGVWpBG6kJZvcVrVJlGz1T2KV57Vp35emKlEuNEfVdqOVoaYhug1Ln6PJ5BN9jhwNc64Nu1wfdrmZdo0duMMqN2RglROrkkgUS20c5Ybgdd24Xr2e0UrkkUb44Pk/4MLlW6SdXPHS3Kr9yrCEzVnh+LE9ZHenqku2hgFbNeXdvbMmY0tmOPHRKkFqoqHY/ab3DYOuUaUIt4ZFVQNVzyyX0+MsrTHd19MMa53lwAAowlXl8ePaTLHbDGIm8eqblbtIGrReEil1rUpF6JOFQUhmqCSAhiNj5v11vB0/Z2bW4eEhH//4x+u/P/nJT/LRj36UCxcu8MQTT/ChD32IH/7hH+bJJ5/kySef5Id/+IdZLpd853d+JwBnz57lu7/7u/m+7/s+Ll68yIULF/j+7/9+nn766Zo69UYaT85e4Csvf4KHZzc5F1fsh2P73ZsB4+0f8UDiZk70NHpSM+pDVWcezAejmGQtspSBdYnsyjB2MIoDVuOBvPb2XxnTQXxMgawAI9XR/u0JIs7mahFnEm7dPyL0JdfFY6rDf5AXhFS2TRNPAln6b+VT9pPPRYsuSzaU0d/IiwQf7msyUmgbpcRGqo5cux4KepyNx1uvb5re4MXH1A/lyBaHZRnoSmbDQCvBsPvxu7iXI0rg4bjDP9z/Qz7+xEP88voLycNcj73sXiajGXx7c5+43ig7q76ZBDFSDeEJd0jw8E6EycC8O2FUheILgox019zYpO7SwCKEATZ9pM9N/Ryno1JyC7WCLk5FMfqXZ1yVxgqdIUBjC0nSrrwklXmIaKIUQBvdx0w7VNOiIogmXrp/WgxjkEBmLEpOh47Tdeb2cTEe8fjyOvtxzUJ65tLb78RCNL3w5MhkNsXZGXo8OlQ1GKQ1ZWjlIrSSGQi05Lp+uEfWdDhw5d3z6V5sCtzUU2vyb5UUjkDY6C6lw+dmZZuOsggHgPQ1PbhVeS7lxGewDWSN/x4j5WXrpzA1zRqdsnQEu8QbYRmVnBGGOveJqBytK6EWDONrmPxt3ksiqR4bzshoUWnKUHI14p+y5e7lCCLs0fDu2QtcO7vLXwyXKSbd1s0z5AjSwrAMhM2ccGvY9iLxv22TfZIVVw+4SZpalXK4zNA/Z2tRy5TZVUb2oGSd+32NuW0D7nt9V5VMT8/6GsrWy8HWMr+w6ANXtqTvEWIYAcaR42cPbYXDUNPjCtHWmvIqQPXpeHOMhlgDgijZWLm6V5xLyzPpmE8PFzky305QX9fdsDGQwprWZc5Cevq0w348JqAewH68r0trjB2T6dnz38g7LMNGH8OvM3aWA11dCcwk16T1o6zrk14nLCQTUc+sdRFalCXsaYf+fC2uPBnXhLbKBvVIjyJEtuWFzti5l6ydv4kxTWifqk2mZINUCgvxJpdUmWaPfZ5o8iWMXpq9qYRcMtpX2ab6Zh2kHatTFR3J1TZFn9cbtweWUOvenpvcEiSzF3pTIhXbP2QO05zj1HI0zGhCYsjKLDpOGiDg++z9pMyuhfTMDcRaUCYKmkKLNu6mTD2MtUZ5fd/12bDD07OX+frH/jv/r5e+lLwR4mANClvAh13h6KGG2bXztOsNZdOND5CLSvlMjaI156Tp4vYszrbKWeunrM3+ABRjdBXbF8lQqgCGooBUzroGrPJsDASJxaT2CliN8kLdb0wZWWHCOAPGhN9sa1FQRpak8Ryqli3ZrrO7lKJray5CN8SayjiUSCiF0Ojn48QPVy/thMnn9hrjcwazfud3foev/dqvrf92f5Hv+q7v4md+5mf4gR/4AY6Pj/me7/kerl+/zpd/+Zfzy7/8y+zv79f7/PiP/zhN0/Dt3/7tHB8f8/f//t/nZ37mZ4jxjRON6uNy7HjH4kUWoWcReqXckllKYSmzO4JZ6zJww3yygiHgR3kxYWgNW6l53uGojCCUmunSQ++cLKRoBComOSxjNyOhbB4/hR2Qcn+sLe2xFQ89hRaZ+HClLUDsjThcU36n9JIRvVdGQ2LqUWK3MZN2wAx5Mwt6pVwXqR0K7zoEKZBhI6b7Lnp77UL0lc3lIId3PjwZpC+eeKheS6s850B6ZmXDTNTnLBVdUO5m/Ph6RyuRx5o5/+jCR/jUIxf4k/UjlIMGkgI+OQlppuyszaUdltcWcHBo7CzGWNrkkkOockPQyToJUhKEQJnIEaUvENUTJRPUHNh29tqVkJrCITZB5+SFe6i6fjGwzCdywyzVI4Yydhek1hgnKCiBIplSAhILOQikQGwyKQWakGu3QaSwTvY9Rv0enW7rnSv9bkdz1VMwa3ucrjO3j92QuNAc0Vj0+byuE2Zge4c5bSCzNuafUEiEmmwGVOZoladVLyUtkG1fU4tpL4QbGeWHWmSPey+9/bbHUrLH8/1WnU9xoKswtUcfSt4CgN6IwwuKO70PB1h0o+dMLez2dpsyAiSeXthYUqv7YBVjbAfzTaLAYE0VsUnO1ymfd9wk3v1NGgfVrYDJCD2BWCIdiUiyRo3UV313W/7XN6IEzoTIe3ae5cb+Di8P+7AJtjnW95ejs7MawnEDm07ZWf7Z1WaIf54mNyw+zwsiuhZpvWtAVSpI8KPc3mttHCqA5QCVnxQl+zcj9XOtQFqZgFSTBqT3EKfn0xYalQVPgqq696xeZiUHiImURdcwf94MWfJWAMOUhec2FkNdAE/Hm3VECfVYcrUB6J73Zl7zqeEsr6Q9IhY8RGA3bLiVFtXj6Ew4Bgs38n3KusxYhI51nqlXbFH52bTxvpCBNU2Voi0myd+71pBfl2gguZ4B6xJqsuHa1Ci7IdOjwNXC3oz7AOvj6XDZnHtBtRLUHN5rGJMaRpNy+xp1p33yg+iN9WpjaumSt7Q4OryOm0tTj4NkgE/1SDaA0EEsoMpCYxHWXo9YwqEzefWyThUaRUiic5AqVlKtkSiBZdgwk4HOfLE2ua1rUiSzKa35aSVIsNt0bLKuYH0OtJLZpIb9Zl1ZhK0kjsKMUDKzkkmSIOT6nii5gp4KYAUSo2eaBwGc9FD7bMeF0PD39/+Y//2hL+CVgwuEDp2rk1CiNvj7PeHosQVnX9mDaze0/gn6iionfeqf5cMBrSImfw+6gzJCgEoLx++72KQfhkIWff7QF4a+IVAqY67PWr27J1ZlXo1PrOufr1EZSjuCXnoqK/GgtLaMiD2GnFhjHZtLQUGsmEkpEoImvweB2PYcDy07Tc9hP2ev3bDJDcepZa/ZcJAWtOlO7tV3Hp8zmPWBD3zgVV35RYQPf/jDfPjDH77rbRaLBT/5kz/JT/7kT36uT//AjRa43NyyE1MBqLkkdsWpnmELPe+LsrLWZU6kjCc92GLQV7lhTc1DOxx6+1A7uTM7EmsnZQJW1WPUFoDpKeuAlC8IUzPAfOLfMDHYLePf2ywveUMsAr7In/TK8jHUT0Qpy6loz2lmWnNfmLs6OY4+ZjNJZFtI3etqaT4EvUSWodMoW/vOvVOe0MVhah4ft2cY89oKXBv2WAVdSBT47CAklsJW56m+v3u4OM+l5X2zW3z7w7/D/3LwtVzvzxK6SDZ6RSo6eW/ONczP7RGP15RhGAEtEQhhnLhLQVLWDkMuiLi5YambeSkmt7FuuUbPWuGRdeEQ7ypgHXTXZtsk7oAwjJRZ8RrBmuClsbKiAI0BXPb4Xn2XmG2SFmN2ydhNwTr0ORAN1ApSWMSBdTImlnghajHrIbAX9XueSdpKSDwdp+vMnUZAjVdFpqlA2nAISPWQclgkU9gU9SYQqB10vQ4a20xuFdVQ2VsuXYfRvNfBlVRGsKrW4AZoTY/kbQvz7e124eT2u2LdtpHz+4yPKPY+H/QRjIpz0ivLx7RzHkUY7PP04JWGwmCAvYJ9UkE/TZtUuHAzKRwdiGpJ5qmkzCypLB5txMjkuzwZUKJeW8JxntFLY4BJZ69K2dgnWWbg3/29+V4aiVyNG967/xl+q3sr67RAklRmUynGBl4E4mJO6AeKSTUK+plNDiT97J3dWxGqySKA+WdVlMnXA6nnRu1KF38IA7wMJC5l+8Cf3t5turDo6C2mlvt7lUlDI5TJCXnyLNPisRQhGCtGHz9XUEsCFGMC96WhSGJma1VDOZW0/y0YUQKrPLIa5tIwl4ZnUs8rac8UIbpn3ORWGVeEapcxBSS8QeujlYHMGFjUl6YGJi2swTIqTVSqNgWyXOGgTBrYD5lVEfMaHlgTb2vELwyw0iRebeQD7BvIXtlY3PngTsVTwLmNYDBVlryRxjaQxdbfDbE2UL2u8SY9qOXM2taWKVygknZl5PYSaEmsS2PHQ8OBJXAvpKcvjSYVlrauQX2esSl+HOmxtC4tC3uWSGYZNnhSIcDSGIF7cc0662PNw8AmNwSE3WbDcZpxNMwZQgDOcJgWlQiwH9bMScoiF32fuyKsS6rgZjLfsKkvm4Kdf736dS8sePfsOl9x9ZP8u+fPUNZtleZ5szztwPp8YPH4ORarNeXYlDilKDuLjISwBWRVv0Zfh8pYB52sh6ps3kocv5+Yn1VOyrDbZA2fakPS2qaossWfy7LMIJnFQVBP5NIyNoSyr6H276TLVJ5RlybJKJgHJjcslEbTlFNSFt4wRHbMAD7lwDwONThCm2vqk9bkzDJ0W0nMrzXuS5rh36axDJGrzU0O8kIlYDJwMRaWYXZH1DeTOfLITPNZgJF15UySKAMztkENB096k4B01tEAo/MWLZq987EbTlrGaicDGbHhKCPtdErLPZl4MZV06OLhFHan9E435w8+sDU1SRxpumGri9X6Z2sovyavZBYk1kDn3xVFNwOij7AMGzIOStrjOaXaJhfQTUK2z2ovrllIR58a9b1ACMW6U2WgL5GDrDG1wURDGnXbQ0jMKZVOeyfW2b0YZ8OCr9n5BH/y+CP84tH7GPoF8ViR94QQeuj2hc1DOyxv7sCtg0kLuoxdiAzV9N2HT9qF+pusOxpNm0Lrj6HoLNpom14CahpvtYkkyF3kMM251OrnsMozclaG4XRSLi2j7NCljni3wrrj1hUfW95auJSoxU/O2j1JKRBjVr+sYBu51NAEk5xm7UocDnN2Yl/ZedgxcVpgnI7XGq0Ie2HNxiVgogzgk7HiPgqFrphJNFKLZZf8DWbS2jCmFI33NTDEAI80AT6qdJbCYHT+mZTbygFl/Y6MGZVsi3WJ2XotWyDX5KVMpSB3khe+EYCtqazQwZ+p/G0K0OUyymkiatqtvmTmwwc1oVDlFIlSQUl/vGI2Hc7aaWgk4d/QTAZ9jBLojVU0fte6G+9Kw8Yk1xKKHUHaDVDZTqj3vB9jLg1vba7z8pl9/qS7Ss7qcwgqbZds7KzdhrBuYbOZfODTY1m2ftkN7Jd9Cy7nPIEbaWGi3eria5ZtvL2zXZIaFmeTQ/QlWkIvY6EhWiwoYGXLob+soi+ueEe7Pv8EYvZf/hptjfGmDei5HHxvZ+bwXdb1p4Gtc/d0rXnzj1Sy+QdlIsIyRDZl4Pm0y0vDGSLZzKHnBMlVbhgkswiqNGhlIJr/a5X2VZZHUz1jYWzEO0t0up54wFVLZmm3n0mmR1gZ8ybZeZXsmO4Rkwxao7SCMLoPX0qx/bgCWa4aCSIo72esvHsSC2ksrKrcBl75mvTXZen8TQwnSASEubT13y4xHUhMTeCz/RfMS22VE32BhcBcYFMKrei6vC6RlYWPRUkclVn1d20l0ZWoShTci1E9alVpkljlWW2ugLK6utIQyVtqBPfWaiWxDBsO8oJL7QHHqWVjyqV5HDhOM1MwBYJ5za5zy/lmt9q/7MahHjNzGYkduZTqL9waUOfBJfr+1XdLj7XP7fu/FGZ8zZk/4/+4+CRHR2dUbjdQ15EikBYTM/hnOxgGk6xnyG7ugII/pmBxLoNiO65mcaDKl6CsNZAFl3iYSV2iTK3S5RHi8TWgOAHAGy2+vvhNy7i+MUxYWlEfV6ItlxmkF0pbxvUzGUMgoOny3kjLAk2uTOFoNdImNaSSWcReU5VLICdhFkwWO7WreY3x4KMOD/hYSMO5YMmF/3/2/rVHsiTJEsSOqN6HmXl4RGZWZlV1z/Q0Z2d6hzO7y32QGHJBEiAJfiKWHwjs79xv/A0EAQ5AYgGCYM9U93R3VdcjMzIi3N3c7F5VEX6Qh+o194iMrEp2eWS7JCLN3J7X7kNV5cg5R2jBVeIHLXE9Vql4wwU3MuKG9zgayt1PAOHNZOEAVv9478XjrKwRHPr0DMFEHInCKo2FBXvMgatqF3sVrZJka3E7dq1uL71JLjtG9PEpAFmA/wbasLE8HMTz9r4dKVSfR2PUJeIw4Hcz+KmrSgDGrJIUxu/6mRTPXYJe3vGQJeHMI254px4mdvxf1xf4dXmF39YXuJEBdyw4mzeCd63R7/hhjS3V/HDG/+XVv8P/7J/+EnKokCxmzqvmh+uVsrPq51egwUZH4WbQW2sws5ydpTJDu2XELXEzhnewSx9Dq0qwGR6aOXxeBVIpJsObulN2wpYuojeMqJ7rZ1Ez7IU/rpkLuUmv0bmEyUzgtfVsrQnMCWtRA3g2H5rCGQurmfKZh2DHeAOAOxsDPoGc/Dn+yDEgWXfbghHKthjp8bGYITiJFjwWUW/GolCFMbscXOoYHwZq+D8gLrN4HvaeXv6W0bwRak8qscc43u+yl8bKcjZS4AgXifaH5GyfApAFtN/wWNMU/wUu/OwxFX1eYswg8vb0pN6M4PBU9MTBzeOLtEKZxHfYax30In2tM32KZDWCl5bq3fOEG97hTiYsSFhE1wzFkqdmDt8aDvwQkSnhOmX8692v8LOX74CRIUlC2iBJq8Jll8D7SSvcQLCz+jnGB3hdwIvpY+1E7W43c01/6/tLsDHL9VvvDHmWQSvfj8w1W6Bq+/7N4wBczqiomf8MnWu4+vFKYQjvz/uco91JU1sjWtK5mKn/J3LZPMcfEMrKZRyleUi95gW/Lq8A6DlxZAUpzmZxASDWJIAyrpbOHB5AeGZl4mDn+NjjzYrYwCttjGDNqYQiPwGAxcamg6lQlFEqYf6eIVgkYZVm/j6SqlRG0iZXuo0qm9tRbgUBMFYzvR8pY0cDBmRdG6Otjfv4FJlZvU+x5zEFVXM5kQfzTUKKnCYT4co0l0ch3MmAFdZsDIQd1fDLmi5ypOisns4bn99jnXHiMYpfR56iMx1g4xBSmL77ueLG8ACw8oCXwwn7vOJq0ALFaAXhwhk3ZQcWwn0d8W25wg3v8IYPeMMzjjwYEEdYxK14BEsw0Nr85HOV50t93vSxMdOA/2L+e/yXP/sVZFc3jTOdjcsjsLwg3P/plZrBmxJFB21u92vVHMi2L9hZFlTFFlfdPOXv1YOi+VHVOTJVQEq73lbzziJyzyxqMkNpxRZ0c5w3OYHNub44oaqFJQXF0JplWcGH/JxYEmTVbrt5qEhJZe9LGbCUjKW2rHphlUSunHE9niIvvvT6/FB8GsjDE48DqQTts7RgR4qUX4I6asRbcBbgaJ1CnGFTbeHpXfAc2Frs8GgHOzO0s4lCJSBtIHGzVjdY1O+88M3q2FbhjwU1fgdM4mCDQJMiipkF+msoUH7/Xf3E4MDJUzaC9/BWtq37RWNlqWdYVe8s22+t8wpjR2UzyDuI5UCW/+1xSGeMScGtF/lsFMomLVwlxyTh/xIxXmS9sLUxgBqIf1uu8HV5idf1BX5Xr3CUjDvW5PVov+Wyo8cPFTON+NfTgv/+p/8O1z+5A8+iLKURqDtB3QPLS8L5yz2wVxqxD9YBaPUJhT9vj1EAWaweJkUHfGdQRdfD2nXx6AZhGNX1zGNUjwCtDPgA7ZNN3xmx797h993kV0ssOmijkvpnMUGsc4d269CfUoVQWbt1sBAWzpsKOgtZy+uMkwxhCv9cLX+Oj4mRVB64I2VcZKQHoI5AwYYimpBUcRaNgheacIv5LEksPgEYCKLwkRdNLk2jXeKoQJSDGU1mCGzZVn4raEUVstfo+qixlnqAJNkWN3+p3lkKm4XpUw9nZ3H8ChvH7NdXsHlkWVHT9mOGGHOuzacDanTAVfkyY+j2izMqgNbu2iui3t1OYG3UjaFBJJioBNtOoOasJ+u4e+QJdzxiBWERoECwCscxuzw2P0QMlPFlrvg3V7/CdFhUCuFA1qD/6qzeWRg7kQFzA7R053egUf+YFUSE2wncg03xeTbvdD+vmeGSVZQd0kV0DIykBi1paPMYLgAtaqp11s+NW+m9vRq0y/ZdCqZRgGo9fuaNSPpj+glcLs/xB8ZZ1mjo5IyT1zzgTb3C1+s1jmzd0m1N6eDDIZ2xo8VAK5P+pbOVQYCjmcKPVHCUOb7PPZVcYeJAlgPq0dlZMiZi7IjD+xfwAklnjwKVHwLm6WRjo6e2s6+3YOwb4QBqPNwb6mQG+N6l/NL4/VOMTCn+AVsP4GzH/H2EA91XBJcYjjBZoeWYV1TCrP8kOfIUoDHwAGPR2TpiNLawz0tewD/yFCCnm3of0jl81k4y4HVR2evPhrf42fi2y38EUyo41yFYpitn3FX1dXtb9rjjOf694T1ueMJqIOgdS4BaJ8vjVmieBMDuV5wM5DzL+r3y1kwJXyXCf/vZf8D4coGMAh6kncQ2ztYd4f4nA+qXL9XIPcxFbV3T50FW1Keq/+IxYFNs0b/RsahaLpRWju++K1N0VAcASkobdlJA3Gpq2iTxPl/VxvLqf5Mkn9jsxpQznivpe40AAGgH+JqQkqpdinV9lwt2scddnXFb52hi8jHxLDP8AWIiPSAjAYeLVqlO/6xWQbjhEXcyAVAz6JOMGDtQxKtoJyAmAmX8FJztwHr7VAezMjWQy5MLrXw4X9EGfQIgapi4ithAhg0jC+1dGwbXGOCXatQd4HGGU29G6J1CnnL0nT98smdwmKnXbsXnv107PCoF2lvX6vMKXt3wDje8D3NMp+Yq6TmHpxrbijwALVGj8iQS1VWPVbQzny867s2DabUuT36+/DTfIucVEMEJ2t1MXfssPfoB/bNe0Iz/zf5v8b/7p/8e/9eb/xy8zkhn9TSpk4JZp58MmL8w76x1bQBWJmxMDy+7G/bVCJcd1kYM97a0SK3TE1WAjOaRKgDTiusxM9aCGelSgVYiRrQmIj4QE/R/ooO10mw18ZdsCUV28EwZW7WqJ01ZNUnIGRh8EcYJlRIKJ9zX0fThCStnHPISxy9B8GzK+xwfE85i0kr11r/I/bJ0jaOV7cUW+ARBQd6AIgJCQUYBmgSRWLvi2WWYzZ/Lr0oHvhwMAXSB69etdzT09Zp7QDlII2iMLGC7RvIt8+WLX/W+vnKGk3uDATq/PnWj+F5S6PPiFqJrkeM9TYKZLRGE3R9QcZYBi4zBktP5P8XxIeixdHHgDAW0kiig4YXWviDmjB5ndhWfY4Lhpcf9ilohpghbF03/RAc1f5hjMmHAn4/v8LcvX+Mvzz+DcNaiAmk1uc5A2SfkfeedBaD3HIkdKttjEeM9bK4hap4laO8nambwvmgXNCBqY9LvGYa9VgCnQIaXO/nm+R6zhES8k27qcDMHzQhhCM/Vj2+CeAdd0QSdRU2Th8QBdDmzwc8FPPHr5Tl+mJiIMFPCgIxv+B5/vX6F27rDmYcAFo48IYNxrDPGVLBLKzJJ2KaskpGtCREALJIBzCYpVFXCDe9wRUt0MKzmi+XNrWDr5Ou0xrrsxmRsHg6yK3jWivEO7KvUUF/rzKzR8N4KZZ/pKlvP7ZmGYGG57FCbpORNscTnGM9fPhVlCbAlDPhvcZmhh9upJKgBOoOxo4yTVDA0/9OO9Rz7f9mwOhsgCai/66lTmThQdeTJtumCxGHqEm+G9TLdR5F5JM0T/Tk3xdmlFWce1DOLBENSYGzhAbMRAhYeNn8DwHW6BxKQbL+MxJvx3/PZyZqbJTT5qiYS3//Yv0gz/lf7/4D/4av/Gn/1eod0pgCDJOmpL1XnqdNP97j6ege5vTMzeILq1/PDwj4QHQs9TxJvXNR5O4aPloUWXawIv6ZoWDZCr+WUGhDmDVWIxc4f6Nwz6lwGIXgz4VTst7DiCMSWJ2VVxPDQQC8ZtfAv2b4rSawvS8kYxxLz28oJY9Z5y20R3q57TKlsju3HxKdz5T7RUOom4bPEOBgi7gNib/x+KyvesuBORmQIXiaj0qFiRcadTJroGoLtA8hygUy6OZ8vYM9mqgjAgJC26K2GvvcU3Yr+AnZfE2zAGwfe9HMIO0oxSYx4HPH/FJhYwJZe7IlF6MmRAthSXxD7zZRs0nTJoR6DkSom1GBoKZU/xzHru9ZVpI0UBECwIUYq0bq2oskEAG1pu0srDmnRTg9W6TjyhCNPeFMPuOE9TjLghjNO0iZ43SYJhtYPdYwyJXyVZ/x3n/0/8c9+/hq8Yx0EE8CToOyB02cJp58fINdXCNrSpdzQKw/SSUAqKyurB7VYWpXCK9myZWc5NZYqQIuCRzHZ8hADfFQivOLQVcm9EgGj4kZl3Svt0gZxrAQpSf9VZbuo7NCp3xTMllMdo7uhnw8A4np/juf4mCCrKO9IOxG56TvQgCwAWKTiJMACbTgxU7FuUTq+LMiW6Oq/OGcvklwil621uaU407FL4v01mmDrfOPAVU/c74t+Hno5e3IBDMFVgvkyPUy8PwUmFrCFqXr2mbO0mhdYm4uSAScdhhFMOJcJ6ucQiv0DsOla5x5Y+tkGMMXnsyUREuCUH/+RKgZ73jvyuuR9NZ+Us4woSDgLmZdXm1MdRHXJ4Q8RiQgHyvhXu1/j1YsjZBAnKEGyLqLLjlBejJB56nb+hdyQ215tDCh7rt9UaRXuzb+enSU2hwi0cMLuWObzNm2r5o7U+n20zwS2FXZHymLeAYylRQpmiR2zhjc3RSVUitNXuP24O7D1HP84olqynkG4lwV/W0b8rryM7oVqczAESM4GZKikMIfHkXfVrpLCGxbQvMTv+61323YgS19fjGmjzJ8d1ZhnVkk48qDMoFgbNybWKg3Q0jWtsYnE1QztuTUK6dxyF6iKpO/Srt/R5pRPFcgCGjsLuMxr0oZ9toqyjvw5oClvVrgXsBbqZ2rAYs+w26U1jrP7qu06CZizqTzOrB0Ke1YXANzxjJu63xABPBYZcMM7U6ScA8xQM/iMq7xgn1f1n4XgXAd8Ww74tlx1eZAayHuzAZVOJpyEUMUklSymZFFrHd0HOmed5eMAlBqAWcaf5gX/+5/+f4GrskFUPM8AmdzwZW7sLGAjMRQDrNpchZYTXeZFkQd1AJieBNYMS61X6JzM4iSHlHgYOAAqYiv+9/MfIWSIng+lFdE8y7saUtHfxwOCMU2F4jO8gINK4LsR9axrzrLm6P5easJp1bzoXAcUO666a9LGEuFj4tO6ep9gjJRxSCNGIuw6ryw/2RO0PexJlLL5hg9hoPemHrAi48STykFi8BlwEmX1TKYp94vf9eWrJCxwbXraJCJVXH8uIY0D9KJ1UMaHENeeX3YwPBotU/2zUvzz17pJ/LaTRpssPpWJQQf9FPeBftJLZhT4OAg0dgaG6lNWO6lhicHfX8PmNZA7X6xVrPMdLQpkGdV3+z01fAcARMX8WKcYyL/hK9zwhJMkLEa7PkuBSw5VdvjDAVozjfiv5jf4P//J/4jh1QIedWUhWQfu8gI4fjVg/dlL0Dg+HLj7uByUTfutdFvpHncwyv2zWnKg0kBBqlopWHjAykMM4ilz+K14hJwQCCBMv4eQCkXl3am3Qa0VwoZJFVidgdicUKpqws9lMA+thGpa//s64W3Z49R5VagnyqeRpD/HHyeyjcEZwNB5ZTlw4DK2AqBAm4lUSShIkcQUK5Q09q0+71XZSz+sALJABrLTphLoXfYGatI4fXyLBQBAsTuXM4PPS8o4o/gHNMKkAyUej5mnP/Xo/b8uuzwS1GvpfSCQM+R6eedg/9w/C/a4A1Wpu+8FEkIzkBdpoFd8j83iDnD5nLNyxr157BxlwiIZBRQyn9JJDl12+EMBWtrd8IT/9Pq3SLP6NOp4bPPNBKyHBL6aQbmrcvMjIvseAfIH+oLK5rXNM6s3yiVLJLyAUkXZ0S6JoM7by6N//wbUArbzmM81PXXxsd0oDaRi/36Tbbh/VpGMlRW02Mg15PGPfI4fT+xowIFGjJTxu1rwH9av8Lpe4bbO4TuUSDCngkNeMKc1cojFutS5RYPLlr1BVQ+KuhJkpBJA+0nG1sgKgtaFncIyZQTjOlUcUjEwyrq1eyGDVErohu8JCrYAzUfYcxvPUWC3GYSzFFx2Nmw5SzOA/1TylMvo7VwyNfDqfZ0Ze+uUvtANOBhoqmYggMXer1nXDGolD+g84YoUt8jx8yIbiMWdfY4DX9FYAGqpw52B/JlHHKtKV3dmJq/+WBNGagSOIlqs/nbZ4+vzC3y9vsDbcsCJNad+wwd8U6/iXDsbU9CJCJfrlJMQVgAn81n7mBypP2++yDP+F4e/wmdf3KLOEkbqfZogmbAe1H6F9rtGXY8N6fIcoLGC+znJ86Jqvln2N5UtyEVsHsKsXdZvywS2/NE9s/zS8MZZPl9FEd8JAg522eOp9xTmBnSlVT8nrZ1/FsPM4FtVh5wxzCmINyyEwk2O7Kqjd6WBnh8Tn+aV/ARjR3njlcXdwu4sBXec8A3vceIJbMnDZO1OvWOIVz8SVH64Ioc5fI9wu0nfZbfD0Tw0vMOh68xPQnF7EsLZqh7VJokH5vBwei+Zxrzpsi9Br56l1U8QT5Wp9SG9vBuobzynpOn6/faBX5kN+DtacZ3ukcDNs8SALZe7eSUixSJAB+y+m8yl7tyZei5fW20wv60z3pY9vi7X+F15iTd8wI2MOAoZeNqqVBz//XCA1udph//ji/83/rN/8vfgqxqGgADAWeWG9z+fIZ9dq/EhEJUHb6X+gJ3lZvB99YG9ysE66Pcu087IsoFX/xFOZQyw8GyzSzCyijTvLB90/XkbsGMAd/aWmxwWat9dvVreMSOqUnuXol5ZOekkzNDuUkrxb/Rfp2hHW/bneI7viIHSxitLuv+KMBYh3MsYPj7uZeFAucvPlEMiKMZgZPu7BziKSQ4uux2mDlwBEHZDpbv1f9WKjQ5KeXie7oW8S7Dn8oroH/H7veTwqcWHzOurjcUbz6luWOtv+1/n+3ygiomsF6F3CLI5xnpXhqmuH89MCu74ApHsGPqtiHW+tEQXULCyGOP4zIN5Z81WbFNvEgVPt2bwPzRDa0cD/sX0O3z18ka7J1EDgCSpjGN9MUB2EzYO5yKtSHDJzvIqeOzkdgCCnXXpoyXbW9hCnKU1/QDaXOJJwOY93Wdtmo14kmH+jpvlnbR5J5ozGBO4WiOSRC4xtuYjdu1KB0avnbfXc/x4wzvYva5n/KK8wq/Wz/H1eo13ZY9913nTWf/HOuPMI27rDrd1F5+ziBbbveDuBTgtyOq3qE+WWWVYrjKhBrsHQHgw+WMTMU7myXWSHJJDt/FwZpZnF0dRqGxFU58AeokcRc3uL4vOvYokGEnUGnH4WtiNv59qzvJY5CAW9CxMHReKOjDG356zrVJx5Kr7CmKqGwrQMAG44xTA4gTeHMP4HrNFWSUHcHVICw5ZZabqvbYoyA9tVnLiUdfDcKCsfUbqzg0AUYD+fLwL7+BVEs5Vm5PcrDPu6oQp63Nv1z2+Wa/wul4FUYSNGKCNubQRToUa3d+wMrY8B44makQ4SX20KdiHYqYR/8n4Gv/5V38P3jF4FAQG42AWKYtpfZHAn70A0gU7C2jsrP5fb73iflqPsrO6+ZAIyXKhlTPmVLFPi84PQlHET6uCXhAgVQlgysEpD/8tISOk9ncq1mW4x+HMX5gqgUrTzLvHMDOhGDtrtA7wa81YOONYVMWiYDtvGgx8VzyDWX9gaLc4McM9isd8wFyl4jUzbkRpvkqV5ZCk+eKn2gDhIJZPEqtRLwHExQi0isjOWEE7o/IGkh7bZxOEnVB9UuII9SoIozxAtfYZ7eRY0dq8eqeM5plFG7pr++yneWo9Nvi351w2Y7+1e95Zkx5eveirGJfG797pRQHGNmD7JADAKhe6GDjziB3p4sKfA5pccaQaCxF//r6q3PB1eYFfrp/jdX2BIw84Cm2G5B50ZGgXD+90+PtO4iNl/Flm/J9++j/i8JOjDuLGYOIJKAfg/ouE85++BO3aAim6eHhnw8uqhINborMNsQ/i+rSCUhJMrEvmVlqhxuu2qA+mm4FZOtB3csOOlSWmg1IKrkkT4SCXTwQUnwduSUXkR6z3IyHsBmQ/H5KdE+4p8ETz8ed4QsEmnUodSNLLCxmCe1HPizueA5xyiVEvOSrW3VBdK5yRlQL49Soo0ID3wYCSweamnjWl29JkIsCGmBKFyCrhDwqgeYD5a5Uz1FhLPeDlLKxLJtZTZWb123Vpjk5x6zIf2TzX/yKfs3MHMCmg1YomFekhOIXOkw8wJlYDOwa04ooDHA6U9IUXf+/KCr7f84R3dY97aWa7/a/rATyxhNG7R/2+4FamhJck+Iur32I8rNrZ0HZWsIH3hHq9a110dcN9UPYNeoSWZA8yzAz+4ljZwK4GvNvkgSrCeP2Be5snGM68ciDMN4W2j23mNwe0pJtr7Kc43Kw/zTzl4mU94GvXaA9WSnrk9z/Hjy0YjJNU/KIc8NfLV/jF/Vd4vVzpmggJs/ljXcYqGbOxYkYqm2K7wmPWlIqnsNNYJOPIc6x/e0+90Rhd3gTHLTruZMCRh+hqeLm+dnmYr0xH4njeZXAeOyKMIFSoIuEkFSMla7hVcJKijCTLW/pO848xmT4lUAt4yM4CHqpMPLd5lSa8oDH25SIS6pzNZ4JM+ZNiHtiRSg0TcSc3HTBSxdk8tHyN8bYcMKaCDMGRZ1Sk6MrezytezHVvLl+zq9F8wcvhZP/OGFLFmTOmpASAgThUKvc84dv1Cq/rC7yrO+3CKROOPOIkGWdjaTnL3AGsk6SYV0+WA+t58v3OgS8S8N+8/BvQoai3lPvxOjuLAB4IZUdYP9+Bekk80HKiOAA+SXRF/l7B4vNRSN4lXquFfQGV1ohKVT+ElDiAqpDAo+U/fj9VIC2NJOCRqrGzfM7yoktHZABTAFptcUjg4t13CaXk6Gp4rrrCqZzMI41NAv39bFieJuLwCYVKP9oOrybvAnRCecsLfl0P+FX5HHdG01dEegiNr5rp9Z46en/x1rZG1zyZ/LA/wD6oO6XSWVsV1MAq+3uEygajE4gDXN1Y3ntnZdB3Vr69uuEX/+XfTzV6Bp1LDZ155sb2IzJmGjA64wzYyDZ3VANE1OclEo/t8cyhE3cZiL9+NT2z0261Ta12MQQQE0ff4aNwjoH/vo74dj0o1bYeTHJ4wJEHnAW4Ye3W4ayzvnOjV3CA338Cf5l2+F/v/wP+7T/5G/CL1p5WslglgnD38xH1q88ATzIuKxFdwhG0WhvAWxtawLt8bAdzBKWWRBlXqagGe+UcnQMBA7H6ZIa3g3IAW/68bB8PTKrvbMjqnVWXFJ4myhBWKu3JZIbeVQqAVZh8sZGs29jvtfuf4x9RJNqySlkE1Zb7Au3McysjbnivMjDJWG1BepYBRVIYhbfPaMWU1rlQvbGUT9oDE3rfZYnFk2o0sMrJJW4JPvjj4r+h/Z5+xHGZ3YewDmf6+Hx0+fdTjZ5BR5v/GkiXkUw66qDSdl4eoMb8DpBvWXHteFaoT4a3YGneWa1ZiLK2GIN1OZzM3JlIGV+JdB4jUnmJmLS0SMY9jzjWCScZ9b6MJv3QxEi7aDJ6uaGyz7iBrr/n8ZppwD8bX+OfvHwDmWRT9Y7mIy8S+GoPpO3SVoCuyt2SgHjyAqjaemfZmX8JOtm84TLDvito81ts3xHJQ3e/lxuiez7mGgbgPo1i803tMiTSxyUYYhuLewXaOl+kRG2/PcePO96y4Jflc/zV+SsUY8BMqWy6mznzZZfWALEyGDd1F2qBkQoYapFxnXRd6uoBv98bhTe7lLwp5K3wNTeFLHo1sP1S/QAAO2LzOcrG4FJpkoNiXqB3j9+Rtp19ExFGStjR0Pnfttypn0u9IP+peWddbq93aC/G/W1NujRPZWh+qoobzWk8H9wRMBGrpM9Ycy/pHFLBxTquA87YG3Fm9aiak8oNR6rKwoK+fzbkxNfgq+QgZ/jrdzb/HC0/9lyngnDIZzOHp5AtLqzWLIsBWec6oNo57V3hPcderPEWoOfldVo356R31XT1EoBoHPB94jpN+DfzL3H96h48NCnfpku6COpEWF4OkJdXTa0C2NzEqli5VK0ALS+yxzY2LGHHgsiLyHKdlRPu6oS7Msc1L9nzHgnfLL2VuO3nurRK63LYg1hovzGtFEoWqo3NpRsPncOcmVUThsGKaJYnZRIUSfj2fAALYUw1tvdj49O5ap9w9L5LBQ04OEnBfyx7/Lp8ZtR8pequMuDOWuMCCEPGVQYskmPyABooUq2iMqFiQcbJPLY2rA9b4DrqnNBouVUIRzPEAxSUydQ6hQA+KSBYV/r9HBNE7m57ueFlO3V9zdM+tR4Db6KbIXTS6yc+l1860y0GSGkMhqW7nEYqJi+UqHLNBlSN1i69n/SVxdUDiRKLC49VMu7rpEbwdVTARlLIP27qbmMIr5TahJNItKFdUePcrCLhpeX75PuCWpkS/nwg/Hdf/L+UndUnGdZt6vx5wv0/vQId9u2NXokQeWAGH5X0zgBR2VlolQdBW/kIVDfuz1tFoiLhpu6ii0fQY31y8QoGt8fD7BD+mCcSXQJSdWCm2lXNoQwtFkIpCcwqNSxWGTnVAYuZHHq4yXKFGvo+x3N8V/TSNUYDDgoYb3nALe8CyIrFn8kIAWflUAAbLjkEGijioJYbxmtNnraWDvav+Wu5TYLJmdAWh5kUxErUQK1QVKEtQlgkQN3GXNrm3q1fY2MAPVVmlsdj4E37FQ+l746BONOt4Rptvu+ZCSrTYEtOuAFVEJOBdOCGyS82rDkYiNVRdiqUiVV8jLJE0seshd3XUw3hV5gpPHQ9UIRDSllshuHuV/8+IGQiwmeJ8J/uf6PsrNy9PwEyAHVHKC9H0PgIOyvmFt/D/ePoxnneJAZxUPy2B7kYKvMD4SxaTSbq5poOoIp/9jmblue4YG/JxWN8MdfENNmkhgo8qjS4GrDl4cmg9L/lOX6UUUWL6H+5/gT//vRz3NYZ+7QEA+WFycESBCfWhlQnHvG2HEzSNWy8sU48qXLEGDTO1FIFiAIHus5VUMJtMTIEC1IHZDSP30zN9D2Td9DbjmtO+tCCcZtrFHCxnMjO5ZESWJSZpZ8jnSm8+kT52rdnwfYNu3Rbnnbechl9szEAoRByAKtnoakHsgJ+1QDEClXiAMBJfJ9SHBugKUN25qumwOKkVgOdBc6cVlRJ4XflYJd3PnTZH4C478yst+UQstddWsOC47buNkbg+7ziejxv9gGDUFhJAb9dtMnBiUe84515hVLkZk72YBBuZDBzeJ/bYDkR41K1810xION/Mr7BX/zkd2DzzZKEMEjvWbg8EuqLuTGIe0aWW7A8wsTaSA4vb4Eo7qM6MwtRSO+bgnhIom6e8/xHu/qmcqFcMV/8AOns71QAJIRyxRdz6azsLFQCrUmBLAC8JtSSsJoRPNvc5Q2zAPX5Olctvh7rBYPtg8fgOf7gyJQAUaaLSg4VBX9dz/h1fYU788lyGmYWDpNFnwC2jB5NH/rwZCQRYwfevGc2wMOlISNx+GmNUHAhuhzaIOa03okId0avDCkiAaOxsvqOf9la3OoiW0nHc1dN3mzvJ1DhcNW/a6R1oG/P9beAtU0nNc1fusRPKdBaHtbJdzWK69yZaFYdOJwtJ619sSeRGcoiGg3edtaWtq7V6tk+L2CQsntAmFNVVJtzyA31+woqjrhOC86iI5D10IO3oF1RkaHa8Y0Xmny/SX1PE/6r+Vf4L37+9/i//+4Kcs5NSpGBsgPufpYxv/4c4+kMWRYbhNUMkC48TrQ9OkIaQkRWTVBfGaoCoUvnDzLjQwWkVl/Mk5kaC7XuIlBZBolOOGRNSMQSopAa+vOMaGPr+yces05TLhSSRKglK8sk67XSdwxyH5N71sVAzqzdFp92Pv4cTySUKQv4csul3vfCAWS5T5YONwpa5W7R6Vd2RUIWf2WLXnI2xNior/GESHN6ZXr4qetwWQ9w+ZBH0PFzQay3AChzKxkrq+/45/wSfb/ezx2Q1weLPHlAy3+Tzyf9L3GAZ9v9UOfhIi3BEyD2dzV52QDGghzHHYDJ2ZUxARtvnKXlr1EWkUkQrfLt4CZBJYxjqnBJokBlpgSE39+9tWPXNcmKmSqKSBCGIK2wUaFzpEtf245J3+vYDcj4eb7BT1/c4Jd3X0AqbSTiPADLVUK+3iOXCqn+pC7SN98kfo7K5rE446gVMtCdkwQyJEmlgCztF3lL8wesLP98jhfq/NElOZsL5nIb43V2xxIRsSq45SRxfPsxYJWsrGXrUvkcP+44S8Gva8bfrD/BX91/ibfrDtfDGXNWVta7ssfVcA5fVi/4XZsaICwz0orP8h0As8wAY+UZbnSRTXbmwJaDE+716moQlQwO2tkQjGyNdDYKBbQmJL62diZXgmARvXXgq8J9atXryEGsBGWIqk0Loe9seJQKkHppefTF20tgqH/sU4m+8Zha+Bt7SZqkNMM9fwW7buw9ScJEHPt9R9Wkec3I/YoWvJGryFWclfVtuQKAmEcA4JDPkfMe64xMjNnzHV+LdOw+GMPVwa/rfMJMBSkJhtS2xUkbjbGlv+e+KiPsbT1gpIorLFihTDDPs6+6c3WEWcB47m3jugKnjCof38gsU8JXifDfvPpb/Lvpn0OMCHGp9tAOvIR6NWLYzcC6mgpFbAHFALomJi43TEnzomwVwUxWCZTIh/SttvZiQaqEtSacDBjCoOu3khB5FqB2KtrciqJ6JonanAPNvdJK4OE9eZFAQa1i81VGdE3UxaLdt2ufOQFg1Jww5Iq1ZhzGFQmCY1E/tCqEjxcZPjOzfrBgCE6m0U5IqCL4dZ3xTXmBk0zR8tZNFFkoBn2gMXkC3OoMFicDNnxQ8Yj2qWh+KMkqHhWEY0ednMzvZJEUxvDuldWzszx6eaFTdB3Zj9cYoPXYvnjK4dvnVYvBjCL73+LHsPfNco7USLo/d51flj7eDBObFxptGHhHbkizV8WcseXAlycLfSVjRyte5BM+H44YqGLOBVMq1kWEwhT+bVVD+G/KC2NoaSXiLK1r2GoeJquZZq5QptbJGIXFKlgfy9LKlPAnecL/4Yv/D+Yv7sM7yxfmMgDrFeHuT2fI5y9hbbuiSi7MD9hZmy4e4XfS02sv5IbYSjhKzUFT7RlvjUprA28wtDparevCvduhUJMb1i7T4I6dReh8S9pcNGZlRpzrgKVahZIzzlXN4E884iwDpDwPxc/xcSFQtksRhvejuzUwu1hF06XpxYAIB3Rh9/0fAGWS2CdlKLPXpWUezrjqoS8iTUQ0YW65u8Mqyhxu4FXFlp2F7nP8evHCiINY8Rr04qntvnjK4dvXGxFf/hY/hv28GfuSWudCfdyOoS/kgY2MrGfg9XYExZIAlxZyt5/dJNwfU3P5Jnl3SREZw8uLZmceceQZR56wyIBioE6RjnlnBQg2v5oK1nMXLkXk8NT6mEhEuE4Z/3z/NfKh6CK8n2sStLvh9QDZzbYzJW7lEXbWVurXEChvjf5Aboh4q1ba7ToDEGb8AIJBHLKMbn6KJIHbvNMSBxjLeFtganSvi4d93yQVCfedolqnRe22WMXk8M/xo40VFb8u1/jt+hJv1x0SCX5zusa7dRdyQxayLnB7jKld6x4ZjDueLW8ZNr5YU6cW0NfqGdhLxkaquDJfpYqEBXmzDjuyFmNPknGSHIoSZX618WkkxmT/2MAxoDFs1NAdWEU0lwGZ568W40fkWOvOpNYhl+wsb9Ll612XHH5KQFZvm+K3PSurV5kAwCGN0QGyfUYDGXfdMdZjx6EiGLtOlxUU8kAfa9yLTVlVmt+43cexzpb/Jqzmi6TbV8NL2FlZ/fyVwZgt3xlSxT6vGFINaXXt5q8jT7jhndn6TMFMn8BB9ggfSrutMa/queRNAb5PzDTgX+3+HumqgGcfzAE46OPzUwbqnCFXezzqL3K5QLroaLjJe9geq+6Vxa2b4aprA9/Hd2VCsiZins9AdJ4SQvxTVlbrFr99LUJKmFYEeUC3TfOjULq4rUulYM/ImmIuE04oRc3fEwlWy4+80RnQSDgfE5/O1frEwztFAFotv5UVvyqfh8eVtx3V6sUQXlmLgRbegQFATBYOZC2y1f322nSn7QagsmESSdBG/UAr8k7BwvJqRu8F5ZexA1oVEjRd3a6mv/ZWt328r1vgUwk3rfdoi2rZMrFsMlSGWjPGVxAwtTbCkNCYazKY4lgymoGiHzfXmWdinKR5FwBqqFgv3uMact0m1oGc6qZzJUvCXZlROOFt2eN1fWGSwx2OPNiigcLo/ySsgzaa70wDuGqAWh8bMw34t7u/wr/86deoB24ji+cRA3B+lXD+k2s1gw8KXN3KDXszeJcbAqYJ90Rku/jXvw3ogoJUbEmGA4MPkgylCLTHDOSi0g3C8foGYjmdtpciAgCKe5kAKbHidZywlIylZnjLdJ98FXT+/hPmczyHNnCwcx3AIhU3vA85oANaLi0qJjv0TnVa//LFmzOFJLxPeqPdxvRwUKo91svSHE/oTbCdBeyFEsMbNl5QPTCml7bOJ+4FRlDmmbO0HhqpP+25xk3rPZqL1CNMLCRjqDVjfAUBW1FKDf31Vk3e+2PUukVJB2aI+cyUjukQz4Hib0Gbw/x1ozH6nF3nn62JiPqU3POEewPlV0koIGOU6fEtUKYY27b7P187+Pz7sZGR8E+Hb/HF1V10NrQfoDcJKDOhXpuUI6oMcvGvveeBhEPsAy8xNnu8983St1JLHPrJQy7AMiDmr5Bs9HPNZpvQZO6b97fHyJh6IsnkGnZtm2zDz5XLbqTP8eON17Xi1+UVvl6Vpc9C2OU1PIZ8PTlSxavhPs6NY52x8hDPZTCOPONd3eGmqg/jKsoCTVAPpUUyFuRgZd3JZKBBNo/fARMqXtIZGYKzZNzwuJWx2fefJXcWKWKetC3vGYmNfCK4MtBscRCLKORyVbSTN9D8j0YDsvrorVFmGvBDdvr+Y8WlAXwfKkHU3K36mh8K3qhEXffHRC7PTFig1jbKxFKfZ32N874krFQA80/rchYvygNaxHWgy4v61Yoizh53MMylht6QZp9X7POKTIL7qj6N+7xilwsOwxKMLQB4W/Z4Ww54W68UFEMjBoykgFa6GA93JBghqNJUKh97PvhrZhrwF9Nv8dUXN2A3ge/zDNHcQhKhzgQ+TNtmJWGrwpDKrUHWYxLDPk+y7aXC7XtMpbLWjCkXk4RKKFSAVsSHWAG/SnRB9MVagFjiORbavGmLPgfqWtHG51uKXMqtWVDVY1gESFnzpHUdcL+MOK5qnTMQY6lqZbB8D9+sZzDrB4qzaOrvaPc3lfBNeYFvy1Ug1442n8QnjGI65ISJtDl6NqO9bMmFdwzptck+cThTq0kUXa4mUcF4LHYXqyvXpruPFtB8PkZKwczS1+p/q7QOh08dvLqM91Vc3Czx8jGPBGzgHUf5x27w76tb3hHKPWpSvC6HpEO7fUxKlTXgRbuGLFYBIRyN1XedTniVj6FJn5JN6HbBr5Jwb15sb8sB73iPN/UK3/ABNzxZVw/gjgUr3ANMJ/4VDbQ8mmG8M7U+dp/+6VDwv/3Jvwe9KOABMcABOr6VPeH+qxH82TWiNS2gg/elPtzN4PuB2xH/YGK1FutePfcuhWvVhgirZBRODcySljCQZlgtmfAqeT+Ae0WdpCUafRJin+Fm8Ppz2jm01oy1bg23vYJ05hFnHp6lH8/xvaJIkxgCwFG0OurXfnFpANTI3SWGA6lsbHAIhCSMq/0+YAmyg+TG9nHQpJmO2wIKH15EDBdTg19C7qMFtDWZSybba908XAK0eurg1WW8T0Kn4BA9eKy/38/SLsHJ3f7v5XpEYiYAWqGmSAJT/E3QuacNY6TG8mYCLfZ8BWE2j8fBuiVH18QLxrEnI2cZcZIJR9ECXYGbwncsLSiw1RvDr2YYX/Dxfo3KzmL8+f41MHFbTHc7jEdgPSRlZ13I2KW7r6vxNo/EzvEFfzfX+HsaA9hBwcZa1OPSHccOmEKHu8Xf3P7esLMuX9fNO7hIGOI3s3o1tp/XgM5i7Cz+xK6f5/j+8ZZHfF1e4lf3r/D2vMepqhJk6NamZx7wIp8xpxVHnsKbJnev8aLqZLlKX2z3znYrcjS18liMiaUgl4IId6Jd5WYbTxxQuDZwI4zHrTjPINzwiNd11MKx+Rot3VrpQA6+WDdDEdwIYyTCzvIWZ2i58oLBF164W7/fT4mNdRm+7T1LyxtcDcjhhexFe89VK5rFjP/6RVI0AvE8pYIiNx2p4jrd45D0HLrOp42BuzO1EnF4/34+3gXT170Xb+uswGVa8Srf4zqfAhi7qWoU7wqLDMY+LRiJMae68Z9V1pYyjwvr574uV7jjGbUDsrb7qwGlarWiQ+sink9/v2JzpoSf54p//cVv1Ds4IRQqwb71YTsT6m5oTbEeiWiQBYTcMHKiDTtLG2VJdisVbmDWmrFYw6lNV/dO7h7qFGN5NXVKp2JxJlZtzwMdc6tjHxMDaSH10xLbBxXASuGdRUlQVgPXxwa2FU54u+xwLBOWmr/XfPXpXrlPMHZEGCnjyCt+Ub7A23rAsU64rTvc1tkmDe8Qoi4SR57VaPeCzeOmigBiwtCOd4aA2yADINg9s7F1lIXT2Fr6mda5wR5KaObvq2xvGcB6AYaNyAFq+cTgRvA9tRX4NHTmlwtnN0wEGmCnr5OYFE3N3MkMa8hyEqnZpTOuVLOvz51kCqBqfQRp1hakjaGlVYltt5mRapj5HtKC2YCsgSoGo43PSYGzhQd8s17h23KFb+oLvKlXuBMFs+5kCGbeKio9XOHGh3r8FmNsqQ/Bx1eqXqUJ/+3VX+KnX75TE0Q7DcQyXsnAckVYfnoFcglI2wkdkNUBWwA2ZoiAeZk0kCtkGXafClBKRuFGVw5j6b4qwSYn9Faz/lnUqLSNUkug0lUauHm1EFObpNyQ1wdt+8KzDczF2GL3dcR9HeM4Sn1OMp7j42OALr5XYXxrXXFXbtWsfqxxBs8qAwhywbhSJlewdaBJr7I5ON7vwImzezKJsX1aV0MPATBAojBCaPLCKttbvXS3c01G39WvOT3pulDsOywBeeJeWUArDHn0Jv69ob3A/MPgfmdbmaEqFtRfrNox9P6TCe7JZ22uTfJxGb3swGWH/d9ErZMhATHvAFZQs6MxGPhVJWn7cx41mZHJGhC4/YGxy6QVzRitKOSMLZbeEv+7Y6aMP5te4+rqDMnNcD1OFALqRKhXF9Vv3QldlTsebM912xFzTf+w2P9s4c6s874nTbiYa3qDXXADqiK56ZnG/euieNOxs6Sba0SvX+b+A92Q3pmVMIA7NzDj43fzc3xisUrFr+tL/OL+K/zu/oUaKXPCqY5YzN5glRx+Q2/LAVUS5lRwMGN4NqBhohLqkau04JDOm2LtRBVXtAQ4oTlL84DtjeBXY2m9tQ6Jh+Rm8do8ogepVgOvAISNCqD5y0TafW4kXYs7nHESVhk7lGl05xYaJmE+yopV2IzP9b+zrFAzl8bE+dTjsiPjtmOj3l+hhvgj5ejUrq9tCp0dMa6odB3b9VjvqHUn1GZmpjriKeYKX3MfWXPfMIHnAbd1h7dlH69z6akDYf4PAA5pQYLgkJeWP0nGPqskds4FL8cTMkkAXt4cy6NXPLk0NXWKlh68W0U7O07hwfZxRvB9vnugjL84/BYyapGFBJsiiwdngKcEOXQqFQ9jZ23/7gCsyyI/gL6zof5tNzXhrkz49nzAbZmx1uZnDAOx9DvQiiawacZzql4mj3YbRf/aHtdOhqLyf39d3BIw2FqHCckALVesrNW8ORNv1ikfG08fdfgEolrHvx2pHvtva8Kv189w5Cl8cQDzKjGUO3yUJFkFpAQA4ubvi7F0vDJaDeTyUAPEEjr1y1g71lbb1q0/Vg9VeAeqkJl45RFNYrj9rFbR8NtPpb3t5fZ5Ryn/LeoZ1XaUSw29o6EP/AlqVOnmhGGeSaoTn6jiKp1j0M7UOnP4osEBymTV9Zu6024y6Fl3Cmje1h0ScetIQypxnHPBaHB5FcJN2eG3yzW+LVe44Z35Z4048ogjD1hEu3g4qHWKf0o9vmMFt06dl8B3xUwj/mK4xb/96X8EH6qZCloY3ZYn4PzFAH71QtlZl9RaB64uEkBU1o6GVdptJADdIM+CvOgg6RMbS0JOHGaF1CUKPl5uKuQuNaztOQe8DGNs0sPex6QS5JTBhcCcUKueTaW2roar3SarQnL//ud4ju8I7/g3UAJD8E4It2bgW01SCCCYVw6AAI2pkS3N7UGqKik62jl8VDpZBkHNxof3gNv1Ec6U+5p69Fd0EB79OV+ToUkM+/D39gwtl8E/dUDrcvuo+w8w+V3/PDVmFksDtfxYutSTof4yftwyGBOV2Kf98RVj/vo8RPb8WQYUbOWG1UD3RQYQSTCAdZsoiiqwbVx4wJ15oZxlsA6HOZLSaoCnrz2K//P3i86rBR/vnTVQxhdpwT+5erOVGvpGkRZPyj5B5gkbYzbpvLMuwCs9IJ4soHU27D+7Z2dVTRic+SQ2tge41icz3WPtuzrWFbpKt7S5Seesi3OcAZQEYbuuWc8mFu9aqHOQSHNo+/RT9ef4rmAw/nr5Er+6fxkdLSsn7PJqz2+TRIZKhXdp3RRateP6bK9J1n29PT9ZkdXXu1fpHEBH85Bthdj+FmheOG7E7QbwfSz22EmidVHYe6w2ZqyAdetWYMulhhnYKA5gf6uVhi7sZhrtcxuD6TIu176fggzRc5tLnywv2B9oCu+wMMinrfwfaNLOVRJO5pu2fT4FSKmg1hCMK5cZ+t9uewBozuL5786YgT53fLNe4SyDzmcGeJ14xLuyRyIJNuGcCgaqIUu7Gs7RTMsbHdzWGd8aO8vjJLYOd5DV5tNM3sBgW8z/GHZW3wV+pIx/s/8l0vWqDaW88NCxtAAAROCBINO4ZQ5fdDbc5EEPpPCW99TuMXhhRJBWLbQUbrLKmP87EgDQ8p9U7PMu51O0eSwV+4zOjiV5V4iEDVs4OQGAKboPp6lCKoHt37pmrKvmSD5mua9nz777rnjaiMMnFDsakJDwu1rwi/VL/GZ9hbdlb9UJXUQesiLN3rL0pu4N3GoD5CJ50xbXTeCbvEAHkDAa9wWxVULdTNFpu17x6H03fODqQS33f4pOVx2QFQbwUAP4Xo536ZnlF/WnMuj3VZn+d/QsrZNUeLeUPlYxeSY4zN/VP0tZdr0kY0d6tY9UMaYS0h/XlQMKdrqxYr74NvVea8DomAoOacFiRporZ6yccVdm3JUZN+uMN+sBv12u8bq8wLu6w+v6Au94p+2S7V9rIkCtai7tfFitS+fHxqs04X95/Qvsv7gHz92C3FlambBcJaxfHkCTAbOPGRw668qN3oEGaDm9Np7v0X9lW7FoQpapMRiD6isdE8uBrV5e6AN83Q7YVIF0JqQVIVFMBdp+1plVDo7ZT6lsdO3EsU2AJoBVCPdVpWGRuT7Hc3xHDMbfuWPGt/WAW551IemMHhKMSf9y4+eFBwO3+rE6bZqHONMncmhLjJ3h0UAxYwHZ6BlegfE5bc2W6KHpe8dw3/zdJxQuLVSSTf+57YMc+PhYAOSPGYkeZ2MB2DxeLOG6/EVVnLklYZCcIAFQcuw3wtAXR6x4Qkbjd/A8oe86uf22YowKH6u865l3NXRJvLMAz1Xnpbs6454nnER9tM62TukbB/j6Oe5Ld/zl+3ln7Sjjn07fYjisrf25/qC4rROhHkZQtkXxZXKAtjH0aLLQHqeL8ZmA8MxyeSFFsoBtJTs+C22OuZi3Lqvfm/mnf/6iANKAzxTb4NcoAGsGoWuEADI+gWvmOb5/VBF8Xa5xLCobPIwL9oN6C70aT7geTrirE96sh2Bv9sDCITV7C1+DLjJozmG+sADC8/ckoxVx06b4CmDD4pnQMUBt/FqRIm9ZjeHpPk2JJJQP3lXvDU9YkdTbqMtn+iRWm0x0zZpAVpQVA780h3GpXV+w1m70W7nzZdH7qRfp+/DfwZBNrtPHgUbsrFA/YtupvTdJ35klzkn0mH+Wj7hK55jzD2mJ+eaQtOO6n1dvyx4sFBJEt0kZrQgTqhMIXgxnVEm4rXPLi4jxcriP/MjlryNVXA1n68wpG2+u2zrjvnZsL9FmJUADW2s3Z/q57TmQkhS0McD3aYaVkPBnw2u8uD5Fc5KQ4XW5hSRox8A5A/n9gE1IDXsmVj9H9RYsRK1BFot6B1cKY3UAmMdiwBpFUca7x4cnFrxYb0wrIwGktZuDuuJ/so66/rez0WL+WknJAAsBhcAnbRkvTEhZi7MpaY5UOeFUhg7/+PhC5adzZT7xqCJ4ywv+cv0cf3n+GX69vMR9nZqZIjWJIEMvbDXb1bPiyDNUnNbAKu6qFe6TtYt2uHVr1mrygDbZtBN4Z9RclSI0fbR/boICLl4t4XhOwgDeY4Wag/uis/c5uQSEdL88bVDrcpBvUskG4PWeYRPRpopxCqPl7QDZd6YEtNLVS0OdngsgKlwnGbvHxBoDDPGYf7b//Wq4xz4t2HvVzZKMIglTqjjzgHdlj69X62zII1bkAFgB9z5RENSptutmf+B7srMG/GfTr/DPvvgWdRZ0xTztlpEAHoHl1QB5cdga57h3lg/eBmRd6sTBiPsOSsXGirKoeNWhzRM7ANG90LXgqgdvuvBUrOog0ElItiwu6robApZLGHiG0gAtYQLbv1K0MlI5mX+WAguFm68Rd9KR53iO7woBcJKK17zDN/VKF36cjdUrwdLR11J45ThosUiGe2DpOYgLQETHBZUwV3hN1at7yhzqpYft5B1snvEEuy+Y2GUV4IY/preegHdgm9H8HdrZAlsPZWlPHdTqAS0Am98FNADPI6OBgQBCztmGOwemtgUxN8MN1h06xp5XPLtiiwLvZD5XefPZpUt0R5O067ZTgDiZGEUSzjzgWCdLfluC229v9e8GhfSw7Y/vx87KSPhpvsGr/T14kM1q1jEfSUDdpYcVcAcMNxKOjkXljC3xT9No3lkIgMm7A/YSLGdcRRJjSYO/51HpRg9ucbct3Y/ywkvvmaXMLOicY39XaYbwLH61XsKWz/FjCwbjV+fPcK4DcmK8GM8YUsXCypQ884BX4z2uhjPelR3elR1Gqnhb9rgx1r8rC0aqxr7JUWR1powWQK07uzR5mBZHU+fny+H368wZ/ZxkHrMSkkJA2VirZIzQ4t+OKo48YulVCiAz7FYABmh+ttmaNOnrYHI6vVYmA21uTV7ogJb/593/3Pj7qecuwOP5lW+7d2RUO4IaxSK3iAEQedwVKUnhJNkK3XZfXKI3mO/zgJGKdggUsny0bJoZ+Tly7ph+iSRM3fvnvYOhry2OdcJ9HWNNc19HvC1qOq8AlR5xz4FYEgrrtuzzgjEpwDWngiFVsBDe1gNOPGGkgsl+b+1+30iMq00HPb91ueqHi/k9wDlSxmdpwZ+8fAcZHLRCjPMBGhliJmN+KIP38GPbg1cWj3pnFQe9ACqMVAWyav5xrgPOZdC5IiE67QJWpF8FyVpSp6L3yWjhzsSK30GN2eWF/ZjTwpqFGpvYF4RQYMubZVEW5MG6KzPhvGrB1fOkBIkO8B8Tz2DWDxCZElZU/KpO+MXyU/zy/DnOPBhI0fwo3PzOwa0drQZY+WsUzhqpRFc8D6+iOK0XQOh/+/CKqwMUQPOpWLE1UFyNSXQJXtSLFY93uus9s9r39fcbsHVpRvjU4rFJoK8KOwNtRN6Y4FeR2D8jYWMq2Hcd7I8do1W/+sqVD+z+ntYBZozXA22RfLYmAl7NmNOKl8MpDHzv67ip0AMIuu3bcog2y3c8GzU44SyXVOTWQOAoxryThyDl+yJTws/yiv/y819C9hWcG9DkFWbJas5bPz+Axiab3VQaHuvkYc8HO8urENUf13+pAlITTmXEXZk3kyp8IrEqRF8xicHYzBMl9Y8hquWpNGYWFQO2erpBSdbZ0I4BEyqrLhwAzmUI+uxqvl5Ku3hGtJ7jw5GIUMG44YzX9Qrvyh5VUoAUAIKd4aCWeyP1ne/Ca4kY7rfkEUAX+k53WyzAv0dgLEibB/wSYDTwHQiV8QPw4pKM6Jd575nVvm973/92Gd9TlRs+Bs5cMrOU4aZzTpMftv3j3lkeuQOw5OLY+X7vOzZ5oaPvZOjgFW/2a3uezTvNJR/eGp1IsHLriNnfLpaorDKEjF6Bs1Ysa98FA0kby1kxpY+baxIRrhLj57sbYJCtLC9eBPBIkN0IShdrkcu5xc/eDWDlAFQDvNrjNpfY2L4+0syjN4Cn7h+6f2QXx+ZxQOeXAK8QycHmdUy2uc6aJJP32nrFZBuAPhbb90Svlef4w6JCZcGvphOmC5Ps1+crfLsccLPucF/H6HILIGwrvMucS8H83Jm6ZlWHpOwZN9cGdI17lc64TgsmqGG8qwlWb2gFZfjc2Hd4F3BvorRY17nrtGhnQxvLehsO79auXV4VsAIQdhnOtHJ2jdtmsN0HHPiS8I5y+d1jxfinHo/lVw5i9X/3ksMBGQNydDfckVveCA7mkeVF7hue8Ib3uOE9jjJjpIKX6YSJKk4yoSLhyBNu6q6xP61A691u3Rf2bdnjtswBZPl7TzyqQokYh6x+wA6kem7sa/i7Mps/sLP+mr+aA6ULD3pus/p2HXnCHU96vnZ5EYudXxDcsWayvtdWaR0yMxGKkTjeFz0AOhLw5y9eazHfvILdo5cYECIrtBAkJ2C8ALM2Xlh+y+1v7/D+mHdWF640qdYAq0jCaR1avlM6trHPJzYph8m736LLhyIXavNhb8sSlxBJLAap9wT2aZYJZR2wLvr7h6xeWa5icQ/Qj42niTR8YlGF8bpW/OXyM/zd8gUAM8gliYsSaItRl4oFsMCjeegonOUeWbu0GPUxYUXe+G35QvEk2YzHKcwUvaW2b0ffbl2Ne3Xg77sgZhLs+soiEJ1AvMudM7QYWyPFy3DfrKccPtj7dioQZ2weq9Bcdjb02JFNiBfjR2PJqVcWoFXxRfoKBW+SEl8MhK6805j7wNubLAJobWtZwStncV0NC6ZUNgytM2tScVtnnGTEu7rDUWYseDhIMAhH+15vIKCU7e83wb9KE/7nV3+F4YXKPzxjEWqLex4I64sR2O+2OiT3zurlhugH7i2Q5RkQOdBV7blCAer6JNfnGT2IlWxQ31QYOvaWA1r+PkBBLLKkSasQ0EG6UgzUtSRwVd+SauwsQDtLeZvsYpWlRzP753iOi2AR3LPgm3qFd1Urlg4KZOIOrNIIpo0DC5JVhgTvTtiMwN3cu6KBGG4Wr+weXfw5I4cNdvGCSjIYwr/b4Y7egqEaQDZcjCkENTbvOxgCiPuPMbEAhG/WUw4H2Xw7N0AcUjz2WAxG2rwcGvzVbtgOGJDVDXLumebRFzoYyuBhe0//XC89GszbzyWGzuKaUrXzrQM0zTNqYfXhOrk86ZF5VKDFNEHHOMPHA1keO8r4k+lb9eGInWLrcp9SEqFOWZOG93ln+Ub5+3qAq2NuNYl79zq2a8RYUb4N/Y9tUkEDwXpgqyuE9FKUDUvLfxpTSxZip6lkQ9iZe207uDsnggXs2/0cP7rIILzIZxyGBWOuONVBmxN5MxDyrmE5mC9vygFHnsIA3rtnK6A1BGjeOrIP4Qe7M7OcQzqjQsGPBTlUCCpZTLiTKda0OyoxZ9QO+HCWj0sNq63ffD0MmKpFMm5kwGsecMepA6u6Qr00/6wqEkDFKhJd/LyxkytNPJf5GO+spxq9zcvlv75bI0Miz3FjfMcivNv9TBXaiXLSnMCKFG+qFsabukPzCwetEgQVCUNScOq2zritczCxQqEkLb/xrsoAYr4onJSZte7xu+UF7uuEq+EMlta1vc9z/bv9dkgV93XEsU644T1e1xf4pl7hJEMAqCya66h0v9uPMF9k0ANv6MdipAyXc+6I8Oe715BhO6/0tZyYmwaCjMPjhWz73q2/o4WDW5fP2fwU84iBSL3UcFswafNRPG3XjfSMKgfAHOByw0va/ku1zV+S0Yo10hVjmHTO7DwfAaBY13cvwNytE+qlV+QH4hnM+gHiVs74j+UlfnH+Kf76+BPcrNpS1JHpvsuDo9AuLcvgjXkioAP8Ehe4TgjX6YQrWh6YJOr3pGBpjeDoNNWqGE1frowt7R6y+Psc1LLPVsaRgljuFaVVYh9sDJQTlxxuF0aXBvCfykSwlRu2bidAM4AfDam/lOMpcHkBBhJ3Rpdq8u/sKh+8x1Q00eQhnuMOgPHbfrD3ziBOCfeKebIBeiAOfxTvnPemHPC2HMz7QLtonsy80RcU/bk1hRzSpakfrxufacD/dPoNvvz8pnmZSDeAu/xjn8FXe1zKP/SmAVcBYF16awHB0mpgmDKzwISlZhzLhDNb1xTyBMKBqm21xCMSja6vwsbTxO8zwiw+fmPVgRqAyQ0TSkl2LPRaTCSqDa9DVMDwPQbt5/jHGwsK3vKMb+sV3qyHSFIECow6c0bBBQWunLVFEJU/95I0GJgg7pfFmKlgorph7Hg4Y4vI2MYGWEVTCzQpUxh/278e6PItUMaRgVbSjKqbNE3vFNlKDmP7ZWsA/9SBLY9LuWFFs4F3A/iMVjTpR16itnhvjzVmnkBNyIsxK8T8nBzs9EYBKgHsOlt2AEgDQ1Mw/7T1Occ55esMZ+iJKHjqkiOXUTs7y43mFQtqv9/BOC/0enfDj4mMhK/yHQ77cxRMdP/6vrX5ZiRIzwLuQoCLpGC7uG8gF2/+bixfk+tztk6gQL9Tep9GvQgeYahdTq0PAKtu/vHHO2BLN10NdRXM0n1MUF+1YgkCg7ZZ1XP8KKNv+nGqoxUs9CQrkoyZldQ8O5+j6OkglFqaLJits3bfbd0ZV71axNetHmNXzM3gAKPCo1XcpNyAMiv43skY6oSTjLgTBceurBGW+9KO4M08Buia9Ua0ADtSY2VVtK6HFa0ze1+kVcWFF+glgAlf8z5Fhclj6/H3KWI4xli2vx964U5EuE6a5+xI80hl06m1jStE7njG6/ICq7ScpSIF26pn9N2WOdiBzrjSRgCiHTTTEvnx2c6pwgln85VeeMA+r9axMBsgps1qbsoOq53L1XLts3nRDknzojmV2KY7nnGU2aSxGWFwD5O8otnHnAXms9a81T6GtVeFMVPCz8a3kJ1WIcKLdwMYaQ7EOQGD+zkyQlrYFfc30ec6mwNsihWnNzOHJUqxLoFrzdop0EAqVbFY8b+by8JqRVpuk4q+NsAtNkALDeTayAwLkBaKTu+RTwnZopO0YZYxHLi6aiXHOiSRYEwPz/H3xdO7Qj+xqMI4csUvy+f49fISpzrg22WPN8s+quJnHnBTd6iSwvSuj35yOPGIDAlmD0PfM0GlZFrVaObhALAgxyCvn6dUz7PpmhlbeSGgi2EHLHzQWg3gynAAQ4KuG+/rFqE7o6n27CyGhJFi+66ne5q9b9tS9x+gE6DTcftBD9AJ2TX+/aTt4R2G1IRwiteM1hZ9lYwxWhU3VpaDXiwJ2Srk/WtWHuJc8gE/kWDORSvq3aK1CuHIE97WA+54isWCb597rqk0aXt+anePh6Dlh/bpV6ngn798jbqTTWYRVh9ZzXn5xdSkhu8bwHsabT+Y+8scmHIddxFQaRXpU9eqt5cSRgWB1DsLaJVyB7t8QHd5YerotKljZOnrbaBO0qrlAgin6NbBllhkG6QDuHwGs57jO0I7MQne8Q63dbbK5YBTHTagtzeFSNRDSxoFuvB01mICgiWlneokuh0OwctqHlw+ezW/LP3b5xnBduwDsBlTgq1lSY3n/Lbe27yzLy4MlDw1j8fEFpo98PFUpYbA+7ftsrthRsJAzc+qL9r6GC3SKtiX0k0xILP3uknk7a7V4wow2Zkdv56tlbq97ONTNckhoGwtLcQKhqSFE+nmGoFL5kcsxgZ0M3kRdJ5rwOUeUSbwZX/HD+/TAzE+m803q9uInqnFmSBTfmgEfwmaOXjkz/XgUdvB4Z3lMsOoLvfMuACwOnDMkojNd/Xf2bOBecvO2gBbAYz5YxS3Xtl2BqWb0osd34/ctc/xiYYXMX39d7dOOFUFiwon3KxzAFn7rMbdZx7wulwBAG7rzhoTKSvL/V/7hkYJKgt7mU7IELypVzjJaHJCxg3vMKFGkT5DYr3be/v62tOBEmVmDbFGnuC5j457uy5/YhAmYlRR03K3yGCol1YvG9Nt0H9q6q3zRg9ouRSvZ2k9taL8922w5Wv2npWlf7f7M+m5MRpTDUB0+fM8043+3eNKZaQDjjwhE+M6n5BIcJ1P0e3W55c5readpvPFkacAsDJxeC1WaYBYW58Ibov6aGUjDBRrdDWlgsIZc6oYTQa/z8oU7IkkhVN0dT/xaE3Whgaogo2FDtxwxgoF9DwHTmgyzY/Z7xmEr4Z3oF3VYn7S8frRGgIBMg1A+oCcjqUV9x+LC1ZWHONVwgZlrTmIFf1cJEQbn0i3aiFR8CpZ1/gAqXhbvHfgq2dt6Ry3Bbn8K1JBuyh97jRQi7mdkwI9hqfyHj+xR+LjX/kcjwZD8Js64u+WL3BTdiicMeUC75CwN8HpIS3RhrRKQhZGNuM81Z9P9nndgGPsrROPYEoYUbCigSCAJgU6adRNxcOBDX9NBQHSjPZaNdQmBKjZImDGiSJB02X729labFTdfkB0c0H91y78pwxkPRZ9AuUmiYh9LbEvgAv/K6NFZ0sNbmSHE0+qLSeB17XmtIZ5IYCNKWZPkV09IYUENdfBSz9nvIXtSBU56SDvi2k3he/jvo54S3vMaVX/A0mo1HzXLkHW8ZFV78ce01dpwn/96m/wf7v6F5DX0+a5XmpY9wOGeQJOZ3tQrNVmbcltzl3VnPS2Mog0JSIRSFV2gpAomLVohWfokf2ocFs6ngCqet4K2fsTIVXFlhwYk0ybVuvxO6pPBqKtZ9kyNSa9iJK2o6XMIFIa7XnVIXfOOhGfcGEc/BzP8Z4QqLfDu7pXwMr8BdzY2ccjN+peJQc44fJmEQqJsYCgF44nASYVMyAMUG8mZ9EwlEk8mEzRc3KiRjbpTabp4pZtfBQ0AM1z9K4ZThRQ2ECb3kfK98MlAHTJ0PoUYusBtr3ncA4/eN73EcUCvEA9QjIYBTmkhwPqxnOCOtDJeXIEMQmh3UeKxAPQ4+9SNQXWtJmMsnycXVEflEULZ5wh0URgRGP0Xf4e4PGq6sce0x1l/MnuLf5u/EKTgo5wEMN2AnhMSDkDxUrKjqwxQ1I38/d3PDkwuhWJ2HWDmEuoUiTlER34RWiLf2UBt+8mtmSHoAf7fcQpTxKgHybiHwybrAAhASV9mDmh+DH0TrqcQOkZyfqxR4WufW6XOYpm56rmz0RDANC7vAKY1IfITvq3aR8+nq/yPQCEPxGgxZJdWuPavxNd23mzo5OMMXecZMQOxuyCytUyGJCMavPRaLdsrOIMwSGdg0mzy2t0M/SiO2DyWSvSXyfGUbQj9wgx1YTJxKhjAUOHhpMwDil3wE3Fjgb0JvD6W5WlNVJ+MrnMY9vwvm3LlMCdz9MlEDMgo2rLjZBers5EQ+tODGhnyjUNYcitTQIKxty6sft5kliApGbus9menHlAZSVmnHkAJx0z93kNeaIXWRYecFsmFM64GpYAOaqpTOZcojOrsnf0/PDPWYxlOKaKKRXcrDsMiXFbdzjlI04yYrL9oudbsSKRSSSFYk3kzQNWqZhp+OA5oOb6Cn79JN/i8OKMmnY6NidEZ9qNDBDQued9frk+zvv9vvhibCpJMKDLUTMBnDlVddwfhLHWrL69hOZ35fOxAJJsPsq6rTzYWqFqXgQYmwukpODk26/vATcGlowIw3iqNr9VatJLwOYx3QRKHEWXUhOGrEDW9GCl8P54BrP+wFil4hs+4G9PX+Dvjy/j8SmXzeu00pGjPalHRcJEpckGgtaqmnTlPSVUMCAqLcsiBlwNNnEwbnjCFa2bBZUb+Y22IF2RgoF1lozZpCTVgLeRBKOdO9754ySCDMRFnW1FpRrrihGNnaWXU+sO+BQG/4+Jnk7cg3LoWGcMRgZhBGFBM4EHmg9M9iv3wrx9RwsmSnHsb6Wx9M48tmSTGAfSyoKzsk5WCZnTitkm+woKUBT2PpaMIVUM3KruyajC+hsF9zwhVcFbOuAqnaOq1k9cuQNAgdYBEwDOsmJPW2DqfTHTgH+z+yWm6wU8jIAlABupBAF1zpB5uvDN8iRDusFZ7xMrHwMpGaglukqJVrNi0j9tSXssU/iIaaW7HTgHtRS1IjM6NEALtjlWWUkVqK4BtyzTDRFRtPpNZn1FK2kXR5d4cUISRs5sP49wXPX4XY+nDT3/OZ7jfcEQHEW7+9yssz8YCYtHMlZmkW2rdAZtOgf5u9zgmwwmYghg0jKXFbK4yTxwtlbrPc4w2BzjrB6XNbp8OVOTEDIIAyTYrW6rV+w2x3YRmJzeL8ho7Kwe0NLf/GkAWb3H1+VvcDaS2Bwk8I7C7f2+/rwsPAAKVg1Qo/9iVW2XliWoBJDQGLipkwJ5N0P1GtHPCdacmfq6rNQtDVL8DpOo2txB0PmLRFo7dgqRYYCjfXMBf5/frxeJ5YciI+Gr4QZ5rpCuwh3sJd93OUGGDCwdSBVPdshsh18FQ8zlEakVVJr03HzGOIePmJ6yF4lHQIiwi8/nOTuuZImEA17+Evs4EjTSclS3ybbJp0Ddw8nGBIHOg5IIUy6x75/jxxuFlZ075tpkxqQG0GvJeDGdscurFj9NApZIovu6y7JWyfhyuMVt3YETRQE0EXfNqIqte7cAVjSxsvOtV5ewJOyoNs8lkH2vAcKiDK2cFOQCtWJ9ivFMz/kdMc4CY2y1GGnraduR+LEjwlkYI/z7xfjK+l0D8iYneCpAVh+XBu/AFqzyxy59nnp1xaXSYhVW4/PNYw280s9VqwJmtU2ZSXDkCbemPPLCu/pACwqn8I5eTTE02rGPgomfOzzCm5QsPOAqL2Ah3K6zyuY5YWd59ZAqMglWbowj38br4dT2AxjexXOXMt7UAxLU2gcy4ATBAcWAUVUzObil+0jBzhzrovefC74/MxE+S2d8drjH1+lVY9SiH9el/T0k0DBAzueLD9S8RFvVJi3yM7fiPjWASXwxJSYbJNb5j8S63AKjFzJ6LyxuYJlKH3U+IphyRf0kAMO+pFUt432pCGpqjztIlxaAL5T9kgBUirmVSD9HWDNN5oRxVFD0+85Uz2DWHxhnKfjr5Sv83fEznMqIbFWPZHTImhMGKsqiEYkW10AzP1wwRMVj11VBElirIF2ym8Hw9rd9VKjh6g4Fc3TH2zJ++scqKACtbCumPiWqIhut+UkEEwH1sqMDbaWHQAOENtv3BCeEPmLbLqoXCQn1Qluur++SLhGMNjCvthDY0YpKCbXr6HKSMcArmEY8QXCSjJ1PrJ35rtNwEwRnHsOod6SKmQrO0PcfeYJ2hdG2tO6jg9oubx/wWQhnazywhJzRfjtt2WYO0qlxJgOkHV8+lGL0LYH/bHiDz6+PeD0ofb2ntwJWLR8IfJiRUga4A4D7ioT/fcnOMvBKBCCrrsPlhgwsNeO+jChJzdcT0KgfZIMuiVJdSYJyK9DnPLmIqrifBpZskOLLEJ8k2Cv30ARjZAgTKAlKyUhJwClhyBy0+YUH7KSo19YToLE/x9ONIow39QrvVmUAJ9IxmkQX/UyMAWLjhMrBXB7ogIazcJwx40FQT5VeLkZoBvB9CBT4GIQvwO/tJRtd1AzIztT8tfpl9CX7qADGIrsAHMh/Rb8tArqcf544S6sZwm8fv5RRtte3+1r0lmDpsFAAWIwU/pnFiiG+v/2+Q0qAAh9950N/phijT6BJbrbyr4NUAktAkjcwEfTcKv8FIs1MvkoCU5M++fb377F1rYJ95EDS+4+jH+dEhFfphN284j5N242gdisJkHEA9Sbwj8Vm7ulQJQAQ0rSGUgO9BNGtVn9jt/q5AMYIxvK1GopZhmiyEKgV2jzUvUY2m2Nzje+0LK0GVAlESeXzVIKtVUWvWc1aPvD7n+OTDmejuzend1GehxIA15AYC2dMqQbTZUxaXHWp1olH/GZ9iVeDslkqJ3yWjwCAG97hkM448YTP8h2qjFF4X2z9q0bwLkkzr2AoUBUeraLbebCCfjTsMSsVL76zAWMMwkwVZzeo78aHRVpBdrRiSWP5Nplh3sDmCFaSh4MWQAODnnr+8lj04FZxsM4iQTv0KSsro0oxG5WKVVouc50WrKIG/gkNdEpQaeEqGUit0zqAkPfp96bojAkA78oen41HmxM0d1qNVezBIAzEuCkzEgmmXFA44VRHTKnirk64wqKFLiHMiTGk1hG+SMabZY+rYQHygpEYd0UN6lfJ1olx7BqgLW2/kOZj2UpJo821TYL64fxH963u58929/idFz8sX/BiviSKvEEyaXGeEsI360PnWlitaFFfcmqPd9cDVUFaabumI50nhEhBL/t+b4AFUsWKGGvYEDEAAko+59ha76LLoTO0enk/0OYvKvr5PIk9RpBC+pgApSTkLCgpYz+WYN19bHxaV+cTjJMw/mb5SVTKB2Ls8orDsFq1Y4y2os6mcR2yDtT6nFdDjjxvwCv3T/LOIH3sSLXIGYKpA1zc/8IXqkfTB/sEUrvnGIS7rtue6oSVpnspVKvS2ty6npjNONGfb5+zXSx9atmzcjwAAQAASURBVBMB0CaykXIMZonoonLR9ke2wRVwUMrortRMCPU9OZhZPuifWLtuOFi1S2tICP09DDWZPfEYQJjTw736kcGYUsFAVSm8uWBITZZ65hG3ZVbKrQFtYQIflarWRn17vNUU/7t04/78Z6ngP3n1DXhAUE59HREU2wzInEFDt1f7zoauFfcBXDoj+MqIjoZW4SbWA5JWYC2t6jjmuqlG9GbyVJt/FgSRJHjlPRklNzxMuKueC8IbJa02YFu3DvfN4qr32XyxKhPO1dgOVjF9JId9jufYRIHgrUkMgdaxZ0wKORVJITN2iaEbdytLxz2WyMajYbPQ8UYh1HmYeAykFu7KQG3PSfwj+97OrwfNR8tfu16AHrUbP/twAqf/q8CjFkZ6f3vxPGUg633RuhtSzDtuBO/RdcKG+2YBuu+dXZWIIRdjuY8zfkyLsYgEChq6B5ZXytmArGrnkwNhPT7k4Jc3sxnMAyV1gGgx/7aFB6wGtBWkB+dEM65v/9TPZuuH9lj48ztifD4fbSHevcA/EDYHZQKlbi3S+Y3I5m/E2G5f1J2AZM/bvFARUhhy8M/nmv6kBbYnsX+v79z++xxP64Gu7nGqeHiRCFkO3hIY7aTbGGEOLD/HjzMq1P9qrRnHdcTKKZrOnM1/RkSb4yRq68c5lc168pAX3Ff3VG1y4ipqe1Il4U3VIqV7r+7SauNNwjve2fboe69oCXaOx2qd2DMkTLgBY2VBMIGjY93SzRurASRXVAJ4cEuMXopYRdfnRyGchDb5zEhpawAP2agwLvOXj/WL/WOHF5J9De4m74MJxtt/zQuqiiCDcOyICr63T3a8dqTS0gzvYrmiCmERtTvou+iOqYZn25CUUdWbwDsD0GWBZx7wphxw5gHvyh63RT3eplTtvTqPFNb56CovcEuEOatP1uJeW5JCinhTZrxZ91htHnNj+Tf1gJu6j2I+oL7Ha+fnNlIz/PEc14G/D8Vq+3BHFX+yf6f5TdfVz3MIZ0SphQkB+Xvmx513cHR6142FF/pTVc8sEUKpObqpK/gkD03l7HOpdp6QBRuPx7S2v3vpfPtd+pm9l3CqpNvhczOjWQEUfZBLUgWLAfGFE1bWdcrHxqeHMDyxOAnw6/NLVE7YDSuuxjOGxBitm4K3/zzWKSj/x6q0yTmtVn3Q1ywyBPq9SAYjhVH3ifX2sTbXwNZ3x9k2akieArzqW5JO0AniJFlfI76YBnbUznM/53J3X7fPJgyoObhfxL0ZvHfAewrGiR8TPavIo1VmbLIEYUcJB0uYvCuGA0C9zDNMLA200gF8DcYVgGBVXXpuOHjlk0TPrjvzgNsyxy2gCciRJxvQt8lLNoS777bowOkiOV6n50ij7bbKhb6HARylPjD476Pfd69Sxr968RtF4p3a6uNiXzHPCZge7zT1wPx9w9CyjzDAyzsbppWRisor/J/6YWELWlVp5u5VrGMHGkDF/aAsUV3wz2gmhzpY63vIunkQcE5wY/c8qCyrVh20Kyfcr+PGqPWD1Zjn+EcfRYDbOtsisWLMJZgzzuAUNPaMAFh5ABnoQAZqOVDRTNyVfVWQ9Z8t/t4nSepN5dU7q5lNOzhRYB4n1GTMxeYZT5gIwECNkdODJX3qUOO7DOCwZ3sQq5qh76fSzbBnFXk0uaFGAmEAwUfGYnve93GP2bUxm7dAk90HGsDpIGfbFvtcacelfa4lEnbr2+fzk4Mk/nkOivXzlQOnavjvx047Xfbngn4fuu+wxOs9x7TfdzMRvpzuIFnw4LTt/pb0gcShn1ekQ4k2369/e2dDL4R4Zzdm8y1xxpUH93NK66ILtGIIdV/pRRPfjXTxOEEr2+oyQUBtIFVKulfFjOlZKMDLAK8/QcD3Ob47so3HRILTOuB4ngK8cr+jnBh364x3yw6364y3i3aFu6sT3ix73NUZb8segF7nvzx/hrflgKP5+uq1nPAy3Ye3FQAceY6x/ob31syKUZHMGkNfd+fgFxVM4MiRPBcBFFw4pBIsrgnNYwtQ024HsrThlf9+sXVsy2e0I59agzwo0GM7Z1SRYNb0ANal0uQpxqX0MHe+xgzZ/DbtQm/7kvIG2FthBSZpTcWOPOM63Zt5uuajE1VMVqjPXW6hEnaJXNVN4Eeq2OcFL4f76HjoedE+KSB1X0cU1udOdUDhjIUHnOsQssW7OsX3rJxxV+f47qvhjOvhpOsMIbxZDgGkvVkPONYJN7zDHc+mmBki50nGDHTvY4bm97r/eEPWeCz6xgEzAV9NN+AR6LgiwVzaSOAJysx6X7gksf9+IpMgvic/YoT5ejVPx1IbmOVyQr9wGtuqJXsOuDXz9w7Uqh3LjIC8SBjDuzdYLOSc4SVWhPGf6tNrVfWKMKmCxSbOMW2bmH1XPMsM/8BYoe09lQGjevDCwNtlh/s84tV4jxHAXdXuCwDM40hRbpCzr5RimI16OFHFIlk7ipC3SWVFyCWZkeFojC1tW5upaqVDuu5RdhKdZMDOmF3Rvc4uAp8gUvymbUxEDbwSQSaKyWNEkwX0RkL9IPopTATAw8nADSATCDMNBuJI7NPwEBGKytLJJHxHmW2gLnhTD/COhs7KApqkMIzKRb2vLgFLZ2plALWTN/h541Td+zpG+9sKa78uen5ydwxWyXhb9niV9zjl23jcwdaRGAeqQdHWbfVzhfQ+VeTvwMIzCH8+fw05VEhK6j21Slu4QwdVHhNkHLa+WZfhWvHeO4sk/gaTIq4BMAHM3jkwYSlDVESCXpuUaiuZYoDmrGbugIDNQM5tuyKJMINEoMt54gchbqlqpw6MANcEEcYwbEFAFtIk8Vlm+BzfEXo9KpPGO8gxgFMdsFLGLq9IAiw2Z4xUw/fKwYUKlw02wMGliSpE0PFNuxmqfM1la0SN80OWNJA0oJ1s8VKQMNgM4dInvzxclkjdb+qjL5r4+LOBEgRgEht5jP4eINBDyeFTjR6IcR8t337159BUikxn5kctWNeigOEaawRdH9zLaGtEivEcQMw/UTSR/lzot4uDqcW+Uu2+1w3ji2gC440DWPQsSJDNZ1ao+e9CAwplwLqbKUFJt2XEVlDYElNfCysn4IP7E4RXwxEYTQbhFWCgSxrI5B0J7rH4aPhJ60ASWhU6omPlOt7FQmAiVM4tWWHoQt2Ywz5luixDD0Uz2O23NxICe+0m+dlsL+Am8LCkQEiQ8vb3qazU1g+fCOj7HN8/7sqMm2XGeR3VoLsm5CSYcsXqjCxR8+3B/YrqGP5G5zpo86qsAMSZBjXtloSTmbjvjGn1ph4wUtHu69bx8CqdsdIA9frVPOeOZ1vDSvhjOTjVAwhAY4hGgb0DGU4mYXNJItB51tq8MJPgxoyuR6gPcAIAUr+sRIRVuDOAF8wgJKTW9AkPAaxPRWrYb2f/GxgchWnATc3HAGDUTqQD8Ehzm5N7M4ub8DevRSdfOJiv36NG7V5Yf5HPSCS4rTNm8wQunMHUju+7ssPbdY+l5sijWYD7MkYRLpFEQYWFsMsrRjDerbtgFgIqk2fRbp0DVZzqgIWzmcCrdHFHRdllTPgsHZHTCgjjiAEQYEHCRIxMOj9XNDP474pVKjKAL8dbyGiDOBnQc/FaEs1DZMia/1yq6nrfLJhXFjNciRLGo48EFQWYUta1YuG0KWK1bRCbKynmpL6Go+AX4FRxNYoXEBF41LmMvWtjp7jx+VEGtAmUAFoJGKCFpyw2HxGk6mvXmjGPBSsnDM9g1j9cZAheDvfYDysWznG2FjPJLazVbh8Y5lRwGJaoVPTSQR/EF8kYpYYpeIK3x63hieFAGKCg1oQa0sWg2UIXZAp06ffExGEAiHYn0gHoRgZk0YHjKrWBbzSUOpha4smEb3dr6eqhC9D0YEJ46nHJOPLtd1pujYFcd86OGCs0GTmBFPGHSgH1dRSVrFUybuoO2Qw0MymNek5a3fC2tIB2v1zdF0AywErj7tverqItaj1BGU1OeDYpK9CSR6eQ+4B/Zm2r+7q+wEQVh3TGZ2YIX0HhRdCSYAU1L4/zh/ZjQsLPh7dI+wLQe5hXsCrBeDEUOQ0WKlshoEsyZPuvcjOERxuc2eQVju5HdbvLkqXfhi7itVZtUN+FBnz5a6h2g700tpa4L4kAUhKYDFIQsaYMulg71wFAuSjjP8dzPAyC0vSHVK35g54zLAnZ5gaGAk1iYPaeFggQY33/WQAs2Wim4ColZDNsbwBGzGvmg+K8rb5NehJ9bIgFLgXOG58DvXwWJPW2AzBRa6iR7PLaFPX6fUAPk4xLI/VPJS4ZR5egXG8IDyjo2JRlFMUMT+qYkoFKFN6I/biva1+VIbr/ImDdke3bVQqodgmp+z43OF+gTD8HUwtow8rS2/YLAJM1SsZRJmThMITfNBzo3i/wpgAfdzxZ9Nuu0wk09CWYR+IxSUc3r3h32sZcuihS+GcE+mZjfMj6+s9thZAAxPz9/cf5PORzjn9d2r4msEVqr8X2FIL6P7bvj6lO3N/suWDyY45i69fVWFhrzchCACqKARzeMIRFwaEhMV6Md1hoCDXHgKrAQT6HrPzIE+a0Ykrqb1VBG4/fO55xlc6oknCgs32H9vHumTvZxo8+T3G5VwVhErY1qHsBoYFdUXjfnvhnyZjROvNOsf5VZcHBrolFBFUqdpSwQjBCmzp5AQGScNlY7lMqyAMPLV28q+FMI1apWFGDGeyKGjd/rwDuTI6mhQqx53PYmNxZ3srQplarZBzyOZoGzFRwSuqxxpLsvCmR04xU8dV0o8qSOtv6xYp0WXAsU9glMAjMCoqd64DDuCDZax3QTFasP1sHeECLMkVU4j4Q41QzXoxn3Bur67bOWOeMV/mIuzzhlZw3HekZyhQ8kO4r91X7EKA5QPN/L/y/ykfwzvwV0XIDq09ZYcXOq5y+H1t2M0dhawIPgJij0+66mon/WIw53LOsBKhaoI+iiDPBDLQCvOuubnhvGp+K/5b223hE5ELqi6wEAWK03mgCJSNAv1MYSJOxnUlwXgeMQwXxx4PHz2DWHxiXu1oRYDXY9YkBUFrs3ozsVrtIAKXsXuf76ORwYjVKdMNEAAZ62KEihE+WL0ycxpvsQvQLcgNqQScWb8vuE9QOvHmPVzj6TiAn2ba41fcrwKF4MQW4k9AuZmUyPr2J4PetsISm3rzDFNwR+1dxMnNBn2cXyTjxHqvkMEjUCrmZCXYeWv7cnJrxXeW0WSx4QuHnkFbLdN8W0Y553jnsbLJWAOGZNaZq8lY1QjzxiDueo8PHSgvQmW5uZYkAIHZMGRkfblfMUAbfF/kWh6szSt63TobduSUEyECQMYNSBqqZMfafy9IqEBcmhz6oE9uZRhwyDq4q51uhA2QoLGwA1z8AsA7KPFCbbKBdOmKCYDu3rdtUqvrVXvEL40RjdlG1hK4kCDFksO5uTKg1oaQUi8rks9tzPMcH4vIMqeJMKjbAQV+RCTH/VCSMdrKzJEypmIQQwbYqklonOhKwsYRBCe6T1UguDeDSS6d1udPn2+scmHdQQx0BG9DhYFhPxix2qW+wA7Rhw9tfAzrvyOY1T4+Z9fua0QeQJR24Q87YlzDJ79cAxboOFgO5eoP33iOz2vF3BlYmhkjeNARw8/bw2exkp378fW9Xbs+5Z1Yi7eQ6WOGm2Bx3zyMo2XqDGsNv03jEhnjvW0mgD+5H54LtacE4FXAaHgV7fNENZ2ax6/wezicP7m++0Bb4wiBRFpZ2jNIzmjZnr6jRrW2jWDIgjtr6b2X9Lge1AtCSNh/FdEzo5jIC2GT8JmmXJK3mw1pI6a/v5/jxxgrBt8sea8motfm4rTV3njSjsrTM9B1AsDanzo5iSgX3dcStybgqEg5pQQZjoQE7IkymAgAQQBYIGKFqAm+GtLN5ake6TobofBBdtG0c2FEN7yL3yRqJAXGPrYwrWrAihd+Wb+9q89YKa1oEwoht4d3X7YkIo7RCvOcvzr7xeWakjJ4x78XuT4GhdRkJhEyEVRobeLUxf6Rk6/pWAPci+Q5OhLBcVUacZLL8dkCGhAS190UbqWIeiskVOfKcMw8BZHlHQrc28JxlrTkArVMZMOWKnflQH8uENJ4xo523TIJKFN0y3WOLjcE8JMaUKs6cAei2/ia9xCtraHA0GfwqWX+/JG2OZudKhQBSkej9HGGGYBXlq+0o4SfDLTBXgIau+NFFX9xwA3iPx0zgbZynfn6yOUryRb6UEDYqKjfX4yokLYXyyssjc9yGiSy65kilMbHCIk0XZe2rjbNAVoUUgprQ206Lwk0CaNXcCElAyZ4QzY98vkrfY7769K7IJxaMzt+IE8510A5qJh87lgmnOmJINVg2364H8yyakIjVK4skmDdAR7EFY0QNrbKHd2MYactLrA+uGI1oNSppY+jo/hYjvBJLj36WV/jS5jM11m6wd8An/x4L+KcQHzNJjZSiojPa5LC4txUxVnjni2R+aMk6gAgOedEqRV6CYeWDvR/LADU7Cu9qiYozvLzCwCDc1cnagqvJ89q1Wx7Mu21OxXTkKbqHHHnC23JQI3hknKxRANAqXysIJ2N8+L+EpsX/UCQkXFHB54d7SJY2AHYDZVQpxgy6rJh3fllhBO+P46K6IL1poSCtuh+rGaDKJVgk7XNg+vDwMqnN04RYfbQaU0vi/Rv/LW7/+ufEJZCsksMmbycz503q6fVcMH+O7wiFLhQGcrChWPXTfXGKVSq1Fk841TFYnEStq91gHlqwzwQUPMgw8KFbshXJuDR+9+15LIIY0l13GRyG5g59bFP+7X3qPqf/zN63wgGfT3Om+Tij+kxapNJClQI90Tmy8yVxKamIepMRtCAykPp3qtxUj0nqCl0BaqKdCw52hcTU5hOBFuvCEF6a3x9Zsur+bf5dDrh68aRAgbaC5tPpe4Gx9QUzyOijAEqCyuN349rYS/2HAyHbk5xaQuCxOa+6v7t5qr2uQ8msC5ROQRTyye1nt5e3H9fJ7eNWWpORhlO257H9m/rP9DuWEEDaXAPRMYNFWQ7PeNaPNxL0ej4tI5bzgFrVh2ZdVTrYG0EX8+4EgGPR3GJOJeaDxQy5PTI4vHvV42pSFYmpPrzpkRq6Z7NS0SZXXkTvTbbvLIc5WbfvKYB5Mj9fLQCf3N/VWMH+mHphVYxWkN8Zw9T5hzuTHY7UCu9A8/v1qFAD9F514EX4x3x0nwKQVR8B2B4L39aRchSBRvTNrJzooM8t0sz0R2L0RYalA6kmUwqNofjJyMQ45HPkMWcecJ1PAXDNqcR84QCpNxm4KxOOZcJSVdK4N+AK0CFcGxpMuC967h3LhG/PB5zqEEwuloSbdRefWVjP9fsy4t15hzfLHm+WA+7s9fd1xOt6ZeDcEPn0yZpiKTtLcBbGiPxBpVEVxi2fcey8yA50xny1xHwkKYbmKGZ4WiLugfVY9L7Bfai2XeeNyg3c8jyIWx5CpJgCUauUuO2Ke2z5oXZPr+R5UP/d3RyVLnIhtZFB+13+3aIFlg0xWNAmMPtCSk1F4+uHZ8+sf+BYJeNc1KSODKC4LyMA7aqwy2uY2wHQLnNFpWUnHvFqOAaFMxu49TLdA1BAxMER91hiaMJS7Qphq4ZAsKHiBrPGzhn3zDrJqAMSOHweLmm7+n3UWq+LTgjO0ArZoXHoZ5sMVmHMjxioK2LtzIE/7mRw+f3fVW3xSaCXGXplg+GTpcTifEcrTjSFpnykHG1snWYLqP8VE+mtJZxVEBP2SD07K0Wl/GznUi8bYlGd+pRKgJX+PEvCkNQ7YZUEMLBAFzojVdzUvVbPzM8kQ3BtjLBF+mPpcju2RcPjNQqnNQPAdar40xdv8evx5y1DvVzYA1qZyO+pefR68V5q2PueuHeWDfCpAFK1kxNRCqlHf5pHxds/1iSD5EAXaUIQlYjLBMnes9kNHbNLv4Mg0tqmew7lIOZqnl7vRQae4zm6qKIJiAMWArLW68r0GwyMckB7TBWZR32d5GhAkWxhUyVhdh+jjk3lrJ4EMrYWmVxJESn314IVQHIsTPRmgMrgiwHnGd4S+nHwiTt2jmPMztBycKvaF2TbriqC4RED9Soc9//YnQ0vv9+Thg+xjAAE9wlow5zA9ok06/sBFYUyRNjmccJsjdi529niPCfbp9VW1gnOkmrsLCYHTduao1/QiiA8s8I03o6/s7LCr0tgLDGdjxYeMKBqK3UCkjRJUF88a1OF+2k9vr+SezhCvXKupzNu83X7gMfyPK+EP3oALuYX3Xmtkt15afnJqQUMsvOWHh3LfdrrWVbhK2LPgVpx58HP7efKy8fitVuIuN/83tPrea758cYqEj5DEIA5GcmDMQzNBLoy4TCtqJzwar7H9XDGkKr5rmoB9K7M4VPkzqrecIQp4UU+YZUBk1RUCA7pbIbaY4AcO1qxIMfa9zqdYm17RWuYwSfShlQr55gHJtTIVSoIY1fwdeDrTgbszF4FgClLdA5xhucIVZfsqBXZWQQzDThLUTDLOpY7YJEpRc7yGKD1FOL7bpebmHtTjZESTlJxoBFnlNbkiqwLJA+4kxFXtj6YqOLEY+QJh3TG6/ICFRQG7wCwwnzNMuPr9UWAWaNoh/UzD3hhskQ2RlZ00gVhsQLcuQzWyEqwcooGBvrbE5BLjO+FE4as3tVnA7juDaC9WyZMueK+jLgaF5yqAlVXeTHFzIAb3mOXb4IwMpJ2k9+RkhYYjD1NHz4eEJxEkFCxo4xdWvHy6oTbdG3g1cXAq5U9yEDAkEDDADmfm0/WZVz6ZvX5koFM4W0FK9BXRBFdjPUEU5dQZZXVd7JBnTP1ApLBt5maD6W/zHOlLjdKRX2GwzvLpyPLiajonJcqwAkmYwRkYOv6DqQMlKLeXguA9Axm/cPFhqmU2HTC7QDMGWGqGJ5Y5leUwbiVGatkvBqOYFKQ6iqp3vzEbvBuBotQqieoYLSruJcN+mDSSwwBbROq79XPdz17GC3CPTUo3q90XKfvulljA7F8jZjhxoHNZHxFBSTH9ZjJZCufSHhV5n3teDfeWeJgn+6jCRXuEZCNKrpLK0bRAf1t1a4wfStbP18cxGLkNgGgNmNFZ3F1pdWFB5zKGLpxN+70yMbO0vOD4xxIAFaecF9H3KQdrvN9SFlHqljMUyUSDFGW4ER6rKPS84jUsK8U7Yjw0/mmGdhe7lL/+EyNavvYueJSQzc/dDaWD6YOdHklwQbvDSPL7kZbXDeAH2zwt89T2i4iUZB+UCY083f3yRIANrj7vqJKoCz6Uzrwi2sCJwEyI1vXqe9DpX2Of7zRT+sOGPTrI5WiiRl96mMORiQIFmRUVp8Tl/qN3dxAJJiAYAezjfQDfJ5oskFnael6rG3EQG081M/XpKYl1j1M09Y7AWTZYw5k9el54BMdmFPByF29VIsMH79P/9hx6Y91GdTtAR8VHarTETijeZ0p404d0xhnYzb0bOuoHRiI5RI/93D010bXw25bnAlIpCbjTBQyJd1WBNPb1xPaNEDUO5QzzjRggvqtqKSUzYhez434LNGktJq8UCC2xn8cHAR0MXs1nNtJ877zgNAqC++rePfP+bwh3XEiAEIB8ilhi1q1Od7jAGW3eOq+h0AbhXnvd+XFFwe6evBMujyGRLf18qdI55/VWA6f0MXxHL9XcL/uMZA1E+H+fjJMlnDYnSFo58WQ1FR9tuv5ruuS7UXa2RpYnViN5QG9fg/pHF3XAV0nXtGCRTIWs95oXQb1NVkEd8jGvhrwWVK5lzK1Ghjm45MDKEDLZ7zbnnbFa0byq3BcZieXypEV4+3xkRJWVDP2TqgQJJHIWdTz9Y9bCPmueN/6+7FoBXuCU2QqBDvKYKg0MxPhxN7FL5vss+BkeYEzbxmMKzpjEQVs1jpgND8stTMp2IFi7eGsLLXTsIIGVWV82wI+oalUAGBIbM0LcpAqFm7PawFvAKyjs2/fuQ54t+46D2HClFWBdC7NluXz+YgzD3hb9nhXd9gNK+5EwaoJVbs35hUjtPmXx3ft61WA1wL8aQauaMEX+yNuBhuve2WK5RVCChRJJmC4KOZ/l9SwyTzaAqt7HXVeQczNOyuHz69J3v29nqNR9xnJC/pquaJLQwEPZP6SNh/53FRsU+w0S9WmvTOh7jQ/C8ckf58pV5DaHOab5Kzwj4lnMOsPDIYOqsUq5ivrEJiEkLvWkkUSJqoYU1XfEs4Ycw3Q4lhnIAMH6/bgcrIk2sEwU4V3jlhl6IArjuRD9ekl9OZZlHrrC0u2CSW2XSh05/5whmCRFEDWYuZ8fkn17W3nrtLhdNUNddVXbxsZorxXc/zHiEtW1iVlt3X6eHwQU5mhdH/X8Bjx5C+YWGjG7B7azamGmW+RJhHs6bouDTzziPs6YuGMRN6dik3KaseM1fh3zsWYWoQiYzC67kmrb2duXl6LdWHcJcIoFdfd4iE6VkGTi7mraj82uPfMLAD4fDyC59axST2npKtC08OW6Y8N5JsDYyjSAzNdUWbWqhWJzcLuYrwnp4rYgO6VAkA7dcCAqwRpFW0f2BPFgN1X6h3YArXBmoQga9KvAqMmQs0JpWbkwRpAPK2i33M8wfD8XLvHtYWdJyV+njvLySVfbHORLhgTVh6AVMJLyxlZJCo3hwEQ+lkJjS0kwcCp0I6FDoqQNA8mgTcL6UAU0b9JPyg+r/dL8qJKt5aKS2ugfi1o4wg15q9vYw9kbXvk/fHjkpV1aQDfs7IeC2vWGuF+aSytK2EGo1ICuaSg+woiQRI7f0DB4CVqawjvSpUg4WOiwKSZ+ZMCUyk3aZGCj01eWOwzVpMvDsTK5DIWeZWERTSh0QJQ+1UOF1ntYNOs6TE2W8/MAoB9WlXSTroXm5TCgSKvMvcI0iPJQB89mnr5hFhxQyiO3vs+hKAs3aicO9DloKHAkhVyrKwlF3Tx0T2w1ReKSLN2AYAEMAsoJbBIM+F+OpfEc/zAUeFgFkBZYrxlTuBKSFmwrhn3NGHMjDExCqs8q0gKFQnsc7xz7j6vuK3aoXs3rvF8NTngRF0Rt/P6rQYwjVRxoNXsUbTwfuIdAAW/moRwiDWyN7Tq1Qza7MrW5CSYURszWNqccxLvvCfq90rqlcUARlBIC1c0ltanGo/JDL0be96oZFLkMxWCVSSICT6G+iddUcEJKuccqWIE8Ib3NjcwJutkeVP3Rrio0WTmVOfwzTpky2c7P6wplSiGnescLPKdFeFv13nj54aqfsJX06rsrDJgHopZLIzYD34+agF+SgV364z7MqrywZhdswGwQ1JA7abM+Gw84iSTAqhS4jzrwVNGK95/TO66SoJ3y/yTwzv8wnIfZ9yqN6/lPxnqGZxNmUK6cnovO8vjfUUYNJCr2bAQhM2fCgAPeLzYw63Y4V7BPgdRBUAKaHmeRKxMLBKVF/KIYF5Jlxt5nkfcij3982379cuGgY3J3c1ZHxGf7hX8RMLblq8141SM2ZIrzjXjYGDWsUwYbNHpBnV3rIM2U5N2AOgq5epNkcz8fZUBV+kcgNYN7wzkYnj7W9+eZkquZ8qKNpDsYoLQ77xkcSWIGSnCWpPahNBdJPq6NghGpwdb3KlxIpDDO8oW8aAnV+34WIruKhUjZWRbPLMIVgiqqFn+1mssYaSy8TgDnFm1PdbewfCxgZ6FrE2tPudA1rmqueGQGIu1na2sVfPL0dbfO6cVag6ccGc+bmqIqEaMb+seU0cNXw3kdCaYS1YdoqpdFeux8ElzBOHL4RY8sXqVxPiqAxdxA7Q+LP0ARGTrddKzs0gC4FKfKwGqelKV2gZQ27holx44l1NtbfWXqmixAKS+uj1gxUqprZk2bWyJAVRSfXhso103TBDWJEo4xZ4UWJXp0yEuPscfKTTXTajs0kIgJ0FlTVIEwMrK1BFSU80E7QpFIgFKAwAYSMnbpKtZuHswVUmYqMTC5iwmDaNtl7o+x++7Gvo0MRgo1nyVtiwuQhs3WzdFuxR6EMZ/O4ChB6/EkhJqAFAPCD01M/iPlTw6M9h/t9icqoWzi/Uf1G+xXKDh+r72fdnAKfdbA2Cdku1YGoPBP7uYabub6CYS8x9Mzcvi4uc04JFjuxbzccsmaV0k4yyDspahMkkm46YZzpMvzq/vgiR9qxMIh7Roy+/UHX2fa6J44o996DMvv9POK6ELc1wxWbpem2S+jhuGVauD2Ge4Hbx9hwNYXvvrvtI/27c7fkOXJDzYzH5O2mysrQceyYOe48cRqyDkWNsjD2U/DFb4XgacxwH7UcGrITHeLTssebBOcFmL7mhNiNxj98Qj5tQ8jQCTE5oFShVtNKUsnnZCn1hBL21wNWAxn6XJOvGCvAO7dkDUdbHmQDtbIL2k86YTokftLgRXFfRs37MAgGAiwgrBbAwu9b9ti2bd4uYvpb87P2njdweu+uiBrK2/lv6uHWVUqB/UYnYp3r0+cjsoc7f3WAa0wJWgtiQ1tU6VZ+O9HfIZXAnHql0Md2nFTd3hts7h33vmwZqCaGOqeyvGH8sUxZRekbLUjN1QVP1kEkRAgan7Mm7OxT7HWooqhHJirOYRN+Xacqp5iNxn7Gx4PstHnCWjomCGS06tIcAHwot4Kxiv0oovp1vIAHSbhFSsuJIQ3dJlzJAhK4BV3/PhEiWe7eNdYZ+8GYh/1woIt47DwXwSgCrbHNLlYp7U2QRI1RlZWiylbt5REMtYWhmRC/HgOZJurbLAoI1J7P1pISM42HetCZIqwIR1GZCHipoZXD4eonoGs36AuK8j7tcR55KDHeMUycGoj2wo86mOONUx6Pj7vGKfF7AQ3pWdta1W/bEP9De8wyGdbXDJYarY029PolpmloRqg/1oFH43XfTQiq6jCtiwuLwd7tmQdQUy1NviJAk7Yv0beqKO3aLQu4KMtDUIvwSwft9ugv8Q0UsiXWPusYp2twhTXlFdbx9ekdjRijdyAKBacxBwB+sKY6NN38UwkWCmBWdbWABmjsg6YDMT3pUZJ/NdY0k41RwLl5x0AXK7ztjlEtrxe4y4yguqnW8+6Xsy7IbRcyrYmamjUr91EePnj3e59ODHBtUuXJufiPDFcAvsKyQNXXvy7esl0eODuVcoHvPN6t6rd6QBXAXw7k5qfLidUPT1BkCJmIQRG/kGgDBsTFWBAJcfOjU3KuN+331QxCYRFqXVOkvCmGLa/co3m55NeZ/jo6JIMsmWjhngDtBKKgkRy4CdWeP5uxgzWKBsz0SMQbS4QaTSwbMM1nZb3+VsLvas2rbBAQvNv8WAEP3+S8AqWOyyZXE5kFUNGOn9TgoIQwBVeun0l6ZL8xJtQatLAOv37Sb4DxG9JNK9oTzY4A7ffxckK3uN/q6BKu5dvgNWhkMUkmwfGxvLQcsRbMUy/dRVNOl0QEuTjeaf5gwtAOYLmrCwHschecfChNE8t3oQs92qp2gm9XbLiVEoo0rBTOrfwTZW9+uV72LYib2GCNinBRibN9ij73Im8KXM8DE/xotzp4FU0j6c2xMMP2gPN7LDtxrz6uKztYJtzN8L2UcPXj04H0zyuPF2NLaYT4v63U/zWniOHyYEwFIGvc4T29JJx2nK6k0D8cKIFvu8Q1y1Queclc2fkDEZAxNA+BexUPjvOgDgQBagyhH3VwJUvcGS1Eu288hiA6uqqUsWe/2Y1lbQ7Qr12eYZ73johfcRZrEBCl84ZRRxFPdnO+0XUR+kPla4ykUXiRUlQIve6/cpxGMsrEug7dLrC/BiCFs3Q5VXsqjHE9D8cFebbX1fZogytOJYMo51RkoqL03EuKmtSYA3NRup4kU+2/ZpLsFZ1yxnHlA4a1MrScikaqVMTWpYOCHZ2JdIcBhXrNYsacwVS81xPsIK+Ql50xHRo9TWOdw/v2cd/nZ5iR0VJYdkCcKINmwTW5ukDwJZXmw6ScJJMt5wxYGAL8db1D1Dks3HGWpBAgkjeBn0H8ZBTaPQrrlQqFwwtURECye9b5af18Y8prUiFZh3g1oD5IG1YNLNY1QZSNSYVKYsgRVugtBruY4DWQLdfhJEd3eM9pzNQ31upN0Q+0J/twOtA6+Dbu2nfHxi9DQRhU8oqmgXqaVo61vtEEIhOTzXhhf6hTaQUnvZgAKn0Xq3w2ZCVze3p85r6UDK0vLqhS/+nJobhnrSFrP+99G61nnb7f55f021in4VgktvezP45pVlDCX710sMeyDoU4wekFtRVWMv2n51FdbfLjqA9R2i2CSlgE7Gi2Tc8QyWFEaJ2QZ8N353maEzsTIYL4cTKrTrRi8/XOpgSW0Ks+f7MuqA3l38qyUgZx6w8LBhjykYNmKp2brW7PB3y+f4zfoKb+oVTqLdb8KHAEklp7AE8TuGjtEMNQEF9oa5blfzUaZGezyn76yY68Yz+iSEeHueUdUOhGA1PHTwCPDqgYIAVCX8teBdPbrKdnzeNsuMwT2V9trAhrMYnda3rfus2jIREe0stBqFGnyRWD3Hc1yEd5qrTBAbv6VbnDn44K/1znVsSa2/Pxl4xdJkzS1hcMAqB4NnNJaWxGdquDdS3ynav9v/9jFILzl7vPtN/niTvrXPBqzoCP2nHakk/uvZWJ/6ldODcBWMatwAtqSjQufdYq/sGVWNyaQs70U0mc3EwXhTA34KCbygMbEIEl3MVmndjglQD1BQd66lYGv1I3W1c9Blij1oIqIdEas9f+YB7+oet7yzDofmzyjtPHBJquYSH54Tesb3QIyUP4Lm+iFacR89EgQ8KDp4B1xY8aLvIhhzTcwP3fyyud993ubiaM/HPNI9dkG62n4ed5OtHxtr0f6QzvUcP5YYCJiGgpw58FgvluaBw88mZUGplqMU7cB+LmpPUThhsuL71bBgThULD7izDnRjqjiy+gtdpUWBKxmxmJpkpBKd71xV4p5a/tyJR9yxft6KHADYy3RSKbyoYkEbE5UozmsXxQbU98bv/jvVuLtGEd8L7+fuWvEiB5vKwptXAYh1a4L6JfWF9yoc/z4mHmNMve+5j4ne8N3VD84iq8JYZUvreew7RsoYocws/2Vny2Oq7d8qFGCgA1kMPS67tAZw6eqgkYoVxZVc4be9fN1lYyNVvBzusc+L+feKyc+p878SjLnGPKdFEPudxq7yHJuFcLvMYfg+pYpsnlsAMGQ7b9YBOW2dkG/WHd6sB7wuV3hTD3jHOwNQ9Xxa0TUN+A75xB0LztaV82hS3Z+NbyGHqnJCT5ofFDEInBNkSNrR3eem95l/brwUpN1e3Hc/K5SEavhErV6E3+ZP0uddCSHNd5m+F1lSEetyaI+JbGT8qarpfBjAGyvLizLSmcM7k4scYK+qXmFuc+jHTtPAMzPrD46zZLxZ9ig1odaEnGHJMyOldvInCJA4LtZdLsF2YaFoVZpIsGPrDMGaSPhFrPfZvEUQ9NwxBnu9eJ1pdemR5eHaZr8fJrBWGVWNuw+UhBWE0RbE7mERcjOoqeJI6svgZopAPwjIJ8fOcgPIkfJmguirOGyr7YkYiyQb/GsYXV6lBTe8w5HnkJI6c6tpzAX7tG6qCQ5uAep/VYWwOCBlC/61qrGv023nQY0Q3TurWgLhfgJcdaJQVkfCYjr1aShYOeOuzDjzgAzGl8M7rDIY6NlMGv2cSKTdPVbBe6sVvs8yCD/Jt9jtFzAdHrwufnZCM4B/X7gJfP/3pW9WtKqFSj+MYkskF4mCNP1Hf2ra4A2ijV/WpiJum+jVh5CCJK26SDawLFlOUUm3O0uU1x03q2Zq+czMeo7viiqknXhC+qUsXJXf2kmYkvGlAEp6Ug3JeVZWuZcmQxmkopCzcprsLIOji6FL0VWKxgFiAQimlSYMD+caNg5VD1ilAMYQ4xNs20xppesgB8nQbvVxMjJla9LRUvcGcsU2PHl2lm6z7sM2EFQ/jqDuuOicnsgNizn8OM82XjuQ5MfD2RTKyNoCf+ppA0BgIBQ6QEoBMma972zeIem54V2oHEwNgEwomFkOcAEwQ3f1bKuk/RIP6azJqwFaBNkkNTpEywd5wL2NwYEWDGOF0PjgdeI1BkJUr98bl6ws32kXcwUgG6k5SMyQ/ZHPupxr7DP8/T6H4LG5hi4ep3brnxHgln8POVRpPmR2jJ7nmh93TJbEc00gy0FqTcHKoiygpMBWqRkL5+j4xiDsc8Fk7KxzHTAMCxIEV8NZQQEeMVPBkSdMpGqSKhRyREBBLAcyPD+JbtlUsUgOK47RJF7a0VAfV+WJeWuZCmUkxhxF4pajKCEgYaaKyfKjGx5xRcXULIhEf3UgGYIdvFiTQnKYbW07mNQwgWIt602hvNthz4h6TNb3WKfBHyrnYZjFibHIHmtW5WwsLzy7Z5aTDNQehnGUBmr5nHHsukROVNULi0ckqO3NHU9xvBPUjuazfMQdz7itOyVopBJg41kGnHkIiWEiLaC8GBZVoABqx5NYCxKWK09WZLlfRwhUgbIUs1WJzpwmg8wVe1pxqgPenndRjHN2TyJ97TgUzdFBuCkzBmL8brnGIZ+x4xWfpSNOMuA6LbFfPsTKqsLGeoN9j+CGJ1zLij8dvsXV5/eQPKm/VC8DJLUkAYzxNGZgHIHT6eNOAo/eK+vChsWbVfmcwJyQvUDSnTIkEobuKAIejZWdCZyp0Z4MIGP3xkrtXywwfW4SRLd3Varo36pU8c8jSDYdvTGcc2759vdp5vMMZv2BUQ1YWNdsXjhANYSHhFSvSwKijL2ZLaYs0RlEqftKuZzNrNs16R6Z1uZ/ZTKQFRmTd9SzZbAv33zBqxKxHJRgf0yrszoAuvGiAjA5Jo3G9HJQq1FR+2Bs7X6cnfU+meFj+u6nGL6dTs1VkM8TCorJxGUxwJbd5t2hRkVEsMiAs1W9R6oYU0GtzY/EB/nQpYt5sbEyd1bOOBXvFiPaXICzAVQJ9+sYFbVbzJhSjkTD/doAZQcuPGBlZQVVTjjlihfjGZMUHPOEt/UKp+EGO1nD440BMFUzGJXwD3ssVqk4ivssJBzSGa/2J7zO2DCxAsgiRFcP6qH4DcXWXue+WZcViQu5iFYInCFC7fu8kgADn3zx7wmISzxgA7w97qb1Ndnf/pxfGP3XpzZ42wc9SIYU8E4QrxQ9JxjP8R3BBizUqgwQzrBymIEekkCiY/kQAIbK0AA/vZP54DGElIGlXVeBQYBEzcHR/UrUk7GZxbu0EGjrl2qsoCGqxY0NBgNjVPygwFYxZk+mZjbfeyV1w0SEX6ptf7i3VHtlf9+Boqcevp3upNR30k3QeYdhi3Ef9rrfKbZ/M/Q4uumyg4/ZfDddYujGzKnb335eqFQ0BXDlpv9i55LPRe6luEBBRT8PkqQNQ8+Nf91rqwhr5Zx0XjrziJLOGODCUYrjrAAMQPR+blbPRkhEGKlgHgvuL0+gfq7xO4+ZwHcJwQZ76mWFgvZaL374ZBZVD3thnH46b4XrnBjM1NMaO2N4WPeoLSiGhxeFAXTyodcJIJIgtGVtPMePL6ro2iJnRjVZoVSC1ASpfj4x0mRrSAFOZUBlwr1ZXMy5YKkDPp+PJsVi7POCwgnvyh5zUjuKg8nIVNY8BoMHmMLjd5dWjFBzeO8WB2geUyVZ0Re4TiespmC4Smd8lu7tdc38Xb17E65C2qgA+MmKvwsSdrAOdpbTJLseM0k0GNHPAk6iHlpe7NAuiAb8kMAbWDVGTmpjs4FXj7G0HgOsfsjiff99o3UjvFRKOJCl97eeWQwOv6ybDi1wRqzPwzc8WeFE3z9RNfl6QaYRR55DkufxMt0jg3HDO/ME1nNipoLDuMTxXCXjvo6YkpI6zjRErrEb1Oj9WKZgCAKNYShCWKoWUtQTS+eou2VCtW67ibQowqy58cvdOSSKLk8snHDLMw7Dgq+XK+zzgut0CtaZKmS+2x84G5EDgDHZFFY5WbfHr65v8ffzKwz30LGYoEwtyyuUsUWQIanU8DIeaYZFj+Vel491k4IXmAgOPPn8JUoiYHuOEObvthN0b1QoqJWNjZwoEn8272ABNnOkyxVTNfDLTOK9cANnaVWCjOY1aU27fBPW+tzN8B8sVtHKhnBSetw6ICVG6c4HR4bnXPBqOkU3Q2+HW00iwnXEEKbcOxzSgjFr1aMSBVi1yhBVDh8AVuQwgleZIYdc0UGt7XYPyOax5ci7dwlZjQuYrUrq3QzdJHDscvgddbITTx5IvbSG99RS/5iMrN/HyFErIB9Ois6Sceo6sSj1mqPqBAZW0oF8TqtpxRkvhjOOdcI9j2YcXzGnEgM9APVc68Ao9zq4X5vxoXtnJWNhAYiFSJGEKZXwa1ursrNWb28L4BYzDoMywI48aWdDWjGibBoMTERI5hv2vuOrrC3BCuAVadOBL/d3+HqEdfXoBtlkjyUChgQaBsj5/PhO7n2zLsJbx9oGaLeNjrbKrNM9BUW2a0vr1e0iytO3gdk7FkblHd3kIw/vk1UeKGnFhXywdhqtiA7WAIgSahKUlL7XgP0c/3jDpVjuuyaVjD1l/nfw1YNer7tcrJuhglIOOKgEMSOJoEBAMmBEtQU8bVgwLCnAMAc1Kgx4hpu2C4q9Q4Gti4W1JCSqIX0jNEl2tYWLg1wuUBOYnL0jtPQFvZDQkGC4KJ708cdkZF12L/yY0OPz4bmmCqEYMAiYubvN+ZpcDLEvtWSl58mUClbJwdhNVDGQtrTP5j7uklCgSVXFACz/vsp6jhGxFvASOumqHmP3dnMWIds2DwwsAMZkLC3rpDtA56q+wUCGEaiA9x5fgZj8FJihTQcOw4KjFU6CjQU0hJRIDeJTev+e/oBvVu9d5agbMWJfiUswuVW8L5FYZ135ub7tcIhNlVuLJ2jMK/9el3Lg4nX+hxCEDSAkaLMh/uNdD8/xDxOFk52HCJsFUaS5yQyTYDAGxN15Qq0J01CxcsJ9GXE1LrgrE16NyhJ5vVxhzgVfTHdajKWKQ1qQjD1VkXBiZVkdecZ1PlnHwhFjVobOiSeMVHCVztHcytlbb+oBV+mMl+kEbXrlFhWtEL+aOuAkOUApf8xfH5JKm5ucwRU2K/Yv2z9tYEX2HQr0zB2rM7r/GfKQ+qZW7wGo3gdcXT7+vhzkY4CvKLJ3gJWPkX5fi+06NyYQCrSJ1Vm4zU1Qn6zVvJ4A4ASVk7/jHTKxGr3DOrFLI1S8Li8wUsF1Ppk8ccJP8i2+Gt5hrAXud6aSUcKZR3w53OBtPSBLO/bHOtl8xJr3ePG9AosVTu7XAWpzKMYwtX1ohbwhq3djdC90T63E2I8lDOTnXMKTWHMg9RlOJHidr/DPZu3qfkgPc5Dv8k6r0pRO6ju84ifpHn/24lv83e5PwygdBWG6zpmQivln5QQZB/PBqh+kJW2aYb1vfWHSdKoEsDWmEmsWtCl2dHMctTmIjNnFgFbSTH5Ia5/HURT2PR+iIuGlJd0FJ0l9s0QEPMGIAhLfq0sXnbdqJYgMGPCeXPCReJo6r08sgs5olEEWwnIesK4Z5/OItTQ7U/WW0N1+X0fclgn3dQxzVZ8Y+k4OrcVswx5de64SNmrm73ZIT6KVkhOPWI1x5c852JVsoPLHnIUVcjKbDLzzIdAmgxEt4QmfLJHt30+YbuKg1oc07R4hlzR9/SqMNSZTfc3oLCz/HNsHvn8XGcIzy8Mn5DnpAHtIWzt5rWhTUH/7x4kkpIUAQgteLXm4L6N1ksqhSV86UEx/l1YnzlU9E452Lh55Cg8EwNq9Qyv5I2jT/eWxSHEOEo5csaOKr3a3av7nSYZTU2H3B6tMeEfDD+jFRQRgfvg6O//CBwt6bZaSQyuORK0aHgmEeWdR93lRcW+f66EsLX1NKs07y8GsVOAKj5ZsGIU2PsMm2k2i9RzP8R0RjByX5glQi7K13BfBw32OAO1yuHBjegLqZ9F3txO4D9YWkErgKIgwrFudUGxLMePVYvOPywiBBnIQpIFiQBu3XE4GB8LaheD5f28zET5Z4tvm2/505xqfFy9ZYo+xxgIjsaJpNbsAoLGy3BcmPsdux0i8NIEduvkoACIzvR0vWDrS/bt8HCQmLWzfr9uv54D6YSnr2LlHlbdAkcsOHeRarcvhKtnOG4rXuVA0gZC/Y4nq50MR9e8cwLgaljCqBRDG6nE/eaHCH3zPh4udVcEC3j734EGBFU5i47q5pr+RDsXCo3NM/D5pxRQvmDhoFZ4o/tYLgK19COwafI5/DDHmCiJrcW/MLAhpB2fRPIVZFSUi2jFbwS0FEcZUMVBVxn/VDtpzLtinJSxRzjzg23KFk0yWX7B1KZxwSGeMJvEDgDueQ07ISLjj2V7bPLQmqvBup64UAbC5XZBCUgjo3OHAlY9nzixaTboMILxf3T9L30sY7R+gOYs3rmL7TzuV21h3wX7SV7h/bg2GlrNELxlbH5sLfR/VSur+a4957sUYKUfB2ZUkbv6eOhJCv99GK37dyRReZqsMOPKMN/UQihNAiRRHnnHHc+Smd5Y77NIaPll6PtTIfQ5pwZxWvMr35rGm51s2XyzPS9w/WIEp3c6cGEPm8Apda1b5fK7x+vt1xHkdQn5YhfBiXLAfVkxJz+veX/hURrAQ7uoUyqUTj7gL7+qt1czjx4IwEjpvuGpSxYp/cfgakgU8QAGtC2meEKmcb0zAkGEUaP3gPr95X04U1Sfu5ioJPysnD4grVdLF+1yt4ooV0qL/ZmpLFEwrHpQIQBdTIFVlXwFexImd032QzcWWQ4UyplK8Tte0OY7xx8YzM+sPjAUZK5s23RdwQtYOU3Xr53XAYV4gQjiWCe+MleVUx0SCXS4Y8qoTgzDYrl6nS+bENsymoHZOVLHYidSAKkPqbdBxEKvaY14yVMQ8PwBgEkl0B+njJClAtSrqlbW7OM+8Av0Y5VUfp4+qOvz/MzJ5m1V6AGj1GvfH2tkCwAqJVraRXACb5MI7AupnMrI4aLXGfncGl7/GB//bOkeysUkoSTDlgsQJa+evxtTYc9qMoIGgg31HkeaJcy4TjuuIpQwYcjsHAa1U3KwzTvOIm7oLc0f3O1iRUF3aKr6QeN9+1irwaj4tf7p7Y61Yu5PGq+Skj/OYkYdslFo7Lx+RGj6g2DqIxYCAY3Cm2io4UaLnrnuHD6Ru2O4AlEjQX4nQ5B6eWLg/lqifllY/bJJwo0fo66g2XXwbtM1PhhM4dXLF53iOD4Q2+9DrhaI6SSHFEtZreIT2e1454yxD0PDdtHtIjGTJQ0JbdOgaw9lRIYgC4NJ1DU+L/TmnsCd7ziVuICePyIbd6Z9BBAO+tlE6hhaLFewuLvmGEWyfkNgrTT7yx4pEOsf4tgBbtlbqiiQe/b5g2+eCto7t8XFA5/ti8wSRwIeabN5mLu2MbYJgSOoZsoiWnbIVrTyINGkgIbBJJkKiYHJD9zsDYEoFskKXjYckqFVBK+2YxiFT1N+sfjwl6zk6CMc5NJB694idLzr0+uD8yH6272V7//Vwggzy/oo1EcSNdi8l6xdsrAefIOiuP2lzgqDbwgaSkXUwaHzDi/nPVvfxGf3Tnk/45nU4dpwyHYBFbH6NHh2R2RljtjHP8SMOZ5+c7qe2vkkCKboOlKxs9VoTdmNBNV/R0zrgRa44rioHHBJjJwX7XHE9nBToooovx1tlVdmJ5Ebu6o2lTJwd1ijaO1A1pgIIYk05UcVN3YfRO6A5yB3P5sfEqOaXdUhr2Kc4mOVNJ1akeM7jkApOksM6Zaa69c/qllwK9FSMlHCWgkPHzMpEOEnBiByKA2c76Xt7G5bGkHbPLe78toDmJTtSjlyoz4k+JjfyfMq/e7CZ1WWRnq+sWMPryVlaVdQGxu1gdiQ4eddvEBZJWGxVf0ULFslhiu5eZtWAwR2tWCnjtu6MqXfGKgNe0gnX6T6O+ypqaZIvCv6JGNf5hJu60/yGCgaqeLPurfiecbvoueWMrFIzWBSwrQZ4TYMCYSsnlJqREyMnzT+GzHg5nfFqvg/WXiLGzboLxjAboDsQ49tyhZ8Pb+331mC/XTYCeOyYrJZjVQNk9bgD/3L3G9QrBr5JCmJlnc+JCdnnB1OBqAl81or4RQdDADbwdwM7c8gEkVPkPpIIYNbCuilU0shBuPE8JpjDAzV5e9I5UP2tjGEl/rhtRqJWJCK1YOGeRo8uJ4JJDaG/3ec6BuL1xASp0LyMlT2W0vebqJ7BrD8wVslYzISbuqTBkwxhwroMKiUagcIZpzoAdQjt7pQKSoBFWjldOT9wPHXJiH7vgBXWcjsGd7slDlaN99PstdAANsmFI/IOYnlCs/mdSAYsAIBgtBM29MRinhU26LPAXtNXM+SPCmg9pm3vJ5TL7eqrKb1PFuD0XI0VrQMTmw9AlQRvPTxSwct0H49r5UgNEn2QT12yCOhC4r5M0QUK0C4dzsrqUeu+gyEL4biOSr8tI+ahWPeptElYdAGTgMRRwTjXAbus3l1fl2sc0hm7tNj5UWyffHcw2AC/hBvOeJUqvhxuwbuudbidM2IDuWSVCco4mDnMez5c2BKqbdIRYY/nVUAlRdGbkmySgUAi4cAULGvSAVksEYnHiVoSIY2FhYc+w20Q9/xF+vuWAEbXDkKpT6n583M81agmLwagCwCIJbV2zoLAJaEOWuWKDofiuLHYHNI64hFJzBt9+BgDAC5ei3lBlNEFSRBqSQRDkMQ9tVr0oBjBKuoGTzwGUYRxuQNp5I8bgGaASrZPkYvv0++UPyqgdcm6cgl+30Gr364e0ut9soAticeJOPpPfRXFoCbtbMiYaUWBVjYd0PK1g7Pkep6OrzfcNwvQYyQGnKJj4V3aTK1WjFu7SnrP2tN9ob/CO44hGYPY5IhHnq06n40Z2ECs7wqXGQqAs2hydkgLZGgL7XitFUIkeQKR7Ae955tsof/es8feq9L1bq6hSxkHun3+0DurzQ0dHNYBW30B5ZEdsEU4pb9tk6yIfiM/ywx/1JFJm0t5LiI1tS7KJjOUNYFmlRrmxJiy4LyOYYfiXeDOdcDVfsE+LwFkjUn9jg5U8cVwC+9eqB0LlaX1WT4CaI1GRqnYpQUjKnZp3TCyRqoYUXEnE67TCTtUnKBqkokqDkb1cNDKVSNHGUI+uEoGqIZP1p0M2EWjJtmsj72JlcsLfa5yNclIKXylFHBS0Kf3h3XJ4Sp1Ywy+St00vTrL2j7jotD/PmXIx4QyhdrfBQ6YKd3BvysjI0GL9ysqblkNCca+OyOMHW253Yn1GFazmrnhPSpIbVPsOX2fHvfP8hE3dR9Nq7wov6MVOUlIFb+iG9zwDglauHfF0JxWHHkyf63BjrVunxj45BLBu2VUm4MYawlDrjgtI0q2piQ1hUH8NGjOMg8Fu7yGvP7b8yFkiCwUOdLVoJI2JyKoBF6+s3Cvx5uxI2AyfzgHYHdE+LPxG4yfnSG/HNQrK+HROovarSSkD1mtfEx080idCcNX9/jpZ7f46eEGQ2L8P77+lzqXJCilmRT4AhHEzo2NlB7K1HLATbdVb1Mxa4qOcZZWGINLWqfCpPic5C4N8qXsSuDZrgNCqFaky5c+Jp7BrD8wsiUEySi8bIlpHlgXDgRQYqwl41wHrYCU0Yzoagy0+7yiiuqKtWWpGADFuEpLtL3VgUU7HTlKDgC7pFJDpemqZncyo70R1eQF7ok1mJdT3cgLL8Mlhn1nQwgwkiP/5pvlyYwIzijR0TAZ0NV3AfH4oQ0Rf5/P6hlaPdD2WCSr/1aotJChBpSXcSeTViO6oY+hJuieiKYwWwKu8wknHjXZAOGQVWp4YvXMWjnbBKzeaUPiSBoyGOc6YLS/i5kf5rSl4d5ghznrwD6mikx62XulI1v3w9G27b6O+Bov8CKfbEFxhreqrS7Hw/s15FUEZ1E/gwrCQSq+Gt4BVwWS87b7BWDrbYKMSQ0QkwnLH6tM+HHuq+mP+ZoYhbWWBAxbwC/ex2if7/4jVp0gY25RBcilkYBJGO05ez5a7nang9hnuRxE+gxfFHDzxCclfq6WP8d3hi3x9BIQUfYvDKjtMl/mZCCWdi/NxBuZ0ZjUAbAYxZ/IASGtlhcQkksKYayXDgwfqGKAVrpJzEQ+jOKVU+OJkft1ZXt3vrwOLfSya13sxL53iCq05+wGBgtQ4L4hCgz485cdDX9IQOv3/ayeodUDbY+F26k75i7Yyi89FgOyegavWOIhQqhkrCz7Gi+aKRtPlMGHdvx0/+untCq2boczvJMBqGyML32ebFtVyu4+JomauT9AXSEGsWgtknCsEyZjdUy2JhJRQsl2nzwMPY/13NGCks5XGFnnkQtAK96XSSvZvdTw8nWX5uwx913MNYYuClNboG++zH9wt9EX7xe/ta/Q+cMArw7UCoCu/4JLPK7/LQZIigFq9J7r7zl+PDGYlKrW1AbOLtJk7CBj5i81I9m6Emhrpc/nI0ZTkJx5wL1MuBrOeDGeMKcVO1pxJ7MBWeMGyDqa56p3vFP2Z8IEBa8c7Pimqu/SiArvXgjoeOf3nYnljakyVdzwFMX5HRXteEite17P1HLj+B3KptCSQdHF0IsM2fxgIQ3UAnRNu6IGq2kVbOw2euaT7sPWAc+ZXKvUbrlpr3PQ4REv38eYW/1re/BMfX31bwfZqghOaNYlB9ueo2xdLasQzpJxJ+rb62DOkedQkSQw7mSOY7SjFXc8Y8KKlFVeemQ1YHeW3Y5W1KTNP1bSOeZk3sBX6azFfU44pEXJG8kM/1PrWOms8mpzU2FVKYWapGb8/9j782DbtryuF/yMMeacq9vd6e49t8nLTZKkTYpOQNRQLBUktChKDSLKKEMNS3ghQhBqGWVYFYFRhoYY4TMKm6eWJT4RDcsqn/AqHg8oFeQlbfLoMiEhk8ybefvT7W51c84xfvXHb/zGnGudfe89NzP1QXpGxs2z917dXHPNNcb4fX/fZtZoGvw6g102x227ijBKip+FjrNuqvJaLHwNTYSv4N52Qe0SzzQPRtcsO5/TVcPkpUZusGTOGr22bocl77j1gJeaOdI6YqM+WTvAkMtze3BZajhSpzzKEIGYBpliHhfPC//Pr/hv+dJmQ+0Cr8Utv/sj3wpugH5cStrYAVWtOF/YWbb4qKQ9N3diPjxTyuTbfHRExUBJFcWP2MX8t9z4d31usnmGddUDXmuy1Oq67YOmWj7q+F9O7/VpMrxLJQpXkm20HSkOUkOj1/fJs+qaIgUzVta40DZdcSeBLlVFg5syXRPUw2SThmQQY8/oRJ8KwGWdkSYbNo6PuXYxe2wN0evWeTevrIhj4uJOBG5wQofLZne6Vm4ksRFbCHWBAHa6GQZoDRRZKWyoqwCkR+lYvF0zd7tfJ7F0M/a17HaMw2Izlvo5pm5IWtD7uyy/M825dp9C7jC3EnIXQieP2kUWflsAoohT+jVaDGxSZnWJY+J7BTbRybxNVSkyGh+Zhq6AVFYo9DFo3HIfNBGkq3WyRiVIl+2ENuvMregwXzZNmwo8aOecdTPudoecxjkJv1NMqd/A1X0KS4Acex2sJHC7OmNxvCbVI914vj7KZO4dqalwYe/zHF8L45/T+O8jcCuZBpxSZKTRpLz73FaMjG7IFForEtSXRHb8SQbdOcPfZfy3UcIHDB2GmD0snM3jg2fR4/F4vNnQ72wGe4zhh5lOM1xTUIDsct1nVtb4SjMZchL1aOxREEzElY2eQ+hH4SGVi5iv1pjhY2uOrkHDd8m5wYRc8mOyQIvRVwxh8IYkH7/Pm8kkA6jTo2bn+tyDBHy3ttdXMLaTIIUNdRWA9CiJh2/XzL1ICBkKon1BpYz+Z8dd3oNTIHEn3BUMKizAYJVZvWrC73IDYbAcULPmvpxn8zuxud5SJyuXqLzO2iV9EPLrpBxWY+9Dz3DKPlkxZUl5HFIQkzjaqA04B6XAKLhQBkjXsWabQ0c2Uuvfx+eBNy4kDBgcTbt0OA79lmbaDd4kjrLBH8Agh4TwhpL1N/959Deb98vfBgP9N7xSxs/l2F1rxm9meMryNye7t9lrX/lao/XKzvljKOvTf7Spou8zBSKIFomiTQ+84INQVRk0Sl6NsfPvfQxs+4p51XJYbfFoQ2TiexbVlsOwyY1Nz3maZc9e/c6b52sUV/a+JkFcZm8l2xd2UtFK0NrFqX0KKGA1HTXarZk+z8oAfazPJtvVjtpkKXV+/LjW0XpGAS197WkGrCzUScEpTfgrieV780Jn9hoINSErNXZrFI8ve+OxCbutUft75qvmtXHdc5X00G7fMabPv6sMcgCy7JitHrP3F7NNyib/q6m043VDCuNumsOqEr4AUfq69nlFGhdpXM+h17AAq1nNy7nJn8eJX+W6x9alWICveWhLjToLHQe1MpPMhieJK35ZAJMqqk9WULVK14cine26UKTVVuMcVVsSavMDsG7rkubunVD7qF6O4rmMU5ZpUsgf+5+zjVVq2UrH63HFB7rAT2+e5tV4rJ5hLtLkxx864Uuvf5x+BinbkJhEb4cPYUFY/hHq2bF/loEMsLu25HHiN2wk8n+786X8Vx/+RuS0GdYQe2z+b2d9sfSV8UtlaaKzeigPZZXln0cNl6JgGR+WLz2W8hyuHzV7vBRfv75/dIjqMTPrkxxGiRyuJUfqNQoXAXGx0HnbGEqaTMhfIIDDekvj+wIarWOj5ux5IoniwOm/IU8qxvbRxDmNTLXoW5O4tRKY0oGjxOYe+U32Y3I7unefixAzHN/kVIYOr/LDDKJ0ubNbom4z+0qjbhNT05JLXhjc0AvZj7i1Yd0H+3n8r42rgKtPhI01Bq8GB7IBYHujiasj0uUo2wTl3Oh7NXNKM6/0xQAziGTtfl0m69rFQuWOWYq4oWLqtZPV5U55l5SGvai2OxRcn9l9barKRG/FZxLdkHgnuTOR2FDhnW4qujRISQBmdaeTeYh54+JZSUMSxyscM/ctt6qLne7/m0XVBufxol0Fo4BvUH397aMLPj45KSbw5MncdOQqM/RQ1+A3uxP2zoco7CCKsNONAMrGX5LDV3vVhRXAGcRSW67RRj/H0Yo4CJIncKcac0fRfcv4tfKkbZek8yBVXguSK/HtkllYEl2RKPbJ7ywMj8fjcdUoQSMMAJCmxQzUDd2TCDE5xNYmstTCqZ+KyZpBjeGdE6oMYqVsDJvy3L5W7nHunqcyv5mvhknKehx1lir0+XUnri9ywrHwTAEKKUBKX4ATA9UzUIWxrSQDdMP+qkeoDNArDcRhvhgDR2OQqCTuMvJ43Js7rgKuPhE2luwdjyvvZACyrhoRTZvajrCSsT+Ver8MZuuepJ+bQIunz6xdoBgz62tmWbPzVKjprrGtTP6O73X+Thm0MpZHligaEdaOxSSr0ZopyeOirVVuZ29d+ajy0AyqJlFfN/Vcm+ZGTzu6UnZJRvvDO6fzcj4/EY8TBVwPJlvOqim+HS0NtuYYgOSdJkf1PW/wUQzo23jsH5CBSeJg3+djdAJclvrtPF7Ivlq5GMgXs13OI6XnzmOMceP0zY+aK270OnoBjT+DJO7hhs7j8WkzeoF1rxsLH0TTLM0T1IGvEqGKhKDAQBMik6pnnX1UvU8lWMj2+wdhy8QPYFLE4fPeThlaLYEKSzQ0qdkiG3/7vGaAAh2ZF0PjoiYf+vUOCysgNCRql2ilYprXCpU6qg3Lgo5NTslT/6bBcH5skTKuYaZO/X7H7Kyp89TOs5FIJ2qfUkzccxOkdr7IEE3KZ55TYz/dcbLg+N8xW2qoOYZkRL2v1h4Vu15a4zH2ylLj+ZTXM1d+VrbZAOZtJbERoRZ1u23yXLYRKSFeS3EsMqvtIqlXricVdcnUd7me0Rpn6rrC3rI60li+C7fVx0rF7XCJRzhLE078mo1UbFwNfkvCs3BbFjk1sJPAXQ7xTrifKia+57DeFDArZtP2JsRcR5sKRUpNLaIpeKqa09vbXt/DNlUs+4ZV1+QwE2HRtEVuuI0VlU/ayJ/M8nvpsywzXFlrJhJnsecX22v8T8vP5sX1NZ6dPeDzpi+zcC23KvVaO/SOL1t8lH99/cuQ8wa27LCxxDtSBb5FEw2Dz/5Xb1IU7HS5Rh2LPFzSuiZsjNmoJIvnD+7zy80zo+R3K2ryvqQayQyRnFA4Wo89xQw+TSAFTTH0vRDzz/nkDOucYSMZ3NpffpwAvZrg63vIwRWGej3ieAxmfQqHMbIsOQTQBSR/ebo+lJSGLseC9knlhx4hOU3qOKo2paNQu57gFAy4kCld8oWmCXlxGMkHNUZVh1F7TaNuTCCPoxmBNp0Y+i055la7ITEDWio/G+Ju4WrZg42dgsEm/zEN11loq+2Kd7+0b5dxtf/YN4u8HWvK32w8ZPAoewukDKkqXQarjBo99jWzf1dpwnFYKstJPIdhzfXqUhMnpcFLKrrzY9+ykYqtq9WjIA2LXpsqSCZV9DleVnXiwT8cWxu8AlVdUvjQOhF4uz0xq7rCDjRGR59Br8vJpFDIo2nxy79v/PlogoymoQSEQ9/y7OKUj06fJdUO2Q4IvqH64nOi4dul2aaMXI27E7bhT7ngx5WOyM7kn2THXdplfYuM5lEDqcReI4NXaiuQY2iTdS1yASbabRCnsjCxqlTAzOhj1AW3rvMO602ieB+Px2NnSJ42Zbc4Njlhyt9fA7vtvz55vR5zgTDxvdU5WU6gbJ+tVPRZWtjkrnjHYCaexGcwxDb5ZFBLZ3wDxBwD20of5wp7LCD0KNu3H81bClgNkMabfSvG/t0DI2vcUNwFkfa/Ym+XcbX/2Dd63CAnfOtRpJN2xKNj1LJv1KzAGFUjXyuGppoxuCe+K+BV4ztmrqWXQE/ADPkTjikdvfMqNRRldbegvlbZWMNlNMWuISskbDpz6HXnHQRvnqHDZ00GU72TUkjo+9QQjNbpZ78Ies3FDKrCqFn7Jp+P+Xwm8SSnUtmjZsNpdayNkzh8TcShbKy87jxkAv9Wo1yk9rsMf7f/cNp9vuqxbvcPTkZGuuTe3+j3h5/bgDMK2JVPAnh2fB2He0uxvAhm4vuo7/fx+E03kji1PYE8sWevrOhwFcVYuQ6Rea2WJk2lRvDzpmNS9TkkJBXwaD9VrBP12KuDegR1EkpdEjM7NOYvgTJ8dr1X29ywPcxeslq3eCxNzthV09EaE3GcpQlT19PmhkorgYZYGvNWy3TimbpYnqcmG3Rjfll+YGJJUqaVIyeVq6+UNeTNFjVqeV2AI2V09cUreDg3AzvKftd/B2ALAKetBFWtpOLNpJ/h7t7XbgdtrCcROoQJjpVEagaZpLHLbG2bOkcrkpU0w/e+Y7CONbZbkWZmSaAlTV7EGVPXommUTSE+WNrswm85jXNaqegksEht+RyfDGs2+flP/EqTEjMQeRFnCn4mtTw4SzMWYcs61hzUW7wTLtopyblSO5s3Vl3FUst02RcuBJ3rzDNu1nQP7YHanI7YxUA1kixuvdZrq9iw8Nt8/biSCDkeW+l4OUZe7o/48eW7+fnTZ7m7XvCx5TXmT7S8o75XPreJq3m6fsDi2pr48QbxkGpH6gTDiN2Yzuvc0BV/o5Eke29dsS7mBoyTLP3DcTMs+D/d+EXuxy3/7tq7SdV8F/yKmjTvoiC5HjJ7FGfMzqjAlkuiiYZRgWPz0jLeiuTNlz7n8FyQ199+UGpR53JtHJIFSgAI6W0tU4/BrE/BqENUwzJCLp5d+fR8RonNm8iPNnJgm1PPNlWqRw8KUimG74o5Kp7S4TB/CRvLrE+HYZHoMpPHUkQ0UlU7sUGE1rGjL+/y7eMR9rZjBuLU2V9jTOcNuaOvE74mNaX8HmwSHszTPbsMKSkdC7J/lZ6kN5YfXgWkvF0zxaFDsgtcPZxUkhev0rkZomyV7lwXSee+X1ZwKfvJOM7ioshIN6nmKGzUqDnpZ7pJ9c6CX4fINtWsaKhTJDkPvtfNfpYibqwL5zLg6IWYdEODOCZ1u5P00UeP9wnvYFr1HGY6r3W+ANoMnk1Drzp4cdkXoYKRBv+txlKawgKcusi75nf40YlOcKnSxIqUlAEiDgWbaq8m8DbGvllXMbLGfiamtQJ8zOaD2MQoQyFhl4kbTHjFjyZfV27Wp/TD78gAePneOixXlK0yekwadSRsps/HZSbwj7vlj8ejDPO/ktLWY6cDZl55Y48s+8kkXL14Gt8X+r9BPd1ItmGsH2XRDBdnK4Eakzj4HQN5lalnc3jMS0kTpAoogW6Cq70Lfn9LpjI0indkNTqG3NTE2E4Kjg3gCqN/x0AW2P3Ms2oAkK7Ckd8M6HoUaeLu+9v18tqXFo4ZWzadhYy1K/tKCpBlBUcvu35ZZuifRL03rcnVS2DqenBRP1P08zU/s6qc58yKlsyncyNDd3H0eV0wVUIhEgGIw/uYnzevT6WoUFZWk3fuY7lbzOtU5bSZo8CUzwmNj+4ZovsePUcVwrV6xQtBCuPJZfBKfT/yvB1c8QrJB7YLUr0hgLaLaCnr9g0eJqMfxsWDPcTt/e73Hpd9s7gK5Np7jdKnGX8JyvtxytIZJVI9Hp9+YyPGmM11sZcS5uG9UNWRulJ51rzumIaeadWVx0+qnln+vU0VIajnUMcgWZ9kGw2VAqYMYCg4scz1ytS1mnaXG70LpyoS7xKrNCnsHU9i4VoWrmMpNQ2JjVR0qNXH2PcqINR5/4xk1ilS9pjjkCv7d5PXq+CEVjyLLDvbiPrfTp1jI4m5D7QpFebSVhJ1AdEGeXrnYgbAFPAK2VYl5e6mSgnNwmSoJ+znXdaXg6z4AJi4aodVNZYl7sgJRVjmx7QSafJ7qJ0DSXQZdDM2maWv6/NQahhjqJlNir5mLE1oMzLX5xiOpXGx/N5KIKHJgzamruMiTbkTF9ThgqlLTF0Ceu5IoymJLu6w+DoXmPuWm/Ul9/sF1+slp928eCE2PtJ5PRrv1Gc2Jv3MmyyRFXGEkHBO/61DpAmRg2zs3uaa2Nhagjb0rdF/2TbcOFoyD3rtThwc++mVKqEPdT2/uH2Wj3fXef/FU3zo3k1WlxOqOvKD8nk89ewDFu4l5l7dJW/5FZ914y4fmB4RNg7pcr3jteENDHOzcw97BVuq+2iUEKw3GiL4Vr3PztKS/+urv5NffPA07atzQpswmqLUPm+qBn9GRIbkeRkaP9bEB62DZNSMMU+tsqYl+zvEyW6jRpnRo+dKusaJ1WmSa9i3EVjyGMz6JEfteiahL50PSdb5ymBVr4U7wKzpWG0b+uSZoPIi8zNaVG2h80aU2jv1nRqHZ/ou7KYQGvPHWFpzty003tr15WdbBMzjonXQCISRHxYyGC2abG7sXzI2ge/El6jb8c6oMAxzx9q6GsbkUcP1XabWGFBKZcq34dkHmMp4C235G42Hnqcc+9D5MK5kJ7HQiUMuBEMuxsDAPS0ubIJfyaQceUlzcZG5b1klNUk0b4Gp65i7bYmw9Zk+DOTu0mCKaJGyNXCRUzm2sSpdBYAu+h3/pSrE4n/T58VGZQaOpu44btY0WXO+iXWJwQXttDehJ4nLvgiXJapW398bTzIpbx7s+lF9PDzX3CUd9Ihvsqwwf4y5OyHO5ZjYoDTbGK+Y1GW34BgOWDf+Sc2NtXMh+CZSNZGqivT1tIBGOhFTOhia2DWkF6phu8sdfApzTF83v0+LmU0jjEqGv5VDBj3mxEC/laEofCj96vF4PK4Y3imTslBM9pAbGW006qChIxoUQWHUADRVS+PUA0lwNL6ncona9QUwAcp8BwPzxwCw2vX0oulzgUSXZ1bdE6UCgEQ3mMKbl5aXvJHPezGTE4L+PpjAu8HPb+9cjGp9JVjmJsqAVw+MLKvpx4DSVTK/q7yr9L2PPwO38++bjTc2Ld+F1+x47T0Y/81lAEtvz+wjBnNjW//NLwsGnyxlToQi36lcpEao8nO6ETPauVSONTIkHvq8oRRc8XEs5yTt+i+NjeBLGlW+rfKJSejK59inQBtHBrTk0BIogFYglZX6TfIEEZQtbvdVWSUchxXSJMSFAbwaLR3l+MaJhm/2ke6gVK7cvcz7DlyQbForpFANGLNI8YcsP8uQalhoY+ON/v6yN2qo7Fy1w95/GKO1aIy7GePxTd/n4/GbekxdZBJ6vBfqpqfvQmbrSy70habS0A+P7vEWoaUNgZVvqLMXa+VS8VT0TlhG3RseVFtlZPluFCgVtA7Ke9apaxWkwHESVpynKZvUcFhtSOKLTM2atuqx1O1IBS2hEIb5bgxUpSxHHD/GpIkwJOZ6hKnTKmLqkhq3O4WbFs7ToXXIRbJEQB0JCihUGFrO02UWl9U3Ye/LNLCwbJ0M5edtZnFZfWQMr4mrdszjx8PqEpM2dqLvpXGOZdLm+jINCY2NMxsQR4f6Y3UCG/O0zAoczwB8ltca+V5uUGsBq5eMiVW7yDJNslWBBnbYc3vUhqCTwCpN+LA8yWLSgt/SuETjElOndWhDpPEREqxQcHNuaiOvDfxZ6DhzUxZVWxrstoepS3qhSg4rn4gZqAwhEaOmGc6qrtSslVNJ7SbX3MHJaK8B81qB2+Ow5npYsXCeq7yBX48r/t3yC/jw5havbw75lbtPsLw7x68CfS18tL/BTxx/FjeqS26Gu8yDcOgTn3/0Cr9w+Dz1mdZ1qXZIK7nG0EnbGFous5V3mvn2e27oS15DSEnl8vrH3W6KGPHC8fz0Hv668MLRTVIVsA2b001Ytk9xEBMSfJEa7iwYJoO34CvH4H3lgJzWKEH/5rxAyiBY0OMxFcugVnGjrhgKYI2aMI86HoNZn+QIKHU+GSMr5k83aEcSp7ReQ4+taw7Dpr3xuqBElKE1pIroF1z9r6riyeRHyVF+NKkEl5iihnvW+bAI3CPXgYu0qOZ56rJxXl4UjJob8wRnC0hDos10YT86bit09qEhz7DRrwlXTtC79NtBSz6+fZzSEdxgtjgGo/YTCB9VlrgPmY2BrDHwZh0Z7RBljTw6CRo7y8wOAxpDm8SX+GFbHEABx0BiI7V2zb2CXU+EFUFSWdxV0mfyRDWPT96z8bUWpHkCtphzyccTnSDJ7zAz7OftthlkKiYzRIuMxvf0KbDsmh1gTFNuKnqTUGYgdCPCgchYmXflGDPMGhJT53hX8zoHN1bEFxpSlcEgs2IwnChLDV0IMPJp2B9lItdfchU8ujYmnurzz/m2z/tRnqkfsEwT/i93/nABlktclsuT9VAVF7kGJiG0RMOUC7w6pxmmofntvEpGEN2DWUdCtPocJnSLhY8eCUNHKX9x3vykPh7/RQ+f5+lCQrRKejTtOcdgEg8YemtflWDhHxmltbnAoR3uAlxkyGcM/xjIYWytKpv0GjtL/bQcEyI4BUbUm0mPWY2BJRu3j0IZ8rGWBKrRfswkiXa/8XDj98XYKWUYZX+UK3tzmhrf7vZ+vgrU2k8gfFRZ4tWm77vvx4As3VPK6Izve4upTNAjbKRCcpOpY7eJ0WSvsh6fmyRSPGwU+NJ9ROd8WaPGHly9M/+0AQDZseUwT6YMWGKSQ1Hphq2uIkPjzTspiYldCjvAmEOLlCQqd9Rur6MHhpibNx4OSmEdECrgeljSzDvSWUBsPn7oAnKZ0uzZCRPZGzvb+UJ5GV0blcPf2vKVtz7Kkd/QSuDfLT9/ZO4rw3fVLugxCIauZ4gbutewI9vYAbFGYJUBaoVFnP/gZNQgGSkFimfXJyCrfTx+c4yY94Eh5FTrXJs0TU9T9WptECKHzYbGR9axppfAJPSIaNDQNDcyt7Fi5lsFw0eer6DgxyaL8AxAP/TrwugJuUFy4jXhUG/fqKyQrgDyKjF0RFHfrYVrR8yqKsv+YrE62WSmaUSb7klUojbJ65qtBAbAgC6RCZg4qJ02eAbZoIFkOgYWk+yAVkkG8MrWgdoF6vxVMl9dA7tAGzc7zK58u4FSPs8nNh+aPNHkiImkgIp44oipavXKNk8K1nBJZDnlSEWykd26KOY6JjKswaUpT6R1UIvWkxtqlpZqiNVZ+q81tkxWajWPPq9nEyt+pX2Kr5x+lLnT2uo06ca5AGcu4SWVmqp2PfOgXmt98hzWW5bZtH1Rt5p+29U7wUnjn1MOXbOaexqUyTfxkUt0fTqcbOli0CC2XkO1JiFyY7rknYt7fM70ZZ4OkSM/2z1vkthKzw+tnuenzz+DPgVeuLjG5fmMcB7wvYO1o08Nv3Z+i5cOrvN89YBbQeuf5yb3cMct6bUZbCgsYX0TDMymtxr5min1zxsZxos25zupOPBT/quTX+F+avnR6+8iVSd2wVCu+BAwJnDZXuZhgBchN9rKHiC/TgagDKgyOr5eInq7i+h6ZiBYtmWx+0rNznDwsAflm4y3b0r0eDw0+uQH1/3xyXeihosZRU7imNR9Aati8syqjuNaUyCGCS1S+z4vEBWtVLnDPkTXdhJ2qJ+thCL3sDH1gyTM55SQhiElBLSTM6QVhgyYSel+tHgWri+xuN5JkZmAgsRNlhjaSGIdhMEA0ToLiaST/hUT8z6gZP5WY3lieY1cmIzHo8gMDbiy/zqJO4BbJ5GN9IX6a9p669QktBAzU/exnn88uRuTzswTLRlETTH7QuU1ny1L/Tjxq3KfqevK562JUnpMszB0xcjHYxJDUHlicAO9PIqmfPR9KAWHnbmJj7Qp0EVNP1SzX721TYHTdsZlnBbDRxv7wKEaUkYexBUv9MLH+6NCOZ84FbDcCmveef0+cSqkmsFY0I260Q7wHmcT9FX6n6s+52S7+XxOGs+XPf1x/vjRr3E9XPKR7S1cHM/Oo2LKotIgFw95kk5m/q6MrzL57jcr3PAY3+/ebl2L8jgBSxdCKFLDPYXv4/F4XDmSZF9GKAWq/exyCgwZfBjL2pM4Kh+ZBpN66fBZBh3yBnNIsRuYlcX/JA+9z+7cW40uYIdQ5S6tY4jArtzoeHCl8PAZRNG5UO/jMug1hqgsaGT8yiKDPxUMYFTKoFAkEUfrlez9O+wlTRxpf38YyNr9HN56kzWWPgrkYxo9B0K/10ixx6Tyr6MXx9hYwKR0lNuNAZybWy6V4tNnibt5ZAUnVGihMstrjndJ/4ats0PHunaxNN/sfI9VEN6nIvsEZY5J8qSkDbDxnqRy6sll6Ydjg/iYi59WqhKsYmMfOLQE5HXqOE3CeS62QmYEO2DhOk5ma6TKm+fxU47XGudGTZGrPsSr1p/d+0pwPHV4xhdP7jPzLQ/iYq+j7HYfu/NnV56vnGYZGMJvNlzau8/+88DOLyX19O25MTwev4mGAwWpKgWmqioWllaV7SXqELk1WxbZ8GG9YV61apidAhfthE2sWMe6eN1NvD7eFAZ3u8MCWgyhR7rvrK2x7rry/V/k9G7z2hqPscRt4dryt+Voz2lJ7sbQ8k5KTWLJh+otHAsDeOqkJMrVToEsleQJ+012q787EWqc/udcIdNH1KOqy184+9lUJR41iZ+6UOargCt1jjGlbOc9AGiOVRqkhxvpSSStQYjZmD6yko5V0pWsFeFCHBvxtDJIBNvRed2I5yJ5NuLZGkEhn5dVqnL9EnKK4W4YWBJPk2vPTiqaEZA5dR2HfqOECK8+Z9Ms/bT1Zuq6IkW90x/ywe4JLkTruBPfFh80I1YY6Dn1OYwq17+gtc6iajVN1+9OXNMMzNpaooRXlxt6KTOtPKu+YZsCTYhMK2UdTip9frvf0WTDZ8zv83mzl/nC5i7X/OyhFMkHac0Pr0/496efx/3tgnubBefrKbKsCFtHWDt86/Abz73lXNPg04ROInNXc6u6YL7YKisrUCR5Zal4FCDLPB7JDRB400aMbxUbuEwb/s+v/nb+0C/+SR68dEzYitY9o+GiQJRSiz2K9UlJIzQPLxmlvidIlSPV7uF1CgpTS/0KnH7R7LZ+t0H3KOMxM+uTHC1q4A7KzrDzr5RzyYuJykOmVU8bA5uuIrZKjWxCXzaOs9CVyFIYJvCAJhsWdtYeMm6m4kDpcpiBnyWKWNd76vrCwqrzRldfQ0bGij5TdkcRtwySB0sHqcvmd9BfGxXXFgDtfAxyvRJ9+xAQ9ebfnME4/o1kgvYtePMkEIustdePDLs7jy9U3i7THscMsQws5/OsRu8doaRJAmVi1y2+Sg/ViyYVphI+e2vlz/eGX7OUqnQ1uhRybHFXNgYT3/GgXwBTWl9xUG9ZdhP9LGOWE/lE8J6YPDEpQ6OPWlj0WSfufdaby2AOfZmfR8RRZbaQLRxtCpz1M9W353SPqwz0O4lcSscH2kPeu3w3Z3HGu6av8y73GreqLbULXPeJ33rtI/zS4WdQXYaRfA9wDgnqWSXBQVWxYwJ/hWb8zUZoE6u+oZPEz6w+k184fwb6UXt+RMct31kBiRkEDMNt4nJ6R6Ckd9iw7rmxuQR2qB5jyYhLZFN4D15Thkrn1HE1cPd4PB55RHyh2RfGkiMDWeCD4DIYVHkFDvreI9HA7sH/yoCPAliJzywRGUAHXAFxDEox3y1EASwlkkhmCfUFTFDAJRUWlsnQYAC7xj4nY9BqDCoNgNfubQ6KZMPAH5WCD3K9VNan3fnqrb5lUt7p1Zupsc/WOOxk/3ZtOqby+mnnfeRjzesKMgB3tqezV1dmWyIypOUCpQll57XPxaWmVw60UWtoAMxcp0yu/DnG3CqtXSK4DifKuFunBlJFdF79SlyFc+pTotddlsmJvg9r1kn2yrIPy1hdLh9nm0Kedl1pyNj+J2bv0F6CXjujczAeCaGVyJ044WPddbap5lq15Hq4ZO62eOeYITw7e8DrzQm+HXw9TNFXfvdOGydj/5E39ct6eLgodEkZFS9113hte5i3FKOF4Kp3kmSHkTteh5Kti7t49U6hsSNHtKJg/PvocxAvmb1lJ+DxWvPpOIJTxn1MnuATTRXp816urjRF26H7usonaq/NbJUcKvvppFlzWG8yU1K/k9tUcau5LA3vqe/Yplo9YKWipucoqCVKFMcm1XifaPIlaI3buNcU9aM6w8CunZT1EeMyybBeJHFMXCyspnFTOYomGHaSmcLOmtAAQuMUrIqjL5d5SelzqKQQGdha3jm2korypJYMaEkqjytWKZnR5fF00rMpjXH1thrvn+0YTlPPNINn+lxaSwFcpF5JMWgKoY0uM+BSlgFMXSJKZlyN5hud24ekelCJoZ3vJjfXp65nk9U8F2lWCBLaZOqzPFPZdFE0PGR4H748z1mcF2/gLlWcxjmnqSH4Lcfe4VOf7VM8S2m0ae8jF2nGkokeF3odL9y2NMq803nW5IBArlkSbT9AGcEnnji8LBLDxuu+5Lhec1yvubM5UIA29ExDx7VmzectXuFzpgpkPRPmO0DWVjpe6Fveu34nP3z/8zltZ/TJc95OWJ7N8GuP7xzGHREPl6sJD/o5d+IRnbzO3AduhEuuzde8Fo7x0RLO9TGlYf5W87KtGSScewv4JqnBfELlkr/l4CNcq1d879mXk+qJ1jVXlFRFlZLTBQsLiz22VgJQk/mUgSkD6MxbK3SKO0gzsLZiQwG78mYRKl3QXK9rFV6XKImPXvM9BrM+ydFlWrz3ogpDnyn5yeEyKys4YRJUyw7ZQ6LuNTVk1E0dm7orUJJBJpsYsgeGAVfR+bwYqLbZTOBt2O+mY7Z0EqAAWbYQ2GtZKgnlsebP4Qsra+yHBAOQNabiJoydNejDyRG3NoHrQuBL90K9qcxI/eEvtQFa4062gVudmJ+T56okELuPLnCSN/K5U1IWsZhlhfmzZeig7I+xTCJk81o12Vf5oM+SQoustWQXM8U0Wq7KC9fccFva3EWJfkOdy6ClZHN/DyvfsIoNje+pXcW16YrLbqKfYWZbWaeiCiM2giiIpXt00U5d3uBsU6VFRPbUssKiy1p0gPN+SpMZVlele3QSuZtaPtof8GPLz+anHjzPRTvh/tGCG9cuSdUZAHNX8znTV+CoR14L6plVgXSDCSKw1/q3DfnepGba8bFe3N6sCCQ1fJy4iv/90S/wlfMP8cd+/TMQP2jLnQgSGfTp9vIiO6bsTkRBLLEOhC5EzrnBi2WU3FEWqAS+1++COHZ57E5ZWeU134bR4ePxX+aI2b/IecV2h8JVoRGVHyuIHXyCBN5p06TyCTMIN7mgDZOUeXHFJ8VApELld66AJE7cDhMLBmaW+TwZswpy6h4ufy21Aveo4bhukuyxGSwz9k8GucbfDIMFLLjB/K8U1Eo5oFTRkzGgVdJ0S+NiAOquJOTk/7fb3AjcSqNj2l+nCtCFdv8HdlaWKQ70m12bCN6YMDN+iZImmc9hn89Aj6YRks+hgo2pSEZ7Ar1ocqSlUMYs5wv5HbVSUWWJaOe0QaOsPc+06mhTQCQUYMwhBagcn7gBaJWccqi396LdVotSd+Wz8YVtuE1VYVhVPOxxGSWxkshpanihu8FL6xPaGFhPauazFkF9V2oXuFldwiQhy5DRT7Rp4vZ4hW7/37252MCth0AuKWyoKJ7Keb5w8hrP1vf5/0xPtGlh5yRfAfuYluFKYwDXZIMYYJUliOVOY2xsuJxKk2WH+GL3ldErPMaxPm1HjX7vQ05pM9aKNdOtgdmlwLxqMwszMQtdAToO6w2TrAzZppplP6ETzzpqqMRBUCWJNd4DiZOwKrVJcFqzGOPG0gpbQmnYAiNPXwWvltIUrzxrrg/sYJUU2ggIKxnKV7X8GMJJasxzb7dOKRJChn1+cI5pBlCmzhUZ4NgjK2XGVscgGZzk29WXq8qqDmu+DmbxFo5lr7W/ZqikUFiJegx36FS1kVhqq0H6CF1+r/a+TWVjdQ3ARWry55NKreLJVjIM/mMm1zTQcPwZbaRW+xo0sKp2sSg0ghM2qS5rwcJv6SSwTXVp1G+TpqAv04SX+2uE6j6HvuNWqKhTz0XymWDRZV/eAZAEuFlfsE01F3FKL4EuBWZVx7avSM6x6mrt/45sVZq659p8zZOzC/X77RsO6y2L0PKO6X1qF3l6csZlnDDxPU81pzzf3OH56gFPB+HID0BWlMR52vDBbsIPX34ZP3f2LGftDI+w7BrOV1Okd/joIO02GFJSEPg0ztlI4gDP3G85mmx4zVkDn0G1AaoE6c1MipF2VK60IClWK/syQ+eGpF50bZq4mj9y8DL35x/l39/4bNrFk/THE/w24vsEfb7Cimmc6PotshOSUtYlW4dGrwlaH+Ec9tUsDRdbenINNDC69DljyHcar1XWfHnE8RjM+iTHmEbvvECVFMjKySFNFalCYl63zKuWg3qrMaQhsqgVyjVj9ohnFSvmvi2sHJtkNkn1yFvxTHzH1LVq2CtezRX9Wr2a8mRiYyxVA0XkGwZGFlBouzbpNTyseRrLQ5BsJIjW58ZasnrcY8CTFhM+fznNf0pPVj5/7DK2xn5Zphsfp4HY93ucBlJAqZGu3Iz7diJtJZUuySZ3aDpJTBmkhEOCSQl7yGwk1Z/DbvpHQEoHwxhz5nmli/u4CzNIQwNaaNyLB5z4NSe+LZToIImj7C2A10W/jZr20QX1FUjVlm2qCgtDxKkGHMrE7rL0UMHWVEDXaa3GiI3vaVNVzKGTKEhXhwhZbhiTZxY0iGDhE8d79FuAF/s1P719hg9tbvNTD57nV1+7RbupuXu54Khac6s659gvmfqKd9T3uHbjguXHGmTjSMGRghpYik3CoBO0d0Og1T4zazy5X9FJ91FY9zVb6fnXF1/A//D6F+DuNbjYa/fDZbN5P+o2jJ6nAFgpl6GJzIBxJAOwRsWoS6PnsY76UAk/XIhYnSOOFD3hcYHxeLzF2CnBMxuryL6CAlgKVEdqH8HHXMjo34BsBqzXfpe0OVK5WIAuGIEOebdV5XVIstxt4pSq3+/JDau9hkwvuYs+uo95Ztl+LVwB4YxVX+bNOAZ97HlgaOzpV06GCGgZut6prF27jK0xJuAwkGrw1Bpe0yTtMvp5uJ8xr8Z+WFGEfgSJmUdLJYOUcHhcVkljjDNjEQxyTPJ7jCgbqrC4imlv2jnP4MpzGCi5koap9CrFyRtHJ8Ikm/njenp0LatdJPpe2aO+pxefj00yeGiAlP2/4J1medh15BxUIVH7nOibrxeTF1papiU2SgZTGxdpnDBx1UMSw/PU81I85H5/wEvrE+4tF8TOs2obJr5nMdsylZbKwbFfMZtvac/mygDORYSL+eDG4NWbMbPegqXlkqjVhCQ+sL3Fry2fwK0DTlJea4aOdRnjiw/1SrEOuK4ruTLK60QplOzn4WO+4oAY1poRW0vQoutt2JA8Hr/JxtR5npyc86vhFkAxfF80LXWIrLu62FVUWYa+7CdUPnI9rNhmeSFAcp5FtWUW2rLXnPuWabbNMJ+s0gDNRvDqYaUJ6l0uMc/TtEgPkQrvBk9fe56GIfm6E8/c9ayyGqV2kSbP/hupID9m6nplaDHMlcU2BUeNejGNCPT5vjr2gaVWJHtqDY35fTWJpR9qI9znv/U7apMuV/w79cxo7dHH7L52l+eEKKgBu9UkDjbimDphk5sTY/N2M8OvXSqkAwOp1F8sspRKwwFc5CKDiONEdjuvSbwmwyKlBjHLlMLKc1oPqAWOmr2fhFUBuhKOs37O3LcFEG0lcJpmTGPk6ZA48SpfnKYtd9Kk2KpYmBmggVkyybWSK/L51imT+KAWYvK0faDOYVc3D5Y8uzgtYQOLquXJyTnvmb3I9XCpAKpLHPoNN/ya6z5SO5WHztyk1DZb6Xi53/LT22f4oQfv4UPnN9n0FW0fOJpuudhM2G5r3DZokzvPz1Jpch/AOjbc6Q/L+2lIHNTb0mwQU6M4cL3WLG+LMXtFQ1/mU+LxjH5eIx62Jwo0bqXjb9z7En7y3vO8fPeE6t2e5e0poYXmXDh4uaM+3eJiBtPC0IghDv7fetwAw3qlxMChue+SqKVLyMtcBrhcHDVaRucsBTTF0F7P7lK6Oo82HoNZn+RoXByKiDoSe0/yar4YQlKqbdXTZL+SXhTIqpyab1feKJSJbapoU1XS7myYrMCom43rM2IuLMKaTWpKH6IpKYMmZasyWKWeKOahcdWwyW0Mfpl/FgwFxNQ9XIBY9yGhbKaIdons93pvQziWZhQgK5cvY0B6/z5jA15LA6n3mEJjw3hLAulyZC1QyLHL8gIa09tkXXsrwlYGDbolgLR5ITE2W+loSMjnPRUgS1/blQV7bEZphonbNJjHv7t5nUPX05hBs/gSZRyT+nFNXUcKmyJPnPhe484rdszbx0kfSZSVBQZyKQ3XNOMwdPLGhUOXvLIJq56b9SW3qzMOnX9IsnOW1vzH9fP87PJ5Xlyd8OG7N9jeneFbz+Wq4idmz/P585e5HT7M3Ak3fMu7rt3jZ2c3qC/J8bRjBH9v8nqTNI+HxrgIyQWGd47Pn77I6mbDLx8/PWJPCS7l0tRTEjzy5bArG4SdSXanA25dFhlN4nmi3mkFWgcpaUel/N09PIk/Ho/HVcOaDs4JIWSrhPz9tgj2kIGDyqcCZHnUjLeYvTuhz/5Y47keKICIAfMhA1QeqLyCHgZghVF3W0EKX/yuDBZKb7DWjP2ybCjUMYBUQPGGGg+rzwXFumXv9/38ofGUYsWESf7spvGrFLDL+i5O1xHn1Gh+PPR1B/BLkLzm2OvoaAuwrfcO+XUi0MvQjVbgKYNW+RgM1HN59xdcyuvQcCypnP+0YwkgWVoSxet8CNwIS5rMhjBWVuVi+byCJCoSE9eT8ua+c4neJfX7iMPO1nv9zJL4nenX5RNi1+Tw2eUQg50p3RU24Ty0HPgNDQNzzsZGOl7oT3ilPeG8m/JgNScuK1x0tJ3nxfqEW/UFB/V9agczF7k2W/FKPSe0e+vMQ586ewATVzZJrryzUDxbblXndPPA3cnhYOYr5DI2S3kzu3f8NHb9PnQcMmpW24ndv2hlb+kUKPIQYe+JH49P59G4ilnoNB00M7IsEKjPqW+ToKoQj7BNQe/vhJNqxdZXbFPNotLAoonvWSUFip9ozgfvJN9lSxNfgAYL/bE5wxK+U77uVZVQZc9YlR36zOIxK5SaQY1iVijT3GwxL70paoditigTIh2atlo7BT6mTlNNPWqSbnJDG1arhJHkMKDN7LF0fCxFtPrBapnxbcbmGo8dL+Dc2LfkQf37wLKyY29lmLtjfs91acDrPG+WJK1Zm7hOva9y86gbrwvisxwyywqzNHHMgpq6yEVqRgBYvcPeMkaWqU+i81ykWfmMV2TPZnxW/0Si89S+3/FJCwh34oJDf85TfoJHa4yOltOkcsMmRGrXc6c/YptqVqnhtJvRp6D/iXpfzSutkWeVVnPTquOZ6SnPT++VOnnqWp6pH/B8dcaxV4DSZ6DS46ldU+qZfVnh/bjlA91N/n+nn8/Pvv5MrqVcsWPp+kBcVfguW5DUgguOVAtpovLebQoZ2B3WkJRrAP1P8B34Xv2rtH5QQEviFTztN2voA3K0YPPMIcsna+IUEGhPNESuIvB7Dt/PZ09f5f8Vfgs/f/EZxEnA97B6CrpFw7UPCvV5W2SGzqPrVG76W4PExWH9clEbXEW5Mmq6iM/1ndm5+KF20nVwYEm76JA67TKOk0PiG62/D4/HYNYnOXwuElTGJSXe0pJDmipyULfZGyvRx4raKx33sptQeaXU9xLUJHFk2muTvJmbgiUV6YQx9a0a8YVBTqi7Gr8DWKmkYOhuKDXVTGNjkRyqiWJfgJqGVLoeMBgI6s/6bw0PdR6soLBuCEULvruZGtN5bYIx4MnvPKcxnlLpfph+HVE6rz0eBgALyLp1lRYag8ySCIf0LMnPKWWR28iQjxWLFNPv6PNDLjMMyIpSZ48slRkqey0Vb7MOZc1t9owtP95d59CvOWkeMAWWLtKhiVVGwTajZkv7CC5xEabMpSV1jlml5omrrs7XpRQw0Dmh74Oa9XphWvXqdyNewTCGWNpNX+GTUsWPJhueX9zjPbMXeb5qOdpjZV2mDT++OeFHTj+Xs27Kx86vsTqdUZ0HfIS0Dbx+/4iP3L7F505e5unQMnfwWYs7/PTBu5B7o+knb9LfEoi/KvGvVFDDbS5RpJNPhwuebe7j68jOlGdSw5S0E+F2J2ObuK1rb/Ido9qqKXw2b89FkkoR9WcD6qzYIOX1SEaR7OLAp8d1xuPxlsPkgS4jBS4DriGk7JeXaLJRqgEMxqhqUyhmvva9H7OxlAGVQZN8MVpqoUnTNJlwkBPiVJo43naZjNDmrV1mkRTvLAOyzMXLDHxt7Bu/69+GzroxqMZAlmPYc+1/oSyJUL1MXHm8gVT6/APbKskgK4yS4TtxRJcekiiO0w9NWmi+V5YtYcfY58Pqx38bwTZ2rhJjWaZ5VhqAKCQCPep55fK5FCcjbzOfmXTDfB3FcxZnTFzHzbDBQ45vh370PHadeRK1VybrNum+RRJUXjeuXRz5t5lsACElX+Ssyv5QwMwA0jokRLQBoyAdTELPSbPiyeqcEx+ZuHqnudJKz4v9lBc2N9nEirPtlG5T4bc5Qjw6lqsJDw7m3KwuOCRSO7her3i5Schqvw3z0CXy8HhTRtZorRFlMwIc+pajsMaFPWRMMpdP3uB5c/PDjZnCjBoqJjUcAawIg0RxB6RjSDe0q6z8/Hit+XQeicRzk3vcmK24u1owqztEXEkqtPrioNpS+cgke2ad1Cs6Cfn7qsqQLoUsF6s4CNss/eu4SFOOUe8+Sy+cug6z1pi77U5zvRWdey7StABe3iVqGbxmQffC18NmR81QZ8Zpk1lH+/5Yje2zRTj0fVmLDMgyZpOxVMdhVZss6zOj99bk6TKoNPZlfuSfx8147xwrieX+MKhV7PXGLKyNqMR74ROr5AtIUqSSWRpo6Y1Wd1iCeco1hPnqWpgUaBqg1Qv7YylNYb9Brl9cIo5Cv+zzsVomimeZyRLtqFG/TBMWfstpnOOdFHArZXAyuMQqTnKdOgRWHfpNeY6Jq5hQMa+Es7ThzG+5lyZFvWIJycEJPbCoWp6annGjXvIZk7ss/Baf66sTv+LJsGbqBjHHoQ/MXUPtDh46F+NhgSLmtWyWNYd+w63mkqPplvPNhLavuDZfc7FtaNsKOq9p7HmvHxshNYLMErNJR5sqDsIm97T1/a/6RuuDNAA7Zl+iUqCU1SCPMMaN/eDpj6a0h4FUQ8xGbi5lINPBlzYb3l29wI/PP4ufr96BBK1gxcHmBnRHFdWqx7XqliqZSGDqlHGX0NYlJ1LAa6uHUj4mC8NKja5dTtQLEkcJvJJRXLGLDqnyE0dleb0dmeGju2sBf/2v/3W+/Mu/nMPDQ5544gm+4Ru+gQ9+8IM79xERvuM7voOnn36a2WzGV3/1V/P+979/5z7b7ZZv/dZv5ebNmywWC77+67+eF1988e0cym+Y0RAVSPBJKfV1pGl6TZTzUtgtoMlxh/WWJptsVz4VjxIYfJhMLriKE1ap2enA7vtimaTQkg/HmnRbUEx/DtDilaY7fo7RLsgmUGMBNC7R5E7wmKUFJr97+GJLKPspZppu0Yvjdqi45b57HY3a+Z3OSCdp53cDpkA341tRAGwrfflPk0E0BWSTDd07tEuzyb4f4/QPXcw8q5zyYeekcRp9vpXAKtU7584maDNFNBacJRMaddbuN04EsWGdi493N7gTGzrgxA/pkfq4noXfMs9m/vZZznzLQZauTkNOAsn+OMGp7COlIRXCOVhMWuZ1S5uqsgG/Nl1pty70TELkoNlye3HO5xy+xlce/jpf1LzKjRGQ1Unk9bjkRzYnfN+DL+HO9oAH2zlnlzPcMpRkj7CFfllzv1twLx7QZXr2U80ZTOOut8d4PArVdkzbG8tDsElVaPuKJML/cPke/h8f+x3Ig2boDKTRY023Pn5Z+9m7vfhcdo0bR5p3k7GUDoY956jaLowtcQXcKpHsjwfweJ15oxHQ77ZzqYSKGPvXJMXGgqmcduANIDIgy4bNCSoXDHSpKv6PNqo9uXlhZDlj4gybd7vNo+buoPNiv7fFGMsSzRfL5Z9tfbGvzHilMKBqf9jXSfHjARRyPMzs0YbjXkPFDYl6xefKvs8MwNT49ihCL6n8l9DfO4n0uZmTUOCqx4zWh2ORfG46HL3Ck3osGdxTRq/fOXeDV5au60m0daMphDnBkCF50ECtimFiMfDwLM5ZJr3HNJsDG3hoxYEVRfb42kUar/9VXllcdk0ZwGoSVvvA6iqWxl3Z21TqpaPgq5pOH9Rbbk4uebZ5wJPhktkIyIqSWKaWj/ZTfmX9FMu+YRNrtm0NrcdFcB24HlIXWMeGdWqImQ1+GDZqLvvoe+KHx5XrUV5rRG+PSZlpv9Y+wc+ePYeswyidcPSYN34qLYr2AKnx/a3zbQ8Z22Cxd9+d5x7/h7v6fo/Hp8XweG6ESz7z4C4n0zWLuuVosuGo2bDI+8Rp6HYM3kGBpLlvOQwbDsKWSQmK0kb6NlWcxRneCcdhXUKNrMaos1ok4VmJAhL2n6kKzD92mSY7ViYRzzTva4sXlAxrTe0Sy9G+25LWgdJsr10qTWhQwEgBKf3vcNyIz5PB1DmmzrMUIwpY3WTHlT0oRzVMHNUhNtS+RFjKKCkdqzWEi5TT0EWZWFE0VXaZfPGwWqWaVap3mFXqI1aVesQALqsnDMhaZgCoBEwxSAJbQvHo7SRwnoZk8o3UrHISbEvgXCaZgVVlr0Xz9W24iLPcoEos04RpTp3ssgx0sMpRFrCtJ2dxVnyxTvyaueuVkSaRhBCcmpNf8zOeDBU3/JaF3/JM/YBnm3s8N7nHFyxe4suPPsI33Hgf/8cbP8Y3XftZ/neLV/gD8zP+wPyS3zdb81ungXfWBzxVHfBs/u/Yz4rdjH52CUtdX6W2/HcvrXmQ1lymDVvpuUg9dT7mm/UFX3HzBd59/S6fce0BB81WA7da1dCJh1QJ/UzoTyLpMFItOqoweJVNnKZdbqRi3de4ftx0l4GZVWoDgfQ24s29B++RypMqN2q0C751LHOi4v/9/hfxzR/5I/y7F96tMvhkgBRIJfrYLLkXhwJZ/agwGa+h+Xsgo/q/sLVEiu2KrWHeNkVOayZlbFGYbd4o9ns9oLezbr8tZtaP/MiP8C3f8i18+Zd/OX3f85f/8l/ma77ma/jABz7AYrEA4Du/8zv5W3/rb/Hd3/3dfPZnfzZ/9a/+VX7f7/t9fPCDH+TwUPWj3/7t3873f//38y//5b/kxo0b/Pk//+f5g3/wD/K+972PEPZFAr+xR+0GG9vgEz5Teid1T50pvm0MTIMmNqTomAb1KzJjOtvAnnZzFtW2MLKMemvjyK+JKGtmB7CS7M/EQA3tpMLM4YHM8skSwr3n3YyQedDCxzbUY9NFe01LB9lqy5DpHu12bHao52gAp3x2h41ISQXR403lS9/tFVEKaEnpoIx17tb1MKPFolEfLSp6nhT4tijbUlyIbuZb8UMHxA3nwijAlgQ57mqM5ZyDMeIg4yySw9Hm0SjWZo6o8tKaCzflTjxk6k55MiSmIXLqIhdZrx4QNjRK68ZRu8hRtWGShgVsE2uoFBQ1hpaNEBLzScvtxUWRnM5Cx2G14ajacD6Zsk0Khl5vlrxr+jr/q8nH+cx6w42RKeJWOj7U9fxP63fzH+5/DqftjC4FzjdT2mVD2Hp8n1F5HHSO++2cO/0RS3md667hVnVONYkDOGTgj9Fs94GdsdTQ0jx2Jry9otWrxCImR+08/4fjX+R3LX6FP3H5J+hnh6Qm4LIMYzxcTGramzf9ux4mxriSh7sFowlbIrhAkXeMmVrWwRmKC0sz9J9csfVpNh6vM1cPk+Ep+JNhg8J+yfI0cWqcna/Tyuc01KDeJCbk26Sa2vej+W5XEDhxHcag2gesyH8b/EdGCXooy8eYV/vP24vLwFQGA3JBrlKVXcAHIO/NlNHkhg3LwIayc5P/HYFTLlNWJIMtvsi8hcop2BX3JhthYBbb3tKGQxlVJlEs5yBPWWNcIYlJBffCJfK6YcwrtVKSfN4GNlbF4IG1i1k49bfK5zCVaXHkcVHORcoFaxikRhLYIqykoZINB07TKzckjXB36qnU509Om1gqNwqjplOf09AEVxha5T16oQ49B/W2+DfWPtL4nonvS+iIiGMWOq5VS25XZ1zz/Q6Q1Uvkfkp8rLvBR9c32MSaJI5tXxHbgIuDZEM/WFjHWjfvLJkRmPstPiinbQwGKSt393yV8+eG+7yVX5a4zHcS3e980eQ1nq/v8t+1X6yJUUHpkw+tNUVqKMOXwNYaZ53vN3798bHLzjGP2Fww+sGpvN0O+PH4tBy109S0d0zv00vgfjsvIVPJeyZeQao2VZy2Mxrf01WBRbXV+UZsn1dluwvH9WZZpFtnccbct5qWnr1cF75lmSYF2FIAC+ZuS4eqFICiYBjvnYEMhugeu0ab59ZoNuAHlKW1RRnGc9ftsHmjOGonTA3cQr2yfCaUmLm7J6cEyjBjG4hVmFwMPlnWbB/7+NrfE9kUXrQGirlhHmUgzJgKpBM9JgPpulEdYnVAEs8y247gIpvUFPlmJyEbww9KDTNNn7qukBo6qdjkc2sWJmQfKgOojNm1WwPWBYiy51mO7GsmGTgrRIt0sNOo9y5xGacDKys1BJeY+5Ynq7McEKCJi1o79kxyGt9W9NqauJrnqprr/oKOc2XKYaBjYOYagptfed132Xjf4x7y9I2S6Imskr5ORM32O4HT1LCUBTf8GnKdOfe6L7hIU2oXeefkDl8we5HGRT7eXecH4+dzeT5Tv91JDnKpBDfvmc4V5GtyuNuhX1MT6CRynhacrqe4XCu4mOuf7JflYsKllMGsDBZJNnkfj1ENJNkuxYbLa4Gtc050f+ZxfO3hL/Ke2cf5bvfb+Z9ff2cBz6TKy0LMr9snXNCNlzZMpLDJUm2N97w2lXVU1xfx2Rsrl5+qThkaKFYLuR711Nq3mUn6nPr+xgvZW4+3BWb9wA/8wM7v/+Sf/BOeeOIJ3ve+9/E7f+fvRET423/7b/OX//Jf5g/9oT8EwD/9p/+UJ598ku/93u/lm7/5mzk7O+Mf/+N/zD/7Z/+M3/t7fy8A3/M938M73vEOfviHf5iv/dqvfTuH9L/4mDvdtFlH3ICsadWXNDiAxqsZeOUjh3nhsE5p43XruHbKufMIB2FTKJ/AYAafP1yTrtlIY/8T8bpouN2FY9C1p51Ui/F92pGZIAzeJvZ7yIuEHucgJ7ShbCwzUFd20PiCLOkgDGmGevxCR9zRnkcZCikzZYQBoDJvLoCNUCbnzYi1E9xAMd5ITgs0EArd2FknJIkmpjQyFBJ1lpFGu41UAL5OQgGxfO5StblLocfvNNUFKZpzPzpbneRvc/75NM7VXNGdcz0ETnyipuUsCRtEZaWppXMBfC5WUL+T2qUiN03imARPH0PxLJk1HbcXFzw5O6dPgU48z83u81xzj8OwLiaPT1QX3A7nHPuO6yFw4GaFfvsgrflAt+AHz7+UXzh7hvtrpRh3yXOxmminPG+yxWV6qYdNX3MW5+W6mfstddOPNNWUDbZ2FN6CpmQabJvoc9EhTY3MJ0hT0R7WNJWGA3ygPeQHz9/DdlNz/lxge3RAvRYmp/3DpodW8Fg6YZKi43AWV8sIpCIfg9N5mL3rvbCwSicj/132/n08yni8zlw9apdowsC+MSDL/LFsaHqgzg8TP9TnJvVS/5H8FJh0fQCjAll+mF9m37dKRgWGN7CIQTaoz2uSNUthHNhWuoeyNMJBvm73sXncMczdb/TVGp4vg0Kj82BSQck/+zJvQMwQnDVZZHi7BJTZNX61OPqtlwFb70cH5N1AGO1zg6p4V6HA3OBllaXuJl+G7IdGbvbsmvNGBhaFnXEFv6y4ctnIXyVvJj8cjt+XN5BwrFNNRaL2W2Ze07w8ka0IvZMiKS2fpxMkVVnu7ql8pE/KowveaSPZqTSxCmp0e1BvtVsvjuNqzXFY0/iugHEHfsuB2zJxiZl3NNTFr2YjHXdSw4e2T/Ha5kg72qjMZttVEN0O81X0Q6NPWqjF/PfGRXyViqJ7pw8hgpO3WGvsruw1pkOAOiAhEBtP8Cr/vBMnfGj7BH0f2B47+klD6ISwSYRNzBfa6AJitGcX+8UWw+F2DNzKYPDOOnLVmrJzsI/0Fh+PT5Nx4tc8WZ2xndR4hGVsuLddlNuTOLaxyj6KgdpvWPYTqMgsz6Hy1L2vzjtz35LEZcCkVhDffHmzV9LCbxW0Es+pzLOpd0vEFfnYMqcbhgzgAGVvbfYewQ0MpYY0pBS63X9tjlW7EFVSeGBqYBJkBuoAQll9YR5Kk9GXpRi+izZGLlIsUsGx96+lnFsTvZUxg5cyh+t9M1PJaXiUvS97DzZqFyETGYx8sJGahoHYcC7qLn6RpjuPbVxkmSasZJItTgYPs1VmbjUucp40mVwloRVT35a/GSNLjznoZ5A8TTbzD45SJ5iP1ipOSlgZgPlzrqQBPNfqC25XZxy5bZZRev2M3AA6mT2MSfGuhasBq/0RMyu6k0hHpCYUgMxu30rPSjpWItyPNffSvBjXb6TmxfZGWWtf2p5w3s/4goOXCAgf3dxgnRpOqhVfvHiB56r7PF0/YP5Uy3+cvptfu3+T1UY/j7ruc2NaP/Np1XO9WfGu5nVqF7hMW+7EIy6WU+pegSPfG6gluD4nGcYEfQRJ2ny4apiPFewCXUWZwgAe9QNA+Vl15Lp/nRuTFbYdKH0UMo5dvBxtD5a9O/OffZRsXD9avyKAhmPZ87gsgXQxEz+afLw9Q7K7Q43fbZ3rUUQqZf+st9l4+aQ8s87OzgC4fv06AB/5yEd49dVX+Zqv+Zpyn8lkwu/6Xb+L9773vXzzN38z73vf++i6buc+Tz/9NO95z3t473vfe2WRsd1u2W6HlIPz8/NP5rA/pWPhE0f1hioMhou1+V5lD5NpUEDKJu6TekVEo25nvi3dEu+EeWjVEwnhMKyLZ5ZNFrWLxTvJkvTyEsTc6TlSlL8uUgObCAPCxDaoLhWzRd1UK1hTdNOkktpnXfQaKeBQAiZ2gTMAWsaeQoSp81eaKY4pujaKrGB0Wzf+MmcDxU32tdLblbZri0NJTRwBhbYIRnElGcUWmTqnpozjf3UBqcp51168jnECSFcWHQ0BNm+AgM8LlgJXSQb4KorP4NlIE+4SE9+VFJGI47U4Y+rWXA8TJq4HOmUZETkJK4ITTuOcVZxw2U/Ypir7CagB87zS5MxJ6Ak+cdRs+Iz5fZ5qTgGVOd6uz3h3fYdDHwsoWTvH3NXUrgKqQtG1iNpfag/5706/lJ947XnaPtAnz+F0y6atadc1bquyD6m0uIhToNHUM+3U5PPoVBZF7qpbl8J3OqmXDX/au07MAFFkd5JzDplNaJ85Znm7oZ86+rnjMxYXhVI+Dy23rl3wyrumbC48Ljqas4brv+KY3NsOXYnxJC2qAYfcRXe5m+JAvLVA8qFZU3PcGRn9bL/qE6O+WSKaFjaSLT4eD4//XOsM/MZeaxonZa0AZdoYAGIFgMnWDeSYeo1c73Ozw757TpSlW2fvLGuoRHxZN0x+ZqbitikSl4pkRJk3A3BiTCw7HmH4ShXYK2/07X6Dd9bg12X7KvtqVKP6fgw6GZBV4XYD6ZCdf8ejZETsgVU2fH6c+VrBsObZ2jGWe9kxhdH601lYSL5/SWRiuI9jYLElHNUIWHGj+xg7KruclWRAnwEtA640gZLh83fD8Vq6beVivp+uS0upqKRn5rSjTsrBASRmrsMjbKjpUk2bGVUxM0pdlgDVPlK5Sht5oeekXnMQ1BulcpFDv+V6WDLJbELby9Yu5ELGl8ImibCVntfjhF/ePMWLyxNiUonNpIr0MRA7DzlowzblUgFB8rkPZU9eGIwyakDI0BUvE/N+4fAmrCypAvFoSncQSJUj1XBcbzFXrtpHFrMtF9cqQqtdjrANzO46qlU/vObo6e0YJVG8ScaNoZ3XH4NyI8BrHwsbbs9fDGuqPF5rPm1HYphDPMI61py1U0127qvSfHQoeH7R6sWyrari/3sQtrk5kuVueU0xyaE12DupmGfWzyZp8va4mWs1yw7jh1BYXVO6YsVhY+oiWwlMGNQjrXimLtHhCuClr6/yxHYka7N6ZSXqleVRBYndHgDc1Ubv1lgPo33dcF6HBr0xvazRQpn39+4/avpoTUB+finHbyFSO3UCqgKx890Syno7dZ0ysnLtUBO5SLPymVhT/chtsjG8yS5rlslsaKSsDcs0KbePw6s2qWbqNV1QWVu+qEmOg/qrJTzzsC0+WXUGMUHDqY6rFe+evMqNsOTYdwXIuhkCx35WXmuQAj46W96ArJT/VxMIztETWaeWi9Rzmjwvx0Ne6p7mbn/E3e6Abao4qjYcVysO/YaI41cudZ25v57z5MEFs9Cy7Ce8tjmkT56n5jUT/wwvhJtMXc9hWPO/ufnz3D854NfWT3DazUjiWfU1ryyPqH3i1uyS25Mz3lGt8MzZSOK17pjufMIkd8ZcUvN33+VmuqUZxvhw7TMe+yFY3lMYUjAwe3Odokxw4W/d+y385L3neeX8SIOqoGxeCmu5sKLyvmH8OtY99ArAKRHB9oQuvx9Ild43IPQTV+TwLhlglo9vn84+1s2Le/NzcMX4hMEsEeHP/bk/x+/4Hb+D97znPQC8+uqrADz55JM7933yySd54YUXyn2apuHatWsP3ccevz/++l//6/yVv/JXPtFD/U86PPD05JR5fTunp0lJCnHZE2JetXmhSKpH9z0RpfzahD/oa3vmvh0ZtPc7Rnf7HVvb2KruelIWF48m60XXZbBCgSz1O9HnGC8AVw31f4qZ/ivUTq9nI6wElHY7ZlONpSJjb6vx2Jn0R7ePqb3D35QGPH6eVT5XSiXWTocfdXKss2TG9zbGGnLtgsDK0gZ52DQx4gbabx7j+zSk4lXWZhPGndQPfKHp6vN5uhR2pIIJR5e0s1Xo1KXL4TlwE6ZV5DC1HPpT7sdp8UYbC3gmPvLc4j436iXPNvdLguLCb7kdzrgeBuPFEw8HrmbidCGzQiJaxyozsez3rfSjJJdIE2KJwwVYb2ukVdN3nBohioc4T1TTgVlYzoN4+j6oxNSKiyTFj8o9SkTtzq7fkY5mbK/VdHNHaiCNjAW/ZLLkyfCz/OryCV5ubpIqcAG2J8L6RkV93hNiXyZzkwiOdzRl8s3VmBVGdnkZKBdHMsIyiedoT5fvJ8khIWVKrUOiz4XVo7EE/ksa/znXGfiNvdY44LDaUIcDlY2hzCwH4BU8qrOfkXOiibdOGUgmNYEB6LJueizzZqLJ4jhLzNthW2XJmAEG9p3WFDzdRtu6YkBYthJ9iDzy8HsTqgw9qRdjxipy88RKK8cgARlvfQW5csoY/2l8+ziBcHzffabxmIVsviVutM8yM35jStlIGWSygiZizYbEOLVxfP+WIXTEzokNk9SoT6wvTK2IL15VY/8KZb4NMkczYTc/k8EjS/JrORoqTTuTyEQia6nUCyqNP0H1WZs2a+a+4yisy3tqXOTAb5iNvFumDhoXCHmrafuDse1AEhkVlamAjLomJiJhKApjAJsvgZQ7vKkWfGWMi+ETtG65H3UTdjbuMrrhjcYY2HIOmdTEaSDWjhG5GoDbVcvCv8K9dsFFmJcmSJxCP/OErS8+JCZj10Vib63ZeX12wK+BqTUcevGfHINk5XEyrEnJDe/98fi0G51ElqJ7xFVq6MRz0U3Z9pWyqpIvipHgE9OqL0EMp92MWehK/TFxCkiYR2vKMrW51wCiKZpoaEl2rVTUouwsW1PUk0m/+9aEB5U+dXkvfug3HLqOlVR4VPa+lcDERZrCxMrNZIG5y+Lwvf26JoEP0r46fzkOM7LdZguSSbY9SdmexOTiXVZkGLClKhJKA91eTVMPdW0qAJUb1F+JIQndACv72Wq3uetZ5X28vj9VV2yy1xXZZmaKNtftb93Ih6yVYV68iLPCjDvxF+U1j9yWcyalPtFEyaGhb010S5lMDE0v82L0krLUvMNLylYpgVVq9FphN1l34js+Z/oK767vMHex4C4Ll7juKw78Lqvs7Q5jXCVS9kXOa4fAy3HOvXjM/XiAJ3GRZpz1c17YXKdPgZdWx1Q+8fziPu9ZqH/qh89v8vr5Ad5r4uc2VVz0WhuJOPUDbqflfB01a941v8uzzX1+2+GHiHhebK9z1s9oQmQRWk7qFc8295lngO1CHK+0x7ith1wrKJClnlm+T7gYcX1Euu5hVtZ+qnse+zJDYAj/yFJEDWdwfOPxz/A7D36F7z/9Ev7t61+CdYIcFDmg61Mu8tG1Lnto7Ty/HYvoe9DQOwW0iubd6fP5CCOB2vAFcfr1DW3ez1UKvIljMEGtQdqrsYmrxicMZv3ZP/tn+YVf+AV+7Md+7KHb9jWeV+o+98ab3ecv/aW/xJ/7c3+u/H5+fs473vGOT+CoP/Vj6hzvnNzhucMnuJ+pvCb3ApUXTkJPl6NFCYpaj4eZ9dUu6gQh26KDNiTdonCX0rCRGk9Sbw+XCm00ZUaQUXgNEJtm7TsMxooWYWtxtzYsqjWMfKNsQ96JLSoPf69CpuKm0e/28+4r5NcpG0Qp7JmxbBAGn6sxQ2tsoLiSUHy9UhrLLAE3eIHZ+/UZfTBm1UompStkwFdkkAiaaSIM3RRkALQ2qS70aTv3y2yoWJ5XfKH6WkET8+RvxeJlnHAQtnkhWnPo1TVMP9HAxNVUPjB3PTVrNlJTu56LNOOpuia4xPVwyfP1fW6HyIGr8+eh3fvgAjDQzHc/h1T05mrKqCaWqxSZOk/tPBvRszL3W56b3CfddLyyOaZNgbvrA/qugt6VoiJVINOIm0Wmszb78qRC9V6lCX2nYJaZBfqePJEKpEy3fVRwJ3hSE0qCByiAdNFNiAj/fn2Lf33nt/Dzrz2ti0m+RAgQJ0P3QSdSeXjyFp2fssHN7m3JmFoMoFxO4rDHJgeuzzG0KXt1iUOcLRzyUPf98dDxn3Odgd/Ya00FXAtLjpsF66h0/3GASHAqObR1Axd3bgeKX5PPc2AtsbB1BEdyKlmocke4l4D5PDknJeFQ8jxc5d9t3qzcACoNrKTdVEMbCrIMwIo9xuWiJeSv2/7HZZZ5Mvrdfr7qk7ViBUYJhaPH2LGoz9Xu48q5ykBWzCBRAYPyvGBeYPo5DGCVx6QwoZipW+KjAURpdD7tMTZMUtpLyOuhL/dtpcL8tUA33j6v+5KPcwxqOdQrp/F9NmXumbiYcQ69R5XZUrVL+NTTu5YQElup6L3K1ueu5SSsOXCJxoX8bvR/3nlg1EkYn0+R0iDR96wFZSdChdPOuujZqV3PcVgjc8dFr0yEVd+Qoi+bYQnaNKESqBJVHQcGWn7NVgIp+nJOCzvLKH/GAH5UcMc5JHgF0UYXSpsqEsJHuwXvXz3Na5eHyh7LQ5x2rHeUE2PgyZ4eA1qvWGv2miRXgVylGPDK/sUAMwO0/OO15lHHj/7oj/I3/+bf5H3vex+vvPIK/+bf/Bu+4Ru+odwuIvyVv/JX+If/8B/y4MEDvvIrv5K/+3f/Ll/wBV9Q7rPdbvkLf+Ev8C/+xb9gvV7ze37P7+Hv/b2/x7PPPlvu8+DBA77t276N7/u+7wPg67/+6/mu7/ouTk5O3vYxjw2vp77jpF7zGkekLPXuYyD4RExeG5JZor7qG5qmZ5aDhOa+1XokTqiryKHfFEsNa8qC1jrBCUFSqWM2qS6AyDJpvWL7YfsmjhMP9faBiTV1cefatyZ6YesCCLTZJ+vQJzqhJNmN0wsDw3w6HS0kBqobm3IrScEuVFEC1rjXlF/zb/KQvasGlhfAwnmi07msw3x+fWnoaCqhyimTeLaj1ce77LObmWYGDI2b6HreVeJpdiarNNEkd5Nc4pj7LcdebWzuxAVLadTPLLOnjH2t5y9LCTOQpdY1sXxeyrIbvHenrs3NicjGiYJbLjHPZvBJPMdhyRdMXubJ0CrzN3kWLnHoPHNfM3G7Xr5XjTja91tjw6PsopW0rJKCQ6tUUTvPaZpwkWbciwe83J2wTTXvnNzhJCxJvedj8QYX3ZRe9Lrd9DXrac1r3TFT37GNgaqK1DmMxADC65MldzcHvHRxzGpbE6MnhMSk7vn15iazuuOp+RlPTc9LA+Vms2RRbVW14jdcJCHQ8Wpc8OHLm1SX2VO4HcAs16csNUzKyur7h0/KfsG9r07JjRmXdEEwHyqrQ4Lz3AoJuGTuW605AiWN0WoXst+wYlLDzw6XGV+DzFC8ywb4udlp0sJEWR9lvDkr7yXfZuthrpEAqDSV2RY5TQV+tPEJgVnf+q3fyvd93/fxoz/6ozuT8u3btwHtij/11FPl76+//nrpot++fZu2bXnw4MFO1/z111/nt/2233bl600mEyaTySdyqP/JR+00PeRd87vUTlM/gGKsW/vIOtas+oZ51TILHZ10xbskoQwdPFyvlqOEPJ0Il2lSuhlLRufAMVogrHvRl8fG3AVQFs8oPS8zs8YAVcjw7JS4I8uz+5sOHQbQVm9zeYEZjNwZdTpMf27/GigVnNsxUhxf7fsU3rG0Y2ygaMbstjjYJt4WhpR8OZfjToQ+1nTfA126lYH+bImEtniMH6sSzL48Ro/Rs0l1fmzKFF49Y9tU4/MEbse5itMs1/BsJDDxPderS66Hy7Kod2JGxQPIVLvA09WEE39J5IJu1GmauoqK5iHzw04iSSJXGSNupaOTyEoirQgr0Wjg06S02bnfskoK+B36ljvxkIjjM2d3+NKDFwD4yYvP5P5qxsUmDBNSnajnHVWtC8Q0dByHtZplErnTH5G2AVdMEHN3ImqHQqtKpdo+CkBhwzruJh+xDuSXTl7l8Mn/yHd1v5dfePFQpYNagekkLgqgOetCGNhkF7uMCok86ctbHJMTfW+qH0cjZx1DIWHr9bjb/njsjP/c6wz8xl5rvHPMfcv1esWZmxUPJcNZg0sap54CyStIVWUwxupfi+Oe+a4ALoJ5GFaYIXo7fl1ULgYDW8iXmVuBEo0N16378DjZAc8MqMI5qhE7aAx6hREHaFzrG4A1NnIflf35maX8G0ePG5NWxmtNaQIOfYodgMyOq8cXaaS9QgHdMljkRu9ljItYIqGdL2tmOIQ+M7HU7J3MbhsVXAhetKVRJJ5Y0mF46PxF8VjuloE6MYUCaPV4KpeY+ZaZbzMQmdUN+WWjKMzkcRz6wNS1CFsixphzVHg8Ae92t48xp3kpqLU7P/Y5vaoTbcJ1oudmI7UaxOdglMpF9X+RCQnHtXpZ5PEvttdYdxVt3wyfYhB8nfA51bPyUZPYUBPfVZogY6P4vOF3tvE3QEvsCnh7aw1iQKg+7qnqkmbxAj8ZP5PXzie7KKvVH3aBWLGQQ3HKlzTfT+zY3mqtATCW8Oj1HvJnfLzWvK2xXC75oi/6Iv7kn/yT/OE//Icfuv1TFULyR//oH+XFF18sXpHf9E3fxB/7Y3+M7//+73/bx6yhRypBu1YtqV3ko9UN2hToMpBV5SRcvb+jS4Fp6Jn4WBrtHgUrLOW2k7CjEGklcJFmtKIG8HXos+eSK2DJRlSqBlqrTF1Hl6uHsRl8yvvgkPf3YxP4MLpYt6LWJgZqhdwgjSI0flBvmBLF5qtx3WFrQcCxkfFaNVhtdEixLBmbupNftwamLsv18u0Xkpg7R4cCOMENZvQ2N9QuqZ2Lo/jutgQWWVI5DunqJNC6Yb0w/ywzjbcaxj4bgGeqBzwdVkwdrPJ6dZFm3OsPSj2iljOp/A5wGNY0LnLiV/k+A5iyTJMdRdDUdcW2xuxYIir/nOdrZ+okh4MN48hPH6o/dq9bNWnXtMGEptEPBIpDH9jm1MhOYJXr7E48F2nGq/0x9/sDtqnmOKw59Gvu9Qf83PI5PnRxi2XXqL+oSxxOtkxCz3k/ZeUb3nF4yoNmzqzq+OyD15n4nsu+YdU33FkecO/uIdJ6XOeRJrGeRM6rROo9H05P4EJifrDlxmLFQbPl+mTJO+f3OPRrgoOlJF7urvHh+zepVg7fMZjA90nXoT6pV1bbIVHl/vjR+Rozs4qv1bgaZwc0srXJJZOQRv723a/ix15/F2frKXR+J2239Dz3ahsXpaQUlpe5IuFdPMP95Ap28f4Sljdfzg1AVmnE2FvqRjc+wnhbYJaI8K3f+q38m3/zb/gP/+E/8M53vnPn9ne+853cvn2bH/qhH+JLvuRLAGjblh/5kR/hb/yNvwHAl33Zl1HXNT/0Qz/EN37jNwLwyiuv8Eu/9Et853d+59s5nN8QI+A48Sueak5ZpYbXt4c82M7ZRNOnhzKZVS6yjA0T3+mCQvbWKnR6p1LAPNl0EthKTe0GGqgZirdZyjb1bdaN6+8damxXF1eMwQuqwxe2SyqTUxx1hId0EJPsbSRkptZQFGysC5I3WmOpoCUX+iw/LFLGfJ86Ly4GZrWyO+sZG2usH7djifk9WId8v3MxTv7Q9xBICFPX0+aNsyWAAHjxurEmU3AzbVrfY10YV8auMqNLY8YZ0JWy3AOyv4AYgKaPi6hnFKCfS5aG2qJ1XK14un7ASdZwKw1ZqF3QBUCUoaUpWv4tDRLHevJOYu60B8h/30rH/dRzmiruxAV3+iPMK2cjNS+311ilhjvtAfe2C965uMdxteaF9Q3Wsea52X1O5ituhXN+x9GvcuOdS37xxtO8sjyi7SuaqqftK9ZtzcGk5cZkxbua15i4irPU8mJ7DbcMmWprrKzsl9XrRC5vZQK/P0YTs02wffLZTNjxan/CutcNwI6/rmOYvMu1OJrMc+rUrnHwbkGgEklXgCsnQPbWKgqPDJ65BPTsSFMej93xeJ25eng0ueggbJQFGhvWfU0v2l9OMrB9DNyvkq4xJj00iZlAYVVJBpzMh8lA92IojiMZ0EAqoI5K3cwnawBWxtJEGL6aQ0KfHuW+zL0ng1wjUkovZpyeGVij750lF6pn2MAEtvtYEImtW/th1wpC7E4dQ0rwAEzZexoPO9MFSMrNpyqfnz5LaAZDfPWzMnauAo0xv29fGFfmj2XPpzsMKau5jD5j5wQ/AtlMSmjrnxcz8nd5zQlMfMeR3zB1fQYiJXvLmIfMLjA482/eSU+iDCtBDdBdhtP0vAqRxFoSG/EskyYNGiDYE7jICVirqMXDtWbFxPecdbO8Nq6Z1urd8tzkHvOTjtdmh1x2E2LyhWXSxaCWDqHjerikyozi8zgrwSQF0Cr/ajH6hka7jzLyWpBEr5Ve4DJNlYW/P0aYlT52D6h6I/bvuDAw7ys3AtPKl4OhSWOPG2iJj8fbHF/3dV/H133d111526cqhOSXf/mX+YEf+AF+4id+gq/8yq8E4B/9o3/EV33VV/HBD36Qz/mcz3lbx5xIOXTBM3UdW1dzUG1pY6DxcXS/HOwUNGV0XnUFyDKJebG/QJvic7YleCo44Ua4BAYPWPu7sXzM/sSALGWDdgTbO+cN09R1pRG8cOrxGBn8pqweGBHvGe8OzctqmllUAI3T2iKOag09VslSRG0gbETUO9i5Yu4eRbgQlQo2Tpg7fW3zA1Z21lDPGKA1gGZSJI563KlIDEP+MisgVbFwbQadpnmOHjXds3G72QHY52o2I6rQiNSu54lwwXW/ITi4nwIXqeEizYon1tQN7SkDIKehpSYqg89b4FgqljIAgVVZN+29LwzIoy8+Y50kOlGvsvuxZu57pk449sKxn70pkAWwlpY7sed+aljKtJjeL6XJx9/R5Hp5k83b7Tpqsz/x3G/ZpJoPb27xsxfPUbnEg3bG6Wama3vyHDRbbkyWPD051TUnNRzXG47rDdebJc9MHhBIvK99jhfPjjl9sICzmmqrNUBSb3ZSWxNWHqmEOEsskyNGD0fK6LpWLQtwezfWfHDzFOf3FxxsILRZXthqE1/VHFHrnq5TIAseBrTKxT8CtkbDiRSCgFESXVRChsfxx679BH/g+Of4vrMv5V89+DIgDECUgxSUdezbiCRbYCiNHr2vLmIu5sWn8kVGaB7DJdRLtLaLdQbFgisSQqdbm0Gmn4/VhoT0qH2lMt4WmPUt3/ItfO/3fi//9t/+Ww4PD4v3yPHxMbPZDOcc3/7t385f+2t/jXe/+928+93v5q/9tb/GfD7nj/7RP1ru+6f+1J/iz//5P8+NGze4fv06f+Ev/AW+8Au/sEz4v5lGJ6n4MG1TxVk75aKbsO311PZ5w+WACzctfk6H1YZZ6Arib7ReG/ZzM4pVXWWUPIzu10nFksGc3O5ri8rcbwvok8TROb/jl6UTl8ooFCTzxe9k3BXpRCey2g3ec/adGWvMi7fVXpMz7dx/ALfKbTL8rM+926FOOI0OL+cnjTyyBhArMfK1youpGduPGVoBYZWTQTypLLgGZi3RrsRwTjPFOhtc7vih5AXIJlMFItVQc+q7omc3qi8MpprPNvd59+RVboUli9wROfSOa35WKOOhGFM+2hgDWZ3oArBKHWdJuJcmnMZDXu2PebG9wYN+zmmn4Niz0wcFlP3ZB+/gtcsDpnXPcb3hte0h97cLuhiovcphzfPtqeaUr7v5S9y/tuCsn3O/W3DaznhtfchT83Oenp7ydLgguJqVwEvrE8LaFxNeM0F0caQb7/vdIuMNJnD9nB+OPsdlCZNz/Ep7k/9w+rlcdo3dVEAvcTYxkwsJ9xDryhYJCbkIcgpc+ShEr/IiA7Fk9LylG5/QST4bzRXqbTHkterjcdUBj9eZNxrWdTdD902saFNFn3cTxgYGcFRlBm18T+1SNgXXjfEYnCnmuLnYEXFFkjBGbZN4WjfMx3ZfcQ6PemnYc4lAcrt+WR57rH41zKx3DGzp+9Tbgxs1IbF/pfgsla+8ONLoOGXvvzGIZQDW7nMOw8gx46JiPMePQSxhzFSjFGEOkxYOTSLzjXFIBrJSYbu1Am0RC1LkN2OJ4D5IWLuePssJk/hs7p5oU5XByt3XBzgKa26ESxa+KwzsiYOpq4ZkqTL3PdpOcgxkxQyedSJsBNYS2EjDRZpyHmfKXs7y2KNqU0DZV9bHLNuGKiSmoeeyn7CONSn5bJdwrXi+HYYN756vWUvNJtWsY8MmViz7CQf1lsNqw6Fvcajh8nk/xfd5E21zcQ77cEmj0LVxIuM39SZv/4q1Jn+uzsHdfsFHNzdp98EsW2J2LraH1xoQ7UZ7XRdMcWH+jG50Ye/0XsYAlt1ntAYN/9kd32al8HjsjE9VCMmP//iPc3x8XIAsgN/6W38rx8fHvPe9731DMOvNgkrUj7XX716cDuFS9ZaLdsqk6tn2FYfNptigVC4xCwp4dClwELbUfvA7tWZuQuVT1/0lHdpAt4avNc+P/EZVCw5q21vnkKMmz8YbHB2DWbypQ1o8rXgW9HSicuc67+/j6HtpTCqrIxrnqJ0fWZYkvIOpC1kVkmdXSyNECmu3A8gWJwpKaRO/JCZiNYqU1xkbx4fMDqudoyZ7+TIyiEfrlakTlilLuHEEutIMNxN4ne8GpUjKzY+xgT4oIDV1HSdhxYlXX7PGJU5TxUVSaeEm1VzEKVPflUZ8IHK7OgN0nTH7GgOy5k64FSbFesTjS8r8+OcDV2MSzYSwpePQBw4l8mRQpMIkhWPp65XXsnS8HCMf74+5SLOsWKkzC6wv6+GtcE6XqqKYOfTrnK6rn9Pd/pBfunia11aHrLuaadVze3HOQbNFxHFjuuSJ6QXPT+9yuzrj17dPKOOw0sCDJI5tqpn4jnvrOWdnc2RVEXrwORbTJY90Dr/NUr4Mwsgm0DeBs/WUzbzmbnfIreqCZbrPaZrxy5e3CQ8qfAuuH9U8/ei/rkfaboe5+8gjybCxYdRQTwPYfOgT91P2lBSnEsNkLDErVtB6inwY3uNiQoIf1pikZvPiNcRKTd9HDIHxUmpNfFGQTWpK48UJpeD3HcSGEY1e12z5T8XM+vt//+8D8NVf/dU7f/8n/+Sf8Cf+xJ8A4C/+xb/Ier3mz/yZP1N05D/4gz9YaLcA//V//V9TVRXf+I3fWHTk3/3d311ot7/ZxianPBTAIloKXPbZ6CuqMFBOla010D6nvqPOwEDK7CrTISv7SqmdXQwjiZw+nyLvk9INGVNEbQNtwM841jaUjqwx+8ynJKcZOtnphmxGSQPTTNdVI0Sd3E1OCLpQqHmioy4baor2fL9Z6NEFw+J0VYYgbLKv1BjYMuZSI2lnIdDj6tmY2SRCM/rdTNntvHSYka5jmj2nGhLLQt+tyrkep3xFVFI4mCc7zDTRFntQ6q6+tzQwwTIMHnEcB036eEd1Xro4U6f05QM/ecsFoJwPSaUQUclgy0WKuevkuEg1S2l4ubvGnf6IuVdvros04/XukLNuxp3NAcuuYZsC87Blmypeuzxg29VMa01P62NgXmmM74fPbtIlTx8Dk6rn5mzJreklz04fcLO+4DBsWIQDTpo1M9/yRH1O4xKr1PFqnPPi5Qlhmyf1TrRTMZrc6Xr9761Gkp3qwPTioJpt7/Sa/F/P7vPFT/2P/N3md/CvPnpzqFXsshIp6Rz7nfJibDgaI69L7crvm/hcMXzWpsdsWLzTSSf//NgAHni8zrzZ6PPGNmRwKiYDlciBOMN8qKCML8l2yrZKxVjbUu1sLRFxCrA4iFLRE0BiaWq0uY3mXcoMqrG3hYEhsgOqmWzQ/K8ECnPHgCwzfrcmST/6HlWMH+eyF+PgfZVQE18vg/+J1fDmizX+9trPtvfSc6SsMK33h3sbYBVEym0GXlWkIiF06Bphv1syI1BSqtQzTAudmWu1ccQgUzfJ6K5Y0qlnmRNCZrUJytxK4strNLnoNPmj/Wz/P/EdN8Ilx35b1t3KaQJkY+zfRxjGuIbstYjK0yV/ZlsJdATO45RVmpQiuJWKZdTU3WXf0MWg6ZpeI9+XbUOfAlXQd5jEU/vIVhz3N/NcaGhTcF61LKqWo2rD3LdMXM/KN0yD7p8WOQm6k8Sl1Jy3U1xvDQWVsiuTNm/+Y9L/3mrsA1jjDbsf2IHvrNfcrj7MT4XneP+DOUMFMDyPsX33n9+5/VgAHmIEyyN8VKNe3g7QtfNF+GTYaI/HpyyE5NVXX+WJJ5546PmfeOKJTyiopHZqqN44bWROfcdRveGym9CnwPFkrc2PSv18F5WmqRe7EwZPX5O81a7PcjLPodd97dR3NBLLumJ7YjMZNzniRmoWuaHeuiFZfSmN1ieuZ5Nm2kQW6JyCNEt7Pwy1iv4+yAXHXroeqPMsOR4XydhmQx2yzfusjalFoLCyVklYSjUC0vR5olgYFaW2MXbW2IIFdA+v87vebmywxjm81yPs8sJUDN8Z5H2q5lByQe23xaTd1tgomp7uXaLNLKXaRS5SzWmWfp6mOedpVuqVk7AqkkA9H4mF21JntrX5ew0hVCO2XYYJKgITqofWizC6vwFYpg6xYXXKOGRqldryuWyybNWTOE9TXmxv6HH4Xr0dfVf2PXaONlLz0fYWH2tvcNbPuN/OOW+nXJuseGbRsai2nNRrPuegK7Vf7SLXM6Pw5e0JL65OmFaa2jsJPcfVmrvdAafLGWlZ49q8LkcIGwdr6BcKvIj5OeUmQYyeTVvz0bPrXHYTnpvcYyUV798+wwfuPEnzwBO2Wu9YzVO8svoI2xaJ+/xxrrZaeSjR0JVSSJstmrKLQJtr4P/m/lfxo699lsoMW1+UJPYxPeRvlcgo1NWAkgRXbnNpBGhhTX8h5XVuBB/gekg1xdPLidZrO40YAaqs1nnE8bZlhm81nHN8x3d8B9/xHd/xhveZTqd813d9F9/1Xd/1dl7+N+wImSp7s77klfqYs3ZKjAERV7TpANu+ovZ1pvR2BcgyvxHQjafF35oXU4mw9bpItDKeaPT5F37o0pgBPCiIs2E8aV+9cfNQUFkzebehYWxCGP3dAKnIroHvVcMSQ+xxUYaO+zRTd23iq3OXI6HpJBGHz11uk0Ya22o4B9YlcUW6R36sGbsrgJUTB1NdCrGAcLs643bY0gksY829eMBF0k4yUHT/JiW0bsbCb3fkjpuRWWLtemrUnPFGpZOnTcaHfsPCtdzKnbBl0sjaw7dI+jDvrE7iTrdpmheSi9SzErjIx93iebU/5tX+hAf9gtpFTsKKjdR8bHuDj15mL4UUmGTPhAfdgk4C1+druthyc3bJreaCXo4hVly2E167f0S/VS5pmPbcrRb8qr+F988zqTtuzlccT9YsqjZHs6/RZMjEh9sneOXBEdXa5RQPssQw68R7NUB829KPK2Jc+6Qg609ujvh/3/8t/NL9p4bq1rr0NpHvpElZytRoeLczqasxoisdBmFUaFihnAsJK0jGxYX43AWJDt6GyeF/CePxOvPGw5g9c99yEaZso/qUqMzMdgHkZF01+DVWS5EVZnArZEmBydN68VRk0IuRsXueZ6zUNqkJqHTelfl38GHwDAEiD7+HPP/LAHLZMLBrLDUcvrLCW8EOgpRXFXb3fBW75JQA9PlvIT/O9nbmA2ZryPjYYV9OOXiDwSA7VyauLybuHjjwGw58JIqCg+vUqLl6XtONrWXzT5VN/BvX78gd9f5DMWlSz7nXNcU8syZ5HVp4Y4HpejVxnsa98fbPvLOS/ScG4mUvSFHTZUvtijgu05SLNM3rq3DoN/TiOevnnLazHEyge6LKJdaxIYpnVndE0dTneWhJTOiTp40Vy9VETd8FXJVYhYZ7TllQlY/M645J1dF4Le4a1yNoQ+1+XHC5nugasyMxzLvmlNect9i/PDSuuHsSBVlf6id8YP00r68zqG5rzU63em8d2S8WdjotMJYjGlPrjUzcy993ENz84unxWvOpHp+KEJKr7v/JBJUscq2wTBO8S9xqLtjGim2qmPiebaroRYGsOs8Lh9WG2qs08DAnX08ym6qTKtubGHsocpGmnPhVtjRJnIQVDYlNViTUGWSZOj2Ohd8WVhYMzXiVQKXBCkUqDv2meLVuUIaUeVjVThviZ5n5qCmqia0InWs59BUxNzguknAhlQJiWfpndimNs+bC4KtlwMrURaYuZo/e3FzHfKtUltgx+DIunGfqDLQaQK6mPB+l0W9JvPP80U5cpHabDGr50nw3CaZtKRd+q/Ym4lniOE8zIJMjqotiZ2IgYbK5NjfgVbaecpM/Mvfb4ukVRuvXsW+KogOUqWvgnypzdC42w/xOYJXXrlshFf9evb/aFhjLa1xTrlLLSjrmaK2yycEB52nGg37BKjXaFEsNVCtWfVMIIydhyev9Eb+2fpLzfso61gQnzELHSbOm8pE2VTxo59zZHFD5lC1HPJWPdIf6PK9uDjlrp5xuZ5xM1ngn/OryCTaxZvVgRrj0+FYZWGHtaM6hOyD7EwpS5UCnrLpI64rNNrBd1dRParLkq/GInzx7J5cvHnGwgtCqzDBsBd+qGgVjZW3b3SaDrVFXSQ294yElR1aouOgGQIjBbuebrv84f+T4Z/jvL76If/zgtyMh6AYoD/WB1AeJy18U55DKD2te9rRS83e3A1LpA3V9lTD4VA5ph1l22NhrMcgOQ17mkrK3XFLWl8YQP9r4hNMMHw8dEUuDiByHFdebJfe3c6qcEmLDO0V+p6EriSFgnfRYPE4sXtWeuw6xAFZqSu4z2DVeGHTDami9IflN7qaMjfq8SeNw+Eyttc2+AVqNcznZY0jzMA36WH8+ZliZZhp7TO6ebCSxEjvOsVRxMGP0KKjVZnqBShJzZwIpG8JC+82eWXa+Ogm0hLJYasJgnaUyA4Mg5vPSSiCgngK3q1NueAWyLlLNRZoVIEtN+g30Spkh12dD9E1e8BJTA8bcPrVYz6VRkK2bE7O2fACxEoc+vCmQtZWO+3HLafKsslFkzPLWqevzuQk7nZ6NqCm9XYf3+wUf22pE7Yurk9L1XtQt1ycrbjUXuQhzPDU/o3aJp6ZnPNvc55XtMb9+doM79w+R+w2h04ksztV3zG/001xOE2dHcw4XG545PuOp4zMWucC6G2t+Zf007b0p041SS30vhHZI87Bo2qIbf0PNODsbdqOx2gRqnlkAN8KSLzn4GGfdjJf9jZ2JXgGnvcIhAT7zI1LuQKRMvfVuV/JRHqcTdRqojkPx5EfsLfJx9g6p9dp+O1Tax+O/3KHggnZ9J75jFlrWvsbvga8OKYBB7WIBUwzgcE7KumPdYMGVpEIALQmUITSWmwdS/k+fR0b+hYKajIfMndL1QQqoBEMTwwAtk507hrUkuF2vKz2+EZAkDxu8O6BHN9f2HA5lII2nCodueiIDc8ue2+dXSgzMMksBs3VXmcK+rMl6xlRyrnu/4Vz3+b5eVAJ44DfMnAFZGmWvQFaNc+Y21eCI1K7Du56KjonrqbL0v8qfj3e7+L01mvLUg8NlIIoMOhmIpVKZNwOyeomsJbIRlzv2gzl9Rfb5zOBnMcmXQDdqa61TzVl/gySO827Kuq8RHI2PzELHPLRY5qMlSB1UG47Cmss44cF2zmo1QdZhYBrV6jvmen3NrhI2k5pJ03M43XAwGTx9VslztzskriqqEZjlo+QNtxrvSkpDAXEVeLDva2XX0biDLIPJ88x13G7O2MSaCzffWWseeo3cQR83WDAzePR1i8xQ9p4mr0Fj5LZ4aI0veGEAsfYLj8fjEx6fqhCS27dv89prrz30/Hfu3HmI9TUebxRU4vEc+44Tv+LUzalz4+OkXnG/W+CdsKi0vghOWIQt89BmmaAw8S2bVHMYNhz6NRtp2Gavu0O/LnvgmrijEjEvxXmWMF+gSomLpHvagDD3HUnUK2rqugKGWXM+4rQBjBQ22NTFzJJS396NwFmqafEFjClWIBnESqKMfE1ZFFbZmL4hMXGRW2GYF72DufOsJCpQ5bRxb0qSTd4OduLZ4AgiHPvIwnm8c6xSLM36MfvLvKSMzZpErVhMxTKWKUIOlXIOEsUnKiCs0qQQGCzFMCAc+TXLNMlrpRq9T13HJjXl3Jmnb+0iq7bhy//Byzz1vjPOvmLKi3/mBJoB0Jq6yHWvMsu7MbKRoOl3IsXXWIEruEgNL/XXOI1zXulOeL09pJdA43ueas7wLrFNNZ81eY0vnb7I02GobYydZQy6jph9beflcWf9jG2qStLxKk44DJtS//7q5ik+srrJne2BSgNjVdjgSRyT0LONFV0M6hUXIpfbhpg81xcrPlZdI4nnrJ3R+EgvnmnVcdlNON3OWHU1bhPwraNa6UZidlcn4H4+mr+dglouOgWQksO1DqmE5ukMZnUnvP/ubSavB8LWah5UhZKMJaxqFOn7XfDKOZX5vRGoLUkLCzVtzH/L64IbaiK9chydwKvxiIs41aeOqESyEpLbTbl1SR2acZlo4C3YRs+Hi5o0aKmGMJAAzFerHEP2lXBJSGPjuwJwOZKXHR9hcbk26h7deuUxmPUpGsFpco6iv4nGRzaxKqjwkenTfc8stOWLV/tI7ftC6Q154lr4lhO/os2souASLYO/E+gG05NY+O3gsUVkg0oVEQ3JTuOywOmkpGaEA2JuwJRn0KFbUp4NRfbVKNYMFEEnbPs5QtkctsBqlJxocjrzztrvVthrqh+cFBaX6eltwdTHuPJ7Z91wNxjnw5DOFXBFctlJReMih2HNiV9x6M2AseYiTVmmiS4SpVjxHPlVoU4bK+vQdRmIcsxdxTi1cTxqAsG7/EkpqwoHUzG6bEXtAtWbOGJFSdyPW369n3Oa5mxSgxlr2jGe+NUOkLnw27JJ2EjNC5sbfPTyOquuYV63CqpWHXWIPDU759npAwWtuhO9XqttYQ9GPPe2C+6eHpAua8LWE1pjIvniSZIawfWOuKnYNsriuowT7sUDVqni9XjAB86foj4L+C2EIjFM+H5PYjgqIt6y2zmKVh+neMRsAP8ZVUc3/Ri/1Gginh9XsfoWtCNhvliilaJkdLfIF0W0anyLgkA15U4BrL1iZihC8vNFp8DZ4yLj8XjE4ZyQkjJ/vROCS/TidzZzIXsoVV5nTBGH9zEDVpQ03YhKFhrXZdm1FjW9U1DMvJUM2KpdX8AtldaFzFhNBMmMIgdIysbt5ps1XOBWuzsGaYaHnUaJE31F58xkPD92VNTL6PGCrmv7Zu32k8kSZXRbAcLcwOJSUGtIfySz1oJETPIu4kgum+DnHWDKsJ2TkeeVKOg18doAmThdHbYSaKXK/01RPtwUQT0xK9fhWFG5NRUtTbYFmLgcsIKmDaa9SSNkoaWJFI3tbABYcNop97zxBjGJsJbIg5zO22cfyvF1MHXdDpA5lq/0EjjNTKwuqb9i5SO117TBw2rLUQatLtKUFGsmXj17DBBd9Q2rdaMJUtHh48A4csnlDbtAckjv6StlcWlcfUPnlaFwZ3uA33qVGcYrJIYpSwzHSOnuLw8PYWetEfs3G8Cf+Eiqzng9HJX77KJQea0RHioC8hd1dK2+9eGU9eWqj1T2fjak8/Fa80mPT1UIyVd91VdxdnbGT/3UT/EVX/EVAPzkT/4kZ2dnb5q6+0bD41g4TaI+9BuWacJZUk/U43pdJLuVj8UnaBXV/9Tk5xPf0Ungtf4YIHss6rymoJcvPkw+s7VeSocqb5RYpIQB4STLEluG4CaVLiYav2Ypvfok5XnF5pKF63ZUJBtRdshKqpL6141qmJh9E89SLA3ljQxJgDY/HXpNBd9K4ti7bIUSmHvYT19XAEqyHNE8pDRZryaoabzPtUYGZcZ2KzWB5BIbidROz1twjgme5LLCQiKW4G4ewKC+Y4P3Wa1y7GzmHnE5tGkw1zfAy3y27vSHSpBINbjIl/6Dj/Elf+fjOIHbP37OZZzyP3/Lc4RssfJMdcqha/lAN+Pj3Q0C+ncDLDdSFxubO/0Rr3XHXMQpd9oDXlqdsOwaztZTuhjoukBVJZ49OeWXb3yUrzn8RT6zuuSJMCc4T3CeCTUWTHUmmxIQoO9Pa7iayEHYclytCpD1se0Nfn15kxcurnG5mQweoblJl5I2llJSRnSM2WYBiNGxbmp+/ewmk0rtU5wTtl3FnfUBy1YBr7YP+I0CWWEL1RLCFvpptguB3WaBaDM/tOA7x/ZG5PnD+wD8/PIdPHjhGovVYKlSmvddLDWPbDZwhcRw+GI/esO7SAczsKU+q8L3X76Hf/vyF3G+mSCtAWaS2cq643mjlPby9/wwsW5acMP6lRspPua9QcPg92gvl9UwKUscXQTJlPnSsIkOqre/SD0Gsz7JUeeFw8CkRbVlXrW0MXDUbOiTp3KeRdUWI14zztZuQyqTfwGkClo+GJLbZGyadJu47H6goI8m9wViUsO2TapLtKpJH7xLxWzcNvTGwmpHXW99zl3AaSm6+bP7m0QwGsuLoShZJSmRu03e4u8DVKZ9D84VFlNwjmmm/nrFSdSoPh9HcrFozlXfLuBM55/oCOX8jpNWLBPQzqPScTvupBkbqTmNC07jPMsUYeG2+MzOMjPCojE3hoTTCdlo0AO7wD0EUHkC1cijxPTjVnTEkWdScD7TfSO1C2yEAmTdiwfc7VTGMA967S3DpMhLa9fzan/CC+1NXmmPOe+n3NkcMK9aTpp19hxZD8Wv77leLQku8er2mJfXRyXGNk4921Tx6uUh8bzBb1RK5LcaMRsmjn4mpEaG6rR3tG3F3csFv8AzXKtXLCcv8vOb5/jAK0/SnDrVjhvV1lIMux5pR7rxN6PZ7g+j2GajePHQR33cj22e5L995av4+Pk1iDl1sM+Fxv7cPaYb7r9EcMOEm+fasf+JpYKQb/dRteE7zwHaKUcBL/EJvLyhbOTxeDxseOdKwwPU80SlIfodTuLwmf1iYJVJDC30Y/wfDDJ1NSS3+Whoglg6X2FsOZMc5OQ+55HkiM7Ti7K71LvaIZIyAJY3nKP/zCdxvGUZG8pCToyS4f66P5MiBRyzubaiqYDmVmVgVCqYuP7dGF/jBo4xtQopZvR6VV7bcOQkXWs1gvlSqc/kkApsr1YSHrNJshBZoumTG2nYpAmJGXDEhOs4d4irIlXs8e4ugdcJ9Gh50mc2sytgh63Neh52Jy3jefmcjmdMACnHNAL1nCseJx5HLxQga5WaIYk371daF/S6Qko3/TTOuRz5YtU+Mg1daeC5Rje4wSVmvsU54bKfcNFPSgc+Vcr2umwbZBtwvc63rgefGbKpzkBW+YAdsfesXMNrHDHzHW0VeLU/5s7FAWGjG2btQhuIJRCTrjO25tqk/qimu4aqupxyldm1H+sP+LnLZznfzlSO4kaA1v5TvwlQVdhY4275+Pe9x+3fd+eJ7IVyt/tNwbHHo4zLy0s+9KEPld8/8pGP8HM/93Ncv36d55577lMSQvJ5n/d5/P7f//v503/6T/MP/sE/AOCbvumb+IN/8A++7SRDGxNXcegGc/HDsOEwbLjfL7jTHgBqeTILLdtUq1zXtTsJ2wBdqpT57zvwFGPuZWpGtUfkPDeBp67TBrtLRXI4dQoaLFAfqEPXlZmqc+r5uHE90zyXBFJpUkcJHPqhtgHYihRvYaCoTRqXdkKqps5B0ufC5TRCciKhKPNqI8Kh1/37JJfCY58n+55MpSMiTF21M89upWPiahJJfaXy/U2mZ0DW1IUCZI2b2iuJRFGz+ODAy640X2WNdTHZN1uTO/1RSae9ni1MzOrGAjZ0TtU5OOK5/b6zkacSPPW+M37BJVqpuNMf8a76Hj/f3uBHL/Sae2bygNpFfr29xWWc0qXAZZywjk1h9r2+OeTFyxMerGZs1o02eXqHRE/XRF5yx/xi9TRdCjzVnPF7F7/MZ9XqB+xxTEbs4IbErXDOeZiyqSteTcdU2aJnFSd0PnC3O+TDlze5t1mwbvP79Immihw0LafrGacXM9KqguQIR+3OVOd8YtWqi3PfqAdjEyKnqxneJzabuky8aSbIuYJXoZPi76TgjN5HKsG1PvtpQbV2pAbkoOdzF69yrz/gZ+++g8nrQZ+nhWqb8NukzCwBUsK1HWnbDmqUtzPM/sQaI+PGT572t0nN+v/k8S/zvz34JX5o9dl85/nXIvcrlfNVMtRCjrwRkp3XcDGp3DCxs4b4OPLFGqX1jJedwhQef7Vsbcz/GpvMjruk8L6NU/IYzPokR8Bx4je8jBYNc9/yxOSC++2C2ke6rO2eBSXhz0JbuiDzsM0Ssb4wh2yRAN1QzrPkcJ4TJ6zTsMlgjEa81ixcW7oaAe2wxBHSbyaBMV9dRYtuQApwGjVhpCYVHTo+MsnpHRsRLpIv3e8mGyROndL/1efK7SQVLlw/LF6i2nMARJiyKxex4yhKLTdE7XqGzokZKoJJLBVcGkvsTGI4+Gmlshi3UrFME+q6p86doTYnHZovVsrmyGZkWbueheuyqbjkz0S7MKukC64x1vTxA1B3IY6NBE7TlCSeW2HJoY/FSH/u6mKgaAVdlMRKWjpJzNEu0xjIuoxaBIyDBxRU7XmtP+bjm+tc9LoIeaeTNkCbAqvNAXc4KL9XPnG5mHAQtry8PuJBlskeN2te3RzRJ8/9s0XRkLsIzYV+SF2VJ/n8QbnkkOiIlzXLVUWMnvp25DTN+enT54kvz6nWeWLfWJcipxi2HbLd7k6k+0Oybm9/jLoRGPover38tulrvOMd/1/+1elX8K9e+fIyaZbNf9LrMbv4KnX2Cg8t04ibz0kpVMjdCJMg5q5DMTNkdFx70pDHXfLH41GHxzF1PedoR7F2kUW1ZR2bnfnevLHqEahV+z5LxFIpBEwSBwoEFQPg3Dqx/U2fmyAJNaBvchpiYV1lls7wXCGDMDq/9dlnZdzMXMmQ0BeQbN2QCE4ZLj2wFVeOQZnEI98rBkDKRl1Eb0OcO6g5+cibdHeDy8PEljG7KTI0RUOGpDRNclgrTGJozyF2H0KOUhd8SLl7XuXiRMs2WCA8g9xucJ+ZCMeCP0s0HzzBnTkcLaDrj0oFMpC2A1CBIxLRZMQetRYQcdkkXYqRvhYS9j4zeChSmAW1C/S4HSCrTZV2vXMjTDKo6l1iGSec9TPapO9rLEuN4ln2gSVN+d07oW3UAuGin7Dp62LBcNlPSOJYbxpcq9INJ7pWIBBzMVG6vJIxpTbQdTXLVOMPTtnIAS9tbiEXU3zn8DEOZruWYhiTyjrebP59A4nhzmNGHWVBeEd1ydHRr/H+zTO8/+KZhy+60glxpSjYWWvsLnl9KS8zKgZcyh30EUYle8e0c9v+MT8ejzR+5md+ht/9u393+d08qv74H//jfPd3f/enLITkn//zf863fdu3ldTDr//6r+fv/J2/8wkdszJf9MM+8hs2YckqTVilibJ2RMMmZkH3rMbQ2qSauW/xLnHWZ3li2HLoNgUU20hNkFT2z42LbBDO01RT1vNFdpGm1C5y5LZMRnYoUxcLqATZq8n1RK8eXSkzqXy+aNVKZJATLnO9M4RnZSsXl4OhxLHIBusr0QApg85OcrP7LGlzRffmOvdt6fB45r7ZaT9bc3nmmnJux8P26p4hdTxKyk16ve88N7u7DFxFIlsZiATqR5wJAD6RkmPKUOshSm44N1KCBcCQuF2fcRKWBfA6DC2BC5bSKPAlrpjw3/stC5798dMCer/8ZSe5Sd/yXH2fO3HBz6zeyYubEy67Cef9jLvNIS9vj7m3XdCnwCaq9O/uasHxdMP91YzlekJKOTzF5GRePQ1j9NzfLPgfzz6Xk9kGnoLDw/fzVJiXxkkYJVAGhBvVJRdpxkG15eXNCUs/oRPPOta0qeLD92/S9hoWcjjdcjLVQAMRR9sH0mWNX3vSNJF6z3TeKkuo93gv2qQPSVlbyXPW1rRtIARP31Yqa59GZBKREEgVxMZlpYYrhuUkBnP45PCdQwJsnuj5ss96gWeb+/zC6h289Mo1DjbgW7SBvxFClwoz2EVBNtuHWVlvN2027a5VLmV1+QY+uHqSy5P3c+xnHHtoXE+8qJnkdVVrlrxA2Doxfv0kmmaYRwGtvCNljyJLOPSZLGBrm4tq7l5eJ7OxCFAsprMFQBrXRvZy/6nSDB+Ph0ftAid+y2FQDfPUdxyELX0diOLKotH4foeRVboLjjJ5b1NN8Em7mr7FkwqzytybjKILmvJg+mpjd7W5KDE9+jJNRlr0VJhFlnAYXGQrulBsJDB1KqdoRRdF7RwMfligZn2WKHg9dKXTDTBxvtx/vncdbmQUWe4Sh25XWx5RbynQlESffaYGyq8vFF2wVBEBn7QTIgrG2aJqMjuLEAbwo/LHEgtNj65mlwqIBZdYpglVX/H5//AVnnzfOWdfPuOVbzliUvccuo6pc7wcI3fijEPfFn2/LrjqDfBqPOal7hoP+gUvrG9o5HiWAkVxHFQtv/3o1/jy6cf4jKrBE8qiGHRl0E5O/nZvU81ZVE157SIhqPRx7res0oSPbZ/i1y6e4KKb7KRqBqfxzH3y9MnTZdZS21dMa11cKxdZdhOqfA22qeLeZqL024uaZu0IG0e1huZM6GcuI/UyoDricK1XOm5yyNGWa9WSj7a3+MVXnmZ61+uE3mpShRvF07JtkW6kG8+a8Z3xJslbps22YVfsy33Ff3/+xfzy+W2dOG3dGBVGpRuRnHbwQy4yRBDvC+tLRjFohS6b/7VjKFLC8bHliT5mA97SSXd6nlzi8Xg83nQomKVMly5WVC7SOE/yanptrKng0i4jy6Vi+j6APTm9yEsxce9F2aaSU9VKupHoRW2x4MbsssbJ1HX0+Jzmqn9zSPHW8tmjUV9DGU69OCqnPKGImakrUDze1vXiM3AlzJxZrJPfpyv3r/fWml7MzwpA/R8tiU9JNTJqlLiSRpeQrCZWbpNJ9DSURL/0UQTte4+bMLkJwSBrGEsrk3h6tBARslSRhijXqI7ntF8p3PiiezzlXuOeu8nl4oT6vdepNqc0bkVF4CJFllIzcZEqp/nqcymAdZmmnKcp69Rw1s3oJCBC8e9sfOS5yT2eqc449kHZW4WpoBeHAXF2jWxTna+LhM9m9MbeOOsPuLddKJBlqZoF4KTImIy1FJOnCpnZ5xJdLo589nBb9+pxIm0g9C6n3eqG3JKZHkJlosOlANIgU8fMH3Iaj3n94lmqleD7NjOy0h4zK+o6Y4CVdaTH400Kin1ZuP16kTy/ur3Nne1BWSPKfcrTZWRKXGYeymitsdt2X98JJW1qDObZXR86vrTboZcBaX0MbD3i+Oqv/uo3DSP5VIWQXL9+ne/5nu/5ZA51Z0ycJgjO/ZajUBdP37nPyYWxJoqjS42atbs07CcRpn5If5tkn6vodH98pz9ULyxmTH3HhrqkGb4uh5z4lcoVTQGRFQzz7Hk1bloDCj6lnqUZx2Np6omthFGzXeeSLtceU9eXvbY2RIZVw1K8gR1lxu0QuZ6/UxtJdMCFJKYIrViSYCiN5f30vf3xRn8LeLU3Kf5QsiND3Iz8iOusbmlFcmK8vYehNB/8jx2rbKYPcLs65TTNi0F/J55D33KIsuxWMmGTlMTw89/8LCKOW++75M6XHfDz3/wsB37Dc9V9vEv89Poz+fDqFnc36kN1v52zjjWvbQ45305ZtjXbrmbWdFysJpyez0nJFwArhFQSeQGmsxYRePHuCXFTcTad88vHT/H50xe5SBdsJfAZVWTiKu6nxL10yEZqLuKM2kWuVUvqWcxrUMUr6Zh7mwVVSCxXE9rc6Fq1NWdnc1LvCU0q85vfeFLtOb6lCpQ7Dw4RcWy7isvlcG5DlZhNO9quoqojXeuRZVW8pNpjwOX9eW7apyD68zyCF2IIuOjpjiOf9Tmv8LUn7+fzvusVvuDHX+bpaxf80y/4vbjo1VKlSxBFjd8TsG1hvdllZaUEIbxx7XOV5HAkWS8MvAjHH0n8yEc/i4/c+g98cbbY+3cPPpfZS5UCTRXq9+UMkBrNd3svbyCW75I2/FNmsIsb/Bvzfy6KBrdlk3zxwxpkgGBZl3YULbr+uegGgO0Rx2Mw65McFYGpcyxcO8SGeo2IXkcFmnzuIGxTRfCJ5FyRF26kYtXr/Sa+K3rhmCYc+Q0+d9PPk+L1Y116xHHktkU3DDBFTcmT67lIQsxfgGn2P7LiYrxIaIpG0O4L2kGxjvlGKMaIZq5rnZLaJeYZcMLBFNWf1y7sgArmrjWVxKZ4RTnmrh58UpwVDOkh3ynrQJvGGkJOyBgmgHHilMkuS2QwQpfP6ypN2EjN9XBZzI+D00nQvLLM8ySQ+OJ/8HG+8O+8jBN44r2XnMcZH/q2W3xu8xof7Brev32WQOJWdc4q+211o7jcVWq42x3y+vaQj15c52w9ZdPWdG3unC82vL454P71A7509hE+p15zMyyKrhyUthzQVMCJP6B2kS2aTHMQNniXuEhTfn19i48tr/PSxTGbTv3a6ioSk6PtK7YbBdKqKpYCwzllaLy+PKAJsaRvXmwndNHTxUBKOtv4zlFtoLmQwdRP0AnISwGKQqugVz8Trh2q3v0nzt9F++KCA2NlbVPRjhOlSAyv0o27EY32UcbYpyTla2ObqmLSa4dbaqP9PcmOMa/LBYR1IHRBcykvBIIef+V2rvmd7rgexABwjbrsQyfkkd/e4/Ff6PB4KiINg6l7cInG94UB7DJA1IunRpA813snCjilUB6Xcjc7EZg6KSyrrVREPBPXk8TcoGBCv1PfVxnUEhItgSGBz9L1BpwAKMBVLwrC9OVro4/sRRlZ4yCNAtChMnOHy6/kCHjCqNOvryHl2Pr8s0WHj7EEwYCb3bWmeIHjMAeqtIcAjB9h6YMOZTx4JINWWnj1EphlxoN9Pgh0Uud3sIBrEN7V8rn8Ck/JK7wo7+Cnnvsq+CVoVje4Fe5yN1W83p/w/2fvT35kW/I8P+zzM7MzuHt4RNx7331zDpVV1ayuZrNb7O5qDS2KGiBCWglaaydAC0IiRe30P2gjgoIgARIgERChjRbaaEEBgtiiyB7U7GZVdVUXuzIr82W+6U4xufsZzOynxc/suEfc+zLf68zXJLKuAe9FXA/348fdj5vZ7/v7Dg5d/BArMzuW5susnn3q2KWWq3HFEAMxeXI0AK1pZ3ax5bBq+aC54h03s5bWmkbqCtPNrPs7mdmLFbuoW64zwa6PV3HN9bTiduqJyRhXzhlomrIjRgPSnNdFyScCiLKbjUlY9y1jCuRs16IWbxxJLNKM+obb3U/oRgouelzsyGHLql0R2PDT8R3SzSVtfImkl7h4jUvpOCfnIjF8QwLuPcT3a4yF+VQPjSwAbL1tkXDUn6fjlP0lJ2tOPbacNEv0/tJxPMb93+81U96uK3/uRtJML+anZIydA/vckdQVPyxrVDcFEAol0dB8fy1htC9JhnepX1INnWS8agHA4gJYWViS4orFRy7sIRxsmEFMWWBNdJOEA3Riqg7nEo1mPk+b4o0kC0PLzpOSKmgglqlS/CJn3OeGxpmaZF3sSYBFvbJ1gwFfGrnwrbGmsoVO7IpRYl9Cq2pieCdNeU++hsXFG0Z9nIFbsKZlxFJbwYzU3YnCBKr1SmZD5FYdE37xrKrst4SjkchH4dVCVqj1DsBF+Swv2ldcZ89V7pg1cON6fvxvPubHPAbge+45T/2OZ2nDHw8f8oc7Y5E6UdbBApt+urvk5WHNfmwYx4Y0BHa6WuYb1yWcV8RlqxEAjQ7fR7yz+f1wW0KtVLiLLb8/fId/oJ45B/4bZ3/Es3TOi3jGF/MFd6mjc5Gb2JNwrN1Ewprvz4Yzroeem13PfG2ozO3o8V2C5x3NIMS1QmMIizaK6xLXuxXn6wEfMsNdy+wCrsloAXBqTTDP3oKYJoffOyQK85miXUa9x42CBmuqaAN5G+nPR7O9XXk23xn46+9/wt84/xF/7X/zY/7av/MTROGv8gntHfwff/e/SzgUb+CkxZw9IcNEjieRgnW8CUQ/VaWIER1eS1Ot5uvUOkiZx8CNHsMisrplfVVnr6kqSuzx94uSehyq95Wv4JftoXRhZ9mdq4olFzq4S5AWU3o1w3cFAou/15ELcVK4ZflG3llvwaxfwbA4UgOLLvyezpmk8Cb2xOzpfKQtznGzeroiqtjntnS/j/KPuaTtZW2oqR45uyXeNqtjKjuaI0vLRqXxVgrtLJFUUHxg6XjUxayCUnUqTAj9A5HqjBQ5XaEtFpCr3jZojXa1xMOOKmV4fRfVibImE07YR7WYcJVeTJUJpgUEq9pq8+jKiya9JgM2AKLMkmlKh8aiaU86BTJzozaxXvo9T4rWvPqSVVYdGbZ+WNIMP3iD1vyncslV7vmPdr/Nn+zeYxNGvt+/4GXc8MV4zpg9UzaQ0mGMhy/uttwdOuLsycl05Yhy2Hd81p/z7+e/yN9rv8d///Hv89/bfMqZdAsN13yzzOj/ib9j37SMOSyxtDbhe/7w6n0z91PhrB9ZNzONS+znllevzuC2Mb+R7YwrssOcjhRdACkGiLuDTX7ToZSKTUaD7a5VjBJaO8VQfqpJQ/xgn318OvM3nv6E29zzD778iO6Fw1V54XCMppWcbVIfjtfqP9M4mffUwTg27LLyV9vAv/Tuf8L/oXvBH/5nH5fUw4JowVHm8fAn3C82OHYQanfBZC9LdXzSDS+xvYkjnfbhudbzTW8rjrfj6w0vQii+WZ2b8ZqRDCNhMRyvTRVLn7Mxq0cK+CRlw1GT+bTI5GoK4aSBVqIBE2Ki7wqI1RHQJTHQJH2ZfCIpESjMXktQDNzflGQswOP+bcawEgy7Po1jFwzokgJX+ZN1Q96w1tTAEYdb2EcV6JLlKLIAOAsIVjaLtsQa2FVZWYvHlph3oy8oTWVaLe9NaVIBhTU7ldszoRR8oeiQOzcRSzPFntgYCFKAlgbznvlkeocX84rGRS7DgUNu2MWOWArHOdvnmxF2U8c0B1tnVGyTLkqcAneh40/1KT/zl/z26gt+u72lJZywtI7+nWs3WWOtdMdjNogyq/BsOGOM9qm2IdL4XNhWnsOhhcniXnOXkXI9ahZQ88nBmXWBGe6WOPqaXOT13tpyz4vwZEgWXGyALXnzER9+fMf4L8x8ls7xqx75gw9xty0uzkiaS+e5JOa+qYD4JuN0rRFI0TMpvO8d720+4x+EPc9eXBw70K897CvWGveGtUZO1prTu39F8+SN51qf+G1y7p+Lscu2T9+4kZvc00g01UjxO6peWRs3ErOjc9FYPP7o4+uKD5Mxu2IBUmztqfK1pqhGTplRxpYqygfs4t+WORBq2If9PheW0lqUp27Py9zTlxTDpAZwz9hcvnWzqUeK/1atZxKpgGlHH8SqXqmKiQalLybsp+dxq4EhZZ54Xfx6v41RpYZgfr9VyJ85kgQqsQDMFqYavNvtZnr/1N9w7gZ6iVzldfl95tId2Lp5sYGpdd7jIuHcumGRLvaSeJFX/MeHH/Cnw7t8OW45pIYhNrQu0rrEzdzzalhxmBrm2ZOGAOMJsNdm8uwRHy3saPIGEDml72eCT4syxHeJzWZgSA2fDI/52XDJO+2OP23f5e/f/Qa3sefFuOEQG/ZzwzAHnm52tD4xlWP87PqCw6ElT35hXunoyI90aUaHg6CDJzeKdvYOj4eGl7On7SLnj/YMY4OIMsXGWL1NZty1ht8MnubK4wch9QpeaS4H4jowHXr8BHGjxA9G3n/3mvfWd1xPPcFl/sbjH/OX1j8jqXD59w5H+xHgX/7pj/g//8DqnSMrKyNzMuP3nBcG6D1w6uv6BZdxysS9p1J5cElvwvjmJvrpFq0ka1b6ut6rgU4e5OS+b7AcpZiSITdHubwkjgytytISlg6NJHv96tSAriQwf/3v41sw65ccXhw9gbWbufQ7m3xTWICmLNalnXIwNpSDMVdaqNFo135k7cyMMavjOm2WTm4vM3NhYd3kHudMfuYlsz4x4+2Lf1VtyGXBzBOzdSiq5MSXDXqViQALK6tOdqlIUmrBMCvcFv35UGN0ZeaJt4UoLZ0QxTGDzmxdu4BWYN0ih9DIsbKvXYvl3ye/V8lH/X3UyFCowKPW1119qur97PWdUouh6ukNwFu7kffDNRm3eGSdy0jvIxnHjfTL4g3wxV8758P/6HrZTO5+r+WJv+MPx4/54937vBg3vJpWluI0rXgxbLib25KkJ+zHlpzNEJ1S2AEgFm3qQ+Jqt+LLl+c0beSyOfDb7Re85/e0Ily4llEjtyVdql4TZ35cDNxv5o4vhi13Y8fu0OG96cJvx47bfW+ymtGiZvGQk3BxPixsLTC54RSD7aedEkJmnr2BV6NVE+ohbmySCYMuXlmilG5IJidHbmB6nPgrv/UJf2XzCb9//RH/o//93+O/8o9+zD96+hv8ex//N3GTFiBLy6T+Bt34V4030mxZUqJQaO6U/Q83/IP/0od8HF7RiOfv3PyA9vOAn4que6G0noBaD81zoJjKl8aI2P0k2WeoIsWHRZakDmrxckJJeaM57+n4+g2It+PP6XAiBHU0YtLinGXpmLsCyCgs3kXgiJiMMKuBPk1NKFUDsUZtljkpkPBFiD1oYCWzbUpElqaJcB/IsvMyA1f0aMJexHQ89P6MRWZXvadqAb4whRViAdpi+dJkMutyjosPFiBaA1P8AlqBMXmlNAJO37v7O7fj7/IA6EpkYnmmurYJtj6dKJQROHHpopyfFHDQAKGtGxbgUBE6LLlP3cCgDVl3uBfn8CeeP/hr/yJf6Pt8ru8hP1TOXk207pov44bn84p99Dgx0HJIgUNsmbInq0kN52Tzd4qOCtmVF2iNXK8MU8Nu3+FCpvczT/yOM2cC0g5PVJP3VMmISVmPHmpzCtzFztID41EmOEbPNPf2jMkVI1qTIvSruLC1AFL2pNqRFcU5JaeC1KRyTTjIrc2nzlDM+6boTtHsURdIqzPe+27i/f/qNdf/suPi8y949fgjRBrc//Mxkq4g35Xja4lA/5oT7puK2wc3+QnmVy2ffbDl3B3w4vjZ+Ah/58o6cfLQpQMtrx2n/l3Li6xejYuniciRdfygeXI6fuFa83b8uRge2+/e5J61G0k43ilzf3B58UjM1dpDEkNuSpCIsvXDciwnSi8jk4Z7frJDkfMldfR+ZsgtvZvo3YwvjN1T5lA1aa8z8758H4yllXnqDwzqGdSzkch0IiVsSnMfsSZ7tc9IJ79PZb6uLKeGzFaUtfPmj6Xz4ju4V0vrvdWGrY5kyXj8wsr6VY9OQmnIT1xliv3LEYSz+stqucd+z1zJDXie+hsu/b4AWhOfxhUAl25PL5G1i7zIHVdpvdSWtV7Y5Y6sjqu0ZtCGMTfsc8t1XLGLHWMKBMmcNSObMLKLHYdooNIwNKTRQxQoCbKIIiGj0RmIFTKhswAaVGhDpG8iUwystgPBZ9452/Hh6ppnk8kYN2Hk/3X1F/mD5x/YOQ6tKUdGD06JyS9pgyk7djc9Ovpl4fWjIDPoocONZe9d+hPaCJML5OiQdVya+32IjN3E7aFnUtCDcQt1dGYYf2dAlnoDxiQ75m3gyZM7nn8YzIvrIvLRe1c8Xu05bw8El/h4fcVv9l9ym3p+ND4l/G7mB3/nWRWr8J+8/xsltd2S242VpcgwodP8ZilzsVi5p0r5eSxBkXu+VqdDsxT7HAOUTwkwVODr5CecAFZ6BKoWxnAqbCwHNdGw3r8mvy8AVgnkWtjCUthg/sgGk2QvTT1GtijnpUHha5aE8BbM+pUMh2Mjo7F83MygM/vcLpTeOmY1mQiwJIe4Ar5U2ixY16ST6pLFMinV39v6CWeWCNsZAVWy7ddoBLZODFzKFo1bR72YG8wkEYqHhRqjygAhR0NeQLJbPRqs9xIZ1QORC9eSVLnTmaGYxFcT+ERcZIfflK77EOhq8GS1dJBJj13yep+ktRufl4l8Kj8zztIfyTwNd4VO3S2A1dpFelEu3Qtuc8ONdotk8If/+jv0buadv3/H7vdaPvvXz/mT8QP+8f5DDiU5JLjM54ctX+633Awdw6ElztZB0Fz8WBqLJs+1U65CaCKrbma374iDyQ4/H875e4ffAIxB9tvtF3wS3+XLeL4kGN6lrnyO5m82Zs/N2Bsd+LaDLAyrFo0O96IxumebF0S8Rti2IXLIHftdZyBN1b+HZMB8sqQyd/DIbLLB+bzC9ZbckRuIZ4lwOSEuk8487Wbkb33wE/7G+Y+4Tmv+8r/zU/7H//f/EAf8l3/8J7TXmf/LB/+qTXR1Uh+/ISurTvKnHQMnSydcErTXwo+nd8ibF4Dnxbgh7EsHoWXxqVposkvEbFmF6rHvSSpPcKfadS+FSWVj5TKrWqcBpE7cp6ev5f3OYtTot+Pt+BpDMP/DQCJIIpYk1VBkg3VUc/Xl9yIjzAVYqQBWVE8o7kv27+M8HTH5WiwXbksyAB2sOyrLXoZOQAqgdSr5rmwuh4FU9hqUxDF5MKmxgipINunRYD2QS3NF6cTaK5MmIkWSaKdCIpe0vCOo9XXHQ6BLFxlkpiYunt7DsG2TwGQ1mV+V+ylCLEDS1o0IahH2dc2VTBDo9Y5JEyOeed8w/d1HzF+c8fnFBrlSHv3TPcxXPI/Ks3nFnI/v511s2c0dY/TEwsCyEy/vr6+m/JQQCsGFROOTMbaK7PAudvwsXgLWIHnid9zkDXe5WxIMpzKZVd+1qI4xBeboSaP5gsZgEg05uCJD0KXjalZQgnfZEoSncA/dFJfL2mMglswC2bw2TBlR5mtv/+U24/pkjYQWfJv47nbPhz/YM/yO573Dn/Kb+x0/XT/nD377ryN/X5G7HtFgx48Jjd9gh1yH3F9r6nqgZQ3wA1ylNdrsAYyVXWr4peFz/IiWEJFF0n7S+KhPV/+0POUpeKX3kSxRjklTen+tWY6rLGzkt+PXcyTNxDKPdpLwmjl3A1ktDe8qrbkIe/ZlD7nPbWFhGXhVA4Uqm7Q2drPKMofVOX3Q1jwTC3BVkw8b0uL1a3JELUFVfpk3Z+UekFPDoTy6gFhH315ZTO07sZCpSZVcbmuW1sNxNJIt/VylBElJqUNksTHpERqX+dBF1rJalBDf1qh+Wu/6hkcuFaYWzCRu89G6pRdhzql48BqRIcvIRZlQdtktIGH9TK5yu9Q6t7nnNq34ZH7M83lrHrmFsOCKvLuRxCE15uPcJrZh4MIfmNXzTLYMXeDq0Nv0M7sjo1MUGi1sX6CYvfuQF4VB8BkvysVqQIFtO/Jb2+e829xyPa94v78hq/CfPvuAcW6I0TFe9RAFidZwv9GNsXLa0kS7C/jBmgOi0NwIYX8ESOpePLVANC/mcW0ySOczN/ueO6c0PhGjeVXTZnQqQNbOfH7TSgl35gvsZiF/3sGTO97/3gv2Y8u72zue9DtyIag86XacefMr/my64Mf7x/y//we/xZdXW37vH33CP3zvN/g//c5/m/65mi8w2NwdMxyGY3L7Nx3ijiCXc2jXoq17TV4uGZqfdPwvn/wP+QuPnnEz9/z+P/kO53uWpPVjHUQBm8wjWL2cNFC0rEdSbj9ZfuTYeLl3isox6dB4EUtIlktQpqCFea1C8ddSZBYDtr7BcvUWzPoVjFkTk5rU0KO0YkbvZ2FcJGCdqz5OWh5jhUJTTOFPR6XyWqfBLxN6W+5ncbSOJJZaZEpqS+lIeqTwJlV6AdzMy9yWTsCxCw7QirlUVdpu9dSqUkQvxXMEMyFvZaaTZCCX9cPL87PQZ9vy/L9Kym4jvkgMTZo4qHU1KniXKQsXykQxqJfEjPmIbP2BjUxs3cCztKUXM02/LDTcXXaLSfoTOXAuI0PRV9/8Wx0vdM0n8xP+6fAenwyPuJ17puTpvRlkPj+ccX3oGcfAfGhgMu8PvEW41qjRHB2aHOIzfTcvPiOuSXT9RFbhjw8fcD2v+M31Mz6PF/zR7gNu556X49oKieR5vNovn+MX+zNe3myYxwCTw42OHA0kkSiE2ZhZOSh5ZSXv7d2KtpvpVxMpOXIW4mxU4YT91OhwN4FwZ5PXfKG4JyMH3yIpkANMjxKb93d8cHHDnDyNT/yNxz/mL6w+Z5c7/uHtx/xP/n//wQKlOuCvvPgR/967/4oZv6dkxu8xvplq+3XH0kmgMKRss29dOwWxEIZ7BUEpSORNnZF6F72/YNbnqOO+7EOWx3DSibh3vFJwLN48Jf1RvsGk/Xb8+R25FAZBMk5ZvIdaFwtTSxafqQpYZXWI6JJseDqqWbsrAFNlTFU/FEssLOAODqeKlOOUplu5X00MzBzUL2Ca3HsuRcWkjFK8uERqGqAaNg3lcUJb5CPmzVXPowJgZXqtm7Zf4XvsxaGqy/PFyiYo4J0u74u9Z0tXXZQpezoXaSXRSmSvLaEEvvSleTKV1ysys+Kajj8jvtwjL83PJCFcpx0v08xN7BhTJqkSXEJV2M8t4xyI0ZFnd5Qpl+Kiom+aDRjCKSGkxa9LvP1bVXg+bxlz4FGz5y73PJ/PGFMw+Xo2CeMqzAvYuZtbDmNr7K9kxYdmZ0VNkVUTbUOqrT1mnBp8SIQmodlYZCZvB7IzklQWZLS0XIDUg6wiUTySDSRLvdJsZ7bdUJjWkQ9XdzxpYHaP+CJt6A6BR3OiyZai7FQX03fJeTF+P2Xi/TJj8RMp71D9dlUfzntgFF/jOq13evBYeMC4emgO/4YDV7DtuG7JUvS9Hb++Y1C79kf13OYV1XcX5agAcY7b1OOxsJ/TYCqTtdke2hruJl+/zSurccTCkcbcWEO4FMNeMk4z2+K7aml8tcSMS9VcG+UVoNppMJ9fBC+JTo5BTYOabK4CWxnl9gGrci1Wa0wn+zhjNVVViPl2Wf1QATDP2hVPY0wF8m0CWQ/HqdE8wJlkDjoVvlumcXmRx8/kIp30vEyJvQZaEufO0iJvteEqrZnxXKUNu9zx5XzOdbSgqFjVNCpchnEBK99p7li7ia0/LAFSn06PAPOSOu9HVIVXh2DrSJeO2+zSfNAkNKuSjBky5+uB4DJT8lz2B87bge+tX/JR94p96rhoDnzUveLvXX2f211Pmj151+Dvju997hS580gSa9RUUEOUtILmTvCjpQOmXo4AvjeAxEWYniTcOpIGTxKP7yMhJNpQkiRDhpBJNy1+5xZrlLC3Y7tyuYad8PJqw9/4jR+zDSO7woYeUsN5M9BI5i51XM3v8/mw5cfXj7ner/hf/2v/Gv2/FGivYPUi46Zozftsk7JMMzrb+yYPg64esrIqcFV9srwzg/jgwXvyuide9szrYOmCtekukIPwwX8Ycf/BBT8Nl6gTPm6FwyNluiyfoTtZEx6uDUtD3zZ7C7BVGcNBXlt7rP4xMMxFJTUW5iJlOjBQi+Map6UO8rqYvlsDSL7Ggnkcb8GsX9HwBe3u3cSgDWs34sTi0rOTRRKWcAUsMpZTV2QfdfRiE2+N/c445mwGrAm1aU1h46aFxrv4RNVz4djtsHODSzctEsEKjtX7rxe6McuicSoxrAVLNYbfuszW2bH2OpELUwpMsjipUYGPhu2/mhHwrB2kPC3yD8eRidiWxfKyGD5WttvWDZzLyNqZXM9h3aq1M/Dx87RZpIm7Ypg4aMOQm2IYHxhzw6t5zV3q2MWO4BKP/ExwiS8O5wxFshfHYJRcKd9Ub7vHXLoBPmQ0GEurKymCXT/jnVFx3+9veTmt8aI8n8/4289+i5eHNYepYTi0pMEjTebqbGWSU1Fu71akXSipeILfC+HWg3jcLPfpt+XacKuIiLJdGZh3N3TMY0BHb4j47JBJCDvBpVKkXDvmrefsgzvu5AwEtu/f8vHFNZftgYzwndUrvt8/51nc8sd3H/BHL97n7//GD/hb//ifLl24P9h+zyZ2VWSMi278F47Tzcbp706OVNg6Cqh1l3rudGbOaTF7BBaK631gSo46cXfSfagd7zdIEO1xJ8c5WUiWSZv7f2OZzAWV/KutxN+OX/thcupszCw1Q1hxukjcjJVjAJRIjfguXlsnFPNALpv80j3DmDfVU8tYirZeVODGldn2dM+jHA3bnVgISWVhVXCs3r8pXwKVY4phBa+THvdOQeysOjGmL1gHuyqJwSSNiSp//NV21W3tAtW0rG0PgTmAXsy/pYJbnY90xCWNS1A6sX8Lyp22xjSDYuDuiJqIes2sM5FAVBhSZNLElBUniZUz4+W7ubNE2mjG7tQOeWXdVBALk4urM5ZWDfbwjRW269b8cw6pwYmyTy0/GR5ziA1z8tbYiAaEDV1TAEwDprQ2alSQWRYASjLHTYiTwmpzSFM+y2Cb9ykGA8Ois81rEkh2nCp18IOQWke7nZjEis52O3LejfR+RhEumonLoOzzI57/7JJnv79l972/xPN8w7P0Hu6PFXmmiI7ADCmbxPDnNC+WcY+F9RCRen3CFjUW26TJWIKnfzzZtC83LWbv3L+w6n1+3lrz4NjLWvNV64ic3Pnt+LUeGdtXzUppdEtRAx19favJe1OavcACXmV1i1fe2k2L9+xcmEAJA7h7mWh9TQA0JlZt5r9IZ0uqYWVmzVpSxoV7QFZlbNXUQ2vKmxqkF2FIx3XCQDAjDozqjXkmyl6V7uTabsRA7Pr6r7KBdGtpSCQafJH8Hb9kv7oq5euPpPmeUfwKm+cyWgA9IYuFYoGtSbdyYC2R1k1snXCblau45ofTuwzacJd6qzkROhfNRJ+RtZ+WlMC2FAQOY8u+jGf84fQRL6cNL8YNL4c1WYXDHNgPLeIVzdZ40ihFCi5In/BtWZNV8F7ZtBO7yZot2gnfW7/kv7b9z0jq+E/jd/hu95Iv5nN+dPXEXuvkkdEtNh1mXWJNeF08CgRtlOyBs5l86AyoDyYJrEoIdRDXStpm+vd2pOSYS1Nek0CAmB1Nk2jbyDybTqq5c5ANwHIzR18nhTDA9Lzj2ftnfPTois5Ffrq/ZE6ewdkTTznwk90jvrjb8up6YzXaLCUMS/ETZmVSG9hJkcOIOo8737Ikt8P9tcU7Y12Vn3iHOocGhzaeHOz33Dhy646MqWK5IirMawG81VtiqpF5I8xnYiw24Z7M0B6nx3OpnsGnKpijOOcYUPKQnVXXNz2ptcprdyq2bXEnQNpJc9/sW/T4XF9zvAWzfslhJuZHP6qGtJgtzilwEfamUS7JhqcU3mq0WKm9/uSTq7TdasZbAa9JPZduXLoedQGZcYvWPaGlaCihAFqTQNLREwsDrioA1IsZwTfLv9NSoNi5Hr+MlicoVDPFWRKNwtbBYzJr193zy/pVjUrTDc5z4dRS/kTY53lJSfQiNBoXltisbjHG32VjsrWlAzWrMwPI4kd1m1f8dHrM8/ms0HJl+cwsXt6x8jPnYeAiHGgkcR1XHFLDVbPiNmTmnSzdcHwunXJBFjquJQk2jSUHelH6zZ51M/Fb2+e8310z5idcNnt+tHvCpzfnTFNgumuRg7eJoXHsDr64EWcYPW7vcFEWeZ0fjk0xM+IzvCgchOnCNhUxeq53K0s8TKWIDQqzFSlhX+i4GcIe3CSkZy39O7ec/8ZzvCiP+z2tj0R1PGr3NJJ4Pm/56fiIP715h+u7nv/tv/Lfob0S/vqPfsQfnn2P/+vTvwXRvKpknND5F7CyHgJVZXIVEfAe7TvyKpBDKcrLvLt6pvy7/8Hf4t97+tfsxh+tWe8grksdUVlSYprvU2ot+aSyrovq6dCl0XhvMq7FiRYw7KG8cOlEKIbC+mpG/Xa8HT9/VBPzWv/60nEPWHhI72ai+iWx0MnRF8VV9lVJppOTa66ysurf6joUEVZSJBzqCKWZUR8tgKoudXcFtRTz1VKOQFll/oIxuCqbiXLf02b74qdFxZCPJu9JMk6FTpQVWsJGvrm08BcNJ4KtNo6uMAIEmMlLSqLJ3I9SxMQDqeRJgZZUiJgXVVTPqIGbtCqFZcDcJ2c8k8nAMeloFyKds+SwITfM3jP4hsmp4f/1gyhA1ilAIl4hg/da/EcguInGJx63e2OOz+Z182paczt2pORIkzdpiQJemassw6lJQWZrmqAsXexls1o6vargopD6wvDLwjA3luasJr1Xp7ZWZkvKrefvZtAEee8Jm5Hu0R5BWQVr+mSElZ9xZPbZcxMTLz9vGP6/gcMPP+TGfUT4RGn+YYbxBvTGQkbijKZfwMp602Uk5Z4iaAho404Mb+01hx38ox9/j99ff2gHedXQzEcpx9LEgONaUwsEPXneh+BWeezy0eob1pNaPzz82+ndcgHQ3q41v9Zj1kTHqTrDvrs1/bRaa8zqGXN4zV8W4Db19wCtzpltyqANczabkSxSmFxxqSfqGrLLne2xS9CVqT1MLu5V2bqJlrysCX3xdEwIM46XyXPhZka1ufQ2ewY8LRYyUteGGcc+lznWzezLa+klMePYqdVco85ApBFPx30QK+lJzfXPiZlVg68yDzeHMGq0kJdSP3mMUNCIJ6mRCDyJhHkUfxLP+WR+wnVaL2Ed9XNbF7P/Xma2fuA29fzp8O4S5hFcImbPn+0e82pYIcBhtgRcBWueR2cAFqVJkqQA7Yr4TNfPpMqyLWPbjWy7kYvuwLvtDWsZeZbP+W77AoD/z8vfZJiDMXqjLGbhbjTjdYmCGx3aWKND2/I+RUNP0lkmXntcKLJCdwTy42Xi0YfXrNqZFzcbfJPNy7eEi8xzoGkiXUjm4aiQWsXXRoq9NJurizROZuHlbs3decd5GAgusW0TvY9czStejmuux57r2xVp8NaYGU2q6Adws94LEZM5GavqcmsM8GRmvMt6UP/zBnKZP5W791O9Q4OQy08t5wv3wSME5rUdo4J09rjKoCqAUmbxDl6GGSjfv0lYQDOU4p9Vn1OW90+yLmCjPvha6YN6SiLkprxHsZ6PHP2zvuZ4C2b9kiOjzGoT805bMs6SPshmupiFmbDIC3s307jjJF8T93LZsdSOSSvREPbC5jotNAZt2IjFc9dFYsNcaLiydNNrB6MpspDeWQcji7LL9+m7qPlnVVaWdUaOrCePMuCZ1dHqxLqUKrNmGnH0LhBO+hvfvvachfXV+eZe+uFajUMwl7P3BPaauMKxkZm1P5BU+CKd8SKdLRrz67Rin9p7QNbKz1z4A72bFxZdK5FJA8/nLTdxxd3cIaKsuomxacw/pM1FRlE2q+7oRdW2icv1gSl5nE98fHbF99cv+G73gudxy3udacp/cvOIw6El3Ta4ncfFkiToMjI4ky+GI+CSNplw7ZYCo8ah5gBxZRPG9DTi+0QaPHN0pDbRNcbSarpI8ko6dIRbh5sFN1vBIhF8gvZGeHWz5ve+92M2YeJ67hmSpaBUyu2f7Z/w07tLvrjaMt+1NNnzv/u9f41/9/uZ1fNIuJ2NyzAnGEZwguuKgPrU7NAJOG/AlSvdCecgeDR4tAnkriGdt8ybcK+gykFobzPf/X9kkI7UOuJKGR4dJ+Lj/e27qXURWSRGHL2t6jz9xmLDVj+XlOQMUBRv7Jh7RUrtUtRZ9xQFeFtjvB2/YCiWuKQY7KEFPIkYK9hM3vVo6C7HZEO7/MqmtFyXsXS8ffFD0vK3iEfEOnQRX5KsBMHWjFbTUoMf5XZSvK/sWdryU1GmcuzaYY96fG7BCq8g9/HgXI7nSTRigFlNmzKg6bi+/KqBrNNR/bTqyhZKUVEBkQZajQlDAAEAAElEQVRjllX+lsMxa2bAZJKNm8kIu9yy15ZRA1MOS2GY1KA7yDRiCZVB8gIc+sLu3mdLsJ1KamEIkejLLv7ENLVuxJ2zks8FpW9mUjbfmfN24LI5mG9O7tgEk5Jcj73Fk08emQysOvpYcC+BCIHcKm6QRY5R50V1BuCog7TOSFA0Cil7xGdCW88royLkIRwNfJN5aUi2//wIh6Hho8srWpcYShFW5bKTCldjx82k7IbPyD/8EPf7l8gI7hpk/wL0M+AO8gQxFpAvLK/Dft4vIuT0306sG+6crTmdJzXuXnKUOsFPysV/piAB9eb5FVfHzf0COJWnPDXYvZ9oeHyP3wis1T8XGcfy3vPg/g/XlG/vK/J2/Bdo7HRmUmGo4JAGZqUEhjgyxrwaXcOsnkNsedLeGWMrtcbkKfYnWR0v48Zqlpp8ngO9t2TDXkwA10jCy2zhV6X5PmlZN9S4vJvC0AKTAA7q6SWxr1JAqiWEMbj2hSk2qGcr88LcsiCkQC+RfWlCNxLp1JrTjWSucksjmSduz04b1i6yde1rKpFZE9d5oBFnnlr4b6UJX8cpE8shRbxpoFZd22YSqCecfF9Pz7uXAA5epsQn6YydtpbCTgEuoUgHB7b+AMCn0yP+dHiXL8YtXx62Sy3Z+8ghNrwaVtwdOmPyKsyzJ04BF7I1RHTpPrHIK7zivDF+jZnlOFuNPF3d4VAet3uetHdc+AN77di4kffDFb8/fIfracU0BXIWZHa4oSSgl3nfTdYhyG3Vr5rhPJODm4CeJaZHjub2yN5KnTI/iWzfvePR+kBWoW0jQxbSGBa2sveZ4O7TfZZmdPkvHAwYiiurnSTDYWz48d1jzhoDCJ0ozw5nRHXs54abfW/2MtHk8mHnCDsljGpg1kmKIYCuOkhF9v5V+5fSZD/9CbX5flxDlrX3xHPhFFjSYIbrti4V0OuBKmV5H8p4uD4t/o4PWcru5Dn98Th1TbKArCNgpm+oeRbwyuk9axY9PdDXGG/BrF9yVAClytqAxecKbEEwLbhtSq/jirX3dBINcYClwzGXKnfGOicbN5aNWy6qaeu23+ZVAbZ06aJPWGpiX6i3cGRT7TRYVyMLa0n0YpK829I27EiG4OKKRhsmVTYu3/Pg2ueGCY/PytaZ+bt1rsM/t65GHWlhYtnzWvSt/W1VJv+mlCAOASa2JQmlF/g0N/xkfszLdLZQcwHWfqJzkbUfF4+TSr/e55asjp+Nl3w+nLOPLa+GFXPyjHOwSPJSKGqRThhNQQldLMbrgvfQhUgsPl2X7YF/af1JoYB3rN3E373+PruhJc8Oica6crXTe7rhLT81KLpKpNFQ+CyWBlXpsmmlxPcnzi4OjGNAW+u0CDBFj/eZNiQO2PzqZsFNHJOkfF1oIF23XE0rPlpdMWbP3dzROjikhkNqFiBr2pt3mB8FPxwndbDNOHOEEJCL8+MHe1JIqJP79FovqPcQ3OsU22q4g71eFZjXjtjbcdTZ4pT64/woyf6jemxxUvDXBeJBoXA0/dX7EbUn4/T4Whf/AmTlpaNROg9SHvAWzHo7fsGoAMopu7aCQiyA1DEpcMyBLLb2VFDLlS750kQRyIUZHDA5Yf0OiCijBkucK3+rz1+9uaoHV03HndWKjhEDeoJYI2RakGN7JafcsKQGfp16cM3qTNaoSqeZIOYh4b8FFtYvGrlK2svzenHL+hM4FieAsX0EWjUJZxC4zY6rvOKQWya1oA+AxiWCWuEYTjzKFPOsURVuUs9d7JizZ4gNKQspO+Y5LGsNtWkC4NT8QOreEwsoyUXy2PvIe811MWG21OWfDZfMMSweW1XyURwNXhtaGMEajvPu6SY0B8hnibafzWzXu+VcUnaIM3PmGb8UMFJALGBhO0kCHY/+JFEdkwa8WHDBnBw3U8NuUNJ8C+kTXHqODAHJM6J3wA50hDSbD0nfnbySumYU348KXi3rjztK2MvPpSvN/cPkpgR/lEIjhyJ/OVmTlnXgtHnypnH6mF9wqS9rTQW16gd2CqDl07WGt2vNr/mo/q+9xMXfqqo8PNYQN0uUmZWfmLPHuQo0+SVlvTKgTmWJYPLFavLu5Mj2TcVkPuGW9WUuNiizBryc+AarWZI0kkt64VGmWCWJnaTlGt6UvfiuWIDc5H55zNoZwFDBMDNRTzz2AJFeDKSaNRWWFnTS8CoPXGWzWWlFcJrYuhbPt1PLnK4bp0ntd3k2CxmtDZvMqJFz19+XQopNyFlnnuWOl+nM3nvy4sl85ge27sCsgau05vm85Ye7d3g5rpmz5+rQ4wQL5EiWaHwYLbAqJwun0snm5dQKoUu4VbQ6xJUFWqyeqYzfTT+xbmY+Prvid84+X2SOj4IZpd+knu+3z/l0fsQPD0+5OvTG5rpucQe3zPu505JKKKRNsd9oM2EV7dzqGzE54jszkhqaWyF1Ji9cP9nT+MSr/Yonmz19ExmGxgJRvDIdGrSPOJe5veuI1y3NtUei+WRJsgaKn+z6zEFwHsIg7DZrfpgczmfev7w1I/nsuBttPRkOLQwOmR3ttdC9gvZWaW8y4ZBwU1pSDNUX25Y3jUUN8ov3OPdlfaXBUdYfUYUs1jh/A2ilypF5drLuvrYmnaYZUoG/4/Ms55zBRSU3xybY6Vonal6ZNaSr1lcuFdIFnMhKT5//F74Ny3gLZv2SY59ndppLRzYsyYMV2Kox6Hexw4my9lN5XIvXwNpN7HMgO7ckUwy5pZfILneLh5YdR0xqoIGdGNpdKb1wnCCB4q9lV1u7mClWn6vSJS8+XjURDwzQql5TXs1ny2vRq0tiKsfoxBHwdCc0xFHnpcPwbXc4lteqmVjcXGo6SAXXHqYhXrrMTjNfpIbP0zn73LFP3QmQNbJ1g0WNl/jaT4bHfD5sySr0PjJlz+e7c24H+zxjdsyzJ0UrPjTKcSNZfkrIhMYMeFOyFMFNM3HRHrhoBz7srpbP/gfdl/yT4QM+353bhF8kGFIAJvWW9GCvvTxPl9DsYXDkdWY6N3ZWbuzvcaWk9ybee+8KL8rLtAYgDs3CxlCVYhKMFTFeF8M/N2OLW+1UROHlYc1n3QUAZ81IVuHZeMbVsOLlbr2Y4LvBEXamPffTsUMhsVBr1z1Uqi3cp9oumnG597MCWRrkSLF9MJOJFmllpbrWrsRpZ4DTifykE/GQWutfX1hUZOk2FDLkov+u/z2k01JuJ5fi72SifmvK+3b8ojFrxtyChIw7GruWzbcnMxXmjxQPR3ucP/FIqfbqdsFltcTAGb94aFnUhqXzZXVMmi1M48T3xBhW9gU43X/U7nv926wGENfGnsLRmLcwvcyAPhGxFJ2jXL4cszCxTkGsqGmZ/r4NmWEdVdppv1PeGVnWH4+8loboVCyRCmNA32m3FIlHICvSijmLVWnIdVxxF839JZTi8G7umKI3tpoakKWp+JmdygyPT47zeWG3eZdoXKLzM72PbMOwMPce+x3P4xl3c7fEqkvRTEoqG92Hm+6g1m2L5mOSOmNn1a5sbiBvEmdnh6LWNmtnjZ76ToqKnWMq517+q3ITKyRqI0A4xIbb2APQeota38WWIQYOc0uaFdKAxIibdkVunyDPSI5Itu63NrZISK5gEveLh4eNFDkFsjimLj3c1pS5vjZNKkPtIRBV5/h7BcOD6/arGtEV4FNYmiyn8vY3Pq6uQ/98sd+34z/HMalyqw09loYHLEBW/b2uF6Y0KFK8HMp/jckKaU8SDe3LXeuJMTeMNFzrellj6t651j+Niwy5ZeNGA63LdVqBJ4Camj6XpPEWCzPpFwuQGrfGYuMCcJN7clGr1OPUn+a3le3x2Rr3e400RNbOM2vGI8xq59GgbMSx01wkfelbrV28uEVFcpvt/W3EkVRpEJzAWpp7CfCnjfuM8izrPXlowvHY3zE782BsJfFFvOA6rvnZcMmLYcPLw5oxevOJim6Z73M0TymUEuhh4JLMgg6euBX67YhfF4Z38eP0LhN8og2Ji27gcbfntzdf8kFzxcu0sdAtN5BwbNzEy3TGp/MjPj1csB860m1D2DncCC4J2SupBT8VkKVRaDOr82EBWne3DW5wVp8EmC8TEj25AffOyLqbCT7RebsmxzkcS4mQuHh0wLvM1d2aOASaV57mVhavLPtPrd5RaO50mcPjl55RerTNfCGwXQ+oCrf7zvwr7xr83hr3zY3Q7JR2l2l2ETcmq3dyZklKfzCOTCp9bU1406hrgdTI27Jmywk4BXpMWn8DO2qpfRy4fH99WoCyB8b0D4Etredcm0CwvGf1efSEvSVZ75nUn24ej4ElZbMIR9b51xhvwaxfcgya2ZfdgsM2oMNilGALxtqPi0bdcTR/h2OhMadqzl6uMAc9kSRu8c+qz9G7+USeaCa4x2c8FgtOarfcH4GyQu+FYsTotNB+AxN+OZ/zxUTdvgFOlK3MPGZi44S19GSUqPMCJL0sk3MvQiOONa9Te3+VwyFEEvtcaLYn6MBDbXzA2Ae3CT5P58waFsmn17x4A8zqeTmv+dHhHV5NK16Na3ZTS3D2HkzJm1n67A1sSkKePDjFtxlpMhqti6EBcEpoE84pbYj4Tvloe81vnj0D4DwMPAo7Zg1c+hsGbfjZeMnt2DINDbIzGYabZCkuqhdJnfBdk8nFdFD7zPQk0b7wSLIEqPh05vLRDlWhCZEmJKYpgKilFyaH95nd0DLcdvjDkQkmkbLgGOouAs214/mrLfup4fH6wLqZGFNgPzfE5DnsW3QwH6/ulaO9Udq7jB8ybs5HdL8JVnQ8jEo/LSpOqLW16/2mzvibxpFddSxGThlW9w5xyrRSNSCtgk8POxGwyDxee259w3/cLypeM+t9C2S9HV9jRHRhn0ppVsSTTW2Vh1iH2rhClVWKZMu8VVlYUhXUQCBoNhYXuhQ7ghIkLbJELSDO6Ta/yrGtOXc0QgcDtGqyojHCbD0y43ZjEitCJ3Fhm1XLup7EikQr0BQHrqhHIOlQGheB4g2m314BYu+FUNN0gQXgss3YfTDNYcyIuwx32hlgWNhxZnBsRvwZ4ZAbruY1h9QwpMak59VwXx1TDOQk5GyThqayOfVqG70qgS6eWc4Xb0ZnPpHbduBxuwegc5FVMXVeuZGonpvUM0VPmh1MDilhHwtYkk0yZx6QauuaiLGOg5JWCgc5+jKuE/1qQimBAy6TYlgmQM0CXpmiJ47WFa/MLLKtN6eNAD8I+0PHnByrJtK4VPZLxv6Iky8+XoofIn6M+ElwMVp6YZ2EnQPNkN8w2S5g5Mla8w3H6br0cH7/hWvNaYHAvWWj3OcXPfkb/i3HX9+CWX9+Ri/CLImr3HPphkXql4sEvDbXLaVQOPMjd6ljzBXscgQcKTtitnm6kUTnInP2rP3EPrU07giU1XH01JqZNHDuDkfG1T0zeLcwr261KX7BytpFBvU8SxvzBMYAqiv1PPE7HhevLYCrtC61kHCVV6SSSn4hMxm40sCztAHgsd/ztKhgwMCj2xxJYCbqmhlUWIvSyWkl9e2NWZOBaigNHi9i9YvW5o29/lNPLzDCwG1u+DxesssmMXzi7xi04UU0lcm5O+BRbmLPs+GMl4c1NztjQ+XZoYNHovlVASYFD9nArNI4pybTvgqMPtOtZ7brweTqTml8onGZy+7Ah+trVkXa6CWzL9fTPnc0Evk8XvAPb79L4xL72DKNoZi+Wy2Tg9VKosemCE5pVjMhJPa7HucyNApDAdqcgz4zvae0FyO//Z7VU3Myi4Qv787Y7Tt7HCwhKFd3a4ZXPf7G09zJPVawn45AFkCzt3+Lmml67kwJMk5rhrbH9XZN5X1YEhHDrqQhTpgaZcoltd1sVWQutc4vWmMs4rcQ2UuXPNvmyMVscqlSExrAVxv7Uhr49u/cCNOZKVRcUrIYQ6o2rV5jQpXxxjXwAWilUs4zHFn3S6J7LqyrUyysegynI7CWTr9u9TxEC/Al36g2egtm/ZJjBm5zQ19ou88KUFILgjE3ZnLoZrKa9CPhOKSGuEjkStdbMnO2yX3OgZnAbeoNcEE58wOtRDx1IQm2+9NQOuaRdblissoCUlWTxXp7ZWgh0JbI3J223ObV8rouxTbAtSPiShHTi1Fhr/NAL9bpWCJkFTZOmNV04GaG/+2AWZnMXZ5f68g3DwqaU536bY5c5xWDNuY3Ruax3xXDxJHbvDLz8uERn+wuebHfMEbPNJk0RLOQsyNN5Tkmt8STA6RNotlMuJUWLMb8a9qQCD5z3g886Xf8xe3nfNBc8Txu6dxsKTEq7LXjk+kJP9k9Znfo0F0g3Dn8BIh1LmzCt9hzWUfafsY55TB4ZAwwO7TLzFubWONZ5uzRnq6JS6IVmHZcgKaN5vU1NxxuO8KXRt31k03IboYwGP23RVEHzU7YNT23jwKHs47z7Z7GZ+6GjmkM1qW4czS3jvYKuhulvYqEQ5nMa4cC7k3qryUS/ryhBR3KujAKpMz51bvK7leKB5XXDXLvdbbf0C1/Q1Gi9WftOjwEpr7qJSzg1nH1kPo2PDzG2/F2vGFkLG7dPJWy+R4WSj+wAFihSA6jGgtrVodXMd8+dPGuSqWoSAVcso20Ha0tEpV6rVZADJzts0oHHOwaNoNzh0MJpXpXLQwtuNddn9Qz6XEX01f5CUuGYnkd9TVHQuleV4aXSRPN01FVi9fjtzPM9yu9tv98uPZk1eW2UTOjWix6LADfSmaSSGE4B/ap4yb2XM89h7klZiElYzFUtqym8hypvBlFTqhtxjUJacqUUzzKvMs4p3QhsgoTT9s7zvzAPncESRYWgHnbXKeV+ZdEWzfcdJQ/6EL9L6BZk/FNRkSJ0dY8zQZo5dZYw7lV2tWM93nxCAXMiwXzyTJ5hieOwXwgp+KTlWqjBrsOsTnRTTD7wNQ7YhvoOlvvaiKiTpa66ycLPfGj4oeEi7kkSJ1O+se59xvNt3WtOWlMUA69NDXgHoj0FTVCee43rDUPRn38w7XmK9eXX/ga7p/f2/HrOYKYH9VVWpvxtxv4vPwtqzDTWDrhCQjVuMRdMtC9OQkAqSwt+z0s/r0xV2ZXKeYR9qlj7Y9+QlZnNDSaLHG92KhUCeLWDYzlHKoZ/KDerEzUM+NLsJWNnbY0mukkGdurqGCyGkNrWNaTkUZgnzv+dHp38Y26dDc0+MXbt/MNd3nkZc5M6uglGxCoibVrv6VPp7zfYtznhNKVJokrLpCdC68xsqyBJCS1xxgDLXLpYwmzirxIZwzacOH3TOrZ55arecXVsDJG1qFBZ4dMDn+w+VvKfJBakNEfQfciS3ZzUT3sA8NsAVEfXN6QsuPd9S29n2kks3JmzfI43LF2I7/RfQmYzc1VWvPvP/tdPt9t2XYjV4cVeXb40SElOTD1Zd8+UhZ+a56IU8axQXPxA0vFLH4WtBFUFJk9m5Vdd2MKPL/b4Fw2VhaQZs9qM9E1MzF55tlDFsJeaO7s5VbGbeyFoJY+6KISDkqzS2Qv+MHR3JT0xDtPbh2p9+SVFvWKHdOPtgaZV1YBsSqQNcb7HlmqC0v43hBB5gjjZLWSc0cfx69gEL/RNB7QxiMfbkiNL/5fpalUPDAfStOXJMN6U13PagMmKeIhe7c06Je19OQ49T2tGBwUVU8hB4C8keH8+jG//oL1Fsz6JUcDxfw90BaQySZs64Q4MdZUZf3sU0tW66g7Ue5SV1KpjrKQLGIyxFIszMmX7sjMmM3w0BL2Kv3UpvxqAmy/56KRV9ZupiFznbuFfVWpufg9m3K8oSwkvZu5zT24gZbMWhKDOj4t8rSNzLzj52XRy6rsNLNxwqTKqNChNM7fA5N+VcNM9xMJM0TuxS9djV7CazThKku8VeEqrxkKYHjp9wza8CyeL35lr+Kazw7nPNttuNv3pOhJo7eudZUQegWHLQhz8RfJVnhEr/RnI6uuJFY6pQ+RVZj5cHPNNgxc+ANOjKHXMVtHSz2/v/8Ou9RxO3dMhwZ3cMVgV0it3t+INpm2t8/dfEns9ctUPLO2iWkLl+/e8t72dqFkv9yvzNsLeyldY7KN/U2Pu2po7gQ/HwsLP+pS4IQ9+NlAtXkjQCANjpf7gOuMwaGDx98VA8R9mdiHjB/LhP6wQ/GmUam2KS/a71rIiQiabILXKeG8dSVySZjKjU3wuRXiyjGvSwxsAY4kFQ/Lh5Vv9bb6Sh+sr55Uj/TY+7dJtnjce6bvJ3/Pcv8xb8fb8fNG9eyci08GVI+lwisRxWs2jFeOvktZHSKZqTCDRY7SOS3zUPW+ympy9yBCysHk5igiM0EdTmqoRl7waPNNEWqsukMZi5zFbi8nXtYhL8pcuv5BEqMGIJY0YCUiJjFRaEmsXT76DCnM6AJkRTXQyyH3wKRf1VAsWVAxc9xQpB6oEEoJcvqcVZY4aWEqqLG0VjIzi2Ofu4VVfcgNt7FjP7VMcyBnd4w+z8su0j7c6mNVAfHRkQVCF2mKrEJECS7TuMy2HWidJSFaA1VAKFJO4cv5nCmbJDXNblnHRMvceDo3ecU35Xpb0qxYGFzaKalT+s3IphsXKeVhbszbqxwqOBNnzkNABrcAWae+WfU53cziL5JbAEeOwmF2xiRQIBqQVYNKXLQixCVdgKxT092f/0FXhEoQzeU1ll21iJ2Lk5K2VDrk3tYF55VcuuD1fauHu2d6W8cvnPd/zjX8VYCUnvz38Gj6tlny52V4hBZjgb5MZ6zdyJNwx+fzRQGTSuMDY+Huc7sAWbMemyNBkgFYCG0BrWKuOXpwFdes3MSjZs/azeyTWXaAsbUu/GEBmRKC10wv88LqTQiNZNoSntVIpsHCrWaM3XWbVwuzrFfHp/GCXWH7XPodV2nDxo2LT9esnk/Tmqf+gJPM03CzPH/D0Ui9mq4bQ+soofSauPiWapaHI+BZizvxXE6spL0HZD302MpkshqDrZrvO8xDuZeZjTOp523u+Xy84Iv9OdeHnsO+M3/c0VlNsfjE2lxvjQAhN8q8zcbAXdJlLVkwq9UZqzAvzbTzMHJIDQnHo2bHY3/H++GaWT23ecXP5kf84e4jdrHlZt9zmJqlPk2bRO4t9S93ihvcwtI6HarWpM6vWppbV5oeZZ+/AV1Hgs+MKXA99IxzoAmmhFmti5TUJzbtvLCeEwY+GQvY5nVJyrwtc31Wmr3S3kT8IaLe0b/y+FlIjZAbSL3gD0KMaktECUJZvLeqpcqckDEhKSHTfKxtHhisL6N266YZHYsk1/sluZ2MsYzLGiRqyYbLSNkArbJ/kFTYYeU6r4SBh8EhFr5SmuwVuHIFsPKvG9CfDsnlPVQttivWFKvEqmVPAbY2VkVL7Y9Slq6678h1vf3536GH4y2Y9UuORmQBIzYy0cvMjuOkftoFcWhhZzVL8VDv11A39fVxQufiPTrvPrdmFo8BZvvc4d1AEimdjYa9OtOla+mEF4BrxLPT9p7JPJipO64WDSOUzXZdiDKJxzIzZs/n8ZKE8K6/5bGfizeV+WM1OnGVI7fZuhytSFkwfvW03drZqKN2NRrnlwXrIT131rSYJq/dSCuJVDpK1YD/edxyPa94Naw4jC3TrjXfp0PpWhdJR270tU2lKJBAd4FDEsKTxJPNns5H3ul3NC5x0Rw48yPvhBs2bqLvJrwou9zx925+gz94+T59iLzcrdHJJk8wqWD2NlECaGvPLWLJIzk6GP1inph7bCPdJy5Wpu2+OqxIWawLj3X+N+uRrolc71bo7Gh2QnN73PzmANLahBNGpdlnwj4T147xIiBZyMGTW0/q1dKrFHyJpQ0H88pyox6ptmOZ1E8KjNe6E2VCl8NoCVQPOxPOvd6VqB0JZ5p6nGO+7Ln7qLUi41irvC4BeaBjf63T8GA8fHx2D/5Wn6M8vnYpTh+jyhGdeAtovR1fY3gM8kkqtJIIkpnKtZMxJs9RIsjCzjqxTi2FBUtRUx93ahJf16zKxq1MHiexSAntPGYVi3oveIsUwCchTOqpoSX1q2WSdQPCWklMWhhIIgXKyqwkk7Jwl3sycCYTK0YzgKdI+EgMmhnV0hzNfk6/lQLEi0NVCVLfV1l8surzVYP4OqzwsPe3PZEUzgVMdJKZkqUTDrFhTp40+YXlKzU4BI7eeg/njwzMjqgBt86smxnvMutgkp7ORVoXl7WupiNOGvh0vOTLwxnBZQ5zA8kZpZ+jV1VV5ZyQuEnFIJjS1JEsaChrYch0TQQVhtiYmXHdwSq0bcT7zDg1kE0276fja1MHlLhwF7V4l5i03U3GGnTOAkA0YBJ7Nb+Vxe8kFkAsFyArZuuEn3w+b/QmVEViYQwvrFwpa2y5spffKbJAWe6DCKkPTOfeNuonz/GanPweU+wXj9fWjYdrzemxoX4Rj4+pf3rbOPlzMaJmwFuKIdAXw/PKxHQoF2Fve3ykyP8SWYROMi8nk+YlsbWjIXFIDZeNMZxi9nRupnXmVHtqqTKXIJExN+wl8TTcAkdPK/MGDDQSGbShJbMrMsNtaZJs3YDTzD53i21Lxi1NaPPsbXmZzrj0ph65zStqSJaBXYmnbl8AsZ5Ld+BWhSYPZU1pFmbUVV6z05YfhJf035Lv4ptG9fStNYkT95rZ+z1v4DKcCEktnbIm20/Z6sxLb6/5Wdzy0/0lr4aVSQuTwFzXFjuO+oKjlMPP20xa52XuUK/kjZq8fHYQDP0YU+Cs37HyltK7CSMX4cBf7n/Kd8IVa0n8MF7wj4eP+Gy64Kf7S66Hnr4tjfc50PTR6s99wI/G6M3rbOcaDMSw5jimhNkV76sIYV/sT5Jjfj+x3Qz0JUxrP7ZMY4P3dqyuMUsVJ8rd2BJ8pl9NzDetNWICEAUXy1oyH9c/PyhhNyNjIveBMFrSniTIsfh8heNPSVXJYqwsPxZLlTkdrVTmaG968PcBrYdD9bhund6nsLQWOQd8NSh2cixJek/2d9rcWJottZnECVi1BJW4Y00Di9LF/nE8h+rPpfLmdaquX0ttlIGSrqjOmIA1BCC33FvHvs54C2b9ksMjeDK3agalGzdyldaMxfS1sqfmHEq8dnOvA2J/8+CnRW6IWrf7Jq7o3MyjZm/d7twwExjF6KS1G0E2Dfsx+tbTlo56QugkWae8XGHVxN1L5ib3fJm2xUNqWja/Ne510IZnyY596Xfc5tVRz14u4li6NaPCy9xz6Ua8Jh59S6kgYJ0NVyb/WRNe5CuBrNrVaCWzdiMuN+Ri2F/TULI6Ppsu+HR3wfV+xTRazKobnEWHV9BBMGArQloruSto9lSeLGFFRvRs25Hez3QuEgoo+U5zy/vhmid+x1Ve8Xm85E+Hd/nscM5ubBmmhphs8YjnCTeYtjwHCAdZ0GtxSkqWRqgHT7jx+KF0LTpHvpgX5tbN1HGYmsUjp19NCLBq5yJLsTaNm2UpYqpHVlwZSu520Nwm/D4iKdC/csyTdUhSB2kQ4uxQb+9PNVM0HXrG1Q7FHA3Mgtc7Evc+tIxOU6HlOqSAVdad0GJOfwJqlWOIlIKLjJuSdaFO5/4KatWnfUgQkxM54ldMpjU1RIqRewU5K2178cY6MQQ+HlOPk3tNO6yPfTvejp8zpMjVIsEAAokMNIupr9wrGjyxrDGWcmh/yypQ1opULlYBYg4EyfR+tuco6XdJHZ2bC1AmgMnhKz6QCrhU0698YWnVlJ265tRkxF32CyOrxYCe6qsV1bPLJmnvZWLSZgHC6lexNjGiwqFEtDuU/lvUT7mT9zaXV/ZVQFbhcBUpSKKmQ5rRfqJxxoS9TT23U88wN6Ro0geJR8l6nbMkVT8qPfqJ1NkrA8mk761PBGcAZ91zrN3E1g2sZGbQwF3ueRU33MaOKXli8uTswCm5ywvzWB0wi4HtBRzJpc7R6HCjO3pbefNprMytMXnzLSnnGBpjhoUiPZQyAUs+dmVLGv0Sg179S2TOiDrCQS3NypViI9r7oo7F78SYXQ8CRnJegkXuXx4PrhVVNB3BrKVxsoBVFplQmyGLT5mw7MxrwUBtUtRnOi0cHs7xX6N4rka8y3EKcFzXsNPu+vGcji9xecoHgOjb8es5grglYdAUIy29zFz4HbfJapR9bplzWPb4j8Keu9RxV5hVtYEOllDdusjVvFqM1ce4Wva0WWsolWftJu5Sx5kfF+m4qUhiSTw8fgGyOnY0hTnl2Oe81BX73JGQxQvqNq/4Mp5znVbLea/dyE46cCyvNatj6w+8TGd4f8sTN5r9SwnP2qtJCUeNzCidOD4Mt8zqeOKVs39Oflmno64jX0ci73D0Eti6kVudS9MpMOTGmuPlPX8ZzwBofaJrIoO2yORwgyzTw1G6rOSVou3RAdyvEk0b6duZKXr21ytW5wN9O3PRHlj5meAST9tbLvyBH3Rf8p1wxdYlPk8df3//A/5k9x6djwRJXPTmpfXZzTn76xW+eE25QcgetFVoMrMv6FpQmpDp+5ndbV9YvIIrqYNuBgTa9ch7WwNMb6eOcSjha7Mx0scCb9S651F/4Mt4xiIZV46NdLFmiDozofdDsjldldx7cgGyXFGtSFKkyuubcntWwgDNTgn7kmAYM8RkzZIKUDlnnldfxcpSRb8Om/iNF8nrk7xLejReP6lP6uuW8rZLPKbNU6XzJ+d3lACeAFk8WIMqQFqa/DnIsXlf71bN6BWrlwNHf6xcrod/hoLoLZj1S46qYR5yiy+pC17y0gWpOnIvxbjdC0E91/NqaVlnFQ6pJbu4SAnNV+3IyqrFgkkJrfOy9YPJQMS6EoMa7dRJXgzdNwVlcaIn6Yf2vIM2XOXNsbuS1mzcCG5kKpLCer/H/o73/R3bIj0cVHiZRhqRReaXSge/kQPbb5mme5pW6B7s0t7U1XA4GjF/Fu+MmmvRwplzN/AinfHlsOVubq0jEAWZivyCE6RZDdHP22xdaVGT+J2bn4gOAddHnDPz5N7PdD6y8SPvNHf85f4TvhNu2OfAn07vGZA1nHM3dWy6iWFqmKeA7xJpcke0vMvMZ8Y6ym0mtPaHeAh2ngnCTkp3QfAfjzw62+NdZr9fMRxa2s4WO+9MXjgnM/rc9BN73y/gjJsLkh8VDQW4nPJJp8LjR6OW5pL+4VrzLcmNdVD8WJhZhyIxnKIBWXO0DsUJm+pNoJakjL5JT/7z0j4eTOSSdZHO3GtwnUy4daIvFwk1inZhfXH/ccs51u6DskTc3tOfgy0U+aT74QC1TlDyJ7e9HW/H1xiKAT1Vog62URtyg3CUqZtvVTI/CnXG+NV6DCFmjxYQyXy1WI6XtUJm9nWqRULnJkTs+RVZfKCEo6F7Q5W7UeLY/cL2iiV+/WhYb3L5liPgBSaDW8nEmZuY1fhaUeFAwnOU+SlWEHlR2m9cpdcvnuf4BcwYX/n1TeRpWuFDT4nqF6knX3zBjOyDWoLtpIFZzXOkl8heW3axZcrFH6uAUkdpIcvGUB3ktnTIC7CinXmHER0SciGs5sVk3hhZE++Fay7cyKyOl+nsBMgKtD4Rk4WYiM9o8seX7nXpjKq3dETAGMCFNVY39eoEuYis2hknypACcfb4UK5FMR+vnI2B14TI7MICDC3zcz5+FCaNSJBs/XHJPppq21YLi/rvJY1qkRieAFm1KFjYVCcfnhw/U9U3rDU/bzy45ET1uP9+eKhaL+XTLgrLWlY72q89RljO+bRgeM2ncTneyePqpVTXnpPb3o5f32GARyoAUsNtWnHp97wfrhm05cv5nLsCas3JWyPdHVUkj9sdt7EnqbDys/nbZW8N+AJeWaO9o3MJHzLBmc9WVY54McbQWkeSmvLDTOFt7u9ltvPjmEi4w0AtkyKaH+Mud1bX5GZRtAy54TqteKe55dLvC1hma8jLfEaTE1t34FnastNxUcVsZGLjjF12q8YZHtTSDt/z0GBsLaux/vmDWm8aR/ZWXnyzHJ6NODYSuS1A5bkfcGQm9QsRoQ8zwWVrLGWrYSjqs9xldJ2QNuPK5+ecsl2PPN3c0bpEVMeLw5omJB5tzBblrB35eH3FB+11ScJMfKd5wV/tvmQrjpcZPpmf8OW0tbAzUXofOWuu+Wx/QcoO30dbR1RIq2w2IV4Jq0jMja0vopa4mB2anDXZJyF1ih+seT5dKL99cc0Pzl7gJPPDu3d41a6ZxoZcvI1tevWsu5lNO3HRHfj05tzklhWUqk25tsjZl7kacuuhccTe298bKQwnsw+xFF+FQ22mQHPINLcRNxQrlXRch7QEXUnQn1/HfJ2R9Qg4wRtrKMCYWVFPEg45evWerFPmN3niZVyPo3pMNkSPDZ46Ss21MJIfrE/VKuDUr9jF4jNZSBqUBpHV0yDRUj1z+83W5Ldg1i85GjGPkYRwk3t6mXns77j1K25Tzz51CwPLwK2JV/N66UZXDbEBWs2SRFg17J2LC9jkySRxS0clqdAI9yay+tgkloC4o10YYrMGclkwzITX8zKecZt6Rg2ceUs/6TkyuLI6cCODNouBbzVrXEukwYzVvQi9KN8LN1w6Ryf//C6tN8lLvDg4WQQa8axFyiJg93eSl27GLnfmW1Ym9mUSLb4guVG0UZu82owExbeJto1segMMY3IcuoZVN7PtR572d6z8zKOw553mlt/pPuX74RqAn8RH/MHuI3bRZJ2X3YGojp9NF6Tk7m1Kc6vIKpK9FT/S2rUTQmaeC3Msn0zODt7Z7nhndceUgzG9KN5aYIUF0IXEWTcyJW+FUTx2ug2skQX4cVPRUZfJPYeToqZ0xfNsj3FRcRGaXaa5S/ghHiUfOaMxIs4ZmPWmYfziUuV9xd/Ld+rnmsbn2ml4WHnYZ+pSBe7qCna6CJT/1WLj5HEPC4k6US969PLvqiU/Bc8qYLj8/hBoezvejq8YTgxAysCggYbMWiYmCYwazCOrgi6Yh2JUt0ja698U82ys65KqJQR6yQvYZM+jx3UKKUSdYhy/mL8LWSyJEPEFDFNyMZ8Pxdw3Ihxyy6iBpI7WRZzk8mogFgZZC0TxR6uoApZVjtZYCpEgcOFGepFFAvg130XM6XIFtECHISkjcCg/41c/+g0bUAO0KDCfLM/QFCN+MNCxvoZJQwGgClOp+GEd593CwqqNEgfiMz5k2gIS5SzMwdP4RBsimzARJLMq6cnv+FsunTXSrvOKL+ctcw4oQu+tqXabfGGxliFlbmqyybWVJRrbeSUPsjCypCpSBNbtxLqZTBKUjd2RU722jC8enNJ6Y2gsr7fOiyfgHRSgSsS8UoK7HyWu4DJo4l6zoLKAjZFV1w/rcMsJGPnGoQ9axw//tsgovvoQ/Lx5/OQ8H4Kh9x4vJ4cop/zacy7v18laIye3l/e0SoiWv71dY/5cDC+WytdKYl++iwlh60bO3YFXYjJCa+Qmzvxoc7sonZs55BaHsgpFFsYxHX0scjbAgHjgkFtaIh6TNlvwlOdMhsXTqVqbbNz4wKz9mN5+m1dmcwKLl219XB01cdya/VWVYfdpJC6piVd5zW1a8TTcLMc3LynPLIlnacV7/kAvsHXmfDiTmDV/q+nr/6zjFNTyYh6NvSScG+k1LjXMoA03uWftRt5pdya9mxsOXSLOjhgUguL6yGo9seknVIUuRD7eXvFRf8Vd6ngxbmglknuTFW6aiSfdjsfNjt9df0ojkYzJGv9Sa0DWjHKbG348vcNNXHE1rbiRnqzCq2HFi5sN06FBR4+78zZVdWoKkrWlFuYk5J0pYtQLzmWa1UzCUk5ygMMHZT9yMfPe6pZ/Yf0512nFy3bDpp8Yh4acPM4nvLfwrU078f7mhphNMeOi7f1Fi9dhC9HwXcLA4tdkjXxZGMpacRw1QOZU1eGi4ovpe9hH3BC5F3SVSj3zddaaqjD5eWDXw2b7V4FjCyBV7nbid4kc14nuKuNHayBZc6fcPzhbT4Ms52bsYL13fqdBKAt54ORlnjKac5CljhIt631zPLdlH/INx1sw65ccDZ6tzLTFQD2L49LteRzu2OeWfW7NwNDZRDumtiwckV1qF1lAzFZcTCUiN6kQc0v01vFcu6kkTt0v8IfqeVU8SFoBJ/PRG6t4l/RYap6TvNB4Lb3EUqucFhmjBhxH/5RdAcBepjNeAmsZbSJzZhw/qjKpY+vM6P6xr55cFj379Qi03844XQQcxiDrJTJLZOsOC/0ZoJeJx+2Oq3bFzdAhPlsHIwjaKO5sZr2ebIL1icvVwEebKwCmHLgaV+znlve2t6zDxNP+jn9x86n5C0jkO80Lfre5phHhx7Hhx9NTdrFjl47JKV/cbdntO/Lkl5j0tMnQZJouEgE9BDRamocrTECJJj8cn1gFGD8Y+YuPPuc7/Su+nLbcjD2fjQ2a3SLz8KJs2on31rf88OoJVIlhYQOkVsid/ZtSRGhwaHDkthqtU6i3lgCihdnkohnFN3dFljgbvVaymnY8pQXsudddgHsTOvL69f5zteYPRmVmAUezwVwmUWfdiOauSFLqc7sii1L96sXkq24+Aa/seCcvqzxGnU3UwgMgy6gmX+t1vR1/PofH0UomYPHmKkIvMys3WZe9AFRBSupcLjNwaWxU8/dc2FhpaZwIOQuNy4izMBCtlfXJtR7LhRtOAC2RvBQWJi/0lppXJHb1nFQFV9aZXI6VC1tJRAnqmOwMOeSWA1agKEJvGVxEKLJ58/Jale9qWqC2XzQqzLQF3gWeGp6VgTgCXwLPMFDrqwGtNx65ACZZDdAK4giai7w94haBIgRJrPzE4AOj8+CsSWJMTUXaTNMmRMwsv28i22Yo77FjSIE5eTbdSOMSmzDxbnO7+J5duD1P/YgHrrIlm805LAUowG7qmOZgaYnJdpbaGHjmfBFzzs7mSy9HK6lsG9FYiNt5G3m6uuM8DOxSyxgDd7G/t/N3FBPeZuLVsILaMClgi/qTzWuV6xd2rPlpyHHjm3VpMNn5lKbElE2WmOzAxvYqHfFiZPs6+6kWAHWT/ob59+t2z0+ZWaegEuX3aA2he3P8KTPsm3bpHxYvb1prhCPx8OFa83ap+bUcsyZaEdZuJCFcpQ23aUVD4mm4YZc7fjo9Xvb318kSzCvjqnWmItiljk/3F/RhZojNIimM6un9XB5jF9dij3Iy7lLPPrc89jt8qY9qTZJK872OQRt6mRffYTOnz2WPnrhKa25zz13qSep4HHac+YGE4yqtmTRw6XclJdZzm1YLkLeRiS/Tlku3py/vyYUbycBFSS2cSYya6b5lNckvO6ripMGzdZlJMxsys84M6hmS2afsc2f2NO2Bz/05/WrigDVPqnxwUyxGNs3EXzz/nA/aa/5seMLP9pfGwgojPzh7wayOKQceNzve764ZtOHS79m6A72z573Kievc8GU644v53Azfx57L/sAXd1tevtpYwvnO5ILNrU1Q45NMeDqxXY90IfI8OaZsTDLfZPa7nq6fmHtdQj6SL9K+LHxx2NJdzKS44YvDlmEO9KuJGD19N7PbdzRN5OOzKzZh5LPDBdMY8LPJG6UR4trSHHEgM4S9eV6ZaXnZHzXF3xFdGtYq4JMSDnkx0/djItzNEDNSGcF1XYnRAK2UUDcXQ3d3fw7/qjXgtE56A4BlQNL9a/d0TVkUKoXwoIHFr1cyNLfK+osZN8ZjsyVjpfvJeqVOXlOSvO4Jac+Xg7uXvnvKzDIWs9z3g0wgVa1y0tj6JuvUWzDrVzD6wvCZxC+Mp0u/48qbaWHi6GNiHiOZLMI2DIw5cDsbLDxlT3CZmB3BHTviMXtGjLbppWxoNUDaLNGzSY8MsSE3eDmaJ6YiPdzIRFJZOiB10p9LgmGlCO9zx6R+0WFP6pfkkLUfl86KyQrt6m5FWJeuxqDJGGv/BdFQVeaWU8dawLmZNZFZRwb1PCueYSs/87S/49WwYr+amcQ66m0XWfcTZ92Ed5nvb1/yQXfNVVzz+WELwDv9jtyZIeUH/TXvd9es3cjWHzh3A0/9jgTcZniWtvx0esSryaLRg2R+dn3B7rpH9qEkAVqyx/ReZHUxsOknbqRnSsf3NGdB+oR6jyQzikfBBWXKgUdhxyfDI+bsaNpIjI6mScTo6ZrI+5sbgliMrZvsuOotoja19nuzY5ncNVi3oLKvTA9dALUEzT4vevJwSPhDRMZ0lHpUY8NUOhQhHKWB94x3vxlg9fPYWYu0Q7nvZRKhu1Lam/nYjXhQ7ZxKDb+q4BBV8gMNud1+v8u+sBlOmbz1/m8Li7fja45QJISxsJ0SUjaVDTGVxJrib6VYwqCK+WsldYypNkvcEqFeJYaGp5qJvEghlqsQxUFullSrjBS79qPBvJbn0/KsDYnMUfo4EpZo9iBpYSVNxcuxGtpHhCG3tMVrxQC02cC38oXxGCMazPDYGGtfZ75wGCPrHVg/hX8B+BAYgB928KPvYFLDiO3mvrlvhVvmC2NNC5bSVQ2V99qWJlJmEyYzgG8yCZtqXMg0IdJ6e48uOwsNGXLDXbTiax1mtBSU2zByFgYaSbRuppfIxtn7dVDYa8tNWnFIgTFZ8MzN2DMPAWa3+Buqg3SmhD7ShsQ4BdIJa0sVKOa8UiUBak2BpI6Vm7iJ1ol3IZOT4L2Ss+B95qwZzcsnW5MGCrAfWNITq1TwaHQuy2YddwI2qTVLqjzRRTPbJZ12vfXYBa/GucIJyMbJmvP1PttT/6s3Dn3zHSRbx9+PqYBcb1hLTthWr6HIp8f/eUzkcpzTzvhCYn671vy5GA5H0silG2hJDLnlNvfc5hUfhVd8v33GdVpznVaLLDDX7h7gNS/AVOsj+9gS8zHhMKswpIbezwwp0LimKEQyI4HgMvvSoF37iVSK7F3u2LoDGzcWc3p7jtu04rG/M2lc8fazQKZkahDVpTapNdSQG95rLDWvGr5/Oj9i7SbeDTds3LgA+2s30uY1t3nFU3+gF2VG2ZbvUSazz+a5e5sTjfNfez64N6LCv/0K+TsH9G+u4N94ZGyWX/Hw4uhK2e7Laxg08jylpa678HvWq5HP5kuuz3o2zQTnsG0HphzMx6oduGgOvNuY59QPD0/ZpZb3+luejWfsY8u7/S3n7sCf3L3L9dTzqNlzUerNjONZPKdvZmZ1fJnO+DxecEgN+9iSVXh5WPPqekPeNTSvPH6Qxe8qB8idcr4ZeLza81vb5/xZ/5jn+w37yVJwh7uWkZbweEBv1/hojN/cKc165rI9cJd6ZvX87NUFw74ltBZCkrJjtZrYdBPvdTeAKaO0NGNSD+NjXfyuws78uGrzO3XOjOHreDC31/08GcKQkKgGBh1mZGmgHNctrUBWjJBS8QBuyoEKIOVYmunLc8L967E23CvApQXIOm1uPPhdYi5qGUGdHgNcBPydcv6TSHs9LU19SzI8vs5j48gYW1JSfOvfycWOpqwzKuazjK+BA6VGkxP2VXn/qtVANYZffITtA/tG4y2Y9UuOTMYLPPZ3ODKfxwuuWPN+uOb77TPuUk/OwpiPb3VwhuYmzAh+SGGRd2RNRPVMGXo/c0jNIvUIzja4NTJ3FksrmjUs3Q+AGV8m/XZZQOAYx1s36RX82iczkP+ofUUjiZts3Zp97Fi7kY2bluJi40aGZHTWp/6Al1T63QZe7XU20YZmeqnw7jcY3+Ki0EngwsG2vP69Jp4lymsc+aC9opHE9bo3loIonY/GjAsTF83AO90dWz/wfD7jLrYElxlSw8rvedzu+NnhkmfTGe+2tkhsSjfoNtsEb6bvF+xiZ50UFT6727J7tSK8aPAHwU3HL7i0iXe2Oz48u+ZqveKLfss4my/JsGsJ/UzsAv7aId66600bl8CAzw4XPH+1teLUmddK0yQuuoH3+xtu5545FuP8DuYzkzW6meO5lAkmN0e0XdL9j9YlxZ2kePjBJvZ7Q9V04ymhU7JJXHpL9/iqoXa9qhZj3l+kNddShGeBpLhJi4/YsUDwBcjafjLi9vP94qIwK/Lpv9Um6GUtU+4BaIsBfC6NGHeyAOjxs0Qf/Af3C41fVKi8HX+uh6I4gZVMCMpt7jkAWzdy6fdMOaDF+L0OV1DVyo6KJXzEjpdLcq2tLXP2C9vTWFzFUysHkmQaTSYdkSNzt6YnRvV0Mi/glZZjVDhIxFyl5uytkeMGPJmxNEZqElZN/3OSi0zSM2pgLZFQ4GaHgVezpsIwU8LXMgz1wAZ4D/4KtP/qnu83f8aeNT/9+PtmRvqTx8COr/LP+rrD4+gFtJzXrJkdmQbzszkrfpdDY00q6RXvTPjZuETvI2s/0brIPpm/litsu0ZmVmHmJvbsUsvGjyDGzs4Io3pUKebJPVPxvVGF27llPgTc3h9l5XXD6jPrdmLbjgxNYBc6K2STI04eFxLZW4S6ZgGneG/nHNVzO/fsD13BoNQAKl98U8LIlAOpRL+a8S9LQpR7eC7uBHR68NHWDrOLxvaVaKlR94ZiJrpZoVwncto8+apR1oKvNRMrxxNWFom+ZhZSsWBAVnsdi6H9/bXmnoxd1eQlr7/ke88p9bkfYGfy8LaHBzlda94uNb+WYyYRtTKtMk/8HbN6bnPPUJIDP25fMI/vMmfPITWch2EBsCJmbdI5M+/+ctwugNaYA1L2wlmF3ltzY8qBs2a3NEeasobUtWBWX4CqBq/HJnsde+3whYHvseCqtcSTtMPI03Brie7FpD6prSEmH+zpvSWDOzIfNq94mc64TSt6mUlqkri1KKOa2oQcuQjCmPMSZGXYxD/jnP9vv0L+Vy/tO/i3D/bV+188/mc71i8YXhxrMcDQmFpKJ4mNRLxTvt8+M7ubcAfA3aqGkdnn8257yzvhlkYSPxqfchN7DqXR8Wpa8eKwpg+RP7t7QkZ4r7/lw/6KfWr5mT7iuWx5HO743f5nXLqJWd0CUp6HgSfdjuASf/Tle6TbBn/n8KPN57mxRkgu7KDD1HDteuYztzTL+iaa1UoS0i7Qv7Nn9ziSBod2meZ84sMn10zZ83w+48W8Ic4eLem+Wjyz+tXERTfwqNnzal5ziA3OGwt6emTp635vSb7qS2MlYEbp2DyaW0dqyvVRUtE1CH46Tq4qgkvF8D1nM3yXfLRREQHNFjICaIzkm1tks0H67n7z/k3sYD2pf+p93lQDqdp6V58Wq4MkZfyQcXPZ95U9SXerrL6cCLfjUZ1y8jhcAaFO2Gj31qvTceJ5eQwXYLFVOa2H8snx1B+bWqL2bxymNqosra853oJZv+SY1czQWzIbN3LuB24KWryRiQ+aV/xkeoeBBk9eOiFZBEfiPBjS/WJcM+WwdDXqQlFNep2aMW5WWdhZ3uVioijMGCPMy1GjPqsvwJbF4QJsy+Sz04aNG7nwB8Z8WlBEzp0ZONYN8qaAPVdpza7Sd91IIzCoMKjj0kqjZSmYVEmq33zT9C0uCnURqGmHiZG1JGYZoXnF++GaLxtjWl2tjgb4Kz/xTnPHmR94Pm/5ctqyix1elJupZ4j2/l3PPZsw8U57x22hRO9zx3faF2zdVBKuIpMGVn7icbfnJ9Mj7q5XyK6mEdoXOrcGLiEwJZMIXXYHrsceEeUuOnTy5CbD5cysLdpl/Hbm6bktYl/M59yMvcW+l+IiJcdmPXLRHbgIB15O9jrVK9OFklfZUk/KZFJlcTWZr8oLK8quCC7pvWayxGL4nlLpIJxM6jnZpJ4Suj/YBr7voQnGI1mQe+XreGbdu+1kIrduh5nP+8n03CpGt+1uMqsvJ/zdZItPnZwL9no68d4Dre6BUYWN8hDUOsXlTk5xmZhPdOHmxllfD29lhm/Hzx1JzQy9JuX1Ehk0kFRoSZz5geu0JhbfKid1E2SPqUDVPjXW5S6ys9oscWJsK0GK1LBcpghNYWJlzB+rsrdOJYZJHF51SdNry1o3YX6OnUSiK00brNjqmInlS+OLJK+VyKEkZAGFMWx9jojQY8Be/bbUwNKfP1z5r4M18DH8RvNDfoc/ZseGm++dc/P0Mfxkw68ilcGJmfXWtMMsSqNKJnLuB7Zu5E5trR/C0UOmcYm1qyCWydDnYqY7xkAsn9mQA61LrP3EqME8tNRz4Q90znh5Ndq+cYlVmLkee6ahMQl7PILwuWzkwfYVggFQo7cd5pQEkreubp/Mit8r0mXWnTXKdqljTIFcs78RclbaNtKFmd5ZYw5sLky9FTV2HrI0SRZpgsi9TXDdSMvDOTKrrTOayxpUJ2+Oa0hWmOMJoGVwqNb2dJnP3zi+snDgCGQJiObFgB4RNJtnvx+VZpeQKSFa3vDl8Q+OeepBUp+a43vwVbLC5QH1T28CrLQ0gurj3i41v5bj0wjnhXXrVell5sPmFZ/MT/jJ/JhzP9BK4nvtc37MO7yMG8YcaJwxn4LLVp88YBim7IjqSMnu0/uZu7mj9ZHez1zNa1b+2Lx8FEylMObG6hQxD765eCYCiyLEkRepoSlXzAZjKzNkGGQ2T0ZsLTB1SMttXPGd5gWPve13B23Y5w4vmc/nCxpJvI8FX/US2Yjjp7HjKq/5MFyzzzODZtbO5ulGzFt31LkERv3iRnw1Z2/+zn6RGYtC+o/3qF6+0c/3VzkqU+upF1qZgMx3mBlUuSwp7VdpXTzIrGH02N9xm1e8nM94OW94Pm7MT0o9t1PH7dCRWsdHmyuyOrIKd7HjkFsuwx4c/OX+E74X9vQiZDKDRi79nu92L/CSeTZ+h2Hf4nfOUnoTRT1i87+LIJOwu+15tD7QFSDr1e2aadcuRvBgCpTucmDVzay7abk212FiVs8/vXmK8xkJGR3L/sIpMToed8YmezWv2c0FADwrk3MW8yOeZWlEuNlYvwDqzU7FPlRbo9wJm0hKvZE6BxIMzBr1qEQ5Hcsev7CfUkJvbnBjh6xX0LVLOjspofO8JLnjMPZTdTmt7K1kgJkkq2HUlePX7653iAgyKP2XB8KhJTfO0uXHZEyy2gSS09cp9257CF6JKvmUQXZqCq+1bjr+tMeUZVbsfdOScljBLomAL8wxsM/HKTxMm/854y2Y9UuMhOOT5JYvVyOJ9/01AD+bH7FxE+d+4OP2JT+dHnObehqZocgwjJklNIVxlVWsc12kH8El9rGhLWDATexpJJPdzHkwD41ZPa04ErrQcuuo1NyJY1EAsHaRlGVJnwIWk+Bn8Zyn4YbH7m5JEtkVKeJVWvPY3xnAJTMO+DRui8HkNWuUBsGJ0ImZUX6dhaEuCJlM93cO9xYF/s7hV77vqgvMGR3eT2xc5GMisyrv+Ts8yot0xpgbEsLa2QT6Mp7xbNry+bBd6NZXw4opetbNhAvKnL0x59TkP4/DHX+5/ZytM1eXxGTdst5zSC3/eHwP7hrrEJRJ35KkDHjRwZsMsABnL643zDfdMjPk2XF2cYDzA4/XB2J2dMFik5+PZwwx4JpMOlhlUGuBd7s71m5iH1tUBV0lUmsTYW6MkQUsaVGiuoBZcARnFuP5sk9PnUOyxw3umCb1mr9HWSxSQu92yDQjfY+0DVJZWuNkOnOgUuCtflDw3iZysAJHrbOhUGbLIn/MQvvygJsSufW4lJEp44YCtH0Dn5Kv9DQxCgr1qb9qLJN2onTfdVms34634xcNRbhRWQzFPZkzN0DuuckrSzGSiPoDN6yYcihydtsTmASwmI6DGbdrSfxx4FSZS5MFh6URYQbwnRzlhVVOOONwJ5evFzV5Y6EmVuZWI1Yc+ZOOd/mmss/G/F0z2THVMRVwbtCGNZOl8xWB4W3uyEDnRxq1YA8D6ijsoIQlCX51AZFLcp1GJeKZCEwEDkohYv2zs7HeNKrssNWAc4lWM+dMJGCjEw7Y+7awFY4g4iG37FLLXexQIGbPEBtbt529H0mtwFQVgs+s3MS7/o6u7C0VA8ZysH3Fs7SByeNmOXpW1WVZgWjFqgiMybMfWvIYlr9rFtp+hn5m1ZjZc3DGotsnY2+IV3Q2RKry8jZ+Ml+dXNCpkJf0I/WCpiPbqMo8VE4aCSfNhVPT+BwEp4JGh+R0XGfeAPqoZpgmKxBCQLxH6vHjiRRe5MRnS4+SjpNuN3WTric3JMEfZiR51JfHpsIay/kbNvW+xp1/3lpT2Vn3KF5v15o/D+MPpw94PGc+DK9osc3ZWmb2/o4v45ab1LNxk5msN9fm81vmHXjgf5UwUJ22MHqFxididkxie1yXHdk5djkUXy1HcIm7ZIFKFbiqsr+aIA6mZhm0YdCGlsRVXlv6usCg3vbheb0c4/lsTeTzYOBHUxrtl37PTerJ2PrxIp3Ry1zkiZGdtuy04WWe2WlrQV1pZis3PPVmAJ8l00mzADO+fMFcWU+q+frpmDUxa7Im/u819H97WL570+81/PPKRDRvYMe7vlnOc9SZtUyk7jOepQ1XaUPvJobc8un8iC/mc8YcOKSGm6kvfmmJOZnVTXCZP3n1Lt5lHvUHZnX8pbPP+K3uC36zecZfaIxA0eBNoulGntTPMzf89PqCfAiEKIRDbYTb+UoBqrRVutXMO6s7/uj6fX70+TvkFy3trYVvxd5Ct3gMHz2+5i9efMFls+ef7p5yN3dcNkYE+WBtdimf3W65fbVGnBLaxGY18kF/XRRIzmqjfmJQyHcNkgRJckzDnSlrkC5rE1j9k5sjgOUKK/h0fk2tw20aZIhITJbGzkmNEQIWLnN/5HGEcUS8t/s6Z2tRSkUaL/Y3Z8CWegfeG0tLCrVJjD1VwbBl3Typu1xMtPta1B0Xj0VS+HXTFVVRccfGU37YceEk2d0WyMWvq9SI91Quy4OO7+W9n99g2XoLZv0SI2bHPxw+pnczv9k8w6O0Enns75bUDq/ZFo7QLJOyk4Qv2nLvTKPeusQelg1iyo6hdDKdKAdMm46fITfs81HbDpYAAkcpYe8m06aXlmstLmZ1RfZmk/+sRtU0ycfxcngabswo/iR9pKaFzOrZacNaI1OJXH+ZGtYhLl5ZXqRM9nlJNnRV5vVgURjViqWZhPxeQ/e3D0f2/t9c/Uo/s9PhxXEmPSut/mRmtA+f80U64yb3tJL42fyIV2nLXUmmjNnSIVuX6ENERLkee+6mjndWd5w3nn9x8zN+p/uU3w53XLh24axtJZqfWlrzclpzt+8gFvN1B6lRJJ8ARkFZdxOf7c/5yZePSS872mtnoFeA+RH070T+8pPP+Hj1ip8cHjOmwGVzYMyBH1y8wLvMly/PrcPRzzze7Lls9kvwQNNE5lUg3TZIMYI//lcSAStLqRYcasCMS1r8S45d39Q5XB9wo4cpIzEdDQqdf23S1GlCp+k4ocP9Trn3NlGnvOjDxRWduHNGg3UGni0+VxlbCFKmGed7WvSvnMAdy8JwvPOxhf5wAn5IobXbzNzwtBNhfzi5z4MJWn6uEcvb8XbYuvBZPCdI4rHbIxigtXITOR+9q1YyE507Ab0UcZk5e7xYEq2XzIwvFjy6yA8BhLxIAZsS0e3UG5xUqOR1LanMcvNScSWgBKSCX2pdvklNIJiLpN1xLHKARSqvKhzK7aFAZwlrxjRaUhHVc8gzjTN5SHXLymiRG5q+qwrFjumDJiJJ7GCfST9S/uij3+bqUcuOlt0fndN+BiYx/Gbm719nOBFaAqFMkkr1E7tjJ5by6CVzU7xsppI8mFUQsaTJ4Ox1jSkwJWugdM7xbnvN03DLYzfRi184a61mepkYaDikhmkKZlZbN5UlVbV88OCUJiTu5o7ruxX5EEweUmTSGQjrzHvrO87Dgeu4ImZH7yJRHY+6PU6U3b5DVfBNYtXM9H4uLD7B+YQ0Dh2F0xTHY9of91hLdR6t5u+SuMcw0iBockh05kVSvUfg2DE+mW8rK5iyIS8fxukHRTXtXXxBlvXk+F5Vv4/j4xVUca81SU7O4cHN9zracG89eo14VQGqh/d/UEQ8HPe8G5cnfjt+XcefDO+TZMNf2/wZv9N+XjyoJj4M1/Qy87P4iM/jBZd+z/vhmqSOL+YLMsKFP3Cb+uVYwWUet3uyOsYYlibtnBqG2NAUX799mS6j9wSXOKSGmD0rb82IMZ6ZbNynRUaYEPba3TOC9yhrN3KjHTmbnH2XO3qZyaXxf0jNAk4cUsOruOY3+y/tPLIlt9+lHofyG92X7HLHs3jOth1wwKU7LDXNrQb6HFk7A2VmTVxnAzs2TkgovQSiJsIb7FL2OpFV6SQw/M+2RFWavzsx/V5L828++tZZWW8a9Tk7sWCw7zCzkWteuMHAQTZsfU8jkZfxjM5FNt5SIp0oH69bruYVn+3Peb/b86Tbcdkc+Lh9xX99/Sf8RpM5k46MLu9XAhox25ohNzwfz+xkmkxulPkMk6wJtDeOHNT8fYF5CvzRF+8z3LUwlJRDB26E/s7m4l274XY18r3Vcy68hQLsc8vH7SvWbuSj7hX/tHmPMQVi9HifWbUz3z1/tbAFswp9iBx8Qz4EZHRHgKWOk9qGIn2znrPJ7fxsk6mrHJDTeV0gB0deN/iU0Bp4VdYFCQG8X6SGD8ebbpfi9agpIa42lfTol1VroIfMqVMf4YdeWsudym3uwb9Pb6t/qtJ7pSQ8ltftK6P65DasJswFVFu8sLIdw0WzfBEtLc360MBRCeTesNZ9jfEWzPolxqyePzx8zC525HPHd5oXbGTm0g34kPlZfGQTqR74sHmFE+X5vKVxkTE3xsgqV4DrlVgMesdoH8uYAl2RG7YF8Gkkg4Nd7Fj5iawrSzSEJWHCzi0wqV98tHxhbt0WqumgzZJoGLPnoM3ix/VqXnPdrnkcDGm/jmtm9bzT3LLLHS/TGU1IvO/3vOvv+DKdMWjgNs/gEltnC8OzFPECg07LgtGIv7csJM2MaiBYg+fqf7pmrZnu7074v7nG/RuPvvXPsS4AHmNrfSdM9HLDXnckhEYiT8LdIrP8rdWXuALqPZ+3vJw2bMLIB+01j8KOv9B+zu+2tzxyPY41N3lgp5mxNHg9ysu04eW4xjlF+8RczkGy0NwYOyt3Chk+/eljZPQ2sUbbWDe3QjjAuA88X2+5vXiJQ/nu6iVrN/G99jlOMv/QfY+X45r9ZjCjTp/4aHPN2k2MpUvWhcRdEmSWIn27j9zXTrgG7k0wJqvA5msRqpRRRci9Rw/BgCxKypSqMa++YlJ/eJucdioqBeOhnPCrJIf3JnA5HuOrug8VyILX0kNOWVmnBYOZIRtDJNf35rR4Oq0i9PiY7I8SqYpsvSVpvR0/bySEZ/M5U/Zo94ILf6DF2FjilJvcl5TayLkbEGCfW7xkYpGfL7V4mIpc0Ng0AKLOJO2OwqIyySCSmbKncSbjiOrBQ4t5qoClWkVkAWocEPGMao0Vy/KTwgYTIsZcieo45IYz37Byk/ljnbBhZw3s1Ro+IjMbmdjREnGMauBUK56MsssZJ8bOakTwuNI8OX5hs0ai3uDlU/w/+pDpLvNnT7+Dn4XuEw9fzMBzrIP6q2Vo1XEE14xReuESgZG5sNMcmbWbOBRZyOPGF46TsE8dh2T7hq0f6N3MO/6Op36kl4DQMGpkxuSoUj6LgxqYJaU5suzFFdxY5HzeNuq31ytI7t7G3k/WtY6zY990jP0BAS6CFRYXfo+I8rlcckgNc2tXkBdl246L95mIEpwyZWyNuYf2l1HXmocb6tNk2rrWgDEFg5iUPWcUXTrr4px1kd+gVdAqSzx+GguItQBQdsf7a8nD9eNNf394jIfjHiB18u/KLn7D/WqnW7K89t4sx3nD87zpvm/hrF/f8dlwwav4xFQcl4nvN8+Z1dFLopeZ98MVu9zx6XxJUuH95gqA59HsNR4ytHo348kMKTAeztjPDV7UGJkU6bNz9H5mzAZ4nTdDFUQxZPPpehU3y3HfDTclxMNqmpqe7jiGTwGcFx/g27xi0gKkqcnhPCNRPbupY8wf8v3+BRd+z0+nx+xTy5kfuU4bLv2eq7Tm0/kRT92e9/zETo8J4oOa6tyLMGjkZfasJXGblUES3gkOR+de/yKtpWXPZM36ANf/87MiVXR0X0Oi+G2PCmjNTGzdxKTKe37iN5tX7DQwlERhMCDqWdryyfSEQRseP77ju81Ltm7gPT/x2LV00iy10pgnXqbEoI69BrblOtnnjhfjhpQdvkuky+NsIwdP6rWwbkFXCT7v4HpFD0yPLEDKTVJ8BtUCqVaeZ5sL/m/ur/K981d81F/xUXfF7/Y/5Ynb8x8ffsBP9o+4HVsuNgfuho79aJ/x47Bjn9vldQJIY/I89UCUIxv4NE0PTpIL5SgtTLo0XawpfpJw6AVtfGmwu2PdU2SANKGkGn4D7VwdWe9ZmBhYlo9N+m+gMrknHzzxulpqHVu8jQxQmyF1nSpWKsY85ghowcLyVpHS7CnrVWCpoQyokqMndCzHO9luSbJzsibW139db8GsX2KMOfDp4YJX4xonmf/WxUwTXtGqeWO962/Z544v4gUZx7vhhjE3DGoSDod1XNd+wonyTrdjHy3eusoOM2Ja38Y6JbM6OokLVXdWDxlbLIL5lPQlzcOVDXkvM07M7HcuaSEJ4UU0EKqyjcAWm5ez6ejnzrP1w6Kpv0v9srB8Gh/xfrilOzHsncsskFQZ1Ca6nswMkBMXzr8mN/TiOHPmw5XJJA/zv3WOk4CT8M2+pL+CYRr0hkuXuURJZN73N+zyNUORgtT0jn8yfsjjsOO73Qu+3z7j++EVj11i6wJNMWjMKINm9ipc5da8ayQXI34zY49nEc5ARIm7xuQYAnmdcDeB/pnDRRgfWdc5HITmTgl7++JPFx3/SfMdPn18wXe3r/jrFz/mO80LdrnjLnXs55bgM/PU4EJi5Y2C/TJuCC7jXcaFTPI1z09Ay6a5oOQ1xRDMY2VJ50vH9KkapS6x/B6cpXZo6VCkMvl+k0k9Z5MVPhxf5XHycJxePw+vpdN/P/S7qs+hJ2yBYvRb3w84LnRg70f294sYUcwIuLLZykJoKSB1Un+tff52vB33RlLH7dwzpAYRpeme490Br2qSQ5mYJbDLHeqEMzeaDA1XhIHG8GkkIaqsvXlmxdwg6ELymJOBVU4SqfgzSvWuQAoA1RpDi4yXZMctX4JQ/q0qZBweMyXfZwOhtDRQwED9Q264Vkf2ZgAbi0Rx0sBY/FVu8oozZwa/onoiYLOvTVRLQQxagRpL/X3YGXeitCTgpVkp/ckl4U96gjiEK5BXwA2/rPn71x1OBK+OXrT4gGXO/MikIxEDNrIKO215Hres3MRF2HPp91y6AytROnG4MnEpSkSZ1WQ61jQ9ZrR6l8lthhZ7D2dvsnMBbRQZLUVXMsTebnczuAJmBYW0C3zuLrhd91y0Bz7sr7nwB+aS4jwnCxFIySPOUsmCZA65WRIsxWkxoT2dKE/+g+OGu8j56hy82FQJS4FRpRXi5OhBWIGlAnJ9rfXi5xnrfp19iDx4Pff+dnq8N/y9VAxVgi7I632aU3mG8pqZvehxqV3uo/VvBrLd+/vb8Ws3fnj7hJ274HbqOA8DfmuSw0FNTdCQ+G7zgj/OH/BFvMCL8n5zxT53S/PjLnU0Pi3A1uAa3u9vGVJDHEwpMSYDp+fkWTXzYgi/8vNi5A1WI0XcEoB14Q+LXQkcAS2Pcun3JfFduM0ryHCTe17GM340PiXh6AotJuFwKNdzz5gDu9jx8eoV13HFR90rXs0bXsYNHzav2LqBq7TmWV7jZbekvUPh66qSUEbNVIukhH1fBk08ckfw63Q04rmQFbMm9jozA1uxGue0zqkevf95MLU6abhwVpNlySSUjSqdRjKRpDCo4x+M3+Ef3H3Pwq68ydF+s/2SbSFHvMyWbO8ROgnl9QpelNviEd1LZOsPPO3veLlak7IjtxHnlDZErl5tmH0wed/FhP+8Y/2Zww8FSPIO9UpzB2Gn+MmAo/6FMF8EPpdLru7WfHp+QXg3w+qYcNz7mfN+5OVuzTgGPnpyzcdrC/QaUk9wifNuYEyeW1nZvJpA5hO/Rnfcp6s7AlhawJbqDeySkn2pdVTI3tLd62NUZKl71DskmgRemgbmaE2Xr1u/wD1VCWAFxcNi5ZTVe7ou/Jz1TNS8hN/YrFnqm3oclnoIThr8NWn49BTL2lyVPHZO9bk5JsBreW/runVSR5HkG2/B3oJZv8Q4pIY/ffkO42wpPY0k/ub2T3m/UHp7mfl+85xGErepZ3KeC7/D5RV7rHA48+Z91TlbEMY+MCe/GH3n2TaBc/JMYqavdWQVxtz8/9n7sx/Lsj2/D/v81rD3PlNEZGRmZVbWeIe63bfZE5ukmhJJmaBlyDBsQCBkyTAMGLIBQw+G+eQ/wH+CAdsPhifagAFDgvUgQ7b5QJqSKJHsZvOS3ff2HeqOVZWVU4xn2tNayw+/tfc5ERmZldXVTVuXsQpZEXHinB1n2Hut9fv+vgPRdKO5X0yGQ7vmwNZ46bGoAaIlUWO4jBWXsSImw6ftXX60eUuBNUmctjPVUHdKNT5pZmOCn0dfg0UTUpax4km/0HTD5LExcdc0CvYQqFOi2qObeJFRbnh9eLGEFNmkHps7G17sOPn/i14MvFgOTEVPIKRER6CVQEeiS4aTOOV7zTuc9TOmps1mzJ12O4LFx8iRafBAJYZl7ijFZKixWDQy+J3pBU1wrLxKFftguASij6Qo2FPH9LHBr5P6nsyF6NOo7x4mX7eF9qzkSTpkWZe8XV1QTzwRwx2/4d5kxefxgBgL3l5c8ufmnzE1LctQcehrJr7DmEjITWwJMEbGBmVfDXK6AUEfbh876VY0qcSADXuTpDHavQDVyvdRPUsKD/XrJ/WU0512N+RJPO23E8Y7v7rgGEzob/LuugnsGpK0rgBWex2IvcJLshG8/mLv57Q3SXN1MgeuylRuW+W34wtGFy1n26mmy2XG0zvlKQvT4Ag4Cdwxm1Eu3otQmg7J0vGYDIUMoSLKkgrOEKOm6Qrq9yckYlQPRmt31+YgRXQSiVE9sJIIwXRUaHPFZOmcQf2o6uSos3zwMkw47Wb5sklsQ0EXd0XOpi/GBD/QhCyxKvtukmOVY+R7LCbBJHtLBSI9jKww0G2evdHEXZOHY1rSscXwDCsFBouRDmiIqQX6PQbVn+3Q5oma6A6vx5KISRltm+R51h+MDAdDwhEyQwE2kqhyorBDaJNu8BMyApleeg58Q4iG1upuPSahAZLJLKytwS8FO6wrxW590fVA5zPTQagdK4S2tyxcQ+8UpJyYjqlvWbUlKVnmRcP9QlOzWhyV6dmYDHYKI7g/jvzzKE0YgZhr95O9YiOlndQhy/YG81ndaBs1wu33IwNeHgOAtLthj2X1EhuLV8/ZKT+X6/e5AZgaXsv+15R/GPGofYDvVevX7svVY+69fy/97nb8Uo7PTw+gmtD2jh8UD/AS+M3pJzzyZ2NNUtmO365+wQ/bh6xjic8gRGk6VqFibhsOncoLvfR4o/54b08uaYLjfDvBmkgbrAIWuenRJ5X+lVZZuxGVBc5tQ0ySGVMKLAEsTE2Vg5GOjNqkWBLrWHLSz7kU9fe6CFN+ur6rSXzVisuu4rydcFLPEEmc1NqUXfYlh75m47SJq8zRnaricXdHG/pYTvs5R2bLwqqccJU61jHhIa8RiYURFqZ4qe647p8ViVRi8Xv1znCf/18CWcMoxed1MuCBZWrHVMdlLDiPU573C74+0RRELz0fFlq3ftIfqG2NdDywWyBxZHrqlEbgcUiqDJlZfOQ3PJxdapIy2X+xKTA+Il5tEcxnFfNPtL5wG6jvqhl7shp8Fb3QVxBKoT1EmbyNpSscL5YzPlsccT6bUUnH1DTcK9esupLS97Sd4+3pJb8x/ZQq18ZDOqc3ETGJaJOm5m0Y1wxQaxVg5yWZ15qhmWJClp/nNUcDsgRClreL5BNoDyQyAoPRelnqvN22b/4Bjh6OEaLZqUb+JGNPnbJjZl1bV65ZsIzWLePjGQGr/YbKldR2YZQ7DsFYuiYPv2OUcY6PTbrX2L89yavX7OvjFsz6CmPTF5w/U3ru0yT8kX2EM5E/P/05993leKG95094IodsYqkXu92M2nFgBEOiM2xiwd1qzWVb5gIid9QzK6gODm88E6ud5tJ0OBPpo9mZKwal9B7ZDTOjYFlATXWXYTLGtHbJctZMiQhTpwlKp82UZVsiwFGlxrMLX2cz8zVtsqxjyUWYUkfP14tnnIcZwRimcokBljGwSZqABTAV3mhRmEpBJfGl+/TcrFn/sxxWBp5joInKBKiTZRkLuuT4evGMg0rj1Y9MnRMiCyyRSpqxlOpIbKLjMpXU0dNhxy7Ukd/wYFpwf7Ji0xesu4IQDVspiI8nTB8bcmOEUOpEmxw0d5OmCgokD/00kqx2Xbve8sn2Dj+b3tcNivQqAwqWWdXy9fkL3vOndMlyYScaw2x7rE3KoMuFwE67LGMnHBgn96FLockWep840ExHNlfuUpihjS67uHRfQIhI131xlyKbNr82MP3Lds6vjzyBJ/Pq+0i6osAcOzjjj8NEztX77HcctEAUokv5b97wmNtxO66NLlrqtTJY10l4Jgv1u/LnzEyDk4iXXn1PYkmXFKCqpFNoI09IViIOPc+7ZJm4liZYlWslMpM8gyHRYMXgAJGIkzgyhgfZGFGlDBO6newwAyltlgwKCqZte09CvbisRPVxCroFqVynYSd50zkxLQFDF02WNxqOrXbVkwheGgRNze3SDtr2AoXYl8ComFK+TcEcT8RJB9TktiMx6Zbf/CmkGX6ZYUR305E4Nj9VSmmJ6OsuszdjJf2YXiwkHP04K8bcbGlwmjyW2QuDGfLMq89WF5VBlZLQdY60dPjlTm4RB9q/gX6ym88xEP2A0mvC2UVfcR5mFEb9NIdggcIF7hSaJBWS0Bj1BLMmqnIcrgA5+42C/f3rfgNlmJ+H5zL+fvyoZQ+5EV1XhsCQlLIp7xetNTDq6l813qSWeN19hi73a9YjYa/ZMd4oV+9wfaRr3+e348p7fbvW/FKP+PkUDko2C8vPzZ0xTEr3qRsq042g1reKJ3zW39G6AU1jH8KsNrGgys2PqWnHJsP9qmDZ6DrU9k49f4PFSdS53PZsg2eW95NHXqXIq1ASMGxCiTc9z/sFxqm88MheakhVsnR5ztvEEii5CDO9LQlnzZQ6eO5XK/XlSobzTUXlexZFQx08h17rnYfFJXfcmtMw5zxMWYWKaTa+X4aJskiTxaOy7DYlNkklhl3SdeQwS+u+aDgsRlTWHkmjaTyQLVTsn1r1cpMR/ZsMK4aYkvp8odNnlzQ5cmFqfmfyM45MTUyS1TaJ01CxyF5bC9Mxy3tdZbHB8zDjMirxQRU/PQ/dBZ/7O5z7KdWiY2I7TpoZ9cTzuV2wXFeYT2dMHwuhBLsVmmOta4oLbYi3R5Ew0TCsZHRTEouUQRdlfX+2PuQH87cJlbL4DIkmOLat53ix5qP5M97xZ9TJM7c1y1Ax9w0X7QRjk/qWh11FkezQgM8/7zGThjXpyro01DxuDxga6hgRBZ9sGiWGKSVVoliLlMVo8v6Ftc91Zcr1+uSL6p6bGiD7j3uTU2kgLOytIRLTCOZBrhMHiebwHmalDuxqp5f8sAZga+95JEGTjiOjf/SbjFsw6yuMk3rG/Iee6GDzvvCJJKxRieG3qs+561YcmQ0FKjk8l8BlpmQOIyZDk8zoPRG8oTQ9m77g6WZO0zlK32s0blRJwRBT20c7mipidAGKCI0EFnZLIWFkZw1glhXVNS/7ik0smDpNtDMk+mjponZbZkVLZTsWXhMYvATuuDUAy1iNRpFPegXpjuxaJbSgi0HeNVmBhXE3glH7k7ImcsB1+uQAKn2VzsaQlghcWWi+6DHDMCJUCMemZyE91m2y95XKhrsEnQj3pcWjLDQQuqSL5GC4PyyiAJXpeK865cDVbKKaLn739G2224LurMQFob6fcBvBdIKtYfpY2VDNkWjErR0kIELaGiKebWf4aXWX70/f5q3iksfNERetflbvLC741cnn3HeXnIcpFgVD577B+546orTbPOEYmzsUspvcR8AqFxHRyki5TVYN/tSoN9NPXWZEDV1yUImhNUihZ8kbAVrD2Pez2h9fJBG5Ponvfb/vibXfxU6ZPjuwra5Sbl/+U9f15fv6+92ddovjKPkYTCdvx+14xdj2nuLEkAx0h44LUeaukchdp8mkKgNQyWFNok5OTdnzSEj2G4nKBEZwoobv6079s5xJYzS7kCPaRVnA1ijMZUTl7SlCL4mCfpSaZ6eIEeyK0dIkp4WDCTv2VwY9YhIKq1K0wR/SSBq9H5vkaKMDo+vO4OMyXDphD8gyQpbdvTy/74Nb+v3+xbd7PMl8JVZWTDsGkCBvdKy4N/eJqJxvIpGCiDHduD8cXm8UmGU21rBahvwvIjRJvWuGz95JGBPAumSpo+NZv6DrHHFrMVHoZ8q6ikGbJn6pm8xQMfoBamqtQA8JQx+FczflhZ8zsw3LvqLJ4OSiqLnnl8rcjh4BXA5NMVbbrwPjKxkQs7eh3ZcYsnvxg6HsOIcOhvGDObuR3BHfHWPnt6jv1BsBWsN45ZrCK8Ck4f4pP8G9310rkl4aueO9f2a+9u/t3zYUGjc91b1j3jZOfvnH4ieCTC3rdw1LSTw2kcI8pDIdXyufs0hb7maJXyUdd+0qm6QvmBrd66tPbMjrh7AwNffcik2W2236ghfbGU2nYBYoKyvm9WKsT5JlG8DaSJkpL2f9lLltRpnfVDT8Y2oaXQ8wrGPJwm5ZhgnLUFFHz51iSx8tU6egWh8NXbB4G5l7bcQf+pqJbUdz8EpaIoO6Q5v9z/pFBsrQxkAGspoECxMI+fo4NPaNmxq72uVlp4pS3BvXG190vzH1PYU3rmNAUxcNgkHwyqlmYaCSlg6o9tdGoM7vxwO7pRL1E/Mog63eW6sq6bik0lpCEudhysJuueeWmFmkiZ6LXpOVf3Ryn+XlBC48xiRWHybcSjAzwTbg1+qRZUKiOVAVinotKdAVHcRkCUEILvFUEj9e3MdKZBkqPt0csek8le95Z37B18rnHNsVp2GevbwKjvyWF3aO84HQ61qRDKQiK10k1zeyW3vGPXwaGjpCtLv7RaOPVU9HfW+SzQnryYx1j4iQQhzXCCkKDb16E0Br//fX65/xd7saR8EheXn9GiSBXzQGAcz+nxnW1D3walh3Bob08H5KTPk9YAyPGZUqwpgSuQ+SDetXtl2GIF+KlQW3YNZXGtvOU71IFOsE4tiYCZ/7A77vHmAlEjEEa8YUQC89M9PyvF+MG80B2DISmUrggb9Qg/i5Y9t7LkbgSjvb0QpF8Exsh8uSQyMJLx119CP75zxMx8Vp0KWDUnvXsRwpwM4EHk0v6KLlab0gREPlekw2eQR4vD0EYG5rDtyWJqo57TJUGIkc2xV3zRqLdsqH4QUWxubI2wRfoatw0xgm6ev32QeiYOhY7+SZJW+2wAxA2lwsc2EExHbHjWNiow6hI3ERB/DQZeP9nOYokdP+UI197ZpH/oypaflFc5dt8Gw7p7GyRy39xGJPPWkrFBeJ6izh15FkwK+Fdm7oJ0I/BVuLTha9hbXhxMz54/lDzidT9VBoCx7Ml/zawec88mccSMPzdEBARpaeMxF8InUQKk2Nir1OPGP3W/Ikmb9GdNIKxU53brq489EaDAWtxsamIDqpp6hyQ0CsIeG/HKAFOwNEa3cTfEq7iVzv9MWdi2u/35/AJWYQy0pOa9T3WZv3uapkoNLmmXk/6n7ojgfGmXYowIaGw8CCu5V/3I7XjS5a3AZsmwBDJ56VibwwcwxJU91E1LMKBbkKAutYMMhgh667iLJm59lb8ajQEJD9+/VRgTObDJ4wSgZE1KOrT1YBq5SoxV8x8x1GKT2dZAZQMhhJLLx2/1d9OSb36vPV4y97LTQK6SmlIyRN322jMs0mpmUqLcJVW28rUIgavydS3rd9+YvqVY8JNyYkXgWiAP3bOzj8jcGxgZ1VYHR+JV2ZE4bjhr01SP0YGZlcgkoTQT+nbdDm1kQ6FjkF6iJM1CstGAVDq0D0Cdka6LSwcFtNqE0CttP5PbpMwuvzeRQFusRGCp4Xc2rnOWuntMEyKxrulysOTE1Jz5pSgTgkA7BpVIsPHdg0glCMgMsVWffA9t0znLVRaUfCsLnW9WUEu2DHztI3RZsoXwbQYm++vyIfzIlMX+oUe82ds2lIGo7NVc8sbZTsnsPe23Rlrbmy7uTvr+yGbteZX+rh1zB71iPBsXQFly7x2AUq2xOS4YPyBV1yzEwzSsoqIxzZDc/7g9HPtcmhUh1aoxy7FWUGgeJc2PZ+DA/p8tfLWHFQ1rgYibQYEhPbjfVJSGYE2rtkeWEOqEzHgakJUaXzbbJU0jEzDaf9nIiwCiXrvuB+pSmqn9VHrDpdPya+08TdPK98ujnS5qwESqNg3U+b+9TRMzUtn7Z3eVSccWQ3HNtuNKpXphKUouDOVDSBfADmrjfebxo3AVKvqzOGmsTccFFer2FAWV5x72qe8LLa5aaxD3yVOKYZlHxVnVRJpEsRIzL6iT2PaWQEg64Bl7HCSqQyHT9t3qLLbLdjt2JmGj7pjolpylmjslJX9JgHHX1vSaclGF1vpk8T5UXEtppG7rZCuzb0EwiV0M3ANmhKeKd/f5sm/Ghxn5lr+Nn6Lp9dHmBN4qM7z/m1hTbsF9LxfCBxENmGQj2CbSQVEbO1hEnUUC1k17jPssLBB3gHZOk6E7zKIEmQLNgmaup7gjFhXdLonTXUPaR4ZdPyRYDWSzYrVz6sjA69blx/7BcBWftsrWuGjcNrG2qrQZEjYT85kdFjTBlXe2vacKjrdVHURpbuASDto8GvaNC8atyCWV9hVK5nk6A66ThKDvCcFzN+4QKFCUxNi4oHTF44lCl17FbU0VNJx2mYKTsr/3zsViObqj+w/NHZ22w6j7GBkCUXRhKFCVivO5XS9HpsqwkkhpTBpgnLMMFLz5Hd4KUnpIIqLyy6yOiEve6zYXkSJq5jUdQcFxtWfUEb1GBxFSq8PyUkQ2U6NqHgrJ/xYU7O26Tc1WGnNx+6GzHbAf9pjmERGICyffbV8L0uSGlcBL5Mt2X/K7zcdVF5S8ILdCnyJMBpnOTJM42f4/ebR7zoF2Mc/aHbsLBbKuk4sms2vuC8m/K1o1MuphM+OTkifV4x/7lh8iJSnQbctleU2yq44raWdmHoGqG4RIGtGcRC6FLBx5N7rI5KLrYV9+Zrvj5/wTeqZ7pxQMbO2LovaYNjUnRczjpCJyqD84nU5IXLyxhPu7+Ljk7oq51RPLCLWA9q1kz24XppYo37bAQhWfvFXYpriR6vHcOisvsjLz+HG8AuNWxPY6dcb9vFtA+v8QprYDjcsPjl92hkX12j0A6/YwC1bplZt+MLhoZ/gNtGqlO94GpbcGFibpQExCZSKnCoTMASx5RAR2SLV6+sZAgiTEyLSRFxEEvhWb2gCwabpYQpqom4lURpenrIDKxEIX1mbykbqI2eFpW5T8bAEf27Kd/PikIIbaZ3pgTeRIpsHNxmVnAXLW1y+jz22GMpeu7YDYJKC4dLqxSVFg5g00v+R38KY//YcQAW8u3D9wMna/93bzIGsOsq6HX1sQqmKWgXUmKVNKWwT7qiDay4F/1iZGiDMoAL0+GJYFplZhnPUbWl8Y6L7QRZGYpzwW8SbpswfbzSiTadEEohBC0+otd/yQohWU79jLZyNJ1jWrTcKTYcu7VK3BGGxOYuZm9RG2l8IAVt/yaXSCG/v3ueG/sjGV1vrmyM24GZldea4fbrb/uVuVUg+3i8FtC64Tm8+r7X1xG5+TncdLxrt18BqRhArJcfO3bKX/G48T7D7/bw1dvxyzuSAbsNHP40kazn0pVcFD2f+wNK27OwNcZFzsOUynS5wd6wsFuMRJZhwsJt+by7A+g1u6TiyG5GRpeRSHdo+c7Ju9S9ztOD/HzZKshUmJ4D17ANnmiEO25DFBlTEuemUcVCmGgKbzJjjVLlJNu5rbkIk/w39fin3Yy69xpuEg3zomHqdM6JybDpC+a+4WlzwPvlCV56umg5tFtWoeQsTrnnlxzbFcto6STQJkMlkYURDk2By+wl2DWw32Tb+WUb9VfrFK1h+iwOv17DDF9NriivB2m96fN6LSiXIjY3VUoxdAROY+QklnTJjrY4h6bhNFX8oHk0probicxNN8pYN6nkg+IFm1Byv1pR3Oupg+fTi0POHh8y/cwyeZ6Yvujxy4BpwvCkSEawtaU9sHSdegInA+1CFSnRg0THZ5Mj5kXD8/WclISH80s+nJ7wtj9nJi1NsnhUudQly1k7oem11tlOe1KrjaNYqieVTwpoRacgzRjmNOzljTZ1+krBrpGxnFBQZgC09sdr6h4IiHdjY/6LJYdpl+h+fS25SbFyUzP/pjropiEyel5dP5YCWWYXxqKnrjLxfSYw5HVNG1BcYWeNvsKyu4+CX7tjXWnMvOG4BbO+wmiCzdT7RHXScyRC9CUvzILCBo6LDYd2gxE1NRwmhNJo5wH6PMlWBIRNLKlix7FdMTMtAeFZM+dnF8d0wSI2YE3EGY1LH7TpsIvUHTaOw6IxACuV6TiQOhvF90xtw9Q2rFzFJiiQdVRscRIobGDhGma2oQmOynVjV3+UKgZNETl0W47Mhpn0eFF5oUrtBuZZoCNgEYz86cJZg8FizNO9MrUMVmQErUKeIAKJTQxsEpTSsjCWqRRfalG4aXQEnobIkzDjR81DLsJ0pGnbDCr+pycfse210/WNgxd8vXzGO+6M8zjlvJ/x8eYBP1sf8/lywcXlDD4vmX1qmD6LFBcB2wRlORU7BNxtQ54MDH0luFonmz6BKYWucSybgmnZ8mCy5I5XAG2RwSxD1PSR7DEAUC8cZ50hdWakjEavk7vp9wAc9PtQCv1Ega7o9DmoWW+WGQ5Ddlvt6xP28LNY+/pJ/XWphjcZ9b7CvDddv8/1w+UCbvf91cfsU2L3J9odSKWa/pF9lRgn8UEOs+8p9qpi5Xbcjv0RoozdMbeJVBiStWykxBrtgJdZktahoNCQlltIDxIp6WmzVXqXHC5FptJSWOX7rEPBeZhol9woy8qIdmT7ZHbS8by5HrqGBmWi9sliUsLZQIV6OxmJeKMmwm3uyANUtsdIMQJlhVHmgDNxZIgpUJNokh0bKNoUiuO+TYGcXVMjEDEZYvrTvKiGLvXwX8yA2ZAUCbvpQDvcCrg5CRSovOOrmgAHIuuYWKUiJxH7DOil/BkYfr45HpOJ75Qb7pRrDkxNnTx1KDjtZpx3E1ZNSd0UsLQUl4JfJ2wz+Eqx2xgnMP1u8zysBfo60cZHb2h7i3eBmW+ZZACtkD5/CglH1JSzvOPsS8M2iqYWZRpVspZkEhLDVcaTZDl7fmwyyhAbrocrBcTevHxDrzvfR1vBI9vppbvdUAS8dJzXryO7J/Pyt6+6C/Iy7nZlrbkO1g2SlZTZV3v3GdaVJLfry79MQ4J+9n7Tc/RjkOS5sFOeoA2Re8WKY7fCigJaMRlKo/L0yiiDs0uOO26toHhmxj7vD3jkz3jLXXJk13TR8Wh+wc8ujumDHZm1pVFvxT5a1qFgYjsmopLxqVEJ4FCraKq6+mgN8vGF2dKhvrwWbdK8X55y6LaEZPisPuLR9IJt8Jy4GVPXsnANR35LTMKJm46JhwCbWDK17cgauuM2vJX9jL2oCLGSSCXgh3Un02e6FPDCn5lf7yBPbFJHSEmBpL2x8+AyNGmQ3WePr9hrDUPxpSSHXzQMhmWquYiJ52HCd5t32MSSOrmxppmahh/WD/nD80dZVmr4lYNn/KsHH/NR8YRl9tH6pLvLWT/leT3n6WbOycWM/mTC/OeW6ZNEeRHwq6C1Qgaxkmg94epczwZDO9fX5k3C9EI/AQT6jePzywMAjqZbHlRL5rbhrluxMLX6QqPBaHPbcOhrnIl4G2h7y2VroTNgE9KSgSyVEJo9MGYnj1PSQCg0vTCUueYZE8qH7vX4Zu6YWK9iXkUQ50h9/3pAa6h/ricZ7tdFb6JEGf72KMPnlb5Z+yys/TT7NABdVnZsL2E0e9+3AtgdTJeg0QJrr7kiQd/35PYea3YN/zcdt2DWVxghGoy2aTExMnnWcjd4zuuKz+Kd0Xjxo8kzAsJZP+Osm9JEx/1ixdfK5yqnyCkiZ/1EGVtlp4mIVYu/E6iD5/PlgXpmSWLZlmyNpw4eUyacKbESKaUfvUYsiU0sR+370+6Ijd0yNQ0HQ9xqmPO2Pyd4ZYY97Q7YFp6ZbXEmsA4lF12FkcQ2+BEgM9m8dxVKHsqFpoKYQJW11UMaYZcCdQpEdKEo/ww2VPvGhpsY8CJ4DFVOThy6GpsY+EfNQ/7p5gMu+gkfTZ7ylyc/5j1XMxePFcG9puBQOWGgST11inTAOhqWyfNJd5fH3R0uwmRcqCOJszDhrJvydKOdA/WGCVzGCd9vHnERpvz+xQf80fOH1NuCblngzh2TJ4LbJvpSsBMzytzGFI18gfcTo/ryhQJP2rVI9LPIYlHz9mKpHTJfq5dN2klbCwkqbTUtZdkzcw2F7fkxsN6WhN4SOkO/tfTVjnEFubDw0M2EWOwmRr/Kzy3uTep7vlQmJlLYbTSuT9yvnNSvTNh7MYBfQpY46sm5BmhdeQI3FB+DfnxMz8pv//XCYeha7OnN9/23hvTCHXMNBcoA0s58+XbcjptGTIPUNYMD68AkWeresUwTBVUkcdetNSgkerZRO9hT23LHbrL5e6CNjm1UVo+zQRMRXcBU6pu4aktlsSO0wdJHQ28sM9fSxoQxCpI5GdiuiS5pP7vHsA4Vnem1y04PBrZRk6wG3651KOmtekUaSbTRaaqiJLposEQCJqe8WtpkmYuamZeScAzyCRk3/X2KYzPvqwT+vGoY0RTIjkCXEjbf5vJucABGupT4rJ/zeXdIHT13/Yp33RmHps0MMi0aXidpHF5Pj4aPtElok+EiTtVHJksnQOeVTf68153KSgcPsyZ5XvSOOnke14c828zpO0dsLKY2uFWWDjrBBIj769/eWhOd0JdCLBl9TLTZkSjLjnmpktXS9JkJpK8tYLASNcRGAs4quDl47bR9QQqGGD2xcxqMkaImNxFJRqX1oeCKBEGGMKgMaF0dWSYSr0UlXvkwDcQbAK39guBV33/h2GtZvwpFep0Xl8jVNealtWbve0YscLfW5NvHYuBajXXLzvrlHSZk754Ift1z+GOQ4DiPUz6TxMSpQuTD6gUAp70arJ92M2a24Z3yjGO7opCeBk+T7UQ2WVXy9eIZ921DVzq86TlvJjxbzbN/le7ttr2nzf5WTqLKyoNKFAf53yaUWBIXYTJ6+j7vF4QkHNiaSjoWbqvPMcx4y1+qemWqqpJfbI8pTM/MtkxsyzZ4ntQHOBNZ9wULX1NIT508pel41h5w2WsD/shsODItU0lUIkTUa7ESR0iJTepGMV9JYv4VG95fNErxuX7pqFNkaiwxJabGYzCjHNGLpU6BH3UTftQ+pE2O9/0JH/kTHliHFzuyut7Up2vfgmWTOrqUeBwKftQ+4OftPZahwudkykO7ZRULXvRzfrS8z5PlgsIFnIk4o7Xh87CgjgUfNw/4+y8+4tOLQ5rG064KzNIxfWIoLlTCHr0QCjM2I6LLe5z85qe85nQzneCi0yCsYT0ws57jmZJF7lcrjos1ViIhGbxENlntNFggHHkN3Xpu5sRDoestXavNvRCEMNHmvF+xS/JNjKzg6KGba3cgVPqcZKh5UkL6NE79yI6XLTGpX9beGOqbQUr4RoDWq8awNo1ekzetLQM6d60OMtzM1holhzJK+EfQKwNXSWM/9evQyN+rg5KTTGwQ5RbkRMO0ty7p3iPXTdl0e6iR0pfEZ2/BrK8wjAyobIKQEFHJ4d02YduST9p7CmjdTXxYvaAUlQOuY8kPV29x3k359uwxC7PNx4usQsXHzQOMRN53pywmP4N78Hfir/FiM1MNd9QO9rb3nMkEkyUlOPD0eykk6pmlDC1H189praOSlkICx4MRpOmIyfDAXxAmwmmY81lzh9N+yqordVGyujkG2ISSuWt4rzrlV8vPczIiVKKGh16syvtIWGTc9P9JWFDDhPu6CdqLxSebfcrIr1m9rM5jZJkcT/o7/J3zX+efvniHTVPwh/NHnL014zcnv+Ab/oT7NjIXf6OfVpM6TkPDabQEDOdxyiaWPOsXfNre1edgeu65JQurTKd1LNnEgm0oqFxPYQKH5Za5bfhFc5enzQHb4Pnp5TFt6+g7i1lb/IUCG32l29O+E4wFEzIavkf17EshVDKCWN1BIh52TBYN7x2d8/7sjC5aVn3BxGgBVCctbNqkHYupbTXpwzY8mlxgJPHJ8ohlXbLqDW0jFGcG22qHPhntjIRC6BaMFNLiAkzQ5zUAM2mY0Adm1lBcyF7LIsadHNDIzqj3iyb1fVrtNX33eNsXsLDevDhh7Cwk2HUlrj/8hm7E0GW4ctsrjn87bserhuTaWEBlVSK4TWAaDHWwXIaZ3mcCR06BK2WGGk7aGbX13C+WytIyIBlAOg1zRBJHZkvpz2EKP0732XSaGqjeU0k9G4P6VhkShekzOKbX9LBhlGToMYRYEMTgJIxyR1CmUkrC3NREL2xjwTJUmmwY1STeiAJzoDKXwvQcmC333RIRNUF3Ai4nSF1lSpHTsb98p3pgXr3OuH1onoQ996pIIiQ1x20xrGLFx/VbPNks6ILlWbFgOyt46C64YzfMJFEI3OSn1afANgW2STlfdVJ/ylUsucySGyuRqWko83veJkcnlj5a9QSRROk6iuyRte6V+XDeTAjBEoMgnWAaRrmEQc3fZYjSvgbkRCejEW8yEMtEqiKu7Dmoag69JmG10dKIo8fSYzRVMTPsvFEgtRBlfsvEcNl6mr6gjQtCP8XWgoQG4gakI/qOaKM2TXLX1taMz3EohHSt2UN/9pspw/f7r0nIhvG8DGhdH18Eat0kM3xpfMm1RgZAaq+Jdf1u19eaW+/F2zEyHhJEwW96Dn+SsLXnrFnwoxzuAfBOecbUNjxrD9Qzt1/weX3Ibx18wgN3waFdMzUNIRkuwoTPuyMAPiqf8FHxhHfcGc09z99PH3FRV3TB0mTJocREiBrw4Y2m05Z5zeiS5dCpLHCR09aHee5Ff8BFmHFo18wMmSmmzK+7fqX77yyRrLOv1yqUXPbVmIxbI9lCJbAMFRe9EgjmtuHD6oSpafBozTIToRKTfX11LRtqlkCiekMD9686vFiapPN5lyJejKpKJI7g2jr0/KC7y3/w4i/x/fO3qDvHOweX/I173+d3Jj/jPbvivtX34HV+Wk3qWMaWZdQEx/NYUidPSDOehwPWsaSJCgJOTcvCbsf3Wt9PNXUvXKByPZXrmNuG8zDlj7eP6KPhk+0dfnF2h6b2hNYiK0dxbnCbDAzNtaYZOroKYOhcF62mpUcn9FOhPYBYQCwS/SwRywgucXy45p3ZOQAxGc67yUi4iEkIyI7RbToa4zCSeLc658hrzf35pZJENlHoO6HfWMKFekUiiSgygmih1FoLdB30SwVjRqnhvvzPsJNmJG3QDHXPjXXN62qf66qUqG9YSoNH5NW16Yo88E3GGzRqkskbr+G5JtQva2jay1WSwMg3sHvrbR4KdJFtWa7+HdOrxc3utb7567gFs77ikIQCWXlDJAGKy47jP05ULxyfXbzN2bcm/MWHFb86/5w7fkMTPetQ8JPVXZ41cz6YnuaOyJqFqVnGio/rh3Sl488Vj/kbs+9TPej4Oy9+jc/XB3SZ1jsUJNvg88WrnW5r0kjhHZhUZLrwKlRgwUrDW27JwmwJGGbSMpVeUyliwffsO3h5yIFrcCbwll/y9eI5C7PlobtgahoKAtPM8lLvEpX4jRrvfEEaUZnFlx3KhBqOb7DISPm9PlGX4rIhewbcUuBx73gWDqmT16QNp9TTPhieLef8vv+ALlk+8Xf5RvGMR+6CY7MZJYgGoUk9m9SxSRplGxAKAufJ8ov2Hj/d3KNLhnerc/C7iNoh/fHCT7hbrSlMYOY0bv5pPed5M+fZes629fSdI60dfrWT85leJ4S+UslP6hQsGkZwhm6eUe0AklHyYtrx229/xm8dfMrUKFD1pD3QYiZMeR4OuAwVF2HGaT8bNwJWEk4Ch35LPzNUTk0bl62hmxf4DYB2SdpDBbHag/z5BkhL2fmdwA7AEtmBWrArLIbJMSd9DHRVNYTPh9jvrKekXYw/yW59pMLKrhsxPBfZ/e7mx5IXqz3frBu76kM3nqsTd668B7nIvtdWIv9sErdFyO34wpHYeS7ka8E2kclzcBvDsl5Q33M8mjvuFUsqq+a4LZazdso6aKLPwtZMpKOwPW1yarRrDW/ZJV8vXuAk8OPNfVateploLZ0IyeQUQ1EvJNEGjs1QUkhmBBQSCrKAgmFz02TZmeAJWeKRqJPlmTnAoHJDI4mZbbhjN5Si3o0+A2I+o+SCJizpDDN0OPVt0efz5YuPkCJhkE+SQbx8nOuAk8XgJY0S9i5FltGwTqUmeCWvDCWBGA3rpuCxOSRWwkWc5HW+0c8gSxAFIYyBIsrEiwiWSJ08F2HKeTdV5oKrweoGfjBsnpiWxjomTsNmChNICOtezZPXXUEfDDEY6AymlSts0cTOK0SEK5HYGvKx61YPeJH1gYfzSx6Wl3gJNMmxCqWGn0TPRlSe0kTPNha0ydJEl3GkSGUheoczd4EPaH1BSBbTJeBzYvGCUK5JpieUuvhJzHvpK9iRjHN5kuHs2/99vnYkT8Ky9zjDCGjtXjC8llX1uiH7X/dBqDc43rh27N/3SzyHLBlJ+WEvmdNfW5tuxy/fkD3vnoQCWq4OHPw8Uaw9Z5cLvv9t3UOHQ8O7xRl33AaAF+2c87bi7z3/Fb518IyPJk/xEjh2Kk182h3yaXtMlxy/Vn3Kfbvmv734Z5Sm4//15M9xvq2UVSuJedHQRcumL9gGrynpGO75FdMRcDBMTUOdstl8dGB6QnKUqWDTl0xNw323xEvPJpbct5fct5fctWu6ZPlZd48/2r47Mnsr23O/XHGvXBGSsLA1C1vztfI5R3bDe/6EgsgmWaYS8HtA1sDKslldMtiW/EnG0IRvUvdGjCmDMDWeLmrTR3m3kYvQ8zw6lrHgPE75pL3LOhRsmoKmc/zs7A7/ufkm3R3Lz9wF7/tTvu4vOTQJn+yVNMWQPbmGxvw6OQqGppfjF91dvrd5RBsdM9vwVnHJItekC1OPoKKXwKf2iIezpco8va7RP9w85LydcFLPxlTbBKTWUCwFt84+Ux5MS5br5Tk9xDxnqYQPIHiV9EkC6bXRAgl/p+aduxe8Nz/j/ckZXbKs+5LzbsKBU0n9ZSpZxop1LDkPM/WUNoFVX6rUUAIfzk9xJnKynSKSWAPdtiKU+hxyv4luoYBWN9dwLESfT3k2IDaMUskxMTDXaoNs8qW6Jzfx9w3exRplExvzxc38/fPoTVnDMYG94fx7FStrfMxApOBlyvteauIVZlZiZBenrFQZ68M9X8x9KWESiC6NDGxtXr15l/8WzPoKIybBZkSZlC+2bG5mm8Ds84htPeeXd/gvfsuxfeT5aP4sJwI6DIlNX/CD5QNO2xm/Ov+cR/6cynSchymn/Zxnds577oL/2vRHHD3Y8P8++3V+sbrDui0QUX8eJ5HWWiLq1YTT5EGSyabzgUK0cIlJqKRV00ez5djUVBKp9yDSmfR8VDzhPX+ikbbJc2Q3VwCvkIRNcngiMxOpRKjEjZN/l8LIzKrEfWndeUjXkwLVC6WSlzXsQ4qIR2m45yHyOAM3dfQZ1HMcuzV3qi0Xmwl9b/lsecjUtWwm6g1wEuYc2TXvuXOOzZaFsTQpjgmNU9PzPEwyw0kX4NL2ENyORm30/Q7JsIkFL9o5m76gNZGZa9UXwNc8b+a0vaVpHaG2OVFDL/xIRq1FZXx9ZmWZXjvJyelC0E+1YxB9UkPejGhHhLmtObIbDu163Ih0yXLSz3naHXIRJpx3Ez7bHNEEPRfvT1a8XV0oRbcv2KwrSEJ3kKh7Bavaw0QslH0V5llmdG5vpoSKFkIy6LL36arR5PP3FZOVyCgF+TMZr5n8b5IhvnSb7P9OjzeaHF6TtY9ykKEYzIbvArskr9txO14zUso+DiPSy8hIlBDxy4QEQ91M+OShpVsY7hZryuxFJSS6aHnRzNm6gnvFkoWpcRKzJLFgbQoOpOFDf0o16/ixfYuLdkIbbOY/qZl3MJKlfHFkeZmUzeFJI9U/kT2j8vozkR5Hot+7eLxE7toVh2YzShAr010BvCI5DZFEkSWGLjtjgTKjBkaVy2XDlxkxpZFdld9tJK8z5goCEIkpYUQwSeiJ1BGWybOJJV0yJISYDBPTUrmOuvPEaFi2Fd4EOq/pjhtTUJmOQ1MzkZ5ChJDSaLHhJbLOBu8dQxiJTqRtshCK0Yxf0yItm1Bo0SiGwqikvTA9awpCFPpgSTmSfDiHhuQhBIIlR4oPzCfAqIeIGr4zMnHHDSdq9lxJR0nHxLSsQ0VE2MSCVe7y19Fx2VWEqO/o1PcsnGDcnI6HdL/q4AHETaL/WOC7jwg+kmwLqSMVGWiszasBGbn2D/a61uP/bn7cngTjT328Znq/WfL+BWvN3m37Una9AyOQNUo69n/3Z/QSb8f/n429hGeC+h9WJx1v/YHh4mzKd3/zXc7en/AX7n3CR5NnNMkxsy2N0+v0u+dv8+nmiF9dPOXt4pyF2fJucUqdPM+6A75Xv8vvTj/myLT8N2bf4/CdLf+PZ7/Js/WcTadlpTM6P637gsp2NO1UFSRe65PSdFyE6VijHLpNnsdKLphw3y1HH62Hbjm+tJn04wx/bD9lYbd8UtxlOa/ooqMyHd8sn3Bgaqam4bfLT8d1BHa+wgtzVS1iRbBJCKjv71w8NyUNvtHbT2IVG4wIITOuPJYyl9zX09eH5PSpWLqkbKw6JT7tJzwJh2MdVpmO9yZn/Njeo+mg7y2fLo94q7rPhZ+yjBNOwpyH7pxjU7MwLcemoBRHT2AZW0J+DyoJLKP6JXvptekQPCfNjIXXhkwTPb9o7zI1rdaUaH1R5M56nxS8bKKjCY5NX3C6ndL0lq6zhJXHrC2mk3H/azowXcqSQcF0EJ0Zmyug920ODf10t94kl0gm4VzkndkFf/XoY47smuf9AStXUZrF2LR/3N3hPEy5CFNO+xmn7Yxt8KO66MA1hCTcLddax0tiuykJ00h3YLEdpCA0R0JznDCt0B7FcW1xSwW4VDq3NwkPDZWRMbU34RrZ1T374VRjs93sap4MaO2PK838fe+sN1WYvKqp/4ohaY9ZNTyHPWVQkgzeud2Bxjpwb5+qfmK53z/USCGBl91jhvtF0ZpwWMO/xFbuFsz6qmPwMUlp1x2OQK/Fx+RFh19bzlcL/svf+jqnH0z55uIFB25LadSAe9lVfL494KKr+J3DT3i3OOHIbjgPU37QPKKQwCO35DfKx0zvNvzz6ft85+JdnqwPqHvHaZpiTaQvNxS5uz01LZVprpgtHlntwISk6YqWhBVN44tEzmOBJ4405Eo6vAkURBamY50cHYYmCi0GS6KQyEIMU+PHFJDBW8pnHfqrvKhuirIFBcIiUb1JSCOYNGyuK9ECZFiIhiQQgKfB8El/lGmzuvGP+YrwEvhwdspZPeFsNWVdF/zo7D4AU9tSJ8fzfsF5mPHQnXPfrpllZDjkScRL4LP+Dk8z5fpRec4mFDTRETAcGk2FOet2E+jUtRhJPCgv+Vr5nOd2wePtISkJMQqYRLKJWKhhvekECQpaacBABoV6sK12N7oZJI8a5qbMjqoN7brg55d3OC7eweXP/temj/lm9XQ0+QwYzvopy66iCY7z7YSYwNvAzDV4iVS2o5CW/8l/8nf5i9//Ob//ta/zv/qb/zqpEo2adQlvIqE3cGbzRJSfb8q+Ddcm9mSNTthZoz+kSl0ZWUKVXgdk7dNsX2UM/4bjTSb1/QJhNIDffzwoUybHy8e9gur6YjCO4ebb4uJ2fMkxpsEAMJx3Cb+J2Fao24JPHx6zPfIcF5tR3lHaniY4ll1JHRxvVxcc2O0o2XjRL7AusTAND9ySYhJ44g95Uh+w6kqVGuY03ZgZQILOGyabwwJZVqiMWk0jDKNJudZXysiyslsznUQsESsthUQ6DBEhZLmAoD6QRe6aK7SVRm+pwbvqVV5UAwh1fYSkUJiuOAOYZHRylQKHIyXBSgA6vWdea1Y5nrxP2kjSV5kLJkkc+S1179m2nra3nNYzQNeQHsM6ltTGMzc1M9ONBvvDlGCJXKaKddY2LFytYFUGzbwERBLbuGM/eKO3zVzDkduwiQXLXo2uxpQgUZl0HECr3DUdwQ8BYl5Tsj8JObSCvDGVXgit5aKpeGYPxiLhvl9y7FYImpQ8+Lc1QZMM697noFlPYQoMb+G+HTD/as/X5h9DED7+zW/BsUG+d5+UThGzwUhQ0L82+RrIz32QcQgZzkxjh/+KxFB4mao0UJhkOM4NF9vgbTJ02L+MNP36oV4hF3zpOckr2FX7fzvv9+Me0/eN1rHb8Us9hvAdGZjieaScMG23kcMfdxQXjid//i3+wUeO83sTvjXTJnuXDBPbKfunL/i9kw/4cDHnN+ef8tBdsIzV6F/1k/YtfqN8zLHt+GuTjzl4uOXvXfwqP18dc15rE8RHg5WK1jpNurW6LoRkOHSbMeF98Pr1Egh2Rcwqk0o6DkyNJ3LXJs4jrDPjt5LAJjrumjX3J0ssiWWsmJqGI9OyiY5DoxK9OsvpFqblvlUWlqovTAasdibroD5W8OXl6gMjKxLH0JBRkC4KaN0EkA21kEmGdepZRsPP+2MuY0UdPYWEsTF/z6+4P1uxbe8QguFiPeFH5X3enhbj8Z73C47shg/9Cza2ZiEtXoRNSoSkpveWxDmGTSz17yTHgavHefu8m3Lg1BqlS5Zn3QEn7ZyIcNpMNdXYaMNkbhv85Ix1KLAmEoKnb61uhodmyb7fVQ6zsu3Q5BVl5CZITpnA2qhXeeHQSAHoO8vz7ZzPZncoyp6F2fKOP+NtfzbavtTJc9rPOeunrEPJsi/5+eWdMXnz3fk5X5+9ICahi5YfPH5Av/RQRNojQ7RCmCa6w0AyCdMa4iSAS9AZ5MLp89mfUwd5PuRUc1EClTG7tWigFYe0Y2WJ0Qs0xS+ufWAHSpH/3tixyH/jdZYqr7FfuV4LjdLBvcbQaAa/56k1vC41hmeUD+7XSxITuF1dm7JUcdh7KMAFMvhqDb7C4c3X21sw6yuMEIUiA1nE3UmiiKoZKexuG7jzw0RxWfDD9busvlny0dFzTfdBUwRnruV5PeefL98hLoT3ixcsTM1pmPH95m3aZHnolvxq8Twzbrb8UfmIjy/vc1lXhCh0werGOsfW3vEb5rZmatos0+iZSUuVo9MHT5WOnX4tJgWqKulZpwKbEjUGIpzHISY3UqDGwQsjlBmw2jcTLLMBe0gJt3c+KnV3Fzt7HYYY0glDSjQp8jwYLXxInMQp9+2a92xHKW6k7Q5/E+A8T8w7s3MFmQaz3JlreGd+weW2om0d67rg09URC1/zsNTElafZuHftSo7sZgf8kahzF/5pdzD6AUyt+gEMiSnTLPEELdKMSZS2593ilGO74nF7h02vC49zkVhEQhRioxB19Am3kTyx7woObJ7gc/IDCUyb0W2nwFbohba3vGhmrLqSF5sZ3y3f5i/e/QV/fvpzFmbLolAfgqfNgj6akRm7akt+Fu4y8w33qxX/g7/7D/mb/9E/xST4q3/8MTLp+d/8O3+dYAzOBcqiZ70p8zmfN1JxbzJPCTE5BcMYxJhMpd3TS+5OPH0Sg9wwopN6Sn+m7KyrwMDV340djLFLMHQlGJlWVx+z2z+OBoZpN7kPTIgdq+F23I43G2lgzAxdwPE8S7oZFO1omR6qk4htLCftIe1dx91qnVk9miJYmMC6L3jaHJBK4dBuKKVnmzzPw5yAMDct9+x6TBB85hacNjOa3hGTFiMxGZJrlO1rNb3OS0BI4xrh8s+CNgTMHlVESQOCI6IzrChrKzEyXyX7QRYSKGXwydoBWaC3wcvXU0gxwxv7dLbd2KUT6jq1TkKfCgwlm3TEzBQciMcJSGowsiKyYnCoVf9BN5qdx6QA3ND5L0xgUdQ0nSMEkxOUKkrbM0cLt1UGw7rUUJkOl9smhkSPpUuOVSwzoBbxJuBSVD8QDJ5wBUATEs5EDuyWqbQsU0WX0w2NVSZsSpCCnkQpaad87IoPdZfROkRl0Pm2MZkp782jEKJhEzxtcGw6z3O34NHkgof+nEI6jm1HRFj1JTHJuJdug+M8HuDtnNm7Ld/44GO+1X+PLlhSJfzknW9hfiTE3mFsxNmettU9xehPstcNH/1KREii8o3hmnj5YmI3pwNX5OF/Vuwsrsowrv2K0fR9rxEC7PmRvPywK0bwA+tqzzcr7b/G2/Evz0jk/dSuCax4rbIobBOZPe1w/6XlZHWXP/h1z+XdCe/PThW4MJaZaziVGa21/PDiLbbB87uHP+WuXWGM+l79pHkLL4FfLZ4ylcCvlZ9T3en4XvUO37l8l8erQ5recZGElSQmvqOyquwoo9Y+ZU49bXOAyNQ0HNmGLgNWhYSxkdymxEI0YGOoCXQeFNrkKQjq/SiBSjJQk1ROF5PWNJXoznxqXLYmSfSEMazKi2GaGVmvasDDq0GuAcjShr6qOsbdawpYo7LDPoURMBseZ1FSwaf9hJM4o44FlXR04jTdUaI2LCTw9fkJm67g2aWGS31+od5PB64ZfXs3sWQdS97xauq/kI5C9D2zqLqmko7n8YDH3R2a6LnjNhy6rbJ8B1mh0XX9rJ/RJcM2eI7K7RXvtQ+KF5z2c34s9/M8n4EcH0mdEKqEabW5ZXoFqUzYNUpADeHzU1dGloVk084QPAIYuo3nyXLB4/kRJ+2cF+2Md6pzfmf+c75ZPcGSmJmGSjr+KL7LaTujzmnym9Yjooqop402YUrTc3e24r/3H/wef+mHP+Mff+Pr/K//zb8BRxFnIn1nsUWv+wBJNBfVKI9UWe+wL8vX2wBYWYHAy018uCIvHIgYg4XECGjt1z5XPLTSTtJ4vVk+YhDXfLOGLtWXbMaM9dH1h+2vSblGkpSIe17CJuzYkHEAAQagK4NW+6KUEQDb9368lRn+ixkxGkUTw95JN7j/ZwNG8gbRNpHFpx3F0nL2+CH/xW/N+ZWHzzgqNhy4hiO/YWJbln3Fjzf36ZLlW9UTPrAvWIYJP2nfok6eb/gTHto11fRjPiie80fVe/xo8xaP14esuoKT7ZR1V3BYbDktZhz6Lff8iuhE5Rtmw327xaDXgSbyObpkOI8TFqYeO+xqHO/psCOQo6DaBQvTsjCRaTZ8H+JsdxbsjDLDl963fPyhQzF0JYauhgJZPU+C5XnQbnadPE+6I07sHFs84b7t6AjMKccUjkNTYEk5TtioWT7a4R6AJoCFa3Am0iQtEuve8fPVMcuuYmJVBviWV9Bq6VYszJYi7+TXseS0n3PazcbjH2QvrhfNnNL2HLkNp+2Ms3bCWTOl6R0HZc3pdI6RxI82b/FiM6ML2f/LBTVFnESSE0wrxA5skNHTZCg4QA1wTZe702GX8tFPQMrAxPf00XDZVFysJjx/dsCTiwXNh463vJrUa9Ru5G61xkjibDPh6cWCkJMn7iw2/Pt/8PfH/ZBJ8Be++wkhGGJv6ILgXNRuecoT+p5OnDxZ0Q8gFarXthb6nv3Scyg4VEWzQ/y1wNo7f2Ikm+Lo/f8kXfJre5Cxg59ZZFe6HnAV7Lpp8WAornbd9Ku/ZGQzJGCUegy3JRkBwNtxO141UpK9dLK0Y9QMTtF7p5AJieIyYFtDvZzzycOCu/M1le0oTU9le1w24z7rpsQk3HUrDk1HGz2nYUafLHfshrm0vOtPObIbnroDTrsZy7aijZZtr2yg0nZUUWUJU9uSDPgUMaZjKv2IA7e5URKSUCdHKTvBYUhCjzYeDIk+WXqEhTQZyEqZkbXzZNw37R5CQl563/J9RkliZmjtA1l9iqySYR0rYELPMavwEZuZRY5XzGLCPhWKdEqXPiEQqLIEMmWj9n0JS2BnslxmQ/s+gSShj4bzdkJjHd5ECtPTW/Uim6SWUvoReGyTy6yrYnylQ1rgJssMK9OxDSoPqYOnj4bS9mx9gQCn3YxNVxAy8m5MJBiTJRs6Z6ew55+VGU/DHlJ63YOLvsm64cy+J7iIs8rkboKjaT2bdcWqLumPDDOrJvU9KkWZuA4R2HbaRIpxgsiC5WVH2c1pTMk2ebahIm0MsY+kqOt5NMNOl2seV0KS3OWOA6CVT7gbO9xDgb+3QRd2L3447rj5393lS49rD3rJc/F6HbL3Z/cB6yv3edU680ZPMrPLbscv7Rj2iJIBrWRkDKhS89V8AvVQXgQe/OPExYtD/vlfLNm+43l/foaRSEyG42KtIJDteLZd8J/Fj/hXDn/GA3/BsV3RJcvHzQOsRO7bSx7aDb9RPOHIbDh0G35QPeQHFw843U7Z9JZNq/NTGy2hVBnyPb+iNB2HmSFcSMATWNiambQjkNVh2KREtQfGrJNT4EI6bbTn5PY6OTYp8Dws2OTY00o6KglMRRlZO1VHHL2xFsYSkjbkjZix0T54TQ0Ne3hVEz6Njfg6BU6CsEzatI5Jg6M+cGc8tJFSHB1hfB6b1DKlIJA4j1PWsaRLbqxZNrHk2K1oRdsdpel4d37Oi9WMtjWkZDhZT/mJu8vEtsxtg7GRizClS5ZLV3HXrnK6345Frb5ShYYAJMvU6vt16LZsoq4bTfRYk8Y1xIum7BpJHBcb3vOnHJiaT+Nd6uAIUdR6wAaoIPRC6HNTOyZcreEjg9/vrqmgNUzK8r1k2KlU7MAX0bo6Ac/rOUYij1eH/PHzB3z34G3+6r0f8+uTT/HS86HXxM5VKPlsc6ifUzRMio7Tesq6L/BGgcH/xX/yH/Pf/I++iyT4K9/9Ma7s+V/+W/8G1sWxWVWVytxrYJd2uNfAx+65NZpszp5ZWeIsqc0Fkhgwced/eq2RP1zHNzY+YtKTb99sHr4YpHqd3+++dPAa4DU8B62N9p5Tyq93uGtOL9xnXOlBd/+uJ7rHIaFYdgDXLoqXV66Brxq3YNZXGDFls7qUdIMlujmW0eAs7czO8r/yInD3u4nl5Zw//LWK977xnPcXZ5Sm465fc+DUaO9Jc4CXwDfLp1Smw6bIMk54HmYcmS0z6XnfnVFNOx4VZzye3uGsn7INxUgVLk2fux3q1TSVBkuizZPSLNNJLYmFaalkhxyv9zojNpsEBmRkdx2bQJW75F0KdAQsQpfiFWlgJdnHK1+3TeowmBuTDQfDxC5F1imyjBVdciPV9kW/4PHmiN/8258x/SdnyO96+Fv3wajfniYoupEVFTFjYVAn1UtPjRoWLqqG9bYgJag7xylTVm1J6XoWvuGFn3NcrDlwBywyu20wofysUZ+ps3ZCTIYzO6UwgTo4ihDooqWJVplRqxnrdUVz6GiOHc+6A350fp/nJwvixo3wtvSCbTRlapjsbcvosWR6BU2NvtCRsWW6YQHQSSQ1lpPljMu6ZHU+haXDbQyb3vCze3f5w+4RZ5sJ92drzuoJXW/Vu6suCBs3TiDPtp5/8M2v8+f/4BcYPb35vW9+SNg6xEfEQNtaYms1rbJLo3Hjrss9dIcFMVkPDozpHuMHP6B0livGhiafna/YgA+xtn+aY2Bi7U/Swyw5gAlxb1Lf0W7Jr3fv8ftPbR9wGIAsueF3t+N23DASZG/GfVR0SHDLexvJLKQMnNg6MnkutE3Bs/uOg+M1h6X6ZE2Nrg8xGVah1I2pXWUmlaFJjk3uDHuJHJotzmuRsfQ6H3fRZhlhxElEpWUBJ8oCHoAdgCIDR0Jiku8/jC6vR8rN0tk7ilAAnsBEEg7NKlQpoPonhsHrKh/HkaWE+XgaKC6vBLlCLjw6Ek1yRBx1OqRPH7B5eMDlbxncNyKx3rL4foQ/vA/NkkSTHz1wziAhYwJjSuqL4bNnVeF62k7Lhz4atp0ymVwGsza2YGJbSlOOicdGIl1yLPuKkDRJMiVhazw2p0taUSCpT1ogblpP23pC1RKSyhhP6hmbTUnqzG6OiZKbcNmAN+i/wZNtWHNG8HRPBhB9lrYloDdsm4Kmt7TbAlqD6YQuCufTCc/igm3nmPmObe+I0RCioe9tfj49SMv6jyt++N43WX5dPbU+/+Rd+LmQ+nrs4oQ+y9thlN4DVwoh0PlX8pqz+7DTy9+Pnib7V5jZO/D18+VPCGi9ZrxUrAzvNTtg62oSbhq74HqnvULj+kvZ//76z7fjl3ZISGNzXZKMfjZXzrUBp+0TNqpqpDwr+fFvvsfpt6d8dOc5RjSQY2Lb/K/jopvwneW7/Lm55ZvlUz4qn3Iepvysvce5nRKKZ7xnVzxyS6bmJ7znT3hYXPKjzVt8vjngdDvlsi7Zdp6zZsrMt5wWU478lo1fM7UND9wFlVM28H3bMljrbJJQJ8syGk7CTBNRcxIiKMO3Tl7VJPkkPw8zail46M6ppGdhAgtjx7T1CNmnMGG46vMLuyb7kGD7qiY87Br0uiZFTiM8DovxuZyEOSeZ9VzICw5Nh009B6aiJ3ARA5g2m8/ruhySgOg8XmYrgC5ZuqipfAe+xpiIiCVGnRtXbcmPV/c4LOpR/vewvKDuPF1yLMxWlSZ5fdFwkRnnvQY+Xfba0MehCZfNARPbcbdYcd5NOWun1MFzXk+wJtJGy5PpIW2y/GR7nxfbOTEaVaOUPU0DodBUy+QFgmjqeScjiGG6bJ+SG+CmVoP4ZJS9RdJ1h0IZW1IEFlXD1LWs+pKmc6w3JT84fZvT7ZT6Hc89v+S+W7KOJVPTclxuOChqnrs5q6bktC7pOg2zeXEw43/8e//p2CAxCX7nu58Q/g2HOdBPtl8VFEVQWxgYGVlDKNcA4ojS6yDtDNPFmqtSw71xYyOf/H26xs7at1SRq+DX9UT3l1hZr5IZ7t2W9tbEmxr7sKt3EJ1rkrs6rwxJ71f6QrL3daiTsmpNLXT2mjRJdpZNSa4E0XzRuAWzvsIYCgzpI8Q4ToNpn50VhsZfLjwCuG3k8Kcd1YnlxWdv89m3j/j2u0/4lcVT3qtOsajn1SYUPO8POLTrUVuuE3ZPhdJ0Z6bhvrskJIM3wySo309NS0xmjE0/j1M67CjhMDFyHqZq8G423LVrjk3LwgiRljY1dAk6hOdhwt2M6j+wLdNsjt6lQQOfmBq90NqUaBK0yWBNZBM7pmYw1NXEw/3RE+hToE49MRcXL4IfDdzXseTT9ph/ev4e/93/4+/z7f/TEz35/0FDFE/9t0pdmEi5o2HHLoLNBscxy2KGZKypb1XiF4Wuc9m/RWiDpQuWi7bi09URIonDombuG7wJbIPn+XY+JkoO6S1T31LZnjY6nFHzYu3aQ2gUMFr1JefdlBeXM9JFgVuZbIYo+ate3G6blH2VY19BJw7IoFbSrkbMLCHJE77bCBIdNRW1SfhnHr8U/BqajecPq0fEzkJtuDiYEhuNzOVQaV5SDJ0DcEXP/+Hf/SsULvBb3/2UP/yNd/jb/9bv4rsOX/Rs1yUxOGgNbqt693Hhcfp6tLGtHYRkRSe5a5/9FyZ2mDyBh5eliWPn43VjryNxYxrhaFh4wyR/vRNxpROSb76iP8+T/X7XH65M7MPkPR5z71i343a8agz41QBoqYHo3iYkoX5zZpArJSTpdVmeR9xW2CwXLO9NuHe45F6x5sBtMSS8qHH4RnTTbIkYifRoEqLLwLOXwMw0o+QtmZy4J5oOlJKM3knbpP4efabWbyTpZhzDRLr8T6WDSQKBoCE/wDp5ptmBam4CXhS4atPA/h29Qwko+TNkMEljzclOKC9nn0YiMUFPzD2oxCYaNnFClyZ06YhLeYvPPzrk3q9/zoeTf86B7zg7/hVYQ//HxyTOiXQ53daMnVuRRJ/05+G9MCT1FLM6L8QwCN4TIQrBKKvpkgpB5eiFDaOkZNMXudOtzTPJx3MSCUmLGpuR8QSkXghRNN0rejZNQartmF44AEHDmmF6RsnE7mTbbbAlbzzTnvwbdK0iGXociMWsLbbVIiR0hmfugBQEeqEpC/2+NVDmg9oA1MATzKfvYf8/kZMfvk1Mwux5Q/vM0fvPsXZF1yYtN4NckXegTwEJQzxBHtfBrKtX0c1jMOvN7IGXfj3e5zXjCuNq/3uufn/TWiN7z+4Va82ripCrt+396f2GyfVj3Y5fymG6lFlZgFFgS4vLBCGpBHff6ycK0kWmzzr8P7Scv7jHP/qNGd9+98k4lz8sLyhNz5HX4Krvrt4G4IPihYJObkkdPctY8ZjIkWlYSEeVGb2PijOezxc8aQ5poue8m+h17hQoa6KWoT6HhRyZDQWROp+8M4Emqc3HkW1ZmI5l9OrtlIR1UqBmcLzy0o++wF56LIlHrscgY0ogMDKyuhRpUEmgRY3gDWa0ShlS1Ycm/CAJhB2gFYlX6pfzzGpaZ1uSOnnO+hkXYZKfzwXHpudgPI6yhhnXNZMlliZ7ScYc4GXG9bYwPXdnGx43h/qY3Ch5mhY82yyYFwr4nHdTjvyGjS+V1GAa9buSnvMw5UW3AOC0nbLpC6au5cJOCEk4b6ccFRuaqOFhdfBcNhVPzvQxs4daY37e3eEHl2/x+OSQbuOzKimfZK3BtJqga5rMtopgG8Zz1fRgQxrXpsGOw23zIQ7UcB1JpCicryeEaDhfTWguKqQ22FY4q2Y8vXfA7518wOl2ynsHZ9TBc1ZPsJJ4dj4ndFaJBPm9fr4q+M++/hF//g8+0ca9wO/9ygdIbem8R2xEvNaKTe31OQjYLo11GZDncEGGdHIjerD9Jn7cNfLTPlA1glS7ekbsK1aslClOr1CKvDRuWrfewApuZ7GSHz/sEfYeO64x+9LAcc/AuB5dSXE3e49Lu3/jmr7fgPkSDjO3YNZXGClph5MYMzMrkozJMZ35xM7nrpictzQkHwqUl4HjHyS2Lyq+/80P+cVHR/yFh5/yO4tf8G5xSpcsy1ixwHBgNuPkrPRTN4JUlXQs7JbTMGMZKlahHD2iAiYnUTTjRDaYLYZkRk+ojoF2q5p0K5ITCqFOSuUtJYzJhTEl1jFRJ0MlUQGwPJF3Sc/tQiJWRG1zU6I02h2uU4/HanpIvjLq1NOkQV6RWGdJY508P67f4jvn7/LpxSEffefpTgKRQP7xlkhJJUKT2WshA3gRQ5t2TC0jMf9LHBQ1s6plXRdjRz1k5+51VLAvRvUee8GMEAyl7+mCpW3UA8UXPUWxS3cMXhcTgLlrsCYyK1vikeGDO2fc8Rt+tHqLvnMkp2+SrQW/2hUYpk/4TcI2CdulMapW8kRPSiMwFJ1kQ0WDadWbUIKQjMN0MHsMfp0yFdYQyoqi1gmq6QwUkeQjJLA+EYPgfMAXPdOyQyTxf/sf/SX+7uTbPKwu+et8zItmzuebA3661m6/9Kpzt8NOIk9+yaQrchX9AIxS3cfJ8RXFhdn7vZiR8Xjj+JMawO+DW/nnLxqq597vjO9uv7Go2P8nO1BL4m2D/HZ8yZFkvP6VeRVJOQFJSFdA110DUAEtJGEbmLyAfuN4cXyHi7sVj+aXvF2oCbz6jjgKhEL6bNqucpMWMxY2jkCRpW1NcrQ5lRfUD2PYZA/srIGBlVKWuRPH+ThBTlbSjYgT6IEiBazskgtTgjZBj+BICoBlICrkfaPN3fWYGWCaIqWglVruJgbhSJ8ZWeS/32LpMfRYTvtDnhR3uJyVfNCdMXcn3LUN8c49moOHJAocPkvh/QhaJWQ0Zgey11dCJANULtD2O9G9Tn0ySs1TUmnihoKYBGc0ETL0hhQF4yLWhkySEpIJoxdWYXpEEoUNpEnPYVVT2Y6TdkYMRlvNsgsQGTaOyuzVJoSEfJ+Y9mR8aTSFHfwsopUrLC62mkLll8Ox9ZyL1mFzM6aPAjaNHj7qdxswtsbaZ3i3hc/uUT6dMXUtc3cChy/YhA2rruOsLbQoiApmmSt9jSwzvD6hvtS5fsV1db1jnV5z51eZ637h+BOsNa+4303eWeMDBiBrWGuGYuF2/MszEsrKCprekESv52R1zpWYxtNkaLAnESQk/Dpy93uJ7dOKH3z7QxbfPuXX7j3FmcA9v+KeXbIJJV4Cn7eHHNoNR3ZDSMomWoZJTqHV0Kg2AzGP3Fk2c++zBHuQqwVK09ElyyyHVRmJnMQZM2mpc6N8E3vO44R1KjgyGwXLTMdCUjY176jTlpC0udJlEGnwe3xkG8qcEtgRdJ7NjZG5GCxCnSJdgmWuZ0qrQR9z0fCNgb217/c7SNs1dT2M9zuPcBrmoy1Lmyyftsf80enb/Nv/lz/g1777Oe3vWuLfMmCHwCsdWleVhCSsk3oUW+LYnA95nTl0W0JvmLgOYxIhCCEYtq0nRMEaZe823nHRTnByhDWRqWt5WC1HM/7zbsLzek4bHV2wGhDTlricvu4kqL+va5m7VgGt3tFtPbSG86MJF2HCSTvn2XJOd1FiNmZk/ZoOpM/NbRTActu0C7Ya5Hp5Tred1g3Rys5bVna/dxtB+oJNEJrGE89KilOD2wi2hZUv+YPFO2ybgu3phLOLGdYFuhcTkk3YRafNpyJCb8DpO/+//3f/CmXR8dvf/ZTf/9UP+Nv/zl9GNg2pdhTzjrb2tK0j9QbZ2Py6MuEgMQY9jeCW7MKvbmriE/eArAHsusGHTQYvxVeNPyVlyhXz9ytMLnaA1hWWcL49S/uTmCteV/sN/GG/ET1XxugFzbWv478duPsm4xbM+gpDBufNCBKHqS3mD9WMIQXCrrM5FB+qFdbO+fR5xK+E7eM7/P0PD/nOh+/w1975Mb8z//lo5jcAWSEZrATq5FU6RyRiODIbOn+OJXHZV5x2c/po6fMkWJiAN4GFq7nr1xy6DQtTszBbPvTnAHjUrLBO0CX1MVmYSCHCA9tdee110v5GJZEqv7TTOIBRhpmJ1NFwHhMLSdj8+iORZQzMJFGiuvEhSl2pvgqGDUkmP+/v8XunH/Czp3eJQfiDb7/P737nZ+Nmrf1XVJM+FcsnfWQd1UvESiIm9f3yErIEUWiiH7tCd6drYoKm83TBYk0GAKOhqT2hM3qhbhxma9jOAvg8CUYFhJyLzCb6GVmJTHzHutOCxEhi6jumvuPD2QkA606ljdhEfxCITpdIv8mnR4Tg86Y6bzIYzh/RGSR61SZL1I2IbUGWSVNCOsFtheIiUZ1H9eLy6qnlV4Lb6PflmSZ29ItEFAitvtZuawkzg7ORe/M1TiIntRooVq5j4RpmXnX1dPlza8lyW26efPKsluxelwJ2rKzrMbXDRDpES2N5pRH8FzGz3mRc98u6JkFJItkI+TXddnbFw1BYpeuT/7XH6DyRrjzmdtyOG8e+MVv2YxQyoGUGm/PhBNzfiAz31ULGr/V67ZcTfnZU8eTogPcPTnm7uKAUnccGIGs4Xp/RjOFvTKQjmhoBmuhYB53vBt8oKwkrKqFTOWNHKT2ldBxlacioVNPlk4hQina8Z+bqdd5ngMqxCxPZjmCUUEiiTUKdEsUe1pxQubsn4rIn48DVEhTI0r5TxBO4iAWPtzPOL2akZ/D8m/d4nN4msWa7vANLhb68JC4itMmqNwjZJws1f0+SMCmDW0ljzCe+JaWCPqcRmqHZlAyhM6N8gdwg6H1SACh7EoYomCrhnK7DIgmfAa00/Gwj3jYcFbqYdDG3dg3EMuY9iGjseD4/kpWd39iwd5QBNMnz3oCOSpaINFkSkmXutgFXp7EbG50CWxq5Dm6r61UstKOdckJR7BPJtxhzwrS4wGDZ9kIfa5xZU5oabwxQjKlGWgQNBQOvxp3M9QJiV2SM46bvzfAG3HDgP43C4bpf1kuNELkqG3zVn7xWNKTrL/WmIoHbteaXfZg4yAwBol43RpAQFdC69tnvriWBPmIwTE4C/veF1eO7/INvH/LOhy/43fs/445b86g4U6l5qHjRLzCyM9uemUaVECm3LCRSSU8tnko6AsJZP8s+TE4T8ETl2i/SgqltOLZrZTLtSQiHPfw6FjxJR9x1K95zp1S2xQILMRwbkwGqxCe9JWC4a7ZMJTATg8eyTIHTqJYppZAb4D2BPZN2YJYb85sUmEgcGV2gKpJdvSJsU0uXInWKo+LkNFScB5XtnYcpn7bHfOf8Xf47/9vv8G//X/8JJkH6h3Bm5tj/+YIuRtpk8GI4j2plsonKohrqu3VUgkLcU+D00XBQ1EyrhvW2JATBOW2gxAREw7otxrqmbj3eBT5bHVK5nhANXTSsm4Kuc1SFNrBD1Ma9nUQKG1i3WmP5bA9QuZ7pQU3XOh7NL5jbmn+yfp/NpszNbVFwqda5xnSM3tJ+Da6OmpQ7lDSJ0aJkYBImq016UiJ6oZ9lVm4OxgobS9xayheW8hRsqw27ZC1n3MGtDEWE9p4QKwV1pdPPJ/UG4wN+1hKjUJY9EeF/99//azgbOCgafmP6OX20PF4fIpL49Pkd+o0Dm7CN0X1UP1xECr6Z4Xoa9mii66sMEkN5zYI1XsB5ws6N/BuVKQPoZV6xTr1uvKqJb3j5thvGvr1KNArUDbUosgdA7jGxxsPGvdv3aiTJXU2VZTIy9YLl1evfDeMWzPoKYwQPY9SO5ABo5R1GikkBrfxhXVc3RSLG6rbWb8F8HvFLYfv0mP/4awu++7W3+dfvf8zXymfUyWcG1oaYTE4j1IWjS8pvumtXzEzDsVvx8+YeJ90s00PNmNowsaqXnpqW9/wJR3YzUnaH/sAQ22pFgaVCwItGog+95S71lJJBLxSEWkaNbbckToPnMpXcNVuC7DoXSuuFmojJXlo7Oq+e4VZglplVn9THPD4/ICVhtqj5D/+Hf4FH80v+2vd+yOFfLlj/TydUoh2Rx2HBJpaa8jR6fSkVOaAywzo/RyfapZgVBTFq0lQfDCEYmlWJbC3SKDBkt/k1N0KY6XskncDGUheR+4sVhQmjsfKyrTjwNTPXctFVOInMbcMmqOHgfF6zlorQWKJNtMOGo9eNPySNqs1srOh1gzvIDmM22otWRjTc9DoZhImek7aDUMgIZpGUsrufsmdr1SRHL/iVTkQSoJ84Nr7iJ0cLZNrjyx7nAo+OLjFVUkCuVUBPuj3px37ClNHXcUVxZwzJ7VhUQ7Fxo/fVMGFrFY4k89IykPI1h83ukNcn5DcpPsbJfB+V2lUD42tKwnX5drpemIy3620D4DpM4MPto9nhWIXcVhe34/VjqEVHDnZCQarM+B0ZWojKDfcBLZO0qRKVMWk7QZYJ0wj9asIP77zN8zsLPpiecuTW9MlSScRLR0KBMkEldCp7UBl8IYGJabnop2yiH8EbyWCWlzh6Rx1mBnCTrMZj53PeZcjMZpaVFd3PGNl5XYUUcaLAUwI6tGGiLPjENhoaHBPpGFIO+xQQlJ0lohbtYEYm8LA1NAI+87ku+pJlHUl9g/+J5ezhI37/WyW/2r3gg+9F2l/0OC6JNCyzf8ng8mXye6Sfjr5j/R7Y5U3AW5Vihmiy7F0IjYXe5C6vILlnFAsFf4Cx8dVby6xosHn9MibRBkdpNKGyziy5QhTkMhIpyo5WPKnPLN48IQ9AlKqRRPcqiWyRsCtyBybqcDvkuT4DXoIypdSwV8a1ZehYj6BLn/9OJ3tx7Nqt7YzhrErge6yLGBNYVAlxkS56Lchzd1fZifk6GKbNm6Z5yYlQV6+gq09q776v/Xl41D57S15//9eOVwBPecXUK+76IV/xN9L+Y/dAsPHxwg45fpNi6nb8V3tkxocklWOJxKuTHWjhCCpj2itOJUA0+tW2sPg0UJ1ZTj99yP/9a8d8+P5z/vpbP+Jr5TMe+HNO+zkxyRUfppCtPE7iDEsaZX4Ls+Whu6CJntN+xmk745QZEWEbvAYvuZoncgjAXa9JuqXpaKLngb/gG8UzFkYbqQvpCSlbi6S83gCVGO7bluPU4vNaEkhsUscmCSEJVhIzGRQhkYAGYc1MpCLS5uMOtinqmRXZpMAUi8ngvxWDSSbXPJKPp42NynRchorPuyP+0cmHfHp6xK//s8djoJIkmPxemz8ONbafSMUnMdImqyxmCRrClW1SKqMp9MPkOdQx92dr+mDpel1v+tyY77MR+7oudN0J6mVVr7Q54Ged+hi2BnpD7Qqqecu0amg6x4WpKPfArD5a2mjxNlD6nrcOVnxtdqIEiqYiRiFNA52PJOuQIBTL/HqDJi0nm21IBtuULiG9rj3Sa20VvcnrUyCJ0AOmTfhe10XTgWktEmH+acJvVeoQCq2LylOL2+iaQ7Ikb+kXkeR1H+QnHX1n6XuLc4HDSY23gTvlhqnrOC7WvFUsWYaKe+WK827C52cH9H2hPr4BbJPG1zCsp6DX0wgQ7zfxh7onxZ0iZQgouUldMoJaKgtO121WbrJM+bJj/7G6Pbpym6S0W/evcQaueHJl0A7YSRPT7raXltuB9zCs5fk+AzcIk1Muv+RLuwWzvuqICYKCWViTF5GohcaelwnOjEWtoreCiFXEOlcqpoPyUruc5anj8afv8re/fo9vvPuc377zKd+onvFh8YJju+JIumzK3rKMBetYYohU0vGeP+E9f8I6I/yDj5SXkIEsTYjqsDzpD+mSw0pkYbYcmS1WOipJDKzAMoNYXaaZbWJgmYRKElagEO2KH5qOCJzEkmWsODJbFlkTcB57utTzwBbMjLCOiSopY8uKocLR5RnBA50EzsOUx9tDqqLjzmzL8WTDga/5+G+9xV86eIIxWpociuMnXcdn3R2WsWJq2lFmqQtNZBNKXRRQOYc3ganrmLiOtfGk5GhbR7go8OfqMeI2Kp+Q3B0wndD1RmNlsydUu1Z67juzizHBauI6jootD4pLZk47NN4EnreqM59XDc4GLpdTQmcIR1BPDGZriCvBaRY8kuQlJNuEzOpDC9Z9bfLQBVE5SKbqOugrIZTgNrp42FYnHNNBsdQJyG/SSAWOXjvq3Ylj87bBfdDw3p1z7lVrVl3J6XqKbCwI2EbZhVcmpleMJIB3VzsL+4X3yHwyV8GlaMDaP9vUv5hyhC5XJvQbE0UynnAl6XAYezXTPlPrpeIk6stM+TGvlI/cjtsxjLGI3wNuR4ALQMYNyG4nlTtfVkb/AZUAC67RDabdGpaXB3znzpTjgzUPJ5fccWvu2A0TaamkZ5CjqE+iRUg4CRxKx2GxoUuONlm65Ma0KQWyehyJgGEVqxH4KaSjkh4jASe7l2MziBVSIkqiS4k2qQTR5OKkB0pRscc2WZrkqKSnzJSTOkVCgrmxFKISRQdZeSa4ZIi5whuKnW3yLDuLs5dUxY+ZfPYO5d9LHH2/5740dM8M0p5SyiVnseEyVrTJqTQmv9e650vUyal3FmQZYsKbiDeRLkumQ29IjcXUmog8MJz08xmAm12DAVH2bJgaFsV2/MhdbqLMbYPPPH4rkXUoASicpik2eC02KuidaAfdKktPN5K65uxmWN2U74xZuTJPSdLnNYD1A5AVHRpOkv0fB3mIRJCGcd0Zk28tJBOJW+jmCXPUcjDZMnUdbbBsW68MYAHps3H9my4DQ0d8+HCGyXbvy41ND2N0T/dnNdJeMSBXbr7xvvo1vbZwGT+jXAy89Ms9cOt2rfnlHXpdZkDLmJxEqvYnQ2qaZDVJkuzvk3ZFqASV0yWj81axjBz+WChPCx4/ecT/+f1jfuP9x/zX732fB1nR0SWnrKyMOHfJZtm3glt1TkM/tit8GXhudR/8eX0wMoyWseSirXIoRqCJniO/4Z5f8sif8cif8dAq41SJAMrKJSU2SRmoXiI+1y1TI3RJZYigIJMFfK5ZANYp8iJ4ppliY4DTPIdOTc90731VP96EMYGpGJrUM5UhqTBlI3ltuCxMS522fNLe5Q8vHvGzp3fpa8c/+fYH/JXv/liZWQLyuxVdCjmlEZrU80l/yCZqIItBvbHWlKOljDLZEpugEsSJ7XT+rxpWdUmXAZo+6iTQtZ5m4zVwIwr0gru0+XcGfMqG5QLB0nrH/YMVMfV4G7g/WVGXnm2vtdPUtbTRcTipeXt6ydS0LEOlz9ckYq/nUX8YiN6QrFH/3yCklmwQrsCThER0ovtgk82+ZdesV+WJNvP9JhG8Wq50M8FvoDxL+E0kWaGvsqdWArfWxPf8FuGWAtEQJgk2pa78LpF6oZlEnnSOybSh7h2bplCQ0EbuTte8NVnSRqfLRCukMsvyB//JvabKNavc3bjWxAduVqXAy3O8FQWj98Gufc+sL0Oz3WNYX3kue8DYvkIl3QR25dvTG3h1DXXPsE/QxzLWrKMsMb+E6HhZzXLrmfUvZpjBq2EoMGJ+98UifRxNesVAsmZ33iW9qE2IGuhj9FOVLDuyrVBeJvwGmrOKX3z6Lj/98B7fePic37zz2RVQq5LATHRC67LHlCYOdty3ayyJOnP/hsSPYXTJskyTvCgYllHNCS2JIIGFiVjIWnN00kU4jZaTOBnjbvWfXnd1NFQSsGbLwnSEBMtkqZPVpBFZcc8UBGlZp4jPjC2HZWGKnC4VWCf1X/nG/AWHvs7pjB1vFUv+zfl38SIsY+DYFFzElh90D3jaH7IJpWrzxVBIrya4qWAZKtXTZ58Rl3fTE9cxKzrq1hM2Dn9hcSvBbaG4zNRVA6HUHaEk0fRAA7HcTSTeqFdMHy2l7TlyGx74i1Em2iWrXjIm0gVL3Xrdd7pEJJAkESaBsDAUL+wIZI2sp17/Zk7O1QnfMk7ikvL3Xn2yBhZXV2lxYVul4krQY/kO3Cbh6l0HXnKUs9tkau9E2QKFC9yr1sxcw3k74XI5oTg3tIcJWw9gVrraNR9i0q+NZAxihFGDu7uY9Gv2OpG9CXYssmy60Qj+tWNftnjT2JvcryQTmt0Gb7yNGwqBa7fvs7WGJlrKBcQVHTnkIiNdncBvx+24YUj+N6IYewiDxME/a7jmrp2kA/AaQX2R9C4JsmxM2Vp267i4POTsaMbxfM2Da6CWk0SBmo/HzDwyEikIiOlG9tbwzPq9APOQhBaf9yZCm3z2IFEmVSkKCw+pUR3qAbhNwjZ5XAo4STjiCGy1SXCSEHpKCSorR9P9OiwmtUzFEgl0eV3rs+lvkV9/INIlwRI5LlZUNuJkiZNTZuuSb35yiZWelksmsqFJl5wEbR51URMJo0hOFjR0ydJGp8fO78XoN2YC3hr6oGl+plavwwHIGqQXA1VfkjYW9tmdoGCVNfr3nIlUpmNmNb0K1Cje5s59iKJ/jwE81w8/eegLwW52DFjJkkZlXiVMlvaNflnZo1HYbTyH0BLybRjGdMSBSTWAdbu0292mWjpdc6LTv6++Lh2F6amDo2k8thZClXZG9Vf28EOb96aLRnb/9uUYcvU+sn//fO6+ygj+teOma+9Vdx1SofJDdjjbziPlVaDTjnW1u8PYMJFrP48P0v/dAlm/5GN//0UGtMbGG7tzOjNIRo+/YS+W0w8HD73k1MdochIplkL9fMJ3P/86P/z6ff7iO7/gdw5+wUN3MYZIHWVqaZ3T0APCOhugF6KBVVXR8tBf8Elxlxf9nIt+MrKQvATmthnVI0d2w313SSUdz8OEFstds6VLhqnpsQIL0fAQUKVIJSrZqwS82Oxp1bMwwjIq2LVMkWW0tBh8MgSEn/RTumS5azYsJGUP34YKl43jA3VKWfUhhBTxYtmkTvEGAAGfNCnwRT/nk8s7pCRMD7f8h//e73B3vuZ3vvcLFv/alrf/Z3fYpg4PHNuSp6HhNDwgoPK/KJKDv0JW3yS6ZGkyK9jkSXDqWg5LO/ovtp3T7LHe0l8WmI3B9oJbiZquB4gFmGDp5noM22ijoA/CclHy9sEleroIhelZp4IDX3OvXLHsKuau4ajY4iXwpD3A28C9O0suy4p6WxBbq82oLntGBcXN+ixFUy9fIdk0Avum3zVykih4pQ0S9Z42PYSsatAmCIQyewfLjs0zNGiUIayP9SvRJn6C4lzXVdNDKA3JOrqq4ulRhKMO5wPzmdZtbXSsupK2dphOCEFllBJu8GkchkHJLNkWJQk7ZhbD5ScvK1KuN/OHa9WiypQvarBcV6fcNF5VCw3bgP0aRV7+vd4nQdDPZWQqD82uLBtUgkXaWbTk442srL0m2QBsDbfF3MBL9lXo4M3jFsz6CqOPZuyCAPo1Sb6ObJY2wJW4SZEdmtsrmk2fiDazwYcPVwSTEtVJwi+F7umEXxy/zw8fvc2dh5d8eHTKw8mSB8UlPgMz3vRjXO3UNKO5+127xufKOibDOhUayxo1cSokocuLzzMOuGtXHNk1d9OGUtSM17PraHhRBlhMhmWynCbHwtR4ibrIiJqib6KjTg4jmpq4jBMKApWvObYlm9jRpUhAj1+KwwnEFKmTxUvP28W5Mq0k8rY/51+b/IT7NnERE/es5SK2/LP2Lj9r741phZtY0CVLaTo2sRgNJ+vo6aMWHNtQUJqe2jisiXStwywddrsDsvwm7QAJK6R2t0MMpcbE4tRosYuWd6rzUdJ46LaZFpxGltgm6oJwbiec51hYYxKpt0htSdOAP6ppfUEoPcW5TsCmBb9J6kuTY7yihcGY1wT9OTmy0XNUDxMLEgwt7BI2bcJtE36trw/RLkk7g35qxiIkFkJ7lOjv9BxNtxiJbHOSCc9KTKPnsd3uAW65kz+a1d80nEGcI7WZBrY/geevcr1bLoI4q4te+yUv0mGC/0KvlJvTDkfa7Eu02qsg137xMDDlxs7DNSBrfG+EcW74spTa2/Ev14hDJ26/KE+op0I0iNmtNQzSQ8kbDxQ3FkE78iZ78eXrYpCVuS3YVggrx8XkkJPFgmpRc1RtmTsNERmAGSuRYq9bPJEOl78OgRgpCS02gzwqxFNmrF7rKzQ2u5KWkB+nEo7d5WZJOAIJoUmGbfIUsgvdGKSMXTL02ei9Tp4mafnhTM8km/8ObC8Lo9wwkeizifvc1ngJiGxYmBe855fMxFAnmJpAnVqeBs9ZqEbj9y6pP4tDC44h2bBPNq8FQh8tTiK9qCQlBIO0ZjTItY0WjGRcPYqMzCWDgkTRA0bXgJAMC1fntFyVtDiJCL2yxIS8/vXUxlMHRdNFEimqpAQfMVVPsJbobO6e74AnkhCGTuz1eWzYtAZdl0yfQZL8emVvHtSU3jQGhESrRUf0Q6BB9kepIE4ilVdQtIuWpnewtiMbS0Y5OztAazSrv2EYyeyUXUqvft3N87L/83Db6CvyJRsnwy79Klr28v2EG9ca8jV7/eFp7zqFa0DVfjPlpjUk7d9PxvngdvxyDi0041iL6I1qRCOdjOlnkiLRZSXJ0GhPCXqljA9BECpF1lpEEkyeJ4pLoX5+wH/+wbf5Z+++w289+IxvTF/wQfmCR+6Mt+yKQ9NkmxJDJT0nYYY6cinFY2Y2HFWbK2DXMkzGpD5L5MhuaJNlGSech9k4156bLTPTYGQNBCpJ2WpEgaxSXDZ6TwQSFzGwjJZDE3IwlfobBQkcmsAmCedhwiaWPLSXHNtuzzolYAi876YsTMdJEPUJNoOPlGUqni61VKPiQO1fPt3eAeD+8SWHpSai/91//9s8Xyz49w7/mC0dmxQ4tiVdCvyoO+QkzFmFSmX5g1QiTykxyQ7IQteC0vTgYNMXVK5nXRcYE9muSzgrKC4F0yuIVVxoQyA6iK3eTtKfTZa5h0bY1AXVnY675YZlX+IkMvMtE9upt6bpaaLDEmmSY92Xum5LYla1atuStGbqjyHMDdLIyNICGaV6w5w37InVWkVBHtPvGh2j+iRqY35gYfWlAq7RQT8VolVPd9voY6XXdcrWYE8TyQnFZRoJJdFnT2EvmM6yrSLH9y758PCUhWt4Wi842U6Ja4/rBfrcgOrz9ZLXotH/axjX5tnkLWKtXpsm3qxIgRHIEtFrVRMPX6FMMTdN+K8Ye/e9rigZJMmjIemgUhnvcPXv3KRWGZozo92MvV7DMa5FSUQZV3YPxNp7acM5ypAK+YbjFsz6CkPTDNPeSZZPgqhOGiRzJeUgXe8QxnzhZmpvFKAXcEpljCiqrDEbKhMrTzzbT+/ynXtHuPs1d49WHBQNpdPCoukdq67Am8i8aKhsx8KrfiEk3VhHhE1fqNzDqt9GTEJpNX1q5hreLi44tJvR4PHIrnloL/ESWcZhIg94Uu4e6Gw0lZ4O9QOpk1M2VtICYx0LLmPFk9ARUsOxVSlEnXo6wmiy6MXyoWuxPKeOHkvi6+VT/lJ5wh1TsYoNkPinzYyTMOd5v6DJni3D6JIlRBm75V0uLkALjT4piNdGx7ot6FurwNHQJR88P/LQpMH8GTuV7SWfcJWyrUzuJFmJ1NEzzdr+oeALyVCKepZ5qy34WGeYq8/t7tbQW4f4SFgE4sZdKQLGRS1ftclmL61On5PpFMgqL3KEvVNDyC7kjnr2RXA1FKuI6RLdTOWvsYDNO/qCpYN+FknHHbMDBUdXXcmmL/j0yR0mLwyxyEmMa3bm72kHaO0PGTqFRkjOaJfCiE7S+8PIjkl1g7G7gF5fN3UoXjex75scDt13kZu7GOM1ug9ocbWQuPakBonNjQyrvFC/RJ/df7qDF8ztuB2vGEMy7o7dmzua2m7XxUP2W19cPVGHxyZ0s2Ikn5e5Q2qGY6fdPLg19JdTnkwnmFnPtGoobbgSlNFG7RAXee0o7Y4dFDPY0kWVJVqjwRwpoXMm6iW1sDWl6RB0vqxMx1y0GGoyTckScSRSDvcAbapERGswDG32juxzglSTHKuk0R8T2UsyJI5+XAbhyASEjTLNSNxxa96xDZU42qRUoCe9MnzXOXI97l3AMQkdCmRp6bNj18R83wGEaoMlBsGEnAoYbrj2095tw7xhEsYp20rymmsk0Wc2AyjAqKeGMrO8ifk2Uc8sJJ8nQBCiMQp8FlFlKIP0T/LfFa50VQfJwMC+8puErYcqRL0To2fHLMtGrgMjOGZ3/mQhp8Fn36xEmkR8pedOGy1dtFyuJvhNllr2Oehk348kvSTy3jUK87wsr2Lm7rO2btqcw8v7tf1fvnLI7ssINMnNjxF9DVeaJ/uH2Aet9h6zvx69NPa65LsDXv39l1Gm3I7/io0BlBpGTIgMXr6D3FDvKFZ2Hj9pr47JTfZgVIYoQwMkKltT6sTscaI8M9Sf3uE/f/eAf/buJX/u/hM+mj/j/eKEd/wZ9+2SqfR4ibznLthkCfoyVmPz25Lw7pJNdOBV7bKJfmyCLMxWG+/JcxLmzEzDJpZYiZzHigNp6CRyaIKqSEiE1FHnIKmZEU6D5yROOYmBA2moU6CQyFTUf3ETHcemzlJ1nQQvoiUgnEdNM5yZNXdMRUhblhG89Fr7YJmaAisyqkougoZ8fGv2lNJqg2FiW4wk3i7O+ZuLPwIsT0PPA+swGD7uIj9qHnLWK2g3Na2SGaSlxbGJBZtQ0iSn609eX50JrEOBM4F50bAtPZfrirRxFEtlEdlm15wnKZtpmCSKJNqUF4gukbzaA/S5GTIgBEaiyj7dEi+BqWm4yCb3s5zcHqJhXRd0nVqQGB8JwZAmgTSBeiH4E/XSSiJjut3QQIlW8Ns4SguT6HMNJWPCYchKE7fJwFaX6Oyuye/qvN7kedCvND3RbfW+YfibSY3mbasAX/AZYInCrGizEkg/u/PVFH9qs0Imr0MhXVm7R3nvXsNE18uY6xqjjZXr1+sVIEte+llep0yJaQSnbxyv8NW6DkaNtdCwFmUpcrq2Nu77ZO08stitc3mvEPd+d1PjxfQqL90djPH+41NPulVJX2KxugWzvsLYpRkOZ7Ts/azKbkm5gO8j5rpeVRL0oslRURii1hGVHJp8wanHhDJtTE9G2S3N2YwnDwteLDrKqsOYSNc5Qu7EGqsmsSJpNOaN0VB4TbIwJqpG3QUmvqO0PXPfcNFVPN4eZmCr5cBteatYcl6cMjPNyOi6by85shssKUsN1cR3SLYaEhcXpqaQQCGBZZwQe8PSbNmkDQ+s22nPidSpZxkDR8bx24Xj2/4xm6ReXOuY+F7r+X9e/gV+vLpHZXs+nJ5w7Nbjhl71+goeDZ3zOnpWoSQmlcactxM2fcGyKznfVqy3Jam1I5VVsi9Vyh/HFbpj3tBLNlwXo12L0mgqy6Gtmds6SzA1q7GOnhf9gp/Xx5w0s9FQUfUaOokmm8Am0sYpIl1E2juBWBj8UmeM6HcTxhBPniwEJ4RCDd4R6CdGAS4jmQrK6Gvi+qTmhX3uSjihXQjdQuiOemTS48qeedVRN56+N6yakjZYnjw/pPhZNRbB/kKw9TCppywpuYr2vDQXiaj+26hbzRUpxzB573ud7A9rc43evfy7Lxg3el+Nv7vhOXK1g7HPSLiS6JFptGkArezuvhLz+ZMLuGj3Jv/992W/aLwdt+OGIeS1ZihUrpxD+YfEzofu2lqjmxN2SijZAWRkhovOR3v3jZLXG6GvPau5ZVMErI+IRGKweenLRY+MeYq5ThesDcSkhZSRhDUKYNnsjVJHxzJ3dgsTKE3PzDbUdqu+jpnRNSRmCYxSQ5UV6uvok6XHUkqvwR8p0iRPikIjPZ10zI0ZGyaJRE+kTYlKDA+t4b5d0qVAQn22ngfHj5q3OW2nOKOb+Ulm25IgZGftiGT/SkOfzCgzjMlQB0cXVXpYd462d1krkTfCcW/DJ1zbHzAC3Ul0v1EYlXkmhMJ0FPT6fmT2Wp8sm1hw0U/Y9J422N2xUgY/Jen2JHtRYROhypv7ZrevHUGVPSZp8jqP7fwVZfT0Ghhcw2sZ1oNhXhxlIQXEKio7zEYKH+h7S4xCG7RYW60r7Llj6HrbhpGZRWaJjOfuKy8aAdlbS64BUzLc5xWPFWtfNt59o/FatOvl58i1y3kA4/YPk3IzaHhvh3Mm7f08HGjwJNkrNPaf2q3U8Jd36P5r71/2fxNQi4d+r1YZGuwDJZSUrSZyg72LJJfDLeIOpI5Wi1+/SdjHmoxdf3aHf3zvkN9/5z0+uH/Gu7Nzjv2ah+UFlagFCsCh3XBkN5yEeVaNrLKfVj2qPyoJnMeCDjsCWW2yY+2hSo8Z52HGXbtiYbZAw1RC9urSUeUafWp6Wmo9DoaTMKWSjiPT0CVDi6pJpvQ0ybIMOyuW5/0BlWn5pG8pfctbdsplrFWCKKoiASjFE4jEFDnPzf57bkkotZkRkuGBv+C/Nf8Bh8bycWf5pgeD4Yddy3eaD7gI01FWuIkFdXJEo3VUk/1+DYlVfn4BwzZ4nOS1VHQN7rYeu1TfX9tAea5qjLFm6IbPcpg8lC2bCrRRIbDt9X2+XyzzOoeyqE3HJoZsUL+kS5ZTO2PuG1ZtSd/rJte6QAwWWqOMwMOOctFRu4LoPH5pNOwq5bolKajUztX8fWjYR8+oFiGrT0wH1UnEb2MOTtHH9BOd2KJT4NU2ahNTXezkfslANxOCN9l/WMbmSrdI+MOGw2KLIbEOBXVw1CcTZhdC/VbEro2yjUO+bHL9Mjbxk9YHQt6DDf5TVqDw0LbaNLrefBfDqEoZrs/xd2+oTLl+zDGw5Hr3fHfc6+O6XcoVo/fEGAgzHiJdrYmGveVgZREHBUtm1ul7ga5Rdvd4yI8Zau7hGF9isboFs77CkH3PrOG2IQJ3oPnaLEUcuiL7Hj1oihAh70G6pJTJvT1YhNyd1cdo9LfgU8J0gtt4ugPHZlGSZnt0ekl6HmewZOzIJqj3d05OU4OKqqMo1PTP5I1ziII1ifcPzrjsJ9TRj130gfFUJ8+R3eBTpE4qC1knR5c9srrkRlkJqIZezXMzZTi2PLBbFsZiGQyG4XEIdKnhJE74UfM+P23u8/H6Pj88uc/ZiwW2DDy6d86R37KwNXWm347UW6ALurHvo6E0vcrkuorTZsrpdkrdObrOEYPSEqLLVMnhAsvoejI6iUZLjiLNG2mT8D4wcZ3GpCe7S5rMdGlDpBPLMlScNDOeb2dqaiuJ6qAhJWhWJbRmTG0ig5EY1LQQRgrneJ7lImcw3A2FGsZ3EwVF/TaNQNXgpaXdDz0/+srk9A+VGfZTkCpQzVqqomO1KemWJWbSI7OaZV0iTyqKS/2bElSOaRs9bwffLO38vrrCSFZI3uWiO9+4LzW8PsHudyti2gFaMfKF43qnY3gOQydir/NwU/d8mMiHLsFNHY5B6pL2gKrrnQjgSgdnTEVMe/e77ZbfjteNfcNFQOXs6WoBPFDx056H1vj4PLfEpGyVwJ4ccZdktw9GkeOWTQc+gOkMsTR0hYLtu2PvPYdxJ6Lf9+J257ZJYBLWR6xV+bpeUrtmy2FZ00RHP3gMShgZT71oypN6XynA1+WExaGTv8/Viah3S0iGZIQ6BOamzzIT7aSrp2McTeD/v+z9aaxlW3bXC/7GnKvZ3emiuRG3S6dtMm0y3QAGJ4moskoGG0t0Aj0+uMoChAQS7xVyYQsESMhIYDo9OgtcAllA4cL5BaHCXyxwCRuoxBjyYRsbO+3sbxf3RsSJ0+29VzfnrA9jzrnW3ufEbZyPh0nHlCLOOXuvtfbea885xxj/8R//8Xg44olbcNotebxd0KxrpAgcLLRsvJaBIWjWPoSxNDnZQx+EIpbbt75g60q2vZa3e6el5aABY4hlbnq7RqBiB4iYfCHWBgqjjCwfyzglBjBWVGtMMLShYDNUbIaKwSmlt6gVpHOtVfvir39fodBOXNdLJiZO68QJdTF7bvsJUBVLQhIAB2R9xxCdW18CRaCoHIV1dF2B7wqk8FAG2sHCVZH1KiWQA4gsKp+Fd99m4xRiU4/k0DPu4TcxsqY2IB1u7SjW+3bjKeyrHbsytTXXjstvOf5+w3sL4/PXbMz08PTew+S4Z/blV8XQSg+f44yA3wG0kIl4s59Gj2HnGgoYS0w2xj1hSC7aCIKIC5hOWLwZmJ0K3YMVn7+95FPHz7O4u+ZkueWwblgUGoX7oFUhV13Nsuw4qrcU4rlTX1GKCr8b8bS+oPMFl72Koc9sn2VMEiN3aTXRfqe84shutHOiBI7tmmOz4ZZpsBI4dTNKcVQ4qtgkayl91hJeykAfS+E7TOyIrveoCSWNUz2wVwfHPbvlxMwZcCqTEosnU3fDmpIvKzTBryWDNQvb8dHlL/FbZpfMZcETv+WrSuFN5/h0f8ipW3E6rDLgV4rLVSbnbpFL+4F8jJHA1hW5E2Qam1Y7jU9L2Kel2aD+ueljVUckTLiYKA+Lgaoc8vVWsSuJfo4WF4SFaXFBuyuWYeBOecmjYsVV1fIgHOAai8Pu2K6wtfQ2KFvryGFbITSSK0+CHcMBPycDJ6kBihkimaOD5QPP/GEfGVwG23qKwjAsJCZ/1J+xLZRrT3nlcLVhmI1sr6uXguoIN8KwCPQHHnu34ehgq/NuUPLDZ966zex1hUl8GZg/NtgmRM0svaFGGRw79zevp1iRgtFSwZxg2R/TqhR475UpN423SeDrTQ/v7hg7lWCJD09Pi0BjPmXqx5i9x4Qsz5K6WwaJ50+TevGk91Kx8gzM+iKG9zFgSJNLInjlJ6LOqeNACJoVEckZDgAzeLyYSMgKMETsKqClY9HR9wIyxNrWEAhOMtWxOgdfWvqVxdcqVu4LCDa2Yd8HQZyCEa4OyvYpA11Z0s4cUnqM1VKQMKgnOTjD3eUaH4R79YV2TjIDzi3ypvtwOKQJJUvTRgqubsgz0XayKaNSiaOLINgr/W1KGTjzV9y1a/pg+Ln2JT7d3uPV7QlvbA/ZDiVnGxUdD+cV0sV69yNYdyUXg3ZqtNEAOgxXrs6iiWtX07qCue057RY8bFacNzPNkncKZOn3GB1Nm7LN43eoYNZYSubqlMlQVpuW2LhY+qHGMnWOJBi6UHDaL3nSLhTIAlaLFmsCF+uZlpZ6kD7quNT6hUl83JeB7lgzGNOsdxqujhuDaKAwxN3AVUK/UtaWGjbd5NsjQVx0siMQ0zw3cHyyprCeJxcL/GkNJnB8f82t+YZffHif2VnMwhdag15sQuyINrKypt09ICHsk65YoGtEZNe/ngJP00BjusFbyRuwDAP7mfZ3NUR2GFdaUjPuzLnEZjLGbMX0OunJG46dbOaMy3/Mbk43bPPL+AzPxq+6ERJNNAv46n8RGhnBKtA552N5WZrbYSwXSTpy4ifepiGDVwkUIwJN0zLE0EBhBV/Z2GY7znMJWUA8jammkY+irZiA6yyusGC0OQoBtWsRpOnKQtlYtqUyg4qr+zI7VGtqhmCo4n5r8YQgFOK0RMJri/KCwIDQY2jdXO1Q6FiaHheEt9wxT4YlF8Ocy75m8IamL2nbktBGrSYgoE07kn1J2lMBiaWNys7qvAJnhTi2rmQzVDRDweANLgJZYeqtpT3C7O0xEzBL7ThgY5lmLGk3EcQq0M8fPQhcMGxdReNK+ih6VVUDRqDtYqYsMCLqVr+wJKweTMDNJHfrTfMpz8NiBKqSOC9oQOQrsnirDHq6m8nI+otzalh6ZvMOYwLbpiRstYPJbNUxL3senx1QNOTybImBmdqZCSuLwP7+e+1vGQHa/cev/T4FkdLXFKKQ7y9La2pSrjE+tH/IzshL+B1ijJ1jpuBWmPyb2hnYQyifjS/JkXQV83yV8XfnY3J93GdMZGoFK9m2SLQDIiHqCpkxGedAog/lrSjAnPRZvajG7xX0jwqai0PeeL7idL5gXndYownypitxzvAwrCgKhzGB0jqtFolz1AdhUXc0XamM1MJR2Zg4to5F0eGCcDHUPO6X1EYlPGamZ2Ub7hXnrItzeiyNr7DitZsijlksfTwSp83iAjz0NUsZWHtlgdnox4OWuD92q8gSa7g0G14q5pzYRRSXd7Rh4MpreeMdU/F8LXxN9Rkul7/ITAyHZoan5MI3/Kf2Fv/m8qvY+IqVbblVrAGoTU8fbUhphqwBnLqwX7oZ66GmMI7eC1tXMnjLG92Cy67mfKvi6wwSGUu6Z6o21QguJHmC6f46Zd7V5cBBqfqRpRlYmI6FVWa0lYCVXqtzgvDp9h6PuxWXQ81FO8NaLe+kM3EuicYzAdxFCaVHSk9/5BFn8JVQXum8mlaRpHJ2TUiAFy0xLNYaZ/jSxAZZantDjGVA47diq3FJufH4WIbYLw3eQr+C4dgxlJFVd9CyLB3eC+1gebxd0FYFb5wfEj6/pNioPrKyySIrWSRX8HCDtMqYwBvjjFBYYsnVbkUK8XpTeZWbQCYT9YbDu6xMSWDVtFRwmshPfz/t9dJjHk2cWok+AmOlSoqXpvYnMbGIfoUoMB7i3xIBPm80XvUJDxFysl9Nb7hZCuYp4xmY9cWOwFjHmtpn5sCBDHYJKDPLskvNsCjIVRjdVYUMaPnoTGHDONeEqKcVVO42VgrophWRTqN1xr6UHUR+qn/hLUgmyESH01lCaXJnkDRBz1hSlwP9QrPNAGUUkD83HeduEbtr6OZwZLe8WJ7GnEXPpZ9p96dg8RHcAuJjCy7dHCoFxL7Q3eFBe8hnLm/z2uMj+nUFrUF6kwGbFGz1znLRzdnMqszGSuPK1VwOs/z369tDHm1XXLUVziuAZUzADUJIWeoEBs3JmjLAKLY+ASl8HaAMLGqthU9GWIGsIf9MS6yPIsCrumPdlZRWa8yHziJbmzW6xAveG0IVkF67ffhKnfYEUoUiKNAF+NoTZh57YSmMRF2SwCDCsNCOhIC2RYfMxjIO8ORuhhhY1h2ldWzqkk1VMjtp+MCtR3zm7DbmcYntolCvhfqJ0syTZpwMfmRmRXHpzFxKwyejaq759MDuZg67QNZORj3ofX037KzpuXG87Qa+f0x6H4wbuDZzkBG02g8aSBu9XHt+p8zwxijr2Xg23mbss61IjsLkcR+fSz3Ax5NH/ZOgzkICtAIq3B0m50i8VtbVSlM+aiGlsrLEWJ2u9enLBmXQ67VSgO+NllXv2BqhoaIwHheixpQvsOIZgqUVRxNKfGQkAdQycGi3el0CbSgy6zfg8nFdKBQoowTWrH3NuVtw5WqetHMuNjN8b2NiYZIAip/DBaGNJYPFXrqwix0M07gcajZ9RedURysEk0v9p0BDiCylvKUx3tOQXlsUQMIEysLt4BMFCmwVoj+TgfdBoh6mowdtMhJEGciDZOBKguqNheSTSMjlJ1kTzITsrwQblMndCiYl5aL/4MuxNCR9lsQCTq+XTbRAWWiypy80Y1/MB27NNzxp5sjWqI8TgVINIMJ473LATtZbv7aN5nl18x4v+8/tA1nTXxKj/t2Ma3bgXdiaG86/US+LdwFy7dua6eOBa+/v2fgSG8KOX7STWAcYHKlsCa/d1DG6F8DENkQtX0BF273KpaSKAH08lqOLtrqyPkQhbagulf3Trec0t2q2qwFTO6z1eG/wTvdwNyjNc+MkJurVbokE1uvov0vAGI8I1LWCWVXhWJS9+tATEOyl1RnLouNqNuPSzzm2Gy7djD4UdKXltr2KvrlP0mF44EB6NqFg7WvO/CKXtc+kz3IpKmyurNymb7lrA0dmRi268VkRFmEsSzYxEv+v/YzXhhM+393hPzz5cn7uwfPKQl5u+ZpbD3L5XuvLWEZYs0JZWL2oTEoay6Jl60rO+jlnEcTa9iVXTc0wmHHzTQmpMAKVwexWmijAot+7StmAKTyVdRTG03otb9QmLQqglDJgUf++D5aroea0W/DW5oB1WzH0FjNz2GXP0FuVTYk60OIFGqvgVhHoTjymMQQRikJ29qgExvlCk/XImLTvlwZfigJVpTDMDd2hxAqVKBBfgj3Xe+HmJms09YdRg2s+YIrAyZECiU8uFrimoFp2LOuOty5XbD93wOKh5JI4uxVMq5pcYwf3sbzwWkVKAqwS66qwiDUEv1e6fq3k8Cl/T0vSkxbX0855p8fTc/tgUWqENT1tghukoXHMDecL0TZH/yDNP0YwdWfsYROpvPCXI7vynsCs7//+7+f7v//7+dznPgfAhz/8Yf78n//zfNu3fZu+kRD4C3/hL/D3//7f58mTJ3zkIx/h7/7dv8uHP/zhfI22bfnu7/5ufuiHfojtdss3f/M38/f+3t/jpZdeeu/v/r/zMMZHx9xrIL6TEYHRGdLnJASCA8GPyKjbWwTG6ARx5E5GpBIz0XssDgyigFY/UvhVIBUg4BqiGOv4PDAKtCYHekhvT3BWNxF9HzGwAUJnudzOaA5Ue6oPltIrJfiSGZuiYmG6zIaaycDa11SiUruXbs6lm2k5iBRUMmSNKx+0jfrD4ZDX+5PYptdzULUUhZYcSqx7SELbWHVkfRQg3ngFsxwm05Ovhirf0qu+5tXLYy7WM6z1uepAJGCsar/g9T4Nc2W12VbGtuMhbv4ps1GMaLJAbonuYslLiW76t82adahw1vDS7AnroeKsm2Mk0AwF7WDxnd0h54hX6iuNYDr1TPtDj5973NKrWLwH6nj8asAWHt8afA1doeeL1zpwbwPFVv92teBrNVoMkanlFbyTQXjrbMXBsqHvCig987rjFx/f5eKXTigvDcMc3Ey72ZRrpekqxXa8TwncCgFk0mJexXrHjkxaQzv94JPdK7G0plmF/Uy6MQoiO89TN+xUK/40Ou3THvcgJoybehRa3K8nT0BVYl4x+V1CciR3ry/7Sf4g7z5Q+lUyntmZ60Mk6i8m0esIZI01YdNAPKEJErscRlsTvcWk5aZd28ZykhTIZAcjXXoCaOU5nxti6DrfYRglv3TqvAQmAJGobfE3vGdnaIdStaeClp7boFpULQV1FDw3eHwwFMbTBavi8AitL2iDMrscRvWzYso2BMGJds+69HNaX2TQx9iA7/KbyZ8zOdghGFwsJUyaVUN8rMupRRUvv2hntH2pAVY8X4h7SiollxGsEctOKciO9tSEtQW7Iu+qoaLMgYV0dFgWRjgsGjpvEVciol2XBy/K5t7Ze3TfTx2fQPB1IBSBUPqRxRXZW1Q+foYidyBMSRhfkbsXZntpx+9dosaIL3VerrcVdTXgnIKaRTFoRvzxHNtquaIvUhk7WZcxA1j5Z3xsR9tjb1+flhmmv6e/JyBrH8RKv6pew8hcvGlkfZLd05/6nqbfQTxtGszdCFol+5Km6BTwuuF1d9jQ+cM8szVfykMGP4K9Vn0kCWbsTia76yR3MosAhUqiiJYqCnpe2rsHj4jBuhAFtyXHCRL3chsnog9QrjU+qU8Nw6KiPwz0K124oZqkXzxaKQDR39L9DYfGNwaGKoAJdLMaMxsoKscpyjoV0Q6xbRurHqqW1ls2tcYFd8pLAN7sj9n4mlv2CsslZ8zwsXrkzC8oZaDHUoljKVoW6ZCofSs0vqQTrSp5KJ5zv+aWbVhIoA3wueGIh8Nh7Nw+Y+NqXm+PeNiseOXihNOzJf6ixDQGfzgwq3ouBxV1t8GzcVUkBSiglQC6rSu5GOaRfdaxHmquemVCnW9nNLHawzuj32VkvKT9wFvdHzNgkBnVkitNgoVQBGyhOsoHRUtteqaS5WXUJNPfB06HFRfDnIfNiiebOYMzVHVPYZU5fLme4SqvulmdiXYhgBOk1/gmFEFF3a12bZ82D5mOYRWQQZPxvlAfXJx24ewXgptpJ0MJo63pDhTEcrHxiMZA0B17inpgOe9o+oKrszlyWcDMs5y3LMqetx4fUqw1BgyFljfa2FXeJlmVxMraY1kl+QAY9+AgMnY07CfMqn2Jlf14J8VB2X7pcSLu3UmtvNOIlcY7r3nDMbk0WaafaTx/Wm0Go9+o+MWePZv8LS4RahIuMfpM7zXx8p7ArJdeeom/8lf+Cr/m1/waAP7xP/7H/J7f83v4z//5P/PhD3+Yv/bX/hp/42/8Df7RP/pHfPCDH+Qv/sW/yG//7b+dT37ykxwcaPua7/zO7+SHf/iH+djHPsbt27f5ru/6Ln7n7/ydfOITn8Ba+3Yv/ytuhMk3lIOMWMKhAFRQqlwuDQl5YgajbXGTMZF4XHAhoksRGBBikBGivYmbktPWnpn+mydHyNkT5yOzp2QUbkuYW6YBCvSBEMH/UEwmU1o/TmibkgfrQwrx+EJXthEfBRa1i1/6V8qgXQhN4MwveDgc4pDcpcMHwUXwQvW3hDO34NzN8UE4LLa8bwHDbcNn/G3aJzP9gBnp1feoul4md2gEGLwKvHuEue257GteuTjh/GqGMYFhsBSFi8wsbeueM5llULmqAkIJ0murehlGEEs1Q0I2utu+oImCjAvbYvHMTE8lSmdOZYcPywMeVgcMweQSkaumHj+Pj61yvb6maceaYl8LoZKdDSWJxNdz3RibUkV88Rpk4CefIdbNDyvdXMorzS4UbaA7ELpjZXr15zWn2xJbeZZHDVebmvC5JbMnms0YlnEOpr04BRJRcFpSR5wEXPl0zAjySnSAbqoJ353Iu2BWBoUmG7tgrwcqbzduKC3Zcfj36bg3XmP899Qs+dRflMnf0RCkwDWBXmHvnF/t45mduWFMbQ0T/CcCWTmwnS6FBMRE+zGW18ayX08s3U3XS+yhuL4mbKyc5U0vGY+XEAlgNq73VEbL7vsI8Ro7WbebSmw9uN5w1dWYSgXPMcqwSl38RALaClzLDIdgMeLY+pKNr5UphFCKI0x0tKx4AkRB3YIQVF/xqNzi58KTsMBt436T3nRsDy1EdlOYdioUGqfAWWkcrSu4aOc0bYEY8F4yK2pqZyAGD6IOsUQWcBK63S81TN/j4BTUAyhjp6UilrMXqC0rxLO2FWtbaTdkCQhWheej3ReHApghgkST103Jr505JCjgFMsyBhO7EUXQKl83fYZA1mDRzk/KHHe1loqIB98WbAeL2EA5G+i6As4qiibavGqcLztTKYT07WT7s+OvJFuQSyx4upM+/ZAJ0GKy1HZsTSwPebdg0Lt4yTSeesWJ7XhPZYfTC0/+XQe3no0vyeH9yM6yqdxLJ0EGtXyUE/deuxpO/bFUWRJQUCtFuyKY3mvZoYNkR/zEpngEG1T+xJWC7QNEH7S6gGFhFKgu4786YLoIZqUwKeZfYr6bYa5dFL2NXVm3hm7mwQY6qVQD0Klz9Zo75tbJOjbs2LKwHVduhiEwMz1rX7MwLZ/r7/BgOOLANFjxHJiGYzto46iY/Lj0c/pQYEX9+SbuvWdugcdwJkua8IRbdsMrwzH/tXmRt7pDfunyOd7crLhqatqmpL+qMGstW7dpzwS6oeCim6kEC4GF7bL2b+sLCuO5cnVm/ba+oPUFr66PWfcVm66kd1pSP/Sq1xgmpf6+AF9JJkuMsimSG3Kkx12l30VdDcyK/lq1iRXPwrQ5mdL7ko2veNwu6J2lsA4fhGXdKauurXCbAtmoMUlgZTABygBd2thUF9jXAbsdiRfThIUSMAJlo/POlyAWhmjDukNhmOmxJrJ4g4FhoRIryTeyrQJ79v6WF25d0DrL4/MltBbxQn3S8Oufe41PX9yBhzX1E32PrlJdyGIdOyV6cqyT7VMgl5RPySkK+EQ5hZ2YZrITG3udnfU0tlZh8treOeKmxP21pM31+Gf63PS9Jl2rHb2s9FJWj5eEIyQbk142RLZ+em7vzeaKnSkoBtf8jvfKznpPYNbv+l2/a+fvv/SX/hLf//3fz0/8xE/woQ99iL/1t/4Wf+7P/Tl+3+/7fYAGIffu3eOf/tN/yh/7Y3+M8/NzfuAHfoB/8k/+Cb/tt/02AH7wB3+Ql19+mR/90R/lW7/1W2983bZtads2/31xcfGePuR/qxGC1s1msMr7MUifdhLwkT0yOKSUCF758fl8XAQDIiCWuk0p3VfGADoA0QE2LhCSFgpjoEwsBRlNUSohUP0t48APMk4agYyKpky/DWok0ADjfDtjUS5iS3C96nP1ZQRx9B7UsduTx7D2FaduFTsM6vPnw4LSKFW1D5aTYp0FbVMWwGFYFi0vLs5xtw2fdbfpryoyg9cExAYK4zVj7wvOhzmDt2xdSW0Hem+57GserA85PVsSBoOptExjgFx+Ami23ETgsVAj7F3MHJQoW03GTVZFbHWH7IaCq77Gz4SF6Vialkqc6oaJp8TjxHG/POe0XgHwSJYAnFmHXQz40uO3BebMYnoFsqpzLQEc5mrE3T6YFQRbO45XG5w3dG3B0BrKq2g0IjhZbBSMa49VS22qazPMhea24CuvAcfGIleW+v2XVMVA+6lDZo8kB6Cqu6W6WamcFcZSQ3InHZ1vIxgaA2khltmmG59+sbtMrL2NfAfI2gG49D8ZHO8K0JqKAL+D+OF1rZPrx+YNeWJ800dPz4/lxjGI29+w02WfRRl5/PeyM/Ar2NYkcGU/WNcn454wmUQpMx8ECXuiRemcFOBmAQRGAGE6N0mPJ/sQbU06HkZDk/fo+FMYmZvpmtPrZntG3hdUu6Jga5VZ5GPWf2k7SjPk0wtxKYyiC5atjwBOfL7xJVY01PJBmJn+WtJPuwI6DsuGMBee+AW+s5GFQ/6nNk1LHxtf5s64WhJpaJ3age22UvtuffroI1aSvqJkYxPTN7ADaqWvOm7zkSEtus+7glC0sevVQEHAoJlwg+qEHZiWbdEAsEEZylY8Q+kJNhB6gzQjgGab6CskEWB7w3dvPbMqJqIqLROy3WQfhsxkHmL79OmepuUgGpQQ0A5XnVAcd1jjcOcLis345cgEZMu2N84z9W8m9mW66U6Cih0bNN37d/6xM3aArMlPfTzpZ/HOIzD6Ue8w9kKbG8/ZSXqEcW7snBFG/zAHGpMD3g0g9mz8jz0kxRspgZg0e2HX7/EaJKh2qUGCI2BH4KMQTO+0E3vSronl66b3eMxYsr4fd8eXNkMkd0ZZi+Cg6kKOOXypWka5oRLk5he5hDl1PPNxjhv0icaMpXRCTgj7dclFNeP2Yo0Phie9xitHxZYHzSFGAm8Vh4Am4dsIyjxfnjEzysaqxOEQHruVSqGIjawkjyXQhIqNr3JM0wXLJ5sXeNQf8Khb8Wi75NGTA4arEunMyExlsiaD4Jxh01esh5qDumHjxmoSI4H1UHPWq05IbQbWruLhVllQvbMZbDImoT4m6hzrPfSVdvv1hWQWdU6UTFjTwYCvAsEGZlXPzA4aj0WbV8oo4ZLKDZ1oo6uZHViUCmBdJSkTbzQ50RtMO365ZhBMZ3ELj+lUt9fPvXYp9PE79xDKEBnDgq89HA4ao/kS0wlukKjLGHBzoV/quYnBG0zUapTRppoEQnnot9qQ5bBqcQeGh5c13Gn5wHMPuRxqvvDGLarLyAic632s3kqlsyFrZSWN4Bz7JMLFZB3ke+5TpYeMtmgS59yol/U0ACoLyQvXpFZkopP6duNp8Y/ZA6+mVSpPO1/Y0VzLGMUU4PJkMgh7a3yXYaz3J0DU9nv3gdEvozJRh3OOj33sY6zXaz760Y/y2c9+lgcPHvAt3/It+Zi6rvmmb/omPv7xjwPwiU98gr7vd4554YUX+Jqv+Zp8zE3jL//lv8zR0VH+9/LLL/9y3/Z/+5EE4aMxSUyV0bj4EbnNx3hyF4a4MKbUxQRoiR/FtsegJtoaF59LkygGHyYLAerkSPRI/Z1dbZA0CVP5R3LIPITG0nYFl13NRa+ZDkPA4nN5YRqprOPcLbOIIZBF0Utx1KbP56Vz75XnvDw75Vax5sA2HJcbTmYbDlZbzHyA2mkXrSIgVl+jHQoetwsetSsuhlqzF65gCIbzbs7p1WIEq1J2IKAlJ26c/hKvLbWH2hOWDrf0DEvPsAgMi4CbBy1DrAKhDFBoEFEYn40dwEJSvb2b/OtZ2JbDYsvSdsxsz9G84ehgQzXvofL4OuDLEDVStAywugjUT6C6MFknC8iBX2k8i7LHFpr2SQGJjyw2M6jgYSjAOKXygmZtukNRNhcKzsmgmZHlrOPsdEX1JGZK4ldbnQvlJSqu2Kf5moKLSXA71TPJf4f8U2/4TRvpZDNP2YKbgCxrxn+FVWHFpzG99sfbZemzkWHcvPeCodxmNp+z+/PGYD3+zKBxOjAHPKPhezZ2x/+Rdgb+B7Q1ftzwc9nV9PnpBA0osBWPk5RAmZyybz/252Uqn5UpmBBfKzmL4hkBrFR+7Nk9fh/EzegNMBgGZ+mc7uUjpqQ2x0+i8pQAaX2J9vIL+fH0rxCXHfPE4F3ahsNiy9x0VGZgZntmRU9d9dpVrwiazDEBiQyywRs2rmTjqthxUfVafNTT2nZlBKtG7yx9BdP3TKGsAqIGFaUnlGpXfBltQBm769qQhfMlZsstylIDLfcoBAosBRUFJYUUlCLURlu2F+Kpy4FZ3WNLfe1QhDGgEfUvbEvsUCujDzD5jowESuu1dFUmgWdmeWsWmwlLCxhLWYrx76TyWhYDzabGNqOjC5pFt5GdnGxMJrftzNc9GCgxt94puSGTjTkCVTcCWWb3XzDm7W3I7os8/akwHhL2H5t8wLcDoN6OGXwjS4tntuZLfogQvCfsg1r7EigTPVK1AyDeZ79HBq9xSCpbdMl+RDZuvwvq5gqRGKOksuAUY6SEnxnI+0yxDRTbgG3QhLGPgEQ/7h357Q6RRTpMFsw0YI4gDUDfaJySmEw+CFdDjQ9GS/SGOjeuSqyoPhRcujkeg0M4c8scm6x9zaWb0YSSta8yqAWqG/XQHeaqkqXteGF1zmLRIpXXRMSkUYqWcOu9DNEurF1F40sWtsusrK2reNQtKY2LndINb24OeHi15OJKAa7BmfyVWhsi+1f9eF/FeGUV6FfQL0dgxkdtKVdpFYeW+cV75yyFcdR2YPB6j3y8sZU4btkrFqbltr3ibnHJ+xePWZUttR1Y1W2slhGGvkCcjFU00Z6Ih/LCUGyE8lIoLw04CDOPP+rxK6fVRAuHX3jCwlEvO8TqZ/MldEfg5tDeEprbWoIYrM4PZXpFzehYvm8btW2+hP4gQGd48/SQR5sFZ5dzkEBVD1y0M/7jL3459WdnyCAMS+gPdQ4XW61kGX2ciQ+U7JMf7Y5M1tzOfvtugKZ9iRXIv2tpaAS+Utyzd2zGDq75gpORyxivv7w85bwcE6WGXjI+PgVGd9/33keLWs1TPzMnYNx4Tsbf30MG5j0LwP+X//Jf+OhHP0rTNKxWK/75P//nfOhDH8pBwr1793aOv3fvHp///OcBePDgAVVVcXJycu2YBw8ePPU1/8yf+TP8yT/5J/PfFxcXvyKCjCm7J9KjRsQyZURSuaEdJ4Ly8HciAUSinlYsQQsSSxPcaAhCFFdQNDg6YYn5MZkA04wyKMXfW8nOZ5o4JhJatJ5XHecsGhzQ2uZY3xxsoL2q2dR9ZlG9sDgHNJAArS/f+Apr/E6b2Y2vMOJZmY5ju8lZZR91UTa+polUogPTcLfQGndqOCk2PGkWrLc1fSjwgwEnBGe0K0rMINyabViVLUY8Plg2Q8Xj9YJmU2kmulBmkAtCWXvtLBVvkNgxYDGT79RVWoMeot4IffLaA8RSvINZy3G1YVW0+fMe2w1LGagFZiL0IXDXXvKWPcCXo4g+wKoqWc9aHhYr1jIjVAWIwTZCsREFtK5iZnthGA51xcsg+M6y7UsK6+i3pQYhjPOluIrdBwUtW4wbiRlUP2tYMF6r9njjMbda+sFiHlY7JUHqwCjAVmyhaLxSbocJYOsnAXJaCsmpiiVImm1PFIwwXtyHkcH4dkDWZKPPjpcIQZwytPKSmqzDwlx//KbxtMevOX6TUpwpWPU2wYW+tv4LUWdi//FnYxz/PewM/Aq2NTusqrA7jyf7/m6WK/6cJu8CmmnLTK90XBSSl+io7LzWpCPc/rX332fSiZvEHIldkwFfw4hMTAOTKLwePLjW0lurH5PAQan7awpCAhpIyOQDW3x+rBLHzPTa6U+026FDNa+GuHhrGShMat4CM9PTDCXdoHYmeFH76oXB2diUr2Je9FTWobL7Qu8tm75k6KwymQ0EtLzQFhPhd+JXFj256XYTrOQkS7K96TslluJVhWNm+6zRaCUwE08phkJWFMxxFCxEmJtCLxMCykWGyhr6YmBtanoptOSoFfyg2jTiAraT3NHX1/FNe7W3g9cmL36wO8GmBNUU2d3jySBmErpP16L0aqPmA94bLUWJ0zthLtOEmxmiGPVO2XoKGMjfg0yew1xP+o3zeRLYmwiT3gRkRZAr/0SvG/C75Y+ZXc9ecDBZs/vjbW3FJDCZrufpld8LnjbxBZ/ZmS/xkeZ1kjvZG1n6JGmNTpvspEtIZCCKgAu65lUlXgXD49ZtXEh9I/TaA1loW6IMChIwg+wk+cyQAn7Utpg9KRQzVpXktxaitp8E1WgEQuXzc9N5HQbD+WbOK9Uxz80v8bHeuTAuy48sTKddA3PHvpYuWMrg6ELNw+Ewg1mWEJ9XvV/QChSL5zJ2yVWtX8dxuVGbcgc+Ze7wZDhQrUIMXmIjlej7GRNwcSFvXZmT/p0veNQu8+dpXcHDZsVrj48wJmCtp+8t1nqcM9iY2Bej9Jdg1HoPc7KIuol7vOlQHzRqGibmr8YMQt8XnHdzFkXH8WzDyja4YDiwDSaCeEvpsBK4W1zwsDzgTr3AB6Gyjso4HuXAQsE7SSWFfiKhkrvlqt6VK+MXn4kUQpg7TOWYVSom72YFbtD9PX22zJzuwW7192Gm9qa6VPJGsdX7vX7BKCusMQxSctocItazur3BSODzn3mO1Wdiw66ldt0lSJZVCSYlV6I9yrH4xJ44VLphuhZh5IsbE1nFHrzZBbf2Aaz0GFyTWQnRbkliZ92UvJkyMW+oRgk3vd7+4xP21c4xO+97cl7Qzy0p3bYX20yF3vO5Qo41EwtTJYV21/U7jfcMZn3VV30VP/VTP8XZ2Rn/7J/9M/7gH/yD/PiP//j4ufY3xhCuPbY/3umYuq6p6/q9vtX/5sNILJ2K5VJZN2s6pmWEOfhIZVeT1s3eI8boNUKIP9W5Vcq4jA6cS9TeECmjE+dnur+LxA5DsdNDLVm8PLNmotOo1xbtPBE7HoUiKJgVayVCY9i22ir3tFmwKLqY1bD5fhzZLY2U+bGZaGvX1MEwAVmWwMy0lDFd67xRMUbTc78449hsOTA9X1u/xsz0/OD6N3G2LXN9fAhC3xU7gGJpHL23NK7grJlztZ4RGg0wAh5Te9UyiYysECR3SSEINrYJLgqXn7OxBHJwhr63BG/wXihKx73DS15cnvHS7Cx3JJmZjpk4DoxjJoJFKI3hIPS8rzzFit4vrenfcOVqajPw1uKAT1d3eHK+ZHAVxcYyLEbQxPRQngu+NPiDQeeMU6HN2jps5RE3MVaxA2N7ohu5bSTWV8aOhKnzlCeCmMBi4GDZcHE5xyZDAWOJQ0gMP828mS7kDF3WzYogbf5WfNgLpqMjlf50bsfPFmN218o+kGW0+8lOe1ljIl2eEdCarrnphr6/PN9NUDAtr5oG36R1tn/Rd7hemN5TgSmw9WwA/33sDPzKtTV5nwvj+rphNuujO07LBOmaPC/xUqkkRERGItc0yxcfS6+3U9o0WQPk7Jz+9JFdun/86HOJ7gsxS5c0l1KgFAahd2qotkNJaRQ8SswqQQOKgVEvsYgs4XSvEpBlACsOR7p3aj8KcaxMw0wGanHcKy4pxPMz/Qs0fRXBE32fzk1qYdCyPReF6ZuhoOtKbc4RN0pJwqepEyBT3azR7hjj43M+V814b3BON5YQBGM9q7rloGw4LLRleiGeQgKFlNQyp+Aewm1Ka6lD4Ng0GNZ4u9VyELNVQf3Ksy47ntgF26bCe4vvtRthElEXp8mPYEUDxjh1QogMAKtBWWZeRYfUxc65DCMmkxgJ4/SM96D01NVA25Y7wNAUvElgVNbiigHEFMjaGXuOc37T+c8YQKDaaWJHEE1fcGpz0t/p93QNATER0Aq75+XPODn+vY50jxKyN73OTbbm3Vxvuuae0bK+tEcUpc7J9TSmjPidypC45o0Zk4/7I7FNfNT/i+CxyqKIahSJ+qp5/cpYMg0xlkn6jClw9WD7qL+X4hlU5Fvf00QOJdkSiR8r+rd5fpd+3H+90HWWs2bOzA4cVluGMKP3lnv1RfbVS3E0oeDANDgMLlRUwfFwOKSJcUmSQ3k0HLCIZQ2NL7lVXGFFmbF9KHIiBa8lgXfqNcOxoXeWqycLgg8KhBvU34ugVGk8W1dCCU+GRWb5HhQtfTBsXclVX/PakyP6TYUpne6/e19RUTj6TjV4VZDbQyF6b10g9CqQbgq9774gV1SEMpYZSsANRhMzUQ/j2G5YGE0kWfHM4qZv0AT9veKcTVXjgnDezzEoe9cYj18q8OcvC4pLyU21qksorwL9Qssf3VzwM21uEy+urC4JrFYNR/MGH4SLbYHrRZP0Qq4aMa3qACOwvRPjGZ1BEBRg7Q+E7iRkf8NsLeZckJc3nCy2vPrGLRafL1SE3kR9rVqwjeyW4E/cqhTr5ASKTMCYCHjlcnf7lLW1DyhPWVk3AVnAVMooWD3/HaVWQtitXpn4gNfwCdk7b2KLRjbW9Ji9jzS14ZPYEXTuiQRlOSdbR0b26AABAABJREFU7vfkV9K4SVP1bcZ7BrOqqsrCvL/xN/5G/uN//I/87b/9t/nTf/pPA5oVf/755/Pxb731Vs6i379/n67rePLkyU7W/K233uK3/Jbf8l7fyn/3YeKXkkZGSRP172klVZGdtfOoZ/T200IAzaJ7dMIm3yxodkODhhs6UPmYZbfQHQntrUB3FAiVU0bXIDHjKRRrzXiYHuza5I0gWN1QJDHDnP7tBovzhsumpltoIHExaAvd2mit9ZNhQW0UxDooGhZRRwrgwG4BYu25aposTaf03mA4tmtesJccGE8lggsD37T8Bd546Yj/z/rr6NuY/Y6bhveCC5Iz7LUZ2FCy7UuGthgXmlODaazTr8arLpbzBmO1q2FVDczKgaoYcF7bqdd2TEH7IGz7Ems8x7MtX330Ju+rT7PI+y17xYFpOJCBI6OduABKsdw1PWVxET+756RYY/E4DJUMrBc192cX/G/ly7xydYdhoXQ700k2OraD8lLoKksoYpllX3J3ueZgteXsoKR+aGO9fGB77JAgmK0akizSOwCxlfqwCPjDASkCpvT0zhLOK9ULE3KpUIjdUVLWPZW9JjFEdSLC7qZInIsxyxfi58jHGFHmYQha/jQMMZYWKEetrumGn4GsuDGPgHBAKNQgpn/7pYc3PJY32/S8yG6d+LVAh92f+9dIz6UHpgH/9K3EY8TxRRR7f+mOZ3Zmd0j+b/L3PmgVhGtebvbrwu5j04A5rd2UDZPdOZ20slJCJV8mxcbxpV1N7niqSrdxLTli0EMGJkwnuVtuyszla0d7F5zqBHZDgStVyD2xWq14gi9pKHPpemWGnXLvOjKYDEFF4lHGlhbpqYbWgemoJUQpP8/7y0dcHdb8QndfWcATVlkIel5i7xbi6YkNSIapo6gniKTOuXofkhC8xECmsB5rPD4GiHbivIWgzUxEArOi50695qjYImjZ5EL0fVfMqOUFzPFt+ACYe4bF2mE+WcMXVgivMDMNhgZPwIqnLy2rouUNc8hFt1TWL2TxdkJkbbeCsyY7lYPXBi511dPUBruO+iw2MMx0PkkP1kmeG2kPV42coG3ZDYgNuGAIrVXGcNozYzCm8y69n5DLm/L0Dvvzn3Hfl/gdTI/Je3KyVX7EiqxRf4nJsTllzBhUSMw3S5pVkzKufR8vcP2xa8/rz5wjyYffEFSkZyaBwrVrPW2k1/E3nPtsfGmPlDxMflMEh7NfFeMNZcePxxAC4hzBWggBMyTh9xDBEsmxj/owaQ0k9payM6aJTBlGozK1L2aIif3I4rRdyJ3YiV1zgxl9UEDZZankMJUtpw65vTB0BW1fcNbOmRU9B0WLE21CVUa7cOlmzCLtJnVYf+xWNKFgJgN9sDiE2vRc9jPd+0wfK1DqnJivTc8H5m9y7hY7citXZcWd1ZquK+ikjKyjEEvGNahvneWqr3lTDjGiyfPWFTlB0wwlb1we0DZljAVFG98ZjxvUPngvlGWMU0xQOZb09XYGZkF1pnqD7ya6jMnuQk5+ixnj0tK42MG34NheRC1gjwtCJZ4qeCpxHNiGlZ2xdSWDMRzULd3S0s8sm3WdpVO8083Lx+/Y9IFhpoCbtwZ3QCzL1JhXBAqrJY9H84arakYoLL4eK06CDdjGxFhmnFi2HdmA/UroDtJki1Om1bjq6GDL2XZG+VqFGdR/sVGKpT6VXPZqW3Z8+lxOmDvs6s+sjzuxWzn2mQ4fwHhAE/FMy9en7Kn96pT0+5QZ5ZPG1JQ1wM328YbXuDaSnRQZWWakzygZY8glj/E+A3t+Y9pn2GVkTUCx/Hv6l+Ilxuff7XjPYNb+CCHQti1f/uVfzv379/lX/+pf8et//a8HoOs6fvzHf5y/+lf/KgDf8A3fQFmW/Kt/9a/4A3/gDwDwxhtv8LM/+7P8tb/2177Yt/J/+NgtMwy7mf8QIu1eRgNi9yZWmuGT7lraGjfkcr+QnJC8Gq4HLPsdf0CRzmGu9cXtCz33X3xCbR3rrsLF7olG4PRsiXl9hm0gZTXUKLAzCX0VCHNHPeu1jNBZ3lgf8tzsirnt6XzBECzrrqYPhlXRQQn3YrlHJY6FtDmj3WPp4sZfRU0pYzzHZsOx8ZQi2Q7OxPHrll/g3x59JW8mdpZoG1kRBZl8kFx+0fmCTVtmjax0L31nsxEJgLH6s6wGrPXMyoHDWaO6JKJ6WFUEyQrjVGC+KLm3uOBWteED8ze5W1zQh4JSBg5Nw1I6lkaYSYHBwBAo/s459U82rL6xYvY/B46rLV302H28wR2Wr6ze4qjY8rGzA/rB4Gai5R8eJIKPMgimEdxCYIDtpuJyUVMWDnPQ07cGP/PM72xwztBd1Mi6UIMiQv1Eb8mwCPSHCmTZ2kXjEeg6m0WcQ0GqUIkPMNaMTwQQk8B7Xg5TND8xteK6yNkJa9DUm9lly3gHw6AbaVmM1/NB19KUkRXXU5jspuIt9NM3/d6GZhDH90tg3NBl8vh0RMctShbsZCJ2Lz5u7nkN79Nun40bx69mOwPsglHsxa9pvYk+I0H25tPUw5g4Jz6M1O4YUcdwJB1x3dbcMK8VqIj6GweO1eEWawK9szukxu22Qi4Lsp5srp9nZ00EAxQeW/rorBuu+ppl0VFE++GDYesLdayNA9uxikBPQaCUITMIHIYkG18QKHCICVHXMKQmufq8eO6X53x+dot1PyODe2bMZOqWoNbJBUM/WKa3jIDanlhrrbhGtDlJZ9F66kLfo8TEVrpmaogyeM+ybJnbntulapX4oMK7tXhKLJWsKLiLfJ1QfHTL+2avcsWKB8f3KPqe2YMCxxpYK/iCNli5ZdfMTM/PNjXOl4SC3FFsqn0mQwwivTB0lq4slFVWefygrdWLZU/wgmsLpDOjILxq0OeuZaH2SBGdfzTASkY+JVqu6RHG96PHhbGUPR8zcdCvecApcpZJMLDng6VyqmlwMJ2PeU5GWxOvIURkyN200b+HIZO3nQLzdzol3pcEIj9thL2POwIBv4z3+Wz8jzOmSULvNb5I8UgGisMuoAWqmZWc7vhTTNBr4WMjJBm7q7txLkmUXtBGFuwGrFPgdmq48nxU9ouEwBBZWll7EXLliBH1SSV9jn1j5GUUDt8UrKuKuhy0C/vqjONySx2bTxnxlDH50YSSMihw0/qSmQxsfEUZO5IvTMus6nNS/sDY2OXQc+YW2r1RPHeKSxx6jSO7pRTHabukrnuc07J135tcaj0Mlg2VNtTyhsOqoYp6Vb23dN6yHiour+b43uTtyw+q3VfPe4aYROl7pcWZMiZQfIxvKnXmgxf8uiDU5BJ2GUY/IVQeMx9YzDrqYuC42uayx5n0HJomJooCxzZue6bn2G54MBzxXKWf3cbvxBDovOVUApdekyVmayg2Ks4+1ILtAtVa4wZfCtslsUGUYFqDN9B0Jd3Msu5K/KYYS1ZjcsQMQrlWGyMBTGps4rWxCUB3qPFO2jP93CPOMBypztejV++xvJz4+BGQso3OvWIbsG1QAC6VvEewKgNWPk3QEcjaIbN4mIq1hwT2Om3Kkst6xeRzrjGy0k87OUYkS+QE2NHa3hn7lSk3JWD2j0+HTpP6kIkF6XPm8sK9dQ0Te34D2LXLAGMnPsq41g3YxtPGewKz/uyf/bN827d9Gy+//DKXl5d87GMf48d+7Mf4kR/5EUSE7/zO7+R7v/d7+cAHPsAHPvABvvd7v5fFYsG3f/u3A3B0dMQf+SN/hO/6ru/i9u3b3Lp1i+/+7u/ma7/2a3PXqf+RhiStnzSZfdRTAK51NUy/pwmeviQjiHNaPqVer+4UkYqadSGcHjsNlMcNP07maJxcJYpGHwrdiWd21LKqOu7OrujmlvMu0UEdLx6cc3p3wRuPjxguKu0yBCTUm0LfR7XqODnY8NzyStlXjXbUOOvmfGD1FrUMPOgOcUbwXnVESnGReaRgVWJnNURjEIMOK5GWisEScKjWR75FBF4snvAb777C/3c9p3EzsApmWesze6o2A1tX0TvLMA0wMoWAzHIzRq9eVQOzqscILKuO2g4sio7KqGhwaRxzGzt4BG3l/mJ9xpHd8HL1mAPTUOFyqcrSeI5MrUAWUPydc8z/+kRltv7tlvsYjv4fNT0DPgR6AmVc1Y41t+xP8fr7j/mvR/c4W8/ZXtWEzmrpp1Mgy5cBbFDR+gAX2xmH84Zbx2v80ZrnD7Rc5r++cQ9zqfomrgYpA30Uke8PAuFWx+qgYbup1dgBflPpRmKUwjplc6WOKCaxslKwEYgbfBjne9TNEud25/x0g7dGAygTN/HEvkpOmHMINtLTx006GDOCWpO1FQoDzu5qZ73DuJExlWq20/AxkBUZNYEYP7tmKPauNXHm0jrNZV3pmHjOjjD8s/HMztwwIk6lI66z7D9kmng+cme9jJ4E5JRnYnFNAuMMZCU9xuxRjK+/74h4C75SwVU3CxQzR2Udi6LDFSYLuBvjOagatsuSq80M31p2RMZNyAGKrRyzumdZdgQ0O+280LiC29UaK9qyXN+2CrDbaHmLCGjlTlMo8KWVKQqOVRHkMhG2Gya3VoBD0/DC4pzPdiVDo/UYJgrBJ/aUFa+MrFh2Ps3aTrGUrH0JFNZTRGZwaWMzFKM/NQnqKWIElwCzQ9tQm54ju6UWLaos8FgRKplRyxI5FvgK+PDs5/kAn+GMYy5/7TH203NmD1c4VL7AM2AQwBHomNcPuDye8XC2oulK+s6Ci2w0r4ztvDdlJnBBXQ7M5x1hBqu6xRB4eLlCWr0PoSAn4SACWXNHVQ/0fRGnphCyBiXR32AHTAN2Sk937vEOoKXgmGoxJq9470vdKRkc7UaA6GN5nRHCjs8WxEyc9ImtMSYyHL5IMGs6X/JnjK8/ff+TY28qbd8BxNKa3Vuv18CtZ+NLbxgg+VyJcbUfgzwlIZeDbtiRP1GdH3XRgp/Yh3T5ZF5cyEBXCJJZK6GY2B/P6EPFyZl8TG/1ut7qOTmwjfuBj+drgyR9H9IreBUiSG5arS6R1tBvKtp5RzdYXpMjzMpTm55H/YrSOAyBDTUL29KEMieWa9PTB0vjy7xPH9htlkFZ0NKHIuv8OoTel9wurzi2G232FEvznq/O+KfNb2J9NdMER2T7BmfpCg2/m75gVSodqDQuN7B60i54cHFAvylhiLFfGTClI3ih6wqVugmiellxiARs5QkBChtGl7seMEZBNDdYLd+MsZExWtL3/OEFh1XDQSxnTwkWBfUGFgIlQinCTODAbHl/9Yi3hkMu3Uztk8TGI8bx/OKCz89OeHi2oq8qBikwraiEStSgIkB5CcPS0B97wtylZpsax4HaXa8VJikeliDYrUrp9CsyszhpXPkKXIxhTK8T1seGke7QcXz3ivPtDLtWZpdM9tUkfWCbkHUbbTuCWUkrS2Lcr+WFYUcvK5cZiq4tGSbITGIGW12vAVRiRb/Ad9YLnhBjAgJSINYQuv46oLXPHr7GIg7Xq1huOk5fbATu0t/p8BDwRsaYJt1HyKzgYHfPGcG/3fhoGiO92/GewKw333yT7/iO7+CNN97g6OiIr/u6r+NHfuRH+O2//bcD8Kf+1J9iu93yx//4H+fJkyd85CMf4V/+y3/JwcFBvsbf/Jt/k6Io+AN/4A+w3W755m/+Zv7RP/pHWGuf9rK/YsfgrFL7cveAGAXvcwpTdgSuG5MdEezJxA+737l4DTSmwqvBioq7l2M3AVdBtxItLzwJhBNtm3q6mbMsOlZlyyWBda99W49nW37tyQO+6vgtHrcL3toc5C4WlXXMC2VircqWZdExtz29t8xsz6Ptis1QcTXUmCJ2WpLA3PYcF5sswAto9iNqZR2YJoorjtPPoS1uHUIb1EBcRtXYlEn5ivlDPnnrOT51cS87kaV1YxvZqGFy1Wt7dGK7Xl0dGvCFIIQBpFTdK2s9pfXMioHDuuGwbKjNMAJZRj/zzKho5Mo23C/OODQNd+2ahbhMGLLALCLrHo8LAfsfNtnflQDmJxtqOaGmwBqDCx4bz3HB86Gy43+6/ZP87PJl3uwPeaM55JWrE863M9q+oNlWCm5JoF70VOWQy17mZU9dDKzKltevjui3pSbpamUGFlsTGRQBv3QcHG7Vv+iNlvQ4QdoEMDGyAYNgHZNOmkwc5jACujn7p163trAN43HTIaJrwgw7QBbWjkwtr5GNgHYs3D8/BxyQBRatROfrBkh/f3N+u8wE7AQa+4KF+9mKKclkCjbvdvnYu7ZGUm//Hn4Vjmd25vrwcf/a0TvJINZ0Ij4lWIFdgwLspsl3H05Bfi46EA1CMGM211vVN8kdkebqTW571biqrKNz0HkL3jIreu7Or7gzW7NxJeu+zl3+FNiJYJMdchc+H4RCPJuhitnqgtoMGWsrxDMzvXYsjB9wCDZrZdUy4JDckQnUtgxoZ8NU+dIm3UeUKXVSbng833Larhi3Fj9WsQU9v/NWywgNmdHK5CsixO3NBsR41WQ0ntoO1HagEI+gtrMUDQCKVDYpAwemoY66kyXKptbPbihIgEwguEAXAr1oMsh7wXgBDJYCY8rog0qcT4G71vHhxWu8WR2xdjWXQ81FN6MZCpyzDL3N5flF6bF21DcsjKOI3RIvu1pF4QVNgAWwfRSSLyCUgWrW633LEgGi2p8TgCbNvcwK2zcZ6d7mNTCZvjsA17WJPtqEaQLEmHH2x41b9+wb1s90TaV1l8qbrtWQcH1ZvQdbs/MzgW97194vN8wSFG8LWD2zNV/yIyUGw0Q7dDoSG3E/2R674u5rSqrcycjuyv4e6LpN1ScwgruDYEIEtiYBbJjMZy1dF7xRvT5XJV1frShBFJwwg55noxas7QQ3BMyVzYBaBtZSVUl8L6EzdIOlKrQiZTNUnMmCZdHiB6Ni8KajFMe5H0v7AFa24chuKGVg42sWps26vx6j8ijSQgGnw4oDq/HOXXvJgVHG70KE4+Uv4F82/D/Xv5Xt5WyMRQi4psBZz+AsdaF+vCFQ24FNu6AdCrbbhL7Ee1x7tSWprDAoeQBvMWZkAReFo7CeqnD5Hljjc1UNKIgWgoJidd3z/OEFHzx8izvlFXWMd7Qh1wULM3AggYWx+BCopcDjuW83LKXHBYOtPW/2R5yUG72+L1mYjuNqy6fLO7x6dsSmW+JmSrYIJsavkY1XnQm+MAyVh9qDBzdY9QVmDY9XPf7KUmyUfeWsdvzd3lejbFqJHRTVN0lAWUqUuJmysrABqfT+PH50QNHLTkO0qU1J8bZEEDZJq6QuzTvdoJNxjutm6ovJtLNosi/JHg3DeGwsN9yxQ/tA1g7TOC3UmGAJBeGdKlOm4FViOaZyyH1f0ktuGJRKCjPglNdduG4zx9Ax/5zaKJnsBzclZwSy3ui7He8JzPqBH/iBt31eRPie7/kevud7vuepx8xmM77v+76P7/u+73svL/0rcjhvRhE4YKccKgFYmX44jYxvCESioUhdeUKqZ0iaHXEC4OKki1TIqaEIVuhWQnsi9IeB4Uidz2Zd0XUFpfXcWRg6b+mcZVaoxtVlP+NWteFrj17nallz5Wq6WMJhJbAeKjpX0LmCh8FokIFwXG85qTcUxtP6glIcHYUK69qWhelYmpaZKFSemViodkknLteYd7FrSB8KLn2JQ3jstKNHyg6sbMNLyzNeOTimb4vcxaOMXTQK43CD0PTajhcnu9n/Vqm+Unq8E0ztKa065auqZVW0HJYNRdzB5rbnVrFmYTqO7JrDyFs9NA237CaLvLsQsCI5XGpCFD4MAfONFfbftXnxdt9Y0YeBUiw+OhwpvLZiWMmM3zpb89XVz3DmC07dgs+d3OHV7lZuCX/WL9i6UkHDckMfLKfdkot+RjsUdM5ya7bh/GTGuprhOgO90VLRMjC/u+GFwyuO6y1fOD/GRCqyawpCUEfHDaPGje2h2ETnoo902wRUpVJDuE4J3XfyQ9gRf98ZkWIr+5mIvK587BRm9IbJfikV7yJgeOfAfydImD51rZSFnb8lxIzi3ka9Q68N5HU7vXai0T8bOp7ZmevDB3Nzxm1qR/K8ugGk2pnr6TrJ1ujvUyaIOhMxLopOy5QyHiQCWXMNQPxM1/XQWZwxGBNY0OGC4LxQGO0o2LqCue25V1/SlSpKrmWDmpDovGoybr0GGyayqGa2Z170WpIRTC5XN+KzVlYpQ967DURAK6DSK2PnKBd5sz4YZW4hbGOWPea/qWTgsGo4r+f4wWQqvzVjCbr3MrZHT85tGoNEYD3EW696KCaDdUNMmkQbJj4HVrXpmaWuWTIwNz21BApG31MizWFgA2ee8KnAz9z9EA9Xh2w4YPtzFTxwwAWGHhfSJqP3wIhQUfC+oueOfZMmGLa+5Ml8wYWb03v9Xhpf0ntDaRQ0dAhbV9G6AucNLgjzolcGhI2l/U4YLGC1BPGg7pjZnvN2hkSbHYZR89D7uFVGIMv0ZAZHYgGTM+ATIGt/fu/8fcNj0yEx/ZGz1ZMT07kZYXsHcOumsb8E37Gs44Y/bsaa81pM65L90vV0WHp+Ot5D2caz8SUwpuBVAq2m0icpNtlnboV4XA7mvepnwchUD5JL/nwsV08jBf++HO3SKAg/Dl9AfyBs7gXc8y3lbNDusZ3VPSL68MWZpTrXa9lWcBXKwMo+lewk+nNziqCaul1TsVlUvDA/p/UFW1eyKjr6YHm9PcYjUd+3Y2FalqbNDURuF1csRZlTDqEPBRd+FjWkhqwDfMtecdd2zCILtI8x39fPP8/X3v9KfnL9/giQxIqHQpuVOG8Y/BhXFaKv2wzFWJYYE/OhN/jCU0w4E2ICvrOEymELR1EoiLWoO0rjmZd6021MPgyFoTSOMBPO2xnPHVxxUm/48uVjPjB/E9BOwQdmy7FVbd+SwJGpsCL0OKwIBMM9W7CQniY8wYrn2Go8krTIXDB8Rf0WL9VP+Mni/fz0+RxfWvqDQH+gjamybEmA8koI1uKWahS881x0NYdVy+qg4WJbUGxKfBlws0A40RJ3GoMMNouJ2xaKTaA7jiDpLOAOHPawI3jBmEA7WMLGquh7EW9zQ+7ymOax2iB2tIJzae7UzsSqH0KAKQsrJfl9AqxuSKj6QOg14YORHemhEQGKjKydRL6MSZU89/3Nyfy9dR4mTLB8bmZkBsTcEGNB7lgKMG1Al4GwRMaZAF/6/O7f42vGx4RRRzXlnd6DYswXrZn1q3mIROHC9EBkkqjQq+xN8oRgMhoLGCfYTZTg+Hym4UkSA5/QeeOrS4ChFIalMCwCbh6gCIRWMxjOCxebGXcWa2VQGdXs8EE467QN0WGx5bBQwOYKuOxnbLzlspuxHUqMaNagKgcq47lTr7lVqvZG40sV2I3C7zPTU5seI54uWA5Nw0x6OixlBLdmeCocDqGREo9nHSpmQY/bhJoDo4Zi7WssnlvlmoNFy7kzWKPlibUdOCgb1coaKrqhiBngeG9cWmy6qYRBgRLvDSEIpXWsypbaDixtG9v1Oham46RYc2i2sX6+x+AzOGdQyq0RUmMu+uCxjMDWW//LgsMQqP9jx/o31Wz+5xmL0DPD0wdPH41ELal3OdRScM8KB9Izk0tAu0B+tr3LxlfcqtacFBuOik00OBs2vubBcJTf56WfcW/+PJ+6uMvpesF6U2NPPC+cnPPrTl7lueqS/3L5ItYEFosWK4GmLOnaQvVPbEHYmNjNMtB7dSrKdUCGgOm8djLMbWqnnszEMZrO8/3f94PzBF7dRHnNayFc2wz1u00v/R7Q/BsYkKkeX39/CvAVJpt+/jxCosz6BDRP2ViBvI53QbJxk382no2nDWHP1sS1dP0xGTNfhJttTfYuuBZwa1dQfUIDlhuC8AChEHylWhWhCLpmBi3JCiHQ9gWLsouYtLKRAtA43efqCObgoQMGXyjzyhX0XvVBCvFYq2XoC9szt6qZNQQV7E7C74X4WKqnouJ1BLUchqS8WEQH3CMMBEwIdFgK9Lg+FFRxX+9DgSEoiFQOatvi9a2ovXHBKODjE6qQ9oLJgo8gl5LqFCazuQRDQTgF2/RzzExPLT1WtFQyib0Tr25IncQg4HB0mHCJ8Bb8zF26dcFn7ryf0ID7lKd62FFySiEtLgx4HDWWQkZH2WJYClR4itS1Cs8TlvTOMrcdJ2UfdS8H5tLTBcuVn2lZJJ42FDwqV5y2S7ZdSdcXmHngYN5wf3bB0ra82R1iRPUpDTBYixtM3DoN9Nq5MTFWbQASqJUDCN4e9J/O691d/Tqw9bSSi3zKO71Y+lbe5Xja68i1X9hZmClYuuH8MZDnmtbYNEDYf61ntuZLd+jUj9Ui+27U0wDVfT8n+mYyZWqIIH1EiAqTk+7KskQ7oU7sz3Q+7nT9nPzuSmGYqTj3cOi4e+eS+6tLBm+4aGcUkUlU24EnzZw33zymeKMafamonUsEw4N28SAUAV8GqpOG5UwrU7ah5NXzI27VG75y+ZCroab1Ja0vWA/avXjrSo7KLc9XZwAsTBsZWJ3GKKHEBcPG11k+pYqtX2fScygtMxFKhB5NcNugnWV/8/Fn+MXjuzzpDqJNHTV/TWwC4oNwVG5VSD1YLtazzGDNOspO8K1lSF9bTJBI5ShKR1kOWAks6o6jWrsAFkZL/4qs/6tJns1QURcD71s8YVm0PF+dcWzXWQ5mJj1L6ViYnmNjNGE/CMu/s8b8ZEP4yBz+xAm1VQbWPfeAJqTKFFFGdLSHH6je5KjY8NrlEY/cURSkF9xMnzeDNg8xkQmsDwbCpuDsaoE5CBTWUax6ujuGMHfcu3+G84bHpyukK1TAfRWQQaguIViNhbsTLV0sVx1usFqFYgJNo75IsECnP301+uP5nyfO9ZB9f/FhFFy/ITbYr0RJfwdrtBzwKTFOcF5lUkQ0lZckVhI+MNnQd7q627R2DRQW+rexedPfJ6ys3WMn7ymxy2LMIy7EhgG752Q8IgF9KTU4iX38xFXKJBwz2rF8883k8Xc5noFZX8QIQa6XT8FoSPY7G06DXxj/TnTDhOBGbyN3D/DsisfD2N7WAYRMAVRaP/jK6+RIm4OkduSeu/MrXndHtEOBNZ5l2eERHvdLjsstJ+WG+/U5V24Wu1lY1kOtbKxiQ20GZqZnFZlKSSzx0s3wMnZ7AqLIrv5TBSRPhc8C6KVoyDGTPpcdavbD5k4aqf2tw3BYNBzPtyrwDpTWZ5H2q6HispvRdcUORTGIiiLv3L/YUWpR9RxVDbeqDcfFhoXV7PhRpA2X4jiwW1xcVekx0P2in3zWJgT6ACWBEmgCnErNK//3JX0oWIeK+3LBl9HHz25o/MCl73DxOpvQ89AJ577mlg1YArdie9zb9or/sP5KPr25w6ev7lDEOvn7swtul2vulJd8df06LxcXWAIfqB/w/ys/yM+WL3BaL/jA8UO+/uAVDkzDT69f5rX1Ecuq49Z8oHUFvbPYwmFMoDcBX1rEBtrGMlyqdpXthKIx2M5DFzf06RpIm2sUIc3zPG3IYfLvpvF2ztbEubopYzDNmIzv520CjnfIlkvYpc/m4GEfjDJyfdMN4xrNmXQz4tU5kx7I2lnPxrPxtJGDh2tPhBvsCxOHYW+OZ9vDBEWV7KjEOoWdl9jRPTDj9dKcTqVlO2BDUFbVoui49DWDN1pKZ7Wb4NaXzMzAzPaspKGzWubhgzKHU7fBQjxF7FQImjG2eFqKSNAc70mJHluIw0dXSjUYo8OsZHkKhYGA6HTHkkGPwcQOsx7VR5wVPf2gClwmsrKAyG4uVNh352vZRwiJpFNtWV7bIZetl0Y7L9bxc+rvg4I6KJiXSiddIIvrgup8aYn6GsObDJc92/98j4E6Ms7OWckbHJtLQJMVQ4AuuPwV9cGxDkIbLHPRtNjc9JSxrPHVcIvTfsGpX2SbvipaFqZnYVvu2EuOTIsAt4srvmBu85Y5YFuU3J6tuVddqJZmf8hlV1Nax7xQMNIFE8suY6Ve1Mtxg+A7BUXFqTA0TpQBvL+/S7q5k3mX/kggUGK43zRu3PunINc72I79C7/dHv5OzCx2n7/R1rCLm+68dJjYkEmCZFoQ8MzWfOmPnTUSYmI9PTldO/vxSAiT0qN4nPcxuREdl/3ESNTOkgAMfoxRArHbuj5pSJ3lRn9qqIXuQOgPVL+1PGmprJaKPz+/YFF0WdsX4MsOn3BvccWb91a8+fCIcFUgw8THLwJUHgzY2cDJ4YZb8w1H9ZbOFZw2C9ZdyWYo2biK+/U5rS950B5xXG65GGoMgTvlVbYxCdQx0ZFL8cjMaFI72YI0HFql0aFxgBLbVM/x/dUjPnTnTX7icoFbC5hAUTpNxhcu+/KgXWPbYS80T4u28KRuiCGg1SlBKKuBuhwoC0cIkqtuDquGme3xQdlYymJWYMsHUZZ0ecHCtLxcPY7gVYslsJCBSjwLgYWp1eL+nceY//Vcv/N/u9UY7E/e4kjmGuOFoBIrBHzwlGIwyg1mJr/IZ56/y88v7/NwveJqW9Nc1uBEm4EEsGuLLxg1gQWG3rJpK47mDVXh4OSSlw/OKMTzmfPbcFZiYrd2XwbEQnusTD03g7BwLE+2iATWXRGXhtBf1kiIerdW/ZgkH2IbVDNSiGzh2E03xeiBXaZ8iDFmCKqNNWVG+b21Zy1izVi1NZVZiesuVbAoSLsbXOxoBsOO/xcsUD5FN/gmG5Sp3vtg1s2srJ1DEgbh1cgEO/qKuXosvbSMU1iZX1zXEjbRv0y2LIJa73Y8A7O+iBGyw5RUE038Zrh54kypvUyO2af53eCtiA+jsYgtokZarbZrNUPAdnES2qD/Yic/3TDUSb9Vbejmls2g9diVGVjaURuqNj2WwJ3iigO7pQtFRthnpqcLRdbCAqWvluI4KdY86g+w4rlXnnNs13GD0xlpCVkovYzn98HkYKMLVo0E2mlqaVoMPpeQpNdcFh2F9bnrU+o0uBmqrJcVejMGV6KA1g64hwZCpXXMiqi5IoHa9NwrzrESMgPLBcPStJn1ZAk0wTITRxOC4pYouNUFQxPv1ZmvaULJpZ9z5hZ0oWAmPfdCRx8GHKg+WIBzunhOxSea9/NTV+9jaVtOig0vVY95sXzCfXvB7zn8z7y2POK1/hbnbs4vru/zixfP4YLh7uyKq8MZ69lrfHX1kK8uH3Fw1PCNq8/QBctte0UfCv5r8yKfW99WzRbjWRQdV33NalYgc830XDY1bq7MtYaa0BhcrZmUYSaY3iIuYHPGPORNMVgZGSNTICtu8DeCTvGYnQYKU0FFEe3iEQXKptnlHJOHeDOn196/xnsdGXhKINp14GqH3ZV+iUFDzi7kjXzyMxBr/MPbB0LPxrMR4n/7iRB4SqB8U1fDmy56wwHXgLFYzsHY1ltLwESlPAzRzkCKpL0XAuosu1IbgsCEmSQj8CTAwnRURhlPaQmlzoXjI0R2VGBGz8ZXGAIr0yjwhcu20xCiUHrI5/sg2dYMCGUsbSzEU6GdBa0EbBhBpNJowJFMetJmTN2mQipnT29R0vfEWHYQQTQrXrPikeVViIs2LozC70FiUxQF3QyBIQiFKICVcBsXtHRyCA64ogmeITR0Yc42WFzoKewFAw0uDARQMAtoYnl7EyyvD8c86I6oYoLq0G45NFtW0vLV9RtcVDMu3Jw2FDzuVjxulzwKwqLo6OqCvrjkjl1zx2yoZwMvVU8YEBbS4TG8NRxy1i2URS2qQymuoCoGpNCgrLWW4PWODxQQdVRCock5car/JQmZCYGx65MliEWI2ovBxX/6hUhaNzfM8Tz7d0p0GX2yG8CknbEfpOxc423Oe9oY39D40H6MEa4/Rnz7+6XtYbKE9dzE63s2vlSHuDDR8I0+lUE1e6Y6WfnnGAjnZHoaMSkvIagEVywvSkLvOdBM7kuUQEm6VcDYDTUDqoF+aeiXQneIdtS+21GVjtOrBYXx6o9HcOeiU9ZU7y3vWz7hK1cPubpd6z60XeXXKa12AayM+rTLomNpO4x4hmBZFB2vr4+47GdcDDMWtss2yUc7db++GK+XuhyKMnMrcbmETpm72rAqdWTXZLvQxFt46UuaULAwPT7u6V+9esAX7pzwyvoOGHBOKMtRx6qoFWBqfcFFV+OdlrenAD/FcRgNOYMzBBMwVhthGeOprGNZdhxUDQdltC3GZQBrZZV9e1RsKEWrT14sTzkwDcem4UAGysn+UolQx712wFH+h3ZHA5j/sM07Si0ljlEDeDpc8Pya0vE/nfxHfmr+Pk6HFW/1B7y6OeZxs+TxeoH3hu26IvTa8MqWnsWiVVMahCGWu9fFwLLoeHV9zKMz1bQMVsXdTSexVFG1goeVo1x2HC+2vHl6SIiJkrA12jwgdbwXBbRSsyuVVwm56VUeMc7ITK20XnaYsWEEqm4axkQAy3OjXvB0nT4t+b+HGyRMQf2zGCs9rRHWDcBVHpMy5PT4ThXK5JxR80rvd34PQatTpmXuOSEatGzTF7Jrr2Ksnu1bmDz+LsczMOt/77FfagjjZLwpAJkGJ4lKeC2TvuuVZMOR6ttidt12geos0K+E4Uiw8x5rPS52hFgu2hgwCEdVkwVcQZ30rSuVgRWEPhjV8Yi6TKkU4shuqU2Pw3Du5rSxvDCVvH1Z/QiL525kB5l4nr6Gp45Zc934LRtfZx2txOBK4r3HZsMiAmuJufWGHNO4gsI6rAnMCt1tLoeax82Sbih2kQ5BjauQRRTTwqkKNXypW+HM9LllOUApQxZ7tIQdVpkPJr5//SwzcZFRZjjzcx67lXY88SWPhgMu3QyA54oLPOfR4Fke+zlNFLrfhJoH/REfP/9KfvrNF7U00HqeP7ngpdUZX3/4Cl9WPeLQNPy62efxwfCh2Ws8ODzmzf6IczfntfaY82FOvyx4f/mIPlhu2SsqHAem47XhEIfwFatH+CCsh5rCOJ6rr3h+fsHc9nhU2+ZJN+fRdsUjYNsb/EbwsTuIqwVxWsIqvba6TZtesJK/Ai2N3d2MtUQ20m2HaTZQez3nGR9Zjeo4KTU3TJmOcQ0lIEsGP3ZPnK63FJhMRzQGO/pYexv2fpnhlKUVJufeGHCktZqzF4zMLJlkImwYKbbPxrPxXkairu+XGu4FxDcnVtIvsvuY7D4voo4zMQeQy3mdZi9dBb4GKVTc3KfmIaWWBwZgZofcHERfUXWvLocZITKjUmndzPYYFFCfSY+NOiJtKBiCRQi55O3YbhBCBISILK3xNaykkjxhCKp1kmxLYnEl9tZMesooxl4YLT289DOGoIwyiaWSAK23bIdSSwxvYucI17SJpmAOKFA3lz6XEVq8goUSIoYyssoCwoCh92RNlXTfmmDZhBk+wBAaNl7vFXhWxhNQIKsNqgs2RCRetSlrXmlu8eb6QDtjmcBq3nJYNdyvz5WdLAPPF2cEhLvFJVduxpXXEp0LN6PxJa4yHJuN6meZLrMaLr12nDypNgSg93r/l7bjoLQU4uLnMGxdwaav2ACDLwn92CHWFzErIDFYD1aFbmUOUqM1Fj3Q6L/QkGjBQUCQ3ZKpSTAfkgh8zMiHZAdEtPxxevz0a/aenMTcsTXsLKnxhH0jce2Aye97wcPb2Jppwm4/EEg2JscKEp6xsr7Ux07HtBCj2yiwtK/jC08Rb96zF+m8oA2ocmLYo1qCYfSVxI/sFogAQfILRXUWMyNrFeiPHMZ6mnUFAg9lSWkdy6KjHQoGZ6mij9/6gothzr36grvVFWezy+yrpsQ2wHk/Yz1UPGkXDBON38O6oTLKjHXB4DAUxrEeaua2o5aB0gwso2ZWGqkbu0PZwk0oM3sLYO0LrHiaULIJA02wnPk5fSg482Sm08o23Jlf8eDwgKEpKcsRbNCuido8ywehjeLswUXqaqp584JsDH6uNtf1Rku3JTArYzxT9FSxC3shDh/0cx4VW46sSqbcK89yTFXiMpC1NNc1gEF1gC0CHymx/67Je03zjQWEPh9nEtIGO6CWFYPF8OvrnpeKT/LIKdj31uEBr/cnvNEfA7BxFY+6Feedxkt3Z1eU4rkYai66OduhZDuUvL4+Yl703DpacxoE11rwaDOrQrsTmrsNJ6stLxxe6ByxHqlUQN85IVRBtYRLxm57AkWjGsFEvSzbedX0GsYuhtckUqZrbrIEc+wzrWDZqWSJoNRUL3hviFM/6p00gxUGkF1/cOeAif27gWiTK2om1wuE0Q7eNG4CnSYAli/YdTH3X1b03vsqxUmCL0K+zh758W3HMzDrixjG3DAppuMm9sm7ZYdMM+Nh1M2CSZCdhBgn7XCLNjB7BMPS4g9htWgwAu1gOZo3nFRbVrbl1OtXPwTDZqh4uF3Recu6qwhBcucLG9lLSS/r3uyS43KTHftpNyqA41gS14eC1Nv7lt3kVumbUNDEkkEfTO5gmNrcpussZQAhaoqowXQIT/oFzaDHJu0vI1r/ve21Y1NReIbSK1CSskQpwPC7wIZBDUhtBo7sNhsxGzPpqV1vFyylDLk8BWDj0/vwXPqKDm3nq0ysJa0vOXdzPr25y5vbA37NwUMW0tIHWIeC14cjXutPtHzSqBjyZ9u7fOHyFpfnc+RxhR+ELzya8+rJMZ86vsNB3WaxxuerMw5Mw/3yjJfLx5y6Fa/3J2x8xZlb8EvhHg6TwcLHbsUnN/fpg6UUx9bXnPczDsoWa3uWRctJuWFhOmrTc+VmfLa6A8CD3uIv7OgEBTS7VBmMqK5WdlpM/Ls3SBFUU2EKQPlJVtzIhNYeyK2l94HeCStLF0EyGjF49V7pvYO7ed2lTjdPW3/TQD6O/TLD9Bh+ws5K2QTGYOJaW9ow7vXX5HUkY9HPxrPx1CHvNEmuJUve/vC9i+9cZ9TNYrxIiP9FKj2inYiKjeBLQ6gdVTkgAoMX6lKDhkoc26SjEZShtfGVivI6CwnAkcheMumnZ1Wo9mIqcUvvyKD7xEz0+j5yY00Q5qYnibj3wTCk4sKQNDwMQ2y5kY4rZaT0G0KSaqJxJUPM3qufp6BW7y29t8rUsgFvQy49yBdKbJ/pbYbISPMRPBvi59HvN5UXOiQ2Rxy/lz7eQyFEm6mde9tQ0iSx9lDwpC+56mtu1WtK6XABegyXvubCzbV8UjQZdTYsOe/mtE2JbCzBC+ebkovZjNP5gtpqmeVJuWVltati6vK18RWXfp5b2D8OSwKSwcKNr3jcr3KZTRcsjSuo7YCIpzRDLGnU9u9dKDizCwCunCG0o2Ofpr4y0y2EClgSuAVyF0KJSA/mEYS3onPdkpMj3k8YWJOAQmSUgkhJw6exsibfpSRNohsZxntf+LseN9umfSbWTjJkYktyPDF5/Z3AYWprno1fPSOxRKYyJcl3ujFxPvk9Jdhh7Gg2DbgnpYaJnRV8rFQoJZcfTQWjh4U2DRkWKuBNGfDt2Jlw62dcLRqWhTYPEQkcVC1GAo+aZWRdtTxfnTMzPQ/aI/pgaIaSwRsaV3LRKhCSmjvNqi2NKzmutrw4P2NhVFalD5baDAzGclg0lEa7xybgCuC2WQPQYalwOfFhYwVJQ2BNjcHT+JJGOtZB98ZjM3b1a0LJTHruzq6Yz3queou1ytStrGMRmWRbV9K4giHq+abvSQatNAkpMR+rT6QIDIOhLge6oeD2wQWzolcd4ah/7IOwsi1HxUYT21HrK9nPY7vBB8Ea1QAuRTW/Eo7QRnDKAP3/smIIgfInO5pvrHj4xxeUrqUEbGRxWYRLP3BgCuZS7YBatZTcs4ZbxnHqt5iod+kwvNaeUIrjA4u3ODpU5lgCFksZeKW7zZv9EQAHtqEPls8vbvO5xS1OtwsutzXNtsIce04OtnzNnTf4ysVD+mD58Tc/QFUNuctjVxV0TZS1KUp8abCNEII2LfAluRuiaaNGcOfGjoZvs4/KtMTwJlbVTWMaZ0yT8NPhfUyiGHa0TGMMFSa24MY4Z89HlDCp9noq80pyHHRNQ3gKfCVJlWRnppVkYTzGl+zIsuwn+4MZ7+17Tbw8A7O+iJEDjNyxMNIGYZfem7Ih9h0m2E2T/hqSufuAToxxoomD6jKwfMWwZs7ZS8Jzty+4vVjz/OKCF2Zn1DKw9RWXfc1mqHjSzLWszJns72mWXUXSTQSuROCN8gBrArfmG56bX3Kr2mS9rNr0mrEQH+vHI9jjfS7PW4eKja+1e6F4ShyXXgXoXTC40FPhKcXTB0OPofXCqVvxqeY+n766y2VbYU2gil0ML7oZm76iG6wGRiYgNmjCIAhSOdXQCibXYhurwYPqdwhHxVYpxaFkIW3uWgLkOvk+FDu6Xpd+ngXhm1By6pT2fOnmNKHgys349Pour14ds+4qfsPJK/neNLGkcmla1rH17wJ4qXrCp+d3eGu5YuuM6ocUnuCF0/Mlj/wBnwU+dXCH+weXzKwayK9avMkL5RM+NHuNA7PFEnjgjnhrOOTJcIvGlzzsDnjULfFBuOqVvu284cH6kHYouL1YM7MaYKXW64bAouyo6oH10cDwuFKKaAQJQ2ytnFrbAoRYExOK1OkrAknexy4VXgGrm0YEtMIwKABmDWDHIEMnyqgfFI2FuAD9kEUZc515ynzs14W/F2A5Z0/IZYapHDixBxK1Vq/NTmCbO0/tA1mMvz/LmD8bbztyRE90/HftRmZA7pdMTce+rXnH+X/dwdlhMAawbaC6EDpKmkNhuWg4qAcOypaDosGKpw+WNhQ56GgHLSvLQVAEckIMXtJLX5kakcC87FkWWgZvRQXHbfA4MYiEqHcVQaeg5XkC9NisvSiizNou2iSPUESgJTGgkn7WNlScDitOu0W2KTZ2I0zags5LvhUjRiJgU4o33r5CSwpDfM0AuYvhgKVkiI2OEuBncjbdZ10v6EJJEoQfgmUTVCKg8wUDhs4XnPZLLroZvbM8Pz8nQZJDUP2wpD2pWjCOQ7tlUSxYVxWDF22aEmUJtk3FJqiNOK0XrKqOwihj4na55sBsea64oBKFC6+8MraaKF68dhUbVxGAzkWdkmh3nDfMyy6XVpoJmFlahy08fe3xhY2+TUzaiYAtwc8hvAiHJ4QPCnIYCKcWPvUcPF4g/vMQziKg9TaBRNIZidpAY/JkMvcDE626kJOLOJ81U8YW6nv/fjlj+tpEu5DlKNLfY8Lk5vP3Pub09y/mvT0bv/LHjTHGDU2pbmJmpJHA3aTlO7EZudP6znl7gBZEECZebgh4EW0aYiObt0Q74AZy4xBMQKzDecOqbPEIb61XNEOp+k5FT2UGHrWrDNDcrq64X5/zqF/RxSYit+oNF/2MVaFNnY7KLbUZuFNeUopj42q1S95mdlaqvgBNTrtgYsLBcebnUepEu6/vVJvgmZlOG1SJpwlFBvgvmHFoGjyGtde99Ha55mSxpe2KSBrQ1zwo9PNe9HPWvYIyPnVjlxjkO4nSASHbF7EqIl9Yz+FMO7HfqdYsC+0mb8XnahqAQ9vkeKXHspRObY/4rAFsABcCpQibEHABZqJcV18Ir/+JI9ahoEI/70U348XighdsoMSq8L04NkHv2RC0f3AfHJvQ04TATISZCLcjeWBZd8yk58fPPsijZkVhPIdlw7JouV9f8MHZAw5sw/3ynJeLU45NxzoUvDI/5sfsr+XneB4XAcD33z7lN936PF9eP2Tta3789IO0znJ7uYkJNUM/WK3ws46wUCXNYRaTYCtLsRbqM8F24Cv9KQFNmO+vl7ieduROYjWJxjxhjFPy70+hHE2F4fO+PwGcbgK6JiyxDLJN318671qlidF1GsXjBcHv+4fTmCadPilpTHYoAVljpYvsnJfujXHgSXjJJPYJ8XrsxkPhhsaPTxvPwKwvYnhvwMegfFojm0s9GJHZqUFIulnvFFzEa0qa+5MgWjf/64ZLggIL5VXg4DPQnM9584WK8+fXNENJZQZWtuWtZsWD9SFnmzlNUypNM6L/idYq/WRhxRK9da0z7mF5xNkLc95/dMrSdtRW26MfmIYSl0GaJpQ0vsSLamOtfY1HxRQ9nqXp8GgmpAs1l36uLcC9cOk1w+IQfqF9no+ffgVnzZxuKCitw1sNQLZdxbqtGLzJwZAtHF5sXh0iRG2igJiAxFbxSQTxKLbXNVPhx5ibqMSxlI4Oi4/t4F0EozyGJpR8vrvLuVPByifDIovmP9yuWFUt33L/5/n6xec5NlsOJLCwLXdNiy+fcOlLjkxPj3BsNzw6VGP96GCFC8KijEGcK3jrYsXm0YIzt6QqHNZ4Xr864pPFPd53cMpXL9/kNy4+w6FpeH/xmGOz4Re5z6P+IAsND5jYUctx3s0522h2vhssB7OW8+2MtikpSkddqpBk1xZI6emOAtWZULSSGYFIrJmOII71jv/bZ/41X3v6WX5u9T5+6IX/E14EMVFdLO5Uu07RlB5vxi4bKWDIYO1oGPAx0PDKyMqihzcF6WnT1i92Z4RpoDA5N1Hjc0CR1t30/YRxA94JEsweiDU5JmUfApCYXc8CjGfj7UYII33/XYFV8cCsm/VO4NX0mvn36G08JQBObGHTQf0Ehrbg6mBJc9AxeE1WVOJYDxVXfU3Tlwy9GUsnwuS13OQF4rbQW/1lY2Y0hw3H9ZZqEnjUUUcxBRlDsJmJpRqGRfxdMCFQiSPEMvchWNpQaglKcHQxSeGBh8MBr2xPaGIpoTFa1heA3hX0g1VGciqbNJ4wsTVA3GNUEyrRaEIgs7IABacSszT+TPfMYUZwL6Quhvq+z9yCJoJbjS9zZ8VNX1HZga9cPeR+ec5MBipR/ZdFcJyYhi4YavF4tKx+4xVw2lQVAVFNK9F28eu2pt+UNF6TR0YKLrsZj8yKo2rLneqKF8sn1DJwbDbMpOcRKzY+tZfX0h8bf29dQdOXuN7gvFAVjrYvGAaLiSyFALhBk05upqWsJouFGb2x5gjMCfw6KH7rlvvlmzzpjmn+/Qr+3YpwdYSwRksP3XRKjetkZyJHXyw/nJz1OEFzciLZnTDJvj8FPZKbn94vH5z8QcjH79qa/cDg2kvKJACYvq6My3fH1jwbX7JDhkn56355UPKVMhOR3Xhketz+yOz2eOkQ97SJzUgyChDXjugeGYx2QBMBiY2ZggHKsOtAOcGvS7ilbKLn5+dsYxXG4LV7eWUdtdFYozYDB7ZhYVqO7DZWhChAdeVmLEyXH5sZLS1MzS18EBam49LNuELF31VDSplAS1G9rVH+ZGAhA20GvAJ9BLzOwmLnVu03jUrDoSXrq6rF2iWg7m1dDMoU85bOF6r7GybJhd4gXhPIkr6D1OBKNBlUFQPLsuOk2mYgKzXpSnq/Sf9rYVsW0mZ21iwyhJtgOUJtQymSG1p1wdATmEngzGkivgklr7tVlkg5NA1rs8XRY4Myi089mKgF3IfAJsAv9Hf4r82L3LJr7pdaWbKUjrt2zQuLT/P+6iGf6+7yC9vnOe2WvLY55udOn+fj5VdwUDbcnV3xDQef5xtmn+MF23G3esTt4//EV89f5NPNczgMH56/yqFtOHML/reLL+O1qyMWpTZcWZUtj7YrZWgVjtI6NrZmKDzGKmmgdzW+MgwzoV8KpjeYIWAGr4BiArSmelkimkB3MTGS18wkXslxy14p8HSaTJvGSVorIxiV9Ih1DU7Ao/h3Zm/tidPvgNg3lTNmMG5sBDQuzRtwiSlofs1v5FonQvFachgmdmlaxbL/UwK7PuG7GM/ArP8dRoilGfqH12BcptEz10Gt3QtcQ03xHjEmx9CEMbDIrJCMeIYs/BbiCeLBdjB/K1BeWYbXDnmwOOALR/cJlcde2cykMR6sS6+jaGgo4kfwCZHWzkK+MIgDXwbeDCecni+pZz2zcuADJw85OGq4XVxxSMOhabBBg43Gl3hMFk30QbWo1qGixLH2cy7dnFO35FPtPVwwHBUbLJ4vtLf5+MOv4OGlAjjO6Yx3YeCqq7hqanWIU3ckL5r5T/X8SfS98HmBWBu0k4gdOCq22fgkXZdSBkpcZBYUNEHbswO5TK8SRxMMn+/u8kvb51gPNUYC66HKovQnsw2/685P85tmXwBgIYFbts6ZCo/nlkl6WwFnNhzZLffnl9yfX7K0bRZtfDIs+FR5l88CVeE4nul7futyxVVTs+4qXlsf89mDOxwWWz5y8GleLJ7w4fpVmqrklf42hXmBwRu2vuJqqPj8+a3IwBOurmZYE1vcD4Z+W7IFTOlUbLKxBAPDEvx5RM1j2ZGM05P/62f+NX/wsz+KAX7D+acB+H+/8E0EhwJaTum611ymKcPRm8zOElcok2uPjZVE5cV5NQJKKdx1zPZ06FInjRszmNOxZySCmQTf6XlJhub66ZnyO4mRMijNZKMXdgzSs/FsvP2YegyTFXSNdQg3T8zrgNeN9ict5klgPQYumsWbznEcFGsVYPUXNVdlzflspUzYzmTtAwk7+sB6jSQ9ld62JwdY4nWfWTNnW1W5TODWbE01G1iEgloGahkF3IeQ+haKyroHFXrvUJ2TIRR0vmQbSk6HJR7VQhEC58OCVzYnbNoKG1um4w3BeDpn6YYCH5MmGePOiMFksZuQnV31TbV0cmaUXRCCZJF5EzsZCgGPUW2waIeSnpjHZyDr8bCk9wVCoPM2g0azouerFg94sTgHoBSYi40Z10Ag4ILuuw5YoOX1q6JjVagAf0rkbH3JqXWcoeLts6InBGHdVXSDstMu+xln1YLaDLxYn3JoGp4rLhiC4dwuMP1B1N9Usfzzdh63bqFrS/2MEgheGPpCBe6t/s2g88tXENp4i0UIlEiYE54D+SB8ff3TvOxf4S25y3/40G+BXzSwXupx7DrlNy6juIYSO0vid52DxQxixXmfSwtDDgB2rgm7CY8psPW0sX+NffDqpuNuusz0c05s1Zgtf2Zr3sv4N//m3/DX//pf5xOf+ARvvPEG//yf/3N+7+/9vfn5P/SH/hD/+B//451zPvKRj/ATP/ET+e+2bfnu7/5ufuiHfojtdss3f/M38/f+3t/jpZdeysc8efKEP/En/gT/4l/8CwB+9+/+3Xzf930fx8fH7/1N7ycIU2JwKuychvNQREZr0s7aD3pvuu70MWMisAW5cZUPiIm8UB+B+OgnVVeBYSF0J3qOlAFKr3OzUxDbiO5rtRm4N7/krJtzVPVR43dgWWjZYSoVfLW7xcJ0UbtWSwDvFJdZssSRdAJVozf58ffKM45syblbYsTzgeoBVQR91r7Gh5KZ7XNnPyuBGk2GNFEepY/UkcTwLcVR4ujFYiWwMC2zoMn6BPyA+u/G+B0+wnqoeNIsVPcXIIqVA5qIt1GI3ysomLamslQmcpJ8aX2RgawXyyd0webP1fgq6x+nahmAi1BzLA2nngjSuVxq2WPYeMODUNH4kjO/yPHcw+GA3hcc2w0vsWUTAn0IdMHQBsvGwzqUWAJnfsHPbl/mXz/8IK0rqIzj7vyKr1w+5Mvrh3xF9RYv2EuOZ1u+YfZ5Ogyv9Lf52e1L/NL6Oc66OVtX8rnmDuduzm+Yf46vKM5ZmJ6vnb3Cb55/FkOgEs8Dt+CV7jbn/Yznlxe5yqQwjjvzK2zUFgPYzpto16wmVxYFvjf4etJ9c2YgFBQiWm44TNi8IvjCcGMH9+m/NJIA/HQ4RxATuxz68fwpE6uIFSo7INHEDsUEPy6M8dB+8mSf0XtTYh9GWxF18XKck2zSBGhT4GrPsITJsXtj+hrJRuX4MTbESm7VDbvOU8czMOuLGCKjwxoSoHRTB4Md4cW9555GB8zoa4iA1iQYT4dPA+VUW22m4tt6TLENmB7KS6F+rAiyuAha6RrF2wRiBZWaiCQt43WCpS4PJpAzMGZt6V1Nvy7ZzByftY7nZpfcKq4oRevPb8e6cQxsfK3aHr7MIuvAjtE5HxZ8Zn2Hzluem12xdSU/f3qP04sFxgT6+PGTZlc3WJwzuNhWS1JXKOIi3LnXghQhL7BZOXBcb1kYzR5o9kZymV4TSmyE4JpQUqIZjATOrX3N57s7fGZ7l0edZlqOyi3PzS6xaHnNi/UZv2n2BY7j976SilpSqYunDwEjQhPR+gOjrdq3ruTF2RnPV2dZx8tzl8Oy4f7RJcuy4259pRooduDxdkHbF5xtZ/z8cI8AfH5zi1939CpfXj/kxeIJH6pf4+XyMZ/r7vKF7jYX/V29/7EbytAWNF1JXQ7YwuO3BdKq2CRekF6wLbrR2XF+Jm3KxNT42rPP5WlugK+5+oLO38TeiiU610QUp/pZxkfJE4e0XazvNhNHPwkx+lFc0e4vLnbLDJ8yUjYia15NN1v2AoAc0Ou//eBg2r0wPCXYEZ8M3fXnno1n46YhTOfLFGzaG29XanjN1rBz0A7rl93nptdLwqA73ka0NaZHxeE7KLays64U/J2sj1RpOHFoEvggfvKaAaQTvLd0xtAXgTPRjlVz02GCp5aBRWQ8IVrqkRhYSWQ930e05K/xJU+6hXarLTqGYHi4XbFtSvU1I+KcNLucN/ggMXk63qeQ3rzZu1+TPaewnllkLwNaSohQSY8BBkyE3wIDBpNFdBWc60LBmVvwpF+wcVpiOLNafmnQMslD2/Bicc4svo1KLIXY+NU77fUo2tUQoJZUtmg4KBoObJPfXxgUpFrNWspYXhhQIfttH/Vp+oKHXkvrz/o592cXnNi1glr2giOz4cwtY6OYZb6X3gT8YBicpTBe7XRvwAkhROaul7GTlCSvNyBxAokBCigZKIIy9MQ6go0OP0IQi4RU+z41ANEHSM8xAbScSu7vawPl8pH07yY/Tyb/njL2O0PlFSl7P/d/5wZbI+O6uul5fb2bcYhn453Her3m67/+6/nDf/gP8/t//++/8Zjf8Tt+B//wH/7D/HdVVTvPf+d3fic//MM/zMc+9jFu377Nd33Xd/E7f+fv5BOf+ATW6tr89m//dl599VV+5Ed+BIA/+kf/KN/xHd/BD//wD7/3N53m1g3d1EIImkxM8Yg+uJfcYHw8XS8FyXY3/hiPi75gYsobIURAS1zuTx0T7IH6LNAfGLZLTzHrqeuBYbC4SigKz7zsOShbhqhptSpb5ranjeXKZ73Kkpy2S3pns25uZQaOqiY3dDooGm4Va1ZRX+l8WHDu9NxbhWphLU3LC+UTZqbLQH4TSm7bdRR5D1SRubTxWsDuEB66g1yGmBpXzWKzKkvg2GwpxbOUgS7uPZdGX3s7KJBfWtUlXhbaWfGsm/OkmeO8YKyHFKtkvdegjw2GFNkkncXSulyWV4rjTnHF3eJC5VwIUdOryqXmKv5uqaSnw8S/TfycJX38zCqJUnDp5zwYjvLX/mZ/xGvtMT4YBRcjo0vvn+WhW/I4yq6UMnDp53y+vcNPXbzEF05P2D6eQxB+sXZ88u5zHM+3/NqjB9yvLrhTXvK+8jG37BXHds03rX6Bjyw/zRf62zwZlnxme4c32iNmMmBjaeBb7oBD01DKwLHZ8tpwQh8sv+HoFQAedIe0vmBue16aneFWJlfj9MFy5Wpe2Z7wYH3IQ2DTG7gqCEUCbFQDytXqtIg1ucwcRjBHTAJ5jDKk9tZLsAYp7M1awcGpy2PN7voTAWuUlbXfACuuQYHYyXTSBOumscexuXFM3/Ke2Rx/D2NljlPnL92Da2yxSXyl/p2C3NocggzKqk8YmaJlBHCfokZz03gGZn2xwzDSBu3eLLlJJytOsmvU3unzO0juXrAxWRzeRtqhZcehyWytSAPeEatOtewyQUP9yFwNsaRhbMGuwYlkhlacrDFzOngFO0LpGZyW1p0Ouon1tgAbhXpxWLNlJj0HxuZSERNF1h2i3fiaY866OUYCD7YHnDZLHp4eQABTDzinZR+pFGLbVnRdmsaKljuXhIwmALcZ0QcxAWs9q7LjufoyA1N3iwuWxsVOJ2SKco+lRB+vYknLQ3fIq90tXmuP2bqSg6JVAfViw4FtstjwV83e4JaBUgwzKSgmO4nBoHCZpxShFIMPgdv2KtOeb9srFqalCSVHdsvzs3MK46iN47jccKe84k614tFsxZvbA540c9ZtRdsX+CA0ruQXqvt89eoBH13+Ei8XF9y3V9wurnjcrbg133BhVS+t35a0TRmn4SQgjuWm4rSVbW70EojCxyHSafXh/3L8fr7h8aeiLDP87MH7xvm9n/HzAaKg7rTsUIFhD94RujhljRkdtARkTWm572Jk0cN0/ERvLrFE9tdZDsYTgyQ/MT6XarslyuZNl286V7sXTq6ZAns7AqzPxrPx1LGD/NxgV649hiY4gtwY6O56EbvnC7tOScrM7WACKZCeMrn2wF7Zu57EPWNqk3ZAKz85Nz6eWMc+RCcnBHwsrdt6DR69KAOhwCmwIYECTy1qz7QSQLW5PNCGgsthRuM0sLgaKrZDxWaj2iZitOQgsYd8ENXKit1XvQ+RRHoDeiFh5wOLCVTGsSw0QOuDpTQDFQoeOjRB4oLgMLmzoSXggpbmX7g5F26WA7zSOOaJ5YXu13eKK+YCRoQCo52l8v0XLBoEqd3Vsse5dAjKLJiLBnQ9Rkt4IgOiEM/M9lq2Yys2RSwbHbTkUsWKYQjHPLIr7lRXvFw+5si0rEzHwnRsXMW86GlFSzR9b3C9hTLNwzj8GPgS1PeYTML4s4dz4HPwC89/NY/lFk/CCeGXSngEsIUk4y/J3qZsnUQQq4/fWyrLUp8rOBcTz7I3l8PTA/+njNxAZCfbPfX087SffEYm64qdqTUmW8a/Sa6ijKfvg2p5Lb8D0PZs7I5v+7Zv49u+7dve9pi6rrl///6Nz52fn/MDP/AD/JN/8k/4bb/ttwHwgz/4g7z88sv86I/+KN/6rd/Kz//8z/MjP/Ij/MRP/AQf+chHAPgH/+Af8NGPfpRPfvKTfNVXfdV7es/XwCYYg+WndUt/N4AWcT6nvyMjZD8Yvf66kpMTafnZNjB/C4It6FaWatlwNG9ohwLnhecXF9yprijF8epwTOd057oaai7aGX0E0lMZthEyi/b1yID1QTisWk5mGw6KltoMzG0X39bIkirFaaI6DFyEGUvT5vLAI9NiYznhqZ/RhyJr/aZS8Y2vOXMLBbKCx4mwiEBQnzUPhQs/owklrzXHbPoyv48iPt/FMnFtaqXxiZReQf4iUXwZN4uAAlshljw6yxBMLsE8sNusw4h4lpONNDXcUu2sUQO4wWBjEqXDZG3jHsva16x9rew2V/Nqe8Krm2Mebpb81nufYWlamiBsfMEDd8hr/QmnbsmBabhVXGniaFjwcLui2VRIayiuDMFYTs9u8eS5ljfOD6nLnnk5cH95wZctTpnbXpPx5RM+WD3gophxrzyn8SUHtuHT3XPx/Wts9mA44j/1R/z0xUvatMxop8fXNkdZh6s0TlnRNbGz4wULq3OkEM/gDV1b0q8s9ePpWlE/yFVGdZndBMzKYucBKW1kZsvIXIQx8Z7WUFqTTH4SAS0RZVXadGyMcabsSj/GKkluJTXButaldEqk2fchd2KdmNDMcVb8bBlsYvecwDWiwD4TOukMT20XJAwk/UEGt/x70Mmajmdg1hcxvNfMw05nAR80CI/O/U4JIuwgpqn0IkwZJfv1rSnDIoJP6KwQQSyJYtzkx3ccobA3eSb1sMmPm3YTEAdSjABX8oISmGV6rXtNxqn0gnhhcIIzgXbQDfXczTOtdu1rbltlai2lo4xC54me24SSM7fk9f6E19oTHjYrnDf0qPjjxXqWuxL2bYGxgbLSoMR5YRgMfohaWaLlct6ZCZgweoBSqNNqJDCveu7O1WC2vqD1JT4YZlGw0URmlQsmZmfUGHQRyPpse5e3ugO2ruK43FCbAYdhYVtWtqH1JaUZstFbSIVBdjp7lGIxCJ6AwWBFcAReKJ/wweWb2dDO0K4jynhzPFdd5s38VnHFyjacFMqA673FeUPTlcrUkjlnzZxHzZI7xSXHZsux6Xi5OOWjh59i7SrO23t4L9ALPlg6QcUnKx+ZeoK0Jm7mca6H8faO/3QD/cGv/L8gHr7u9LP87Op9/NOX/s8j6JXmeAKAnSPckEnQ9rI2T7YsCI+uqdxlJ9Fy94HkPOll5/dpxn2XcTX+ca3Jws5xk3/T56Ohy5v2U5hg041+Wv7xbDwbbzcUXPa7QUOKItJ8Zn8qJZBJAS1gL4EyMRBTuxDXyahvIBGMnT52w3t8u3k8Aa1SED55ezvH5eDHjH+bTl/Ah5REUS2s1qvq1CBavr4wXdY9tDJQMpaZDMHQhIpLN+dimLMeVJvEB2E9VLR9qeCUhGxTTKFvMqB7ogJcZPwvJO0v2LU1MZMuolnzRdlhUZbVECwhCIUZiAWbuKC8LBPBJiBrTD5xS9auYvCq/WJFwa7SDFQy4IImeBIDrURLC81kH7NioqOq/kgCOA5tw+3yCiMhCtJ7CgJz02ELz9IqAFeZgbnRUsTEqvbx3uEU0GqGkmYo2Qwli1XLTAZm4jg0W16uT1UYebvSuRzLZJxYxZJsGAOApJWxM59KJFQQKn34AvgknPk7nK9uY9708EsBLtr4ZO5GoggoC/CLGFH38Zg1sN1DhRi7H5KWWLQ1Nzj/u2PXUNwILOwdFt7muRvtQrYZo8zEuykdfGZr/tuMH/uxH+O5557j+PiYb/qmb+Iv/aW/xHPPaYD9iU98gr7v+ZZv+ZZ8/AsvvMDXfM3X8PGPf5xv/dZv5d//+3/P0dFRBrIAfvNv/s0cHR3x8Y9//KlgVtu2tG2b/764uBif3NHLib5RbE6V45F3AmZvSJrszOeU3IhxRMrR3uQ7hYgjTyd7eRVYvA4w5/Qly8lzl7xweMFh2fDi/Iw75RVGPBfDnMaVGch6spkzRJkREXDOUBSOEIQ+dghM46qpebheEoJwvNjy/OKCW9WGZdFy5Wo8wiooa2smfda/dcHw2C1ZG2Vc+WAykNUEjRMW0rIJCvak/VD1tVwGwLS0ropMrkN+bvMiX7jSEva6HJgVA3WhwNfD7YpNX9IPliJWShgbcD1ayVB5SAzWAJQeExtZGVH74YMoK6u8wgXDOtQc2022I6kCxgfDsdmw9vVOXLMJdYwrtrn7+ZlbMJOeSz/LMdunN3d5bXPE6xeHLOuO99WnuTEWKBPrxfIJDsNMOm7bKwUJMZweLDldL1jbQL+0ysCdDYgE2qZkc1nzJAiPFksenyyp7cB/4n28vDzjw6vXeKF8wsvl49h10nHqFrw2nPB6f8xr3QkbV3HWLxiCocDz6vpYiQ9BWdU/8+R5CuN5bnXFG9tDnDeUVu/BolCwc172FKVjOOwZFrVqPRkZk1QiGgdbwfQ+z/M0KYPRZgvaxT0QYqwjqKbWtYqU6fAB0IZlMgGaFRCKQUViQKaXjM20ZPA7TbDGAybgVNT3GvEK2GVVXd8Tpl3dJUyAOzM5Pvl0k5JDCYrHIYotYMmJzLQnXBOOZ88mytvcq73xDMz6Isb15Pg+cJW4uTd8IXHSxxNvRElBga5gjXYCKSMTKxqSDGQZcCXaUjTG/4minwOHxLRy6t/tUNMT+EU6TkYHbgJWpGMTyC8DFBsQL4gvWC9mPD5Zcqe6yu+/FMdb4YCl6ViLir8vpM2dmta+5vX+hP9y+RKvbY5yt6h2KNi0JV1T5gx50tjVjoU+Zsp1Q9RFEXK2RvU1yKskpKDIBspq4Hi+5bjc5vfZBxtbngsl2u698WVmkDWhxCGcuSWfbe/ypF8wtz0n5SYKTXb0wXJkN6qLIkMWFX7dWTwNt80cFzxWDC6XkRgsCmy54HEM3DZbfk39Zn5fAAtpwcKxVdDqws2wEliaVuvypedBcUhpHIuyp68sdTlwZ77WrpV9xc+sX+bANrxcPsbi+erqDc4OF3z6/A59V2hAAbhOERlTOtUwcYIfSqSXKESZNqLYaSq2q03DGcv/6yu+Gfv8kDddbWeuG6P0DtoOhkGBrJs6G5roeDmn62gYCNM68wjw5i49abxT5vxtyg2feu6kXPAaSDVNi0825ZSJhF0AC24Ax979fv1s/God16bmHnSVywtvClKmQr3T5/dsjVGnJ6/vxE6RXSDLp7VvRuAJRhuR/Y9wg3152keY2JnpselaKZkiQZ3xvizZzCoWtlObZTSYuIoOeh8i4CMKGAF0oeDSzXirO+QidtVzwWjyZLC4FCikz5x+jcFC8CYCW1EOfh9Y3EMVRDTDPiv6nbJ6HxlYHsksrKSTZdAyw1wG6ZY0rqQU1fhIXbVcLA0pxOHEZDbBZdArzKXExxJ2H/dgE1E4C/gQcHjm0ueSG5dKViKzIJXrNKGIWiSqu1LguTKqD1laj/fa+XZRdNpRzFne7I6oZeDIbhECd+wVTV1y2izwg8lfbHB6o8WOTka6z2n+BClAZgQ5QvwJhGP1XB+hmFQP4RQkXAJvQriAMCiA62twh+Dvwx0HywAb4MFzwP+fvX+LlS3LzvPAb8y5bhGxY+999rlmVmZlVZFFslisIqkiKZmWu0lRsKWGWmjLsKyGZUsNwX7Ug+wXCTBgPxnwix8kwG30Q0syackvDTQMP6hlm+pu2NaVKrKqWKxLVmZWVmae+9m3uK215hz9MOZcsSL2PidPKt0GRJ2ZOLn3jlixYsWKeRv/GP//fwg8BBbWwdLGnhj3Kqi2G/29b/iaTv2ST78AEBvWmo8DnvJao9t1aX+oXTnHq7Xmf7X2x//4H+ff/Df/Td566y3eeecd/qP/6D/ij/yRP8I/+Sf/hLquuX//PlVVcePGjZ3X3b17l/v37wNw//79Afwatzt37gzHXNf+0//0P+U/+U/+kyuPS4xXD85VGeM1YPgp1/8+pjKlYHiozMrnHKhDVu2e93qKbBkpexUcErfrhd/AwQ+V6rTi8u4Jv/uZGTdPLrnsa9yhnevh5oCHyzlPlxPWq4p+U9ickZ3+8udubW/aJZF0CZZ41yoiUTgt5py/XvNjN54wD+WgM3ijMCMnL5FDWacKKtOVmrEZTKhOg9GkvURa9cxd5FiWLJKr7EWYcMoUJ5Fjt+ZpmFosIYGL2PAPLr7AN569Thv8sN5kEGXRVVxuanP5VRnmYV8EgnMW3/SCa6KN50Q/tL2vxUFTMVH7W+Xl4Co/9+YmTNInzoZWh25NIz2dWHziJFIBkW7YH5/Hhh+1JwR1XErkcXfAJhZEdTxYz1l2FT939wP+Dyff4HZxnpLkcOx6TvQMB3yhfEqnjhMXuFDh2C15NpuxuF3x6OBgoO0f1mtbE6Pj/WfHrB5P2bQNTyujuYfoeLqa8r2z27w+O+On5x/xS9O3OfZLjt2Kk3rJsV/wvc29wQxm0VdEzAGzcIGowo8ujtlsSpa9fQc3piueLidDNfZk0iKixOjo2gJXRNrjSHnhcD34VpLepybNMgN5XAj8u2//Pb5y9i7fmn2Wv3XvDxPFm+Z10r7Oa4rk8TWwWfaBJ5dYJ2pxUbFN0ouVP29fD1tDrKQbfMUEa3+diSTK6gjcYg+kIu/b9gCucfIkV+OPKquG9ShqwinyyRiqMmOx+z7bD7d9X8GGtrHBPm4R3LZXYNanaIONeKY6jVumGI6FF2X093VtXJKOfelaGJBlwcPzv9jhUioIjUDipw8UQRJKmrPesO2Auj1HXmgMtBKk375GItCPgpcI0uZrhX5RcrqesJkV3KnOmbs1x345lOU20vEkHHA/HFFJoFXP03DAPz77HN95eoe2N/572xe0rdm3a++gE1Qd6gK+UApvToTr9TaTLs4mIRtnMmYO7BbnuEhd9txsFtSuG2zBszjkoa4t8FAAPwgmBnW8353wrLdFrXY9U99SJ558KT1HfjHw5kvpcTHyJBzQSMeJO6dP2eI4BjcTuDVuUwl8rnzMozC3zI90zNyGSsNQ6VVKP2Tws8bXjXLJreaSczdhEzxH9ZqDckPhAm0sOO8aPuyOOQ1T3iyfcOyXfLn+gC/deIMnl1PWVMTWk+2AfREpqx4RWAYHrWmtDeWm122Ux5tlsY1NzjBIjBAUWbdo16F9j+s7/kz/TX4mPOSb/g5/y3/ZKEMhIDFusxGp1Fa8YxBP3BdRvK6pbs9x3dNjOsmVJ9NnzDoQ19G4nqOdsn9/hoqW/UuJDLp7r9qr9ryWtZ6GoGPc7Z4HZO3hXXsn3H1OGICsoYLxeS9NOI5625xkOthY50rz++/NBzvXlteijG2MQLH83M7aFLa5ltg61n1BXzlmfkMlVjHkMB2ogshSKy5igycSMNOLDzfHPFnNCClBEqIn9GmHFkcBkqhZdyeHvb7z6dZrutUJbBk+7PU3yvtgG2qJKcmiBJxpeqnb0fPq1eg0EaPcZyqMl0jpAoWE9DVF6gRkORhgsWWsKIhM/GZQMNHRZtCKvHe/1xLl2C1ZamU29Qks8+k+AjitBrH6CjNZmbiOadGyCUYNqoueygerEnCOTSy4SNSaI7eicR13/AW3J5es2ooePwBZwED9R6BL1VlWHe5BauAEeBPuCNzCNtFnwANgswIeo3KK6BLYmFZWrJB+gobX4EsBvqzonQBPHXwd+MZrwAp0bUmTYWOTOtk4g/1S9MIUsT/v2RdWwqT3feGgfd55n/P3x63Pr9qnav/Wv/VvDb//zM/8DL/wC7/AW2+9xX/33/13/Kk/9aee+7r9pLdc0y+uJMb32l/+y3+Zv/SX/tLw9/n5OW+++abtd0ZSJrs6fqMSqmuq2a9Qk7YXMwBiks4vilUKKVfGhqhaEbHfArOSdHh3xKvTnF+dKX4D3dOGRdPw7flNvnHjTVBwlx7Xp5NEKMP2XBKEWKqZVaXhqyJJszEzWByuA/Wep3rM+cWUZtJSFT2HzQZOYD5ds9ZyiFfA9HNzlVaHH2KArPe7iDWHbj1UbD0JB2y6knflllVuuQ1zv+YiNPzu8nX+3o9+nJiqgGIUOoxuvepKNn3B+aIZqsq8j/S935G/RIW48Tami2gOh5XiC9Pd8qIclCmxPXJuBIsVsnPhQquBVpklXgYNY2UQiX9nc4cfrG4N39VZZ07tOV76mZOP+Hdv/k/c9itadcxcTHrAxRDnrDXRLVWYowS3pnYdJ9WSW9WCTh336nMcOuhWlT7w/Sh4HzmarDms1zxcHHC5qlm1Jcuu5MFqzv2jI2rX8bOzH/LV+gPu+XNuTy84DVN+0N6hjQWV62ljQVDhvcsTLlc1RRFolyULGnODFzMciZ3jcj1FfMQVSth4aK3QoJtD82y0px8PKS/82bf/3mB49bUn30eC8uuf/VXTjfOyNb7KQHOO/5xYUJ5kVgbzK3Emr9JjbKLsUNiN48YRIHaNCdbOcTbaro7rYSDm8bWHTej1WIUlT2S7ngjbSn7YEYvfaiszkpnQbXI0nzMmmaNRAUD0oK8qs/63aU70epQxN9VtH9oXg9M9S9x9OpR3ySEhHe63AYw5GOS9lwzaIuoU1wshQmigPbYOV16mrPZ+0JwChIy4Ss+w1snIOt1lQCsHFJ7BCVGiZVj6qVActnzpxn1+cno/ofVLZm5DpyacOHdrAC7iLT7sDlnHkq+fv8Fvf/AZ+tYjzgDC0KaNbr6G9HvEKqtiyt7269IcUApbFTPlQ5wOm/jhtjoLTooicnNmPPrCJVcP11G7jpCcFsHojxGX6JI1F6Hhg42JCt4pL5gW7bAY+HSOQ78esjzrWLGMph/WqefN4pyz2DIVTy1juURH1ECngUvtaFWZO2HuOk6j0RSzmCRscChLLYyyKT3zJF4f1HGruGTdWAC06CuO6xUT33FSWeZ94js2sWQTswC94/XiGf/q8bc46yZ8/YPPsFkVAzKaA4zCRbpJR7v2W2A1l4g68ET+7A/+R75y9h7fOP4c/9UXfxUlcbxTHYPYRSKbFtYbaDsIgT/Tf5N/p/8dHPDzvWUif6P4ShoiCQB7Hhg13pwNj42CgXEw8py2o481em3meQ+bQWVHuNBebK+5okm0v+DJFgAgBcS5kpC9Dd6r9qpd1zKN+qXaGCxPY/C5ulkpcB+7cu4cl0+laRRn8EFSdlJTtq2xY13LoK94pWXqOnlDtP19uJyslZTee1zplSuOYwmuNiekW6VpCk6SGG9Uyz9n0d6NTrmIE3p13N8c8uD8kBhcwgPVftf0oTOglmiFWWNLgdh7W4ecGtV6OGb3dg8XKrZWTctu0O1wEinE6IDZabHCQCzF1rdevel5hcaE6b0J+uZrkaxhldwbRXTQ4VpFA6QO3YYNgVIcnuQqllpQJaK0GghALVBLYB1iOq8leDJe2qmjIiQALYnDR7O171PWuIt+ELeflLYelWLui32aLBUzVvmx5hHrUHL/4pDQZyQ09ack5BxLIfS5Txaom6DyGvJjDn4RJp87o2tL+u9O4B8BH0SQc9BLVFq7k1HN1amfwlGEryo/9oXf5XU+5NHRbb7tvmpFWQ/mwCmKmYw8d4xd+9zeWvMxINSVtWZUIanXvHyohn/OW6aX7q4149cNgQbDff6EONmr9gnaa6+9xltvvcX3vvc9AO7du0fbtjx79mynOuvhw4f88i//8nDMgwcPrpzr0aNH3L1797nvVdc1dV1ffeK6/ptdDa/T741p0tvfY+2vIS5Tz+VqwMy2bw/9OxtRpZjbqrKsQzqU4ASJSkzX5DeYZMsl1M+E+GGJujTfF9u9Zv49loDokJjPFR2DaUReK2DYq8rG0Z9XXF6USBO4qCbMqw2HxZqZ21ASmLkNN/2CUiLLWHKqUwDWsWStJVO3Se6ARs3Lzoceq156d3mTZV/xxvQUJ5EfLY/57uM7bNYlVd0lfUHBpwCqDZ5VW6aKIBkALaOnpY1mrlodvhuB0hL6MQqFD9RFz+3EiJm7NYsUfxwXzywe0XJIjHdJ0D1/llnS5HVEHvZHvL25y49WN3jWTihcZF5sOKkM5AsqHJZrfuXo23yhXBMUaomcuIqpS/qVqiy1HS53qUoJHLvIsV9y2VecVEverJ4OTJZWC95d32JatLx24xwnymdmp3gxYf9n1ZQniyld73nczfhmfI2owrfOXuOHt97mVnHB56rH3PYXHE/e5fXyGR92N3hnc5tVKO2e53vrlbDyrCYlk6qjbjpWbQNrh+IITYRekF7wa9sXhNLkHcfjIwMzVwyvLt7bHTuJnDVUGWUsYEwH3De/iqmMsetSmKG78cxQmRV3tbgGAfrnxEDXJSt3aIRZ2H18DnbG9QBQp5hmJM1m43MHpNKBMbat7BpVbUkGoQ1bkMQ0Y6/Lv0x7BWZ9iuZdQlFftuVF4DoBtlFJnzqHFm4IMjKKuSOYpqO/U2CgToaKLBBiBe1RpDsCvxKKpeDXbGmCGVkdwDC29KhUieVyZVfcvi9ii0mooDtQwkGkvrvkT/34N/j56Xs0rmMqm2ESzRVEp3HCItZDddGzfsoPz2/QntVIazpX0ifgo8jRf2oOtBdiEPreW8Z845Dectk2iG0XF1MmJ1PkxCnOK0XZ01QdN5sFE9/SR4d3kXUsk3ZWyTJdXwbglrHmw/YGz/rpQPHwEgcHxDXF8PgYyOrUm5ivRE7DlItYMh9WWpI8r+llRSJL7QiqHLuCWkoiW5HJE98xFWGtSqdwoVsBx3kK2tZ+yZFfsC5N2HhWmLh97Xpq1xNVqNP7H/g1UR3f29zFEXmzfMKv3PgO331ym82isr1LGanrjtIHSh/RpqUtK3OLwiatUAquE/7t3/tN/ty7/wMO+APPvo8K/Fef/7WhVFWGknRFQkT7pJMVIz8TH+4uBOHhc2elF2UqP7Y957U74u8iO/oP+/TAq6/dPe91gceW3jW6lFRNuA00XrVX7cXtE4FZ2xexRYRGj+0E0W4QqR76cwZfR313/PdwXEpuOCB4CLUSGnCdWPKkH21IdHQ59ta7QbeOgKzRa0iMs+it6jhWSnHQ8VMnD7hXnlJKpEzATq9byfO1FnRaUOQscSw520wI62KryZQqlvMaur1v9s+ChaTHOKa1CMPF72ozps/lwPlIkauyEs1BEPok8N7j6bSgS1VjESzICA3rWKYseBiMQMB0wTzRBO5HQNZYb2sdS1p11KPMlaT/NP3XaSACjbhBFL5Ix09cpAR6DAtqMafFhkCVEjCNWPJnilVglUncvhBb/1RlOF8lParCkzBDUI7cks9NHvNkNSU4m2jFKz5lyZ1TKALB+wTyOwOz6gb9ovLGF9/hp/rvsK4bfvurP8vl+0fwwWT3O0jXKVEsIJxGuNlzVx7yun6EQ/m9Wx16UMKDkuuzCZ9mrbn+4SvUxTEWxovXmv1zPheY3n9oNN5erTX//21Pnjzh/fff57XXXgPga1/7GmVZ8nf/7t/lT//pPw3ARx99xDe/+U3+s//sPwPgX/qX/iXOzs74h//wH/JLv/RLAPyDf/APODs7GwCvT9zGhlTXuBoC2xhkj174XGOq1AaB6Py6nJQIOuho2TWosUqyJk6OLVxeP2w8DDKDIxMr2MYbuWp3Zy2KbK9T1UypHDaf54rOtI649JwEKBY2/2ptwuqxjCy6imf9lNMwJeBY6oY3C6PHGWVwyXlsOPamMVVKP3IujCyiAYoXseFRO+e8a4gqnHYT2uh5++ktlpc1Lom4x7SG+OTG3gdnur/BEaPDZt5IjCahgdMEbNkAFh+teMcrrog4pzRlz93JBRPfcRamgwA8WIVZVDc4L2bapEeHWCfgWMSap+GA9za3+HB9DMBBueGw2HCcpFQa13EWJrxWnvIT5cOUlFdqKahlu2l3CD59mZ3aetKII6DM3Zo2FvTR80b1dLifp2HKgd9wo1rShoLK9xwULSflgju153F1wKToWHYVy67kbNUQVDhdNXy9fIN5ueHB5Igfbx7wU9VH/FT1iM+VT7lXnvH/Pf8JROw+AbS1OTq2myJJ1qj1nV5MI3hjya5cHZwd3A140S2TItp4+cbx5/gDz8aGV2+lGyE2LnKM4dw2hhnGnyVedioxs+52ANUubTfUKIfj12d64Y7GNgNwnMfHDl1QRqDVfvXxNWZYecztjM08Zkcg1piJct3zkgrQSON8nNyPBYP00RggyzjEy7ZXYNanaN6Nth8ZbX2eEHUWvR4C5ucsGIUjNkWqftnb/Ay/j9DN1GkyiO+SUY96DLiaCf1xINZW7aXOAg3fspt8CdvOB7YISEwByVDCSwLIlO44Ikctx8cLbs8W/MGb7/Kvzr8BwEWc0OE5DVMTRXQt62jlrVls8Y3qCRehYbGprJS4E1ybyoOdBS3RkwTlUiVL8EQHbdr5uaW3SrHotlUvDuMEq/0UH3FlTIGHcDRZc6taMPEdUU1AGEzbC8cAtjmJnIYZD7tDLkJDKYHa9eZElYIL29Cb4+HMbQaBxbWWrJOgPMAy1DyJU+7qGWvMIt0jlMlpqcBzI9n2Zj2tLn05M+k4cp5Oo2EskqgexOE6fHrs0K/xolyGBodykBw6lrGij+Z0cuRXzP0KR+Tx5i6/s/osfvouP918wC/e+yG/ufyi6ZCJUvrA8WSNE+VksqTtPeuLgmJhxgOxMJ22rzx7bweQ+urpuztZbnXYxibrKsSEpjrHt9wdfj7e3y4E/qp+xIvaFYBrDBC/YFP23DZUi4yy6CnAH2cUxoH9EJDDbtAw/jk6JvrUP6MBffq86OdVe9VS29Vtz8DJ86LmPWTmed3LSUqa7Ae6cuXXnf4+2qTkY6xiSohNJCRNLScMCZGds+vu+w3BR9z+TgpwQg2hUaQJNE3LtGp5Y3LKj9UPAdPByhW1DigkpIogZ7Q5Aod+aVpOvR+0VSTRVfL7bAMsHYAsFTc4Q0tn67YWo0E9gFrYJs1pcs21IK8pjIpeilEVg1oVmHdKgWl7tJhW1jpWLGJNG4tElYwExKqkSFRDAgGhSpVawI6gPJjG4lJLZmzoNRJFcQgufZEOR5MScFlPK6aJqSJQp8cEW1e8mj5Yphm69FgjPc4prS8QTE+rkJjokzJUeVXOgMZlP+O+HvFaqdwpLvjMwRnvtDdNHwvFp2BDMNH9EB39psS1oM6jDegcDuWcGzxlTcOcCxb1EQxVwEBObGUOk7ZwCTwueHBsrnOPuI0+LO3x7Hr4Eu3qMLo6Tj5RG14zjha2J9rXzrqueutFlwSjPZ3uaR69ah/bLi8v+f73vz/8/c477/D1r3+dk5MTTk5O+I//4/+Yf+Pf+Dd47bXXePfdd/krf+WvcOvWLf71f/1fB+Do6Ii/8Bf+Av/Bf/AfcPPmTU5OTvgP/8P/kK985SuDu+GXvvQl/tgf+2P8e//ev8d/+V/+lwD8+//+v8+f+BN/4hM7GQLbIBeu0fDVq1SiaysFr2lRkQRUEyWJOdu5huB1XyLF25qQpRyyedSWepQYFHtrwY4UQ14XEgjmSOBUD1qn6mCvW7f1uD2P61IVb9YPDjbv9zNzYM+6u6UEzoJVYGXTp2O/GJLJh25NKYHb/mLk/ueGCqen/QH3N0d8sDwipAT54/WMp6spl5dmYKWibDam1VVWfbrlwqYrTK82LxspgRL6BPQ7NakVu2G21pbRHlOhLHsOypaTasEmFkxTxZzH9HSNZq/DZ8mO7MPfCIvY8GF3g7fXdzjvDYyb+I65s5jnVnlBIx3LWHPkjTI+dT2NVCAWv4ylUrw4JlSYJZbDSaCRgqDK7eKcNyfPcKJchIbbxQVgoNtJcQkN3Czts+T3NlH7C47LJR+sj7m/OGShwnLRUBSBh8s5H8Qj3r044cHxIfPjFV8sH3Pieigfczqz7/br8Q0WrVWP6aKg69xQ7QygjXHdJBdDpCrt8f4kg6Tbf8qvf+5XUA9fefYu35p+lr997w8PY2YYW6o7ou32eERzZRUGdjGIxXsDtPBoiAj97r4uhC2dOOMOOebZT5rAtpJyPM73KL856TG0BIztPjYCx/fWo5zkHGKi8ZygCbAiP87ONVyXaLG95suvWK/ArE/RBs2s3DKfPH/D0UbAIFI9Wmi2v48XE0esfKIUyqh0F64sNkNnGW123NVO4TqsI1URk3tySA9xLQNgBVvASuKIVjiqyFJngcXmphLubrh585LX5ue8MT3l9fqUX5i+w22/4iKW4Fap9NaE09fBQKwxAFNJ4I3qKU3Zs24Fv7Hrydly1+X7xVDaqA60M40TifYawQaJJgqIfWjTNckVWWaz7qjrjrvTC+qRGO8mltSuI45G5VpL2ljw9voOT9sZx+WSo2JD6Xo20YTg3d6u8TRMmbv1kGnoxOMk4iRyERre727SiOlptXg8kZ8u10xd1kXZzQ5fakenDVO/Mlt4VXM7VOVczZnk2LU0YttxgGO3HLL6h8Vq0AJbJcHLUgK3i3PAFuMbxYJlqPne5h6/OPkBv3b8u3xw75gfPL6JiHJrujTHRxeoXE+Iju+vKtp1gwShuoDohd+5+Tm+9uR7AyD1jePP7XZCL0nbbHTP0sT9t4qfQWFHM2vo4vvH77d8z7Jt7f7G7ePaCCHY2ZBl4GovyB+ykowmfxiG8FCcsffcdeNy6D75Ba/wrFfthW0vPZb7zXj9wPqt5L/zmjO8bHetUZ8ykOk1Su63+6m49DK5/vfhsIwJeDUaCLYR1JwYyYMiA1bj4GP75kPgEyYQD3qm05aDasNhuWZerPlM+YyZdGzUg/RpbfEJ2DEQS1B8AikKlEO/ovCRPsiuvhcJ1MrxgozogwG0ssG71W3RnSzkkLGVtB9Ig98XgVnZDhVKYMBTIZHxzNRjYrTP+hmrUNJ4oyV6iYhaTbMpYm3bSktqejwxgV72eQWliwVncUoh0eg86bnbrqccgVjj1mogakHpOhMgTrcjABsMrGokUMgW9rHqZVsFa9cnLTAZHnMSmTpzW1OEJokSP+kP+Ez5jM83jzifNzxbThFgWnbm+Diq7nraeUJfgXboJegT4UN5nRkLWq24f3kPngJsGMQHnYy2SgE4h9Nb8DvK2/wU37/zY/C0MM2sBwAXQLfbla8L6q9U0X/CCfu6zfrwuFwNJMYv1avrx7UUxHSO51Zg5WH9aq15qfaP//E/5ld/9VeHv7NG1Z/7c3+O/+K/+C/4xje+wd/8m3+T09NTXnvtNX71V3+V/+a/+W+Yz+fDa/7z//w/pygK/vSf/tOsVit+7dd+jb/+1/86fpT0/o3f+A3+4l/8i4Pr4Z/8k3+Sv/bX/to/20W/yCkNLIDO8QgkgIttYJsr6WGXqjTOpiSqkj1nx40NqvJjMQH/6jCzKm/7RYm6BZ5GFVkDpTx/BLVEAzmZXrK7bqQXSGBISEhv5/Ptdt8l6Vyuh2JlbxZ7TycN6/mK877BiQ7aUW1RDEnrxrVpX90MVUwAy1iziDXvtbd4d32Th5s5XaooaPuSJ4sp61U1MEPAYpToGRIPITq6zlytYu9wZRzohumrSmBf0mmMggaxmKYKlGWgKXten50NWlYbLYxe6Je4wWYLFlrRpEhh6iwB79H0GW7zUXvEKlRMfEdZmDu6T7ELwNybREw2HlmrZxk7Dly9E7vsGls5AhGP4JL6/z2/4HPNY+ZuNbhHltKbHI0vBr2vLjlH3i4uKKVnmSrgzvsJj1xkUna0tadLoN9B1XK2bvj26V3eaj7DsV/SYcLyX6l/RCMd3zx9nbb3dOtkdiWYQ3HwSKGID2hwaBTcaWF9qsjmBlYw4sH6b9hu8qP3/I0f/6OIQnHZmasg2Jjpo1VnpbEmXW/uhiGgXQKoRs7uOc4ZcITsbqiJSqh7lViS6MODjFHe8+3FQ6T4RRLja+/5XZmU9JwbgVG5uVFclD+Wt3PHXCmpbDW1cqGN7K5Z+6wXCWl8k87r0hzxCcqJX4FZn7Zdt/HJZYLXURCvEV7M59HSpwBjtyprKL2T7QKjuaONH0uBc6iEWLLDS3WTnlh4usKyGX1v4u7mRMiwS1W/DTqy+LsWEEujE/rDlltHC+7MLrldX1oZaHnOPX8+uHBAKm/FUWo/UOKMchhTlsM0tX7y5CF/39+0Us4hOwHSWblqrmIZrqm1ATy29NTkYJK1IUQYgC1xkbIMiAQOmw0T36WMfTRbcRKgFXs68VyGhnfXt3jcHgzZ6FICpesH10LgysJ2ERvWruTYLyklMHUbgjo2lFyGmt+6fIsH9RHLWPHh+ph/+eh7fKH4AaVapVaZwpVOAx2BRVRO/JKp6FAdEFTZqGXQZ65lKuBFkhui0W2cRKZ+wzRprZz1Vr67iYUJ8Scg7zw0nPhL5m7N0zDjW5s3+Kn6Q/7o7W/z3/Zf5WJTc1SvuFVf4olMfMfkuGPRVXx4VtG3jr6BYg3/1U/8EXwb+ZnTd/nG8ef49bd+xS543P0TfXYoc01/x6Lkv5avWnnvJ2kfR7kaBx/Pa1FTxu/lJkwVSbxw2dl4ZU0IO+iaf7ATRGyzIHo18/GqvWrPbc+LiPX6fv68bLuIAcxjeuHoHfJaswtcbdeYfGCmhwyVTfmpMlow4+ygvL6Iyu6YGNH7xkkTdYpWitSBadMyq1pmRcvEt8zchgPZ7CQfDCDq8Ti6NLf3mgAt0cHt9eZkwY/c1Naa/duX3nv4HZAglinYvxd5JzfcCx1++qRvUheB0gUiRv/LAUyvDq8OJ0obC07jlGWohlM51KqwEnUdsLUqHWGCw0Y5bKTDi5mNqAo9njYWfNQesfA1nXou+oY3m6fccM/wKEocriVijoatmqB7KZBrdyJmnGTrtVLKVt7PieKwwKt0PWW67k0sqSQQ1CV6ZK5ULpi4llodKy152B9yu7jgC9PHfDfeZdN76qJj6lurSnOBoom00XOxqZGwJBanuG8fcyq3+a27J4Zf/UDg+wCPQDKglfumIhnM4n343TfhPsikRBcKTwA+SM/329d+mvaieVwNsnrZtQYYgoJxd7uOyr6PPV95fnsBr9aaT9B+5Vd+Zah+uK79nb/zdz72HE3T8Ff/6l/lr/7Vv/rcY05OTvj1X//1f6Zr/ERt35RqPxYZKulHnWScdM/JkfxU4dDS2B7Rj5zWYWd/TlonYiFWaVs6JCrFmq0ZlW4reMUp9IJWaV1wWQx6O9UiiULoxfb6AVy/ddhVzzZuGFVrFR2gQuxAxXN6MeHJ4YzDYr1zq8y93OE18kF/w6h6bmu+tI4Vp2HKu+ubfOv0NTZ9YYkHFRabihidmYb0jpyQz1TDwQgkCqH35tae4pUYZctA9hbQx85tl3IxcX1fmKP7UbPmqLSEd45n1lpYzEVrcYpaFdnw2WLJRZzgJfJ+e5MH3aGxS4oNR35FTMmHA79m6tohAT7368H1/SJWfE8DP1m2TKmMWnhNvJtBLYCNmjP8l+sPkmukoRe2jkWO/WIwzzoNs6EK2RGpvK0rs2JD7XsKiayKksIH7kytuqsLHifKty5f40ax4HPlI7xE5m7NF6sH/PKtH/D/XJgeLyneiJ03fawm4MsAhcWFsfa4teBaGfrvIPsjltKW5wHHe+NncHTPLu5tZxVZo+qq3AY3UpHRSpwfklFVlHs+u0uuMYvTZISVO5Jjd3+YKYYvWktG579alZUNW/bnD64kXsaA1mBIeg3jJb/+ZdsrMOtTNCcv15lzNldydUkI4PcGvndomdwqMro5KtEbOlBueZEYWaQPj+fJ31mZrUx66klHX0T6wmgN4nUbuKjgEodbEx1PR0GHeJs46zLQVB3T0ib1i76m7mZ8tn5CQLhQ05wKyKCTlVt21pi71c5m/TOTU8JRT7wohyqyDKANJYayXcikT7a7ZRoMlW41wIs40Ayd14EPXRWBwkeOmxXVSLcq4FiFkk4di1Ax8y2LUPHOuVUmfW7+lJlvB+epUgKNtEMAsYw161SpBRZouJRZ9pA45rbA/Wh5zOPNAafthLNNw8/Nf8haoVETvX0aWiIG4kdMmPe29KzTSp+NLNbqmbuWJjtNkRNrmq6vGwC3TSwH7a46gXE5+1Sl+7+I0EjPw+6QRlq+UD3kD5y8z7fP7zErDLyrXc/crzkpFqxOSu4/OSJcNvRTISygbQr+5hd+DenVBOJHWbvBrjn1y+1kmqo28gQbwvP1HV62XUM51Dxxcz1opftBut3uayf0MfXwusosyMH43uN7wNYOLXH/56v2ql3TnhsDXwlurY/uajNcMy5GZecybJzIKOvz30uu9v0hYHFAGfFlIDpHdDJswLddflTWn99r53w2bxdeKZJeH8Amenyohs12UKHTYqeoIDefyntr6YYKLS+Rw2Jteikbt2uGMq6cHO/nIkg3ohZ73b6Zy+CAAeKC/fQu6ZgU3UA7t1PJoG/VqaeMRjN8tpkiKMf1ygTssfXXiSa3QhOO77SgVzfcp6jOKo9RWwNSalOB865h2VesQ8kmFNyrz+jVKtRElFWim8V03wuBKZE+ffCcSO5TMFEM39z23ngi5ajSuldPSPVheX3v1FFKTPdfaYFCTWemCIETv+C1yRmPNgdUztajQiKV65m4jn7iuGymaLsilg/RM0f8+3NcLUircApWXvUUJQm/j/sqaUMhj0E38HSeNkYt6CXIBbDinwnIujJE8uBIfeUFa83ug9eda+/Me8HA9nzXvHb/fNed+9Va8/u3OdlWeoxlT7JL2riNA+mYkR+4AlylSo2xxk4s3CBbojn5LpiIu5dE4bb9XHQkcecUvTropkJ3CG5jGr+ZDTKmGGZpE7gKSg0Aw5AMycn59C9gDJRypJulqTprDX0Cw/q2YB1KogpHhWlD3UnmVQutuOkWtHju90c86Q8MyNKKy9DwjcvP8K0n9zi7nFBVVp3atkYbHD5r58Ap2jlI2oB12eMEzha1gV2pMl+EpJuFAVyyBbKGrywKrlCcjzRVx2vTs2HdKNN60UXTXrRYy5LhGYDr1Cc5lYrvrl8zaqK3PX5O2vsUS3gid8vTAWDKgNgi1rzf3eQLlVH9Ow34RE8HiESi6iCjAiTpFEv/v1Wc814PPoFbmfZo/+z9w6gjZBOx1ntuFEvuTi4IKiy6irroKZytGa/PzujT687ChP+l/SKfrx+mSu6WPzh7m+/cvMvX28/QtQX9YHYl+KQ/Nm02tH3BYlIQQ3b+3IKpw3gYT6LjfX2WItEU+0QbZ9JHc3FvO3NwX/1Tvtw/2HVwhy241ffbPVwqAlDnrFpL3IvjpEHPbgRWDYNKhs+UKyz149aLfFohzRN24I6Q++he7MisjMApS35u32DYV7mrrxkq51+BWf/bNJE0ce8/sR+UZ+WzIZC/imBnwfcBydx5I3aCEi2sXJdRB8iuIKgtKEN5ngNXKEUR8D4iSVCwqnoEhoAhRGcJmwQgbTrrGk7MPdClIMMJbILH9yVd8MyKFk8cxN2zcLpHh1JRgE6KAYEvxcRknSh3y3OO715weXqD8kwGOooKlhl3GBKeOrbr7PEw0cHZJFdmZbcpcQa+RZWU6VCqoh8oH1GFPjouuoZFqGiDZ9lXTIuWB8s5q7bkzsElE98xKzZMXcuBX3PsFxy6dZLrNarkQuwzd2kz32lh7iCxGoCuqI6zdsKPNjWXq5obB0suQ8N7/SG+PGeOWcA2Ivi9yWetkbOYq7Yc55pFGwVHT5d6yrFTuhg49lYefL87ZkmFl8gqlJxUC+aJ715Kb/2CSOOEi2iC9m9v7vLZ6gm/ePADDvyGy1Az9fbZM4WynAW+dfM1Pjyt6C88/QSKjWXm/H6mQmCg5Y17tE9jgZAMBzwS49WJEbYUwuGco4MyP3yUsdDxYykr8iJNqv3nd7Sy9toVzayXwd1Gl7v/+bLew6v2qn1cE5LLzP4T+8HrDr1QrkfB9nUVdt9o56SDscj4LfYyb+PXijMtEidKMJQH7w3udym5kJMlGdQKaQM/BksytSJEoRdPjI7K2WY9i7tn4XSHuSz1KasRE/0uV2b5VK078xuagw3teoLbMOheKLtr7rCsBnt8cNOSfKAM2ZVML9QcbBVqgFY6m4FGQhsL2pjArOgpXWDR1XTBMavaBOIYoFO5nsa1g2MhGBDVYkkV+9y2rjjXDa6BOTG1CSXnvaftCyZVRxsLzmKN8xsqtT5UAPt6gz3KOldtqbDBBPRt/Y1DFrUR09lqXIeIaTR2SUq+V2HmWyrXUyaafYn130KglYKSwLN+RvQrXq+eUUkwZzAXqKSnThXGvow8nM65WHtieUosOtx6hp4XSAwgK2ABrED6bew9/lAuAGusFOPc5LVCQKVjW5GVxNOuJAxl9/dRkga2wctucPMxa83onHsh0ZV2JUv9cW08LPdjGN11tXzVfp+25zk/55Zo54Oe1r4ZlWoSj2b3cbZ/Z/3T7WOjNUHY0hRTJa7vbK+OCN6BFoorhNDA5sQAp+pc8KstEGUnSOdLwxMYxNzz4DFgS4ZjXRi9HgaHtQyWgQForrDHX79zytdu/JCfaO4zcxtO/OVAT/da0mIav3f8BRdhwkfdDZxEfu/yNf7hB59lddGA00FndjAYGQXi0jm0V6JXnA+0fUHpg7mxb5zNSWUkBKve0pAAMLA1Jn94MT1BEXMZvz1bUKdytsPCqqamo7LjHItdxKzHa5qGT8MBT/sDTrsphQtM3ciZHWUZK24UCxrXMUsMEy+RSKSLnofdIQ85ZO5XvOkfA4EjV1GIMCbRm9FIZBk7LjQSFOZOmErJo7hhHUpO/CXHriWoGPCmwkb9UAXXSMdtt+FRrJlpxUmxYFMXPGrnzMqWuuiZ+daYI74lqnBYrFmGmlICD/ojIo4vVA+558/5k7e+zmVf8537dwzR9JhOVrqnqkJV9LSTjq5LOtODg7uiHlwX+Hd+8D/y1Wfv8I2jz/Hrn/9VNAF9Rmu1zpm/Oemjube3HXQtf2b1T/mz3W/juOrg/tIt6gtZHYMAPHw88CVyPUsl04BJ75OrhIeEvgG22WkU0vhM771reJL+ZShkNH4zOD04HqbjYzI7ftn2Csz6FM0srD9mp5E73b7wYmqSXD8SLD+Uzqo4m6MLGSG+kigdie6ULC8zN90FCC69PgP+0cCg0gcLIkoLJJrS7MK74Gh7Tx8dhYt4F3BiAce6s5xsSE4budKpLi0AKb1NpNme9jRMhyDDPrpwFqa4BCKVEjgNU479kln5mHUsmLoNtw4WnB7PKS7LrW5XmzIv2EYsZ2WKFaD22cUrsRllVaMNSg0ChWWsm6ajLnsqHzisVhwWa1ah4jIWdOroUyBVuMhF1wwBwHG94qRcMPdrjvzSrjnRVZxEKgKVBqZii8ej3nQSLkLDTX/JTW82uV4iz4oZpQt0vbd7qcJ765s4iQTe5Q9UF9zyE8buhpAzHpEnYcI6lpxGEzI8dOu06Crr6Jm7jqmDY9fjWbFOFVnLYJVjE2/VWln00agqHlJwlD/X4+6AH7Un/Oz0h/zs9Ic87A+JahmfWdI/OfILvnTjAQ9uHNJfTOgvhXipxMrhu1QzPqIPbSPU0SbeOdRtLQMlBHNJ7LdVc9e2j5uc96mFe9nE686n+wHMNe1qYGAL1b6mVq4e3KlyyeKlbOPgvAgNgNjLAmOv2r+w7aXYSftA1v45dNTnknPVUImUMnRDDY5kACfbNO8CWwZCs+27w+bdACSV7Hplc6stb0KIMiRHfAKDQOmDieRqsi7PQFHhbB3JSZZcpbRSM9jI9DvFKlEzQOZQ1mJVqGVyEyylZ1q1rJsa17rhmrP5yYDj5aAna0emzVocU6HTcRYbWmBYlMHWUInU3lxku+hpcYQRDcSJ0oYiraGRxvdMfEstPbWzDLVVRBmVzxwMdahuzg5aGy2Y0jKV1gIiVVaussAklqaxonDaTxFRIqe85lumUjB2NwSjHDqUVSzpEx0QrGp3QouIJYBqCUwEGokIXdIoE7pY0KunyBVWiRwf1dGnTqJpLSmILLXiPEy451rulWdcxhqFnc9Zu47bzSWLpiZuhNj2aHtpgXRM+iGuT8nCcP1aI4JKMNkH7ex1TlP1yp4zwc5gGU7wMQeM/85ryTWvkb1A4TntKo0wg9LPWWvGx4+A7Txmd4C35136q/b7pr0UjVUjIsXVvn1Flzftm0b7LXMolARqbauyRNMwzHu/HHuMqqdyUsAc4iwBHT30M2X5esSvHcXCjKCKBGzZdaQfIZ03FT1loIqUAB+eD9u+n/djsYBYQTdX+lmEw56feusj/k/3vs694pSIG/byF3EyUNM9kafhgDaZPD1mzmVo+OHiBqtFDReFBd7BjLWcBy3VAK08QJ1aAN86gvfEIhKdoL2YG7soWmBi72mgarAB78owuLOjQlH3eK9M645byZV9E81RPeAGDWDT9NrQJJaGzdElj/pDPmpN7uS4XA5rkhl6RC5Dw0HSx8oUxezQngsTpq7lIjRchAkdME19JgNZxZBUUoIqi+RoeLeoyS7ux27FoyDMpOfY2Xq9VPtyl6HgIjbMpGXuOhqBm0nnq3YdN8oFl6Gm9IHb9SXH5XJwancjwfCp3+BRftSe0GrBV+v3+anqPn/oxju8/fgmbWmFD9J0VHVPXXbDPmM23XC6KE12JxrgGkrBF8Kffec3+fM/+O9xwB949jYI/I0f/6PDmFFsHMko5pcQ0c7ohV8OL+/gPm6SQKcdAc3crtUUfsFc4LbPGy13RBEeTwE5AZOKZPBb2vtuEnX3nPm1cUST17RHQfc0IsXmiTxutc66pJ8s+fIKzPoUTeD6bHlue6W917qLjOkgeTFIAokZuAIGfRJIHWwvoJeohFIIdQa4GNwDNAhtX6TMeBxcNKIKXedxqVorBAskogptV6TnrPzSua0wYR88qpFp2THxBpCs1XjYZ/2UZS5nhQEka1xHFz2PZc6PNw84jRNOw4y313d4spgirRsyKkNpcQDSgmgUQyiW9tlDbRPMMKhi/mn8c++VsuwofaB0FlyUElmFik6NXthHP2TNV33Jui/wLjKvNhyVK46KJUd+RSU9TQoyXKJN5IzN1G2oUkluq55KAq8XZ5y4ni+U5wSFN8snlC7w37c/ycXlhMt1zYfLI7rouV1c8AfrxbYkVyNmwG7BRQlDpqjTAo8J61ZE3u5uEnG8XjwDWoI61urpMPHK875hE0vemjy2ICllO6yCzgT5OzUtrYDpuFyGmvfbm/x08yOm5YZHwfj0TqLpoUTHj08f8v3bt3j3oqS4LOnPhaJ2g7hm/k6U5F6YAZxBINqPnJUCg9gbDFWN++LvV10L02N5Et9//kXVJzvn2X3tPrg16GLtBBJydSJnu3HaamKxO2GPPmZ+gea++6q9ah/XXtSf9wKR/b3GzgM6+nunoGS01uR+eh0wpjq4VW1BrvycEFLiQxJaFhJANTxOghHSpiYETwiWHTV90rypt6pgwTQBc2Vth6PVYgDts2tsBsmKpNu0lIoTWSQNkYpn/YxlV24z59uPvKW0jP52SS9r0Fzdv2cqKcEDzvd4MRFe74xW10VPxCqxYv6s6uijJY9ElMr3NN7m5lr6VLkcKQjpXpmIeyGBEsW77WOeyNxtmEjkBhuiwpGzytwfhFts2pK2L7joaqIKU9fyRtEN2ibGGBBybarFfwncSppjpQt4lGdhiiLM3QoIiTbp0nUom2g0yONyM3wGgDbR3AW1c4oSsfvWqucsTLhdnHPDW0IsYMcUWB84KRc8nU05bT2hjfhNwBWKZpFdjek7SV/MuI+DBRU5wBhKOdR6YPo+d3ZwMp60R+cYnuOa568+9Nw2Xkf2T6Wjj7JzPdecZh/I2mv7iRbQXXr7q/b7v2VDqsjzE4AhbKmIket1eFJQrrU3g6rstJ73eTICV5UEdm3XhCwGry6Zb7iEeflE+WuFMI30hwF1jmJpA8p1NgerZ4gDLNk/ujZniW/UnhtMrUYAWn8A7XE0M5EbK+7MVrx+cMafuPXbfLn+kIVWXESrLn3aHeDE9vJeIutwYK7siVHyk81HvNveMo2sy4JiZR/Sr2S41lilSjTEBMTFHPI0KFo5ukxFXHvcyqGlEh1WpZ9jQK/QJTkAFXxjYI3RECMnkyXH5WrQwzUgq6CUkpD0vS7ihCpYciGq41F/OOhjHSRN3Y1mIEyoJXCruEj7fWXuVgOAtdZyiBuyrMqj/pBFdEy9stGeSKSWkojSaaAUz9RV1FIMLu3WzezLKQnc9pEDKbnUjhIjhjeJdmjxVGStUIolOm76Sw7dik0s4cASQ3cqu+bH3YHR34vAUbFk7tbM/YplrPjO8h5RhV+cvMMvTH/A79z7DP9o+Zb102AMniY5Q676klvTBatNSXdeDABr1n37ytN3d8Cor5y+t4u7pITiVpM0rR+pYvFb/g4/Hz8aQqKXdXBXVSTLE+2P5z39ux3zhuftG9Pebkef6mPWlO1nZMtU2Vv/rk2cDODz9v3G75ulgxCSm2S6mP4FF7TXXoFZn7J9EvBwJyDPqG0WWIxAlUQUc5Y8dZbBynZw7RjRnRzDxJmzD7lTqECoTD8Kxvs7pe0K+s6b059KyuIKm3VlG84gxN6hpVA0Hd5HikKNn1wEvIvcmlzyVvMEL8oy1lyGhstQcxlqLvpmcPfIgFevjnkSWryIE37Q3ua3nr7Js48OaR56fDdaAO0jDdUDYA4ltmAorhNclxaJKqMJ6TXeSkZ9okU6UaZFS+17VqFkE4sEYplgbhfMUcqLMm9W3GoW3K4uOfIrZm6TRHD7HYvbMmV1l7HGSeRecTYI3c+lZyYOJ4JHmLlzyvm3eNIe8HeeHnL5ZMo7osQbQjh0rDVQa65y24JaXoRGHG8VSyJLYusGt8R1sgUupedRmNPpctAvmbs1H6QszcS3iYceWcdyCBjMzcQWl3El3Wk3JapwUlzyU9VHeIk86g8HXnsQx9S1fGH+hIe3Dlit51TnjnIpxNLhujj0PZuSZCuAqIp6h9jKnfq+GLgF4D06zpbvUwyva88RMd2eY+94kasTONcE7PttB+Cy1OCwqcpjdQ8Ei/tBzmjDt/N3pjF9Gr2wV+1VG7XrgawBZR7myiFZAtuqQdhapMvu5sPm57wu7QYWmnQK8/nsREoInpgz1ZIqlNSc/CSBFholaR1aZTCJquidDvP3cbE0TRAtEm3PKkzbWAwaGzYHlkRk0EdsteRZmPLR6oj1RWOb0zDaTMlwqds1MtNVEq1FHDag/e5aY5pZOiQlRTAASGKi/5nmSVBHUKtwNoBOaYqeaRI+r6WnSrRCJ+ZCVexVDnVaIKLM3YY+Cd1XREokFVAIlWzw9SOWoeLtZU27LDmVCdrY+t5r3NJodkAtpUA4dh1KhyI4DFTrcWySHuRCawLdcI5aes4TsFU6q7qy78glTTMx8CrX/OXKAxXWoURVmLiWW/4SceauVWCAZNaBuVGvWExrur7CbyKus6ScxLQZT/d+jNbkjLK41KljQiSTi66IQ3c0tuTjAanrKlfG7brXXwGCn3Pc886TXK32Aa6dtYZr1hpGa03eFw0J0pe4hlftn8+2v995juGUuaVdc8y+4H3WPC0csSqGAH2o2Ej9akj4CVuKk4zXjN31ZTz0/Eboo6BFJMwiEjwqiguSNLUY+nAsQfoUB+h2yA90pfSYRHPDXd9U+tsdxzcvuXWw4I3ZKUflis81j/lq/QG1BNZaMJM2OesFFrEyXa9YEFR2hN+DOm4XFxzWa1zrcC1IMLDDPiTmxE5yWextrdMCtBViX9JP7F4XC4frUhFXb8Li+RyIIrUiPuIqi2cylbFpOl6fnVEnh9jCRRZ9nW6m6TMOSfe0QL/X3uK99U0KCRz4DUfFki4WZtKCJS+iCj7FS7kirXEdFWFwaLf7YOvSWZjwfn9E5DzR6iMXGnnTbzhyJgoP23Um/1zFNU9Dg0/rXDa8miU3vEUSsL/nzzlywlKVoFZddtNfcr8/Yh3LAchbxoplqGijGY1M/YbXy2esY4lHOfBrQun44eYmx37JV+oP+T/e+m3uLw756Nkh6oVZ03J3esFBuSGq47Bcc75p+PBWSdvWgFAurC//9p3P8wuPxu7tb22/t/ygF9PaKhw+Vz0lMO9vVV8F4MvhAd90uw7uz23XJvRHgNV1ju77xz4nFtp/bMwgGdOHgZ0imkGiJSf6dgA9BsqwFgxzwXDMNSAYsnWKzrpjuL356AXtFZj1KdonSnLtW+LCzuZIUhZFnZhDiFg2I1ZuoBfalyyD1W30BnTF9C3G0iqWwACu0BgNTztH64uhumqgybtoInc5AIkyCAwi4IpIVffMmpZJqnIqxKgPh9WaL83v81b1mFY9T/sDlrHitJ9y3pmd67IvWXR2QaUPHFcrbpYLLsKEH7Un/M75Z/jB+7dpPigpL9M9SfTIDNJJTqI68BtbxCIMWRvX2n3YRiH2i3eG0HsX8S6yDiWPNzPaYDer8j1tLLhs7fpElON6zXG15LXmnKNimegdNgFdhAnBmZWsaU1tN/RZ9L4kcOxWw9db4vEiHOC45xd8afYh/+/Jj7H6sGH59Jhv3p3wSzfe5UFwwJq5q8g1pD5x00oJnEhBRzDHSISAcB4bZm4zLLAf9DeYuQ3HbklF4NCtuFldDnz4gOM0TFkneg4wVFxlfbNlrNhEz4PNIV+Xz3LsF9zz51y4CUEdpes59j1PwgFvTJ5x8+A29+961mczioVQLhzSpy/CCRqz8PM2mBkm3oIh260hK3t+DM0wt72qresm5E/Urp3UrzvOfgzA1R79YwCw2A0Yhkl8qOhIP1MAr5Kypx9nq/2qvWov03Igv/sg206d5nsRy7jDNts+Cj5y0DxUXo2TKJ5h3clztRaKRiEEN7AddTQvq7qtwKnKjguoOMUXligpXDAQK3FKat+n5MLSTDtS5e86WmICoIuOLs3tzhl1b+pML+pcJzzYzHl2NqM4d0PgMS602XEylG2GX9kGSlYGL6OF335xEgedMCdKHz3LUA5aYN5Fgrph7RHM7bDxHfNiQ+M6HDrMkZtYoCL0qaIp63+BzdkRwROHxAqAT6FDJZ4DabldXfBeeUJ3UdCtJjw8KPnM5JRLFaCnGonzOrEb4EUpMDv17BipGJ2xkoAT28OcazMkNzyRWgyQKyUMlVdrLZOOVw6WZKjKi2oi+L06ulBzvzsy9yzZ0IoBkY5IIy1LqTgsVkyqKWEm9OsK1wm+EyQHkDLaSo13zANNCiR1NB0BWp+4Suk5m/9Pdo5rXvOCtWYfuNpZa8aJv49ruXQG3bqKvmq/v9sYpMoO69Fdrb4a5mTd1cmBoc9r6Ydq90Eva7/fjgLeMavEHmQH3IoFg0mVibULHERUTcnOrRyiimvE1CcUXC/bediPQDJNcUEJlCn2mSjdUUSOWm7dWHB7dsmt5pLX6jNulAu+WN9n6nKCuqeRfqiKytq3QYWIG0CIUuB2cY4n8tWjD/hW8yb+SYH022uRzHgWMQOrlIzPiRG/3pqv7CSEIknzFyjilrATHOJN79gnM5TjyRqXqnMniX4Htg6tQsmdKrn7acHjvuaDzTGdes67hpNqMYi9H5WrgUUzd+tE/be16ixMOQtTjvySuV9RZYf24AZzr4ftnH8qn+PdYsFlaDgLE+6U59yefZs5kY321BQDiBU0ElGWGghUvJlcEjcaadJHX6jFUxZLtXRqWsJt+p5yOypMd/hGubDPGj0n5SIl4006pZLAeWgoJfBG9YSn4YD32luc+Et+rHzI//7u9/hN/QmeLqacTJbcbi6pXU+vnuNiyZdPPqINnsfnJX3v6GZCuVD+xk//Gn4T+erjd/jm4Vv8xmd/5fr5dABsRgCQCNEX/Eb9c7ZPCy+gum9f9PznrkuAfxx9+GWaKppF6fNrneycK8sdDXIT+6fIYyLF8Fb0rNu9X9535VhpJxmV9pcvGRLCKzDrU7V8/1/YTbL4u4xm3uGnbH+P0bjfUYdy1Fg6Qu0I1RawyoBWbrEwEAu2C0MsIdZqWYzOoRtPJ1CUAV/EFGRI4heH7UYn4QJlGQY9rGndMq82TIt2cG+MKtyqF3ymesbUbTjtbgzoeFDhvGsGAVyAVV9SFz1363NOigVP+xkfbo74vcd3qD6sqM4YOvQYz8hWvTgTbfRrxYUkhJwDjF629ktDrGaUlj7RJoMPdOJ5tDoATMNl1ZeD84WkgKlyPZNE+Yhq1UuLWJsIIo5lrOkkmKZHNJeNuVsTokCi68F2r5DF3COREuXYL3nrxjN+t55Tngn9quD99Q3enx5TylOmojudyYtjQgXAMnYEssi8H2ibYEL0nRaDlgoYlXAdrUS4lMASc2BZx5Kpawf6S8BKkoM6Fn1NKZFnXc17yxN+UN/l5mTBib/kNJhel0dTANNz0ixZzwsevVZTnZVUC4dro3GrYQtoWdImPeasggPSFyiIt94i3lvZ+3XNCTsuHvvi7yI74yKX9+Z2rZbEmNM+Fkz8JC1lBcf0wUGvIW5/389OKgyZCIkpYHlVmfWqfZq2L/p+nYZWRpg0JVAi5qTrTQMl+t2+vENdTJtvYMh+q7fMmyYhVXpHAJw3anpCsxGBGHOZrYFUTuy4PG+WRaDyPaUL26kcmPqOuV/b5jRRC7skeL4JxRYox0CtxkUO/IaJa1nFiotQ83g5w194Bn1cYZiTso5Lfj+XnLBy5c+WoWbz2W4zIXZJ63lMlTTLzubtDG5lbS8h0xCjVTKJaVkGsTWklB5FjOaSqIRgc3glPVELSCCXnX/7cez6k1akdBxNVjwqatwa6BznfcN52eDdinJvqnEiFJqNRgJZwt4ojpEwVHMlYG5UkmdOiymgSvTxNmlolRLsPGpwXa646qLHi7IKntNuwjM/Y1q2TJLjLlgs4LD+MSk6+tqxnBeEjSO0YuvMSFcnM9rHFMMtqGvf5bBdFrfTb/a+0t1xtPM7OwmMnQfzd3DdWrODJf8zBBf5nXTvvfO+KQPHOThgN6HCQDPc0yt51X5/tesCWdjdWyT3aPXekusitu9ybtdlPdOQ6gIdPb51qB73ea7EJlkzZ9D07ZW+sYAp1EKsbN0ItSXdy7rH+UjrSmLlwJkgOCGdM/90uh1PUYZBIUHQMppebhUoy8A0JeKrFExchpoDb/t2gNNYsdDKzDRS9uLYLbmIE2Z+Q5tMrLKjXoXR9j5bP6E82qAfFjYE8/qRAvuhWm3kgmcJS6MkaqGDkL1WuksvVECS43yKLzOQVbjISbNkVmyGpIDR6yObWLDSyrSQ09z79uUtLrqGm82CebHBiW4TQOoH5/NGWtZa0WrBZWiG5zONvFWTVYkpoR8Rc2fvJrzWnPN4c8Dvnd7hT37mG/ZVoQQCnQa65K65VmWTuuPrfokXRi7t0KGs1XHil5RE5qkvTcXTap9iDx0q5W4UCxrpOAtTk0khMCuMPrmOlbFpRJnJZkj+LLXmHy+/wB+cfZ8vT37Ek5MD/kl8w+RoJHKzXBARjvyKqW95djTlydEBYdXQT6BvBNd7/u9f+lepzqw8cDw2rBp4tK4k8HcYFWMX9xgN9FQd5FW2L9sbry/TXqAlfGVN2get9zW9h/0iV9aKbUJlzyxL2OpqsZ0fskSSuRbKtupq/C+fQkfL94iV9TLtFZj1KZrRIWSb6Ru33DkziJV1gK7ZxBiAFZEYITjzaPLG43W97dBiaYtCqEebb2VYKNQlimEF/dRcF/waXBB656AOg5Oh98ntL9p2EXTQKSlLmwDKIjCvNxRiWe4b1YpFqDjdTAzYSqBG5lSXsuVmt8EbfaDo8Gny/eL8IZ+pn/HDzQnfPr/HB2dHrL57zOyB4FrdVp/BCKndbt6KlVIudSvWq1Z+XKzt/odmJD6e9L8ooO09UDEpbQIMKqy6Eu8svCh9YFa2nNQLTqqtkOA6ljhpOPKroRorkCuZHBFHIz1T6fFuSZuoe7UEynEfweFQGoHb/pw/ced3qH62572zG0zKngO/Ya0lH/ZzPOe8xohqmH7vNNCpms25Cjf9hnXSiimlH2iCD/s5F2HCWZjyneVdnmxmvD45o5aey1DzpJsNrieZatmp5/HmgIiwCiUz39Kr59lqyjer15m6DW+WT4aA00lMC7unKTqaomdyY8XyNU95IRQrR7EIwxiQFDQPE7JPY0UEivQ5QzBA65pyeNib2D9pG4+5HUBp95z7lCNgp9LqShntznukQ8YUrb1L3glCRguJZdhTFPaqMutVe06TXDaUwalxyxuOUfAtcP1aoySAOaLqDbBxW8DZIWYi4sRAqvHGfDR+ckY5lgyVTBrFxkthCYIMWIlEVF2qHdpO6s7bzOVcpC7MqbCQyMT3tNGzDkUCtqxKq0sD2CeoRVUs2FFH4awqaFJ03KzNuOMsTHi0OeBi3dA/nlBesg0g3N49GeURXG8OXPm+ihrApb3d00EIPiEnmuzmQxTAj6jyQhdlSAJ5iZQ+JOelbtAA69U0BGvpB+olCDKoTQkFpkPl6FJ1rlVS7eCOJKdCsUTLT8we4O9FztYTCm+ah716LmKNsGHuGKggdn2maxLARPwVJtLRYxRFJ3GotLqMNW0sWWvJk27Gsq+Yl2u8RNOgjCW16+1aNSWVEJahQrFMeuVsv7DuPQ/93KoF/HKgqoqYGHzUCYWL9m/S0R1U+I3gets75DEwgFZjh7YMRo72aEIKuoe8yTg9PEI5P2mT4X+7m/DnLV+7ccAe9f0Fr/u4Y3eAs9ElKVYtku/Jq/b7r33aykFgJ9HuHVr6nYAdtoGqRB0qeu2JvDbIdu3IT+Wq1jyfxpG2bxkpq97YI8ERS6t0zY+JmGGIc0l3KdG1Afpg15adckVMDsW5SFP2eBdpo+e0nbLsK26WCzr1nMaKZaxZq7mON2luXasxLZ6EAxrphqS1VaIqjQTmfs29k3M+uDGheeS240ktBtMEaGSaVb4PfmPaWgkvSmDXSEvZ2/ooyZV9/O3N6tZkXnzPoq9tviXSa8HTdkavjnVf8qydcFSu+WB5xIOLub1uEjksVgZyJWAvm1qZxlY7JMA3Ug7VXmdhwk1/OVQBt0mexKEULvDB4piPlkc8vpyZU696nsYKLy0nrqDTyIPgmErAi1W3TcXkVy7U9LAyjhcUNkmvq5LIRYSpwDpN1FPXsw4Fjsjr5SnrWLKMNctY4YlchprXqjOjFSKUGDW0SrTIMgYa17GMFb+9eoufbj7gl+ZvD7FQkeQBSpQy6Yd97eiH/PDmDR4+qwiNp58IxUqJlaR4Yn+82bo/0Nx3kBpn/1zcAlrwYuOrF0mtyIh6uP9z/7ExRn0d5TDHQ9fpcT/H4X0LVu0uZFfArtG2j6jEMVA2nIvtINnbb75sewVmfYrmXdyd5C39fC1HnWR3+zxAixiRLu2unMMHRdqYuOoOv7YqrX4i9LXZ2sZyq10ScpajsffxaxNb7L0inSRpophohmY/Goa3Fry3xUOBwsehMgtM62oVStrg+ej8kD44Fsc1bzZPzSEqZS3WzjSaKm+BifGPhS8ePORWccl3lvf4x4/e5MGDI+SiYPZY8Bvd5dKnjk/WdfUJtFoyWKVLUOPMO0E6klhvqipILiCqDE6FfXB06XvKYsR5cQRofMdhYeh9H50JqCfhW5u4I4fJ4SNrVjXSMZWeRiLHziblZRIsLEUoxVHg8eIoxVOK5w83C36x/h5/YPIu91874lF/mFxHEhUz9JSyZC6OgLJW5dhth+ix62lEOJCKs9hSipUTL2NB4zsWseZRX/NRe8RZN2Febga9smWsWPT1ANZtYsEqVNSuH0BKAFJx17ov+MHFLRZ9zU8d3OdLzYc7jmK50sCJUpc961sd6yc19bnDr+OultwQvyZA17mk2eC2GjQhQFHY2BlXZ13r0nHdY+OJ+zlj7HktZ8pzxuC6l46DBhm9Lru7Zf44o+Pyz/0MRJ7f9wOQV+1Ve05zortmIwLPrfC4lma4+zxBEQIqDtGICzZu1CcdwgJiSHT2XHmVQKCcbc5gV3b9U0mbd9g6Myn4QlEi0bk0ZNS0GrEAxI8ymVa1Y+5/l5uaGB1dU3BUrHBJhDeKoxefNp8mNF/5gGrgZrVg6loedwd8uDxicdnAxlEtUwCV793O/diORxeMsjLMWarbysmUQLKNqAwnU3QIrGKUYbMWU7mCosPXVEikdj0uvcZAKZtNM9Uw0ylcEoIvBlF4pXFKr9Alh0OPVQA7skajAVSfLTpe9095vTjl8qAZxHtzRZXTiNeOKl1fDzSjHW4jkUKMtrjWgE9utp2aGH0rBQv1XIaadSgHgxU7xnTM/ADWmei9AV0GUo5bHx3PNlPaWHCruuR2cWEAoJqkwTBtilUm9NNIv/IJ0NLduTPvh0USrcG0aIS0LowqhHFF+ttsVpDMa0hcIeJz1pG9ef6T4AUD8Dz6/ZO2K9pdo5/PO+erteZfiHZt0JmqRYc2clgfYpK9NlSqp/2X62MSfTeaXA5U1UlKwqXA1qdESOrjWRZl0P0dJfVyda/0gFMKb5IgNAxmVHXZoyrDc1HNEbfwkbroUWDVGqW78IE++C2DJDpWbUnnkyaui9xseqbe4pUP+xssYk0pPV1yywoqrLUa5vPO+aQvqyypuefPKVNC942DUz64fUy/aEy0Pq0Nprdo2kOxtESR6ywJ4pNYvbnHKWFitF/1mnSzhJwNd0kGJgN4YPNfIZF5uSaqDPrEUU2LslfHuitY9hUfnR/iXeS4WTErLBaY0HGnPGfuVszdimO/HGRHvFMqCay1otSeZaypXcciVtws2kGXcq3JLb2vWLQVbe9ZrCqODtYsQ8W3Nq/z1foDSjoacbxRwFQmqeI3ElA8QoPyJApPY8NMOk7jxCrAXMdaOo5dy1qVp9Fx5AK3nRA0VV7pJb+3eX0Qo8+VaFPXJp1jHUAsSImkFKs6iXywuQHATzcf8LX5uzzu50kYfz0Ix3daMHUbfuLGQx7MjwgrRz8TQtbcdGKyKnk+H82/ORYYgJ0QDRhWZ0uL2ypJSq5WekHbglYfj/DoOP7J1MAoVnmVH49s9agyML0HWu38PVqEd8awHx23D0Dl4hLYglxpzMeKYU3KxSnZ9CdvxmQ/afsx7RWY9Sla5cJQGQUMm4zBdeC6jnfdF5QcR7QPiCriHBodrneJZ+0osQkwlt5ArQNPNzUebzcVtGEo3ysWgusx/SwBLZWq6WiqjvMLs+n2E0UyYu23lVkiRoPogqftrXtsek/XFYTe0T9uIArvdJ7/pfgCv3DjPV4rT3ESOSq2IuQ3yiW3isvBTvV3Lt/gH370Fqf35xRntolM++MhM+4SPW18T7ONb5EohqjhH6EVo1FGcBsx3npePCPE4Oh7j/eRXoW298TohoWhD566Mgpl4y14GLjnqpSElKUph4kwg1gz6axKSpRSoBHLkjcaKEWYu2oAsnLLgFatBT9T9rzuH3JRPmajntM4Ya0lc9cmRw+lVWWpQqMBjxhABhy5hlK8URfVyB9moQtdcTo4lXxh+hgnylGxZBPLYbF7uJnTBk/lA23wNL6nDZ5NsorvomfTF6zTBmHVlZy2E9bHJV+afMixXxCzmHGa0USUou5pjyr6WqhcooA42VJAweh1XhB1KeAewT85+C6Lra7OCwCpwcnweXSQ9H7XvnYv/nne+4w3XcO5rsmEq4wylWndGMwbxgvdKOhQEjDwCQQOX7V/cZuXvYzZED/o1X4/HHMd2JU2z4nyJ3lnEp1tNHoZEo7qHdELsRLTY6xszo0FQ192KZkwgF0efBkpfGCzMajElaApQ2EAg72nOR4qIQohUR/6KMTgDRRaFqDwLAiFu8HrkzPmbm20cGfOslQwcR1T1w6ufw/aQz64OGZ9WePWljXP+9phvzmiGOYm6TjX6/C8Cw4NHnF+eF6ibcpVetvARSGKucFGdhMmABETR698oHDZdCPNFxgApUCPo0R3QKwKE8Q3uh0UJMfGBGRV4gcga9tXnIFcOO74yNwt2OgyBWolPY5aQtKqNcuNTqFIUu32WgYXKk1JHcXAuKgQ3JqgQiWBG6VVUzWuSzpZ1vUWfU1QwYvRhQqJhCQBYOuRo4+OPhhVpktAV9+YyHLjOnNXHue3RXFFJNQ+OSONHHPHU2kCsvJPC5wtK44KgktOYTVwCDJJn3oNXAALwCJPgdEYe85a87Kg1HOOGwo7PgYc0/y5uAoMXAvUjtecfxbg7FX756tdX9x+hcYEMEigXLffUgUx7V4Jaa/WKxQO+gx0pUMHrcWt1IPExCYZgVuutwqkWCe9rB1Knr3OOyVEJYrR1CdVR9Tsvu5YtyXOKVXRD46whY+s2xLFQLCuLXDe9P2KIuBdx6It8U65US9ppDfjkERXf9ofmHRIag+6I0oJRsPzJgh+6pcc+yW3/QVPk1zHrGiZTFtW05pisb13rtsmeiRaJRaA74RimbSAgz2nVTQXwyHhaeBD7Dxl3YOabvHhZI0XZVq2A4Mkm2yZlYXQhoJCotEDVSh8YFZ1TIuW29UlJ4XpS930lzgx2l2mV86kxWnkRC6ZyoYP+xtcxGaoerrDBfeKUxrpOPZL7hVnnHZTztoJ9zdzNBUOvLu8SaeeszDjj86+zY+XNaV4gkZqKQYnQ4tfIhv1PAkHnErkfnfMzG2Y6oY7/oK1ek4TA2XuVnQoM6fEGLiIjUm/hK2cyo3C9L0a6fASOY/N4ACfxf3zPyfK4+6A03LKF+v73CvPOA1T2iQXE9Uo/l7gx6aP+d692zxY3iJcevqJuXCG2lP0/V4ssLcejal7fjTWAilZI8ZIua46Kyfyh5/j9UZGkit7BTX7v19bibV7rI6PHbfR+pHnkPE6kvXr8jFj44fhZ55eYga/EoCdjK8kZv083TUU+oSx0Ssw61M07yK93+twXJ/pGBYO2AYZMaV/82sHx520gSpAcUjfG+Alglt1eOcozx11XdDPCrqZoz1w9BPjogODQ1poQL1SVX3aZAMYuDNvNrSpw/TBsenMLantC9rW0y0rZOVxS4ffgJYGJPU3eiZNx6KvuL85onYdjfSGaLs1J8WlbexVWGvBtxev8+1n9zh7NqM4KwYa1tBx1TJBOYAYnEmwBbBY6k6AIQrFRukn9llNFN4q0VDMhTGNuFyivOnKRFuwSiIvajQ531G6QDfyl4/qqF1PFwuWkBxOama+Ze7aZB2rNCI4hLVGOmx+mIoRC1/UNtpzlhahE9/xVmGRVkBppCCgTMWArAgs1Vw/5i7L/MJEKuoULC21ZaOR26zwPE6CwpH7/TGbWLKhNO0WdVx2Neebxkq4fcDVS3r1dMGCkMWq4XLZDIFYiMKyPeTJ6icoXw98afKBUS0l0oaCdW99RpwSy23JuVVp2CI90AxzACBidMMQU7WJT194AXUNIT5fO+tlKq/GHPHnHbe/udvPTuSFSPLn2d8IjuIn2T42CBrmYzJwpqO/x9l5QFQ+kSvqq/YvXhMZlWfv9MWXBLHG5S35mJEmgVVKOSTm0iqQPiDi8BuxCuHS9BuzhqOOBOC3KJHifRiqslQsk14VIdHwrGKpjzaThQihF2LnoXdIZ4kYvCUO4yRSloEuei77mqI0x7xaemrpmbh2eOteHY/aOY9WB6xX1QBk7QTyo3E4/htIFb62ztjz9iFd3xCLKeCQ0KF+BdoaIB81rakOKQx26aNRBV2iu1hFURhoDHF0QapWFZ2z3g5NluSBWuyzFmIbNQF6dPjanGRi4fNbr5G12lETFzmiTR9bKcQRUUqMxqiQfAyhGp17nJjpCASUGR3OG/VdUAsu1CWaJENwlTXNvFPEd0MiBGDdedrOnEBKQFW5CDWr7iZ+rtwqz5EE5IXohj4jokQ/6uP2hsOcfKWKNlWPqIBkSqEroJiC3gZuwC1svn8m0J0D94EzrPT7RZPzGH16AVK1M2RlOHpYG66jrLzoHFwFvkZxg/2d15qd7cjH9ZhX7Z/ndq1e284BkWuT7GM2yUhrUZIG3uAEmlvSvxlXQqq3/XosZLuuZAH09DMzSnLiIxakZAp0/ciUIsUl56sGVaFtvVXx+kjf+7S/72j7gq7zdJuCvvCIU4rSKOf5uE1v+nyTsuWgtMqcTgsuwoSL2PC4mwOmp1VKYBMLStkmHp71U87chGZi1UOLWPODzR3eXxyzPG8oNlkHKV28WLAujEEtY5j4jWU7XQexBnoxwfc+rVVJCN7PjF5ZV1utYoBp0ZrmVcI+VmGr/dtGSwosOtMurorAtGw5KtfM/TpVJdm5Dp1pUGYQ7yI2A5Xwpl/Q4QfGjSfyZnHKbW8/gyprhblfUbuf5unyx1n0DYt1xWk7oXDBEt2zLY19cDTENBlzAmbqeo790pwHU4B36NbMXcsH/SH3+yPuFBdMY0eTAMbsdtgkB5CP1oe80ZwyTYKYpfREzE03n9cRuQiNVX0l8KtTz3fWr3E8W3LPnw0xk53D7s1pmHK3POPHjp7w5OaMdjWlWDjihRIawa+vgj+Dg3uakLcaws4KVfKxqUJr6Dj7McnzWF7XxC3PTWh+XLsmIWMxy3bB2ReAH7vrDjpY49fKLnC1u8aljzumPY7/zvh6lnL4BIz/V2DW/xotTfzXtv1syPOqQPY7caeIph29232tqCJtQLqI2/QUl57qoqCbebqpBRsGYtkkS2nARdsXhgW4SNcnbrIPLDYV67Zks7RBrlGQRUH1zFGdW3DRN7YQ9VPFH/RURWDVlTzcHFC4wI1iyYUYETwiwyTyo9UNPlge8fDsAC6su8XSJvRYQi7NBUZW6Nt7WazAt6lUNznlqbOFIYvB56DK9aAbR/RWPp2xbl8k8cQi0AVPVQQmlS1s+62PnrrohwWtC34owz12y2Q57lhLZC49pa3DdOlr6jQSJeKekyIbggJ1lBJpEiUxoKxjZK121R7ogPf7KRdxwlvFM46dEFFiLtdNfW4qFSXGj2lkxV2/ImKLwnfbe0zVU7ueeZGoItEzKbokHKmJ/x5ZdqUBmReVTcoqSYcAnj054P+x+Vn+lc/M+aX527xWnvK94g5dcLR9YZsMxw6ynvvq9o/k5OITxTDaBkmTf5VN9hZ4uPWKP7P5bb7cP+Bb5T3+9uTnBxvhnbYPbF3JPqq950vyr3UnQMrR0eixUdwyzkIOjh5708B40t+h0uYATOxFr7Lmr9pLtWv62Pa5l1xrxg8nyiHqEImj4Hi71hAUouL7iGsFXxr1PZQMbrpZhxdvVWQhV3ql34Ue7yxg6YOn70YCXK3DrwW/SVm6woZeLEGqiHdKFz2LYOK2E9exSYuGYtQKh3LeTzjvahabGlo7v2W9kx5jonhkvclhDKbmOqMZimJVPFSoHCPhNqI3ktZEAJ4i8QGEy6RBFIlJ+SMHYeKUGB3qlMr1gwDxuEV1eGdutB4lqhvmn4nvrIoJT49SpSyxYl+HXXqubr3+e3Yp2MzzfIFREiNGVew1a0gZQHYWS1otOHJrGjH6YVBF2VL9zKFXgUAhPTO5RIFaeh6HA8pUUVanz2taJDHRKSw4dqJ0wRGiI2y8fZiKQcpp3Xq+He7y2XnFZ+pnHPg1pZsRo1V1SdIYvErN2FtrJPXpbXfGyOAC0iByB53ehp8N8EU1sbF3gd86gqeaOkxgENfaA4+u3vbkAPWSc/lwtXlMywix2j/HXrDwsefbx9dGp36VN/n92156GzEWfBaxmCODVeNkSIyoJAH4VHmVK9EH+qCI0RJVrhreJMDKWCNilb7lVmtRosUUrg5DAhUYJE+6zsAq56zSqmtNgKpdV6ydmWzEIGjv0CiUjYFAzpn7X+kDVUoWHzcrPj99wtytBtbFMhj9ehVKTrsJbSy2VHBRHusBnTp8FRO9reT97ia/dfYmb9+/jX9QUT9LQT8M+pK5+mpgKqu5sUtvQJ3r7F65VgiT9OK8h0xMGRGbK/N9OUhGXLNiw9PWXPsy42IdjH0R1A2uutOy5aReclItOPJLpsnhrxyBWDMxnbBMbV+ouRvedAtm0rLWkqnbMJVALdlpXTjC4eUxHP0u5/2E/+HpT7J6POVdIJ4IJ9USgJW21JQ4ZIh9HJKSKJ65BI7LBYuorLXkMGmTrdVzGqd4URax5n4/59CtrRobOPGXPOznPOun1C5wo1wMYNwy1pzHSermMlRlBRytFkOhxWk3ZRUq3i7v8Icmb/OZ4pSIYxFrGtdSEpi5los44XPTJ7x9dJNHwdMuJlQXYkk+n1kobKm0vW4rtNJ4Up9MYpI+poqz71u3+qZX1rPrJFb223W4Qwa2XpT0lwxUpT+zTIrkJMl2sRBSgj8qMqIQQwapLZ63MW4LzT7IlQHt6Hff1z4nNk7c6HHFKhfLl1+tXoFZn7bt95XndcCxWBvYl+7d1c72IqebUWAuwTaGRMW1IelIRVxf0E8cXbQNfN8Y2rLpSkIwmp0v4jBBKiaS3nUebX1KcQvVE099am/bT5KooVdipRzMjOqx6QuebabMipapa7nUmqftzKqcMEejZ+spj5dT2lWJ28gogE+82XQRRu+wxc+N93QhiYhHJVf5ZCqIX6dKKp+PMcqhOo+WEXXOFDCiw/lACAVlaRmXSWEOJy5RIHI1lvc6lBgv0wVm+uFNfzlYlMfoWErH3LVMJQzuUJ0qnQYKuR7MAjh0DT9R9nRASRbQFzqJrLMIuNgYP05czKUWdNoDHU9jz1wcpTiWGpgSqaXkSIQjIKiy0R6S9e2TcECnnp+fvsft4pwn4YA7/oJ/tPo8H2xusArmTtJHZ5WGOVEQHH2XXC9bx/n9Ob8Zfpx7Xzzjs9VjjqsVpY+sW6suiE2kmxWE2lEuwrCw5+9cxSYsUt8U/CDKq3kj4wSZTfg/r/4J//bmn+KAnw8fggj/9exrXCt6yAgMzoBZbmlRySLNL9N2JmEY+t0V957R8zvVH4yOHwcW43k57mYtXlVmvWof264EuM/fqFx5Zj8QhxevNaPAXGLK0Od5WmOqpHXbzLrkLLslBDQazc7tidiG6AhBMJV5yO5OhUkS7upzeaWqTMAqRBO3rVw2GvGsknsumFveui9ZdiWhc1bGrgyDcgdkz4CWsDc+04BUB1oBcyS+idypkS8D90DPHHzvFrJU6INRDV0LIlYcpIKk9cH7bXWWl5iKsSVt7CMipnsV1NHl+0Nar12JxwKZNgqdBHPSQocY1GIlfeG0VkvBTZ91SuwmFAiRMDhLZf3mHNB0akLwSGSlkQrBi9BppBSHx1GLp063t9etZsAy6c3cK0+ZuQ1LrTiQlg/6Y877CX1yNYxaEnULnmq0oExEIQibi5p3400Obm448ksab0FqHwyY6ws1zdBCkFa3FVmjvcP2u01zf+pvKh5oQO8gP1Xg/pWOnyn/KSUt333tJzlf3oT/+QbII4QVI6V4u9Yx4LtXJTkOCl6uPddTcXzIJ39sfB909/GXvrRX7Z+/liuo9g2pdo4ZVWdldsgAXulWZyc1iTbLxMKZU5mYGZUWbgCvBiOqNDnFlOTI2oKxSMFsneRPIDnhKuHAjK+6tiCm2KTvLbIVZ3NoDFbmoVEGPUbtBFeZG7v4iCsjTd0xqcyYCKD2vVGgfc+PHTziJ5r7zNyG99tMh5vwtJ2xCqXt61V4vJkNNPFpYUnvO9UFF6HhR+0JP1jd4tsP76IfNkweJf0kSXFL3r6n2+nCFtjzG7W/U6yDs5+hz/eenT1q4WP6t00MnLcG0uSf06Klj45VX7LpC7wzAG9ebrhZLzipFtwtz3GydQFca8mTcMChW3M/lJTSm1suIVEUHac6TSBXx023Sgn7yFRyvOI5cp57/pwvzT7i75VfRB96urM5330w4/Bn16aFFZecOKilHNL7XhxopBRPI44DV+NY89ni2UC/P40TPMrMGzWyw/N2d4eb/pJjZ1TRY7/kRrFk6lsa6VMs51honUyuygRAFumaA5tYDo7wvTou2hnf5nXmbsXPNT/ixF+yiDVBHTPXctNfchEbTooFd6eXLDcV568VlJel6Vg3yfAqD5eou/NuHoZWRZLcKs1FVKMYA0WcVWAlquEnMry6jlaYW1QoHNdWa6Z5Yj85Mgaycvwyjod0HOck0HqIc2Awfnhu8mUsvZKeJ5FyMsUWMGdS98kWq1dg1qdolQussr7TOLOx367rTC9CTEclgzuB+HXc14SEiipuHai6iGsLJHgQR3cgSO9oW0+/sZrXGCN10+JdZN1WbNYVIemTSOconzmqM8uebI6tQ7kO67yzPjkdCVHhsq144OZMfMcmFDzaHNBHNwgurvqSxapGN95EEbNWyZ5lrahVWmVh8WEB6CyAylbpRkG0Dj+56Pjzv/mb/Nz9d/itNz/P//V/90fpaof2plOiAtoLoVCid7gyUk1aTiZLDorNICDcRU/UOFjWBjX73kHUF2EZKz7sjEc+d2tK6VmI2fre9guOR5z7gBLR58oXeHHUFImQQXI8lKSH1eKxwCGgzLxy269YROU0MpTidig1jlqUTiOOQClGQwz0PI2RR2HCW8U5bxbnNK7lK9VjPuOnRJ5yGTd80J+yiSVrX9Kmz37c2DV5p2x6z+X5BF/19GUBrbB8NuG91U3ulOccF0tOJku64JiUPd1Nz+rOAeWFo1hF01jY6acMYJPEmACtBOiGOBynpfDl/sHQ1R3w5e7+c+4m27G0U6G1/XtbJmt9IgNHW3HDqxlx0TQxO9nSNfbaTtWVjsCp0blz+fkVnS7sNThF/avKrFftxc2LVT4CHwNCfYJ2ZdzoAEzxgoBcVKEHHwJSZXq2IBUQxWiDwXYt6pWisKRBm3QMtctAluBXgl/bKfomvW1yHKSMaQ4wQ482eC67OmkvORa9ASc58OiiVYnSOyTRHHfoHzAA7FngWNiOy21VFqAFosdws4ZfhDu/dJ/yxoIf6uuEgyn8f+6Au0DiyuqaQp+yk4qKR7ziXZeSJv2QLQ8JzMlUuxajFA7zhgodnovQsHEFtfR4Ii2eVgMz19GMysniUDd1/Zdl1Vlbz0JJ/9UiODG3xFx9NRfL1LdqVumZEBFRChxe7Fij/tl5ApGVKkstOHIbDtlQSOCuXzKXEmVFq4HzuKb3FkDkz97kimoxrbR2XeIKtagwCN2q4KybMHMbmnQvYxJ/DlNHN6twrVB3wo4T7PhXsTqA4f44wUq5K6BCb8Hdwyd8tr9P1S8412POT27a013J1Q3XdXu57cPPo7fvCOmOwbb0I1e7DOvE3tuMg4kr58mny4HDdd0hP+b2jEpetd9XbSdwfZnKjisnSB0lnydEMwvJc2baE8XSERPlfOxcqG5bfQFspU6SEHymAyMQKyU0JnweN44ulsQ6UJS9hUDODKuKMtC1gjgrc5LBiEipmw6ZWhVsXXYcNhvm1QZH0h50kWVfcXdyzueaJxz7BYtYcxYmBHWDk91511C5ftjzr/pySER8dnKfI7/kcT/nWTfl3YubrD46YPbA4dsE1KnFKPkz55gmj0e/SfqSQS0OCgyyKNKntTYK+AjBAO4sdg/GFlEVFr2ZNZU+ULpA3zV0wdMnHcJSzBG48j2FC0x9S5e0wS7CxLR/XUenBYtY48X0pKayIYrjpluy0JKLaPcnYEyUqZh2bzYbya2RwElxyZ2Tc+7fbygXDtc5fnh+g9+7+TqlvE8jbXJo30ZEGdA6dMbmWasOQNZCKwOy3MbcE2W73p3HhlJ6zmNDTOYpIbqh8OAiNEMhwtyvLVGkuaIsDs7ui972Eeu+5KEe8A3/Jp8tnzJ1G24mQKtVP7g9lhK4US953MwItxyriznVudBtPG4TB6c+XBJ1z+yiHDhk0yt1ZOF1UR0c3GXf+Co3l5L3+9TC/aS+y3Hz1ce2rxH2ga2duCPHRmNHw3Fo9Jz4BtlzLQRLGu0FwLFgqMAiGr4Qk8KMigHbA0DmLS6SzcuvVa/ArE/RnIzcbsYWmk54ruvAWLT6RQGJG3W+UWD+3JbKhiUo5WVvGlNasDn20Av9ukSXHrexgL69Y9nurvdJVyEtEBdCeWGlwO2REmvFteZwFSqlPGgpk/5JjI4+wGVb8+7lCYeVpddDdFbpEwrOVg3dpjBueGpbTZLtxJ8phPm5XKbresW3cahkIVWjAfz5b/yP/F+++3dxwC+//V0A/tof+deSG5USsYlDo6JlxNWR4+mKN6anbEKRwKtI6cLgogU20Xau58BthokwqGOtBWUscUQaZ1a+FzphJi1z6fFi1MCNRhoZaaRd93WhLKNlSkxo1+5PI0VaLDqCBuOV43AucBGVoGZvu1blQOBAaqIokchGOy5iz4NQ8nvtm5zHCb/UvMOR6/hi+YS7vh7Ege0zCa+Vp4M7SQ5eCom8Pj3je+e3WS1r5tMNMTg6SsQpE99yrziFBi7nNZdtzaTomN1s+X5bsLqYMH0suPaaD55rrjPQ5LFFPDnkECNE5ZvTN/i51Xu5ApVvlff2wCq3O4Y+djzxfBDgBa/LQe++XW1ug9DheLiPKrWuBCFs8YIh5srB0HPe41V71WSHgjQCmoTn99+dwPoFfWt4WnYC8+e2fCkKro1pznaEVAUcew+dIMFOFGZmZR1jArHENi3S2hyh3rL1WuiQ8IgeXBUGeomm6tk2eE7bCXUy7VDNG3/Hpi+IvdvVWcjgFAwBV06GDJWjbnuc6fwJogUwg9tK8RMrfrb7HSbtOfPpGb/z+a/BN4FnE0saKdtqnLSWSqE0ZcdhaRvqXl1aomOiCmbKhRIkUrF1iI0IPQ6nDsFTCJQaaSmpNFARB2O+oErxMWWdiiU8AApxQy1QkSApJNKrOS6WiQSy0S0TqSdDPwUqBnwFIhuNLKLjUThkoyVvFM+oJXLTLZmJT6L0Amrr69yt6XHEEbjmUOblhqebKadtYc64UWztFtMam7s1FNDWnjZ4ShepJiueHjn6tqBcgr8mDtjKP+Rd+mhXTspQLBxncswTf5tCD3gWTuASYxhKZOAJjcfQx601wgvWmhe8bFzN+7zzsheA5P4re+vJ6Olrz/Nqqfn92V52D5ElEnwKsq/p0xJ1WzEfIxIFumiMEkhmPtBXSei92AanJnA+Yl9ISmAnymE/VUINrrNNsxYOLUbuc94qtIoUoZZVT4zbXVNZBsqkfVv4yEG9NAp6YYLn82LDo/UBbXAcVytqZ052QR3rVLETRpu2i7amcgXzao2qMC3Nlf1nDj/ks/UTPmqP+dHaJFO++8O7zN73JD31HSYJpHUmWHIkevu9vFSKlQ40ejDwy28MGFSvaKW2F64N1mnbgnISaHtPH+xaJ2VHHx2bkMyqxKDpwpm746xsuVkvqFzPcbkCjFlyGRq8tzXAq7KOJUGEmWuTjqOjlI1Jn9CDMzc/J5Gp62nEkvS23ggb7Ylpbg3q+MN3f8D/a1Nx+tEhbtZxMlniJXIRG97vlbLoKNwumLWVXLEq4QxmHcoG55T7/ZyZs6qxdSxpxJwV7+sxQYV3Nnf49uU9Zr7FT4wGutGCJ+0BkwziRQPysvPjxHe0sWAVSg5Loy0+Wc+oXOD3mtf4ueaHAw3VSyTgOPZLnoYZM98yK1tWZcHqzobV0wl+gwF4m7TJiLsz7rA0O0laDDKMHwAJwQAtl/cz+vzKrOswheeJv48T9OOX5YBq+FuuXPO48mr8OXLV1gB4jRKeO5pZ5L2WEkeC8RKxeHzQU02vTQ44kiocJVUoapTt/PMS7RWY9Sla4eKumyHsdkR3ze9jUbePC0Kua6OOOHZQU28ZErwQvSMWicubJtbYOiQaz1c6aA8LFm3JamNlo64OxLU30KqBfmKUwnE1VTiINIXZoBdlNAv16Hi2nPB0MeWg2VD5MFA/ogptVxBbO+/w8XK1ygjUGgILsUHieiiXEb+OA9UQGBBvicpXH7+zU73ztffewfXYNddpMU26XFrDbLrhZrNgloQCx8KKm5i1QKyddlbGe+A3A8d6mQQi52LiiZUEHOaqtVbHTKyS4GXyYZ0GnsaYJvCOkuyMKHiEpzFyP0x53S8pxWCxUkyYd5rs0pfaDdVYnSrL5KboEs/8MjQ8iVNa1jQSuIgta+nJBb+fqx7TSMf9/ojL0AzXdrc+50a54LSd8HQ+4eZ0Qdv7RFNVDos1nylOOfZLy/j0DatQclSuqIuebz59i/jdFwNLGkdUqJEvAuIRifztm7+MROXLy/f5pr/L3578XHrt3qZrf7ztZcb3MxFby+mr1/ci8dTBinpUcrsjbshocnaj568JHBLFPGUqbTF5VZn1qr2oOdFrNIKeMw7GFYfDeHjOiV+01uxUuIzPzwC+Zu2U/F4SQYMYINTb36F2dMFMJgDEK9qLaSIUmVqYQSR7i1gphTfh8Ez/iCqsu5JVV1EVfQKGZPioIXh77zhaa0ZjVEaP2UPba3aduXVJphqyXZNEbd1xLcO6klh49nuRQDWfNmpeqcp+qMpqYzEASGBC9eO1Zx1K8FCJ6caoCl0sTABYenMuTELrgtIjVOmTv8y0EVFWg5CzuRiaMyIIwkqVSy2ZS4dPwZEf1hxHr9FEe8WqsWICxzzWfTotaLVgqSWBnkKUVgM9EZ9Wwxt+SUHgMja0brvtnPkNE9+yDgWrumRStoQotNEMW2pnxjKN60xUvinooqfxHd5FHq6OaZ5sg9wrTQxolBGIag+2wDN49xaLv3/E//KTfxiRlvgdB98DOAdZbZFQ9m72+OYPG/q9b2MwPbl6WS+1Rd9fa/bfn9FjGdTaPwWjNSjvsV6137fteUk3YEshFJcoSGk+zhSonePsV8lmPCFVlETFr3tccLjgrbo1MBiD0EAoBapRoi//dBAqA7JiBX4Nfi2ERvFLR1/FQevKnMe3roUxJop00r8V0QHIqop+oHIDVK4fEtRPV1NONxPiTFhPreLIizJ1LU4iy1hxgdERu+iHeflWs+Bz0yd8tn7CB+0N/v7jz/PekxtsLmrqH1VU5zpUlCAyVGfloDzT2F0wBna5NIohGAPF9UbNLFZJFJ9R4J4SNGMNsaAmA7Lpi8HFMWR3VoDoqH3PYbnmpFxYbJo0tLroOSpWONHBmd07A2oC5l7rMDBo7kJyZW95GkraayYVL46pVNRa4GTDvzb7Pr88+QE/P32P73/+Ls+6KZ16TsOUe8UZ98MhcM4XijVlMsjqNNBIQS0lS22pBebSUUnkxDk6lKhmJNapsXx8cmp81B/yuD/g7cVtztuG+dRAyrMw4VE757SdcKcJQ2XZed8w8ba2fbg6svUWK0apfGDZlXy0POQfuc9zFqb8ZPPRoCkW1TFz5mi5CiXeRWOhzNdc3quoLjzlwlF3o4k16nauzWuAS4UVmvSCSc95b4BWUVjpXq7OGhfHjNvgYPgxK/8YnB7XVWQq6yg+2sHIxnvGMUg7JDB1oA4LunUUHoG09v5cTczoaH5KuMRwy8pRQUCaK8b5p5dpr8CsT9vGN9u5bSfL7oUfV1E1lOuNS/vsW/w4Z5Ksz6ACsXDEypswby30E8fmSFjfMrtLWflUsYRlWDaOxaom9OYSEqNA6wiTSKxsY+5XbkhOIiC9sDpruN+a9tTRbGWCrKKsVxWrRUVRBcoy4NNk2nd+qMoaOmjqpK7fTvr5XuayRddDsYqW+R9twrKonkTlm4dv8bWn3x/A5t964/O2kKgBWjQJgKiUatZy62CBE+W0m+KSyGPW9ypFqH3PJhR0atnjTSwGuqETZeo3TF3L3K246S8p02hsJC2cwUqT712bJoagcchGALTqeBobTuOUksC94sLE5bXgW5s3zLmjus8df8ld3zFLQvFlsmM/iy0PwopZOudarXKrkcDnqkechhn3/CUO5VGc8mFfcx4bDt2aVj3LWPPF6gF3/AWn5TPAynSnrk1uLoG7B5e8Njln3ZcsljUKPGpNg+u2W/JT9YfEuePby9c4LpYc+A3fvXebWMxM3yw7GY430SKpYk7ImlZDc2rVHQj/9fEfgtkvoJsNV3h+142N0SZs+z57x2QqRy47yJsttuNpJ8vmrglGhgk+Z/23/Xootx8Ws93XDdVaqa9rpUNm/VV71V7YdoJZ2e2HL0U7TGvNODAeXvsSa03eADlBvduKvxdCaIR+mjYkvWw3KgoEoe0LNMgA1hCEWOhAN3e9bMdLGhv9uuCyN+2puursdaKE1rNsa5yPOG9gl6oQR0CWjvZyw7qyP4WoAVq5MlhC1l4KWI3tJTycEb7T8Ltf/TLNZsG7vIl8F3gCVr4ToTNkSEWgVHwVmFatrYuhTIHXtvLKJ3AqqCOobGmHEgbKZJky0tmx0ZMrqwyUWkWHCByMwZZRi6qpMspaQFglFyhP5MC1FiDheNgfWmWyv2QmLTMXKTEBeIdQpuTJQnvK9Bl6UjxM5NgvWGvFQXKWXGrJRfSstaARE7hv1XPTL5i5DStvwcRGzWTFKEHKrGqZlxv66GnbAgWWoSIiTKXjlr9AK+FRN6dxHVUVeDKfoc4yVpL785XOm5/Na00E2QCP4GkD//MUvuNQqeCjHk43wENgxW4ae9SubLSvWyeuCQq2R+8kz+HFyZSd1+blVPbOMz5fXmv2r/fjh/qr9s9xi9U1MUdOoI8ZI05Ao011TpDrnNPy2tCnmpk+mkhz6aEV/LInlo7iUtDS0TeeWAt942hnQpgI0VnlRTcHBJvzCyhWgiSNXFxOZlgFlnPKZl2gZc+kSuO1SNq3zqiDmVbnndHMuuDpxbEJsOgq5tWG+5dzHt0/gig8vT3hpFryk9P7nBSXTN2GZW9UtL50nJUN83LDa/UZT6oZPz59yO3igm8s3uDvP/oc9x8doc8qfCeUlwxUeC0krV/b+GYHYAaKteK67T11fapIzoBhTIU5S0ecxAQW2EnWbUlZBLquoE2fve093ungDlz5wKw02ZiJ7wykig6PgT+N64YqtEWsmboNc7+ikY5SAo10eJSp9HQKR84S6s53dGpJ9LkrKMVTJ80sMFDrSCYcOUvO3/Y/4g8273MWSx6GA56GAz7objD3KzrneRpb5k4JauyRW37D3PV0GqlEOHaRI1cxdRVnccWR6ygFlirM6VjGjrUvk6bVIU4ib86ecVyuOCkWXIwS8quwlU5ZhZI+AZXrUHK6NppmIYFePSE6Fm3Fd07v8GQz4/H8gK/N3mUtFXO3Yq1loqzapqZPDB6OOtY3PfUzR3kZcbnQYm8J0gQsiXcGBmXDK69G4/UmOKPe2zj8uJbH8PNYKZliOGZ2Pe9UIQ5JSUuIZLBs/8DtuYfqqwRkaznq++jWeC5VYVm8Zcdt74m9h6Z/18mxXFcI8KL2Csz6FG3IribBxZ2qrDHcOf49I65ZZHEUIGyP3wb56ne/zXHgryLEqrCgovKEiaM98LRzoZsLqztKd7ez4GLjkGgUw5gcAmJwQ3ARVwVUETkMxMsSf+mM453HlkJ5LsizCtda7fBpM6ebR+JRTzHpcV7pWz+ct1ubfonN9mxn+pS9cCkWQI1nP0wG0RwM3SYmuqTuiiOa0i5/6/V/BfXCly/f47fe+AL/t1/6o6CWDVEFWiHWitaB2cR49BmVr1wYHEt6dRQSWYWSIon3li5QpjJTMAepLhZEJywT13wmLY10dChrZBBPLFniaHHODS4eQaOZnye6hxdh5iIXahkEL8pFrFjEmne72/zO5ZupIsFxUT7jyH1ISJ5ZpVp1VSOOp9HQxpk4gpoj1Wmc8EF3Y+gnXpSZtHTiWceS0zDjh5ubbGJB4zpOw5Tvru7x0eaIR+sDpoXZ+X60PGJerjkulxxUGwOfFN67POHvz36MPzR5m5m0/GT9IWdhgkM5Kla8efOUs+bgxVlgkeTmkUQ9c7+O2OYpRJvox5N75om70YYsj50xR3w8Ee9lImzcbF979bquf3ioQIFRYCDDc/m8eXLO57lOb+uKfta1glqv2qu2bUO3zDTdj6vKyr9n0Gv0kt0Kj7xjuAqI7a816o36pd4RSxmy8bGGbgZxFgxMCmKaVWw37AZc29tobxs5qSPaOqSVQT/EDga3EWTtjcYHrIuKWCnaRFwSCY7BoVEJ4hK9UEafM/2iObmxPb862aEauoBVZcURICKpcufhDfifSh7fv0s89sRLh/+REsJT1J8jsbeTBxAP6iNl2SOYEP4am3+LVFll21gdHBg16X7Y7+mepYy6CrTqES2psM13UANmAs7cZukQAlkLy4kQkwbWyEuESpRWE2VThE3SUXkWZjxoD4d9wMavqeUi0QktY+9xFKmCC1FKTC9zo461loNzFBhtsCQQEHr1nMZq0KcpJbLSkifdQdItqShdoHE9l11N5U3PpfL90ElP2wnvlye8WT6lksCt4oK11vkOcDRZsS6qj58/B1xJEI1AC3oB8T3k/ABOJ6nvnmNVWZcI/TYy2R87+z9HXe557/3Sj7M3RndekxcWBsCK0UP7t+HKY9cd9Kr9vmnq5XpAdzhgJP4OBmRdFwjvVGjoFtDKyQJv3CDXp0qpRD/DC7Eu6CeeMPG0B45ulnS1HFALtFCY2d0gBg/gZj3zyYbFpiJ2jiDeEgEOIKKpEqsPjnKUMA7R8XQ5sXkzOFbnjfXx1lGeevqpJdu//uQzBkg0ULvOjCVcR+06DooNR37FreKcZW0MjG8s3uAfJCBLHtQUGyHUugNcGftFcL1VTmshQ+JEC/BroxeOYxuNiu8kxWFixlgCUpperDpBI3RtQVEGvI8URTDQZVPRR3PBzXN24SLeRW41l1ZAkKrYoghd8JZ0F1jGakjAlwQ8SiMdjQRKIpXEYescUCoRA7mcgVjuBRNW1vyNrIGOE/+MTp+xVk+njhPfMU/JeEQ4ct0VEZap80yTQ+VUKrw3BHCmkYVGpr4jcE5Qx+N+zs/Nf2SUUZeNS+yMbfQ8Wh9w0TWDHAHAYbkeNNHWfcGyOxykdgpvoOjj1YyL7k069fziwTvM2OCwOKyNVhUMxspyXulniZ3l0tAaJbSHKm8v5iGSM2zOoZLiWpFBL1iqEr1WN+vqfb+il/U8YGv7ghf/DTuAFbCT3FdhwCKG+Ea2+7vxeUUhjBlrytapEwOwB53hVMxyRa5FsMKGnpdur8CsT9FcKrMTkVRRmG2f94Cs/QUD2Oo57D2W2/Bc7kBuG6QnIWstPaH2aCGE2tnCcSB0B0J7BP1BxJXBXtu5re4U4DaOEBx109nkX0QOjo1jfbkwoXjjvifqSMqgx9psZSUaGFUshKAFvVOmh2u6zoTmNThoHdJZQGGv2VIWJTJYseePOaYe+jbiuyQ+mcs2M4CQ3LUIwm+8/iu0xxXdzOFWgksic1bGa8BdMbWJv42eSfKN6tU0SQ58Sx8cm+htEUiTXxc9GymoY8fNcsFGC8tea8F5nNC4jtItB6vYmfS4ZA0bEZ5GBaystlQ/0AG3fcdx5DxrbXmaFph1El784eYmq1AyL9esteBRP+c9f0gpvVnpasfcmbPIkevoUl+ZOwE6TuOEb63eGKiSx37JHX/BvVRN9qg/ZFOWLGNlQsqxTr8LlespJPL+4pgniynz4zXn/YTT9YS49hCFj84P+Xv+J/lgfoN/ef49Xi+ecau44Gl/wI1iwVG14smEAYS8Qnu+ru+nPi8xAVgxJqArbl9zDeC7P3m/MLv9MpnvHAPvBPxcG3hc0cHSES6Vga3xMQ5z6Bj9bcCsXL1Hr9qrNmpCrtzN0WjqqNeAV9dsfbbHj48dWt6Bpb+Si+H4OKOxu2HDPgBZlQUksVKjDwpbUCmfojfnHl8G+nUBTqkamyvbrmIMOo0rSKJLWkg5+dGZllKcQFn3hJCE5hOAlt/X1pDRmMqA1eia8nglBSQu6pYeQKrMknPQd5EP78AHc1wZ8RVQPUEnD4myAo1DVlG94kqrvwoqFOkmRDW4qcrVyslSvUgUkZCAraiRiW8Hkfiojg0lpUac68hUxTJpbP3/2PvXWNvW9K4P/L2XcZlzrrnW2nufc/Y5py4+Vb5guwqDcQzEJDHpGGgJOkrnQ6PI6U7UUmIpCIHgE+ILSLSQ+ID4gFotCGoQ4DioW2olUqtDoOmQxmkwZQebsl0ul+tUndu+r9u8jDHeW3943nfMMeea69QuF8RQ3q+091przjHHGHOM8b7P8/yf//N/8ldjmwA8WilM0qM4++7ui+C7V4Fttlc+afpUcRVm+GSolcejWceaK9VICQaBqCKNkuYiLWHs69dIZEuX4Kk7pctZ8JlyLHTPiR7GtupFhDfmhE/RMCkg3pVr2biK2nj6aOl8RfKyCK/6hnfVA26aBZ+ur1nqnrmGbWxo9ZbGRDZ2gl/eevTvsjUBUgdpgHQNyZBSRLznPqOfORMyAr6HgcK/IFuz97njuz20NcdQq4/dz3TZeDW+bUe0u4AZoJQyjWMamxT92ZIwnyYUNRJnTH0uH3baprBjkpQS85QgJIyP6E6RVob62uBnltlzAbV8IyxelJQcjsCPBmOlzLrvpWYvAb2z3Jtv6YPJuomK3lm8M+isAeV6S1xLAt50ivlG7JJvxR6lU8f9szWVCTzrT7hnBcQ6MaLxe9+suW/WWTi84SrM+Wg445evHnK1bUmXwsgqZVSxEjCulBkq0p7Yu/KAArNJ2C0Yl3LpYRobCpk+Saf5qd0jf7aF6KR7Y9AJ7w3GRFzQGBPH5lu1DTQm0FpHaxwxibZuqTaJ0VJlNlFEsQotJ6bjJkhc8Ka9Gkv7GsVY+RGBdZSfjSqPRER/jAYwwCYNvOtrYtLcNx1LlfiktWyiQyuDcIIjGs3cRDYp8CIEHIpWJeaTNXMaLzUqodOAS5FzPYC9pJ05TnXHu+411rHhRViM3RpjUqxdw2qouYot88rRWnmvD5YhCEg6eMPVzZzoNSenW0ImYTy/WfBiPaf5tOdzs/c5NR3zwmSOGpe30zru5EISo60Zgaw9bSpNilHYWSHHNkaPSTW0QtU1hEAa3DiHj+pnjUn7/bmczCGylMd0H2NCfj95OTLvD+nCBSPLz+iudHjna+2+fz6PQsQZiToTm6WyL6eyBFA5VN6XdEBNEiclxqToy4xXYNa3MKwOUicO+w/WXaME3tOOA7BfXqj17oHK26faEusMWlWaWEv5n/Jx7C6SivCiB7uRhVKFzLiKCu1kmyLKnXRCK+g2Nab1vHn/msZ6nq4WklkfASyZnKEFf+ZRbcBtrJTxlWDAK9SNZUNLsxioZ45hU8t+XPlucuxiAEynMF3C5Frjcg0E5EqYbgdkKR/3yjCLAJ1yARMjVaVR0aCSkfKUuSI0MNyLxFNPXcmC7oKhC3YEoHwURlRIiiFaal3qpKXkUGdmVp8sIWlWvqGLFUvTYYg52+ypM1V3rnsWygujKym6JK3XYy4xqVQlLc8PRqU8N3HGOjZ86O7hkpEabyIXbsEzljzzS16zN7xdXfDArFimgaUaaBWjeLx0G0nMdc93tk9YVS0vwgm/0r3FJ+sXfG/zEUvdsdE9lfK4NOMyzMdOIA+bG7538Zivbl/jyxevs1q3PK6XfLQ+5fHjc8yFsNqu45Jf3NZ8Mb3Flz7xkP/iE/9AOocg9eU3Q5u7e5CByLRfSljGCHYlYTaGBM6jfADnpX48f34v8wDHtbNeskxj95nJ5zS3arxvlQxOPzf5OWYwOBJoKMauHqMQtWHXdrYYgsQtIcZX49UoQ6tELL7kyzznUwbH5Jdb5YVTXR8lAUoyYqOiVqPIbynhHTXjsvOhHWPb9dKlUOV25EUjpOie+sGgbORk3mN1ZD1UO7Bb7+ZZshDbCDYSnd5pYJVMXq9xWEwt5exhkEzLtLRxZGKpXM7u2XUYmpR/6dxkpABZalxvck0+zyCtUbGCLIhLcqjU45MnVpFoIMwSqYkYI8coou9l3YvJjNqLwsSN422wOjPNkPciiiFYvNY0ygvQhcIguiE2xdxOPdsXFJ5ElRgZVUbpLNB78BwRGVJFxHITWmLS2Fzqtw01Gxo2uRTlVHfM9MBAoE6BnIDOT5OU3lfKc8+uGZJlG2uexxNOzZbXzYpaeWqV7Wiq6GI1Zs9PrHQovHRzXnQLhqFibSKroWG1mqE62a5Pc566OU+vG56dBn7n6WMa1ZK0olbXDGGJwiJ1DCE7wHeso+W98i+GnDzJD2lKCJhVHvDJHNoDhu5AnL7RGO0NY8Jj+t4eQfeu3R+WFE9jDzWd3/mHkmdi7zu8ArW+bYespfp4Z7QyDrR8j4pOTwPdmPUai70IefsCehVNnokPprzMK+MCaojYrcZ0Fr/Q+E4RK4VuIFmF9opkIm3rcpk16Er0s/rBEmeK1nquu4ZuqBgGS9haVJWZvVtD+9xQ3YgOl89EUW3AnySqVroUdt7yop/zvrnH6/UNXay4UnMialybWu34sDvncbfkyeqE1eUMu9ECZGXdylCT/bX8lbNfJ7YwjQG+HiSW2dmhNDL3zZBL6xWEOo1l9torUqeJSuZ5VOCSIpiAMSIHY0ykdxVGJ6p64LTuRk1JYNKhXeV1vRpLDS/8gmh09v8Nm2S5ScLAWmpHmzM+Jh/XpURMiUDCfsySZ5TGJCVJFhVZqkSbgdNAYh1FdxGkbDECL0LFLw9vsjRbfkf9bNTS0ig8gZASjsBc1Zyohi0DTnkaM/C6GaiAVj3iK+51rsKcuR6otees7qQcMxrO6o4uWNpMUmiMZ6srem8ZfH6Otoa+8aPkztBbusuW/2b4rTz51JIfPf8SD6tL3mrv8cH6nN5JHGRsJCjxV4AdGFzW15HBlHWljCJRfIJdySHaiN/R7CpUUjiIEw9LCw/HXfN3incdblNsSdHSS+xVgqWJ/ZMkZbYjSY3J1WkVyh7wPfn+I9CbzeqUmZUm8dHOduXyRTNpQPES4xWY9S0MoxKhyeBUmBiEKZU3s7KOdgyZBuhHHLCkhLIbZ3YEsUY/q9B7QwlKRFhQmJkKuxHASAUjQXmYiOsaSDYRtgbdBh7ev+a83XLdt3hv0IMeAxKVt+eTW3740+8xBMuj9ZKLmzluW5EGYX0pp1BXFb3TVMsBUwd8r9GdkW6IIS/ecnpUKzCOXe15uVw+7UTfY/4X4g70KJcuCJilfMRUWYgyAkmzmhu61yLhvsM2ku1BLhudl4WoNR6tAn2wUmoYNWU6aKQsZIiWmDRN1i9xSUTQ+2hFONK2LLWAODEplqaTNuIZ2IpsQQcWkFvTyvAEXArElJjnEsDLtOA9d5/3uvuA0KC1Sjx3C37t5jXuNRs+s3jOJjbcVFcstIjYv20vWGZGmEPzob/HE78E4LPNE+aq55+nT/HCn/AV9QbnRvjdAc0b1TUAv7x6i194/hZvn1xzUvX8ysXrPHt0irmo+OjJDGyieWrQuQ1x+9zi54bQwC9cf5r/18lv5XeffIWl3mKIvPvsPvdv5L6NmYe4E/GnBBBMgCwfhcru/AhkpRAhZqdsj+04WXCnJYbfLJh1ZBwDtJj8G7MMewu9fCaaybbje8cOwg4pUwefeTVejSNDq1xyPoKlxdbsA73lbZX/Hsf4TB63NaAEyKr0CGKNOxvnsEzDBLvOfygpRy+aV8CUaSglvYnkFMomTuY9rXXSeTBqSYqMGe5sB04db59fEaJm5Rq6viI4Iy3LYwatOk0ICt0EtImil4UamcQjcIwEFVNm1qiRF0FNgaw9fb+E6GYFVBzGBI/WQgNSvXisQ6Xw80SaRWEKTJyvUu5hlYBVPutcxSwYIf1ZknQ0TFI2aFXMSRRFjJaghNXUJ0ujvAgFA01OolgVqFQg4UY0ZKqVFZHy9pRKA5FAl6T07zpHfeX8trHiop/TWse9yuKM5SRJsxOApd5S5wsbUdzEllWUOqH7Zk2lPI/9GdtY84JdpjyhRnv1bFjyeHPCspZmMc+3czY3Daoz3KwtaGF7j/7HpiZWLcm+zpP+Lb58/xGfPH1BvVGojedyc8qsv4F4mcsH4z5QsyfoX0CskjzZsYBTeW+cN7cX5L0Sw38h6/XH2Jrp33sfkRempR4vdRzSnl16Nb49xy1/Y88G6KPv3QKyJr7ULS3fQzYHimmn6KLRI9q18r4KEbWN1C6inUUtLbECFaULonT8UzhnWUWdyWQxs1+y755EdzFGTRxEhzcF+dk8N9QXsvwNpwIuhFbmfZxFHpxKp8PBG1ZDw1XVcmq3VDrwaDjl0s32ktrXQ8tqEOCMrZFpGtmJ3c/SGKQLACXvhyprX2VTWGyOykldEcpOwr4iYbfiICYjISMalJcSezCkSphlUSVS3iigcSpRVYHaBGZW4oTCzCqAVqUlQXFqt6xCw1Ve6xvtuQozKhU4Nd1YdhizVu+5lo6GFWkEtroUqQ60GQ/1fwFOdMv31S6DVqUrOyx1TYwDfUnsII2uzrTju+vHUn0SLUsdcQneD477GuaqwqXIReq4p1saVdFkvcWIgGNGeeApS73lQ3+Pm9Dyvzr9Rd60V7znHvBO9YxH4ZT//urzNNrzpF8Sk+IizWiBtUmi1wZEr0SrMTO9u6cz/r/uO3nr+6743PwDXqtuqLTEkzEqrA10bcQtLH6mqVYhNyvb+UuQfZsC+mpISJOrHfiVxgSims8w1vCHn/80nxs+5Iv2TX6q/W3EXOF1FNQ6xBWMlmqsg6GypMsU4NrTeCt/H7L493ai9u3rnsO5C2sobPUSAx1+hl3ML3/ItRk7GY42++WN1Ssw61sYZ9WWzUPFA6WZdp5gfPDUSOO91TEEbgUiY8ZQa1JtCfOaMLPESval/QTMKQ9hZmWNLJHsyOsAyoHOHaOSkUfU1+xYIV4zm2+ZV46Lbsaqa/BeS4v0/LnQJMIDxw9/+j1++Pxd5nrgg9N7/E/VZ3h8taTb1LK/IKCVuTH4oSW1ATULxF6jB8m+lMltHNh1Qnvp8BEtjJl+n9Auol0cGVkC9sfdrPMRFcJI19Qrha7MiCr7GYRFxORARy611I+Xxb7onRVxd6vjGEiUroYhKVysWPuG82qTBeOz1oibC1tLi2C6S4ab2GJINNrxwKwwVSQw5A6PiR43LvAuRbRS1CjOdA/2gqd+ycw4tllIvsmdsF5sZlQ64KNmE2s+dOdUKnBuNmgil8pxE2e85+7za9vXiUnzen3Dm/aSOgc7lQljNxCQ+vlfXL3NNmuIGR354OYMHzTrbQ15kW+faKo1VGth+8UK6puEegL9PUW4tPw38x/g3m/b8Fp1wzO3JL67oLnw6CGghtxy1phxEVVhlx1XIe2DWN6TfBDrXrIThZH3MmBVySAeyUzAZM78eoCvtHNg0mTxnlJxJ/H83udKma5suP/e+KGXbav9avymG432uBOY3XImJs96Kfcor0/H1D+ZAlpKgdHEykp7dAOw05SCffwsHTpCaWdzijZi0mnMuI3zIypsLV2mOl8xeEMMktnTeT5Fk0jzyNvnV3yivaRSgWs/4319zko3+KEAWpJsUYMiBgtWWFzJm10nqXz6OpC1H3cZ83LukijZJU0K8KHSxNZEL2tR0dgaDMpUef2QjH2qE8pEVAm8gBBLJzqT/c+Ys7Jie6SletqzQwGNiZHWiC0SgXhheEUUgxLB9JjkdwVYFZipQUo8CbnDI/gkOloAISW5zQkaFTjVW9axxuqAj1mXJh9v68T2RKtGm6ZJtNqhYsKqMJYnXvg5KSkWZmCpOxG2z+wxlYNDEC2Tp8Ny1BvRKnEztMSoGFy2Cyph10oYC66UGxjMUKPWr+PvfZL0ffClH3vI2fddYm8sm3++IP2ddzDdM1QYUOESyLIKoz9UFmxGIGsPxCplUntA1jeBEanJ1sc+pPj12Rom9mRqaw7fO3j92N+7Nybn+MrUfFuOZNROpuFlqkXgtq34uO0Oty3srAmQdet9gwTqMWHXHuMi7sSSlKHSUkmhAoSg8K6SxISSskNrI431bF2Fc4b+qt2B04OmutDUF2Jr+jMBmkwnYFqsQc09PmhsjgOGYHiyWbKwA1ZHnvRLLro5RkdcNMysw0XDTdfQd5UkR6Z6i1aMVcoAl7wo64zO9qyAF9qlEdzQPo0xjooJ4z3/yRf+Ht//P36Nf/I9n+H/8m/9GDEJ8UAr6faIF4AjRUMyiWg0SidMFWmqnvN2y9wOWCXC7zeuxSWNzutspQOXfs6slOPAWOL9wi9osl7uUksS/jq2rGPDqe543Wxzl0MZgYQnjN1pD4GsMkRbSxhW01LBB3pGRUejLH3yOBJLrTjXnps00CXF4yAd3SskWRIRKZWrOLCKPXMt+66UISTFTRp4z1csteO320vetDe0yvG726c80DN+Z3Mt8c5wzZnd0ihPX1n6YMcy/xAV3VBxttjyaHMuQKqN+Nxd2V/V/MrqDT7dPKdSgTdmN1x2M1zQzCpPf9/S31uw2WhONzvH41bjK6VIWkscq9OEocUuWZiEifSH11/gP978UzTwg/5DAH5y/kNHr/fRuZsrYY6yrGCUKdrDEI6SbPY/l6zO320CUBWbMvn4lG1V7NRY4TL53Ah6RQSJisjNn+yzAI0vM16BWd/C8NFg+pfc2Bx0GTkShJQ62dRWhEVNaK0IChbdKFWMVdoPptn9XgTVQi1MqEgi1knEBvPDMZYI2oTRkRebGettg/e5Ve/5gKNG94owi7z55iXft3zE6/aGue55YFaE1zT/OL3DM72g6ypiyWAEhd4q7AuNX+yOLQGFQnuo1rtsxq5kRYQVtUsYF4V1FXLpWS5BK7W4+KzwHjPY1Q9oV7N9WLN5aIi1aIJFJ91PSlZcLnW5YDXBeBrrIUGt+zFjbtQ+RXsdpAyv0S4HWC1r33DjWxZm4EG9knKQbN1WoeXKz+lSxWfrJyzVirmJrOJuv6UrVIViqSNGdXyqfk5Ac+WF+jzXA4/VKd9xdsG9epvb1EqL+0Z5DIkuVnSp4srP+eLNW1z0cx60a7SKXIY577kH/KPL7+TN9prvnj1mULKQX/k5V0PLxte4aNj0tZAwombWOprGs60jfmhZfAT1KlGtkpTVKunQYreJOI/8F//j/8DvWv8yj3/4jL/2B36E81+G+tqhNwPqZiP3qrKkWbNbEDOIRchA1uB2IFbJnh8bpYHCEcdqR4tVR/8ex9jJUE3mkdp38AsYPX1tMufGjMOhLUnk5zRP6yTaP+PnR2SA/UX7VXDxanzMiEnljO5LjMMgZhpwT22NUmAnQFbOHoow6W5tTnv7mfxefLFc4o7OWeSRNZK9ngQY0CqydZVk2EMGDdpAQECoZBMnJ1ter1e5o6pnVg2kueJ9ztmoGu+0CMgrsSck0Fsj4vCmAGlqzIiX0vYpAC3afPn9DGjJazuQPW+4K3POWhiJgAqGsKiysDGiCVZJa3UNY8IEcmCJISnpvAVglM+O3XQxkOGSgSBsKaMifbQM+V+lAnMzYHJ5IcAQLR0VHsN9s6ZmoFIi9j4+DvnGaZToj+A5M1sSik5VJBSVCqxpOGu3zIxoTMakGJIVbauY8Erjk6GPFU+GEzpfMbNOWF2p4irO+Hp3nxPb86BaZYl2xEYFm0tfpPOYYEuaqgokG3HGEoOlvpHmL0lDNAqYof0b1O8kzv/tx/w7+h/wPf0Xqc8t//cf+P20X3oH86Vz1OUZariC5AXYs3aXHJiysaIkSUYQq7x/bIw25thCv48K3QlYHXP24fZcujMTfoedmex7L7v+cWDVKxvzbT/SYUKs6O4cvn6LjXW3X7V/gARG74NXH7O9sLPYlTIBaohUVw7dR9ypxZ0YtFOitTsY1CD6vl6BfmODjxofhbmKTqKPqMGsNM2FJN2HU3CnET3IhFEBwjIyP5EAzYecRA6a2ig+XJ8hp6VorGfIjUZ81Fx1Ldu+InZGAKo8x4RtJjZHrrPENTqkXeyVIFmEYeUTZijVJfl6ZG3GH3/3/8N/+pW/hwZ+13u/gvLwf/7RPyDAnxe2czKJlIRckEICKwlmM3OczTo+vbhgGypcNAzRYnPTKp+0aP7m9bZuPI0a6KPFJcNJDlqLhtY61mOJ5SY1xKg50z2NkoZTEUm+ty+xfoQkjKk+ibMyrUgpYJToZsma2ypLlQJdCtyIsgyVks7sFZFTXfPaRPMtEnkSOm5i4l1/xpf7N3mnfsbvaF5wX3u+v/mIB3o2gm2RyNOwZK4H3sod24GRLOCC4d6DDTPjeHqxFPC0GrjyWrRDo4BeS9NxX6140p6yWda8f3POadMxe93xq9sK7RrmT7WQMI5OBLXL2gFjN3djJAk2YQ1/rvtgJ48NfM4/3u3j1j4n8/XY/C3JmYmG1TiHwx1273CMIFTKoBxjl/fD9Wa/7PDwGhzEOYkdEWcCdKnpMb+J8QrM+hbGOtS0L25rIO2Nw/KnjyuH0ppUWVJlxs5RasLG4vDZG521ySZKgotYxBWRxbVo8+heZ6E1RTKB7baWroNBSYcpk5gtevSip9vWnC86ftcbX+M1u+ImiK4TwMPqms/f+4gvm9f5UJ2yqmqSVyPry7wQQ+NOwC0l06CUUGsLK2vajlNlIEu7NE6yAmSV7oXFER2Di6LHFCRz3p1p+nvCJtODIlxXuJkmzT1V7VEWjJb25z5qKqNGkGvja1rrxpa/IWetI4rOWUJSnFjFwvQje2obKvpgBeSqAi5aHIwMqK/2r4u4ev2EF/GGgGGhPPe1dBfJTSWpldSmL9TAA7OizWWDXao4tVsWVoC2dWhYh0bOxfT0yaJJPB2WXPuGjRfQ7Xpo8VHzs+odfunyTZ5t5jyfL/hwe0atAy/6OVtf8aBdM7eStWnOiq5ARR8MvbP0JkCULHk0ucVwADcTMcxoFf/7X/v7/JH/8b+TIO5n4bf91x/w/z7/7fyd5odINxtS1++e1c0WXdeykGZQMnkvHTymNeIlEJtSaj9u3kwX8EMg69i4gzb/MmNvMVYHmYtyunEXUMeD7cefpcSQ/Pur8Wp8zHDJYLff4Dm55ciM/x3fVmc6enZM9ioJDp0JNXl98poIn09enzgzJWmStDhyztncdbBsl6jqgKo93hma2vOJxRVzPUjnoOwIL0zPG+0NL9SCGxqGXhpRlKyd9grbKUItdq84SJIckfm4J9BbMuRhB5pPmVk5+trLmMoUzTYrgW8Vfpb1woIi9UbEl6uItqC0lByKkHu2y4WpFA1Wx4yT6VFLK6HwuWNu0oFaSTMOh2igeKTrbmt2rdZFOF5z6eeS6DBrtmkgYahUZJappLroqSDy9jXC6Coi9J5cTq8DCQHJhixhX6tA0AKJrUMtQVHO/pcs90ec86w7YeMqtlXFjW8wKrH1FT5qZtZlMeKAaTJ7LBpCVLn8MuZnRf5JsksTqoZUV6Tvh992+jN898//MulvK8LDgf/gd/738MkzvvYd76C+dkryVr6dF5avGhOIcp9TPACx8r0d58N0bnycrTlgYn1805FyHO6cineNjysZS4f7/dilYfLmK0Dr23pIZ7WsmXXsudxronOEXbOne5OOz4tpgHzsgRrLq1QuHcr6OpPkYLISh1SrgOmldl063ibUoDBb2XA4rRiCYdU1omlU57K4dSUMzhr8POGWE8AoJ0biItBUjmUzEJNi1ddoBeuh4mIzY+sr5pX4v52vRHfKiL8/bCsBxfzEXStVJOXy7L7OXvKydGM3Q5IuuYLcj9qTKsEPvHh3D7D4ofe+KvvxgAG7UYSWrHso3YFTStA6mtrzcH7Dud0Ac1w01LmKA2AbqlH/t9GeG9dCBWd2O9qN0tlQq0hrdsytkLVl+mSokwjDF0DrZcdVHOgSVGqgVQpTEvdKUylheX3oEwsd6VUkpDQ2FmmVdDXcxMBNipxCZmJFtmmgS9Jf/iZWfLl/k2d+Sasd75oVhsRDMwD1eC4Ww32z4re0H7HUW7pUs4k1vjI02jE7dbzZXHETWhbzHmsC9+db1tuaoKTpwP16w3dWT4Qg0C6kMqefc1p3zIyDT8Ovdm/hv6Kprz7+2qTCnIddN/eS7YigVOSL80/yg5t3Rzm6L9Zv7u3jWIXKCCpNgeZjo8QoPh4Bx3Z2pQi8j4kYLaSakblfSohT2gO0poLwJZGvYpY2Kkl+tQ96TRnzI/tfI5jENzFegVnfwnDRSI10Cb5LTeso8D7pRnAHHXDsQKA1WAOZubJHUSwjP2xq+vBME8lKMsVhlrMJHsIpxDaiN6JPYroSfCRCC25ToVZWHrw2kmykNzVnp2veeGPFOycv+M72KVpFVqHlmTthHRpmesDqwDvL56xdzeakIcYKNOhOylVUQCZ3El0R7RV2nbWyYvmOOZBKYPqIHiLaT7WWuAViHWZSk9GkyhArqZUvwZXZanGWvSYuFGRhyNpKWV0JLmodqI1nYQYiim2oRkH4LlRZTNByORi2RgyFUWkU5l3l/sKN9pyYfhTxnWd67zo2aCKnuqPNqsghiyqCKLN0JThRcRSVD1Fzr9pwE1r6aLPjLwDbZTRc+xlrX9MF0fbSKtEYz0U349HNki89e4PVxZxqPuCC4dH1km5b469q9NLRPbDca7ecVD1Xm5aTauCTJ5f4pBmC4V3us2KBW+6YDqERcX2vFTefgR/6p7+6Z5Q/ffmc/8Pl30e3F/xt8/nMTGBki+zduUnp7d4Cre/4ffzMbfDqG469QODgs0dKEUfm1XR78nwbAbP8vt79Pi0NmZb/jtur3TWQYyQ5/ktWBLwavzlHSHq3ZkJ2MvbnwcfPhLT/3Gu1D+oeRs6TmH2yi1vPc7KMQFhoJIiQDrbCwk0KKVm2iJj7IKV2ycpz772hbQYWJwPn9Zb7do10nrVsQj0ykrRKnDcbhmhwtSWlkuzJ7Moo4r8ksh4Lt7Syxq+WkCAjTNhY5fsd2phDW6N3bebLdwek02LSxKiEQVABRGkrr4rDKuV8RkfqDBq5KCUTRkV8LnkPSdMF6XpoVMxgl+xjyMwtoyK13mloFe2TIVkUiUb5XTdFpMix/O7zSSuV0ETpMZUqWuMYosUnyZvH/LNLOjPEjLyX5FkzOtL5itVQ83yzYNhW6CoQknQh9M4Qe4OqI37e0VovHQuDpdZhFC4OSXHJjIEaMaWKoiOSDMQGhrfh5MEL0pPEL33JcvYFw2/92sD3/Bs/z9fOXifley8Ogx9v5/7zrCZ/3P71qC2ZzoNvydYcOR4TW3Hs0JNpeaysfW9obs3NW3+XtPcrQOvbdsSqLEiSMDzKypqM0e+KUapHYpIYZFqKfjim+li7Hd1xQoxlhsmK7m9SEGtNrBWh1vT3lNiNjRU6VgaOtFe43nC1mYn8SZTwKiXQGy1gT5UIiyhNqCZ6idGAagLdUFGZSG0Cby5v2PqKVS8lhB9ua6oqMGsG0Ss0whwOIbN/cwf2aIRUA+LTmUGhwy4ZOXY1zNUzZoBqkzB9GrWNCxhQEp2/cP4d/I6LXx0Bi5976zMCnO3yK5ikUJWAdeJPJtqZ443lCk2ij5LMvl+v8cmMMikLBhE6DxURJQkHFI32GCJzMxCD2JxWOU5Nx32zwhBHO6JVIqDoU6JRxUXVR7WypsMTuEmKTbR0yTJgqAncNx0QMHg+CCd8uX+T720+ZKkHzrWnVYqFUjgSc1UzN/AsbLmIW5a6FjH4FOlSolWKh8bx3c0jbmLLudmwUJ7L2PCrruVZ2NIly1IPnOmAS3N+S/WEWkVcEnbaXA/0yXJPbbhn1wDcm285qXtOqp7H7Qlrb1Akrl2LVpEHuud76kcYItszSeqcVxsWZuDFJ2eE5sFeZ8ojk2335mEMktLI3Pqp+/8mxMTnN++JZlbzA5NYRe3FTbfmXm7UM3191M/6RjFG2tmcwuQaGV2xaLbuz/NUmFp7dpQdVpHyXEzsNLsK0FW++iROSiCEmIiwPT6mj8XheAVmfQujlJUJgHUHdl0evKyFNY5p1kNrEW0zJXshC592kdDI69qnow7PPm1P6qr9LD+UGsJJQG809ZUW9lOQoCPME6qJpM4IpTWDULGFsIV11fDwZMV3zp8y1z1XQTK/WoluxjbWbEPFzDg+e/YMqyOPmyXBa3xoRadLS0DRvID6SmH6hO2Y6JdkpzqksauU8nH8R4xS8lKArLjf1bBc3zSr8QsB5EwnGfuxdj0AgyLGim1v6NtAOxOQqbVetLK06JD0UbpV+ahpjafJtF0Bt8wo6FtrLyUTKFauwUfN1lY8bG4AcufC3EExGQJKWFZ0+feEUTuW51WsWKeK69hKZ0Tj6FJFrUR4vtVOWrorw0klzDCASzfnIs646EW7JCbFEAzrvmazbkgvanRQuKDwXUXaGKpLgzXgm8iL6wU321ZE7bcVJ6db1q7mpO751OKSr6l7xDrhltJ9pj8XRpZfJIY3Hd/x6We8+8sP+JFf+MreOqmBz/Ufkprv2714R3cdNV1w81yRkqXpPS5U+f3V+JsVf7+14JaRHa5p5nDUxirYss4LtyLP5SPBf9m+gGGHF2UqdKIKoJszbq/Gq/ExQ+05QXc8L0cYisA+CDXJrhdbU8q8o1EUEc6jRzgMxLVo6JX9pzqinML0KichBPAJFSLY5NXY8EP5rOXoYDCWRT1wr1pTqUCXKkIujtOZ5eqTsJLuNSLmu7YNMeZuvXnOqQB2C6nPrCzPmBSags1Ft6SUfOwArQmQldIemDBeAGuIlZyb8pBye+lSZpJQpF7jvSLYhK0CWOlYWLSyUhKgSpFG7Q6btRpdFJ2smBcPY0TLKqEYcocqpwMnts+3Vo26W5JEAZ+kDD0BPi9VJUDqk2ZABOUtiUo5HFq0rohYFTKQpai1w+bkSxcq6UjopSwxJTme8wY3WNLW5EBMMXgDTqE7jdaihbbta3pfkaLCO03dOoZoqI3nrOq4Yia6MLU4x6FVJGl9ERYAAQAASURBVB2JVU84CZy5Lc+Xn8B8/1f5t/6+Q19dkurA9daQbnx+YMs9POKLJRjh3rLOq3Lj9ueQOpw/TJ6DbxUMGufi5Lg5Lh5tTT63lyo9L7bmmG2bZNbF9HwMQPFqfFsMNS0dmpRnfcMx+l93AFQF3DpkvkdGpEfBXvCpUsrl64poNckqYqXxM01oFMNC0d9TbN6MpEYCV+0RpqvNALxXDL0lJYWx0mSjv2hRTSJWAdWXJL0a1+OkxRaYxw3D44anNpGqBKcOU0Wq2ksnus7SdQ3OGaoqYEzEOWnyRFRgEqFJYs/yd9U+l6773XcswTqQdX9zYt5NkvJh5/uplPjJT/0oKiQ+t/o6P/eJz/LXv//fw/QSmxXN4+R3djLUCdUGzk82ojsZLB905yyMAFdQmL86J0TknIUFHMS+IDbomTvhzG6FlZW7HMakx0YfZVzGmpvYAvBZe8VcBRq1DxmEFIsaJEZpNJq5SlyiuYxzrmPLud7QKs9lnDEkw/+0/m4u/JyA5k17yZv2it9SedERhrHr4VwbPvSJq9iz1IoKRZ/gWZBzWOqO728/4B17wesm0qoN7/lTvu7v8Z57gMl8sp9bfZp/7/wXecPcSDII6JPl+XCSSzMDj/ozlEp8enHBTA88mp+yXrWkqHi8XfLL/Vv8ntm7vG7W6OYRLhl+fv0p7tkNczPwudce8Quz1/a1RjOAszeM2i3BBT6YCsYnIUj8V+e/G05+GJwjOcfRcUdJ4TjKfC2IaWHE521H3SwY7eGtssG77GAS304l8WcoYK7efb/Rf1KTfend58djF8B77yB5zr6stAavwKx/OSMmofuVB+NQjHEacCi16z4wZYCEiJRKKIKRDMa0XEIof/nhyzWsKsliiAKzheFM9mU6hel3Tk9okrC1bCQlI0GFTRKM1FIeobU42nPT88CuqJTnI3ePuR4wRFahodaKy2HG0+6EeTXw297+gJVr+BXewG3mVNcqCyMCWdiVEkxkxBZAJYWKEeWTsLIKkFV0lXJpgMqlaNNymGQNcV4TKzFm1Uqo1qER0VuVFCkoGBJp0KTOsOkMw7xiO1TMm4E3Fita49Aq7nUEAQGmfNL4nJWOqFEg3ubsOMD1MGNmHI32nNkNhpTFFjU3YQYGBgwuZU2uBA5FlwzP4xyXLK1yGCXZkU1scMmO7C6QbMnSdONrMSmeqBM6b+mGCpfr/vuuIq4qafPbyLmmjcFey/uxSTBohqElXmZ24VlkrRJ9X+GX8qyuNi3hLBAW8ixiEmbu+Z63H/PZk+e83VzyUz/+w1z90j3+t//0H/OWu0SRqbHmjT221V2AzdG20NNxLLN4VxnIYebhWCbiKNNrekKMAcL072MBTKGPp6TGwF1ospNApXw2sUtV5F/H1rM5W/FqvBrf9JhqYE3/LkNNfpkCv9NNYu5fk5/daLI2YXn2sxM0gr0ZACsin9pnu4M43yp3dEIxds5Vetf5KmkE3DLi0SiVRnbRXA/oJCzgSgUUiQHxdbpg2fiaSgceLq8ZouE5JwRXofviPLITgWf3Wpo4UTvNrAOdrD19rBygTWxB0ipLAAhYZwbGMksFjPpgARGq9+C8IlQaVxkq41nUItirkOsxnfaJLHybVGZc7dh2Oou0g5T2VVrE3lvt5BblMsUhVqAdAY1IwCdyDxU8mk2qiGhsvrZGJUy2U9UkmFFakjbltZQUa1WLfk0wY7lKcJo06N36BwJk9ZmJaxIERdhaUpcTFW3CKQjeEBv5hoOzpDbh63xFNKiq58HyI+7VPctfO+OffeqH2N5f8KM//o+YXW75cvxOfm74HaivKWAFDOw4aLdH2ruid4xbduWuxf8b/H10mwPg7NC2pCOfKR9Ncv5l7o2fO7b9JGEyvqTyG6/szLf1SLnZ1K0yo8Nu0EqjSlXIoc24S35hygKOcdQ/PcbWKIyOWBtiLd3GQ6NxS8OwULilwi2gfxDRb3YEr0kbm9lQiB9lyukojA3EoOkvWtExut+hdGJ43qI7SdSrqFCDIMMCcCVMp6Q7aoJkDG4ux4z3HLNlj68CfrAMQaE0hJUd/bHC9AJ256QEzCqvRZNZWsX2xCyXkitMyHamANTl+kY0f+uT/y7JaoazCnujqYzEdGNDrHLcBKlJNIuBKleUxKTxMdEryzZULOyAS5oqIyTb0HBadfShaMwEVqHhxPTSsCoanLIMydKliidhybnesNA9Sz3QJYMhca63uGR4EWuMGnhNg0FhM/owXW0F2IosteE8DWyio1MVRkWuU0OXpDTw6bDk2bDgjeqGPoo9+pR5xGXUYo+0p1GWuapZ6i1usmZVCioVeRRO+MrwkF/cvM3/+uwXcOmaSkWWuqNVjk2U4z31S3w0bGLDtXL83OYd3t0+4Gl3wkU346QaeL8+52Zo6bzIyZSy/+hyHDTU/KPr76ZWgberC2oC39t8xEfuHKMiZ3rLp2cXfOE037eUjtdlHgDC6Ml2JUk5rUAqXXbHz3+DSi9gFHOfTvesN31M304wgYlNOTb3D+1S+QxlrgORTMZhFwNNd1GYwxm4mlaUTZtjFTNFEl/ym5FgeQVmfQsjpoNouRiMXG44Pnh3AVnl5+HDE6MYpCQPoumj0HJNpqpOsi/JKtG/UhBqRawVdiWOpVsmdK+pbkRQcNQU0aDaIIc1iVhHaCJm5mlbh9GRZdtz3mzH4yz0wFwPXPkZj/tTGuOZGcfzfsFV11KbMGouLeY9129pNqph9khT32QB+DzRpwZC5e8oAchOjFe87x2QhVKkyjK21AYpL6wr/LwiNOIom22iym3lQ1uuecqGDpJTJKcInWFdV2znNb2zLNuet0+uBKDK38UnjSZRZ+ZW5ywr14zihVaHMStudeDGteMzcWY3o4hxQNPFik1saJUj5JXhJtY8DadjR5HX7TUGab+rJ6vhJtaEJNnzcmyXDKvQoFWi0pFBRxodaSvPRVR4a4lzpNNXzMGFykAWYK8N9YWifZEYzhSx1gRdM5jEs6B5oRb43tKcdbx5fsN5s6W1ju9aPOWT9QtcslyFGb96+Tr/1+/9LP/Px5/jDz/6H/ice8wX67f4qfnvQClNEXRXIZBChBBEI2ucDmqSFZyIld5FZb7DydrTrjoEqO7Yz63Kqkn99yjcPg0YDgMPuDNzUfR5pqWIkIP7aRaiBPbfIMZ6NX5zj1vaJAf2Qx2+Ln8c/Dz8nZ1HQXZKfBKWola3nbKcNEk50EhGHPukc0bZSzc6FXfvowCbxrmTTAKbUDZiKymLqG2gNbsUXOnA2sWKdWgwSphLm6TpvMXo3Jk3GurK058oHIZqtTt+AZp3Nb3ltcP38s8pqFXWkwmolZQCY4gZzFIJlJM+ElJeOblTSQmgFYGoiV4Th4SvDCFqahtY1h06g0kpQcz30CgBuH3UDNGMcJd0P2T8vQ/CVsBCmxuTaBVz8kU67lqCgOWIBsomNmxTRaM8C92PgNoUUnNJGMdqArWJELzJTDkBH60KUpaTKqLTwlDLZRIqdyMbNc16jemENRcacXqjMgSl2UTFVtVErzGt56TtaY3D6sj9es2picR0Tff0s1z8nU/wcye/m1+pP4+urgmPW/ifga8AzXV+ls0OoIyJNGFpqQLojoHl9Pcj4+jr+wjSrcYhR/dz7MVJ0FDA1rvAqTvOZ7xrxdZMdEfk3NhnBytelbN/m49xHbqrk2FMImdyyMCaCLt/o/LCw4BXdBcZbVEySnT/Kk2ocsWJUbiFxs1EpkIkQUpiNdfx6Un1ST4Fs9GEhaadDay3ImMye7DhbLHletNSUr0q5hLD0lHQik5vqKUz68gSznq66brCNYHZvKdX4LYVsYjHO5nTyiv0sAvW5TiyjhWQaZSd0Lu4xnRRmlhlvWMVRBS8rBWFvaN8BB+xtZb7dikxnj8poBjERbajC4+1ga2rqIzYS5E/MdRa1v5T63k2LKi1xydNH+wYn/jMvpqbgUY7LtwiS6S09LHi7epitCObWDHXjorS2V2YQeuogYG5Eh2sRlkaZTFKyg9BShErDOc6stFbLuMcELtyHVveH+7hkmZpe4yKXIUZZ6Hlnw0PqJTngd7wFM9cDZxry7m2bLJOFsCZFtXHLm155pZ8fX2Pn63e4e36gqXu+FT1PJc0PhuPHReaIU+MSgXeaG5YmIGHbUVjPE+6Ez66WXLa9sSkedwvudjMoDcQ4fnlCb9i3+C99T1+x733+P2nv8BSd9yzazah4WF1JTpcuRyUonN91zw6NsdiRIXcEMuHXexbmF7HkvrH5FbuYmrt2YHp65PfjzEvOWJDJj9VSsTD5D3cskcj63jqF04PrXe+WqmsyifCy45XYNa3MLSKcq21YmxZNnnAUpqEIC9RBnUoTCoUPgF6zDbATDIcCgGASp1aMuAbxbBU6EG2d7UssNWVxm7EeJSTCbNENXPEoNEzL1nYxnEy61nUA2d1x2m95WFzw1J3PPGnuGR4f7jHe5t7fLQ5ZWYd75y8GEUHQ1K82M5ZbRuGwYpWxizRvS4TpH0+6eoxyRymfGlUzPXlY0Z8ElwYQ5rVJKXQvYNtP17T1JgM9IlhMQOoG2mT291PhNkEKMgEI2FrQfKGNGiuO8P2RIRqz5qOZd1xVnU0Wd+j0SKOnpJi7eoMeHmIUn5RkHwAfM1TdUJMitcqYbS5ZICaIQcUNyhczog89ye02rHQfS71kNKPjgqXDF2y0lIWS+m0qlXkhTvlw+0ZG1+jVWJRO0LWMrE2MDvv8F7jewvXFbrPC05UGAdmo2guxXmIWd/M3mQ9g22LayPm1PHa6Zrfcv541AN7rbrhJrasQssv3bzJ8NUls2dBqLEPf1ScqKyTpaZsBxBAyweUG0iD2+lp3TUOHa4ps2T62nSUrCD725R5utfRY7pwK3ZlhHyDNVSVjh6TRXoSjJQFeypyKNtM5veBTtatzh+vxqsxGWpM1R53VhJ5vr2MnRk/MNlVElBFoVA+SXY4ZwtVEkyanEGLVhInZS0X5yShezWWrJcRq4SpgmglVLIAGxuorac2gcZ4GuM5sT2N8qxiQ0RxHWZcuZaVa7A6cl7vxGtTYuyKGLwW5m0FfiHfyW5gJ9o++cpqAmhNtbKm5YVZuzIplZ3KUk8imi9FL4uU9bgG2aeIwacRWNiBZkrWwKhIwdB7haslOdIa0ZBqjbCsQi6lFCDc4qIkU4yWuk8pTdyZzyGCCjUpKeZmyH2iBMwKSeOw9Aki0oVwE2usEmF5hWg+KiIeTUDjEY2usStLfu62sZG279GgFFQmjOur1hE788SgiEFDr3NGlQxsSVBmunxZyrXr87rsFdGK5MG8GXitXYkeWGbpDckyxI6nwyXhvedU7z6EdSI2S9RWwXaA6iPgGjHqGqghieC7Ks1FvlF53bF5NXW4j7KBJ5oiRz67szW7/aSy3ymE+BJg2F4podr9G1+/fWr7v0/t0MvHB6/Gv2YjVN/g5pZknVI7VrzWtxPuaaLnC7u1MeWHufhOtSU2Npeca0IjWljaSRIerUamuopgewGXqptErBTOZwZMZCwZTCqXiCuIdcLWnvWVaKe89tYVD09uWA0NKSn0oEWYvcpsW62IVcKfRjjxKBPp1pV8rdygqnwmPG1Z31e08wFzEunWNclptFO7OavAbHMCRyfMVuRSpEFF2rHwY/ahu4gZosRmUWK4UWy7rKk5maKcdGU3G50ZXznxEBR+LgLw/f1EOPPYLHxvdMQFw2AMNkqTqtLEqsiPDNEyM26sKqm1NBKxOnDlZ8yNJDueuZORqfVcn9ClioWW2GoeBWw611taFWhV2FtDtFLEO2ThI5EhieZWpTyXYQHA+8OD8Tx9NFz5GZvcSfGL20/y6eY5b9orHpgVS91xEz0LHZnnZ01P1tm58nxH84z7r61wyfK1/jUu/Jzvn3/I99SPuG82dElKKDexpgqBp5yyiTVr3zAzA981f8y73Ws8Wp+yWbd4b/g1+4AnNyesHp1gr4RyHYYZX8/dML/0wUPi5xT/wfkXmOuBVgkA+MydYHqJL3QsoC87MGrvAuX4KOz8jlKFNAJZIUizknggzzJN/pfYZtTofrmFfVIgMtkvkxhr8hpkVuPUgE1+Hux33N2BXRSgN40VZFFPKrNKpVa1e+0wdnqZ8QrM+lbH9IK/pHbP0drW6duZFqh8xJRyQiNtP0OtD7oHCOsoVjkrnTtuhBah427yg5udnlgn1P2epnGEoLFG6qjnzUBrpVXp1dByUvXcsxseuzN+cfUWM+NY+5qNr0em0oebM4YgOlqbvma9aQi9OLz0GjXIhPZzcJ0aW24XwcTDSaViBuj2qJWK1FbE1qJ8JBX2TjauyehxEmkvui86QZWFFoeoRBQ+J49yLYcYzZj9X29xg+ZZMGwWFcNCtEleb1fMjKOPIqBodKRRUtM8BEvMzKzeW5RKbFLNkLXTqizc2+mKuen3qK+b2Ei78lRRqcCCfmRwVcplEKsaswmtdqxck7tBSUvdG9+KGHxSuNyBsbWeEDWVCTQ2MHjDypkM5OTFRieClWDCz0Xzxp+IaL6KUpKqHXilCZ1h6yxfW92nNqLl9SurNxiiZW4Hvvj4TZZfUzQXA6nNHUR82F+DpgCO0Shroa6gctD3pBIs3pVJnDIcp1OmzIEj2YmP7S41ea5ulRgeGfuMr9uBQClhKu8Vyu5ISy9fP4NXEuNNQWv2r9Gr8Wq8zHhZ0OqWR7CPYhVB2tJlCZUdjlhAKuHvlI/K866kdDw/+1IWocYy8jHINqBmAWMDKWq0lgx1ZQNWR0IUcdraiC7gKjY8HZZUKojQezTCVAJunHRxVQpx5gdLyg7mqMWVHaJQZ+2sbF/GYH86zdLk3/SaWi3lM8UhLR+cguLluukM1qQs+dBmHbCpXUtQ2rkLW0gTg2KTNK7WzCsBNRZ2wKow6mMplVlbKELUpMzMClGAJpeE2QQIKKUSXkmpoFWeIRkcGpdE0N0nIyXseDQxM7KEBVzAL5DS+SHasaRxSFbsX1LZNxfGuM3lLkYlrA0ErRiiHsEaueYi/J+C2JmkhRk+XtKsjxMRZ9kHzeUwk33qwPNhQUiaSieeriLN5QeY7ilp0DBYiANKbYAt0IvzUxb0lFBaSyIsTDLd5T4fnSrHixCP6mUV3/6lbE0+5jfhnB8DqMqzfNQG3QlepdsbvxrftiM0iE8UPuY+T+KHown3SaC8x8aavJeUItWWMKuIlQi7xyrLigwCZCUNxCRFbkaPbCbjMkFsI/6mimbUHJSTkh+lRN2tajCJe/fXPDy5EX3bYPDOjDrApelTMgl/EvnM937EZ5fP+Wh7ypP1CVermXRt95qoQDkloPvzhk1vqJcDtvE4p0lKAC0dGO1KMR/1jYDzOrNvpokbM2TR95B2VSiFdKDTLm4BiUEKmLV1xEbYafV1QCXDaq7YvhEJ9zxm5sWG5kkekmLrK0LUNNZjVWIoKAAwBLNrXqUijQ74pLlyMxZm4MT0aJXwwbCiYRsqVqHhXrWWMj3teC8IU2ppOha6z6CNMKeCdiy1JE5mk3XHE3ApEEjUSrHUjuchsEbxq92bfNifjxpVaGFrvbt6wNXQ8onFFYZIHyu6SkC1IRnO9YZP2OtckSKx13v+AU/8Msuw9HxP/XUehTP++faTXPgFv8KbnJs1bY6lPl0/p1KeLtX8o6ef5cV6znc/eMqz4YQvX77Oh0/O0U9qvG/5tfM5alDMP8pllBXYZ4rwbIY/SUSb+L+ZH+R7f/BDFronJMVC9/zS5Zu0z9IoXSA+wAS8lMk2zsspWUMYegHlpAsvIUh5YQhZ0F1xZ6XKdO4ewx/K30UvS6l9ExATI0sC9hPsd+1rYl9iTu5NMYYxDiLPYZVjJF3iM/ZtW/k9x00CZGUJlm8iNnoFZn2L42iWK3c0VFOjULIcJeCedpKCUelfZQVFBaOYotKaWBuUVmidMutjd2A3kwfKdpL1SEYcelRCux2LBC3i3fOTHqMSTSMlhSFqBm95cbOgW9XQG96tX+fpd5zwzvI5zzpB1msT2PqKKoM4L7o5WiVa67lazwjrChHDUJhBSe35hMESTXHo0p4QL0nqzKfieUKxjOKMZtBKBZl4yWhBsEGyRzl4kMwMMnEDVKuECgp3qnCLnS7MmDmPyDlGsIMhBMUqiIOuVaI1noUdpEOIGRiMYciOfR8sRptR36SUP/uoWfmGhR14OpwwM47XUGP5oQjCS7bAkKi07N9kC96lipvY4pKlVp5WeV74BZUONEq6V124Odsg2aaQNC5Ii/PWekJSY/eW3ktmIc0Dvp0g/F4TesVwJs9Jqsp1l64yY/thBYO3XHazkZm2HSpiVMyagf7XTjl7JlTYuGjQm2F8ntORhVUFlevEs66DVqhtN5Yd3ioxnFJrp9mIcYffIDo4mk1nsnjuB/bjmOgKHcu87835NMkilLV+isfmhbyAqNKOdxJZK15pZr0aLzWO2xq1C8LLczpm6u76nES/asqgnO6vlJuoDLxOnv+QGb7al3brOZAg7crL8lyIVaKqveC4NqCy+HmImm1f4wcDXnFpFmzOa86bDRtfARUml9qVsratrzLWFOmHiuSkq19h/4j3uDv2zj6W1ug7p1KCjSmgnHbJEaXH7OGoRRknaxqTfaWM3WX9rKJZGavbl1uOK7ZYh9ysI+6qc0R/Ua5RraUjYMjdBEM0FF0xwRsVKbOBpetuYBOEMTw3w8iaCllHy5USwQx6FX/VZyH4kBlZNkW2sZaOi0oAry5U+JERpwi5W6PV8r7WouXli9Go4l77eqKwr6S8kLH0UE18g/HZiloE5jMi5oIhJWGC+QtFs9lCWpEqhXIJkoNSSqmm6rbZr8gKswph2uE8O3F4tQOZps9++TkCmd/E+Dh7dKcO0fjf8Y8dvqX2fx4CWXtlWuO+05ENXo1vx7H6NEgCMN/zw87QOXGRNPvxCdwGruD23ymRtCbOK8IiA1m5jM/0E6bO5LDR7uyRrKUqC73LtC1VAzH7oalKuBnZqQfVGexrW+7Nt5Js71vWfU2M2ffPhMxYSRnh+TuX/PZ77/NO+5wfOIF3lw/4/z19h0s7Y3vTkhqRLNdOSWfYK8swaJgFqoXDDRozSInZyDKNYLwwyrRPu6AbRntQGlhpJ1IopdJEpQRerpv8Hncd2nNTK1Mbkq7RMaGixs0hnERMG9A6onNQH5OCqOmSlG5WRnxnH/XYuErnREgpP9QqEnIDK03iws95rVqNSYpKJzaxRvtEr6uxZP0mtKMA/FwPPDArqiowRE2k575W9MlTYdAo6XaIJ6ZEhaIi8oYRpu372rGwPX02Dk3WGn7RzbnYzHh9thrlWB67MwBOTIch8SjLylyGBY/8GV/ePmQbKl6vV3ymeSqPNpEzs82d4D0xM5Ndsny1f52vrF7jQbPmQbum85avX98jJcXl9XxMtlQ3ivapRQWo1gl3oggRqpvE/BF0ryl8qwibOf/l/X+b//jT/zhX3cCXP3iDt59G9BDQ3U4yYdSVy5INeyBW0YQOGcganLCxQmDU8JyMb6iXVcYB7rUvvD6xQ9NKlb2qlKlNvG0yDru7j3IqxeZMY6GSFC3b6V18NL4nO2UPNSb7COHl7dUrMOtbHcUhGmvG9e3a1vJg3tUiNwggoIzOKL7ePTAaivi5doJoR0qHENHIKh00gMy+UsRGxA+Fqps7GLaJdG/gpO3xwWBNYN3XrC7ncFXRXGiWOfPQ34Ovndzj9dmKt+bXPN4uue5bYlJom7h2Nb23NNbjo864QEIFLXXmRTwxL/SmT7sH2ShpFuKEVaZ9RA9xDDIkmzHRusiTv4hbCrCRSwcyzXfUOoop4wMqdxVJcKMgM7RGB3riDBbQUPeKeFNxFRf0TqbGvWbDsuqpteek6lk5ofGqHEyErKvVB0vnM5PKSlniSdWzDRVNFtIt3SC7nEXZdTyUksPSgeQmzFjHZlwol6Ybu3pduAVPhxOuh5kIIrtKBDJ1GvWzRrZW1CidMI2wI5RKeGcJN5owy1TsKonoZlIkE0W/CUAn2mXP2ayjsZ7OW55fL+hXDajE9tmcs68r6hu/Dw7lcpw9IKq8nR0jCXozoBUTqu9vC8Tf1VWn/D2l2U4W61t6WS9ZenWofTINiksnw2P7G4Eqjn32wBAUzEolkk4S2Kny7xue4qvxasi4FXQfjDswWiAzjvL747O8CzYgjfam2LIxs2b29UMKkJUsaK92z7wRtlaaSTlhTBqtIkMwDNsaeo3dKurMJA4zuGxmzKuBZdWzck3WhIJKR/pgCVFj8tqWypdMOgdEapyDKrEToM/Zi6jZifSOuowTx3KvA1GaYNwTUCNNrttkvStMNRXluKacWmkGcYCJjHqRHpIydElJmUgLwTga46XMTgekWkWMZkLAJIWUG/rMShNQCeosCG9VlASV2m0LjJ2dYtJ4tWty0kdx+AufttZSghhRbEPNOtT0QdhdLorYhVIpuzxZ7yspUj5PZaX0EAUxaFIvZT80SS5OkOuaVJyUMyRsE2gq0YHxUbPpK0IvyRjvK5orMINnJ0YmfexT8b8O5kGGzHKHpTSWVUl3pIMF+2MTJNOAP/+bOv3TzV+6zPe4rdk9L8f3cQyHOsoa299iZGSP8/vV+LYdypOZWcfezPFJqRkffemDIPmQlTX1d2Y1YVETG+nqmlQGsRIkq46XVcFO5FlJZ3bfKGKNJNuVSJ9IIYJ0MUs6+4kJkk3UdSBEzbPNgm1XQZJ1KDxw+FQJw9NK1cnnX/+Id9rnvG6vqVTgzKzpY8X/zCeIUROCwrlG4hEv382sDebC4M4t2EhoEzaUmAJI0lxq1PnNdrBoAAs4l0Qry4WxykRAqwxoqfxzAmQBKOfRGwcnNcOZZf1QmnLprSbOFdqQE0Hs1n0TcSqxGhp6Y2mMxBy1CdR6/+aXRHrpTDtEy4UTPalKBdah4dq1vFBzzqstp7bbazxlSFz5ORd+QUDzqeo50BNIGKVYxV7KDlNCK4VWoqm10ImIQ6s1n6yfA3ATWvpoea1a8cydcNp0nNQ99+oNBolxXDC02tHHikvmXIY5Xaq48At+/voTXA8tr7VrjEp8pnnKZZzzS90n+OLqLX778n3u2xXdJKP0UXfKs+0JG19z3bdcrWejDZvNe8wi0Z1UdM0M81XLyaOIcWC3Scp2lQCVi48SYRb5P/6zv89v/Rtf5frHav7xf/4Z/sHmt7D4+Zbmohcgc92hBiexSVOTGrsr0Z2CWDGObKzkfZYjCOWmyc+7qr2O6WUxiVOmsdFdo7CyRhuidgn30cdhZG4VO3Urwc8u/hYbNrWnTFj+lPxS/gyTMvn8aslJJelm/M3ERa/ArG9hiFhrMRCTBWQKXB1mOe4aaQLgHDIK8+eUj8LOirJwaq2ITmG2ithIx8NodhkKs80srUqALL9IzE6kJnrTV2yuTlE3lua5pr4qgJMizKC/HzlrHE+3Jzxo16OQoIua4GpWXTOKoscoGVplk3RFDJItL2UEyu3AtpFKrER3BRJ2A9plcKowsnLGQoThUo5dJBuhTNZJKSKvk4x5CeRKKaMIzye0V/heDGhoxcErYpkjRRLAK9LGsHUzHqmEOzEMreW06ljYHh+FnVWbwBDMKBTvc6mf85JFd92MtatzkJFotZNOXTmoaJWn0Y6YFOsoJTQxZyZcslTKs4nSIkyXLlaxYhOl1DOiRpZDTDCzYQSxtEq5FEc8CG2CMPqSGEXVBmgDVeNROhGDlL5YG6kbz6x2nLYdy0qelbWv6Zxl2FYwaPRGM3+kWTyO2I10ZiRkx6ey+wBTCd7KPAgx677JQ67qiuS9aJscjlJiOG0brQ7ax941pyZOmDjz+5/ZCxqmu9AT5lU+5RHQms7LwyCiLMpMwNLpfnKgjz54Pb/2KmH+anzc0IUhdSRTfiuzDnysF7Bnkw7fzC8UZzzJ7yPw7JUIdBrp/pQMRJNGMCtmICvWiSrrfDhvcF0Dg8ZupLNuAZxiBX6WaE1g42pmdhhZBcI8MgzekJKUoaUcxMi8ScJqZBdwSPeo/E0mXzMahSY3IpmKvRcHM5EB9QyV5fVGqYP14MhlLeyYAmipmMG8fC3GuX84IuAUPlasEHsYkqYxnjrrNIYkHZ4KyyqO4BGSrCDRpQqnzMgotipQpTBeR0thWknZYIyyryIKLIkSm+++fKaIyLtohOmV1K7E0OxALKUgREXwsuApFeURzftKNoFNaBslIMvMLqUTxsZcEu/HrsAuGnzQBKchiBhztVLUqyQ+QrkPFHuQf1fjrZNfcomTEPbyM2JM1iE5ci8O2Y0Ha/r42l3jwIG/9fsxW3PERuxs08cda/JV79h2CpCN+z+0S6/Gt92YPbkbUJJ1P972q2DC5j0SPJfAuMr6WJUeS9P1dC6VuCem3PE1/0x5bYyMnfpQ4HODplF2xOzOW/d65zeZRAiaD1+c4nsrFSxaOuTOTzuG1uFuGjCJd958wfcuHgOwjk1m6gQ+v3ifiOIX9Zush5pnm1pYsomcWIT6iaZ5bhjODP6kMExFC9J2Uumhc7OqkuDReb3XXhhbZLCLkEafdiw3KyBWsTtkWxSjgB/A+qGhv5c1wLwiriwu1/JVVWDwFqMjRqexe3llAj5qrI5c9jNO647BNdkWKBodxi7tm1iDgwfNmlPb0WiRNwEBuZ70S7ah5mFzzZnd4KIlZqZuH2u+uPkEH1bnfH/7AZ+ylyxjz4tYM1eib9WiqFAI9w0aBYHAqe54zV5zYjphg6nATWh5vV3xWr0SQM3PsFnC5sT0OCOawS5aPuzPcUlz0c/pveUyi99/vX7A+8N9/sHj76Hz0u34jfYNZhmM+9rmPsuq5435DV2oWFQD1ZmUbF5tW7SO9K6SBIwRLTRJTkWqNegafCtxtF0n/tNf/vv85z/334l5+Qo86xf8jR/8UT75q57qukffdKibNckJmMW2Q9e1yLAUKZUpE8uHfV2su+ZuAa8OKlTSsXl7F5C1B3rJPo8BU+Pmk/eOVwXswKjDEvhCtBrjprT7pzww0fAe4x91AHopduSKlxivwKxvdRy7ydMSQ9gLxI9n0pM8pHuUYMTpmj6USRy6FBP0aaQJli5sYWYZzizb1420SI/S5lU6DQEm4b3h0aNzzPOKdqUxW8k4ALgThZ9LKaJ5vQPg0c1y7JjhstDgqmtw3uC9JjhDdAZlI8lNaf7ZSOVAZ2yXPnnAkwIq0ftKVkGXcolg3C85DMIUSFYLa81qVDCMIvmHYEDJwJfrFiSLXAVIW0XciLiilITsPp8MksVNiuRg82KOc4bh1OBnmkoHFnZgiGYUftcqiaYJjIDfuq9FE8ZqnDY83i6lbGSmWRq5rnM9SH14smyCsLAKi6swuVotBi4mabPukhl1suZ2YOsrXNBURgKVztvxfNZdje8qlIkoCzGKQG8CdBUxNrCY9aSk6J0wzU7nHSf1wOvtivN6y7N+wYerM9Z9zbavSEGjnGL+oeb065HmhRNGXbkPVt9+xjOnVBwQPblFUQAtraWsKYS7mYvjvvYBqeMC8Xd//NiifXQhLxn3EoSWxTW/Nz4ve51AdtuoEmNP1+Hs0BF3+7z13qvxanyz4zAIh0nAesd8OmSUlGdV7c+HAvqMYL/fOT8pi/26uRqZUXtgrZI1Z7WaoTYaOyhhLuWEb6yz4GcNei5AxmqoCUnKvAtzaPDSATAGRYo5iTEBsYBbSYxCfYeJk6SQFccwSTSx0worI5e+j2XSOi8CZY07BDggBydyH1JOJmoHePkZrSRO0sRp24HaCkLCbStCUIRWgg5TxVxuqEfhd2FM6fEWAqNmZdSKoBIrJ0mmWCkaJddVZy0uj8ZFYWH5aEYml84/5ZLstLEKC67SEiyVpBVIWYvEswrnLdGbHGDmyxHls8oktI5UlYfMQlNAUzlqE5jbgdZ4NqHiZmgF+Awml/8rqhtFc5Uw2wlze8LI3nuQywKcS06LD1V6Nu5sU3oJQOcwEDj8XX3sPo7amo87zuS52isdLK9P95cx65R/PxZojKXuxwDYl48PXo1/zYbdHLm5pVrkpViDMvZkULSWTuJW52ezlEvvgt3SdKdMvQJklWRIzAlBkLJDPxOAQPcQG4iz7ABliRDTZUZwgDgD7wzhQpK7mERqAionaB+crfEnHeezLb/rwbu5u1zDY3fGJtb00TI3A/erNZ85fc5Xrl7DNIHQCZgf24jutJSa9TB7Cl3QuGWS5kgm63tlWT4dcofEksRICbvJ1SU+7q5B+T4FvDoGZE0SS7ESIoFfZA3bAPbGEAeN85rQepqZo7aRmKDSUoXiowZNZlNJ6eC9ess6J73XvsbqgE8Wm9HHG9cyRItVkYXtqbRo7JbSRJcMm9CM51ZpjyZxYqTj3yN3Tkh61NNaasdSaRwJh5QZuvzdQlK0ytFqRxdq7tsVl2FBqx1vNDfy3KpAHy1rV7MNFc844Y1W3rtyM/ogQNXCDixy7PPe9pyYFO/fnPPs8oTlouP9m3MerU8BeHKxJEbNmw+ueG22ptaeB4s1H67PWNYdn16+YOUaXnQLAfqeLkgGhqX4NM21AJpSJqvYvK74/M9/dQwxdIL/zX/982y/cMb/g38DXqxIXUcsza1KvLPtpDqoyKek+I2X4KlG1h26wUdBZ9g9U4fx0hFg6padyn7JjuE13f/Be5OYp4C7089ME3iiISzrxVSGQMXsymXAS4WsszXpbviy4xWY9S94qANtrPGBvhXkq/2fk9eTydsabm8/0flRXgR1CSk70LKQmsGISOHA6OibjXT+iDcL5je565SRYGL7MI21q6GGcN9xvhDQJSbF8/Wcee0YvHTO6La1ZI17AzmDysqgc2mhcmrMXqB22QvjdmUB4wOsGEWIk9EoEw8cN8lw6EETsti7KgBI2rGyxgCmJG/1BFCLYhSLtovpxDj5uRjVWGVQK4vZJZNLIILCXbY8j1paquvA6+2KB82aG9ey8XUuvWC8VpJ91mPWBAsvtnO2vmKIlvv1hpkeOLE9LkqHqSFatkHKDM+rLY2WDogVufY7GTahZhUaXNI8aDbc+IabvsklhnHUtEIlemfpOxFkF1a5wthISpHQW2zjWcx6msozeENbO15frHk4v+bU9rnEpOJ5t+D5zQLnTHZQoLrSLN+PzJ4O6CFrAhSx0VIeWxaxPZboZIGdBhVFI2vMMEwcr0MH7C4weApg7Uun3GZlHfncrpshow7Q+PpkqMTYtPQQjLo1Jov7VCsmscs+jOdrOM7ceDVejY8ZtzQU9lhZR2zN7T3sgK1jm0yZXjHDHElAFRWl9E0Hk1ui7z6mHaIPNdRUPZPMO7gF47yLBtIs0NY+H06xcRWVEXH4mBTeSblhCtnOJGDQOx2soEawrYBZpBJogULKDHcMlaKdmNepQ9Q5xaxtVVrt7K7RLnDbxwhKZnJ8cfKmytdDbAxjGWaK+UOlxDhC7CzbpEitwqjIwg7M7UAfLC4aYtrZmlLWTywlg2Kztl668oakmRnptFRrL9qKWejd5yx9axxWRWLSJBWF6YuUEw7JEFHMcwOUPtgxYSNEaYVCGBPem/HLppR1PVUiBY02AmTZrM1pTWBRORZVT6P9yBDb+pptXxNCBmeTBLT1dcJuwlgaOt6qkuQbbwJjA4M92tLkvhfm0x5b6iCbzeSjRyfF4bp/uMnHTLXpOY2Jkbu2L+Dwywx1BPwqY6/knrvn+qvx7TWO+TwpgTYoa/ZZWXA7EC62oQBZE6kFPQTAiL+s8lqrVWZ67I49TRKoABRAX0uDqpLgdieR1Absi0rill7ih9AmwhxSHfFbi3aSEElGkXpNnAU2g0HrxCfOrvie0yc8rK5zoyVp2vTCLXDRsA5Suv5Gc4M9j6yHiisWhJsq07N2bFoVoLmQEshYQ30lrKwxSC9LRvY1zVA6tSe0jztNLBDgfQpiHcppgFzjeUOsJ2w0hVSC5gSoXhuiV3RB4xtD3Xh8iMxqR2Ukye6ToTWO1og9bYxn7Wt8ZvvqnEIGKT+PoRJGrJfKkFoHkS7JscrCDCxsz5nZjgl3EGCrJNpBqkwqEpcxjVri6/w9u6S5SRXrJPHIqd4SMwi2Ui2tdlz5WWaOSefFs2o7VsBcDjMuh9ne5QpRc5XLBZ9eLIlPW1IdWavERtd4Z0iXNfZGE+57ntcLQlKc1j0X/Zz3n5/z+tmKN+Y31CbwmdPnuGh4j9elgUIClRSxMvhWQEYUXH9X4Gcv3+ZHvvIr5ZHh3mbLf/bzf5dZ85S/rT93Wy6FO8KDW8xIPXldSdCqDubosZLg8vuRxP4e62oCcB3qLEL+u2AL2cebxkLTL6JIe7Z3l+jfbTfGOIlRX640xyqvpxEDSONnUpXnWVIyL18JwP8vMzQpZ6Pz6qM0Y7tb2AFZByNptR8oTMfhw1oywokMFuS3J05dUR8vgbvpRIsqWSAIcNO8EAqln8u/4ZSsmxTAJNQgrJtYR+qTQUQHlaD+IWheXM9JOTubvBZnfNA7BzNl7a5B7QJ5tTNYKqOu5eEt2XwVGIUTxRlV0hodhK0DQr30EZ2zHuV7p7IAjNmPPFENY0YobywIsPwqwY6X49othEZJiUdftMUMsY15YiX8uuK5P8FoaW+7rPpJG1xD7+0IJrmSKc+Mp5DL/mJSfBjPuKxmnFQ9tQ7MjMPlMhJNYmaddEeM0kFqE2vJZsSKbahG8cRtqLjo5oQoQp8uSGmGNZFt17Dd1gI0RqACYyNaR2I0aJswGVjpnaWpPG8trvnM4jmN9qxCw+Ww4KPNKe8/P8dtK1CJ5DTmyjL/SNE+G9B9vjdJrv2exptSx412GXm+CJ33Ds96mo04pNVOALF0OF/0/vZlTBffO0sMke+iULfYU3usikn8mybP+pQqW8qLyjHGIHYKXClk7pm4zw55NV6Ng1Hs+zi3lNqPpe8ArEYn5aitOfwl7RygO23TBCxQCGU8IcFOFODGbGVNLgBOaCBZiM0uSaCCALymlnI4pRjZV1sn5dEpKbEziZ0QaDl2BrLG78gEGypAFwgrJ6kR7BibjpQvoJUURJROd9lu7DGDFRRUYA8ryTdAXkt5UzVej3FEMFlXJeY1QFlIVnQvk81Om4LoDJsoSRGtEo3xVDmbHpImawoDSsoMVRbVD4aUJFSREsCGzthRS6vSkZCUCOojGlsKCWoK0GVVyOWFeuxu6JLJouwCMhWQUavEEAzOWZLP39kgcgNKfCCldy5QKYM5qXvuVVuMigxJGM4r13C9aQlODHcKCtVrqhXYTUT5Kdg4sTUvA8pMHGd1F3JU2I2Tv2Ey39R04Z8cWO1vP57iXRnzW4fPLPBDW6OObcu+jSn7O1b+XuzNlLGukAf115HxfjX+NR57DI+DhyoEMCb7Y/s2QsXMtDd651OV5ykhHde1IjaaFHYJzRHYzyWHuzkk+9U+MSwVfiZMJ4BwGlCdobqWB1vnmCE2iPbv3BNWdlzzY7X/HPe9gP0PqjWfqp/zgbuHSYkuVrxe37AJNeucCH7Wn6BV4vsePOHZYsHzswW9N9x8uJTqEHJHRg/mucQn0twjjedWgnyVv48KQiRQvsij5CRvWawLsDXRAZ6y+lNlCfOKUEsJvl0p/CKNPmQBA3SnSV7htgY/s9RzoTkbHaUxl5HGXF3WOJwZh486xxtSyr0NFV2oGIJliIb7zYaF3bDtTwApNfRRY72cq7C2PESYmx5DylIo0nHdpIhLAw5FJZYUl0Rm6TJaNrHiOje0WuieijB2dG+04wQpOSzJ+plxnJktEcUm6zW+SHOu+la+m7P4YBicZegs6aLGdIqgFG5dSfOxlaG+El1gTGLoLRdpzuPNuVQP6cSFnbF1ltoGvv/eY+kGqRJumQg1+JkabfhwFmk/fcPnX3vOr/2uN/gv63+L/93f+VnO+40808Dnho9Izfe/3JzMDRgIYQdqTZlcwF6C/xir8lDP7hgTeBoT7b0+eX8vFlK3PjutNik2R+R+dtuMNqb4Pepw+0mMZCfYQKlSyYuKDrnBULFPEfAvY+RlvAKzvsWRFCil7vYPDgKDdJgxUZMHdir8jjws41/lQc816XtggRaDk0xpjysAVjJiMOaPIu1lYFgatg81w2nCnUdYOrRJRCeLpBk0sYW2cRidRLsiCIAVoyY6sWZp8oAVMGKK9pYsfeliIIBWGreLqrS9TdgulxDk2nFilO9dGG7lp0ber4x0dpxSHWMGx7KzV5gAJWM/Mocmk68AbNqD6dKYlQmNwp2AX2gpQ2wjqY6kQfPiakGImreW15zXGxZ2ICK6VcHVo6ZLSKIt4r3Be4MxkcEbNqrmQs2Y1Y7Wes7bLef1BqsiPmlWvgZqbpSICUTUWAYC4ItwfG61K5dHmFhGR3pn6fpKWhAHBfneUudLaaQrSlV5UlIsmoFPLi/5rsVTWu34sD/nSXfCB6szXlwvcNsqg5YGs9LMP9QsP/CYLkwCubS7P2VMu4VN3pdunXAL6Pq41rMHelnjfTz8O0m3mLvotOM4smAfvn+7jOj4qR3ff/6RJkBXeb0EFBy8dte+Xo1XYzrUGJt/zDbT5/uO90YH5sDWpCPbZbB6bx+jQyOf0S6fW+4ga7tEqBV+ocbGIzRBpmnuHquCJtmEzY0pxrK2PRBL7esbpZ2tGU8n7b21K+sgn7tWY7JDe9Au7b5TyUQqDrKe+X2tcrfcKdLADigr10dNzmnKbGBihwqQ5mVNMKpoi0nmdwS1TIKg2HY1KSlO6p7WOGodpIwniY5YDGr8m5TLyGMu9Ysap6BzCWsCVkda62mNG7Pz0srdMCi7u1QIEAWMosFGxTGbr3IJe/npvSF6PX7JFNTIJle5y6E2QtGubOC07rhfr7EqcuNb1r7mZmiEkeWMnITXqEHKC+trSWDt7nWx49NnYvI6k/eTYax5lLaXiGj8kcBgPICaBN6TbaZTRQwYadpx965x8Ngc2+Bl1/2P2+5Qr2R33IOj3nr/1fjNNka/ugTPdzATk9G7rrblffJaFoRyrzPbMVaaWIuwO7BXZpdyJ8PS0TAZhZ8rqY7ooD8HEpiVziV8O8DfzyBVpW5c1s2wEH8cK42NUhQtKZC1CuAT1QXP/YnoQ+VRa8+JFU2+X71+DRcM5+2W3/76BzztTviSswzDAruWcyuMqNE/0/n77ZU4l1giZQBs0sGwNLEqwNZUp1Hr8e9kNHHeEGZWACuXqNaS6E3zEjOp0X6kAMkp0qDpt4Zh7tlsGlbznmXbU8+FBbu0vZQZJi0sJ5WotWfICfEhGmm2kfT4GsCJ7fFJSxfbKM2tDJEzux07rkcUXawISdMqx0BOgCBrfpeZvy+ycHtAca4z8KMidQq4ZMemJAABzYnpObNbGi2MYk3iaU7W9M7igiFGhTFR7N3GooMizCNpESRJ1mt0rwjzJH7HoEm9wW8bTj7SDGeJ4UFku2roq4rlouPr63t8eHmKaiL+gZAUdB1AwclJx7/z9rs8qNa8UV/Tx4r/07/3B0n/bM5/9vN/d1QD+qJ5YwdMlRHjUabWyFycbn8H8WVvxLgDn9VEO3iPvcX+71P/aW87NZYGj+DT1OSVsuExiZ+f+cmcmI4R9IoA6RbOkczu86OmqJ6crzqwcSZRhOBfdnxLxS1//s//eZRS/PE//sd3J50Sf+bP/BnefvttZrMZv/f3/l6++MUv7n2u73v+6B/9o7z22mssFgv+/X//3+f999//Vk7lN3SMD+xUK2saWB86xIcO1eH7slP5YXLZIcVhkUA+WU1sLLE2hEYAHsr7QHUDs6eJs68GmqvI5jXL1Wc0/b2EO43o+z31zMlhgxq7elBFzmYdi3rg3nzL6bxj1jiMiaSgSb0Gn/8l9tBTFbJzNplAZYHeUQ3JHUDS2G3Q9AE9BDEEsBNKzNcq1ZbUVMTaEq0mzixxXpEqQa+1K+1yyz+ZMDoUBtaOiaUm3bwKNVhFsH2i2iSaK+lasfgA2ueK6lKjNwa8xm0qLq/nPN0sWLmGbaiwKrKsO5Z1z7wZqK1H60gIwl4LQeOdwQ2WrqvYbhquVjMuty03Q8P1MONimElpR7TEpOmj5cqJs7/2NRfDnBsnXUDWoeZpd5LZYOWxk19CZtHhd9NaZeFduZSJuvK0lWfeDLxz+oLvO3nE0nQ86s/4cHPG+zfnPL88ESCrM6i1oXphmH+kOf1aoLlwIzgoAOQko5cdADUR8T9KrY6Tz2cR/3FMSwyn49DpOsw65MzhdO7cOe4KQEomUTOKvo/jIF5KYxdNdj+ZAFhl+8k+ynt7wfg3IXD4m3W8sjO7MT4tJdFxmJ2bZME5fL/8fZuOKP/r/eYKY+Yu25tii4o9KsP0kjxpLwTIcnNNf0+y76FJqJnHVHHnDBU2pkki/m0Cs8rTVF6yy0UTK6hRO2k8zTie3MEF2U9S7BiUKQcTJYue26aPTKzJ+qQAo0nWkIx0DE6VHu1MSYzIusU+Cywe/jtY9yZC89ontEsiKnwj/+xGyuqUk+8bnabrKjauYohm7IRbG09tPJUNGB1QaldiXnQRxeZonLP0Q0XnLUMw9KGUswvzqpT4dTlLP0TRbSzdC4dopEyldCuePDkxSZJrql2mdBoXNwWYDKRVNnDebHm9XtEoz8o33LiG66Fl0zUCZHkFTqO3mmolOlm2izt8KhVveXLfE6g0LeEhL74NMAMWkJbAApVqpM7pAJid/ty9sXt9Ol/U5P0xWv912privKvJ799gH4dgVTr83OHvat/WvAKxvv3H/v2eJMCVAmMmsUpxWPYTvR8L9oIwj0Ic56IKSbSi4g7w0RMmZdJZEzf7SW4uwEx9I2Xgw1katfGqdflMLkNMjHqKmAS5wZSee+rFwGLRcf/emgcnGx608uGQNJWSMrhGOy7cnMf9km2oWJiePppxPVy7mndX93m8OaGtHfatDet3PP09lZtTMSbmVRZ+l4wMWTpFYhhhDqcduzey7/+C2JS6IlmzW6/yPYnzitCIfVVBdM/qGzC92mNeJiMdHrVX6EFhNhquKvxlzfWzBU+vTvja1T2ebJasvQB5czuIjm4SncRZLi1vjWPrK66HlhfDXETZdSAiuoa1Frt84xse96dc+RkuGRrtuGfXVCoQ0NJpMFZ0yeDQvAgVN6niXX+PXxne5OvuAYDcEyVAUdH/LR0LXTIY4ihE75LIr1yFGX00LOsOoxNt5Vm0A+ezjqZxUCXiPJCabPSdXJfSAC3phFkbmieGs19RVKuE6RS6U3BTES8arq9nfO3JfTZXM9CJh29d8oPf/TV+z3d9hd/7XV/mP/muf8zvOf0yb9VXVCrwXnef6nHNf/Xpf5e/ffI7+UL1Cf7WyQ/zUw9+BHV2ilqeyL/FArWYo+dzdNOgrOWo7tVk7o0lhnvvTQKIqWTRkXE0OW8m8/qYrVITO3IHGiQl/jluHqtQJvHP3jkwlhPu+2H7xyhx0Eg+yV2xgX0ZFht52fHrZmb9zM/8DH/lr/wVfuAHfmDv9b/wF/4Cf/Ev/kX++l//63zP93wPf+7P/Tl+3+/7fXzpS19iuVwC8Mf/+B/nv/1v/1t+6qd+igcPHvAn/+Sf5A/9oT/EF77wBYwxxw73r+xImh0zq7S6hB2KCruFa/xQutNoSJmHFpaJ1US7M0g7/12BUcTMxEpWghDTRxFW1NBcR5qriJtrLr/T0L2WiE0k6YR+MFDXnqG0vw6yCCgPamv48Nl51imRp7xk00twUVrmknU/xk5SJQgp56lB5/brJVtTstnGySQxQxQga/AoF3bAxhhsiOB7zEFF0opoFGmm0SWbGyU4GNFfpGxlzIrn42qvyAlpYm4nLyWOac85FZANqjUMp4p+ULilIiwUHnhhFmiVWNb9WDL4xuxmFFdcdc2EcJezGVnEuAQqKSpeAFtXMasc95qcuTC5I1XWwXrRz0lJ0VipefdJc923hKQYvAi3+9ztCxjvJ4CeeepGAp9i0IyWDlKfWl7yzvw5c9PzXnefD7enfP3qnNWmEWbX1ki74h7mHymWH3jap4Pco/w87j3PlICjAFRMMuYHQ+pX2YliHixY06z3hIU1Btq6OPcH8yfrTx1mIKZjb9GeLsp3BBQFtCpslmm5YXm+Ut6vBPoHx0q7baevpxz4iYxFeqWZdcd4ZWd2Y1xDYd+m7NmX4kFw5L1be5RnOlPap+2Vd8dUlEYjUiIrJ6F8EtujJCFhe2ljPdzX+Lk43kmBmgeMjQRvcjmaOOzSyU9zs5mJozNOIiYTin0gix14JEjS5EQVY0n7OC/LcpGJOSqXw6gxYz65RvkCj4Be1oNJSonwcUzjuUlQs/v46LSlyblkPZWCr6iobgFcCekGpgOYQVhsfqaItSLWiQhs+xqlEBArlwwu8noOFYM346F3j4OSJXUsP1RsEVZCpQOtdfk0U15epUhyG6TlvdERlRlcvbd5mRbvM8Zd7VoKegQXVS3dCeWzOYGrEkYnTuuO82pDpT1XfsaNb7jqW4bBCrPLKZTTaC/NaOrruNPJOnge92wNu3tCqXOkBk6Ac2AptiStgQuIl5AcsGs7fwvQmqzt+8c+mEOjo77/DN4ae/tRu+fm6LaHgcz+eYwMLBiDir3jFFszPU0Foy5bPsQrFvC37xD/Rk3EPfNQeiz9G0vfXtYGxvyfMWNQrEoSUoMapLIitIZY6Z1kSNa5C5X4b8OJwi0EVACgQbreXhqqFaM8Q0lIhEVkNh/o+wpVR1QTsFVg1jpO2p7Weh60axZ2YGk73qouuQxzQjjhKsx4r7vPu+sHvNjOWVQDnz//kI2vaUzgoq9predyM2PbV7jBEjuJJ7o3IrHWzB5JDFAS5LtrufPtFOQOhztQUKVJMh6gqYnzWqRSugF8P/qzqbLEWuK4aHIZY5LOeUTFcK5wyziJT+SfkG4UepA4LQXF4FpebCr6sw4XNSfVIIn2qmdhhjH5jvGjbuLGSbOq1jhsUmxy8gIQJq9KuKR5OizHr3NmNrsyQx2JaNapJkY9MrGe+xMMkXOzYaEGKiUdFRdq4CZJ5cllmMvlUR6HYWk6ulhx36554Rd8sD3nWbcAwJrAvHJsnGgLK2B2KrrOfVcRO4PuNHoQeR3lFKpTmF5RXwp73C1FfFw7he7Fn4lDg9eg7g3MFj2fPr3gU7MLAGZGANGrsOCFX3Dh5/xPj76D+YcKe+H5yXv/JlRWtJ5D3MU/h4msEKB0Lex6COE4Y2t8vvbn7i25lcME5vTndETY05wqVScH8ZNKjL7fng7lZNsC4u40qtMIWqF3YHXRnZZt2WEBpbvzFKjKx452Cn7n134duiu/LjBrtVrx4z/+4/zVv/pX+XN/7s/tTiQl/tJf+kv86T/9p/kP/8P/EIC/8Tf+Bg8fPuQnf/In+Ymf+Amurq74a3/tr/E3/+bf5Md+7McA+Ft/62/xqU99ir/39/4ef+AP/IFfzyn9hgytdsLp5QEcNbOmZYEfl+0wGbjSSJmhUaQqZ4YzSHWoEZGMlCXESuEWGjcT6q528gDoIA/l1TuWzVsJd8+LMzQoOHO0swHvc9lgrwXtd1IyYleatGrRCexGYddgOqlf9zPJpPiz3HK2V+NnjzpISbLN00zNGIwkaXNr+oBy8q/QcsdgI4OEyluozI7imLM9fmYzu0pKNUIlJyF0YPayVGJ7Ejookk+EPAGjVSLgWATry20LoH3EdgrTKTqn6KMmRBioecYJ8VSON7cDc5U4rbosqCuvD0gbdGFpTS5AUvjBsKWmHyybumIIhnnlWDthK9icEkpJSUbdS/dElUGplJSUMTqTH7/MalOAl5IVWwXqzHjwQaOSaLGcNR3ftXjKXA886s/41ZvX+frFPbquImwtDBq7Eg2D5gWcfc3RPO/lHk102/YYVaW85xDImmahyt8+7O6x9/uGfy9LcQThObZ4qzxH7gCEbtWGx919/tiseDllxU4MUbG3GO+BY2n3b1zIJ4ZhBKxKx8yElAWZ7BXdVWr5m3S8sjO7oYrnDONzPMJWe/bh4AHd20mxRYzO9AhijcmSA1tTnn2zW2NjxS5jnVGU7lzKs8euVEFBK8FHjOJsT5MhCkRfcRAXxOSGJdqL4xOtZOdjs9uf6C7eXZqlcqOR8e/p+h9LW/XCGk27oCyXGyZART2WxwO5fF9KaQrDK5mcFGFyvD0EgRHQUiqNQslolTvy7YDJso+S5VdeOgqTVO5iCxtqUgNYqDRUKtJoj7aJlEopjckx6gTNyAeKQeGw+JBwRlhZlQ44RG+llLGThKnlvM6PSS41QO2SMYcXNirQCW0ixoSxGYnKAUdjPPdryeSvfMuLfsFVN8M7TfI6J9I0uge7heYqYjd+n/F77PoeA7KSBRbA29Deg7cS2IS6mMFH9yF+NYNZIf/LXwfu9s/GDXZ/7Dn8B+OWntcUWz4Amo5/njtt0mh/yn7zz8MuUrfBuAxgjFRCPv77vhr/2o5bSTHzDXyKqZ92V6lTjk3Er8syIEEaM8WYWatBNKKSleTzyL5PCTOAbwVIqFZp1FP0M7EjdiWyI7EEwUZK0+39DmMiVRVoFx0+ama1Y1EPY7L3qp9xr97wTvucgOZnrj8zrmfP+4Vo0VaOiOIrq9cB0e/TKnG5bVmvWqLT6CrCoNG9HuOZ/p6SypGMfU/LeaU7W0a0YmH8TkAMpbIsiiXOKmFdeW6XdtqdtExh9ZIEg6hS1j7WmtAmaSJUjk1O0mRt5BQheUVyinWYEYJm21T4XGZYzzwL24vubgayrBLWrCYxBJtvtSTSt74iRI3RkSFa5nag0rs1U6vIXA+02nEdW7pUcRnmDMnSRwG0WuWolCeg6FLFQg3j70MyxKSZa3mtdwuaLCzfJcu1b+mCpTaBtatHPeJ55cbzMnXE6ESMin5rc/MBRv8+tNleW7Vjis8kcayQ2NRuFcNpJG4srgpc9TO6UGFVYG4d724e0IVdF8jLL9/njacRsx5Ii5nMLzcpX0/pdoWI0SiTGXnWQt/D4HbPgH7JboVy4ffjKaX25/hdWIPa95umHQmPJdJT2b4A4LDzCSdVKBJYp53BSexVYZXtS5JRkwHrJPNdYvDpgREGYhLvg2+icuXXBWb9kT/yR/iDf/AP8mM/9mN7QcZXv/pVHj16xO///b9/fK1pGn70R3+Un/7pn+YnfuIn+MIXvoBzbm+bt99+m89//vP89E//9NEgo+97+r4f/76+vv71nPa/8KFV3GNijGWGMAkc9h+uvbbf+V+qjLCwapMDC4hGZ9Ho3SQZ37PiTEWbg4sa3BJUVNRXkhFffUqx+bSHOgpg5RVpHjg57QhB43pLKov3oCBT/QBSlUbDUq0UJpcJqlBWcoM/86RTR+oNKuhRjFe+ZPmuUj4xdrrac+wUqrCiSnBRgCwfRhBEBQG1DBBmFaAzC0YMpIpq7KZSAISQgy8pLWHfwKSEcQpU2gVmUUQedUiQBR535YqR5kqhnVyn/oHGRcXghVkVTjShlbrzWnusDtxrt1Q6coW0Gg9BS4lmkPumTCRFhessyiRC1tYa2oEi/KtUojGBygRiUgy5O2JrPSFq1n0tAohRoUzCKAHIks/ApBdGXYia3llhe5nAshn4vtNH3LdrPhrO+KA754OrM9ZXrXQJcxqz1lQ3ivlHiZNHnuZphx5CduLzQjrRQNsbBci6I0uhY+A/evbTfG77Pl+s3uS/qn9AmIZ5/ow/p/Pors47Go4u3EwW0o9z2kswpCbHvmvTCRiVJoZBsgjsMIQc/JbFXMQPd+DqGIMUMd69g7w8pfY3w/hf2s7Av7q2RqkD9p46Jlq9H2Tv6SAUBqLJLKxiV9TkedZAgf0PaOQjIzZ3wCVlAd8kzUTcWRz1nogKqkjdeNHCyqCF8jtW7zg/s4MekxKh3Qg40ONUkNbpNFGYXZF9Ie9JcK8dd6IFooFS1iV2QNa4PmXx+3wZYmVQWo3BS7RAyvpbE1BBhFAZxeUPhw4QEUArapnyhUE2nn/BGFLC9mLHVZCW5iFpQhRmVUzCwg4pih6VSrTWS9MQMnsqTkTzQQ6YFNHLSaYgwFSwYuALaGWyxlVKUqpDYgwYnTcEr/cI5cIyLtc1lzmmHPAiib7aBl5vVsy0YxUarn3LTd/sNB1zIkz3mZG1iti1R4WY1+18wLv82SmoRYJUQboH8zP43RF+a6BptwzvzeAfRdIvPwSugX53Aw6z3Gpyc6cP2OHLx07nG9mal97PZJsDW7OXUClzVh2AYIn9uT8FwseXXj5IeDX+NRrT+zwNfLPW7lEZlEni8JamL4zMLFVYWUpBbgBkBolfYi3dzCNxB96DMHhbzXCqsB1olwiNGksJk02YfucrlSDXn0TO5mKHTe2kBD0pWusZguFyM2PT1YSg+drzezx984TvXj7lvfU5N0PDW4vrsdu4JrGopJOdC4bKBCobuLk8Ia4qWTo2VrooJmGKaC0giDTtyXFCl3ZrfbEhMWHcpIHP6PfG8brGSnSOlA5id61BudzBt3RQ1yqLyyeSzXYmCOu5eSH6k+6UXZI0+67CgENsbul+eGnpvCacSVMoqyJXbsb9ekOVgyKrRTA+31npgpt0LikX/coql4nHpOh8RagUl37OtZ/xoF6Nj8dC9zyNjSQsQotLhhPTMdc9RkVa5QhoumRZx4YuVQDMdc9NbLnyc07szue6cAtWoQEY457STKsygT4YrInjs5CSAhsJJ9KgRNZFuSHRWdwiA6RNkqZeILY8QaxSblgg1TPPNnMqI7bB6Mimr3HBsJx13GxbTt7TNFdOCCdWo7duXEtl7hzEKUUvLYu877oYatIwYQiX+Xr4u96fn2W/I4ZwSBYoj2FJ7k/s26Eu1t7+D8aopzXtaDhJ0o1YwQHQVRhbhwyscvzSPHqPOOK51TBrtJd3NQg7Mr5pMOunfuqn+Nmf/Vl+5md+5tZ7jx49AuDhw4d7rz98+JCvfe1r4zZ1XXPv3r1b25TPH44//+f/PH/2z/7Zb/ZU/6WPSklZ3x6bZDqOCbRNh5EsRqoEyIq1kQVUMeqSjBodOVNZfJBoZMGrtuIol/boScmit/1EwJwOhJtKumDUiXo5EIKm29RZrDV34hiQgALw54Hm/paqCmw3DRvTUl/mrHjWnmpeKExnGe5F0iwQFmDWGu3VWFaRlABEdpsz4pNMojj+aaJhkiZlhXlyhjAaBdXLpLUuEOe1CE7KeiidCJnoZiW5NtFK2YYuQUgOYrTP3Upy9kUSFTJ7SlZkLClBrqcO4mibQWF6TfdAjMsQFM86izs3LNuexnoqLSUgJ3VPSIpV1+C9JiQjEzNBSjmqyfRg7yXr7b3GGBFFtjrSUaEysOWDoak8nc8CjrlEQ5kkoFXSpI2VOvp8D9ymkiDRROa51vyzy+e82VzxzJ3wwfac927OubmcQy80XbtW2LVi9iSxeBKoLx2697vnOGXUXE2ArJCyzlkJEHfMB2JE+UyzDYH/6Pqf8OOrf4IGfnv3dVLr+cn5D+32f9jFsDw2BwzHW4HDZOEeF98icnjooGluZdclGDjYbgK+SgODSRnh4aZqorE1yU7IwjxdnSfnmzuYjUHyqzGO3wg7A//q2hpN6W6U5+DhBurWLwfv54BGF90rPenCWZydHdij9AR4zS3XjRMbFNJu7ocG3GlCNYE0GAHRDZhGQHg/ZLQngyrTEr3YRszcY3TEOYvTFtMxzh8Vc2dEnzsT2UiqgYGx8cgYNGV9RBUntgZ2Tn8s6/oEXM8BydQhVLm3hY6RVFmUNiNhspQRj2UnKc9xLc7bjqUl+x+7+I6M4nwf2LeH03skiRXp7OODQuWywxAtG6+Jraa2IvJbkh61EXH4wUPMbOZdPVo+dpRfYgaeuqCkb0zejw+Akucq5lLDUm5S9B9LWTRRgZPux2X/0elM7hCx97by3Ks3nNiOTai5di3XQ0u/rcELsKmdQg8Gu07U6yTCy6WD8Z3UJ6SMJ+Y/RnBSQTKouISHHvW5nh+a/VNO0g1f/eQ7vPtd3wnvGtjOEEBLbtixo4y2YLQHh1757n5NX9trhT55PR3uQn3M94Nd0uNj/Pi9ACN9/LYCfO0ehl9HBcer8a/LGP2YfJOzT7XHsJgGxuW1Y1IoQfw4ZXblxEnrkZ1VglHlI1qJTVH52CINosakM3m9REmpUbRSSm06Lc+7gVBLoiTMEvp+T1s7uqHibCZlc+tty/MXAkDZa0N9qWh68Av4eWc5+WzPpxaXXNUtT7cn1Fps0KxyXPRzOi9J3VoHXjgrgHxCOrkHpMpk0rxKO7Er0cpP35SGJ2mcUtql/W6rkSydkeM1JT5oHEGMOK4tKsS9e1HYvjqk0Z/UXmIolTOm7mQfmB51iMnxXyYVmLXG0fAiavqFyJHEpKh1YGF7XqtFY2zja+lOS6IPdmz4YVVk42pclG7rpZP7WdXhkmZmBjSJSgW6WElHwljjkqFSQcoK0bhkCWhuYkurHJdxziY2e49sqx0nRkoGn7klz92Cp510nXTRsHEVWsfMIpYk/ybbpt7Jd1MmoZei7QzgnSGuKqJNuLNErLLeWieNZ7AQ5lGe4QDUkaYdaCvpHrxxFTfrGdsbEW8bekv8cMb9i4TdBmJl0KWaiIP4pIBMRkOIkrhOEyBZKVQLpEjKoOZu/urjv8OBTdp/7VZlyiE7rGy799n8I96Okfb+LrGNYp+8U/Y/iblKYnTKYpzaRFV8pvHzk/OdmNtkEqNs0UuObwrMeu+99/hjf+yP8Xf/7t+lbds7tzsEdcbSu48ZH7fNn/pTf4o/8Sf+xPj39fU1n/rUp76JM/+XMxrt8AtkQdJx/zscYWUdGwJmGdxphVtoYUEFueGmL6jxrlSh6EKpzIApZQ/Ky+KbLAz3Epw4wqrC3BhZ8GYB11mGwaB6LUDWLBDrJKWFOhFOEp945xmfu/8RMWkuhhk/b96mq+fYlcJuctATM2Nra3BLLW1kZwm2AhaVh9l0QtMduxsW41c0THzc1ZpTAINyYRIp6ymJfVZj1twWQXwDw1Ln8sLddSnH0Z6x3K2AF9GKYdA+ZTaWTDgxJAXoKtd1EuhFqLYR8zhhBk13X6GdIWw0l04TXlM8WGxGKm5MitoElm0/Bhd+MNIatoyJUHuMEL3G68RgLMbKfrROGBPx3lDbwE3ucqVUQukkpRoRUmZUjauKTqitISTQJ5IBeH224vX6hifDKR9sz/nazT0ePTuDG4t2imolQpzNRaK9EN0SvXGifaCzs1500srKE3OteKad3wKx+oHU9STvIUa+f/veuM5q4HM+AwsHelh7ZbrjA6ImmYrdax+XER/rutUdgJU6OMbhe6qAUke2Gd9jtxCn/ffl5yR4Pli0VU7zyX5elRnCb5ydgX91bY1VURhRxXlh4tMeYWUdGymXsUvDkP2St1HINj+j08xZyUKT1EgZL+tsmAF1BrL6fA42EryGoEXYWycR7zWMckWxTizvbXhjdiOaTqHisVriTYUelLCskPMxg9iVUBtinUgV4NIIaAFjk4+pRuJ47pDXpd212Ae8kmhYxGxrKNfX56VBbGios/0tqcM9sJud41gA98LIjGTAnz2GZ0mc7Bg3ajw35RJVDrCkHEcTnYBQcQHzyo16jAnp5FVL93o8UlrIlC09uVYpQdKGqBJK6V2JuhZGVYwKUycGZ3agSkmrJoQNPEwWPZUgg1mpFrs1twNzM7AODdeu5WqYsVpn9m8APRjMYDBbg+20tLZ3PksJOEqJI4WdlU9c5TJ2qaspXyahogQWyUewPaf2Kff7j1iqFZfqhHeXnxU5rW0FygBut9/xJ/tDTf7lF76hrdn7/BFb84188495/1aZ+8uAWJPf5XTScTv4anz7DKV3rO9jPkUIon+VpgvYkeehrGch7jG7xuc8ItW9SkGMEtwjJdkpJSmrQ0ki2GlJHCvxuYfcHMT0sq/QSulhrBN+GTlbCLhhTeTFZsZ61ZKuaurnmvpKYbaSXIkW+vtQN47LYc7D9mYEba6HZtSSvdzMAAHvu8zy0SYRbPZdBy3rM2JvksqJlGznijxKrPL3HRLWgeknsigxNxYJQQACI0CW8lHKCU2u3DF6bJolGYAcu+RjSPwkkiglSWJ6uVemV8I0skkS+lMMstzOcksHRbipuB5yUmKpWVYdWkW8ko6HPmrREbMeHzUuGmFTo+iDwQdDiIqTZuDJZsmmrnPnYc1b7RWtdsztIAkQUi4/FJbVkCxDMrwIJ4SkWdPQZRbCZZhjSLTK0xopRbzwC7ahYu2b3AxL0Xk73kOQpJ7LDC2QklHvLNpGAbwymBWCBhuJc6CO0vkyQWxFU1jVAV3JZ6yNnMx63lissEpKLDtv6ba1xGtB4S9rzr6iqW8CymV7WTrtar0r9dNSNjo2mLFmBDdFhbLMRwtVvR/zjl9yhxvsmFxqnxwzMipvz9t0aNfy70XqYH/jlNn6ZbsdICX7un16EvfsntkpeHVom1KOG6MuDMSDRIwpcfaBfSvb/MsqM/zCF77AkydP+KEf+qHxtRAC//Af/kP+8l/+y3zpS18CJCv+1ltvjds8efJkzKK/+eabDMPAxcXFXtb8yZMn/MiP/MjR4zZNQ9M0R9/7jRwBTQaU90GsqbDi6NhOnooJxVcoulnY3JaSvpRLEcTxSJUWIKZS40MkAIwm1MJOKllgt4DhXoCtpX5q0F7R388T67rCDOKQxhZMHYj3Ir4T9Ll5e82/8/BXead9Rh8rHrtTujcrfrF/ixDrzM6SQCMh4NnsqcKvFMNpIiwisc4dN3qF3bIrMWQ/0FAJ0baadPyYsrJSSpSyqxTF0c2uLXqrsZDBPMuw1NLafBJngLAGBKCaTkol4vNRjGFQCj9ThBZCK/uYP4nUN2LZilNdmF2mT8yeRexGsX1N404ULlTcxBN4Ax4uVzTG5xprKQE5nyVWOrKixY0t59V+aaYzIppcJZJKpEqhdMKHbEkV3Khm7FYFCJDVy+960ALSubwwJCU+/wysldr4RS7+f297j3ev7vP48RnmRSW6YBtFcwmmSzTXEbuJmK1H9y4vxGYf+Z/oY90q2clAVlpvSV0Hzo0f+6J+gx8MH+1a2to3b0+sY4DwXVmJu0bRmlMH2x5hZe01biiLubm9qE/F30fmnhYmINPXS8CqJKCNllsgVpkMKdOdv2GQ85to/EbZGfhX19ZElABOHIBYe3Mg25q9l8rcYVJemJ2MklQoQExxUiB3lJnsL7OPRj2plLPobQSnMRsJBvwsT4zejPtPVqFNILWJ6C2QsEvHdyxecG43hKRZxQa/1DwNS2L2bkbQTMm5VhuIWSg91gJAjTapgHHla5ffs63ZsbLKFiVySDnZUOajsEzHqexLjyWNSmJvi37e3hJSkiGJPVsj4vNp13LeKoJFdE4QgWEzTOxfyeoiGfpqI3bWz6XDVkyGITVwAif1gMll6WgpdVE2MajEMAgrdwQopw9FLjnEiHUTRgVErwj5wvVKSthTAcGi2utarPL9KYmaApJok9A6UuekzpWbcdlLMKq2GuMVyhlsZ1G+xvZLtGtFsNevgRWwATUw6lpNmXTjfWMCZCXJcPsNhBt4dsrV4wd8/eGnWaQ1H/AJeCK7BcdIM2H3jI+AcEF97shiHx3qAMiarPNp+nc5/cN4QrELNsa/jwQSd7zGZPup6O7e+/9/9v4t5rYlu+sEfyNizrku321fzi3PyZN2Gd+oTBsbyLJxGeySwX5BNOLBqjKV1UJ0YzVgybIRCLkfjIRs4QdAstQvCAEC7KxXXA9u7KYxTRtThanCONOVLtt5OZnntq/fba01LxHRDyMiZsy51rfPPnno6nb6C2nvb615X3PGjBjjP/7jP0jdS579W27b7+rmS+aESLahsfYg433Cdp8HDgsQOTOzYiBRnLKvysCiDJpGZZ3HbtE5ox7TF93SMKwNvZHIwgpUG03hC0ZTvtwyEBYus7LOnxwhT2rsVlg8EepLzfRwjdDeUaC/e7Xj3qLjjad38GeSQf7aeFwQrjsVNVdWasXQW2zlqeoBJ1aBoYXXCoFblS7R9McwCTwQ5yFlkcmo/5uqorsAzh+uZEuyK6N/aFU3Vga/F+BQxq8OHikFDiILzKmN7xY6D6qe43idifmSj+dBNhWXohULN6uabllxWu+412xojGMDWedXwSKbv4sEnLdcdzVN5Thvl3n4WNmeY9vSRoBqFyrWZkydO3crTVMPFRvf4IKhDRV3q2t8MPTxSDFkMlZI7FYavBPBSuCy05TIVa0VGNPcvO1r2lYZdlXtMVE/y3vtA2HpIDjqxUBdO5wz+GbADZblqmPV9Jwtd7y41JTJzlse7Y7oneVyu8BvNR2+empZvyUcvetYPB1imml8xpXVd8LKOH+D6mCnTHZQQCsynCSI6mhVltDLlDVZtkPLDr27c3/pWftQzFWT91smVaqVLZl+C2OfhCzpM/GNpNiu2p+nytTEfFPS+5POF/3cbKPZoNP1c7b3BWZ9z/d8D//xP/7HybI/9+f+HN/4jd/IX/trf42v+Zqv4ZVXXuEXfuEX+NZv/VYAuq7jl37pl/hbf+tvAfCH/tAfoq5rfuEXfoHv//7vB+Ctt97i13/91/mpn/qp93M5/3/R9lgbc32fspWdzyojK1UrTKkLCmopaBKicxEqyTpZiZ0VjNJe+xNhWJIdne5ugDqweLOm2kB/HAhrp9T+XsXMXQOh8tjKcbLsuDCBphn4L1//LB9ZPOLItFg896prvub4IW/ePeXJtgJvVVdqp+kYHo1i1FdgesFdW7ozj18GZKdaGKVToTds/KvphbN7lCi6E3HxCHoNTifkbYcptgmmwi3MKMobYkoMklHffHoZacNVZI35RqusuBXKMmsMx29Ac+0VVMwRd31OttU8/6oNbO+ZqHFScdWfMrxsefnskrNmh7E6MVjjWTe9Vh80QfXKekOQyC5w6lyIF9WntRBcHOKL6pBta5E6AnxOkI1VQWQT8sAgg2heeLp9TtNKjpqO837Jo/aINy7OePLoBPuopr4yWh7+KlBf62+rrzzVzmG2g6YHAmI02lY6W0RNMWZpouI87FrCbpfzwpMh9bP1N4ExfHR4h0/Vr/DJ5R8o3o9ZimFZxTB9N4ZnR8eLPlOWpb1xB6LBPxOWLp2OCSg2DtzJ6ZAQCCExJ8n3vmSHeEH1EGwYB23UWJBSTOu23c4zN7R9R/YG4yWtK7cxZnRWkhEhOnaYgdEhN4XRUjjJmhZCrjYFGk3Hgr00mF7BLeoQgXrG6rHGIzbQVD2taNW7j5w+4aza0MjAgGVleu42Gy5XC3b9UkXEPROQSjpNiRcPvhfVwKji61uwsg7ejsJwym2edgjjd+91qOt1X5PfaZM18dK9FBcwSDEejM8rGXgMMT3bRgZC/Bes0FwooJVY2HrB8dhDoIqss2ElDAHwhs4vuTg2HC9aFnZQMCoos6q2Hld5dUQGMxWqyD9VIuNWtGBI6hdxYHKuYHcENK3Qz/qgZxrgiIGaxjp2rmIz1Fy0S7abBbIx2E6rb+nfJca9gOlexRwHZD3A47uw2YJ9B3gC7Bi9aMb0wuJ5SQhaRGTwBAfwBPncKfxb+I3f/81wJPCWwKeA7hrkmjhxMVGdK/r6+F3y/Xhmm8wVz7PdzUUM9nYpr6vcJy2b9WkhORfJwS3ulYAkIPC2fUW2OeM0BdcPAll2DKoDUz3fyf5mb9/S8S3PZyI4k65DepfBnerKUK1ruF8DNgZA1N8JsX6D6QRn4N2Hp/BgQXOphAHbRsZUJbQn0B+rPdXf8azPtgxRW+nNi1NeObnkum9oY7Xv66itNfQVrjMwGHonOr4NUXe3U9aTeME1KkNiBg1CKKkgFoyyjMzlHbiFwbaW4DzSFXZwuleDU6C+0YkqWINYryDInMlbupGxwroXHbPTnOurgGl1HqwvY5BjIfiFgloQbdOyQpwNsLVc9UcMd0ag6iQCWkYCF/2SIWtSVfFxSq502/Y13VCxanqs8bSu4nNX99i6mtdWTzmxu5xi6DFsXMPO1zzsT3jaKytuZXteqK84H9YsjVYK3LgFS7vj3K3YuIatq1lXHUurTLvOWRaV46jp6JylGyqaqBu87WpcbwmdgVhoRgS8s3gnGBuoFwPHq1b1r7qatq25f+eKs8WOVdVzf3HNveaah90x72xPeHy9pu0q3GA1e2lrWL8jnH7B0VwMmNZFHyeM70Hhk0wquMf5U60DdK51XqcB52N1UM3q0p0PpBgeAK8O6todSjG8oW+NHWME39I+odh+wuCdzW+5WnNZyTD6SXld7LelrTS5jmQbRcBWvIxVDD3R933+yep9gVknJyd87GMfmyw7Ojri/v37efkP//AP8xM/8RN83dd9HV/3dV/HT/zET7Ber/mBH/gBAM7Ozvjzf/7P86M/+qPcv3+fe/fu8Vf+yl/hm77pm3LVqd9VLT3wuVNRdLjRGS87qxAqk/URzBBoLh1uGSsaSuxoRh2JBMqk8/gK+iONTJhYxWlYw3B3wFxrqVtfQ/eiw64H3EVNaAL9IhAqr9RL6zldttw/2nB/ec1Hj7/E2rT0wfLYHWEJnFY7Xj294Op6Sc8CrlOvJxtm9ZUKvWvlQ0N/rAOtbUNEdONPjvsk/ZJJOdOktVSkF+p5ElpbOBmDg118/4xQG4kVUvbzdX0CIgqDMMRrUDooWQvMx5Lx3Wlg84pB3oR648dqjGH8Jz5QXXvWDsxgaDuhby1td8yXBgP34aRps4bWwira2A4VV2ZBJ5X6La2NwsXqPASJYJSAaUUR82pErIOxiaiF6aLGS0xBta0+B7cShnXALz1mNbBeKu33S1dnnF+v2D5aUT+uqK6UpdY8DVS7+G/jsa2n2nX8t2/8Cz569QU+tf4wn3zpO3FVk1MNJTHqEqBF8YwGR2i7CSNL3wmDF+GfVt+CLCWCVcWgfUjTYT6Yl59vYmrN879lXF8CYZmhNYlIMv1cfA+J1VVuUmhjTai25X4ybpPfm/Qu2KBpPs/p3PxeaLfzzDPae7BG8hwxY4qUBpAC8uoY6DuROiOZdTTv9wl8SVpRvga/8kinwu3BqAaF1J7QKuDjqogCWWUPLSotr72qel5sLnMkdhs02rowAyeLlm5ZK4TRa7pD2UynTCWTUw913Mup7MnYKoImKTiihuYUwApzMAtGIy+xfgY3DhOSmATTuUYRL5lgRel6TAQOR6NtvI9uAf2xIJcx8j657vRbAqZXnFC8ICvwTnCu4eJYOF3vaKzDiAJwNgJAzho6qbJ4u4rwx4uLzzxFS5VFly42Xfuor5SE+yXNn4WsgY/Ok9SeutJ57rJbsOtrhk2N2Rp9Xk7tAzNUmOEM07+K+VhAvtYjlYcvBfj3K3jnFWCHhIFApyBWnnvTXBzycw3OgUuo51Oghv/5Nfg8sAAuge0W5G1gCwyFkT4f8A98lvm6Yr+bxm2RGWY02iEHTzdrB1MBS9AsMGX7Fosn7OGJYxPIxsNt+4psOZUoBYmtRYxh6rjGTuHDtH/A4fciMUcSA6UMaJb7pWVGCD6M+oXRTjRRD69aWWxrNTU8Bry9aPV004HdNlErSg83LKE7iwLeVYjMUP1u7rccLTsGp+Llm13DW+EUEQ0w7rqarlU313c2F56QVjMe7NYQbMjyKCmtKekImyFWX7Tx5Usec7qV/ZhqFmobJTem9rC4gOkdvpll7FgVEQedD5LkSWZXxXc4FbiynswMk7iPbQNuqYEOt9DKhvigQZc6VkBM1zsIuydLHjktDAXw2vopd+otRjw7V3PRLeMjjJXTvaEyHq2jJez6ikXl2KF6VT4Im6Hm/mLDynZsnFbX3bompipW+e+xbXHBKMtcPH2weIR3+lOu3ILrYYERzwuLay76JQ82R2y6mnXTs+lrQhCaSjN0emfYbhr8YDALp5IrXqhqnX98X2Nqx3rZUllHCCpovz7p+cjpE15YXHNkW67dgofdMW9tTnl4dcTlxYqq1u0ZhOap4ehNz+JJj+nc+F744rkndqJETevSnE86wuk9mr8zRsrCuhNf5JmSRcW6veD+PMg0O27Z9oCxyfyUjBj25pp5GmJKjfWlYHzhCx1KIdRKhgUAG23FYOK4NCe6vEf7sqoZPqv91b/6V9lut/zFv/gXefLkCd/2bd/GP//n/5yTk5O8zd/5O3+Hqqr4/u//frbbLd/zPd/DP/yH/xBbpuf9LmmTPNR52VVRUcRJR8xRbxmN7N5jgg6gwQrDymASxa8aB7bU8XwN3YmW8LYtuUJIe0c7RnWphlN/Glje3yr1cqURAWMDdTPgvdBUjnXd8frRE75m9ZBXqnOMeN7p7/Cw1+d1PSw4rXe8ev+cd6oTds0C1xqk08lABi1lW+00Rc220JyThW8ls6ckOhRx4O5iLnm6B0nk3rkpkAUQvKZjQZxY4yQxeMxuwMYUTS1jK+Ng30YNy5TnHlkzZiALPUqIEZ8opO+WOln0J3D9qmH9DiyfOGaWaX6etvWs3w1UW8PunsF0hq494vO7mhdevOCF9TWnzQ5DYGkHnrYrJQ1Zz840WojEWexWdEY3I0BiejVKgisozcQJLTIk7FadjBCrQWQmQABZOT7yymMq43m6XfH4yRHhScPysY1go06G9VZ1wqqdV+0SH/izn/uX/LcP/l8Y4Fu3nwcR/ulr3x3v2cwBLFIMxXlC1+m/uM1co0hK4Cq9EyWodRN9Ng3cxUA9qdrBbHk63zwiINP1k8E3/ZXZPuk4HN4ms7UMo4MYiGmEsTDBoVTDIHn/2/b87ffaPANMneG5E17MD/uO9miUaPlw/euNpmdnm6VgG4ZimVvEak4xaKKl03W96XRDt4BqPWiWeCWIUWaQqXTstsZTG8dZs+VuteHY7DRq7pZsUuUibxXQWu+4NguG1qqgbUzHTlX+ZNAx0G4Z0/xLoENicnjy3V2ah+JvT2NpmeKeWtCAgsT7lSMwPuQ0GnUOGYGdABJEHZ5k1KbHlYCreCjjgIGsPYZRRlt3EoNCu3KQmT4/GZQ9awZNjRcnuKHh6WBZH+1Y172ytAhU0TkJAcRYBrEEPMGbyHaLYFxkACSwKmtrhWKISpnuKTZhJpelrQqcHWuUfzfUbLcNYWupthKBx/jMeoP4BjPcRV4L8G3wjXd/jbPtY9546cO8PbwO/2qleglypSkZk/sx3h8JCchyhIgUChtyTuGTNYjVDsMlsFOjICnyTxz3+VwTf+DEgJ9+nyy/qUn+b/+eFd8PzjU3HLZ0DPb2A3IAUeYrhAOd67Z9BbWkyzTqWqVBsPBRUpsH3J/VktPuAsQgyAgoS142DyYEo9qMGAhoAD9YodoGWgG/iOOzg8XjwOpxoDvS+aa7oz5NqGC4MyCNJ3RGgSgvhIXneKVA1qrpudwtaJqBza5ROY6hkOMIQG9GR79gfNg2gvpmHPLzHJFsaQ8wAmlBiMyt0fbV+2816uCmwIf0DjFJUkJUSL9kwFEAWcXn5DMFdA4zSCbags4nslU7flgqmOUWgmujDMDC4lZeA+Sx4FB/seCxsyyrgXW1zqLwPhiO65beW9oolr8bbK4A6YBhsHhvqK1RIKuvaYeK637BquqpjOOk1uJXVgIuCAvjWDWKTPbBcuUWPOnX3K2jxIBTjSwAG1liD3dHuXohwOAM1oRcyb3d1WobtFZtb++pm0EzJLxgao+tPH1k7HlveOH4mg8fPeX3rR/gguFRrxpdD3bHvPHkDtvrBWEwDF4IrcFeWdZvwfJRBLLisxp9WlPMhaW/ccMYGwXhn8moT8c6RIA5tM18GYz+kMikH82lV/I7Xwbpo+GivkvIc2G5/ka2MIzzUmZ6kdlbmYFZ+kDJX0p/PTp+EaAOzENCz2ofGMz6l//yX05/iwg//uM/zo//+I/fuM9yueSnf/qn+emf/ukPevr/nzdfM+2YItppU1rHjEo66XAepRvGL6EeUw59rTTXTOOLy10j9Gs1ZhOQBUq7He44ZGeotsrY6l/suX+8YXCWbqH0UCAOCqKRjL6hFs8L1SUAT90RX+zu8tbulM5X7IYaI56X1pe0znIZBfZCgN2mwXULhnWs8hGj5nkimHX0DGj5gO1iOVsXxkkxhMOR8rLFF1CcJxgPg8d0DttZnFjN5U9pMxlU0HQaH9M4NdoSMq3aDCHmy0NVabqhaVXwd/OyGv/NpRsntvRI46BmOs+y99jeYnqDbYWua3jgznAvCc2p47huOa7bzDSwRitD9aaiFy3DbmNp4IBMhAHVEZCcPy8BTX9xqP6XGyvBDEdpUg2sTna8uLri0e6IJxdr7JeWrN4SrZbVq+MkXvuQ7Ufhe9N7Pnbx+alQ++aLJMHPSZrh/FkNDrpeBd8nzy3pK8yotHOxw/I5S5FiaG8Y1POxDqRuSLF8BoKl9XsRhjIyEgGonBO+15/TMQrwQKbbBxMiwBVGB0MgVeoI2XJ59s/7vd5+r88zkAyE2VxTGC2T6DvFttH4TeNdEqfVg0YHyI+GC8S+b5XtmtKyE/vJNeCXHqJ+oq/BHznWTYcPBlc5JA6ScchX7YxYJj3paux8w4VbcTkscLEsuIiWUXdeaE2dr3HoLd5ZpI7jpyOzmw62AtAybhyvKNhZ72kmpXvhY2pwYhO7EvyI25qYahjQbaNBmSvjxmejjOSobWjQoESMtvfHYzXDEbBJf+MHB5UPiDM6JwzgnGUTloQjwS48jXW5wiGQAUwnBi/g0crD6pQVNgrjPZ0HT0hAXIgGaUyXBP299WJgXXdsh4btrkYuKpqrsd+kSsOSr32BnMDJC4/5SPsF7vp38QLvvPg64Rh43IDY8doC7D3sGPwKKUqeQtziQTqQC3Iugwz6TxXmp8PtHBh6lqPxrPXl/gfmonT1e8ETmLIxDsw1N7K6ZHq8POdMtk198IZru21fWW1mR4UQR4LJ8hvkULIDW6yzhbRDkY4kaXs7PV+uTj4P7EfHVouPQHU1FhRJ1bNN52lParYvCcNRYDj2cDJga4/vDQl4NzvBnWnV76ZybLuarquw1o+ask51Z+MrDxKQYAg+IvVelLk0yGhXx4BkCnpPwGwrOe3Q9FBfe2SIzJvkx8T7le5FeS/FB3xj46Jx3E2ZH9nmF8ksLRMD2yXLTUTGjIAQx1gPzaXqs1bX+pyGlYrFD4PJ4vp+6cEE3M7yztMTdkPFK8eXGPGc1jtqU7NzOrBf7Ja4oHNa72xMjNGq64MxWOu52CwRgXNZsqwHFtXAVdNyb7GhtsrCftqtWNnxL2jY4Wk/fge1EVpXcVS1PJZ1DIIpgNX2Fcump+tUJ6vfVQpONl4DaINhsVQfr6od3nuaelBSkcCLJ5d8zckjvmr5iD5Y3mzPeNQe8XB7zLsXx+yum/iQgeuK6tqwels4+eKA3bnRn03AkMiExZwYWCPok9BGNJMFRjZX2UTYr1o4y0wpgKtJGvAcvApBAbayzyXwqQS+wux7PlexSekXxd+eM1Bm+lcp2DVhZc2Pm45THitPiNPtwmj0HDjYze0/OTPr91KrxdGfpI5txk53aJKYREHStmkA8wSrQnJ26wCr6HpE8nPanBF8LbhlBLLiIDishP40wMJhrmqNatz1nNy/5u5yO0HafcyDHrzhcrfgYrfgd67u57SPPlgetsc83B2zHWo6Z7ES+Ni9t2iM4zeGl+iGintHW66bnsedhVBjBtFBNHX2+DcZxamkuU5GCgAxxOof6V+qhgdTSmaqAjIfKJxGO+gF03m8VRG7lIIcXHQcrN43Y/RFkRCjKkOIGjA6eVQ7fZbBxApavTpu23tGWUupumS8Bo36h+woLp4M2J1ld89qzn3f8Li/k6MCZ82W03rH0vY82h1hjefa6iDaB3DGan69U6s02Z4JKZeYSSFenbk8Aaey8JJE78Ede5ZNz4PtMY+u17jzhtUjYfkk5CpmWgFMQT3TxeXRYfvU4jW+9eqzo1D76sNRGyr+fkmj09jEeRiGfSDredqc3ThnZsGUhVWytObG23s5IzBOPPF9zYN+ILIhC0FeMw7kI4BFdvjT8iCMgF92cot1qKMxXmvAmAIdvW237YZmxeu7LRTvBYcd04kjK4UxMVa/BUH6oO93HDfLIESeb6oY/c2aHRpNpwrIRiJLK9CsO5bVgA8SQanRL/JB6AZLO1iedGuObIcRjw+qrbEZVKDXBYMQeGl1hV0GHm4MzhuWTU9vLVu3wGHH+YXxessgQwb2YByjS32s8h/jtul+TW5piAfxPs9t4kwGrNJ9ySmEJo4dngwijHqYOshkLTCJcokR8AlW53IzxAIwobiIEmBy6Hw0KItb2V+WrVsSToR107GwQyxE4thKg5FAZywDmt7uxYyphTNgTjyZ4p+Y1BODM03R0XoMTaCyjk3faDpIa6m3MU0ozZNFaqU4h4Qetg3X58c8qc8IYeCcO3AZoA1APz6X/akmMrn9AQchXWw8Rj5A8miZziuZlTVbR/HeMCsmwrjJ88w1uZPGY03mmsTkK057KIUj/00Gfzrs7Hqm736YrJMSobxtX7ktvzdmajfD2OcP+Snz70UAMdtLxcAerDCpkioJ5NKiEimFLg8dmdGiXbO50OWrh57F04FQCZev11x9BIINDEee6oUdVe0UkImV5dIBm3XHnbUyfs4WO84rR+8MIQjtttYKqzC5Rk3zNmNgOFYMtB1ZD9Itxorn6R0tXx0zqCavbR2md0jrstRGlt9QSiyhtgRjlKhgNFMn1JolI7FKpPiA3XnCykzPJeP50nPVYGv6S5yTQh7ejBPV7hJylkx/JJqmuQLXWYYTBybQbWrOgcY6KnH4YDDiuRNZVH4hWFPT9jrQb1tN9fPe4J0QgsO7eI+D0NeWtlGt36UdGIJhG4GxrasxEmh9RectO1dzp9lw3q8waJ2/xgx0vqLrLFfdWIRHJLBsdCwfnCF4o5XgC6DFViOICVDXjlXT0w2WF4+u+NqTB3x1BLLe2N3lUXvE29enPDg/1irzMQXVbA12JywfCqefd9SXQ9bH0jl4BBxT+mx+7sXfvD6/GwX4m/7OiRuHAvsluFz+S8H+uZ+UbsGcRFO2ksEfio422yaY8XjzQgj52fhIxanG5TkgY7IJtHeKCRgm+r6PZBEhWNVZfRavZd5uwawP0IwE3N0+VgOadrYwQ+WB/e/J8YU4oA0KLLhAsJWK7kZjx1ulj/brKBDvUS0tE7WyThx24fDLij4EuN/ykTtPAdVpckFwQbjaLWi7im6nRicenDd8bnWfSjxDMLyzPckigL2z1HVE04OwijnJ59sl/WARG3BHji5YbCs5x7ysRJUG3BTZt50fy5om2mZ2Np6RKDsDs7KeiTGYziELO74kPiitdwgErw5NSkGECATGHPR0bQZlLSWh2mqjaZNuIbhasFs/of5OBrc0wV72mN5jhgpxBtNXnHdnbF5uuHfacLbYcVy3nDXbXHVlGCxhLVosamOjjlfhXKZzHYiap6CziffbV9C90lMfd9TW8+DymOtHa1ZfqmjOg7Ky4iRtXHSahqC5//H3SO/4708/jvQDH+3e5FOrD/PJ+39k+izcbJRJzLoESuZHJhNWVk4xTKwsGI0ua8eBunzmmXL7jD6xt/zw4ny5s/0OaV1lenfQqMN82zzAF44IJehVpHzm40n8IGieP8rSu42W37ZnNQHC0o1GUeGEH9TXyc5zbKUREtAU7RDwwRAWZsIAThU6U5UpcSMg62vwC49YT6iMcmHWjrOl5vsNzmo6SYBuiKW9ewNOczjCsfC0Hg3Yq15ZWQDOC3XU+AhBy7JDnL+8Gq+h9gp6OZAdxTg8Ov0JQAFGICv+8HFMnRmSeze8uFkxxT3ElB1xQUlD5b0lzgVBdH4oxoc8bqfgQwhjymRiLvVAqnhoBTOMOlGSz1HMNSGKxvugzpkXxBl2bkl/bFktOhbVQGMcC9sj0dTz3kCt/l3AKLOgSC2cM8Im2Edcb9JUbcAdO0zjMCZw3S3oNzXVhYo2a3Ak3YMkcO8Q10O4QL64hn9n+Pff8IdYrK5ov3QCvyFwgeaQEplUc5MgsutCKJ8tyrfK4G1KJWRqd6X3JzGEJ+BP2n62/AO355xrknN2SMPxwOs9WZf23Ztr0j+da27KWrltXxltPhccZGWNK5kWBgmHt8ssj+hcxvSqYA1i0CJWRfDPG5sdYW9NrsJebZ3q6liBoPb1+oHD9J7NyzWXHxG6Mx1gggX7QktVO7quwg9GUwydqG3sods0PF2s4iVqgB60eIWtvOrxtAbpZbTF9IdkgCtUKvaeFyf/oMTBUYa+eC2WolkoKg8jvYsi90XKeipilUC/OoJalYIPvhJYWEyXqhlKEVyXCBwyZfem5qO+lw0MsYK7OMkVcfO2cb4wHuyTQHOp1R/7E8Bb3MoQFp5e4JE5wkjguGlZ2p7GOu7EwPuD3bEypvoKEWVACSDGa7XbIHgn4IVuqBkGq6GDINTGc2+5obEDgzesqz4GsSrWVcdmaLjqF8okNgMGy26oedqucEEYnMli9V1fUVXqV7iULhrX1eueuhkwJmTB+ioyGu4fbfjakwe8tniKEc/vbF7mSbfms0/vsW0b+l1F6CyyNbma5fodZWQtHnZjemF6R0odONKcn557oY9F2PMx8ntU9o95K/2iGUFmIiNRsrLm723xvWRKTVhj6bMdAzXj32Lu8Ki2ajqGkXGuMYltqfumwL4U9kEqnLD3M6NUzl4zRM2soMzK9xHovwWzPkDzQZDrakzRSPpYMHa4OZiVWtAS4MEYJAjSj4Oh2TlqEXxjcEvJnULTysaOKF61spSVpciRDIJvAkfHLeuq46pfsBsqrrYLtudLqgc19ZWw8lrpsL/neO3snJXtuRoaBm9pXcUuOhDLStMHroZGQa6hwprAZlfRXjeEXnuqrwPtHQVhmgutAmLcWJExveTGgWlV+F1ZWSG/2OHQyw36gs8HhhLZHnRCMc7j6wLyjXnCKTKO9zm9Rpym2miKjabrOQIyiBriXgXRfaUCyWkS20PpS0ArMp7qq0FT97qKXWswnWXTHvHgNUN3YmmXFSdNy0m9U8aFN2yMCi72QY0Bs0XTeGL0KDsSUW+ljFanidc6MCuhPuo5WnVadWRX07xbsXoQqLZEMcow6siIphXmMcOD9A7vAj9z9m2x6mYFVTWNzCXKVhkZnzkXz9XmUcJDgDDFQHzDQL5HpY3bHkoxzANsGowPOC/zlJOcohXUsNGNmDgPPh0rAgcacZj93gQq/Cd3mG7bV3ILoBXlAsU7ElfOGIz6ebqz4AkyprFnY2wIWNR50OjaqLOQRGnTMYKNzM8ohqssL62EWxtH5yoGb+iGimFXYa6tVl0KMTVx5TlZtFTi6WL575Re6INQRV2tzluuhwUuph26oWLo7OiA2IBbxnE7SiBpKiDj+ERcHtNAsog4ca656UbP5+zx7pNSDLRwiSGUNzm916EIdKRxIioJaEqf3lNvU/SfOK7H8SVed8k0G6+7eKw5xT1QO8a0Qyf0ruH6RHALg6uGmHY4IJGZ3YtW1vIB7RN9BNfSudKYloJR5e1J4NQAUoFpPE2tRU5cb7DXhnrDOF+muTfeH3EOoQcew7ZBfvkUfkNol8dwDuEdwL4BXMcLSPeVqfE///68bR7JZjTgi42KPzJZNN9k7/seg6soqLB3Hm17y1JK6nyuSecJs/3SXDN3GtK9u22/Z9oYYDMQPPjI9klBxbLdGAgsHGiJ4Eqq2lZHjaICnMm7RfaWBj0M3gpuZVQaZSUEU1HtAu2ZwXaB5srTnRg2L1dcf9jjToZYpEIIJwPrVYePRlXYqSElnWalBAPmYc3Vu3eRAdXm63VcquM8JYtAf+YJjdr1pot6uo6xCjhxKElVqA0KanSzW5Js7QjQ284rM2sXK35rDt7IGJWo9lNbMFXBzNcPbmEyqzZJxeiJEnCWxtrpOojzN4LtVXYGUWJDIhLkflCM47YLrN8NdBvBDMrUGoLBAy0ND+WI/shwdwk+GJZVz0nVMiw2ADwNKwZnqGpNNfSRATdmagSC0zT27a6mHyx15eic5c5yS2U0TbSxQ2ZtbYaG1lVcdQtWdc/gDU00OBKItd02sTsH2l2t5+ks0hp9ro1jueoQYNX0tL0SNwCs8XzNyUNeWzzNGUdvXN/l4eaIq82SblOrz7yxVBsFspYP4ezzPYtHLdI77fM3jaFJtqAEtlILEdgqAaQSxHJeM1hu8pcO+TuzNveJgki2NZ4Z5D+wrgSycjZVOffGfcuAZ/qrDCy1IUoplZSOOEkpJL5rQpZgmWhwCnocL0il7Kznbbdg1gdsCi6IprsdYmUVVN2JeHVanwCBGOkgIp+mVzFuu9PjuYUhVFqxol+N5cG1RK2+7H6IEeSjgbPVjgfbYx5eHXH18IjFmzVnj6C+1JesO1FdLVkPvH70hJebCwxaRnVhB3pncSj7bF3pyJ7SRXwQdpcLZKP536FSgUFNgQQuk8OEds4I+uj9CpERFKZ55nPDtEShTZyI030ttlUnJqYs9h5TjYNP1uTKDyBNBHpduIBUsWR68TyrTdAy87FVO6i2fkwpLNhY4srBLP6OAHY7sHrgMX2NGSzihG2/4vGLNbt7G7armmWlDsa91YbGOq5twzWaHOFExeRDjNpr2knSWIJcFSumoyRjtr6G3UXD8s4VAgytZbUTqk2g3vq8j+l9TjcsIz/KUgv7z8MHqOe9v1hXbp8ot89iZaVnK4UQZtluAoEn5+UwW+uGbeflpPc1sEbgWM+bls8+T/6WxlwxZs9un5S3KOgCMQFj/I0/77bdtrLl9LoMhJbpSXOjp3Cqk1Oexqdk0BTGsfigacwiSCVgQmRnjc64bxidch93rz2LetAUs66h2zTYS8Nio6kbBHUsfK3bnjVbjm3LNVq624rHxYOKQB2NWbX3hBAMQ2tVH0PQiF0gC4ralhnIUY5lxdzj2R/T8oazsSbfm9KSS+9wBOydMn9LjGGPxeTGlSGOVSWDTB0ZJoC3GRirGobioAncmgM6gAyeepMYWgp49q5me2QZVj19PVAZjxBY1T3WBHqxdFT4DgXloj8mLtoow3j8kf5fXL/otQ+tpYrVcr0zVLHKYfoNuQhM6i8hxINfQXgTdhfwZAHUYHZgLiBcEaM5+89q/gjT3DthZSWreP/ZHh5q5YbPs/M+7zhdPOMbDyvpPsqN2+1pa910/ve6tmyOhuf+Cbftd2fzpY0WC53s2VcHtJz2ioegdlCoLcGKBomFmD4oExmGkaGhC0KVBJ+F9tTglprq5lZQXwi21WDq9r7h6qugf6Efx7LeENaO07sb1Upqa9zWIlsbtffGecu28XwxEFNfwOI8xKIlmrIdxNDfdYS1w9WC7CzGSRyPRkArZyEHPW6qxK4nGNclzUnTFynOEaAQF5e5IuVMBGMMYV3rYSREEXwYFkYzcqzeq2EhGQRITKt0/pwlwAg4mGFM9VYJAokFRGIWTAQWk2akOFg9CdjOKLttEPrIJ9iGBcYErASO6o7KOC6HBY1x3I2AljWeTafsq+AF74UQK+WKCYj14IWhrXB9oLMVgzN0g+V40dF7w8I6auuIyYk0RisHnu+W1NbRO5XEAa2WOPRWUwh7Q7MY9Pt6wG8bzM7AccjXlgAwiff5teNzvmr5mIXp+dL2Dm/vTvni+RmXT9Z603qD2VjsTqgvRXXb3nYsHuyUXFKynp4VpE92BmQgMwf203sVgazcR4ZB/wU/9YeY+U1pefk56UaHMA2oldgCTN7PeRulGIpjxWOHoo/NWcRJqy3YIkgTom9V+EiJJKC2pYLIoUrnjTZPkIk9k8HliByLCRj7jEytWbsFs/5TNGsVaS0rFxZAVphUEJmJKaZnNWegpOjckCodukwJbBqDWxi6kyjkOkAYRFM5RI/76PKI3dMl9cOK48eiKWatTnZdnFjMIIRWo+B2qReyc5UK9YoK7/XesK46rGipWx+EzdUCdmasMhUMptUo85hWwJielTp1GOm5GsU4QLcUMzHMZF4NEsaBJYukBMQpXdkvwjgoHDD8s3+SDMlkbAddWYojJz2q5tJP0vCmaSVT2mnexgfMRoXhTd9gBovthN2mZtMf0d2pWa9bmsrRVapLdrRQ0PAqCK42eFNpBMmiVOkQBes7MuqdHSV0cmOA1Rdq3m7ucO+FSxiMgmG+YGS5pI9FRNSTHkK8tyXb6gAoOwEUZerw6YWYae54CWTlbWT29wCltnyXZu0gU2u+fHJNxfNPm4QpGDVxCmQckPNqUziioQC00nZh3C5vVJwvt9ncJBImsg637bbd2GKZ9T2thIPsrCJyfuA13ZtrYsU/q1EMteWsaBCl0WqxGnwBgsnGzLZtlIm1MTSxwmBKTfSLUYsEZ+h8hVQxJTEyskRQ4zgItXExVUBTGPquUoHeBO4EyYK3Uk4DgXEsSYxZVwDzs3TwvXtFNCLn48Zk+zj2B43AawqP7G84n2uIjlOeJ8brTM8gGY4pdbBkY6VDHfoNI1gXqFLauFeG1tAbelfjloa6GbAmYIOCWnVM2ehCpZppvckWbPCi2rZCdIr0Xw6kQGYOVOeGK7titW4hpk4m6YDMyMrPKlXmciAdhAHCRXp4QHqwjlHMvfy9BzpxZDDl1SWQVWxz+C95nN9jOxYtzN6pveUHxvfy+LrxdN89AGo+18znDZktD8X3Q+c+0PTWhP19bttXTMupO0E1TGTed9O/Z+WbpgJWC5u1r3yT9FDIGoslWDIGVKPjGu3+ehswTuiPya+5eBiOhMuvCvDqTgH0yxrpBL/yrO9pVdTNrsZHbST1HaQ4j2pb+ZUHGzRNbLA5ZTpY3WfxRDB9RXfXwekAK8dwVVFd2eiriA49Ztyn2oDpdNUhnFhc0BTBJHRfZokk9g1oxggdhID1nrCodVRrTHbotcBK1Oxtx0yWYaFzbrBM9JFz9rRTEXoregyCzrO+GoNImtKZPqd+oXqLx28K251BnGHoBNcZrgdD11nunW5oXYU1noXVVPVV1WNFAaMdys4aehv9Tk17lCqmoQ6GYAMGz27TMDQW5w3WeLrBxi4WWEf5muuuyazhZaWBl0fXa7aXC/CCiyydrq3wuwhsDrGa787SLypaLywWfWZovXR0xTeffokTu+Nzu/s87Vd86eqM66slDAbpDNIKthPqc+HorcD6wUDzeEwtDF7ARLJDYffryjBNMUyfc9Vk/WcGx3/z6N/w0d2X+NTiVT55/IfxzhP6XrdNQFbpD5Xv7DzFsHiXw9xeeUZwP/t3N0XO09yeADgpWFaBrOmp52ZkXRVzaBmkU8aW5PfK23GfrCOcgOR4bvGCryKIGNe54fkrj9+CWR+wpXSlQyBW7nCGPHmkSSCtT+ivPvhxuRp9CamML9EQIg02wuknJuZMA53B7AzNU4O/qOmXNU0L1UY1ttq7otTbaCv6GhVdM4F3tifcqbe0vuKqX2SNrdQuuhVGfK42FbyADQRiZScnSK/01ZFFxAjsZEekYATNqhgCWvJ8/jLOqjNoiptMJo2sndU7ZKigIqd07FXbKwZ1TEyFHASxmv7hmlH02FuJlQynQNaIwqffOTodunwEuEznaJ50iKsxQxWj1hW7jeHivmV53FHH1MhF5VgvOs1R7yo6IHQ2lls2Md1Qp9dg9BoTPbqsNmZbsI9rnlZHmOs0aUdWXB/y9aU0wzzQhfH6S2DpoLMswkTPJBtJxbaHDKZDrKy5U34gCvG+msRn8qyBOzsZYXRi9qyW6YJSwyz61SSNrJsi74E0cKf3DU3TkjD1bcysn9622zZvuZ+V71jpUE8NlokBlN/xor/n/RiNstS8Vk0islFpJBEKcyqI3WkwxG0tNjJygoFhVYxLgREElsB137A0qpPVecvgp+l6rauyYYskjRb0YPGFEicj5lEALPPfkEAhKQ3MuPFB4GoPENTxkVT9qtDOEucRb+J7K3nTSSvmmiAR3JGxglewMoqkmpQ2meaSMD1mmP3dW65zqt2BhMjQcoJ4w9AL7dpQNQ4TS51XRgGtgOqcOQAXAUoXDfl0aBmdqOwYFQEU2Rp2pkG6EVjS51P8hsgIDKTjOGCAECNgk3E/DdBl357Nsamfp3nr0Fg/c+SlXJZAr0nkbP8Qz9UOebwH18Uvh+aLWZszMfJ9O7jxuK50LjSwGR+YzLa/bV+RrT8JanfZA07gM+ypzM6KQJeyhzSt0C0sbpnsNgVPTDdWvgayXAdo0Bti+lwc/5NGID4ytu6Cu9dTW4e7XKpuVQBZqv7e5dUKt42p5bUnmFHbighk2Ve2vHTnimU1cLFb8Li5g1uodq8MZOH0+grsztJvDP2ZgzownDiq82gbR+cbdE6ptmM6oZQ2dpxLTFf4LTErYaprpb6JaiP6DChK75TebAVvBVsHuiND0Oy5nDJv+xRoBher9/pKga0Eapmg44gZ0rst4zwbyIxYGwMKpS2gQW+tHmk7w+4FoR90Huj7Je92FffvXXHUdLQRIqiMpzKeo0blS4bBUtWOfog5ZE6DT1RxYugMvrXQePogOGcwMRthuehpe8PVdsGi1qIxPghnqx2Pr9e6zGuQi8pjUiXLmBJodwqWBRugM+wuF5ha57W7JxteOrria44fcmx3vNOf8qXdHZ62K955dIa/qrUo2FaoLw22hdU7geVTR3XtsNt+ZFfNgn2T5xuZ3tmPjWy89OxlcNB2/Nfn/5Y/e/0/YYBv2X6e0Hb8zPJbihevyDjaq2p4wD6J/yZZXvnBUqznxlay+ed6WTD2lQRKCSq3owvTQRSk9pBBrmzrRZtGQsBLmrxgqhEOEgRXRbugBLckRPF3YUQP37vdgln/KVoSUTzEJLGizCwr2dhOVT5AO5FqcJSGXjS4kSy+FuaC20YHRNDOVF1Z1m8J63c82/uG3Yuqg9KdRj2UJoAJmFaoL0zWYzALRztUPO1X7FzFpm/ovaE2Hms8VgJP2xWDNzy5XrHbNCocHzRSL71g+lR6FOyuqPg3e6Fsn9hBPk8EWW+ssuSeXtIzDxmpJfUzgoHiPHQDMtQRACMypNgfjPLlKV04VYksVkBQwG/5RHPj9bxMgKy94yV6cQK44rWabqB54rOOlukN9bUOwu2LFe1pT7Ps8d7gvGCN52jVYkygNTW+swQJOGNgYzRaYIFeinMTJzeiLpnQX9exWpmK2AdRpl9JK0+fkzZCvr/p3s/7c3kvSwArUZkl9ve0nTmQRvh+WFl5H/af4aEoxGSgjvsY9gdtU1Dly8MG7ReU+d9plRmNnsm54ucyXxyiARch6WCIVQ5DFtfyDoyFLHL45QB3t+33VisNFZl1XoNqIJXdaFK4IDkcRSvH6kLkdHrO0cAJAqYz1FdQXwX6tTCsiQ5GAdoKyDCmgiAgVkV6d75m8Ibe2VxdV1OgAjtXa/S3qxl6q1WLInKcWEfpumW42dYxSVw9A1lp22QEjscZ79eh9y+OYxnJi0ae8xnwy2T/NO4fnGvIQYJJOrKQnalqF/IcchDIKo9XPqMytdJ57DYWNHEqDG87Yegsw9rAwmBrTwghXk6gqQZ6sbgegtPx0QvqPKYuNhsj032QgOp0dioMnFIQIDGzxvsxpljKeJCQWFjxJFIOusXDkdnidJQyEDF/H4DJXDX5CyPoM5trDhzmmQBQ+T4m2z39zuLaDjoYkd23l/I+v4b55/mxyu4gjEGWiISFqCln0qY3BXpu2+/q5lbF+OOD+h0zUDeze63J40i2uSJIEyKg1R9XtHcioLHzo55fCkhDDtAnwGXuFCurV+U6TMRzdi947Gqgf7KkPrcaBLkzICZw9eAI6QymF0ITYOEIKw/nOp4FCYT7Hd/1Nb/Fveaa1ldsXc2v1wNvmvtUTyqqbTHvOD3v4qFQXVX0xwF37HEnHrPVIHwKMNitqA8TTf6UxpfHd8hp+XsBoCI4H5yH4DXwYYY4T6AaWzHIK6EiGC3qVWreDYVOcjpfmk/TMl8nMFFBwyRB4CslLRDUF9C0w/HifS0Z6JIeVo88tjXs7gv9iTCsBLczPHQn+BcuubPaYZLWorNUxnPcxAySnVYcHHpL6KLPZYJ+Tgh8awgeXGdUkF8CbrAaVAmaqgiaor6tBrq+YrursTZgao/bVLhYaVBak+0JGbRvmM6ormDtsFaZX/cX19ytNnxu9wIP22PevDrjwcUx/kmjYvlbob4Wqg00TwPLc61Ub3cDpMJkRplXStwo7IxMoIhFzJLuc5JWGRx0PWGzJQwDH91+MbspBvho9xY0f+BmUDm9n3uBNQ4vi98PVtXNNlvpxIRxvjvkPxnJ22Sdt2L/xLi6CSxLy32VxgWy7ZVAryxbUaUAX/odxWe0L72fdgtmfcAWrOZASxefQlky06COfQKvpOhcEgeUMII/iU1URtcnzJdi8PQxqmsGpdI254H1Q6f01AU6UNiAO/GkMuKgxqpER2C4H2gWA703PNwd0bqKbV/jvOCsx3mtYnG9beg3DQyi9MwiMi4+Gktx0rAdESCBHDUX9rSZ9GKSE5/SMWOqS1m2My0r9zk0iQD0A6YbcFWjL0Khk5IEgkvNkpTVYMRjDfhKU0BVOylQbQP1tdsHsYpnlhy/BEhOrqdo4jz1ZRe10GpspwNzfWXZ3Te0dyq6taNtHMZ66jg4L1Y9LUSxTRjqgImVN9JgEIwy4koD3bbCENNzkr6aWwpmMNjW5b4XCnQeGKmzUacs6biFFLErBjZBByzJ/FIBq9T2tL0+37T+BlbWTVF1s3/O9zTCAzdOFM/cTSQPtj4OwuNgPnXmE022ZIXsRcQFFThMlx3A5xcdTeWp/Pjzb32L2/YeTfte8b7kfpP6q9nr+2MaW1lpZ2bgpI+5s07nmmS8JIfE7qDehGiwjNfmF/GFSMcrInxuFbTCVBA2Q43zhsHbGLfQKJwLFV1v8X2MyMfCF4kJJMlAjgaSiSlte/hFGUzIP6IA6BlF7idgdVo2LtkHkhKo5bW0ejCqG0l5zgPjv2JiuqEOf6KpxfGfDJq6sgdiHUiVvwnIKpeZLo7xzuCiMLzphGFV4ZYeVwecNVGXQqtAUTscNkZIDd6EnM4RfHG/Si0wooOa0ntkdKpGdlYo4KvSwC1QqRzESNvMxaVhPEj8YiJiI4UDnw3j6Zwh82Xzlm0umS18j/Zljtt5binnlfRXimXpX9FXM1hWXm75cwPkmSe9kubAz7ttX3GtuohAiYgCWTB1jlMT2dfKKpkfC0uodT/jVGjc9EnmIkwKKAGTgHCWX4hB1GHFyEY10N2BcDzgLhqWb1fIAP1Z0GyPncVejA5A8FA1juqoY7c7oro2DMeeb/6qL/HR4y9xz16xCw1vdXcY7lieXq/YdiokXCHglZwYJPpKT6NtvLF0dzzuyKvWcMwusTtykaVckT39nmjzaUB+9C8ysBVBjRACWfsjeK3u3Uu2t6UzGFESk+5rGZZmIuJuenX23UJ1kjMbNgEQIWbBeAUZHeBWhmEJbmkYVoDA0ZeUdZRSvBJIl4YHM0RW0k7Y3jf0p0J/JAyh5jEn+HuGD51eYCSwqLQqYe8tC+tYHG14Iiu2NJGhBcFFBlVrCI3X+aOtCFUgLPSeOO8JFZpCGkS1tgSuNkuCF9ygqY8EMgscyEBqrjRJTG8cBLEBaz3Hi44j22XB9y9c3uVL794hPF5QX2lArL5Sf9l00Fx57M5TbR1m0yFdD5UlELOE8v0e5/ZJVlECshKhYtcStltC1xOc41PmRb7Vv5UVhT5VvVy8gzM0ae63zIP7823m77QnMnEP+VP7+8yLN3gjIwCe9hGi1tU4b8+zWaYZAMnfnM7dgfE9QsDbMMrkzOexyIVIMkbP227BrA/aCmof6Ocwi4ZMhNhmgI64UcNAQjG5FBHNiYZT0YnEQ3Oug5EZApsXK9o7QogaJW7tCas4evWC9AazUwquW0K407OKOk2bvtHqUl5wsSJV31ucM7jrWivr7Uwui2sGZfyESq9TxRlj2kEKtOZrhjGSHn9/ITJeMnFCwWAb79+4bUrV2PcyQJzH7AZ8Uyn7Le0zcwJSJDQZfYkqPayMCmgGBeWaK6+58Ynhlc4zczIykFVet+z/DrzHbnsWLgJavVJmmwvoTiq6O5b+NDAcD7iVo6oHJa01TsfNmJPua0vYWtUp60VBkoikp9LCoagCkdkYgRzxEQLemknfHCs1hnEQNQbK9M9y/M1OjWjVw0SpNpJHrszKOpQbDiPgNTOmbmzPSh98j3YwenFoG2HqXMDoSJb+bfyZE0ZW3FfLyxb+F2hUsVKjTazHWj/tIu+3EuRt+73VJP83LiiA6L1tDwExaXUoSkeXjnKexmSS2pTSIKpzjc73axX2TZRyV4cxxSACUWmu8BWEpaeqNO+jd5aAxAwNofcW76KgbG8VuBpENZgCOeocoqGWnIxSNytdY6nbkH9wMXZPwIIUhWS2Ps8bs5e/vL0+wODGoi03pbOXzyaEce6sJTtHGgSKOoaHTlf+xlx6/T3mmhCQ3mF9FIZ3qq9pW3CNPju/MPjG42sVWhXAxDEpXVuIeloqYyWTfpKG9DJKWz4PzYTRiTak/hTvSd4lpc2n51L258m9K5YZ0RSevM1shxvZWOXfZEQ9a04oXoz33Z5jvz0Abra66M/zQMnkKg8sy0ZXLHMusXDCuP52rvlKbKEen2sIQaU7DthUOUg8DxTaIsVQxkCyePCNRNs4jJpZEcRKKXC+GjNGXKNaWf3RGEAPFnYvDxBg+WalOr6V+ipSedjWqgPcawAkNB4xgeNVi39Z6HY1H3rpKf/F3c9xYnYsjWY0nFUbPrS0vHrngt++XuBcM74SG50zXK1M+ARY2c7glob2viNUYaxiGJKfUd6weItCHIMLTdhgNONhqpvEaEenQJLzSK9zYNIZA6htrFxajZkCiahQbWOKl4yvdGq+0iIp1U6zXvq1it67lVYP7k9ihVxvYoGQoGBZ8jMDWTqlvtLq7dutRe7rPjI0PN0pe/qF42uO6o7GuKx/5bxhvejoB6UuDW2lYJZHbdzI7E1MbQaBKkCr8z0JABP9cV1rkYXLw264rpA++QdjpUYzCD72c3F6U3xvMOvAuu542B3x5vaUt65PeXR+RHi8oLoW7Eaor7UIWtUGbAf1tfp4plWZmpwyGNmLE62oZEcUaYb51fEB+kGBrF2rgCbws9XHAPiof8Cn6lf42eabiz7lyVGGmQj8ni8U380bWVhAkjPKc0bqh7Y4RtFKMkMi1ZRaeON1MvYZk/z1ZJPFPllkrSixJWSR+Hz/QvGZOKbUatfltNG4HYDYgAzPP0/dglkftGVjQ2LqkhnZJEVqVLBGO4Qd00CCjZob6eE5XzC70EklVTK0scMNOiARoN54bKsI5/XLFbt7ErWwoDvzmPsdIloyOwwWs1OhQY2OeBZHHYM30Fd0EjCiFSR8rAftnMG1keJZRslBHQ6L5kpXanWZnqljVEQ30uSgApHjizsRza8SElNaxGGCfuc2BwZLdlbv8GYErCYt6H8Z4EIyoFVtPebYYHod2McKhgUoFgf/kakV9oGs+W+YLTftQDN4bFtjjyuGlVBthfpKaO8aurs1w4mlXRtsox6bAMZ4zSNunPqLNqYdVlFgryHrCrhYjTFFZFwjebDxjSnSCwsHSZimfqY+nSPFc6NoBNEmLCtRbbW9Zsr1s4F6LnyYwM30HhXL50ytycCbnNEcyRgBqrT+IEVWiKy8Qw9t/LnpbygG9QxqlQ5HtDzC3OErnLIQBEm6QLe+xW17r1bONcL0HSn6dDAyDYxANl7zoZIOlBTrRbLhnLbJBmSv804w0B+ZrIsVRPVLZO0UIPeiINQwOuNuGagahw+iuovx0p2zWV/FexkNYT+ba+KcIkFGQMsxaWmsz7p28STBFBLtJQshB41m41k+Z3mzmL6fAT2Ri+yseYSV2baT46TIe0CaFBgKRQXDsLdvWT1379oOfS9PmdIOh4BvjOrd9Gjq4UpwS4tfBK30ZfXm6a1LhjAE/MggCnGsTw4fM2PVME01srHfpXmiZGOl55X7MnnHgwZ7HlfT9vHfoZ9fzC0y+74HCE3ArekJ35MVXOJd6VLm3w/9jvm8dVNLwGJxzOxI7P2OuMuh46brvJ1nvrJbMS9M5B0OVTSbF1dKvkc9ir3jA9XGE6wwLAXbaZVbKd914jhfx/E2KHA0rKE7i3NB1NHd3g/Y0x750pL6Sp3Z9kWPudfiWovphf7UaVXDhYdKU9Ct8bx+/ymDN3zT3Td5ob5kaXo2fsGD4YRaHCd2x4ePnvLm0SmbweB8hekEty5BCaG5jOl5neppyWAZjgPNE636nYWqYTJG5fS8wZMr2Hk/MrOcy+mFe21S5c4rA4giCUUq3MLkdzsD2SJjqmNAU9LtCByk4k0Sgsq4DCpfIV4Zs/1p4PpVw8kbXtPYi0yV/PsSw6yH9cMB21u2g2Z/2NZy2Z/SfajiI/eecLbY0nkNOC3qgdrqHN47w8Yu6HaV4jytwfQxVdSEqL0kCrYakF0Vqycz+X1hp5k/QcD2ZFaWj6mTdidU1wra+Trglp6w8FSxqjLAG5d3ON+suD5fYh/VLC41GFNvYPE0ZP+uvtbCXmZQEgQRaNQ52hOS7lx61hmsBEnphXmZJ+x2ysgqM6jE8E8X3xJtjgMV3Yvgfk4xzH1ixsqavKvFNuV7nnyfAymGk3mhTGMtbMUUzNNKn8VhM2u6aFL4VqVrPuh7jUTWttF/cxmWSUEtHwVZIh6ild4D4ZAfeUO7BbM+QDMSIhgV1OmvrU4E2cnQ7dLAFKK21qS0rRFkCDklIMTIRrCCb4zSdJcSy8xCcxlornxE7gO7u5bdfaXyulVQEENAXtlxtG65ulpqpSKvLB7TKaCV+khlPDbmpu76iq6tcZ2WXiWgqYUuGaTx9yStjC5G3Y2oXlYbB8nCXs2DMymf22AGQ1hW8d6kkqCML/IsXUMpvaKRcJO+z4Cs9NJ6j3QD1HZEpOctTL+IIgvU1wPrZMO3PqdqlPulSoUHUwrnrKH3cDKqqw7TOuxRhVupcGW9Ebpzobtjae8a3LEnLBxShRxhFQOmdgTrcbXR6hyx3LBOToJfeGg8zhr8lcE3RLqyiY7p9A4koHEEtgpnIT2bYgAFIsOLkcedBlEjI2urHLgBxIwVKg/kho8ipM9h6c+MtTJNt2RglC2nauVjjE7uXqlbKUCr8rtlH/iKRk8ZTU8OR4jPLgvAm6gRZDxVTDXM9+q23bYDTcfSiNQIYwowHO7nmaKu65OTnXWMTKHLYRR08FYN55QuYrugUXjRcX1YKYjla8YUQwGOB5pmoGvrMTXQ71cdNDFgAjA4gxsM3plxPPYyvjzpJ6Wf7Mi/hVCkGKb7k6aM5AgYwVuwXlQGIB9MRocvL0t/orORnAZSxaLJnR0vLqYWlAGqZ7YMKARs7+E63pch6UhON98795fLpklphy4gg8HUUa+vB7cT3FJwK6u6mtbH+xwyxiM2RIdEVEswAYZpvIts02CE0EmuXkQtpNLc4y2IY62Y4vfI+Dc9m7xoNLIlgYgUu0lhSc/H/OTQ7wVhCsO9PP3ztmTET5YdeAcPHTt3u7C3z3zOSintk2uNB5b4N4yL4vZpfAjZcdDnqOmk8/t6274C25z1XrYUQI6fJ0BWquSdbNzeYzCYrSMYYVhWEQwP2S7KrKEIYinDCIa10J1q5W27A4wWBunveEJnWF7rfm4F5qUddTOw3VX4tUMWHlP32MrhnaFpBtZ1z4fW59xrNnzd6h3u2GssgaduzfmwphbHxjdU4nn97lPetJ7reom7iIUpHDn4YltlqNpO7WDbgn/IJHgzBkTIsYucXTJnxyY2zyS9sBinfELEIKUlakEQD73DCNja5HHF1xKBgLEirDJYCp8yEgRsn65xLCCizwVSgaruNHD1mmH9NiyfunHQKOzUHPIJsDh3mD7Q3jG0Z4I4S98f8dtdxYv3LjhtWo7qlsY6Bm+ojeO8XeJjgbDOqPnvA4hXQMtX0ZfrojxOfCZJ89h0kgEWExl83uocZVvBd+XcroBJ+h1H9ze8eHJFZTxPdyseX6zpzxdU5xXNU6Ha6nOrrwO21aCcbSNTzTk+8dv/gm96/Dt8unmVT979dlwCHeMYPQGyQJ9bmD5PklaWG6Nse1rBN7W9dEOz7wtMfJ0Dx5hniO2dQya+ybicid+U2phaKxnczQDq3CSi2Df3qVEHztvpNYuLP9mP44U+W5UzoPL5Qt/PNHULZn3QJmTx67IzaT65wdcmf3cLg28kRzZytaVUhTq+IC5GUF0TAaBG9Y6GtZa07S81uupr2N0XurPAcDxGA8LC88Lphm1Xj5fZxfSMQf8t3zH4pydcLQK+DvhYUSPTlJPOVnTCZdLTgZAmryg22xMHiLhJTD8pnRlfaYd1gyL7pjJjmqUp6I6DHwXFQ6yaiFEDzbmpAVuKxRPvae8iO6vo3nNGFcVLHBlG0nuap10GrA4+7gB7ABZM89mft4WA6R31hcfuLOaowi1VS6u5Eppzob1j6e4a3MoTGj8CIqC6YAaofKbqIkrNrpcDNpazHa5VcN5byWmIOTLu0FEgDXQlo6MAmjIrazaoBp8igGHfONKLHL/fBFKZA8c+NDAfGtBTFCFvMzu2Z6LBJgEVw7WHI+NlRCx/L48bB+hMqy38b4ifPTkCla8p/0uaOeknBYxRzYDbNMPb9p4taygWy6Izkd9bkwIio5MxSdMudk4V9XzcNlgioKVpJa6VrBsyrFXk3TdFP60C62VP74qXzI0aH+KhuhbCrqGrmkgpj+9V3iVM3g/mc01azHjMUvw9Ax2lz2AAq4wvU9uYCljcr2SkpuCLTOeGBDrlgTJdSzlEBTS12vlJUZc9RhUF+JGO4QLWuQNg2awdArBm1/G8TZzH+li9tzFILAlvO3BtBLWWVlM4bIyKlkZqAkkSmCVoCfbKY0xQcLKLgFbUnSzvn3gb0zdMXGJi5LV4mCVQNJkPosOWEBwRdGB3ZKNjvt9N7C5JH4qFe5vK/sf3SnGf94+4C0UEenKGmXOx52ykuSVf87Sr5LSjkrm1d0HjOp3WDjjbt+0rpvmKsY96r1UNc1CyCBYfsq9iGhGeWIhCjXjxAbe0eR7wjQIRvpLRzo991zWCW0J7R0EJu1OfwKN+CicD5nGN3Wn2QHvPcfdkQwhCOBGs9WoTRTbW4A0+FwsxfGTxmPv2iqX0PHbHvNXd4Z32FIDWW3au5t5iQ39s8d7gVz3eC922JjiDPK4YjpIUS9SocmAKfayUcuVTkDOOOeIDtvOxiFLxHsV/oaiwnlsI++9mCoIYD4NKc5jO4Ru9mdaFkTlTBQWsUH2oIOi9EmKGznhtJhVcSbIjlSgrequ+4+VHFDBbPnHjGD673BTsWjwZqHYW21p2dwW7M3TdkncGwb1wSW0d66rnqO6ojGphClBXjo0JtFIziNWqhEPUvUpjWD2OSzKAdSPTWtw4WFlAetXpZCuZDe4bNDW+h+Es8JG7T7i32PBbT1/gweMTeHfB+qGhulbQq77W+1bFvqiFyDwyBD7x2f8n/90b/wIDfOv15wD4py/8l9PnNwEm2W/eE9qOMAz767JucMHKyuvGd3CvunL5XWSaYvgMX3Pqi0zntbzOjmz+CXs/+j1jMH5mhwUKjbx4aSlVMfpDJVs4AY9IBLRkPE/OZEki7yYSg2LAJXhR1v77mKZuwawP2iqvHcuRmVepUpqvDcNay9qWzkKKLNtOn1SKhEsBbOlLFNH2bnQw3FKrXdTX6lxsPuQJx/oSydYSqsCdVy5pqoHrXcw1K9ME48tYX4F9FAfyYRR5bO8Y+lPoTj3+yBWdOGhVh3bsqYGxs5leijKxo3MRRCeKEF8cb+Pv9cpCy9VU4sslsUKUQfOXgxfERD2mGBnOL3lpXOb0BYHBId2AxDTNyQtRAAZZ8yNG5BWYGyenPHCVA8ehwaxc9zwVGGZGhHitpGEGj782uKXFLS3VVlicC91joTu19CeRGbFUUCvU0YoQoA4aDjEBu3DUzcCdoy3dquJha+l2NdW1Puf0kNLz8tWYMy1D4RiktFmRXBVnYnyLZFZWENHnc8jYP5RemI87Ut3nVQwnKYbPiDjsRdgzoDbf9tD++iexrcpWpnKUA7CuZEydjQO1t9Pj5p6QbnnpHDL+1Lzolpl1257VEtsioKltySCRcb4JlewZFeqc6MdkjBAKdmlsElBsILIPXaXzjul07ulPAjRxfOg1uLA8brFGqwjpcWUPWDLdGEkVH4WHrTAsNX3ALdCqVcWFhBhJTC077kJmfE2um7jOF+9tAucCiDEj0BW3Ve9rrCCYQzaB7BBkNHsCZMSJTVAGQ5QH2GPrTLCxYp4LjCDWPIVQDu+/175MQIsoVWB9wBjBVwZTSax8RRYcdk2ULIgVqDQgEMZxLCKISftv2fS42rBxC61W1ZfPSDVYkIpgVhBWGGrEt8A1yBakHY2GaFTvNWEcbNOzQdCLy7TkuG2RXjgJsOi/iUZXOvh8Htk7/w3XdGifZ801h35f6rN5/Wzf1BdSAOVZgTMp/pWXXs41t8ysr8jmF360r1JLNhaF85rGnUPapSbNBRpk843FNUbTzG0KmuyDNkn3aVhqsL3ajPNVdwb9mUa2q2sdl/uTgNzreGG9YQiGVdPjg2BFq95WxmMkcL5d0nujhUMQLvyKB8MpG9/wuD/iSbfivFsBqsf46tE5X3PyiOuu4cnlmqYZ+Kr7T3i4WfO4uxNZQaMDbmK6G6ShLdnDo7ZU2k41dNFg+xAzN5wfNbRgGpQ8ZNOlucQ5tZkBM1hM75XpBjpnE2BQlpX4kDW1EsPMxCqNptAVUjKAYFu9zxCF4nc6h29eMiqhshmZwJNgVyBnnlQbx7oPmMHSnWjVx65b8sAZ3IsCJ7CuOo6qDrMINNbxeLsmND3GeFpb0ws4qZRMMeiYbYZ0s+M/r/5jntNSwCr6w5mhGsBXgeEoUG0E30B91iojq1vRO4PfWdYPDOu3R/um2oV8j8SD6UbtsI89/dy02uD2i2qf+DANhEu0FQp5mVzF3Xnouwkra9KeNdbOi/YcCsiU/lC60OQzJR/pEJGgOGYJZE1W+QKMKrW54/q5nApESZuQUgclywlNgvvRHwrJXrOF75SyW9L2BUFDT0rOXnkfWNYtmPWBW+pIfhT5C0RdkKCDv4t0e9uF/ABNr7m7k+hbbFppTqOmXkIueaspfQEjilJ3Z4Fw2lOvevptTbCB+s6OO+stF7sFQ2/xZYrhMGpqaSRaT+pdyFpLdhevJRh6r8KMOVrj1SQXE1lWLuQByLbjoFrql2THgmKZ0RRKhrG8b470imBs0KqLCRxKdNQEZCWE2xVdfQ4Q9Q5pHSxsHgQmkXfIk45SHsOEPjwBtRxTYOuGyG9Gzcu+8T6aDB47aA633Tl8Y/GNod4Ii6eG/kjoj4X+2OKWIbMkQh1yqgcCvjd01GzrgbPVjsuTlrY32LYCMTTnQSuiB7BDrCqRUkldyMZQsJqqmdF4YTpQlmhNej6TH2QmQNZBXaz53/RsD233XDdRJs7CwehEYrHMHREZ++uhSPpE9yrsD97jNTCyTiYzg2h/SmmGcbYNvIdzcttuG4wGRQRDQkRlJA2eZjQ0jCvGXR+KohxTQLUMmmjRgtHIT8BRCqKw8JjK4QcLJmCWA8u6px0qvDM5NT1Vu02gWqmPHuJ6KJ0I0XogdSh+3/ibU/Q/fVcwazrXjPNH3CwdQ6KRFrEOKLYFfQclXqCM66boWfEO5wdRNO/BmWiw3TDXJMchMYvKFEmfBpTptlNA4sBcU57ifQyRes063yWmlrGqy2n6gN3JyAxvNJUoVGp0hsjWzulvsfpUbw3LaqBdVLF6onYg2xInWIsMJwR5EeFlqD3Zy5J3QR6DuSaYwiGY/Kb0cCuQGsJSP9MCu3icDmV5+alxD8Ucc+D7bOx+7jY7Vpjtn74frBzKOG+851wDN9sSUs4dsz6UnILinQ8cPt9t+8pozcubad8v/uZgYQnMG5OBmIkmqvdgTR5n68serNCd1gzLGLiM2HGyD4eFsrLcIrJhkrNrVbspLJTBiYHhKNDfdbz+4lNWVc/OVThjFMwyPrN9tn1NP1gebk44v16xtANfd/wuV8OCynje3p5w3q1i8SrD0+2SV44ueHX5lO2dmt8ML9IPlsfbNUYU/Ohtgz+3sbI3ozM+m1sS4ylPA17TtfF+mnqW/z0j0j0fj0LU5hocIoK0DlNbgrXjnOb1Xple/SHTBxXbT4EpUR9MXIjzomTgwO70B7hFJETs1Efrj2F313C89aPkAOP5ciwgLretY/2Op9pVbH2sZO8aHnd36F+xnC5b7q02rKuO03oHwEW7pLLKjAte6AdBmRAgvWSCg5IkyPay9GRtNXHjZx9jFbYF0LmoP/OEpedoMfD21QkhCI8fntC8XdNcQNWONo/4dKyADFPw79dPvoo/+PS3x2qDy9fG55NtrNhSJkpa6KJe2jAoK2/yuOXm7zMA62CAH8b3NfWfeA1T4AoO+UdzHa2UWRUOsbpKE6cAufeKBGW7RTGN8VyM81qAEFOPk12WNNHS3zxuRPtQvOArP9pfFhV/lxB1PJ+v3YJZH7BJycTxPo4kI7BhW6+GYV3kDZdGdho3LZp+mKi8VvJk4OuY3lFUKnFL6O866uWAd1Z1k9YDL965oneWbdvQbzTNUAbNXSeAWwf6O06rTWwNpjXYDuxGq2KkTltfQrUx9CdCfxzwJw7fDOArbExZNE7wVhHw+rqYEPzYkU3OQR+dj2DJEZ4kap8HbyECKDq85GOKsoREfMzjLV7WOOhPXmqv1ZywMh04Dr0bCbgqUhvTBDUBtUA1QzJ1NHko0YFJP/VgBPcZFuTM8ZHeYXuH3Q1qDNQ26qfZWOrY0K+FYa1RMLeCYRXwMWU01Iah9lywwprAatEznFrarcG2BtNBtRWM1X7kGhX2VM0Ar4NqZTMbK1fIKNgHOtAxRrL2GHJlkrQO3lIaUDMAK0cMiyjiXPj94K1Lp52vL2mwZUi60B0o9ytzvJF9Pay9VI7822fXkYBiP52os5ChoNolFHYls2Pfttt2qJV9JIJQ2VkOaHGQyH4d03DjXzN+TizZHGxIfVtiamKhjwBoGe2lx1SeEDQ4Qu05WnW4EFNAUtUhP6YM+Fr3w6DVdF3Ua+xHphYBTKuRWdeIgvOLQLA6EYorKhrG32C78d3LQBZM2Frpd2bijkTILzknyUExqgGVDPtU6XWMfIYY9BkdnEOpkEkI/j1x92QQpmsOBZCVlxcPKo+T4/hRfn2eCq3PbBHUkkEBIBODF8EaZYNXCmypTtr4N1SRtWAD3gRaanUWrcMvBDdo8EyrKBrEH+OrVwl378M3BLgHPAnwmw185vXYXweQlkhLOAAM1gRZI7wAvAy1g9BBG4C3gIcgG5DuBgaSdogxQlxY4ZPb+Ox7uq+XNf4JZdcojfzJ9sLEAXivR5i632yumbJJ9Lu+t2NfyenscbHcdE237SuirRa9ju0lS2PuEFs72mnz1Nnsw4x2kemcgjgi1CJAhVtIZPRrn3ONMm2DjUWmChmH/lj1fO1xD0EYjnScq+/sePX4nKt+waZv6JxFJHDVNlxcrXDbCtnaHEhv77cAfHZzn8Fbdq7iSbtW7eIIfN1bbwF40J3w1uaUEIS6cvSDZdvWDG0Vi1ZBeydgt0J9FYte+APZCj7NDWB3fqqXVaYY5myOmYORfJOkg5SMvhBIFfMYHGIMpncqAh8rsSeAIQf8PVhHTNWPc36sipjTI2PqpK90Hq52IA6aq6B2QSgE5X3ILCz9Tcn3IQMZElQ7bfnAI65md1dF+k1fcdWesXtpSztU3FltWdiB47qlEsd5t2JwlmEwNCcdfVVrdUKHVjksfMQsG+CLeR21O1IWebIpjIuBtZOBejFwZ73lum24uFhRv92wfCDU1yFXCE7EkaQxbfrk0+m5P/nKd2Kvdnx08wafWrzGJ+9826g3Wr4XpW+Y3qshMvJCmDLzUkt+TvYXiwE5vY8lM7L8Xp7b7heUS+/m/jmf7SfNgazR/4nfJ/rAMs47ec4pGPPF/JME3hUYDjkFOWWrZF/Rxv4b57Tcx72ozSeoLrQEvDfviw9yC2Z9wGZqP3bCCS0RVBPJU20E1iYztCSyYhLFD8gMEhXW1U4UIgNqWAr9iR5TYqWA7sxjjnqG3hJajZSvT3Swf3y1pt0pkGUahx/0JfJNoL838NpHHvHq8TlP2jVffHyH3dMl1ZMKJJam9TpoSAfNU2V1DTvNvw5NiEQpHZRDDaYV7C4Ogu9xv4LRKnxBwJqA6eL4Hl8Y1dCSqQ1qIPf+JC5eINiHTxSBmcHr5ADTiaREtwuwKgNZiVI6AbJ0YpJUmcTF5xYrbJWCgROWlmE8RtnSPrNxcDTeo1xu5zA7wdYWX1vqxtA0WlbYLYR+LfRHwnCUoulB1/WGx044PtlRVY72yNEfC9VGcMnqjveg2Q38d7/1i3zT49/hU+vX+dkP/VEFeJJRciiNJg12Nj0flHJbro/vxl70IT2/G5haNzlpe9UN43kSaBUODfjRMbqRcbU3wI/LU2SxdPrLyh/jsnjpaZ40TK/ToACyDbn7eS9YC+Ygwnrbbtu0aZQqWXqzlWGskgeSGVqloToeKPZfU/R1Gecbt4iniYCsWwSk8VpO2ylNql6odbntalxv8/WFWAk1WPArz8mdDSfNjt1Qc7FdMewqzFZfDJPmwQA4jR6LF/wQwZKU2sZ4veJ0DoTit914w0anyrjkFMT7JzruzmApJqysEjR4VgpwCnz4sXppHttn93zCyEpAlo+/cwJkUVh6afFoaKYVea6ZGZg3X+v065RBFqt+GQHjM1vLW4mFaRgZW7XkoJSvVA5gG4Rm0WNswNUB11hMX+NpgPvQ3IfvgLM//Jivv/xN2m3Nb776Ub3o/+0+wZwDmp+4DxgZYKHHkVfhox5eDkrMegP49KsxGOaQ7AEVjkGcB/aa3HzT5qzetP0kYl3ONcXxysc1Pebh7xO9kvm/eKwwX1Yeo1wmjMGX2H9C0Fv4Xt3jtv3ubudP13zYbwrbq2B5QOEwF/3WmMk4IM4RjEWCaEC4ALxM66gFqo0wrGzMGBk76ZAjJGB8wDfCsA74hWdROdonS2yvBYrOjncYVB/LB+Fis2R33WDeXbB4rMH1INDdCXT3PC+/cMFXrx+pRpaBJv6G667BecNR0ymYHoTH3TouFxa14+lmrcF9X9hudaC7q4Hx5iJMgvCY+I5HaQgbgRC9P4VPkICMBGJFIf3cjIwFj9L287khBMS5qMUF+JABnZw94hTAksGrsoednics0pyqwZeUIml3gVTYqRrAN6jPNUyBrAkbLWp2iSuyVbxn8ajDtjW7exbbCbYzXPsVj53BeeF40eGDUBnP3cUmPlfoInN7cIKrBLOxWd5Gep3zgyGnpouMcz3E5emnDtr3wrLnaNVhJOCC4HcV60fC4nysDJyqL5vOq6RNmQkU76l3ws+cfRsc/eH4btjRN0m+RAijLT8E9XFKranSRwRyVcISyCpbma0C03mq/FsG/cs2Xxb71GRukQPbHzwW+xk3B+a8iZ0oRFtHF/oYAE1MuGyKRD3mxKzPcbuCGDDaS9PzhiBYO8ABGbKb2i2Y9QGbsU4HGUmCshpCDiJZaE3pqfqITa8Gqq9HhBzSYCJRIF5f6iT+PqzBL0KOUAxHypSSIITrCgSa05az9ZZN2+CcwZhAc9qyXrYMJ5YLOQYTeP31R3znS7/NC/UVDuF/Xn6E/2BeZdMfqUDrpWB7RVoT6FZtdADxleYpD+uAb4JGYkxgdWEVBEs+QETbgQIk0nUhVswyfTT+LXiKqkdGN9aor0GCn6YTwmicHhoo0jnTIDQksJG9CST/9UyBK88o8PgeTYIa8qRUuwRopdOUoNb+heZj3Pg74l/xIL3DiDLDqoXFLyy+MixqM7K1VrHk+kJwK9Vsu7hbI6sBgjqq/RE6qfuQHcr/42/8i6kQojX8kw9/d44o7etJJUdqnGhM8Pw3T3+F/3z7Bp+qX+GT6z+IF3sYyCr0sSZaWXtOzA3m98FtZ7fvEPhWHrMYQCU54VAAUoUzM3E6GFlXs4FZGZZhP02EeOCZB57TDW+9jNv2Hi2zUp2ONxI8IdL3s4MSog5iHFPTmBvMdOwTJEfTcuq3ZUxDTzhLDWERX4jIvrIrx7Lu6QcbC0AE7MJTVwN+IbQsQAKnZxs+cvSEte0IwFvVGe/IKb2r9cQdYzpkPJ/p9V8Wo6/R9LZY8aZuNfI//pa9V2oC7KQUEkIBNAWUbZXeO4FUtWcvsp6Aopvez+zohQimxDFjL82wcBYmbKwD297YRkdxpNqMv38v9fBAu3GuKUA0cUEj4oMjiFG2VtSfVKa44AqmVrCJvWVol0ZTCKkIVYNr1tj2Dt6+hnwTnH7HY/6r9l/y+4dP84Qz2leWfP7DX0v47WOQJciWIG56ZSJIqFEw62X4z4Hvdnzt6tP43vHbX/91alH/xssIF2jKYfLSpBjn51pZz54/puuePUDv3VWZfShPF9+/kdl8+PgJEJszsaCYh+YM4vLLfK45+LLctq+oFpRVKblQEqN9VepiFcFXDClsOq7zjGwTK/g6RulNBAWCp+l8tOf13deKhYZhqQA4QLscbSWtXCuEOiB3O+6stry1OeXx9ZqLJ2uaNxtOHgjNeaBqVT+qO9UXIFSB10+ecmxbzPKcB90xna9YVT3OGzqg94Z13XG32fBgd4xIoHdW/102SGc0gLHwhKXD7yyuCaNmcSQa+Jo4tEb7PDK2dH308VLQ1hdARgY+ynt5IGA7sevjvBQzSUzvccaMBcKjLiugABTx2tLndOgijT5pFdk+YJzQHxWMY2Bx4cdUuzwfFZ9DGKuRF9cpPlCfd8hQYe5W4A3iDLvNkicvVlyftWyPalZ1jwuiFShPLnmyW1Fbx5VZ0G8a/MIjQxTht0IYQpx3JDPhSl3MiR6TgWoLu7fXLL5+w7rueHBxjGy0QEF97SegZKpeGFxQveA4dya/XEogcvZs9ppn9F1KO6HUYT4wjmfh972qhTICX+PGenpTvJ/vMfeU+2Utr2QjzPct0oJHDS4Zzxdb1vfOxx/noYNgFIxBfzsyNnOGCul6yGZMMmHyvOUlBvy9JvOYMGa0PEe7BbM+YAveoJZfUfEt0U0dGLxGnVuHtyYbfVlcMB3HTP8mQ3E40jx0u9Wn7xaB4dghC6e6Ur0QjhynRzu6oaIbLE2jcOayHnjx6IrGON6wjtp6vuX+FzmrthjxLMTx2uopb56c8camYfCaFiBXcXAvAa1tZMV2YHeq3dS94GAQmgsy4p1FsSVODjkHW4+TynAqiEFeKX38nNJA9m50BIvKQfaQAVgKXPqAxJEwzPWcdKN4+jSAp9/gDw9mhyigcf9JlYkSpT9w1hvbTQ7QJI89IHjoBswmpSBWVNeGemHwjWFYq0ExLLVCVXdR0b5gFBBl7GMmlrs3PfyBdz87FUK8foOE9o9R6HRd8Y9I1hsD+K/f/df82YtfUUCs/xKI4WdOPh4PWhhSNwFZJb23ZFilAX5mFJQRhbl4/LhdsZ8PUJnRESj6YBIyTP0y0bbLIgdlWmG+H+X3IsogQcfmrDGTn6kgxmOsp64dlfFU1h9kjN222zZpE8ZQ6sSxU/owjteR3ZqqGc5TSebOcQaOGh2fTYyG+Qp848HGXG8n0HgWzYDzBucN1nqwUFnPuu6w4rmQgDGBV9YXLE2PEKjEc1rvuFwsueitVj8KMg55yc8Oev4g6Pw5xApZ6zDqZhTGd/o9k3TDNGyawmjKRuJY7nxiSE1vdDa6xnZgrikXBf0vjxk3tCkbK/2WAxdxAMAYT1QYq4H8g98fTjGfa+aLo4OBU/OmVyc5WC21ntIRU1Au6Wq5tmZYC8GugTOQF8GcIt8Q4NvgD4Z/zzdvf43lZzvM646l7Aj1OJDeNA7qc1yA1PBK4COnn+Ub20/jCfR3Gr7wyu+D/7UBFiApP0Um78oENMqnlNk4fniuKVaNjK2DwZS0EeMcVfyGdLx5pHsv3as41PR6D6xLx55vE8VpxASMVQaMuQ2cfEW3EIgBSDPaV3Mx+LRtwcgK1o52rBnt+RClJvJ2CVyJfo5q/0JoNZ282ohWY28M/ZGhc4Jthd7D0FYKYsT++PbFCVdP1lQPa44eKTtKU8QC/ZHQnWjwXByYreFJu9afIB4fDJuhofcWazx+qHBDxXLdc2xbHskR3WAJQdhcLJHeqAC5j/fHqx9jeskOt3TxvuTgt47XEsC0mmWD95mZJSWQBWTqY/BkaY3iWeSWwA8rZO0siVUSB480Js+HY6EQpnOSFMtQRlbwMqbqob7YsNDfY5wfUw8n4u9h+jkBQek6y3P6gAmB+qLTlD1XY1ycD9qa3SA8dYZ23bKolCHXmoraeKTpGbwh+OgDbyvojQbZ4r1xqRhZX9ja0WfMINeg17n+kuGdxT34qseIBEwn2W5JhdUys6ywLdL8rNqVxb/UDj2vDPrMQL68T/SX54yqZ1UwnANbB+cbOZwRM28TYLrY/tB+h7JQ0nMuty98vElGS7nJrEp8sduohxXvvbdM+q5Q2G3ERyBBqyh7XfEcsblJuwWz/lM0o0ysUNvshIvzGv0taHIiHvE2Og/6RFMp3VwhoJZcxnJYCf1xFO/r0NKka8hi270hVIHlacud1ZarruFoGVjXPTZWAllXHXebLY0daIzjfn3NxjdcuiU2Ik2vHF1wfrrkQqAzNcFa7C5SQKNDXm2h2mrXck2aAAz1lbB46jN6noLUkiisQga0kh4LAhqcV1FD7djagU0awGML6b0PU6csUWdLQHCvFYPVM4eDQ2mGN7VDwEpqh4hih4zUcvmhc81ArHxd82sdAtIOGFqoLLapCLWhbpS1NaxUY2vxVGguDLsXEkhDLihQd47/06/+Iq9ePcxYnAd+/eQjeXCcXltxTWVKYQh89PqNKSDWv8Wk5OwcjJpEsIQJkFVsB4WBsRdpCNNrDGFSenY8DgUYOT1EiviUg7bmgacqH+P6fJrshBTHiPngOYohyjwMUSsvsQ71ZykjyxhPbd2tg3Hbnq9FRkmwJhsgkoyRMloo2glDKdRZBkwCI4BLAq4UPNKy64zOcQKyTKBaOJZVT+csdRWorc4zQqA2jpXVucdKYG16+mBpQ4WJL8px3dIuKlrAiVHmz0COLAfRazAxuJFZYqK6j3YXRl2NEryaAVll6pVP1en9dH0RYCzuG8WLrX8nDJqbWkggwX6VyNmGU8DomebaAQtyvmp+7IMr5p7QbBXzVaVlmuYirdooEFNnrBrOkbXlK2Vo2J1g2xXD+iWCfBj+MwhfDfU3b/hW+7/w8f7f0fy6BvjOzRkP5EV4AFrRMGocHLoTPqB5/UAfYr9q1AxCI2SiGx68V9MgROocszmiBLKedRvzd517ArPlwg3PJ+4m02tKzMj58sn1z88vTPo4hGngJM4/6bOg8419nmrLt+13bRMbsnzDXqAQpvaUFQXeEztLDEnLKaS8IGPwlclAV654lgpwxGCJ+JC1B23wmD7QHZucfiSDEHaWamOoroRhu2JbLWm2Qh2D5/2RMKyhPxGMKqYoS8qAX3geb1Z8fnePhRkYgmE71Gz7GheEEAQfYOdq3tqdsXM1q3qgGyo1l23QQEaspJfAj/JfrqBXgjii320XGTwlY61gZElV0lgSi60ARcoge/rr/Ci1EQIMDtM71UAW9pi2ZeX3Mi1Q2doKAkq636KBhlCIbrsalk+9/haSr8P4OV3XJIOlOH/Q9RKguuoiWFRjeoMZBNtW7LaGy7sV7VHHetmxCYKRwLLpubPaIcCurelDHO88uFqwG6Ps6xDwjNIAoEEp06HBrejzmQHqp5YHxyf4ztJEvef0/EzrM4Mq/06BrJ+bwK2UuTPzdUIZfC9aDo7lm6LsqpHlNAOy8nY3sLKsHc8TgednZqqkZ3QI/Dq0HUxJM3NiQjpEYd/kjJT5fBf3zfZXJEOUYu4HC2vBaFIUc1wiDWSml9UHaFIF5Wf8tEPtFsz6gM1Yj6+MaswWUVkJ5BS3xCBRsXOPbbXT+hpgasT4WA59WAv9kUaiq2vyoOQWYWKwmNOe/+yFRzTGYUWrgDRmoPMVF+2Sc1ac1C2NcSzMwMY3WDytr7hyC877FYM3HC06usHSCvRLz7C12EuT+2O9UT0t2yry7Xq9rmobYq5zyBH+VIUCxpffW+jX+ptNn2i7TCtNlBUFyxbvoRTfScy2Z/X2zJAr7ll5jLQN7Au9P+uYMqv0AAcBmGceo/w7/wwzNhbjtZXRoHKf1mE6RR+t1ehCvaxwqxq3tCzOLfW1ltitesf/+d/+It/61u9QOc8ffPe3sz70m80dfuH+t/Czr/7ReB1hLOc694niJJpYbZ9evMq3bj+fAbFPN6/mgX0yYRwQgN+rYPise1dGGlMkIqRB8fAxkhGWf8dBTzauluQsj+9zHnQZB99SHytNzIhMSs1KAHxcxnhOYwJVrI7gb2lZt+05mpiofwH7409KcYPYN9XwNkPAS0ozLJgihTHha3ANWumnJ/ddXwVKw0QWnjvrDVa8ArESsOJxwdAOFVCxsANWlInVB4sQcMHQBcvO1figgrzOGwbRc/jBYNrC4IpVjcxAFoA13dThGIE2xvEofg9GDeRkaKXASpnl+6ygRUqhni7jOcZ14hgwn2uK9RDH8nLBsw54wKqcGJjvcYhDaNX8tGH2IV1bdF722uBzIEPiuG0qS6hqfCVU7TGmew33cZDvhK+782k+OvyvfEP/Ge78uw3m3QG+Dj7HV/PojVfgSwI8jQ/+hpx8Lf0FbODzS95+9cMMr+jDf/CF15A3QAGxiIxO2FOzuaZY9J5Npvf9IGNr1g6BUTeeL841EwdFxmOEcZE+jrx9sV3aJkAoUd58mSHrMj53Rutt+93ZgjriAqPtNd+kMuqsp8B78lMqk4ODQUwMyKPvenRSE6Ch28eO5xjtsmRXSeGkxn/20nL0RWHxJLD5kAbqg4H+RMeaVJ1bAlTXonNCMkwbjxF40q1Z2p7N0HDVNdQxaD84y6oe6L3lol/ydLfi6WbF9rrRwL8JaHESUXbPEOeHQVPR5s426N+kQykJzJ/b3iJQVfvLYBpknfsKpb/hPYIh4JBuQEKt7KHSN0nHlzFdMB86jtVmiMLw9fhbgtF0w2Gp8jHNhZv4WDenncf1aZwvN4v72G3P8l2POWswg8X0YFtDe9XQvWC5vGMx1lHXDheEZT1QWce9046nV2s6E/CdhQHcymu/jZItwQLRJpDEksv9FbVtOqHfVRBTFoPVippVLEYmgx/B16g5lsbwvedYAknz4Hh6fjL2o1HHmfE9y8c6xGpg3PaQVtacQXlobrmJcbUHbt2wvZmtTy2mGaa+X9qWakfqZ18xNSPMyJqb6IUzfY/yXBaxgRRMzddiA6Hy+b56p5rZzgj2wG24qd2CWR+wTcYC70ekdW4MJypiANM5JAQVUDTCUIG3kp2KrCs1aJVAvIJZbhEItSfSl6DyvHT/gg+tLrh2DY0dqMQzBMNmMOyGikdXax5dr3nx6JqX1xdsXc3CDDzt13zh+i69s9xZbNn2FV2ro2C96gnLgWFtFTfaWUwXKVUEqk2gvi46qRlt0LktmqoR+lrTJVMeuHR6j3L1jCzQy0h1FTK6/sw2A6b2VoeQS6qOxuhsQC/BorIdYn4VgBZEAzfRmm66tnLfQ5/n9+0QiFXuM3PGxvLuASKrwW7AWENdV4SmYvGoYTiu+cRn/x/8ud/8hWwnFHM3by3u8U9f+66bHbfSGSzEKaUf+OTRHyL0Ax/t3uLTi1f55Mkf3gey5kyt4vOeo7DHwiLf4zLFMF/87P6pIHx0XrKRRoxCSja2ymhC1nZj3GbO1ioZH5SRBQPBhOx0KFsr5n3Hd1aMOhYSRU9rE6iNn9Ctb9ttO9Qmw1so3tw94GXsR+I8BsFXyuQKFZmRVfZf8cr+TWBQqBjnGQlg4Gi946Ru6bzF4jFoFLX3wuAN265h09cc1T1HdUsfDJV4dr7mvFvigmFpewZncIO+yKb2UHnVXAEYhMGZbJiYPmC7AiBIjjzFX8bvOeJXxbGtALASiJSNr/kxhP1jvu8WpmjD3nUWQNb8XIde/3yYwvoLN2y7t/AGAGvvNx4AseDGuSo7tIQx6NIPBHEYW4NdYO/0+K+2fNz+Mn/w4t/zyjsPMW965IlD7ge+8MKrfEo+Bv8ReBhAnqJq7vtgls7PHtgi/h34rdfhMvDg5Q/p+rdREXh5AOyigZHuxyEHoPRYKbad3ZL5Ljc0SbcjAWVx30MGfXkdKRiSQTcZt5t3xb00wvmxY7/XDq7fS+wuQE4zfD86JLftd1cTCcq4qoqKhdaMTJMU8Et/E4gFma0lyS43ogH5VE0tjQ+pD/t80uigjo6+r01OayNAfW44+TwcvT3ga2GDzXbTsA6EWplTapsFBm9ZXuv+3VnArpSmc9Uv6FzFebek7SsGE2gqBUkC8PDqSKsW9hWutdCZqDEJ0unxTD/adUmjUW01slZTYpkIYdTLSuNeGXSv7NT/mIDSN9j+ZVZGChaHmP7eDpjW4Rd2vMc3+Qzp+E5TskynchU2Fu4A9SWDD1DB4rFqck1t+Olxx7mSDGbunTddlwmYdqB5CsbV2N5iekN9De21pb1c0t8d8McDxjq8N3gv9M6xWnRY69mGhmCMFhgx0DeC2WolXF+FWPU4Pr+Y2ZPMftuCubb4xmsAK1bT9AsZxeRTIa/sNxQgDUz1ssrAe1wWCoBJ0nMTGTXTChbWxO4qAc0EXplxmdzUR8pruamye/E7bjzGZON936JkH09sq7RslhY40ckqfKaU5ZO2yWmccW6bMPzT70j/JIzzWhC1N53o/akD1k4F+9+rPQNCvG3P00xMK5q3JJg6aQVNz3Se+nqgunbUG69ifUNkYm0C9VWgvixeRj92GgyEzmIbz8vrq1wNxEjgaljwhYu7fOHxXR49PmbzdMXF5RqPsBka3t6d8rRfc94vaYeK3ht2rmJwFu8l5sSrQy420Kw7ZOkY1oHuVFMfNRVSmFIKR8HhfA8KA8zHqlLBqkB5SndT5yKxsg7cx0Pgx7MAovJeTwzH/W3ypH0TkAWRWvzsF2rv2DdFOw5NSoVjla7nRiDreVp5b0JABodsdpjza+q3z1l+7gnf8qX/bQLSp6N74FNHr+9da3lNk6o3AQVwvUf6Ae8DP3Pycf6vL/wf+JnT/wJ/KAIRP0/ejZKVdYBdFQ6se5Y9HtLEkg6Tbufs+7ht6qtTBye/b+UgXlY2nI+ePu86DtQhbme0kqEGZrSUdACcF3qfZtpbJ+O23dzmTPXcDrEay67sArb3mN7n0tTilcZvemU9qRZV3CFMx26cQWzgqO4QQvbzO19x3q44367Ybhf0u4q2bTQTzFuuhgU7V+v84g0+CEPQvyGMzpE69AHbOKgCvg4apc/A2/7v23v/y2EvpX5F1tkkHTg6JIfGj+lck/69h6F4sB16j+NkdxOQVWzyvo594/YHgKzJ8cN0wbOu68ZW3nQQ75HeIzvB0rKwT3n9yef40Dtfonq3xSx6+P2eL3zth/gVvp23PvMR+G2Ad4AtMNw416gBtIPwBHG/A5+/hP9RkP9R4I1r4PPAI5AEiB0w1tMlHzDe888on/9ztHwH53PLgbkms/QP2Yvz62B0IA7uEw58SS9mpiGCRFtOu73csoCfs/3kT/4kH//4xzk5OeGll17iT//pP81nPvOZyTYhBH78x3+cV199ldVqxXd/93fzqU99arJN27b80A/9EC+88AJHR0f8qT/1p/jiF7842ebJkyd84hOf4OzsjLOzMz7xiU/w9OnTL+u6xQZ1wA+NW8X8Eezon4xpTYypWS6MWQBlpb4EwpTHLJ3lItgoAWSA9VuBl/695/iLA92J4eJ1i69Us8otAv7IERYeKh1/pDeYLl0b9Hcdq3VLZR3n7ZKn7YreqR6W88KmbbjcLnl8ecTl1Yr2yRL/pEGuLPbSYq8M1YWJ+liSGSYSIiAyhKyzlHSAQsyk0fmyuIdlEFlGHcFgDaGyhLqaLrPmxjksB3jL5j3SOmVu+em2mWFcAmth/CxO0yFNp/fR1zHF3iWf0sXfHaZ2vC/Auvy5OEd5vfO+5MF0A82TlsXjnsWFo74KLB8Ejj8PR79TY95YMjxYcf14xW7T0LY1u06JE4tVj20cdjlA4wnrAbf2StyIWUqhKu0BlUMo0yeBXOVYfMDVglvaPZugZAzmcT6BtpKkG+JzsjKyF8vfS9x3nsqbgapp1sgekCUx/TABZ+m+JmD5eTNUZteUW/LBk59VgEjvrb1FtP3GOdBXSfZI7TWf2FeleWHG9z2dM/tI6XvCZlOGyg3kF0QF4G3l8LG40PO2W2bWB2x17Qh1NXG09WHGFyJtGA2NCQvcBWyrDoZttarGsNQOXW9iCkYVBVbr+AIvPKZ2eDGcnV6zrjouo9Pw9tUJD945w5xrhUO/9CrYu+yojeOyW7IdarplxWW/wMWe3roKa7xevgmsllohpN3VdNuasLX4GvpTFTYMRqnCqTpEHmB9ZiROjDNlZpHLe6dKjbRhRM+HIrc57iPoxBkExOrN06oceq/F+fzC3AhopXVzRD5f3JcBGN3USnbWATR8cs60/fya5vTXQ4ys4q/M19/UEp25D3y6eZVvvf5cTgf8tfVHcGL51MlH+NlXYnrhgWsfac/FCOTR8s39QAiBvcqFqd0k+p4mhTJaWF72DSDXmIaRJql03nSxxTEMhZBpegenKcEhHXNvsitvwPhvHJwZc78j0yVfoATVy4rMFolOhjGBqlLxdxuZWTfd89t221KzxoOt9pzwGzV+yuaDplTE9L1gdW5J0Wntw2PxAwyxskwgeFgudA5pfcXgLVddw+ZqibQxql8FsNqvjQQ6p4ESVxk6X+V3zXnDWL0zUNcOHwyu138MmqruFnHCFNWMLDVC0pxzkDYv4/WX2mCQUg1T0CTsv9slEFEi39GRCDchPZMAQnk9h4Cn9wsY3dDK80zOeWC7yaUcALkmgBb7c8k8av/MH+CBHq4D/mHNW8uXOa4uqF4beCL3+IJ8hC/w1Tz5jy/B/wQ8vQYeMmdlyXx+AwgD+Evw1wQeIdTxXXAkMAyG4v2Yib6bYgCfvUMwNeIPtcn68p2b2TraL8e5pmxp2TQafuCE5aLkFCSWcOrjUmyc2b/xa3y/dCr2mQ1s3odz8Hu5/dIv/RJ/6S/9JT7+8Y8zDAM/9mM/xvd+7/fy6U9/mqOjIwB+6qd+ir/9t/82//Af/kO+/uu/nr/5N/8mf+JP/Ak+85nPcHJyAsAP//AP83M/93N88pOf5P79+/zoj/4of/JP/kl+9Vd/FRuzOH7gB36AL37xi/z8z/88AH/hL/wFPvGJT/BzP/dz7/u6TbQ1tKKZxBQiA5UB77PQe9x4fOWTExyCVkMcogRCU2XwIyCZ4Z5aMMrsEh8IZbRF9N1dPgksnzgIgetXarYvSR7D3SLgjjyyVJAlOB3/TaxYawZo7wbsmSqzpxTaPgZGemeV4e6Fvqtwg8G3VtkdgGmNvm9GAzYaVCe+K5LT8PV3kLNG0rXLBBSQcX0otHpLOxb2/YjCXk/aVnrMwr6PzCxQ29q0PSFWKs+3s5QRSOBVMXaoDRuQPmBFMGtlxlU7lYFprn3W1Tp0bWluShkXEyBrPh8kQFOELN/iPdVli+k99qSmPzYMnVBtYPEU2juW9q7BnXi6wWCXA1UELAC1G1Y6sfuFw13X+F61tGQQxBSTXDplEy8nCWAa1XNurrRao+q56bPz9ZgGqM91vO4JSSKzEIs+n1lucapJpSaNqMOrEeoMTh0UfU/37VCmz6F01HmAf9ZSttFeHl6pzSbTOSgVnZvYi+W5Z9lFqs1GBMKI78Xo75S22AhgyX6gv5yv0r7pHAZNU7bofU2aw7FfhPcRfLkFsz5gq63DV4ZQ2zwBQOw41sQBcDoYzCnn4rVShnitrpHW+0YYVgYXhN6CrwOmcYgJHB3t+NDJJQBvX5/yhbfusfjsktNzRbB3Lwb8qade9SybnnXV8e7mhOuuZjecKkXXGV48uWY3VDhvMNYp0wwYYhqIVsjQQSsYYVgGuKODlLmGBNAFARNL24bI2sqChTZV1kKd+oH8YijzbP++HooK5Op5caKeM9/2BOGfB+B53pYm8QOG5wRwmacblpTi8pyHgKxyn+dKKyyWH4qglICYGa/hk3e/nSDCR3df5FPr1/nky38UZ+wYjTj024s2iQo5h7Q9DAMTsffyWgqdrD0jIG/D3n4HIwkpum2m6w+NeYec/OS0Uwy8Y1XNwtFI9oaZ7ZcGcMcYHUr72pCZW2mZhMR2jOVmJcRbJ1gTcqGG23bb3qsZ46MuodGKq7FNKuPe1JWKMSFV87Nu7KupIITS9LUvSxTkrJeO44Uq8l71C84vV1RPKhatvhfDGsLCY2pPZR21cVz3Db2zDLHqofeG9aLTikZBkPg+ANmgzS9ffP98BSzJKQP6O+I/H/K2e1HBmfGl9wjK8uV7bQJsRcclgW7PACWmxygcjmcd/71attsPHUvGP3sgVihWFOd8JghVgGs3pLNMQKwwP46M6wDVvbqEN1+E/0X4d+tv53fufw0ew5MnL8EXDLwh8DkIl1uQLwLX5Eo5By8xAVseGYasMYOkMHF8sCm9UL28GSOruG97t/XmuSbt855R7YIJli87nSte0x7DSg70oXK/1OY2hRT7TvaPc3zq+5Iel2Byevt7/IzbBpCBpdT+wT/4B7z00kv86q/+Kn/sj/0xQgj83b/7d/mxH/sx/syf+TMA/KN/9I94+eWX+Zmf+Rl+8Ad/kPPzc/7+3//7/ON//I/543/8jwPwT/7JP+H111/nF3/xF/m+7/s+fuM3foOf//mf51d+5Vf4tm/7NgD+3t/7e/yRP/JH+MxnPsM3fMM3vO9rz4BF1E7NBXFstPEK0CnpB4Xa5vErGMFVEdAqhhMgBwV9o76NbwzDyoBEhtPWZz3cxbkCKG4pXL9cxawOsi/QvehYvbAhBKG9bhSwcJJZWd4qK+toqboZ267GGo/zWk6k62wxrUnUX4q6S6U57EC8corNoIF0twjUV5J1GJNT7ivRIH1xL1NV4GAjUyfpIyXbVka7WebavD4Jx/txHz+Kk8eLH30EERgc0jvE7Ps4ehKyHVr+0Bjmx3SO+spQrw3iAlUbqDZjFUYobXgmkiF5XX7gJYh2wCeBcXzyaNph77C7muHIMiwNrhfsDuprob1j6e4I7tjgGo9dOL2MvlIWaRD8YJCFI4hR/aRkJ1Rktp+4RJCIPmbsL75WvUzTB5wx+bmqlILe41HyJ2SQcFosgdH/SP9CmKbVCkxSCcvUwXwjS3BX37+9YH/5r9y2AFGnxzw8V+2zj6fHCzcdr9y/8FtKGZbJdoloFvXZgkDSy1K/P75HwugLFXZI0szKRbFyYBPwkrNXAGzlCe3h6z3UbsGsD9gW9YCvTaxkqINc0tZJUcGQcqLTQ7VFx0pOtYyTUAIqJIqZeKsoe6hDBpus8bSu4j88epXdF044/qKhvgx5wqg2gr+ocLXmKp/ULd2ywgXh8eWRphQGNdgX1uHqgS4NKKiD4eLkECTowGpDjPRoVH8y6sVog6Q0xQIx9rFTl6iuCscVdOb4Uszpr5OXz6qnEkoG6Q1MpWeJge/t916srBvSRWE2OMwpk+UkVToKh8Tdy2s5FBE5BGTN1+Xls/2kmDit4MXwM/e/Q5dXlmBsNpYz4m+KSS8PhMUxgxYzkHaAfpjel3KgLqi7eWJO62MFmL1SsvMBOz3L54hgzwfrlNqaJqAMZNl4/2O60wiyFfuWzI+9yaJgZcV0RPHRrYqDdTBRByJSa70TjN3va1IO6Lfttt3QqghmKZA7aqJM3h9hOm6kVBIYnWoYI7l5LNLx2xUMrUTxNqIOxDubU4bzhuZCsG00Jk1kdrVGwdwgLOyAC4aAsG2bPPwJAWvAB6/geUqB8uCdibTeUBiLTOjq8zbRHIotmGJdKPZPqRUJ7/GzVzrM5ppkEIoB46dj+4ypFHgGiJVvfvzwHlPN3jggN3yZHyf9rvk5wuzD/FpuCqakM+6tPnCc/HGID+0C3Dvw716BB8Kjs5ehE7gQeCggG5AnCE8JXIN02glg/z4WQBZOU9r14XmUhVX2/dj/SyArOw+FpV78YQ5klfs9q8nhR5BYWSWQFeI7maWbDx26vLwDfaAEaMeTxWUmaY+M80jwKBi9d6jbuebLaefn5wDcu3cPgM9+9rO8/fbbfO/3fm/eZrFY8F3f9V388i//Mj/4gz/Ir/7qr9L3/WSbV199lY997GP88i//Mt/3fd/Hv/k3/4azs7MMZAF8+7d/O2dnZ/zyL//yjWBW27a0bZu/X1xcAGAqnwfBYK36JSm4nMZDazCD19c/VjQMtcHbNLcAosLwUtjkPlYuDQbcwuRMkmEFvhbwsHrkNWUv+jO7uxXbF1Qv1y3ArZJ9Hli/eM2iHji/WBNilUHbqt5RZmdtDc4Z1Vgygcpq8ZCr7QLvLG4TxRGT3g7kyrtpWfJJ7E6UEOCAIZ6jL8ZLIQNZ+V4ZcE0ERbxFXI0pwY1sW0ZQcD6eusgq8gKD03nVMU3dhKmvkFINm2pcdqCl+WzOKFUB/YGjt9WfsK2PEjD784+Egq019zcOjYGHrsUUf+PxqqsO01nMusLFquq2E6prYfHUsLtv6E89bm2g9pkFDmBsUFt54QiNxzuBnTp9KtwfEKf+MI1HTMC3tQbiasEtBMRiOx/F4pP4e5wko72ff/cB8KgskKDfFSoMsVhZFoAvfZ18Pw58LiVXyvTCIrh/ELic3/65Pzx5DmMxrJQ+WQJ3uY+m33RAXb3UFAv5XqV1kPCKtDwImg4agay0nQSUcVWQBeYV40PU0wp19JFsyH6nsbHwwG2a4f9+LVeJSR0ngBnGChClUZXSFIKkyWX6oBTlDNn5JgSt4rQgo6RuW4ETLlrLxbvHNO9UHD3VCWj7Uop8BEwvLB4Yhs2Si49A/6JlaXsa66gqRwjCEEXi7y63DN7Q1AOLeqDtK4yJpXY7g9lpqqMOwprrbruQwanx+qcvR/r9vtKJ1O4CEqP/KvhOlneYGuAjoLN/jwSxQsAixkwiH4r9hf1BYT4wHwKyDlFpU5tRq+dpi+8ZtT0AZB36rTeCajcxyOYg2fPsU7bnue5iMk1pn7gQ9VEc0nbkUsXzHPB5amG5rrJTRtshIOuAE7sXgTj0O0rHIEXOyseXB91DgzkZXE6gV8m0KsUOD+0rxLmkjGoYFX6XSE8W0UqGIkErvxl/8Hi37baVLQdIC6f9puqvGejKO87G0fRfdL5BDcQkfotAGBRgap2hvVpgrw31Tvfvj5iAuHYj+L6iPQN3ZKiMw4pVNhmCjyLxy2rAB8Fal6PsOYAZK03lgiJBU03SPHGQaVP+oGRogRqxiQlczDGSf3zabzTwD841JoA3amgV2wbCYXbWnC41B4/SOcsms+3LY06+z1ceaOGGz/n7HIyab3PjiufcR4XaVQdrB1+4A7LUB2J2IJu4fkdOCywnxXSsHAQCCV7nnMFN16eW3of4ThwGsgqHL+9azDXv7Ucc3q6ca+bnZ2r/zVsOdqb3eo+dPL/ew9cxjaprZ5c0PpBSC2PK4W3g5H23EAI/8iM/wnd+53fysY99DIC3334bgJdffnmy7csvv8znP//5vE3TNNy9e3dvm7T/22+/zUsvvbR3zpdeeilvc6j95E/+JH/jb/yNveXGhKi3FMbqYiEoQ9xKzCIxONH0QbewuKVEbRxlJeXq4q4sT6Z/3FJwddLN1TnA1wpouUaZWvW1prb5Gtq7QnsnMBwFwsIjvVYU9Gc99483PLpa6xhrA7Q2VrMDEzWQ1m8awtsnbBd6rgyGCfi1gm3BRjvVaj8PjZ4nXbfKoCQnH8XCnQZkTJ/Gk3HeSf8SG99XAfGCWxgkqCZWrvAII6vHB0wfx6gEAlZCGPzIyMm+Q8jbHUo9k37AdBZvahKbqEy1vzGjA0BUgqV+qkHmw/NU3HSSxl3Y+ykV/8u1S4OKw9eDx+4sw1GFbQy2E+qtUF8J3YmhvW/oTzx+oc8NEzImGVI6X0D11Jwoo8cEWDvqZa8i4RLY9oahFeprHdODBFxjlI2aYh9oX5UYe1efsfBLymB7YuCl+5Ruk4FAZMaHmd8zSRc0e+mFUgKT8/mr/HyTvprMio7NGX152+lz2CNfJHPQT0kn87mm9O/DZPmYwjlnF5c6jzkVMdqI2W+agWN60OhjpqJDaCbCLZj1v1OzeH7f2UN+84WXqK5i5MMKzsr4wAu2iNIdZaTwFb2gHHDG/UxMV4wDbSfgrToVlzE63sD2pcBwoiqFpjNUV5JFfcULfWu56hesK809r61TR9pYNm2DlUA7VDTVqHQYgkCvQJbdCDGQrlTSHs3FHsDV5A5ZOv7pt+rvURaLcVBvgBCot35vcJ5U1yibEZ2goUDEI4OotpE+a8DFqnD6A8b9/fiCTNa9H52sOeupGKikGDByZcN8rftA1p7Ae3n8m6i8sd2YXvg82lkHKcsHJtLEzoKMlOdje0Yga9cpK8v7/QhFkVo4OVccrLNG1k3XYmbLZpGPUqz5phTDMgqQqcYhgJdR6DM6wJPId/yX9vdVkSYrTIGqtG3K9aY4n4z3T8xInzXGZxFsIwHzPIyN2/Z7ugmBu4sNj9ZHLLq0UEYadzRScv/LTnJkORVWStoFCuNm5uyLgxBUiNd0RJ0tGI7ANzFYE1NCsvhqgN4JnbPUJs5HxisRSpLGiepm2cm4QgayUpq9xOUSdU3UaSl2KQyhFHBN15/2t7Gq6yT6HgEdyZ/3bnRxE+OJ0sucUrWD/t2Lwh885nuAR4da2PswvTgpvofpqhuPEQ5cy/z653NNeYzn2R8gaWbJJQpYPUa9akFDtemfGhPKriv6XnktgSmQVabqQPE3fT7gIKS+vTdH3GD8l/vGde+tSTdlXE11Sgq7rphX5scrAa08v8wchfF6ZovC9Jgy+ZxCqILgb+73t+3G9pf/8l/m137t1/jX//pf762bpxZl3dBntPk2h7Z/r+P89b/+1/mRH/mR/P3i4oLXX3+dyvrR7jRAVVa2M4TK4BaWYW0iszwKokNklgvV4CGkIPSo9aSyIFEqwYAhSigQcAvBLwJdLbilUF/BsILNKx5/OmSwCi/444GXXz7HB8H70bOVQeKYL9kJtjuwrQJstidrXPVHwu6epT8ODCcBv46+i4/zYBMITkhypInNM97g6B+luSOQhd5LUGvMrom+XW0wRpDBTDMBRG1zDwpomdGGTnq/I3u0vI7k7Use2zQjRdlZUtsRyEo4SBmEhxwMyuNomAm4h7hmnq6clk1a2F8/T52ct0OASlrlA3Y3YAaPry2+MbilwXaGaisszoXu1NCdQn9s8IugwJYNCmClc9sAlYdeA0tSearKc7reURnPEwls22P6jcVuxyCYr7TfZGDSCjax73o3Xn9iTKVnWrKX/DiC5nskmlp6EFS8Kb0wfU9/5/Irs75x0Ecqlx/yjWT+fXqMTL6Yz09ll8xFEPbPrcsl+kXFOeOxUu2VfIxqOl8FQ05LFA++ntsgejhjiuDLc7ZbMOsDtr/2of87/5dP3OfB//Ah7vxWT7V1UDBYcEClncdHDS2AJL5Y6n0ApGoS3poIDmkkBFFDX7bC6h3BtoHtK0L71S3r0x12MHSbRkEoJL/MwYJdOjpv2e6O2A2aSuhj3rn3hm2v3SCBWUaCpn2kwdPrRYrT45qOqGMSIz6FsZYi4AlU8vG32xat2NgrldfuwpRuW7QJU+s5ACcdcCLynVhC5X6HQJx5RGLvoM84r4wD3FTLYoacl5PHlwNkzU/7QYGscrCcT6qppQk1DXrz6/Ze6ee7DrrkLc5Aq7IqCEwH6gzshsKJOXAt6d6FkCMfCg4VAoblBGpl6nTc5CxIAq4OOCjJcSkdRCjy6ovjFQ5HdjDiQDwpfR6B19SM1UILgRg8TdGHZ9u/t+228Z0nv8X/8AdWXP/mCcvHWp0QiSZWMnZTGq0UxUdSl459NLcwGjepb/vo+IgTpIf6GmSA4RiGO456OWiUurP5/dVUlOhHVAEXDMOgeln6msa5LhiGaEja6GV4GIHzdE3pb3SeSk2TDHSVvyW92ylA6hidrwIMe08n/nnsphIkyaBOuf49jvvlzDX5d6ZxXsablE6anLLJ+d4HkDU/7ZcFZKWdiweWUgETG+EAQJfnmtJZS+fwEchyheMx2b3sEKPzOd22uDnFtrmV9+4QkDXfxkzBq5vmmr3rmbeD8y8TAGxyvIPnC9OPpeNSBFg06yQe/Hauee72Qz/0Q/yzf/bP+Ff/6l/x4Q9/OC9/5ZVXAGVWfehDH8rL33333czWeuWVV+i6jidPnkzYWe+++y7f8R3fkbd555139s774MGDPdZX2RaLBYvFYm95Zd3YrzwkfblgRZ33WtMDk09h24DtRudTgaPYbyKwmtlLYbT5zQCeQKgkSy34SsfaIMKwhv444I8di9OWYbC4nSXUnuP7G04WLee7Jd5HratO/Q3T63lS5bqssWPUMZZB0w/xUF+CGVTTt/fK1KIK0CswJoVjHSS+D+k9DlBtx+/l3BEYg+8aiYk2vVWtMIYwHgvG+UdUB3WSwsa4TqLofAYy0jZJBH42HohzmN2AX1bPzv5IYIsZv091bYvxdHZdBwGR4pr1eLMVc5DjObJAZPDYwWN6g2kNtlFgy9fKplo8EfpjUeH4NbhlwDUQ6qD/TNBnG1PRgheG3nK1W/DiyRV3jrbszhp2oUEGS7Cimm1RoL3a6o9QXSd9dlmMX0SF39MzuElUvSRUxBQ6/GzbsnqhCHvAVukrHZrL5qysZz33GwDEvHoyhxzwd2I22cEsk5SWHEGzYFFTIPtvaTtGO9KMY0EZ+E+SD74KI1uLZDeKBgWDIEa1H4z1eC9Y699X9d1bMOsDtm+sF/z3v/8f87OvfTP/t//wXZz8v1ecvjFoSVevVfqUWheZIulFyYNoch6iGF2quuZDrP4nWrUhQH0uLJ4oKHT9mtB//YYP3bugd5br0GBqjzMBEyMcGOjuetZHO3wQztslm1gStTJeWcjJqfCGq11F11X0u0pTTGJHCpWmLRLlKqptGHW9kkakQQcOH8YXQGIufYB6m3owI415DlR5xtzuwlnIhvX7SZ3L9/kA0PNeQNah5TcNGjNx9QlYU4BBaVkGoW66hv9vMLLydRa/4zmcqr0qix6kH5CuV0bWnDJbDsY3AVlGCHbU6Lrxvh5af0C7bOq8cPOkL0ydASFTYUP5OTv1/x/2/jzskqu678U/a++qc8479Nvq1tCt1gASFrOYhBEiHrCNsbkGzM0v8YCTix1u4lxjEj02N7lJnjxRnodA4tw4jvHPjn2vEw+YyBmM8S9xfAEPimdjsC4gsBEgkASSWi2pp3c4p6r2/v2xh1pVp847SAIh9Vn9vH3Oqdq1a1edU3vt9V3ftZZaRJmuIugkjfeE3FiK6QXEZPA+hxh6H0I+tC08ihUNk6dzKUvZTS4xBX/10o/ysY1jfOj+pzO+u2B81gWngPedEMHBZ0HZ9pLmWMXuCUluw3szhWI7zO/VIWgurji0OqVxQuULnPX42ne86M2KpxxVeGCnKaia8KM24jFecJHr770wqwuaxtDEUMZsGEQDJuEeptLzf2vo523ZkGh1aTa+2EV35MX+Ab6AOZHOS9tZD2DI7x/TyRSgkm5Cb4fvNfb6dZcxPC6MrP44PWHF7+Jnjf6rvlJ4ge/dM08wxJsm5MrqS1qcp98wzANZEs87CALpvvaxLfehv/NdFtq9XX1WVid3iOnum+tHPbe+XZa1t9KArmSYbBzdLBUaEeAA9sEFK9573vrWt/Le976X3/md3+Gaa67p7L/mmms4fvw4H/jAB3jxi18MwGw247bbbuNf/It/AcANN9xAWZZ84AMf4Du+4zsAuO+++/j4xz/Oj/zIjwBw0003cebMGf7kT/6El73sZQD88R//MWfOnMmA10FkbonUeDwuJH+PqU3s1NGMQqhgMlKhXQOluSSsOZOB2s7zqQpueA9uJDSp2npMqO6KYHvY9Srk352GnEf28IzjG+eY1gXbs5LZ5oiU/NBummBnCFSHAkMHH/L/4sE0kSUcmcLJRijPQbFtqA4J9UoA0PxKg9sqQn9EOyWG0VcTT7kZCAEaxAo3IZhEwTnpc9gjqLUgQMwRZlJIZ7pthYSwdAhOysTkNfHhNT7MZ31AQ81/3ppsI0jVIIWZ/2LR83Q8LttkPgAviQWT+m58xykdLjxW79Psq2TDpH47a/D+IPaYTDTg4sP12Nphp00Oe3UjSzkyjM+GfFfVqqFahXo1fHZlWFs04whsFWFtXVUhn9qZYsLGZMpoUjPdEGbb4XdkaqHc9NHuDo46V0RSSErKn4CszJBqwSafHOQkew5yqpWhaxcz/zmFF/aBrH4KlkWsLOXM74giSmhHfR570oX60Lx+6OqfvHzIx6o0K73CQiniLK0TM3lF1Losh+jGfYqd1QGnDcHxH59jSY5+D0Xh4m3a/5ppCWY9RrFiOGrHfMfGR/maV/wF/+k5L+NX7ngRa3+2wupJF4Cf9GVHpaDjUBOamWLVi502UW1mkRgotqLXxMHWsQBkXXnJaca25pFmJXg+Usx5/HHWE2jWGwRovGFWW6qY5L3GYkwoa1vahu1pyXRzFD0k8QGemkDNdUGBuBE5zDAbFRGtTqFYItKdZH1UPIQqE+I9xU5vgS3gSflJaI2tfonY3SSh5UB+IpI8mlxUQ9v6iid7Lnw7BiWdqa7nbZ47z4L3czHtQ9fV394ZhHTH3h+T9zH+WyfCVYqQ9F04qOrgJe/nLumDWBrI0uNIbTSoFvftlfC9M2HrbWoCnpMU5qom5sTKygs35WnwpHYoMC38uRgpkzyGut9My9LHRDYjMQbcFC6yVELoVWZBEqtMPUY7dylPfTEirGB5/vgBnnbVKe649Ao+efJyyvuKkKekbuflvnGs8/No73r3OQ7tTUUbNrgWgKyNtR0KcWy7IhQPqdVqKD4ffuQzE6txgmtCbHpyRnsP1jZUjQ3FRRpp9UAjWXclRnFiZyV9pr2BmQnZe25SboxUdIV6XtfkVVe6dA1q7cthQttev6f3XsujBbL6ukaDQJ1EFj0wKL3Z67q0ruk08cPXtSeQtYuu6dwrdT2d7VGXNa41OLRoJpa0/XQAmn6bznD2n/B9qJJUGvKcdJ4z1PMnneewc83QHV/WU7GZAq7m+tDH6N+ExPyMcbMRl0N6w3CWumY/8pa3vIX3vOc9vO997+PQoUM5f9Xhw4dZWVlBRLj55pt5xzvewXXXXcd1113HO97xDlZXV3njG9+Y2775zW/mh3/4h7n44os5evQob3vb27j++utzdcPnPOc5fOu3fit/82/+TX76p38agL/1t/4Wr33tax9VJcNJUYXwJxHEOTyJ1R7WX1J7LA2lEaq1wIyBsI7Xlf3SMd6mdXvclpI/27Dmd2UIJ2wmsZJ5XCNVhz0cqkNKhSYyrwrH4UNbTOuCU+fWmM2KsH3czD2n9eGGI1ec4fDKDqe3VjhzehW/VWDPW8pzQrFDcK5G+0KmAaQwh4R6JtQXNfjSUa+D3TLt4y1Bv5TniISD9jlLTC6dIzcb7yYymK3EnMQ+hgOSHefSxCTbve/ES6p05/M9lBR2CF2gKhEZkoM8RkJQ9KIf0pq/n9dJ2QK5emFyRHs1n2a7IubzSsSFBGwpu2FhtEnnItWkou2klHtLicRcU1JFvMYKvrQ5DLE8r4oLTAz1SgC26pVQSMCNAgjZrIbk8Gf8Kt4L6ytTqspSbzQ0Zwr8JlSrAdCaxXDboq75vj//TV70xc/wieJybr30L+FSxc9c+ZM2AkSLEbwL4b/egsSQ1xz+rr/L9LxpIEtt342VNVdRXunUTuihdH9DIT0MXTsr3/PEwI/9aCZg+mkq3UP7k8i2D9ABsojPTWLzZyeNJdpw+vracySHZch1h7KTgn4CaBqDyMEqvS/BrMdBCiyHjOVamfFDl/wu3/u1f8Cvv/j5vPvTL2N2+xHWvhASuScqbgJ+8pdfQDMSqkOeYtMwedjH5IShf2nCe2+ErcvBPes8z7j0YWpvuP/cIerG0NQ940CgXvPIpGFnWnKSdYBI3wuNZjNLU1l2NkeBiVUZzHai+wYFZmJ1EQBnPaMtwVa+tWOSkovx99L4kKskPoApxj1UQAn9Oiv5Rzr0W50DsrI3ITbIkyTzk6sGsvQEs6gCYP+93tafcOYMi94E7jz5C+gnjIfdwwkXMbH67YfAsN0MjP51JAXlfEDFk5coTbxJHGGfV/lKEoilJ9EBNhbQAlk9sCtUCdmFhtQDunRYbtuml2S3Rwv2/d9D53Pv9qQJPA3X9Y5Px5g4qcf+NY3WW98FwyyBDq0MTgGKssFahxWPNY5JUYdXW7W53paylF3EYBiJ44hpuGn1bl70tHu48/LL+OjDV9Dct0J5LuiL/NvULML8exWaUdApxbYqTw5h0RuPnx0Cf/GMo2vbOITzsxHOmZDnxCtdA7gSKBx1bdlkBHSnnqYWvDPUs5A3JeTICn1oEEvnLbEVOeQlbIwvESQIIRTtGER5yJ0N/aaiIHHznCwEsgYBoV4n0ms3t22g3yFZBAAt6kPSvdfn7N+kXc69kInVP37BvdmvxPuVp0Hnh78EpeczM7sPYgHzQFY8XNT+uDuujHe/r4t29b9jPdRdP8viPlNbbbf6+f5SN5p9pQGydAqv2rZAQNgjgLEB1ArqtC04UohbMrP2IT/1Uz8FwCtf+crO9n//7/893/u93wvA3/t7f4/t7W1+4Ad+gEceeYQbb7yR97///Rw6dCi3/9f/+l9TFAXf8R3fwfb2Nt/0Td/Ez/3cz2FtmwDwl37pl/g7f+fv5KqHr3/96/mJn/iJRzXucRE9xzZO+nVgZYVKe+CtwVmDOE+x46giW8TO4jrdEhJkxx9ZyJFFBrXSvJyc8m4kzDY8bhwZU41Qr3nqjQZbOqrNEbJlYaVh/eItVsqa89MRdRWu36xXFGVDsbHDzs4haoH6UMPxpz/EDZfcy6Wjc2y5EX9x0TE+8/DFnD+5hrhQBs1UAcjyaXlZw+hMsFlGZwvciJBcfOQjI4eQQByw2+1DYJr56VPP4SHtS/vgtWvSAC5IYj+ZMNl5G8AqT9KrLRgUHtM4L9ld1sHKUS5VE0AMy8L1fH7v58MKc0SIlmQTpWig2F6gU81cj2co2gTtxNWpQdLndPguekjqUBnd7NT4wuBKix8Z7LZhVBhcEYCs2bqJgFaIVmo2Lc1qaH96ZplsxOqexlNteIodwW5DvUoOX/3eO36Lv/GJ92OAl/AZEOGXTnx9thPaatHdiTZVAiQlPtfX02cOi4CYFsjS2ztteqysDjg1cEwSx3wopL6tksY37Lhpf+u+c762ErRH5xUWH8iGuRJ81Dcu5tFOIYZ42qITOsdpXAf4WDAi5RfOYzIgZczT5wTBYEwoEOSXYYZfXrFimFAEj4d4JuJ50+GP8u0v/igfeOaz+eV7XsoXP3aM1fsMdkpLvxM6PxhfwPaVNc2qpTwrFNuBjRXC9WDnUs/k+ad53qX34xDu39zAeaGqbEBka8FMQ0JeZwN45GtDNQ2hg2J9iEOVEI+axDcByEoVQEydFEV34jK1UJwHO+suxDQDRy+MNQXRFYEyKnFBm/O0RNAqJ3/vAzxxcs6TCb02MOwt6ANIi2Q3QGg3LzPMK5UkfWCrv/8gIFZ/fM4vPr5vACTQSCsXQ6bXSqOqeTTdQyWVQc8lfXvXrL1KixhZ/UlapM1tBa1yhGFvT76O2G82ZtIgpcOG6ntS2vDBXtW3bACkcZCToWrWh++DV9ABunLBgzixY1JceKBCU7STtkisDio+/zXO0KQEqMsww6XsQ4wIhTe4WDigAF40foBnH3+Az1x8CR8/c4JzD6xTnpe80Ae6BrIHDNQbDl8aTHScpHwh3oYFYHFsm0tXz+MRzlfjwLhKDpMEekXw1xWAE5o6hA6G9VwAQzretQhkpVwT4qSTFyuJuGQckRdD6Tpym56u8ek6jYT8KpW6cRJyM0i6Ab3pOS+sXV/X9L6APqClG+0X7zmIrtnr+D6wtaj/ge2LmVjqevbZ17wulPZLEdoQ2IH+2vD7BeebA6nyhwEgS9o2hmFds9ttzgv+XiOte9T59XEtU3eX/j2dNAxZ12i2ft/o6PXfMo2jU9H4XphQesQ9iEfwueDInte/FAAGizv0RES45ZZbuOWWWxa2mUwmvOtd7+Jd73rXwjZHjx7l3e9+96MZ5pxYca2hm377Tfh9eGuC4eg9xNRaxXaYCKUJIIwrJaxbfFw3eR+BMMmPc1OG+b4Zxaq2RQCS8MHhXW84ZNKEKW3bhjQl44bDKzvUzrAzKzHWQWMYr83YWNlhvZxxL7C9OWZtfcoLLv4iT1s5xURqLjenATi1vcbW6phmahDXhiT66B9Jz12xFV5dBcVmyMFUr3iaQ6FA1OghS7mV1rPt45qdHp6cHiY7hspwHjv1IS9YdAqFynjKqaLBj8gyHQRy0vejGFCD63qRwM5yrmXsJFspAUdDzv6kz/pAVv88/eiJeMxgASjaqaMzP2rQqm+r9WWh7QIQQC27U4MVbGlpJgW+CMniR+cMzUSoViKoNYJmHMCterNkdrbAHa7z2qKJACvTds584QN35aW2AZ63fS8Y02Uq5fH1Pqb7E3cYPN998nd5/tnP8XF7GbeuvBgnhk4lQ6BTHKsfXgiL9f6cPdOzlWSARab1YUy0nrbnvJR2/rydKBpl24fz6j7IxXhMnSJVpLWFvLKLiLZTEewiIDIEA7tR0+u9EyQ6YEajOuuqwvZs2l1kCWY9TmIwlAIlUNFQAuvW8Nc2PsVffs4n+Yuv2uBXHrmBX//U8+ALK4wfDlTZkNE/9jEDSsfsKHgbSuaaKvwAZ0c8R553ipdddjebzYgHtg/lZO4AvjExZ5XgRj4wqppgN4jxjMbBY2NijqyMLZQNrtZ8xLhIKgSfckmm336dqhjGaibRg9OU7cOQF2NG2jK3EKm5RJaZ8h4YCaEg0C5ooWVlRYBL9ISfKLNR5r0FUfYKL9wvs2lI+kqhf8wiVtdAn7uysGDvsEQNYu0GbkGX7sqCdW0+r5trn99rquxuQJbyPvUNi8HQQN1X/B3lYw+wCA+eQ4mGlB8+F+QJuk0MT6bPBpArsTva7R3Dw6bfcZywDa0CiRU5UrnZcNs8utysNY4ildzUb8XsAAEAAElEQVTpx9wvZSkDEnxXYBCceCzCSIQXjB/iOZee4qGjYz6xczl3nroMzpUUqbqPMpqlAYynWXF4MWHBERfkzQQml21yxdoZKh8q4dbOZPTDu/zQBMA3glGe+MgXSce0BoMIYBzeWbR48dnbmZ/QCJYlxlgOASFR3tX8kKYbn3RG6FMi6KarQCF0F/x9iaCQJDAmAzy6rdBf5NJv0tneXxE/Sl1z0GMW7N+VhaVe9gVi7XUtoixM9pi+F4F5ed7Xi3X1/ac20DUg2Ieu0ceqU+yabHlIFGi2J6AptEaI/v3S/p47jkL9qo2K5N3Woe35cnw8je/ccxHfAssHvcalPClk2hQU8VnyVsg5kSCyfHxm4duYFNuNLPVKmJfz85KqjRXhWGclO/yQGEp4SKhXfa7knphQfuwwhcPNbJiHjeeiizYBOLc9RsSzOgmehnFZc/HKFhvlDtY4Hhyv86yjJ3na5GGcN1RYcHDYbnPt4VOc2xlztrIEkyVUbhcXDGZTR/0Wq627WrLRLbXQrAIWxo9IKJ4C4KMTJ4FTqdKdSrGhn+3Eak5FTyQpLhGkcq2dkvRaz0lMSgIPrR6La+QcIj8X1RFsDQ0UJZtHhwB2C2fQzdE7JP38totYQLAQ1BqShaSDzjUxZ7PNjX9WY7Zm+NKGMMSRxe6EMNNmZGhWDNWKROZWqIa4U5U0E9eCKmnZElXRRy95Oi+771M5i8od61eTc01Fu2bQ6eHbMYYIUeG77/9d/tqDv4sBXsQ9gPCe9a9uo1xEGAwv7BMCIBfNmqtguNt3kyTn2qbb/5A5IW37fgRKAqlyjmCtp+I9EK9SraR9nvwMehturEh6puK98+36M5FzSACX6t/G5O/jUU1hG6zpP0CLZQlmPU5i1Mqi7DwElsPG8tLxFs899jv8jYt/j9unV/FrJ1/IR++5Erln0obxpW+jCMnuUmnRZgJHnvMQLz/2ObabEV/cPMwjOyvRU25wjcVHpeFLT9MEZlV5JvxqvCmZrrvW8C5dCIMiGtpOQnLEJuZUUT/QnIyX7udcKST9qNOcm6i2QgdwwkuoaDjz0RPv24pTPibLT8377CPogDw59nsRILMfg2E/QNZuctDjVPu5xPeLxrUI5IJBACt7EzP4NQCSZDBzYELNbUz3Vbftg1Vq/26MrMEyswOTdp5AoQtkqXY5zCgZEdK2yclKtdchtUnsK1GvA7/hcB66jKzebdKhWy7l94m2rzfJsiateEheCCOeUdFQGkdpm1A51MsyzHAp+xadlUM/QhaDFThRVFy69jlesnI39zWH+YvNYzxw5jByplAL5rYDXwTPe2JYrVy6xZXrp6md5dxswnYdwjq8M3hv4nMSgFtvgzFhp4QHTKAe+Xbxa7wyuNMDIi1IJe2ublgCagEZx9yfqrKiatvmXQ0hD2XOFZL66eUM8gzoGjVWki7SA+03XiAHAbJ2k72O28VxMpxyogdcdVhZA+cbALB8f1/SR32nB0o/LwKrhvZ1nBe996jfQuf41E637xw2fG61f2E+LSEz/zpjMAOdC1lHzbGpBsdBl5E1sL/Tb2efV/u9OiAAWSnxuzXBieI77ZbyVBMjPhj/0yYAWUWrIaRxwdCuXQcwNSIUQgC0msBqzeHoZbvWd2Wbw7deEWaHPXZbKLcCS8tZ31Y9q03wpBtPeWTKJatb1N6wsbpDaRwrRRXY6d7gvLBWTFkrpkxsxeWT6ERp2mqNlbes2Iqja1s0zrBdjqhlhBeDqcHuSCD9jtuQslStsF4RSgfeWkwFq/e7DGBlG8YHfQFdveRNqPyYWPquADESHPIqHBMYBnGMwTs3x1TqAFYQ5oqh9TDEcEBaVp1meykHcT73AENrLxnOOdv7vB8AfIhl1t8Pw0DWkO3jHVI3yHb4nfqywI8tRWlx5y3liqUZGdxIqM8K5Xlh51IbQMxkV5dQ7jS8+c8+yIvv/yz/76Gn4xq449DV/IfjX5uvs8+EDRu7Y9QhnM87f3eX5VXd1wWyhu5bsoe0rRTBzL6dMydGbdf7+7+lBGYvIgBoIoKnvf4eO7jP5O/YRL597dtKYXzq+bI+hzFqJnHGI5KZaMLQiyLoLCO0jv59yBLMepzEigHvCCkXw7fqcNj4o7FY1jE8s4SvKu/m29c+x4NP8/zW5jP5jQefzyfuO0a9XQaKZB1oeM1I8KVn5epz3HT8Lk5XK9x97ihbVcm0Cl+dCBjb0GBD2dqZ4MaeWRkUTWCmQHnaxNANiTm6wg/JjZN33kMBZtvkkrTeBg+3iaCanUIxbRPUN2VAxVM8eQ65KiC56RNwk5RHyqGFtBTm5InPkuO5e5Nc05/sfPfBTPv2K4/WuHiUfc+BWIuAtd1yYqlkgz7dhwRaLbyeLqi1EMjq57Lqt1vgWZijy6Y2fW/DImBrN2CtN54MZDGvgFPV0LAPWnA1TaSS2yUQK1Wo0R6IvM+KakOuytEmOEwVTmJMudCCUkIujR4++xza633IGTe2NYU4Ctn/hL2UpRiR8NjjM7Dl8crmFUZiudjCUXuGZ5en2Trs+Wx1MZ/evIwHz63jahvm3ZS01wLGU1w048r1R9hpSs7MVqgaFQorIOLw2OwA8YWnseSQD3wwLPAS2V+0ueaKGH5LLF+eyqdDfo5SzjrTRF2R5rr8LKrnG/I8IuEmhPfR+GofqxjyMRRemIwRT6uLQBkEqX16lrWu2e83xpdf18xtGgCw9OdBppq6/73Pe45lLyBr0Vzf8YrrxXjvex8wEDrHxPcLF/R76ppuuzkIs6cv8jF5HdReh+9tH8y7Jb222vOt7A59vq7x1UEjg+4J7xDxWHGh2MiBfrRLebJJaZrM9An5str1mvi4DXJuoJQ3SWqP3XHUqzYXCHGJkQ65EpwroFqXEIa+KYzO0LKiLLhRDMVIv7+1hmsue4gjky3OV2NK0zCxNYVp2GlKzu+MqRrLuXLCWjHDiM9A1tjUnG/GbDclj8xWARjbmpVRRVVZmiNTmkOGerOgOGeRBpqxx8wMxTb4BopZSGzfjEN13mIn6BZxIUKkGUkbZRIN8/CMBqdHPRaq9XD/QhXF0F9gDbf6Qmq3uz5IjpfB7QPvk8T5VJzLy/y5/Ro82g8ja+D4DgNt4fj30Zd+7W9PolhvnTbZnhnYnobRNMhOGI81hqIs8CslzaSgGRtG5yzlpmV6UVh/2JnHzuB//bMP8r9+9P2ZkfXuy76OX7r865Xu0N8D6rdAuzaAHLEBcMfKlbz4/F0ty2t0ogtk6fDC9EwO6q2US3jvW6wZzHOOjQQwQQY+vdahQje/cASpukW1uu2BLkiV2sX8V7mPxIDzZDA8nUOchPQrMceWh7h+JIYb0tFfyU5ynhARsE9ZglmPo1gxWKCJs47B4vC4+PRaERrvKcVSiuWENHz3xqd57fpf8OkrN/jI9tP54IPP5tP3X0p1bkSz4lg5ss1Xn7ib2lu+sHkRO3VBVVsaZzDGYY1jNIJqK1TtwINbbbCHKmonGOOptwrM2QI7lTYXyTRSgwvBnTfUqx634nArDrYNdkZ+WhJdMIcI1uEHq8NWdPhVCjshUTJdC2KFcBc1QamJIotLD6wGceh6HzTCL565yXY/7KcDyCCb6qByUPBqL+Bq0ZgG4uARg/Qn1KFE7ENGxxDjKl3SowGydjmfDi0Mn3vGiGZkAZmVpRf4A4o3U2c7527/kqcyeyXnPGf9DuNmH3WzZlaptxJBLGvb8EIbn1uAka1D6McywnApBxATmSguLXYRfPwXPqd1hmBEOCSe68cP86zRQzy8MeaL9UV8dvMSHj6/hptafOkpViquOHQa5w3nqgl1TPbufKos46AwuIoc/u5Lj4wanA9Gs6tMroIrPugMG9m8vpKYl4UW2KqFDpNcCM9CRQ5T1zkmoQskgFoHpYVnyr+RF/9K1/RvpKdrDOTmvv3g9QlYOBd0tz16ffH4EGcOCF7tBVwtup4FbKs5h8l+jKFBNpbWBQPn3AvI2vOc7dtFebJ8p21/sT98/bueMukaM/9b7o+pv9mnDvKg2/bBu+2DDRVHbVRooTUuE9iW8tSUxhuMi+sJRzdvTopmiGs6LwJloEOI85hZQ+k99aoNRm8qVmUCGJQLV5mQN7fYJOfTakbBMU7hY3qFUGDooovPc2LtDI0XNkrPTlOwXk7ZaUqcFxovnD6/xsmz61x15DSlbaidBQPGe85VE+7ePMJWVXJksg3A+e0xTRMennJc40c19WoRcv8Cs6ZEnDA6Fx6VYsdjNpWtkhglCqxLLMsQfeKzbdOMJTvnjdDRVUE/+dweoU2ZosUwH27Yl8yU2cVu6YUVqgZtNxrIGrIP+uva1L9081wdONR6N10xAMItZmIteEXZYHlfjcwq2NrBWENZFrhxyeiREr9q+J7P/TYveOhzfOyip/OCU5/tsqi27mntkZ4DRqv+7nW055faceuRlyPTGc/dvpc7Rie4df2Gtm0CsqC959He8n1SgL5PA+yrNkyYLotskY2i+p0Ls4+VOtt0KlHHaeZVOjzdlhRu7Nt2QK46nVJfJSKLTtcSnJktEBiuH7Aht3Cym8SkXMJRhYuntI7CLsMMn1CxkQHTeNcJP4Tu728sBodjYguOmh1eNv5z/srGR/nIlcf54OnncWq2xmXjc5TS8BfnjnFuOsaIp7ChMo33wup4xsg2VJXFnSwDtXy95tknHuCi0TYPT1f53ENH2a5XA+XVBpzBzAQzC8kSvYk03ZENiRJXHb4UzJRcacpHBRbCBOMvN5XsFKUUIhDQSaYYPeUBJXcBFKtTaKHPebFCezVhDSmBoUl6P4vuvuyj3UIAazfPwxAwp/dpoCpuWxge2L/WobBBLQtArMEY7n67Rdv2YmN1ztUulIDFQFbWKt2xdJMOzvc/H2KhJmMrc8auZmVpr7c+Xxt22D02fIgvilqbQTPr29jx1DwxtqwH6xHrMSZWlYrgVZG9DoGdNTINK7Z6vCzYpVxgYuKz4XzL0kqiP1kkJIwXWClqrihO8bzxA9y3sc5ndi5lqxmxVkyxeE7N1pjWNk4XHnygb5VFg5WKc43gN2OOlZHjko3zTGzNdl1yenuF2pVhYSMSHv0GjCMkZK+TFz96+ksfGAAR/PIx/NDEMMEETunnTCd77wBRvn2MQmVEnwExvG/ZW0O6JiMFSvzANo2q7Zflsh9ds7BJX9fsc9+QTvK+bTLk2Bl6vxeDqr9NehW79pK+R3w3NtZc+17bPHcvSIi7QAZDC+fG2Z5jT1ZWf8zps8icDhoCsoaO1UYEqM8Gcm5GVVwE4rNL/HkbjxVPKc0SzHoKi+kh9rmKoZHMDglOhpAXN2yQdvvMhbo1tUeaYI02o1BMxFSRXeGkM1/mCrTaqN0ukJHjsvXzrNiK07MVam9YLSrOV2Me3lnjzPaEze0R1WaofntqvMpVG2c4OV1nYivWixlnqglVY3FeQvL4uqSubHCamJA4uokg1urhberaUm1b6p3wgJhZYJUlo17iHJgJ9MlAJ+oZG/WJa/OlAvhCqNZa+yexshL4lSdWaW2fsDZVeesM5ASsHd2zjzl2Fx2ykOE0JBHM2Jco8KQzzv54F42tkzZgwGbq2z17SLYrk85Pr94jTQOzCru1gz1j+O7Tf8hfe+j3McANp+7ko6tXt/WvCLmyAIacyP08ZFo/5Gq7jcfXjvesvRQ/ecm8jdW3mbTdlD57n53+eSxDv4U0aGVLhbWS+i7Ti1qezAGS8dnM+sUFvdACWl3dKs4rsoJKg6R0ns+5ulI+U7VeSwx5kaCjHLmaKA4ow1wlpYvtgr1UNwZrHLPahpCXfcoSzPoSSgK1+vxQp1adRXSbJO/ZMTvmVSunuHH8mzzQlHyquozfO/tMtuuy08fhlR2MeA6VUzZG25yfjnl4vAoCF190nhcc/gJHyk0+tXmcs9MJD8wKajdCapN/vM4GI8PUIYTQjcBOBX/W0kw8bgT1ms8l0+0O2WBIxkSuSqik9TiKAmgigNXQUnIzAu7bhO97yWPweOfjd1MMuzG6+sfrSn99cCpNNENglO5z8Hw9V8ZeCmgIWAIGgay9wKt+n9nz3bYZZGOl1zhZZ9qsBrLS72TA0Bn0NEDXaNB9oICsvrHTNxhUXz5OwB32lTIW8vYFRkYCrDpGSQK7dFhhrOTWGhjk5O9WQqn0TnjhYDKVpSxlf9KCWt3tCr7I4e9p25pYri23uKK4i01nOOXWuHt6cfCMK5kUNSIwMjVjWzNrCraLoI9WJlOOjc+xYmc8JOtMm4LN2uC8jYlAJT9XybGRPHpSB33jYuJQV9IaCDHsQy/OMhigry8+h3OPq4vMrBQKonRNvAn7kMcBYN7VCNnjnF591vqxD07phf1u593PtrjgXTzoXQyv/QBZg/pHvZHOhmE2Vj5HOkbP+QOhhbuec4H0+0hA1lAni/qS9vfZ0Z8dnbafY1W7Rdt6+yTGfAiBmWXmfldLeSrKpKiZZQM5GqqJEVKYYKDqA3wM+Y7zrPiwPrcE8MpOfagYNwp/4kMuKldINnC9DX24FYeMHRiPnxWsbuxwZLLFdlMyizrl3vMXce+DR2jOlUglOZ+O3ZgxKWsmRcUDW4dYKSpmox226gB0Nc7gI6BVlA1NZTDWs7Y6ZVoVTHdKdrZGMek81Id8jB4R/BlPeT48JLlqrk/M3Xj9jW9BraR3TNBJqco1PjBUTBWNcBcYOjkvo6cFHuL9B8h5s9K2BB4msGC3eXvBPDpYLXAvICvJkD2xCFQbArQWtVUMsoXj7NtfuxW3Sk6ng0TFRFvs+Vv3dJhYjRfefcnX8Lzte7lj7Wr+w7GvUYPc/R73r0Eaj1Q1VDXe+27ES9/GGqpemGRR0nc1plzcbAjTMT328dA5pGsnpXzCGsTV6yOV1jT2p+x5uu1yapVkN6V0EhHUyraS8V2MQMCXPjti0iMhJkSxJMf/qGhAV6TeQ5Zg1pdBbJ9JMxD8bJCcb2sshtJajlq4pryfZ48e4P+ZPI8/fPhazsxWKCQkj4YQplSKY1TUcMmU8UrFs48+wBXjRyil4dLROc6sT5jWBaemFteEvFreEGjEBC+4nfoAaMXs9W5TaCYwPeqpDznGJy3llmsnFmmNfmdbQyVfoolx52m956NHI07+WVJi3kh1Dm0PeIPT4nmoet9+uzhIUnbvoWnCQqBx4fscUiJ9MAtC25hf7WBekqQYexNl5yLSzTZ5/0JvQX+c/X4GvDC7srFSP0NAlh1om/pMH/vJ3vsGbJzw5/Zrb0MfWI0Taifpu5qAO+fvnLP1MriCltpr1cSe+tATvZAXZwDeS2BgQXTCOEYx6XtpGow43BLEWsrjKKb3fA1NaykkURAswooIKxYusue51G7y6eJS7tk+wrQpQ56duLiwxmHFhxDZ1QZbNlyysslGsY3Bs2pnHBrt0DjDVhOMj1QRVxsKJjK00uLHVOE5a1bAjTzFpmCr5DGhY7jr3AztBbU5TkIjH0NsBpK9x9eFpcP3lAhyPAanyoGSsquwEd8Pzeh0KvPbOt5r9gZwekPZExDS24eArH2BSKK2zQM+c/105v9kKLSfB3XUgjHMhxWq40R9FT1d04Jm3WP77KvO69w1tW874U+6rcwfM5cnK7WPz4TEcYpJSd9bJ4o/yG9gKU9KmRQVO2VI/C6+CbmWfDRgI3iSGS4aP5DuvJQiJuw0JLu1O+BG6Ycawu+accib6ErwJYgTzCiweO2RHS4/fJYVW3GuGnNmtsJdD1yMuXuF8dnwI5we8TSXzDCFY2VlRhn1zMmz61x6aJOz0wnbVUHVWNbGMwC2ZmVgcJSOogw2kLWOclRTTQuowoLMF8EZPzscGFZ2x2Mrr5JXS+vw8LRzRZwrnW1DLN0o3B9Ttc+41Km4yIIvIs3FRvDOIzY8vF4EMQJOAD8HaHXYMKmfSApIefAGwaT9All92W2uhi4raLfr3I8MAVlan6l9Cyu8p9dF441yx+RKXrR9d8vEWr2K91z2tTE6SRZHqGg7S42hs7aoXQCzmqZ7vO4zOfaj3bVbcayOk1/JbilZ5qNUdOGsgRviCfnz9LESzqFtpM4hCciKYDcq5UMAsYOeTOsxacAX7bHBfvIZ3ALACX4UySyVIKsOU6ZcwuCcMCochQ2pWIqiHriYYVmCWU+AaHCrUcCW6aw0Qpt1GfOc0vO0iz7ON659ktt3ruZTO8e5b2eD07NVzldjzkxXsOK5+Oh5Lls7z7EYmni+mQBwqJgyKmqKcUM9M9Tr4EpDsSWIhcaFcENTEZPuhgncNAGIqLxh8lDwyOhwwk6JTh8VYrqc3iJMYpl1SYZEx8hQn/UE10kKTwg51B6P3H/vKeyDRJpBtR9ZRNt1DpomAFiumZ/4mqZ9b6SlSM4BWOrC9KSdxtzf1lmgm4ODWKmNYk4tlIOAWOlVxYQvDC3sAVmdiXOX2O9dgaz0PoFo6aeh26Zz9YwE/eeyB4GOERL6JiewTp+R7gTti3aRFNrF0EIbveIm/I2KhtI2uUJHSgI/NnWuLrqUpTyeosEtpxeLA1btiIJLjOfw5CTXlKe4vz7MQ/U65+oxO03JrCmY1iWCZ2VlytpoxnoxxeCZxVXM2DShgpp1IWnwCHysrouaGiXlVYRcBj7oEaHYJuZWjPtFgcjtBcypgbzLazu/r2t674eYWrnvIV3TO1kfINB97kt8760CsZxrASwd9pf0URpe5/w9AGsRI0cWbOvc4110xW4gVv6sTzTUx9ybxSBWp8+0T9SY573Uu59zwfZFQFZ6n8813N8iwCrvT+ul+DcXpmi6nzvHQGC9dPr1Ud+kryCyf01M+h4fIiFUhrLiFhumS3nSy6qZ8UghuFERvua01IwgVgpz6rCQFMgV2rZrPmcDE8kXgp26vDY1dQhB9CsB9GlGHj9yjEY1052S9dUdDo+2OVeN+czDl3Dm84dZ/5xlfNrjCs/soqATmtpgV0Jlw4vG2xwqpozLmnPTEee3JpRlw2xacGgyBQJDqyga6iosypwXdrZHNLUJFRQhxxAmp2Wz6mkmIU1KuuYk4kKC8GDLSJv7SiSzsog57UUVsQpAWKraHu9hYtHAvG6KTt3AFg7r8jA9z+uKHKa4KOxsEZD1aGU3EKXPD9iNBbwfRtY+cmPNHQctUSHbXHQ/azFw6+GXAfDcnS9wx8qV3HrxTV17xPvgdHfk34teE3ll24Uw0vAntQt5umZV+DxU+V3bWUM6yebJeh6wiu0625U9BXSjWNIaYAjgUx/n0jT0IlPS69w2wu8xkVw6Ie8+rNtcypuFek23wwWAKxUY6qRrScuURrCFU2HxIQVL4wzmAM7+JZj1BEuftdV416mMCAHkWpcxLxh5nlV+jgdWP82Hp1fw3x9+Abc/cAUinsMrO5xYP8vVaw9zpNjCeeHheo0HZ4d4ZLZCaRyjcYVrBD8RmtrQrIdEveWZAGxBYGcV2x4/hXoSJvDV+2F0rmkn8JSLSC2+fFpX+laJ+ARAQWZlJUpjniCIn+NE10HAk8TJM8UwdyRSLUVPsvtlO6XuF1Fde0wsXzdQ1y04BeG9zuuV+kqglRgGmVhDYYhp2xCoBcwBWZ2JUoUUpn2ajaVBrD28GnnS3C+IpSfmXYCsvvcPGGZk6UlbjVXHr3eos3MGQWxje6BWP0Sxv015ITo/v/ibzuQJzc5KE3RKaBiZWSmhobFhki5i0vdkUIxMk8M/ik4G7KUs5Usj86wt36mMCGFBN6LgmPVcYk9zvnyYLzaH+PT2Me4/vwHimRQ1h0ZTDo+2mZgKD2y7kq1mxHZTYMRji8AI8AUhr8lIwEkIK6ziNFFH50lNTjBang95SfL825kLyO9zKEh8/juX1gsv7IewzzlTtOTnXD3wel8HQcqI2b6lnf92AbG8xztHLgDSYQ0MgFPp+DTX95lYaridY6XXpjPQ/k2d3ydDbfsg1p73R32/ur+586n2ol7j+4MAWQtzZGldg74lUdfMtdd9do8fvB5tXMRj+7ql811J/xivHC2eVC493B4fdQ456Xsb1h5yrApkcGspT01ZsXUApqzgSxvW1DFJeVgnx7leAxJpjk35b/I6J67xYlSFt/H4yPAotsP6drYe132FY7oTws9ndcEXzh/mgZOHKe8ec+ghodj2zA4J1Tq4scfuCPJAQTUzbB0xuCNC7Q1Pv+hh7tvcwBjPbBbyZTkvlLbhyOo2Z7YnjMZ1TBbfhh1SB6ZYy3QJf3Y75VqK2/XcmB5NT1u90bfPr52Fe+WK4OBvPHCaHMLeOlykk9R8sDpgXJN7Q1inOwlMgt3YtrvJXgBQ+o47wEhvntzrHNAFv4eAtL3GuBuINdDHwqrvfVupvz+NzXmcNbzn6Cu6Oap6srCCo08FAKQlMTgQ55CqCWBW07R9DgFZKtxwjpWlGVx9wHIIyILB8Yfk9bQ2U7S/WsCONmWKAqs7a6lYkVAIObgTQJsNetrnXQPg3gO2u83p8MLOQMl5tDqRM6Vrq+56CQxSgs5yXhgpx/9+ZAlmfYVJArcSoNVPJj+WkquLkmP2AZ5e/ha/YL+G2x+6Akghhw0TU3GmWeULOxfxyHSVnabg3HRENSsQA+W4YmVcUTWWqrLMyjG4gtEZYRQBLVN5Ch/K2doYO68XvC4qvg6Q4NrPycPhBUwTlKHRiRJ9mydrEMVf9BtOk0X2OH0J3Ix9IKuu8XUdgCwgg1MJ0OoztPLktXhs3utqWz0ZArFS/9Cb/FqAa1cgS0+4e8h83HXPWOlPzjAMZPU8CqGd6rcPZC2SOaaV+u0tYJolICtNoJkKC52cgt0qHpKrc+S477yPDojVOZ64Lb/30ciICeBVUl4IHnIjIWfWEshayhMlCdxKgFY/mbzFcNgY1s0mR8xd3C5Xc//2BhBDDvEU4pi6knP1hO26pPaGWW1DUl4BW4SQ+MYbXCM0NtB57U4sspXKpMeS56ISvyfpLMKUdHQNxIc76JMOYNV3mvRlN2BLv9nPwv+g0geynGuBrHTODqClhtQxynYbm5+/eZ3zK9nN4aHmchnYlkGnfOw+dE2/SR+U0t9B/72E991y4gvOmW7fno6c/i2RdpuojvrXICq5uwKmug4U1Ljbz3u99q+hA3Kp1wBYJfQ2SAK0DD5UI13KU1pyYEQ0ZH1hgiO5ceo3FX80VvCNAgKEHH6XDOUMYjVhPdzEUMNO/sMUJtcIbloijXD+fMn2ucOsPiQUm9BM4PwlhATQMULDVFCeE4pNy3Rnwuy4jb/TYFgXRcPKuGFaFWxNR1y6uklpG0Kls4bV8YxzO2NEQiJ4qQz2vIlrunBdIXE9mBmZWZXtdOmuJfXj4UpyWoli0+ecjuVmil7x7ZpyyH6htz2+D4CgxJBfG5g9zmXQpGPs94Eo7bDXDg4YZigN6Tptq/QJA+ltH+TJtpbqU4OhvWmlU1FRn3NQ9+4ChvWBr8G0LXsdP2+rzEnMnRZy3rbXlQrHpCqSUjtkZwaVCn3rp2/ZDcgCKGKl0KHQzd1ArPidz+tLFm9Pt2HBe+Jl5+c+Aro5R5x0bUGvwKsOwCUqesUTcIBR73zZXvPZCZOHasg2kjG+DTFMTOJ9yhLM+goWzdqyYlR1RFiRES8eeZ57+W3cc6njI9Or+OT2Caau4OF6jQemGzwyXeXMbELjDNvTUSxnC64xGOMoCTGqZq1mdrHgxhY3ElwhjM55ip1Epw0/WrvjkMbnKh/dRWT7muPQAUQwtcvUXCArM7S3xLXg1pykCcYQlFYCSlL8sxtou0+ZL/eqgCznWhDLKe9CQotTzHScZH3j2mSAMAx49d8PsbP0Ps3G2g+IBd0J1nYn2zmgauGNGTqnDLOxYFcga7eQwoU5svL5ekCWYgXmErNoA0C6AFPvuOw9UF6CDnU2HScK0Er7DIGJFfd52/5+vXgoQgiHKRw2Vi00Jiy8rPHR1pZ4G8KizS495Uv5ChDN2grgVlsdscBy3Hq++dDnObPmua/e4MH6EI03bLuSzXrMdl0ybQqcF6rG4l2cb5wghcfi8MYgpaNZAW9NztVgp+Tw9sRUMbHcucthWXoeIuuX/uOTqhe2DN/ea2fb0LMXOxe6aFmaQ/1A233KHCurB2R12Fj9tipX5WBYwRDg1X/fH25/36IF/yIQq/+a1wPxu9+nqll8TjU41e++gazex4U5stT5ul+v2iZt+7mw9bmxx31mvv3Q58G+kw5K/aj8WK3hRWBjpTAZAWNcVrGpjlr+ageGupSnlnz1oc/yZ0+7kSN/0WAqF+bQsUUa01aC02t3vS5U6yqgZdI7H9fZPv8um0LCnC20hTu2LVILxXlh/IiEROyrcO5ah1uPa+VKMDNDecZgqhD6l9i152dj3GpgYVnjMnA1LmumVcEXzm5Q2sD4XR3P2JoGi7muLFQGqWI4uyeGh0VdUkG57TG1px53nwJXhHuQc4hJuGgf16HOhpCnYscjW1Ds+M4kkZwmuSp7lLkoEm2XqPeBDWfDM+xN2N60xaTaDn07x6fjh4CsvcCdNAfmY3r2Rhyfvkt53uwng1eyMAflfkG21I+2x/rthkCx/px+0KgcDdypkEMg0vAIjKzawXQWwgudG2Zj9cYzmCdLA2y6fXoOk+3UGeSCaxxK4t9x+rSnSjmxOgV1EpAV1zXB3pHs/HfaPlJ9plxyGtzq2E6R4dixj4zPjC5vfBvaGdOrOC+E3PSeqg6A39gezNG/BLOeRNLPtWUQVmTEV5WepxX38fUrn+fz9Sp3TK/k05uXUnuDEc/pnXGOMU8LICNQe2G6U4Yyul5oVhxTCQCRK4XJw1Bu+S4DxrfU2gRceUueyIMx0bKOAsWZDpDVmag80evj2od9CIxVgFaKK/FFnlm7bfqA1n7BrT6QNZ2GyavTJF6bEXxC8PXs0QejdLJ/BVzJnFLpiQay+iBW3C+d7dKdYK3pfH7UIJb2OKTPQxOvBrJUwvc2KaGa5PuGab9tvh66xopuT+840cqjnaATqNZN3N56HjJYlcdGnqhdSvhuICV6TxN4igXvGBpOwKYECz4bFyKeIiZ+T38j0zA2NY0XLC56LJbmxlK+MqSfa0sQCixHjeei0XmeXp7htCs5WW/w8GwNhyACs6rANe0DlSp5Og9NZWNeE8GVvsOcLLZjbhZR86nWNdBW4NFAlm7jab2p8XMXGFLb9Oe+CHmhlze0CEG3TR8h2reuif8lIKuuWyNGN8o0An3igbFL79ydYcl8ey1CZ15vtw8bN3NtOzpqwEu8SBaeTw1s6L2k8+htAzqFAQCrtz8fK+rrXNROX1tfVynpgFXSHXc/p1aru7pAllf7co4TfW4vyvDy8StxwZYQh+DjH9jIAg7q0JPB2aU85eTb1+7n/h+4jXf/xtdz+R80jM7UwWAvQhiP1A6pwZft8xPWN9KCVmna8eBMOCaJONoiUUUIH09OPzMVyrOG1ftDJMbmVcL02h0OH96i8cLW5oRmx+IrE6smgjSx8MdqAK8ema5yanuNqrGIeJwzOB90i3MGZ0JBktI4JqOKWW3Zjs9C9j04wRUeuyX4Mub6jWzfZK90cgglh30sIuJsuCZTh2NN4zFVaGNmPoOEc9Jna+03GbsNhVLoA2DQZVL17Rq9P55/UPq2UL+fvi3SYwXNAT66zVBOrd1Atv2EFw61WcTu6ocYpvdaR2mwaYD5lu65znmWvwcXCBxMZ8i0ijbogiiXoWrxeixaTy4I3RQfWIZzFSRp5/9Feba049/rY1KoMNK1gVIalo6+VPrGpNBZ6fjWwrPUgl4tW6vtw1uf2Y4hJ2p4JhGQRlpAKy6ByrLBmABUJzDbmjbn435kCWY9SSUxtYAYflhwuS241DY8q7yT48VpPnjmeXzq7GU08RcqkR2yOpm1jt606K2DJwPjqdagGREosQ8I5VZkVeWHnhZE8GSmVZ7II/04/HBpc2U18+yrMFGm9XpvchuacL3P5UUHJ/c+oLULLXUwNjsBWbMqhBfG0A8AsbYDIMnApJIBrmTBaebVbowsvR3oAFm9MMM5JhYMhxX2Qay9FrB9hZDOqc63e2JCacGj1GwRiAWdBX+HjRX7np9ouxN0hw6b9sVJOLVLHonMyFIGdHb0x0VbYFu1k3PHiwFhAoac8D3nyrJxYi7iJO+CJWKL1rOQqsCtFbMc7uEQSgl02qXbfClfqZKYWgCpAuK6GFat4xLzEIfMDp+ZXspD07W4FvQZeyiLpp1ytb5wgITEoumZK8+DrYhztNI16jVHUXUALfU+edjz4re9jt0qGHb3pRUdCoxIg+826wBa/e47eq5/RgVkNU3UMcoQkB4Xf3CCUDfW94Y4B3QNHK7n3AVOjHzWId2Q7o2EFfKcM2K/0gGxBt6nt2pbn1lCr/nCcMKhMcoAkJXOk8+twgjzOdo+dGigV8Zg36udvgoNVuUTDXzdPjKwctv4ORsCPjyTonKLhCVAqJibwts95BCupTx1ZdWMuPniD/MNf/UT/LOXvpZ7P3g1F3+ypthKa1jJoYOQDM+UnDxsc3lNRfjh6FxZPjin0/HNSGLUhjA6I0weDL+vzSuF6rptjh09C8DWdERR1rhGwPgAZMWfYnXIMzqygxHPZj1iczrCRQO6tE2oSOcFYxxVbTHGcer8GrOZpd4pw3rLBfvFFVBUYGYxmXsDdqe9PwlES2vD5OxIYVLOpsTvIV+WnUYdZQITTRJgFfVMZmA1KvJEzbedx23IUZ/YQAYVI6omAqtsnN2ArP3IASJW+kwwrR8HQR9NBNjrnItALL1vCBTbL+usz1rWkthlA33NhYU6kKYJIFZM+N42VjpQp3FJh+tcXaqN7+vZ/mUkG6p/HhhkYuXzKIaU1n1pGRP6Zn4ZofRTtpuM+osLueS4zxFZCciK6y9XkMGrdM6UjyunaUmFtiJjS0zM0yXpNYyjsA0j21CaZmG2oSFZgllPUmlUPq30GUJIyGEz4VUrp3ne6Lf5/fWn8/7J87n9/iuYTSVT0uvGUjfxF1m6EA6yZXBF9N5Vkid8cS6AU5DX+dJ4xEuHiiigaLeAaz0aWQkoT7n46DGB8EAmWuduk1acHNIDKxC8Mm7Aq5GROvaeCNNE7H1mZGkgKxsdxrRAlRYTxx/b+9ROTEgOoxlaQ8dqsVZNlgPhhPnaGKa7DpWD1a+LpD9x9oEqmGNiJdk3kDVgTMzRX/cDZOnFVjpG2X+dPFcJ4JozNiL7yvb2pffpsyF4FdL1K694MjYQEBufIxMSv1vrsBJYkEX0MjiEQkLS95GpKUxDKc2QtbuUpXxFSEoUrz8DGAxjEa4td7jUfo67RxfxmeIy7j+/QVNLNqSdF5yLlCqbwKYUDuyzERUcEK5TEbf1dHdp79opkh4dic6SPNI+kNUxLNT2RSLhv6xrPGqi6h83tG1I0kovjL9lZKnFu4ewko4Ty6JTZeAr/pfmzN2YZ30V0C8n3gex0nb9mtuSxzjnjNhL5kAoPd/3dIXoD/O6oT/gfbGx1Pa9gSylmwIq1HVy6Db5nvSMh7Rfh2XMHRc/d0I30o+dro4w4Xco0SgIxUTSEiAwtTySAaxU2dDI0nHyVJeJFDy33OQnv+pW/vxpl/B/3vUt3Pf7V7DxWU+x7TC62n1eI3XDkFLuUNOEebUfXheMVMHZ0Gb8cGAxIbB5QqietcWJi89w0WSb0zsrIfx8WuBnBtNITl3SjKFe90yi4895YWdWZt1R1zZUL6wtK6MKcJw5u0azY8N4mjhfzUyuiGuqMPZmEpLMm1hJPc2b4jzYMPage3zOFZZvS3S4eCM0pWBJDn11C0zbRpKtMaQC9HxsaAtGaQCrL9pRL9Kydg+aN2pofx/s6X/WbDB9fNaDC8Cn3ZhVCz4Ppnk5SH/9uV7nLNbHuLCIEMgpEAZBLZfWCh6qGqlqqJu5ezDHtMrOdFnYLket9PpamPB9zj5SNq7SM2nc3s5XR+w7U9IYdU5gbe9A/E2nrz7lEI7Lt066FmKYrnb+95K/J0ALE3KihrDegDkgYAqXlyzew7isscbTeOkAXPuRJZj1JBYNaA0xta4uLJevf5GXr3ye9228gF+/7/k8tLkKhHAQ78HNLNQmeweS8WBqwTS0uUxiTitNdc8//s4EH2PpCYnjkzejDS9Uk+FAfLSmfM4lIlSASQK+BkUj9Fr2AWj5KqDwvokTWIqPTsrFuQA25cN88MrYAF4lEMt7H0ISU94sayNY5hTldeBJTUDWbjmxYJ7qKtIt+dqv4tGfFRaFtPUn5yEQK77OTcKdKh39ClMD50BNmooGPD8hizIGBkI91IJM99kxKlJfCvTKzK0EghndF60nIQFXMfQwecUxwRD3kYmSgSwVd59CDJ0XyvigTGzNOIYZjuPK8iCT9lKW8uUWDWgNMbUOG+F5o3NcVZzmz8fHuPPcZWxVobJV09iAvTQmhOLGBVSHyZuMDCCBPR3baUDXoJwK0qTQdgUWoXRNOq7HCg59D+gaDa7sZissarOrgRF1jXOxmlVkLmdPeOqvZ2Ska0sgVwI79A0TCfNw9qz3xqml7wjRzYYcJ/qztOfXDKXh9ovug/T29z6r7XO6pPd5X0ys/vhk6KuVdrsMHJ8NgHndMve5b0joz8qImHtF6ym9uIrftZAtHF1YhAhWhZ9R2F6YEGKYQg2X8tSXsZRU0uBMw0tGp/h3z/wlPnHNJfz0F17JJ/7kGg5/GsrzaU0jNCrZecexZ4Px2awADlYe9Dm/oWlaI9dURLYXbF8mVM/e4mmXPUxpGk5urrM9K6kqi/cCtSFXgAbqFXCHK+racnZnwqioKYqGqrJY62gaw2xWUFcFD03XgWizTA12Ozrfa8CDnQpm2q4jXQHjbWIF9rg56onGxnBJT7Rz2vsnjuyEb0ahkqG4tKZOdorWST6DVDq8sHXex3k8PX5DDC0tGshKx6b18V5sLA2U7GXvpHZDn/O5FLA1xHjaD+i0YBwHArIWXUv/envjkxw664OutSZ8RyLdKvSprybkrZS6CSCWU19U6tvO683Be6OBLGOiLWEQt8uXP2d/xe6tWqNE/dPRLRlQ8nP2UjtWOn9ztrXSQ+HeRR1kycUTNHnAKUAsOf0zSysxt9KtjfZVdv7XBmyDALZoMMZjbUjJMrIN46JmXNTYYrb4XvVkCWY9ScXuUikv7/eOsRRcU1i+7/BHecnK5/iNMy/gD09eE70fwQCniSVtCfRcuy2hbPqM7KHIoRsqd0gKLUxrJL0gC9UfwFRqoe4JVVW0aDppT+aAFGNar7xRrC7VftBrAIsnd83Kapo2tHCoXQS2vPcBYDLSsgjivRFHu39IUiXE/nuYA7L2DWJlqqkZTjq4KNRwgXdm19KwQyCXnnj7IFlnMd/d1zGCFvycOwnd9fZ+fqw5IyAc52KS6f5EncemfrMdZlam1HbfQ/JG+O7kXHrEeoxN1QtD28KG/A7WOMa2ZmTqQJ/1IbHoxFRLZtZSvqLFLJrL1H7nwSJcZIQXjx/g8uI0n54e457NI9SNDY+dJzo1Qn/SBD1DCkv36jHwdJ7VHE6Y9ovCdRIgljznCWg7gAd7qBCF2jvf1xwTaz/PrwLaMut3SNeksSZlJy2Akfbnz70b1Rmi0nl9/dcDskQfM/Q+tyWOZ4CNpebhuGHwbXfIA216+wZZvUOf99q3qB91UB+c2u38Wp+0ukPpwv541PV51WYRoAWtPgp/ybAMD0qKRE3NdU5GGytBWXGkUg6FhLyNS13z1JexREeCeCbec+P4IV54zX/m3qtW+NkHv44P/tnzOPSpgtHZWKnPCm7UGrAdp5+FnStqZkcN41OGYltCLqloZ9qpp14RpkfBXH+GF1x6kpFteGDrEOd3xsxmFu8MvjZIIyEEMKYhaVY8uMDaemi6npkaEB4lax1VZXGVwTfBVqEWpI7hjVMJYYQ+9JmSvqclVbEZq+R6MvsshGf1blhc/5rG08TKbhDSrTQrgq1SagnBi+9OBUmvJWUELZCVbRRVtX2ADUQCA5qB/Y0bZmT1+1rEmEoypMf3spGyEz8fMAysDdlxA/p2YUhhet0PGyuBQdp5rsEkp+Y4EaRxeGuGx6jGLhHICg6mAXBvDvzp2URDtlf6rJ39HWe+DNs+3isH/7xd1nGo2IHvNUlkIQYWpMFOk7FOR3fpbXPF3VSeOfEeVyjbS7GyXMydHfLqtX36MtxLX3pymLxtv4f0nHsvWOPzimjWWFYWX9mcLMGsp5D0AS5dAfGwmfA1kx2eVf4u16/ew3899UL+/NRlTM0o/HhS5Y8p2CnZO5EWaQLtQ6UpipDptnmdlH7rMY48hxhGmVtL6SoZWhSQEqiRJsend0QkPCQZVBsIOUyT3dD2OIn5ugHnM8NqjpUF5ET1iZ1lLXjXpeY2ccLX34fe7yKIpUEtI2BsC2INAVj6ejMIJN2wwj6INQCILUwgmM83sH1oAlbj6IZdDCz+++0GzqePm+vD9BgA0h6Tqa3aYOhcWw+QSiCVNh5MOzGn7cH75tsiBwa89W1MuFGTc/oq40NgrYtVDFsgy4gP+bEIhoclMSn97obZUpbyFSZ9gEtXQAys4JpL7N1cVp7lU1vHOLW1BpK46eQQluRVT8ZNClfX4DToeYMMCHXzkRAWpHsNfBH205ur2uSnC9p5vWFggTyoa8gLZ5+qFmY0QoNWqX08v57r9ZikPWzOMYHaphfxWj8kEGs3AEtvy+fpz8W9L63TxVB/e73v6ZohnaH379Z//7h0Szqfe+OW3nZ9vt19iHFMtHpl6HxaT/VP4ckMYUSx4XX8bPoq44MgMSm2BrIEcn4swQcdk49ZylNdUtXziRRUNBg8ExEOjWb8sxPv563HfpNfe8WLuPXTN1DdscHkoXBcAoIQkDoYpWYKUgnNRTVTV+LOC/VKqEIrTWgzu8iz/sKHuPH43QCcmq4xayzjsqKuDbUDKmmBKBuWTK4AiQ5ABFxtsIVjNA6Fl0xkuhfjmqYxuVgVXrKT3Y3CGInrOABqsFtCuemwlQ8hhTEMPa8DW5yJDM7HdZ+4lnlCfAaTw8QbiYwwZZCraueakTVoh8Dc9o7mSrpDt9kvkLUXG0vrhP7cuR8mF3QZZvs8/8Lk7tmu2gPEGmIz9UEt3T4BgBD03NDxGjwb2j+kF/s2FgrESu3SeHSeLO20bxboraHzzu3rjV+6jK0OyzylY4lgl6l8d81AsnEGbCoNaGvM0LY5ThNbSz9PKZ+wuPBceuvbyu/p+q1HYrV3q3Kqjooa74UmFn04qCzBrAtETJzpL7Vjvn39Hp4+OsUvjW7iD/w1nK9XYWYz1dabYGTYaTA4gHZRBbhCcDEppBPmwSnCj9nECiKDFS/671WbfpI8bwRfBkPIx8TBwgBoZWjjoQ8iPuYucQ00DTlXVn+CTgCXD/mzBAsmhhHqBKzWMMe6SsBW07TvdWL3OPlInggHACz9vjepdoAsFW4IAxNnn22lr3HoXLoP5X3ueOe192DA+JgDsfQEOWBspPd6Mu70lSbeBKwaXbGwW70wLVo6QFb2sqltua/4qpLBh2O8auvbxZN4pAisLICiCHTZJEY8q8WMkW0oYriHDjEMt2VpZCzlqSGJ57MqlmePznDEbvFReyX3+CPMtsrABFZsXhOZWbkCrlpspcVSypk1+Jh4FCurv2/ICFBv+3OjSJscmW6lo25BEtSkdgDRQFYK49AAVQaqRG3PWWvR3v/crg+cZQ+168zV/ddB5m9fNFAVX7uMWlH7YrvO8QOb/cB+9WE3Z4jevlBv9I/Ram5obPG9hzmnWkfXaOea3q7eDx4X3+dX1Vc/X1Z7vFfHZPQqXIFRSwfrOwCVCJSmibkaA4iVGFr927KUp7aMpcThKMVi4vxQilDiubZwvOXIR/jel/4pH7n+Mv7jgy/jDz9zDcXnJ5TnA1DVTFpDVWqBwtGsN4i3mALcOPyY6xXPxvMe4mtPfJbaWe7eOsLZ6YSqsVSNxblYFEcAE8K+momn2ApJ47cPWfxIGK3NKMtgcNS1wRhPXZtQHVd8y8zygPjAJJOQfD4ZKF6C7ZIMaqMYvwmMakrTXVOq57uNRPGYRjCxIImdeezMIRHAytURFWA1lEC8E2aY5um0HdRcTfdY2DuksN/+oG0Pcly/D61z9mJgDZ13vyAWdPIWD0rTjxfUA9ljsuvbWfq4jsNdutsWOX1Mz6aDrk5xPVvKqWPVq7arFrLGE6kgiq5kGPJnzY8zp1WJ/TnbnquTQsjkR6pHVIBU8C1XL9RjSHpNI0sOKKPNpIAsEU9TW4wNjhjvhaKoGcfK7xNbM9rtu+3JEsx6iotma1mg8cKqGG4cVzz9+Af4lbXn857PfTUP3n0EZu2v1kxDNY80KYV42ZAkMVF4A+tKsjc9GCRxkncgtc/srCRz1T160o/j9VZwhQkVVZQnZG5Rf0DJ1Rm9D5Nh+oMuMyuHgxAmqsTUMibk1WpArG2ZWgnAapr5kDtoV6Eq7xZFEXeZQSpq5/PApNpWtDAZyJoL0ez3s6h/JbslfR8EsWBuwt0NxBo8Lm7LE6QZOqc6VlpQKgNZ0gOsFLCVX420HnAUcCVklpaLXoUMbMWKHcQSs+29g0RLNDHuu7QN47LO3vLaWyaEEMPSNJiYkHcsNaWph42xpSzlSSRdtlZgapUIV9iGi9Y/yyfKy/iYPcHWmZWcuBeC3siOD9JzHsCjXB3HK10TjZPWy01ud5AF+mDOCCOLQzoepQSdp4Ar55QnWFqQQo8/Oyri+5RXS+VS7BgEQ/NHnnPVpBvfz7F/h44TPb7wPi+Q9fgGGVl0jhve3sqeAJbat7Aq7uD41TkG2/dzMPb11nzfi4Gq+YqHQ0CV1jtpX/aCQ/aGJ0fLoK6hrU5tTKiIG74Oj/OGQuqc9F3iXx/UWspTW5ID2+GwIhgMlQ9r3FIsBkMpjq9feYivverXefAKz0d2ruQ/n7yBD3/6aRQnR5n95Ec+5DwUcCOPNILD00xgfO1ZXnv1HTw4O8TJnXVO76xwfjoiVSlzTQgTjIMCEyoEugKKHVi5twj5qcYjppOWPTVbczlfKYWHIhrBtWQQwNRhHSexGnsOR5fgjM+AUx2c8D6GXKXtzgbHfABoAAnVHvMzDdhZ16mvbZrAyAr7vISQtsHQPQXgiAZzYps5p4n3u+uhoRC9L4UM9Ssyx2SaD8MfOHYIxOq30xEwvW1eM6l8D+Ca0wkmM5kXOm2GgKx+Ww1K9dosLLC1V+VCfdoOqNrPgxU+tMwrpZsikDVvd3UBqI4eS9s0oAX5eYPWDjJNm+Q97dOVp1Pf2Z6Pz1RiFicmYxvFFRvWcX1lQpi8SGBeemBlVGGjripNQ+0N4pSdvIcswawLRFKy+JRLy2A5Zse8aeMTXP/se3jX+qv4fz99FcVWGVhZs5i8Pc0ZqdqHZt5ArhLi9WYJk1sGjJR0HgCRNmRwYF7xVnClVcnvdrH5k9JIaPd+J3cXPeVAYlj5xnUn0wGASaT1h/oU3mIkxLynCob5QnoLyE74X6p6KC1Qpq9JH5O26YlNhRlSWDox2LsAVoPegc4Ye8c8VhBL9bmIYtvpqwcEag929kDYCHx1Jmq64FWHiTUPWOXEp4qNlStuaEPFhN8f1rd5sgqXY7/FBtCqKBpsotBGUMuKY9QpIdSGgJSmDjmzlrKUp4ikZPEpl5YgrAm8aPwgxy45wx+PruWBhw/DWRtAqMjKavUC5AzcehpImE3en7Z3jYWFovvvi4C3Zncdo9q2eat6Y9nj/L6z+B4Y9yAQoy84njjpO+nfo95gevpE0rbdnBkdRd5u6ABZfUbW3E0b0DX9722+6fxY1P59g1j6mIHTdRoo3dGOd77/nIdReu36hoPMv7Y6pGdoKACrMyzx6ti0PvJZf7bLBY8Yl7dZcRm0GrjSAG4twwwvCMlObN/m8DAIVj0jpVgqH9haFQ1XWsOVaw/wLU//Nb54lfCBzefw/7vvBXz+gYvxM5NZUV4CoOXHYE9s8Q1Xf5odV/LJ08fYqQtmtaV2Jv/uRuMqhCdOLWY75OdtJj78bUmncqKpwMwCI8qfsTQTn9dx9ZrHjxxu4pCZYLdMZo+40mMQzCw+VwXYnWDH6LCqAGiFzy7mxpp7DuMzGkCrkEfL1AFQcDbiy7qyoZ7DEwDlmAetcn5dlZYE30ZSwHxY4pAsCF38sknvfIN5sPT7gwJY3rd6MhXN6vexqIAVgLUMVn5Psh82Vt/eUm3mwgq1jWRV36a7fy4/1l62XjoGujrKStdmU/rKJ9upd825OFa0SxPQlKsTFup8vrXTW10mnYTuiMqLZdrj07Ma8mbFPHMqPxaFz1XfQ/tgMwmwUxWslDWWUCTLiGPFLhPAL6UnHYZWzKVVYFk3hq+Z7PDca/4L//mS5/Fv//xrmP3FBpNTga4bAK2Q9E1P/AGsknZtrRQGnhxP3ib0XTCR9SU+/ImRlenA2thJYpivSKH72dO4icqm7gINnRDDNMFa225LisfaAGTZABBmIAvaCcn5LgsrX6Npwwo1iLUIwMrHxUmpH1aYPAG9CTRfqgav5vpccJ/6wFQc00KjQi/2++fTYxg4tpMvy6rzpj77jC4dEpi2JYaWAs0ymKXCEbuhhj1AC3Iyw04SeEsAstLEHMEuMT7/AbmSoU2fEzPLWSa2Zs3OGJuQD6KUhonUlFIvJEksZSlPNtEMrZRLy2AYiXB1UXPpkU/yidVL+dNTT6M5NabYopPAPZc8J84Bnu5Cqo/XzIWxH2CwEQzKIceLjpdd+t1tnx6TZvnm7T2jCKJHWfUNUa/4Vq8lICtdQzp+kbdZBnIxLgKw8kt3Hs5jk7jClW67fEmDgFzqY3dd0922QGf0+x443+BZBq6xz8oaDBlUnui8LS/y1Rik3a7/FoJb0M3h0z8+sVLytlhYJOXCkqRjOkPAeaEwQb+kCoYWT4HDiJv/XpbylBUrJkZkhN9BgcXhcTgq32RwK7C0bAhLxPC0Ar7v8Cf5zkMf5/PXrnDb5rP5b198Pvc+cAQvBc2qZ7Qx5carP8daMeWuzYvZnI0AqBpL0xhsDB8qyppqVuBnQrElAZRarxmtz2gaw2hUM90uqc6VFOctEEIR7Q6U5yUnlC62hWYcjncrjsY67KYJOb1cLL6k1nXJIY+nExKl55SmjKwrIRYh8R32r53FZ60J4YSm8Z0QQw0q5EqGIuHZTbaJU/uTaFtiEbtov5ULDyiDLKrHIotYYosqEvbCBjPjagi0GpK5IlPqswayss7ahYE1tG1RInfYH5Al0mFSwYAe08dlVl732jp6tO9ImUu9Mn89w06ZbrsMWPkW2ErHuEKxs7QJG5+xZDZ6YmivjXBAzKOX1kUJJ/BxreO9xGJZDTbaU55gM5W2YWQaHMKqran9kpm1lH2Iw2cq8lE75n85/Oe85MV38c+OvpbPn3s64zOhUklK0OtF6ORnSPHlcRGWngNT+xBi2Jm80874JDjXpgJRD3aI9RW8ArIWVgGBlpZ7AJkLMexPnMa0cdg63JB2rKlioYgEdleiEBemndQSVVgdF97HUA8NZA1NpAOvHWprDCtMZV/nvAD5egb6o7cQp53YdH60zph0vz1jwvc+5/P2x9I/Nu2X7jnDokQySDpfsVArGNVvr60GW/tsrQCc0bK18nm9ArqScu2NPdLl0zZjXAtkRaqsycZG+/syEkJCSmm6HnTZpQLmUpbyJBdPqqkGK2J54eQUl19+mv+x8kzOzC6imBIqG0ILTkmngxbQiouk1FbcgsTvEv9LgFF/jkpzhQKycpMBlfKodI1PnSmd02nQM2Y0uNUZTJqw1OB2A6d62zpA1twcPvy5C9ToP9p7S29+H9IDA+NZKB0dMHT8gv7S/L1X+wjC+d6m5HnuHzeXd2TR+Yck6TT153vNvWqTQ6r6F5Hvdzqlb31m8YC0LtO5F5MOMtLN4UhMN/04m7RL+QqW5MxOBaHAdDCBXDgNwzi2dThKY3nBqOH5o4/xVzb+jN9/2tP5jYeu53w95srV0xwtN/nc1sV84fzhkPPGNkzKmkoszgsro4pJUTMua04+Mg4/dgfjQ1OeeexB1ospO03BXY9czOna0FSmTTrvY1X1CortcKipQrV1NzLU6476UIOMTUhG70Ooobdh3jUVITVJWv+ZLlhtGp9zC+XIE4ihhLFN7WNfMeRdFbUChgGl3XREh5WV2kr3827HP8rwwj0BrIOwvRaBVen9EFiV2usoFR02CHQKYi2ShZXZIzkg6ajdcg73pU8gWEQe6B+j26XiLKafj20g5Us6Vun9rBec7xIG5vTPvC2V7Sfb9tOPZBkqpBNArGQgtXZQCsPVxyc73ykiQC6S0E/2no6nBcF8jGIR6zGFa4uTxFDkUREWf1VjKYyjkIaxrbHFDvuVJZh1gYpVCcoNgsGyLpaXjhv+7TP+I//2O17Brb9/E5f9kWXlVANp4tcPdW8edgUY39JyIYIjrgtseYmLa70oh9y/L004j/Z+xEV+35g4kHHRn3QbF0IMjUBVZwaVpAkoe8o1dTSAW5ImMIi0YteCYKkyoZWYi8t1Y6MTI8vadnLahdYKvck0AWAJyLJq0hXZO0RQn0/fHtd+Z0PlYENf+v2A0QO7s7FkYHucWH28Dh2KmkIJwz3o9ZEnX2n70JNyeh+BKWdDnoYcYmjasXdYWrlyoTI0So8v0u/Bh4lZiJWkwp+N+bKMCZUMTYz9HpmawgSvePA0VKyaGaWpc+6sXVTtUpbypJYUbggg8d8IwwnreN3RO/jQ5Co+fvdVrN0rlFu+Bax2eSjCYirmcOwkz91DJ6hFohcJZaujKpJ4fCe3iT70UesaWoeHSLfSUra0euNL79ViM/eV+ollt7POSE6XXh95gT8EYnXu8YCe6ANZRrfrH98bf7fb3v2Z3797hacF5+ktR4aP6ekayNfR0TUdvdUbrvS2K32jP2vQSushDWRppwragEBvbx0lLROLDGSZSB0R8Vjjoz3l2jxZMXcW0jpOJOqZpa658KSTZgSYS31BcG4nSTZBMjodwpWF5dvX7+Fb1z7P5+oRn6su4Y/PPYNTO2vBCLUNdWNxHi5a3cZ7YX005eh4k/PVmAdWLsIXBjfyXHb4PC++6B4O220+u30p56oJs9qyNbUhd68IznpMTBzvSjAzsNvBV+wqsDsGXxiqtTB5V+uOwghuBMWmUOxEIKvxuNLktaJm7ms2F9DOqyTbQwKQpRhZ+TYN5GmcC3LQqU/60p+rDyr70Ed75rKaA6IUQKWZVDr0L0k/f1W/Hx+LY3X2DdyICODsKruBWHH/HJB1EGfKUOV3kWEQq/+qomRa4Cq6GXaxx+ZAtCHprwfmwmFb22fo+tJ4UlihJirkEMNoY3USvmf7SJ0zgry+COd2qU3ZOWXbT9JjApru6J0gpevaTAIj2+S0LGneafqKeBdZglkXsOjQwyYDW4bL7Qp//9I/5i9966f4h1f+z2z/9hE27m6wM4dLgFNaFaU1lwdiBcPgxdBAVYg3l84inh7oER4cb01I+E5QQklEGSnDFa0O6GdMrCxdLWEoabtO/h6rQ4m1cQJrAcEO4JUSydsSb2074ca2HUbWHkws6AFZacLuAVlz1Qb18entEMNKiXS+j64B0Dl/VMKLQKxFHvVF4ScayMpt9TH9xb6IApra/Z3cWUInFNEV0jE0QAFYnVBD35Zkpj0+VC9sDQwxHkzwNIhxuYJhaRtK67DiGduakW1yQt4yWt0pR1YpDRbHSGqMXSbmXcpTV3TooUvzOMK6FHzN2r1cfd1D/ObhZ1PftcL4TDAc9PPdkfQY9vMyxrkpGCm+N2/1uohzb55bDwJcHVDVoBO/JxlK2j7E0BJD9s4OhaNEJ0ouWtLz2HeArHxYfx4e0BXpPANA1sLqSlrUpkFdo27irqGEPYfZnkysvG+BroEOIBfO3z22fw/mQg/n2vSALK1H9Tm0oSC943Q/xne2ZUArFRkxAayy6n0hQed4LxHUCmc2ktZ16RiXQ+KXcuGI3Y3tEsX0H5bOtnD8RApKPC8cwXXFfTy9PMWv2Zfwh6euYXM2whrHelmxUoRUChNbsVFMOTtboVypmF1sWLlki2dddJLLy9OU0nDx6DxHxutsr5VUlaV6ZIKrPcW2wdQtkOUtFFseU0MzjvmrGijPCc0Edi6G2cUhn9bkQWnzZUE7HXiPi+vHXEE3PnfOxuTvKYQq2hwmF7JSfcWQxDzPDoBGPjkSvO/mzxqax/Oc/9ifzQMlY++DV6mae7KLNKtqzjaK2/rAVYd9paMPBn6Dzndf++fYLaSwz8YCFqZs6cuicEF13JzdNfTaB7JUFEyfoTUPNHXHMmdzedUu9j2U8D3pSG3X6OMRZf/Ecelw3Dwe5dzvjDMyrbwFN1bjUhEtiKefrgWJdlMCtVJFU0+oUIqjaQxl0VDYJt4Kzyi+r5ylh5PtKnvPcEpuueUWUmhV+jt+/Hh70d5zyy23cOLECVZWVnjlK1/JHXfc0eljOp3y1re+lUsuuYS1tTVe//rXc++99x5kGEv5Ekjy2oQqJ8KqjHjVyjn+20v+L/7qm3+LL7wKNi8r2mogkryaPlc8CZN+oOT2RXxv0up7i00MMUx/aRGXc6noxS+LJyqdeHEg4aAkFlWqOpL6jfmv8gTdp6km5lW+YSqW10jsswkVDl3oxzcOKYqQU8tIeF8U5KqIOu9V/w+iF0lNnum4ws4DWYbwOU7mmaUkcXyRupr+2kVy++dVm3DO9KcnbDUubeTEiXb+2Ni3BqqE7vdspDNm0v7EuDLxnFZyAs80hn6Jc2e750+LlDyRJ5aWhvEzoOU7nvIAYIEvVZ6RWFVHCoexwbNQlk2uKFVax8g2rI+mlHFSHtmaFVuxVkxZs9OQK8tUlNLkv6W0stQzT21JyeGtGAShxHJtMeV7Lv8Iz33JXZy9Fqq1dh4D4iLIt3hyqpo78Oi0ISBpQ39BTDsPCd3QRR3mx4Ljk/j4n6d7jHa6ZC+1amCkPW5Il/U99lrviO7T5TF77xHTFhMRY4LTpANkxQtP87Li6XRZRnpuV3M8fd0xv0DX831HH/Rv3aL9Wm/kz9K9Dund8r4uU0CWZ+B7pqtrunqqPZ922un70/Fs6zGoffm9dA2GPvDVfk7GnP6dRIdJrPwUPNkeiZ5rYwI4NbJB/wBY4yiNozQ1I1OHXFmRsWXx2EGKyFIuNEnr/UV/WhJjyyAU2FwJccNMePHI8L9f8ie88xn/hddf+TGuv/g+Ll89y2oREjfvNCWnZmts1yWHD22zeukm117yEEdHm0xMxY4vKaXhxMqZsGYqGxg7/MhTrTuqQ8Gp4UbQjOM60oHd9pRbwXFuKii2YPywQAN2Rxid9tiZi2kqwkNZT+IzHdJytXMMARTTOVOTc11qst6RDErRAbxappZ6dtNjlvMhSXceS7aHBkL6kirrpvd0+9y3DIUBNvHPOagq/HSGn07x01nIIex9eK3qVtck57+LYJfvh0w68KmAlm//9H59nzRAlq5V5xxemJ/KMBhW2NF30vah/6xpE+6n4xbYXp1zD70uArI6dpA6B0oXpe6HGFlJB3Vsv3kga65gVlovpd9kamfJBdT6KVnStXa2a0ZxdPSHMMJku7X95m2xvzaiJmABUpl2LIptLBJsJ2sdo6JhHFlZRjxOKdGx2b9tdCAwC+B5z3se9913X/772Mc+lvf9yI/8CD/6oz/KT/zET/ChD32I48eP883f/M2cO3cut7n55pt573vfy6233srv/d7vcf78eV772tfSNEuD7omWqa9y6d6kuI7ZFW4+ejv/9lv+He4vP8SZayzNWCG7ErzgIbQwJEyU2nUX9/Gh04wqvWgMnyWEfGiAY0icV0ZCf1/qfNjDIT0vRJ5088TsAhgFzFFWO6GGcZ81UBT0PQ5e5crydR2Mi6IIAJi1uwNZ9ECsdP+kPae38V6Z9n0fxMoAljXDAJbqtwtOqTYawJLed5YALDtglGi9Ky0o1Tr5VJ+a6ReP1WPN6L/uM0/yLTilwS1X0HoJcmnYCGBJ77g0IUdveN5epM9pAg6fpXCRjRUSGKZcJAKMyxprXE76PjI1q8WMia0oTGBoGfGU0mBU+Ielzbe1lCBLPfPUlto3mQ0shLLxa1Jw08r9vO6rPoJ/zhY7R6S3WJJgWMTQQtMQF9qq4wVqY07X5MXzLsekfocALq9fh5w33YY+O1iUEyV7raWdb1Gv+np6i+LOQNJ1JeZwdpakhbo+R+q0u7huz6fGoOZ6L0bN4zI/RmHeGdK5IQN/Q/s727o6KQFSvn+c/qB1WP+8ygk3dO7BiAaZb6fH1amEq4yAjt7S/fc+ayZWB7glbQ9/ohK3C+Tk2mEoAdQqTUj4bsSFJLsEZpbgAzMrhh8uwmaXspQkGtRKDu60XTO4DMK6jHnxyPCWo7dz87EP8q0Xf4wrV08zsRXbdcnnzx5huy7ZmOzwjEse4ulrD7NqAth1vpnwSLXKZj2mMI5DK1OKSY0fO5p1R73mqNc81ZoPieMVa97uwOicp9z0mJmn2IKV+y2H7oLx2ahbXGvEi1PPJe1zaivffYaVJFZWmnhyyhSV1F36zgtte2TgI37WrKyh+T6JBoES+0nLAkBrjpWl2WAJxErOlbrG7+y0AFbjWtAqOeQTW6vpAVfJma/b50uW1v7RpAIxwwyvvJ433c/p3gyBWHagCrwGsRJgNacLe3/pHGgbYkAHdsCptHaINtgQkGV6Y++LAus69pkKd+zr5iG2+Bw4lrab1u7qO/xy+wgyZUKAafdlWylVm67nj0+sRvGtvRWiWkJKFjfxodp7unXWBwJA0QQHTbSTrHH5/djWFOIYmYa1YtrJN7yXHDjMsCiKjpc8X6D3/NiP/Rj/6B/9I/7yX/7LAPz8z/88x44d4z3veQ/f//3fz5kzZ/jZn/1ZfvEXf5FXvepVALz73e/mqquu4oMf/CDf8i3fctDhLOVxFIOh8g2lBO8LQEgfJHzDyg7/9YX/jh+94mv5lf9xI4f/XCi244FecshHSpSYZS58owVbUo6mLnNIHSoSKPEL4s0zA6unXIDh2O/0OQFYrum21eM1AnV6n1w2kkEwEReArMi4oihCf1WNbxr8rEIKnysNznm6U39DnoB8s1S7VLEwsbF6Sd87k5lZAC6pSbLTf7r0NMH1zw9zsPduubR2razRAa56IRzSAlmdfFiqj06IoDIQBkEvSWAa+KIFr1zKi0X6rBZJqv8c7pGuvYgJDGNJdGt9TvxujWu94tHDkACt9WJGIU0GsiBUlwIyM0vEz30fF7Is9cxTWwSJBUh0CIwgXnh6UfPG43/GH25czSc/dyXjU4Kp2+Oyp7zxHQfJQo+1ntfSfAE9CCqtyhZ04eMRnQN952X+/LG9LiDiu7vzmDKupRALbRxFHSLQhhNq4yPnUbRtP5I6ovN+GLTRc71exAcgq69D2uOG5/tBYGpOBoy+gWPnvhJZ+KHbvqMP+kVK2tchfdgPWdftBkMx9HGmq5da3ac+9/RUB3yDwMgSol7w8ev1+S/pFysuX6aNRoCJ23SIIYAlhh8udc1S9ilpbk4V0If2p6TyqzLiq0rHNcUX+erJ3fySvZH33XU9052SS4+cY0Ucl47Pc1G5xSXlOXZcycnZIR6crXO+GrNZjZhWwSS1axU2pl6odgp8Y5BNi92yMdG7x1YEZlYN0oSwwpVTnnI7hamH1CaicwMZshFPfBTaUHPyc25SYZHoOGntCrL+mUuE3pPBUPUEdKQUKv21epK9ckhFW2Sh9G0ezRBuGnzdRABLpVYRE/RHVQNhuyCBQCAS2L+OcHOIjvlcKCv20bsXuaiR8+G4BOA0jk7BowhS5WvrdNIFuTrVCodsqz5IOAQg6lu1ayijOmYor5bptuvkmEr9D32VfVBPhq67dw6IDCsFfs2Ni65user6lI7RVQwTyOtjMve0DHKFKooyp8/ba/M2gsEx0jTlM/V4/CgCWtaD8XgHpoz6yLoMZI0iA6sQRxHfO28YHyBq5cDMrDvvvJMTJ05wzTXX8F3f9V189rOfBeCuu+7i/vvv59WvfnVuOx6P+fqv/3r+4A/+AIAPf/jDVFXVaXPixAme//zn5zZDMp1OOXv2bOdvKY+/lGIZS5GBrCTJG3OZXeWfXPaH/Ktvezf1t55m84TgykD/bUZCMxJcGSqNuEJoSoMvDa40uLHFjcKfLy2utLhxEbYVBq9iepOh0pH9GC372a9ZWZ3NrcGQ9+kE7eo1eR7EGmQ0gpUJHF6HI4eRS45ijh5BVlcghRWOSiiL8Gctvoh/1oS/IrG1BrwHsWLhHJAV2ViZ1aSotB0WVocKy2B4YOdPJxRGbYc8ie6W1H0ohDGDlzl0UIUIdlhVophSrYfEC20eK+lRYoV5wCvHbss8FTZbHQows+2rKz2+9FAE70JmZhUesa41MAiv1gYvOIQqhta4XKUwVC0MlTlWbEj6PjEVpakppQ7GBY5S6tDH45Av4akiT4SegaWu+XJJKBk/H9JiJCyg16TklWv38i3P/Cjuum2qQyjWpURAup1TnJpfUiXc9s+02zQjC7JBs1D2fCZ30zXh+LnEuYlNJap/PabOq2SdI9YGHTIewWSMrK4gKytQlpDCCq1RDhTTzqvSzrG7eqgXAVmddu3fnNNEL6al29fc35zx0R7r1V9HRL8Z0DVpV3L0oJxk/bYZWGrH0AnFYMAQGQK8VH95DAvGrY/xcYGfc4roUMPkSMn3MjhQUqkQwUdmVjgmAFs+Mn4D6zeFFxpxbc4sUsbrpa5ZysFEA1ul2M7nJAXBhnhmOeF/v+RP+P++8D18wzM+xXo5Q8RTecPUFVTecl91EQ9MN3hga4PaWzZnI3ZmJc6FxZqI5/D6Nocv2uLIJecoL9tm53jD9mWe6pBQrcVcQN5jZ57R+QhkuZjnKrGyBJpRGGPnmaW7LmwrXsf13NTn6oUpf1ZiBg+zdbuOdaA3t/Tm2rS+N7TA12MAmTuRJ3NjUkBWVUE1C44QHdrXDxtMYYJRZ3aiS4aqwENw5Kt0LR3dp0IPM3tLsa3mL8iggaxcPV7fuyEgK9k7Q/quP95FETCd88z3H/L29r5LaPVOn0U9FPIIrb2kwVEtA7aXrl6oE7qHsaD2x+vzrT0EdIoezEfNxDVW7MMVvUiXFM2S1G9U0pKA4FwJXhXMSm2MxxYO5yTmeiTbTg7p6LLCNKzYWSyYtT85EDPrxhtv5Bd+4Rd45jOfyQMPPMDb3/52XvGKV3DHHXdw//33A3Ds2LHOMceOHePzn/88APfffz+j0YgjR47MtUnHD8k73/lO/uk//acHGepSHqUsShSZtq/LhNetnuUVN/zf/PjTX84vfeRGjh0/zbWHH2LmLOerMdOmYLsuaJzBe6FuDFVjqWuTf8jeC94Jzgu+EeTkmCt/swlVEDvVRBYMdGB7J8kizE8OKslhZmURgSzX69B5xJocupETwOsqhCJgDW5jlS+86ghbJ0J4paliSeEmlArue+NzXLFeBAOTh+HYH57BnJ+S4+lTondDD9BKk6eaFPONgARehc96UlVDWaA4+wlid61oSDvxzTYKTl9naUaxWTPfvhNK0bsXEr0CxRas3e9a5kVaeOjwRMXccgqMSlUL5wwMS3dCTp4I4iLG0JaWtb4do/EwcjFviY9GRQCxjAkJ341KYFiIY2Jr1sspKzYkQTUSjIixqVm3O5mVZaPRMZI2XHEpT5yegaWu+XKKWTD/pO0jCp5ZTrnq8o/wRxddyce+eCVrh3Y4Mt6i8YaZszTOUDsT8ywIzgmND3om56T1Ud94wuSxadn4bGukBBkwTtSuwY27OljiCm4u5L3/OTaVCDCIft8uevMielxy9toVqkPRwHKhIlfKW9kfa0fXQJ5Li21Yu3eKzOr2XB3gqRduoY7dT4Xb/vZFt7bf1WLwSm0QaMaGnaNx7lfX2emrP0yta+K9MhWMzidjtXuaPiNLO1L6lQrzWIUuM6un47PdosLasxhiWEYKBYxMrMjSsuKRyLwKzUM+rJFt8y6G08XtUmdWlkSwyyYlu5SlPM5SiqXxLjJuQ/jhTeOGl5z4bR5oaj60cxUf376SqSs4VR3is5uXsNMU0V4oqWrLbCeYpK42SFpHimdrZ4RrDH61YSbgRgGod4UwOhtArJyfyoSnzMxcGyUS5zhJzdKUlvDjxuNN2O9sBLJqF5hZMSl8W72QaMj71umu5/nYNlSIm19LS3Ykm/kk4WkuHgI2Hk2uLAVk+apqq6uDAqMiO8v59r2YnAxe0s3KKRpiUasEfmlWVsrb2GdRgToH7TF9MAlYyMTSbfpAVg886ji498Ny06+d8/T0b/ruesd1cmRp22tA0u9ivzab719X1sNEEki0AU1PN6vfanasJRsq5o3T9hC+tYmANiw3n6sda1s0KzwbyW7K+iwhxDHfsBQhP5bzQmEdRbSZjEBpHFVjQxgYrWPGeYM5QI7HA4FZr3nNa/L766+/nptuuolnPOMZ/PzP/zwvf/nLw3X2Vife+7ltfdmrzT/4B/+AH/qhH8qfz549y1VXXXWQoS/lcRQrhsvsGv/40o/w2lfezpXFNlcW6wCDVGRok0h2t7Vt/+8z1/JLf/JtTB6qWoWRJIUUpnxZavtg7qzdxPkAUKlJWESCxzvGiOvfolgbk7q7tq32ZgBupeSiV9/Hbc/9JYwIlQ9X1nhPw/6H988feBWf/PT1rGzNIHrkO4neExtL2BXECtvj+znK67Cx0TbYG7ya6ysu2rcvMXzrd/0h33PkjwBoEBp1sOslJAj7222VtzTe8P+cuZ7f/r9uZPVUynegJutOCIdko8IVrYGRJ1sTJuDshUhKIDPCwo8pH5vumwGffmjWYwoXnESSwgvb0MIQnh/CC8dFzbgIebLGpo45TJqYI8szMQHcKqVmYiomUlFKjUk5sx6DV+6pJE+UnoGlrvlKEyPCmoz4+tX7eNY197NhajZMsHLcgsW9H9A1ettHpkf46BeeSbEVeS5zzoYhYMsPgkW7iqfNlZV/d1F/+HRiPR9HAyFfl7QL9tRlYZk84xzfd+nHEAn3wBOjXw4wvN89fy2nHj5GUdUkBlOrL5RHm3aIe4JYvd1+UVvVYG/wqrcj3rpqVfiq6+/hBZNQ1CFkhtJddzsK+9ttjQ+fP71zjLs+fCXllu8AU4NAltIfHYaV0jn62Dxs3a/WQ3l7cp4kh0kApFBAlpGWFRw81ymsPeXJCqwsKyFso5BUHcqF0A1C5dycM2upa5byOEnjXYelZdHV0YUVGXF1UXL1+im+cfVe7qxW+Mj2NXy0voLaGVbLGQ9urrO1M0KsxzvBlK6D3dRVQbNjkR0LAvW6w1nDqJRg0D8S8mdBAKNMYr+oglPeBODf9wDwDGhV8dlzBGaXnlBjeGGW9LY/v6t0J921tpATYHu1nu8DVxrQSv0PyX6iUzSQNZ3BdJqZUmkdlD/HCco3DYINIYFJtF2UwJ1+5XfX7bcDWg1VLBwKK9REisjE6twXoJPyRSdy184XLYvu41DfnfOosWmwqs/10N+jtr10H1E6oJTWl0Pgl+m16y0VvITfqCsGUrVkR0t7jly0rWMH9can9nX60TZUjwTgQh6iyPaSlo2VcmVVJpMBvJccOpxOMypqnJdQBd40TIqKUSxeUnnDivkSgVl9WVtb4/rrr+fOO+/kDW94AxC84pdffnluc/LkyexFP378OLPZjEceeaTjNT958iSveMUrFp5nPB4zHo8fy1CX8iWQsZS8fAKwnrctZHbtsfWQ2W43LzQo0n4/5/Ho5s/ynbaZGuxi9Q5dUtboFWs6RBm9RhBsly6rvQHe463hstVzbJjJvkogL5KRqWNJ+a6yS4BWDpGhCyTlcesJWE+Gedz7GMQebQbPK4AR7DR8ftGjflYboMFxB785enkG5LoAVmiZ2Ff9CTiVoO3QYY2amCNDy5W+a2DYlA/AxyqGHqxHypjwHbBFzHdlQ+XCwjpGRY0Vz0pRMS7qnOy9iEDW2NS5euE4AlhGfASyGtZMuGnmAJP2hSZfLj0DS13zlSqFWK4sAEZ52yJm116T2KiTzTT+t4ttMJ/7xC8+TudT0YZOZj8N9IWeUw1zneaFcvCsrpUzxlKE63+UmISVBJrFPmJf3bDCOMKh+6wW4rnd0P7dZM82A7omfk451I4Xj3YJGyxVz0k+a6+cA7Hye7ReHVjsD7zv6BWle9r+ffu1x8IiSd8gQddIZAen3FjJeSJ4yghkFabJIFYCrRJby8bwdoEAZImnjL97kaWuWcrjJ0NrXp1jyyaQHjhiJrx03HBd+QlOlI/wvodexMntQ0xrm4FaJ4KxDWsr0xyGJCqfodkJa0LxUK15mgm4Miz0xmccpiGu9xNaFV5yvqzM1qLFkRO7VQJLS2IfndBCJcm5nud2xcrK+bqGQgcTyBWZWXpbp/0BWViDid+jzePrOuTHguyU95FB1QefJOdcNHhfx65U3yn3lTpmLtywB2B5H6Jc2sGaNmwvfU7HoQCx8EH1q9hXOsE7PWBmUF/J4s+dc+hx9VKqaEZWBq6k04cmGiTZywk0x8YasN3m8l4ZiQzfZO8MnLOjh8IHpwzxoZDB1KcOLWxzy8UDHZkMIB6cAVeo34D1mXnMuAEvuMog4xCBUhSB2pJIZkY8a+WMkampnaEQQ+0sa8X0QNV3H73lTcgv8slPfpLLL7+ca665huPHj/OBD3wg75/NZtx2223ZgLjhhhsoy7LT5r777uPjH//4nkbGUp7acqreCJUQdwGyHjM7PgFavut16LxG8d6TEyJqL0GacHW5VSMcLnceE5AFcGq6jpk5cpVCE0MMC5OBrPm8VAnwihOsFVIZ1w7wNDC/9+Ow91NOvfuZNi9NsLWoDhDjvJskRpbrhwbmOPg2kXtbuTB9HwrkMm3iT1fEnFjRQ+ZN/Gx9aDeOcd7WQ3xNIZdF2WBMTP4uniImL8x5sgbAqBVbsWpnrNsph+12BrUmMsOIYxIr+pTS7Mv2u1BlqWeW8njKlhsHgwfYC8h61KIBrbzNt/v6jdPCXKAP4vQX9xNb7wLk7U+2mlEIkdGebW1kCPO6QOjqnbSQ1pe0QNf0Du79LWrfiqedz9Oupl967NGKJH2j3ivdCl1Qas4T3gO4tBe76zH3GbzyBXTyZSk7zdg20TvS6px+niwtKU/WyNRMpIpgVgS0FFPLSp+ztpSlfGlEV0RMn1OV9CNmwmvXHuKfnvh13njij3n+pfczHgWWRshFGpgcs9pSNRHoGjf4kcs5Tpu0XosTUKrAlkL/UooKcWFbYLLEwaS51ZOrFeYqubUKQddRufE8c1Eirv1rz71AqeQ1uhlYT+8CwmTdsYey6jOyZhVs78SKhE3bJlZu9ymno3KUt6dOQFes9K7PXRbtMfq4fihhn2EV7akwt5oW2NI5sdJ1azaWDitMOSH7QJbSZQf6S2NVxbo6VQrTX9bRyk5K21F6QH8dQ/pTjTWHvKfvRdtuqr3+PbWpVmKb+NvNplf6yUQd5GwvF7FtbajcZ7aTwsYE7Op+UqRLIA34VselPoqgr3zpsl4zRWRimaS7yHaTKIax8yHl0MgEhlZhQkRLafafAP5Abq23ve1tvO51r+Pqq6/m5MmTvP3tb+fs2bO86U1vQkS4+eabecc73sF1113Hddddxzve8Q5WV1d54xvfCMDhw4d585vfzA//8A9z8cUXc/ToUd72trdx/fXX56pTS7kwpfI2P7BeJNDhhxItprBCFWK4UHFo6bfpl5l1yWvZm5FMzI/VOCQlYbG2M7k2K5ZLxucPftE9OV+Nw7UUcSJVbKw+LTVfVjYs0ufe5MnA9t4+LWlynVvySrsfUAwx8qTsLTl3x2MWZevkWHBpE8H3E73PGRIa5IpGkM6ZlYyiMEH7NuxQAIlVC8sQsiEmxngbR+0MpQ0Gwsg2rBRVNi5GJjCzxqZlZAE5ITyE+2NxjKTBEqobrslsEAy7UGWpZ5bypRTXSYAdJrxOdaokWfe0BtP+kK+BfpIY0+YsmZtjJbN9cx4tvYgn5IhZtbN9jGF3mTU2EsH0wlkyiNWOqXfgvphY+9Q1addccqte/6Jf27yJ9kvAMNIJoPthg4OhhUm39LcvAr4SwKUtZCGyfxN4FcIGnZdQfZCQ66o0rWXdglVtkncg58gCclhhgccQ8pCULPMzLuVLLx1WFvPsLYNgsFxZrPBX1r/Iy1c+z/sOv4BfvfeFPHR2Lberm5B3t6kNbmbBeNzYYXZMKNLjBVOHB8zUhMcp5vbQ4byanRI2kp/PPN2JYOpYkb3pVUuPIFY3J1ECbejYIf2cSHl7YmLF9llEAqDd36bPoaUfldKv3J7+6roNLXSq6m1qKwLOtWys2I9vGkhsqsa3x0CbQ8tFZz+QE8kn6eTQEhWypxB7FWa4azih3pYcKBlwUtt7DKk9ZS7EMdkz6fyq7W5MLQVKLUz9oqVvu6VXZcP1j8sJ3YVujjXdNt3yIjL8BFJOLImgbIeVJRH89e3xqWAWkEMLh/SdqPOmvFkhF138PmsTbCknIfedCfYUsWCWiTrORntqpayonWGjrClMcMiMTU1h3IEcVgcCs+69916++7u/m1OnTnHppZfy8pe/nD/6oz/iaU97GgB/7+/9Pba3t/mBH/gBHnnkEW688Ube//73c+jQodzHv/7X/5qiKPiO7/gOtre3+aZv+iZ+7ud+DmsfH0bHUp4akgGt9LAp9Hpf4JWSFhSLx4nSJt4FnmSq0JHiub1icNmY8DAmjRfTfcDqFcPlo9OP5jKzNN5x/+YhNiqHL+JkbedBrP1QUlvgSTqfk3Rsh4E4c+/mvbcaxOqPIU10zsLhYpvHKmfdBGmUN0AbF9JuzxNtBLiS180XdJPCp2qG2psg5ITvpESGqbqUTZULPSbmx0qe8lFRMyoaxrZhtZwxMg0OYWIrJrZio5hmr4LzwsQ0jGOI4UhCFcORNEwk5M8a8TiBf08hWeqZpXx5RSuY3p6D6poEeg2FjGTjJxkDan/eZzqf+84VVwrrdudAY+qL857z1ZhxTHq8J4jV0yPDINYCXTPYVu338/ZHB8Tq9a/zVo1NzWOVHV90DNu+l10v6DMYRfuaQ9ihC2ApgCvs88qJ0jpNMITFPm1+rEhPV6xfT2karIRqhkXMkzU2dUyWGyrqmlhJ1yqAq0Cxsg6UXHQpS3lsslekQgo/HEvBNYXlb130cV6y8jn+y8NfzSdPH+P01gqzacloXOEqC06gkVD0og75r4rNWGypIkZ2xD/lrBZPTgSfQwkrcCV53jNpvnUeqVsHelutLbKxDC0raxfdoAGtDoMnT3ZuvmK7Oq4jOpxxL9GsrLoOQFbTrjF90wTwSiSDW22eLImFr9q0KmINvnF0cmylUMMMghk60S45bDCmdEntUqihtcMAVhKdF0u/qhxZnSqFus1uebMWbFsYSthrOwd0LQKyFuFpQySE3nnzObJOUtXboVNxs6tfVN+mLYzScfSn98lmUv48nY4l+4hMC4CF1CxKj9ECWdn2ivrM2wjMjmKex5QnS6Cuw8HjyM4yEnSbiQ6WUsL2sQl5hZuYZ3g/ciAw69Zbb911v4hwyy23cMsttyxsM5lMeNe73sW73vWug5x6KU9xOVOvBABD0lpOMbD6sh9W1m4eDSNQFMFzkSbeNNGmSbmniMWadsJI3vI4oVarhuPFmYNd8ICc2VxhA0IZ+dj/UHWLwYqEMrxtCLhaWGkjKkwxKpmuSpTfn4D7XmdXwmH72MGsh+t1pFF5sZRhkd/b7kSdgCunZrTE0HKlb0MOi+5kTBHDDFWeLDtqGVnG+MzKKqJXQVNkjThGxnEoglhFZGWtmlmkyYawj4nMctL3iVSMpMm5TkoJfWN2X/xdKLLUM0v5UsqOKztpi9g1ufs+WFm7GRmJXZWq4uZt6rjeYlu0B1jP7xLBLPPYwCyA6axkDPOs3/54tF4Z2KZBrEHgapGuUQBiPq79QkgsrLyrM74wtyeHwGORbTeKVZt6ulHrmuQ40UPqh1gksMuS83plpm/qIwFZBiACWYqRFX4qKZm7byNhVHihFcfYJN0R8mOVUZekPFnpryR8trRhismA2DeDYSlLeRQyBGTpRPF9MQirMuKm8TbXH/9NfufwCd5z38u5Wy5iWhUkr7Y0AnVINF1sCnYnrJE1rp3BKVU4qAUBaLcpgqw3BDZWTWBmKbaTrlro+0WodpFO5TtVuVCaHgtrwbEHcqTo8ELnQnhhAqGKIocYSppUEsDlNCjlW0BLwitlEULYYh5hsaZrGzVNPN6G9k0TQKxsVykgK76Xvm6L9yfLEEAl7Z/vfe606fdBD6yCeYbdEAtLve+0tdLbR6tDh0S1y7/BtD1xKkzvHNneiXpZhW7OFw/p2YCiztUrnJWBLOWI6Zwz/eRte55cMEGI1eklsB6LYFOJE9zI51D5lMIlEwPEYwuXw4dHo5px0TCKxbKSrlstZqzYCodQOYuJObgO4rB6TAngnyhJqHFNtXB9+aUWO9viz+67hP+2pGw/LvLBz1zN5NwmvnJQhwpN0jgkhWR4kNpB4xGnKj5pim161eytTLltoG5azwKAr5VHwYZ2ukqH8+FJtkUAs5K3oq7jBCNgDU21w4cfPMah7Ycf9fXPfMH2XQa3s5Uud/Ek2d80QF1Nk+UidlZfNCW6Y1yoY4dCFdvJHPxWycdOXsx/qz63+8n2kN8/dQVyZhu/ncBHNf6Un0sxr0heh8hc9ratsoEhzHLJ2WM9EsMNJU3s1sf2IQeDs9GAjbm0TOkxRhAJlNkQeWp4ZBRxUdMwLkoKE8qkF+KYFFXOWRJosw0TG5K+j02VKxyW0jCWikdOCkeqbWr/2A20Rys14dz+IIuop7ike+F47KFdj1ak2ea+cyM+xWNnoiwFPvvwGsV0OyzSdc7EVtUEHePS9+/bHdADr7Rx48OulGNEt9VVChP7KrGzUt8+AF8irTNBXDI+ACO4puSLWxPG1aMPa28QqkcafF11AKqFC3ItQ00Ss2u3NoOdqPvY0zVznaT+04K9sjywOeJTzWN7Ju7ZXoXpDr5KwGIcjkD2ftcKrNKvMBdiqBnDIu1+SddgVNXE9B5API1E/RSZW5KYW0ARdY7BURiDEcEaQTDxs4+MrDKHIKak8JbIMsZjpWFns2Gl2X5C57R07qWuuXBkr0TxBqEUyxGx/E+rD/DMq3+N/3j6q/nt+69jujOiqU00/j1SBSALD6YBMwtrNmeFYtYiRTmvawS0nCFMJGooJuXHEgk2R7IfBgCnOXNvESilQCwAV5gMGEgEsr1LIWEJkPExPFLNfUPPxyKWVrJ1miZEkXjXhhimsQzN8bovMVBGsCsl0Y9VIDMwldKtNE141eGEXsiRLdDaUpqNtQjE0vv6IJVRZIIEYlkz354BPdY7xXwKleHPu4YU5u3zeir30fuOWjKE2tYPG5QukJkd+Hbg3Mn5IwmAEtVHr10ew/z+kHtYHRtJADltS9KJluD4p418yUQBD158jmzBS2BRFg7nDMaGnMPh1sQ+vLAS7SQAhwTnTIxsabzB69jIPUT8k1Cb3Hvvvcty6UtZylKW8iWQe+65hyuvvPKJHsZXhHz2s5/lGc94xhM9jKUsZSlLecrJUtc8PnL27FkOHz7MI5+6lo1DTy6Gd+PnESGHx+F4uJnyvvPP4j998QY+f/IozekRZmowlWCmYCqh2IHV+z3FTsh3ZaeuZV15T7NiqCeGphSaEW3oYAw5lAZM47FTz+hcg5nFEMAUrujB1MoZkR3p3TF3GD+KweNGNrJYQl/ZSa8jS3SouWv7y9XX+22U2Z4rtTcO6hpfVSHqJDKq6Dj/exEA6XNiEEO38uCQqGiNnJ4F6FQlTG2Sk7woFEA1wMLqf96LjaU+z4FOke208ByLZKivdLxmYsXxDYbl9861KJyw3S8tK0q62zRRoDNG5dzv5LlK4YUqBFE7WLxIjl7p53R0hWT2sDfk5ySneslFtoKjxVlwo5CmRTyBqZUqGqak7yMXEsB7YTSuKIsGD5S2YVLWrJczDo12GOWIlvA72ii2WS9Clffx9jn+8Y2/xZkzZ9jY2Bj40lp5UjKzTpw4wSc+8Qme+9zncs899+x5kU9lOXv2LFddddUFfx9geS+0LO9FK8t70cpu98J7z7lz5zhx4sQTNLqvPDl69CgAd999N4cPH36CR/PEyvI5amV5L4Is70Mry3vRyl73YqlrlpJkMPTQOwyWS+wKf23jM7xocjc/vvIq/mh2LUwDQ8vUgqnBTEO+rATYeBtYMYlFJXUArsR7TCO4AlKdIpMYmbltm/h9X0E3CTxhnhHkjeBLiy/idl10ShKCsU/Zb74sDXhBYENZm5O655B3CEBUYlup7R664YSaiWVCf4M5sqAFuYyAUZXgFVi2EMBKx+nwwT6QpfNm6TBO3Zftfd7rtolK2N/PmTUAYqVj2nN0z7NXapi542MfndzAuiqkHkP66/WRUrLMXZvKlbWI5OSt5LQL4TM5JDLnGzbgxvH5IrG0YjtVIZ70qi/dhofNeWFtPKNQxa626hHFaAfnDUYqxqamwUQW8cFyPD4pwSxjDFdccQUAGxsbF/zCAZb3QcvyXrSyvBetLO9FK4vuxYUO2PTFxAXY4cOHl7+dKMvnqJXlvQiyvA+tLO9FK7vdi6WuWcpu4vAYhLGUvHjs+LGr/yu/cuSZ/PSdX8uZzx+mPG8xdUz83nhME9lMPhrotc/MlRQKBRHIUqAABGPd1G14YU7wPhTSp7clFlCvnTeRkVVEtlMz3FcnN1barwCywXPqzbq4VdPg6xh2LQaMwzeuZV81TQsqJSAkXqPYWAc1hshjTQtMxRB3TBFTr6jBJcBlrjpgOF6MmQexhgAmnfNKM6H6QJYxw6BT/16p+xv6T+Odb46JoYeD+bLUtfX7lIG26Xvqn5ceaNaTwW0RZGr7pgN6JUbVfF908l4BnSIliZXlCiFVPPQSch6La8Erb9rE7l4CKJwLZlmfGVn5XoiHIhQy8Z6QksU4iiJUg9cjXStniHjWyymGkPDdxUFaQv7HVTPDHSAB/JOLj7qUpSxlKUtZylKWspSlLGUpS3nKiRVDKYFKYhAMhiNmwv+ycRfvfuG/5xUv/Qu2r6hpxmCnIQG8NB4z820Fa5W/zjS+DS9M9rcLBn2udJiK/mnQaCj/7oJ8Wjm3kzWRkRWBrAHGVLywdt9jFefxdRNCDRPYkcIPcx4t14YE9s/rfGBjGYOUhTrek/NiAbnae6rwnnJlQWBiQciPZU0AslLlRP2Xrz8eq9hWCWjzJjKyEhhWWHIS/QQeGjPXt49gl7fhj/SXATH1Z+juUyGNOVdUDCfUf+FLjdcgqm2S1HcCkNRx84yuLjjlC3U/1Vg6eYNTWwVQJRmsXmhSXizwhTo2gl6dEMeiDSuEFtxCgVs+JXu3PlY3TL+NeIwN4YVifMiXZXyothsfPCOe2oc8j7Mm/NbGtmatmOaE8Cm38EEq8D4pmVlLWcpSlrKUpSxlKUtZylKWspSnvpRiuaaAH7vqv3HbJR/hH3/09TQPHgohhHVgaInpAQ5RxAVQy9nISBEykGUrsDMXwC7XMmvERSaNxAp/CcjqMYO8AnW8NbhymCfSr1C4Z9XC/YQjdnJpJfSBmJDdQFUF4KeT36rHIIIWsEqV85pZALdGZZfJpUMJBxhFKbRxDrxKx+Trors/hRSm7YqR5Y3pJGGHATbTQI6r0G7+lqVqfx3mU3+MQ+dI7KuhfgfCHgeP7++LYFh/LHlbAqZ0v4sSvsf3Xv0uNXib2rhYSCtX+owFs1Ji98zYSnm0Etur8AH0dQT0yPpQCE0CSizW4WqDKR3GuIg5xgIPJhYiUfG7DsHFmxLYWZIrGLoIeO1XnrTMrPF4zD/5J/+E8Xj8RA/lCZXlfWhleS9aWd6LVpb3opXlvTiYLO9XK8t70cryXgRZ3odWlveileW9WMrjKYGdJbni4WEz4VtWT/KeG36Wjb98Hw8/u6AZmcDOqiMYk5J05/BCn9kqEFgm4U9aIMz7FjhIx0UAx2vwxtEFXOJ+VxjcKFQu1FUREyusI7uF3XUuvrc/VVXvhRiGcflORXbRuap6jLPQt4kfvWov3SqjVa3a2/CnWVt6nCkPlwaU+gysIUaVXVCtsLAByLLtcZnplNhPmmGV2zDPmErjUEnVNQNLs8zmWFj5bwDIyr8BdXs1MKf7UPvT9nT+kIS9+5sIIYARfM1J2Af61Cyu+Jfb90IMXdFlYDWjtk1mYuU/3wJdI9+Oo4iMrFSqt/B5LGI91gYgqyga6toigDUeK6Fa79jWTGyVq703XthuSlbtjMPFFiZW43VDSOQCeVJWM1zKUpaylKUsZSlLWcpSlrKUC1mezNUMDyK68mFNQ+M9W77il889m3/1W/8Tx35fGJ8JwE7Ol2UEV0rOC5SSZSdmlq08o7OOcrOOlQEJ1QZ9j0nlfAsgQQvECPjCtIwsCUBT6itVRZTIeEqVEslA10AYYtqeqhkOVDLMVQzrBj+dhX3VDN+4UD3Qh2N9ArqahrlKhmUZL0XaEELfzbUlIhBDD6Us476UQT8mg/ceKcthIGsBCytJF/RRQFbKj2XTfU599voYYmH1AcC8P4J8A7BHB7xKoXeJwRbPM5fcPeU3M+p4LRk0k+HtVoFjEisKpqGlPjUIFllaXYCq/T2n11ydkBb8aqsSxqqGZTiXL9rk8D4ys1I4oS/a58SV4MswODeJCG2ij5WBmWUKBwLGeMpRHV5tw7gMbKtJUTMpKgrjWC1mGPGs2fB6dLTJqpmxaqdMpKaUGr+1zQ/e8Cf7qmb41J31lrKUpSxlKUtZylKWspSlLGUpTwmxYiiwjKXgkBnxpo07+ZVv+3EO/817OHV9STNJVJP4530HFAjhhDGXlosAk5Z++J2bBz8ykBVZRd4KvpAMUITztLm6cvheH/PoAysa4OrLIlaWjwCTtQF8SgBW/xwpwXtKCu9cC2SlyoYoRpdz+KbBz6qcYF7GI2Q0gqJoQaOi6AJZieXUz5fVDwE8CJCVWFgDfYXvIbKubGSb9XJdZVaeDOyTdl8evwLLcg6vNE6VE6sDZHUYW/MMr8SO0qyxEO7XY3EpICqBUC79tqTtq3sv275yyKLKG5f6yNvTq6MTghgALd+paOjGPrCxhDbpO4TqhQJiQ44sBGzRUI7qkPRdfA4zHNmG0jYUxjEyNTtNyYqtGNs6hxZacVja3FpjqdmvLHNmLWUpS1nKUpaylKUsZSlLWcpSviLFxrA2zdAqsBgxPGfkePd1/5H3Xf4M3v57r+PiPyqYnA7tvA1MFYn50b0JQJap0ms30XSKnuqAWEYiY0daIEkBHynMK4AAgvTjCo0EdpfuI58wgU3q815BU96HhO8+hvs1dRtGN6sCO8vayAAbSKStAbsEZHkXgKlZ1R6T2Fkp6XtdB9AsX7+qVqiBoP0wsfQ+DWSpxOwZyOqBV537Spf9NAwu9dBM/Vb6+9U5OoywfpseCBVfh5hYbUJ5FNikQh5TUnZ1f8Qp9hUtYJVZYtK9F5m1lYA5dUzIz6bOE0EzZ/vtyNUQJd4fn++Tx1sfQCwnUDrEughihd+LtSGUUABrHKOioTSO1XLGxNbUEUnbKHcYmRqDZ8XOmJiKUpqc/H0kNcOVFoZlycxaylKWspSlLGUpS1nKUpaylKV8xUrlG6a+pqZlHxmEgpBL669t3MOvf/O/4fB3f4Ez11rqFcm5gBIzxTQhkbU4j5364ZxWkPJah/3Ot6yrAVaQKweAFljMsEoMrCSpqmIvtHFuTJqV5T2+rgPA5F2HkeUjY0syGGNa4CmBXCmhe7qectQdY2R++bpumWV1HYCukBSpTS7fB7L67CnNbsoXkwAwMwdk+fS+A2yp+5vzX8k8qKRYVinXmc6j5fU4TbeCYR6nzoVlFrC59Dk719X+ZQBKJ5ZX5xw6PjO47DD4p1lduWJhDC3MebFiKGHOgZX6jLnixLdAFtBWAS18BvicBVf4mGdLsRs9YEPCd7EeMR4xDls04ScVKxxORhVGfM59VXvDajFjvZxSmgYrPieAT0CWEYfFUUqPXbiHLJlZS1nKUpaylKUsZSlLWcpSlrKUr1gpxeJwND5URiultcgtUHn4qnLMLz/rP/Czx17ET//WN3HRJ4ViOzYygJOQx8qD1DGflRCM9BQauDDvknqfkppnRhY5ZNGLYBYBWb38V4NthkIb+/udC2wpwquv6xa8Sn2nKobOhfemy2HxjUOooSgQG5GbsgzJ8BsH0ym+rvGzGeLjeaQJVQ4DcjHPxlIA1qDodomRJdIBsjQbaw4gNApMmgOSeqCiPj5dugM/iF5Kt126R7vkwxrcH8EgDWL1mV6JGRUqB7bX0jKnpHeOHlDaAct6x0dwVoQAsnoFcsVrS4nfvY2/3cK3wFt8pMSBL8nhhEBI9u6IYFZI9p6oZkWhGJO2wYhnFEMLjXgKcRTiKMUxtjWFhDZjU9PEgdmI6oanfP8p3Z+UzKyf/Mmf5JprrmEymXDDDTfwu7/7u0/0kB53+R//43/wute9jhMnTiAi/Oqv/mpnv/eeW265hRMnTrCyssIrX/lK7rjjjk6b6XTKW9/6Vi655BLW1tZ4/etfz7333vtlvIrHLu985zv56q/+ag4dOsRll13GG97wBv7iL/6i0+ZCuRc/9VM/xQte8AI2NjbY2Njgpptu4r//9/+e918o96Ev73znOxERbr755rztQrkXt9xyCyLS+Tt+/Hjef6Hchy+FLPXMhfP7WeqZVpZ6ZlguZD0DS12zlK8cGUvJqhl1gKwkpVhKsVxi1/iho3/Of3r9j7P6hgc4f6UEYz4yTnwCr6CtGNdjXLnCtPmYiIyamOw9AFnqeBSLC3IfWqRvm/dAkF1zZWkALLOyYr4syKws3zQ5bNA3YZ9YG5K9674jm6oTQlgUMBnDygRWV5BDa8jFRzAXH0VWJqGNNQHIKgsobPjrVSicYzft9mdNy8IyplO9sMNe6lQjlA77qnPeOeYV3RxX0K2AmMexmIXVfkHqD+b3092PYnzNsctyMvf420rMqB6DLbGuWqYZLVAG3XtBBKZSgvcEeCUQSzGrTNP+XnNflszgakYeV8ZcWUUIL/QmAlmFRxRwhZcWBzQO5wTvBSsBcDY5D5aLr54VM2PdTlk1IcRw1U4jM8tTxlxZa2bKfuVJB2b98i//MjfffDP/6B/9I/7sz/6Mr/3ar+U1r3kNd9999xM9tMdVNjc3eeELX8hP/MRPDO7/kR/5EX70R3+Un/iJn+BDH/oQx48f55u/+Zs5d+5cbnPzzTfz3ve+l1tvvZXf+73f4/z587z2ta+l6ScH/AqW2267jbe85S380R/9ER/4wAeo65pXv/rVbG5u5jYXyr248sor+ef//J/zp3/6p/zpn/4p3/iN38i3f/u35wXjhXIftHzoQx/iZ37mZ3jBC17Q2X4h3YvnPe953HffffnvYx/7WN53Id2Hx1OWeibIhfL7WeqZVpZ6Zl6WeibIUtcs5ckkpVhuGI/4jee/h//tjf+Nh25oOPU1FVd936c58f2fYe0HvoC7+RTnfvgsD968zQN/d4f7f2jKPTc3fP4HHZ95i+FT/9uIT31/yZ0/WHLn3yy4880ld37fiDvfNObT3zPhoedNMtslsanE+zZccAG5ZLCKISz+3AdNmiYkfU+hghDYVylHlkh4bRowEbRK7KzUFlpWVX41zK44wue+8wR3ffcJPvddV3D3d17NF9/4LO77nufwwP/nmTzwmqfxwKuuCH/feIKT33CCk688wclXXs7JV17Og193nJPfcJwHvvEYD3zjMc68+LIAfmnQy5oMZAUQqZ/0nTkAq5usvQsO+T47bJfwwNzPANC1EJzqAVjeCM3EcObaEWeuGXHm2hFnnzbi3FUjzl4d319Zcv6KknNXlZy7ouDclQXnryw4f0XB+eMFm8csm5dbti61bB+1TA/ZnK9qKB9W+z4CVjEXnLMt8yr8BZabK7o5uUIYYQC6mlECzyKI5SUzunxkYqXQQnHxnluPjB2YkBcLiCGGnrIMCd9XRxWTUcW4rHN4YWkbVosZR0bbrBWh8mYTb3ppGtbtDqtm1rntE1NxEBHv98oy95UlN954Iy95yUv4qZ/6qbztOc95Dm94wxt45zvf+QSO7EsnIsJ73/te3vCGNwDBA3bixAluvvlm/v7f//tA8HgdO3aMf/Ev/gXf//3fz5kzZ7j00kv5xV/8Rb7zO78TgC9+8YtcddVV/Pqv/zrf8i3f8kRdzmOSBx98kMsuu4zbbruNr/u6r7ug7wXA0aNH+Zf/8l/yN/7G37jg7sP58+d5yUtewk/+5E/y9re/nRe96EX82I/92AX1m7jlllv41V/9VW6//fa5fRfSfXi8ZalnLuzfz1LPdGWpZy5sPQNLXfOVLGfPnuXw4cM88qlr2Tj0pOMofFmk8Y6PzSoecqt808rjB54+47e+j6f9gg3J3R3R+A+hi1L7mPQ9Gv5NykMVtzk3nysrgUxOAV0R9JIUWlg3+KoKidoTyNE04RhrQ14rJZIq/M2qDigWGFtFYGgVBZQlflxy7gWX8a5/9eNcPyrDUCIi5+JgmwPCBv/H/X+JO9/8VchMjasPXiU2lmZi0QOXEoiFYjvB7mGBPWyqbbRg+6L2ut8ING1ePuLN//B9fPPqp7ASQlyb2EETL8QheZvF0yDs+AAoOm8w4tjxJc4bzroJ/8cvfC9HP9nMXaNOzA4JvGuBrFCFsA079Cmxe0EbPpgSvqu+UnVCL9BM4oWakCcLCaysfH9HDjNqQuXCeKvKUR2rFgYW1rgM3/HINoyLmrGtWS+nTGzF2DSUpmFkQgXDUprMzBqbiolUlFIzkoZVM4XNLf7nF36aM2fOsLGxsfiL4UnGzJrNZnz4wx/m1a9+dWf7q1/9av7gD/7gCRrVl1/uuusu7r///s59GI/HfP3Xf32+Dx/+8IepqqrT5sSJEzz/+c9/Ut+rM2fOAGFxDRfuvWiahltvvZXNzU1uuummC/I+vOUtb+Hbvu3beNWrXtXZfqHdizvvvJMTJ05wzTXX8F3f9V189rOfBS68+/B4yVLPBLmQfz9LPRNkqWeWekbLUtcs5ckqVgwvGo8fVyALYH19p5PfKiRo7zVyKgdWYm2laoBaMqC1R74snVOrz2qMYYc6HDiPLSVs13mzREL4YAqVdCF30lgarBismBy6OZYyh3ge5K+TzFsEb21O7u6NwRemBbKGErurkMJO2N6isMAem2pQ+m0WtNdMrBy2F8dZbjtOVhtcU65zdbHOM8p1nlmu8cxyjeeMVnnOaJXnjVZ4wWjCC0aT/P5l45KXjUtePrG8bFzydRN45Yrj21bPM9vwnWv00oJVGshKyd77ebVcEe9LAqxUu1y5MPbvSo8rA5sLk0IUPdLEyodlBLLEwyhULkzJ3a11GBvDBo2ntA2josEax6SoWSkrVoqKia1iXqwWyCqlwXnBiqM0dQCugFJqrHgmpmLD7Ozy5c3LkyoB/KlTp2iahmPHjnW2Hzt2jPvvv/8JGtWXX9K1Dt2Hz3/+87nNaDTiyJEjc22erPfKe88P/dAP8TVf8zU8//nPBy68e/Gxj32Mm266iZ2dHdbX13nve9/Lc5/73LwYvFDuw6233spHPvIRPvShD83tu5B+EzfeeCO/8Au/wDOf+UweeOAB3v72t/OKV7yCO+6444K6D4+nLPVMkAv197PUM0s9k2SpZ1pZ6pqlLGVezj68xsXEZPK+C2R5UVjJED6VGFu7MZ36+bISOwsC8NPQBb+cR0RC7qwIWgkSwwubkNS7k7jdZOZWOle1arjYPn5BW7c/ciWjxrdsrF5Yode5sJJoYCmFHfaYWPthYA0mb9dde78QxBruj5wLSxp4pFrdtf9HJQrEAv0q7WdpWVZtbqwIQvWSvePbHFpewJcxlLDD2PIx3BDcKDKy0m8gvkp8NcZRFA1NY7DWMSpqSusQ8YxtAC5LE/JfpcTvzguGkOzd4li1M1bNjLEJYFdJhRXPIbONlZQEfv+/wScVmJVEej8y7/3ctgtBHs19eDLfqx/8wR/kox/9KL/3e783t+9CuRfPetazuP322zl9+jT/5b/8F970pjdx22235f0Xwn245557+Lt/9+/y/ve/n8lksrDdhXAvXvOa1+T3119/PTfddBPPeMYz+Pmf/3le/vKXAxfGffhSyFLPBLnQfj9LPbPUM7DUM31Z6pqlLGVAKiEhWF5EhQtGlpaWvaoY7iKSwg5z/5HtZG0OL4QmVCeMua86YkLCdxqHxDxabecR7IpVBZ19fMO2GhfyYnmkzXWlwwp1Unc9pig5X9Q+QKyF4JXeHKsNpqT/4v1wrqxOv2QQK43PFTA23ZDOx0Nyzqz0JUhkiKnk7zm0kC5DS4Ng0LKz8nsLrmjzuYk+RshhhSlvFhKTvUv7lUwmFd4LOkuViwnfbaxc6Hx7A9vE7y7nxiqloZQGi8PimJhKVTIMSeBXD5A360kVZnjJJZdgrZ3z4pw8eXLOI/RUllRBZrf7cPz4cWazGY888sjCNk8meetb38qv/dqv8du//dtceeWVefuFdi9GoxFf9VVfxUtf+lLe+c538sIXvpB/82/+zQV1Hz784Q9z8uRJbrjhBoqioCgKbrvtNn78x3+coijytVwI96Iva2trXH/99dx5550X1G/i8ZSlnglyIf5+lnomyFLPLPXMXrLUNUtZCir2i8WhbQmM8T7nyHo0gFbnGGO7oYYZ0ArnE6sYVxDCD2M1wtzO2hjWZzrg0fYx4bBZDOAfVO57ZKMNLzQGX9pYEVLCaw7hi0CXqgIYWFt0wgmB4aTsvcp+HdaS/jMEME0dPxRumNvbAL7l0L8Y5if+SwBmpeTrGdBqQxx1snevc1/F8bmyBa9cGf68IeTNKtK1R+A1tvEolpZVVQvTnw0J3ouywdgGYx3OGUZFjbWOSVljBCZFzcZoytjWWHFMbM1qMeNQOWWjmLJiK8amxsWba8RhxFH5gjUzxeI64agWT4mbvz+Lbtvj+BV8yWU0GnHDDTfwgQ98oLP9Ax/4AK94xSueoFF9+eWaa67h+PHjnfswm8247bbb8n244YYbKMuy0+a+++7j4x//+JPqXnnv+cEf/EF+5Vd+hd/6rd/immuu6ey/kO7FkHjvmU6nF9R9+KZv+iY+9rGPcfvtt+e/l770pXzP93wPt99+O9dee+0Fcy/6Mp1O+eQnP8nll19+Qf0mHk9Z6pkgF9LvZ6lndpelnlnqmb4sdc1SlqKkx/QRp3JiqWTunZBCt0eIoZZeMvi2gmH3vRRFC3KJCZ/TEBOQNSqhiInf+wwuQqU7s2vCqYPJ7Mw4VC0sDL7sgVimB2ClqoQxp9Z+QKxB0CoCVgmM6lRGtNJpp4EsDWDltr0xJnGFcKw887jdp20/ozxnVI6sFijthBTGPFjpfmWQS2J4YdFeV658WPr42m3jSx/f+1y9kMKHPwFTOCTmzTLGU8S8WEAAtIxjbTRjtZxxaLRDYQKQtV5OGZmaQ8UORhxrxRQjnspZJqZi1cxYM1MO283I2JqyZqZMpMLgMptrv/KkCzP8oR/6If76X//rvPSlL+Wmm27iZ37mZ7j77rv523/7bz/RQ3tc5fz583z605/On++66y5uv/12jh49ytVXX83NN9/MO97xDq677jquu+463vGOd7C6usob3/hGAA4fPsyb3/xmfviHf5iLL76Yo0eP8ra3vY3rr79+LpHpV7K85S1v4T3veQ/ve9/7OHToUPb8HT58mJWVFUTkgrkX//Af/kNe85rXcNVVV3Hu3DluvfVWfud3foff+I3fuKDuw6FDh3IumyRra2tcfPHFefuFci/e9ra38brXvY6rr76akydP8va3v52zZ8/ypje96YL6TTzestQzSz0DSz2z1DNLPZNkqWuWspSuNN5htgwYB00XpBpkXqXtmqGVZD+gVgapJLQflaGiYRIjgbGVWFj9UzQusLaMabMRaVZWBGx2jtfYgeMfrRSnCygMrjB5nJ0cWFo0mWxBOGEH2CICT9AB9jLoZAfOkSoE2pj0fNGxQ+PLoJnQlMJFdmvgih+dNHikAhdZY4mdlb6sNC5nW2ZZN0SwTfKeJYYMupL8G8sVDGOOLF9ENpb1mRkGYMeBjeWcCdUKjcdaF0MJobANVjy1C19a7SyFNIxsw1oxzUMopWFiKtbtDs4bxqailAYTwwtH0jCSJlcztHhGOOrBRHPD8qQDs77zO7/z/8/emcfLUZRt+6rq7tnOlnNyshJCwr6DgLJDgIBsgkReQARBFMUoioAIiIbtYxMRlRf0VRYXFFBEBRQkrC4gO8oqCAFC9vWsM9PdVd8f1d0zk5NANgiHPNfvd5KZnuru6pqaru67n+cu5s+fz/nnn8/MmTPZcsst+dOf/sR66623pqu2Wnn88cfZa6+9svennnoqAMcddxw33HADZ5xxBv39/UyePJmFCxey44478pe//IWWlpZsne9973v4vs8RRxxBf38/++yzDzfccAOet2Rvf/9yzTXXADBhwoSG5ddffz3HH388wFrTFrNnz+bYY49l5syZtLW1sfXWW3PXXXex7777AmtPOywPa0tbTJ8+nU9+8pPMmzePYcOGsdNOO/HII49k58O1pR1WNzLOyDgDMs7IOPP2rE1tIWPN+5fUu+YHb42g0DzobusGLZH1KL1UxXb1uaioRGyyxmIjm0VdWWNQcWLcXhel1SBg1UduLRmFBRAbiJwnFuA8s5QCk6S5WSBKUg2twUbGRWIZ63zpYuWisXSSWmjS/YU10cZzIT7e7JDLp49aLW1krabwSoW43Fczf1+WkAUDLb/TumZCU32E1lLEp7S9ltOHz2btvgwRq17TUyoTsgAIAm57YzOmt722XPt6J7qiIv7sMnqRS7er979SqWdWnfZotfPtgkSY0+CRiGEajHbLNC7azq2T6JxLRKXZ1AA+jfryLJFvUdYJn5GvscpCoJzolTSzDmKsVfhB4KK3PItSlsB33ll536UWBjrG14a8V3sfaEPOi/CVK1vyKhir0coQKIsu9wIzGry5loWyy1NKEARBEARBEARBeN/w6quvssEGG6zpagiCIKx23nzzzQYP06UhEr4gCIIgCIIgCMIgo6OjA4A33niDtra2NVybwUdXVxfrrrsub775Jq2trWu6OoMOab9VQ9pv6Vhr6e7uZvTo0e9YVsQsQRAEQRAEQRCEQYZOTLzb2trkZngVaG1tlfZbBaT9Vg1pv4Esrzg/qGYzFARBEARBEARBEARBENZuRMwSBEEQBEEQBEEQBEEQBg0iZgmCIAiCIAiCIAwy8vk8U6ZMIZ/Pr+mqDEqk/VYNab9VQ9pv1ZHZDAVBEARBEARBEARBEIRBg0RmCYIgCIIgCIIgCIIgCIMGEbMEQRAEQRAEQRAEQRCEQYOIWYIgCIIgCIIgCIIgCMKgQcQsQRAEQRAEQRAEQRAEYdAgYpYgCIIgCIIgCIIgCIIwaBAxSxAEQRAEQRAEYRBx9dVXM378eAqFAttvvz1//etf13SV3hece+65KKUa/kaOHJl9bq3l3HPPZfTo0RSLRSZMmMBzzz3XsI1KpcLJJ59MZ2cnTU1NHHLIIUyfPv29PpT3hIceeoiPfexjjB49GqUUv//97xs+X13ttXDhQo499lja2tpoa2vj2GOPZdGiRe/y0b37vFP7HX/88QP640477dRQZm1uv1VFxCxBEARBEARBEIRBws0338wpp5zCN7/5TZ566il23313DjjgAN544401XbX3BVtssQUzZ87M/v79739nn1122WVcccUVXHXVVTz22GOMHDmSfffdl+7u7qzMKaecwm233cZNN93E3/72N3p6ejj44IOJ43hNHM67Sm9vL9tssw1XXXXVUj9fXe119NFH8/TTT3PXXXdx11138fTTT3Pssce+68f3bvNO7Qew//77N/THP/3pTw2fr83tt8pYQRAEQRAEQRAEYVDwkY98xJ500kkNyzbddFN75plnrqEavX+YMmWK3WabbZb6mTHGjhw50l5yySXZsnK5bNva2uyPfvQja621ixYtskEQ2Jtuuikr89Zbb1mttb3rrrve1bqvaQB72223Ze9XV3s9//zzFrCPPPJIVubhhx+2gH3xxRff5aN671iy/ay19rjjjrOHHnroMteR9ls1JDJLEARBEARBEARhEFCtVnniiSfYb7/9Gpbvt99+/OMf/1hDtXp/8fLLLzN69GjGjx/PUUcdxauvvgrAa6+9xqxZsxraLp/Ps+eee2Zt98QTTxCGYUOZ0aNHs+WWW6517bu62uvhhx+mra2NHXfcMSuz00470dbWtla06QMPPMDw4cPZeOONOfHEE5kzZ072mbTfqiFiliAIgiAIgiAIwiBg3rx5xHHMiBEjGpaPGDGCWbNmraFavX/Ycccd+fnPf87dd9/NT37yE2bNmsUuu+zC/Pnzs/Z5u7abNWsWuVyO9vb2ZZZZW1hd7TVr1iyGDx8+YPvDhw//wLfpAQccwI033sh9993Hd7/7XR577DH23ntvKpUKIO23qvhrugKCIAiCIAiCIAjC8qOUanhvrR2wbG3kgAMOyF5vtdVW7LzzzmywwQb87Gc/y4y3V6bt1ub2XR3ttbTya0ObHnnkkdnrLbfckh122IH11luPO++8k0mTJi1zPWm/5UMiswRBEARBEARBEAYBnZ2deJ43ICJjzpw5AyJoBGhqamKrrbbi5ZdfzmY1fLu2GzlyJNVqlYULFy6zzNrC6mqvkSNHMnv27AHbnzt37lrXpqNGjWK99dbj5ZdfBqT9VhURswRBEARBEARBEAYBuVyO7bffnnvuuadh+T333MMuu+yyhmr1/qVSqfDCCy8watQoxo8fz8iRIxvarlqt8uCDD2Ztt/322xMEQUOZmTNn8uyzz6517bu62mvnnXdm8eLFPProo1mZf/7znyxevHita9P58+fz5ptvMmrUKEDab1WRNENBEARBEARBEIRBwqmnnsqxxx7LDjvswM4778z//d//8cYbb3DSSSet6aqtcU4//XQ+9rGPMXbsWObMmcOFF15IV1cXxx13HEopTjnlFC666CI22mgjNtpoIy666CJKpRJHH300AG1tbXz2s5/ltNNOY+jQoXR0dHD66aez1VZbMXHixDV8dKufnp4eXnnllez9a6+9xtNPP01HRwdjx45dLe212Wabsf/++3PiiSfy4x//GIDPf/7zHHzwwWyyySbv/UGvRt6u/To6Ojj33HP5xCc+wahRo5g2bRpnn302nZ2dHHbYYYC03yqz5iZSFARBEARBEARBEFaU//3f/7XrrbeezeVydrvttrMPPvjgmq7S+4IjjzzSjho1ygZBYEePHm0nTZpkn3vuuexzY4ydMmWKHTlypM3n83aPPfaw//73vxu20d/fb7/85S/bjo4OWywW7cEHH2zfeOON9/pQ3hPuv/9+Cwz4O+6446y1q6+95s+fbz/1qU/ZlpYW29LSYj/1qU/ZhQsXvkdH+e7xdu3X19dn99tvPzts2DAbBIEdO3asPe644wa0zdrcfquKstbaNSWkCYIgCIIgCIIgCIIgCMKKIJ5ZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEIS1jhtuuAGlFI8//viarsq7ys9//nOOOuooNtlkE7TWjBs3bk1XSRAEQRAEYZXx13QFBEEQBEEQhHeHX/ziF8yaNYuPfOQjGGMIw3BNV0kQBEEQBGGVETFLEARBEAThA8rdd9+N1i4Q/+CDD+bZZ59dwzUSBEEQBEFYdSTNUBAEQRAEATj++ONpbm7mxRdf5KMf/ShNTU2MGjWKSy65BIBHHnmE3XbbjaamJjbeeGN+9rOfNaw/d+5cJk+ezOabb05zczPDhw9n77335q9//euAfU2fPp3DDz+clpYWhgwZwqc+9Skee+wxlFLccMMNDWUff/xxDjnkEDo6OigUCnzoQx/illtuWa5jSoUsQRAEQRCEDxJyhSMIgiAIgpAQhiGTJk3ioIMO4g9/+AMHHHAAZ511FmeffTbHHXccJ5xwArfddhubbLIJxx9/PE888US27oIFCwCYMmUKd955J9dffz3rr78+EyZM4IEHHsjK9fb2stdee3H//fdz6aWXcssttzBixAiOPPLIAfW5//772XXXXVm0aBE/+tGP+MMf/sC2227LkUceOUD0EgRBEARBWFuQNENBEARBEISEarXKhRdeyKRJkwCYMGECd9xxBxdffDFPPvkkH/rQhwDYYYcdGD58OL/61a/YfvvtAdhkk024+uqrs23FccxHP/pRpk2bxg9+8AMmTJgAwM9+9jNeeeUV/vznP7P//vsDsN9++9HX18ePf/zjhvpMnjyZLbbYgvvuuw/fd5dtH/3oR5k3bx5nn302n/70pyX6ShAEQRCEtQ65+hEEQRAEQUhQSnHggQdm733fZ8MNN2TUqFGZkAXQ0dHB8OHDef311xvW/9GPfsR2221HoVDA932CIODee+/lhRdeyMo8+OCDtLS0ZEJWyic/+cmG96+88govvvgin/rUpwCIoij7O/DAA5k5cyYvvfTSajt2QRAEQRCEwYKIWYIgCIIgCAmlUolCodCwLJfL0dHRMaBsLpejXC5n76+44gq++MUvsuOOO3LrrbfyyCOP8Nhjj7H//vvT39+flZs/fz4jRowYsL0ll82ePRuA008/nSAIGv4mT54MwLx581b+YAVBEARBEAYpkmYoCIIgCIKwGvjlL3/JhAkTuOaaaxqWd3d3N7wfOnQojz766ID1Z82a1fC+s7MTgLPOOitLe1ySTTbZZFWqLAiCIAiCMCgRMUsQBEEQBGE1oJQin883LPvXv/7Fww8/zLrrrpst23PPPbnlllv485//zAEHHJAtv+mmmxrW3WSTTdhoo4145plnuOiii97dyguCIAiCIAwiRMwSBEEQBEFYDRx88MFccMEFTJkyhT333JOXXnqJ888/n/HjxxNFUVbuuOOO43vf+x7HHHMMF154IRtuuCF//vOfufvuuwEaDN1//OMfc8ABB/DRj36U448/nnXWWYcFCxbwwgsv8OSTT/Kb3/zmbev0/PPP8/zzzwMu8quvr4/f/va3AGy++eZsvvnmq7sZBEEQBEEQ3nVEzBIEQRAEQVgNfPOb36Svr49rr72Wyy67jM0335wf/ehH3HbbbTzwwANZuaamJu677z5OOeUUzjjjDJRS7Lffflx99dUceOCBDBkyJCu711578eijj/L//t//45RTTmHhwoUMHTqUzTffnCOOOOId63TLLbdw3nnnNSz7n//5HwCmTJnCueeeuzoOXRAEQRAE4T1FWWvtmq6EIAiCIAjC2s5FF13EOeecwxtvvMGYMWPWdHUEQRAEQRDet0hkliAIgiAIwnvMVVddBcCmm25KGIbcd999/OAHP+CYY44RIUsQBEEQBOEdEDFLEARBEAThPaZUKvG9732PadOmUalUGDt2LN/4xjc455xz1nTVBEEQBEEQ3vdImqEgCIIgCIIgCIIgCIIwaNDvXEQQBEEQBEEQBEEQBEEQ3h+ImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARBEARh0CBiliAIgiAIgiAIgiAIgjBoEDFLEARBEARBEARBEARBGDSImCUIgiAIgiAIgiAIgiAMGkTMEgRBEARBEARBEARBEAYNImYJgiAIgiAIgiAIgiAIgwYRswRBEARBEARBEARBEIRBg4hZgiAIgiAIgiAIgiAIwqBBxCxBEARBEARBEARh0PLII4/wP//zP4waNYpcLsfIkSM5/PDDefjhh1doO+eeey5KqZWqwwMPPIBSigceeGCl1l9eJkyYwIQJE97VfQBMmjSJQw89dJmfv1fHuyzGjRvH8ccfv0b2/XYopfjyl7+8pquxViBiliAIgiAIgiAIgjAo+eEPf8iuu+7K9OnTueyyy5g6dSqXX345b731FrvtthtXXXXVcm/rc5/73AoLYCnbbbcdDz/8MNttt91Krf9+ore3l7vuuotPfOITa7oqgrBM/DVdAUEQBEEQBEEQBEFYUf7+979zyimncOCBB3Lbbbfh+7Xb26OOOorDDjuMr371q3zoQx9i1113XeZ2+vr6KJVKjBkzhjFjxqxUXVpbW9lpp51Wat33G3/605+IooiPfexja7oqg4b+/n6KxeKarsZahURmCYIgCIIgCIIgCIOOiy++GKUU11xzTYOQBeD7PldffTVKKS655JJseZpK+OSTT3L44YfT3t7OBhts0PBZPZVKhdNOO42RI0dSKpXYY489eOKJJwakuS0t7e7444+nubmZV155hQMPPJDm5mbWXXddTjvtNCqVSsN+zjvvPHbccUc6OjpobW1lu+2249prr8Vau8Lt8vWvf522tjbiOM6WnXzyySil+M53vpMtmz9/PlprfvjDHzasf+utt7L33nvT3t6+Qvt9/PHHOeqooxg3bhzFYpFx48bxyU9+ktdff72h3A033IBSivvvv58vfvGLdHZ2MnToUCZNmsSMGTMayoZhyBlnnJG1/2677cajjz46YN/pNu+77z5OPPFEhg4dSmtrK5/+9Kfp7e1l1qxZHHHEEQwZMoRRo0Zx+umnE4ZhwzaW9zsYN24cBx98ML/73e/40Ic+RKFQ4Lzzzltqm1hrOfvsswmCgJ/85CcAGGO48MIL2WSTTSgWiwwZMoStt96a73//+yvU3ms7EpklCIIgCIIgCIIgDCriOOb+++9nhx12WGY01brrrsv222/PfffdRxzHeJ6XfTZp0iSOOuooTjrpJHp7e5e5n8985jPcfPPNnHHGGey99948//zzHHbYYXR1dS1XPcMw5JBDDuGzn/0sp512Gg899BAXXHABbW1tfPvb387KTZs2jS984QuMHTsWcD5gJ598Mm+99VZDueVh4sSJXH755Tz66KPsvPPOAEydOpViscg999zD17/+dQDuvfderLVMnDgxW7dcLnPnnXdyxRVXrNA+02PYZJNNOOqoo+jo6GDmzJlcc801fPjDH+b555+ns7OzofznPvc5DjroIH71q1/x5ptv8vWvf51jjjmG++67Lytz4okn8vOf/5zTTz+dfffdl2effZZJkybR3d291Dp87nOfY9KkSdx000089dRTnH322URRxEsvvcSkSZP4/Oc/z9SpU7n00ksZPXo0p556akP9l/c7ePLJJ3nhhRc455xzGD9+PE1NTQPqUqlUOP7447nzzju5/fbb2X///QG47LLLOPfccznnnHPYY489CMOQF198kUWLFq1wm6/VWEEQBEEQBEEQBEEYRMyaNcsC9qijjnrbckceeaQF7OzZs6211k6ZMsUC9tvf/vaAsulnKc8995wF7De+8Y2Gcr/+9a8tYI877rhs2f33328Be//992fLjjvuOAvYW265pWH9Aw880G6yySbLrHMcxzYMQ3v++efboUOHWmNM9tmee+5p99xzz7c95t7eXpvL5ez5559vrbV2+vTp2XEUi0VbLpettdaeeOKJdvTo0Q3r/v73v7ee59k5c+a87T6WdrxLEkWR7enpsU1NTfb73/9+tvz666+3gJ08eXJD+csuu8wCdubMmdZaa1944QUL2K997WsN5W688cYB7Z9u8+STT24o+/GPf9wC9oorrmhYvu2229rttttumXV/u+9gvfXWs57n2ZdeemnAeoD90pe+ZOfPn2932203u84669inn366oczBBx9st91222XuW1g+JM1QEARBEARBEARB+EBikxSxJdMHl8fc/MEHHwTgiCOOaFh++OGHD0hrXBZKqQHeU1tvvfWA1Lv77ruPiRMn0tbWhud5BEHAt7/9bebPn8+cOXOWa18ppVKJnXfemalTpwJwzz33MGTIEL7+9a9TrVb529/+BrhorfqoLHAphrvvvjvDhg1boX0C9PT08I1vfIMNN9wQ3/fxfZ/m5mZ6e3t54YUXBpQ/5JBDGt5vvfXWAFnb3H///QB86lOfaih3xBFHLLP9Dz744Ib3m222GQAHHXTQgOWr8h1svfXWbLzxxkutw2uvvcbOO+9MV1cXjzzyCNtss03D5x/5yEd45plnmDx5MnffffdyR/kJjYiYJQiCIAiCIAiCIAwqOjs7KZVKvPbaa29bbtq0aZRKJTo6OhqWjxo16h33MX/+fABGjBjRsNz3fYYOHbpc9SyVShQKhYZl+XyecrmcvX/00UfZb7/9APjJT37C3//+dx577DG++c1vAs5cfEWZOHEijzzyCL29vUydOpW9996boUOHsv322zN16lRee+01XnvttQYxKwxDbr/99pWexfDoo4/mqquu4nOf+xx33303jz76KI899hjDhg1b6jEs2Yb5fB6oHW/a/iNHjmwo93btv+T3nMvllrl8Vb6Dt+s/jz76KP/5z3848sgjl5oCe9ZZZ3H55ZfzyCOPcMABBzB06FD22WcfHn/88WVuUxiIeGYJgiAIgiAIgiAIgwrP89hrr7246667mD59+lJFg+nTp/PEE09wwAEHNPhlwcBIraWRCiazZ89mnXXWyZZHUZQJLauDm266iSAIuOOOOxqEr9///vcrvc199tmHb33rWzz00EPce++9TJkyJVv+l7/8hfHjx2fvU6ZOncrixYs57LDDVnh/ixcv5o477mDKlCmceeaZ2fJKpcKCBQtW6hjS9p81a9a72v6w4t/B2/WfI488kpEjR/LNb34TYwznnHNOw+e+73Pqqady6qmnsmjRIqZOncrZZ5/NRz/6Ud58801KpdJqOaYPOhKZJQiCIAiCIAiCIAw6zjrrLKy1TJ48uWHmPnAG8V/84hex1nLWWWet1Pb32GMPAG6++eaG5b/97W+JomjlKr0UlFL4vt8guPX39/OLX/xipbf5kY98hNbWVq688kpmzZrFvvvuC7iIraeeeopbbrmFzTffnNGjR2fr3Hrrrey0004NwtGKHIO1NouuSvnpT3864LtZXiZMmADAjTfe2LD8lltuWa3tD6v/OzjnnHO48sor+fa3v/22/W/IkCEcfvjhfOlLX2LBggVMmzZtpfa3NiKRWYIgCIIgCIIgCMKgY9ddd+XKK6/klFNOYbfdduPLX/4yY8eO5Y033uB///d/+ec//8mVV17JLrvsslLb32KLLfjkJz/Jd7/7XTzPY++99+a5557ju9/9Lm1tbWi9emJDDjroIK644gqOPvpoPv/5zzN//nwuv/zyAcLQiuB5HnvuuSe3334748ePZ4MNNgBcm+Xzee69916+8pWvZOXjOOYPf/hDQ1TVitDa2soee+zBd77zHTo7Oxk3bhwPPvgg1157LUOGDFmpbW622WYcc8wxXHnllQRBwMSJE3n22We5/PLLaW1tXaltLot34zv46le/SnNzM5///Ofp6enhBz/4QeahtuWWW7LDDjswbNgwXn/9da688krWW289Ntpoo9V4VB9sRMwSBEEQBEEQBEEQBiUnn3wyH/7wh/nud7/Laaedxvz58+no6GC33Xbjb3/7GzvvvPMqbf/6669n1KhRXHvttXzve99j22235ZZbbmH//fdfaZFmSfbee2+uu+46Lr30Uj72sY+xzjrrcOKJJzJ8+HA++9nPrvR2J06cyO23397gi5XP59ltt9245557GpY/8MADzJs3j0mTJq30/n71q1/x1a9+lTPOOIMoith111255557BpivrwjXXnstI0aM4IYbbuAHP/gB2267LbfeeitHHXXUSm9zabxb38FnP/tZmpqaOPbYY+nt7eWnP/0pe+21F7feeis//elP6erqYuTIkey7775861vfIgiC1XhUH2yUTad3EARBEARBEARBEAThbfnHP/7Brrvuyo033sjRRx+9pquzWpg8eTL//Oc/eeKJJ9Z0VQRhuRAxSxAEQRAEQRAEQRCWwj333MPDDz/M9ttvT7FY5JlnnuGSSy6hra2Nf/3rXwNmKhQE4b1B0gwFQRAEQRAEQRAEYSm0trbyl7/8hSuvvJLu7m46Ozs54IADuPjii0XIEoQ1iERmCYIgCIIgCIIgCIIgCIOG1TP9giAIgiAIgiAIgrBCXH311YwfP55CocD222/PX//61zVdJUEQhEGBiFmCIAiCIAiCIAjvMTfffDOnnHIK3/zmN3nqqafYfffdOeCAA3jjjTfWdNUEQRDe90iaoSAIgiAIgiAIwnvMjjvuyHbbbcc111yTLdtss834+Mc/zsUXXzygfKVSoVKpZO+NMSxYsIChQ4eilHpP6iwIgvBuYq2lu7ub0aNHo/Xbx16JAbwgCIIgCIIgCMJ7SLVa5YknnuDMM89sWL7ffvvxj3/8Y6nrXHzxxZx33nnvRfUEQRDWKG+++SZjxox52zIiZgmCIAiCIAiCILyHzJs3jziOGTFiRMPyESNGMGvWrKWuc9ZZZ3Hqqadm7xcvXszYsWN57NFOmptdBIOnFHvf9SWaX/WpbNdD5x+KHHDmg9x0y960vhZz+FlTeXTxOGZevj5fvuQWLrj2U/SOjxn2T0Xbp6czrNjDv2/dnJ4NYtr/rTnva9dzxi9OoNJpaH9OYT62gCj2aL+2ibnH99NUqHLZxrdy0m8/j/Wg6U3o2bmfT2/xCDfdsjcjH+nn9Gt+xea5nnevMQVB+MDQ3WP40Ifn0tLS8o5lRcwaRCxv+PD999/PhAkT3t3KrAD33nsv3/jGN3jhhRfo6+vjtttu4+Mf//iartYq88ADD7DXXnu96+39xz/+kUmTJjFz5kyGDRu21DLjxo1jwoQJ3HDDDe9aPZbFueeey3nnncf7LWP5+OOP57e//S09PXLxJAiriow/7y/eT+OPUoovfelLXHXVVe9aPZbFRRddxOabb/6B+E6FtZclz6/W2mWec/P5PPl8fsDyYrOm1FJLx2l/rZmujQy/2+sXHBZ9mY5WS84UGPrAc1y/5aGsP/E1ihWfruJQ1n00ZOPPvMjDz+3Af+eM49K9r2bWaU/zxwUf4rVbN2ZufiTrPmppu2g6L8/ZmN7po8it1wNtJW7Z/Sd06Ig+qxj9dEDP5xfRZzrJTS9yxMTn+cfBW6IeaqPQ4tOS03i444pZ+jWjh1rmZ/Vl0m280/ZWZvuryooew9ttY8ljrS+/5DaW9vmS23+nur1b7fN2213R73BN8272ofeifw4mlufaU8SsQcTDDz/c8P6CCy7g/vvv57777mtYvvnmm7+X1XpbrLUcccQRbLzxxvzxj3+kqamJTTbZZE1Xa1Bx6623ssceeyzzRkIQBOHdRsaftZP3+/hz0UUXcfjhh4uYJQxKOjs78TxvQBTWnDlzBkRrvROhhUrdPfCmR73Iv+/clE/939fYZv//snvpP1xrDuKt47ag898RLwxdj/VNhbeq7WAtM/rb0DGoPo/nKqP50/ytee62TSlsapkZDkEZy8zeVnQVvLKi3Jej6Y1ejrzhVKy2fOvIW5i5s0fTnzvpnBUz8xNV3ozaGFroZQFtvFgZjYdFKwOAh1nivcXDUtIRAZaCchFmsbUsMpo+6xPz9je26TaWRqAMLTqmtBptxRYYmBU3EVqP0PoEKiJM6llQYVYXrQweteN07xvrGqMajs9YTRWvYX+h9YitEyxNModbjCK2mpyK6TIFyibAoNEYPGXxMGhl6DN5qtbHwxCoCE9ZAhXRqst0mYKrjzK06H7W9xfToldfQy02lv+GQ8ipOKszQGw1Bk2vceKspwwakx0bQKCirGxaJiDGS/pNug0AjWnYh4elamttWL9OTsVoZQitt9Q+83bfmVaWAMMQbQhWo09dn7WULcS2tk33HQ6sn/veVVYmW24VZom+tDSW9TsB0CsgpqX104CnoGzT71bhKZv9/3b1CJSbkTD9pla0TUXMGkTstNNODe+HDRuG1nrA8iXp6+ujVCq9m1VbJjNmzGDBggUcdthh7LPPPqtlm/39/RQKhbXC6DIMQ/74xz9y4YUXrumqDBrWZH8XhA8qMv44ZPwRBGF1kcvl2H777bnnnns47LDDsuX33HMPhx566Aptq6DIbgoBtmyZwdZHvcWv/7s9Xf9vXb79zUPRoaV/uGLeVj4b3dgFseWX9+/OxtVFPH/XxoycVSV4wOO6Px+G1YpSu2Xokwv4w5V70dm9kPiXw+mYWaH1dY/w3zkWb5Sj6S3L8L/P57szj2D9x7pZuFkLumpZ59cB5/7xs1SbFcPLC7juyoMJei1WK1BgPLC++x8gzivCZijsOJ9ths/gv4s72WfkS/zh9a3o6i5R+HcRXQUU2Ho/aAU2XabA+GB9i1W1slaDyVv22Pk5vrPOXavwjTXy7ekf47k/bAqADmvLrYY4DyYHxrOYPMR5i80b8CwqF+MFBs+P8X0nmBijCKu123JrFXFVQ6RBWffFhsnBAlhQRqFCBdbt0+9TeP0KHYEyrkzaPn6/q6PVSb1893+ct+iqwuQsxoe4xXDC7g9yUvsTq62dnqisw5mPfAI/F6G1RWtLHGtMrLPjVNqCAu0lwoeyeJ5B6/R/J84pZSnmQhSglSU0bhtKWQp+RE7HGBSR0ehERDFW4WuDxq2vlaXghfjaYKzK3hurCXSMTgSrold1wpWKs/8LOqSkq3T4PexYmMYwb/VFUXUbjzejIQ0CXE7FlHSFHE6kS8Xfsg0y4bKgwkz06zV5+my+QfwDJ+Clr4FE7DQDlmcCLLVlS5KWDxLhMMUk+9Rq4LreEttrFHprIiFAgCE0y9+ub28PLww6JkyYwJZbbslDDz3ELrvsQqlU4oQTTgDc9L/77bcfo0aNolgsstlmm3HmmWfS29vbsI3jjz+e5uZmXnnlFQ488ECam5tZd911Oe200xpmUAG45ppr2GabbWhubqalpYVNN92Us88+G3DpZ6lp2ze+8Q2UUowbNy5b929/+xv77LMPLS0tlEoldtllF+68886G7d9www0opfjLX/7CCSecwLBhwyiVSlQqlexYH374YXbZZReKxSLjxo3j+uuvB+DOO+9ku+22o1QqsdVWW3HXXQMHsJdffpmjjz6a4cOHk8/n2Wyzzfjf//3fAeVefPFF9t9/f0qlEp2dnZx00kl0d3e/4/fx3HPPoZTiN7/5TbbsiSeeQCnFFlts0VD2kEMOYfvtt29Ydu+997J48eKGi5zloVwuc9ppp7HtttvS1tZGR0cHO++8M3/4wx8GlFVK8eUvf5lf/OIXbLbZZpRKJbbZZhvuuOOOAWXvvPNOtt12W/L5POPHj+fyyy9f6v7TbV5//fVssskmFItFdthhBx555BGstXznO99h/PjxNDc3s/fee/PKK680rJ9eyI0ZM4ZCocCGG27IF77wBebNm9dQ7txzz0UpxZNPPsnhhx9Oe3s7G2ywwTLb5e9//zudnZ0cfPDBWb+/7777mDBhAkOHDqVYLDJ27Fg+8YlP0NfX947tLAhCDRl/1u7xZ3nGkOU55uUdv5RS9Pb28rOf/QylFEqpLOUy/e7uu+8+TjzxRIYOHUprayuf/vSn6e3tZdasWRxxxBEMGTKEUaNGcfrppxOGYcP2zzvvPHbccUc6OjpobW1lu+2249prrx2QUj9u3DgOPvhgbrvtNrbeemsKhQLrr78+P/jBD5ar3YS1m1NPPZWf/vSnXHfddbzwwgt87Wtf44033uCkk05a4W1pwOAiHH5909784tZ9qJQD/N6IV+5Zn+ZZMe0vGYJuiAs+uhrR+rImbs7T9qrB64vwqpZqq0fYrPErFlPKUVwQE7cU8Kqg4qT/J0KJ32cxxYDCQkvUksOrWnRksR40Ty9TXGDAWppnxrS8XqZ1WpmWNyq0TavQ+lqVttdCWl+PaH8lpP0/MV0vt/PQyxtS/tVI7rp0D3I3tzPsjjwdL0a0vxzR/p/k//T1fyLa/xPT/p+YIS/HtP3X0DINmt6C4hxFfpEi6HEiz+z+lre5RV9x2oJ+2v8T0fFCxJCXI9r/E9L+UkjHCyHDngkZ9lTE8KcMwx+3DH8MOh/1aH/Cp+XJAoWnSnhPtRA/3Ub8rzbss63kni2R+7f7K/yrSOu/8rT9O6DtXznans7R9mxA23O++3vBp/VFj9aXNa0va9qfV7S+Ck0zLKWZlqa3LM3TDS2vG4a8EtP2WsSQV6oMfb7C8CfLjHy0zKi/Vxhzf5VR/6gw8pGY0X+PWede+PXL26/WdpoRttP2aIHWqU2U7msm91ArTQ800fJQkda/FRjyeJ7Wxwu0Plag6bEipSeLFJ4p4T/djH6qBfNEG+FT7fQ/3UHvMx3MfWoEc54ewcynRzLvX8OZ99ww5j43jDf/NYqXnx3Df58bzesvjeTV/4zk1VdGMu3lEbzy8ij+8+ooXnptFC+8NpqnXx3L46+ux1Ovr8uTr4/lkWnjefSNsfxz+nr8c8Z6PPzWOB6cviF/n7k+D87ckPtnbMQDszbi3tmb8Jc5m3HX/C2ZEb+zn9OK0Gt93grbeb06jNerw3i1Opxp1U4WxM10mQK9Nke3KWR/c+NWZkVtzIlbmB83s8iUmBW18WplOC+UR/NSeRQvlUfxfP86PNs/huf71+HVynD3Vx3OS+XRvFodzquVEUyrDmNadRj/rQ7njXAoby7jb0bUzlthO7OiIcwI25kVtWV/c+KW7PXcqJUFcXP2NzduZX7cnP0tMiUWmRIL4mbmx00sMkUWmQLdJke3DVhocsvdbhKZ9QFk5syZHHPMMZxxxhlcdNFF2ZSWL7/8MgceeCCnnHIKTU1NvPjii1x66aU8+uijA1JFwjDkkEMO4bOf/SynnXYaDz30EBdccAFtbW18+9vfBuCmm25i8uTJnHzyyVx++eVorXnllVd4/vnnAfjc5z7HNttsw6RJkzj55JM5+uijszz/Bx98kH333Zett96aa6+9lnw+z9VXX83HPvYxfv3rX3PkkUc21OeEE07goIMO4he/+AW9vb0EQQDArFmz+MxnPsMZZ5zBmDFj+OEPf8gJJ5zAm2++yW9/+1vOPvts2traOP/88/n4xz/Oq6++yujRowF4/vnn2WWXXRg7dizf/e53GTlyJHfffTdf+cpXmDdvHlOmTAFg9uzZ7LnnngRBwNVXX82IESO48cYb+fKXv/yO38UWW2zBqFGjmDp1Kv/zP/8DwNSpUykWizz//PPMmDGD0aNHE0URDz744ICLl1tvvZWdd945q/PyUqlUWLBgAaeffjrrrLMO1WqVqVOnMmnSJK6//no+/elPN5S/8847eeyxxzj//PNpbm7msssu47DDDuOll15i/fXXB9yNzaGHHsrOO+/MTTfdRBzHXHbZZcyePXupdbjjjjt46qmnuOSSS1BK8Y1vfIODDjqI4447jldffZWrrrqKxYsXc+qpp/KJT3yCp59+Oot2+O9//8vOO+/M5z73Odra2pg2bRpXXHEFu+22G//+97+z7z9l0qRJHHXUUZx00kkDbo5TbrnlFj796U9zwgkn8MMf/hDP85g2bRoHHXQQu+++O9dddx1Dhgzhrbfe4q677qJarUqElyCsIDL+rJ3jz/KMIct7zMs7fj388MPsvffe7LXXXnzrW98CoLW1taFen/vc55g0aRI33XQTTz31FGeffTZRFPHSSy8xadIkPv/5zzN16lQuvfRSRo8e3WCsPW3aNL7whS8wduxYAB555BFOPvlk3nrrrawfpjz99NOccsopnHvuuYwcOZIbb7yRr371q1SrVU4//fR3bD9h7eXII49k/vz5nH/++cycOZMtt9ySP/3pT6y33nortJ0Yl2roKfht9xb0jQ8Z/pCP92KRRRsqWl63VFo8UNAyPWbhpkWaZucY9lQv5eF58otiqkNy5BZWKcyO6VunhN9v6F23RMsLC+jdsJ2gJyZs8dGhpemtMn2jC/h9hq6NWmh+vZ/eMQX8iiEuKIyvUKGL3OjavB2/fwl5xIKOLdYAykVS5SLLmKkK8FE2SsqlkTru+tAqF3VktduHCRRRQRHnISoowhYImy1xyWIKMfgGXYjJ5SM2bp2zWqM4hgT9lId4BH0Wr2pQVdCRdYKfraVNocB6rr7WU8Q59zrOK+IArAfGV+jYkga7KIOLsIpr4rnVCquTdtPKtYUFZWwSqeX2lbavMsnn1q0bFzQ2tOiqwQsNOozRoUkit7ysrftnNRNbi15NEcgj/cXkF5kkMq/uA+XqBTaLoLMajKewXhJxl7RN+sUZz722OglS02lknkrauRa5ZzVYz7UNOona07j+ptP9uzKx5yLDQm1BWxdBpy3Ks3h+jDUaz4/R2kV3zc01MbezFXJdq6WNUhbHJRZETVnEWBoFFigvS4mMUZRNju64QNkGLtVSa7QydJsib1WG0BUVAQh07FIPrSavI3wdE6g42359dJVWNkn1dMsNKolKq6Vbxskylx4YoZXNtuVhiNEuhbguZTTdR310VjmNyEq2HRKRZjcGxISmutxtJmLWB5AFCxbwm9/8hr333rth+TnnnJO9ttay6667stlmm7Hnnnvyr3/9i6233jr7vFqtct5552UXwPvssw+PP/44v/rVr7KLuL///e8MGTKk4eljfSrHmDFjiCI3GI0dO7YhHeXMM8+kvb2dBx54gObmZgAOPvhgtt12W04//XSOOOKIhjSOffbZhx//+McDjnX+/Pncfffd2RPlHXbYgeHDh3PJJZfwyiuvZBfho0ePZtttt+XWW2/l5JNPBtzTsJaWFv72t79lF8D77rsvlUqFSy65hK985Su0t7fzve99j7lz5/LUU0+xzTbbAHDAAQew33778cYbb7zj97HPPvswderU7P3UqVM55phj+O1vf8vUqVP59Kc/zaOPPkpXVxcTJ07MysVxzO9//3vOOuusd9zHkrS1tWURAum29tlnHxYuXMiVV145QMzq7+9n6tSp2awR2223HaNHj+aWW27Jpoz+5je/yYgRI7jnnnsoFAoAfPSjH22IdqinUqnwl7/8haamJsA9Rf/4xz/O/fffz5NPPpl9v3PnzuWUU07h2WefZauttgJouKmy1rLLLrswYcIE1ltvPf785z9zyCGHNOzruOOOe9upqi+99FK++c1vctFFF3HGGWdky5944gnK5TLf+c53su8W4Oijj17mtgRBWDYy/qyd48/yjCHLe8zLO37ttNNOaK0ZNmzYMtNdDz744CyCeN999+Xhhx/m17/+NVdccQVf+9rXAJg4cSJ33303N954Y4OYVV8HYwwTJkzAWsv3v/99vvWtbzX0kRkzZgz4jubMmcMFF1zA5MmT5cGI8LZMnjyZyZMnr/J2DPDvynC+d9/+dD6paX2jjL+oDNYSdpToXjdPnIOgD4b/Yz6qtx+MJXgTSD2SjAVraZ29qLZhpWh6YS4EPiQPKKxStPY03nC2vBqB1onIoLCepjin4oQGDdavUzKU20a9tY+KrRNe0mXKiT+pABTnnHAVFRVRCaISmJx1KX15g/UtqikiKEQ05UOa8lXyXkzei2gKKqyTX7jKbVzPXi0v8Ju9toOuAK/Pw+9T+L0Q9Fr8fvD7LV7V4lUMOnIiElqhQydmeVUnxhkf4hzUWxVZTU2gSt4r48LhrHKphQqytlbGRcOl21A48SZtRxU7cSzOKXRBExmLDpO/qkFZiwotylj8bk0IFFaTGXmgYnQIXrUmZthUnPSs+46ztFAnjFjtRCyrQYc2E6qUlwpgiXCVHnO9kFWXXgo1YQxra/0uE8fSdRMxLLBYbSECm9VDo32DMT5KW7Q2KAVdprjKbbMk3XGBrqiQCVmBbvQMS6WnmERAstZ5qaGJrSa0HhUT0B8HRFa7FEtl0MpSMR55HSdplzGesmgseR0maZgxYZL3W8EnRlPQIToRnQIVu+0TkNchZesnwljtd53XISEenjU1gStJZQzxMi83LEtE/7nOqjGgoM8uf2SWpBl+AGlvbx9wIwHw6quvcvTRRzNy5Eg8zyMIAvbcc08AXnjhhYaySik+9rGPNSzbeuutef3117P3H/nIR1i0aBGf/OQn+cMf/jAgBWxZ9Pb28s9//pPDDz88u5EA8DyPY489lunTp/PSSy81rPOJT3xiqdsaNWpUQ2pER0cHw4cPZ9ttt214mrzZZpsBZPUvl8vce++9HHbYYZRKJaIoyv4OPPBAyuUyjzzyCOBm59piiy0axA5YfsFjn3324dVXX+W1116jXC7zt7/9jf3335+99tqLe+65B3A3GPl8nt122y1b78EHH2TevHlMmjRpufazJL/5zW/YddddaW5uxvd9giDg2muvHfBdA+y1114N05+OGDGC4cOHZ+3V29vLY489xqRJkzIhC6ClpWVAP6nfZipkQe07OOCAAxpuApb8bsCZn5500kmsu+66Wd3Tp5RLq/+y+oe1li984QtMmTKFX/3qVw1CFsC2225LLpfj85//PD/72c949dVXl7odQRCWDxl/1s7x553GkBU5Zlix8evtOPjggxvep9/FQQcdNGB5ff8Cl4I+ceJE2trasj777W9/m/nz5zNnzpyGssv6jrq6unjyySdXqM6CsDIYXJrSV+89hvVvDRn29znkXp+PXtCNXthDbk4PTTOrDHtsEW2PTEct7IJqCFEExkAUu780EipObjW1xgY+eF4tOiqJjsE64csmolMqZFlfYz3t/k+iajLhSqURRjUhy62rsL7GBIq4qKm2+fQN9+ke47NoA5+FG3vM31oxf1vLom1CeresEG7ah96kh8IGXbSM7aJz7CI2GjOHrUbPYOvhM9myYxZbd7zFth3T2abtLdbPzV2t6XPr+ovYacPX2HiL6azzoZm0fGQuZufFLN61zNxdI2btYZm5q2L2RwLmbpNj0UY5ekcEVFs01nNRXF7V4FUsQZ8l6Lf4FYuOrTPjr/MNSiOsGkgzPhOBxqpau6ZikPEVxnOiWZx3gmBYVIRFTVjSVFs8Ku2B+xuap9KexwR2tQoEMcpFrEVOPFORzSLYnJjm3md/sfvfC2t/OiT786rJ8qrFq1h0lHxWrStTcX/ZepGbuEBXnIioQpzAF4KOnMCoIlAVhapqVKQgTv6qGlP1sJHGhJo48ghDjzlh6zsf/Arg4YSpyHpExiPQTroyicl9+peWTc38oWYab6ymYnzKsU819oiMpi/KUY59IuPRHwcYFFXjU4l9QqupmIDuqMCisERXVKTP5OiJ8247JiC0HmUT0GdyyWQHTjALje/+Tzy+YlwEWDohgrGKGPe+av3MrD+ta723Vmx15tMV2xXrfRKZ9QFk1KhRA5b19PSw++67UygUuPDCC9l4440plUq8+eabTJo0if7+/obypVKpQbQANx1wuVzO3h977LFEUcRPfvITPvGJT2CM4cMf/jAXXngh++677zLrt3DhQqy1S61negMwf/78dzwmcDcPS5LL5QYsz+WcwpvWf/78+URRxA9/+EN++MMfLnXb6c3R/PnzGT9+/IDPR44cudT1liR92j116lTGjx9PGIbsvffezJ49mwsuuCD7bNddd6VYrKn8v/3tb9l+++2XGfn0dvzud7/jiCOO4H/+53/4+te/zsiRI/F9n2uuuYbrrrtuQPmhQ4cOWJbP57N+sXDhQowxSz3mZbXDsr6Dd/pujDHst99+zJgxg29961tstdVWNDU1YYxhp512GtBXYdn9o1qtcvPNN7PFFltwwAEHDPh8gw02YOrUqVx22WV86Utfore3l/XXX5+vfOUrfPWrX13qNgVBWDYy/qyd4887jSErcswrOn69HSsyDtX3r0cffZT99tuPCRMm8JOf/IQxY8aQy+X4/e9/z//7f/9vQJ99u7Fxyf4kCO8GGnikfxzr/hly0xeiwsgJUtaCMajFPRQX9zjhagnfN+LYCVVJ1BVKge9hPe1ELJ0KVYmIpZSLmqp7b31dJ1LVCVhQi4bJXqeVVlm6YJxXhCWdRFwpoiJERUtctMRNMbo5JF8MaQoiCrnQzcbnxfjakPciSn6VtqDMkKCPZq/iIkqSaJKSrlJQVYZ4S7ehWFkKKmbLlhn0lPL0mRyR8agYn95EPOiLcpSjgK5ynv5Kjr6Kj+n3UWWN1+sRdCuCHhfJ5VVJhBkXZmQ8F6WWtVcq/CV+8GmKoQvPqotIUtQK16UcWgBP1dY1TuxS1rp0RKOIA4UJwAyrLDGP4qrhkab1KYgN2iYRVSpNgQQbJ6Kml1RQg7I2S6202rpua1XtODWo5DOSj6ypRWIpSxbd5tIJa/9rq1wKogc2Tcc0KpmQwIJRSb9PI94UNrYQGLCKyLooqtVJIUn/q8Q+eS/K2q5qPQJbZ7auajNCauUEMIPGIyZGEVlNOQ6IjKbgRWhliJKIK18bolgn0VoGDFTx0Vh87aKs+uOAJt95lBqr6DcBeR0R2iBLGUwFp9QUP7QegYopWyctpQ6UeRUSWp+8avSkrFoPDxcBGKgoEfL8LD1xRRAx6wPI0mZZuu+++5gxYwYPPPBA9jQcYNGiRau0r8985jN85jOfobe3l4ceeogpU6Zw8MEH85///GeZ+f7t7e1orZk5c+aAz2bMmAG46YrrWd0zR7W3t2dP4r/0pS8ttUx6AzF06NAB0yYDS122NMaMGcPGG2/M1KlTGTduHDvssANDhgxhn332YfLkyfzzn//kkUceaUiTM8Zw22238ZWvfGUljg5++ctfMn78eG6++eaGtlvSQHl5aW9vRym1Su2wvDz77LM888wz3HDDDRx33HHZ8iVN4utZVv/I5/Pcf//9fPSjH2XixIncddddtLe3N5TZfffd2X333YnjmMcff5wf/vCHnHLKKYwYMYKjjjpq9RyUIKwlyPjzznzQx5+lsSLHvLrHr5XhpptuIggC7rjjjgZh9fe///1Sy7/dd7Q0oU8QVjce0OL1o2IGCFmAE6wgi64agNbuz9NY38teoxRW65qIlWwji8TyVE3E8lSDaAW1iKwBAlagiIraRQk1K6ImlzYYFyxxwWDyBnKGoKlKa6lCa6FCc65CZDS+NhS8kJyO8XVMq1+h6FVp9iq0+X2UdDVpExe5UtAhHpaCCldrxFGgoM3vSw7LCSOh9RiWcxN0uAgWn8h6dIUFIuuiZCqRT38U0B8G9JZz9JQDTMVDL/bJLfbw+8HL0hSTdtS1NrQ48cWkKXZLHFR9YEsqfmXvk3/S9Dxla/0hKjgxK1dwwsPqSDEEaNIV4lySMmhrPmCZGGcs+BriZIFy3mEunTJV4lxqpbYuLTHNGrQmOf5U3LIWg0Klfa6+fZJlKnJd2cbKzajp2ySNMwkeTIUuwKbbSnMZQ52IZ5pFYRHDgOZfKTwUGkuMxuAELXB9qGxzeNZFYmlliJPIpxhNT1xAK0PZBKAhND6h8VxUlNWUYz+btRHI/icR9NLKa2WpGh9fGUwSYaXrvLXSiKy0fL3Xlou4Ulk6YRpxFaOSFEhFxbjUxLQZXeO6lMPQ+hhq63howiUF97dBxKy1hPSCMDXATVmaD8jK0NTUxAEHHEC1WuXjH/84zz333DJvJpqamthxxx353e9+x+WXX549DTbG8Mtf/jK7+H43KZVK7LXXXjz11FNsvfXW2dPapbHXXntx2WWX8cwzzzSkEfzqV79a7v1NnDiRW265hXXXXTdLb9h4440ZO3Ys3/72twnDsMGv5B//+AezZs1aZnrLO6GUIpfLNdwIzJo1a6mzGS4PTU1NfOQjH+F3v/sd3/nOd7KL++7ubm6//faV2uayWN199UMf+hAPPvggEydOZMKECdxzzz0MHz58QDnP89hxxx3ZdNNNufHGG3nyySdFzBKE1YCMP4180MefpbEix7wi41d99NfqRCmF7/t4Xi0+ob+/n1/84hdLLf/cc88t9TtqaWlhu+22W+31E4QliYHuuOh8iYx14pW1jbNvGotKBKqG6CzPA98DL4nGqheylhaN5alaKmHqh7WkiJUYlAOZgGU9iIqaarOm2qoImyBqskQlg81ZbN44423fEAQxfhDT2dLLkEI/BS+k4IXkdUxeRxS9KoFyr0ueE7oDFdOiyzTpSs10Wpks8sNbTeJMSgC06H5Cz8uiU7LvI1GUTNIgHcnEIcYqKsYJEWmqV2/sIri6wzx9YUAl9ClXA7p6c7AoR9Cj8HuUS52rAsaZwNdT761Vp0+lGlDNSysNfIprvlyp0XpUcv/nc9FqbacOr4eopLCLVSKg2cz8yWonTBEZN3tBUkcTqMwjLCmJUirJbE0ivaxyKm4SeaViV1zXRYIpW9fVbZIdm3ZVF5iEJvXssolw5aK08AA/aSA/EbQUYBTWQNWsXhnFU5aCDqnEPr6OWRwVCbVHXoc1U/VMAPKyFD+NopBEPgU6yqK7HBptnUm7E6k8fBWjcaJfOQ7wlcHzLLFV6ERUqxg/67s5HZHXEZ6tRUzV93WTCXHudT7pjMZq+pKILYCKCdDK4FmTGcunKYeeghiXahgnwtjyImLWWsIuu+xCe3s7J510ElOmTCEIAm688UaeeeaZld7miSeeSLFYZNddd2XUqFHMmjWLiy++mLa2Nj784Q+/7boXX3wx++67L3vttRenn346uVyOq6++mmeffZZf//rXq/1J+NL4/ve/z2677cbuu+/OF7/4RcaNG0d3dzevvPIKt99+ezbD1imnnMJ1113HQQcdxIUXXpjNJvXiiy8u97722Wcfrr76aubNm8eVV17ZsPz666+nvb29wXvlt7/9LVtuueVK31QdfPDB/O53v2Py5MkcfvjhvPnmm1xwwQWMGjWKl19+eaW2ecEFF7D//vuz7777ctpppxHHMZdeeilNTU0sWLBgpba5NDbddFM22GADzjzzTKy1dHR0cPvtt2f+LivDZpttxl//+lcmTpzIHnvswdSpUxkzZgw/+tGPuO+++zjooIMYO3Ys5XI5S2Opv7kTBGHlkfFnIB/k8WdZLO8xr8j4tdVWW/HAAw9w++23M2rUKFpaWthkk01Wua4HHXQQV1xxBUcffTSf//znmT9/PpdffvkAQTZl9OjRHHLIIZx77rmMGjWKX/7yl9xzzz1ceumlYv4uvGcsjkt4lTjzssqo816y1tY815XKhKyGaKxlpRQuS8RaWhRWKmYpF4UVNmkqrcqJWC0QlSxxSwyBQfkGPxejPYPWFs8zFIKIjmIfY5oW0eRXKHohBR1maYPpDXJe19KXciqioFy5ggqpJjfc9SLW6oqkAfCUoklXMbjorJpJdyJkWUVofbQyDTf/ofUy0+zYaipJatb8ajP9cUAlEUm6wgKVyIkKfWFAV3+Bxd0F6ArwuzR+rxO4dOL9hEmEogSbiENWOX8u66lM2DLKeZ2pxPMsLuLS14rQUSwn7bZ6DOBzGIzv6qZDFzGo0uYwdYJWOgGAVejErN34kBrY28Q43KlvuJREo7J0RWiMSlPUUhNdWiWQRmXVZdQCSadQSQpkbbbDzHHdKiemeRYbuPzF2K6+a4W0nevT/VKxrGICvMSoHchEpdB69JkcGkuLLjsD+GSddJZBgP4ocJGDCUankVfOpD1CQ+w7k/hkuYcloBZ9VTE+HobI6IbtG6XI6wiUySK0wtgjryJnVJYdn6tLgMl+u6H1M8+vrG7J/lcEEbPWEoYOHcqdd97JaaedxjHHHENTUxOHHnooN99880o/Ndx999254YYbuOWWW1i4cCGdnZ3stttu/PznP2fYsGFvu+6ee+7Jfffdx5QpUzj++OMxxrDNNtvwxz/+cYBh67vF5ptvzpNPPskFF1zAOeecw5w5cxgyZAgbbbQRBx54YFZu5MiRPPjgg3z1q1/li1/8IqVSicMOO4yrrrqKQw89dLn2tffee6O1plgssvPOO2fLJ06cyPXXX89ee+2VTWEPzjPkhBNOWOlj+8xnPsOcOXP40Y9+xHXXXcf666/PmWeeyfTp09921r+3Y9999+X3v/8955xzDkceeSQjR45k8uTJ9Pf3r/Q2l0YQBNx+++189atf5Qtf+AK+7zNx4kSmTp2aTZG+Mqy//vqZoLX77rtz7733su222/KXv/yFKVOmMGvWLJqbm9lyyy354x//yH777bfajkkQ1mZk/BnIB3n8WRbLe8wrMn59//vf50tf+hJHHXUUfX197LnnnjzwwAOrXNe9996b6667jksvvZSPfexjrLPOOpx44okMHz6cz372swPKb7vttnzmM59hypQpvPzyy4wePbphxkRBeLcxQElXKHfkyL2SpEhZ2yBkpVhrUVpDLmiMxkq9sbxaOmHmh1XviZWmE9ZFYUGdiJX4HJmcotrkorAqQxRhiyVqMthCjCrE+IHB82N831DMheT9iGIQMrTQy/B8N+1BH3nlIq/SyJMYTaDiBvNrd/wuqiNQMYGKnBk1mkBFaAw5FTekRq0qHgqDdZFZyWxyWTQYJhO0YquySJrYapdGlhhjZ3W3mhhFXkVUEqNscAJCb5xHJ0JHmAgcofFYUCnRVSmwqK9I1+ICelFAbrHG70uMzNNIrDQCqm7GwPS9q2DNY8p6EDYbhpe6k7ZePZFsvTZw6a8xqNi4PpWmGqZ6UNKdXNqgdX1MK3RonZ+Wj+ufBsC6VECbeGklTWl1GoaW+GSlglnivaWjusg1ry4FM07KJ6KYtQrjJ2FccRqqVXPbt54T1YblelZL+yxJ6neVT8SgPpNzUUxGZaKWsYqyCSibAA9D2S4hWFlFJfIJvNgdD+64VFLGoKjGQZaya5IQPoPCx/lY9SdRW+kMh4GOXVSXcvmdWlnySSRY2p1qfdUD4yIjDbbmtYXOviMXzegErar13CyHuN9DuAKubcraFUhKFAThXefRRx9lxx135F//+hdbbbXVmq6OIAiCsJYg48+KM27cOLbcckvuuOOONV0VYS2kq6uLtrY2nn1+OFPZilu+tT+t/3wT4hgbLxHhoF10Cb7fGI2llPPKqvfGSnyxslkJlyZiLSUSy3qqMZWwGcIWS1yymOYIlTP4QUyxWKWYCyn4EXkvYmihl3WKi+gMemj2yhRUuFQz6FS0AjKRKBWrtDI0aZdy6GET/x13m1tQIR1eHyO91SNoxdalVb0aNbMgbqaa+BilKY3pzG5xIqilr2tpVbY2C1ziNVQ2AWUbZOJWmKR6pSlhgYob1qkYnz6Toz8O6I8D3uxpZ/biFvoXF1C9Hn6vE7d0NRGSUkN0Sy1ELRF9TOAilyrDDNvv8DLHjHg4a+/6lK9UuEvrCmTioosUymcz4AG0eGWmlYfy+OXbU5wT4veFDX2IdAbGOkHK1vUlZZ1RveuLZEKpVbXXrkzSPzxVi87K+iYN66ATv7E08qv+c59M9DO+rQliuhbNZQKLKRpGj5vHpkPmJO2is+81TRM09WFiQJNfyQQbjUUrQ15HbFV6k+F+N1Xr8UjPhjzfNYrIajryvXjKMjzfTYtXTtJqXdRhaH0Wx8XMhH5E0EVehyyMmvhX1zrM6m2lnIhZqe9VKQgzsUkpi1aWnI7JeVEWxeVrk0WHue/WZCJZvZjl/Ltsw2t3/G59T9VSCdNZGrUyFFSURV6l/aa2nkuh9DD09cScuN2TLF68mNbWt581UiKzBOF9xkc+8hFEYxYEQRDea2T8EYTBiQf8c/F4gq5l+B1p5aKxfB8Cf2A0Vv3MhFqDrzF+EqWVGr6rZRi6K7C+ImzyqLQowlZFWIK4ZImKFlMwUDAEpSq5XExbqZ+WXIUh+X46cn10BL20+X20ef0UVBVP2SyiyasTs+J0xjbroq6wjWJXOiNaGpkVJ67eqbjUZwLeTIUi6+ElgkL6eW0b3lJTndJy9SmDZZN6YTmhqmyKmTAFDPDSqo8OS2/e02VaGwIbE6NdKmJyHPXrpu2hsygWRdk68Wh8aT49HXkWhSW6ozxz+5uZ19NEz4KSM5fv0gOFrSQiyQTuvVdWPP7KOB5/ZRy2qlFVnfhE4QzWKwoVuygnXXVCGUnank4yy3KLLblui1exxHlFtUXRtiDEq8SoyLg+V9d3FM5oHVxqILZm62Y9lyKYmX3Vz3Zoat5YOkzqkKQnWpUIXcl20m0aLDZLJySL1sJzx0dEZiivlfPGsp7z+LKBW65iUBXNzFeGMUN3On8tknJLZh6qZFnqyRUYlK6V8/yY18d0sHXrWwQqZn7YRNV4hMarGbJbJ2hm0U661g/TtD/XP52gWI4D53+VpAJqZZ3IFvl4OhGhsGgVU45dGmya3leNPWcYb9LvyImzfuJvpVMFVLnfn7EKT6ceWS5CK/Xm0liXelhnCl+2PgWiROBVGJzfXD0uanH5RWcRswRBEARBEARBEAYxxqpaNlS9KJ0KWfncgGismlC1REph4ouViljQKGSl66R+WNUWRaVNEZWcH5Yp2GxGQq8Ykc+HDGnqZ2ixj2H5Hkbku2jz+8nrkJKuZiJWfYpeaL0s3SiNvorRLqrJ6MTs2wlCWpkGs3cAT7m54UyyvSoe1cSvKk4inagTmjwMIe5m2iRiVP2Nflqnsg0om1yD0FRNopLSNKqKCRIBzfnslXSFFq+cla/3W8oEO+XUlAAoqFpUVBoN5epjySXplRo3/V6JCkO8PrqNi9TpzhcIjc/ipiKVDp8FI0ssqpaYvriNRQua0fMD/F6FrijqNTvrgdevYFauLi3QpdzpWKFCF7mknEUSOgSvUjOfV7HFBArjK6ICeFVLYX5EabbFK8foapz4YqVqFTWRKvG+cqlwZJFaTrBSWKMwXiK+aWcQn5EKRqhsc0rZJA0xEcwSbUQn+7TKpTQaX2WbyLbv1aKzFEnaoWdrwpdVeGm5NLUx0XiyXEXtUiBrM4C6f6xRbrmXmM1rSzX2WRiWKHqh80yLfSdSRgG+jumPc5TSaS2TPqgTj6rUf61sAgIVO/HIKmKjCeO0z4CnDZ42hJHvBFQvJow9Ai+mHAcYq/C1wVcG1/NcdFY5DsjpmFgpAqXwPEto/MQnq/Y71SS/3aROHs7/DhKDeKsJdOTqiAuBC1TkZlG0PjkVYRKxekURMUsQBEEQBEEQVoJp06at6SoIAuCEjrigwZiaV5ZWKN+HXOCErHoRqy6tMIvGCgamFC5VxPITU/c2RTUVsYqWuJjMSpiPCYohTcUqQ0r9jCp1MbzQzfCgO/PAqp9xEJzAU7ZO/HFRWDWlpYqPIcrWCVSURDXVItE0JhOydDL7GzixyMNmaV9apfJWIzXjdp2lKIZJ+qCbRc7drHtJGlWYzM4WJgbuofUborDqo7xC61M2AU26glaGQLnyXkNkmSGm5h3kfIlq3mCh9ZNURV07tkQA9JRhiNdLoCJabJmyCejwewitz9i8MwrfsjXH/JFNzOxvY/riNhbOa8Fb4OP1K3SUhUjh9alaNBEuWkunvlKJH5dK3qdRWalY5FVtJjiFJdeeQY/B64sgMs5w3qQ7qkWI4bnoLGWSyKo0asxLllnQxmZeX5mwRH1dE/Goru5Yi07eGM/VM43Ycvu2WOsEL11rdidUJcIYBnQqtmmXZogFjHIBSgrnu6VrdbK27tjSWSN9W/PgMs57y8SKcuzTb3JZ3zNWUY09yrFPAZfCGBqPQMdZmmDFuNTSKJmd0PXTuhkGrSKKPTxtiJIoqzDWaAVW11IEI6MJkpTCyLjzgU7+N0Y1+nChXFSk1UTWI6ciIuNhlMr8szRxJkinsyF61Gb1TCMivUSY1kuIV6nIa+qE4ndijYpZV199Nd/5zneYOXMmW2yxBVdeeSW77777mqySIAiC8AFCxhlBEARhbWBufzOFWX1kMxlqhQoCl1aYRmQtLa3Q19jAW+oshWm6FpD4FimikqY8RFMZooiaIWxyUVg2Z9FNIc0tZYY29bFu80LGFBbR5vfT7JUT0/a4TrzSmf9SegNc/z9KZ2lSucTIHdwNcTprYbrMUBN4AhVlPk/1MxmmUVbAAD8jYIlUQ5+wYblu8KuqJtFSsa35YKXpjTUPKb+WQqjccVWth5f4aNXXK6diqtajkAlwljJBg9hl0AS4CJZqIqB51lDQIb0mT07FFFSYmN3Xtp8KaaHv0Rn0sG5hIVu3BSwY1cS0ng7eWtxG1/wm9GIfv18lsyIC1qUTppFXWYRTnX+UTYQa5YEyifm8tqjYiUdRXmGVxvh5ct0arzeExPw9+187Q3fXN5NIqiVEMheg52ZfTFskTZG0yYyHbmEihmmViW2ugqDSbdeJcDoCa5w3Vr2AokwifunEc75OINOhU8KsdqKXSrS5zPtLJYKecj5fKu1IWQRXEp1lwWhNJXam/r7yXIph7BEbnQhOtbTS9LeT9lNfG7RxIm26PE09jI3CWIU12qUNJsb2KkkJDI0mn/jHVWI/E7TAGdD71hnDu36XhsoZJ16h0Na9TqnNUOhlv1kgSzV0+/Tdb1QZJzBaFzcJTqzOqaiWJrwCM0WuMTHr5ptv5pRTTuHqq69m11135cc//jEHHHAAzz///CrNWCYIgiAIIOOMIAiCsPbQE+Zo63c3oMrTkApZqT/W0tIKAw/j69oshNlMhjUhyyYzy5m8otzmUelQVIak6YQGmzOoYkxLaz/rty9gXPN8RuUWU9LVLJXIqxOiyrYmXtULP+VEoAlUTKBjl3qobWagnvpbNelKFnmV+ljlqEVsuW3aBvHKeWglN9+Zb5affeZha2IZ7vOyyWUCWXmJaK2q9aiP79I4USmwNSHJEFJNorUCFVMxAfPiFkpeJVsvFeXKmfjlIs5QUZYymW6vQEhstZu5Limf+ijFShEqt35BhTRpJ+gZdIO41WLLSf19xuQWML44l4VDmpg9spU3ett5c/4QKvOL+F0eXsWSKjipkTqqlopXi35KhCeTdKskYglciqDxXapinM8RFH2Criq639VH2cSPStXrTkkEVir+QM2sXitUKoIpF06lEr8slx6oEp+sZEZP7XyvnNeVbTCRz0iiqJSxmLr+n/lqURellfprodz/1maRV+54XGNlfl1ppFki9rljsKg4iX70XGRYZJ141RPmiYwTcVNTdk/ZLCqwpJN0wzqD9YrxszRDcKJVGHtYm0ZkedkshtYqfM/ge3Hmt5WmF6ZlcjomstqJWioGDT4GrVyUmKesE7KSNgxwM2/Wz24IZNFkafpj+luCRExWJvHgAqzJBC2TpBIvL2tsNsMdd9yR7bbbjmuuuSZbttlmm/Hxj3+ciy++eE1USRAEQfgAIeOMIAiC8EEmnc3wPy+M4JjXj2fI1wP0wi43W2HgN4pY9WmFqS9WOlNhnck71LyGrKewvqLa7NHfqSkPhajZEjcZrG/QpYhiU5XO5l42apvLxk2zaNHlTMCK6wSf+jSoVOB5uxnN0oilVGRKXzfOaGgaZttLBaZU7EoFq6r1Gm6m69MRQ7yGbTgD90ZPrFQUSz1/QusT23pBLI0Ec2mBqTDlRDo3Q2HZ+oTGJ9DJ7IbJDX4+MYmvjyLL65AmXSFQcSbe5er2nbZDNWnTMBMCoyw6q8EvLEmXTAW5dLbFqvXoNkU3E2GcZ37YxFvlIUzvHsKsBa2YeXmCbo2uukgnFdMgXukkbc8q95pkWVpOxaBjiw7da69qCfoMua4Iv7vqDOFtnTgFWF/XXqfprvViaxJ5BclMh0t6u3l1EYV1AT4mmRExe518nta/fpZDq5QzxU8iskxQ89Gy9fvxcIKUqpXNymXbr5Na6j7HgikZ1h0/l3Gt8xkS9POfruHM7m5Ba8OI5h6agwqd+V5G5xeR12Em1PaZHAuiJrqjApXYZ0S+i2avwsKoxJML1mVuTxOVSoDvx7WgNWVRiRl8KmhpZQm0yaKpAs8JyemshgC+jsnpODOU93XstqFMlvpoUPjJ7xnI0hjTaLJ0WyWv0iBM60SUS2dETP3gKj1VJm//+Pt3NsNqtcoTTzzBmWee2bB8v/324x//+MeA8pVKhUqlpmIbY1iwYAFDhw51U8wKgiAIq4S1lu7ubkaPHo3WS3OTGFys6DgDMtYIgiC823zQxpr3E825KnFbC7q3vzEaa1lphYlAsKQ/ltVk4kGcV/R3eJQ7FeWhlqgthsCgcoYgF9HWXGa9tgVs2jKbUblFFJKcqhgXQVRJjNTBCTTp+7wOMxErUBG5uln+0vTBNPIqLZOSrufMphuFnVTo8lTNfL0WxUS2rbhhazrbnqeMs3NKjJ9SQSsT5WxtLU/VPKtSA3iUJkyM4OvFu1Q8Cq1HJfadYbYymDSyympCnMiXRrkUEvGipKu0eP0UVEhJO7+x9JgLKkyizmozwqWi4JLt5urvZz5eWhlywFCvx9XX66Mz6Gad/CI2bGpibkczb40ewusL2+mZ3Yy/yMOrJml2SdBWKv5g69LytPs//UqNUlhlE+FLYQKPOKfI5b0sSktZWxO14sRsSoNCk7m6o12ZJHrKaoWObM3fKhWvrALfOlN5yAQkZSHNUdTYxBtL1Ty7bCLOKUBbdJREJqbHkl4G1gL8nMBnUuE32V1iMJ/6aalEeLNJXRSJ7xagYkVoNJHxiKyLpnITOahMYGryKpmIlUbqlTQsppgZtWvlBKXQelQiPzHStxijnfeVtgRBY1+IjUYnEVpxnZhlrGqY+dCYWl3S974yTvyLFXnPbTeqn7WTmviVznCosbVUQ8gELZNEEOZV2CAqLy9rRMyaN28ecRwzYsSIhuUjRoxg1qxZA8pffPHFnHfeee9V9QRBENZa3nzzTcaMGbOmq7HKrOg4AzLWCIIgvFd8UMaa9wsaaM/3Ma84HD/1x3qntEJFo5CVLtMK4ynCZk1/p6Z/uKLabjBNMboYkctHFHIho1q7GNe8gDH5hXQG3VlURdX6VExAn8kRWi8Rrlx6Xkm7B0apUJOm0qUzEQIDBKzMc8cOvG11kUku6sqJXyYrm65Xxaul8GFZmv27TiLBUpP19Ia7FkViMv+iRiN5k3gWOfEp87Kqr0ciMqTiVmi8RFBykStlgixKDSCvIyrGT6KlciymyDzdTLNXps3rJ6cihnh9DYKfVycG1ketpVFYwRKeY6n5fNV6LuLLqqwOTbpCWyJsrVdYwCYtTcwc3sYrizqZN7cVPS/ITONVRJZmV1eFJJ0vEbVM4lflgfIsNgarNdZTxPk8ucUefncFFTlBS5lEcTLOD0ulMx9iXL8l8b2qE9KcopH6bVmIVJKumJRJjK2UqfVz5TqX8+pKtpkKYsqCSRQ7VaezJdpj0s51AlrijWUTwdhNammTfSQreCTLqfmQWYgSs/eeKFczTdeWghehcal76W8oTaPVytLsVYjRLAqLVIzrd5Fxv54w9DCxEwI9z1U+jjVaW6IktEwpS2wUWkHej7BAJXJCqqdrvyWAaiKw5nSErwyRdWZigY6pxD5+4rmlqc386Q5dkyfKjqtiffIk0VdWZ8eV9ksU5FREdalTNCydNWoAv+STbmvtUp9+n3XWWZx66qnZ+8WLFzN27Fh240B8ggHl3wv05huz9f+9yDnD/o3BzZTQYyv8v9m7Mr/aRHvQz1eHP8BPFuzM1qU3mVicwQ8X7MCE5hcIVMy1c/agoEO+OfJe3orz/N+cPcmpmDNH3sucOMeP5kzAx/C1kffy+65teKZrDCW/yjGdD9Pu9fO/s/cC4IyR99CqFT9ZuB3rF+bwiab5eEqedAnCYOLQl/Yn94WYaMbSRZb3goiQv/EnWlpa1lgd3g2Wd5yBZY81YzkWTe5dreeyUJ1DGX7IPPYszU4ujBRVIh7qHUtfFFD0InZqmsYT/esyIljM+n43/yyPZnwwD60MT/auR6Bidm9+jW7j8XjvOHxl2K35NXqNx2O94/CUZeemV3mhOpJZ5VYCHbNNaTpFHfJo7zgAdm16lbyCJ8qjafd72TzoQ0u0miAMKn49b0O8Oyymu2eN1cFQ5Q1+8YEba94PlOOAuOBBMmvhkrMV2kBnKVwD/LGS91aByWnKQzz6RigqnZZwSIQqxAT5iKZShY6mPtZpWsTYohOxWnQ5M3IPrZ/dzKbiSBqFVdKVzAunoMK6tEKTeVaFeE58Ic78oULrN6QTZpEddYJRGnWVfp6WrZm6myQCpLFc2QSZ4XyaQgg0RGXVp0Wl6ZH1264Z1rtVe20+8wFL1zdWu1QrVROU8qm3llV42mSRNaln2OKoiIfB14ZK5HyR5oUtrq7aRWy1+7206v6GaDZNTciqn7kxFQ08z6VRhtbNnJimILpYL5MJc0EimrX5fQzPdbNuaSHzhjYzraeD12cPJZ6VdzMdhomoRS1lr2a+XidqWVBZtFPip+VpjB+Q85WL0qpENeUIXBpiEk2YimXK2CTN0B2Z0s53ynjKiWZauW2opHMrwHOm9NYZauGlopauGcBbpVyZRLDSJJ9psEFiKJ9aZKU/ndQ/LK1y3eyFUDOhN54T0tINqzDpWzlLNfKoGp+qSaKkjKYawaJKkarx6Cnk6fB7k505USm0Hj1xnnmVZnrjHEUvRCftFhvtZmhMorPi2EVmWauIY4XnGTeLoRdjjMbza/5ZXhKZZWIvib4ySWqh67eR8WophHWzK2YTKyiLn9QvrU+fyWXCWF4nwlYSBZl6Z6W/KZOojCsSobVGxKzOzk48zxvwdHzOnDkDnqID5PN58vn8gOU+Ab5aQ2KWl8dvzlNqSXOuFd+duxOP3rwzHzru3/z1zzvRs18bL/1iUx5baHj9nL9x1x/25MEJW7HoX52Ew0Nan81xyiHD6I8Ceqs5yvcN48R912XWc8OJmwzNr/p8YY9x9DzWSXmdkNN3u4uWguKYP30NgOJbHifssh6bDZ3DK9dsyk37VTl2nx+TFzFLEAYVflPePdVYQ+czoGZe+QERKFZ0nIFljzWa3JoTs3QeL5cnyLsnugrFP/rG8Naz6zFq29m88fIYqhs0Mf+ZYcwoWxbv8Tr/fXEcr48bTXl2E6YpJj/H465N2oiMphp7RK818ccNhtIzpwkbWHILNbeP66T6VomoxbDLei+T931u/c9OAATdij+s20lnqZcFj3Xy7AYx26z/OL7y3q7qgiC8z9C5fDLLVHVNV+UDM9a8nxhVXMyCgsJ6ybl5WWmFdf5YWVRWYvIelTR9wzR9oxSVdoNtjlCBIchHtDX3M7K5m/Wb5zEqt5hmr5yJUh6GsnXRWLHVtHhlSrri0uVUlUISgZEKTc7ovBY5lEZYmWSGwN46ISmoD/khnQUxCYehJjalN9RlG2RRXWkEUsP6qW9U5oMVJZ5WQRaBVe8p5SkntGUzFSqTzEpos2irtByQpP4l6Y7p9oxbN43Iavf7aqKbcuJWJgygsrr1xHnyNsqMtdPZ4vqrOfrjgKIXMrqwiOFBFy2JqJVTMQVCUBHYxkiZXOJP5GlL2bprzlSErJ8hMRUaAxUzxOujSVco6SqdQTejCotZv2U+r44YyuuzhxLNzeP3KOerlWQIQl2kE6kBel36YaQwmeeUwnoBJq/JLazi9YWQRmfF1pm8R4nYZF0/TXqH68uJbuXEJ1z5JUStNCIL7aKtXOpgkitZ5yllEwFMp75gifeVSg3llTNRN6mCkkSGqbp0RJtuNmkLt500NdJmUVmuXrXzoLGKauwRx67Ruis5KrFHd7FAX5Bz5u/KUDEBofWYHzYxu9xCZDQtfoUgiLMIKBO79EKVpce6HEtjlFue7FYrS5Tsr8FTSxs3yyFxXZSiSzWMkhkSe6McBS90/T7LEFXZLIfOBy6NEnSibypk6cQUPhVbK4mBvftt+QSqj+VljYhZuVyO7bffnnvuuYfDDjssW37PPfdw6KGHrokqrRQux7T2i23z+zC+4q2+Ng4+9GEmtL7AFLMpxbkhP//7rrT3WMqhT2GuIr9pH1bnWFwp8MvNfs4ls/flsb5OAApzNdV1+jBeM4u6SgQR+At8Hl60AR8e9So2MATzffw+KAYRl4y5g3033Bwbacq2MR82myUge588oVBpfrhJwihXDo1abZFgoY2zKToH7mfV97GsbafEy5gLIV2vvg6eUll5Tyl8vNXSDrE11GfyL0+90rot+X7JsqujDevx6i5E020Hq+nmdlX75Tt910tjae3zTv1Ro1a4nqvzNyMsmw/KOAPuAqLWy9wTU6uhK8yz8aZvMi4/j/vtMPzemGfeGEuh6sYnvxfoDLHKeXRM6nyGv/Wuz1thEwB+ryIeGWJ1jnI5QBvQ/Zo3yx2s07wQPIvu0+gQfM+wb+t/+HnHMDCKCEN9108vLZd8n0ZvGWuxq/CbVqjVFgkWW7PMuix5HCvDOx3nsj5N16uvg6or7y7W9WppB2PtgPPbO9UrrduS75csuzrasB7V8LrxGmZVWdV+uTLrLq193qk/Ltnuy7sfiZ5cu9isaSZPtmxLi5fexLu0wtTkfWlCVuqPZXxFtcWjd5Smf6SLxtLNIb4fEwQxQ5v7GNc6nzGFRayTX0hBhfSZPLHSdMeFuhS5MBOXMvPyLBUu8XJSiVG7hXKShlefupcKS6XEAL3euD20HjHO4yf1q4rRVE29X5ZdqohVT7zE71Cr2piWmrsDmUCV1g9lCBPfL5OsF1udmLM74c2JWHXnKGso6Wp2w56mWNYLcKH1Eg8wLxMN0vobq6kYP4ksU1Rin/44oBz7VI2HVq2UTUBJV2nz+2jRZQqJgXxBhQTU0jfT46730go8l7pWtkEibHlZO9dH0HX4PcRW0aLLdPi9jCksZHbbHF4eOYxps4cSzi0Q9ChU5GYz1DUbL9d0ts5sHbLoJavcLILG9zB+nvwCjddTRcWp6Vbyj+dBbJ2wZBT4OvOksrFNxDKXRpql+CVRh+n+nMeVzWbpxLhy1tYM4ZW1uF2oWk6hqvPdii3aqiSaKxG80uisVDhTNSFPJR73qVAGNVFLVxTV0MdYlc1iaC1Uqx5h6OF5ht7WXCb8pH23bALmV5qY19fkhKei6zeh8bLZBLFgIu0i1zQY09jn41i7CLmkVp42WKuoGp0tAzDaiasaC16iJRgnhFWNT4tfyfZprE7SB534avAIiAnxslkNY8h+165P2+y4NJrYgl1KSvGyWGNphqeeeirHHnssO+ywAzvvvDP/93//xxtvvMFJJ520pqq0UhgMMZbQwqEt/+Kpo8by8L834o0316P343lMAG/sn2f0A5bCvCrTNmmjc55lwUttdM40LHpoJBOfP53CPE3n9IjZ/xzJ0P/GLLbNtE4zdEcl8ossZgG8+v1NOWbbzWibqfD7LcX5MXMeG8HuM05h6FuW3OIcH9JfBmVR2qI8i9YGrS1aW/JBxHYjprPnkBcxaNYN5vN430b8c+E4/rugMzt51ndgrdw2PG3JJbMegOvwvjaMKi3mrFF3sVmutErt+FrYwzEvfJpFvUWiyEtM61xYZD4X4XsxhSCi4EcU/ZCcjij5IUUvpOhVE6XYmSZG6cBgPPqigCh52hFZTRh7GJyRXWw0ldg9SalEPt19eeIomco0Nbmzirjsu7DSXIz2LLl8iO8Zoljje4bmQoVD1vk3p3Q8T34VI2uerkZ8Z8b+FL3aCFCJ3TH1hC5ixE2hGmOszmaYWHJmiaKu4msXRpo+NSp6bvrf2OrspJOGRutk2legNu0rZIMt0JCr7SUXKalpZ0GFdHg9bBJUadPFVWqD2BreivvoNl5t0MVQUCb1fEzKQYjKLmgCLAYoW4+yHSiq1c8Sk/bjJZcD2ZOxAeunfR+bhHC75eFSii/tNstzKfTklHtO4qEIlHaXXlYtZQ1hVfmgjDPgbnbTAX/T3GxmbtnG9NlDWdw1hOqmPtaDxRv6tEyz+H0xi4YWKPVB/7wCpW5LeVozP5+zC36fotRl6JneTGmhpUyO/CJL1QR4ZbD9sPCRTm4dOYx8N+gIgj5L71vNXNe1E8Vu8CoeP1I7urFGkfxv3RPX5IneqOYu1ivMw6Jo033MCDuYXh7Cwv5SFvpeP9Yo6p4Kaksq36Xh7M1Bhd1bXmGYt2rn2IVxld/N24ZyNXAXdso9yVWabGYf33PjmzuvGgLtzne+dmckY1Uy5XlyHrWa0Lg0EpXM6mOSi0GbjTfuSWhsNJXQx8YqudS2yTTiYKPk6aiXtINv0NpgjEZrQ86P2aRlNjsV565yVNys2PD37g0JdO3hSWTcMVVjd2no2t5d3KZjRPp9aGozFWVjZXaTlIxRqGxqcJvIMSpZ15Wr83hRdd95khrjJeXTyAYfi69iiqrKUC+ksIrjrbGWbhtSsel34fbtY1MLlaScu59JDWzTDJIIlV1v1FM/htRrSWqJsWVZZ/10fZX8pXWJlzLWLG0bOs2qIUnrQeEplX4DLFu2FAY7HhYdOT8iZS3W15jAy8zc0SzdH8tXlDs8utfVlEcYTGtEUAzxg5hivsqI5h7Wb57PuoUFtPu95BL/pUB5VEyQGFObTPwIdJxEYhkKLcPlpAABAABJREFUiaJR7+OURjyFNk9sVSZCuWPQWUpiSlwn+KQm70Hi8xQmflPpPupnQvRwESP1olGciGjZ53VCWXq9m4pRqWgGuEECsnWBxNDdrwlDaXRKcuOuMU7wo1Goa/bKBCp2Zvh1dUvHlDRKy51jDaHVDaY6WjnT72Yd0xfl6A4L9McBnrK0+E0MDXopeRXavH5adX/djHK1WSHTVE5w5+xAxzRRoawDek2esgnAQrcpAO7eII3YKukKBR2S1yEtXpnR+UVs0jqHV0Z28t9Zw4jn5Qm6XFhTNqthIvCQilmJ6GMgSXtNxFelsTpHUPTILSijyknbGlDKRWK5ajsjK6V14v2mM394pZLoKyzagvFdf1fG1tIAs3Nh4p8Vp5+pmrF7avpeb5KVvLfKBb4p5bav4lpXSgUu49ftJnlJlJZ3v0dl3P1umKTvVSOPOPIwsfudmthF1LuJAQK0MhSS/hYZjzhJD8z6kFWEyb2sTTyzrFGJHuihtQUdO7HOaKx19/mx0S7VNU15TcZ9YxUYjVGWvBcRxu4eXXsu0qsae/QnY7FWhnySIlsxPk1+pe43V0vDNVZRsT4lVXX9TKf+czWvucpg8Mw68sgjmT9/Pueffz4zZ85kyy235E9/+hPrrbfemqrSCuNrQ14FWdTLH7o35vG/bspWO7/GrIfHM3Xqh+hcaAmbNFEBCq/OZcjz61CcG9HxnE/TjCp+f6LuB4bcopBhTysKcysok6c4p4pfCYgKmjinKM2pMvyJgPyCiHJnQG5RxIgnPPwHLdYLUbFl6HNpvm/yFMarTVsaFRQPfaidl7YeztwnRqBi92Q+12VpWmgzRTm7r1apsuzCKfvyYAKF9SHOQZyH/3aO5pmP/pvNcgtXqS0Xm4DFD4yk9XXjTiDJD98qNx2q8RXlHPTmFXEh2X/Bus9yNptBwk39mjzJNLiQ1+RkVDdeOQzZvlQE+dDduOnIZicxHULQb5JteMlsFYVsdosor6j48KMdhvLZA58mv4o3Ws9VRvPUfZvgVRUmcOGuOlK1a89kELCezcwVrZc8YfBdIrf1LHhJzG3akJpE4HQnE+cDapMTPmivJnpq7S4GUhHT08nNnJc8dfOiZIpWd8NX8EKa/Sqj84sY0vYEbav4wNxgWRAHvBG1Zz4JgYoY5nVTSGbzAAitpsu6AbegQ5pUlRjFgriZ+XEzsVXZk7JM8ErMP1PqB/d6zwZXj9rFFZDNtLPkzDCpINgoljVO/5xNAa3SmUiS0HOcQFeOfAosMeOMsMp8EMYZSPuNeyqmULxYHcqM14cxfN2F9Exv59VXR1Hqt5jA3ZT4C3spzG3F7zMU52qC7hgduZOk1RavbGiapfB7I7A+QW+MjrQbKzxF0BvTNMPD7zdEJe3Kz1DoKm6qHwPFObhwfwUonZ0TUW68eH1kkXkjm+ib0eye4IXgVSDobwjnynDbcReRYZJ6gCZLQ1hQsszacDbDvPIqtWXFasqvNZNf7Ma8+rHG6sCNlR5UfbB+sn+/8VwLyYVr3dPP7EJ2aVpBup/kgt5Pxx5TW6YM6NA21Ccbj5UbAyMNj48usd1Gs1ZZzJoTtzDrtU53vZA+jTZ1da/bdza9uFNGaoa3Oh1nqK2Y3qTo2nuFzb5rpRPhU7kbE9dlEpEr+fN0GvXsbpzSz3xlyOmYFr9MIT+DwioGAlss/Uaz2BSTlCAnojWpCj41cc1YRQX3UMlXhoAYC/TbHH3GGfZ6GCdcpoJXnU8NkPx2E5EqEfTS96nQl1IbL9I7IxrKNYhlDa8TIcwmY3wy9ihcJINWScTmSkQuC4ODkq7QO1ox5GUXwdJg9F4vZNVyjDCBom+YT8+6ivLIGK+9QlOpQt6PKQYhncUeNm2ZzfBcVxbxUzYBVZx3T165GQrTqKQ0GgigkDwcTWcdBPcA0PlgeVm6YSpCpaSij5sR0dXVU7YuGivOlnnEjT5W2TRygDLZtrKolmS7adpg/WfAgPe5uoe6rv4uGioVzVIvLqjzzkrq4SWCfyrGFVRISE38Ss8TBpu0hYtgQbntRNYZa/vE9McBkfFqaWRWU459t8wqtPEyX6OK8Rka9CblkhnxdCXzBUtTOeMs1dOda3PEyUPriF6Vpzt258aKCVgcFykoZ0Seipktup8mXaHZ+pR0laG5HtZrXsCrIzp5bVYn8cyapxYG6i9zjU+Wdoh2927pdYT1XL80XpH8/DK6L3TibEwy7iT3eTHJ46MEq5yan0Rv2TQlMU7WSc+lhmT2xLoHStmTB5td22iSMd+qbKw2Xt25tm6/6T1ZduJPrgsyEiP6VChLyySaXLa9MHYjgE0ftmlLOa79XgBCPCrWjUnOxN31hdDW+gfKRZcRa9dmNqlnYJwxfF2ZONbZwzZPO0FNJ/eJeLXfWuZZlwSI6GS8qyZCXE6Z5GGfQitDf5zOXBplaYaBCvEUWcptGjSxZGBBuJSghGWxRg3gJ0+ezOTJk9dkFVYZgyG0MQtMhWe61qXpTcWsF8cT9FtaX3Wdv2W6ISooercYQfMM1ylKcyPigkd+YUicd8JT1OS79yUfv88Qtvh4/QYVQaAgbPLxypawxcOrGKImDyx4FUO5M4cOLUGPm97UmtrsJDoxdPTKipEPa8zjwxnZHzuFesnj8VUmgsWBwuQUcR7CJkXYAmGzxZQMNmfQxYj2tl46vFU3Ei3piCgJ6NGRdX9VizJ1F/eJWm5y7oYtztX+3E1cTUlX1iZilF3iZoXaDK/UiVl1F/HpPt22atvxqgYdGlRoXNsphfXdCbraUmTuRxWdq3hxnVMxLdMg3xXXjDmxtXBVBcZLTppecqOltbvZ8lKhq/Y6PXFm66aCV7o8KRP6iSCok8/TmxTPutxzzwlhSoP2YjzP4nnuJiPwYjxt6Sh2snPTy2wcxEs/uBWgy+aZEbbTZ/LJU4iQIBfTqsvZRUVoPRbFJbpNkSZbIdT9xFbzVtjO9GpHg+FgjHZPkerDqnXUcNOQRp5l0YfJ4J6VT3wG6qMJGj+v2zaNZVITTs/WXgPkkqi4cujX/AGE1coHYZyBWmRWv42ZVWkj1wU9T7XjhZb8QneuyHVZlyoyvJlcd2LA22uwvsbrj7G+GxdM4N6bQKNDS5zTmYiPsm55ZInz7iLQ5JKoldgS5T1UbNFVk4XwQ91TV2VRkaJ5OtgZTTRHjemI2fGkXi7pOUsnD0oCMHn3wMIGTphXvqFYqDZEja4sgTKkM8Wn6QbpmFETcpaol5eed+suWOse6NcLUyRtkqYjZGNNKmbVt4W1DcuVTcecZOxLnxQn0RNYiHM+vRsqVi0W2p3fcovAr9isjvV1B5KUpPqxQmUX6xZqN8lp+fR1vQBGbRmASdIdsmV1D1ySMCInhKViV/oghiTSXEHRL7FuMJ+h3qpHGFXw6TIFQuujksgvzzPkiTKhKDW2rljfPSBRIRZFV1ykKy4SWq9BmPKXSIvyVO1mKxWjdN0yTe3BErjvRmEbb+6X+bCk9lolUWNKJeJVnWDmJTFZUZyELggfSFq8fkwOjO9ucpc1YyG4630TaHpHePSMVVRGhuTby5QKVYq5kPZCP+uUFrFuYSGdfncWjZ+moPWZfGaYHqiYFt2fCT/1flgAITqLqipb5z9Zf42VPmyERKRJPJzqxaHQJtdcNn3vZ+u7FKgkKqsuMmvJ1MV0vyn1UVX1/leZoGUbf2/Z+ko3bGdJ4bn+QWq6/dSfKlBR4s3V+Dt07eUeXlVi3RDi7yXHZpJImf44oC/KJXVVFJIsDee15b7fivGZF7YQej4FHTakLtYM7112RVnlKOhqdh3cpKouuk6FlG1Ad1ykbAMXSWZc+l3aH7K0Ss9FbLV5/YzOL2b9lnk8N3QUM2YPQc/NudkP9RKRWum9icKlr2X+WTa55/OwXjFJO6y4MTGujVF4JCvTEO2tTOx+A2kDKgWRycJcFenvwCbjtHL3O3VCkLIuUs7V04k+VqtEfEyGrrqHNMQqM4NXLHENoHCeX8m9mYueTPpO5GYz7I+S4BajXeR2Kjh5NsveSMUqrYybETP2Mr8rY50wG6fpilUPolS0AhurxCg/6arWJbEbAzbt957K2jDnx8TJF+P5LiKLpH9qVC2tEAWxT86LshTD0GryOj0XJMK0a1DKOF+6WOksyyedmEEn4rK731r+B5drVMz6IBBbS2gNl8/Zi8fv2ZzO2THFuVVUaMgtDuhZx4VpNs8IKczsQfVXUbHBFtxJSPWVsb4HuQCUQvVXsIGPac6D1qhq5MKE8z4oha5ELlKq6Du1OTQoC0FvOsUDGF3rvIDLB9YKjMXvN9i0f2j3wzSBE4SigiIqQlRSRCUX+RQXLCZvsYWYoLlKUyGkpVChGIQ0+VWGFXoY4vXBKs4q2aFhyIfnMHvoULwejd+nCHrA77MEfeBVLH7FoKsWr5w8vS3X6m4CS5xTA7arI3eTYpVL8bDpzUfdBfuSN1o2mfYVDwgUXui2HRcUKvYSUcs6YctYVGwIeizddtXNuwMV41XBL1snKNXXKT0Jpk8u0qcX9VFaybLsRiJdnjyVM4lAlt2QpQJfUHdzqQGdbDOJ8LK+dbPieJZYeUSeQQcuBaaqfHw/xtMFuk2R2Hatsp+JsZrFcYnuuOBMRP3k6R6KODnpxnUjfToTC7gLhq6oQL/JJWG77mTar4Ish9vXBi825HUtbRJAKz87kaaiVaCihplu6m9S6kPaq8qrmZsmdUvD6WMMHi6UPk5GOy+J6cS6gUwQ3g6LG2/+3juOGf8dRqnHEvTFEFu8iqba4vpQrjvG765C5B5YWD8xjg/d2IHnrrRUGLsI3pwbW1QcJ1Nl6+TCy4Xz20DXQvABXU1PomCzK7m6SuJuqlVYd47NBA5n+Opuosj+rO+EdusBvkHnYnJBTM534eqBjmnyq8nT/lX7rRQVFNbppadYQlfdE2NdTSLHQnecOkoeYkS4aKTIXWQbr3aOXRInZtU9/a0XsQYWTz5UNeGIpI09hTJqoLBlAWPxqlBdSmrbiuIpi47dw6NGMasmUFlja+JWMjY0RIzpuu9YN37fmelvvcCViHKq/n29+IVyY49K/sfDaFsXzeWmFVfKUrUBxlZW2RPKWkUlEaryyY0ZpOk+yXhZ9wVGKPzk/B7hfGxSj5s04ipULoIrTc90IplJojAsLlhc19I3qd00WxQR2j35XiICKxWnUkvfOleYbN82uTnTOB+adF2bRGxJSvsHm+64SNjs0guVNQOFrEyoV5icpme0R/d6EI6oUmwr01ys0JSrMqzYw7rFhaxbWEBJV8mpKIuuKtugQSQqKefNlIobXhK9aNCJt0+Osg1cSqD1adX9DRHzUJtpME0hTKOnUsGl2xQpqDAzKE/7cRpd7+QUXXeR77aRCjfpTymNBnHb0Fm0foxaZiSIqTvfBioml0Sm1c9umF5/pgJX5r+l0mvBEEwt2iRNMcx8sxKB21j34NVLxIoKfjK7m6aoq2gCorhxMho/uSbti3IUfGfGnUa1+Rh64jyVxJcsTU+LUfTEBddGyvl5aWUY4vXR4fVQUM5EPqdiCta9LpuALlXMjiH11MLWjjv1AivpCiWvwpCgn5ltrbzYOYK5M4bgL/DxKrgMmkTUIo1o9hKd3YdYufOXE7Q01suT9zR+VxkVxpkZO1bVlAyrnJ+HUe5+GYO1iSiYmM+nD09M4hmXDloKJ0bVi1tWuwd4rohKg7jctUEyrqdjoorJzOOd+FV3z1l3v1W/Lra2vomd8bvzrHI+V8ROYDORS9ePjDuWVPyJ0URWExmd/Y4qxqdqfIxR2Ehl20ivSaxxy6LYQ/vp+AAqsSQCMlsDZ41giY0TSI1VEHsEXpyMUS4KK5dYFZSTKKycdvXrjxPfLKUJdJz140S1ytLmdV20cWw1gVdLL15eRMxaBdKT6Z/61uWhn32Ycf9YjLewFxLDOr+piN9bIljQh+pKlidCk6rWPJFUbCCMQGts4INW6HKUGDS6J+NeX5i9d+sb96P1FEYpdGiSQSodsJKNJ09l0lx5J/5oonxNuAqbIGyxRK0xFGP8fEwuH1LyYwI/puBHtObLDCv00OKXKXohJV2lxSvT5vUxTFdYVTGrRec4ar3HeXzIOLrCAr1hjkX9BXr781T7A2zZw+vx8Hs0QQ8EPRa/D7yquyD3qu6iHJITQ6AaZp9QNrnoq7uOU+mJJHnKnmHJ0hbda5U8OXf51nHeq0VtWVDGEjarAU9ZVoYY5W6kIpfnXbvYtzVBKxOzFCaJKsiml00EqCzyKlkHkqcF6fFG9eKXG1iydT3cjUtc+5xYgW+xyawiNvaIjYtMU9qdIPs8w/yoGeha5XYAMhNKnYRAm8S/AGoDZ5qCmLYduAG23+ToyZ5aBQQ6xleGyHr4KiaMXUjsktvLfGFwJttamWzgdq/9rD5B8uQNIkICtE28IaymiufSEi3JCJpeVLkvpt7E0WNV7IeFtYG0f7wctvL60+sw5M0Kur+aXbjrcoCuBuj+EFWp1kQVqBmo4s5hmESk0qloFZPkJDnRxMQ1s1Rw4fqJCOG6c/bskyzyhtqi2tP/5IbJd1GzJnDRViYHJm8gsGjP4PkuXVknPox5P6LJr5LTkfPxUDE5HVFQISUVs6piVk55bNk2gxmFIVRin9B4lEOfauRTDjVEGlV1szJ5VTKhS6fmsnEi8iTHnPnO1B53Zm3f8AWm7bNkheqKpmkOLlrLojw3c1MW0WUtcW7Z2tiKYEi+S0NtrEl2nj7sUcmNr5vNyV0QNyyzdfcDyQVqdkzJDUr24CjtMnVP4zPfEoUzptJOXCIdk5ONW5uMOwqstYRa02dywIpf9C6NyHrO70ynglDNQ6sxNTBNQXefpWbN1WTmJmuVGz+sxSidGEOr5L5FQV0EV5ZqaMFXcRJNpbN9mqwWNhkjXONZnBeSn/gnGBR+KlSp1NLF+UfqRGBL66xlrPnA89/y8MZU5SWFrPS8nNN0j/HoWh/M8ApDhvTRlK/SnKuwTmkxG5Tm0ub3ZRHpsdVZZE4qwDTrVMSK6mY0dL8TgyvfFReyunnKklOVTOhJ046cKFITtlKMVc6Gweay6600Lal2/UUWwVX/ECE9RXvJNVz6kDFND3QRZi5dMI0mq/fByryzlkhR9OqWu33XxqM0vTEV5tKIzizyK0mbrlofTUyoPMrWWUyUrY+Hpc/k6I4LeBgqyXR5FePjKUsxS9lUiYiQWn64NK9C4icMSVpXIjagNLHRidDuY1D0xTkCHRMaJ0jNC5vJ64ger8Bc3UKb1886wYI6UauPqvKyFNPUKD71cKrUzR4JZMb9BRXRGXQzLNfD660dvDh3BH0zmvG7tXuYFJGJOsrWeU+l9y/Z/Y7GejnyviJY0I8KkykDrUWFTryFZHz2cMtN0hH8uictye/BfX22LnLKPUBxdVDJvaPK6pWOZam45bKYsst7iGtl3D2lrQla2tUrG9fS403GRJ1Y46RezqnPZCb0aefNWOvvrs+l368xOntwoVVtGUa59dMBO4l4trixzkSgfTduWasw2oXXB7lkFtDkPslCZjljk+XFIKQS+5mnlvNydj7N6bKcjgmtzh7UhCbnAgi8xuyddDwNdK3/xKiGgIV3QsSsVSAVs/4wd1uGP9GLt6AHorof2OIecl01cQtwr5VyopZKOpin3dNzz3OvdaIkJ5+nIpYTJrR7v8QABdRErCz0P0kXzGnivCIsKaotLuoqKlnioiVujvFaQpqaypTyVUpBmHki5Tx3U9EW9NPu99Hh92a50mlefIvup7Aarqw1mo3yszCt7iLVnSR9+uMcvbGbfnZRpcjiSoHeSo6e/hxhf4DqTQSuboXfD36vxe+ndsLIpkKta58lSZbXp0vURxqkarx7r9DJryadTjUqQP9wS0lFQOMTkxUlp+I6Bd/WPzhwOeUmMRR0V/V4JgkLTSOprMKaesHLJu9VdhNh6/K2bZJxkGzOpacaWyfmJTdS2mWlu9x2FwKMdcblyjNYT1Ot+iyOSxjsKt1u6jSE1nruSURC/Ww3qRtJmr+dmnBq3HqV2KcvyrmbZCyh8ZyYGyvi5EY9r6PsYsE96XDLi16ITqdPtpoQd5KNrXNF0enNlyWZ/aUuZD6ZISxOnqyn5Vzjpx0nURKzvmhqg5cgLIW0f7zYN5KmGSG6v+Iu0lKxvlLFS0WslES0yoSVdDzRS4w/2XlP1USstFydiJWWIfkvW5atm9wkeclDk3wt8sr4FpuzqLwhlwsJvJjAM9nFvaedF1JeRxR1SFFXa59hkovpEH81/EwUiqF+DzZ5Eh9ZnRi4u9SZ0HiUY59K5FONPTfLUKghdBffXsVFbOmq+7+24WSsWXIMGViBAWl9bkWyi930eWmWxph8rcZXRE0uVXJVRT2fuoisNN0xOV9lkcy65uaUXYwnqYANEWiqtm7D+2X9UVc+uwd1Y1eaKlLrmzYRtMi8UuLYo2wDGqcQWHEUyZNnaikT4C6u643rXRPV/jfUjP7TvpNGYMXJQG1tzc3KUyYziveSz7C4CQaSddIHb56qyVjKppG+qUdJ8mAlEbuSOCxM4ruTClo1VdGgrard4MMyLoKEDwqh9RLx2y6XkGVHluns6KEUhLTmy4wtLWS94jw6vN669LhcEn3oxKTU/LugwoZorFpKoUfZ5FgUu2RoF60TNUQ4pRFY9R6lodENN7B9Jk9JVzLrh/qbeBdRVSubRmCFxssM59P91JdJI6VMErUV6JqAVU8qYHmJMJy+r98e1HumOv/Tqq35XaXHXu8NVtu+SupkM4EuTd9Mhaw0uyBKUsd6yGGsi3bps4rIeLQElZpnETZL7fJ17DIQcMbuGsuSkWd9cQ5jFT1x3vluJWbdfXGOxVGJPpNjdLCQoV5PlnVQouJmPjROUEyFLff9NKaFBiqixTO0AAUV0R70Mbq4mJfaRvDfWcMw/5+9f9mSJMmyQ7F9joiqmps/4pGRmZWVWVmv7ipcgMS9vAPOOOCIizOOOeWQX8Jf4eIXcK074G2Ad5EgLtEg0AAajQa6uruq8hUZEe5ubqaqIoeD8xBRi8iqbGStHmS7rOUR7vZQ05fJkbPP3vv8dkI6KDuaKoBiYSEBruCsI+AFDwW3BtREGL86go9zFEGCSW4FIHHiCKoBVBzrFvKug5WiIiNJczphtZXxHKlmimKL51skBmp1OaZrD31pT0Awm9WCoRELADQQzZhesiqIVYVQihbV0DWoWUrCamsUL0ycakapbB0KCXPNuF8nlRhWCvllhMhKJqWESg6T7qM25AFIEjgVBcKAYGplY2kxUQhrQ3KYFBAVIYyD3t9zpzS5zEYkqAlTWiO3S1LNR6uBwt5x078Pc/32JJlHMOsPNKiKAlm1Nu8bTyzeRYMnAnJSEItZfz8HschAK6Zoqxt//z4Aa2IFr64Iy7UugBXAKpCpgnYFeVzx4uaA5xcHPB0fkLlgoIqLpB0qLpJ2qZh4wdN0wNN0CEouQ1koahb43RdHDMJ7fB8dPq7SMYKHB9BFEg5Fg+qDAVwPZcCbZYc3px3u5wF3hx3mNxPokJDvCcMtYbg3pL9s/Un6NZ3/3cczYYoFpj6grysZAW7VwQzpr8pbVaX/mvGUD1h3FGAWrMgvZPsoBBYJY3+QSQ2EotJejUkGQQOwuJucOzAmVtvG1iLSSU4SUAcJaq4kq+oyQXJnV7sCUhURWznhdflunQx9jFBw6aGOONUBBzOiRMKm8rdIxqFql0eWipEc2GKsVSf/Ma26yDdJRwUhU41q12iglss9qpAacAIAaUUyZnATuldIMMW8G6TT5ou9d4FVG+3kJvOWgOSQHSbbDoAtEPE4Hsc3DG1JLVE0AfC7Yw2grF9ryd6ALDQgKv4+A7G+JYBVBqjcuwewzO8KuYJTxX634CIv2KXFGJDKLsm28J4sIdrxggtaQpLlEi1NGP4A5w+EPalsJqGikgEHyYABoUiKKsiSiYSlqqTstGYsBnKVUwaWLYuLrJpKQJtf3zHeCWjFkzZFd+CWzvlAHbcG4P+1Y0eLxZAWF2OxTx54YD6KHRpCMNBNGlsP7f39j+9/xDAHrnw+7QGtqID7Ct/jsf1ddX/EihJeiPiuI1kxQYHNhMUZEYy4/8SqxC4lIggyNB47S8I7QEEAETXT1R4GBlahgVqEFrddBQLCpqihi3wyUEwlN84Xq5qi+ZnXfSBdRyV1NrbnjWcmjpPa9h9jzfd2PMkPVpw18JX9fwOyBsabH2Xc/qyCP37AB89ucTnMeDI94Mf7l3iWD9jZXFzBuK9TrMUHWvHh8BBsLe826Amp+mEpYyeZ4TiADShUTELYSwl9+O/V5IhMVb2lRDAZeOaj98vyfViMIXmsrbvhwAUJZQM69cPBpJ6B5bLDatSbc2miH5O+f7u9kYraXphaRY+0BrNfX7NihnZyO5kCoQpjqVm9v0hwkZaIQ0UIxzIEAMakxZ8xFAeMXVpUXiaEy7xg4lW7D1ocPR8Tr9pZ12Rpp5Kj+9xArZNvFcZtusB7+U7JC7wgiQJkO8xYOAeg5ew9P04fCmo9YOIFV+mIm3zEDy9f4988+Qhf/vqJSQ8VXNFcDRETQ66eNA4IM6o12pq+FNBpMRqeADkZk0lAkYdb2UGMkSWAGFGAIKiJza9SlN0U5BHNp8LbSsgAr7O1FjVZYs3w/jj6FG+XACrpb9/HWK6Z32aprQCyeWNFALo+nHFYhLCuKr9XJpV+eiwJFzbbGAnALUAyWKxyO5kKFEkgKhBhsMkHXcZYjcI9pIJTUYajM39XMObauiBn+/65AfxFMiaXdUBMZOC0HdvA2kSCDTRdAPBZs63fNR7BrO84Kiq+eLjCdDJGljqpKS3eBvUJBpEysIasQJaBWQ5cBYjFUBZW5sbG6gKUg1jB2rLf1z1jviKcnhKWG2C5rij7AkwVaVLZILNgNy54cnHEJ1ev8HR4wEWacZVU47yzThat4qJBaU+nqHi4zGo8m8i/y9jzgqfpHpVbgPOWvdHBIbtWOwc1eakZJ8k4lBFv1h1eLxc4rgPu1xEvDxe4O+ywPAzAXUa+Y+Q7Qjqan1a1hbUv0uM62QRWabOP0h8uA+uFeb5cbrvk/deOaz6iTDbpuqDYk8xYiuqkzrAKhvjfeh+wyNY7qzsmCAKUCqBL1AsGQNBhdR1tLC3fhss/AEiybh9W4dBdoFhMfNfRm6zflh0YErTlvgPhIknbugKxsGKrAqy2KJiLLoo8OUHVikOVFI1VNNhXTZerBvoFKdghXglZasYCoDAZqNZaSuuCx7sv6oJGi0re4nn7XSnBUd4mMY/jcbxrCASHZUQqHZAlW9nQ5i7qmb/MgINZ8O/xO0CsjaH3GYgV79HHmtci1LB9ks6wXSnpWrMpmIYVN6N2wRpYfTgGXpHR2qc7eDVa3HEfEJVZvd3p5ruMgSp2PAdQABgbp2PkOIPT5SIC64JlxZVTzTiWAWvVhONhGRTgWhIwm4RibnHGiyHvIhN5saJduw5viPNtMWms52//rxojra1S3DWDaX31rGhR9RoowcelprbPVnn2NYhjU0B3PP4gtb+DfSxA9MqQ9hxV355sJCDtxFCsCb7r6BO9uWYQCwYpSGcG6kUo2FWDfzVMTlGhUsJSzXQ7rieDaMu8JbsRXHKuRrjKlFNWmp5lPz4/r2HobvLEKvpZYeYsFdWNec/uEO2y2K7q4/j+jmf5HuvzdVOM7YGs2x8lvPmjivzxAR89e4Or8YSn4wM+nN5Et0ImiXlOmULakW+kFXs+hUm4g0nHOpo3lnnmmARwFk14j3Uw1rwyd9x/y2WD/YiOhFbse10usOcZg7RuaACC3eGSpubjlMOkfsdL+HJFh0NgYw5fQG9JCs9bn/dg1/nouy4CLWfx42Wq8fcGfOvYSwBwFJX/OWDl2zvVtAEJ5qJxJxvxYOACFl3zOlU4GdkgPsuKsls/wLYPmSqQVhQh3C47VeeI+sv6NS1gHHlopAa320DBkFbsROWHSWr4mjUbDw6T+DGpJPU6HfHBdIv/cP0h/v1nH2D+zSXyvcoOeSHtemhxwshXlhsZIEQDJDN2v70HPRgrfS2gpJJC8aILAFRSny3myJlRNQ/iWiO/9iYrMJ/lPm550y9LdRCJj+0jSMCr+2whpPcux4fHMotjIbeHvh7FTNudmeUSwapVkaXyZl73uBWSQlTzz/JkjhqqJhZTWVpO678T1FsrVfXQssAtvh9J41dKBcuawKys8LUwpmENDy1AQS8xsEuvG5lZvFh3Q1FjeKohTwUDk31f9fvgdIC32ZK/azyCWX+AMXKBDKwSQgeyfHHI3aq0B7FSMrlWektSGCCWMbICxKJWzZXkyYfKCNcLwumJgVhPBOt1gVwUDBcLhlyxGxfsxgUDV1yNJ3x4cYsPplu8GG5xzccACZw23AMGgAYnN3j04KYJyB8mwUjEuKQVT/kAN43sR4BZZmL4ruAxS8ahjmFqWEF4vV5EIHgoA26XCV+f9vji7hJ3tzvU2wHDG2VxOcDVj7ckI44viVbJJQHrhWB/c8T0B1gjFuh1Vt8s9UVzuUEs5M3QXyqapNAAKZ04yfJcBaJqotZpzKrf4h2lTO7A/bbMl4tWwM16pQCSRUEvQtBx4aCXCLAr2Kfv3m2snQvGQxmQzR/hUKcAipwheJQhmFnX1tlgqVuDUAW1lB3FVqJey6iU7LSGSTyEMVBFIaXvZq5YStrQtL3KVYWRvFpHJUzpG2D4DuAK2RY0JRY3f6iE7HH8wxiJpMUUB7J6Vpb/ztzYWOy8947/DmdgNSZWMLJsDoqZnbv4k7T6uO6MkToBdTL21VCRWJBTQU7q4zGmgqvhhMs0Y59OxobShDxD2aw9YKDHWJtPC9ie76qY33EwEQZUXNCi1dTzxL9LJqr9fQ54hVdSziEnO9Yhvs+rME6WdNzPA+bTADkx+MTqv+UA1+Zzv2GHjY0kBmgN0/oHkVsKYHO5LdSZYtFNdrwi5pcWi3aJeyVkifTux4W8KNIAGbHFfQ9coVv0B/BljwdRy+JaPJ8FA3/3rrntXChQxcJgS+BJWkc049YG42CSFSu1+VsMjazCdi+3u2o2n6zM1Uzi9XwyRPMK0Xt8tvdq50OVPxJcAtI1n48T6/nI9maodmIdNHNw7BHE+ocxTp00p5cWSibcfZzw+heC8ZN7/ODpG1yPJzwdD3h/vMMH460VsnX9f2/rKp+LdzSb51UJYKY3hPcOeolEC3lUUJCsg2FuzHghzGZq3q/n/bMWbNlPV0nXdUdR9o+bXzNVY3khjNx9RFMFG75vveG8b3/DwrLHFsnNl1WRgHiPd2902d1skihf8znQrew060AozRrDX1fA1tGwhofYQCq1X0TLn2DgKs+4WxVoVz8i9SWqRLhIDbCqMPYWq7H8VFdMltD0fmB+jzB0Dh1qAecT7tdJgXAy2TQEq8mnX5cLBbUGvQ7XfAyvtNG7fHeg1mxFZs/LZvG1ryChYEgFOzbv5WdH/HD/Gn/29Af4q79+gfzlAC/Oo4uTKvlDFO/XSsDTBMgeu9+KAlrOxmJSEonr+JI2E4GVpeHrKCByO/dGhsVCB69Qu26HBqRF7HP/UNK8KaSHlpt7/hi1ee7fp8dC1aSURcGjUjnyMiq2nQLMq7HnUlaLFGizKm8eJZVD+ucMvQbEaWyP2JwMiItiFCBF9S6U9N5krlhXRimMnAtWK9SQEEoFcrJCPLSI0zy7xDzcMnZ2f66mbpmys7VGy6k0TzvVHLnjsQ4hbzzHAX7XeASz/gBjyiuOQ1dd6KqcAPAWG4u5Vct7NlYi1CE1TyyXE/YAlq3qhYGyY8yXjPmGMN8A8xPB+mQF7VfkacXFbsF+mrHLK67HEz7c3eKHu1d4ke/wJN2rTNC+ZS7b8t+3FQSJaoz/3Si2ggRCkbOVORAd7d713LvGQAhzQaAFp76isKPFJkij/Aagpf9fsk60BYpwP8v3G3TXg9bx/QF3ZcLL+RJfzxf4m9un+PLLa+B20G6KRwKfDDGvXZW5x5QGmw9G4HI342XNWJa7dht0n1mEsNg+DaERZsxdZWqggv/n4Y8x3FunRIHRYe3DsyejLWlVI0OJCV8lhtImw+pMLWqsKkK0xvXXkhM9DBgz9me0mbUDMZaqbotKm8tBQF0Jf37/Af6v0xexeOiv0fZal007ZADY8Ywf5Ve4phVflWdYq4JZI6/ahjiN2lmQGIkrKoC5Wwwd66AyD7RkEkBUBrJR8OaSkLnG90olIhq0K2kAr6RGjAnG6BI9mX0NsZ9oz1kj0dmGBEYG00HALE7Lb34Rj+NxfJuRuGLtO+mdIyDnbKzwxfLvf+ehYl0LN3LCLl8O0IsAycrCKhPip+4qMKiEcBh0gZq5KoCVT7jOR+x5xsSLygntO7LlmDaaOqDVRk0MJP6m7n0EQn0H6uMd7d713DvPIyHkGYyWYPRNQjIKXF4Wz6HJtFdhTLIawKWxqwcMdHolrHttR6/S+Iw3px0Ohwk4pTDADTZsv/s+t9p87AvmIa94EEYp8+al/r9L0FQF2Kq3vaFqQsWvlvdUFunrldr23kNmgFroYgrZI32TACC6LSqLuOFO/h6hDrCK+8v+Nzmi+42IW9vUdj2aea0Wcr6aL/Fv0+UGjHwXYBPJIlqhPlPBEz5iRMWD7DSxrsrEXWvCQgks6l01QLsPFuF2TSVh8Mq4gZsQu0eFdDFOrRqNOI9k7dAlcgn9X8EtJk+3tkfS3xbnkqGWhGusKXZO2fZZmVzYAMaP4/s7nqTD2fdGi+R3H2W8/oUgfXTAD56+wZPxiKvhhA+nW7wY7nCVjgHgOLPGZX3ueeUWDwBCUtb7mLpn6SwZFWuA/j4PneqAApV567rMTYaU3XSUjFrben8BzMcwK8vKQKS+kY6zqyb7jAFrGMf3DH/3raqoMTk5MHXOAHF/Lz7Lg0B1Iz/0ibIHpbzQHmws+zwH0hZJuK/TW5LHBMHOOgq+XvdRjB15xVUGHorgXkYcrLFR5orXyw57mTFxQRHCdT6ZV1Y77n6+cDLCQCW8/gCEkuFU1BCfSTDxGtspwrizYvKTdIjr3HzTtkytHTF2tATQqc2cMhItUaQaqOCJ+bLtecbz4R4f7G/xry4/xvLbPfI9wCfS7n96IIDnHHYBNQZlAJfYfUagw0nXRMWCjhfzKkBW0ZdaQUlnQy6Cmt2vkEFFQtGiMUuAbN2cSYBVCwvq6aXfMZUnqsS7DWkFILR4qgAV4nEywgEVikneCyP+eo0r3drDhn63GGth1FUN3TdDCLSSyTZJG3jBtm3x1gKOscW6t1bLiYxxJUJYFsYwFM2LuKJUYC0Z07CG75wPN4FXIonmeZUID2WAd5cvQihiskOpOJQxwNcKUujx76BYeQSzvsNQnxzBf/ryPXxSXC7klXFbrOesflhDDhALHdXRfbJkSMbI2oJY3gLcQayaCesFYb5WEGu5BpZLQbmqkF3BcDXjYrfg2f4Bz3f3eDY+4MV0hw+HN3iR3+AmHTcgQp9MOyPKgaEiHJIPABFAzsetVNyXAxbRJOFdXJOwHHrH74udsnurhrgWeLbK0FGGqAYBrSOIBjV9bGdf/h1mHGm0NrsZeznFsTFqA+88eO4HzJJx93yHL394hS/ma3x12uOzwzW++Poay6sJ+U1CPhB4wUYuIqyAFhXgi89v8H88/Z+wLAnr4jRPnQRk5tYha2nXkxdCOhLGN0B6MJ+ZEXjvswXpWLRbR4qsAjCmViQCAnC1yRc+6Ysxs+x/cuzLKgbO2rL3azw2llbntRVsLTdKTLoTevzNIJ5BivKvhHqf8Sf/5pf4k/zHbaL3SdInZwsKxACzJsKOwl9MM/43P/xL/OP9r3GqA14texzXQbXWrg+HJg33dcKOF0tCckzyBYyTZMxly84CAceibCsdytI6IUfAThDUQmbKS8jQlioszX/E26r7PaSfoXcmg9SDBykqg8c6xEIwvj927nuG1qOFyeP4XUOZGYKvD3vcGPgg7Un9z6SEAWK5rDBYvR141UsKvbLYgwsgm9+2IJbOUaJMrLFgGAp2w4qLPOMirdinGZfppAbBtHYgwpZ9JQG42HwotGEDW/30rTFDsMiCYoVVahvE+a/0jt9dub3ozAWCe93leDz3ABp1si77NAflMhFWUVC9dgUXZ8J4okKkc/E6aDI1X2QcrkccyohDGXC/TLh/mFCPKZhbUWxAV021gHm43+H/9pv/HrUQamlnSQTaddZab/fML6oEWqHm9Quscy1wcV9Ba9VFfAdOBVPLz4OQfgDb8XXAVVR2fQ1uLK3Nc/1xEDbeWf6+yA/tediiP4A1k0lQBWRm/OrzF/gVv7e9yHHC7Ff7fI05DTgdUsGn11/j/eFWk/KqctHCjNXuCQUyAUhGpmL3T4OZnKFXqrOz/APFQFLfj2rhmyOpV3KlypvI/q9234io/5ViUwRQk9jqPilTmywR18gk0alXt6UnhIE4+eF0+Rhrvtdjxwsw1vDSrYnw8F7G618A+Uf3+Pj5azwZHwLI+mh8jYEKrvkBbukx2prfmTdMNdbgzeuqgUCamLKBNmweUG1NpsbuFIqOnj3mXfz64fLGXhYXIAy2ncP9b2d6qbfWbPtJmyJ9dJG2SYaxlQies7H8czxvwJks8V3MEWdphUVKFGuXKHJ6wyEtujvLjABTvag80ju2NmZZNq+sh1XPX5Uh/LV07/WarDWhMAcpwbs/6lrVFv5QO43o6l3aGtl9LauwGYqn7twxkIHKC0plzKQsPAe19Fw19l6SGp0j+26PLkG9Tmo/MPGC/c2MZz874E9vPsZnv3qO4WVCOhGqKUWIY7kTc+0CBbRI9pg+E9BxaQytancPM8QaIRMAKb7MIVDRBlZUpcUwYxQT1HyE3HIgI9ZHmiRZcNPKgTKTLXYTaWVBSUm2RrPci7rcSsiOTXQ9dFoT6sKgVfNFYaBOFWvhuA+8SUDcGwJIISyVsYqSJsQkilQaWSE+39ddzkYQWLd6bi9Mon7IJFrcX5NZrlbtjMl105WwCgGVkZN6sRVo8ZWhXTbnkoAEjBbr1pqQuQTw7KzNoQNQ/y7KlUcw6zuM6LAx5/bl0SeU4pgSMA4bNlZ0J3SfrMyoQ/PFkm6F7kwsscrKumecrgnzEwOxrgT1QkGsdFGwu5jx/PKAH1y+wQ92b/Dx9ApP0gF7PgWINaAEaKXsJkUlfLJOECwGGHhykdA8S3pGFqCT+a2h+wUUPhNeXXSt+je5fCxWjfFuGAWEowxhOumtbX1fRiq4Tg+bYOa/O6Swi/a1jcXV05jjuLjiGg8YUJDGiq/GK9zuLnCoE14/vcDX7+/xxXyFv757ht98fYPDVxfIrxPyAzWDQlbJSP58xPHLEVyAbBMYLwZaFUPHLZkIny6bR9JJMN4KxjcVvAry/ar67iogaYuSAJpIAtQKRhUJRHgjFQzZIQFeVfd2td5ZoyUKBnj5/SZo7CzZsrQYtO14aNpuFoYs0HsdgKTOz6eXqJD+XjhpgpGqBSnBy3mPL4fr8KSZa1I0H4JDGbG3yhVbhWsR9UvzKuEgWg1chXFa2/Q2pIJKWn13Lx7//roPindT03uHsNQRF2nGYuBUkWZ06n4NfvKqsIFZrToIcm8IDbY9DT6+L4+ZxeP4FsNnz+KLjXjCanY927cDsDbNRM5BrJ71G4CEPlYHwjpZR8IJKCMgWUEsGgR5KLgYtI37Vdb4MvESrbjZqrQOWvWLkmhtDsFqscYlVc4g2TKybB9BOEn7nUQCpPKEvoK+8RtVhbCCoyNchUrHZkkhJVwlYbF9SaiYaN1Utjn2Sfc1B4Clx7p2AF0voSQWTFgt3RAc0ojZjH+PuwHH/YD7MuLNvMPtcYf1kMFHVvNZW1gbrgG+T1gPyVi3aExbanGFqi+QsQEvuGi8SSeNA7xUazTS2L26tqXG1OKOS1ftjP8O2aFfDzcefyeohfZZRo2LBMIr8A5uebIgAKjQhr3Vm/C2bZ7d15C4r8nYXQQE27davHCmAjGw1ISBVB5I5v9TxY2r9T4yUYYtzL0Ej5AJqs+LAoB+D7tvVYJEHBIgFvbKpKoGdFki3wNj1DpP9bKoCgpvR2esbO59W489ju//uC07jL8eIGmBMOH0NOHVPwLwyQNe3NzjejgFkPXB+EaZXEAHtOg6ZTRgwotxQPPGChCrKzm4sbuPQ52CCeRyvkMdNx6J+nnFLCMylprf8ncCgImXWCtVYZy8q6fnI7SqNIpUyluJo5AINOVJr0AZoc/N0rofelfsxc5DMMGkAUN97uOjSeJr89fqmPdFvBEQLI9ZzSi9mdU62HSo4wYQXGuKOeAiLQpWdHPOWhW8ejo8QDsdKhDlgJh35o61KRUUWzN7PlSFsKaEJadQjzBV3K8TBi6Y7Ny4x9lt3YWh/o4XLFRwxBBd7ne06PkSB9ISFjFjfO946IUFa8QyJAXEdrzg6sMT/sP+Hn/2q48gv53AM5BASvLJhlExLF82QOt5Buoe0xcHZWiFATw3axYkgKWBWyzmaW1VFCOSkM3bHtfECogaZwnBCMi+INF46CwtoPkJQ4C0WGMuvVn0SthbuRi4NhPWJSFnB071h1bSOynuBcJb9j6GwpXKOJYhugsCCKk/VcDMGbVx16DHLg7KLQSwaNN1i9dga1KyJnCqQYhgrihFuWj9ekeboahCJnMFKmMlMS8wxloFmRhrJUxp3cwjqn5RFl0h//4v+LbjEcz6DkM7n/kdqfRFAPrlyLkBWc7G6k3eE0GGpO1CzySF7wSxnpgf1rVg3VeUywq6KBj3M55ePeD5xQEf71/jk93X+HB4rWi3TShF2qR6EGU7edDx5ys4KMUOekXS0QWf3+WVFcHGJvmtmeH29/PhjDAPNG7w3mvzS/fcQCcFpaSj89rk6dww7R5XN3JFD9gV3tlEoqqQ0i2e8iG0+cukXTpub3Z4/cEef3N8hl/dPcNfffUMx5cXSLcp2Frp1BJCqkA60lvV9S4262OdrGS5IFBlDHd2D3XdyiiMCAUCjiZTBGlsKibQUpUeaqbw7It4n2R7VhZrouGdD8VkgxsWl4FUkoBUDNyy49B70wArSzTEfbTYqw3G2rJLIpqp2sHbxEkEqQRiwZoTDuuIQ9V2weE7s6rZZTEQ1uUds2RbAKUNuOWdpTzYa4VE785EYh0adRs5EgYLENbV0EcVRpVWsQtgC0CtpMlux9DS5ysgGQNW22eGps3qsxX3vGVrBS3wPI7H8a6hjI34IxjADchKAVS9ZfLuANY3sbHOQSz3wxqBOghkFCAL0liwG7Uj4c2gHYku0xGTmbU7mORwUptr9d4OsEkovncOevVMqACA7D3v8sry1/spEbS4olIubD67H9Vf43HKYotLHsMjywCDRCtc4tj+9QKK75Aem3cRF9nKEx2gUxCjgDGjpMW65zFqUpDtNGWcLge8ebLD6/kCrx4uUA4ZNLPOz2IgVUP4wF4cQXvsPDz3oFYZdM5Psy/Ez/43oDCYWj2o5fGoisUNCtlhyAsdzQoAyzxHuDPPrd0x9IAVde9vF7shZHHcDpB5gPX7Gu1xf5+jV5aDgIDKWthYDMisxixx9pRf4UhchbFask4ksVYRq6b72saZfhBEQuQ7xnbwfigrOGKZnv6t21YBB5NMJAUb3V9byHyxDLxq962yvfqbwNle7ZvzOL6v498/fITnf6bf3+WK8eqXjPLTA95/dounuweMaQ0g65qP8b6+YAwAA80hH4vug+8AshyQSahRlPbXNFP2hJNsOwUei8rWmLRzmTPsvUbaG677e9yIfrLOZz7UfH40zy2twvZgWJ9XxHuIDaSSzeNvywXr5nN8f0Lu2L3P2UcjAPeIcnnj+b44W2oDasEAcJ/Q7W2LMO7XXfgRVSGcSjYWS8Z9GXFpEkP3ETuUEVfptDl/0eHRfKu0Q/wEJsFl1teeag52zGU+wbuDX6QFa2UcMJq/st4bxzoArAqaSz7hTd2hMGFHi/mm0VtySlfOnA8FTt9g4gU3+Yib4Yh/tf8Yx7+90phlHW0N89C6RdWJfa2E07MMqnuMRUDHk51wDzZaKIAuzw3IYURjEY9zZICUAVfhnQUAMafrX7QYoJakSXvJ8i3bQc+5eD2XJ7b1FyyGr0vCOiRIYXChMJTv5+wCO/hOtAMAUlRyuJp3lqyMZJ5jXrx01pnnZyAonyUwOQFmBnK1NRWDczXQqp2GWsnI/8Y6L5Ykom3SG6MkqTiaXUwVwmEdsEutwc+pZgxUwyJm6CSPf5dWN49g1nccBYLldkR68xqAAVnDsO1W6GwslxWesbE8mVAgwIAtVk+s0w3j+JwwPxWslxVlryDW5c0RH97c4idXL/HpxUt8OLzGnk+4NFaSA1FOB17qZJOc3hyjTaQAImB51cJN4EdsKb3eubBP9s/pvt+kcdXJK731GODAVd48tjNttQMF4UEEDawHTGBp3l7s+2KAVnh+SWPELMitba5kzDJt5ItMFTuqGKXgBseYcI8yYB4SPhlf4p9eTfjyxRV+9fAc/+n1C/z262vML3fId0mTjArVJ3fyiZi03FjdJhT3SqlEYBGsO70/JA8Ybhn5flGpYQdqkdRmVOj/JWxkIk3K2fTd+pw04MsnWEtanKXlOYKeStH9y3Zvis5XasIIeyFFmYHIthEJjlbPFTxDAFi+k55YQPT+L0PCsQzRcv24DihnHTy27EAzg+eifieWSCySlOZaFYgaUg1pYLLqhbaCLZhrVup20ntkFfXpcvp1dDH0SqB4UqqtZSsoDOfTJqO0QIAmN/LKXZFkwNbZvfo4HsfvGAJBPSXQSRMQAqyRyLnRu8cUxlsglie0ATY0T6x1IqwXQNmplFAGBbGG3YqrSTtePckPuExH62zlfoutiFGhrZ2lY+MmW9T772FsDQoT+ITWMVRf5+BWO/5zmeI3SXOdLXP+GOCSYN48lqmabMuPg+PzqsUAB7p833SOlXgd7EyETNKkFF6EWSVFtzpIMgN8TYgmOLlVX7cmwk16wIfjGxz2I14/ucDL4x53DxPKQwbPupj12ALpQKAuLqCrDAdgZO+p2e4RZqRE4DnMSALU8opuuDdZddsbD5AlAN4Zt5cXdlP71iz3jKWF/nkDtBxkDemh7VJsr2OqbW6QkLQ3cNb/jtjnoG1tbCwA0UWqd6rSzTtoafN+FAcl7jMHtESMlOznj3RBTyB4dziGxKK9CmGBMoX9+0BdgkwgkBg4bACb7+85y0rsu+ePe/dC/U7Z90ra8TyO7+eoAL48XYEXQdkxXv8s4fSzIz54eoeb8YR9nvHj/Ut8NL7eeGT5GqSCwbSENy3QisCLJDN655CN9YVmX/s7yKRATTYJoK7LBiBYVQDUl6crrA9d3rI1ZDdAiFewTlyYeAmfLe0KugWYXNXhXRh9v3sP13PgyjukO4uqX7sNWnY078EGavWfBzSVi+cafmxedA+pZvcZaq2SUf21/ftEvbYu0oJFGCNV3AyNUOCFoYfOc4hJmxaFPBNrq1jDZY7690D6uoHUNuCuTFZM3rLf1powIyNLCV/LfbLOllLCDH+EMfiIo2lYFbacTlBI57F3gYHa6VIlrxiA4brg6fiAP736IX79X15g/Cqpj1ZtcaMmAFkLMKtwA7Q+r6DjHChMP/MRAMkMKkYBhkoNFdCqLU6tVV/n7zbgDIlAq7S8yuZucRljNNURBds60MrMLHV7VuTXXIuimAYxsMljnX2KWqAo62mgol5nhSGLJ5YITyuszTML2qfKGntZU6/UqWuAMHCP40wCKYRak6pnoMqfNC1YV2WQFSFkrpjXpEV/FlUJARhY13qtEZc2FRiT+ow+lAEDFwWhyJmNxcAtBaUfuxn+PY4KACtpS9Cct90KoyU64D5Z3+iNRQh9ex3VE+vhPcbpPcFyXVEvKmi/Yn95wg+e3OK/e/Y3+NnFF61NarfQcr2500AHc5f1KsRA2hrVmVuNftp5aTkbqwOztowVA6I2gUEw4903XzUZmG6nboAt72jSM7M0sK0dKLeE2eRt3alnEjUKolOhXX/ubCtG64y1o0WDkiQUWbAghTxtQcIgRpG1IFmg1YWdqPHkDSvA9aPxK/xi91v899dX+OzDG/yXw3v4i1cv8MVX15DXI/IbRjL6lIiCVuJznnSJhrOhgukk6lEzJCwXjPEuYXyzIB1W9cuyDZCojFV6jytAv80NK4qKAFVEF5BgXtn95kmIFGVpiTOonEUlBnglCoN48m0CDcCydq+SRD+rURYiKfHqhdjOtUmeICSoKwWrqoBxv47BrhpsEncmh3sitHso4SgZgzRQai1a2V6KL4q0Qp1ZqwBu+MtJ2x1nLhi5WOeNHC2PLToFhV3vT5WigFsF0BcgA7BlYIEByVA3LV88bml6j8ysx/H7hgD6pavV/LF6EIs6oOD3sLHsVwexy0hY9wpk1UlUTjhUDGPB1e6EH+ze4Nlwr/Mp6bfOk/kiurAPnyjSJgoibmhdMaAik77Kv7HnXlq61mtQQo9ROGjVS1r4bCG8PU9tIU5GffehqQRtOhQ6SBEsGFQsxro5WRKXu8qy+xJ5l7kMieNL5JwelR0yKjJ0PnPj8EJiPiLFvJgcfKvIxgPY2YLviTzgvXyHj8YRd1cTXi17vDzucTi4x1bzFhOv5XQ4zoat1FdlGdqZMhHqIEgDIZ0qaKlbUMtuqwC1XG7hLC2mAKmEPB5YN0QHqfzXTdGjA7Vs3+gM0NqCUd3v3H7v2b7kMYgMtOm23eSIug+S0FhVUJZWm9u9Z6X++DWKfAZNZuh/VzO5qlV3XMO/2HdAJYeOvXkxJeQ9VtgICNBAJzcCJjiTvkYOpP46/vdZJVu0sxRFl9MzAOsx1nyvx9/ePwGI8OYnjPs/nvH82R2e7h7wdHrAj/cv8cn4teUAs6kcmv2Br5sBZRb1nQqrgUOHOgUQdqxuD8Id8EOxDq/CGFgdCtnA48HmT6CxsfZWhPf3LLDGT+y5TQNhBirWja95cPUeVgnKtHI/rAUqmdx0O+wK2ADCv2lH2onPgRU/tmLAc8/CB4C3WtJ258KBrDZPNBWJj14yxqiYSDtou1/WjheTaymQwQbYzCDs0oLRPIeY9PELqrG9Qx0xwb3PCoZ0sjW0BJDeN5/yazNQwUMdjSGmEsnV1roOli2ScKhjMOt83Q1SgocWbQfUytiT5lQQRqIFfSOSAcro8uvua/xLK5YNVLBPJzzNB/yr6Yh/959+iOGLQX0G/SQmLZrrnCjglXB6NoDKJYbPK2heNN8oVRntzPa3KVfgDp1VnytWPEhsBSKJgnsI7j1GEpS84EUE98ru2Vxk4FV7C0RUdnhObJDCGkNqB9gZa8tzhD4PD6N/ASDN4qCKglGQdouaEGRrey1o1gEZjV1m+6BBSzS/IwAsWBb1zqrVVEJeBLROmx5v1soYkt5XIhLkAJSMMa3GTNZ8q5YBF/olwMRrsAvd+P7bjEcw6zsOBhTBHAfQvKjRu3lkbWSFmb+xU6EQwi9r3TOOzxiHDwmn9yrqvoIuVoy7Fc9v7vHzJ1/hH1/9Bj+evoyg4z5TPfTsC3jvMnDJJwCGontXEqoYUTbywXeN6BYnuUvc+a1kooFercNIH+R8W8WYUS4NdK8rGFDhANNI2LBeqqj/lVcne0BMTVinzeOuv76UU0yoI8pGH+/sq56B5sGzkZgB5aWuGKAU3V1acMknfDi8wk+nL/Df3lzirz94jr+4fR//5avnePhyj/w6gWcgdIFeIbdRoUmF/3hyWQ3UqjmhTITxFWO4W0Cn4hcXsFRJqwGm817RFvoCrVQnvcEYEl5ab+m73UCaTA4iDbiKYVQu6irnDAXJDCuNhFnljdjQcbW4TBB2NK9lXA6QycpmHNiq5Q5mRRth83DQLoGtitfuL8apZmNlbf1rwCq/KEJYSsKQVFz0sKon10VugcITHG2fbmAvN4p2sU6dMH23gq8l7nG2+7w9tjW9VC86Y4E8Fssfx7cYBBgFNQFmWvpNssJv6lQotg0hCknhcgWUC2NiDRUpV1xMM57tHvD+eIun6RCU8IrmMeejZzgBVpm2yq2XJ8jYV7188Hx4Oq9gVIsbDexpo/euCjN5NImj7yssCevBCYav0xQwUNP3No94Ew0HoICtNL5KbsCGPZ8NsPOGKdliWQ6AjFCJ0JpFtGvqEkRA62JeKWYAQm27l+mIZ+mAH0wDXl9e4OXpEq8eLrDeD+ATNx9H6UCcdsIauNQxm8RBTSukpSMhzaU55ftinrXyre/RxTwRQfx/0qYeAZSYVMOvSC8dVEzrG/y07ON6qWKwsrz4XBogtzkWxEdHgaSZBdtn+3ehNiDLr2OTCrYmM00+6/dUuw/8Pq0mD4xkox0lBECppHapAqyiy/PMxbxa9Pg0EiEALZcaeQd1wNgqAZC17Ve7kOGxBe/45gwysctP23vicXwvx8v7PfBDxptfrnj2wS1e7A94Ph3w8cUrfDS+wmTrX2dWLaKdAr3w3XtLLZJwrEOsyQ7WlKnaF7BXVCwmEHQW1oRlI/XrRzC1jC5yqCMWSQFq9a9zICukhdxyjIpmg3IMWbuBW56D2DGEwbR74Flx0f2bgpVFEuu1voDi+wN4Qd59p5pZvucoIGf/bn26+k6QbljvHQ9dpujDc6G1cnRCTFRwWpXN2Ztvq8drUvaLgdiX2VlRFASHBMGAtVO6CE5QIoF7bE0mBzvVAdn8tRw08SLR/cq4SDMGGoJw4F3kfX92vBigN1mHyrXlmZZ7FhB2MGP8Tlbq8faGHzDSijQKhuead/3p9Anwtzvku66QY3GgVi3OAcDpuQJa+cs70LKi+QULULRoQ0nlKKHBsHmVPH8qzgjQwgKIlOVk4BYgkGwsrdS8lCVpfhWycpPl94UVYjG5oDfWwsbeJV4vAC+EUtQb7VSzXiPLy2tlA6QQ+dJSkgJi4kUeaUAWtfUBL2RWMR63ff9FFyOhoKFAxUpN1rSrrWSYrTBjUkfmit2whgSfSX2zMpvPpJNhalJPNiverTXF/aeg9VbN9bvGI5j1HcdAjHS1ol6NigC/q1uhywp5C2T1ssKalY11+AHj4QPB8mwFXy242C24mGZ8fPMGf3z1OX65/y3eS3dB8/W2p21/Vpv0xCieOoF4VR3ANwJYjS7rCzx+67noKIctoKX0UYeAm4Ze35PfMiTtqxO+3wMQ7DHvmOJmiosF1ArGoU4oINzVnb23HY+zvqqYtruIyQcX7PmEp+mASz7hkuZ473nAfZuJpufwXsYIPgAUIDMq7fv5DX40vMQv97/Fl8+v8atPnuPPvv4Qn33+BPT1iHTQzhR9wqEnEU1+aIwo6vyo6sAow4BykTC+WpAeFtBqSZ79T0KG/PssxWceWVbuljahSbJKgyPrArDYPenZUDeZKrtKjLHrYBg22wMBWGFdpxykksYEiInVNuvzVLbJtBDmmnC/TpvrkUlbDV+ZD9xgCeOOBEeZsecZ3tXwBO3qdCoqNdRCjHpckZ0Pr4zXNaMYSwsEzNaeOFMF5wUQWCtZCZZWkQywbkMZj0qNhd27zkbx++hQR/vuiVUaS3wnnE35OB7HtxlMBBorZExAKQ3Iepes8AzI2sgKGSgTYbkiLJdA3VXQWJEH9dG4nk54b7zHe8Md9jzHon6VZp6u+1Ph8ivveMUQZDSW0zcBWA4KNKyb3nrOO8oBMODBPhetGujDQawqzWfo/LnYbweKYHFY3KSewg/Joa9FEnoDeyYHxGTz2gUJJAOcyTNQwcWmu1OTk2z3xRbPfvhSUUm0aQmavGGgFRmEMRVcygk3/IAXwx0OFxNe31zgi4dL3N3vQA8JtFBrMuLgFrqwoyh6LFzBQI1iCkMGQjrWaEJiu+UnszG1CNblyZG9TiwXr5O47zysENrndz32Yv+avFFf5/68/iLfhtSzfUE7JgXQOuDJiRRshZZKkQBurocxDUZekS3BZKrqmWvXVUTvB7L7rVTzERKNd5W8kQGFsnKtFH43gHluVQWtiCsggkW00u0sLfdUVJlRRYMG7ftAHWsemowrQ1CfD+mYMKp9Nx/H93swgP/zL/8f+L/c/u/w/vM7vNjf4/l0wKcXL/EkPwRYpEwllQkNXEy1YPI5YcwGTN3XCYc6BXgUzCnzhuofUzsTZbImqjjVIYzTJ15QSdfzsMf6tVKVhClM5gezj2hKC5UZNrDM/Z98eycDxvy1nrn0+U0k0J2Ez1lXUWDsYsc5kOWFSACAKGAzmoRudusI20Z0MjT5osrnVpz7RAVDmZqVRR9rHAhQv6qEpVPXMAnuV+12uIquaU+mMBio4lQz9nkGqJEBmGvkY0UaM8vPy3IW3wHEtjQPSzjVhKs8R2ys3ikKmk8tlHDJJ1vjVhRI3E+w+47RJJoKJlYc67gBtPy+2tGCIeu1/e+fFlwNJ/yL3Y8x/9UlhjeMNLf4JKxMYyH1IJ6fjOBlD/76DrQWK6KIFQJZQa1kYBAJaCmasyfSPMsLDhbrBGq9gmAEk0oRk3cPjpIK4kbpJe8VIT00kpsqZZJ1uq8U4JQDXFSsKFI4ConOrFvFmVyaW/lrBIB3NFbv5i63g4F17pkVuaipaNI2ZiNueQZYwIN9ZwuDuWBdGdlvayPPZALmNQN5VYAt63V3EsFaE8a0YnZDe/uQKa0hM9SGDt8+Zj2CWX+AQSSoU0LqgSxjZsnAkPxufyxnY9WJ8PA84fAh4fhBRX2yYtjPuNqf8P7lPT69+hp/tP8cHw1fhyeWS+6cgeXMEG+Pmqgqg8iqMA5g+fBJ/misJGBLFfbRT+ie1ATrqgO7etmUT4ihubcA2Q+XNxZQmAT2Y6AVRcaOPsyhm4+qi4FdYGhdk9re+mTtrW0PmPCa9nhZrqLjxp5PeMqHTVWqB7KCqWXHdokZR2iioZNwimA9UsFonSPfz2/wyfgSP99/gb958Qz/7tWH+OsvnqF8tkM6krVet3vBK+a2UK86yxtIpGbueq8w6jAqS+vNSdupWwVGilcB/OirTs6+LYKKVzKbF4llFf2kBWwBMAOu4m8H4ZMxFwxwQ1WgqkoDrKR2x2CVjGYyjEigyCrsWLVaXSbCXKx7IdXwvTqWAbfLDocy4f18u7lPqhAOdcRdmTDxGh0G15JM8tF9tBDWop04gKQgVuXQdQcLi8/aqEMrKJnq5jsU1Xze+l4tVQEv7YhTTGZYrZKZQvbqiz41EsbjeBy/dxBEPRw2QJaxfH+XP5axsSQB6wVjuQLWS4FMFTwUjOOKy2HBk/EBz4d7XKVjgDAuuYv20saycvZR/zfQAKy2zzpWNP+7cxaVP7Y5TlCAWLKJD13CEWwYK8BAQfnzc+YkngQ5i3C+aE6dzLCxsogELMBiVdCMEsBT+HrZvm7MjiF4oDHi8kArLmgJf6TUcXx0ravbyiYXGalgFWNFO2fH9idRxQWpt6N2Kn7As+Eeb/YX+PJ4idf3F5D7HH4ZZKcsAB90cYcQxq7kaxMChJPGqtNqXXXbufE3EiQq4sHOIp/q7Ihs28J+rs6YWueAVw9OxQnq/n7HT/9aPy7qj486gKyaCX2SWD8QJFi8a004lYwlWSJ4dtctot0vs1S4Z1qVfv6mUPU5kFUqQmLR4D67i0njSK9IgTGqelDWzeEBDgYW0JqIuN+cX5+KxopXYEyT4ta64HF8X8f/evef8b/89NdgqngyHvF8uA8gaySdhUdbl8QcL63joCsx3tQLfL1e4kk6hHrhXcbuixmvq8+Njr5TXv/6IowFpJ1d31HI887lzjrpi9PKyp8bA0s4mFt7niOH6AvUnrP4/vvai0nid5i/4cDNO6v3ygIQIFbLtxDn0IeCYvq7+mUhgBoFv3S+aV0GdbHc+40pM6zirkzxePyAMNccPkUOYFWhMNhe2MANXjFhiTXmAGngHxC5gtoA6PDCVAGQWdff/nnVFhHu38U2ZzqRIlh5xEidgXfziR1aPijcFXi8Ig6AVfY6d9fW95FRzVKnYLgquPjxgn8xfYpX//kZppdsxXhAss67LNroZL1kzGXCuFak1/dK3RIBVWkrARFQEogdA4myBcUuphIQrPmWnqiNGbrO+e4/1YNYaHkWEA1QtJCjwanP/6zOobHETgsXX9e0vPRk+cVgRXSpZCb4ujYr3lm3dj+aHivRoEpr1GVsavdHdqzVLWDEpIp2YiAC1ELgZJ9VNHatK4NZvJG9vryTG2LNgClflpLUK5JLFHgcLOVQw2i82qijfs94BLO+40ggXOxPWHeXyCm9W1Zo/kZhCufdCjNhvkq4/wHj4SPB8mwBX6642C14fnXAJ9ev8Murz/DR8ArP811MALMk3JYLFLB5PD2E1t2ZWAA2vwNdQEGbKHp5HaAAVA8sOVhz3lq9/9v3aezeo9tuNFv37GqdPNa3PnvjmQVjrRhw5lXtIwalpUKDwp5OEYDdaNKPrYpq/KsQ9mnGUodoe3uyCtKzfMCPpy/xPN2FLHFHC0bzSmrnUs0pWSpYBoxSrOthxiKtE0yiikvS1rRP0wEfDq/xRxef42+fP8W//cFH+MvPXmD+cod8x+CZQjbhk44kNCAoAbzo/VIHnZzLMKBMjPHVjPSwRKUBxpBylpbqvR20Ip3opYak1SdPZWdJMLVcH97YVSZPtETYwSphMXN3CmCqDrCghLfMe7VSotc5pIbUvVZvvpAHVkmYS8K8Jnx9vMCpZHywu8WHg9LMvZr2cr3Crx6e4ViGMLDU4KuVDACoVYwW6ze50l2REWaFBJ1oMyrWyjhCFwdjat0OV2GwfW5GgVPJWSQ6KnZfJFuUCRY0I8/mw0BhEDpS6QLj43gc7x4MQh5X1DyCid8tKyQHsrxwAgPNCWU0NtYV1INxUDbWxbjgZjzixXiHq3TEBc/INu+tIMx1QAVhpBWZl/CIarN1Y2n56Dv5hY8DmrzOn6tnf4cnhY3zv3Xb5OvDeK50768OhMGBrvrOz+49s9hmgWqfmVGwkoIWhcjYZsoVC+BA2jbE5gcxpsNqiYuzuoowLnjBk3zAnmZlm1JVA3xbGTvMkKyQoltPSCJYYWwJAYI3RoIRBQNre/QrPuH5cI/bix0+v77C13eXKIesccY6IcZCGWjWSTZno6AV2RJQEyPlAelYwIud4fAPYQOx9N7SBiUwhnA3D7Jdl2B2NeDK59345R2gVh8n7IawNVT3dGxoc4H1vw4Ei9dCd7F6ocavUSUcV5UOXeYZlxbAvNDxUEe8Xi5UAptFW5XDYyJtPy/2wzxxuPX0BACiCleZrB2jI/aN3PzdwFZ7tzO2yANsnGZlnBVh8/jCpuNhQsUK0iYEeBzf5zFQxU+uvsJcM67zEXueMZmn7CzZLEea+Tlgcj5ImLxrfkHNU7ZjPp3qYCydvGk0BVhRndewB2mNc7aNfA4GgAWwY5+9Tycr0nMUrjcgGSjW+z2LyI3mHZBzIKx0+QajSQd3dFJgith8HxM2btjUR4sGigWodVbk9+04e6uBYGajQg3gmw0xGGmFdz49yhBKmSKMfZojTzlJ1uOBmcAbm9T/Pxg7S/2ztOmRd/+Oc0Tb7o5MFYey21w7N4u/K01K6tdlMebpxKXrAN6aJa2iHrN+zbhODSxkP09m/m5xLKw5DJDUx/T8bssIiGt4w2qDMOwLrj4+4V/uPsWv/uOH2P02ac5k/sM1qwqlFMJymUBlByoFfPuggFb1jq8UoFbIvj0pcS+ppEUYlBrPa0EETVYIApk0Xzy4CkxK2CoVfSHDpYdEpA0XC4CVASm6bwK4zRuDMC/a7f0yzRhIO4CeinZvh0DZxoVxKtqAh4TAhRpDmywns+MSO0w4c4vQfMT6fEns70qgXANwEyG40JMA0GD3RWXMKzDm0kBaIc3tSJBZ14pzzbFmVKKAy3cZF9wK/t92PIJZ33FMNOCHN29w2F1/sz8WnQFZJis8PWXcfcx4+EGFPJ8xXSyYxhXPLw/4xZPP8bOLL/HJ+DI02edyQu/UtyP1b2qTeasqFDRargNTgE6e3l2idQxMG0Cp1837SNAv4LmRYT/6AOevOfelCjCMauyXVzAA3f6OFhRyTysJffXR8FoN0A1scJAuAih5l5AmxVQteMZdmTDXjNmC7st0hSfpEDLEPZ9wiRmqv5d474gK0KLBQdRUUqWeYxgZeofEHc/4Ac94mg54nu/wo91L/PWT5/g3H/wQ//mL55g/v0A+MGhR7bKeD2y0zRUWV12fzQRJCZImTF8T+LBoRw4AVCuE2YwDFbhCtjTNN09oV80nKbIP5uaZVZME2q/Jjj5XvfTkc13ngaXAG6IrIlx66MmT/13JnrPEyPJwnlm7YoDUJ6Cq/vr2YYfjPOD2aodll7GDdtqpAny97vH5wzVECC+meyxyxFpTMMpikiYFt4gU2CpCwJqQk8k4rBJVicJwE8ixKMpcAaNwo2QgARfUqNYLEgZoBw7vDuOm8RBWXy1fEMG72ejxlw4EeByP45tGAuN6OmHJ07uBLGfDbIAsBaXXHWG+IaxXArkoSENFTgUXw4L3dvd4lg+4SQ9dLOgSZbuH3cxdEwOtvIXsCWYE3zGp/LkVCdmW916tK9JcrOxI4Iyo+Nxu29/EJ6F3vMZN3vv36+NN1NZvT0DI9p1VSZZ+/bMooKW+RSW2FxLFqFjDGFwKOrkUwOVo7vPoMe+BxyicuNxnED8/tk/ihRSlVrHF2xXqi6IyNzFwQsHFq1SwkwUXPOMmP+DNdIfPL6/x9f0Fyv2gMndbNHfF1gB6Qn4IoGaEp6e2ywVoqaBqC39UBbRMug4YqLXx0lKQh1hiHtb3Qq+EtPvTCxr9c17J1oU/Nu/HRurRcJ3YF1u8B+YjBrz5NgqhVL0TnJVVK+MkjLVUzKPO/aAa9/axDirrEcKSFkxWOHNAS0+kmuJH/kCWKlW2uNPkQ44hmtBIDfVJwNQzgz0ZL+37ZWG7kN4jDgo7PNhAXYrn3S/L86rH8f0dBYQPhttgq+/TKYAj92fqWVjAFug4WtHX3+NAF4BYDyUIYKCVSulakdvX3b4tlxS6D+nEKyZTmBRhnAzcGajgUKYNo8sN3HdW/PZcxn1tHdRyoM2ZWp4z+XcNaLYnbnau4NSykQW61HBjAG+gl/8+dueq70hYsE283ddV85ct+OXNsBYzm++vnZ+XHvxjqtENrpcePpShmYDb667yjAuew6cocQ0JoYODcR5AAUxu8zzt1r3YNicn+1iheTVmDYwdNlAJ/yO3+4ApFo51wI6VJYY6GpGBsTOI0vdnBFBQdZ9sjeyd53vg9ZJPYTw/vb9iSAV/kT7C7tcZ2U4ZC1kzKkKZgOUqgdcLDEsBPSiY23ftlaj0dHmUdZEPx8HkiYRdL07G+tUZVtzkPQovpCxlN31El28xNYsZiBnA6/ulkoZ9m6ypQokDxZvqSNzbanuAyBvLmmJdImbxEttICo456YEFyrxydpiRFTQNJ2vYhfa8AFI69Q8MrLOnq4FpZAUgld8nZeoJobJ9t+w6rpWxS+Zfbff6agDtTIKR1/AA+zbjEcz6A4wxFdwN1PyxMr/b6N2BrIFw/0HC3aeE00cLhpsTdrsFl9OMj69e4xdXn+Mnuy/DG8u7iPT010ueMdBBWUTd5O3D29I6/dMBI23TWjbm8a5F7wEnl9H50M6GtZMHvs3OOmdabWjEAvS18ajpdzlKbwrvjxck7EgBrMUAKbaJzSWUGjB1W/3xRitstC507vk1UAV4xUMZ8Nl8g89wg+t8xPN8j2f5Hu+lOxzMc8wrDL3BfYJgpMWC7BEHmvCm7nCUAW+s4nHJc3RlfJ7ucM1HPE93+NnFF/jb58/wr3/wMf7i8xeYP9s3plZHCa2sMZSSg0Qm7WOgpoQ6ThhfJQyvj6DF9ODFJzA15FX2FSDZEKZEVoFok5jqvd1PSxfkDFIGFSG8pmoCuFhHCk8eokCsC2kH4mrGhp3lwJxXCMgW/8oIg+rISzN9n2vGvCYss1HKB23lepSMSTJGu99PtpjSDjDUHqsMqc3A2UX1KVcQaScylcVo0qVdebXOsFZuWQbrJLlWBPX1dFYRB5XwPJho7YCsZgjvdG9fMIy0RoDWJAWP43H83pFIMDP9Tn+sDZCVgPmSMT8BylUFTyuGoWBMBdejemM9zYfwxlK5HG/AoZEKEi3IJsM+9z90I+xgY5kcDtBFcYYbUyPQhB5wchmdj+ZY5fLAt9lZ50yrc7DrnIV1PnpT+H4rmUqARb5dIsEOWwPcgpbIuAE32esbE40aqEYqC7krE2BJ5gXPyi7u5D8pjlva50NtAjJVCFYsyDghYwGH5GZUYSQSKvY0Y0q6/WfDPd5cXOCz62u8vL9EuRveYmpF8cTRJGNpgYBCDpiqjxafVlDR+ExVGpuXOiCJ0VhasKmXXABnC/QA0bRoEhJIf5xt+z2gFdezA7TitYjCSdwAFvsaK6z9ODOrSmNlOZNXErBUVsZUVwB05l2RxkH0xxAsPUKtuofsiYuQykTE6vIm1wApgzgALct1AgymilXavUuQOI8NqNLtN4lsY2d5HIrujAasPYaa7/cYUPEkH9rfZ56wLv3y4SqI+zqFR1YPeIRtSLfuWbo5MlHFnrWw5z5XwNtAFkMw8dokgLYemmiNrrHeIbqi98TSxzz2MFze2JpDqV/XVpTEjgJ0x33ekMcZ/pvzIy33OS/aO+uqdHHMTeOBJj2MgnrkTHXjn9UDWEcZokMjAGW2FS/IW0HI/PBO9W3Prcx1k/QXocjzFkk4rYOZtSs42a9f/ZwOST//tLYmXyr90mv2UMbm92qAg3t4AcBKCZxPEZiXouDZkIt5haUAzpyEUdFUNLGuEMYlzThiiByzogGbHnOv+ajnaioYXhRc5hl/yp9i9zcDsl1OqgBlA3lGwnw9gNZL5C9ah0MfVKpZoljcLwKpyipzcEsrEVqsgRdsYDHNCyZRMZAotoS5ulghKaFNwhYiXGJIhSCFlZVbEb5ZHrfUt5TBwkDt4o8xsASI4gytXUdEB6O6oo9sF1D6+av94rFTVzQay4vtsHclYUFduQFaRFiWhJwrmBGG72vR+LTUJqcduSgr2fzW9D7PAK8YSL+X69/B/B14BLO+82AQno4H/PoZ43JK0RnonR0LmVAmwuGDhNufAPNHM66f3+N6d8L1eMIHF7f4R5ef4ZPxK9yko7J+rMsHdwCPS+Hc1H1HWj2ZkSLILNgGG2cLOYjkwFXP1gqaaw9oCXeeVtzKpNgysLwrm4fNHsjqQa+m13dwyTy7BBE0Gu21vW+gFYWa5rwPrE7ldbT/ZMytCJaspnKHOtoCVumxi1VWHe2+X6f4slUhLCmFTn9AqxYpM0hnJj9nIyur4VAnJAju64jbutsalvOMHc+4kSOuzcD3l9ef4V+/9zH+8jcvUD6fkE4Enrft1oFWaXZQqyaBMKPmAcLA+OoEmlelzwI62doymdSBVidqAbjWkJLAk15H8E2aSFUagMa2OI8zbhIbnxBTe1xPnqP+3b47dmZJi3AHdFmloBalqK41BTMrrkclMwvkbnGlLLseAFN/BU1RRQh1VWaASjOsimKmz8wVKyl1fC06oRZpzWCZtQqmBHWVWUUnN2spfZG84oQAtKpJjbza4CCot8He+DH49e2SlsfxON41CNqO+/aCML5pseUtIAvQRDkDyyVjfgqUq4JxP2PKBWNacZlnvBjvcJMesKM1Kr9ugu735UDaTc9N3ZUjS7Gg7/2l9GMlkufGiFLgqge5XNrXA1puWl27uca9InoGVi8t7J/z59E93ntk+X70nl4hE+zBNKpIUIPwbIbfsU1jo1VLLvoOiBVKl3eJoc9Bfl5dYkAQzDVBTJgvUNDI5cv9/nk3Pf8MAEi06HmWhBkq8T9J1veGf5nKQXayYhxW7NOMF+M9Pru4xte3l5D7BCrWkCTOXWAv+reBUorVaLEuUUY6OqAlrcNhlG/RiiLGzop7VLQ4I90HnpvEB6hVz17k8Y+224g8lLaLc2dyBWhG8TID4ggivSdN870KwErUZB0GXpXu9cUYIqa61Puy2vmyg5B4Uv/2Yok2KaiNzQXtbCuA+RBTyNIBlSEKXDpEdkrEWGH6fWsMR7vXXe7T3+ddAvU4vr8jkTZ8OpjDdII2g1K1wNK9rmW0syTc1wl3ZRdMp2MdUDwRR+ssPth2HbhKgHmcDhvJWgUFK6dYcc/n9yK9gsKAeKoBZJ1LaL1zIWzfBiqo1MznVSLJ2KdTOz40s3PvFgh0bChiazr19trL2VrFFqkJZbNdlxX6cGbXLAgzed+Ov9fnj7B5iRhbNvmMP3aybpOZK0asWG3923eDm2P9mzCyGmvfC6MmBtKMU83xHvfte5Ie4jz0qhy9FhqXV2twVKDdDYEGZt7kB2RWU/i5ZozxvE7OLIKBFxzrgLuyw96ULj1oWME4iqlvjNk32n0EAAO0IRgYSJKtK2LLDbXz5SmAt//26d8AvwT+NP0Iu78aMRiTqUpUTQAAVAfQskf6+n4LaImDUNBJnAnErCwplmAkQyjyGhJRgMvKb8TG/BaJQlFMyE4gIDSwyx7XggQQBDtpccvjMy/6vrUyTsXkeVaAlw70EuvSW9wzmbrtefou7TE/fBN6RZJGVc9HMLfiwtkBZS2OaCEH6jkmFHYufSz1/9maFohoLjcl7VqYk4OVzoz3fIlBjzLDv9/x/niHdadyDyF8IyNrvWDcfZxw96lAPj7ixZN73OyOeDYd8NPLr/Dz3ed4mg5BB3bpmqPsqhk+BlvITd0dyDrUKbogtcm0BqvKh26z6d2PdTCAyW4q8Fu/98ysc0YWAGyrF28DWV6dABqVFjAtu7zty9U/V6HBasczUMd4v09w3pXFg4y38O23N3DBHjMOdcRDyXgo6gOTLbhCEEFCJYl7LJIx8YIbeWjn2TTxA63qV0YLLmnGjlY85SMuaVaJolW5jjIYyEfqb2IV+Jt0xI4XvMhv8KPdS/yXpy/wpx9+jF/99jnKF5MZxXfJhlVsEYbw5muSAeEBdWRML0/gh8UmWOmMDB1/tBV+9dW9sjtoqWF22BIZ+2z33fKFOWkXDiGl8PYjpCCsCYixrJsBtdMpmrbCVvCIyv66Mh5WvTalsCL/JKjEkUSo/EMD7VpTsLKKg1vCWJaMunAEFKsvQKouJpglpCViVPQqQGLBkArmoh5YtZJRmgWMFNReQCtU2RdgwtpFBgWJWmfDUx3AEF34dVmEggG86Xb2OB7H7xuXacZnGa3a9w2MrDoQ5mvC/ASQmxX73Ywpr9ilBc/GA57lewPaFThZJQW7KokxX50tRBIAi4NXXjTpGVxkC+EeXFqNCetJ9SKMDLFvo4NT7XcHtyieeTvR6Lf/LiCrZ1z1HDJd0r/ty9U/J/b56gvS6ue9nKQH4Dwh7CUhvenxKoyl6hEx+ZHaHhrQhQo1zqWKyYAqsaTHO36NVJBR7H+9NromKBglYRad94rotrLo+wiCHa3Iyczi8wNe7fb47Ooar+/2CmqtrQos7YT4Cdb5ja2wQQxJA/LDqt2hbBFNpEhO+GfZnC6EaIpEqj/oFvj9NQPOOxvGBSQJCS0BrYshOsDKfxz0CqCsPQxp8Q2iBZK1pogLUikM7DUskQFTJuUztrezulwKVEuCFAdFJfIWEQJqO369T8xrUjSupSi01IbpQYFronaXejwxd6xI/sn2k0TlvNSDWwKA1BtG77+KtPlGPI7v40iQUGoMVDBZPjGSzh/haWvr7vs6mVfSbmPsfr7NZACFS9LOGVY7XmJtfihTeETpHFaCpQUgZImbzoLCmKx51TnY5ftUhMFJy9wTq03KAmX97OxvfY/e55OBdw4iDVjic8dOzeKG7V5AV8ZkfUvxovu+Lc47SJPgTLL2HfN99pjAxlDx86RdJF3CjzC7D8sXoWBdMVVlR9nmT7UBUN4ZbjS/xtfLDqeaMLHmiL42HkjXo7s4L21fetN+PaeMDAXV3bfM920pCaeakWztrICcbuckGaeaMfGKo/mrsQh2mJGgMlDt/qhdDhMRBpTwXe4lsIMVaNiASdC6Af4GWvE0KQvxf/X0r7H7Rwv+3+mnwF9OGtPWli/VQZvf0PMdpipIL++0K7R7VlZB65JscQwIQMuHs6tordZwxxQx0hp5hC+xnlhjY5HlOX2E09jmBuw0E2SyuLEiTNnJwLnDMuJqOCFzweJM4kraRFA0hs1rik3T2gFdqX2kXnyNybqEkYityKIMMRbQ0hQ6YUsjpN5kwiA3ty8EJDFTeEbORSWHqWLMBWvpiAmiVi6nkiFScKQBu7TE8/147Gb49zhWFHwxX6EOlmDEou8MyNoz7n6Y8ObnFekHD3jx9A5PpiM+2r/Bz/df4JPxJa75GJPeoU4xae4dNLH/e6PDo+QAp7wThmuRnQIbrCogwC4AMUG46WBB06P3UpKmiacO+KLopvBtx7mJfFQk7FjcP2tBCm8u4G3wK4ExQ5F8BYsacOdgh+vinSkz8boJBE6fbTIXqKkxtb9vyy4+283oFsldkFVW3PN8h6d8wHvpHgNV3OCEXVLm3L2MOBoAd6wDjhji2u1owZhKSE0+GG7xn29e4H9++jFe/uYJ8qsMnrGpoFPVScnmrpAZ1ZQAmjB+TUh3JzUjLIB0RodIWvFyKUwFQObp5pIOr5DHyXdalS2YnaXlkzJFNQDRIQPQqoUwgErR5dAvo2FijeraVRBqVcmSm/PCJCjVEj+/Nh70tcpH0cHDg38ppBOsJxWspYgC9RRD7rqSJe0Aks3sfTEgayna8bBSY1q5l4lXINqCoqJWreS775bfd2z3ed/xU7vC/N6vzON4HDEqKu7LaH56HXj1DUDW6bmArlZc7k6Y8orr4YRng3pjTbRGYjFLClBhoDV8sZwV5Fwo94NyhpMztYAGRPWsKge7dA/FwBn3RyBXJGyk6QriUAA6Xsn/uybhbzG0yOe8rXSxB9oQ+6ZDIyVhJSCJ+lWFYs302w5SVWmeYZnqJlEj0tjSzaoKNAW4pQmAx1OW1lHM1wBkANsFz7igBRdQrxeWNZhzMyWbI83zzAAThkqPMglyUgDsMp3watrjN7sbPNzuwEd2F/0AtVyiHhVdu9f0/stIDwSe11boEAcX7TWgzi/EthMg0xlTyx7XRTUF8BTnTJrHVgBW9qvXaHoWVgBI2AJZ/ZQr1QEr/wwDVgXGeqK4J7yboANZukvG6nIpiO2ffg+tsk3ePVePRPG8ToJuAFqpBA2/Cjq5lxehAbarJZfO/hK4/YOCw86e7BmQ6O65x/EPZ1zyHNJxH0wudeuVF8k8sqbwle2lgosx0IuxnnyN3ftaxeu64ngFhfTQ/ZJ6Jq4DW3vzzmpaYcRrgTYfO9jDEAXTkJuBOLDpRL5Iwp62csYZGdzlKwWM2byY1Bi/yQJ1G7ljLWVUtM/y0TONepZW7QqVPvzcajG0MeLOvYTd28oL64ukMNz37vWT2aNUYWSqmEWVDFUYu7SGUuGuTljTiou0IHPBXDMe7DhqpQADe7XDzgDLidewxvAmWrrvfSduiXPvnRf9ujmba5GEQ5lQhXE5nOzcU6yHawQX7c53lAEDGqPQg9EY2E+OO3rprl90OrwuyL+o+J/yT4G/uMDoLCgBAM2jHNAaiyC9uoN3ONz8AKZkMVmh0MYYnUoXT9aqDbcctLLEgsSZWgDgbC1nH5uEvtpztZMV+rDdCNZUVW9fJsGxKAC0VmOPVfUsrtLe6t2MfTu8ImxdYvSx1H0oi+d3ai1DM0PGGtdJKyi25ix2vGxMe49nRZnNKVWNbVwxryk6XYp/f7ioqsagqLkjCky8ovwdpIaPYNZ3HIsUHMsQXef0xmhAVk2EcsF482nC7c8L9h/f4cXVPZ7v7vHDizf45f63+HB4FR5WDpbc18k6FTZJoXcn9IntiITbugspoC+GdSGdN4i7dxb0v/sOJT659OblQJuU+/aygPtssRldN2DM3/Ou1dO7uiIuhs4DjWUFtOQmDBnRJI++H6njgvnrtPvHEAHhUEZj72hXEJ9wV2EMXHCdj1hNp6+mlOtG0+9yxkMdAzj04O7bvk5H3NYdPqMVHw6v8IP8Gk/5iB0KRn7AThYcacZRhqiAeXD0QH/JJ4xU8F66w4fDa3yy+xr/8b0P8D//+hM8fL7H8DqBZ5s5V7SFuYFAK8gkgRllZOyYkN+oj5ZO5UmrB0I6KRKBVgFXUS8tttV/hhkTSpe9GJDl7Vw9vSCAVzIpigS7LX5cdeIG8V717hMkm1TDW8tkhsWCcS02UZMCZrPp8HWxo/fsUhPWkrQTVdFrP9cMqayTMux8VWhXEla/klKsrXHVCZhZsJYEkYrEKhkqQkpJ4BrdCt03qwphTApSnSqQSBcWOWlgZ5EAq9T/obESGRzfd0AB2UfPrMfx+0aFYBVuDMgzIEuYIJlweqJA1nAzYz/OuMgzrvMJL4Y7XKYjhmDIsvlrZGP7NkmhS5ScxbRCjXodYPIkiSABbgENjOqBgNUTKPfsEQfCGoPKwTBYsuMxpLFjGkB1LhE8H87v6Z8v0oFs3ePnJvO95NHllgyKNCi2HSxRY6rVFACEM3bE9j9RxWgJYrXz54CXVx4zuV+ZJnKrxV4BxbZH1m5k96i4TEdc80lZVxAkLNqBEck6H3pLdYU6ChIGZ3jxCXueccUn3OQjvtpf4je3N1jvBvDJwDObMwndvUZWC2LCah2a8z3AJ5W3KwrEAInFC/aT1vy5HMhil3tvryoZE0sM2IkaSOBBsmVp2XUJ4A0N1Op/BBYvPXbWDoyyirYnPBCTDYl1A7SkxAtlVRD3dBELZL2zuu2ju9r7Al+g31Gy+EF6KiGi3mPKBhGDUBV29WNMBnyuxtBStgs6PmF3f5KvxZSH2NI/ZW89xprv9zjaGkk9OQWjsb/dG8s9cnVNq2umoyk1oltht4h3ZpWrHfrmGoCCX278DqCZfaMzWxfGPh3V6gON2brUYQNIuc/WjhckqQGMBbCBlktMHbvIi+IDFUzU4ttIa+QcPRNrhyXAJpcU9kbwYUBvv2t+8e54k0jCK8vZWVqEaZI4n+dVPaLG6udG8H6uDkXl59zJ/VdJ2rkOhBWChzLEOtTlWaswDuuAzBX7vOCwDniz7LCKGm1PXFBkQuYafmT7zojfP1+Pux2rF3CXqp8DNrCq2n45+4oktlstZt+VCVfphCRW3CG7fmjXEmRkASMxLOYDCct3k6mGwk/Z1TnSFA4JVbt0DsA/vgLwM+D/xT8B/nyP8ZVXOmAeUkouoec70FLAd4cAsagYw5hEjd6JWoEemoeoJtyrL2KdB8WQJI7XS+qKOVVfE3FNYOCXA19QplSl8L/agFsWXj03ymamXi3+OK4qxSTxhRsBwp+jFsvFYqFvPzo5jtK6y5MBYAxgtfOwGlMri+5TQjR5ESFISWDW5mPJpIdrVSsX5hpFG8+fVtEmX178R8mqdrF7sD6CWX9/g6FtnButvgFZwoSyY9x+nHD7RwVXn7zBD2/e4On0gB/vX+KT8Ws8z3fqeYVm8j7Qiuv0EGyscwPyo3Xn88lyR0snbchBHY1JwUavN/YOHQ4WpaABbyvgyZD/IgkjYfP6d3U07KsRPb22lxv2NOctFO3n1Lu9bU3oz7fj4yiDvV7f48GRSXC3Dlgl6eQPwZTWSCzCDNK+yBOvwZLqu6M4u811+oeyw12ZtAouhIkHHAwUPNZBAa10wI4K9rRgkIJBtOvkUQYcZYguHcc62PynLY3fJ92HF8MtPpze4F8/+xj/6a8/AH81ID0QyOR/PlFp1ymY7Eiss9mEHRHy1w+gUrTboXBQY3WyNm+oqpOyGvHaCXUwygGpyBy8yuETvD1BJn7wyoMXN3w9z16JgAJpaOAS4NsGqJCym5yOWlknTKZofd6DoQWqvV4Lm2EgR8IoQEsweiPela1wUoP6W0UgIkipApWR2KvmDOIKtrL/UlN4GKxI4Crmq5UALmBQUK/BzUMLML8JjwwW8HWxYnLfb1goPY7H4YNAGLm0ZL0DskAGZN0okDU+OeJ6OmGXVjwdHnCTHnDBMwZsTd61gLEEGyvR1oBcu+c1looyjFqxIRhZ3fdSn/Pva2NrOSsK75AjAjAQR1+fgbYf1AvQ+te3GNA/38MjvSdWPXt//5pzE3oA4ffA8PlDwaINC81Qn5U4PP1WURDCO/SKtGTMzkAAWh7bnVHTm8YLBCdJyqYWvSIrsfmAKVP1mo/Y0YJMgsGuCBuDZyVW+ahdn0XUI0ZZXhWXrODlPp1wlU74bHeNl6+vQA+s3XUJTUYIhI2NT6tCBCAjE6m8XapN7Hou1WfEKtqwRXKXAGjXwsZkIr92nifwVnYYwJVLFd/F0or7wf528IrarsXjzv71ax2/23F2WxTYvVyVUegmu86Q2txaHia9G1UXK6VfJwKRaSjzIV7VgV2asBC2PnRkSaHvIZH6aSVL8JIvSKFxUo+ZjbH1GGu+z+O2jnCzc7cx8A6D4YFr69rbssPrsgeg7Kelvp0Snhehm2RcGURJnD0rAUbteY7fHahx6ZyzuqqBPv4ZfVOnHgyroPB38m1NnfeXP64s39aFsAoF4yNkhmY8rsCT5i/Ng9UZXiWAq94A30dPCtDjaAWSZICOg2Kb89jtmx5jjkK9G6TveIF7jfm5yeaF5d0gdR80RqzCeJcUa67K0h15xVyy2qeQYJcWXKUTdlnP36GOASyeJIeaxcGmasf+UEaVKXLZ5FcQt2dJuORTA9dsLexKl2PVYv5Aagjvc2QD0LT5V6h00PJD9yV2llZxqlKfS9q1vWQgDRXpumL62Yo/4Z/h9OeXmL4iUBGUwW0ZGLQH6MUeY62gwxEblL/30vIAwto8SwywIhGb46tP8mokz8bIlm2hTIjAq0kZDR9wyaFwA7Qc1OKChitUAFbQXyWBRazAfpY/r9Q8PluFQ/+seKcZfD/I2FtEui2NVaJkhAJItthr7C0IgcYSp4q60FIrGVFAG6oMgBEfdK3zsAy4GBaUypg5YUzeKIgC0Pq7jEcw6w8wfr7/Av+fyy2QVZMCWW9+kvDmFyuef/IKP7p5jfeme3w4vcGn01dBjXQ21iwpdO17OkWXQtcWL8aoOu8aWNE01r0Xli+CfXD3OADcVwU9Ljt0fkar2vgEXWPC0BvZPbRcvtj7c/Wj//v893Pdfv9YHyzOt9cMd83wnfXL4NWAXm8OIJhXDDWIrGXAyCsu0ozMNfxKeo29A1nJKgEcwbG9ziWS92UKfbhTgisYB5nwPN3hkmY14KQFhVbsZMEMo3bL1AGMGYD6BTxN97jkE675AR9Pr/Dvrl7iX33xMb78m6fIbxJ4gTK1DNDyiXDdKWouxKhpxJ6B/NUDaFkR4Y8A78ghVgEGgGZsYs8l2sgGNXEQ0ArUAZHRkFUbmtmWhAGwuLzQk4gKNWT0RATYdNLiFZBVPWYAoBbSzyn63VpqCjnPADENv4FYlTAbaw5Aq7RLv28AQGpqSCazNGYZmT+WEGEtYtUFkzvVZvw7ZQVrvZIwG2OLvS0YWlXfg/pijkMKzOl9siCpHNh0NpoYPZbMH8fvHs+GA349AD2QFYysp4TTexUXN0fcTEfs84LLdMKTfLCCR+vE5B2VMmTD+GXoa1bRbm7nVfje8L2X6K3oFlFAyJx88T6bQbn7wzloA5xZ6DmQTf54q+IrEdUqrGfA1Df5aJ0DWu6ndW78ft6AIcxNY3vKrvTjFnuvf8cBZwgocKCG72zgd4mFv3oq6f7lDsgie5y6/an2nmTA1lxV+pJJK5zB3KJsQKXu2YiCShUZhEJa/VzQ5kb3RSMyqbsUjOOK63zEl+MDfnu4weH1TllaVQsnwdKyvK5mncJXIggnDATwYWkMLQCu/dPzzYB3fzLvLMdjPR5ofc0W9+SxoQO0DIdp4Na7pYewh7wKHQUVaa+hqjEiJIOh29fXqCF8k/I5W89BrF5Cu40vflCIeBpG72T3Etl8b4Bf+KzE5wACrVbD2DUKpnHnoyXxb/wIoRAg7ygcendD9kQGj+P7OmY0MMLBlX797XmB25MwVRxK62DYW28sljirSqGBOj4v73nGxAtK4QBgzgGg3vLDZeOQFjt6w/eeGdTvhxvJM2Sz/ZASwptXcTCAWle+AZOxxVwN0csv++05iOUsrfORrHjRs7d0f52OUyMHm41MEAwqpPDhVYP3Jvl0n99qx+o2FEyCuSjLNlNFITIjeMLRwKK5WFyAyxgbY2uWjKUkEOVYs77hi2a7AgSwuNQUedRq809CNauMZj2j3ltNcum+ji6L9CIuQ7CnOa7n1+tl3C+XfApAi40tOJIDfmRzrq6N3V8tFDPme3asIypq5M3VGFo7XvDh8Bq4BPingj/hn2H+s2vtECimuoj5PoOXPXKpoNMcIFYwtLoGCMpEog2IFXkUdUhOqbaAsfe7Ybx3/yUHrlqRjlcFkggAFdpO7NDXU1H7EkZ3fWtWYMnTDwHWNSHnEjGPrOsvqAOr/Hgc1DJqc7CyJB5qXYoNyKKVGqjl5xF2TkgtW1I22xVWogCzfZ+EMKSir+GKUhlDKsHgGrm0vKkmfHte1iOY9QcZRbjrNqc3cJ0Idx+/DWR9tHuND4c30YnBDRgTVALgssKRSkiRHPzwDoXeirZnanmlRffHTRxzVAiUUdXAoURqyFfBuK9jmPx6krPYa88NEKPjYFf16wGtbzo/+pnvBrd67yzAJjRh7EhpuL+L3TVbFUHbvkpXNajYgXDB6q7lGnOn8l+kVtnRL453wmMU0sQp6M9u7EgI010kwA3IowJRVELyquxj/450MtP+gsGqDAMKRi4YRU0PFyQAOQCtkQoSLRjyikuecZWO+OHuFf7l1af497/+EOtvdxq45jaJRZJhlNcZDJIRFxUYXh6ARa9j038jDOLthHUViP7q2USVYKaANulS63aot4JlASZ9FEIzr++Up2xac2y8txAyQymEtaSQ+AQNYGWcVjWWXGpWKaDo+dcKgNJvi1j1KqoRFAkQ/HOrJguAd5TyTIeRUlXPLJt015LCS8vNLotoksekC70xrZt2uXpPW3BH839YkKyK6X9nTGYA+phcPI5vM9xzIRJ3UonxfP02kHWVj7iKOKMsp1nMJBo1ZIUJbniOALIcsHIJcYBW7udh+7MBcgwqIhi4a8MN1QWE2Ra5DigRlI0F4K0qY2OAUfzfA1rvPD/oASi89XvvneXPCbRr4SopgKa+O6P/vnZgGKF1tfJtqEluwmpAhXdG7auMAYBJ65YXlWd7RbF5kcUe52KM1cbG8c5BxzoEy2elNbzQkgHs7k8TUn0wIM3oXxkRBXsyg+hJQa1fj0/w5ZsryF0Go7FpAxCyAkRNUEMTSRgE4IdZF/N9DBFqBvFAM2Z3xlZ/N1RosaU2QGvT7RCRAzlkFH5Z8Zy9wMNS2057oy/8qx8I2n/eNcrlUe5R1bOxnP27Ma2N32W7g9WkGYKQ8VemAI7JioPBdCeXstr9AoCg91nEIFt3AcaKBkUnRDV9V7DAd6k6i+R3fnsex/dhuNfehvWEZj3iQNZ9nTaS61ZckFi/TmdKC3+ds2ZUFjfFY/EcGmNnoBI+XD3jaFvAJiQ0zyaXHJ4DV0wqPRx4xc72LXyBJYXEsApjsfd4B+mKbUe83ry998pytpU/FgqODuhaKm/Oa69q8ZzJZY2z5ACYdN/63AkbD63BcsDbstOugFQwsnUytG2kJOCamuTYWFZrbUBWpoqb4YhX8wUAYJ9n+yzB/Tpi4gvc5AfsUyMybPdta5XhINVcM3KnLHAgq39NQsWUqnZE5La6dUCz2JrerXPc9P2coaX7kSw+em7oCQ+AaAjGUaTz5lwJFc/yPX5x+VvUTwn/HD/Fw3+4wsUXhHRskzMJMN8MgFwifyna4RBAyA6FVO3ijwFAshhskkMSiacQhRolFUToZzQgq2rhQoExRGMsN4H3Q/QfdGGliNo27FPXQdKOgwxYqtbRsN+GpLZdeMjr4jgZC8uJmW4LoIBa193QVS4VcFROFta8yht/5RrS/VIIKenOi+VUTEDiasUaHdrERMkBmQsOZcQ+zxrPvuV4BLO+40hEeJIP1uHAgKyBcPtJxqt/vOLFj17hx09e4slwxEe71/hkfBmMrGamuIZU0A3eB1pDKuim7b3vlE+gPYjl4JXTivuxSMZRmlZZEW2ln+pkvYakT7uxUZvou+Dj5u9Ax5yyb8hGBx3np7bgdbaMCh8sNE+sanRR9/2K7dg3MVlC5lrzfuxoRuJqPmKaNF3lE8xPEKeSjX6rkounwwOYNNG6TOtmwmyVLZ9UGZCKk4Fr3pb2VHQfV6qKOFtwV9ow42m6x4JkHQ+XMKpMZlDpFGNfXGiFp1WbLllNN2/4AS/ev8PPrr7EP7v6Gb7+9RPkVwnppN0FnWVkFwQgwQkMoRF7GKC12kzZ02VJVHpo5vABxyviZBO1VYtJQEyawHjWkMT8QaCTuCjg5QtprYobS8s/Aj5ZSvNjsckX1b1DfBVuh2SAmC/S/LzNNWFd1QckOh0CkMJmgEitut/5d4EktN4AjH2lx5qzVgrmNTevA66xmPBJuK8kMAnWmnShAcHJokLmgiSa3DnlvghH558+GXLd+uN4HO8aBGWOUj0Dsm4Yx/cr9k+OeDI9YJdWXOUjbkyq7kCWyuIqBmMDZeNNsSW5LqkWUABZPQNrA2JZ4uLMon5UYawdACYkxmTpFqP2/XbWi7+WIrmQVsVHA5dax2lnpnTfHzTp4jmDvtprRbrfQXAIrpeEMMQ+R/d7eWtr1iwEYqCGgg+jT8RAgCGVFNTapUXhB2NWtf1s++zHTl7pRGOGOrCixyI6H9kcqN4ShB3rtRpR7NpK+3F2D5Rxt0jenDuGeuswCyZasN/PeDYe8KvpGY5v1CCeCjoKXXduSc8+KGHAqIBWle2LLCa1vK35iSgji9rrq4FbZMUSK3qfyw4bUNVizYal1T7Z7iF4/SQW6SqNoO0x+W7A5Z7Nm6V6sxNpTEQ34I3vpftlAR3Stn1MvHhvRZ1i3zcwB1iqMmBjMtp6LABmizmEPjl3ebAazKtHqETH5v4u7s724/ieDVcVqDXGuilAO5B1Wy7ib+DtQrPPK4c64nm+39h3HKp6OvVA06bgfFZ89mK1m6ADDehyplUUf83z1r0Gff7vG2oA7veVY9u9gbo/1gNafcdFQAEm7x7ooFbPWHYPsB7ASiSb4y3Cxm5S6WKvivHPGA1YaYDcGnlNeANb3HX2mJ8THwOpvF2Pj8N/1WOy26asYMwlqywRzVspc8UqCVf5FGDXQxlwmU8bKxigKQ7YivYFHGvZtao9DcdMor5lXtSvIFw4E8vmJ2d7uSG8xw8t0jhze8ZoTxUQdpb7hUcWEHmiA3/JrrHnyXoOzT4Hagx/ww9IqPhHV78B/1jwz+hnOMoVpq8Ig6gSoxZg3TNAI6heIn/+RjscusEv0FhaZggfjwNqgJ5gQVAU3Io1CwBYzBOAEkKl4q+X5LkQRaCizn/RQ4efu3lNkTN4oyx4g7AuxlRTptDaAVMGpNGiMTisyWyf9Ktrx2Ff40jlu31wwoLevnaeKmlnQ5IgqJF1PqwVqFXZYoWMb08qmRxSAazRVko1GGdvySe/xXgEs77jKCJ4mg5x09VMOLyf8PoXBc8+eY0fP3mJ96Z7vD/eBZC1s1a5gAceDibWENUGjkmuHz4ROgDibKqegaVspWbuu+k6EqytFhhcP+7DW9QmlKjyXPLJnusCyjtuuHNpYMgIffHeVU6ig+FmUV03v6cuwR/QDOe1lW6ySo8HsmzHorrcE5rG3P2yfAKYq7J8bvIR4X1yNtxvQGmsK46idGWvIngClrloq1TRlrUAkEwPHwAfMWZK3XVWptaARpFOLJtukz72pN0RB9L26lc/OuFfXn6KP/+rHwBfDJAFYKLosKE/OtvMxCBffDhDqxiCDujCOpEu22vV7obwuavaJGvIvPudWCLtMyRVo4M7XdUzCZ/0LOvYGMALjLbbXkMrqeyzMhYhyMpBuRV280MzgLfkrlRrlW7MrL4TIlXdHoEUiAO1z/bdAlBXDTTEyoaolePYhQXzCuSkOQrn1YK9JvarJThVSlC8L9KCk+nZWbQimLHtAlQsufDFkyZWj+nF4/jmIUAsxn3xsewZx/cEOwOy9nnGPs0BZA0dcKKdz8yAHDXmYgevzmfzkJeLyg6cP9MzsLyC62BYD2zFvN4Btm5K3V7jci0J6ZVLEftYcg6Y+X6f/20Q/eY9Is2zqz/Gc/ZW/xkJNbafoeyxVbouW9IkmA6M+f4SRJOCqp/gQNTEq4IRbx1Jn4BqvFyFw59MqPmBudFugFtsbC0GWKsAui+gYNw5UwsASKpV1BHAZT+0i6VXuAvGm4LfDE/w1asr8CGFtL0LElrR9V8uO0Cr1DOGElpl2osYds3CaosdxvF7R+JKBUuL25Xz6TwAK3+vP2gvcNJx7LpLLIRQvMAh/r4m4yibgk4DslwK0aSG+kO+yz1cZMUT9X60OIp2b7bTSUCt1o9Fv6cCq95TkzqKf4vEEl1x+aFYXGkAsYNx7ay28/k4vp9DWbf6RfW51POGWVIwsnpGlXet6zsALnXAnucArzbrlw5c9zzAFQo98yp8sniFdzDspYgNNOK3cgfvhgg0qaIXx3sfLo9TDmIt3e8JmiAfZdis8ZWp1T7P8ydnU7mBewCBsvXAUo/bEuxc12HPkrGjGYsBWYCxnLrP9m59B2sIxV1+1J9PvW4pwKfMxrC2+WkVLaauluOsnYdS7x/LJDitGVf5FNJCZWhNQNbz7Iy4Is6Ak/DO0nNJ4Qew2Pm+4NLWr4xYm2tu04Cw1u23AWeHOmKRhKt0tNWxeRjbZHWu9Fmgaouxe6wHahe0+1tBSc21LvmEAsYvLn+L9UeM/0l+gtO/uwaXd3hoPZ1AyyXS1/cKaAHojd6bmaJEtczne6oKeGG1plpECoJZId9Zx8QWzw0AAreCjEoNSbfveIKn25bPJNa1xSZfZeuiCAFSs0xpOxj4skkK22POX4CJdKhoMa1aMAugq69NCTQvswKTkxpkJYAJlSo4GcbFANtx18qoLGAhFDuWyN1LCgnlYR2xS+tba8XfNx7BrO84Kir+71/9E+x/WyEGZH39TwTXn77BJ09e43o44b3hHp+ML/E830VwAVqgYSwbHbezsXT7llDYRfXHXRte0CZZ9bzKVgnQz2jG8K2jxvmk3g/vXgi0lqv+uclu8DbBe6VYtWdszKq+s6F3GXTdc7HJrh+9P1e/3X7MkpBckkE1wLCx20cPyEHfhU78Gky0o9SJlJ0FQD2WhJS9hWZs2a4to3giYEkARLtu7HjBTX6I5GLiVc2/uyrMItptMlFFJUZlxlEq7mlUc3+asYuOHcrUOkresPF8uLnhQCt2+wUfjG/w4f4N/sX1j3H6zR75jpEfdJEq2YB/WzSfbrzyIMhfG6DlEzNTW/4yw62fIkEQPRPwTp1JgwCMYeVsUxb9XOkAL6noKusS3ZedqeXB0SdXLgAtjHlNWrGu1PTjq4JZfn+wLaqSU1WFsBQ1+j+tGW5Y6IkTeedFq6T0FFqdkMW6dABEFSIMZg0Mgpb0lMo4rhljKshcUYpW5yoptXvlivt1DNNOALhIS9OB++IMMM8vDeKPONbj+H1DIPhPh/cx3Glb5+WS8fCBYHp6xM10wpRWXPASZu/5HQtkgmyYQX3BxBNzr4av3eOlA7MAnDG2dDSfrLYE6Zci5/Org1f+Quk+16eHYGBJt4/Uerj1DC3fv+Lv69jFbR+wiXjnYA7QvInYAC4Hw3qAS9kxrYujg2HVmDPJol+x1WIRlRWOVqE+/9TGFJOYEn31mKlg4hVknf+ygV3KDNNJtQrhhGyd91TSuUK9NjOKsbXcIF7j4toVzLYsraqMAq7IQ8FlOuFyOOHXb55ivR3As7KBxU+ovTUALSQMMqgpvLc9PxuEFivQXTuqDlZZEmEAljN8IdBEgIGQHXr9hDxmbZla/ngAYOILeNImH1aJjAV/7VhXdj39x/e+iHXSrdySBduPAOtgCaA/Udt+KnAHZSeDAoyKsCSaVKwhZ7KOnKT+Y84KnG1d5wm9dsVENBMAqZxUm2mZl9JjrPleD1d2+AhPXTAOdQpw6lDHADu0GEFmeaHDzciBVpT29Yt3wdPtU8wpDnTtOoP2RVIk1y4njHUQN/Co/xz1uG2G8X23P5cs+jhncei8LG89Hq/XCmeoYlo33947yxUiFtOEwth9kaxfZ/ta995aoxnM98fS9osiVwEQbDdvNOXXzsEiB5NOyMH8B4CTsaAAM3q3boNjWuN6zjVHvD2tGUMqeLPssJcZmSrYbFYU1FOWnPvPgqGLdxv7NIdEEACWYjmhSQ5BHGwhz7fuzIPNC7z+Wd4V3AsxhzpiIe2qqOQFtc0ZqbQ80451gXmeGRnAE4odzpsB5LgmTI2h9cf7z4FPgX9ef4rTetWtD+y1hTA/22FaK/iNKVmYIn5RqcrOqp5UkEoOCyCZGvBVJBhaUbwwwEosJukGAVpbLEPtYhMD66W9rALpqL8vxsxiCOaaNE5VQhkFMgrSRdGmVJY7+a3Jq3VOdOBqMOCK9TRGbpawke17kufKFpf/6wWVOA5YvqbLLiUPABZLbdFFSQt8KxgD6bmYixq/6/1tIGnIZvnRM+vvcxyl4N9+8QNcHwXzdcLrXwDTT27x0c0b3AxHfDDd4tPpqw2QFaws1NZp5AzI8jalvfzOZVTOxGo6+NaBwieToMPKdkL3icUnPWWAUVRL+gnbt6H7m5FoCbDLJ+keQT8Hspx94n9DnPF1Bl6dLe17EKd/bU9h9oAEwNaK6oflQFmFoDAhWUVqlYRkrJnVg0VVw/YLWWw/gEFU+ueEaJdtghiXdDL2lZo4PhtqgFgA3gLpbssugjGb2fyOF0C0knC0Y7ukJfTnCQuOkAhwPRNO7xfgvXSngfZG8PSnD/jnu5/i5d8+BX2VwDOFBluSMgVpFMyXDH4xYlcF+aVVHkrzzHJ5oJvEExDmh1Stuuvn2yZ4L9Y424kEzRzeFuxR1fZswksZoKhm+8LeZSylsLKUCoEWnVn7xpxOQV/qgFJZEf+iUg33zcLC4LXdIF6BEFiwIdtx64wlK4GyTr4FrNcLABEhJwnDQkC/O3NJkegwBOCKSr2JoVYIWWwRYre0+9h45XAwueHjeBy/b6xS8cXhCuMKlJFwfA/IT2dcGZB1mWY8yYcNkEXd/wkSSTPQgKxeRugA0/nvhAbeNCgJ8TeA6N7jYzM922sFynTyFuVN3thYTbBqvXcY5LNj0W1ugSzvpuV/+//KjGzjPL05l9r1ssZW3PQujDp1OKuJDcXw+a35jqhIejB2FqDPrcLIm89rnR4BT/Q0+o20BjsbVLVhSc0Ru85Z0SfJGFDC2B2ivn0QoBJjFaASYUTRNTgELAUrGYQmtJmH1J+pYk+zFmwmwe7Zgr/Oz/DwZgd64PD3CFBLNOaUkUD7hCwwyaHrLCheB48xzlLqaVNVVAZv/8c95HM1aGMOH8+fg1qAQ2RxDxrG6VpTuGE/KppUpKulid0Xq4GRIhpfxd4rBoq5NERsHz28wGUZ25vOYqieGurYZtxJCfU+dPZi82oDPHSZ9BBkIc2/ixyAb5wTk270HRofx/dz+PfYAa3IKSQrG8ga5TjgdCgjWte6AUi6Tj1nXrkErmf+AW1d7p0HAWz8rva8BUMYEoCUN2wKf9oOqAr1SDCEFcjSz2mSM6CzQjnLLfR8GKMMNaR/s+S3rUQsp9L90Dyot1tpkkR9zdwtSt/V1f0oYzuHxuAq0hjPuw3giGDTnsz+pT8HAxecSka2dW8fqwCVB3qRQ9egqzG1JMCB8EQz8PJUNXf0IrFvp/dO9vtDj0kLtIkEp5KVX5aAbAqeRRioiIYnIZlEwUOdMPEaNh1+fznwqMc5omLFAGcVIoD33l/tiKEBph2g5abwC1QWOksKPy43ha97An4C/En5I9C/ucBUGoBURlbT+HKBcSmg+4fGwvIivQNaaI8FgNWztaxoTiCgWBdj0vdQMWDJBlUAVcAu4xdlZFE28IjNQ74icpzDOm7m83pRgbFG8c3XHVQRxvNihZZq/lmwvxVUMyDL8zAmSDIPL7aQZflUHSwOV0RXYpjUXpNAi5OFwNnWR9C8TgkC1djNglq0mDSlrkMmYB7EdSNN/n3jEcz6A4yH44DdJePNTwj881t88vQ1Pri4xSe7V/j57nNcp4cNkOWyO2djKfjUQKyjUTD7gNJP0Nr50PTYBoT1k59TQ70a40by3mHDgSz/bDdO7/XhFefLfsT2dPtrW1CLS1icAtw04QtSTE4OUinL6u3tn/tkMWpUis6pzRUN6OGOSlSEcZDRzoMG3ombbr0w4TLPG1NeD6ZeeTrVwTocqmeVdvky1pdVbCAZiVakJAB2GzqtVzf8GhzqqNeIF7AoIFWgclBPJi8xYzCW184m9AUpQK3GNtNz/JQPGMeC6/SA9z69x7+4/jH+7X/6GPmLAVkIvBiY5Z3PIJivGVQnUKlIrw6gtUBgK3M2U8MVoERokD7FwjsW5QFM2WSeFBQSEHiRMBGEVwJgC3wDzDyrIEtmg95aAF7aROyVCgFFdSRba2BvNV1Biv4XCvmHT4jtEnfJDxnAJ5Z4JMSxik3imuSkTXWqNSpRv5owd2ZdQKBiQ//O0A4xA1UsNjEvSGaqWSPQn2zR92iX9Ti+zViWhDwQTk8Bej7jZnfEZZ5xk494nu8x8rIBshxsdTaWBHik885qtPyhXxh1n+deQYmqMY4EEnOwy/oMCAMhOz+Hmk+WptcujmrVYNjjXsXrR5HGvPLPBmBYgyf2zevKzbR9Ae5pf+lAr36c+2S5ZNL3feOJBAqWmgNFztJyfxfD7rXqbQmVABh5y0jzjrjOqioBt6wYaQ2wcZUUAIR3/iJeAQO0fArWuM8RJxYz+HdpYrbr5L4iQoRBVK5CBGSxfSEKUKt254QguKAFOakn2P7Jgr+dnuCLlzfg+6Ser8444obdlJFAkkB1AB1nK4hIY+Lajea+GlEsIb92LgsXCx8daOXnzOZ03df+eQRopVvqmF3UkheqNsUH4wsdWwzBKuhZU67jd0g45u0oUpN9tnt+wQ7KCih+8GYaHLBwFai6XZMeRm01H9s2k51f277YtWVjdwk1oNkbBbh3VgOJpZ3/x/G9GwqKcHjbelG6gvG67DeSQZ+DBy5hxzF1csApnXAyD6vmx9bYVf67NlDSdQxbXnOsAyoIE60bj61gC1mS6gX08+E2Hr0hfOv83UCuAMtsfvO5K7kEEbxhqTmg5Q2d3F6lL8S7f1YiseNo5u5AA65CRigc36m+mN+PPnc6//651BAWlwcqKh+vTSGzImFKKx7KEEDbvCasZpi9VsbFsGgHcCCYWu6bNRdlUYXBuxBGA64ceIoO3EShyvH3r5Vxss6XD+sU+363jrjKszWSUTkkW3f36HAI9czywn8VtX8ZuOBQR0y0huSwXX+KLvNqG9OtSmxerJXDYxoW/3Y8I0mz2nFAy3PWF/kW2APlZ4R/tv4R6M92mF5ZsDAPlOUyg57vMaxFOxy+A9BSxi815jCgjCfvXAibt0VAzDrfw7sFijKyMm+7JQLhfcUFyAeC+fODZ4CKNroSIdyvIwa77gDAJ4bMjJUA4qAGR16FJEBp8S/Ssl5+6DHQQblq58Tju97gDQgrBnhV0tzJ6igBbNmJkAqkrB7FYhLIwhTm7qUyFhIt/gFxj1bhRwP4v89xFMFyP+L2x4T5Fw/42Xuv8P7FHT69+BqfTl/hOj0E0OPtXBMkAKa2nSG6EmrL3ASWxtbyodUCo3VSR8u1CSmYSUKxsHdvLX/chwNhXjXxfay+eEINSq0JfgEoGMUyINkqS0GyLZLuVYZosYr2eEC9NqLbIumE40y0raZ9WyXy6kYkBx1y33+W06cza5e6RILLNMfiv0LBJzdvH9wTyxKVIrTxE0tUTX/fTDb9/LMBUH3XkpN5lg1UWpZo/mPoztc9xgC0BqoIA3QBClVAMtAdY6KKPU4Yslaq9i9OyFTxr4ePQb+eQPfUFr5WRS4jFNBad9iVCn590CSDoSXi2CUGWEArdMKFJxBs7As7H+F3hag6SAV4scoDWVIVyY5Ed0GfNMl/J6h80ToTEtXO80pBLg/eiyT1h7Fz7Aa8IUOsHBUOn6BBmqgIa2KDhYDcJnWYObwUMkNHu29JzLxQ07S1CKoAiZUWGwll0goIU4qMTr+TbCpNNVj2QM4scHPfRVIsQh7H4/imsQKoS8LpKVDeW/Hs4gH7YcaT4QFP0gNGVqvySPYdyDorTKwGYlVQdC7s2Vo+GjtLR0iu0KQlQAMYfNsEQZLNFN8BYVvJooP0zvwCxdcHgMnpIeCISfK2fAO9PxCdPb4dDpCRxeQGi7X39Z0M+797yZmcLbKk+2FLhggwoLr5gSnYrvuWDaRzUMI7+vTnTKT5IdU4/xLnq782KxRAao9mgFZkO68a6wFQAqT5rzj4Ayi7FFYtl+7aDKTJScoVw147Jn7GN8BtRmfjZvsCSNJ4g31GFgGOcyzkN8OquY1kYa3Pjf0bIJUXKKBsKLIlRKsci1a9/b7rFu2hJLEL1JhZen+TGzl2saJUiuTOQU6xz4a0e6IKtRt9e8PrOwTmR0LBAm4nqgl3HdAqIHDS2EO1anyknjGIoEO7ExwMtCsGbBJgflsmmUUDg6sxyx7H93d44l5kwLEOmCXhtlxsmFGH2prbVFGjd5cO9r6AgHY07OWGQ7duidc7M8rWqP4/h2df+9vf4w1JfA0fhujdsfTMjNS9tgrj1PlmOTOr304vt2zgVdv6aJ5LLf9wn6jWvXDoZIP9a12amKj5F/Y+W3MU9BthwL94/pgfS3+uqzQvXu+O6xYV/XmroiDTaCyowzoEGcHnrTEpY2qtGl/mMmCXFhytadVkYJJ7YjmDzUcVwsRryFEBVbi4rUoFoZYBbgo/8opi71kl4VRGA7VMvtld2EUSTmtGZm2ctVTNqQYqqoAg9W52Y3jAzeGXyKELyPyMiwFaanXj10QbhrGRJ9Qu5toAs7JnHH8+4F+Un4L+/YjxtYBEc5U6AsvNAF4vkb6soGXdAlpxgiqQ3LPEnquwArl0gI5Y4cbTaGr5DhCAD1nxngrAJ8L4GkiLBrGa9blSGKeistHoZFgI+ZaQZsIRGWUsSKlGvxEAm46GAOCkNgenAsBa7TCy7m+Q9nxRVpWh5ewsV7cQFNjy10VzLQcehZCS3euVTH2j3Q199Hm4iAD8Nsj9u8YjmPUdx/tpwv/+n/4b/I/Pfo4/evIaP7x8jU8vXuKj4RWepvuYULVSUlUKYGAJDLRxU0bv0OeTkmt/+wlUTfX0iz93dNSEGppin7CdKeJtmY8yoApj6vTsLhvUbhHb7hG9nNAN4QFjblkVREEkRcH1tefpw3a0gNUChA8H7XqtdC+x9GDm+6ev4c02ek26B4aJ1/AMyyihKVfKprO7TPMvFXvMnVxRq+c+PEhV4e4YJIJqQjUvEsJJxtj2QAWV2K6pUmR3xADPcUxHZBQUjFBAK9kk7cDgjG1r4WSMNJcd7l6seH93hz/Z/Qynv7nE+Jp9HlWK6ggABLpJ4HKBaSmgu2OrNACaQCSd3YSkdTokAkk1emxtk7oj+gStnthrI2tINul5Wdkrw/Z+FglQi4tWJKqQ0k9XahPyqmCVSzorSJlylRWAWg3skoSlqHF8eGaZ5FJvZZ18xfMAIk02IhtVeYuwWKfDBE7FqLtAYUItCSIVia3dcipg8y2YC8BJsBpTi6EdDr3yCUZjRpKzt/5uk/bj+Ic59pTwRx9+jr+6eI7n0xHX4xFP8gOu0hE7nsFAzOHRaRBioDYFeDRLk6sFG0u8r99WUuj+UdGNULoFNRrrKbACA1xWY/n0oFP4+IiCTz1Y5OAbgEjvAWNuiflgkctbtkbv3zQa2NZAGR8+dweIF8CdS/8aYBVAQne8/eN+DA4iuMyQqRrI4KC3faY4ICgm+Wtssh5U83m+9xZzuWhjnOm/qyWWJAKFswhVqrJ1qKi8keycSAUR6xwGnaeyrxVAqCS2JsDZ51b1NqGKn+0rLvOMvxqeo7wekE4G6lg80OmOgAmgmpFLBebVgCOvBIvGBdu3TjXUCiAugwgwR8KPkQz8EgtHPUvLr1LgRsCm66EnLuLv7xItqsZe9PMPap2jxAsnxgQx43jy4glhK2l3RpbAdhLthX6MBH0DaxHHQS7F+fTIGWbFwo6JuTcc+WZAoFjz+TpM4MwybwrwiGT9Qxi9iuEoo4KkVMGiDBtl9zAeyhCMoXbvGLjeeb/66H2zesaps6562bm/Pgof9nsAUBZn2nq2dUr397in1uJWHp3M0D/XcykHsny9DLR8wNdYGz/gLsfR3EPBrd7svS+ceB7mbK8KxlJbHgJpxfZozAXenD8frhzZGNxbbOs9dxMqVlH/q4cyxDViqhiphu8vANwtU5iDMwmOq+aTVRinNYMs73FLk4cyKChFTc3wUEZcpHmzD0Dn+1rYfKla7rPWhCmvmGsOGaIzr/TztXlKH7D9fvPPKMKhiAHUk03loGfnDXqdil0PByj1/mvb3rGyEvsuhy4XDcnhNWH+ecafzj/G9X/MYcJeBpWNLzcjaNkjvbwDammAFmCxKyH8s5KvJ2ASQyvKOJvLxUPvALQkdXmO4cIKbEmoU6gq0FSr+Siy5g6lMrAqkMWzglS1EoQ739Io6tiHrBTFnmqhgrSipo9ZrlST5mTCfRMRIyUYC40EagFDaH5aInGKUBiUqypnzGZADeo11iWL2W7jIt3acK4Jqf6+VV4bj2DWdxwZCf+HZ/8Sb5YdLvMJ7493+GB4g+t0xEhF0XvIGc9IQR03+f6qXIWx3VGGAGf8S95PeAA6VpGDPm3L76LWKtOoB3k0oDgw4xUKB1qArSdHDxal7jOLUGiS/Th99NJCDwDvGv741uie4xx5mu/Bo+9ooqvGdVPh0OPjTZAE0OiY1IwUleXDyEJ4KKOCfIwIKpOZEEbFA40m7HDg0lWOnKrriUB/vo51ALijFFcgccVgFQQWVi04GNo7tQFsGiTXWAMDbbHi7LobPmIcv8JwvWL66Yp/Pv0Ub/7yKcavGemkE49kQq2CMhHm6wSe9xiLgA5HoOoSxCviVtS3iZYgzMo+8iYf5JOtTlBgy2PEPss6IIa8IxIXfa1PiP6Y01zhBSyXfpgvCzKCYebnUllOrPRZq4KsRsOlQhYUHFiD7r/vj8/kFVpR6KoMmmnqhC/SJl4irYyQVRqIVjiDLija9l0dU0FGwbHY4oGVreGmmVnsHqD6CGY9jm81GIz/5uLXONWskq804zKdMNGKDAn5mgMe7X2tc92hjihgZKh0gbpFf++b5O8PVpFX8c/AFsbWaFeZRh3IY9vq40qwis4YUMAWcHrLE0tc6lg3iVQPBjWuy9uj99LyY/EI4dJA314PaoVYkurGz8i3FSCdjaiYdr9rPFJW1lKTytwN26ggDAE8tn0MhoRtWqSP4RzvdUmBv34VNX2vSCGL1HuCbFpTcEb9qkoUI4gQnn56AA3Q6hltO1qR0wN4qkhPK/46PcPp6x3SgxYGQLZoF40fZWJQUSY3WfMRciVEV/HVUywQalqGuJ4GClFUxnX+FpIAiQIAw9ZLyxfusSBvWKnVXOx6nUkuFMTSc+kSdtT23mqsRQey4jPs2Lzrr1DP0HrXnWk76XJB7+BYOeIscbVjF1PDkLHxzOKBVO6up0N5dUUYYmuL1e6PzqrlcXxPx45W3EOL477ur8LhlQXoHFSg7B61RKCwynDp9LkZe79O6dlW4WdEbV7uOxYCzcrDP98L6lFotzXdxIt2IwQ24JRvEwAOZcLeFrUKYlArcjsYRNo1vJfQ9Uw0l9FpLGlWL3q8dZNn9E2xzk3lPS8Z0BhcrsDxmNcXuv3v1s1xC2r5uXWpoZ+7zAW1jAGeOWAEIKSAun/tGAFEs6sKwsQFmSoyF2N0VXgDK92OG8znBl51bC/3vGIjBsxVfbMWYTwUlb2167vGtquQGsXbZ0y8aLdfu+fC69lympCTUjJgajHlTwnCR/Nxy7FWGZyhBcs/LZUsle0e6GStDHwwvME/vvkNjr/I+PPlE9z8xwReNAbVrHFrebIDLUUN4YsHCI05oWoh0ufOvbRg8aaSKl08XpjEPAocNqgYO6xo7sVri2nk8UWU7LIUvQZLdSTKtlEJsmrXwFS72KQ3gcV6PT6Pg1GI6ZU2op8vDM2lqPNNrj0Qh5a3OROrEjATkKuen0LgrHmUMjMbw31eE8ZcNL7B8/SMXV7QNzb5NuMRzPoDjPfSPf748nMkqrhKR1zyKcCk3kvKh0/QB5nwpuyCkeTMG2DbVRBoyH+wfNC+zD34pa8lQDIGrI3tRHXzPtXVbzuenH8W0FUaLInwoeDV9kbzioj6saQ41m8CsvrRA159ZcXZR5vP2TCTGAtYUVxqkxpTxakOOIlTaptR+2pI/alqheq849VScyDG/bHfm2Fin6AczVPAmRBrx5bzbh4uN/R99gpEogqWqnLNjqHlbYMHlJAdwvY8dP3YGl16m9ofDl9juCp47yd3+B+mX+LXf/E+dp+lZvw3EEoVUCHMTzOo7DHUCno4aZJRRA0O16KAFtwQviqgJZpmRatx8smZWlJRlDUhZGBYlZZBBGgEz/YAUNBraQWkaHZDFQFKVQiWOYfm3r8fUS0XYDUKrtjfjdmlwQTdrUSePCS0NrO9HqXlT4AzxbgxD5m1olIhWEoCpRIyRwVMBWDtRKZmhmyJW8HIjQUx2GLr/B5/HI/jXeOCFrw33INIZas+1wEIIKsHhBwnWCTjaJVKACjU5uRzMEYZXL4WbJ5W1VhaQGd23lXFg3lkld8eANswtHrgBxQSQt82nx8D1be8ExxocpN4f983AVn96AGvXi7pwM/55/i2AZXyOYgH6FxNJs3zVC8KLfZ+gRqaJnrbfluZb33hSI99kRzvBfQarVakMahjIzN0c3+XG/rxrJLgrDeCyTXpjKEFMS+/1nkqi306pbDNaPtYMZDghgVpFOyfzvjL/AK3X10i31MsugGb3gUoOwbJCBYBLVqhiNPdE7qFQFwV0PJOhzYRb5hO3pzEP4TafUW+sIbDYYjXClpiQFWr2CGHkJYXlKKG/Q4YhsTQEMhaLely4LKefQa6WOOgGyO6+W7Qtc0wGSAJanQCttMjsJbmdl7t+Pzb7x41XhB0ZqDY/UMkmzXl4/j+DQbwlGd8VbQV2rEO4XtVQVEg97WTe+c6yNkaQhkziGqsk4EmMwzz9+6ze18rjwf+GPdAmD3X+3Y5+OOqk2IKkkNRfyaPM24CH90N7ZgcaNN8QwKsUl5O63DnRvB9d3cv/kcxn9r+DLQChDCMd3mhF9ad2XVetHelSwxqAJMrZHzbvXTSgZaTmbx7TleF8GR4wEPR955qszPpGXVzyQ1EAmGf5zCCdz+sXrboYL0qR8j2RVlbq7HnHHj0fVltvfpQBgM1JQy7L9KCIhQm8E4YcHDMt8PiTZA6ENW6Gp4koxbCk/Sg10YMeCQtCLuPsueJR3RS1C5vVFZWNV+t2ux7DMC85BM+GN/gHz35DIdfjPjN/Q/Cm4oyDOhhrM8uMCxFi/4+RIwAoKsQMAdzi6oZvkcM6mJXFydUDSMbVhR1hvSad9mo+lMrYy3uI63nHg6AdYUWoMW44H24SoW711lBiQgBTLGZwQcPICFIAZJsX7I0n63q9i2t6BmKnAoIGMLFWGkMcDV2FhnYSLoOIYkmW9718tus53w8gll/gLGjghfDbZgRDlSClfUuIAuw5KIOm2rAOUvJJ91F2gR1PvTL2SYJR54bzdUmqG7S90nkKMOmhW4/HOyKyR2NFebDfx+pNNaSv1+2vibnFf/z0Y65617Y3ci9b5g/VyRtkNu+urHxfjG2lE/oEy+oVfXcPiZecJVPrZpU0fTbWAEDnyCqpGMITmegmgd8mMRskYSlagBpn+OSx4r7qoF6zyfzJjNjeUViAKva6PkX89JS1sWChBnJDCqb1HJHC97Pb/Te+0jwP+YV/xkfYfdZatXjBNSBsO4Y9GQALxdIa9GqOdD5aOkbCJZDGN1MIoFDgFpRjXCQShAJirCzGXx21hW6yzEA9UjhYrTWQlb5sPNmt8y5T03/GJnXVlST/DaLyoaFlArU7LuuSYVXD3RCF2V6QYAE89Cyfa9a6Yadj6Ukq4hVrNTMrolcQkhg89bKxKhWaZhrRuJmCK8b/PaT9uP4hzsyVezTKZKKhBqsrHcBWYDOrYsBIc3MWu83/9+ZSe01b48C98xrIJAzrZzJJAY+KNO+GsCkYE7fdKMfDnaJdPMDGrjV/57R96fTcc6W2gJQb4/+mJvpfTvi84S/2vP9/NO/5txvy8EjEfXFWiRtjj1TxchrVB6VXWMAGWkkJGo+YRUtFpItXlvzlU4GKrSJrxkq6yFq0pXBmMy68jV/TAdDveKM5qVFokmONgvgzbnPVKJBys+vgL/iiq9xjXxvAKXAWn8TakYwtFDNRBceKqTNf744N7SmhfEO1CKymGT3iVhRhTufrzOW1nktxRf5MOAtwNJAD/HWiOssJkl1MO7sPT1o5vuvXl/SnhSKYz0vnoh5fwUiCLKqtn33+oTF7zX7XAUo9Bvp/nRq8G/Jn3/24/jejolaMn+oU9h1nLNZa1c893vLIXl/rSeVS02xdj33b9IaaSuqLLYu7xsr+eMhfewM45MxhHzt74COGnovVuzjkBz6cHmhbzukejbnJggOvoYGNnlRn6t4My3vQOin6V1NqvSz8ubYegCvlyG6LYwPB9hUZ+bb0v1TEE7/PpTpLf9jBx29KD/ZZl02uFaOnMcVHR5jXJIIKBC1SkJdGXnQx5oxu4JqDkABAEiBNQe0MldcYMapDsi+xrX7YK0JlcxPSzS2ZFav4LUmA60qErRphTPmPB9lkiAcMIuaw9v193xKr7t5UAvHOZsp4ZJPuLTuu67aAXT9ox3qW1MAB7R2tOCD4Rb/i+e/we1/M+Hh4Tn4byVUHWVkYJ9Bz/bIy9r5Z3kseGdFQgv8xsiCdzC05U34J1o3XikWawvicwH93Xq+aAGqENZCqGKyvKQG8MG+cjCr0NsxyWWBfkuz5zmwmGqv4e7veK0Vh7RGorFpJWNy2XEtBMliORUU1HLVTlR6OrmitDWS/1QAqBw51Klk7L5hDfeu8Qhm/QHGjgqu+QFH0a51Tl11qR3QQBqmitu6w6tyGeaGDhgF0t+xp1TfrF+6vpoCwGjETSqxM5NWf68j/a31qTFcgKgMeMWhDzg+Zr89BAbKoZsw+2DgnQMZ6UwyFcBUAD/f/Lx3YAm9uwFiYxc0NECQ0UvrW4mHUlbVQJ0t4VIK6/bYyhkbQYPIiH2asdSEExol+zpVhHmjBawEVhqsTZx+VBOvcQ03zCxjdS2SMEijXzuglfgY50+vqXIqjtZyvRqY5Z07nPEHbsflgN8ln+K87X9wwv/ABf8hf4zdr/X56FIhuhigOmFXBPz1HWhVAykqnVVv0jbsYhA+FVsPs4Dca4vEDONt0W9rdZROaujZBGySRwdoOXurAigEIW5miCsAVrbIKs03y4Mhiv6Uor5aVahV3w0XrBmtii7dsdWWnMlqO+0dDquDZRVIEjJDJmWdFGsri8raKISaz05mlRHOlIOdNaDADeAzpQiumsjjcTyO3zsylJG1hjOSywp7do9huKRtvo91CE8lB4ycjdV3qPJFhIMg/TJNGT7t74xmKu1SOwekwjBbVNbmXfk8oQp2Vw8geVwRoJLGgHfJEFeLdw5E96OZZHfAyDc8v/FyQVtcBThnx1FhjDS8XSVUkEBUBma743NAL3E3XKcdqxeWYEwGaV2NRpK4ks3s3a6V6HO+H5lqgEvcGc0XA51cLk9d0gcATGucP8V8dCG5gk1mCAOzVJbGYiApNT9PLz4N1oEx5YrhasVfUsVXX94g32oEEwGQOrBQMrIAeDgZYGXxoVp1O9Ae/ZUqTAbYgB1YvCCmyCUIliOSFUraq98NaPmF8cRBOoDLwpB3pgXM90wQUo1aTWbYAUNkF9oTgvb5fiNQXD1xNprHQ4vHwhIP1UomVTTmBYl634kek1h24fdF6dhZ2kjYEh9SEKtiy5B5HN/f4TYm513QXV44UFFTi3cUyAH9vu54CSmbAw29MmFgBaM23lbSN6BoLKxzq5REFYOBHg5ChV+W8FtF7x7I6m1FHPw6lAngJrkNS5buPe5zWzYTRsu3qhXwm9KlgVFe2G+MrcZUa+e3NXvqGVXxN3EU1v39fY7IFtucDQcDJf2cVyQ8FPfhrUikputVGCtp1FcQq2CurTshADOD14ltrirjAoA3yw5uFeCEiVW0qOVdEx3QOjer5yiIEMYzoHERDlnixJa7giJ+rDXF324CrvOtsbRKCvAzWTeZhdSKp4jmXsr6My8sAY51BBjYYQmAEHY9GUPcU8c6YEaTNz7Pd6gg/Hcf/i3+5Jc7nO73Fgus+D0RlusB/LBH+voWGgAkbi7t1OvsrHbTxWzvRSLvYKuLM8BIA+F+6TGotFhEIuCVTEECUFK5XjXChMa7dt4jBtXuQd8ZIBYjVBGeV2/VkjoW12YTFcCC5pFV4H3JQo4obOel+nfM4lrxVSngTC2RFh1dbtjDx5lqyGq/zXgEs/4AYyBlJy2i+t49nzAaO8vBGUeLj3XAfZ0CxAA0AOywBPPHuw86KOKTrfo1eSWiSUb6jn7+N+DeT76PW78tH9o6dcCAgh3NGwP4dnxrSPHONePncrcAo2jbea9pz7fV4zBsfBeLqwtYpQtE7j/SByKXNTqIV7vP9o6GPtbaGF3FwIVC6o/RU3cPZdRATeuGwdYHuU33D9vvHS8bvbwzszz4ayvjjEqsP6KVgptUUaAeFyHP7HyZINwBWsAoFTAKrTO7XHO+o0WDAID/7ft/jjEV/P/wKS7+JqsWuyjAU0bCcsmgMmG3FNCbA6KzoRmho1SArXU7wbynbJIinyeNWluwafgR3WsBW+hTJCnhW+WIf9WJG11LWPfMUvkh4bCOOA3NhL94x0MBpKohZyQYXnn34GK3nRsGw34i8akAKtnrBa2MoPvjfxKLJb/OJtEOh2/56YiaNYJVtuMLA42FDFhFqperPI7H8btGoiZDzhZjvLtP5MeW9K6izUXmjtVaQSohM+aPdx/sPZGAM9aSgSMA2uscyApwo5OG25x83vVP0ACTTAXN1Lu9xn2//P39aMuhdizOOumBL//cHqzqj+ldLK7o9obeCL4H/YAtRLJ9rW+DDWiI7XeAh8s7xGJUeDUZ84FYwFI3LC434tdz43NSbYAWmk9JQpN2uqRR4OCW+zGprG5Hdi7g4IhOyuHbJQ3QApF1Li4g8tjaOkFmFLDRyH66/wrpfcHneIL8plWNIVBZ9ggUSchlAE4LNt4i54i+kHmNeKyxI6qI6jYZC8l206Gi7t8toAU4kCVW1PHPQvu96mcvtTFJvHoM8QRA1xSNrdV2GehiTXdIdPa8iCc62MaaroIdnmf9cwJjp3Ubl15u2JgOsGvu2/Rr+zi+v2MRLTAfZTSpVbO5GLhEIx1nxAzv6Br2ri7pTIKJ5k1RGNB76rQpzLfGNi6XCxYXVSz22f76PidxYEv3rQONQJHn9GtuzxWcwZXCn0o2+VCft0zd98PVL5FrUd/dcNtky9lW78qRolsjdJ3XP+ekBG3Spbldn4/0QxUYJVQjS02YzSMTaCyq1XwXlzoYmFSiSUUFhYeWm8Az1gDm93nGLi0qK4/53nNJ3Q8/X5MZ7js7KqGikOB+neJ1g/nB+j4zCU6rWoIkEvMjXg140zxLfblmZAIeqgJ0I6+xdnDw1JmAEy/YYW32CP0c5t1ABIADWtQArQLNz+N6EAEbdp0azn968RL/9JMd/r9vfor0b3N4VlEF6sBYn0zg42wew/3c6x6Q9ph36gCMnWRrpQC7fEL349A8iKpsFziwed5tXASoq27X1zk+AvyyHMZzM7gU0OOb5z1itgwC1Gxx0pYXATOIhacFQO6IB2qkGfJDTWtMgqgnWI8xPLQ8Fqv0UmASQwAptc7PRE1mWCpjBsCPYNbf72AYgIFmvN57GwGNYvuq7nFbLmIyrUIxoR5lwI6WCDL96DXei1VctLryNkAFe2zwb4ZtyicK1xzrewUQ71BikwqtMWH35u89ZXgW1+06M4nUQ8sW2/5cb1QOeJDcBs/zKkwPgvXdDP21C7aBQl+rZnjuGVEswAb7zaohVUhZDVTBDYkBoFWKBx5wmWatJhBr69q6i/c7yNhXmpzWDBjAEjKOxh7ySrx3ielbEzMIhzqhgvE83bVKVQ86QgAqYRjZui8Kmit7jjqP6/2fpgOG6TNMLxaM/2TFvxx/AvzlGAANiWqWl6sEKheYRNTsUGwmq4786EymrCbeVh8KWqcM56EKAYnMCB66LaX2xcsgBGSfQVVmSMUQ/qGrVNiuSNHrO9cuOa8MqqRgl9NVK4EWapO7D9utMNI1/Td5ukNockOr8HvygKLtYjlJ+Ge51GStDBJCYtWwC1SCCABJKqqUYAiOrD4JizBQB/UWCL3K43gcv3sQgIEqBE3W0QAoHcWS7AcZMNeh5cn22gKCCCOjyQD70WAmTVTcDxB4G6Dyx/JZrOmN5X3fHEqooGCKJWoMrz4594U2gPCAciGvgnbka8DNfvWAsifx/TiXHvYgWN/N0Pd57QJEL0/c+im5fIPjdc5Sc/CMpDGqATXZXUljSkaFkElOJOu17RLABlapvLsHAb13oV83/8zwZRUDrEi5Wu6bIyDsMQPkmL8lNNTOkUtI3BxeSavVbqbGRlYXxYoLXpDoHmlfkT6o+E16CnydkGbEwrgmACMD+wEZAjotbUEfp4diy1HsIPfYOgO0jBUHbtVte2eAtLDHex+tBrL5N2O74Jeq29sw7ESTBF+cu48WFYr3tq21Q+gfc3+R2EkHtNDuVekKPLCij+95eGX5cXf7qN/lCqpsNc/mnbM6KxKP4/s+jsL4/7P3d6G2bVl5MPy03seYc6219zmn6tSxzqljlVqpVMyXWOT7MO9rYgIaEhXBSJAgiQQMeCEIQqEiiDd1peCFCuVdEDSKeGcgV1HDi8FIxEgC0TffG42VxNI6llXnb++91ppzjN77d9Ha03rrY851fqxjPj2uDnuv+TN++uhjzN56e9rTnnbd9g4wRVDots5YQ8oqW1xzJwMA+DeyfglI3db5BNSJ2R1RPyuKyTO9UNfDyrjKaO4TOBsnWZDcJDlIFxkKm0hX440Al2fBoFf3jRkcHJe9ZbuwRVAr+kxZ4CDXXdd8V1Pxcl2vxowNEhao5cRriKAe532CQNuWpOLBdHAdrQl1kDQ5FgWC1pqQrBDRbZnxMB2cnLC0hGTsOzKxXItpM15zyA6aU+msPRtvZkxQ/J1tNrF5prZOIY2UjZXlq8mXzOF5yNipf5VvVWPYjuOpmOZWOKliA2gpqaQ6QAkAT6oVMbN7cpUOAICPPPwcHn30Av/j9Q8iH4AYZF6vMtJ7LN3wsAzsLAAgA0v70HpFQ7dBNr/D7Jb+uIaKh2LgD32XZNIuQpfS/I0kzTV6hyBM7V3hZ8nSBy2JxNljHlgxtkFr6As1+l4Sjg2oDAsC8GVvnPVcNN3QAzNMTzQ/KtE3NN8pGRM5bdaVpQlSG7On3qzdg1nvQFuaovazZGdk7QzkiD/Y2za7YDgAn6BiaiHFAclgYnSE23OSqxijDYxYeLU8BKCHIFabsE8LrkRzngsSZixgvVAeP7at8Lt/jubAHQSWN7+ebHeyn9SByRWPx79ZilKktzxHaxeyOCDHipCsSHJd9z4GxSIVqjumlTEOde6MAwgOZcIBWnr2Mi9YW8KryyUu84QH0wFVxEUoLyyvXu9FF3AkwKRj3NMSgB4xQg3RLjofxiA7NGVoRUHKuCIuXNXyuqCRcorDZxog24ljQsbXg3TAh+aX8Xef/V1MX17x6+0vAZ/aaeWM1iBFUHbA8tSEVC6xo9hha10/q7XO0LJytM7Sgi7ARZoh71bpA7YmSDqBS2m+SGgw4d0iQyVDRf6hoBLFEI3txQm6hEmP+liqmaXvS0mDD7ulzOrPo7sYTaBOU2bKCyy6YJskAWZlTbLSoth5AK1ymHNTlhgSkJQey2gDHYrbdUaaGo4wxkgCUqvK3roHs+7bW2ilwQHwyexDFP9mozYG51o+XlHzaEUaGExRK6vBmD6Af8dGgffIcuosGNqq5CkXTHvTRagBxzgNYmyF3/vn3YYl/nsTJ4L9PLcY4vybbMKh3tS5NkmvZlvQNaMUFNKofQTS6Lh1wKuPPyvqzqm6HadzNyflFi91UiYnatDn06AHF4jsaSxjz74yyu731LYlUMeqlgK1XVt4g4w0oAOf0rrYrBKJKqo0wJ6vngypKbDP5Bt8yeXLSM81/AHeq4AWF79N0HJD3SeUNiOXZhUOEZ4M70B4cMVYWgHQanQE0NMObbEsMN+COlrSYyx+eHMcNCqObn9CF5iyh/Cx2EK8NZymc7jR6+fgR82vSTo4xz7QBgk8kg+7PrWv/Lw/b+qT9GdcDHhs5sBK0mdbEuCsO7OX9+3d2x6Zn8F0ZgaUmSYIwOccACfrbKYYErCqJoVwXXZI0nCRFte72rJfeTwCYcCYmeHFQGJw2kAJDxCndchoIeuYINWFrLhtE9C6LlcFhmPosUc/iC36ZTHbpYvhx0qEvUphCefzY6EzuTQwcApyMQC+9fdiv1xaJUwe7APZWAzi8r4tnkqoeoZHq3BHVlZtXSeN1QeTNDxe9jgWTTWs5jt4+ngTVGk+RmTcHqwSZqxenKTiybrHg+ngx1hrHopW8Ti1JTyuE/ZpxdoydmkdtbnsHLw+TU3VTJQDJmNqqW/8MN/avVFQcEZxeRkvXla1uiK1O+M4Z9EMqloTjpZlk6ThKh3w3PwYH336j/G5L3+Am9efxdVnGlKxmP5OsD41I99cqhxLqWO6YanOtgbQHZbAzhreW6ohWPm9ts6wYhV3Pxb/id/PnCoOy4RkgXsASIXBOZz6QBKAK35G5tYKtB2GavVDa/xOLVmde/+0gFaji+r+WKPRp4ZXJsNM/2YjBJQmaKlqpkDJQC6eQnmONXpXuwezvsBW0fDIkPedFGNWrcMPvraE2zbj1XKF27ZzRtZWiPCcBtT4Y+3VaFQ0vKfWRerq9jgESWaszgjbAl4Fyfvery0ZUg/bRtwIkYnlgrKheVlbA6Z6lUOcbMfzIJyXaDrZX9v0xC4Qn5yRxTHYp8VRZAcCm6YWHjENOdv9OvtEzmiDVvPoosVX+WBsqV45o7TZoxbxWATPaPBdTNNYWlf56IadGgOkZD+ql34fGFFgxBz2ugtt6mSfAouhjw0jPiOgNb+3YPfXVvz73UeA/3aJ3atGbzXR2eMzE2R9gLk1rXC4rdhRbBLOCtY0iy6481AanBYAczZEoxItmSNm23mahcH6YpEFqaLgWJjUxSbFQ9H0WlZoAfq56qpph7QhZHbBJvEGw2050fNf4fY2AXuExyZnaaqnlUTvcKaTiZ5CVFXgHahoLWOyCbnUhJt1xj6rGOeunQphF4ZJInX5vt23TWtoONp8PUFT9RICs0lYzS7jpqlOFp3oCGIBI2uFbTWnhclTnBfJFEU4TrvjOHyVpLoTH1P4eIwJPS2en8VUxRqORiZWMrbTMCYGJhGY4my1DZhwP6bTscUS8+yjBS5DP3S/CFAlG//VgirUt2L6Wa/3NUagY7qmlyBvGbWaU5jNTkMZvPBjnAZ3CE4xjYaQEs/ZoItBBbJMwN/sUWkJhzZhMnaAMg+as5U5Bs6wM6dHxBwvA1UVOGtgbeMIaOXLivxFFb+fnwU+PwG3sKKyNu/vE6TukK6bVjhsm6fLp2IulgOgxcq5CGBq6yOjoI/ZJIuYKysXXnmJdoVl0gebUDXVoWUrihD7BAWxyMzl5xJeO3CFAGgJPCPG9bGcYYbu8FTduKWYzmv2SSlmoLKaPu/V/CIFsXLSBFINaY02RX+DITJ0395VrQK4rntj/vR1/nXddWZQ7itxCrxHMXMC8pEddFtnB7IU0DCWZ83ucHK+ihkcQJ9jqa3VJT4IIpktCNkNg2SKfU+GmWp/TZjzwTNT2FxA3t+nE//Er71l/40uTfWY2K9oE0bJlT5OZGtR85jAVtyfY74T09LlcY1AQP+LTLVYjW9pOraHdXTT+ZuO7Kiopfzq4RIP5wMuJr1Xk20zSTFfC56SuAtsrENIacyiQNiyGbtYOblAGVqPlgtd36JZWqr6ZQdjg6m+F4PvBWvLuCmzs794Tl4P95tTwU2ZcVNmFNya/tbaAU7pGoGLKsAht+o+7G2btThZ6wWy9B5aCqcF2siMu5AFV+mI53ev42Pv/0P8+796ifnxJfKifkjNQNlpuuF8WCCPb2CDjx7xGCIhJp8SqhsybbBaQAMCWSvqlLrtATpLy2ygtObFsQgAAbbmi/jZphvCrA+b9vVLDKmEsGrAaZEuySJASIAZga8qkFUlFarVS5Bi5AZGjKqm8bcZI6pm190AlDWhVsE0FeuzfWeBmlITannrENU9mPUFtorqP3gtWb2GKhQJFCt/YmlkZAhFEKm2hAUd8Y/V/JhDTToqQa0hz/tMBCKeI050BQmFa6aWsNi2ndY5DYBWNgCtNMGFsYJobMie2iLfBT13nm2rixV1tTTPfKTO9IX+FI7bz03mVWk9isT0vwh8EfBLorn6tYUISdMSstt2rBlrTdhlBR7mtMd12eMqH+xeNAes/PhmsFmJhXPnoU1ufByQbF0HhRUwOUavlis8SAenvWrEp6IY2yrL4gYeANASWOlQP1PaNhczbAkVT6UbzPMKPAPMH634v8qXA7+7Byv9rU0AJKSnJ8h6halUyFF1TRQMCrzTUjug5Qi9bkNtLXdFm8AUaUGGVuPk2li5SoxWC6fLSoiYw9hScXHeKxea99Ws6lMyYWNLW2wp3BIzEI0UDzZO+O7MSCgvy4tRI9SqKF3XwLg8VZQilm4FIFWsJWM3rZZGrG1KysJK2dKHWvLSyBuf477dt5PW0COoprzkC0IuACpUJ4vgUASbqJnERTUQgHxGXe0ftbQECGyezpwaAax4jrhm6vAZxeAJpgCj0wIwTco0PxTGd7BqhSBtxYXBlElBJMhsdbGirhbF3X1b6UBXTImM544aXJMtxunwxeul0yQWrIp6Wt0Gjz/0YnNYFQsyVQ3kzGn1e8GoPvvHMWQ6zOrHcnVFUNeMQBb7MIf7zkDZLMaMEi1rj2bFU6SALCj2lQ5LFgBNGVrr5r4IGvayqP7FBZDf1/Cp+hzwcvZ1LZ+7sk9A3SHVg1U4VKCpaYeCcyB9oSytL67Rfw+uhZiooyW992J3qvWFeQSvTl9LeML7b8feqA1p4lItzvIiVmoLf7dz48G6rYl//TvpOzf93QPKPE5Jh6Qm6qE2MP0p3isGTElc0+fXfm/3tuZd3eic37ZJmZ7WKLPhxZTqKF1yMOaOA0YbX6ULpXP9nHw9G9PvuKKO2rEM2mYDSDzo0EbNLG6nqYaLf+bavGRHBfH4Thjo15pt3iRQTz+CkiCs6h5bvM7of2yvH4DraVGSJRbx4jjMsqK0nRf56ePT2XHF1u/RV5qljMXBrE1S8Gi9wGT+T5Yo3N4zOyj2TqYWxdYZjKlNcJEXH4+1ZlzOt86gytLM7yn2jFRNU27Kj4sA5i6tbhcABcl2Uv3ZWltCqRmXecGUCm7qztcwBwMqCtllVTyLoTbBHqxGKHi0XAAzxrFLYtItydMYb9ts2T/qA7GypNr0fm9SCOKwEJM+Fyueyrf4wMXr+PIv/iP8P5/9EkzX4qLsdRYsVxPSM5eYDgtgFeDdHyrVAhGwiRqmvBKqGwI6x1vgvyXLkKka5D9XWdcfj8bnr2EtGbWaIZMe/JeiPgqavg9mwVsM4kDQSQcRtCrW3aRAHpt3TyOePUjD4BEs2LOzTtN/qhZIGuOMuiYtunbJpunnZIATitjd7R7Megebs5FQh6p7T+reJydAJ8jhx2eA1YUs/t7psjb5LlDRchcnRPVjsWxqaYxkjBMv0Evxsm9MV0sI9FxJNjk3pycDGMA5Tp6RORVbgbjw/dImR8Q5mW9TSzjZlHBtY9UQVkkJOkluMMSdOYrnk+EEUZYW6dUELkhfBUw81yi66qhUTzWckpZPn21ifmW90nuUT1MpCdxldCqynkvBIZhhQPh+G73qz0ZWYK4l1KT/LrA4qHW0e9ghdEZK5MQo8h50jTR9xr54fgX56YrlyxN+bf4I8P9VhtZkzvLyQPXTpD1A/vxjTQMBLE1Q0HLqTgYrHfJRCBGHVse0JQbZnaHF4wFKsTVQSoqoOK8BWVJ1skQTN8Y0lJUMLgPAijmR/ll/0NzxcIel2CxvfdeKyeY02QTtFIVqnc9AW5NOyqkZdRau1VVEgJqQJxZcUNp3o9OKhLU2LKJFB2qzyMRbn7Pv230zNpI68SxRrUDW5HpUgM53ZA0RdMhSMaGqGDs621d9cRMpNyDGGUfoqYNAd5BhAFVsBJnYN6b7dUFWY2FJ9fO3JiihoEcEkia0QbvKrw29KmFkzxDIOtHHQmd3OQeo9bStc0GhKHAfhelTs5T5OgGiLK0mMrB5B/0tgQNizfqmzuVk4JA6J7UJbqxy1HQmbZ9jzrABAzME9Hh/2WcGdrZi+gDBr4bWZjRZ0Qys0zTKFphcfQQVINV7nm36nKRAtWvT4PhNUvB0uoXsX0F5TpSh9bkJ+dawniaaroAMaTvg5mjOQAuVATeAFi0JgS0HvQKoypRD2zYyonwXB6+I9vTPucjX3wH8+W1NBtCLwZETjDI4CWzSMOgUa38FrQUIKoJb9oL6WaxqyO30fiuTToQ8xuaaJC1GuI0950yde1vzrm2lNZczOdg6ko0pg8kWVYe6w1U6nm4T5hWmfl3mxefra9uP7KzYqKfEFOotyDUbQOFATlyvtoR9WrDUyeVIvCodQhEr9EBwMYA6+hUe7DWfAhjT3SJBIF43/xJwYgVDoJMDItAFwKvBa7/6b4uV13cuRdL38bFte5cq4be9MnkHwA4yOQHhQdbA95Soq5zxcDri1WPGbWCx3JYZu1SQDOy7WfbIqeIiLzhWXSPU0gGwJ+tepWpScbH5CCRMUj3N0bNbGtMgCw6VGpjN/bPVyAbx80PRdMVdWnFpgBpCFgzHZqkZBxkL1zxZ96hZ3Hc61BlVWJkTuMU8aLWpf52Q0umcR1tFX5o+EtMNn52e4MsevozPfORp3H7uWeSDaU+1hroTLA9npOsLpNeenGZUtM3kb58R5EFqytayTBcRca2w6LcoG8tMlOn7woAfCqUD0KA/KyA2AKucDVhw6m8MwHhGil2XBJF3ADAigMWPNIOF9olphIALzTcBZLXPqYVsYJj3x0A2EgJSYLI1qH5zNsBvrUnBurfY7sGsd6BlNE0FlE5xjEDWk7obEH5GGAh+EVQqYeJNtkAnsBWZRjM6ysymKWwdoebEywidIs+Wk0zKqwNvoVLFHZRcgKyu5HRc6mZFxPucJlZFrDrYozoAbDKhTla/LlbsivRQjo1XcURC1xPv18y0GFaCIuoey86qxljFAmApLDubhlLEa814endw435oE+Y24SI4GUwljOKXAHokvamAZE8DNGMm7HdzTQM37KmDdwU6Ge8Cuj6McTDcLpBpx2GJ2ts2mwOp43klB7wwv4aveuZTmD9S8X+tfwX43QvIy8qaKjOABxmoWuEwv/IErHAIQHPD6WRs8sKlNU09TA2SElrICW8AxFIQG8wxWCswpb7AD8DUSWtKPfVoYs1glQ/YhAroRCmtT9beVnUmWCxRWVv6xsUYTyIYm7SXYuBbcA5jqqHekgYp6lhIqlhKxpyLisKbUVpTxQQgkRl4xvjct/u2bWTkCHoKSQSyji37vKffibHbQ0UpBJ0daS5S6pFyn6uUtr9NR3TH3oCuztqyn4jNvQDTDanfRJDLWFgGYJ1rZMIQGEpQm+AMMDv/1pmK6ZQ1bAuYs2/oA3W8+Pk2DZNjE9MTy5ntkwEMZECRsSWbcSI7rNSs5wkOjgCeQkHosDQttz6FiTBDBX4jsw1+bs6D492KIvjU+qKwe7+ftig1Dl53QIEpjDGZQJQeGAGtitpUh60nPOr64KkEfPDiFeRnGz5V3we8PEFuFNxRcytYLydMtUFujn0SJgOLFyQIbwJ4FQCtvtgOgBYZWq6r5Q9LB6/ONDKbYPdjAJxofg3gcqI0+2YsLQTwiun3m8sILRj5AGiNN7SfF0xttz4k6WuwWhOQNMCnwR/A1dDubc27tsUlD0GrpWVnSVHiAjCh7rBeBPo6lIWhcgpZJC1hTovqDaEzefv80LBPq78n0wsYA+LODDPAJmYaLA6YWPqeCcTHrBU/VrCBAMBCVNvzKdurZ2vMsp4wztino2eCjODWNm1wu19kf82y4ogR5IqZOGz0FcmYW9oE6oPFxiJhFdlBrJsy9/sHBbcm+kVeKCkPdp76WrwnV9PRXzvTa2NbvAK7NKylg1M8/6FOmgnRkm87pj8m3a50zS1AZV6You9picbgWo35dbCq86ySSD1jZiZdpWMoljVhdr0QvTfHlpFF/Z8LLCGN1p5zVBToM/hADjiGCocP8y2enZ/go+/7Y/zGRx5i9/oOabXUuQzVfHzqAnJ9sCImzdlZEFEfCejSLKxuGGhJ0loXfy/VAvrwIP7QGDSpQK2blQp9JvNpPM4z7BvkIMyGkYHFj6nZ1ZKYvAqG4E0vmAVqEMRTOCkAgOlo9S+bNM92aVUg2XpjJIBpsjVnTc7OSnnrwL1xuwez3oE2G1OJbWmT5+I+qTvcth2u685psZy8qb/ESTbqL8UUDEbUvIohOiAV6cD8Ec+a79U1hUJLwVA5oGITHaRHHnzSDWBRF9kTLCZcy++2gBY/7/v2vpwTgAc6uypWLwQiwGXAnxnAIbc9XIufpyUXSeyfMeqUcOPAVR1EEIFeMeKmdL2jpWYsMmHOxdls2+jSVT4o5RkzDnVy0U3SmrOBZl4O18dqjCxVAI+simJGxSIFF+mogE5gMHD784DW6uNOUfgFWlXxSg54YXoN/8fTn8LyVxL+ffoI5P++7OLo0oCHCVIvcVGswmHpz7jeKutDTj0vHNVALPHXsIm0P3bNkHwDtKquxNPauuh7FbSpoVzEiIU5YhtGW8tAuWzIe32yPIIuAdAyv6Cx7+aQ8HqbVd1QKQO7fqZ7cEwS9HNLMWyrwKv9SGdo8W+pyVgc/blaSlYReAO2uMC4b/ftrTSvYGqtGiiztoyjib6zWh3Bnwq4nkksPsEWgRyKvbvzjg5IKUgjDsYIeunrczN6F5TvDrljDbawJVsJUEp7EaY3NgOraFM6ILMFtPg5/45AzXlbI9IcVI9phzw2yZjbKomdGTVes6cdDseCg0wrQoog7S33NdCka5Z1Z45BpoqeKsI2p9VSJxkFNz0vswGeXgkAQ78DEA/qbvW01IyEyZgLW5CP4NEpoEVR+J6W2oNLKx6mA17cv4LyPsHvy7OQP550jQJRMG2nDonUZs6BTdIS0g7Jkg1AT/zf9UhC2XMFjtQYqG0wXTHT9HBAKwFtCsCTzeNbgLOZDZCJi3E+UOgLfekfD85CO4MjOVVseGj6e+l9Aez6B4BL/Hstet73rVWr7nJd02R81u/bu68xOwDQ+eG67LHU7EEDdf61Mb0ZYR9dj2TXLNLsiGWcd0IaYpQwIVNrK5Qe18ddsF3lQQZtLPT5ep8W95ViZTs9jxW7AixYO3nQP64NCTbtrQjTNlvF52BbTw/i8eFnwv0u5OhBoy2r65y/4uMqfbuMiqO53ewTA9/RL3LJEnQBePalGmqwtuxp7/vUwbu1amYJUw1jXx4te0xSB2aP6lAZyGTstcEnEvWbmHI4S9WiVejAFuValpYwtRHcjPZOnw89pmvHNnjq4k2ZsdaMfV6xTwVra3htucBF7jrUT9Y9nmCPwzThmekGe0v1XOz6qyRP27/AEcBkftDq4CmfSWYXOaNL+jP6zHSND12+gj/+kof4w5de1HRDsq5awvJwQrq+Ql4fASuD0vY7cRvUcAJgnUk3FC9kYlIrsPma/iwXTk0UPKwJpVYvKgMoGOW2r0S/Kayd3Oeyc9Cetn7OaKR4i+oEdwu7bTO/iY84Azj+aKkPx21h2lwM6tSqafrCrJYyIecKkfUsdvFm7R7MeodaZ2SpZsltm/F6ucCt5UyzMa/7NmgwASF1Dr2KR8zpJksLhkQnWyiS9URgS33u5g8DjQ1ZWvxsiBS0epL2NlBnrZGWSQNDAcQte+pERytSiTfORUXCbevphPGcCT3aEceh502v7uTwnKzi0pkB/UcRRd9JkZ2kYi/F37NNSSnVhzp5NIT38XAmnz3SmLVvxatwqEBh7pEBcwQo8ktBTVa24rXsDcB8Uvd4kA7IbfLJYidlFOtvCYu6NRtAq/dPU101MjHLigfpAEzA//H0/0T9SMK/v/0o5Hdn7F4zaL8B64OM47OX2C8F8uRmCGOfAFqDsGEAhZheYV1iXrUDWpofC0lJS9GaH1N2QJ00WpBvxIxewrFOmjPeNM2w7SvarmI/63jEdBAHrugg8PMWP7fJvvU+wsAuLKKTb1JHSA2ZAleaUikayaj6O+JMXpMYZdZYfeuE/bRapZbuWGpp5AmD6M99u29v0LioIxCxtoxbSy+M4A0BlHWT1rxNnYtsHH7vEVqDrwgSEUzqArijH65hguapTw7o+N92kvZGwH0LmjANkBHjc+ypCGgBGF6fY22tG4cngmDb4itR80rZ0g1FUmeFGeDnzK+w0hvAKgfI2sB0YkvCNNAECudHXZqyuZYIsLFvaAqca2wguWPpoqocK9uf40CWVpZeoRGWgMr5MgpJcxyVXV7PMLR0dbK2ZLiTCpTvZAUS8MX7V9GeFfz++j7g5TQAWjInlMsZuVbI0SbquwAtIHwXAC0+N3zf0KscQjylggVPeIqWgWIpFrLq57WJA4rUQENuaLkh5/4bdFsTbYo+VPpnsDX9K5oeN0FkChMli8AWbVMDGpIFgHQ79xMsZZ/OLFmDbKUJxKli9+3d2OgxaPC1V4fby4pDU3ZMDLDGxjRE+gQMgBDcylKx1NlTCRmY9UrhIWXa08UM8Nqn9QT0YZbJGADvIvEVbSg+0rM6ulZtDQ8zfaau/VRdI5jyKwRqKHAPQOcmdDZXtANRAysyuSLbjeeJxxqyYzCy27g/JV/IFKOmctQNi+O1DmCbXvfBdICTVOwTsLYGYA4Ao/5diuoAl5pQRLNrrtcdLqYFe3T/ZRLVx5oA7NMRxfyv1UkByasdkiiwtqTZBmD64whM0sYwjbBAmWWTVOwt3fBYJ2dzTWR2JZ2zdkn9s8tUcCiTF3+aU8F12aGkpMBo6zprvXCa2rNbkCiyun95RMZOCm5b8pRYgou1CZ7JN1jmCX/56c/h0x9+L5Y/ukI+6nzbElDnhPJwh/Rk1vT44B8A6JXgAWVtWeEsVVeHFs6q+nmzyvGelZKA9RKos3gaX9kDSA2liBK+mKWSDGy6FBVkNwAprdKPCetaYAdz8ebmqurxW7L1DEG1GUhrj+mjQZEjsW2KBoJIIGjSUxDVvtJui9k4CxUKtOJuSWgNmKaKlCrWkjDlalWD37pfdA9mvUONk+jSlP1yXfe4rnsHmbrgee4/9pb8iYmi5WycpJwqa0+lT9g2QVKfi3ngnOhdsNFYV/4andoaUfNzWlWec47q54ogSmkJR4sUXGwYM0yjBDoCHmm5AHTRuGFixTz72CIoCMC0uXpZ3E4R7iKTWaqWqq1AFWW1rcgerZzESsEWGwv74Q3pjGYcR4bXKGIZ+xsdj260QgQAScENq+BBTS+WMF5aBhoLA9j5rGLjhSQgHcHiAltH47ZNKCjYGXhJABQNqJI8lZOT/YN0wPPzq/iqZz6F9a8l/Hr7COS/zTYJCtY9IE9npHKFeS2Q28PdgBagrKyYZlj62hy1oU3K7nCGFuCTJ58HUaITUgHytQnDLwCKsp3WmvCo7nWxbqAbjgnrMmGaqmmeSK+ESNIaHQd64HQWkkZKvKpVnLVtcoY0FVZMrQNPxtCqtaej1KqRk7XYM8GoiDSkYnoVqVoErSFBqdD3DsZ9e6uNjJnVFtjHwMZimqCyzvtiICafnSunHplH1EoCdE4ahcz7vMq0NSD8lhHS++zYCkT1n9z2fBGci8faCsRrpUGzX5v+e0VDhPQ9aSPY1cYx4TbsT2xb4dGBpSRjiiCZSB5EMvAi2RqOdkMrO3WATkclBfClA2jb+8VzM200VobsfaYGWge5GsTAf7UtdFQ4XsUse7JnRx2+GQ0rJogtdlUjLIXxAtQtaE15AiKRXWxMPuvfCrWBO1nxIN/ig/tXUL9I8AftWcjnE+QAnUenBuwF0nZItUFWQ5XuArQEQAyeREDLqueGaRwuKs9m4JD/W9DT0xvcqTmUaQAqUURTeVL4LP7tp+02Z/M+NupLjj8Q6SiYfyeDHEurgGRY0ZPqesMC6qH131W181QR3Ld3d6NTDowsKgDq/CN5gJWBbWYixOBvrLqeEDWuWOkusH7QxdbJyGFfuFbOGNf2V/kw9NtZWyGwj1DQib4EbQK1cRW46uytrR/FfTwt0P0mPd8xBMy36YUcqxgM4vYx7fBCjp6RcyqYn4b94phpP7r/lqDFN9i/KJMCdJs4SXEpFDKSqDtFf+fJuodIwy4X1Z5MVSvEGUhwrBm7tpo915TwxeyDju+EWarbXVYmpGA722TbJDTX1WIAajE/hkHcSbQ4Um3iPiE1tHZpxbFqxXmeJ0tDziuerDssNTvpYCcFj+2zp6cbLC0r+GSaw1f5MBQwuMDRAa2LdBxSOgk28vmmjwZops3z+9fx0Q98Fr/7wpdifqQZJGTylsuM/NQF8nFRdpZlmeiF2ahwqGzuHdjDbK0hLQ15aUirakkuD83+Jv1XLhswNdSSsQqQkq3X9g2H96ndKhcabJElIR/Q0w6NgSWN6zTpgFaCgkwEuioXMHDh9ybqF7GeBCGAZBk03N+BMfOlWhLzv8JvsgiQBJIqWhGk3Ew8XvXAJgO0cqpnfsl3t3sw6wtsxR6OjIZbZBNf3OH1egmvVNh0KazpebqwnNPq4FOyKC8jrDwe0BlbMeWQKYXVQCf+MNn4A6Zh4iQeJ1OnfRLIskmZJXhziE5E5tWgfbUBm06AKo4RegWVc9tFkfdY2XDLyuplYevA0Br6gGpChutQyWU8TsPeGXHdsVtawiy9DDDHZm0Ja9lpxCBMwBR+j4AWDVCkWidpbkQgBiTaZHZddr0Esjkj1CFg0YCLtKAytTABuVUUvw8VOYBaGfosHQXYNX22UlwE1D2ODbAnDwDwdLoF5lfxfz7zP3D8qxn/eflLkN/LWmHDJpjjUzPS4Qr5873CIZuyn+gwjqwsgW3aFNwCoxUNQA1MCytPrrRYnSynx4Ld6xbhyDopliq4XncmbqkL+PwkIR8ERwCrsbMIiIlpdpHtldbw2NrkK6udf2oeqdAIvrlIJsLoj1thOMOqMloOOFtrgpyN5r1m7CZgytR5EKXVoous3rf79laaLxSgQMIKrYZ5aLODNAQ+Gjq4Qeem61o1FzEHOggRtaX4OcEbgk61dU0tbusLI+4jbQhEbP39WFlJgLB3P3d8Xf2qezsBqsKxo9bVdruY1kLmF4ATWxLTFyOYN2yDzmrT6z2/zUTATOEEPZ8t1Dl+XXqwp/kwfdwBOMEAIJIZRmeDKYq19VFUQKvbJgKLaAps8Px0EpieSj0T3Z7zYvMsa4JhBK1y0wVpVsTQKiROxhTp43chK5Bv8cUXr6J8keCl+ix2rwjywYA8K38uFzNw0yDFkKUIaHGYfaLmd3ANEk85jAAX+OwLp2+ir0hHIB98U5//l9pTP1sD5ChIRa+5GjuLYFgHzezeM1OSn4meS2zpNBZr1De+BIzXGBC5TdaKBm2swEiXbGl9V7unI0frvv1FaAyMXtcdAFhaX0NpHaiq6KmCS8uasuWB6zEozsb0xB747oHxYTsH+Hvxoyi0vg1WXKTFX0fWGOVF4mfXZT8cgwB+RvO0QlhxrA6S9dTn7fVooFjPv5PVA+Q6H3Atv574JLvgu0R2VgTC+nVwzd7BrjlRrqSTHKghzLFNYuBghRVxCfcB4r6K62eJFneZrSIcALxnf4PrdefsrDUw05wRJfSdyuAzkKHHz1lAqzatBn9b5g0xYsIurQPYSRZXSQWXeTFGmeC26F8ytXgcTXHVQMKUigvXJ2meOrlLwKECr6+XuMxHZCjTi0XAZikookCs96NlwH4PvL6sHuXgw9F3Z7rhlz18Gb/74S/C8tID5GNDNhZtnQXlaof0+mTVeNFZsyw9C+mTfZzAY0piBfKhYPc4o06C9QKoO7WHBJLSIsCtBVGkYV0zalW/pc4AJrUd6ZCQbwW71xQco19Fv4vkgYFFHJoY0aBahUQkOBst/sRb1XNKDfYpw4tr1bl1aZkqaDPHopoMTYLk6tIsWhFesFZdFx7XjHzPzPrf2xgNra2nGLJCAiMFRNfjhK6A0OQPVES74+TMv0nGydDBnQ14pNtvWFmbh5boc0xDZNSCulkaZTk99tYIsY8U14vnOifhtmUyRVE+SAfPenTkVFdIkfT+oPeqFF13zDW4QmQc6CVnNXUwQRXPVfuMkQROtFOqqDbR3pQZV2lCDUyiSsAqXPReVlSRYTInO2tufdyj+LGCX+LHpjCnR9pbdqYfgC7CL2LAnoFaHvnSlNMLFGcF5iZAOiC1GajwCh4ZDQ/SAS/Or+DvPvvfcf3/2uG/1Q/iqf+eNf2vAHIhOL5nj/1akV9+rPpZUTmXAohGpXUQC7Bqh7ZYjw+E2MRKR6By4hXIqsCTii5qrD8tgrVkBQZTxVonjUofRRlcDxNq2TzrwclwcZ/WJ/Lhp1OVeRX8I3WGxIQf+XeyDRgGT1oWvkJFC2vtbK0mDaUK9pMa+rUmpBClWFtCqtkMwn27b2/cuP5oTQHz1ZlYZFT1xbkCUB1EjcGHyESKaXD82x3iDgrFv2OfuvNM5lBsFJy3pYwfX1rXzYpVE2PbAlYpfE49KrZzWozOTAt/GyPNoe8cw7oZG47BkOYHBXJW9PHUeXoEHGGAT7Vzqr3qVZ8KWVjN5gFpQ0nztSVELrLezxHwUxs92sco5E8gLF4S0w95bLH/CHkUs+G85snsaAewmkkKSjhCxeTgHtfwK8QiBr5mQcNOCp5KN/iSS2D5oozPt2ewf7nbATRBuZiQa4Pc2GoYw6Tc515HnjhP06YQwDojEo8eoRbaGwui6GIfkCLOYGjSX0sRyAJgJ7YgHx6U3h2EU6b+uW9GvwbhssgAdvsUDyZhBwNGpes06lg01KaAVrP7l3mRCAG4+yDKu7pFdhCBnIOlcVF7KbKwyEBcqzJZ5qzAD4PiZGaxcf1MIIagTAm2aC/riV2Jlfo4R0VdLQZjvUohFPxm4DxmN0RNqXid1BbWa6x+HRHIisWx/Jo2KZfUxuL4AMBt26mfBE2v5jwJdDKA3wMb/3OpnJzj/bMAaqGpXEgMxKs+WA+M9yB7B5kI+vDeqmZWxdHOdTQ21D6vaEkc6DqGCogpNSSBVzNkeyrfYopyOEUZcTdlHuxObWJB5uapgMeanbkV2z6tnj3jYyww5nD156sOtl+frSlVHMvkelG3qae1FiQ8k2/8OUiNwOwlZikOPjJFFOgs7ILU703rhJGrdMSz8xN86ftfxv96/srYWUCrDXUSlMuM6cGFVn0vtTOzGFSpDQ1Vwa2qQJQApp2l2wmAdCzYvbpCCrBeJpRZg/gEkpanBMtTGeuuoib1F1sD0q1gfpQ0KC+ArMDudWD/WkVa2uhjsV9Qv6waSYC3h7ZnG1CRQj8O4FLS3GkDycxnQ0MlWYH2sEivUybNmFm2Ziyq69hMX7hubOobFaTbtre+5X0722bJ2FmEgCLbpYlXFwQwTMLURFrq5AAXJ+84iQOnWhWqm6WTKVld59hJNFScwA91HibbrXaWG63WBVsju0ohLRqAGkA1Gfob9bOiCGJ8vbRpeO99tvNG8MxBrTAOzIuPelnxHDo+zQ0bS6Azd7+PJaPZfaHHKAcdCY4Vc7uztGFRvtTcq3ZgBK94r2MJ4NrEIxLDdmaE+LdXetTJ+2A6YEvLONQZj8oFntQdruset3UXtASSVS5Mnt7KKIqOoVK1dyjYiYJcBAsztCTt8/Nr+LvP/Xd86V99CdcvNJS9oE76b71MOL5nj/rwYgwPW763Rx74Tz3DM68bPXIVP2ytOxcVGr0O/7qTo2O4toRjyVodsElPE6mactFKUtDMj9EgZcwdZzqJf2YTswzCiZs+1G4U+rUDbVXB+8bzN3geOBsXE3xWjiWbbk0Qg26nwPF9u29srMhEzSKCLAU9dY7PEudqOrAEuzStTPxvP/aoY6X6Ss2Bm5gKF5sgAGzhXHRgwnoITMUjmJJsXh/Oi55SJ9L7ZNPCsF0H0WT4nI3i+Kd97hUh42c6DiNwly21IgYrYjokmbQUu2fwYZwi9D3tbp/OuvA6x4r2RobzdEF4+H4hLT/Y5QgsRhF/WN8iM0LHtV+LMrSyA1qlJRysQiaLC8T0xrXxyZAhuKT3TvtNexzF/gloPUy3+JKrV/Ce5x5heagL5ZbE9EgU0Go7E+fog9mjw8wLD/bB3/P71vdD9WRO7Wfcb/sP8P6WJlap1jTHGsEoA7Rq6FfD2Ec6CbUfd3gIQj8kfDZsG/ep0i/bnJnaNqBu7Uw4fQ7EmIjoNumuEo737S23T3ziExCR4d8LL7zg37fW8IlPfAIvvvgiLi8v8bVf+7X47d/+7eEYh8MB3/3d343nnnsODx48wDd/8zfj05/+9BfctygdwoD1ddUUwymIf3NtqtpFx8FvcKDKWE3UkKOO3G2dB1Ao6l5d164VHNfGCQ17WT0bIWoDAvBsBABh3R4YS1DG6j6wuLhtlGSJWS8RUIp+EMfoIi0ebF5aHioacuxiwD/KrPC6ByBrICWk4R+PvRWkj4WAMlQ3zOfodsriT9KwS2sHmUCGVnLR9V1esUvFwSm/jtTvE4MocyqDX0iNLP7b+zF6nyfTslot64Q6fX3MKnbm00x+LQmP1j1uyqxMK9rdsF/MWjjWjNsy47bMONaM63XnPgCv97bMeG25wJN1D2obU9fYM6PsbzE//Rh8tYu0wHUdw/jG+/NUvsWXPXwZy4cOWC/FFz4tCWoWlAc7tHnizew3aisIHz/btloxPVmw/9wBl5894sFnFjz4wyMevLTg4WdWXLxckW8BrAltSairoBXB9ERw9ZmGh59ueOp/Njz1vyqu/rgiH4OdQ7AzYSHlNicu1BD8sLh9X+j1bYOvxMALbV1aBZufiu2nNlPjTQ21dCStua0F1pLflmbWPZj1BbYEwVOGxC5Qse7btkOsTkEDoKVt12GiAjAAWmxbIAvAySSqnxUHuFh2lT/oWE0ospN2Vgkxnj+2rYZVGiZdpk5OCkxxkiDCfgeIBQSmFRlc6EBZ3Ga7P7W3YlWTxSqKREDLSwpv2j6teHq69Um+cLK1X+XSkoNSSSou86JGIJQGHSII9p7GYWkZj8qFpxjSgWR/KbjJbTnxx4gWJ1oyxqKjWZoCkrd1Ntr4Ho/qpWvl3NYdbtuMBdmPxTF60nZ4Yjn5QH+uZlkdtOQEn6XiqXSDD+5ext/5ot/DU3/tZdy80FD2mitdZ8H6IGN59grtygAtTsxWXraDVQZQ2e2UAGipM9D6An4DODHPe1jw24RZiuqNLbHscOnftzDRstTt2cmYk7D1IZVwHG6zAdL6ce3eVAHzSFoVtEI+rjkSjOw3MQdDtc8Wm6TXqqCcsmZOHtv7dt+GJhDs7dGjVtZqWlk+PxGUh7hO0lgplamIvW2BLOA827dDF8amAn8mHYCigLn219JJ4ly2AZdONKwiaGS/HdocZaOJW6O7QCwAVgmxp136ucJ2WxCN15jQBXY7EDBWSeyVGsfGwEcHpDCctZhDyGtVIeU22LEtntF4PrPtxzo5YMZ7P7Kj9d4Q5KTAPO8Fdc9YfXHAWJoCWtRjW9qEo6XQFP9O1xZkm3GMlImehrHkM5FQuyZb0yjsXlY8nW/woQevYPf+6wBo6b+6E5TL2ZwEGUaEFZocwAoglkQQi+/D4NLmuB0Ih9Dt9TWZtEznBHDeJmCzLzafb8AqLvbfDEzT19Jf8ykiiOZQModBbU8EtsnEqnaftmzG+/aFtb/+1/86PvOZz/i///Jf/ot/9yM/8iP40R/9UfzET/wEfuM3fgMvvPACvu7rvg6PHj3ybT7+8Y/jF37hF/DzP//z+NVf/VU8fvwY3/RN34RSyrnTvaUWNfMu0tL1qwIwFQEerovjP09JxJgqB4wBEwCDjaHdubBqhGxkUg26h6wgB7joO/tN0IoB8xjsZ6YGK5tHWxVF4NlXBnj7+PRANVMC2R8nCbTRP6JPpWvwoLEVM1RkFIKPoBmBMfplUZeJ8yzPxeuLfln0HWJbmwbUtVhV9pS9tSbzYVZjaSlr7iIv3icG7Nfa/Y61ZtwUTcNjNfal5RO/qkIwS6+mF2VZCGQxEFTtPkXb6MCZPQ+T1M7isr7dlC5kH4G163WHQ5lwW6Yh0PSk7PCk7N2XGtMGxV/ftp0LvjOoT/vL813IcfMbWfDs7gk++PwrODyr/lDj9JuAss9ol7vOygJG0GoLYIUA/hDwrxVpKcjXC6brBdP1iunJiulmRT40yCqQVYAloR0z2jFjfiTYv9pw8WrB/rWC+bohrWGtUkk2sNOZKR0qF0Z7ZNclTX0ytM7M6v23Y2+mKSkKYHVbp9rFaQVQjCxQ0f0kgReuaUYEYIBG5XneOph1n2b4BbYsCXthVGE3pL5xQtpqSHna15lVRa9KpzoUnNhni2Z41cJWB/rvdrKOERNO2Ey70z5UwPKJd7J2UcNAnT22yfPHY0UON2LhswHwMoYWgSp+t9XT4gTuul+xf/aaEeL+mR7N88+bsatarwZy26ZeJRKbcuZSsEpGFTKrukBh8pWtsqRmaN72WjMOpeeBb8Uvb63CC9NKkTq1OgrQRx2z2rSiIem0W6HO1Bqu286PkcO5subdmYZWF9GsqL7mr1Dx+NSqQ9YXRlP21EMeE/Mw+V/Igg/uXsZXf+B/4Jdu9jg+foi0clJUQCu95xLTcVFq7ZmJGobUC6CC8Nm6VsXvCvQx1i+qGKik50DdTKCir6uBQkvJmHMBxd7dnymbyS86EyoW0aMQ0s/PtYtGGESrcXBTywmXBtRJlE0GTUlEEUPERCtdNVh0IYjCN9PJCnPD2rT649oydmeTce/bfRtbEkG2h3dbuZAsn62wuwD+O9y2BsFKQLgFphGdCXc8xkqCWxApgjUUjN9qclXbLmonxpRE2gYHy9gvO+e5z+L5I6MLgDO/2KLtUrsZwA/h6+5oJVvVlXA+2pLK8YZY3T+Oy8Ah8qpeTRTfLwb0UIMpMhqyhHSflpCzjtMuVPMC4MypanZQYa0AxNm1VP+094tjnO2YnZWlOl08hvi59NpWZFA7TdMvEpKlvFEYX6d7blMwibKzJjRI63ZplewgJaz/T+cbfOjhq/i95yaU405TuVtTEGaXIGVGKsUArAZPvwODKT2yCyt7rsPQ4cQh3dDGRoMdym5iUAOAa5S4nloVZKZO9AfvNMC+ed/PtulquDECDCkdFLkXpm9UmM0ym5o2B/dbotu4b9LgwaYq9ty2LpR9396ZNk3TwMZia63hx3/8x/GDP/iD+JZv+RYAwE//9E/j+eefx8/93M/hO7/zO/Haa6/hJ3/yJ/EzP/Mz+Af/4B8AAH72Z38WH/rQh/DLv/zL+IZv+IY/cb80ZWpyxx6As5Q0W6NrPO3TihKeXRX+7pqyq63Tp1RR2shY0oqEClIxIMz1rwIeuq4+2DG8SAfIpiqYQ1obdbCw8Zm20iYAHPByzawAQM0Gxuk5u9bthSy4bZ39tRVlj7pWO1kHEfGtOHyUY+H3uy1ry/wsrv1n6Pmv624gIkDgAF1tvboeWXCldWmYY53wIB1wMGYTU/b2sqI0wR7i2SVHSz/c5dWBp10qPvdPUlx/lixUNDgYxWfgxqRYyJzSdLyKi7waqFScfdVlXOBgE8GupSWveEjgiufYmUj8RV5c32tKFbdl9m2SNDxZdpiSpiJer/r6uGbsUsHjtMMkqst1kRYPpldRDTH6zLdth10tuEoHtWNNnyGvRikN1YggBB+fyTf44MNX8ekPvB8P/jAhHwGgoc4KMNWLGelxBuoKUAg+aiGi6nv9gWAUP1T72cx+9cAFhsJVUqE+x9T8e90XPVAS1jNuV2zbxm11QM2OyBA3kdoBr7MLRxlPQbBLK7u3MSgjCmi1rfZEg7KzikCmpn5bVgutBU0s1Xm73xu0e2bWO9BqaziCqYI9fbBABoR/lg4iUNSQn59rcRFLmuoh0HBpVPrxewSGDK2MGiKrowAjJ1LShblvQd/uGNICuX2MKPhkjDEVgiBWFHdf3KBV/06jxf1851IQORZMiWP0JTmyblEA8Jp0jJc6eaRa75OAooWATrT7pGl+MVqwVK12ONmEPplooUYiuqBmPCbHh9EmOoLUKeBCYp9WFynkYiBqGXQBw6lHhGp2VhYXJ7d1xqN6gUf1UosOtNlYWROu275Hs6D73rbZUw75/M1S8ECOeCBHEwvVe3SRFrwnX+MvX/0RvvpLP4XHf3nB8WlBmTXdsOwTlqd3aA8vNQ8c8JV9L1ELjzhISD0cohHVUgw9RbFP2KlIp8cS2DLDVOqol8Z9pIil+8XoCDo7K0z4PBa/O9mm9PN6JAHcxsA2ph1aamJrCtZp+kly7SxOx9T7ilGu2jTlEFsQ7r7dtzOtNViKIAEN8XSyIW0wAFjB7z3LXEX4Xl93dinbVkKajgWBrIau1cQ0wpiux3Q8AiRAALmcBdWhlC2o1UGzDizVzXdRU5I2ivuoAIAer4NP539z1Y8PTzUU6amJkaHGI/h1cwxbZ0VxDKbAlOOWngaEzoqYEx2AMXU9sqh4vTxns2OtxnrKFgn385kTwm2AzqgoA/MtDSkc1RyjozG0VihriwwvlQ1guqul6CNj1LNs5ugV7KCVtXiPZqm4kAXPzk/wofe8guOzVZnAWdMNWxbUfQZ2c1j825jW8anVj/vfMfKNztiKrOA47+P0PfXO2Ah6CSPLZ35OkXHlrzffDZ8HhnD3EILTET2NcE69DO0HgyjdT9HnalO80Z3V+/bOtN/5nd/Biy++iA9/+MP4J//kn+D3fu/3AACf+tSn8NJLL+Hrv/7rfdv9fo+v+Zqvwa/92q8BAH7zN38Ty7IM27z44ov4iq/4Ct/mrnY4HPD6668P/7aNRaaAvjZ1gEcartIRV+kIAJ4qxjVqZC9FfS0vaGSAGAEdAlkx04DNheDDgzcEtatmeGz1cZhWSFF3fhYZXAR/LmQFdZL479za/LbNG3H5DkBFsApQ3ycW0eIaPgb+OU61ic+R8RrnwNDiPpxb+Z7HTVK1QAZG1hpblKSoTXCZlwHgO5TJQScCS8mC6VFnK6bxUVaFGo567mrH6FrAPQVSM1cA4FgnT1OkziOBr2PNONY8AGJ9XMbPCJDRrhC8Wlt2cfnJGIVknC22ln583ONQJpSacFsmPF72eHW5xKvLJR6ve7yyXml6bevphgfzpV4tV+43RfBO/djeX5cJSiue3V3j6gOPsTw0+yRAE9F0w/2kLOItO+uutMKQubJtwzKNciz0R4Dud1TV7wL1iKNdaRiCNJ4VQ38p2qmq2Skn59/4R6kAaUGvkkizxJ97C7IvtKWAg1p8rQtWewaXrNIw4ZLpNx3XMWvsjdo9mPUFttIqnrTq9FNOVp7fvXEK2AjI8HUsR0vQaxTX60AWU8h0++TnJJAWDRHQQagt/bJXEyyekqLUS2VjXcixU2XDxM0JOp/5JRaI5yTXQJ/dstPOtcjuIjDDFqs7UiSS18/852yT6j4tPva+f3B0ZtE88izNGVk+sYJC3bpop5He5xVTKhZVEAebOM6ejx4cCDoovM+xoopei3ifaHy5WOjPhl43Kb9Ar5h4XfZ4bb3Co3LpYNfRwa5dpy4boPWk7RzQStKwMwN/IQseyBEXtrBhiuSz+TH+6oOX8OV/+Q/x+Esq1kt0QOtCsLznAu1y328gAatNuqFebLjRId0QFH1vFikn0LQFnppOhrWkwaD3c3Nylh4R4GdhMpbaxlQRYIg+OHC2SkiRDJNyw8bJQI+2W39aiOrXmjwHHICnCmm0TfUG1pYxWq/7dt9OW20NC1oHG9C1p3R9MKYns8UUOaYIAh2AEvTnFfZ6KM7RulYT0wL9/A4+aeO8xxS3WFUQ0HmcKXMEXQRtWDhHmIg6UGMynI0HgBXKLos6Ult22rm2TWeMzOYUPk/oArHUhuI5BH3BHwGPbcpNZGLxPnHMSKmP6wSmyydj2sR7wKkmAoTxs6hb08K5WriGLgDf/DjxHkXAi7pjS51wW2dzYBSsWu1Ya9C1IaC1BECLVQ6T3ecdyqD1kqXhMh3x3PwY73vf6zg+0zSNwwCtOkH1s6bAoiBgFQIhHeXBZrtzr9s4l9v3HkBpDEz0zYfjbEEqjACVbLe5C/SK9i5+vunPib0Kn8d73LYAdOu/+ajLc9++8PZVX/VV+Jf/8l/i3/ybf4N/8S/+BV566SV89Vd/NT7/+c/jpZdeAgA8//zzwz7PP/+8f/fSSy9ht9vhve99753b3NV++Id/GM8884z/+9CHPuTfca1MaYoIGjAFkIAUA9LU0eI6lEAV0AGstaYhyBplNoAeiI1zEEEEzoPAqNc3p3WoYkhggewk190KcyBBLGpjFQhuTfKEaYex2vlWy5Z9Z1/IsmIflRG7OtC1TQnktjtZPauFLCuCWucqshM0u0oH81F6GmJM/dynxaVofMzCvQCAm6rMpVnUJ2Gq6CzVwSQAfl9VAyu5f0PNrSQV1+sOT9Yd1pqxTxTk7/4nbT3vNdnbBMwme01A61h6WuKhTL4vj8X+JSMKDGmi6MBp98USniz7QYKFul8NwM0y48myQ2uagv/68dKzoggAHtqkvweTXOHzcV33g6bWsU3D86L3uzlg+vR0gw++51VNNTQxdMCCLrukqYZyugbzRmmWujEIMdjP4ag9IEP/yOVQGPwOgJJuGHwVDhDOvN98ThuUGLyvwRayCJh97gyuEPD3oD/s+22AqIhXfvfgjANyamtr7SSAUtRvuiv4eq7dpxl+ga2i4VFNxnwJpUgtOpBgYn6bp2c78UY9qIO952dbLS2n4W4osgW6+OakumV8bR+MmDrItEAH3qSLskdmVjQUkZKLBuzSAmwWSwSoEvdFr1yYh0msDmmGQGeacXuysuIYAh3p37ZZCpB6GuCUKqYNm80nWzuxl50FtUCUZv14BfZJqbWsODhL0TQTRkwSPJpOnQKe61AnpftLL4kc73G2LpHFpZpeHThzFlfJw32tSZADU+wiLXonpaJ46ohGHW7rDCTgAY4jQ1AqZqy4gFZYpAG+SAuen1/D33z2f+HVv3qJ1157P9IfNGcnrQ8y0jN3pBvWqqwto73ScQNsLV6JNlkJ9QZjaYUJ0iPWPfUwOsitCeqSepQhRAEGICwBoRCUbtLg1Q2bmE2S/vhKU3ZYReueOo/BqII0LfNeodRgm6xbAkQqalG6rItYN7hTsdpEneWtOd/37b41NByauGYRG3VNmMa3XUpFVlMbFpZic2qobLUBZZhCtp3XqdNExtbWvm37sE0THLSyBM40GphZ0sDqg0OKYyMzTE7Owb8Uyid4M+hd2bFjl2nLuD1ZWT6GIdhxrjFVZDWGEzUZ49Zk0HU2VwCYjF1TkXCswCTiWhLw8RUfE94dAmaxj6sFfFyLYsOM4PnJ4mIlyahnVS0oFdMxM3oFXEBTBBs662tqqi3WxPRYBEDrICXHI0nF1EZwcJKCB/mAFy9fw+0XzTgcHkBeh95rbNMNbaXczIb4IPN56t+Jgz3B1qALuYOAEoEjS2/s4JHu22CM37vAKdte4m7slXXR/57bpoafGDcIdkc/lw7cWfCE49+qQHIHffUy9B0ZWneBwvftT9a+8Ru/0V9/7GMfw9/+238bH/nIR/DTP/3T+Ft/628BQKj4qa21dvLZtr2VbX7gB34A3/M93+PvX3/99QHQYmMKcxRYj9pZS8uApAG8qsieQeAVCGu3E0z/ipkhvj51gEnbdd05WBU1jLiGfVwueuAWAYwPvhFT8k6uLaxh9TqCP2H7MgMlCttv/aOo5RsBJMqfbOVV2LZC771fHRSL3z2pe087jJ8vbbL1+nh9lDPR1Esd27VmDaAYGDlLgUqsZFzmI27KTtP3mFqMwMKCpvChTDjWjB3lR7KCm/u84mBsqqVmT1+cLTi/1ggKVjyxNEfa17WmsI2OLUGrlHQhTvYW17+XqbifRTCMAd4a1vnHqunph6YFtPaT6kBHRtmTZYe0NuynFbh9iKd3N6hN0xqvcMSjdqFMxARk8y1v64w5n/eTk/1OFvRn9mE+4LmLJ/h/3r/iqf+R0Y6aTg9RCZJ6MUGmrAF97XyftyH9vX3XUwv7tuoP2aqn2vYEqZiBgmYA0Qh2qQ+jOlXIVskQcJshdfxMrB9NpJtN2qJoz6I94nsDv5qlw7ek2lgahFI75ZcdjiuroM08GeCVDS0dsZlxLNQgfovtPkzzBbaKimtLz+qTV08PiEDWNkUOGIXemUrHiZwT8khL1WNwvyjmfSGj6OI5kfeyueUx1e8YWFRblpRXKQwRj0izjQypEhbYW4H3c41AmutmoacbRuBrJ1aFbzOGFKHV8eyMMX4fjRgNm9Kju6NzU2YcLDKhAvCrpxgmNBzr5BM978WhzriJVVsaq1iG6i2BtXVTd17NkH0iXTsy6nKgS3fmnfg5qHdwqBMer3s8Khe49uqGswnDa6SKrykQf2vRiSO6uCafw9nGNz4vV+mAD+xexf/5/v+J5a/c4PiMMrNaZrrhrOmGYYIG0CPmtXrUgYysmHpIdpaoqExfrHNyJgDW4JpZMaghnG2jk+GOSXjIwudRfJ7ihDGNMR6LAoa6nXhkYfClG4A1eYQB6H8ZZVhKVpZWs7SgNi4y7tt9e7PW0LDY/NvCs+OpFAHI0jXEWLUvprc125dARtSdIqBEnSuv1vH8o44AAQAASURBVCfd5kQmFbdlKzj/XLNyYUMQQZfRwWYVRb6OaYbx9ern6ADTmPh3vvE6q4E23I9/aQ8m+6cOVgtVFUddLQfQZOxzHMnOktIpY6m6qJ+kYk51YGIJWk8XxAhQRf0b3j+y5PpY2jlMxN2LUFhvejpkvwYX2EdnbfFamPpZmmq1HIKMAkFVpiiuYKphDimHyfW54rjlwCLr6S0rnsq3+OIHr6K8b0W50IV5E1i6YdJ0ww3K09lLNsocbFuFnwrAtx4ZPwNO0XawWuDQtnYFZ2wNz7PdJrKMz2FKLW4nDqrJ1pHYVtX1+2Usj5qGvitw3F/ftz+d9uDBA3zsYx/D7/zO77iO1pZh9dnPftbZWi+88AKOxyNeeeWVO7e5q+33ezz99NPDPzatTt2zA7ZSJ/zLdeVa0wlQU5vgcVHWPRkusxRcpAVTqiHQngZwjEUmuIalELyn/tn8GkXX2ZQxNvo8t3VWQoCl3RUoS3SpPZDfUynb8LdXQG9DX/l66wvR79kWqTonr/Kk7gc5FjaCTJEowPGMfgMzZUhq4PXGquXs0/Y6CfTwGP0cdj/tPMeacSyTAUfVU/Vuy2R/Zy3oUXPwLzTFbjbG1D6tLsuyT4tmpkCBpyn0jeeM2SsEt45l8nTBWPkQgAm5d+kc2sFd6hpfYkBbNT/Pi5yZ3Iigy3XcLDOulxlP1h0eL1o18abMeFz2mt0SGFr0r54YO4sVDSlbk6U5m47MrYu04Ln9Y+yfu8F6CQeGmlXirXMGmGq4YV9JzFRhuysFMXzX0+WjreP7zXzeNv947jb+42e+T9h/+H57TuCU+bXdDgBa95sIeskq47Es+B+P1YqgrBnVqsGX+tYhqnsw6wtsSyv+w4jVJrjopzaWGhhdOJISCwC39uPixMbKfZEOHplZcdLbTsxx0tZtxwd9q8GxBD2sbfVCsp0iwMR2Lr1QUw/707xNrYyMLOC0amEUiY9AlhqF8Trisc9VNOzXzwqSneEUUzAZceB7TeVkTnnCoUyebqgGNdCjDUyL+3OMuUiIqYGzlCGiUmARiKqvb8rO7xeZZPw+HlPvSfbIRkHC46KAloJa+xNQi6mfvN9kEUYwa7z3HUgFgKfSDb7s4vP4f3/J7+PxlxWsl0qxrZOgXKYx3dDTPk7TDaXWAdBCBVDrMHlGVpY7HhuAis7WwolOOjDFPPIT58KaM78CUEZwSqoBW7VPwjy+b9c0MqJREgJbZAh0JyPqdkU22Vr6ApKfrzX1fPL7dt/uaBVNwQFjVPnj6eyavrbQ1ypQTgd2tfm3ttP0RLZxXdOZTZEZRWYLwr7nfP66sVvblDcADpZxm21VxXNMEp1347hs2A+h38DIphpYXhuwTNBOLFs8duwrAAfm/FjmzDVs9L+khbtggVqo09Hs3Jznu1B+H29PzTwDnBGq3MoHJAM2vbqlgefUceRVdEF5caA9AlrF9nW2Fh2CO0Atpn7yfrMq4jm8JwKlPN9OVrxnusELz7yG43ss3TDB0g1F0w3nzJvZAattumGwGx3g2joSkZE1fs/HkJ/4WsqCyQo6Cd4wchyPHa+b9iykv5+zV7pdAK74XqwjPM3GiXAwmmw/fmb3+CyQdt++4HY4HPBf/+t/xQc+8AF8+MMfxgsvvIBf+qVf8u+PxyN+5Vd+BV/91V8NAPjKr/xKzPM8bPOZz3wGv/Vbv+XbfCEtBlljFgAA130lA4vgguv22fYMHEddXJcsCWvpbMBHsbXy3sTT47zENmZk6BywT4uvRWMwfzaB+JhuyP2jNvG2omEfg8nHoAe5Ww9shxn/2CavRB8bfaso7s6xHDSy0FMCtwF8HVOKia/Wh273eD2UUQHgwB9T7/ZpxWU+eoo2wbmtLAlt1JQqrqaj31tA0w13ufh9nqTgYiJzTvBgOrifE/uWpOJQZy+GVZt49ommj1uKZZ2MPdYJBZpK2IwZps/aFHypaThfB6v4nppscy7dLzXNrFL1ao9rRqmCtSTcLhMeHfZ4+fYB/uD6GTxZ93iy7v1Y12WHQ50DaNrnUgK2CtCuAbQVHOqMJBUP8wEfeO/rOD4tnU1ri642Cdo+6juGttESPtvO5rXre89coQxKAwlwfdHniwz7s/Fzzh07BvhBFpUuBIKtah2UCtXjh2NsNbusH3SNAJj/J5omye1Y4TDuZmuCego13NneNpj17/7dv8M//If/EC+++CJEBP/qX/2r4fvWGj7xiU/gxRdfxOXlJb72a78Wv/3bvz1sczgc8N3f/d147rnn8ODBA3zzN38zPv3pT7/drvyZaAWtM1+CyDk1s3pVkM6sYvPPQtSVIEmc2CN7K+572/oPMgJpJ3TYDQV2KwZPAIPfLW0aStASYOJxnIE1sKG6XhTZU9yX5/DKHxvjFgEsbsvtAa1AMl5PL8+brN+7YKg5rj4RtTQIr1MDYG/aAUwtXC36TNrrraUgThZtWmxSVcF2jSbsNyWIaSiXpiVuCWixQsxas1NxCUbFyTQytWjAIkWa7TDoCWgU7XHZ47ruXBCeGmrxORuYWvbdVkeDwCSfuYu04NnpMf7Kw8/iub/0Mm6fE5SdOKC1XmWUpy6ATCfjdCLm35gTLgHQYhTDF/fWHHyyybY1Qal9zJoxpzyavUH6I1DlANnmNbcdoubb7aJBkOa0WVQo1beJ54k7O6uJ667QqYiOG2DprG8j+vAXpd3bmdNWLcixYvzdcg3jqXL8bYR9I9tKBeTFmUAdsyWzZ/z9KijRq9AxXQ5ntt2CPydi8Og6TQwaRM2lyCICQmCGwBAaEroTRPbUdpteeXAT0AnH7tfct5u3DAUQf+8stmhfPf0wMLdcNzGASpPomctmkU7AYTXwh6mdtEEx0k5Bd++7dObFYkEX/tOAWGftEYzaPhOrOZtdv+wUiOQ2sO+OdcLRbNTaMlYwwDYyBmnXO6B1ypwjMMnnY5KCy3TE+3ZPcPXea6xXcO0sjXwntN0MpuqcoEAtTtR9Ib8Vf9fPxt07sNX8UAM7KwJP59A5bjMAZOcj3LI9Rvh+C82OfW3j+f23TgZe3+QcUB1tz337wtr3fd/34Vd+5VfwqU99Cr/+67+Of/yP/zFef/11fPu3fztEBB//+MfxQz/0Q/iFX/gF/NZv/Rb++T//57i6usK3fdu3AQCeeeYZfMd3fAe+93u/F//23/5b/Kf/9J/wz/7ZP8PHPvYxr274J2nUz/V0OWPTnMyFmzXllOoQYKWu5zYoHvchI+vW1sMXafHzXqTFUwwTVHD+bLqgZ1BoBUbXBq6TZ7YkqV71MPoR9H221x+D/hrE76ATt2dGShyPyMJi4aytP7XbaAZH4fjoX3GsSCLgsaiRy4waAK7lFH0/jlXUyo2SAMCY3ums6WQVCg0EI0A0Jf1ukoKr6ejf8zi1KZOqQgywImus+6L7tOAyL64hnKS5nwRYIQF0m0XwKgJe1+vOn4mYCnks2Z+/qLnFe8N1PzNxkgXxjuuEWi37gWuUmnC9zLhednj5cIXXlgu8vDxwhlYsFLCYcH+Wbttj1o/e484mvEpHfODqdRyebcrI4gJMoKndc4YXxmI7B165z3PGhrmPxM/g/oWAAfceTHEwKfhKJ4SA0E4YXrYfAyy6EXy/FECzAciSEPznR1sAijbR+qyZMNILaPl26i9JUh8pvY1KhsCfAMx68uQJ/sbf+Bv4iZ/4ibPf/8iP/Ah+9Ed/FD/xEz+B3/iN38ALL7yAr/u6r8OjR498m49//OP4hV/4Bfz8z/88fvVXfxWPHz/GN33TN6GUP59lg1WrqqcKAv2HT0YWQSqCXBHUmtPqon9AT/mKqDhbTEmMP0g9ZmfxREos/24jDswJZiXAi7TgQo6eeniiaRUm9dLEKwn2KEVPgyBwcq7FHHW+5zmiaDzQ0w1jBGVLI/br27DieGz+m1Mv0avCifoZKxpy8X+sp5MzAOxNaPEyH+0c/ccWBTG7AandQLTkyP45YJKVWG6MjksD2NMS9Tg9GqaVFY+1T/ZkaD0uFw52HduEJ8bU0nSPDmjFcT6iO5Q6PusAEGq64Wv4/3zRp3H7lw9YntJ0w+rphjtlZ5Fea+mFwteldtYW4NGJKHAISzfcov66fY+G02DVELU+F+GOTsXghPB4MdoQjcdmok8WOejHiJUWpYvFN7HKhmLAW+h+6/1eSmfc+ULx7c3b7/p2b2fOt4oOIEXwZpJirB4KjfeUs/hoaZW7UaQ3CphHuGObkugMYDAlEQNAEasLbkELgkCENCZnqlZnlsVtYz9Un0uhGEZmKT7O79fN+dh8sUy7THCAoFGwiefGwGWMQv8i6yv2PYqye4l5wG0FUzPI3moYGVzOoEEHribT3ItXp6mYtqgVBSM9kkm2Z0sn4+r7CoWZ09APgpQuFh6ukeAbbPtjyziwwmHrlZsXe19tnAhojUzzNIAqGRVTGHOmG37gwetYny0oOwWyWgLaJCj7rA4DH25LL3QgitHoOI/jNN3Q09zvAKXQQopmG7/bspskfrY9XjjHXSwsP+4moBKhrc4G6/3DmXPyHjbA0vItoHLHb+S+/cnapz/9afzTf/pP8eVf/uX4lm/5Fux2O/yH//Af8KVf+qUAgO///u/Hxz/+cXzXd30X/ubf/Jv4gz/4A/ziL/4innrqKT/Gj/3Yj+Ef/aN/hG/91m/F3/k7fwdXV1f41//6XyPn82vnt9J8zR9An5jWN6ce0O3pbjKs753JuQHrI3AUmU5MJSQzK+picR6JOsIVUYrDdHhbr87raXbmKzk4FfyL0+s+ZXbFQDjXuzHIPWSkWHCZ10YmVg/iB/2sje/B+XGbxUK/IEqcxP0YcM4G9uk+gallNirKk0SpFLbVfIW1ZszGktvn1a9LwaLJQa21ZrdLzBS4LT2TxP0fC1gkaSprQjCo9vTEXS64XWdMqeIiL84cIyNskoqjBVsA4Go6Duc4ljHNkcESQIHYOZEQMgZjlpJwXDNEVK9rWSastrY+rhkXpqt1vezw6HiBJ+sOT9a9+kY1h2yWXXjOq5Mzos3ivTjUGVkqnts/Rnn+iLLbrN8FqFNCy+mtpxrqYISNNvP0uUC/aQhvbc7Wf4kZJjGoQt/nxG4N/lHYj30XgBm+kY3VMmIEZVP1fQS7YF9hlS5kzyr0TgAQlDWhlrcOUb1tAfhv/MZvHIQPhw62hh//8R/HD/7gD+JbvuVbAAA//dM/jeeffx4/93M/h+/8zu/Ea6+9hp/8yZ/Ez/zMz3j04Wd/9mfxoQ99CL/8y7+Mb/iGb3i7Xfoz0bSSXsVSe/4vI+kEtIA+cQNwNN2PYRGIOCn36AJZSObIBACEFT1UXY3H6hNnjKxs6bKupdUUSmIbUvkCZRQALsI1AN2I+DWingBnw3FQAenGhLnqNRiKmP6Ywn4VNNAj+KLjnZwGzf7G/PxIT6YI5C6tmKTgybo/GS+dWBPqKtiZllZqCZe2GHBNq6Z0bRouRqzprLEayGIGZCalx27LlEZmFwBLbxSrONJ8TmTkJEH1vG7KjKXunPL76nKFKRW8d7oGoMwxGo3clEVIsPK26bPq1SmlV4nheO+kM/GeyU/wly4/h49+6I/wqc98CNOTjtKXy4z16QvMNweg2vPl+llwoUKmGzYkE00nxbRCmuVZVxM3TKITZ4OXwQXUuSo1eUUTtpZ1soWlAeo/PUdLdAZCJIX7DZ6iphrWybapNkenYK8aNILQmtKMRfvdpJ+/IQFixQJK0usTwSpaBlmmcXY/Ae/+grd7O3N366lZAYhpHQLi40zABuisQPh3GBhJXTOrAx4Sjh2rcFaIanZKP49vG/opOF30e18jOBR+gBEYAkxkfGAycgz6ubdcFo4Rv7dfn8+bcVwikEdR+zgedLy2jBYyYSRsr+OZhvPXZqLoYPqfMg6246UVFXUhp4EXgUAwGTBGRw9NmcK8arITXI/MnoPaut5ZRF7S5hnQPhpwFZhZ/JzplyrOq1V+eU9vywwV8V2ApKwqxY0SKhqOMJ00aHosWn8vMjoLgoYJcFBynxa8d7rG+555jFceP4O0qLloDWizoO5n5EXFj30gpUGaamxhqPjhD2oAtjaBDkHXP4n2gUzazSPmj8PA1LLR4oJ+c/phP/Rzc9nWwufeZYJQvB6xg7g9VftJwV2YmHCz+5cAyIZZcieYdt/eVvv5n//5N/xeRPCJT3wCn/jEJ+7c5uLiAp/85CfxyU9+8h3rVxdyF1QTEI/sVa6HaxNAUvAh+muuY/l5acq0IjuG4vCl6fkObXIGmIvGt4y9rA7uRyCLf6/rDhcYWbQRBKNGFgCbF1efN/Zp8T5GP4tB4xznY/elkoZk7PczVFhsOAGrIpgX0ysj6FWQdGysXztZPdumtoQZHRRLOB9II4hSkQZgkI0gE/ux1oybNquuk639j3XCJNVBI0BZtdfrzisXxvPVlnBrQNJkgRNAiQpMNSw25/Pvoep9ZpV3CrZ3fWKTzykZV9NRQSzze2KARG2HgV3JKiHWhCmtSDW5bVlqdmF8ZQoGjdDUsKwZWjBBReaXJWNZeuYLAP187uLzT80HMIV1SepPxfTCFIgcR/MV1bffobSEq3zAZV7w8D3XKPvdUBunZUHLorpZx0UBrXNtk04ogAnBEzmyOb81ICfXGAYMGGIlxQhwhaa+iYzvYSLvVGcx34ia/QwG+XZi9ini6k2D/9xn60vJavaMdo2i7tnOy/VdDf4aDSYvcU3ApEidvA121jua3/KpT30KL730Er7+67/eP9vv9/iar/ka/Nqv/RoA4Dd/8zexLMuwzYsvvoiv+Iqv8G227XA44PXXXx/+/VlptfXcZwAhjU0nvts6Y6nTIOznpUHDryCCVueos7F5LvkZIflt2tg2HxjoQFWk2Ma0wHNRD0ZXSWFmP9h39p/bMt0w7n/uvPH7cy0KwlNkP1J4zzHO+N12HAh0xVK7BP0oOsjoB502oIvQaUnZblSYtsg26Ga1XrJ8qx1zsPLmMU0wijAC8JTGm9LTGT3ihA54EcRa7LgHi2q8vl7gtXKJx+UCrMhyW2eUppTrJ3WPY8uejujjjYYZXEwkA7QKMhoepCOemx7hK97zh8CHn2B5KBYxF9RZsD6YUK8uekSinfl3piRtTzdskNJct6pcALfvFdy+T3D7nGB90CC5Yl0z1qL58kgNy1MNh/cC61UDkgJh+Rij3G0TlQj/SjstMXtuu1UZWINYfAseBzBGyRvQSo8yxHKzfKY8leg+Yv622p+WnQH+bNua1kaDzWivQCPKTAdTzLanA7bNM0bWxlZb6lwjkEEADYhg2gjyxF+2i7Lb90Nqn/TvzqU9ecpUG8XN4z41bMt0w7h/ZImdO/65FoXkmYq5ZZvddby2GV8Xz5deoZEcLDHnzoHD0KeuB9idGcGZFEM/N4GLNGiA8VgEuwbh+E2Em2keMZ0xVjmE7dNtsbgdLhu7Q3u3tq49Scb2urHJCWMVSQHTRoGdFFylI95/8Qh4zxF1Bw1KGOhU54Q2TwNA1f9u5uN4p6KWVmDb1glYL+3fFVBnQFLToh02h0OAsgPKJVDnvhBPJUa329l0Qgnn89ftzDZ0TkLfZIuIxWvzvwJnBFe7xwG0ILvsHsd69zcGxL1Ceu3i65GhdZWPbkPIzuIaluvOoYhUHX2SQadJero3MwpmKa6t19f+0gEzNP8bQTAADsANQfOWBuH3uH0M9DtQYYzRWRQA49+Y+RCZXl4kSrrESdSjirrEDqgQ2K+zpyZGBhf1sSI7K/5jBg8BxKgV5kAbqn8fr5uavzdFRfInqTiYDIqn4qF5tUKO5VoTdlm1so5FK6RXqCD80e4xZVU4BvwsalnFbJRjJeur+Hb0a9iOprXFY60tY0p1YAAyCyWCjWtN2KXigvD+jKQ6BOhSashZ35c14+Ywa9piyTiuE4qBZI+WPV5dLnETGFmRJbiTErKTQiommgOUF2nB+x5cY3koiEyqphE8tClZoD7Yo9i4zx3fu09kVQbpG3kqYfBVNFAfjmH2hZP9oP+LbqucyQwM9iRu32Szb2mDrXINrVh9nowu2jjR7ykCz2ANK9R78MZkYtil+jblV95RMIuVO7aVOJ5//nn/7qWXXsJut8N73/veO7fZth/+4R/GM8884//OlaD9/2dLYdIpkEHgnY0VOJaWcV32zkjagk9bQe7YhoWgnTNqbG3TGiI7a5vfHRejQNe02kYe/HwWgWD1xC3KH9/H9MJZVk8RpBB5fB+vN4rAAz0dcQBbBiab9oUT91awMkmvrNL72k6iLhnVQSFuM0nF0/NtiHp0sEl1r5JP5ntPD+3n2Rvja9vckFqKICdvTu5Hj0I1TyXUSou6uFjDpKvHS07D1e8VJDnUGa+tl06jZdqhAoLjM3Y8c88HRkXrVVYu0oIv3r+Kr3jxM7h5oaHO6AK9+4Ty9L5rZwE9muDVo1oHsDZNakM+VEzXDflWJ8K6U1Cr7AAIUJeM5ThhKRmHNUNSw/qw4vieirpvQBHkRwn5Rif/3g8MTgYQ3jPCcY4Ki/6dOxd1PAbFDKWJT8jRODQ6GrxHJv5eanKdgPv21tuflp0B/uzbmqg9pel1UVpcm8/t0MU/I+FvBD5t21akHSAYMzKVyOAaBM7D9jxWPLdYX7ZV7uL5GsSrJw6MMhlT52J6YazcWO27+H5g8vvnHWhiSgfbIPbOYJH1N0P7lQPMRWAt9nUL6pGlMGwjWq0pBpR4lCjMTlAr9h/ogu/b5mBlG4HNtgmmifVD9cuSg1rngMQhtd56sTYNlETH1QNz22fzDCgom2NWKCNgkoKnplu8/+lHWB4ysmuYzSSo+wldO0uvGBiBpY5cbQenIZWGtFjwBBqBrpNFokWDEXXVcVqtyEfdNZSLhmaM4XQQyMJz9SZ3/bjkju+3jK3B4dgcl+DWmcvrycXaqoXOo0bbfXt3N/5ur8sufNbnJgbQSgANCJbEv0xti4WmgA5WMT0NYDaF+PzWmVs99Y7AFdAF6IFxDuZ7z9jgvmlV/azNPFdbwnXZO3AVheJnC8Lq2rbrZt222YEpT8Pc+DuuK4zR32BWixfbCoCPM+ICOBPHP45hPCZ9QFY5BDBUcZwtK4R+DX2+LoXSg979/AmzaHof+0rm1ZSqp/Zd5BXZ3i+ljx0D6fRHjnVCQsOD6YhJOgGAx3s4HzTLxbIlSAzQfdWvYZXD7RgxBbLLvIzAarPPkq01uHb2oko1YV0zjsdJU9NqUhypCpZFA9/XhxkvX1/ic9cPcG2VDl9fLvD6eqEV3uuMR+XyhDAR7z+fe0Cf5fddPMH6AGcyPQQtZwWsIjOLvs8WwDonFh/34Wa0CVU8kELAqG2qyStY1Nzv0u1OgSse95y92i4pBqCK/lAAsIRZL9gAaA5WobO8yCYW858IbEGDSEw5rOtb943+VLwo2dwcpQG+sRF9o21+4Ad+AK+99pr/+/3f//13rK/vROvi7goGOWhVdz7hjNX7LOXhzMTMlg0FjimGkYnFiZr6WpEhxImROercPrYoPhgF3jlhkj4LjHpV5/obK3AQnDon8s5taFTI+GHjRBJL5Max88U+ug4ZoPTnyALrrKs+NgR8slQsVamjU6qI0ZZZqgonSte8mlLF5bRYLnj1SZ7jm1Fd6HJOBU/lWzw93eJhPph4ex0cHDfcUp29tRgAtQQ6784E63uVxclz1ZcAgHIBEdtNmfGk7PBoucCry5WDWgSsqJd1Xfeqp9VmF/DcPpO9Gky/n89Oj/HRp/4Y9UtucXxKPAWwzoL1auraWcA4UY8iUobKB3CrNeSbFZefL7j6XMXF5xv2LzdcfL7h4uWG3WsCeZJRixqp43FCWxLyTcL0WDC/mnDxmQkP/lCwe9S6FlYbJ3JOvP669Ekf6JFz7h+HhNGEVDQdEWGC19fmZBRGGQReUcoYWtWqrvgCpyYDwN4IXrhv2/ZO2xngz76tiUwnF9m2KHPbOAW6HRwYOnccgOuMMcWQultAj5LTUYm2jOwfAlMAhjkdGIMdDqbYHB5FYLUv538Dft1NfNFS/e/GcUDfhraAjB+Ez2Pa4dah6o6VMYi4cA+VveK5E3rwKOqRlZZAdlsN94Glv3VxDt+Gpc25r2qWJD8OtcayVLcROymdTbW59wBcV4vgkr7uf3lPHWRsveplBC7JnoqNrI9j1RLrBLV4j+moHU2/kdfDfsUW01DZVAz+Gu2ZFWXfHYeWYOysHByJu2xN+Ieg07hUzNcN83XDdA3kG2Cyf/kWwKIOUy2CUjJQBGkVpKMg3wqmRwnzIyAfN8K5m3Nv2VkDOMVtBhbW2G8J3w8OAseJjCz7Pv6lzfFo9yawct/enY1i4fx9xvUvWVRdOLzrvEa2E6UzyKSiNAcb9ZcYnAX6fBj9nruYV+ynA1CQwW+ITCySAfg5PystuUg8Qa0xqF9dk+oc4N9JBQb4bkAspkcCcOYVhd75ffSXeAwAA7gFwMeQx1L22DQE9JlRQt9mTqsx1Po2qtu74NI0tijGTtCrM7iU0USGVCw8tctlSA9sTTBnE/MvkwNQWuxK9+M5ahPMSX3RB/loUi0V1+vOWFS9aFWFePXC1QCpo4m6T1L9NYP5a+ukAffLcjG71AujrKaXpfOb2Q0TD6dNLWtGWbNmcqwZh8OMx7d7PD7ucb3u/BofrRc41AmPyoX7QYD6vdv0Sb8vUvDMfIvj07UDNHQxkgDUzbqrbX2hbYA/vo+AVmmj7iIDhAaSSbA35+I4wnR1QIGkaHuCDXPQansswQYgA6JJGfylGLiptq+xs9IqSIuonySbfvpab1wLvFl725pZb9ReeOEFABoV/8AHPuCff/azn/Uo+gsvvIDj8YhXXnlliJp/9rOfvbMc7X6/x36/fye7+o42FT/tzKgs1X8U29Q+lq0d9rcJOR5vW7Z16yAwLzxWS4yTNfPDgS5syBz5nayI+lQx1zv2aWesKqabdXZXF7CPgvSsaMj9jy1jF57FLQtLtz1lXZWWhnXpXWKPBPPeKCWzn695tvqcihnKaCAWp9VS4Y5jv9sIKZIOHFlusf+zFCxQfYBkv2St0tKNSZbmNGCIRqJ94oSYhtfpZMrXlQsSLiKa4FAnJKmeOw/ArymK30PgQOnAxmq9KiVaTOMU12UrZmifn1/HR174Y/yP5z6E/auaflczUHcJ5cEO080BKGUD0BhgUxuQxfO4+4UBaamYXzsi32bUXerCv0lwvWTcPpuwXmWsACQ3yE3G5R8peIUGpKUhHxXIouPDCdeGulNnw8Tv31ebs1N3Cuhw+AMUcCfmhMsqatRSOEnRfot1oFXx9wS3NFWy3h3Jv28n7U/LzgB/9m2NBsM6wJDQTtmaARjKmwdry1Rl0AzoC4ft8mFtGRLm9lidkO+7eHoHxngu/056Jc8tIOZgigEmnd3VQSuekwBTjOSvkGExs2Vh6W/wlHUVga84duOY8Xy9utEbtbgFGQvRGWF1XNWIYvqfjYtFtimZQSbatmIj32c0XQ82TVeEzS2r26Tma0WJ/zPqDcFSR7H4gb3XNLVP3KHSu7MauKaML92vhHurNrw7bgqUcW408M6OlsFnRFlZWZrFOZQV8iAf8OzDJ3j16hnkW0GrDUg637Z5grh2lk3kEp5qfxmfdOtDbUiHYnO3gGmMEGB5kLBeavp8RdJjrgnTYyAf4bYimXPhi/l2/nV8Nhq708KHsV/Qy4k6jbB73NBcj2RojNbbtTfaNuHdEdiw3duad3nTdLnmwVaCWkDPTIjVz5eWUav4+hbS9Z32aXUWVqy8nUTT26ZUHRArtes9ZVQXmj8BsVoXpmc2BYXjB11bGSufVwhmlLP+AIBBt2q7blb9rq5HXFsCgm9G4IR6V2z04+J1M4MlScOxTe4TZFQsmE+IA9z3ts0OevUUTfXEfIxCYEjX972y+Yzi94b3OUnFzWrrlVQ8U4NBcSVNNFxMy0mlQI7NNtXvtO9qhcnSOoQq7wsY6BnF7V0DC8VZWVNSwKszu7oA/UVevMoh+3MsyuxbLHvhuE7QelJpWEvUquuRPFWUYnqfIiiHrGvzfRtA/dfSBRYbB0rC7NKKh/ng4OocfOGdZSLNUnDddritMy6SVnSsT6+o2QAwNawAmtuTN2xq4N0f0otB1xLm+xxAJTtPBIzcnzl7DnQ/iyZwWPQZOJWC1qMJvTcLkrjPxGu0bepk39vj3jZoUlpgQJ+gZQPh8gkON/bdWFkC9e/ejrF6R5lZH/7wh/HCCy/gl37pl/yz4/GIX/mVX3EH4iu/8isxz/OwzWc+8xn81m/91hs6GX9WW0EbJtALe/BjBDv+jVTZCEAt1JtwltcIFp1DiFlxYQt0cPsaIgyccDlZH40FxfzumHaoVQ5HwG285g7c8fwRyGLbbSb1uP9Wq4nsr8gCY96ys70gfu7IBHPQpXWmAkEYttPyveJ/OYExchAraOxScWBotch2NUZVQcJ13fn+t2Rr8f5bJGusehhT+drA3GK0PqGFCh5d4HetPcJ2sMojCmKRraVlb2/XkPveEm7KjMes4mGLmJi+yfTDLghfhiqQynBbvXplRsUz+RofffqPcfzgEeslegpIFpSrGW3fIxxskf3UNbTC960BtSKtFfl6wfRowfxowfxoxfxkxXSrIBWWhLpk1GNGfpJw8XLDxSsVly9X7B5X5CVEMs5FtBmR8Ik59qFHK2LkAXFbm+Q5kUsRr+whRTztEKzO4Q6WXTJLDNeuLXAmaHjf7mh/Ee0MADSEqoPSMCFoGwnBn/43Cv9GAKq0NKT9uU8tdHs70AHAqxCepCm23pcohs61UpbqTB/Xb0JPOeQ+5yLmbDWch/tEIItt2ryP+y8YbST7OaTqgQyCvh/PHZlgUWcsao9FEGwrFRDHOabgESDs64MAWrWejucFRKjBaIwtivp3UfstLtLfCTAwt8T/9XQRvyfGBo6pNApaiaextwaraNWrCOv3Gu3u++hxfV1jtnlF9msm2JgM4GJ1Q47AhSx4dv8E5enSC3MAANlZ0+kydlwDt5MoNZnAUhvSUpGOFelYkI9V36+mfVVEtQ9LQjqKMbca5hsNmgxLnDd0KHCmD+N3cnoDx9dNAS2g26fOzrLLbH1Hjr2Ofze5d/bzvr0rGiU2AAzASay6F4EsAl5k7nhFOwuQ8jPuQ8YWU8ri+rZrD3XQh0DVXa2E+Y1tluKfHwjooHnqHYDBj2IxLQJ1/BezOMh4ukpHD0Yzm4Zr9ePGI4+6YGRWxTGNY07NrdhiVce4LQBc1zFoFlk/XIPH4NOcegVKHodsXjaKuc+BgUfGFP2W63XnmSf0PwhGHuuE63Xn+0R5lUOZuvA7j2caXdT89VTBkq2IVfbzUtQ92srbMuO2zLhed57mmER1vrxiOcYgUgSymOaaUl8DlZJQi0ByRVsSltsJ6+2EVgW1ZNweZjw57PDa4QKPl73paF3gQF9YOVkn98azlmzcdmnFdLV2ZlaMlYgAieQIn5jtyzOv30JWhrKv7HUNNoXGPEi5+E9Swr6xsmFgUJ0EXBo8bdFtU9DE4vVt4oOjblY8V0HPWGlw1GlgkTkBQHw/ZrS81fa2mVmPHz/G7/7u7/r7T33qU/jP//k/49lnn8WXfMmX4OMf/zh+6Id+CB/96Efx0Y9+FD/0Qz+Eq6srfNu3fRsA4JlnnsF3fMd34Hu/93vxvve9D88++yy+7/u+Dx/72Me86tSfx8aHv1eC6/nYPY87IwUWT6zUp9XuFPHd6m2x+SI5RD1d5NHKBbCqYS9PO/5IzuUCcyKOTKx4XWRKJStzm7cIAPsWIi53jtMmcrIdP4rHR1BrZ4bN0xShk+zO0i4p7FiQPY0uNhpAFaXs4zFL8R+W0mGLT85z6lGpZI6j9ttSQaSghkU6y+oOFRrtXFoBqtm/nkZCaq0+K80nbBfTFCBL8c8ni7gkqVjbhKV2QJGReEZPrtcdVqP7ruaYTKniqXyLmpJNzquysUilrjvk1HAlB2e9QVYkuydoHSS9SAte2L+GF154FY+feh67R01TDSdB2SfUix3y7RGoG0fVACsgdXZW96YN3KqQZMsfU62VikH0kAt3id+1phEG9OiCUhy4j52oSp/ALbIhFhFpBiy13M8jRpFtQA/8sw+AA1eS2nlnwcrO1gSgJOQppFxtAIX7pu3eztzdCFIwQivoqXo9aitOU+d3nD8JSkwyVgocz8HWxdYjczY1YzU1BLHwzTECe4zN+xKYWPG6KLTdwTmgnrDLtF8xPfHsNQSwbby2Dr4QCGKbwGpb2lYIjhYUooZXa2QRyYmtFps315ZGcVo0D4q0JijSy9FnqV6FSgRITIOTnipDYGINjhQDKy7Ob4s/AR1M0xyzcSaAFgO5BLQA8XREAH5Oski7gHx0MJjuk1FF70SVXv1wl1YD+qr1QyP1as8628DBSanKMLNrodMyScHD6YCHD29w3D9EO9q0ngRtErRpgqxbFjDCe2MtoWFgbnHydr0Ru0IGK85grH0BbhxCRrPRD2GxeTvzaGt4jIG9Ne54uq09C345IThyYjis7w3NbVLf59wO9+3d2OhzbPWunH2FrpNFwIqttoR9OhqA1SVOnM1V4ZpYsZhSZGV1gXT93IspeUA4iLej+e+f2/B1QvNz8bp4PB6Bc2jUh/T1K7qvocBa9mvqgu/MVOjVBrfaVsxq4etq81fUwjrneEdmWgTDejBdhelhfYgphmxdSmB2sX5q9V5bJXP6DxWCQ2GGRk8hJZDEtrbsesDJ/KjrtsOj4x67VICkTCxuU1syzavkaY1jNlByNlZtPejvQvFmE3dpdR9l3YwhWWS1Ca6LSvSw+NZxVRH3y92CtSibiqxzLaxkRZYWTT1sRXRal6YASYJqIAIQUY2tm+OMOVUVw88LHq97PJNn3Mpswft+D/qz1NwXnaVg3q1e2W9oAoBphndVNNy2FmzRuc/tJ5JWKAtKzWgP2KcOQG1tjnerAoBVOmzB5LQWbAQ8ZbElG7t05hiQ3l3psEQToJmWMpr6UkI8j3aV3/F8i6BNarOQ+/rnTzXN8D/+x/+Iv/f3/p6//57v+R4AwLd/+7fjp37qp/D93//9uLm5wXd913fhlVdewVd91VfhF3/xF/HUU0/5Pj/2Yz+GaZrwrd/6rbi5ucHf//t/Hz/1Uz+FnM89FX+2W7aFeQRflOoZmE0tDY4AJ+I5rZ4Hruh4GYQP+d0cJnNGGDiRbBfT28qK2xbzuckeOrbJ/0aabRRHLC1hbgqA3CVS71pXAZAaKibar3HL4uI19yqFMkwkPGY/Tg2GpQN3GQm5mSi8/QKTHTdLj8JQ36o05fHTaANwIwGMEaqoTUWxxVm0nO02GjOMhzQcbIKfhHTehEMAoHbhWeEErfoBYguLFUtLzhwjNVYjHz2CvlaNgDGastYdplSxayueYAeWxy3ZIlT5oKl9qS94ShUwBVTHPRs42I3asSk490y+wUee+Tx+/Yvej8vPCvICK8MqqFcz0uMMOdaNkxHDF3dM3twylomtNsGWpKl9pU/uaBjFDQdHoM+YnGjd+eh2WCfckC6COnatpY3jEc7lfpKJBFOYseUGVHOeLTLhbHI39L1a5n3r7d7OnDbBmIIHwFP0ABjANKbQEbqI8yuZMK4ZJW2Y82MqG/kx1FOKbQtSbVs8DsXSGaXnnLxld1VDl7OlUNzF2eogVAekIjBGEGzL4uI1d22XcRHUpdb7e/46yVoSaUhNHbAmmiKXmoFutg0F7icydO261taFayUAXLQFit13lttSM5C4Pmhnx7mPhwmsm/NAcHDQMwt2twMwXS+tp0WmAIL1qqs+PRqrjuyrag5WhjpRBMaaaPCFFXJ7KmNDxeTPM8e9mA3neKuDqeys917c4A+uHmJ6IkBpptUhaHNCOyZIYXmnAFh5274fm7QWRHQjaGUBFgnR7nNR9M3hzwFWbl8k2JJgjzy1UMb9t8eXRkCWO8YDmlkNyBh/HUqEfuvOwX3789vobB/qhFqVRRXXqYc6ObOKa1INkmpwlYAJ08coa0LJEmrNFmjq2pR0zXioWpTBqwnWzlaKRZb4Pc9FcXgyubbP6ZaN5X0MvkT0p4Dzci1AD8hWVOxS0OZFB70I0m0zK/S45xlYPI9mtpR+TOkVFPlar2n1seQ9WyAoljUTBfWBBNYzilUN2b8JBUsQ1Qdg+r8VmODZGlEfba3ZWV2rjXszG4Wq+wM2ZzQddUBtUrLgy06qpzMSyNLxNZ2xmpBTxW2ZsMvF/aBjVcH5ORcNhpD9tapeF1MNRRqWkpGTBuOXVbdtTXAs2t+cK0rhxAn1aeaKeqs6h9hVBfVtSlxvJ9QimKaCzz+50udXCh5OR8/00etQsRj1g7r97KmIBZf7I1pcTrr+iPR/pZ4AWlJbB4iCozFIrzTO8hrJOCma1fo/qQAIQGXpwRYLxAO63mhiWSCir2NcBNVALgzmZDiPHWjYjmCeNICujAf8yf6a+mf9uAIpdr1T+KJov1pF6Nybt7cNZn3t136tah/c0UQEn/jEJ/CJT3zizm0uLi7wyU9+Ep/85Cff7un/zLbFKvkAI4iUDfVe6jQ4CrFiwkVaPPVAJ6iRwQVoGuJFUmZUBLGcXrtZ5NaWsGCMDDDqAJxW63CdJIsULFZilmll8bjsQ2wq5juCV3pO8iLtvJzkWmBgRQRcKkobq6DEY3rJ4XC5nEBZ3RAA0IzlhYYFvK6eI++VVezQN2XnYBHZTG4okuZ97/PqAFNtCSmdpmL26EyPVq2SsaBHN9ZqpYsthZH07r1pcxG4AoDLvPSxDD/saJxv19lBLdK/Pd98zahZBvZWkoardNQKiSZKo2OzIiPhuu1xhQMWA7IKBLP3QUFDAHgq3+CLL19FeeGA8rt7TLdmL5Kg7hKwm4FlvWPx39xJQBX1xMFFvc2wFRrai+ysFZYDNBoAPg8CPV6ztEffpnZbt52knW6bulOTCnrqpHUTjIokgLnx4KQLAFuMpLGfTcE3EVRhXr+Az0hOsSbbfQPu7cwbtViJLs4JTCMjYMRFEYEUQPWaCFwwFTsyuACdH2PVvMgEIwssttZEf8IBJIqAG8/P/RQM0v1oy9jnQbz+DmYVtZa2XLCos+XnRQewIjDF6/ViLMHx7/2GRbzHa+U4J6lQNjQBqL5NFNZNBEICQ6CzpFgdEsbE0gg7F/MEpCSdwnquGWIndiFjGcd2dVvby7N329rZZbH67gCYhjFZa3JQq6H3l06QLpA7eCYJmKFyDGwMakkTLJjcqVMHs0+jBA0BDfg8Pd3i9x+uaC9PaGvHq1oWjYIXhqhPBgqnIeo2gkKIxqH1hbgv4iUcqx93YF71AeuA1elpu4NBJ4O2KdiaCH5RO2tgZ50zGIzUNH2ekIBaG5JwDVVPNPTu27u31WZrZel2gmvay7zgpszBcR/nly4WXm1NWWx9PArBc72ZUQ3k6OeJjQU32LYphXEOjoBVfA8wTXsUlkcgD/QAvH6uMJCc+Edc08a0wi1oxX6j9erwMS3Tr6sl7M03q+jbesqajYWnCEoawK8Iul0YWWDBNAZhDAxzaSW7j2vLSE19lAfTwasOAnBd3SoVV9MRx+OlA4+ABlBS64DmasDTnDpje0r1pB/qG03O+Nql7o9VCG7XHYoxuFhMxnW5IFb10Kq6lw56aZ83z41V/GabcsX1YXYNrLJmTHNBWXOYOM2HSICiV8kr5wEAFkFdJ1ynC+wvjmjQdMdDmfC4XOCpdItjy7iyzXehwBq1l0ny2E0Ft9P51bum4nFguq95NoD/FnSzwNTABtfq9etN47EkRQVSnMZ1zD4MQRUHunrQhfES1P4eQjDObHwN5+c+kRjgfpb22X/OdIFaD8A4gNWglQzr+bE9195RAfi/qI3AS4ZqbpxjLpFa6yl4mwmbTfWS1gGsiEwvj2IEQItijzRIvWogaavrQAfmd5HhRCFDNSyn6YKzl6Q9XVRX4xfOgINTBLEoHp9Qh/S/yPoaWFmt628RHIs6I3pMG/cIcvl3ySnDp+MvrjnVjYl4Hnev6JJMz0q1q6gNcoPZxAv7GEQqbjSGPY30FGRkJUtWKWF1Qp6PCwnVEdC+xOeB+gXHwG+tzejFaMipMx4A4GadcYPZwTJA0wFdhyWpMc5ScWxAahUJs9+DnSA838mB24SKZ6cneP9zr+PJ0+9XEXZRFL7sEurFhHwdV+AhIhFKyZ4IwdvnMEc75m+LA1ltBLQiIMUFPZ2FPuebk2ATcXQ42MU2TtT8zP9yom7QHWvTn1CDCsTzsgimUZi3AmB6YU2otbljvJZ0NqXlvt23bVNqtzr5ZYAcxm3I0AEwROZio2bTVgcrAh1b1pMu5lIAjjoA1sDCGGP6X7UlVQzkNKid4vVEr5/z6xbEYv+qNGTrP4EtHxvrb5z/O8i2ZWV1/S1nIUFTETgmhHeGdEj7zsd5M3cRLOyaLuL7Sehng1jaeINItxmsDhYrSnLclNk8jg2v6QRTaeQ79QhvaZ1pl4JdaX6fMDwPyiQbi7CwspSwH3H7mrAGdhWgc6FOjzbSUjCH50hadoBwsmNSo+2IDqJepiMePDhg2U8qws5jZ0GbMmRZByzLmbnuPLwREoRTW9PCLpvjArQbI6A12BP7fkhj3DoV2+5sttlGyB3wMgdicBqAjniF9EKvbmgO5NlJ4769q1rUydpW1ePrKdUOIgQmEAOtlNsoTTBbID4ea0pbUEdO/IxYFf22dnF0AlgRxIpMXn4X5x3qCzLwsbTs+y8le7B/aRkzTv0VVjWsaIOP1Ito9fGJGSwRnGIjUy2+n2XFbZsxQwtARRKA+xYYtWQjkMXXB4rO27XRj4gstizAdd15AIIsvEkKFjHmlDGvmGHy9O52CHwf1snZUQBwOS2diSaa4YEVeDgfBn9j+xwxSH6RVxyr9vO2TK5n5dvVzkrm65hZ4tqbzsaqDmQtq1Z6lypIqVlAGMhTQSkSsksIAFmKdQLaCmAVyJo0WwIAGlBuMpac3d4e6oTrssPtNOMCi5MoqJE2p9VF6tmyNC1EYvPusGSJQe83SjX0NELb+Y2E47e+SOt2gEDU9tgDoMVTkJ2VJARPTLieAJYuZLo+lp2LWSqotqMV6UIDpMAlWti3bf8d/GJ/dPGBNvN4/HzUaH2zdp/f8gW2cmZl0MXi2vAZtZu2pc45iblwPE5FcbfHPFfFLwqjqxj94tRg3acOf2MUpYum96h/ZG25ptXJ03kHwGUQVgSZ2G9PuTRn4I00tnpfNqCf1EEUsLQuINn1ATr9mPoAsaokP+vjp8ebCTTaOSLVWKMjvarhzo7NljfG82CVPCj2zr5y8aDHJDW3OzGTsbZqSwMNtzbBPq2D6KP21YSARYUcD2Xy+7qUjGPJuF52eOVwhT++fYjPHR/itfUS12WHAsGhznhS9wGsUqrzjtpaaAaK6iLh2CZUJDzMt/jgU6/i+N420G1bEtT9hDZt6EomunvC1joD5vS0ju5YdECrf3fn4tz3a6NjEo8fPh8cmDDJpo34ISfkk+0BoIlN9Gp4Yx+baWc1ALUksPTsWtPbYdPet7+grZ150GlHosnXJb7pJUlP40roIuFdOPwUNFLNkQ5HEXQf+zIyrSYpg24Wv/NtfD/+6yLqw3bSgbNzC5mzABe6KDvf6zFjyiX/3b04qmG/7Tm34BxtDM/FfjG1PO4jgH8W+6zbc3xY4bQfSyUAGV/tzhzbGDFP7hjEe9qF5Pt5Y/ognxOCR9ye205h/RDHQwBdDJORBXHQixWobtcZT9adOgh1xlLViq+WpkEdr4Lk4J8LEiOIWNvxd2nF07tblMsRwGkiaDmhpdOV89l59a3OtW8AaN21vQ3LSTv7Gfe5w86d7cdgx6J9GW0NQawGuM3p9/y+vZtbMp9iDmvE6Cuw6NFNmY1tVR04SVJV1qIyg8LWu5CBuRnBHvomTDuMAFDU0JpNZzb2h8AVQZuldssTsyn4GQGsCIQx4yXqE/drjQH5zrBhGuWFMNul/+CijlOS5sHxaAP3aXENrLguvkpHz2ZhKibHgn9P/bBx8btPy3C9WcYxZpobx56fxeAEta2GjAybONaa8GRRTar9tLr8yWLpfYAyoghUHWt2IGsLUHGsjiVs03Q+32XtG6so8t4eyuQVEd03DcDo7apA2LIBskrpnwHAuuhrLQgoWI8ZrVj/UlMB+FWANRl7qWlxpkDgrTXhuGY8Pu7wudsHeFL2nurKVpr6R8yuim1K1ZlKDvDApuMISp0pfjUUxAptSCfcEAEk2Aa3FbyeOLFvtr3z+MBg12R7HPouqQNT1C3WwI2CX5xqCKpJHY872MYh+GLHbaFwFvQ9tsd4k3bPzHoHWgRtzjXmcfsPIQAZGVZeGz1vna+jfpazn4yVBeikx31Py8EmnwQBDFU7mNMeJ9zB2Aw1oTugNEvBsWUQm44VFs+JFnLxvR0fMq5Y7XAbfXFh2TYZWygP6Y1M86NY/tGq8cV9szHBiNTPUlAkuRGM6ZcZVQ117sb7psydkp8KJvSyt65zYpEPGpKEXja4eP8sEoHsYvJAoPgKTsCp2tKJ+CKZZQTwamMefjdWPO/q0TY1MjQANVXUZVbj0tRAHCftE6NaCrpW3EIjTSRqR004tmObcCELPnj1Kn7zuRVlnpAPFt2WhjYnYJ56qqFN7s56YvrHRjtLWus0YdJvHQSDVw0cgCg3DCFKwK83kW4YwKVRgA1fxfZDtT4JYLdpAOuk9Ek4/lg8NaTQcUQXa2x2gqbjw0t2p+a+3bc3aUw7u6tFsB7geqBDSR4Ma13wm1pVBMMVGIKLngOx2l+PznufWq+qp+ekvlJ3VCJ7qIF6XBZ9buOxAACCQT+pAzxd+JUtniOCWnoY/WwKfWOLLC1qM61hfBkppgg5UzNrG0uDKxDVoaWM6gCSTjHdOfL0RPT1Z4xOs18N4kEvoDsOpSWIVB9bXtMkPaymdiI7hKUAhqXMb5jFrDLJtclQoTEw0OJzEvftrDMDsiqdQHgQpraEmgUlJQM+OytdpHka5JZpF1tpCRMqnp5v8ZmripYTUGg3zEakmGpo/fJHvwVaUz+us4Il7Eb7AFtQb6PqtDnh+M7I2mynhyNguUlJZJfoGLBPGI8ldbQ9ve/WZUa6ma7vB/Wj+SXf25p3f+P6mCzPiuzMHaCzilYX8EwOVPGz2YscJVu3ao7FAtO+alVTjkXTvfa+VtXzoXX2FmApdlIxSw/CLzX3/WxNXqWzZzknkSlGcCv6Iyx85QL1XNvXCUgahKXvRJIA/RWm7W0BsIQe5KZfFNMDI1hF8I5ZJ3sDx7aZIawUPpABBLiQBbdtPg3W94UtqoFXfs3ITj7YpxWpNdyUnQbATSfrUCfXtiJYRbBpZ0ysKaSu71K/5iSq3ZWaglfX6w47q2DJqpf0V6i9RZ0rFsaas5I3DkXHhX2ghtaU9PmJtpHHYz+OJeO4Kmt3ThVzLqg14fY46/SWKtY1q9i7Ma4aq+YBaDWpD7GrwEG1drEKUhPUXfXtl6Ki8cucjaE1o+RTnz4Cpu6/p6pz82buf9PgtMiY/XGuDaxesyE1gEkB/BmlVkJsY2tvWsg6aeo3Nen2T/0o9aHMTXG3xZlfvrCBFr2iOH3YFtzXPpcG1AlDYS1UtYkiTRl0FSeIVLtPM/zf27ZphYOuVEuACRNS1E8nW53cPIIdFplREwXorCROygkjC0uBq+KgVo96aHUOTny6v64BGZHQ/WX4u20Rjd4FcIz6Wr0i4wh6xEVybJz4Z1mdsXXufASyfCxtLOrmOEubOmPIHmnqwbDCSWwxNSaKKO5k1WqGDkip4zRL9c+ALtZby4xLRFBvjOZw8RCN5cEqG0bRd5+86+TORmohrXIDYq2tlzqeUgEqcGuRDgfPgqFYVj1/y4KWKi7nBTfrDOBKDZMU1NyZFkvKuJAFD9LBxqtqRUmMRocpos/Nj/Hgi65RLp4GnnRwpyVBm7PqQ7EuuMiYahhzxbc0W6PIOtWViD/QUw0RDEcwIKxm2DJptIFu24IDECbpKMA7HI/vq3VNoEKHNkn7A+nbCVpuvYJHtS9VJRot6X2ROmpn3bf79mZtO5c5M8hWEJ6qBmXYZNQ+X3HREVhCW4Aiwl4KaDUXFO/n685GF5Un4ycAbrbWmoJNOPNTHVpkLxE04/HINONnW12uc+LokUFwksqMkUFG5bpYSZEhhhjxjmXv+1iJM4iG6wn9I+AlCPpWBKTMBsWqttwPAFrLmFCG+8a+KrvJrndjp9IwQXWgiKk6Ig3SOoC3BbHI3gLMTjWLvKM/e2T7aKHaEBlHw5w0Mn+L2a6xokmxKntqsyazL+xftPHaDz1XQsNVPmJ+sKBOe081BGzOzqJ1SrhY9sX4GSCrYQwT0zaFtIqzdiV+FoEyvRnjec8BXQ5whf23x+sXrS83wRl3VpoBZKmN6Y36w9UPGgFr/e7ceuu+vbvaLMVlI1iRewuuxFZtnRznntoUCJikuNA3oGDX3GLgVXBjFfWA0yqKBIxmKbguO5XSMMCD6+QuOQI7xma9HmxCZM1E3awZWkCLovDOsjXw6lBn7NPioxAzVmJ/mM0wVNljgB897dA/R/dD+BmBK53HOgurV31UTWISDkhIINi21BlzWhVUMbYax4HFpziOa014XPaeTaL2fg79UcDrSdnhWCf3C6hhBUDXs02GawcQZExWJNHsDqaWepACmllAJhcFD5lu7s9ImTHnXpn9sE6g3uJak+suEri6XSfcHGfsplXTzddJs06MeVUN5GhFUI8ZaVfQViNhtKQV8Xh5uarvA07RAlkFLSXUa8F12mOeC6ZccXM147rssMkGHXy8tSYsadT6Gm1EO2VmnWmDCDxg87b5Sq75FQIuHtCXIfgxHJOp8lvB+cEenZN1Ode/vr0UnSciQ8ttUATI4nfRnyL4FeyU0Oeq6BUhK/7E7tA9mPUONYIngD74Z9MA7ftYqYLv+TdWQaR21raS4RKOuU1JJMhVIMP5o0j6VphQ+9zZWXqu1RlV1LZidbs3ExHdironP+coxMjjb/W5uA8nfI5p72u161SwS8uoko68ulYY972tsxu+0hJug4gjKdc6Riaaa1ENQAX89W9DbVrJsDbBLpWhOgswVhdZWh61r4xiS+CRzCokVQQ51NkXEMnuy02ZLfW0YZ90UTHLCtTJK5OwkogahjGysVheOnPQI0UXgKcfJmlY5176uEBQJeFBPeAiLx2wDKw3QCN8OVVcpSM+8MzreOnBM7h4WTWwIB3MerNJ3XWzajeG/Fyb9KhBg4JAtkiXKmE7dKDK8tjZbZ0/DRzrl2IRjTvYWc3YFy1ur0CWNMCUnwE6FHdG0M3Nd3DLFklIyLl6KXrcQTu+b/ctNjKfAHX+qWMVmwMNDnP1fbXqoQyBBmpnRUC+YUwBp7aUn6P1KHospx5F0j2dMPSNa5uYEk/AiuCaGNBwDqCKbSvqzqvdronYj+joxH2Y6gaMgJrreNg2tKGAzi+1WSlwOyYDDYIOYEkj463b+sZj1xi46qBRaeJpeTmwmdiYoilm07YAVAebWNWQ12N6JRBQhB3QeZ86WpOl7QgaViTXx2IKIu+fB45b/w7SUGtyp4OtNE3ZFwEq2RhSTU9LtSEn0Uq6qgkXQVp1osUcuKf2t3g87y1ybJO1qHMSq/idb0SIBJvHIFwQL1DnbtA52R6WAJOdfwCUHEjDAHrdyc467WHXQwmOC893lgnWt/A+RGFdSeH3+wbFNe7bn98Wlx/JFkNc0xNcoJg72TYAsNQd1jpW7ltqRjGm1D6tQ6VDFjMiY5eA2cN8cMBFtauUSTRjA2BB2UNxDd01bbtPoGBYB7uYbUINYrZDm1zChYSBqD9VDNBaxCoVBj3hi6TsKF6XstBGdlbUyEoYdcH4ubLERpd6W/lQhemnAF5ZSvXmPlazIwXAFmQbCAsWbI/nowavV0FvluUx9WJRvO+HMnk6qrN/a/JjLyVjkQ4gsiDH0QTgHciydcWxZOxyGaoSHtbJwbPFUg6jjwIoKLSWhP284nadsBgji2sC9mtdM0pJao+qoC4ZaV+saBQguaG1pkytKmjHhNaSBlcOCWkR1LkZS0vn17okFGm4Oc54vOyxXJwu4kkwYYrnbdUMl9t1gqzQbBGmGiIEPcaDdB2tN6rkftfnbpPgAQtP9hpKr0OrBApcCB7BF9L+2fjk4ANFO2Xf2TJh8H8Q7FIPHJmvZAF/2i4uxBzcggb66ZOBKYa0sYD6R9J6QOYttnsw6wtseQPQKBto9uoWngKYNN1waXkwMJyEYaDOVvg90mIjcwsYKxUSICGdVvvWhu17n89oXHHCCLTZkZEVy+pqZMG3s/hD1NbaViPUCMQYOaawe0ZgYRHYC+BgT5WM4JuOC0Es/xw9Dz2Zw7bNu+9gjE7ItYrTq+k8Ufgy7rdPK2qZPY98NYO+l+qpfdHw9Oscf5CTVCDBdK0UnGQlRYpuAthExJL3JTqcfH9O3LPV5MaCUY1SEp7ITim7tt/r9ro0wdPTrV7/VIYIUzaAMrcRPF3ahH1a8PzlI3z6IXQBbSVfVZg3oeUEqafP3AmNNogg6pJceEEasQj6VZwYuxqv/iHdVoUNDVSj49/gE7vvwyhBAYwd37snAn+8xK6t2vyb0KmxaZzwUaGRn2lj0apoxMgm6waglISUWtfXum/37Y4mG3BH8c8AIqE7MKV1Jq6L9BpTp9p2W+F3Lh6j8HgLc2ZfwPdUxK021jYF8lzKGH92Di61NDKyzgQumMonYYEfAbvIHEsGcG11rghwdRYWvxe3iAzUxNmK47K1m7w2ZTeZBqb3Y9ymtOQwC+0xvyNLK+43GRuYrKvaMlI2cf0AujEtMI5tbHweqGvFgIfIxkY3Cdfe+9JBsIbmjKxwvhbskDNUm+sDLshIqeq+DThI8wzvfVp1Tk8LVmQAS08zFHWcHOi3cZuk4MF8xOvUOjA0rQk0ZTyZ9sYbtTDvQ8hqOmMTwmJ9wBK3Ay7DQI2f33F+B8FCGwIv7APT1nnaCKr5AyUYcnURDtC6rWl0UO5xrL8QLVuKGqBi4VsRcwZbmXUwpeJZCGRpca66KTtnM2mAPQFV2b70dTKqsYQsIG6gT4L6KdOmIivBp61PQMBARbd7WqFXdjc217ZtAy/MdmF2RobOIYfWqw8CvaKhr3FRncnFDATYvrN0ADBWgqSPFgExVlTM0iVPom9GrdrhGiAD+4zHpF8RNbNquD9T6npkZOOxKjqzQLKojXp9udBiUVwXmMYudavmVFCjZloT7PPag/8mGk8dLOqW0X85rIKbVYP+t6uObWpabKXUhKUkJAGm3H23JA1T1oIEh2Xy/haCZVU1s9zeNBWCb7mirUnBrBKKXSBgQg2Q2bZbTcs2zKFtSShZ/cDbMuFQJ0s17PcrSfVsm5uy80IHS0nG4g2T6nZ+fSvC7ndt69UQuw/j9khGcMr7EE/Dyob2Ge0JyPYtGNlhtE1oSEWUMcV9Oa48hoTXCO9T79vwXVN/SYq5etYHpGZ+lhjRwOwWFQPeYrsHs96Bxgmoo+W9siFztkkpPZfypk5GEFdsPS93RhdC9KhBjASgRwZmSwWjISgQN12cWDlZR0CELU6WTCkYSuh6JcBJARpJ/neIPgSQahinjVECMPySooZWrF4YQaxsv5YCsQlm8nTArbNBEJFjlW28mXO+tIzcame8GTjl9wUyVHKpsBLFFtmgYT/UCavopE6jMktBSsoSQwUgFakli4Z14IugGUEpRuAHujdGkJOsLPbznI4Jjc6yZssJ746mGgXBWisOy4TbdcJzVyPgScHJ1+sF5rxiBnVzKijaywqaMyqe2z/G8lRFzQqoc2JtOamWSe8gAJv08vhbOEt/Dd/p/hidkRgyt88dhBLAsOOhdKxHNmjTgtHzFA2gpypCujMTaLY838DI2lBCHNRKUINULAUxXGYpDHPct/v2xi2K3wJwraqCXimPjKm2eabU55YBOPL0N2nIrQsBM43PwRhsABiY6K4FP2pYqLsOluj20fdm40+Y/U6tz/O5IYAsZDHD/27TC88x02NlrHEEtEUNrYo+N0YLwqTAauMcK35tQbqhfDwUuJIGFIHrUhHsaujVc7l41GtVO8YpTkGiNgCNxcI1UdA/o0GSOYUEaCBDpUpWS3RB+NZt/8jqCkEEGAhn25Fp1+8hRcYFpYoystgHt1+CVjOqqKOy1oSrOTwJCUitIreK2zbhSo5+jwUE6yzdUTQl8SofUfaWohFiWS0xbds7aGCVnAnw3o02DbYGd27Wt6ct4YI9bO8OAHDah9iFzevoMLylfTA6Em7LNj+Mt6NBct/+fLYoGH5b50GUvTQDbVIHs4DuQ9yUGZd5CRW29SFaW8IEYEEIskMwWQA86igd6jRUCIxC31yb5gBsRV+EAWau5ZeavZoc0MF5IAS523b216wWB45ClcO4nwJOjEj2Rg2ryMaijMm2UiN/f0Ml+UAomGV1Rm8JPuI+LYCBY0ubeup6G6vVx6yROF7U8ErU8wJQoX6BphPmEOwm20z9k11acZDu+q82x5eagDT6hlfzgtXALn5Wm2Bdk/sq0fbHioXJbNlxnVCMjZXJ0FozSlW7VmtCSlUrGG6ek+NxUhtT1RrnXFFKQrWARTtmnWQFwFQVJDkqitKkAZNOwO16Mn+g+ZrcKxvmHjBZalY9YfQsmjjubAWqa7yUjKnY/m8nsyL6QWRrhTboBqODVWJpeZGt+4Z25c7zowdtQhCFXzFAM9jOEISRGrYj2LUGf0t6AMhBq2ZuTtFKk2RvtWSOWKg0iXO2703aPZj1BTbKrsa0NgBYNlAOUwsZDY0lYUmbRdM0w1jpg9vfWh41gRxAJ06CVhH174YkoZ6BNuOErJFaRgFWj0Qwg31Lk2VqH8GjgmxgUjz+uBiO0RcFiLRE7uAABKDrjRhZZXP9i3+uoFq1/sZoCscSAOa0ADUw4mBpHVDtqbVtITj4/tTAip+t+mvswrp1m1+dQYHevRmom0qDtvqiAQB2aUW26EZtvbqM9rEOKYXOALOF/qFMYGW8hnHiZcoOHaLjccI0FeRsjlZNeM1FIYsblIu0aBWqNLnGGgsSsE+1KbPoYT6gPLOiTjOkMQ3HJqopA4czg0odLS66RQYwST+3CRVmsziZr12XSsJEqWQsA9KIVtHJiCBTXOy3EAkQ9DREAmN13Ne3C5EIMQ0vNwoNwCpAUqOkgBc9neYOX2NU/W3Qae/bX8xGWIjUe7Yi42KeelkEQWLqMVP6yKRtgSVEoIaC3BE2EnQAKzKhtrpWp32O4AfCQrfa+kaG32sEiTy1z8EjGRbOeq3jdUdwv/pnI6gVga43YmQR6NJt+3c8JvvLaxz1yxh4yVjRUzqFfQl2d9sEnYkbx6IaYqJaVzqgg/aZAT8MprUmPbVINM3PmXZC5zb5nOlMMzQ/FisX8l7RXgzaWm3sZyNbqgrqmpByQxIF4WpLOCQ+i/3pmEQFqav0kRZ0ZhYLEwjUTrZ9RUu5O6ICtR93ierGlHWzh28IDEWbYkxaZ2ohOBNn9n9DQCs85dIPd+e+5467PXfsowOJsj2DIFbKvG/v3nY01g7lNQC4U8yCGhRbV0ZRD2Lk3AEQAlj7tGKGpj2rhuzqa1oGbld00CUWnYqgFs8LQQ+4W2AUoC8yrm+BHnyvrQdxVaKj+whRP6sXkBrBus4g6z4bAED0HEw1vEpHB4AW81UgnZlFjd5yxteIfhiD7V7ACxgYYdpHZWzNdn4dN9Uwm9OKtLEDzLpQCZLm47xtGlQPgJMF6p8sO0/5K9UqHoaJb5LqEitAr2rIwFi1v1OquFlVB2ufV2VQNZVfOZTJM0IOy+Si7znM+ylVJNH5aLb9aYNL7QVWcq7KyqoAmqCsGphPWRm+2Be0YpGE1ax8tbX8DECq6jalBmRN6/d5s4iagSLABNSacL3MeH258AqWaLDq7mM1UEAza25vdnh6QUCWTm7FW28tTPbbrzbp73Qjov+zbW5LLCivHwa/kIQDakVCejBEzgWAzKej78O+kOVl/R9E3qO9bOjgXA37FfR9irLGW24aCbwXgP/f26hbRc0rIuoxLZCTrOondWppXHw7iwc4AXoy6bLhPAAcQY6RgUh75XG3aYqR+cTv3PiYweN7AlzUU/K+ChzUimW0o7MxRqw7CMVt+TcagXg9cYyVrQbvs+6jEYptKl+sZsgo1WjcmosIMtJRnfuo1OtZNK//xqIcHNdJrMIipNN1k2ldJZ1Db0LFFrvRJywqvY6qgJxRgmsw4mR+rXZOOjjUNFmrOnpLzW5MuoMhWAspyBr1qFUcQFkOE4oh4SlXvH5z4fuuuxtgp9T0Cq0WGUHNnRRcyIJDUEm8SIsJ826VE22yxB1tm2qY32Dy8lrjcOYVENbuQFy765+qHxL9p0MxCJVw0o0RCH/8mqUswitzxIm6CXoqIgRpbaisXpjaqUEIzoQTANrw8X27b3c2inZTS4QgRgQ1XH8KIWrtzz7nvL4A3jK1nC2MsaoUQTIXBLe+xBQ8zhNbgfmYrkhgIiatdUYYnPEVWWHUZnGAAx1UimPTr4Of9evi30hY2TLVuE9p4qQfLU2v4Mo2fREg+KNnT8aU2wYTmi3um/WzE0rFmcVrS1iqWBHVPkbFxm+tyQpQmNaVVKTG5LwYwEgDyMaWjZkBdOBr1C4P4scB8ItC8NR6jPtGYIt6X40r3iaoa3eGJDUcMPXsiWysibSCGm1qL8xpQsOE6s4yoPYv7QzMGm8EgDewH7GFhfXdG8BtwQmzN24WQSXals13b9aXEXbCkFoYwashih6ZWGe9qDa+lH68+/bubTspASBKvr5l9buCNARmq81BfFYPZdLKeLCqhqm4VizQ0xITerpeMtbnukmJ0+8s2wPFdbL2pteVbY0dM0QiGAZ00fWtxm9Cw635Ug7GSXV7tU+Ls6uGwH5gdo12q2FOR2RUC6p3UCwFf0nBJ3i2DdCBLmzSCXlu+j53+TZdHF79FPqRW8Yvx+kyK2OKqZu8nw4Aimr80mfYpdVTDq/X3SB1MufirNm1mkh/+IyVBxuAOVVng3vaIYPvG6YvQSkAXnTmuKrGZEoVxzVrNcAmg/+2rsm2UR+tVUEtGhgRW1drABgdvF9t/6lZSqHo35LQ6CusqUeleoRGsyVKwvEw4cm8w6vHS1yXPcok6lsi9QycoZjXhPWYNW3OgSVL9+O/t9roB1FXy7uo1yW1AiW5LpfV/RrS+PQ4FkxrGKol8ufVYIyoLGe2adACVehZKcmyUhq6RIqdr6XT83t2TQo+lGDUEg5+V/TbfLh4f95muwezvsCWzzgHAPVJ+kTUIxR5s03xHOlteVmgg0FR38I/syfKkf9AVdW+9Ql128dtxaBt21IqydTq6XzFGVrb5kyqDSvrru0i+HUMICC3YX45I878nHpdEdBKG8PHfTmGQGfC7RPF4rvAIw31sU5Yg6OFBsypadVIO/b1ukOF4CIvFjEpmGLeA85rb2lfNM2RKYsUladR8FRG68Pa1LDsMsUvdeGwmsGh8CKgwu4UTGwwYGtNqCV5talWEgo1SZCwLBm3acbr0lTcPq94VC7wpO7xVLp18JKNDK0YcdpfLGgTPDLvbUo2UdchMuAtRiSMjQX7xCPORq2VohEVFXqUcUKMh2S5WdjPwsIUPJM/3vaBRHZWz9Yxg2fxdDoVBNSYXrL1qQh0NekaXtzPo0EJMhmrpcm9g3Hf3rQREt4yoLZspYyKImnYjtuQzcn0dwIwQAeDIvOqa1GxB10vS7+QYTtnWUXHI8zB59qIDWzAsgBonROD75bivLj7drtoiVZsUy7DWle6sDwZRWQvEdAiRDeAEL4PwR1lv1FioDXxtI6hIAyjpBCgNYtic5tqYvBqY1pLFlwZr5FpHX4c9qX1VD3agywtrJ9lYLR6KpCBjg1knuvnvhYRsVQROHiKJu6AeIXEqlFe0UtBkYS1ZBxE7ciUqqZspIxidjzGNKhJxmuapGCaCpBmYGtrElGgO5Ckrcg7ANfN6vBRn9/90Jt9tm0DaLnQ+7lutP6UR1s0XEkLH8gIaG1Pq5dqrk+Lx9BnqVWx6pFv0P/79q5qBEIYwGXBp4rsv2M65l5hzgKz+7ziUCZl3DStRMesAKab9ZTCubOAbP3LNSy1p5QVtfrnBQnXRUEtpp8Barfm3IPO9IcOdcLD6YBqrykaTwDH9w/sLPaNflgPfrfAyConzCmOmwb1oT8wA71uMTtJIVZS1z6MemQFgr0UXNedZjWE87j2Vtg+VqfPG5Austooj+KB55o8zXOfFrWTrft9h3WHfV49DfRgGlY5KePpNgBOUe93KdmD461q2vycqmvtstIhReBbE1Rj1CVpOr+vqpM25eK2arVj1aJX6+mHFgQ5HiezH2plm6Wva9AJkFxRV/Nh7C+okWgAVzNgq1c3FLCSuCwBjMlN2VlV0FZB2qlP8WTZeSB/p6I0vn7hb2StGdfrjHYzIRXAA+1609xvOAG0ogg8mwjO+kVnmgS7JC2e68zGLWBctQEMysuZ7Xnc3P0kDZ4YEcB9KbUx1cAw4fU6uGXg1wr77cB1zNw28bHvcVY7tn7mQ5bQ7+1baPdg1jvQCLQAKvIOjJofEYhaah7AliqdFhvTEKOO1lYMkBO0Rhs0Dqsi3W0AfyJrKzKxPD1MTuEssrC20ZCxIkdMU1T0Wid+CjrSgPaUQj1GB6I4Ni6UiJEWrGNYNk7a+Avcpj8umD0SVTaGgvckpnvOSaMPBBAVfFPA6kE+qhFvFE0Wu3f9nIx6HMuENC0WDVFBd4J8Kc4oIIjV+8a0HgJYnq7YetWPtSZn96HAq8pEFpZIw7GwXKw4I6yZc1FrZ3DUxai6qWs3LWZERBr+qD0EoNGf63nnwOHcKUjOwKugHlnD5W7BMo2Tj4rBv53J+m7dLFhOOv1PYTVB9+CCkxCPI2G7rTPAidi2Ecv3jpGOk39bD5Yvi4Fo0bDxGB4NMgfDgC1ljDWc+Snet/t20rzSX+t2IYqvRyCKekcOKgQApSGwoQI7qYMwI/BFsIIYMZ0G/o2srcjEIvh1lxB81+bqP5lR5H4E5BJ3JAOKtiR+DwxAFMdGpAdwtkxeyFgVcRt+2aY/NiQfkyGVMoB7HAey25oxm5n+LFCgZpcKFqsIZZCECcZHoM2Ar5ogqRpIJqh9nRngkT6+8TrJrCKQNqRvmrNQG1l9ijwRxIsgmaD1qoxmX/SN7Vvh4BiDJxLm1LomHC3A8KSpkvtkjrfex4QcgkLUgCs2lgIVD66bm6R+i/govnm7A/DiV+gL8BHYspFoo56I7vDGpxvAq9Zfnz3GBtA62/W7vttsR/av/XTu27u4xeCxAlpzByk8S6OhVBl0s/Z5xVKz+yicO7I0HH2NnrA3KQpmLxzN51ltfawsluSC41H7CU0ZXARmgJ5aOKcOai0tY0F2fa/rsrN0x+LB6CxdT5jXnSx1Ma752WKmSqx0yFyCfVpM+3g6YYVxnzktA9Oqj3knEPQMExl9HvfjlB03aAHbdjH7pb8fA9t6vJ7iyDHiGHcmnQKMLCrF+7NLBZiOeO14qcCUaVUdS68gKAZKsU2put4V21q71hbTH6t9flyV5rEUBbTyVDxThPdbwECL6saua0YtdpXNUgorlF3V4IC8CNBMd6kRdYlFMNbUX08GKK1iix6LDKyibuvcTKRTUx9rVSBvrUkrNvoYNx/ftSWgTnjteIl0m5CWNmaGvCHjF6e+0BsJxDO4H4pjjT5PPE54GY7VQjBEfZsOakkxcA1yVoZl+KwEwCoeLwBsSD2Thfa5B/eBXkdOd2yCTQV7bOzfWzdW92DWO9jOVWEi+HFb57NR4yUg3IBNgGGzGKVwJtOGreWg1oahxX6caxGY0PMSWCKVVtlYKlLbJ02l3FZLvUhg9cCCNBrRCEJZWh4/30k5YWCRZeU02wicbA0HIuU3GB1UQDbC+HdoYHGcL9Li7C4aDTsgAODRuh/YUvzBxJQ/Rqn8nIBXgnEBTLRhu7Vmj270fdXJONbJtq8OUDmDS0YWl1ZENIqvaHlZz3O3iDvLpPsSn+J7hYtx0ddrwmMA+WHTqh5lwnXZ47rucSELrkCdthWzrEgy+/gWiJbkPTejxMk9RCr09tDDaT3FsGIzwanjIFUjAzBGVvDibBvA0zyqOooKVIlHGZgLLhapGCbj1HyiRrVTGE4bjYdPzkzxIFiV0NMRq03+dOTJyLIIEaMWtQryHfnp9+2+3dXiwnZYgDYFu88tATg/xxTAuFiIc+kWWIrAl4JaBGp6OwdY8XixuaC5A2sd0IKBQEy9UCbUCIgRIOpjMY7LSkAOmqZ2wsAygIbR4Mgu3lrLynFCB61cXyogE7Hq3rZxnCcpfj11gIi0s4eafVrTOZ196ABZdDD8Pto5GpLvH20r2VSxddBMxzVJKDoC6cdzFpd0fS3RMViskIiDX60zut3W+LOmH+h2glYzjgDSfsVqAb+lTljShNnWAcmuI4nBlGEtn1NDOec0DPMoV+xngKdzr+Nhtg7DyXG5kZwHshhYoX3BBsSinWpxrE7PdxaWs++lX545HnZX+ZobUMuknfeX7tu7q5E1v1jQU9PmVMf30ir0LVV/1zcGTiUxHabhadN15IqxINGcjh6kZeVDwKQ5vA9tSF0EMABYbAoSGZBdeqra0jJmFF8SRrkTBpbZqvSgynXdnfgQ9I+oWRv7EwPohzo7YypqZVE03/07AbbaxYOub7Bf+j6dlTqJjVpcPF5Oix7TmKwE9rZSKaXBKyhGHbN+XJUxIdMuApO0JwyQkIV1XDN2U4FIw1oSdpOCl7WMbKzSBBNZeCZl0tABqhqiDSVoH1EmoTUtBLCsWYtS1RAYgfonAJRpZetlBbQq2iGbHpQoUMUJ1jI3huI3cR63CuNNan82U9PjWp9KEzwuexzqjIt89Hu6TytebZcKZopWaky3gqRib71wSAVky8iKbSut8oYRkO2+6P5KgS5ywqmk0YfpayW3PaxsyB1C9spZUIy2Jfo7PAfXQi0AWraNNKuKbamQEPWFEqDVEc1eCQQtN6QVqBNUOiH2IxrNt9Duwax3qMUKGWx3AUk99zzkl6OdTPQxyg6MrK9tPjWPB8CfunFB24G1CH4BfTJO9l1kTnkUYTOhx4hvaWJ5+uOE7sc+k4LJ429bFN7bXsNwjeHcWZqDZVFQMjLRljp5Pv1SZ8ypuAGcU/GcfjoOa1Na7oNJGVoAcCwZq/Q89YtMvYDmwBLL4JKW7eMfADhGtio0KsLjMXrmuiVNnIXlx7Hvj5anTjrwcZ1cZLHU7jBW6qyAoIwg5YZaFMACqbhJp7j1MOFwodG5R+sej8tegdg83isXawzXNaU6VPXjWv8NV9AnEzt6FOLMfhIncxe+GQH8gUrLv9Gg+ec21cfteXxs5lXLJ5fWJ2iyrcSOuzVf7OugYRj60RqAKqjptPLnfbtvdzVP6TsDbGxbr9LX+SpaUGHcfsueiqwvMmNi63NbB3jOHatBGcIxdTCyvTpTTDUJyRVK7ZQtxtcqRB/7wnEZt437nhOnpyB68WmnnXy/PVYKTkpnMPXji3S9sSimz7FnuiGvn/3XiHkPbpVgBwC4kK+CgBr9zrnb8+1alMeeRLUgW9VFOo/XATD43SHI58dh1NyOX6qNl0Xkkzl6kZE3TrcW/aa4B0ui876WhLWKMTkyjpZ23zbzfkx1HSplxYX4W/EJBptCJOjufTmv38mgwh3f3dEPxzu335/ZZwiYxA7RNt0BwJ1d+9vxW78x9+1d3qiZe1127mcQIAJ0fbvUjFnqEKRl0DZBK20zrSqjDXMTU/ymVDB54DmD+lv7tKK0KXzX1+5rzbjMR1/LQpL/dT8DXQuKYBBTF8/JlrivZPNkLJ5FIsCJFIz96G7b5FrDMxgE3/pLySsaAqMeF4AB3PJj2/kOdXbWV/TbIgjGdEcyvO4CvWgT9mkddM/YZ7ZsCHcSYCnJ7wvZckkaLqdFAwk1YbEskKV2G7GW5KLtQPe19B6anpgz8GAZJJp6HjNCRBomkEDQ1GcpTG9M7quUmjUOvUqvulrFWVdeL4raV8NaHhowbkCbDKgKVQp9YqzQ6uLcb2d2NTdLZ6yeRnjbJjzj457wpOy9SuRaM64PO+QjRmbWcLPO3sK33rY+kFcztODMXXP5W8XHov0UBD3GvnYbbB19rgatWkgTymFOquXVEnoFQwMh3b/C+LcDYOHx5X19M4bbpt2DWe9AoxYJAAdN2EpLOLTpZAJO0jCjDIAWxCZttCHVMFaQcycjADT8fN4AStuIQQS0GCXYAl7bfRPSsB8AZ2SRoVUsYkJDuRWlj6mP4xjUcJ5uOC5k8WNEIXvuU08m7d5mWb0qCLdHS0AQ5uf1RwH6KFpZm5w4MseihmiXlyG9hVEKfaOljR3oscm7NDEx+TyMi2tgNdFURaldYDBcVqxgeCy6CKEgPKuSAJqCog4HcSGLNKymlSV9Zd5ipYhgMABlct2uE14/XuDV+Qq3+9kq5PTxWsJ7vxep3jmJtiTYkAHfvDWmH+lKPJamdXQ/Alkh8rDVLPHHl9eP4BRsnCGnyPI9UwTR0LL0aAU/j5N1hTpwm+Oxi2L6MQgaJq3+Ccbmvv2FbBRJB+CgCVssMhIbGVsRbADEGC8YmV1ow/bcP56rQjBt4LCtXlUEtMh63QIl233F5qsIKnXWVmQ0hWIhdqwOfHXdq5MxAH+yzfugxTTgDKNoT2KKJvs53IuNA8FZhcL8BL2SROAqgIrSmW6xUUA9J/K3+n3wgJd0yQIAg+2imHy8B10DqwvzngsmMZIrZrdqS8O4x9T2ynmtwdKrTS9rO6nGiGtDB5Ggwvgs/X6bZqxZqz+2cJ6CUf+NY3qnrRGcdy7esBmz19M5WkTl/K9s3g+sL9oan9rfRDsLm0U84r7o4JV1yb875xicBbEC+hU/uwe03tWN6zMVEi9IrXlqH9f9k1ElStNMgJjyRzmNZd2htuQaVZ6hUAm4JKCeBqVr0/TFfV43QfReXOJJ2YPVuql5S92nOKfFOTRKn6jOUpdumVNxQIsBAy9kEvyQQdrEXieEIl6IPlCvRqj79+8ikDVLwYIx1TD6OtssHQqK9/N0OwkBHlv6I225khf68XgPCfhVhEwUnnPbB3C+r8GfqKqLJgm1zVhan3ePqx5TpGAyW7CUDMmlB1hMGB6J1SbpExEUVXtQSp+/mwnI98rqRjyoSYMeAk0fXFIPfoRrr2TkreKC5FLEWUqNlcEb9LuL0iuGC/r2DWhz9VTFVixwk9WfelJ2Nrb9t7QYQYHXeXuckW+06NMwp7bWmVr1bU6255hazFQxxtc2cP9Gto4sKvd16CNFG5LCdhWoJhUTgyMuv0J7ZWbdHzn6PNBtpFpV49b9J7q22/duZyuUOWfnbM4ceGvtHsx6hxonh1gWtrSxakisCNdT4ZpNhtkmhvG4sfrhVT7Yj2p2sCsCXf08fBryMJHGCZS52Wmz32yJsXztdNtN/6tFS2iIyNo6ngM53sLqqQZGVUL19xTf26YV6nHVEHoKo026dJ6431YAPo4BIzdZKm7r7MDUbLTrpWZP+eMvKwkrLFGIVw38WoFbzJqTDhOFJMjZ1FirPtbohK41WTSsM7OmkJ9OwIoGYJKKQ5m8wgigGiJafSS7YOLxMLkDVFeBpzWkhjRXncCP2SZJpea2KliOEx7nPQDg5mLG43KBOo0VJ/m8LHUaSu6SZmod179vltsQy6ajT6z94YBO5gSLyPoiqBXPdeZ8XnY2TJ4dyDqj6NP6JE3dezpt/P4kFTLutwravgXAy4SzU9jHoyISrv++3bc3bwQbuNKgs7+G+W0EMnqacRehFegqpiK3vs/alKk5pxWt6bwkMHaWFAe6+nngfYj6WPH3W0L6GxsX6QTNPHgTADPONQpeVQO04MBWBD3Y3kowLzKqKpq/V3ZRB9riNRBQ6zaKABwF8T350PsfW6GjhYYkBWsQ8VUGV3adrCTwFaoI1BkVx811PgewoosGqx4W7ZzZEQMIB+0tft4Y9FDHxqc2OjT2fJFdNlYSo/OS0Iy1VZfk7J/m1FXtv2ubFAIp/filJHeclinj2CbXAov3kkAtp/ae5hnRH7y5rYHtExla8V7xrS2wpdnm8RzDdH3mfGcAKncKwuu4vduY6GTc0cVhvwq0KWznHkfYJ9qat4/y3bc/Z20LYkBUN0u/64LrDLZSp/VYJxdYjxqxT8rO2TycP4aiRk0ZWmStJGmezTClgr1U12tabT09ZrBoupzrDm9kRgC4b0TpE82wUBArip6TXhKLZcWMlsi2ylLcN2LFwri+1YqJvZ9bcIuN6Ykc9ijmrvvp9UaNLq6lYyokQa4U+rwNoEypV51f0APfzqYywGWSgozOxjpCcL3s8HA+aADHfIwLLDjK5Ay8KWva31qU+Ut7QLtYmqYi5tTH4LhO5n8IdlOxoLpla0wVpSQUv53Zj0uh95TNWiZjZRHIIghlwJQOSFPDkACsSQXdaRgBB6rQgDY1tEPSeY+V+ARoXPDwL7NUBGhzl49Z6oRjm1BbwqNyMdjBtSYsxwkPbqG6U9beML1w21S4+PTzcyLxZ/SyhLbWbdVmYbaxGW57oqTLmW2ZqujvzbdqefxMrNKhs7AarN/99RjID+ZnC3BFG1ns2isgdXMRb9DuwawvsCURzLK63lOVUwaWv0YZSqg6Iyv19/8/9v6l15ZlSw/DvhERmXOutR/ncW/VZRVZUpVIlWSTbthyWQ3ZhGCLYkcQBP0JCZIICFCTHTUECFBD0B8QSD1Ms6+ObJAwJEgNGyZgWKakMkUVq+qyHrfu45y991pzzsyIGG6MR4zMOdd+nHtsqE6tAPZe85EZGfmYMWJ84xvfyNCFoqdejUj72re3q0MFHfW95YtvtLMUzHH6aogoeGWPAHjt6a17o7IB6yhQbylJdcOABKxcAKrbioLhVzOEEtMGpFq5DGYUCQtsn29uFY8kspO0PxGBN+No44zGwc5pVASJopEhrSM1JI1YWSRDIk7J741XhVGQqveMwiNKUVKHl2oHaeVBGY99t7Tsjs3aslcLMcquXXMTJZy0YuG5yths+9aS03eteqELFOskTsSgDBVVVAHFcG9J91lZvkvE+Gq5w7lPm/vjz58x3dSuXWpxUfarCLbsoDfenm2+1s2ySXwPFjGDGg9WFpNPsj5+hlTtINx0anziveEkODgWkS6+ESjpcDHjCHAFtvnom+UcfEKP4RD3Tnkcn39eXvJz+y43Iigj1nSkds9/ZBVhK9I9WEYKLBE7SOX6Ejxgjz3wz9BgBg+HI6OPfon1qAMEMrFxGQ9gGlt7EXFr+7R5A5VMNN3sVoJUIkwAukfxxY5FVlbsLYU+t5X+hn01oMz1XrwfcTwEyBvgESNtsAcrsBHPy2yxATQZxnIa9ytBq/WxXRtdv4f7YXO52SPWtBAm0TczQV0H13gU/Eg69xvTivW7lPrN4zROquU3GMTMolMlwebkTg0DkrLuSNYAsexEDDvaPK0ade+cUEnEgs+lOKNs3+ScEizRp/bkN/imrdkDQrilm6WT+xMLf7rVX+iWYx+7dhO0Cn3JpbFniQaQdXM7jBcUPo/nAgzn4aqfsJMN+TmA8p1uDtqAvDIeIGl7xnCaSKp3X3pGppDxwSLsDsBZWMZ0sgqHznBiWceYzqsd90W5SEqZZiOUIEC+D3ZfernyPy5cPNBva2nzjRpChgWaf27SIRakFhZN3mS62DnGCod27OinRIF426dpqqFpW0WWl2WwAIEUgGvpF9l2a39iWznjSFV9woRzn0Z6p1ox0/s1f3Gl7AF883WagjA+fr0351Ywp+YyJUsvrrtrrTGh5L4BrJjJBdwPpTpoKds21JaRSBhdl3Vy/yNnHjiMV1e3Tgkpd9fK6muSgIcCS0g8XtMAo1AhQFQjUCURcQdAqzGLpHtiBfpDuiFnln4zjwqvgAZaCHQfKjqqXybAo4Cnlk1zbhPaY0E+q18Sij5dxQvcAPK1X7J3MG4F4vUzYpZq7sarIFzbjA7x625WjcfWBgQN4ZvbBftzg8SNtGqlQwOxbF/bL9plGn0wBeCqqyXMel5Eyq7DJ1UyBJ7BrG+9jclEAZYQQVj7iMYa8yaWQ/W8dmobYXgrObs6A2mbxhjLqD8lwh7HZ9/tS9X23SI8srK2FFlyXavB1qrIkGjHOP420mJV8QCr6EGbbW2yNpF5OT8SsEpZX9HR2IvO23upItIlHXLPWuOEVaO8Tmc2Zybci4yOF+Uikal6wGOd/PpYOqDpARRVujsb+MVbZhVo5JObCLwxwB7r7NoxCewpF5ZmYs2qi1h+OyBpfUst6CxVQ3pPG9FFF9/do9sEL9dNU4ertiv4gkaoa8ZSMk51wqWXzYLIruO5y3c9ydjXnlz4HDs67Kbtq3fcyA2P1TvMEbZTIjZgim5WARTwaMu4ctZIlw0YFEAtlklVIwwRc/Loh0cQtuKH6Lcnemri4DOP770vwRRkX0EZr5yl5/bcPtQMOIkV9GI13I2+kG4vi0mr7Ecbm+FphghsrvAj5hBkMY0kWw/eWnZsgazt73jPeop2LGplGTjWAHdMQF3EQnfgHTDYBj2cQ6KtxlZkjsX0R2MsdWItgBKrP25ZYPbeILwEdmBtc72gTCibi30M8Hth+jREjKUXrH3cO7sXDuQpMl792oQ0RgVWDJyK6TqA6nH5mAfjlzGCOHZ97PsenqGmoJakQe5T//T8bs1jNrbEotE4DgQwobeEloTd3DiNVPtwHauyBHMYu9mB7dN0dXCMtEbcsDXhZlBINbTveABEN+2Z77dLJ4yA1ZWDEjaj68+xv6y0cw5ugV5qQnzJwTtnIpz+c/vutkxjTb920Z+tfei8ArK+9vWrAleXXgZzihgPCk4tsfp6ErBiTm2jOWsyGrJdAFxy9RRFA8Ue6kHTD8f6+Ra4dUgVnQkP/YC7tPi4Y0sKlnmgtQNTalrBUWxFBJVioD8ytPyvAlqretbR97Fjd834ONKKNQBGws6SY03K+PJ7on1aWrp9t4IEuAKccOB+Ur92zfc+kojzi/9VUpfMDM6BIdf9OYgBeQOyjKFtvklOHQjPigjAZyxVAhmHqWr2h2xjGozntWwCZjl10MRYVwUcp4Za9WqyVCkkBagSs8igrHkIuRMcrBrGSr+z9fMqwQyeGLRI6iAX3q61k6zDGZD0NQNJKokGlx1vSaA1gQ8dKXfMWbSSj2n1a/3YZ71uCbUz3i0H0GNGNmaWzasxwGIiX0+xhW9VefcKK+8Bctj6D6/j/tjbm/dUiA/bb30bteeRoaX+DjFEyF3v1S3/R8Yl95DJgCo4mcBtXYf4aRSRs+H7vcewX7VnMOtbapMykC7KVolpE0mdDqP4xrxvIIjpWWUPDANiFT02Irg2MYdF+DY9I4JPpPsM4GdKdaTuaYTCtvEytzeeIkslNH0sr35FPVQ0yYjAl4NNPPqwY91qllbo771feVRjueCxfShTS9uqhLe0umzM8ZpF8XUXnERDT4SaMs5UnFgkBkMYWkVh8g4KQu4Jte+cKT9fMfpnpWHbQt1ouRSeiYuWzLW5ylINa8voLPuuLUs0vSWJtDPQm1Y+XLVPzRfnmsANsvjOLEKKgEQpmoI56nQQAb0L2+vU5nEvA3rUQU7xbpCFDrUxEQLb12NH3tJc5UJsJvCbE3DQzBplYq+7v9knqxPrFaTUQ2H7Wt6T5YabQ9KxFeEhCDXWJnYDwHzccCODFr8XA4quosjPXsVz+4YtUZffnC6MXfMKcJCKFUCJ/jUwgA2zTbZGZKiMW5hvAHgammMBASJ2RbsdUBOF3bMyhwxsSWHfkbJ+/SM27S7TX4r6XdZH4wgSDbDJBd0dYLk9SWwTA0dKhzs4FuQwkA0DIvfryxEwv41JR66RXQv7aymWWWYg0UIMySXmcLCCawbEu5A7jxQQcGTgQeZ5DEcSBq4ZUMYD7GqdNuvnzpp2yckZY12BL+5WjRLgTppaaPdHD9XHGEA83u+quUIXsaxjMMdLWHzjarJ/pve4p6uLffMuX6/tP24jDcbEYMZ7bU3sY9/dPpWdrnex9/vfa2Rf7WMeEWRzvRlGqGQIvSHPtuZPSmusGrQO1sgazSpcG+DUmNC4eFpaBLJMAB4YgPjSC0paxvc81ul73doYtF3N51A21CELeFNCih8A394yHSaSKuEF2zTCSABINNK+bT0+YRABLF1yA2pZISiM63QLkLIiWQA8/dDOVzI7yoYMIEyvukkVzBBBefNdRCKmYEpV9MswCAURdLv0aWMTjQgxpeZ+Qrw3I2vEUjjZwcaTAld2P5cuRaok2K7XNI/g+6oVC13IHVBxdgKRAFWmlRWrIpsPm3Us4o+wF6Ey4pLZilSUFdwyuCmllGiwcQjOvAIAnnn7uW2n4Ap1iG6WASYq9+GphJW8kqEYxeTi79HvSYnxcr7gkKow2XjCYzvgoR7wZjkCAO5JqsznM6GcR9D+yRTDpwAtC9bH7JQP7CPi74AlRPFTYBIQnBJgVDnEVarhPmNFxhP6oADOJdroCRuYZjZLl5Di80TgKvQNs2O83ceCQe5zfaLZegazvoUmQn3JJ8djWhW8GiwqZ1xpiVXQmIRXzrjLKwoGm8e2iWlqrpGFLQPLQAabtP07nahNSFAiwCbmOAyJMbCMApvBm6iE5ID3DTiV0TFThYnAA4MGmyD6Ux0m5p50X7oJQtlxYiXCuF1se8MZy9+KgTYASX5ht6qYxIh7ZGWB7e9gzfk+ZsSDAbH9e4gYjTTEoR9QNcpyzOuGuXWpxYGsSxu6X0UNBu8MBKDOy+5HblVDes+e+pAAcBYngrs4OtzFWNDctBStOh0lOLBMQNPUERbh+zf1gMc+S44+axpooDyDJJp2usy4W3SC3nrE2wFbzfn9ZM6s6YU3Jn57DAwoAq5ZWbvJ0T7bOBEcJm/rS1AAmWT3NsU+i/7BjcqGnmaYQ7+sqZAq3uh54mwdyzVg7eu5PbcPtejUS5WgtgGvss55Nm8PHo45NhpFx/jN2zYR/DJ7QdiCQay/d4KmxIMGCMZDO0rS9brvE9lW+2NERhYbiBJ+jJYmQtiCYTb2yhkMQkHbXJ9bIFQ8jvURt4ttr1cSWWU9gGd7oCq2CCX49daVm+mP7UXvTZyd4vZ270P/3c6B4ffOmd8GImKwqgzIag48jcIiPVzPeJ77qduCzaKlolvZ3Mn7uU2cCQFhdLWawpVgAJ3AXdIMTGPUKhrbGmGw46RZ2v1kJPD32ZoNcBQZVLZKx3W7MRdfzc8cwKXgI+3Jalef3RpbeO+BFB/zzjnAsEN75GsDbtmx3cbpxiGA89y+m83ZRRjg0NBK1IC3sppsnrRCVNZ8bta1p1Xp9u95m2lg2lmJJBVRUrLGGroTo3P2YHTy9aP8jWmKd3kd63Mds5GvRtV49vMbrCv2ohhRCxcYrK5YIOVg/pH7AN3BpT1zy1lhvniU77KyvcR/Sp4RsqrYqvkvAl4Bx1xdULwbQBZ0igGplJi4K/C1elbE3h4Ji0vugzPrAplgVX9FNLQErCzUHbiqPTvzLpFUrsypo3iAJqP3URymdQITKTAqy/iSmwNWSftdluKZH4Au61t2IMvtmfkm8oDIvDR1YV2tAbCSB0wD7mqnqjCxSPcjXYj3mcc8b6LvNkfae0tRTAw+5wCMyd85NxzSiiPJvfrD5TV+//QaX53v8OpwkWvbEvIFyAvjyv54XqX+7V20sZ5gW3G6/fmTzbpPwzb4NRL3dZtqyLePQU98DtZHcqdnZXbTdKwiUdJtknFXQtqh/WSMKOCAVgn9Wj92OnZen1AY6ylM77l9Qov52PEzoylG5g8wAJiquhcmpB51oSK7CBgTmVdU2K2E4iQ9Kaocx2IOATBYWhHkiewlm2z3QNAcStM6mKEgEgA9h6qTPONIq0cs4rYrZxdtt2bHyRgVEuOx9tUF9yCXAWUxBdOMjF2DaODsb+MBypiBtMW09MGin+XAlDIaNIqxqJZVTA9ce/ZI2Dg/yU+vO4F8o+3asRIxLi3j0jRfW/+NsrcJSy2Sl96TVwtpNaPVLAAYkxogDMCKGGlqAmRFkAvwqERcRHMTOvC78wE/vbzAu3bcgLZukPW5vvSC5TJJxEByfMxjfbp9SKxXKAdj8876D+PfLcdDqRk++TICRXZrfNwY6OcW5fHjmKHs2Bqt3dBtPBFs88Y3/nUMfYD3h/yf23PzZjpXsSWwp2wYoLIXMTf9pKxwTyxWsuUnjUe7cbr9OAcgKFPfADHJ+jfQibfAj43RmgU69tvEeTym+kXQJVH3NL0CFac3AE23tf734/c+wBtbyjds6x7kcqBvB0DZXgbQJb02idgZc3Z8v8abcxKbZXiGsaNELDcNYXe1A9DX8X5b/2bf9+CXs7IUkGzdSqnbFGh9aj9WVMSO02mksxvyEgAVQ1MoM5D7yBb3cfDV3GnVdZdacGqTi8CbxlrH9jwri71DnG9vPajbm/j+tt9/ZxP8775b+4xkJwrbez/7bcMxLOK9OcaHzuWJsbz3XDqGw/jcvvPN1r1WdU9A4mvZjcHAMhF4WZebb1ICw8vWoJ1HsRFnfUJ+p5VlHVyDP2LaWqafZdvamABoypqmxSkzzPo2wMz0s+I8Jufafe1+5TdFfwBDsgUYWSRRu/cKvLrRIkPL9hvf7TWwBl9kf+1l7DbuCN4F/a1wHTsIx7QO4O6JMWYlQIi/kfweih/TsTRh5JXUvFiV+SvMW4mUkjvu5xXHqWLKHbUJkJUdqNJ7nMzuAimpzWo34AUGWHV9iaDBjDA5K+uKNPDBh66AvoJWKwnLytbzDUj6XhhLtAGnmBiwPs5Z+s8MmmVx75UNZwbKWMvc5RUNhMc+49Qm/OjhJU7L5H7c6TKjPBDKqYuW1a2oj9/498Ms1M34fmBy3vsuav+iLXjKlfDxxe17OCaH7QjXbC/e2zrzv3g7jr2ttPe7R9/IBMNv0oruYTv57uN9o2dm1rfQ9kCWTUDxc4t8RH0se1+S5mSzGge90ZY3viJv9jGGVmRreXqgpjnahG1OgTGyjOZq28Yc8hjRMfrspICcnxcPccNxbgVABehawyqhq+ht97EsLMAWuKNh6GDtWVfAYGzd+u5WyqFdKxOJ3IrGp817r6ilEaiEhgsL4JSYXbOqq0EQXSgFGzXSDSZPazGWUgJj0hK20fDagiAp+6pywrmWjbaMOyraLCqSNDKy1KIsrDQYWW2kKNqCnxWQcgovzIhYx2JMuJNQbvcOSaAYn+qEt+2IlQvmcO9Nr6Ah4aEe0E4ZqWHoWoUIhU/YRDKJ2ik+Rb+9IYTok+IVOHRDn0urbQDAXrfEy9DaX2g/Kohovz+GTqhQvymNqDeUCrthZCEE7m6hA3YchqYncej8+jI8t+e2b/vUW6+iF0ELDPaTzR82NzgLx+cl+SEWBYYabfcx4Iaxjdb7sXloTu1tjjOJ2dLfR5SXgA2II8yysZKxVLy97mMUet9rWAk4Jb/3AkuXTw6KQMcfWVux+TW68d2tlEPYNQa5bYzbRyeHMdINTXzf00RhUvIIaZ2mPzMKeUSBfk/xhDgS8X7b8QQMG/e89nGvrI84ZlnFKpNK7ZDbE0oOZtn5GysMPieSz3W2nTOyfFC7+d721z6FLVA2rG1grKkSRE+GVeyX2A45zssAIqdOOWXp6rZia/hsf/bdboJSt7rYHPxpm0a8PaK9VgzQHpRrcXe9wXaf/UX8e2tcYsR3Oz+373prCuS7Ri4NMfZ9YHjVeciqtZlPElMPAbhwOCD24pAr3q4HlNR8DT+pXzKnupn/DDwxoGXlJGlzGgidAsPLfKBM7Iww+8z8DwssG3hkWS0bPwijONEAnrQQFmckTuiUcEirV4zf6mONH8tgtm2lWKJw/aTbTVSdeWb7tYAQHIIW016v+JZAvuxTN75LRkdVoMz8woman+9F0wkjK2/cvz6uKTTI4XZC+pvUH6saREnEWKow61rLqA2YS1O/NKQXWhXDqaGqZhZ3k0AhtCqL6N7Ux2BIQFcBDQBglT1hS0lnbBg6pOmEaSURfVcX1ZlKqomFzJJaSABKFxZS6SAVnafM6JaGSOKTEAH3qpdl9+HtesC70wEm61Ko43Ka8PoBSDUATH1nM25VWI+aVnvf5yMq8UbfijmAeuE0eG8Q7HAbMflhc5y3HocWUg+3SwS6NiU8trN+N3bGbLx9b34csGF3bQCxTuh27z6yPYNZ30LbV2HaN1vomiYWgO3CV1/bZFV79gmvOegjf82wGPU0q1ftC3CLpOgC2/S7TL6762IwsrSGuKw8wduce+O2wx0ZW2gmpdRKv0o9VWaVMbRWLliBDfNMAJGGhfNmv8bk4NbN64xRUWREeOS6ZGIsWt7cUgWtdG0E/+x+uWYMyK+RXdOipdO9YkqSSfuR5HsrY2v55cBwhDqxClyO+2taV/GzpQ32VmsJOTFybr7QcPaAglhrz1LR0KoUMsA9q4Mh98dEFa3M7Egp0OhDHUCNa5dEKq9NZqWDSvdoi+hmTc7Ws7aq2CQA/Gy5A50y0mqejfX5xOr5QxM3B3dtr6fVw6QY7UWYrK+623kPTqONc340BAxQl2odDmLZhK0RiQ7yaoUIp+3TAWEY4QQx3kloumzK2arH8t5I+3N7btr2LKF9M4ZTC4DNLVZUZKRa5NbAiZi6BsAFuQmqsRUdbN3PtJsk1XH0F4XdYzrdkE4PwJCNkzwjN6Q7btPfmoNXg6FlOieS4qgBErW3BmrZfsZ/ugVc2fhY7WBkuYm1FRs4GFajjwH+jfnbtpM1uRb6YCvwsk91YRR0rDQi8wZ87cdGkCIgQ/wd/jrec2N0MYs9J5LrM6bmMTlayXSraOjzGqcBXvkuEagKgFW8gaDt5LlviVWjUTpunbCqw93DXG7srIqEUytSlr1HzwFP25oPrYgdubpGhjx4cQUEPYUg3bA1IzPp6d3VxvFuCPZbiwCWs93cGUFILzGnQVgJV+OPv9/n9p1rOf5mArjTFTxaNTBsc8LKyVP8rC29oHPTeUrBCQ2+WlB90QwRQIoY3RcRaTdB+EWlNSSLQYCspRe3NQZkAYO5dWmHMHZJ4RtB/O6AmPlCsXmGABcXne8QZs0xiSdwK3vmkNZtgHtHQAAAaBqjjzX8cO3zrMLt0OyFCErFNEY7xkbTOBzTmHRWbfGxz34unUl9kaEJtrKsze2cY6qoA5E8fExLLbRtU7AVMTvlUKro8ULW/1bxeMrNtXTP67UuZ8ywy6UhhYqBbUmDpdsJVLrMVwtAa5Jldx4TnjGsBNjCyK7Q4krC4NIDE5AWNTOTBvQqVL5Ej1m6sLISPNgfBZ0oMV7dnfHZdPJiAW/bEe/WA84PM1Kx9VBCv2SUBwbVG6wsuwBPtQ8F89/XIhPKbKyZ1njMmGp4ZWt274Gt3dn5VOK3hPOyPhPARNv09+hPqZ8kwX3y8foyz9cW+jr8PG8tFT7UntMM/3/QnA3lhoQ29FPXkLIUCwW6Yos54VE/wthGMWppyDqAK+aRfXbuCkYE1ljMS7ftRq598kW2RSCinpekhoxzSgpuWVu5bGi1NnaLFiXqmKlJKqICYK69FZ7kSNO1Y1t/43qzpyeKuKOAfpb7HtMJ7bWBWFZhsnHU75JxXHYVRebcRgRJx3hpGatO7muIZDf7rInxYB7MukuTcrhrT2jKstqfd2egtqQpIBJVb00cDI7MLGNfAUhZnchOysoimPfJVWcRuyX7udYjGV10TvJwjiyN8KFvFxuAPGNLL3isM9I5IdUbi2UHdyJwxh83kUfa6W4id5YWdn8N5AqREdpvhzHZegR7D0bZMcO+FhkxsEsqN8KjI2Rj8z5pMK9szJpiSJZiyPScafjcvlGLaSIAPK3PmqWk2z9LQYstCrhHgXOzARs2T2AHRXA+flZ1nh+pYiMt0RfOGKygqHNlY7T+LOAQGWnKRRvnyEOM1vprPHTESO2DSPaypwHaOGI/m+tiIFvYJqZRxuqP8X0Uq7dzaWpz7f64fpf2e+1oDQacHb91cls19LLI0xAtFREKnpH2G1NIHJjbYUC9j+vemTQNHYHpq++NmRX0UDjOdYJAwgICN5sNLrFHw+08DQCNdj+CiY2lOhdV2s7/vrEdY7d6/5j5db+IN3vA2Nq0va252ve6a2eL3frc+73BMI7H2dlut0PBdm1s2eaf3Y9nW/NdbiYAL2tfXW+qoLgxpwBd32OIhhtzMxHjmCWQXsOcZECHFTmaU/OqdunmQyvN0ggN3JJ+TPaEldFjoulxTidcWnG/49ILShpphlH8fe8/XbSquGkID42q4Q/Z9pc+Ye1lkAf2wX27rhgBfgewwC65Ye9HtfgAVmkzYMqaaRNbH6JnOfp+7LPoZ2FkhFiLbCzLAHloB/GD0HFIFYdcUVmqVbrwfmquySs6WttA+wbQyhUvpwXHUpFTx928YsodJUugm4jRWpLUw56uMkosc4SIRQIlBj5SKArCJPq+6nNY6p/bCYavs0lTC30b+6yGoDMsgMBhjQ1QYQF4WGwzZQYdm+h0TQwqHZ8dzrjLq9+Hh3rAV+c78KmAu5AQLrWAThnzg2acALrG1/Ff6TbemHDfIwj/vkZ7WwAMG3Bze/Zt9u/NR9rbts34o63B7rX5QJEZZj5OdPX25xr1s2K/bQt0fYAjdNWemVnfQjPh8qcq9Fl1vD2oZRRQUKg0gjGpmCbRRC2kvKkToN2Y2CEw2FixPDvC34ytID2ATcrgnkIbv5OIhLGgFNCKxg7R8G3L0+4BqP11mkPJ3IaYFkhb/SwydTkD1PJmjBlSfWKfNx+bRNXDe71+8VwmalhgxnfQbOdUcV8WlNRxqhNWNicjXWmj1J5wyNX7XXpG0ai86W2Zg2GvS26bXPTGhFq1imNLaG08G72mbb55I1TkjYGwz9GyR8A94psZfUnyWZjwkRlp6pqZQMIEgyxGDAxduWhEaFbBzY53ywHlBOSVNRcbAfzh4fU8BWB9DLhllTxCjvb7osxm2Kx/urWQZ3VabwBKwzDyEEp0Zyk4KHrt2L63a6ypiPYdAc7OuhrDh+3Yc3tuV+DQre+zMZU2QA2FSoe2RhwAU8NI67OgAut39ujHSoKMAUjF1L0oHO+i74Hd6+MMi2bGVtxWKuWNEGHClmkUz10ct8Ge2gNQe32xjB4q/m1BvwhEuTWw+QG0CSC5ltUGVtu2yCaL1y/el2h7DEwSZlmXilQsqR4xek5g17wy8MkZWlD9GT07ZtU+0zmocRJQingDYnYA6MM57JYGwPACInaiIui7TVnXizhSDSnYGoKyh/XJs/0SvNIuMI7ZNIhmbLuG5EEnIsbSCtIKJAskBDCHDFn7eW3NDZDovbYmbAoEofkbx75lhzyqHccWbY2+3di0G+NlGwzg7Kyb5/XcvvPNUtH26eGmT7W2CSW1AWjxCDBUlhS9kkRjyXSrDESR9fBWjN1SDKVy4ag46MfQ7WrPyJl97bhPz462QGyMVDeU85HsiId2QGNyRlJsUolu+5BvfCJgy7TSDBYLckc/qGNo744A7vUPyEErTzsseg/YvzdgrXPyTBnLhrGq8hclHewr61oQ3TNPMIJJsl/BXVrG/WC5z0WJAw/14GBkZ2HJmR0yQDL6MdZ3Iqnu13rCqU6g1JFTR2dhbKXEWNfsbFG3U83S0sO16sFOWNGQRRfSCZoeqCAUBOCSRQkhX8jBqXDJBrBFAmj1zAMAqyTZFxaYbwTKQMoMSh3z3LBcCtY1gUrHi5dnvJwu/kzJvUh4XCaAgV6T++HpnFBOTZlLGIGIj8lKse+iDSL6sE2KjcQvsX2iSy23n+V6fyRdyX4uZj9MgsX8nggy7ZZOvq0tLK9tmwKXdjkC2MUEv5/A9rVv+5HtGcz6FlqHgVEC6VwCC6jbItiBGAGFHMgCvDyu3WwTSbfWkK5YQg/1INGEGw+raaFELa3rMdvCN3mpWtCo5iGRG3UmaOhq2XisOVDFVmnx6afPGF2xeQUQvWazVujw73mrpdWYINpctOkz7hOZa35eGwNFTjseBkYM7wR5X6jh0g6oyBrV6JiIcNQIx0Kj+uCpZlhlD2dR9YSWrOLLKHubU0frCVNu4KaMLasCovpZBKhuCq5KpnNP6DWBTecKkLW7bUQQppaLi9sN3zoj4knw1vEggCYFRhuB1YlqGvEXdp0AWY99dme2c8bjMiGfCNSeiC7fanFC39aF31Y03InAj8+xXZwHICn2RYmuHQXbftdssnZKbMag6gJWeGZLqY3H5zAhs2wPmD7X2JY4nFanT5q0n9uf3GacJIOzPIUBrGlF+iMgdq0qAwEADEaP97eNHosd2s7Ri1ZB2ojE2f5MumZiX0Rfj3n0bUAXaCg7cHD8DSSK1Q+t7dMV35dyuQeS7NpZ/4DYDt6d+96RSmEM1ud+nw3LSicGsz+s83jnUd7ddRD1/iSSylUdGmwiidhz6kAHmulcENCalEhP6GCSyradCUnva1Ig0JyezgJuSiBKdMksUAIdm/0zzcdxwTRYEnWuWD+362RA1y5S7KCVAZPWRfxrgRtzbngE6yQlNDmj2667BH+EAWyp6B/Vom3Y2wDabxdX9jf6ubVf/I5ubPDU9rTbb6PteO0Y7I9F2NqhvUnyfuKxnsGs73S7Vb17VYaOzWVRSwnYzrNzbigsINbSVEurk6Qe0tB7LSR/TdfKjhGbAVkGhmVizUrIvna0Y5vGUwd53wB07UlIPKovWgXGSy9S/ZAJmTRIryCcpeXJXNg2AevoDwCa3qf9Nj9uRAi2msINpPIpvNW50sXfmYsW9mKd3yfs2wbIsu1ItLesQmPMoJFzJge2Vp48C8UAvBrYUXd5Re2ShjglSRtd6sHHGpl4tWcsdi2a+JpFbVFk3y0qd5Jo2IyUGLUmn7p6S6KNpZpYUjldM0VM3sQYvo3AU5dUc04iy2FBYAggBQjABVb2ld42TlA7oP9sn2bfEXpSS2Xp7Jlxdy8VCV8cFjykGcyEXBo+vzvj1XTZ6K491ANaS8DUMR0rptTxuE6YHgj5MiI+H+3z/DwBFuuCVarEQLsoDP+x/UTTFIClETUxHygE8vGEPeLtestZV9qV3RviAUJylGex87BAP0OyWPBpVd6fwaxvqdlC3hB+YOhaiWj70xUyjAJsgNGkk6NHixnoWkbbJq+7rDnqYQEaReKt2XhME8oFz3crmo24oDKlIjur6cL+Kl+dkqb4bTW0TNzdtvG+w6rXqhRmAha+rZUVxeHHta3oSFdAndCoqzOzYoqkRWViNZO4v4xlLKZH6mVCYaFjr0p5riHn3FMv0zgvqWiYkI2GravMBrjO1tIyTsvkNF/XwdJUlJy7RzosDaT3hL6mIepu0XIC0NTJJYjA4SolZ01METqBxDGaMwGCpBaWroYHgB6PlNptz4hUohRAy4zepRc8nA44niCaWfZoREbWOPDtyTZ+/kSp2kixdSqrg0Psf69orRHQAjb55BF4Gov96+g5MZBX2dbPhrf/fHJ+KhrSIfpYOtnDUg5ND+C5PbePaOMxHuwr17XSuWSftmbNICAHVGCpcJZeOBYiljpn82R8RKNIvDUXkoVorThDazeGGIl3cXoMsMrF4jeefgDDaKuh1cNViWe9SY/Ua2Ul1G9pZUVmmaVJggTw2lfPNRAqKZuqh+u6ZajJcfJuLMbCiql/wpgYbGDmca1cJ0mv2ea8eKTe6NIfgFQplH4T1qZsZRpRVBGQJZ/zbeE50gppMK3YuzW6gl401upStLNJOsakQFec4Fwny05CUhhRxjk2vQaWUmnPc+WEdS0o63Bq/O77ytn6/Qhb84F1vwFBo//44gZCtQG0tps8BU7tPyfGSLm4iU5h98u40aI929+/5/adbZnGmt3W9CU1rE2qBBpYZMFgTyvzn+tgVFlLaicqZy+s0TUIO1N3cXkAWAEcdM0oaYFy3AJ5vXgq9Ki6Z/PlqakOMA2tLut3SqJja8CWpGF33GlKpKUddk4oIU1M+utXzNuX+SyaVO2wST+MQIYctw4QKwB15qs1EFbVuYpsr8y8qZI4qaZWJAX4+MCahj50FJP1HzJtrIiYAXnmC0604twnB6fsesT7V0hAxVnvTW3btM45V3QQHtYZxUgYaTDs5lRF9B8VD+ssKaZZ+q4kEih+bVTmBI2AmoRhZXYi0WbN7OwrrTzo6YMNICb3XQC48DsQWbmEVBH0bMWv2cynDvyMKsIlyXNLELH6aar48vjo64KVMy5csPSM1hJo6pgPFVNuaJcZ5R2Q1r4L4ti5fIPF/McCWV5tDPAkpp9zXjcml4flos0x38beJ9nWrmXc3kCquP/GTt26LIxt8N9eEwaw9ZHtGcz6OVuGREnN0b8EBN61qTRSm9H9e6PPGohljkABcOrCBiohH1qONVINAf1Bom9yyNc+qn3YsSMttYeqiodQ1dAme5s0rVn0wV5PgTk1gC5lV92giUWBdts2sq2MiRXTCd9fuXAcRwzMVmvKzjOmNG7EGEOkw1IuL714RCNW6rI28v1VZ8AAJ43+zKU5TffSBJSblIGVjPVk4wpjzYnBLOKXU26SbmgVCnsWZzMxeifUtQiQpY6Ck4VCBSkYbhQi6XQZDCwrb8sJMuEmjNRDTTVkTRXhi0TycayYc9vkkQ8NNrlmj3XC8jjhxTloWu0pth+RDx5u4JaZFRoxtDIiDQDJvxtT8r5qR9zfIgF7A+CsrLCPPCJDCH7zqLmTwuPaWZRhNw9TgxgfdyLVIPQwaX8TI/jc/sS0hCFeLvPQmKcdUOEhjG4OwRV4oouWxEBVBm+ivll47MXjrcKegVgGohi45MemsV8DObjmJd4xgDNLbbEWpzJbwBs8cxvo2jYBrMLYMcA1YDCxtumE17+5jeaVvh42bntc1uPYOY8UxnGu1hcAYdby7rPQZ4v3zMBKteHMEjSRgrCE1mUNbPacAuIi6+rRbyIAxM7+ahiRcysY4kVMWpLPgO0cGSc/m893ZdXHPua08PiMIA+d2SFdFDuDuHTkxJhS20gnmBMLQHQm14zJtBn3ANaNt+9t73EEvP9btsJOjcaXe4DpY2zNFdgFhgvB37A1vh2Hz/f9ayKAjI8GGufGDZ92jZ7bH6s2kQqg6zr41KYNAyuuvytCEILYGVumjdVhlQkJJQTK58C4srRFA6eG/pIc86CgltmLjTaWza8c9Lww9L0MFNvqyQpfduh/jbEbA+xWM38MECbWBZMzsTJJQP6xz8KoCuCU6WXdAqKGlAr73/12lu0CDB0uA8HsdWwZjAsIlz65D2dhGvPPYvaJA/7qa1nmzyGt6EQutp9I5takaaCPVYCrc5twzCsKNXw2n6SaOwbjzLTKZi1sVXNCOjDeXg7osogA5op1tYAakIr6aY031QrpTG4LHOTa+yxqR5hER4uU/WvmjWSxI9mJq5oTBVG4sM+dNr/6cTRwVlT/6+V8wWktyKnji/uTA3ouxdMzFg3Yc02Yi+iQLbWgPELE33eZIu73BA2tj24fy6pifxw+PI+HwL3ZifcVygKwtTUh7c9/grtiXAY++Xjs385fcrsVgatIsNhxfey+f2x7BrN+ztbADq50zb2OLWpf2XdWUWQNqYaAaV3IpLN2yUe234pV8TDtLWsDrd/mtEeWlrGSbNuJVohkOwVjIvy/HvO1QUickNPqzCybqA1Es2NCe+xIriE29LmKfnOdS2Xglhwv4UihiiPEMGcknDVXfuGyYYJFwyzpmcX3jYKMfj/iAh8coleWltjxyLOfV+8JtZOAOamhtxGlqJrKkVpGVSclkydgOFtrbQJMNR6phIlYhBN1khadEmNnQRyYIvRd7mmQ4azkOQFY06DkWoTDRN+1WZqbTPZmOCDejdp9yiw6KpVA8xYhSsR4MS0OZi2c8a4d8dPlBd6tBxxzxblNwDmjnDAmdzsO8PGT+ocmc+YhOKhRGn5qvouLfXsZnAV5H0GvUSGMw8S8d1A2/cfvzWfT+0fqaJpBjRUM3Unq1xP4c3tuT7UeQBwrzBCbazgpkCT7jPllz6Iy3SWZgwaPybQbTXtrHN+ExfU5xgBAjGFlQJFtm9E9w3mjY0XwyoLQ74gZhbr75jGdcP9z9pQ/BeeiPhfT7RREB4f0tbEMHDCjDmLRHgRUnwSDCRaBMdNWiWBb3wVhNnpZGJorZi8JjFVtlZyP2Jopdb32CjQm1uuZhvj7bqocejjJr40I8krgo/eolWZgl03NqtcZtROB7cQXRXltIR11MAAnLmxIgRy2tcCJ6YyYTXLAT5zkouzpBqm0dmqTp7rWnoFKnmbox7g15p+nqSEmpmvw6GrbG9/tgK6rvomumVpPAWtmW8gAqnG4yBrTW7lJK7wVoHlu3+1mek0lDeaIgUIFwNon1D7sQ7QjQ/pkgBgldQ/2SmZCQikC7JsW1iEJEGABzqxsIAfR9PiW1RB9oHhcYASnzY8Z8+Y1oORpiWgOZF16ARJGmp4G7mMQ21hYmTouXJABz1yZqOm5ZOyTA7vlSe2ul+lqpdC3SZiYjlYKfZjge8ZgPlslwz24ta9kP/zI4aslGlWE5d7LsS3gdUhSAT5xwtv1iEIdR03PBCetNCnjMPAyEeO+XDClhkvTdE4Q5tTwrkoxqLVlnInRlgn3xwW1J+TccTnPQB1oBq3C0OJikxmk2JTJpRAkpZBp6GGtuq9rY+l6WdfTnp1BQJ9YATIMv4MYPBm4JH0fjisOkwToEzHm0nDIDXdlRVGmn11TsT8ZOXeUu4q7ecUxr1jXjOOZQW3nzxKAj5VZ+ZAo/Hv8IAus90n8ulTJ53p3Rj5CE/LK1lhGyu67W76RB+uVK9F32TQxmO/xkwBiaSxta5/C9nAb9vEG/RnM+hZbTDE0A2HAilTxEw82I9J8bXJOm1QEYBuhsD5XFT23Ch+Gzq/IIyKCgdLLfttSrjbBdxbA7Ja2VpzsbdLNYEH7FTQy4zKhepVCzzcXPqJHPGRcYZWrbK0YcdmDXVJaPWlaiEV+BmAGsgj+Kgw3BdAmNJhWiIOLtBV/NHBvH2k34HEzLnXuVjOcqaN2AOZsZAZQxlrfjHAbSEVJXZ1KIR2tLWGpppmlk6eK6vaexqiYRtoHK/BkIJZcNL1Y5JFxKWXLStWFROBNGNENAIc0QxYQiwXF76fizocx0O7TgpkqzjzjH14+x+8+fI7GCXN+wKUVpFNCObNqZoXn2J0MQ3k+NMl+CNDCZiImxgYcwg1wiBgiRJlovAe2SBWrQ+niu4N2a+wsp8Px8FU2wJedYpyY1QC76PveuetwUcvn9tw+tjUMhmms9gYM0AmkQtS6jzOnxk/Bm7O1HFwK7CgmTxsEsYI4o08X/8SIMueQKu8sLXQF8q+j21bhauAlrMws+d5As6Tj2TOgEmOT9rjVtRophNb2YFcH6bUarDFjZhn4Jsdvg11GjKRaeESq+aHX3SYGY3PFaxaPGXEYYEwxTYGypGwq6DkXdFSkoQ9iwFUAodKYwhzIaj3pOPU7D5zQ7qrQFriJgJVt2MMbW4l64MLQe7/ww97YtTGTTBjaj7Ybsa87Kme8aUd8vR5V12QVsG4lpGpOw63F7kcugD9SCP7q/c4GxWYAU7RP0Ub4Prtjbxf1wfbgBggVH5qN06FfXW1Pmy8/wT94bn/MWmPGmSfRNGXCuzYqUGf1Pw6p4qFpOlkAjhIxlpZxbhPuy+LgiQVq7b0FfhN4k0oYM0xM/wqAFkIaxwGACV0YY/oAz9RdF9g0oUaqoep9hQqEmRhdGcUxk8OOaYAUMCqpW1u7BfWba2ftNbT29kKubcKkoJClDRoYtbdpgPkSaQNwDVsn5AABpOBjNvsZ2WJyTXOY3616+6i4HuVlkvo/oISZKk5tQgqEi8heqyrbYvdUmEjGyKpYesaUJDPD7nVSUKsGlnHR6oa9i3ZWLg0t5+FvtDTApwTxRxY98yyTH3XyqoV+u0iAK0A+pzXgGwxnBdn0RitgyVGcIeCZBlHS1PDZ3Rkv5wvmLNfoi+MJphv2oiy4S4v7q7UnvLkcsa4Z0yx6WQBQaxZfRwP372U5xfYpAu9PtD4lfPVPAPVXzzjeLSi54+3f+xyvf5tcHsJbsDF7NtZT7Cwz38NO7TfQfoHNIpJ4q601vgCizTQ758uGHACyLkDdhs39Ce0pdZfn9nM0Y2HdKlk7xPeybytpaU0BmaGB4dR6HpOG9b0XUo/Cf6YZtd/OgBoATjn1VEiMqLVF5WP1DzMGY/+uOdvdv1+5jBQGGOsqbfRbDLDqgZFl730fp9SO62evDdCyCUfGKECW9+3XTSsBmtFJdcPGsj7cOAS22aSGcqRiiiPl6TL7KDtJauHdtOJQqt+TFhYBADDlwXYwEMsYWSnoiDBE7FdAMRqMrEDZRaTpQkGRKt9TJaSVQCsNJpNpmwBSinbqAnS1hJQZqejxGdL33JGyUM7vVaPtsc/4ar3DTx5fYFVG2sMyY3pHKOc+xrLXyvqGzauFAJv+PJ1xHxy5Sm+80WmM6Ftf9GGjRLsqjZvXCOMJfpsYY3gkyf/5/jTO77k9t09sxsaiG7ZmVMAbqWsRbGcYiDWgnVidz0CV/aI+RvJt35hSZ8d0f5sGMDXSHAcAt6/yZ/3H/QkDqDLtEUtv9HHz0J8CBmAVGVn2foxd5+IwUXi0G9vjjmqQkVE2ztfeE8S2EEb5+NiHXQG3A3qMYVsUUDNgzqLb4bzMfns6kAU74nmksSod6Z9wXD7amt7lOzbbYl/Yv5hKAN3GgC6b2xppGji2c1pid1jQCZQgMppx4ZwZlMZ5AZrO3iac1tkDcEvLSIuAWT7P+on9vG30uf94M88jHHe/3c2dxx/v42OCOrZptDU3xrU5drBN+79Pj/G5fZfaWSviRQAnpuqZwDoA/2tzXknC2Fl6cQBrTs2ZLIDYj0UDu3t/xjSdHuuMUxMpChF7V72uGIDQbQt1LN2C42NObLxNidynEHrKngb2rV/zoQaBYPg+Kws4c9Hq3NEuRJ8iauvasUw/KxmIhhGYiaL7t4puRfF4SRHkzbZStVWu59rLle5ZzOyxdullsOKCfziqSzZkDB9GzqE5OcJAnKOlDwaplTlXBzsvrThYafchgaXyX64gYhwmOQYzsK5Kkjg0YOIRZFcx91hOnhTZiNpYSmYb6/LEQTycRvBXQbFuqYW6LwjoM6PfdbE7mUFzw/F+wefHk1y7WnBp8k9YatXTaxsnvG1H/NHyErUnTFPDVBqmLMzCvibRBu4jeG//xnm9x/+5Bfp8pP3iDCy/tOKf/rV/gFd3Fzw+HpDWD+z0Hv8ixpiMLTy+462NjkMMRbEGGMUDWIwgVhx/iPfJPlufaWyI4ed+ZHtmZn1LzXOnQwQhgkfA0F4yau2lZxy85CopNXdU8eicnOI5JiCt0hEmTJ+4MTS2NuVbyQyOspx4UFslJVCMgEUztuDTyAG3ydSALGumJ2JsrNgc1NPPYwQjMrNcPF4ZW3vB3WjUMjFmrDjz5AwtYABiMe0xyWzoEY8pVayhMuRETVNEyAX7J2pekXJQnbei72YES5IJkJpWaWmWoqIRFq1sOGkkwB01jbQboCVgVkfvGawVDCOzi1e9tonFsbCqH+pQkFXE8yohECBLL32qBGZGL/BIOXcCSkcqHSmUdreURSIg547X0xn3WvZ35Yyvlnu8fTwgJ1mEPFwEzEqLTuj7CdFpv2FiekoX69YiP+C2ZGwzc5puNXXqboFT2wj4bh+LLgwf8GaEnRgI2PHoW/PHjaHlk7kxFK6OF8bw7GQ8t49sEQTxdMId82fPELbqU4DOBzsAidVmWGodABdm34iX8xBSvx3EG4tqY4j5gt+AI5bjO+sJQKPkAA8Ruy3ZVy00IMnYWLE1O54tlMLKLJ6rsZmMsXWLpbUFtDoqsjO0bBzjbHWcSsmx3mIwCHrNZR5hT//IYNQ4Lg590wD/bH+biKTCrAJoekCzL6LFuMPsyaBM+HU1AXgQNiCgg1QUFrPxH2izUDUgy/vuspOfOkGOkwSwojDxRZFXSoxDrpuqw+c24bIWTdNPWGtGXjQ4swOf9rbHW3xIY7uZ6hFeBvvyJJvJrtOtA+jHG3aWf8dPAlp7NhffsjXst+7Kjn1qRPu5fbfayhmPbXatrCmAQLYuNb/CpDJiIKGrDTDbsYTA/NILahdGloEd1kftycXDDSABhp+TiPG2HiSNmCwQPgL1Sy8OYglIsxWSrwFAu8uXbXGoniXlEdeMKs9eUXaUpzZi6GV19RNsu8MTCEEsihU/k2ubcaTV/SEAAqwEoCprhUO51umqD2BrN6wqbSLG2jJWZGTuuGBk5RhoN663BvEZLhJvi/AEmWMLE5Ze/HmwQlXnVsaaISxac/ArXUerE17PZ5TU8XYRBiCzFI5qjcYaWMGoVAktAzyHidVBi+11dvCqYTPRcWHQIvanz+JbpEroE4OzpBu2Fx2Yusy9uSNNHfNc8ac+e4tjXvGT9YWnzt4VYZxNqeGQKo5p9YyezgmXtWBdM0oWIPDcCvhUkFdlBkf/Bhif8U5P62NatAlP2AdqANaEcyv40d/7Pl78MCEt2AZbbqWw3zwedgEqXzjtDoqdncWm8qEvETK5vXKm3M5+bd5z+Mz8OQrD6Hja7t5oz8ysb6Hd0oKK6DggkYvG5CVprVVOYlQCU8t1OZicinsJIuQAXGx+Q59V8Gyg/TopdRGBdBFbZ36ZzpNFONKGpWWpePbDBlS4UBlfVj1EJusBaEWh+hgZEbpvubpmOVyrKBRvfwdDbZxrZJzZuL3yB4b+le0XHbLJSwnnjfi9C+drymRkhpmBrJ6iOYw/IOWMX0wLptwkUpFHNKt1wnktuKxFtW6wYREYqGXiuwBEw8pmAwOqyAcjjZWaW4dzkar884W4ThJUxeGQ3HMSvS3NaU95zITMJFUNi3x2d1jw+XRy4/51vcdPTvdYTuJkdCZcLgXTW5bqHuOCfbN0hg9EKAwc26P5tyIh20gJBECLBoaGA7R3XDZsq+C4+TZ9bCvvd47UbvK/crICMBb/Prfn9r52SwvKpoVoN9j/DlszCpJsmVr2uiogNkASXaTrd3HB3XTbmJZox17DnMoYABgH2+ap07ovqxPlVQTDeJ3L5PtsKwba8W10zaPzQ4/KWhSDj0Lx9jempliL13CkfG/Ph8K1j0tBT7EP52sAnzG9BLAbzdNFw/WJ1zgTY8rN2V9ZpQIM/KotaZTdHAryla0xzRiQYIZ1zPqCd2B+PHhkY5ld6fttoPOzRs+Zho4jG8AYrrsFZ/Scjml17ZZzn3CqE9oq1bIYQK2yePeqTnGMn9o+JhrO13Mz3dyXd98/Ma5df1e24cY+V7bhPfvctDX7fp5tzXe6rVxwarMzoSJ4Y5VSgZGadp1u2DFn0RWyFlmjJrVhxZBMa+hFWVxny/qadB1ta+aY2VB7RuXkwXvXW+SEizLDLr0IgKY2xY5l52HAmwmbWz/jWozsFFvb22sXfW+zb282x8TXY/rh2stG6N2OZxIstl0EmXqwm6aHZT7U7XuXnTHmqYba3yHVUMFwS2aIPqX5eLWLjyjXWf6ZXIoBh0aSMBJFSd3vjbHylp5dYsWA0bu8bp6JkjqOpQ72dM3oi+gbokkKHKsIu89fpqFkEilmpuz2EVzrlxPAkgEJTkC7Ywey7LN639FeNeRXK8pdRT5WpKkj544vXj7iF+7eoWjQfm0ZS8t4OV1wX5aNXta+MA0RVH9S7Gs6JaSLDvIWaGW+yLfCFt417bOkjl/787+H+S/+GO9+rW+1d13vEZsxPBnsueEXARii8eHzjX15n53Z28xdNkz8ngmORDlIpp99AjHrmZn1bbSOofnh4uebCW3ckUQdlUuoYjhyxJtOCgAAHrnmnmdNoyqfGJ1thMAmsRXblEDb3sbTGC5OaMwkq+IRW+w/UmstymDNxOH9XGl7zjPVTfqgXbOMcd4dci1MG+upFiseJgj6HqPfZiTiPbhJ+6WOxMMh8vK9OtlO1IZ4Yq64tOL0ZblH1e9LvL5mFNaWffuuDoawtMLvmclL2kq+OQ2fIpREZwZQGF7uyyb8rHotNYEq3DBwYqfiGujCcWJgwDWb9G0ioMxVxRsn1CVjOlZ8eX/Ci3JR8feCSy94ez6Aa/Kc+XqepLqHpf7FSfybVuj7QOTa/vq1JE396RLhZ9DWIQsRctmPNw4eEzmzan+sTRSCg6/31PYcJnQOLC7WsEMf/dn4PmXSfm5/cpul5RGzwjFbEfVNml1wIkxXKYIrthgFaJMGaNpTMb1tn8aYoOC7BxLDcfXvqMQ3tEIIxtbdBoBisMHYWObc7EXos32ObVokANf4uk4rZN+fYRpevHF89s2ZZAqngbbnaU5KvAe3pAXkPMa8IVpbIyaR9XsAHjF2DTONqsdKkQ5eqjB870mmFi1BbsBZi/MjSL7XidCANWCsdx2Gs4qDcY5K0Ll86PuRlVNXAMxOPWBnW+CFhq2hLE5ZrRm9AblIiv6sKSzmSF5qBjr5OfWakdYxv26mzW/sO/iMftXcxoRN/KWaqI1grtkaxP14nHwIYuzn/P1n/p5Df/vvo23Dzh6F6+7HfvpUn9t3pBmY/9BmDF2rEVjOJFkEANxxN0ZV7cn1hOzfnJsDSS+nC4BgP/RZMobU0guOebCaTPf3ErIhDAAzNpexhI1dNCVhT61sOrxykKgdLKLvmqmigxAALOEuL1fzsLCO4XrDGR3nPnkAe1VtKNPPAjDSELsEwE0/eNX9TCvLBN0Thn0dxx2LxDMPMMwYXitnSSvUALtcy6Hte2EjMZCPacKoKG/sK+lzWyDMgKuDy6tsF6sxRX+wruim/tc5PC/Gzvp8OuFNPeDlJELwwu5WLCWkDSIBSIw+Y2g91rA+TxjrfYboXJmQO8GD9cbUcTavrst7YdQXHfhsRS6MMokfQyQ6xHeHBV9oemHtCcey4lwnvJwWzKnhdbngLi84phXHtDqJw4gLKTHmIrpi75YD8oVG4L7LuQmraDexfopOsBsGUgP5Ht+ny/Pw5nzEz37yCtPDDc3d/f4xO2RnAzZ+kWWmYATorEL2PtPFxsJBMmBvp5iwrYgY/0Z71BWs9EUEPtlOPYNZ31Lbp8VZsxRA0aEa1TsyiZh61ZxyYAgeAjJxj9S6UYbWyqoaC8omr8jGsmNmdE/zi6+BoKsFS3foHgkpqeOY1uEQaB43EtAtMhHK1u7bJurAHWeefKLMO/DJXm9E4lW4fsXk4FaseCjjVRYXDxHIs5e6DWmPgBscMyQ2vigSOa57A5IYxkINKTGqRm6chaUU60Xv29ILEsui4b4sqJywUMG5FUy5gQG0Tk4VBmT+koW8MhF0RrGCJNxSALUYVFgMhOleEQ96rrG3dot5S//wKnoGoqz6WYLrlBwPq1T2KBVviXFOE17en/G948MmxfBNPaK1BJo6jpYnf8qYHvvIG+8YEzkU3LoFan1Q7P2J7xmgLimB9u2VVtaNfTa3+srwYDMBixNx4/g2YdP1vsSWuhM+xxbYcoF4tgPJfX9mZj23j2232FkAArgzKhgCllLYPdqL8B0Ar8BKAEAD4DHGkIFKBhTZXG3sJAsIeJpfeC3jMqaYCGFYOqGlUBRq44zUdoE6OPQA4Gquvv6MUTltcWI9z0aD8RVF4geBNW0ANPt+fCuAk+l7WaDDgKyRdjgq/8TrnDbnA/+sAJqmI0mZ3fsd98Yq+wGjWuFG81LPO1MHUhI7gWBnGRIoMTDL7rYdxPQYDTlJGHoVEQSJbW9rbI6LYA10IatOB3SNXkoTNpmKBlfKmKeKu7JuUgwvvYB7AhKjZL1TlZBXHneHoXNoGNjNn8cHVsfv+3p3DT64xr7q6/225n0dXoFeHziNjWNioBoDQwwTT1yf5/ZdaZ0lE2Np2VMIKzS9zJg4aWSMREmSOTdx+HPFlJqKh7MHdG27IeWhlbl7RgWwtDwqfYNRAQfC7LN4PAPRus5Hc6rDHzKhdgW3LjwyJmIQ4tKKpyJOSYLQiRn3JOtWE4A3f2fvMx3TinOfHES3lMOVs6ecZe4u0C5auqtUH1RQaoKywCgE7EN6YSQCRD/kosc16ZMExorBPLY5PAJ/G1IAEypLFUQDx6LuWEw9tJRNAA7eNR5i/SPVNLmczTmI9B9z1b6Gf/eyLDg1xrmsOFXxv7LN1VnvUWJwTxsAiwuP95ZeRgDfDYCUQKCLfGHVDE3cXUAsoL7o4MxI31scxLo/rLhoca15qnh5WKSgARjnNvnzdSgVL8oFr8vJUwwNoDQAEBDtYiIpjvD2fEB5IKTADKbGV1XJqTFg1Q4/5OfE78x/uiXBIjcHxJIi+pP/8Qu8+B1Jux++xgCdNnYj4bYmcLBpt8zCZp9oe+I5RRu2WwMY2MWk4v7YAly+TvBtaQjrm27aR7ZnMOvnbFnv3MrFF/wjNW9LJzUR8Rg+swln0H2TA1ZSNaM69bdqKVZjTBlA1ZlwoHql0WVaWRLJ2P7abGKUsWcfw57C2vSJ2kccNjneYLxIFyxcnFoLGK32miHlAu8Y3y8agZhJJkyhJm9F4E1Xa5+iOIQiTbera7RljMUiIfbeIiuPfQ79s7IIJHIzJVZQq+MSzj1WW6k9efTLFg5zagJ4meFoGXNpWCpAfThV3EXcvVdlZyUWEMuAKqM9JMeF4ILhVqVCRRU93YPlMwJClTwSg/IEngQSx2IuFXfTikuVyoxf3p/wqoxI3KVPODWxJmWWbQEgPyaUUzdvGk5xjS2RhJE+sV1NwPzEX9jE+JGQvlUr3G0qwFswujdmef/s6tjYTur7vwZkdYBGcZr3poY8t+dmLYXF5ki1g7OX4pws/uvWE474rGlWRTFyY06R2p4ofm4LfYbUbbXX1gYAdB3g6IFVZNpedpwYBTZQbM+WioCVpYN7+mEAqCJDynVJdF9jc9l5AKEAiDoZ8bgDlOLNsc3xImMkKagSUzpZt41aV7HwiN2LDAZjVE6sED3L2LKyH7qykwzIsvPNxMi5IXXGAolE59TReoKV1AYG8CiphTQCID6XDVvjbT8vXaUlYltpyhamZHYFN5qMPSfRIGkpgUuTwinKTmAAlfNIj8rdbSmtJOK7NoZbRu2plfkH2wdszdWpfMBJCR0RX9sat0FPHB7A9T6IAFUY3y3Ai8MO9vLZznzn2xANTx5DnBUQSimwoECY8uqMHWM6FfUb1p49BdELGfGQ2ohVDQF4cNfareBD3N6lO3rCrMD8Ehhcpq0l1Qe7bpsxaTU9yahgdJK/mQScMUF0C9i7nlfqG58psWRkRK2s6OdYVVXAtKu6+xDGLr6qQB/8DXsf9YZNCF7Oa4jW2zEMwDpzFkIBj2Jixi6TfvXaqU9Yg20xFlwmASFPTcgEKyf0Tp6WadtYdUcjQtyXBedWhiA8xE6/Ww/oZWiYXYJLaPfHGN2kBT84s1RgnwGeIfImgIBXEztjSDQXB/glvoTYJ9JtLMhMXYCsfujgu4759QWfvTz5WFon1CrP+mFe8XK+6LWSjJkpiSTMD45vMSlwZUBWzDpaTBKIyYNvp8uEw1uAqozVgugbQGsXzH+yfdPqhh0o1PBn//zv4e2fO+AP/8GXeP07N7qPyz/NWAGGT+XAF4Xtok16TxDFfa3I5ApA2hXLWMXhOQbReABaPY0gTI+srU+wV89g1s/ZEgRwAmQyEiHCQFPlkYIY0yvWntFoiJrvhdHts5VTyGsOIJOCQFaiNUbhrbmYo4NjkkddUnPB+L1YvE3glq8NDBDqqYkb1PG23W3SIO38M+QhTkqN3QjH72ivomNixmNETqL4u1GXLDXR+onXY8WEtSfPT7ecdjNCcv3GL63tjFki1txxE6KsQIEKapJHggy0ihEPr7xC3a87AJzr5K9rU2OaO7rloDWSiDoTYIAWQdML5XsHRCySriVnKVQ7NKfCIhmcAc48aJ3xX2avajWXhmOpeFEWPJYJd9OK7x0fNBIm1/zcJyy9aMoL8HK+CPB3IuRFRPCH6jBvJ6I9M8uotD7h0/Xk/p5IgvljzqLaNXPkruixtyqJ7PsnXE3e+hjDmFRj8g8Td4xIxNPn8e9quxupis/tud1qBPL50RZXNu83BXYsRc+q/QG2SB4R8Q1AY5/RVoPqfSDTvroeYLjC0H9ijAi+pQbu/W/Xp9gcl6/6lu3VOYAwYWMapJ1j0h8p7fqMY/bzxhB+l/F1fz2crXH+ERSK16NilKN3kE7Ha/cmznxRh8xArBSuTaaOOYktYh5Bp6z/mMc1iuwIF1aHOHwUUnIYJIESuwYW4ABJIRG7NOZEbAasdsfuSwS+HNAakVZfGId5DgRPaScCchL7OqWGNWWUxLgrq6e3AuJ4GGBKJJqUDEiF3ta3eiD7Be/+Pel/fh5PIERPtWBrntqVb/X9DX0VYDgLtwIum74VT7ta88sPbNvfE6f+3L47rUHmQlujPtYZS88o81nmo15wSNUBCQD+8FROgKcAdgXDZA43jSUDVSL7x0AsWzefW9lsN6ueVcFO+gQ8xONjgF8D+8bCysHvQWpYOW31bD2YLZ9duhTMiqlztl1XZhZo+Dce8KChoxgDHeM4CRfV0DLfRnCWwbYyv+KiqYiWfmh+mFWa9BRDfe33L/ooem3AA6Aa90HuT3Owq+PSDs5kO7UJc6rIxLjL6ybN087X9Jv3195ARbt2a8s4lIpFpVMKSRXdRAlLL3iRF3ROOBQBEUtpqDmjdg2qUygGklhBKtb5iVwuBYWl+qH5O9o4yz4umVKA9lnF9PqCz1+d8GJWBl5PeFgm9J4wlYacOv7Uy7f4pbuv8XY9onLCi+mCzpJq+KJc8FkREOzcJ5wx4dJFSuVdO+DHp5dgJpSpoeSOx3XC5WHGy3c7nSwHtKByJX2AWV3Y0r7dnoV1s+AVPfmd+HbyPP3o7Uu8+fELzD/LAMszNHSuZH8HnZyt9REGwAAttReDRaVyLObbBD/plkTLTbul+0V7dEUoUPkCO8THtmcw6+dsHd1FzR3F1/S/CVIVz3K5ZeJQOqkDPx2HXK/6rZxwZ7njEAZY0ahAZXkdmYhJKR8WjehGl4XJXVw/FnsRc0DSMdADe0qjCo0TUu4eWZg2/ViVju4Ak+txhUX1niIbX2d0BwVnGsCX7F98G2Ob2ZhMxPEWgmtlbnswzrFKiFwbBhS8s7TDyMyauOEdH5Coq1HIKqw5KM+WAnmXV40KSbWPOyacFORKxDjVSVlaC5aaJRqvmiAgSNSCDI3CoOkyuY7JpmqUphxSw0aQ3N8nBbLy1tHgzNKfdjXPFS/mBS+mBXOueDVfcMwVd3nFy3xx8fcOwrlOyLnjMFUc84qH9YB8khzyvRAi3RAhDDfn/dUMY/QgTmkJqPeE9RXQZiBVgP/oE6a8zltAyxwy8CZiAd4mc20iDTaB843HLmxnaZ03+9k7jc/tuX2gMdjnV6sIZ2zgzEDVqK6JpNu87tX7GEOTMTTRH9HILAGkLC0Hbnas3KH5RA5ibdPsboz9FrBDJiQvqyFjKTMyJpIaf2I34jUwqGjMC2YrRvqgTG2RGbBndw3x3u6gt1zTIRpv1slE6Q1s2VdRtHEZoGZA20jTH9cGJNfCwD3TozD21oIMAmNCc6DMxherjk2pedpnIkZB8yIyBK1cTIRJWVogRrdgCcEFdzdAlu1NvGUGI7xmx7Vk640Y2LAzfl1273PumLNUj8qpewn4KTXM1Dxgx6Chp1OEmbW2IoVMuq2EwzieArbss5uAUETbbmxIktbSZgkKecrkx7Zbx91/dosh/DH7hTHa9wR8uJ/n9p1vNkecNfhqgVn77qJABSDzq61hZ127Lj2jEG1SC01WI6YJAoONZXNPSc11/wA4kGUpg3u2lgFXsbCS7bPXFAZEfiWyiFYNzj95LQKgFQP3gAAYo9qfgUejyrvp6PprrXLoLCtl8oBHrsilTxt76UWpqHtKoQFZ8X5ZYCr6RHafVhafYw/irTwyTzonZ2Fdanb9sni/LD1zZRF4X3pGSd1F3p3t1Ud1xNqT6wUDwKUSFspSxZAEuDxhwpyr38PjlNF70gAKwD2BmxgOblk0piYFC+/1b+oieaL7rEsB7oG+ZmEVnTX9fu6YXl/w537hZ3g1nTHnhnOd8PVylGenNExpRSLG/bTgFw7vcEgVD7ZeUr/tRV5cPuVNPeLSJxzSilOf8WY94h+8+RI/efsCvZNIsOSG01pA7wrmd9f2RwZn1dx5pBh+k7b3UW5tAsLbH77Gy3+g1X3d/7tdxf29zRdM6pMRfTD9XQ5GW9NpARRbZsQAEGEAYdF/Cq/RdgEj/jR7+wxm/Zxt5Y6HfsDK2VF3i/LGidPK0NpCWkrcykI96nRYK9Q9LXGyvHKnoRIai4bVPi3QAJ+q0Qr7FICnww2K6oSSWqDeJgArOl1P+lLSVoAh4IZWlkYHzAjFyh4m/p4wdL38eweSJIrg27Poa61c/LOFsxteEa4PaYcET1UcbDirapVlfGBMqcp5XDk3zXPlp9REf0vBQavK0knTPY1dp0y8SUVrl16QiTGnOvS48oKLGopLK5izUKF7l6ShaWroawJXARGJk+pZ8WBjJUhkwqp+ANh4VDqRUN86Gn3iISCr1FwkCL13kmh+mjq+uD8pC0vo2C+nC+bU8CIvuMurApgFb9sRb1eJ/hwmqXbzs0tGOUNot523k9ut9okaWfuJeXmV8fVvXPBnf+VH6Ez47T/8Huo/PG77iWKHm85GMOFDzSfVW87G1kfdeehhkubrffyG8I19n9tze09rzJ7KbdWdor6VA7P6muw1kkRJ6fbz78wgZwmNqoGMkWpOGAEb28/S7qzPWEFQUhmlWQTeUvqYCQV9sx6S7dSOGNPqxngbRgok82BVGcC1FcUfjGLb3pjSDvKBnLlrnzW11cba6hT6pW3FRxPkN2F52YTdtkUwflQTlBVfVvkAmRKG5hgTENPiGw+nimDaWfBgDjMBqaFwAidJw8zmTLD0mzOjdsaoSEgjcGJglSEixsiyz6zx+Lf52NKmDVMyEMtsGQGUGcdpxV1ZHKicFZSbSNLySZ+hRauYETFyVk1MplGp92PmzU9O49hu32bC+ZcbvvzsHRiEr97dY3q7WzI/BRipKftoW2MjeGoBfwtve8rG3BiL//Cfwa0/EW3p2YEodOCxzijUMU8XTKnh0mUtbWmEMSg7B3DIJE66MrYc4KIRBDfQxIBpY2YZ60eK0NFIWwtAGhCYv8To+psVMCZhTlXX+wPEin3YtpdWgGT+RUJKW4KAgUJrl+rdBmyt5heYcPqN30cslmWAFLDV3gWwCZS71lawl/sKhradAVmWRhiBN8umsQydyLAChs6yXSezr8a6MqaW+ZMgvbawdMIJVRfLFug6V9Fbm3MD87hvds+ygqMGXr2rBxzz6sL/U2q4n1a8OR+w1AKgoRbpq7y8oOSOu1m2nbMENdaW0ZjwcJlxnCrqccix2PHv5hW/+OIdfunujftcqz6Xc6742eV+bFtWfP/4Dodc8dPlBd6sR1xqwdIzXkwLDrniXTug1uy6cA+qjfwPHz7D7//RZ+jvJtCLKrrJ84rLOqmkyhPgqQFcHaDahJUV2y5Q72wpy1Kxz4MAPBMpQBQeTBWA//X/+Q/x1T92hz/8nS/x6ndJxdvpk+zeBvxKdCPL5AlwzHyjTp4CKgPGNrAS1gu2XSQB2M+jl3FMd2J3gbAPtWcw61toK2c89tknqxgJKakhcVeU3aINQwix9gykbcRBctgTEmt+MkZ5XWCwuoxOizT0s3wbX3TTyDkPBgiAM26iUHoc+0UpyTE1cDCttuLqoKH3tW+xwoa9B4ZjFCd8TxcMD7GN71YFRAO0orDxRBUrF1xULTAaHSu5awvoWAY3XjurapHQ/PpK1IWFHaSLASMjGR24qSGyyogJjENqyNMFi+pvneqEC7EAWsRIhdGan+yYCTx3HEY1kOaMLcBTG2wCahpYt8nBJpGkOebHDkwdqXTk0vHi/oLv371DZ8KiEZg5CSurJCmVbjn/D/WAS1VtMy3bfFon5ItEy6l1INGY/CyCfiuKYZEHY0N1Bme6ArT2zCwm4P71GZk6/v5//2cw/TSpIPATTR3LzYS8Z4WFxb6Aftcz6D7ivQEF4qS9B70U3PJ7J3jpqHpoAv/PoNZz+4hmaQWxqq09OokEoGmaYmg6SQJqjbk/0wBybIHrbCwYo0t/l9qPgTsWkInBDEuwY0R9rC1Hy8t4h37j2AdgNvZxphVvx2LV+PYVFoEBzFmLQFb8Cxh4xJv0+aF7NX7hQ1R/AH3Qb/eM3+FsCTBn47SAzFXFSYh8QNf+3fFTsGrY1eFsxuvNiICaCCqnzM4SW5WVxfqXRFISemID9E8YcxjC3/cBKwoqOXBl8x9B2L+FJZU9yb95qrifFk2RlWuaqWNK3RkaBGFvL0GuwdYHa89IFbqIlknY53W5EGHgN8brC3RbaO+DJ7sJnIDpKOXmf/pHr4WB/D7dx2iPn+gyfk5QR+ap8drbCHBFw7O3NbHfsJ+fKgh8S8/yuX2n2s/We9SecakFOXWpesqEhbMzcCR1ubkoOwB9hrbrd8s+AKQKu0ltbLYBK4DVHYzapvZR6E+EtKOMR6waa2Lkps1qGS2dhr9xCMWuAGx8IxOBj3In+/FeetFMEMlukWshk2FMAezYBt1j8GhK1f2Lc59CUN6yO0aq4Ma/4aHZC007jOwwY0UZG2tOFR2Eh3rAi3KBSZoYwGXnHAXdzc9Ygrh/C/OcF9PiLQMrgoVe0VLtzFmfJdkx4d16wMvpIvezGmOv45hXzFMDDsAvvwDe1QPeXI54exGg6MW8bs51aRnrMmGpBZdLQW8Z5yS+yXFecZxXvJ4veDWf8b3DA16Xs2uirZyBNuNFuQD1gEOu+OKw4JhXvCwL1p7x1XKHr5Y7PK4zHpYZLw8XB90qZ7yrszDWSDJnzq3gq8c74CcHpA50Klgz4zIXnB5nHN4S8rkHe6NNhdvJUgsjkPU+mZMPCcMngHMGsoBVvchnhTp+791r/Oz3X2P6aUabGeuroin/jLR0UO2e6GNCBsZgZvMZYzNGmIJUG1b1LTMV7FIEqMY10d3MLHextzHoZX1v+rI0wyfM+VPtGcz6FtpDP+BtO25E1QU4KZ7yB2A7wUJZVylUUNIbZ5VB7vLqVNthVFRPytPXRmqjaEKlzXbx2FkjvUaVjZRWq3gYW5zcfNzhHPaaV3uhQ4lSF0wYUZIo+m7k3KF9dUNDjOFMrYRR4dEi1QLYbR0JOy8zRKZXtklLxAC5fP8dqLU/d2e1aRphVQOyKiCZIODkRB2NGAnCejukisQJr6cz3uEgx+4JSy2oNaGvSUEPkh8yeABWgPzKYzAg/sBZJx2oIDEpkAWAKgR4OzD6XUd6uaLk7nng01Txpz/7Gl/MJ/xsuXPK+OezRC4OqeKgwogXZYIsqvdl1+ZxmTCfGOis4uYMio/FU5UMP7LxLiKRmogcz7kBL1fUZUbfpyuqiDulpym3NsFvWFcONMnMymGSHQ6QbveUD/ShSIL2ZTrM4zw/sN9ze24QpuqiduWWOLsDLeGBMugjpwjRBGeDJR1N2Fhw3SvbN+pqAQru6EI3bhePbcBToe6l4q0Rtlok9tle1yq+76ANGZVoCLkP+7kNpkQ9rsHUotHfDugCy/cGLNk2dr4gbICs2J+l+ROJzY1picCwSXH/eE2uNL0M6FOQsYcovrHkLCjWSeytCCLL6A+5YoEJQZOwg02XUa+4L0A383U4uVuNdN6081A/0lKqe2GpVDVL2ggYSJmRcsOr4xl3ueLUit+7YxZmVqGu0gnCVGMQWh8MQQBYW0Kum4u/AT83N+YbtS0IRR3grmuSuaG3fA0+aZ7Ee1lYiixtRODNhkTmr9mRPR72TW2Nbh8j7Vf9PbfvXDMwuDEBPaESo7aEF5MwceZUvbrbqU045Ko2hBw8AoBu639d63pFQho+iwVARStrwn1ZPKgtfTEe2+zzckxzj4CX6WZFHUAADtAAQ4/2kOooRKTAlWWuAFZoiz1LJqY97vUGRTKlARjsKEvvi8CTF/DQOThz3/gNlzYLyBJ+aOc+qW1tO33LtCEerEpr3euRndrk7LopNRfHNz8vtsoJibune0bfpXJCVxDzmGtg1FmK6NZ/Mj/nsc24VFlrtK5pkJ2QE+O0TnicZnz/7p2k+mnWyb1mc7woF1TO+HJ+xMNhxqlN+Nn5Xp7LnnBaJxAx1ppxXia0mqRKOgGUGC/vLvjy7hF3ZcXr+YSJugNZl14c7PtqvcO5FTzWGYmkWuExr/58mI7bsax4fTjj8/kRn08n7+d1ubg/fOkZj5d7nC4TqAKpEvggRboua0F/N2F+A+RLH6AQKUDUMbSyWgeqOmxpe58+2IK/wpnQjxP6XUEvSdzAYwIXOfbPfvwK9789IVVgfQH0KTtreTp1zF9XoPIWdHvqsGMJND4zrayuK8hILHzCRlkBrX11R9/Hgi32eq+1FfvV7z+2PYNZ30JrkKp2pz57Kl9M/bOJZy+QHtlYjUd1kDtFjuM+k6bwVU21A8aEvy+ratEVM0xR7FFy5i3qAZhIuYFM6FsK7wCGxpgaJ0xK443nFAXWQW0DaMl4++YHsHLBRHUj9N7Usdq3TOwRkM4j4mGphQbErVw25W+v7lW4/u4c6OuuVU6iUL1pkFlFkX1z4WK97xZVugM8knGXF6RecCLJpzdWQmsJrWapMMUazZbONOodLhbp52BlWrFd5o2eCSebgPR8Z6C/bMj3FYfjMsrmQqoVfj6fNkb0mCtea7nal/mCI1Vn/dkioaSOSUs4n88T7i82E/KWQsp6HjbJf2x7X744S+rM5/MjfukHX+H3+hfgNI/jR3AqtI/KJY+Tqzl1PvNSiDQoDKbbbA0AK23YPrhxGHMuAjD2HC1/bh/TTNNv5QxiDlHl8T0QWEzaIhurK8gEYDPXehoeOFQ01O/0KTcNJwDOkrLXjKG1BQjQY2kQESSLwZs9UwrAJqhimmCJt+e00aTCFtCyc9lcN7by6IMhJmK217bGqkMRjZTIPXhmfe6F5mPbMLDC2CNrbpsSqWmdASSMLU4pZncrZxRNvASgpelDJUqSIh/cSdhZPq8pE9bmIb5xIHtt1zJWP8RuHgPQM8Cz6KGU0kDh2bqbKo65wp4ehkSXJWBSMacqaacw3TR41c2sqVB1zZjqsDVXg2X9/FNszYdNAo55xauXZ7zlOzDl8cWT+773S9+E8H5gySPqRLe3t8O871A7W2P9PrfvZusQEORxnTVzIIAapkFHIolRIdXuYqqh0TVHUGDoMlVN6XLR9p48fdB8F0s/G5V3CUvPmJPNWeRjAbZZLMdsPsXQNIwsYEs3dCYX8dDqonH8KY39V85Y23btboyjFvR3xzp+MLGE8aXbaOAa6uP0TpsgugFUlkVj89alCyBk1732tAm22773aQnFqSTrpjFhzgO4s3O9AK4TJjZAgxo7ssSstiCSLM5tl6aoWTvOxFI/9LHOopXFtLG5zITaDNwCcroX2ZEsAKlldFhbWYgId3nFZ69+gqUX/N7DZ3g5C6trbRmVkzC/iDFlqTT4ejp7oYKJVDORM3734Quc64S36wFfnY44XWa0lsBdCmpNU0OiL3CYKloX8fr7ecXnxxNMCubSC05tlqqG+YL7vCjwmPBVvkfOHcuBkVYSHadGWJeC9Jgwv2FQDWsmtTdRK4tqu7ZBT6QYXn0f3vOU0e8K2iFLwD5hE0z5p/7xf4Cf/MoL/Pbvfw/l/3YUG6HBi14y0oUxPdYtEQLYspn3gFTIWrnpR7WdfxP6cd9P7Y0vDXi7bQSvfAmpepT0qbqUoT2DWT9na4rYv2sHp3eunNDToKjGCRjoHlEF4Lm/aJOzrSJtFjDGU0LGqKQnxx6L9ziRxVYCgytqnABj0Z6Yr3Sy4gToUQo9BZnoZek5ad8GXG10PswIadREqn70qzRDY1tljaZs2F/KyrLtIxgVox0dg2m2ryzSd39t35jT3pW5ZeeOtM1dl21II+Qj5cNaB+HUJhe6POTqxklSDSs+n07onPCwCjvLU19yB7IZC+1QUBNE7RImBiYApYtj0UgiAk2jCLpw5QTRy7oH+GXF8fUF81RxN69uhObS8OXxAS/KBQ/14EZMDNKCL6YHHEmYWfGeReD1sc5YH2aUc5jMgU0Emj7VufhAIwZ6J/zO2y/xR//PH+D+a6mkiGuf9HazCbwzKNHGWXhvxDs4E9vUFmwm8pFiiY2j59taDYMOued6Tj8fo+C5/UlobNFwK8gBRpcn2Oc807wCAE/H0/0lDVEevDGnbY9hVQ2JVdBc50fTeAK2IE1ssTx31K8CAK+wCPbUJ2uRZUZQAMv6hLHOCJm7A2IGIvGNOV6AZgZpQMXGhLCfOEkJWwbbsI17MNBYa4BWjgw2VPYdfcT3PjYeoFcE4kzgnvXzKGJv924/NTBGJSoitYs2bgUqj3kFg7Ca82K2PQq9x5t/dSAJmljhkc38FfS1mGSbPgGYO8qhIefmDGBmCerclQVzqlINF3Atlyk13OUFBd2ridmzqQFvcXp6Rl8lzZCesCfftq0BA9yBr5c7PPz+S0wXSJrhxx7CbIpfcote4MOsKgeqPmJlfwPX88+Dg/Pc/mQ0YfQIAyYRI2k1OlvLnts0KtJpVfVJwRvxQbpXPTcWlAFZlrrmLB5qoilkABcPZpABTC4Cr8C7re9dY8sZR9sAQ+eEFSHVEIScV/ePLCtCxib9liRFJEwiZdJiUqbTG7NRgJF9EnV8LahvY1p52uhGJRK2WfQNrJ9Ln3FqsxMaXGyftzIwALxy3iFVPPZ5BFoCE820A/ethvtgWl9dbYz7bjTGuz9Xa3K9t1pkS8sOwAEIwBUjJ6Dp/N+7gFCH3DBPTdm1pgedNT1ydt8iQSor/uqrn2jWkfhTb+rBx2O+Ve0Zb+vBx/Pj00t8fTri8fGAtmRwJWBJSEsSEgADSMDysiJNHafcvFJ87wmnZUJtCTl33M8rSur48u4R//jLH8kz1JPqbzUJ+Ju+UxU2c30sOLxNmN81BV92RsAyUHoHWpN/H5q7iYZe1u5zAODpBhNYbhoA4Le++hI//Yefo3ydkSpv9K56AfpMwONuv48git20F8HXsW38J/GhgIrtn0L/4S8wQK2e4TbLZAw+tj2DWT9n65AKFpa3vCLjLq8qZGulbIdmVEJC30eNIewpT4ug7pNBwXASVs2TjgYBMHbVVrPKDUBgFBm3yqpaxDQKSdEYlffi3zhOB89CRNqE4Q+RIbBL+TikNYBcCbNWLnQB99Ci0HvHFryy/TM6zjxt9jMgzPLeo5ZJbA5kofv1iewrixatytSKkfekDK5M7MmTRoWO9+TUJtyZ7dIfcow4EYCk1Ts6ZP7q1MCFtKohBivIFsSdJP3QGFpV0hIt1dAmBJ4Y/a4hv1rxxetH3E2rR7uXmpET4/V8wX1Z/TkBgJfTBb94fOuMLNNUMyO89OKpH50Jj+sMelckh3w8JPJ0OOCjWlpPlaG1SxQn7acqHdq1ZMKfevEGj/+LCT/+ySvc/8FBrsnTRW02bTNZ7/W5Nu+fiE7sdLz2k7ODU+ZbPQWUhe2e23P7UGMMHabGEkOe0NCRQJp6JuLiAzRhA8W9D7EvBjYZA4sBZz8BA7QWCYXwrNMQXo+aVqYF5QyugXeMYxn4FI5DgLCHrs6V3CpE9pKBeLGqU/weEAfLxmzMrlvbAdgIvW+0+cL+Cewiub6fAmzm0OyZYtYs/YU02BMrHRJGOqGlidLuWkcwEoCzlOJYTWQ5Nrvuoy+75qR4iugbelXDuKPt5poZLGnbjLG/2hokBk8MmhvujhKV97RALXBzyG2ksWrfc2p4URapYIiR+j9YGAo06rmuPYnzUsP9Y//PP3gK6IrtJrvpyUZ4OV2w/iDh8fGA6V358MJ9s3cc4dM7Pgk4XWlIYuhg2XtoPGZ/Ljf7+/ixP7c/nq1qKtdlFZ0jIsYB0Mp1Vm27OyOrc0bOocIrsfsoEcgCRkrguRVnY1nq2JSbr6WjiLzMoSJSLmvr7oBL5eTb2murprgJxOuz3JhgkqamBVwwgvlmF2J2S9TgtSrlDhjp+zWAcLUnD+7b+a4a9De/IaYpAkDmvrERteetfhhtU/kM9DO9Jts+VqEH4GmGi7LLas+Syql2dAnEh5i2aT7MUaVqjL1VkTztcukFSxMg55hX/7yajnBPIwWxJzCzAlos2nvE8pwB+On5HsAWYHyzSGGm1/MZLtauLK2X+eIyPC+LsNlObcIfPr7ET0/3osOl1/jxPGM9TeBzlgruHciXJPpQDQAR+sToM4PXhHZJaCgejHlchbmFRij3FZfLhGlqWsHzS/8t/PRyj5+e7nE+T0gnQmoSzAABdMqY3gHlIWSZBMF3qFYWaYoh8/Wa5r0ZJ5DziNuw6g8bQCUgD4O63M+vvnqB4+8VqWaoWo4RIIrA003bssfQ4nb7LJebfhCcTQUoEHWjL5MgsOWDn98eBGOEzBg54ec0w/8/tyiGe25FohsdTq80UUCpQmULvVEFL0aVXawdu3QGXQBPNPKi7cee/e/IL7fqiVskPvkxhaklfVtUQ6jG2OzjQA8yJm7wqn/YglvWIqCzT/VLpKmM3LGgjHQS0pRHTwGRSEAEr0b/SulFdvAqAniDqjvGePa0ym2Z3spadaXL5L9yRmYxVEYB7jCNqFFJMgJAADySYJpSJXWvJiPRLZngD0kqNt6VVaIWeUabElpVkV4dO/cRaZDrM2aNNHVJGVmEPWb501bWtE+M/L0Lvnz9iO/dP8h5NRnL2hPu5hXHUvGPvPyZs7JKanip4/yiPOKz8ihphV0Avcd2wM/qPX7/8TWYCXOpSMT46emI2ZhRPlQeJVYBXKV9fADU0gflya+I5T6e64Qf/9FrpJ9IznjEQ2VCVwcsqQP9VJd8w8VldcBDegd2BiHmem8+5y2jY3yBayDLX5MCX8+exnN7f4tPSO0SaQQDDHE4rLJdZODavOdgEuBAzAZUQgCmoE6Npe1hux3REFq36omRiQWGH9OZWjzSED0ip8c0QIsBBddC1T8b79W1GD+oKw0u33eIodsJNGxT+yyVENiyd9mPSw5exf3MBoxzCWmVPIArOyciEWI3prOlc8rZakrj7h5EzSi7lwZmefUqzqiQfosHW1RUPTUwgJWyaHB0HTGpVL6lVFP4Z5iXVkM0wEvI2TzW8AVIdxV3xxV306K2NHmVqymLXs1n88mdnEQsABZ13KUVh7Q6c0LWPwWnLmkkDGF1EVgqSZ1JnAW7x6zP3Z4p6zfwI2zNe74mNV21Jzw+HEGndLuSYtTNei84FnSz4nZxnGpvtjZlvL/plHyobcZkXsWzrfkuNqtP0LrMExkyjwsoMdy9o877Liau69NTkwrn7puQgiAKNBlIcl9Wn+sSMU66/p1S83lHUgxVN5i0OIWOxdhGMXhsKYqeysg8tIGjn8SjSp8VXLJ0NAseTLS6LtK+RT9gUqZqlBWx9b8Fs22cVuTJGUsx0I7kbDYnCfDQME46adp3MbPD0ggnDB9QKqBL/+cq3y8m0q7XwcT8RQet+f0Q4LFhVnkZ+7xDwK2q+9WelH3dUDmjNkm3XJqkZtaePPhtbS4Swi956x/Ge5cSo7fJ0wilurtc38+nkxfXAgQMPLUJP7m8wMM646vTEY9nYWrVVdI5uSbwJYEWEjALEDZwgbxPLMH8iYGVQGuSOTMx+NDBSxGGVWa0OaGeCtqx4TCt+IfvPsO78wHn04z69Yz8kDC9Tbh7IxIt7fsA1oT8mDC9ZZRzU1BJL4ilGFoBLGNm+cPWgXxLQArbKoa37NRYYOlfuI3rTPinfu138O7PHPDf/86fwvG/Olzvb91EOwMaQXuz8/weH8mWTaab5QEuG4vaLt6DYTZ2dW9c+kblDaDXsKkflcf5GRng/bb0uj2DWd9Su/SMxzo5JT4lRiJ4tcIssMi1sCGNvPAtA2i8rpp3bJU6ouaIpxxyFHsfE40LJNpnGEysopRimbxDZUPum+Pvm/VhYrQTicChVPsbi81pt58DXSS56caEsoltpqqMgz1ANqIj+xRG67fpQtia9Xnu05beq3TfvlsVevUUZDVEtBnHysmpvcbOku23QNa5TSg8roHlkXdOXgL2qGDWq+MFRIwlF2XwEJCBptWfShHKa8lyP2Lqy1Kza261NaF3wnS34svXj/jHPvsJXk1n1J7x0Ga84SMOqDgWWTh8eXjA59OjGyErrfzl/IiJGh7bAe/awQ3VV/Uef+/NL+CHP/0cKXW8upOqKg+nGYc3BKrXz4pFyKmxCCK+r31KCXW9Tg91Br6eUB5IJtrAzPqYBf8mggFcg00fGIMDA7vJm+naKJhTZIZIKoWEoydzhr6Jp/Lc/qS1yglrF1USqVw4tIagTCNLQQTgaWzAYEdZRTz5dDyLpsllleZs/QRQSDnERhfL+hhA1fjMmFiS9iDfeGo3aMMGu9U2FQRJ7KjZr/ft54AySR+uQ2UR+QD6bfbTq+Ki79gDXKprtRd+gArtbj7f9mNtDTaMWM4wHiOyuTY6YcAGyDJ7ZM3S9phJNL8gbAyGBCBAjNYlt9n4UxIdZRFpJ0ZKOpKwMjXxX26ErkzgVBrujyu+ODzikIXtsfSMCxMKOpCkvzst2c4+bjkvYXGILV568fM89wk/vdzjzekORFIBkUFY1oxywc1IrbOxPirN8FPnWK0kfUlIi6JVn9jF+A19w2HsQbn3LPL3DsUeGBvVFp9tzXe1ScrfeO/MURYNIdM7fTVdNPuBXLxctJ7YU/ksXTDOT8boiql3d2XdrKnPJtBOopNrgMo5CHPb+rL2Ib6+tIxZGV6JRsEnmzPt9V1aXUMLSQMvYYymOxW1qsyHAAT0k+Byd5/D5DSMeBCZTjZO04+VrA6ZZ30cdr11Pi/U0HuRwls6Z9s497Iwibr4B8pWs2bnJNUiJVjxbj04U84ALbt39tqkUuZUMVPfpO9VToEZJuextCBI3zPWnnBeiwBeXa7BcZJUVXuWjqVi7UnSWQMrb+Xk2lymG/yiXPC96QH3+YLHdsBZj/+2HvFH55f40cNL1CZ95dxRq9h3SkCvBFoSyIIqmUVgnIH+ooHWBE4s33cgX4SpZY8DdQGyMHfwmiTz5a7idJnxs5+KHzG9TXjxhpDPwPFnjDYBeK3rmIVQHgmHNx20DvF3dH2QTBNYNbPY/J2nskueAq9CyuHGjwiavfJe/vwPP/0+vvrdz1G+TuNzm9pvmcFv4l/sTcXutRMHdJFJPt7dGsy0sOK4VHKFgW3a4i1Q7CPaM5j1c7bGLD/ONrn+1Bkyeb8iWX0ZoAWMlEENcw5wy9hYFErhBt6eof6FOi72ORlAtN12mxstzKUpOM8Gopn4n9FgDXCzFo3TvtIhYIvu7CVpZZ/BlIqA077SVDQIKxcfRxRf3xxLwTrXodK0yniMrIvjKOgYIzOdCRXbSiITLKc+bZhxh1SvAL1DrppfnTdplhsj2iXd1JylQg1LljGctJLMnCpSYbx8ecH5ruDr5Q6XWvA4iaG/m1fMWSMr6lReWsG5FpyXCVNpmEpDTvKsHErFq+mCP3P/Fb43v3Nj+thm3LUVVjq5pI4XZcFn0wmnNuPHywu8WY7oTHg9nzFRw7t2wGMfegC1Z/zw8XP81h9+D+3NDLqvKFmo6+tpwqu3jLQHq6JzsRdDjAKIt3LF958lACnJ5K6CxczAD+7eAn/hH+K3/+gL1N99gTYlJJtQ2wecGvdYeRgc+4xl4u2Fbu8XHAQDqfx9NzbD6NNAM4820K4/346fmVnP7b2tQ1K6DTQhsKdEHJI8bKafAQSQiAEDsoBhc7xqYWBgASN4YBF5YDhE+2032lgKTMWgjAFhXb8TyzZ0q3w7+0FhC67FfgQQGczlmOq4EYPfAWTdQbzAkNoxrW4dy4IZ4xxHM82tziMNMAJcMgfQ1j4FcCsy4ww0jM2cDEsBHWu7AKwxodo1YFLnbwgWW+VeSox5rqiccGmi/7I2FSHOXbfT4t3Enl5SW0ZKXf6RCMlbqtLr6Yz7vPi6eeWMQlmeSRbHcVbNnbVnqYzVRC/rkMWJXLQqlS9mmfBmPeJn7+7BlwxMXcE1Rq9Z0imu5sjwjPfdvO+25DrAsP1+bAainSMBvJgW4Bff4KuHO/Q3M3oSFtaIIn+ErbkFQJnt+AAT2W1N+HvV3/Yh0X6fGId/8Ny+i60zISdhRRqrJpvQenhmHBAH+7o3qXaWsZ0SDR0jAEBIgzP9LXsdtZEABVpI1sDJgiHKFJJ0RxqMJ0A1h5sDaAOs79jr1EpV9urB5JwrGpOv26PGa9S2imytjDFfGhOrhZ9FTDOc0HDpkzPOKstr+xmtLEW6DEwafUQtsnHdbWw2LvPfJFCVHbySYHN29tpDnaXiq9omG/+pSkGsWC3SUgjn1NBJfADz+RIYx7zKHNyy+xmNk7xnQu9SYRBQe5gJORGWKmM9o6C2JP6Ano+lDhrwVnLD59MJf/rwFRJ1PLaDVg4s+L3TZ/jZ5R6nVfSsiKTq5rpmtFU0ElEJaARqNOYzJnDqaugBnoKGMIB2YPBRr3cn6eOgk+KafJ1x+uoIeiiY3hGmt4TDzxiHN4y8MNZ7wtoJ1IF0SSiPwPymS+Bc7Qz1LhUMTSuLWVhZXfSyZH3yLQUNGFcC8F9/fY/jH2RJtdTiW1c+BqLNYLkeUesKuC6MZX4Rj878uKzroagzHAuU6DbOvgpwh8sWMLZBl+B3GfNL9vu0a/cMZv2crQF47DNqT7jUgilLimFJ3dk+B524TaAQkOcF2GpPeZ+6rU1mjYXCWdtet4OQFFCK+leRhQVY/rbQcld1hCaqDkYlbJlZA2SSsrWJ2Bf2t8Zrk7dVGLQmgJVuS0P9ygCvCFytLHpjsu3YzsXj9+DVjo3lERb9965NnjpozSo2mjO2ckKl7Pncckxy1pX0O851DVGQxsNRmVNF7RJhrizRhZ604oeO//U8qmnMqeJFWfCiXJDRcbmb8NBm/OTyAqc6+TW8tOJlbJeacb5MYAZK6XhxXPB6vuCzwwlfzI94Xc74ojw6oNcV1LOIzcvpgi/nRxxSlcqbKmoplPEFX+h3Qk3vuM8iSvnQZvz0dI/2ZkY6JfScsawq4vv1hOkda3RitzhuMtmj3ajsERfvT4kgQiZZzhl8yFKatiS0mZAS46vlDn//7/4y5q+kZO3lywnUGdSA8tiEDrzD2Lycrh0/kUcWCKTAFHu53T319paxoL3x4Cf+he9ZU3sc6OqfNmk/tz+ZzUCDzjK3pCQphjHinKk7GONC2r7/NXhj28ZKh9GGWBPgxQClsXAZLCzbTkA2S4fw1EIMTa0NMwuswBEhO+BF7x1v3BfhvY8BI8Xd0/1CX/uKtradi8dfgVdbNpZrbHkUfMsw3m9j4+vaUw/nt9Ej211vG8MmqGRBH53bO1tKC4FbATPhoFUDzR7PIW2oloukjldZsxjoZ+wr0dsh1JrBDKScMJeGQ644lBV3WdJ3jml1QM803ESDDZipSVUrY3Ao6MeACr6vHuxJ+jxYEOpUJ/BFtFE4JbSmYNc5IS3mSGDbgmbJVaPtG7bF940NOSUgiz3iROhZnsdzK/jpj14jn3Wdc5d8/k5rlzT3K2zI1j2GLMOjGqTXYoBPfDUmZw4/BViFQ0QQa/99IMPr4Z9tzXe9FQWyMFXRyyrVv5tyc1aPzVsmoG7NJEpsG/MdmoJMEbCxtLUa/JXILjIALepQ2fENoDHW1KwV0m2fCPQcygIgbWRYOqQg1pW2bwDtTAdq8x2wAbvknDn4B+TBdqv8njbXLOiHgd1HgfooxmpLybQdo47xdj63rBvTMfaqirtzqhokt2vnml192PlznXAspn21vSd2fNt3ziuWZkVExiI2gn8ly7XrnSQLpCe0PvQymQklSyXEu2n1+1dSQ0kCYh1SxX1e0Djh63qHN/UOD23GH51f4mGVKuQv5wtezhec64TeE9aloD9MSKc00goBoEg6YapATyTsLL1e1AR44iwajphYQLBK4LlLJKkSaCXwXUdfk4NfaSFMb4D5HWN66HoMAhcABOQLJMXwcYCXaOr3RK2s1sEGbjnwxjfn92/aHBQC8L/81d/F218+4rd+9D0c/st75GUEPDAujVyfK78lFgraHcT8o2C7LDXRAv0cbU7U16Ib/dkxbvhDe19osz3xJ+FZz2DWz9k6hHFjEYq4eLO/Q2B9W60i0k7tvQE3PnlyQUwn2KcSdhqRDhA20QvQmHCjuONEFVEzKymi35kAHrnjccI3+m0PrCdr8f1mQU9SZSkjCiymIc4O0eIa4JIKt4ftDbAalQ9HFcKk22WFdO06rpy9IomBiB5N0rHGay7XaUz0lnq3Pz8Dsew62ndGq7WIhoFEjQmXlvG4TlhUuBEA7jJ7SfKJmr5e8aqcUTnjq+UOXy13uCsCMObUcc4Fd1qF4+V8wfePD3g9nTGnivu04JAq3ml64FfrHb5a7vHV+Q5LkxLzh9zwh/k1DqUqdTjhUKr386qc/fy/mB5xSCum1nBqk5xzYjEsVUqkt5owvSHMD03zqUNzIKtLmdregXQtxP/Blgn9rqDeF/RJaNNtIlDqeDldcP9n3uHh5R2mtzOoDVBongipMlLt5tlumy36O/vEfROQojDJMqsgI3YTOa4maGDLyOKwra9tKHyO6/2f23PbNwGzkkdxmQldn6POKhaqD1asimd/ZT1B/t4WpVH0PTKNfFse7zuT6zHFRXlywGgcmyA2IGpmkUbiGfDCel1thJ1TCoAc7VYzG8AH5H0Y64v0cyi4krFNJ/TqhzyE2y1QY4BVnO+NySVpnEmrPcZ+kqf825hFh2swzLYVDgeAZaBZtJlxaomOln1ngS0R5NX7znKMpto3jZMzAaYkYFHRNUXmjsId8yypgedWJDU+DSepUvLU9jlX3JfVU3Im1btaesHCklZ/bhPOtXgKYU6MBzo4G4RZmCLWz0GdHiLGMa1SQUoDQa6T0QnojN4IvReki2ozPgVkMYsd+qaOQwK4JPQpSVSZNLqsDLPpswXrXNCWDDC5w5BT0gg9jx9ZbBH5tTjcEwBVdDjMMXgSe7qxnx0mtg0oFsfy3L6zLTJvmcnXpXclAg7dJUju8ur7WLDd1r+HsjjgEv0Qq0K4zwhZW0YPwcpEEsCNulggqO9xXQ0wgjju44CcGGCZK5UTehsi8YUa5sSb/QABrcwPs2Pcp+WqsmA8pgV091kgY5uEQ1px6rOzrjpD0xr70PJSsA0AajhGSc2lPRLEYTedsH2VdBvTuU4u5t84OYhlqaN23S6toHHHGp6BE0/O2rL7b5IoXQMRUxq+3rhXcELANDWU3MR+p+56iK8PZzQNisy5uS9hAKUxtd7hgK/We3y9HvFYZyRivJpVrmSdsbasz1hCvRRQI6SVQHXMb8yEXmRCMw0sqMg7Mw/dpWnYCS4KZDEBjcCFQYXBLck83oB8BsqJUc7szCBOEE2uDuQTYX7bkTTFUILmDdSa+DhW4MqArHHjnk41lJsz/u0/D+7SHuixR+S/+9EPcP7tVyiP5Gl8V6DQXkt4/9i7DbEgfvRbdrbUfSIW+0y4Yv/6ONS34ghiGdBmusL2XRxTWAB9atzlGcz6OVtj4KEecGnyo50BlFAG99KL5HjnFROAUTGwu+CfCbDHCTam65nwoEVi98BM0clWnh95PaXmpbDHWMlFEu0pXDkhg1B7ezLFrySJWCRmB7Xs+B3kIFRkR3n0nV35TaKvvaAlcV72OlcxQvvYZwfTrJKIXxuM9MbOQi22Y/g1YhFvl8l0MLvMGG41xraOQ2S5WZ+DGjx0y+y7GsQaW99Cfayg1qmK4fj8cNLo9opLL6gQrTET3nyZL3hxJ1UFTYT+XTtsRP9HKqosOn58eYmvljv86OElHi8zliWjnicpX2vX9r5initKaahVNN1e3Z3xU5IqJK8PZ8yp4XuHB/za/Y/9fCbqmFIHiiAx1EgcjEvByzeEfLpOMSTNHUdrkmZ4q91yOOJnJJHyPotwsVFsiUVEeU4Vh2nFYx7Ch2aI6pHQpyRgFkIU4n125cbkua9u6Ns93c3m+w2Q5YO0vwSjBj/7Fs/tY1pnSTNs6vRnjPQ/0gV+YxXhDYAWESMp4GG6VnFxHtP1JH3KdKoGEOMpimQAzGAwWTrbJs0OYh8MXAOUPcWEjjHH7pv1bwCX/5a0T9GWHLYn9hNTFV20PayZrphTaiNMu1Gu8VZo3gCqrgykgj5AifCv2fFC1JrCsaPu1VZTbIsvRBAr6pbF75zttZuw7J5VtUHHXL1cuggqk+ulJOqYqWGehCVsTKkl6FIO8X5pjRMe24xzK3hYZqytoFVCr3m7aJ46cpEUwd4TiIBDWXFSIWNLM7wvKz6fHv18JOjGinLKgpmZwCthvmBbyTDeAXckntBmvDXvb2wNwJTcznh0WdMsxBFsWNNYn9ml70VYXBbQsd0+1dbcBOE+1A+C7bgBmt18/WxsvtPNbm9j0opwMh/cTauD83OuDmpElhMAvF2POGT9DqzzhGzzoPOCAUhREL63oZBrc5SlnNua1YLGYy0+qrxH9pStwY0FBsCr8U1J+jRACICm9239n32gRbZraDQ0eQV02hfJ0uunmlt3looXCnYl2jK2rHqf+FcE6PW5tOIAFyABcEuftMwO8yc8A6fnoYPFye1YTt3Zs3IPugNP8fOmgeqchJFrvljjhFmF3hNvdbkSNLWcpdLhaZ0EY0iM2sSmmAb0i3nxILullKa84pgr7rKApRmSBvnbj19iaWXIClDHMVcsOvavznc4rRPOa8HlLKystiTgkrfaiB2CVHRIumFm9KOkGdKhjUBSTA9vuu6Y+0g1NN2sTkhvCtKZUM6EcgLKRdcQWdIZ20RoR4AqUE7A4WsFraQymUiZWPDE/J3eJcVQfgQDkLoRzL9KLX8iO8V8lwgyGYny9O6A40+SC6tb5giU7XSzDdLl9hgxbTAE+WWw2NohO4a+JmAb4MduWwPMLI0QCMDWsJckCylhxLGSEz6yQj3wDGb93K1DEPlLFbE8LuQlao0SOgWDEWmq3gcnT3ObUnPqrOWzAyYuSyOCCt4swA+5Dp0onfilUmEAlQCsu/EYuGWAFCCTlC1OjX4rwpDdWVDWxMCs6nwAk5b+HeeVkcL2Fy5CWw2AV9NJOzGPvO2e0SjC02NbO2czSgaA2Ws/lkaOukatD0Es36upBNDKrp+V9Y0imInYyxRXBSetr3OTqPIStgeUvaUTd+sJK2W8WY4eCbPIzdt6wAtlbV36hBflgi/yI+7zBZc+4S6vOLUJlz7hx8sL/OHjK6yaftF6wtcPd7g8SuladEK6JKQmE38vDJ4ZjYDTRcZHiUGZsVwKWk3IU8NX8x3mUnF6MRyOSyv4/dNrvD0fABNg7IxeE9K7gsPX7NFyZxzZ5N6VlXUrzfB9WllxUs+7FESb/AB8tdzjZ7/1BeavEspp2x2np1H9q/zwzZc2Hp14d6VmLVrg39H2ewOuNhTeW8eInqt2Q3sj8Nye264xxA4Y85NTR0J3thMwGKPCohqAlvcRgyEa5XXWkW4z0gPlvYAatr/YB9u2MqkDvwOVAJVnGOMxcMvYujYG/x3QqH5olf7iD5kggRurUmgAjB2zIW00lSz1LYJ1Xl0Qg7kg9mfst2fvRlH7FugyERAUp0j27H3Y3QiiRSzBziqWvLfrJALMlqKeN1Hz6g6OjQ0ACbCYCM7WIk64aGVlAx0XTrj0jFn7q+rg3KUVU6qonFG4eYrKY5vxsM4bm3tZJtQ1AVVT7aostK2oBQtxaUgikDw7rUp5dEod5zIhaxU1YEgEvF0PWGqR9BAmMLPssyTkC7z8uF9Y/xdYWVeg0A1bs/mKxmui8Z7Hn3ObcPrZHfKZkNZtd0x4TwT5PbbGOqcb4FZwEG4HWXBbN+s9x/iEUT23P+bNwKjeE3JpMAH0koaQe+3Z15ymdQQMoCoGGkYgdctQiuzcOVU81tl9HxMUt1S3RcXNj7puTjfS/EY63KhWuGrBijg22SY5IBebAFbsfpIFuz1bA1ZEJGuKIJx0EM9r7UPUfaPVpcEU+9zSCAuApgvGpZeNLlblosAc0Ihc/9iuT2+0AZdiZohVFZT7INcya+okM+HSd8gEgHMtyKljoYwp9SF5omO6K6szxqwgAKB6v2HcTdlf94cVr49nfDaf8L3DIyon1/99rJIqeMjVn7uv6j1+dH6JH779HJe1OJPrs8MZD6sQP95eZrG9PeFyKahrBi8J9FCQqvguVBW4SQIqYYIUTlI2FgCp6k5wphAyi/1YRBQeizoDHaAXTezJu4x8IpR3hMPXQHkQnSzW6b9nwvpCQLN8JsxvGEUzUMTWGXDVRwXDHlIMuUOUzQNy9LGl+Xb+Ce+ZUcEE/oVf/T08/OkZv/2jLzH/V/fYGC3bTSS1tS/9LAJaboN2gXtjlV0FXdQVJ94CcmZ3b9kqO46x3hQ4Mz/NIYUQcCHb5xPaM5j1LTVL3SKdlBcFpCyKYE7GygkH6oKQg52VBcDR+SGmOBZ8tVsFirqhCBvIhVZwUBFEyflOyOBNdMP6y5lVlFFagjo2IbUQ+tlEwthKaDiozpa1W0ysDL7idhnjSo61dRosug6IoVj70Izya7KjCct1DKAVj7z52oXpZFEoo7kacAckF1nclAGGVF4yoDHm5k+JcTHAqhXMCvTYmCtnFdUd6T+mVWAGyM5l1VSQMkk05gTRrrLjT9TwZXkIgpRiOH66vsBPLi/w09M9Hi6id1JrRmuE9TRJNGO1SULospT1dZEJHpckTkJm8NTRWxEj8AI41Yx1yjhMFb+5/iIelwlfv7lHfzth+jrj/muZqM4zgZeE8iCTvFf38BK1GNTb2sC9Py2CaHpZ+jp+vm8WdSEGuBNeT2f8+l/4IX7np1+gvnmN8vg+p+JGY583r4cVIxU3ohLivCMYAlzni+/xuwCG2Xuf5J+9i+f2Cc0j32BfEJIKZacANBm7aehYBfDHIQ75PzoqFhWGBjGcLaVAkDtGLFHbBgG+nImF4fiQprcZs8tgIROctWYAlmlWZQetxvfMW2Dq1u/XdJzse9/fzxZ6/tuUQmCAVoxtFcFNgY8I+im4ZICZpckZIAUeDLFovwiMSVNGrB97ncAe3LEIuzkzrDbNUkPsM8Iob09h7WzHPqQKBvtawUDBTB13afFxsTIDTn3CY51xqhPWOsSAe4ewsCrB8lvHvKfOQGJNEdQ7QCz2ZhXwCzNQF3EYLqnjx+cXWFvC5TyDl4R0Tpgueg4vANZ0k3yRaPjG1kQgq3cwPxlCkCfAvrwCtG5urnO2BAq/94Ov8fXpDv13DyjrN7A19Mlrc9/3Kiru5/HE0M22RFvzseDXc/tj38yHALbaS65PVViC32HOjxkLAJyVZIH2wSCC6ztddD6ZU8O5FWVqNQVrVBcr6GMdVUfPAjIlbdPVoxh87QknTF5J8S4LAGP6XYcAZJn4uoxbKqrv+7eME+v/ZZJJxtbZMQvEP4cAeZZeaT5B4+LXrfbBDjbJmJjFYb7fxRhK6tcZQ23W4H9Bd//Czt/Pr+UNONc1kC0khuZFO0xSBNAMEWXVPq4Tptwc5Go3ADBLRW1qHzIx5tJwLBf80v0bfDk/+r291IP4H12ArGOueJEXXHrBm/WIpWf86PEVvnp3h/VSwJ1QDhUPhzkEnwRsrWtGvWSgJtCSkCokvbBBgRPlL8uiQfYtDFrV/nWWtMKswJUBW5BAiwFZfJT8N3pXkE/qv7wVIGt+UFtdCH1SMOu17JrPwOENIy1NAJ7aQbUPH0flVBCBLGta1OnK//lAGnwElW4FxkkDVr/5B78I/q0XmM6E1EJQMPote2f8iTR8O6bbNfXLdtyVEeuJTK5di77OZt99JA9b2xSzVN4fJLrdnsGsn7M1jKpEMQouE5OUmY2C6161UG9b9cmzI2dWQzSgU0ubK6kj8bbyB4BN2VwDaDZRFQ552B0BWBlGTN5v89WjYTFDYMeJqYb2ecbQyBp9SCqlLbxtkW5P+IWLL94vvbhBsTTjGKkBpU2lkT0tOIJcZlAkwjHS8x7a7ACaG4s0zmFl0dnKeeh6rBrhtjK/MqGLmKUd81ILTnXyaBQzYdbyx60nQA2RlbS18V6Uvmzg5BflUYGsisd+UCH7A37//Bn+8PQKj+uE6pVGxJHsLQuQpSmFXDRykRlM7E4HKiEtScQR7fNOwCQOLi8JXBrePB7x+OaI9PWE+WcJ0ztg/kpmp8sX0heds+hlvatDL8vYWCFqIZU9eNxQoi191doNVtZ1et92Un+sE37zt34J+acTXi62zSdMgMMjBSUzmLePvW8DkJLr6FEFYABgIYpClsK/224DaH0jT+e5/Ulq6rvD0tOs2XwnBWiG4Lprp2A354NBqktFYQVh/SfSRD9bGLutYCdZNgdGeDOOMS8n/841opw1tgOaQirbXmcq7UAt29c0smIfmYctNAaW/VgNhDKAx20kbcfoVyxMJLybVOy4DUOk3VIvoeNbenYA7VY6oGybBts5jK3qWB1kCldZquUmtD6uGNHQo4lMuD1w15QlkajjLq0KZHWsLI7Wwhlv6wEP9YC1DaaZXUvmNIAsAFZNCgnyRIZ1hqSDYFCMGGKTGD5nXtYJ66WAzklYT4s4DwDQjhh26wLkhbdzpLGw9v821XLxdCP/Dzc3DMdae8JPfvYKdMpQEsM3szW+mP8EVMn3DSK7N8Z4fYxdP3xjm+f2nWyTAlNEjLUJqDOFtf25jpTAmD2yBs8zKehUqEv2xi4ga/6CMa0ErKqaoSBMJKmGuH3YDrmiqmaSHb/rb9bW5Pv5slAX8XkFgZrP6aQawAZUDcCqpH4FYMl5Dt9ln+puQXOTWxHGVtpsNyRKBgBnXRhxoKSuTCsDC7tm6jS/1hcehaiAkXnj41IALAbCGcCigBQgNm/taeO7SZq2Msq0Km3rhMt6QMkNa0touYv+FraMOECmCNPCelku+Hw+4U41xh7qwa9xSR1vliPm1PCuZzzWoRsNAO8uM9ZLQT8X0KGhniesjzMod1CWa8adRLtqSaBFKhbKPyBd1JYywJnQJ7E1nADO7ExgAApY2cJE7XjikZqYSHS11oR8TignGjpZFwmGcCaRJ5mB5RWh3jPyQpjeMaa3TQMpuuY3HWADstz+BM0s41p0W+h/4qS7Z0nt/Ka1ZyyPE+7e0SYl85Y0ypP9xo+jZpYBSU9sy+n2caJNZNJrpbafgRFc6ezRHb8sO7sWga2Pbc9g1rfUDrlh1UnUoqTGzDJmkJdoNVBHm1ewgABIo7LeNaXX0hdjykFkcFkfsQpf1qhCp1EtI07QA9hqfhw7VkPyykXWNkaAOvai8BbxWTd6XSHSoKLtjdMmJda1Szo2lGA/ro7JjJ4ZHa+0yMKSMtZULBN7rhKxiRVSAHh1QblnCReNRE267anZfqYdULwKSCIpTezUba34AQBFV71mbOweHp1eLNokh1TxolzwvekB35/eIqPjbT/isR3wh8tr/MHplVN5p9Sx1Iy1ZpwfZ4l2VzEA4uFBmFdatpYygxuckdUPHTjo9VwMzNOoOQDuCY9vJtC7gvKOMH8FHH/GmB472kxYPtNStYuwslwvq2vUvPGIWFhlDxawjHrHJo3D2geAo00p9oD0ixYbwJMYoq2js+ukA5SuJ9+nnJH9RD2iBypxreDTiPTDaSL7qPjwvkdf4HEe+3TG5/bcPtRykoXVYE3ZI2bsI5u/oQuJuFgdAJeBO8AWsNHdfLFrLB8Dumw+s/TDfRBE0vs4/AQG6BWBLTtOrDhYqG1miE1FQwXrNnUGaaQK+j7hXBq2+ofWuvySPRUxXhuE8984GKEfBtSx0gg9D70sqxJo4vWuZ6IMZmO6VSZ3yAhSzMSgK7Hf4rjZWCoPKxu1sxKN48RzSR7NV70v6piTAFn3+QICY+GCtRcpHlLnUYmLWGQPe0Jd8gh+cASJCLrC14eGBeyCCO0ibx0MuaBjtbteMrAkpMWcCyCtkqrYDjLPCitLPrcLH1MK7TXre6tEqw/H5p5/WBg+ID405nd/ntK1U3Gri1ssrI9dmEeHwGyJ4YFP77Qbftw/7PdJANxz+2PZbPaccsNcBLy41OKfRY2qlTKQBghjmR4AhhTGzo9JJMWLOshZRZ0JU17Eb+HkhY6s2Xrd0pvn1JCo46yyHQA2vs2iFXuHf0KulTXkRq7n86JjszR8S/Wa0lZ4x/qdqOGxzaE41Va6xNboMbvl1CYtpjHGZuyrtWfPiAHgKYjRjzuoPpaJxJtGVklds3k6FhVmB+DC6Ffny1JhcG154+sBQFGwyn05Y/ayiMUzN5SsYGSpmFLDi2mRYlJlwetywSGtfg1WznhbjzjpuM5tcg3g1hOm3LDWaYjd94TzMoGZkF+s4EbolwyqCZwT+NDEDqwiXUIrIZ8UxKohvTAAIwJiCZAla++wBliSAFyNwJP4Pli1EqJult4WoAPTW9XIOgFpUXuTCG0C6j1Q7wnn7zPK0vBv/O2/jf/13/8f8d/d/SP4mz/434od6AJeUQSxTKsx+ircgZ6u9KmebJ9YtGRKDX/+134P7/70AT/88eeY/+uRZuj6jbf0pvbMrLjYe2rbp76/tT1226u/s8Hz5GcxgvvvM46fYLCewaxvoR1yxaR0TyudLQvHtsnzlgqCEhktAVTZ56LvJ0EDq2xyquEXYjpcxsiyqkX7FkXLra2ccKfUYe8vAFkWnbDjTBhltKNzcKC6EXyPx7dos5WUH86QglXUce4D0QdGhES+30Zq6o4iG8V79y3pvZBI0TZH33L5pb+Czg0pmzBixkJDvBLQaEovV2V+LUUwkZSyBeCAltG9E0nK4ZQajnl1cf4X+eJlzidquPQJb9sRf7S8wlfrHc5qHF5OFzciS8t4sxTwY0F6VB0rkrRCnhjo5G7XWBTri6mDSgfXJI5G0fu0JuDQ0NVhoUaeRjg9dFBX0KUAYML0QDh8LdU9wNCUwsDIsgn+ll7WN2mG8APirCTGfVnwq7/yR/iDr16j/cEr35T68K+u+1GH/8noxA6IwnsiHTYJ0+61j0M+8J91nNw3B8XHGYrn9twgEVERBSdnJyWdl+M8uNefAkY62x64QvhM5uexholzr+lwiZC7Mbiuf2hRtNxaB6Fgy6aKQJaN1T63Sk9itUZvVsUWYd84PtHOGumIVvER2mfdIcfxDEj7dBH53UKqOTx13Ywt10J6ugNPup/qctB7AAEAAElEQVTZPYHEDBgjNB7AoJwjbyL11pelCBqA2G28eh5CRmJPrxEnS56XWZ+bQsJYqJyx9IKHNuPcp43DyWiopAVWLkkcg9XQHQGuOGM7WaZ4FfW9pRwGjRM0khLrGrGlDqQVDljJojcraCQphuXcJBgiN13TOWx1vHMofu7GA7AjeRam1PD5Zw94dz6iv5s96O443hO25n0MrJvA0jetxGhY4od2fbYz3/lmQIqt4+fSlK0rqWjIwLkV3Ju8BncUEh9G0uBEoHztWSqPunRJcqZVXN9bFomBUhHgMqZu1UreIuPBWPUXdNQ0xnNkIGGbIWJAlDPIVEfL0h5tXS+r9sHEimmIjdOGkRW1elOwj6abJTZwMMDsHM2vs2bzszHRRtCCHGAyX27fEjjolBnbaaRoLi3LsRIwMWENelkmZyMC7aNPP36X+5WT+B73h8WDH4epYkodhyIay3dlxev5hJdl2aScrzyKZ1244OvliM5JQCyIRMDaJhHK1+JnlqJ4WkUDKxdhf/eHCbTonJogmSQLDb3FlZAWA7N0DW9phjrF9wljrVx4ZJwcupiBFNY1q/SNTr5/PhPyhTC9E1uTF0ZeGX0itEk0stbXwOVzBhLwr/3tv41/7b/+PyMB+N/g7yEtHf/HH/xF6T+KvrOtRVj8nX2Luln76oamFfyhAElsIcjym7/3A6TfusN8pq2eJMRP2qb43WD2Au633GJZyddbQIvfB87Zdkn274mubY7ZqqBrbD8RD/CzXDbz5T62PYNZ31KznOOVZKK20tYTdRxyVY2qAfR4JMJKaJPQda1KRiZGpoaurJsKmWQtlQ363qIEc4BhOydkGmLn1l9kVJmBeqgHB1cAeP54TDO0iLKnE2Lbl32eQ+TEPr8FZDVIxMSi+PvUu2iMHvu8Ec03kG1EUxImWvUYooUVRS6rHnsJulpuvGoB6TVdkF3HLFY+POaKRGLohsB72gCMTR2nnDpqSyhZIjV30yqORG5Ox55Tw6tyxmflhPu04G074kfLKz+eHKc4Xfmxznh7OeC0TrisBedlkhxzcwQaABC63BiJYmQGCiMf5Plhnfhp6jLBJway5JBzJWDqSKWjnzPorOKIj6HCRwJ6AdokRqY8AvPbriVqu7Cy+i6P3EEtfpJqe1XRA/i4xTwTfnq5x+/+v34J05uErALwnxR5fsoBuBmh2G2vr9Ubh1UbMb/qtqMSPjPwa//6uT23j2hWzZBCCgOR2QwJZmxEz6HrSF9gy1xqDoj47exOj4EmMRos+3c5zobBZGjuWExHtpgdn3WBnHSMdh4GfLl+FIZQvEFNG2aXf47NSucpIMuqKRIZhKTbM6Fi6EcBcDs12GumwUW+T9ZUeksZd8BK7Sljq6tlW7QuKzyJsmd0Sg7g2XgL5PpGaYD9PVGIfMPUJhInT4JpY02RiTHHYAkLeGXHs9RDY9ytPWNp4si2liSl3YTc3bTLIpm6CeYKaEWFZd2ukXMvh06QtBcm8ekyC0u2kuikVBFVt0qFTAmcCno+gHoRZtZyAfgC6ufByuo70Xe5WOP1x7BwP8pWEE5twtd/+Ar5QnAf+Wmc6roFf+Cj2+4HJPd+dOTPwlP90u1+ngGt73ZrIM/smPM2ze9Q6oZ5aZX1MjHQZR5MIEx5sHq20iXSrxWNsjlKqufKmnlkMsgxa89IZR1B4dRw8cIP4sVGX8PmtFkZTGvPeD2d/Ryk4JQxvOrG5sT9ow6WBeDNh1ndx+ojOL8JmGe/hqYlFgP+l55HVXgYMJU3gfxC3c/Tsjw6k1+fSdMwLUPEMkesPwCum2jXymRLWpfARU7sGSAAtGiGXQ/g1eEiIGUIqh/zivuyYKKOu7xq6qbs866NyuDG0ju1Ce/WgwvOD4BSKtrG4jNELBlKLWsVW0Y7FwGWDKhYJUCBTir0DlDTeVVdRwuep1WCJvJaqiDnk8jN8MTO/E1zQy5dROQfC+ii924dTC/JJgHmrwXEAqRiYZ8EKFs+A5bPGH1mzG8SfuPv/9ZGU/ovvP1t4Ac6RgOyrEU/50Zj5pCZ8p7mcis3O3EjRl3WBWw2NWKlOsfTnkTwVKVE/XwTtNcfrzDidiBXw1VhLAYsErgBvmLqYgSrNqCV+awGVHrH+GS/6BnM+hZaAm8YO1Gsz76XyWRMXhKpbhsGlEw627S9QxYDlLSi3b5vAJuoiOV0RxDL3mc7hnvjY/LsTA5iAVrxQyfBGA0HthULAQC8FY639AujBRtAZYj/XpTyVqtdSuhaZZDs4FlH5VAGGIRTnx2cq6px1UEO/Bkd1qi3Meptwr2A5CEXEoqupB1mnBtQLNLE5IDXoiwtS/dsOtnPpeHFvOCLwyO+d3jw62SRqhd5wSFJVOTreoffOX2JPzy9curx6/mMRIx36wEP64ylZTxeZjyeZ9Qloz8WUJVIuelkAWFSySyCiMRDi1AZWNxJ0gszgwrEociMPHe0U0Z6UzC9I0xvJFc8VZ1sEqHeEfqBUc6E+WtGeWzDoVBBxJhH7pU95MB4MjoBjIk2albdnNB1gmTg9XzGiz/7Nd69OyL/N3fAm5uP0ae1XcUR0gmYFIniOGHH4Zud+QBAZf05ONZJRDo/MQLx3P7kNkKcM8dC0ssbK9BjhUhkK7ErBs5EACWm7RmreEpdI8F6lDBHD8ZsSGMIDkVk9goI1XVgBhAJ0JUxmFgmZO9VDMP5xoqFgLLTI4il8yoZY0gX0eNzbLa91WxubgpGkV0T2pYwZ4j9MnDOtBejXlYUqSeN8PtoORQ74XEvko636v3xiossgJcFfMT6GYhGyIk9UHJflg1LiyFpNEXv+7lP+Lre4WGd/Xk45AqC6HtJOkvCqmnsvSVJP+8SMad+w9YQxN7EuS/qmChrGIk8rYAyg1cCXTS98CLpHtQBUAHTAX16Dc5fItWXyGcgrV8D/GM5Rj8B3AZw1UeKod6AMT/fAm987g5/nwxgyIkecsX85RnLZQL9qCBfbj5Gn9h2tsZtyBMR9P12sRvg+hxugWH0bGf+JLRDaiip41zLBijKyjCy9asBWpdWcIHMq5b+BpZ+POPDK6omXc/HbJIeji1orwE1nWT/Q6oe+DWNKwCYc8VZK+NZ/53JUxlNL8tE580/mlNFRkemEYA3/wIM3CdJdTym1eVMLLBu4vCxWQD91GZ06PF56GUZQLf04qmC+3OxJufQvXr69jjdiQXxOpXU5J6AVTqmA13YLWstWGqBSScJIythbVv7NpeGzozXxwteTIsH0OU4Al69yAtel5NXeFw5a/pgQu3ZKxIufQBZa8seyG894bSK/7XUjJLbRif5WCqaBkBaTWoD4GmA1GRuS74GFtAJrMCVvgbUhMwKniSgH4B2YNGjOnS1B0B/LKKp1QjpnITV1S11XY43PQKHrxjTo9iKXgj1JdBnQr0D1teMdseSkfIV8N98/qv4p3/4/0HSp/v//eofBVnhESDYH5NauZXTh2GXnnq/3/wj9HqpyTrif/WP/g6WP1Pw3/3BD8D/5UsQb5/FG4/mxx03aHQZ4CSv9/Ir7IW5nu4Ycv8Tttv18d79on0jPGkHb7WPzegEAPy7/+6/i9/4jd/Aq1ev8Iu/+Iv4l/6lfwm/+Zu/uR07M/7tf/vfxi//8i/j7u4O/+w/+8/i7/7dv7vZ5nK54K/8lb+C73//+3jx4gX+xX/xX8QPf/jDTxnK/6SaOxQIC3qQp7IJEt82+k8CICUHt4TWOYU+t+mCZpTm1DR9cUQ0LL1wUtAp0/hnhsCMQQdtytCa8GP3scq/Fh4NFwDGlmJs45xSvRovgCEYn0alEHN2GpJPpvEzwBhak18bMxirGtJ9Eovlnl+UHr307NUfTd/KohyrRp6BEa0H4N8t2sfDKtWcvl7u8G494LHOkuPvJdOlv6Y59Tl1vDpc8Kuf/RR//vPfx6+//BFelzMs1dEM810WI/uT9QV+eP4cv/P2C/zeV6/xw598jt978xq/+/Zz/PabL/Cjh5f4+nTEu/NhAFlniTqkEyFdVPivwyd1QIwGqoBWfd2JMZnmSU3gixiaVDraJYMeCsqDAFmHr6TCBxPQJxFGXF9KRD6fISmGi7CwqHZQE0FE08pCa5piOFJD+EOpIB/L0lIjuLSCh8cD+ptpmxseDOEntxjmvmVzLBR+6/Ob/WHrUNi/HpySbzLO73h7tjNPt41eFAb7yAAdS3VPN7aL2k0RpNkXL7GUN7cb+r2lsZkmIwEbmxEjwIZvRC0rT7AzsAeGg0SwisIYtj8sO7etJLo06yOKqbtwOcgdEwObovZVDG5souK8h9bgIFPVgI0BTZaWYjg2s1YAtImIRoql2I5RpGRVe3VpE5ZW9H120Mr6YzbHSqqRfX444ReOb/G9+QGHVF13jLFlMDz2CW/qEV9fjnh7PuLN4xFvLwe8uchnD8uMcy2ux9hbklT0SqBVFs8bgD4CV11AK267icy21+/BNBhZa3ItrHyGlkXP4DShl++hz38W+MEvgP7Je5Q/dwR97/ug/mug/oV4Nkxe1dAdiuAkMPB+p+HWnPseJ6L1hGUp4Eu66SB8I1sTB/FtA0xqZ/wvvukYn9sf59b6NuuBNWCa09Y+dEihJANogJHqbFIZXlV953Uay2iioU3lGlLKVDKt2kk/l0qKrKyhhFfl4kyhKTUHVIwxe0hVdW0b7vKKV+V8lT2ycnb/YQqSImZ/LFATU+gsu8MLXDHhkFbMCryZLEw3+8GjcIhl3cQ2pbZhPNn1mPR1IqlYbsBd1mDHxv5Qx31Z8Pl8wstJmFWZLOsj41LlnwdPMGxwTh2vjxe8mi+SxdOLZ48kCPP2oc344flz/IPT9/DT9QVqT3iZL/iF+S1+cHiDz8sjXpWzj/OYVxxKxZ2y6xIx5lI1lZFwWSecl8k1staWkVVuhZfkLCkAonNFw5743KRglb1uR2B9CSxfAOtLxvqKsXzWUb+o4EOXflZdJxRdSC/J/SBaCXkB8kKevTK9E8mUvHSpmFgliNxmAbLqPSOtQHkklBPjP/6f/R/w1379L+H/8cWfw3/8j/zv8Td+5S8Oe7Nn/Roza9/M/9kziD+23bBJ5dwxf034w7cv8VhnKbjwMOPwdZegUzjEe1MCO66AqVstBumdxdW37+O9lL7CfhGQ0m1sf/OFOI39N0u+Xb8fap/EzPov/ov/Av/6v/6v4zd+4zdQa8Vf/at/Ff/8P//P47/9b/9bvHjxAgDw7/17/x7+/X//38df/+t/Hb/+67+Of+ff+Xfwl/7SX8Jv/uZv4tUr0bb5N//NfxP/2X/2n+Fv/s2/ie9973v4t/6tfwv/wr/wL+Dv/J2/g5z/+KkhTwourVo9YkpjMr30jETFGVgesYCI3XZOLmTew8Nl2h4W1egY5dNNhHDKzbfpIM9vN4RfIi8j8mytB4NlFQdjJSobqzWj45qjkMGexrgHsDJEyyNTdzpvAmNVIxIZXz18JlEbMUQNCUkjLT1cBxBCdGYroj8MzhBmj9TYNaRnErFfaztnS9GwCTlWCVlbxpQ1yq2/LhP5zWC8nC54NV3wqpzxugglOlKXT1rlYyXGpb/yNMJzm/D16Yjz4wwQ0FrCw7sjUmKk3MFdHKK2JMkzVzZWqlL5yUKtlk/OplNioQ8HuswZsZCIflbkGHTKKI+ihTW9Y5TTmOjrUfLJL58D06Xj3/jbfwu/8ff/R/zd+1/B3/zBPwOuvE0v3DgZLCkmyEZFuJ7Qn3Ii9p/HSZoJb9cD+A+OmB8kTcUX77ttbzOp2EvLEiDlfb3iIrbRBOyiFk9EzqljK+Su/VAfr0MBMh1HGOpzxNzbs515ukXgyOYnAM4wJWZnYNl2xsSxuTCBN8+vRdhNeJxBmKneTA00vDdTd7CKMECuPcwU0wKJWKYnBZXggNbYw9lK+pnth9129p0BNzFl0gThjeVk2mL2GelqK6vdtO9dnF4ngJgGuK+oKOcAFXzXuV7BrAjgyZ7qeHD4jKQPA7RsmmrKuEphJdfDtZrV2ZvTqLhrAu+AFByx+1RrcraZlFQXLRMA6J2w8iSgouqNMCsoZXojXRhZMVhgFQodiOfwd1wcbBhPDNU0AVAFyEqLMLJSFZ2snhN6uUOff4D6T06g/x3w/V/8EY4/6/j6v/8S+L9OwP/wp4D+DtQvWztjx4BeRN79/VB7zybMWjn6XUFeNYC0B/XixlfHM2MyhrQ53n7B/54KUU9+FgMmwdQ/uc+zrfnONquGniDi3gBwVjmNO8D1mHy9r2vqWYGilZOkKiv4Axqpe7VLcaVVgam7vMr85fNY8kAHMNLrEnVnOpXUcGrTxqdBkF+xPi2NL4q1m29g6+qJGlbOuMurF3sCsAGRDLCy9bjJkSAAWIACWskAMKiuYMgAYUnfvFO2U5QasRb1iqOO1gi2F7yezg5EFepYIfa1pI6Zmve/9uxC62tPm3RDIsai9zSm+L06iLbuqU5urxMxHuos/kYt7gfl1HHIDcey4vV8xmfTGa/Lya/PXVqQCuNlWXBpkp7+WOeQ6ig257IWtCa+UDl2vJgW1J7w+HAEqtrBqn6HznOUdJ3gABaPeTAruGG6WUBg9rBkpVQpMMK6BsHckabmwfuk1RAlhVHTDVfR40qLZJPUe2Fw9Vn8HGpAvoi8SlqBljL++j/xlzD9mQpaO1LtSJER7JM5FKzqkk5o87exm7o5ASEzJe2NwI12My0QoEvH/AZ48/aIr453KKmDHjLKSdhm7oPEwz7BIYh+0NVwtMr7ALIG4OVFZt5TOXGThRL8HvPTTHhyY5P2/z6xfRKY9Z//5//55v1f+2t/Db/4i7+Iv/N3/g7+4l/8i2Bm/Af/wX+Av/pX/yr+5X/5XwYA/Ef/0X+EH/zgB/gbf+Nv4F/5V/4VfP311/gP/8P/EP/Jf/Kf4J/75/45AMB/+p/+p/iVX/kV/K2/9bfwl//yX/70s/ifQLNKIVI6OweHQ9hXlWSyzOiosAoVZSNUaNTPxIMBZVEGm8QvvfiEBwygqlB3Bybn1cvnAtgYhcaEOY9jAtiI1APbCoJmREZ6hOhx3QKxVs4u5h5F4o3ea/02yGQY2WqW5ggAa0hpiBWkIt0ZELpxPE8711jiN0aS/Hoo6BjTbSRyNMZikayOIeiOLNfjmFe8niR68aJc8DJfNhGwN/UOl15w6SKy+1hnWTDkitqlhG1nwuM6Y60ZeWoAE9bTBFRCLyxC7Y1EnL0S8kmcAKqSP27VPpgImFirR0G9P0VpOkmqiIFYuYuBsUj7koBzRnmXML0h5JMK8TagF6DeEZZXhMuXjF6Av/J/+Vv4V//vIor4T/3k74Fax//py38G+zK1V5EKA7K+YduAPvj/svevQbJs2Vkg+K21t7tHZOY5595bj3tVUhUqgVBLSC0JBj1odSMZGIPMgJ5pmdWYyUYDDIOYBoRhAsOM+SX+CIM//BCDphtrQ93DY8zmB68/NLIxAw1dPLr0AiHxLihJVN26r3NOZkaEu++91/xYa23fHhmZ55x7rzSiKrfZOZkZ4eHu4eGx117f+r5vaeD+wOYah6/5PN65OkP68YcYntiTAqMD48Yk7pNvnYA9iWhZYc3keyv91beTo+09znmOJetJvQW8auXiOeLaF9u4jzN3D5XeLdLnCsZUBpLUJMV9otyfyofKNxbfJqAxD/bXgNE1/WZb1s8iZywVMNNtltvZpdPt4Lqa0dECVMEKFXVfspjDr/YBZQ4vnbCaLn+gVbzzpO3YON6vxdoMf+nB6OCc/rEw29ag1lIAEiysMt2vnw+jFKr7Pu4AWTEZ6OK4gHUu5SXeDSQah83bssoJbT3gTLssuvYgaJXe1x4C64pVGMSKrheTEK4KIJm0sm0dct27pJUEaLLRzOcV1CLA/BsrBbXQMgdnUiDLpYVJ44zPj6ULyP0Z8vk55KsEv/qj/xq/8elPo08Z/+zLvxaf/spfA/y7LZB6e4EvrJuL7b+/GxP15UNdzcdEwDZOSB++xn7qUP7jgHBotxcc3dL1skj9DY5n3TrXnwKtnvs8K0jaAKYvur/78QUxlGUVV2yfTUw3WK7auGjxzwJQ5YNuOwIsTFUvyDrDCoDlOaUqHRz0cjDqPI5IEhqgSz26XHKo56GmSK1PbbT7djCwynOpUWLtFO/7GCjVv/0cnBwwl1B/T4WX7nx23mzzqgJ1XI/jIJkPhrHJzBerkgOEq7ULN366ozFrZ2d3idQ4dJV69csi9dRqP6fe7Ecu04BD7qr8vOYf7WfHS7QpQhiifSbZik9R39/TcYPLw+KFNaewshhgLmAW5MwY9x0kM+KQ8OjBHq9sdzjvRpzFGQ/iiAdR85zrrF5Zb+TzysKKMaMPGds4443rc52H+qJNoxIvE1IQ5E5AMwOs8YQnK0hrGoTuWmMEWR4yPxDIJlePRQDLBGsNRYgBTIxwUNWKsq+sYGKgFhVBGIt5QenxS6fPS9GOimHEaXneKeZVLd6X0883Q+SY431iG7dXqXYr9lYtvtT53JlR9Tyo5jzSxqKjHGgBjixGGs/g5OClYH8jjti1l1aK2OZAvg0s5/LgZ+Da4rnZ+Gkd+XC9G1DrPXlmPXmiGeQrr7wCAPj0pz+Nz33uc/htv+231W2GYcBv/s2/GZ/85CfxB/7AH8CP//iPY57n1TYf+chH8LVf+7X45Cc/eTLJGMcR47gYFTx9+n4Y5Lz/Y8WUsgn8kKNVCLRzyBAKMvLqJlPGlk64AUtb1dbUEFJQYMaDtABTzlwaONV2qkWCAWO6mG2r9E6NVcaXgmJ+LJ/QW419aEC1BVgjBXwt+LSGwMGSJJdpuMGiH6ethBxLC5euW/pYtE6LkdYJke/LD9vut/qTiTPTtGsLkdQWta3nytLa3as62ulji8XkvgvZKLdJac7dAWc8LQEUhLFoi9+pRDydN5hKqK12vZqxT13dHgCuxt4CimC87oCRdRKYAZmiMrHyYmDoIFbb8UMCINEmwr5JTFjAfdZJHlAj+DGodMTALG2Jbh5Z11q14KxsrNwD48uE6SU1ReyeMn7jp49NET8DvPybFnkhYMCWTfDtEMtabqHq2odxu19W3UZ/PJ02+Ny/+DD6x4xuZ0/dtYD3KoVY0vq8CQ/d8nv7sAiKTf7HwNtKK95M0JVeW8xF6F1UIr5Yxi9XnAH+04k1gZdU2ZlDqTBCKFVy6PKC9t5qY4H/7pI/QDdl8Tl+YXAJFuDHu+K5Ebob65aGpSVYOjoRpEpG/FhFvDOf1PNaQLWlM6Pn7A4EtfKYFqzzubqyzKjp0IjFQ6vuswGEAMBN6091Al5PAWvAixyg8uPb3wUL2w3N9s7o8mNGS0p8K266DnZc0HOq3ld+7jMWieNonpLe4UqLMIt9gV+tKZs8j6FMX5cFugywLLL1FshyJpLPrRpPSKvpZGdNagCvb9O8GjPrnGuFKW+7ziMQJk8uUKvJacvIG4Y8AMI54dXdW/jo4RcRSsZn5cP49KNfAwwAjbRM8sfsLB+rGHNLtYDsv+cIA2OOuHrjXBMlqyPeGWuWm3a5Ru/XEG0l75hhew6VLdeeg//e/rwfX7DDLUdaAGQujI5L9ZIFgCw6B0djU/WckIQ1ITyyNxlM4ueAlu4zIBNhOLFWSyUgBIF7/84SMAMYGrbQIklf5vqO5nXOg6WY76buczZAynIc349vd523tn2p5+45hucMbmMSSJCwNJcaKNXjeB7W5idgb4rCVVVTGWRUFs8ru6Y+FnN4fW7GuomK7/9qHtCHjMm6A7onr0sMPUcDUAtYHmOZBPu5sximjz0xIGueo64LbC4uhZBT1CV5IcguIlyzMk8zAOnxeDjDO+EVlEEgXUH3aMQrD3d4ZbvDq9tLfPDBNT64ucYhR7x1OMd+7nDeTQpu5qCKj1AghatRO4ooaBEFEjPKyOCREK9oMXtP0I6DB2Maka2hM4FHLeiXTjT+MFRumAg5EXjP1rUQFSShonlNmGWJN0wokVAirOivgFbtqJuhRYoiNfadHMy1Y7s0XQ3pttyiFH3NC6hTtPi+ftiBn1ys0UyQxUT9juHvo253pwxRbnobAzc9syyGvnAhpvEOXj3ent8Lxqt3DWaJCL7/+78f3/Zt34av/dqvBQB87nOfAwC8+uqrq21fffVV/If/8B/qNn3f4+WXX76xjb/+ePzpP/2n8af+1J96t6f6SzoWc3XtBOKL6J7zSroHqHRwn3FjEnemErAs5I8ndTcEnLHcg17BCM3+GIJo1fQOQEIwUIhXVRWg0VobshZ40b27JCM757MBrjqbyAHz+pIjA3hb2Gdg1T2lDjkKElgCnBusL+dYVue4L/0K3PJqz/Fx2mufjYkFGJgv65buwRhZm5jwoBuxibNdX6VHO/uq41wTlrmE2v3D6cvXadBW59a+VoNRxG5Wmq+bTjpYqV0/SA0TEy9otvmVcELt9lQTDFmALK9c6HOi9NuZgb6o0a4QgplVyhRApivnw9IKt78kDG+LmtqSUm5zT0hnwPSyIG0F3SVh8ybwMw+/HN/0i4sp4j8//+g6oah0hPzMSsWN8QLVdBFCHzLowwcc+h7x6mgae9FF+42WuUf7aiZXwokqBJrqSbtfT2h88qbmnz/vLLr7cXL8csYZ4Fd2rHHghsjYIJbNBmMzSXP3+aLZASNq9tEC/8dAFrAs+tW7df1lqvsjAQnVRMBVzbWTImQ15y8eWV4oWaSLbtSu0kMnAi1yST9DL5CsDOCxmOC6vK9+nUjsvBYQy58Xu0YrDzJajgnAWAFl9bw3N9G/Yd/5466GyxfaQTgffu7RgCqPCV546Tihp1y7PIq9r8lWsN6xcbbuum5A71LFOQeorHRJcghayFF7KWr0zqjsqZWksF3A++8FQMTCPrVEyA3gRQB2KWFe2L+ULD6JtUTfA74EKUEX5qUD8qagdDPCEyB+Bnjrgx/GZ+ZfBULBZ/ER0JsAdgI1f291j8eT7vOMF4k1ljCeJ6QQwNN7oBgDN7G1u07lGJA6jjun9tPGGpx4/n58QQ/PBTZxxtU0rOalFkRyDyefgxw8SRX4UR8sZyEV4Wp87gbpgHpoxWaOXDofWm5kc5MX3RcJon2Hacl76voYpd6vzqwClDl2mTaYS9CuiCZd3IYJHNy2RGWS2pRqriwrL6g7IOcehw4wFRBSDoiUK0jm5+mMrjFH8/PSa+PNrfyatAy1ypsSbhpRaQ7Vh7y6Zh4zkgSUxNUWJYk2rWrzFf+sXDkiJCiFMHRpNV3s5h5P9xv1+iuEnNWyRDKhJMs3EiFcs3b8q+wlWkB40jwBxChXAa8/3uD14RE+ffEBfNWHP49NnLEJCefdhCEkfHh7iafTVu1XYkFODNoHhD2rQbudoDCAoAXy7imtFRCCpmAvyFvrnpssZxGgDAXoC5CXwjwaZpV3SYSojD2aLyNPpRqa556Qh6UAHXdqGO/HXcnJj0eVEjaeWHeNY+bwXfv0zYKCp2RdDNtXUQbKPuLpbqOqotHKfUf5yCkfrJOg06nHTnkYnzxvK3KV5fc2ThW+2c1wVfA/im/+XAUxXyBuvevI/If/8B/GP/2n/xR/7a/9tRvPHSOTd6KVz7HNn/yTfxJPnjyp/37+53/+3Z72L8nobAFffTssoERyL61QJ8/rNOA6DVUC0C58266Gjsq3z3sForMJF0A1dnfabfv8wDOGoN4a2zCptxdl89uY67ZuUOhd9jpbTAOaCHggKEegVYZWOFxemLHo9VtJi8sJj4G6tgLvAWvpHKLbD9a1xAOdmzMCytjy4NgaKD7oRlzEEX3QbiypLOa8uTCmFKvmG1A21subPV7dXuJhv0fPSbt+dAe80l/j5W6HizBiIG1zPlDCmXUlvAijsurMVNJBs2Ry06tpwJTViH43d9jNnXqXeCU9M8oUdPHPljkUTyDWFfKqMWeXAapZYt4K0oOCsi1KuY22g0KYdx3yIQITgyatXsRrrh5Zw9uC/lIQDwWcBLknzA8MyDor4BnonxL6p4K//BXfgf/xK34rPvXSr8b/80u/A3/t1W9bfxGcdnvSDLExgX/W5E9kVYkTT2VA9rro+C+/4t/iV/2az9f2vXcmNbfMdDeqLnUifv4MacXyOjpnB7lW1fEXTr6+eMcvZ5wBfuXHGmVaNYtlkhpvVOLBcOPzuaiZ+CKRW++ndv2TxWTch/sBBjSsKijI5B3yghU3ojGJnMkb2c15F5aRz+uheYyAhT0GB5gMeBJayd0FWgxyeWEF9bA2iteuja0MUpb3215Dey8OzDmjwdlqsPdHq/3audjhCFL9q7xDlYNKsPfiJsx+dCbBJqo8Xc19i/pgWYzemgFxsPVDoGLGyeqp4slXK0/xdcKUVXKYC2HOjDlzXUdod1uCOGPKF50OVgnVuWnF+LFFZek05pQIrdZH0TmVlxeU2YzjsyVHSdnEPKvcI+wtqUha9ZYA5AHIW6B0CZT3CONjhJ8S/OJPfhz/8/634X/e/W/xCz/9ceBnBcBjAHvoyljWBZSjIf5BPWuupfrfzacEKo2kgl/1ytt49IHrlVnt3ft87odfbNwxd7UA5P147+PHfuzH8Dt/5+/ERz7yERAR/sbf+Bur59+vJiTvvPMOvud7vgePHj3Co0eP8D3f8z14/Pjxuzrn2Tx1xXKIyt7w80nKHvIC7y71mLIahbf+T844GkvEPnfwRkaAMhVrId7ynrEEVZ+cuPmUgZprgypAQarIy/reC9Yu+8tgDKzrbd/eJYJ+XN23rr0X8KlUHy/PTdou7S7lGziZp5e9zxz1X9F/6tu7zi2idXpc2FClgoI+jm1N/LWTFR+urZnU1TzgkNdF2GO7lGh5pXpUKaDlABywTH0ihKe7DXZjjzEpuPZ4v8HuasC875CmgPm6R77qUK478JMOfBkQroICRGkBsoSAYixbygby7AjxihEfa6Oocdfhjf05/v2TV/DTr38E/+pzH8Jbu3M8nba4nAfkzEhjAJ50amHyhLD9HGN4m9G/w9i+zhjeCBjeJmzeFgxvCzZvCrZvCDZvC+JOYM0oUcw6hUeuMkS7QKCDMrvCjhGuGd2VGr/zpMyubiforoEwCnjWuMOzkkKKFVF8X60pPEn7z9YKx7c1+zpFoDJDZ56TPheC/mM6PWdbjuPPV0UKMyQyyhCrJcrCVFLA6sF/TPjQJyO2f/cBtn/nIT78T/Q9trFQmVrPjjjVB6vtAPwio6Jnyzm2+6BlsXRTLukgmL/+tuLMc453xcz6vu/7Pvytv/W38GM/9mP4si/7svr4a6+9BkCr4l/yJV9SH//85z9fq+ivvfYapmnCO++8s6qaf/7zn8dv+k2/6eTxhmHAMAwnn/uVMKJ1CCmFanejJGHVGtWHU1/19wW8AhZyiJvwDbx0kAhQmaHvo3aWoFQna/e2yrRMqFueFhqvLBOvv37D8+kA1FaTIejsnLNVafznLNrS24OOvg9FXjYme/S9OEjXVlu8va4ztTToLQb6fq567AVMG0VvXQ9cQ0iVSeVVktbPJBdtz3vcnr3jgrNuqjTrlDWIJQiuc4/P5QeVhXYWJzzoDnip2+NBOOCBmWcUEBD1Os2RcZ0G7FJXAbirqUdKWkUnKANsCFYRKwSZeK1frl92lcOVCJDJilKHlckfGCi9QM5ylYzIGEBDBtgSPQKEuCYWXoUJByAeBMFMEQGAs2i72jOddcJeTRGpCFIX8D99xW8Bf2kGTxl8mOvrfNTJ/XgUA7kYdyYhtw0F9wT9VUH3dsR/vHqEdB7w5tU5Nm9roFJvqhdIGZpSllYEbKY9BqSKaKXktvOShT3SHp6yQCLd2F782DWJvM8+To1f7jgD/MqPNVUWKKjywiJyw/cQULBlMW5fOgy2w6WCEWXFnqpSuvrYIjtXEKpYZdgPJuiQK3gkDcjkoFIk55Ydn2cDouGoIyEWoK31h6zXA9pwJFpSAug02G5fO1DZz+rB2LDDqvk7Ldv4c56g+Fc28lKwEYu37bsqDgwdTSTOiFhkmfrOiwgmBFylvimwqNH7xjp5DZTqOYD1Myqs/jhzWeSVUw51DUIAIqfKBBahxS/Raf516GQozeMlYikC2FwpQYCu6LxVoEiXF0/qvbD23CJZkgU2CQegFVtlZumV5TSC5zeB13vI39ui/OQAygJ6vQBv7gB5AxXM8tM8NXcem/SemNPvGj5Hh0nAe8bltEHpJuymHnFv8YDW9+0zRxNrKvPh1Ovv8PyqMerIUwXAIqM/3n51zOc/3fsBXF9f4+u//uvxe3/v78V3fdd33Xj+/WpC8t3f/d34hV/4heoV+b3f+734nu/5Hvztv/2339P59+bjK8Bq3T9mLaZG1iZSzq5yFuohxwaEWcAjn2OGkKovlO50iT2zSe8ALX47W2pgoBgTrAWvHDRqASEvpmsHQmVhzdmYUba+d1N1B5j2ubciSkEpER2l+n48B+koK4OLkwJghtQvDC2yLo5B1S3kihGuRY7ZfAkBnaOVoNDXznKA5gmpcPW90muhgFnHGWOKakljYcyPHY1Rt0u9+WKSmb0vPlehuU4aK5a/xzliSoJcCPvdgLKPGgxJjdN5T4BYh79JZd91UeDAWK/+uFUVMmFZI2dCvgC6TcLlYcD15QbypIewYNzM+PQ7r2C/79X7N6sUXbsTCsKlAk0OHJVeOwwqk2oNgJSo51UiqecWob42XRSd52qHXWP97pemIlQEYQLi3nMbVMkgRHMc92sEsLDRZCEO2KJjudZ0s3i6nLCAQgC6COo6BbGAJf746wIDIUC6COkiEEi9u4IBWkElkBJY841jgAeABEL3NKN/khSAIqB0jLxZQDO/RY4L8qdSo9qh8A4z9+caR/GlZV+dTMnk6PejML5i6z3neCEwS0Twfd/3ffjrf/2v4+/9vb+Hj3/846vnP/7xj+O1117Dj/7oj+Ibv/EbAQDTNOHv//2/jz/zZ/4MAOA3/IbfgK7r8KM/+qP4xCc+AQD47Gc/i5/5mZ/Bn/2zf/bFzv5XwFA5WdNxD0u1OVJRKYBVUm+72D7hzRK0nbctwGcJ1eCwIFSKMKAg0BlP1SCxZUxVI3WE1eNsgEjLmsrC6Ax0ctCqNF5Xjps4RXZsDN6dkdXKVtwzqx7T3kcrl2z1/G073dZA1/2nvKLfUdYugESNaeNy3ZkE2zAhiSYFSbia3uayVFny0TdrG2dELjjkDr1kRM64mrVtuUBlG8m6dQQWk2JqMD4k/URf2uzxwc21Vts5YTvMmDr1Mnkyb5EKY0pLF5FtnHEWp4VR0BVUcytLGIQFRKxeJAXgmeqEwbN23oAokJXPLZGYSasrnUAyg0JB6ApKJtDIWiEfFdAKk1UsEhDGAsqC3Ael4A46E4UDIe4IPJ1IQAU1GANYJn4HrXx4R0N/f6eSj5bSejSBrTsJKkAURu2iMqaIlPR9tZMfHQUiPY/1vhczeD8OlqDjE7M0+10lI7Lett1ellxFjh47Bs8gN+bw+4H7OHPbUDzXFh/2mMM9LglXmyKqTUuPh78ugyAmUSMIMggRi5G8L579GD4/O5Dlg5vEZSXJc/lcc4e7v1YGV6BNhMDWpYqa7chYZnquS4HI9+bvuY0ZhAWc03NbCkXAAmL5cX24/xSJVEaadwEMWDy+/BgEqdJ9b2lfSmvI7+9jfe09bqQS1JaABJN1CANQ2507+OdJHhEq43cTZ5zFhb11FiZk1nP1xiIeX4m0qUnHeblKoZm7CADLMvexTUhVarIUPuDAU19vILihL4q+lq0rIpIBWY23o3tkURKQCHIwP5QASzAKeJ5B+S0AB+DpI+DNDZAKkHeAvAXgEsBoB7cLfLwobsCek8AQ3fL7qQcs8cqZkYMZ+du1WM3jx7P4DeDIgMLVYU7EGrxYLcZjzCqlbfa3KtTcA1kvPL7zO78T3/md33nyuferCcnP/dzP4e/8nb+Df/SP/hG++Zu/GQDwF//iX8S3fuu34l/+y3+Jr/qqr3qhc/ZvujM3q8eSPR+54JCiNYgg675OOJSITUiYqrJAgSJdG8f62CbOdd3nIFgwxlCRpZP7PnfVaN23ZVnApcUfl+pPt/uYJdT8w5tpAajdFqcSTU4p6KhglsWyJImbuy/SxLbJlm/jnldVloiCjqWyzjx3epo22OcOF1EL3qWQNsCCYJ8788taWLm5MHboa67mndA9D3G7kTkHPUbQa3gWp2pNMuVQPR5zYQQW5AJMBoLFUCqjGFDz9ZwZKQUQCWYOKFV+B+0AaJK8MCpI1YRqVTbY/C7BwsKs4FO7ji29ABcJIsDV4zPQ4049rLYFl69foH8zIu4J/axr5ukl9bYqvRbko9chCIjXJjs3pi4ACKtdCgiYzwh5WOIDJfPK2pQKYgGaC8W9MYEdrJqB7lrQ7cqq2C6RQPNpf6mW1erg10kG7nE88a6FwwDaDApWtWysFsgigkSGhADplIFVt+Um16k5yc1JW5ggHWo+paxpqv5iy4Z4d8kFrYGo1f6AVaFztX1dkJ4ONG1RxT8nFPVFWx2jyceeyYA+Gi8EZv2hP/SH8Ff/6l/F3/ybfxMPHjyo3iOPHj3CdrsFEeGP/tE/ih/8wR/EV37lV+Irv/Ir8YM/+IM4OzvDd3/3d9dtf9/v+334Y3/sj+EDH/gAXnnlFfzxP/7H8XVf93V1wv9Pbfik44Nskco51uoEoAt7r6Cr/9IyOtJKwFgi3J/KmU/qUbVM+gCQiWuFIxUGGBXkaTs3tQbuwLptuANPu9LXx8/s/bhB/FwidqXHAV2VJbaAlZopLpWc4+P5Y4Hchysrm8gW/v4eOsqYEYzphQpU6bmggmhJQk2cXMvvgWQs0ei8i9xPQahiwBYZq3MxPi4gjAZKwYCsx4ctxllb33oASikgZ50F50ME9gF8YFABPj8I/uVQQJuM2Cd84NE1Xj27wgeGa7y2eYoHccST7QZPxi3GHHHejZrIpAhiUX8rXyEUUoZWNJPDWSm0cW+TFamHFU+ARGBm1c1jYvDE+hozXJRRgThkQrdXlhVlLaRRcm35EkjUFFFnEZ418MVDk4g8l45cP/eTci5pEKWTeu6j7W/RbQv5ooTr3887TlYg/DhN8nNKOngjKbiRtDSPH/19W6WB3m3Q+QIe93Hm9uFAUx0G+pBIlRfCOtod+yb5qIwjsOX+1gQDvAJ66k9y0Mzjj86/Ls/T3HyJOYsMogGa7Bhtl1t/PypdzLXrbQIjWEdfN2/XbTUpEtHF3/HxAHuMFiP2aK8ToSqtZIiSWIUBaTsS6mvWXRJNjmnX3cE+B8ByE9/9mucSIcIg0pU4UQZB24jXdQIp6+GQOvVMtOsjDowV3agk1iJF0uTkOgJvBQFiAYeCs82E827CWZxxYcWUQxcxpg5JGF1ICvIV08fpx+cXX+e7IKrcy4ss0D86N2sXdqstqV4l6mlh1z4xijUf4USgGYu/Y8PQ0vtJX+ueGFSsyUlSM12SApLdkslggoJYI1YVlBpDTkzEKyDr1ER9B8jV7qbmhHTndqfGCmS6cZzbgodt9rwA1Ind3Pbak0nK/XhX4/1qQvIP/+E/xKNHjyqQBQDf8i3fgkePHuGTn/zkrWDWXY1KtmFGpILZwPwu5HqblKZIUYSAsjCNWkAmxnUxQsEZbWZUaJkzC9HKGH5KYc2cJVSmFgBczhsMISlwz+q9tc99bUblIJA3j3Jw7CyowmSf1bjdWVqRF4aTM6i80LANUwXsArQQX83erbGGF+Xb9+CM3tfHh5iKvp8xR4tRSl6oBATz73qp32Eqsfrl1uNaYy+fP6o/mTB6kygWUMOOk9oMrJg9iuMxgQtCLvh9/69/gG/45z+Pn/iaj+Ev/O9+MxKHSlRoOxUiE2hmcIJaljggYet5tnldSjPVHhcHbC5J54LplQxiwfx4g/7NgLgzBQcTujc7dE9h/lM60fRPCfMDwnwhFWTy/bqkry346pwNlEDVtzdvNA/KGyCfF41VTQOruCPEPcxXCwizygv7y6y5DdHSQZwJpbKfVGZIYsUa4HQXw2cNKaDNBthuILFhZHns8dxi9RjW8sMTQNathyN9rTcBacEgaj4vv87P9x7Wx13nOBZHqdnmGTlQjTP+2dp1lXB8HFoX/4/38UsJZv3wD/8wAODbv/3bV4//pb/0l/B7fs/vAQD8iT/xJ7Df7/EH/+AfxDvvvINv/uZvxt/9u3+30m4B4M/9uT+HGCM+8YlPYL/f47f8lt+CH/mRH6m02/+UBsOos7y0qHUzXDczX0xfs9FYVc52Hgw6B+qH3FFGdtqpTeRjifp4A39Fo+h6UFkBWnZbOKDk4FYg16SXShlmCK7yUM3MR9ZbYuCEB14dse4ehRbphZvxOoXXz8WNHmfoJN/ZStjPYZZQuyZGLjWgjCVWeaXTiH2MeajVF31fxYwYzZfEAoxLCyfreLL2UhGdNIQq04oJy7mQVqDe3p9hN/aaYADW+SOgmLk6HYJ2NTLTRBSdVOWaAYooPOD1N7b43MVL6M9mfNkHHuMrHryFR91BAawc8KHNFd44XOg5cEGeIvgqastzuxckqaF7/5gVhBILPKxAVjwI0pYgUbTqYtr3shFQX1S6mDSg8UE9stouUmESxL0gHiyoBDVFLL0lr6aXZzdUfBaQ5dKOsnT2ODVEZNGhF7mTSiqhebJJTngCri43yIUxXg546EHoNmDoBEjlFF39kKVSfZ931ITh+CXS/LtlkvfXrlhh96OO+zhzehDMx6lhHJGYCaxwBZyARUbuC+uurhiXEQzUESyAUBLWx5ub103Y/dUrQKvZF4DqY8VGNWm7ChZSWZybmbsc3mODFn70fQgdeWlZvIH97kbogHbmcqYTbDFP4pJ4alhOGveyyeRdptleldm6A9ZrREvzETdaL2LSQmnkNyRaXUAA0QZaFhr0+HIA0V7fG81gqLRnP3eYsxr0AjZ9FrZugGStyA3s8YVqhi0uGULA1a7DVb9F6DIenh3wcr/DhhMo6rmdxwnXqYd7q0lm0MQr3wqj81lcgwGcSxzgpJJDl3F73EOUJbko9pw1LOHGSJ4yqmeJx7ESNKFQ8FUTHX2vBZDRDuZBp0Bb35RlTq1S9TtiDZr19zPAIVkVTpZXUgamsdP7Zgx3HW45L48x9cEm1tRdv4tYc+sx79jd8XP3seZ9Ge9XE5LPfe5z+PCHP3xj/x/+8IffVaOSAKlSvOupQxcUPKmdaLkY62dhrjqA1EqU96lDF7g+V4RrPtI2MwKAQ+rQB7dOWcAk76juj7dAFGBm8AXV36rNb6ai4NE2zNUKpaOMwoQtzTgPet4DJ4yI9Xz8/Wzjof4NKNsrcl79XYv/ok2kJjQSdvvyTjmimOS7NItVB7wcbNuGGRdxwlPaGJNNVSUu0wxUgGDqFWPoXnRjLbpPOWLT71GMnTuXUJUlPgTA/+X//Q/wf/1rPwYW4Fv+6adRCuMvfOI3K3s0L3mVZF8foAIKZI2kVsQaJ7kGnXLZGnZAlsfSmSBdWP40MbrHmgM5ONE9ZXSXqE2kHLTipMwbyt7MSuqxNbZol0FnZoEJpVuAJomonll5WyBDUdZvsnXDQXOasNftwygIB6C/LAiHovmSdUikLEvhmFD9H2leivvK7vLcBGuFSTuq3xWDYgQ2gwJZLWBl76dujwawOt5X/f304W6+xq5h46G8yiVuO+0TcYS8O66PG/Fi/YJ31QW+3Xfdj/1sTd79cr3LossLywyfNYgIP/ADP4Af+IEfuHWbzWaDH/qhH8IP/dAPvcjhf0WOGUp7dSSeaZmsphywCamh+8Y6WTqzKJLKDtrqBOCyjVx/z7Zd2wWxNRh0DfoxM8pZVh0nBAOTVDKi0pJZAnalr8Bbpcc2HUAAkwMWTUA6M1HMWOSBNXkyhlR9TQOwZdwMhM7OKqQTqJsxthKRWdQs36nTkcuKPqwtdpfABaBW9Ntg4KPVoj+VAZsuWevigMeXWxTrPpInK0fPZp6eraNGWQcBWC6jXSV0wk9zhykT3t6coQjhahywnzq8dLZH5ILLeYN5jkiHDnwZEa8J4cAQ/0baV61/rNpvALVqEWZdBIgZwbvRrrdLl0zK1BqVOVa7fEwaaHi2Dh+jgLKaImbiavQLeAKi+nLypKQBopYLvZ6wfRDRTQNE4hVbS5gW+u2xdrwLKB3fBISy4KV/WzC9vkHpNjjbA8PTgtzfMck+wzTXKa1rwotUBkE939smaLIgKHT7JHwEYLX79Ot7P3Tcx5nTwwEdgVfYATJAKYvGEn/OISgABg5Rnd9nWVfPW0mhCGmDOweFfBt7DjgCrLBIDZ3Z5L5axdhQzm4RK35UwAqE2iGQ2kU71cp8oFK/G62PSGWTVT8rQkSuLDRnUq06H4qen3YhpHqcljqfZfHaEiFkEjBxE7cFrcm7X18IWyHnAsAHALwKYGNf8ANy+TxGeQsxPFXmQSYcxg7i5++ykKwFCAWBaF3ZbMBzSvZrJpTMyAXYxw4iZ5hyxJwDNt1s65CIkgNK0o62bvq75I6W6B6a6rmDWYYnCWm80dhHWnix69h2leKZajcpj5OcDNwy+YYXElpZuDO3lrnQEbMGsLp1Gqd1nNGbdF1AJlis8kTEPjmmVaV7tVdRQ+J8FSEhopuBOArKXVj4XbHG38kt4FMbG+4Gr+yb9oy84lSsOYFp34/3MN6PJiSntn+eRiXf//3fX/9++vQpPvrRj5oBvMvcgDmrTM2LuEOXlscsV3G/puozKKSsXe+8Lj7P60+fCz232EQFm4aQMOZYZXuA5i7FslM3lXfvQAC4ToOCZKTSxMmaljAVbMNcFRsZbA2WQvXVjbwYsHtBZCwRM8JSdDcbloES5qwdxwE1sUeHmkNEzkglrDy5AGW+vGRNoVze6KJF9xmbys00OlJWOSOXKmn03K3nvDJ/P+TOfLYWOxYv0rdxWYTwDf/8F6qFAAvw63/uP6j82QrjgHpnuVKjLYbcYGbN0JxLAOltDk4Eti7pWuBWIKucZSAIaK+Aft5K3Vc4mKdVC1RZ0SIeBDjYce3C1cKMyQo53Vxr+74527pjEGOaEVA0xngHwngQLWhNQH9dEHe5rsdpvpmzCFuHRBcPCaqHll4bWebJlq116vvYdWsgi2mRHt72GgfK3m1N9Y744df9edGgu8Ap/drKyffgBIibO/QDL2DqyaI/LGfKVJ+v+7yrOHPHeFcG8Pfj5mjpoV75ANRMUUT14JswA0E7agDAVeox8OKF1U6inU2S3j1jmeQU5PKgU80NkcG8JBi3jYFnq7yjglsB2iY8FQ0aDN3/oXQrfy0HufzNbXiuksP2XHwUIexyj7MwoaNcu4T47wCqqf1YIh7P2woMbsJcASwfh9zVhKwwVfZaoKVbyngUWHIDaIUjvy4mQcoBl6Zrn5KCS5J0wqSZwYdl8qRFeq/eVFHqF9q7aJAAGM0P5IO6UPiPbz/C9GQACkE+BLzx5ALTrgNGE6ubrjwcNDD4JMtJtd+VPWWgSe6oVi6ElRlGGeqdFW3SL1olD5MxrCbXlysNl2cB5wUC93a0NalYdVI0g/hW4UFQ2faqS7oAUkB9p0aIXbeWCpKDVQx0EdJHSGSAeTE/ZDJjxKNOHo0GffN2wuZNNWWXQMibI9CrXbDftbB8jkmzygNPAF51mxNAVa0utEmvqSx9u3dbgbgfX9xD83KPCSYzhhmV28LWq+g+f6tcgirg1DKS3G/Lvw4OglXJgv3uTKggAHF55nrDE4wMNzo3M3ZaWr4HO1rb0h1ABbl8eKfBQibFO1r5CqzajqyMW1EBJDeSeAeskqg5r7/n2JyPj2RSTYdSRBajYk2eTI5YT5khGFDkQwC+DPx1gvKldkV/cQD9s4+hlA5jEgS6Ri4FJYXKaIJJ7dygdyV5EJg0wa5NrSL70wQ503O93G+0e60AOAd2hx55DsrStTmqVqtTk+AUK3Y4e8rmO5Vl2LHJALaiyU+loNv5sxVA2mSJ58aA1z9bWZIFsvdHy4Ve4VfLi9aP6dwqQAggZi2c0NELyBbrgYHAWjCh5nEDuHQ+bgsRNegg7gviDlUKctzQYzkZvPdY08SXF4k1J48ht+/jfrz38X41IXnttdfw+uuv39j/G2+8cYP11Y7bGpVkqGSNSRDNq4lJMBcvHOgNMaeALmYEqAR5bAoV1ZfXgK6phGomD6g83fOPTcPI8rxm6TS4sMT8b5fYpazyxiSMkqhK7HykEpCoIBs73y1EAI0jMwLOSPOKuQSMZuY3S1h1ZR+CHn9nAF9smVRhxINwwCwBF3Gs/lyzBDyet4ic8SXdDpFL7bS4z/2K0eaTcmvM3nO2LpEKZiVoM7CW/dy+101Q3173zOo5Y+aFlcUEZV8Xwk98zUfxLf/03+nUS8CnvupXoWSuDaJiLNg93ahPVsOyggEFlTVb9B+JFRucTTUv80YZtFt6Oc/aUOqyA0+EvBX13m3yIZ4FeSDEneYrejNp/NB8Qmoh3mOXsL4OorMvz14wIYRJUEb1zQIDNGnRxM8zXjPiXrsWVrXJKAj7Uq1TOJUlZ6ksJrvPy8Ie9mINGtClAmBOELZ85AbAHDym0JLr3GKPQtYoyuOfVM9HRwGxxLl2HgcgRUCsn6U0+9L9Hr0Pv/y+PjjOXW7YvwBekG+lf7eOE7Hstv235wMsgBUJUI4ZYcf7fsHYdQ9mvcfRsoEWIEtXG8G11lyAwqptLgK/7MUW5UMDbgE6MRZD/VMJCEZx9aCQSkBCqJ1DWvrv3MC9bqLonZCyl0OblfJACTlo1aMQ1UnbKb9zWe+vYFn0twaNSim2yoJNHA4sHUqHnfSVfbaJ8+oY+6w0/o4KEjRJcFCrZZ45841BSFlNHHsQtmHGYBTiqTnfJFyTKADYSML/+a/+L/j6n/kF/MTXfAz/wyf+C4wU1aOEBCkFrZCbYSKSUWRHqKywLMkAsEyAymIyU0VSdlN6UBC7jKurDeStAWFSo8T95y7QvxlwZtWMtFE9uoNTdFgm2XAQSwiwTjAikDeEtAG8K1XpANloB0NYe/S4V6DLq/thFPRXgrhrpK0M65xhi3zbHzWTi1fn66R5+5cBiBG03SpY1U70+iGisrAio/QB0gXTsDdJho9TeYNJLXU/RkkObRJwi3Hj+zhuq1jUINAkJHgGY+s+4bgfzzvaAmMRl8jpDeT+fi61U/bT4lGnX91FRuHzJJPAfaG88YfL7/Q4Wt32Knzrh5XrgotrouSLev2fVoXNYLFKQSTrELViRTWFhgok+XkYi4pUEu4yQV8PedGD7DrN5nUS7f1JjVlsRvSCIsYKMxitZZ5V5puQFVoLAgRMWVkMWEvhtSizhcirwNcA/FszvvLhvwZE8K+ufi2QO8jPvAqRJxCMKCVZkmHIRNFFvC/MfcKoUoJ6E2DtPxWAMgg4CKaxg+yDMqeiYL7qEXaEzsCvEi1OOJs4LXN6NWv3fVusIV4kGXXNHWAdDAGn3TmIVc8xmbnvvCQ2Dqb5jm4whZrfnxlrRFTm0XVaDDmuhPtxLMlYOkQtleBVgDkVawg1LlYgsJVFHBvQ/1KMW0Cw47hRc6A74sl9rHn/xvvVhORbv/Vb8eTJE/yTf/JP8E3f9E0AgH/8j/8xnjx5cmfX3duGd/Kes67LkxDGFMAmL0yZ6zyXC6EP2gG1NWAPVpjflR5ExrhKEROFWrDvQ0Zsu477Es8K5MnW82yLIgeAslD10PLmGS4p3IRU2V2Rc1VkjIi1ELLPHbJ5bWWoZJxJO+kCyrR6FHdV9u7S8o4yPthd1evUUcYZq+fYrgzq1RuWIolLG8+CbuP7O5QOswRc5aHav1zlobLCHLxzueVUIiZTenge4qCffj6qiElFcMiddnsXM9KPwJjcuzcgZ8b/47/5rwAA3/jPfx4//tUfw1/8rm9TIIiVmTUeetBVNI/FpiCdLLZlyyMch3PfrCYt9BiQNoL8QIEsmQIIQL7ICipNASUAnXc7b5p8hBmAqMQwmLKDkyB3dhzAihsLsKTnZI+JEQMMjCudkgbCqB5ZpRM0IqZ67mEGeC72Pq0A77Lvsszn2kTKALyC6h18a5Hg1KhgF6+BLP2gbwJaKYNS1seZQV0EdUGL+R6TDDDTN4DlcUBVK9DcJ/dLvDvlAbzKf/wzfo73tdrPqfdgoKMz256nQLN+rYXLo5ipRTPc3KfghXK5ezDrPY6OSp2cnIG1GL63i12TITZgkxsJsjC61bLfFudWrXZzQk8mXNbRGitmqJG7+3sUVj33XNQcsBTrkGjbb3gGU6nsqo46DNYhyX2svBI+GLG2lS5621w0++tEX/c0barsEVAqsXf+AIChpMrSClj8ux50BzzoDricN0optkSslXC28phNSFZ1CoihNFJH7TKViyYpnoD9H//KP8bv/yv/ACzAN/30v0cRwl/4xLcrTTfat8YYTfoPtYqgAcFRclTzQJR1gCgdMD0qkLOMNEbwGz36K52AqTC6K0K81gk0HATdFZCuCfMDTwDszUlzXLHJmbTSkTsFwfKZ2EQvqidnqTKVuKfKyAL0WP2VoLvMCxWVUCsXLjHUtrxm4OuVCzTAzKlhkyAFBroBsul1kgfWdNtThod1H6d3XUdjROggVp1Un6eacHzKp4zgazt3LAlMm7wANyoHN5IKuyfEALZ1cMK6AvGshO1+3I9muJwCUJBHgShf1CzbCUwm2MYfIRArCBSObnsvvhDWoFCV5AEr1pT6cIUqPxHSynW24oGzv3xEKlCTd2VXJZOBeDwrdmyh1vOrkS7S4rul+9Peh8WkJ27qDsC6By9dD4N5ZDkYJ01C0bN6s7h8HXac6rN1/B7ImphQhsN9LnUsEgEMCq58BfDVD/8ZvqH8FBgF/dkBP/2r/zeQf8WQw7DMic7Isip5OyfUMCeoHaakfRz6eB4E6ApKYtAuIE6WnEjbqtzizWSFkAErqXz9vJuChceIEloQzDofRlliR0E1jW8ZxWES8CTr+dEBOEL1Y6nvvXlfdGrFuzpTaIU8xkXisTylvzRz9AvHmlqq9tdjnVi8yCL+1hccPfY8idTxbjxBoBNX6xmHux/PHldXV/g3/+bf1L8//elP46d+6qfwyiuv4GMf+9j70oTkq7/6q/Hbf/tvx+///b8f/91/998BAL73e78Xv+N3/I4X7mQIoK59U9Fu3lPSuTBnBSP6UDAlXUuXwtjP0brlKbrBpKDIbN33olmmtOvuszghckFvRfQIlfulEqpHVu+MLLsxO864Sj2KMEYDa3RdHyoY5hYj/j6YrFNh0dzh6ax+VJFVgliEgQg8CKpjcyVJgNQcRskCMx7RDgAWJQtldFbkP2PgIApSeX50ZgvnjrIVMfT9DzzjnEcDtSIyCLs8YJSIJ2mLfe5wnQbNrSw/ce+syBrz+pCta2FcGey7f1YRvQ7bOGMbZzw+bOvjuTB++P+gHllEoj8hiDFjv+9R3ulNRt7Ivb3q5HOxzbslAGRAlkToulWA1APzQ0H64Kw0bCH1WTzTz5smRh4E/RNCd41asFDLEgOG2Ivy/pwCgrlXc3cx4Cv3VFm7dT0P1LlKgkCiAKwewuGwns+rL5b5MtZcKTfeZ2YsTyAgqyKFk4AnUiZy1uOtYi4t926D1Z4ex/HluLAiAppmYJyURcwMzAnoO6CLQLCVGlFDLCBTvxjAVTz/EVBnLPpjqZ/oJO+kBzXhvOvEb8mDbmyDVZF+eeLo7+OQVtczeg/VYlBz7BKa881YyQzpGed+PO7BrPdpzBZAAGdcEYrRfF2usei4ybryEQ6pQ2LW1qyytDdfdQSBLvjd9NwnQNd5+9CgwJX15DRVZ0i50frACRdhrFWHgxklnbG2h/WuhV6NPpQOXdP9Q4/Fi6+WAIF6DJSqEa97cwEaADzIbQ2tiSb5y9Bg4yytgRNSmDX5MGPGYn4vHlh7TpXR5p0UW1P7yczggeW7lQvjG37m59d685/9jPnIapU/uRzDmUjZ/bHIKucWBACg14kizLY9a5KQzgvKuTGkrqO29h50Mo07BbKqn4h92XlC7QTitFvAkpAsJtVQWR28Qm7ACLnkI0plZNGopvGtB0rcA/1lRpgKJHgCZYFGAESqXiBkHif1Z8MUuHPECBm0Un5DXng0lCZrw9kHL2DA3o4bFRXN5u94AapHyskEoq0M3KYN9+O0CY6sz+W56Lr1DQishdn9uB93jpaxquQQLy4sMsLqtQGdqwWonajAeQU4reR9WPanf9s2jUSl7te2E2g8A1Cld22Hpp4yOiv4pCaZEJD5PKL6cyVZmoV48UPZzqGCbu6J4t5Z7q8FaGLlr3NflioLgTLTamcpKijknb70murC1brdAvX9RSrWSREgXkCxpZuhafGM6dpJQpSESAkdks7bjDpBlGqabtddaAUm1aqkKxmlkUWQA0wC6Q3RmLkmKCCTk0wNQGX7oGxGvXI0rwuq1NxB/XpXtHNcAKrGpUCr9N4Z16avCmZZtbtlWTmeX2+lJq4+d5wBlI1Vmb/tE7fFmhOA17sYC6urffCuF6Ce47NigQNTzz3aWPOM07h5vBfa+otyfOpTn8J3fMd31L/do+p3/+7fjR/5kR9535qQ/JW/8lfwR/7IH6ldD3/X7/pd+PN//s+/5/PX7t0LWDKnhSFUmVI5oBQFsJJ5ac0G6uvcqjeYS8L9tT5KnQfVK9eL7a0/FoDqpZXsnOYSqhzPtzkk7TYYuSCiYDCm2D53eDJvqpIFADi40fziCexxzL2HuZ6zejIuzax0u4zFwzFb3gNonnTGU8PqSvZ4rLL1Mx5RMCuLyvKigRKueMA2zLicNyimWNlTqV3iVzkiyIoqBgpyUYCLFjl7Kowhqu+ydFQ/1z6qBxgApBRw/WQLPI0Ie6U+UYYW5I8k62Km5z5huDeWBFWH5F6lhfmDE/qzGWkKKFMwbwFRj6MgCJM1DLH8QjFH3akXmN3YXRjVH8mBr2K5TO6pAhda5DBgs0P9WXqVNfo2pdNjxD1ZF3Yt1IepaAzLpXozVrmgLHM3ZQFnVarUOEhNTubjtrm4mTtvMIKPnrcPW8Gr9vnawAQ13j5zVJaZ3AwUnos0QJDLLVfjWYbuHlOa93Uj5nlexLpWEtz0O9Mcqzm341Npclw9j+UYvl44YXd957gHs97jOEgwY8CyMjbPQuDCmI1R1IcMEZXBcWN26xP+IXe6nS/km5vVJ7Zo3TemsiD6Dn4FFIC4TpL73FcAyRlc7sm1BDhWCaAxugoTBko44wkHiQZYLfLGAEGwif2AWBfyXn04C6NO/l2uCcau9LhKA17q9jjjadW98HgES1kAWDvcXvX6nBE5Y0tzvR5J2vevXbA8eXKgzBO6XBhFgJ/8dR/FN/30v69r8R//6l8FLfBmTCkgXXcLK6utFPti2wwJQUAxo0Rvb5sH7fqRH2bQJkP2AcjaWpZnqh4lnFC14m7AiCK106AHIraJyA0J6+RhC35OKiEUBpAATKxU4kKV/RV3Us+/v9YOHxCj4jZAjPuHkO3X5R91CG7K99xwt50MY1iArKYqcdsg73xY7Pi4u0pAPv8fTY5tAria413vfmNHzfvC+jUnqxByx7n5edwCYFWdvAXMem3fXS51P76IRzKWUetrAhgOamCMAjD6RVD+UrMdCGSgjG/n4JUPFy96p0I3SAdQ51MHmgQKNLUxYulASMttThr7kiUTBEEAKgs4gasnlzT78X0li2vV/B0NGGZeIy5vmSRiY4UXT0bSjZWW7r8F8/zaejHGYyY1oBrBQB6LsR6X1Vg+Axi1Ed9ngH/x9V8FZXoJfk7+M8h/IGAPMO+Ri6DMYWFl3Zhr1wvrKvN2GbszpQZRuV9irapbZ9vqh9Iu0lsJYOO35SCW/w5Ze2b58cm6KEoR616o6EyYFj9G30eYrEOVxY31fOdz/XJ+N8axfK8udJuNuQWyWrTtlmHV4WW+vnsSXs3jJ+LDDeDpNjSpjTVHgNYxq3d1rs+V3OCW63dLHLsfLzS+/du//c5mJO9XE5JXXnkFf/kv/+X3cqrrY4ZFgxVDQS4qq+ZOqhWKKx103lXmlkuvD1MH5lI7IR5SBAHYdjNEBFfzUBtb1ThUsMorSgOiDSEt6hIyGxHrOO7bXM9DLY5sQgLM6+py3iCJ+khNJeCQtagxCxvgpMeZS8SDcLACfKnA1JN0pnN13uh21vAKAD7YXSpbCITZcrgznhAoKbOKZgTruB5QqpIE8O7t+tadqdVFLdpclBEvxx12pcdl2lQ/333u8KRs6nV4GA84jyO2YcY+d9hbDhhLxiF3eHzY4mrsMaUINo9KATBNESEUpBTUe9ek5e6dC3h+ACuuAMXYtBoKfW2uv+bBQSIgXxTINqPbzgihoAQGbxMSosYZgXpaHRZwCZGQg4JD3sGQsxZWeGWyLmb6DvQHvY7zGSFtaAU8lYhlPR9lKXYHaLf1sOQpDs55LuXm7YDFHv+QjvyZwiQgi2GcljhVlTjissM1y/x4KMkAp+fsCj6VpVBdivo7WvF6VWgBYGbWtx9PjgC3W7Zpfy5PtPHzlhcfx5TnjEXtmsEtWGp8s3/L81jipceqZj2yArVeYNyDWe9x6EQYlg55uekA6FUN+5mEMaVQJ+BIBYELdtIbCJNRpMPUJA1DSE03DKXPTohL9bnxnqpVEu/8IWqy2HHGeZjg7dLbjoPuc1WgZrRdyCpFgZnEU8GGMi7CAb17bwmDpeBBr/ReJsGGJpzzhIN0VVcOKNvL/3mFg0mwKz1GA9IcFPNKSfUAC9YyuARtWWvsAzd910qHbj8WBRXVQDGhBE04tL2t0nP/h0/8FyiF8Y0/+xn8xNd8DP/9d/2X1Utgf7kBjWz0XE0IWqPE1ZBlUqnIchTkiwzeJpTEgBBkU4BRW63zZB0FZ5vsZ6sM0DLhewt0/71Wyw3QYjEZoPt4TRqIAIAm1koFQc0ZGwmJHqs0+5NlknJz37Lo1Ln4pC6nK9E+btBrjzyyVi2/7LGUQbMdu4tgEa2w145SR6bvLkm0wCKRrJrUBqdmBrxtsj+avO+k1x4/bMc+mTy0eVQLaNlEDZwA2I728aKT9v344hwCm+NFF1itx5QviPyrUECQQiuZIFlF3OV9AoYX7wgL81UqKFbM30pXHqEBdvyW9Q6L/tMBKoeyBIt00R8VW1V70xNf17CBaL0xfPX96HvtQwJMbhcpo6esRsISqjF9J3nFKtb3pTEuG5PYuxQKFkAKKOhYF19Z/HlRWyYHrLAwGhz4Uo8wawFeMrLsUeTzkJ99DbuzR/jUx75F569/D+BnAaLPAzJhHhlIxYoktMxZzZxxPDzeiH6YkL6AugKx1yNaF1sRjQ/GlBLGqjgiFq/8Of+9HsOBM1kSD5fbV4Zq1u65IKxM5H1f5I1FvADSSsRthdt6g9XX37lmPkKLapw5AWT5Q0X0DROpvANQANHnc1rvr4JN/nr3Z7zrrO4CsOq4Gzi7+frn3/YYdLsBsJ3Y/n584Q6mgos44byb6lxWZFGH+BCh2v1O1SOAsKhHYGEgB4gkK5xoAWVMEYELhpCq5+ImzpW1OrD6XQ0hWVdYfcyBLO8U23PClEMtVriKQqV2KiOMQnhrPMeUVWUxhFRN1F2emMGAqC/wpjFRUslgNEm92qFc5aH6XF2EEZELPjc+qioVl87vSq9AmAQUUsZWEcYH41MFBG2yzMJgA7gma0HeUcYr4RozBxykwwM54Iwn9dQiwXkc8VK3w770tWPjwKm+n9TE5n3qcDX22B16pDmiJIJMYfkCOzKQGDTzIid0L8RicZqBEgQSDVwyglCJCwAEGBvrgRbiY6/NxACAuKDrlL1cZs1rsA/VI7j0qFK/7lp/z70bveucrewpNXUHYJ5VBTwWhANjehgwnymzq7U2EWeNsei6XwDql/fvYEkYNb7RqVxt/eXQ41sOtBSJluZXnGQBxJjunjBbJcXK6/eW+bvamCjIVov5NXdpnn9WM5ETp7WKBV4QMs+s59ovsIoZNxQmd53SKu7Q8thtaxqx3YlUI3hfA1Vc67YC0S3jHsx6j0NZRgFTCTrxGpglolRQlyMcUkTHpXYn1G4jahDv1YsNUGm2PWcMnKq0MBjk7iBUZW/RUi1wk/ho3QjHpv2rUoCLVV+4diUcS8RVHpBKwDbMdd8PwgEDzxhLp6btpPLAAsaGZ7xkGnRPJDak3ltBtDIyScAsapCoHRTLKuA85D06yjhIhyyEXRlwkA5XeYPLrJTibZhrVec69Q2AV6rRpQODDmS57PKiG1fdVcY5QpjxF7/728znZHGUuX6yBa4iaKLaWtw7fVChheHDSn0sQamuZBrvvBVMH0zgczP1nRl0liCJ4SL8eK1gVmV4pYXVVaspWasFaSDkHjbhaudCTpZMHBmkSxQgCvhACDtCscleKbt6vDAtgBilpdW9VgwIiHrsMAnigVYT/YsMb3O+nN/NSgWNE5CyJg+BIVMH2Zg0EQZSMa8METVnMc11YRSn6PtEeTzp3WIC/9wyxttyjxMgVMu0IjH50VFyRqL3jRD0PjrOwZ4RM+/H/QC0SKFAigJHuVTUt8rhACxVd2i1U/1JrWNh0S5VXg7xznyRyiIttJtx8c8yMMcYUAAqIMQuM3ROOKGyx8i2K0QVAJpM1uHmwcTaNj2ymuFGWoyFVd6XsTFGri8dO5TqaUVQK3k1r89Vouj7AICB5tp9UKv6ygqYRDtkzWBE6GuTec44Dh8MyNL3ZJ+DLN28AO1elUnL4CKfRToU0P/yJaAfF53DrgDG5yB4E9NhBoyFSy0zy0GdZj4RQjXnrX93QD5TIEtPhoDO5nG7H2pHQf3Q6kL9BttKgBJpqSwLTIIuJxlDwlKTJZ4XcEtgcUyapEIEbQdDzUQAsnbslEWLRi2Q9wLjZpHlONZADXdLWYCvzMoeJgNQWwYxmSyl7kon92eBWSeO7Id/X8cNFtdxYtImHo00dV1lx81YeT++oAZD8FLcoQvZ5HwLE6t2p/XiemEUmzNSCgihVCNxES0Ax5AxZ0YXNHdBYcwUapfAIoQEVUQ4aNV6/Lam6KmCVoyzOOOQYwXFAqncfMyxmtdvwoyeE67moQJeDmhtw1wLFxv2hlKE2QrpWbg+3lHGLvf1XNyC5cm8xXmY0LG2APH4VwErEK7yxtQkKmAPKPV5AOgoYS4DZtEOsmc86rljBgi4CAeMpcNBorLEmu+sX4+pxGp6P5WIJ+MWl+OAw9Qhp4C8D8DE4IO7p8Pmav0ia75CVdkBLDkFLA9wZhZE/XsBgETX1KUXlLMCOkvgIOBQ0PcJOeuideg0j82BMR8iwKIAGWnRPPeWywQgba3TeqAlxgmQOyBG6/ru7K15WSsLaSyK1v22dG2xRRua8GQ2AAU6XweqahfPbzxw17W+F72b+VFj03KNNG7p6zlLbYZSWV2n8gZ/zAGtZwJQpxISO99nsLFu32fz86iQUdlm7bk+Q2K46mZYBHD/rltGa8Gyik92LVbMrBPbFysWmbhK1xFNfLpnZv3/YQSSpo2qgSuZMSW9vGSfai5cO4boQnh5XmUYjJ4WA3lgYXcFktqaVo/TyAuBys4CULXrtetgCRglGsurYBsmFCFclQHXaVCZiDAGMyz0/XeUK822dwaYaFCbESqTilHMD0R15e6XVezczmms+wxQOYjuP6GjVAGyQ+kqi+uSN7jK2mUkUq7AlF8DlUsWDJzN6H5hZ1XPMmMKbGLSz0gIMQApB6Q5Ypwipl0HetrZhLiYJvrKX9jMDAtVpYJTXUuvz6WXEzYvHZAzIaeg/lWArYcFPLLKCItpxe1aif10bxGluQrCjMo+8kk9TACIakcQ1bkDZRB7HRm4BRTRiV4Dihil1oCsVlLivxSqOnI37fXMkdtKiU9Mx/rw44n6+PmaBRYFsnJeU22zaMWeyMCe2wODyx1bZsFyLCzAUHtKL6i9vpGdiCf0blR9x0tlfWlXiaozJWyb40rE/bgfzxqtd1X7tVoMBvQ+rRIQ0nnLm2mQJenaudYSG5vzWwy2ZX218kIANa4Aiwyx7UiYBNXsNlpiNIExl6hsAQPffL8OTHWUVMqOYsypRRovaGSTNld6J8KAovMHUJuV6D41ThCJeXO5/1VGklCZXFOJmCwB4gbU82tQjB3m7eOLlArO1a+4FBDNiHwJogKRS/CohsdFZqS8R55HYBRQKQuQ1fqZNImKJy3eebDY32VbEDcJpSiwX00g7ROkxEvr82YhWausrbTP4o17nCjgrswuANV/S4y9Kyb/IJOF11vE2aaeIDiQdTyxNXPhsfSxnlu96fxfi76cQL1OgTVAjS01NhGWWOWIlSM+t8UaNHP0bXP+8Sk9G/t69jhOBm4Z6wSiOf6St67lH+/Hud2PX/HjLEzqB+gsU2NbEQnmOSLGjJRC9Rxsm2vMc0AIxuJioBQGm21HsGI8G6DlOY/6/VI1OgdQrVA2cQZbfuHFELcDWQCrpfBw0SlrShlcGbvUoQ8ZgBY5/Dn3ywpcqgpkl7fVw2pXeowSK6A2loiXuj0AbVY1l4Cu1/lfuwfONb7t8oCONRYNPOMBH6xIsqb99O6xRVmL8aVDBmFDs8kQ2bzEVIHiKhgH5pgET2Y1d6/5TQ4Yc8RhjpiniHSIwMyWQ+jnJECVlEvEytPX51dPFfMgkG4pTpQOcJ+VvLFcdCOQrbKxuj6Zv7NonbvT93ixHfHkams7oTrn5w1QBiA+sfklOLCEysRKZ4TcUTMHAwiLAiYeCigT0sygoqbgdR7LgHTQPIxUNQIYwMUGQmVZgBsbVXlBUOWHxyRnDVmRaCkmyRKfrBOizpkEqkbLzQH8eHNSE3e+Sxt4lA95gcVlkO8GyAKWWLMC6vRf3Jeb8dfP/5i9dhRDK4hoMeM4blRp5C3xZJXXePiW5eMHYx23mmOcjGnPOe7BrPc4AtSQvAsZyAHEBQJg6EQ7iQhBfUrF5G4BgQWBC6a5U7CIC0YDs0qg6r1VhDBYACi0GCu2Y+kI6MkEVaTfQZ2DdQNJwjiPE8YScVk2tU2ud0DZ504neDNYB2U8CIcKmAUUXMsWoxnGZ+hE7YaJZzxWtpUzsTwAdFY135A+5hKWAsYkQE9AYK1obGjCo7CrTK0naYsLM6u/yoNW8I251T7OuUNHBZdpwFuHczzeb1ZMuZxN7pIZ876DHAKodmIi64hhDKAoupjnZXKuCUUv2hXqogDnCcPZhL5LSMyIsWAEVPIBaBAadZ950Imiu9LJt0SbrJtvsh+Lk0oDJQDzljGfL/I7D1TCTVBjQR60AhOnJRmJs1XLs6xAnhsyO3EGF1XvLAfaamJCdKeGnIrcfOY24OsG1fYFRkvNPfl88/stQNZJlta7MaBvADQ5EVw8X2qrDQ5kvdtJ+358cQ6vHmv3qYUESSxN51ZdPVT2qbGA3buRCRCQgvNkgJexdmIt/EoFmNpROwLa3wIDr+zYlQEAQkFWxpIwpirJ00VQtlgX7P04oDZQqnMLQZAkLoASjI0Grp2oAgQJXJkB/tpg5x4pG0PMTEptHxEAGwMsijK/EhhTidoMxZIUZ5ExqSVAW1AhEQQRjCVgn3oc5miFJwLkgCJJP7FCyDPU17AUNacVqsCPMlDXQFZL6/euf6UXoCsIfUYIGUQMCRkZC4uM8iIvr0bwEwCRCqK302aNB2UpqJROCyYVBHO/EkJNgoSsCxG7lATmA4maBLTjVKzhDOQG2AKOX3v35LgqGhy/BFjiTHNMkpsPP3s8I9bcdvxnPvHukaU7galTp/sCb+F+fGGMTVQ265TCkhtklRP6Twe4nLGVEtfO50QEMaZ5BwW5J5MZhiY2MMSsPXLtCusFFzcwj+YRXBtXkbKbpjzgkKN1/Ss3jOWVJRsr6OVdFTtTrXgxPYuCa97IysdcVDbo5+KFDgWvMjrk6vXreYw3KnnAhwpIqSewvm8fztByg/gzHqskcW7Salea+GtGeLfGxWtYvcK0a/1UIq6nHodDhzwtjCwelYUE2LydtWGTsHY0p2TCflOClF5qt9vK0M0aZ4U0Hy0dIMbwpViwPZvQx7QQM7ho470cMM7WNbgryNuMPIfqsxUmaEFcmiKFaCzhWRAOAIoeL20UBKGiTKwwCnjU6ypBkDsyJpntbyZgI7X449L8Kkc0hrFd1LVyXATCBmQ1j3vzp+BdGF1i6F0OPV/yLotEC4PoaEhKoGnW47TWJ8/hGXySCPAiowGvFvaTIIyCMOYF4DvOgdq/27B8o5mKbXIUbypI6Pt5Vmw5EY+O47fH5srMMtDrRXKjezDrPY4NJTyMB2zjrOBRDmY4TmDSBZsz9gBlBZUiyJaJiDjNVj+1mUKtcjAJ2OR/3pnJK9UBLvkoUON3rpNipILMZmredDs85A49Z8wmi9S/EzZBO2rEU99WKGB2KJ0lEMqoepLP8HY6rzTfy7zBk7zFy/HavFUYLIKeEg7C1UvrQ/ES5zxWgKwew6SPTAUPwwGTBGxkxgPeV6ouAFyEsWrfPWjuSl8ZW7Mwnk4bXI4D9mOPaYrIh6hyDB8k2vWvmr1rldZRegkaCLyiEZJ2zGChygjK5wX0cELoMrpOGWwpM4bOunYlVtNE3y8b28qAj9xRDQRiZrnCrFpz8x0JY6nykOkBI53RqtuhBIW7JYrqzQsaiNuqI7PTZqU+rpVm305/kgFLngy5V5eb3Le05VvHsyZmr0ocP3bidSeTFWCpKtxImBaQiE9UapYNn3GKPpnete2J5EDqdcWNAHPjtffg1f14F0M7PCVEzhAJ1YOktVpoiZBFyKQEesMVo88smKs7W2ny4lVtTyqqibsxk2BsX5cYKg6jccpjno9UgrFhtTqtf6uEvk2Gjod3RmSTCwoVjKXDvvQqwaeixRpEK5YYQAaVqiQEJFGJ5DlP6BuAzIfHUCLBhhISEaIw+pDQc6rGxL0lUy6bVLP7UAG2LCrlH5PaC+TEKi0Xvc7Lm9JKiEr7qC7m7EMwSbgtrBNZlyDU+aR0AhoyiNVj0xltkQvQASUTSuXpY1loeoE7UJWzV98colrgIAEoqScjSD1PSrfEQz9PAACbvEPaB3U7aiSGq8+UaL0Pu1Hd18WTjSVZOTrmLXfKM8eNGHDXxHzHIU7FGnIg7o4J/Xlije//BcAmf90zmVb3INYX7XgQR2xiwjhH5BQxHhagpzRgkxBQcqsV0nmOQ0GMRb1qWfMKnzvcwsQtUBx4ilSQwNUXy+fRyexAvLj+qDtgNhbsWVy6my+KCrrRpT0VRh8UxNqGGS93uxqvvKlURxlXecBl2YAhtXM6oF3SHdgqwjiPIzY84yyM2FCqCX6n5lO6X2RkYvSUKgsLUPVJFsLGQLkAroX8WRbFShHGy/EaA8/Y5aEaz3ecq92M26jo+c5453CmAKQzrUVjwroQbRKtsJ6LAABqd4YyyMKwMX8rngGQxgIhgKJtEwv6zYyzYbLCusaYkgP2e7tmiReGUhTks4LSGds7GwAVLCeVeuqLEsVV8VtCHrTTYZiWfMYvb5Uq9nqOYQLKrDLDCp74e25ZxkejBWYqI8usSrQQ4zJIqewunhXMomT5g8+fpNdt6Yx4FN92BwV/hx61+dWxxG/1gibncRN4WY6hnqJ2zkVAjBUTaiXfx3JPQFRZE3f55DY3ro9dy6X4fsJH2Lwz79yP7cNzsFYxUwtIdAyI+WsWVr8z5Xzt8qLjHsx6j4NJ8Cjuq3GuYOme54ybUtiYhTqhCzSAKG0XIGJwyEjegY89ABiribNWT2ndnUllHLlq0l2S2BkFV7sALiwvB6u2YcbAaSVZYat41I4epg9/nM8AoLa0HXiukkbXpPvzADCaVJCN5pzBlYXVNRXvlrLrgcIBr0JzI0NUoO+aBlzljb5PO/4sAfvcr7pZPZ22eDJucH3oMR46lF0EHVgZV+4/wgoC0UTKRGoAEgmiviS9VNaT+1B54SefFch5QjQgqwsZgQXMgqFL6GLG9aHHvO90XmNPSjSglE6rKiTK/hKyYNABeSJ0e/UTKT2p7M8ouxIAqt5Zyzm3vzv4VZMXM3Jfs5VovRCusw2WgFkTC53kHeyyG+/0KMW8UZqMuh1tteKYattm4S9oVLh6uOiEDuDmxPys0RgVrxgMLg/Ezcm+pfbWRYX/fWP/R7831/uZicn9+KIfRLooV7zUiyGE9uvtshJ4PCI19VUwBoAVPFQ6R+YPynWhFmxx4uDN0skPlVG79AE0zy0IChVjhcGKarpFRxnRPYr8fRgARiTVLJ4hOIgbBWshpPUBiw3I5j6NKgtU9pXYe/fzru3XZelqq+9P6fcJEUmsl5WxntnOe4Jgkghnc/l+5griqe+kAlkRc4paqZ4Z8E64BJNO6zuu0vVmDvB5WsLCnJIgFUAEFMhCXxTICupnw6QJqXrcFMxQg2CftxQc07nfY0/9YNx7MGgS4sUTCVCjWAeluOlWddv9WN+nyj/qZW7nuePXn5jb2zmzsoBbeeCp0d70z1tg8ev/oiDPHUWNmoS9wO6WVxyBfFiS09uuk7/KY03793Mf8j7WfMGOVsq3jTP2scN+VBDF8xEBIFkBKGJtHMFRVjmMlEWaWISAJjdRRYegC1k9em2bBEbkginHmrukKqnTLoUJ2iG9o4I+5CqHdP+tQ+50HrL3cjUNOKSIPmRs44zUqx3KXAIG61o7i7KyLtMGV6lvmkKpR9bAM85YFSkdZYyWPz0Ke5yxKjscDPNzdcVLT0tnXN1O2bvBcjHPUwK4WrE4Q2wGahF+wzMKSM/Xznufu5qzAcB16nE990pi4IJCbJJuWTU8KhYzIFQlhl6AJ9J8pWwKQEC4DDeLBFBpYekLEAS8yYhR7VgcOLw6DNhdDQtQQQJJbKmCgM6TNh8ZAzAGzBdqv0LJmltNOjk5CwoEU474GRDiQQswnJWRlTv38FIfLkCVMpTVwB44Au7s3EpQljmOQRePIw2QIpFROvXk5WTgVTbj99qUxcCs47q7eyoCi58WAEiBXF6B0hay6dVCxc/Fcp1WfUJEmv9kUiWXdzis2xNgzRhuzOvSsMZoiRdUjFU2l+cHsk4+iRvx4S4mcAuAVXb5rRYxTchuQK4WpK3rIF8PvEBgvQez3uNwgCeSglHePa81GV8+wGWxrQGDV/dNF3KlePpkWUQ7UMVgwcEm4jHHurgPR5+4U1eBhdILAIOBXEVoqQZYNxKmgi1rJWOWgBnakWMsXX39KKzm7NAqvoNWHWWc2Wvbib/6bxklt3d5iPt90aLr7clljEM1b/RzucxbHCSaBn3pfAgsxvZMLvnocD32mMYOZQygSfXmnO1QzpDylq8i1mocteNHHkQBLzu30psR+1A0iHSCMGRsNzOiAVkipMFdvIU8wF2B9IK8kep9NRzI9O1WERCV9BUAiDqRJ2ulLMToUkEYF4lgiaqTV7BKWWW1qm+La694VzPedoj9xwwzlVmeOwJjli4fOoGipd2emrCmWfXjYY12tW1310/IAmwZe+1Gq9oTo61S+d9eqQmTgKcTevGj87lr35bz3/66mhxawvFuEqTVjl9s0r4fX5xDfZvyUigo2uEHQrWvQBtr/IvR3p4F+l/g1nurKcQYQKSb6fNaZNHFwvEt7ubwgM73DnPFCnoR5qJeV535aDmIBSy+VAlr4CkLY3RASTQJWLy19CwCGt+t5nyqR9bq+TWgRSK1U2O07Cmb1DCBKygozXVoje2TaLORyRlZmXRx2hRHtJAhTaXRPgVbtIHQGPPqa7zaXtgmhQBQ0I5SDgACZCbN9s4JmpQGjW1kAFY0qbjPL85+AtvUG4y9lwVEhGALYjJ6qTb0aG6sNpPwOatZiJ6ccwVWrcY6AQBWcx41+2uLL2IX8sbUmosiaCEcPdF+C06cTFNxv7OjbXv+cvS3HcIr+ncjWc+INbc9/W4k788zbvuc7scXxFCZm3bF82I2cwEHLXrkFFRqToDMrB55sdhSbLnnvEhy6jYcgu53zgFDTEglqG/W0b3ee6c+CSgSUODgFWNvnftcmqjnvrx+bphZ7tW1T2rLss8delYPql3pkQrjaVI/p4Fz9dH1BliRC54mlfq5l/CxMsSl62yxwwdbHAEUyLou6uM7F7VWech7yz9KbawVqOCcRkwScWUSQwAYKGFExBnrtdrnDoEED+IBSQLeGC+siZUxnlhQuqJdDK1a6mt9YLECkc5AIpalztxpJUI69b7Shh36+hI9VzDGU1HvsS5kHFJUIOtygIwBNGR0Q0JOAaJ9m8BdQdcnjXtDRuojSh+1i3oCwoGQEi3nF7XjoDAwvqy5TP+YcHiFkAdGOKiCJEyaE6WJgK0VYwQK7mRUUI8mWFyxXO4oBBznGisVigEulcnkTKys8kKPP+SgkOc8kdWHYQIoL16MRAQhBqYRMl8C4wAa+lUOJIcRSKmegxADbDEtsHZ4J9JjEIGKfXYoCnwJLFE0m5dcEHf6eXosulWJcjTuiner5xpgcM2oWv64lUVlbK4Va1CWroX19R7/Vvs/sb/nHPdg1nscGVoFvuhGvM3nWqnNATkvC3M3UXTqqAgQQoEII4QCN+9lEoSwLPInqzAAwFgCtgZoZVl06UtXP5VfnAcFmBzxd5+tWZZOWA5+efBhKrgII17udjXJADQwAqgTspsleitcndR7tN0Pz3ms3iZe1TjnEZME9GYk76ws98zqSLsAHlN1sxAYRdlghbwxIJgEnSUgLr/0TiDXs0oLSybtKjgt+uoWGQY00VAPEADWoVA6A7lsMlbZh1dHAEQBdQWbzYxH24MmlZb4TKnDYepqhxhmQdpkVeoLwCMtCYJ9kaWZjDmr/DCdq7Fi3ANhIoSpVJBKWHXnpTfgaiTI1t5ccaAMN0CspbMFUBlRVTpIJkVR2q8GOkGYjXo7y3rR3oI57RABXe91wutiXQndCmTpTaavE/POaifUExTbG+9JvMOJICRB3JXahn698fp1dyYxng95caWZsH1i94n4uSZfunnv3Y/78aLDpX09Z+xJ1NPD5H0Vb7CCiScGiiWYdKTO+4aT2N9ixQEHsVLDoi0GzLufQbH9O9OWSOp8R5DaaMMr7a1kEVDgrKeMTZhXSYM/73JxP+cMZRTre/X3nxBNCuLssUDFKukZCYQIZXY5iOXAmseennI9V/X4Ijt/Ywr4IhFYDOFpidMen3NWX0xkly3UN7T+7HxRZ/OFGPjkCKN7gVTQy0zXwYLYFQwxGbCHWgib/Nh2XSWWKjdUP65m3jQA7djctXQAolbTF3BmqQp7skAFygrulv21IFk76PiPFYuKqhTDWVgeb1xevwLG6s5OxJo5WUWeb9/Otl2KDid3bp/1HXFBjJNXgSyLi6di22oXcvzAiX0vm6wTh/Xzz8W+ItwNkt2PL+iRbS5zGR+ToI/Z1CFkIEQAs84X7u2K47UxgJIZ3Jiza7NqqQqSIaRaOHeZYQGht0ZTu7SoJgoIUw7YmDSRjQ0bSDDmiKs0YLSGWUNM9TidKVaY1Gd4Nm9fhuA8jsa8SjiPIy5nLbQ/iAdtWmUMKI9DkRJS4XXRXmLN4RzICs3iueZKwrguA95J59iVHk/SFh/srrDpJ2w8PoBX3lqMghDUoN79f933ci6dAo0GljyIB1zEEW/QOcYxKngkpLLuqF/o0gkQBGHHy7qz/dBI5+qyKQhnCSWpIghCyAOWrrcCZVVF3e7sbMTFxryHDwMO+96eV6Cr6zI2w4yUueayIlTN4UPMmPuMXAgpMegqgCezbzHWLyeNPaUThNHYWwdlZIVxKbrnBqDx+EjG+nW1iVfuSlxi2gp4OuH9tKzXrdnVJHWbYyALbgCfpRba6+uDFebbeZ8JCAEyTcBuB4zjUmTJGSJaLGpMTrW1IwmQCyhlPS9T6CyKFf389aMlSLFiIRMgxbpX2jrP87rqcdmATiI3HmvvmZN/F1kz3U689C454Co/wvKZnMyDmpB8HLteJI7dg1nvw/CuemfdhMuxR85czcZVe8zIRHVFG4KgFLY1FoGCSToMGEE165WVKWKyakZu7qC5KItq4CWwMFRKiDAbu4vM5J2r3PAqDdinDts44yKO1Vi+s1bos0TsSoerPOBp2tbAeMZTpe46yOUU3Qe8R08ZEwJ8zRugEkNnZblUZRbtIBWooIMmDBvMRtUlHEqPA5SG25umfVd6nIWxJlqjdU50jf0u9QrWhYK5ASQkAN5Jzn2mave/ABTLWsrWaLfXQSeweTE1LB00qERB3MzY9jMGo+VeTz2e7jaYp+Xr5JUt7jPQZ5QpoOSIdKbnRIm0w6HLFLxS3znFlpB7BbNArIHA2qg7XZdEabjFmozUioMHgxUI0xi3twtzA7JKx7U7CHulYjoyQzwKnK7pbjXgMs+gy2tgu4H03ZqlZd2l6uRuUkPKRrX1mdiz7SPzeDIeCCUBRVFGnRhNeFZGVgWyjgN9M24wrG5scMdzaK/tc860J/Z3TLm9R7rux/MM76rXmSdiEYIU+2aTLVJtS0Cg/qcLOwtkQIUBuoudxMJiAnTxfnx3a5FCvbt07/qN9IKCe2k5G0sAsGgX3lQYMwf0rO2/AViCkVWyJ9qJdiyxsp9UolgQreMg4MUjlX8EFGTiWo22WiYiNFHysyngyqrieg2zNk4hZQ6k6jnpVXg1A2ZZCi+ZzKVStIAk4pX+5ZJXmjzD5rAF7FEgSR9zZjBNZOCTgVL+WhattEeVsUdWpttkZrwlNZVf2D6CACFDMkGEqyxeQaLmPDwmWEdc/50zwBYXhBc5YgXASsPDMxCunsMaF1pjS/WWXO+7Fl1sDq8G8Mey+HpxsXpccgaNAnQREsKNeAGR5fBeSfeiSQ2M/pmceAPm61U/U3ihx0G3Z8eaJWO67fnnGC/y2ttizT249UUxnK1UzO/Pu6cTeSzQDnU5sTKyjtgSUgjMygAlUxzkzLXuycRAZw2voIUPLS6YGsPm4ALtOMgkOI8TrpMyqFSWmCuIdbD8xJ8DlAl8FifspAdQcNGPtUgzl8XQfiwRWzOGv7C8wFUbnpMMZui+4Rm73JvMMeDCiu4HibbdjN7yEwDq+duwsg7S4Une4u10jren88bDWCdHL/wXqMVMZ+8RSLXo5McZvSFXS2JAwTbM5k0G9V4kBa8kiuYtbbwoACwvKYMxeaJAhgLeJDAXUATkQlAyQfLSlY+joB9mPDw7YNvNtUlWNaXnArjML2j8eeV8hzkHbao1R8xzQB4DOBaEWNAPqcrey0uMlFjzIdF1QZ4YSIzunYDhLUJ/qcbwEgRpoz6NPGtu4xJDZZvZfalpUGUuO0jmBZkSLYeZDbCDAVwrnysFojgBJKXGIX2+AbI0yAOpgLJrXgsQGRICpO+UndX6/8YIyhmSEiSlhYmlXz4Is7GiPe4oILgCxuTE5H08bkgpj2IXToNWz2Ig33i+Am/rx081c6kh7jjOGOCqsIex/xvfs9U+2t+bv48LZXeNezDrPY4iVP2iNmFGFwqmoImBA1oibqCnk0N7/5Vi7Kxmf27i3pomdtwAWwIDl1DBqrFEnEdF18cSa9UZQK2c1KBBVAPCPnXaNTEk7GwW0YpFwGXemO58rgaNgAJPY+kWE19aZIRuiNhZ6w33w9LAILb/iKdlU7XlL4WdmsKb7DAjmF9WwkE6XOatdke0Vh3aCpgxp/Mqb3yczrBPHZJNzMyCEqTScgVLouEgVelFu3kErSCgL6BQIJNWt717obgUpPl+9zGh44yn4wZPrrcYdx0oCIbNjFIIyejc/TCDCMgbxjRkjH2Hec/giRD31kExqbyRzQ9rfFmsUk7Yf4DRXTkFV4BJO1ZlY3dpomLoPMNmdwPpOu04BeBmNcFvQrbkwveXGzaWa8gb2i2VtuWtTca5mdSJIYcDME6gzaAVCGuNi3GCzPoZCmk1g4CFassMiZpES/X6sg/NEyACeNJOHcK0BKGKDDVfzlPVhFsm4huL/VNFjIYJ1l7D1Wvl+DWwpOoo4NzmO3Y/7sctQ20c9D6KrK20uaiMrUo0BBBzEHcvqeV2Xcu1lilALHdXsCJw64qlgJUSWxSgT+Da2S8b6OVbO9iz/Fz2pJ0Oo0pUSlCZgCUfU4mrlu316CRIEup5u0zRJYQsUmXt0V7lHlyAJhcHUX8sCLClGd3KFJ5qNX4GY7K4FikjSUDHM0QIe+lrEeUgncZRWQpWXoUkNv8uB7Gsy5MmIzZ3CgD/OwSbhyxOHVU7iVRmwyQYc8A4ddrCnIDQKTPN5R8hlgpo5iDIgVFmlT5yQmWAudEqCEgm56ACzGdeYLFmIFCw01lqsPdjl22532h93uQ3ApZtavykowTCGo0snagWFvDKj4NPVMSJNHHIGRSjMbTsZFO2AomdCvMCarmvYwWpqJ4kuceKvRnKBQwLPA58PWesWT24AvVu32zZvik4PWvb5qlTa/97IOuLZ2QQrq1LoFteDF0ywGK5O3Ky7wMr+4ZoAbJKIc1VCiCkhfe+V7ZUF005UhiZ1dtQjd7T6jwiFWy7GWOOFYCKXKrMMBXCVEL1t/JuhYAxb7nYOp/QGzsLUOZupFIVK0UIV2XAo7jHwElzMVvSu18wAAymJNmVHo9nrf6+3ClA01HWzrpcbny/3M4EUIDqUdhrlz4SXIQDNqSEAd9ukc83ChhjGA88aw7EwCF1dn4zrvKAgILzOGLbzej7hDQH8zIrKJGX+ZcJeVtAyebFaHGjK+DzGSGqBBAAQphB/lllZVQNXcLDzYgPn12CSXA1D9iT5mHOhgMJhs0EZvVFG7qEMUU8PQy4vtqod9aoWvYihBAVxHKblfHQVbCUuWhTNALCFaN7SuiuBP2loL/MKJGwfyVgekQIB5UR5gG1yOI5nHQFNHJVtZCorzGEkHv9x5mQcwBPRX22SFcveoGkAmPVU6os3pD2oa3ZwqVUpnJlQHUBsu1BKRtrywp7DGVjpfX34OQQM6MsUgv8N1Qut40bjKlnT+63AlkvEoea51eA1vFrVgtO/beKS6eOcYLh5YDlixT578Gs9zgmLItaJkEfMkbWrh5E2g0EMUPgJVCy+7YJLJkRYq6SgdYQsW4jhEguG6TqEQVoYqOgFiNYRcC7TO1LwHXqMeWo3ZosMEQqSLS0w91nZWFlMB6EQ+1S6BRf97DqKNdEgZsuIk6lZVFQy9uud5SQRU3gvepxXQa8nS7wdrrQ5Kx/Exuaj+i9pe4DUABszmpCH1AwQ1ldfm4Z2m53TAHzbLBZFMDa10oQ8MhryqgZwbsPVrc1n64zUvnhuVan/DPjWDBstaJx0WmgvTwMmOcAYt22ixnbfsaUQvU/K0KIMStjrMuabCTG4bpTYC0bUJNRF/10IPCIygoLs94yJSyLU/H1iU8gsiQppTM2mT1eaawNiq4nB/VlyQqYOWW3+mXJYv6u4JZJAoMlDnQ0sTIpoJUS5CqBAltJpSyeKbaNfwvIgSxPRoK9HTO/rBRbkxxSEdBUFmotoKDci/qM1Az5eCY9vfmauota1VyYGL7dLYezz0raz+yO7e/H/WhHBi2yMugcmUlnVSa7r4ICwfCtRNasmWJZik0YDja1awav0CpopFukJsa5j5UzqNwmPcGkd4VrC3fvzFtogXPnEjBxgBTtehVQjKGl+4zViHeBwlQ6yVXilyiARGOBKvIU1FrOS183ScS+9NiXXs8y7BDNdL5eJYuXZImGejPG+lgG1326F5gu/Ak5257YAHfR3yk50I61HNnYWmwt0UtXKiDUljaJBaHTRKLnDAE0pmRNPGGymxhK9egEDLBkAfWCEgpko8y9NDG8o6KzouriNFFlblUpB/T3epW4uUk81tgcJmFZnzvWc3Iqtu1J1BvFt1dZo27gc6RX1QlLInEj1vjPUlTi4fHouLjh2zBr7KBicznpZ9UCWgZaVcmhaGzE6jxeaI29nEf785nb3wKEtb/ftT9ZLsVzH/N+fMGMQ+4wl1BBoKGbsR97xJi1yE7asCjNwb42Oi+RKUd8PskpgJomUoCC61p0tyIC5dqxsGfNG4pZmjjo5F0LOyoYS6xFmT4skkIHqVyKOFlHwF0KiKSPn8VpJf17mjYYOOGt6QLnccR10k7no3WB/0B3rceTc4wl4s3xAk/nDbZhroVwALUgoueorF23Omkf3/CMD/dP0VHGS2FXvbIA1G17ygCx5SldzU+KMAaegQLM5pu1Q1/JENsw41G/x26r4JLapTBKLPpdnhmyycDMkM5ijOcvm4x+k3C2GdE3gKObum+7uTYG++DmGg+7A/a5Q6RSgcRdUlVRKYx5MqVQn7DtNQ887HuUMah9SyZogxP9LHJmXI4d8j4iPI7aAb4XpE7n4rhn9O8Q+qdAdw3EfQGPCsp1e1a7FdJ5P+4J84Vec/cHhhdgAOSt5il5AMIoyAMhbQkkbAoNNDHFBpGCVbBcqCwTJNk/L4TUoknLmBJRJUgUSBdQzjbgUoBxuYcQWAGtnHHrOAajvLjyvGCWnf/z5Ch3jltff7zdLaAVLDeNy7m0uaVYAKpLmiZ+UXucdvF5nBO1r3uOcQ9mvcexMJjCKglg+6IzA/McEWOu3iIA6qIb8O8ZISdjFUWtpPjko10wymriBNTskEmpqfvc1WDDEIxW6QaweKUIYRNmRGJMJNhEBW884Pk2Ph6FPQZKpsFXiaNXFwaesSs9xhxRbDK+LgM2NFuXw1S7GAJYWFkIuCwbPMlneJK3eBAONTi5h1Y7AgQbmnEo3aoNr5vB++s2POt746IJGwCKBdKTwfjNtWdRVlYUXcxus3Ym7BOIgK5PSCmoZ0AoiDHjfDPhle1OwcqkUhgRNU7s+6RJWCi42Ix4ebPH03GDx/sNpilW+WE/JGyGGUOX1GPrQcCcQmXwpSmg7CL6NyI2bxGGx+pbNZ8Rxkc6UZfO2tYy1DzYvVWA6m0CWeSKur0yA+t6PQtcMlEnoKwyvUo7N9CKiqhJvMDYWVpJ0A4jDNl0oDmp+Ttgum8LdilBcl5W1USgEGxCfw/ViXbc5lJ6atOT9Ft7/3JiIrdBxnRbHrj72CTVBu/WrIc831rWA/fjftw5MtQUPQkvgEEDKAkBJQdwEEjrmdcmtKT4dSmEyEunWTbQS6BJis9vPqI9RoAd3xZ+EJu3F5AN0PimMhOVzkesEyI/LR8Dp9q90D3AFHTSn7MENWzXcitmCYjQ5MdZWtLEv/Z6jUUbmfSs4hGPRo1AsL6uQ8EeVjQxybuDd/V9Ua4SmjppsCjbCuuJRMi8saxwgljAQTsRgtQ7sxQFnMg64nYxYdtpXM+F6+REJAhRrzRzQR8zNnHGmCIOKSLnUOWHIWrcCgZc5oGQC1cZaskMmRnhmhF3QDhg6Sq1UXCryhCpAet9senglizbCavBsLj3l89tx/N6wRqkaoGstmoupSJjwqQGvJlrsuCJid7UFvgcICPAqh9YITvVdOyXNtacWoVL+8ttseaOOHT6nLCWbpxKOl50n/fjP/kx51DNxNtGH56L+G2s849JwmvOIjU/ibFgngNSCgihYJyjsr2iFqnd1aFnnYs0B7p5PsElgiS1OztYARzvaHid+ypXdCYXUPR9SMBUNK+6iAf0nLDPHaaijaGSAUXuDXzIHa7mAVOJS2E/d3g6b6qtSttoykcLZPlQX1/1/j3nceW7WGp+U5CttJKxmMgzCg7G6i32mHoDq+rkjCechQm73GPghNc2l9ilXnO6rsOUAtLASClgJkV8xOYwV52EixkxZvTGunIVTS7a+bGA8Kjf40E3oueEbZgrkAUGdqnDW4dzvWdmBSXTrOuILio5YmefOwWBoECM1ur30bTrgScdhseM/inM+kNR/3QOjTGjdVwULZRLNDbbtd4M04XGqTAK0lbZxKXX+8TVJplk8cqKAiqMPADzOSEepOY3VcTUrO/twzT2NC0FCxjAxaIEw1p0Z4gxr2BFEGSGdAwZAiQPytDKuk6REECBNedph8hSTKmSLKl5GyzPEjs/tLnG8d+nvlwvOu7YRRsrVj9XQBOdLLAcxxkq6y6cXkDzkHxjH3L6+M877sGs9zhmUZ8PlUkENUbsEg5TVxfYuTF/96EBw+9zAmDVbC5WTLSFrNMt7VPvOFtr3FIfV9ZWwcAOPK3N3hkKXPlElsww3sc2LEwuADiUDjNptw6VFPZmMm+AlAR0ohP5VRqwLz3OeNJuGNKhKwmBBUBasa3cu2RDMx6FXdWQP+B99dECFuCrfZ3rz3vSYxykw1kYcWVSyLMw4VF/wFk/49B3akQYDYexxXXZZjXqLVTpubTNOLsYMXQqEXUg0hcAHatm/0u2TxFZ2+keQgcmwTuHM8w5VCArhoyOC67nHo/3G1xfbbSSkQg0FIhpwVNmHKYO+8sBEEIYtCxepoDwNKI3Ku5wWRAOOovnDaF0tCQW9q8EDWpVamhMtDwAlAlpA6QNa+U9iVYTAE0QnPVgZoPKuLKnfbIpMClIaTTlYu1kyWi3QwW54G1muaHb3pY4HE3oXiU5luTdOVbV+ud/2fG2zvK6MYEeJQjHz68mcDFQQU6fU2VoNK+FzQH3gNb9eNZwAD8bC5cgiFxUWk1u2q03ZHs7Ve9r+H9qDtx2+lt1srKfysJaJHvOd3ITWzdOb83e/ZyC+XrpeS777tjBNx3uVdVBGb+zLP5WHRqpohCmEisTOJCyn1lUzh1k7fnlr4nIGLiAKSMSY6AIAqFAnWQ1Mcmr17FSgxQkQ0ASQccJYgWiztgDXShIsaBkleUUGEAvokbpZr7rJq6IBd2Qq0S0yjd97WzM7gfdCCaVYiaTxx1Sh2Ldj9lkh0yCOQccUsQ8dmrmXEh9Vuo9Q0g5II3qX0JR7wTJBBoZYQR4AuKoXoQAkMyb0W6O+q/6gNUMwZMKKPE2aqWWBKAJC/utfZPQ+7T6Ivq9IMu/dWt0A59YfUfQBXvO4hjz2rvktnl0BURJ8/OXP9bo3+8x1gA3wbX2OTq6FC3Odx9rvmBHkoir1GMqAftZGT4iZMbdglKwak4lhCpHdxn68nVSpmnfJ5SiHVRVbbBI/DoH9aFxIbKCH50VwX1o06pSc5dtMKWHMbcquAJY0V49cJkEY7LCfIdFog6VHO5zhykH818s1RP4kDuMKVawqQ8J74xnYAgedtqsSpUj6uW7KwPAymY6N7BpFrVr6TGbIiY1OcpCLsjgFXjl72GWWLefJWBDytBy9Yubvw88V3nih/pLlAvC29MZnk4b7JPmGm9enevyOgdkUyzEIWN7NuKs12u5iQnbqEX961m7LiZRQCtyqdYA+9xVs/xd6vH56wvspg5DzBg2M8YxohsSHp4f8HCjTa42pjaZoBJVxKJyw0yYdx1oHzC8xegvoTLCp4Jup8WIdMYwy0uUQMgd4fBy0AJ6MtDDmFfVG9jqEDwR4jWQ56AEhE0BCYFGUpN5XZAsDC5NpeGh2C1knCl0PKw2tgyC0dwN4AoW5Epeug4GLdjL0AFpAO0OCxsrRrVduYudVQ9eFuuHYyYYeT72AoGGcdPTqn1ft53GLQUaZ1i1cvdWXih3HQ+4eb09AJ2QFdY1RlMEe9EwdQ9mvQ9jloBD6mp3oY4LxoYNBWjAIDIJBOmC1McC1hKmFECdINrrVD5YapvZnpPKN+xu8ABShzCyLcj7kNCRTmKuTQ8kVd1erOPJNijQdRFGzKLdQiIXHIpWDg6la+i7aph4SRtc5QFP5m2dJL2qEUiTHTV313ObESqTyim6r8Sr2uEwixou+nbe9dCNGDc0Y4YmMmySRa94XOUNBp7x4eEST842WoWZO+ynDrljlR1mQqGgC33rEhG2GcNmwktne1z06jeWbfJnEnxguEYk1bKf8WT+YQmXpOaVSbj6Y81zwHajFOzd3GGaoiYX/uW1atc4RYxXA8I7EcMVLzKUKOgPOnHHvSDuBWFUz6puX1CeENJWmVbhYBMJL9UZZQAI5nN9TALAMzBfEOJBjeRrguCLYLvxyEAlAVWt+Q3UHDa5AxV0QiJIH1E2EVyGaoqobWYjMKcbQFY1f68PeAS7Zep6VjW8+lbdvs0Lyw9fdAhqy/vSSg8bKc/CUkDNz1pw675yfj+eZ2TRart34fNqOtAEf4s1sIWaM6r0Of0hQsjGBhKj14uBYamoN4V2A0QFl8kLHs09LQZVdaxgVAilxpVV3m2V0GhxQdu2sxVW1HelM5+qqWgMyIGVZUyxmsN7cYQNZPN/7qMFKCPLwblAhA0IWx4QcYZAPbSFyjUydhA5INAEQlqdoxeCCAnEen7MgkkiImWchwmHLoIgSCVgzgFsssMKJjboAXWCGDM23YzeZDTO7CWoDIehrAXvGhxCqeb5xcC5UtQXpOuygVlsHRWxzNm2EkxZTXrpwDb/2zzDACUFsTihNvwgAXgWBKali2FyoB8q2/cPntS0F2SJSFbGcEnq6WgZxM1YI6iAq1+eet5NCKgNS2wDKkXNd2PQ+DNZ8QNQkKuUE6vfY3TIEoZ3C+Z4rLlzo1+GidyBqTbWHB9eFhDbH7+PNV/4I5LOq7kw5szmq2v5RmGVRZdgskLNRbI1Q4IxQ4s3rwoF09iZYmFha/lSzf0NHYRKxvBNxv70fGOfO+uyvsjVk+jc/nTe1OZV53FEEcZlGpAKY2OAVwmEIKWyzJIwxqTqDC+0XM0DIhd8sL/G43mL67lXn6dpQGe51vXUoY8ZD/rD6podpMMsAQOUWZaFEKoHVqoWLgBWHXgdqPLh/sD+XCtRLMI4WFusIlwbmATSZib6GiUZvNJd4zyMeBzPkIRxOQ+Yc0AMGePcYeKI2GVshwmvnO31ehZWTzIuSBIQrAgfWWWEnjdmIQyccE2C3dzjrf0Z9gZkPdwc8NJWCyQX/Vjln1fTYF3a1Q5HOxgzChXIIQCFEJ9qYYSS5i/drqh3lQB5IEivTKtuVEZy2hDShiARoGLxhgnpDMhbIG3MN3gmUCKUiwLpC2irNi28jwgHAptEvkTdZ+4ZIbAW7d16JMCYxuuJb9X8gGyd0ypTgsXyoBJGpAzMSddWAyBdALa9KlPcOysEUIyn2VnH41hiWGTFwnrevMUL8c52vDGeZzcntqn7bZ6/C7w6jiutL/AKBPP1BFnsavKi1Sm9YJy+B7Pe48jQivGhMTrMNuknqxg4CwsoVWooomaLAMz7IleWz5T0dV2g2vWCjT21+GE5HdeDQ6iPbzmjWMtaPckeYNWrz8KYbXKeSkCxCflhFBTWCvu+9JiTyhwRURfTT+ZNY9Jb8GTeIlLBo25vBvAJG5oxScAkAd6m1s3fgwFUG5pXckJuAgSAFZtLr5oCdF698NbuB5nxkPd4bBWPL+kfYzyP6EPG43GLcYjIhfHW1ZkCWqqjAXcFm82Ms0HZZA+HAx72B6TCtRrkLLZz65IyFvVeGXPEZ3cPcTUNWm2yzi99n/Foe0DHGYEiYiwofUYOUtsf5xSQDwHd5zv0Twj9E0F3DYRJkAaVaJBVuHOvMkRvWa4JB0E2y3WhROAJ6l8VBGVT6uMAIJGs0wfWE4UZv68uuxhDw32vTi36GVrpENLJOxUIF6PddjrRmykiQgB1ETI1NO5bGFri4Ni7lRqe2uf7AGC5yfwNs/bnKeifAANvP9C7Obv78cU2CgzMsvlbGjDJpXlacacKngN2K9o95l1ps22fC5uHkO5LfataQo4sVfymwOKPRwiESk1o5qJaZ+9q5aBbFu2wBwCDnZtAF/ClGCjHCxPrYL4qDlQdslaoB9bEKlBBREYGI4HQCVUAhA05JjAiGELnAL4U6C9AD0WlBI8B4A0QvW5X1gAmkDL77T1HCDqaMVPGIISDdBAQLsIBqVPG8CFH9EETrf3Ua8Lo5vBcELuCLihbeQhJpR+iHREJGu+iAXwMlW26rPJyHjDlaDiKIhQxFGtfX0CkjAsJJhO1+bMUhiQCXweEAxBGVHP36nMhsA6/2jnXwXb1q1ozh7zRiFe/xRhebgiv+8HNuZKWY9VR6UF0+xxpoFstvjiTy9ugB2MKA8rOYl43IrlljtZDv0dQ68Z4nybwBnB61+NZsek+1nxBj1E6TCVoN1mhKgP0OTwEwTTr/MRRYwE13WWlEEomay6h81eaA7perTHEQPKRdPuLfqyFdW9WtfytHr4ANOcoymr1uADSIogX4wMJrq0hVc8ZQ0gYc1RfXOvM3rF2uL1OWrR2RQmTYMrBpIfmQdXN2M0drsYegQVTiuhjxiF3eGO6MLmheXrxpE2mTHboMkPPryYJFayiJPiqH34dH/rxS3z21z/Cz/23rwHdMvFlIRQwJomVsTybFtuL8VnY2GtarD/nEYfSqU1Ll3CQiJfjDp+fH1hsyDjkiM/vHqjSggRDUIaymCRzCAmboMwsz/VcwXNu71W9liN2qcPbhzMEEnz4wRUedAe83O8xloi3xzNcTQPe2W0xp6CdCa97YGRQUdBEtto1UUzpQkUlgXLQtXce1Au4MIGzIO7VJwsClIGUETxrniNBfYqnR0C6EORB1Jtqp1LGEgF5OKPbzspMu+zAM6G7pDpnSgDSGSFdE+Kel9gAaJwLtJr7hKmyiMVjnn5R1uysQMp4Z9bvSS6gOYFEUPqoypShP5Ib3uKd5aoVt1mpsqtSrVZaZcpJlUoRzcH8XG8b73KeF3/vx48d7fNGnD+5s+U1DoJJA2BRUWXR8TnTsqx4oXEPZr0PYy4BY1bgpPpWkSYKc1nYWCJUfZiYpdJ9xSopXcgrLy3vqtSHXJMDACbrUFNEJkXitdK93AEOwHTm7zFgxr70SHb3KBimv6eigoqrPFTjxrEEXKYBDME2THg6b3A1D3gybmt3patpwEU/4oPDVQ0KkwTMEtFLxoy46mbYjvZvB7YYyuTyCV4DQqiymtnovLMEbHg2o8WMV+gKuzIgC+NLh3dwEUY8HVR++NZ4rknGHLGPPZgFD7YHvLzZV3r0xnX7pJ5ifcim5W8rLRoo3zhc4Mm4QSDBy5s9zjo1T3zQHRYTRVFKdtcnUAoozMiJkfcB4UlE91QZWP1TwfC0gJIgbBjTBdeJv0Rg3qq0sNJve2VnpTMgD1q5CBMgnf5NWy2j0zsdeDSgi7UyUjrSClfTjRBHk5RPNj5qVaNoikiW/CCYVMRoty43xJlpyMdpYWcdU26rEa/dfg21lnKxQrw8Y6Km9e+nKgB3jeesUJ/a1w2ph2/LJyb9pgJxKnFqZYb34348z3AvDAd9ACvKFQW3CLD7jSoDlYHaTKEIqxKNyirv1TVMMTbSIlNzuZ7LEjVBKavbOUAr7sESi2hzdO321xzJz3uSUAGtZPGPjJk05ogpR4yps9gptYHJWZhWnlhFNDJmWqQeC3eGAVyA8BHgS8+BbxDIlxNwAOhnBeEnPwQ5zCBMECRkkcqASsbS0iJK0U6JJCCaMEtERMHDuEfPCWOJyKKFEAEhZbbCFDDEGZuYakxvuzVWSQ7nlew/Q9kH16k3f0aVkHShgKCMa4KuO3SqLLpGJv3MSyHI3MgIZ/UriaOCQpw1iaCiwJ4wrPMt6gJUu+HqP/dipAxQAKQToFMdBx2oGsgrw9jnQqrypdXCmpZf1tOesgKJNNFQo3oFWiHQSjkKhFg9GbuoC3tPIJiXSrcP/0Lc0JJ4orC831vH6tyP9/MCsebZW9586S2x5lbQq401x8/L8tT9+MIdsyiTU4EnUsYor1myHMzixCTJEAWwEGTx5aNlXUIs2p2blXmrxRGbg1sGlQFZ3NxlrijxLoT73K3UJNsw1+1nY/Sm0oGDVDlhz1k9gzljG7STeaRSQStAma2H3OGdaYtdUu8tNarv8XAz4pAizocJh6nDdVS/j1f6HQBgoIQuaGFkkmiMqdnYuRZnoPLKSQJ+3Q9/Ft/4538eJMCX/cPHyGD89B/+KHpKqi6RWBlXB+nwJG+xoWSxJFWZ/Cxx5c0FoPoCn2HEgZXJ9Q5lPIx7jKXDy/0el2mo19stURzw60hBLYbgvMo2RW1g7Jo/TRucxRmvnT9Fz5rrXKcerx8e4LOXD3B5pd0e510HjAyeGN1OmVUOHMmekTcBLICwYH6gE0w4qHl76RjTOSMeBHEs6C8zwqjHl73OoyUQSscqQ+wYEhUAC4PmOBIEeaPxhGPBMCRME5CLyWfdK9jW3unMlCgj107swBJpxDqigzUXKr3GGe3eXhaPJ7GiSXGGu66jVghLKeBxhjBDugjqO2DUeIQ72FkrZYrbrNxiAn8yl7nLM8uZaO+1YGEm9av442ka6/49JwXuZmu1r7Ul1A2rleM1wlpK//ynfQ9mvYeRwZhFddsKXC2G68UqqWk2jXU0A11ugK1CiNEYVoWQSAGtUnRxv08duqBgFqCI/2x+V0W4Mruy0AqkSiWgENVzYeRaCWYSPAwjZtGqsksEe07qSWL7905UY4lgCxxMggzg6biplZAhJDyZt3gnnuOMJ/SU0FGqGvNsJVYHr8rR7/PM+Iq/8CZe/fFLPP6NW/zb//ZDQFxkiTrhk11rbXPX6tML1IMrsJifSsIDPmAXB7w5X6Cjgk1IeDxt8bjbKu3WdOU+zuKEgTNe6vYoIAMJBRdhqdw8TVu8M54hFcYHtju81O/xsDvgKvV4PJ3hnfEMV+OAMYXFKytTlTNKJiCpMXrpUBeeJVINZ3EUhKmAarIBlKwU3LwhjC8B8wNBPjMK7Y41MTkv6F5S6vR83SOMS9UCsEn+jNBdM6jFFI/mH/U/sbMpUhNjb4suhEVDbosZyhk4QCsUR1UKnDJEXFVMlPr+nk3g38t4TkPF48n6RjLRJhCCxguseU7Wrz2e1O/H/bhtaCrPBhItnW5rbwUIcrYYwA68LLedr89gvxfoAlhsX3NhMC+VdE9MqrzEbm7BWjpfGgaPdz/0fRIEg1XiWZbOtG46790LVX6irDMSqcxmEcKYVcqXhRGJMZZYm4EEYwi7xFATKV+8Bgg6EM6BeAH5zwmv/obP4OPy7zDRgJ965dejPB2Af/4yMh5DZIRb9IrFGy3x2DwHrZJ2KGCLa85Gnjlil3twVL+wQ444mNdJbJgHACqzYMPeFVETTe3qqMcaS8TeGArbbsYmKMt6KgGH3JmlQUQqtPbKMjmjMmfNG7IF+rk22V5JC0tQU2FnoapkAygDUDpNUmg2gKkT8EbXDGUOlR3so3RA7gg8E6SGWGs+0tCBhUq9IXWBD7+Jmiza/vYVrxSVPQYFtBCDMoH1pkc1gV++NM2cvBRNFlbWs5Cs93k876FOeY08z/C3fhvQ5b/ex5ov2NEWiZMVOLxAnpKC3xyUbQVR/yyIAla1hkimJDFADCbTy4mrXYoXgpNwBaoiZxxSh4f9XnMQkwQCwHmcNLfA4s2rLFRtLjWwWqIE63qonf8W3y0HbZIEXKUehxyxS33tfMjmlzUVVWb0nLBLPYaQ0YWM66lXA3zLWTZhxnUacMYTmAUHiTgjPXYG42CAiY8sjLfKBWYJ+OCnrup3iAT44Keu8E46r6yrAMEZj40nlzYS2dBc33vL2hpLhwyqxaAKTtGMEK+wsWZb3oRrV3pcp8FyrLwCxFIJ6jupC3cMZh/j/seH0uEijNUE/p3pDP/2yQfwdLepxv85M8pVh+7tgOGxXv/cGfOqU+YUJ8tPzjJoZLB3W49aGMn9YpsbR6jkMJn3bipwyqB0jDD1ACLCyBp3IqEMhOkhkLYCBCDvItIm1ZxXghZOIEDpRXMqQ0UoMygHxH2xZldkBRptJKLnpzJHzmZALwSebe1AzWKdaGFnRWN8NQUTmhNg/lkuN4TIurNhKRqfTg25TRvYjF9Cm5S7ALNVjuOFEmC5NGbKX7v+tq/xTobAArL5a1bkAwPDTDVVX/Muxj2Y9R7HZCZ7brRaBOiCVYhJwNatSA0UtRqSRe/tEJYKiC/e5xwQzWiRra1qKlxb2yZh9LaId7quspZsNNTSbZhrZ442ASpQP5DzMClri7Va8FQIe+nq3bYxxP469fUcnpYNzrsJY46IXLBPHd4Zz3AeJpzxhEdhhzMejQFgLn60fGGVrqvV58u8xVf8+Tfwtf/3z4IE+NAnr5Ak4F9832sAYDLFguu8qX93lCrbyw3lJ2OYeeA45wkHOWiFI0z4QH+F3abH07NNA/qJdUzRyoS/trK1WFv3MhU8SWeInPGhzVUF+q5Sj89cv4zXrx7g+tBjGjvkyw40EcKeEbK+5RIFZReq30g+K6DEyBvC9EAn1DAC8VDQXWbEQwYE6EgngNIR8pZxKME8SgicGHkjGlg6AF3BdjMroPqUFnaGVcpTAMZHhLgP6Au0CtECK0yVdlusYs8jg+eiE2nQDakxRoRP7LmAqIAmrWpIF7Vq7pTZW9hZCC2/1O/do8r6HeM9yzFecF/H1YdTVXMy1EBALzwjv5/v5358YQ4HgFQOqImKL+5ghRIpDm6RVecFxNapx9Zo+sNMgR2sYlQQyj1Gii2wlWFDBorRUqCUpQrfUamAj9j+YT8JqHOrS9S1226sN75X2idLgjrOGE1u4v4qsxd4bK5241wxiaGr3pwPliUi0AblJcL40Q6v5l/Ex+TfYKINPrd5FZ957dcAP3MBYIC6pGRMJrtXfzBjZPn1wiKhCSQIUL/HJOp/0nGHM5kwx4CxW8yH/bqwyQmDyTBbtlZH2jdxLAqCncepep1NJeDJvMH1NGBKATkFyMhApppUaO4iCiDZPVE6vSAcjZ0bAc4ETgKeBJyU9cQWMCRoIpGEwdbFUP1MRGWrxsrtOjX2L2OLkOi/QkDeAJxscZoDCBFAB0HUSZIKhCdISIBkbV6S3WjWgC0/J0KNO26uQd4hl1mTBmtOcjs769Tk+h5Wze9lPM88f3zOJzA3XfTf9t6esfv7WPMFO1zlkIt6xaoChFBIfwKoFnYlEyQzuF+6GwoAMbuTYp6KBABCCLFUqfncmMgzCdg6o/dBi+L1fGz7fe7Qc8LAi/zNQf5kCLozhwbOSCLVDgUArouyra5zD4Z6GG5Cqv6DCmwtYNHVPOBqGjDlgJADLoZRi++mpGibWbHovBsgmGmdS2TpEVBwkA6fevrl+PTTD+BXf90b+PJ/9FaVQ/3sf/4R/OL4Eva5xzZM+EB3De4Kzmms+5pNseK+wGNRny59v7MV6jN2pVcQqxrJCx6FHQae62sCFZzxVFlwXiTa5R4jIiIUYMxC2AbNYzZ2jLFEPE1bfHb/EG/tz3A99pjniBAK+phwtdugXEd07wT0T8yfNwBpC0wfyJCt5idlYqDTuIADqX+V+zKysoH7S0F/VRAOxnzKBSjaBR3R8pshIA+sMWkWUGddfc34nSdC3gpKHzCNESHmiv2UbgG2Sq9yxNJrHOA5aIzTm9BM2zW/SRtG7gl50POkTOiySmhpFqu5LIAOMZtBveVDsoBxENHcZuiBLgIpmUn8LeysE+OmzcrNCfq2Obs+3uJhL1qjOVFcP8megq1lnhGb9HGLTaTpP9FNy5YbxXzRS1ubEL/guAez3ofBUKO8bF8ANV9UGYW2vg0KYln1tB3amMcDg9TfPUfxakMsatTemgpmf44KOgO3fHSU1euK1GsqcMGD7nCjkyGgFZwREddpqDRgn+x9wtyEVKssgUttJZqFtCUvyCrmA5gEgUZMEhCMPzUDVSP+uXSB66JU2W/51KdXVY4H/+sBn08PsaEJD8NBPbZ4xqF06CkjWJU+W0IHKEAGqJeWV0Y2NOND8SnOeFQJYiR8uA+4MmDMTXYBVLaD6tqBMwPmznhEhrblvQgjihA+Pz7AL1y+hDFFBC7YTx32TzcI70ScvalSvLzRikLe2GQv0Ik/axcOrYYDaUugAQAEcSRlZc1aTaBs15gJeepUtlEY6VpZW+mMMD8U5B7AxBgn9elS+q6CWCVoxUKiQKKCYJwF3RWAZBXyZpLPPWu1YhZ0UlQLX7VLBmJRY4zIa206zRlghvSdsrPcO+tZk3prAu/+KD7pLWY/t1Jo340/1o3J+lm7kBMTeTN8H7UC4XGpbtDspxwdT+6r5ffj+QZBrDChf6svin4flTnrIBZwY0lgVXrtfKhzu//uW2cz8dVOhFTtGepzUJ+S1ssh2Dmp4lcq49eZV+3Q6nrEbCxg7+inx1gM2D2WEdlCT7dENEZTsiREsXhBAoGNAVbglheEy9JhHs+BHfBWeQmfKx/Egc7wDh7i6jAABYhE2BgYF82EPlhaqAAWahLnVfAA63wLBaPOeEQnmrQUEM4D1aTOAUN9vRm5Qz8zT5o6ShXw6jlBAFynAU+njfmaKasgHSLooLIPiM3znRZNKg0vAHBZIFDZVhQAuOl7VmaWzkdSF5/Us1VVCWXWWFI6UpZWAJC1aMcGjgpDK6/1n5hMkEAlIkgPKRcAXgL4zGQq15DwBCVegsqkrmeClYejUBN7qkywuZcssZAQ9Pwd3BIByl2xxpMGLD+Pp/RmHr/x8tv3fPshj/94N4DWqX20CMRxFb098Kmiy/34ghwdaee6GHLDtCGEYIoQy0OkQL9XBShTAAVdO5IV3Dn4V6ogBLGl3PIFrfOZFeCHkHAwwArQroOVgWU/B87KTLXXpqKvU8VJh7GEug1LsfnTvYEjDiViY82ZLuJYOyJe574W+1sblIt+xNU0YNupsfsmJnxoe63nZ0WV66yNrRA1drVgUbCizowOY+nwrx9/CG/8+Kv44//Vf4Pr1ONr/9l/xM99/Wv4S5/4TXjypkrzHvQjfu3Dz+NR3DWficaFg0TMORoJYck9DqVTsKluq3FjwzMgGnPOWYGxAQpq+cj25S7C+GB3hUvLb9yK5VHY11zpMm/w+vgQn90/xG7uLU8TJBJ0bi5/3QOihY90Lkhn6uU7PyzoPrRH12Xsdz1KozwIe0LcKfjUXYv+2xeEfdEmVu7jGDW2SNRcQwIhbTXnKJGqTBDAEssA8EiIeyBlUsSCBHlbIEEBNI9D0hfkHpgS4fAKIY7GzrI8qkTCfKa5U/bO8KQsM04MZf4aANPM2q5IEajsnVJe5md7bzJOKn83hjABkBhBOUNS09n9jkK+59Snnltbqxw9/y6ZvHflTQpKLoDeipllx1j5Zp0qtpwAydr8pxKuiRabFpcuHjG4nmfcg1nvwyhQH5NUvCqyeJiIJRrZOoSEUABb3KaZK3MrGnMnhFLNZ8cc6oQaSSmmQ0i1A5SPJIwOhC1PdbHtz48lYqZQF82BFz+T2SjCvl8AtbNhKorib8Ncje13qa9SxfNuQuSobV8bw99D6SpYFqx7h7a61SrHQTr8fx//WvzE61+Gb3j1F/HV3/g5fPwfv1lv/k/9ui/Hz+4+go8Nb6OnrHRdS5DcWN7NFb2ysaFFMhjACDQv3RQpq7midLU74/HYlR5ziRgtiLwcr6sh5C4PeGs+xy8eXsIb+wtcTz3GOaKLGVMK2O960C4gXi9f/HQmyK+OCF1BnlkXB7GgXHUIB0LYE8IEhINO/sPTgnAo4FSqEWHpAxAIJTLmi6AmiKSVc+9UGK8I3Ou7ns468PmkFfROkDY2WUSVJZYtWcWCVSNOep9IULPGtFWWWOm0GsJZqyXeaYKcess2zRv1FhnatbBO7FkrFDHopC5y0hBRRBbfLP9bf3n2F85H65f1vHhWu91qsn7GDo4TnKMEokqtRG/kcjwRH78taSb8F8fi7scX6XAPK694a4zRUpbeYuaVBU1M/IlSuDK32L5zbGAMAORCNicUsChwHrlUs3cfxUCjjvIKcAI0DhFQ514GrcCb3DwPwJpl6DGSmftmY5xpzDEfrZDBRWqS4p5eejxj2tq80ZF1+xUgieAzhzN89s2P4bWffIqrl78Zn95+Ka7KOT7/L78M+WcZ/fw6HkVC4ICeZig2I9pCXfR6Z+H6XqMjLqTHZIt9mhjM6CQhQdnaXbgJqrixuyczG2OXMVTqss89nqYNdqnHlANyZjAX5EJIUwBm1gW8nUPpgHKh8otSdK1BLJCJ1ctqVlCLk8WMUVTuUZZyqwQFgoRVfuELSzd7pwzwpGBYBiN3DOqzTmBshvIwIMvZwhRAeQMqHwDljwKvMfCqAoPlzZdQnr4CCZ8B5ce6cC3a+ZYcuGoM4IQtucgwKeHybVDzd3vfzp71Fm7tdhUQe348aTXaQsq7iTWr1z7HDp7H3FcM/j0Va44SiHf3pu/Hf2pjoBm9MZyIxOYPUYmgV8iFzPS9QHLQrtRE4M68fM3n1719Y8wVvKpG8twUvA3QOu9G+5tRRGo+5Ebto6kiVGqda55zEVXp4XNslvWNGkhwFifNVUgLLedhwsDqAzyWiEkCnoxbTCXg3Obdi26soNqYIl7e7DBZU64H3QGBBI+nLbZhrs2yAhXMRXOFA3foTLUyS0AXMsKBIP/mAf5v3/G/x4P/eo+LzYg33nyANAXEIeFxt8WHt5cIW218BQIOpUMRxlg6PElbzBJwxhMGTnX+B6AgFQOjMAYmzDliYOuwaHYqGRpXPPfx652NtODFfADGBgu4zBu8k87wZN7irfFcr2HQYhWzFmVSYRz2PSgWndMTYRpgUnUBvTzhbDNh77J2Y/mFpwHDO4ThsaC/1C6G8SojzKUWpUtgSAdIZEgAcm95cr/kHLnXgknLEqp3ganTJTF4K0Cvvm2FGWUQbUbCov5vrJYFhzmAZ8bmHZXUg/RY00NlZIkVfAKrF1iYAJ7pxvFPAv+tZN0GJYv17bzNpLlQzqtta+7DhuqUZn+n8p9TseCXUHoIWF7yLO9iYF04Oc6HpIm7J3IlAMYqF6BoXtvu6x7M+mUcAQU9qT47BgU3clnot+6L5UMSI0OlIRQWym6MBTHmWmVmXiimgHewUsGge1p1WKrXLvVQGYf5WTR3TCoBYJgZfMGcnUpbkPy1EO3eF0dtxwplTl2moZrvXnRjbakOABeba6MOq+TjSdrWiTlAqg5cJ+JJOz+VDr+4e4Srf/Ey/v7jM7z+XRd4az7Dr/nJz+N//c++HP/jf/2t2LyTMHwg4SPdO6vrfZB+Rc9dQLPWTF5/32ABtAIVsJQaDDxYOiV7QzNmXuSHG5pwkB6XZYPX54d4ez7HIXXVVNIlobtDr+vkKJgvBGmrv8vLM156+Vq7I15vUJL6i8QnAf1j7WLYXwr6a6PgTi6TIJRN1C6EcdF2z2eM0lm3jrObSDfPQJ4CcA5QLCgbrVpQJjPrFUjMmEvE4QMB8RDQXRfEnPF/+lf/H3zdO/8eP/HRj+O//9bfiswBgChDK7EyxCBrGiuTebSQSjzaLlIARJoOhkQLO8v15KuNG5pSO5k/z0R6NO5kaDWVnvdFZuGVhQpiHU3SnkDYPzl6bT2f+yr5/XiOQSYnqD5RcIN1izVYquUAFkDLV2Re5QxSDYEVw5VlO9tHEYaQGPgk2sC0nof9JGkkeMsoVl4LtHRNBGDFjuW9RMrojf01QRciY9H4GVi7N3mnJgDo41y7/kXz/gik3oEMwQyqBZX/H3v//itLluX3YZ+1946IzDzn3EdVdVf1NHumex4ckUNSQ1KUTEuGBMmSAAE2/IMefsCEABmC/xADBvyTYUOkYPgByzD8DxgwbNCwZdgSH0OZI3GG5HAenOnprq6uqvs4j8yMiL338g9r7x2Rec+5dauqhzB7zgbuPedkRkZGRkbstdd3fb/fZba6tqi9niPTZy/5x//3b/L+64HP/8QzxrvA/rd65IeR3s+ELVy5M7ZyBZ2aaLGepzqJ2DlCacWcCmoJSihMshqjK5MtkEmVvQmNCTZq4C4NHHJn3YvLvpLYym6OZanmlNwbWwqn6Caz2U6WUE4dmq17rjs662J4tG65flJcxNhYmP8hwRU2lS0k1VvF2gzhWbwd20VlwJYmM0PGWSJhEoySIHjAZZL2xHmLiz+D+45H/jJc/OJLRt+z/+0r9D/fwY+/ARzIIbaOWPYmJxd+ucBs0S9nMnTVFWC4ZmdpuqeAoOsLfRWLvgrS846x5kvu9cGxPsR7wCphyRvujTWP44/F6CQzuGjz6MrGpLJ1q39WixdODZhIxtCvQJa4bM0kgHn29H3p9loYwDE5sgY23cI6qWCTQ0+khmG1Pl93YTefQCuuJ2e+v7UBSCjegnXh2UlmwgoYF260joA4Dqk7sVCpxfW+MLhcAcLW8/MxdUw5MCbrQl87/FWZYFZhcNEkfwi+gIDf2l3z+9/4iOGFp/v+wPW3HIddR/xsgyRhvnJs3r/jWXcwEArHXR4Yc8dN2vD5fMEh9XasHQwusnNTA52SOvZpICPsc8+lP3JMoeVUnaQmeVzLEJEF3KpAVirEgts0cB23vJh2HFNotjWdS2w3BpS1RgE+4bwVvlLnkOia6s15ZX/smfYdjN6AoIOje+3o7oqk8MaYWH5eeWI5R+5da0JVC8fZF+m7Lx6NO0oMohV6UXAFB3JBYHaEkIlDMpLAxqjHrkvWfTOXTLjPTJ2Su4B0yn/4n/11fvVHv8ff+bnv8Vf/1f8msbfiny+yyBQhHzgtOLzBdrKZ1SxW/AJerfOZmgdV/0YzqEO8P2Vn3Tfuy42+5PgyCpUHPYCrST68yQY7jzvCiVrmjbxqXQCqrz0vsqyeb2ys8l5flkH8CGZ9zbFzI11ZdDtR1GVyqUi0aoarLTdBkyHd1SfLOV1VOzLeWVXZfhrjyxUUHZZkYdRa9cjmsVVkch3mgYXQmEn73LfJr5oLgiUbF8W/BOBJONgkTo/PykEdUwksfaUIl2QqZs9Vd2zBxIlyPQ8MLjbjxjoJz+oZpWuyvWfDgdwr3fcH/v7hO/xP/lv/Fsd/o2N6PeBeR57qnvze6euP2bqCvI47ZvVc+SPPw91JVSNR2v9KZHYmcOwltc6HE9gkJtUw0jUZ4aa4jtn7XPAiXvDpdMWreVsCXuaiG4lqEtKYrGNMN0SmLGSCVSIEht3UgKw0OZgc/tYqGJvPlM3rTLjLhEMyg0Iv5N4V8MqqF9kX4/fBKhdxWyoKa2NxLLmQBCSTtIY+MSerhOAV16fF6DlkDtIDns3njv/w1/46/8Fv/N9wwD//w98i7uCv/ov/plXpopIPy+Ribe9Zqgh1ovMO4mnlQWI17qkJtZxSbu+btIvUpUpe1jKmnwj4BPcu6h/Sid8bGIQTJtXbjkuFM3DL0K7KdDs5pkdA63G8w+hKNbv6UIlkY2VBu9hEClCBLPYL5Wf1YKrbOaHFIbMakrL/skvFKpp135gEULECSwXXgJYszPhWXGlrlfK+ncTGSBqcJR0z3haUNdYV6YMVIAxMyyoMPlr3PqRI7gJZpHRWlOINWZhD6gpzLLHxN6j/BP/pzOf/6XOGqwtihHS8RbrPSJs7dBvxpQiUVBq4VCvqvYts3aqLYgGyNEtJqgzw9+TW+TCW8948x7R0n5Rl0RXVvFMOuecu9RwLkCWidD4WxndJ1BRcyIUtVgAbAd+nBcgqSalM5nUS9hCOunRrKoB7lZarl8bAUlcfLya+gTfny7rAzKXo5pWcFXxtDpKbd65ulJkNbDaEv6T88p/5e/zp8BvcDRv+v//Cr/LD2+/C/+t9HJ8g+YiLs+1D2kdbIahlp41xtYo1mdO/sQTK4km+H6cq8cewyK8CZL3DuG+XD8Sah8bJNm/bXt7cXyuu/BF9vMfx/58jq3ARxtJ8wubEWPytUvKtgOGczV+aFkBL59KowWdybSaCrZNHlQKOKeKtSJGT5whc9DNjCsTSZbA29agjiuOinwglb8DZmrQW2XNpRpVFOaSu2aYgmZw6ZnXcFRBoFyZez9v2WW/noezLiiAbP7Mr/l3HZKqT3kWyl+bRNeXAy3EHwKZsexM3XPipsaY6l1pMq2bwHwy3fOtPfsonL56QXwwwOdL1Dj+Zr2DYRn71wx/wS9sf48nc5YFZPfvc88PxKZ8cnuAk8/5gEsRa6BjcbHMq2kAtY4hZV8R9DuBtzrUYswD4WYVNYW85UWZoLLB9Ya2N2XK4S5fIOjNlT8xGkLjoJoYQOcbArQzM0Vt87BTNRZraKWnypLsBNwsyl8LMXgjHWiCh5S0M3n7KUiip3dSlFCTEWQfc2t/Dj0tuU0EN9bScRxT8rWO/2aDVj7HL9JuZrku2JpgDzmUuNhM8g8MHHX/lb/xn/I//1l/HAf+1P/gt0gD/y3/93yhnT3GzFC/i8hkqkOWxeJPUgKaKxxYWs9GE625qpVBOpPJS5HP0Hc1b6zz3WXc0rH6PK8VK239hHH8VS5Xz0fbxZXZ1X46iNCuAmh++Na5VkKruruRSDRA7B7q+5HgEs77m8FgrVO8yQxeZoicC8xyaQW+9jnFWNc2z6YWrNECkSDGcUUirf5Z1czIzxs5J6ZCUT/To1rEiNdN3oHT1Sytz3tSqF8FlBrF24lVKmNU09RXFv0smJ4zq6H2kd4mLMBajedO1+5J0dGLtdl9MF4zFxPE2Da26sXHzyXEBfGf7kt/83mtufnyJzI6b11t0H3Cjg23ku89e8N3NZwDs88BdHniRLvj9wweMOfAkHEjOlURjZNLQwKla5QCb/GfNpetUKnLF1BhjvZtNtlhm0aN27PPA67TlNg3M6rgII51LTWoZuwknymHuTCqaHTF60pxhduCVFD23+x49lm5Po9DdOMKh0Flj8awJgg4m79Bgcr/UrQ197e6uAIookJaFagWaXBLc3mjCWmQmvk90fWToYqN79yESr47cPt1w/HTgz/7132uEKwf8+T/8PdJOcUma2Xx9f1lNNnZ5WpVCijxFzFjmdLKulfKc36TcrrXjdUJ/oKPhiljy1cZ9ScS6i+Hb9OjrcT4Rnz99X0LRnpP7J/o14PA4HsdbRmVnOVHwGbKQscTDcKdV5lomCc2ALN0BAatgCrZqLECTIqQM4gRX5jUpVXZZHUFlIdWR1bWOfEBjU9XiTpDcGqQkdQWMykzFjLYu3rOKFXPEkg9VIWGsp1wWSl60dAzuzaRcjAGgklCxLrR1VC+Rp90Nnz7/nOl2gnzNeHQwKxJn6O54tnnFs/AaMO/FWQMH7Xg170ilG1T9PL7IIOu5rMAZRARj+orMBIp8U7TZ/znJRKR9BxFffFS6Ip0XYyqIa1LL7BISSot6LTHcWTHMmLFWHJumHqJAtgTBj9aZySUscVCa+aoWaXYuwNUpYLTMbXXuOpdCSwaZhTj7Bs45n3EhF1mqAA7vEnlITNuZ+IuBp88+4xv8kCu34bl8h++//3O4ATR3i7ygvP8b6/Uaa8ocKhWlOSkKnL3IuwXMOmdkNUn81w0s94x1rLnnsS+TQZyzfdfjQfDqgecfxx+PkRCehCNXw8jHxyft8SoXzMmQgebPLKv7WzFQq8gEAehWxY7SxCpGTwgGSOXSxb12P0/FvmIoYLx5LzrGFHDFoL02rKpWJ0CTHCYVkpr6pHZXB2tGlYstSlbhetowpsA2VFuUHqABWYOLLUZ0kjkC19OWqNbIqjWAWuVNwSWehkNjS1V5ef29k8TzzYHXmy2Hp4IeA+qU+CTjr2a+++Hn/Mrlx83QfZ97btOGHx6fcRMHNmEmSGbrp/Y+Q8mPsriiuInMObT3BQO9xlUH39rUq5fIRCjWLtp8f9f/EsbCqjlMLN9rzEvMbUw+ly2vqpYENpWbrG90uNEajkAtiihpsAvIPoYz25Mio3NRT3IYdRaTXCzAUSmguBncbO9nMYv2mrQxH60oZn+SSk4lk+Wz0SuhNCYIwZRHqsJFP3HRT/zl3/vtkxznL/7+74FAd2uxMpubi4WCtWVJBWjUgKy1NzDBm7VKzVdqgX+dU6y9s8S93Te4djSs3pXn44sArC8px1vely8HaJ1vW0PqPcd3Eo/qz3sLS/f8vcqJvgw76xHM+hqjShDe6+7oXOY42+n0ouTihWWtbK123LpCFW+LVAwaRZTQJXKGGfPJUhVCqYRUnXedcFxeukIFZ8nCACfsq1HNK6sh9uqL19ZUqKa+BRszYPS8ilv2sacvVN0peXZh5kl3bHKIqI6tn7iLA7exJ6trRowXYWzv37nElT+yc6N18ZDMrIGEcOlH/pkPfsw/AG6vt+jRJm19PvEnvvmKv/ze7/KNcM2snuu85bP5ih/PV1a1Kd0Xd35s3awcGU8gicNpBbWWziFdjo0qDGYUn9QVg3pLOCb13OVhaYErypNwLMBg5kDHIXVs/GxdU9gyRusYImB67QjSZ+IhIHe+TfySbBEet1aBcMmRepDsCggECK2aYcxhLQHANN1yZ0iKZBr636SHF5bAzLedJTkKSZRhY5KiThTvlG03s+vuSFe3/Pjqkr/5p3+O//rv/FaVpPNf/Oz3cJNRf08ot66yPMqDq0qFBodMQmujlvKbQFExilfnkS6gk10vqpVjshr3gFmnFWo5W7R/wWz8VRb1whvdCx/cV/UtEd6afDRmVnvdacL4OB7H20aFlLZ+ajIPKEW90mRCoIBTBehQqGxAFRC7UnEeFJN/W+XdCiuKsY5EaXGpAVql2m4Cx0XaXl+zmsaK4ay0ToO1A6MTRQvT6lgKJt4ZOJbUuigOLlphAofHOl7NOTAVVlbttFiNhhUD0HoXrcFIAZtySUR6mfhg94rPyEzHA0SrAOg28eRiz3e2PzbGsAqjWjX7Lg1mMF99F11sSUTbt9i5rLJMKefGqcetQLWAPW4G9TT2WE02cokfg4vNYF+AWZ1JbVxCIiXJKGu8OmF7JUdj/tYEQLLNRbkzH3SpXhQqJ/NMq4K3xWMBjDLIxJLcLjUz22dv8SxPvj1nDDnaNWLrEkfn71C/5y71/GD3TZ6O32WUgR/Jh7jPDRRTt5akUy/iZVKsJu1gcaV2k2og1dmNImvvLLdIP+5bSesDj6+P53zzt42vGGu+1Ob1Xn5LrFlvByxFmNVpfRw/ncOXgsfzYc9n/oIperzPpOSs6FHXq1IWICtgnOqxWGMHNc8urItG9asejHZvjXMg+MwmWOG0Sst33dQUJftoYFMohfe7uOGimLZ3JZdJImS1gv3NvGnNpwYf2fqZV9OW23kwqbnLJF3LF01OmNWk3Rcu4ZJyG4259XLc4SVbB8QC7sDSZdGaeHkGWeRgzdtREsei/HAlJj55cmDcBtITx5OLI9++es0/9/z3eR7umNXzMl5wzB2fTpdkFa7CWFQrM0/CEV+k8kld8+VCrbFV7Tx4zB1jDhYTKR0XnQFZFdCq/sfryWnOlVW8yC/n7BmzZ8rBZOxlIsgqHGJneWYMpORMPVRzsl3CdYkcA9oryWOKj7EU4LHrZ76yAng4mKG6n7XFpEIHLI2vCiOrNCgJx+XAa0HcpQVgzUehu1XiVhifCXko0vjik5X2gbt9AKe4IeFDYrOZ8S4QXObX/8x3+Bf+7j9uOc7f/pPfJQ8Kt9ZFvhZ0tOY8hY31RrOpteeVF/DOuhuuR9Y32Fnl4rTiSvUNroX8rF/PM7i9L18N0Gpg1Oo44U3z99Wo52URBLwlCOlZkFr/ugrrcqbEOSUBvPvHeQSzvubwKDs38WQ4cjP2J92b1h4m4jKifvEwadvYdimZCXy9lmvHKi/WHXGMAUKEXAwCfWQXigwvew7FbD24RMzeggbplJKKcJeGFmyGMom+mC8Y02k7XeeMplwRfefM52RMWxwDr+eN+Uv5uYFZZvIr9AU4GtYL1TJqtycnyhAS3fNbDmNPFxLffvqav/Te7/Od7gWzBl6lHa/jjs/nC7IK7/V37PzEU3+gk9S6evRiyQZaDBzx7LO5/HWSOGqPV6PousJhrVWLGasGHbU/Mef1ZKKu2GuU1uwFCASjb6fkSLOzBcGQcH0iHYNNtm2iMrqsqDAlo9ua/EOKl4muFvI2UUiyKgWzTe4V/CjsazvfHro9xFuKsaFHe0W9osmxvxk4uB4fMv1QDC6dLQR2w8R//O/+N5As/HO/83v82i9+j//VX/rXrBWvLPtfDxVYYYKFYipWqbCuB/UCsomxTuy1U5Z3qAbbtlYp6oReOxp+ycn8XtAJlmTsvuHecYa8BzTT8wl5XXX4iuOxiv443mU4lE4SgzcGsCWoJfKr2O9Q4ousfi9DDRTL2QCtmsc3XEBonhrBZVOfFMlDjSPVxL0eTy2W+DPGlmLd/OojvhjTVk+otl1hktYW4wbumHwvpoBgviZSWF6VfWXOVMstumZl1ZFX2wQX8dvIXJqqXA0j396+4ok7kNW1BiGH1KMYaNiJFU482YofmOyjdht2KEnEuk+pFZNikVkK1T7AYqL5Z9kxxVXVPxeAMZ+w18rnWyUjOQuaXTF5B0Lx3YzOGN91Tou2aFCF1FMk4zV5WCUY9fzLElcc1iCgXTaryqg60FnIk8WyuTK7SjI8j54o3ljBwajFTkbcNNL9tud3nvxJfvStj6xr2W9c4n8bJH5ekLP8hWavtaNl82ts1RROQbAaa8DkhrVyLqttW8HkS8aah17xtljzrnP7WcEGzuLCWzC3LzMeY81P99i5iY7EVRh572LPp7cXzfaksnClqEWcKIRcChHO5pPkIVKCgc07EjKaPc5r82L0IbdcZ546tsPEVFQkjWVViu/HFOh9sm7oGSZM/ncXraPe1s9svXnqVgsToClJ1vnFIXZ0zook+7ljilu23cwQrGnV58cLAMKFFfkPseMQO+6mnm03896w5yKMrQv8MXa4YLnI+/0tnTNmFFisq53UKzD0rN/zC+9ZgWKMVmT5cHfDL158yvNwhyfz8fyMT6YnJBXu4tAM7604YV7H1/OOMXeNDVZBtFGNjVa7Cc/quZ5MLTOmwLP+wFU4cuWPXPpjsUoxpYkRBnxjdVUfsKzCmAP7uDC4jqljP1u+mgpLy6SoYgWSOt32xXswL+sJ9fakOiBYgSNmCAqxWpRMpjJxseQvlPhT9itJ8VrndYpdipb8pgBtXfENLmBr2gvda0famG9kCoIbrdGJdmZazwWMY2H6dZH/43//X0BQ/sx/+UP+xi/8PP/Rv/Uvk1MmbsXyr6PFvlyNQbMuoP+JYkOW/KYWSlbdDNtYezqu2VneI32HHs+RrtUocekc3DnZt//JTuAPxoO3eRaX9UJtzLIG/s6LLKI0sK2GcJWzbarfJiwxbpUPv+t4BLO+xvBkdm5m50c+3NxwN/e83G+b2WKtbAN4r0BakoZoenVN9s1qFjRn3MoYvoQEojqSK2i65wRVN6mFVZFd0UeDVXYnCVz4sRkHggE3WYsBoDet9Zx9Y2TdxZ4xGn33ohs5pI5X45b3hj1XnRkSvhh3jClw1Y98MNw1LfpdNNP4ThLPwx0bmTlq184VQgus3xxu2H4wl/fu2IWZf+byR/xs/xlOMj+an/Lj6UmTolQvrkEiCeF1vGTOnks/cuWPOMnNHN4VvfltGrhNA4fUM7jIN/obrvyRp35pm2tn2DTmVWdeZZKH1DdPsCl7bueB2pmrsrLm2VtCkQW/KeKWJHbzopALyu0hd0raWIVCvQFZOUhpDauFkUWThrgpG7tLy2NtwreLqJoqWscR6F850lZJQ/FzOQbUwbzJpCsD3rKax8Gum9EP9vzVv/IvEad/BZ0d4XNPN0M4lhOzWqS/KWvQZcJrUkMpckFdAC1YJv9SkWhm8GdDq2fWQ3Rb26gxod46dPXzoTm5AVEPTNwnmMDiVXTve9dztcYOzrc7+0iPicXjeNchmFyik8RlGK2AMZcWp0pLVKDearksuGht2JuyqoDG4pTaz1BdxlNuvcI6onaRY7kVUgGbpHT7A8iYWXytcFs3wAqSSZN6KJYgVEbWXLw7OpfpiifWMVkH3cGbTOQwd6Ts6H1i180t9s0ljniX2biZQCauzdqFlnhdhJFul8p72/t90N/y1O8RUW7TphR5LDEIsjQOUazbbf18g0Tr/lRZVWKffSodtWK21u4XfqJ38aTTrp0raQWTWX0D/GI2ppYWAHHKnsrkjNmYbykVIEctwZRyfktJt3xJ0vxGqgdzlXdIkYO3Toa1QFIW8VITltptqSUb5t9Y+w2oCP5oXo7qCyAZ7QLJQdFezBhej3T+Y7rf/xl2L4X91RV5dPAjRT+/Qd1nuHjgBJhaXWvt2m/Ak1CZudLoSfe9QJYboZrBn41yy9z/+pON7nn8vu2+aPv2+AMbrR+ugNxZYvDWQ/iC7R5jzR+PUQse7/V3HLYdYwy8PmyI0eaTnJ0xR0sRQZJ12sYbwKVOzS6jXtNz8dWLQJ+QmnRG17y31t0Oq+m6+f9aI4/KsMoqZCkAfbkeTXWSibppzaS64uNrTTwsXlSPxcHHxr66OQ4IcNGbx9UhdkzJ40V5Me4ILnM39by+27IdJoLLXM+bdpxJzIPrqjvyrDtw6Y/WubCkxbUDfBLHZcmjnnd72NkcXXOtSz/yXgGybvKG22R50OAiXZfL7xYHPJkfjU+4KWSAD4drjrljLrLz67hpYN4hdVzPGz4/7NiPPUMXebo58nzYcxVGnnV7LosaxphbgX3uSywxc/xjsuLRlAOH2DEnb11ys2M/do2BnZIjzoF0FxamHjaVpqNHkj2mQZFpuT5kBveQt3mdogVcAarcpAuopbkVSySVx+Pi7eiCIwdBNt7YUGK5SX9jhIDcu9apN/XC/FSIR8+0SUx9hx8SfR/5q//uv8Lxv9MxT4E8OziW+OhLA8RCJrinHkbza1wP1YUlXDuqn4BYcsrOasWVokyZo+U6jWUsLeeRvHiWNlDrXXOeLxhaWMtrAsB9MWadF73z+yonfs5mgbMUZSxclxyqhG0Uy5NrHyNdvbYAZo8yw3/CYyMzHw7XHJIFj5vjQM7me+G9SRGsQ6FDpJjVabCuQKl+4VIMzKXl1pUSHIqPVvO9Kr4poQSLOvIqoMRs3l0OZXYGZpm23DOWAFHppp2zlq5TChxmm/x2xRvqdh44xo4X7IzZNQ/86OaK3TCxDTPX84atnxtt9yKMfNDdcuWMSuvVQKxOjCmGt+rRN7vrVlUYc8fgZp75Pb0k9nngNm1ImGfJQGyGjAA/np5wlwa2pb3tTTJK8uu45ZB6M4yMA9fzhk/3F8TkebI58mK7471+z7Nu3wAwgNu0YV8qGIfccxstIOxjbwEye+bk2U8LqyBGz3jsyIdwMnGluHynAFLBqIS1fl3fpC2ZqECWTfi+TviZZcLPagGhGvkGwU3WKUTFwaXgJzNlRCz5cDMgMF865tFx3AXGvudmiAyb2dgXzox801wSES+LlPEMnLHrtOA2ZWKs167AKongVEde2VmwmMFrRqsE5LyjYQG6RIu3gwr6VasSX/YlLZLc8/hDk/4971e/Xz17bv34o8zwcXzZEcT8C0027piiL+bitC5VlmQIKqWAoK4BWgCUW0zUPCGMkeURnwpYVOTwqmXNps2YvY4KnVVWEVA67Z6yrKrksAI/xmxypOyaD2EnVgQwY1rPoex/ToHbqafzVtEecyjtx+04OpfMd6QATNWM1NeJy0U6TVz4sRUhKtNs4+bSSSswaUCRwh4zFlm9te/SwFTeN6tjJJiXZfFzyYWBNqbAfjbD+CFEDmFkWyTp9fjAzIcNtJICZvlWUIrZF88Yx5zcipXlSLVosporcj5bNOdVfKnVcOUUtCmyvfqYS1o8QcAqLzQQqxr74kpnXI8VUbD9h5kGnkkCpLRYj0LslOgdY7gjhB/iPnuKfHKBJIfGPequQW4RjfeCTeuxjjXLBEtdIZ/N2bLEGljYWU03pUucWj1+wvB9VxDrfHyV17zre71tG11OxxuPL1j3o8zwj8EYNRRf28gHw21RQTzls5sLYpQT5QiY351muzDEaW1sCGMxg6/dDhXy7JGcbTsMGNtsTSoYs8PnaqkCc/LWWbAwjOqcXb2a6t/B5caSqp3ageazGEocmQvAX/OWKXk2XWwNMqy403F92HA49Dy5PPCNi7vF5kWUIRgz90Xe0fvExi/+Wk/9wSxHiuwvF4+swc0NiOokmU+VnqbNVfKXVbhNG8tBipqlqlVcoZ6M0exKAJ51h9IAxMC+6zhwTB0vxx0348CcHDFZfDjsB25mx6thx+snG55vDnwaLtkFM9YfXGRWRywFoVpsGVNgjJbXVRscV9YJ87wAnDmatNDdGXAlGXKv5J1dENX0HWfgkYuCzNYR0B/LY8X7KhzBj2pSw1SK8CXuuKhIXLrSSlpAHImlM7oIufNAxolDZ6MBVXm9GyFERT24yQo1Odgx8R7kyZQxuQscQs9hSAuJanINmAOa0qWSCE6G1sYmgibLXerjbdyX89hJNvP4WsSvj3lvgNVbivVvU6c8qER5h1FfKw/tf01AgOWzwVJLqu9f1hnVt9n2v8QgC8tLrF3nOtmdgWjKG9vo6vF3HY9g1tcYCceLtOMmb9i5iW9vX5FV+H2e8zKaCLeax9YJ1fvC1uqjmSAefaNwalzafYtXUrJq7HYDWvj/TpQoCsmokd0KzBqzeVN5igeJZGYt0gRKp6ayAA9iE/CYPIfSqnZOviUOc/bcTBtu555Xd1tjVL3/gotu5Om2a3K1fewas2rrZwYXufTHE735TsaSPGhjTgEtYJhExR6r8sIq9bPHSst2MiOhVW1q+9yYHbdp4PW85eVxx+txY8lA0e/fvtrx2fEZ//jyfd57dsez7YHBR3oXm2FkVAuQ9TzUKs8UPbXz13HqyFlI0ZOiM9P6g8NNtqBPfbaJf3Rt1ehGh5vN0yrsy4Q/gZ+V7k5t0p+WxKFWzuuEL2q/o6dVC4oHW12ca5CiQacFED9iyd1oAFeMHg2e2AVmP8CQEK+WJE2LzqNO7JUtdjKc0KKKnk1868nPrR53lFLLygy+TOq2zUpjUifze3TgX3oif2jT80n7XV5z/v5vSSBOQEA9e+6NfT4Aij2Ox7EainDIHZMasPIkHFGF12w5ZGeALywZq1gnIoEiJXFWXa8XW64VN9fkiTl76wwEBnA4sVs6O1QyfhVrolqnQWswZ10Qc4l3lZWVi2TOYc/lAmAltbnZzOyNKTwlY78eJ6sWs9vT+cgQXKv2z9kxU5hcYp11e7cARSa1MEmHo7DIii5ayzFWCSBY/D5mV4pG5RwgqHb2t0LUgGAsrVk9uTCwjqnjGDtrd56LJBCYDj37uOFVv2O7ndiEuRjb5+b3ZXJCaedhzsbUSrl2qYRY/G00icXv2eQU1X9RfZkj14WTJE2e7mZap9ulQLKAV1ABrVo0KfBkYW2tF/Zql0l9F2PsaanIn4Nn0ezwcwZcJvuZSW4hHEBK1T9FqnsYOT1cha3gFPpmrLkXoVmBWyexaOWddR4/qifX2cM/sVjzVYGxc7bwu+zni7b5qsfyOP6pGX9/+hl+K36T9/o9l34k945p65mz4/PZ/JukABjV06bmJpqt6J5DUYzMwnJPCcygyRt7q9yWh9wzbGfi7PG7kazQF/uTWsRYK0mmHMxzVnQBpQrbavDRcpHkm+fhxs9FLtdxO5tiosridt1MzI5jDNxNHePcmfpgsuIzwPeevODVZttAMCn2KWMM9C7ytDualLywnhPmbbhxx9a8atJgBW/6lrM4yezcRC9m1ZJUmAns3MQ3+htu01Caa1l+FbNnWz6LFyVisaYWdF5OW16OOw5zx93YM8++mfV7r+QkdD/o6V8O3Owu+OzDhDyd8N4mzu1mZtvP9D4V9Y0QkyflpUtw9V2uipI4Wfpf1/8yF5XIBP5oXrzzweMOHn+wxYRihu2i4EYhHCDstcUcN1s+E45qypJ5BVypkr3Dj6viRVZc7X4OaOdPQCEVY2DFrTGBXbTcCcDvob/LJ53fUy+kja3vU19Auc6RLxM4RaJDxgVgsTdZmGN1yDlgVVjsa5+s1vhqeaR9psbcglO5oTM6mNxnBr9ugPUFMr8vO4+fkw/etp97cxI5234djh9iep03vVoBiGsWVwvxzYdr2f7L5EaPYNbXHH/78D1+/fo7fHv7iit/5JubGwNGYuDuaN3lbLFbJBDt+lZ8SGhw6CzFmBaTo0WjT+KUnD132dH3tmjXobCzOquIrKsZFjiKlwdWzZiTdTus3UGqN5bzM3dxaMEDLEHZBpMe7ueemziQsrXkDT7T+8Tz/sBlZ5rzY9Fg19fWCkc1ZQfYuJmdG8k4jrnjOm9bl44LN5JwbJjpClfVy8R74ZZOEq/Tjn3ueR233MWhyRmj+mJoGBhzx6t5y8f7JxxjYD/2TNE3OWNKAteBy+97wj5w996Wzz9K6C7iusx2N9GHSB8Sva8eZMYcqCBkTJ6YnNFUk5BHj4weN4mBVHdlMj14ZHaEg9hEF0vFYhS6uzLpz5ZY+Bn8lPGHjB9zA6qaxCDl5oNiEkMtxr5FFiS0LojzVsyTK1swQS13CEdbmISDde+oC/+0sUkmZeDCfNhqIJMyqUhJMJbKNeV9axXPoTkvk77IaVfD87GamK1lbeH4poTRRk6D2E9kPDTpPwBkvROwVNdx72K4uAa07gO34A39+ON4HA+NH8Rn/Gh8ypNwpHeRizBZ5bcwtJrZe2NNgdbKtMt2z2UW/wuqB4rd06qOScX8UABCQlnYv6kAWLCsteqeaheq2po9qWss1q4UR8yT0F6RMT8Uxar4Yy7mxC7jMM+LbTHrrfuq77V0YZpPwKkgiV6SsbrUMWrX2E1mRp+L75bFpkBi6yY8ylE7Zu0Y84Ypb9oUYarnRNIDUeGYLamK2TFH3+IElALs6OivBTc7pm1gf6nQGZsh9AnvEt5pK/YsJr0GZGUVcnLkaPvVKJBsPSDREgl1kGaBbL4f9sIFVPJTAbOKT5Yr8vTzqnirtub1YlTbHKVnVVp15oOSAw2AKgS40rkKNBpDuF4ZGhSVhGqE/lgOfnUM9RpalWLb1VkX4E1auE5yoHU1PB8rLxMp+ymo63LhfhWj3beNh2LNQzWTdyxifOlixwrXu+9YHmPNT/f4R/sP+bsvv813n7/kZ3cvGdzM0/7YgIyb/YZUJIIKNu8DWrrXiiguZFs3q4MoiDoDz6scKImt3bKQD4ERKxbc3G0YhpmcHRfDhBbwyrraGrO1Mq1qV8Oojrt5YAixyRHNOsU15lRUxz72jak1amhSxmMMjHMgleO/2h3xPvPNq1uu+uNJ98J9NN8pp/Y5p2yS9qfhANAK7VfuSCeRWQNHXexHKqvXSWY406QZaznyQbjm0h8Zc8dN2vAy7piz5/W8acypqcgIqwLkmAK///o542yMrXn2aJZmxj4nh9wEwq2w/VTp7zLj9x37j7ZMz5TcK9cXieugJq0bZrqQ6ENqnlhzKbikVJlYtm9Gjzs6/NHyFcngD1IK4ULaevxe8JOQO8XPghttu+4GK8jPWphZauCVquU1x4ibavdyO09hBVqt5Wyoot7sSnAWa7Rz5pk1CHELucM8ruIqR0ngk02+flS6O8GVos58WYo+av7TeEWd4pLDTeWtAzCVYsw616nzaGVYFUDLFuzrnIcTBvMX5jDVDF7zsm3t5v6uMaliUV93Lj9//Rd5CVcw6qzQc/p7/QWqTPG8FkVZcq5D9yKpXM7/l43Qj2DW1xhZhf/8xc/z67//J/jutz7nV559zOAiH22uyU+Fj8MT9mPPnHxRT7lyvUrTm7sum8FdZcYoEMqaC9BsnYOm+p7ZMXU2GUqRE9aWrxljXk3la61a6V2YuCqyB4cSXCptbx3HFBrIlbLjoB2348Bh6nAus+ki3kWuhpEn3ZFD6hh85MJWrByjmRg6lK2f2Li5aONdCwoZ6xyYVlSbqke/cGMDvswU17GRmT7Yc3d5oJPED/IzbkpAqJ5fr+YdYw58crji5X4LWGI0jR1p8ujRI5PQXTtchN2nmd2nsP/cM77vmZ4o+13P3ZDwu0jfR7rScniKvlRHPHkuEz/A7PC3jnBn+5RkEyyABl+q4UIOStjbwt4fYLjOlmRUxlW0JMJPNulLrU5UpN8LBNcmihxc6aIhVq0IQu4cceOYLwzMcjOrKreeJBnZF5kjwvgM0qAmgRRsYaIWzKzqT2ut28Z6ZmkUWqzAbhfjMrmvJYOtwlAn/FKl8K5496xN47HnStLxE043lgm1VE7OE4WWG53N6SeVBHlgO13Nz/LmPs4rGo1Utw6cj+NxPDAU+P7hOZ+8esqzqz3f2NwQJHMZRnQQbt1g4ErpbNSkh1rBZ4yFWZMRWBZo9oe9Lrl2S6sK3idbJYg2Q16TKdgCMZUbo5qXd6XbbJCMOKxLVfGIio2BZJV1VceUjAkrpUNVEDPjHXxkLkb0viQOVRYvaDHVzQUnFgYxo3bFOgeuDVQd1vyjl7QC42ybjkxwE50Ks/Z4dlzrR0z5kqSKcsTLS47ZE3Xmbg7mVYZ99hSdVdCjQLLihmRb5Hd3MO+FuPNmmt55Zp+RLuODecoIZpSckp0PLZIeewNrQe7m6nVlC3n7UK7JA9UtTCxjAevS4nztf5VKFXwNZpWvXtyyKFVX4oKzs6VOUF9jzhIfFhaqtuNTWboiAsQN4LPJxEllwewWNnrzx3gg1qzn2na85bX1W24T8tmGFaBjKcKUL78BWk0O8ROmLOn6l3vixUOJyL0x6aE3eOi5t7zmEcv66R6/8fpbHP7hM/7eR1vuvt3zs5cvCwNJ+ZnLaz71qeUk81w61mU5ueWgxgpF5jLPZJvfNKwtIexHjsZ4yrNjpDN/Lmy+7n1iG2ZClxqIA540mzl8yiYflKQEse7puzAVC5XIPvYcS8F9P/fWrGkOeKcc58DtfkOKHh8SISTujr3lDt3Esfjd7krXRCdqDUDK5PSkOzK42eJWiTHPip+udV43NUiVG8450LnIVWFtLWoSz6QmH9/ITC+RLMKVX9hdN0VC2Lo6SuZY2GZzsTFpfsrJEQ8BJocbHX6UxTLkQti8VrYvEy56jgfrJDgXsCntHIfRMw2JO7Fu5jk70ugt7heGL4r5n0XzP+yuDcDKAcLeCu6WLxjwow5ktm3CwXKDsIduny2nqczcwuxVV+NInWdXhQhfGDu1Yx6gXbBCfZkrW7zpHfO2MLOCpUW1A6Fky6XcrKVzvK19/FiuyyDEnZ03iQ62yQQicVnT13W9n3WR5HNP4YSzAsjaB/iEQbXaZu2d5RykZIV870uutzIb05VLel7FqSytkdjpW3w5H603mMb35TBvU6ys374xo8/2XXOgdf5TPDf1vlO02u4hFvSXkcU/gllfY2QV/ss//Da7X9/yuzcfcfz5wJ989ilX4cjz/oAT5VN/yTEGbo+DeZA4M9urzCHnE9phnRiiNN8Lap5f7iFNgng11L58wVPy7LqZy1J9WHfaq2PdYSpIajdGZWTtY89+7lu15Dh3zRhQ1fHquCWEzPPNgdt5KG3TpVW4a4e/wcfWVr2TxEbmVt0w3xQLDBuZ8AQ2bj4BsmYNJHXcZZvsN24m4djngcHNfHt4xWfuks+mS47JqhtTsi6DlVIcszP67G2HjM66VYyCmwzZH6+EzavM9vOMH+25+cKTB0e88Bw2HQcoHk9iso7CWEKsAlJvQEkGUrkZult73MzYy3fmhP6V6cfDQQnHbOaHygmrJw127G6yJEOSyfBqNVmDkF0xPKyTPfYeOQjzzpGH8lBe/ayTfZUeKszJPE1chLSxz6ilE6M/VmN6lm6KVWZY5Y7rfEOwibkkQlZtWCUR51WG+1rW+tIFUc+eWFNt6w3wFXXiddL9ylWMOmmvKg5twn6X1z303Nl4UMf+OB4HBr588voJ3Y8CL8dL4nuO9zd39M7alosoe+mLj1aoIcRYPwU4EMngnbGxKhhRqTG1egI0BmcStASMlIXOZ/riX2id9k7pieuUqLKFgSItlGXRbssw+zv6sjZzHGfrmLUNsXXG0ixNImEwlhaAy3oVepRAWnxLyrkS1myAdAJkVQnLVBjCQUDpmPU5Xr7Dk6eX7J9bXJw+8aTjU1L+GOWGpCsJTbJCE6USXGV+OUAcrGNtOBST5WJSq96bbCKY1KRhMLkCVrbKbK3CpRQWCtOqVZT9CpsRFt+SqC25eAMY8XZ+zKekJB5SgR7AmUGzioFXqy/WCiKd0Oxi2qK/XaB2fIVNlVQa6NVeU8zrXayGuMvnOwHHHkD3VYrnj6xiwn3bSz3odWJx5p217HW1j59QrPlqr743ZnxtGfp9cegx1vzUjj/48Xt88Pfg7uWG352/yd3P9Pzsk5f0LrELEx/uMp/JJYfCApqmsv70mVg8lMSBq10Oe0GO5jNk9rfOZIaV3evUVCVdNiXJ7ECF/dEAs6GbmbPNt4OPjCksbNTs6V2iL13+bJ7PeLHOtbE0XTIPQc/d1J3k2oexJyeHLwyk474nz57dN6+5mYbmB3wsHlW7MHHnzM/qojO/XaBJDC9LVXrSUPweT4vv3ik7NzbgK+NaMce6B3bs6Vv+8yJe8DLueBoO/KnLH/HpdMWPuTQf4hx4NW5PZZilqJFmhxw8bpQWO8LeJpb5CYy3nuE60d9m/Cwcj6UhRxDSxhEvMrk376tcQKZQ9lOL3bkzTyyb15XcmfeVP1ohJhyUuIW0NSaW5RuW6wzXBiCFg+Lm3Ar0qS/nS0qu4gQNrnhhLYBWBbk0GAMre4s31eR9LYmLGwOkqlKkyubrT3U1QTYArr+FeWdFF6n4UCm662FhoZk/cMl1MrhpDSKxWh+tRs13mvG7xbMTRcqanbUu4tfX1LnXeXCn7L7WAKuSGnx74qvHpPXhP+AL+c4xZh1LZDl3thZ5U5Zvue49Oz4HttbHUR/7CuMRzPqaI910PP3HieG155P9N7n5xYGfffaKy26kd1ZhTtnR+UQs/kveZ/MvUpMbuiEyC+TbztqMgl3MtZroKmhgpVgtuvE5ekaXkdmSmFqBiGWCDZIJLtGXSbsmH7VDX1TPMdpEH5zJQ6wabx0VY/SkUmUZk3U4dKL0Li4BRx1bP/Ned9eqEJ1ENm4+AbLq8KJsnJktTurphdZFymi9Pfvck6PjvXDLrJ7fuP0Z3pNb/r3/5G/x3q/t+du/8nP8r/+9f4kRkznu545D0ZmncfEgA/OL8pNNxONzob8V/Kz0d7b433ib2OPWE7feFt5tgV5+ilUK0qZ0CvSQNtoq5n6isKws4ORQfEsq9bb5YRVDxEKjrZXuNIRSXVi8syrzSoN5o627Q1TJSH199UbxxwJcZWNltWRIIRy1FbAlVgYVyE0w4Gqy8+AKgOQnPfFWaZNsHW6VSNSJ2ps3iTpZ2Fksx91O6FpDHgLEdYXi7H3Wk/pXGauJEt4+aT+kFX8XSWGzaqmHf26MeH5NrY7tkZn1ON5l6OQZXil+dNzOF4zve55ujvQu4YvsImvxPYkBCgiUi/xQnMWQDOhU2DG5gljS5guF0rXKLuqsDsmQRJkppu2uLujtIq7ykFr1rtK7WHyzcpFYAI1dBSYrrB4umh0ZiykWOSiyDvO6ys7ed+vmJhW04skpkFWHsZBnHObjFMrx5no8WjoI0rF1OxLf4dMPP2Lzl2d+5s/8iNfjBT/4L77J8T/7gPSJR+QPmNOBGE2+runUkL0yddVZscAXc1om6xQY9pQug9aFtnXxOR+udA/02pKIJukrcghXGFxrSbhVqm0XUhbEIlAZs9kDwZ10zAVaFX2ppq8vOtu2Pl8X+S5Wz6yV10iJNcbwtetOcsNGzVRal3NUw0IF18ou3mSrrsGpWlgoSYPWw63fe6s0rBOKsthem/HaRXr6Pm8rQLzLkJMfb53WH4w17/D+9bUPJiFf93M8jn9qR0qO4SbR/0PBH3s+ie9x+Kjjm1e3DD6edBr0LuOcI86e4CM+JHJaFluhSySnZA3I3tu9HIuPYhWSOEy+lSvQDBS5Ys5lXtfM7dQTgyvdCH2LSxlrvNG7SHC55AoWx47VEgVb43tXYkw5Pu8zMszWiS96crFq2Y8dl8PYGmXVQn/vElfdaH5XYeYijASXGzOrl8gxL9YpWZ0xssrH9WL5z3ymMplLHJnVs88Glj3v7rjyR35n/wH/4PWH/MrTj/nl3Y8I7hv8/fEjXh63LVaJKNMUmI4dZDNiX4reihwLa6qzOXx8JvQ3pTNdFoZrpb+xOTp3EHeOuOVkPjmZ68XW+ylB7iEHbZ3W/Wgxyk/FYH02ECz3Jaepc7eydFKvy/pYrFB6A6ZiL0jvrMAyr7ymBPJgnQqN9UuzTakxrPr9qpeSsyys43Cw68zNywxrhRMljLbQiblI8r19eDcJjKHkaqCBRhYxVvOyL9HFr7gCU42d5Syuqi85TmFoKXnJeVa52kkDrBPvLCCExcuxfZD7Ci5/ROO+OLFWrdTcro7zQstasXLP8/YeFqTWsWpNpG4A1vnLVtu863gEs77mkE1CsuPiR4lwcLw8PuMf/vzAN9+75rKb6Hyi86kwncprBLouMc8eEei7SAiZ0SnxEJBj8TapibQomkzG4bpslVM1n5Ip+jb5OlGeFUbYVAwPK1urmsGDva4vFYv93DFHz6afeTocGXaRV8cth6mz4y3HHEt15WbaEJy1Ho/q+OZwY90MS7XCaLaJST2zhlYxBwO5OpJN/IXCe1RHwtqU36Rt88i6jltedBd8q3/FRRj5i//R7/OX/ne/jyj8wt/8lP3c89f+u/8yTpTD1BGLznxNo3WzTSSpx5QNRSJBmdC6wzJR9TdKHIR4UaQUFQiqdFSPVc3VKhq5g7RVNAjTUej2tIW1WtbUWqPnUGiWUiRAbqk85GCgFttlQq03fg5SWshKA5RqEtGSj3KMfizU4KglqSpyDxQ/GsDkff1MNim7oxD20sC5PFjgrHKVE4pnZXs9MNGeTO7nVYh60d/TsrZpyNN5kvGTn9C/KFF4KDE4lxm+uU0F9E5f137WxOP8I+ny/ON4HF84QkbU099m3Cwc45bPnwcudlY4cS7jnUKU5bIScMVAVgDvE9kpSdQW/9G1WNMuWsWYwJVsqWq+3dkVQ3jbfONnRGiGswZaFRl0uairOXyE1pQj+MQQzK/qGDticidrllwkgNa2XfFi7d0vwkhXPEvAmMaebIbjxc+rygvXjVBiZYJh+0241lXwmDvGfMHBv8dVt6X7M5HLX/oRf56/yW4383/+S/8qv/Pp94gff4DI58QUyanosZOwdBAs4FKZYxuzCrv3q2mtikkl1pI9Ld9TW/CVhVxWQb0WAMzCR4oW15QyxZbEQtfJpcgyza7m4QZIBVZScNo6oyUU5TuXDK4V1RYApYJ2DTzT5X0klUQjlQ9RZa4rvy+tQF2UxevrHFTi4VizGMCvt1/HmtUv60XX2flYXvyTn4S/KHo9GGseeu5LDIFGan4cf7zGZmcFbX/MPPttpbvxvPpTT/mD73Q8uTgyhMi2M6PwPV0zGQe7NUIXiUVGHbpE10dmn4mDR+8CbrQqnRXsarwQmIy1KcGALfMKpjVQCj4zJ2NiPe3No6rKAJO6xp4Kwbx9a0TYhYm7eWCcA9McEFGeXhzYlc/w+rABTJqXwOSORfUypkCcTaq+8TO388BlN/KsO5ycs50f6QqQddSOjrR06FWPJzcQa87O4o/mlrdUf6xZPTdxw6tpy8f+Kf/MxY/4E5tXvLrb8qv/iz/gL/+D3+VHf/EJ//N/+1/jJVvzucTmirmasWdjYzUpYDRlSSrqixoL1BkgJUnxbQ5W9AjdnflMTZfSivOVJVtZWcaqEVJWpK9F9RKPnHk+1mJJLai0n2Wk3lBN9dqOra2XPaROWnd0yW4pamjJbUpeVZlYxiYWXMotfubCQF5yHIqEfumSqEGK0bzt181qzgipxMkkdLdmo5J7mC+Kykbt2vWTvhl/1nK/h0bNX9IqxpznPG2H5y8VVFwBx86e/ycFZq1xqrr2qNJP4RTI0rPX1I+cl+/wZKzjz+o1bxRhlLbeUFaezWW/X2Y8gllfYzhRuk1EpUOisnmR+ODXHXef7Pjhn+q5+OiOp7sDvU8MXWy6aADvMhrM7C95Y251F4lj1zH6Ht3XFqmygEpqnQm1sy5NtUPTFEPZt7RKRO9Sq37EIquohotgbc3fH+7IKtzNJu2r7cuDy8zRm+F5dPQbq+jcTEOprNiVuPUz73d37PzIPg3s3MSumLpfpy2+dIGaNNAXT5PezcVs14MsevPbtOGT+Qk3ccOLaceUPD8eL+muEh/11/zKf/XDlZ4Z/sJv/gGdT0wxWFWmVsprMlAX+UHJvZ2/cGcTl58yLkoDiyhm7V02r5G4sapDHU1bXbKOpBC3StwK3inTUyH3UqR7kIdys+Y6mSt+FLo7Y2lZ0uBK61pLUrIHDdI+Xw1AFjBo5owuagsyqbckQ6LiE81wPuxzk5DUrMYlxQUh9RYUuhtHKOy0eQdpa+8ps8ki/XyGvuiqUnFOHW1eK64YCMub7Ky2XdlnrVJUDTnQdOP5LYHkJ7VIP/8c+g4a9Huee5BKCyfH+uB+H453j+NxtCGi+JBRsQp5OCi7T2C667j5wNNdzWy6GS8Z73PzzoIi+XMmYRBnJra+z0TviYJ1rdIVWsECzBhOKwgOVzxOUgGFpMg4vGjFX6wbIEssAWNX7cJki/Za+S/gVzUmztHkjz6AoEzJW4wsnz9IZuesUcicQ/NcVIQxd2YEL9pMekVNshJxzfskFVnIpIG70m3qkAaSbrhLT/GXPZffGvlZ932+Mf+A99yBP9P9Nj/46E+QQ0+KXetcaO3sOV2ouWUBFlo3QW1yiIVFRFl8VwbW+ou2f67tVkrlfJGHqy+AECW5qSe/sGVrm/QmeRexBEBXx3k276iwSAt1SSDqc829IC/PrRuOsJraJVOk53Ys1fNFMqSOJju0eFbVFvfEmnvn+nKN1lhSF8jnm50cUAHaYGFnrWWKf9Rz7xufQxF9d7+Th/fz8GaP44/neLI9lsTS1rmXHyv9nef6s0s+++WO3ZMj4xAYQmTTReY5oLMnZ7GmVD6TcyLNnjh7uj4ybGacz8xeybcd7uhsLqm2KKGAGdmhar6M2VnTEedsvjxMHSlUEF7Z+MgumK8Vuci21bXcovprBZe56o9MF57bcWifs8rzgk8cDj1x8k3uuOlnDrN1XO9cNq/FoGzCzJPuyLNuzyF1JFzJWaZiadJbx3VoTao2WKfDXqxzelZHB4WhbGDWi3jBq3nLlAOfjxetodbWz/zi9sf8lf/T3+Df/t//HZzCL/3NH3OXBv5n/86/ScrWcTBGb53skxVI1JW1aNDWeS9utYEAZp9ihYOQKyNrKWBVxtKQlbgxoIoiflApjalqHCgxI+7UugCOgArjsyqNt/eshIAqe4xbaQzbUD2DCxBWlSMuQSq5jdSCiNL8EU12T/OCtEJJnbNrMd8KHm7WNvdJVvzR2F61i3vNVYxRZsbxboZwsJ/djcWd2dMk+lXN4iel+ie2UUGW+uf5vCvl2Gu+cu4XzHJMb3hn1fjjBJOdpJPXLJ7BXy5AvEsH3rbNF+z6XmYW2HlheZ8HVSsNyFrF2JqT1+9RT19fl6Anr/8S4xHM+pojRX/iVdTtM1d/CGHvuf6FJ3zy3Y4nlwe2/cxumBjn0CrRwRdzvmTG8EMX2W1Gui5yozu4DeWmWlhdZEGjIzrfKiDeZ4ZuZj93xOw4hI6npetgZWTN2bcV8uBSMfNNfGt7zV3X82ra8uKwa9XzOHurzosFnyl5Lrzp7i/CxFV35JvdDbvitvc07Bsr6zobw2oj8wkddzEDtiv1mDv2eeAmbfh8vuDT6ZLPjpfcTgOdNzDud/wH/NmrH/CPfvWb/Nlf+0FrKPEHf+F9BIjJkjR7A5uBNGQoJuwqZozoZpN5VDCospxUa6WjJgIGPJksgWXxHwQiaJHjSbKqee6FiFUH4tYSl7RR8laJO2fVhNHa3catMLzO9DeLhlwS6AhpcI1uK2cJA+13bebu2QlxY5NSlV53ByXsM34qUp/VpAG0Kos/KsPnUszhBd9BPlhXxu7Wnl/Tbm0nC9Cma4PC9XB2fb7BzmrMJbl3YhcwQCsX09G67z/KCsUDgNzJQ6vg8NZKwUrT/gZ+pyeq17I9jxnH4/jSI2d3Aui7WRmuwc2O8Thw+8wxDDOdz3RF3p5LRdS5jGrpkiQO7xNdiDifmOihSKRPqotq92pOBlJHtXkxuGRxQjui8wx+blLHVCrtNSiaSbtYl6gwMjnPMXUcVsBQTm651Ssg5RJd8VPpXeTCL14lg5sbK2sshZEg6WQhKaJFUliSgSIDGTVwSD13qWcfe6bU412H4Hlxd8GHN9e8lmf8MH/Ey5TYh0suj6MlMjmR89mkWqR3lRGlzkAqA290BfDYYnwt47Pqcv2DBayoXlIlWaggo3pjXKkrTC01iYh2NLCrSc4nwR+tTbpbrU9ULJatGWCVNartey/xx1DJInuUhiNVMM7NqzjxBjhW5s1o3pIVWHMOtLDL3LRIFU/GuuhRn3pjm3pe1u+7AsBkteFqfm5yQ13Zvj8InP2Exn2A3Ns2Xy343zbuizWPssM/3uNu6tmsEnPJ0N8knv9DZXjZ8+qXO+ZvHRg2M32wfGPPULrcCSKOELJJvlWYZ0/fJ0LIeD8xOSXmHn9wCyu0XF+SBbMUdCSniLfsNCVH11nhGWCKgW03s+smLruR0GWm5IlqHr6NeVuAqI2PfOfyJcdtx8txx37ueHG3A4wtHCdv3q9JoFsUK0+3M1f9iMOsUd7r91z6kY2bmxx+V+LKy3jBPvfs3HS60BPzy5p0SZUTwj4NjBq4jQOv5h0vpy0vjhccY2j+X7978z7f6G/4lf/qh43EJgq//Hc/4Rt/5Za7qWeKYRVTKB2+Be21xZU0lDkeyyW62yqDo/iYFQN2V5UcdX1gLOAKJNi6W5t1STVKrx65OGMuqVgMk2Qglx/FwK6tEi+E+cKe8zP4oxDulM3rTDjkBpQYoKZmizu4ExWJlFgTRhoLx/KhEhOjAU9xIxTCXpOvd3f2Pm5WqFL5ko+sFS3ZGxu6f1WAvwxJLC77I7jk6G5Kh/l4BmTV7wJOYkfzsjofxTvrjW3bvsTOf+uKeCY3PJ+0V7lP6/L3DuOLgKy3bdMO4YtM4OWB36HFmSYdPAemVnMS1fZgdVyt2KZnr3vH8QhmfY2RVUhH3+jwxsYBP2V2nyn9rePmxY6Xv9Bz/Maei83EbpiYoi+yQyEEkxtOo921V9uRTReR53CtF8idt/a4GTRokTZAVo8GsTa7pUtV8AXEALLuyMUY3szZi5+VSya90MDNvGlMrazCOBvDqw+Ry4sj02AT7aafCS7TlS6I73V3fDS8ZuemIhXsTs6LUXO1yR97iWagiHKX+9LdMPA6bXk5X3Adt3w2XXA7D3x6d8ndsW/+XTfjwC/sPuP/8u//WY6p48//xvf57C9e8bf/g+/SvTRQTrMxskSKL1TRmueNoi5DUOTa093ZhNhuqpOqeZnQxDy1EGmUWslWYVgq1gYEJQ9pmw3Q2qkFIAHtMv5yJu08+S6Q76wjSA5GId68VLrriBR/KbzgR1cShtNJ8QTFLolIDrJiZa3yqlnxx4SfMzm4EsnATRn1UrTrFsRaMBTruuhGWgt3Py3dSdpYIeht4lfFGhXIkhScGyOm1QQPy8Reg0Lp7CEpvZM31U9kvEPHjvPxTsemq4m8PrRKAN/Ar94WGB7H41gNVUHjAjRVTwFJSrc3v77p0HF8zxMvrBts5xNJSldQlcasqt4ifVe6Dm4nRu1hrpJDad4QSGVaUdhICsHAMdvAodqh3vysgsutqYcr751VzPi3XOSKebtU0KrvZ1L2JYalZgSc1LF1M5fhSFcM3OMZolzbudfbx0s2WQgwqZnNJ3UNxBpzYJ96pmzdsaboyx4iYxLe+688v7P503z/Z3+GXY7Ib2zZ/aMJLy+YGKnNKkRWC8AqnZNsc9sIblo6+lXwWgrI0qj2GGM2I21+aYDEClCqQHru1LzMlEXG6BTpE9o50mRNTxoIls2E3o+Z5o3mBE31/dYI2puxhlK5r4vOJiksv0tUXPXTKh9Vki2GrfBijLATq0Vo5r31+pXzBOGhuVBoUgR78XJ+16TCkxc0pmH5AkSQnN9p4f8TGV/hbd6VsXUOXrXvj3tizeP4YzFqh0Ko84gB5n7KXP5Q6W4d19+94PYXRzaXE30poMfkmYq80DnF+WxzdPSM2bHZTgxdpA+JWyDlATcu2Wr190MpMnePhlzk6nY1ep+ZCHQ+NQP64DIbLL8IK6Rg42di9gSfba6OfZvrpxg4jl3zHA59IneZPDvCEEnJsetnpuS5m62j4wf9LZcFuKqsq2bkrmJeWehJ45KNWEOrytqCCmRtuEkb7uLAq3nL9bzhZhp4sd8So6cLlpe8kg0/unzC9//8e/ypv/VxK8T/47/wPt8cbvi+ewZAnEMBBFdzsTf2Zu7tsbQ1bX84eLNHqUFZLB90k4FUqcrES/HDT0su0+bIDPjTOONmA7LSRnEiTF1RqWxN2ZIHJV9G8hWk24A/CHmy/aaNBYuLUeluYokBNGBL0hJPWgdeoZi+L2ysNWOo2q/UY1s6rIMbs1mnBKmWTIXVlUmdwyXFz0KeoFv5IvrRitMulhwoKr4wqE+GQDN7L+f5pJvhKkdTYTGEr+yscyJA2+9qH/cU8t8Ya3Csjqwtr/sjGWuZ4X3jgcLIg6ywhnQvr2sMrPtOUw3Xq2vzXccjmPV1R5TmH9HkRoUW2aXM03+sdHee61+8JH57z+VuZOiM85myw2UzW0/RM44dwWcuNyNPt0d4D65lh9wGyIIUJ0INWHeG7K0S7JUJyF1NBJSjCqpbDrHjohvZhZmtn0kqHEqL2Jg9TqwlOsBuMKPEJ/1oAaeYw191I0/6A1s/M7jIdzYvSrfC1ICrowbzKFn5Y9UxacCRSSKt5e3rtOXH0xNezTtezxuupw2vxw2vbzfMrzY24faZnIXbNJCD4z/5H/5l/tPLX+aj4boEmoJeO/OSQRTpMhod0qkFhGB3hIuBbr9UNOoCuFaZKQlFlXTWiRNsOxctOEjCvoee5qWUt9FAxqC4IRF8JnSJMdeuGVoW0QYY+UPG7yfcMdqE3nmcd2hwyzXUGEFL8qqdI/WO3HkDvlY3uh+1VSBqNyoVA1abj8mseK/EvCRaLpo5fJN/lM/euhe295fmwXVu0C6rSf58cn+rjnwN3HmPpBJV7mFlnYNE7zTuWej/pMfJftsEbBfX+fG+8fhjxvE4vszIxTtvvXBQW4z5rAyvCkvrvZ7j1Uzfxza3ZzXZryKkJMTocM7Rh8QQIuxgPPQwFXlikgaW1C50WuRdCVuI2povo+pQOvNm9JHO5SIBtE6GczbTeJGlwNGFRNDE4M3rq5rD9y4y+Fjki5kn4VBYWEpqsJMjq2vnYJ2AJHWFlUXrWjgWWeExdRxzYEyBMQaOYyAfi87Pz6i+YPpHO3gNn73/Ic/Cgcsfj3SfHhG5BQ6rla8uoHzxtaw/5RCKTHtZjLXFWS6xZrW4awkG5bHK9KqPl5iEgPaFXlXivtTEU40VZmSlep0UOeOckJhsfvbOpN1ro/d7qsm1u5SWZiXr4dJSzdb62YRWXdeSQDspHis1PKjFm+zLOVsvVnXZTssxyXkcqHNrPU9r9Ka9cB1r1if+NJGQagT/k2L/rmPNT2aPb32P5c0else/K8Prcfz0jDj7lo/AKaCFKsPrzPPfUvqbgetfDKRvHhiGYk+yGU0pokIIoFmI2RFnx+gCfYgEn7m6PHCtQn7ZI5OUArGcoKgKUFjBKjCrkENGO5s/VTO3uTf/Xt+zDTMbP7MLc/NZ3GfffLXGFOhcIpb5PYSECFxuRkSU49QxJ89uMJ/ip8OROXuuuiPfGl6z81Mr6M/JF4WIa4AW0BpYgcWOI4GdWE5U85w5B17HLZ9Nl4wp8GLcWRf2qed47Jhvew5Fgi5D4vtPn/P/+B/9Mj88POWf/Xvf5/f+wgf8v//9X+J5t6crsdm5TK5z7Cov0d6K3doLukkQBX/0hGNeMe9Wc1vCvBtLalBz0soAdmWONfKNqUmaxK92/8uQdlaUyZOQd4nsAK/4TUSzWDEsr0xkK1h0SPhjxB0tcdLO2zphziX2WHG7ytkb6FOBoSCkwZM7K9avCN6l0ZZJZ/2cF5CkDDfZY84pblTUGVBV8yCTxUJ/rU2hom5R6pyDSepk2XuNQ/exsoB1Af/eeFLj67qIb1/8KUProdeVY/hCG5QvGCdg0wOg1IkB/Gp90sCnB95/DUS+wRjOq9fXfay3qWvaCnrpV8v3HsGsrzEShdp6NrGsq+b+CBefZMLecfvikpc/33Px/NACw6Yz7fohRA5jzzQH9qLshomn2yNOlNduBzcdMsuJ1UNF8hWIoy/dQ4Q5ebb9XBIH+4qj+qZDd6ukoveJbZGJbHwkquNZf+BZdyC4xM5NXPqRnR8L28ruxlkDN3lz0qnQuVzMgF2j8VZtuxdlVjPdfZ12fDw95fW85WYe+Px4wevDhtevd/DZwHDtjNYq2OcplZSkZtx7zF3z7orJ+KuuailCRqW0dB+smpEnT7iTZQELVI20pNKJJdpk64K2ikAuGnCqrrt9z+VmK0a2YReLXMYkn/0QjSWWBBkdflwkfGGv+GNExgRzRCZFRgcVzOo86p0lHcWjSr2DviRvIk2ykjs7jqr7lmTeKogjDdUUsUzEKq0aXqsc/a0FhxwEvSht27XIFt9AYqB11awT//nkvq5clJ+tbe16GxEzfa8BYgVo3TtUTxOq+yoWXzBOFvZlwn7nwHBffKqT7fk+vsxhPYJZj+MdR0YWYIOa1K+uYbWFWX9rEq7p0HN87um2EV/8E4PPZsTrHDEGUvLMGLA0hIhs4SgdjKVzFaxWIVBXGiYLNDZwUvN7FDV/KlIgayaV+9UAL7v5vCweW0EyGWHjrfOtE7WOUs5YvNUDCyyxmHCsOxWKK4ymApKVU2DTFEouHXKrOe9YQKxD7DjGwHjsYe/xo8nQVWY0vyDpx/DxB+jvf0DqD0T/Au8+RXUgZ1uNtanH2TysClJWUppMynB6b+sCWJWfUhbTrbV4lSmWD1LnF1GWxa6oNYBBTF7izEfNGGtAElx5fzcVICtmJObWYEOSvbk4A7YWrw97w8oUbivOep1V5liRMjZ2GM4kjwnrhFtjzWp+FF3ik7GgZflsq2t6+XJ5cx493+b0kqReqqc+jeXkrRlo68r4u4y3LOAfGus75t7F+5d8v58IKPUVPsfj+Kdv5OhW6zM9YXFUFmS3V7NCuXNc/+IFd98e2ezM09CXtbovYJHzmenYkWbP3WFgtzGG1nvPbnnlt8wvNuahlWjgsVZz7eor6BSdHLVpqYjN7R44zsF8GLMjBeswGyRxTIGo3hi9aqzfjbfizJw8bmtdGa/6kY2PTBvfjN6f93sGFxlcZOcnLv3R/K1K049OUmNageUy7fypa6zgwc3NazGrY1TPj8an3KWem3ngdh64mQaOc+D6Zke87uk/98ZW2loecogdEuB/+z/4F/Eu8+2L1zxxR96TPWAM5bwCAsVn2GAsbF/Wqc7meh0d3Z2xsGBdCCi/JMUfC2PJlzUDhSHLqWWGSftAnK33cy7FKgxEkyGTvYc+I15xIZuKaAoWv0qc8SOEQ5UcJtzthMzRQJrgUe9NWu492jn7PHEFDdgb/gABAABJREFUQsVcCvWmVpKYIfg2gZqpO4RRCYeMFGsUxLq9m1wyt7imJXbWWAMU9pU2ltp0WY3haYX7e0fpmGsXRjmeh+xPzgv46++lPg+L3LA+tvIOtnwoP7z/rzlOiqDrdOoLdt2ef8t2J2DUyRNyCoq1nd6zv3vWAV8m7j2CWV9zSDaQpNLriye/PVkuSjfDcJPxvyuEY8/tz3vSN/f0XcS7zOAT/fZIHxJ3Y1+6XPSWZHSR3dXI3egNDa8rpSoHqVeFM1Cm+m95lwneqg5RHZI8vYtG3S2BwUwXhSCZPtjkv/XWsvapP5Qkw7pCgVFsb+LOqhSSOWZjbs3qeeqtQ4g/4wUmHHNB8T2Zm7zhRbzg9bzl9bThZt7war/l+maL+3hD/6oAJtWQXCgMMMdh7riLPWMXYIV7OJ/NRDGf3aH17pks2FbfD6tULRptYgGHvE2QFUGWLGRvSZOsUfX1d4AF59AlpjHgfEYVpimgoyeMghuN1urmIuGLRY7iHcREbRdujKYMrla0pOjg3api4YgbIW4gbQ3VdLMBcBqEXM0gxdhay4XKCRugv8n011atzxeCS3YdWTdEXU7hqluH3jNXLef7bCaqrxM5qTpIXm17LjlsE/6SdNRJ8mQiXE3sb5WLrCbMk0nxy8oMV5vXgNDYCCXROJV7rJgXq/dvi4n7JvVHYOtxfNGolb/iP4ToKnkv90k2Lwr30jpJTc8dejHjfcJJwjtlEyKzU+boSdlBtEKE95luSMyxAEf3Xpd20du6S4qlkrFwDG+zm8GX2OJECa4UGDCgyTuTNwYxTyzzwDJQpnaxyghzDtalUHQlh3etwu7ODk6x95ywbliTBg7ZpIXHFJhS4Dh3jGOH3AT8scxpAVRnRGa8vCDpxJyvmfKRwd0ycAc8AQLirGjxhr6rxr0kS/fTEmtq90dUIVLk8BU0sglAFNQp1cT9ZDrV1fcgFtuTOpPvqPlGEh0uGkuiAk6161MtRJBXO1ZMblcKCK2y6oRcWVneZCQ5gHb2Ul8ljA7yiuHVjHvLMa5Pj5+K1BFrQFLZaa0bYnlN+6xfOM42aqfR1l/t4XMg9k0d4vIdcbbYXz//Loe2jjUPvP6dxgkWpyfJQMPj1tusn7/vOB9BrD9WQ0tX1bWPkD2hq5+Cm5Td54lwdNy+2nD3C57uyUjXJYsDPrELCbdRbkPmeOxI0XOczJd3CInL3cgtML/Y4A+uMIfVGoqU7ndamb3VH1GFY3SELtEPkV6sSdWcHXk2KaEXAzY6n9iGmUPsTIZYiusbP3Mzbeh84kl35Ko7NtXIzk1madKK7p7btCEVs/aYrbthVmmyw1RukKSOzsVWjM/qSOIYc8cxd7yMO15MO44psI89N9PAzXHg7maDvhjYfuaQCPOVdW+tnsZ1TMkIBXP2RWKfiaXxidSqRd3cq8n+g5ZmX+Amh5urQmSpEEtBCV35jrMXNDjrYtwtLJvakTCVGHOy9rTgbH97JQyxesbjvNJVRl0S3OTwE/hDKdAflG6fcdOirJBUCiihFE98LkoiB51r8j3ElCm5s9xWg8V5k9Wbb5afFY40EkHuLD9KW8vr5LC+tsv7Z2nqlf6m+Hk5iFuLk4s0UR/wwcIK8bLkP2+AXg+ATm8U8Nes4FrIr7G4FvHv2/e7Fu3P6zL3zPnvLKu/R2Z4Ukz5srGkyfvf3Ietebh/nbm+Pt9xPIJZP4HRWDtJwa8WVCupmJut6cfV97Pp1n/hkulnrA1Etz2y9ZEwZLzL7MeeOZn2uveJq+2Rw7ZHp95atoJdwK5eAyVgOPMOcc6OIGVHdhkpSUVteVs9SWo3kazCRZgZukMLBp2LpRVtb4a+WGC4jhucKEPR4HkyYw7cMtC5yJU74CQbCyt7btKGfe658kc+CDdmnJhDY3Ad5o7r6y3yycDwwiaBVLoP4qDvrbqyjz1jDE0eOatnSt4qG6l0AhEWQKuws3R2uKMZEM5bIRyEMOZFSheXBb5Th2hGauIES/dhX03hZWkvHtQmayCERCpJ4Dh2pNEjY5ls1Sbm3BXZRmf+MNp5C0Q5Fx27tGtGnbSJ3RYF0ui386UwX1misTZxTIOQOtuuu7OOhiqCVIYZ1i53uC5AFoV95gsolpQwqlV+VpPLCYjlpMiO9E3a7BveJ/VaLZ9DFRVdAnGbzOX0b+ceZmmdv8VDWu13mAS/qOJ9334fCgprUMsYdacg3uk+3nztl9GGP44/vqOueam3aLtwlkzamlsIw2vz0hqf96SrCAM4iQSnOG8y7TnawtqVzrNDmImdR9PS2GQBOhYgpBbhxNXcSXCqFVMwsKWwqwRtEhFF6CQRKgNLckkchIRvi39jVQUEM5G3T2igluTQOkyJVBaWsXZntaLNzo3MOVgnX7WkYs4m5efWmym5snj9Cng/0cnBko0Mc57IeiDrTFIt/ozLqqytPwu4R2X4CsWQXVeehnoK5Ne6lBYpelsAn1ZPVVi66Zbz32QpBcjSdMoQVw9aqP0m6fCgbsXSpSyAV+/jlt9rASKXbrp5sPdvzgFSEqbWxt32q2DFj3L82Vf5u31/beFKvUZ5w1/kZP16hkWdLHjP58uaMJzEGorxfLtZVjuvF7G8M0vrvjV3e+KLxlcBld4h1nyh/OT88cdY89M9yjxQrTTQRdZVR7PaiEJ/l3n6e9DddVz/gkc/POJDgh4g0XWJy81IX5QjceXJ5UXZbmbmbUcuQHpLWmuhFykg/emFl5JrDUCAphSpI5fCxMbPTOKZk2fjjcHbu8Q3trc86Y4ESTwpnop11EYfx9xxFwecmOz9kHscynU0yaITZeNmLlfywjmbt+M+9QwuNnbvPvccUscxBe7mgUPsuD5s2N8NBmT9yJm3UwGMKPP1JhRpY3bW/Cs77mLfwDZU8CGZvPMY2jmSAgRWvzEmhz9KW+e7CVg1hEIrqKX4LJZSdLKEbGf5Sw5L4TqV5pAtBtYiGSWe94k8eSsmzd7ym9GX7rSl4UidRmu3XAcEbzFs3TV2xUxSv7JTUSV3jrSxYn3LcXZCGizHcYn2HLhyDgzQqnlMBfUra6s207JcJ4IuRvSVeOInPS3CnI+z+Ver9P3cOqWOswJ+9WMGVv6ZumyLnMobHwKcSgx7sGv6G8f9DtuU7+2huGFA05vA1vk+auHupLHNOj6dH8/qsfp8K/zXU7E+JQ9JO+8Zj2DW1x0VYa6Llay2oGP1ZZYJpy6ZNi8UNztu77bcfK94TF0mrrqRbZjpfeJu6ltlYkC4vRy5nTzsPW4UXHJoWQSrV+u05x2xmOCl6Jm7wNzPiCiH0NH7xK6brGW5eC7CxNbPXPqRyzBaJw+WasbLeUcsycFQko/BRcYcuIsDz7p9SUiUMQdex50xumTmdd7xOm35fLo0Wq7f8NQfmNVzSB1bPzMFm9z1dc/wyhmltCDyFSh6/8LouLfzwBSNdrz1E7dp4PY4kFPpBpmswkESq2rU72NyzagybWxxHg7S5Be145QU2UzGDPVdDcwqTZanpVuIhtXCvHSj6nxCtkpK1l2F0SPzKsEoCYnJE8U6kyXsgdKFpS3sY8YJxN4X2aBNnmFvxuzzhbfjqCbA5WXzzpE6o+T6aZkRtOpSnVXJu5tYGgosfih+tuvVxeIrdtYp5I1RjqnRbte/1+cr2FXkLGSr1rxRuVjvUwTtAtqFhyf3N153z2Or5OFdWVlfSnZ4375ZAWRSftfld+5hmT36mjyOLzdWc9v61pTladSAHxDCwfwypjkwPhPYgu9nemeSDS/WSco7pXPZiKx9ZEpWCLA23tIq7CrYPSRK9moy6FQYrN5WtN4pXnKRHxqzta/dCVdSQqCBUIfctYp4kGxejpKJ6piyJTK+gGNJnUnNJRLIHOmasfusnjEHNt1MQojqStOTAmodPeFYmVAF+CnJ1rafgcSUhZQTKSc6NzGpdcfSLO2fned6Y5eRpHUnzKEASaIl6Vhu+tYwpnxvksuXWT0H1wvNGmeENlE4lwmdWsex5Jpv5wnwU/fjivSi+XBJec+ycbYCmEnb64ut+GZFGFlA+tU8lTszhrcW7KtYU/8TS0LcmE8rsNjjTa6uSzL2RawsO2+6vEnbfnXSKvBaYleTHr6B4NhJUu8MzHrXOfiLYs0XbVtf8lXm/IdizdkxnEwN68cfY81P/3Cn95WuE+aVtYJ1r7O1p58yFx8r/ui4Pm6ZfsYqpN3WJOHeJ7wzb98jHXH2DF2kC6lsNxP3AZkAlTKfUIrtK7A1gxZfI19em1QIKqTs6L0Zp4PDoc3v8bIbuZ0H81X0kWf9AYey9VMDwcYcGEsxwz6qFe9fTVu2fiaUlt91+zn7k+29ZGb1ZMwM/sV0weAi7/fCIJG7ODDl03T5cOjRlz3bT6xrOcLSuVpB+syT/sg+92awHw0I24WZWT3HaBJKsiPPK0A9L76qNaeR0WJx3FrBOviVjQgsuQxAtg62otIYT6m3GNHkhp4Tn+fcqeWTQcEXwoPPzMmOJc8OnZYC/dJpkVa4zZ3D0aFdbsAapWt7A1QLYGa5lJywnaxLrzBvhemJ5Q+VXGvHanEnddbJvb9dfWb7Eltsi4NnuM4ML4t/V1jYxi5BpnT6XZ3DB8d50X51zG/IDteqkeBanqRU7+KzCbh97TU4OjTcX8h/cP6uvp2r3bx1vHOeszree+KMfY9veZsCiLXvECwmn+Vg65xprR5q7/eO4xHM+gkMWQUOAbROMPW/6hGRKqBl7Jjwj4S7m4Gbn+sYvxX41vNrvrG9hR6O0bTk3mUGl/noyQ2fuczdfmC+7XF3fuVfIQaYzYKODpz5TcWgHLsB1ydCn+j7yK3vuRwm3tvsuQjmhzW4aCwq3QCGyo/Zuj+ZDDERs3mjgPlvOQzAGrHtruPAs+7Avut55vccc8fn0yXXcTBWVQrcDBs6ScRizHgXB5O5SGk/WyajuLNOhN3FzPeefM7ruOX1uGGOdpM/DQd+8+ZbjFMww8taGY826aO01rZSzM5Ne22of/aCr1LDlJdJXRQ3F7DFCZQOIBWIWv4ZqCRR0K5KO40WLWCSxHYB2HBpkVvU/SvGBLOqlckEm+QhKy5lch8Kym8AnJuVcLSuhK2lLvbZ4tb+DofCOKsGkKoNuOpuopnZV8A1YEBUxgJg5A2gaZ0HVOnjSQCo0qc3bgwxpqJzhYngVvspDK0zny315h9GWAye3za+TleqpXJwniV8wQu/4lvWY10rYOvfj0nG43in0RJ5OSsQ1o5+S+JQAS0/KpvPYRo947MN8cpztTmy66x4EUsbdil+KZfDyF6UaQ7kyWPdRah4S0NhKhs2A9kp0XsrBHiTLE7JmydjME/Gvsg/UjFmB1C1boMxW+dBV1harZdKYXgldSSWJGTjZ2bn2biZmB370qlwzp4omTFYQ5Ksjt4ZC61KJzXQ/GtzaXzh+szzweLWWGIvwOAin46XxOTJSZapohZOVJbiiQqVFVGZTc6VSrjWJLOcxPp3peDXhbus5oP6cwVGVWaYFyW7jLU35GROan4oSRsItWZ+AW94ENr6xTXwR1Qh1eJGTRJor82dXRMuLtdGvTgqcOXGvHxGVgtfLbFm5UPZjn19qUs53+tk4UHQS9paq+2kJCGNoXWC9tGq+BQfry8aX2Jdfe/hnf1y758/8SFn4NZjrPnpHnnJRxr5sHz5WgrtjdFXQfTCqt28zITfEG6uN+y/tyTU7+0OxoT1ibuQGOeAc5lNsHk17hyvjh2ZgDuYZ6NMsrD+nZZmEqXwMTly70ghM/lA3hh7SYokfdPPbEJsxd1dmHhv2DP4yFU4tsI60HKQ1/OWQ+pK4T0Z0IVy1R2Zsj3/QX/H1k8EZznIIXV8xiWX/tiaWd3GgRfzBa+nTSvS/6mLjzmkjjEFLrvRFCWxI+0Dm8+9ydWrUqYUurVTuiHy0eaaHx+viMkRoxVmnnUHXsct14eNxVAonYqltE2H1njFK8yCPzokWTE9boTuUDrypbqOtu+8zvm2D2fPF/asBou3zfN3LSOvhfGMyRu3ytCbF7AqZDUga+lgyQJmVBaSd3aN4Wx9HzMEIUvxK1ZFcsYdIumyN2sUb5JDiZmu+Dymzg4mexZleAFG5l0tohjrt+XeUuJFUtLg8JPSv45NZq8IGkoOnixsVonh25iqTZVyH6BVmdbnLK0aS2oRPxXrGGjeXm/4C5ffNXjou6+V13zhWBXLHgLItMbS83EGNNV1ydoz66EC/0nhpYBbtT51sj+/vO7LxMdHMOtrDF/MMMxYUUslYumC0IJHfVQpgInRaP2oXP4wEw7C7eGS3/+Zjumbnm9dXPPty9fczkMxQLSq9rPdgd0wcb3ZcNgNpLtQJF/lDc8RzQLwZPVMkyf2Ht8ZMAVwTIHgnhCzK9IKx7PhQO9j05b3LuGScsAYW3dx4PVsE/3GL/TcTjJOlH0auHImn3w1b7mdh7bNj+crfnZ4wUUY8WSjCfuE7iKzBtxYjAs3GfrMN5/f8O3NK37r9ptc7zfMU+CqO3LMHX94+4x5DGh0yw1RZB4ShaxWYZDZjAptcV+Q4iDkYC1cRaWZ0lI7N5VJuoFfwSb9St1uiwCAYIkbQB8STuDgEyl4tBN0XCrbbqo3vyyT5EMToSoyZ1zIzZC+PV4/S9k0DVaxwcFwm22SFsiDM0NIjKLrptxotaJKdmagWINbZWXZ8/dc8FJmv6a5r5Wguniqq6Y1or9clNYBrWaRRYz7hteWQ50riyD35vMPjIcm/3dauL9Nm14/xmqS/apDVMkrQOtk4n9MMB7HW0b1h6qSVJUF0ILVbbLy0VK12IQXSNDfmPxwmnteXTnSpeOyG7nqR6bi46HYPL7pZrqQGENg7gJauxy+bRQPrZQ82TvE5SINMN9GV/xRcmFJbfyMdxW8MiBNsidiMsO5eF2JKGHVvMOLAXezega1yusxd0yp3qCOuzTwNBzoXMRhsngnGbriN1UMi2sl+mI7chWOfD5dMM4dOTqLg+q4njalg9Ni0F4XXu0xWUCk9pyURK6C/2WB2GSbKy+Jxvqthr91rbsCvVnJTrwzObxIbh0H27UgLLKPBX18+1yayr4dqzcs11gx4QUD6Kofjp+WeKFBimRfWteqpeChIEVaUq7fNVPt3uuqLvilnNyV1xcnrKzVilfOXg/t8+jJ51pt03y/5O3n5/SF9z76jpHqwdf/pBlUTcZR9v0IYv0xGCUfWbNAlzJHyUlg5Uu6/HSidHt4+nuZ7rbj9rueVx8GVIVvXt5y1Y/MyXOcA1MMPNvaOv/57kDKjsPQM+878m0g7Mu6EswTqhTddSrggvMYYyhz53roMm4wH61xDuyDyRuv+pFdgIswtbzBiXVNj9k1Ju4hmZdXEAekVnAHY39dhZGMcEgmF7xLPcfY8eH22uKIm3kRL3g177iLPVMOLQea1XyGj9OW98NE7yPHaBOiOiVuF5/EuFNyr+g28fxqz5Nw5B++/pDj1DVJ5bNuz+/efcA0BYsrYPPUJFYgOFqBP28zsvcGbCnNsDxtaOwsoHQr1Oa9iy+S8tmMxcXXzrNLbnP+z5XOf1KW3Dnb2eu6RM7S/LPs/ZZcyEVw08p4voBzlp8sc7AV7pcZUpLFLxUhO/CFweVisTopks1aXBetsnd73/4uFxn/YggvqqTOcof+OuJSXgrIxQuyNreqDUkk68Pqk3qf5LPYoLrkPCf3npwyrZx9PoFTQKu+33neUSTvXzb3+dLjvjiwKm6t48QbMWMdviqY+ba4orpCBL/geFZg11eJgY9g1k9iKLYYzBR7itXiagVoAQa4FEBLC/K8/Uzxo2P/esPHr7/By2/t+PkPPudJf+SYOm6mgcPcMc6BoYtcbkaCyxy6jljYShVmrUvXKodwXtvjXR/ZFNnhi7sdHx+fUrtB5WST3svd1jqLJEcIiSfbI16UIUSuuiOxmCj2LpWKvnX/6H0iI61isvMjnUvcRWN3DT7y4+MVT8OBbw+v+Gy+ZM6eTYhcPd9zGAZSSRjEKZvdxK++/wNezTu+f/OM8diz3Y387MVLfvvuG3x6fbnIClP57JPgphJYOmBewK0mqReKRM8mwNx8qaS1Is+Vklpu1OZZUibv3NFuaglKP0QzUBbFh0gImdmB9pm8ETiavrwrRoU5CC7awl6DO/XNAqt0JMUdYmONaefsWGUlkekg7mx/foLhpdp7iOnD3VyC+WCJaP3bFrarDlUtOdMzkGqdQNTzJ6cPrAGs8wl/LUXMLCCVeERy0ZifUbq8a95hWrpqnVSb/ijGSUC5f5OH6LRvq2y8wbQ43359Xv+I4tbj+CkbisUUqd2HVrFmBWjZtnVhaSCFqNDtDdCax8DNeMHhquP5zqreMZsP4ZzNi9B7awziRJm9MZPWb9beslRvjZFafBuDNSAR4DD33O5dFZwUhpRy7M3gXbN1ox26iGDV+d7HBnp5yWY2vmKPKTTfkWr4O+cexba/iz0bN/PEm8wjZ7H9bieiD+TqfSVK6BMfba85po7rcUOMnq6LPO2OvJgu2I9DKZjI4itSJYXKAgCtWMDt66pvs5pvm2l3fWy1IFzT7BdpYHmdw7oXihbSazaTXwG8Vd6Jdgy+zPXWCUuoHVzb/N6ulTrvZ5r/oK/y81WLbmdxTwobOBxNmg4FyEpLbKuJV7tUKltwdWmeglGrv09izdnfJ9ueT55yCnQ1n5ayE+F+T8c1i7rK8f9IxxcHsgdj3QM42Lk0Yz0N3CdDfBw/xaNe7mXOr3MulN8rsFs3byA7yGz3uJ/g8uNEODhu7za8/JaBV99++pr3tnuGENnPHfu5Y/CJTZi53Ixs+5n9tmO/2TAfPXLw5vFb3/OU4m9qgKnYpeDIUTgePdJlpmB+jlyaNPAYOz7WJ2SVZgh/1R1bgWTdoX3M3hQlLvFy2rWCurGqZGmCFWY6ydymDR+EWwBez5u2vaowJc9n8yXf6G95PW8BA9Z23YwEZXqqpbmTAS15k9GgdLuZP/v+x1zHDa/HDeOhwwXrvphVrBh/DKv8ZTk1bioAiJqioTYVcQXYqcBTDoILVhxyudjYF/8/bfMfrViSimdvlYifF0ysYKtGAsCKRiFE6x7ZZVJnpvgSjSBQr6NwrN8pS35Q2b6NpSQLGSmVLrtJEa8nn12SliZUUF1YwNho1Xd4eJlbLmMG94LfRzQ4K+CPGTdVYK8cWmX7lJzGfB5Pz8GDQ9aTaN1eT3OeQmY53b68yrlTQKuqUuq2JVdqqhRZ+SWn1X4fGl/wvD6Q35zEh7o+WD++Bttq/Ghx/AGv4vOxYko/CFCdn/s10PolYtYjmPU1h7rFX0jR00Vcoeev6b3LxGHgl8ldTXboJyHsPYfbK35z3/Otb77iqjcfrSmZCeKcTFYHZo7e9wtmXjsJhsIUEtEmDQSYYuA4B/Z3A/kQkMlBAt2a1E1C5vZ2Q57NNyqGRX9+uRmt5TlizK4A4JscA0aCZF5MF8zqea6eX9h9yutpwyf7K+bk6V3i0+mK720/5YPu1qrn/ZHBR8aLwGHu2E8dnU/8uW98zNZP/Jcvv83NYcOwmfjzH/0hPz5e8Zuffsh0DIuePAoSHW4y0MjNdt5z0MI4YunsxHJz584tdOz6ndVFLbTEZJ2ctIp+oRJLMG8Y77R1SRGxx/GQkxSTXDs220eZCIqHiUox0T27qyVnZG8dB7UPsAnG0ko22Wdv11U4QH+tTUNemVyS7HcAN53py4Um67RJ3Z6rzA+qZ9Z61GJBYWjV8/Jga9s6jKdc3so16WHTkJ/o5l1LRJZAyNurJ/eN+ybBB6oPX2c8RMddMyoqWPhYGX8cX2vIavGkDRqyRKRK6NYgRVuU2Lyl3qQlfrQ45WZhngY+nTxXl0f60vo8qWNSR4quJULep5OeDHXWd9VhFvPKqiNlR0yOeTL2LMmAIO3KBONgGjubw0VxdSEliobUcIysdZ6kVbbxEaeOQ7LmJFs387zbN3PeVAGtNPC8u2PnJqYcGLx1D07dzJw9c3J4UT68uKFziU8OTxhjRwiJjy6vuYs9n+4vrMhSgawKWKX601jW6mp33OV+Lx8IWPlEnMcaVo/r6byxdK20xbgULzIpiVsjLhXPLy2V7lrBr/uwKVsa6HFuum6XVoa5TMfZm8y7xKcGqmXw0ZqO+MmuRZOxsySssPiltJ1zuuBexZr2ud84oLLParp7Dn49ONbbLjHWQMczdKyCXatYIyevf8dx37wuX/D8VxkP7Oc8SXgDA3wcf3yGrLxLU7mfq5fQ6n6tVii4s/knUmTTwuZlxk+Ou7uOw+0Vv/Nhx8+8/5oPtrdcdBM308Dd1LMvhfY+JDZdpHtyx2HoOYbevKCEpUFFBS+KF1MzmVoFK3FK10dCSFwfNnz2+nLBDIq/VLeJPLk4MkbPPAe6LnIxGMg0J8+um9iFibt5aL6JFaRyog3YfjVvCS7RSeLKmyRxP1sBvvMJ5oEfHJ7xp68+5oP+jkOyDu5ZhX43MWYh9g7JxVeyMzratz94xTf6G/7Oi5/l9jigR497Evnuxef84fGZFeOjg+ia55EkwR0Le6iODC4u6pIaE3IhTWQv+CjF89A1xp39vfhE5VAL9bSuhhLNriQLaF8ugU5xm4QPRlboQ6TzidEnEsGKGoPCscyXCcLR4nnunYFU2LFRGLqaxeILS+7lpmR2JdG14om2Ana5JJ0d63yxeOz210q3tzm6kg7CIYMX0sbb+apAVok5us4lluXK/V5ZZw9pzXVaQNJTeeGaZZVZMR7tMSnsrAZoqZpvsF8zrwrwWFUpNQcCy33ell81wPJdgKUveH4FaK33fd/rdZUjm9m+3L//FTPrXIXSijDrx2vwWq2H3nU8gllfc0guVcmsxZuiLkjXQIEBXXi70NsXJNbxyOwcStV8znR74XCz4UcvvsmnH+15dnUwCrBPZkRbropYqK5S/EC6LhXZhu3eiwFYU/QcDr1VAkaPTIKfrbKsTsni0E7R2ZION4n9/d5IjNZ5rw+JySeCy60CkrJrLdOjeo5JLaFIPX+QnvMLl5/xzz79AX/ff8T3b57xyf6SYwpczxt+4eJTfm7zOQ7lh4cnAASX+ejihl+4/JRZPb/2+c/y+rDhwyc3fPfyBb9z/QHf/+Q5efYWHAvryk2CzEI4Cm6sC3mBTTn/maaN1pIYpMFZIla9s+r8s6butkp78RcowJglhqCbxHY70YVUqkcB7zJ9SMQhkqInOTNgdpO291YvFpOceRW05KIu+M8ryKrIFHECPpjnQP9aSb3p5jcvM6Gyvkr1pYJXkorJYSuLlCDYOdJQKvCqjX7bgKc3sbV2zZ7PMI1Ke3bMb9BoU6n+4w0YK8yrk8/ravChAYtaMvMvBM3OD2F9UGdA1hfRWNfB4W0mh7bB2edcv7fSJvKH2r5/mQn7cfwxHipn92VhaJVrrIICSvmbNSBdwCRnCzM/g9zaYjaOgdvDBXI5sxnmxvpJ2VElKrl6edQ1nbeOuGvrh5QdKQvzXOQTUVpVWbIxyRqInwrTKtn9pdvYpA3eKanI1hcZpTSfrayOmCFiHiSvdcN7/Z6Phhs+FeV62nA390R1jDnwXnfHs7BHUG7iktBcdon3+j0J4Yf7pxxj4HIYedYfeDHuuL7dmmxiDWSlknQ0v8pipxHOkkLqPY95IdYEJS9rt/Uc0QChuvhbxS11QMiELuFcRsR8zpyodfkORRYiDlGPpLQwwgo7oxUs1mBaPd6Tawwk2cGKMym+L/L/6sdYizKtg1Fe2LdubYJR5++SdFXylKseWwU4kvuOo15Ub8z5903e9T3Xq+3yWPHkXIzhT1Gf86p1bZbypWPN2SGe/H52aG/dzztu97bX3HvkD33fj+OnasiquA60Avt6XWu/LPdjW3+KUDvk1QZD/W3G/YEQ9o7x9Y7vf9Rz862B9y729C4RNkdeHzeMU+Bw7Oi61ObsYTeTixIlZ7Eu66XY65wSo7NY43N7rPpmxew4rnOWWdC+TJ5ZmIEX0wU5WuF9OgZi9Nz6wWSIKowpcIzB8pWS6c7Jl7iijSV1581c/hv9Db90+WP+5uG7xOwYY2DbzRxT4EfjU76zecHH01NeTDucKM8uD8zbkSkGxjGQSpOQ99+75Z//4Pf5g8N7vDjsOOx7CMqf+/YPGXPg73/+EeOhW76zVOJDXti+bhbcKjuXLI2Z1XLKFbuqgUAFoK8AZvveVUuhRdp8tG7EtOSktosQLP72IS1FJAENJmtPo+BGK4xVlhPFHzFXtYkTI0nM6XSNrApJcfsJV1hIeQjk3pu/ViEe5H451rCH4ZXS3+TF/6vkz27OxI1HBfyUGyuxjtp9vn3W9Wc+y3XWMVLLuWhdCs99s+7rRLgGhLJCTohfQKrWDXHtF1yHd6fHXeJWsyz4oxxfVOh/4O0rc1x4qKv8eV5UFwHrBxdQ7I39f4mP/Qhmfc0hUUrwWN8EJpVrnRtkVb1cYQEWaEpL83LD+RmG1+av1N04jq8v+fTDDeHJROgSqWqSBZs8dVnzzaP5i7TOfqKNxiqzGQi2qm2ToAjhTpZj9fUmghwdyWW6rnQeycsVOGfPYe5Iama0QTIuKMElfnT3hOvjwOtpy89ffc4vX37CB/0df7h/xqtxyw/unvJq2vLh5obv7T7j/f6Wz6dLBjfzfnfHbRr4wf4ZH+5u+Pmrz8kIv/aj73D3O08NVLpMNulGA7HIdn7daFXjWkluVNpys6zN0oFioC5IKIy6Svcs39lCdVxJDittNagZ6/uMLxLDpNZOuAuJzTBzBFLwJ+wurR4dLAsLSdZZ8d6xajUuyeizfsyEo6O7s+vEj7aASYMzb6/iqWV03WW/9b1zKFTjYJ/V1+SiBMmHgCwDre55oiZMeZl53kgGHGVyVwTLYNcU5OWFJdmtLXZrHHWCPnCK3hhfMAG+C3i0ptE2ZsLZ4+txPuk2hoWsnjv7/aHXPo7Hcd+QUhFcuuGVxFsKCCJArSGuAC578QLIIxZvXBLkWMF2IY49+4uAG5LJ11ZM+JzdkpQrpAgifunsJ5SKpSzt4RtoURfU0jpeNcaRlIVQFrIo3hdQfnVTZIQ5L7HOleq9EzVfyegZU8fzfs8H/S07P3E9b4pE33FMgcsw8azbs/MT+9QTJLP1xti6nndcdBPPhwMK/PD2CdOLjR1cX07CyvcFyhxbWpPnAhqtq7510avOjl/q+W8LYjtpzdeozg2ynJuF/mZyDOcWgE/VoxpwrieEQIwdyeXV9ZHa/ClOG5hWO9Xef4GtEI9a5IlF9jFLiytW+ZdFel/j62q/Wva3dIy0z3TSdfEtQNaD+I/w0DPLiayTr8IJvQ1OF9GszvUZAKVfFEROjuct40uCR6fSwFUCuN7lfbHmfLMHYs1PjCX2OP7/crS8o64thRMPvramqV5aa3lRzItBvD0CmHWF+wTCQfDHwM3Nc26+veH5kz3bzrqv931kmgLz7E8wi5XarYBW4JypGRigK+vnrNbR8DB13NxuiceAHLwVqqOxlVIS89N1al65s0neNChsEnH2xOjwXkn9zIQVLazIYjd+ViGp0LmMQzmmjk+OVzzpjlzHge/tPudX3/8Bf/fzb7OfOrNkOV4UGWPmo/6amD3PhgMH3zFlz5wicXDsp44Pr2751ed/yMfHJ/zmZx9ydxgIXeJXfvYPuepG/j9/+PPsbwd09E2q7ib7KRH8JLgJUAgIaVOaeKRVDqPLfGq5jOMNplHNYVZTepMqduW7yWY5EKUURDYKQ6Lri22Ky8SiBPJeiZtkxZ1YGoXkkm+VtYcUhhn1uqrzkj/NDQzBpHQ6zJAUXwBVdULuDbxy1p+G/rUyvFa6fTbmoNC8ft2U7XeleAKf5juIMcbq9uoEF/Mb8sIHixc1hqJvkgzeNmqzF4CYkAAaXMuTpCbt62pg/VeI4OpLsHK6WK28pRv7faPlKve87CRmnLOy1uPs4QdZYOug/QUx7Hz/DwFoj8ysf4LDFngFjCh0Rsm6gEy+ikEKg2t9T0OTUEgBanOwxeEQle4gdHdCfx0Y3/OMTzK6S8s+qt6aso9kCb/EVXvcstJpkrkyaUqyZMZNNsGhpvlOG20dnvTgmZMQik/HnB1zqQinUrkAwCem7NkyE7NnP3ccp44fJ8/1ccPvbt7nw+0NP3vxgl+6iszqidmbIS/wPJgUZJ97XqctN3GDQ3k1bfnN1x9y8+kl3aeB4VaIF+bvpV4bI0swnbkfrXJcZXZ+XJKqpYJh5olaJkQzRLefta06Ygynqi2vVarcQdxA2ip5k+n71ICsbWcGIvWcXG1Gk7RET+6UtBHiWAJIEIh2jkmlMkG5jtYSkDWFtC5C5oQfhXB0+KNvny1uHPPOvufhmPGHdEK3XXfS02CtbVNnEg45slTz3ibnqwlWPpt51pRY6uW4Rmsq+yw3QIuq8fcrWm2lA5efLRCCXdcnchO+WEvetluO5c1Fv9z/+APjbXTe9T5OCAAVyF4nu18yyXkcj6MtWFdd8Kr/kUg1Ii/zPZwW/gqIZeBBueadASxhVPxsC+k4OtLWEQc1yUR7vZxVJYtP1gq0qWMpIEhjxLYOexUb8mX+q36E0fyxnLdglEthwMgC2pIRR7Z27lhiMidHTJ67Eo9ehh0XYeRpf+B9ubPOiYUNALBxc+tcNeaOMQekJDWf7i+Y7gbcnSNMkHtIWcpikhYfKpDlIktlMi6ffV3ltgq5nlbBK9O3buPWhRPa7zmAdqBBG3tBBDoH4In5CniODztEOnKOqLslhxskjYjOxsyqn0Grb4lJHE5BrVWsqeBjMraEFUVKLFVaMQSFMCoyr9jFcorprT0oUcXVa+OL5sD7gJgHH19PvHW/K0CrsrSkrJrFXqPr+Apnkquzg3vXHOKtwNGXizVvTQIeiDX1qQZmw1uQwcfx0zby6JfiOrYOM1khi7efYVSWoKsspvBgscWvOp+qsTL9lNm8FPzk8Adhut3x+TcH+m/sCSEv+U52bRJM0Z+8X07eQJKQmedFVZKzEKMnjR6OHpkcPgK5SO4KHi0R3NGAFLd6XgXSNloBJDukeb3YW3tnc2dtEDInj2ANsLTEmU/jJbdzz/W05Zeufsy/+OHv8rc++zle3O04zoExeV5PW17ttnxv9zkXYeT37t7nx/srkgrbbuZPPv8xf6I0rPqdFx8wRc97T+748x/8gD/cP+P/+fd+GTl4A9+UQjCgFUrcLPgjrVFUpMSGVmwvn6n8zF6QfkEqZQXSVMCrOhNIonVnP7dNqUzb3Gd8b90kAVSF4+zNRsVnQh+JU2gSVT+W1xWfRBfEivdJC/s2N+VJk4vbju1fjU0AOeOmhHZmF9PfCfMraxIQ9tDtjZGVetcIF7JaW7h5rc2kFe7VQe7l/mJKBXzfUtxZ2idwyswSMSbVmk1yz+tPAK26z1WeY99nuVf9GtCqcWoVn+5jgp1M9O8+3jUGrYvyb8SRilvc1xirbrsuzpW1TdvXOn9rn/XN93jX8Qhm/QSGJDUaojdAQnFLF0MtiX0LIDZaAbEFEvt2HbqwQLLS37qCgAvTrWd+Um5mr6csr7q/2mKV8lzt8JfMN8oV5N/NxUuqGPktF+Ry1ao334x5CrAd6ZwlEkD76V2mc5ltmHGS2cceJ0oIiRg9N/PAfux4cbdj6N5n281c9TYLTsnzG/lbrQp/fRy4vtkRr3trAzsVY/NoQJVK0XsrpUuhAXM1CPhpmbQlLvd7m7xl2YcWIEsrYu/Nt0qyPWZmiWUx7m3CTgPkwTqWsEmILElWMyP2JjkcfGQI1pllvBiYD/bmOThSr/jJ/qmAC1ZZcXNuwGg1fm/fIxWIWnxR/Kxkb+1q42CLjv5G8YeMnzNvoOJ1TqxdGjvT44vm0wX8PRNIDUb2M5f1yj2T69n2y9+WcC8MrZoFF6lUkEWC1L64decv28kJTX71mR4cX7KS8YXjvt2tmJf5HoCtsS4ex+P4umOVrNdKY81e35AcrnLzluDW154ECXvcF9aUdSgS0uDLAuShxR6c76Z5SpUFdPtXiil1sdLWhcW0fM0GJlhFfYmMZR4UY2MFV4srvlX6c3aMc2COnoPr8D7TuURfPCOTCp/qVZvixhgYx468rpAnY6m6uIBK9mJZPLJySarKZ1JX4ssaOCjnpuFGpcC1hNblO0MsvtSF3wmQ5SF3CsFea3IPZ/MoV3j3J5DNU8JHmeCF9HFPvP2AHH8AfIa6jPPJjrV4PYqrIKTa2qNWkO9jADkDSA2I1GIiXJIXKR2houLu20e99lz1d6EwA2sw1mUDyomyC6j804q/luvrTeCtHec61qxpSo2htUqixJ3FlVWsWe1a6nfFeps3TtHp+Cc0x7eP9UAx50us/x/HT+PIK3DZr+b9tYdWHXVaWuUSpPICb5OYqM0FIiY79JPQ3wj9a8/h5pK7Zwl3UYFzUHWIU5tbYbkvkpAyzCrNGN4k57aNi1U5Ii1RXgqoEPbGYrJicym6u5ILzVYQDn2i6xKHqaMPqcWMGTjOoQFZTpT93LPrJpwoP7h+wuHQsx97Xk8bfv7qc/7yB7/Hq2c7fvfmfV4dt7w+btjPHf/4+n3+zHsf8+ee/ICbiw2H1PG82+NQ/vD4nCkHfuUbP+JZd2DMgb/x8c/x+nefs/3MES+UeGlySdHlM1dmkx9ZiiXBCujr82Edx8vXHCxeeFde481/q3k4r8CEpelV+dpLzGnnMACd4kMiqTCUc1evI+8yfW+vjRrMKL6QJBqoIXZ8dc6VlCFmyPl0aqyAjeqiytBiCj9lvBPS7AhHRb01znKztrylxlzLl0xmeBJ/St6mxccrdcU7DAjzkut8YUFldW3aCafFkgpGLXFnBS7J6maqRu+qEO0GlMK4UucWVct6H2dgl23LKeh2TntkdZxfZTyQJ701b1mteWqu+tD29fo4Ab/eAYf7QouX1XgEs77uyAIxI7mAWPag/S7L2rUNYTFNhdMbKpZvvErZnAEs3d4mKzfZhF4rvo3CL8vk1CrFSpnYCmgVaYysqp+u75sLcCOpAELZgBGXHFNvi8o5mtb8op/oXCKpM3qxSw3Iiuo5RtOCXwwTowuMs2nZY/SMc+DWDfxYL0nJEedAOniIDtkk9OjpXngurqVd6OuKde6xqkb1LilsAjdDOEC4s8icw1JtgJpQFfCvLK7r+YUlSKgr+y2gYOqKYWJv7XDnKzVW1lVkuJgIpWNX5xO7YJxYJ0pIiX/n//B3+OVf/4Rf+9M/x//0X/832W+3+Dtvx3rn8LNVHJoZczQjRfO5An/M+MKsqosPLdeOBiENBcAKEEuXquG1MrxOhGMqCxJYI/cqxgKLW0fclIwy1olZl+vxgfGGzHBNk20nW063r5Oux+6JlE6TC7BJfqX5b+h8RfEpP73whSv1rzChfymg6b4JuFVn7PzltSnyyX1uC4B10vQ4Hsc7D5Xi8WDxhTWgBbQuh+ubROTkFmoYQm3IsGJV1i54orVSXLYtgFm9L9v9uU74y7XfJNUrafVJjKsy9lyIAloTKSF5O/KcHUmg96n4cpmhuxeTsYto8c2yibz3iShKSp6chZyt4+4kgTs1BldODi2SdIJCFNzB0Y2r81s+W42nWluiN1Yz7bzUc3MCZNW/9XR/9XvQep6Kv+bavySv43mJOblTtM/4PhlDShQvQud64APkgy3yL41sf+UW5zP6Wx/w+v+6Zf4HP4PMB5Olz9mKWFUm2NYEunxnUXFV5iHrY7cYkYuBMA5ymefCqPhjkbHf22pcoEraC/i1eH8o4AugOmDL0A7LHCIw2s8Vw4LKtGoXVLuiy3HXxEKM2l7bqevZsa1Zjeshq91Ztvt2lvLp27/z+EkWNRrIUE9NDaHQgMA3Kt2P46d7RGcgQikWypr+UJkmiZZEt4YHtdMp6zVgYdUEY6C4si4OB/PR85Pgj8L0LDA99cSLbM2kCl7AGrRSFqZuNhDHirPLvNPsT7TOs8t6vCpIql9t3BiQkvqybj96NDqiQD/E5h2ci0wue2GcTTaILMxfJ8qYQiuo3x16xjnwar9lN0x86+KaX7z6jO2zqSlKMsJVOOJQdm4iq7BPPYdkaE/vIh/vn/B3Xn6H44sN4VVg80pWMcTYVpVYQI2HU7FKmZXU2fG4eYmrDVgs8QI1xmwaTPVT2b5+yifLZCvEu/K6Iin0ljvUAn0aFOmNgeVFLa9xmeSFKQbAug133rMXmPvOCvtb+16lfL/JCW7OaOfQ6psVxZRLb8kV6u9uNuWRPzrC0REH+5zqhLh1zFsrOvU3mXBIuDHdL7MGNDizXilKHCNvnHrKPSQvPMldzpOOta/VvYGk/Mzlr3XHdlWYjWkhwXKitc3KWha4XjsYCOTeIAh82XHva95S8L/v3N5ntbIQHZbtWxFfWRhZ9XTWc7g+tfX6rs9XHOMdxyOY9TWHabiBWZEKYjXaoCsMG04DSD69GBb6pSwghBTmUPnSu6z42TrXnSQUVJCDe+l+J5TStSSkvuU5kyuV66gEnOw9cfCMvVU1sgrPNwe8ZK6Go7U7d4ljMuAKzMgdYOgMUXJOmYue3TklJ2ddSdJyx2oWay9+/hkqeFACX2uxmsSqGrGAfHeF6eQFP9tPQg2cSyKigYVxFWiL+ByqweIiB80B0tYYWfOVEi9NXuh3kRBMU37Rz1x0E71PhHKC/73/5G/z3/7f/Dqi8Gd/7Qds/Mx//N/7l3l92HCcOqYpkGeHjs6oxlPpdjgJfnSl1a8n7LVReU8nt3KplHa1KHS3au3Y2zr/dIJUbwFtvvSMTwpYWeSlX9bs1nZaZp16fa3ZG+sKST5laL3BAFC1yk1S80IQR/XLqsfehmPJgNe7eJsc423j/HWrP98wgK/J3X1vdV8AYvn9fIK3X3gjRj6Ox/G2oYWhQwJx9wBaBS2RwgTWip6cXLQFtKZI0l1FUygBoDBUk83/cLaYOYs9pwe42mwV0tavXf+qdbEnICOoOHJQYnYkdajCNphZ78bHAuaU56vssLxRjTki9lxO0iR2qtVvr96ELHPJ+ajHuAbST2T6JcEonVjXfnpr4Ku+RwMA1741K/Zaq/SW6ngOpcV7ryYv6XIBssxnpvMeLx1OnsEvZX7mL3yfvyj/BRsZ+c1f+dP83dd/jsPrnvEOYppJKRKrHUHzzFw8Wlw1tK/dsu75rizpXD6nm863Xc2V9qUYANZLaTKyWoe0udthHVqelX9b7CK8Bl6VnxNWbdH750tZXdtSrvw3GFyrv1fAlnV2c6cg1gk4pouvyemb8tXGu71uncC887YP7P4k1rz7ITyOf5qH1IJozTFyA7TUlXl/HRPqtXHPWqTJxee8NLgo95g6rMhe1tdhL0xXnrRzzSPvRNK2uvZy0MJGrfejWZ40sL0oMap6xLyktK3bNRcbprwCHVwphhw9aevwvRYJI0VBIY2l5Ytn17abySqMxSTe+UxOjmkKTKN1aHx5u+MfhW+w7We23UxwmTlZsSRmh3eZw9SxP/aM1wPuOjQQzs3CJi1kgtwVZQn2GW3OlUYi8GPJR8ppcZElhqziqbSYYrmJi9qkoS6Zd651PNcTb9x6/ijzucUZiFtFN1Ywybl0q199bcEnwLMJkU0wOefUb4ibAsyVY3GDFMWJoNOSeEo2Bm+zUbEvhcbOWrGMcnANoHcRvNhnmDpToCAl1zuaP5bU4kQda0sVz/L5Haf5UdZ7r/n1aN7X6eyJKtl9I384ixeuLPydLKyu9b8I0tUGV4sy5SQGuHqLSFszWcz6EpP5et11H/B3n19Wy7+oB/BG/NDVGum+5lbVtqYxQWsMX+WxJ/s8C9cn7/8O40uBWX/tr/01/n/s/XusbdlVHg5+Y8651tp7n3Pus1wvu+wy2Di4bB5tuo3dUQOxeVgCokDH/ESwIEIBKVIihFEiiBKBFCBB6pDI+YVfkkbhYSeO0lGipiM5AXVMutuYgIN/2CQ8jF2AcZXLVXUf57H3WmvOOfqPMeZjrbP3OefWLR6xz5TuPefsvfZ67bnGmOMb3/jGT/zET+DJJ58EADzxxBP4O3/n7+Btb3ubHJgZP/RDP4R/9s/+GW7duoU3vvGN+F//1/8VTzzxRN5H3/f4vu/7Pvyrf/WvsF6v8Za3vAX/5J/8E7zsZS+7l1P5UzGciXj01Z/BeO06uqcOlY2l+li6UM3glQHqb+4UupkcBwAEBW2iuJ7Unc8GhtsUIxFr2qjSKgFoKV35+9SoFzfpuKzBhUHO6oHFSYW7DTaG4doA7w2YCftdD0OMBcQhHI0dhmhzhiMZRGsY1qSW7/qet2COCN6AXYR1Qg1etiNu7e1hCEu4NWWjodUVCB0jtlFF99UJqE6W24juS2RG6OQ4pUOfGrbkkCu2FwxpCUWi9IqBBgF+SQgLyT6MB4y4F0CdnKv3BqaN6JzHlWYDZwIG9VSv+vAzE+bdF3/0k7ixOJZMkInwCvIt2xFEjMP1AiEYBG+wPmlAJyJ8aU9ImHiQc8ugXuqQOCbnL6DbEMQRwEgGIhmC2Bj4JWHcMxj3BQAjDy2prGreobY41ZJvM/a7QJhYbWBTsDoPLur9zK3WHKC6D5DqRR65nAha+jQ/rS0lNhei5wIlq3458rj0M6eHIcbBzWPExRL2sBffYKomBcmfJABr8uzMViIlX6JaKjXaKssmChCmcPqI2TG/dSG7k15en8UsuM7M5bR4H4HYG3iyKkAvRq+1XhbVRjorDdEiMGWTkXyOIcBYEUlP7NtIYugjEWACyEow42zApmkRYpNB/UlS1bKUUmjWGam80EvwYAK0/DLd/3JdE7+b7lWqq2QIOy01J4liT6JTMKsBYsfglgEbYa0EGGRlvdEZhiFCIAJuAI/gabwST6LjHod0gN9++AuBqwD3ErxZGwFIoxICo/eNiO1HKeWA6raYkSomXrkXqfwoaY1QTIGQ2kEyEy1Q6ZQrZfqxha5jyiJa5pETqjM9COClwOcBdB2CXf3BHnD7CoDfB/g2StlhPZGqn6ZIOVzc12zZZPsn/9hHDjYuIpyr46IL/l0MhsvxWTSYQD5IgpDE+HANaBGk1Dhop9hUBs1TXyIbyz8RzU5d1/U4BFAoydYEksfjAiSwRS6hThUWpexaz1Vtq0lJglRFoknmVGFBUW1kYmzEVEUCkQoJQOwI3Il+1+AtWhdwsJATDEwai0R0NqCzsmNpZOXAAJbdiMFbjKNF8BbD4CR3FAz6weH5uAKY4EcLVm0vdgxaBNDtBotbBu6k3COeXXuqLJnoZA3l2u2a4dbQxlXCtDJjscHGc5GSYdmffKclqWJ82V/y2cKslfuUkiUprvErhj8IsPsi+p4YwJ0NWLgRgUX6JoKkbN8IsrO+1qD3S4RDA3tA2IxAcyLnawZGc2wKCzgIizd1vHWbABpjBmvqJHHqtB6dlLQnP5NAwMVtRns3wPYB5BmpkmNu29gAoZUKlCRNk+btVrZVPWbrlMJY5OKK0rrpLEArnVsCpcLsmDGWqpS0DVFJ5lfAXKrWuijxYC7zUl4/6zP1H6djmjlwlRN5tAPPqO+jqc4/rd3SNjzbrga77mHcE5j1spe9DH/v7/09vOpVrwIA/PRP/zT+/J//8/i1X/s1PPHEE/ixH/sx/IN/8A/wUz/1U/iCL/gC/N2/+3fx1V/91fit3/otHBwcAAC+53u+Bz/3cz+H9773vbh58ybe+c534uu//uvxoQ99CNZuUxL70zsiE26fLPGIUiiJWbpKpU6C828jQiZl/WBVI3UYSYAWseRVJAuqAEMonxV9LY0IUukgqrgkldMlZ5XLUfR1XWBndNsgi/rmSRoBuyb4ziEYyV6EKJ1H+iAZDR8NNr7R8g/V2dAsetIsiU5eXw+NrGWDge1E+2TZjthrBzyyuot47Tn8evso+t/fR3No8vVKmSFLiYguwHOmXCnImdaYst7abSoz42wxiun6g0Up99BrJ1Y6bley5HERQW2Aa6TMxbmIG6s1rncn2HMD+grI+/XXvxRf+CtP5WqHX33tK/Cpo6u4e7LA5qSVLiwuYtyz2F/2OFhuwEyi+dJ5DHsWftMgrCz8Rq41tiVgECPHoNFo5ko6UoaWxFG1VvRvdIQGGPcI475mckaxw9nQw8D2jGYdc9mMjQHf9on/jNffehIfufY43vN5X4VgrM4rAjxlpzChvibDjHpRxPn5kDcIuUtj+nvGJDt3VOWTuzoMXmgfNTV2yyMLYDcovHO/0/3k0qUt2YvL4OL0uPQzpwcD2IwNDuIMTU4T9xTjEdOFwql5JpM+AVpERaQ8aWRNsupVBrQGoGS/lW1Ki7lq27wdl03z67NFjxkJ0RowRdFdYWgHKmE8Ryb4aAuwDEjChwDSE05+bgwGMBEcDaxjEKR8orUB+00PXqzxaXeAcLuVZiKV/2SLAkCpT8g6YLHyNfo6V4BN6upU2420z3S7AGQJp8Sqzs1GnABZxsrOpcGIx8KNaI2F5yAg5B3gk+Eh/CZ/HhwP+Bg9hjvPLLC526AfCH5wmnX1CG1A6zw6N4JBkjxxoicSvdWfJCVCFVs7n3Bik0XV3FQNFmOFVZFG1Gx4aCHt0FWnJzq5VoBggoXBFRC/DPgSAG8Gbr7kKayxwvGvXAU+uA88fxOENUAjisbYZEYhUxRQL/J5ut22YGNimy/oa6iapC+I4iSfOw9QejH8wdZw59LXfG6MJBeRup9pUlyMmFG7qRIoqgmUQK1TTIn0CKl+q2jMIrNNc8I2CnPKjlQ6hdf7qcEsKuAMqs9nNlIoNpTnsVJ1fklnygBg1QJmMhgcw3vx7z4Ic2rhhNW73wwgjUmkk6HDZnQwJFq31jCcjdgYxkBA1ARz8AbGMjgCcVRR+0igQa91IzpcudtgdY2xSsbHhvMbKWme9AztGtKd3MtaVkBCRlCZgMRUyh10m+JTkp0FxCazV0CsKvUHSfOq6AixA8Z9RlgAYRWBhRAKiBhd47FoR9HIsgEGHm0c8S3v/lX8mV9/Gh/74gfx7779S3Cl2+CZg30crTsMfYM4GqwHI+X7vYHdUGZ3m16BR5/Ydg5uneaMlFQmreJkXtlIfBKWWkEzAt0d6eSeNYWp+OU8ZRUMC53BcGCEPRYLKCob6TNyHjBEKPG4fi5XXREVQAuYrntqQMvow1CzguuuvxQzmSLtZ9KFMs39BPTEal+6/10+bP76eSWGpSz9dJwiO6h+T/Zjtl2d5EzXc4rRNbcx82uobcPFsDsA9whmfcM3fMPk7x/+4R/GT/zET+CDH/wgXvva1+If/sN/iL/1t/4WvumbvgmABCEPPfQQ/uW//Jf47u/+bty5cwc/+ZM/iZ/92Z/FW9/6VgDAu9/9bjz22GP4hV/4BXzt137t1uP2fY++L+IWd+/evZfT/iMbkQn9x66gefZ5ZbNEofITF4ZLLaIInn45fPonodSeImVPkCZaEZLlBHLVAEH6FdVEI31oSbdNkzU9IGrwknFMABbrAhSspQgjIfYWsQn52iMTjsdWyjqYsjivNRFOyyJSGUjqJLJwHiEabLzL2ZMHVse41q7xUHcXnfHYe9mAD3eP4s5v30BzaGSBbTh3ATFeBPFTJtkEQf/FCVLWbOFWjX8KNNRBTpB8M1v7KpCXMuU5u+SEmbZYDli1I652Gxy0G7QmwLPBEJ2AeqHB//Z//Qrc3izxJb/xSXz09Y/ip9/+Jhz3LfxoxQluLOAd+udbbJo9mBs9FosRw2DBUZxnajts0nUSEPaidBgjCCsjRgQNJMK+gd8zGK6KjoHtyyIhtlob36rg/Ew/hS3BnQAUDcARZgC+7WP/Gd/xsV+AAfCG5z4GEPDTr3qrzBsrdGDJjGktfGTA2WyM5V5GmcOV2HvOPICy4WciwG4x4jvGPWWYz2o5e49jW5nh/FxqbawcV8233xYPXQYaefxJ+RngT6+vYSaE5zqYk5NisGp21nxOsZZJpT8zgoLyUxMuGWDJTOJ6G6AAULoInLG8uH4s0mJvtm0+PV3gp8AkMRNz+bgGCuwrpjEIzJzL3NMC02jZiAHDmCIymxjAzhgFvwxClMz8qhmwsB77toeliPZKwNP2AJvnlrn7rey8AHpJ7D39nXWyuHqeK42H5Gq3LvYmXyqq8g+UxaABjGW4xqOxEQvr0VovJRdgBPaIfAf+N2/it69/AT7+Z14KRx7rT14D/ndgPPSIoReH5xmIFmFtcWJa0NLDNRHBy8FiJFkgp0YxEYjE4IYBWy8oWMo1wYitlMQHj1zqnyuGKlBO/FS5L3K9Fjw2AO8D1wj0pYw3vOSDeA39Nu7wFfz//o9/FneeeQC4dQPMnwGZEzDXax2dMFnHTX0Nx1LKUc+4vDhPk5TODADua7xQnGvb2HZ+s/3nNSW26zCW4ORFOqfL8ad+vPTlz8HvP4jm+ZOy5GcWljzp+ivESgKlDmYnvdvKaykZDpXgsBLIMyEn2omB9qgK9FFsmryGXD2SEswx66SWbShWUz+BXDO7mYEw3U/yK80xISwsQuuAjjAOEP3FZY/OeTSW0KghH6LF0dBh8E66HWoyGRC/0rYeMRJGY/P5hGBgrReJEADxmiRcms4jXiH0vAB5M5VzSdemiXRuNIZhZIkUiWdYOsMq0DGuqntDAGlyOrbIbK3kX6LTEspQsddQrfE5JeUlme1XkK7wnZQXuoWXJXywMMseq2bEQdNnHeQ//9P/O775p/4bDAOv+5VPwRDj//VXvgjX2jWeX61wp19gPTRSeuktjJH4dxwtNmvRUIY3MEdWKk1YCBJuI7FNaFMcUmLPrI+WiAiMrP0FADiGNokp9ziBWH5BAmTpPbR9AZKg8zD7bX0+zh21/iKqz2QQEdtt9nwf216LcTpZ0lt1vKExP2iGISSCzAXGheOmXazguX9JgHM618k+UBZCoKkfmu862ai6m3D984LjBWtmhRDwb/7Nv8Hx8THe9KY34ROf+ASefvppfM3XfE3epus6fMVXfAU+8IEP4Lu/+7vxoQ99COM4TrZ59NFH8brXvQ4f+MAHdgYZP/qjP4of+qEfeqGn+kc6hDKaAnoFGWasESB9p1TqbYHdi5/qdckGC82T8ooRFUBVNqwZWCaw1tdWAEG1aAYjZ5CTnhQscrMhomrRrlkE9ga+dzjRhb41ThhYqleS5l5HUn7nSMCsyARPEliIWDxj4UQs3keDlRtwpdkAAEa2WNoRr7z2PD788B7iZgHTy0NNUQOcKAvodA25w4cGR8RAan+eszzVdaegIdX218MEzS4nAd6UKXcRi+WAm3snWLoRrQlwJmKIFkO0OPEtjscWG+9wuFngH/6Ft8J8c4SzES6KxTM2wrQBYWPhjg3csWS5w/NLrPcXss1IaMZS9pEyCqEn+J4AY4VR1jHQCrAFF8HLAG8ZYWEKyyAFR8SlA4gXbYE6UyElqVXm3TNef+v38jQ1AF5/68k8HxFE+Je80mRlMmhTAlMYDYAAWWPiipO0XjHSkXHeubAWQtw5Xoi+VzVeSKlFLjNMi706lt+xr8zcSMecn0NakF2OM8cfp58B/nT7mgQ4ZTR0So8qvibN0dq/XMDXnFpUpPfTUZL7SfT6xGyMZR95zmP6bORnoCpln59HDr4ipBQuGIzeAfAIioqk5Eg6ftJAMRRLfoYtGFKWSACcETZXZCmV6NT4RRAcBVxbrPH0fgv2TvxKupAESqegKvmVdC08/Zd9TVq4Vfd2y1o125WsMWP03hoBslaq0WJJSicDEwITxugxhjvwn7qC/hcW4I9cB1mGeY5hno4AngKZE5AdwR4w3mRNrLhu4NsE1BFsxXDOgJ1XlpZeR3QVsGUYcCxlm45EmyzNixRV5vVF5X/Tbc1lCRa0B9D1iBt0Cw/iGSywwTXcxp39BwA4EAyQOi7WHXC1DIPJlPsMlMAgjexfyrXk2XnRRf39jBcAbp1ZZniGr9l5nNrPXIJan/Xj//zgx/HLD7wMzXPHOoeknlkeE6rWJDM2e4oFtrDcS8XIbCFj1EaG4m+oWtjkdWXySxM9IIAccjk4CDBpnZ6f5+JMEtCRy7zqOZ8DZ2kExXccwhXAtAHjaHFipGokMGHhPFoTcDRKjZ41Ef3osp5Wqq5wJiIQwRjR3hpHB2Yx7qbz4MbANQFt43NlyfN7KxzGa2julIoSuX8KJnWM2EXYE1OtjwG7ESDLeOnYl2IYJtHxreVWmFCaN1W3KDoCj9O5IDGQ3JvYEPxKwK6wYGFkNRHURJCJiMHCNR6LxuOg6bFyA058Cx8NXvvrnyoEJAYe/7VncWtY4Va/wu31EkfrTkovWWRUusWI/cWAuCCMSxHI98GgX7UYBgP0FmZtEI4l8c5WBeiTu1X8K+lT2kHiPXYikdIci5xKapAFBtgS/EJF4leAX6nu1lCqcfzClJL5PsotjLUsEE6NM4Gumkgyfy39HmevAapbVtZPNZPrXmKTVHK6qzLlBVespPPcAjpte+Ym68f553KyCRnQnp7klgNfIL7aNu4ZzPrIRz6CN73pTdhsNtjf38e/+3f/Dq997WvxgQ98AADw0EMPTbZ/6KGH8Hu/93sAgKeffhpt2+L69euntnn66ad3HvP7v//78b3f+73577t37+Kxxx6711P/Ixn5ZqeJmUqqglgkBjIzJGXSECk7DqjRqgORWpsn146nY9F2LSwRmOfTEws1kpuczkyAXgGhmOt09bVRTzWQ1CcD4MGij4TgLbrFiMYG6WBBst5sXYCNVjM4QEsekY0AP5pZdxSzoO8YLRxFjNGiNQbr4LAODQwxrl89xvN7HZZ3Se5hIKCJAi6Z1EEjiSfGrBNGgWAHfdBVz4NJtaJS4JEyJco+S68HzSr7pWSnY8PgRUSzHHNZYWKgbXyDtf7beBHAH0aHEAgcDYyVMh4f5AszRjRM4tIjbgzoDqE5ArrbAJOU0dhNYgLEwqxqCH4JEdO1yqIzChxBgDe/EgfFXUTo9Iu1DLPUwG00wMaCtWwRqXOjpyw8nEtkIvCR64/jDc/9TsalPnL98XyPKHJuuSvHMWBrS+lgrJxAfh4k5SbPgyl14kBhZuXnRCwfoxhk4mkXkp3jnIX7tvr68iDs2F8O1Lc5jO37mgNY83O4HGePPwk/A/zp9jWnRvU8Zk3Gem5l0As5ez1Ze8gDhkStys9WTH5kO1My+7H5uVT7KGudaVAuzCsURmz6TNgSsAQj2liBYBsBdUSYGyICnHQYlc5ktfNhAn7k9whHLF1smUT/g0VDK0QLz6JzuVwMWDcWri+BGQgTXQxitc2BM1CSXovJUFbgVcZZCFkvps46plJG0TPR79AxjAu5rJCAXMrvo8UYDXwEQjxGCH+I+OlrwNMHIEOAPUbk5wEcg2gDYwJCY8CegF4SJ3aDfJJGWctSPpHOW/xk1HPjrGmitlr9T1TmFtu0xgHIaVIrkpShp0AsMc1z+WUEkwcOATxl8AdXHkOLAYfYx1PjI8DzAHgtXzLrfMxlGwSQKXMqB9DppqZSEPU1NgV+la/RdZR8p/VaqMzaC5U3nAcSzRf7F9lfDva3fGjHvs5M0Fz6ms+p8W//x5fiC37vGFn3lKI2OiB9NmgiyJxt+TzRDn2uEuiiDODEAibPGj0SYFKJeuVgCIW1VYFUpM9rkkepG1QByGwc2bOCYolJE8tns9vSxlpJs5y8ssd6g2gYjfOqG4icCDnSapKgAu6NEy3GyIyFC7AatzUmYowGo1aVOEcYR/ndNiOsjThY9NhrBrx8/xY+7+A5fGy1xpO/+xC6TzupqMh2VfR+xZ5pw6e643rUdbX6XNJqk5jAQPWPZhTbGyzle5xYsDGt+2PysyRVabmDIXLVCZz4MNcGEAHLVY+ryw2udBss3IgIQuI6f/T1L8WX/rc/yLIpH3zNK/H08RUc9S1ONi3G3iEeNaDBIAIYuw7HK4/lXg9rZS8hGLjGg8gikLB/R6vsKQJiFyVhwtXcjIQ4EkYTYUYpOXQnAlq5DcNuxMkmEDA0gN8TnePoBMhCC4DltdAAbA3swEiNvkgDV7aUn5nphCRwK9dlAgMxTCtNgPJ8pZH8UR0HARL3zAGwBGzNpFYuLPA+e2bzx3eAcKd8RbrmOcjEikRMgKmLndJOf5TXqxp3V/tNVTnzxMwfWZkhALzmNa/Bhz/8Ydy+fRv/9t/+W3z7t387fvEXf7EcfF6nOSt32DbO26brOnRdt/P9P8kRF9ViPWXN0wRLzgNA+u/UBEE18RKzq6L15m2SyKouzOoMS5oME6eRjpkXR5CHdlvWnSGlAknQdrboBiujJy3WN9KZMEbCRgEaa6V1q9HUdmCDtTcwjjHEwoOMTPBscolIYwKGaGFCI2WLocXdYYFRPxOXAdEZRMsZrONWQSYSFpEd4sQxJlaW3YjBDo0y1YLQbEFq2J1knSWYIslAkxr+TsAs3vNYHvToGo8xGtwZljmDI6WSDl6Drs2mQRhFvBgAgrcICmRZK+KKHAnGMcIiSstj/e5M0A5ioTDNJGMuXRptT2ArLYYFzEI+19AR7JowjgZ+yYirCHQBzcKjab1Qra2BN4xIDva41LTbDfLPlB2iyHjPK78SAItm1vXH8e7P+6rJvMlz1lm5DmPKIimiALRzIz8TPawZWnUgnDXo0n4YIqB4n2WDFzWOZ7WflT/0XKvAaPuOpoZ9p3O4B6P9uTD+JPwM8Kfb10gbc42Qa2buroXDtsC3er9Upk9XH4QCdGV/NQvc6wXL5Lmt97HrVicW7Wy3+fcoZYVgAJ4Qo2R9PUk3XDKSTChul+BZSi5DlekR/EQ1R4hVg9VgZAFchmgnbdm54dJIJe27Yu8me1yCQL1RrExhfdYT8zKXDiqQVQd0mZ5v5HtlA6CNcJ00S4lM6EOTE1upg2OM0jPZjz04Mog2AJ5DjECMIwAPYwYQSYBGuv+5NkaSF0gBaE5UqB+yhHwv0gIzschIu/7mshnHME7WAKyli5EIDANKQRsryzgGGB9h/BHoZAT9msNvNa/Dky9/HP3JAvhvDvgEIB0Ne/kGM5BlCiurKkkoycCZQWUga73RaSCr3rTMv+pz9wJEbRv3Y9PvgZm1cxcvIBC5HP9zj3inhTk5LkFzikeIcxI9A10muZHU4RCiobWtbCnb7Co+iVWcoaNO4hHPE+nFUVDUJhozJ5HtZ8Xezev6Opapj8fIAT1xArSkkiRWnfnGYDF4YWGJnhYXeRQb0FiptjB6ZM9STdI0I1bNCB8NhtZiPQjT68ZqjWudJLgbE+Ao4KV7d4DPB34vPIzu0zazUuvgPTWuSgL2QhzQ+xoFYMlJ91SJwnyqCUcGBbTcK927lCiaSKbY8g8EwDLcwqNbjGidxxUF5ZJ8jI8GvXeIIPzf3/5ncTy0+OKPfhK/+mdegX/xTW+GHbWrsOqToYmAdmQ3xxbmGYt+1SK+ZIBtIsJgJckxkmj9jgJMuSNJ0Pt9gl9F8ScEYCH+izu5/tBpGeHCYDyAdn5HbjzAJKL2OaYLSl5IVUcAjJNJFK2wlQGg2Xi84+P/b7z+9mltYAAac1TzOcUvaTg7rTBJRJYYp+CYoVPyKjLfafr5c8a9gDvbxtY12QuMp2pSzU4WWPLLlS/N68k5cK4MT4ZuSzvOd8e4ZzCrbdsszPtlX/Zl+JVf+RX8o3/0j/A3/+bfBCBZ8UceeSRv/8wzz+Qs+sMPP4xhGHDr1q1J1vyZZ57Bm9/85ns9lT/xYYjBNwawM6ChzLJTQvC5tCKt4PWbTVkJM/upb0/E5vKCUwP6ismSPiMPmyLv9eKtGikLTsQlEE8OJJSDp7K0WoA9/UKjGKUYGsAygoswLiIEyXLbjjNY5dngaOiEkZXbp3P+3UeDMVoMbsTCjnjm5ADPHu3Be4OmCbAHIzYPGXFOunBmjojO5BJJMfrSRSk2VJyDIQRXzj/Rd0OjRq/h0ipX2Q1J7B1dhF16LJcDGhWxXw8NDjddvqWtC9L9xFuMg5PuUExg3T4MKrQ5GgQA1AUYJ6L3tAgY962wyHoxztKBRAwxcakbr2vwiVlYZ3VJ6chojkVHbLhCGCMQrDjqftMiHDvAE4glI2Q3JAywUYQnTeAszkhB75Gx+JnPf+upOU85840MYAkrq34wUNHBK+Ot7Co2XADfiqFVZzRE26GCcwNnlmJe6CdwdYfBywH3DACbGMhUKsWVEZ3sZPYzf656bVeNuW6X9l0zzi7H2ePSz0wHEYOXQRmq9XxLz0RaIVAOOOT1CWp1ej7neVxtwPXUFlZT2V91TowsFr+LSUJ6DoK5JbYlMjiSQa+0SJ+fWyDJSkex90HBLGk0wSCr4u+QUvwhWDAoZ+EJpTQxLdQDRwQTcDx2OBlaxEiim9IG+D0NJLTcDwBSO/O6xKXWf0mAScrZpGtkoCofLIu/vL6zqgPpGOQimibAqF8cg0Hvbb4V1nBuvBK9EVIsj4AVFI2DGt1IIuNhDciI6D07Rmjl7SQ4TEGWJ+DCcJ4wtHSdIgLulOdRTOwAL0mUkAJP1i7FQyqhKQLASbfSjOl+jaB4FxR/H/jNVwDPWPTX9qWb4acAxM8AeBZAD+JQ5hcpG3kO0tRzO891LVtJoChV7+cvQT+kPoJyg5wqIuctx9gy8iZzAGyyvT6fyS9t28mLOM5kbF2Oz+6R10hqLzU+yPYXO+YrcCbbo1Jo01iCJ89VBlTqxHmKZ5IGkoIvyeZMjq+MrWxb07UAsn71QATnDoY5gRAU4PF6Op7AI4lWIBYYWw/nxJY0VrviegNnQ75WQwxLhcUVo5xYZPEPy2ZE5zwWzuOobyFi8il5Txhji8YEHLQ9Vo8cYTi8AndYgCkKpBU58nfKQ1EA7Ci2NoJK57qAnCSZMLCakjyRv8t72UzluAhFMkWTEdExTBOwUjZW53wG9Xw0OBo7jNHiaGjhg8VmdPi/fcPXgL9epG5ojGgg99BYnSxdQBwMzIkkyptjoHueMN5aICwZNop0ShaBD8gMsujk3ti1zXMldFauyyn7t4vgZYR3DNMbGCfaX5IoKmLwTCrRApGIMSSVJ2zk+4wOaKzEiF0kfNt//8/4jt+ttIEB/Myr3lqAKJbY0Pgo1Sg+FN/hrMqqmEl1lUyIKF1FoesHWJFYqRP4QPFrdRyU5/w0vs+vz986Dwjb9ozXo4qPTvmMSawkf2eSQUWeyWskzD8/ByDKPrdqbjFKXHePgdIL1sxKg5nR9z1e+cpX4uGHH8bP//zP40u/9EsBAMMw4Bd/8Rfx9//+3wcAvOENb0DTNPj5n/95vP3tbwcAPPXUU/joRz+KH/uxH7vfU/ljH5EJuNOIdlB5ESldIKAWMJ15VSCe/o7V+6luHWVyTLrFoXQ7zIOm3zwFMXgZpErBAjAFAFJmJmETXt6uk/0Z3e9k0S0noaVqTAIuBRIQhkQkNpUTLpsxBxC9dxioBBYL56WOXWm8KRtwMjYYegc/WnitZce1EXEwitxWgVwGeeT16IrQXKYxq5PjqstK1ilxeg8sy1dghAlGy4DFakDXiKMavNPbzBhHEThMjpEBDINDOGokKDEsJX0A4DXHM1scLFc9aAUcEqNvWgWXCHYNuDWJYGHUy3OpjXFx8CX7T5MOKrZnNMcEkJH26UHmGXmCOzFo7pZ5lhYAyYmmlvOFJZgmxXYryESAM0Uj66y68R3GNpevJEZjNedgFPjRrp6Ihal1z+NeMw/bjH/12rYAIVNkqzHNUgpd/rIc5IWNz2U/A8izjt4CsWpTWgXcEwCrfr92DfMFktr56cv1s1t1OzR5Jk8ZWBGZkTlZCPFpDCAHOkBlu8uhFesFW55m7aL4vbTQydlbli6HHIXhKwLlAvgElOYpzkQBu9L2EJ80areqGAjRaKevRQCHFCHN7iUgC0/DM/0XKueZygnLLUQC7uQn5xsiyZkI14ac3AmKiBHENxJxBrgYkOYggy0s5VTykBff0+DQtR4EoKcGwVhET7krVgoqiumlCcYzmT9UtFySX5XGJIRgAI6ly5cZUzeusq/MNI7QEvoewr4KwHMHwHOdGv5DAHcBHEPqFMVPkKk0snaNMxa/E18zeSNptdA0gHmhvuZeP3JOoLHL15zln9JXdglkfe6NbF/ma7CcWI85gAaQHcCkYmQek1Sjjk1yVzYiCfSBkpysE+lUPpdAmTpBP2F4hKTvWsmppGBZ7U7qAAuomY4AqV5USE2fGr02L9pMMUi377YN6EeH1gWEKOtzZmlaFZTF1ZiQf69fNykxYoGuEe2tyJSlUo59C68ntr/o8enrHt2zDRIYhUjgJpYEerKjATBKhjCQ+xMDYEGIDWcR9wxOJaH0xBo2KDpTya8SABWDDwvOpeyxAXgVsFh4XF1usN/2mXgwBGEqHw8tmAknfYMYjZRWctJRJHTLIIkLbUADAGQZWAaEwcCeaEl7z2iO9HuIQLOWbFXqWJmadoVOSgdDqzIqLUQonpBBE7+0CCuWa+ki4kLmCABwF2AWQcgEowFvJMkuc4hzUgXVXEvSOl/03Ce2agMn3RzK1VaxlBcCAmRZm3V+T5X1pc/V1VlJGqbWBk5Alkq0ZMIKi686VZ1Vr+VegH/ameCYV7zU4NKOw9RN5uptTx2DZ9voEiXWz30qPb4Pn3VPYNYP/MAP4G1vexsee+wxHB4e4r3vfS/e//73433vex+ICN/zPd+DH/mRH8GrX/1qvPrVr8aP/MiPYLVa4Vu/9VsBAFevXsV3fud34p3vfCdu3ryJGzdu4Pu+7/vw+te/Pned+p9pGBLUOI9qAtfUXjKQkgBlZ+U69fpzNUCzBdCS7TRoqQGtFCCrY4iO0F816K8ZsAHaQ0Z7FDPgIdZEF26snRe1LtuMiZGlx9YFeOgY8cDDtAGx14V01IzDqEbCGyn3iAZhaPLiPDoRrZVuUnJ5/egwBoO9VjS3TsYGx0Mr2lBBqL2IhDAamC6gXYwIzsL3CuMGygaetIMHQBMxyckgZMppFk40jOgYUcVwYeU1s+exv78RA2+kbKKxQa4rSJmHMdIuffBWgKwTl9Lcsm9fc6QZtBA+trHFNHWNh7l2guM2wA8W44lDc8cKw8yRtrJVR6FBAEgPk2KtWJxaaoeeasXb540IJraSiYmNWJT2rt7CFsWwqA6M8VuigVOsJpJOOK40GDid7Z6NUwurCApSy8JJG74GtCojTXoO2UBeRCQ+n+uFN52O2TM3aVWP7Q6hzrblbj7nAVdpMae/Xw4Zl37m9KCMJOjIzwoKSpQAmDm4tQtcTouPtMmpBUjyEdBnvWyTghC/IISF+CApnajYPZhM9XJsLs05pqwaXaB3EWRZtJ4q5jCCBjVRfE5kEYlPCRJnYiYuG5JSxVSal1qvj0GSLY2Rz7NG/ewhydMmig0OJp94up4SeG3XrUzXwRYlaaRfDxvOWUjxqwxqItpuVGKDrAgsYk4AcQRISa8hEkKw4NFMbYXei3yymnCqcR9ronTLtSzA3WhgVG+EfQku0jVOvjiqpgIjl0sm/RVpKy8scbbpOmUHCdDKfjntmxkUk6DMCAGvnB7IQ0AsEdwqnadSFHKOr5kMNa4cZR8xBQzpROQCJ1O/3u09+I8XbL7nvmY2r3b5Gk6fnZW9n3WCyVRcjs/i0URl72Jib/NIicLE2oLa1LTttuRimoOpTBGYTHgCV+AXI+nuJtYngKpbLZe1cvqIltExVd0OdVenngcFs0rcUrZLQI4ZpMRMWDUE9AYcCT4SfO9AljEYhrERbctwWh49RoMxGpiGsw0GkBtc+epkFs4LE1iRqSFa3B0W6IOTfQUDtBF+BbhNuX5Aqz9aAg9yjm4dBcSzouMksiOi32t7uR+hlWs0XplYqnE71/5NxiyVNcZGAKzopOLEXBnRdcJS2/hSYm+IcdSLKP66b2GtVNqMvUMcLUwTFIEg9CeCnJFhiWlMRBicJPIXEbGhKXOZUboU6rxMbGATpNzUbURCRa6nYjmrRqMZCXFDGK4AsbWiD9xGUBvRLkcwi5RLaAyitYhkYQagOTQ5xmmO5RzcWqRbyAMfufY43vDcx4o28LXHp+WBgDYgqV5om8ykYmNOA1nAFGhi0QxmCiBjdK2ln7c0fUa3AFRp/7QjoX+/Qu+7Rp2Y3OZfcvIuv1B9jmavzz6/rSFO/rsCye6lrPKewKxPf/rTeMc73oGnnnoKV69exRd90Rfhfe97H776q78aAPA3/sbfwHq9xl/9q38Vt27dwhvf+Eb8p//0n3BwcJD38eM//uNwzuHtb3871us13vKWt+CnfuqnYO0F+0v+KRqRCXZthFKYaLW6mp5Qe+coKmc4Sv7M0UTaceU08kan/86xQJVZGfcMNjcN/AoIHbB5gLB8xmL1TFBDomCVThRxAGpcwFVLdLmecQ/wNz0WV3owA703Rf9DS+JAAAJJTTQAS8LSCkpdDdGo5oec+qZvQCTZkaUbwUxVJkS7iuh9YG9gFgzTekn8jqaAWVFL8bgylEplZuKi4QQgtRJOLDOh3kKy44A8OEuPxXJA60pNdGDAWREaTkCW0WyEDwbj4IDRKMCHwiDI0QF0LsjfZCIsaQdIByyWA+yK4a8YHC2WiE0DdywaWHag7AhyuWeltZIvTbNQ6fqItR5fsxvDFWC8GuGXjOZImF/S5UPPqZ5bVUy8dRA0cJGyRaHNpveo2pcuiBLdtlogkQ9C1x09yNoCliXgLAnJ18+EGv97Mdy7ygwnAcJ5+6LKaF/QXxStr7Ioy6/PdpKfwxccDX32jUs/c3owE8hTXghOFxoKOoHLQgn1+6d2Vv2x5WHf5msyZqKZRgJCS/BLyotrv5L26M0xF2FfPX4OOtTXgFW/LO2XxI7FVYTrgrCQos12LT2HaUGd7Gl6LFn9VwaCdKfeyz6siWgQwag+S9V1JfCIWIB6QPxZ1MVw8pVcziWDAzSzSSmg0GAja5XUia8mwjUCsNVfizEszZGjmeicRy0vFIZF9R1lVEPPq45dSXTCDDFgIlzjYRogdoTBNWArnW9Zy9kzcFdwHiBdYzpclTDKtiuztLT0sJMAygzik20t6jzxNRHSpiq9aCA1NhGAK4YxGdL6HlN9ckb2xVGCV9YaFqRAhMEhguoSxfRcEIGySg4m+78nX5Nu2/x5m/x9AV+zBbw6c9RlxOkkdny+Tnpejs/O8fCjt8DNASh1kE7xCEWwtTKnZ/FFil22zj1SPa1sWLZMLo1zkm0WZIEweajSNvknnUoSnkrcT9wUF9DLCEFAvUl5bhIrSfdLTIDGCBxMriDhRhIG1AXRsCXGshVqV6oQGYPFcd9i1Q0TlhYBE83cMVisGmFLP3eyh8OTDjFIp0PXBfQPBMRDIz6yjVIS10izqugILiVqdX0aOiMdDaMkhtCIeDkbjW9I7G1s5buKSdxddReNhdxbx/LeQrV/mwi3P2KxHEDEiNHgpG9xpDFNim2kugYYeies5bUTW28JZFnIDJYBL/FeBIAuFOa2ZYSFrAVgpMMiBVZ8lVQCR7SOYwBMoGzDUsmaGatu6yw+xQxaNjgShqsA2CCQHDsGI5pcJ5r5YZmHphc5FYrii9yJ7mPQihRmvOfxrwQximbWK7/y1NzODC1AGVmUSwsBTHxEXvvvSvLHCFiLU6WF2wDnoG8n8E8D6N36VDj1eL5goGvO1qzfqliXdangrv2czsboudY+C9VrDCH0RNzTuCcw6yd/8ifPfJ+I8IM/+IP4wR/8wZ3bLBYLvOtd78K73vWuezn0n9pBvprIsQJQampv5QRKQJsi3B0TTUs7irHOoQRSoQeDkLQeGOJAhiviJJojWSf2NxmHjwOxsVh9OsKO0+PWFFYxqlKuFxvC5gawedmI5fU1rI0YBslqcBsBqyKLJIYSTjIdRoGsRePRqJBtCh6iAkLBWxgThdnkpMSQ1HN1zmN/b4MjLOCNleCCuHQC9FpumO6ROkgx9KLJFK1QVlP5HBsGWcod+wDJWOTuj04Q/r2DDZbtCKuMLEDKnMcgrDNpPcuZoTUMDvHYwWwMuGVw6gKW0TXOuivp67aWsdcN2GsGHI0tjjctVsseD3Q9nnceh4sl+sMWtDGwJwa2l0VGtJypsuJcOAd5ycGBRXcLEXAnFXXZE7gxiB0w7imDokdeLVCdiagWEacWN7odW+kwBmeKYdPFDqvRJdW4otGXOnNlZQEARg1m604gRAXMchbUNuBUyvgCMIgLBQX3QtfdFShcABCT89li1IGtJYqfy+PSz2wfFMskOQVopRdRb8O5nENe2LHjWaySd8soxqCeoCQfCJ0ERXaQx9qvgP6aLMybY560J8/H4XoXmgCygF8C/kqEW46SAAkJlVebypQTKFDdrMRWc+p3uEoBJnsdtVQvRIOgzK10Q6yJaNsRAxrEQCimTHT9Qs164nRDqkUWsyzYrV5XLMDbVESey001ck1t5+FsqM5b4r8Y1Rik7ySK8H3wBjwa0YKpg8AawJglwIwBGhfQapOV0RvYxmNlPdYmoncNQm8RvJQGJr3EzHJIvoZmx9Prz8K6Y/ldgP9SLmLWqFjh5R7qJwFuAeyBcAWMpe58DfAtAGswDeLXTWU/8/LJgCRKBLhR4d0e4B5AD6E+RBTgtJ7DxW+xMSBrlOV+TinjjnExfOgMpOnFGrt2X31/l77ms3c88+wV3PShJNfrUbM/0rpnXl6UbFzyL+kz6WeKS7axQ1A6sVOYPxHapS8DZ5wFn2HL/lLiMK1JKdn9VOkCASKke2ICcVQ4PSUQrVRdsGUFtCh34iOiQrJpZZ+55DwatM7nbrgAskausLMsusbDK7gVosQFibm1GZ0wmQaL0Kle7n7A0GjSH1D/RWXNnoaBVmUUN56S09DKUNGWQr5PCeTiJP9iFOSC6CTGTmI1s+fRdR7GCNvKewOriRWv3RmNjWjcmGM03zvw2grTD9BqE/3O9FqoN+BOnR4x2sUIszdgbRbo0cBvADuIhIoZBJwzSSWBBZCMTfE10WmjLiqgpQBbMhlplO+LAoGuEGJLiBsLf+JAnmA3Bu5EAKy038RmswPnkvokrQJWbeDPewu2jTnoz04dbAKykr9N/jA/OxonGX0OasAqAWA1I6sGbYwmC9NaKZUqalJGPls5+l0A872MVKlVj/Q8bsGi6jFhZtWnouuIXDrIKEuVXQBYvc7Avfup+9bM+lwfYT8q4qEvVKWGGbjSjK+sv047mFMIaHY0FaA1+Qzyly4ZCQG0ohF6JpMAGe0gxzt5NOLwlQy/NGjvCM3SeKF45kUrysT0S8LJowz/aI/FSphT/aYRw+2iMJg3FpxmeiTAiwDsajEIIESMMRqlsgpbq9fOF9YFEXc3jI13IrxoIloT0FmPZTOidQF3jpcwRrokhmhgbcQIBeCSQa0NSWJrQXS80o1K9eQpwCBPBf3W4GKxkvNOXUvmcVgIpvpqNVAaLKjXsg/xdcjit6zlCwqYyU9G23jsNQM653E0Cp03OcO9doQ5AE5aj75v4NcOfiiLaxqkW0juKlW16zWjaG5RqLqkKEhp14zuOUJ/kzEesBp8zkFGEZiXe3aKmXUK7GLkTEpiZiUgKjAAAxojMIzyz3thJ0aGZNCrRRUAkMnPBREBKYPotD/8H/eYGeU6ENhe/sv5PlyozHBb5HMZYFyOc0ZsZ8BrXWqYfslgzfwhBrbG0hWzY5uk0MTXVIfMi20U9g0IGA8Y/XUpe7YbZc5G5K55ebe6z9gA4wEQDzxcIwtjH8SmGsOISNGNfp4BRNFAIcdobNHD8lFstAHgE/vKRCmFIBG3FTPFyo71aAzBGkavHaqsTV0AS1BXA3D1fRcAK30ncqNIpaPqzGWhMAGpHbp0z5Jkz5wpl5lluluOKvCekhnVe+Vg5URlisi1tCaIf4kWZGIuyWxsAHXAaKN0Jq6TRICwn5PAsqmCTBWET2LuCQCjNFdGwK0FnAyt+CYrFYNIDAo5SQthX10D+BFgtQfcgFQXPgvA7wH8FAh3AGIwQmWHSZC6uACbA1C8CYQ97Tx1DMZzAD8HqR0tLK382bRQlwlSMSP+BIzwLl8DTIKfUyOBUhctM7wcnxODDIp90qZGAErQbMsaZsJcVxtGKPZoFzNj3nU9H7sCtCa23hISu0S2A3xrMK5k527DMKlrHEFjihrUolJBr+tVE5C1o7Ld0+sJHRD3xV5wFHCLvDKbiWFgwI00DwFkbb8OLZwL4gOI4YNFSIl4Yi0dlPglJU8iA350WSA+6UgBQDxqgDbCdgHcBMSNNGFCFBJAlklR8D+V5kVH1bNdtoEBgpOEy8QmV2WGqZSdoiTreS+gWQ1wLuZqkuzuWRInMRKsC1Kip1IqfnDAkSt6mIaFiaVawBQI7KI0pMk6yvKjazzsjRMcuwXCsYMZDOya4I4JOCStoJGkd3YF6nfYAJEkhqW0uNAvPnVllCZfkozvnlM9tFZPQbUs3YZg7sprYaHvEUryxQv767wMRJ5z1oCdlCXm+QzsWDDtHhS1SgvI8eekqZUelBGLJjUghJVYYox7GbtYWROQal5WychszIseL2tezfaTTYEpx6vndlq+7NR7vYdxCWbd58iaWZNVvgbuVIFaLA8oayfCbbTwaba9LGQSUANgkhUpzkMALQDZSLKVRWV3i6XD3ct7rK8DJycO5iQh2Ca3NwXECfh9xnDTo7nawwHo1w04yOLeamkEmYixt7L41awzDQbhqEHfCRAVidGrgLshYFT2FTPBOVlgj95i1GxHYyKuL06wtCPWoYGBBiDBavJIM/GRJhkNEVAsQV2uzc+ZGka0RvSk2mT41bAbBmk79L3FgNaKM3MmwkGyMWOw2MQmZ8y9NyAAvregIwfTm2zs88MXoeUOkIxGKnkwwLIdcbVbCxsNAoxZki4q1sTCUOtGBBfE2VihAY8b7ZiYjH8kddLC4DJjCR6T8U4dZJojYdwN1xjjgcyN5DSjoyy8meYhABgO+Esff/+EghsTRSp5o2yUOKW+IAy5CNoM4FHALIQADlVQASgzi+TGWKM/qew/bbelPG/y3g7G066xnTp7sc9v6xBazgVIDI44M+zb9n+q5vxyXI6zRkFT0gvTPxmYamXh3GdBtqt8TbW/mtQlxoym8g4KYrBR0GKjZWZXPcYFMI4GNCrrx1PuYATI4ju2QFhGmEUQAEq7wUKZuMaKD43BlECHICXtg0VwEdaIZfDRJKJNbrDFEEDMUJQyPYiuhiHGwo1oTMAYpWMgtYzIZooPalBV34KaxTphxulzL5qCyOK8RSSVAcswLqJ1Qc+bc7duKccn+MxkJsQkUxIMMBgt6eciiJ/OKYExKngr3x3B2YhOy/jTCZP6VZONFeBcRLSigWhSAt6bIoSvUwRRRHULQFnWLDl5QlrGYSSQSIBWwlmTFAOTAWEpQNbL9sBfDtDjI3hogN8E8MEbwJ0AYAPwLLXEBkAL0BWAXwl62IAeGoQl8HQH/NbDwBqQ9ogjBNBSu53ArAlTa+ZrdgUAky2rF88a9+FrzmT85jl+OojYGl69gODgcvzPN6QUeTYDUqJXpU8AiJG02J5Ir35P72eCbsp6ABMga2sHxFlsA8jc9yuD44cMxgNhIhkPrJ5idIeh2FhTbG9ay6dmIbkEryURkU4SIiTxS7jqYfc8oiewd9lGcapuUKZP3FjQihFjAbU2cDkOSElrZqlAGXqHcbTYW/ZYtcJiGrwBBdYOiZgaiZGADuLHFl71hgFskI2AGeX6QlN1lQVymVVsGNxQBq9ii5zEFlF3BXq0WyGxlhYaRrMUfazEHvPBaIWLR987xFDoMVJ6SAiBwL34RE56wlVcA8ulZD7FCy4ljMQPG2Is9gaEhRfdrcMG0TkAEqO4tQJWWsmSZFRqNnPSBg7q30KXShTLNVMAmkOx66GV/EdYMvyCsDiR+ETuVZE/oZCWGPNnZDpX8zNBKJUoiY01t8t5DlbgboxSFjgrMaQe8nw2mDC7MpvLStk7tDNxBqGr40581AsoIZwzzuYEmxqD2AZob03oA9t9TJUsrckAdSfEU+Pi+OBkXIJZ9zncXVvKqIhKqWEuq4JaGCoPf1UyKL8UDa28OEwAQdqmYCKnBycjz9kwZDqqB9o7hOHIwVwZwV1AtIxhVcoE87Cy4AYTxqO2LNJdEas1hhF8U94jgFrtAOUJm3Ur3f5yi/HaYEo2wI8WwUq2oNFsSGQR5m2Nx8Y3uDt0OFFBQgakq8bgcuYji5/Pyg9StoY0CxVaQfITUJfpj6n0wzD2Vj1a56vzlPM3YAQSva+YSg2jZs17C3co3lXosUo8apLnRKbksqQa0Kx6XFussbAjhugmLeMDG7Qm4IgJfd+ky5HSTSPWTFh6yNaGBqNdCAEaJUBMOKotl4PUFr05AsISej8IbsPS9bABXMrC6KKADOEv/e778R0f17a1z0vb2p995VvynNOT1zihRLkUGTR4mHHAt9z9ZTzRP4XfsA/iXzWvRyRTNLHOGqeQ+qmlPJP+OjeqZ3UzvGCJ4NnbnN5omnXYvq9dr1+Oy7FtmN5IPV8NIhPU96ByHGWhkX3NeQBXzQLZGrWn7VAALUZmwwIaZPRAGAyoi9JW2wChySdaXQwy+hEHW94yUlJmXdQFti2+xgCwEYgCbvnRVsAMcmlIvoYojUdSGbvVrobMsq1VNlcfbPZTgklJhroGCfN9q571tBjLpLEsYCtBR/lMWkkLMzcxceU05fzldJPwe0J+VFDAi/6HZKhTd1ueUvUVbEr32TYBCzfCUUSAdGqUzSgnUABJzqTblUs3QQXIzMFv0alEvRCdrWmJAQSoJmMJuoyX7y9a/Z0aAPtAtw+8Hnj5E7+LP4PfxLDX4sNv+hLcfv4B4FdeAuAzkOivOhcCwB2AG6CHGuD/NOLBV/4BVuEu/uDkpfCLDvilBwHchUYP2L14Sjt8gWP+PJ25qxfB12z72DZfeOlTPicHb2yJPaBrZOK0+IZ0lChVI1z7CZ6updOYBL+Z1kPKtqgDeEwqSVLJYd0Zmy1hfd2oDpLYhuEqYzgArnzcYnE7ZhZvcmn5HBK7U6tJki0iyNp3uAoMNwOaKz2sZQzRiWxKwxXLVMvvVCvXmAgpBojCVtW1/3p0wrTSWxeCxC1gYGw9DA3CzoqUweS29fDeIgwJVZJYwbqoTcAol4on4KpOCgCA1WtnC0QlPaRrp8BiydX2iu6vNLFiA5F/iQS2DHdlgGukjD2VUgqhVSRU0tdsnaA7rIl6v2lAG0lCsWPQYCZljJm9mwEPyDFJOrwvtAN8PzgYw7i6fwy/b3Bnb4VN18KdGPg9aRpiAknnyeRfgaIJTMj6VwnESs2+0v2yvSTQAKAhYK1zICw0vlkz3ImsP9iWtVA671OgzsyW5/ctSQxRMbLKvKwmaEgxv+oEx6hBYXke4QOoH0S/0VYSK7pfWAO0TSEGpOPtMugaF11IG6u6vom/mJcaV+ezPfFfrSXnr23Zx/yz2/wVMSPeJ8v4Esy631E/EHV9udajT4Tgo5Yk6phMwHnpVUJFJ/W0mDqMeTCsoAXVgq4Q/aTmjsVIKIwhFSynaIpzyABFAtDEEXAUQfaeWhgT4XtX6t2JYRwjLj14sIijBAJOWUZJbDAj/750K3SulNgFJjx1fAUA8NydPTGqLqJpvbCiIIBWZg14wHjO2eBUnzvRf1LHZ1I7YEX+o2Nwx9IWvQloXMii7IZKm/dy/tXTxcjlhaLHBWnERChaJqagzxgN2EWgZawWPa51azQUEalcu8Rpeiz9zoI3sC7mskYiBgcD6i1I9U1SfEihiI0zAUaDDb/UmnMFN90Jo7tF2NxkjPvSMTG6RP2FsrOK3sHrbz85bVt7+8mZsS8BUim9YVAvAu/fcvgr+LaTD8EA+NLwKYAZ72m/WIz8NkBrB+i0zVBuzRZcMEtxJhC27fg6by66f95Fz50sCjFdEFyOy3Evg1FQlJz5kom9TQh+6mvSi/W+cHp+1sDV3NdUi/Ea9DGjgG4xRSL1eTJNFq6okV5SZqhiV54EqIqpMyzJAchANBqDdKiKTHnBDggQla4/psRDBIxDZr0yCEdDBwA42bSI3kpnJheLiHD1AM+vM9HkTz23KcCi8o+NJAygTDOjgJowjQGTabSJwT1b/GlCJO2XlbxWt8XOtziSls0DjfNYWA9LDEYB/PJXSJwx0Kgt1nPpB6lIdDB5HZGuL4N39T0g5G7BKUgzo3Tx8ksJukwlAs/RgtkA1AD7AB4GHseTeA1+CydY4VncxO0HHpCy/QS21aAsEwAH4qvAyyJe8ppP4c39/xfX4rP4zfYL8Euv+78Avw3g2RWA29sXxzts+cVs/BzBenE2lePrLxfNuJ/la+pn9gUGCJfjs2DkYJqmLHcuzTx2BrSzkZpH1fuDguvbmCNJ7B0AODLCwsKvZP3q1iydBx1hvBpx59WE+KTB8vlYGErZ36gIdrJBBLCX9WuwwPohwuaREe21XnVt9YNOS7sCpFROuxwmvVxp6CQJY2clZhlVCyuxs2IkcDTSjKoJ8N5iCBajMp2clos3NmBv1eMEEEHymAAkiSHIsgqi0ylbaiJndhIFSHmkERYsW8UDE9vXIWtBpTK65GO5jbB7IvSewDkfbI7HmAm9gnSsPjlGAz8qG3djRac3knQTDkqzqGw9NxFZhAxyL20TRAu4HbDxDkCXQ9r9bkDYN1jbiPGkAW0szEYF2ntSfTPR10r3IDacAiMZESoWD9Ag98z2GgOqb1x8RpJJYckY9wQsbbzENMGKbc8d2/Mcrub2ZN7OHiGjgJahUp1BBGYpARTyiD4LSSc4Rk0+lp2lMkMiEtJwKpmXF+Vejx5onHRNdFWcdEbZ+YX81r34gDM0syb3KSW25vs+51iJSSl/pP3W/u/0d3DeuASz7nNMatKB05MtZUNSiZwRo77VcbD8V3c6PFM3YfI5wIwRbsPFwFXvG9X0YqsgFov+kjsuNdx1BjrpIUUni3G2FqFzCMuQuzJRLEwl2wiNFSqM3ilCL6LpMf9OhmEhZSSNDXA2YqGsqGeP9nD07B7sbQd0EXzFw5tSh56vNZdNlJcpALaXrE5ojYBX1QMjLV41m22EhgvL6BYjFs7LuVDMmf7UiTFnMfRJYyZgNHDHUj4jmQJ1jCNAvZHjuOT0IcBdF3BjtcaB69EYoc111mN/0aMxkhUSii6yIzU2wlrRDCNijK2DX9ts1LlhYX45Bvsi4AuSAIKNOAhzVHUHOWIMBwS/zxgHFdFsoJ0upXY/zZWPXH8cb3h+1rY23W8u9d5UfxExgsYAGj2e6J+agGFPhGcm2yF1ltuRFTjz7/sYFwkYznUOO94+FVikBcuFz+5yXI7to8ytORqVXtaHv2ZnnRVMz9+7B19DQYRZ2c3PAaUMnMp5UgDMUIFZp4BdmgiGit9hlAYaKQBgDQ7koQrewLUxb0MpCtIESqomsySJisQYPhlbDCfSaAOWwV1ErMrqyrVu8TUs1y8Z9KQZWG1qkPU7MjPLAM4FOCOlkUYNQ/0VlQSVvMIMIIifzppV6TyC+HC2mpVPH2Nh8y6bEZ3xGcSyJmZxY6ONWQQDVeF7FdV3RtLe0RpET/m+Z5F7LZ2cCMQrwGaMBBqp0yMGgLS8P6hviom0gAhjI2gAcATcpmt4lm9igyXu4ipwglJmVM0XypRAAtiCHGMVj3DAhzjgu9jDEbCKQKvIKICM2gHb5/cfJdBDp3550ca9JGQux+fGaK9tAGclIAaKTwCQpU9SYyr9meKNyRqFT/0iow5yE+A0L9nSqohJkqSyjbIDed1tGMtnIB1Wrwfc/XyL/rpFcyTJ1yTWnXQXEwuKSZhLw1XC8Usj+KENmkY62419Qs0B0wYhrPYGaMQOkCeRBfGEsLRoW1n/W21KtRklLDbahTx48SVu4eGagEU7oletrPS5VSN6u2MQ2ZTbh0sQAU0T9LYRYpBEfi4nJPEjdaJE1vCsCQyJE1NJZSD5XFS/QlruDaOxgJEKmuVygFOtYR9sTprnZI4CWPVXG4IBBwOzSeXs+j0aVuICS7xHkOZfRhIe5IQN1nUercZQaYgusviarhHfs3ERfmnhe4vQ2yw1DQBBGWGAXA+NlCtobE+IUL3fQa49EzD0froNo3tekvV+n2F7QnNc3s+sPpJ1Bs3n9nljDmQRAGNEroYYhhl04kGbQXSCk6xKDIWdyBEgMy0NNyUOovR8Ge2wO2HMv0CDP1+iXWQ3F1kLVm+fIhPUa8tqbUSVBAvxDiBMP5/WFRcdl2DWfQ552GjyxUv2olrIpzpRliyBMK62AFoZqZzDwnqMlAGJlNdpdcaSItAeBvilw7hPItRupINdWEDE+oKAV+7YoLkjD7tQPYtRzaeuEzJ0BL8Cxj3CGIC41BbYXup7Y29ze1b2Ul/soxGAqMp4OCMig0n/qnUBCyeC6KlDCK1VkLUVMCcvprl0PJyIJ+p6W5yeOquqpldALKgxFiPJrXzQdkKLrbPWgHQpiSB06gQkc2PgI8DewB4ZNIdaduEBM8qxQovsLHNHp0UELMO6gD03oDEBBozOeqzcgI1v0GhXKwDKRpCLMsbD2ohVN2AMIuALlqCQCUDH4JYRPMGe6FdGQsUNSw0IjwnNYZqTQut1J8BmX4SX3bE40NASyEu9ud0QEBnv+byvAgC8/ta0be2ETVh1rSFmwEfQMAIh4je6R/Cl4yczGPYb9sHynORsxPxn9SydYUxTvH6hjMQFs47T/V+c6QUUozsBrtSIn2mQLwOSy3HRkcpFqkmTs1l58SFGO+k01noFW8d8PVdrHGRwbL4xKc1fO9+minRSEN2hlMVr0sT2UnpWGnbMjl3wCSmDbnW/TdqxHJeD+j4q+0/sLAFqZMcxJSL0YFaBmsYGYQYDwFi6X4ngO01uQX2emQFMmIkVl40zO7e6HinbB8gmPzi/3bKkttn+R5CxiAGS/Bpk8Z4W5JzKZVLJg2b/c1crAsgKu1jKF0WPsTEB3lhYitVUEJ0sMIGc6HY1LkgXRzVkWZxXS0ZjBGyKQPJxdVE6AqYv9yQxtLwCWqnTbrQRJjKiW4OOI+ijBr/+wBvw9EMPY+QGt37nJcDHAOA2GBsQUjfcct7EAxAPgaeW+P07L8d/X70WB3QLv8ufBzxpgNuAlBjm/uan/crkyzjfEF8o9DnrWXuRxsTXXPqPy1ENKVHO9FJk6ZO0xq1iC4oRbO3EhqV1z3YtUt0mCcZvi1Pi3MABucOhvmQ8I7QEdoBdi11obxOGmwAeW+P4pgMG7ea9FvaO3SA3nWDtfjtcYfgHBjR7o2gzHbelDM4yTBekjI6A2Et5HyzAbQT1Atr4owa+FQbrCGDwDswQ3V9IeWHqmmsTcytY1bKNaA2jtQE3F8cAgE1ocLdfAAfA6EVEPlWmAChl+frsUoR260O1Xkz3WW8gZL0fG0JQ/xqTzU3DMtBENMsxkwRSYy3AZBmVk75Vppn4dT8IKzlunHRQ38ixo9E4w+kpQPV5tds9K2OavZEKGhtwpdtkLWCZDpQTSEOQrvUhEMhENMuI2Eogl7oXR6c6zElKphG/bzZGdBjXIueSJWYsgB4lUQ/AHTNaR+ivizZwezfdU5FTKaQObB/b7Pe8Ql1QJtTSKmktQKnhVYhFK1hLD/MwBhO9YIol/tl6TmnxsQNgStVau+KV2TVlU3AB33HeNpltnUC+nRtiMrfP23eOoy7BrD/BsaXUUL6ZygGkL3QuBp/3gVOTb84mmYuwJVDL9hHL5wKis+hvEPwCObCwtx1MANwxYfEsoz2M2UFMHR8mDw0bcT79VQJFg1HZPACEvttXWVwAWEiZ3uCddMzgkLMDVmu2pTuIgTMGY7A4Hlv0mwbkSbp2NRFkxdBF1fxImfMEItlBGUUkdGOrmRsmQrQEv6CsmQVWyq5Th9/GLESfQbKKlWUpwlHQVrVFLwuDQXNkxEgCRYwwlgAsthp/NYzYEuyex2oxYOFGBCY4rblYuRGHNmSAKxTLAB4NYiS0TjIeBBHqHU0xyGEFBVOFLp7APb8Sqi2TBJCSAZJrJ0YW8A0tw24KtZcd4BeEppXg01uLn371W7Vmn/NclFMsTouUSks+Sk24ZgX/9f6XASHiieFT+A0jmllnDaqN8TlA0twQngk8bemyUQMA24zqKeM8WWyceWrnDyo/L4ORy/GCB6N4/QRA5de3bXuP+0++BmU9VXYmtrdZRwzGKHieNobobqRysxMBvmptkF3nxArU0EJKzwNXC88IKRWJ1bVqoBSilE5YxAqryIi3MP5JbPkQLby3uiBmbWLCE6YSK3iWQKTEDgBQWsurn5ROS1RK2QFJ9qRuYpZzR8WcDa5ZWSQi9VL6oRlzJiCQtDbvy71JH8oi8zbZMvmd2ojGeTgTpUpD50djIgZlp0kZZvE1WdvFRFiSEnBjWYgbiWXHyCy53GmKZB3AWmafu+NyOafcdt2Kz6Y0ocyI6E5g7B+CfusxYASeeclLpZvhHzDw9Ahpa9iDqSiGEEdIfdEaiJ8B/fbLgKXDh77wTeAuAL8XgA8DODkB6FgnTTg10c766/xx1ip89nee4yUovZddvmi+5oxjXI7PnjHe7QB/hFxHdqpShEEUwUb1egIXLaC0xks2ys4+W+8v/V6BWmJqNI6oy4JR4pVkD4xH7g5HzGiOgOZ5C7+wCswwwn5EXBBGZR8V28eFsesNxjsdMtuXADiGXQQ0rUeqMEyXxCT2nhcRdGRBo0G/Vo1adU4hGByPFlYrUKyLGEeLMFLugGitxA+RgSHYLHzee4fDvsVa9X7lVgm7K6QGJ3o+iW0b3VQeJTak5XNyQeOS4Jeyrd+TuCKBOeIvOVd/rBZD3k9QgkDrvJhcko6+SQ8ydcnlyEAgNHcF8Isda7MWYUOxS7Zd1xoqn5J8gtvzuL4SLWAfbS6hZ5ZzsMRYOI+TvhWJmso/20a0kyOTanKp7wRETmXUhiNe5k5o5PgGKMw0EkAwkvhK0QaWqqKwINi1+lrVcKSo8aLeJxMDvu3J9+ek/btf+ZWIZoZ2aQyUMKfivfVaosY/owJZ4wAePU6Ni+gFX2S8kPVc/fFTgB3fc8I/j20fm8VOk7tF0/d5tm29j3vxfy/Snf3cHRkI2jZqYCtOf6ZFPTEjiS9OP1vtf74vsyXYTucTGe44YP8pj71PRbR3JPuxeJ6w94eEvd8nHPweY+/TEe1RhOtjrjtOlNckMEvaEtSMjOYkYvF8xOI5oDmUjIkZhZVEo5FMx6ii8oEQgsHQO3gvdMmQyz5KG/UYDQITjsYWd9YLjHc6YT8vIkxbxAsBCS4y6JTQ+QCYkWF7eVpCZyR70QmAJXpZDDMoqKP3lVRbpG19riM3xBijzawsABiiYL2NDUI59kYy5ZtyXDPI/smLRkhzBLnnG6HGujWBn+lw+/l93NqsENnIPxAaE3DQbERw0jeITBi9VfaBnN+VRY+lG7PYJKBlGy2yMCNFypTM2ADjlQi/HwQUNNNALAkoml6cREW2yMCfX2qZpqUJ2DNhU8wAT4kZYslAGEJ0Df7l/pfhB/a+Du9pv3jiIHJmYtu435LC2ghuOcbpGvDyuVPBdU2L33qsGvSt9r3LwM+N/A7TcTkux9axy9eUDZAyh5j5puxrLnoMBWwY2D6fWexfexTRHLJ0xvXSKbW9K3awuw20Rww7cNG3UDsysSfptSi2NYm32kG7ISYbpozgJHbOuigP3iAG0kum/KjJGjR1pQKGaKVEZKOC705atFMGHPQ8ks2r2MrJnwBiG9lIV6ZaCL5uviIbip+2NlQMhdQ5sRQ6pESGpahgmoBZSf8y/9T7YLz4NKuZaROUIXxssTnpsPFNThoxJDnTWq8BjQWjKjVR1kGnIJiAXaTXiVyaL3OolFWyAWLHiG3M5SATbbF0Xyr/k0a0EWx6RPcs2Hwc/InbwC+PwId64OnPgPBxgG9DOhLGqU/gCPAG4OcA/xTwqwD+vQH+HwT8RwKePAHoUwCOIejY9rWSXNCL4Guq37e9P/mTd793T8DVeQv9rc/rPez/cuC//Jf/gm/4hm/Ao48+CiLCv//3/37y/nd8x3eAiCb/vvzLv3yyTd/3+Gt/7a/hgQcewN7eHr7xG78Rn/zkJyfb3Lp1C+94xztw9epVXL16Fe94xztw+/btF+ciEqg1/5cZsrzdpyTAPtnoepPJ7+WPi8xfKU/Xn6rHxwpCuGMC3WqkS/iR6NKmzuA0CkuVRoI9MTDHFvbQwt2Vf/bEwAwmawLH0SB4Az86xLUDjVWoSwAtAuIyiiB8b6XMjovGVbo0a1kaT4UE/EisYG3UuAHoR4dnTg7wzMkBfv/567j13AHWhx38aDEO0gExRpPlUhhqr1NJtt7D0EryXe4lobC3OCews911kiCIC84JmUbjGWdFj9eaiNaGzPqtG49wkiEgBrzGbl6Bo1B8/lyjkTzlOC+dz3IxYq8pIFrq7Ji1kpmUxABhEA0mn0PU5loAgMHArEVPyx4LS6xUv9STKM0l0QUOnYBTqfmXO2G0t3WXBxCUwyRmG+n1KBBrCN/25Pvx7R//BXzZrY/h2z/xC/i2T7x/OmlNWjtBQJ/IWslU/RsD7DDiW+98ED986/+Jbz3+EEwM02cOmDK0tj4gGnPVcUuqynqhYxdwNLnGizufBFCf2Yl9ZlIm287eIy77nBMG5uuGs8YlM+t+xw4wi1JWVlPaE+HFLWPr+/V+t9J6USZqvSkYdhOxeobRHhr4pREQI0g5Xqa1AnnFT/W+cDrIpiA1yeGY4ZYiEJgor+ShDyAj7Emb1mS4ZJEcsGzEGgUmjEbakBsCBm/hgxVWVi8gClrp/lGfRGkvjhL0BM6dTyZZa/1pB/kjATjJKLO2SI9VyeOoteVS+wyMmmEQICtme2S8lsyocTWA6G/p10MBoF7AInYA3ybElnBsGqwfaeDZwFRPdsqcA9L5cfBCtaVOBBUPWknL57p3Ddhix0DSioHN7KqwYsRVyCWl+f7ZijkBcaRhocGK6pmETibEuAIoGLi1XDQxxNilkkIFspgAsmp5DMs2qQuHChuSc8AwanzN+fXp5KqALX0vO5tzxr2WA5467v2OSbYSW59HOVb9mTJf87y9HJfjvLEjGM0ZcQB1qeGusfWZmQfZF/Q1IAlOmmOGVTHfpO0kIMYW3zh/acvixniAR7WznMCjsrhhkhLrXEVJSfOJhfkKKGOLcpASomiWeLWxICgra3oS+dK5nA/lrkSzBWEyW4ERIZqYdUkiKDWBMSASQCuwyYkaYkJkA4JoaRFxcc0RuawCQCltTNec7rE2IrEk92kgg1GTMrX+Zn3aIZJk7iMBLqJxAa2V+5bL7hOg5TjrZKb7kRInSasFY714wEQLI7GCazCfDRDhQXED4hFmPATDKePNC1iFhOBVF8CkF84AbQB+BsARcHchxxmPAdwFeI0s4HUKUaLJz4va4PRkvbDxx2jkZ75mcv8ux4XH8fExvviLvxh/+S//ZXzzN3/z1m2+7uu+Dv/iX/yL/HfbtpP3v+d7vgc/93M/h/e+9724efMm3vnOd+Lrv/7r8aEPfQhWNUO/9Vu/FZ/85Cfxvve9DwDwXd/1XXjHO96Bn/u5n7v3k66Na2SlsJz+4nMZuvwl6zxUz0NksJ01X0jbZdmU8jieKn2NKKXQOgeJNQE8okoAMFKCWdbVhNAqQFOt+d0JZXZOrdeXzxdS0cGOwCS6tb5zwtasTqwQmUUnEYPYwOAtmkbW+uPYZL2sfI8IIBclpnGiZetUh9CHFp9+/gr8YQN324EaBu8FBGcy+ylpSyWWSgKxiJGbLhkviRxiLeXT7o+g8rXKvVGbzBp7GMAsvDSy0rLHNGoJFWYhGZQX9OZ5kZ1JyW2u7itFKTeMrXRNNGorzYYQ9hhWWVkrN8BRhDcRm+DQtT6X/nfOYww239P0vaWOxcKgI6xbB07kiCgyKTLlKLOzEwCKiFzeHnVemlEYWmwIzaGIwPs9xrgmkUSwAn4lsXa2AHk+3ejqzpOnnhe9gerbq1gIEJ1gH/G/fPr/g79095el4RX+EGBteFV9/kzplDOrS+Ybz07tgrHM1uR5BWTdc4JkNk4l85nL/s/4fGp+Ux//XoAs4BLMuv/BVCZpHQCoBhUbTEoNay2TCscAMA0yTpcRVhPlvBpZQIymZzRHAe44TFg2yUDW7bXrkTLmdcY5ZV2bNYNvA8OBOJdE3WStpY4LqSXfW/ZgJjgr2YG6fbohMcDMBB+sZgEY3EQFSAQ8Qirx04w8M0CDge2pMK2AUt6RFqcWuYNFvYA2XrT4kn5AjISTvkWz3AhoBa6CDNlGDHKxQcYT3DHDbVi7jFBVWlJ+kiLwTIThCoFbqSvvg9MyRmGCJZYaEWM9Nji5uwA8we5HHHQ99l0PzwbH1MoUsyou3+rMiQIiEiurai9mkIsdI1oWDa1GvquooFbKuKe26czyt18BaXKYEGH7qr1y5VSZoJ05owhCwiiFV3XhWAEqa6UzRwinA+Rd44JAVrq/k3HOx6aUVs7PRHltuo/8DJ/a0eljzZ/nU9unf1vO5QUDcpfjc2gkX4PZRKu6UlVADGG3jzjla87QW6grGHduw5I8MIMGUJgKo+9aJM31GwpjWdmvGwLasnDN+zKkWqqM1nnJdhMrIKT7zGtHeVhjZkIJACP7ydGNLLS5OpmKGZUvdSZYKtcoWeHJdSVdD1fOZfQWpuGsW0V6oSr1CoIK1ud9iJ8znvM1Y8t9KrodkqWGlfvgo0Ek0tJ5k1lqUHbW2Dsp2TCi4dgajwjCGG0+N0n+lIvltBCxEOZvArmMlvgbINrCFK6/swRGst5HsaseQARFL9105ZsSH1Klo6VTcdmHgFkDmAIIJwAcYKJoD/AAsAp0necQ9Pu7yDj1DJz3scn7cmVn+po5UHreyezatgrA80v1tpe+5kLjbW97G972treduU3XdXj44Ye3vnfnzh385E/+JH72Z38Wb33rWwEA7373u/HYY4/hF37hF/C1X/u1+B//43/gfe97Hz74wQ/ijW98IwDgn//zf443velN+K3f+i285jWvubeTnoNXGpdQ5Epnr1rUA/LcVY08puBUNevVv9RVIXVwPKkisTPdLT2cGSLa44h1Z0SOQxOuoRVwXBp/CBMzNQ1xx4TmUGVCNA6pEw0FUFOZDwfEjhAWDB9QKhgCZC2rHcZNG0QeJRDCYBBaA2MEqKrZWelnAmOcC2hswKodsWxGRCbcPVzC3XISY6gNjr4I0SN1mp2NXI4dAOMjyFci9wa51LAuYU/MLDhWEyfdBJPIulPR9RRz9cGiS4kKJRhwIF3XS0zlDkWPCpCKElYyQYxy340nhAVAgRAXnL//pvVYNQNaFa5yQBafZyY0SXc4VdiMBvAEs5QGIM6JoP7gHchGILoCqqnvjFpSmfwwWwBBtRi1Asf2APUpBpPEWnME+ANguCaabIDOsRYAEfzCoB0Zv37tcfwf6kZXVx8/9T1NJsNcJ5iFmfW64z/Y3fBqm80lUyXx58EETX/u2kc62Flj5ivmAPS5Y9t2tD02qveb1kaTuCctfE6d0+k4jrXs9KLjEsy6jxF2zaJtteWAPClmts2WWtWtpSCaOTk1AXdEGafajAYNMqRdHnL9um6QqYNnTHRS9Ls9lIy5XwrSHRsRiScFWpbLAQfdIHpTTAhM6IdWWEfQDHmULAERY9mN2F/1uD24TEElglJQSQCb1JZ11G4WzIhWmAAJLQ8NcrY6qpAwW+RAQxwG6YJewKwxWGy8AxGjNSGXpKT7kjoPJv0VCrJedip+TIFPif6mQCYZ1pMlwV6VzMUQLTbBSdkgE058izFajMFiPTTSitxJl8XGBHTWI4ZmkmGJLYMXAeQieLA5WPALli4gJIsECgTjBeyShYJQmf1S6u6hZSIhUM64xEbL4Q3BjsLAM2PJxCV2gtyUNJ8MOEZladn6VogDdw5kPdhvqSHfOnkFzHpBAM9Zi/xtxzlvd7scxZaP1sH7LgdQv5dBY+x45i/H5dARd03qXfO9fj0nW05vu93XnLHPc06CAEk+UOWvuDqVhI7hnEUVs2brWRbQTQHhYaG+jNE0Hq0LapYk4SHl4rp4ZZNLEUFSAtG2HptgCnOVoJqIelL6XCZmFIAM1CQzHLW7HzEQTQFnsr3Q8pjUipuZENhI4sIwbGJgVb4m2/iEiucgR/fNvL1EOsWmkGw0dUWD0VfYpyRPDIL6PQmwpBNxCoIEyCpfNFsALqJ0tZL7FR1KcKbBWmI/p46HXPlgkAQnkZHXHaynAIqgwLApAQgUweR6zgnCJvMmC/YH8T3w5QujEaXV2nmDZj/vYdyLr7nAhvdaZnih9ytfw+nvS1/zoo33v//9ePDBB3Ht2jV8xVd8BX74h38YDz74IADgQx/6EMZxxNd8zdfk7R999FG87nWvwwc+8AF87dd+LX7pl34JV69ezUAWAHz5l385rl69ig984AM7way+79H3ff777l0VcZ0DJnWifZZsL10Hq/eqNdFc9yoxqaZdyxhb11FVrFMzu4gZ7W2P0DZY3zSVvizg97SDbSCYXpLW7W1Ccxdwa+1sOCv7PnVYB/iO4PcI/TUCwyCsYtZhMh6IhsBwApqpXyCtJknMpaSfW5qKIIu+EyAdaRNAoz6DWMGSLgJWytdTsrnWeUyd+Mygz6WF6D1t0gZi98mIhm20hNBIosJo1TQ73Z0BaBHQqizLGAyIbAaR+mBVs2rExos2WC53DATqLZpDQnso58RGZGkETNASvkZxl6MSf4a9CFp68b8mILJBYwIsBazcgNYGDMHm+9hHkzsJi4+V7o6uG9HYiDHI/WJiWC0vBUlynpeM2Bs0R6XKJjZSWRIWEpOGltAc88TOydpB4iKzpFySHxppdjWuDNxJxHs+/6sAAK+//SQ+eu1xvPvxr5zMKdZYhADtYlw9LxGgEED9iN/oHsWXbH5/2vBqPmrxdwBZN7t6P713mj2/41k7b8yArPpnPoddYBqw3c8xStxbv1wWeqd9T73NlhipThTu2uascQlm3ecQnZAZfHhKdFFLxKCG74Lf0JydNQe05tl1YMsDgC2LJIYsWBmT2tzcpSSfbLV9Yip5zo5EEHugv0pgx/BLhr0y4PpqnUv3+tGhHx28t/CjAh2BskE1jbC4Hto/AhHj9u29/J7cSr0HCmaZgbSsghAbRugUbCGALeWuw4A4hNARknik2lCAWHVWrOKJGviAYMHwbBCiQYMAS1J7bnQxT6HcF9tHGKtZ+VPaUnKf/IJw8mjEjSsn6JyfAFgJ0BqDxXpsMAxWjGMXc/eRoIAgABh1BrwfYJU+HQJr90Spn0cnNYNkkWnZ7ETIMbTSldLvVRl3XczwIGBVaBno5DPEBhSANlQ6KlGLYTU7l0tptNyQrXxOFksMWCk15MTOmkxwNdzbOhmWCTCby+c8O/XbZv7ZC3yOZou3bcysbQ4hH2THvs957C+ZWZfjvCGNG2YT7JRtn0/Ae/A1ZyxgandQr1fm49RraT/zBVFV1nJ6J7IXitrYgpOWY1lcR8cg7UabSvdE3FY6TSUdjsTwBQQAIeex1w4gAJtNu72SPwnQBsqL6mwvE2BDhFiBW5nJPLEj+pPV55VLy7cjQuy7CNerSDzJOdfBmuhZFhbwnMkGlkBuPGAsF6NolKAAWEmvK0SCjxbBa3BlGVYRu8Jcq8CkNoKU2cCRQdGIH3UszABleGS2hMpwJSBLOgnr/YPG2lWGPTMOtATIDiWoFnaWfjEoHaGh96fYVRLwikSgl605DWYl37It4102mvx1T75mvulFHrvJ81Au69xtz9vnedte+poXZbztbW/DX/yLfxGveMUr8IlPfAJ/+2//bfy5P/fn8KEPfQhd1+Hpp59G27a4fv365HMPPfQQnn76aQDA008/ncGvejz44IN5m23jR3/0R/FDP/RDp9+oDXC9/pn7hbkQfEQBwmbzo+5wmP+uPcJZgJZF/myJIYDlsx4UHE4eMhiuACBZ23fP2szMaQ+B5bNRm4dwWZvNnFFKigDQZDPDbUoydwxGJFFSZ9mRxMarThQI4KXEZuPgEIKBdaKTm4CspK07DA5EIvsRmHBns8DhyQLxuIFlZasmHUajTFOW800JeeNVb9jLuSafnphzbAmxkbgidOLzWEGc1FGQIqRz/CLAuvLdti6g04691kRYlVJhrQDRL1CW3IGkCmRDIimiIBE7AXtAWvapx0+2nFjsv3WcASrPEi8FJjiK2G97nIwtnInwUVjBXjUayRNCb7G6usb+QgDZzgWsDZf8koIl3DBoMIWVq362bnKV5oLxnMsM0x0xPYFXPPGTMHo9APzKgI4sfubVby3PBWP6HKVhNGmfngMfRSc4BCBGvPfaGwFmvHbzh/gNegn+lX2ifDaTV6JUq+R9VvFPPWjWkfqssaNSa5v/uidGVv4Q7j12oXKsOZs9g1vV2jDbFK4+m7a54LgEs+5j+GilDMIHKbnaFozPMyN1RmTLODNTPiuJOmuxtbtshMtCcMtxUmnKvLa27mpESrMclwZ+UQCSeBBwbW+DhRtxe7PE0aYTEfhRRc21nWvJ8LI0xggC7DywOsHgHdZr0R1gbSHLSgUW51QcFxstn9NriWk21wvrxMxiwASCt1KGZ5ws9kMQo+9MzOUejiL66GBNRGM4BxkghhkJxsd8C20fYUievlovRr4DoL9iwDc32O/6vP/ABr13GdyyJoquYLAwaytGVvVdNkGE4S0JoEYrD9f6LEyJURyjBAaiBcapJDMis9QMCwU7LDlTc2GEVRbUuCMZOiPOSkQoDYgBt9b7w1SEHNMaxogR4lRu6NQ4pQWMMYBzQuc7TwAxT7iEGE0XSvelkbVt/2ducy/7q/7pyLh1NR/mjuQesO3L8Tk8IguIjxiRaZTbgKzka1JkPAeRqnEmG3A+T888u10T+IyDA/l8T/maeuGp++BGsrJSJsDgjrFoPZwJ2PgGg3dZBF5S/XrtddADSQ4QMVbNiBANRk2wJLuWfYcyjdLZM0n5XAJ5MqhU34Lq+c6lcFVQI18da0dBGUaTJ4YIRrhlUNhLATzOwRoFzh0DpWvv1NeEjsArj9Z65IJKDWKc6j5KYAPR8BpVf1FBLM9JBFn9XSOlIMYoYyFSdfziA9KNyAEHkEvY2Sa8S+akZPnrlS2qZEEK9jjf0pydqr5HoNJPSyWtQEnGpQD9ogyk7Aum8/VF8zUXMfD3eZiJryFs9yuXfuZFHd/yLd+Sf3/d616HL/uyL8MrXvEK/If/8B/wTd/0TTs/x8wTfZxtWjnzbebj+7//+/G93/u9+e+7d+/iscceU5u/Zd4TFR1f/fu8WCR1NzzF2qjZJFXwWR+rZmXNnyGp8IhYPDfC9RbrGxbjvth3O2h5eQ+0h1F1b1FsDoq28KlkP1C0qAKA2xIHRUtALKWUqRtgll1pGRxEiDxoaWACsoxhXZ8TvDdqXgSgWQ+NJOyPW7g7VmzeMoKaqMxRAcHiaKfPXmVmKEiDLTairUv6XQT1d24jG4sfkDU8azyTzKKxAZ36M2dDrjQBiqzLEEXORcooDcIIwBsYbWhFGlcRAzEmLa8EEOl97KQcFESwa4f4nMWtRw2uLjdYuUET76LVaIixdCP64ES/kVWr0huwY7iFx6obsVA9rcjCfAMDoZPvL7acrxPQ7z9IGaffj5K89wJYgqC6waRxodxjMwJxTPMOWTrHLwTAG1cGbhNLvARI0oZn66PisDERY+cCRkYy+JdXvxzY8+B+AA/D6WfxvG6GFYYweW7Oek7PKzOcH2IOaNWEluq9bSXxE6H2nQcon58TAbYBaSJJsO2NM46xZVyCWfc5jAfgPQBXAK00uLJaiamSUFRN2RLozO9sWnd+fmA/eS0tanZ0KsisroCifZEALZSa9/xQ64/YSLvY4QphPAD8vgi/270Ri3ZEYIOTocHJYSc10skAaJAggJg8tGy0nFBnees8BuvgBys151FAMPIKZikziyIXyqmW0EjGGFk0ODpkGnPqisGuZL4BgKOBD6Jd1WhGAwCWzSgdoEyQtrLUKjgmN8EE7c7lGSZlnAYxptAyvXFlcPRyxoMvuYur7QZGW6MbiLB8Zz1CahUfLMKxw+K2ge8J64MO/Z7DEByMpuhb59E3Ac5JJ5UwGNDGahkOg1stRkpMBEYOPmIrAu9+CaVyI4vhM5G291VHqaAdr6D0bIPFLaA9ijCcbLmBibHMMQsk3R5KAYURIU9iFiF450qp4by0dl4brvdxnknnGjDeVeW7zS6+gIV83clwJ2slLYguYNx36m9djstxgSHd/DSC1yC+jBlwpEBO9gcXoKhv0zjJ7+H02mLymoJF5wXMsgCv9jQB4KpALP2wUj4eOrFhsYVka5sAZ4Nk04PB2Fei7jz/RwVsUsAG0Oy6kSAml4FEKkBWQPZbJSuc7BCQOsUCyZaiZIonz3q5p5ENGBGGYr4LjQkgEi0tZyJGBX4KMFaArfQhStIAel2xIQxXgb29Hp2VrlYJz7AUYSkKM0uZvjwYuA0hBoJvHUIzIpDJQZA10sjFqBAzaxZfkhao1guzOQfkBFJsID6gYrdlqgKX+0NRy2h1Arl1ad4CLSdMZZR5xcTQ0g+dN2qjiQAyRhgnKXEym5M0/yP9y4a69jXbPnTqks84wMXGxH+cmaQ8Z//pEuYBy+X4Ix+PPPIIXvGKV+B3fud3AAAPP/wwhmHArVu3JuysZ555Bm9+85vzNp/+9KdP7eszn/kMHnrooZ3H6roOXddtf7MuGZyVF1IUmzqJTVABQ/r5iaRJVCbPnBVc+5dTx5/uO5cdpp8sa+jmboA7iQitQViUeMSMXM5rtvYq7EyoLZgfX/ZtBylPDAuRyxB7lPaBzBgZFxFktfmTFSDf2oi9xQBrosYHogUVUtzAhM3QIEYCa2fc2LCUZKsNJaO6WYYL27fyLXXZJCA208biYxIz1/YCdmWGl9pSbhjURcQg8cOyHcFq31vtZggAQ7BYEKMxots7DC59MVk2xVRFE1mbLE2jKN+9HQBnIPIqkTFeIQw3pQO8Z5NBnsgk9yp1sAfgg1TmUCBwE9G0Hvtdj8YkFpnGUNq5ODogLiKgyXnyRsoJFxpvrqIw4CraT2L/yvmJ/bYDEJbyXmwAo5W5sRWgD8wYDiyao6jzPk3a8r3I3yW2YWNAUViNUp5aWIdC27OSvB8qYeddI8XgcyLMnEF8VtyQnumt+z/n+BcVf981Zv5xUs4OnF5HpveraqlJfFRyi5Vfvti4DK3udzDAmWo4dRxAWoBWziQv1qvttnR82nacyYjyb55dT/W98vv2XZEutvN+U+CTdJ4qVDq35wUAAsKC0F816K8ajAfAcIUR1OhYKzs97DusTyogizhrnEDbyArzRwEUBvrgcHfoNMNuJZvRW2AwoF5bx44JyEJ+SJhKOYMInJOwxZbShjWqcPCkZbgKPsZRtLt6LwY5KCVXvhK5eU51q5IwvGSb5T3JIEXYdYDtA2wf4TYBdi2v9VcJ9PgxHtw7wsKNWFipL1+6EQdtj4Ud0bnS9QOGETo9fiBlrMk9HaNF6wKa1sN7IyWbo7SvNR65W1T0prR1H8UBRley5NDOiyBMGj2x0e+kieCG8/ZRA8n+mkF/YBEbUhq0QWzMxBixIcCKsWdLU7aiMUDbnM42bqWo7pi4pzKE2ze7l8G10Z2cQ1lMANUCaseYs0sush1PjPwFd3A5PqcHZ4YvTs3ZVN5cbb1lBy/koNDAYduu6MzdTij71e8pCyyvb/GJJItS3xFCRxmMj07sZCr/6IODH9xEz0n+JZ8DBUTKPQts0Aer2o2mADWBSil5JPWvmPiaXBZn1PdUtjWtq2n2FUhbd1KWFGVdxmJ61J+QsAASrlI3NaGgiRPP0sXXM4yP0v1qlHJ7XBuw1wxwJsIpgOWMdCl0Jmi33oTbcGYyJz0vyr7PSImKjYhBAC9oyU4NyBcAEKBAOUBM96dEQ9MJkjO2uh7IemNadhgWhNAZBQe1q9c2TQ8CmCofk8E9DSgutBK+YBDwIpjns0oIp3PmnINd9Fx2+JpLzaw/mvHcc8/hD/7gD/DII48AAN7whjegaRr8/M//fN7mqaeewkc/+tEMZr3pTW/CnTt38F//63/N2/zyL/8y7ty5k7e5p8HY2kF216DsT875zExX59R6ZW7DNT7JYxZp1vGFGSPcSUB3y6O97dEcB0kU19fB1XpsHkBXIH/e3AqrqFkz2jssAHkvYIZbS2md3ZB23BW93+VyQLcYsFr16BovpdpaOQFgkiBgrrR1jXT64zaBbGIXo1ffkuykJ9CgJYZDpX9IKGLn2jwjxS1+ISWHObERktyAXqcX49EPDoOXjux1N0NrIhauaNWm3kqUSrsjpKHVWlhwFPQYQUgB7kQAQbdhNMeM9pCxuBXR3ZV7TksvAFlw2AQniXklJwwqVuxMxHpo4O+2oJ4Ay1i0I5ZuFH0tE5A6DscFix5Yo1/oaPT+CFsqNoBfcS5xZ8eSyFeQLbRSnpkbXDXqp5XUkPUcSUsVF4RhjwRIJY1hTEpQz4CedPOSXzbILGBOsY7+JGtATcUVuheG765td8ZFZ+xr/lwQzgatdh5bf+yyE1tenqzxtuwr/5lwjBxHnnF+Z4xLZtZ9Dsk26grv3EVI5TginU3v3cGm2pZxy5kPYLLPbZTBnBmvGFdMlT5HHXhUiCkgxmJcGowryQKP+yo4HiXTO64bHLWdLNo3TtreWpZZxlBlcQiDxxQNkBAMDvsOm9FhGBziaAqIlQKpBLaps06AXAKpcvekuutHKp1ToVlu9P54MUrJQDKTCCUaqYdO1Njj2GZwy2ggkDuJpHscGGYMpyjZ0RlsbhIevn6Ia+2JfDVpX9UTfuIlM7/XDRhvrHFsGRgNDg42eHjvLm52x7DEOPEtfFgAAMJoEQcLGiVbAWhWyHA2/pmem9YYiVWWyjZShiglNgKJA7GsgVpEJNGe4RP5nsY9gKJBd1duemyNzKExyvFS5t3oF0Ksxj4W7SxrQXPtrNzRY8ZszO+ffulFH9uCF9ry+7ZtX4zjXY7Lcd6QVXT5d9YCKZWTp1Turk3nc/sCp1AnNyanhtnrDDVC0wNMyghP+TJ9y6huSBJ6Vc0TUnAojBaDdWCQZL9DWlxqYJMFd6k4QhaNwt5b1dfSYCOBWDw9j+x7gAmwlao3J4Klyefogjn/S/qPWSxddKuMpVxiKI1SLCglzCsQqF7YCdM2lnPKxyb4lcX+UhIkQPEx9RQZI2CI0LiAsPIYDYBAaLsR+02PlRMtMdHZkpPhWAKywkLTm1Mz4aqRF8zV/Mulhvq9cxLx1wnFgJTHqx8PjXze9crsshJGSsfDxGdPjk6/Y9JQk6DsLDqd8Kgz3ju0Pv7Yxz0+g/c8Ln3NCxpHR0f42Mc+lv/+xCc+gQ9/+MO4ceMGbty4gR/8wR/EN3/zN+ORRx7Bk08+iR/4gR/AAw88gL/wF/4CAODq1av4zu/8Trzzne/EzZs3cePGDXzf930fXv/61+fuhl/4hV+Ir/u6r8Nf+St/Bf/0n/5TAMB3fdd34eu//uvvvZMhUHzEtnEKcOK8FoTqAUqyc8eEmYFkhfWbD3C2T8plwWXzOkhOKoocKwPHKEzMCYukil/S61wFxXpOdmC0DNgR8AvAr6gkalsBbtgxVqsBy3bMbCIfDAZv0Y9NvmUhCKs3BDFSrvFoW4+wP8LH6XVzJHBQtlK2kyQyJQM0KSA+jqoyq9BR1iKOjdj/0Jb4BoBUqIxAXDLISSI8BoPBOzgbVR5FmFH16JwvJeR6v8xIsKOI60eNp2LKA8xiQIm1GCaI4Pp4wFjsDRm4CrHJJZmBDTbewRJjPToMg7KyWoZbeizbEa0JcEYYcVbL4KGyKGElcjAYlG6lVR+h48IKjqJ5JlU7VVxmpEwzLCQBJqw80RaeVKs4YNwH2BIGbyRRNMhFU/rSqynP6mfICLs6V6I4C3i9XUFiHbCys8bUKn72bGzTyQKKX9oR/+fzmH9sV0n8RWx/jTfMyTgzvGFn2f0MZ6h/rwG009gFpIEOVYBtYmhV8etFxiWYdZ+Dk4B2iNK9Yj5pZ5TbSRakpibWw9Bkgm3tIJJGbUTr52OGwOb1fH2oecCux0rGNzOa9Dr9kjDuAeOB1C2DxBiiB8xgEQLhxHXyPB5b2BNTQBRCNiaALIglI8vw3uLO0RLGMIIXfS0aiz5HOn5yAumcjJcMQgGxKANe+VYbaeEK1qx+7Vyc0IozYEWcM9TJGZyMLfbbHsvG404TpftfJ10UoyNYpV9nVsQoJxdXDfoHIlbNgKUdM5AVmLBnB0SUkg9DjAeXhzhadfhkdw3HmxaPX7+Fz99/Fgd2AwBYhwafPjyQbBABdGLR3JWa99iIDhaUkZW/84QZVdkIMyJn1M2ILPAIYsCLNg1slPIQGITAiC2BNxq/dKT15vKZ0Fm4yEBM+gWi9SClhyIGz1FBLyIx8Od0muKUAXkRx/2WXUzYjluCj1NM99lzdootqevIe7DVl+NzfOQFQabDzyfilr9rIGsbADZftJ7la+boVfX7ZB6n/Z3jawBkdmhd1pAYT6m8MHXDo9TRNhAiM0bjZJ+jkbJogoIakIVutb/M4g2EnhvVYk11CTRhqeZ7wuWckt/JYFU63xkwxw1ybiuxlvK9rQKJ1FCDwOruSdi31sPZmDsDiw5jaZVejscZqOHGwq+kdL0xYXKbGwrIKlzsQAbYcwOGxuKuXWD0DtcWG9xoT3J7dc8Gx31XvqZROotRLk3Xm1pd/4Q1Qen7ApKeIil7SwIOLgwvwzkBxyyBBbx+BVa2Tw1eAAPiCISp/IGUF6Y1CxVGhzEAn93bm1PQ/GKO+wWnzmHWn9r3rues9jVn7e9ybB2/+qu/iq/6qq/KfyeNqm//9m/HT/zET+AjH/kIfuZnfga3b9/GI488gq/6qq/Cv/7X/xoHBwf5Mz/+4z8O5xze/va3Y71e4y1veQt+6qd+CrYSgX7Pe96Dv/7X/3rueviN3/iN+Mf/+B+/sJMeCRNd0jPBJdFfnJQYXnRofHKqNH0OaM30hRAw7d6W3tqlmTP3T2l/dddGnn4ma/el0mzIT+Ol7GxcEfy+NDuKHWAPRuwveik7j0YaVgUjTasGWxhYVVd1GElwX1n0uLLo8TRfRTh2sgZ3qbyZQQ2Dvct+ywxaxpeqHhrpJJ4JA5bAxLlMznj1gUQ5UV9rP3Eg+N7BNBEhSKdaq3GgIUYfbNZBtApuWStBUmLTZuCvF59DtjQaqUv/E6EAAPorhP4RjwcWwgSOTOiDQ/SUG1sBwHp06McGITX/coymFa1gzwYthCkdmST+Y0JUIItU15jZ5PsQFtLJXU4YwICiGay+KTqC3wP8nnzHqToFnczNpA8WG4B1fSH32qALVdlPmk85ToewtiKXckNEMKTcUL5AzoAUOQdYC07Je5O/mB0/5f0Mzu7Cu16gluNO+2+mWtz185vngd6SuYzRmSOxyqtlyzzmqcsJT213j5d4CWbd78gLhijOIZoZqDT7Rua6WdAvbhsbaw5ooQQmk4U6ZOEnIED5fdJFoDLs+Zj5EsrraZHIKCUFbKRsr79CGK4K4p3AEApiHGILIBr4pQUMYDdSApfaqCbgKwEsoRVAjCMhnDhEFWWPgYBRygprQI0NT+uCU7AROC+Yk3BsVKFHE7SzhVUDkwIXAzAYZCNcE4RSrAY4lfyFil4MqIaWCxgbxrhnEG4TTKu6VYORzhY6wl6D51/bYfHKO7jWrRHZYGl7NBTyMUYWna7oBnxedxcv755DHxt8+soVPD/u4Zo7wb7t0WiA8UAr3R7HQRyjPTE4eBKIlrG5KRlrjGayeMgsMkLWCzAjgQPn78N4+R5T0MgNgTtd/WrpYegYZpGyStIVMX0OlhGdkdIXna+s8xUsYGauB7cG5Cw42jK3606GwJkZiVPjLMbWvewHODcAuVcHMjHayQnUejvzw7/I4N3l+CwcNSiVmFe0bYO0HfJi7KxOhfPXzpvrsgChDBAlPYlJ8MwzXzMLSubHzsE2UW6vHhbI5RcUkNm4nBpaODkHyc4mQigVICX5SKuBAAM8GoQkyq6JjSxqTuVcJ5dfAUiE0lwDKIs9YogOYVtMmlyvBgNGEiep1C+xi/LbFS2sMQHGRARjZcFtdXkRCKYGawBwY7B+iYW73mNhPZhJSgop5iAmsLQeaU3AnjvBVbtGYIOjrsM6tFiYEa3xsHpRKys942MSQx4J7W3k0gyKmLSaT3Mis9MS+FiV9ks5DzTzL59lAykXAbKvT8LxFGRORAspAwqc1yK5rCgDUSmAyDNOA21TynJrJtZFF+P1OGvbF9t0X0Dfbrp9dQ5bTMCpcelrLjS+8iu/UubPjvEf/+N/PHcfi8UC73rXu/Cud71r5zY3btzAu9/97hd0jvNh10YaUhmzu7qjFoKPyajW/iE9k+fMk3l8Mu+emEqxtuQut2ozVj6Cgny+XkMBlb1NNtvW10TFHITyuo0MauT5953EHX6pjN+DgIO9DRbO4+6mE0H3TYPgDTgYKeNLOr+j0eoMBhwLUwvA1W6D9RWH27wH9loZodqLCfzKmr+pi2zCPpzEK1bX1SV+SzdK7lXuJKiyLJI4EcAHlG43a3ffMiwx1t6KBG0z5s6GyXSaXuImSQiIGD1pVaLItqT7WNYFsSFsbhK6qxu0zufGVj6KVrJV0XxroiRqkl7YQOAFI0bK/m4TGhioXzQR3EXQ0sM1qgs8AmZjSmKeUOaUTl0zyv6FyaaSBAvtzF7NmbAAQKwNu5K/YfhVmo8GJgDNcQVoGSUqVOudrNNodLGQKlEA7XZIhZ3VNsD6jITKWQyti4yc+Jhuf6F4Yofw+0XGhUoB1SYUVtZ2vztP9Oe1BJ3e9qxxCWbd5yhMCy5tO+vFCHMRXdQhYBHOpvVuc0Q52KiOnSh6pjxs+Z/SWEWQVRaBdhCDNckoA0V0sHLebCVz4BdGOxZSXmiCIWLssSxU2SK3e3VrwG5kUhpUgrpIgQAhdizbD4m+S7lrIYVSBljTTNNEz0CbLoxtj9ztKt0rNhKo2I04g1Q6wvo+GYZzoilSi7+nn6S/90EeE6OC5rGFlMAMhLCwMIGBgUBjADcWh48vcPh4xKN7J7jRnuDAbSb7TT8bCmjcGi9rn8cVs4axx7jhjjAuLDaxQYBBZIMAwr7tcXPvBCebFmFoQQHo7oiGynBVu6VE+U4AgEaCW5c5AZ1vpIAegFwf77w4BL9icC/14+RYGW+c7ysn3S0qARaC6GcZH5GJIAYAaxtbA8Dq75EqccRxy/yugo1dZSAv8shGnOrXZsE8IdPpCfdm9DHbvsznHYHG5bgcFxhZdHQCaHGmvldbAok6tWveXnQ+U7KdVP1enh+2VOwzU+m+t+V0Tl2Pfj460o6FuqBJLNwEzOliPqYHMSK3Oc+3ogKbCqClvwc5WU7MpuRUqPI19TVReeaT3p9JDKWcvS7HSll3zH0NSSbfGlagSe5jHSszI5eGyNqaJ4kpdhCWgNpttoT+mkN/jXGgDOBOkx8Ff5OTs2BY43HFrtHRCDKMpRkQnIj3MkQDJoLQUsCqGTF6h6ht0d1G1jFJW2QSMGqX4fQ9phOYAPp6byUBBu1qqD4kL1y5XKuZ3ntkn0/SDTFOyw3l+5HgWp6NfNOFDQKgLHeraDKBYH/0rubiAUOd0b5XXzP7Y1IFdjk+64cZCOw9qGlKWd955eg5qX4+QyuD/Wluqjg812u3+ue2EVHkUNLaisrfxGldPwepMdH0zWuyxByqk/W5VFEv0xD6A8K4n6pLIsIqwuyNAgARYwwWx4cL8KBl6yxxSPJBFJKdUQsSSDR+G4NF47FYDtictAKCJV9DAA1q0z0yKyitoWNDAiZZ2TZonJLkQ0JbrbuhvsdDOv0ZaEdZAnujzCzpXAsIuAQArQtZD9glEXhyIvWivjIz2AKE/QpIp0AqJAK2QhAY9g3WjwY8oCBgBMFCSADOikaXlBrK50IwiMcNFrcMwoow7DUY9yz64GAp5koYYxi09LCNvBZGI6zrXvbjV4y4iPma81qCim8AVQwuh8Je1qSJXwGmKkfMcjdKeDhhg8Xz2uxqYMBSDtPB2jwssbOMxjXKmiOh65Vk/jZ2Vj12xf3nAFMvduK7ZsXXr8kvyPf4dKMHbPeb+VmmU++n9WHeP6EA4fW2XOb8RcclmHW/IzmLEJEF4Ldl1raVH1Z/nyo52tYWl/W/qoxLkE8qC/CZSKPxQCQSI3IgWYPmiNEci+BfYjPlQxgqINbSYFSRvJQhTtlxM0oXjJrSG9sqMPAEt5EMCLzUrJNPAJRuFgDbqy6TZwE9rArDQ1lECf3OIiakIFVB3YuDq68DueTQrYW0BBBMD4RKBD0Z+NoB1IbfR5Nbm7eNx2YR4FcWfin7ZQf4pRUNlMZgPHA4epkB24innruKB1eHeOX1z2BlBhyFBe74Je76JUY2WNpRAC0KMBQRNTpqSMA1i4iBLUZ2aJqAL7r2h+isx2/+4cMyRawET7kOHCSGnwTAcyfIkyp0yOBetlMWQADMRoOMqM5gZYBGdkzKNEwi+9FBwVO9x1XGXEoM5Zg5sEwBRQVQkTEimJgWIRPgaGrMTxnQ+s96UXTOuKesw7mslNmLteGvX+ZKomwGTqcY+nJcjguPPM8YBQXZsqI45X/OQrJQ5u18k/pjOcOmvmbL9omZGa1kwAEpq7AjC7B1Sr9oCmKFCjCvF1gJdK8ZXpTAKV3IGq+2KcqCP5X31Rlu49VuReiisaBXEqjo71yhWoSyIK4XfbWvqdyT8RrDGJR9ZvIpl2YfgGplkUpkcP7bEMPaAO9strtGfWd02iHKMGJnMFwhwDCOThbYawZcXx6joYAhOmxigz46RFAWhLeQUscCcsnrBEYgSZ5YG/HQ4hDWRDwbpGSq9qeI6j61JXpm6Saw31Xf02xupcRXYq2FVNaeD4Is3ssBmOphccEesWVQ2r58hqgBk4EsdVsIJ8EDGFGhXPo9nvOMXNRev0h2/V78QwYZsB28uvQ1nxuDIqQZlTG7myDU0ie1YPuMbUFbbFzNnphum4CwhKCm/c43pHKcFCBvm5uRVaaiHKtmX7E6IQH1MWHKTsSkrcYxS4JfafnZSgTb0UbYRhpjHA8t+k0jnQnTOVexCHkjv2uiHYB02SNGBGEzllCatbpEzq8A/eRJ4yDO9jytncdlsa9s1eXo15R9wCAxFFQ/EgDioIls4gmYZU0U9rLevK4ZYcDYawb03mW5khzXRSnnNmOsYqt041GSKhY4fJyw/9hdXF+slX0l2yc2ViIGNACOggVHgtkY2EG+/3FtsRkdFs7h2mKNu/0CgQld66XyJN3HSDBrk31LPpcEFBrO+mFm0Oqg6n6xVRW2VNKuvjx2rFppuk8vgFboWCqQrhlER+juRNiNysYYklB/jCVhH2SOMrS7IQGU4hqtRAFboHHbY538SExjnu1ajqdfEx9bPcs7bHx+ftKzlGSMUF6fHCcx0Hf5DJr9nB1n8jtjdwVNclb1v/sYl2DWfQ4RQ9UU8q5Sw1MfkmAkO4A0+etSw9mEm3w8Ldqw2xlQwtQiS1eKdcS4MuhvEIYDwuJ5g+aIxbgm45XaoLdSVhi7VK+ddorSTcMjt8+V0kSULLzjkknQIKPOWgcVf01sLdF9IoSlUjQhhsgMpGgAJBvt5b3oUDLBqXa8Km1Iul+ppTlAoIW8bwYNnjSrAQiA1Qc7EWhP9fMJ1LIUcXW5gQ8G6yOH8XmL9i5JpsQCsZPWwscPWgTV6Nrf2+DhxSFWZsCe6bFnpNTwrl9iiA7HvoOhiJd3z2NPe8Y25DOoBQAtBVhiLDDitatP4aXdbUQm/Oatx7C5YeHWDL9IBloXCqzfzyACmLkrFlXfg6m+lwDQwJoBkU5SoS3nIGg6C8vPEaJj8IgMTEoNf6VbQpCMuZEPswJY4jGpLLQumq2gLYa1XhRdYJzKcp8Cl3F+EJIWdJhtm4K4LcZ8ftxtDuIy0LgcFxnZ7tci8Dsj+/Qh6PxMmewUbGC6KDlrIUHVhrt8Tfo9ykI9NISwVP+xVq2QqvQPBO06pIK8qaRh/lzF8m9yCpV2Si49qQKy9Ht6VjMIE5Sp7KrnX1Fn0nvJEQLsA5NkySkQj6tjVcAN6dqVgt7qBK5AAKwQbVXpxhnISr8TMTrnETuDcWEQ1gTb6zkZ+R4ZhGHPZBZY23rsuz4nRxobRDMlOgQ2GKIDgXHVrtGwOE+jpYhpWEhg4hDwkuYQB24DZsKz66vwSxUd1u9qojGW/LjqV8Y6Xo3Jf0zvlVX/HS1NWOuZ+WD0+0tZXEJeM5SMr7KzeGaAST9ADqAGMPtAvALgQCfOMYC7AK0B9Dh78tfHu+A45UvuBQmrDjv3Wbt2d17QUX/00td89o9UJWK2LJASYyQBQ8zKrmJlq569rpoDXNM3sX39Vh13su18E5ruX/xKbXj19brMmoocysRPQNakw740rApLwngAjAfaMTwnOSgLvY+bqiuuS4Yr+QFdXzstT1d7EyLhsO9wsukQvBFwaTSgQT5rPEQyxZQki6yXS+yS/F7xgwS06RpSAhnaobHyR6l5iZHrEaaYNLJiAAvnMXonOmDeoVuK0bUqug7SqgstayQfYXwEDYmKJt9nnieW0F9vsH7FgFes1tnHOA38oiO0RnxPHxx8NFo+yIirgP6aalPaEme1RlhxKdayLkiDK2+zFqZVZpaAVSxd7tNcGGVuhEWZU5MSOHUNCbwyXuMXLTFky8L0ZcrVgmyA4YDAZLC8xTA9gFgaXtFY6QODTpcbUkROqhgjLMnRn1myLBeTnN8Z251VGrjDzaTtJtueJcOyLd6ab1IDhLNx6rPz89p2nhNArTrGPfirSzDrPoahOOlsNw0yuEzOs8ZZpYbnjCyCygVQgi7sskirBip2YHR3WWi2Vxj9dVLtI84L91zikcr10qSq2rUClbHQSZkMRRZ0NSmbAbhR9bHqshEFR8gT7CYxfpTuWSPwRvaRrsWkrG7ahBNDTMCz0CoNOJU86rnbwGiMZGUoKMvMSevx1slFNcrMSsFEymgYYqHr6jVf31tjfIlFf3sP7SHlBbwJkHLMPUJYMa69+nm8/fFfw0PNHRjEDJQ94O5i0zV4eriCT55cwzMnB3hkcRc33JEcrxIYCDCw+ndggqWIG+4IX/Pgf0drAn6dHsfykxbjAYPbqDomJrPkjGc0R1yMvC7KVVNeOn1okGc8MnsiNga9dYjLmDuITTIe2o4+lbpwKqOtkhICxlLJYmh7dWIjGRWl4xYG4pS9xec9O/PGCvOxRYNuYvzPe962Gd8XFpOcMsiXAcXluNdBxNNFGrAF0DpnYr3A+Zs+Ko5GjpPxHIbae8obUmC4XmxQ6KSLlLGz5hym+menB5psVwNHybbXegoVoGICclvuxBDN+4iYZMMnTOi0nwzQTIVxAZRyDNUbjFoWMrEpKYmgtjKVPrCWUFhTFvFAwkh4IlETmXKctWhHhBUhbFqEQQNQDUSzr26Axc01nrj2FPZsL4CYnvjK9PDO4Ch0uDsucDx2OHA9lmbQy66CQlD+O0IYB0sa8Pl7n4F9KOLTdB3NXW0WYnmyBmD9bqUTlOxZc0hSVoPqO64AyvTZYIyUgRiuklnI7Kz0fSXGVQK8JozCtAYiXf+QBaEB6CaIXgZ+yAEvUZ/w6WvA8y8B8EmAbkNYWmcEEOdpWO1anOfX7sHX3OeYX8U54dPl+CwbUlrFhZ0VzVbB9emHSsxCzLKONMUonbteSWstBqDMrGy/qxko9gslQX2RQFh3mWORbYG+Jg7mSZnQSowTFoThQBhZTFrxwUBki9EAJy7CjxboLUxvRJ83GsAqgKVJdAGhuDCzGBi8Qz+KRErwRpIiA6kEi947U50jpj/rmCozzJqCieT4yhQWVVgwYFHOg6o1PpDZWWOwGFUAfjM63DEL7LVDZvbKcQvxgC0Bo9zjrAFc2z4iDI91WF0/xo3FMVobcikjAKzcMNEdXvsGrQ24srfBiQs4aRbAxqK70uNlV+/gRneCK80GC+vx27degjGoSHwwAlilJIl2doydfrk5RBJ/YIII+0vHY5SkBwrJwjAhGP3+vTTNYqe+omVEjZtjS+CTcsxhX0XhtVshOgsbIV0LazF4iyzxQNYUGQhDkrhvZtIq54BJO/2NMha3J/137/LUPrZUimw/l7TB9HeeJx7rj8zXbjue9Z3r0cnzvuO8toxLMOs+RlSB6xf2YSC3774XICsxsiwVA1bSvPIjLcQTpgYGIsH2DLcGhmvAuM9adkZZbySL7frCfKKArHNBMWl4qHFta5F4lKy4ZWmFmkoZBnlfSt0gnRC5Cm7U4OdSEpv2zzDarhtGrqHufiUtbLliYAHcqYqGvpbYAHaQrnyhLa9xLIBVossa4iyeO0SbXzPgXAf+kmtH+NSjLTbHLcwANCdSthH0fvhHenzjyz+Kh5o7aCggcKkft8R4WfscHmgOsbQjhujwu0cP4IHmEI82tzGy0wy5aFAFvamWGKkL4DV7gq97yUfRfrHHr9jPl3u+DOBAsk7whDDKd2WHmHXF0veTgoPkSMV5IYOF7kRaGLMx4CbmkhFAgsWoGmyWxJFGp/NonH4/lCKOVGpojBpj8dw2BnzLya/idcNT+I3FS/Heq28UP2XonlhX9Thr4XUmK+p+Aood+9h6LruOdwlwXY4zRioLeGEfxu4FxVkj+5f6s3TqRybG6At5AeklY5ro/wnIADBh6uQERc3CSvZKj50ZoDQ7HwX50qLHBImVUuOLnNRIPjF9Np1LYv7YSm9MD1QCMsCorkjajwEjEpX4rbq2xNbNZflAZkAV9pX8Tnos1TuW1yDgEjOwtxxweCBdtShIG3WQsJmIgXjg8Zqrz2DP9rCIiBDdK0DO8YpdY2UGNBQR2OD5YYWV7XFgNohsqnMAuPpc+koWZsSr9p6BfTjiU3RDXlTWAgOSsNCFbUrsbGP9TRgX+h0blrVFbFLQxxOiYfqu2ZB2MET2DSmIQGJn1RORCCAL8Aqgh4FXtKA3R+y96jaYDE5+8zrwSx3w1EMA9Rpdnt31cNs4c5190UD9RRpn7vvSt3zOjOiywcGpxHoaM4BiWrZ+euxkCJ6xfTrOqXKlBKxRFbfMR6pKqUsN87nPjpVAGSqNIUQEnDDuGYx7wsjKpXReQSACooJQQxMQvYE5MXDHlGMrtsj6wHlda8XuxZaBQFifdFLabhlxVABGxd5z0iSBYhrz5MQMI1fFAARWP0m+KJfJLdIEvMZIbJABNpDeIyslk/WoS9ZDJKyHBstmxMJ5dN2IwbUihr8QIgM1BpxYWblaCFlDKy4cTh4yaF3IrKpUwOGjRWe9dmiXF3tIGeHLD27h2c0enmn2cXi0xEtv3MFLV3dwrTlBQwF7tsfzqxU+frxAYjiZjUFzV0BBiR0LQkKjHtQKIYLV39cSNqYnjUHlntm1NInhRm9/+o4aqZnnLoocz4IROyrajk7KVJvjICWKlkCtgd0kX1WeJTYGlLMvFTuLCHAONPrTINb87/OS+Pfa2GrX2NZwDsjf+zTxrz9mWsLz53drGXJaD8zWBHnb+t/8VO7xUi/BrBdjpEkRuRLm5d2Tcu44Ltq9Ji3kk7Hd9hlWY5cEEOsZQYBbM+xG2Fke8ntN0SXVv5gILVoSkXNduae2sWEpaDig4FOij7qI2DH8gvK+bA+M+wnIkoVv1tnIGifpSeDsBNiJ4UnXkYKktHCuReDTfqlHpiwbBXWy+GJikLVioAdvsWzG3F42aiolZbcdSR14ZJLSD3UQe9fXOHlEdrZ8xsCOjNABwwHw5ld/HI+0t2EQlVHFsIgTYGqPejyx/CRe2X0Gv9/fxK/deTn+u30Uf2bvaTzS3Eos4zwCEyIMQoWePry4i70HjzH0DmSkZj4aBvcWYR/or1k0hwbEonvmF3rPW+RMVgEqdYpEYW5ZFY8PQZhedpB7CAOwy6GrzEMDhE7Oy60DMHKeerkCl6QMUdhZBFiD/+XOr+EvHf0KDIAvGT4JGIP3vOTPnvss5HKrLWNSF37mTs555tKCI1HY56Lw285pfh5bfpcXzj61y3E5do7KRpb1wa4U13nvXWBkYc4dviaDF3qc6nBGdRJjp+bdYwpuJJ+TFjt6mPQ3x3K5sUHurDQpW9OOq9GVQIEqMAnApPlI7oLE5T4SkBMmohtIedusY7Jl0ZWPl65J23JzLHYjAWUAECLBGUmecIKtuOzSKLDEAJyJ2UQ1S4/xQJG9Y9GLjE6SQ4/dvIV9uwFBtFtUyn0CTLXk8ZLmLq65Y9zxKzy1uYrPmAM80Bxh325gk+BvvkWEJAifxr7r0eyPCF40u2JU9qw3iC3E1w+6CE7M3QqkTAyDSYdDRtHbAmXwL28nrng65QiAE19ofJw2GMhuiQBqwLQCuX3gCcbnv+Z38EXxwwiw+PAXfil+//arwE9fAbACsMaZYNYuX1Md7uxxzjNYBbfyPJ3jm+ZvVbuvPPP2bS/HZ/dIz2xkcIgguwWkmgNbKckYASmdgiR6z9EjnQS8cyH4KsbJSQhXNH7zul1/T/aTAp+WV9kGiqXfefp3ArKGPYNhnzBe0SoTLTFPPgmgXMbkBwt4gh20Y/dafEfoONs0M8q2sRUgKwKg0SCsrdooATtIS+NSGRu0m2F0DOOQq2AAlGQ7RHqFWHZDCs4AiVQgiRa/4NwNsQCGBKYondldRKeVJultqwwtoAjBd86jcwFHTUR0DL9nwM9Lw63QGpjUnb1uBtZY3Pn8FQ6fGPCq/WMAAmDtuUEqlIzo/vpYBMxMt8YTB0/hqjvByX6Hp69cwTP9AW60J9izfSYKNAi40Z3gY+ElwpCDfEfLpwntoVQRsZUGVTRU3YehcbAt1URSUcSaWJfvIcU4zZEwi2MnpaLSuV0nqJbXspGYlhZ6772I8pvWwPYK6mnDKxpZtH9DRC43TNUoqemV0ojJGHDjhDGZO7hXzi1VptzDOBUHzd3MPexuyjCfg9+n9yVfsSxe5t0IJ+e37Thpf7WzSmu6MzNEZ49LMOs+hiFBdVOQnrMJM92sLKS3LUMyr1HfgZie6haybcwmXQ4KqvpsYWcRhmuigWSGxHDSa/Jl4Z86gpDnqW4ISXBve2E6hSVnGqzcF8a4jPArg+YYsk7MQYmI75FXHRCv7b4Zoj+GKviP0wcqo+wbLppZVrsXxbQ4Tl34KL+fsvRaJo7Y6EOo4FX9j5kQKQUfBmut++6sx0HTw1DEJjRYtiOO9z36Gw1sry13AWweinj9wR9qltxkQCugaHIByEytlenxxOoP8fLuOfz3k0fx1HAVANCZES9xh1kEfj5ENJ7xwP4x7lgRUAzBYDRWkjaLgE0E7MbCnQiQGJYCDkZfAj/j1Ml7zuv5NEdgZH6AFQyMyspKgCBQKlktEBYGYKBJz0Gar6SGXksPE1PrieFTeeobAE9s/vD0/K6Yi/fbxWNbmWEJIPDCF/41bnyeMd72/mXAcTnOGal8SuYZYZdu1u5s1nS79NL2jNi9+5r8DJhyDhRk8S1lEZSTFjVwPuk6SKjAJmRpL0BZXnYL44okUIhNSZzke2HL8ZJeSel2WzvdcPq5TP7GV+eYtK/S+aWEUXW/6vK72qeBxd8kEEvUAUq55kT83cSsJeKjRWMDxjYiLK1qpsin/B7jofYuTGInKaAFBbXSSEythgIebO7iqjvBZ8YDHIYFAMBSxJ6REsWwhRJrFRpbtQN6ckIqjwYhaDmhAzxbGC/fQWi1zHIOXilTQUnH8lpaexBEOBnlM3ndkm028jROgJblKEK8eSRHD4AaYEHAdeABehaP0lPwbPBJvAy/f+VVQEeAN5OPvli+Zvp8zHzN/Y778VWX47N7mOpZqDV8bbFR8l7qsl5rQ5w/JrnxOeaUDTZPYoXEJM2Bb72sV1ArszMh689UcVGPVBkCPUR5Q+KR2NCs+7q6PLUvZlNserIrpoHo644G9kRikhRYm4FyVQlY2LkYhG1MEcAojoi7iBgt4I0wflIpPMozX8diqexQCACynRlV4sOWzuuJcQQG7BrwSzq9L2LACDOscSEn3pNMCqtPIf2yBgW2rEk0YrHXodHyu9YgLBRQGgMIEUwGxy9b4c6rDZZXNrjeneBqs8lNq+QfIQWyhhid8bjRHuOGO0ZDHgs34sBu8HnLz+AkdIggBDbqmwyuNBsc7K9x6/l9sJdFhFszVs+MYNNgc5O0ekcAx5Rotpv/P3v/GmvLlp0Fgt+YMyLWWvtxzj3nvm/mzcybDz/y4bSdBhtTYEOCKQkaqVHJogz9oBGiBUJYgECIP0ZCtvAPQLJbXQ1CggKbpFpqVFKrq/GjyjYuuoq0jU3mdTkfZObNvDfv+zz2Yz0iYs7RP8YYc86IFWvtvc+56bTz7imds9eKFc8ZEXPM8Y1vfEOeObPrFCE+Buf5RWLkaVArauV2tybEOQHGZiRIYKwBuM2MYwAaHJNAEhMQvYPvDZlBzkQxXyeKjwNNR4RzUsV9hw52fpZp6P8PmFDFeheJtI93O2V7drGzdgBZZQbYVEbaVezbFHOrZMLb/q7SrsGsh2xWVSI9pByRQhpXMBKDNlXJkKDVJAqjMG72rOlDlf9RnuNFoFoy/IqSaF4JVI33N4hq2/4s1dAu00E0syp5iYNNEM2xcXlimoCsFnAbBZ8qFZdtgFz+LXfjZHpJGqw4nUOsZBDzHSNYDroyuQShZ7Q3COFAkC9mQtd5nLtGqmn4oN0uKYWrrkZkYF73aFxA5QIaF9BotY57iwP0Nx2W8ArMEfyTK4l2U0z6V544pRoaoOVHb+ojfomPHn4lfQ9wOI8zHLpN2t6ap4jX+2O8uHwER80G3kVsehFcXPkaawK8j1gDWK8cXAv0RwxuGNQpQBWlf3xrD46kodqzUK0sR59yCmKPXG2syvczkRw8gLmD6xyq3sCr4joVxDLw8fnFO/Ht6y9DyVt4fv6OvN5F781FmllTm1x64M8srMukEQ8AsQLkmzx+OYhft+t2hcbJ2o/+Ag/u4KaZw2j5YLzdY2tsFzR85i1w4DqdJBcBhZ22hpH1MGi0X1csdwoSMVKq+ADMs20KICuBUs4jhkrZvFrOFUpJLtk5o/OT8y4mvWqbKfCg+mLSOYkCvrEFTkAI0aHrvYy7Frgh2W0fPZiBykd4ZtHYogjvham1rmrEGaGDS9dCR30CvUzvyimgZSwtW1a2OXV4qj5J3yMILVdoqE/bp+smxjI2OOnmaHyfNCQjR/TOoSeAHKMH0PeVOAsNJ9tLykxgl1kIAHKJeijrQZ28BHBaCqj9s+ejvM8VIQaCSyz08irFa+UVQG8SXqZ34Iv0BhiEV+lp4C4BLUSJ/qL35kHmcQ/wLl7GPk06DLtszXV727XESg8yMRN2CE8/w+XysiBV+TuADPBOO5iSRTEch9MYWQBZW+dIBjookNM4bI4FzKnWQHMWZU7am24rDW1HmncS+rmkg/UL0cpKQQs9P9fKP7MxKRgyz/tyHVCfS9odvMyBbTxKQRrVinVMiIasK3GBNU0aEJ8jwoLGqtFUMIuTG+Mp+TBU+Dz2mwSJJHhfnwob13VSlT3OCwauVoRt1IeJLBahM2F1p/5OcCkLxVcR/Swizhz6BaHacMqyYAJ8RaAuoj+ocPJuj/6A0Z/O8MrxDTxx+wzPzO7hLMxwFmbYxAptrNBFj4XvEqDlNBKUslLAOPYChHXs0bFPYNi7b94FANx543igJ+ZbZcj10nl+k327+iTfH5BK2QDJFqR75+X++l7wJRChhzC9uIKwpzVl0UDEWBNiz5mVVxG8+ZsVgVt9JL0TDS2iIgPFgcZC8FbFPQyZ0HkdSvOKCzWDccEYP3rfJoGhK6Ys7sxOmdpNAs2R5naT2FQ5dnC+j3beV2nXYNZDNq6QHsSB476vIsGV6YT6YO8DstLKGD5c5pBYhCDKIF2fyxPmWmQmVprwY1hKnXNaH0wzaSZoPhsba2MaGkBcV5ozrhHzkCMQfkVaXREJPHEbGaCTI8MaxdeTtggvRcrVFCMGYpA2CLmoUR2N1hJLZS3R4XIIC5ZcawJiIPTksXECXrm5AEeIkkoxqyQHvPE54tErunFUb3Dr5jnuAAhzj34j1u7dj4pOVink3qkVG4i7F8AWIIN9TT0iHO6FAxy7NWrhQ+M8zvBadwNHfo2aAs76Of71F78T9+8f4F1P3cGi6lBRTMK9fXBwjjE/aLF+koAmoKkDYiR0qxrhrJKurRmBAfYe7Aj1OUmEyKqO6eDiu5x6apVcYgFmSUfLQxcrKRnPm5wCYgN8ek9Yyhx/4pHvBpjxodWLopl163sufjf2sUa+hhP4XVTatEyBrF3vJimDJhnaBwUfrtvbtxlL0RAQa3ud7QexNdifXphWnvi5nIgYmNXqT0U1w4FeYhGVs9+MfWUT0ajCrmk/kLQIi+Kyrl9GxY1lbH+JK1A/A8UFwAvI9KcHeAWKSwAtKPbpnFLApASykG2NOWHJDjFyNJdIbKOB21Gcm54EWiIKiUlFEDaWFIyKKdXQQKXGBcznLVZoRBNHAaKbB+uUVmjNnIZyWQlsyWmSVDIEYR1rzCg7HS1XOI8zNNTDU0QbKnz63tPYrBvcPFqiUjF7uXUeUW1l1QT0hwTnFYBjIHQe3NrNYAQATE4cRyqAq+LZGbC59PlIE9zRlIot5T3kH5lUyJ4igBUQl+DPHOLLt96L17/5McQAbD5zDPwWANyDVDMcORfF+cjf315bg4tszWi9yV2UAZZrO/O2ablytSsC7Dt8kbHDXGpnlemChYbOlGMsgQNNMwSl8Q/AdvC9dGyNXcLQyugRrvdobxI2t4D2zGN+N6Ja65y+LxxqErChnxHCTCQ0wkxBrCITxbJOqBdQhKKxnUwXiXSeyxK8Hb0rKVW+AbjJ7xRFleJwCnrUnDkMCpw41fsmZRQlIfeG4Fec5sfRq+ZfQrjyX/FlINXdG0JksSWuB9BqZT69IY4Yq65GHxxmdY8uyNhb1wGIDoEJtYJe86rHfNHivHPoDzz6A0I8lQwNdoRYO8ARqHJYPlVL9kwP0LLC2aZBxw6eIp5oTnCLPV5rb+Dl9U2sQ4U2elQUcVhtUj96SEV4IBe28hThwXAuYuY6HD2ywXOHb+J/4g/g3skttDcc/KZCd0DS/9qvlimS7m0EXMsDdp/dewCD+0qBUS3F/wQLE61fxAy6KNAZNVXeKi7LMy1yBtZHMHkbsALIjBSZdwC8A0epgDio4l7M1+gyWECRnZLawzKHgd3MrMFx9O+OuV5iY45/GzHHxn/TPscSLsnmTqx/QbsGsx6iOdKKCK6YsXLhaIyNyEUPYKG9NdxOJm1MlJ0Nzr/JsYplU8ezZyUyfAtUS5KKTLqtCb0DyMK19pBSXh5rUo0MTRmMhOpcKw0ygWsHOiNUZ8q+0n06AOjzcfya4RXxrlaMfkWgI6QBBSiyDCmfo9+wGD6bDBsQR8jUZAXf7CXxYPROmGhi5AgcJcWDA6HvHbyPCNEBLuZoBjEqTTEENJWCWWm8Dk8cnmFe9TjbNDhbzgAAtQ/40voxvGv2Jg7cJqUaxiJ9w/SvsKVV4vDV9hZeWN/Gtx6+jNv+TEAvRNQU8HL7CD53+jh+/QvvwqO/1ODxlvGl730STz33Jg7qDiE69CGHdo4WG3zb01/FU3OJwq9Cjf/t7lN48dVb4LUHNRFwjD4SKDhQJDhjydUKWDaiq+bW+XmItUwc+hlQm2h/Sq8hhJmDbyTvPonAYwRqAYjO46cf+R7g2CIYw7zxATB0ySjCrsFva/klSkUD2bgNF06tOPG9nCeOIhUXHfe6XbeyEQpHBUAZUU+iTVd5mMYT57I5g0KuYGsmjk0MuMBJQ8S2tXRx6DVNMYMNnLKJpVS0zaB6Yo22lAIyg/RFIDGzhFlagWkG19+G696JeKMRuaRzAOsN2L0I4D5A5wDHIjUhB0USEMfIYvCw89f7oJV9rZx67iuZ9MVAcGQphZIFRArAOJfTP0ScXUqsMxMOmxaVi2iDR9vKtM1TxL3+ADcrEdK1VMOSXWH6V2O4hkE4DXPc7xd4rD7DgtoEenlEnIU53mwP8MqdR3DwgsdBYNx79ghHt5aofUw20FpT9Xjy6BRH6sD07PD66ggnZwtJ5fNy4yOrZiLne1YCluSyE8L6fMBLVNx1vMVMYq/Mj1iIwOuVA2uAXgZeehb42Qar/3gsDsfrAM5WgH8NopcVh7bmkq/RDohgYvvLvZtvWRri3nO5bt/QbayRVcqeTFU1HM+DlB2UqxrmnyZBrKk2DjgyMrjOSJIs5GS0srHRdYzZaUSYO6lAeBMAOVTn4rOktEOSALVoJKkOklZft4B9shWCkQ11GSGgBjdq3yoWrSorgLSWbUPSI1cb5gGeC9PYKrzHGZJWMPWUzoEpZ4TYOaUCSVDbxgCC+S0yp07nHwEfOdtHSEZNrITlXC0JnWeglnteVZItIkF3oAsusX/lMZDfQnQps+Rw1qK+FXC3d+juNugOCS4QnD4T0QH9DY/1bScyIgvGu7/pFXz/E5/Dgd9gTj0CCHPq8URzgpN+jjZ6rEONO6sD3GsXeNfsDmauQ2Snf+WZ6LhKAZTIDh6Muevw1Ow+/stn/zf8991HsHnjJhCd+CFzySyJQEpTN4CwWjJ8y+jnSCwfYtFpBoQIxS6DXzYvaE7Mn/VSud1pUTQL2Gu6Yag5B8QMwCFOfxNhnpD9G3uvSsDY2FnOb/l+6X1JQX/b58RLdpkxvTA5D2VXRhhAOoVoOME0W0sA6wII3/pte59lEPFB2jWY9RAtMmVNDABDHY5RG0dAbHFJ7S1BrIJuyAX9cOwIDyIl+jAYjZdHD6EN7K5XAOmAknMxnrinlAA1DKaVJEAGku6U66T6HZxE4KtzEd4zg0BJ2woJWa/WjGojTzQRwJFQLWXwCbPifK0kbtLwyv9yNaw8wS3FhPO16MBc0QCUICfRY1bBwZZkQm6DrVM2FgB0WtUwRAdXMXqlxR7VGxzVG7xZHWLRCJjUBY/Pnj2BI7+Bq+UcBIySjujY405/hC9vbuPp5j4eq07Tby+2t/Fr956V0u2H+Vw9RTxWneKmX+JetwCvPWIFHL3U48ZnG7zxyDEODjboeo/1spFouAO6ow2eeuoE75u/DgAIIDwzu49fcB/Al158DNw60DwAxx26vhF2nc/3KswZoWHEJSURTfYqkMhAF0iNBg+exRiB0Dj4VfmsEuD0RlkUY/BbHvSHjsVbMxsvDU7ZePQ+jVvWPuD8Dg5W4Olz1Ocw0dP3XcZDDODX7e3RxPiP3pldbfBbntUMJh6D2YUuojwZL5fb54FLbrbGfpuwNQwkUChpWiA7G0AxZgOJIJNSy5xFRnVdC7iQfjZxXmMUlymKLh/b9ZLDRnwM0HvgnvWg72Hw4wDuAPjNGejzzwH4AhBbAJ1MNnk4ERwEeLg47yJ4RVBbPTGuiCaUQwgCWtl+PYkQLmAglqSEkBPmFjOh8T2aJmDV1wlMCuzwZnsoqYZKf3PgQWrHKja43y9w7Nc4cG367SQs8PL6hpxamZJDjAPXYuY6rGMlqRcOaE4jZm96LOcz1E0vgZPWCyuKJDXl6OgeblfneisJx36DL9Ft3Ds5lPUqBpqAEH1y/KDxPwEsGaxOo81jjJ0nY6QWokG2NfKMEHxR9RwpItYCuA9wB9y7Adw9ADgAtAboPgTI0pz7By2fO9XG4K8tvsjW2LIHlaewQ18DWG/bxhUjVd0BkGRPLipKBWCSwRUhL9pIu4dGY2NqlNdBOXdCHhNToQyHoSA0BNBqThntMaG9yegOZTztNTPDXtVUEETHenvlvWoAmw+QNHPLc48qSUIQ/01tV2yQ5mLOjjODBOm9prBVUpnQr+X3oNtSWV2QRDrF8IrkmxRM1GS3AqNac/K5Qi1pksme6i3xLcA9UJn/pemOXDPIa1q6Zo80VQAreDW4NZTXCUzYdBUqH3H86DnO71eolg71Mmt+UQT6mXwPDeP93/Yi/vATn8GRl0q4Tld0FDFHj/cu3sCd+hBfPH8UfXR4+eQGlo82mCmjITIlVtZ4jAoQtnANxmP1GX7w/f8R/+vt9+Bzn3w33AYIBxFcyU2JOn8IcwGjqjWjXslvpm2VmXms8jwZZPQ6X6jPpMJ9rAk9HLjh/Py67PN6tTFOUzWl8jEE+JKO1dRRs02aaqjglgBkClB5L+/DVL2Ry8irlKvvAKnGunQ722XTDHf5RTtiNFs+ViERUf7OxfO971iXbddg1sO2iTkQcxEbvYy2z465VHIuKreNfhImJ0eiPaET6nHak43bvYi3J4p/zOCPrSfHHzovYSbVQcKc1bEg1OdAc8pJ+LU/JPQLgHpBzMUpyZF5EwFmJ2J64hSwCMXfjfi//MbP4Tu//EX86rufw//9D30cofLJiJkzM4jA67m6MLwGc5iIGcEJGBMaJEFFNm0u1c4KQVhNjgJ6ped6jSQDUl0KEIPATFhUHRoXMHM9MAcOqxo9O/TR4aBq0bHHby6fAQC8a3ZHdLQQcRrn+KU3P4BPv/gMfBXwzU+9hnce3EMXPX7jjWew7ircPljhK+vbODhoB/TcmgI+fPhV3P3wAT6J9yHWDfoF0J80OG09yEdwIPj7FWZvOrBr8P/Ft+K/+qYNblfncBRx7Nd4x+F9vECPwd+tQLFC/8wGfNgjLGsRPjSD7wGecQIwXaeG1CpYkkwwTLuMbQAPSJVr5ObgUpXP5Zndflfe8mg1sBuAGr9mBpzS9O8XHsYcfi72M3XMawfkul3ULnxkL/GA7pyAyEJ5ZydszcR5pAn3jsmTgT+u0EfaAm7HoJqeH1ca9a6QmFy+k4CHjU2pelFE0rUo7eKA8cUNgEeAxx3cdzG+9YO/iUcXr+GVdz+Bz938IHDPAW/cBPEJwH3Wv7L5bXHeiYk1uk4ggy/RQBhg8I4zAzEKM8uTfAaishSkow3YMnH/ygVhCVMEKqCOQXSxmFC7gMgOr3fHAICb1QqNTM2x4QovLG/jtZMbcC7i0aNz3KjXiEx4ZXmMPjos6h4n/QJ1HVI1RUC0TZ6oT7F6ssZXcRvsvfT3xmMTHEgDE7RxqJYEJo/P43F88NEeC9eBIKXbj5sN7uEQbi3oZDzugToi1j6xGwyUMj3GaAGrAshM5npDySmXyTBrGsboGQIgRscAq1NkQZoePFan/5q2S9qaHc7JlRsXz951u277JE+AwVzIgKfkNxRs4BLEmjxMCXhVbrh8yj4ETppRSYMwSuaFsWr6I62mbtXIYx5jRY/RDoIcrA+cK94FSU9kTwhzPUbIzFlW3I/qKECV+ibONF8Z6Beyr1hLamEakyyDhcT/YEB8L8+IDSdZFDh93Qs2sqWpG3tL/BsGedrSKXaaXgmmlE4nRALxi7hz6DqPed2nCoHQzJIcJBkKwXtiVD6iDw6Hsxarp9ZYtQv4ljC7L2QH9oTQCCM2vHeNP/D451GT2JugKGIAIbCHhwRBDpoWj9WneGJ2hk/ffRr//s334iOPfBVPN/eAgo1VVneX7yxyLPr7gd/gu29/CZ9/9nG092ZAxXDzHrH1CN6BOrmX/WL4YMV6GHiPEDsh6Yka3DK/M0rldtcJOGnApJE0UsqhB5zXuVHQNMwKwMyJz9lHeUX0eREBeLswZ/RrvZ+UUw2tlZUMR4DWlo5d0d5KvygHMGn4/QH2YUCffEExB0Oy1TZnKkGxqYDorrFmql2DWW91uyhP3ZoaiUEp20lKYXl3kedetH2jS0bWYDJTRJQNdJBz1V3ZpL0A8kt6q4mrhwXATnW2oogQug1QnzOqdUR75JLob5jLQGhVDtMkv5LfXC9MLjNIxMD/9Zd+Dn/xV/4tHIDf/7nPggn4v/3hHwApXdh1aqgia6oCD84/IfFEOvjr+eukuGRuoRewj6ySBRParkri7/a3chGb4MHRwROj03zwSqlrEYRD38KBsQ4VDqqIp+cn+OrmJj5/8jgcMe4cHOC5gzcxox6fWz6BT33lGfCbM4RFwPPd03ien0bceICA+qDDHQD/efY4Fr7DzWqJY7dORqmmHh+7+WW88twNvLR8EhQI1X0PnHiEGQOLANcRFq8xDl8JaL9wiJ/6o78Xv++bvoCP3ngRNQU8Pb8PV0fM7sp6q3tzLJ+JUu3QBvON3KPYq3GutdILS8liGXwIvaWNbvLglcT4x7T2IuVQvbohG3FEsX2r24UD9CUG8C2K7ChieanjlMe7BrGu24O2i2yMNZL/LrY1o8+lrRkf2tah0fNeOunlcUZg1sB2jY5ldsKAcQOzqJfIvesZaCRdjaICX0WUPonBqqZfjB4UPCguQE8Ah++7j2/bPI8nqpfwwuydePV9T+HksdvA6wdA9CB2oBByKmE5RJWfy8B3EQCCyxMxZmQkJo1rhBA8CEG6iwDRsWKESElHK7JTphWnQzROtLZ6dqgd47ja4DTMcGdzCAJjVde4VS/hKeJOd4jX7t8ArzxC5fB6OMbrOAYrK8rVESsAd/wBKhcwdx0a9On2OYp4ZnYfZ7dmOO2OxJnaOGAD0e5SlkJ1LmLN4W6NT73vnXjno3fw1OwEjhjH1RrkGX4l4srdukJ3rPIMdiRLN9T0DvbZOTRWBRGSk0ehmOwTchBkwPa1exIgumgo3hd7oIdp7W9pe6tszY71rmxrrtvbp9n7EGUAYhV3B5DH5HGmCOn6Tr4PWbzT9mLw7BkblYog5mBlTD/zmm5oTMxYyUrVUuQtuhsMamSc4VpBBtNB7GW+D86ZJGm8MHYRkIL1wRM6y3gY2Rw4RpgxuiOn0ihyTn4tRaMApCqIFlBxQaev5j8ploFIgGfEiuG1wqvZJRGhVxCLpBq8FGHiFEBxgVWjWI7r+mw8KWplcQ/EQwbPI1wjerghCiOoIUYXxdh00aHW9ELnA2ofUhX3purRVHL580WLs5sNlk+LIbViWlwB60cJf+j9n00Mq3zLs3iKMbUiO8ypx/sPXsV7F6/jS+tH8R/eeDccvQsfufVVvG/+OjwJkFWmqXfskyh8ZIegvz1yY4k7+qARAK6E0cxwiAxsbjM29wgUHcJMpGT6hYBUodEgl2b1OdOBLu67ayWtlLXacpghBcdIWcNcUbo/RvBgR4gzoCepFk+dMq2JE1srgVpOnWlmwDs4jvjT57+CD22+iuebZ/CJm79XZGcm3zGaXH5h2/W+la0oNJc1FvO7PqW7aOBaKb9iY8WkvRpti7idMbaLmANczb5dg1kP2/zEQG8PiRmHshXpVOnB2eO8T6YslBNsfRDKNKrBNvYgGV3YTrEoO5r2Y7okoheYIxC1DBLRC8jBinxLGgfL4KsDrh0/1gDPsmPieiQxYL9R6mdFCJXq5vWMb3/5i6krHIDv+tIXMx3XDBYbCyvm/rHrtI+B02cTiSxz6eGgYBaBnaSTRAChd+i8R1P1SQA+FgOpdxEVRdQ+JLArKPBVuYAjF3C7WeJOe4gvnD6KZSf5G/+5ewzrUCMy4bNvPg7uHXgRAMeIpzWqUy8pJQtG7xmtj3hjdYjPVwJoHVdrPNGcIrDDST/HC8vbeOP0EPxYi9i6xMRqb8hA3i8Y60cdqpXD/G7A7f+lwf9v8wG0H/J439Eb+NzpE6CX5jh4hbF4M+DwVcbylQrn7yD0h5zAv/qEUJ+S5vvLvWtaGfi7Y0asGaEWAU7TKgNk7AYV98acPqO6hyjVP0pn/DICh1MD29Syy9Bn91Dud+pbYf/gOgC6xutR4dymnV1uv9ftugGYthOF+PjUM1cgS5hEpQbrX8LWlIchm2yVx7P3ID/cW4xEm7wwtsDhcaVUK2NuwZYyVSMxscxW6ee0ngJgMoDJWM+O0HKNNeZowxwtz9CSFMQghBTw2NLDsv4Zt/IdNn2vcjlBU3XMSBM4MiIRgnPwCk5VKgBvHeGIBchyKgjPEjixaHtDAQvfYRVq3G0Pktjv3XiAnh2YCW+uDoSBXMkFcesEjCIG1+JzBeex7BvcaQ9Ru4DG9Tj0LZgJm1jhXrfAsm3AB0FC18rEwkzEh2MtcgWul1Lqixc9XuwfRXjC4XazxJubQ9BJhfoMqFdRAK0zh+5YGb7KSvAbDYhwvndei85Em0d4gCrRrUx9HgEgZBb5CDRFVB3NgWoYYfgyXXLwvaz92WqX8CwexNbs+p32/35ta76BWyl7AhS+SMRO3ayLWuHc7mr7CuRMrrvnWXdBpEj8htB6VnZQDn6DivF9ZAtKTSQg2wWwsI1iRegPgNjICxLmDASCW0TEOSMsMu7ABAHaILIqjkj9F8C1kkoYmuI4kOPAszKtij5JNkxYZybrkrNWKP3uevkcK042zdIiy/sQa9knRwJHJ3pYqp0VokOA2JE2iEzKuqvQ9hUqH3DUSADeu4hVX6OpemAW0R07bFYOYLk+6hmrJyOemd+D18E16IkYQ8sXRjAUN7amgG8+eAXPzu/gt86exv1ugS/hUThiPN3cx4HbJFDLgDDbrqaAs26Gm/M1Nl2FrtciWt6h7zyipnKGw4D1SSP9VQtYKcF2Af1iLf3nK4heZSc3Vuy8gIrVUtI7QYX+pgbRTFrHCpjZ/SR9jsNM7lUdzM4Ag6C9SqiwVyF4Zvzp00/iz5z+BzgA396+CDjCT9/+3sl3Zdz2Fn+DPR87qg5ONTfx3pbi7VvvaYk02QGxZb8S27J4ZqNJH2H4TtjvsLmg7msyk2VPuwazHra5Cad4StwNGK6nqexbv42rIk40rgwk4LS+6Evk9MK8su1fPg+iJgSNcmTEOqWGBEvlEEZWtEpRVV7PdZrLrbusNlmHy6+QBNurDaNeslbK4+zEeNHa6A6ExfUfn3kO3/3SZ1NBiF9573sytbmshBWMDly8NHa5joYvkjk8RR41tfIbVzF5UhyA4ISu61yEDx6di/AuZgfCBcyqHpVygB0xapIqgjMXcKNa4SzM8NLyJpZdjU1XSQojE17CTZys5jg/0VrAdQQ2Hv7cSdpMA/AiSJnz3uP1+0c4Wc8wr3vUPmBeiU7JJni8+vpN0BsN+EYPqhhxzuhuMPrHO9SHLegmsLxVYfmuGvXdCs19wuLFCr/Wvw+/cfud6E9rHL9CaM4CqvMA10UcryJcqHH+lEN/IMa2OZW8cus33zH6GWH+JlCt1OlqpUoMgFw4QJ3VwbPGeu9DBPVBSkdr5HAgfFi+IxcwtC49YO9pg0n/2KGY2P3uPHX7XfPqFTQt150yDuV+r9t129toytbseHDGkw6a+K2Yc+1qW6lyNuGwFPbBJMj2P3EO+vuAHWvLoONFCWRxcWzkCabtO+lwsQBWztLQeyksQpEz85gBdhuwP0P40iPo/tMBfu27vgO3+Vl8NT6B1W/dAF4FgHMQZ70sOb9CdHyqaIT1S3mpZVqyakolJ9PsTZTJNZEIqYfImgYCECRFxLuY0jKIJDWEAVQUMXM9WvY46ebogqS3g03ofY5NV6Hd6PROy45Tq+OSB1DJjYiBsFw32PQelYvwjlPp9hAJZ+dz0NIDsyj3vNJU88MAVwfQHOjmDt1ND79y8BugOnF4Od7Gq4sbiK1Hc2bl1TmlEVF06I4k6EXRHMTcjxTE8axWQOwBwINCBRdILiDpkwYwmThnB2FiqV1hKwij9zPZmtFzOv480d6K4fmtsjUZn54YD+ynC4Cw6/YN2koBeI5IVQ2VcbW3jeZr+xghU6wstkD9SCNnCqhNGSKU91XOFylKxblqKQLvKVMkrQs45kEg34p9JM1F3XcqdGUOMjR9PUrgFj0hdiKAZbYH0PdP09xdrxX0OmMHAxxFGN6ClBaYoZ7Aqimc+8h8nvKEZU6d+lRBK9N6lPOkRBgQU0DgjhFuEsJBRvJiJKw3NRjArAqofECILmlknW8ahCCFrryL2PQVbszWqCjC1TLmn8wP0G8cNo8Cceb0egnVk+eYmfjx4LZHBPYJ0DIh9/J3AJhTjw8dfTUt69hjHWvR2qIOEbKduMQZMPvC8jEAwK2DFdZ9hS44xOiw8RVaJ9Rt5wM2tyoADrEC+sMoTO1afFjXqr3aAK4luZ6VyOIwmw8jwGmskVl/Nq+APkfK/vapQrs9qECcEULnUIXs96diVwWoxd6BQsSH1i8NSBsf2nxV778CXw/KxiqOfSkQqHzf9gVH9rWiH6a2T2nB4/ff5neU/Th7X7eCqJds12DWQzRHIr6XHgozHlMtpfbxIDqyLydW9mkhgmKZChuicnLzi8EaKACdKdqiDvTpxdU0P2MulbnapBFvqwRhIr4ZVMrOCROhWkUcKO1XDAsnYCmlbCgAR4FBa0Z91qO55xAbh3/9zPfDfzvwkTtfxCff/xz+mz/4cdFfIogx0OMJui5dKvTQXPXD0PEUqddUzpR+AjE2cJB0C0AcDUeIrUfPgFPHw7uIRu8zIBWaaia0XMGRVOZYhRozLwyuO90h3twcYtXXIuCr+lq1D1huGizPZwCTCAE7BqqIcAQEx3CLXkroMqFvPcKqQrepsK4DfBXhXESMDuuTGeZfbsCesVk4MEX4NeXUms6jagLmBy3CrEe7aMCuRnOfMHvDo9vMUfUyQejnBHfsEzDlN2qkK0bUe1StpFJIciI70U7za+0jK3dcE0JKJxWDbQ4gxSj0uz6Aul6ArJKZVeaLl3/Hn9/qdsmKhhe1FFUoBuadQEG5bMqpum7XbaKR/XcJRza1cbTsoojdlK1RUDZpFE2d2I5JR8mcAmMQSU+BEztGIdBaFvgoI3clCOZ6Rn3O6TfEfH0pxVyvnziA+haEN+FfugH+t0e485V34PUn34muJbiXGHzvHMT3wNSDVOAvBVGsG9UhTA6U4VPJeaPUTwncttQ5u4GWMhikoi0R0COAVLTZRGSlsiAQUlVDRsdOGFwgrGKNZd+gj05o/9aNxOh6j641URgkME1KyzOoFnsCEGIghN4h9A69Cgmb3eo3HtV9DyapHgZiZbrpdUcH8hFVE8BVRKg8mLwAWktCCBVcpFTdMcwoVzcuKhnayYvTVjxakcHeq4D/AhTmIL4BdgeIXvNjaAXgDIx7AJ+BOAqIGVnZv6NU9rTz4ZxpsOxr0sYv44PtZcj+vTYc123YyLOITu9awfTmLsFev9A3GaysY1/J/BoHN4r9bm+vf9T5pSjyJd2SwMRZH4uH47IF5PO+kYIaoVZ9KycpZ1wJAyfWgDdt2B6ghqS6t2OwMsGS5pYHEIFqI+v6teosabA/FbkqimiABQSScyPVxRK74XpWOziszGrOfaxIgH8lGHArc3IpkCHMou7ISQZFxYCXqpPcOQTHaFsJoGvIPAFasyqghfo0Wtyqjy4BjzPf49FHzvBadwOhdghHDtSKcPlTj5yhVnTNUQZsNrFGAKEub4C2kmkl6YgCeN0PC9EPdgwPxpprnIU5lqHBrVqLh7DDb5y8E//rZ5/D40+c4OZ8nSRfgnZY3wtaWdcB3VHA6iACTYSrAzgS4qoCrx2iJ8RFRFgImMXeaZCkqFCoKaNhhgSimsQBxwIcLVh+6ZllAUvD3MNvYpGhonbeQK30fjg8v3gnvn395UTaeH72jvT78J14iPG9MDc7QSr1xUtAOG2e5pl5PzsPtSsFmYfsrNRG88uUumh+lB33iu0azHrIRr4UdyuArBg132pqo8s/pBRZgAACrCIIMUSIzuu+HJJxytS8fIyEgDqp3NAthB7pNxBQKSBFx2HORLFdtcwVNKJV6NtQMg61HY4FaLJziQ1J+VQF32SfQrckR3CIoJ7hOhkEuCX8qyf/EP7b9/0RrB9xmL+SNbdihYEzZ1WNojpbZdTeDFxsJPdbqpDoTxZBYYBYGFkIJBEtx+AgNFYiRqeDPkEE4B0xuuBR++HgvQkVIght8Nj0VcpJr31E1Cojp6taxqs6AkpxJmLESPA+om56cSB6Bw5Ocot7h56BflOB1x71XY/De2JoN49qf9+vcPCSE2H+c49YObS1T+kbCITusR7dEwxaeVCQ9JD1k4wwd1i87jC7GzVHX5hY5tCaUGa1FuMaKwL5zMgzWnROyyEUcibiyAUG9RHoelDbAX1A0ssC8juS0PnhX1mHcBUae2IsvgVtED2g6eNfKaJhxvDaD7luV2zkigdnSvNkcqMrHMDGxkIcgiATOtixizE4TcTLXaQx2MZuSmnmFuQYrKe7TWkiHRI7K2kyaLTdnAd7hwaReg/Jm+P8rqYTiwChB+IZKH4FeOVx+FcfhXME3xD6+V3E+g0wnYJ9L4GRwTimE1Ozv8Vx5Xck5m9U52m70RAsJHFCYiCAHCIxnDqCjhgERoxOQafcDLwKmkrCINFAd6KN44ix6Ws5T58RPfK5QqL3EQwgBqieAAGGAfUO6Alu7VCv5dr6A+3vtUN9IlohsXPgIMVVciSVEA8i4iEDvTKOKwYfipNWn0sQxPrPtXkCmoAtc+Kc2GdEBwqHAB4D8TuAWwCeILibavtfvQG88ASIXwb4Jbmo0AqQZTZmK010+HdYPXd6cr6zvYVjefnuDL5PrXPpneLa1ryNmqtGWSGsgO4gDUTaIKvhgmd+yuFNvxUFp6bYWEwqQzF1iNE8zQIDrhcAy69IBdmR7FPyM6xyXchjSvQWnJdxKWoqYJhnH8J1GbCwffjzCtVSdYA7DICmynS6OpsL6360yFV3ROhnAvTr0J4KgLClHEaCCwqGtTnbwfWF7Q5AfR5TlowwWKFkAgIzI9ROBOkh+5QKuQpKBEIPD+8jQiR4x2h7DyJJPwyRcDRrlfHLyU8J0Un12dkG3e0zrNsaXVuhW9ZAHXHcbHC3O8Rj9Rk8GAE0qGZoy+xveSNNKN6ArN86ewrvO3wdt7TqrTGxXt3cwMubm7jfzvHq8hgn/+NTeMcXAl757sew+cA9HM83iVRQ+QjvGTEC3kfceOIM77h5H40LcBRx3s3w5Tu3sH5zAapIQL9ZQNhIxEyqIBO4E/8X0HTESk0FIzHhIiiBjP1M/B6TsSkzLWJNCDMH6jUgZf6XUbtcdh7+1aO/D2DGh1Yv4vmZaGZNBvKBNJcQQscFAzlNfy4liLaaadYN9nPxcaYKjGyxu8ZyLRNzxxIcLIOA42u4TLsGsx6yJfyqpBQCu4GsssIbdkycRg+XVEwAUG0DWroTACVtV3StcmSctMyo5hQvZPLqNjpJt3PRF9DYVq4HsOJUISLONLqglSQs2mGgRmgcukNCqCkLGWr52TIKT6y5y5BqEAKI6KDfMeozOXffiiELM6A7KOi3ek2WAtI3rhBJzNcSvZxjmFEerAw7URtPTEIvrqP0bw8EcnDeoe0riSA40RswI+CI0UePRsPIbfRoY4VVL7pYkaUsrrGyztYz9L2TiDcgOiaQ6/A+ptTCGJyknlQRVIux4c7BnVQ4eMVhdk/6qjsUp62+53H4EqE+k/ROv3iCWcUAAQAASURBVHRSkaX3EhmfM/rDCDzSoZl32PQLoUDPI3DQY3PDoTuuMH/doVoDtValnN/ldK8sgp5ZCAKEul5TRzXFEDPrW7m3fs2o76/hTpfCxurDtnNRPO+TlQx3Ouhv0ex8334e5hAjQ8Ljz8n5w2DIuG7XbW/b9UxeBGQVTsWF+2QRmWW3DWhZBbtBqeURE8kmOuyUqampgFbNdLAesnNCEYCJtWo6G6MAv0qQiMUBESYoJfuzlcJo+08hvw7E90BYgflVUKzh2h6eOlBoAWoRq5jTFw20UtsuGhqUtTHsOijbm2Rjxo6dgj3CdFOwXbWzKBICiX1wxMLKIhGBJyBVPgSEqRW0am4Cskgim+a8xCjbctHhTHL/LIDCkSTNIjGxAEStTnhGqNZyztYXbu3QnAoA1dYyBxA2tAgmp0DXPMDXEWFJMCFkrqPY4Eb0tlwPOBVRrlZFV47uH5NH9AtQfAye3gH6DgDf0+PZR76Em9U9rNwCX1o/h/BLB3A/8yhocw6K52JnrPb5WJdx5IRc2N4y9tPXyNbs2iVf8Tqv2zdEcy7uf2Y15fAqQBawG8gyPaCtyukFKDzFxBpqLZafIWMvBPSpl4wwz/o9SRbF4snMuSIq5THbtI6sQqoFRyhYBTut0N1LKrNfk0ijRBNkz9IqUF/It6ysqayhVK1l+6BUKLNXFNV0qHYWe9bAvsypLa4BQItWIRW5MLZKYgVHAOCkRTy4HwQZZyMQIdXYQ3AI3qGPwtQ1pu2s7tFFlwpcBXI4cHKxtuzJozPc38yxbiqc+4iqirizOsCv4Vl89NZLeLI+ASAglYOmGiqQteYKyzDD3HVJLN7AqtMwx6dPn8HrqyO85+DNAfB15Nf4lqOXsQwz/PL5+/Dq80/g0ZcZBy+t8eQnF3h5fgPxnaeoXEQXPFbLRkA8cQHx3ifexO+5/YL0JcR+vuvoLv5D/S6cvHYkdo0AbiQTpt8IxcpV6ssy0M+BsJA+jzXpcnlXbC4SmNB3Ihgvz3B+XtkxYuOAZfGQE2EQSNH3IZLDT9/6fcANzU4ZA1kXyKs8SJsEsi4haZR3gKFfowzKS7WtOWZeNghqAoP0w3L5Zdo1mPWQjUzHhAyKBRLzxLn8MJeVdPLG+fMFaSMUVYvHHGF7/oOiwLo5BwjDyCZuSpG0QbZaUWI1AdkAlNpZFi0ItZQ+7Q8hQokMeH35zTAA9gAS1o84nD8jIuJ+Q6jOZVLsV7kELbEaBTWo0RO86Zt4WebaKICWrxAaqWoCZQBVqygRC0th9ASndOJkILV/xOGhxOyKVrlQwSS3QR6M2CGqGDyTsLO8lq6VfmKErsa86lGzCMC3atGWfYMQHc5aUYOsNZre+IA2eHTqYEQThNQIuUVHQufEuehz2gYAhODAXXYALJoTK6A+JSzeYBy83mN1S8qcVyuJOtSnMgC3SmvtA6Fd1/BnLj9+kVAvOnQA2r5GWAmLrlpxcr5cANCKA4eK0M8FyIpeDL5fRSzuLPFDX/5FfGj1In796fcAjvDRV76E32zegf/OfQRh04K30vmcpJpaSdrynxu+F5NIPzAY8Lec9IkIxtZgfkUnZZCiVU7CpvZvDtlIO2Ir6lD+u27X7YJGNssvJ0lAth3lhGPLnlze1gwYROU+IwDK2h0ZjdLDp3MDANZo8ugaEniVqzcBYi5jRYiNRtAZSKQkhwSGmSZfPye0x6U2xrB8u527lGknEQ2PEUQtwD1Aa3GyAqU06uhDEt+VYA4nfUab2FqFo3H/sdqblJLgih8AUM+5P1lSZ+SgQAxOCoDYugC6ICmFNq+z6k5d9GAWFjCQU+A9RQG6ohMyhoFYNj3RY8focoVFx3CmsaNRftdRukabWPpW2Nn1eUS/yBWLKYhgbh7nNNLfuaRLaX3g6ihE4egRe5Hscn0x11fnzxgesQKid2C3gAtPgj7MePz3v4z/Mv4PeN8Xvoxw34EfJbzwzDvx89/+h/HGb90Gv3Ks8F+BnurFU8nKIvOMrbcn2o5XZ2v9qc23ll1xgL/o/dy3uylb8xCnct1+dzUaC8CT+iD270IG1tUeEPYupxamZ09lRTTAzgrMpxRwXVeCAEVwBFAgS+agji17RNa39DzzP4wdlZmcyRxlNs2BFEViL/bIdZK+KNpXkmHSH1CSUjGpjegB30sgHM40/5C0rHI6NKM+BfoDEuFx63atxkpBUxY1kJFYviabUo5/CmBZIS4AOciv9rGfk6Yc6jo2fncO6AXQ6h2jqqRQVdt7VF6yREKUwiCu6hGYpNKhl2wSR4xFs8FB1aJxAffcAou6T+Y/sMMmVjgNc3TsceBaeMcpnXDDHq+1x/h3r7wP3kV87LEX8dTsPg5ci2Vs8MtvvA9fPbmBw1mLTaxS9UJAKhl6MA78Br/n9gt48wMHuLe6DdcvUG0YfulwfjqXNNBA4GUFt3LwLSH6Gb5cRXzX7S9j7joErQD8zPwePvqkxy/f+wDoXg2sHPhmD3fcIZx5qXbosn0DiXA89TIHkfujvmMtv8fKfF6ZM5TBAmbJeuLaAd2QHZme+3HF3bJNpurRzt/esnYRgYBGf8umfv9OsMquuVxu+7L5Ikb7KLvnCkAWcA1mPXSr6iCRCWAa6SwfyPGDwjb12vMg63ogknStyikrqRi4YeVtdU9dLime9EMogz1MQKwduNIXkEZORoDoJlUuRThcRwmriw2QxIh1u37hsHyasH46gOuILkiqgoFafoN0zvWppik6QqVlQV0fE+WTwKjWAcRAe8Ojn2sERTW23DpIGgEArn1ihcVG8qGTLphGymOFgQFIUY04fNGIHHgWpZK3Vs3YEAOQ6ob5dgjjqnZSOWTZNXr7pf/bIGmKq67GuqvQ9w6h84hdAWU7RmDIMtPuIoDhEFQkP/ZOBlcPbG4JI8tvZPCd3WUcvBlSOVmrytGcCP26vUm5mtfKy+C/oQRKghh954HWIVYMmgHdEaE71OiPExH/+izrY1k6Z71i1MuIetnjh77yS/g/fPUX4AB8593PJ2zmY/gcaHYfPzX79uxIeL1+n/7L78XXSf9jPOkv37spKu1wZfMUR/sqjZz5VsjLhvuYPo/rdt3GzXkeTnC2kKLy8/YMw7QNx22rQg1BI/dIY/z2XKOwMemHbGsMLGAIyGOp8IN0FS5SyhwNAy/2XtgwURw8VoTuCOiPWEqgR4BmlMAVq3oLiJ0xZ8gRwB2DYg/iPu2XesgLONOxMKrobhtT0AR6HY5FF8a0ATP7eftf2W+pMwDJ+gnQqleyjxik+hR8SCnb2svwpGkhADoFsVJmCjsQhCncK7OXo9P0E7u/+jdQtnkEIBa/RUrnEubImpoB8OdAvYwpfcaq2/oN0vpg2R86AvUOTtPZ5VgiNC8Cywx4BS3VaQDJPfCtiCzb+VH08F0DRxX8uwO+M/4avunXvoD4/w54kSu854jwzX/sM3jxnY/g9cf/oHgnZldKp34wB8PXre0b4686/u9cvwSf36JjXbffPW0AZlnayNgnuRAsLX6zVKlyc/vZGPXA4Jkj5uxv2HcFqUofhE3zFxg+twWQ4zrWsST/loIVWu02ZZYQkt4WV0B/CHTHCox1Ygfqc2GDNqcynrWHToCNeQa05NjGwkViHrODVFY0XWAGfAfMTwP+wq//PD762hfxK889h//mD3wc0WUp9Kz1K+tTYKkbVvhcbHY2mk3MFcItqJDsqFdmGENALMfyz3MCfEIQpm8IDpUXEXhJKwTWXQVHSELwgQkHdZsCI43vcWO2Rh8dNqFCiA6Pzs9RU8Dzp0/j9dURnj44wXsP38CxX8NRxP1+gV/46vtx98u34NaE/+GJGzg6XuN4vsHd8wXWywa+Et3f5+8/jYPbbWKF1RQSuHWzWuGPPvsZ/I/um3CvfwzVmfbJnQZxFqUQyTwAa4fFK4T5HUb3hWP8v/BR/MBzv4Ujv4FXkGzhO1AlmsLz1wnh9QbLD2yAGz36UMGvCbHwfUVeQORUHOR+mZ0CxEZJgbMhG5Cd6TYTonfDhN5xxhYMkC2WlQzJS6T47WyXGPsHbUozazw2jPaZdPRo+/vA/9l37trfk5fwEKy0azDrIVtT98LAchnsuXQjmnQuxusA+UExwIddEeWIAqKV6XqDZmCY42ScXCgMi8/ivsayCil9Q0ANpykgUsJUjhsrMQKxAtojQnuDBQyKEJF1ktSD9qZSa50MCKEhVEugOyYBu5YOzXmEX0VUSw15RMCvAmY9w93w6GcO9TLCr3rQJsDKnKINcBsHN6sQ5l5BLQXpLL2ylvOQCibWJ6NutmpZLYlQbuvQcZWMQF2paGLw6INHU/Wonc+pIUyJkcUQg7Fua7SbGv3KBLuKW9ITeF3Lyk69HmKprNLKcuokddCoygAKQUoZPDfHUhWKfRZj7xdyP10LuEqE310rQBb1JKmGaw9qHSiKgYweubR8xSkCESvAW/ppryyIjuE28vx8aPmVQWUOFJ8/1L8KzMrnMAqgRU4ArpFe1mAwHw9qlx2kr6CVddGkvhSiHjv85ugnsKrc13igph2fGYlVed2u20XN+5DfkYsqU221y9ia0VcG2Ci/+pCSpcpZ4GR8GgngFVsjp2oI2TCaKe9NjsxbajsX+idpt8rWMYcizABUCp5FtWNOUlKiOTtKhZdJqYJdHSeBXWeVpNhYyZoOV0mVKddF0fxTIQ0KYlO4cuDKyfmlghdUaLAgMQUmu1nPj0ECaKkYvMWdvIsyqWaHGIDoKOlo2a13xc774NAHj9A70bwatwiwLU/3mBMbC4CUp+/s3hbPA2emdj/TqDVlwDBpUQbxnf3SaTAMkq4aAfROdCn1MRgAfmZbYM4EdN4htomisNZ4RqiaHhw91ssO3J3DdxHYRHQ9Ad2os0VITOY9QDFBL5zviybfF7UrbHehreHSGRi/iJfbx9Y5Ta1/bWveHm3kRDOPQuY2Jl+mlQ5u8bkEq5JtKcaOQXqhgVslELZlb3RMMgasVlMHMiA0nr+brqv9bhVxw0y0svyS0jjmVfvKbxjVMiI28pukOovt6BXUggO6gzwH7A+0eAVLgMR1LKyxCPyF//jz+Au/8W/hAHzvf/4sAOAn/8gPCIDVIulACotM7ailVGs/DlLXlaU1ICw4uR5wvlZbF5GSLRQJk5xZ4jQDJETV8A1OK6wDgEOY0GRsfMDcd1ijRogRN+crPDU/wQvL2/jS/dsAgBfCLbTR44n5GRwYn777NO689IgARDMGtx4nrx/h7PymnGYTwUeCFN5dL/C5sydwu1niRrXCzSLXPLDD3HV4/PAMbzz1SArCV6cE/2aFfsHojyOqJaFaMhZv9LjxxR6r1w/x33/8O/HN3/wSvvPWV1C7gEfrc9y8scTmZIH5m4zmNKI5meH8HYzYKMjpoMEr0Wcz+xQrYQ9XK2B9wFn3E+I/hU1m5xnQCQK4okRqT82StgzEijF/tsd5Smblqu0qNma0/MJ9mu3eNYdUF3ZnJs1oP6X8ytS+Bvb5ku0azHqIVlPAu2/dRXv8KPzJmd5o3npQ37KmM9mB2DlnozEAsgZz2mx8UuSGIZESkESfi1Ul9ZBy/jgxwozQqwaK/R5rxuY2gZ1HvwD6G5I/4O7XqE8Ifk0Dwd8wL14cPY6lrfVzD995zO47NPd7Ae2Y4dqA5h7Dzz38JsCte6CP+XqZQR2B2h5u7eFnFeLMozuUFEViAXpiAxFjdEjOj73dierLkEn3RjW04BAJ6DUdsK57dcyAyBWo7oWh5QP66NRoOKzaGqtNg3ZdIbYeA1X0hIBTjnqRnlMQ/RT0EkXyLcFt8vlxxQlkXEeHfsHoDiil2oQZIcwl/9vSQF0rqaGhyQaQeoJfSjWoJOi/Ri5xv1EG2Abwa4ZvkZgGvmVUa4ZvI4gZnz5+F77z7ufTeG1jUATwfPVkvm5HmKz0uU+3YTRpuqhdOuKcJmHb211Y8Q0o3j2pI7ZTm2SHY3FhBOO6XbdR8xRxc75GmB3Abdo8EeCvsa2BpcQBiX0FDIGsiQkMo3ilGVtjrW3HxAkgo6AxIQ1ApLGRZTwLC0mdFv3GCDgGrR3cJgsE235jNZykMQHwQCApCuIC4DcRfh01MisphX5NcJWU0KY+FqwGtTURoBA1JdzYzU7GW2RAKwF3I+qBZRfCusQq24LAam/AUnI8MblYouueGJ4iIkQDJDKhCw59XyH0bsi8KhunwxffKdkgC5aUaS9lqmS/yOkWifnggWCMZ52cGpMrsQag++40VV9TYiw1CCw2J5VD12CJdFkEhQjXb0RW4IuM33z2g3juOz6Pp+gU77/Tg59l/PqjH8av9x8FvgAAKwDqYU7aE/v7FtiaS62VD7pXhPeye9rhjBSHuW5v81ZVEZN6vfsyRsbL9hYUkTFMWEKEUivL0glT2+EAsydEn0GmnYFAzsNUqdOY2JzKcCUD1SOSrEh/gFSlPdZ57mtSKiD5bExTEfBWMMzGcJY5sAm9mzYgq/5wfwhQYHz0jS8OArofe+GL+dpgY2C+Ht9lPaxc/RuakimWQETGtV+dkQbUThpz2evY6rKgvCA0QIyErvNSIR1IKYMhOgQGGg3Oe2LUPqBywt4CIfkyFQXMZx0em53jq6ub+MrpI9h0FbyLEqzvbqO9UeG8a/Di67eAOiIcqZ1fefgzl9hN7Ai88QiOcbae4dX6GGf9DJFv48n5KW435zgPM9xrF3hzfYgvvPoYUEfwQY/+pEZ936E6B7pjgOcRnQOWrQeowuErhPos4uZv1vgMvwP8zYQP3HgdX13dwOlv3cYjLzPm9wLqk4DmNGDxRoXTZx26I4id9uL/LF6ndM/8RoP2HTAHYf0YIxUjayQbBm3xvJo5tfeis2e48MljBFkBrNE7dSX7s0NuZdySVADl7/vW3dr31KGtQuHWD3s24uK6Js6FWIrhkNPuLN6dq7RrMOsh21G1wZt1GUKOQ6f9IkfDUjn2PER5Ppz3RaYzNX6Yd2kJlRPZ8f7LbbwItXYHLkXCTTyRSQZlr+BMaIDuKKK9CcR5hDvq5KHshdbrN3mCCogGOCADR33G8BtO0Rab7PULB6BCc9IncXHqxLHw6x60yRXxjK7J3oGcVpLoAlxbiR5X3aCfqZYKCxjEQNJVIRVOHNMfXQ+JlLsI7gmhkmgGEYMqK9tO6KNLkQyrcLjuK2y6SoCsZcHIiiQ6IqzU5UpL67L+1lFiX5FpwGgkCQDijMUo9wzXEdobmRXnevlnqTfs1GiqUGF3E5o+o85Iryk9FYNrlujHmpJD4teQ0rVavSWJYAa5Z9V5ENYCMz7x+O+Hv7fEB5dfxm+6JwBmfDC+huf9k/hXzbdtP2yAAlv5n0UldlUy3Nl2gEVXaQ+VcjEaoNNnNWxT1PlJw3LtiFy3S7TG9VgNNAhGTsfYMRi3S05Y0r5t9TEYMt4fRmZln60p96GpE6GmHNkkFAB/AQJ4IDSMMJdxi5qo2k8GxgBl5D4D97kCUU69l99iRcDcSUntJLbLoDYIkBVCCkyROgpsGkyRBdQKTp07n5m/nG1qThWhyfFAIpQ664yQCocMgByoiJpLYREPTxFeA1J9dAjKyOKuNOJIAZQkqFoEsRBzkElARBpE+5OIvabYWDVgY0xQFNsvcgOyjqXn9DNo8QCkwJHcU1ZmtqYS6n0w22WanRbhFaHkANeeg+JrwG88jq9U78EnPvhf4ZnnvoDq3R3u8iN48dXngP8A4DMA8CqADQbU9MHcqgw8TNiay7w7F623p32t0vsm2cHIr9+1rXn7NBog+CNfBMjj+mWZWRPOcvk8le+PBQJLpm1eUQ4Xq2LONB4P7adYjDU6fhHL3Hcoig5YEARAkkQJCwWyVFfLxizXadBWxe9dz6hXjPUjMp5XS0kXq5cCZDRnqs8bOYGBdm6xdugPHPoF4deffA6/96XPpoDur7z3PYndlXQhyymtFqti0hR4cE4rpLyuFVIprzeNwwDcWn6Lc+1sAtCLBDrgEAhwOu6vuioBWFFvUO1EV8vpSOGIUanNiUyY+x6Pzc7w2uYYry2Psekk5bBXdlfXVXiZbuDu/UOE80pPCkCABJgsm2fO4IMg9j4QlmczfHnVoJl18D7iZX8MR8Iwjkw4vb9A/eIMdCOCFwAaYUyvHwf6Rzv4RQ93xGhvOqyf9bh3VmH2pkN9Biy+WuE/r96Jz958Cugcjt4g1KuIahnhNwHYAAddBNDg/EknaYOQoP/svrC2LZXUdVJ4rD5luLbwtTpO9i7NWwqwNT/z9sxEoA+5wm6I+fddheIuo5e1E3DWP5cBhDTVsJQsSoD1PvDrgnEhfdf3OqUjFudo7CwmSunCZTbMVds1mPUQLTLhiye3cet0c/mNLuFIjNHZqajeVhRk1/bjB0xBA64kOmK6WXlbJCHbFBUnYwrZPsRwOE/AWrVLZhG+DuhWteSn9xgYHABwGwyF42OOlJgAIiDH744q1Kc9XGvgEUAGZkUBUpKjYZpT3gGVB3UBCIyZI4RZg25JCCpezhXnylhB9FHsHKknoesCOeWQnYi31xExEFpfoWl6+CYP+mWFkC44hKDpHpz3nZyGnlLp4KiCg+VEP/VLB2HHsRiD0CjQZBowQNKTiZWIXPZHBX22kxLq4SCCFwFWtj49E1bCORCwJknDUTZWtRKxS9+yRtu1MEDLqFZBJgMVgToG1gE//cj3AEffBe46IERwb8In0HRCgom+T0YCzeFww2WTg+NgAbbnYzo479xman8TE6rherRz3rfLUUhO5MC45QHcnMBrltZ1u0xjJtzbLDBv+4tXTu3qtmZynT17mgSx0r5lYZqUF5Pz9DttbwcDp3Sf9h65nmR88wxyEdz7xCAeT9xIGT4l6zZXigJSmiRJ8MZtsmYjAAmM9GpnYLMuzvp/FNUBkXCiJyB6L1X+qux0mCPGzClYYeAWu8JBMnClk8pXzFLh0FcRlQZKJMgrlQgJUpUwsnwfTwWoAK0IUOYCZacnZrBrwMiqCl2scnnhYMWqYDoDWq1LWb61ImBcnIgBaZpyyD4HTgzIItWJtHtJgeH6HuAV2L0GuuNA/58bOPnkozi5cQuMAJww8NUInEYALwC4A1CLSWbWwDmw8fwKg+7USzBedhlbs29/5XleZh+2yZStQZq+pS/XWlnf+K0aVzOcArSAnc7yltM5sU7eN08y2c0RzsCVADXRS/GpAStrsD89vtd0bq1GblXME/uWsg8h+88OcJzJ3BlAKiRiouop4FG8e839gPos61O5LiaHmj0l4CnpWkEYvdUqoF6KRtJ/9/j3ofog48P3voRfee45/D9+/8cHmkr27hFzGu/ElhT2IQEQnPrFhPUNfEtp7AYWBkoEAwME4Fi0CSOphKFIoXTBwzuxK95JBkkXHWa+T4WsDusNln2DuZcOdMR4cfkI7qwPsUlajZyKizR1j9PlDGGtjkjIBjfOJTWPZxG06FOhkdh68MohekZoPVwT4FxMoNvmbIbFFxq4XuVSarlYvyaEhfRN2HhgFkSvugoI84ANZqDeYXYXADt0oRZJm5n5R6KlYvI6JjEQ5gA7RrWWZfV5RH2eHy0XJL3eslNcr5kqJHIHsdZnxaQV0vRA5g4UWICsrs9AVlkgTt+PQdDlkuxgA6S35nAj22LP106AqtDOSrsYr2f2nyjZ6THoNHyO5TwGdukCfy5VSCzemau0azDrIVqAw/mmwa1OK7ZNsbB2PZgRcscjYagYN2pbleCKz+Ogy1QOuy0nZG0sEmBnc8OjO0ICT2Sg1cGbkQyK6fp4xeyMseU20Ep7hBYVQh2A1g1YRVKFA4OouSHbxDphjmXEhVOKYa58QpL2YSBW2TdlHwVJfUOIcJFReUIz9+jnUrHEq+ZHrDkNQE6dDDNwrMwpCgA6Qgy6bi/pJHCMVllaAJJh6Fki6puuRt8Vr1WEAlbSh66X48BJ9AIOklZYAE1+LSMPU3aKXC9srRIIFEaAGPD+gBEW0sGulRTQeBjE8VPgilSbyx6NGIT6a6CZ0alLloM5gc40ZFieI4oMv5ZyVDxvAK5BfSP3oOsE2Or6YXohyeeBXpZzMG2TXc912rb8O3rmd7XJAfwSVX2G+7CqPNO/DzKJbBC/quMwjUtft+sGQECMNnjMQ9j9qOxDnNKMZs9Btpzz0u5sr2qLh+dDeSKigEGsgDBz4pgYeBKRxm8Ze4fpumXkk9V5MYA/wAmQYoyigMTiSukWOq/Mx8rHzGKnUK2SYZp9FmjdY2sKZjBkLghfSQESYdAS4HkgBi/HHqIfiS2mpo0JqvUlNiBANZ8Qcn0ZliqRffSIYcTIshR6u167pt6W0+DmmeD6wOmKuU9TP+m5caFBCZijKAzfJEQM3pKo4gggOpCmmtqcAwXAmP7Fom9dD/ApqO+Afg5+6QB4sdG5QAeEE7A/A8IpBMgqwF4a6WUlp2FwC7ZbOvcdM/pdbRKg2oVa7doH53dhqk0svwaprpu1xnQVrdnci5UVYkV4jJl1mbnQrnUYUqW10hFUARu2Mct8CAK4Fqc7EjLbqExfL5bHymyG+ClS0dACq0haihYAZyKgFnDBr2V/YaG2o5J9WyA9zIF6hZRtIkWSODndsXFpPJJz0VPU+aoLjAgnoFfPcDECAfjpp74f8R2E9maFwxeEsRUruy6dxxesNdfJQSzl0qVAfraH1DNiQ4Oq7AkssHRsG+sDASFfLxjgQIi9Q6/n3hGj8lEBrQgCcN41qH0AEaNnD0dSTCSC0EePVV+ntMM+OpFUIam0y0wIvZd0f88gH1WzS/ssOPhZgK+CgGvRSbGrCEBBmBAqIRFvHKoTj8O7BL8C2ptIWSTVicPBK4TzdwBx6QEHhNYhKKEAGw80Eat3MlYRcGtFCD2hvRlx8m6H5sRh8YaTAL3OE+pzefBiQ3Abee7YE/y68HO8aK1xUQjGAkTGLupnRUVORiZZBAa6HtTmbCKMfVh5EfLnse+z1xZcbeDf2s9UwbrLNMog1S7/66o2aSrYcylmWdGuway3qtlDYQwUa+MHd6dhUH2SyJI7eonc1XxsKKU1l8KVlfIAatGOMmrSHaoI+yqfJ6X1xTjY4GopACAAJsoYhZ5ZBwDssJ7XcimOEWvRJTH2EENz17nYH+RcfMeDlJIEaGm0FuBpHTJ1KgYtMoAIaju4lUd9WmFREcLMI3pCONDVKogxqfLk3sA1uQa5Pr/WqHNwYGawZwTHaKlGjA513aNyMVWZ6jqP0DpAoxXEIqwrwBQjMkAqtOs6rRw1j8LYMrBK87gRWY0gUK0I1UqdDHUGBJgD+gUjzKOk3/R6vvMIaqIYGv3nNKrCTOkc/bkTFtYKqM5zBKzasFKskUvUB07Onj/vQH0Eew9yDpLu6SUvvPKgqgK7FqJgzMKaK9lZRIB3wxTDMWg7USnH2j621LhtAVoJ9MUwRWM0OI9LRu863iAvfSJCnsDkS+7vul23va0A+fenGe54yBIgA6QKU3va1C7tteHBSpZSJs98AkAcIajWBPWZqGPgWlSQJKWTRAzeSTsuBduW0FcuHddsVGkJynctVSMkJOHyDGgpuGX2O13UJWyNrkchAD3Bb5xce+XEhtZS0S+dixOf0hy9EmwyZrBF69mujSRoxiw6Wo4gTgE0GBEoV8NFoVHp7NyyrRa9F9P9MqcCKdCSHK0eSew4AVl6f2ItKetwnIAzrliE7ElBLPvL4uTaOVKbK+wKMIkhO1uBLBHLtX4JcG0AxQ1A54C/CxirjQPI9aDQgqkDWHXGykj3mOmbvJDRvd3zCti9uFTbeuVGtma4OG/2APvfu821bXlbtgQQkc63yjaV0nSF4GDJdAXU34jyPpeAlqyMzHTVeRWBcyVYGuonJqBHfZP2SLIpmETqIu8HGYiycUzPzW9kLA+N2puZBIRdJ8dKVc0p+zOilaVSFzq+W5EjG0tSFXRNcBASDoFa9UmUKesANKfiQPiNnFeYEfoFCWkpcva/akp2hxuHpIUM5OAH5f4IMwFdLH2xTOVkyDIZXwmxiTKmd059PYeo9i0o2ORtrCcViGfJD6wooI0V+ujQRY+gtqYLHgTR3CLIc7baNIiR4CoBsaz4CCWfQ4CsJDq/EeALjfgRKkgMf+qxeJXQnLDKw0BZcYA/dXjks2Jz/IbATgJZ3Hr4Nyup5r5gxAUDxx18FRHuzOA2JFkphwGrm4TNuUd/IKmIrmPUS0a1Ev1fufdQ9h2yzYP5ytpXffZby+fat/IuUBTWnlt3oHUnbKyuF7kCYHv+IDdA9zWcyyWmXdGGBBX9EKW/d2Vj7bURNufZJeVCo7+72iVtzb4sFGIgjv2jK7ZrMOsh25XnDDuArEm6rzoceyl3sfg7yWjBoASuTegoAvUSUnVI84MNyIleBmGwpJrZRFceuDw5E2OQj4Nyck4qkscaZXfyPaUqmkNUMKRSd2ip07LCR5m3vreVqWws7KH6zGF+1yF6QmuRgxmrMCEDNaTMb6DsSOm1GHjktPJTnEVw9MLEivKvcwwihnMsFUTqCF5Wss/yVlilFhIQMDbqCJBN6rX/tToLK4XVdbK+VRQUg62phQtGmKsRXquTM4+S7uEYvoogF8VJipKYGgMBK4/6xMOvSDWyZJCnXiYFwp7jlAKSohJB+pQ67SBPql2j0TgFpkj/oevAliPuKLOyxg6HphiOQa3JydUVc6r3DeiJzZG+F+8dsD2gj/c1+j4elMfv3VXP77pdN2tXf0wut8Wlih4A+Z3YBcQSBnT1knLuOyBVritYutGi5xZI4MI2IG8PIAVGBueirYzWQyegZbWhMrVkEPEzkGqAhPHW/icbpf8UjIpwLaFaadRZUZDoOWtOOhZzX6YGmq8W7VzlN3OiImsgRfEjA4vMaWB2KGWi0jWarbegklXNLYC0lOaijmRiZRWgYmJjVQpkASklvgSynMuAFrPYGpZcF6k4bCLvBWvbUtitJL2lGslNZbgkws+yYRkCJ0B4IFHsigac7L6S3V8Dq8g6RO7LljD11Dtw1ZfuIlsz9fuu9+miQ41sV3qcru3J27ZVLgJuR6qHMbNKrZ49zKyLbEL6PbLMdSvzDfIcKgVAbVwngHtApDUoCaBHb4wsEWJPerCFtImB664Yn3yXAaigIu79IVLVVd8SEDV1OqWD5bF3/YjH5halObffSBpZAi7M39cMEtcry8tR0rlSJV75vY2oVoRYebBXzVkd66SoUkzB+qzvyAgzB6cMMSrG8ugppbPZvzArxnGWTAzu870ickDFYGLJLnEeAcCakILvbe+BCkkjCxANRjiodIrHpq+w0iwT09siYjRVwMlyjq6V3zi4ZFTTEOtZWGGtR+wyo4GafDzeSDB9docwu5t1qMJc/LDZGw4HrzAWb/Y4ebYS29ECFB2qFVCfiiC8+LgAVRGhd3BrkXNBwxLoqSP4VsAqNghzl4DGSll8Brqme05IbLpYE7qFFkPbMKpVRHXegytCe6NWNqH4StV5QPP6OdybJ+C+B5eSJ6Xv6kgIAPosXqqS4a5CWVO+0MRcZ9Im7JJjKYKdaSJG9u7ofjXN0IrIJYwi+Ul7rkffY2vTqc1IwPVl2zWY9ZCtLJGdHtgHoe4BA8Ny2ao66WGO0z/HipIhKJkogk5HRO8AEtDKhQwu2QueRBRLWqtNugEErXDUHTIwi5KvralxRrPtbYAuXizJp2c4piQkm3Qzdk36gOx8TPZFBkfYu0wNbiPqs4C5l8p+m0DojiRKERsGq2aVQ3Yykr2r8uTerQEXHMJcJBZjIHSBQF5ArLrJKQ7sNaXE8toLx8DYBOzleitlR7lO0gO5UgOleSc2yBPL/Q6VVGvpF4w4GzoXcRGBJoKqCF9FOC9CyUbzjZ0DrzyqE4/6vrC9xMkQY1KtObOyzOAqZdb1DLdRceTyfgBSoh5O0j+0/4ll4kSQKpA0AK+Kz2OGCbbv/9dqgr4vnSOVei4H9ql9uLxNuf0UdTYxt64djut2xTZ4ZAZCvg/QHsTW2Go7DmkpgcBwDKfAikNQ+m6gExFJpSdk+5JMqjGGdFnUsTM0EG1DDQ4YG8xYXhZoyeelRq9I30vBCt73attMbldf2CQuj18UWLQFndiSEAE0Aviz52LCp6yp4uAJWIJm10W1pUAqp24glvflGFwarOKUbb8EcWzU8Ull7DXKb/YNGDpSZqcMyGIDsgwIqxXIcgJkGVBn7F9jZLmN0+pQum3ByErl6hMzWz8bO3swl9JBWecg8gyreLJ6IBRj7tKCoZ5n4Ttv9te87bU19r5c9C5e4dz5iutft9/9rfYBoOpi53jcLrn+rmeYIgOBdXxFAqQZlkqtAIzNf6I4wgBScQ5JY5axNFYy9oVaPyuw7wJykNXkSQiJXdVXAoxZESVjlQJQOT0NzBLQHTicvZOweTQizhhuQ6hPZayqz0wsXuyNCxbwJdQhIkYt2GT21+RZwJK+xkB37FU0XMTl6xXDryJciKrXq6wg9X24ylphqRiSE0ArerF/XPp7yuC11P008LUSQDH2EwcJuqdTVY3fEB1mdQ9HrMLwhBAjVn2NoL/3waPyIYm/N1XAuq3RdaLFFVovDp49HwBoFsTP6FxOOwwE1FGkTtRGmARLmAOrJyjp9YYGmN0F5m9GzO8ExEZ0nf1GwMn6lFGfA+0NUpadpGSGsxq08qhPHfrDbDd8I3rH7MVnAlNOA3Vy1xCBeikOFjXyHPdz6XffAfUyoj6PmL95jh964RfwofOv4PlH3o1wUOFD97+M54/ehf9n9R2I98/Bm032d0qZFUeA88i6m0PfZzI7ZesF3AaftySFxpuU9qVsI33hrWPS6O+4pbnP9M/l8Uqm/ni/Y8ZZCqxxMRe8RLsGs36Htguj5WMm1gSCGb3kAKe0CktlVGPjeolsRG8vEwCWZSbaaykgclJ2bvI5FJGCuIhwTQA5oIsEhs+RdjUufk3JiWGvk3UFkkJDqeoUE0RRl5FR29JpS/3Co+/F787JPjS9wG8imlPR96LgQD2huwH0nBlSQieVaoFmJA2UMDQfqWK6Q3QR3HpwJcynvvdZaqWKQBhGyEjzxSnI/aKoA/SaEp011sg6MFwI7+tAEBsBssKcU8qIa+XcwlEGslwtQFYMDqEXq8e9A629lrml7GAEiUZVa6nmkYAs/ecCayUZKZduzxtHkkmLAYxOAS27O54F0AJAfZ/vSxmR8G74nLsd9/Oy7TKTsj26JDvfuX273QN0AcUzPDhOsd9rh+O6fR3bhbZm/HzveG+yZqNsRJzfMXM8kgC8/nE23is7c4uKbvEhrzbDy1hNngFN2wZcArCsCp9VzEvvl+SGJIfJGbhVzDUn+4GQDSMwMRZROnf7zQUGNoq7aMXcOAMiaKijhazXaMCDAVnSZ7oNFIyCEyYxYeCcwJknWJyVVRHUPiDOKX4AcqqO9htB+s3YbNbnsYamuisYpmk9JsRfprGzMpVlBQI0Su465IIwcdshhWrf2F9Zr2BmkznFdkPthIeTeNLglQVUsqNg6a+jwdaei/H9vmy71Lqcj//1ahe8u9ftG6cd1B3gFvKl1MThHePXVeZW1nbMsVI6t8+/k4JYCdSKDGcsf33nSx3DsRwEqw9jekbmQKdtAQE7KkJoFHjqIUwxsx2NfJY06wwcrG87tDdZ5s0SG8GmCXAtYXOLRA9YmWGulzHTbyRtr1rL/LVaQYqFFOdPPaM+60GR0R2LZi9FoFpGVMtOClSZ3pcX4oBrHEIt0QWZF4tfZmQEVkBPskmQ/Kjk25W3BwBWDnEm6YbcE6Jz6FjcfQuEkGd0vUeIhKYKcMSoXcSqq0VDKwjYZYEU7yLWXYXNpkIMKqfSuYHdgQN44wW8YrV/ALiKQO8QtcK62AatBOxYUkoruZd+BTT3GbMT6dfuUAgXFID6jDG7zyIOr/3gNgAOCf7Eo7nvELQIFgWBS8PGg859yhCKM0Z/SDltvhafL5wQ/IbgV5yO5zskn8ivA/7rV34Zf/b1fwcH4DuWX0rTi+96/XPw87v4qeajMHF3NlvmokwyBn5OmQ5CuBDEAi4/dg9ApOHfrTYFaO1r9pwruFwyt8bH3LkL67RifpYANyAx1K+agXMNZj1E84iofHwwg6BtkmJX/nbJfdsAT8xK2yVw5XRAGaK5xOrPG2iiVN80kTRAhjmBSnKQPAmWvG8BsixiG3uHahbgjjqEQGCje+okeFBOt4yMkz7bHgBk0PZrJCN4qTZiZaHykl9tE1YGfBvFeYgQQCtIdKU7FEPGFUtkv5cUhXFJ3XSoHsLZhRkMcSxCcBIF6bxERIhBmqNPGzdI3xBxQ7t3yFUWCXAbyjoE6vi4ToxxArKUmWApG2HB4CaCamFkxUDo2lpKtptuykZArPo0byf9IlGJMZDlOs5RJHsmHGk0iADEIaAFZMfKkZ64XnAZjbB7ZcDWvhTDfT72g753D/G+bjn2nP3dJBI/+peuo/Clxiki107GddvXHCyN6yFtzY7tr2RrkB9le+5tQpQBB9svcvQ4gTaEMpVP5jW6XTExLhlXCchSwS2OBOcFvI9s1XMp2a6siYWB/bJJlIFYrOO9HO+SfWtACik45xy4LHLBkPEz2VFNOWRJW0CVAa1BhcWpQ0UkTSxLSQcLmAVjPyW6tY7NqodFNkGUoTr3qzKyACSQKQu65u8JyNIxPRV0GTGymAmhc2JE7ThBNCAlQIVkP0yPxo2ALFmHh0wsSv8Bms6Qnq9xh6UxduwYIC8HpX1eKsVweIALfn+rt9vR0stXAGQj27J16LHNum7fsO1Gvcapf2T7hwGoVXqealOizvWBq9mY9G7p1wlAy4LSUpxpB3CVdkDFeeqYEACv6YoJyILOtxwA57TKHKFaavqiBob7RXEaXqU5FFxqj0kkORyDVl4C1sYQZa0iPhPg3m9Et6paykVLINihPveoV4z6LMAv+3RdFAXQch3DH3qEhlAtA2gTkApZadDFdwG89nAzSckLtRMh+kZS/lIFWWXHcgUB68yWFZkBmaElAE1sWCuWe0mZIyBWAVUlAu49Ezh49MGjqQKCk+q5zITKR8k8IkaIHpuuEk3g3iOeVzLeu9E4HChpAgMQ9q5OFCgQ0Prkg7mN6AkDWlzM7CWZnSH0C4f1bYf+AMn+hIbQq8C/b+U+12eE6lzSMZ0noJd+oo2DOyFQVP3KKpMVLJhTrSjpBfsNpyJdlv7uemFbA8CHzr+SMJYxl+RD3ctA89EEVNFF79G4uvsVwZuL2qUCKLuAad6zfWF3dlYzxRhz4CRRtLUfO19fHLe0XZds12DWQzbvojruO/L8djVLWR8hsmMh252TkIEuTzEZp5KRpXVFJumKSJNoE15NbKTxcVmOJ1HxPMkFaWqiPvWRK/SdAzURXEXEysMxwyVnASNAq7jY0umAOS5URGdp+sWburaUyoahk6VGkaKyjaJXUIbQI5dpjQ2DIuVoMmQ5BUoGxHWQvo0OpllLXtIspGKHHn9DqRKX38ggb+Lu9j3OWOnIGDgAVt2KSSJOYcHoD3NefXYugLCIoCaAHKPfeNCyEqNCEGPdEeozgl9RFt9liTrUS046WZbeUQJZuV8FXCQHLVSgNIhBmWRK4KSxr0gdvsG9mUoxTO8Edt/rqZS+PW1rQB6Durv2Y/fpkscY5pxj+DwTsHNQvnYyrtslG5FNDK9g4YH0jI1tzVW3B4bDdQJqjWU0mIWMWgkyXaRJRRlwySwm3TbqRBWigWigign4WrVAHs+z9/RZSv0tV9nnaE2c7xBMYGVCsQaICJRU3ZVtVRlLTIEiRgKCLEqZdm+AFhddZ+diAB4BsCq0WvXLHB0i5MCJCe1HjTMUQFMC3706TybUy9leGDMOXtjIMUh0vpQhsBSfXIZe91ECWUlSYAeQBXVcoRdM5rmN+t2APNLgUelcU3FjyoDC+P7uutdT6+5rW+tdwdZc1pEZ2xrdPh1u37le25pv+PYdN76M/+nR96M6PQc7D8RefJOxGHwChW2uPHY0h9/3Osbj1KeCaQUgA9wJhNo+Dptds9MMxTmWqxfj+JgIQMFAJ0YkYWoliRUNIHc3GKe1kzS1W4x4EACS7Ij6TPRjXVeculZVtOqrfjP0W/qFpMC1Rw6LNwn1/S7bOED0ZSPDNy6xsohtIOfEjKE+grsAt3Zw8wqx9ugPK4SaCzYskkZxui4LivSFHQMSM9evBNCKjTCjuGfp2ujAdYBXGZIQHUKMqDwnAIuY4InRBo+2r9C1Fbp1BbQauAByoLxgBlMgcM2JRWzgVvapSHWAKWkAC2sOKci7vk3oD5z2MZIP1R2qRMxM+sO1sl1zP8vaGOMaAOoTsbuxNttIqM60eruy7nwL+DWnau4laGq/+U0E9RGfPn4XvvPu503+Mw25EcDz/snEytrZSnkVQNNMJ14uN9yGBzZtok25S5znElvvrxu/V8N1Lssk3hsEoqFvZHrCg2ON/qY0wwdo12DWQzbvIkBFN3LM5W+B/U7Dnt8mKxdYG93sVMXQ5cqFeWOePE4CzUYgkkVlJe1NJnTEkjFnlNeYJsRaCREacSbAdV5EGA80DS7kAYsUBHIKEpX6HKVAL1C+5Lz18qcqKVOlRY2dNb7m4jpdxyAFHymKNbCqKGEm5xEbTZhLKR/mPABey5xHVeR1vRcmWs1JeD05GY5TWXT2aswdlBWm193JdVKkVNHEnAIiZWQtWKp2GIVWzyd6IBxGYBHAwYHXBHfu4dcklFovIFpzIvcqVY5S41ytGNUmSvUWi0QYkDXGZ/V+DACt8e8DOntx78pJk7HnikF6crBWx2T8/F+FkbU1kO8SPB0v2nWI8YBf/huvo12QN7jisa7bdSuaM9E8a6WQL3DBc3Q5W3PRZumRtvdyYIu2nQ9gtyOfdFIIWU9LJzxmgwb6CUkMmIBWxlBLzbbJd6qKZ0BN8b087tZF2dhlnzEClcbvsi2k8cs/XE8qMCpSpQMGRSAooAWYXRgyl+0v9cWxWSPMLACeCa/n2WI+rcHYlEDEvE4JnqXv6khYxcJUxUvPR9jYDNRi3zhqhUK1b3AW3UYKBKU0Rk0tNPuzBWRN3RMeAVpTK5SfiYaf9avp0tjF73QMJp7RrcPua1uP//T78CDj/4XOhXXHtS15W7cZdTmVuxSfvmRgblfbC8ZOtUIHa7ij8RwRSMiApwHzCpDlZMEKlO+u+Dlh5tAvnLKXVG/KNLYwBKaYJGDdH0Ssn4RU/J4H8MbnKqv6z9i1pH6NVDYHmlMJ/AKaTl4MwZsbctD6rAelaoiAa6UiqztrQW0nYF+IGdCyuXHlgeDg+giqvWpq1VrZT1Iew0LGZhBk3C1snH3myvoTyYaQU4Cpl2NFCLtXCpA7OBcRosNy08C7iKYKqJzoD266Cm3rBchaeQHKKvUXtAq7IDoEONXxDZBgf6+av/obRbkO0bnKfRdnjOi1/zckIv61+XxZv6y9AdHDUt9G0jwJ68dFawtAYl2Z/IpUR5Qqic05DSoauxaoz1nlXFgK1QCgGGHVCatlgGvFWP70O/4g3LLFh+5/Cb9JjwHM+GB8Dc/7J/Gv6o/oM1sAWqaZpX8HbK2RT5Sf02yf9r5zF4A+e7cdpRdOrrvDnmyBTVPbTjCf9wVzdh73CmPONZj1kC0O4MyJCMgV275Jfz6o/i39GmeU26mnb7cDnwTe7QGyyWc5odbfuBKGkOk62QAjpbyVnqlMoe6I0B9R2kcZmR1M9keORloeefu8x0weM3rja3a0/aKPV+miUIbNrrJL+mJhztmRUgF4KvrFnA4fSNIIK/EC4kyE4eHzQA8gRTFiha3UxVjLfl1Hqdyw9RUFiT70C85RGTMaet7hMIIXQdI6zipUZ5QZYA5oWqA+ET0siZTLgO26UuSd1fkzoGzHFJ7NeLP2GdKAO5iA2L1ypOGL4p4NgC2XUgztN2NlbWvX7BnV9vy0awDdO9Db6SiwSxhSaVPq6r59TzksbrRO+rLnXK7bddM2eMX2VKF6aw+qf8tD2Xs6efg9DnwJpJQ/lSbBNjcmsDo6ycGI+V8CXxrR2hicQjFOl8eZfNcYGcgqT5h0nC1PcOq9vgCckABB/h4sdQSUUvSh9qYEgMpzTkEmBzio0wbpJzBypNyYaW5k04EMTqkNGth4ZS5ELegyVTkyNizBmghQ65KYvN0zqQaWg1VgSycsbEuyIUW/T7V0/cPft5zdsr/tetN7UT50Nl6Pbw7eMluz1x5csE2a423NefZsa/uedCYuPq/r9o3Xnj9/B6p7FmEezY8ftFhI2S6rr0NS52y8bPB3sN/RdwW22JMyWCkJpAM2FkowwwIiaTyLGcRK45p6un5DUrj8KIAWPVwVEZYVXKcgR2EzRD9QgTQnY1j0ko2Zg8IynpkOYJg5sKtRn/Zwm5DHsRBBXZ/ALJh/U3ZP1wPeAXUFCh4UIxpAxc/lX+gKu0iacjgRBB8EMpRQAADMDtw7cBPRdg595eHrIBLDLkpFQwiAhbpXxpYDR5ErSQysCE3rl/tBvemLUT5mEJDK+sdYXJZaaPcmLDhVk6yWlHUbi3l4bIRo0B9xrqir++sPRfeM65i0ugBNGDH76hi09mDPCA1pFozIq1gVQ98hZShZUL9aBVAXVZif4DbAv7rx3UDz7eAYgb4Huh4c9KR3sbLKZ76s5q7tKjbosqByGSzcanuArMvYq634HU1/3ksKsDlA+dnkDtzlr9PaNZj1kC1VM7yMobhqhZG9+9LDFoyslFKnVUVEBwt7z6+sdA2bUOnD5QIjwqLfhH6egSzXC7jll1J1glgqj5i+U3Mq6YWxyYaEgg3COiAzJDtzhAvkgZkA5jzJs2VTzRhZJcpdgH7lYGGTYddFVFoSXF78TJ+Nmuud2AeFQ5X2oyQsDpLiABJx+Tjn9JK6VgZnMyZy3Kw/giI6kcqWq7MW5jKAW3VI18uxjC4b5yxssJ7gTz3qEylZm5hXGy0nu9HIQydRJeolzRKRR45T4XBclDW7r2KnQ049HAzieo+8y8LvZXTuss75FV6jLSBYj7HN2Cr2q5+zjoxZ1WL1wncca5cMnHNCeo7Kbcq/IFx54L5ub792JezqrXye7HnVkxg40BFpfM0g1Q5bs2cCZJWdDLCJOuaVQI4xSYkllV6AfUkTSJVHLSVRiVBkx7F3c3R6mXlF2cZcaMupeGfLzrGv44FFJ8ctUvDGKqRGUNIBsz4pAyblbpKepR2DxTaUYNIW49kctK3tkTUc2fRY8iRyIAfACrop45jUsUoAI4vtMvaVseLExmSHb9j3U8t2tH2/047fbdxNZc+LSQbha2JrtnHcK9iadG7bB9xnJ3albPBo20s5GNftd317sz0EWisPG5HkT0rf46KUpX2BkoLRMdDL2bdNObYaC2t8yGKOFb1LQc1YEbpDh/bYpbGmzC4AdKwH8nvlZPzxOgeNtZyC3+jyDQHk0ZMEn2mj1cRb6FxdBqZ+TjJfjkisLa/BeAPoByzTDqB1vrjUP4TMxNrXJN8PFFoBtSLDE6GeedQHDqEWgkCyj1qxkG1cTiATRAe5CBr4DUlFv56F1RQhIvOdAwcxmpJyKJVoIxM2XYXIhL53iIHUzot0DXUWLRGWlduQVOv1GpjXIdcvKfuqXr6XtjpVqVRGlgWokg/ISML33WGu3u46ud72kQhu1DZpWqPpC5Av7GrnMrkAsn9jZNkzZMeT4mgsbCwGuBJRfr8JcKsOXHnQYi7aZyFKZ7Yd0PfgXhy9QSVD75DZWebv6OepQP9b1K7CzErHR54TXeSTDH3q4frps81NLCV47C/tabt0RHe1azDrIdvk/RhHRMrllxRZ3CvIOwKyQPlYloI3uW2RPgGgYAIxMH7AdCLIHghzSkKK9bkMBN2hOBGzu4xqnatLWBQkVd+ohOmUz4lUuE+ecteLEWTHg0m4RUPS+L/rRb8AICyrbAHYiuq6nkHLkPqFoqDm/aGCcFbxqZwLGPijIFiERRtkRepJwDBA8scdAGVg+Y3ki4Ol/8x5MLFgiyhZJMIqFopToppqlUYiPINaQnXmMbtDqM+z8ZSogqZ2BPseB5VX9ka/3QSglYAuA8F420CXKTqDjh4N3kQJwDJQa8DKIuRlaR/Dc5xsF1XnuEzkm7AFZHG+vbJKMXiXn1Pa1NTAPvq89X1frv11u25TLYH84+XFsgsnDXtsjT3XAMa6Q2nSs4caUr4XSRh08jg60bG0QWRgP5VGX8uk17QxEnvW0hJdETgprl0mrFl/cRDFt1O197u8zh19sasx0ejqht8oisOTz0G0GmNDOZ1yxNwdM7TKQAEFEn/VJu+R8tgFZM0qSP+NAZWkB6lamKlfjE3LSBqSEq2SlBIpUZ/7xIJbFLOTKWnrxZPBWx/SN6KtxXrNXKAyOxArskkCI2sC2H/lQyB/eTT2TwUqtr/vuPFb4NWVVxhN7ofvTFplh62ZArcuHRT57WB1Xrff9maZIpzsghNAa1/wsWiD6uGXAaou8YgPggSxWLmc7iR/gwbvQ2wIm0ccNreEFVOtJOjhOsAhV2IPjcyZ2ZMGa/WQTnwRAJo2pnYliLHpH2cQKcCjgXro+lwBrEF7jtD5dHHKyuChoP2jIAgYoD4O+lOYqTvuwVT/hgjaSF59tawwvyuplP2BBNxpJuL0NtZbULtkumxVk+9z6h7gUkER9ozQe6zXehqNVDDc9LLzrq0QVxXQU7IxwmxyCDMuqgfK+ONVAqC9GeFIimkhQsC+KAxf6kWkP8w10L4UUX3bd6pUOYMK+QuQZYWvwJpBM4861DOoEjtAWq2RmcC9MspWTgTne9FFq5ZIFY99K8WvjGlHPaR6e0RiAro2wJ+34hvVlZ4HA1EqvFNdAV0PbDbC1gJQphfK42yDuPo7pU800MjCtu+j/lD+TqJc4EfPzQO0qXTgnYxfnTeM9YQHgcnCbMuCieVc/DZleyds20XtGsx6yOZoz0C/vTKAPY74VKPh4L4FZCE7z2Rlzk0MF5oCFrfP0crdJlaSkyoRNoE0Cm+YqQBfFCAreqA7BsAGZMlgEhrSQYeSQ5Hy1i2nGplt5IIMKvFcDJTrKEU7bECO3sEhZrkRO3fTzbKIz/glL/6mCo/Ifb8NaEUpB5tSMxwoErojTql9sdoWhbcX22n/uXQenDW2SgDEHApWo9oq8DfDIPc9Kggo0W4Z9KkXIxDmjLiIgGfQ2qG56zF/E6jOhSYbNVojlQklOu7XrJUci+u+AupdClrm7fcY57Th6L44l1hZA3ByipU1dU+x+91Jz8dFeeC7ftv3Sl7w2yAKMTrGwFmaGLgHA3y8iA533d7OjRKV5VIrjz9cYRugBHQSkJU+Q8bktI3oVeV04/FDrpNCtvEVqUCJHaus2GRaFmKTZBXT+wOg5doL9hby37KISdqfsmq5Iy1yAU2XyLZGAHy7wPwiZ+BLB+5BVKN4t5GvPW9t2xSbKKDli/0Glus0e1ky0rYCD8Y4K4cKLm4UjdYDEmvKUjcTm0rtlqVyUrEPE3oXRhaE/buWal5iVxzYqjYFpxojAa4PKnxfnh+u1sbrb9kZRTDhIVNYRi7v1ctnmx9ZsC/NoTBha8rPhf246PTGzsau9a9iay5oO9Pmxwt2OSNpg6velOv2u6G58sUr73EZ4HuQFPXCn9gXMNxiwY+fM2NmjQKTrCystKk52KzjjaYvpxRmAmKVfRT24hO4Nmt15eJNuk91wrM0CoAu5zOZ5Eq0dVoRfw+NVle3AABz2qffRLFlIWogVsfWyMN+sgqGU21fkD5G0CagOuswd1qJkaUKIYCsb2hDoh3CAuDWx5Uwn/xG/Ai/Fn8t1gxaeiHvRkJVB3Sdl2q5AJxjCWxX4m+4TgL11Ou1BgIcI9YMxxaop5TZEg4i/NpJ4J5yoAoOAoJBiACuF/DNtUg2qF/IvQ1zBbIcgCis4NiwVJIHxBeqIog4xTbYpFhaB7eWKu52/dVa/CMyIGujBcESOJntrjDuIvy5poc6gOHE1hPn4HskEMk/9D24szxX83Hsb+H7GHuraFv+zZTkSvpterG1q+jcpUBjeawCGL0oMDpOaUx+jW4/AKmBrIlaBkhZ/1lA74pD1DWY9dvV9lF7J6nlu+8kF44GgCJ9a7xe3v8gGm2TVXMwYn5ak+CqPpiWX1ytWEq7Hsmqs7ua8hFt8FYDEoB6Lawg3+kBddJsx48VIdSKuM+l5K3fMOpzeSBTmmSlIrOuMAr7qMwj9hYxg/dFpBgppc53EVhpHzgAEGppmHHq11ipveDMGkBhOKlD0jRBVzgQKQ9YKnwQS8VCieIQwhxJrDGahllQTNL8lBLIAuDOPer7DvM3BVSkxJYiuJD1sap1hNvEoXMx7oYJgA/G2AOS0zdgZek6W9vtMdhlJIK9/t3FysJoIHYTz325+ysM3MPzRXG8B/MytlMZkZgUg94o3r8UfShzw6+ZWdftrWi72CY72l5bM94P6X+jktz5+R/ZGuTxDygmOXaeRYWr0tFIQBYhMUwTGGb7UNBLJsJTAJTYOCtYIqkZqi/SapVddezYsWEgxcRtwhkr+6BcxJyq+u7qSLJzjgzoXNdT7jMuxpEy/b/U0Up2NBRgVaFbNRg7zRm0lHZWcM9+L5w8uy62FBUDsgBQK45AtRRHAFyBuAIwA8VG993B9RtQv4bKC+/oCJu1Dvsm25rcn4PVzP7AaM4LAIf6lwGsAZwCOAeoBZcXZYzwXaysiVPceu73X8FvW9tl57bOqXRAipX2pTNet9/9bQBmpUrgb8G8YgyAjQGtSzxPxDq+lKmGahuk2NTIHug8uDmLYOdS+pkBEVKMSqumborx3+mYpr+ndMSAVNXOmFzpvDyAVjJO2EGEwDkzfdN5ekggJIg/FKKTILHXrAlPW2PXpJ7sZUYQDQBTjPCbgLj2mJ1EEGvKnKJ9UlG4CDwESkEjsqGYxf8w4XUA8B0Bc00P3DhwIPSRhNlEgK8CYiQ4F8F1AMNrur+AVa7Va7aMES9VIbli9EdSQd11UkHdbzS4NGNJJyxsj+kDJxCDFciaC5DVL6SvvFaGjw1LaqEXkM3VQR5xYrAWJuFAwiReO9T3RX7FtabpyMrSEwkWp9XbLSVe2MlCgnBthNv0+R5aX6PQKfNyMdLl8sAQAA45xZfGAFbxncvf0vNxhfYgQ/me7JUL0wvt3nEuzJBOhWXOtbUPM91A8o+23gDKc0X7/So+3UONcj/2Yz8GIsIP//AP53Nmxo/8yI/gmWeewWKxwPd///fj+eefH2y32WzwV/7KX8Fjjz2Gw8ND/Mk/+Sfx4osvPsypfN1a3NXbO5glAIZMlKvegYIVJPuGvHhxFA0FUvR1YCDMsSB5cX3HWsVBKLJWFSTUWpKWgfpMXn4Dspp7YlCsuiEYmN2PWLwZcfRywOFrEfN7Ac1pxOx+wOwkYHZfP98NOHitx9FXexy/GHD01YDZPQnzt0eE9tAhaNpFSXVM6YLu8i9+mqxOzasLIMvWcW2EX0fUS8bsXsTsLqRcb5s9MfZIzpFRa539s2ooHVLVxpSL30JEEntKBoYUDDItsRRdQXY+QABXUh0yziMoiD7W7A2Hg1cY8ztxoCEgjCyhPlcrBbIm0gX3tWH547Kv9qQXAkPwa7DD4p4VrCwmUooCba2/b9I95YQkRuLDtn27oNHgaperjtFAfw7D5/ehjvs2b9d2Rtp0Oh/225oSeLnqM8ajvzYHHwMNKGwNhs5IOqaBKsFEwa3IhoJNpp2hkdnEyFpDx0dK+6o2jHrFaE4ZzbkA9hZh9RtJe/cbYaTW51Hs0EmUSlRr1v2T2BmLGI5sjRhI6880GO3uKuv3qbG1ALLS98BJdNavWVIoVbMld2phr9M4jDTWJ0eg/BxGyzhvCyAFnTC6N6k5idZLURMCtQ5+SajPLHhVgXgO8GOg8F5Q/FaAvxWuey+ofxTEhxjSLnZ22c4e3Z9eWENArKcAvB+49Q7gsXcC1fsAvAfADQAzgPyAlVWmru88gcm5xISt2XXiV20X7WPid9Zr2EoLeavO6br9rm5u6oW7QpphavvmZGWVxHSMMUC9fbwpV4mJEGs3cIqzjIfMY21cNyDL9Va4SL5LZge2MiaYchAEZCwmoD8QIfFwINUM4zwiNJyYWSBd5zAziahHCu5HIwJAU8Q9pXSvxAqbCg6X/TqqYDf5G5FUONTlrouoz8WWze+wEArOKFX0s8B6rDNzzI7NDmCt6id9K0BTfW6VEh2odeC1R9zIv9B7wRej6GpxxXn7QCmQ4gK0cj3AlRSrok7OqzoTYXgLnqQiJ52IvdenEtgvwcL+UP8dcJYc6AmIkLTGgwiuYmZk6TnFXgA5DiSMrNMK9YmcR30GVEt9dtRHqzZcFMKCsrNY5ygKZLUBSNpnhc9ZBOQzSOXyPasqkFZsp/Hvo8D+VorhA7Ypv2gniWE0HozTBOXD6O9gg+Hxtnyd8RxjYh9b51as8yDEhAfuuk9+8pP4x//4H+Pbvu3bBst//Md/HP/gH/wD/ORP/iQ++clP4qmnnsIf/aN/FKenp2mdH/7hH8a/+Tf/Bp/4xCfwy7/8yzg7O8Of+BN/AiGE8WF+R7cAN4yEPEybZGxdsAkLiOWCiHrbiyioPCWHwhwBG3RDI+LbruecdqGR7VgrtXMh6W3VUl70zU2h9NanMhi1x4T+AOoQQCfmWRCRPSHWpFRgyhPKorme0ZwGLN4IOHwlYH5PHSGv6SDFJBw6GA76arQ/HiPetQNXDnATxgUKQPg8mBBLOp5flYAWoz4FfCv54pbGEivN0dcsB7eRf5aGaMCWbzOLy3VIqYVmWMd9khkESM5MfyAaWWCgOnU4eIVw8Cpjdo+T+LFVUymjDq6NKUKTDzD9HNlvCeBjHgChsh4ykFWyspgzjbr8zcQR7d7oYJ6MQJkbPmJlDdq+Uapc/zITtilnZuwYuO1lk/vYM3hvDe6XuA/Xbbtd2xlpEcqYfSvaA7EyWMeEzMa1iC9Q2Jry3XBI7zqVARcLrjgZP1OBi072a1ogXgXTrYJu9OooxMLO6fWU4vGTE6coKSH1ktGcRVTrDLqZQPmge8f7GI8RBoyQ/ugK/cqJ+5SijEX3kNrtBGhp9Lisnpv6ySOlkZSAVWJuRQMLi2Uhb7tVThs2N0AaXy0NhFUDxW0ExKrPtRpu8AA3oPgIKL4X9K5j4L8A3H8B0HMHILwHoNsQtpTbMcYVtiZ1DA9+yquO7BIcgAbgW8DiSeD3AfjfAfjfM/D9DDx+DOBpMC0AMoqzdrrdL+38izTiLvztUq/ixXO6B2ITX+ZQb9V+r9vvvvZWsO4mwKnMyN//8E+6RFPnpPYhKqtpcIzBsYUpZUC/sbjyvFeBCGNg2VyLkHWBbWyrFBBpBHghH0EHPbpbAe0Nlsp4M0b0kjqXinFMOOvGDIuN+jeaKsm+YIGaptZlm/kwKslhQXwDy/w6ojqPaM4j5ncjZvcYlepN+TYXYYm1gErG5PUbBZU8km5UErbfyFiPIIARehm7ORL61ov4OwBYUSxjQRUp6lbUyprYMkraiqKFybkCrvahpXMaWyzOBMgKcwUXo5y7t6yZmjMjqwlwXthYYePBGw9uPbD2cEuP5r6TvllDfRI5TrXRjJU+A1k2p3A9w20C3CaIaD+Qnt0BoGWP5ihIzxas9w7wHlSuPyZjJJ+n2L+ulxjau4IwaZ/Fx+KdLVP+Lt0uQwyZOO5wPWAq2Ln7mMV2to+CwXUVO/ZAaYZnZ2f4M3/mz+Cf/JN/gr/39/5ePglm/KN/9I/wd/7O38Gf+lN/CgDwz//5P8eTTz6Jn/7pn8Zf/It/Effv38c//af/FP/iX/wL/JE/8kcAAP/yX/5LPPvss/i5n/s5/LE/9sce5JS+bi2BWSkyG/GW0HrHbczksptsh7fcCyKkCoD2kmjlJ/b5ofRtFACMkRyBaiNsq/aYEGZZ3H19yyHMBN12PdDekGNWS8DYKrGWSLcBNOa4CBOpqPrhbfLNwlzWz65jNPcjGj3/Ld2NETLO3uXBZtTYE7j2qQpFqqhkkZfx+qSpjJD1/MYERFwyltQLeFdqtETtf68gVjKclvah/1wnywbCxV6Yb3b/pKxtdj6M3iw6WVGF3h1mdwmzO4x6yYMJgBldHwP+T7/58/i2N76IT918D37que9HcH4IDE52GqaBLF2eBCy5MM4xZuBK/zmO+NOv/8/40PJFPD9/B/71I9+N4L0AWWWaYXk/R3nhU6ysfSmGu9pQLPdixyItjhcDWgNjsc+RKBzGMvVn8j2+1swatGs7M2xbj/CDaJ9c5UDj5zuBRzouwCZhBVJrTkRhfySKXtgnndOxA4KOdZZKaJqLBtKHmR6zEBwvUwfN3sn4R0NQyiZVdmgbLxXs9xt9GePoFS4nUcq4FIdmKhIgtigBWWzjDmPS1pTdy3LNktlndpIkBlCjmNAiB1z6Ynxi5HGd83dLwbFtmSC2z065cCaSQwKzO5yF3td6XyywBALxAuAnQR8A8AeBZ5/6IqrQ44VveR/4lwD8p6dBOAd4A6Dd7gDtBLLO2JVeyNkey+82mM8BfgL4QMT8+87w7e4/osEGv/XEt+D1/hnwG7cAvgeQli6j0UR8/L24j+U9ujIYVLwCV9n4MunxV2b4Xmbda82sb8jWuB7n+2zCjkDwA9mRHdIfWUO08I1U7whA0syKlVPdXp1TjthZKV08mv2gJGae1jPtXKAY9wEQNK0wZ3mwU/0lKxISCXHj4RcBYR4QegJWLmVNyLwfg7mzC5JaRd3w/bHUKvaAL8ErR+DImgZ3QR+Xeq+OhOHjABg4puO030TtMxm8JbghRasAAesEKGRNqzfbZFrDyJI0BLhAQCvyJ4EduOYEnnFUEfWeYMLrIhwv11UtKdlXFyz9sLArrEF/JyuZLEDUgiPRi7vs+qzPnFhyrPdAK7jHGYObmICs2DkB3gKBumx/3VpALNN3NAICBQWyVir4rvrMIvrOcF3U5yyDMZLKTyoDw7lKO/R+OtY0y/ysszdNrdLHyayswb9yX+Xf0fOQnou9Psn2+3OZtsXy3ZpnAgQeZp/YcbQKdfm+7tKILNn6PPZ/ivMGq9zOFU3UA6Euf/kv/2X88T/+x5OTYO2LX/wiXnnlFfzAD/xAWjabzfB93/d9+Pf//t8DAH71V38VXdcN1nnmmWfw4Q9/OK0zbpvNBicnJ4N/vxOaR4R3UR4iTcN6IHYIMD25KBDay7bE1uryy5mok62kntXnAX4V0zqujfAb+VevIupzxuwOozljdAtCe0MZRq2Iv/cHOQpioEQ/I6xuOyyfcGhvErpDQrcg9PPi34IQZg5hJqwt9lnEXs5dqLQm9CsL7TijvjMnQv8NJqPeC3XZlhfR5/F+y2ZREBciqpX0kd9IP8zvMpoTiRCVZYFNZNiArNLfST6eE6Po1xIFsaodIIi4u64X68yMi41QodvbEfBAde6weJ0wf0MAxhLISpooAP6Pv/Xz+HOf+Vn8njc/j//zF34Of+aLv7AXyBow1kqdLCABWUgDfAFq9VoZseulLG3bgdYt/utX/h3+7Bu/jI8tv4Q/e+d/xp+++78MBu9JVlZ5T8bvhtvhfBT3cIv+PtUuI1hvq46elbEzsZWms+Pc7LzTAE+jfdn5p5LCXwMQ/Hdx++22M8DvXFvjwBo4KSbul3mcp57RnTpQV202JkCDDzoJVFaQ65SqP45+mj3qJD2kWkm6SKhJi4PI9sbIyiCanGSspNBIdyjrx5oQEgu4HJMl6p/YR+U4YpPYEj/e6oOhc5Er45bLXSE0TtPjwpStgQFsDNfHVIFL0mZU20MZWln/K/8bY2Vk/5H2v4q+uz4vLyeQ7If/YgOEhThBriPUSwlWub4IKrEDxZmkEr4feN8zv4U/EH8JfxD/Dh969DeAbwKoqQFWZtaW0R3alvFzOA1kFfOqSEBwQHCgxwLew/8ZHw6fwreF/4QPxM8C7wJwkwCayUUmdham51wT92nnKzXyl/e/L5d9OS92JC41/yvXMT+MsG1bB38f6IW/br/DmwNP31ubG+2aA5l24M65Fu2fP10kbl6Ms1J8yonza0HrCZ2dQWaAzkUtrY0KP6ucr6WCVjau1Xl+zh4i9dGqP7N0oKVHOJXIQUqjs3GUMxCWNAuJsj6jna/ZEg2UcKX2wIK0l5G/KMGM8l8CQZACx5ZBUp8FNGcR83uSpVGdQ/WpKLFyJeWQs6xJT7nPCsDG9QTqhKHl1pRSDrn1Ou5KlXY4gAIVlQEJ1UrSBamT/Xv7HrSSPXHWzmq3M1bYiz6WpBdq9Vy1exZECgsVfK9FkD6uPei8gj/x8KdeAK+W4JcOzQmhPsdAS1L0gxXI2mQgS/SyYgay0v1AuneZhT0KwluzuYWbuIdAvodJ27oAth4kxXDM5LLFW/Z0zz4m0nov4x+NJWsE+7Av499G29pcpnjuBlOCqdfkCmbqysysT3ziE/i1X/s1fPKTn9z67ZVXXgEAPPnkk4PlTz75JF544YW0TtM0uHXr1tY6tv24/diP/Rj+7t/9u1c91d+Wtqg6hGrs/f72MCySWPuOZqynwVPNUw+9Dh69vOizEOHbiH7h0B05obCuJY+8O2ShswYZtEMjxmhz0+HsWRmIqnNCcyqldGObqwCakfAbSSN0PaMCUgUSuSb9WzkBTMrr3BNR5UppnnUl6YUuU3O3mAVp2+GygVZUz6jWQdJcWFMMowzg3RElIUl24ggQA6QpIvZW2bVYpFy0tFjPTQ/kJRqR2Ds+57z3xyrEeO5Qn5Aw5VbiDEp6jA34gpoTMz76+pfSmOgAfOTel6YejWEbPRMZKOMcFTNnNQqIZSmEZKmEuv2HVi8Ojv/BzUuJlcWWZpgMA4bphSW4NR7o7bnYFQWcuqwrTNwHUQ01sLvYYEm8UA0lU06zyjvM55CeA9MumHJur32LQft62Bngd7atqVwAu2a48LeNYWEz+h2/MlRAfQRYbOeOwdLgXIASaIQRHBsBYVyvTkiDnE7ncrQuzCXAwl4nyUrC4QAQU6FPglT9iqI4emmCW56ZRc+RXmlsGQegmEQiOxqWwj8GCy6yNeVPEXB9BLOTIiM64aMolbQMvLLIP0H6uqwKxuV+aTiRT6wnKhhf+o81dTM24sS5VlNWlCmXUmzSRJQkSj0DDsISN/gUFQIWtALmENu3we42AuHSshLISt95GEiJEWCt274B1jzDGR2ih8c5HYItVYZi4XwM78EgDTYtH43ztPVh0MeTbcc2F7Uc6JjebivSbdc0tjWE4XK799e25m3VFr5PgtQAtn2RfWAVkB3zEoRySGDX9naXOCnb1kGzQEbvpYEIfntnpK9/me7H9rqW6eoFEyjp2ur+Yo003/ZrZRB5wM0AnHm4ToXGD/IbnjS4dDhxHas2rmWZZMBFzoES+1i0hIdpd5NsnF0+Tbl+ynrQxTrXB8RuVsug4yZBWFraQQukioJxJh3pVFerPG8KgDffDKRgHECBEBZiY1Gz3DuIL8ee000omb0CdNl5SkAmaYlZ6nxhK53qkJkmsASm8vwAASlFvj+KwDwCgcCtg1sK+4qiMr9YwLLmfhZ5T9cZJU2+WisjK3LyjamXfxfNoxJDSwvFDO5TyUC07wZe7WJklSmGNPwsnbNHdkX72h6hXevsHef3VCTdaqXdnPgtHSvZIBou33cu++yUw7CvL2hXArO+8pWv4K/+1b+Kn/mZn8F8Pt+5Ho0unJm3lo3bvnX+9t/+2/hrf+2vpe8nJyd49tlnr3DmX5vmiPH04gQvHD75lpSFfNhJxs4HbuI9zayRYgDvBcWvWpkMdgsHikC9ZIRZprECgqRLHjShJcL6MUZ/UwbWHk5KwbYqLtgpGynRi8UJoSi01OZUjAWYQS4DA6TnSeBEtU1GjBkUYtZi8g5wDjyT9EIASl3O1z+dXiC/b/WHg4B7LCEOalwGkCLQHQ4BLUuHISsvW0Z0CiYXoAZJGQOmn8UVp/5IGikMVGcCZM3viJgxFcZozLiiAHzqkffgY298LhWM+dQj78nnMb700nGw9MKYl1FQ8MrYWIFBIQC95pOHMGQiOsLz83fg29dfTsd/fvFsHtRTBMP+Fo7FGMiauF/jdJFB2ypxW34ZThoe9D3j8jxHg3iO3O04v+K4xEA06vue9d+u7etlZ4DfubaGiHFcbXCvOXq4qi1vWbu8rRl62LokyrPvgngMsZaJtu+QmELWomlqaWp2fwDEeQRs2FLHwXSiXAHksEaSLaDgLeCQvIPCObIX2Y1sDcyJQB63iHIaO/JEfQsoG3dD9ksG+Ic4SBEOGuggkgpdTMkZM2AjavZJKr9OxZx2ZBPKg42ZXQZk2Ymk0uVWiZjzPrL96OW4LwIvfOt7cAv3UCHgi3gO+CrAS4CggNOg8oralXSOhYNWAFmZjSX9kVOMIsAdpGrhCfCFG3jh3R9AeLbCjFp8/vT9wGcAnAMgFYRBBrWmwSpkWzN5sybWv/D70Lt44DndhBMwCI6Vx913DM7O5jWI9Y3fNtHvd8yZAXLb6/BoVBqDV/oeXSV9abDfAsjaao7yPNL+umFV3OgFJHJFel9KbQqiXWXjKDMG82vTXpKU6XQ68PdY/BLIHLw9duiOdW5tTjQjad2miu3l/Ht8ud6CAIV92fWCltddthLUGAAc9jvSgOX6iGoJUKF/krWDNW0c6quBMzsr+USAUwkY18q+Y7TzcqK9hSj+UBRGFmu1W9cTYmPAECF6hovZ1loA37QxnWpXSdBE/qaqhU0G30qbbEAWz6KIuq+lIEm1EoJEbASgrE+lUJkVUUkFAlTgXYoGiB23Qlam43nVNlX9PS0rS9Db/bN7TYXwu/lD1kr8eY/vcPlzfID1Bj4Y5BnR8yLmxMKyYL/sACNfaGg3y/Fi4O+U26VjiF9kn7Mm6+XalTCYX/3VX8Vrr72Gj33sY2lZCAG/9Eu/hJ/8yZ/EZz7zGQASFX/66afTOq+99lqKoj/11FNo2xZ3794dRM1fe+01fO/3fu/kcWezGWaz2eRvX88WmaSa4VsUHd9pKHZG7R7+uAMQhwHXRcAR2mOH0IgAPBzQHkMi4SHPnFmj5WEGhAMZiKiXKhkW7WUvA1WY5WvwDSWno70hGl3VCmhOpZrgzkGmjBqZkxFkwmsiiVx7oXFaK0Ekl6Madu2S24ztl0a3E3pqgDCLHYLRdQPQHUmai00YYyWGwQUAHZI4PKDb6LFipQ6KQy4VXMvgGj0kZ91plHyllaSWGTDJ548E+BnV+V++9/sBZnzk3pfwqUfeg5967x/SExhuuxfICgqaGZBlbKw+gPqQQaxywqOD9CdufQ9AhA+uX8LzB8/iE499L4ZsLPs7fP62WFQTrKy97ZKRhql87ikR/ou2G6caDiIQyeng4USmMNTp+3Xbal8vOwP8zrU1zCTP0ltjana3nUDfW3HgYibDSPodYaaR8g76HSlwUEbumNQ5qfNMnFSHJRXM8BiQEZJGIwvDNsw0uNJqVNYYqFOnapMzszU6ZqYoqqUXTnQRbd0qHYCnbE0CdQDqWYYyiIPmN5wZWlXeVUo1tD4qU20mACzrG2MuGPBuaTWWkug0/WMqZYA4ArQC8Crwm0/grL6FX/6mPyTH/jyATwHAmwAthzdhH5BVaGIlvZKoNieBWLoNRQAbgN4AvngTYODFd78XPAPwMgGfIwAvATgHKO4PhthNsltT3qaL2hCv2r3ahK25cJ2p9aZsj/3ddR5jG3Nta94WrU4q6SzzNEcYaPhexMy6oBmbPi9I/+3erwOid4O5T9L9m0p3QgaN5AcIw6cYJ8fzxVxcBOBaAJd+oWnqpIwsnZM3J1IJ16oSsgN8BBpwYm0ZIyjNufXcycbUWLxaNgYbCGbnZwBgkbkggZf9ttTS1pN8jfkoyHP9srkQgZUenwGQE4YyEzBXH8P60cnjQF2+Nir61QBBCqQUYdmIZ5z8HCgg5tc06BvfUrLb1oe+lcxwv8n2ylsRrMqALJklp36MlBjV/aHqZEXAn3rUp5pCqMBitZLiKSKAzwnI8p3I6ljlS3tGpBORbf5YWmXcotn+0XolI2vHPczalDT8t6URTGm9C/1/IwMAe8f0vaDzLj+JGUkH1faf7m+2lZJdUnwf2aEStBozowcsLEIifaRni4rt99m3iXYlMOvjH/84PvWpTw2W/bk/9+fwLd/yLfhbf+tv4b3vfS+eeuop/OzP/iy+4zu+AwDQti1+8Rd/EX//7/99AMDHPvYx1HWNn/3Zn8UP/uAPAgBefvllfPrTn8aP//iPX+V0vu7NEWMTK3H0L9l2sqfG60yt5ra3n0KJH+QcTNyNPSHMHdpDJ3obAFaPE8IcqRLFAJkloFoD/YoQF/Lku05yt8uS4S6oFpRGmJ2lSRDQzwXc6Q4dZvcI87thJ2LORFLyFBCWRWINBVDlB/1kQA1P0Je3+s9ePhjCritogNdvgkaJHQK7ZLzA0jdQR4sCJN2GGU6jPqGodoLSYOj1R32Z+wMWIKtiuJVEIMS5YEXGs8EZGzSJOADBefy37x9qDEFZBeZMUOmcTTGyyrTCPgobq+tFH8scjrIVwu6RHH7qsd+PYRoOTbKytvQECiBrnA6yl5U1uJ/DSdFku8gBuOTycZRhPBhvRSHL89J7eOVI59ugXduZ7UbE6NldMZJ4SVuzY9PtI00MPA90DpZiLMB+aCgxV/uDPI6WjoTtyvVA7CkBWhQwEOk14CoWzkgZBDCWV2iywPnuSSkV44+lwgvQUrJK0yle+C7bS6/7tI/lOMKSyhJZfAmxXXKEAACVrmZOhW6afNjCLpfOXzpJZSqn8u2OUwAqV0jkHbc6AlgB/DpwvwJ+8Tbwaf3pDoB4F8CrYKxApCCU7sT6Z5uRle0ODLwKUR1AjO5NBKgFcA+gLwAvPA7+8g21PSuA3gDorq7TFw7ExJyKhvcvn+Tgw/5X6EFeh8vsd6LtA8Z49D0tpOF3mtrPdfvGbSWQNbYbF/kh+34v2FMX7sNtz4MssJv0BpkBq2hYMl3tWAVglQOFw2c5aS81ErAICuKYJmOsge4IWLzBmN+RYG08yDtgknX9moGGEKq8X9u3AHDy0nMksAU3HJL9MkFsOWcFsKwv/QQjbtxfe8DGXX4bMcOFCFrJvoU3ICgBRUJ/xMneAkjFVFK2SOGTmJ/geoA3Arw5IrG5BCE0tHIenIpmic9naYPSj7n/EXOgxAIpxsay40atslgyibtjBioGbRz8UnxDv8rn7SKSmLsIxavupFYqTALvhe7aVVr2k3DxfXP6QI7XK5ncZdphScywz9YuSjEEMqhJo2XlO1G8I5fyMy7KavCjBeW+J77n1EOb8Iz2N/D9ir8PaKOuBGYdHx/jwx/+8GDZ4eEhHn300bT8h3/4h/GjP/qj+MAHPoAPfOAD+NEf/VEcHBzgh37ohwAAN2/exJ//838ef/2v/3U8+uijuH37Nv7G3/gb+MhHPrIl9Pu7odUupIf1wrZvnTEqO56MDNbVVRKrZd9+i/2NlluURQYpnfQ5Qr+QUc93jPZY0jpE7ykPJMZeMYFe1xIQSStdAFYNxPUaEdF3GjVUHBiilaXrgZGEffuNQ7UcCfKNsxYAwDnppsF6CsSo5pFVKUyl281oaiRH0jacsLoU9HIdg/oo+fFFx/kuFgbAJUepJYmcE0HovAy4YqZLwYy3GWUkI2j3hoKmzswiaOXhNkKlHVTkKO+nbap9nZy/crl9n6IzMzKQZY5gAQ6SsbHaTkCskKNLW62MMgxotPLXRN9LVlY58A7oqRNA1q42eF/Gk6CJ8xuAT8X+E4UWxT0ZHCg7AqVxyBG4qWNOfKft36+di+12bWemm6d44TuR235bc+m25eA/oK2x38t3nwTMAgTAkcIgyCmDNge1d8Uo6JZG6MTRsYivjYM5bRBpbLVtSvtlDFlhIpWD59Q1iCUYjC9mR5z+RsW65jzY2Gq7TCB+cS0mHlzs2kUGd5aZrwEWVkDLqvJqTIVCMaSZfdox0bRlsWIpcd47BbOyndkVDGCKIO5B8RTEAcBd4I2FXtwKwohaAdxiYKytPwe6jCWoFdVuqwajgVyTrYfQEHownQNUA1RBEM01RIRtm4E9aHuBrB3XPth+auHwx8uM6zvX0blfaa9o3/rXtua6aXNAfr4jaw4ZIHScCx6Aq9iFkdbp5H5KIIuKuarOt+R7EaQt2PVlSrcFgb2lrBV+iwV3LWAcZhIQAQPNfZnLhzmhOwRm94DZPdGcDY1LxUGsiAhXmu5WiYaWVU50c0LXZy1BvwHqwJmdVZpG89tccY5ln1g2w1RAeKuPJ+5Ldivkazmm9oxqJRVcZX7qUmpnmEvwgoAkYxKBnJIP+esihFSrpAMEglPbGmZS8dCpjU0yK57hO0ralubvSdBEtbMcklZVmGcGl6SJEpwGUCxbJRyLw+OWDvWpQ3MCNPc5sbpYmdzVmlNKoQucCptdRPAYkECm7oc9j4NlvO2Djtv4XSgC/SWwZetOsbKGgftLzLfG5z0x3m+N/XsyWbbYVPbR2JSEnHVidmrgyxX+EaBg3XBfqQBB8e6URQnSOldob4XU06D9zb/5N7FarfCX/tJfwt27d/Hd3/3d+Jmf+RkcHx+ndf7hP/yHqKoKP/iDP4jVaoWPf/zj+Gf/7J/B+zH09zu7RSaEi2YIex7GSRCq1BEaLL/auW1FDYuBNlbyQol4OaGfyc5NWLyfy8FDQ9jcohTdsAhF1Ap8AoIB/UIrUNRRBPoKkAas0WIzZEnbJFcmsdLfLojx62dSTrbqy86aGJzUMJhGVjkwILCMUZ6Sw5QMpdF4YYZR+qSfu2zAgpMUj57hN1GovBFwUEpvRKLyMjnQYU4FMdFevyFYBZLUH3pMMyh5cNC/jkEdJRCwdDBofD/t41b+dv47dr5SeqGBVqWTYekdfQR1AbRuga6f7nsa9ulwwC6ArMHy3Pc2CboMkHUhK2ufXlZ5ylz8Np7wW/9EmzBN7yQ992NDMVq9dNhLWvBg3QuGjuu2u72d7AwAsKW0721XtDW7Vr/qc7nH1ogtyGXLbSxOrFT9Hj2hn0PFdnV7S5Er9iUTWU6R0AFAXQQIklYh59+S6CvrhJ2k+qEEY4rz3wPa51tQjHtRdNgGbEzK2xhokiKVzmywnZdWyIrQ6oE6RkOqPUYG4AXDAgkAF730T/Q6tPaje1ACgTQaswowP9njMiAyBeal/VpU5RTACon5QRFJnB0FK8vs1jg6rssoRrHVfUBZSGTc78M+1YuloP1rnldBdy5YWVv3Q79PA1kX2JrxssvYmqn1SoDqAlsztIXTx5s8zr7jX7dvyOZKcVZjZj1MQarL+B02Dy+fMZtDMZLgu81xowUeA2fn3vSGxrtWtpVpHAEyZoeKUiVE23c/l5R1YqA656Rt290QEKq5z0kLMKouY6yA/kDO0/Ui58EeqM/ynNQC7bEC4g2gD4T+gFAtGbWyoRzzQPA9egcXY95H6ZuMB9fxdY/Hg2gowvam4/tAHcOv5HrmenCKBOIMaIFEs9eC6plVhmQTROYEiY1DBFS631R0RG2wicaz9p9rWe8dDTQaYf5Yo3Y+qD0nACCEA0Y40KqFANxaRN7nb4gelu9MfJ/AAag2nITe/UaAPCM2TGlCD7q40HJDmfZZMoVR+E8FkJUYxeU2ekzSdyFrpsnzXbKyUkV3O/6IlSXbFbsvbddF7+NlxvnLAFm7/K89yyb3s6/Z/HBsp/QZG1dOvKg9NJj1C7/wC4PvRIQf+ZEfwY/8yI/s3GY+n+MnfuIn8BM/8RMPe/iva3PEmLkwLWr4sG1sHFA8QIzpY5bj5BT24yCitcpaijWhPXJob1AqCW5oOvVAdyznkMuoMiJI3s8o67Eagf6I4WYB8byG31AS/YMh/6zfHQAFaIS5VVRMgnwHdLJfMIpIB5itgQTQk3B5kLXBwgAizk4TAFAfExOLrRpVISCZnCwGnAfYexGP1HRSFyJozQjsAQYaNZw4JgQFL0wrTGi2xaS6cCS4UuF4ddJADA4S+ZASuwIw2ks/YAVdpZmegB266D+bCFh6IQWWwb3rdwNZ1kZRh1Ry1mi1BYWWC5BroJu1B8jajqjncx6cQ9EeJPo8zgffapdwaiaPWwBfOwEw4mtH4xLt7WxnAICIUakO0Fv+uEzskMsPu4CwfbaGoFF1/e5JU0B07O9switjbko5SKluxVBp76VOvGMDUMXgllJqA0p9ExSTJANqCiArXYJNfJ0Ogltj5A5UZ8wqLdPHMRpDVABrAG4XdtzOVewqI5LphZjdkwk7s0OERKbBBDSFWSEFtQo221ZTmzSYQBrI1yOnagL5PItzHDbz2sazzZBAmtRVE1ubDbfUeMS4G8ga7ChfsKS4aCohwvC3AsgapF7sBbJ22JqJZXsv7rJtZA+uuu3OferHS7O4rts3drsMkPWw/suYSTKh6ZM0r8yRTqADCjAAQyAASPNzGS8ACsLqcuVYoXPjWMu6zX1Zt71B6I4FYJndZVSbPMb7jtEHgoMCJRtNWVO7lKoGUv6cZFhqAcnCghDmBL9mNGdinykYy4tyxVkg220FNmickjYAu0bNQCfL7ihxFMpyKqk72qDjPGHmOH1GpFxYRf08JmG8uTbb3pKskIAuC6gPMnMyWGX214gJgPh37bHI0HBEynzxa7X3Blg4IUT0CmRRJGFknRNmd4DZfYbfZMPiIBIuBmb6TdZatj4Bhn6OLMC2PUsAFdL9SCnvqZOL5cX3rc9Fk+qWxT0ds7KK88xM7sL/mWI7utF2D9LGKb98eTtUAk9lNov8KL+V4HL6i+IYul4O5GVfiFgld/S3VKDmku0tZ2a93drjzSm+MK9SR7IOum9Zu0pEfUcz0IaNGUPGRBL6rYiYa4lzfYnDTP6JsJ46HySTXq+IOipoJF00N4w54FqkQcMqL6VBrwieAsiMI3uoOU/KWd/9MgGjrDJFQTo65d0nh4URKpfLwtoxoKBC5XLE2tgCBC2/KwbDtEdI2WFh5nTwzMbOb0L2XMyjYkK/QGL4hJke31JdSECsnKbJEiHS0ujonAi/a0pnMh5mkIA80KrhTxUf9z4EE8tKeq2mZyJGUNeD2m56oJ4aZC3yQKN/JZDli/RC9xYBWZdpl31/diwrWW4pRVIda2NwjXPEZWDmPBGz3Y0H9ouOf92uW9EOfIu7VYmUXGEmcpn2Vtia4j2xd4GdRtOVUeQMINPxJXoZE6mHCrjqtmYHyuiujokpQyAMj00o3kPDogpbUwYTZCOkqHHeuFypAF6A4aRMUwPj/5+9f421LrnKQ+FnVM219t7v+3a/7valGwfjtgm5+QIEEgcUBeeYS34gFPEj+j7ycZITTmIpQIRIFCnij5EikPhBkECKoggR8OXz+ROU/LK4RUR8Dgki4WAMhzgHt31MaF+7+73tvdaas8b3Y4xRNapmzbXWft82ibtXSXuvteasWbNmzaoxajzjRqRmU+6ednkoY5H5L5DBNbOQtmMyHoSQCtcTdw/JdKgeIPkBkg8KH/Vxk+uDU4KwBTaOenISC+gw1WPjrYGq15+BTWt8IW/2IV6TcULlN9N0JK+Rf5Wm2kAsG+MKaHT091GBrKPKkYtloVqrFZ+7b5Rscn59dW/h6z1kN0/lS68kj6QfmRTHSgaBrXhWc4QALfsbR3Dd17DjAvI3Mbdy2I9AYJRwHJnuJwg9NE8NAsYzyvt2s7LiIMDUcMXY3A7Y3RKPkvULYjU1npFavgKDuqOZAsVb42S+Q5z7RylV45leIExnAbsbhPGCsH2cwPcChg1nOm/7chMN9gEUs+N7BxqZOPm4t3ksAYRtQowSCP/sjmSlt5AxSYGknF0+oiRKSRoE37qm/Dc4IMsst0SO1L6oQa5Z+No4hl3h+d6NrHhASH+mCwGywiYgXBHWdwjrF4CzF5Mod9h4G+fg8MA1gCygIuiLsaY9vmg8yoOFS0BWqu/pLbMqq6wsL4XunHgkoCq3sXDiuqB1r7quaeOt/l4tz6lkVrvW9iPuu4FY+XtrrXVkOYFZj1hes7qH3WMRZ9RQpXCkX+B1J+8x1W1Cqb+0WWJVWllCjleVYzrpNRa7CihAVkZwlcGklZqIKjGjUTu2ThhvMob7KvivlQ54IjaIiyaNkqkpsKVKLc8XtrUW3YAQasfM4jwllgCLYwDtEmiQeFoZgPDjpotMNvaUGZpYJgEEBg3IYIQFdRTtRiFaNDIiJ9efBEqyQtOZEx4ACVlgmgjdzBsTnm4m8CpJDJOk5r+qhSrae9NGyzNViHb17o2SOMJ6aFeuAh2xxAqj7W4eI8u322gZKiDLrLKiPwZ3rrn3gmtht5v+/AJhrl0JW0ZRnvc6xLLrCkLNebt3PuYE+vb+JngFrizuTuVU9pUbcYvpjBB7m+IvRjmW11hdB8zMLE+ckgLQjQtK+nRAgSyvCCLbECFrf6Udhe6jKAIszbfR2uqeuhdFAJhV68+oaKKns/nSrCBwkgNcxqwgGmSeSswsI8az9Zz5rhPestLLdulUbmVj6eJoWWB2AbRYwmaxDD4PZZzsfpWLJQovSisWPhNYwKwMLLnHJMoDxCg0lYBmnDxBrY8cKqb9JsuM2yvGa6i0nGlxa+lmdarf7U1dTw/xmua6xUrUr/SSW1017bYa9b3uiFTOXx+gO5UvpTJzM1woXc8OJ7IcFben2RdVcy5BsoeDEFIqSj8nBGfFBxjIcVVR7c9lb87ZYwOAWg8FUcJDZZGVuP0NV4zdDcL2cZFtVnfkuu3jkhk2aqZ1Uq+QNDjrYaPPGscwj4F6R5Bzl4xXAqIM98UKa/N4zIlMVg80VMeS5dXS2HbGmVexhOZw8hFgNLS+ljOPYgG0tP4aQb1hCONNcckkx2/NSouJskIp7Iqsh4CcEIxG6cd0hpwhUizSJETNSuOgZUwzAhgdf0L5nlaS+Gp6bAJtA4Z7hLMvENZ3Gav78j4taRjByU8kMmLYNLGVbVwOFGLO7oUFtHJ8qQGyllwLZ66HqqDJrrcxYJYICygykc353LG6n8da2Pr1ei1rqwVZadYuIYcZWupPD9iye5hSslLA6D4sNVBJlqOuyaxOYNYjli+MNzFcOgaSEuBjsjQLIAdO8+dNa0md48BhP9l8DSBouVsk3sUBTlhgRmSJ6zSdOaIWBcjiKFZZ5g6S24cSvgF5YpIi5TwG0JCwe1xg97ClAlAxKi06vGBjmmmd2LKXLgSxWmbOx1lMOZGJB212QAgI2wBeBTCF4vPdLgwVIDigBrJyGtd64daueQXVp4kRryZQCvonQs54g7C7QUiq+eAIsPmK7/Q5lECAADpL4JFAW8oBeeMVMsiXM7k4ULEVytqSg2q2j04aGF/NdMk05BYna7urL0gpaxYyKAU4ocIxaNM++O8ZUPQbloZ4Vhvx5XOlT9zdrO0j4r0YJPNK9YarEF7u97kRIGYEXQXpeUetsyfx4lSOK5dp1dDjdk566TojE/OGZrzGVTtWCNdrfEysto1M3yHaXp4gQXMd3U/qek5O2+q7n+MK+uMMUeUFxnQmizoHhTdew/U1NYgmx7K1l/Z5NpyEzL9lQ6aVIKA/k1g2IZLwdMvu1AJjKBY1FZCl9WhCvnZhmEu7Y0Jgsm4ADKREOQW9geNsAsCEekoQVGkCYKKsTbeYW1V8KxsXAECsj7dWWR4Ma/tPcOECyoafxkmUJtUF7iVUllX6b/aCSoVZ0PeljTot/ujPf144fgyvOVRMCD123VmX2m4f4jXXbP9UvjRLILd+9mQdpMQ51qwcuN4Eqdeap6O1zJIBl+j2sLb/81ajFu7DgViAAhYj5/0eB1F+D1cJHALGG+Lyd/YC4+wOY1oD29vS1tkXpFObJ+T3cIm89+SBkShoDEbrhO23RclOOZYSFTqY+Y/unSdRpl98VkKXmOvbjBY2oBUPUejfEvASggBZQyiKUYvL5RXA2dpNv09lTGlMOc6iKNoJwTLXPqZJqxxfxFCwsbiR50gD1XKb0hJKmiVyUHd3s8piYHdD5BfjQfFSL1VenlblnulM4i2HyyhA1vOEs+cZZ3dT4dEGGKkcxIEQdoy4TarUL2O7CGRxc751L7TLekCWxT5rgS1mBE74f33uw3jLg/8HH734cnzwib8EtnnTyEdV/OD8Xux913JTfidNvaOSvV23LPEgBsSycGGv6O7bBa5cn7JslPd+lA1junLUQ1hnncCsRyyX0wphe02/QgsS1wJbvnhLmGzziUJMWp9Vmyw5XhHKxh5lsXrtAyVJaxo1+ZCBSSlnHPQADspm2fmy0k5AL4Cwux3Ag2zcp3NhXDRqym/rphJ6y2xRNNaljxIUsPGL742N/21o+DiBxoiwEQo8obgb5vvbOE6sLi/I8TtalN9fCzem1e0n0YIgsQBaHMTibQTGmxLY2LQblkXELAloBDAqBZiCMJpGEANQQEEbKgXhynM1Qm5v2GZmsVDtOIN2E7DRgO+5js7rPUBWzyqrMp9trLK67oXXJcLHmss20mkPyJoxh5aAt8Tb1sHSsnVCzMxjyT796+n06VROpVfGFN0G+8iyxC98scNsletrZ4vANvYk/2YWjy19ZPknygLKQkNSeujdqat7ZMFHjxnoAiCdKU1hIA2SPrxyYc/ATPnL1mCO61bJShYHh5ufxqjU/XBMCENAAtVgArtLbBNszXFNjzk111LdRjWWakkWGCC1NiMFtCzTE6DjZ99NKEjIu82lGFv1sQgJIq1xBQAIkLWDZBZccDX0Hfc8lyHhASzge67jBAD7zFNOJkG1ufdzw9PWlp84zXKZs9coR9ev18jR4FRPSdJWOaKtfdr96viJ17ysS+ilW0tc3KC1HIrzS8yFJrVztBN3pzQs66By42pj/Sjto516PMSytuW41AkTI+ySIyVafyAMVwwgieULC5DFBFw9IRZb6xeF3m2eENe61f3m+YkwnRG2N0tGW8vsF7fynVdmucoII6nngj6jepb4MfByIFNxM5TfmK1zjkEthedEmIdQrLKoKEMyj/bFgCzM70FjQrwCbAIMXCygxxuE6Vx4Q1BrK4tzzJFk3rSs33AdlT/ihnNsKLPkAqnBgybTYv1NXGJjchCLrPHxBNoRVncC1i9qbLMrzzjLPYkZkSd89+/+Cr76s8/id26/Ee975p1IIc746d6iVs6t4mYmQxmQ5UGslIrXCjP+35/7/+FvPv9hBABfe/9Z0DjhA098g4CRWR5ychFM5iH9jgJk9ZbkdQ1arlG6CpGGn/YMC5aswNqwKnnv5Y6Zso2YwXphL04xMXKon2PLCcx6xDIh5EXArSniMSUBPsV4odvzTU4WSDoCtmcIUrfiAXkj0wo1YpFUXPEsfogE89Nrzbc5igtiikKchitB3cNOiP32MiCdMcJO2k8rIICzWwihXix5g90MmQWnj1sIIZi4EIN27+zHnBmYJuSMfIEQASQO8/Exd5NUnrsFsmzjb/HGel5yub2JEU3jniCanUSIO0K4KYxzOpe4MTlmltGpEZi2UVwMDfxjHT8FxYBCNLp9mBHmhY4a0dc/2TxMoMutWLaNDszqWQsmiDvnApDltQ89q6yqK9fUBuZyTCyIpbZb5rxPyIeru0874RnA0rhbHV1P8pv3X3Mqp+JKgg/+Pt9oHixL9TvHbap2eU0jFFdWKOwaaAo5eltp84ECzBtv0A2v/RkdNNeIaaQMhAHKS+z+XNrIy5bQX2ek7UwOaFoE2B1TZSBnmdJ4g2IsO3cvtthaYJfVq9kneDmFOyx+Po4M1kyHAmrp2KwpxyBLLltuvjYBPAXlfZTHq+XF8j4jiNcALgA8DuAGxELrCsDzAO4D2ALcWPK64SJ9/izAKpBFU5qDWS2vAeV3MXMvzBsa+b5olVW68nDlqDX2kHzsUcux678VME7lZVvOwigWQu2JJhC8WLhgeZ90qGR6z3mPJ8epuBh6QKtXlI5RYtn3VmBC55Ysildm2XvGKDGVLDj4/ddFjDcFyIobxu4WYXeLETZqKWRy0sTgCFy+JuDytQAHxuoeYXVf+EBShbwl4kgxIIxiXSxxdRXjaxRLRoO8NVEeEytenoEJ8s3eeIgZyBI3tdJ+10PEW2d1StiZy+OQFRjru/J8uyTKdhCyMshcDiXpley1Ja5y4dsFgBD3TUDkw5zcdmRMFyURGAexyUjqGjpeMKabAlTGDWF4AJx/XrJDhpFzfC3KllBy3Xf//q/g7/xfv4gA4Os+/zEAwM+9+V39B88DpX1ako9MJjLlVANk0eRArKRujXruLZefqjwH33L1h0U2ckYAvVhZXSBrj6XusVkFu8qP6yTKcnzFy0e9LO89zCG77VqYHtuq2l7D+k1lrbBmZ27b7SbkWignMOuLVZLa1gIFcDGt6IEdSFfA9uh7U2qtXPlRIa1ZSHCbvoCcPjVPOi7CRm4jIStnp7MCsgz3BchKa8qmo9Zu2AlTMz9vy14oQoAjiLYRNyHETeJK4HE+xjO/c//4qinnKK4gMiRSl/xY2eI2hD6Pc/kt4xC6Cyq76uXr9LmIETdJzZRDDog4nkMY9rkG3NdUvwDEWmFyEgzbu9H3oYs9vztz2bBn9mayaAh1Oz7Mxb0wOSBru6uFCxk4ZB9wI8z+XPsenPYBwEGrrOoh3LgeVTqAVrUOumDwnIi366HXhzawYavh9sdzfB9HxOvG5teD5v06lVM5unhtbd6wUfWxVLq8Zg+LmkMw1k6zPtrryK0hQs2bfBt6vd8Em2s2Tcixn3wXPChjnfSxCY3uk51rFqW3Yq76n9cqoaekEt4owgQlLperxVi+Ij8jzzfRuQ8y6N1NLBpSlp9VguFmfQ6r9cBAQsqHMoYlyDxJnz0oR27TaO+eIoAVGDcBfBlo9WrgNSo4fZ6B558E8CmAvwD1WWx6HcUqIU06ViIsCJDVca/xYzzjAR1+36Gh+6yyqs/qoiNKbz3Q/gqH3AwXY5XY+1i6tsc3aM/99vb7VF5u5bXru0g31q0h1nLpWfocdR3KmkMz/yqh9DCgJdd7Ynj43jQmhF1AvBLrre3tAbtbhOE+sLrPGG8Qto8JvQuWsOpcZJ24E0H7wZcxdrcTkABeBaSBELeEoJZZNIqSniMwBgFcwiBtDldqaTzWe2bLGE8782rQ4zGgykScXNByywKue2VeD3PLOSdOzobDg1omJ3o9AQGYGMPliClF5ZchK5doEistG1vjvdOaivv5KHzXArlna2rlm5SAOLIG4hdrO4m9rO93kjbTIMYTac2gHWF4QBjuEc6/wBJrLJX2CthpYwi8/XPPVuDR21549sBk0ft790Jv/NABsiSGpspJHsSakhpLlAn60bPX42uuPpmH/KMXXz57n5W3igeyZp2k8r7g6ld1lh7QP+theWJvnd4exLkN7/OmyYAWlbqVp4q179e6++7dE/fJsb1yArO+mEU3ute/pjNTltqZCQP9DaERvGxFpujxeCYp08PE9WQLhWiBhLCNNwTIiltgdQ9Zi5En+kgISU1zk5rqDpCAtRognvS4fS8xvMoxiaVCeYNrlj0cCJTNdzvFdvXjBDIEHEAgQlJ3wwxUWfYU2++6hZMttSYGRZbrOloPnybXA1vmvkkT51hacccYNpJBcrwh/uppDUxnBATRTPHAEuRdVfMcJdivLHKaMRAAiGnC3/7dX8bbP/csPvLEG/G+N/9VJGptyoWwZFdKhhDvMSFc7kBXm7nbR34YQpXMwCywvLvhQ1hlybVNN6+jJbxmZo4lIKpXjAh74VqOowQr3LcHdGBode9OnVM5lZekPIxMsnTNMZschiOaR9yYJPuUxdGYuUU5msZq+ZtdGdQN3idLMAAru627jZDnKcVFAbM1l0Fq047DAVLU7rjaYpvjpJbNIdNmBoBQeFSLL/rm8itIqOjM7G5ufGpgS+OoJBU0mDRgsvDQpBkkWa21RAMvPMcANOtbzjZFJuysAbwWuPVq4C8AeEvCathgevYc+PUz4A++HMAG4m4IiH9KADDAgtNLH3dAGiVL7jiq+0rnIWfKEdTAzkNaZeW29h9YLtdcV0e5/OXKjMqNyC7zG/u916Meo1N5RZfXDXewu7UqyVWZszL3ocsR1z/y/HP0bel4Fas2iSKdRkZaEbY3A4YrRtzIutk+hgzqWGzGNIjAO94gjLeA3a0kZEQ9U3aPM0YNfk6JEK8ou7WbVZK56O1uENZrwuo+iXvhpNa3llld+y7xElWmaz1L7FmmBB6i1lH3wpXmrNU9u6+P2NA7PzYz8KP0h0ZGnEbgPGY6OVxafDCNlazB3DmIfEIKZMWtAhM54Zc2ryBkdm1X44gZ7QqQjO2snwDCRoCs9R3JQhmcU4hXLHlFy0eeeAZf/7mPZfDoI696Zj7WqNvpZnzM8pBzNzTwqrHGktiOnfiORPjg438RIMJbrv4wx8xa8laZxcw65F7YgMK9Uikj9/GCJVyh4p3Yz68IDa919zPQyv9Zn5q5kMMLWRX3ftswLT25e6mcwKxHLBESCLY1491bzEKLDsTNOrbYJDWAphfwseeXHc3KihVkYkkzDlQuHmkAxhvi7xx2wOquXDvd0Owd9xmru8IUgiL4uxsoRI5RsiYmBYnMvc+DDKYpcGnQ85rQjB5iEaXPnDrjppZENE0S1sOGCECikMcmxx6Jc5S8dn/hYkTkh7Dpd8+6IUwMulLNz0A50G7csoCIF8pEmBCuCOkCWStiYJrdwwS5wPLsdu5//b1fwf/2+43Z7Vd+c/Us2XLLxjYxwm4CXY2gSwdkmTVTCEK8e6VnHZeJt6vXs8py5x7GxbDa8HeCm86EZGedNXOFOuL2nljPCD3JnMxm17NzSqQDZhZavm637VM5lU4JBrZcx5U9g0502KX2mOLXEHCUoAMU0MhrXHMKdrcGLECsuRDGrZ7XmBthB5DFeFQt9KRWweRcE6psfro+ZyCRbWLRnDcTeCIQMRats+x6Tmo1FrK7pLdCJa3rgfF2OAGl0xPNjs94zWxwAYLEc0ks42CZG2kCaCDNIKznI8ArZa4GYHnhAUEqpQsQngK+Cnj8L38eXzf9Z9wcH+D//qqvxMfu/znguTPg/g2Iu6E5wZ4DfKF/AeAdaLoLGu+Dxm0Bsow2E9VKlGo+6aRoj3Vo8aJrKPVl5EOF3JDMLPbsWHW/cuCheY31d0G4yYBmy2taAWLpHu6aU3l5li9MNzHcHw9XtHIM/e5Yue+9do8XyV4FYLu+Osc5rzHOGfI2rxok7MkD4Rvb2yRAzGjyBqo1nNenJezQhEtktFfDfPAATN4SWJUeFhtqd4sQdgHDA8bF8xPiZRLZpvt8ThPh984W7mNUMryKSKswB7bJfVcrrZafZ0DLi6KtOztDPEcYmFIAaQZGe+bdjQJYWezkqIa3YeQqwLxXsDMBaSXWWCCNd6RjPV6U0DPTubh4muvncCWAWrBEJTZMzJWxAAdCGBnvf/NfBU2Mt73wLD7yqmfw/je9szfaetH8UBfISql2KxzVGmuaQLtxzrPsPQZCQpQYWQZY+dAr5NwKgZlVVgayZgBRLUvt27PlNq7J5I4Fn3OMbQOgnEwkFerfPb7X5U963lty5YQQ7llOboZ/jCXYZtfcseRgsWbRhcOHbOY6Jva18I46GDnmxAxmadQyn2bi2gJIgxCLuBHgRZBxmWUWfI0DMF1IvKcwikUWB2B3U4Gte4yzF4Q4SkBDFQwmwu5mc1/VtGeBxm+gdWITq/bDEN0JWdvBTts9dzugwhimBA5JrLMctEuByibeSnLpzzsMVDQTZmVVSxJ5oWeK6/ujH0mybiSNoSWAn8QDG7eWMZIACtisJ30vYvFmKeertql0jxj46s99vDa7ff7Z8lxOmCtZpBTI2o4ID66qgO85Ffw0Sfw3oNbq9Qj2dayylEgf6/s9r3OwyvHXLgkZ1O+LrcVZLDolwNfOvuEZwkMwo1N55ZUMrACOX8BtjHUq9XhAW/adb8FZO+gneN6cdI7POl42JTlzbCrXetNy1iDmlICgQFZay3VhCwkOy7Zx134mqVPpk3RNecut/CSO12TQy69FaPsVitESd2k4uxqSaHU5KKBFNKcJDjQif8zReGKnKa5ogmNIdl1zCixCATu6H5NYXadJYzGupG9jZH0vARxiiRkGQPxZzgE8Dn4MoC8HnuFn8af5v+Im38fIAz729J8DHgNwfw1wECCLzwF+DQhPAxcrYMWg5xk03QHtPgFMFjB+UoxVeFIPKyqAYv1bKlH+3JvBkJbI6hG85mCNPdcey2uA7hrM87p3nY3JdXnhide8IkpcCpx0zcJeoG5kDDnWuShbnGC+Lg+Vlk4eqMdBLH2ni4DxgtQTArh6LIh74agKdBbeMa3luqRZ2ld3JTnTdEvGK2wJcVPkE+M/lrhpPAfCRr1KNI4Tq1J5vCDcO4+4+HzA2Qtj5Ya2+ExEqvxIRXZMlBXOOVaWWRSRuO4V66LyOQO0qvAnNmBcFDbMCJsJlBgjR8BlNQQE0MKAbJiQIsTjhjjH3MphULwHD7tQMoOCYSpe7G4n8Erk4PggIG6B4b49bwNaEAS0ND4Pjas2MSYK+LmvnMfIMh7eWmd5q6wlIGvmVrgTK+Ic6F3HL396ecY+TS6ycCyNeyE3YVrEFbGZIn4v1yvuVOW+22Wgy6V1ca8VedjLr3qJ53IsrA7wZJb45tHCBKRY7juTqYy3PQSPOoFZj1jCsY6d+4SHfZqMfKOldrFsQujrANVETStxMQyjIO4W7BCRch2OEvhvd0OuGzQjyPZV0uf1HcbFFyQA43gRymbdLzpF6KcAkBI4HigzmzCyZBIBlQDs9hHqWc2BpH/eIqurMXeEZ2IQBHkPGrkxOUIyt+YpnwSI9ZY/3hte6pi3VhXkOVmFJ44m0GmA+C0QRkJaBdFcaGyyuCXVshRBIwdDVgL9kSeewdd/xpnd3n6mArKya6mmEM4WWVcbyV7ogCsZMq7nk2nOY8yB37tA1iGrrBn4uDxcR9UL1N80VNc+5D1R5kTRNMhEqQRU95mDGNqwOQbdttVzXzyVUzlUjp6+B4Cqgw0tnT9m09ThNSZ4mNVt3hD764JseKaVXBfUqnY6l9/DxjS4AvR3i1tvbBaRLr4HqYZZXBSbRdcBDWbaxN4uy/ELJICQwKRCCgXZlNPC1Z7XUNPWYjmw0+PCG/xOMahQRxMpbYpIQwCHM/CwAibLSmKbkccB+hOg1wL0mAgJ99ItXOEcX8ATwAsALhngnWKaZwBug/AG4E8B+DMMusGgTyXgt86BTz4NpEuwc0tkYC4ceIstOF5D9uxUvxPjLdWxl5jXHLW5vgZzaUql1DiiyTbeXDf+yaE1fCovy5KV60vywAFXo4PlmOQ7S+WYy4w/Ge9o1nZahbwHYyLsLkJ2RR/PCNvbsh6ixlj0buYWt2mnMX/DjjBB3d6cx4acU++RgcQqyUKjTCqvaFbDuBOAZloTdjcIcRMwXCZg3LPQAgFjQga0Wrdrjd8k4cyKp40BaClS6e9UGGmOgTxQtmoKuySeN7r/Z0cAaJcwMDAigtUF0+SG3WPkLK7KGALI7vSW7d4s13KGOrfHsHFPZwmIDNpIxvawEfAwbjj3Ty7ozAGIQqZVSu0tVq8BsrK8DP1tvHJi0HYnsYMtOcmMH9nAUS0fLQBZ7Pmat8rymcQcoOTBYDnm79l5xAPnDxYDROG8k1y4g9J/1HKPkphsUW9tMeXjee1ymR/z/ZTVs/t1+Ng1ygnMesSS3QzbYkGz23IdF5EDJVsGOcGgaPWMONTExcq0EmI1XKXarc4IyCCChKVSHR4IEds8IaaiN/6IBMi6ErDEUoJPZ+rSsJL6cQMBrszUVxcGDyK8pBWBzoQwxg0pcdPni24/TpB0tWad1R1zfZCgz69ADjMUcQeICHRO3Xfm3Rq9Fr0qnn74d8n172zvRciupOKzLjG4ksV8UbfLuJWB2TxJWeMxnQHDA84mmDCBRAXCMALve/NfBU3A257/OD5y+xm8/5l3OgauWghlZMN2i+/65K/irXeexUdXT+ODN78OE6gGhViAKyICM4NTAg1DIdoGaNk7aLUTS1ZZ+l4e2c3JyjFAVivU+3VAqIh1rrLUv0r75epxOVYJo43AVRHsmRbqVE7lcKEeIQf2gFfHIFfH393+5/mam9+PcqVoQkKNYGW+o67stumxLLrTuRxf3VMgSwWEnP7bNMPqyp7dSox2k1tv9qnui2Gk7O4OKO3Xh2NAXLk79CEXz3D9ASYBtFIQRcjQb0DllPLjAK+pKnB9iqvh102l8iPOu0sGsShdRDBbYby4AOhVYHoSaXhMAcQdECJwFoAvB/BVAF494YXpSfw2vx1XOMcfvPgngY8y6AUAfAnZAa9B/CTwDIC/wnjba/8znth9Hp943Z/Ap8Ibgc/cAD94DOC7yCbXzEAIeU4xs1pTEwwErQbD0W7gQKysl5KuHgVk7VlrtPT98NrsKYIqq60DvGbx3qfysizJXr4lCar2X2Xj0QtFMm8MjbsTcqgPX9oQEo9UbO5qBnHzNuFImM5DZQVk8gYl4QVXrxYasLqrPISUr6hHiLU9XTDGIJ8IDES1WnLgV1aYmEixQwZmshXSpG7cUFobgPFcwLVhmrIFF+BkA78vNRf3qIPmXdKADMCIm33IAFW2BtZA61ZSJH1mwnguCnuJ2csip02SnCrsisURjQnDJTCdx8yH1voepjPnNriWdxE2Gu6AiqxYeGhtZW20KLPIyAhbysnAJOQK8jO1c6BmcvaQDTFWpXYvjABlGRAzudvCzFBK4la43YGutllu7BpJAKisrqxrHU+VGsAqMlG1fjyQ1ZRryUnUb6MqS+udHJDVyDCExgqL53SjKFao7Ld8e9omse7XOueyjkr3XnXIluO5+AnMesSyCtNhv05+aYSK1sQv/za0OfiJ2Vsh0g2LybS+n+q4TKSuCBOQLiQjCJMCWROwfRwYbzDOniecvVAEAUAIaFoLMQs7iaEVd5x9sU1bbEyqCBjy24LUbh9TxH5X/KwByLMFzMGU2TMaeJPUtJPFkkvTr4fdJKDYirrvLVvP2HhV5xYssAzI8qE/gns3DVE2UIsm0aKQWi1cfD4hjITtY5SZWlpJjDBKohEaxgnf81u/hK/59MfxkSeewfuf+atidpstHkqQfxjBnhhhTPiuT/4q/j+f+fcIAL7m6pPgxPjAjT9fiHaIAAVhDgFFC5cSMMRCpHtWWV6rkEEvR+g8kHXsUrgmgX4UVD/fMqmkyJ14WOT+qhvXxy2WVhagO6UCv3ptnsqpNCVSOmKOv0S8Bi35M+bhKpCv3e+GWUiFLdebQ6LsDmdxsoAiNFjW3HglcTUqN8FARdGhcbVo8taoWs9tqPM9tU/CcyTTFU2MmNwTK80SN5L5SJSOyO6LqGN6nxhiDhyrwPXtEGm3OrdYuK8BWfVQqpUT5rzGQC0dZ9ktRgwPVqD0GkzrLwe+EsBTAE8AXlwLz/gyAF8FvP72J3ExXeJyvIn/68GfQfq/z4DfY9BHAOBTAO5BgKkBSDeB1zCeeu0f4u3j/4lX7z6Ni+lFfOqNrwe/KgL3ziAujCmPMfIexgkQwU0uA6nIBhbuHPJ7qgbDTdOXjNe0518Ket1qxq+zdB1PmlkRt7ephINr3ONUvuTKiqb+vvg6ZWbZ2P5euKbd67R7HCsH5jkTIa3cug7A7kbA1RNyg9UDLmCV8ojtY4TxAtlqqig+pA1iBbZILLLGC0Y6T8DAwFVxKeRYFPcZ/FKFvilLrP0cH9GUKfp7OiPEK6PLmnhr4mIFlB+Uy2cFOopcAKDIFFZVj9PE2WskW6vpOHOk3HcDUKYzaEKuKEYDlyWjrABajOksglRzvgawTQDfoAwGpgjgDMCG8j45RZWJTAZiAcCMf1sGYhDUjZIQN4ThUuRENhnQhVDYi18c8ECq+lKFHaB67FPSTIUsCcP2ZXP31lkeyPLK/QbIkndgx8s8bONkVfJ6T1ZoHncGcj3EUq8AqKX77WvX76m0PXMjtD1YsvfEmAG07Ntwe6MWCDU57NhyArMesZzROItl1StkAEHvHJcg8PllG+rNENPQ1J/sOfNakno+Q9/MfS4TDsJwmUAj52NyLy5ubEr845X8ni5Ek7G6S1jfYaSVmPWuJgleuHogapwwslhXmfmrMjQBteq0nhUBVleU8Vw05pKZRE1xwcViLCqhGJuMhj3fZiVO0oUg+2gFtBAgAeF9I0e4rrVg4ryCEr4EEDiDOL0sG2EHYJRU6iGSxjURU2VeT/ie//LL+Nr//nH8ly97E37m7e8CTxF/57d+Cf/7R34BAcDXf+ZjoInx3mfeNXPRNGIuvuYJtJvw1jvPVvG13rL978CNP19AK06u/26yxVgTbk/QHZDFFSG3edXM20PEuVOvp3XpXuKFWPh+Hb62most6Oa1F0v9dAS+ApmX5OBQmPfJNOtUjimR0lGa767bUT53IAi8KV66VeTcLMYBFu6nG6Ywuo2lbVKSZNnKWnbnPp0GwUaCxhS0JCRBlSLi3uACnDc0u8qAU/XPiKMqUAbZrIYRwgu9elD7KUFu24drF2wD1Gm2QDBld0PhY02/Dq77loG3tzUACBnj4V5dhroXsgJrERwfA12+HvQ1jPTOhDe86lkAwKe3T2N35wZec/vT+HPpd/HG8ZO4Gm/gt9JXI33i9aD/yMAnAOCTAH8BwFb4HCeAJ9AUMSXCdkvY7gg7juBddK4dCmQtWaiTDHzRdrtxILj32dJouPqd4esfmB0+Gk+aCenX4DVVH+cXPLRSZqnzB6bRqbx8ypPxPsaLiMEJ/RIL9SEnVc/CvVeuY5G10JRky6NilaV7semMsL0VMJ1T3jd7ms9RgrHHrfAMyXIo14dtnWjEkg0GA4WCKJWHSwVfGJoUS7uqYEzcICfKMr4DiHVWgirxPS8KpKTOMUvz4la3wpzoqrV2GRNwFiUciknoupfMMlt0soq+nwICImdT50jFMnonfHg6C6CRKwstTIy4mdy+P0CTDmK8IVlxzdUyrSHxfxMyX+cgHjkG7DEB05pVMWUNifIoXqEEnXfXm4zEqPcw19n7H67HamFn7yAV10IPeHlLum5DtPjnLa9E2S9jmuNk7QGyDgZ7B45kUMeVKvSJ56VtX/ZtGR1wZYq7maxP9T08FtLdr+Y92PEM6wRmPWKJpIugG/TPobr2G8ia0tlCmblFzZlIJSg3wvMsDgngQAXX56uEuBUqkt33st8zZWIUVZhIa4jWY5QYW9M5ZXPfuBNrruEyCdKu2QEtyFsGGMxix9zlHFMwd4iwZQyXdn7+MEyQmFkhSIqNfQw2adAiLkSLEUBQd8NAAvwZENmx8vHyz2LJAIoGDG6ZvzE0/e6LWW8FBlgttcJWxvhv/ddfwv/+OwJaveNT/xVxw/iZt3wrvva5OuD727/wLPDGui8VkJUYYTsh3HmA3xmewtfgk5mnfnT1tBDxUM9P6b+2lbULc/fCWdB3b6FF7Xg2goYdO7JcW0jYV2jh3lT6uVdT3h53TCC7IDJ0POZ1gIZZX6fvp/KKLSFrJJaADWA+YdkRsz2lM9dtCdTnFEjCAmmkuh0aVbFh9U1QASp3QBrlzzLs5ixJg4ZIZMrAlQgVtoEvm6nMayx5id8Ms7ckZnV14CwkdIfDg/Fp8YkLn/eDlggUnLuh28TO6KCdOpbXWBMtr+HmNflC6m7PopggPgdfMOjNwNvOfhNv2/wORor4b/iT+Mxjr8Orxy/gTdMn8BW7T+LzeC1WPIJeYOCPAOC/Q4CsSxA0tRfvQHwH9IlX4/nffTV+8w1fg8f5RXwSXwH8HgOfAcD3UCONTRftmSrabHO3fM6yQHXG82ggq1OO2j5fg9csn2uEmusU6u/1uqVZ2yc86+VbbocH2N4ecKHgOfd4xbGlBag6VjFVrJ0jLEvm18taThbnyfEPU3DbHt1iOPl7MwG7m2KpG68ExAk7OAtcAfHTmrLlFVitq64C+GIED4zprJAmjiUsL6+tHXM95Bxz0WIwAm4/R5qkZEkpbsrgLNVr9jzSLIIJYhk7MhCRn70CPhSQSatQj9VKfli8L5o4K8s5yviGSfqWVqTB7N1+YmLEyjop4OyuPNt4o7gcVjx6BMJEWaYza5u00niXgZHOhM/SKLGSW55r48tuTHzsJnnu/nBWQ3vMXE8My9xOEwPbnSTBMuOHNjSQusLLcBR5p1Luu3NeDmIiIHbiZMHmil1rxzobsEctS2vW5hEor6OjNDlU5nnvnAfC8pxlf3zPta6NFlg7ppzArEcswbt+LC2mJc1GA3pkZpBY0mu3GvIlhtEWm0jtxGFIQMBd6ScBwJhASYndTjTVYSopaMdzvZw0jokkKiomogMDk/hp725In2lCEWJMO683NEArjKTuhHrKAKzZRpyK1VkgpCEg7kgFI+rvj9UiKTcVg8s2JQAPxwAygG0psOURQkYGGA0lsmfJfVnYsKpFuAVO5Axq8Qy0+to/+jje98aEj9yeB3yfZe+YTPuQQJsJ4cX74HsP8MGzrwamhLeMz+Gjw9P44NnX5PGdzd00ARYrK4aZVZYR6pl7oc0JI5ihQ6RxAMhqif7DlN6a23dLxT4rsKlxD8pE3xHc6hjKOU+8czszZlbaeSm1Lafy8izizqY/lmjS4obE7yhcHQZmsRH2tjPrVX/joQoJ02BL/wG25BRC+LKG19w6ku5ImAAMkjgkWl90LYnVcNm8Z+WH3qTS2tt6TlRrztn9Nc9jqJhtQmmivE67ikJWGg4UHs3SgHc3rFJPL23oDvEaq+LrVq4rCxcyRDFhKHsKkm3wDHjN5nk8HT+DLa3wWTyN5/nV2KRzvJCewBojPsuvwXPpaeA5yOZfor8LkJXkBVO6AqbPgT5+E3xvwrNf+WbggoHPEvAxBnZfAHAHEvy9YdgmMJiQ4K2ySB822EPXY1fFH6Glxz+S1yzXOlCOXixyS6fIqH77Jggz3jPjFR2+0eUlB645lZdPCcQYz21PRgKkpyTJe65bEtQc1mQU3SMfO90bPlOttVCEfIv15AVhKxZeI27FdQ2B8t4sRbHaSmux1rJMtwAqxX5ai5ud8ZeKJwVGupgw3goY7hXrWY7IsZ0AtdbSkymW4waWgdQCykCkfUUtsywbohxLoF0Z67AN4j5o8gRQzMq0hIkd4IeK55osRQkIzNKO0RnlvzMFPAAJal/oMzFhfZdAU8B4A5jWlJW0TBCwz8URk/ennwNLBsOzBEyk71MsvOJGFUoaS9neaXlanvHypbHMSrFDRe9jgCG2O9BmB0xTAawU6EKMyImvfDEFxMy1vZaDshJMZabaWsu1d4RF1sHHWrp0XxiWY0FuKu+lMqLRz0WL/Pb3Ut2mzHjeNcoJzHrEsqKpAEw9UMCXh5mwDj0FIBY3nXhMOci4Iu8eaDASTWPqumBYQEFKDIoBq3uC3NMk4FXWdAdGWgPDA8oBysdJ3DDSGeHytZIWN4zqnmhBExPA25KRIkXK6dd5A6yUAM/c8ewR/V7dEQfyY9pmBMkWWZJtBJMy9KQNWvwsHac8vsdkyXN9UZit/OZUW5Xl/feCqxqzgFr5mYVSfOT2G/F1nyug1e/cfgZhZLz/Te8ETYy3v/AsPnL7GbzvmXdqOyimsx7IuncJXF4BaUIKER+4+PP1/RsG6bX9FIIAWiHon40VwYOq2b0wUK298EBWTwDplWOJ2aEApn5DVDFxdAX4KlvHnnuLf7h7Ji8c+GF0AKYPbOjvk8Gwh9hnnsorrwRvemPz+aUsrZBi+9zZOVlQBGjQbu2Q749tMq24JSOxskQwilsVHMy90Ln7poisZU8R4o7NgsOMNyhr02mEuNFZn6eyCTaXFVJGYjyp+/jUnpMDNa+xxdzyGpfJJ7ECWpSJD02a5ZCcu+G13qH2xWFxFh+kV7X/gCrghAmMHehFgP+Q8cnXfgVu8CV2GPBxvBmffPEZYAx47skvw21+ES/wq/DgP94G/gAAPgvwJYh3IsUAoDRqNqgvALsd8KkngU89Lv1ILNfgMwBdIZsytF321r/ZileJa493eLCrBbKq5z+S1yzX2ttEW6GbHbm5vuUt+3jNYqDuau/X8BoTHNrnO1KgOJUv3fK58THc+PRu2SJLZYMq7MkRHiEPVTr7IkBpciwKz2p/BhQZgMo+atgIzWdH8yQAvAAiYevIbVSewMhWQkA5HyZIhsIrwrgVM6zd46LNDFtSN0YdrkHd3xWwoUkyJdr6EwCLqjAo/vkrg4TG/HYG1DCDxkni0+4mhE0AEMUirJFLbMwIUHCOJFugBZ03YMsDPUZOa+OrmW7BLLTEjVAmCd2XeL7jDWC8IEx+XM/U0k15efba2RB2ZwkYEjBGhKsgvHkSvh53kpl4FpJG6d5BkOqQnGYW1SYLOi8d2o3iXjgaWukGwQAsDa9SBt3zHi/nOLlIi32vFH3HrqlOtWuDXTZfHP+oXQlpvt/p3bcNEePre0DLZBmrE+a8qFrnDLE8pPpPsAiIMc81XAyBE5j1yGVF03Kq8LYwl43udcsRDCYTMQCY5tr27JO81I5O/OGSwREYz51LiLp8mDsIJSCtCLsbjOksIEXgwZcxdo8nhA1hdTdguK/a8BEIkREmF88rCOFPawAIGDTbBo3+mReGgiBjGAKyedPSmLFaaMWgcVrUOotZGEYgpLMoAJkfh2sCWqadt1L8vylLcBa3oLuhdDG2kAjvf+M7QQy89YVnJdD7m94p5tIc8N43v6sGTtRklpLMr7CbBLjc7EC7UYjylLIWvVCL1MTGspdNck0IxSrLjnuNQ6uNUEu5/I4fAciS53rIzXfFVJr2H6K9mRVV2y6X836TkwNfAiUUj+3FGvDsVE7lUImUrgd8dgTp4647RpjxvGbp5ljugJGOUbLYmgsDYJtZZIstE1AmqDsKAbvHJBYHjUDcEsIWWesqRl81DTB+I/cEarfDAyVvRHm+oWueVyw8CRkBZFV4qMaeCY8MaHlME+jw9b3vfQJ4BOEOeHsP9F9u4Vn+Kjz75jeBpwH4OAHPArgC7lw8ibtnT0qM92cBPLgD4Asg3giQxRClSWJg2okFW9gCfBfgtfAW3gJ0BbABWeaX4zrYWmXlZ4Wj2Y6XGHjl6bwfT3R/zIay/Xm9rfO8oRmvelhe03xf1H6bMNFa/rbv/yH53ql86ZZMY8zSpFd4mUYfjK3YKwdcDXMIh9godlksdB32Xy1IAmewxJTgds5iOUWNByUAelkTeQ1ZGBVVtNMk8st4O4B2hHgVkNbQWKaUgauZUtqANJUXMiBl4Jfb8/liwBOThDepTTOpvIuUgHEChiCxfQkgDkhDKECZZapkiBGCAwEA45tc+I3Wza6c7XxQMarusMTUImZMHEHqVpiBvZGQzuR9JH2+6ax+f7QTPsxMoB0p3y3vI2VfzjJG9v5MOVHzOfdjST6r6iDLgGC9ZmLQdgRttjLO7nnzu2iNUrLy2vGoLPt05KD8O+hzdXiEMwg4Zp0dWo+zeMFHyK+9OFmzOhozbpbMrOUvvs0g8y817sFsxg1+vsbShpeNlpLm7CsnMOsRS0QqFikhiMkiUBaRWUlZOWRR8hKWo3yIe9cpszAf87AD0loIYxyLW59pI6Y1YfMqYPvECKwYCQHjyKpxJ9BO3BYNqDLhJEU5nlaE1T3G6oGi5j0zXVIbKNvMhiCMwaPjJpxkxqAwvxGnfFw7kQhhJ0AXr6hGsgM5lxgcv9NttS9cLLdgprOdtnKQfxLmnSji5970Lm1EL7Vx8UBWMhAwIYwS6B1jAk1TCWo4TQpccfkMHUnOCbFZU26ChroZ+jhZACrLtuqcs7DIzb8U2j7g4dZQs6HvBRBuAx5mV0k0xJXcea+d6BUl7iV753JdeqnG51ReliW7BLQbLlu3PSH2j608HK8xLZ2tkbx5gpIo22M6wX08B6bzJJo9kMhruhmCAWCNBbKBYxwE+Io7LFs22c7K1j954kGuM+5iL/T4jXFGsNVaLBUFUUV7DKC6Lq9pFChHAVqcAFwC+CPg2a8APncGPDnImH2egSvo+QDwCsAGwAsAPw/ie0Da5cQiFU+hCUg7Aa7YeC4gDNf+Oo9hz9JaXcF9ApWVUhU3q7shf4km/8M207uuWpu051yHRxzTDy58ZrGdazV4Kl+KhRhiwdR6KywV2ycvzYkqHEojz/TqwO17jRyFGsDKwndGMg4HqTe3umxpm/uPkoVdM7UnDUaeVuVveAAMD1iSikRg3BJoGwS0MtIUgLRixCvKFl6AA1pQeIlYP6GWEYK4NYatPai12zxba9nrv6eUXeEsnlZAQuKQ17TxO2KAsqxl41Gsoss463ni4jHheU0L4Ng72TEiA5SChgSQv7ALGEcFBC+KlVaOq2X922hmRqY8XuASTsCyUlalNw0Ye+fyUjIus+4ypUvYjqDLjQBZBvS2VonT1HcvbIEstxdj5wllnivmRluUYa6tQ0rJpeW4Z4nMaH1HTsrWvuZ9Y4BWc79KPlp6H1bFh2ix+Tab71a3xNX2Cn1rw++N5ol39pcTmPUIJTFhQnDo/B5V7ywmEQoq2darFheO33ccqtue94J4KIAEBwGopjMUQSMCYUfZfNeIUTT/7EASSCsywoYwXJa6YSdalTAhZ6aCAlpTlNS305owrYF1DFg9SCU4rwMNMgNTQkEBYjnEChdREgCnHc+FQszgMZXshgjCXK8JaHnrrKX7yHA3jTgBKDssTgBH/eUFk16bDNGe7CbQTgGsBLHG6jCmrLqx0oJaEzROllIUs8ryQFbHvbCY2iITqTqTxRETuLc/6l12DJC1GKOuf5/q3B4BYDHWCfS64DZt/m+p5DoMpkPc7VReyYWZhH54AGu5NuqdCfrz8FHcSR6W1wD1uiDhAzy4a0xYMKssMwydXJ0IgBikSUmsrs+S5N16We+TIhA0sG60zFedeB/kaHP+zNr44C7w72F5UIjNOkutYkFlLGwcjgK0VEo4xGsMSOtWmgC+AuEFMHbAvVvAvTM9eal/GshSUgADvAGlHWjaAmkCpSTtzyw/VDJseU3bFx8nCyiCQg6US9VQztwL3XBU43jMZmmBNRxTr3/lNXmNr3Ykr2m14t61I/f1Ufp7Ki+LMmHPuj9UejJJjgFYeIUHzZes53suVwBm+7diyeP2TdZ/N1XZrP4HiMWPxYAK1j9tz/GB8YZ4fiT1KBkeMOKVWBClFWXgC9pGVrZPZd8dRuSYvl7xIvW0f0bqyM4rHXPuc3mvHFiV1strUCxdJwENE6lME0CqYCEb07y/Rt8Sh8u7Ihdfi1orrHxja7BuS2IMJxmXFKTdlBB3hPFcxmi8kAD7SfmsudmHUSywTIay2Jg56HtUVmHKLBtjqkUTy0II9J/Vh6jxcYPNvZASg64UyNqNtazu5janJErlagwW3lfOkumFAnJeQ+06mPd5Vqj57NVr9iZ7PVg6saAl9M5C/e4+EZU8WsU3y4YM5Xof/L1YxSPzKHFx9mOG0ka1N+RrebGdwKxHKIEYUfzDjnZLq6yFjinVhubAdYeabDeBZMRciFwW5gOECahpaVoJyBI3AkoZiiouguJjHjeQST8IkfAmqT44o2UF4aAg11j6IVp3kWbiljFcpcxAZmaUgcAxirVSYFWAc22h5VF0oDt2xCyWTEQI64cHtHLxRLBNGR9QrLT8ojWi644ZoCXPXPqdXUkn0YyH7STWWOpqOOsLNWOQWMC/Jdx1SsA6FjdD/auyFXpgiyz+AdXnMjF7SOGiPXbA9fOgSap/f7265DZpPR6T1HXQMqfl3ygEV+eqtVcR54o38ozYn+SLU9lXiDjTg6ODd7aAzHXKS81r7BChylplIJMlBeEIwPGOLAyYm0hCcVeINh4oLon+vG2kqJyzrjEhhweoMhvC8Zqqz+qarm2WeCeOnvuHXuA1bBr3KPUfDtDS0rMMy03tcUtgQAKxSxB3xn1kBUYO0G7Soo5HmkDTWECs7hxseA3rvyXibIBWFSvLdrzIA9FaYfnfXxQg69DYH7ue9uFG+9aXCQuN8DBzJbS+HOhPxgZaYeFUXnYlgsv7fRQvEFUQH528J1tuoWuVn6up69tiLJ+qcrnfeCbACTvQCUBxSbLg6yZHXIgyPg0SPH51R7McDgAPlEEsya5XaDiNAA+MBMpWwjA5RpNYZYVJs4+mJPW7YWfaOL8K3ld7dgMe1NWQPNivl/GqSPcCSvT3q/Y8DCoyQ9KEK0v7B2s6u/9xPk6sGRYZ0sYqIEyMsCNMGmdsPBfw0GKZpRU0JI0kCfMKFsm+aDyXMg+vgkLaYzAvGfXWdbjUNSAry0q7SVwLt7sCZHWssshnM/Q8yVtlVbyqLuyP96yy/DgvlYZvzBRUvXXVysfATF6qQ65QfewheVql3DcLLczXtK2N/Bxtm237Gbg8sl84gVmPVBITAlkmu2swjofQhi8S/H0bpsXGIJkyCEAAprOQXTyAsunPQgQB4YqqgO7qpYfpHBgvdBIrETTXxLgxzYalQkcOqJhWJhSgxNXKVl8lJe/wwLkdtuaZAeBVlMcfAY5moeUpoWl8Qw1ueVeQiREwCR8edGz2AVo27ovjW1ZjRWQSZ5mB4edMYTYC7OnLVpCol60wjBLgnaYpB37PzxZD8QdvGJcFBmWWYP+lYR2fELOLoVllLQJZIWRrvgzetJZbL2V5lM3Znk18la0DmAkTcg7d7+Ugsoav0phbfULRRpXbSNceZg2fyiuqMNcb4aPLSzmvHpLXmLWiAFd14NHKAspkCsuOhJp+eheGvHgCMm/JgJV9mmuh8rCZGyIju62AhE8Vuu46af0LQRpIKIJJ+6y2efX0hlEWOUOz7AbhBeEAoGXX7xnf7nlGrQxp2hI3oAngCUSj8NJcacpNQxPDYGIFsrieB5UWu+2IO94qlfTP5nRx3/Bj1wBZ9iz+2fs/Hr08anNLvMaP21LdY3lNw0eqa/NcPKq3p/IyKnvj5rQubdfgJXstQHptKX21PuWA3vvmtyclStt4AKYzqRicVVa+h9tfgQRUmS5E2R62wPpF2VePN6UNGoHVfbkwXgXEK6k7XhSBnKOAYGGLrFTxFr+2xa8siFzsrwxUkdBVDlQsrfYRdHPjNHfDsfAZEbFIwTiTL9Sbo3HjrMYSyNZZYZtEUZ7q8z5ZEwUFj1p5C0CYEogFDEyJQAoihp3EvpzOCOM5ZScZDhIvK3tsaMyzMHIVekNuLO8mB5BPE/7W7/4yvvqzH8fv3H4j3v+mv4qphwTpvMqgl42NhWDZTRIn63IjAd+JigUWSv3s4eMzGVpw9xbIsnkdQiUbVVnd3V6tpfv7FU3NoR74c2jZmqzUSRpQ2l24tpVzfT3bx9lezbWZQ0X4cAeeH5mcFd07N1DZb738Pa9hmXXybXkpim22QtAMCAeGtYOMXze+lc/4cZ3iM4mkM8KD1wy4/1TA1asCdjdIUq8aoQ5AGhhhJMRdMbk1/3TJQCjtpjPtjwFaqmmnsbhxGNOhJIxC/L2BsGXEHZe2k5iGmt979zkcsMJDAK8GYDWA1yvwaih/QwlkbvUromdmqGMSf+oxKcG2++gNnUDWfRcqKMlFvferf5ptkiymQZJ7W/wRmpL+Fk0IjSn/iUthQtxMCFejEGkDsuwv35DKZwjiWwMUAp5Svn8JBs8F8Bui/DVAlowFMsGutA/OKuthgaylMX4Yq6yD1loHzs+sphqiXgmsbo/SN7u3eq2AvMfk91ROZanYhurQOussm2vzmtmXYy/0IBawuxGwvSWb3Wkllo25m4RauZGKEJENhWzTFN1Fvl9+g268hpHTf+fYKgZoMTLdrOI1NIX9BtQUIzGAY5R4i/ZnmvTgiQPnzTbgXB6M3jPPx5fcJfPe1Cf3vUq/ybfvBk5V3yd1HRz1zwFYk+NPOQYW1/cl1/EKbHXCQhZkqfAqLxg4a70KyWtprn1WVlkPyWuwMHyHwMPeJS8Frzn2MRyv6bW7aOVyzexQr+Tyoz/6o/gLf+Ev4LHHHsPrXvc6/PW//tfx+7//+1UdZsZ73vMevP71r8fFxQXe+c534qMf/WhVZ7PZ4Pu///vxmte8Bjdv3sR3fMd34FOf+lRV5/nnn8d3f/d34/bt27h9+za++7u/Gy+88MJD9TtS6luxtzKJd1dfit90ndIAHx5smWVE8z95LmTXFQr9jRsFmEbWPz3vTDI4AtMFMKlV0PqO0J7dLXGDAwPre4zzFyacfyHh7HnG+i7j7AuM9Z0i15gHSu5GwzcqJa6zRjGF/CzGqlOOcsu7swVU+U2jxLyllICUKjlglgURgFnQ2p/R/dbbwACtsE2L+4CirF44x0DYJsRNAo0sQNZWYh6v7yac3UlY3WWs7kmcsnhZXq5YrhW+7OOP5TA3Oi5/63d/Gd/zu7+Av/jZj+Fv/7dfwt/8g383s+jL2RtdETkr1UDWg6vCczoKfm7D01QN9oQ+412o32srF7XX7IMGWpkiPw8ftWc7xD9qt/WF9jrg1SKvMfCzacrcDP26z4BXpLwvqwDrirdr3T17sl45gVmPUAIx1jQWYkaEbhDnh2UQx5Qjm2ZFlznKZ1oTdjcCdjcJu1uE7W35nM4E0JrOxb0QEKYQdjWQJSe07QiZjQngRMXcdo1ayB+KAMNUvlumkjAywsSq/SCEHWfT3lkJAmIhEjgE8CqC1wP4bACfrYH1qvwZKKPEpLJ2csycJs0EuFNCqH7XHtCyvh+1f261JQaeKYAVdpMAaApSWZBC0oyEYat/Gtw9bCeEyxG0HcU1UhmddN4R6XZz4kxnc8rmILHGJNWxxXHRsl5lEDAHNjSC7X+H4l4o1m9HjosvNod0M9/db++xyJoR8Otaruzpk7+Hz8TRs7xq58ReucHX5QN1T+VUABCxZDOskI/ePP8fz2sM1MixUKLGKFkJT5jOJUguR0KKErskb1pMYJjKZtWaBNy64/JXxWQwEthYQPqsvFlhkq1taR40vnmcEhPQxRGMqrzyv4MSwQqUsU6VQcyAVlZuoN5Iur4fRVPbOl7BYUoSC9ieuDqfN/6aEdcnFZE4jK5+vlkPTeOG9uq5bKnsQCwrHgTMQpfymiqLlKe/fnyvUfx8WLp8X5uzcw/TiQMl8xKqjvnSzomDPfBr5lSOKr/6q7+K7/3e78Wv//qv4xd/8RcxjiO+9Vu/Fffv3891fuzHfgw//uM/jp/6qZ/Cb/zGb+Dpp5/Gt3zLt+Du3bu5zg/8wA/g53/+5/HBD34Qv/Zrv4Z79+7h27/92zFZoigA3/Vd34Xf+q3fwoc+9CF86EMfwm/91m/hu7/7ux+q3wGFR/CiwFpokN8zLgrM3v2r005Vb1ZnD11tSjcxUhTlBxMQJgFNhg3DMvYVEES8PXY3KVtYrV8UWr+9LS6HNALnzyecvTiVeFeMDFSFbXEbNFlFrML09yDxfXsCtvXFxqUNgM1UBwmvL26O23tRS1hRgKcsM9AuzdayV1hkBYY7VxQYUBlr4V1nPnuYrlFixK0o2cNOQa2dgVqM9R3G+kXG+kXx6pF3xDKeBnY0e2ZvsfPVn/14BigCgLe98GzmlR6w81ZZltF9BmRttnk8/brg3W4O/HqZKoRlq6zW2rgFtuwe5M6jkT/9uC8tVwfwLZWZHGR8vndenyOPo+9D06fqHRk7tzXNXCthGtDL72ny98YicCZLN3Pg6IzTOLkZPlJJTFjRlFNQAsJAyBMyXtw2zTd2Cf2g8L1LFya3XyjVglKXhhQFyJrOAkxLkVYCXk1rAZDSIAIHq0uggU0WuC2tlLirqyAPku42PghIt0ZMFwnpXgCtRCMyXKL40jthwwSLsJPUusZgUpR7WZwteZZ64TEhu8AFUiIVQhnDVuPkS2Bwat8RYC6HNCZ5h6SumG02yuwOaNcfelcdRp2JbyrzgBuKZlp7Z01VMhoWQaXugNn3ctGmu+LB1kzUE8uYxSA+41GsspYyF4J8nCwUE+JDJrRt6VSbEWXn6unr7NXmte00BHO2LqgITpWfNwMplOtzhh4GLFaDtV+0de7+Pc2Cdj4TcYKmvl1+llM5FWbSzLnVUVQg9D63kZYN7WFLvUv7xdOrdvMGVZ4gpxSPW2TNbIpQ9wjhHwiQ2CTeIsvWhnfPDeqmviPwOkksDnUVTGvhJT3tYtbi6mbe8yEwVzGzevTaNrQmALIEQJTmuaXB7QB64gNULoeJCw01EKwZ3upxHhaQYIA4lcbmu193U9T8k1E60oJXlibeC3PVAzQdt/ub4s/c2/OzKx3uAVmuWfY/jik9XrNUp/MO9t1mrlA5UC/zSXeJ38QDs7XsNdfWpcpdnTr9aPtCe+qdSlU+9KEPVb9/5md+Bq973evwm7/5m/grf+WvgJnxEz/xE/ihH/ohfOd3ficA4Gd/9mfx1FNP4QMf+ADe/e5348UXX8RP//RP473vfS+++Zu/GQDwvve9D294wxvwS7/0S/i2b/s2/N7v/R4+9KEP4dd//dfxjne8AwDwL//lv8Q3fMM34Pd///fxp//0n75WvyMl3UteQwrcUyqXodnJ5rh6Ydd1DHCo3ctLfCjJbtbSW9tPpZUo2IcNqyu5KbtR9nXKJ6a1AlkTsLondH77OGH7KgFULj4nVkMWDN0CjlvW9rRWPqKZ+RCBybuq35CkVsGst3aMsJVEIgCyMgDA3KPE+CGRsGyTP1pLoZlMKIgDTQAjQT3CASKk9bItSg8wOWTdU2ded8XeaUAtWyr/iluTC4OIJIOEswwjI24NyArYPSYyT1qJy+dwv7zL7MmTyjv+P1/7Jnz9Zz6Wp9VHbj9Tg1hw3x2QZVndabMD3bsExlGV9g3Ak8TlkDSJWC5NBnfoeFfW8MdYZe2Rg/YGfz9Uepe2sjEws2KrzrV1O23XllVO1vPbPndNCdfi2ml5nJOPbP1lwMzqsjt3DV51ArMesWQBw7Qd7SS9jpXIw9jJ5cnhNz5u4jkhXbKBCBBBE2O1ZaQdYXsrIp0hE3KxzJLnCTtC3CD7f2cBI7pJqCVuSOjeWqhfWpc6YYdMFM1iK1iARUIx34UJHOhqD8SiSzN7MAMTkFSwoCmVmFQ9tztngQVqzstFMMQ6jAnJNtxOy28B86FCiPVxOagiFfAL1L0njd6euaDwLShJ05SBqmxtZX1JuolRqwiymFkhyD1iAI8BzC56ZkqV1RZiFKus1QCOUUC8hljPgCzzKe8JHUvFEbSHKcsBQ2leZ0a87UU28zcLWf17ValjWwHBPwuhWKgZwBXKcQ42F2Q/cxIsTuXYQuBmfj4Cr3mYeUezLzDpmv1howO22WPZ+HMAaC1xNoyPJAW3gEL3K5dCL7S4EiaSyE6xCA+ZRjvtnw8cX2n8bF1zXb99XnsOAoSOK/BiIBRgJL0/oGQJX2btF4RE6iif8cBJBp7sk61b/eI3ma2QZO1lnmr99zfzdVOp62imoCjuOJEoTXI9Bhk/8AJ1K6RlV80AAbRKX9rMhXWa8Gu6F9o7OlyzXxaBrA6v2dOHo+i83qtVjLS35PZYte7q4/ltn/jMI5UXX3wRAPDkk08CAD7+8Y/jueeew7d+67fmOmdnZ/imb/omfPjDH8a73/1u/OZv/iZ2u11V5/Wvfz3e+ta34sMf/jC+7du+Df/hP/wH3L59OwNZAPCX/tJfwu3bt/HhD394EczabDbYbDb59507dwBIAHjZb3SECYsDBDja0MgNviwoRzLA1VPGt6WhYZWg7TOt9cBbAqYVYbjSZFCuj55up6EEfOcArB4IkLK7Rdi8mhGvCOefY6zvqwWqASpnlEEs8xIZLtU1zqy0jI+YQiWQU0AUD5a0JQxX0OQjAtBZrF3pMAmt80BW9R5cSYLwUGRw4DI2SQaZRoDCBBooA2Tl5bg+9yyA/Ktp5IzcRFZyN/1LNJdRjWYlRtyoYUcKEmg/yns6mxiUAq7GkkEy6biHLZd5keBiXzLe+1X/C8KW8fYvfBwfuf0M3v/MO4tM2IKfiRGmEX/zk7+Kt975BD66fj3+j/OvwTRpbKzEhR8xS9gVIiBGcTG0+RiprBPysRztRk7e8cc6wJZ8d+cfsswA5QWe1KXxusZmHkJHAG3VnqmqML8/O5mnkpUciNbjZ9l+oyVHxP177yknMOsRSiAWq6DZS3qEncOixZXd1BaLHS8bvRa8qphD9JNU8HeadB85SopzABnQYoXDzX9c2ijpc7OAT1InbgG6S9jdXQFBN/CT9DOpiW7WmgNVoD+JzaWMoHpmJcZuE5eV2kF3asZUJyFQbG58U3n+AkTpbyQgkVhFNWNPZoaaNQSagSp23kuHT5TUso6RLABaVZYObxacgSn9DfeeYyiAVibSxnz0uiCMk8apmMyaO4yNsQJZ5Nr1VlkzICsaEXedqjQUnc1Q9QDue6/4OA8PG+jdEekeYZ8FJQQKYe6sYW8+axuZVjDxLobZmqAF0nrtHxqPUzkVV4hYAPyXttED59G4NFH/OFWVZhYmlVuAxb8CaqBXz3kXds9rvFBEExA2wLTV3TLK+RyE1ANUCo6Rq3MoSw67+gDBEoIQkK0JioZ4z8YwSGPUMVkQXmMPq4CXId2zhvb11fGaitY2vCa34XaYHjmUiuW30eRsOYzCa5hzX0XAc1bAIaCf+tzmT2OV1QJZzd4lP1c+V+rPyjG85pg6B0sZp0NA1lGtNTwFPV4Ft+4cb5rzrkfry6nUhZnxgz/4g/jLf/kv461vfSsA4LnnngMAPPXUU1Xdp556Cp/4xCdynfV6jSeeeGJWx65/7rnn8LrXvW52z9e97nW5Tq/86I/+KH74h394dpyQdG+c6jV4ROlZ5rxUSXxkr1vvn7izpzZg36yywsSIG2fx5HhOGDXj4ACMN4TWru6JvDKdEzZPiBxw/jkJ+C60pVjFS+Y9afDsRcZwKW5yXolOo46LZXwHsuJEjsnndAZcvSogbhjDBoj3kj536S8CdF+eyt7ZaHRjNdS6iQsTpXyOdgk0JPA6zveXjr51Aa32Z+8dK5BVWfTYOwRAoW+xFyZGmATUSqsggNYAnN1NoBSwfZzEEptN7iMFoqAhbMyFFMCO8HNf+S7Qm1Gs3jLPLf00i6y/+clfxXf/4b9DAPC1+APg1gb/38f+Ingcy5h6AJVCGXObX8wgk5X8OzKrLBuvBassUc64MWvKddeTBxxniVyOacqyrzNmPKaam67NAlajAFo2ZE07GQuw7UUzF2eAlO3pYrlf9o5x3ymRALnXGK4TmPUIJRk44gdc3dOOArRaa6qFktvXeFez+sYgDIElmk04wG9oKccZMVAqu2FMJT4WB4uTpbGjEsCrAk6R7oPDTjOGRGD7qoDxhqBcaaWpYDXdumjokf1gxQKKi9UYlYyB0wroZbiVZ7NYVsocQUJcjdCNrHWMAQpBT/pJZrjaoTZZ46QCBo0JgYJ0v8nENb/4sLBZAVqsffSaAeM0AYXQDlGEhXHKgoK5fJrlVR3csCHCgABXwyD3miZxybQSo/ydrdUqq1w3A7JC0Va07oXdQs2nGwfpFx0NYh10L6y0IfM+LJnMs7/O941cWw3B71qMeIKMup/7zHuvS7RP5ZVXuMdrlNYfx2tcW8fUr7R5Da/J95Yvi+tRp7UBvgZKkZ1LhecYH4LqhpiR3XTN5ByQuvFKzk3nlF1C/OYIrq7fuJdMVH73JVpi0ai3DyAgFtmD27NH92zK11peU4QI7dASr/FjZRp/04C7Z5lffCSwmXmC/plyiFEa95ZjVt+sxdh4bS1M5Xr5Geo5QhqT0SyYc8kB9KNu/AsRnQFZ5I7Z773POu+Ke8p6PA8tAau7WK+zHo9qt7muw6fasmSdWAEDR/KPk4XW9cv3fd/34bd/+7fxa7/2a7NzrScGM8+9M5rS1unVP9TOP/kn/wQ/+IM/mH/fuXMHb3jDG7CmCWnVue4hFYTVnsmvdweMk4H9ds5CipQq0pZSU6GbQld7gJaAArLYhwepyAhR7mT3sCDw6QwAiUI9bkU22d0Sen/2PCFuGLsbYoVFSX6HLePGZxNSJE0+xTPaICBJGYeYATUXysVc3Adxh7Rg6HGjoFmU/a3JKyVRSNehr5ScOVb/pqQkKQBBMsuGnQKXqzDn6X6/2ivtzVuQqK1uLMxZ9HlXv6opIjGQSAk8ENIk7plnKSHuJAaaZSa2fUAYgdV2xP/227+Mr/n0x/GRV4klVqJYxXIs42M3K3LUW+9+ooqx9ZbNfwdfNBYSgMobUwG2TLEC2xvIH3tAq3UvzANjvNrqoMhG/jwW9l17JsHM22rPdd5zaBG08pe3xxyv64Vhqe5DeDg+4uakgWSm5EzerdDtFa9jUn0Csx6xRD/aoSyKWXkEDYf4ldamjZ7wVAwieJah5z1hqyamBSVs7pfEvXA6cyg5IQd1T2sFszTGVbwrdaa1AE2IwKTZDb3VV34WgsQ+0YDywRB5t+dGkPZ83Kzc7ygz3S/IFEg9BwkBSbKL5zAgVBTKpH7rE6RCq7wKkM22Q7Qx8szdcFY6FlZ1u1RAm0q7bcyZUQUezJRFj2sQX2s9m78yZzePDEJpWxyDCo4s1lasGwhmcVlUc1uKQYCsszX4bHAbbtoPZNEe98K9RNqNif88UI4Bsq5djhBqauDpiCb9lPWvUue9aelgINbDModTeUWVaurp+uuXR5xMRA3Jdb+Yag824mzxqqfLJZWgTtVayIdtY2MAkYHFpmwxd3ZTnGgW3GRtaT0Ay+6CQXmV8TKe85QUCaFzHAGSH6MajTIGpEqHLJoQ8nejjVm72bZt79A1TioI7jX44uazLeTrOIHDX+f5lBdWtZ4oS/Swm2uVhYSz1hLrLG2c5AUS9N04vkZEEo9xMO233aMIAvYMNZC1x71wH69p6xy7NI4Asr4YpaeE3Ff8q/bXVAAYoUgvnTV4Ksvl+7//+/Fv/+2/xb//9/8eX/7lX56PP/300wDEsurLvuzL8vHPfOYz2Vrr6aefxna7xfPPP19ZZ33mM5/BN37jN+Y6n/70p2f3/exnPzuz+vLl7OwMZ2dns+OBHHgcAuACzeeSuLIsWQq3UV+DEs83sSTAboHZ5rpl5SHqPR+RE5ope10MlwLY2FpMUTOXN3PYFCHxUhQT402RT+KltDPeIA3uTogrBVomYLifREGtLnGz8B6hJLOyPvnYikhSV7xXWGIDGw9jngEGbFlbI4l8ssS/jV5OSZQCSCVcSEoCaEWAdhOCJlHJ7Vc3tPFprLN6t1W+RQ1fYCdnVVZaU/1w/hwpDxDLYwJNGsB/DFhtEv7mb/07vP3zH8dvve5N+FdvexcSIigBf+cjv4zv+Z1fQADw9Z/5GGhkvPfN71qIx4g8xpaw5KPnX46vvfMHOcbWR9evbwwFUDxbXMw2kY0SshGKj5UFVEBW7QLvEmB1ZSDaD2QBe/nMbP14Yr/UXGd/dVRp+QJ1ZB0lLbXLojvW9snzHysJFerErUxt+5RBOn6dAPCP4Ml5KoAEXKyIiBGsXvEI78L56wi1swXiLLNKHVQbWTMftJIzSTlvAwvAByBrxtNAkolqLT7iPIglVtwIuHX1JGG8oYL8OmG6mUo8rEl80eNW+sOKypuVlmWuyo+h2UXSqjC26nmIspUUk/SNo5gB80CYVgFsfzGo9sT5P6sbHscADPWfHPec0rILShralrD7Qm6cl7QcmdFp9qjsMmjgkoFX3kUwm8KWjIe+yBjpMdu8EDmQK2QtA08J2GyAcZS6ycXWclkfvSltsforhNvewRJjtHfV+9u7RjolX3dk3aof1aZnLgRV8eXg5pdRxt5r7BBudms3f5plCdmGqAiC1Xhg3t6pnEpbgtd+A3mtLpaXVHCdr50WWKiALMy/e5fC6rzboJllo8XTksQl8kmjHB8vkNOsIzLSmqu2w4jsrlhlMeTylx9Dj89i4eXnETpo3fT94SBKFI4qoORNL6o/z3eqvwbIMmAo81+HP+wrBwP7NtkLZ1r/KquZ9cVle5yNSeFHcgNyIJfjE8zCZyzrbnaFtOeXpzsKyFqay0ZrF/6uvQauUX8fr+m3TfV1Db+4TmH33NaW5zszbfpD3ueVWpgZ3/d934d//a//NX7lV34Fb3rTm6rzb3rTm/D000/jF3/xF/Ox7XaLX/3VX81A1dd93ddhtVpVdf7oj/4Iv/M7v5PrfMM3fANefPFF/Kf/9J9ynf/4H/8jXnzxxVznIR+grLs9Jcfry9d02gGypLi07yVu6nf7pJ+h7N1R7ZtM0cwYLqcKyJK+QjLnTWX/bPQ77uRzOi8hSziK++HuJsBD2eeJXAHJlHgjYPN4xPZW0LhbhPE8YHcjYLzQv7OA8YwwXhB2N4NmfA/zNc/98SlyGYocYsft+1J2QfXCMOssTKm4dLNm7LNxapvYt/fdV/wc8DKp3dNnvnXgnQ8pQIklmVYq/CzsEv7X3/tlfM9HfwHveO5j+Lu//Qv4nv/yyxguGcNlwtc8V2cvfPvzz4qM1D6Xt9SyzIWXO/wf66/G+258PX5z9SfwvouvwwfXby9j60FdL38EEhmI2R0LtVVWHociE+UxXbLKOsYiaw/P2JvB8NCrPCRftQBZe9or9RoeV8ssVB9zgGYGqch/UoltSpiDZbn/5fM6cbNOllmPUAIxVjTOM1c8bGHRctebpP6k7GaOswB2Dbpe+bPm4/KXBiHscatIqO1xPSGMAmSN58B0IdkOwySBEtMATI8x1i8QVvcY0zmBw1BcPJIIITleVgTCBtlMmFJnM84saL5ZZy0h8y14xxqXKkiEEiJxP9SBkiwm3qIrFAsE7x6Sx8f3JwFhSkiW4RBl0e8VJrz/vRH9BGAsgBX5GFjOZZDVmkqsrVQ7A8g1lhrSW3epAMKUqjmQ208J4CSAlp0bBnH5GGK27GqJdvEx92PvhmdJ+/aQZZ/JbFUOMWi3EaqEIxwmkl7gyLGyKksFR8Q9U2p/Z2Zf6ju6v9/a7FRORQtpfMYv6lxZbJvmFfLa4qrevvnMSq/JGwu0pNOArAGiMVfzc3N7TytxMwxb+QN5pAlV3C0m4VN2fGaR1DzLonWWPTr5MWAHpDiCpTem9l6NYLGYJYy1RZZ22J/gPa8oX+t/67NYmm42Vw17eXqeuHY7r9yJ1MrbXBU92MVAm2ilpIhveJOVYG42MqAtXd4HWC1V2MN9Dxf3Ovf343he01Y/uGY7+71auVJu0dOgz3Bm4zXtbU685ujyvd/7vfjABz6Af/Nv/g0ee+yxHL/q9u3buLi4ABHhB37gB/AjP/Ij+Kqv+ip81Vd9FX7kR34EN27cwHd913flut/zPd+Df/gP/yFe/epX48knn8Q/+kf/CG9729tydsM/+2f/LP7aX/tr+Lt/9+/iX/yLfwEA+Ht/7+/h27/926+dyRAA1phybCcAQLuuG+upShHfFbqPnzC9+dXSOQkDAmAogBYpjbLA3xnEcqW4OkLACwA8Mlb3xCp0uBTekN3OCeCVKNyH++LWllYQV0CN17u7FXD1KsJ4kxA34iLoM+nGjZdRxBVqvCAEzfpOSTw8MpWuWOHcqkmUmhYIPgCYasCkpZdJrYrU+4JjyPVokmejiRG2U3E3BB0kVfsKm8xkrqLJvR+zWCNPkJapr3m1IECs+gh4+xeerQCrr/70H2D4CmHYH7n9Rnzd53z2wjfOG80ePBYbWYCscPc+ps0WHzj/WuDGnxfwitSsGgBWCnWY8qYFe4hEFjKFvvEob3ziZSKTkUJAtsrywN8++eJh388eRletPQ+MtsHffXMmiDgeyKGOU11usHAvO2dTIpT5Z6GJYOGMmHMcbW6vdfKVtFP+ji0nMOsRSmLCClPOhAEA2ZT+YcqCZdZ1NiA0lYUq5reOeemEyoEMTeOd435AgCed4D5A+3gOjDcZ6YxBOxIGsQJ2txiru4TzzzPWd4UCDg+EyBtzyZpykvYlvS2DNE6JgCcyvS0IPCkQljRjBzmzVgEEdKwUwMrX2HhF26xTtWBp1EqBSrKlvBALs5DP6i0o7+kQwqXSA7Im1aZMEzBNOfaVfz5jcDlLlAejjMi2AXZDKBocQIK/kwaCn/T4EIFtELdCJei0WgFna2C9mhPtRmvWcy+8dlkYu0ed90cVN6+l/ZahIT+jWWssAVfVuayVQXdMRFPB+R6ekC9aHJzKqbjCTCVzriv/Q6eOs+Y13lIIrXx4ADgLLrYU7Hr7U3JtggcPotQIW2knrQXAsmxTACHt3FqNhZcByC6MxkvkYImD5WM3svWVAE/3pc9G6+a8JtODBAV99LdXmhAK4GXtumQRs2IAFHD8XmIByBJFhlho2Ca0sh4g+VesfQ200n2D8tjqRqogs4f12b3ycR9jRMeELDajcy8sn+U5l90Lr1mWrukd/2Lxms7zVdVoXnfGE4xndI4vttmeP/Gaa5V//s//OQDgne98Z3X8Z37mZ/C3//bfBgD843/8j3F5eYm///f/Pp5//nm84x3vwC/8wi/gsccey/X/2T/7ZxiGAX/jb/wNXF5e4l3vehf+1b/6V4ixCA3vf//78Q/+wT/IWQ+/4zu+Az/1Uz/1UP0ONOcRubRz0Ie7aIOMH1FaoOqo/RrLdbR1+yFtg3gOYnXv5djM6p4CLAHgM3EZJJUhEgFhR9kDZFrLRWmQ8CVXTxAePC2Z2+MVYXWPEK+goU8YkwaIt1iL1t9pLVkWiQNW95LQPRRvltLpAmhVdFe9HCraHmjmuiedZSfY6PvJxyjLFGGU+F8+hm1mWzZmh0rmwc4IoO0LkF1Mc8KAQ+/dFB9E+Mir3og///x/c4DVM1n2ef8z7wQx8Lbnn8VHXvVGvP+N7yxdM9nJgLVJvVR2E8KDKzk2DGDeoXQyCb8xqywP8pjhgH6nYSgyj7ekznJPw789CBmaMWh52aOUZj8ijWI25rO1d0T84eVzuh4t/MPCtT1LfG+51fIbM5Iw2YlsufsQDF5W6mBq+8oJzHqpy1LMLF86PulVZiM/ETt1++5S7rcBFR7cASohIrs/GZjl3DBSUFdCjWOSVmK+O50zwpawviMC/3iLMVwJkLW6z9k9MOxEoxEmAm+V+J9LW5T0cQIBA8A7VrBL4pAIIEPFVJUIaUWISvBmZryZEUJR5eaZVYuTCYAymHwp1+1U4+Q1EcY/ps4Ky3FYuLqufmlynlIC7cTFj0Yxb/WZFzFOAjpZZkLvmqFmxjURpbqeFQXmPMCFxKAYwEmzgA0DsF5VQd+9Tzjre2pd8Q5ZZdX9oMJE3Lx+KKDqwL2qTfyh9l3dGXFuBYY8d+q1RHaNu967SS0KHwSUeFmSrOC6aWhP5VSOEjw6G59lC9R53fnFzd7KruGmugOUKutFxUoyqOU1cApKiVWWAFlxI9entSg6hgdQrTgKWKV/PMm6s2xJuW+mZbY4iqbgyPTayQuhADtzlxHHa6jmNRQk6y1V9KJDlztDKm06XpOPATzz89N+NzyqrmP9T9nVyCyyyPYYxEVDbZpY/w7NgquaJ0YEm8FxPLhYbgFEJAoiVbbAubsDTfwRbX5/7MUD8703D4+4bLmx5TLTTB9ZujzmUF19DdWbtt/t3q/tk/IY1+qjC1ivgMIHXHcBmd/vec978J73vGexzvn5OX7yJ38SP/mTP7lY58knn8T73ve+h+nmrARwBm1mMbM8eGW0zDKzfhFLFd830wqld6jjNHXjBGFOFwwEjltGuiLsbopynVRxPp3r5aowp0meNUUBosZzYPMEYfvqCRwZ6SyAB1HSi3WvWHBZexl9oaJU2USAg2QwjBsA5BQYKrd5AKuSHZzCeFZ62WCh78rFzxK+phZTu0niJa9jSQ6l43R03CQb74pc9EEtgsmYDaDV1tcxYJXD3v8V7wQAvO2FT2TAykDAhIj3PvO/AM/4+6MAWUkSctGU5HM3FUW/3ksskF14lmQB4C38ijNYMP5sx8xizowaPJDl3QtDcC6EmCn6F90Lj6L5ZX20IYGObaMtlSXVgsFMj1+WPVmZS3nrMFDFy3J8UN0D+HvkmNvOAMDaL5lBm364rcax5QRmPWKJlAp62QJZ7WatPe4KLxG2anPb2Yj4zUwHjZ1tnmxSaawPA6wMiDLACQA4itsgWIAsmgiru/r7pmgzzj4PrB6IJiODVROyS0k1SakQV/NaSCsCR2OywKTpduOOEUYqC0OJbLVI3ExnP/lzfSEsOXMKSGY8k6THzRcDFRDVCDOkGnmGBlRPmMXyqjbyTaniZO0mAbMM/MpAj3uHniEwi3YBQLbIMq1BWxzwyYEELDNLLm9iPgzCkM7WYAWzWqJtQFZ5iDKXF4O+69jP5uOjglj+/p1ySLDwmv4WNPKWV/bcHtyaBdV0xDi37dsJqMcA5Xdl3WXXxFLnYTMOncoro5BXVS2uh8MbqGNiMbR7WjvInbqL92rXlW6OsrUUHKkjlrgmgIJRYpEFAKzWvfFSY6MY6TZe1ayr3BcCqqS10a1/Biiq0mRyWnVjw4d4jfvMvIa4kGAm0TCyt3oq95Y2eTbIPvgw4MIOXGdXB5SYJVP2ryxtZFOBtl23sa8UKZ0bFHkYlQW4tWkDYYGLYwTHqPyopsmze7g5nvl5FwHszMcDvOC4ciSvOXB5W7fdi814wQI/XezD0jPTfLoIP6/rnMrLrySolb9a/hxNNpb2Ho3L8b59WBaavYUukK14pA4313B13h/r1ZMfyJ8c1IXwDDkrbRpEdgk7ygmm0goIW0KYGGFk7G4RNk8weJ2AkUA7Ek+QlbRLO429tatpfxoEzBKATOJore8wVg8Yq/upeH74faEbG2MV4p6m78gSNRkN9SFFDLRSwNEn5shAkirELbshr8T13u5VSD3Nxr//Iud1u9dlo4ka0JJQJ/aSCqjHE4NjwHufeVf1Hhf7pEAWjZytsGgUIMuSYuVEV9sdeLdT5bkDcf28rqy0Qh5TCcov7oVZ2aIunbOQK/4zW2+hHHsJlNKL47HETx7pZr69Ev6gldvb4q3sk6FIuuyzRZfxItufMbIVVt569HgYSnvXecQTmPWIJcJZVVSB5R5hVvd8eoGsGaBGu7YUC6HaIOWNLLJlVlIgK25V220CRhAf8umcMd5giV0yMIb7sljHGwLuDPchWpA1AASEkTFpWmBzCxG/biA+AGhktdpCsbyqGEVZRPadJkbcHjds2Ry42SxSoArQsmwbOTWo8wfP7n5tLBASyYgRRNszYMaED5YxAdvdHMjyDMszcaedEYuBVOpPXPy7rT0FrrJ7IlHOyJIJNDPIND/rlfzZfTvaosoqK9dZegHLBPZahLe1SFsY41mbM2LfpA92hLbaLDnG79uq40nM7+UBrGrT0utuQKPNscqu7aUgoKdyKtB9gN84LSlLrlNquaM+DiC7M9hPd6oqBPQayrFCdBMTppruE2lmWXMtDAAHsQAG6TEUEEtc+mmWbS9vnlgEEMtcmIGzZlH62A75O4t18TGlsjhDERgAVIBWcd0pdGiW0rxDMMgUJjlD8TXxhxz0Vse6uhfN31dlPeH7qNcrCCXt2DM5oM6EJpugFm/F6Le5Fwa4+2POa+COL2yk8/2Wnv1aA+Vn9OLsvh7/siHOc6RlTJ0+7lvD9qxUz7l99buAmr/vidW8bEtRFPjNtX5P6npl8XX9+ajCbKKiZAPKsZ5IU9EDV8waFnuEcxxxvl0rBJf8iXKG9ekMWSExnQmQFbbCb4w2pzUAIsQrRtyILDKtpKNxCwwPKIc5CTtIHbXsEtd3cVmclJ5yFAuwcdKMiCFg9SBly2Hpr8WJ7DxjJIkhlDygpULMAnA4U65qTCIwA2MSLIdI5BNQva/VMat40KHS7L9nSh3veuj3CY7HZTkMpEkDG6uj9jGNpagyJuzUCmvU4PdExSKL3FjFKPKVt0a0xCTMBTAEAExNPKxQ9lQ5RnAz5zsK/8oqq2eRdQ2+wYu8sFcXi3I/7Dm9RZV/jIXrZvsSKv3w9xPlFSo+Q+ysAZ2MmPd+9gn3veVNobQPIFvtH1tOYNYjlBwAvhNM7vDFe34fstDobIZykHeropuoWntc/jgKcR6uBCzyAsYASWsbLiwIuzAHABgvGGklhD8NwHgL4EtpPA2S0dAYQLwCVvdZ72FZMOr+tIvABAvzhAtjia21tNe0mClLGzRxAymAljy/CFEMZepM2Re90qQbUwrGoGpAC8nGTom6c+krHRCwiqapBGOfHDDlibJda99VQ0DevRAoBB0o86VlUMzZlTGDq2aFNURNkR4KkdZ2zSprlmWq8sdvJ2Az6Ne1MtpnvXhdYZ06/bQhCnW9Ur8+5l082kCEi64inTa8kM2AMkLOoCAHrq3yTuVUOsUCwF9LCM519vw+dHmP16AhtST/Zhsk2xuR0HVxPa8vDqPwFjNb5yiBdQHTsKMEf18jx0OhoG4lRgJHcf0o96itrdqNM7v4MMVirM4i2CsHg2g7UuwtvHIQ9VagqKxwfX95Bmhliyfb7LebT7jznIrlV+Ve1NzMn1f6MxN0ssKkPJ98r/tOZm5NSlBz4ozgFH0H3As9TW4HdeHn9cCrQw1cn9csHVucJ81xX28RrDrwzJXllb0n2+zYd+IZjzqVl1cJply/jpJ13kgp1Fmvh8peK/pybglQ8bSgii1IQFqFWigmCepuLobTWrdXWxTF+QQBm6LE0eJAiFtG3BJ2TMDAlau7XVOFX1GrLfPCCU7BThOrVRdhdyMg7iQ7n8kGVawsBfjNOgsxalY+3d/3lFR+f0+hruPeFalcEXYTEgfJzh4KoHUtd8P8Mji/k8rOjw0qQwa1sgdf846ND+UYzNC2TJ5o2Y0q64O6EtJu0vfg+Jjdw2XU9eFMeErZwo3IxSfLQJVab63WotwPKhO5LIYc63fR9VzJ9LQjF12DxlZWUdeUd2ahdfxzdu/VO4jChwnZ4MVnMW0t1ZOtmWZDyKGu543CZ8Cai8tV8cGwdxvWLScw66UsPYH8OgJqxyKmKg3hmgVfVDS2Z2FiGQrELJcwrYWgr+9IPA1x9yMl5ioQKFKaXQfVTcO01+kMwEYVA8oI1ncFsDJrLxMscl80nonPaGXpV5UcAAEItLSxxmyWV65iaM5T4+bhUefo4p4Y0hyigERJXDz6sUE0vtm+rFx2iQoLlBLg3Quh4+K1CAgLFnlNZMlsbcc1ER9Cqe/rWBdXA3LskhCyRVaPSHsgqwCjnTnph8DONc8wt6CimiEdUbzlU91Wv35eHw2B9NfN3IWaNg1orXy9nbDhrTusncqVUP+SutESZDNVCRQnoeJUXopyYG1262tZTB1dalQH2B9aWDv2XdzZxQ09bnRTG939zXLK2mIUrb7yHqBseMjahfAYpGLt1Qbgze4Y/rjtfe1JqOZFh0rmNdVg+Ade4DVA2VDrAFpw4W5jnteAcB2rTWKNrZhfFGq+Y53qbmo7QJYd94JH8PXnjLdKbZ413P3shXMg63DQ93ynffMvH+DeieW2l3jNodLwgl6bi92w8aWm7sJ1S8LBTOnSjPWpvPyLAUDZzfC6ysCqMfVoaPds3nvE6MxsX4pKZjkkpM/iYlkoFJNb1iK7WNZDAFV4FADgQZQjcWONyIcpn1ME0jkw7Uj2ZRMBQT1NLoWnmNKdJgW2tP24AcYbZY2FSepagHkDbHYXAWkgrO4lzFzM2yEYAhgDaJoKlcrKA+u87tNzvMHybiTKvYP9JwCYcogvA7SMrhTgY79XSXWOue63l0fIZTD2c8LILlCst/J5zQifGoBM75VjY+2SGgBwGQ8PSk2N4UFwc7MtFrs3EECmXFnrZ8huhnms27noZCST6y1QvCkSHjUW4XVibPXCHlSliwXsa7DpS95/FRmoe5l3KbS+cMEDclbC1qiAyj0yaKbzM7skXnM4T2DWI5YIrjfnvUI0t95aKk2cosq9Kx933/Wlz9NlOiawcotOj8WtaA/CJsk9GUrURXsQtkAa5NnCVhq2rI2i6WAFc6hMSJLrhg1jd0HYPiaZ9OKGJAbWrvis2wJIFDQdr4dvkcG36pmXaK9tiAfMXBeziWvvWkIxjdV3KMoPdWNZ9F0uRMK7sOy1yto1VllEAmQFB0B5P/kY6jbcvbN5rLkqesuurGVXrYxab+V0wEMsGocWyPJusv45cx0b0z1EdwnIajdDRxL+NuZAC9Qu9WMRyNK50gKgmeFXprAdYNj4pZro5z7Z+IT2Xs3920LycNdyYzmVV2TJWvd9pV0jB+rOvnfAhlyM16AlxW49uXVh14tQILEvMhplBDkANFG2UKSpvp5Y6rAzSmMjX9rutCLwGRVga9K06ck2Y+7ZmGvwygFO1TMfAjLMErjhNQT0ryVHxgmF52a63Xai0y/X9l6rrMnc5rnEYfG0t/0e2pds9yb3vux5PahVhK1WUKr4lLbRZincO/+oPdH5uUj/rbO0ULFf9vKatuzlPf54Yx3vzy/VW2hvplDx40RNvWP7fSovuyL7kQhg1HXYYRqPagW+LwTDQmmTXOwFVKiswbQK2D4esL0lwEm8ksDvOVGHWpHwIPzDwCUDpLL3xwTA4mqtpD4AAYRI42rtCkDmFSkmnA+XAmiFCYhXws/CKIp760uYJNA8DwBGT/i15D06pE8B4BAFfAwqIzjASIL0N25wxsu8C6jJIRq3K0BdDgkAl3iwmY/qtxq4Qnk3k7Mq84/QKsqZRVFD6v1ivNFbpVmZGEiTWkA5PmTtMbJFVnYrtPtMSUKruH7wEMX7xOSlGLKBAMUIniZwCM5KW88zSexgQGWikK2yunGyHJCV3QthNHkPjzqiHFYmLl3XOehBu2PLIt+p92KGJ/gQC15+qviTtuU9xiw+d3ucFJOtnql1QzyinMCsRyyV6weFon3tCe0eQGg1424z07WA2aflyBtcFLe5fE9UVlyUlFiMhejljegkjVCSlLdxQ9g9lkqw8yDEHiyEOl5R1lwY0RpvEMabhKtXS9D4uCGs7gLrOxqyaacLQjN7sG10A6mvOS9PYipEtipsBF9T3nq7xoCs2dZbzYWNZjHzQDLeqemI3xMkIINHBr61zdp7MassC1powoXFEQHEZtMIEVGdtTA/JwshHgbpS1D9y5hKm7px8Vm5pBPCBDmWzc1M22C3aTXER7gX9ghrBWT5zz1laSN+EMSqhMq6QgtkAchmsrlfjTDgAa9auHL3dH8G9OY4PrltzoQ5E/UAsE993Gv/VE6lKdQADfMp0/Aam1idDcrSbzmIZj52eI19bdelA0pyPEKnSc84lm6IaILGNiFMUWi/dd1ru4OLf2W3TCtx7RhviEuiuRrGjcY+sc1yBVjJeJg18D5e4593cXw87SEUFwpuznWG0mgOMe2vZyBSC2T1um0ZDLNbIJV3ki0pqPSNHM9piylYfNyvfOtyzcyFJQNS5Oq4edjymua52f/ojEn36an98hLxmt59loe/qwnPYRDadqx+C2T17tm5Rxe8ankJdZ7nxGtetiUr17VQjKKUncVaUjnhUdwRH6L0+I8PDs/eK0D3q+MFYXtL5AoOQFwBq/sSRzFFyUyYVpI9PWdTVznF3Pw8UD2tXAemeo1OZ2KBZbGzAFTuVkzq/m7tKZAliUTkftOaECYFuJZcKbOyOuU4g7ySBBmV+7nJbqa4ztZAyv/Ncs4DWqQJrkjd6wzMDA7QcjysFxh+b0wtoqp/+VP7khMHTg689F5FQE4Ykt31DPganTWWAXoGYmnM38yz8mDq/AkB7Dxd2MtD0wSKEWKpQTCfSFqtspLfgvK3QFaW131/PX/rWB4dWxatuao9S+869EMe9GJlZYVZCenTu0/r8tlPxGA8y3/KdQSVqRQryHX0d45p7doyGay069Gv/rMvlROY9YglB4D3mQxbTYgHC5qFXS2eUGsvq4lqC+bAYhEEuliLiaUSAZFVIFDwJQmx4UjgQWOWRCpxqyBxs8YLRWK9X2uExoBCQbxZ5mHYMS5fR9i8bgKfT9htAtIgZljDA7XOckHgwUAaJJ5KjFDNSmFufpx4EOY1XHGJZQXUGzhbPK3A4UqlEWqFBtuzE4HCwmpSIKtr7dUS2YlB21G0BwZQjWMhykZwtS6YhbDad9PI+BhbSZHNbN3g3BOZxfebCJYrntWUli1jh83FRtvQjZNF5dgS4T0KyGrqHYw944vV6wmfzTvel6mtXVuygWkIv2/XCwIV8S5tZHPZTl9Kw+yAMTfvfP2TgHEqBwrZv1ZB0tbyGyt3ulrbe9ZzBhQO8RoWEiNuDpR5AAfhNUaHBdSCaqDVZN2DxQBoB9CAueCvm6PKHc+W0ATsbgLjTQaGhDQSYgbzhZcYkOaTm1ASTbJlMuwV44Nh9ABOGZ/8eXDD5Sot8Rp0XqOvtyRY+MPZKku12T4zlnf7yRtbvc7H4gpG4GoBpeq434MY/TReA2fJS6GeYy0/WDhX5t0Cr+kdpNmXh+c1+8rCfqJft+nLnv3IYvtUnrflPQf7QEA1767T91P5ki2BWLKLsVrke1e1XumC2BCafoTy8TrzqY2l1Itrmi081KNkPFeXvQciLW8fJ8lcuAUoESYN/s6D4hSTgllGtwk5mVQa6jiMqzuE8TEC35iQhiCK3gnY3SLwpVrwUpFPKDHSmjI/CjvJum4gFkeRo0DA6l4qMgqRGBmgJlU8hMbLAiDSmq1beILIXV52TFQDDi6+r8gMEmczBQKvqL9/tr6gBhXLO0Ml5xQgzDbBDUW22Fau/5IEpLmxzk/S8clgmPFjH+gdKAHfgQJq2V8bhsUey9PgEMCsoQhiBIICfTFkeWkRyPL7lY57oR/H65RZfK2lfcbsuvozG0Es3qizlqn+Xq3BLOv7zaPVk59pgMY/AwiNK2LDe5N3Rw5O8U+FN1ceLJ7/LcngnXICsx6xZDCrFwTeJr3PmtDW8Z9L5xp/3L2+uTpBaGQwsVriFGGgmI0WIGs8D9jcDtg8QaARGK44M4Awyr3SWULYUjYzjOp6yEE05EjqSjiqIHChpp47Cdi4uyVMIcc5UZfDrHkfCXwl/aS1Bgu2QIzqcmj39iUvaBNMPFFw59oxmmkjHhINBhzxdwSlipVlQdgBZPBKCTCPI2gYnBbcESZvWmwE3Hy7PbOzc22GGpt7MUq8LJ+1UM8vAVlZYxypJnbtOPrDPY2Alhkv20f4D232F0pPO5L9stu+eALqiac/1xBwq7uY5ZAcYSa7NxcCTSYkQwV7XZDh5GZ4KodLyKBoh9c0c3pe9vMM3w53T/avI0A30Zz5lLnxFVrNGchKA2E6I4wXKhCMZY1a4HeOJkjI75xh0DZaKkzkjDqDbmaT0K20RontyBBQS4E11vsEUoArOsArlfuA9vOameXVPprFvqL7+ii8pnOtWGU5oKrZxHJKEuy2cjPUkwZkkXbaabWrmCmkzzITjFxQ4xAwy1qYr8Vs82znCri5wGua39XnApDV+/2SloY3yLFOX1pe4773jgE4DCg7XlO13+Fn2aTx5NL+si5VdnUttE9e2BNKY28xC5BD9G/ptgu0gAlZLpnWAoivLoUwT+uYrTtMmE5r8QDhqPzEMrOPyO6BFrg9rdzeTLOs05YQnpgw3QrgO0GyImrGXLHwKuMzXRBSVL5ibVwpcAahzSnOFSglDInePzob6wxEiXsep8LLrM188eydSDKq/gCrCyKzhDghSExdKtZZ1mweezir4j4+pO8O+wEtu/9Yx9YqXhFOFvLHp6nOUuiurQK9ax1yQd6rMC3WBbXGYhfOJWc1DBEYBpGLhqhJStwzeSArhFpGgs0j+97wuSPKXiBr33I9dB8ng+1V4vSAzYoPlTVpCsa8hwq6b8t7Tpq34fiRrYu8z2PkMEN+Ls54JOFwWA1XTmDWI5ZALL7aRKIFMfChXxneZFGOyUf1Qm1jp+dri5LOxFkoHtSa9cM0HxcBm8cJV68m7B4XQjbclwCKpn2Yzhm8YmAnG/zhfhFY0koZyxrgB5TT2VJg8C5guCTEK8JwBQwPhDl4tJ8jMK0J0wUwnVPx996JBdawKaBWjqtlIANQCSmz0tLYID7dNnQVoDXbAJNan7VjV8a20nA34z6LlQVIJkHz76YB2I0iXBAJSNWCU4AQ7qAE3IiygVzMxdrL4l15Ymz1higMtEuwcRDI6pb2+AKQ1X0vRrzt3ENuiA6CWx3XjtpE1uLKteeoKyj0iH8bH8usZDk2hLrtsz2/10ScyqnsKaTzJe99Dig14MEJ9zGvV77XAvQ1eA1QQK32BEkQ+DQQxjN1CzyTqmGHnK2WksRpROS8mQ9bxysMCI4o1r0GaCUSBckIjWECpzl019u6NFdyIINq0p66jBgoZPf2PKulW53HNjwI0NfkAa3ZWMreYW9yi33AV7bKcgFxY1DrLEDUoQmzEAj2nVw7QB2H0UAucGmvpYtAAfgD6Z4lNHOrzMH9QFanLNHQZp7+cYFWvbLfOtn9nm3YF9bmgfYqay9Cf/xaXnPiM6+Iws59sKz5BanwGOsroN43ArXyfjbvyMk19bqvLIls3ro4PznhjlrQGk1m0lhYU9mvpcEyGXKxlNqqoG1xrlz/LAg8K10Po2Rl325DjrVlPCKt5Pt0TohXwPYx5Ucjqn2l1KccKFvqOGLdAXwswyAANVAKotBkFEWQugyWy6gef2aQeWeorNFmoczWTmxhXYR+i4GDjLnna9TKM44nLcc2q/s0K6ZkByQjb2dMcgve+2SIhQ/ZraZUYv/GIGTNW2WFABoGyWQ4ip8oVwm2kK2xKKprYYzisXIIyIKTw01+b4xMrl3clqAeiHlpwceK33SssiogqyeLVbyI6nszQPCWk809Pd+JdbxMq1fzKipyEfXrZOW+TtOk59ukPvvKCcx6xBIhwf5AvRgmrnRArgrYcqhvdU1bH6g3kZ4Z+Eu5/91MYdNaLLJ2F4Q0iA86iLB7jDGdIwNIWZs9klhfWTwSvXfWfgyM8QZhdU/a46me4QZGhV35Lf1B3oB7MCGtGZMG8bWhiNu0f1OfHxIZ5OqVeuHteWu6yIr5rr+ubicT7AxyqVXWlJR5qfbAfOW9YGGMp50jbcwr/2nnfaD4to6Zzw6hBrIciJXHQIl01ji0rrDNuPQsstp+zIgnFn4fyw8W6h16h7MYYN02OnXdNRn0sn54RlQR7vJpQa3zuaDCYGBwtEm6/9lO5VSskGndaX8mIll/S7yDyjyfXVPfzX8AKEBXB6/qfTcrRgOy0kpjnmha83QmAklMZU8uJ2QBZbBKG/bZb9JK2rHAvFWnFBijSfvjQKXKsMnWdgSYVRmliF6OqXVE2Zf1jvb86p46BthvBA7pg2irmQAyJK0HlPhBoPbldur486HzprNSTudcoBrIavrQdTusnrXDa/actgMvOZD1sO3tU+JoWQKyuhZaR/DNmdIFKApM5Tcn/vLKKUdbMyzxkGtYQ+Rie8ecAMRNOLevSi7JULVGonmw6CUWHsW1Rcw5QYj8dp/6PSu8CVUykjwmChjFLbB+QSqOt1Ywi15KAGmAeGgcRh6KdXAYgWmNrOSe1gRsi9t6vm9nvbEezkAJw7kOkoQWCTEDQFWiks4elyOARCJn7AuJQpoQJAnoRU6pf63i5MxK/ukAdjmmsL+PV5LkNvV6A6WiKl6utnX2RuNzU5JXHdRDJQRR8lvbUSyuSAO/m8VWBnVjzJ9scbj2AFnVsxPKPPdA1jVpax0/svns1sfMHa9bFuSwWekdtn2Ry0zYA6lr+QYKfDWAl/XXlI9NezPe5vaUbWD464ztCcx6xHJOk2iZYxQ0uJc5pFcceLFolYVm4vvL7ZjXiDYEytcxwSKtRaiYNM0tAAxXtnknTOfKANQ1MQc/TCpcjG5xqQ+63AMYb6QssCARaD3Vi0SZDnGZ7Gb6KylxtZ4KbcZAJAA9Z9/1Sm7pTXa3OGysczBix/iqwPhdrQIKUc4Nu/aa6zKQBWSrLEyTMuZay5DdOMyaanIQtFlj2bu192ouIvl6KvWJshkxVPuAEB4ayOrGyepsuj0B7blNHEWMHnKjbZq+VtsHNETUbsML86Xpy5IGQ0xsdVPmgqy2rh3VtUC1vuu1DhU0Tq4fp3K4DHCZc3txEBaL7go6oHRbq8tr2votfW3rKN0QRUf5BFBiJQI5m5RZmeR1pGAUUvmdN0Z6j7TiIvwwCSLm+2Ad4rLuOSomM9UbpUoDSHqp8r+DvKY3WsbnqoHxO7MFXuP5R0dAmPEoj8xpwFzKwF4N7hXQqH3pPKNx1f3toP20WFwmgAGFhz0kkFXmXYfX+L7uo63HlpeSzu7pw7Fxug5a7gLHgxNNO/O2m3d9Ki/LMgPX2yzVueI8BMp1LE1me8fYUcjnPrG6sUGAlAXBOweJNqAHQFZkKC3P8XrVAjdsCTywuBiOZQwoMaYzynJKlmcYWN1lrB6wAFiW9HELjDcYIVBxU2SRUcJG2k2qvA9j6U+25NXOTmtNQuJdDY1nqyxTrW+vaGIWZY49aFakO1kgkIyBxTZbIjStcpsZmAAilncVaWadVb8zlL52yuw6MmbNJW5j0uDt41SeLwaX0V0/E4PGqdapmCWWv2eT5V0s+EhkHzUegIZWoWkS0MoyGiqPomEAztbAapC4ZT0gyysl2jhZHZloX+lZJF6n7OUlC/GyZA1xDs1TXb8EErljvVAqfT7HOSaWl7m8IfoM0HLA8mw7Yvs8BjjWySwOlROY9QglEGNFwLQuwNTe4olKjiuhhw5qyrsdyBOMuP7NVHxULVaJTRTTWNDECKouIA2OR6PsLo0pxC1hOmdg7WaebpREoADCjkA7ypMzrRgIDNLJSKxMQLMFV8ILWx8l3S4mZL/zLNTohjsNlAPX98cXXUGrV6+k9dVPbwZMZXyq4k0e9xB5I+Z0tUUVqHBKxf0D0E1GQAVM5Xu5xs3N0J/zgSNdbK28OQlBA/r3gayKsB4DZOWxcX3qHe9aA1glzM9do1SB+90xf5/WtHxmlWXfdX14rXaVwRB1/UyQG6HaH8uaE0IWHLwLI7vjvo/X2D+eyiu0EETpbVbAh3mNflZAhh56GF7j1wXXvxl15k+fprmYpnOOiSX0kCo3P0BApmnA3DLArSNKACZkvpXUwtEboxmfMxfEDJYYryFIvEagjsPo8Z9Ac7eL3pgcwWsqBQSqV1FrIdE50X7v3YKTJBgxIM1cAtuYNlR49OK9CDXvyfX1YUN50ZlnZN7SB7L6MTWofjcdpsDtNbPfnXn7EvGag8XP7XxMDvSUOXO+0slg2NZt7+GPUdNme7zT31N5+ZcAFzMru3V5C5OauIp7nPutoJMvFdhipY3PCqjVzzwDWpfmJBYLIccfEKha87bXYzL5RdeXs9ClJEmlpqHQUEpiPZVWJDG11iJ7kFpZre8ICDZeSPxGDhJGZfe43s94FaGyjjLli7k7hqlxKdR7U5J7ZysxoAJ+ZoCIlQAwa+Ip535YjZ/JJgFIMWgIkwSfed2PG6IfYxkvyX4eSl/sHS2JVd79fKl4Zb3JOkGJkSrzMziVXD1AgC5ALKyc54oBV2RxtExOCgKG0TgVt0NAs8NHcSG0GFnjJFbLMQqYNWiMrNUg7oX5IecWWS2Qlcf/gHthX24q/PCY4q2x9ll+A1gEtMSCv26rvonrz75zVoU78lVonkuv83WztZXK89n6i4pc5K24zPJN9iIHnt2VE5j1iCVATVJbf2D73goenpfYcXdtbfVSvnb9zF0bXpA2jbX5npeJooTQB7qFBIKf1pK5MK1Q+YXnDZOa3+ZYWWt5bhMYwgZYvyjtb28T0lkEDwnTGWN11zIikrhXOcKZ7xNKvyTQMWWBw/cj17PFtm/T6rTcLYv2wAiZz3kWNux4hwn1Ypu0JUGCHzIXbYHFuzJQyrQSppkACkBFelNjCJ6QWqZCy1IVNBuHnbdNwR6LrDZj4bUtsvYAWXu10Q+5oT4q+cFS282mPwNZnog3cyiDVy3YZZsvJ8x7018OqK1N7P4BMu/zJszATH7oMTmVV14hoDYDn51teE2v3tLmhTo/yIMNzXHfDjXrynfB7cUZrJtUylmlcswruLWmmx4fV9GUIoDwoKia8umMMMYADpNq5ynf0JvB5/a1z5mGq6UR+c2g648pmw8XR0S8rXxu0AFZDPSArXmTR9yYoRtZ3ex7Vw57pvydHS+xvrp72THrfFBwisu1PMvUjL0WWd1N/HUsshbn6ReH1xxV9rS9CGTtaWtm2evX1EJbecPf9KdSmNh3uM9TedmXmSVfzyor130JFkqOryc/KyuUtmqzh/RCtsR1oooPZAArUkn+oXGzglknGfmzOFkM8CD1p5XsyTiIlcrqrtzr6tWUeY8Ekk+YzkmsvLQ/wwOx1gJBXeWV92g8RmpcAUvSJ8K0IsRITlnunj/Kw1XHDJAgiAUbEYgku9+MN/nxDcroqNB69nyjAyxSYmCXgFWYzY294Qv8c7ZFZShKSb18uFgL+8RYU6r7ZzGDFQAjA7ZMSQ+o9RXyPKPdCF6vwEMs9X0ZJ/DVRmQuQMDJKNnciQhYr2buhYsWWZXsgRrIaod2n1KwpdFL4+jOt5+LpbF07NWv9msLxSfLsjVccIWF9hlFTnIAWK9OBV7ptM2eX1qPHSJV8bgjygnMesQSoYKqWsMAgJnc56K/Z0K5buzrOD1GpIAZsEDNBG+FByrXlHu781wIq0+lSYk1y5RbYHqvYiElAXbNVDcN6hoVgXAfWN8FVvfUumsipFXANgwIkzCBsHWCv6GuRn8tZoq/H0p2KY6SRWS4cszDLx5HFHrWO1koCCQEll1dMFph5VH3fW1qWgDOVzvUVlatn3lA2RyYlsJn+LBr1ZSWLZChzRGz+vPEeQHIqqypXkogy8+5hdJ9Tyj3NmJ6DHMtWgLf34UOdARutmdndPvdMoeSjRD7BY9gAhuXduweCmhRYCzGOziVU3GFAGT/OA9wt3OW8j8AhdfM17bjK+11hAZQoO4cPwgaGLDkeBAHcf1og4aaCzgBOV5jBqW0TlAgKweGT8IfJgqZN9Gk7QGq7UbhNdzcT/vkNejmRlIpfY5eonYjZCErH5iBXI9esvWYb9cLOq3G1rvM5/64+dS6whu9oiCCZbbMgtyU9LfnJdrkHxuQdWgOvpSl5YcAZgKjKzPe4fd4vfq9++W9Yd2H/n0aXtO6eJxArZd1icSaXZxrC/5HKD1rrX3xeap1n2ms7OlzO947ATVdrtstQu+0Rg4zUru3lXulKJZN0xoYLyR5lcWnWr8gNP3qSUbcEs4+Lxa6w/2AMNZZeGlCTgpi/Ge4hCYLYXWH9EIMZXoZRs5Zey32Ys/1k5sxqGJSeWDFWcAJuIUZz7KIzTP64sOJoLxLsdBi8Kocz8WHVrHDDShXFQOyDMAygCk5cMriVynYx6tBgC2Vd2icBGQyAwAiOdZYNedPC9/i+2BK/iRGADwySrZDBs6GHCtIazvIAAEAAElEQVTLK2UqBU2rkCd37GHW0ZIYwvVcP9YtPZcwf8cP3ScvCzlwb2YEYHVUlG1dEb3BSbVvM68VJy8BZX/FTh7y4Nh1gr8DJzDrkUsk8dfOAIIvPcHBvgc3QfxCyRt3mk0WOU8lDgk1vriu5IljG91qwpbNvAnyaU3ZFJcm+UtrAa1AqAK2pxVy+nPaQf3WOVtN0QSEDWG4E3O6XDMvnM6UeE7CSEof5FzYSV/JfLkB0XJsGXHDfXDDb/TQfPdjQpBU7A2gBYKACh2Qf7H4buRYXFyI8Xbn/LcbeJm5WGqZoAkUopRYfMCtbiDkiTFEDSQfRWNhmo7VIFZaIQBD0CCU8sc+KGeHOGdm6QWQ/Bt13/xY5nrNPO0Qx+4QHnHuYAwHI7atFaPve08AQVkHM4GhnU/wY4eKUM+uA4rlFTftEQv67dalYI/8xyuQncqXZAmoN7lVoYUfnbncCsUCLHR4jdGKA9qxgxYyxmusPc0oCLO+sg1Mo+QAIVtlsfIg4y9Wj1h4VtgIckwuHpbFczRlCaGsVYkXYg/AuT1W/tC6j8yeyX/vVSXHdh06w+BrWHt1igfg7HOayjP0NrbJWf+2AJMBHvad3EmL70JBN5eS6j27tSMUNwxrO9Pa5j5e443mPvr1MIjVOfnHSTd7fKR9rqau/10JBNfo9z7gjv17bHmXX7d6/hFxjVP5EihpgCpu9QC5ANjHljYm416rk0P8SJskmikTWN0NSx09rrTYe5eY8gKkNH8CoPJCUitfi5M1nQHTDQP2gLMX5N6bV0sH1i8C588nxB1jPBdlcBiBSZOSZL6yY6Q1gbYKYmkfLMNijgXsLImFDzGSxqWiJV5SgVeNdTBMyK8VupapMgebhyjke5ZE1etoYi8aoEU5421dJ4Nzh4rKPMQMjEniVE2awd0MPLxivzlWAVYGPMVYAsLbPRIDQyyZ4YmANJV5amAZIKDYOIFoFDmJGVivJFbWeqWyEhrZC/U87o3lHqusqhyx1LpKjkPF8/Ze3Llji82d5vlIkwUsxek2HuaNDLyVldXxvK5O6qC0iV1bjWthdkPM9zt+o3QCsx6xBECFWzENbQO4Zn/bxlLGgn9nCy1y1/jJZJMiOhDLTeJk8a86rnc+bpYXJnzhQJhWwHgOjX+l7Q4KWq1UQNAsIhyBtGYB8NRaa7wh7Q73gbhhWHpbS30br0q73nVk0qwfFuzXT1wTQFIUIht3NZCVraogz9tqg7K2wluj2aIxQMuutYCKhIqA5/FqtQIHCo0JtNOgYz4IorfI8lZ81naC1DPrLDtOVMcn0D6VmF5UBYHPc6raJOic8nMt1HPtWCCrfli3mfcb6ba9RykHmqmBpfoZct8A9+x1f2fB4/3n7Jk67TbHRfPE8h5nRF7fWQAQGRREuL2WVuZUXpGF9B8Tal4DvwapzFm7wFtlubUAoHYjrOZ94R1WKvN4z2s8XYE7NnsAsbBNAwrdB0osxwAVCqgcjxBwmEljkcg1YYecjMSUKHYsZ8Bl1z7cscwfmmNKd0NPqeGfzR+ztrIpjB+TZihYx4m5eX9lfK7La5C4pCc3hUprkdUCWay9ygH0qw42704qFNZMZSMdPe1seI1daueomWvHAllVcfP8YN2HLId4jT9/QIjv9rPdf1Xn2rr9/lS8xuZt71pvJRi08onXvLKKj5d1xH6MiYqHQHWCj7q+1MeMr3haU7XkBPMKENKMhhxVzkkCYBmwZYBSdllXJUdaA9O5WGWFHWH9vFhdbW+LbHLxGcL5FxKGDQOsoJhaXCERIpzCXuP3ElTInqDxqmhuOeKfTwGt6YwwdMCsbBlse2sDB6KL4QiAIX2aAU2k1xDAOXZWM95AAchmlnAsl09cWX6VGzuQaqlk5T1UHpmA3VjHuPLzZpwESDJXRLOksvPbnQBZxgeJyvhEBcAse6FlB2gBM3OzB7I3DHESIGs1gM9WWT6S90BzDxY77vc1jyjLXJvmeh7es7DuGBdUMr4Dm5aKN4TxLoalgsrGCp4a3zEAtJZ5ihzGel1br5eUq34IFDdD4sP1m3ICsx6hRFXtcmwITQVgKWhlljKQCTPb6DTggrcuYcs8QcgZCNtSZR/snaf6kxhIQQSL3WOiyQim7bDHCbqed5Rdq6bIGC8ksPvqngga6Uz6T6M8kAkc3v/VNBphElfBaU1ynQkDAcC2PIdZruRF1KGpGSGmemEulSpOCqx91U4YQeamfkpy75lZdb9PSKhdAUMAdlzAJiNM3orL99uIeNYYmJ+MOwfUWT2CuBrmzBxGnHPAzJpQd+da82w9IGuufaZy3BG2vXGtOsJg/v4QpQ1I6H29S98aAk3ls0qU4AmuI8CtpRc3bVR9IVkTRTPB2fIw95O4AFnexZAWFu+pvOIL+V1yPkhu3ZH7TfvX4gxccL/9fN/DSwj1ZqbpbK4nX5DBKrH2RQ6U69sEkAPv2v3TIOsnauySnEU0OUs11H0lxzuQIKCLagSNl2RXxIbeH9x0HkunuPfpsun2hIQc4+oa92B3DZHGKeEyB3ydmfs9gOodhtJhmxzU8FW3p7F5M5tvvv+9ueYLlSGagzGYHTgayPpi8Jr2OuXHe/vU8CFft+YJ6J7zY9Oz0GI3ftyCVZmPyWKoSMFLpWg6lf+pSoTLAJbdghfKdeYANeuvPZ3UfdwnnQAqWcYfy1+N3CTWkCs1Tbb5bAoQQJUWju7xwOAorn1gkT+mCwbtCOsXBMgabwIcgbPnCesXGXGrVleDWE7l+FleQcTFuleEeJVxCMAN7YcCbDRqRnb7nbjQ14C9Xh+tbOatWkj7xCiyDkeCZZjPmdI9SKUDRVOqxxIQ/oDCw0FcA5eNImhvce6FNCaxhvIxr7LMA2SAxeSdGAWU8rJOjI6f6bHExbPFrJBtLq6GYtmVamSRYgCzAWBrYBjA6xWqxGteEeNpsyl5/Po4YJW1aM2EI/YUvZKzVtYgZNtuG6ohF/88+RiqPlb7vIaH1XKNjnfwc9TRA12jZlWVLbyMFhByPLtsle/+ZvJjuD6QBZzArEcuEW6xAgXIggMRDG0GyuJurbLQgAv6kjkSeKByXovXasuBRvjwE9cmlIuT5QWG6Uy7vkXlcsAkRBoMYCVZG9MZg88Y4YFUnM4VBLO0uAZADYzxVgLtAmgi9TUHUiAEcM5CkkErF0wxTEU7vkRT53Gx4Bais9ryxafKbT/hGFDnHmzC4gGNORlhzRoRx8WMOI1j0UB4bXmjyeAYZqbH0HSz5YY6h4YAy2Boc2pRu7AEZHlCts+t0O5rx1sCifrYTKjAnt9Hlnauz56DgHZzMOtv03e/Jgq45daiArqVT3jT1sx0Nv+xxtZD0ZKfyqlco5AHD+SA/Svr2wfizvXnm61FIKuzaerFMzm0QesBXVkoAYqroHbGNkOArJ8UVUgZIJly4UAwdTeELicEsRamRJL8iZFTvJO2n2MteqALynd87IZjiuM1++qQ+159tt/R8BrjC8fwGnY8q9Le6z16LoaZz1hF5Znt7ThhZtnhlHUzy9/SXDVHu0CWdaU5jN7vFsjqn3YNLrTzsLxmgff12u4BTnuLrc+2jeav9+y9eqVdD3Ie0Y9TedmUWTr7HqC1D+RqlBjs6AahjSnk1qYl8zDaU12L7lxs95qVcO6SV5nlVY6ZZfriHRC2BDpjjLeAsAF2txgcGOs7QRTnaqW1uiNAVhiFv6SVc18ktf4iFbhV+RE05ApNmm1deQkZjwnWFtQFUALEC+BW+FQeqw5NtxjGS9ZvWXlkIBUU3LKsh4QCdiXOsk8V+sSKWt0RaS1isXAO5GQ/r9DYX8y9EJtt7Y1i7RCVsCk+tu80wZKS8NlKY3iNyMoSoMrSnmUqzVSYrb+Uv1kMLoy7fA0NA3hKoPUquxdW4Vc80GalF/S9IyPV74e6x/P1S8XkQR9epl2XB1wJ91teqSzSeY8tlmBhi2o52jdW5CFq9xl2PhT5x8a2DXdEOv/Yt6thVrwF/cOUE5j1UpTAZZNnJaPNQVzElJCxLtY26LtZXlXH1aLLEM7sYmgEl1CDM1Q2xWad0g00Z5MMyP7lRnzZ0FWjR0bsdRKmMwY0WOJ4QxDU1b2A9R0gXgoINY1AvEnYvUo0JEEtu8Io9zET3RxwMbEE9R1tg27CjQgoaaDa+g1lMc58wfdom7JW3LcTLIZW3c6ssFvl9n5ngBqrCW0q56KuZq+l8GCUEbQhZuLfBbGMKVVJBgIQLWth417otAuZIDsQy4+htOfmyZI1VjO2LTC01yLrEYvPdrnUts3VKiVsK1xQqTtjWvv62wgVPWsudoR5Bi5niy+PDEhdsoA+p3Iqhwo1n/6H0RfdPRRlSU23yrpVXqJfM22wNeQElZnGHIDXovZ4TVai2JrxGQxR7mOXePDJeBzUgiutxNoxbglxg+xOyAmgFYBzseKiiZAImb/kjIbuniGDYVzua7whEJhm4Y6vXfJzVAdpJlz0eQ3q92ubwVk9Lpp5oCjSUt4MzDfiDOUhUqcPYum/RnPt/yr3wnZO2h7GH2uALO4c7s5p69Ih+nzMuSNLd64v1PHWh3sFmiWBaE9/89au4rH15yKI5nmz61tXyXcqL5tCQNkrU6jnXS+u78EGO7xjqXCxPDWg28sj8tlsgTptSF8thhQhrUReGDaMsHOKbp3jcUOYzgkpMtItkVHiAyEy4w11N1S+kVaEbQSGK/Fy4QCMF4R4JXuw8QI5Xtb6jgBfcadZBZ2s1T5DGiivyzSU+MNxm2ra7RUKveE2CzdPSwMktlgeI8AALOR9sSZMAorS3rsBuncBnyAFCYxQgWXlXWr9XvB3z8NM5mn5mwVjtxhYllHXro+hHg+TaTLglYp7ob+vtzRWa64sc1lmd61DwyBeK+frvUDWnH4uy03HlqrNnqtgW64RA6u7dq4hzxCbJeX8fE9paWstx1hreYvnfwTMgrhHqWJ7Se/FMo8fjmrfeGw5gVmPUCaDsi2bYasB9cfa4jc4WRguBBGhCB1VcGuPdOaJUQgCsfPltnq5L53NF6FOL2ta6kGYogV5pwCMa9F4xA0hnTHSecLqxYj1C8DqLguzGVkJekRaS8fNHFfcRpRZbMv9M30LBVTz/aHEEgR+41PdegKH6vtRGfD8JYEyQwCAo7Po9YoJSQZuWmBC7xbogU5nImxEnxKLGyEpsbGsIInBqyD17HykAmQZsbYkAT4w7x5rrB6IJeOwMF4t4WnLNYlQbveAl93M8rC9LRdiORNG3HNWVmtABUZVwoFbfxkAbp65zdxRaRiI67Gyou6FFFmNadJLIoSdysu35I2mKU6KdKqfBzZbszWrtMAf660B34d2vXMHCOnds36Q6nt2jbe2dU+cokgMNJFaaDHCVZD4ixaM17TS5sYPZE0464YKQG0F5p4leK05lH4w59gofQBp4fuRhQnw8bIeidd40Mn2E56fAM2exIHmFLLwmd0IrIoplIx/+fZNEMi/m43+DMhqeI37XpVDvGapzsPymgPXHTrvY4h050HFR2jhuH7vHG/BqqpPvWPVRG46So5knBQnL/uyOHcNWOiVpeyEC9ZC5WbNeYu31YC72VUOzf4s0HyNmLUpA9M5YVoThkvGcFUvNGJxFYwbkzEI40p4RpgIvELOup6Du6+BeAnwDmDNtD6dIccEPv8CY303aSZDztkD/drruccX/kl5D2hWZLPxctfWLpVic7U41HYvHyxeRYiczTCSMTLNCLyHt7BZJTtAy/pkl+0DYBKQk12Zm9++uMD5ulTqx1Ar/+H6HILE2RqifFp7vu5kmRP1mCXIWkusGx4icmKsfco9YGaVJccwP+bK0rnuGryuheQxbTbgaDWn/Nxs1mPb7yp2Fuq6eauZTA6i2uWwlcsMS2cNC2H8zANnNjUSZY+VDHKx7Ql5ZiW6r5zArEcsgQio4t7ognHuhtU5nQRprcHpptqaqAayKAcmtMwZwVK9+oBrTtgWv3Eogipa5q7paiTsbgghH67kmGi8xXd9OoPJOwg7kqDva3nOdCbCRbwXsH6BKiZDk1wTd4zVXcqxUTwwx0FMfwE5Fzcyob1rSAtocZRni9sF4vqQG7QsSAQIQbeUxn6s9oGSviRIEERAwKYpFTdDPx+GMPPxziWEMncy2BnKHHPCSQVkqZahADXk5ptdU3+XZ8OyJZa/X3veb8abNnPdaxCiaxV332yN5wSASmPenOsK68358p1yvTq4IebgVUBllZUBrgABhYmrMStuIBoA/os1Vqfysikew5ID8q8vvFCZ5wb0NHvrCsiC8Q09F+AUB/V6qmg5ylReFKJIkoykQV3RoZ9m7WsZPiH8wzIYSt8h4O9WE4mMrlnddFPSmFrsntGv8wEZ9wlj2fDZZy9uVrbYXRjahyu25oGcKbC5xdEaYAbApnK3tlz7BHmQgGWBhhoLgHy8OWZ7kRbI8v31IFZuo+E1vu32e+fAXiCr5TUP/U6OLDbGrSa/ERJ833oa7tl3V9+undXtna94SfkTPtK8b2dSMqMhp/LyK9V8cRuLJXAh19XjCUVpYntLXwIt06nOPqbdH2Z+4pStlTtUlPbTiiSA+iXj7MUJNCakVQCrRZUoMwhI4uZHSYTfsDEaLm7qYSdt8wAJ4K5yRlrJuYvPiCJerHkVFIjAFKiKIWyK9ZzB0HiHp682xJ2hEdnGiMOcJhcgyclslRWvVSx9YiJgUNpkcX8DgUHgIJ4t+Zlal0Mq+wIwF1DMW2X1irZjsbJo1DhW3jprmsApieJmmoDVSt63z3JoJbsQOjdED3j5JFoWQ3gqYFj7TDk2llf4e34FyHvyfQg1b+P8uUwslxJHVeUYi6wjim+7G9uz10/jTY5H9eIjd7+7NlsjAC8jtcUS0mVAqmnbX5cW0KcKULvG0J3ArEcoFgAeQEOYdfHE4voF6EQKQdzIssshyjkl8JWZqTfocYJGm30gTAxmcbGQ7B466UKhrkzI7omsAsTqrmg2zC8dADAKMaVRF6JdN8jmm88S6DJgeCBg1XQmBCyMwODcBDMTMcHEL4qhxEBJgyyC4YqBjVD0ykLLwDv166WOEJDr+RNmarq0l299wxV1xgRhetfUmJvPOphFk+AZkgXFG2IGGnP2jSzYuA2EXee0+FB3wkx47d16ICuGPIcW3QqNGHmNfPUgfQJ+LJB1FDDjCZUXDNo63X5Q/Z3a83AMCfPPUI+DaQ0q81cHPGeArGOZVSwcuWqvAM1ewGTdJJY2AjGCHTuVU1koFV0jd1Tneg7IDcdrlBflpeauKwJwP4C1B6hawCPTLwNxqaw5Kz0Lk7hBtpLKbWogd9vjFhBYOxAZ2EnWXNP0BYbgOGMhIORdT1zxwJttsEJQUGzUkUnoAlq5qQVeU69ZXq7bO2/jlVxw32uUzAMZdVws5w4v8S3c/fMp14mW1+Qb6Dlq6lV0nspzUGeO6Ud1fEbnDvCaXhVaqPfFKO2YzM43/Zj9pqpu1/rXn9NDS8JC9ezNdTP+3Gx8jtXJncqXduGeVNdTrF+n0ILSxAn++f6ZpkD3Tirr6O8WrOUg7n/V3gxyfLhixKuEsBXhJ+wSmEPeXwUuGdTTikVmsesHqDu6KOPBhDjW+7i0knuEHWN3M2A8F5ppMYDjjtVCC7Xhge1xR57JF73wFlWVRi6p9p2aWb06rzwcC8oVr5C3nUJW9E6aZIhL3UVAIytFevdoDpgHig/4bnxokqDsNEjmwuJ2qDKOuQK2hgM+NEtQYMustmJUWdQBWatBZOnRgpg1srcFe3cGAHmcPFAT5kqaCsTdt1Ra2tsrjbGAV6QdvLZTuryle77tY8OLsoxSr22vuPf19ymQDJOo3BfdfXzol6R7RpHp3T4moFa4kpOfjignMOsRyoSACEIYEipfXwMaQhDXL+hEsqyERrCiTSTLFuiALNuEezCiAbCqjYwKKBJIXTQa00oDE4Z6EoedgEXDPTHdtfSs45lcB6hwwUC8FIuw6awI5fQgYrgkta6SlRl2AEdCiiQZC5VxpSZOSrDgiFMh6AKkEcbzEr8r7MRiKyizYAjgFSKDduVZuoV7xHdhUXBhBjbWpPW9kHEUwfGAlFlm+R3klECtdsy0BqqFyK4dQMUIKiYfIzA0FlmB1DXRCHS5pjdf4AlYhyjOAa76+0EQ65jxaonjQpyFxWv95qjqX5vN0N2DqCLWPqh7uYZyuzOLLJeNI4PD7tq8bqtg703XAyPkuBbXE2JP5ZVZGIQADfrqAdwMOpTNV9mYYb5O4PlLL6uuteHax8Ix3YQI3XdCghVCDtYet6wJQqT/psCAHWJoIFrU8REVyOJBdAwRECCLTDtf+JKt0Wz0OKkrYcpV8rOnQRoTzTVmGajy5sxc8K/Da5aWtMOsDYigQIAKSdcDtIzXoGh/G0UItehIng+Op3kwq8frKGBmiWFCQ67jr3G8xh9qz/sn2ccrqP/9kUGs1vLhSOG+d98eGGxt+nPVvq6qV/Mp7h2j+bHy6RQkHb5djHNOvOblXmKzxwMg1jHypTpe8Q4rWQhV2jw7P9/3SUWeryFPIrziOMyBMVNWZwAsqUI7sQBZvq3kvDgmxvpuwHAZsHmCEbNlr9wvjLLYpjNguC8WvDlmIoTGbx4PCBPj8nWEaS0xfocHwOoeAw9KvF+zBvP7XBoIoYmpNaMRHq8hVPVKIhI9ZlbKzTU+SLjJVoeWMxOBcmBvk6M6AfzZAXJOHlqMlWVg3JhA210BnHzm9ZWmtLdjXh4yl0Ef+8rCsTADGKTeoNZVu1Ets4YSR8u637ooGpBlmd3d8SpOViUjGG/UOe8NARbG9WBZyD6Yu8P7fx8svX1Zdb75bM7lOdybQ61s52hKNffZ/c78icoeKpZrqlAu1mbkmieaEpNQeNo1PFZOYNZLUOKQBKSyRWYLKmeWIwGyHFjlA77PgCy3kIy41RY2emPCnDABOaA6JWBiksC4KnhQUqBoy9mn2zamcSsajvFc09ueyaykEcXd4ypguE+YzjWr4U6eOQ3CCOJGNBs0CQPhAAwPgOFSsmYEDdxrGgJKGtBRY5WkqPdem1sKyTWayUT6uoeK7yPw+84lgIKmtZ362okqxW4reBiRT46wA86slgoB9xZXRGKtpYIeJQavjdCH+l5EotWINWH2Ad9zBk2/GQHKXGmtsRY1bv3f3eyHDYh1FLG36zvjXGLAdfrD7ns7//XaGbDk6ztrqy6BNYsTsk2Re8bmvna+ysZB9W9ZX44waywgmSKsfwnTNTQQp/LKLQZmtXEfzKypNpV3a52g5+HOo1rnZWOysFHqLGtiABOLtVSkapNk2Zws7ohvmyaAiHOw3DQotfMxtEZC2FLOahiTbTbFQkuySol1sLkqhl1xYSSzuHL01seTNA1iisAUIMBWsn6bcPZF4DWO/C8qWfytZ2CZ8if7NF6QAS2Uvmd6KV84BFCSoMQEo9WOqHqhtGPdWwOp8rt0z/Ea99MDWUdt2Pfwo7mS5Uhes3iv46/vKXi6loD+O82/27VVG8YvgNnzt/zK38fc18t9C7ibs+ayrDWJB85Ijzhkp/I/efFrdSkW1uyauUuVt2yp5zXla9p2j9n7kXojsLtOkjCRzNPExVrWaDZZwHbHmyzQehIZY3fTnYvI2dUl0y3yH4Cs4AgaK/fq1YTNqxPSeQISsHohKm8RcCtEAcGyQga215Z1GSYxEvDOOvJgyOTVrM/CBlkJ04IEEsTd0fc85n2yuDjGrXxi77AFHXuxqoA9yn99NxYry1tk7XalbXMLNCuqcRKLrbN1AafMFdAAKWbJPkhTCRpv7Q0KVZiMbR5O3gOmB876edkBsnKiLHs8pZtLoVPqMXXX+LIn9vASaNXla7135fZ0i3pw3xY3vxs+YnUIfg4i4wVFxrN9hWun5WnWXEQtEwLZImtWnDxGhpM+JH+6Bu4FvOc97wERVX9PP/10Ps/MeM973oPXv/71uLi4wDvf+U589KMfrdrYbDb4/u//frzmNa/BzZs38R3f8R341Kc+9XC9/5+gBAQMq6lYZumnBZxrA3Mj/3abxA6QZelhOYi1kwA9yChnEcodSJbPqansDlg9YKzvMYYHjJX+DVdJ3BKDuAhubwVsHg/Y3A4YbxCmC8L2NjA+ljCeM6YLBq+SCBebgPEmI90eJY7JpexceQDGG4TxQrIPDpeMs+cZZ19gnH+BcfYi4/yFhLM7Cet7Cav7jPX9lDOUxJ1Yia0eJKzvJpy/wFjfFaI+ngu4VW+i5WsPXDlGAWluGmQugAAwqQ948kzEFjHNGRVQiD6rdmZKQrh3mmbWiO+kPuDeLNcELKvrQCuxvooyZ6yNIYKH2nzWLLJywHcizDJjquWbacRyPTMH7f1R/cc2l/0xZz5aIfOYX99e40Gl+b2aNlvrKH/v6lgTz6e5hxFkHyBxBmi59j1YV7lToYyTZePI/TfrLdMy5DFlIGrgd/0LMWXee4qZVcqJz/QLgRCis/as/hpaleejbdb82q2BrLKWnLKltz5R1oD/M9Aq7FiUJErPg7lomDASgWldLIDTSi2uzkTgSAPAK8haSZDMhGsGnycBrtRtHUEsji0bVRgZw5UE9h0ugXglvGTYSH/irliGUSpCifV3uOIci1EsxqpB72+urrXh4rxhzLwpWSZCV8tvyns8zB+zuCYpqcBAlfY+u7Dn6wiA8iePlBFg1ld5vgAo7uzB8V2bH9SZRzYH23kjX9jXPfRnpeEJ1wWy2nm6VKf3uzfPe33yx3qKxdqtVOu55+P2udsxbK6d/S3yUUGTSb9T4PyaHlZYOJUvjZIGA7XdpiI0Gwwq+0X77c9lgNyXTqysyrqzUWruK5QYNDLCLmUeQZMco2wlK3tqkOxf0ypgugh48NSAO88MePDaiKtXRexuUlFuWJBqdV9Ka5FzwlY6lGMLqzxBLO7v6QxINyZgSBKUOgC7x4CrVxO2ryJsXkXYPE7Y3iLsbgbsbgSMF/J7WovMs7sRkM7M+8YGCHnvyaaEng2GEgWGgnrNuc4lbO8PmLfprJFzQH6gyBemUD/CEriXwZCYQbtJgCTABXOPYD3G2x2w2co1owXKDHWAeGtzUJfCYQD5bIQ5yH0oMtBurK/VT44BvBrE9dCsssj4Di0CWZ7nVnP5YUvjUrhP2ZFLb+21x3u/2/aWeAZqWbC6PtTHbT/nLYHzPXSf1x4rPLKZq1zazXuqqk+O30JkKW8cIPWPEOa1XFuEestb3oI/+qM/yn8f+chH8rkf+7Efw4//+I/jp37qp/Abv/EbePrpp/Et3/ItuHv3bq7zAz/wA/j5n/95fPCDH8Sv/dqv4d69e/j2b/92TBY4+0uoWMysYZiqIHPi84uKIdjLbk0bsxCehWOqrLEqQdplqcvEsQEHTBDJwBchT0KbeGkQgWK8CNjeJFw9Sbj7FYQX/yRw503A5gnkGFnpnLF7PAFnCTSSaC6e2AJBNBa5bTUT3t0kbG8K8d/dUmBMCX72cx9V4LlKiFeMuGGEbRF+4k6ODRsB4s5eVEBOfduzjy3KWM4IeuOXfnBNqKkt9TQSPcYOZO0HMYoPuSHnpqUIJijon2ckPhiiWnPxEDMzrEy8VwPYb0iMmVVxS4xo1/Mrg1haL4M1ru6hvx4Y5UGizFwbInhIoFiq0/tdnr2+vmJS+TndmvFCun+Gpk0PCHtBvwLV3PW5Dy3YxTopDOzSWFk5g2EAQkgIgRFjwipO4vbUMU1+pZYTn6mL2b+EkCrtdNnsUrMumslNyJrwGuAt7VTHF9ZxT6D2awTADCxOg1nuEsYLYHsbuHoS2LxKUqEb8MsDizXwwEASiyycS0YRyzbl4yuktYJj54RpDbBa9BqYD6jQpEJSGNllqOJ8zsCtuC0AmFgDz3lNv3D1cXAVt7EafXHjWB2uNOh2rfKalBwP0E8or8m+oCpVkW0InDBr88du7EMm5A64+VXdSw+Q65I7Pps3x5TO3Kv7QVX/vqi8pu0Xmjr+Gte32VpaeL4ZeNd8b+vM+lmBZVx92jQgsqy5jEgpA9en8vIsIj/sEe3aNZQBbPTXvdaplSWNlaXtV69ZKlBL/2hMmVYDEBBrLeDR5ZMB97+M8OBpwoOnCNvHCdO59CvsABqB6WbKMXkBYHWXsnshB+ER4wVpCBbt/hbCd5gQ7wcMl4SwEbdEUsVHWomyfneDsHtMeNnuFmFzm7B5TACuq9sR28cCxnMJVO/DTIjyJKmbYBn/zIucgqNyWXS/5YKF7+5dZHmlxTB74g0DZuVLDkTquhf6WFkWuH29yrIPnZ8BMQooZdZVQMk6CAh/WQ1l3nmZKAS5boj1fXzc4NxxVeQbGObdC71M6ICs0oeeUoYOWmVVCv22OK+XRR4C1xdyz2/Hq5txbq/uw562Xb+9d04vhIGXiXzfKjlK2/Oyj4HA+V42XInLby7HQXCZEN017Xf3gBy5GAocWa7tZjgMQ6Ulz/dnxk/8xE/gh37oh/Cd3/mdAICf/dmfxVNPPYUPfOADePe7340XX3wRP/3TP433vve9+OZv/mYAwPve9z684Q1vwC/90i/h277t267bnf/hJRJhFScgDGUy+sXUAR0yGkqy8eYoJqgpFtAnDeZup4H8IkmmwUCIO9bAhshms7Ng5jqhqmDXBnJpvJK0BsYbwO4xxvhYAq/F/WB6EBGvKINXdGMENlEm1uM7hCFhurdG2Jh5rQBUaZA2N+eEzROMNIigsLprrofyLAnkXAaNgJZu5w0ZagZZBb3Vet2Ahrm+flkwmSVPnPNBQo5FosWASKoQtHl7PATg4gwYImhUn/Fpqqy3EIVY5/cSAvh8JZZYRnRNI6Upc6UekC3+MlDazjEjUDq/Yq1taIWKo0xp7dmy0NtcWx07rq2XohRCWz9b5UboBLD6uCPOhEo7wUpYW2Ekay4qMM/PGxTgOkLj4XGxzoplR2LCRYwJZ6sdzlcj7p0ss6py4jPzQgCiZaf1a5f8Wi5zvtqYAEDw85dKMhADcb32TA14Mo/xG+oljW4LMJiChqA8rlhhQWOa8M4SjSh4tUrq8wfgbBLX76uQte45O672dxwkM67Fygob64AsvSqNOco5H78KxH2ydbxSsNRdGpp834Zm9PgIcJiMBgKvYv18nFx7rHuOUNPKIQoP8eQ7GVPVyZLnVMh7loqeekUJ3PfMvOuN8Hwg9pc5+DO/6OCG/qUszb2qTX7Dh/L5lifaGnQ8ZXZ84druszq+lt0J7VrHmwgFyBrChCEmbP84x+5U/vjLwFmwpg74W5WF4zOLlepkUdpWMYGBzlqhRTpXdUNdpH28VA4CZKU1YTwPYhV1y+7DGG9I/4b7amk7EZLFW7SYv1dUZcDNGdRIvFJW98WSt3QYGuxdZJt4JQHmadLPBPFqIcqhUDgSdrcErOIgINdwKZnX45YRJlXCT+iCSWUQCFX8KhuXbt3SX6EhS++3fNJYQKqKbh2y0HJAVmWVFZTpEuk+V+eZt56yIPA2P4ZYwKntTt0HWbIfjhNovXJzSy25BmcqHQOqeFvW/8zX3Hz04Kyfx48CZFW/e3WaA1mevN768+eW4mvN1yXKWiMU2Zjmz+VjW/m+V0p+XxdOBmTHd/ztDcyLyOEi2PXHZCyJtcV5jzkHvxpQ7MhybTDrYx/7GF7/+tfj7OwM73jHO/AjP/IjePOb34yPf/zjeO655/Ct3/qtue7Z2Rm+6Zu+CR/+8Ifx7ne/G7/5m7+J3W5X1Xn961+Pt771rfjwhz+8KGRsNhtsNpv8+86dO9ft9he13FzvkIYLBDWFzFZazr0wu4HpguFImNYBu5sB28cI29tCEKdzRloJaDPdmoRIMAFnEzDpDB4J4SoADAwPCMN9QtxI6vK4E8KbtXca1ycN4tKRVsXkT1w8hEDH+wFjFGEinSdQko0s3RgRIiNtA/jmiNVqwu5yJQKIm/RpkHgjmIDdLWD3eAKvlTGFiLghjFupSxMQ1WWkxFVB7XZhtGqPdHAQxLpG6V7TMOy9bUcCx0F6e2MNpCQWAJdbOT9EZJfTDFYB6XwAr2LRkrO4OgJJAvhWLi9KoEPIAFsOprkHyKpAph5hOwZI6VxnpbWEklhnmAu/nrD7TztXNVoGejE4Ygtkkf9O+VjWKFhdB2RVYJcj0DXYpcfM2tEEcRWoTVgHcfn0cbKca2HhrRIri5lwtRsk8+ep5PI/gs8A//PzmlWcwGGFOqhv2ZjVoAMAUFaaTGv9OxerpjTopgIQRYZtHgZTLpBsYDWIbtiRar+pxD5MjXDv1pG3YLS1QtpOUqCXdS8LArBKoADwloB1Er6zC2iD/OQNUQLSucZ2tDVJAWEC0iQPY0oUACUGi2E3LQC1Z1+5VF6y/A1uo31MXTa3i9UA1liL2YU96MsPgAfQWN3WvYBJSdxwslbV38TmFNycsvtTqcPNXJs9k/t+NAi1KGTXfZC+v4Tv4QCSuA/I6ltrad2F47l+hx/V96jXkixIO8euHdlUUdM+UQIDGKdQ4gadysuuBAC8SiAicA+k6NEXb8Vi1jEeCGiDvle8x7ejTXQUndWa8tiRAljeeoSDuBSKcly9SM6lgfWLjPUd4PJ1hPEGi3VVhMT1JeFrNBFoEl4VtsjAVvZ40RiNac0YbwLrF9UbZkdA5Lz2kgakN2ALKDJKWkmbwwNG3MJZebV01O2Jt61SZf/vRZrG6MTE6o9vDRKU/bYACp25MFP8cN0v9Sip4lWNUwGp8pxxoJPf74+TzKdJA7pbH2Ksn4moxNIC5LqVwhUpFWssk4FWseZVjTXWSwZkLfGGNqlBtQaa/QvN361de0yGQza+nttfqOcVLD0Zr3edTt9ZmAnfz4ZfVfIlI7u2UoLs7/y5oFsSuyYrOzl/5n3LNfdj1wKz3vGOd+Dnfu7n8Kf+1J/Cpz/9afzTf/pP8Y3f+I346Ec/iueeew4A8NRTT1XXPPXUU/jEJz4BAHjuueewXq/xxBNPzOrY9b3yoz/6o/jhH/7h63T1j60EBLzq/BKX8XZhCkQowd+Rj4EI43nE9vGIqycDrp4k7B5Xq6hVEiFiImBUYnwZcpYPuhvFxY8BPkvgV2+FqD6dsAXATJguI2gTEa4kcG68pOIC1wgWtmjihgC79XkALiYwJckCu2LEyJjuroBVwupih929NegySsbEcwHCpjVh92ce4OJiiyEkfNnNB/iKW8/jIu4QwPjc9iaeffFJvHj/AtNEmHYRzAQetUMJABNWnxvw2v+csLqX6gVwzUkNOGLR0WxkCy9XMoHxcavyxrLlTjy7drq1xh9+0zk2T8hOkVQIDKrlyc+in2QCmgIglsnl7HnC0x9+gLjRh1AmkAm0AVkxFAIaa3dAA7LaoH2tpZLcf/849oh4121Cx2lzm/D82xI4ajYtLzRaWkpD5VlQKvJ1FOAc7ge86vdFs7Wo/bbvPSurHsE1wmnzvx0jE8D9mJnFil6XfcBXDbEPjhhbRhoVLAyQJg32HmJCjAJkTSlgiNPxQt4roPyP4jPA/9y8hkA4H0aMeX6Sm8NuI6cbtDQQpjNxzxgvGquowAISmdWVuowjANgGsZJSYIsvNA5GYEwAGATeETAFsRBWwUEqoaE5TtaWi+XWAwnT0WQIiBAgaxOAwAjDhLSNwBgkeKi6gHAEptfssFpNCMR4bL3D7fUlBkogMB5Ma7ywucBmuxIFcAoaaJ1K/0AIDwJu/hHXAsZD8pr9VllzXjO7rkdb97SZziLuvnHAeFEEE7jg9bPr/HeneR0ugVv/z075E8EGoJvdyQsD1umKvnY2+24OHFWuYYHFJJYVl0+xI+a9NlHxIOqMS9gSzj+viWg69+sBdOzHh3rnyndbqu3YzKzXvGDgzlcWk+4hsmImnzNJhESBorGygmqDGAARX8vg8FS+9Eq4GAUcMNeuNl7WQ5Q26Hu1/8pAGBbneJcu5Tq2Xw3ZYjitKLumM4lyFCTWTyCRW6YzyURbgHnktW5Kk+zWNAHTSoRrStIeRwG/to+zxNBiAq3Urd2Mi0xxQsjKmzTIok4qqNNGQ65oP9KAyoKYJqWhgcEd4D3TEudW5uvYb1OA1xe7oXReKt7CzQLqdy2wFqyy+u6IapW1VWZPVNwHLe7vNKIKrWLhVOxYYvkboipfqIBWRCUwvM9amFjuE0KJr2X3DyReLaReK0AFZFW0+yGArL0K/KpdGySa1Wnf5ez6pXb3lX31OudmWTi9R1PTd29NVeaTBnCncr6KrWUWmj1DAc/ruMhfBhaXPuny/f+zd+bxekz3H3+fmXmWu2ffRIi1ltiiJdbIRogt1K6UUqUqRamlpaitqlpKl5/SRYtSWrQqKYKW2tVeKiQhEblZ7vpsM+f3x5n1eZ57c5Nc4rrfd16T+zxnzpw5c56ZOXM+8/1+Txhr2H9Y7CGrJWZNnz49/Dxu3DgmTJjAxhtvzK9//Wt23nlnvz2SLam1rkgrZ1V5zjvvPM4888zwe0tLC+uvv/7qVP1jJWsX6bQjASvxUOa7GLoZm9xgh7b1bDqH+cHX61yUq3DazKvsTLPZzs77Jqu2ws5r3LTyTx4zMFHaplibwq01A+jiABcyHqm6Irq2hGWbeDyepyjmHGhJYbdb0UyDHnipIOiif2MOLEpKCgKRSWncghlcpJvyFPMOFPw35doM6Iv1Gu14fHv7hzi84X/YKCwsbL8NXP9m6eFR1MlXgm7sTPaAq5bszmP/3YlUe/evDsPOpZqLYVyQivt+V+lEu3yqi5cZC9qoAhEmrHRUfilrs9v0l/j2iL/7x6bMTJIoXP+q9mJ3KrfKHcdFcfn8GbQ9sx66YN56mGdTFbkZKoyQFd4wkkE5gxtH1ellFQkRq+INWrU2Iiq3q3XhegWlOsU3Jv2dQxteqTi+cjzMxJHB53g73LlyPH+6aS9qluqqHUT5wCExCIhbg5TlD/J4gehlR4OCsO3Kp5Qlukkbq6uonMSgJLRI0dF3239L7sfOUv4MhqYf1v5S2Z79mXXVz8Cnv69xLJdSRawjn/DBwqJUayx+i3UmLZjRyQTCVTid/ia+hRXKf+gOHzDM22SFwk1Z6JRJ9rLmzYeV9kB7obWh1uCWLMjbWAXlzzTo36v9N23hW8fgHuQRs7rSaL8vsrMlU5arwsGJtsHzr73dRv6PrTPLsFD+v6CEsKSw3yGWRizfE+0b8F7z6Mq35WV0aRWaKC36o+IVCQupkhZfFxC/EQRPdmW7AfAcizGbLma3+v+Fq4yUR+huEj98r8rNRQOPrdyMwgeNKDewjCDRlwDJFzlKJS2M/M8VgV/DdBLb9pRVPtD767007DT2bbbKLFl1mZTFw4+lv5IfxevPjMXqKDtnuujzkgP5aF3V/CreTmVplPfHyf0kPpfvOywrduIlLLV8MSuMn6UjfVL6ms8sNuCk/YG/6676uvP7EqX9mbzjVlpUOae7dZnq4jOV57a2lT+gVdEzmRN4sPjfbb8fAT+GDqElJtpYXZmXxuaUtwqgfJdzk1+ZsXDQB/npqmSeje28eQkThozwn888fxb3wDU+8DTQtn8vjYlbgct+MBO8mZDECEhB/xcct5u2sPPGQjKMm1VmjRTeI8oG+UEbRp+TcZCCkCmqbIylfJevhPtizFqqIl5WVwRl5AqRBVbJJbTcSjmRKGVbvmDlW2YFsxr6s1gSTJJV7rERnLPxcyzofzJpQoHKUoRB4ZWKPKCgupAVD5Qf3gSJ8oeN1cWxd3Uux+sXHEeVPD3qz7p7PtCxOpR3XlWuuco+PLbOijqf5NipeszU8n4t3MaKvkcTEZTts9xgIlGPqPzQU6Zcc++5jgWsgZthnLq6OsaNG8dbb73FQQcdBJi34iNHjgzzLFmyJHyLPmLECAqFAsuXL0+8NV+yZAm77LJLl/vJZDJkMpm1qerHhoViSKadpSkLx1eGdexiKtWmyA1J0TbSIjfE3CgDN8LshzbKhZolwZThOjwJPP8m5MXcMLQygQw9G1Lt4OTMmZFqNT+jl0pTGOhRqnWxG4rYtsfoEcuxR3ms6KihpaUGmjM47bFYKr6Ln3Z8NdT1Y5i4ysQxyXhkB+bMcXWa6WqxtFmvNF6tRmVdBtttNFk1lQ20Gg9OA1MdJqhvxvJnOKl+Nkfqdvldpnq55W86uspr3qImVfbwrXRZ/mRAXvN5aLqV4XbXl5TrSzaen79czHO1pj6Vpy1ItkB7KmGRFcReqzZzoenE/Ru7St7UKkSsajfbHt7MK7YLE4zZdYebYbRT32U79IQda+fxx/SksIMofzteofzH49CV3XDj+XVw/IEVVsKqi0oXxHgZVvTAYt4gYN62+Q9JiThZinD2QhThG/JwgGGaC8vysC1vta6T/sYn1c/Ap7uvUShq7SIdlsKKW2L5eCk7FLFKtfgWn6avcdqUb/GYfDiOP6yE5uvBc6EfMNcugPbfVOuC38dZNm6N6ctU2sWyNI31nVgNmlwxRT6fgg7bvB3Hv4zjYpbyK+C/GEErcDROjb+jkp8pfp9KaXA0tVaBrEqtqrG6JWsX/UlOVMWsgnG6fBDtKn+1XfdEyOpiIAPV+5pau0B9NxYXnl+Iya4rxDwPSNsuoZan8AezRA/l8X4hXr+gj7TKDiJ2P44fV5w1tkCtsp1VhKK2abTSa1ioYb3Ucl6zx4bfu3txU2GRVX7Msb9hi5cPDog+l2+T3E/8WQeCWCJh2wexRQg+mz2G71Njl4+GsP8RPtuk06XIMiZOzKJFl63X5QP84CQqD/xO7BqocvupEMKquF8F53b4QtGKPb/FyrRKOrL40BgvkJR5yVKqAS+DiZFolV1TSqOKVhgQPggG72bMfV7b5t5hFY11qudAYYDCS9t4aQ+VMu7vVoEwv3bD6hNaMPv7DC1VAssw21h5BuOOCgFaR+vKBa14PCQFkQCE35aexoqPi6r2F1D+Qr8aidhcXVlthW6J2rx5DvK5HhRjFlrFkhG5bN/iKi52BedEbFb2cPIrpcwgNKAm69c7mKUXfxZ3O9FOgLHEcqzonOtCyEq6yMfO4Xi8t7JLpacWWRVpXRH8TlV+r24tisvrUO1ztTKDchN9Sllfrcrqrqiw4ArSq3nyhNdt/Bkintd3M3TTRN5JEJ54oRFB3Ksl2IcV+9xD1sr+NJ/P8/rrrzNy5EjGjh3LiBEjmD17dri+UCgwd+7ccAAxfvx4UqlUIs+iRYt45ZVXVjnI+LRiK4tNapbg1tjR1KuWGVjkh2ZZtkWG5ZtZdI4wDx5Om6JuoUXd+xa1H+pQyNIWYUwrN6XQQQD4rIlx4mZ8N5FUMEiJThwrCFaYh8xSi5r5Kex5NRQ/qmHhokGs6KjBsjw2XW8JgzdtRm/UQanJw81o3KzGTWszq1TWNT7jjkanPLSlsbIlUqkShbwD2p9lwNHojPYHFx522iVrFXqlPT1HUWiwyDdauBkrjAPV5Uwp8YfCIEmTuIlXmvX6HWeVMpM3q+p5KvalNdpRjMk0Y3Xzz0Zho0gpC0uZv7ZfeQszmcCHnQ0m1lZM0IoLWdEMVComvPhvswKBq+zhIJqJomzygfjNTHWzlLVPYruwIUyC8qpbAqwuzW69eRumovqHxxO7EXd9rLFtVFla7DgS21ll5YcxsmL7ClxCg7d/QRtZoOOBC/2HKiC0xgpimShlRCwF2L47iAwxukb6GYOlFINS7eiUCu+L5jy1KNU5dA616RxsYi+CeRhPtShSrSbQbSBk6dg5H72xjh76Pdss8fMeCB92ghiHdofCWWlhrUjhtqdoaa0lV0yhlGZQYxs1gzvQA4t4WY1naz9YrunDcHx/i1D81SjHw7JdY5WlCddrh9A10rI9nMST0ZqjLX82xEz0UqBbqtwP4y5sVFkdKAqrvL5V9a2rYkGT04Hq5p/lL7ZS/pg0smHzb1e0F9PJvlERG/DGHvLDv2ZFhagVvx+H/YlKDByq9hk9aZNqbR6gCS3R1oYOL1055Xh8/8T7i+iYEkJWvJ6q6/VVP9NNfxWzCE7Uq/ykC342hS9Y6fCUUsrzf0ZNlzFIhc8MKbvMIqurzz7hM65HdN0GXgCrELKqzu4WnMOBkFVWZpA/Ee4j2EbH+hhfywj6nyAtmL0wfMkfs8BXJVPJwJXQuOcRhYrwXQOdDsis0GSXedQs9cgsg9RKC9XuoPIKNx0JxfHnwaAPCy1x/XcvgfdHsD9TV1OGmcxLYbk64YJQ7X5Y9R4ZF6SsKmMhRWVaWCBUFU9WFfQ9sX+TX8UFMq2NcGXbhPGxHCf6rd3YLITBX9dLWm/FZzMMZj0slkxgeN/NUGdSRshKOdHiuxUmxmflQpbvEVVtBs4eCVll53RX/VfC8KFaPxd8Lu9nu/pc1o/0mGp1s2PPbmV5y/vq+AvNSIQNEqvUMfiqddIwoNziyr8eovFaZBAQL1MFLzSDEC2ayCigh6yWZdbZZ5/N/vvvz5gxY1iyZAmXXXYZLS0tHHfccSilmDVrFpdffjmbbropm266KZdffjm1tbUcddRRADQ1NXHiiSdy1llnMXjwYAYNGsTZZ5/NuHHjwlmn+iKT6l7n15vsQ2apg1XycLMOLRtmad1QUWwwAdmzSxXZpRqr5FHKmrfkVqlsQBH/4fybesK6xIqdV2UnX5AWuHVYRUVquYVuS7OyxYHGEu2dGUYObGHgsE7UcM38ZQPpXJlFddrojIddW8IrWWYftsZOe2RrCuTzKeMC4vgWJK4KXUBU3qakoN3LALm1akcL7U+3jglYnFGk2jVOzoveepQdfwIVdYDV1lV9jrOoCDAcpIcmz1AhiIX45r1eStFg50L3yup541ewF9bLQvnWWpqlbXUMiZvfBtZXEFljVZm5MJpkIMhH9NnfT9WbbFC11blxhmXEH3Cicmp7QdhsdbPG/aXsplf+Blxb0QOS9h84wrxWWd2qzVpYJoBVClk6KlfF8vt+3aYcHd28LQ2Ob5FlxdwKLeP6a1keSmkc/2/gatgLY7LPDNLPdM1GqY94adAm1HVYJni3Y5Ef4JAfgB/3wzyoOx3mYdpzItfyikF3nPhbvOAaKM9S8SDjP/i4ZtYoXbDJ5bOQ8SiUHBqyOWrqOqBes7KzhlLOgaJlro+UZ6xOASxQtsZJubgl2zxnB9dUENcLwLXwFBS0DZRYGxQmVqFnY+6dwdv6UixAfJS5WgGr39eoLgore7js8lbgD/60pcio0ipuGdHa8EWp0ihUaK3VUUhTWx50N7yPxwcBQd1iD/lV3RKifXbbn6zFva683FQvCJt57RDOrNTNvhJv/Mv7ouB6Ke+fKtqoLJ1Y/1OeL/ysK9KjAUPwAiV4UeILwxaheGXFXAxVbL/CZ5O040YCQ1fEBKaQQByICwLx87U8H5QN+KN7hXasyNI3ni/Wz8SFBOVbICUtEQkFrmBgrC2/n8O8rLFK5r7tOcFgmTB2oBEtYlZZKtrO6dTGxdAXpqySxs4rnFbz4tjORwfmpUwltKV8j5HYsVi+AYIb9bHBvi2PcIb6dJtX4dIehUspa+N4/xE8v8cELe0oKJG0plJmzKRcMA6jih4LVj3JFsw+GIhOKSeacMS2zPqSG7m2xmYWTJxjWoPlW1kpFcXTCggssCzLBJn3NGjfzjgIAG8rM3N8VzGyAospK7n/LoXY+GFWTStrC6usPMp+u3JWldbT+3H5c0XZ9/g1WTWcTLVrGfz2rtKfB3mD/fh/k0YBQVuX1Se4D8QtuixQnoossGLaRejqS1CGKUx9XDGzFi5cyJFHHsnSpUsZOnQoO++8M0899RQbbLABAOeccw6dnZ2ceuqpLF++nJ122omHHnqIhoaGsIwf/ehHOI7DYYcdRmdnJ5MnT+bWW2/Ftu2udvupZ4u0RdM+i2h/fzhWUbNyrE3bRi7Ul3A+SFO7WNH4nnn4zjfZ5kctRD928FBdbmobvj2Pn2QVA/Tohqx0UJaO4v4oI2x5LQ6FVof3OtLUNuYY3tjKJkOXkh/osKyzllzRwfMUOS8NaY9sbYG6bAFPQ7FoGz/8jDkGz7VxvRRWh2XKLig6vLV3zenw0mFAYrRvnugpLNfEDjMHRKzTXY3Cu7sm/PLKY3BpX93vzlQ3eGNRrFWs5yyPFZl8kPDwEnHEovWRqFXUmrb2LEODjriKmXckwpQFfI/FGgjFmPhAJPHQ0X1TrZIqN/D4OdobAwwXK3pgp6zOKuqoKuJjld1E4wHeK6y8Yt8rhKzAKsVv7/i+g7hZ5mHJH2j41iVxIcuygyC8XswiK3I3TDsuti9shTd4QfqZbhhiKzKbtFJsqUd5kBugKAzUZva/VptUG2RWmnuKm/GvoZiQVdGflF/D/mfo4r5RNkCpeJnigc5buAWLFQWbVLZEfSbPoNoO3KxFZylFybXQWlEq2mhb46Rd0o5rnpddB8vWYJtj0NpC5y1UUZkHIdeiqB0gmnFyTShqOzoOHQQj9YOoxwPtdveQ2hXdXcoq8VwYS1dVEqtsrjVuChqt6MWRKqucjtkr6dj6uKjlaSgUHGrL9l8ZFDb4feN9SddCVqUA1P3x9JSqL2IU2HSlKK5G2YFIV21f0H1/V5Ze9dpRsefzsnUVAXMr2quakBW8PIkJWRYo/yEwelzw+x/A9vsgS2m0uBp+ZjGWmEZ4ScSH9J8lw8/B93LLltizTnnQ9wrXQpXMa7xLrOiFop8njDlbMWNqlTy68toJBtBaGeGolDWfbd8YwIhVZiZ47ZjxjvJd5D1f/AoMBpwOU3CxQaEtjV0wM/OCH7i9BKkOFbohWkXCZ2yTx1hYBUJd4Irv2Qrb8wWvovYnLTFtYpU0dsGrECJCoS5+P40RF7rKqSpoQfRyPnwhHt+n8l8+dX/9Vw3+7moT+D1w/QuCwDu2saaq2CC24yCoeyB6Bd8DYSxupRUIXHH/Dq1RJdecI7ZlZi60fPfCKkJWl26F5dZcFe0dbVd1jNTFTMPlRii9/rKgWt8X+141LnLYv0TrwvOpyjlRfgzaisbDuqzMMFsYl7l6/RLbaHy3Q2P8ESpPKmapFb/mV0PEClgtMev222/vdr1SiosvvpiLL764yzzZbJbrr7+e66+/fnV2/akmo1L8ZLPbOeaIE2hfWguuxuq0qHs5Q8MCj/TKItpW5AfYFBqV//BsXNPcNMa9IzCvC6jyZjjxpsMXsQKzvSBfwuJEgZfxrakszENMh017ro53VtQwcEgrGw5YxjYD36ellOXd1sF8YDWSsl0as2aw0JLL4Dguju3h2C6eZ9HemcZVjhHlfBPCrCqyNrja492OwcYSK6/DjsBzoFhroTzPCFoxIStufhy1m+5efIpTdiMo/xzMCtjtvSmwjq1VDLA6sbq4pALxKhC1ggDFFpZJw6SV2lKgCzH1XyXNtbsSsvzOMJgNJp5myumqDVb/ztvVAFgr06lnrLU7FwDynomJU24mG7pUxtfFbrYJgSp+nvh/E4MHqzw/FddVolxHRxZaCuOSa+todjgLP0aWDv9atm+BZWkcX7xKOS625ZnPtkvacv1ye7sX7JtIP9M1jrLZd/Ar3D1uB4rtKfOgV1SklthkVmoTYFYp457uv18IzmsvcI/t5oV9QMUDSnDOx6/3snXa1uF30FBUFEsplnc6ZOvyDMh2MrymhbznsCJfS6vKYFkeGceI3/mS7VsvmpkKtVYUiqBVJMphg8PaieWe1qwo1vpv9oOBnz8oSalo5qgq97nkg75epfgUopJZy97RJ9+AdoW/zkspsqqI6iJKRCBeBaKWjqVrNBaKItrMFqndWB9RNggIjzv2uQshq6sH2rWlywdlf992l+ZxPafkj75XZYmVqJOq/FytP6xYV75dLD0pcumYBQHRA76CUMzyPyv/egvjMyp/1lwwf/08tjJ9ztoFFxE+7dhKV3+WCAb9MeEgEGkAtG/NFbduqbi2KR/0m5AoCa+AcH/BBiTS4sHLdRcvpoOBd2hRrKGUVRTrIitaVSLh1qRtY3WlHVOA8mP2eCmNlzaWV6oIpVqNmzWuf+kVxv09uO+D2aedx7fw0glXQs8xruleyliFeRozw6JfZysQirQObwZWqZt+oqxf6NG9MxjfKIwN1qossDThGMKEMIm9tK+ybSh6xONleV6038B6KrDM8jzjYhi4HgZB3MNjUtH3QMQKxmmBwBXk8QUrc25aKNdDBxZZMYuv+OzuQZ17Et+tS4vCWFpPXvZX9IU9pfyaCDro8r5/TfrQ4DACjy6/zPjzXnAthd4+8XaIjZuMF1hlJeKik9LmGS0YE5W3n9KEMyACkYGAE1z3ZcdtBf1erH/7uCyzPi1o/8IoUez5w2QvYxc6eGHREB7w74AFXcPMEU/y6/d2Y+B/bOoWuTidLsrVlDIWxYxNR71Npz8NrJfWRmiqcbEc44IE5nC8koXudFB5CzunsHPK3KRj4k0YZys+uI6fTJb/ts4hnAkkcJ8IBunNHSnaVzTxUaNN2nKxrU7SJQu3oFjaZpMv2HglhVIlCn68kmLBwm0FVSyY6dlbLShonlkygobOZWvcni4Wr7/fiNXSiS74s475rtEacPMeqjN6cA0esqO3Fxrlar/jSJ4UuqtpaX0SQRoDbGWOz7+pa9csyvUiX38PVNHc0EtuhiebRzLPXtHlA7Ybu6to/+4azGroaouVXg32whJeriOqr/KjXMQFrOCNWkzYCmJmhTei+HTqrGIAuxo3zooggpQ9wKcsXlwylAfy7/e80Cr8dcFGqBU5dC72cFA2kArODcB3s/SroWI3XUU0gA8e0sIBg58/sT76q8q+B9eUtmL7sPzry7esMjdsHeYvOrG35TYopbBshWVbKKWxbUXKVrAyT8nN4+q1FwLXlBJm33pVD0f9iKAtPHonJuCaoNxOFrWm+a/vWuei2KJ+Hi8tH0N2iUW61TP3Pd+t0E1ZFNOKUoYwCLy2NaS0P/tg1NdoTxnXP1eZWZhKmLfasQeSMDZJ/MEt/ldF10oYN47kdp1FTSGboiOdxVYapXJYnot2FR0FhetaaNcF5Zr6WeCVFDqvwXPNvTavwIX322vIFNvWuD094KMWG5XvhGC2LCt6rvJKfj8S/gDmvyjFf7voVT7u6dggoMdYyu+j/Dp42g94bPoZv3szD/aYOLzzO2tYrnKJWiXqkaiZ8o/bf4DVik5SqJYCulSIqhu/j8cf8MuEregerBKCZrjvNXkQr0aVAXny4VuxuD3Df0vL12o3b7U0QC4HxbLnhrJzPfpeKd5VFbGC7WJlrErwSlxP/vr4Z5SOtrdM6UEZXizGSCRseSi/D7N8kYtcHk/n0evwnhbcT6Wv6X0yTglUKikQlFtlBTPBdUGFwBQXvlS03nNUVE780TAWDytyRYqJWOX7DvqK8AWlEd7NSxIz+C7VmolNrBJhiBbLNdsG14BVULh+XF+3RuFmNKVajVXyXeEd0Cmwcv4YyjH33iCshFujsYrGWssqEL6sV65vpYXJa+eMcBW+/ND+8cXdEIlEj64bOmiv6D6hleryvh7kjbYzGwazGaLNxFnB5FmhxZvrRRNqlVsYKboWxYKxU8n1vVT89MBCq+QaASssS0XCVtyiLwgmHwhS/gyI2lKoIBh80Z9aOWHJZ9ZrxzaB4OMzF8bvw6sQsnpijRVuE6esreJ9QLXyqopV1ajsnldrHGbqUn2DxMt8qLR2DLcP8kXrki+0qLDkj/dfwRgzPmlBfGwVNxgwFmHGYjF63ojHHNYkYg7b/kVVLQRQFyjdB3uThQsXfqqmSxcEQfissGDBAkaPHr2uq/Gp4J133mHjjTde19UQBEH4zCF9Te/Q0tJCU1MT/319OKcuOhL71CyqIxbDNhCvlAoDaofCQBCjyLHC0BphfNZgLGnHXqbGxG0vZVUXwYnSqvtV+/nt5AA6sJRPWIwpcDOK/EBFsc7MQBjMUmhEfijVQalGY+cUbq1GlUxsnlKdeZGfajWWWqUaI4ClWhROO6Q6fMOIGkVuMBSGuNhtFukVxtXQDgQtMFZNsdh6WhlRTblgF4yFr12IXoRoW+GmwC5Cqs2N2oHKNgnDBfkvoo0gRXJGd0/71mpVhuza1C8hZpXli7+0D37rsKxQEItNPuV55iWS66I68qicL37HhSqto9kJS6VI2AoEq7iQ5diRZZbtW3d5XpTHP0Zsy5yj8XLSqfB8NRaEJKwIdTVLwrURslblUljNKms1xaieEolS5S9b4upxJGJFwnBc1IvqHgivkTdPcN2pRFkJ98IyYSt0uy2z9i/3kAmD0CvQwSRAcSMA/y+BEYAdGQKgQBc7WHDyJaxcuZLGxsZu26lPWmaNGjWK1157jS233JIFCxas8iA/y7S0tLD++uv3+3YAaYs40hYR0hYR3bWF1prW1lZGjRq1jmr36WPQoEEAzJ8/n6ampnVcm3WLXEcR0hYGaYcIaYuIVbWF9DUfH4HlbRjvNR43x1JGLIgLWQQDexV9jgpLhvaICVnaNtZawSDbc2KiAlQIWHFrLRVYQykVxtJKTLjkBXGE/c+OEaKCeLooPz0VCECEVlcBXgq0o7E7TKGlWt/dcKVFugWcdo3tW2OqkhHMio3Kd0v028IxFlpWMRCZfNd0f//KBduPkRXEygotxjyN0kbQcizli02Eeaq1USgqBV8Dq5bY+uo/OsabIB6+ahXxfruMxxVa0viV84xlF64buvmRckzcrOBcsX3lIoiHhb+9pTCmoYDWxnUwbrEVuCVi2gvHTlp0BS6HoYtsUsjStlXdGgu6FLLWxK0wyGesC3u+TXL7SlEq7nIb/141hrOqYtlYJqYFVn3x7eJWf9FxxD6HIlfc/ZTwnAqvOU3koh4/h/11cS+xeGiXQMhKWDpXO/eUXyHftFHZXoW1dHf0STHLsizWW289ABobG/v9gwNIO8SRtoiQtoiQtojoqi36u2BTjuU/mDU1Ncm54yPXUYS0hUHaIULaIqK7tpC+5uMha5co2P5IMwhJERcIKqynYgPmmDgQBXguswBRsXKJDdLLYvWE+UNrIxL5lI7ciuIuUZ4dlRffr3JjVfUH0FbRF72caL9WXuFlNF7Gd1PSJji8W+vhtNhkmxWpNo2TM1ZUVjEQ02y8lIXnGIssq0BUv5gVWICXUr6wpcDSYSB5IAoQX4JSRuGlFXbOqAo6fgzVRJGg7bSuFJy6EgK034Z+EHsTHytqwGrCVUIoC8otE8CU1qggxpXjRL97IFoF313PCFEljNDlelFZtjJ5XdcEcnfsSMSy7VhMLg9tpwiDxcfFq+C89T9H5yCVQlYX8bFW6VIISeE3IRRVSS//G66vLFj5E4XF1wdpXboLVnWzryLO+YKvCVOgE4Jz4veNt1Nw7pXXvcyyqtz9N4rVnBRkq+a1yoRb3yIrPNd0tL+wjpaxzgoPs7vwOGX0STFLEARBEARBEARBMFjlMVvLBazy4Nyh9RV4WRtVHnc2EAOsmIjglxPErgossoJg54lBemCZYSk82xeBXBM8upprYhArKRhQl2oUxXoVxqxSLjidOrTMUilF0R8gB5ZJxlLLuBt6WY2X9rDbbdIrFU6HDgfSgXWVts3A2+kkEq3iliX+wN/L+LEHy+IWBzGNg3jGkTuhqaebUtgxr89IsIoEh4R4Ui4+9XRSKz9gvrKUmerDdx3siQtdl/sp+SqiYyetsaqJooHLYbys0FKqLE5bucDqxGJvBemBeBW39vItvipcYbtxK1zlTIXQfbvEt+tCxAr3U3XbboSpVaWXC5jh9Ridb6G7blm9KidviMpIThxXuW1yw0iA9vyJ0QJLrcBtMC7Uls8Sn4x16Yt6DubkD2Kthsfqq3CK6ipsF4iYJQiCIAiCIAiC0NepYoGly620gvSYQFAejykM3h6zhkkEgAYjyPiuglHepBWMcjHil1Z4jsZNRZZaFbF+lB9YHsxkWTbYOR0KTfH8tmv2rVwzeUloSeXPSI0FOuOhcjapdoVdwLgQGj86rKLG1hrcaJbEeIyhQCgA89d1jKAVBoRXCjuvUZ5xI7SIrGQCLF8Y81Jmf+VxskybRWmr7cJW5poYlmf7ohtUilRdiCuJwPNBrKvAcsqNWWO5vrWW/yPrTMqcA64brbcts9/Aist3MwSiWFmeZ1wPgzhasZhboSts6MoYiVeh26Gi0hprdUSsVcXG8tuqQsiq0nZrRCh6riK9XJQKjtcmdj5VWnlFVlJJsSrpZljWZvHg7fF8sTIS10Xs2g3P32DboFwryp84HuULWXGXRfDD+EWTFfWEPitmZTIZLrroIjKZzLquyjpF2iFC2iJC2iJC2iJC2mL1kPaKkLaIkLYwSDtESFtESFusO9K2S6etTGyiYBZoS4XuWzpuIaOUCe7uW7qokk4KBPHBrlUWEwtC8QiifPGZs6vlt1yTx82YGFkVQaT9TaySsbRKd2qcTu0HWTeiULHGiuJp+XGyUu0KNJRqwc0YgUk7GpW3SLUoPFtTqlV4DqRbAsErdkyBG6QGN+X/9S22AvEqVdKJ49QWFOv8WRYLRthyciav5ZqBuPIigc7yZzsst1ZJWN90MX6Pz2IYNXDsc2CQVy7uaH9G01VYdwWCWDJGV5l1VRDkPTi3SsbvUgWB2oMA70pF8do8X1QMAsAH5XpmhsVQZA1Es/jkBIEwGnMvDFxhVzVbYVUhq6vZCSsaQ1Xm6ULQWqVVU0+osu0qZ6APzgdVWYdqsxfGBeOg/Pj1Gcaei1+H5a6A/jUXpHlBnCx/u4pzWxPN9O5vH4jNUWJQPw0pz5+9t4riuwr65GyGgiAIgiAIgiAI/Zn4bIaXtezLoq+NwV7ZHlnDBG5c/qyFgBEHbIW2LZPHF3bCmQR9a4z4jHGJQXLZIDpyyYuCV4cB3XXZAFuDthWljMJNR4KU8iLRxiqCkzciVmjR5O/HcxTFWssXxaB9PYVVAjsHhUZNYbARWayCRWqlhZfSlBo87HaLzHJFqh3svCbVrn2XRU3nYJvcYCN2AZEoVdRYBVM3y/WDcwezDfqDfc82x6GdSLSyc8bt0nLN8SjP7Cuc7bCKgFEuJEUzC+qwXRJWWDGrnFDM0n6cKkCVdBQ7yxezdDABQLA/HbmGRvvR4GqsQgnVmY9ErZJbKXAF51e8Xq5nrLVCCy6TX2fTkVVXIH7Ft0s55vwsF7KqBHxPxHTroYjVE/GqIl8VEauaK2B88oKuhKju1nVLN5Za3QlZOmFJGdvGjoSsxCyFcYuqmJCllTJugcTSrLL8+PHrYgKX2ZcOY2yFwpYD2tJoxz8g27fsdDTK0ijbw7I1OtfBvC9f/tmdzVAQBEEQBEEQBEEw1NkFcCx/sBpZuITiACRmhQMiS5eyODpx18JwkByIWGUCVrAdVA72owGzET0UoFwThN0uqlAQCtwcrRKhFZPS2p8pMbL+UNqsL9Yrig2KUp2HnVNYBRVafaiihdOm0Lam1OiBBqdToVzfoiRjLLnsgrFeS7Vr3IyJ0ZVZoUl1+Pt3ddmxmfzGvTE8QmOp4ijclKmvlwI3a9wfnbzG8gN7lQsqXbkWVjNK6VbIillW6aDOnq6Y0XK10L7rYOA2aFuRoBUXo4KA7zFRSwWxtpQy28RFVYi2Kbko10OnnMhqsIqQFQ/4HrqyJYSrVYhY8QDosfatKnCtygqrbF1FMPRqllZKVf9RY9ZSQb5w5s9q1lXxelTUj+harnY8BNeiCkNTxWcfDF0IY9+DWUbjgd0TdSdWRmw7k1dH+/RFrTC2lgVKm+vT/M7aVEhplC96rY73pohZgiAIgiAIgiAIfZgauxC5aQUE7oVBenxAGoulFc2E5g9gY3GKgAohS1uqcvCcGJgTChzaUn6oKoX2xR8jChnBKJgN0MSqMuu1DZ5vQeamFW7GuCd6djQQL9WCW+8H8VEat9YMju28EZRKTS6kPZylKayCH8MKouDyBQsnZ/ZnFSHtB7HXlm9ZFYhqXmT9pB1V6Srlb2+Cx5u4YEFdtVJYrufPYEdZe5aVA0lrpa7cA1XZNkRaifJi1lbxMmKCkMmjq25PMPtesF5rdEcOlc1AOmXcAYslwrhZwd/YDIoVx4ERUcM6BbG1lLEOxLaS7pEx18KKgO+2qipkdSViVYt5VRFwv4tzOCyfyvVxEavathWibux7GOOqC8GmwkW3yv5NmfFtgvUqITolRK5AB4uLVmHGcsur6FoPA82XWXiF5fn6W7CfUMgK8sSFMkuHMe2wtRGyLGOVZaXMOaQsD70aboYiZgmCIAiCIAiCIPRhMlYpGYcoDPBu1ocB3yEKAA2RVUVAPJ6UouxzZLFRKWZF+60QF+J5fMxAOWnxEgSA17b57DlQqlXkB0KpVuOljVVUqs1CFUEVFV7aWH549SUoGGset97Fqi/itTuoUjQQD2ZG9NJQrAM3rSjVmthXyjWWYbotqKBvveXXzcyaqFGlQHSLuV+qSKCwShonZ6xslJsUw0JBKy6iKP9rzLUwbBIdb7yoXtFnXdXoJ2z7uHVWFXOXwMUwWX5g1WUZsSGeHre6AmO9BZHllm9hpR0bVXKNe2HKMdZUnm95FFgFetq4F8YsCKNZDIPzNCZklYmu3YlY0ef4wRK1e3BYZa5/VQO6dyFgdRv8vVyojAtPqKqCZFhmtXXVRKxy4a1MBAviWiXiawXXcNm1G7e+Cq5v31gqzK80kZAbCFcxUQsw7oMxYQwP46YYuBqWWXcp27fI8s8zS2ksS5POlCobpwtEzBIEQRAEQRAEQejDZK1iZOniW74kAr6XWbxEsbHiwoFx+8MPuO6mFF7KDG7dlLFKKjQqnE5NsU7hdPiGUS7YRY1VUCYAeugKV1bJuJjgD4Y9O9hvIGKBtn3rqhoo1WkjZGU9cwweoBVOpxGzdFrj1bsoW6NaLLwaD3tAAa9go3I2dl5hF/04WKXIJc/NGBErN0SHg3WnXZkZEn1hy0sFIpdOChSB22QoVpgZAbXCzK4YWKDF26E85lSwPuayFW+v7oxTErGywraNNghd1mKi0OrMlqgzDjrloFwXMilzyMWYwOB5gAWZtHEHtJQRrZSCdAov7YTnW1A3VfKM1ulqtG2jLF1miRUXtQJ3w5iQ5Z8nQduF5zFxgUdVF7HKP1dpi2qz+1XLm4gXF3zs6req1ubd1KHb7bogLuCVW2EZYUlVpitiIndMzIq7EQdCViw+VtwyMm7JFdbDi5WtQae17xaK/zfmWmhrI3AGroVAKl0i7bjoXPnJ3TVrEopsnXPjjTcyduxYstks48eP5/HHH1/XVep1HnvsMfbff39GjRqFUop77703sV5rzcUXX8yoUaOoqalh4sSJvPrqq4k8+Xye008/nSFDhlBXV8cBBxzAwoULP8GjWHuuuOIKPv/5z9PQ0MCwYcM46KCDePPNNxN5+ktb3HTTTWyzzTY0NjbS2NjIhAkT+Nvf/hau7y/tUM4VV1yBUopZs2aFaf2lLS6++GKUUollxIgR4fr+0g4fB9LP9J/zR/qZCOlnqtOf+xmQvqav0GR34qWj2EPYVmTZEnc/tGLCQTggVbhZi/wAh7ZRDis3TPHhjjZLt7FYsiMs+Tws31rTvFOJ1q0LLN+hRNtWedp3b2flDnlW7pSjebzL0h09lkzQLN1O0TrGonWMRb5RkW/03QWDJaMo1ioKDYpiPeQHKPIDFbnB5m+xwQhZbhaUq0ivtHBabSP8WOBlPby0+ayzLnbWhXYjoKimAtpV6JwdujcFA+4gBpdVAqsAbhrcrMZt8CgOdCkM8CjVGlHNBKlXuBkLzxf1vFQQPJ+onRMuZLEfpJrAEQgmceubKoLIanhZ+fuq3ED7ApEJmK7C4PDd1g/8uGoWOBZeNo3XUIuur0HXZtHZNLqhFt1Uj26sw2usxavNGhdEy0LXpPFqUuiUhU7ZaMefeCCYgCAQrQJX1riQFXNtTQhLMetCU7+ymG6BG2LMYjC+hO2wiiVugZUop6w9AwHHs40g2qW1k6r8W7FYXaR3s41pk6gtwjaLCU9Bu8TdBYnv06r8/c0kEL5g5UV5Q1TMUssicj1U+HGxdCRGQ2TFBeDoSMhSvnuhwlhkKVAWKKXRWlF0bfLFVPWTswp9Tsy64447mDVrFhdccAEvvPACu+++O9OnT2f+/Pnrumq9Snt7O9tuuy033HBD1fVXX3011157LTfccAPPPPMMI0aMYOrUqbS2toZ5Zs2axT333MPtt9/OE088QVtbGzNmzMB13aplfhqZO3cup512Gk899RSzZ8+mVCoxbdo02tvbwzz9pS1Gjx7NlVdeybPPPsuzzz7LpEmTOPDAA8MHxv7SDnGeeeYZfvGLX7DNNtsk0vtTW2y11VYsWrQoXF5++eVwXX9qh95E+hlDfzl/pJ+JkH6mEulnDNLXfPppsttxAzHLUmGwd21Fw724UKAVeI5FodFh5UZpmrdM0TzOpnm8S8umHsUxeQojSng1Hjrj4dZ5OMsd7OUOKmeRWpTGXVQDeRtrSQaVdbEH59GOhz2mneK2bXRu3cnKXXOs3EyzbJxmxRbQuoGic6gvXA0wS6HRxMDyHOMC6DmgHbDzZqZCpwNSrb7gkPLQKY2b1XhpD5X2cDttrA4LPagAgNeWwm63UCWFmzGWXW4GWjfyWLFLnmW75WnbrYP0zsvYYvv32G7cO2y/5Tw+t+N71Oy2lGW75VmyZ5FFEz0W7aZZOEXx/h6Wv9gsnOiwcC+HZZunfGFgddWn6qwy+PuqiMfLiokx4e/eg5F/bmQt7+0/gHcOaWLezEbeO6CJ92YMYP6BQ1l44EgWTh/Kwn2G8P60wXwwaRAfTB7EB5OHsGjacBZNHMSiXRtZtHsDH+xezwe71fPRdnV4KSt0QQwttpTyrbqsqlaD1WYuDIQsbatQrCkPZB53A0wETC9fYunRrHuxtHKxi+R2bkaxbEvF0m0sPtrWMtfO1jZLx5mleZzN0q1tlm7jf9/KZtmWNsu2sFn2OZvlm9ss3yxaVmxqU2hIXqsVdCWAxUSs0DW4PG8gQHXlKhhvO981MKxLIFIFglrMLdFLabxU2W/guxV6weyF8RPbMgcXn73QsszsDp6ncF3LfO8hSuteuvo+IXbaaSd22GEHbrrppjBtiy224KCDDuKKK65YhzX7+FBKcc8993DQQQcB5g3YqFGjmDVrFueeey5g3ngNHz6cq666iq9+9ausXLmSoUOH8tvf/pbDDz8cgA8++ID111+fv/71r+y9997r6nDWio8++ohhw4Yxd+5c9thjj37dFgCDBg3iBz/4ASeccEK/a4e2tjZ22GEHbrzxRi677DK22247rrvuun51Tlx88cXce++9vPjiixXr+lM79DbSz/Tv80f6mSTSz/Tvfgakr/k009LSQlNTE/99fTjPOaP56ZmHUzu/xXflCixWrMjqxRcBvLRN57A0bSMtOoebIOz5oS5Oq42bMe6CToci2wyFRqj5yMR/crOKVJsZvLpp425YyircGiM65QcYt0CrBPlhLqqgSI/owLY9OtsypLIlPNeMhEttKeyVDnbOBG0PUWbgrDxjPRXgZSA3vGQsOUrKxONJa0h7qHYbnfVI1Rcorsxg5SwTr8r1Z03zjFti7XbLuHe7m8mWxTsKvBcD3CrD42qy6wEvnkjjzxtjsxsayl29QjfDuBucIrKO0ZhZEjXgxdwX4+IUUf74TIbx4O3xPHHLJu34wpAGVfISgeLDWRsBPI8VWzRw5nf/wFbpxf5xm4I8X11xSbZdV2lmG4trP5jGinPXxyq4UPJQnhdas1WbubCrgO/B5ATERKxyl73yWQd1F+JdGJA83k7l25eti/KYbfMDLPb62lOcPqTSct/VlYZwQTqYn85FYaPDtitqiwP/eCYj/m1m4Kx0bwzq5LedlVwXBm4P1sWPKyF0RcdWHuw9zGOTOE89OyonFLJs8OyYGyGEnxOzGPoB37VtXAtNPjNzoXI8bNsIWUqB47horbAKHbx+5NWsXLmSxsbGyoaM0acsswqFAs899xzTpk1LpE+bNo1//etf66hWnzzz5s1j8eLFiXbIZDLsueeeYTs899xzFIvFRJ5Ro0ax9dZb9+m2WrlyJWAerqH/toXrutx+++20t7czYcKEftkOp512Gvvttx9TpkxJpPe3tnjrrbcYNWoUY8eO5YgjjuCdd94B+l879BbSzxj68/kj/YxB+hnpZ+JIX/PpxgKyqoib9a1dArfCmDWOthVu1qFjZJbmrbJ8tL2iY5SmVKcp1mvSS41b3qCXFYNegcZ3NNllHvULNHYO0GDndDiwtQvG8sIqaZx2M0hNt0DNh4rsR4r6dxwyS23sFxrIv9MIK1NoDelMEdtxGbreCho3X4bavI3OMUWK9f7g14+GHlrK+HiO9uNZKVRBYRUVVs6CnIWucakZ2EmpYKOKgbKBL/iAV+tRbNBkUiWC8Xh8oex7SqmKJRtb6pRFnbJwbA83bUVCQJxVxVFKCFbBQZYFdC8vs7pm5K8rs0IKileRyFFBPPi7L4Ypz5xLtcqlVrk0qBINqkSTVaTJKjLIKjAgtjRYRfNXFRNLnSpRaxWxlI6EtsAqi14SssqsqMotrRJ54nHiurDASrRfeRuq5G9s5+Ct1mHUKhUuDcoyi6US6eF6yyxNlmKQRfh3kAXDbQ93cLHidw73q6K2Sbghxtxoo2OtYrFVtt4LBM64kBUXwcL2iX2PCVlhHfyLRjtG9PJSGp3yr2UnJmRZ/oken8VQgdbKPzTjZmjbHvZqWGb1qQDwS5cuxXVdhg8fnkgfPnw4ixcvXke1+uQJjrVaO7z33nthnnQ6zcCBAyvy9NW20lpz5plnsttuu7H11lsD/a8tXn75ZSZMmEAul6O+vp577rmHLbfcMnwY7C/tcPvtt/P888/zzDPPVKzrT+fETjvtxG9+8xs222wzPvzwQy677DJ22WUXXn311X7VDr2J9DOG/nr+SD8j/UyA9DMR0tf0DYbZrRTqTJwsFY/lpHxLrOFZWjawyQ31Z6vToFyoWWyhbahdZAaewYx/2oaSEw38g8Dw5XF7KuLrKBM43SpCqmTGr9YShZuxUYvqKTRpSgNcWhXU1+RZf/AKOhtT5Ec5NC9tQC1LYfuzAXoKrILZkZshnC0NWxEET1dakWoo4HkKnbdRnkI7RgxToUWJRtdomjI5bMBG4XapNq2aYFutTRwtN21jlTR2XodWTyGK6sJWV+lxultvEZmLxWevDD4H+KJQt+6K2ghOytUUaxXD7NaqlkVhkURWRkElbRVZGblaYfnpHaV0dBxezPqpXMgKRS4qhCzCPGVubjEBqtwaKbE+/j0os9yCrbxJujp+5Z/Trqa9lE6sWptzCoC81YV7Yaxt4iISMbEyENzKrbL89grL1ckyyi3UPCdWflzoIr6NTsQWiyyz4gHfyywKQ5HQzF6olL9YXihkgUlbHfqUmBWgyk9MrSvS+gNr0g59ua2+/vWv85///IcnnniiYl1/aYvNN9+cF198kRUrVnD33Xdz3HHHMXfu3HB9f2iHBQsWcMYZZ/DQQw+RzWa7zNcf2mL69Onh53HjxjFhwgQ23nhjfv3rX7PzzjsD/aMdPg6knzH0t/NH+hnpZ0D6mXKkr+kbDLU76RipGPSaBUV/pjhbUWzM0Lp+mvb1FKUajZVXKA9qlhjriVS7xk2BHXfpcyAItg5mcKq0P+CMuSMFVjDh7IT+QD8RbDrQLnyRLNWqsPMO3tJ6mhvqaB5QoL6pkwE1OT63wSJWjsiyZFkjxZVp7HYLy9dC3AYX0h4UFTgazx/0Wg1FLEuT70iBp3yrLo3S/gA/EMAUFD0bl14QHXxsy8P1XS5B4eQ1Tk5j533LEl9wq6BMEAyss3o6jg/FKaXQmL/xmRWjfDExaFVl+26LblZR6/9YXblwecTc6BLlxuJ1+V/fWz6QEa5nBLNQOFGRBZYVE66suAgbF5+Sok3QBuUiVlU3wa5c9lTU3uWzElYISuXikE/GLtFbeIDdZptPFSIcSdEoVo8K66pY/rDu/rUclaPKrNaCspLbhe0et2QLhCw/0HtwjSk3LjbGLLLigd8Vfqws3yrLL9ayNEp5Zt4KVRZjaxX0KTfDIUOGYNt2xVucJUuWVLwR+iwTzCDTXTuMGDGCQqHA8uXLu8zTlzj99NP5y1/+wiOPPMLo0aPD9P7WFul0mk022YQdd9yRK664gm233ZYf//jH/aodnnvuOZYsWcL48eNxHAfHcZg7dy4/+clPcBwnPJb+0Bbl1NXVMW7cON56661+dU70JtLPGPrj+SP9jEH6GelnVoX0NZ9OGixF/R5LKAzMmJnkMja54TV8tH2G5u01ncM9Sg2auvc1jfM8GhaWyKyIhCwjjkQD10Rg6BjlA+r4zGbGfcl891L+Z6tsXcpYcwE4nQq1LE3bwkYWzB/C+yubcD2L8RvMZ+B6K7HX76A4uESpycVuKGKlXEiZmdNUrYszoECmpohbstCuZYLD17mQ9dBpE3tI+e6ITovN8o6aXm1zpbSZ4dAyx1uoVxTqzOyHyUYri5f1MVB15js/RlpPCCy33DTY6FAksGNLQNw901bREqTbMYWrUHR8gTNQjKoLWXG32ITFlR3Feou7DIbBzH1xplygqYynFWuXsjbr6nu5q13g7he4Oqat3hOzwFhKxl0Jy2coTLgRBi6X5S6FcXHPvy6DAO2m3VTkhkmyzLgI7fntG7axb5EVbhO0mb9/LxUTK4O8voCFHy9LOZ5vlWWy2bbnC1ka2/ZI2e5qBX/3d9V3SKfTjB8/ntmzZyfSZ8+ezS677LKOavXJM3bsWEaMGJFoh0KhwNy5c8N2GD9+PKlUKpFn0aJFvPLKK32qrbTWfP3rX+dPf/oTDz/8MGPHjk2s709tUQ2tNfl8vl+1w+TJk3n55Zd58cUXw2XHHXfk6KOP5sUXX2SjjTbqN21RTj6f5/XXX2fkyJH96pzoTaSfMfSn80f6me6Rfkb6mXKkr/l0YgE//NydLJicIjcky5Lx9SyYatO+fSdeU5GaDy0Gv6BoWFik9qMSxTqLYr2x0lLaWHJ5th/3xonEKM+JxIRwoBxzTwrErMQ2wexnTlCGCeDuZTSeY5Yg7o5ywcorrDablsUNLFnayMuLRzKqsYUxQ5azxSbvM3TMcmpq8yg/gLSqKZGtz9PY0EFNxpiU2RkXp6ZEqrZIqqaIVVfCy3pYeUWqTeG0K/LF3nVKWtFWa9wLi2CVjEjnpqFYWyZoheKN32bVLBK9MmuUnhinBDGoyhdfXUqIHd1ZQeoorlVuiKbBcisELKgUtuKiFiRFLRuNrTS55piAaFlRzKsyIavLmQsV1YUsf/v4Oqgu6nXpMtgdcQGLmHgUE5Wc1RReekIi1lds/5GglGyPhFWWSgrLcfdfrVTlujJRKyGclV3jKB1tE2xnEboTBkHfcYxlZCBg4VtnKStYzKkWiFZKGVELTJptaTJOz0XCPudmeOaZZ3Lsscey4447MmHCBH7xi18wf/58TjnllHVdtV6lra2Nt99+O/w+b948XnzxRQYNGsSYMWOYNWsWl19+OZtuuimbbropl19+ObW1tRx11FEANDU1ceKJJ3LWWWcxePBgBg0axNlnn824ceMqApl+mjnttNP4/e9/z5///GcaGhrCN39NTU3U1NSglOo3bXH++eczffp01l9/fVpbW7n99tt59NFHefDBB/tVOzQ0NISxbALq6uoYPHhwmN5f2uLss89m//33Z8yYMSxZsoTLLruMlpYWjjvuuH51TvQ20s9IPwPSz0g/I/1MgPQ1fYeNnA5Om/E3fjxsMunaNqz36lALsgx9VZNZWcJpd9GOolRjkR9g4WYUltJoR+E5xl1OO8lBbug6GI+146eFA+HAPcsXtgDjeuQp45YUDMh9iw98Sw5tB+X4IlVRQUuKjjaHd7RiWGMbmzcuYaumRSzKNfH2iiEsb62lriZPbbqIqxVtuQzK0jh2iXTaDIJd16LUnkGVFF5GY7lGtMukShUCzdqQW55lUMGf1c1vHM+GUkZhlSysogs6ZlVDJCrF41j5HpFJVNSu1dKDclQgJgTffWES6NYqqyKGllEYTHv5SXYXApirtYmb5X8P8selnSCkl9Vp+fULVsRELOheyAonMiBKj+friUi1JkKWT2iJVWatFQhsWbu45oWX4fozU3ZlvecFfp2q/HyqFMCSroIqFKZCwSsuUtGFqBUIWsoXqXRMKAvVzEiUxtbGpddPNwHfiVwL/QXAtjROyg0tshw/bpZtaVK2i/qsBoAHOPzww2lubuaSSy5h0aJFbL311vz1r39lgw02WNdV61WeffZZ9tprr/D7mWeeCcBxxx3HrbfeyjnnnENnZyennnoqy5cvZ6edduKhhx6ioaEh3OZHP/oRjuNw2GGH0dnZyeTJk7n11lux7d68jX+83HTTTQBMnDgxkX7LLbdw/PHHA/Sbtvjwww859thjWbRoEU1NTWyzzTY8+OCDTJ06Feg/7dAT+ktbLFy4kCOPPJKlS5cydOhQdt55Z5566qnwfthf2qG3kX5G+hmQfkb6me7pT20hfc2nF+0LEje+vzWZOjOsa3OzNNUupfDPwQx9rY1UZwlV8ECBm7YpWTaFlE1H1qJYDzgaNw1e2kNnPUh5JqYNGo0yg9i8jcpZWAULOwd2TuF0+rqKBUEwedsDz0oOjpU/OA4G6ipITxHG2zHxmkzcJzPzGeSWOcyra+S9uhpq6kyA97TjUmovsKJVsczNovM2uCp0Z+p0bFOXnI3docErYLmg2hVWAZoXOvx48A44ymVt8bSFM78ELfnQQslzFJYvNnge6LYSVskMzAMBRgWfzQ/ot50G16NcXwrzmB2av5ZKWHGVb6L9PEbEUNG2WqM9bT672pw7wQyGJQ9KLqQcvM40P56/I7bysNB4PVCD3FAJAc9XflwsSp5F7RsF3M72KHPgEgjR7HlKJevtp+GLXKGgFVgk+cJOVLPgU2VU91UJXvHwTOV5456RUR5lfsOs4j/zB3Kd3qb7HfSQllINNW/lsJa70bXjRUKVFRMAK8SsQOgK4tyZy9ZYVMbzlrsrxv8SiIdEsbBUTLgKrLLKxbCEVZiOCV2xci0j+AYWXgB5xy/EssHxEsIXwSQU3U1aEFRL9ySXIAiCIAiCIAiC8KnhnXfeYeONN17X1RAEQeh1FixYkIhhWo0+Z5klCIIgCIIgCILQ3xk0aBAA8+fPp6mpaR3Xpu/R0tLC+uuvz4IFC2hsbFzX1elzSPutHdJ+1dFa09rayqhRo1aZV8QsQRAEQRAEQRCEPoZlGVedpqYmGQyvBY2NjdJ+a4G039oh7VdJT8X5PjWboSAIgiAIgiAIgiAIgtC/ETFLEARBEARBEARBEARB6DOImCUIgiAIgiAIgtDHyGQyXHTRRWQymXVdlT6JtN/aIe23dkj7rT0ym6EgCIIgCIIgCIIgCILQZxDLLEEQBEEQBEEQBEEQBKHPIGKWIAiCIAiCIAiCIAiC0GcQMUsQBEEQBEEQBEEQBEHoM4iYJQiCIAiCIAiCIAiCIPQZRMwSBEEQBEEQBEEQBEEQ+gwiZgmCIAiCIAiCIPQhbrzxRsaOHUs2m2X8+PE8/vjj67pKnwouvvhilFKJZcSIEeF6rTUXX3wxo0aNoqamhokTJ/Lqq68mysjn85x++ukMGTKEuro6DjjgABYuXPhJH8onwmOPPcb+++/PqFGjUEpx7733Jtb3VnstX76cY489lqamJpqamjj22GNZsWLFx3x0Hz+rar/jjz++4nzceeedE3n6c/utLSJmCYIgCIIgCIIg9BHuuOMOZs2axQUXXMALL7zA7rvvzvTp05k/f/66rtqngq222opFixaFy8svvxyuu/rqq7n22mu54YYbeOaZZxgxYgRTp06ltbU1zDNr1izuuecebr/9dp544gna2tqYMWMGruuui8P5WGlvb2fbbbflhhtuqLq+t9rrqKOO4sUXX+TBBx/kwQcf5MUXX+TYY4/92I/v42ZV7Qewzz77JM7Hv/71r4n1/bn91hotCIIgCIIgCIIg9Am+8IUv6FNOOSWR9rnPfU5/+9vfXkc1+vRw0UUX6W233bbqOs/z9IgRI/SVV14ZpuVyOd3U1KR/9rOfaa21XrFihU6lUvr2228P87z//vvasiz94IMPfqx1X9cA+p577gm/91Z7vfbaaxrQTz31VJjnySef1IB+4403Puaj+uQobz+ttT7uuOP0gQce2OU20n5rh1hmCYIgCIIgCIIg9AEKhQLPPfcc06ZNS6RPmzaNf/3rX+uoVp8u3nrrLUaNGsXYsWM54ogjeOeddwCYN28eixcvTrRdJpNhzz33DNvuueeeo1gsJvKMGjWKrbfeut+1b2+115NPPklTUxM77bRTmGfnnXemqampX7Tpo48+yrBhw9hss8046aSTWLJkSbhO2m/tEDFLEARBEARBEAShD7B06VJc12X48OGJ9OHDh7N48eJ1VKtPDzvttBO/+c1v+Pvf/84vf/lLFi9ezC677EJzc3PYPt213eLFi0mn0wwcOLDLPP2F3mqvxYsXM2zYsIryhw0b9plv0+nTp3Pbbbfx8MMP88Mf/pBnnnmGSZMmkc/nAWm/tcVZ1xUQBEEQBEEQBEEQeo5SKvFda12R1h+ZPn16+HncuHFMmDCBjTfemF//+tdh4O01abv+3L690V7V8veHNj388MPDz1tvvTU77rgjG2ywAQ888AAzZ87scjtpv54hllmCIAiCIAiCIAh9gCFDhmDbdoVFxpIlSyosaASoq6tj3LhxvPXWW+Gsht213YgRIygUCixfvrzLPP2F3mqvESNG8OGHH1aU/9FHH/W7Nh05ciQbbLABb731FiDtt7aImCUIgiAIgiAIgtAHSKfTjB8/ntmzZyfSZ8+ezS677LKOavXpJZ/P8/rrrzNy5EjGjh3LiBEjEm1XKBSYO3du2Hbjx48nlUol8ixatIhXXnml37Vvb7XXhAkTWLlyJU8//XSY59///jcrV67sd23a3NzMggULGDlyJCDtt7aIm6EgCIIgCIIgCEIf4cwzz+TYY49lxx13ZMKECfziF79g/vz5nHLKKeu6auucs88+m/33358xY8awZMkSLrvsMlpaWjjuuONQSjFr1iwuv/xyNt10UzbddFMuv/xyamtrOeqoowBoamrixBNP5KyzzmLw4MEMGjSIs88+m3HjxjFlypR1fHS9T1tbG2+//Xb4fd68ebz44osMGjSIMWPG9Ep7bbHFFuyzzz6cdNJJ/PznPwfg5JNPZsaMGWy++eaf/EH3It2136BBg7j44os55JBDGDlyJO+++y7nn38+Q4YM4eCDDwak/daadTeRoiAIgiAIgiAIgrC6/PSnP9UbbLCBTqfTeocddtBz585d11X6VHD44YfrkSNH6lQqpUeNGqVnzpypX3311XC953n6oosu0iNGjNCZTEbvscce+uWXX06U0dnZqb/+9a/rQYMG6ZqaGj1jxgw9f/78T/pQPhEeeeQRDVQsxx13nNa699qrublZH3300bqhoUE3NDToo48+Wi9fvvwTOsqPj+7ar6OjQ0+bNk0PHTpUp1IpPWbMGH3cccdVtE1/br+1RWmt9boS0gRBEARBEARBEARBEARhdZCYWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRAEQRAEQRAEQegziJglCIIgCIIgCIIgCIIg9BlEzBIEQRAEQRAEQRAEQRD6DCJmCYIgCIIgCIIgCIIgCH0GEbMEQRAEQRAEQRAEQRCEPoOIWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRAEQRAEQRAEQegziJglCIIgCIIgCIIgCIIg9BlEzBIEQRAEQRAEQRAEQRD6DCJmCYIgCIIgCIIgCIIgCH0GEbMEQRAEQRAEQRAEQRCEPoOIWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRAEQRAEQRAEQegziJglCIIgCIIgCIIgCIIg9BlEzBIEQRAEQRAEQRAEQRD6DCJmCYIgCIIgCIIgCIIgCH0GEbMEQRAEQRAEQRAEQRCEPoOIWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRAEQRAEQRAEQegziJglCIIgCIIgCIIgCIIg9BlEzBIEQRAEQRAEQRAEQRD6DCJmCYIgCIIgCIIgCIIgCH0GEbMEQRAEQRAEQRAEQRCEPoOIWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRAEQRAEQRAEQegziJglCIIgCIIgCIIgCIIg9BlEzBIEQRAEQRAEQRAEQRD6DCJmCYIgCIIgCIIgCIIgCH0GEbMEQRAEQRAEQRAEQRCEPoOIWYIgCIIgCIIgCIIgCEKfQcQsQRAEQRAEQRAEQRAEoc8gYpYgCIIgCIIgCIIgCILQZxAxSxAEQRCEfsett96KUopnn312XVflY2PRokVceOGFTJgwgSFDhtDY2Mj48eP5xS9+geu667p6giAIgiAIa4yIWYIgCIIgCJ9BnnvuOX7zm98wefJkfvOb33D33Xez55578rWvfY2TTjppXVdPEARBEARhjXHWdQUEQRAEQRCE3mfXXXflf//7H6lUKkybOnUqhUKBn/70p3zve99j/fXXX4c1FARBEARBWDPEMksQBEEQBAE4/vjjqa+v54033mDvvfemrq6OkSNHcuWVVwLw1FNPsdtuu1FXV8dmm23Gr3/968T2H330Eaeeeipbbrkl9fX1DBs2jEmTJvH4449X7GvhwoUceuihNDQ0MGDAAI4++mieeeYZlFLceuutibzPPvssBxxwAIMGDSKbzbL99ttz5513rvJ4Bg4cmBCyAr7whS+EdRAEQRAEQeiLiJglCIIgCILgUywWmTlzJvvttx9//vOfmT59Oueddx7nn38+xx13HCeccAL33HMPm2++OccffzzPPfdcuO2yZcsAuOiii3jggQe45ZZb2GijjZg4cSKPPvpomK+9vZ299tqLRx55hKuuuoo777yT4cOHc/jhh1fU55FHHmHXXXdlxYoV/OxnP+PPf/4z2223HYcffniF6NVTHn74YRzHYbPNNluj7QVBEARBENY14mYoCIIgCILgUygUuOyyy5g5cyYAEydO5P777+eKK67g+eefZ/vttwdgxx13ZNiwYfz+979n/PjxAGy++ebceOONYVmu67L33nvz7rvv8pOf/ISJEycC8Otf/5q3336bv/3tb+yzzz4ATJs2jY6ODn7+858n6nPqqaey1VZbhQIUwN57783SpUs5//zz+dKXvoRl9fzd5EMPPcRvf/tbzjjjDAYPHrxmjSQIgiAIgrCOEcssQRAEQRAEH6UU++67b/jdcRw22WQTRo4cGQpZAIMGDWLYsGG89957ie1/9rOfscMOO5DNZnEch1QqxT/+8Q9ef/31MM/cuXNpaGgIhayAI488MvH97bff5o033uDoo48GoFQqhcu+++7LokWLePPNN3t8bM8//zyHHXYYO++8M1dccUWPtxMEQRAEQfi0IWKWIAiCIAiCT21tLdlsNpGWTqcZNGhQRd50Ok0ulwu/X3vttXzta19jp5124u677+app57imWeeYZ999qGzszPM19zczPDhwyvKK0/78MMPATj77LNJpVKJ5dRTTwVg6dKlPTquF154galTp7Lpppvy17/+lUwm06PtBEEQBEEQPo2Im6EgCIIgCEIv8Lvf/Y6JEydy0003JdJbW1sT3wcPHszTTz9dsf3ixYsT34cMGQLAeeedF7o9lrP55puvsl4vvPACU6ZMYYMNNuChhx6iqalpldsIgiAIgiB8mhExSxAEQRAEoRdQSlVYPP3nP//hySefZP311w/T9txzT+68807+9re/MX369DD99ttvT2y7+eabs+mmm/LSSy9x+eWXr1GdXnzxRaZMmcLo0aOZPXs2AwcOXKNyBEEQBEEQPk2ImCUIgiAIgtALzJgxg0svvZSLLrqIPffckzfffJNLLrmEsWPHUiqVwnzHHXccP/rRjzjmmGO47LLL2GSTTfjb3/7G3//+d4BEQPef//znTJ8+nb333pvjjz+e9dZbj2XLlvH666/z/PPP88c//rHL+rz55ptMmTIFgO9///u89dZbvPXWW+H6jTfemKFDh/Z2MwiCIAiCIHzsiJglCIIgCILQC1xwwQV0dHRw8803c/XVV7Plllvys5/9jHvuuYdHH300zFdXV8fDDz/MrFmzOOecc1BKMW3aNG688Ub23XdfBgwYEObda6+9ePrpp/n+97/PrFmzWL58OYMHD2bLLbfksMMO67Y+Tz75JM3NzQDsv//+FetvueUWjj/++N44dEEQBEEQhE8UpbXW67oSgiAIgiAI/Z3LL7+cCy+8kPnz5zN69Oh1XR1BEARBEIRPLWKZJQiCIAiC8Alzww03APC5z32OYrHIww8/zE9+8hOOOeYYEbIEQRAEQRBWgYhZgiAIgiAInzC1tbX86Ec/4t133yWfzzNmzBjOPfdcLrzwwnVdNUEQBEEQhE894mYoCIIgCIIgCIIgCIIg9BmsVWcRBEEQBEEQBEEQBEEQhE8HImYJgiAIgiAIgiAIgiAIfQYRswRBEARBEARBEARBEIQ+g4hZgiAIgiAIgiAIgiAIQp9BxCxBEARBEARBEARBEAShzyBiliAIgiAIgiAIgiAIgtBnEDFLEARBEARBEARBEARB6DOImCUIgiAIgiAIgiAIgiD0GUTMEgRBEARBEARBEARBEPoMImYJgiAIgiAIgiAIgiAIfQYRswRBEARBEARBEARBEIQ+g4hZgiAIgiAIgiAIgiAIQp9BxCxBEARBEARBEARBEAShzyBiliAIgiAIgiAIgiAIgtBnEDFLEARBEARBEARB6LM89dRTfPGLX2TkyJGk02lGjBjBoYceypNPPrla5Vx88cUopdaoDo8++ihKKR599NE12r6nTJw4kYkTJ36s+wCYOXMmBx54YJfrP6nj7YoNN9yQ448/fp3suzuUUnz9619f19XoF4iYJQiCIAiCIAiCIPRJrr/+enbddVcWLlzI1VdfzZw5c7jmmmt4//332W233bjhhht6XNZXvvKV1RbAAnbYYQeefPJJdthhhzXa/tNEe3s7Dz74IIcccsi6roogdImzrisgCIIgCIIgCIIgCKvLP//5T2bNmsW+++7LPffcg+NEw9sjjjiCgw8+mDPOOIPtt9+eXXfdtctyOjo6qK2tZfTo0YwePXqN6tLY2MjOO++8Rtt+2vjrX/9KqVRi//33X9dV6TN0dnZSU1OzrqvRrxDLLEEQBEEQBEEQBKHPccUVV6CU4qabbkoIWQCO43DjjTeilOLKK68M0wNXwueff55DDz2UgQMHsvHGGyfWxcnn85x11lmMGDGC2tpa9thjD5577rkKN7dqbnfHH3889fX1vP322+y7777U19ez/vrrc9ZZZ5HP5xP7+d73vsdOO+3EoEGDaGxsZIcdduDmm29Ga73a7fKtb32LpqYmXNcN004//XSUUvzgBz8I05qbm7Esi+uvvz6x/d13382kSZMYOHDgau332Wef5YgjjmDDDTekpqaGDTfckCOPPJL33nsvke/WW29FKcUjjzzC1772NYYMGcLgwYOZOXMmH3zwQSJvsVjknHPOCdt/t9124+mnn67Yd1Dmww8/zEknncTgwYNpbGzkS1/6Eu3t7SxevJjDDjuMAQMGMHLkSM4++2yKxWKijJ7+BhtuuCEzZszgT3/6E9tvvz3ZbJbvfe97VdtEa835559PKpXil7/8JQCe53HZZZex+eabU1NTw4ABA9hmm2348Y9/vFrt3d8RyyxBEARBEARBEAShT+G6Lo888gg77rhjl9ZU66+/PuPHj+fhhx/GdV1s2w7XzZw5kyOOOIJTTjmF9vb2Lvfz5S9/mTvuuINzzjmHSZMm8dprr3HwwQfT0tLSo3oWi0UOOOAATjzxRM466ywee+wxLr30Upqamvjud78b5nv33Xf56le/ypgxYwATB+z000/n/fffT+TrCVOmTOGaa67h6aefZsKECQDMmTOHmpoaZs+ezbe+9S0A/vGPf6C1ZsqUKeG2uVyOBx54gGuvvXa19hkcw+abb84RRxzBoEGDWLRoETfddBOf//znee211xgyZEgi/1e+8hX2228/fv/737NgwQK+9a1vccwxx/Dwww+HeU466SR+85vfcPbZZzN16lReeeUVZs6cSWtra9U6fOUrX2HmzJncfvvtvPDCC5x//vmUSiXefPNNZs6cycknn8ycOXO46qqrGDVqFGeeeWai/j39DZ5//nlef/11LrzwQsaOHUtdXV1FXfL5PMcffzwPPPAA9913H/vssw8AV199NRdffDEXXnghe+yxB8VikTfeeIMVK1asdpv3a7QgCIIgCIIgCIIg9CEWL16sAX3EEUd0m+/www/XgP7www+11lpfdNFFGtDf/e53K/IG6wJeffVVDehzzz03ke8Pf/iDBvRxxx0Xpj3yyCMa0I888kiYdtxxx2lA33nnnYnt9913X7355pt3WWfXdXWxWNSXXHKJHjx4sPY8L1y355576j333LPbY25vb9fpdFpfcsklWmutFy5cGB5HTU2NzuVyWmutTzrpJD1q1KjEtvfee6+2bVsvWbKk231UO95ySqWSbmtr03V1dfrHP/5xmH7LLbdoQJ966qmJ/FdffbUG9KJFi7TWWr/++usa0N/85jcT+W677baK9g/KPP300xN5DzroIA3oa6+9NpG+3Xbb6R122KHLunf3G2ywwQbatm395ptvVmwH6NNOO003Nzfr3XbbTa+33nr6xRdfTOSZMWOG3m677brct9AzxM1QEARBEARBEARB+EyifRexcvfBngQ3nzt3LgCHHXZYIv3QQw+tcGvsCqVUReypbbbZpsL17uGHH2bKlCk0NTVh2zapVIrvfve7NDc3s2TJkh7tK6C2tpYJEyYwZ84cAGbPns2AAQP41re+RaFQ4IknngCMtVbcKguMi+Huu+/O0KFDV2ufAG1tbZx77rlssskmOI6D4zjU19fT3t7O66+/XpH/gAMOSHzfZpttAMK2eeSRRwA4+uijE/kOO+ywLtt/xowZie9bbLEFAPvtt19F+tr8Bttssw2bbbZZ1TrMmzePCRMm0NLSwlNPPcW2226bWP+FL3yBl156iVNPPZW///3vPbbyE5KImCUIgiAIgiAIgiD0KYYMGUJtbS3z5s3rNt+7775LbW0tgwYNSqSPHDlylftobm4GYPjw4Yl0x3EYPHhwj+pZW1tLNptNpGUyGXK5XPj96aefZtq0aQD88pe/5J///CfPPPMMF1xwAWCCi68uU6ZM4amnnqK9vZ05c+YwadIkBg8ezPjx45kzZw7z5s1j3rx5CTGrWCxy3333rfEshkcddRQ33HADX/nKV/j73//O008/zTPPPMPQoUOrHkN5G2YyGSA63qD9R4wYkcjXXfuX/87pdLrL9LX5Dbo7f55++mn++9//cvjhh1d1gT3vvPO45ppreOqpp5g+fTqDBw9m8uTJPPvss12WKVQiMbMEQRAEQRAEQRCEPoVt2+y11148+OCDLFy4sKposHDhQp577jmmT5+eiJcFlZZa1QgEkw8//JD11lsvTC+VSqHQ0hvcfvvtpFIp7r///oTwde+9965xmZMnT+Y73/kOjz32GP/4xz+46KKLwvSHHnqIsWPHht8D5syZw8qVKzn44INXe38rV67k/vvv56KLLuLb3/52mJ7P51m2bNkaHUPQ/osXL/5Y2x9W/zfo7vw5/PDDGTFiBBdccAGe53HhhRcm1juOw5lnnsmZZ57JihUrmDNnDueffz577703CxYsoLa2tleO6bOOWGYJgiAIgiAIgiAIfY7zzjsPrTWnnnpqYuY+MAHiv/a1r6G15rzzzluj8vfYYw8A7rjjjkT6XXfdRalUWrNKV0EpheM4CcGts7OT3/72t2tc5he+8AUaGxu57rrrWLx4MVOnTgWMxdYLL7zAnXfeyZZbbsmoUaPCbe6++2523nnnhHC0OsegtQ6tqwL+7//+r+K36SkTJ04E4Lbbbkuk33nnnb3a/tD7v8GFF17Iddddx3e/+91uz78BAwZw6KGHctppp7Fs2TLefffdNdpff0QsswRBEARBEARBEIQ+x6677sp1113HrFmz2G233fj617/OmDFjmD9/Pj/96U/597//zXXXXccuu+yyRuVvtdVWHHnkkfzwhz/Etm0mTZrEq6++yg9/+EOampqwrN6xDdlvv/249tprOeqoozj55JNpbm7mmmuuqRCGVgfbttlzzz257777GDt2LBtvvDFg2iyTyfCPf/yDb3zjG2F+13X585//nLCqWh0aGxvZY489+MEPfsCQIUPYcMMNmTt3LjfffDMDBgxYozK32GILjjnmGK677jpSqRRTpkzhlVde4ZprrqGxsXGNyuyKj+M3OOOMM6ivr+fkk0+mra2Nn/zkJ2EMta233podd9yRoUOH8t5773HdddexwQYbsOmmm/biUX22ETFLEARBEARBEARB6JOcfvrpfP7zn+eHP/whZ511Fs3NzQwaNIjddtuNJ554ggkTJqxV+bfccgsjR47k5ptv5kc/+hHbbbcdd955J/vss88aizTlTJo0iV/96ldcddVV7L///qy33nqcdNJJDBs2jBNPPHGNy50yZQr33XdfIi5WJpNht912Y/bs2Yn0Rx99lKVLlzJz5sw13t/vf/97zjjjDM455xxKpRK77rors2fPrgi+vjrcfPPNDB8+nFtvvZWf/OQnbLfddtx9990cccQRa1xmNT6u3+DEE0+krq6OY489lvb2dv7v//6Pvfbai7vvvpv/+7//o6WlhREjRjB16lS+853vkEqlevGoPtsoHUzvIAiCIAiCIAiCIAhCt/zrX/9i11135bbbbuOoo45a19XpFU499VT+/e9/89xzz63rqghCjxAxSxAEQRAEQRAEQRCqMHv2bJ588knGjx9PTU0NL730EldeeSVNTU385z//qZipUBCETwZxMxQEQRAEQRAEQRCEKjQ2NvLQQw9x3XXX0draypAhQ5g+fTpXXHGFCFmCsA4RyyxBEARBEARBEARBEAShz9A70y8IgiAIgiAIgiAIq8WNN97I2LFjyWazjB8/nscff3xdV0kQBKFPIGKWIAiCIAiCIAjCJ8wdd9zBrFmzuOCCC3jhhRfYfffdmT59OvPnz1/XVRMEQfjUI26GgiAIgiAIgiAInzA77bQTO+ywAzfddFOYtsUWW3DQQQdxxRVXVOTP5/Pk8/nwu+d5LFu2jMGDB6OU+kTqLAiC8HGitaa1tZVRo0ZhWd3bXkkAeEEQBEEQBEEQhE+QQqHAc889x7e//e1E+rRp0/jXv/5VdZsrrriC733ve59E9QRBENYpCxYsYPTo0d3mETFLEARBEARBEAThE2Tp0qW4rsvw4cMT6cOHD2fx4sVVtznvvPM488wzw+8rV65kzJgxvPf8hjTWRxYMK9wO5nYOx0Ux3Glh54zHiwWXWlVis1SGZV4n/8k3sUdNniVuJ//OjaLOyjOxpg0Hm8dzDsvcerZKL2bzdC0fue083jmSrCoysWYlzV6BZ3KjsNHYymNSzXI6dZEnOkdgK4+JNc3UqDStXo5/5wcyqaaDlLI/noYUBOEzRUubxwY7vEtDQ8Mq84qY1YfoqfnwI488wsSJEz/eyqwG//jHPzj33HN5/fXX6ejo4J577uGggw5a19Vaax599FH22muvj729//KXvzBz5kwWLVrE0KFDq+bZcMMNmThxIrfeeuvHVo+uuPjii/ne977Hp81j+fjjj+euu+6ira1tXVdFEPo80v98uvg09T9KKU477TRuuOGGj60eXXH55Zez5ZZbfiZ+U6H/Un5/1Vp3ec/NZDJkMpmK9MZ6i8aGSMza9oFvUve/FGqnFTTc0cjxF/2F6+48iAFve5z2vT/yz/ZNeeX72zD42l9w2s/OoG3TIkP/6fCHE95mVE0Lc+8cT9umRQa+4PCrc67jyFvOJT/cZeB/LK4/+ENczyJz0yA6vrqCxmyOnTb9Pfv84Ry8tKbhXYtLd2/ljK0e5ie3HcjIJ/Ns+asb2Cqd6t2GEwThM01Pnj1FzOpDPPnkk4nvl156KY888ggPP/xwIn3LLbf8JKvVLVprDjvsMDbbbDP+8pe/UFdXx+abb76uq9WnuPvuu9ljjz26HEgIgiB83Ej/0z/5tPc/l19+OYceeqiIWUKfZMiQIdi2XWGFtWTJkgprrdXB1R4Nb6Ro3aTE4+N/wR6t32CA3Y6dhwH/+B+XbnE4O059jVSry0elRkb+s4MJRz/D/c/uwUvvjea6Pa/nmK/9i7tX7MiLv92Od0uDGfXPPBt+/03+PX8blr81lKYxK7FqLe7Z5lcMstLktcXwZzysk5fwYccI9Fv1zNz5v8zZ57+0PLweRS1zjgmC0PuImNWH2HnnnRPfhw4dimVZFenldHR0UFtb+3FWrUs++OADli1bxsEHH8zkyZN7pczOzk6y2Wy/CHRZLBb5y1/+wmWXXbauq9JnWJfnuyB8VpH+xyD9jyAIvUU6nWb8+PHMnj2bgw8+OEyfPXs2Bx544GqX52oPAA/NpKOe5sEHPs+0X57DLtNfY2rNIi7VsODLmzLkPy7/Grw5YynR7NYDsLxYi/LAK9i4Gh7v2Iz7/zKB2i01rrZAw8piFuWCKiqKrs3QBZ3s9ftvoTz4zRE3sHiCombOSAa977Hi4HbaPc0Gtct4mfV4tzSYJisS7VIKPP+zhflsAUUdpY+00xRxmVe0aLKK2Co4TvzjBBeFTeQZYAG2glr/Hp1SFilsMsrBVr0nqLnaw0NT1C5tuki7p6mzFCkU7dqjqCGrwPWPKeXX3QZspUw9UVh+PW2SfYqFhV2ln7GoPAbL37Y3j6+3CNoJonoCYZoX/tofx76THiNBe7paYysVro9/LidYF/8tLKxed5ktarfLtqi2P1d7a/V7B/cKML+FhQr/rg7ldVjbeq0Jn76zXlgrJk6cyNZbb81jjz3GLrvsQm1tLSeccAJgpv+dNm0aI0eOpKamhi222IJvf/vbtLe3J8o4/vjjqa+v5+2332bfffelvr6e9ddfn7POOisxgwrATTfdxLbbbkt9fT0NDQ187nOf4/zzzweM+1kQtO3cc89FKcWGG24YbvvEE08wefJkGhoaqK2tZZddduGBBx5IlH/rrbeilOKhhx7ihBNOYOjQodTW1pLP58NjffLJJ9lll12oqalhww035JZbbgHggQceYIcddqC2tpZx48bx4IMPVrTXW2+9xVFHHcWwYcPIZDJsscUW/PSnP63I98Ybb7DPPvtQW1vLkCFDOOWUU2htbV3l7/Hqq6+ilOKPf/xjmPbcc8+hlGKrrbZK5D3ggAMYP358Iu0f//gHK1euTDzk9IRcLsdZZ53FdtttR1NTE4MGDWLChAn8+c9/rsirlOLrX/86v/3tb9liiy2ora1l22235f7776/I+8ADD7DddtuRyWQYO3Ys11xzTdX9B2XecsstbL755tTU1LDjjjvy1FNPobXmBz/4AWPHjqW+vp5Jkybx9ttvJ7YPHuRGjx5NNptlk0024atf/SpLly5N5Lv44otRSvH8889z6KGHMnDgQDbeeOMu2+Wf//wnQ4YMYcaMGeF5//DDDzNx4kQGDx5MTU0NY8aM4ZBDDqGjo2OV7SwIQoT0P/27/+lJH9KTY+5p/6WUor29nV//+tcopVBKhS6XwW/38MMPc9JJJzF48GAaGxv50pe+RHt7O4sXL+awww5jwIABjBw5krPPPptisZgo/3vf+x477bQTgwYNorGxkR122IGbb765wqV+ww03ZMaMGdxzzz1ss802ZLNZNtpoI37yk5/0qN2E/s2ZZ57J//3f//GrX/2K119/nW9+85vMnz+fU045ZbXLspWFrSwsFJvWfMiZX/wz601cwMLvb8pNy7cHBe2jXT6YCJv8PodyNY1WJwA1dhGtQNmaQbbN1wa8ygEH/ovBr3TS6mUByNolsEDbcPm4e9jy+lc5ecZDjP1LO890bsR6cz2aJi9m2RYKXm0gq8CxzKA5q4qklBGabF/IcrURevKxvykFBW0xu31zHuocxLP5elp0hqI/yK5VyohGygxiP3Jr+F9xIG8VB/NGcQivFYfwcmEIz+YH8e/8YJ7IDWRO5wAezaXI62KVVlszmr1OTpy/F4e9fSDH/vdwvvLWURz55pEc9t/DOfbNo/nym8dw1JtHc+ybR3PCf4/muP8exYlvHcGJ/zuMr7xzCCfPO4iT3j2AE+ftz4nz9ueEd/fj5Pem85X39uX4eTM45p39+OLb+3Pwfw/k4P8eyIw3ZrLPa4ey18tfZPIrh7LHfw5j95cOZ9cXj2DCi4ez8wtH8PnnDwuX8c8lP+/w7OFs/8wRbPv0keGy/TNHMP45s37CS4ewx8sH889c74pLVzVvwR7/OYyJL3+RKa8dzIFv7cf+/53B1FcPYfIrhzL55cOZ/PLhTH3lMPZ9/RAOfPMgDnzzIA5/ewbHztuHo9+Zzhff3p+j35nOie9N5aT5kzllwSS+vnAiX184kW+8vwdfXziRb36wO2d9sBvnLp7AuYsncP7infjOhztz0ZKduGjJTnzvoy/wvSXjuXLptly7bBt+0DyO65aN46crtuCnyzfnpyu24BcrN+OWlo35XeuG/K51Q/7QOoa72kZwT/tI/tw+hL91DGROZwNP5m1Wep292k5vF/M8m7fD5YW8xQt5i5cKMK+UY7nbQYdXoMMrsNztoNnrDNPyuoirvcQCRlgqape8LlLUbvi9wyuw3OtkkdvBEreDD13zeanbyRI/rdnrTCxB2vLY0uLlWOl1hstyt6Mirc3LJZYOrxD+zetiuBS1G9ZxdRHLrM8gixYt4phjjuGcc87h8ssvD6e0fOutt9h3332ZNWsWdXV1vPHGG1x11VU8/fTTFa4ixWKRAw44gBNPPJGzzjqLxx57jEsvvZSmpia++93vAnD77bdz6qmncvrpp3PNNddgWRZvv/02r732GgBf+cpX2HbbbZk5cyann346Rx11VOjnP3fuXKZOnco222zDzTffTCaT4cYbb2T//ffnD3/4A4cffniiPieccAL77bcfv/3tb2lvbyeVMn73ixcv5stf/jLnnHMOo0eP5vrrr+eEE05gwYIF3HXXXZx//vk0NTVxySWXcNBBB/HOO+8watQoAF577TV22WUXxowZww9/+ENGjBjB3//+d77xjW+wdOlSLrroIgA+/PBD9txzT1KpFDfeeCPDhw/ntttu4+tf//oqf4utttqKkSNHMmfOHL74xS8CMGfOHGpqanjttdf44IMPGDVqFKVSiblz51Y8vNx9991MmDAhrHNPyefzLFu2jLPPPpv11luPQqHAnDlzmDlzJrfccgtf+tKXEvkfeOABnnnmGS655BLq6+u5+uqrOfjgg3nzzTfZaKONADOwOfDAA5kwYQK33347ruty9dVX8+GHH1atw/33388LL7zAlVdeiVKKc889l/3224/jjjuOd955hxtuuIGVK1dy5plncsghh/Diiy+G1g7/+9//mDBhAl/5yldoamri3Xff5dprr2W33Xbj5ZdfDn//gJkzZ3LEEUdwyimnVAyOA+68806+9KUvccIJJ3D99ddj2zbvvvsu++23H7vvvju/+tWvGDBgAO+//z4PPvgghUJBLLwEYTWR/qd/9j896UN6esw97b+efPJJJk2axF577cV3vvMdABobGxP1+spXvsLMmTO5/fbbeeGFFzj//PMplUq8+eabzJw5k5NPPpk5c+Zw1VVXMWrUqERg7XfffZevfvWrjBkzBoCnnnqK008/nffffz88DwNefPFFZs2axcUXX8yIESO47bbbOOOMMygUCpx99tmrbD+h/3L44YfT3NzMJZdcwqJFi9h6663561//ygYbbLDGZdrK4vq7ZuBm4Jh95vJoaThLig2gAQVH7f4vHvvHBDLNRV7pHE2hKcVdr27PiBWa7P8yjE+dituawuq0GJsqcMk/DmZM1uOfr2/C4GYNnsWsfx2BbnewchYbOTl+cv++jLRclj82ggELNW5aM/nn54CCMbmVfPM3J+J0gvL8xQ3+apQHWoG2FB2jNHZeUT9fgwLPBssFLwX5JkXRjwdt58DphLpFHk7e88vThEZa/l/l/y002nz1krv4UuPS8uZaIwpa8/Z1W9L431YUEDyVKq1xAB1Y8liAUqAUnmNRshRFR+HZFtpRaEuhLfO7aKXo1jBGG8sugLSfL/TeDL7HytAqKNfUIf492E5bJr9rQSEDvzlxV3YdnQwrsDY80bwxrY8Oj+0P0yY6+V1bOmoHy6+zBdo2GbVt8gR5Q3McpaM2szTK0WGasjRKmTyWZU4y2/GwbQ+ltFmAtOPieopMqkTGdlFKYylNxi7hWB4WmrRdwlKatFWi0ckzYOhctkn3WjPxZnEYT7dtFH5PWS4p5ZKxigx3VjI+u4Amq4AHtHo2Oe2QVS4NVoEUkFUWqbhFlAYXTVFH9l4pv6FatccyN8UKrwZLeRS1jastbOWRUi52zELMRmMpD09bWMojq1xsNEVtkVJ+2ygPF4WrFbbS4fZBbeIWlanY+R1YKQZ5A0tFWyuKuueWbyJmfQZZtmwZf/zjH5k0aVIi/cILLww/a63Zdddd2WKLLdhzzz35z3/+wzbbbBOuLxQKfO973wsfgCdPnsyzzz7L73//+/Ah7p///CcDBgxIvH2Mu3KMHj2aUqkEwJgxYxLuKN/+9rcZOHAgjz76KPX1xsR5xowZbLfddpx99tkcdthhCTeOyZMn8/Of/7ziWJubm/n73/8evlHecccdGTZsGFdeeSVvv/12+BA+atQotttuO+6++25OP/10wLwNa2ho4IknnggfgKdOnUo+n+fKK6/kG9/4BgMHDuRHP/oRH330ES+88ALbbrstANOnT2fatGnMnz9/lb/H5MmTmTNnTvh9zpw5HHPMMdx1113MmTOHL33pSzz99NO0tLQwZcqUMJ/rutx7772cd955q9xHOU1NTaGFQFDW5MmTWb58Odddd12FmNXZ2cmcOXPCWSN22GEHRo0axZ133hlOGX3BBRcwfPhwZs+eTTZr3tLtvffeCWuHOPl8noceeoi6ujrAvEU/6KCDeOSRR3j++efD3/ejjz5i1qxZvPLKK4wbNw4gMajSWrPLLrswceJENthgA/72t79xwAEHJPZ13HHHdTtV9VVXXcUFF1zA5ZdfzjnnnBOmP/fcc+RyOX7wgx+Evy3AUUcd1WVZgiB0jfQ//bP/6Ukf0tNj7mn/tfPOO2NZFkOHDu3S3XXGjBmhBfHUqVN58skn+cMf/sC1117LN7/5TQCmTJnC3//+d2677baEmBWvg+d5TJw4Ea01P/7xj/nOd76TOEc++OCDit9oyZIlXHrppZx66qnyYkTollNPPZVTTz2118pb6razyR7v8v4fx3LfT/Ykt53ib29vScqGmg9s7v3D7lgbQXaAzd9u3A09Bobfb+OmoPEdD/vNDMVaRapDs2KTDGP+WqJtpMPIhzTa0jh5zYB3UuQbLZNn0xrWe7REx3CHpv+ZgazyYNS/8nQMS9OyeQND/1Mis9y3jIobX5S7d72swjSlq6xX/npthCM8HQpWFXlj26RabR5ZsQVfanx8rdo2jlXSWIVSlBDbf4Um5VuPBuKWtixwLLSt0LZlRC1HoZUKhR8VOxytgvaIdqBDEUcl8uHfmwLxKl6hQOyKi2BBmptSvLJsJIxeg8bogiHZNpYsjQQnbUf1VjoQrpQv+PnffR1DW0Zsi28Trg+EuWoiWHB8SodiWckydSjhC2QW4HhG9LKNAGY5vjuk5WE7Ho5jhK3adJGia5F2XLJOiYxd4qNBdRgn0t5hhVvLOx1DqLMLeCgsNI7lYitNRzrDCGcllrMSW2k+cuto8bJkVZEBdgcNqkid5ZHSXkIcKmpNEchp44qbURobaPcsFruNNLv1tHsZsqoAQNYqUtQOrt/YWasYuvDaysPCI6uK2Cqw/LJIKxdLedh4uFikcXFRvijmb4sm5Qtepo4aG21chLUOBS4Lja0hpRStXs+tKMXN8DPIwIEDKwYSAO+88w5HHXUUI0aMwLZtUqkUe+65JwCvv/56Iq9Siv333z+Rts022/Dee++F37/whS+wYsUKjjzySP785z9XuIB1RXt7O//+97859NBDw4EEgG3bHHvssSxcuJA333wzsc0hhxxStayRI0cmXCMGDRrEsGHD2G677RJvk7fYYguAsP65XI5//OMfHHzwwdTW1lIqlcJl3333JZfL8dRTTwFmdq6tttoqIXZAzwWPyZMn88477zBv3jxyuRxPPPEE++yzD3vttRezZ88GzAAjk8mw2267hdvNnTuXpUuXMnPmzB7tp5w//vGP7LrrrtTX1+M4DqlUiptvvrnitwbYa6+9EtOfDh8+nGHDhoXt1d7ezjPPPMPMmTNDIQugoaGh4jyJlxkIWRD9BtOnT08MAsp/GzDBT0855RTWX3/9sO7BW8pq9e/q/NBa89WvfpWLLrqI3//+9wkhC2C77bYjnU5z8skn8+tf/5p33nmnajmCIPQM6X/6Z/+zqj5kdY4ZVq//6o4ZM2Ykvge/xX777VeRHj+/wLigT5kyhaampvCc/e53v0tzczNLlixJ5O3qN2ppaeH5559frToLwtpwb3s9u/7mbJp/vgG1H3nULnUZ9EaJEb/NMuTlIkNfMsvg14rULS7R+F6RpnlFsstK1H5UovbDItnmIg0LCmSWFWmaV8Du9GiaV6BmaZHaJUXq3y+QWV6k8b08NUsKNM3Lk+oo0TQvR93iPHWL8zS8m0MVPerez1G3MEf2owKqpFFFD+XGF51YrKKLVfKwSh6q6KJKHlbRXwouVr5k/hZKqLyLKnoQy6uCz/6C1uB54GmKXu/FObLxBROte7Z4HsqN6mXli6jOIlZnEaujiN1ZxO4sYedK2HkPu+hF7eBqI5yVPCzXLGGaq7GKnmlbPy1oByvRtkBgCRfkc42FnOVvaxc1S5Y1rpGrV1d42sIuEC356LMVfM/r5JLTWPnI+s5pB6cDnJzJ73Saxc6B3alwOhR2p8LOK5ycws4l0+xOhZ2zsDss7E7z1+q0UB02qt1Gd9jonI3b5uDmbEoFm0LeIZ9L0dGRoaUjS66Qoi2XYUVHDcs6a2n1anqtjQByXoqWQpblhRraS2lWFrM05+tYVqjlo0IDrV4NeW1T1BYFbdPhZWj1amj1srTqFO2eRU5DTutwCYSsDs+hXTsUtR/DDYt2L0OrmyXvpVjm1pulZJYOL8NKt45Wt4Z2L027l6bFzdLhZVjh1dLs1tPs1pPTKVq8rKmDV0NR27R4WXI6RauXpaiNBVm7TtHqpclpmw7tkNM2ufBYLHJaUfRdjU2d9WpFUhPLrM8gI0eOrEhra2tj9913J5vNctlll7HZZptRW1vLggULmDlzJp2dSd/f2trahGgBZjrgXC4Xfj/22GMplUr88pe/5JBDDsHzPD7/+c9z2WWXMXXq1C7rt3z5crTWVesZDACam5tXeUxgBg/lpNPpivR02tiCBvVvbm6mVCpx/fXXc/3111ctOxgcNTc3M3bs2Ir1I0aMqLpdOcHb7jlz5jB27FiKxSKTJk3iww8/5NJLLw3X7brrrtTURDfHu+66i/Hjx3dp+dQdf/rTnzjssMP44he/yLe+9S1GjBiB4zjcdNNN/OpXv6rIP3jw4Iq0TCYTnhfLly/H87yqx9xVO3T1G6zqt/E8j2nTpvHBBx/wne98h3HjxlFXV4fneey8884V5yp0fX4UCgXuuOMOttpqK6ZPn16xfuONN2bOnDlcffXVnHbaabS3t7PRRhvxjW98gzPOOKNqmYIgdI30P/2z/1lVH7I6x7y6/Vd3rE4/FD+/nn76aaZNm8bEiRP55S9/yejRo0mn09x77718//vfrzhnu+sby88nQfi4+Et7Ld/55ZcY+WrRCBdlFktKA17c6qnrspQ2LljleVTc+skrKzsgnif2UZVbTnnJ76q8PkH+4K9XVk7531iesEyl0UqhtCZjlegtGiyH/ACr0hrMq9KoWofWUn6CqZvW4CmU8kwdbYV2LGOpZQfmWRjLK98iLWwjFSvTt0yKrLACM6aYCx4aZRm3RjyNthQq7qKnFaAp5Rw8NL0l+42pWcbb2hfTiNxJUcYtVFsqaU2ljcCG0qE7pIq7YDqEboqhRZbtW2W5fv6g3ZRCl/DdVU3beQ4oy5hoaaXQjkZ75q+xUvMgBV7ORjsWyvEoFIxcYtseJUtTdG1WuLVAWy+1kqGzZJxVvZIK3f4AHOXR6mXJaQcXlw6docWrwfMtqFxtkbZNXWw0KTS+dmkEIyyKnk3WdsPzNadTrHRrcbUVWoJ1kCFllbB8K6qcTlHUNpYKgvWXwus5pVw8bRlrLs/BVh45ncLCC/MUlYOFR1q5uMrD1ibIfDBpQ2Ct5fnCW0p5eFpjYdx4e4qIWZ9Bqs2y9PDDD/PBBx/w6KOPhm/DAVasWLFW+/ryl7/Ml7/8Zdrb23nssce46KKLmDFjBv/973+79PcfOHAglmWxaNGiinUffPABYKYrjtPbM0cNHDgwfBN/2mmnVc0TDCAGDx5cMW0yUDWtGqNHj2azzTZjzpw5bLjhhuy4444MGDCAyZMnc+qpp/Lvf/+bp556KuEm53ke99xzD9/4xjfW4Ojgd7/7HWPHjuWOO+5ItF15AOWeMnDgQJRSa9UOPeWVV17hpZde4tZbb+W4444L08uDxMfp6vzIZDI88sgj7L333kyZMoUHH3yQgQMHJvLsvvvu7L777riuy7PPPsv111/PrFmzGD58OEcccUTvHJQg9BOk/1k1n/X+pxqrc8y93X+tCbfffjupVIr7778/Iazee++9VfN39xtVE/oEobdZXGrjmw+dyvqvFo0Y0I2AFYlBsQJCUSOWVCYihZQLWFrTregFCZGnS9GqWlps3wkRq7v1sTK0VubQXM389uTz39qQUjZuJtY3xEWsasfjBUpcbBs3EqSUpYywVfLQtgLLMgJOIHAF+bQvzsXjYbl+MWXxswJBJ/isPR1pW5723R0JywWF1eL4s+r1jpw1Mr0yjJGGX0fl6shtEh26GCo35mIY5PWbTdvGZdAqqjBPPD6aIumeGB6z307KNqKZpUm4HxrFTKNcv1zPgqIFtkZrjXZV6IYIpg09T9Hq9q5lVtYq0l5I43oWnlY4lofnu4/mXId2L0MBm5Tv2tjhpbHR5HQKPGi0cqSU58fKMtt5KNq1Q4eXMQKVH9OqqK0wThYYMcz186coUdQ2RW3jaYuMVaTDTRnxSilcLFK+MlnEBg+/bJu0ck19MBM+FLSNrZSJt6U9bGXcFC38Ovr799BklBvVD2Op1VNEzOonBA+EQQDcgGpxQNaEuro6pk+fTqFQ4KCDDuLVV1/tcjBRV1fHTjvtxJ/+9Ceuueaa8G2w53n87ne/Cx++P05qa2vZa6+9eOGFF9hmm23Ct7XV2Guvvbj66qt56aWXEm4Ev//973u8vylTpnDnnXey/vrrh+4Nm222GWPGjOG73/0uxWIxEa/kX//6F4sXL+7SvWVVKKVIp9OJgcDixYurzmbYE+rq6vjCF77An/70J37wgx+ED/etra3cd999a1RmV/T2ubr99tszd+5cpkyZwsSJE5k9ezbDhg2ryGfbNjvttBOf+9znuO2223j++edFzBKEXkD6nySf9f6nGqtzzKvTf8Wtv3oTpRSO42Db0YCus7OT3/72t1Xzv/rqq1V/o4aGBnbYYYder58glPOtBTMY+U9t3M18AUt51YWrhEhlxf4G9ES8CteVl9mNaFW+bfn3eH1iVLXE6krEKi/f1UaX8TwWLhtQpUJrQUworLrv8noF8b7K8yhl6gmgFMpToDyUUmhboUomtlYQSB6lwbdoiqyQAD+QfnjrjFlsaSJRTysjnml0LPCWsZRy2hU5XSKjkhMtrQ1hcH5AeSpZRw0qPIl8ndKPLaY8khZbQaB4z1h3heu9aNvAYiuKxwXaMekE6bbZ1vNjZKmSwrMJRTdtAyWFTntG8HM1pDy8go3nx9bq8Hox+jvG0qlYMlZQSmm0Z5F3bVKWR2shS4eXNm57OBR8oanDS5GxiqTskrGK0l5o8ZTCM5ZOnhUKR0UsbPx4Vb6glfccLKX9mFiaNjdLSrlYaDzl4XoqDP6OdsL4WDkvFVpsWYFbqmXcSl3/ZmKjsVUhIWqllBvdi1QUc6wY3ICq6OyrQsSsfsIuu+zCwIEDOeWUU7joootIpVLcdtttvPTSS2tc5kknnURNTQ277rorI0eOZPHixVxxxRU0NTXx+c9/vtttr7jiCqZOncpee+3F2WefTTqd5sYbb+SVV17hD3/4Q6+/Ca/Gj3/8Y3bbbTd23313vva1r7HhhhvS2trK22+/zX333RfOsDVr1ix+9atfsd9++3HZZZeFs0m98cYbPd7X5MmTufHGG1m6dCnXXXddIv2WW25h4MCBidgrd911F1tvvfUaD6pmzJjBn/70J0499VQOPfRQFixYwKWXXsrIkSN566231qjMSy+9lH322YepU6dy1lln4bouV111FXV1dSxbtmyNyqzG5z73OTbeeGO+/e1vo7Vm0KBB3HfffWF8lzVhiy224PHHH2fKlCnssccezJkzh9GjR/Ozn/2Mhx9+mP32248xY8aQy+VCN5b44E4QhDVH+p9KPsv9T1f09JhXp/8aN24cjz76KPfddx8jR46koaGBzTfffK3rut9++3Httddy1FFHcfLJJ9Pc3Mw111xTIcgGjBo1igMOOICLL76YkSNH8rvf/Y7Zs2dz1VVXSfB34RPhpRc3YsNOz3ifuRrlmSVwBQyEJW1F7mqhVY9OaiwJ0SouwBjjHb9cI6j02OKqirWSsdCJLI6AypjaXQhY0T7LhK6yz0prtIsZaHsf/719lYIWkbAD0e8RogJzIhLCFpYVTQvnf1bBDIWBqx6+hZc2ghWeMkJRXNRSkagVWG4pT4eB4+0ORb4XY2YFx2u5gZWUL8T56UF/G7oUuhgxJxC1IDTe0r7YGcxQGHdP1EqhSkaICmbHVL6ARckXwrRfhi9+WZ4KZ0m0/LK1b6mFBSpvGTdEC/BsP3C8RlsuOa/3xD6AorYpeRaOZ1FyLTP7IkZ4S9nGpc/VFq6y8HwhykPhaUVRG4Erqy1s5eJphadU6IZY1A62Kpq8gdCkPDwUec8hY5Uo+hZVNh5YJhh7UC+LKGC7hXFRtPHwtHFFtJQmqwoUtZGVXK2wschaeTPzIZZ/7zAiWDBroqtNwHjzg5nZET3fqiywLusJEgC+nzB48GAeeOABamtrOeaYYzjhhBOor6/njjvuWOMyd999d1555RXOOOMMpk6dyje/+U0222wzHn/8cYYOHdrttnvuuScPP/wwdXV1HH/88RxxxBGsXLmSv/zlLxXTon9cbLnlljz//PNsvfXWXHjhhUybNo0TTzyRu+66KzEr1ogRI5g7dy5bbrklX/va1zjmmGPIZrPccMMNPd7XpEmTsCyLuro6JkyYEKYHgslee+0VTmEPJmbI2rwV//KXv8yVV17J3/72N/bdd1+uuuoqvv3tb6/VLH1Tp07l3nvvpaWlhcMPP5wzzzyTQw45hBNOOGGNy6xGKpXivvvuY7PNNuOrX/0qRx55JEuWLEnMyLUmbLTRRjz++OMopdh9991555132G677SiVSlx00UVMnz6dY489lo8++oi//OUvTJs2rZeOSBD6N9L/VPJZ7n+6oqfHvDr9149//GM23XRTjjjiCD7/+c/z1a9+tVfqOmnSJH71q1/x8ssvs//++3PBBRdw6KGHhjMzlrPddttx7bXX8sMf/pADDzyQf/7zn1x77bUVk44IwseF5YLnKN8iyxeyXB0GWbcKfvBx14tZbuko0HpMAAuDswefS8nPwQyCyvX/BjMKhsHW/cXV0RKICP6iXKOgKT8werhtWdD0xPYxlL9twuUw4X7oC25am7yrEYOnxwTHXGVJtKGXbNt4XcvXJ9rQdaFkFuW6qEIpGdzeC34Xf3/+7I6UlxkEgU/sh/C3I/b72fnVi1e0KiwVCayW6+/X/xwXXi3XCF6BdZRVwk/zl1JyfbiU/Lwlv5yiic9lufFy/HwF8z1ehlUw1mJWyf9eNJZaVsmIY1ZeYRUsVEGhChaUFF7RuOH1JhYenqfIl2xcrSi6NpaComvRUUzR4aXJ6VRo+WQpjw43Hca1yukULoqitin48o6LMq6JqkS7TlPAWGm5KAraIeelyHsp8p4TCl+W0mEcrQ4vzcpSDbbyQsuwvL+NixUuRT8gfbsXvewJgtS3eJGbvhG2jHujp6M6Bhhhzq+j7rmYpbT+OK5uQRDWlKeffpqddtqJ//znP4wbN25dV0cQBEHoJ0j/s/psuOGGbL311tx///3ruipCP6SlpYWmpiZGX3cJo5+sJdtcNAJVyRcpYrMGBjGYvJQdiVrBMLCKRWoU1yiG50V549v6llMqEHQSQci7ca+rsEoKxKjqx1s1blY5XiyPbZn4Uymbd85zeHP331TfZjXJ6yI7f/8MRs750N+nqXDc6qrL+pVT3gYBQZwtyw8IH7oZKrAttG+hFfwlYakV+CBG5QbWV/iWTMaqSYUWd1pB89YZ7v3m1YxN1dMb/KZlCD+76FCcnGcMcGzCgPzxOgZWVgBeYIEWrLOi9aG1loJwckrlB3bXkYVXkK4thef4Yq8dpIGXivJpJzKG0zYmPlfMd81LaTwH36oMdMrjsC88w1XDX+yVNgK4tWUYV728N5blobVCa4XjuFhK49guu496h53r/8cgu40FxcHMyw/F04omp5MhTiuDnTYG2W1kVTF0NfS0okVnyHkpOnSGoXYLdapITju8mBvD/3LDaCllqbGN1VZKuSZWl/8ZwMXCxqPeNjErA6sqwHc5NBZbQVqD1UlRO6RUiUY758fjUiYIvB/U3sKjzsobyyzlmjhaflm2bxGWbyuxy9aLWLlyJY2Njd22nbgZCsKnjC984QuIxiwIgiB80kj/Iwh9E6u2aKxS4hZZgdVVKQo+rm3HrCv64ovrGuGoLMh4kL8ihpWFsZTy01UQqAiqu/qFVlb+tkF8qFBwUJX5YlQL6l6xr+4EJE9X+lH2EqFw151r4arSygW9+GfXt86yLJRlhbMaasc2Vl3KMwHLLQ2eEbWUrSIXUIiELa2jOFnlAeN9MQtLUaqBVC96Y65067CL0WyGyjUxskxcKx25PsYENcuPHxYa5wRB/JVxCQQjeFnBDImWsagCwI4EWBNrS2P5qphV9IUrP39gXOV7HZptTFNCkdA1UymF5fnbalBYLCvUsajUhq0UqZhq6KKxY98t//y2Ubj+deL5v3Gwrqg9Xu8chVuycJXCsszEBYWCg1IaOxu59BX9wPyBW2LwOQjmHghZNtp3Q7SM1VYslpWppxVaY7WXMtTYBRP43bVN/C7sMBC9pUxZKeWG+0pZJVxt4wXHhDbB4r0MltKkIIzLZQLEW74QZmYUdWOWWTmdIkWJtCqafa2GVRaImCUIgiAIgiAIgtBn8fIOVkmHupJytRGyCqVoNkPbgpJn3PPiweEDtzbLimabs4PYSv4OAougko7+Qpmo5GfVgRihEqKX9pQ/nZyfN7CscZNugIHlTpRQZV/ldJUnJhYV8w4/W7Fe12WsgrgYsLTYQP0HbtU4WD2umxcT9cq3jwtlrhtZaWmNsm1IOcZiy/OFLlehLMvMfGhZvlAUWF0ZcUvHjOqCgOmhbOBrktllmmlPn4JSGsf2RZTOtLEW8kxMKe36qpNHpDi5ZQKEbyaV/shmRMG4sYaB6oltpyJxC/AFu6iOZpZD7VtcqTAeluVGMbPQKgw1FsaCU/giLaiiKV9bxnXQdiNLLnQUY8sEfgcL5ec3VlpWwazz/CD62oE5L2zFI29vZsQmxw0FqMCySimNZWlStotjmzTb8ii6FlorXM+iJl3EtjwKJYeVrTV4nomV5ZYslB8zSykoujadbpqCdihqJ3RxjItYKVUijZuwbkopD0t5pJVLUTvGRdE/ATxtkfdSlDwTe6vTNQHtA1Er75pYWpbW5H0ztaLyhSzlYuukhZblW4IVtWNmRMQhpzUpVTIuhWFcLCqw/CkrC1jYWoeukD1FxCxBEARBEARBWAPefffddV0FQSD9kYOd98IYRCHBZ9+qRxUjAYZSYNqijfWP1qF4pbAiMcUXsnTg/hVzKQxnnnN1JLgEwhdUugvGt/MtcEILJ5+KoO5dCUU9sdoK6loosdmP8tzrTKq+rYqCqJs6dLGvQLxyNfXtS81nL1b/uJtgeQwvqAian7CiKt+f5yW+h1azpZKxqHMclGMbSy1fKFSeZ0QtW6Ft26QB4RR/fh2CGQUDrSv4PvDNPAPfTEcuiBBZbkHoxhirWNnfmEWVAnQxEhRVlQb2RSwjKClTTri9Cq21lPbPG0sF+lUUK98XSFUgeMXWe6EgBtpvd22DVQrcLP1dWn5eMNeANtt4mJ0rP9aWsc5SWEUb3W7h2b6m58SVQdAqbn2owY6sGY2FoqZFK5Tj+QHvlf8zWyjLzGZoFDYPz/bIe8ZSylg7RTGubGUsp9LK9UUlbQK2K+0HYjdufUBkFeWLRe1umvZSmrRlLLFSlkvec7CV2d4IXkbUKifnpbBsU66rbWw0eW1mV7SUDmNpBcJbChcPDzvw31QmLbQoUx45naJR5Vc7Htk6DQB/4403MnbsWLLZLOPHj+fxxx9fl9URBEEQPmNIPyMIgiB81sksVVEw7aIXxbZy7Eh88DwjYBWKUCiiSq5ZXC9a53pmKblQLJnPPqrkuy4GllRBcPWS58c/ii0QBmkPxCmldRivKVwXWIVVC6TuaVOPrtZ3tVTD01jtOayVHdGyoj1aWjqx2/Jmac1hteWwWjq7XFRbZ9fiWXd1KXeJ1Dpq4/LtutreM8KWLhYhl0flCqhiKQoYXzSB4q0gYLyrjUWeWxYw3v+cCEQf/MbB5ABlgeOVB6oUlFG54MW+B9uXlWPOG/z9G9EIjzCAexDEPaif5fr79aI2iQext4KA754fCD7I7weAV35wd+X5+UuYfZT8fQWhyYJg8sUov/msUCXlH7tfX42ZLTIou6CgFIl/qmS2MRZvCkoWFHzFrOSrbBp00cIrWWh/QYN2lUnT4LoWxYJDRylNUTthwPe855D3HD/WlOdbZnmk8IwYhSatPOpUkbRvQZXTKdp12ncxtLCIXPpK2qLo2ZQ8PwC9Z1P0haYgQHyHb73V4aX9dBMMPphd0UX5342I5mqLgnYoaMfMkogvguGFx2L7LpGB5VhOO6HFV09ZZ2LWHXfcwaxZs7jgggt44YUX2H333Zk+fTrz589fV1USBEEQPkNIPyMIgiD0Byw3ZiGkYy6GhSIqXzSCR66AyhfCoPCRaORFafGZ9AJrmkAAC4SuuAAViGZBPCyoKuaE8bhK/uyFgXDWjfhUEWA+2F9XAhgkZ+wLd14mEFXJX1FnT1duW4XEfsqtsoJyunNBLK9j/DjDVb5VlqdB/z97/xprWbadh2HfGHOttffZ55yq6nffR9+HeMnLhyQrFGOJlBTZQiwIsWNFyQ8hdpQElozIBAIDBKJIvwzpjwIYUPQjsBDCSgwYfgGRbCiErIRwxCh6RBLJ2NST4us+eO/t7uqqrqpz9mutOefIjzHGnHOts091N+uSLTbPBKrO3muvx1yvOeb4xje+kWf7LaDWcdT7bABlC2opoGWgljHoZqCWgVcFiDLdNZwAop73z1P2Zv+k+deCXTFXQKsByhQkMmArSgGOSAzYssqFCmA1IBeWxzagdVHZsFbTrOsqgNaAWAkIY/2uFQ9Rqxw2VRLZQCueSPdxcBCLQBMVUKsAW9kBLiioleyfPy5RwS0AmpYLrfI35g5P0xmSkKX0McbcYZdWNyoJtm0E41le4yqvsc0rBCj45SyuLIRdVAAqChtba6Xfc8A2rgpw5v96StjloYBbro0FaMqgp0K6LldALpUPRwmYpLOKiVwE4hP0vD5qiiHwMYJZf+7P/Tn80T/6R/HH/tgfw/d8z/fgz//5P4+33noLf+Ev/IWPq0t37a7dtbt21z5B7c7O3LW7dtfu2l37jdAOLwHxLGhqGClLC1NUYKMFgJx1lZu/qYJDztgBoOCM6zXNquzJ/HNWQGQG+pBV2XMQqz3+KXDHQZ8WMPN2gql0ElBZgGOz9L8T+zmpc9WCT8/T6DrVlsDXqf2fYm09BzA7WZBDcv3noFZMwDQZ6JieD2rFPAe1DGjSCpd+LRdAVRIDgaSu04BYWAJTMl+vrjMHt2b/yrYNWytVUKuAVC1wltr+NvtvQa3mt8LiMoZW2a+nvKa6HGKAVm4YWxPAo4FXo23v55YMwJoInplHDWBF2UCtTMDEoGhMLbF1mnVlYmVo2d+YuTCWsjAylD31cLzEe/ECj9MFRjAmMJJUQOggPR6nCzyOFwYy6b9j7gEAY+4wGvMqZgWfshCOucNkxwGAZ/FMNbQMAGtF2l27K0BTE/3Yfgz9nZBEqxpu81DWyagC9dmYYMr0+vAQ1ceimTWOI37qp34Kf/JP/snZ8t//+38//vbf/ts31j8ejzgej+V7zhmPHz/GK6+8AjpRSvau3bW7dtfu2kdrIoKrqyt8+tOfBvPHmoH+bWkf1c4Ad7bmrt21u3bXfrXbJ83W/PPS8pnpCQlUFyu3TKtT4ImxnTIMtMpFVwvwlK6qgSWtiJSvDwCBDbSRms64rIqYbwGwgBspdg4wSeAbIM8N8Ol5YNNtqXrP28b1q1owb7ntYpkEmvV71uy3sm/f/hTA9itpYjQmACCGJAckExAC0IUiBC8m8E+ZVeg/CCBs+laiumcmoiUEkN3PUgnRRdWhh6y6WKTremsqJHo6aRH0p9O/AQ4Iield6bFd+6o8q/ZZAur+TFdL/6koPCU9F2GU3yhZBUXT5jI98hnIJtmE5oNeBxY9H7F1pTegLNq2QTW+PCuuiMgTFKQKDpqa4PxkDKykvwkLKBEwkWpuCRSZEei20XTOOmDKAbs84CCDalHlgCiMx+MG+9RjwyO+0L+HddgpACXaj8fpAg/jJZ7GDe6HHc75CLYUwH1SRhaTYMwdOkrl70Vf58LbuELPCdu0QkdJqxXa34OlFU6k+lg9Jax5QiIFtXpo4YSDDBgoYrRHxUXqD9IXLa+tDHhAUZln/7wLwL/33ntIKeGNN96YLX/jjTfw9ttv31j/z/7ZP4s//af/9K9V9+7aXbtrd+03bPv617+Oz372sx93N164fVQ7A9zZmrt21+7aXfu1ap8UW/PPS5tejqDcITw9gI4jTHDHmE4fQoMmmBfepvQV4XdDKJZgDDXATwOEVfH3BkhbsLZasImyAXGL1MCTANFtLKY2xbH5S1mqcH27ngvaW7/FKjneALCWx/bWEMdIpMI5SxBtcf6z9E5f53npls/rw2y5px8yBFJBra4DAoOES1qiCsWTojXB7zkVMXVy3bNkFComCNr7DqvrN++PEFQ8vQXAgAp2+WPUVKtsQS6TqrcqmvVYup2L1osJ1j8H1AIUKMoNINYAaWA/pm5Dgqaaot1PrsdAqGwy1ya3ywmKBOkwA9Qo63qUdf9CWkGxiNULDOyqt49Mb0vKBUABw/IYELMJpSObZlbALg4GQAVcpTUO0pf9BQOfRwnYpVVJ+0t2QSdRMIxJcEwdzrsRUVRkPoNxSD06yghBwSWGYMoBzIIVj8hC2KceK45FIN4BqATCZKLwk3RY8VQ0s1yYPoEQrH89RUzQ9ZNVNPwwmbnePtZqhstIt4icjH7/qT/1p/AjP/Ij5fvTp0/xuc99Dr8b/yN06G+s/2vR+Hu/C7/1R/8p/r3X/1FZdp0P+DPv/BDenzZ4qd/hf/va38D/5fHvwG/ZfB2/7+xt/Afvfz9+z/nPoqeE/+vD3401T/gTb/x1vJ1W+NF3fy8GSvgTb/43eDet8B+++z9ARxn/7hv/L/xXz/4F/IOrz2ATRvzhV/8uXuY9/sK7/xIA4E+8+eO4YMJffP/78aX1O/hD548R6C7Sddfu2q+n9gd/9g9g+N8kxG+eBll+LVrEhL+Jv4rLy8uPrQ+/Gu3D2hngdlvzOfwRMIZf1X7e1ujVV/D6v/4e/uXzd8uyUSJ+YvsW9qnHWZjwOzZfxU/vP4s3+qf4YneNv3/4FD7fPwJTxv9v+zn0nPG7zn8J1zngJ7dfQEcZv+vil7DNAT+9+zwYgt95/kv4J+ObeOd4iYETvu/sG9jQhL+/+wIA4Hed/yIGAn768Gm83F3je/o9+I6tdtfu2q+r9p+99yWEHxPkq+uPrQ8ZI76G//gTZ2s+7sbboOlS46RAgqeYpVTs3q32b5ZCmOcglgMTBjiVdTv36k+wmNoqif63AZsoplIpj4yZpUwZZ4U1KYPO3nONrCWY1h6Hef4dxvBiBgJXwIq5MIJKCx/RnrXH8A9OkloCWsaAK6DXbHkFtso552xpZzeryD2vH3ptlqDWBErcgFpKQ9LrIgpyGbCnJC1eVAh0lk8FLWfAFlDAl0LeK8CRzBhcCo4Zk6t1VdvtqH4XMjAqS8GehLU/xGIMqQpqCaj0QQEqKbvNQbWufP8CY/qxQm1Fc5xgml3KEMugAk4tG6MB8AQgBrwqYTaQTTzT1lhblOycHFCELhcD2EhIn6Ok7yusKuKYAnZJWVkMQcwBx9ghB0JHWSsdwtL/CGChAmhNEnAV1zjmHpN0AEVMBlxtJ53b7mOPwLofDppKGMEYpxU6zsiU0XMCC4NzV9INO2RcpxUuwhErisrKMjArQ8CUscsDNjyW+7qmya5fhiN6SRhrtkqGBHS2zodpHwuY9eqrryKEcCM6/u67796IogPAarXCanVT2KxDj44+JjArrLC66HHvsr6N/8eHP4C/+3/7IfzAH/nv8BN/7Ydw/a88wD/+T78Hf+d9wVf/1N/Aj/2Vfwk/8Xt/Kx79zGuIr0+4/EcD/nf/49exnQbspx7bv/46/p3/4WfxjX/8BvJ5wvkv9vjjv+cLePaTr+H4mRH/+x/6r/HKOuN/8df+XQDA2Tc6/LEf/Dy+/PK7+Gc/+j34L/6VA/7Nf/lHsboDs+7aXft11brzFTpOwMc0ngEoRuaTkk73Ue0McLutYQwfH5jFK3TDgNWqPht/Z/tpfOMffQGf/hfexld+/rMYv+MCD//Ba/jlveDp7/kq/tnPfgFf+cIW+7fPkc8ThocB/88v38OUlJo+/dI5fuw3vYyrhxeQXtC/z/i/f/5VHL95jnSZ8Ls+93PYdAF/+ed+BwCgu2L8lbdexStnWzz6qdfwj35TxG/94k+io49WPvmu3bW79vE2HlZgEgDjx92VT4yt+eeldTsCj9n0qyojyzWXxNIIlxpM5T6096OpSAg4u4krWATo/kPz/RQTyfcTuLCPKGVlYaUGnPLjeMph2xdnlXkKYNvH+YloN7owB686Ps3MWjThj/o8GvDWXs6CpS1oJQZmKSbUXB/z5R38krJcCuhHDbtOkGeMsJsn0YB8M1Arg6YJyPXakAgkGHDGXEEtZH0mLP30VrZWOWcFl2bgYMvgWgBchcOWb15vMtaSkDOmGoAqi7GmDEAy8JMsL3LG0hL7zcArIRWLF2NweYqhphlWBlmbnuf3k6MokGXMtcK6sseSpLklYv0KCiYJ63oSdB9htOMa24xS3Sd5f6gyuCgaM4wVOFpxRAJrumFSCGc7DViFWATYEwhs1zgVYE31qiYJhQnVtpQZ0U4kg5AlY8y6/4Fj2R8AxFznfT1l7FNvj4Guc51W2ITRfk/YFJ0vwoQevcrCY8orZGYwZWzzCgMlTNIpS0vCafTwlvaxoB7DMOC3//bfjh//8R+fLf/xH/9x/NAP/dDH0aVvS3u52yIH4Fv7e/iD//rfxv/y9b8FysDZwwn/0d/53ei3wHHqsH6PcO/lLSQA1+MK/+mX/xP8i69/Fd1eH4b1Q8bqwQG5A55en2lVhfd7/N1nvwlrikAv6J4FdHvgfBjx73/2x/DsOwCJjElS+Zckn/znvwPAJAlHmWbb+b9Ty9v9/Gq0tn/eB++Hf/6gfn3QPz/Or9Y5/Gq3X6/9vmt37deyfVLtDACc8Qhh4Cqu8N1f/jp+2/nXQAL0u4z/9uufQxi1nHO3A1abESBgTB3+Z6/+A3zm/ImKmBLQbQnhLEIYOI69TqgOAd84voQOGQgAHxk8AX1I+P33/hmOLwGSCRlSxtEscvLfcsyNt4zVp5a3+/nVaG3/vA/eD//8Qf36oH9+nF+tc/jVbr9e+33X7tpvxDa+EpXR5EBWjLUCXkpoxcJPVthrgZCim2UgizO0AAWm/F8LehXx9qYKou+3Sa0r6YQLIGtWxa+tVPi8NEYiSN9BNmvk8zPkyzPkizPkixXyWQ9ZBUhnIBwzJLAyeU78AysT7bZ/sHXa9fTziX/tfokMQCLAf+8YEgKkDwq29dpPX45O/8mqh6wGyNkKcn4G2pyBzs5AfQ9wuBWYm6UxigFhKUNiVLaXsfaKSLxXlzQh/5lAvMisCmIRjW+E42FMuvJPxEA7NP+kMPzaKoazvpZKg43Iu7Ri8mgE4W1fUTATiXeQyUXos28H04FrlrkgfLTPURAmKd/LOlHBsHAUq35oYvAmFB9GzLaZbZ+MjRX93BQr45Ea8XnUiodAEZRXhhgBiTDGgOu0wjH3CJQxhAQiB0FVsP2QhwJqHSUgg3DIPY4m8u5tm1dWxXBAyowpM6akaYdZCPvYY0zKAGMSXMcVxhzwdFzjmDsVoLe8TAex9qmf6Wgl4cLKOkiHg/Tad2i1Q0C1tAIEAYJRArZ5VYTkP0pFw48tzfBHfuRH8Ef+yB/BD/zAD+AHf/AH8aM/+qP42te+hj/+x//4x9WlX3HzyfofuvxH+On/+efwE//wy/iFX/4i7v9P94gbwi/9Tzq8/rcYm3dHPPx9AZyBKQZ0CdiOA3YCvNJvIQw83il4lWJASEBODHzfNQKAn/zLvwX/q9/5OYRnAXlQg/Bou8HjHFR07sj42YmxphpxWz4MAYKtdHg73sfnuvdxkA4jerzCe/TkTgdhAuNJXpeHFdBym2uayoPXU8bLnPCp7uKFr98/GCf8zPEz2OUVrvJa6YjQSggJhJ5Syb/Vl8X6QhmMXKiUPcXS52RYbbL831Hq4650zA+H5db96bV00TomLW/6vetv4AdXCf0LshR+Zjzg/739MgBgxRPWNOIgQ7kG3npKVlZVMFBUhNvObd3kJbPXqrUWIAi2jJGRwUbxBAJlBEj5DgADpXJcP6bvh219fw56yvhs6LHhF2OvJMn4VtrhuJhjMYBzJtznwXqtoCeDEYjQIZxMr22Bv0CsjioSDhJvOGqeMhVKznf93ZexPTPB1k3NPsKJSQUvnjFevo93LMpf1fZJsjNABRe+Z/UQ3/ot9/GVd17F+89ewup7J+QeeP+7GedfJ/TbhO1vYpBoxI0FysoSYMMThID91Gn0MhFYgJwJeH0EA/jmP34D/+VbD0BH0sgtEfZjj73R15EI7yVCV3j5KFoX3giCSRjXeY37vEcEIyHgzGwIYBFAEA7SFWo+ABAJuqLsoJOpMxJcvuD4kkXwbkp4O92zCGVXqgNF0V5ZDBsE1abQvuhfgpQIJVMuffZzz9D0i9TYTTlxbW5rJT2hjDdSrgdB8Hp3hc+G/MLjxjsp4ivjKwB07O8oIVo1olZ0NZCfUf3s9rCjVHRQiBZjadt3WGS9WZchM8HgoHwDO2b9zddrn4MAwT0OL2xvswiuZUJc2BoCMBCwomC9FmQ7A+0Hn0yvbe0JEymIiYyIfEPixjfn8tw02xbNGP+rrd3FqaeJsHz/5t/vUoI/+e38tR1I7s0qEwKoDJ32MykQQy0YBVTgxVf3VMLy0C6eI69O2AAVmgYmlWWFynqasbFOAVm+vH1pXAi+ZV4Z4COdsa+WKYLNvpXlcgsbqxUh/4BX5EZa4i3LVLzbQQnjIeXaf3Jb50CEp+wZU4m4piJSzPV6ZLFzZVDutTqkSE0ljRE3Bhvbj7YMJE3ZQ86grtPf2tTDIg7Pll7owvF2ebJpixUNLB/XF8+QsaBmpmrG3qr9pMXgVqYCqbmNpl/l9qQwvrwCoZj9tJQ8U7AvTKcCrBnLS9lQtsyfK38Eyu9Nv4N9L0wtS030dZrTIjJSkZPETG/L0yOlE7ibXgTom3OurDCqt48EIoQxd4V5dYg9jqlDyoyjkNpwEEYE9JJMr4qRwUhgHFNnqYW1KqKDVikzxNhbPet8Z8oBIoRD6jCEpCmFloZ4zAE9ZRwbgCyjamj1nKzaYodAWf+yYMU1dbCniIBsqZHap0DZ+ps/tI8OfIxg1h/+w38Yjx49wp/5M38G3/rWt/Cbf/Nvxl/9q38Vn//85z+uLv2Km08s/8ur78P/5299H37kD/zX+A9/9F/FX/vW90IIuP+5p3jy7CW88veeQGQF6YD91QoPDsB779zDv/fyv4Zd7MGj4OqX7uPeNbC97nE2Aun9Fb7ze7+OKQe8t73A9mfv4fIbhO2nBd0B2P6T+/g36N/C2buEbtfj3/z7fxQ5EySz/SVIstGBBOf3D+hDwtOv3tdBqrdB6izi/J7+dpw6HPYD8O4KwZBjR9YVLfaIDTB+5x5/9/f+n/BqOH+ha/jv/NN/A+k/f72i0UDNOUelnIoNTs5AzIFK5QinbdaN5oMRGcrd5mPPGqEMIjXHe/657QNYGbvxt13jJ37wP3hhUO8/ef934i/9N78TlAnCAsfeKDXUUxYgwCp9QMUB3ZgEAYUMYnN+ODfBK6XRdp2i5kxSHJAuKDDWcUYf6u+Bcvm86UZ09n3FER0n9JSx4gkdZ9wPe/zBe/8tvu8FM7EyBH/78Bm8Mz0oy1Y8YaCIN7un+HL/COdMGEWws0T1SxL0RFhTuAE2KXNPH4KeGAmCq5zwOHczSq4Dc9xY1dD8pmCeO1h13pSkfuZmu7KPxsAzFBRrQbMetc937dvfPkl2BqgO6T85vIavfu11/OCXfg4//VPfhZ+/eg0CYH3/gMPxDGffOADSAQzEY4d1BHbXK/z1s+/ClBmUgPH9NVYjMI0aWMmHDq++9lSjddOA8b0VVs+A8VKjjOPDNf4S/ffQbQGeGH/pm9+vfpEQREgnXEJlYtavIwJlHJ6srfM2s+szhlUEc0ZKAXEKwDZo5LIZ/9sxmgRIL0f821/4uy8MmP/Ye78F+R9ezCfQS80RO5XWrpRxH5jZitLXRX/LBPU2W+Ob0unlnu1Q7A4D+c0R/+u3/j4u6cWuwc/sP4t//IufBQlVW+Ln0dpAtoX+uYiRqMOlPoUa1zLUmUPA7ABg3Y5tPbcvZNv7ekSimhwwe0W5CM9W+zPhu1dv4/XwYmCWQPC1eIltWpdlgTICZVzyAa/wHgOpbzHZaQ8EBCR04BtgkzIVdcUAZS6OItgLl1QPv7x+ft70MVFHLTRwYuuDtxlW7e/eWiuijwzNQDMWwhLgumufvEaTTXqNiaNl2GTxAC2egxZEMvbSDdaU62g58AIYu6uCZgXIwk3QqlRVdGCm1baapdbZOt4HSy2TvgOGXhlMrVD7UhNz+Yh7Nb7ZRVqcvo/5HznNcLFbH+sJ5ZoInbjm0qyYHYwhA2GkwZ5IvfRkLCeSZr96PymLAnsiCm45Iy9lIC9E/1tQKzMkRgOsQrnmJOpTiN+7BtTS4wIkTn3S5UJU/JByHJrb84LfQU6nJdaNdTtPb/T9ZSopiNUXs2OQAnR6LagGTxjzB8KRKrNRysZTW+Cph27zS0piA5wJQVNCDRAUAmDZerm35976k4HiDFC06+aueK5+q6Y7agqir9MKwUtn9ndijFGdwgDBLg8KFiUVhicTcZ9kDuskEEbpEK3yoYvEO2OKSTDGYLeTQEKYMqPnjCkzEjH6kBCzANAqh2AgJkbmhJ4TkDv0nEzUXYGzZGL1qxBnwT33uwIyeooYJSBLb+mIx0K4CBAc8oeHqD5WAfgf/uEfxg//8A9/nF34trVdHpVN8y7jL/6f/1VwBt55col1Bnb/4CWAgPe//1Xw32H0W8GbP96DUsbwEz1+9m9+D7qD4CwJhp8EQBlv/vUAyhmbdwjv/b3PozsKzmPC+rFOezbv6At/9pCw+hsXGC8TOAL9T61A0QdHH0XqiCK8Bgh4NY41yVd0AMjdmW7ntM241300VGRaDEyPv36Bn/2hM7z6AvPKQIyH71/iO3/q/Xm/TzQdNKEDneXCZ8uH1wiNlWI9NQNcUqqZFo4EzSteUGuIGkBpAaw9vH+Od/7FHp/6NrxN57/M6PY6GubgI+7y+ChAGswOtQCblH8y/52AyRySuq6UAVdIFBwLagQUGBNQUJCHQ0bXZQTOGLqEzoCvISScdRN+89nX8X3D7oWvwT87fAq/sHsNnQ1qPSd0lPDW+jE2fMSb2OIgHR6mcyRhvBK22FDEOUes7X65XT2IFEekp4wA4L3U42vxJTxJGwDKBGgZfUwZA6UZa23dsEl0X3H23ZlqfTMQA+q0BGMT1O8ObKFER6YUMOBDVBu6ax+5fZLsDKAAbaCMbkv46Z/8LpAA28MKnQDTO2cAAYdPbUBfJ/AEXPyCOgXnXwl49LXXwFHQZ0H4JgAILr6izkV/Dey+8aD83u31eP0WgADdDui+ukJaGc3+m6EGC9DaGmvUAQRscmoAKh2PhfvynTKAHJvI6em2f33Ae5/r8bkXwH6ZCNv9Cq98cz/v98lGzaTZ0knYxn6vftXaC1l8Xuxq7kAtbc/880wwFxVY264GbD/DuHyBa+BteEbQAkTNebitadGS5jyXf2efUcE+IdUuLkBYY7dsjQqOEVQbhFBAMWIBszGA7S+Rscg4443uGV4PH14c9rb2KF7i/WnTsO1MrLYb0PcJFxgRwdjmHgLCGU3oKWNALpNnv1QRtfhWMDbZLjOe5jPsrcKUsssqo09Zf5WJ5ozE9vY6mOeXrTLlbJvCgHMmG8pymzIYSKZNUzjubM0nsW3fPlcmj0jVbGrn8AvNqZlWlqe2+XciBZ/qygoweVoYoOlpdgyvdjirPuj7T2n+nXkOYnnfFnN/WQ1A35X0uxa8OgVazbZdosUnWVWYBzXcv3ieksdzfhecOHdUgENXkto3QY2GngK42LE9KVpalObXSwrAZSw7ZqALak9vA7ZEUFhaqlgO8ntif8l0tFpQC8ZiKwAWBEVLq7kh9TfMgS2/Ls7aaq8X12siBng6dlqurVdJ9MfUgT1UUKsMwtqRamOgdptjw8Kzc9Hu6/FyaPblmK7vMuhlE0JJFVThdlTSh10KjkbGk7lvxrmuLwEz0kI78Du+iEw67jdg4SH3iJlxjJ1mHgmBAUwWOMnCmAx1SyAcLc2QSSojXTTbxsGjLujf49SVzwSziRDsY4+OM846oLPMoDF7OqNWQmRSna0shDOzz0fpkIRxGQ445B5rnrCTgDVFHOz+DRRLBpXfZl1+wIdtHyuY9Ulpuzzi3/rqH8B/9//4blw8FHR7A5r+8gYcE+5/RcCTIPeEl/9prmViBVi/n0slBiHC6pAAJuRO375wzApEdPqUr48JQlRQ4HDU3GaeAiBAOC4qiAA3JtbUGg/UFzXMtvF1FuCSf7b+hKNYKdAXmxwx5yqKuOx/23egDGiEBJlY9SfZcs4D1Zz40NCHffs2WkLNZ/2x0JFLFMEHQbehy8l7APpnhHfTBYAXn1zzCHR7GwxZQacSEWgcBmGAuXG2WhDLQS47jwKCdQtnhKHXa7ZMkDv9Cw7IxvxKQUB9Rux0EDuwKMuLldG17QYDh14czLpKazw6nmPdOCtMyo56strgAR8wCeMqn2F0dlXYAXlColzAIgA4CnAw6u0lIjIBB+nwJG1wlc8KgNVTLC5EQMaGj+XYAyVsaVUAqTWPgAzo7ZkPpKqeAYI1TYXNxeb0MCqY1ZNq7jDURiVRJ2PKd+ysu/bBbZKE/+rJl/DOz7+KYat0dwjQ/ZMelAWrJ1KETc/eM3DIaOyUsgUBdF9hLxWYgUUigTKp1BQRKhqcFF1nguv+gOfgQdIEVJrFSU4HLGRha0oAxpykJIiFk/8rb+Th4rKb2/bn66itARnThbzC1xzUosWeqO6lAauqrSlfab5SSXVowCCy5eFI2MqA53tbH65RAniqts3n/eX41ofW9nn/luDUbBlQmGyl80u7afZXWhtG/jyqzcksIFJRWmZlbzEJmLOBQy9ub8fcaWlzrvMXgkWFQ4910En2KD2i3x9MALJVaWrALAGiQU2DZDABEYy99BibNFqmXCAnEikBDQAIkjFSKParM4/HU/xr+iXQQdM4PBUzQJCppnMGKMPDpjQFa803UIC79klpm693oOmo4JGznJ7TRERTzVws3cXcnUHVir17OpsDWcWe6AtMLRjlAEjLuAIaBqjMwauiRxU0hbDvioaUA1izx9bTr5aA1W3n2WptLX9rFheR+FuC88WfWIApNzqxuOziwAlQ+nED4CoDPQD3XQB7w3Mjlq5JZoWl1WhOqV/Z6FA5s8oYdBJj1TM7kXpIQWqHHVBaglpKra3pqcmvoZT7S43jVIAtYAbWOPuqtMKEQklNLOmXpFZYl0nRxPL7VUAx26feJwOEoINfSSskVFH5pMtyVwfyAsaSbketrU5UPrqIOxVwyudeOk/IfroZRdhd2mm+gVwU6/Xy1ERxRrR3g4A8qA7qPvXYZZWfOaYOMQWkTCASjLnDQQYNgNDNd38XBxybrJRJtCDQGDvErHI90dINRQiBjVAQCCkzOs44xg4DR2RLefTUwyiMwY4ZJSDmqp215gmTBPSUsMsDXuq2AFRGaMWTMrcIeJbWWPOEcz5ilID0EQTg78CsF2xP8x7/9lf+NfzSX/wuvPHOZBNy1AFfoMJ4AoQDTs6beZbPrv/x2IJOAA6p7g9AOMzBqrCL9nuzfMFEouWxZwMa5sDVid9m+6I5SPSi7bUH1xAadOAsx1x0eGYMGoPKNqJETbFT8URjaxGBWnDKd0lSjQo3SD2R3q8yMRfMZqxOpy1GiBAOwFU+w4tOrl/tr0o+NuCDH5VnpmVmSRPVLpUxzLnKHWYOhINbueRiU5O2CNXEASzCoGwO3Y+YYKVAOoGwIE0dchBQl5FiAHPGFDLGGPA0nQN474WuAQAcc4ftNGget3CJHJyFCU/SBofuKQ7SY5tXeJI2yEFzwkO4AhAxNA/rKIytqFhhzwoujQi4ymd4GjczbZo1T6rNhlCed41kVIYWW353gGBy/bHm94P0hbWlyl7K8HLWVjLdN4YY0KVzlDsH4659UDvIhL/y5Lvw5Kdfxfm1ObdmL+B/mqHpNEaTa/lpH05Tw+YQALEd7LX0dds8jWUe5PiAzi8n/TPgSubbL1YlfHttzfnZiBJiveWYp2xNQQbccSOos8ce2aWT/hKV/UABnLK8IgzinaD2/s0dOBKd+B6lB1DB9l9J24SjHsvKnavv0kzUywOB2qHFZ/dV/fQEjRMyN5F1/cZZEWoqTXEDbPk1iQYSsphvLcrYyoxj/vZUfY3CmHJAssm7Oy4999hLjws5IEKrPx2kh5jzThgByrMAoJYiV2iJSRBEkEyr82DMLkCf+870NlsOFgElMu0aae4QZVuvZXBFUfVItu8CE12GlmOPIBQWl0gFtV4MC75r/xy3s/cEfLVTjaM2vbDVyVLqo31VFk8LWrWaV8v0wVOi7Q6UAJj/5sta8XZA2UK+bs5A1wEhQNZDBbCsPxUcfw549SFAqsK4ek6rGRd0a3aIzAZo2+bUqifBLpqbHMJsAF2mKJZ9B1TwCjCOZbKxk3AD1AJOgFrqxxT2VUqQmNQpaEAtSXpscqZeC2o5+65EPqgGvk6BV9zsy86xAl+orK1yeagAXNIw08p1LwxUsuuDOpfh+mwUjUabY5T0vWporfvuZ1ERg3cfSswmEZS97P5SQSC9O8bSY6nrkKUfMsiJXzqmm8h77lAY7S3eJJ3+7gCZpiJWQgNFQoqMbawC7wAwxoDAyvZ1P2KSUALr2UTY1V4IYg4mwK77yEL2SDDIGVkkmkGcGIEzphTKvo+pA8Y17q/2yEKIOWDgVOzoqgSGBpzxCCYpaYZsvs91WmNl/lYWwmCg2SSd2k2Zis37sO0OzHrB9l9cfQf+8Y99Ga++M6lTYOAV0AAnPnF/ziRiBlx5k+V+bu7jFHh1ErQ69b1lEJ8CsU79butIVkPYHQXfmF4Czl4MxLjoRwDD7Dxu7fvCmGqZWCrAFiWBJB24hWgObJmRmLGsXGTQByygjlc+Y7eZoEBM06pel3AAHqULAM9e6Bpc8qEO0GUAtAHXbRZRiQq0UfO2xCzrSFp/c50wv59uINjBLjPinrttlFgWNSqSSCMS0QbXYNfXSs2KEHLSXOwkLyZOzCAEZGzHAR0rC8yjA9s4YJdXRcBwkoBj7nFgBbYuvYhB88gcJWCXV5pGCCqDezahYxc8ZgiCV12zh8CNgEfOeygtN2fGmmqkYbSZy4BkooWms4UIEDAIgAbIyhCsKGEURiCtdCZCc0r/Xbtri/YPjy/j4c++is11rvbE58K+koNbz7M1s3X9802w6cZ2Hwa8us1Tbuesp0AsuWXlBqDjCDzLZ3hR9ufACTW3uj3sib6fui7uhJCdS7aB1if3jUN0Q+xW6sS/TUmh5n/xY5CoplVzeI7ALg94UTBroFgm2eXIhbWM4lSU88XiL7XrubPQXp+6Ltk+0f6TxX5Q7ayOhVSCRuV6GCkvEeEonTJcXwDkJBv3xxTAxCBUzcQxB0wSkGCiuGCNNlPAKFp4JSxeAi173hnzyh5dA8kE/lePmyEAafKgFh4wvawSINFJ/wRCB00VCSQl+NKJQKBMLIZoQIXYGFyCKFQuWSApgsE6bNDpZ/2u/bpvcU0KBDn7ZhkQBurYFRgY+gJWOGAB5spOcQADmDNWvLleVquP1VY4FDHGFdf5DTVznaGHrFfIm5UGTlsGVgtgLV/zpg8fKg5YGKTPWZnqsW4FqU5tfmJZm/42W86LnebmPGb2gJqlNtA62BMACNf0wkw39LSWKYgzUAtQtlYICmrlXNNA5UTqIZujkOvzUZhubQqLi/636Sz+p0039DPLVFM2W2DL97EEtcTvXwW1yjXLVSOrLTbS2qGWRTxja4mof2N+FCWpz54BWq3vpMCuPcbOys4oNs1IRkA0UkST/QIBQgY8FsO52aZhSefOb5XqJwP6PSVGtCyODELMDCbBceoQQsYh9RgoYrLKgc7oPeYeSVTIfZ96tVMiiBK0MNAUNBNICDk3QZ2QMMYOOaSS1h6MhXU9rezSU2E3d6zFwBTAIuzzAM6qc3kWJkwIlaUlCROACQEsqhV5GfaYRHW/AmXE2yiSJ9odmPUC7Zg7/B/+3h/AZ/9JVDZNKfV5Io1iCVK1g+pi3RkY1QBYy/TAG+su93UKIFu0GyDWkoHV/r7cJwnCQfCt6SV8Oxg5M32u5fG8nVrehoh90I0oVFiZGGSAFgADuYBS5tdn4bkaDUf6yYEsMiCrAbT8cN1B8K3xAYBvvvg1SCjCg9QY9mofpAGeqNgTp7oqOCVlgHTgThgIHhVpwSwL1AjB6LAK1OVOzDbqdwe1iAnSCyQZqNURJKnwvDpZL9YCqXjglBpALGnk4Xpa4SA6EI+W933ICmStaVJWlCRMYK2w2DzMCYzJIhOARi52qfa354SrtEZPSaMF5JGEmjLo252qsOHL1rbeaKmNAAACguh+PBQzlZuQAbpjZt2157ckjL/5jS/h3nu5KVcNnfgtV17aj+fYmtu2O5keeGP9U47S7avfALFurLs8ZjOmk5bdvk5rfDtSmUsfgNuvyanlM1uDajt8guyT+hKx9u0aR8qdyWYFaf5vpuIF0AL0MnCEXYOrj3zGN04lo0SjfWJvh5x/ID/vcivmwJT/K+fp5zZf3v6bpS66Q+O22G2ui+S6k2DODLHMItO/0saklTSzO1UgqB8jGFOHaEGTZEGPaAGUDglR2ITaFWTi5mEWaITcb3eCsr+8Bco4WgRagS+776QBJHckElSMt3p/qew/UkZv2yVhZCL93UE5e8G8Kmc5Z8hzh4C79uu7DVcC2u41JexUmqHNeakLFcgCKhjVhTK+SWBjkzRA1TKY7Mdx8Mr3BdT1iapekz98Q69MrFVfUglbEKvVqi37svbcqdJzwKpW3P1WUMvxmQKk1X363NllYVxbqaSOmU2uQCCKP9h+puT2W8r5UUHsUTSZ1Lbb4Oz9yQbVNKAQWCr4lOz+2Hh2K6gFzDWwmG+CWtnq+vo9dFYXZQVCiRqmlhuHeX/hZ+W7dXtpaZRVt9mvMZVz0m39maoaWyUVVlxTq9rk2XSnuS8OtCnTt/kMMtkas8nZ+9D6Pw172fqZ7cTIiBCVueXHq7ezALpuZ01AvhyvQ6lo6Ju7fhZlgCOV6yaRMeYOx9zhOg6aAh8ViIoxYB97XKUzvNk9LddfwaFgwflqWwBlWR1jpwXjRJATwCEjJQazYEqhFAUTAPupRx+SBpwCSuphFFaGmgVcshAGyjimDufdsTCpQ6eMMIYGWNBUVVQfrbM+hxLY+bDtDsx6gbbPA/pfXoEnVXrjZINFy9AiAx3M8fA0tjbNbQ5A1Y+3aVvd3EZmL3RpS72rZbuFhTU79i2gEono3OnbOTNq+3PbcU9FmlArqJT1m6gBkQ7MhUrt6XOiEz83XrOBzm9Pi/RnN84opVZBqjvyLK6XnfoVNRJpBshq7PycCCjIvzQRAo8k3bArpgNW0wpriiEyLMpTB2RnZQVzJHIWpTnbdhJs5wHqbBiohQGzEq0v0mIOiCkgWwSCvRSslZUdJSBDHQv/5yBXFkawwTGDMEKdkTH3OA9jiWyrKKL2tzMarYJYVPYJAJfhoFEEAEdhE4onqwaiqYTBohABjAkdtLBspc0mYQyUkCgXllYQKgM/zGjetbt2W5skIDzrzLGwccJtDFDtRplAoc6MmknzbWDTc8GrDzPGywfZAqn7vrHa8tjLFWQWwf62NL8us2VLG3dqO5k7U8X22k7N7niwo4jGo0ldIaDqo5hj44wA0QMXAEtgOQw2V87fvnG2nKPbixvn6hN4FDtS0kB8W2q2LY5n/dcGY2YAVvP7TGyXmmvBYmkXVFhZNlzOqiO9SMuWmiGuhWLnmZgLk1dgoJewgloUTAmxwo4CtSkZgAijN8ZU+a0UF5EymRcSYwhrG0y3MYiye53hFYUNTxRTz1GQKxGDpYrHZ6iuTbB9O0urfUTzDGG8a5+0FkaBHI+ngSxPLbS0PgAorCpgDj4BVdzd/ZRZ2mAzP3cA39MNW6Co3adrYg39TSbWkoW1AJtuTI8Wfb3RFsODnADIfJ+qs6s6xPGMMW0IaQWkgZB71SpyEEI6IA2CvFJt2SLi3WfQxOAI0MQVlMgagCAX8Y5AGIFwBHgUhKMGw8Okuso8mh8ZpQb3WzliIiBlULDqwWZnChPM5uXV1phP9TymljOmRICua3S1GpYWe2CfKqjllQ8d1PLfiUq2jN6LagTK+kuJZZbZ7yUdkZvnsdy4aphbTS1fp2VrAVA2nAdt7LkRgvmAjW9NZL6VzjfEWFhFBwtqkxzUEr/ugrJOZaH58fy+CZxZ5nIB7m+VoJLYtmy/m7A8crWveivJ/mnFwJx13M+ZECgXoMoBoZ6iZpDkUIIqx9zPtBo1rdB8kkxleCAAOWsVxcBa/CsZGyxlnrHDAKgofeqwCrGAZ57h4gSCnhJWHDGloHIHoppfa5pwyL2CWOSFSj48vnAHZr1AYwjSAORedZNcIBdZwDGXgV06riyfnOEiq8UAnGgz8OvUpPsGUHVinVPbt99PAJ8fBGKV/tIJRsC3qzXHbunOJ8E/a5QWy2ZRcIJkLtTVAmyFDGEGmRi/35hZGqIftkXeae6YKGNg9cKn3ZOK+9dznmuwiHtXNvCCSpd1Em4Dqjs+BnzX9RqKbGFs+bMIGzz9n858LdVQr03uoIaSbODupIJawLclWg7ohHuyaAOgrKacBYfQ4WncKH2WcqnQccw9EnNhQzEIYeGdTQg4SIcAsVKwqrvCpIOzD+yH3BctE630Meh7zsqs8hRCAKqJJSusWbXSRoHuTwIGm80kAIE0dRLCSKSZ4Mm8sgwqhuCu3bXbGkGQ7d3UyTEqENJGW5kaWzPbwQkQqWm32Rrodh8KqFoeo/1+cnNZ/La0Ye2+fpXfEQcHy4Gp+TxvN9nQ8+2kmbwXYIvNKbCB2aPyszREAZYpiG2Kg9i9Hr8N42yZJEpznOa8CgjVnlm5x1LsYFluK8+G3fYytiCWf29s6hIQU50QqtuIVFAL3z4wS6DCtsXvQgYJIWYVro0SoPpUWta8E0s7NDYUOdOpMdQJjAgFmhI0vTAKF4fBI85RAoq+CwTk3+20tUKV3R8DwbzCbwKDTAC+s2udoKwuQgILIRNZWjuBxY5zh2N9opsEVHCqbQZIoOv0HzAHsQAbC+gmIOUg1nL99vPSjylC8mZXunCaieUgEy32hxMA1qljL0GrUw+4z1eDVgiXjpAGQlwTpgtC3BDiGTBdCNJakO9N4HUCERC6hL5PWPU6x/OhPbBWVfUhbtWpsOSUQqkuF0hTmGNSMEArEhLGKSDFgBwJMgbQkUGRwCODRyAc1J/s9kB/Leh3Gf02g6OApwyKDIqmj2dMLAGVKn7EtfLhjRTEDwK1soD6vgBWknIFtUIwO9nMFVwzstwb1z5snCcHtgpTaqGjBSy0tKT4WOLIUJumWICuxg8Tv/fOZqs2dCYj40ATEyi6SLyBXtnt9/zzch5DQAHHKKNocvnDwN4/ZxULCtNKuGXGWj9J53TSHsdPN6GwuDIREBUscoAoZcI0BYSQIZlwiFVL0iuwJ1Dxb/ZTX4JhPSUDm1CIA2rKlOUFACFkhJAtcyTMqtgTBXQGboEzMhGiVUyMwugoYxUiWBg5d+g5oZeERFXiZcodjiDcD3skaOZMEtWZDJwx5Q/vV9+BWS/QVjwhn2VFVm1goCmBklYYRJISfVC0Nzc0UL4xEN8KXAE39atOOh0OsJzYT2uwFvuaCR6eArFOCbE3g8i3Y2KZQSqm2ux7BmTd1rdlv4B6/k3UiYIBWA5sMRuoJYbCL0AtZ2o9D9TyY5HpUpheVPqACjK3tUmCPUflhOwY82N5imeJRlCNCJBI0cgqwBbVgdHTNgimjdVMcMujITr3yQElj1utuJTKZhI8ygubjjOupjX2MoKFEZ4za2Z4zvfN6zRJwpPxDDFyAbMSqSPQdwnH3GGXVyXvuv3ngoGaFijlmjprq00PnCSAKeOYO60wSISJlJ2FRvMrCYMplQg6gwD73kNTRkajxp7zUYXhJSBBxX97U89Owkio9F5QUn0va7dg2nftrgEAOsqQzgEXm4gmn5DmojshFIp9IIugVxDlA1ozpntTu3Aa4NIVTvzcIiCz3xaI1ykQ6wZQZOOcVPD3RZtNU+v+gQWQ1fy99b1cXgBpGNTVcLTaIkT5dlCrvWhyE9SytcsRXS8qPxdkvL35OFTjI/P9VIegnmXpnvueTUR8ydYCHJCyz4Sie+I71IkzKrDlWpHFqUF5ZASaXiiW3zHmDhEJJPTcJ+KGY9O0DMEhdciJGqFhpVxzzjapDuihk3llanFha/l1qf4YI0IDJa1TnaD2K1mau6gVMVCrcd4txbBoa/l1BCFILvsAlMXFUJ2ubM+S66MonKWajyCtzjsLit3Zmk9s668d+VYNtfLXWFFFANybZyu0gJUDD22QxFs7B18CEu3nLCXdWlYD5EzF3W8FsZpt5cQy7euiK8s5Js2XCwPSMdKKMF4wxnuE6ZIQ10DcCOK9hPBgRN8nhJBxf5gwdBHrLmIVdN62DhPWQb93TYW4mEOpgBpIMOWgbBPXY3WWS0lLIyRRsexovx9SjylpalgWwjF2iIlxnDrsj72CXfsO4ZoxvN+DowJc3VYwbAXdPiMcMsIxK8iV1L+UnGcglgNdHwhq5VzZSuY0ECVAWEEt1+81YMnJGQihud9s/ktTRKCNmrfBG57fv8LaymZTGTq3KUwtMpaygWHSPCslM6c+o9SmlUKKrlarh+bzqUIw8bkGMGNloWid2TMr5tvrzdXF5ntVcAyFeQVC8cvKtF+kAIESyjSh+b1eG2cBIpGKsdvDHlMAQbW02ufNq7P3NAGWHp9Fn8ExBxws7Y9Js1L8YuRMIAkIXdLgSzbjbMLwMTGyAH3IGKE2K7ACq61UShZCBOPcTsaPncDoUJdlA9omY1H0Qd+pNY0AgONHgKjuwKwXaJMEIBEo58LIoimDx6glbJnsaU760jubSnS6IZ0bEtSB+pSBAG4COS0O4Nv6MqdVtoO/o/Ye4QQKgEVtJZK2PU+/KkuZbP5/3/8ifrS/XcPDB/blsrb9/C+8ie9Jjxsn7ER/PgjMKohAs54LYSYdaJGz0qy7oJoAVpKYmJVBdxuoBTTaHs1ARgBPgh//ue/GH9o9QEcJUaqBSpkLLdQrJvn5+2efwL739j186n1zUm1Qm1GjPY+cUJlVhAJstWVhxQbcsm7Jw7exmN2uSR3EM6mtsOqHhQprdFcGFZF4iGpwZUtHlAD89Z/9Lvy+R5/CEBJWXSyDq9/7QBnrEDHYRMGFDMfcYR0mLeOaevzcu69ppKCAkwJmHUj3acAoASzqbBxzZ0CRVnbyqoHOPAjQtAuvM6mpIDq479OgzyHrBVlxxNGAKSbR77kDGDhKp3paUCcIZX+smDWysrpKVY4wo/H6+QcDuYIIpqaPd+2uPa85kw82H4VA7Y2XYLf3l2D6JjZJ0/c9zSd7BYDA8gPqSvXP7PGkE8vafQKNbZKbG8ipnbbHOvEuFJQE+OX9S/hJHm+uc0tXThwJjx9f4tW8w0021qmNbnk3l7+3gRiftIsL6Lrj5qCW25UPAWoBNQUHOrn+xUev4j+f1mDKyKKCsK7TpLe+/vWuze8DYXe1wsVemol4c26EAv54+kXrOJB3z7ez9Zfr+qS82M9sV6s9XrE9qKBWewnssrl+iadr/NJ7r+A/2l0gsKDjjJbhBKhN7SiX8djnG8kixkRa1enx9lz3632F6mJmIa3OpFZP0ymEMQiV4zAEgWqan1dq8sc/G+jlFZ+kGnUVtzVgiqBgdRRGB8X0tBpinjn2fiSCFFaXnhMVcd56C53ToH1w4nqr7XXXPnmtiFcnqPfs+UIcVCeLG42jNugLqI/i4FZsPptIeMlsaIPGTRXEEqQHgMCQs9WvjIl1AsA6WcWwXW5z3twz8mDpgmeE40uEw6uC6dWI4f4R69WETUhgUnbV5eqIQBlTDrg/7PHSsAcArELEMXVlHvigV51GX3cTqg0qzrg1r8DdzgH98y4N6DnhkPtZ5bltXJXv+9TjkDqMVtX72WGFp8/OFZjKhLztEJ516HadsbeMwbVVBle3S+BjqtlBGSBky7Y4wdTK2dLrmjl3IAU2nY1l930JapE9H0Uk3u8fkfkx6nTMgK2C/sviXlM9VgGwME9BtMyZGahFZFpiUveDCh7ptvp8yuKQbaAMhHmGD7nvV40fZQvoM6mOF+v2JfPF5l3qYzV+V/Msg+q1I99HpmL/SuHRVNd3Bhglt2OaZtiFBOIMSUHZUUI45t78ngoSePBqjKGASP7MpsTIkxI7yLSxJOu5h6CAlohWrqcgyJkxQW1s0XcUhiTCqos4JJVgGThin3qchamAb8ECQ9dxVaocOhur54hj7pUkBMYowMn0sVvaHZj1Am2fBqweMfg4VeQbuDEhpylVo9FQdal9s4RqWiJzgwSjAan0M3kFkfIi24tmxxUbsBzhXvbrRj9vo4W057Fkg4m+/Wff2uLpv/85/MfD5xfbogojttdi6QRZf77z4ahA0wdFg9rW9nuB8s+cC/+ektJvx1Gpug5qSQAog/IC1LJBpjCfmhTEMhcn4OzhhM/8xz2e8VslxUTLs+p9ICIEaAWiU80N8ufFnNNZ1Ms9msY58EiBDdQlWEB6Lb3EuUcGZiCY1Oobbn/LtmTVjjxfnGAMLGvJ7hmT6o7ZriUbyPXuCo8eDQaGCRBQUyMBIHif7bN7nqSgGwXNlZasUfhcvBj1dqYUcMydgVHdbALRU8JACWyAkTsaKugecZB5KfcpB2yjCsCzUWABFKfAq2sEzoUS682jc+xWCbBcdS56Xj0lQCz67ucpehM9zZDLAC+408y6a89rMQd0O1LGb1tgZDmkZEcMpEkJJ/vcegE+e1sgGS3IIL6er4tmZubHtxV9QrgEwpaI2G2+9G1jvjci9Fcjjn/rPn4mPLi5+YnX5zbNq5d3OsZS299ld29djtllnPW1/SsGB6UEylRBLY8AnQK12ok2NY6aM76I0G0TLn8m4Ej35/hU07UPM5LcBwpzzy9ee2tn/XCAqFAf3Nb473NQC+U7zddz58cfRY9Yu29r8xuPZhdM0C4Zue0RANsO+12ocwxeXIjGcZg9CG5LzSYWn75cSH1XclZ9LAWjlG1Rd61VB6lARRXI4iJEWVsSxmhaJURc0gzrJVawLJgjkctF1VRC2DKfEhJJYYkJlJGlGIZul6yqYYaoCkAD8i2u0l37hLU8EGi9huz2dSEH0NDMf1KqIFcR/FaggoK9ECGoLfE5c9uKoA7d9Auek04IoAJZzwOxuBn7WpO1AK4AqN5Vp+DV8R5j9wZhvC9IZwK5nDCcjzhfj3j1YotgVdY6dnYV48GwR8cJK5v/Peh3WLGxslYaAk3CuN/tLFAaiwSFB2s3rJVlM3TM0MrZjEOjS6TZAQEX4YAA0WJDdpwpd8j9NRhSQK+jdLiOmmL1ZNrgWxf3VKOVBNtpwJP9Gsepx3jssN31oENAuGJ0h4D+WY/hqWD9JGN4GtHtE3hMJTNoydxSYCbfTD10HSxPFW1BLVsukJru58+D2boWNHVgy1lds3SQYuf8ea3LK4Dlz549Ei1TC9XeANAKiOxpij6R8UeH6vPqzN8yPzffLbqOsj5fyOYbeaZLhPovra/Hi4eVDKiyZ78wtIDCbyl98OsAmG9lyzw10fw1jrrsEDts4wqH1Km2cArIiSA5IGYuusJrmkoBkyyMq3GNnLUK4WTVesfcQ+z4NgABAABJREFUIVmxLZlYzwuNTJso+BSCsmOiaXQFIQRW3azRgimbfsR2GgqrMeaAkWrxLPetNBsmVc1ga1PuEFjQi1aBV73hiA/b7sCsF2jvj2dYP4YK9iUpecwasTAHIhsrq7CgbKIbGKWkd9sC17SRFskWsYkeN0BHA2ItIGdl4zSo5ikgxQetG3D1ifVv2Z52R5z/s+N8nYLuh5o774Picn/PY1t9FNBtqZklUqnTQDXIHlVAvglqmZZWAbUCAfbXaa1l8C++oVY27GOubC00Rts/ezvhaZzEMdrJeNnWnQMTxmVLSyipj1T6VLejmurB7cBrk1yPHNg+9LvpahkrTQEr+2csrQKKMWqqi6ctmvGTBB1h3HHJWhlE0RyfuEgZsDW6YJ4Si2qaFa2RgGkSbOOgFFnWQTE2gBYjY006xR9sAG1ZWpMEHNBpdSpVR7FHgoohdU2SnjXaxiJVHF5Ux+uYe41wc2VpMWX0mEo6owrDqwbXmqdZyoemLLL1zypU3YFZd+05bZ96dHsf61EBrRbQWAD4DtYUVtDSjW0nczPAyr82AFZ5PKUZ+7w1E8TSmUXz/n2Ydhs7a4roH8XT67Z20ft42/5u23/74bauipz+rdmnLJYJ0IBaaj+kIDUGanlgqmAmJ9haoqB/ODZMO99/M97jxMcbfWubGYjZ6u5A+CNmoFBlZ1HZX/sIzhhXzfMpTEX7o+yXmnVteWEdLtP5mw6WAiigwmLW4Jmv0HSGmg2pLpNUn3W1YxpQILveKQOjpQSBYKlB3HRF0JHemcICJimAUxICmTZIhbz8kdD9OEtKiTQEAqmNgdh3LU1OEIBy2QdBGjuiz4+mt2sVX7H3tYNWnfJiAyVSf2drPrFtvLAqhYeDLnAgq7BhnEUjKvQNnJ5XW1DV583Ugg/O4GoZWl04zcQC5mwsBxzKu08fDF7ZciEU8CqtGHFNGC8I433C8RXB9PqEy5e3eGOt/siqizjrJsTMGELCmAJ25myvuohX19d4Y6UZJZswIgnj5W6Li6DXzoOlPSWTkMjY8NGClXrteqTCuHctwwkBPSqIBQADJTzL66J19NaQMEooshRP0kYvO1TLdY0JL3VbAMC97oAH/Q7XaYUH/V5Ftu+r9tHDwwUeHzaYMmOMAcepx/7Q42rbIzwL6K9WWD0CNg8zVu9H9NdRgS1nbZX0QyoVLYVEC81489TDlGt2iy0voNYy9ZC5glqNf1mYT9RoS7ZAGNFNjS2gpiAmNMF5qnaqBcBA1R903Sp/nvx5BtSfJoCC2VhBzVwi7Sv7s1r619qypkCJHqE+rKh2qgQSCSXbpTzPDnJx3QNPYprEzfkbusQTIWUqzL3JtNd0W2UB95SU2YRQxnyVYVGxdhEtfpWFS0BfElX/DFCwkwVxCmob2aeWmlYoAI5T7SCFpO9WFzHlgJ61mNaYOmQhdJzxbGJMwX4Txor3ml1jMNSKIhIIu7yyFMl0g3H8vHYHZr1Ayw1iTGJglqUYUkyngRpHuFMuL7RSF53SW5lbAOpLastmQry3OAeFueVGyoE1HzzaNL4PAyoBN6oIzhg3i219XdVnmoNIzwOtbrCyWkrzh20n1p8BWa4h0IBaNE2Wfqg07BbUUnDFjPMS1AJqGmIzMfau6++YT8rby1bASD/fptOlFG3TfBLvx0wKqNzI65ZWB8urGdIsF1tYQdjCNHOgyucc4tfNJhs+mNl6JVgTAM5q1DwlEWRxYCtBq+ww1NRXMz4SBKVKmXtF9lfZXjaQ2mOQg+V7ixp7QEvLXqcVrtMah86o2+YcuJOxlQFP0kbBrqDb+SB/iD06G3ijMAaOCCTISTW09BZrRKynhF0esOYJK4oGUOlEJgtjaiPyhKLR5ZMWhqZD+jYJXCY99BEG7bv2G68JMLc12SaXzmY9BcIUMEKq0946/b5dywKVxcZtB052zPYxAzFwc7x7XrBi0ZavgqdMn9qWZqe9uAbPszXSrogbp/srbWUXbZCoAbWUecuq50ENqGXfyYzFDVDL+liArcYeV5sEjZSfsDW+kOZfy3rLywGqaYPSXJsbDCxzAso+CfP1Yc9dMRjNesuuLDWz2s9AjWj7PppoeBHt9W0EjQON5tluDyx1XQO3pHlXkjBiK7puGlqjaBBFpO7OuzkiYC+9gV2jnYYe0Ks+CYBslabYJAY83V39plyqP3XQCb2nWOqrQEiL4KM/FxGkJtZY5Bk13bC9t3ftk9nC0TWvgrJTuq4CTw5k5QyJqW7UpiO249Xz5t7O7mKGXJwhr4d5dULgdhDrFgDrVNDXAaw8MKYNYzon/XcJTJeC6aWE/sEBDy73ePlsV5hXMTM61pqjQxfRWQrWy+stXlkp++pet8dFOGITjljThMtwwJomrHlEDwUE1mTsLChI4N8d2PbA6JomPECtJAdUkHuEVmV7OVwXQe4BGQfpCmvlID12eYVtXpnmqgJmT9I5clA250v9zjINNE3xKB0e9DtMF5qmGIVxNa0Qs8qcbKcBV4cVtrsVnj4bMDzqsX444Oy9jPWjhOHZBD5GYEoKahFZWmJVGKTUZAEBM0DLb1ObgePC8zM9LXeO2sIoQJOGCACLVESgSUOUeQqipxpCLDXOljXVCctj1AjLk+1nDmqRuYRiTCorxuEgVda08zaJvQQUo0qsqB42lSSSkh3TuPJLORdhgHNVrcmeDezvHkGF5KWegwd6cmZE2yBnO/dEoCAKdOVetR4lYCerAryWa0JStB/LPqzaJiY2/8v8zy5DIiMXvUqg6zRlNYSMw9Rh3UccY4cuJPSiml6OLGUhcA646I9g1lT6nAgdqy/lfti97lDIBa7n1YeE7Z0A/K9Nuz8cMN4H0iqAj2YcYgJNsQwKtzYXy/PcdH+5Uq7AlqeHlKqHUumb/nvbfAJ923F9YPLWbt8sXwJXswhMC1wtvy+3NTbZqWPMWrufE/s82T5MtP3WbZtOeelZsRS/rjMQawlqGZOOrTIle1+BFtias6PoJumtjTZAThpwAPASrifP0QdY9xT8N2mPbfsomihSJxLkTi3qhJ7qYOxpHWorLEfc04jsN/YUkawDMdkAreWLpRzDJzcl3dEre4mU8vUSbPLtv/uJB0uVtT7noLRYL5Guwu+M98YLZCFchANeD1eq0SWasrjLPR7Ge3gYL7GmCed8xCEPmEQpuWMOZUKhmgUdBtYIQY+MbVzhvDtqLrp0Jc97QqiaCBzBILiwfUDGQQYMiGBkJFF2Vg+N2in1N2Ntmjcj4Y6Zddee29YhIq0ACQyJ5prmXAMXp4a/GTgBGwuaiZxgIdKK4tzfgBqWj6evc9uw+zzb19qaG6u1x5W66IStmqest5DC/BizNguQLI7xIfr7kdsi+CTmMGg09gSoZX9dRL5MgK2/M2CrZUvYqRRgC1jcs1MS6M1+b+2+ssEKKoXSxQr+ANV+AAXw0qPidgCs7Lfus7CucnM5mueyAFp+69h2le2EyR1mqcd1u4emM8XzQF3ePGfCNc1QhIqA8y4NECEMFHHORxA85VyZUdu8wi4P6DihNzAswzUzGWSeidhWPkEKpGkWPUcIFDgjGHu6FB/JCJQtbdAcQqh+lqc9ZgnoKCEApTqUQNCZO5b8Bt61T2TrtxnSGZDV9zUzIlYQ4gaQRVrpW8RT0Op8lppxV8Q0AAEgGBPr4uwDmVhLFhaA8vvt819AOkY8YxweMA6vEMaXBNNlBu5FnF0e8OnLLV7f6HwvmjatHl6wDlJA6AzCWZjw5voZXum3uN9VUOhBUPbTJB3WNOGVcI01TTOtq5ImBSlglHsQk3QYJeCAHoOBXUXqggQ9MlZImKCpWRuOReNuJQpUT8I4p4h7dMSW+xIEnSRgoIR7rCmjT9JGtfSQseJe9YUoIoPwUrfDJAHX/apU485C2KcB2zjg2bTGe7tzXO1XePd6BX5vwPphj/VDwfm7CcP7I8J+0gJmMRefU828Bc9KsQCbLyQtE+i3rQU/C6gFVNb0Epxq2VhACbbP5Gn890UKIpkfpgOj+8712Zn7WdVuwbW1PO3VyQBuxpylhQpSuY6iaoipQSkBGlRAq9RRtLkRAZV4kBsTytVAsjG12IP+GYVoUDJngpmtrH7WlAKGEPFsXCMn1aqCAJIJY+ywy8Ms5TVAfZZj1JRCEdVw3KVVvWDJrmUn1RYKCkiWJ9YU5FgD9kQompQAkDJjSgFTZnAKRS95CBOyaNGWgTWFcMVxpum1ywM2poUaqC4f6C7N8NeknYcR8UJf9LCbQMdJGVk53wSObjR7cgtTqwFzvLX0T1/uwuvtui2IdQrkyfVzYU25E+O7ui0F8BTg9RywaVaWPNgg9kEMK0ejTwnOtyAZy/y357G9LHJwMrp0g/2VgcwWlZ1AiYEQQKJpb2K02cLUCjrr9oHphgihG3PIfGJPqMnIPoC3LkY7iNPiLwCPNhNghsEWetpjmfTbRGKhq+URah9k4T5sGWD9VjXReB/bnGoaTFyYq9FxKq4bhbI9dDt0to9sLDCuxsQjFQgCMrqsXr/FdSGNDHhOuP8bc6fLcsBL/Q6HocemyL1rxOtxvMB1WmMbVnggmmaoNFs9XrR0Qo0MRHTEFhlnBJJZKVsAhYEVKKtgfAYCS0k5bAdj/a7LMzRVJdBUROgHr5R452Dctee0nhLyAEAEPCVLW5P5OHhr85e5GZPKcmtl0tiM+QWJaNa1iZqODafG1rq/wppaPNpzAKsBk+jDLG/WmDlJNsCVU73tfbIJaNPP+pP+Rn7e7amfWh/zZR90F26sm3MBrYgtIur3wZlabluo6YQBViUNsQWw2l6U/rutuaVLZb+zhQCoMo79vpdLXO3HDaCqPbT47ydALbtP0t42C5Is/2kqIIrOVr0fTb8JVYS3MEGkPgp+rYpX4ds29sh3KqiFW0iBKC0lrp/XYUKUMKtIGyVgnwdN/6AOGRMyNEXRu5uFi+MDZLAxAZJN/F2bMZjhdfavi+Tq7cxoi8e0zY/kIBxTUhBLSKct8xnHXfuENcpQqZK+r3Nvz/ZISVMLvcqhiKYhBtZxJ8Y6V+eGqQXAAQSwCbtvVlqd0AAOnUM+H8R6rtwG1X3kQEiugfUmYffpBH71iMvzA16/uMYr6y1WIRYWPaAM/W3SlKmBY9HA6jnhsjvgMhzwUrfFK+G66Kh6uqADWYMtB5RxtbaK0z0yJjAm0x8KEPQ8mbOecQmvYK0npXIWjCBV4qIwtrKCbcdMSCBsoMdjCFaUcMmVCZaEMIUtdrkvzLAEwiQdeom45D2u8hmexg1AGT0lbHjEtYEUK45Yr55glwdMOeD6YoWruMbTcY33P73B4+0GT3crPHo2YHh4hvNvnOHiWwnr90bwbtJCZpnmelqADrAuDm+AVmFNwR8X9+la6R1ufNrGzrVEBqIZY0t48aCU37QggTA32lT6m2plcc1qch/S3wc0vubMTyNIqvZCAhVAq/QxSQWXik02QMuDSvZAF8BL6nn4cK3sLdSMGbdxDsiVzJnmZTEfK2fCLuo9FQeiCMAETFNNLWz9kEAZfdC0833sTaPRqvRaIS4SZamVbJki0gjzg5VwkGIAcUaMDOqr3q8IYT/1N4queAEUbQpobU0Pzt/jlNmkXLiAx7s8IH6EMu93YNYLNKaMHFTDgvajMrJS0kqGKc1XXk6uS7gT5QUXB3/8BfSXP/DpfbkmVKt95e0EUCVc0xqpTWd8HhOqBbSWv/vgYCwz8cjLbAL+4Rot9z0DrXwyLIvlzXmLnZNHljxycOp8Tn73a8mQ7GKJxtQKOqEsTC0fzQIV3bOTbK0iegl4MLiecNMF/9wAXQqKofp07pC0A+YyMu/LSqSeykAEmyjMnB7fhue6WtVLMZ/Ox7SMQj+VoAO4NJe7CPUmFG0tkpqCWCLvuWFrkZW/dZaWR16IZl1FAHJkHFNXaNgxB+ws5zuL0muTRcEz1TTDQ+7xNJ7h0PdlMhANCDumDllSoaEzXLSQTRcrIeageigkSAKjoBN6UiFgFX8nwKN40qFHtJRCIFQ5RU1VgUbSe2hayoATz+ldu2tNc208TqLs35Try5fzcuVTO9C/DigsgwzFiTnhZQDNuNt89naCaSU+OXPQpeyr2c+ytcDEqd+Lo2S5ysWEvqCtafsk/k0wG4Da8xaptqiAKh8BIlhGsG0CDmabcHtamTkA5AETP1fU6wSa2YRZOtny/E4t8uDIyVvT3NdmvyIwAXUq2OZtoJbvpb2tLT46T9+wZVw/A6ip6VI/e/Dkxu++XXn+qDrKLcsMvkyvQS2WAHXKM0qVXNcimWxCLtk0tOxatGBtFBV+Pg9HY2RxYRIr0yub/6bsqmR/qVw3NixABV2CAVdeOcpCbqXKImzy7zpZ7T3zwiW6HYpw9V375DadmzWVC4vkSNZKdIA+wCmrX8Gkyz1bwYGsBSsLIQCrAflirSBWmePjwzOxgGp7vBHKfDL3jLQmHC8Z4wPC7lOC7otX+K5XHuP1syuchQkrjiXFjykjSlC2B4DO5mrn3XGWkvfZ4XFh5btY+5omHKQv74Ozoc4tjZBJsG6qUWcRVHkIKusMdt3Wxirx+aeCTxoQ1QugYNdBOmzzgHMabS6qshmXfMCaElYEY1VqAaMJGZcUcRDGg+EdbKXDVV7jKp9hTROmqQO6nbJxbIzpOeH9aaMB207HgE0YseKoAd9Vj3xBeHx5jm0a8Gxc49kX1nj03ed4//Ea/aMznH9jg4tvJqwfHtFdj0U+R4iKRI4XLFOfa07e8GdHxCogtiLxbUXNNtVw4eYCPsY3Mjzlhuhyau1nE0QnkuJPleeN5/ZMwaM8Jx9YqAEwYMklUgQzIoGnNFIuRqQ+z6bTRd5PxszmlDiZoIBdICj7Sx+ieQGUIDPfCkBhIopnzJgGcU61wvsknRYksZT1Kan9GlNAaliMgJ+fNNfWToZQAC4K2TJl1K4yZ+RM2I89+pCQhbDqo9lKvV89a+Gu9TAh5oDBQOZj7nAWJkxZNSJXPBlpYK0yLjwhSUBqNJE/qN2BWS/QjrlDf02gKdcX2oCsG4ygFrlesp385XaEO4tShWeDfuN4nGJhtc5J0VHBzFk5mT7YAkQzcGQxKW7BNmZI3zU58tVoLftc0gT8kIvvZfmyXyc+S8YJQKsuE19mrDhKuaZ8plQj0KfaLaCW5n4HoAvmcJgoJjPohpaWG3fP4/brMHcS25ztGcjVXP/C4ipRdVupHVg9Mg8qTqM4yLYAtbRgY3v+OjArtdXcNxc2FMyosBVQ00GWGMpCEgK6SpW1YHI1KFEHdiIDvljvlwTdhxb8877pdXL9L3GmltkKAYBADdqvg7azsnrmksqhKYIqwquUcI0MHHOPqVFVjJmRbND16ELmjDHpOutOB+AQRMu0Gz08k+ppuUjhRJpuyBCseUKgpJMj8VQPTTUEwwQ/OwRSwdHQWqi7dtduaUkYYYRO/nwM9LLay5WlgTRm43iDOritEVj103a9BtlYAjntPj2I0II+tv3NFGn/r0Eyyg9LAMY0o0qwxKKsaJymZfsIYNYM8JFTx4eNZQ06UroudTyClLGTsjRalSfuycmONLbNATITza0sJnMYHIQhswWyYGvZOcyjw25rThy7XP46mb3B4GrHXp+dC5VJuqZf6Iplck4oAYm6G4Kbi3JYanaJkqCh3cjN7zaZroGQOslfglo3llNji8T6WQJEFdiSsrF1zrS36jRD7YoztZjUiRCzLQpRaeqRA19Rwkww3re1HPtyb9z+aDAFpUKaGKjlT6eAwMjleKrJlcvVUz1iBb2y1nAvAFYHZWfd9qjftU9QY6gwdxeqrmJKmlrogJUDWcHET315CaRnqOCpPSwhQO6dI581bC/gdiCrASduAleN00+AdIy0IkwbxnhJOL5EOLyWIW8e8Porz/DFe4/x6uq6VD8D9F065t6qomUE5AJovbR6ilf7K1zyHvfCARs64kHYYUDGqhGUTkI4b1KYJuECYDlgNQlja1Wzt3mFg/SlSiEAPMtnWJsu3kAJL4drnPMRaxOyDjb4BAiCLdvKgDVNGBEKoNbbb1d5ADDikhPW5NsqsOUM0LUkDMiFxXIZ9uhzVGBLglb5jufoKeFZPEPHGfvUI1txKq/U+FK3w/1uj6u0xnHdYZ96XF2u8ejlczx9a40nXzrDk6dr9A8V2Lr8RsT63SPC9RE4Rgv22+DLpGCKV2L3ZwlogjMnQC3g+Wwtqn8p5ZuC8b5NA3SpiSJLq9V+FJuTqQJa7o/TIqOG0dgiUWCp3bk930W2xZ5x1y8WUK12aM97AeQa1nDuUTSwYH9K+rzbOn/HGheBR0K0oH7Kqj8FIfWVWPW0StaKBAxU9YGTEKYpWPppj0Puiz9FiUCT2cfgFl+aTujDKJG1MBdpPwCg7xNi1qBJclKCdBi6iFWIIBIcUg8mwSF16IzZDCggvU+9AtO5Flw45h4rrhk2H6bdgVkv0FYcEc9N+D1WRlaJdNAcTVbsYzGbaIxDYWhZDnopjdpus2RhtdsC8yi9r7cEwtrf2360n5kVvGLWSiVe2c+Bq7bfvrvlRGkhtqfrNCJ6tzZqUmd8kg641lLpu9nc2fUQsRRBA536zgZe05eZolKt3YjfyghT+qwkXUYuZhjsc6up5aAWGTvLZsx+rWbAVjmZeo1uXD+qg9782qEAXM7cqnFtFIfEQa2qqwUDmagOlKUKVI3MVxfMnKlkVNisx83BdLFgGZ8mCiidLScYeGWXluv5+AkQUJhaev6ojlsxgJgJ0qvDrKzCQ+ywSzW/2vUSAAWXJwQT5aTCwjrYxAdQMfZJgjK8rJSt9wMAcuzRc8I6TBhT0P3b4LtiAMjoCMUgtJrvTgOHBDAIEwLWmDDZMDtIQvBSzXkAeMSAhLFwyO7aXTvdAmWdADloYv+kHeObVqKIs+azMVRb485HAUaarZYgVll+wpY46kG+Yf1z8wvNPxNVQK3oDi6Aqw8Cq3ziuLA1wAfYmueAPSJNX93cFoALDQDikVOGB6NcAFeKptlz+uDgBjBLPQSTOQzN9WluYamCaF2Zz7rb/t5YPAf0/LcbthDVGfDr0URySY2Gwik+CUdTaITq+K1fm2vNzSEKEtb0kc1W+e/+eHqU2/Y9A69sN+4IlOfRbyGh6VvbFynHKqAeoQZKQi3S4SkZlbUFeOpeBiMaOwJQMfZk2iWzVHLvjwnCd5wKQytnQkcZnUbpyjWahNEDegJSSdYJXO5xZw6ublO3jyrqgoBU+nzXPqFNoKlXRECKgJhMRsu8ciCrbLPwJdp2tka+PKtsLOAkiFWWnwKyyvtn6zGMhcWIZ4TxkrB/lbD/TMTLbz3B9770CK+vrrHiCR1n1RulhKfxDIACv8CE/bTBK6stXuq3eDlscc4KXK1pwoaPuKSpVLXeNKeWAIAEGYqFZMDSCQUTCE/ygCf5DAfpsc0rXKUz9BSxy6sCNvek1a6P+bwwrL45vYSN9WHDx5K6yAa49ZRwbuCX9kM71RuAFmz+mAQY7S0diNCTBmf1O8CY0EtlfW15wFXS/vaSMIVQUg33qcc+KWur46SsHTAe5fNS2GjDytp6pd/i1dU1ogS8d+8cT185w9PPrPH4i+d48v4KZ988x/k3Nzh/O2L98ADeHoEpVqYWUSV1eLM0VMo6HknKdXwG6rqux+VU15YgYW2eftj84MdvsjoUpDKj0aQf1iAQqrSKjfkKalVbWgJ92feLGVMLpCwuNf2mj+zBJE/986yYRiAeJAgOHPm5wfwvclPszDHUCvIWzMlJWVYxs2YGeWpghoJbUD+HrZJmgmo9jrGDZK2GmE1zuNikwnIW7TsLKDXBny5DEtt6DGGfpwhiDAghK5iVGF3IhaG1j5p2OGXBeT9izB2YppLtAtEMmCyMjuYZLFPuAPnwgNYdmPUCbcUR8eVJX4aWkVUMR/NSL4AtXUY155zrZwAV5GjBqrQYKICSLlhycj3thBbbt8tum1gzKyOsCwqoMSuItdwXqkG7rbWOyKkUkA9MC1n+7kyE4qBQM9mzmbYHmJw95QwF1txqiCjDKvVV1ywlNfZtVKE9pphgq11XklodpoBahuBQYFs3NyCWU1+lDM4zQNMHODSPiANPiyh7C3A5sFUGWLgDW+PcPjhrqqBdL0aZ+atRqdeQS9UMmeeq++DuA7wsjEkUZYM14BVlmDA8KmuL/DMVJyQHqYN8qMcq3pMBpyJQMUahuT5BCiW6XSJTEjDYwzDagH3MoZRaBhT4Okb9FzkjcEbPGeCMKYdSTtYjfjVqoMd1uvsxd5YjXgdl/UazaKJrMbigaAtfZWGjC9+5GXftdOsoI5+ZsbeAhjrfJ4Clk2NrHcdnTof9NP8gJ/UXlxpY5GAXLTdvQbFbnmkHaLx8t3+/7RyeYy5kZpd+JbZm8VUcmGt/kGZfVICSAhYpmoECyotYFJqr7fIAyzIttBzC1rHJOHl5O8hpUMuPSzaO+0R9YTdOnWirnPRc9lZzfjNNFL1QZSx3jM8BttkzQpiBWrBodTHhVPdJ7QEaMKrgWRYjLOu2gJUd5xRTCwCK3rr3x/uYVaOxvbWKzSrzyi9NbkSmNZ2IkYgRRN/LZON4XGggRuHCAs7mQOo/fSymrEGTYBclgxCtAhqAkmoYweghpUaKg2jqx1V40jVHkjG1WlOtdubO1nySm1ZW1+C6RGMfEQO5YYCXzI0FK4sIINXcknvnyKte53bWbmVj+T6auagum3/OHSFuAuIZ4XifsH+NcPhUxGufex8/8PI7eG24xkV3xIaNuU4ZV2ldUgZXHHG/2+FB0DTCS97jHh9Kmp6n6g2kVT0DGEmhXQBAEsFg/RlL5gJwEMbjPCiAJQMm6fAkbQDo+zdZ8R9AA6i7rFXiXC7CQeSD9HiSNtjmFdY8YU2T6W8ZsOXV2/gApjyTmHCB+QmE3t79JIIEINj59CA8YFLQShI2HPE4rVXHD5P+5Qn3ww6Ph3Mcc49vHh+USnFPJhWRd4aWt0kYZ0FBw4twxHk4GmNrwNN7azx89QKPPr3Bo2dnePxowOabl7j45XNc/PIR3dO9yuxQMo0t98eaZ8vAUy2AoXaQ2mB9C2qVZdY5Z/o2fihlmfsgerFmz6AzwCqoVR5DW1+BIm8Esn1IecZvHCerj130tKSCYAAVEXe1g6i+Vud9QjUwaN8dzLSz2o7OqhkaYBUzI7Cm/dHE1c/KVAL32VDDNhsFgpJueMwddnFQcffoQShnxeu6lEy/ODK8gJcgq63rzBbbVDEEQdepzzNFJcAwCdg6v48KrsXMuDcc0Ina0LMwGeNY/aNncY2zMKnde97Eb9HuwKwXbZlqlLwFRbwKIVBewhmIYUCWnGJfefOX+gSIBaDqXgGKWp9yaHxQacu3t/nHljIoq24OXi2Bq7aVHGM/1onr0hq5j9hmehkNJX+m+dQ6TFZVwsGgwlbI2g8kUY0PESD65N2qM0ing55dY4nxJrAlNookA4g8skWkTC2R2X10UAvGzqpaWovUkEUkAcnP1ZwNO19nHLSOSokmGBUWRHaOaCb3en0EJvwfqDK5HNTy+2cRBALMqbTSrJYjfUOQUFAGUGVvSVlPSGm0rAeHBCD7YO4pJDZoB0P/iSwywWX8VycDZnCCABNhbKpp7FOPMWv1jCkz9mnAaNbJhTod3Io54CqtrWxtZymJZLRYLpcBACahEo0HOnRWQj1nQrZqUkWoMAMTBQOyqIjEA3o+x6wU2kAZo932NWWsbbLm4sLagV/hC3PXfmM0d0KXoMgJ+zH75pHKZenr+c71z6kiImhAG7QTsuXRbNsyWcP8WGSp6pauLsuAzanWOkPPWefb0gpYR7N9ztIi/Lwb81rGQ8qoSIPOC3zM1s2lAoDOmDgFbDX3mErAy8Cx7I6k/5v3vZ1E37DRLQOjnSo04Nfc1uDm9mZ33IydFH4XMcCJmmPU/dwQgLflRUuEzGEguLWqcwKfZDeTfiMeFX2SMpy2QBk13wUzZpiua/1nvYaaAlj7P4mmmXu6YMwB0XbuqX/qqlKpfBslGMDVTAlRK6HppSIEltJ5hulFitr2YOuQXZxMWulSIAW0YnvckihY1ltfAKBDQkcq/KvV1z/EO3fXfv02gY4ppt9LtNDE8ubAVusnGOhAmzPke5uaIdIEQVRqBPP5ve/jNjYWKYiVB8Z4yTi8xDi+DOw/HfHG5x/jd778Dl4drnG/22PFEybT/FmzVgbc8IieIh6sdrgMe5zTOAOvegLWDXjVNgeyytIGyMoAtpmxkw4P06WlZZlwPGnqnq6rA8w5HbE1EOu8089aHbvHho9I4BJQZZI6ZoBxJWdFkJuR8TbuY80T7vGhiNAzMs75qAx/mrC2EngBCmox0QyYWxOwhqAPe1zyWFIiD9IjCeOt/hEyGF/rX8E3xpfsOur5MYmmHxpjK9tYl8Cmv6Wp0i8PW7w2XOGts/fx5HKDhy9d4MkbZ3j3Mxd4+zvOMDxc4/wba9z7asT67R14e2ikXep8pTxHnToC5NIvLahFdBPUUsegPF/ts+pMrdtBLT2nYr8NFCqphszNvpv5DQygclaSPzdOIgBKMAFuOr2Am6AU1/Jji6dWNsEuathelR2PJhVRiiOkvpdZiAjIaCnvKRi4hgKE5UTYxpVJmUgBstg0FyUTpqnDIXUI0CCJm38twOWYA5n2V5XMEUFjbAApFVnmLWYN6pQUxi5iyox1F/W9MK1jrzY63zYgWGpsTwkphxvr3NbuwKwXaPs0YHi301KmbUnb9q9P2GdV/oz9dEOrBHUdr4YotQLhrC3BliVVuHV02uZi7au+VCMpRgt1grhkXd1wKPwFLL/f7GKLkpe2HJjaZe2+TlCfZ5PTAmqhRo5miL6CSBJtciqioFaQGiWnet3I2VZdOM3Y8gFRck099AGXG5ZWC066iGbDzirAVhlcbQBpB9MG3CrAFnAD4CrO0QlQa5ZyUybllQVVQC1zEBRkosZpsXvnzpEjXX7KWWZlZ2dhdRZwbBD+5nbfYGC5A9JBgbMMo7qiUnqT/pYZhZm1SysFshpmVnSxXfCssswxd8igGXNLH5f6bI0xIDEjhaqrMOWAKQesQkRnwoXO9EqcsGIgk2CfBpyFEYG0AkdPaSYK30NF6H259kFdnyLSOHuo79pdm7cpB4QtV12mJSNrEbksf20cuuF4oPne6Cue0jOcoRotUHVyMG73rft3lu+MWbRcrz3aSZt4ql+L81ius+jmqS7qMrm53myT5ocymV3snADJbMLosHGsTmrF54nkwImV+9ZB7TRjy9l3WSs3+WS4sp+o3uPZOdr+27Fb2uu6vE/zyXf52zqk7ekuQC1p9llAoSY6vYyuLoGtJlG+YE3lM0HnP1T1N/0RJA98eIAEwHJ+XIAt20bMhmmUBAWU8+AKeQDMDKWnOySpulmAAliumyX2PRkrS1CF350R0V6DZICkUC4gp66vLBAWAUgrHIpp+XTIEILpQ6p1myQgQKxioQm8Sy6sLtXPYmTrZXbReODO1nxCGwlqYJuoamW199uD4Ez1c2BQ10Euz5HP17OgdgWNbwGybmNj2TuVB8bxfsDhZcbuTcH01hGf/dRjfOf9h/jU+ikuwwErnopWVR807W7DRwTKuOQDzvmIN8MzrCjhnJUNpSx5QrB3i4lUqB0VxBrt+yTABMIudxjBuMrKZtrJyrToLB3YTqaniMuwt8qBqQRHL8O+VigkQjAA6iAeJPUKicHez6xaW8LGLJsQoMHNXV7hkHusTRvoKp1hkoANH/GZ/n09b4qmC6YgdY9aJypBGWiXBFwiI2PEQbRI0cEGvkCCT3dP8WS1BqBsHe1rKP16ls+wywOexg12eSiV5gCUqt89JTzod3h52GK6CHjv8gLvvXqOx29t8N7nL/H0uwacf+M+7n31Aue/vEd4fwcaJ81Yap89Z2OLzgkoRq146ICWP1PL9EOg+Ao3nvkCai0M+In1CTDtLKhd9f2fYGmJ++8CtCQCTz0kXgBUSSAdFXulwJbaUpp0HcVFm+yZamoK80plYFBTFP35hoJZABATY+gcnYOmMcKNo9oGP/8HYYtj7mb+jhYpUJ0rJAJPxpYWKhI0NAF5ACRIYzPdX9X9uCB8TgE5a8A/hIyuS3YcFZwPJgR/1k2l2uEhdbjoj4iioCKzptxPErDiqKm8HyFSeQdmvUB793iBi68DdJhsgrcwGoAZhIZ50XVA4Fqd4VT631LA3V7UUop9eYxTxsnXY42Ey6oD+g7SB0gIjXg76raYG6QbbQlePS+651GadnYKzDSGlsvm2ibNvpu0gXJuduwyMfZocttPUdlUyTriEInNwe0+NdezVKfwSLgLvqcMxFijWyJoUw8VHMtFTHMOanFxJgsrrwG2ShZJE2aegVu0EFJuAS4bXGaRf48eiE3K7R4Ux8RLzDYDaHkGqHFgZp6X34rqlTGctaUytZ7OMcsBN3quEJmume2O9DIazlPvaaz7oLayIUtl3HVKkd2nHiseEDPjGDukrPf2mDocpC99SNDSyVkYYwpFL0AfKRXfHaOmCWplEE/ZzegMIAuWdjhmLSk75g7rMIFFEEXAotGzY+6QiTT9kBQ4Y2GEUmrZqiPaZfSo3iiqg5AbFsBdu2vLtk0DhmdQYdNTNsBbOwY6gLSMvrct54WAewUN0DyvpbXAzxJQc+cnkFXKYk3vXtqKMhQ+55lf/vQB60rp03P20Syb/dTu+9T5LvfTAkP+R3Ti60wtgihQ5QGDZr8+vBJIGaeCun6rg+bHN9tWghxmV2+CWifOtx5sMb631//Es9QCXA5X3QJqeaXFAmoRKqgl3s92T81hBBBeCMA3vXKGlgb1mmfT7EiJnvszxZhpdqEV1fXpSG58hGqWzYaiOCFJVAPLtUVSZteGR8yM2ExmdF6hVSgd1GqBLBFCygFSKg/q3IwgBSBzPR4tDKKpHJ0BV7lAfrpvTUnKEJKim+WRbk2PtJgQsdmYVNK2Wqfmrn2yWlsgqWhl+Vx32XwZE2i9hty/QB66Iu1QGVfPTyu8DcSSjhE3jP1LjO1nCccvHvGdb72D3/zgm3ip32FlWlgBgoN0gH2+DHtcGmPpnI94wHs84BEbuglgeZsgGHNGhgJXWjkwYJdr2iCgy7dZwZqBUvnsItmBcg0uArjk/QzkAlDmmPfCoSxbY9LUQhqRwfZ5KoHXDNIKbaZX5FUVmQRT6gqg5YHOt+MDbHmPV8J1YXzptVLtrTUlnFO0AkKEHoQAuz6kYF65zUh4M2wximAy/OVgUh1ZCI/yxu5Bj0fpAk/SBlkY3xrvW58C9mnA1Gj/Pej3eNDvMV0w3r+/wbtvXuKdty7xje84w+brF7j31Q0uvnZA//AadBwrOSOjjH3Fz4oJiPHmfAJQ36oFtBaphOU5FDkNap1Yn/RBsOc2l2edkusc6ztEzIWh1do+Z1cXfkNo5F08u8Z9WrNZMFALiZB7Yz9DC26pzZJm3mXvWnkMq9HVAl3uu9jSSDXIk0mD/KLPVBLVDVZGsV2SyNjFAaMDXAQtuJXM3xRSAItgbC3tv4TmAvgwIwQKgpyo+G9x0pMPISMayDUhQExH67wf1X7mAUNI6CgjkT6LnbCSCTJKBsyHbXdg1gu0MXUIR4DGycAOB0g8V7jeCCIChr55cXJ58bxSw0kGFqAvVmz279Ftf+mX31smUd9BzlYKYnXu3KBsdxK8usVxkOU6TUSmpCa06/N8388XfT/h3PjqYbFt4/gXFlcL/NgyMdTGGVQSYBR9E021NL8W2HK2VgEOSR0zOlUVRjIk5iI4j2DojAsAs6cfzicGc2DLZs8uRuiTiEKJba9Hc54GRhWgx2fh5AObzDS1atFZKoMxDMCbTVLc+UDNF9dHy/PB7Teq114HscW9a/aV7ZguEl8cCQAuAO+C8SX327cXQjZniJJW0NinHg/6fRnoWmaWC3O6FoFHx6OlGmZjdmUhxMRIaUFLt7TDKWs1KQe6jrEDd2IpJhotz8kqWZkWGZOUfHVmQc+TVr5hYGXlcZ1qnoQwSsDaGVx37a49p6XMoAhQWyn3FCMX9moFe4kcxHCQajZpOtWkVN+pIIG9jDjxfRZ15cr2XTKw3NFZtg/z6DdAjXfpRvdv2LEPsDW3LaBll5Y2SW72WaoNLAEFdkCr6ZcPsH7pfIzz/TBZKqHZorbcubO32G0Xl4vhIEwRa731mtqE3G6+gyMFnGu3OxFYugFqUbOuuO2yTV1Pq14iuJXyZ7E8UubgVKCKKvDk3SW1aW1qIgElMFJY2yVS3vTcbI9/LiCW2dB2e7c9lFWMfcoBa44FAFI2lk60FXAiBGp/o+LwCUxHCxqskEzIxTGTmnbYgFkCdVSINfCWxYumsFYAhgBgC8ixnaMyPaKdZIdUUh9hfY1QHZ48u8l37RPbkmr4ApjrYxHPsy44gM4trTCEGZCl88+PDmS5LtZ4yTi8Qrj6YsKnv+shfuC1r+FTw1PTEs1akdCe4YtwwIOwQ0DGy+FaU/DoiEuecMmE3ibCTIRJsoJXIgbMaOXBR/kMj9NFObU1TQWsats9PpjQOuOS98hgY9xz2Q6AVi6UXr+LAcL2/pS/wljTNEshzNB5qKYkJlyGA3Z5KGl+x9yXStiAMSzNp7gMe4yiTNCD9HiY7gFAqaC4biq8PeCdpl/yEfctcyCg6ms5MYkBBFABugBgQkYSwQTgkp/iSR5wiQPWNOGVcA0ABXDb5RWu0hrvxw2+dbiPZ9MaHSVECXhltcWrqy0uuyNePbvGe/cv8PCNc7z9+XOcfX2De19Z495XDugeXimoNSMQsD6PXQClDhgnBbXa1jKWuTgjDfP5pp1vhsb5+rrD4i+r/Wj8r3aZ+YZtyuEyKFgIAbZbtRuYT4/c82rspRbxQVM1kMzXQU0zFJi/CvUFPS6WAES1Al3I1m+dZ1AkILp/oqBsIMEh96rvnQIkMnIn6DhbCiwBiSpoZX2AV35koBhmt6+JtO8uch8qC1znInpeKTGoS2DWAAqRWPEGtZWBM3ZxwEV3RG/+rzOUhy7aM//hK73fgVkv2ChB89NzOh39AFRDqe/ri+WlcYGCJs+YWO1f/9yCK65/5Qqi/lK36xiIlVc90PFpBlZrmJp2a+CuXY/n+7gBZDURmrKsQdiFDADxieVsY5SX289fKZhSXnQX6aVmlCiyAO1g43lxWYGRoonhA2R2J0Rm1/BWUKvrlKnlKYiAglymhEcecRCZVz4sgGPj5D0P2LKBtX2X6+92O0A1V7uIFja3imr6IUEAFy0Eal63X38HkNxRaKodArCB1++31P4XEEqQuzogtvtl64Pqa9VnJgsMqNTfcofGg4HXJYaX96UApKig1DFr3ndMXMCsXRywzSskYYxUKd7JAKhj7kqa4SH1mloYGSkRmMWIjAKRjMBealb/jhJASSx6ztjFgIE1/bA3p2YbVzgLI5KBXUFyqXB4tDTDRBp1H6zylFY6jKVK1l27a7c1ndg8n5lFRCa2ai9SkzblUb85E6v+KV8EdcwTW9a+l8t1mCBdp8VDloUjsLANH9aXXgJh7f5urlzXuW37U0GbZmftNRG3MeV3qd9p0YkG3CraGG6PiKuO4YkuPxfUgtkSZ2oVe2U6XJ6a4X1oKx9Sc+9uvd7+u1Qwp51ytNdr9iygMLxupF9Kc524nqAexu6RHZNsowJqSXNpuaYeznwFaY7DNH8c29vb2p+mtYGUG+mHTephwfiS6WMZQyvnGimecsAonYJPlr5nPInCriqBlKyp8NnS6FVqwFI8SNngztAiiKWnsz0i6qh7+mEgdZCmHFTrRgKIlIEeVHUS0dKbMlFlcAmZWP0dM+uT3ISgwu8fEDgGoFkinlbIfBrIaoISs2yMZv5f/gZCcl2sVxjbzwiGLz/FH3jrF/DZ1fvoLXUIAK5N1P2lbos1jbgXDngzPMXa0urWlLAhYE2MnrgwjbIIJhEcBNhJwJO8wqN0oVWrba7nulfMuYivA8q6ChD0FMGmyQUoIPWAE57l9WxdPaSmHw6UdDtL4WXKRTrCW29MqYOlGt7jbJIXlYmlIFbEmidcpbOie3fIvVYjtP0NxHiSzovmahbGZdhrOpadx1YGHNIFHlHEK+Ea5zRhwxEbSGGocembYG33LoCwIk0/7m3A3tCICcCGIo4SsJUeb/ZUKjneD1u83F3jpW6HXz6+hGfTGo+PPX55+wA9JwyccNEf8Z33H+LN82d49OAcb79xiXfeusCz37TB5VfPcO8X9+jfvdL0QxElcTiDu9eMJYoGaqXW8bFneZl6uJSsaQgdtfCU1OVEjZGx9UT02Xcqq/twnnbYMrTgdqfatlJsJHh6oY7ZVDJozAezMZfMl1USC6oWsc+bpPaNQTNwS6+ZAk/ZdKmQ1O47iEaigfvsbELJWpvLJzikfvIxaUpsMF02cn87o6bti9srQWbY/MKnRNbfPkNF4QnUZQWwoD62+lKEKYUi3QLosfuQcMap2tbU4yxMJQATc8AePVao7McPandg1gu0MQcM2wwpiHMj2GAREOo6BbJawAmoL5d/9r9LI9QCZIFrGiFQgYzyUhPAoYJYwYzRBwBYJ+c2p6Lojb8tSzpncVy0H7kjpBUjDYQ0qCC4BResNC8hDbpMrOpdHgBK+o8j6iQ31+8cBTwCYQTCJOBJEEYBRQEn/UsGQnk6QamAlAUUSDWvnE3FMCHeDwtqCcC9MrW6TgXj/d4XgfjFABoEZBSnWo2K7L5JeTZuS0Wst8QmGotogl7+RrQQKAOuR7IBQJrStioIb/et9BU1GlCeAfNOyjGrMywiNVXR9ykNa8vJAzOnSspz2OqbEJrvnpYo1h/XAkuCZDTamFVcN5qT4BP0LCq46ZU8GLkwsgAUvaxo4FEWFdQFJ7stUlJKNMVQaa/cpJBkY2tlIYy5QwhTFYRHHYizaNphMBFTL+HsrbfqNjUx8q7dtdMtCSOMAmk1/LzZuEDMVpgCcABG2xIW8D8LW9N+9UDJjd8a1IC5glg+jpQh7KZtuLV9AGNLlutQu1wnhtIRctBJpTAKzV8MuJCAktZM4hNVVPFU36HU75TNFiWAkkZT2Wq5l+gqWiCoUYByQMTClTXF78R53gZqATrBz6LXutVLk0XqYWNLyGacc7ve7HzZhxMHdqhIls+N3wvxdXAz/RDQZ8dtjUDtKNROAfb4cqvRRuV+OrPYz6Ps17+S7qA9z3Jadt1np7k4PX892rMq1RWbyHoFpdS+6F+UC6u3nUqqhgJSXPZZUtqlnjO1nTHZA9/eUxjJHAmxa+uPVxJWfSyLcvu+Yw66noFiPSVLS7SHnzSNKkNOqjzctU9OUz2fRmqkHcOJ1YcQsWqFF5Cz4QOBrJNsLGr/qi7WdM44PmBsP0U4fumA7//i1/B9976F+2FfuuDgzKvdFS7DAa+Ea2z4iHt0xIoSBsqFicU2fiRI0cKaILgSwuO0xrvpEhmMZ2mNwVLwAlVG0WAapa5xxRZEBIBzPmC0tyGJaqye8xHnNM5SEb0KtfbDNVmV+TQYwwyozCkvQHTOx1LYxysaup7rNq8wSgcmDXa6fpEWLFIdvMfxAldpjQzChkdseCzpj1kYO+kLa2yXXfA7Y0NH3OMD1hRVf8zOn0WQ2dljgsFGwwp2EXoA6yBIMuEgE14Le+xCh630uMpneJISEhQEfK+7wL3+gMfjBs/GNfaxV2CiVzmNz54/wac2T/Gte/fxzdfu4d23Nrh6a4P7v7TGvV/cITy6BsVkTHGzFV3QuUQXFPA6jgWUvVH5cCkQ3xa3aQAtfScw97UXrC4yqZ+WkVVtKqDBFxvDW1CL6z4pSnk/PBNG/SbVxqRUNYbddroQfE0ztDRC1GXqA3l6IWaGLSY2vxXgycgACdjHHk/TGVY8YU0jRtMNFkA1uSwbZZ96BbksbdB970Iq8GuYqTwneTg1J9RrJNnmBUGQYgAPETkTuqD6j9FArT7oM7+PPfqQMKYOQ4i4mlbYdCN6K7LFJLhON5mVt7U7MOsF2sBO411Q4ZzK23WWemYvUPuyee5w25bsKmD+uf3NIuJurGTVA6tBc94bEOsUgPWB4NWCJHJK/F0MsJJABkwRpg0jboC4IcQNMJ0L4v0E9BnUCXhIWrqTpSC3A+cyMRtMNG5KjGjlQ5kEYww4TNU7SZGRjwGIDDoywoHBExAOhHAEuh0QDoJ+J+h3GeGQwZOAUwZNqkdGydI2RAAKSuv374ZGFxbdbUytlEF9byLxXHS1JMFAn+Z+k7LxboBa7Q3xe7oAtup9gDoAvq6DXk6JpWZy7wNqeYZsIu0AFAwkch6yaJ9LRUXXEEuoUYdmIl4qcjB0G/ZUQitl6yKJNlh7CiFRXUd/M+cTCliWSL33KzWORgAkMsZch61pCshZATQvOcvIJUoWLM3xmDocm8oYWTS64d5tToxJCMyMEDLGCDAxmDOGTkXhAyvjakwBHWdECeCsEbKBE3qu40DnOgfQ6Pkh9zqxYkGGRexEJ5EBchvR5q7dNQAoaQw3HpRiCxpGKJYTu1MDvgEut9ma2URljg5I0NQACXMQ60MDWM9Z78ZrYPt3/T0xwCr1NUBS/q0zwBlggEIGG/1dd6Fjgdvc4LT23FTdgaZzxlRhD8kEiQQvOc6RddIXFeTiST97YIWjjpmqjyGFQVxumzSFORwY8XwFcXAIM/Cl2AS/v1JtVQFjZhPw54BaC3Do5gWv/VQ2n3VEmrmEtDupE/Mb+2+e1eIM+MSZqMb+WDfUn+q9KGdHVRPEu0z2n5hdq8tQQC2yHRUyNDWm1ogXZbmfjV8vAiQ3lWahz4aYDXMmLUEspUNUS9LWi812BbDys8pkWF8wEWGFnjIJAmsiYDZgKgkVNjAhY8zqEITiaaEAV36caPqMqm0ptiyggxUYu+WW37Vf/00B+KxBj2WmiFd3GwYFstZ9dcCtCeG5Qu8tkCUM1cU6YxzvMfavE7afT/jUlx7iv//a1/CZ1ftF1H3KHXqO+FT/PtY84QHvcI8P2PCES4o451qNsBVyzyKFlfU0C65yj2eywuN0UaoP3guHkt63oWPRt3IQaZIOa5pmwFRPCRBNF7zHyv5IoCL0rlUGVcNrNBBLmVMRa+tPaoCywBnbvMKEDqOEAjyp3Y7oAexkVQTjlUHGdlwtMjGZjtUEZa49Gi+w4gmrXgXld3mFbDpjbNkAV7LWz2BMOeCRXOAy7DFQwoZUQF81tiZM2VMRpYBcq8Y2DKT3gInQEzCJ4DJM2MmESx7xcrjGZdqX9MmX+i0+verx/rTBs3iGFUc8mc7wbFKx+VWI+MLFY7xxdoV3HlziGy/fx9uf2eDZ5y/w4OfPcPlLW/DTXbFpyFCwdegV1Oo70GGETBNk5hPJTZbWUhvrFEurbS0BxNcDDNCSam8xn3LNQS3za0A3bGzLwHKd4+KDwV0yC9lkGKFDim/dNmqwA57EmFmEtFhPRdx1meur6TNo8wqhQko4xg5jDrgeV7qMoPpeLOqHMWAyc8WeioFcqgNmFyU1msssFtxRViQAxBgQgto0Fa1XQfghJGRSv6zrxirdIowJQCce+K9ptR/U7sCsF2hX0wrhmG9GP4whVSol+NvgL5h/T7kCUi0763ltCSx1AbIetDpho4lVDNJiOzmxbMm4mh+vWU5A7hlpTRgv1IDFCyCugXghiPcjwkVE6BLWqwkXfcT5MKLjjJ4T1mHCOkSswjwvmiGFBumU2uz5vICVxOZK+c8KaBxThykFHGKHKamY9zh22B47yDGAdgHD04D+WYcwAt1OMFwLur0CXN0hKbgV7R6680EGLmSLfHulSgO1qKkA0zqNRA2olUWnti2oBeh+Alvahd2TFtgq7CmZRxtgDssiHbFWS2wGYY+UW+ohYE5CC2rBnIXkvgrZIGz33Fl/BkrVSIE5NG2ljaVjZE4yEZBD1fiqHkPzbNk5lQgFoYJfhMKwyAGgRJCkKYNeAl0MlGLOxYHwaJxHzZJRXWMORUgTAFJyw6KOgINKrqOVScD2wvQhA1HpsWCgQ0bMjM7uTRTGCrGkocQc0IdUIvvMYpUMK0gbkEsq5F3qx117XhtzV6tUeXMgyxk63hYRymp/UAGstn1YJJVJJ5lhXon3pF7esnn/2m7eWKf5YJO63EHZvStCHjRqmAcFrmjIYNbKOQNnDCGBjUXZsQqLukZDewhnFLlgt5QlMK0jsrk1FXZOyjxjgqasentjZCAxMBHCgVVD00CuMCq45f+QjDFsIBb5gNvmyPk4CTRMISnXxEEvF3p+LqhFdbN2n3Ue4vdF6rHLTWhQKbMlhMb++Nht6zy3+iFQjkdM858IFdSiphvNafvZnUw9hP5eHIfajdk63oUW1CrHp7ocQA3uSPM8ODBoSKPPTchAIz+rls2VDYgCGrIMfK5g52k6WoUDlxnMgpgttdAuhO5Lb2AG0BGKzcggBNK/ImrdmZRFocBhLn2MHjm6a5/IxrEJerTsWgey+h7y4BIydOYr+NijAdYbQNYpNhYA6eYphdefE4QvXOH7P/UtfPflO3i52wJQJjxTxv1uh0ve45XuGg94h5f5gDVl9ASsSQXd2f4mSGFlAcBOBE9yV8Tcvd3jQwGNnHHVUywMqp4ikgFQPcXCVnIGFFNGsr8+b3NxdWVLVXALlNFD/RZf5nPMXV6BSVlfA+2KHtfU8CAHSgAfS/XqAEZPY6mCyJSxsn64MP4uDZgk4Gk6w1E6TZlshOJ7itjwEVfpDBkZT9JGpSwSKyOHQ0mPzMTYAQUAVHAvlZTO3gY8F5H3IbknwgaCFUVkRFzShAmMz3WP8SRv8Chd4HMD4Sqf4Zh77FYDvnl8gCkHXMUVshDWYcJnz5/glfUWb9+7h3deu8S3PrPBsy/ew4OfP8f5V6/B18e5b8wMrAZICKBjAA5HtKmz5elwllZLEmm1tNw3c0CrnRcBdX17Z0hvcAnmV+bXfE5VMmEwtx9Fmzj4Ppv+JtF3qUkpLIDWkpFFNcVQvEKy+2dQkXV2CYOMOofIGtR/Gs/wUrdFgGpQafDe3t1ImAw8WnVR/dyk2zow55lNxbfzc0kEZ6oVQfhI1l/NSsqZIJ3aJw4Z2eZMzFIIB71lt3SUcUgdOs5Yh8kCi6zZNLzQT/uAdgdmvUA7xg59goIaMc6BrC5U1Nhbo3sBwCLp9UUqecItAu3MLmperqDpJLIeSjrhSRCrMQiyXOY428LJmAk6BmVfxTNGXBOmC8J4n3B4RRBfnXB2/4D1MGHNGlE86yecdfpAXvRH3O8P6FhZLUmU8nqv26PnpMLZIKwsWlKFses1y2YUnDYP1DSxKQczWjSrtBGFsY0DDqnHdhrw3vU5drsViDPioQeedeiuA7pdh/4a6K8U4BquE7ptAh8TeErqeFA2YkONoMLooKXipLhD4gNfLTMrxrS7AWpJ0HvojmiT/ncrYwuYs7aIGpH4Bag1A7p84u7gmDs0DaiVdHJdRPx9EkMoA3ZxfEhQRQFRJk2aew7fcXmG2JycmYaaO2xsqztbTJptq6ei+4E5PckdTLJXSJAiQTKbuHtXomU+QfFqhqOBWf6c5RSQox5TbRaDOKszEdWp4C4hZ40WEGlp5CwaUQiUMebOIgoJHXVYcTSH2oTj7ffls82ior1OS79jZt2157WYGcH1r7wCro8fPplr26ngSPuMuQ1qbU1hdjWNCSCGeAoA1W1OpZnP2tK23Fi3sTVk4FVPBlgR0gqIGyBvErp1RBcSOrL3kBM6Vo2ggRPWIYIplyGDISXF15dVkV4bH5pOe7oYU3ZYoTC2VMRb4YophwIaZNSU5ykH7MYe09gp+BADcGTwaKzhEZYer2nxPGZNXUzZJqm5GZiljJFF+HV2XXET1LJ7eAPUIqqTddv1DNiqF2B+b2ixzAC4G6DWiY1nwBbmzwCyR9ibvjp4Kc0WzjrGHNQ6lVpYTsMd9yZwtzwNu0zPZWQVxyfXFL+6Wzu+qF3xEd3PUwXcDQA1zRIRggjXukDOSjNbKsYsDgauZmeGk+/P9BuLjpYGbjrKCoJ5Cjy4XuZyclwAN097vLM1n9xGbWB89gOBVivIxUaLdDiQZQ5+kSOx8f1WIIuA3BGmi4DDA8b+DcLuixM+89YjfPnBu/iOzUNsgqb5ZZu7b/iIN/uneC08wyWPOKdYKhN6VcIErYzdamNtJWMnhIdpoyAQKfvpQdjh5XCNXV5hzVNhoHiqXoKyskZoyt6apqIzVcAsuyxritjKoOsAGJALc2kCY5d7XPJounXaN2dQ9aZ72i98GEiHgx27p4iDCUkEysg1Mlx0uYKiCJgkYMVTsTsX4YhncV0qm+7SCilskYwltssr7HIs10aZaDYPRodBEkY7zycGfvVIBqAFrHnCIAkTMTYUAWQlINvzE2BkKdJKiQxl3GRknFPEy+GAT3dPscs9nuQNnqQNRgl4qdtilwe8fbyPfR7wbFojZgUuPnf5GC+vt3j38hLvvnKJb3zmDBdfvY+Xfi7i7Jtb8L4ycbQqcgc4S2t/VL1i861K6uGy4qFnjNx4NzBnaM0AsMXywnB2zSyCF+kq6+iDOhOH16JZ5tOZf9OCwJ5LXiq223xGN9YFUgqe2fvooFIWTfmLaoezib67VIIwQKkGUY65R+A8YyQ6CwtQhpQeV8E+d2Nr/xrw0FlbkwYTKVY7iL6ibJJVOwsAyAKJORNCsHc0a1DmYFXkh5CwClGDhKzzqYFTDQJ9hCD/HZj1Au2sn3Bck0Y7nGV1CsgislQ0u+Epqyi8v4Q5F0HyWYQ9hPrC+X6GXplYQzevTrioOgIsAKzngVflsw4g0hHimjFeKHV4/7ogXSTQWcL64ogH6xEPzvYa7aNaoeBef8BZmFQU24Ardyi8bXgskQcAGCgaYBWtWsh8FHLQIoNxsCognrfubZdWCJQLWDBZjvAkAe9dXODR8RwDR4y5w5PDGbZjj/1xwNX1CnIICFtGt+0wPOmxel+wfpIxPI0I+wieEijq/SEwWuBKSFMQ6wTCRsUSDdARooBang4IVOexrURJVCqJzRzFWQoJZnndla2FCmARzQZf180Sm+yXFERnarmz4JN8G4S1IheMLWbLmSzVQsrgS1mMQWU7aUGt8k/q/lqNGv+dfYFtG+pXMatKEUAkHGKPbVzhGDtNObWysGMKJZrmbZKAXewRk5aj9eqCUwoaCIoMCuomZcog0VKyBID6hJgM1BJCsMiCAFh3EdmAqI51MjJmjzq4uLuew1mYlOUBKtuwi8PLgIE+WgTirv3Gaz0nxA6gEHQ8+SAgy21NS89vQXYsAJJl4AXQgEmnwPsMZKebdqS01p6UZc2H9jeLUuZO09TjOTCdAzJkoMvoVhHrLmHdTboZVQbviiN6ziXFa8VRnfsmdb+nhI5SAa8C5QJYqVj2vPMObgmUAewAVwt6TRxKIAZACaBkEHbDgF0cii1SxnDAlAKOxw6IBJoIPGpqfHcAuoMgHJQdTMkDJO31hI7vzmwqPzQwjtsBv64elHBbkzG/B/Bx356HZs7Qsq1g11x32kxuW1ALximiZtuGmVVSEGf+tZ2Lz1NsPx6H0Ql1UR+rl8KWz1ILa68qWAW3jSfAKmm6J24LZ5dal2e9bjEHjLmr2owGKCe75y3vT1N9zJnMoVQXTM6GMkdCBMVGSjJbEzR1saTFGsMLovZFWVi5CYyo0fRnOlnne8oGwJkdFE1B0oBLmM3F7tonsAlugllMoLMzyOVG9bFae9EAWeJzRx8PTgFZPeN4P2D/GuP6LUH44hV+yxsP8eXLd/Bqfw029vmaIh50qof1erjCA95jwxFrCwj2C5vVExdW1iQZT7PgYTrDQRQIOkiPNSac87HoSrXNmUoKVuWih6Wn36QD2l/vJyjhnEbrQ303AqlO1yWPJXOkR608XXRODUXgZts1RU2lMk2tyfK1eph/Q8CUexzEpChI0xAHYhtXamXFl/pdEZoPlIt/dJ3WmCQULdZszOHe2CxT1mDr/bDDZTigp4hRAs5NTwxgPIz3cMl7nPMRWxnwgA84RyznuDZfx0g7xTNbE2FDmoa4oYhLA7YO3VM8yWs8SRu8HR9gvY54ms7Q0SX2acBVXGFnlRA/ffEUF8MRj+9t8OiVC+w+tcbF1+7jwc9PWL+9A03JkTRIRwANqqW1P4KMpTWbx7Q6WvAON06Nr9vqaM0yZnCDoSXuc1HD2NKog61j70rRh5wfC5nmBWCcOSxS9DuLMSrmVgGrytDS7XwVjnoOKYY6HWvsO2VYhd1svhBjyiswiWackADB2etZmVJecVkw0+YqtQ3sVeJo7Ctfn6EgV/J16nk74zwls2mk9j2mgFUfSyBSLLsmmGzLJoyIohXrsxB6uUsz/DVpZ92E7YpNG2sCiBXIcjaVtxgV7PLWAhVNqfVZTrBH4N34GIi1ZGLdCmK1xgiYg1f23cGr3DPSihDPCOMFYbpHOL4smF6dcPHKDm+eHXDWa6WBPlTUdEwBIoSBIy76I95YXWETRqwooueI+2HfDJ4ageiN9upVQ7zcrVN2g+V/+2DuVUMGSlo+14ZUz5EHgG1e4SC9VqkzwAtQscmLcMTL/VZpjDzhkHvsU4/H4zmeTSpceIwdtmOP3WGF6+0APOswvL/C2bsrnL2XsX4/ottG8GHSlERjbanGVK6ph9yAXR4lzsYMaFM5c7ZBy7ZxUKsZXD0NsRUZnDG2CgPL1z8BapXvdssN1FJwSsrgOtuld8PDMv6oNnpabfSuDtY2wJNpeJiTRES13Lmzt6hJKwSqGLPYdTKxZQlOebVuWORhyqyVDDMjRdbBn4xKK6yVbeCTfgM/cwU6e0mYskVbMlXnIrJReglsdFkdhHXgPU6d5X0nHGJnzrSUqledaOrh2ijGA0ebFGmZ2URsegdWEdHKNeszcQJMuGt3zVrHGWNH1R7cBmR59Ttv7e8533D8b7B+gQJiLZlYt4JY7vCU74sP/lsDXnn6YF4B8UzZV8NGU9N71uk0c4WRkuh4FrxyUncsE/xAGSuKM1CYIEVbqKOqFeKTcjIwpgh427IoXCL4Vb9JyudROkRj3TjgBWgFuYESznjSiaKVLp+EsU8Djhsdr2JmBbhih92o7K1wCOi3Ad1OU+DZUt/JCpMUFMu1A9tx2X4u5+1AVrm9njpP5T7d2AaoYM+yuc3wPkiz8SmmFuEGqNUytXSXVHcu0NTDNoXEtpPZHaj91j81tbCkxts56PlU0Ksst/WK70AoGlg+HypYqKVuqMC6icBns6kQy5rUYiPRL4dNzGsxElXRmgnHO1M5Nx1iGECtz6BAdbeyCIKlHLKBe9muM5MgUy72PRi8KmRajsYwyURg0Cy9/vSNvmufmDYLiDPo4hyymVcsLE65MTel/MXcd7AxXxjIK8b+5Q67NwnXX4x484uP8J0PHuJT62d4o39WBM0B4EHY4a3+Ec5pxIYizjnPQKwMZQAVv1iULbUTwePU4yAdRoTiGzi7pKeESYIyi8yH8CqAqm9ly0XB5TVNTUpgzRAZhbF2RpUxsQBl7ardkHIuB7MJrkPn4tSAgloZGW1KYoAUvydAEHhf2FPIGphXhhOjl1TE4be5K37Mt8b7BtoZu4W1mNB1WiOBVBxeqABaek0JT+NG5SxyQM+p+EN+rXZ5VQTy1zSpjpco4JaFMfEBG4o2muTClANgjDe9b8m+ewy7h+CSIi7pGq/xDp/r3sejvMHDeA8bHvGVwytYccQ2THh0PMduHDCEhJfWe2xen/Ds3grvv3qB3WcG3PuFe7j/CyP69w9FnB2mzwzmAmpJ1PsnJWiz0NFqg/8LUogzqG6mHDavkf0tYA2R9keoago3UkIGgdWU93a/hCLyrstUGN6BMA+uANWXA5mPlERBPUCLnCVCHgMSC8BAWtvGtv1+7HHIvemsDUU/i1lAq4wwJGz6qaTCk9k1LbRlp2NAFUhAEyH3dl5krDM4IEjqbybWIGQJRlHRlVQ/ijXRbMiYouIGqy4isDO2uGiQe7+OuUNudI4/qN2BWS/QrsYVukMuaX/UdTdSNUq1O0BnTiHUiGTOp3Y7b12AbNbI6+G5INYsutoYIgCzyW0BsTpG3Kju1fE+YboEpnuC+CDi7OU9XrvY4aIfNafWu0IVyBo4gXvBg36PB/0Oa55wP+yx4gkDRVzyAed8LMKLSgHW820Nhad9jRaNWPOENWKpIBcgAO8xFXCrRlh8WQo6qD/L6xLJ8SonK55wv9uVQf+Ye+zCgHvdoTC49qnHmDtMOeCQOjwdz/D0sMaTZxs8ebLC8F6P9XsDzt/JWD+O6J+N4EMEoqUUpqZcept66C1Xp8wn7a51UkAtZ+E5QFWYV+4Q2hR+CW4V4UOb9otAXPTDQK36vX0MKlMLwIytRUwl9dCfpVptwwfWhWCv8VO9MmVxmlrBX0+ZIReh12eUXHBQYNGY+pyW9EKbd8H0agAT5BXV0QIr6q/GmzBaFNqdTh8gfQKg94UsB1z7JRadoZAhWfPSxSolEgm6TitJZSFkq8xxjJpGuELUfnU6BZhsEB5YxTuDWKl0o9tOEsDmnIx3w/Bd+4A2pqC6S6QTOmp1IqyVancAnPm5HHee25ggfQfpug8Gsdrf2uUtauG/WfpgXBHSGlrFdgXkdUa3mXA+TBg4lYmNblLHzEAZRFDbECZ0llKxBLI65JIm6Jwed0CAyrxK0HGjIwPNyNKsDSkpgvDlFKUsyzRqQQnpED1FGISDdOgoaZoINAVFwayAFUfkjksZ6iRVf+uYOhxih8NxwOEQEHYB3S6gd23HY6qajqaJeDL1sGkzlpb/vQFqzZChxZ2jAgbNnjFHgRQ1qQsEBdSSEjnBDVCr7r1haxGa1ENfTX8oXaO5rVEAbA4+zUDa20CtxTmUAI4v9kxPb1KfBSmAlB6lZeQle96cfesajAJUlrCQOTUL+23BoJy4cWJEgVxj5CXRCH8UVr/OxJ/VNkthpDsjkO0+MFXnwC93m+p+1z6hzQPnRKDzDWSzbgazxke4rUjUEsiytML9S4zdpwiHLx3xpbfexXfee4g3hmdFx+nlcK16TDzhzfAUlzxiQwlrAtbEWpUQwIoYR5n7PldZ8DT3eJQ3N1hXPpcDLMVPuGhkAcAlH3AQnfetacJBegxICKjVBr2tbbtLA3cmG69UKF7T7QqLy67JOWUcmoFhQxFJCMemMrWmMyYcJGAE45wmjFDmlVcdBNSGeZoiBOg54pAHgEwYPmuQ85g7PB7Pca8/gKEpY59ePcEuV80wQNPePY3RgQtvuzRgxRHvx3O83j8rwZ6rdKYpj8QF2NrwEQN026MErChhEr1nofFlVJCflLVVzt3+EuGcgCAZa8nY8DNj5B1xP+xwldf4yuFVdJSxjZZ+KMrcefPyCvfWRzx+6QyPXj3H9jMr3P+5HpdfOyLsxjJ2ysCgQOpHH46l4mEBtNrx1b9neT6g1a6/FJLH3JT5uuQsLXh2laYbwgMsWS2AmJEhqZWOxW2bS7hYxkvZfXOomTi8M7ssxQ9CwOWE2AddPjHClkvQDEBhMSYwuk4zq87XI1bB5VBEGeO5OR6j+HEeLHKigdZNUBtF7jtFu14lUqh9ywngTo1qnBihSxYbE8TEIApgEkwIKk+EGjg6pA73h8Ps2fugdudFvUDbTz3Wk0C6oFXtgr0BMdfUwWxesoimIBKVqncA4Dpbs/RCZ2KtBshm9dx0wjaSAtwCYtnEDRYVjxvG/hXG9tOE42sR4f6E+/e2eGuzw0vrHc6DTdhTV0S112FCIEFHKuZ+Ho54tb/Gq91VyVt3EcTRqLM+QHrUpG2FrmuRFgBFTNHBL4hGD4Ym8rFG3VdvKSZ6jhGXRrkFgEkY26BpZQfp8SRtEEhwoB4X4YDrtMYuD7gMQOoYx9zNALddHnD1yhpPpzUeHc7xeLvBt97fgJ/2WL8zYPOO4PydiOHJhLAdlRYbkzJ7HNTSk1LAxNJMy6T9FKgVmnryLbDVMCdcX0sawEsvaP3sg7pYVJ8k2T4aptYy/ahha2mQ2BwagbK0WkcGjT/jxwJqxUMXh2+ZAK2IMVDTCskHfzsG198ZUgAmC2CDo1W7zAExsQJZxrA6TN0sJcibC8Un4aJhlYyZRYksjVGK09W+N9nvn4N2BmilTBjJAKuQioYJQyPpYE0lzKLFChgqSr1LA/oulUhg0VVpnom7dteWbcoBXbJ3xZmcgE1qmkqs7WQNdXzxZb68NCJNIwyqS/GB6YStfWmXNwODL3cNrLghjJdA2mTQOmO9GrHpJ6w7BbEEKEUdAOh7RFLSOwaOpTy5AlCW3g5BBBXGr/J2K3jlzffbpgcSSYmWu7h3Z7YlGxTWoQp7s6UauO0ZKBVwJQnhDMHAqoC99DoOIGOgiFE05X2AgiLO6PJ9TxIwbjocUod97LGfelzvB9CB0W2DajtuM8IhgceEot24TD20ubXenBn/qf51W1Miyqi2puxEd+r6Wu04b5SkOZLmxkCgjGX7flMoHmWMdbbWLEVRoNFqAVrRd/ghjTUrtYvz9W6eRfO5eS+aU5it4ybUriNllGqX2aLXfp4xVVZe20pBAbvPBAPE3PmYw26zc4U4uUCNtGqOkNWmMcAK2dhWMgNZCWSaQ4zcvAMK1Gbbfb0Sd5bmk9lcQw9kqYUGZM30sZzhcsqXWABZuSeM9zvsXmNs3xJ0X3qG3/b6O3htfY1Pr54U8OR+2OFeOOC18AyvhT0CBBtSgKOHpg8GEDI0fa+3936bBVfS4SoPCkgJw+GYNgtjkg6gWHwJB64AZWYF1FRCX64Bw4Vgexm/swFYFeDSY2olP28MYBLUqowmcaKVAFPZ5iABo1R3+mCfXbMrwVMKU/l3yD0mGFBnOlprGnEkBad6TgpaE3BuOmQtI8uv/Qoq5bLmCVMOuE5nduyMY+6w5gnH3GMdlMH1/2fvz2Jly7LyYPQbc64uInZ3ujwn28qsrJaiaIxtMP79Y10bLCRsIWQh2bJkS35AsmSpZJAli5dCQiDzgJHAfrCEZAzCvGGJN4PvFb4WcvNzfa8NPz1FNZl5MvM0u4tmNXOO/2GMMedcsffJyiK5XN/yntI5e++IFSvWWrFijjm+8X3fGFAlEMzULh2NgOthhvojHGrE5L03QthpOz33wCWDTRhqhmd0pN5nzDh0Ix5UZzhyOzwOB6gp4NF4iE1s8MXNLThirMcGY/ToqhHPH05YtQOeHi3x9nMLXLyywK3fbbB8ayexDwA7nxla3oO2O7Ay0gm4ytACPrCPFiV/YuT1kQ0uonnqckhFrOG55NBijDEhTU5Y7PdZnQ3z+UiBHUwCRgWzMJAY1McaZ9MSSzekzs1ySRjD5HG6W6BaiJIkWwnkc3VljLd4KIsisL4vBUasocepx6LAIQcxfxdGgtg/eB+TL6T5PO6MDFCJNNf8vhq1jfhK4tQNmPUBRrTyn36xQARM+oWLMRu6pypcAWQVjyVDbwCoKvCiBS+awqhR39BR+pJcy8QCrk1AREpIGJe5he7m1RG3XzjDRw4vcNJssapksqxJJrCKAhpXpUR8VfVYePG6ul+f49ivceR3WFJfaMP3KxVjovDWRpfVgGDJvD3f8aS+DpogFAtFa41r7K3UYSRdlqimkgE15fX8CQ8YnMOGK5z4DTaxxZramW/ELtaotbJ0GVqAnGjMXcCtaoO+rdAvK1wet3h6d4mzfoGnH13g9GKBx28vsXi7xvKtBVYPJ7SPd3CbETROYGMWzCoEAnLOgCQg67+nSRNVl++PknlVyhCLroe8B3hZl8xrjeKdy9pvcJ6wbeItOnTIekNYWiBKrVfnQB0nND5N6el7wWnS1kwqTeDpIyB93K6RJWS64iYComUuRCoLFKPAwJTZVZAuhFa5KytUNrajLhK0W40cqr63GSOSBB7L8TiKlCR5lUSHYZJWzGbMG1m6JUZH8DGicgFTdGgc0Kt3SuMmOJZEbxMapbNH9LG+Uj28GTdjf3BaBGkiYosgKGC1z/IlmgNZ+hjpTwCAc9oC21/jpZKTGpSvIeSZl9J/OdZQjjdTqz5YJxGLwx0O2h6dn9Cor4cwSNTDSv1CCFKtrkmKIweuR+tGdNqRyiuQZV6KDWT6qTRieMoJC0NitFX3rCpacZyRhUoA3OSFwt6SWFiCY5TimSzmDbgLHBBIYluHUaUwEjsv9aOZ2KXYI40fRKLvOWLhRqy8Q6gdhs5ju6zRTzW2U4VdX2OzrlFf2r+IajOpr4gu4md+ipSBTZ5DLgnIsEYlz2Re6YkpK0o++GvYWuWw1yX5IXSOLe6RmVw+fybpeS0q5LfQz47MryqJOeRxPebrQK0UxwrA79rOiBayintCHpdtxMdGd6rX2Vi+nChg82HMXGdNZNJR5R/593w9LO4AIkWXz0kr/BBGWzC5KQv7Su5xub8sriSgNErzGgFXfXr8ZnyVDmWwUNvOGVl7QFYGqDHLJ2ZAVuOwu+1x+aLD+tWA+x9+hK+59Tbutpc49lssvUi97/hLnPgNHvhzLN2kc2OWFBqQBSABW+aJJQCRpKHGYDLm1cC5S6FJ/WwYYLWOrWxDQE3ZX0c6EAY0+oV1JPO2gVLZFypL0DsDylhYWYGRgLVa5y6v7KuaonoTyfpvSRM22nGwQUQoGCVOg6anqMCbPGcG8CDAwQm/ygERW7zcPcEb/S1sg6xZ16HF0g8C2pGsYct8SY4xSAdtOKynVjpvA3g0HuAidNhUDY79Rq9FwCa2iBhxHjoMTggIuaujAIdGJACAEZIfhmK+kuZecp1GiB+aqfI6Evlh53ucaiOAe9U5vujv4CJ0uF2t8UZ/gifDCqfDAuuxEfmZD3jx9hnWB1s8OjnAW/c7HP3eCie/P6A5GzRGqFLEE6jyoPUWrObw6YpcawxfxC4NMsZAknPiq9vb46TJpXkRQ/ID8fflDHjZ2HsfRAIcMksrFvkQI8XXJFO0sDghsbNcoNThOAYCLmrU5w5+yJ9JCLnb+8g+deHs+xrDWph9fBtY1UNupKVeXChyM3b63hVmzCwHSh3n3QTEBjlvc6Q3ACNOLp26166GwwStn0Z4lRV67WxYO9YO1EEN7D3oRmb4pzOcfqFQV+KDFJSRNU1XgSzv82P7wxKUpkY8XAiIpV/CxMYqW6GnpOYa4CoFI4Arh9ARhpVDf+ywuwvsHkxY3l/j6+8+wkvLU+k2aGg8e/VbkIr4wo848D1u1RvcrS5w4kVOeNtfYkUDOvP/gQUt+Wnoag1BYkd26JV+u2PR80b9PXCuWHiKWFKPletnbC4PRpM6cVTqiRWTRr5hYK238iEF1LCKEKOliBUGnPCAtdvhNIpx4s7V6GjAJrYYuUqVz9NxkRaqCz9K50ViHFQ9HrTn2CwbrEOLy9sNnjxY4dFmhSfnSzx62qF76xCrtxgHb0xon/TwFz0wTrIgncIVlpZ99mX3w+SnVSScSPIDXZmn7lTy2Ky6YFX3EgQjo7la7QS4YhTPOWmQAFtMtM6OTw+hkB5mEE3fHlox0EzCJmGRKNrx5ft+DmxJZCfK51NKQQDppjEG6R4Wo/hlkXYknCYnFSeu00LBzHiHwWPqXJKVyo1KoNEVTDIJGmIIQOCK5fNiIHoNEpFFSsgkdzwBPVdoqgmtTsoGAO9Cjcap9Ei9cwCgR5UWFFK9cHtR8GbcjPlI3wHvkHyQdL64AmRpU5Fn7sjYWE2t3W+Le4/2QKz0mn0QS3+xx132wQotYVoC00FEfTDi/nItcokCaDKfEHOmqpTV2/kRyyRPj1i4ATWCdm7Lx5BaxNs0B+vYJp1OAwiTsqWEJSOuQpaAOHBasPsCziIgMbwm5b9IkZYTc3fUKrrTGdVUAhVYPCC1Q9SOpbLekkOlDOSo0jSwxy5Wgv0waddfAS4aTDjwPcZazMeHhcf2sMZmbLDd1djsKlQXDepLoDmPqLYTMExSxGDOxrEFS8s+R9K/5X15HmtmzKvyahcfN1MChNJIqJBdQS5+IsFGV4zi9dlnsrTsM9b/E1DFSIWVtBddbCdWCs8Pq8Dm9G/dn4XW8jSKhXs0vyybo7XoEYMwfUdZ2mtDAYkLYdKmBiwMYr1hUXaSSm+UTYMSI5rBIm8niFSYWarbEEawdyanhQLA6vVGLLwsvd+T4pONQTEhBbqb8dU7Kg+u26QUSXOlFkrnCo/r1R2hddjd8bh4xWH78R0+9vLb+NDBE7zQnmHp+1SsvlddqDfWhE6LyQ65E56BV+a9NDLjSfR4HBdJAmUyQg8W/ytYcyjpvOcQ4RD1OZmzjenkKaYiR8oj9L264ncHnuUTZbG8LraxYrgDIEWKfFkdFOTS3MOT7MdiWq0Lcel0aDlVBlPMXmU0vy+uUufFNbdyllqIve3X6Osa7/Ahngyr1Fxo5Xsc+B7WCd6kxRGEVhlYSzfgsN0hsMNlaJW1JsdzEbt0DGOssIkNHEUcuh0iO9yrzpNU8yIukgzRJIh23YR5N29cFDQ1GVmAL5NpOgC33YSICes4YtUMOI0LrGOLY7/BWbvEO8Mh3u6P8Gi3wm6qEaLDcbvD4f0ep0cLPLmzwvrFDnd+o8LBGz1cr/QhYxkSzWSH13Y6TIwregaghRwLZ0UXiyk5El0Bqmw/oIIQIK+3PImhqB9R1mZaqqTAlpGtzHo55U6ZvCd5VJDcjUaC3xH8IMBT6CQPqvQ7EnWdNUWPafBwZ5In96saw0qsUqChzalnlkkbo+VhjNTNUA85HTsIIjfUD5oTaKc2NRqIQ5B1mPNy3E07IXJA7UQl009yXI7kMxJCgIf/CvKiGzDrAwxjZrEnuMjCyjJpITAHsvYfK4f34NVC2FhVTi7EAFsXQCVDqwg88rfuWreNtcO0cBiXhP7YYfuA0b/c48Xnn+LVoyc4qbdY+AFLpZ1eTi0qF+GYMQWPhR/wXHOOY7/FkdvixG+wdD2OqMfSTUqxzd9H63YRdK4IRBqkHE5jg4vYYR1bXMQFAkslpuxa2EcBF0b2aWI98RsFtXLCkSi6Kgcxg8Sy7e5oWA+zAlr6WRFQ84QaG2y4AirgIi70fYNKHBvxQYpV6oZogdMjyjUixmG1w8r3uNdc4sVli4vjDk+eW+Lxyys8PVvh9GGLxVsHOHhjieXbI5rHG2A7CKBlyeR+Euq9mgtmyRDZdjaMPTGrwttEm1EfRsz3WEGXTeaFlqhYC9j0GIqqAyXpIcOlRICdXEwCJ0aW3Z8p7TD/LrD6YVEGjGJx788yCPuZgbD0q07wNplPk5dOhsEJ2BRle45OFzkVdpEBN4ihZvApKZmiRzA5MOvEbSfnZGKWxEcmYZ4c4GVD1u/gNDl4HzGwtpetwoydBYj0cAgVKopJqluZyWhRtRtjBbhJqiw342Y8Y5TMLJKsGTNZoY09z8Yrgwjc1JLwvJ8OhVeAreIXQpIShhoILWE8AMJxwOHhFiftFp2TgoAxJQeV+ZLGB5OstzShpREL7XTbQthZBKAqDoH1XzRARp8LEKlHzxVGrtBzlRZyM+aV+SwqCNHSJC3KaYLT4ouBE5YAEIS9pQIynbYIgTlNxzYVQo9XALFRPFkcMCQvR2GWjSxcheShFSlJHK2YRJCujY2bsPIDhnqHvq2wPaixPW6w3TXYXXrUFw2aC2Fs+e0AHoNWfYt7pLwnDCDRa7jP3tq7YfQHp1UsFf9b58B0g+6bWBna6fhqrLHJXQso0iCkPBLZZ/7sc2ziqIBWKr4gVZb32VfprfbO6gpLS/eT2FwsYNaUmoUg34AqJ4zspO5N0gQgKHjKDLn3Upl7dgnnR2UnGK14pJeTzWwaCNErg1FAL/PGgjI1QnRwLiDqtXXp3PIIygzcsyu6GV9Nw0Hm97Lg6SAy8oQulAys4nUkRfPQOmzuVzh/jRA/tsY3vPgmXls9xoP2LAFCLzWPceI2OPEbHNKIlYsJxAp5l8kna8eMi+ixY4+LwtvWF2wqKUQIKLtjkb45iHxOFBlRixxaGIB4PiXwCowdV/CUbRzKYrtlHJKrEBqKGaDi/FzhdIGagB3PQa1azwnIwJi9hx1LyZiSeT1LIzuS8/E0pnMuR00Tai9eq0/HJc7HDqe7BXahxqurxziutvAQBrN1Lexjg5q1c68VWijg+eYMF6FL+UyIUujdRbv+MQGAI1cY2ONOdSmfBzuspyOc+HVS36RO8hQwQtbUdq1rMEa9Tr6YeATUlHvj0EV0PIhJPg04cju8Ew5x7De4U6/xuF0lUMvmq+cPz3HU7fDkaImHt49w9FsL3P6dEfXFCI4MRwRAux06J90OjXlcAlopJ+KrgBYUOCoK7Qn8Km8eEnmdyHYlppmdy/yk1UMLuUhjQBWAZ3poXbl4jLSN/M1KChBQyEVhR7kRiRjA0eFiEtAypA6ZBF9FxJjBp5SHhOyLZcWdFAP1yxwrTiAXMWbqHtIAmM7IsTTTYgY1ARwcqJIiC0eRHzoXIf73uZlC0OY4PSocN1tdc73/vOgGzPoAgy3hDiwMnGnKpu4zuiIjSQvtOQW5aLlAXC3ArU9Ax3uysYDMyLL720CsijAtPYYDh91twvYBI7y8xUeefxevHz3CSbXBge+T1MFavrqKcVxJYOpoxKHfCqDkdqgRUyeSrH/P3/HAjIZkYQ+SU92xxybWWLPo4NexTZPnWOjKE4OLpoTIjhrsRva4cJ0ENAWrapoS7ViCWF6VBRA8EwZkCSNYum7URHAAlkSodTI9dCPeDRJYBjVpPHRbnNUrXIQOD4cjMadjj60aKfYRKdmIEInIUbXDUbXD3fYS/UGFi1sd3n1wgHdeP8DDp0tU7zRYfanF4RsBi7d2qM62oN0g7KxA8/tEPbOIOXW5zLkj5Um5pLzq/VCOK0wtA7oU/EqsrICrj5VMLdLJRGWLyQg+rQoKUMvkLSF380hSxWjHAKGsatKRJYoF1Rb6HOXEhnSFQToRh8mq5fI3BaG9xigdMAKTSn6qRJsGpBuhdBerMU5etebQZItUC67JTcqgpNLClsQQEBXwqyqpjg+TSUryQiYnG1CjxYhKE54KQY7BSVMCz3E/y7kZN2NvaNJui7P9roU2rgO4AAEw6gpc11IwAa4CWfsgVvG2V0EsKZqEhjAtgPEA4KMJtw/XuNUKg7dxU/oOTFqoaN0kskE3okKQv2lCR7JtQwXbSRdIdjYRueJvWPsEh5EdBvaYIF2uzJi9/O4bFpiM5VkArZ4rhEjoqRKpor6/yDl0ka4+XeW+mHPRxo4NSL0rxEOEWM6RAtYcUXFIbJ6GJvSuRs8VLkObALkpmocKZotdn67dhGU1IDRb9F2FzUGD9e0Gl9sGbu1Rny/QnjOqS+u+G2TtsQ9gGKsXgHW5pHJxX1Sb0+cOYFY6BjJTa5/VVSI3hIKVZD5k1zC1EtsXGaQqdpKnZKuAQ1nAc/FcCUqV2Foxpc8wpHT8KB7X+T5GSkcMzpVz88QS03do58L8BQrRIdg20cn1txpTkSzIH3bAkLVdPhP5jntVAQAIkRSMlA+U2en1KD5WBUMjOziKmKJHJFZfm5tA89U8zNQ95xJyv8yaRwG6xioAL30s1oT18xVOPwbUHznH1z14Cx9ZvYtb9RqHTti1z1XnWFKP5/xlkhU6AA0ZoJNHBHAagSdhgTU3qZs5AJzHDqkrecrjhWkVFDQ5cjuxMIFH4DrtV5hR2cbERocpqUKsIUNiSYES+GTSc/PHKgG48hwMatsHZ2zbsQCyOgrYwafmVCWLCSTlkyusMAoJtAEjdVwH5PcAYdmYZDCqkuXA77Q51oRNaOUc9go3gHpsgbD0Q2Jq2ftacxIHxkXocOh3uIgL+CDdGJ3GwHVsEcglFph4c1WIZmpPUnjyZJoZBTX3qgdZFwK5bzjCccQdA/m0WddB1aN1E97aHuGsF4+wo6bHa7ce4+lyiy8d3cLu7gJ3/4fH8u3elLUiOaSFyA43u7k6qmRoEV0FtOwwLXX0e2CXmcjn01EQK4Kck+9ZkLk6MYjtPewYYIUThnloZWsWeR92FjoVtLLcyLGATlGep4nAdvOWIFmU2NSHClUb9X6VJ627IBgISghIZ8/luaNQyyDlWUJkkFgTzSDe6/YBqSt9CraBwL0HNREcCFRHUaJAmFrBRTA7VF6IIpH1+6qsrMgEH98/RHUDZn3AQUVyoeU4zEpfzIAZcKcXEaiqwIcrxFUnX5wCyBLpB/aCD81owGUCwl6qKf2Rw/auw+45xvjyDh99UTqO3GkuceB3AIQJ0joJAgd+h0O3xZ3qEodul5hXNYRu2yh4VX6LrdKS/xbq8I4pgVincYlBW5UDqm93BmRNySTeO/E+ieRk4nQxtaYduYJjThO9BDenDK0JwCE6N2KllRl7v45G3MYGHQV0KTGZT6Q1SRWn9hvs3A49+9QKOPITDOzxZnMLp2EJADiblqoxb5IvWB8rlZx4lSU6LPyIo2qH57sznB8s8Pj2Eo9eOMDDV45w/qjD4s0VDr+4wOHne9RPNqBtr6BWnE+cXujgpL5rV3TgAK5owcG5AQEwN4pP9yHN7qNZpVwBLKlmFI9BJ2CtNMw8s2xRnhJtJO23HKMlAZJ05C4fsMxSPR44L66MTgvAaQWkpNe6ERgnqZZHrZanSTiQgERcoeEwayvM2gVxih7wI6boQCZR9Bo8osgiAe3oaKAbIFXzZOIohsDy1RffLAO0UE0gQCZniLfXED0aB8RiXghijoDotLvijczwZryfUTJtrgOurgGyyDlpJmIG70CKR/nv8gUZ2MpAFqXvO1fqh7UUT6xwNOHO0Rq32zWWfkieWDZ3O4ihZ0Ni5N7SlJhXVhmXOZpmB1ICWXYsE4CpALFMqp6AJ0RJynTS8JCOplaAsMWjGWgHdlptLsAjXVRJdVv2W1GQ6jRyTKo4IroRFWJij9k6zo7bkyyyHEZM2gVrgsQydiKHPPeLlMCYh55JEoHshRTYKZBGqCiirXc4rHr0TYXNosbmsMHlcYd+U6G+qNGcVWjPAtxWG5RwwRovP2fyaR0zA7XK7WagVgENmaeWLaivBbXKK5IhJPNXnBnFG0sLBesq34np+fmeOBcgZjdMCQvRNUcgfySmIxX7kPwzGcBzwcqyQ5303mHEGeEYuh5IHTC11C1SDT33gKssgOJUM4FfYq+EWNF8SLM6B6hBvCOJdNIlU5haXNyFZuLMZA1HbmLNV+uYdS4HpPO0IjEzeaENY24RITQO6wc1Tj8BHHz8Kb7h/hv4xOohlm5ATRMO/Q4nfo07bo2lG2drbEDBHVVEOAgb6yx6vBtWIhGkKXX3a3Q+HRU2Kuux69gmq5HST9RYWQ0idnBSAClY7l6/nMZwtbFTOaPT54MlzRQw8pyNtY/5GyBjPlrG1rLHJFcKGPX9asTUIdErqCwXX/2IOXfTFiWJMMx2sUHnxBPLOWmENXCF42qLe510W+/8iIUfcOB3Kb6Nmuy3NKHnKnUwPPQ7bGKDy6lNx9BqZ+0D3yujKshn67JMM0TCyvXq6eiT1HPkKgFcO2V3ncdOOlky4GjEmiusaBKZJXgGAMr1sHtOpIiHLsBFhnciWW0o4Dx28Ii4W11i4R/gvw8v4OnlEpu+wWu3Rtzp1lg8P+LhwSHeunWCW/+jw/HnRvjNBApOWFoqPaTNDhgVjtwHtOzD3vfEsiJ+iHOWMO1tV459SWI6TZMcsuT0eiOUgBbK5lpQgEjzD9l6HhMpUpIZpngUVSIYgehEAj+xWKrAAbXaHbABYQyEIJYLtYuZRKDHIfFQ/o615lcTQE7+hsEZhBzbCVleGPX8HADPmjdJfhbTNXVJIUPE2I4VulqA1RAdooto3I0B/J/aCFFvKPPKYgWy7IYuF472sVQVqG3ARyvEts6Q/7OArGeBWJDFUGwchiOP9fMO6xcj2tfO8Kl77+C11WPcqjdYuiEFBA/Gcb1FRwNWbsChSgjvuF4AngRe5S9rqXUPAHoGBq0u7zQwbaKg/Wv9acO6kdQ0YUU9gF6rBw41hwRSmbyw4QDPIt9Kkz5JdcH09a22tq0pYAwVNrFN7KqLuEBNAQ+qU5z4DY6ohyNO1Ruvy0GvQN0hAYdgREwIPGJEDlqv1qfYRDOmFKDLQLbz2GETW1yEBR5NBxijTy1zDdxa+AGvrHq8sDjHK4dPcfr8Am9+6AjvfGKFJ292OPhCh+M/HNG+s4U/Wwuzr1zQVl4+4MpLdb2sMgDXg1rKoCoHKdA1m5gN/DLm0z6ohaLyUIBcDOT2s0YxBYED5/ndFtLWdtbuZQAUlbVF2UsrVSOgkzgDxCTIf5CJLncg0woAU6ZxayfDlIRybocsElFKbWpjFHApsgBQiMXEDZ2IJ2E4so+zIGMTN+u1CwpAkWPUdQATIzIwTCI9HINH6yWp34UaFYnXjoviZeJYJvubSvnNeD9DOz0neaGAxGUCfM195BzIewGyKl/cy88Asp4FYkFBLC9+WOMhYThkVLd63Ftd4la9RafNQQwycGC0bkogUKvyigWFAuCZz1UGXRlZcmIkCcfE4oFliUDZOUpOX/blKIq/B0nXUdZquIFZgbWTjhDv4fRdbY4ZY6X0dgGwiLNkflT2VkDuUHrIO3RuRMtTYsV4hRI0d4QH0BLQgmWeQFQ2jzx/4nbJW8WALkm4HHYqmxzU30SYXZoMKbhVuYBjF3BY9Thud9gdVrg4abG+22B7UaE5q9A9DfDrCW435MJbuk8EbIEVG65jl9v9UT6WFrNQUEuen4FaBqqUC9/yplMw7KqfVoafSikhosUinr8/kiV8Xl/baRj7uAS8bNHOSNVwMBfJDZJvSTokLs6BCcwCRhI8PCb9elK6dCY5nEk007mWPi08jzVy0jBxCbNDgMhTnRZbmJEakAR2ScI+RQ/nJj0ugvEb7fLejK/ukXx/SECs1NhjPwEvCuVM4vG2vVfh9BPA8mOn+PPPfx4fX76NpetnbCyTFZpEz4YxZr2qNHYAnoQa59zCUUxeV5YrBFjxQda1Nqd5MFZOC+9qI9LRhIbGGXBlDCz7vhn7ymRvBijVyJ3QpSBAqQvfTplM8lpO51Ge0773k0gU5+ysiLkPl+UbUd836jwRNJYYK8qsVqz7u42GAqIbsY4tWpqkSF7vULmgFijSLXcXawGTICyuGCnt2/IRu2aOGJehTc2PTKEzsk8+W4EdWn1f62oYVSvnU+dGl5+DE2DLxcREs+tv3Q4BoDMZWXF9DQQ9dOJpHHjAhRtwmy9T58OPLx+idSN+s34em7HBo+0Kjhgn3RYfu/Mu3l7s8IXj29jdWeD2b3l0TwbFp+S+d14BrX7IE3kpOTQZYXkjl4BWqUIBMviVWFY6lzODYpTvGpADRvGdm/ltFYBWxjopFXCIMWd12WDZD0UDvaDKFHsT2YYnzXc4m8C3WmAEZzVKiA61i+INXL5HzMwrNwGJHMWAG8Ub1Y0AVxafNIfzer0mlU0SC+jmTdlCiMHDOZE7TsHBOcYUhJ0FAEP0WFRCNtihBsUsQ/5y4wbM+qDDFkEqDZuxs8jNt/UOtFogHixnskIA1/tjvQcbKzQOYeGwveWwfpkQPnGJb3jxTXzi8G3pxld0czMQaOV6nPi1SAndDndcjyWJDM+6jRjzKjBjBHARPdbaWhyQjiOG1CeKcDFy1cFhRX0yZwQAh5iPi2Rf4gMhBo+NC1jHNk30u1grG4uxiRUiRbQQc8IAMUoco08UWOtwchEX4ljifNKBN0pvrikkBtqSGJ1WkRypv5bOH0sy00IkoGtUIG/DVUoyHocDjOzxOBzgUkG3s2mBdWhx2osnV+UCnl+c48XlKd49OcCb947x6JVDXHyow8EXG5z8wRLdmxdwF1v1XdOVtJPqPVnzgGHME2UJar2fFrShqA7MZIoFqAVk/be7ytIildyVC/7ZxBzkPiarOrAigypBtQ4dwgJQQEt/T9kfKailjzGMoQVQJK1AEKaQ6GVaCSDwROiti5QuYqKxGQJhUnlikn7YKJKVlNgEAnvOz7NeGz1GA88ILAwxiHeKU1DLOwcij5YYU3TJwNOTdCGpXECMVplTeefNuBnvZ9iizH6/rlpIBKprMXl/lqxQf0/PXQdkERA9gSvCuCCMR0C8O+DB0QXutpdo96rj1k2tUS8qkdlNWKg5sMjwNOYlIEn+9WytzzMjaYJPRvH7zk7WiYdBydMjewXxLHufrDpP8p31LmJgTt0LzSDegbWlu7x+Yg/W/Ro4RsSodEHfcyWsT5K5JrPRxN+oZWGg1QrgEeQa+OLa1wQs1NDbgK6gQN6SHSY4ROew4QaBCVtuMETxneyjAF27kA1UD6seR/UO667BxarD5rjFcFyhOffontSoLnrQMOo6JX/+TNJ8Q02arsaa8h4yoCqtxvPvCbu5jtFFxbZsINU1LC27EWcAj+0miTMyiGb3raULs0TCMKjCRD49WfxBUMNdOUbJcYvCSTo3iZ3mhUi6ZjJAlVmq4waSMu/Fmj1Ajbm8juUx6V1fbC+XU1ZeZGGcKEmnTOIOREQSwE06KmpCz+XJ34yvtmEqD/YkPlnlWtDtfb8AwEGBrBqnHyPUHzvHX3zxc/jU6g1lQk1YuSEBWWL0HmdG6Y2uoUf9rp9Ghyfqi9XRmOZzWdcTVjQkAKeU2JmSwymjVnyWODGcduxTdz0rToOyT9XITmxGEJPUzzzkRnbo3Dwnsu9ZUAuMwFmSCIhXlrGwRs5SRE8ydQbdgQFVQCHpMvBMj7Us3OwUHfCaPx0peGd2JzvNtwKcSu+2QCd+k+upxZvsk2WM+W0ZeFW7gM3UYD0JiDixNEtq3VR0iavQuqlgp8nadOn7dIwRLhnBL9W72HzOxLxetttwK2tpt0VNAefcooMofzrhH2MHYbDp0h1mI2D3TYR2vKQJgSe9x6T41boRB1WPh/0R/ujiDh5tltiONV48PMOrh09w2PT4/cVdPDw+xJ3/0eLgjQEYpMth9ARyytba9c/OmQKLquUaECoNey411tJNzTvKPCHT35jHm2TwznNmpO1PGVozySGUobXHGk4m8FOW51pvD9LF1C7Us46XkxJDMgtLcqjgRZ3iJt2npHzCYVDk0YVc708SRItFZayOAI12vpCik5PzIS/FGGYgTA6+ko7SjhiVj3IskRI4dtTuVD1z083wT2WQLd5CBId9gqoO6yZXVeCDpcgKzeTdbtAv162wALFiRQgLh92Jx+YBYf36iI+//ib+zK0v4rjaJubSLtZY+h5LN+C56hyHbqsglnQhPCTG0uUbZeSIkRkbBtZcpa5/j8NBkjt0bkyTZ/YTkS6DIzyWFBK4ZYHMdNfmlWWGg7GQhthzEjiNflsliYiZvBsQJn9nA3ipZOeuiCN7nIYlTsMyHW/nxgR2bVyPEEgMLF2PjkIytB+BJE00unRNDh2AQGLkeMgDeh6wY4+l69OEf+qWGLnC3eoCT6YDAMD52OF8WGCIHp0f0fkJH7v1Dl46PMU7dw/x8ENHuHhtgYMv3MbJH4xYvLGGO98UHQ9JQKjKC6g1Tpk6C2QQlShP0OWEa4muUWd9ubrRUQBg1t0qsbJQglxmqgikbofXVBpmxoYGRDl5XYoXUc/NIW+LvJanoGwyZYKhkBryRMV3T/Zr1Nsp+mLxVKlppczQHF3y05Fj0NcSZKXi9AgM1IrFMbN+MesIpE5aQqGNZGyP/JW3qlRgh8pNyfi6Ykp+JjVN6GOFA98XUeZm3IyrI2FOka/3ygJS/CCnnQqbKlcL94Es2nsdXQWxWBOdqXMYD4DxdsSd2+d4vjtH50Z4rT5P7FJzjgPXo6FRZHAUUCGiodymHQCisnyl65GwjwITNtwkQMAq20BmL1qSEzRhsUXhxF5YVAJppxhEYDXMJpBWvu05BwHcItEMLJvghQFjoBhJymIG8FFXezlZcdiyw5brdOwVSfdFMDCSMHVlcT6hAidJoqmYCTLNEYDKFr8kUF+ggIkDJiLUPGGCRxMDtlQjssPSEbaxAbCQqn2osGGHyskx3OnWOGp2WC8bXJ506E8qNGcLdE9b1OcDqB+RbRFMlu5B5GTRX3ZgLoFTA6rY7h/ONxBBDW6L+6sEtUrpoQJH8lTB0ioQptTtsGRoGVvYYhHT3qvmmE16G6u6l4dgQFGxrRUwVLVqh5e2N3awAazG9rUKtEkNsffalHAYu8zeq8wKjPHvOSUNcgkpJTyafyc2oHw8BOeE7U6axNj975QF7ynMv/s346tqsFNZoXM6qRR5RrkdAfCEWDns7tY4/biD/9QZ/vyLX8Bri3fTdp4YJ178sqxjIZCXjLaaEkBClBvvRll7dzRi1BTT1vidmp7b2j/C5e5+QCo427DuhjUiGl3329w8plzE4gPPvvQ1CfC1iTWWbsTITllDGbxq9DgGNZ43ZYadk/2sSc6tlFSWvekz28trQUcOJF4Tq+38AleJkWUduDuMqQDf0ZBkWTUFvDMcotfrKV0IBZgS9YkWV6KoWlplbE1mg6Jf+ifDCo4i+ljjqNqmQtQmNHiuOVePYmGPmd+xQxTQU2X91pDLUcRKOw+PxiBmj5E87rhNYsRF5sTsgoKCNQEDsxjZF/HEkXakpwvUNKFzA47cFgd+h9ZNqP1dPN0tcDZ0AICTZoNPPPc2Pt8OePvoBON/a3H8Ryo7NLDMLQTQ2u7yZHrFf5if6aE1Y2eVQFTJ0IoZKUp/m/ewrcHSe8iFSEAXab7ji/gG3V1gcJWDAEUBrizvsWO04xQwS9QoI3ssaFQFlNqkKNjFk2wTudiPeShbzmWXplIGoqplAGR/rOJyuInEKF7zp5QzRYB7D/YM1wT4Su7VoISE7VCj0kZaDMC7iCk6aYJDz8BVrhk3YNYHGE0V0sIK0J/JE8hlxktVgY8OpFuh2wMTCiArgVjAHMjSABVrwnDosbnncPHhiJOPPMb/7fnP4cX2NIFYqbVrvRZKsEoJD2lERxErJ1JCV7DGInPStz+OC1zEDhdhkUABD+ko4nQCsza6BlRZm17rOCKHn7uQDOxn29pIrXI1kIW9VZaBZ7WfsGKpGgzssUOTthEgS3638+9RJ0lG7SYc+23ytwoQBBgAdrHBqRuwogGHbpcCtRw/pwoUwKmK4CByzI6AQ0Qcc48NDzhxO1z4S6xjiw23Ap65EU9q6c7xdFhgMzXYTA06P+Go2eKlg1M8WJ3j4e0jvPXCMS5fXuDw8ye49btLtG9ewq23AkBVwuLjuhJQq/LC0jI/LUsW9kGtkgsNwKizwN4EbdsWj80m5Gv/zj5aCfxBITnU7Ixd7tSRkgS9160aL9kcl6tzHQp4aQJAysJCJMTowNElKqwUowm7IDTslesBlu4r1kUj2iQfheqaQTD5ukY9L+iiRaRVAFU53wLJdSAvDCvyAJQuKxWqCASH0btcFYkOlU7QO9SILqB2AY4dKvZiyhlpT5Z8M25GHt7Zl7m4R8pKYglktQ1Qe/A+M3gfyCoBLv2Z5e6E0BDGFWG4xehub/Ha4VMc+V0CsVgBnYXrRWqn1dSGIiowGhKR0/76bwKwY8KWxQC9j1UCBQisXQxVrkfZ30q8roCKJvW1kvOviVEV7JNyWxs5CbEkaj4MPHMUUeuXPWAOficjcJjEj1MRJWqC1tIE8eKSo4i6xJrYY0cBDQIampIszC57BfMY4WQiL49Ld0QDtkYO6PyEBQ9J9l5RREUR21hjTa34BkaPEV5ART/hqNnhoOlxuWhxedhhOK7RnnboHteoLgbQOOaqMVlCTFKEMBsFGNBzDahVrsDZAC27bpzvtWcxuq4DtArwKVWtUTynksP0HnqvcUoW7I7KhwaybZGSCdm6BLzkfcmKIPoTrEmDbjOx3B81hcTMYv0yGdglwFLM718AZxZr7DvNQhFPf5fXh+1NozKGWTslpmKKYm+pWCLebAxhShLkWAbk87sZX30j1rL+u+KdVbKyiBKQtb1b4/SjDv7rzvA1zz1MHllmyH3HX+LEbXHoRiwpd6srgSwbF9Hh3bhMnkrmjWsFhDIPKCWH1p186foEAJfdygEkhpO8NycZoaMs7zMGZQKt9HHz9wIym9eGbdMU2wN5OZzOd+9yynGz7iPvq8whhmKRbVL3XuNJrUZExlYz319ACv7SYTfgyO9wGpbYDbf0uAhj9NiGGhdjh22ocdJsYf6OjhgL9YRtnbCmV8q4qilIF/exQySHx+MKCz/CQ5qLXIYO0UmHyDEdZ0j+Zufq67jT9XVkh3VsxfsYJqEUtt2aawWuHFY0YGSHlkIqAu04A4B2HWvkuNwR457folOw7wWcqtF8wO/RczjdLfBou8IuVHhucYFvuPcm3jq4xG+vHmBadrj924RqPQkTjKRJBznKPsUloAUgSw4LQMvWVLGwaUnsruKm2PPbmilfjKFleU/hPZzQIgPFFEwqJYfyuABdYonCcJPasahSxVSyxFBTUfEHBgTcrClIl2YDfQMBgcSEPTqRGVo4iEUcdMiG9Bb7HJSNhswmCwBX9rse84TE4uKOQXWU/JGBafRwVUQcvR4TpQYHiV0cHZpqTID1+xk3YNYHHQz1oIjzZNQYWW0LPliCu1qALK2WCEDlZgnEdWws88UaVw672w6XLwP8+hrf/MoX8PGDtxM1tI91MnQ/8ZvkGSVByKSESrmEVcc5sbFOY4PHYSXdLHQytVFWTQCpslgFxSul11HEDrUY8AJpbWvyP2vrChTywiKlKDXnu7jQrhkDHBw6rSruuEYDwJNMtk3qeuLQx1qTCunMcT51aN2EY9WFm6lkYEpdUTwx1lODdyETuFFbOycT/IrG3HaWYjLGl+MV5lZHhIaAwBNO3IQdb7GJFc69mOof+w0O/Q67rsbjcaX0X8bF1GKKDstqwOvHjwTUunOEt14UUOvg87dx+3d6NG+vQf2QJ97KC7hVV6B+AA1j6qB5pRWtjWukh1cqDrYd8rYzAItZEuU9QAshV4kToGXV5eSLJd8HmcgzD8vaq1smzcX2uZNhrmY4oGBgiQG8JQhuksl90sVBYJeq0encGOiDSHKsKkAxJ0NFQUK6d1Ss515cw4g0iTMoERKYKV32yIQp+HnFMAIVOUxR/nYckxl9WR25GTfjPYcBCddIv6iqpC175QTIKkCrZ/ljJRALGcSKDWFaEIYjgG+PePH4FHebNerC2N0M3Rc6X7aY0Cj4b1LCEr9gZDbWjr0CWeL9UUo7KpOaFL4cJtkjZGBrIo9q70sj8j81XNUTnVgWTFRsa/5ZEwMT14ig5I9Vkbx2hIPnqMmSk6+8VritaxSDMEQB4yxmRqIUHyMIkX16z4E91iBlM8fE4CIwGpVkAJb4FHaakKmn0sciIjqKmFgWewtUWNOEjmq0NGGqpBHLGCsQOFXraxdwu9sIqLXMoFZzusDicQN/OWRGsBZFZM0SQSFIYaWoRs8W/dcxr5LssGAhPRP8KgCttNS/CmjpnTp/bvY7MmNL9y/vr2/JJWg1T1rsOb3xUsyICdDSzZP8MBvyl96Otl1gaVTCRRepdK8Wf4oxPO89iIywpUug146z/FDym9zdkEAaqqhg/hFIqc18E2u+qgcbaPVerCxHiN5hd7vC2esO+PpzfOLe2/jk4UMc+41aeEw48Wvc8xc4dGPyPfKE1Nk8MKNWoOA0Al+cjjHCo4Z06NuJcQdqmtTPdkoFbWt4sXSSvzQQY/dBjzVoYQEKTEVdZHoFhEpwyySFAHBIYwK6AKSuhuaVJeeQvbOMnWW5h3U8BK7WgkWlkacGm6PH4vuUZXMZWCtl+OXx2jmaiTyiSAPHmD0Ra5XcAcBRtcPEHpdTI93eIOvgR/0Kq2rAYSX2MlNU30UIoLUN2kkeAg62Xh6rIB57E7zIOLW7/Sa22MRWOybG5IWcOi3qZ2RezOdRWFJHbpc8HY3BtYkt4IAVDTAPMQO+RnboKGCIEm87ClgW12pJDO8G1HSKE7fByvVo3YiFH/FH9R082q2kQ+PY4qXlKT588BjL1wb89+YFvLM8wN3/TmjOxgT2O82JEqAFPMNDq4xngJEAkh/dDNDS55nnuZF5ZNljJbi8B2gJkyk/L6xmmsUCCpKZmLKGxuIGZX2/CeBaurv3oUpAFggYowcHV8Q1eT/z/RZwTEE7A8Z039atkCLEAF7zLVtDhiYFZjsB+RHltYhiAQMC4uTgKqkIkbGGXUSIDmMQIKv2AZWLGKJPfsfvZ9yAWR9gTMFjERhmyjsbpJ4lRytwUyWgKlUz1T/oPYGsijAtHPpjj/ULhPWrE1589RH+zN0v4uXuSape1BRwXK1xx1/ijr/EbdW2tyQsIg9KIJaNAMZFZFxwhYvYCE0UHp0bkimjVVCE1VRd6SzSaecKQBZUDcIMtBLpSUwGiEY13WdgGZBlPliHbgsgU2+BrKePJF5allhtYitMG4roMCXA7Xzq0McKZ5MAY766FAqsJhpeZYqD6tMHrtDRIBURlq6II+Wvh3VSqSmmLi6BM2MrwAz0GUs/4phHnLgdduzxJByIaXwrpvE7rnA2LfHucIAhVtiGGo0LePXoMe4uLvHmyTHefeEQmxc7HP5hg5M/6NG8uxY/LVvY11UGtXRyniUa17G0rgW06Oq9W8oO5QPT+5LnAJftB5SYVyYpTK1lbdK2CVJ9tMouHQASwg8gUVStCp58TKA020gzMMoAKQQo86oCvCTCEblKwVqJKM8/3dIkeDRcPixiJPqwmAcrSMf6uwMiR5CXaoOvQvLimgLDW/AkhnOMXajQ+CBd0JQTPEUvrJsbz6yb8R4jskNVehztDTF6r7WjKV0BreT3+WMzIMtJVT+0DsMhMJ5EHN7a4PnlGY6rLQiZ9dG6EUs3YEkDFm5UYMY8oeZMLECArJ6BAQ69smet5bgVNEwSSODcoAEAWFhHFWIxVRDUaS6BVibtkPVoNg8vY5ZtZ0CUJ0ajzddNXgggebgwkVb5tYiDKgEXFQtLGU6qn4ElLkUQFlqEiQqomxzRQLDADhMFVHComFGZPF9PsNKuiR6cOiUyDCTUbSBLiJoiWh7Q+QmTEz+t0jR+ghzXJqh5fBSQ/aTdYlkPuFh02By2GI8qNE8X6J5M8JsBueMAAd5ArSDNSGL+LOhZLK0rbCvKANJ7gV+cH7vC0LJ7l68CWqS/p83SvQJc0RGWz+9haiWAJPEqe2ZRCQRFZH8qAhJ3ULflOPfaKhOEdBj7U375hB1QGZ80ZsYAkAdYwTSKMTHUxI5eGglIkTGkpMm+v1fQjZvxVTPYWnfvP76n8hhOKpy/5jF9/SU+ff8hXls9xnP1OQBg5Xo8V13gnr9ARyGB6xGyxg3MGPSLFpixYcKTIE5JQeV6TbHWrwEcum0qegOAh0ivG4QEcABIc2Y2Uc/z98gOUR93aXs5L2MjRhBWNMEB2OmxRAWsBHzKgFb5d6esMzuOGjK/jpwBq7r4OgK5rpnyF6vqFo+Vw87TvFwBFNeDU5xZ0YBzjZGlPcuocvqFH7Fmh91UFfvh1EXYxjbUiI5Sh1wrliR/L5UjLvyIy9AiKqtGVDj5s7J4ba8ZWdbXjue5mXVAjBrr7PGRPXaoYN0bRckzYSj2adfnggkrJ35ptRIFWhZ5a00BZg8AiD/YGEVGuQ0Nahfw2uoxlh8a8BvL5/FwcRv3fx1on45yXI7gsJDcfLMrPK7/GIAWkLf1Fh8M0ELa5xWQCxoHo95YFq9Y53d9PamEPxnClwFNASapZBSPa9hAEXvMV1PWKjyLOc6JrNPyJ2N+2TyRZPUTskolXj0WPxBizblG5eRARD5Jkpea37BjcHAgH0AuFipPi51yQfqpgq/jPIZ+mXEDZn2AEaJ4Al0BA7wDdR14tQDXKhFLkg41Z9yXFrqiqkJi8D4eemyec7h8hVF/+BzfdP8hPnbwDu7Wl2miWVLA7epSOvgVdGADsfZHZMaaI86ix2lsMXKVmCyHbodNbJNZuvlVAdmvZOQqATulZBDFxFcGoxpiRIpi0hUuZDaEFAZNphSXHls1sg+XJRnWpc7e3yoGBnw5ijioeuxiLbpyrqRSwCF1XyxBt9wKvRUAhEYEcthRnTzBrLWwgFwTghphRhRyFpa2xB4QnxieEDHhjusxsMMF17iIHU7DCuuqwf16hSfTCk+nJdaTyEM6P+HDx4/x3OoSb906wuMXD3HxaoujP2xx/Lke9dOttFkHxHvNtYD3wtLqB7B21byWpXUdoKX+Wu8pO7TKQwDgrjK0gALQcpqvIDO0yHEyiBHz3MLK2SSHsZhobVLT7wYbySTq8UYgRgIHBbcsFwoCXEULmumelH0yc+rwIU9QAZiJ6CT4AjBwKmH0pOekT1XlH3qoWmWIkeB9TFRZfVr8shCT7FDYWQ6tgrg342a817CeENcWTYyR5QTIKmONFUauAFkKYoGAqGyscUUYjgF3u8fzq0vcadZY+iEBQzVFLNyAA7dDRxNaNXU3EGt/MAMjGDsm7FgW3kJ2IbTapt0SnMLSOzGpIkuXNlkel+3WWf83SaElJ4xiPSRrTjaGFxUgV36viriQtsQEjpECK554NhVWFDEVYBtBkghjgAWVnnmwyLyiT3OD7dfmJzlLkV5Meh08Iiau4SAAnnRzFNlmkSvNjsmTHjsBC94igDCwQ+8q7LjB4DwOXI1tbLCNKkFkkSDeajdY1QMuFy02hy2Gkwrt0wrt0wl+N+be9I4AEg82mgJIiycJ+7nC0rKVNRJQRWxXjOcJwQzQ0s83FREKQMu2fwagBZjM0OZnew9IDLJEoly2MIQVZXdFEXpS4sA6//Me7qaywgBKLEE2MA6cLMXSNSiBtn1MiZGNdQEgVfSRzz0dtB2XxBWZFshWaiCKQijWRJ4ALSZS8R26GV+NI6oBvA225BKAyQvHgwpnr3pcfrLHNz7/Fp7rLnG3vkxeTrf9JW77Sxy6MakRIqA8K30f/fs0OpzFdtYUarY2V1aRgTXG2FlR7rKemE/E4o9FOYk19tWo4Kw00xDG1lpzhEYZtHUB2EQAvfpXmVqkJNcP7BIjqy7W8HU5OSAbwIPmr7fnbHo0Q/kajFHnXwBo9ZpuuErH7pmwRvb3bdRHLL9nZmNJfqaFEAWja41B5ag0nzG/q21okuE7IAxlx5y8tCyO9cqQm6JH7QMugzTgOvQ7XIYu7d8IBJb3rWOLE79JwNjAHkNYpkYsJkL1FDHEFjuqsaIh3RtrbpJ3slOCQM8eNUWMbGpryacaCeTwrkdXP8GJ32Dpetyu1jgLC7y5O4EjYTrXFPDK4ina+wH/45sCHlb38Nz/Qegej/K16Co4dFIAMYbW+wW0gOs9tIqCjiw55oBWGvvyRZP163MzVQskviW5iOVBsfyXQagUO0h+H6MT1p0W3ADk/UaAJiEExBIBYnFJkfMTlpY09UIO0YUyJb2nMphT3DSn/5oRFhE0UW6o5YSFLMUeDyBozqQNdpgwTBWaasJuqhFKz84vM27ArA8wrlC2TVq4WIAPFtJNxIAsB6Htp66FGciSDiSYyQp3tz3WLzhcvj7ihQ89xtfcehuvLR4lRlJNQeWEa9xxa9z2O3TEiQKM2WHJ3z1HXETG4xR8fEbQbTKEVJz3E2wz3jUj9n3vq1Jjbd1HchdDTpO6eIuEWQUEAHJ/UQt8ASFaUAyqt7fkQsS8Y9Lgy5fe/EtsgrY2tLUmLCN7XIZu1tLWuqmEYoY69Dsc+7Vo/knMKT1F7OICI1UYacSaIk7cDp2y2Gr9JgfOyY+AWuKvNXDECr0AW/4CZ7HF47jCsT/AcXWAd4YjvDscoo8el2MLB8aLh2c46nZ492SFRw8OsH6hw/HnGhz+0Rb+UjuPeAI78dJC5UHbHjxNc5bWFUCLsJ98PFN2WDyWJmpNMnLnjmJf0RKV+eSc5IQOBQtJJ2szh09sMHmNgFjF75HhJgKNhDD51AGQXU5irWvgyBUQi/boAMhz8h4Kwc0NffUEqZw7J9KJ3GjBEkBYW9vCS5XB5BtW2SK7HyOh8nKtrIJoizQBtXyigO+toW7GzZiP/VhjC6K6VuavmwNXzwCyOCXHWVY4LQjjIWG4HXF4ssG97hIn9SaBwSYV79wobCyaUFlI2ztMCz2BhY21ZZExmIeUhzTRsE6gRFe7FCYhMrEysPJwChLZ7w6sp5rnO6u2M5BkgzLVyOOlvF0wjyjerPoqW3AzWbVQqu4mcwQh+XNZ3LCOcXY+ASJBNHN6QGKfY56dUUsTWpW1E8TPkohV/hgQETFCKtkV5+TPk6wZDUwiSLypSFjXDQUsOCBgQM8eG9egjSPa2GAdWmxCgwnSZZXAOGx2aKsJ60WNzUGL4bBC99SjOZ3ghgnpjUk67GIi0BSkeGLX8lpASz+JAqh6puxwj801A8G4uJdL2aGBU3uAFhTMSZ0OI2AMW7s37AbIrCxj4CIt1ikAHCgdV/r+cMlwkE62bHJGPX5LJCPnKjf23teOhSKlJILsfbiIp87ioFyPyMIITyp6ZlR23+v1YL3mrMewnwDfjK/CUUqhyO5ZvQ8cYVxWOP9QhYuPTXj1pUe4113itcW7Cch6rjrHc/5Czd45rWNtDLq+rQFsGLjgGgM8zmMHh5h8sJauR0Mh5RdW4DaQxoaZutu6yEOlRhQxssMIl0AgYQU57BT0aBBn7B5Pws4VBlB+XTlGZMmhxC9W4Eue9xrXdtesx7wCWtZpFqy2I2wsLesET0lSt1L1yhKTFjHkeFc0YlApoJ1D2Vmwc2NCzdaxwcgex9UWmyB2MAs/4rDqMbHDemrQugkTO5yNCxzXWyz8AEesfq0BU/RYh0avlVpwRC8SdL0mra5JreDaOlO5LLDwAw58D+uAeOxNPdMgYko51cAA2KvyZQ4vXGCBpevhKWJFAxoEDPCIsUY05QugIKUoi0pASxiCE7zboqkC7vhLvDHeEqlhaDFEUdnUCHipe4r23oT/9o0RD6v7uP9faiwejXJJu+qPx9CyNde+5LDczgr/FuO5ULMAQgaI8tpU/L9CZU8RtSiASJBiZrgJcJPGBuBK7kARKecZWaSFk3UFLHYdI8lhe06kgPl+BNC6EjYsRFvOaGCX/s26MGQvoJlIl0gamowOaMQ/SzBAtWFw6vMYHLomzxGtn/B+xw2Y9QEGXfn0CbRagpddbot7HZDlngFkVYRp6bC543H5ISB8ZIOvf/EtvHrwGM/VFzj0u7TwvVNd4oXqKQ5pxMpFdESZegtpk5vUVzEmX6xR27+m6gnFxIyyLiOZXTUPBOaPVbbLtQShw5SCki3ikt9UUS2xikXZ8SOA4IsypR3biob0d0l5dVB/LprQQQzmAxMGVNjEFu8Mh9jGBo2b0vHs1FNrExuhpiKzA3quUlIiAVOuxaG2zN1RDU8SiO19A3uRZ2qHlwDGCJ6BfK2CW3Lt8mNLili5LW7zDi9Xp3gSlnijuoXP+Xu4CB2euBWeDgvsQo3WT7iz2uCo6/Hk1hIPX17i4uUVTv6gw/KNHdxuksnTAfDK0tr1QD9I5Vwn0yuAlnwI89tXH59NXhGQTFAnckDAHD2hNFEXkzTSZK7JQzm5a+KZ6LOlPDGWlXFZkbNRbu33AGFgTQ7kGNMqgjpK57SbKunk6YZ0CpWP8MsJ3WLAopKJkplShcM+MqHRFkBbujD2J2V/MEugIN/d6BSwg3XpiIAH+rFCW09qbkkpuYL+3YdKO5/tBbSbcTPKsR9rAFBTS1OI5I/1FQBZKisclw7DMcC3R9w/usBJs8XK92jV38SBsXQDDt0WDUU0xMrEkiGlhgwURc6+WOJL5YuKtpqQsvn5cAKAaG9Flv2xkAArm1srjvv5WlE4KY+L0r4sVosULL9PVHijKQCnBHiwAUteFQERE9TYWyUga61ke1KDdGJM7BCLYhEDKa4GfQ/bfySXYhEYmCjCMWvl3KtvDKGHxKSKRHgTeQ4BVpSNiKl4rAaLtxlPOHY7bGONc9fhqVslifs21Ji0qLOsR7THAdtFjcujGvVxje5JhfpiAk0i0BSZOylLawKmYIXZ6wEtu38LoCqBTLa/tC0bwiivmfmQKNRTAFqpur0HaOUbSQEue0r3n2WP+ZrtA1oSG4RtC2LEhmfxYooOEzsEq3RA2CVUR1S1+H6km5Hn4BX23xuYFZRSSEgddim9WE4xA2V2z1ujkSIHSoOhjODkUXkzvhpHWrvZfK/rNnaEaVXh4pUKZ58IuP/KE3z48DEetOeJIdPRgHv+HMeuT7I8WyoKhyKDOWt2eDsciHWHcmc7GrGBSJsu4gJ3/KValUypQI1CjVEO6U7u0jxunlglGwwEbNTXyV7TISQbkxbigTVCut2awXjJejIGVblOt+eDgmh2ZLF4vmRs+TyZzIblLSZtrJWlZuDW/rYNIkDjjAiQOr0jIJL4T63cgA+1j/BkOsAu1lj4EQe+FyBMO9gaeLcN0gTkqNrK/nxIRvojO4wsc5Z5kdl5A0hyxMgOl1EaKZnhfOWCyOnhUPtQSAaj+tNy6uB+7DfYRLF0OfZr7FhAtJXrcRqWWLkB3nFSxJg66JB2hRm+3H215lAdmVUOY4QQKg5ph1fqJ2go4IvjbTwaD9XsPKKPFV7unmB1v8d/dhFfwvO4/19qdI8FHOFFLSkMM2i9zZ/ntX7DNP+8S8mh/i2yQZOlUCYC7AFhSZsK5PzHpRJGNoon2ZZcyd4CKHAqslBQbEHXdZYrlQoVszOJuu4hq4AFWX9Mwcl+ypveFfGHVZ1i+7dJQf+22OYGBb2UGcARoAlAlddbZOysorDDk0MkYOgJTTvB+SCKFo2dfXj/ENUNmPUBR+kZQYsFeNnlL0NiZZWMLKTn7EZkkk6F46HH+r7H5SuM7mNn+Lp7b+O11WMc+y1qN+HQi7n7bX+JE7fDIU3oaN723KcFIameHbiINU7jAkMhvQq6sM6vm3ekKP8uh6NMI7YOHQDgnZow0tzgsKGYjGzlO1x0DWFlammAKfWxFvBKvbaNnHg4DAx4ODHfjS5NkInxoiMBWdadKioaDEpmlIAeQ5SJ/dDtkoRxxw4jee30IVLLMvC6dM75eg0sLAGhHiOZI9cqRazBqDGh9iIbXbkej6cDfI7uYVX1eDIs8aRfAQBW9YDVrQHrgw3eOT7EWw8WOPr9FY4/N6J9tAOFCHgPM4kn70HbXZYdAs+YqDEf+xO1PiaflyYVUEArMakwNzrc63Jon3VZtZBKA3CdMby9FUWtRnj7nslzLkjscBUjLAN4dKCB4AaZxPsogFZwsojwLmKx7NHVk8oMXeqskU5REwmjzqYJl5Eq4ikQlXzzSo/LJmcF5KpiZp2Cxw5yL6zqAUOsENVz56DuUbv3T6W9Gf8rj3zDUm1AVgaJjfGbfk8bG8glG7JXGfuByAqrOzvcXV3iVr2VYglFNE7M3RduQEcTGoh3U1mlV4ggfRVGlmKFFUsMoBKmkozA+asjpMwsIXR7sabg78DkhAByByuS94gsHd2kwFJervz6mOFxicvFW9njPHsXGRKbxHcosIODmMVL8wc5jn3GS2SHEdkXJbBLU8q8O6J0xKs0blYUMLHHBEKAJIEVHCpk+YzNnSHNlOV1zXIZrZHBLHQcAY4jnBtA6mmziQ2e0hK1CwnUAoDGBTRdwNCMWHctLg8qtE9qtE8jqs2UACMisUsgElAryQ6vAFryCV7pYAhroFEAWum+5TmgRcAVHy3drLzP8oMFgKUL7OuM4e1zgG2mCYhdXXU2kISjjsLSCiK/MM+siV0KAY4YdT2h8gJumnSiHGRLxuI+SHjcjLVmT+rPPSBKlAE8W8VHliYjRPI5CjNZwNzGxStm1Dfjq2vM5v1kBk8Iywrnr1Q4/STj7oef4NN33sIL3SkOvJiGd26HF+unWGnX83IYkGX+UTsWaeHAPhm7O8TU2ElsSKZk7i5euCN25v8EA2IdBgI85yYgg8oJ7b63nMAK5UuXZYtmrl5TxJJiMnMHY1YoT5cDZmDPSR6Yz7H4Qhbby0/pNLv/mvRawzJ0Xq7B6FyAOmKknChoYd3YWY4YDUfJY+DRYZpdI+msG7CDAF2tG3FcbVCTyAEdAuCAk0ZYUoEJJ37Cwg2qouHktQUggV3bII1XWj+pNC9/3r3qzg58j21s4MBYVT2WbtBrPSYw8SII00oKOx6tG3EZumTj0roRZ2GlwGRIxZ0dMzxHIGLW7dIsYGqacOgGRCYMzAmUtFFr7mnn9aA6w9LJMb41nCRwbRMb3K/P8c33/gjx6whv8QPc/y8VuscMICJ2FRx3kjttd9cClDPmVfkYXfN38fqcE8nzyRB+b7uZ5DAVfpAsJZgh/ohl7mQFFRKgK1YARULUYB/rCCJGHyo4PybgmNIaMI8pOgHGCoCJJmTavfm0Oko+XW4ElOQnxxIMSNPDstwwypeGAMSa86JvIjAJGYF8BEc9P/vORgLgMBEnD+L3M27ArA8wZh1uui4BWazadHZOfqZEA7KQdnKTm0dWaFVW+KLD5UdGfOjVd/Hxk7dxr7nE7WotKLTf4kF1hgf+XLTsBGVjGYAlwzoUnkaHniusuZbkolhYA5iBWjY5weinECqvdSHcH6V80BeLI6ukGMhl3a3kfaCvAaDIuyUwnhiOCSO5NNEbhViM3aOypbKkoyN7jCD2vQ5wPQaucKdeS1c7pRNHJozwGciy86Aoi30nHZ8uQ4uaAnqu8GSSThm3q+wlsI6tatel+yEcNAiNiQ4t5o6ZnWZdI72uYONeVacjaBvjS6xowImTTpQPp2M8qg5xVPV4pz/A5ShG97UPeHD7HOuDLU7vrXD5Sofj3z3A0ecH1JcjQFHauHoCVR602YHH8csAWl+m8mAfoFUesAdglX+TdSrkJB+0yXlfcsj6PvuAFoAsu7AqgYJj5e0YBgd3UcHtKD0+BZeAy0NbIE0e202LHTXwLuK43el76D51IncjMkW2lF+SJu5WdbCvWyRZ5anckJlBVYT38r5jkC4xRFJtcNp+NrJ08LB7xLrX3Iyb8cxRxpqqKoAsjSVF98LsmYUC3JIHYjWXFR7fWuNud4mlFzN3DwGyDt0OB9SnYoSxsUoIxToU7piUGeslsTdA2gCBAmxIHT4LSd6g3lj77Cw7BftZgl1ekWaCMtg14QAy+95eGxKQlWUlkShVpC0JITAqBATkc7AOicEAM1bzYZoQyGHpxtTVTqZ2QiAkICudh0qzawRMLGxie99tbGDG8RZbR/YChBCl4o14b8mR+uSVVNQaYOtFmaf228mLNJThMaB2IUlHL2OLjWuxdg3WrsEQKpXtRBwsdhgbj92ywXBcoX3coD0N8IOWbZzXwhyBxinJDmeAFpAAMLmsBVB1HaCli+fZdjNWl33y9priw7axLznUK7QPaMkPvfPc/K1mwFEgUO9ENsF2n1GyNkjmytFhHCtMowBIncok0jLJLotVvu0fl9gVqURy71g03rKCe5IM6LFEp98nVisHPWMmkMu8kK/EUPdm/P/fYFfcM4AAWZ3H6YdrnH6ScevDT/CN976Ee80lbmlu4RHxoBbP3aWbZsVnzVUxGjDDwNvhQIArBdwBKTAHZekYmCX7sHyiLv4mDFyjKcrpHoyNNrPqCq+HCMqWJZS3NZBqv9ge9N6vwdhZLkFm/i7bjDwvqJjBvfhrpcuWWGgGZNWUAb39YbN9TWIkD7tee8drjDE7VskZAsBI+Ymdk0eUHIWkw6EpYyIoSQBrDli6IUkBDcQaI6V8RoCmCaPK1CvKrJfAhOiE3WaMrz7W6GONikJ6H0eMQy/r5lZjnhSwPKybvIFWgJABRvIpDl7GOuVNHhGPpwOsXI/OjUKsICEVjPCola0lHeYDwBEX0O6GkM/h2I2oOSJEMaQ/cjvcq4Rl+MZwC5vQ4MCLb/L9+hz/+/3fx3/8BsabURha7VOVHC5qgBdwMQK9qjksRyqDa/YhSOOKNcue4oXkQ9b1WMwFlqLwT4zcEb7IOwAkX235AymPchPDDwS/E4BpPAIGgnRfBxAXEd6JJ1oEaYMaQtVOGI8qhI7AK23QECSm0YRssWIhjvVcOP+zYr+xw6y7oRV93EiIvgD1puxLPDOQj+Z3SfAKvk2TB9VqQ2B4wVdQfLkBs/4kRq0GvNo2M8kLzeh9v3Oh3rQJyLrjcfZhh/ETG3zqhbfx8cO3caeWVuhL1+NedYEXq6c4dIN2ddBuGxAgK4DTz4vIeBIbXGi7VPP1MPTbWFclS2sX66Rrj8qoskmpNHQvvbAMrLJRsrgcMAOyHICSe+IAdBSx42zCCMhED0JidXWUKcQjpH3vQPmYACTpn7y1x8r1uF+fYWSPd4cDXE4iKzypN2hdls744tjNP8u+OH2sMEWHDTXwYYmlG2AdSHpI9eBQNeNHbocNt3q8uWrkNPCMADqStsOA6e2vBsaWgHt+wIkbcOK2OPFrnFYrPG4O8FZzgneHQ7zTH+BpvwQz4ajrcdT1ODvq8PjOITbPt7j9WxUWb/dwQ9HJ0DvRhavscAZoJWotcuZzbRKifyeA6npAq6xOzAzhdZmePLSCbEsu+9nIvhTQKhbw0slQwSpipddCvKomQnVJqDby/LRAauWaEo3o0Y814mkDJsZ60WDVDEkSmEhRyrqayTxK5hmK7SIkKdqTwYCBGAgMjxglkarrAO+ifrcIQ/RoifUeFi8DbyWNm3EzvtxwTtozl4AVcnyZoT/6076RBmT1twjh7oh7R5e426yx8EOSg6zcgCO3RUMBlQJEmcme2ZPWoXCrnWAB+WqU7CaLOiVLa1IpSWqYgNLjKhu6l15YhHnSUmIX9pwvL0cxCEAFxqTxyfZi3lcmAakod0IENOkil6SGAFAhYCKvF5TQ0ISVF2nEJjQY1G+k82OKM/Kx5GOvCmAKQDL2HdnDqYTGui1OWoCZNKFoMWFEJYRQjScOUvU12FBAQRlREypLxmyKrwhYUUDHAR2P6GjAzvXY+AaXocM6NFiHBrupBoPQVhPagwm7rsJ20WI88Fg8cqgvJ1CQduGJpTVm2eF7A1r2QRaf5j4ytw98FYDWldew3CtXJIcs718WxPcJY2kUIChDFtVkIFIU5q8bZbtYZ2CIFegMLKa7vJM7baw9Gh/SZ52WHKyxpTidJHncPx5G8eR8G2bxb2SNR85L4cRCtBXz7B42BuPN+OodbDRMSDIcG4ezVxucfUKArK+79xZeaM+w9L0Wqxkr1+OOW+PQjcl315ZFHRF26pMVQHgYFngcDjCyz+tmZWUtqUfn17PcQYB4XfsW3lBeWa0GJgWQFgZyZ0Ngnsxa3mEz3H6iG3Owu9K1EMgF9bqcf5Af2y/bO0DW70AG0iiDYUB+jZnB74rFaylPzMtM0r9JPcEsfmln+FSgt+ufm1HBiRfsgd8lX2BrfJXM3lkkeHCSx1ihBIBK/ikpAS7GViXxDkf1TrpFKhvLvJGtMJ9iFDjJETttEmZAVqe2LaaAEUscTl1+N7FB68bk13weF+l+2MUGgSY0FES2ql5PkUZ5HybsKOLETeoLDXgKqGmb7HOO3E5ezx7v4Chdw5E9bldr/Lm7n8d//HqPd6bncP+/VmjOJ4n5XQ2aFqAQweOYcyRgprJKgFaZ5+wDWtflTXaj7Bf+Hct2zl2VHFrIg+QYyQwegO8Z1QYILWFaMKZOnotVvqGnIN7LFWVWd7cYsLlLCL2HbyJCcBg2NZanBD9wZibr8ZqkUMIvwwXJzWKTz9uIACbLF/xLvL0iAK4ExHITITplbmvzLmi8DqND1WhHzKEC1wHeTWDkfO79jBsw6wMOdpSBrFJWWLlUsdw3fLfXhc7h8vkK568DzSfO8I3338RHD95J1N971QXuaGeREzckQ8ZyGBPrgqWycREbnMcOEU69neQVCbziCqBsIm/DgJgdHFY0pOf2A0uZVNixjMU2Nqm74nl73CodgExGBvBY1aWslAPzKmLq/IGQIGRPEkQlGOp+EXHod7hfn+PJuMI720PZ15Lw0uJp0qUDuRJi+7LHl25ArwnayB5nYZHaqgNi2msg4egrrFyPAMI6tli5Pnl9GXPMzqdWUNCqQuWIgLajBWo/oqULHGq14Y6/xLv1Id5ujvFuK6DWWS+B4MHhBY4XOzy+u8Sbzx3h+LeXuPX7A6rLEeSEoeWcm8kOnw1ozY9JJqxy5W/b5Qm7nJivbUNbSg4JIFtoFavyLD3EnKFVJikGekXOGu9IoEBwkxwXRak0DLHKtGZIxZpG8auKZhrPOhHHvTxJwSpu9pKMCE2KWDpzoDjOSZdYtSYODHCh+7ZEwiGztZgJbSULhYpJvLr4KgvyZtyMNIgEyLLiiFa2UgWPcFXKrj9jRRgOHfpbgL/X48XVOe40azRuEimB0vQXJAWTqtiNDWNiTWwdm8RvkEGQpum6UGfr9pdnOk9Z2mGAzgQvRRQYmDU71QSA6ZkCmM+bJu8gzI/VOl4bQ8uRSFlsP7OEyN67eHNjE3gtrsjxiJzQIVfZCYyWJhy4HtvQYD01eh6Eo2qr8XKeyMm+coetWk2SbRtrWGGH4ylK51YiBOfQkBoJxwq1Lv7luggo55gxkcRQuy77OLl9Mo6UncwDWp7SPbD2Ddah058N+kkSp4NmQHcyYbOqcbHq0D2q0T0JcINOotYAp5AdPhvQMnBGfs8eVjbvPwP44nzt9/2zLFbYZ5BfW4BkGojmgJa+VwLBkPdrcnT9V7J5ObrUQCYqI5HAujzJ8sIUa/Y+iJQI+OI97Vw18Gh+ikQxi/rT22nJ8SVGc7rEsj4hBiYWqb3FIXB58jfjq2mwsTsI4Nrh8sUGZx8HDl49w6fuPsTz7dlsDXzi13ixeopj16eufmaDAYjhu63l3wgrnIZl7hqe5vaYWFZJ+aGAiEkPI7uCrcUzFGCAg2dG2a3QCtopB9A5bYTDIU3YFGxIk6HVFDAWAJIxsgDJUTpjBUMAKdvWCsuAgE4dCSvN01WbFXv9/u8JKEMG/sphgF0Ze8zUPpvYu9Q13eKFR0TnRtQxJHZbZJeu83G1kb+9KGyWvseoRuiyrawxt0E8hBduwDY26EOFKXo0Xozj+1BhVfXJp1Gk/LJPR4wD32Ppe2xCi40CXt4zAlfoSAAmRxFjlBgoskS5PyIISzck32IBxirsojCAOzdKl3j2eFdzqJoCTnmpTWd6HLkdPBhPQi2sLMqsuhM3JL+1NTd4sQZO/Abr2OIsLDDGCo4iXmyf4n9/8Af4f3yjw+PNPdz7/zAqzVXjsoELEXQeco5E9GxrlvcCtPbULmlaLwCs3BEeKT4ZoJUWMCmiWI4m3+t6HdE+JYAd/IoQao0hjhArRogO26HGuPA4HRaIDWEIHtPkEQcPDA6h96CR0Jw6NGeiSrFiSwKpVG7IteaBOpIMcXZy+XWRBLAi/VKkNLuMgckQX+JbmOTFclnVy4s8wlegWrkBsz7AcC6q8XZRKfdm6l4wTMrkwuWKyfqBaNgPP3qKP3P/S3hl8QS3qzVaN0qXQn+JO26TZIXXdSocmfEkepzGVlrjUkRDAWv2cPoNiygCjAYgmzCALIkQSZ92ldA724zZre1tKQ3cT71tgtl3APJ7j5U03RLYiRDTxJEyuybvgwHtlGgsKXvceh80FDAAACMFTkfy5Tb5ogVy02/LMcwDlgUCp1WFPlapCuQR4RxjE6RrS642TYhwIkUklwJ+TQFrrlFDqlGd0aiLt0xafMpzhPgWjAL4Vedidu9G3K7WOKqO8SV/grc2xzjfLdBVE145OcX5Yoc3bp1gOF7g9u94dO8OcGNAJJUdeiftaCf1PSkn6n1teDFRA9dN1kgTegK0EItJeg5oXcv8ikjsrMT5sIW5Vwmifo9IuzlRlIkSwcAtpGtJQcCsMXo4o9yaZ4ncPpgmjzF4mGyLbF3lM7iVvbM0cSCkG5UrfSh1lioBLYAqBhPDVSyebJF0ciZUKj8M0aXfp+gwUCUsr5txM54xiLgw9C0WAiUVff4CBbcA9oTxwGF3l9He2eH5g3McV1uRtVFE5wYsacCSxiQr3C9GADKHb5mw4wpRASsPxgCH3CUug0QGLJiUBeRTgk9gVApkZUmUnhYlEVkCn/a/HeYJdeW0MccMSqld+Rzrfo2pMlNZQ+Z52z6fU45VFQQ0AiN7UkAWYrkokzt12XnvM2MsjhGEtWb+WnaNCEjxLeri1ytwOCqQaNfZE2OAh+cI8V6NqMAJ1JP3yydp71PpROqZ0/4rili4Aa3rcE4dLscW/VShchHH3Q793QkXiwVCV2HxyKHaBKlsV3PZIWKcz/npYl8Fqp5pCj8DmwzQynzffUBrtt/ig8+7VEDLNiHkJiPIb2n/EoBW3lgR6r9Is7dLvidRPi/x/ygSAU74XXovCzG8/7wrnqNi4yIRgH5mpHUphtyDxjycWx6QrlduYs2fxPjsZz+LH/qhH5o9dv/+fTx8+BAAwMz4oR/6IfzLf/kv8fTpU3zzN38z/vk//+f41Kc+lbbv+x4/8AM/gH/zb/4Nttst/spf+Sv4F//iX+Cll1764x2U3m7sHbb3Gpx+1MG/eoFPP/cWXupO0bpJ/HfdDivXJ8VHQxEdmaSQ0ZHThkby2NthgTfHWzPbkTG2OPEbVXLIenvU2BAQ0UB8kBwboE8JtLC/B65VlhixY2G9mv8skPMOTyzPQ0GoUhXCNpfkYrsvJnvbcuSClYVcNE/eVijAKWR/yADMjOhrys9n3lkRHxSIcMiA2dy7K3//zDN4VBBpSX0hN4ypUH/otokR17pRjdO9FDXchNvVGoB0kQzOJeaW5S4Hvk9/j+wRHOGo2Ur3Qi2EnI4L1BRRVdqhXYGsTn00IzvUbpIueLA5xWGnMIIVjEb2iTG2dEPKszo3JlaXdLacMHKFzdTiwO/gleE3hsz6QwQ6P2KAV2m+SAs7ClhS7iTvKcC5HjUHIRN44DQu8QXcQXSEi9ghssNLzVP8pQd/gH/352o83Zzg9m8xKDBi7YFlCxcC6HIj7z1jChcBpCz8m+fVl7FlSdsFFnxgjwhgg0BAiEKCSREuF3qIAd8HdE+AqmdMrRQqba0HAnZ3HdbPtdgua8SKxKImeOwuW/iHDeoLgguA3wH1JaPaxRw3Cy9hA6FckLmMCZKXscYai12TxJ/U92qHZBbPkFwq1lLkEaWaLfSQAh5HQgyShDknzH0X45Xc/L3GDZj1AYZ3nKqRUjVXnyz7AhgrC2k9JBXWxuHixQpPv5bx4JPv4JvufhGvdo8VxNrg0G3xnL/A0o1Y0ZRR6L2FsEkKT+MiVQgHnUQE5PHF5B5ntF573JhONYUkLywDRUnlHeGSZlsWRtoKV+nsY3GPAvMqesnKsm3mLC6ZkIRqGzHCpfd2kISgNFyXn1K9sYpFMqLU0HNSbXC3a3A+LLDwI1qakoG9XS8AuFRgaumH1OZV9pdb5Rrw17kxAWIb1aQbbbZLFFphazn1EAAEaDPTeJNQel0syOczvzYB0O5h0t2qoxFHbodTv0TrRtytLwEAv9s/hyfrJarDiJNui+WLA948PMKbzx3j9n/vcPjGiGo9gZwwtKA+WhjGq4CWfVB7lYdrPbSumdAB5ElfZXtmpjuXHBaTf6Q5oGWSQsPYUhYir6GolYGYASoKOmEGIAaHPnpMUQz7AUkqDASLQWQ9VEgDAU1U1LiQHbJ+fD89nkg8ssrXWpcOGKjnRDeuSYexsbxj9FMFV0ugjmkRhqKL4s24GVcHkWa1lryTmGjmDXClxTNDgKz+yGH3HOPg3hrPL89wUm1R6XzU0qSFjah+Hhm7LYdJCncFgMLsYF0HA1xaeBBxWqTb3A4gMZ0MnNr3XCwPPxY1STM7L4spFkv2cQv7vQRwdA02Y3GV5vIRlGtR4BmwZbGGIZJIM3AXc17xeQRLXFhWHn2oUDvpqCQeRrZ/2emgBru1C2AWdhuKc7N9AwJGGSBmHROnZAMgMTKwsLUIjMkAMzhUKhEZiVClPUP3P782dt6eRNxYuYCWJ+3iG7H0Ax7iCI/DCtuxlgSnmlAfXeKibXGx6rB4u0JzHuDGKPGilB2G9wK09oCqsqBxBdAqj9g+6QLAuk5ymG4O3XcJaCEvzGXXxf7SjQRDqbLEXEGuGEllOoRQgqIMKXJESUiSzHTvJjWQKkk2AJEMpiSA5kWOEmnTv82n0gpR5p0lORGBXJidkrzuJtb8SY1PfepT+JVf+ZX0t/eZSfBjP/Zj+PEf/3H8q3/1r/Cxj30MP/zDP4xv//Zvx+/8zu/g8FAUA5/5zGfwS7/0S/iFX/gF3LlzB9///d+P7/qu78Kv//qvz/b1fgd7AbL62zVOP+IxfniLT99/B3dbWS/WJB5LwsLs0dGEFU1oSUBzyzMCGDuWefZJbPAH43OpUZIVcJ+rzpENTJDWvh3JOlik1jHlI/b+tqbuCqbWqHK3QIxeFQ3mQTsqI9bWzjnfEJ/apshtIsSgvoOxgiyWSSwymw8bxqIyRpUBVYB00UuKD5KGWmm9ro+NbDlK9hXbLwRFzNUnZivRs0+G9yVIWMowbYgCJV7ZrvzdpIHgHEMCHFoaUgQ4nxZo3QjnGQs/YooCok3scanM4m1osPCiTqkpYAcxjL/gTpsFqIpH5YNgj6UbMKrn2cg+sbh2scZSvatqlcsH9vD62e+4ggfjMnQzmxszlT90OziKGFhY3AaaJmsaimhTnhgxIjcoe+DPsXQ93p2OECaHy9BhZI+PLt7G9qUGv/znP4H6YoHjP2I4ZnDtwMsW1I/gYVDWa5EjXfHQmudIaaQcaJ4Qpy0KyeF1skQrdOROvQWgBQAMVLsAP0TUFWXWlLIyY+WxPq+xPm4QWJpiAQCd1jj4EqG+YLhJcykLJaQxKCrYpu9jMUlyQswCCanxuwFp5qdlC0gKmttVOUcSKaI0UJGw7IA6KpgFuEoIACE4TORBNzLDP53hiFNl3OSFuI6VpWAXHBAbh+29CqefAJ77xLv43+7/IV5qnmLpeqxcj9v+Ekduh2PXoyuq5E3xZbEuhY9jm2R2nkLuDKE3XI3c9a9GmAUVm8AbnRwAowbz7O9E9S2+qzaRSFKRzUYHdvq7JS/zUQI2Bm7Z92Sf5bUPXNnvBsSZtDEofTl1X9QKsyPG0g84qETyF9nhLCxwCDExrPWb6CmidgFj9LgIHTxi0Xrdp2TCJnEzfLUWsJGdTNQ0IUSRpjRu1OPOUk8D3JwKi61iWnNUk3pOVSNv942OliZETFjTiEO3w6Hb4t3pCDgEWhfw1vYIl0OLzVjjoB7wsTvv4tFyi88f3EP/2w1u/zahPoNUy720uCUiMTx8JkMLV8Z70WnLKkM5CafXFoAWoBOaJS3XAVpawUhSjHQMnNlZjNmEDAgzS+4Vl5h9ZNEhIjEnLNFMUkMgMbJcgHQGsX1bma9IVGLNKXFJ2X8ydmRwIESW2l0IDq4KKf4FliSIIgOQSuZXUIC4Gf8LDkpxBJCENoNa8w3zduwJ49JhdxdY3V3jldVTHHlt2U0Tlm5ASxNa9ccy9WI5b0fIIn3L3tpsADrXlp3/TE7HIGU1ZUl8BIn/1t4sXx566ibI8ydKE/my42Eofr9uzJoPAakpz956LO27fC87ZvO8KgGtsvsiEcvXHQJONepFwkzoY43GZRNkmVI5eSsNsVJAUKriQZ83tlul3lkCEgqDjVl8tBxFkTNrEhQ1kTOp56RrAJv3GA5Esm5wCtaVHmNlXK4QwQQMHNHwhIZGrGMLtAKuXUwthlBhnDwaF3BnscGmnnDWrDA98lg8IvhdEERGWYSECQjhywBaQDaFR2a87ksObdIu5YbXJRJcfK5773ctoKX7NoAJnEGrGYu3uG84GniYmXdkNxyjuJ8xe272HhZTLbao7IIsQSDK7czzDZuvlVW2QUCM0qSAuLgMpAmnBCm+kRf+iY6qqvDgwYMrjzMzfuInfgI/+IM/iO/5nu8BAPzMz/wM7t+/j5//+Z/H933f9+Hs7Aw//dM/jZ/92Z/FX/2rfxUA8HM/93N4+eWX8Su/8iv4a3/tr33FxxMawnjkcf6hCuuP9/jkS2/j1YPHOK62cODklbV0Pe74S5Xn5fX40lGapTOQdQ9mRg4gGbx3NCLCJfBKpGPi7VQXecWo1iaBHWptfFF2RQfMDD1bm3hwWr+VxXUrpoPlcWNljbp4SxYldg46aVjx3Aru4n0la0HLs8pG1SXrykHkliUDqyOa/W379MVjBmKVzC+5Hrkg8cwQRhED1/AIScJpVih9rJPBuZjDV+j8BI+YOgl2NGL0WX2zUflf68bkreWIUXspekzssfACUk3RYz21WPgRW/aoOGSWMcT7qgTSbNhjVug3xpcdOwCMUUsrTj/L4nPrY52O8+m4wtJL7nYRO9z2l/AkdgiBtHu9eieHogPnqrjvaojS6MSvseM65YABDl+3+iIuX2vw//xzn0BzWWP5NoMjg9safLgEnUXwNF0PaAEZrCrrifv5kY3rCv8KaCVrFrtpTHJopvCw+KXZUZCmH9LVkMXDEUIMYC+xzU0AAmGYvKjHlEHsekK1YdRbVi9iJKzCYhMDcJMEqaikBHXcUZJB9qUkyHtJN0X5p7W6mW2LgWHWPIXbqLGPwHVMsYy0uUmEy/nZNZfzWeMGzPoAw6vMkImAymVEE7jKynJAqB3W92s8/Rrg9tc8wv92/w/xWvsurFuhdbJb0TQDshLdVSsFZ9HjYTgAkCeQ/e4ivmAFOcQZqp27ZTCsVey+NnzftypVRbRSYpWGZLRYVE3KjiLAnIY7Fm9j1XJ73sCwMmlJFWvKFf6BXapAdG5Ew/karGOLdTFxv9Ce4qlb4jK0uJg6RCYcV9tUZbIqkU2iNrGbhlyCnselTs7mcTJqNSKfi0gSDfAauNWFglTUR66kiqVdXM5Ro+GgevOoUpKQMBFgLisNzDh0ESsMWOpConMjDvwOb3S38Afre/jCxS2c9R1qH/DSwSm610d87ugO3j48wO3/02PxzgBnvibOCVOrHyTR+HJdDq1CcC2gpZsAWW6oq3EBeTVBwVXJYeJf7AFadjumidZRymMoQhhSmqxZ/DLJoXlm2edZJhohEKbgEYODD5Rey/p69lJxSPmP+plkWaFuG5TNQcjJBhfXDQZ2Udq/AGna8Sp4tD4H3hsw62a813AmMwTJd2GPXjNL6AmIjjAeeGzvAYt7a7xy8BQn1RoejMaNWNCY5B0lkGV7MULKjgmXOjfaXD/BGFkmr8ttpQmMstJczuMCqFwFoRLowJjtjzWemGQqMb/SMdLsPYAC9MYeE5jkb3vIwLBywZTYWoVXCnP20KpMiqfXYOAqMasqCjisdtiFWrzEYgUG0Lopsbmy3MWnYzX/k1y1dhj0OfPWs7iTzgWMiX0CvCb2uk4wXyT1CYQUY3qVHhpQZSdGyFNxySaIAFpiWTOwSA5rimjaCRdVhyfjCmd9h12o4FzEUbNDdTvgtF1i3TRYvEuo1iF5ZoAINFKOM/pY/tDL1XT+/XpAy7bJCBTp65Jf3LMkh5Tfby45hEUdpKTBgDOb023EYn/6eVmiVnqdAbKIjyyyexfn+ynwuKsAF/JlkLiUn+DZuaC4FnZNOR22eHWJ1HFG8rmJNX9i4/d+7/fwwgsvoG1bfPM3fzN+5Ed+BB/+8Ifxuc99Dg8fPsR3fMd3pG3btsW3fdu34dd+7dfwfd/3ffj1X/91jOM42+aFF17A137t1+LXfu3X3hPM6vsefd+nv8/PzwEAwzFwwRXOPxLw8gtP8PLqqfgXKZBlrKwTv8FJYuQKK6ts2LRj6Qb4MBxhF2us3IB1bIUtU+3QuSGxriwvcMocGlgkYdYIQyw9pOhuxfcl9WmOHXAVATDfLBvGwrJhzF8ge+2m7uWQDrlpkLKtdO42UEv8suSxHWf/LPuqNMrGAuZSw1qBrIHnZvnXKVLMLN5UKFGvlZnZtxTQs0/nZh5aIsMbE1PJQEM4iR8mG9xp0xDrQu/0eo/whRF8nXyAWzclqWGM4gNcUUgdtfvosQs1VpV4XlUkRahRJaDWRMtkhGVxH5BiveVFZuXSR2F22bG2qm6xdcImtBj1Mx2jx+m0ROOmJKU00/kDv8NpWOJedY6Ox6R2kQsvgJaQNIS17InRIWCkEYdui9ELYeFsWuKl5jG+4fBLeOdjh/ijd15Bc+FRBwG0YlvDLVrQZRDfx31Ay+4lA7QSk8nyo728ST7YKwyt1P2dWRRde4wtwRx5Xm3SG2y/+A8UoVHVKiE4TEG7IltBJiLFZYsWImnPiphZccVeMyLJDlPNKQBcFyCeQ5LH52PRdZZT0IvlMQHfIjAJMwsg8KQXqAnCep48XLjxzPpTGZ6UkuezyfuzWFmxctjdrfH0k8Dqa57iW+7/EV5szZB8wpHbaYAZZsDS/kd5Fj0exwV2qjW3CcOYQMlwloUAbECWdC6JCXQyc3KRluSFsgUQT5nmKttlwAoA2j1k3oCocpuw9z00Gm9fANolK8uSpSt6eMyZWrn6IivNCJeCqZjienRujYYmXIQFNqHB6bhEJMYaEpCHWGGhk24orlvtBF1znhNlto8V1tymFrceEX2sUNc2MU/JbNwqRULBdRrwJTBBgbER6tflBGgc2CUzfKNTJ1N7zgHW7oWVi/AkDIuORjQ0aZCY8IX1LZz3HabocNJu8ekX3sTnlrfx8Og27v2/Ghy8IefsiBCdfg7bXZ6sgTxhXwdolcMevw7QShJDSQ+yGeJ7dDksAC1brMv+WPMdoafShBQ0Erilk3icXPIycaUWPSq1NYi5YHrcVv528LYvo8zqbZ6SG/uplYj8Qt2NVtYBB1dLVWQavSSZ0aGtR2F0kHi5OR/S8d2Mm/GskbrZWPEEuPp91HjDjjAtHbZ3gebeFi8dnOLQ79LisTMwnOY2tft34I4JW64xwaNCQFAGiDGByvTfvsXGKsqOD8qShMjdqoJGmZmTNheUjKgMkJVSRCADUbZ/2ddVNpYnQIuM6fxs+yx9LI6nAOTsvKjYPupjuvRSTxNC5QI8RwyxknbksQaBMcRKYrMVX4rzk/NmTGlfVgwSz7/gnHYmZDGe9+oxpklLWfWe2Kfr75hT63UpLPk0nxKUSQ3x+yIATufoHIPnoGZDDIcR3kVUykZ2JO9xNnToJzXx9ROeO7zAab3AZbvA8i2P5kJ12woy0QCJAzODshKo0k9ujwmeP9RnAFoJ/Cq+G9dJDp8JaMnblgCXdTI05m/J1gI0/kRS0IhALrMsUhdEzg1H5PF8b9r9aA+UoeXKuXP+J8wt3VK9VzgSSAsqMYjnWiCgcvo9JD3GxHq75trejK94fPM3fzP+9b/+1/jYxz6Gt99+Gz/8wz+Mb/3Wb8Vv/uZvJt+s+/fvz15z//59fP7znwcAPHz4EE3T4NatW1e2sdc/a/zoj/7oFb8uANh9aACOWzQPNnjp4BQntXTd7tyIpRvQ0IQTv8ERCZDVEZu7ApqCbdQz8GY4xDq2aNT+QxoduaTwSGAGDKx3yf/JpGhmw1F2+M4SOJ3zjWEDSr9bHiJzok/eWT2LZUOt+5I1tUtF7x37K35bwP48n3/mTuq5I25uJpWLyR5IwFYJVu1LFgEtouzlPaPOu55C8v4quzJGVqYWZe+p8hotSRhJ69hmdhqUreUmBax67LiW7oIFmGfd22sEOMcJIFr4HtsgjKVEWiAWIAuMXmk2d+p1kpaaB1fZRMDALQNLIzvpWjhJcV8khgJsWodDy0EBuTefTCs9X5caxJj3VmRR3Bjh4M3xFm77S3RuxEAea24AfwFgTGuFTvexognesV4zh1O/xCa0+MJwFy81T/AX7nwOb3/9Ic7evo3bvx1FLVEzuGtFIr/rnw1oARI0QhR/LHtoH9Cy7UomF5GAWCmexGsBLQBzuaGCRdbhkL2WYaIVfgAXFFQrlCkhFl6cmtvMbtPICdBKpN+Ygamo1iusiwOaCuWM3vhlAYgKWiM7zds8S0dDB8CzEATAwJilhlTF8tRnvtlfbtyAWR9gOGKESn2y9ooLiZWlZu+b52o8/STh4Guf4BufewOvdo+TVO1OdYl7/gKHbsSSeEZ1BbJe+91Y4eEkWnuhr2bF+sgVPI2zYzD5nVTgVauu0rgk19OKR9k5xBHP/rbn7X09heSVFYpJEJjLAiOkmmEUZtm36fKzzNAqJVZZKQPPCDd77/JnoKjXSSo+UI+R3Kllg4HFw2ThR6xDg+1YK/vKYVWJFGThxxRQzCzRJtpWqTvrqcXF2GHyo3iKuAmb2KQAXCugZMBaQwG7FFSl++FFXIjhMo1wSgm2SktNAR3EIy0yJdNfCdJWceDkn9YR457r0SiAt9ROVK2f8KXNCdZjg8uxxf3uAn/2/hfxR8sNfnfxAsb/d4vjz6mPlia/5EiM4UNJSSqBqvnEnJD4Ukq4T6Uttw/IDK2i4vC+AK20D1aTdoYLEMqqarBtAnUBwOCwC7W2uhcvH2YS8CkAULALxnSxydcmeJusjTZrpTcWAJCZUxWirIpTJJUxSsbDDMRAorZxQIwuUX5DdJiI4Z1Ts+b3rwu/Gf9rDiKJJUzuSrJbglvsCePKYXuX0Nzf4PnVBU6qbfJwWqrZe0sBdbGgyaCNfJU37BIjyyEzpQBLPubFDItEZupu9ums4I35Hjoq5IR2XsXfnuYsr4pyO/YrHQjLa4ACZ077UsCGsswwx50MiJX7KN97/72MySwtzrMkEAAW2kLdk4A+Ys7rVbVNaJw9p15aLEBZQyGZwle6r5ErDLFCVAPeSqvtBpxYUcqANY+ICdnLKwLoWarhBixK7M8xtuIwi+uw68fmZ5avX0XAEgEePZyLKdZ5ijgfO4zRY4geB9WAFw7OcFqPeFwfIj6s0D5VHy34dKPRpAytfXngFUBrj501264AshI6pIv4JMfNH+z7ArRsWELBAkwhChBUFj6kMk2YohQFibXbsVbAEaEJhhzAPgsLwPwxTVIIUgwhe97bsemJsh2+HrgRBSIktsLuLfVrU2Atknbzff+5wc34MuM7v/M70++f/vSn8Rf+wl/A66+/jp/5mZ/Bt3zLtwCAJMHFSInxe4z3s80/+Sf/BP/oH/2j9Pf5+TlefvllPP/gKR7dqvDanVPcbjbJHmPpBnhELF2PE7dBSwEdZWZRCdbsWCxM3p2OtLOdygsVoDJZoaxJgxbVK4zIcrJsCO/T8xIhQiocp3U+xVTM3SEXdmU/MfnlGqupVGgAc2Zuku8Vj1uBeZ8R7GCMK9sudzMci208SMzwiTAUYLwteUvpYvnTupPvGBg427GUrLN0TAbKkTSJGtlhp50CzT6lcwN87FDThFbN1EMB+m1iO2PwGtiYWL/qceVIfIlKoKByuRlYq9sv/IB1aFOxv3YBl1M7A7IcFNjS19QU0Ctbq3OjyhqFfXW3vhCASnVrmSxQ49Dv8GRapTWCsaGnKCL7TWhSnrRh8TQ+wQYjVQp6NbjjL3Hitjh24wyo7CgAbkDAOYba422c4CwsMHCF5xtpvvZ///pDLN+tsXiH4SKDWw8eW9AwgmO8CmgBGdSyx4u855mAlo3E7EImArBFi2I/uhIRWaCBW7KyKrvgEGs8DAw3ST7CLCqQaIWMIs+xmAPkuGOAlgs5NiW1ihX5GclvK5QAF5C8iwEgNNDCqxxwUrhMVngRpUtKfSYCKk6FHUAtY8INmPWnMryLmEp+PlB0nIIkFxWhP6lw+jGH+tOn+Prn3sTry3ex1G6C96pz3PMXWNKUTQj33mdgxkV0eBKW2HGdKiUBXqVsMpEsXZ9oqcbGCqCZaaBH9skyAMcmUXtsf9TIJouBc1LRFBWWEoAq2Vb2t02xEXO2VoC0E5fFMxedSfKwCc4AthLVT6aHxXEEchhZquIWOGXyrzDCY2KHIUhF+bAW6mzPVWLLiC49dzCMen0iSxW+poApOmwgyd6ICksvVatYtOveRTHL9Szn1ejkXfsJg3loKYNLPpuInVYtDGwsP6OWAiIiWpJ7pHOEmkYs3TlWJIy+petRUcA7/aEwlPRifvToXbRfM+E3Vi8gVh1Ofh+o1gB7l0DTBGjt68O/EkDLU9aBmwYctkgn2YgIVxhazCA3B7Rg7Kt0sxQznR6DTMr6OEsyMUWXqk1XkH1lRXF0OvnqBKrf2QSOaZAhK1uSJhlcmMfbd51YkokgiQ8mh2Smo505mAHnGJMa9E7kgKlCRRGR5ud1M27G/nCk3fP2Yzvln+yAqXPY3SH4+zs8WF3gVr1ORY2V67HSirwxyfdh1ACgZ8I21pjUdDwqCiGgVERFshAVCRxmHk+l0Epy8VxpY9bkOofHq+ep8558BfPekuxv7xKUWAb2nmNcZWuZCXxpJl+O0mvIgKiSseWKcxW8gxJTyvZVaRIR9BxCdOiZ0PopmbgTkCrO9re8f+7s6ojVl1HkF3BSua4RwCSVTCZZqNo+7Ly8FkDEX8ul42edyIhcVqchy0A5HReDmVHpNF6RsgC4R8NSp6+1ocp6aq0mAAC43a7h70W8Ux+CXYXuCeCGCHZVuu+kicf7kRy+F6BVbmc7Nil4cWe8L4ZW/h1QcGh2Y+z9rv9Mxs6zOz9vKIdIs6KLfhjpb5Org+eHJgkGcqt0UkP44v1hBvCWaZDd95w6LUaWrrnOXe0KejP+5MZqtcKnP/1p/N7v/R6++7u/G4Cwr55//vm0zTvvvJPYWg8ePMAwDHj69OmMnfXOO+/gW7/1W9/zvdq2Rdu2Vx7/2ttv4ffHGi+tTrGq+mT47pRReeI26GhKwIqZvkvDISmaX3CF07BMgHUppXWIydLE2FlmoWH+sE5ZQCNXWLq+UIEokweZiTr3zIpYFk2SbFvQHJgom2WYusRG+TuQ2U9S9LWieS5q7DO2zAC+hoJ7RKoAkdFQNpoPhY9W2p/OH4Hn0k273jITM8LeN9EacJTnZgb5gAE/Yq4fSNa3Dgyncr229JAqAKxkqq5ge00hSRBlfZxZbCM7TOzQQsAtWzv3scKtaoNRfbXMSqWUCwrjKgOZAYRaWVoRjONqkzoZdho3ZN/ZS/h2tcaTaQXPBmR5DKgQmJKSpnIxmc0/mQ6SssnUSR2NaDlIV2a9xi0BTuWGd/xluqZnYYFDt8OnDt7El14/wRe+9AoenHvQJPkPtxWoa4GNsBtngNa1Rf/ZR5o7wRcyxP3tUl5UsrHsprLiv/lnGSs/aq4Ui5iW4h9USgjE4MEclKFlx6A5D2RficCXwK1sAJ+2c0XnX407EpN0nRIkDBlgBcr/iAFMatECZAIBAAqUJh/2UFWLNE6ZokfdTHD+/UesGzDrA4yKInZq+A4g/RQWigJZt2qcftSDPyXtcV/sTnFcScXEGFknbkBHfG1iEQFcRI9TNXsX5k/+oppvydJl/bx1zXOKOFjw8MXkWIJWRtuNJKDJoNURR5wmWSAbFyagBcD+mg/IXlj2fA252cvtLYDK/iXIJB8uZP8tQf5zBdkZ4KUjsnRzskqKGVPK8VYq/ZMr27pJabX59WLG7bHQCbIEsCamZOJrY4oe3slkO7LH2bTAge9xGbq0jRkkOhKPlY6yt1aAw2lYJQDSpIgC0FUYC3adoyiJg1auRjilVE8paDbq7+L9Fh1NuKP02y81t/FoPMB6auGJsfADPnn0EAev9/g/6lcQuhVu/xahvpgQFzWcof4fBNAqKxNABrSA6wPANbLFfYZWmjxNqhLlWEzGIa/Lx4Oo1QhYG3Kk46FAQCCVS+UhUhLr1ijvY/rwdI6cqxNuIgGg7IZmAtcsTLFJggwqJI+tqNX9EBzqKiRDRgnSes/dZBk34z2G+UalUcYaBVVD57G7TeDnBjy3ulBvk1FB7gEr6tFRQEXzXQE5N+6ZsOMKk/KWZrI4/VlWfyuVcpjhuwEjJUg0s/MiZXoRUmHCK5BCxetMqGh8sH3QKl0XTSLseaePldtT8bPcvpQqChCHxBxLx128qZnbl10HK4h34770z7xieBarjMUrxagSwIqglDCW2zvdJ4HQxwoNBQyGWAApZtm1i8XFZpAWVGJ6H9JtJMnI70fE8CzwobEgIgjEAl4CShIiQDoerrGkARVFnHuR8o/RgyBm+PfaSzS3J7zpTxCrGot3CX6I4MonyPMDAVqYPz+XJtqKGnmfV2INrgJahibp+xsTi+wxe539ZCQga94vMseniPl9W0oN0+effikf1O1LxjAjeTcKSqXx1hFg3XTlZs5G8PoYQ9Y6VNw7N+NPdvR9j9/6rd/CX/pLfwmvvfYaHjx4gF/+5V/GN37jNwIAhmHAr/7qr+Kf/tN/CgD4pm/6JtR1jV/+5V/G937v9wIA3nrrLfzGb/wGfuzHfuyPdQwfWjwGL5ZYVT2m6OC9fL8iOzxXP8Udv8bSSeHcVkcmpxtYOpJfxAYRLqkIdhAfQA/GoAX0zo1FJ1ax+qgRcOh32jhJPLes6CxF9JAA8LFYYJX+vEEXeaYa8ZqbWCf1ufVJbsYBFIbwmMu5rdMhkMGmkRVoKgCo/e+FeGuzvo6SZ3HZjMvijQFh8t75uQRw7bG5jBzgwVhrR7+8T055zj7g17kRAQ4HfjcDgkwdAmQGXfKyMjlo9Kh9QO2umrf3UeSGjZt0f4KeGKNarm8omHcBkR02KkUUCxYB1A79Fo6FNDFwhRDzZwZ26DRXHa1bYhRp5C7WWE+tMpknLNQA/nRcwqvxPTCih7wuKmHAQLqL2GHkCq/X7+DQDYVdjeSYls+euA0u3AIXY4cd1Tj2G3zdrTfwxa87wfoLhzj6o4AYGC568ELZWYNcUwN/CLgqObzidTDPifJ2mOVS+4CWgD7zXCnFTOb5c3tKliSDn0QZMk0ezrGoQoqFFCU5o+Y7iUUl8UHyJSAqK8u+rvZadlDzd85yxpj3Q6pmMdaXGwmxEtaYKAg03wp63l4DbSRwIPg6SlfD8f2rVr5ifct/+A//AX/9r/91vPDCCyAi/Nt/+29nzzMzPvvZz+KFF17AYrHAX/7Lfxm/+Zu/Odum73v8w3/4D3H37l2sViv8jb/xN/ClL33pKz2U/58PIkZMtBbKrCwSNHNcVTh71WP98R4fu/8uXl4+xXG1gUfEid/gnj/HIY2J7gsUZu/6c8eEi9hgHVsM6o91ERapO551uPCIaBCSBMEAkE6r8iXzyqp25ehVa25f+FJmCGRKr1VGgKw5twBiJDWTFJqB/aiPlebv5bI9SRBRSAgLVlFqG68Tq7DRMhtrX4ZorDSranduTJ06ahdwXO9wUPeonBjbbkONrQJQVrGonHjE2OQqxywJxTY24o+iflpPpyUuQodNaPDOcIgn0yoZDW50YTBylQzqdzpxj1xhHVtsYovTsMLjcIB3wiHOY5c+X6MPr7lOxvs7dtixNgRgoT8vCTh2I+75NT7aPMTXLb6A17t3cVJvUkDyiPjw6hH+4mt/iPFbLvDkkzXGwwqx9giLGnywAC/aPAHHKP+MWl32u9fHhNnE+TH7m8XYUFbzOiHb35a/MKTKEGKi2M72X1SzSfXiZDRa+4LoHJgSjyj3jUhfqqT7z0kIJclfen2OefMMeO+QUqWFim33t2cFzaIAZxwJcXSIwSMEhxDlHxvbMGZW5M3I4ybO7A3iXEVLxZP8d6gddieE8W7AndUax7UAWaSJx9L1aCgmpk35crv7JgYGNV21Sm4fq2RgTlrYML8o0kpy8srS5KKs7Vy3lgsK5hoOJ93giuf1yKoCIrCvWmZJ6WstdqR9yWOl+Xv59ra9vR72/vq7GfOKXGsOzF0ndTRWmpnJVxSTZMNTROcnND4oy0oYVJbseYppHmBAZIm6fwEfXPbE1Kr5NtYSe6IX2bzGoj7WKkUUoGqCx8Dy2U3sEdklY+addo265Aa9Apd2fiNXGOAV+GBMIEzFtC2MLKAlqZDf8Zd4UJ3hdrVG50eUMrdbzQav3HqC+NKA7T2H0Diwd4i1B5oaXPl8g5ReWoa+zD5Ak/ntb7P30z5fnv9t+6HI18ea2Xyun4JKzUsAq7xvoEURKUzkoln5fswueVXNjueaYytHeglds+0MzyP7YshpRAKzmM5HdqkqLwX9OXPyZnyw8QM/8AP41V/9VXzuc5/Df/7P/xl/82/+TZyfn+Pv/t2/CyLCZz7zGfzIj/wIfvEXfxG/8Ru/gb/39/4elssl/vbf/tsAgOPjY/z9v//38f3f//349//+3+O//bf/hr/zd/4OPv3pT6fuhl/peL4+xd32Egs/onJZOVHThEO3TQm+J2AzK+oKkHUWa1yoJ6914S6lbGJnEtP61NaqqTira1RRTOSGUyJNztu4lLfkxlLlOn6XsmegNHsHVNqmRYVyPi47q8s5ytzdUVAGcc5RzJDdAKhZYyog+YgF5FzMyAUDs8gNi+1LWxixAZF/VqgHgFHPcZb7YM4s69mj13OvNYfzYDQIidkWmBKQZeyoZQEQtSrvs+dManqrXidZoChdXPpXOQG5IjtsQ5OPR9fPm9gkQ/bADpeh1RxG5ITm37t0/WweNNuXyE6aXVFIRWZj7EncERZ45QIqCtiqVcjSD7jXXOCk2mDhhySBPAsLnE0LbGKDXaxxNi3waDzEl4bb+IPxOTyOiwS0GaFiRRNOnFgu3KvOE1vMU8Sr3SP8mRe+hCefAqaVlwWFc+DaA22TQSpjK6Uif5Ef2fPl38A8R5IXz3MpG5Yr7edOlj8FeR2lbTB/H41jIgPUdZJKSq3L+9U4wykHIn0/y6Wy1JCThNBNuDZncpMcv21nX0NXSA/zAi3vG0YKUFUNeQmyMRLC9P7N34E/Bpi1Xq/x9V//9fipn/qpa5//sR/7Mfz4j/84fuqnfgr/9b/+Vzx48ADf/u3fjouLi7TNZz7zGfziL/4ifuEXfgH/8T/+R1xeXuK7vuu7EMJVxPh/5uEswSiroSQGvONRhdPXa1x8zYCPvfI2PnzwSBZ8NKFzI+5V57jtdqmlaOl2NQIaWDzenA5xGkVeWFZuB/aJ6tsgZCYQssa9HAZMGRhkoJVp0kvvjJriTHc+JoqqUYvLiZzRUEw3UkSetNPfxWI47H2ZXPHTwDEgB6J9898y8beJ2jTYBuSJKbrUyk/8BreqNZZuwMIPuF2vcVjvsKr6xLoKTOiDsLhqEn8tM1R3FNGHCn30udKj13KKmiTECuupxem0lOQvOgWxZLuy8wcgrWafhAOsY4vTsMR5XEhVQoGu07BMnWN2XGcQjCuM7DDCJUBrp5fDE6EjqKnnhOf8BT7aPsQnFm/hQXum5yIbv758F3/l1d9F+NYzPPp0jfGoAjcuAVpYdFez0BLQ2ks8qHxs7zX7kzIVr0sTZ9oe6TFizLfX52XS5QxmOa0GFDfgFN0MiGUukhI7DZMZlomKJjvQSTvJGOP89RSEnTUD1CYSH6+Ykwv5Xb8Hk0zO4+jTsZmefQw+BZKbIeMmzsyHVOtwJdaAgNA67G47DPcC7hxf4lazEUo+xKNp5XosaEqeTGV0sDlZiibC/p3Y5wQcwKR1QVmwS4wxYdW14qriK24G1KQxx6R7NjzlOZ5gjCyJK4TCU0Sfz3BP/pqV+IApkQVkuHoN07UsXueueS/ML7XKH7O/XSk59HodOhqxcIN6iAQs3IjGTWi0AxSQgQ9jDlROqt3maWag1T7QaHK2wA5DFEAqqJTa/LQAyGspV0t71uKLFlN6/XwN6NpyLdVzFh/JSRnHE1ySqRugNdkCFSo7VABz5XrcqS5xt7rEQdWncwGAW/UGr508Ar/cY3PfIbQO8JQALVTV9YhnCVYVn/AVQCttr/9x/p3249f+mMWeuX+axYPZwp3SmjttE5lQvpKRX2cPpE5S5b6LU5vFlr3XWuwp4xTinmyRMTswjhLfYqB0H9hxBnZXEpqb8ccbX/rSl/C3/tbfwsc//nF8z/d8D5qmwX/6T/8JH/rQhwAA//gf/2N85jOfwT/4B/8Af/bP/lm88cYb+Hf/7t/h8PAw7eOf/bN/hu/+7u/G937v9+Iv/sW/iOVyiV/6pV+C919ZImfjtlurCTdhaXMRBTyoz3DitujUK2tfYhcA7NjhnFucxy7JtgywyHK0KTGybNW/iS3WsUWEAObZRzZ7KyWzcH3PHdcz1lY5jI3UKoPVhknQ97fNAF1Wkhi4lV8reVXZSMlTjn81FRLBYjuPva6GfLUpl4Fc9jNC1uUm4bSlab13/MkD2I5Xz2V2zomEIDHCWHAGBOVjcLBOgV7nZFOo2PsYs6pVM3YDmiymr3yP2gVM7DSP8Qk8sg6Il1OL86lLIJd9xgZUWoOAXvOZjeYx4unbzxREZgZv+VlNAXfrSxz4Hif1Fq2bZsBYmcNEVmJBqNOxeIo4mxZ4czyRHIprbGKFnd4HLQFLN+HQ7QBAwd2At8djdDTiaw7ewvEnHuP8lQqxcYiVA1cO3DWgqhCx2cIi5UR7gJaNEoi6BuQqt/nysWr/dcVjqiyxYfkRTw5h8voWc/LKXF7IRf6DWV5msYeYUyxyyrqSDTJJwWkXRfvnQpGbMbKskOR3YW+pdD5Civ+Dy7GQAA7vH6L6imWG3/md3zkzPpxdIGb8xE/8BH7wB38Q3/M93wMA+Jmf+Rncv38fP//zP4/v+77vw9nZGX76p38aP/uzP5uqDz/3cz+Hl19+Gb/yK7/ynu1o/2cbjvIKJbGyHGFaVTj9cI2zT034yGtv45MnD/F8c4ZWK+QPqjOcKJBl7CWrHBioteEsLTSNekgBJaBzQ6KTDvBoGEoBzrTigbI+3dq9lpReO3ZnwBHmWvXOzQMQgD3QSx/L36k09j200uN7rykfy4tXmfg7x6o914pLep8MyIFdumbmF5aYaRSwU6r06HtEEC5CB8cSkaxt+kKTCjk/J7UKZbB5YmxZ9Ntw0sWxBNistW1k6cLROMZCWxK3WonYcS1fduJUUYkFBbuPdb6uDiKbZI+LuMgUbpp0AVChpgkrGlNC5DnmtsEKUJrx5sv1Y5EdDrfFsF7voZe7J/i2V/4Av4rX8XQ4xq3fBuo4ISxqgBdwMQK7Pk/Wz5IcWpeNiKwNjzobOaFmZA+tnLTO/77a4TAZxQcGeWRqLTL6DwZiBUwL0X7HGuBGErlRtf/Spl7osqxmusyEOLkrAFVaeUSl7br8fikDzoQukAMwIZv06nHFipIxIk8EqgA4nsUjoV47eKeU7r1g87/6uIkz81GCS/KA/IuNw+6WQ38v4vatS9ztLqVzoVa+D90OnQJZpfzOimSAzK8mLZzYJ3YNIAvtikLukAcHM2ctu/7ZV8QW5SU4xIyEDBEZIHXV6H1/lI/Mppa95+gZv+8VU5/9GICKGIpjF9KVbGBPhFlyVEowxQhe/qpYrjs78fmwa+R91BiRWdL7MkpCZq1Bj6k8H/HRMjaXHGHlAqxblZnTI+3LzY4V+tg4AxNFmjOgTp+JI2HKpm7ICEnFJ0y1fN28srwrRBz7DSoKOMdCEmB9n+Nqiw8dP8HncRu70KJ7BHiOiLUUiIgZmKYcX66THNpNC5Uc7q8ZyM6a81xa+mcRingl99zMP0tvCvFtzKNkZrGTeAPSWOJZASKC17hCkBqGXSSGxLp9kMpOM8WEdAzQOJhP215DWtEmFsBKmkJAAw3nuLwnWxfLB3lc3u8m1vxJjF/4hV94z+eJCJ/97Gfx2c9+9pnbdF2Hn/zJn8RP/uRP/okc04HbYekFMDj0O9QUcOzXiZVVFzdGTVkyF5hxFtsk1dopy8m8XoFs/2FMq8AOHUXsdPU5sMfKTWm7NE8ocDJyhVp9fRsEDPAASz5jtiYAsFPm6giHjsJMpbHvdegVtLKcIMnyKHdml/dQMKq4VjUEdPLXfB18sd4MzOJ/BSXcQ21R9nIZA7NmeZHFm2LfAqxJQ6fUOZEYkTmpL8wnODJhgEuMNyuKJCaoGvubtUlHIxonMShQ4YXMMUkBN0G81lo3yYGRgFVRi/pj9MnL1boaLryAZJEERLImWRGETmNOaRQvHd4r8WpDlj4CQIcRGy3Oy+cgNghnYZGYW6fjArUCb5YzLfwgMS669H5DrOQYKaJlaZ71znCEjibExsH7M9QcMVIQQ34wVjThyO0wcoWN6/HWeIJNFIP6P3v/i/jlr72FgzcrtGNM7CxqG4lRxcJh1qghRsD7PA+DAV/kTFeanSDP1+WISDdWypuAhKDK3J9vPlIT0CQzhMz9bgIwEWIgeJoz340QYD7BIgWUeJhYWVB2l6UmFjMdJeCLdV+sABVT/r2MmZQXh5Ij6WKHncbXiQCPxHbjoE/sF5i+zPiKmVnvNT73uc/h4cOH+I7v+I70WNu2+LZv+zb82q/9GgDg13/91zGO42ybF154AV/7tV+bttkffd/j/Px89u9/hiHMrCwvZEcYD2ucfrTB2adHvPr62/i6kzfwXH2BA79DR2MyfF/RBOtMZzRVY2Sto8PDcIDH4UCDglUeBHE/dFus1Iep0emwBLKs0wgw7wBojCsxhuUrz5u00KQWAGYdC+1v88sKPAeyygpETdrNA7nqYZO/LILzP9uPAXsG7o2cJ/3sESbnYRO9SQ6NjZYDWqY52+NOJ82lH3Dge7RuQkUBCz+kCkUfK1yGFn0UXbade6UU3PXUpAkfQAKyjDZtFaiWymq8UHn7vSpXr3IPq2RtQpsApx3XeDwdYBPbFLh2XGPNDS7iAqdxgTVXGOFwET0uWBLKjoATN+FYKzOdE9PDl5onaBUks/19ZPEO/sqHfhe7b7nE00/UmFYVuHKIixrxaCn0WiADWuXYZ2Pt/0vbxVx5eA8arUkIszxkntxS5PTP94DfEtwETCvG7q78G24x0MhxmhRnYodFO2C8M6G/N4GWE6bJgbcefsgMrCtYAbMysOw8kHTgNplToKKaoX8bY8uSlyAacGTcDyFol5Zr2rbfjC8//r8VZ4D/eWON5OqU/mAixMZjd9ujfy7i5PYl7ncXWHlhnFYUsHI9ljSIRJsyYGJAVmDpsnQZRa5WGrgTiedFS2OKMRZfSiBrgse+9I6g1Wb1n8qHzemnGayXvJZSNmJ/p+PlOZBlW1ksceXvlNeIGprTP9uPK64H67WwGGb7ltdy+t4aO6vsdGjXzBXpiskULR7I5yGM6VqZWA4iORxihYmlmm1TgF23IfrEvpLHBciyLooWX2x/cm0yAyckzhkwqUTEOHUmGWEmTPDYxAaDPa+PDSpV3KnEPYLQM2HQKboioKOIlkICPJduwJHfqsRfZG4RhNv1Gq+dPML00oDdXYdYO1kzVR7c1kClq/Vrq9f6H5e/c94+bcfXbptiSrk9F9XsK3N/Pg6LARSB2ADTUv6FDsmnyj6jCEJVTYjLiLCMQB0RAwGTm8kvrpvrUxxJx3bdMZU/CXSFoUX5XAzfUnC0BG9vYs1X72hpkkQeWbp3p7pMrCxgbutha/YNE06jKAIEHDLpsbCtSp/EAGOXyjZiKi9qiDnzKKb1boBLrBzrkGi2KGU+sm9/cqGeSAGEnq1kIECXjRnTt3h/yxU8ZaN2Y04Bc4P2cpSdHc0nS8475yb5PfQ1RX5T5kT7ShQb1uxLts8HYUV0kXTm4rw187DukSU4JB0QR/WwyjYmnmIqjM/OT+NF8r/S7uwAsPAjVlWfrp89ZgUs+7tWg/jE4GNhbm1UbVJTwKHf6vWMel9JccTr5x9ZcqKzsEhgnLC4Btyp13ldQSIp7dU2ZBsbnE8LTKqWsVzNAMBtaPBkWuGd6QiP4woXscGuyI86CrjvL3GvOseHm3ewdAO+0N8GAHx48QjPv/4uzl6twLUDe3pPdhbvqVSu/am/X5Eb2n6KnIhYk2FjexWvn0kKQ5zHLWaRIXKRp0SJ5VGtVUBIDa/Kf1mBUi6wSPMtXC3C2N9FTkR6oxu4Zd5ZbsSsUAPOOZOpWcTLWP4uj4snd2U+eK/xJwpmPXz4EABStw4b9+/fT889fPgQTdPMunfsb7M/fvRHfxTHx8fp38svv/wnedgfaDAB7B3YEWLrcf6hCmefDPjQq+/iUycPcaveYOmlq8jt6hLP+QscuhEtmXm3VAFsuhnY4e1wMNOtx+JjqhUkSV0qEmilE5P5RWHOtDKJoI2y05sBPaW3lj0OFPRdZEmhTcSekEA5SwZKuq7sZx44bVhnEWA+8adJHnOt/HWjNKQPIPH7gPl7jThyO9V0T/8Xe3/SJMmSpAeCH4uoqpm7h0fEi7fmnpVViVpQhcbS3UBjFgADmkMP0cxliHDABQcchggnEAACEW444YoD/kD9hz7MDDAbaC493ehpDIBqoAtVlZVVmfky3xaLu5mbqaoIz4EXYVUzjxevMouo8NIlyMPM1FRFVxMW/vjjj9FruoeXK86jpxTa+4zq10YGyYqLPGGTCg6lw1izVjpsQFWsqGj3QM5P00EUrHKDpPRp006xdQE44DVy54bB2HmHOsifglqflUd4Ube4qQP2tcNeB+wewFWqeJKOeEwCfr6db/GtzSf4+vDcI3cTZ3xt8wL/62//Lu7+yx1efrtDucioXULd9uDLLdB1bcC+Lz9c20m64YqOuwCwaj2bF95ywuEgl3maxAyaGcOuYngF5D21h8UiCBNhP/W4nTa4mTeYakZOjHw1gS5FrHm62WD4pEO34wZWQQZmmoNTYRGJ4Fy4Q2LOSZHB2HLJQbJM1tWDKgQuCXUmcE0oJWGaOtfPGks+cage2v3tj8vOAP8J2JokFeykSm7C4V3Gk7d2eHd7i62OZQmMizTiKh0VaGjkwQjWFBB2dcDIPSYI8BBjYcmgD01bi1X9ALE5XYBMmvj5Mv0w/rwbE4kXwcoGTjWmkqUUNpAHDsqxnY9uGHx4/4utxOEIpwwvPrPNui3ANdACLOo0vbuzdH9NtUvE6DzQUWGaJcaosrnbzCK22ycBhebadLKMlReBLGtNHFjWZV3PQLaMqufbBOc9zVltkmlzSYqppLGb1tbMAmrd8YADd6qpljDpEJ0ADCQpQRvMGGjGZRrxNO/xON95Va3KhMf5gG89/QzT1yYcnibUTgKA3GVw30kUXE70jGMQ71YAnHx9+4bbuj4x/gKAlk72CTK+51GCJzRh8ZAxAaiEqSSfE0glN4D6AvTqnI8d8j4hjcGuaN84F0SJH8z21PY+Oh+yDrVXdWBQtQgJq2ZjyWhFR36mU/6H9iesWcrdhmZM6uRf0YhLmrEl9tQ6oLGydpXx43IVpEGaptFlOiITY6fzzolbStclHQXAouoBdEszND2sFixvou9eQIpEE2qAVNKrLGl2VzQ78HYiDg8ZeydOrqtVFOAywXTzCazauvkG0d8wf+S4+iwS4+K7mC6W+S+LzBM6fX/gVukw6mMZE3YRqIeNwUur06v/Yee1zJYRMsNlOnrK4MQZg2aAWOrhjeqYWSq6AV3mF9p4b/djXwbJSCG5JxboT9T0H7sk7Lpd2eCT4yO8nC/QJ6mUGX2YgwrJ23s5D5Fd2deNsv0ExIrB/5h1ZGn6192hgaEhrdBE6kVXK+HZsEMi1nXFx/p4vMaH41O8KFd4VbduszIRrhPhKlW8m/a4Skd8pX+ORIyX5QJP8h6//uxDvPqVGePjTphIpp019Fi0NaC1BqDky9NlcfnaT/K+5eWcvqP7IsGmRTti+sIOVgFenXdpg9hBqMUyjsBWk32x5YH43F5pGaxZ6BrH/RqlKwZ2GM4CMzvGWqWX6/L38br2x1LNkGh5AAs63j3tdev8o3/0j/D3/t7f88+vXr36E+FkJLDT6uqQcfP1DV78MuP973yCX3/rQ7w93Gpq4YineYevdc9xrZULDbzqSQY8o67+qFzjpl4AkEHYUswEcKqLyUhPBbu6kUENEj3JFlEAoWi1CAAAi6FraLdVJJQohzOeeCmiuKW6YF0Blh/eqL4x2gE0wcOeQn46WmTDlm2oAWDLFEVla62ut9GeXdtEvRqjq4qRXObZy7WrSoHVa8PJRQsv0+i52wfukGmLqWanzAIifiiDqJRk94g6JD1xQ9L/Js0OlFmVjokzeszSd7heU6gCcplGxHK6VupY7mGPaRZRx4FmHGqPbZo0wsE4cI+naQ8AGGvCgQquaFYno2Kgip6blti7+RU+K4/wo+ktN4C/evUh8B3g/3b3q8hjj8fflwGsXg5IpYJuShh4NeUwPgwJ8l1KjXZqrTC0hFm7ZT5AVVBKn5tyuKhwSIz+puCSgG6fUDYAd6SgMnA3ddi/O+Bu06uQf8Xt3Qb10w26XQJNQLcnbF4w+r0MzgnUqnDYORHk4UoEzHIKapvl2mRZz5yekiRHvOoDTCXkghvbvQrtF0ggjSRNJaHPK9bbQ3uj9rO2M8CfXFtDNisBwJkwPu5weJvx6K0d3tve4DKP6FzsdcTjdMBAy8qFVjmZIT/LGwWyAP0JK9juAug+4Zaxd9IURMAqpTXnQtI8xL4klpRG9/2pgVgNZGFYSp+1TsGdeHfseGn12VosNmLrRLDLlnXUlq2nlhasjK2xrnSvJOdlmlkKGy1YAQb+MRpwxCxp6BL0CawsJIzqWNgIB4LrVQlTqwEQkpGgRVBAzvTqVMx3Zk1FIdH1ioClCzirjk2snFgVLKsgVHUcTMNr5oROUw4T2AurgMQJmyFCzAY8mvaLaIGJ47WvwiQ2G/zOcAu8Bfze9C5Sydi8lCcIfSeBjHFaoZ8UJvnUJsOEZYVDm9iTrRNe9R6aNZE3tp08m4uUQ25PSh4rsEtSjcnKh+uDNV0lTFcd5lpgQv7j1IHvOqRRWFNpAvIByJP0S5V8ez8VDrtWm8K5mSDTHIGtm9s2iKe5+KFoakmFpEMyoVZCSusn/aF9mZqlABpz52neawC83feEZQD6Rlk1N+XC1zFdIQM8BrUtJl9hvgkAD7g3Bi/73Hl9bAfu8ZiabpLpRPVoWlGxEFU8bgPqphUga2OiVTPMxF6wSsak5kv0JACWZ47o78WD85DlVr3QdK/supmOVtTHsu8uib1KYuJWpd00yjwFkQEEkEquoZ47kgY9SNIaA+FAGFA9tiTi7gYajtw5iAPAGU9J/ZKrdMRGn4eD+h2XaZSROwHHucEAdo9jgL5AMgkqSZXBwqRZK8L2seMwMXi5R537Xjd1676PAVxP8t6ZXBbcN521TZrQ84xJNbvuyoC5Zi1qoBpfxOjz5M+CsbM2aUapCXelw8fjNd7pb/A077BjSUdErbhOcm23VHFFE97rbvDZ8ALP5ytcphHfufgEH3z7U9x8/X1sXkyS1pcSeOhBOYFnjYBTcpo3W0pc9I/WaYTM5yvAA0Cpvq5gOs3uuc8Uxu5FdUPzV1gBpNoCHjwnf/Y9BTA2UuDKESoswCpLRXTGFYXPq0kZVRapmQicaaZl7eG2c/HzXZkjKtT6IGCt9fW69jMFsz744AMAEhX/yle+4ss/+ugjj6J/8MEHGMcRz58/X0TNP/roI/zlv/yXz/a72Wyw2Wx+lof6M2mJqtzgnLB/f8DL7wKPvvMSv/LWR3jc3WGjUcqrdMTTtMfTdMR1Kj64GqhzYBns/nB+jB/PTxe00Kd5jx7FBcRNCN7SxWLLwSDEyIcwlkSIoWlKLQdTyzk3wCgTO2BV9JcSB3V7Ctc02rUJi+cJLMGt+Bq3sz4tv39a/QJNp8OiNVHwvoZrstQIS+6oWanabZqQSDS1pLKcDsoQgAlo1TyEwVV98BSDkWUeThWJipRCTtWjEgZsVU3rO4LcqPRp9lxwowTbMR64k3vuEaWMUglHSPTrUHo/5oyKHUmKyEAFE82oRF5+WVJwCpBGJK64BuNxOuA63+HH01O8LJeYOONPP/ohbn55g//u7rsYbntcfMRA7UCXUpoWB52AREDL88UV9LHBNwJaZ3LFfbDWZe5k+Pf3A1rEQB4rNs8Z/U5pwPoQSaQ/4/bVFrvtEVPO2HQzDrcDrn6QsXnBSBOQJkZSSq47CtwcBX1EQjl0dSBKW98jH6qNlQqhRkeBoeCbRkUqS154SahUQSWhJMZEqnHz4GO8cfvjsjPAn1xbQ8Sezj49yjg8A4ZnB7xzscMmME4HmnFBE7Y0Y0NLZhMgEm+VgVd1g9u6dRYrAFzQ5Gl0DFoIwTMvR3YKQE6saGgpIWB4SojgBO1I7Hii/skaaKLVK9ACmNbWP5k1ELbuI8691n3aVVjbL7N/zA2Ia/ujxXrWh1XzM4DKwCfRBhNraikRIj4s081ZB82OKlKe/Poyk36XYBpdwrQiFEqYKzmwZawshoBoABSYyr6PBHaW14wk4KPfu7QQqZ85+zGLiH92qYGirK9e2doi1yQzagJjALDJMzZpxm3Zejreu8MNju90+OH8NvKY0O0YyAnUdzKpN2chAlrrm6wo0ALQWj8NBmiFOy/AT7M9rwO0SITSkO8q0kgNyAKcITkeOozdjEwSOJnHjP4VoTugsXctgr9Gac88kKTrmc2x7x3YWn9n2xuy5aCeOUAAKlBJvZS63N9D+/I1AxQKEt7Nr3BF0+L7VsGPsWfgs3IJALhMR9HnVX2lRBXbNPncsuj8NAqRWyXDBGFnXdJRwA0AOx4c1BAlWpnrXuXG5hFbkVDVJ5k4nYigWxqiVf0zjd/IYDIm14SErO67MbMqxBfYaJcWRLerYhInPcRfiYLvKy7OieaWfbaYZU9NT8s+WzNmlqU3Jmb0mgJZQBhQceAcfC69n1Sw11TNDMaOe1xpyqZVKZ/Qu78Qs0TMxzDNsgxGTpNmhWgqZl/cn7wrPRIxNmn24iLGlipI6FHQdw3YrApCVTCsaqFppNncImaq7OtGl83O8pP0SV4Y7wrGq/kCV93RGWKv5g3mmvGV7Utc5aNqNSbsZpWF6e5wWzZ4MV3g1XiBLhVc5HdwlY7yvOVbTCjIPGNLrcjA07THs3zrWmLvdDf49Wc/xv/ju+/iyfc6DFMFZQJvOtHOKlUm9VwXgNaimW0RwUJpDgSdFo5bN9fLyqvPC1bJmf1CTB7NCgwBwQ6uAKgzwJYESljYvQ50AZwlOENqTxXHBNTXkzR8+WGRBe8DKEZFd0lqvJOaoo6br1XlMzSzhb9g4OVnyjn+hV/4BXzwwQf4F//iX/iycRzxL//lv3QH4i/8hb+Avu8X63z44Yf4d//u373WyfiT2LpUUTaE49s9Xnw3gX71Fr/x3od4b3ODPgmQcZWOeJp3eJwOPumrkAEzakN9f36Mj8q1alRJbvFlqEgBwGm+hypaRmsxwDh4WBQXWNJVI2036mVJP6f6WGYIjDJrfzGd0AbultLYIj/eF4zR1f5k2+gQtP4sfVGMzDK1wnLJLXpj5WtNPysO5tZMP+toCD1aRKEg+aC90fRDBwyVoWXfPcpHXGQBwTZKwT3Wzsuj35YN9kXK2B414hXFcO16GyvOnAZrlROmUGrdy9eyUHVfzpd4Pl/hB+MzvCwX+PH8BD+enuLj8hiv6ha7usGOe7yog0bcpF+pYiMA2XUa8TTt8W73Co/ywUHR//LJ7+PXf/37+OTPEMYnqp+17cBXF0AfzPrrUg4tpXCdcljqYp2TFEN7sPx73HhrsYIAAQAASURBVJtySFXAqO6uoNsXdLcF+a6iu6vIR4CnhLuxx2HucDsOwCFj85yxeckYbiq6IwdHA95f+wwHrGKOOVUsStN6uoexzooM8FTEkHg/RR0J94SUNl8SarXy6Q/tTdvPm50BVEcpA/NFwuEZgd4d8d7VrTgGVD2VbZsm0esL8JGlGOrPBy/rBrcsGlkGvPSaImdt1rGvCYobENXqGAJw8Gs9R4oivcxte3s9p48VcYr4pxieXocl6GW2Yo0TrP+ApT2y9U1Ly7W2sOyr6NkJuZT9uhKahtbpvWp2uV03NLYVSO+ZpFOQBo5MbN7u56A6VKSpitanpR+OnJVF3DSyYuqjnacBUes0UquOaNvaPTBNraNqr7wqFzhyh9u6xW0RG2O2bUTGgTNGJA9CdRBB/8jmukpHDKrlAwBf277Ae++9wP59QtmaflYCD50I6baDbDZmnXKorycph5VP1mkPAi8fis9JOSRdnuYqQZCRkWb9KwAqYVYNxLFkYCZ0d0A+MLKu6/bD7FhtaSD+avv141raIV9uty9Gv1WHRLbTOx78AGYBtpjX9Tof2pe1Tdy5tpJJjMTAs6cYKthUlJE5cSeVwHX82dWNB4hNFsMqFwJtXm1ph7b8wAKueEocBOAYFAQBmr9gqYHrFlMGLVAyhQyLGFwuLNq6Par7MJL2qL4C2hRz4iZxYowpoFU7lG2h50XLz/paV58ja8t0gI/afwyUHBme8niOj2/ZM9F3M3+n6phv131QBp4Fsi4VtDH7E4tNCUA1neiWybE3nzA284nkOJJXCZ8449PpaqHlGAkFZhPNfzGfJVZgNJ8rqZ914A43ZYsDd9jXAQdllomkC2vqY8Emz7gtUiGxSxWbNGOTJlzkEcfa464M/rxu84zPxit8//gOfji9hZu6xb722NWEwhJ82pLIAzzNe2zThI+mxwCAb118istvvcLu/c5lhDhn8NCv2FbtvBfphrIgrBd8IeDUP5IbcH67c/pZ1oKPtLAv6oOgmmYvwQqiRPvTbNPK9vGSfeW+ka67TFcEQJCURULQ0YIHT5JqT4r+JDl7jOYwoUsALWhhy+Dh57UvzMy6vb3F7/zO7/jn733ve/jX//pf49mzZ/jmN7+Jv/t3/y7+yT/5J/jud7+L7373u/gn/+Sf4PLyEn/zb/5NAMCTJ0/wt//238bf//t/H2+//TaePXuGf/AP/gF+4zd+w6tO/afShlRwfAZw1+Hwpw74M+99hK9sX2KbJjzKBzzKB1ynA95OO1yncVFJBJD7fGDGizrg0/LIf4QFCdcKhJlgXmRhScncAduslE6IkTnU3kUY2yC0HKDWQovrSEjvk2aZdI+cNMUkoRj4RSqKyByALNPeEkNhkVprkZ7bSuO2CEhkfa1ZWi1TixcGLoFRifwcjKmV9ZcUdaq2aUKpCRs1vhZR8Huh6/ZUgCRpgNs0+c6t2sdyG3LmlkUPnKZ75kc4qWChCTVd5iNuyxYAnMlV9RiM5pvUsTioEKYJ1JsAfp8KjqnHZRpxQ1s8zXscWJ6Dy3REpSOujQ0GqUbSK712Sgcc8g63ZYt9kWoe/4tnv4sf/meP8fLTd/D2roAqo170SNMGNM/gWpcVPNYph4ZwRkotgHsZWkqfpVrBSeH6xPBqhsbQitvXFkknZ4Wp01wYKIRSkzgZJbmmlTsWpN6DReKNecXKWDAP2h5cjVRoEHHB2oreNxVCGtkrXqG280ORV2aAZ4leMQmgNaeEM/jrz3V7sDPLlokxXwCcEua3Z7x/dYvr7qAVVGcVa59xSaM4I6vtGcLKOnDGngcHUCukWtVABqe09EH5Hs7OAcQ2mXi5MXxbuvnS1iyK0qGxkQxmy2E7RtA+QUvTM9YWuAFZQANpdP600DCx7+LPONqV2EccpQ0DWQJjreIgkwaBiANTSybvrMEeIkanny3YxAoawq9Hq4TYKeOho4pZjzRqX8XjMObWQLMHcipIJoBUFydTOIFSEPmlglEPYFYngiFRfavGaADjrKmPBpiRHkumikKSCnKkrok+kwgTM2ZnQyQIk1CCTQWVZkxpxKgg2GUa8c2Lz3DzwQbHu0sBiZhECL5UsQccnkRnUQVUp335ZgwtvZ6ECo4lA8OqztBaLLNLK2wtNidAvePKCcTKFFFR9sVkv3Xlz9GSFQYP8pt5slNedBA6kcCO2iPzh2wHEoGU1StJvyz7qBSAr4f2pWtJASRAsjosGC5zW3bQBRDwZl8lQD4hY1c3LmkCQCRMdNQ0eQtbB4CCYL2nNtp6SUEroM2rK5JnQWxJZDeizhOoIDMpKC5Mq4mTV2E3v8SqsNs4bMsz8UkGR2z2k5gUTDJfLOuP24K+iWQ9Y2eZAHwEueL1WzN6o+9iAX9javWw/fDiGCKoZ69bKi75YjaxIMEomaaB5Ww6BVA2SVIMHaDTsd6agV5CjMjuW5oN71JFYsZd6XGh/mUDrWSd27n39E9LG+xz0XWqs69EvqViykdMVcb9w9x7qn2fZkyq69Un0TSunFqfSjSwSvLHmpFJjg0A5irVGRMxLtKEDw8CRO3nHpfd6GDY79+9jW1qxbDk+u5xleTmX6YZX+te4IVmqQDCzvru2x/jt77zBNc/zKBSxdcYelDXgUsJY3gFajrLkvJA/bnv4jrA0r4tqsDroviZm5gBA2pEdJkJtwcWLhcpGAJY0J7Vn6FmU7TCIRV4cROvaAj9znC63PoGNbCr5lYlkYrYNCS5PIDYvjRLAZW13aKJUDe6A9PM+uNMM/xX/+pf4a/9tb/mn01f5G/9rb+F3/zN38Q//If/EHd3d/g7f+fv4Pnz5/iLf/Ev4p//83+O6+tr3+af/tN/iq7r8Df+xt/A3d0d/vpf/+v4zd/8TeQYkftPoF11R9x9Z8TxZYevvvcCX718KUixphcONONp3uEyTbik4pRT47gUZtzUjB+Xxw10oYqBixsDWS9hwvLa2CR1MGonWpUIYAliLUQEQ9SicF4Yibya5VjqQcxRjyVx5diwmGx59cGwzKi7sl2rFBK/B5ohMEAM1IyNU3F10h4j+15+VoUkLIoBNEH8iZpwpQ1YJqJvqX2mY2XrZQBFOZNHdNikGcfaiTCuVjPsVcgXECdBhBPlswkSWqWQTWqR6S2Z68KL6iI9tVz1KJi5ZxGZFePU+p9Key4SSeWpZ/nW6eEH6gHcYkPFhfwB4DLNAO5QQLjJFyi4wieT5Jj/r776e/hv/vwVLj7e4NEPGFQS+GIATTPo7tCcDKLTlMNSgdyelwWgtb7JOAW0QKSaWvLesXn9T/K4IU5DaqkhVBiUSIUHSQMe5j3CX6M+iWujmPerp7RIPTSHoYbB3ByG2NgGeHIasVCJAxo7k4y4pg+m4FatCuKt86h+jtuDnVm2Ic2Y3yoox4TrRwdc90fXNjItpi2NsPLrsXIsYFFhwi1vAugiEfMuoCBsk+bQ4pivdQhhwvCyTWsRIHIQC7SwL1EqYr3d7PtuQIa9rln19rNaBEqxTGs/8zNdvBogFqsd2vzKxkqO64dxIjEveGmZGD1XVIiGpTghSddfpvaVE5uqIJ8OmAJuiQ6W2bJMNVyLhC7Ni8+MxuDqFKwCgE6vglRdlACQpCu2Koh+nNrXrIxh7x+iqWWNlLV3kUZ0EEBupgRgdAfMmswxJlQAI/W4g1TxvUwjvnn9HL/9lQHdLmN4JWM4+gzUCppmBXcCkHUSFLEbsgK0Vt+dA7TQYMrlzY0PiFoLi3sY+ASGs6Lk9p1OuhdaVjgFWMGrzczj5rayn3ZcbDbITil26oAWaVDITk2LDbAiZqs52EP7crTLJEBSTge8m185I99S3ByUYdYKb0tW1MQdMgnbZ4vJg8OWhmYBdkkPlPnyrm5wleCsoYqEotkSiSpKAGBGghaRyNiiFaqKciATGjNpcJaPgFojEgauC5kU0eLKWrXX/BLCXgs4VQf7Wa+DpPb11FIMYzaYVzLEMuXQmutvoVWg72lpayzdsPCpLpcF5u08rbKhEQeMXWYsNJeAUR/PUvgKE3oX5Jdj3GIU6QoFsZzVxhTAw2B7PBVTAk1TteqnwsQyf2MsHQZNPezVz7T+MlfsywbXWXTWoD7WMRS/ymrXTLS/V51iUMW+DkCV9Nh9bTI6UplQqpJXEDbJgvxN+7lLUizlrgwYqxzjZafBe0VYbuYNPlM9rC1JMbAb7pB5Vu0sxqSsdmOK9VTwK9c/wf/vF76O47/doNvNwhzus6QajhPAAdCShzek3wW/yAGt8ICsU92tVRb/adEvms8ALPWzNJWRgIW+lTOzALEFgCeHWPogBWFT1uNz3JOlv4W/VNsxWbqhGSbT47KMGs7kgRaTSG4p+m0bCcoocKXMLPYID8DlzZMHvzCY9Vf/6l8VOt09jYjwj//xP8Y//sf/+N51ttst/tk/+2f4Z//sn33R3f+Japs049vf+BifvHWFb14/x3V3EORbq8VdJ2FmbQOQZa1ovvrH5Qo35cIBjKxPouUUy8RQIp8goEIGBSsFLhEXzT/mlmIYWzQWzujRQT4CXYCKL4JPtgeaU1M0OtI+n65rIFd8FC06ns8YiIpgWIClM0bAFGZ9ppmVwkC/YJZxQtGBHCxVDQ/U+7UpOmCakU5msNFAwkTVwS3LS/cIBRibPDvTyqiux9ovwKuJ86IUe6bqYvObNEk5WhWNt4iEaWoZyAYA+7IR7S0I48huj4FllQlTyr5fALhkAVMBYe69m2/wlMaWVw9WxtYepf8UAPASEpn4xe3H+HPf/kP821/9LjYvOtBcQUMHvtiAphmYpmXUPDK0otNxTkPL1okf780Pd2846Gc1J8HFCFlFl5VeiyIA0VSAlKo7HjG1w47HmV3VBld2FpY5G5buYd6FZXJYNKIMaM6ED8q6HZHkfjOk3xkyC2IxSkxA/QIVO35e2oOdWbZMFU+f7LAfBzwZ7kQnC42Bu1FmVoe6CCQALbViXwcca+cT36Q/hgbqm/aVMHCSesktNRAwrRRpy6qF3qc2GwKMybRmfUUGlm+DNmmTfdIicS7ivWuQK/Zlzv+a6QtdHgidDoaRvp/DPiJTjLE8BwueNCxBUgcnyLUjknGMVLusQBg8FPqW68QObrHaNQPiSe2GVSO0FMSZJT05p8aAsAqKBlpFu2NReNH2EtaVxe853ONJWXiCdzTwqgS2WKHkoBmgosEaZCop4QpHbKks7sGggBbnPVAucVDp+GfdDh88fYmP3n0b3SHJeJwz0LGUH6+1AVp+cyOwpVdfVjoFtO5lamlXbCAiHBU6YWchArZmfCwVHp6uScaOUxsj/QfAKoBStj8X8kXD23yib4eE0NcKhz/bt20TbCjrMT9ktH+528xwbV2rKLiUBGnC5i+qaFrt6gYTdzKGwNK+mtYSqPpnY2BN3AEhxdC+v6SjBN65Q0HFAHiaoUl2mHYTEFKyQR68jVklFmwX36axTw3cWmv/mt5vjduGZksKSIMRYguMubYOc639FAvKx+UmqWKZJy4wTy2lsffrH1MPjXElUipbqs4uEx+nBQVcKsaD9DOy9iKg0KwsOBHoPwa1rwN3rhdckDz90K71BCCp/eipiBQL1bCe2DUDucQmy3xik2Yk9aP2dfD72VPBbdkGQkT2oBt0W19v3qDk5OmDPRWpXFgHmDbaXRGttYssWsZ3Rba/maVgl/mDPVVsuhGTkg2MQPDZeNWKfVHFFY3qlzeA8718g+/jHa3MmfBOf4uvvPMSL7/5AS4+EpvOfRY/6DiCDyGdIuhnOaB1roXnRvwiWn638p/ELtzT20lwR/6TtPjw3GrQxX0SX74Ewcx2mW/jSFYir6Qd7Ro4zOfiYYSgfw02jYoCW97/EtCqHWvWjc5GMkBfQDfrj6Wa4c9L66ngN976EX6weYpnwx4ZFZdZ8pav0hHvdq9wFWi7wJKu+kLL3bpgow5UCRWHOrjhaNsyslYTsjz3gQpGzk7tNVpwBEPWLVYK8Zx0aiKwUdj3XLpchYgV2sDs/S7WWUbIna6rxjSf+YFGAMvALRNLtOiFGQH7VcaSvVEgMjM72y1TVe2OEUfqURV4iRU7JsCrCcbzBIR95Wwu2Ay29WFVQ+zV+rTBO+p49eEeSZRAPh/Q4zIf/b4ZqOV6Wyz97nlYHOMcGFsFImo/cRZDQnVhAAe8xGVqz+OGgFEjEh/0L1CQ8LJc4Dod8Oef/CH+459+F68+fAvP/qcizKehA7aDpBsWKX5AHnkIgJbdqHCPXfQwPiAhBZGYwcbhPgG74OmGAJyaSgVS+QJozkkFaCaUOYV1yY/BIg7er3kMGmEnjYKwR9wDGKe3nujUqXAHpojzylbRMLHkhesxci/74yophrkTzZxUTn8PD+2hWctgvHdxg1fdFhd5AkFo+kkBrat0RB+YO0CbXzAkvXBGakxcA4bBmDmfaD8lMJKOWcnS5hzYMe0odZQDGLJuXsmQ2nrUsGLfisLxxmaOvrGm1udm68QWI+DrgKifXxg2DNxiNP3HBlCFAA0aC6sBUW3OZ4w10YwSthLrNTRQCoDr0yzP05yYFnUmDp1DQTF11OzVdS+peLqgMXeFySXvBaAiv999+OwppJpeKFkRfELeEWcJyGgC8yJF0HnFQ5bcQmQcFs9jR0CBpGA+ygfUQjhyh4FmfGX7Ep+9d4njzQUuPmaAq1SN6jJolHRDJgU+T1IybLYcbA0DJwwtZ2fJ1W5Az5mnjhuouixAYjsI61UJRkjQLTxp8TiBJah17jjDvhsqheWDuGoOfAFSJTFrar+Bcw5kwZnMBJyiuw/tS9MG8w3QpDhis3n4rgrgNaoot7F1DAyydMMD9+hRPHVxXzc+r+xJAvaH2rvUSUXCFY0L36Xp9VYHXmJ1cStSJeNOdQaXMYqSphMeVLMrFrFaN2MBW+upYl87BfWXusFAsw09SSXCRA2wmphdHiWfAbFse8siqQC2RJh0HWN/xZ+bBewBCPivbyMbLWbI2Ag6oGIk4MpSBKlDURBpmyaAE4Y0oeeCm5o8WG7MqGPtUcP4ZJIqVUHBCRoQy7MASigw7YsuSbpeRkWXJ+xmSSOcq9iRfR08Td38nD2r0HuaUIOI/KH2uFWReUDsTJfER7qtG3w2XrkmcafHt8nyLO3KgImlavxFHjFxxl3pcawZY+3Qqe08ls63AYCrPOLVvMGn01VjfZkP1t0hQbSznqY7bGnGh+MTvDe8QqKKb14/x3/7rXcx//uMYSziJhg763iEa15ZumG0AWfZWefszdJWOE1wFaw/G5yxZVUfLPNZGEgFKDP5Q970r3R/zuSSB5/tPaFlvFR4QN+kAxB8ozQzavDBOAFZpVZqJ5kyIXlI7GUHF3y3bZw8GU/vC9qpBzDrp2iJGN/dfgSgRRMqJxQiPMu3eExHZ2XZpDoTYWTGTU24qVsHogBgREbhjGpRFY14GFAVc9EBE1Jv2hxeunsFZK0H/5jyEY3DWvMkivienLv90ZJV5W21meEUZkwn5kU1kLXDse7bmg36rXJHA5amVS9LoyUUZAOwmki+OXcJmeaFOKWz5QCv7iHXTvPlVw7JzBmJ+SQKYUKIdi1tYtBTEaMBeMTE+rQIlp+39mFpjgmMq+7oUQxPPawZt2gRkQTGZ7jCTd2iDAkfdC9xTZNrm2w1SlEp4Wne4WW5wIF7vNPf4C999ffxz3/9Go9+2OHiWMBdkkH8MAJlbFGIcxpar6PVWlsBWvF7AhbsLKtwaHQKcwQM0LIStUJsFI0aTzUMTouctjk44fAiRtmwUoS11eMWR4iKfK6dGA3TJEECEhEqyUxFGd8ySidDvOQzEaPWhHkG0oOD8dBe04gYb+c9gJD+xiSVS2nEBjM6sM9nCPLTkkixAA5RkNyCFqypDUQCchjoYoCVNdPydOaUBj/WQFasYCjH2H7aFNZfW5XX+OwOdNm5naQbrjqzvoxQOaMBVNbfuv/YdzuXtm+G2cp2nus+2nuGVR00kXhjTAEtSGQOpPQtbDjS98ztKhUC1mBh1XtptttSPiX9pK3vAs0BOmSqfq/teM/pR86a4khgDMownjn5uRZOGGvn14cA3EGKrHAmPEoHDNQK0XRg1dYibNOIYxFbdplGfP36BX73vQ2Gm4RuVyW622VgLoDqkzigBQQHYg1WhSfp3nWsD8BEcRfsrFjVMOyPoKxcSD8CJpEDSusHwW3U+lmx9Wn5OWJY5jMYYAWG6zXaQ+mnolkpJ8fs50keoKnGUH5oX8q2paaB2JPIlqyzQiqAY0gJtJYtQA1NbSbRuMqaKiiMqjan7TWbwCRQzI9JVLGrkk2QUTGuEr9tHB2RMTBwMCDNPFvIeLWl4uPbIczLzZfpfe4uUihHiGyKAVfWLtPsovBR+7cnXgTkLfgxKohl9nMRlEcDtyKw5ZpbYGzNP0DTy/JrrO/dX2JeAFnyHb3W95Jjn1FYqpiX2hh1pokFyDh+U7biR9YeKTcWXPQTrYrfNk24KVtc5hG38wav5gv0qaDjZTD+qjsqW0qY3j0VHNGh54JLZU71GswxPa4jd1JMZN5iN2/QpYK5ZnSp4FE+4tNyhcoJ1/0BlaWK7lSzV9+Vyr8kQFoHt1eWjfKo0+rzRXymV9MWMyc86e+ErZUKPhuvJPC/kTs6UMF1HXGdigdwvto/x+8e3sXL+RKX+YjvXH2Cf/O1r2L3/mP0LyeQFSrZDqDjBnzQ1Mo3STfU72SyobbX2FmvS6WngIgqy3uppWW2QqVQuBkkYvWbMruta3pc7KyrpX0zwIvUbAY5FwYAXrC5LOuldnD2sIFpAJAmiR0tUu6NslV1/QrVgCSwOUOk5/mG7QHM+ilaIsb7/Qt8OD4BAFxmEZ27Tge8nW9h1QttsJQcbcaRpaLUq7rFxJ0ziyIIJbRbuT0H7lEhgn8jwwGtCGzFSIhV7zDAx/oXOmgTfF9HbqKQr+ukhBz0tq+6AE1t0Pf9hwH8nCGIn2OL2EaMri8Br9MB3oAbAItIjkVoemoaZFEvy6oUiqixCq6zRIZ6AIXIPxdKOOr9EBYW40KjFHel0W0TVdyVHjU17RmrMrZNEzZpcsMQj6dycsHEdp1nTHWzWP9xd3Ax+oyKPrUyupZCulcKbgqR+ufzFb6Pd+U65FtcYVZQD7hWNteWJrzb3eCz+REmzvily4/we9/9ED/+nW9geCUiiNznBTtrMXCvPdgYYWA+pdVauxfAQotIoAFaJmhrg68NtjKAsoNZtVJjWNmu3ABYaqI8WDFCbhRaVHJNEs88ZGj1Q9l3ggzGlnrYdqSvVZ33DppuSDpos2qfSIn3ez35h/bQII/HVT7gRgtGWBW8Dc24SKOmCDYwptP5z8ziuBy5EydF+zP9JgCaatECAszCurGiHsAqMBCQXmMH2TyFwzKL8kcRdfthLPAE6zfYH2t5BXytfyYR2Fr//M79HM99fw7keh2wZuvbMGdplICmh0BT7rjpZRk4aKwq4UC1dMJK5J8ZBFPDMt2qjqpO7rODjUSMuWYwVe8naRpip5HtJSCpd0DvL63uaVFntVBCZnEUiqYzkjoUWfdlGmBmv2T4k2M61B4vcKnBHNFxywq0bqBFTrjiMo24q1JV81m/w/O3b3D72RNcHTXdMCk7S8XgHdA6G6FGALI03fDsTQygl8/cFdBa2y9uACzCM2Z7ocrCiILaOhfW0m78AWN4QRPrxJ6nCHhpx6Y/0ib+aJHrhPsfTvMWyDsWw5Xbjs9mXj60L00TNpDM7a5o9kB6qEWNXU3YqQD4TllZMQ0wzkElNVlAscjUyha81znuxFIwqbAE6XtINoMxtoAWbJf1BXg5cAvgDigCcKEs/Jao2WtAlrCJZLnJplgBEWNnWbXDnjSQDanqfVCt4AmEagM3L7NJYvVCIyC47q+Jw+v3lYAtBMCKfgvQmFlHXi4Hgl/odq/5Wc4k83Fdsk0kU6W432iZOQcW8LCH+DrVpFWC5MlRi0xdptGD4+CkQuydZ4FYit7j7m6RkmjNtLQm9ZNAAkxVEomVyzTCKvZWSAr2VCUN0QCwnfooXa2uM9zT5EBqYSlulbjiZto2hlqakYmxKwOu8uiakpVFIP4iT7grPcaacTttsJ8HXHYj9rP5RBU/Pj7GhmZ8lK61IMErJEhmz9v5Fl/bPMcPj29hmyZcphFfe/IS3//WEzz+fpYMFSLw0DXtrHnWILvMfk4YuvfJsKzbfUAWIqBktiRIs0R/iwSdTTNAkz7IHdwY+U9JzYRX0EkWPKPzdHZdrcmz6HICqDDKIL6UA1Ms8x4QRJtLLwvL4+I+kwFfgpFpQa+JwL06XdObG6s3V9d6aCcto+Jp2uMyj7jMko97nQ54t3uFLc3YUnFQp6dGVd1xh1d1i33dCFDFrYID0JyFBDEklio2huiEpdDFNLe4rQ1WUk3IGFsBkAoGyyLF63Lp7TxbpEMGp1bq1vsLm601WzKR/OE8gGV/VrnQohyFTyMb8YGNtNwIysXzHNVAS3/UBnEIKGWVNyxVxwQCG5W6aZD1qfhg3XsOOblYLtBK2gvTqsNcjb7djjyKME7qZALwUspbrdBhBusyjXiS79wQyXfC0rorEu0w/SwxBuTf7esg+exlwIfjE/zB9DZelEscOMu1hQggXqcRj9MBl+mIRBUv50s8yXf4L559H7e/NuLu3R61S+AuAZsB6JdG7t6ytFWX6fcnJWnPiZ7Xe94vdojWn1EwWAbKVIA6JXBNizRDjyLYYKsRDIsseE65L+PWbxi83ekJHjwxWv43S+TB+oX3rYaiElDJow5rwO2hPbR1IzAuaEKfikRLqWCgGVfpiA4i4m6gjqXKAQJOHbnDyNl1luYo5E3s/QujCD4BBdrYbCluRE0nwcAQAcmssp+lm7VjX+hoYfGzOXOebX0Dxmi17sIerGyNMYbX21h/DrjpT9scispNCD4ey/p91O8CsDhPS8EEGvPNbBGjAT8JjEH1zRJVB7eMzSXnuBT3Z+0jCs6bfolVHvR9BftXgk2v3I6+QwO9yL9rBVOMwUywYjFSVepYO2X06ROj9m/ipDZIXm/KVu1Mj5mTz5k7kiDchmb0NIMg4NeGZnz14iXGdwumSy2FnkiqG64KNrBdeHdEm0MKj0pzC0ogLl81vuf9ufWCPViM+1r6fJ2WaTYlHmO0Bw5WxeNE2ObcPN72SbHvsC9/pcVxclj3wdZ8edtAkuZ3RWMDVKBC5QD2DOxVJ0uALJnPm6C4pR4euBcmTdnipl5o5kJFrGS4pUmBrgmXdPRCVQaoWDA+6XZVtbxsPDloJsKWpgWQBUhaXWXyiobWShhTe1RsqWBDRRmwLRvBzgvQ1Em3VQJy9RDhdfOlDspGLdwArQhkST+SinjQz+vAfI9ThzpmlkQheNPGsvcVlmXTfC0LBk06p5cqfDMGiKbyoEHyTKxAIvm1NbmTppvG7ita4GobWLsGeom/I6l/XRK5k4ssINOdam4mssAGu7bWsZ5mkdhf1My6yKMH3wHg2bDD4+7gAJswzJIzrkRsXq7HWCSd8KPDIxQmvJgusCuDV/nNxBj1OA4hlfHF8QKj5rrNVVITn8+X+GR+LM+5FdoixtN0h3e7G8wKvm3ThG9ff4rxW0cc3u4F9MmBnbUZTjUQWIP8a4MANJ8n+EC02PaMX7Tux4Ct8B0xAGNlwfwcAhWtdgyo7xL6O2evfEKEplO5spHiy3CrbEjNPyLNTrWgiwnBp3Jmf5WQJiCNGqjJvNjPF7VTD8ysn7IZeit5w4zrLKysS5oXrCyjrR4AvKhiIEbOkMofpBGOshh0AYl8bFMT7jbhcmvG5jKWEQDPhXZQzFLQzEFhwkRpEd2I4Jbsd0lztf1bxMCiGOtIRGzrZWX13dn0ROszOgm8xDQikGYGK4Jw0ZBZ62nGVRqxpdl1p0TcEhqxmJclYXl59FaNA5DradTXLpVFikanGlhWPSRWJpw4o1RNX2R4ad2CpFVRBOQaaHbacOWEl/XCnZrCoosVK43MnIEKTCRVFi0aVSEilwbm7XnAh+NTbGnU82f0eUIP4JIK3s07P49/P38Nz+crfGV4iV/+9of4g1/6FrafZKRZ2VmbHhhHBavEC3RR+HUkIjZjaAWqbYwEENAiEPb5TLohJSwG9AYisQBFRZhZqmbiIBZVje7r7XSxX3UoSCMTHKpKeeSgykCNxEtAy95qCogP5g6chXh+hkRAlHfLZeWYPLSHdk/rqFUQIsBZWb0DErKePZIVIv56VEHTluJn0V9pztaCMHYssGHgljVj5JhIPKApcQHEsZ9FcWClsY6iFmNs+svwRuFVUhnbelitu94m9hm/MwBrDX7ZcmsGcq23BZqNid+LnuGyT5MDOKJ6Cg5B0JyidtkAxQzGvDp6CqwhImEZSfC0MbCgn+36mLg70Bw+q6gIiF3qVeOqkgJvTL7dqIy8A3ph8+r9MuFfE8GvnDDrdakhHcaeAbtGExJuy1b0uJIxDapGwBmXaXQqxCfTYxxqL3Ont27w8tlTdPuEbOysnCTXMuiTMMJk+zVi7wuG1n0pidLZvemGC0F41sg8ms0RsKjtOwJL/gzFPsN6bCvEZfrQuYbImR8HtYdSo/O6jdlHQFnQ7MDWiT7XQ/tSti3NWr1a9J8OzD5nHjnhwJ068X1j5KrcSUJdBFgl9XBazI1l/U7ALRQHsWKhqZHzmeMSHdgR2VMQgcYKbpkSCkJhmX4XpVLMF7Gqf4D4MBZEsPfWRhcub/6LHJNUdQTEZvXUUskL4PNQO5uemtD7ukU2l30WLT34WNCmuu0Y7Bx7MA7hOOU6L1lppnmZmXGd7kRzmRJ2dcCBBxzKgCtNM/QiV2hSNLdl60Fxk1Ix4XbTtLJAvQVfZk1j7HTuYey9BNY0wypBNtsOLbBu85VIMLjIk9oW1coqG2F2QdIIX05bXPdHLzJiLKshFeznjLkmT1G06obHmp19NaSCIc0CaIHxqD+Kn1Qb6LUrG+zrgI/ma1ynO/T5zq/vB91LPO7u8HK+wFvdDl/dvMT777/Aq2++L0LwOpGomx5pOwDHEajzMhMFeH264aq5pvDnjc863lMEtABETS4Dm9JMPhGkQuJz2Dxtvb2eU7RbloUSK7R7pZ0EVLU7btr0B+BBffOxgj+VZn0/w8+39nLdHBzzKoptHvQm7YGZ9VO2ASVUiiBcpSOu0yi0XL0PmQThn5jxcRlUcFEALNNysnS3g9J/s07+tiTUUa9eGITHh9WQamJ+VhJ3zcjaah69paPJNuSTUolQZDcm1ix6MVB1DQxJg1zqWcXc8+TbSg56zC+PwNQ6n3ziBlbFP2sOgoFOjvOcKGSLQkzYpiZMWRUUMtBoXzfY182iospCIF4jDMaKkv1VXOQJF3lEAuvgyv73at7ixXzpIo1r1tzE3YIpZvuPaYVx/3bvNzTjkepl2X081k5EEDVR+aiilxd59IlJZcKn0xU+nN7Ci3qJHfdanhm4SoTrJIDWe/kGz7odblRE/jee/gjHX7nD8VkPzo2dRV3AwtUjXDC0YpQhRtJlxeX7sA7ZwLz+rOuSeaAcPyNEDYKDGQfDAHh5ZME+uwMSgDEODhOak9KAM31VQaF1pFyuS4uSOCtL13FmljHLHtpDe03LzsMRAGPQqHRkZSVqqdm7apFRONhgGk7G5jGWLRE7EL8Uv4YLi8dmSkDyxyeMrE5TOwIu3YKRuv0cmEKtX/kzO2bTr8KnoBN8/21b0/aK/VmLQFaixrDn1Z+19XdYfMenOL0eiaX42XgvaX1yXQuSj/sx/Z+oCcRXVrCGmpZVAku/SdIDLa3UKiUeq2iSVJZKgy2F1BhiFjRTsImFYRXva3MUG9M4U5XUjgAquhaKMwhEV6tPZQH47WuP27LFHfcYkTHrvR8I2JAAWo9oxEUaceQORIz3tzco78woF0t2FqUwVWX2fZwwtFi/iTdsbWsW63C4wbzQGzmJYMfXsw/Nqa1xmxP6PGFlodke7yk833F7sznx/Gh1XBRZWcD97x/al7JdphmDS5zYOCDj3TFUKhw0zc9SBeXzANNvvS1bnXe253qbxqAFK4DWhOwZDdYESG8VwwGZs484LTYCCMBlmQg2VgEmlcG+fUx7n3ScsmVWwTDKjgByzhOSZsosH/7CAiINaq8m9WsmtmBQWBfGpFqmJMag0DpN0dpat2yRbgjCyElBJ14cZ0LTBhOfa5mB0ytLyyq8A/B7NmpVQguqT7XzsdvSDg+qpQlIhcEKWmjw2j0waZV47zbKCptU10psW/ZjmGtSoCq5n2Pi7sYUuy0bZ1MlMG5m+fxy3HrWi4FUFYS7ucfzwwXu5h5zzc7Asj7G2uHVtPXPL44XOJQe2zxhrB3284CbaYOpCnv4B+Pb+Lg8dnaW3KtZbBnLs/is2+FX3voIt784Y3zatWE+k/hBl1sgr+wT1/BxNeCeTIjkszO01n7T57XIztJtPDW9AK6RQmE99W3M91lMju5hZMnG0EryAkxR/CnHoIwxsezPUFzdNg4BnphiAbzoD32BKP8DM+unbEbLRxbA5DrdLaMJ1PLV9wzsePCISEL1SoY1DFKVE6yaCABnbxVOHrnoURugw1rVUA3IEAa/PoBPZiBi2qCcQ13Qd9dihHIejFHFgh0w0/Oz9AwDpuw5tfOOVF1AnmmrEhKdj4RQwjZc48B8VNFEADCmGqtD1gxYH5yCohScWJVl4g49zSKKDImUiwHo9DqJPhlIWFuVEzZqqI/oFlRbO7ZNmhagUSJ2qmxGdYORVzNJi15YqdrKCbu6QYZUfdrXQaLYdo1s9CjAngb0KLgrgxsIO/+qFGC7p12qmErGbt7gh3iKJ3mPp2mPK5qQ0yRRCQhD6zod8I3hU+zrgH0d8LXNc/zpb3yI3/uF72D7SQZNKgY/9JIzHsrSnlIf+DTaYIOtjf/rKh5QR9IYWSdaJqYxYiwTDkCWDoaVUKcEUurqWYCf4fnb7gjouh41t0O0KHkwBMxNB4UUdeWEpqHl57fcLVUSW1cQIv5YGMCH9tDWTQANrTBEBQNNDjwBDcgCtCQ4RGPJNI9sW2ORmO6RRVkBAWQsjQyKv3awFHQgKZunIIGUSWyASSYD2loq3Br0SaTfu7Pejj8ytyzy3gCzxubP1IYZ2zqF1+hIxO2jH084ZQQDDSSz+d3iM4yx1voxO2oYSAK8OmRPBZW0yi03ZlwFYUZCYgJTRRcCVcwtig6W6oEGWtkgZlWb7F5GIMx1uOhk2AHrNTWHg5kwQoIegxY/iYGUeG+IGJmBObDz7F4xtWcJgIv1TrXDK2wlVYUmTAan6XPa636f5Dt3dq7zAe8+ucHzt95Ct0+S9pBInAUVgn+t7shJU3YWVuszGg0qXNvTiybbRRFcQNhZLbCh47k9C/fZGg42z48hvJ5bHo8pLPdDJpyytxb71WsV2Vl2Xg/tS9d6ZLCnj2mRKL3VEwg77lFYOFg2J43VVU0768Cd+g0JBx6Q6QgrShS3s3k1AAejes0UsaC6F2hScMVaQXIyQGYJ7lsg31pk9Jyrzgg0pm8UkDcfySoYWtsSu4wJ0ALjW2IkY2GF31J837JBlprAUziWAzefKIq9w/yksM8eTYB+UB8sMs38nML5Nx2x7H6MXZLrfMDEGaNWiR3MZ6mSMmo+BgBMJbvvaSmGEeiyPk0zS7SAjcFVgxQOY8iza/hOnLFBAzWNmSWFqnrRE1b22Vg7XGQ5hhfTBQBhVQFi46TYlQRnDrXD3WzHwhhrRq4VYxXQzAAva11qvo+wsrKDan1OONYRd0VYaLu6EZBQL/WWJjzKB/zo8BSfzVd41u3wte0LPPv6C9x87R1snk/CVAIkS2XoQWMP5vG8DwTAA/zGznrdeucqFsJ8E1rapHMDv/o1aQ7L7hFSd2H3wl4Z3lhRwvJV2x4rHxJpoSw9jgpQModJ1qumnYXmk9V1Hq7+LsiArgJN42TQTOAh2OU3aA9g1k/ZjKpZkPA073GdDosB1+6dVDCUH/KWJoxe5kwBDjKR1IoRgKUdGojlUVukZlTCgyyovUyirVKdDe7VJp7UqgA6tTWAIKClYeipLvPOA8XXSvPaduuBfil0yIuIxZqme66qoRmcHEAycEs5FPaXUnXD9wktddKO0/LTJ2RhM6Rofuz6FxcTPHICEjwKsVGB9MoJG8zoOxW3VOaDgVYGRmzSvFhuA3zlhKwRewMsLZrh0ShNGYxG394b8w6AR1A2acYmzTjWDq9Kj2fDHhdaUaQy4bZsXBTxxXSB22mDu9LjD/LbuM4HF8YvacY1iXG9pBlP0x6P8gG3ZYttOuBXH/8Y//Y738D4HzvkwwyuGTz0oC6Dp7oAtE6otRxuGuCD9YJa+zrnhGMFj7BeSDck1kOoDJrhaXxwJ2Pl/RK8+gcAjci38rTWpzkLNuCyORLWjS3XvslSEQnATEBmj2Q49TaAZ4scwygc+dAe2qoxVBBdGbsbWiao2XthwzYh9xnkD2wCBOiGjJUzwSdEBmIZq8fBF27C8bZeYQJRQtaRmwFneQHyE43VCwE4mwgKLHhVRshEuIbjiLpZEXhK7VR8PhivQcXCHCwwATv/NUbgjOIAkln2tPVh/USR+phWZyBXS5FRMeVF6BKIoKGw07ICT+ZIac866BiLuiiwZakXye1j9ZQNOw6ggVx2DxLVBlTq3ooOXDUEtiIrLx6rORWW6n7UKHufGkA6chahek44qAjvVBNe0iU2NCMnBjCBUTHotRbdmwlDmpVZPOPdzS0+eusJyqcJNKsznDMoFfAK0HJBeKDZEQSAyjAqA3I+FwhbpRu2xVimG0LtGgWU9HMm3v5ghspREaBCwNbOdHeyXPfrtk/tDMdzXq3r2z6kG34pWyZazKdjmzRQanpYADxoWyCavAZ0X6ZRtZB6nwtv06TrdJ6WmEmWG9srZoZMnBVQWlbmNpHvmKkgQd1Z+tb1TLc2ph1OSAuwy4L0nmmi624spZppIaFSuPkF3i8TtCadi7EXhlclXGeHVGiQg1oWijHfJtgYtJRHCXFaTzO0ZumFsdkxumC++gnN99L0Tr2uW5qwC2CVAVpSAKrZIUs1B4BD7R3QMh8zawDcguyivyvsJ9PqPNYOUGLDsXRIVUDKO5ZCWEcyrTQ59k2acVd67MqAQf2Vizw5O+tm3mKs2ZnHQ7gUc00Yq2Sl3E2920N7ZSbc1C2Oc2OTXfYjOqoYOSMnYUmLLZJ0RwPFrN2ULT4tVyoNJNf2OskT8dl0hSfdHtf5gF9+9jH+219+C0++12GY5CpynyWov91I5d2YbogK1HQqwWIakPGhAM6PyZ8HekW/yrqJ2SWzEj36MPjXwEAmNB+osAbhyfdBDAG51AcyLawFGeG+xkCaGbWX7dMMqf6uwJaR4ag2s91sqRz7GRLnve3Be/op24Am1HqZjvqZF+tkIhxZ9EtGzp5P7gMJyA2EfWevUjlE7qjlnBcFPqLhMDbRmoVlxglYRjkmEzxUAxFZWxHsGigagqZFZQO4UXLX6YYx+hENa1693tfMeZn49Ds55nYudh3suvcaFTpXyUkEJ0c8TnfY0oirdNQqgg2oM2AwEeMyHZuIYohGyLrisFiqpgknNnH3jF3ZuGhu4ahjVv2e27q3ZesRlGPtF4K69revA16WC/9OAK2WwngzbV2MMRHjUT5irsLIkjxzMRg/OT7G7xzexw/nt/BpvcRN7aVSCoAtVRVCfOVikl8fnuODb36Gm69n1CFLCsimA4ZgGF5HrQWW6Yb3pRx+XrrhuWaLWYAsY0nBBklueeQeTa9t20W6oUcTVjpc8XAJKnTIzaEItFoDtGyZs8XWx4vlNg/MrIf2upbBLhY+BGHX2IQZBMyBzQvAWVkyEZf0QoPCZh/7W3qas7hAIXgAF4DvQjqHg0thTF7+pA1AWQqYr8GuRUQeLSXO5jiFz6cbRtsTrwatXu9rNncrfPpdHCqsL0KzAVaVcFGAxNbV1M0NTa5ZFbWtbN4maXrw70m3lf0twSXmBjgayw3ax6iRbEYD26yPyo2JUZl83YmFvWfMsShcP2lKkjkNBmjZsVkfdq6DaaZotV1jL+/KBp/Nj6ToDfc4auprgrD+TDvSno3H+Q6PnuxxfEw6kSZhZq1TOcI9OnvjwO31TdbhtuxeWxP2TxUL3SxryzT18J19tu25rR+Xr5vbs1X//j7Yl0Vf97R1SuND+/K0hIQ+FFuyR0fS55Kyn1r6WZyrAs0n6TXoa5kno44RlkFiFfVGzppJwAqyzIsg+0H1Gq3FYlcTZ6/o7sdPTWQekAJOll0i25+XF2nbB3FysFcyjK2HZpWsfByruAoou+rM9TV2lVSmP/3+NPOi+UvWrzGzDACr/l2zKWsN417vl/lnA1qF9i1NuEpHPM4HXKcDMqqzsUw3a6PazgAw1YzbeaMB90mZeg3gikAWAHSp4E71p2bOWsG9x1QzrrojHnd3/lyYzIn5K1a9cNbgx6Ms/tSQZhWP79BRxTZPnlJ4KB32c9/SJqvpZCXsxh7HqcNh7nAzbkDEmEp2IKsy4Vja89Snon32uFbtrMt+XPjCB+5xUy+w1+dwQMXTvMdVd8SraYtPpmskqvjK9iWefvsFbr7RgXtNg8+EOnTgXqsb5hWkomLwy2X8+vF37SOFdMO1T7TeTgIY5PZJfBNyMXjpo21C6leZj3NiOwLxoNmcZvuaj4SlDbTvTUdLJ04CjsGzaFDD+lj2IX7bmwddHsCsn7L1VHGZjnive4W38+2iDHUPZSEx48AZoyZ02OBtBsBYOrbMXpswfGtxWYxsVAUdAAHNajA8rWJHJwaNDbBJDsRMYV1zkiTv+vQRKXw6EFuzwVr6glcxPOlj9b6u/qyvGM0wJlhv1y30YccZHaqNaoRFYKtqRGGbJgzqYGxp9AqGfh4BsIpC7kbJdWHdAF5VJsxVACa7roAa/ppx5M5pu1LmuN0nE2WsINyUrQNaBeYcpBNh+k2aMFvFEE6uhyaVDAcH1461c0DNtGzuSo+fjI/xk+kJPi2P8KJeYFdtogJcpwnv5Rs86fa+r1976ye4+YWK+SKLdlafwZtBIg2eZx0G77V21rrdkzt+0mpY197X2nLEQ1/EjFSgulkEFFqJ6nJzJCqWTsva6bBBu4YB3IAqf98AsgVgdSaq7lH8oJsFhmtnPbSH9rom5atnPEpHXGhVUwukacAMFQJOmeC7V7nVP2YKoDz8NYJM1hbAk7FyuVWyAwQ0i2mFBobMSAqCNCaX/cRdHF4BF9Lt1k5KO4bGklpej8VP1q/BuvHq/frP+orglzPB0MA0a3accVkODpRdV9b095Z207S0FumhZpsUsHJxfU8dtGNsTDDmVkmXufVmlY9LAK8SuN0nBZl6KmAQjqbfpSCYpTrGfQGa2qgBMOaWUmpl1wmWXphcC82KpEyqnXJbt9jzoEE9BVBJnMlHNLojnani3YsdxrcY1Z2GBM4ZWGtnrW2IvT9pq+9eFxiJD9Vim9Vy2CReZ+p1zYaSzhaA1n371Fe3OxEIs/f3gGcnDz3rMa3Xs+UP7UvdMuhk3n3gjJ2yp0yXyrSVbI476PzywL0WtKp4knfOmLqpWwe3jHUq81ipgJhQfftD7X0MOug81thbABwcswqJlpYINJ1Y+7yvPUYNzoywyqmteqtUdl/q5ZqNmTRw49fmnsffZFNkWyzeR9DKMkViBokF9YEW5I9TQV8X5Ov1YPdZTJw+Zr+sC1qZz2a226tChvVEO0vui2Wf7OrGGW42vlo2h4ivb5Vlxy7+nlFxV3r1GxqoZPbpIk+LVMMCGfPn4JtcphGXaUSnwvAXacR1J2wnu0+xAuKsrCnxoVRDWFMHM0llRQAeyLkbe0wl4+VRtLX2U4/D3OHx5oAnwx2G3FInXx63uB03mFl0v4ZcNA0x4VE+NpAv6CJ/u/8Ev3LxIS7yhBfTBSonvN3v8Ovvfojnv8aYLjvXdOSNZKlg6EGbzYLZ9MbNAvbnKhu+pi3ArbAslaU/QjOWtiB+50CWvgafJq4fdYZlO1mWCotPZNvGfSddVq0P8Z+cEGA2yTJaEIAx4mWFw89pD2DWT9maTtGMK5oUQZcLmxXVFK2sxrwyJpYNRnFQmDgryDI7A0sMwoA9b3S9xuSSfTdoyKqUrFMQI1V3TWG19MNoHExcEWgTdVtmjscayIqMrEwNyDsX4cjhb3k9W1/WzzJ60daLUY/tCWvq1GoZuypDKKj2fn0No2glAAeVGgOssfEAeBTb2FRzzU6l9msTImA2kbDjvFFGlgvLB5Byqp2wseYL3JQtbssGh9p7ZGWqGTezCLVf9wc87u7cIO7mDV5MF7grMom47o7YZHFrrjrJGf/J9BgvyhV2dYMb7rFnuSc9AZfpiC1NbtB+4fITXHzrBnfvdlJyNSfwtgcNAxZKz3JRsBCDB7AQNTTw6VzUIrync5/rcl0fhKt85wN3BJDCcUWHwZ0Fj2yvBuwIVDEWg7xnL64ci+h4UKUApNk23MC2B+fiob1ha6CGFP+woIkwhaQJUzYpizY5E8sm2DGFsDChU/CEuWllzZpKAhg41RyCGOWeOWMOUV3pt/0czFYkNOaS6CVxwAeMraXnpuvasnO+O7BkZBkQVc+sZ9fN/s5dzxr6iaCZzUnjT5wgbKK4/TndChN1J0i1wqgzFaP3hGYbAHiUPDLAhIWlk3mIzZh1blDDfbPmbK0VoMkAjtzptg3osnVNL+VQe4xaUGRWxpfta6wi1G7pIrbXsWYcapuTbNKMLsmTMaSCmRN2ZYNDHZTRkTBxAw57mtFBqjwygKf9Ht3TEfOl5qwTiRB8PhMaY1WCOgG39KzjeL3aLlw0LJ+e82Lwi3LlOs63z6vnwB9cPgWmtM9llHz9/eqY70Nqw/r+nC5OxWzgg635srcEQiKCSdBYWh3QNK2u0nERuLX2ql64ZpUFT7dpwjaJuLto/I5aSKngaZZA56CMUvM9AJH0cB0uT3GbPeUtfu/HbqnSmsEQ0wFtznwOxPGgMUxwPi10p6xZ0aoTvyX6SrwMyAOnmVwGWEVgzPwhY11F/2iCZJIspFnCd9Kn/GCnYE9NO3KdLeOB9hCwsOUJ4pdcaUZJq3A/t2q2WAYreio4LthxopUWC4FEyZnKhE0W0XkJnPcYNMg/14xX8xav5gsRlGcD4hLuSo+7OuDFdKnglVQlPGolQ1+moNOhdLidNpLWPneYSkKpCX2u2PQzSiW82m/xyc0VDpMQCKYiTGOrXFiZcNlPOMwdptIAs22eXK/L0jdf1a0/C5Zp9bi7w/PxEr+9ex9H7vD17Qu8/6c+xsvv9FIIixTQ2nbgLgN9B+qXaYyvbfeJvL9m2cIHOrdOZckawWmQnS0CyLxAaRe2btXfuaySaActNd79G7Rgv/tMVQA2sKQb+nqq7eV+UpL3FiQyNtmbtAfNrJ+ymSC6f9b3wlwSIOegg7BEPDYB2OgcTAHgEZCRM7KuM9ARPU2uVWED1hCqJQ0oInQOaBpDCsDNUgAxDooAPPfYvgNjEeGIpceNsTUhqRBw7Nt0WHQ/LM7AWivr7DXE8re5zvlfR1NcaJ4tpTIw2pxuGkrBU0WPGaMChSanLs5gURFAzSd3JyFhXwcXMIzXLVFFYqmGiArMqnVUNOJtg3eXig7WPYAJEzIKJRfyt+ubgnEy9pVRsf2aaK76i/kSQ5r9nm3SjKTsrrF2GNIs1aGqRVYU8FT675BmZxCgdngxXeDD9BSVRXh4QMVGB/lrmvA07/GD8RkKCJs04dvPPsPvffMJHv0wS451n8HbARhHYJ51wK1wQXi7WffplPAZMfhVidt7hQ6hkfCwH4seUKHGeFqBUL6dgUw2kDMpBVaPlQEvD8sGvC21TkijIpzJB3BOCGXV+VRkvhI4qUGidjwP7aG9rkXWDxDBlAbkzCDMyJghgRNnTHHCgg2kTJsZhKTrDJhByhoyVpFVKjQQLKOi2u+ShFsVuVlLOGDpPke8u4KQeCkOz2gR6cjYqlCtsNW5RzCKaHk97psCrY/RHJN2XZbrxyCr4QYOKmpPEU8gtbukQOEcgi8gRobYHwEQs84ryZ27or3aPogEVOmoYgaQzaEgsfUGBoodh6QCoqojwgsmXkzptOUVySPh7ZwrZrVBsg9pUc+ryKwTPRWtitki89GJJb0m4IRD7XBbtnJ+xMg4uNbXQKK982q6ACdJc316scfzJxsMNwngKikcXQZKWQZI3lgQ/owYPCNEJXzB67oIYzb7JN7t1xlbc9Klfs9ClWx3ZbFO0NWC7U9/P7kBXwuNLSzf+34C2LV4fWhf2jZxy1womnlhc1jzQwTcSJhqcq0kSx8EoOvMDmhZcaJBi1BNtVcfAyegyqhagDn4ICaTAmqSKTFLBWjV4QfUhd8iBY7gYvNxPm7FQirkPK0AlH3Xk+j42e/QsjoM4m/gWLOvMS0QATsQcLC1Kl241paBUy73wi2gk4mlejiTV0+UfXDTJoZofMU0+4VmmIqp95hx4F41E5NfT3u1e6d1K/WcKi7TuPRr1OfY0IxKTZ+5JyksBUCBRLETm070riydMFYi7POISTNfJk6YStIskEGDGwWJxd98MV34HMQAJgG0BLAy/4aZcJw7FCYcpx7MwH7sxYdRtlatWpRFt7kZN9jkWexUTdgoS+v54QJ9lorzzzY79Kngtmzwcr7EVTriKh1x4IwrmjHpNXwxXeLl8QI/3j3GzbzBn338A/y5d36I//N/9gyPftRj+8koz8eQQdteMkb6XgbdOSqwY6mbZQzjMzZLtITDxMMetJXmsINbwEJP2GxSmgGaxfegmc5btcCgAtTXIloIwrcTaL6Ysatg9ifaqcXxt23jOmkCytCWcVK/bQJqhjK6zh3w/e0BzPop25YY2zTiOh00rW0JxkwsYJZVnzCdJY+MhnxtQAUHNZJu0Ql7hgcqDmTZ8oyKESYkX/11RMbAAHTAiC0O9lP4LuamA8uIhUXMs6AHbR17GqlN7q3dBx5bdcM1uCUCisCGhBm03r6u1o1CisvoCi2ORYyXaHpMmJ0KXVEXFNyqbKpJhRSPtVfR2pWei14zE4bvUsVu3ogBIvKISE8FR8g9PwZwakpSScR1V8Kvdl8HvJq3CwruJs2YSSJTHQlAVrHMDa8gHOeMi0xOATYACyhuMKw641V3lMF8HvCj45PG8hsKLtMLrbTCeDvfAgBuyxaP8gHfefQJfuubX8fxP3S4vJuFnbUZkIYBbIN3ALTODuDnBub7mjkpFa567IN3eIDiIOzeve1n7RCEvsU5gw7KDFSr1IEQGZdjYN1PKpY+pdsBTR/LKLSwoLy6KuZ4VBLwbeGNE1Y/nYf20E5aR1ANplnZspo6qN8XBTMKCxhvbvI6VdB+eoLDSjCANFLbggLVgSwBkwSUMLsV9ZjMpgGnoHNkci3TPRpTCUDYjv2ncWpPWsR6PWrc9/MxhsJ6fWNgdSQOy3p7Xq1L932HOEls4F4Go1BFUvYaw/TO5B5ayp8zdDk19lrcl+mWaJDEKjnZ1TOmsJVGr6CW7sFAJUYX+qRwP4RN3C0dUZLKYsYYN72zuA5DhHn7FJhjYK/K6OL2Ve6U6KPIcd9g47Y654qeDxoEY1yShJksKPPWsMfHT55g/iShn6rYgSzsLK5hNsDL8VlPFGFgb4Py65qBTgEZEnux3NYrGy5YWViO6auuGpDW1lk7AeCwjXhnwuClBpaRrmBmyTcIWJwJwROL8PDiuB7szM9Fy0SLrAj7zZlmrwRNyYOqRYOzI3e4TEcUJK2IV7xqnrE3jTklzK3RASljflUkXNJxEUi3yoh5Navf0uTACgCfj49oQu+mFbUWgzcQy/yWSRk9TaJF1j14Bkv1CoKHMz5CbBOvskK4xWWNXTVxC4bYa48GImb9QWewLzdwa+2vRHbzhIQeFUduWlCRnSXgUqcsrOrXrapfM1DBpHpoVhndNKx6knCJjf0AcMc9HncHmK6v9Me4yCPuyuDH1qWi+oqEWpst3pUmPC/p6O14xpp8nVS4MbUDkHU7bzDX5BUIx5pRVKS9zwWTAlwpVcxzRilJwA8m1JrQ90ogqIRX4wbjnPF4K7ZzP6msS6oYxx7MhEf9iBfjJQDgcXd0NvLH82M8TXtMqHhRL7CvG/xg9xQ/ePEE09jhxd0WH2xf4RcuPsavfveH+P7vfxvv/6uMfCxgItTLAXkqQKkg7mWez1X+VAj+bDsjBL8ojrVu6lM5gIUz65kLtgpuAGgaWXRPlV9brzC4o5PAihW4ikWz4rYE83Xk8Coghbrc5xOALRWdgxKQCqHm5USLKuDRwDdoD2DWT9kkxW3CFY3YUvXJswkw7jmrVlUnkc4AZFlVQst1tuogB414WA57Vi2KrIbnwD2svtSEvHBALPICAAck9JhPjjdWg7C2BrEkStNS92wQmnTSnXViHqMKazpubBkNxDpHoUzQNI/QT3S6zokt+vlgGQnB6rikoqEytLhgFwx75V6E+6mg0oQD9egpe9Ri8nREXhhcAJiCI2DA1roVjXyLAUuagqhgl4ovAs2xML2ru9K74C46eEQBsBQbcubXXekRK1rdld6jEbK+3O8msNhjP/foUsWh9Ji7I6pOZq7zAVfpiA+UQn6dDvjK8AK/e3gPG5rxdr/DWx+8wu1X3sbFT6Ta1Ak7K7Zz1Q3tpoaHZRHdjt9HdtYKwEJ0XHRw9oiEih6yDvaLyLlW7aCkg7mlYSgyQBXuaSy2cyZVrIIox0oa3afSBm6j3dYUIugKkPFMoE6dGROtf2gP7TWNAHQo6FHQoTGh7HWCalVBIqkLIEsnwUmBD2EQyXhOxOig4uJklfTkdzND+inqYTcMWgCMWce8GfkkdSWDF8QZa9GJYAWTbWyL51QUKDLwyuwQwjr3XSezw+d+VoQGYK0xCEZLOzy3XbzeEVxbvydiZG6BpgLRopJ0OgajYKaEhORR6aLX2oApv87c0g8BcRju0xcz8Mnem+PQUXVgzFI5ikbbJfVdbIQwd8viejCaczIpQGX3a9YKyvD1W5XHqQqQN1VxMk0XRapsVmw09eWRsgkGmvEoH/B8vkLmiss0YXt9wHh9iX4nYzyfY2f5zt+kuuGKnRW/j2d9DvwKjF3bxNIhFEmT1SKwhOBM2D7CQyQv51lY0O8Wzoalw9vEx4ErPewUgK64TdIdVnpgAX/JW2XGrrLrO4GFZTJxhwP3eFGuAABX6ehz5KbZKwzJifMJ22cK6wHwbBHzRbKytLYkgWFoKjxgWR9im6z4EQCAZmcG2xzbUvKyBu2zAlYATsBY82EOFtBnCVxL320dnzujje/n0g2jbToE/wFoPoiDVSSAVgO3CL0yZBPs2rf9JggD1fyUIYzjO+6wpYItFWc3mQh8AuOIfEJKsGMzxlZsJsQ/kAj0WyX0I2dc5iNuy9YzSEwmRapOthQ9C8AfS+f+n1W+NV904oSLPOFYOryctnjSH9z3mGsDwSyodSym25y8yuFlJ4Lst9PGA/RVGWxTTZhLRqmaCUSNzV2KzF1qJaTEOE6SSshM+LRkbLqCLhdlkENZXgnHIqnylRM+Ga+wUebhvhaUQRiMP56f4nfu3sMPXjzB/sUFwMB0M+C3Hn0F3/jgM/zG0x/ht//0+9j94RaPfijZUrVLoO2ANMtdJWbwOOFsYafXsbNY7dy5dg8D2f0jC/ibTpXZvJAC6PuMfVWAkhsdr9rbMliWgf92PMsAjIFnzuzSdRdAlvlZMV2LoamGevyzZsmcvwpn24Nm1k/ZBiJcpwO2ysqyezMB2DNhxx1u6oVX9RhNdJHJS+Hu68ZL5lrFu0U6IaR8dYLkmhvoZQbBctELUpg8kworLqMPzgDjhNFBtaWF2FLBhspiYAfa5L191n6JFzRb+5O+WyvBo5Hc8tO+Ioh1rlk+u62/HiZs0Iy0XbkWKlxJci3lvVxjS+2MYu9Nx6z1E5c7o45U2DdVrQAzKxOrU/2B6g5kFNqXYxQH5cidOzOAMLGMVbVJMx53B49aCLuqa4LDGvEeS8ZYM56Pl7grPV5NWwfEKgjHKs+dpZSMtcN+HtyROtaM59MlPp6vsasbjGr8tlTwQfcCAHBbNtimCb/07BPcfrtietSpc5HAF4NoZwEtD/xnVZ1vpZu1bp5/rZpaVDQ/2x6UMCBLH8ttvXJhxUqjC03o3dYJ3yWrWGg/ixi5sP3oIG2UX0CBLm7fN02Th/bQ7m8ZwIZmLeLQHruKppU1cqe6Q+RMHRtbRJNRxjrTPzS2EIGdjdVRgTGurA5hLDpiFQ298AXgY5y1yJ4q3CLRa5vSoSpLa9nWPwf7HFMtK5a2IPYRRx77ma37iiDWuebyEvccUxRul+9bSolwFMQh6QKAaKmdbA4bWnVA618Yck1JrDHqGgiZIamHxsSy/Zo2VwTbALhIvAm92xU3/UdAQLKNsqjsvK3SovyZuLzMX6SiVdJqVcmPeA6pI9A+ppp9vzMnHEqPXd14Cj/p/q0k+lg7dFTw7GKP8SmjDCIEj0RL7ayokfWzaOcelDNN7ASLzbAc34iO2sar/mLZ9NM+bZ3T712DZP3Ark1ssFmy4aq/e/b90L48rYAxgVy8fELCjgepJlo3ONYeW2VBxgJTMqcUQfYtTc1f0Tn0liYPykdgxebUtp2BW3HbXoGpMcyJgeZ72PdxWfRXetQggt7ALfNhBlRsqaUgWoGrmHZodijqUBmwZOu3cxKWlVc+NGAQTRvY9IGtmrsU3zh3P9qPVnzEVnkdkPszYHmM8Vis2ryx4ayZ1MxCQyuM67bM+9HAfFVCwqNOxM8lpVBE+81vkT+RSrnqjrBiHpbxkcCYagPYKghd0gJUpVsE2EfV1Xo1bfUaVPc7rLXKiRJ02XSzi/pLnJolBbEk3O0H7HcbTFNGra26LzPhcDdgnhOmSXzjTS7C6tK0RGbCq8MGh1mOp1O/+aPx2s/7oPI/CYxpyug+6ZFuO9CU8MPPnuCHx7fwi9uP8Ge+8QM8/1VgfKJi8F1CvZAK7xZ0oS5r5CG0ta1a6DyqzV8LwctFOrNs1Z9+piryJmlSv0ODHi1eE4Cs2NfiuGSfJgjvzKyouYAAdmlWzEmKPeQYkul4qfC7Jx8xghi8+mRAY529YXsAs37KlgBc0YwtFWdjGZNo4oS9glQGYlV3AJIbBhP/NlH3Bjh1PjhVjbjHgQto9N0BRUWBxVB4fro209ACWr55FIBfVwKZsBRRXJYeJ3WcmjCwRR4WDC/WtEm9LhGIdW0tLEGtdryrz9Rov9GZWbeYyhIHywzGgCaOuE2jR5BMgF2qibTUwz7NqJxwW7Z+3az6oF87sANPjRJcnflmlaPE6ciqsVY9GuL3X8vPmtbWhQoUbvKMScvhzpzxYrrA8/ECt/MgFRJrxqHI5GI/D3h5vPDytpZuuZsHZ2RF9sRlN7r+lpyn5I//eH6Cl3XjegNP8x7Puh2OtUNPBd+6/Az9N3fYv6dC8ARhZ11smvhhBLTWYvD+ntt7o76Gz58rjmgD+RkAykAiYWi1rw2sIh2o/X1wMtwQzNwG45Uz4PuwfcblFECwWQdqS18kBbuiAwR8sRDEQ/u5bASZCEZWlj3GVhRk5owZJqoeQBZuFYesOIWMD9Ki2KuliTVNKAOioCBMdbCFiJ0Ras1AFwANZLFzoDXRhPwvnqc1RhTUJV+2BqK09sMpphBe7/Pj1z89w0xieuG57dbC97492B2WnmZ0VIQZpYGLWasmEbFHjbMyp8YgvG+piPE8vMLhAuCCT+hFW4xUI02+Z3WQRLA/a6pQ09rq1EExpsWoTsqh9LgrvX4mf36YhXV1nJvdqnqcY83OyIopjda/3ANxao+1x03d4qjaYZbuf5FGDeoxnvZ3yE8mTFfJbwbnBO4yyCLbK2eAw/u2XO+SvtqYf/L9SePFy4mtWUzc10EJ9vu2AJPAi2CGBVTWu7S2dg4orhfBKrM55ngQ2o8i9vtga770bUvcAr9Mnh5oWR97BZKTehZWdCqhuq5vqwiofgr3zsL0lEMk9CieFQKIr5IgWk4mUl711dIWgcbyigWRLGDcB2AKAI4KjBkwVZl8Th/9l3X1cjl+0/JrQfdzjq/1bemD1qJ00FpGKK2WVwhg1QdfpaU9hgJZCpIVFo2sjaZSWqq/ib/7ftHSJ6Mf56L4wR8E4Nc8iuyLn9G5CD8gPkuXqgfLrc2c0SsQJQWmxH+0YPiktuBYM15NWxxLJwyymvF8vMDz46VWRBR7cCwi0H5Xer8fxvjya6mA1bFklCr2a5zlWZmLAFe1JFASFtI8dpinDvOUMU0dSlHfuiSUWd73WbOZ5g5Dpzpfc4f91ONm3OBQOuxmAXiLpm/aNXx7uMW33n2O+e0JnBm8KTjuBvyHl+9j4ow///QP0f2pG7z6dsZ8ITSiOmSUK63wnjPQdSBjWZkfdK6dKYRF0f+JgJb7MLxYPzZhZoldSyXo+cYJTeWTID2vWF/xu6XNYg/yA+b3aHqkgV7Rv7JslVleLaDvSjhO71J/qUDkV74AH+IBzPopWwbhUllZwJIxdND8dItUGJBlaYMFyYXhDUyR79sdNIMBYAFQVaSFMSggZ2XFJo5AEzVfD5Dxfcw1X7O11hU2hMJaRcjQjZi0Hk1rxaNDobtImpF9x/008Ou+53i9fFxEJJbHHWnOFsUp3AwwIAb0wIMbX98Piwh8UUDLqhoCmhao4KOJtbeIRvb0DTPSnTLdOjfS4gDMNWGuuh9dZhMGi36MVai54jhkWFUpqzQ1V3FAjqXDq3GD3Tx4NapdERDrUHq8Grde6WM3qSgkMW6nDW6nDcba4dW8xYfTW/i0XvnzOKB4GuWh9nhveIVfff/HePUdQrlUdlZKUp52uxGh3jON18DTmzYDu8K2tB68bXBmdqDJAScK63CLLJykW8SIw2oQtxzw9eDemFttn1TgwJWsGPcBAdkYmvJBbflDe2ivaQRyzQ9g6aNadTsX+A7jtVUqNDAjViBcpPxxA8C6MMoKsFV9ncgijc0AGAdiwndxP/atrUOrhz/63AasmX1ag0sJbR5U2VKwl33xqs9z393381svL+Fo1yyzCOCZnTDwz9bNYGfOLSoQanDDAC1jUAHNGTVACmiVrEwSQBgGgT2HWAFR9mVsKStBzuEcKOzHUhRrYHOZnoofizodU81+LKPap5mTOi+awlKy72MsHcYi2xxVEH7PTZclo6oQvoBnV/mIdx7d4PgWUHtlZ6l2FroO51Iu/L79kdhap8DW2tbYag5ScbMtp8gpLwGo5RGetTULgCouj/vhZn9ieuLJAw665/1D+7K2KTyvBYRX3FIME7FXujPf4sCDM3akcmHRTJFB/moPq2hbkBykt7bnzWLebM0ySOz3bK/RBxk1PT1WLGzfpRMfJTI+m+wGaeqa2IkNlRBYXgbqK5bMLwAnRbwSlv6KX8sAchlglVfAVfQD7ft1kN/uSwWaD0UNwEpgDFQ9zTA2C6Zbi4SH2Pe+bpqumfqNXiW9dj7u91Q8GFE1c6OngkEF2wub5qEAWZ3q7N6VfmH/uiQBewmYM4bcfNPKhIs8LaROrGKhBcLMr9nkGZtcPM1wLgnHqcdx7DAeeycIpKz3XqvdMYsGY8oySafEuBt77KdeKhnOGbvjICSDStgfBxzmzu3Vrgx4WS58ntNTwaN8wFubPTaPjyJIPlT02xn7acDEGb+0+Qn+/Fd/gJtvV+zf69w+1U0HvhjEL8pJ7NQ5vSwL7LebuwzCxHbP8ljZcOkfBb8EEAH4WBlwfTgRIDOwau0nWcCfFPSyfbivxXAB+jOnQJr5koqAWmlqfhXcf6KVL/XmkZcHMOunbBvqXPQdaODNyAk3dYsJgpybYF8KA5QxYiq3FJCk0VFD3ffKkEmoXpbWaL2AlVyVKojWDyBgSYxQWApiBLTWAu/W4nIrc7sQikcDsNJq/dEm1Gh54zbwr0EqA7kKmvZVpPCa+GIJn2Mz4xFTC620rTkUjXK8zDmPAKGkY9ogNvu1NZDKJvLWh92jtTNjjCvbZlZQyyoL2rK7Orj4okWVNlra9lg73NXB+zaHZDcLSNWlgm2WVKNj6XBQocS7uVdRXqVfl4xX4xYvjhfYTcNCLJ65aaYdSo9j6bCbB7wcW0ndm3LhzLyeCp7lW1ROeD6LcOIvXH2K6RfvsH9XBmrOBN5k8GYAbTaN2hDYWe3if0F2Vmyfh9RrrriwoXAmWo4loFVYQTD5yqIZzuCyz84C08E7AF+s3nRcN83tGNygFGWUFNsXlpHzh/bQXtM6Sj45BgJ4AwVAlE1rlQuXAusNGHLmDjGsWqClvWu9OtXekvcGbCVlrVZl50TmlxUIsc9lwc9aspiW/nb7ZGyhGCGOvYTgHYBmd4yEkqgxeIPP79fKakNE8MvstQnC1/A5NmFFLVMLDdhaXtsG6q3PsUNZpGMmqn5tDaRiAFYt0NhWS5H5dl049G9Og2lh2bJJwUsAzgQzB8Js2jqdUQIh2Z0Ni5ibloo5PynY3WPpcCidgFs1TCuZVJ6jHdtUMw6l85QWs412XhdplJQRtb9v9Xeob82NnUUAd8rOMkDrTKqGf/bleoX19Tw7K7TPG5fVbpyk8Z30EZyCsO4JSHZPGnw8H7M1cX/uUCz61tca+/mc83loX4qWiaQIFQP72okfooxMoIm9mx8x0OzMq9j2dVikCgNQEXFZ36RMTP4EaKmFDpxwm3dGYMVEzC0rxTJHXI82zNfPVWSXdZKnHBpoZf6K7K9lSBg40jJd4H0jvI9khAos7K1pZAFLx7nw+TRE812spfC3SHM0IJ8ao/cc6UBkPwRYukxHv95yvGnx2SuzMylAqeASCPs6YF8HTyvsUhOI71LRNHbxZbJd38iypYpNKgI8pdLE5+cet9OmAU5g7OceB/U9+lQwpJaHY/fa7NFlN6JLBriJVpZpYtWi6fGVhHWlgBXPCfOYMc8Z8yxpiKzpiOOYcTf2KFWBujnjYphAxDjcDXhxe4m7WUC56+6AyoSbegEAuE53uEwjxtLh+NkF+ucJ6aMB06FDIsbL+RKJKv7ik+/h4us3uP0G4fgkC+uoI5SLXtINuwzk1FjEwPmg/usCL/d99znyK4DaBU3h48SLYEvzYYItrOvtedGXbbtYJ7CzYmDFA/0l+mMIQJX6SeHzglXMcaef3x7ArJ+yZSIMZOCQNBkIxTk4aGW8rFTeKEwOAI/yYaW/ZIJ+nVcembiTyAhOGVNWIdGagWNW2cIM1LqKSEw1jAYgfu/ADGuFvEX0YmkEvE+SfH0zBH5cq9c4yL8OnzBAa03njd/L6ynTwM5vabAaxbanGZfp6IO9GHRjLFWPUgHwwR+QyEZVg21MKkstlOvVgK5dGVzjyllZeqzHKtTbo7KrjMI7pBmbNEnFQaqYqua6ozGy7N64g1AyduOAu6nHce5EaLFmHOYeU8la3lbF+1PFJs++vwhs3ZUet/MGL8olPiuXIkYJSf/oUsFn4xWOtcfb/Q6/9JWP8fIXE8o2S854TuBtD2wGSTeM0Yhz6YZv2s5QcJ2pVcMybVSVWmsphvZ7M+ZWiD4sIg+2/YmjwL587ZDY/mSf8RhDXwuvOkT0AWFnPYjyPrQ3aISFXqa/MrBg7UgVQtXpC5PigeYFcGEBBAuCWPph1H6Kzb6zxkz+Z6wv2/9iuwB2rSs3yTnQAlNItNTQOveeYWLr+j4ONavX12Rwrc6vgV1Y9Rn7seNfHpfBWafLpC+x/52m0UjKXrteXWATRADKgCnTBPH96Lq2zLYzjavGykLrRwGlyG7IVDXNcIbpOhprzhhZHM6blZk1FmVhhVLqcxWh3rmmdqzEbiftXBnkzOWxCmPkTp3jFK7FXRkwc8ZFHvHseofjW6JLwpar2iUga7rhoiKT2AcO79+43QNGUQS7zqS1tzG8sZ9sTr4AtFbdL2xNBK7O2CVb/yTyvR4Mzi3379880v3Q/tNukh3S4aCs/8h6Mn/gwH0QbJcA7k3ZYl8HzVjoVdfOgtSy3eBgSVpkNMSskgkCbFkanIFWi/l4SJlbSp9UZ2zFtgB/dLwyHS3fLyeft0pfS3Bo5OR/635tiYFOhYEjL1lZBmrFzxYU8QA8yEEuoDG2YvNsGG7nvqXiqZByHGI3E/FC41hE8QsepwNMX9nuy5YmbNOI63yHqyTaaNs0uT5WT5YW2nyWyzTiUT4io2ohqt4rHwKt4JQ1Sy+cWZh6AmBVDKl4YMXS1RMxno8CEm3S7ICWBU32GpC/nTaYitgU6VPsmzGuCPDACFcJkljKYR0zprse5a7DfMyocwKUFcxM6HJBSozdcUApCeWQMU0Zd7Noe9l1ualbv44v50u8OF6ge5Vx8RHh+vcSHv3WBn/w2+/jf3zxDezrBl/tn+N/+Y3fw93XZtx+NaNcZGVnZdSLfpFu6AH+2O7zg85pZ9ny18ivmE9EhRuApI1qsE0rJtZJH/bRBeK5HTsH5pZ+tpZWtPiFRmQM7DNCOiIAasBbKmF+9QXM1QOY9TNowkBaDpa7AEBZ8yqGOsETofAel2nElSLt9mPqtfy69Jc1X10GLWNuFU4uIh9RfgPBLL86w/LIQ/oiJO1uLZa4iIoT+9/nnb9FG+zvTR6sqIMVoxjmZAHNMPj7YEQMNIsVDGX7drxmJM0wRuDPUg1NDB7QVAtltW3S5MbE0x9Cis7EGX0qHtUwtpV9b5pVkRZt6Rf22YTjTSvrcXcImltLRtxcxYkYi4i3j7VNThjAXBIqA3dKrT3OHaYq1aQ2nYjKm5NzLB0+ubuCVbza5NmjIhdZQLz2DDG+1j3Ht7efAAA+mR4hU8UvP/kJpl/Z4+7dvoFZQwfeCKCFtBLpXbd7ohMn7Cz7LgJYZ6ISPtEvaGBSWQ6gC+SU22BuIoeADrK1Ma3MYbEoRnLGFS+YV+3YV4wsT0Wk0E/T8uLP+X09tIdmjbCKCuvYzToWWotphi7Yzq3ABQAc2dihLVAxKyhmjCjrl82+MHnU2iftuo6Jjy85WQ1ss9S2Yils4bE3fOKerLHF+Z/7+7wWdbAYbV4W2V68eh81s+xnu07PXLPMXOh9BW2Z0HsfUg6tP6kO3Lawaxj7kCh5ddaxgV2NHWfn0iaCBmABCl6Spf+Ts4H9+ViBjM5kqMlTCWOz+2eAVkxVMSFgA9yKppaY/lem5kR1qXgAT0A74HE64Gkn1XT3RVjKb29vUd6ZMF0KO0vsTQZ3IrT7uQ/Outn9XwBI4e4HAOp1COhi6K6BERX20fbHAbTSxZFpFY/J+q/tGE/YVYtzWL3n2A+F7R5szc9DKxCZk50GZ4VR1cAIq4heOOFVvfC0Q6muXnCZRmdHHbhX1qyBDC0d0fSxcgCeeoguVlFAybSyMhijAlxyjIQrFaIXyRVJmRtQMUDYPgPqwk+JzVhZ69RDqwqY0CoCNj2slrlh7NoIKp2DFjzog5A2GA4lglYOXkHkAA6rPqdwDnY+Q5hAtgyV8wLpB7XZ9rqlyVlasn1d+GGmDZxQsVFA6zofGnhFFZdpVMCvBbQmy0Rx+9NIEnKe4lNZVoexrwyEFOmTDoMGzaumnltRKq+GW5P7HYUTsup3SV9qh4oGR5IMhinLOFoLAYkd0ALLZ8wJXAi1JtztN7i5vcBUMnISGzweeuSLGV1XGvMQohm8qxuMyHhVt9imSb6vQLdjXHxacfEJ4/H/nPFvvvd1fP/4DjJV/FePfxfvfuM57j5g7N/Jkm6YNd1w03m6YWRnAThjd16z/DXph+d8ogWjStP6XP4kbk7kkxgPklSAZrNVAexK5HZoofvrO5JbYELwsgP9ytjDtX3v2Lr5SsEmev/1ze36A5j1U7aEhBzAFmNlWUqg0WqXgooZo+otifbUrOluYmwcXacJV2lsIBYIE7KnLsYoCNDAJDE6DcBa5IPHyEnQeDJDsjYYZgym9a8ALe/bzv3c9v4dL9lY5xhWaycpnt0a1IrH0M6nGSrLP7fIjV0f2VdaLLMKLpeqJWDXaUuTiMDr5H8NZK0rhSzTGMmrDVqp2rF2zoKaa8adsrYc5ERVUKzHrmzkXDhpNN2chXYM+3lwR6JoNDwnYV4dpw6f7S7x6m6Lcc7uZABYpIjspwGW2rPNEzZp9udqzxscdMTZ0ox3uxtcdUd8cnyEHx6f4nF3wJ/5+g/x/JcTyqVEHmqngFbfgTbD5zsZ60jDF4qix+hAe40gE1Vqnp7vL0QWVrtbRL11MCdfPzgWntrBAaDSbUp79e9q+ExmNCy/XIGtdRTmoT200Ej/AUv/V5KkycdqY1YBTajXxq5MFR2KBzY6tIp7UjkXDsbY5HZGS0WzZgEMBin40kCYdrztvQnMS39tnF72KevHgIq1NdB0H8ZgIFT8KZ1jWMU+zmEEq7nYyfuYYmggkgwtS7Ar3gsDjkzLxOwRKWvXBNIttTACWfFaRZDK9mEgWKxi5WAkk4uyL7Qv1baMtYPraqHxyWIq6aSaJu44chOwn2vC3TTgOHVih9DsC6M9B62ioYBYVuURgBYvkH11qKLrk2bsy4BXZYtNmvH+k1c4vEOofQO0kBM4pVbd8HUtphqe3NHP3fhMWmDoU8fx5QOjDgHOgFuwbZaLzgJXvFru+14FTOL6wTmQbRtz7AQUe2hf2rZTmRKg6bRaqxBQavC0wTZ/HTQtrYCwL1LE6qZceDrioTb/QwpXNeFsY2TF70de/j6tyJKkKrb0w4kzRkildatw2JhI1VMK49y+Vb0jD86KrxRSDiHaVJZhYn0BAvrZPPec3xFTDW1ZfLWAPNBezefpwa6pVdFSGRPgDKzICKt6PIAxj2mRNbPMqMnOnBvUbzGGnaV/SgBrxjZN2NKIy3R0wGmbJmxodl2zlmEiaYUWnL/TwlERNAQas8pS0QFJlze93tmqCFJ1VpcVEzF5lCHbcZOnIZIG+bMGRSzNsKooPKC2qYTxthAwJnlNDFRCnRK4yrrj2GEuCePYgYtsm5L5g5LxYlIv1diGIHy2u0Sage7I6HcV/a5iuGFc/PYG/6+Pvotd3eDd7hX+yld+B9M7Mw7vEuZLBbOGJIH9vgNSWrCzToTg75NVMemVL9pWABEQg/phjmb2a72PFNaj04A9U1vH/SOzQerzObkgisz7DZQUQ5tAWWohE8CJm0bxuepw97Tu81d5aG/SRm4D08St4t1BB3pABprK8IpFNigfao9tbtpNI2cMhLMgDLCk8sYqIpYXHQEraxJ9X+aIV7Avt2N3MXdNLfTJL/FiIlwt2qezNIsTN0BLfiRuCGgJZlk0I6YRxgHdjkfWbYLzUnKYFtGMc23tKLX8+4oajIOUIBYQCAAys9Ox5RoUpfkWF0A3dkPTPpEB1/ZTFHTapBkbFVKUtIrklQ/t+MYq6YCA5JR3WvnwWDoHEE2ksLKI7JaaPJo+1oypSG64MLOaHlYpCTlLlaxSE27GDTZ5FidGIyL7qUdfE7ad6HCJwOMGL/MlrvMd9nWDbZYo+aH2uJm3+HD/GD/cPcFvvPUj/NrjH+Pf//oHuPmDR3jyvQqaK+pFjzwVoFRQ6cHTsrKmPCqvH6Cp8jKKYA8J1Hms1HKu7DtzHmqIRACN0uqTfTF23h+L00eFwVmjD6DVNuEvDMhUJcJAaouIF/ZDAKwUurGoAwPIAEcE96E9tDdoZt9lDiIgRAK7JqJ8txIF1zF75uSpbsSMGYQO7fGzn1xk9FgjNMat2Zgep79jwlKs3fZvy+3YI76cqGlAUZij2k8wpj2Gn5+vV1nmiYRTNr99F9MIYx/xtQmRswem1qn963aKUZBfh3gVOlQZqohxgNjfggSwnJklcdq8YNY5RKKg6RePmRvolNUBcafOAaV2DCU4I+ZkGGPPqxIboKXphMzkA2jRZbM6FhyOh3U8tWdyLBk5NeH5RIypJNSkzC2de4y1wyH1GDBh4g6dOrXmFN1OG9yMG7x3cYN3N7f45L1rjC8HbJ7LJJ+7rJFeBnEGlzOzX+bXBlVkzNZ1WM/KjIa/hptN8PU82hzG/RNbg7A9pD+y9cAgDrbmNbGfhR2j5bPgxxb7oHZcfsgPQNaXvhm758CSbG4sqX0dcJlGHXPEd1gHZa2NBk5RwWU3LgK1u7rxnRgo5cF27twPkbS5CUhQcKACyg4Gmr9iaYwDCkzfNuvkKupF2bgY0wcjuGLfxYqAziwCYdSUuHWRqJ6qj3+iMcUnvkq8tqabVX17eW+phPnMNlEXODY75glS3GWCMXWbH2bN5AB23HuQ6oAsTLgAYo2c3Ud05hxYUkPRrv9eA+amnVWZsEmzF+wA4L7JXLPb4ImTC7Z3qYbU9aTC8S2t/DD36FLBqP0ba3euGWOR+cq2m9y2G8PLmL4AkBKjEusYRsLIsuF6Uh1FEwovBGQGCoEnCXSgY3AFSmaUMQPHjFoIcycsMLvWEmCS5/hV2eKT6Rp9LqgHQneooMrII8CZcfEx8Pvfew8/+OAZrrZH/MrFh/jGNz/Bjz/5AIcnCfmuggqhDhk0dcA0g2oCkcwKvJnkypq19XmtsjxQDhyx22DyLBb1NQzlMbCIwgQpkWxrts9ezT9iA69kGwLAmdzfaedh6wJMjFRowTo+M8Tocev8rwCWaGRphrXj15nDk/bAzPpjaDHdT4wFacpZVcqtDOKX6YjrdFjQRI3mafpMEpEw8b7kxqlHWQzwSdMWrWVUDGgpJetjA5ohMlpvFFq0PPPIyPKURN2PVNzQQfc112Mt5m7N6LrnMIsM9qqImdjBKzc64TGP0QvT3WjphUvDVXRwl9TC6mLucj6maZV8XUDyzStIc8ml4p9RcI+1Q0b1ksIRRDOm2FhtgM64U10qSd0gdzAqk9Jwh0XJWgOwDkrPLVq5cK+i7qPmmCcFt0pNnh6SlFY714T91OPVYYPbceMVPqzdjUIjv503Wk63x74O+Gx+hFd1i4mlmMFPpif43qu38aPnT/CDj9/Cv/nsa+ip4C994/fx2Z9hjE86IAs7q1704L4Dhl5EegEsdLMAvE4I/o1aTD3UtogQcIuIuzNo7xd6W6FPzwtfRsHX7KzWj/ZbZUCWnO+WiqjzQT+mFKNJsOgDLfW2HtpDe8PWBNyTpyaYcLuBHIBM1DY0L+yBsXEmB7yalkoUis8K/9tk3NIWrZlG11qXMfrNxvGJVQmX4NUy6uv7D+BSCv3e19Zi7u0YG9gVj8+AreSvDXCzfa/nbG25ghIwfajlsdjyzpLownXrDNTj1hcgVW8Zdj+TOxkM0Ud0oHB1D4xRZbbLGL3maDQco603a/ogh+O15Q3QEt0R02asBsTAItjyaimmUr0w4Th3GFW30YTjAWAqCcwtiDNrOstdHXDkTqLk3OG2bvD8eImbuy1e7S7wk7vHyGB8/clz3L3PKNsEJJJ0wz6L0xJTOaJuln325Xon7fVNmq57NqodH/Rga9q2vPh+2af1u9o+sLPWTCoD0BBSNxDWdVu3TgMxO/YQOPnSNxEhb5XUs7JBj1XmepYdAsAZuC/LxUJXaxM0YwG4jyGB3Ma6Mm1e0WStDmIlDcwDkv5mDK2JT1mUxgiS9wJ0W9aISaIYg8pT27mxskY0TbBJMy9iVdl2XU7d48KSRWG286DrZDplaq2BLFtmGll27SNrK77e53Bb8KQRF5r0CwD38QY0Rm+miisaPcVz5Ixd3Uj2jsrc2D2y7Y0FJ7paEzZaCABAK1QVghtJWVcxmGTBdWPWxqJXFsgwMGrmhJfjhQTp1X68GrewYljG+Eru37UUxOi75U4GO1KfBgB4pma4402ZzAZAQJkxoU4Z86EDj/rdnJA76Ws3DyhMuC0b7KtV/hywTROut0fMl4yqVWXSxEgzo7tjXP5+j3978zXs6gZX6Yj/4p3vY3pvwvEZoQ4C5tQ+gfu8ZGdpO2FnWQs+yNn2Ov8oSK5Ee+BjfmBcndopBLvDnsHiPk5Y/97gS7R/OrnimIYVjoXcX4Izs6xwlh/PF2gPYNbPoE3gRSnXHQ/YVflhvCiXQsElVlRc6J8DzVoit2ljReDKWmRhWTVEE2yMzK028LcBzLczlD4wxgBgQKPaWj8mCF+Z3HDY53UUxAb6c6mF62WL1EC9VtEwGJ133dZssi1VB7ac1qzHFx2RuE0E7yqnBZtt4oxIze2Vdnu0Soac3Ag44wkCNk5VdLL2ddBJghiB2VMykutmTZywyaJRclda1ZgY8TIqbiLGVLNUEkkFXaq47ERbYA7rH4uwtKYiy4gYpSRMU+d55gBQa8LuMOAw9nh5t8U4d4volKSJiHD8WDu8mje4KVt8Ml/r4J7xaXmE7x+e4ccvrnH49ALzqwE/+Pgt/P7d2/ju5Ud495c/watvdSgb0TGp2x68VUCrVyFE+iMON2+inWXva0szVM9zWWVjZQhEKyuKwp8Z5BfHsvxoNNrXfb9wZKJhqYBXW/yCA/dD+/lsFcuosU1cTVfRik+wTl4t9azXKkjOJsUpg3edImjC5FGPS0RxBeRoaXBpsR3QQBP72VlQIu6LtC9mYFYgzj4v5CIQwZjTdm5O5tcrOBj2HeM09VCOfTk364I98fmyHh+Fc13rg9kyE0F35nO4ZpbqQcQOQIlmFLsAvqX9zcqeMBHlma2apFx7AyVZ92vppIQWWQcacGb7t+M0B8K0rNbsYQBeEcoYyASI8G7J4NquAHPCNHeYS/a0wwjyWcqjBF0ErBtrh33deDXNPQ94OV/g9rDBvO9Qjxmvdhd4MV/gWb/D5Tt7HJ8k1KxglupmCaCVxWn4ohpasX2edtYKeFoDUc3WLLsltueIF4GWNz8u4HWOzKK/s68/xTV5aP/JtKzj9YiWhmbB8XVr4De5dquBQlLRToIZmzR55UPAmFYN9DDmD2DZKZ0H300eBWgB40kLWx2UZbQGzdaA17kq7LFIlehkybFtqHgKor2eA7Xszz7Lcan/BMLELU0wphLGzBFLEfSUQV3HAvUTA3smr264Dt5HAoEAUlqROByzFJk6/7s3QoLp/2aq6CF6t6/qdkGKsHsZ9dMiceKqOzpjN5NoKrq+L7iBWsQYUln4K7Ks6e7OnLGftfgZVRzU59nPgzO3xpJxM22xnwfsZ6mcmamiBA3GyAxPSQdXNdTU1+UNSQB6HViZgKFKyqFqbGHWubb2ScSYVIPYrsUTzUJ5nO7wXv8KjzcHpIkw3Bb0+xn5UNHtK7oDY/Oc8f/90dfxolyiIOE7Fx/j3Q9eYroC5osWbDFAi7PapXNC8NZeJ7fyRQL9epq1l7/5gjFfMJRX0QIeXs09TJD8gp/pNBkTLPg2bEESdvYXYn/2lhC2XX5vlQ193cSiqfUF7OMDmPUzbKILRZ7LvKsbAAKSRJHyNuinE5F3o5JaFUPZpjqanslMRHUBeAAutNjExc+nGtq655oJxFqLYJdFR9Yi8bE/EwheGok2gK+FEyuwAAGjsTgRWbxnIlaYWgleB9iWxs4iPee3T06RXiwLkSFzEC/T6OLoFt0yHQJnEFATzTejYLRcSx1MYMyccDNtcVekmsbEyVlbs6YjTjWfapUAwrrSfZhW1lwT5jljGjuMhw7zlOVvzmAGprHDNEkFj5wqNt0sVUOSHPl+7HE7DXhxvEDlhN28wb4Mrv2WqeJxd0DXVaS7BJoI5S7jd1++gz7N+Evv/z5e/uqM3fu95Iv3Amih74Aun1Y3XLcz7CwXgvcH5nMMgLOfAJqBNN9fJfBewEo8xKUIb4zA2zrMYgiwHNSBZaQcQMjdsj86jZA/+BkP7Qu0ynDwZ1K2LoAFO9QKVwAClHhlIqQAiJCzgQG4flNHxdk2Xt3O+4UDKLbNunkVv3tsDdHyuwh22ThuwNayX+j5GCutjb0x1dCYWAjrlzNDCd+z7rlmE3lLg4wy96aZFUGudTOmVgSJWCfsxnoyAKwncRZMR8vO0/Zr1yJWNyRbj00nre1PbE/W6yCAkrGLrbCJsbQYDfgShwKtipQuq1XS2MucUEvTNAFDlhfydHhzXgykm4qxjUWra1Q7Z9pvCSpOnxg0E1AJPBE+O1wiU8XXr17g+G7F9Cg3dpaWQEei0+qGJzdiBVIhRKM/7yEAL16cIVX/CEP4ym68bt+RWfzaddfUQ2AZUHloPxftwBk3dYt93eCmXAi4pGC4y56s5vGWZhUD4RIEWT48A4lUxaFKOQtLzRpDYFiOoXemVvQRAJljW1ErA80MZDEfyLIdJmXwWLN1TS+rHav4KxOS+y1RS2udXhhTDjMtNbnsWA3Uin5KlERp6egKXi38iJZGOLIV7QoEIl2314BHBAYj88x8r7Ufk8ENrNLjtuqSkZFtbZsmjJyxTZLKbfpZJgAfGbQJopmVqGpAvYml2zJj/5pmViLGWDocirD/tnlCRxWPesk8upt73M0SNN9Ngwi+U8VUst/HY+lO5hM5K0A2Z5SyDI6IQSJFbiC6WbZ8SvKdrVMINCfQJHbqeCfZKmYzH3d3uEpHjPq89Zpim0ZlEU0V+VjQ72Z0+4p+Bxz/8BE+mh77tf7O008xPquYLsUugSCFsfoshUqypBr64XtmSn19oOKc73OOwRXe336NcPFXPsaf/6//J/zX/7v/Hv/5X/kPGB+rvaTVdnXZB1V2lslCX7gGW3mOcczBr3H7wy1zRTKN2/cGTFac9PVFWcQPYNbPoBVmB16OaiwmFXgHgAMPnrcOwH8oBnwZO0go9gpOkQzyVmHEjIQJvAPwV6s6t9DSwpKNZc1EFYFlZCM2A4esWXUQr1RIvDBQQBjcNSVwbTjku/YaAa6elqVrY/qhv8IcHTu/lREyOjWaCHE8Vu9br1EEC21d+2zaZc0pWP5M7NrEa9dRwcRSscOaVfAwvSyj8AIiqngoHfZzr4BVu0+7eRCQi5tBi5Rq23epCaVqGsictVytGuJZnIuiVT1sFsyaBlKsMuIshqSUhP1xwGHuMWo65G3ZLM79SXeHX3r7E+CdI3iooI7x2e4Sn0zX+JWLD/HtX/oJXn2HMD7uJCJx0aFuB3E0utxSQCzV8A0G8UVbAV6RnRWF4BcC8AWgEh4uHZDP0D+W9Ftdx1MFmaWvkGoYrSqZJwsNHvmgj+bsWFoIo6UeKtX2wdl4aG/SRHNJ3ktaYVpErWcF3yPQJDqBTdDd0vdauXV20MOYPrbc3nO0GVgCNms2lrW1Zsm5n3mmZXpeRyJGT3ZchFWvzV83Ju45wMyukVUxNIDLbE/sK2pt2XpyPq/fXzwuSzNcsNtsLAYtnDxJzWhzgVY18lQU349lxaoSILP1aRpXzg4O99HT+aoBVu3IDUiy4ZDQADa/r6wAFgvLt2oAxb6vVbWxqh1nA3yqMrpKVXALANeEac6a5ijMs5Hz4hw3acazix1wWSSynoC7acC+bPBOf4Onz25xfAsoGwGuuE/grhN2VgoOg6UafsGo9tl0RB3PF0VHeDnOnwWmzgJl9gy1zxF0Irdr5/pcLTB7FP4W4FdMR3xIMfzStwmMHXfYOZAl/sFhAVJJpoEAXoMzsg7co6dZdKvUT5GAcGNLSfA3ud5WzAax+fKoKY573rSq2KuHzz5HoXfA2EZ1kUmynsNLgSaZc8cMjQNnZ2O1cxV/pgRAy3WSsJQwEXH4pRbkaOw1v77k60fx9omB7RqIUaDKgtoxkyVKtEwg95uiFq/1AZi/dp6hZqmEQ0gBlVTC0dc51N7vkQXiL9OIJ91egydN66qCnJllwYWYagiIxu82q8ZhSCvc5slZWmPNHsi3rJJj6TwAX1gqrQ+pgNXHMTuQU3teSiH1b9rwx0X1shK7A0ojgSZCOiakQ5JgSCFgluWNnSWpi9t+FoDNdItNT6xuYFXluYezhGgqoJnR3RURg3+e8Lu7d3FTtnic7vDLj36C/P4d5ssAWHXCHmZjDVuxkjcRdv88u/WaVMTpCeP/+M1/jf/DO/8jbqYtvn/zlqfwnQvmL9hSdqHdx9J1ajhuMs1IXvThfo4uj8F9pqW/Yz4Q0EAuqgDNJH7bFzDZD2DWz6BlkgGtAthxrzpDAhJYFQlL9ShMKiIu6YWH2vufTXibLpQ5G9UnxEZtNYaW7H+Jwltk48BLanGLzDQ21/L7U2CrlS5dalAttLf03E/609fpNQ9kT6efjdJ7LhoSH9hosMw4mXji2sFp6YdyVMY4Mp6bgYjmZFjpYRPJjNctsrLkmGVi0FOVVEJaGgUpg148fWOs2em22zx7lLpPEonqdPsKQpcKLC+904iIpCFKeuFUslTqmHJzMrJE6utM6nyoQ1oTuBLujgN24yDVPVQ7KyXG7m7A8/0FDqVXAC1hr+zCSzriUT6IBsuuR77N4JmQU8Vd6XGdD/iv3vkejt854ParGWWbUHNCvezBm8HzxSnfM+Q4u2p53+gc8PS67YE2qa+QaobA2bC5RxhqXMZtoA1OiJew1UHanI24TwesanMc0gykqUUYvOSs7U86fwCzHtobNZOIYFgaiVRDJRgQVLxiXtSlMDao/XmK4AqsMlArgiutvl0Drmy8NXbXvEoLWYMz6/H4Pha9bWtAln2OwJLORVf7k1Ze8ztap7Fnaoz/uB2Fv3X/8VwccFvtJwJcgAKMfhUbCGWT9k6dNwO5osPCAGIVXdMbEztXF9pZrPvsNN0Euu1UZS7SUdU0+KRpiKyApV5zMhZeSysR3VdhYklwRJhYXunQmGoKZLUMPdVjK1KgxIIvgIB845RxmCTQYymZZl97mjGQOE88JtBIMpZSlXR9mvH1yxeY35oxXhNqR2AFtCSdnZaA1n1t9aycsLPedENddC/b19fh1abhcwC45GDaNucdjwZg+WsImACraHfs+MHWfKlbYQGpXpQrmKC6yWZEttNRixlZCpoJr9scX8CP3IArJIzctTlhOuIq6P1K/yLTMVBZBPABuK6WNfOJIljVxoCiGl1L/2NYBOkjmNOC/b3OnyOw5dpTIXPEWFXGArY2MVyQPUewC+RAljCx9JgV0OopBvWbvYm+kaUkxsy4Q7CdkZ3VQ4L0FpA3u2CyMrLvFpSXhM1W4Mor2uurAZW9amfavdvShMs0IqPJq3Tq09iYe6wdOn2fFRi0Y51ZikkdSo+5Jq/aDgCX3eiZJsbgIpIK712qInGiAXTRAu5xVHsxqxD9PCuImkTIHYCMd13Veb7YB5DM99MofzbXplEySdKomRoJwFDR9QVZj8GO2ZiLT/Me2zThIk8YH1cPZNuEgSqju6vo9sAPbp/iJ9MTAMBb3Q7vvnWD2uu03uYwiYAuSarhFxV7X7fX+UMWBKmMbkfY1wG/dfd1/D//uz+Nj//1++gOODv+ewA/7MPtjgf5ebF+PD9AWVfBT1oE+zUFcWGTCAtheMuqOTvBe4P2UM3wp2w1eKZC7ZU0rTGyfohRGT4ITNwhMztSbi2xCulSxZaOPnj1UA2soKdVOHkeeuUmag5qoBXQjIAMauXEOMRXwHKrlywgFwheMZIK5MEtkEE4VhiMgr1NM0ReexKDkek0ULjOS7cHP+asR/BsHYFZiNibgdQjGCCRj4LkumMSheowUEFBRUoVieW+bNLk0SwTN5T9NP0sA7ZctBAmyK+VIplwVP0ru3azVwhJQIL3c1d6jWgUj2hbFRAALgAfQUciloqFFh0vhKS2kbU8bZ2TU3Epy+z3OGfPb7fKh/Oxwx2EDpyo4iKPOKiuwTZNmDjj1XGL7kXGxUeE+tGA21cdfuvRV/DLlz/Gr1z8CL/2rQ/xP//42xhuOlz+ZAJxQrrowaUok2rFxLJlKSk1gJbfn3NIrJLHue+NQaWT+kCYanRZmPPe9ieD86qvqg8pR5CLYHMP69/eo7I4UOoJU9IqicHR4KQDeiFw8FKIz5TrfWgPLbRYBWeGCGUbI8eajz9oTKsKdsaWtQJjPkmlVtFnkkpM1R9S2+8pi4sgeVUcJrUWuZXHvx2tvdZwXNaX/eTtp2djWyDWyHIiB17WFQYj8GSvNi8zu5HO+PAx1TB+jpIWETyL25+I2GtPdp2yXiMmAqMoQFc9BaSCMIAxqZOSqWJGcnzDgC65R9WXFVA4Vx1f9D6SRsbNFhmjyq8rGfgI1WSsHgk30CzeqwU4BYj9SBo5ZwGwyChwTFIxquoM14AwEObU0oFYj6WWhAnAPEgqYp8KZrS0lQrCce6QDgndDuCcMR63+Hi4xjv9Ld7pb/Du0xt8evsW8jGh3xW5Ir2UiKWZZbCtYYbhgzU1WwP24z07g2a0mfu5gIgzd9uqtqHbGstLJTkGqWh1bj/LzwQs9bcsLsM6FjBZlwH8Cn9m4njpdLQVH9qXsU0AbuqFAxOH2quWErDnwatxF/UmbY5rY+q+Dpg44zKNKvYu7CxjaS1T+wp6tFS2qmMYFKiKjNSBCiZkYXkhAwwcIMdYNF0xat7Guf3EGZMuH5G9CjkARE0pS8uLwXf73uyOpSBadUOgMafsO2NNRXAqcQC/FseWNNMGfiWir9KuVRuL4vItFYyc0BO7jyj3MTmoVUEoOoEc0SpQSgpjxlU6ehrioUp5MPtOKhxuPI30wL2z7nqt0m7ze9PBMjaesbMmlV4BsusCjyFI4uM2id9yKD06qhjy7JXT5yoV3q2IFQBnaHlVZhIdq6JB6HHuZHxTG0VJAydmZwotxz4oQMIMVGp6uUyoGxmkOTNSX9B3RTKJlDAg6ZZyfbaYcJ3uhK03EdIkldq5EzYYE4kQ/B44zB2ez5d4US6F6bY54OXQ7i9T03aklBpdPLa1VpZp7Zzxb+5LoV9UJawMmsWm/+dX30P6K4wfHZ/g//3f/LkTOxaDJd6H7SNWS/T39hOXY1kAWsyoicI6EEyiX2/bfCHWxCGuAGc4Cy7NCCPL57cHMOtn1CYGdirEawOzTWxtcGqMrOq5zgamTJyRuDrjqvJKFBFLoEjSEltu9KDijTUMciYmL0YrexTG87jvIeZF0CqCWu5AhIfXKmj5fsLrfXopll8em2lr2WvEKyrgA5X9xuO1eJNmx2Qg31U6LphrPc1IwbgBwEBZIxaK2FP2KHlixlHv28wZKcz67R5YVahM7BUKZWAvDmgdFMACpIxt6lo/ETADgKlkFwh2J4SX2jWUwpxdBRO56GR9TuAqTsbdPqEfZnSdssXGDpRZyuAyOSC611RDA13HkpGPhO2nwk4aXhG+t/kKfuvJR/irT/49/so7v43f/uZ72D2/Qr/LoJeMsu1AYwfMBVQrqBRwqTKI3Rc9D4M2VV6Wdl2l93ElIOt70CLFj8THubf5RL+qc0iaPsNtkLaBdm007dh8QDfHgbU/rdJheDUVM7TLfv+ouvgP7eezCWu1pYyt0/kAOCOLiJ01ZQUwhMnFDkwxL/VEoqttqYaWegbImBs1sxJa+nYEqiIQVFbH5/sKoNViDuU/krZ+DUe2fr3vJ844nTeatpa9Fl7+tNd6Wu26vFkzwK05T2VRuCMZ301BjomEMdyjgFKzu65RBdJ0HhmXF4LzDlYlPVfRPmHEFHVZd66tZPtck9iHcG4RRLPU9BhojaFY8kCAdaADqd9wkrGOEuaRkDoJFAGSAk+kjgmsamLCVEVDq7EoEqgA3Z10n4+E5901Ptrs8O3tx/j25af49MkjjHc90pSQD+JsUFE7pykPzDauv+6Oxcm2ndvacWigVnMcgjMQbcTrGodrSgZHhssYb0po/l20S2Z3TIskpGosyJJmDx8wrC99u6kZr8rWWVkVhKn2QILrUm3S5MwkQMaPPk3uG/RpVJB90mp5yat/m+8RBd8BeID4wD3AS39BAiSzL3PB8jCWFQtTc8Jkx+EVxk8F4ScQtjQvUhRtrnyuABQUrDLGljG4gOartMqGTQLlqL8dWxYd7C0xMqxgxhL8Kgw/DzlnXgTl4/FlYtXnauwrC0BE4CxRRVVwUs4JqBDtsS1NupIczISMgQoO3GMIvt8Wk88FDFicOOMyC1PrWDvclo1f57vSO8iVFbyq3HR7DZjqUsHd3COnim2eBNBiwpBFg3OugxckmYpcFSlytUo/JRlb57KcGHOVVMN24dTeBGF47li0sSrAiducPOl3DKCryH3B0BUJmFArmmLXSdjB6qcxkGY5RgOm9OKgu2PsR9E6/sn0BF8ZXuDZZo/vh8eV1I5I5d0OdBwloFGqaD5qgMOD+vc08YMIZwEuYOUXyd+kFS7/hxffxB++eIo0YhnHOAOOiU+FEMkj94+Q5BqQTpq4o1V/+kKtb6ZT4EsC/M2GxomcB/6/oAD8A5j1M2oVrUKH5FInWOW7ogwgoWx2DnYB8FzuiQWNrpSwSQkVAnBs09Roo1gyj2xAs/z04fSwFoO6CSnG6EeMXqxF1lOIVpywoBB0sbhRbhelb2mZe/66Fgf5NTsrrb43oxHzz+2cxuA0LCuCNLZBrGC4PAalrWrkp1LyCbinOdTOnQsXemf2CXj113YdTLNkzWyrLFU7hmQAYnUGWBNbbPenz82MZqXrAuIcNEALqHPIL9fStOiESQFlayEx4s+/TuLcpNSOsaVKakGC2uH2sEEagf6OkccKqgmPfi/jX37wi/izv/YH+M7wMf7sN36A/+En38X204Run0AVqBc98lSAUiUVpJwR71hEJejsIH1vs0HZaKyBjnyuGfAl7tQyurBcLzg3aAbFB+IwOJsnTCawaN+tHA1AjSsp2BYArof20D6vMeB2ZSEMzsKZMhCrGvCuD5dgtsomVe5QdlCloKeKmG4YQTIfa1iYNPnM8xqDFy09Eb5vSx2UzN5TUMscCWMcxX6t73g+yz74zIh+vkXm1ZqdRavvDeZYzbcALJ0M+Z70vUMVqsNyOrhEQKpHFeuq13hGBqE0fTNl4JBu56mhHPenx8QGlS3Pi9lEkPX4SQHJ0A+pTWGmZvd0n9ZfNUFdvRBc4QCXPJgktkUvARcdC+fk5sZsk5dYX4CU1HR35g5UgDyxTJ6ZMDwnfP/RM3wwvMRbeY8PnrzEh7dvY76TCDozwF32Kk1IDJR74rtGafJJewO1XtviQxEv8vq9XaYAfL22+/Ux+H5WDodf3ubELW44Y2lr9HsPtDzYmi91E20mCWwfTcJEfQxj4JSSHLyIzUAtSzk8cI/M1UGlTCJxAUjaoLCCRi9e1VPBQEUlVQTYMj3bXd1gS5MH5teC80lBs8jzOtSNB+WNeXSFxiqLmSUFhAF1Mc82cGhAlSqHAeSXrJelHwMETSsKYB8CUAV2VldhAbwKNz9FBOOpVTgELaod1rCsovlMZtssLdIY1pFVNqACGogXQE+yTCwl86BZJVYgrAB+/XqSe2DV2JNlAWHCkZqQfJ8KUCAV2kGLKuwdmd9asc2W2dG76PtFN7lYvABdHcaSgQx0qWKaJe3cmFqVJRXd0v2K/s1FfKOUKlIiVHso6pkBtJJmnkANuAycPu8GGkOLAOoYOTPGOWPTy7W5yJNqWc+4pIKbegEAuOpGdHdyg7lTVlWW1PbaJ6QC3O0F+Lstm1asQMEaDx4kSIphJvxMqu3G7e/pK03Abt7gdw7v49/962+juyNc3im4tk5VVP0r1gif2azzrF5ddsIu064UiDKfiG2Z+kCeMcOnpsjuE2fdx7n7fU974AT8lC0hKVpPCx0mo/gaO6uVqU3L5UqBvUwjLlWsDxAW14HFEI2hNHesXtijaLncsgBn7EcJLCmxUUDwXHXACHwtwIwQ7cirwRXAIqofK4Ss27mUQtPGilU+MrX8czMQ97VzzCw7hnjei3NXIxsZRzFVx65lM8zFq6xUiOaZCbRH9ly8buZkTkrHXXzHws4a8ozLTu75rCLwY2nrzrWVjTUDPWuFqBIcEARgy50L1iiGRS/m5EKIfMjgY8Z8zBiPPQ67AXzMqMeMeU4eqRAdMCnzu68bvCwXyKkij0CaGWlidEfG5jPG8fce4z/evQ8A+AtP/gCbr9/i8DZhvkqS7dFn8NDJgN5l0c5aC8F/0eYCvLx4uOSSMFK8Ne6NsXuqMQfctLNi5UIycMreW1e1vUqEIXSzcjrs1ZliYds00/L7h/bQXtNIQQqGpBwLbtvSDKP+lWlWLZcLMGTjmbWZE2ZIkYrZwDFu/VpVxA5WXa8946bPZftZCL4bWHLmXCLwFedVjl/7WfBi+7hdBLnWbb3UfvaGcfgcU+ambR78Gltz7is7hqhbFTXHGITZQCm1K/H62Xsr+GLXummRSSVcRgtexW3jNbYKhnYcBlQlYuRUXbPRUjriugtNK79mtAQWfRALF9nYWPYeaM5FFXuDklBLQpkz5jELQ3iWlHi7v3LtW4XmY5VUd6uAJOOlsLTm5xt8Nl8BAL66eYn8ZMR8IakMMhFOQE4SxU4kUe8/qhB8uNraweLhsnF7cWvWwBLO2Bq7jPGSRruz7v/Md2uAa7H7xb6Xx/dTuFEP7U94q2Ds1G9IVLFJE67zAZskOklTza6FNdWWXhir5N2Wrb83SQ3zVyxl0fSzKqwK4YzLdIRVXr+iEQMVZwsZuAa0cawF9JfVxG29guTH1ZhXkhZZNe1tQF34JQZQSb9tuVU5jMutSRVA8iwKA54OTCfSJnJNgIGqi71H38XWj2ysKPTuPg4aULYumNWjVVWMJIMBFaMy38yXuU53UuUeFTvVMrN2lY7Y0iQsOLCDiT3NSAoiGrC4UQBT0iWTs/cSWApUgRykssqQlUVryqogmgD8XDO2CoANacaQiwNXoinMi2A9ANxNqrc1Z8ylpR2a5q+ltgM6fI/qzwDyqkFsJgaVJgWCBPCgmR0VoIsZj57ucbEZwQDuxh6HucPNJNduUlkVS8WsTBhetCA2d4TSJ9RNEl3gTottcXLAOBN78GAxESGAU2rMrnX7o9ilVcX3ZSEsmYv8+sUP8H/63/zf8b//3/5/cPce37//k+NZHZMRBYJvZBItJ9UW3S5yi8+YuyM49EmhrFRs2cqevmF7ALN+Rm3NzOlpdnbWFMAoQHLSDewScfDJB+32yj7QxMqIZgjid9asakg0BnIsIV2RmqC5lXw9cMaIJjobIxctPa+BYK28bpsQx1czDJNSgYHzQJYN7lYhK35n3xuQFY1KfGgjxTge16SDS9S5smYGN143E1Jcg1yJKgaasUkTNioKb61VomyDgwNBaiAtl3xQ4fbYbIA/FClZaxo3Y+lEK0udmVErItoA3/YlzkrOjJSrAlurC+1eGy9R7kLAlFCOAmyZ85GVcnHUY9jXwUHY63zA9faI2gNUBMzKR0a/Z1z9kPDff/It3NQt3u9f4s9+5Yc4vFMxPkrgLEagDhkY+haZeNNBdd24AVIny+01OgEE1KBz5QOxXR8TOwygWBSC9+yauEtevVoL69ogT5pyaOAaVUT5u4f20L5Qc3FwnxBWBzUKlqnHZnsAGRpMNBxozBiZZzRnYQGUx+/CyGtj9podlMNILmpRVkFRtpl1ct7YRXBtJfspVY5QVgPH1uBcZKVFG3IOyHK8ZfW9gVyMNhxFvaw4QnH47Meq9jBiJOtr0IXPVv2rIrkwvNlpIhXrtb/1dryUm49AkFe+BSu7bX0NyEFQAccU0AoVDivINRnZBz3dFwFgkqHbmFfrZhdlPVZWSNrhTBJU4dYXAK9qNXH2ez6kGUNXXP+ECiOpzRleAT/cP8WRO1zlIz549ArzJaMMEgFjj4KrnYn5q1+0nYi2r0/21NacRLLP2o3YLy+u3X0s4bNt9YAqwXsJgv0RHIOH9p9mqwpSHLjHbdliXzauB7RNEzZp9nHlyB2OCmyZrMQmTS6tAUAZWhVbkqCrBckHrfgd/ZB93TgYvePBZTyM2W9gVwQMojSKpb1ZNfFBOVoucYGs/kfyDBOrVGgAVU8Vx1CoqafqqYXrZkF5y+ZIYPdZDISyaurxJ2SFqSKI1a4/ECscVrSiVjFrJIVXE6DvVStwglYQpKXPYEWZjImV0UBGuSdlcY8KJ8/sMU1lAb7Y2XMA3NZcptF9nC7Jdb3IIzZJgCm7TzfzxoP0Y2l6wZ2nF7bKhWvQ6lg6HEv2OcZYpI8hF69e6NvoZ8sWMQ1HIkiFW1r5NNo4M7hn1FD/jPsKZEbuK96+2uPdq53Ivxzl2RpSwZOswCC1a3XdH5CPulMGapcwX2XM24T5gjBfSAo9ANwFv16yLrj5ERXq80D8n/vSCU1TOPo4XyAA48H04Mvs6oD/y49/Df/X7/8quh2d2ubQv/tGsUKhAWZnDtkfT4an9QNAUoe95hbscl+IGxGgXRu4/XI2HQP0Zqct+3zzVR/a69rECbu68b9JowfW1kwsA5Vik4G8uKGw/PNyZnZT0QTg1yyrAtJjaP3b4BerhZgRi9tFHa2epPytGYT1Pt742ui663TB+x6+GAEx5ta0ArysRSDLQLSYXhjBLvm8HPyNamtRHStxG9lcsp9GtTawz2jSnV6jrCwFo+KKuOAyEtGl6vnns+ptAUCv0YuZEw5FRN/HIiVtD6XDqGCWORyTMrhiaogDXYlVL+vMBWMSYMv+CjWDwBJl2E+9pEWCcZ0PSDrBeKvb4aofkY9AtyvIdwXdrqDfVwwvGd//wTv4yfQEPRV899FHqO+OGK8FyJJ88Swlaru8KE97Vvh8HXF406qGclEwPiHsv1qx/9aEu29OmC/prMB7Y2IpoJWWQBYQQKk4cBuLi3DW4fF1V46didPbd17y94soHT60n+smAZLOgxyVW5oxcMrEIuKT8drSCEWjUdg3YnXOTA4D4LL+3tLro42yvpsYevWUudbn0oaIFkfTA1zv/42vjR/DMl3wvh5sT6bTaA7KuVEmAlme2hmOL6b3rY/bYRpq6ZtWRN3+oui7Ba1Mq8zuk1WmJMD7ApRtQHVRPyNeS6uSCIiTYLZk1menKLA1O2im58nkYrw+u4yI4LkASry4hghavkK4sLWSR/0JjA3N8uxQxUUaMaQiQYCRhQk8MvLEyEfg5ctL7IqUT3972IGvCsoGrinCWqqSY0oH89n7KguDrVF78GaNULbAdM2YnlbMT6o6UauLwjFq7pvKWny63tJJeP3x3GtrVg+yB2QewK0vdTtw35hMINd2LSyphX0qzsApnFz4eqodXs6Xp36J6mSNanMSTDx8bgLq+lDFMd2AgV3d+PHs6gav6tZ9m1ihL618jBFZUxdnB8YM/LKxzPSvYpN5ewvCy/E1sKtH9RS+NbPXgtATyAPpMVNE1mnBE6vGbp8NsIoSKZZ5ci5jpe2jZVucyzYxHyJeIwOjTL7GmK09FWzTpBUlcwAgLVW0+mfLPNmqXpqlosb9WiD/Io9IxNjmCbfTBrfTBvt5wO20ZIR1qWIscjxdEv/IKhkmahpZY8nYj3Jfp5pwN/bu1yRiZ2WVknyItgJWYuDR7Et4z7nNxzmxaGX1DNpUvPfsFQbV9pprQs6MPhd0qWBLoz+LT/NO2GzzBnnEQjYEUBwtE8oGuLyUdF2TC9rNIvhDWvVwwcq9J7Byb/Gnn6IoVCr/f/b+Nda2LDsLBL8x51xrP84599x745mRGWmnH5Xtsk3ysk3SBkwBpiwBQvywVBbZcotqWcK4yzKIFuJHmz92Y6kByZZQt4UMBW1QlboNlFqV5TRgG+MXmDJ2JumszIzMyMiM1437OK+993rMOfrHGGPOudbZJ+69EWE6CZ8p3XvO2Xvt9dp7zzHHN77vG8A2NnhjvIEXP/Us+k/dQNhW11FrZ+fHUqnhvoBJ83Oq49Xs+RqcAgzkQ1lD6HtX5z/5I16vMx5hXHtmvQNjYMYpL/AgrnPbWpsYRPIX1afE2FhioCeLDwVJqs935NJlL8KhpRGAtLpNVg3Riavm49XtuAHTpHPVcjch6u+AmcTHaVIx+/TYotcqH/OkqO4G4lEWtkbdbSDGhgIOAYmLt8lVayp7fiI9RNGpz89RgoueC2NCJ66rIfXouZjhTq8nwcMq6OVaLTFbuAHHATgZVznom/zQPF88RXQqD/RaGk3s4cC5YtErxTvoe5/YYZeCGMUb24FJ5SGM4BK6MWQgzbuEMQqldQSQokz8lszMvTFocELBTbrYN6ZWAkiliNzI+92PIcsbTfpqfgiNj3mBTWOCV++rxYLQvNrghe2TeKY5wdPNKZ575gHu3noWKZCwyAKB2wDqR6mal5iJLDW0isW8ndkj+mYRM+KC0P+ec3zvN/wCHCV84vy9+IUvfajKRvf4ZNnEGhkgKmAgk+jIvd7YJM/lzh4R0gJO7zlFhgNlJljer74flgtSAlilM1d8RK/H9bg0IoAOAVtuyvyjAH4Dk7xBOuMBGei2rHk+d7IukG0OyzEEDo4FgEkk29VFUONR1TI5A7DMWB4o/lauOreyj+mQtZP6KWHqm2X7sJ9U/W33wCroGWSqcJar1kR5XVet2wjIyc78HO2rnLtF6hbzJqz1iKgtvqfX42DvXxkp39OEpRvE8wYlJpS7i4nk3MzezVI8M7QNxKoAshGEmHwhs2qMF8CMEdVEXgi0mkjZ/TXZRw2e1JeX9Anbpgay7KeXTpbmkQJf2NOsDDPn1HtE3yBKDAdJQNy5x/1xjQPf4cB3ODrcYbs8FFYWIL6F3oPUZHdyg+uYUvuPMAq96mGD5TOaAhCf7fFNz3wBBMad/ghfOH12EmuujF1qiitFf33X8uGr+6cjxw4YMCXxPMeP+tSr38liU8IjXdr1+M93RBY52c6aSukaf5PawnhRsNzm+01qsUmtdi6ULuvRibm6dWZvaURLI3oO8HDYxEa/ryMalyZ5xo4btIgYoOwgBbWAYhIPfe4CLQ6or2JQmauss3v269L8o8Qoyj9bLl5ZE2P36jEb1g13oqgA546GS4oZhJLnZJgfloMYv4s3FvK5yTY8eQ0A7NihpTSRG9o29evrLsR2nkBhKpn3l/w0831kE/fIPgNWuZs9i1G/p4gL9ojwmU0X4fQzUqScA0qTACNL2BgVOLTGVsbgJWLpgA4GAnJDq5F9vtiRHboY8nu37Rst0nCWt3vHGBNnH+lxFHk6lGnsmiQejKjiioPEm2qdTSNJTkPIDZfccsTx8QZHbZf3H1zCwUGPJ1YbvG/5AE+EcxyQ+L/Zzy75DLSk1uc4F1sBsoZD4Lgd8KXNMW60O9wIO+yi5PmUuDCfgDePBY87HiEvklyE8VWL1/F//qP/C17pb+L//TMfxhP/UaSONEkWq/2SVvZyp2KNTQnT3IeRc6O5cqUYu5cYRQnS9KWOaUn/NiQq6dvopvt7lHENZr0DYwAykGXyPQBYVlqimk6bO4YoyAVgWl0gh8iC7raqb45KFfUooJXp0OV5GcY0ikrZdZULntB4Of9en4uxkNrqnE2vbdrt3KnKmEZWleBpN6z5Y3MZYN3ho9yfYpJYdzUEF+qu7csW1fbTgsXcDB4oOnOrACWtRlkL4QQ32b7J1x8Q2VceZakAdY6xc1JFMMDSAYhq/l+b4dvvCz9mn62RPcbksPRDvpf1vbURnBhWDtoCd2SHbgxgSBJgiUxKlYGuTup1oynNzuCiKx31Gt1uNCCLgSYhhIjgI3Yx5Ha8SWndLY1Y+gFmqWAm6m5ICJuE5szjxfPb+LqDV3Dbn+Orjt/Aq0fPZI02O0IKDuQ9yDltUf6YM9YjDDeKkf3CDfj7L3wYb7x0EzdOsH9yTNoBctJ2tsoELJliXNaa10CVDkscAAG6yFVPc5nIM8mBLaC8/eu+Hu/+kYDseWLyPQATKVvdadAkzzWQNMEdQEiVhN1ApKTz7MSkmwUkibqYLJ5WlH931TnY2dV+UgXXpYmMzkzhHTiDWkAFElE533k1fW5yX1/jvvxdwCgZdVfDWo5o+7LztZ+jxq6yTTkXD5V5oviO1VJJnp17rrJbMcQSOdtGWVgj+WwhQCR7NPZUAeNoIvcY1GfLvDyCi5N7n8+dLGYm3V6OYZ13bd/QJMs6LE5u8nwkgJjyzWelH1SXCziG81JgG/X6ifSzROrRRql0gjVD2qTy9p7woF/hyeYcK9fj1mKD88WhXg9ng1oiYWbNpul3bNgCPVDEr99/HpuTJRbdFQdTAGzqqzgF08rn/eFJj2GEdh7wV1zjdWz5HTNEZih+WGb8fqhG72vf5wYRDUqnwF1qMCSx2jj0KgVMAY2XYvgmtUhEea16wQsplFORGCbY77Ie7hVYqUEsy02ybFDZYhdoEZPL0jfJN2SNvnZdBmZ6+IlCwtb2gOYHXEArK8ADpSC/oChr2uoLMcBljy1AXp+L6XqfaquThU5rcy8t+2lFd1DJQ0puwpPXyD2wub/OQZLIHdllVcyg+YrLBXvL3SQ3PFBWEfQcLDA0FLPPWZMBMJeLJObx3Cuzy7OCn9xq53VhV3WpePcGJysP88JyxOBI6JPHODgsvOQJ1tFwcr9VYWJFtsgkDiiaz8h9LlYJVDWk4rxeVlAlF0gA1zlhZBGEieUBVrCLlhE3b17gmcNztKqKOV7scNR2WPoRz61P8LvWL+Epf6YNC6RZW588Pn/6BPzAiEstHDYOw9qhOyYMR4ThSM7v3nYNR4x7/gB3t2uxFIlTxhKAqytej/K8yvhYFmaXnw5O/nn5mRr5DlykBT762tfjxbu3EbZAf+SRwlLArpHhNyPcEHMsynFlnhslLkBivfiw1ygARRHggCIj1H+6nNDz199nPymh+J09Ju53DWa9A2PHhLO0yrIPQ7Z3CGgQJ8BRlvVRQqeTCVBADEHKSzeIRk0UJ95QrG1ylacFQE0ZSdF+oQIPHCZsqpqJZVUU8XwqX6DSxrYAXXW3w9wyFpYIlQV67Vll1Q/TnwPTynoNUNlrpLqP6jHo9npC1Yf7UcgsUpVKej7T6xOmmEcDodoKaFXeJ4eExI0ywVSLjhFAiwHA2nfoUkCHMskv3IhRJTc56GgnQxtBGVWAsLMCJbR+xKjac6cAZ2SHPvlc+UjcZN+TIUrnS2ah4Kak8haCGMBbC1vr8GHDciSGdJSySUv/UZPQtqPKJaVqYsGwoYi16xBcEqVinyT700jv+oTmAniwW+H1/gaOljs81Z4jHsSczOiNAnTCJe/z5J3b0z7qqDNc1v5hiaSKE4GhD2go4vc+/RI+2Qy48+p7dBIun3ULNFzvc26q+7BTqjHoKrnIj2mSYRO7VcdTKM9fM7Oux6OOkcXrxEAK64w7kjCpDGYx83H5HRkwAZVZ1+Rd9jE3AKFmn9v8UzOlDOyQ6raDsYDMLxCYMrGMrWU/UR0fABwXn8G626EBWwB0P5e79E28tqrzpupnDVDV111/VedyxPo8H2VWEuyE8xxt51OuUxKPUoGfngvnJIsRySEgSSWcgMaNkzblBJX+ZEBL/o/2mbC4qUwtQNYMDsLoTRpKBTwqSYWBklwxtizOMABOTqqzQKnE1oyrGjGqbzjP5kaC+Jf4CGsISyieNRJ3RJ4vsgSezMUUGa4HdmODi7jAgkYc+B7cpClQpIAWE4GcgzUZeZRpfTL2sbdYY00C0ij39j0HJ7jjIjbnR5dizeV9zk7icU9q3/b6mDG48n2vPEjm8el6vDvHJgkYcRg6nMeFzAWRMpBhHbltNFW3HAOPBvYARfEBcoBnzoypulM7MGbVydqVDommMKlzFfN8qofTQrEAUgHQTogtxczyApDX4VmaWBXLS97iMqDldT7JoNasmGAxzCR+kUnN2CX3qDux25xqTCqLNSZPz/eR5Fl7DpD8cKpq4WyL0lKqOiOSHqOoQurzNcAOgAKGjcaUAsYNHDBAwMMeU59lG+aRFcnyHVnnX6QFBvKIqcmSwKWawCcWG4KFk6J866Xov2Fh/DU+ZquU4JKuByJuNDtsxgbD2KAbA7yTbLMNMbNyHQHOJ0TNY8SvWeKTc4wYBeiynIYjidwwEsg8GAEt2hNcT1Kc9wA84/DmBk+uN+hiwCunN9CPHs4x2jDiaNHjKw/v4qbf5Py6oSiNBSjhi6/ewrMRiEuP1IhH1u6mQ38MxCWQlglDFNuXU79EFwPOdgv4DmqNgv3DGl7VDKj6OVOopOqDZGMfkNV4xHWLuAqICy1CNfI5uUgLfOrFZ+HuN2hHwvYJBzdIJ0aKjOV9h/Z+L4AWAGNiAcjdDfOige1GK7hlihXtJE+OkTxlw3y7/jkbK39dvfxeg1iURCL5GI0MAVyDWW97DBzxILXa8pSKuXol9as1yGayaMNM/BoMeZKxbiHy/GUpnIBmyAws+5DNzeGBGrxx8IjFSH4fgAUA1lq02qbuZuiUipspv2AMcHI+uKxBL/TbEhDmV2QBJT83mwCyh9YVM4N1LhStu7sUxIAy+cv2KQfEpRsyg03kHCXAL92QjWkLG27M9/DQC2V1G5v8ftXeMSY57MdC101MCAqeBZdyBdyBc7taa6Nu+vHGF/mFXItebypeOcxCA5WfVDp95BuA0i4VpUqOwCI59IzQpByQuhgQ1L/NqnBez186TOWomjNIv2Ns+wYPxjXO0hKHoQNaRXN0SEcQBbLmk/JDqhZ58iw3U7Xyl/fDiSZVyUsfHZ2AC1OqqjoAeWIGK5MhafLraP9pVuAaGbU2T86lNTtV8c3oy8bSuh7X481GAkuzDg6ZySPgideqbfHrA+S7Wi9kzRvLQ0zgXVUhBaYsIxvRKsIwwAnKIOFLr6m7KQJTYGu+jQ0CT5KM7PekwFbUYkmWzoH2glGyL31tlWDMr0hAO87PXdGE58piiQF09XlIpXl6TVaWMf+wBEIggxjVq6q6AjP/jUwImrQxFdZc60YQc5Z4zK/NKVoeK78bk5BG9pCiqcyJ4kulcV1BLFuvWByeM+NqRhZrUpWl7JcQfGRZAVDNbaR/OMA51o5YcmxHZnyvUkfdNdUyDUO9IPLsMUqL+Y6DSFv8DOEh8WokZ0YE1XiI5OMS8GMg1h4EyYi1JvG5KtaUfc/ZWchSw3JQ+8TvOeR837UcsQb9qlPJRZPrWPOuHxdpMWFqNhTRuIjzcZFZm3BSaLXC+9r38l1CyNYSxoCSQqY8ZsqNXtlVy0kXPPFybGhEq55cpoDIagUQzJvWmFqANVMK8DSU12m+YsoAoORDHowLbi8BY7Ivl9fItq0d2+JMHYeMAVWaWEG3KUysOn+RezL9O+V9ldgxsJq/Ww6E6TnZvahVJXn/CvBZPtMgTdQbnhkthJhgdjKywJ92FjJFSdIJecdNNoEfKCj7LOIitZn1ttY1s+U/SY/RUcI2trjZbHERWwzJIzQ7AMBmbMVnlzgbuo/JwTnORa7EBMeE1kf00cP7YnBvShPvUl6PhJC0k2GFoTjOwBYNCmSpTYqpTPLcvUhYHHXwxHj59Aa6LmDcNeDBAQkIRwOOFj2+enkHSxrwQAkpgBjC//vtVyJ8aYHUMLpjYTqNS6C7BcS1SMz5UBqV7PoGJ9slgo/YbBa4sWM9l/pDwhZM9+Y6Vxb0HyYpJEJaNRiPGsSFy119wUKYuOkv8H/98P+Ee+Mhfuzf/VG0JwsEYnCUfIzYo7lwwJjKsbjqesjlOIAWl/T9sCI9lyAmzCyP0gHSTrv6mUkUPH2cGMWiBVWe+gjjGsx6B8ZOWTMGZO20o0FDIhNLibBwwyWJnze/KnbYoVGW0FgqFeyy8WFuX6usrEarFyY/zACX69AYAIFiDAhMDRUjXK541B366mETbKbuKsiSZZIQ+qsFTtOdA8hfAKPXekxBLGNblQrEdNTbzk3ca805MAOQFMiaDw8ubYLZIcLa38o96jVo1kFFJIfCtuurJCG3tnfAEiQMrViSDK8V8aDa/aWX4+5iI10KmaR1rYJqJju0DiIdW8WCszEvE0vXp+QwRCfmiMaasDkn2Syh95+R4W121mmioOXl5jDQJvgQxYdLA1CjBvkHrstdHh0x/BZAYqGzEpCCQ2oc3AjsugZdDDiPS1k47FlAs6PSaUrekFKdqKsSGb6vJvOHTewsna94cLg3HuJnXvogzl66gcMLlOuu2VmgXH0g5ssSD8b+zJarBMEhd43KjIXqPagBrHxcDbhMEB8z8wy7HtfjTcaoM6EtDq2BhKMEVoAkUF0DLr8lCKXfWFwmKwRBfZQ8wFGZVwB08S/7ltWHdSgU8Iw1IVFvLa6kAnAZTKklhfuKMwAmcydQrfn0eQOEDBcg4FJiUn/H6t9p9thlOGL6+xzDqF9TJ0EGZM0HAQJIkVcQSDoa2j2qpYg2zA7AunXlxxUMg97HSDL3F6mgTVGs773c39rIPVDKoKUx+hxpYwD9O8tLWWqyFuutLTrr41NPxtli197aGgma31AHwDPIKwiovmxmkN/SmLs8EgS0AmMiVWdPoASMo9fuaUEOUYE45c2g8g+YAkv2+1W+WQ/zONE5H4mwTS1eOH0C3ckS7YDph2pyLOiifVY8sf1dFWswwbmmf9fb7QnvE4BrTwy8Hu+uYaxGa7BggMHKC/BkkjEAWLgxF9utaDmwx9r1yq4pnr+2n55lDdu6NLHfSHCqWyg+fZEdGlcAlgPtimgQlIFaBrDYfiIIO26wdh3M8D1338vgkPlsiaWLRb0eTuVyxVtrmj9YzsEZLDJlSM8Oy0wKuHpELnYpBmzVkc0sUmw/A0qDqghWBlg5bp2zzAkBBnoZkaBWwkQ4LDHO8pZYrgk8gfHt/vdqDN9kFpz479pFLN2ATRKrG/sM3Qi7zD4OlBB8UjleyEX5XWxyR1wA6PW1tc2BSRfHVKTy274RtharvU10eeo138YxybqDvKzv2bN4Yzktdul6nBs1fXeM7nyB7mQJaiM4OqB3oM6BFwmhifjgzdfwfHsXO25wNx6ioRFLGvB6PML/9KXfhcMvAIsHCXFBGAHQQubs5IHxKGJ9Y4dN16LrGuwSwfuEeNHA77SoHWfzrBZnrjR8f5TBjLobIjuH1HgkLwV0+yj5Hmpt4/A/vPL78dKDm3B3Wrixyl0IGJdi/+Jt33rTCwG5rMkyoKUFmSJH3FMKpapQb7ESZhdQ4hdFzf+iKlaoFF7etJAzG9cCl7c5EpJ6mEiAWNCY2+ACpfoxpKATkk6UVLoZGnW37NNloCq38lY2lAFZDWJu4Q2gAoXShD4MFPDMjmWjyAanHUJalUPMmVnyekYLAV3MaF1AoVKJt0kbKGyqqLhGfWZOny+Si8uj3ibNtjGpYu3XZdLCuoNg3WbYkonWJny4DFTV7Wvris/SDRpw1NRd748dzzp+hAoUrL1AAEHIjYEl5y6Pt34UBpSLOYGo2RV254fkMFZAVg3g1UA/M4CBBMRi0smz3DOjdbKDTPgAEBjrGzscaVeObd9gOzS5zWwEZd19YkJzrgmwJ6SFR1w6xKUTM1yVwxiwSwailaxUQDDnCoBV02z3VTIec5CCeEd+hz//Nb+KP/pNn8D2ab5yYpxUqmvgLSF39JB9lgx70rHjzTAom7CtG2JCuR/1sQi4sl3v9bgekOrXWM3TZo5r806RmekisWJPGUBiksGyTwEqJh5PTDpXF1lJ3TkvYxa4bNJed1Wk6ouVZYOY+kgZm3fOzJLXaxdAqhKQLHks+62mFnlMv+r1mZE+Pwe2cMU2PNvG/p7LDx3xBGMviYYybPM1yr22OE1gBET5V8WNRn2ycufB6t7YXZPOUFWM0Mq3nVkNUAEF0PQuZQYUAzCj9fmYA1n19DZvLJLNd+eP2w3SMJSXII7RLEYsGjUJjn4ixU+gnLAxACWEyH6CQwqk3XGBpIUaK7DZ4rlGIK27YX7j535V+Xdg/6fizQcBABMWNOJ33f4SPvDe1zEeALOvxcOHhafq577zeVNWVQ12XfWe5G0f9wSvx38uw7yqhJ1UFCJWtB7YY+17HPouP75wA9a+w8INudN53cSqBrIsVzEvX+tm22S7jjIMLOnZY8cNLrgVX6LamxelaG/eWlZgNl+nGqzp4dHDT66tPFesQubqDDlW2VfOV7SwYBLnOs/oeCotTJh2Vp9jFbW8sBxzrj4h9OyykmSX84+psiVL+bnMc/VPK9BbrmYxwXKcCOsUmS7dJ08JB9RnqwAbSzfgyG0ziCm2NC4/Z/twZLFEzrd1I46aDq0bsQijgk8efQpwxDhoOizDiIXKE1dhQHCpMn8X+bsBYbXixGl3dheUZTY4YWO1CdyU6MSewYsEXiSAGHQRQBthrfPogFH3uYpY3N7iQ899CR9cvwZA2Iy71OQGbi/2T+GLr9zG8gHD76J4Am8ZbgDCVkGaA4ljm81CzomBYSfHDF2dx6T8TwCudJkSDrwFmxUddWLN1U+WtULPHp99/UlcfOlIzl2PnVlVDrmhyPwYOe+xc+RqO96T+7DmOlXHwiy31O2pPsfqXC/FLcJ+UOCKcc3MeptjYOkkseOATZQvgjGyBPmX6uEAD88pg1gLmwyqyas2XzfJYT2yOV3F3HIQ6mirLBoLCkjI7VYBnWRn+4sKEdWSxgbT1ug5gakqMwYUNXuy+NxZSSsDIJvI7fkq6aheV6fx+wzipZPInscx7Vhoho4OjESlomHBVo7lZvtOWaNfDPHtvAel8vp8kg4JC+cQE8E7MciEExNmm8Ttvpvpu7WqBUoyMrIXvxhLXPT+bpMwtcwIMSaHNoyStBgjC7N1KgNQuQt7FlVF7ZmlmRp7lTgQy3YOCKsRtw62WIQRb5wfICXSSsxWZZiMtevkM0YM35fDxoVDXDiMC8K4JoQg99go7YBWKGxCtDfd06UF9WP7Zl01FDTaccBHX/16vPDi0zi4f8V+r+rekZQFQHRJFkKGsRmoxch0WqombRDEw2yeHe/Lkh8/j7oev8NGZElORjgM2ZBVGDvEsr5LIJCCAaTztDGn6oWyn4Eh83l1tKo8CxvIzN8HOASFo8YqcTGvLUBExfM6HWsCUJuPB/BkCsjyP8akKsuMS0kJUJIRyr9r1yNjCnCZ/upX1zNBmv0Nff3Ilx8nTCX52ecLrNZRZP7jmbFAs70YAChJBGACOAEObd1ApbkPETyUMUUJA8mrjJuVuACDZvpukg0A1efAgVAkHAQAWu3O5rogMDt4p40AsifXnump6LNlC9vQdm4AVs3WIsCFhGUricymb/LnauGkqOPAORkniHdGvt9eWFkpqB+I1wRbEyY5L5ucq5N2+YrL6V965C0OnbtHOHzm/Gncf3CAZnvVtoyanVXOUR7n/MAs1tSvB5Dl8RXwdQn72vfBf8cu+np8OQ9bZ659n+f8Wi3ifcpM1yO/y8V2YMo8jakRfy12gCo95gCSWar0CDigLhd9bd0cq9zEaRG41w56uashKHdm79nnnMmZLA4DWooTlUnxzRKzHa/MLAN2MmDFFaOpkh6arLDuHghgkrPMmVd2rtlbSX/ac5aHLKvMZuDpa5YUs+m7Q5EPGvhmOZUBWwNcNoSXPCTl8zRlxykKEGjXvlNQRq5FivSOEnZoMjFCjqMqHxIvs4u0QMoApXY4hhSxz+Jywuobk0ef77ODp4jWRwQW1pV1bN+MLZ5anCOxwy4GrPyIPooRvHfyPqzaAdtevLrG6OB9FauI1a9R5k4XkuQugNwlTlqF4qwEgQM4id0HIsGtRqSLBmgTDm9t8N7jE3zw8DUs3IAHcY3IDkd+m4kMn94+DZw04s8YGRyAFDS3HYC4ZDTLEWfnK8TeA6cBqZVz8jtC2KVc/BbPR/sAMRD3K6He7iADi0zenxi7GNBSxF/70EdxkRb4v//qt2P12gJ1YNgrzJqxknmel0UG9H5YjMo+WbbfmbTdlCiTc7Y8yfaTqu0fM15dg1lvc0QwdqmdsJ8GpckvFJwyfw2bfCI7bOICnpL4Z6l0ECj03Llpn8gQZNJI5LK31qBmwKCYGV359alU7+Vc3QTQqoNTuZ5SRs0TnlUUDKginsgKjSJbfFWqpAlSlZ5XMGT7y8DrPoArQeaFBnxpe7nWYp5YqMo0CWj1+dQAnhnhe4oTqYe3+z2/P5ocei6+W3YMR9KFqfERiC1GSDJ5PixygmH3Ltj94sLWAzuMLHJDMXkXwMwRiryQCzgXoxMmlAZgOJtVqEzqNux3m7gIQGDQIuLW8QUOmh4Xg3aVCRGLMOI4bDND7UCNPU/7Jdyok5anvN8UCOMKWC0GbGODg+DRjY1o29VoUM5JV+C19GM+TGr4uJWKigKLSBhSwEHTw69GxLbdI8vgS8e4DFztAbLq7K7K9mp1jT2Xk5Eq9+Pq5zWQdT0edbACSDX7yUzAvc5d9nHKsjKI75JJ1RyjdCisFov1IP1wMiuYWwEirIhtLZMjzaYJpfpcOf7kbS6xuDAtfsix9bmSt09khYXZdDk3VwxlX9FzLyBzVe6fp6rLu5kkPzX8kDsW8nzb8lxkM37n7FmWJvuZ3p9pJ8ras6rcD68eOEb27GPI75FMsyzvOTh/LmwnZtpv0g7WYkhmZdn9hD6f7A6jlFdred58FAqf3NCQsFz2aF1En4Ql7VVysnRD7ipmrI0uBimC1AglpHCQGvGSHJJD46hIULjga+XNoKsXxQ+TEu59Dar9MZBI5VQRFBLYz6Hh/eNSbMHsNr7JYv5SrKkfqz/MNNv2Ota864cpCOr1uOUAXQp57evAGeQCSgzpOGBBI5wytgb2cJzEl46V2eW6fBxACvG1Z9YAnz2X5HkBtAb1T7Wf2W02A0jy+JHbZlBrx1KedkhoUcAvU1cAUrhsqmL0pIO6rVGrgvzcJ6vcA0ajQJYASKzA26xhVfVttQzBzNznxfgaXHMwGxLKxwMjF99tZNk9pgCcsVCzqgYCYln+aabv1jQsv9eae8p+xqz2sWOYUb9Hgx038GBs1Ct44UbcHQ7QpYAxeQQXcTKs0EePpR8RXMTCjRnoCqTgEsasSOlSwDIMkt9QhPOMIfl8R4dY1jVtEJUKE6v7hhZc1Pjd+wRSY3h2jDQGUO+EpeVY5uDei29tk+APRjTtiBiEFrRshC34qfNnsFm1WWr7VDjFDbfDBbf4wsVtNOcSN8aVR1xIB8PumLB7ipGORwxdAI8OdBbQnBOGowQaCeGC4HdJ4lBkYExFycEMxMtZ7Nsq4s8lSzbvJyn09OzxP7zy+/HCnSfgX20nxAJyku6T5Y1XKGGyLB58OXdjze80H7QYJPi0bM/qIwxnwBeyp7DJHYECxhkx4Noz6z/hSAAuUivmiSlMuoJ0HCaBZEghdxYRxgvQpUYBrQCkAhwN6oImAYHQUKm47FKDSAUAE/qnuKlEsoWzy6sj634o5ysVjzajGvK40VTrIGCG79bqtQ4G8srSzdAAOxsGMNWAlMvPVTeQy3exBrd6dmhnkkjgsiTROoHYsHOZGypagNvBzOtl2Htj3lmFmUXZKB5ArgwBWo2CU9NLCaJ1V8MuBQyVxNARA8pSsta0Cz+KRxaKDHRUryy9LRhjkarUhsaSbLgs6bMkBJrAkueMatOo29QJqxdWFi0jbt06x5PrC9Gws8Ptgw1uLHZ47/oBvmbxGm76DQb2uWpz2i1lcdy4XCnvDxz6I8Jwg7H2Ca9sbhSZ50hKM2UgYioDeQcHOwd4Ajun0hLpOPknnvwkbjQ7/NLr/zuMhx5ubEFj0ra0CVbJr72zCkClv+g5E1C2qSbfTFAxM14uk/88icjsraisDAsk1+N6PGQwxOtiZJFl1QWPyE5zfs5/9ymAiFW2zLn6DXbi/2D71Q+psKtkzqZq0e+rAkWjrNIa1GFlb3kkjJW8zYGU9WWxB/p4LTQs04GAVrb4ubRegnUznE8hdi6zHF6eq/ZRsd0nwMHcU6WCQCZ/1wbC9nwNZNXnAwCjbp9NfyvZp3m9WMyrwb4AhpFfJQWU6vg4i11EnKXsXD1WrleZVioTEraVHMNkpfW24Ol9z+AZa1W8urn19FjYrXsQE4IUWULCatVj3QxazCGsmh6LMOJGs8PtcJHXOkHZ610Msjj2pGxiILaE2AJxATSOcT4syho8Sx75txe00WIRk8tIa0MRX72+g4Uf8cWLJ5FaAiWfJRY5Bu5jZwHTx1HlFdU2+wCs+rWTWKMJRX576jh1Pd61w1QHDqIYsM6Fh36HLjVoXMx+ibbery1PBi182Nw+VB8aa4YEADtuJ0qSmmklnlfCwFrSkLsSNjRmsOsiLdBqc6yoBf46c0061y21iGqm8FHZXVZ8zv69CmS1mOYM9dyecxmUjoEmc68bR0FBLrNPqcGpbNw+Y3DZc0ApultukhtSgfPfMR9Tc0M4LClixz6D+uZHnO9JBWTJe6Dpu7LYzNrDfLEaivDMGZyqm4G1iJIL6euso7vJ0A/9LgOdO/awbu0GWgVKSI6E+QPK8SqxETcYrY9IJjGNQYAvU6SA5bPYyD0771uMxgC3PAaA9wnD4OFcgl9Kt3VHUpjJ8r6RwMuI9rBHf9ECpw3QMHgVcXT7Ao2PODk9QNwEuNWI8+1C/h222I0Nnlqe433L+0hwuBsP8ep4jM/eewKuJwxrgJ3H9rbD5jnGcJTAB6NYuZw3cJGwuOuQGsBvHdgBzTkQtlHyipRAKQmAJawEYJya9E/GvqZYCZe1q/tGFTDqOJHg8NnXn8Tw2gqNLR6qRVDtX3XlsM71s0I/ksXn+blUMYex3zfYKnB2vntj20POqxrXYNbbHFGRzFzx0Lk0gz6UYOEggdC4ODFHBBTMoIQhLXKnQ4+iUxfaZ6get0lcgoinhAbCzDImzU6pp1bpAKucTiHTuAfyNKpomfRIX1eCwXzYwn0fK6udHcM6ghiIK9d2WXbY6KS/T45ogaVOEObVlagg28IM+KtviU36dVcU0983NObFgHWFEl8AoUUvacAAj56lEjWQVzCuYmU5BXGc3M9AUtEyk/em8sYyv63gRpEijh591coh+IRh9JeYFDayJ45O/pxITA6JgaRta422acmG0+6Fi4SbNy9we73FZmjRRWvjK+f03OIET4VTAT1JDDbv8iFevXuMZxIwrj3YEbpjh+4WYTgExoOEMTqcdwu84Q8kwd45UEylGgBMO3wAU4lfPWwir8hxpJPnZDPnwMuAuAxIrce49oBndKnBR1/9L/Hip57F4p7D9hZhWElL2rBJaB/08LsSWITmylKtqCZg0gl5YthrgJV18KjosgByxd3AO66PUed6dgwGrg3gr8ebDft0WCc8ix8TSZluwwbGo4AXso2A45FD9mqi2esMXM+MLJiXFGdwilG67o0kc0ctVydd0DumScUZ1XHm3leOi+xwX6GSqp9zLGDOhbGOhhP1MCZfX70f8+svo1YIy7ZTSYpdB2uMnANb5lVWwD35myFs3Ay/ZemfnCFBAMjIUlnP7yOXayCNHcLCStn/SrYtDF7We2zX6LRbVJz5LubiQ2bWvfnNz+AR6fnX3lk1WEMMeMZy1WPVDBiSR1TQyxh3R77DgetUmukQkLDlFuebJQ4ZSI0U5saFdJNKLZAaMbjuEbAZVZKSCyfVae/xAdn74crVH84VB+I9i3QicHDyz0vnKBC00v8UHrxxhLCVFu6pEaNbN4jvCo3zc5mCmMVI187h8mkCVWKQ35MSa+bM4Pq9m7CKrw3g35XDE6l6YFroGFLI4MQ2iv+pAysjsoy16zMoAsgadu16NBSxqfKTtOfzWXc/BCD+Q64wxSK3pQCveYoVkO13QNbcS9dNfKBkzS8FZ2iDq8TWDKsQCOpugfbPfLQOMOpxhJFqEr+hWmBa/lLnLnW+YWP+fP14BrdoKt+3PGrHXllW5bnMSkW6FGNsbt5VMsv6nicunmN2rva4bDNmxpaNHj77mFle2Kq6Z6CIngPWvsN5XAJA7gruSZjAKz+gTwEP+hVGdtiMLQ6bLp+v3cugLNzWRWxG/fyNTc6DLaYutGP7rm/gHQMQqWKMDqvlAIYYxx8uegzJ4Xy3ENWHY4TbO8TRo3+wgNt6MAH+qMdq3aHvA843a/BOc7WdRzc6uDbibLvEreU226m8Ohxjxw1e2D6F3bZFo4bk/QEhLgHfEdxI4JMGfivvUdjKNhyKv2N7ynC9FsojC4CVRClFYwTHBLDSovaNuXfuvtxoPubSemhYZsLadfhrH/ooHsQ1fvy3/vdIrx7DjTzxWNhbUNdYOY8rRTI4W1hpx/eJNQtDTemr9UEFpOkCTJtvSd5sxf6asfUo4xrMepsjQiZta09ro6t9HFCYVo1TUz12cBTRuNLVzjoQ1rJAQAEVNoDJowXyPkBAq0CWtFnVDiKI2Qtqx810VU4J0InRQB2rUuw4TCodAGCyQ5tUHV32WNnHyqoZUw3ZcwXQkmu73PZ2hl9kaUY9zCvLE08ALTNNFCNHunzezLmTSmZlMU0SMblHpstHrhBdJLlPSxLTfzNIXLviTQAAgxOJnaOElZdK9MjSqtZaq/dR2BVC344YYZ0KPRof5d7NWtcmMIImI4kYznE2wc2yHJ8E0LI2tbEg9dYyFYuE9qAHM+H1s0NsNguMnQeY0B70uLnc4j3tAyR2uFCqd+siPtM9C7y8RAqM7tgjtoTuJqG/KRpyXkcEn7DtG9zhA6TkEC4caBwnYBbqKvU+TZB19ntEqSEvPIYbC8SVQ2wdYksAS+Xuv372P+I3D07wbz75NWhPWnCQj9Kw8qDYwPWxVM2BKS1hlhiQJhlsTC6bmFFNxldVGPQzdUkGUv+7HtfjTQajSNjr+cZYWVBpdC019+Bc7bTuhdZd0LrIAbX/VA3YEDynPJea76JJDEWKV/bFkO6KxtKS/TLAbmI8b+cvpYPS6cgAG6AwUzJba3YfbNhieJo06HMVoCVXUx6r91V/Xfd9dQmFlTVhpMEAviq01uddHcfuRw1e2HV5kkJF7Z0FDkjkEBBzJdtY3vU5JhLfKyJG40TakJhzwURAT1LmmrzfyY6nySCBtbmsnCMru9TpqjVfI1dzIyCMq0SlYJLK07kqGxi+jQATLvoWQx+QomgNfBuxPBxw6HdgJogdsSTid+MhcBbAjhEXhOQloYhLSR7QJDjHKk9pBcwbCGayO/2w8NUAznzef8jg4JAWQdqfe9I24gLyfs3hHbzWdnjpzm34zucFPDXyM5h3ysOOyRAwzT4tXH0DCFcDcvOxLxZdx5nfESOyw9INwjrS7+3SDTgZV1i4UWWxRQZo4FU2/dbH165HBKFLTS4Ed7r2ddX3aalsIPNgAmTOM8ZPOa8pqLIzeZyyuGwMHLADYYliOm6eUH2Va+2T82X2FISp1SJNGE3mO9Up4yizn6jsp88xVNffTGiNDIACmmV7E93Ougga2GU5irwPDgO0qMGXm2/YPG1gW20ab4Cb5ZmWLwrwWOVyGRefAiIthB1n76Hdv9on+MJ8tvS9s8YA5p9V3hshAwSK2iFTnguUsDPvNSa0LiK4hButdUF0ClxJ8X4VBvHWGpscv1gbgwTPoFE6LBIx2iB+XDeXWzzYreTzsOoxDB7DvSX81sF5IC0Smls7tG1E1wWk6CUQNwnoxJKFe8mfUksILuE8LhB7h3vuAGNy+NTJMxhPW6y2yGCMG4H2ROKa3wHNJoESMKwJ/Q2xUvGj/GzPEmiIAl5pLkOjMLUwjAJkPc4wVtQVY9KMykzbK/Rplxr8Pz73h/Dqqzfh7zZo57LEevN9Pj/VvvPaJbGyk0l9g2l/IQW6FnCcQTnbXhd9kmubZY095gsR4FHHNZj1DgzzQTJPKqPyWhvcXLFw00Bhw1rgWtcQA6EAY16VCVWYM9NPYqbaIk4et7a3rR4/04DZwavOOqG0vN23T/sdwKUPli3wayDLEhJPfEmWsW8k3a+1sn2zr7nRe6WKkCb3pE7w7OeA+X2eTgh1xaVFrFhbRcrhVZJplQs5Z/EtA3ox6HV6PO2+0lDEQei0AlYM9Ud22MZGgCyS6rlIhrT65EoysQgy4XejShtjuRYzhie6bJhOjsF9qZqxE60yMcBtaVk77AJOto0wtXZeNOeBEY4jvurwDTwdTvEgrdErBXlIHv/za1+Pw5cIYZuQGsK4kIk+NYxxndAe9ehHj92uQd8FpOTQbAlOTFmmbcGv0I4/tgk8EVIbkFqnCQYE6R+lGnkSV/jc6W3Q1hc6LYlMJS5dyW7rBMHYWVaVSHpfqZi85/UPS0KXv9I6AQt4R6XFrHxwsqyobqEr+3n0S74ev3OH5aeW6npl+JgXo3GdTFo2Z3Pa6wRcQmZ1yr7rjoVqbj77YNrj82YiSRlIVnnPHlIgOAgAb4yk+agBpVyAmAEPcyaXva50SXx4EY9RmFozAs/+ba3CPrsn9bHm551fP9t5XUsy8C8qmFd3ODQfFPNsZIUXDeeruyWaSX/rhG1Aug+QnM+QpMtYUp81iVvy+sz2hSzoGeo7BWBM5VoS7N6X+5KvyQAuvTibG0kTADXrQhwd4qCr09FJpwInxZhb7QaHrsOOG4wgBL0vnzl/Cu2JJBHsAAT5afHGLyJiIoyDyw0K/GAL5dkbcQWQ9Sifmfm7yN6JrMLVCYAAg11q8KBbAdrZyt549gAHmn4IqpOQBgJljWXgqOFw9UnulRpacYWq7dmSCJQiSrWP6/HuHg1FdBywSa0wXOCqzoXGUJJOomaNsna9mIQrg2utbJykPlBLjDlXsE6HAqqM6uMbsx1HnVPYOtseN3DL1tP2e+0b3NAIaGF9p0b09bZyjSMuuMUSAw7UIyqfK43CWDJCAKXMQMsqkuqLMFeh5IK8Alt1LDCgy6SJADJgttPCw8Rfy6R3Va5U4ijn77cBY42CZiaFtOPuIEX3BlGYVqYeMZlo9R3vIYBTKdLHiW+WsdpqGaknMeCvX7ekEZ2u/80nuaEIp55VA0uB3rqx72KD1o1Yh0HyYAg762xYIrhSnN+MLQJFLJsBiSk3i+qieGytwpAVI44YT6w2eGpxji4FsTohxvZsBZwH+I4QVwnN7R1urARUvdi2SMnJ9L8VuTqNOkcGRrsacLDscdYvEOgQTy3PxeR+XOJzd25j9VJAc85IDQACfMdwPRB2jPYswe8i4tJjWHtpRjIANAK+B9rzqHGwArIUzOJxfHgxwgr5jzgsT8mMXQ0eLooX2cABd08OQGdBCj46pnnMbKc5n9Fz1WZYE0m75kXJabzze/IjjUFZuaJkAiWU5+1cZImpoHxsio8Xq67BrLc5PKQqUS/urboqkz1hHQpzp3FjBq+GFLJ3RgPA2goYIGIj6kLUHjM5YHleGURE2d8oSxhRg2LSHcRALqvc5/3AGEpJq/CFoluudwpwzRfx9ph1O7S7okButU3Zxz7Aa87Wmu/fAowEsGmyse+8anCupi/b31l+qddvVY7a00pkiAkbXug5Spe/TQIcBTgWgKpxEpilU5TLx3IsfiAhyN/WDcw6Ha5Dnxl8DiwGt9Fr5xIgWqt0naRtkGM4ZsRESOqRhTaBBwcaCBy0c2FgIBJoIxVvNAaLE7hNWN7a4RuffQUfXL8KQNrVDuzh3Q6vj7fx2S89hafvJjQXEakhpOAxbkmCxDqibaVNbew8kmfw1mO1AWi0Fq8pv/kUea+s7pGArHm7s1pHpIt4++B99vxJvPLqLYRzl9vCZoJB7XVlxzfDQtufVcJVUmNSxOytZdtBE7lUZyNaVddAg8Q58Eyvp+zjelyPqwYBCBQxNVZHBkCYCY0f8/OetNMhiV8WK8OjZq2P7CYxQGCW0s7bzMRtsBYTDAwJiBPgSs5Jux1Ckh1jZU3nf9kqwZqETBOAei4v+788GGVut+djyQ/yNhW2cGnM2Vr79s8wYKk8vg/gmZ+7m7yqluMjX78BUB7IwKJDAhFPzJwbihgAEFxmggpoqYa5qGSaykQuzVtUPp9ZXDEXdwjI1gXWkTExpNrKbnKBRHKuJmsHAGhnKSRd5DouqP2gugGnd4xl+7Aa8fThGZ5szgEIMyPCwdGI87jCvdMDHGxFrmHgkRsIcQGgSeKl0gdwdOIHOTpJKCyk1LKLmg1VjUcCsiax5vKL6urxvX6Ns7MVXH95jr8EQM33XT9m3yOa/Dn5ENfydcGuLEMoIWsveHY93tUjWoMdSF4wJvW5SsCWGyzciDX1k4J61HXrQB6u8sXqUoNdanDkdzn3yN3SgVy4F/N3h54DWpUAAvKdRipev+bra8V6K5T2s5zF8hRbl9egl+1n4ACzVTFFiRECJlVCHS1SVmckBZ3mrC4Da+SeSJHcgCsHxo491hQz0LWkmIGq2rt3mv9oB0I9VClGSIzoc542PSdHrK9T5ppJJanXeJcyI8vYWZbXZHN3/RzsUgPvCpAV2WGAGOrb33aHW4q4qCxtcpMy3Y+BgF0K8BDlSa/XvY3SjXAzthM1DAAEBUxFpeK0oztnICtQQnBR/T1lXbLwI5ZhwM3FFl9zcAeJCa91N7AbAzYXS4k/bUJ4eofjVYc2RGy6Fttti2EXgM5rPkDSxX2R4NYjFqsBIUQklnjYp4A+Baz8gJcvjsEvHmBxX9NxLyCVG4DQMdrTiPZEpbateGXFpfhkUQSac0bYxBJzkuY5KUkXw7qT4VVSwxrIqjviPGKRX4AjBpLcx6Ub8H/50E/jLC3xE5/+MNKrt+BtOwPBctDQMZdHJfXcDK4UTzT6EGtDsJpBZqdtxRQDqEQUJsesfYSsAMNVyOIr4uYV4xrMepvDkwJN9mWt0Pfc2S5vm3IL3KJJ93mhakMkfsLyyu1ruUw+oOp3nbQsUEhr2/K2Flqsz9V7BwkW4ockFZDcaRHF+DxW1Nm5bjx31UMxUQSmIJKZJNoo21w23a1b30aFfW09nO8fOAeYutPTPsN3294qHBas6pa25r/lKCGyR6/3SBKLlLuEmGm8nJ92W3Lynuy0H8uQQp7od6nRDh4y+fdVO1sL7NbCto/yujEF3Gy3QLuVqoVL6GPtnxURUyhdUHxCjK74ZZk0JCQgpJy4MpwkG6QJBnFJUCzx8BHLwx7vv30fz6/uI7HDq8Mx1q7DbX+OliJ+Y/s88KCBHyCgjHPSqtYBccHwq4jttkXcBNDGg1sG9YSwQZbxUS2zsAn+YeMqA8Q9k3sGq5hBoywyvv3J/4ivPnwD/6P7veAvif6/MMT2HGtfcjHv3AFkikfOL6wCUeVNk05VJrvRfbgo+Jjld9fjejxsZFkfmWStzm85VzdlW87JAdsiVpcg826zhXGkoBEXnyuZg8tRHJAN4CMbQ1VGLU90MBNX6cDoDWHmMmclAIGsM6K98rI3VfHGVlaZPV7dm7n0st6mxgOAAlyxnTPz5F7a6y1+2Ott+bbvDEw+aPIRssWZPa7nZ3uLsM6Q+t4pKDjC5zVkRGEUJNDEK81AylELZnMfNTsnwHwQI2KSbRITln4EMOaK+9Q/S+JjftddAieZ3DiVd4CcgFQTnMgBeZLMNNR8AwHHCG3E8WqL42YLZsJZWohcn3p4Sngt3gB2Hi6W11pTD/YANYxh8ODBCVjmGYgENyDHF2HG8puCWZdG9s16+KYZTLIKMwhfvb6D2+0Gn3jtPeCz8Ghg2eT4uITCCqBVMa9QJx/V9vNuCdWfNfD1+Cd1Pf5zG22Wn/nMsNmmNs+PVizPCgZdmGxiKwbwxJkJtfad2peUtX7tqwsYGOLUoFzyi6gdCHfscEQ7eEp4ENc4crsCWoE1jxHvppYi1tTlnMcYXKYiEX+sUdlM46SbYt2FfWmssEpCVxfOowFMOuoCfS11N9P2OrbUfljWEdGkhTb3F/P8KVBRM7nEON4V364qRl9SxMByn7F6bRl2P4VVRaXhmMoDreu9sePqz4kRI2qQUhhpCQl+YimTmLCNi/yZSkxYKDljGxsBqCDG730Sw3fLExcqSRyTR4pN9tcqckQ5l+QiRnZADFgvehw3O/zuoy8AAP796VfghbMnEJPDreMLLIKYwQ/KKD7fLbDbthg3ARilkI9EEh8cwx8OWCzkzhlRwFGPm63Eoj4FfOaVp7C+S9qxHTIHRwGymvOE5nQADTFLzWMrhRa/0/e1q/yyagVKSuKVZWoUh0cGpx53ZOCIhZl1kRb48c9+K9740jHC/YDDR2E8XXV+DGVpzR7jWe2Eqz94GrMoiVUA5zXBbF8JGZma4dhvOq7BrLc5IvNkAZnqyQT+kkFh7ipYTTT2WK1lli95Qc7rboSJFWzRgOOQJhO2BYED10mlBT4/384CkUxyCdYkdc7U2qdJr1f980BRDzNA7PO3a7p9OY/Lx7DuhRMNvGrRfQXf1r/PAa04uyYLGIVpVU36Ch62FCdmiRL4jJYd8v6iMrfAlc8W5P09HZe5Te02NtiMLQBg6Ys/GqAtbP2IPgaM7LJRYu9Ejz6SQ+Mj0liDYQynACk7RkoAK1PLNQkhqCnzEBAjgyKBQwIFBo8kEg8C/I1B2tWOyiZrRpwPLT55+iy2By1WvsdXLt/As+EEG17g0+dPI1w4pMAYjgK6Y4fNM4TuNmO8NUoVZCCEkwCKQIwMvyO051qdsBa1BmANI/gqMOuR/ECqikVOVspnk5Is2n723gfxS7/11Whea2RiLHn5mx/3ITp1WMt4fY0AWQwkqgzkNaFnzL4ZKEkeGwh3DWldjzcfxr6yIesKXfgyLsn/6iqwyZJL5Vk8G20/dZOJWoIWIZW3JgNoU9+omOfSEQRLZtTHiqZmtskgM7bzK2Oex+ckomZNZsBt3j/QGFDTmFIvrvZ9u+bRx87BQC4/u9byO186A662kftCk8eLN6UCUVCGAqZrB5fXB4URwCistjnLq9MCFSC+nMbsDS5V90vPyyXxXmSXTaIjOY1JHt7xRGJIqNYrthrVogk5hlNGVorKFDbtQQaxFPRaCpMqJWGpOi+mwHe6QwyNyJxuhg2O3E4kEf0B3EAS3xYO40IajMQVkJZJ/CCTg9tJUYYZoJHge5RFdO2DGBP4qvn1cRKKvECffVpZKuCf3z6BL75xG+7cTzo1PRRAmn/49z5fNrIqOmm8y1X1PWa9k/MmA7auY827fSzdgPO4yB6tgPj4BrVAMQUBAGURVTkKhDm1SS0WNGLtu9w5L+lafEmjsKdYisFLGjLLpFaDANDt0sQTq6FRcpkK4Kk7IhrIFXVO9Jh2WrduiPm8tRBvYFrxIK5AOBR/rEsSQ70PII2jexZt5nVlbKuGxMvY7EwMmBLwz/I1ugRqybztM3MrgtDD5U6MluvMfYnr/MYID4md5nSWQ4Zyz8FYknS6j3DoU6PvXfEe67XQZDnmLjUq+WQgAWdpmY9pfmmWwxjbqnERXRIiwFJ9sHaxwcgOrTa3WvgRK9djcGIeX+dHN5pdjptdCtjGBq2LeP/qHn73wYs4iyv81vY9OBmWaF3EB27exXGzw91ujS+e3cTFrsX2fCFyQmX5UC/3XO0n4VYj2nYU1nESpcvt9RbvO3yA964eIDHhY1/4IBafXKE9YSnUE+BGhh8YvmM05yNcH7Xxh9iajAfioeV3kgM0WwbFJOeRIAV7Zsl10mMgM29lJM0/rHCeQVmP8+0CNDiriE5jwr40TL5+AKp4YpR+a1RC1XYGVDGQlI1FzEAsViuUODfvsvclkw/nliu1PPERxzWY9TaHJ8qTgSHVYvbnFTX2GMhnvTqgAAnK4jhWE6+0NlX5GYdL3iQ2ItxES96zR7KJPFdH3J4qR8xBxSa0fC31QnlP5aIcuwSH+rE6ebJqBehyVw/b55XgExUmmNF95x0LrTJSMwtqdph1NKz3W/+Ue+0mWv5sishQTXox4W8RczXEQMFeL6nRFsO71CCyyy1sjWrsicUrK3kkDgguoW5hax0/kjIiHDhLEAMlIIzSfSo5dENpLGCeWaGJcC5hoeaJ/Rgw9JBZ6HCADyyMqZ0DryKagx7LVS+SwIsG8IwNgK5rsBsCWjfi/Qf34ZFwJ97Aa8MxPnd6G66XblLjgrB7itAfM+KS4TYeriPRlG8I45rhOkLYENrzCKcgFsWYkwwao8j13qFRyz1M5tdQxNPLMzz5zCneGG6CXw1Aj0uLlDxmxrzWkcMSBPNjmXcqNBki+z2JBJADh7G2yv5QKubX43o8ZEixTOY7Vsky6TxmUkLrquoyIAV4Tkhksr4yVzJqI1t3qRhhg3VBboBWZgYpWGI/udoXAIA4dzx0mC7G6+X9HKCpR/buqh63Y9a/19c1/wpOKoZ5H+V4cwDrUjepKlYZpFCOL+dgU0dtDl+fv917k52XbroCaMXqzKUqXl5fe2h6sLY3l9geSCQazATnOK85okoErY25+V4JC0tASwnPPAHOcmxiibn5TiiY4jyDKMFrQSUmjzTqBbdRgHxtl46Q4BYJoRlFEqi+WQMC4ugxNpJAHTc7EMSA+Dwt8aBbgSKQAiEFYFyLx2EKDBoIFB0oivQjNQBGYWX5XhhZxAnglOdmSnw1mPVWRh1r9G8PxkHosT7ssIlL8LkrVeVHOHTu/mR/A5i4s7E+RiV+XCnBYGSJor1GjvEmr7ke75ph61hbW77RHU4kXwjAcdjmeHHstzgZV/k1to9GG1TZMBWDsXPBLgMlVogfcp6RKt8ptdmglNfeNsSovs+/T5QkcLLuVj+ohoa8fwGyxjJ36fl4XAYM2nx8ySsGLt1eax/dN2tsZQCWsbXqfMRylblapI5ltWTQwC/LURwxWhTZnRnXz61Rymvk9QY8OUrok3QlXLtOmHeIGAwMVNALABqIZ/OFMucsT8zvB8yvTK9f3xMHxqHvRI6qTWgyMKgbj3pv2sobK7FD8AMO1H/tht8BAM6jWLXcbi/yPhduREgRK9/j+eU9fHDxCgYOuBcPcBEXeHZ5hq9Y38ODYY2XLm7ipfs3BcS6CKAonQZBUuQHIBOeE4mh5EiMvgtwXiT8h22Xj/8zL30Q/O+PsbwnQFZewmicCZtYOp87AgdCbKQBSHsKAb60SzpFzsVpinvkhY8y6iJLtlHhvcWXyx17kYvk9v37777+X+Ls65b4J5/7fRi/9GQOK9m3at4x0ZXj2bTBVuCviieyExRgLHJm0SMvAyXwZJAraTFspIlaJe+niq97FMNXjmsw6x0Y2aydeGKoZ7TbXWoAh+yTBSB3MezUbNH03gasRDhERmZuSfeRCHAxdJdtnJr92Re4VDh2OmHZqDXSFgxqeWE96km9rhrUzKkG0wRlUKpxOY/S1dAmZtOWz1lTc0ptDWzZz4FdYXmhTKJzEKvWnQPALoUrA4Pde3m/yr4tyciBA04lnNrZg+QeNzRiII+FEwNKzyEbbObz1ITjYmxzt496tC4i+SFLC0cWrwNHnNkUiXgiWY2JkKKHdwltGOEd4+Zqi24MuDcGeJ9ACyBuAtKpk4XsKmFxowMRcHG+ROq8MLUSEDmA24hdaHJC/MpwE2+MES9un8C90wP4HtmElyKweEBY3BMpoRtl5TwcAKkhuAg0Z0BzrvpxbVGbu3p0PXJXD63oTfyyHsMAcW+yoLt6T3uC52/cx931IZjC1a+x7NEqB3OQiRlMblLRuLSv6gVWXSCtSlw6xdmkfY1oXY9HGdms3ebQLCUUMGlUmTShNBoxb0QDyM0A3oAVg4rM8N28mKTopt5ZBsaAckRxKGypkf0ERqrBJkuCSEG4eXmm/ipN5m9NCuYySEA9tyrwuga07OsZq7/r49RftRroquNJYQdPX1vvowbVSu8ImiQyc/l9zXqr7xXrvQGAUZMQkyImXRzKPXQqSUSOQ/nEEnK86JPHeAnWUx81Z6AW5aSEAICENUdAbo8OQNmlBCJh+DpCrsBvkxe5IQmIxZ0UEtAw/FLu4NA1YI0zIAKPDskzRid8cAfGeVxiQ4yTYYVt10rlVp4EGAg7AFsqvlgExEbmV0qA71DkHQlSPTapYYyXCxX1TXm7cg99+ZHf4cZii03T4sq26wAufSAvPV/aml/abh579DHSn3zVfve97nq864bB4AN7bKPkFsKE8Vkh0LiITZTfF26sLE+KH5JJDes188INOI9L6aanj++4+PsaoCUm5aOej5QxzLi8nGcBdHapxdL1AswgTSRvACogv0QOU0uY0TmqHCZWCpeBfZbn1cOAq8wwhrCpctFmz3rMgKy6IZdJB2sWrwFakwZUlYzQjp/vhc779tOALWt2ZfdfDOabiQ9vrOKxFejNcwxAZrBN7qedQ3U/Je6W+1oDlJFDURAF4P64Vnafywyrw9Cjiylff5eK2mSRpBHarWaDtQKXRsJoKGKXGgzssXY9fEg49hs8397FwAEv9E/hjeEIt5sLeEr49PnTeOHkCdx9cCgd2LcernMFlGEgM1Y9wA3DHQxYLgf0fUAcHXxIWDcDbjQ7dCng5774Ndh9/CaWFxDmkCkBB4bvgNAlhAu5L+wdOBBSIMSFsIHDRiZe2b7ca1LFCEeLS4yJTxZrjE1OWEhvJw7NOhqyLALzZ+r/+ZlvxYOXbiKcOhxEFEuWmv1k3ghAlQ/p7ueA1xWydvYk6zH1B64BKivmW5ptjC3ZX33PdG2nr3nUcQ1mvUPDACczfW+cTOD2pR3Yi0YXnFurArKQs6BhFM4G1v6UsUMDcKm2CBBU0HOvfk+G2MtEJcaKNZDVqEkjIIEkQnTtjYJaMKNZlT6IkfyY99vPvmc1fffSxA1klpFRcWsAy4ZN1rmzh35y521xaxPGOlHIjC/CpFJSs7LyNbGfAHcGVNljntLk/OaeZUDRmEOTQ2srXO6xVLOsQ0xkJz42CbjQhcLSj0gg9FEo31LFSDj24l8yksPSDxhJqh+BInaxyRWP3vssBdqNAWP0OFx0OG53UoWLh3BO/LTSaQPXOaR1hD8asNJWtuPglYpA1U8CrUYsmhGbscWd/jCzCz/14GmML6+xOlUwywHhguEGodU2FzLzbW978BFywtGcMfw2AlapyCaICTy+TcrtPs+s3JYWoCiB9lfufSV+6zefR3PmigfLVaNee+yJKxQrmuw+gMoen7j22vnIQ0TTCrxN2I8zaV+P39kjgzwsUI0ZvYdKot6zePY4Ze0AApqYr8XIHmbKbqysUcHaGojJHQi5BpeQQRZAOtDV4Ex9TAHBbJHNE4NeosJYCirREGnjdBAKcCSYzfTLaZ2ep/5f01EzpgxAAeqF/fTvqtiYn7fXTfy8alANBuIVtppdk83Z9nO+D5rdQzG0ZyQtCs2BKQf1gEwuyzmdTkoDxMQ9QD4XUQsjXqWHSzdmJnmgct8dJYxJACZPEVHZfATOzUxaP2LhR022WhAlcPLgzomkvWFQG9E04tGVoq1yDe3T34Owu4bkcRFbkbMCeGN3gHTWoO3k5jMBrlfAamC4QW72uHJAiyznc70WVFjfZdbEIfHVcva3O3SepyQg8he3N/HGa8fwPT1cHvGQvKVmUdWA61XbXHr8KiDsOta864efffgcJay8fLdtGGCVIk26G4Ya4DDQRH9at25bJ61dBwfxhpUuiUMGt8wOxUE75BFjxw0OnDB0DEgxh0AzJV/SgAPX4SItsKQBa1d1VNQcpS54mIfWPs9ceb6w1Oy1xrCq2VbNbP1vxfP6eA6MlhJ2mm/NlSn2s5YtOmI0SOgqkkN5X3hilG7dC03VYg24xL84YZMW+bW5yM5NVuDEGRAo90UArfoz4VA8tIoflkMPNYDX49QS+EO/wya1aCjiOGxxPi7wYFhjYIdF1bUQEOngqPFl6Yu0dEgejY84i0skENaux8IN4t+mUtUjv8ORE9bgC93TOI9LLHWbj589hxcePIGLXStFIZXEs2eQBErQCGlI5RkcAG6k4VbXNejPW2BwaI92+MCNu2jdiP/1zvtw/sIxFjthAVOC7EcLJKFjhPMoa3/r6ucIKXtlAcZd8ENVULFhcageLADWJTbU2xyWRzAKQBXVT2zRjOAmIQWXWb1WAJmcKzClzdfDuryDJnElg2f1fmi6jX38KYlZPCXZxMA0VtaWFXGMDPA44xrMepsjcln479hnpNqnhOBSXiQbyFUj8oPqlc2scZGBr5BBEweoBDEpyj5mMCwqWwhAJV+wqoErjC4zDtyzwjIG0lRWKL/POxnacWRf0g3JE2dG1jxgzCUjBjiZ91UNQpnxoq8X+m+y6Mo010q3Xp9DTdlNXBu40yQo9irVjJiDccWnTOSO0+eL1tyXe0YQhpYGSjHSF1BvoSBWZGlfK6bvHqNLWGkwqDtMOWIFvBICy2epdSP6ygQ+OOkm8971CW42G9zrD/DKxQ1sNguknQfaBH+rw83DLQDgYrtAv2vAOy/aap30sUzwyxGrVS8tb0fx+ToMPbox4KWXb+PwSw5hx9pVSv41m4TF/RF+FzEeNuAgso+wU+bWGYPGNG1RGxMwjkU//mbVCNozQe7bzKoe+hmiVCbzP/rUp/DebznBz3/+q5HeOETY4HKVuv5aGD0YJLRaqlPpApbxw6oo+z67VXWiDjrXFibX41FGLqBB5qeRSyLgqCwnao8sWy2YFNFk7MW7UU3BUboKOrBI3FUOl1lfBgix+W/ZeRXjW4sdtCdjzrLE6jmq5uX5qLsj2tffGFmZCQWJQfNX1/KQufcVV+fMs+33DUYlTTeAqjoHk3RlUMgSBBRJiACAIgu85LfF5Z6k6rptGNiYqvvhKQlDiwqrzroZCrgpxx/1vUus3jguKViVClhCBfByuiK2tYPdH3luxI1mh6UfsI0tzoeFyAdHAjyDVhHLVm0PxoA4OGB0pWDiGAgMCglNI+/4mBwG9YqM7HB6tkZ7SpIgkMQRA7L8NsGNCan1mSHsRrlpoZMYYNVwShoT0luZYOcBogyTmk821b8/cPAGbrxvhxcf3AZvWrhhzw7mp1K6G0wRVBYLAX6Tc3nYJeRuiPW+r8e7fuxU7XHou+ybBch3uFYNnA1LHIRObO70OY+EUWf287jAWj97a9cLe8Z3mWk7VAVwQAvY7NCxE7sMYrTaARyofAPZoXFi4O5JnLOElTVMise1xxaAzExqENE4kRwadFMUI5qfMOdFoPn8ZsYTpgoOe95i0AJTVlTtpWW5zVz/VANqdSHdjN7tOE11rwwsq728WqRJ3lWDVlZ4t/vtNb4MmT6jqh8ImNjn/FJAv10SNt4FLwrLjYqHLwA8iOssF13SkPeduJIqUkRw0skwKcMvXwsJKWGlIFYXA7wvUnb7bO5SgyF5PNlEHPodWhpx25/jwHXimxgP0VDEe9v7AIBfO/sKnPZL3Fpu0fiI+7xGWhFibASZ6QHXi3SePSM1DA4MtxpBjtGftcDo4G/0+OCTr2NIHv/25fdj+/kjLO5JJ1zJW6Rg7XqG7xnNRYTfRZk/SeSFyQszC1ziEwD4PmkeIvkBpQRY0d5YWW911B7BV8WDPfN7NwREOPx3X/UvEb/K4cde+DbsXnymeDrWccGYWQroTQ6/p3AiOYwWdBTQIpGTXWpQ4kZG8gYG2g7qnVV/Vz/3kCqvHNdg1tsc5pllYJVN6COkU4IZwhuVN3cOSS08EhaueDXZMNNDAGp8WCbAHgEOCVEnmcgNDlyvzHrpTDHfFzjAUz+ZcB2bcaDL5op1MLBztcBVZIzp8v6rYZUIY1zZy0zSkH3C9HVzo8W6I0htvAvsTzismmHAFXhK4fVgxOr8511AUkWnvSS35JC9yABMNPpp9m2XqriY/XtK6GKbgc2BPQJFBPKI7DM7QgAtpzJF8Vo7CL1oz5PP7WsdcW5Z21b+WodNh+dX9/HexX28tLuNL22OEZPD8dEG7oYklI4Y3RCw7Rp05wugdyCl2XHDwDKiXUur2uASUhI22Tr0CBTxybNn0L7cImyQJyhjZLWnEWEzyiTvgNhq0O8AFxlhI8lHptiqfpzHUf5+hysTk5Hkvv9vF8/i5z731RhfW2Nl+Bm/ybp+D9vrocAVcCV4xVSqDvbRMmN4+ePqCvv1uB71MM+sCOtUqgwpjTEMm4dsUS1A1pAaSMe8dGkdpOI/AAKS2FzLkPlRQBbbn9eYAe3Ex7N9yVnK8qkGX0iZRqWbogE/Nsw4fvrodP9zkKdmHxl4Y9dUr9MyIIMpI0qufbqNPbvv6xxhLCsFw5in1hZvAj3YlRlL+ZLcUmNPMWwvoNel69ZHTRY6Jp+LLbZ2cFW8zSwxlqKMeK2pvB3ymHNRiqmV34nnAmi1IeK42eLI73A6rnA6LKTyvhhASz1LAmJ0GKJHNAm7MbO8AFlevUuc3gNHUsRxlPDG7gD+zBcQiCsgq0twQ8psrVzpHeVj6Ib9HQzZwKzfps5Rdp4JhLv9IT5//zbSRYOQynOPikU9VqzZsxnvSwgmB7iONb8Thq3hjX1l3+eD0OlcQTgfV/BUpOdOARXnxTdvYI+lG7CJrapM1NdIc511ZjxJfBlAl9bODcZsMm/bGuNql9pJcb2Wyw0csKQhgze9xh0zLneU0CJmA/hhwpQq/lxmAj+oKqP29S3NrFxmZ9mwGGHbTCSJrN1hZ7Gk9r+yv+eMMVFz+L1+w/XfBmhNWV4FnOpTYV85RR56FrcwYWgVqeEShSDR0IhNEiCr1/d3lyorGhagyphZpbBe7iUADKmBRxJlCY+Zdd3FgBHi82v3wvkhe7fZ53LphrxmOfYbPBVO8/vbs8eDuMbAHs819xFB+MT2fUjs8PzBfby2vYEuBqwWPWJ0iE0CRwGZOEmx2YAsLCN8SGBlbbmDAV/9zBs4bnb4lVfej+GTN7C+r36LnShNOAjw4ndAs2GETdRu7LKOT95JU5IlIS5kziY9ruuLVxYcBMh6XK+sRxi1kfreoWFwcZpw5/VDfPr5Z7BcD/CU8Mb9I9w+tVgJITXU3sUOKtNHAbTerMs7kRTUZkysfB7AtJO7/W0SQ43nNhWwr479mMWXazDrHRgOKbNwusoTyWkwiTP/hBq5H9jDKwhiYEqh0EalgwrglDtYwE30zgaQZQBswrLiS38b7dfRkIEs2Y+cu5kKgsWPq2XkT9t8gs6TFk0nZ/u5D4i6ZKA4o9jW281fa3/XwcLu575ui/NznncB6VmNJ7mY5deBVM5DKkc7bipdvoJa1SR/llb5Nca2y9eii/ZOW6G3flTpRrm24CIOfafAqMjkEhMOXMwVnS4FuNhg5Qd8/eGX8FQ4w2d2z+BOf4jWR7z/xn20LuLO9hCvnh1hs1lgPG8kqWCh4JLRc0NCsxzRNBEpEYJPOFx0eN/BAzy9OMNvPngOX/jEe3D0miDvqSHRhWvFojkbQEk6TsWFw7gSaq7vGG4EwjbmpAIpAUnlhY9TKX/EJMQYU/lvBbNOhiWGbQOXs/9q+/zmYMrOoj2tZ/edRgXI5c4dHtnThSGgXlL2xPxzWU/q19KP6/Eow8Cgkads0ZEdCFMfDACT7oci5ZJYYgCQPesh8x2TPGOL99oXCihMWmMSucl8Pf9bTOmZ1G+pAssKw9ceL9Ju+6LMySTV+qk8VgE+tSl7uV97Ys+e67oKxOJq37WkMEveZ3NZfc4eSRZ6bLFK9mVsKUDAOJMfyuvl3gufoYBzcv2UGcB9CnkfdXHIzsEkh0SAV1P3yZRHKXc2Zv0sMQiuYpOLbYLEzKfaMxy4DvfGQ1zEFp4Yx4stPDE2Q4vzvsXQB6Tel3k2VeaxDnAhwXlh2TknMfBGu8OB7/Ha7ggnrx+hPdft1ZfRRZEWuk4YvsWzRKviWhkXieEeIOtxxqPGGkxDGOkaYpcC0ugvz+XzD+Tk70dElmYxKCcIrLskZAlHPka9vRVwLmPQ1+NdNurOqQDUIDzAKRtoiE7XrWJt4RwboQRbXVsu3TCRmwPCzooKQkQQutQUNUkKSOSwUMYUSMoTOTdBQkMqC1TWZ92lXYCeJnv5AoXFU9uiCBBUACvbvzXQcgq65bV39vEyUKjkJpZr7HTbpapN6mYntY3KfHs/uz+5+K5fNpMO1gwwY4cBmLC/sqdwdRw7RiY3sJ+oSoCSs8U6f9Lvv5jdB5Eesna3p9KxULyqlM1V3XPzep58nggTj2RHrDLBEWfjMj/WuIhAEdvYYuFGrHyf/bAA8Yo+9lscssgJnw5nIiuEeEu/Oh4DAJ4Kpxg44H+9+AC2scHCjbiILfrkMSTxkozR5byGA4vxu4EgDPi22B00qwGL5YBbyw0+ef8ZnL18hKN7BBoBpybvICmO+AFoLxKaiyiyQchz8CQdDFdOTOI9wfecX1e2JYC1q+FVI0sNcXWxYx4brogVU9sSmfiJGc15QnOvwcvbY7x3cV/WFWcNmk2S5ioemK4KdOyTGc4N3+vTssZYkNjD1umwPi1rpAVUP7XAWfudscYwIJMBHnVcg1nvwBCKrAA6YypSw9FpF0Mvk+TCjehSqDp+IOuBsyeWVh5sWMXCKteAdDk0uWHUiWfe9dB00RY8bF9AMWW0LiHWAcS6LNXGwi3Mh6WANnV3EHmOJr/bc/POIPu6fAAlQZoHBzlXmrzWrsOov3nfNO1MMjd8rz3FbDKX9yBVQUqe79mjteMocHjB7SWpIVASMOscYgCedf7okkgKratXQwkdO7QuZr8SMw1eqQn8SunV29iI51Z1j1fc46k24v2Lu3i2OcGrwzHO4wI3QoebzRZvdIf43Olt3HlwiOF0Aepc6fJhlVnt9kFtRNOOGAavcxXjWJOLz54/id/6+PM4+rx0ZWIvQcON0jXKKhZMhBSkTS0c4C/kGKHjqX6cWY14CxVXTRXy+8gqrbhyPKzCzsjGhvYR+qabn8fxf7nDv3npA0hfuJG3m0zIV8kMCZWJ4eVji2SwmnEtoah9tVgSLvhZDsOPh+ldj+sByMKSIN/VlIrUMFCCdwMcIXc6HLkAImTbVItpWW9M51pjUNlXwhIO8Q+8DAJB92HzYJHLFZBJ9kPaW0GPraBZbShfm83XFXTZXrapv4a1p/flc9qPQddSPcyetz3UeEMGsOpOczT97s4N3w0sE5lfSU4MtAOAUZ+PIHgu8kICo4efgJDl/Mq9L+wzRksRzHKs7HEGKPOCMpPAroUJmfVrPmujFlnsPsg9iFijx3HY4Mh1OEsL9Np1aukHbMYWD7oVLrYtUheAWLyicqzJaGmC9wkp6v0MjIWXNcy9fo03Xj/G4gFZ00S5X0kBrSFl1hKbTIEkASEWIIti9YZY8YSrSXY2f+/7bMxu9kM2uFxAee/yAZZPjfjC6S3wyWL/iy4BXeW8clKy59jyfugT+Q2qvwzIQJWBVvV5XrOxfmeNXWowqh+dFY+zTxYTgrIybd7qUsDK97n5j+QWCkalUiA3YOLYdfCOM3Bk/lNSBOEsNzSPrAMFwoBSCG5oRILLa/GanWNjgFcPKYlD5ukbAXgWYE3AGo8WBbQCKukhh4l3Vpp9Gaxz4JDPSwD9lsrjRdkhBega7JqAWDqyoTyAnR5/0qG9jruWV5jCx/I8+KyOAYoaxJQ5A4dZkxexP4ns0VIx5DcrmV1qMhkigjOLCwAGZWPV3Szt/Y5wqiQSv+e179ClBpvYYukGdC5gTB4r32eyQD0OtZOhdFvs8YQ/h6eE2/588nl9kNbYcYsn/DkaRHx89zys0+Hr3SH6GPDa+SG2XYtxdFI4SWX+c53ei45AERh3DnGZAM/whyOePJTOhS+/egsAMK6AsAUQAQ5SiKeoTOBdgt+ogkQWXBJ7vMoMPbLlirGH3ThZFEgMepTBYvLwqINnipa93QwJIruPyH6XCZBAMI9B+3KhN2Vl4XJsLIsGYXo5XZ9oPpR9tXQBQgki1aSqKDPLidjjWmb4/4/RpYAuBSy83P2L2AIpVN0KGY5l0W+TTJcCGleALEAmkU1qZUGqE2Dd9jZPykgq3SvtaGsWVlKaquioxwzazDtZmLl7DYZF3afJDmv2Vt1dI/+8YqFUm+vWYJQFvolflo599N36Z95uBp5lmWE1rMphWvt5F5D6GHVb4B032HFTgYB+wtyy17QU0WBUCnDIgRxuhGdBv+PoMCaPrXWPUfNcqwD1KcC8tMbkceg7HPkdIjvp7lEtIsws8SmtZrw83MIrw02sNYj8x9P34MUHt3B2sUTcBFDvQGOZvFxEMdprExarASk5DLuAsBhxvNzhRrvFC+dP4JOfeS/WL/vJxOUGRtiJ4bvbqV+LJ6TGIbYiL/Q7mUB9L35ZRlmlVHX1ADDt6mFVire44q5oskIJLp+Jf3P3a/Ab/+Er0Zw4LPorqtJzZta+UUx6CvhmSde+ZKHWtlt2XDJkuSckx77GtK7H44xR/bJsbpDOdQpsJVRSRJp4J3pOqNnpHrJ4rllWtn2d6BjoxBXMU7OwCvglx8gdWGfAETMhkpsAT7bPGiirpY7z87iyOqn7sYJg/VWrE44JoDz7m/dsA1wGz0xmWA8zMGdIN8K5D5YNA63s9SN7jEDlI0bZo6x+TQBnY33z0GKQes6Il2FKDRIje+QY640VNIvZP0slMS5hQeLl2PhYGFlwCFqgO3AdFjTiNC1zJzPnGHe6Q5zsVuj6AB5cAbIMVLF5kiDJRKPnPjo4n7AMYiR/v1/jjXs30JzNFuhR5R49g8bSGYmdVpSjLnRZ4hpUClIWxTNwax8C+lYG7/+dAXxhcxuvvXoLvqOrm43MP3T7hp1aha7mXx8XmKq+CHWDxOvx7h19BTDlx7TAHihiNE9eiB2GZYGOeJKXDMmrsbvkNNa8apcaLNy042DdQMNRwiYusFYQI4NiQLbh2KWm6pxnvoZqpUHxkr1HLS8sLCZZnxrg0xMmz1vjpxo4qrsV1swpK3avtRnXADfxtKqliGYWv88nqwa0dtU1z/OYWj5ow0zuYyWvBADMcpYIj11qJUeZAY/2mh0aabrF1sxryICWvSdybeaZzLn4Lu+fsO4sZ6qbljUUlYUnnTCPwzbv16SpUnyf2uiYp+aR2+LAdZmNdyfeyKDZ0+EUAPBC/zQA4HRc4eXtDVwMC2zHBtuuRbdtkHZaPOkJrie4QTvdjvLTRcBvCeMBYTxMCM2IJ5YX+mFlYJkw3GD4nRStU6Cc34RtQtjGUiBRryx2wsxK6g0MAND5mFIpqDDhEmP7LY95TrQnbnFFV99noTLqfNBQBNoEkN+/LfNelcr8uBM5fJXnGKsq3zdjaFnu40oBKBdcqhg3j630sJxsNq7yrd87fviHfxjf9E3fhKOjIzz99NP4s3/2z+JTn/rUZBtmxg/+4A/iueeew2q1wrd927fhE5/4xGSbruvwfd/3fXjyySdxcHCAP/Nn/gy++MUvPt6ZfxkNmwyMKRWUdWPASZcadCnkVrmFJlsmm7mnVpa4KQglE3Yxi99xOwNXChgjsEvSSXjMlQ8LCksaMiPMjnmRFrrvgm/WlF45xryiMDVJnD+XuCQiA4oGfM68msgW9e/aC6vIIN0EvJpXWOaAm/1usr1aOrjjJlc2opooToNemPzbcaNVjxJkjKZrgJ+939adoyGRDTpjSbAkFI7SJOgFSrlzo11Tx4XBt3Y9jv0WT4UzPN/cw01/gbO0wlla4tDvAACf3zyBN7YH8v4TK+KtSSArO6ju/rGMSNFhd7YA9x4Hqx7PrM5wrzvAJz//HrSvhkmVHazdPbZcDBEdCfVWJ3rfM5wZ9nZJaLbZJTohs7Ie1wjxUUAuS2JsUk+yOPnQzS/iv/iGL2L8yh1SwDTLfYRxyfB3djyqJ+PJ66qf+yohdcKyb5vfweM6zlw9zEBdGFTIc4kBRAZ0ZdNfA4LIvArLHGoV1CxxUxAqsssAlrCL/ARcqZlb6iIFB/V3JIO1BGQRr8Ayv0WttEamyULevJ3KMWasKa7AJp4ysYyAY9vnDoIKMNX7nDtyGbhjrKVsSM80Aa/ma9N9EnjWe1p3LgQE3Er5CmnCsrb3wQx2xdDXZeaWme4P+pyx1moA0rwRWzfm9znLSGl6X2tLADu/yFNzX+kiJjFn6QZ0HIQNrkntg2GFzSjFGbK5zFU3KGGK9Ktvydh5IDq0TcRB02MbG7zx4Aj+vCD6+WVRWMA0VjKPzMoiUHSg5EAsXRQpUTWn8vTf44xHAYrqeV//ZiY8uzzFE8+cIN0c5/7Qj3boq87VPttXxBp7fG+hZl+suR7v+tGlUNbe7HDgReY1qhdr42JmY16MAj4Ik0sYOpvY5vhgBYWiKCl5iK1zLZ+wx+3vi7TQ3KQqJNtaXFUPBgoN8Or9VPKYJQ05JxnYo4eAHta1z9hC0wZWtpaupYHNJH+pi/I7BfcalRnW1iW2ba0e2ak/cj0ajcMDuwmTqx51XiI5XJO9vPpK2eErUG+pDaUMfMo5i+Y1Vng/S0vN4ULOC0usdZrv+PxeeCpd6+VeSVxqaJx4CNu9qfNT26f5XgHAkd/BI2GjXsErP2DhRulO6HcCitKIm36Dm36DJY0ieeQGF6lFzx5HTkCxF7qn8cZ4hFf6Y3z+4jZePLmFl09v4O75GtvTJdJFA3fu4bYy94MJfkcIG0JzIUbu4QJozqXgwcuEg1WHXWywiw3e/+w9LI86xGVCXAL6kS+qk4sI10WZj7WAwk7ynBQIsSXEhcQbchH/7W/8NP7Wv/5xfOTz/6KsjCY69Eco1CeeFmAeZ5jhej00RrmB8Or5ET67eQq/efZeYOemcYBmcSPNfub9VeutPYBaDXBxNoDn/FwudNWvMa8xLUqxuxzDHkcS/1gh9+d+7ufwvd/7vfjlX/5lfOxjH8M4jvj2b/92XFxc5G1+5Ed+BH/rb/0t/NiP/Rj+7b/9t3j22WfxJ/7En8DZ2Vne5vu///vxUz/1U/gn/+Sf4Bd+4Rdwfn6OP/Wn/hTib4NZ2m/3iFwmeUG3JZEQRFrQ/IvYYhtbDOwxJpvYS1vWXWqwSfL8LjO5lLJbeTNZl0ObXHoOuZpRdwpJmkrUXlkNolZ0JUDYPuw4nuTxhsYsLczXCAOWCiA17bhRAKjyGgWdqip03h7FCHe+/dyPq36sBCnK5zFnY027FvoM0Nn5T+6HBtC6na0FTuuyIl4YjWrJXX6PjLJrAcPeg0XVwsjpfbVEw5F0+iiJQ8JR6GayUqcdKKVCFtnh2F/gmeYBng0nuOkvsNPPyjPhBLf9Bc7HBRwxnlxd4Hi1w3LVw69GwLMAWgyASXTlDQOLBN+krDlf3dria5+4g1c2N/CJTz6P1WcXWDwgNOcKXvUMv2M0W/HKcoPKgRwhNg5xQRkosoq668o1Tbp61KOWHD7qmFfc7Rj1xMmM5gJ4eXsD72/v4rue+xXcvHmRW7znf7x/Yp6P3CFr36lmyaSdE7IG3P5dvgZMJnY7n2vdoYzrOLN/JBQAysCSxC6DRUnnvDH5vCA3k3NZW4gUzZqVmETRQCwDN1jnSmbKBvMGcAEF8KlBoxqcMU8uM51nBfGtAyKBc7FnLo831pJBMAIQVYARa0ypvlg162r+DWJM2V12jPqn7bd+LLPDuMSweRybmOVrkmB0/lomaBLNCJcNl4GqYIUiuRxYtrF7NrLPYKLtw94Dkwja307P3hhZVJ2HJ+lWNmfFJYh83SSpCzfgwO9w5HZYUo9RE6AD12FFA/oUQASsQ49FMyI0IyiIp0quwoIAJ2wxBIZzLCa8TAirAbfXFzgfFrhz5xjhnoffAb5HrqiLb4nI1LM5LRGSs8JJALgF8QEoHYHGQwArAK2cxD55x1sBtrA/1swf9z1wNi5w7Lf4xqMvYbnshTnG03+PHmsecjp17DDU9xFjzXXx5N09or6x9bzqiXE2LjCmwsA5GxbYRVnr9km8RR8MK3RR1CRWLO5a8LUCAAEAAElEQVRSyGwqA7Gc5iWdromNjSWF8jH7I5XchXCRFtikxVQlocUPK7ILI0oL67AGV7KdgWaASd8E9OkxnU8NFLJha2vrjGiAEwBsUoMeLoNhApYVGWGtPul03hbPsAJcmcJinqPU+YnlHgY+GZNMgDqf5/QIJ+b4mo8Yw27HDTa8wFlc4SIt8vOW+1nOaO9RzwE7buWf5imyv5AJEeaNZe/PoMXzqGoWOW4BEetGYHXhw1hhdhzJKRkLN2Tm1llconEjnmzOcOS3eZ/34iEu0gKeGEs34M54A5/pnsEr/TE+t3kSv3XyDF47P8Lp2RoXJyts3ljDnQaEEw+/JZETjhJrUlvmTddBuxIC3ABIQEoOr50f4UunN7AMg3TdPR4wHDLiElqsl465vkvZQoW9Q2ocxpVHf8Oju+EwHIo3LhPwF379X+C//Y2fxje/8Wn8H77wL/FdX/x5OYnHbTri6GqLFWNeqdQRRIBzgHfgRUBaBNSU+zpPeeLjEen/8yR+/b//RvzWT3wd3vczhLBN2dQ+g0xX5WEPi5nSYlo9sSwX0lyplvWrLHNvTsQlH7PYZMzux2EhP5bM8KMf/ejk75/4iZ/A008/jV/7tV/DH/7DfxjMjL/zd/4O/vpf/+v4c3/uzwEA/sE/+Ad45pln8JM/+ZP4nu/5HpycnODv/b2/h3/4D/8h/vgf/+MAgH/0j/4Rnn/+efzMz/wM/uSf/JOPc0pfFsMhZW3vwo2ZgQUIvXdIHsGnyUK2S0E62OWKtcsta4Gpz5Oh90NqK5lhMYM17yspxZqrdJncANGeNwpSzc0DrbpxyeOKBOP3XNrZopLqXTV6uOyrZdWQfaypGvzaV8mwsQ/Uqim+c0Cr3s6CmwBaqcgvYfRnuQcJCVHBqzIpi0nuwGKYX1cyTOY5DQIpvycejEQOMYlcsKGIi7hQRoKw9wysv9moOaLq20VbPuDQ73DTb/B0OFOaMOFBPMBFanHTb3CRFvhs9wQG9jgIHU6HJcbkyt1gAB5IC1tRA+wYfhklsdQOH88cn6GPAZ/74lNYfyEgbAHXS0IBSHIRtozmPMKbqTsgRrzW2aO1CYklEakoutkv66pR0yquGvsm+n0dNljowss7jM/eexKv3TzG0g148OAAT52m+ushu7iyGl6C0VyjPhn2XL0JVawtln0x1A/GjHuBiUb8cSoQ7/ZxHWeuHqSgk8kB65hiMcSZJEDn6VEBr9rPKoFybCAUYEpkaciLUkABYrLYof6MDKTK4KhmC9UejqXQ5wDWeEJFSlhfV/Hrsk58UwbWvlFX2+38qfq73q4+1lUjr8eq65pL5Cfb60/xynKqqhYRYC0nZC5AlGN5Vt3PMtAXNKaZhLAGrhJKMam+Tk/SuZBBGEA58RSDeLkHjlK+5qUWWzyMUcfwFNH6EUs34NB1CEhIAHYsFfOV+tjcjytEEFo3oovSnGRyN5wtPnU+JwYF+Z3AoCbicNEhJof7JwdoTqQlupm4A1UxpFcPEgMZncab0CD5FYhvAnwMiisQdwAeALgP4ATg7sr399HGI8QaSKyhBIQNcH+7xvlygUAJu12LdceXJIGPFGveLAG66ilLAAD59prZbvl6Ts7lOta8ewczY4B1MSxdswPJutQKHHWnuqANIuyxgR0ceyyUpXMyrnActrJeVsN4r+DSLjVo3KiMUp2LK+AHWlxvMCpgFITlRLL29tTn7nnm8SuxQuKIGcLXSpXcuZVKs6UD6hEnReHCGrIhQIwV3n0GZjxF6SBIJQ/Zsc++WCVfsnO4/AWqJYh9tcC04zRiPAuorE8WgsWzyvI8U4wM1esTrFuhqkpSo02zQs5JGkS0NOIsrnKOcqiMqAixQZHraib3uvYQKwqgy9e3dp2Aaql4Bx/5HTapzewxY7aZSmntejRuxEapTzfcVgrxrrDrTBX0Sv8MTscltqnFq9sjbMYWr50d4fxiiXETpAt7oiwtz8ubwEgLRlwlIHk0FyRFawVEkgea4w7BJ2y6Bt2uxf37h7JWOWkQNiSFlKhdCRXUYe+EkRUIcekwHDh0Nx26WyJzd6PEqt/96ucyrcMB+IbTF0Hv0Qfm3WrqYWwtIsB7kHOA9/q3K+CV/a4/2TlhYtnP4JCCK3GjakqVAmH5xoDVHda8g5Bah3F1mcN06SO9h+ZkssTsf2Xsq2lHnqqwJX+w6ttrWePEHytXW1FinOZKjzPelmfWyckJAOD27dsAgM997nN49dVX8e3f/u15m8VigT/yR/4IfvEXfxHf8z3fg1/7tV/DMAyTbZ577jl8wzd8A37xF39xb5LRdR26rixQTk9P385pv+Mj6YRiwFUCyU/7YruYJ4xRKxtQ4CO3R9UgM6hB/NIN0v5WpXFJExVj/iR22EFarxrbql7Ymx49/4046SKSZp9Wq3KYBntQI8Fa752vtwLYJOhMJYb7jN9Fv10BWdXx902eNsk6mraz3ac9n0sIATFPrP+29ykDW7PnevbwNdClDKnSEpczvVb2P/3q+BykkHXnDUUc+R3O4lK8SVSTvgijMq8aDOwyqGmVjmO/EXquasuN8nuWVngQ17jpNwCAF7snsUsNLsYFToYl7m7XON0u0eeuUnq/GUBSTXl0iJEQmwS0CUe3Njhud/jsvSeAi5A9tex1bhBQq9kkMX2PhZWVPCG2084elMRbi4ZqJtrHypqPRzFBfNRKRxID+rM+YJNEDpMGn6vlbJPnQ0aefOtJNj/Hk2RZJmszOdRtUpXMVMwFA9SYMDVavB57x3+qOAN8+ceazNDRqjBDQCxbU3hKyD5UxqZSqZmZwjPLAsPmt0DSLdX8nIyNVRuyjyzyFNtPQvnoZtl8xQrI3f8w9X8CBNiIcApg2bxJmcE0v95sFM+XG4jMv8aE2nexgHf1sefD8HYiYFSAZh+AZeCQXZsNkxbue58Su0vXNILgUKSads8HvS8GjGUZ4CxW18cf2QsoRYwFRnQIOXka2cE7YT0Y80rew8L8XrgBCxqxoFGbxQhY2rOwjw38ehDX4r2RAnYxYDs26IaAGB04VsZ/NveNALFDSiTsYM9oV734ZG3XwOCm87DOi7l74VCxdglaJW/A7gBwz4HoadBXAXSDQfdb4LMHwJ1bQHoBwA6yEr7CPfZRquaPWhFmAd+6WKwiOFIpZDxGZflND8L0pvuaA2c51qCqhr8j53I9vpyHsTvMWsNUEMFF7GIjoBZqz1mZb6S7tkfQztnmk2VrU1v32uPieRUnzaW61OTCbQRliaBJ4RMcEpeibYQwtmz9LoVjAVmss2FEAjjAa4dxj5S7rScWJlM5BzdRlRj4b/kMzLspAy8un3u2IwHn7cp+pg2uap+tOi8ROWLlT1QNkxLaGDhgh8rbC4Te/KxQOhXXhIOdqllESphwEtfFhgbAeVzm/GVp3Qm5yV0nrWOxvP8CLNp8bddizOI65xPWleSmAEoeqefZaJ5qvztiHIdNfv3adzj2W/TssVavLCChZ49Xx2O83t/A3eEA9/s1drHBg90KF32Dk5M1uHcCZI2qLPFSKaYoJu++E+k5HyakFsC55B8gAbLgGIuF3Ith8IhnDWgkpMBwkaT7+lYKEr6XAkRqXZ6749KhO/bobhB2TxHGNQsjLMn2/+Gpr8Q3v/y/GTyJj9/4ihxf2FXFHhIAN3sFewcKQQCs4AHnwEF/r0Ar6axOalKqMdPRxENSttPj1J0APSEu6sq9eX7RpEC/1wplPmqWFV2ORROAC5fjkS3HpKBfmmRZaMt5Z76Oxy+6vGUwi5nxAz/wA/jWb/1WfMM3fAMA4NVXXwUAPPPMM5Ntn3nmGbz44ot5m7ZtcevWrUvb2Ovn44d/+IfxN/7G33irp/rbOiKKweGQvMg49GeglH2SzJAxVKZ4kxayOqxiblTaenidwI0SuyCrfhcmUdmP0HPzhEumTUcGsmpjRaAkJfu8s+aA1eS8Zo/bxL6sjP+sItLMEpY5Y6sOHKZVn4NYUyliqWzUwUKqF0V6WbPRjJZrQdeqRRPZI1M2ohT2VX19hT5t0hIbTuUz9j45EJbarti6FdqxB+VeOqXZWpLWUMRT/jR3gjlLK5ymJRI7HPktIgh3hmNsUovXuyO8tj1CFwNONitsNy3SzgOjA3VOqbiinXa9yDji1mM4cIi3BhwseizDgHH04DZhOHIIO6XReoA6ZFNE10eZ9HQClaoAkIJORnqP3HiFJI/m7oKzkXhCl9079iUhOjHXaL+oTQpNnrxKYerXvdlkeZXh4Z5zJj0HhlaLzC+HGcSEdGVL2wrDe6uGxO/y8Z8yzgBf3rGGgczKsuKH/e6IsyTOuElWRDAD9LnPh/lv7e+GywVIquZGRmESTfaDAjSZh1ZEYUdZ+cRel72p6vmTCog0Z27V5zW/JxGEkM8Pmb0k11DGnLFVYxpJJweDwIzLNJE4ZlBr2rp8rCSYNRtLpCXGkmZhbWmCOfeNNFAvdx2qnrPrMtArX4+ClJmpRRBWlTF/dTJ0lBBV5kcQE2jbiwfnggkAlagEMBNaNyAB2U/zIrY4HxYiKxkajL0HjyRejJGka65JuJVt5QbxGOFlROujsECSAzwjtSR+GbpwNZ8sN1TdCXXxDufBLoDdTXDzDPCHGE/+/lfwVbvPY7Nb49MvfR3wv6yBTz8N4FTQNH6TVkiPJAPZ83wFvtnvTDKPR01ys3/Yo07pjxprtLJinXal2FJdh8XmfYew2HgdZt7148hvceg7PEhrtG5EnwIuxlYAby+sytZFbMYWSbsa7mKD1pmMz2EFAbxMmngpT2GC99JBbxMX2RcpKlAPmJfwNK6YrzDITczJZf0+ayyFWX7ADiDkwj3I8gGHhgYsUdbdNZBl8cXAKjtP6YQYYGqT3GRLj7tJDZY0itJEfV6tqZQNkxvObUzq3y+4hedyHQbY1c/tWAzxa6JBQyM2yefrEe9el/MOu6YBRT1TmGUBQyw5oQFaxsYCgAHFo7kG++prcSYv1UZV5p22VHP4ZSZXeGHr6WfoZFzDU8KtcCGAoxI3brgdHCU8iIe4M97AybjGJrXYxgZ3toc43S3E5H3XgEcneUwi+J2D3wE0EsJWPLFIOw2yc9g9KQCU32mO0xKGIyAtGcEltEFjATG4tTnagZ3IC9uLJF5ZowAtcSUd2vtDh4tnHfpjxniQ4EYCDSqJH4B/8HV/DL5nfOj1z+ETR1+Bn3zPHyo1FJMDuoSMsXoPOA+q2Ff5n3MKWFFhXznk5+dA1j5GFKrui4AAWiJN1AK6s9dWINYjgkas+yeHCXCVx6W/dU2VNB+i8viUsYyiWFFSQJpt8yjjsTyz6vGX/tJfwm/8xm/gH//jf3zpubn2k5mv1oM+wjZ/7a/9NZycnOR/L7300ls97d+WYR4kQoElNC6qrlxb4Jq2Onlso7BxhJXj1US8BAoz4LX9Fj8u8WMyIMtkgRdpUXWmmAJDdQfDgUM2LndIeeIErIJR/tnxrA1uve/6mkUrP328r5KTWh44B+bs+aIptyoFZ8rzPpCsvLZI/mpduj3X5k6QAQMMkArZDLFnj4vUYpMWxci9Ah7N9N1khHIcNwki+z4HVhErnwuXu08Z9dYjYRvFN2DhVN6hi4GlG7JJYqOVqtO0zJWwgQNe7J/EK8NNfGF7C589eRJfPDnGKw9u4OJkiXTawJ0FuI3LHZ+aM/G/ajZiihguJG75VcTCR5wPCzx36wSrW1vEg4RxJd06rPrgtwnhYsyLZQ7ilZUaMUKMK62OOMDHiO/+rZ/B/+03fwJ//ks/C5dmrKz5Yvut+GbVL3du7+LfjcBwssC/u/t+/PydrwHdb3OC9UiViKsMD9WTK7/ezQLKbL+SdChjrcqtsuTEgsr12Dv+U8YZ4Ms/1kQUY28BmZQFxbKwNiNzSxTEwF3moJGnbFWTLAJTb0Mizvs1TyVbxNr2tpdsBE9FjldLuQXYMrYYcqe+mrFFJBLFuV9VuWan17zvXhRgCsBkv/UwA/na6N0YWdLE5zJIVn4vyYPdp+wvZnELxvytzNxBGOFV5iExxd4D249tPyqTeJ/X5JzZZfckssvXaufEClgZO4vAGJO8b54SAkX1b5Tfl27IyYYZ/o7KRE7scBLXOI9LnAwr3NutcdotcLZbYNgFcOekY+6gnQyTeF/5HvBD8cGiBFDD8I7RJ4+j1Q5hNSC1PCmEUGTQyNmT0YCslFuiL8HhKeDrgec//AK+G38ff+pXP4r/5sX/EX/0fR8DvoUBdwvAAQQhm8canszrjz2sSj5/OAGpC3h5c4wXN7dBW59jwSPN7W8Wa4DyYawPvS+Gkb6GcWUnqGss69HHz//8z+NP/+k/jeeeew5EhH/6T//p5Pl3qgnJ/fv38ZGPfATHx8c4Pj7GRz7yETx48OAtn7cUPQU0sEYgreYk29hgFwN6BUmkWYhDHwNGdtjGJqsQLu9XPj1HfodD3026FJqP7KAd3G3ta75apmow2RwgBWOTwYlcTrxtN2mRC9G1n62t8cXMvRRgsqRd58+iTilAls2HczuV2s6lHgZ21d7AfZUL2e/78hTr0mfnaaMhITGYF29kB9mi+PNaniY+VMVWRs6pFK46ZUt1yWSDPssE833Se2+jSw02aYEuNTip5IiAMLmK92/JQW3/dj+t0/FZXObctWaiGTPPDN+t2dhToS7OLzNjrEsBZ8MSp/0Kp7sFzjdL7DatTN0jITzwWL3ssX6FsLxDOHiZcfilhPUbCYsTRnvKaM4ZBy8zlneB5pxBERhXQFwwcDTgaNnBE+Ng1cMfjqBlBLUJ3DDiiuE7oDkd4XexFFEYSJ4wrgQUGw9ZQsqIvJanBETn8RNf9+34K9/8f8I//Mr/SlcCKOBUPZ97B2qaAmQ53XZfTHorxe05QFQXXBTIyooQff5xASOgilHz87bjzXK8Gkzbe0yevv5RvCX3jbcEZn3f930f/vk//+f4V//qX+F973tffvzZZ58FgEuV79dffz1X0Z999ln0fY/79+9fuc18LBYL3LhxY/Lvy20YqOOJ0WlgAMQzazM2uTK+i/Ll3cYGYxJD+GwKr+/qpLKbwiVWlFFIbdjEaRrkmppqdV4zXpRla+mOUTOJatBGXl9ea4vsumNfrUHv4bJhYDNB+Mu5W8vbsi+93hmoVR+zPnZtomjHscqMVYFsMo8g9LrtLrWVaTvBIWVwq9wfJ8AWt1lfbv5ZQAEsy79iyC/39rLJvL3OJnf728534Qas/JAlh54SngxnWKqB5iYtMiMLAF4dj/GZ3TN4cfskPnX+DF44eRJvnB3g/MEa23sr0GmDcObhdwTfS4KRWoaR7ERmKMa83MhK+6Jv8dKDm9iNASFE8CoirljArAiEXULYxYz4G5A1HnrsboohYmqQJ83/48f/Bb77Mx/D7z/5LD7yxX+F/+blf41HHrW5Ya5WVBULL//gPdAEcBPAywCuNOOWPCwejHj2Zz3O//FzOP1/vRfv+dcilbRJnrgClGxk3U/1+Bxom0+0e4C4unJyKZnhKtDs6fBxPcr4Tx1ngP88Yo19Aq1ybvFCPE1cBmzG5DQWTecuY/IA1TpCGV9z8kaYMbZGCPAiAFlVwUXxqrIOiDE/OgOtuHSHys9Vr02zx6zJvDHPIggjynHtdXU3QgPiyrHtOiv2ml58Bqjq+6FxZURp7jHpkqiyecVw5JzYaxtsYWQlOw8Ft+prHHRbi8Mm7QQKYJn/cTHkT3rtXN07GxYDc9cwPVdjhJtxsVkdrF2fTeQHDuiUkQUA52mBe+MhHgxrvNEf4n63xqZv0W9bjJsG6Bycyj+koxTAnrMfIQktT1hLXs52iB6nO/F1dC5Jl8NGKrKSIDDcmCaFguQIqSWMS4+48GB/ADwLfC0+jYOf63H2/wXwMcI34jcRPrAFbgJAg4cubeuKeP67PM5mtquxR2Qg6nOCaawJu4TDzxP637yB7jdu4PALIs2352W+37Pwn49L4NQcjLviOt7seXv8Os489ri4uMCHPvQh/NiP/dje59+pJiTf9V3fhV//9V/HRz/6UXz0ox/Fr//6r+MjH/nIWzrnuiNa3bm09ucFgOASln7EmByWfkTrR/RRrFK6GDCqTUrdwbBTC5RNatFx0CZJnL15DegACkgETIvQUkgXdtaB67Wobq8xCaRJ1sbMYAIw6SRux7C1tTGy6uPWxYGr/HVr5lgNnAHTzvK1DM8K9ravXfbNneYsZnQPQLsEhgmAlCDAnP2cqGFUemiP91w6KGY2lb4fQ5raptRqm/p6TUp5Fpc5Jg3sM2BVkyESaEK0MBb4ybgq/lj6mNPjrV2f/z4OGxyHjRbwxW/LzvEiLbQhQIs7/RHudIe4GFscLXrcOtpgsRoQLwKaNwIW9wlhA7gOaM8Yi1NpSEWRc/d0N7IUUjpG2EnuklpgvD3Ch4RFGLEbA5oQsVj2cCHBaad315H6ZSHPpZRYDeQJw4HI5CnJtr6nDGTJxigA0Xw4Eq8r29RkhftyiLfafVfeHBmP8FLz8jVvsBKjrnjxm3o40uXYxhAZ4aSwIsX/7KOcXz/fHzIzqz7XRx2PJTNkZnzf930ffuqnfgo/+7M/iw984AOT5z/wgQ/g2Wefxcc+9jH8nt/zewAAfd/j537u5/A3/+bfBAD8vt/3+9A0DT72sY/hO7/zOwEAr7zyCj7+8Y/jR37kRx7ndL4shixkq3bnipxbElBLOxwJ5TGDOBWoMyaHDbW5O4QZz5q0EJQwKDunLrvZJG7HjlCzchZPrUvnyw4RCS1hYqy4b7uWImpZWO1LZcaJNkFeNYwe7KgOMvu/IAZqzQMSoFUWWJAUTyxo9aemFVtAqL2xMoAkK2vsuJ3QayNcntDXrsu0amOJ2T5Ns1+6j+yrmBOWNObulGbIDBRDfq+UZWtfvHADhhQyDdjOuWePTVrgbjzEK/1N3BsOsI0NXtse4aRb4s6DQwzbBth6MUc0c3OSjwh7IK0Seia0iYSmC5kg0pJxtO4wRofzsyVO7x7INZ4FhHPSDlOc7QNkIQ/Ehcdw4NEdO2yfIsSFVCn8Vn5+6PWZIeL5F4Ab36JViBlLy0wQnQcFX/TjZnyoHTuyhtx0416SjBTkd94jTSQG1q8NWbYRFw5xedmnRT5k1QkDlybwS4aHiS7be5mGg3T72q/Mij31bvU8SHd3Pcq4jjP7hwEY+WPLJHivNWSothWbBa4USAVQsiTF5IfyGVRZnuqXks7ZNZTFlbzQzMuNjTXuiQHCwuLcGXbuK1Vv59WHK58/ClOrljHuYynZyFJBKtf7Zp7bJpuoASwA+TEH80IpgFPuIoki16+9sSIXAAksnlYZnNPn7F41NIIgMcOKpRbvaglhud+za4CAjRbD6vfHDOWTXl1QL5Og6wuvoBczIZLLxZ8ttziLS2xji5EdzocFuhhwsW2RRg8MBDBlTwyyE3EAB43brBVsXYxyABbtiJQIfdeg24iPIfVOpO+jsrLyPCw7TmZYuyCMB2K8SynBdR5dWoIOGTduMHAD2GCNcbcAjUnn7xk1qQKvyIolzpVKsBVOYNsBdfeoXF3e9/FjoDm3yoR6koQrZBL2YbviYzyRGVYx5fJOqni0tznK7Hf9gL2VSvzv1PEd3/Ed+I7v+I69z71TTUg++clP4qMf/Sh++Zd/Gd/yLd8CAPjxH/9xfPjDH8anPvUpfPCDH3ysc45g9OxzN7nReTQQVYgjaRZh+cfKD9koHvACbrF0X1/5IRdiRUUg82BAwibK9/c4bNG4MYMq4svE2CijaM6KAqCy94QIUUZ44sIEIwG7LLcAVLqrv5u0TfaZcm5h9iKeq2ZTOSfyk1wie/2iFJoHmGdUykV6Y17Z3FqTAsxLGEC1XcCSBvSo1DQQX6iW4gTMGVBM1pc05L/tdWaobnYoLUV4As6g/lQsjayk+Yfsp3FjZpNZUb3jACRkM/nGjZO5f/q5UWAuNbnYYjmo5aMdB6x9aVZlapGOAzZjm61SnmzOsqzwwHWqHiLcCDsFyuTazuISXZLjHDU7tMuIz5/cRvfyAVZvOIQLKcCzh8QXB8QGaLaA6zmvn9EQ/KCgkhdfqP4mY3V7C+dYwTZGNwQcLHs4xxgGjz61aDZQAExyDDcauiPztxuBsCG4QX22OpMYSuEmeYJzl21VmNQvi6RLIZMDQnhzcOhxgCyW/7KDpxXHbaJ3EBVNVZ3MsWVWsZycuznBmCmqxRf7WTe8Yi7xceajZeuDOt9iBb8ueRFXrxNgkKbbPOJ4LDDre7/3e/GTP/mT+Gf/7J/h6OgoV8aPj4+xWq1ARPj+7/9+/NAP/RC+9mu/Fl/7tV+LH/qhH8J6vcZ3fdd35W3/wl/4C/jLf/kv44knnsDt27fxV/7KX8E3fuM35gn/P6fhUJhIgEzWYxJpR+uieFhQaZNu8kMLKPOfsg+pcHQcsMCYKbcAsleWaLzHAqywLMxqBtCQF82VUaNuZ8aKjVI/PRIalE5Utn3NCKuBLwkkflIJNg+obFqPwp4CT43isxQyV9+LiWItrbRto3buqCsi+VwqNlpduek1YbOOHxZMNqnNgW2o5B1GeR6Sy4HOrs9ArHn1Zn4+QwpI5HIwsKDqM9KELDmspS1r34kEkVhlH0JVvhsP8fpwA3f6Q7yyPcbF0OLBdonzzRLDRZvNESnJhOsGwO/EJys4wnDDKgukE7AAUOwZbYjYDQGp86DOy3ZRNOdhy2g2IvlgR0grJ7TbtcP2SYfdbcJwzKABoCjHdSPjN574Svz+1z+d58SPH76/JBJOZzftQoMQhHJrIJb32QRx0rVjn27cWFxXBIfkqRgjEtSkHnmuBypM2E62Hg8xPAQqkCtviOnkK9qv8hxwGUjDFZX738HjOs7sH4SpHM5prBGfRdYuecrqYQWEGBOAo/4JFOAm6rw98XJSryzSymstFQRVzF0u7Nm6U6F1JFTL8ZyUmKdW8XrifC426u9VzdSqvbYUYsqPZr+tmVF83ZkQ1XYMZIbXnBlWjPBng6mSyZR4OOq1GqPLgLuBfb5Ouy47VwblJNEjZeaagVhzgLAGMu34djwC57lFrg96XdbNsAxjMzgIuOUgbK0tt7iIC2xii7NhiSF57IaAfghIgwciAUkXpFGKJya74J6QFgQm8QmkCJ13AZBIDMfoxGMrOkXxob5aIksUDxRS6aEkJeOaVDISQWkADQ9An38C/+Gbfzc+8Lu+gK977tM4Xa/wi/HDwH90wBkD2OpJoczjzom82GkBhVxhXqnBLpMr83Plc7jXo6R+X1S+YQDTvko91fP/fKqfx5r9R8GlILTnoYe99BrLemfGO9WE5Jd+6ZdwfHycgSwA+AN/4A/g+PgYv/iLv3glmPVmjUoGDjiPSyzdAB8SzuMCWxb54AiZ1/vokXiBpReoQwrtEQFRGlmxE7aLS7gYF0AoRfea8SUecWMGYwaW7t0AsHBDlhw6lpzGc8qAxlK7pAKAdQc/cBFRCyBmCWL+v7Yeb1GaUhmQ1SLq7wmRp2mtrf+P3E7/puo5+d3ypsQOffUlWdI4yUnmJvP1OdSevQaoGZuslt7XwyxQbBiZAUD27LVCvEfKDY1y8d13xZg9vwcFiLL7aN5WsM6TKaDTnMcULRMvMM1RutztnbB2/SSPMo+xk3GFQ81f1q7DgesyMCj3cECkWilTlDU3gkgAz4YFPvH6szi/u0azkfyFovzzvapJtIug36WydjfQxEEsTxpgOADGJwY8sd5h1QxYhQFdDNj0DeIg7GPnGH7n0D4QP2CKsla3kGuehK5nhI3M7+OKsh8kE3JBO6/f90m7iSS/YS4srQQgpSIzBCbMLEoJ6CLILFQ0/yEFjUjeoCpG7ffTYkeIKy/xtC74Z3CrilPzmLSH40IMsHlmVUoYs1KpfbTq+Fe25cnrbKHKoAx81edkxIxHHY8FZv3dv/t3AQDf9m3fNnn8J37iJ/Dd3/3dAIC/+lf/KrbbLf7iX/yLuH//Pr7lW74FP/3TP42jo6O8/d/+238bIQR853d+J7bbLf7YH/tj+Pt//+/D+4d0M/syHQeuw6Hv0KWQUWBjZo1W4WYxgu6jR+vjJXaSSUAGFADKRt05xDpQAcCAwhgCoPI5AWEsSBhLy7OBMpoMaMUjs6r2fGhqI3M5xymVtf4JAAlWmS1sKQOpar35PJhM6K3VMawqUoNNjlLu+iHHLF015DXTj7RMvpSlg6bllwl/ygIzym0tk7Rrme/3UqdEqxjpe2QJkyWJycAtFIaWMbDW6LGkATfcFmvXYemkwnOWVuJXMq5wMS5wd7vGg4sVul2LFAkYHPzGoTl1CFuh4jYbht9xngjiHWA4EvNENwLDETAcMtzhgEUYkVi8s6LSblMvFfDmgkVHrmBWbB3GtcPulsPmWcJwQ+WmO6ceKVJZ/4f/xX8F1zN+193P4RMH78c/fvpbgW6cJgLeAeSmJog2MvCF6c/5hL1v1OaHhFJN95pwaCJB89fMg1D2s6omYctCrko8rgC86lEz5+SBatJ/izrxd+O4jjNXj5ZGtBSxZZc/6ladTSjMISbkphb5s6vDfKuifuTqOVwkaKWlu5n51owhQMAfA2GyzyJYuwGyemiV44FKN9t9PR5qI3NgCmbZvFm/rJxfeW2ec/cAYfa7y/ufHiNLIhXA8lkSWeZ5Y1xd1WUwcjHlt+MZYGXTg93pBEJMYcLSdgo8zmPLXE5YfMuUWWeAnoKMsbqPxtBiLucRKGJBA1qKCOqL2XFAn6Q5Sp+kW+Gub6QxSAIQCTQQfGddcbVCPVaLTy8Jhckw0kLkHtQmeJcEWG0YrE1QOEqS4AaG6xKcyhPYix/juCQMh+p9AsCNPSjeA33mNjY/dYx/8nv/PG4/eQe7iwWGX10Bv8IAvQLQFnlSNzbWvsJHXvjrp+sqIGvfYOiKvEKJbD6vYwHPXrOvZlGxq66qnk//eIRYsQ/oug4x79h4p5qQvPrqq3j66acv7f/pp59+S41KEooMLWrusXAjEATIOfDii2Ud1xtKGNhlo/jWjTgK3WSfR81ukq80FHGrucgWKAZqWPfT0ljJZ6Bk37Au4XMp4VLlaksaMujlq/2YH26dL8RcQHe5yF97alleNW/WlNjhIi1ww+1K3JjlILIPM1hPalVS3fMqR8pgj/2trCvLxwyYG+Aya8vyAEcJFwpWtRRxod5hiaUxh9maZABKQaxGG4rVkv/syahMrAjpRthUxq0GetXvjzC/VAmkz9fG73Z9qXrfj8MWx36LY79BQyOO3Daz0WzccLt8D276Tf79jf4ALzx4Am/cPQLda9FsCc0poT0pyhDLLcDyGBhwfQIHBZdUlZIC0N0k7J5OOHriAs8dnuCp5Tnudgfoosdu2yJGB3KMNDosLlRiaGIn5iy/c0MCRQffCxkgtgSn3dCJNbewfOPNhs3rFn8eVrRODBoGAbsMwKryJKqK+GzdDp2u0rzGOR3CUnNAuHySGTRSQGkiM6w/3HsAs3rIY3su2+5RBXSx7aN+bfWzPIG3FKseW2b4sEFE+MEf/EH84A/+4JXbLJdL/OiP/ih+9Ed/9HEO/2U7zPTdE6OH/G4dDcfksrTQfu+jh0Mj1EcKGTwx0CsanRSAdTeU48ikZ8BUA6XKQidNMiNBUolgCRj1cLPs3brwGAPpqq6Fk9foZFb/LVp3n6m49vj8dfVPoRSP08Ckz9WToZ1TsslZA5cBWbW8cB7oJAiU45kWvLH2STrq45s+foAsLC2I5HtYSS6t00ecbZODhAUZBSQbDURODXjXrsO66ihlvmBGxX2jO8TpsAQABJ8wuIS4bdDc92hPRFPuO0ZzwfAd54kkNgBAwLmYHKYAjEtgPIpYr3o0PqIbA5arHjsoQKYTk+9ZKLdRFtqUOFcn4kKSDt+XCopNQGMI+Adf+8cRnhvhuwiMCY7osna8qpjrzb8k3XurRoD7hqH+YJ2/90zA5QTnE/a+YMC6r3p2rp/X4zwM4NLtrplZZVzHmauHxQNbh3iyLniUGb8mJXQKIA1JFnAjl05MGfQCZcaUeUHZcSQ5KMypCCmKRCIQivSveCsW30cbNPs7G7Ab4+sRYk19DMCmGvnf5I623fx1tr0du7Cypq8Z6yTHtucKKKqArGIGX0idXMUXrl4/avHJVWxnVNvbuo1BGDXWcMXctnto52tsLNmmthuYrgzNkJ80VhKx+maNWhizohapB5oXuUhs0UUtkrkEIgeODk67SblBmLtO/UrsAsRnmuB7SCNBJ/EmtQlNo4U4cghNxAgFyCwEREgSkaQ6TgrupMb2C1BMQBpB6QHALwD/4SuAzzo8OHgS7jyBXkpAugPgDYAGGJiVJYVyU+oP0GS807HGiiYTWfsjvXZPrAHAb7LCz4nJw47zFpOE63H1eCeakOzb/lEalfzAD/xA/vv09BTPP/88IjMiyRrXOjnLOhfoUkBwESsA29jk+WPhIrqk3bYplQZWIASXsKKhAnUklzmPSyyogCgNRRz5neYFIyI3eR2egRYqPrSeElpjWimgE6s5rvjiion4fBgLqs5Z4kRNUroTAsIOapEQaWph0lDEgeuynNy6KwKYAWJFKml+U5lEoGyxCy5AlORdaQJseVN4aA7iKGX1iKOkHdglZu942o3d5veaWCBS9uIVbHJPA5rq7uiW83hOGYyqC+7ZcF9lhva6gT262KJxhTHWUMxKkiWJNcpNv0FLEUsacm505Lb5njYU0SgQNsDjC/2T+Hf33o8XXnsS470lmgcO4VyvcVQgS8ObGxTEsjmOgNQUQIdNgrggjIdAPB5xe73F0o/YxgafvvsUttsWYxfAgwMcgy48fIfM+LLwz55ykyjfMWid3z4ttMhPH3X9X1eoIPM3EYE4wdhRe7/Hc/N3BdKYElD56V0CwGzRQSS/+7oAUorwTBBgTPOUXEi3HEglhAT1vZr7Wc3HXM5exxI7Jztlrm4JY68NTJ0X2blNWF5vQQ//WGDW9bg8zDtjQSNaBUaG5BGJsj49UJp0NYSTRfTF2ErVROVtXSpvh9FJj/22TIootFKbwGpDdgB5MqwnYqnQX30NrVZSmgrIMkDqYR0FfQXeAFPmVc1wKkaCPKnW1B1Devhc6a/prDbRlqrAjBWF0hq3AEJhAupNznFWLcrXSAldauDYIVWJQj3ZZ/N6fc4m/1pyYudqVN1BA5tVNgTQilrJkGDqKGHHTa5K7VKLs7TE6bjETpOLp1YXSEw4+9INLO94NKcFSLLqAqmRrs0M8kMm6Lgg9MeM5vYOi2bU9xBYNAOIGF0XMMYWzbkElBQITvqwTpD2sCWVI0ry4npoQkNgN5sUK2aVVBm8aMeBMmnXuuxq4r4k49s3eKYXd3JRrEnRnA5rxyJGrsLkjHQ2Kb/pYev91tcAXF2Bn1ci+E22vR7XYzaMTWTMTkAZvfotIV2gOpLPvfklMAh9knkHxJfmK5sLFzTkBS5QeWwoC6sYsmv1sgKGapnemxVDxKcLCJjKEYHCmrJRg1P1MWzU52qxjlEYU2Y/Xzc0KcUUl0Ei87kCCvhmj11iRYEyeGWAkCUWNeA2Pccy8t5IpH3EU0Bvzh6rXzNWQNbkPilLz5IIY3clvR6PlP1sGhpBJMBmcBEmZ+w4oEsBY9J1RujB3KI7XSJcEFyHIpVLpTCQK9kgsLbUJhYgKy4AtxrhvYKlDAQXQQ1jHJ2wlXtbzJKhNiVJgDK/WOKXixEUe4DugN0WfLIG7nigHwFcAHQO0AWgXmRs5u128Nk7WRuKTLyqrhoKMLAmAVnKO3vdRL5hb2T9Ztrfj7hen7/0baFS17HmHRl1E5L3vOc9+fGrmpDU7KzXX38df/AP/sG8zWuvvXZp/3fu3Hloo5LFYrH3OekiGDKIUDNIx+QVpIpAKvYmkpsAq2ZA68bcBTFVknLbj6lEOg5Y8lAUH7mYPVVwGICe52GNXT17tACWbocdmlzLTJCYU4P/ADLgY7+XXCEIgg6RGzYUscxr+nJPzMakXu+bvM5yD+tIKwBZMZSPIOzSIh8bAMyzN4ImckHz4LJmXOYGaZ3N5zlUgvouwwG8T6tmndQ1x1HJoEkSbb81I2+u7JH7Snm7HFsnOVPJyUwN1FDEDg0aRNwOFzj2G2XfjbjhdrjpNljSiIUawl9wyD6/F6nFZ7tncLc/xBPteZafOkr45XsfwKc+/RzcxsMPpE2rJI+JCyAuScCkUQEkbRKSPIClg+tZHxc5+nBASC3AjkFNwsKP6JPHF85u4fT+GhgdqHNwI4EDI1y4vL/YVvI/SEwTBharafnUooS95Dp+4AkQVlXBpm/ePkbwpeZTLCBbTLmoU/a1Z66fe1ldMXKOUx0uF/VVrTL3+9q/o3mMm8a9iZQQJEAaDBzUe1jdH0oyr6TM3tJL3ROmH3Vcg1lvc0Q2CqjPX9Yh+axPB4A+WRVBEg2rSFhgMelbj4BApfNd8dOqfKmUKmqVgkG/5bXpYD63XEF2AEpLWk8JOwXLGpI23Q61b1VZ/Pfw8CYPyUCX00m1m7CYzFzRKg1mXC/HKVUNkw/W2nOrdtTf27oyEVEou5OAVL1+bmroqSQyFmis46KBgra/oQISa525Mc6M0WXmiXWXj/o186pGzaw7j4tM0T5yOxz5HRqVDUkFR83fESaB5ajpcDYAL57cxt03jhBOnBq0Q7TeGWnXakKNshOBGmVlrYHxiRFPHW1xvNzptSzQjfrZcQy/I7SnDN8lwEniRkkR8yTMr+ac4FqZ4MXot8gaDfjKKHt1HvAeFKOwtBJPNeNAAbOYQQbIjaTeV2qSTCSWW/ukh2RJkdBr48JVAQZveZAZHT7266rAkR/c8/O6Wn49HmEkRl68lvm0NKoAzMcpZbWuV0jGuhhGBUMihyoJKZANEc+k6DKjWeIgC2+CZyDVixndQzFRL/sb2QMscUz2PwXCAGTQycCkAuiQAkdjTuFrQ3gDrGopZQG2KMsHp8yuIsmzUcsbIwrLrAaX8uu5MNhsOBTg3Ri75sdloKCdYQ0kZgYEiVW7B2dGlzCrCmCX71XFqgMKiFUz63otpqxcj5ZGLDTOBwMASczfB4Sqmxhj4Ud0MeCkW2FzsYBT/8XMwqrW1m6sJ7aMnAorqwHSOmG9GLAMo/qBAaN2uyAHuJHgO+hcb+8r8uLWSacXSWAImmyoxhEPAJwpajTqP1klSJbuxHvE0f65n6ufrN+BpMUQu99Uvc/2WU+WPMj1SPVdCj/vxDz+SKDavmHgImZAGjA9r+tY846Md6oJyYc//GGcnJzgV3/1V/HN3/zNAIBf+ZVfwcnJSQa8HmdEyBq3SwZOewSnHnlW9WOH1o+AH9CngPOxzWys1o0C4riybl86YWatqS8sGzeiYfPDNSsNzTe0AFLWsAo00QingFH+SUPF5iqMrCUNuXDdW55gbCcQlpAcCgwcaN5leUEutkwoIjIOaMROASwPLv5YmpPUOcTAAZGmHl3yeAGN7Frs+PY4YHmXzMVLGrBJiyyva5WtZsBZDdLV+7BGVB4JHQvAZESHDFZaMUXzEvPH6lLAQD6/f0PycK7EtE5l7nXB3eSEtq9davBkc5b9xjZpgZO4RoR4aN30G9z0F/BgfGm4hc/unsbd4QCvbW/gS2fHuPfgAKt1j/ccn+KJ5QU2Y4ub7QZD9KDewWthnEZkY3WAMC4BQLytRijokhRIipoSsMQGJgG/hjUwroCwGJFA6FPA2W4B9A5IpBRqgHqC32pcyMwsKX5b7mTrfUpAbAUoAzSv0VyHrQi+b76urUomwNSbjLqjoXgd6DnwFU2udM2xB9CqCyp7vgb5ActRMug12cnVweJRYlT2wLLYVJMTaE9RXx+3n7WK/1HGNZj1NscAQldRM0WfLhUN06d3yqRqSNrbJiY0jrEIfd7PQicgm7waijj0nVJT5Z22Cc9MCYGykI0ki98GyAi/J84Tm1VI6t+NTpsld3uGAUwGbAkAlf5/7P3br2xLlt6H/UbEvGTmWmvvfU6dOlVd3V1ks9lqdvPWpGRKNmGJbAlWC6YJGTD8YgEUbZAC/MB/xYZBwaJhS3wgH/wq0LRsioIM2IbVbJFNiwLp7mZ3s1jXc9m3tVZmzjkjYvhhRMSMzLXWvpxTLUhVK4Bz9srMecuZmTFifOP7vlEn6lJZqPcjV0mKzPAhPdd5Ffu8WlGfz7TU8ne5ygJK+UzVLfstrEGg7GPtbdf3WALFxi1Ze35auajGiaw687bysQKHesIOsOo89e/CxCrdSS79xJU/8tTvuXATV/5Qr8dJ4qIxy7/yB767fMBNHPnNl1/l+8+foJ9s6A7C8MoAp9IJyi82W3UZyHJLMkQ8FnmgMD8VDl9LXH50yzefvGDXzXxv/5TD3HPYD4hAnB2bW8EvZohYgDJRxYV8rmkNIKlB9U8W0PdMjJVuW71LzrZp6LaEiCz2XZVM1S1GvUUzXozhi0mieKmTrwU+bx1KdJ08y2K/gku6Xv9bGVntxOruSTpan5QMUJ0nFQ8yZx+r5Y/jHUaiyMxsdJJsMQb0YgyboAYI+cy+Md9RYwfT7kf2VspytMGFDPvYKLI7qyuvgNMqubOfzPq3mYmTn69t1rGFeFfn3pXRdD4KwNT6cSVOgbJ2z7VYcgqgnY9zRtcJ46v5URbwqzx/bjLssIV1eb383e4Tmsp+OZdTu+fC6odV/blk7ahYwMMi8TTAydX30MoN2+mogJRoBsUyI3lwocpAxswaV7WEb8jnQW3Ncp3MoPfz4wU3+xFuuwo2+YnK/C3ttV2wizDjXEBXJmDcwHKhDLuZp+OR3kVulpElesJsXcs0Ct1sx5PCrs2TcvFAkbBOq+rb91totMs6kRPKAdYiipRKwT2xplS1UzLQq9zlDFAZk9ieW721ZAW4ZL0avCDe3QFIT8Cl9hLeB1CS+97BPQd4E4DVjsdY887j5uaG3/qt36qPf+d3fodf//Vf58MPP+Sb3/zmD6UJyS/8wi/wK7/yK/ylv/SX+Gt/7a8B8Jf/8l/mz/25P/fenQwBFoVXYceUOrysMvCkWe6HEEUryOVQnnTm99u7yOhClahv3GIqE12Zrpf+aHK21FWFQWrWxnBqwVHyGlgZP14SRx3YyMw+jSeF6Y0sZrGRZYoAR/qqsCjG4rc68EwOICvQVAAq60ZupulDZuOeK0hmHEOjRCmF9dU/t2tyqzV2FjP3dtsSecp9KoDcalvSMNUa5YzJCQczTK/qk67mgG2jsEi2RxF/cq7akTHLCNux86the/FPW78nnj6DnBu3sJOp3ovruGFSk6g+7Q/0ErlOG16FHc+Xi8rc2/q5gqRJhd+4+Zjv3T7h1X7L/npEjx4czD7xnRdPud6NLNERLh29j+guwq1DlnWyqv5VDpZLcLPge7M38XNBYGB2ueP6oqROiAOkUUm7xMXGTN+TCiE6ZHEVuJIFJDfKcpkJ5oLiFpP5SchdCvssEYwWm+JA9e0qH7s2CvZ38ryNyQr5b2qx3LKtTtQeZ0WV8lorNUxCNt60TTN2/SADOR/DT8m6AD90Xfe9N7n/+WpA79ZcbH3x9HhSCkmaV2gFsCwB7z1j1SOY9UMYRkG1CaxMtlOeDEcf6uLZiWbtsZxq09UxejNddKqVwdWyc8DYRG2721qJOPFp6rIMZQWsltqRwtVqQmEEteO0G8eqBy90W08JKIrPHfeQU1Bqkysthb3FHTNGPQle59rztjtgmbDbSrZVDMqEnxpvLlcrzOcyxHLses4MqztJHON4r0zQZclhMc8/Z2KVv1szRIB9XKtcG7cwuoUrd2TnJgaJPPP7mmBUc3sxjXwL6F3HLb9+/VP8g+//FDefXNC97BhfWfLhchUD7Pa3+nL1gqqr1WLrCmUVi/gk8sHuwOAinxyu+NZnH7BMnQUdAdl7/Jzlgl7WuS1/TdySzRLzJKilOkEzCeVxvq5fQaw3jKIbJ1EMEPPO60SbKxZ3Ownenexr7G4YBW214uyre/84Z2TlAHJSKalGkOdvOp9eyAlbc0/vq0Y8jsfxlpFYu9xJBt1DlpZ5UvOV0gqMFOmhYIwe70pXQclrEq3gSeuzsbKsWobS6dxdwDDzcFylKK3HVNt4pIx2gd+ala8+WlrP7aTMjKfH6HJytjKtTHDZsrvOOyS252q7FVaQqJnEyn2JmYnVMqTO2VLtkLNzFrlfUL9ea7t9Zq+pSl0PnB+77KesjO0lS0cFA8s6iQzFE4vE1i10GJN4Bc/AuBfrsafU8f3pCd+/ecJ8O1R/LKB2b4I8Z8amAJAXrRYHbO6zuAM6KpvejJtvw8Cr/ZYYHITMvl6sIq+VVWtzqBTPr6j177Y4IPk61ltYgK2TG7omAw+OPB9TSsf5GmqmUp6zbe9M0u8hK68M3beNO4CUxfs7CYG013Z2WUJNEBr0bz3+Y6x55/Frv/Zr/Nk/+2fr4+JR9Rf+wl/gr//1v/5Da0LyN//m3+Sv/JW/Urse/vk//+f5q3/1r37h6/ZZ9XFQx2U3V0/FUQKdS5BWVu3olsy4FbZuPulgF9VlL+DEppG0bZrCO1CVHbDmKiYtDGzcUnMjIPtTuZO1/aLe2EtuqV5bsIJFm2ah5lEGDIBpm1AdtavXlfS0sVP7d6vqaJlbMccoh63FizTySF/zFwPFUvNbd7nbIJWhtWh30oWwlwBaZI5rzCugXvnbrs1V0BEg0Ug9WdUf9tppzjKljjEbwZfPpgJZeZvSYMyklYkPu0Pdv8gXL9yU1TrKlD17n6cLbqJJLG/CQNINQR2Ds+eOsef1vOHVtOH1fsP+9Qa57Uw2t7EiSlg8r263eJ84hJ5ttyBecwMQu4bCvpLSxfAA3TEX7XNhPXXCcmXMLT/C8IqVKeVBN5HtsHbo9D4hIcfq3OSq+j6GkksZAaCOpnEUYl3gRYvZPLmDb5NzOXI3PjmdXhvbFFVFQsCYLumuKqXdp+Y+903wmu1QHgC9zkaRTJ4egzXcJaW7jafMrHcB5koRKZ+/2NBUsCzlIk+JX2WNcH6YMx8uLd5ZJWa+S46WxyOY9SVHVMld8HJniUrnXZH5bW5/a1WOonueT7TpZZ8xs6eKXnkFwtZq+hoabIyVfbV6qUQVeiEnNCt1tZjEl2NVaWFG+FtKbQGwzOBw3ac1V2+TlCLJm3WlzLZAVtnmvDNgbFZr7WuFcbU0LKx9XH0C2sqHeVSdfvNLB8Pz7lAlwSnSwjYoFEYViQpUtRrzdMbwsuC2VqZ6F9m5uWrLwQC+D/2NmSSSuNWeT+MTPglP+Gy54jf3H/P5dMFXxxs+HG7rtfz97/00h29d0R2F7mhVcrdYlSBshS53LUw+t6PvshniAoglFcvW2puXrlK9j7xeNnzn1VPmVyNEwU0OFehuc4LhMYme2MJHnORJ0TTk6hxtEchYYI3UsJ202o/kbVK9ohsPRTcOrfn8vVUJeWAizxN1C3pVyV9JMt5ErX1oQn+TEaJSmXDlfGVSTu7sGs6v9YtISx7Hj9UoHYoKa6oAGbCCMIV1VQzhFavuWlek/B2UNQko/xbAA1Yw5r78u8SXIoEr1+XycdvOikUCaI9XazoyO8s3rK8CYElT7Eh57/W61qH5OkvPRJHTboj1fp3N/enk71MGMJz6bRV7gPb+FvbWOdur7H8HqCqJ2T2v2xS1Pq+UrorrOcu6oEob8+cKVGbE6Jb6uXfEasxbvGD2aeQmjezTwPPlgn0YuOhmtn5lhn/v5inh5YALuRthTijUm2SwdC0s0gqrSkudQ9VB7K1woh7wincmZbk+bojHzubHYOCXm9eFrnZiBZMCCuXvkBRJX3tL5YF/yw093+6hkUGhtdpu3yjLb9s5vlRB1m0ePF57+jLHrz/Qh6/li8Sa88dNgnISf84P/S6g2uMA4M/8mT/Dm5qR/LCakHz44Yf8jb/xN77MpdahGDhyHUZCcplBszD6wFYWdm5mEU+nVqQuseHUZFxPmLX2XDphXJWOheXvWuzNReICYt2mMT+nmQ1k4I6BUamydAeJVojHEIMipV8tP6YGhEonABXAzi30JZ6cgbinEsG1w7q/8+OwUebORU2J0hb5y5p/0a6CW+bPayC9qTmabusZxLJOt6dFeUdiyPlee//bxldRTwv+ffVl7liQ5vNRnnb7ymy7iRs4A7JGFyqQdemnCnZ5MU/FkkN92N3wMl4098ze/20YCcmzDwODD+zDwPW84XYZOCwdx7lnOg6IU/PO7RTfJ8LicV7RRYjBES/svbohmt+uZsuUZDmLC4rO5pnlp9yMarFix+JMlSLBpIVF/gc5Jnll2y886Y8cY4ewgmTl+yBpLcQX9Ul7DM3SdPMjVtws9K+xHCrdldyXvEcemitKjAzBgB/n3g5olf2kxELeLDU83/8ejyuSNNdq19/dRvz+boOFN43KvpIGrMoE+XPA6t6fWCm2tPjZivvV59/ZzyuPRzDrS44Fx03cMGdgJIlUICo2VNGQjEnkxdD6rZuNqeVjDShF3laGSQxttIwlmucKK6tMivW1woyCqk8H6qSb6qSe8v4WkErb2zJ5njOpyigBpVB622pKAcRKYlA9ttoWvE1lodXQl+fb612ZWv5Ec96yscr7ghbEWqsxpTNhMeutgFYGqs5NeEtlw+7faUJSvNGKXn1pPpenfqXl/mB5Wp/f+cnM/En8xvHr/LP9h3x6uOTTmwtuXm3phsj3n1zx4XbPFDu+tru25CUZgFW6BrrFwJI42uTmeptk/VTM/AQXjEGF2GthC2GndBtLepbomaYOmfM9U5ugi7F7Hboi+7WCrnbMYjFWDBDVg8YHJq/z8aaFfasb5w1Vh+b58wn9RCNeAti5LLD4cr3hWt7pvdzZqQSecjGnr52MNiF7m57+cfzYj+IDUYER1vk5iSXhgs17IlQwqZdozK28Bilywtb3qWVDncv5ynPl9XNj3taDqpXCFZDLGEFqIEG+5tKUpAW8yrWdj8LWErHCx+q3RQXETqSIetbpr14/FfBqmWPt9aa6z6l35Akbq2G9tJ9F2U8xCWI5dgX0skT0fEpoY3v5SKr3YklHZAUzyxizv82sHbdxXcq9dtkjC+XzcMnLZcs+DNzOPfOxx3nlZpzZ9gsxOS762a4xz12UCnRZoPosKXc55oR1odkmCQX4Sj24LrPOkyMEB3G9Z5KyTLH9Gim181I7358Y7AprK/A34Eqn402xJv+vVrgfOGbz/Bs9rVYU8m4yUeUeP8ShDwBW9437wL/H8SM3juq5DSNDZt/ug2X7xrRZO6fv3JyliKnmK6Nb2MexAlGH2NN3WYqWrU2mCvpnQJ0MfMnKBuqhglijW0gkLpwpTxbtjPVEzN69JiksXQ4v3ERU+3fOP/7iEVxUH/c1nlryun7MINux5AlZVTI3QFIxZ/eN+sVJ4lYHY31JYKbxz9LSyVGJ+b0VEKsU0HsJ2c5kNd63c50CFiU3OWl0hasG+TVna1QqSR1jw8pyYub7JVYWs/alvc58P9rPvHpkZbbd6JYKlBXwbtGOV8sF+zQQ1XETR27DyCHa96F0VZ+XkUPoeX0cmZaeGB0pCXF2NvWNuaiWBJ0daUiQHOITt3NvnmwZ0KjrZVnnszga6CR7szxxMRfrPRX8Kmvs2EtleOnR07nE6IKxx7qIDgmCWAEllImclTErZ0CRKs48HbJR+drUpOCUhdWV+gYAa4eqFdOT1r9RhXlBh/5hQKsatj+Q95yf46Gifn0tnzuVdchagPJTon89r9d432isW95l1FysBbtyvL6Tp6W78fSO19fb7F+a8QhmfcmRVFhSNteTRJc/CCeKI1a/p85FOiJ9qaC6VIGutRPhCmiVyayV4J1/sqNbsoywAGW5FXuWGh61rxKx1hOrAERWGTCwq8hEWkmIGTOmE1CoJE/nwBqsidUs2TdFtVY1egk1kJ0DVtbGdj1eyvTdUtXoJaxMsPwei3a97TQ4N6yucm+LuX7bHbG0yy0U5zY5K4lSqz+fUkfEceWPtSuUk8TOzSQV9rnLSZGbFlru9bKpQNnoA0vy3IaB7+2f8On1JfvXG3TvoVdSp7y83nKYezqXuOhnNn3gdlTkZkWxa5JBeay1Up68dSzUTugOij+u1fK4SzzdzvQ+skRPSjnBqNRbyS1wDTDzU8LNqSY0Jj3MVYlSQcleuyda8mLK/qZqc4hvnqjbAODlQWDLWriyTugNW0x0pQGfgEsZoLP3eOYT97bg8ZB2/Oxx+Zm2AUBabThUim+9X4/jcbxlqK4V3BaIKqbt62JXTyR2TrT+PEs8aSV9RXJXjil163V0Eumy1KLsV5hPIkrAV3Ci9cQqAJFoMZO383UU96383vJ+q5p4LTCke2JNLTCQTdMb8KmNnQXIKmeK+XrrPT0Dnpykyp6qrcsL5Nbco5bNW+5WAbKsw+Qp8FU8uFqwrsCDbSwtHQsHF+iyvLIAksrKUta87UF75tQxp65eR2Hhzclzs4zcziPLsYNFwIO6xHHqCdHXglrnE3MHrGSt02ppOWlebJZ4o07qwt4W9ZB6ZdNHnEuklL2kmuNILN1wqT4lxZsEgZSPU+JK+/fqrdXcxPs8GO3mr8nBQ0Obf+Xkj7sbFhDzbJPi+aXlnp2hlZLUulS9z7jvEu6LNfeBVJoh1DVvs9DJY83kR304lM5F5uQJyWVGlfkn3obRWDmdeTTtMjNz5+YqdSugiHWsW0GPYulRAJACgHlWFlbRfLRKD3s9VK9EA3pCziXKNRsY1lPAeVurFxuUqGbF0dqK2LUmFnX0kujzeZcs1Ss5zNwARLDGv7ZLYQFyavd3Vq+tiNQcqzCxLmRi0Y6jOmZ8vR/nBfsCWJWcrAW2TkAsXb2vJj01oS9WLlP2xJq0q+qdIjX0knja7WteMuXPspUvFv+z4pEFaxHlmAFMJ4l9Gvne/LT6dh1iz20cmGLHPgwklQpi3ew3pOhwPpEymCUCrk9W2Jg96ZBzsiRoMGAoRM8SPc7Hdd4q8sJM4U49pGDm68YQFpInS9OpXdwrSOKyR3CQE2ufzkfwihwdErJXVi7G1CKMkxxjmjhUv5vgZwPe1It16R3y+iCsgaCknifsrFKUj5HapVKTAVpdh/gzQCsXPFTVfBxbqfy5bL6RGtb5/dw3Sy3uuKDgFE25MBgUN0XLfxopZD3Pu4x7wmQFpuqirsnXTuIlNa6Xz/B8uGidkd9Uizofj2DWlxwF5b8Ooy1+u5ngIhd+YnTB5F8ZWPEkoiuTbQJZJXgl+ehdqKypPlM/KxBV2VCpTrCrl5NjYLHyKeHEaBAAPZ3IhxyoXJYmFq+vVnpSWVnCSYBo9ecPfdlKC/a2Y2Fszs+J9txnYKk7MXQvo6Xqlvd68hnk19MZKtAGKDCKbtu1ECx4P+32ANzETQW9WvPFsl0JWDTSwit3YOMWbtPIq7DjJpqcowSBkBydS7yYt9wuIzfLwPVxZDr2aBDzBRkCiFWxp2NP7COH0NeOMpKy9KOAM8Fwm26fabiLTcJxtPcfOiFsTPqXsyG0UzbDwsabMaKQA0NhBJSPSUuS8UDVAuuY2B3LhM4KaMGpHLCMhjarqhDCagZ/X0dDuFO1qB099J5Z9HzfhrG1vmb/CXa9/U1Elnhnn3sPe145ICcvb6rml21rAijrddyz3fvQaR/Hj+cozJwpdQjK4CJJlEFClRxWTBlbWCgrONUyoIqnlua5wICfteufrSkL8LQu7MHmjCqayAWYlh0VlQr6ODDgSgqkVKSEK0Tl6nVlcFe5A2SZkfr9P5Lyvk86FjbnT2c/Os1xsTV0b197kyeWZqDq/EoSpxLH1t+kPodWS4BZuwp6GUjWJlzgKWw3++wSwiDBmLW5y9ScjAUQ1DEnX2NW1I4ldszJMwVPXFwGGgW8ZQGqQgge581fpwKZ2hQnWOf14jNSFvvJZ/Aue2SVood9mZTOR7vW4iPS1OLaua7EGcmXdw7sSwDnm2tp5kotO7WfRgNOKUDKn+S9no3lQHryWgWk3lYdP3n9biAo1+lnPe0w/IZxh/WliuTf8Ztizclrb7xm3itBeBz/3Rpe1PIOUTbeQA8n5lsXkiOos9dzV7uiMnja7S3P8LYOLs2PKqiVASc7xwp0gYEmpRPgmHOTIpXrJVRJ+ZxzmVJomJvcpVx76/kUEZ7kLnrlObNCyfLInEfEXEAopu7l39Ikqkgih7M4ZjlKyWkKC9fAPOtiuMaThVPvLfPU8kS19z7nDvZR15yu3INyf2qBpWFYtaO856WCUHd/x8V6poCOvUSu/JErdyTi2KeBV3Frfoo5xwTz8gUYOVXH2P23IvzTbs8nyxMrxIcNS/LMyfP92yfZHscAydfHkdubDWmyLsX9xYLzibh09NuFzbhwux8ZLmfC4klzI9ePjmnuqq9VHK3BSP2YS2roTAGintopNnX2OPYCzhQnaZTcUZ36/Xw9bbjsJ0JyVljr1sI2YsUWH604n7yQOrHCfdPwqvybxOV8S2HWbKli12RyfG3ikazFk8LEPQeLoIJbmrzN1dk/T0TQmOw1MPZWMY1PubzXLilSvmFO7LWU7ONW17CbFL9fcFO2Y4hptVc5AZvelP/kP1r2mTSvmQzA1ptnOZG24bGN22d5XJGCqlBZenb9D17WnfEIZn3JcdSOQ54ojrEjIVx1E4vzjISKgsdGA17anyYyq0u9+VzkwHG852MpzKzSCvfIajhvlfjVn2qTKcYLvlJ4vSR6YqXqoo5eTK7XS7hXSgjFoysx4ZlxK/uqSgjl5N8yYR9zN4wSXIATjXrbpbDIDEsCYBp0WSszjea8AFdrFSVLFXP1ow0WMadcrqHmlCSlsLJKMPesTIYCipXkoHR2acHA8veiHfs0Wjtk7dingdswch1GbpYRJ8r13FUQKwRPCM4SDK/okEw+M3nEZbmQCsdgyQ5qC3rIP/QySeT44ILWLh8hV8m7gxojq1sTDYkWiK76iaRC10emXo1+GmSVfOTcoJ7Llba4ij9aUBiuXUOxFVzUk8r8OlmVwND8lxRSNLPE7gEzxPN9zifa4qcF1JaK921TErMa6ez6/SHSXc/3e2bB/RP7OxYsYA3G9bE2VQugrAvrZ/kex34cP74j4Fiyj1NQhybzwSjze+kYmHK1uQBIRd5WDHz77LHkSQRxd85j3lcFQKJuU+CqItMTrCsemPwv4Ct7yWuik+LWaHLDMm/eJyUEA45cUzEXqI/hbqwp1xByPGiTjXMT9taY/pxZ1oJ4VX6YwaT7GGIKlZFWWFynHmGnQFhhZZVErd1O1dszOiLSIeLoJCAyYb2SFyQXhArDOOQ4WNqtT8mAKwHm6CqIlZIjRSElyfOlvQ+NDjJbD3WE5Cro3saBegcLCJUUV4oenVS5YJ33a6XVKvSjDyjgvBJ8OX574Pz5VBDG/hM1TxTV4qUiJzKUsuit4FcbaziLHaprYgD3F1pqnCkX0b5Og25xmjW12+havLBIY0mOLFYFf5uM44uO+5hgJzGzPW0BGx/Hj/QwzUKZ39a1XecSh9hzE0frdJfZPoVtNbiJY+q59Eem1NP7mJsVmQSusEtKnlKKyxu3MGd53aLeTMQpahNrRFVYQktpS6pFkaFVqldfpxSPs58UglMDJpI6ZoENq2fXuc1JyU8K4NN6A9difc0t1h9EWwxJ6jieFc1bdUzE8pPVDqVfLU847ThoRvO26DsHtAqxoahWJu2rOmaUxD4N67XXnMPXdcDOmz9vkWx69MRwPyQ7z+hCjYHl9TYXuvJHFvV8f3pi50i5SBI7Xuy3HA8DYcp6c2d5A0EgCSk5Oh/wfeJiO7EdFuals3WEU1IfCbNHg4Ffy9wRk0OTI15E9KYjefDFv6rUcEo8yvO8nwxMChuYn0DcKC4KboI0wnKZUK8cQ8fNMjK4wNgZ0hV3ZgSvs9DdyqqecGsuBSuQBVRgxgX7LRnzCfqkhNFZPMyFfDfrWjwv8SgmWEIGqHLuIw4ySYGYg2nInYYhv55BsRgN6Cr5TkrWtKR0RGwZXSQjCKjYLz8XbzQJkhKyJLi71HvzaFUjb/M7zvfuQbsXOI09Z4qUNmcSpXoMv894BLN+CKMGDWxyCuo4xL5SektHpgJsOUl4tcmntE/tm04UZdFfmFKlmruAofaZblvYQqX6UXxI2km7jBp8pHQjNODIudNOU+Xvvql4LOJOjtlSd0+8SRqwrT6WVZZYqjLHUt3IMsyY6bpln1JRKKBVqeC0Xlirmxgn4Fa5phIUI8KU+qorL1pyM5Vc2PmpXl8LLrbyz7U6pZWJ12MV/8/iFc+DmSVOqeN62XAbB46hJyTHfhl4edgwTX1OGIQYzavK9VkGkz1FtLNVenLKcclykq4s1KkSQ3VmAu8GM4WP5+BJBMn7lYWDTKdMs66LTEL24QIXZaXeWhZZjd3dSTU30R8MNDPKrZ27GgTnfc9H1WVrykCTyWbwloSdTMwxGtW2DQ730mypUsMTii0gCBJyJxO1BFZilk9O4VQn/h4JxZ3E4Z5djZ2WD12SjZN7SPWcUX2/6sPj+PEerZ+VrfUs3rjkGZzxjDxaga3SbTCoteK26mqq82UBp7wknK4LZgPVc5pR91nBqAKY5Z/hyTg1djfwRlUQd9pd8FQGaYysJJJZTjZihdDuAlTFk2t9XKe76nNV6tBF8tH6TqkWYGsFuarPo65eWG3Cc24yL7LKO0tcCdlIt5i6J3L3YoknLLaoHYkBYYuTZ8ATOhnxHBBeoFyT9BonM6rKXgcO2fHW2FhdlRMlFZboOYaOGHye2mwxC5gpb/1gpfpOqUCI+S46m9BKIlHmruRBPGgUkteTOe+O+4Fa8aW9R64yjFmlJCcLWwqF7kxGqPhlbQCivhRWWIGs++oO9QdyCmjhXMarSiyRFexqrp8CkLXbNXSw6gvXbCdJ0bQu5SWZVOKNLc/fZzwUa9rP4hzYakb5XTyAIz+OH5ERVehdZOsXFjUD+M5FUrI56KKzjujXcWMFWrJvVF4blxxlKKwuzL7Euu9RWaG9jw3jCjYyA0MG0nIH9BJLmgliI2tROGnPklavrI3kwj+Sc5oiXDTJ4YyvSo+lAZVgzVvK48qqKmv4zM4qChMnppipa3xW4GwtyPfV/9ekiI3vlpoVypx9hhfpKphV5JQljziXMNYO7NkqpYzit0UGGGMD7C1pOPHwrQ3D3FKBrCn17NNgcsXUnShV4iL82f/TP+Fn/8En/Paf+Cr/6f/yF+gHA8tKs5jIBZ8eLwF4frxgSTZfTseecNMbeEUuLrjcDdbZ+jssHU+u9nzz6UuTK04Dh6knRcd2O7P4xOyVeOzQ4JhC/sSGtHruekEmPTVsz+lk6aLbrpf9LDWeVEZPpyzB88ntJb0vayDQIaFi36aU9yty+TpKOtDI5SQpbrZ9ay4VTQ2DUrsaSlSjpLdD1Uzf05mdyZuGJtPZN6x5WxhhliuwNpl6B1+t8yL6yWLtIVbWfSzlpCugdX5KW7jZ56dK8rKSIHQNoWVNQeaW1FhfLFhOYll5j298eyfjEcz6ksOjDJnWO3hbrB5iz/UyMvfWLnV0K93XzBRt0u5ltIlbzXSwtER1udNIy+oqE2Ixalwfh+pRYhNjI4Ur1ygluKyT/SazsVKeKUrHwjJJlonfJl9pnis6/LsdRVr2mAFMKxV27VBVwLRYj+clsWQq7vlozSGn1J/pyde/y75tV5VakcnnaL1Xeok89Qd2buKoPddhV6m45R4U80RHe0/WzoUAz8MFL5cth9hzjD2fHi45LL3duyTcHkbm/YAGwW8jvovoImx2xggLmWIapgxeRUiL4zj3DF2ELpEGtYk7rRN5MdtVbxpuo8yaZ1bqrVLhFtaKOXAMHbdhMHZG1qtXnxKvOJHqhZI6oVu0mvTViT6b0BcJY5yFOEoNNi1zuiYVMRkDK55N6iFATFZpKBUJsO3E5X+lTq61KlFGqUho1ry3FFtnTDIXkrHANF/z2/ThreHhQ9WBQqd96OU2wWvptC3AVc4BjwnG43inUfwUBVswC2rMHO2IPmR2VgYOMojVif3OXU7aFaHD5u8CZAlq+wmVRVSkgGXeFE5ZVS3rKDVobGGDFaALrMNiAbQMA9DMHKMey9Zr6+KlXOsqTTz9mRQQKjWvruyo9RgFkiqySZfjwZ0OQFCZZQWUOvElOwPSoAW8Vj5WQmpnxTKKeXEvMbPrulxZdwhXwE8Qr76C+wMJ+UqE2x79p1fo53si/wz0FUjikAaOed+gVihZks94jTCHjjibd5n0ap5VKnRDZnZl0CqF1XpAk3mYeJfMV8MD8Qxgz4mC2hevJgE577KFacOWAmMEzMnup3OrRKlNTupxmmTFLipvmwwkcm3My4vgB6XZ1W8xnT1voJKWeFIX+Mn+TqU6Ui/TLqMFtMq1iX3PaxzAfL980hpr6iL9TaONPw/lJEVi+FCseQOA1b7+GGJ+fEZUl9lAxo6ccnOI0QWe9YcKchRbE7D18FF7NuTO6Fku6DJQUroP3mZ/2HbtDZZ7bGTGO4sbQ84p2u7k5bmUgaCSC81ndiBJHd6l2lXQSePxJJbl3OpQ2bi9xKoaOWrHJudRizo2ElnUccwMrU1TlHaieF2ZwK1HcZUI5n8XVpP34vfrJZkvcPYBW3QkquPYMLIW9VkNY8w2L6vxe/288o+7PX/xvYrqwK25YxlF5fOhv2UjC9e6rfLCcu5DHOicSUT/tf/jb/Bv/R/+EaLwL/zqJwT1/F/+0h9jyfL0rV/4ZLpiH6wQv196QnRMS0cMrio4JDUAiFfkIuC7WKeycv2dT/R9pN9k5cuQGPvA3g/EzBoGkN7YVOqyH9UmHz6DVNbd0AoDsMYLP+UY0NivxFFIzxJRhcPtBu8Tnbe4Vv2B54aVVZpXuRIP9GSiLH7EopndLqxgTX7d1RyJO+MkV/miI6UqQ3z3fTjxD64jF4xoc5MvwgxuCj6C2P07U6IQ1boU12JQs7+sMcsIEGe+wTX+v99lPYJZX3K4DBSViiusyHmRAIwuVEkDmU0FcJmpnXBq0F5Aq8KmKhOETYicVD2KFG/O1eDCUmqpplXekAGoUmVv9eMtAFZALDvnGUW3nPeeb9qpHv0udfh8lG6KLr9fdKUeF1lhy8YqJvJ1SGYBtLLCHBDs3q3blsSjXMvOH9m56cQgvlRSCjW3yxXlSY0W2/qgOKdcxw2fTZcZyDIT3ue3O25uNqYTr2COGPNqjKjCuF348HLPHD03jKQkDLuZELxVY1RY5g7vE9In047nSrpEEG8r1ApsOaPCShLiFpZL05tnBSXhQtHBKhavpm01/NU+EZ2g4vAZELJkI08+VVu9IvySDMw6MVTPC+YTQ992cleFEFe6bTs0QWgRsJUlYKaOmWrbUHfvAFoOM0wsFFvnICdLEiP3KKnWJOULyjzu7cJx74bN8Zt7Za99oVM/jh/TUQ3bWcESoXT5M+lZJynn0YZUlyKCSDZFlZURVfYvHakKEENmr8IKAME6p0dcvQbBWF9lVMaXaGVlVclL3qaFpiqIRbNGguaZtereFiO0edwCTfeZxdv7IF+x5vhC9c1amVIrG6uVguST2HtuZIXl/XsSoVnNlW1KLBwlVN8W1SJt7BA2JP0KPPkI9yuJn/6F3+Wb8s/5TL/Cb/32L5D+7lP43tcQmZgS7KMxGUL+rA9Lzzz1aGzec5HxZcZq1ye2w0JMwkyHquCHSIoZDlSI0SEumey9gFbaAFTrR1El56JCLMzcbMwLrD4nyTGFHudygcwpteN8W4U9TyK0+Q4o5vBfb63iCiCa7D3eKxVPybwZz8cbzG4rCJU/65WBxSmgdTbRS75mtR/hCQD6wxrnQG7zA3nzaLd7jDU/FmPJQEnIrKyAAVpQAI6eSz8xpY49Vrzd+amum4v3rBcDrq784UT1UUbrYWudxe/GiI0sxqhSj88/4o0sJHG5YK8ZYGqL0sXo3axUNmIV2d7onjUHGjDwzfTIxZw9sGkM2Iu6pPy9ZkSrhUgvCa/mt1VYUx6tjKzCCCv51EYWojjzLNQ+d2aEfRrXHE4L8DVkJYqpdAow1voSH7U7sS0pMsHqndzcmxKr+gwKbiSwTyP7NPJZuFzlg5lx1rk1p/yDv/5JXZOL2uNDNGlkaUp2iD1L8lxPA9PSE4JjmTvSoUOiARZaJuaKRkA49vghMnSRjQ/sw4B3iYtxJiZHiI6+i+z6hQ8v9lxPI94llsWTgqvm6SX3kKR0eyvW1xpCKZSI5R/kwpco1U6lqFgA5mNPN5g3nIii1Tcyb6eyMnwdFaiSqJZPxBxbMiOpsqGS5VrRSVXKuLnJX5zkYpDlFtJ1aDyTmcsq2X1wFFmi44vlKV9AdXK+7QnAlAv7Nedpi/5twV5ZCz4x+xy3ASy/fqrctwfa7PsmssBD4xHM+pIjqRl8F1pvUgsGG79w2c2ZUdTVziHQGBlKYJBQqbzVFyr/BytYBeCxikXbMaOV2BXa73oe+3hLVdi2SfmxTcKmLzf67pwDxXo+rddbgkR5bqXpNj/KUmJlNUts246Xa21lgAl3ClCxAk73GbG3LWvPabqtzHFpfgnnjCywDi59Dgav4rZuU4AsyK1sQ+Jf/Q9/gz/wDz7ld/7ER/xf/+IfIfiO1Anfm57y6fGSQ+i5nXOnj8NAuult8m9/nFmeocmxu9jz9YvXvJqte2FKnr6PDENg8j3L0aohMTrEa+4alZlYM81intqS9kRznMAfVz8TUZvAl+j55OaSziem4K1i4Uyep0GaSsU6aZUFfnnsYoIFYqbdSiZRCQVQ09zlg9PkIUaj25bk46EJXdMKaJ14Y+kZzTY/f99kXQJJAZzehV77Rb1LmgrDHUArJ5UgJ6+d0G/hTFrzOB7H/UNzNbl3Vn0sjKZOkpnBY5LDvlnE1g60YsLs1i+q9YWCpgAhkC1FT5iwrcRu9X2yUYAfEa1M4cIMK31rjTfssuRP7oICZEneWaGkYAltl8Soq89VWSudG7cb0Kc16Wq9s9b7kwEsbd2uynl1vT962olwffdyYiJ/zsgCavKx5Db05e4k3QBfgz8Cv/iHfp3/6c3fgr8P/Cz8nZ95yf/rT/6r6H/8U6h7zk103AYhJM8cjY21LB0655V4O39UKpPQdwuX/cQUO0L0dt9cwnslBk8M5r2myVqq14KGB43Nzc03ulSh14UotTpe5dTO2G+384BzSqi+hqDZa4tyHmEFtArAWU6X5RU1FjU6Umk9Ee0DXPdsfbCgAaPOxom3VPv3+kWweftNyUSZ3/WtDUHq+JKxpmJqDxzmJNdswci83+P48Rhbv3AbhwpYBHVchw1BvTF2E+wxWRoOruRo+Yikuq62Anagz9+jXoKxkrLSo1iAlJzmfH7tJeTu4VkJgqvSPICedGf9X5pTbWTJqoqHZVqWKxUCQKvaaAvwK3h1VM9FzmXukyqa3clKCpjxlWnW5l0bt5CSvZfi1TuntaizaFfj05LzFJP/9fhMVJhSzyZ3diyqkxKra4E+F9qLnHBJPTs3ExFexS2X/siLcMGLZXdiCr9QzPYt5v3WL33Mz//qD+rc8Fu/9DEOrXLSl8uWl9OW23lgDl2VDqbbDplcZWStRQhFncLRI8ERjp79LgN+3cLT8cjtMrCfe8YusukC3iWm0LHrF1OGPIHPXlwRdonu1lcWFlAbi7hZjLmlUplQrhfiSDWJT55a8BeB3bBw7Ad82a/kRWodD8kNtUqsMyCsLQiVf4uyA/wkSGY2W0OsZKytkHJxJxfwM5uLlMwzyzkrxoewemG969AzDf8XAag0lw7vy4PeYTwYZ9p4ec4wPt/+oZh4B9Sy5wwvLcXSdx+PYNaXHEWO146C+G/dTOdKe1bX+C5ZRbgg+AWEihgltSD5dvzTb9+Fm+rfZfKC1WjR0P6eQeIJEAa5u19TASisstVEfQWeSiXZn6wYVxCrvOdWq943MsPWs6p9H0vTYWRlpYUM4tkq+tzk/bwrYdui95yuWxhma6UpJyFZ6uJllWoe1Tp/tPLCKXW182RS4d/6D/9rfuWv/deIws//6g84xp6/9Zf+uHWwXEZul4GbaWQOnmnuCIWRlUCKD5VTGJN5xvhE71O9j0MXGLpQjZPHi8g8epbgc1UB0phQMePMOBhi7TOoZWCVrvpvhe5o/0o0AEwiIEpKwu000vUR5zJQFCSDYjbjVDCrUlFXQItkSYSI1klICliTGVmumDieV8CLJ9a7TOiNbtwu5g3JxJme/F6K7XvSa+vPudWJ37uh/Sf57zuab+S0glFfa3LPd0l+HsfjoLCBTofI2gVq9Z6yOcKhFC8pp1SZYIFtOiJBfJ3Dz6V3J23GG1CogFwO825qmcJlxKYI4aDKD6s8kVMQbf0ZrO+wYBArYWa9znI9ylqsOO9AWBKC9tqLl1bKoMh9Ju/nnljlx9vGYpviDB0oc7eWa5QVNHRiMSeoNWMpMhAzrh8QOtw3lD+ov4X7fyif/j3hg98Vfv//4rf5z7/xy0ivpLhlioElJubYEbM0MBVGljZgkmByClGLN6UyTQax3HqvuyEQOyGmLKkULHEoc1Y2XS/YaJnvVdb5zAXqPEgunJA/tzl0OG8NThAqa0zzRuV6W8ClfgObGCLuXIagNRY1GNg62kX226R+7etvq4CfSybum7vlgeffNt62zzkud34/TlhmzeUodwCtx/GjO5IKt2HkEHu23hp9HGMPfqGXxJQ6BhfYZl+tMkresGFh45bMDmr9onwFrk5UJA06WvxtS0OnY+rBWQEdXZUNQy5wtIXsCzfxMu6yqiLkBlFdlTpGXC64G3gUs9UJ5GK7WypgdW5/0voZA9xmNlRrDm/PD/k9nVqZFLCqfc7uQwHF7D20DLXV/H3NVUp+5bBCvClByrFiVbYUX992LLlz7c5P7NzEPo0192nzF4fSuURIZsvSuUhInr/9F/8oivAHf/0TfuOPf43/21/8wzixzpe9RH7n9ivczCO302CMqehIi1s9FkshQQBv+YJEgSVP+h72+5HPjxd8ZXPLT1285HuHJ8zRE6KxiKMaOFFlrqL0Q4CPYDlsIRd9pRTWOwg7wAl+UrsWZ90M42iKk5rfYPlW2nticjy9OpAUNn0gRkfsPSw+exmuqpXYWzfEVNDaEnOSGhhV5tYm3hhjy67VhYQErQwlKflSkbt7hwwDmhtf1fznTeyskv+0VMg7k/47ji/ZYMTy17Nj3CmUnF1beVj20yYfKoco3embfUoOW9cyp9yat45HMOtLjkLLTVjVtEwQADdxZMsaMBJGod0Q8LJkPxJnPzq3nB5XXdVaG8VX65e/LKoLw2rRDperB2W/MiHPuiYrloC0z6+Gv4WdVRfs+VvkJZx4aflmxWkdBF0FZur7bECxMiowRfHQMh8Nm/RNIunJHUtYDYrt/a8BozINKLr3lanV+rSU6wDoXcgVkVQBrpu44TpuuIljvfbOJZOENpPtz/yDz07ouT/3Dz8B4HXYcLOM3M4Dx6VjnjvmfQ8Hb6bjlOQi77wIMfbELpEuhcFFRh+4GBZu5x5VYTssbLpQzXydKMvimfoe7Vb9uES1Cb98VJJZCFUCaK/Fca1cIGb6Ps8mNYmFORZtoq4yxpKMZBM/0VQrFRITEpL57+SWtuU+gena3dK0fm2TCefAed7LDBHMCH598G6T83kF4yFm1j2Pz6m1d1ql1+3Onk/ld9FM2GrXXyWhxSixbCLr5o8V88fxtlGAKsXmXhGqR9as/oTRpBgQ02n2x8qARkQYJZwspFYwZgV2TiWBxQi4mKgrHcbuKn5YAIHVkL2AV2Bztih0Uh67CiqVxwAd6cRLSxpGjK0P5Q4+0YJiZbTnLd5iXqjSMwOa7H1qK8Ws9zYXd/I2hclWwLiyVmt/skVaWIArh1aAa1aToM/q63ZOoJNg13SEg2xJXxE+eCLwEVxziR5tYTdl/6k5CiEaYzcuHhaxBT5n81YSdPFEp+iwWLFMEr2PLNF0hF0X6VxC1WKwoMTkiN7ZvFdcBwwtXKe1/AGIUn2vcg1qlSgCziXzWinygfKlbG5eveR8DCnbACRdwapSeGluuOTX67xZWFHl73cBss7G6ZL8HZMHZQ2a77LbQ/FL7okpzSneeE5OQavCJhMeY82P4+hyow/ghKW7qMvrW1dBdrB5rigdruO2Ak2eVc3RS6DPHfcKu2rRruYaTlOWT7vMxIKjOmJya1EkA1olB9nJxJG+FrJ3bqoF+AVHrAxeOx9uZYClnOfY9bfqDMnn9tX0/VgVKqHao7TewaX4ft40y3K7zCTL96HIJ5dcdN/IwiyemLsQ1uey+YxXu5cVDFOLL6WgXkA1u/b1fRRj/l5ilYGmtBrWH3NTq8+WK27jWGPw6ALXwYynBrcWo7oh8Xf+vV/kP45/vDYF2LoZJ8o/fv11Xhy3vN5vTFaoQlqcSfPK/Ktm9o6jNqqyAyuyCOoTcfL87mcf0n8t8uGw56cvXrAkz2c3F4QoeOeYg2foInP0TLnj4ZOrPa9/Go7PR/oXju6wFjroDdRKvRAHy2mKEqV4BqeOGo/8wfH6dsPl7oh3yqYLXG4njtcjbhbcYuCVixkEy+eJvRAHl+tAuuZC7WjiWPHwqrlRiVXtKDGo88jQo9OZzco7yA1V1SxUWt8stVzvBAxqVCwWXpv4Vw3N7rm++/4uT7WFEDiV/r2rquU+uaCs64b2ubv7PvD8A+MRzPqSI6mrHYYKCj6ljt5FbsOIF2Xr5jOz8jKRLtUjCqiGiY50WgloflWlOuJFM103b9P8kkqQKlWQsl+h78Jqxmj72nnn5ouzaMfOTZTOHw9VO2Ycg6YTNlbZHzLNuEpL3Apk5fMWuu5R+wxIGchVgoUdy1cmWenmWJ7fSKh3trbqTd1JguPycUpXyFdhx85PvA6bmkgVJlZ5DyFX0M/pub/9Jz5i62e+f3zC9TyynwZCcMTgYTIgS3LFXL1VLFQUyYFBO+HmMHJzOfLhuGdwkSVdMgeIyTGFLn8XlN5HxiEQniwsNx63CCmCiJm8q4OwtanLhTUAxA2EDVUbjgDBqiIXu2k1uO97dHFIFFwQCp6aevNCSV7w2ZsE1gSClDsDVsNb+wTcYlUKGgNciZpNDN06oTveXp2A04m/oP9fwufqvcY9jKyHZB0n25eE7R1GqUDoeZB5HI/jnqHN/FQKCAFLEObUIbnKep+HVJcLFWXRK2KmpkWKV0YrpStzo0MJSF0stF/XltHUNaBW1xzXN38Lmo18T+XhvYQMAFFBo/V9l+sRvBb/rdPiid2TVFJ4CntrFQNq9hMTgvqMz2iNS9XnSt0KROnaNL7IH0uMKXfp3Ey+sFhLnJ2y/8nUxKTCmIsagVvSP73iV//kn+KDP/WCP/CHv8V3d1/lV/lTdL+RcOnATUzMUVhiR8qt0Etb9PLRlUomqBUo8gc1h445erZdLp6pMbGSSpXUC4p3SucjaXTE2RlQlYGsKsfIpjOSpC40S5FllX3bPgIMwyrADM5BzJX3JJXsXUzdnRMosaPcxwyaaQWt5CwWNV+OVloognhvnaRab8R3Bbfq9nyhYvh7j/c9R9lezx6/aZcSkh9jzY/0uHILH3R7vi9POMS+AvEpeS661eokqXBM1qjog24PwD6OTGI+UM/8vrKFLOfwFbhK6qo/U8zaZI9y4Q/0KWaTdPOb2rgiFTTWVo/JBxcyU1U7rtzBgJ/GEH4nU1VRnKgvSoG6UY7s3MSmSuoknyNWGWFra2KP9cSL2Bha7o5PoiexsCpMeqgeWCnnVlYwSTyRI6/T5kTpAkZScKrVm7d2BM6MrcIOS9kHESzW9GK2AcU7qyhM9nEEb9cckmOiq0zpnZ+5CWNtEBLVjN2dNEV6D2RG3ncPT/lkf8XtPPD6dsNyyP6LQYzdmw3f1avlMg7oE26IpMVD9NWbFlFYHPO+52Ye6a4iT9yBV5utfbeWnsOczfSjY1p6DrdDntKFD57ectxNXF/sCC96a3iVO6a72PgAT9DfCsNr87Banlj0DU8SMtucHYM3VrBolTf6TSANHomWBxV7FpRq4ZIGZ35cyd6PpU5ia/smJpywjE6p4/aUCFL2KYqUoUdSQuf53eWGrW/We4wTSWGbK71JZtjkVW/Mc5pxKmdvwa3yXD5uyUE5v3ecFqjKrWzj23sUXh7BrB/SmGLH6MNJZ6dSFRldoHe5Lbc6FqxLRq/h5BhD6fqXP8wiryta6gVfabxJTcpRvpyla1+pggwSOdLXKshGUjVjjBQtuz+pGqRm/3INxdukJg+56gFmAt8aybfB4XyUDh5FE16vmZWuO2fabZFkAqCntOZ23AXYXKXoljbDJUB5DDy8ThumTNktNNzRBUJy3MbRPF6kBBzlP/mLf4Sojj/4Dz/ht3/JWtp+e/+MH+yveHm7tUpGcsTJr4vfjDxrp9lMUNYfplOO+4HfefEhf/ir3+endi8J6vjBzZV1DvF2bzofWXLScXFxZP+TymEcGV46/EHqOVJP1XuHDVWZF3eZ+ppnA793HA4DV5cHhi4y+shx7jnedGu1YsnVdmdAVhqkepxI0DoBF7NcJXcJXHKCEUo1XWsyUocI9N3dCf1N3ln4rEE/q0zcN76Invxdt5e729XWsW841n1BoVYk3iMReRyPox0hmU/jiT9TmVMl1Y6H1mkQAg53torpUFJTMDEJgFbj9pi9NgqbyyreuZEIFg9Wr0ZjIxXgqM9zfCyL8IzaOqSCWtokE+VtJCR3XSyxRgiNnF1YpZYr++ru6syAIqnyRrvmzOQl0QkE7aonlicRxeH0VGLSjnLueo7Mxi5suU5iPR6YV9isnSUVqSdkNkSXAaU5CU4mhB8g//iSz//21/kbf/LfZfe1l9y8eor7e4L7LxOvlxfcLonj7DEfWfM3rBdVFpQurxvq6tJeD7Pnpdvy1d0NT/ojCeF2HonRZWl59qGM9j6GYWG5Uhbf4Y+yepgIiKeuN1K3nqMa9ZZ7tQjL0jEOS5Y2KiF6wuyyZ0n+KlUwPydMDkRzXKggXU4+NH+ytRJOLZrIOUglgHMGaJWOuG8CtNrX3qlYcrpAf+fxrrHpPKxwXyx5x1gDJ+uSx/GjPbYCX+tf8ZvyMTfLWAvsAHPs2HUznYu8Dlt6F+kkMmln+YtYR9yj9tX8/ZiMObVzkzGg1ACli6zTapsytR69C76qTTZu5pgGNrKsqpH8nVzUcqHyfETYuKVKCqGoQU59pFZ/rpRro9lPkpLLSL4myykuZLnXIqXNVfZpXNleDctswdb2t2qdHMl2LQWk67MZveVoq7Q9KU0Dq9OCegG2lgbIahtMnTDQ3EwPLHT1tbboXozeYyZWTMmbSghlztLDKRnj6uWyZR+GDGL1zKFjWTzL0XyyyMVtzR1otVOK36HbBsbtQtdFbl5tIXkrbCjo7IzZnKQa0Pcu8vt2z7nwM59Ol9x2gxnCJ2cMYajNS3a9xYrxq5FX2w3L69FYxwnc0eEnob8WhlfQ3Sr9XnPn9pz7XEDaJmSxeBGjYwqO7bBwNU7EJ47PrnuT5qtkPy7w0ZqexF4IW2e+XFPCiUOTnvjaptaHt13DQ/0+17Bw3i1XBMbB9l+Wd5Mbno/GN3g9792857w51YOjxLovSA7Qkhe1tl4NIHYf2HVuIG/3TE6sC+w95Mt7j+t5BLO+5LhyRy78xHN2tc1p0NJRz4x6F/WZbhvZsDClniV1+C7xzO+r9K3QcEuAKN0vKiDlFnpdKx++kUiUUaoFBeQpj8skXY530s0wU2lL8DEZXzzpWHgeCKy6vp6/7YoYG4CsHTFrF1ym78azbS7cxDENpFzdcLnE2xMza2r9urYB7sQwnpWKW0BAJLGkjn0amVLPlLoTRlYv0UCu2BGdVJno6ALT0PF3/r1f5O/wizyfL/j09pLnh51VMo4dmtvWmqb6rIoxWstaPZZJP/+gj57bbuQYOy66iZ/YviYkx/U8clzsPc7Bsz+OLHNH1weuLg9M48J+t8W/6A14ylWFuBFSr7gA/Wuhv8m03I2SRrXXFiEsPps7Cr1LdF2EzowcrQJRkgSrmMchU2/nBKKnkzSAWIX8viqFiiAiNRmxD8TBONgEHxog96EJPemKzn3Z8Q7mh3UybTt3nM+mefLWZjJ+aNzxxnrg70fZx+N4lzFIYHAhdyFy2TpIkMxCMiKNoHkB3wEhM1zFKRtZjJEFwGrwntTVbnwVkMpzZ8hzqsntTofJ5Bvvq/y4o5hY2PGKZZ15d1nMKfJIl1H+No6de2VV36+zWKMZTLqvg5ztk3laKtmnqb2XkaAeZe3KiJDBtLuSj1SOWeJfPncBwqTOj5pNe7t676tsPd/vKQ2YtWDCyyvgd+n+y58m/JbncPEUf4TDJz375RMOYWKalZiZWNXsPa8Wi68VpZNT4HRVGBzz3BG2jsFFrrrJwLTYEeKaHC6hI0aH84lhXIhdYuk70tFbkw9ymMssLEnWHr0Y6ZZuUuW1FI0BpoCTiHMpz5s51uTFbk0CvJhsIjYQ7Xmsaesjuk6uRWZ/iqjJ2gU3pdPnHwK0yr9flvl7dilvHGeL/dPX7D7p2bb3Hka5F9A6v65HPOtHe/QifLW75qPxhn3omVNX2VmdROZshTKVn4QzzyUn5q+38xO9RPZpzJK6GZeVGx6ldwEwqWGRI9p5LWfZuPlOTmK5xUxPrJ3UyYBW7w4GBGUWVmE2mR1Lqj6/cxqrEXzKhfELMUCt5CKtJLyMjQRmXAW6Jnz1lkoqloOomcjv3FTzl6IU6YkVrALq9Q9EyyuykqaMkk8ctT95zp3lKq1Bvc8qGY+eehXn0XprJcwipQW0PNaA7DvHZ3w+XdQ87caNKzMvW5fcLgNzNE/epMJ07AmLRye/Fkf8CmDVW9nZ476L3O5Hy2nAiuBBcLMn9QojNed96g981N0A1kwrJMeLZcvtNLDfZ2BQsO9dP/NkTDwb9tw8GXnxlR2HpeflzZb50x3MUuWFiClS5idWFPEzhKMjXM2wga4PXGxmrvcj14eRj3a3bPsFGZM1TBGp0kTNa3nplGUrgKcTgTlVH+BSxC8F7GKjIkVqWBuYYLlDYUSpQsjFFO8s/9mMdpvfBdB6W/7zLrHqfJs2D3qXOHefZ/AbpPJ2Th4ONC0I2ABYJVdqY7w0Mf5dxiOY9SXHlVv4eHjNPz98wD6U1qiGPm98YER4HTZ4US78hJcBhzL6iaP21XSxyA17QsOmyuBSqXJnsGvDQhQ5MVIsnia9BFypJkCdkMvkuHGH/LyjmLVXeZ7mCS1PyECVGC5n224knHhlnYBjmKZ745bKyLKjrpUd4KTCYUyxtnmujdj8kCPC6JaTROPcm6vsUyogvUSimsSjBICIqxUNJ2r+V2Gw55pjJ3UcYs+rZcOrecvLw5bj0jFNHeGYKbmzy5O+rkHAWRVju5tZFs989CYJSdTt47QGom9sXjK6wHcPT7heNkxZGpKSkIKQnGPsg1UZthPPL3cst4PRgBeHvzWd+fAS+hvFTzbZSxICEC8SsTO5aErCIQzshoUPdgeOlwPxaN4mya+VhdRB2DirzIvgRKrkI3+Ya9vVNw1Hliam7JvlkM2IHjFA6127exTKrS/JyP0nvmM0+KYqxUNeWW8zfj+5rnsupSYozTmkAcdOKhCZzfEIaD2Ot4xRIhd+4tWyZckSMWNfGTjUOVs0CuaXsWALzs4ZcBMk0ekqN3SS6PMXvwWXCkBl3QgTCWNyFfneyuAyKK02EMngTplBu8y4VQSnUgEtoLZ6N/5lhtcy+FU8KMtPoiOdYBBlPVQ70KqrEsK2c6G9tv4Yy/VbzLzL9NRmEijJX/vcfT/Rsh35fWSxZE0oSiLhxO7ZlDrmXPRykkw3wXNU94TnFxw/vWCKcAwLIR4IYSGFjFTEdS5pkw3pEt0QDUAKXQXIS9Vb4wpUXnVHvCSuw8jsLMEowKgmm6M6Z01Khj5wGHqTlCQ7v1ssqfBHq2y7uLK0EoJuU+0qpSqE0NH7xKZfCIMnZRm9JRH5s3SQOkFSkcHap1tvuNqXQ982SVaArNwbQTqPBiz+vI+HVp3X37QyP33tXWPNGw7xjtfUHje/dF/R5XH8WI4LN/FRf8P3/RPmpQOBrrB8/IyTREiePvsd3saRwQU2bsnWHQsJxz4NDN6k6xtZ2Li5gje1SyFrU6rCUjoHuAaJFSjqJeRivKtWJ14bgAjwxNXvNl93UZWYbcqpUqMWrTGvrSI3LM8ZyyqRRO74+4L5Ba/nC9UL7FYHFqwgUZpqFWVNbZTVvN/CinYZhEPW/KVIBYtflisgRbn2MzP6tTtk9gLOuV/KfozlmJ2LfNDv+efHD/j27TMOS8/YBaYss+v9eq/2S28dbZNJzGPM8vrUrD9LbHH5NieBIeH6iO8i09wR9x2yuBMPKhEr5odROcw918uGOJo08zZY4T4m81Cb5w5U8F3i8uLIN5685id3LyFvO6eOY+h4/uqC8PmG4bnDTRZ31EEcLXAsT5T+dS6MRBCvdL1Zs3Q+0veR/c3Ii4stV8NEv12Yj56oCTc50o2xxdWZXLY0wTIZvCDB4VKqahOrzLRfvHyPmrhSi/idQ1p2VkrQd7Wg/1ZAq+3qDiso9Xtss/JQQeTewv5Do93uof3OvIQ155fJ0cQ0eXuBphmPYNaXHBtRvta94qqb+DRc1iQgqTOGVvJVuzwlu907P1cT8+u4ZXadVd1ZwSuUBo131XjRke6axZPur3rkFq1tW9naxVCVWWDOxugFVNrIcuq/lf8uOvOBxFxZTZb4uAyrlok3shraJ107lrisQW/puWW0rCtXqLv0LM36s9CCz6+vVGzK5N9ed3nsUI6pr+y5oiM/xJ5v759xjH1NOjpn9z0kxyH0HJaeOWSN+tQRpm41RwRqK3RyEOgSzhlwNO/77KMFfhZ0ErRX4ug4xh4nWqsXL5ctx9jzKmy4OYyExSNe8V3iyTBxNRzZ+MDNxcirecPntzuuX+xgPyC5q5R605fHrVVN/FFYRJHLSD8GNsPC7cG6lnzt6prNduamH9CgVa6orhjIK0s2Y+kgt8ZNp7SJPO6VemQKqzoxKWIM0Pn7Aa0H5YZ6agL/wxpvCghvkQ+WUSb4+yUeZUa24HMi+WiTtMfxON5xdGKJw+gCt2GowFIBtAoLqHeR0CQD9gtW5tQRxWUZYgGQbO5uOxoWc3ZB73TELYDVyrgy36lijlviC/mcBUCK4tDi0VVBsdU8HqjwVSl0tjLyuo8Ye6p4iLQsL5P+rcBN8cIS0RPPytYwWHKxJmhbX8+yRHVn18eJbLFd/5d7U85dWFma9+kksajj9bIhJF9xCfMvTCSdCAmWdCQmIakSQrJ+GamJNdJeDFC6FSpmCp+LJtUbxCuarDmNiLLL8qBj6glJCckxh44Und1bp4w+MvhA5xJz75lix37pmQ8DLH5FJJ3ZphQbSxcs96FPuC7R+WgyluC5GCe6PjJ7n324WpDffLPo7Qmz/pKmW+EZaMQ9o5mLJbEWT+4DtN7gn/U+2NI7jzca5L7lcfvCQzWPd5C3/968scfx38axkYVLf+TCz7yctmz8qZ1JSDaDl87dvTNGVPFbWnxHn/0XwQCekj9Un9+cZ2TTE8CWvkftV3ALre2vCpBVfHNjVoN45E7O0TZ16vP6vi1al+MUeSKYd+8mF+1TAwqZsXzThEvlThF+IDLLysZdrUmU22QMokIc8JmNtYJ65XmLi6UrZC/x5Mda/X6b+1H+XZr3VkCsoqdu70shNCzquQ0jCVOR7OPAP7/9gP3SW7E9dPicw8zR5n0n1uCjKD8KkCUuIV2WrntFvFoM8YkkVnFwgwFZAMvUrUUVLWbsBgSps3hzmAa+s3/Kz+w+I4ljUSMP9D7y0e6W3kdujiOdjzzZGFP4O/tn/ODmiuv9yPT5luEzz/hKePJK6W/yojmvC9TBcin4vTC8Nn9ficCrnqU3hvFunHmyPTJNHa/2WwYfeXJx5PnsSdIRLoV4K3AEJHtmBWNwuyD4KcfypCeWKknMekW94Oe0srPaUfIe75DkjJ0F9m+XX+/7twNa8MPJf+4DwO6JSXfixrsW9Ov+zfYP7dsCgs0mRWZ4omZ5z/EIZn3J4YBnfs+zfs/n086Q8zwZlkV2SJ6Uq+OkbO4tkUt/BIqRuVVMNrJUNL9UIxypVkvW864Ta088AY8KWFQm6rZyUbZxkhgUkuSgkf26LCFZE5hVPrgGiGIKD9wJDEvd/hSAM78vM3wsJpILVkXeyIIjEfEWOMkAHiEbTFqfq1LZKLTc2NzrexlaCIWiWRKNYsS49bao//b+GZ/sr04/0zz5JxVrKZsrGSH49VenrCBWl3+BUZBskOh8Yjr21RTez1LbwZqnF7yerJvih53nmPpase99xDllyODT166u+WhzS1DHZ8cLfnB9xatXO+Szgd1njm5viYSEHMCz7LA/5I6H2bPLOWXoIssQuN2P7DcTl5uJ2+2WpBAuhG6/GsHHpquVUYmlJgvVeLhtv3o2sRsO5pBigqgKSwNoDYN9o94GaL3vyHZbb5zEH6pwPCAxvFv5fr9LemiSfq+Kx+P4sR4CbGVh4xf2sV+7EKo01VqpoE6g4B2tAftqzt6TTrrwla52nURO2I0ZgLIOV+kEPCqgl8Mq6pIlHGWU1wtAljA/ESt05tVps60dd5UUFkwcTjEIoZEb5uJEOZeBWK4y0FqQq6sG+SX5sPNap1+LF5HVD4t8r1pm172/43pV6/swyadU78zXy4bbZTz7TDW/LyWkhaTJ/ksOMnvaLjejESXmJPtbfEKcGpAVGiArNmdQZYrWUTG6JQNt9j68s/P7DD5djBO7ztrc70PP7TRyPPbI3tPvG6lHMXDPskO35DmyeFqJml+Wj8xLx9AFhi4wd72RbPs1zpRbVzxJynvIeo2TbQw+vX9xr9JUwyEDWpZYiM+rl3cAtN5rvAuD66FYU0LR2yrtXyTWPMaUH8vh85flyh35qe0LbuPAzTKycWZSF9Rx0c1ElcaDam0sEjPj96PM0jomA4yO9FlaF7hw09rgqWEmQeP7CzV3KQWRYl8y5wJ7yTvKcA2A4yXisE6ERX63kVAN0zeygHBihZJUWHJBxY5niy7zcEonOUJRkZznDoVlXPwVL9xUJYNH7UFZ308mARiz6rRIUt7bxi0WZ9SAv/PGWA7rImnXv4Jp5bl2mM+v+f2WfKaXyG/ffsR+GcziJvoa3RRqYd4ALVgWU3wA9H3EYkxCe/O7cj5L6ssEEoU0ezTZWkMcJtcDdPaELSAJF4Q4KLKJhMXz6e0Fr55t2fm5vq9OEq+WDc+vL1gWj/cW576zf8ZyM+CuPd1BuHgtbD9RNq+idW6P4Ja0SvxEEPWkXtj9IBI2zlQi6ki9Ke2PVz2dN1PGEMxj9CeuXhOT8FIv0MWxXFlXeFwupgygncWl/vDAutwJKUviRcEvZxJ2tCoypO9WmWE8830Wga6z2BDCmwGtol55gyLljeO++PIeDK878andtyGPFUKDGdDrmwGtGvvscW1QIs02vN+7fQSzvuTw2OT2UX/D97qnvF58rbYGXbsJBnU4NfNZM+PbEtWx89MJSJRwJ/5W/gyIKhN5qVoXUKuat4sFtJhBn17CSbUcMu1Wiy7d2siW6jqstN1i/t4Om6RT/du219o5pDCzXHMsciVnwd0x2C1VnfIeCsvqWI4nik1Pmb3V7F+YWiUogRkstrr0UhEpFY3Cjusl8u3jM54fL6qG3LlyzWXyV2ISjnNfJ3fnE9Ilm9ydgiureiyly0ljjM5kHm69gZrX5skDnfJyv+X7xyd83F9ziD0hOQYf2fULez+QcgC5nkc+31/w8vWOcNPTf9Zx+ZkwvlD6Q8LPWq8Bgf1HVlEZXivHD4Xu2hODcFRhO84W4EQ5LD1PNkd2T47c6oawOMJO6G4rBojrMqtsErq9vT2TxuVTdpl5FVJpktJ8uJKZat4044XRVSoUTpC+z9LFeHcy12SshFamWLpVJT1tTXs+Hpqs73n+XRb+9yUID8pK8j1CTxPfE/PI9jkeAa3H8fYh2Dy5czPXbsMUnX1t5LQanSgeQhZ3NHWoWGckl7VdDgNduiaZOAeiiv9V8byqrKoikauVNFuRuCxbab/11dcqg2SWopx2N5R8LncGLiSkdvIt8IUIVUpYmFn1WPlHat0XT1lV5VzlX1dWUJivWDle8ahIKG2kcgUkgxMAcGVj2XGTWsU7IpUd5zAp+yEMxrpKrrnm/O7y+i9EIF+POEWcrmvYcx/KfIyU5O461y7I5nGnHJeemzBy4SeWzODzmZWxiCeIxa45dhyWgeOxJ80et3cMe+iO4BbFna3Jl53QKVnaDm62FvJBhehjXgspIXnGLtBvAgsdKakBWvm6FZv6UUGiw4nBo6IdlQNoFRtIy11IMb9fY2M1ksLSDUpAnEPPvbHax+XfGiPs4t4KDn3BHOP9xgMnsS+k/cLal3+P5SiP47+9w4mpNz7obvkDu8/41uFD9qFn8LEWnkcXcTnPCKnpqK7KnoF9GnjqzZLkdbKOdBduYiOO3q3yNyu4U21Nir9Uu66v1iPaW+dBLHYUIKms0zdNLCrXOchSAS2PEiXRs/pNrftbcWZfVCCZcdt2gl/9fk9BrUU7kxfiT0CkhKvd43sJzOpZtFttTpzlGMeak6UqUSz2LxsWY4fJVO/vWvxP7HUw/+QmX2rN39vRi83VfY5ETzr7fG6CFUjm4AmZYVunv+RwLtWy0TJ3FlOSEBeP76whSTdkE/ngzAdYqQV6FDQ51CvdGC3uRFOYpAtrZ6gHh/YKWZGyRM/3jk/5/bvPSSoMPnCMPZ+8vmSejMQQkufl7SXjPx+42Fus6o4wvFR2n0b8MaJdZlprLpbnjup+ts6D6gQXFTfnQnyAcOx42V3S7QIxOFxy7Jeebbfw8eUNTzYT3+mesaQRcPi91KKMqLG0wkbo9g43pzr5awayws4RB6GbBImat1GkDUmOaquCV3L3FgOuKhDkkK6z/CfFtwBaaW2C9a5zu5mU2v3jHYom94w73sDVyL3929aU9zKz2oXJffUcXXPNtuBfcuX3GY9g1g9hOBIfdLd8c/ucb2GBo3OpUngdhenjmFJHL6bfBpPjRbEJ9TqbxONgJ1OuhGudGOsEng0L4RTsKpUPL2ZKW2ivTtJJBSMWwCVXD3ruyvP6ZuG8qKuPXXOupZlsC6BUOpAs2rqnnN4ro+n6DJrlFrfqagBcKBWdwvbyFRSsFSRcrmB0J2y0VobYttotnlkp+2kt6nk+XRjjKlp3DUmWiBSKrnfJWpvPa4AUZ0aZMtr1qEJa8vsMgibTpItXXKfEoGinhMt8TUchbRTXR+bZ8+nhkpvdmPXviRA9n91ccH1jC4gQHC++94T+eYdfYNwL4/My2eeEMp5OpP3WJvXhRnPbWUFfe5a942V3wWY7k6LnMPd89eKGn3r2kk+6S15wyTwPOaGwqntM4BaxziGd2ByVabSIHdu8tSyoyLx+b1Qw/bhz0HkkpnVSL+AUrDKQ+wCtdnzRysQbxhslgufbnZm/P+yP8obXyiLhDed6HI/joSEoG7fwtDvwCvPOctl83EuqvlMGqGRmUmElaSSJzdMTazfCXoKxkwpYxeqZZd0M13OXGb14G4m07Czb/4QFnP/VvI8j1lfLN79NOEpjE1hjUfHRojmWXdfKLHD3zAuFpRUzg81xKqEEKrjWAUlSPv9psaaYzBvOsd4bAw3Xc1VmGVRQq8uA1yHL2JNaoamA2C472oooMXliyHNiLqiI6LpKU9BcVS9m8KoGyokjSz0EHfJ9CzaHizcD+X0YmPsug5HWbWs/D0y5ZXqKwnHqcAeHROgX6A7Q77Wyfs9NWV3vkCT42YB69eBESIvn6Aa6PqJq8XXXzzzZHLh1A0dGYvRrVdbQVSR51Peou0D4AJWnID3IHvWvSN0LJB0QnZG4yqdWE/9cXFFZWVgqdeEtzqHFEP5dOhz+Ho83emyd1BIfjjWPoNXjaIeth4Wdm/mov2FKHd/Tp+zDQOciU+zouhmfrUOKyqKTSC+RziWOqefSH4na41Wzl1QuEqspRQrAU2xRvCRu01gL8rN6NixVYeIlZt/arsoWrUBtndUHUi2MF/DKQCqLQDOOAWustairgFX1/KUwm+z9HHMu0LKyiuKiel5lxtcxg2BFJuhItXN8yUsK66yATb0EA64kcSFzvV6fY+G1rh7Kdj09r9Om6f6Yzfilr/e2HNfej90nuyd2zp2fmFLP6AI7P/Fb+495edwyR8+09CxL7hKYrJmHJvOmMhaUoFFIiwFeiBKKH2JvBe4aXwDCmtcwKq5L+C4SF0etcEWBMZFqZ1xl3Cz03qwOeol8ONwy+sA//PwbhODph4D3if2LLf0nPeNLK/BnDogVTHKhTOaU19NWo3Axs8KdEAdh2VntJ1zkGBKxOLL3BAG56Uhe+dRdWa1DlMth4psfP+eT7SX725HwyUh3EJO/5/iROvPm8sccZztHHD3zE8/8pNjG6Jo/ZE9HUQzIiVix3Vusx3sI4VQyGKPFbyeNdL8BtOzLk4tYb+nm/r7jbTHjbcbvOReCs4J8s0nNmcpzbfrWHkub7fNj+y6crjXeNh7BrB/S8CR+YnjFooZIH6O1Qo0uG6q6yJLWL2SZcBf1jCzW0UMNhAJwLp14XaFl/jBz9xXAWhfeLV22dAUpksFyvlYu2G4/NMfpzyvAeRS9eRtAWlZWkS5GxKocZB+XpsJh1ex0IhkszxfGWfUK07WrSFTHlT9wm8YaDI6pZxHPks9dpJJtdQOJOE319fLfd6en3C4DU+g4zn2ubltSEXA2+aswz76CVUmsWk4z16zdDPOMG8F8OlKtvttkrNArsbNZWgS8V5ZobLGtn7nqJj4/XrA/Djhn519ebth+pzO2lLcKeH+r+Cnh55SlfDYBulgSjlxdGG3CR8AvoHthejmwnzxy9Oyj8GK7ZdMFvnpxy9BFPhuuOO4Gulcu03uldjZMg0OnXCXvHHHjmJ54M3rcK6l3lvS0VQoRM213zthZya2AVku9LRP6+WQOBmydVaneZ5wwqN7VK+t80n3L5L8GtfvO+ZZrK7LVx/E43mE4lCt/JCFchw0hd41LGTw5N7ktf8UsEwysYIq9rtW4tgxfgayV4VQAIFOZtccvx8rbNUW58w6E5dhwyrw6H7VrdAMStaysIl1MeQWVWFlgRaYvolYJl8ymathgXcsiE9s3qM9euELEErWSiITcOCTlAkvpnCWitZBS7nE5b2E4X8eRJVpH4xB9/r3nYgC+msPHIDWZUFglc+V+FqPeMl+U8NI190ny3v60uukcxGT3pneRUSOHMLAEX4G6dOzorp3J4Z2BYW4xIMvF7BGWP+YiKRC1RCR1UghllowsEI6eJQoEx5Lg2HV0LnHRL/hLZe9HQu9xR8mVd4/KQPJPUf/70K/3yO9XdCekF0+J3/2AtGzxy/dQF5EUT4GossCuxRZZmVbn2z1kqHvCzHr/8c5t0e/b5L6F/pv2rYv//2aAt8fx343RS+CZ33NUY15ddhMfb6759v4ZIXk2PthaPq/jzzuUL9EkYFPqMwPXosBtGlePKjGjeC+JgVgLHoNEblPHrY5WfJe1kYPLYNeCBx1qob6MWSJeT1lXMQNPAysg1eYoZR4uxu+eU9uTkvcUL63zYUV322Yj1rAKheu0NSCpFNgL2CWJK3fgwk0ZCIv0eZuNWLMUhy15L5gq5gMQ9cBeb7lOAzOefRq58gcW7Wp+lLKX2KIdOzcZe1YC13FrHsLJ/Lg+GG65iRt+cHjCYekJsTTxcBUzCHMGtgqzqNJeBOmsuYlO3tg7fY4vAq6PaGH7zg4Jgg4mRR+HwHQ7mDwvisnBd7H6JHpvXdJ7n7jwM1/rX/Fh1/Gbh4/ZdIEPrvZmEP9qS/e8p9sLcbSQ5iL4yfKr1BvT1OVu6S7pSRF5vhCWSwOM4gjLpXVsR0G9Wvd4r2YBE4Qwe57f7EhJuN32XAwLT7ZHPtgd+H53xfL5Fn/tSAOVpZV6y3l8NBXI/MRz85O2zfjCchwTHpk83gwjS1CW0/+8y6hbWNnB9oGv22v9oazPl9Eyjd+kSHnXURdY9xzrjmfw6T7nBIB7rVLydRYZYQW62mOV4p87Paa6+0kGbxuPYNaXHF6sq+CVP9BL4KN+YFHP949PrCOdukrnNTCI+ndCOObJKUnuwJfRaSBXP4yVVCodYBNrkeFtnFTzd3veqgFVfpgrCy176z6T9BIcelKtOp8GuvXf+6i79rerXTo2shgDC1crHYWVBRb0Nu5otGNZ2GQgqrSEX6GLw3p84Dr1vNbR9Pza8ywNtUMhFP1/V4MNwHXcWKXHKU/9geu44dPjJTfTyBw8MTpSzGmX2iSeukjXJQsO0VrbI0rKk78fkpGHigFvcDa5L4L20A3mexVuekgY62sMyBARr/RDYOgD227hSXfkabdnUU8nicvdkWnpuf18R//c4xYDp2zBD4UR5Y8Ngg0mvXPCshPmJ0LYwHJlRu7+mJONYt4YQQ+ez19dMAyRsV+4GBa+9pVXHJ90PP/0Ce5lR7fPk7rPlYqD4FWIG8/+qx2Hj6RW76ukA0HPW6yLrLTblNbq+PrlWSf0+ybz9x1NMUPPk5X7JvE3mB3Wr/g5nnZnUm//ltXQUNfnTib0JvY9jsfxtmHCK2VwC04TO/VE77jRMXsgrZK7ln2k2II2qMNnCMckf1AQCHMltJjRNYlDUkfIf3e5dXqR04X8g1hN3Q0YEc0FlCaBqGsY1p9exyp/UORexdf5T7f+zerV1WUGbsiAVPX4Kl4dKF02NO6IdLIyyNqfI9IyfWBWx0RHVCHg2VSvqXxvckGmGCgDTBhTWMTi96Qdt2Fgjh0xGVilaRU8oqAu4Tw2gaQyR6jJ1AUz5VWoHQxT3jcv2J03Rl6aG7CrS9Bb0cSSjEjvrOnJ6JZqbj/0gZA8y76vjCzNVfLqi+iBsOJk5EtRgdhbMpE6M+LNRIvKtMo3CoJjfxzwndK5SO8jl7sjYXQcuhE5mn+M+i3qfoL0CyPyZxL/4pP/gqv+hn98+XP89j/9Wdzf+jr+0wl0bwUPSWex4iyJEDlNHs6/SD8M76ym4nwvkHX+3Lss1M+3eUOsqQnGQ5Xvsn/7+uP4kRwiwjM3VYbUE3fghbsgesfPXHzO945PbQ7IpvBelKIdPsSei1wMcRmkj+rofayKBmiKyGKSOu8MlCqm7mAspCt3qLLDhKvF/AL+l7yjdBAsZugm3VsN3p1Y06mkjiPOmFo5T/CiHNW8Zo/aVbVJYXkBFexqbVNKI6taAEeyqiOxc9NJ18XCNLtyhyr/q+b4JDaSKr7Qc8qh8fl3OKt5Vl1JZCMHK/i7/G+WWx61rwX8wgorhRkviSlaYf/KHfEkfrA84bPDxYmnr6oxr9ZYIcY4Dtnw3VuCmXK+YtUIWWOtU1IGwRCM4ZqD5GY7M/bhLv7hlGGzIAJXu6OZrY9HPhxueeb3vIw7nChf217z2/NXuH69xX02MLwU/AT+YMd3QRlea40t4dLjZi3K//rdCTvH9KGwXClhZzEnbhWdLFapxzoTiqJjtM45STi83ODGyHV0HIee3bhwMcx8/OyG2+3MqxcXhKMn9R5J1qlx6JR/51v/GX/45lv8vZ/7A/xv/+S/jjt2JG9KlTSY9YlbFHee09gP0ozg1ZAvuTce3bPf28zXfw9UKufnvRPLSpyRs23yw1LIaWNTKyNsc507Hln1hfXPx26G/w0Ph5ktHrXHY92CLv3E1zeveT7vDJwpNF4X8ZnJ1GNyv2K8C9BrrBXeY+qJIgziSW6pFYJKqVWfzQX1xJiw0n5zFb6wnIruPEqyNrPZGB7IQUTvMLJKgACgsp7Wb2SpoFRZSZE8Nubsm9zPpLCreglcyFy9vIwplXKFQ0+CQhlehKjmxdX7hStdOKpnUcfsXZU3JnXWTle7ek/b+zW6hV4i35o+5PnxghCtVWyKzuQdZX5QSOIJ2fNIOqsuVS15mfwdaMzPlyQjB4TNaCa6bQUdp/Qbo9g+yZP+B5s9H3a3GQy1x88PO/avN3SfdwyvM2gV82R/rWa264Vw4ZGgdcKoE/0HwvzUgkDc5AkjdxuxtrtqUhSvLPuBMCWmvmfaLOzGmYthwX/tFS82F8zXPdp1SDKNuN94fIz8O//87/KL/+R3+Xs/+zP89T/8b9jk3jtwglvu6fLhsvyjsLPaiT/p3Ql9RX2509Hj97ASfTKB3ys3lLMJ9wueSPK+5xWLx/E4HhgCDBLY4JgxScLgApedJSJe1t+Ik5SnJPMlStKAS8oJ8BWyoaxH6JvnCxAVs4GuQ6u/VDWMF82WRKsPVvGrUiBkP8YVWLPmF+eMLFUqOAZk1tP6epH2rQBZK+qza+40e7Eg1derxLmyrxelywL4MsW0GHWppGt+j4POBMnywCypLF0SS8wp9ynly4k4OjF7gWPsOQTzP0zq0CTVgHd9845UmE+twXutpLOCEWXf8ppAl6vs58/7zszhxz4Y87lb2LqZwa2PD6FnOXa4vcNP4HJXXElWJTcVpJAGe4Orf5SQeiFuIY62j3a5wi7NvChYhVyUtHhSVKJzhN7T+0DvI3J55NgNxLlH3RaRp6R/Rfk3vvp3+Fd/8/9NeOX5+T/3D/g//6n/Gd/9jZ9DP/8Q9Z+RZEHSPXyLfH5p2VntF+08mWiphM3tvvvghztOY809r589/4UhN7l7rMfxozl8nicv3ERKjiiOD7pbnGSm1VZ5Pl8QVehcBHV0zrztBhcyu6k0l7IcwwB784QqhuZgBu+3OtScpYBSbffDvY61kF3yhCXnJj3RGkJx6unbMrBmPL5ZZJV8o5ABChhUitt2HavhfDlfq0gBKji2kXDCGi6dFqsxveQ4m+NIKZBs8vy+kUQvBmDFegxhUcWL1HgyiDCrcl/zu50Eiuflh37mu3HHy7TDk5jVLEgciafeCt47N7FPI7dhtPlTTDoeFm9AVAGpHIgvHTnMHkXVZ+BbVhnhkG1VcsfCefYwuzrhaKf0FzObPrCEzPYqrKc+8eEH5kOlKny8u2bjF742XvNz2x8Axb/MWNT7uYfrnu42e/BGA0HiYHkOYmzg1AnHDzOQN2ExKc/Ph684jh8p4SKhXTZx9xCdEi/zB+EwX+M+ocE6MMrk0b1nubT3eZMcUwYAVYXt1ZGw9Sy7jldPejafev7d/+Q/5y/87t/FAf/SZ7/J8aPEf/Av/wppEJatoOLpjgk5N3g/H4WdFZt4dF6wf0NhRVUzIPZDmMQLfdAOfHrMbNz+LqqSk+urrDnWhVWNq9QcRyWDW805126R1Bxa8/Pv824fwawvOXpxbApzSjxP/a2ZtLuFnZt5GXYAeXFrgaOMEhQW9fTOJtUx00qHbJY75w6IUdxJsNjl9reF9dQaqxfz9zIpF+8sA5RsAT4LNUjEogtXx8LK3jrmFrSlw2ABsgpjq2V2tcBWCQ6l5W0xQezdaVAopo8bifRiANbqBWYAVqls+PxDX7Tc95VJhmRfL5fY6cTzeMnMata4cUudUBf13Max+pHE6Iizt9a0UAOAgVWKS6CiaHKmHVdqxdt3EcQRj7klugraKcNuofORwzRAZ0AMfeTJB3s+vrrBoXxlc8uz4cDv23zOU39LYg2gr283yMue4VWe8LP3SUk0XFDCKCwfuhwMyBV+mD4Qjl9V4i6drH7TSO4QRa3o14Tp0BOyB1gIjn1n79/5RP9kJmwi11c90weO8fOO//X/8+/yv/pH/3cc8N//nd/AH+E/+oX/EaKe7pDo4wOTUMPQqt4lb5vQczcqwJ5/H5rtnYmau+yqdvN7JvDzSsSdbZtrbA0My3HUnVUuGoCzJoZfkhjwOH48hhOTEXaaiCRGt5Cw53qJHHPrbyep+iLVffOXLKnDu7J4JkvhUgVpUEj0jUw9NU1M/AloBVT5Xu38dwI42SIxijsBs4SY5YKr91RgbUrSAlmFsVWvH6lrI0fLMjaJfqcm9fBOK4i2GtAnulwwaYlDJeFwZ49L4mEFmvYeJpxTenUcGLKH5eqRZe/Grm1OnV1xZlql6FbdSS4ykGONlcJtEV5Bq1xhd86SgVpQQYwh3FsiuiRnx/KAV8btzMUwI8C2m9n4wLNuz+gWij8awDR3yDEDWblToWbCE9i/qYPYuxpnyrWHDYQdpP50AovF/4S8fTZ2B2B2pJxgpdSzNO/fjZHUeaavJdy/eODnwj/i+F94vncjPPvJI3/wT/8m3/7JnyP2l6AdLnj8HLl3ApWc0Ims1fC3MbDO5/n3AbLOt33Lvm/0yrr75NmfD5jSn5y/iX35z8cw8+MxhgZc2jjhGbfs08BCx4fdLTs38+l8RVDPknzt7J2amLGPgz2HMOZ8Y1FPr4HeNbI6dVVdMutIaWJVislABoRW1mthcSFrk6ZSeHesHlNlPXybRgaJlalV1SdSur5yp8HVLHBMXS2WFzuUkjd41doBvkjXDSRzNS8bctf01m94ltWSpdznc17NUdXArVyAb0firuR+Ul8VKa+SdXy/kJlZPRclz0sDR1V6M5flVdzxctky+sDQdUxLbz/3Mkd7y2MKq9dlY/bq7ytq7N3cyEqywkTEZIZpyevWRWBMeK8csh2LHyIyBsLU8ezDG/7YV7/H1i9WWPMTV/7IpT+yye8BrKvii+OO6x9cMrxw9NdS85m4EdxE9qMFfzS/4ZC7JcZLIY2QiqTwSomXKbOvqAFbgiCT0N8KskC4dMSN4mchPAXdhRpXkwpx6pj29l3SQ4fMWTbZK9orh58M/NFXv32yLviXf/e3+d/9jyMuWOUm3dgrflJYEnc+8HaYt0zOAwpSmO5uc84cljckLW8b72i39cai/L1SxPu2u8u4KgX7tjlniV3nDK503zHfI2g9glk/hOHQagZ44WaOOq8mgS7yYtkxpe6kGl2ShRI0vCRwWX4o5mRnFQ5q1w+qcaD9WwAkCwqpHifm7V09l1b5X0QqmFSCQsKRZDWYPx8RV7sGwj3+Wrrq09uuhuV9ngcFJ1qZYG3HqYX1NzI3AWG9jvKv1ABUHrtMN7b7XijUCa8dpFJxEV7FHa+XDYOL1rq1DLXkYJ3Y7emUHGkpugvsB1vMDp1BybFTu7gokHXjxYerv5hJybHZLPzxr32Hrw43dM4qXJf+yJU75vfb4UT59HDJ/PmGzUtHd8zJRF+Z4EjCKgHqCNss8RiyYeHGtONp0JNqvgTBzUJ/Y7Te5dKROrvZ4SJZFxI1xkCaOuajrXx13+GOZq2sDuZnieVS+CN/+3SC/6Uf/A7T/0CytMTRHRJrZD0bpUJR2FnvIik8b7leP7N3q1Q85GPyXhP4G0Gw9V8zkCwvvPXSHsfjeK9hha9ER2TAESQaMOWsoHHInVtznQxYGVnFZFbUOgvaute2LImEGZzbXrC2Ki9gT2FhAdlrSWpVuZyryP8SUrcprKvia/UQrlzM58u0f+6vVToetv5ZraeXFwOwqnm8UJlg7S+6kGjtPdrf7bKyxWJa7o8x2qxLZC0CCfk9G9BUuhwe01jjfgss1lVfwekrVi9VWlhPWTAtsXgTXV4JRmq8Kj5cro9oJ3R94msX11z4GSf2WQ8uMOS4WBh1+zAQ9x3d0RILUVt0ViArF05ULb6kKuGwpUgccoW+fWfJiiZutuJLHPzaxXdQSvtCVUGDrEyFxSGhN9ixEw5p5PnTS3a/eMuzzyL9Nxduthtip8i2aB6tjbo8WNEu91LWWPEOgNa9YeFLxpo37/SWx/e8pmf/Po7HUUZEuMjWJibZG7hyx1osKEWQT+erprAcufQTU8rNmQqrKXl6H3keLnna7e057Wq+UfaPSG3kVDyfgKzAWI3eeyKJvgJavRjzqxRMSk5S8qdj6tnryMtkPsHRWay5TSM+SxQ9tt5dJYnG5jLVSkckriytDGgV8KiV09v1xhNVyCaDYWXbMce7CoqVPIFiA5BTgbPPpMSWXgBV5izzTjl3KYSA0q1+xnNMG6BYsszEJGxEuU0DThLHaDnD6CM3RRcexRhTUDuuO69VfaKTy0G8LUhBWhxuCAZoOXAXgXTTo50VGUSU42FAgXFc6HzC7Sb++Mff5Q9dfJ+PumuO2vMiXKyKnwxqTqm3JgTXV3SvOjNVl5XR62fwiyIBxlepdjEcuoQ6RxoUDcYwVw/9tdBfe7Q3CxXNqko3CcNrGF4p3VEJW2G+crgF+E5HHDoDw54q4dLBxq5TnCK7gLsd8EfBzc5inFN+7Zt/gD/9//tNW88I/H//6E/in85MUUidQ73gIvS3QvIOv9zvN20nkvW/N8WheyxQxLk7z7+po3uNQ+6e5x4610PSxvti330LOOWutXHOiWqRrCnCtMX/H0Y8ewSzvuRI+Uu5kYB3VpU4ap+7dviqM3+x7EySoMLoyqJy9ZxaMgOrgEKOxJDPUemtzaS6VPmfGVREHWzCyxpvo3Wu36ziK1UCz20a83V2lVVm2zVBSIwKPEDWqgcDtljN1sv193UC0wqYeTRryPUkgIDRfMGSjJIkldFSds+Dghe75/FsxbdehzHaSlDcyIJz5tV1q3ZHgzq8S2y6wKGLTKLgtYJU4sD5uNJ2g6ylfCH7noD3iRg9fozEo0e94jZ2xdPU433icjfxZDPxs08+449efZsP/Q3P4yWvwq5J1Ox78XLZ8d0XT+lfeyTaZJ966PYm+ej2yvgq4qeERBg8hK0jLop24GehuxVw5pcVtzZzdHvob8w43gVl2QlhZx05wJsv4RbCriNcKLpLyCbaPQmCnw0QQ5Q4wN/7uZ/hT//GOsH/6h/+JoePbWKVCH1uaSuB4nN8d7TsrPuqEyfbWkXj3mTifai3ZxNtnVjvM36/Z9ybpLzjvqcnfsftHsfjaEZZ/3TZD0QRgrgst1hNyA+pN7AA85OynaVK0VL2gDLMxBawPv8GK0AlBZBaF/pZc0ZSb2BY8eliNWkHA8A0AxYJqndiUkcnxior24EVPCQzZ72aNNFJYYsVhtX6/lcvFEvciotVkRKWJiXlipysoFeLE0HF++0Wnd1v98CacwXaEipCyPe1k0jpoLga+mYJpEsElyy4nIFVIsk8G6Nkz6zTYkRhLqUgSKfW+VVActISgsM5ZRgCYxf5cNzz8fCarZs5pIEp9fV02fKeY+y5PmxwU2bj+Rz3FnLjD7UKeVSLdQ6kE1OHZ3m9WwDJQFdejrjFkhM322Lb90Lqy821hXzqIfXOGF29WvIlQEq4eIDPgF/1/Gd/5pf55f/5f8pXtp/yj4Y/xt+/+ZdI308sFy9QCaAJv5z5M9433ped5b5EJbwe5z2fPxv3XuVj3Hgc7zAKyH/lZhYNdS7qJfA8XrKRhY+6G6I6DrHPeYGwT0Ptbl6aiCzqc9OIhSkzfy/clD2vDMx32a+3xIkyry8NHcPYUr56/3ocGzcb4IEBULkJa+1OfptGXsad5VSpt87vGTQrnlav44aNW7Lx/FhVLRtZWKDarpi1Sax5ypLVK8YkU3q3VFuVjQRGiZV5VeKgdVZfG6KUUUCsdpTHMf/dPnZQ7WZOjiWJHjgCg8ITd+RWBxw5d3LKy7jjVbwwANBFZpfY9TPL1hGj4zi6tYjtFPFK10fmY2d+vwLVd6BUdLJPY9dFlqXLzGsx+aEo/Rjousg8d3Rd5GIz880nL/jZy8/4mfFTPuxuSOp4vlxwHTc15y3fuV4837r9gOtPLxmy/28pmvQ3ltu4GbafR/oby40Tif7Giimpk2oSrt7em3pYdsL0gbFU+2ulv1F8lu6kLt+DlM8VLf/pb2H7GUjwhIuO40fK9FGEIZFG604oo+InQYLwv//lfx3tlf/et/4p/9Uf+0n+/X/7X6N3kekysNDhosctQr93+GN8uIhfvywWX0xa1372OebcyYXy8/fkP/pGn98GZT3/crbhrc2fssTw4WOybvsWZta9455D16VO44tWvYbfM949glk/hFGQemsBa4blvQRexguSCh/3r3EoN3GslY/RBbwmW+QmVw1ZF/Xs04B3q3n75sTb6p5A0QSU+YTPl9vp1u+qgTy3aeTzeGmdPNTx1N+ycXJSHSmU3ttcKSh+XCaHK8yncEfK0kuip3QrlHuDQpWGyGkbd7j7uxvyj6cwtDzk95OYGxi4fAZeBWTBu3RSvXkZO67T1oKhC9Zh0ke2w8I0dqTo6zpXsr48lAm/leTlf2U06QfZ9NhvIrIN9EOwKrlLPLk48jPPPueXnnybj/prNmLtkG/ipn5+BbhctOO3b77C8cWGXiFsFRmhvxEkGJC1eRHp9rG2Rx9fJPobqw6opwaxOAjzpWO5FPyk9HvFBdauU7lSrl1OXmbY7JXu27bv4eOO6Suuyk1SZ4HRzZY4/vu/8su43cKf+o3f5df+0O/nP/if/A+J+8C8WNUlvM6gWkXb5X5m1X1D3N0J3T4Uy6jed5xM1ryRYWXbQ82c3+Wav0h3kTZ7Vt4Y+x7H42iHss6biQAOnCaOyaqmF96An1m9sYcygFRAnuI1Impz5pIt4VWyFC+XD4xNlefe8qNRDLaSYgCfQZw8UUSVuvgqbKxZO/ZpoPhYjW6hEzNlByssBBxdTmj6LJmXDJQV5lNHOvk5GgZt4FWRIrbxpMGBgBXIasf54xJ7WhVgmQvW4omdz4vmeGFV6yVLMJ0kog7M2uXPyoBH75TOR4J3qFv9SASrhKeilivMq3IhYpV2kRxrUOhAOuseRQbLxiHwbLPn6+Nrdm6unl1zNkUub6UY47+Yd4RDZzG5A/EGQJEMyOoOat6H+XP3B8V5m9Pb6qt665gbB2NiuWU17K1divJ/Eu343UKOR0K4EMI2s9Q0oe4A8j3k177B7/b/Av/Rv/JNrnYv2X/7Gen/45DvRnT8jBhnXEikyVjH5Yba9/gtCUX9AjyAVsIbF/UPjxI8Tv98n91+KNs9tO/j+LEYvskoPcozv88A0IaU5+Qrd8BJ4rPlyvIS7Rhzk4ybONI1a/uW1WuxpueISQvN1zdWCxRjdZ0W0gsQVf19sRzgmIbaMbDPMsLirbvXkc/DJddpU/OUyNGK/D42DK6OJXZVjheRDJBJff+edT2ZdDWdL0qZjUTbXuAqZ1heMtmANWcp3pFWnLfj9ZCtWe77HISeU8ZvdpwCVbzEytAqvl7lepDIjoU+hQYc7CpgBwOjDxxjx20cEKDvImmbpZPh9IqcV5K3oj1Rqq+WLA5NSrezzomHxdeu7t0mMG7seNPU0/eRrz295uef/YA/cfktvtG/4Hm8zB7Fwj6OZuWSOiaUK2edGv/J4Sf4recfIZMzIGtjXdiHVxYH3QyblxE/Z69dZ0wtjpHuVox9lO+3ZBVH7B3h0uOzWX0bd5at5UOpXzvsGivIcp/+WhluEuO10B0Ef+wIW8Vlqxn1FoPdDKodf/WX/03iRzMXT4/md6nghkgcco51dISNMLR5QLVUkdWvoBn3msCLO81/8lqqej++R/7zRobwPXnQFwGP2u6S63PcyYneylY+W1N8kWuBRzDrSw8nNsEtuTR5kbt7VAPENALg+0Qvl3y+XKyGeKVinJOM8oPdx7F2zCgg1YWb8kI5a8RJefGeqtfJbfa3KiOqY8kfcdmvAFmv4pabuKneHjuduci6+LmwvlzuDihdlVFa4IqVmVXalMOaTEQ1T47BrSEtAUtJjtSSgT4HhZaJVcY5iBXPnuuFHDSt65QBiUbZvSDgZKma+31moV24iSn1bP3C0S/cLgODj2w3CyFG6waSBOdXlgJ9QmNOPtQ01doluj4wTT1htnvlvOI762DY+8CHF3t+6YNv84u77/KV7obXcVPvfy/WzWnJXVj2OvKPbr/B737+ISiEJ6YH93tnvlkHGG6TsZ1iDoRLWBOiuIZLUYibDvfRgKirkpE4mCQx5gm+oN/JA7lToguKX4DPwAVH2JaDWiIiEdwsBHr+N//mr+D+7YBziriEbCPh0hFuPHGQu3RT4F7z3fskhmca8TqZP1Qx17zqyBPoF5J63LmGu/uLak7QyiSOUYBzkn/vDHxf15G3ZdWP43HcM0TM62PJ37MhV9AdJlEvrNoLf8SlgUMcTDbAKpsuzKsiM1jUOExeUwWpegnrdhgAUuSDii3mZtyJT4kWebymChsVIGvSLntHlaJDZBBbpK/eKubjVQo4BdQqMa6cv/xUWg8tkxeurylrArEax9v0cB/McQ5iafOclv3U6qglxhS5YY/J51f/y46QY3dQT+8iQa2Tl3dK30dSMv8SVVnZwJIX0aVokuMNLuF8IgZvflvkNa/Pvo0ust0ufH3zmq/21+zcXJu2zFkO5CWR1IClRT2fLFe8PNjknkbNSY3gJ5Mb+lmRqNkjS1cj1mVNJurn3jlkZwGl1LXUG1vLAK2mPpCLKITM2or2xiVJZnYlkCOkT5AJ9G9/g/RrG14++xjZJ2TaI/JttHtNGmbSnKWPcs8UqvdUCe4Drs6lG+W5h+JHmc/ry18GYWpPevcplfVFrU+Sf7tf8JyPseZHfrSWH70kNkSSLDhvINPraKDWhZtqp6WkVnxeyIV2zF+2dDUcc2F8HwZGCdmHNrFzMzBzIdFW40I2Lqd6UpE7vF7n4vKVO5qliazdzVNyFQQzoKtnn0b2ceQmjvW9Tfn5osBImlnJGQBwmswyJXsMb3K39JKvtE2uSh4ya2E3S+1OWLx5C4jVWsQ4ThtUlQhWWFgbMUZyVOXYbONZuxsCjE0OUxQ3BhyuUsgkS1WxXGM5xIfdDa/ijqf9wUCkMJC8cLmZcKJMS4dm39+uM//MFAXXWS6jSaBLyJSj3GCNQvbHgbhY93bXR8QZ63c7LjzbHfh4d80ff/odfn7zPZ75PZ/Hy0zCSFynLVf+yIddZJ+G6un8veUZ/9WLb7C/3aDbSBiSAWqzQ547Y1TtEy5Y8QQ1uaEslruJAikhIaFdvtMJ3OhJo6vFdsiFem83u4BYklnMqMWfOIDbQJwtXkkwdhgq5BTNPq8sk9cEnQjpdc9tNszvL2e8T3ARiFGI12bb8pDkDzjNfR6MLQ2gdd84kyha9/rmtXKeO96PbznvO46a58hb8qyznKgyyNrw2+ZFysroesc61H3jEcz6kiNpBm9y8NhIxIm1TvcoLzP91bkEHZV5VarKsErjEkb1jbk7x5U/siuL/gxYFY24VUZWvfisnqMODOJr+1gna3AA6nH3aeAmbrgJI51L+Jjq873EWgkprWo3bqkgmauLdn/yxXOiTElOOpFsiGbQztq9qtWpt7/9+yobUZUFOOpqLu/FjIsrdqFw4VKtohS2VkIYKABX4sJNRl12I18fX9NL4hh7kgq7ceZ2GojRWf7gzBgejJGk8/qjU1805FgHRAzIGsaF7Tjz0W7PVza3/P7d5/zSxT/jommT7HLSc+mPNfnZuIVvzx/ynf0zY3hdLWgUdPb03+vYfK6M1wl/VPxik7oLyfymEkiMSNTMzvJrZBVjVUnK3Q9dqTbYBJh6m/wlaZ1TUi8GaE0wvlQkCcsFpE4N8IoriCNHh+5HwpDon02M24XjZG1t70zq5xOufWHu+cRZJ/SHxpsm5HedtL/I5F6qGQ/lEdJWj7gfzKvnv+fxY+X8cbxlqBYfKluId5kFWzyqjiizGlMUN9c5XEStxTasTCZsDk9IliwE+gwoFcCq+GiFasxuRcaII+TY4Eksma1lpvN5jlBLAhb1zMnALCcGjJRKf/F3LD5ZxaS9xJjizRULp7f52cbMMAN7umvi6eqhtY63qYFLBT7k6bDYTZQQV6z9BlFrsNrEMQO/lOIRNuTEbhHPpZ9wKCEZ87f3MIOttjXft2xKa+vU9kIV6QxIKh0QxVnjkd5Hdv3Ctpt51h/4+vCysqeL9NMKSqHex04Sr+OW18vG3uMQbbEZBX/t6PbQTcbidUkhqcWHzASWlOpiWZ07uZEV+Er2wIWVlVVArRPikrPY4wKmq8mSenUR3A2qEbiGVxvYO1Qn1O9xm1d0/USIDvV3fUTqNbWsq4em+beyb9/0mj6Aon2R89w99Apcvfm63ruK/RhnfuRHWQMXG5BeEldu5qiewb9iJxMv064WyQsyU9b/U+oImalVAKNJO5ZkQNeinp1M7NycAbCORQ182GSvrqIQqWqP1Ncc5vN4yUZmFjlNP72s9ilH7XOeMppShcSrsOVpd+B5uGTnDSw5zVWU0S14MQDLZxZVa6cSsfymdDGMKjVXOmrPhcxmo9LwqUrhfcnXv3Frgb2VELZFkTk3qzrmz2KQxE7W7YroA8xHa0RJYt0Oj7k4U/yHBxIzjp7I1/uXRHXs3MxPDK8YXWBOHZ8fL1AV+i4SkjUjiWW9rqeEn1IoUafgFTfELDH0uE6RPuC7yMV24mqc+anLlzzpj3xjfMkvbL7LM3/LdbJiyMYtRBWe+T3XbOgl2mdAYp9Gvjs94xg6dhdHjr7PHsSO4dsDF99VxteJbp/wx1ysXyIS127ossQVCFJvOQ6g3pE6y1dQwS8Wn5IXOJaYo2Yan0EqSdDdUqynAeiOip+V/kaYnwrLpQFe4WKdKLXD7tOtRxZhicLmKwf67czt5Emdnkrkyn4P+S9W2fs9bvHn+U9tuZw/S2dB9byoVAv6xRtB5B3YWWtcehf/4JNtGnbXfeepkkRpXm+BqnOaPTmWtbHvPfOiRzDrhzAKRbSaAqJsJDK4AxtZ+FwusuTNPpkfLE8tYGT7/mKOe5NGIjZReWfSQQsAqYJHLgM0YFXglJlZhQN1m0ZjWOFPqhQlcThqzz6OLMkmhZAcRzHT4I1b2OtoVQp1XMcNO29o/xXHk8AEpun+JFm3xkLzLe+/mMkXg8MpZdZUpvf6Ui0v+5z9GFoga58TIUegR43lVUC0uj+ga4eRWZ3RpNUC7DO356g9z/yeXiKX3mom33dXvJq3+Rha/Qa6zLKKwSE7A/I0ge8SF7vJfLh6850Z+8DVOPH7L5/zze1znnZ7vt694uvdK46pt/vmJpI6LtycK16J67jlZdzxyXyVQbUFVeHwcsPm2wNPfkcZX0e622hA1pxwS7QJPqolF7EYmrjs0OwIO2u/29+mCmi1I3VCHA2o8pNV4oHK1hpfRSTC9jNh2QmHjxzLE4gbJXmIF8Yc628d3HqWsWf8YI/fBVLf3fWhgruL+aSZadVM6LVy3kzoJ1log/K/ZYjmJLEwH7Dgcq/U8C1a8XuPLbn60JowavtazjKagoztm+dnLQngY37xON59FHP0EksMAALPQieRvQ7MxcDXH7mNG4KuEoaycpi1qywpcWvXP9DaxVDyXAsmUdcKhJX445uFiKIkSmt46+nnq+eKHUMIWrr+xcoKUxVmOnoXEAKjUP2/QmEii3JbWq83c0Y5l2iy3D8DXYU11fpkSd3n/J6uQNaiztZgmvD2860qgbp/Brbabr4i4NRkmhtZCDg24qzQkT0yb2RkirbkCmRWbL4gkQxY9cXjzAopQ28suehtYexdYuwiz4YDT7sDo1u4dEeu3ERQR4cDMan74GK+F8qceo6p5zYONelRIBw6utee8SX4KeFmNSArqiUVuWhSE4oW5RMxKYdkmUcGtOobwBbf6o0Z7AI1SSkLVj8lugPoXog9hF0ijgntblE5kPq87pmBOZJ8pNsGpO9Q51YZYzvOH5cvxn2jBZpOtpH3AIkaREk0A0xyPxj1IED1hmOThZPK/TJ5LfDl6bHPga42l3gcP7qjZfe0o5iZO7GmTJ/Hy+xpZPPpJ8sTllSaKJncsJfIREeKkpUiaxfAUuwuxfZSoG3zk6VRiwwSuM1KlY1QJYlH+swetS/3nNUhxee3FNjBcg8viSnPZ+X97pMxxuzYcz1nAdWOmF/X5/GCo/Y8ccdKBijNssr1JxUWWWOmsYRNtj3IqjZpmVbtz3LJc+CCsOA4qje1CNFAsabwXixkFl2L+yWHiayNQwZN1UNroeOpv+XKH2ph/MLPfO/wBO+MiRV9JCZX91+6xLJ4NCXSILVA0g+BJxeWC4Xe1djQ+8gHmwM/f/UDvj6+Yudmvt694qvd6+b7tOQ1gOPKHer9XNTz/fCUV8GabW26wNJ7YnTsXw2M3+4tt3mV6A4JP8UVyEqKhHSa3wB0fi3WO8dy2ZF6oTtqLqIoEk8lttqZEiX11vW9m8wYPmaNqHroDykX+IXNC7NnOXxVWK4gDTnf2SR0SLijpzuARE944hn6QLcJaN+fFFGKZUMd56bvbzLivC9OnccneUD58tAowBVNHvTgtvfkQu9bjGllhu1yoAGqJFmedB5jW8+t9yUgP4JZX3LEfOuP2tkiO1cDCqXVieLkOv/wbWKPOD5brqp3VjFfnFPHkCsfsak2IFQ6p8/yjhJEzOBQc2tco/MWeYGZAmvez4LHMa3m9FPqrTOWptztz34h1oGiM6O71LOXyIXrueJQQbG9jpBgwTqXFJrwIBGyft6u3RbUEWHQRBTryAGrbLB4aZV/IQcJtYw/ypq8+Gabk88h/356YDqbKwxczJ1O1Fs1xgk/vXnO6BZ+4J9w1fe86Ha1Mh6SIwwLS3RWTcjP78aZy2E2EDBYp6qPd9f81O4lP7/7Pl/xN+zcxBO3sq+K79ngzFug3L9CowZrXTsHz+F6ZPzOwOW3lOE24afMyFoyI6skAw5bqXpHGnt09NZdozcwS1LRkesKnACo4pwA5ok1XNs54iiEjcPPij9aZcTPWVM+K8eD4/CxwFZJ1p/YqLiTEPYe96Gy2c5M/caq8+l0AqxViod8St61u8f7UmXvM0B86DRtheF9J/B3HNXcMP+95kCPacbjePPQvBoIOJxqls3l7oECQuKCiY6OJA6XJX/7aAUKL7bQLfGldN0ziaDgclmxPJYMhBSgywx/V2m76vqc5qJFykWKmIGrlCUcQa2DnsvGu5VVlbfzkggZhAvqGJvzrt17XQbVsrTQVkV2jqpxy0wpNTPXLv++yhRQEvo2sS/rwk4gNYvQh5L/st4TDABrh7AawY9SmG7wtDvQSeLGjQzecQx9BQWTCsmnKj0sh+x9ZPDRQMBkINtFP/GkP/JRf8NWZgaJjLKyrwRjEXhnDDfNbIfbHPPBgMGYhDB1+GvP8MoKGpWRFde4Ud9UOXrnwWdDXp8TBc0gVVmP13tin0FCzFA+n8MM460LlAuaGcaKW+y5sAjLRYI+IXFBvTPZY4S0KLIzU+Pou1oQOF30tsDte86rXybWvMfi+zTWvPt+73U57Zed5k48xpof2RFzMaKs8XuxnKTH5noDZrIFiJj/1YKBUkkdn4VLeo3s/MzoAlPqOMTOiiMSOUTzv9rHkZ2fSOTGUCqZ/kOWkccsIxcWHdm5ieu4xZOyJ6+xrWY6BgLHnIsMEm1dnAaOqSckxyENOJQ9Q22eBXaeSbvqybWotzwsjTzRI1fugMfYysc08DoXQRbteJl27MQK8KVIX3KWKAlfACn19DIDcOVMEjiI3AGvnKwdeot6JJdCqhWLl5LzKFMuSBVvrvJv8eKa1OZNY5Z5Skf6DSaZfOJ7XsYdvUQ+6m/onQGNt2Hg6XDg+fHCfJdViMk+h5gcS7S4XfKcq83EtrPulvulJybH0/HI13av+dndZ/y+8TOu3JGNm7mQmYHIrRavM3diuL/RhQXP58uHdc2x9QudJA5zz/7FlvG7PVe/C8NNws/JfBmXlAsnGchSNakg2Si99+jYkwZvcaeT0xznPPZoLigsINERR8VPyvA6WqzaOZato98bK6woW7qD4o8OP3umZ8L+64KOij84Qm9KFX+wDrqHQ8f49BZ2cNtvIAlynpg63i2GPOSf5bLXcGuvUoCxkke9ZS6/jzV1J8/ByiWnpPD79isx5S250Ts0xSpFqJOCS7vekCZ+veN4BLO+5EjAPvXc6sAFc26tapOjywytHutEccwG7c/8vgabtgtfkrX6kRCW1LHQsXELSRyjcyQCG5Ys+zBwq4xBAov4WjH3ZFNerEJyHTcc1YQpZkRv/P+kjskZrfipP1TT2jL2ceRaFq7cgWduX5OLvY5VEnJUkz0WJlLpctI3bXFLlynHGhCW/MOIOSCUUbyyeuCZSyulV8SCR/MZlGBQPg/T4yuo+ZL1UD+TQj9OwbHozNfHV1x2Ezdh5OlwtCQseW7CyBzbxb8lZb2LXPYTc+z46jbw8XjDH9z9gG/0L+ze5ApQaa87SOSZ39d7sKjnloE5eXZuYucmXoQdr6cNrz+9ZPxex+W3ctVib1ULmsmazhG7DrK5riik3qHOquSpN/22n02DbjRc0JLVJatCuGBgVdGqx9HTHRPD64Cb4jpZiiCpMzmIOOan1uVjeWomjpIwvxWX2A4LR7cywWx9I8apfteJHfIE+wDdlrPjPASMtTTbOmHfrUq8k0zDqDAPVjROtOT3XI40MUcUUvvlLTHl9wA4exw/WkMx5tCsNq+ormCNmbcnnAhCsDqwOpNWOEdM6wLWi0E2XlJN96Nadb3DADCPfd87javvVvNDKUBYAaUKQwp1zMCkHUFt1gsZ0CodFYOYKf3olnrsMhb1ZkasIScoq/lt2S5kZmtQqQwywWV+QJH7rWys0hujdJI6x7fLGtQBGymQ4bpt+8tsC5cFRymyRpX1b4h0zpp13KaRJJHL7siQZSEHH6pPzZy6ygi2taK9UyfK4AMxOXa9Vd8/7K0iv5Ulf16RLieOHmXjjvV6E2KNADIDr7S6n2LHdDvir50BWZPmpCCdehg6Ibnu5Aaos4W0+sy6cgZEScoSQ2WtGqvdaEmKi9KwhIucPSFF11kRRocko6rFERiENKY8n+d4k+/LXsYz2mtznPcZ54vz99m/ni//oXDCznrfofYFe3ANn4+/nuv05TaeSftZ0Gz7GGt+ZIeqgR8GOFhnwpalVVQRvRjjcnAGTtzKyFN/yzHLCY+pr3NGl3OSIjtc1DNKYMrbAFwnizUbt5ys/Rd8Lfhv3GIF8My68rJ6DRb/xGPquY7b+n4W9SzJ5nsSvA6mGbvsTOboNBdXmo6LHptzi8l9UmNzXactGzevKpXMCNunkZ1MVd3yTA6N59ipFGyT85biieVFTlQlJZZEVTxwlYvxKe9btnFYrFj0VF1S9p/1lFnnUJM+FpZb1uN8Gp7QS+SpP9BvI/s48Dps2XXGnksqvF42HEJP7+JarM8TQ81nUoeXxIfjnp/eveBnxk/5Rv+i2qSY71jI783xFXdr4GOWQAK4rPoZJPC1/hVH7fnW4UNeHLfcfHrB8IOOi29j0sJDlhaGtCpHRNDOoV1X4wzkom9nsvY4OLSzXMctOW6FZF133WlxRXtXbVYkWeFExeJWf0gMryzXKWoPSYqb83sJjtR55idiDK+Nq/kOEVgc3iWuNhM3Hlz5mrRywyzHf3C2va/Z1fsU0c8ZX28b58e+T6XyLvtmdnYb307UKqw5kaQ3sMjKAqr83Wz3XtL5PB7BrC85omrt/rdoZywctzAQ2bkF1Cohm1wVuHA2aS7emFHXubNdqaKUBe4Rq5g40Sq5cM0Xv0gtKvunAkcrEHXMk3i5BidaA1Wh9CZ1HJLnotJdfdWjV3kIwj6Ntf2uAXZL7W5YGFmlK0libRN/2lWkMXxXJZJFZvnHWAJEGf4e0CJmIKskFOeMruPZr6A9v0MZSHzF3dJ3kZe5WnTljuz9yJPuyJQ6Io7rsOEmDLmKFQhpBbae9Xsu/fT/Z+/PYmzbsvM88BtzrrV2FxGnu33mzUbMFMlkI9KkKKdUtmRZJOGyZKFUBVVBsEoFuACXZQggbMOG7Re/WLb1YMmwAD4UXJBKbuiCbcI2DBGUqkyqNzs17JRkMvubee89XXS7Wc2cox7GnHOtvSPiNJkpicyMCZwTEXuvvbq99xxz/OP//8GbzSl3/bpQlu08KSywRsG5nia9J5ton5OHwwmPw1G5l+9s7/LF9+5RP6yYPRYzRJx2wEhASkiInSbwKi9mQ+PK5BJrSQCUjlVrxsq5dfWwikaslVgbOOUGRdI/14Vyv9U7ZPD4LjI7tWPKYKlemCsDSlxEjmZWvXKD/bsCPom83CJaLcm5UpnI+5v+/gxA65n7P9zXNcMSghTsnqVBLy9gH9A6PDWlTPjl+dvc4na8wDApnGfAfEIygOWJZeHrMN8pq5APxqhyvbFtkxFvUAM+MmgyYEwtyb5TYvu2yYJirlvYP6J4jQyMC7UBVyTgWdpnjCxfFtC5w2GeK6M66lRpL9skJlZmfkVMkpi9tczsPfGdhPKYvXY0fBcyc23f2P263+2+Te+zpRoZyLJYFq8wug5ZWZlplYWYHmUpZhmwlZou1jR+oHcVsziY5DIVlro4xvocmwVl7q3pypG36vg8df8Ci/1VuhceM/D3mDF8r+Zrto4zttqUe3nezzm/XODWjmprXaPkAMBiAohYJ6kx1sRJZ6ksry5xZgLY532IAoHRDN5RQC2J7DUvQQTxFqv8DhKty+5EldSOldL4ZA4cU3IxnT8Link4ET9/SH793oMHf18ba3j+HP6iSYpMQM3nbvsCx54+fxtrvinGNB/pJcnsxLoJHruudOSbJZCqi54TZ1KzV6sL+miNOwDaWBWfrDY5bfd4Qio0zl3Pkd/Rx5q5780bS6zD4S4VXXKnw1qG4vXrZGSQraQd858UHy6GI9pYjd6/6jgPNUfVGA92sebY74B5mfcjZh6/TvlKnXwELeZU7GKDTx661kTL8qZ8XCcJnNExZ3Fo8bvKRfcugVVhso7M4Fb+mce0cJLzF2TshHj1/ZPJ9qZqyUWdWmIiSQy8UZ2xci0PhxNOw9Ik537HkW9ZVW3JJY/rHWe9AYSNM5uXvuQzkbu1gXdvNme8Xp9x31/udbCfNv3q1ad8J4wAqRpR4+FwwrvDHaI6lqmZ2MPdEQ+/fJfm/YrFQ6G5iKXokYexfB3irGiRi/Nga+6YGMAkIEqrHEesSjxlRLne/jDQCohW2A+NECuLZQUEi8l0PtpxCUpc1tacpFUWjyMShGEpzLyjO4n0x5h38Cxw0pi033W2z2sBmGkR/0WBp1zMf5ak5Hmx5DC/iRhaOo1Dh2nVswr7h8eLmJ/0decxqfhdt7+99cJBLnRjIeYFxi2Y9TUOLzbZjFpwqz5EcRBhLgOzVDGeS2AlHUEcwTlcHYF7vN8d08WKhevGDh3RMXOmsw5pQm9jvfcBdBP2igFJQ+kiEfN+kheWk8hchFaMktu6inaoiGrykIW3qkpeTM9SlSPLEmeuJ6RKbzZ5dEQ2OiuBswBY6qjFGGjTMRez8t1pvnf736fDIJAfK/c5/z69/5Pfe933MWlSUpfZA9m/q8bYUnPXc+J2yTx/bPUbVDgLq6TZF+YyjAb4orxRnbJy7d6xrFtKLJJPj+Jcpi87HoclD4cTA8rCnE1seNQf8357zM994UP4d+bMniYNeJq0YwWaFiOiGAUXq1IUw8E8ueeqT8XehCk6mS0Ek2oQwUsyKh8neL8NuHYYqb7JVNC3gTB3uEFpLgys8q2weQuGI6W+u+NktuO8nVtHrGmCQpqgbqpQODFE87qJXp9ROtjTdYtNoFFvZoC9TMVjsugvk/HzqhgvUtmfVB+mflmieiszvB3PHRm0zz4g2aQ2gygV0b7jmNl3Q6CYq3uFMGcdZymehCKBiyoFFMlG7IN6KgkF0Mom85B+T0lAPr5OvLBEzDsqqKOSwCCOEEcwqpZYtsuvGfBFllj5UBi2VTqGiFpylir4026GNu/upwVVuisZcMqywDyutc7DATWOGZEGcJh4ZAC2CD0ZAgu6jxHkWDAtNIqYj9ZcTGrRJ4l5jwNPkWC2yRxZ03uYu0QKcJwKJflcbU2apKWTfyK52CPsorVwV5Jxs3o2YcY6NHz57C5yXlHtUtHBbi7qFM2twO1DUW7c1G5NJ/Pr1cWmjovUBGSVBk06FjMkaCqe5Kp8LrQ4A7cqk5D4ziq7LlA8TNx8YFYNtIOxha9IOwCV/L4djGclE8+KD4dgWbnGG9ChrxU0eu7rXyDWTLc+TFJuY8039PBEdrFJa+AwekNJZK3VXmOmmshKOqI4OjFPotfrM77S3+VsWLCNjQEgjN0N21hZ4ygMWMlF2U2cmQWKRHbJFiUzrrIcLa+jM8Bl7FEzorfzjMXj1yxaIjFWSYquVBI4qQxMyfnG0luX8mxSD3Cn2iQQyBdgJkhfQK4uWbdbExMt63+wHK6YrkukEVOGWLEd+hQLc4f1oFpyFwOqpmDUmL90qmX7mtFKJT8XGb2A7Zj7CpMwAezsfVZW0lHXTzn2W3axsYYr3vMWrnROf9Qf08/GTCl7nmWrgQ82T7jr16ZaYfQ7dhJZJVucVZJaehn2GGOncZ7yGuHhcEJUx5NhxZfiPT51/jq/8vk3qR9WNOeCb3Uvb4mVWMyZ2frdBSvaazV641oBJBXp01BPCbSWvyRW0EQaLwLSmnpEvXkEq/kI4LpItQtIH2FIvpDpvZA+4DtHrM1ypbm0OFlt7GT6IyXOAovjllXdctHPkWFkZqnI+PZPivjqZPSLPBw5Lr2kd++4rYzx+poc6Eap4TOCx1clgT/ctoB4z3jJQUHGBUp3ypesRd2CWV+PkRF8wBhMUhWKKzrK6wBja6WRJ/RNbOh7Y2rNXA/JfDAmD5NevYEKE+PFDEI1DEVuaFrqnckaJh8E65YozF3PTntmMlgnEi+l+jFEM4ovMgn1e6+/47cFxOqTXBJIwWHcNgeuuZNiEOlTJcYBG7UOIvPJhJgZmr7sY/9TnIGsMN3m4DUvMtxkogZMA+7CpANjlrRkKaglAsvU3reRkKi1IQXDvF9KIMhtkcGSxLXWnMYlpwkcs+pGxdN+xaP2iKftkqGtqHvIZIjQGNAUPUmeYZO0VEmJX7EHUGWWlqgFhLLYzt2ogiYALM8Y9tOlyOk6SypcO+DawSb1MvEGJCR9elBkl44pGGi7iNxdmXxm29f47fUJRqbTWRerdA4Ro+JeR7edjkMm1nW/PwtoypmrJovrLHuEFwO5vpZtppUG2GMw2HsG8WUC2O34ph6O8aPbJYPcmfRkFw8pEBVp0WnJgycSnVXCQzSfqip7ZjF+JDMrqEpf4piAKiHLEk1u6MWqw10C/PPwYotyA7FMSlJLsHktAV6xeHJhFf7JdO8kMpOhJFsxJUPT683DuidGKoFaBRGTSWZyTp8KGdXkAJNpM+1/emdrHCdE7qG8gqQ2X8oT4BHCObDde9VNI6/RcvJRY8lXFGHBCEAGFfokGQxqyWeViiHGtIp71+yBKt1xl1hYdi+gw4oyuXNY9sHZhoZNaNgONXFw+CwHlMS0UkXyvKxJZhlvAKwmB8zb5A9QBsGmEpEy/6UFiTGEE5AVQnptei5G8x5RErPY7mI2U9dKmTeD+atFby3cr1vwCiVGFbwpgY9T2fnLjWncecbry8I8SQ0zugxfv1jzvAzjuqd1xOFuxzf2MGarfQC65I8YxKUCbJq7xICsPgE2S9faayI0VZIOhjm5E2vtQ/l9wFOVorn5/TqUuR8SkJbkhwIe88ytxc7lxFvxODevmlqtWMMoy4tmri/dEqO6kuFOlSp2rWODEYcBba81F9z1G3tcSOBajSOaf2BsgMZk7HH0F+7E80Z1liSQpuKoidRisaRPuUNUWKYa7BSssvsxjilTy66P8jMrUgqTa/K6On1Lu5wn5WCS95uK8nZfbQ696zYEtyv3I0smA1Kac12EOXPXlwJJVtOcuN1+DpTIEw0h+XQNJa/Jn5lsrfPucLd4ltUSeBrmPO5XPOlWPNyu4LShvhRcRymeR6/QmKQQpTC1YqoQqc/ydcYCCiMoY3K/FDNyrOgNkNJKiKlQn5lYQFGm+C4x2NuAtMFiDiB9QEVwXSA2vsSPamvHdIN5BOMUXUTuHW2oXGSIjmorJpecTq5O0NTk6tqZ+ro5/jAPiAqVu5rzXJcDZdbVYZfD6biOreWueZzrAbC9/bwIMUBT+TLHo7xcOAil0zPO4JbEPQjihcYtmPV1GrlDXa4YGDPKUTtDtPNkUWNtcnPgeFBdFl34434FcawQRBW2oSY6Myzvg98DY5zEQiNeuTaBZ674b+SJP2ABoZbAidtCPQahIY70YSeG6mZqcU5E7ldr3qhOmUufWubOivF8XjhHFfPjSqyuiDOqsdR8wJ+xkoGNenbqi38V3Ex42QOuEpB1+PjzU4qUXKX70R8cqZZRnpPpxBGrgLzq1/TJhN+2zSwrX+5nllvOU7Uim2wGhJ16LuKcx+GI07BiExuTH7rIJjacDksuhpkZI1eRMIchyfPU28fA7wTfp/blVrbfq7KWRCMt0KttkqKmyaD4dlTjhJ6TCa2Sqa6C3w4GhB2yslyaSJMxo2sdvgtI9LjBsd04ujpyMm+Z+4EnT1ecXE5usEw6e+SgMq0gHI4XZU5d5591Q5LyXGngy0gfX3To5B/j+1Umbtnf9oXki7fjdqSRO9T5JAvMHQEziJ6BEuu+NJqtL1PcCerYal18M/JrBvWoWLOQqC6TsgAKawtsLhQGMnRmz4MkIEsT2DWTPhm5WuyK6uyY0bZFIhoNVMsSuYXrOHY7A8PUFwDLJbZYBt+G1AmxSuyzDmUgciK7tOAeAbvxGq+HAOzxGjgm8mH0jRV8B+grIJfAr91Hf/0+8Gks6rRcF31ysQRGNnAe2asm38ucn9j33tqbT/3H8j6KQT8GMFaJ/Wvve37fjIG10aZU5zPzLTd9aaPdO3FKrECa/KZZ8pA7DU5BrD1Gz8GNs7qaFhCr5FtF/2+xRTSBWyknkyEaEzUEiwEZ4c8IZC7ChMTwVUtMhl4IXplVgxkKbxuSsj2dX/aQHGNASS6+FgRHrvv9JlToOSDTP6pYM9ntXqy5BtBKX9Sv/3ncjt8UQ0RYuhYfjspjljO4xAodGzPtM3wGfDJM32nNsd/yZnPK+5zwuF8Rw4yVb8HBEA2A8kIBMoC9orZ56IYkgQ/E6IpqxKNsUifD3K0wF8qznC1ivsL5HHt1dLHirDcT+YUfj5nX5hFJvr8bjlMn+V4rzqP5bDUSuIjzkrOY0byxl5bOrm0TzQe49iE1joqlo/pMAvPUFS4ztXL+kq88M6xqxlxlyrzaU5JAWbPm/TH5mTsWe7TkLtkU3t7smN6H0RQ/yPh8jkV33YaAY+frktNloCqPLhVBMnsuq0um+VFAuEj5n+U1S3r1HKeOim2s+Up3h9N+wS5UdnSnDAtN83hiAHtTebhsb5KTu1rGeKEpv0lglooV9k0iaI/nXCbdrOQtLCXu+C4ztqz049uI3yWmd2td4UUn+5jkKBIV34EMWthh1VbYVuAWA8dNixPl0eWK+pJSHLoysidOuO45MQNdHZlhN47DOXuvYDR57iZlCuyDVzeRAm4azwGw9vKXSX62B1wFwF9lhGXT/lz4mfpuvcy4BbO+xmFAS1rAZkOIlAVkmmueXGqJZVG7Sgt9rxFqeDIccT6YrjkipZ13bntLNLDpMtjE7FCWvi1BoEumEmMnEetimEE1J7G0up1LZwwwKFTe2plfVw4OtQRqN3Dfr3m1Ok8aeGt2vlPTpHvMHyx3JTEWmoE1l2FOpxVvVKdJHy9cxCYxmky3Hw/8r6b+WaV6MRlxUO7++TWzn+lof6Dh8b++IlSyJ1c0GaPJNzIHbnx/pPwdUvoydhqxSkeWbeT3q9+rArnU9bHCSeRYujGwYGBZZmOt44wnacLfxJklFFIXmWEbKuuYmLpUAaUaMPUXMZxxrJoTxy95DgLRKy4koCjvS5Mpb54gk09JBpFc11Mpo+9JSJ1EUqWCqKlC4sCJsbI0GkV3ne51cOCU46blSbtE3p/hu5RQXzfv3TQZTif1ve2fQbeaVChU0gcgyn5w+jqMFzKIn47n5DNlm3IAknHibc38djx75MUzkDoOjh82e05wmgsd4zzWYFVyQTl2sHVNKWAoUird5jsixT9L0zYC1G4oDCerhFdJomGG8SIjqCZpLlVMQliliWrsvKgJoLG4lKUpC9cbe0BiAasGzE9SMC8wj5ZKvGBgTRcrgnMcu11uqDWyuBR8MssX9jtG53tps34F3IfXVvCD8D0f/Vk+xBd5xAN+5jt/gPa/W8Cn38TJmiw3FGDsI5j3m+akMu2Oz2cQq5jUp8fy+zUFwHIsiunezosQcbJfNTZWr966fyW5/5B80dQZyBV0n+ldzjXFG0k/9yTQHCwo82NpibM/zymZ9Vq2ncjbs4/JGJtIQNb0QImhmwApCRaTXAexwRISgcZbRzXWfkyCrp1vb5iEp1S26x6//kX7QNbedT/jZf+ox4vEmumYrA1uxzfuiOqKbG4+kV9HHCEzXhN4nuNEBsiNJSrmoVU/Na+qYUFEivl66cCu1l0wBvtgbWLDnWpTzqEXz9J19LEqAAnkNbexpPL5TQvuO61ZupZXK2hjzTZYp/csIWxTwb93pvyok4deLYHXZud7xuWmrjDZdZP8BjN4lu1bLDeZs0k5jUueWg+Sd/CTnHMZ9ZSglptk5Vv2zspdDDNgVSSIjF3Ys9eWdZff308eUw/gLLe3x1NjMHXFoD43HMuF9FmO+RLpUpxcymC5SeremIcV3avS0Mvu4VCK8zk36nH06tglYDArTS6L33PFsd9yEeY2NwNdtNWC1kqsIc5M8m/MKsW3pjgxtchkPsqG4QXMStt5QXW/y655LqZ8YwLuuCE1FlEDzjQKMii+DdY1MSiuyx7HCiGCd1Z48wakmQeWUm0CohWxFuoLwXXCbNFx0uxwKJfvHXFvq8+dh+Wa4r2I9WsuRvBfTZFhmgPJJP/J4FP6+XVhWh0cc3y9rWM0WxIcyiVlUrDXMacqeZXm82d837+KcQtmfY1jo3ARF3sa7qCuyPB2aOn6EBLM7NKCfs6QwKWeV+oLzsKCqFISjYD5ZmVq8DbUCXzSUfec5ASZrpsndmuBa35WmWIMiUFG5NjteK2+4KkE1mHGwnWl2nGn2rB0HXf9hrvekIupnLDTijZ5aV1ECw6XYc7ZsEj+WmPgeqM6BSwgvB+OTa7nApuoHLt+rwvh9Gc9+TvryO//52vu/NlLRGH+NzqiwuMfOSqLOi+jtCRP9NeNzG6LiUFg90tHjToUdlt+PGrWkSuevhgxloqJWmBYa8NpWHERzZ8m35fsMxDUcdobK+tpu7QTEkUr5bC9a6ywiSiMoJR6QfpJ9yiM/pq9sbK23GeZYALBrPW5dUaUpA1XEXRZJzZYfiPSROTSBEgCWwZF3HjcWFm3Kd9ELroZX35yh+apsyDyMmNakSiT+gvMZtNJPJsUOyzgTdllz6xS5ImeRAn+KivWUc2DLE3SRY/+rCr59FJuk4vb8QKjV6ug+glQFRP4hNhiMTOcFCGx9BGBSqN1JySwdB2tqxPDaaz4Zt8ssOq7dRqkgFHZ8ypLD3O8E5SonsGEb9SMhRhBaWRg5Vt2UtNFM+WtkslElifkTlhg8SXPvyEZmUcVunTdFn+q5MkVS6er42RivE3JS5Vicq+ORuJeF8LpT4cDZiivEL4dfsdHf45/8Yt/mfhzoB8V7n3PE/6n7/8/oF9YEbsFTrY4GUqT2ZwYXjcy3jCdiqQcd3wP833UBFJlCaFTM2mWyXaqZrhvXo9N8cXqorWpDynZiQi7YKys3VCPZ+S0VLDLeSZX4kOPxQxMHRZQxtdAIQZMQCwJiSGsmgokgtY+fSAPbo6M74ZV0iG3gBWV4h8pXumC52I7x2+vaYX+vLEXa+TZ8WHvdZNf0n0qj+Xk6lnA0uFzLwtCXbOvm2LKSxdfbsc31FBVOszwPCZlQe58ngu4o6RMCpAFFOl4LnwD3K8u+YK7X/bfxaspY85BAM6GJW2sOPItS9+WgnefmlHAjJVraSSY7UmsC+BUy5DW5iZVdESWrjMzc8yeZOYCC2/yw1x0z4X516szXqsuAMvJ8ljHmXlqYTYujQx0yd6ljXUxmM/+wB9uHgH2VX+SigRgqod1TL64EwCmsKomLCtIndrTyH5bpBylnuQrOb+pZQSyuuytyJiD5DzE8sjxfeyTn1h+D41d50t8yb5biBXcPVoM5Y9dx07N2P1+6rqen8vH7NWx1oaLOOciLPYK9JDkqInE0MWKy35GO6TPiUt5SrQ4UeZssRwievDJYiUX43OMsSZYZgDvemNaSUjyQRibiESdVvmSAiWi3uF3A7Lt0ZkH54iVQ2IYi/bT4kZWooDlO0LqcGiUJklATFMFKol85uwBzSM/8Z6cfCkmHRrLuG6d7+QGlcokB7qJlXVYzCcV06IU+eTXMvYAsHxfFErn9sORML0rMsKp/Oq62JRzJpWy5ICXj2O3YNbXOE5jw+e6Vzh2u2Qq3hG0KSymLCf0bpeqxCN40mNV8yg9D/wlT6sVT4YVdeoyArZQrhO11ZXX2ge1jxUbRglcxIKXUXyHEmRCYlMtXVtMe+eu5061ISIsvfH1jW7b8Up1zoPqslQvdlrvAUONDBz5HZdhzkWqWjzuVyWIRRW8i6XKElV4HFds4qyw0XJFJHfACjqh7MrY5WOnJtnr1fHazwzjolph9rNdCchBx6o7QJcYaXm4XNVPFaAmBU7UPAN2KXjHSUU8g15+UrmKCfDKv0cVukRZfj8cl0n+Is65DHM2oeG0X9JGz8wFhsqxDTWX/Yzz3czkN73DBfYNbXVSIU8Vgzxxi1o3jhIAGO8Jw36VQkIs3QklqBkehmDP1z69JpqnlhdLTBKwo4nyJsECYek4VgnDXBiWSmg9X3j3PvGiZjWyndNNv+FL80w67eRF14FL103iSc44JhqC9OHZx3kR9P8Fkp0ryYNwYwXkyuScKxG3so/b8QJjp56nYclMBuaup5JAVE9P8qLKlS8ZcKoT8MRW0ZlFtXQdO1ezjU3xZgJbROQYUzoXksH90Zck/wziim9VNo6PGJuqlqF0QazEEhpFSqxyokXesXRdkQualHAcXiINAx1VSaQ2CbBpxJq+5+KQ3QfYaFOKSZKubwpgZfxBKNZ9QMWgDcM94RV9l93f9bz39+GNR8qHvudz6GugS0G66krhMZA5V+P9LjhQuu8xJSBOlGGSTE63y8y1qeJu+rtqZntXXGpTWFid2r3p1bML9eiHpnY/u2DNXoKKVakj+x2lprEmgUui40FzcrEHuk9PLp9h1NKdsLCA1YAsDoslkic/RsQ1JQ+adwmFnRxr0EE4u1yiraM+XKvfOIU+K9bI9b/vPZYfH69VJ9ctJLDuWfWIF5neXwDgmi70n7ffm2LNVw2i3Y7fEmOnkYfDSVqzjobrO62Z09OnlM8kdEPqnL6vHgkq7DAD97t+w71qw/kwN4BcJHnwOpZu1PkGHJdDbU2rJK+NXelMC4DGBEAZ0Jafv4jGO105YwZNu6Qf+V0B1rL0fJbQAy+RY79j6dpUeN+MJII930CzVAlqqogstXvUH3EZZtZ0xAXuVFueDCs+3DwqUsh3hrtA6kKvjhbPUvYXujl/Cdg0lyWGkNlXQj3EoipZ/8CM7Z9aQpWAKbSAWLVAq6PKIyAmyZwQIjxqHReBJkUvy6XGHAdGE/lcGMrFkAxS5ffcGpMNpTif1wA7NWuAx3HFOs64CEZWOAtLdrHm6bBkiJ5Fyh9P+yUX/YyLfm7xJjqTlXlK11n1pBzF5nXXU/KaXLSwxh9jsd4PY8ddFUrDK8keW9nKRK0Te/YJdu1Q8gCVqhTs1SfQKsTUhWwCaGHn6UIs/ooSrev7sBDCKuBc5MvrO7z33h1WlwfG7jmWTUGqaWwpjVUchAPt0WER/1nF9WtyoHIcBzpwM6A1ZWtN/YPh5sL+YS7knpO3xElCPi5yiudZOdYh8DUJubcyw3/M4zQu+PTmdRa+4yPzR7xRnQEWLKxNbTJWl8iJtHuU0Xlqb+o00qnnleqCs2FBFCmSOBjlcW2sykTjVOnx9MGX6naE0lUxdx6x19V4Z9WOHCTAJui6DiVY3PUbVq7lvr8sJue5arJJXbCyDLEAaurZhpr322MAHtRrjnxbAkb2lgpqcsksUyTaJOoZqGWk02ZgCjIry3ERG94Z7jH7pwa+629+uXzoN7+zsYlaKMaMnRolNgNZcxnYaWa6SXm8Y7yWmuu/9IcSxFz16FPVJFcsphrywlgLc570K877uSUcwZPtpDIVd9fV7DYNuqmoLx3VGqqdIoNVKyQovjfKq0vabesCNbaWBQOvNOs106RhHldhnNCdSxNzLMaC6kczRQsMk0k9sgeIiUKsHFo5+pWnvetQURafaYiNBaxs8liSoysJh1z927mkk2e/QpEn0zxJ3zRxToGsPMF7e/ekT/KmzJSaVuVv0o9fd7430nO5mhxo2u9kAh8dEPN+J5O4HCSJt+N23DB2WvGkX1G7wN1qw1Fi2zpSdTbBISLKjKFAKoIBShkwmWVAK9aJoDJ+NjMoMyTTd8RgKklzapUKLVlCDyCpogaUToNmGSsFDKsl4FxbFtS5ocbCdaXg0qcY0WuF6rjgzgBaQBiiZz1YjFvUPY2zRbgnJumiSR0TLMSAB4VKTCbtxykSYZxylEgg0p41/MPwUT76LZ9m/jTy9KPCF+Pb6BMh7oAERQXN8WQEsioiue9gBqjyeefEaioVnI5DCeL0mjMroT+QFGbGWqsV29CkZi62bW5Q0kc7nyF4ht5D7/Cd4HornhCxTkyapeopsZDxd0nP24nKuODMlxItJpmEI8slLLnI86FOF8fT6TDPjZO/s1EwToiNI8zT/X3qbW7P5sBThPLwtl43ZU/lFtM5dzrP7wFYV/c5Alnp0E4Q3FjpzxenUNzn8/U+M9YcnMvheFasec64yffsdnzjjUsVPtO9WpQV8ww4KaXA3ql1LbQO56m4kL5AmQ00TzYoffR8sHnCF3jAJjQ4USoXcarFW7d2oXRfh31/2U1scGIMq02csXQtF2GRthvKsaJaR/gTFwtjq5EAdNyrLK4c+10xl/cSueO3vF6fcpIMzTOQs4sNQJEM1hJ4vT7jNCw5G5ZEhCeDNWBaVW0BYwCO/Y516gS51iqpbgaI1qzE+Q3Lyf2eKkdc+pcL8X3KRzZa8dZ/fsb9P2eqktXf7HiCcvojR+V1MDK37NyVXZrvcx7VpTwnA1m9+n3AKk27mX211mrPx3faqMxLKLHG3gsDMTOAtkvs5ofhpDDWMoh1GWa0seJJZ2uRrCRqY8Uu1Fx2DRfbOcPgYJAkGafM0dNCijoINanRFcnOZCzWu6AHvksKzhU/LdQec+0wFlKCKU8YAnhnjGAsJxKRYp8iIY7beD/mO1Ght20RYwaHmTAsQb3y9MkRTwXkvC6uQvk79lWP56lRnpcj7BX0AfHQJ0DrZWWE+ZDPBKtAnF6Jh3AAUqX4rpN20qLmKjMe5+D3aax7idO+BbO+xvFwOOFT56+xqmxCnC96HlSXRY8NlA5DjQvMGYp3Vvax8Cgr1/Jqdc5X/N1E27RgkUGjkBDzqMI2NraIFQsmfTBteu0GNrFJwJaBVj6h+cauMkZS6XShY+AyrXSftOy+SEjcBJEww8SGI2+tcTfRWEfbYOyrO/XODBirTaE3Z9NHGA0is4a9lsDcr0G16Lu97HcczOe+iTN+6v/+rWxiw+s/f8H2B2q6f91ji0VjDk2pv7mSAaQOgpOOiwgheap4taAx1Yjbde9PHhnQ6nGlYpH147tY82g4KdWfNlY87I456+dc9rOi9R/PLS0cuoq4rvGXHteCb83E3bdaqhOuV/wu4vq453mFQGz8KA9MnQLdYNUJvxvM5DBNZFa9YNxWxCoUTpBO0RgtAEzpt0ERLDnRaJ+a0Di6I0d7D5pzYf5I2T0QYpOqLM+Yc/fO4brxsjK/Q7PDSVJS7DaT4XABr+ClDNf1uiSksAn2z3uqAX9mFTyBfXpdEnY7bscNYx1nPG5tAQlQVZYkZF8lsIVspQ4vjkpNWpeNybNvVC2BlWu5cHO6WCGiDDp2sjUQyj6affSoCEFSMxGtSme+PkkZXGI/jcwsh1Pz4ho9pEbAxzoQmqTRzOzTInLyZTAvDl/AqpF1ZJFh7obC+MoeX81EA2dsAEsCNni8KpV0BYTKx7OvaQRalCcMn/4Af+vbfzfNxzf8to9/hnf0DX5Fv4/wiwK7nsT53cMQZJIkTBMEyNXwCbiWJCFTLtfhNFFYv5i5u5IAqwRgbZLkX9X2twkNu1jRhWqvCDbddwgO7RzSOUsuhjRfBy1gloRULMl+VpO5SacVbIHsZSJBcUMs8vVSKVf7JCVElMK8ysnJ1LzXblR6H1KBJR0zNMIwB99ae3RLKCgNTm4ez5nfX3ZxL+W/K683la9LsVn35v6Xbu7xvFiTfu7Fmudd6vStvAW0vqHHTj1fbO/jRHmzOaOeWde6RgJBHes4Yy4962jr/wd+zVyCdTXEmhjtrcEd7PyGR+6YXayZycAQswzNig9OlSE6trFh4bqSP4Tk3VdLoE8d1Xut9l6bR68VdbJd6aJPqhal14pGBl6vz4rqZC4996tL87Vya1zqVpg7+a1jQ+6C26svMSLP0b16nnQrnnYLenX89tX7QGb4xnKOuRt7VId3kR5r6jRP6IxPhfdpvtLrWMPt1NGq52E45gP/2+meqsT/zFC2y10SXXp9VqLknCWz7HZa4ROrLY9clAeoxdYCUx/fPKbMOyB1zs35qS/KlHXKEy/inIfDSWFj5UYiT4clF/2cdTDAcNNZYX7mBtqU54To2K7tedl56guhvsyMq2yLkor0k5ijzuSE5mkV7fE0fx4W7NXlGBb3urDnov0h+GNd1NNjmTXUQ+kA6EndFZNNibP8SL0Q5o5u5ejuKNWZp/l8RX+c4mWfi0HXfRsPxjU+jQJXC/lwNRe6Js8p4JVzVwr6VsObAFrXnIu6lzenUuHlY2d+rRvj4bRboYFbkyKZMHpqvUR+dAtmfY3jV3dv8dl3X2G+sIkkdwG86zeQWElRXdFwO79hhrWrzXTPPBnOpee1+rxMHEP0Jh2J+51CenVQtN52zFYrYupE1FIXySAJzc90246qGCFmcCskuu8ufdnWk8TfJ3PeTiszLlfzPIky6vEXvue43nHkW2oJppvXipkMvFGdUUsswbS07Z1ey4H5xWE3Do9y7HZcVAt++l/7VmoJfGL2Dh+ozuGAOdWItY71E2P+w5/ZKBdgN2WpSbB2vLJfQc/+W706TuOCi7hgF2veHe4UJtYuGqB3Pix40i15uDuiD55NX7NpG4bBEQZP3QwcLVoLjrsKv3bUF0JzAc2lUq+n4BW4LuxRZ8viOL0/ceatvTpYAMhAVh+QIZaFtIgmOcR4XVliKH2SHYbJ84nFZSFWEW+Mh2Hu6VegFSy+bB0/kmWBJUkxn+PBFyWdszrZ813JzxlmpAcPiwWna0Y2PLzCyspJhDPmmAA6fO368etPgmcnBy9Sbec5FZDbcTvSeDQc8/RyRVWbJM+nLoALsfY+Zo8ntMlxUKQ3xmqCTrIjlkOpEqCVpWhR02LiwGswJgBG0NSlMCYr8nEbTzT5egLOMkgUkq9W9oLykthT6hgy0KEVJI8tScBYUFeMy52OsUtEqQg01WAgl8TiEeUlcuRaY89KRKMVHvwE8ZgmHXlIukqlAx7hPvcqs/+x5ie/+19keNXhLiKvfeqC419ugXdwbLJo0O5tAm6Kn9XkvoEx3DL0lVlbNSGZ52vxw5qeo6ZK+04rOq3p1XEZ54XdPKjJO9tYsQ01m8GKKH1w9MHWARoEV0WaKsl+Bof0gm8NGPKdsX6NUZU6DyZz3OJ7pdPKq0sLzBRPki+JDHFk+2aQ0N2A1EeLQYJcTThUIdrqVZyi6syXsQYcVBeMFX5ydT/t47pYkxbDVwCvzM46fDi/7tqR9yfXz/ciY+fPf0Sh5kVAq9txOy5jw6+dv0blIt2qYuZ63qqfFqDfY+v5zNI60R0BYSVZurdfZPdE7vo1r1QXPOoTk0h0ArQkkbYzOXivPnWulSIlz2DI0rep2O5LoT93P++jdQl30Y5Zy7AHivXqzSZFA8d+W64nyyc9Bjbla9hF88SadiPfxIangxXf10NDVMfC9ziU+9VlAdfu+g1zGVirATL58V1s2GHS/Nf9JYiWZlOZbnQYYzoMQPz8P/WAN//2eWGtfPGfugdJRVJPdN+FpYWUfCSbved7mY8x/u1oCCWXyl0Ia8ZGItORc6UunfsuvT8B4XE4KtLP9/o71jwEU9Y87lc86VZc9jN2oSqfhajCab+gi55KIkNwxE0FKtQb65TrO8XvctzRYt7u22TMngzbp7Yp6h2xzhVoEqPKZH1VF6m2lhuR1/fTXGHKJK49WnukHSzfcewX7kmAT/La1aQWEYXgPcPSsXtFiLVy9EUD0+IsxbrcaR5Gaf7h2FN43IDOXFPIf1b+c2Xf1z02BbRUr8gKrxRavkoG15WCC4wF/vSzqGP2XjdZM6h9MfKyQcrPF0ezbsGsr2EEHL/w+G3c5+es79X8hkLjzM+knoW9TiJgE+9FnOOdFnovME5IErjr15z5BX20CX9Ik/GQqtRDHDXjbaxoqdjGhsYNLF1n3UWQUs2eZYPcNObSFfAHKIECSFRa4a4fykS5jjPOw7yANrPUlWTpWu54+xvgLCzYhrpI6GZu4E69TYHMJXP1WDTtx36bKi3+WmlF0XmrlNdkWvLbzWNe85dFAggjo2tq3J7puFFd6foUUnLRqS8U4ia1Dq5lYCUdgZEmbZ1ODIhcxxnvDnd4vz/hcpgV6Ug2kQR41K14d33CumvYtDXtriFsPQwOGYShamiPaqo6QBBca5KPaqNUW6XaWvtYGSaU2aKzNtZElkPkFrIigusDLrebTZVymExYMY5SwjyxgW07BMTJXgC4MtQqJ2HhiLXgWqg3SqgzPXh/4rm2aj4xWLwyrgO0DrbVqfRwWoUowBbjJBrTtVZJLvV1MEQcz+sltnmBbW9lhrfjeUMRvrS9i5xW9AvHU13iE8PJe/s5HYN6WuMC7T2XgSiPMnedgUbBmE25G29M4FDUsdOhASkgKcGoJSTpoi26Ao6KUBhiYB4nU6ZSlg7aH9Z2fZFYykElFVRy9z07h9zaPReJwGTzQ/QmIcQKLjM3WIEoSQ1F1JIQHZ8bcEg+53Jfx76EqhcEPgeffxv3+RnVAu6EDavhApEv43iMQVJh8rqR+Zbfp4Ar3Rw1JQJt6oBbJWDPYQWe3H0yM7G6VGTptOIyzliHGV2sSnwcEgsNYBMaY/4GTz94hsGjvRgoFIXolNB4nI/JgDfN1Uli6HpNYJSON2PiGzI1f82LTUgFi8EALAOyxk8pe0BVXrxreR0xrVSfGWvs8ViJsbCCnWvuNHXF+P2mBOJZseYQ0DqMNdP5W/IvB9tOP0RTQOvrOZ/fAli34yXGe8MdPv/4PnVtDKqF65hLzxv1GWCgTpeUBR1wHuesXEtIliclL0hF9uyP9aC65F614TLYujdGSR5S5omVGaGDeuvU7b15OjopOUmf5rFcfOjFF9+u+iAf2lOGiM3xsYBKNY0PpvDAOtdmcGsuPRtm5pMVZkTGfeeCc1THcb3jldklD+o1tRvoteLJsAKssJ3JBbnJRm6gdRHn3PXrZDOyPxEZF5hy//KYu45f+9deA+CNnz/n89/7gKd/csHHeETAOhBmcsM8ST+zn282e5+ODJZdRPMrDuqI4sp7OZeB3PEwW6hMc76cM7XqOVfzDOvU8zCccBqW9OppY81FmBe/sW2oeW97Qhft/d0NFbu+IkZHHzyn8wXHs5YhOtq+QjqHa4X6XKg24HfQrCN+pyWnMRVJxPWh+PlOb2acVUglY5c8EhNr0KI8cd1gcSEJMTIQdQU4Uh19dAMmL5wU7q2YkuKkBuuqWIHWNf3C0Z1AcyY0Z8qwANdaYcNYZVzxeyojyRqfFRdE5Eoh/4XGoV/wNA8CKxoVQOuQQXAwXgbIumnb5xRcpmwsi6tpzZSXhKn4pHlfTHK+Fxi3YNbXOH7j3Ve5/6uweb1i3R3zq1iVwqG81Ty1akKanGpIHlS+0D0hT05WMTlxO+77NWfD0uRoSqGVmpzPFZlJFyscSofQR0/nKu5WmzF4qMcl08XcLcSOZ94hTQpsufqRR2Q0Z9wkD65ZMoz3iYobcNyvLtnFmifDEY/aIyLCcWUSxNoZGHYaluxSC94M7u20phtSAKpg6S+KX1auFkw7d+TuJw/8JQHhrtuydKNGHkbabGZQ5ZFbx3bq2eiMLv2dnwtqE0Hu9rKOM/MHSwnJWVjZpI7w5fYuX9rc5aKbUblI7QLLqmPuk2+ACk93S55uFrRdxdBXhM4VICv/i9R0mG+Yz2DWzoAs16Vq9wSMio1Ha1dMD2VIhuy1gwj1pkPafqyqS5YRSn5DR5NCMb042S9L9apZ4SQRKF0UxRFnFaFxhBk056lKXltiVOJCnuQmcsg8VEZJ0XVUYDv8i09e+wbwFPkkkLoLpoSlckh//fGeN57ZHerg8X2t+PTfDbP8dcHvdtyOG8bTixWLR9CvHF1oeMSRMXtmyrFPviFSBLY2j7NvTm7gEohYN91OenZSjwbgJSlJ5u65wJA8tBQrMARxyYTepIQ2V2cQbJTYZwleZnQJcsWjy7yuxg7ABk71Rb4YERauY1CTsmxCg2KFo7xv6yZbMyRQrUppxYAnxNRty5kUI3t5GQsqn4MS6ajlIZ4NSz5L3DXMpaWWDcIWZQD6PSlHmFxLSHEzy2kGRo/GkvBJMP8xseSuU19Yc22sabVCES6GOef9nC740sG4doHKxfF6h5pdXzEETwwODSOQJRFbkGc5j6aCQ5J1ZDnHXqtztVbm+NQywN5s0pti++gDkhOBMq1NgKMDQCwF1/Julw8RHFSsGZ8QKQ1J1BuTLC9y94CsnLBcM63vSdpvSiKuffT6UVhZEyBLJ79L8QZh755+3cazYs1XuY/b8Y03fnn3AdqvLNnNI18YvHXQxlg9d/3GQCI185Co5l+bbTC8a1MRfCwC51HLwCv1RQI6qlJkdzJ63IIVMKrKfBWH6FkHA+SPqx09niPf2j6SwsM7yyeyt1WOI9PR6Wi3kqWGYPkLznyucqF6ozPO44Ing5m751zJrsGKIke+LefeRuuMm+1Z7lUba3yFFJuSuXSchzlz1xdP5D4h17nIEFSoRUtDK6DkYEEdWgk/9f/4NmoJvFKd89ub93AoyxSzgWRjMn5Jc37T4ZIn8BhbpiqTdZwVMK+PnpV01DIUtnTu3G6FeV/ICzuteTic8DBZpJwNi3KPM7MuqvCoPeK0W/D++oghOHZdTdfWxN6hvYMgrOsFp8ctVRUJwVGtLeaYPFxp1pFqHfFtxGVvq2SJkjurS4wF/AFMOihCmHnIHQ1zwb5Pjayg5D2Ks0iau5nnAn00EIygSD+At89XljbmYRL4ERhDhH5VEWaAKPPHycMrs42SvPBKY5TD4RzqXTnf6eNZLXMIaMlBHnY49hhVh0DWQUHfoqS/evwXHTruM53c+Os1MvpnxaVrVSgpluaYVmLbWAd7oXELZn2NY2g9q/d6ZheOaldxrsf8kowMrXnd4yeU2AwqOR09nWCkstYycNdvuF+tS8Uiap4Uk948VRdyxb2PDpdYSb16ZgyjVn2iu87G633qKOJFR5Br8hnLHQxzcpErIlYxGcrv2eBxExsiwsonZlj6NO9ijRPzlXrVX3DhFkW3v9OaXgLzuCD6yyKHmVJipxJML30ynldmEib+WInaO/GMyeeXuzCW4yWvrja1lM3gXsTRS8XD4Zh32ntsQ5MCrHLRzznr55x1C949P2a3bQhdgpedMl923FltmVcDu6HicjdjfTFHt96SiiDGwOrSl9Up0suYCOhYJfe7aBWHVMGWlBjorCIkOaELigyJBjuoJRdtbxTaPFnVFXg/AljTKriqJSuVG5+b6rWnE2gGnJxDnWNYeoaZLeqbczNtj7Wh7VpMfq9ZyDtBo5rHYe4kkseUMZXP7zlDp+d4CGTJNdsl2Yu8qNzwa1n03/TaTEi4ZptbmeHteJERg6O+DAaAD45WG96XY2NozSKV0SMBmwfNjD0WQCmPDG45zHNqoX2REGb2VC5u2BR0AGoJ6AHjKi/syQbwk9jhkmy7gFzTrz/GmBqPJ+XxfEzrjOVLzFEYmWETRoCoxbiVtHRSJzaA7d/AsphKP1qm3+l5qEUCnJzjGNvWO5JTOsbqmp5XuQa1dKvXKnWisi6DpQtkitUGXEXWYcZFmNNHn/zGrDi1CxVtqLhsZwy9gVQAiFI1gXndU7nIEB3dUNG1FQwuoYwWVyTkhaGWx3KsyYoWaySS/ARLKFWohJjlHFGRLP2LJFlhMDl6nrfzfF6qGWlfeTHqGL05rlucToEsJc3nxv6NlX2ufGuLW00Oy3rzGn88bl7Uhwl6dsj6faHihlyd0+XqnJ1jj83x7uvSGv1FTu3Gp74asOt2/JYfv/D0bU5+3dPe92yD8DlnQPgrtcno7vo1QCmw91rhpUtF9t4sNtJCpYfiUTWXvqhGNskbq40162CesAvfpQK4yeaijo2OnCin/YI6+SxOu6fvYs3MmUwwA0rHbsvc9QVMi0mGnlmr2RO4UziWLR6TTWb/Wp/iWu1MRTKXPhV7Aq/UFwB8oX3Aw+6IqI5V1eKJycx+4HE44n645K7fEHEJ3HIlf/Bi17VMDKhsS9LnHAzzH+uTT5fdu41ZreD4QPWUY9ftSeDzsDzNF/Ap29RkkGundQHzcmzptWIX69EbzCmdelauLWSE3CHycThK7GXHaVjyTnuPR90RbaioXGDhe4YEbs68MZrP+3kBstq+ot3VxM5DZ9J1okAntMxoo+CXAz4XTlKh3vyxFBciMmjxRIqVMwuTzJYVKUbuWvvkoxWRdgS+RgZVKrT7sSpsjOE4yvNSvpPVKHbj0v7dfiDJ5wApTteeMLMOixKE5iKWxijZH9jm2RQjbwgnpYh/k8wwq2UyUymf54HE8PB8Cyur/L5/zFLQd8lTNbPP8n0pxAOu+g9f6UY42X7y+43S+3yKB3FIVIkHbCuJQDKTL15ZKd7vdYp8zrgFs77WEQXXRWabAb+N+F3N2e6EvysfZOF76lXgfnU5VhZwSd4WQHoaogWNNBmB0VLvV5cGFIWGjTYlOZg5m0Db6NNCW1M1XuhixWlvvTYytdZLJIobO4xQsdO6dCR0ovTJjL2WgZ02eFVWtOS2ukZNruhjZR1OQlUMgINahf4D89Ny3Pz4ZTDDwKiO719+hmO35XE44iLOx8RmgPv+kjf8eq/KDSOd1icQCywwetG9CkbWgGfjxF067049p3E5mdCr5DGW7nMK4nlS/3tPP8jZbo53kZkP9NFxsZux2cyInUd3Hn/pmJ0701PPlW7e8O7RAn80GPYzOHTjqS49rktfyGiTIWAWMSrERYS0D86FWAtaJdPBGuKkegAUiV9QDPRqIy6kKkXQDYK9ggABAABJREFUJCNME3lU8AnYmk6IbjJ5idjf0+cPM4TUolUrR5xX9Mee7liotlBvI/3ClRa7Y7KkyTuL65OFqdTwuomV/FBKa1O15srIrKy8/WHlvGyXTsMbi+2rSjIOjrV3nnLw+2GWnM5J3c3Jxa3M8Ha80FAL7lWi2svgaYcZ73JC7QK+jixcRzZSHxA66rRoDqVgMEwKIVXqKNirp4++ADGQpR22ffZ2cjL6Ye1CDX4Edww0iwaiQPLGEvyEHxbUF3bvkCTmjUzljzGxYq3yHaJLMj5jilUSOa52dn4oQZJRevRsY42qJI+Yno02hek0YBXahes4Ska505HlkAZ9pesRA/ymM0aR+6XEZUjnHRC2Ey/GoLYXP7nPMXlGXoQ5725P2A3Wndg7q4C3g6fvKjQ46wLVCVU7xo1QeS6bGmksdmgU6AWXTN2LR1SuXbjko1al96NKwJBLcSk3ApkuUtOFa5U6Yw6auh6muJIKHJILHalAU27Ylc/sZEF8sBA//GxnAEorZwzg1FTE9dYavQBaeXsdJR7XxprpQnsqKTmMNRzMwVeu45oiSb6mw8tFLDcRh+hXGWtuiBPTK7w21kxO9xbI+uYc710ec/ezA+0Th99VbMIRn47Csur42KqGGTzwl0mN4ZM3VUVHMFuOnO+nD9ao5lBO3I5Xqwue9qvi/1cldtSQ4gcAEmljRa+OmQvJGNyD74lhxtJ3OLTkPG0CtHLusIkzIo6la4lqP3exZuXaklvkkQvv+Rwt1g3MEhhWml8FA6VyAf4ruzulG2OOOblr4CY2bHTGG3LBfX9pr89FeOpyT3q/ZiaBVj0zCXuKkI1mT6nIyrWcsMMlVtdKBpYpH4uTe30IbmUgK3em3URTluSfOx27NgZsfbtyXVKfzPjycI91ahbiUTax4cmwss6RYcZnLh7wcL1iCJ7lrENVOJ613Gm2xadrF2pOdws2bcNuV6NBiL2HPhXqo3liqRPYebOqiRU+gt8JvlPqbaRaB3wbR59FRqAjNp4wT0y/kIr9dkPssTbgNp0V7PP87a2zeg7OhZ3lHNTe/IIrB86P5u+HMSLnPgloKp3/UvE+zj2hEfpjoTmzONQvU3GqtyJ+9BZ0NQE+o5Jl7ORcjnE4bijkP1edMpUVTkGo0ohqH6BLHxKswzs3513Tv2/oPvwiRZK9hiOH5zF9Pvtk5eXHYS518LrnjVsw6+swMl2/Wg8cfxF8V/OEO/yM+zBHH2ypF8a2sgp1TJ38BqI4YqK0AmWizobnS9exCU1Byn2aYNaDacJjmvxqFwo9uE9sqOyr5URLIh7UEZzJG8DouXWqesDIwMr6baB4Sq2j6c83YZbOxWjBR343SikSG8wnY97LMGMbarpYccdv+MT8HXqtuAxzlq4rANUmzujddk+zXxPpxe19K3IFxDPKEWFMprKRYb6XuYuktZY1BlotoYCGVg2a8Zntq3zq7HUer5e0vbWFj1Ho24q4rZCtx++Eam1AjhsgepDBGFeuF8LGE4+CsYDsZqYKuCAJxVeXEpCoaCXEWWRYKWEm9CvB9Z6qSQyFyr7IvktGmbV1dsqdP/xuSO1oUzXDOdAhmewOBTzDObTyB6AWJuNwk4krdwGZmsBn6mpTMawq+qUjLGD+KHXyyL2E08LaBR3bud+QXJQqxTS5mP68bqTzkiSRNCPDCOLTJD3Z9DrcC0uQRBn7KD9r3JQkvOzYq0hMPsq32NXt+CpHBitcF5mdgwueLXPekbs0JwFXR+bSk7sLdupxCdBRCWXOzJ5ODi0+iD2+sGoFSoHEMIu84I4JlDI5iEQtgI2gaS4wEExl9GScybDn3ThlYJV5PHlKdel8c5xyqfDi3X53RCcJfkqeH320+DlzPa9VF8Ro7Kg6eXdBirEiBdiDBGSJFP8qO6YdScim7JT7AtnYfbyXg/r0z5VCjceM9vO96fE8HZY83h2x6WtC8IX5Fock2RgENwiuszgj0eY0TQWRGATtBW1SWdhupn0mjDy2R9N3CtGBeiU2EFuLIxId2udCCbaYD+PfMXUMdInRKiF7a0laeOfjxrEinebnfdCKwpqV/Hd5ve7HGgT1nlgnZlZtHQxFGY89+R6MwN1NE6rsveaFYk163grZ6QBKQgqf/bJ8SHXpWsOLvOAfwfh6xa/b8VtuqArVJlBfDtTbGt96Locj/gFvwZtwp9owl66sgc2nsCaIScTvui1gwFAPiZGUEngix27Ha805T4cVC+3ZhrrI0Vo1dk+vLhWNzW5kUFfUJD6xsXIX3tz1/WxYcuytSLHTmpDm7kYG0CQ1TOBwBuCQbNsSSjfDvP7PHr9HaZ9gHlNWtKnM+F2Uu7XZslwmf61NNIDo3f4Ob/gzXvMXvDPcY+62lkskZpQX5UlY8nZ1XmLY1B9y6tlbT4rxtViDqUyGySPbpHSJ3bbL3on4ctxD+5Oco2X7lo3OEmOt4lObN3hndxdIcnxR+uh50i7ZDjVP1kvW67nFk3XFRW/AyHvzQL3quXNsn4NtV9N1nv6yQVpv8SlYgcS1zn5PU2NwgFekdcQZuIv0XADfx5EJnPOOIckKq2T07kADSO1SgxE1H+B2ZGRJyk+08khiyBWjMlWT0sWUX+Sb7AT6OMabK/J2ClvLuhgKeGFY+sQQhuV7xnBSD7Gx7u3pS2G7elbdYqpImfpzPW/ECN5f/9wNrKwrTKl0zLFglSxXDq//ukPovgzT9r//umzsPt3uRrBrGnYn+7nK3gLipHD1guMWzPoah3gtRtwA1SZw9I7i+prT7X1+Mn4b/dueTyy/zF2/SRTZyGlYEdzoJ2FMp2rSqWJsR9vGisswS51GXGqDmia7ZBSfdc4zPxTwy5UKuSUec9dDZM8UMVckpn5aRlttir4+qHU9zElB9mdxosyl4yIueKe9WwLJxTBnlWjH1vmi5u/qhwD4bc37rOsZT4cVvXqO/RaXKLnZRywnHjXRujmSu3vY8920MpOq4pvoWGtTPLKytPAiLopB+0zsvmaa7UWc86XuPv/gyQc4383YdTV9VxGDEDuPXFY0l4LfCL4Hvx2/eC7PfcFArepS6FshLFJC4K0S7gY1c/WB0o1JohB7iMcRXQR2Q41p8ByxHr/x6qBfjd0KEai24NuIX3fItiuVhBsnxxiR3iZ/8FB51HtjgGVpnvfozCeW1/5+tHKEeUV/UtGdmBdLs87ylMmkNvFjSY1Zxn3kKkWqJJTF/sHI9FrJgNd1owQDQSQmcCslm9dpzFVTsBVi5XDZCP9lx+Hq43BMNN/29+QUplWNvde8/Gncjm/iMan+KeYh0VwEJDp2w4Lf0FcId4RX6wvr8CSKpgr4XmUsCeryAtwTOXI7ixMJGLJDGRNqmBQ5JHGsVM0bMhcyerC8XwIdVQFxMtsq/57bnwNFepgZWqSkIBu/R5ECggnGAui04mKYl4V8Fw2sUhW6aGDSu90dAO77NV2SYESsgYqgydheyy3N51IWhEw8sSZfUk2gVq+OHl/iSKdVAtTMS8YYagbOZU7aoI7zsOC97Qnt4M3najC2mQaBzuE7K3xYTBljTVmzJrCKToiDlFihzu49mcSagZ7s6REEbSLUyhAdGeTxe8xWoN5flBr7T3H9MBrIPkuep9bhUHNxxBm4leURVsiwirqEWJqUjAdMic3MfBlFrfvVFc/BDNoVVtb0OmQ0AJabMagpOPnMaTiv+zWiMmUxc22syc+ps4NPu3O98HguGCVXEoDy0mms2UtqXv40bsdvvRFUipRr/rDDtxWuq7kIR/yyf4M79ZZ6FXhVTG6HGDNpHZux2Ju64DqJ7MLcGlVIJKqxo+76DZsw4zKBK12sktSwx0lkPcwMAFMD/XMzqkGVIThjcCUwwDktOcm0Y3tE2ainF1+agGTfwbnrTaodZ8ylx6X4EiaSQKCwsC7DnFnqjtirN++rmeVWpmwxD8I+jrnWJpjk8XvmX2DuOk7DiifBDOJrCZyGJXPpedWv94rw02YgRVkymaB8KsZb3jK+Z3b+JlEcu9xbIegiLjgNy1I0so7wI7vL5IXWqOrL3T1+6eItPn36Cv3gqXxkVg10wbPrKzbruXlddVY4aZ56qstUWJpBmDuGdcWj84bquCd03qa1zuEvXZF85znIZOhYLNoK4TjFzWVEnUe9FU/CzJuPp0jyQjTWr0Qtk3C/NHDMtxEXFTdEXO6Wm+NO9pgaJuwekSvFejCgJUqKabVDez/mNzcxksQKKmFR0x15uiPBd9BcRkIzsoM1FXsykcUUKdfEs9SgS50bc5pprnYNU9geTmW7EG4GtKbXMbVZuaJOSQVAB1SOqeXKdZ5X+yfC9bHDccUz8rr97LGt/NXHcyq/zya74ZjPGbdg1tc4NFUYR4Nt+xIuHg+4X614Gu/y0/Ixjj7cUs+traxLhuubOMNLLMh61rDnHoi1DBz5HZuqYZs6SuQKBzB6UwXr0lE5m1Sz+WInSpOALzNqdGy0Ye76ZOKevplJg56Bti5Va45la5UAhLn0fLB5wi75TeUJNFNdKxfZDg3n/Zwn7ZLGBeZVTxcrnuxmfPHiLo/bFX/ggXUj3MSGNpoev0n6/TBJMsrv6baOni2TSj4ms3wS50anxei0mVq7S9UZGE0QrbVuTEFizju7u5xt56w35lGivUsTt6e+TK3Mt3lRPQIxkr7k6sBH+12CMPSgdQJ3BjGTwJh9sRJAVVulHAezVUcbhK6tkSiE2qi5QKGxiiapR6pQ526F0g+jDC9XxCufKuRTqDv9nqi5ZiZv+9La7XloSR/GJMPlid3Tnjj6FTRnpn+nTOojum5sLa5fwDux/R4Ekeu6eOhEP371y5a/ZqnVa7SDS4gobpzMHaOcElDU4qF/BvB3zXhhuUauYuTtJ5d1IzX3IBG7HbfjuSN9ZgS1dVCEahNZRMdW53xe7tPcCfhqjSSGlqp1hBXRYoxuPlQ5DTDgpXFja/UhVddHJpRtV4zhZQSFTM5BaSSSTdujWqvu/LdTQLQAXJpi0qAudcU1WVxF4MRvi7S+SoyyqelwTjy2Q22eYc6OvR0qzts5m6GBJdxJXXODmjF8ZmQdenJNq+oyeXwf9FC2qYFLwJI088Wycyv+WJgUM4N0ASkgXNtXdL0VTIgCQZDO4TsDsFwqeuRFZI4zkIoo+fHWEgl1YyUzFxFskW3ba4PJAAV8EwgRwuATUzjFJzvpEQjJ+8mMqCzPzqDS3qJZDkCTMSkwxm8+eU0L7gRyZV+pyQ1W54iVMMyE0FgHrCKzF8YkhXxucC2wJuP9G783eu1UeyNupNdsZdmTseWyP2S+Fzq59qlc8x8JiKQ3epU8M9bALaj1zTAiyXNVqS8H7n4Gql3F6WBqkfqtiF8qd/yaWkZLkou4mHQZ7NNcad5Uvqy5I3f9hifODNatACKp4ZNjCDXr0OBQKheKF1NWimxDTT/x0PKVHc/n5k1xtDDJa/c7fls6h5v8La/pTV6XFSXZK6tLXchzLHMotRvwGjmWHTPX83RYFVnjsTPZY6sVp8OyqEn+IW/ygfopd92Gd/r7XIY5d/x2r9Cx1oq70k1uvTCXYN69WbGSCvFGUkh2Mwe0E/MTc8kv66rvL8BFWKRtkx9zAu7m0nOhc86GJb9w9jZfWZ/Q9lUxa7/QmalMdhXSWpdBvxWqjaQ51nINF0B6MaBoUzF0Dl2ExG4az1UU8wBOsUM9uCgm7qiFOFdUlP5Y8a3QHZsdSbWdqE6clCYk6iUxcaXEm2obzAB+0rjKAKuQ1CchddK1uIL3aF2N8SYzl5ICxanlDHiHem+vnapQEqhULFWOKoa5MKxgdpqapQRwQUaJdy6opHh7k2dW+uLsF/Gncesghl3Jfw6UKfa3prUdV8CrG+1MsPVCsVx5DpC199w1200llTftpzRuUYWQOkTKGKdEjTm+v60tjZ55P68Zt2DW12Hk7gm2gFQI4PrI7HTg/q94Trt7/C98Bz/0Ic+3Lb6SWr/aRHwRFgTnSuvX3G2vlsHMCV3LvWrN486M+7pYMURH4wKVCwzBpXazQhcseSBNjDHUtmB30EdLQDK92LThY+BYunZkiSWwZ5O8tIxy7IrBfGF8pQDTas2xt8r+k25JGyqe7hZW2XeR892MtjU68k/qJ/jf3f8NPjZ7lyYxwmoZSlUi6ugDBvs68kNPrVY9p3HBaVyW+zaXnnVs2MQZZ2HJJjaWKLlxH22s2cSGp/2Si35m17WroPVIL/itw7XgOlJb2byABUigRVlMUyZ1u7Umj5BAWTiWyS49JmpBg15wTqlXPf1dB2Iabb+T0sEpVhY41OlIZRVJAJRPuvXEfMhywmwUOJlkSnUjKG7X47pgQFSqnFu3DU0srwymWRfFYenYvmKL59m5tdQNjRvBG6W0e8/04Bt9oGQ8t70xBcCc3AxowVjNyCwVBdFcuRkBtgJoleOSrjOOj7/oOPTnet64Dos7fCznSC+hC78d39xj9H5IyXU0kNbvIouHjl1Y8Ou8yrfcEV6pLqnF2EiK0MYKFSks3NxtzyUj9SzB3oSmsLQMsIoFvNLEgrSiiX35TbLoyDJDVSlMKrC/e0bvq5qx4Uh+zLrqKi6fV6pOj9hw7hrlaNyAQiruOHaDyRKdKO3gDawR+I3Nq3xo8bR4xDisa5atQ6UAGRnUmjYfOfTUssJNnTomjg1YupR0ZVmLzwb3SpHR9Gp+Xm2oLM8crCpONIl6YbWmOXQPO5mA45J+whhzCisrfz7SIjvjLLatoNFMYF0TiUEYxOH8CJ5lGfw0btmLJS3E3Z7vSJETSjrJ/NPecPs1gmiw409jkpME0k0AmQRyxVoYzPaTqk1x1+/Hi6knoyjXTbV5y2sX4YdG8DcCWrYx+c0QxsV/KaaU82cCGNkfdp368gULufGPFx7XJjS3YeYbftQ+FJsHxdZ71Xbg+EtJLdLf46+pwAfgW5fv4uUSEiOri57TsOKuXyeAKyYlgyuqkbwMyqqRgEtAvgE2URwr3xXz9y76Ij0c0u8+FdtxWPMoFRa+N89G9dyrNoWFtN8QyjGXtjCxnFjhOudOtYRi02LAkU2SdxKBwBMtzwGesuLd9g6VC5wNC876BXdqk9Zd9rNy7L+9/hi/Z/VrfEvzHm2s2WnFLsw59jua5LE1lQjmzoZuAlyRzikzuHLn9hzj8jYXsaEjNzqpik3KeTTT/V4986KI6QtzKze1+qWLt/j82X1CFNquIgyeGI35q5uK6sJTrQ2sqnbmnwgUcEYFfI8VSTz4rac/EeLCctpYK6IWr5IKshRezDfLcpVwHMAr3SspgdHE0PUT9rMDmUsygbd4EL3tz7cRt+lxm3bfzD0XVPL8HRnBq36YdOO17oekYrtWDh1isWPRmUd210zKTtDaE2fe/IFPbH5vLibejNPNs9ImkDy7mOQb7MeDw5znGQzn56pSct4TUxlOLO/BX3Oc/Lr0Pgsp14sKgz6fmTXdx3S7/NH2+6+1opPsyxFTjqMepl0L96WF43l8tQWgWzDrax0xoclBwWOJsjj7oIhSXwbu/Ro8cff4Sb6N2UcGPjR7zInblqQiGx5mAKtPwaERqzvMpeek2rIOTZIJUCQVe6eSqtwxBRLvzSy+D6N2e6MNMzfgXKpEJ4+RYrqbRvYaseCQfJsYjeRrGYqhYsCxVA8VbJuGp7sll7sZu21DVQe8jzTNwMOnxzx8esxmaNi80vDJ1a9z7HZ7XR2nYwpe+QKgZVlhzVobHoej4okFEMTotmfDkrNgRvtRcoeuik2Y8XRYMkTPk27JLiSD5GiGu651uB58J8V8Ni/2cwUjij0Wc7I1SThcnuQzmzUlCVljrR5kAN9BaB3OKXdPNjyJjkFlL0nwu5xojNI9dUKsHdpU1tK2Ukit0k26MQJb2tRlgtMizYimH/fOKKcSofZokuNpZce3KoZN7tv7nu6OMn8kVLuczFBMzY2FZiBXqfQ/a9wEZGXD3AzV+5eY1bJGXmS/5e4E0CqSnazF+WrG4SkdTtzKs+WIh/vKFaPbcTueNxQoyfHIEMlzje8ii8eOrSz4DV7B343c8Vtm0hdpX6c+AT+5c2ECrBK8U6X25eYtYp/poI548H3RVEApfiqFjWWvsQKISUQksbEiVigxmeL4RQpqfi2DjqapU6mfeWlZ5T4i1nLcwdJ37IaabqgYeo/zijjF+8h6O2O9tU5b/dLzdv2YmQx7ko/pmIJXrpyDnUefpJe5EcvYZUuKjDFL2ROkx4CjjxXbaKzpbaiT5CbtOJiB7rTQMWVE5XnEGLT2++Gpl7k2A1x5kZjp/Gl6l4AZygvMZx1bNX+YHGecAMNk/+n4Jqkwdq4ENYZXArSMMZVjgVgC4aCgcPmzmtuDJ72fVZbz3CyM1RBbGA8LR5hbESmROcrCVxNAZnFsUtx5mXGTb9bLFBQKQ439Rf7XEFauHYenlG8XX0WsydvfAlrf+CMrRTxW3ANcF1m91+P7itP2Hn+d38bs7Z7ZsufERRrXs3KdWXOEhckMiTSJZZSLxSGBW3PpueM31tACa+i0TnLD2oWiDsk/M9M3d8g7VI8M0SxXMvNqExuWrtsrvs/cKCmEkZllHlwtSJbgBd6qnxaT9GyRArBOzaDA5u9tqLlIapKLesadepcYy8KvPH2dz13e58u7u/wfH/wcH24e8nc3HyEifNCvi6KmTYWYm1QlMwl7RfkpK3glAxex5lTnSQ7vynkao3f0sZzawZzHBXPpSlOrL7T3+crmhMvtjBCE0Pti1O42nubC/H39zoomvk1zdyVj7IHEsrKluMUOx9DpKKsbMB/g9Lxvx7xoWKTYo1CvjK3Wtw7fmgl7rFLBP8Wo6PP6JSlPxOZ1F0xaKP2kQ3uW2omM/lNTaVrtRkWIt/woNp5Y+wKU6Myif2wqU8vEA6m7c8SmYlh42hNjZdWXUG1jyXVyfC1s6OQdfEWNchgHDkGmG1QptukLTtJlXaClmJ9tHos6BYzmVE7LbG9ycelryj2uiT373sD7nlsjIeQAyEqfn8LYmjz2Mud3C2Z9rUMwyl8I9k5Vidki9qVEHNUmcP9T8DTe43+R7+Bf/PAv87H5e8zpmbuetVY8Ho5YuRYnkZqQPJ9g5dpktN6y8h3roWGXGFc5echMqSE64iDMfU8lkTZUe1KKy3TKMzcQvVGDs0FiVMdGZ6WCYbLE3CWwxhFL0tFpxUVYME8tdafjQXPJdlXzcL2iFUssXjlac9E2XG6WEIUv1Pf4jeWrfHT2kODPWbnWKg6MevPcmjZP5Gim9TalS8lFXLCJsyJ9DMkHKzOv8rlZZ0Vrg76JDethxtNuwWZorFuWi+Z9Npg+2rWS/J8YK9XK3mPqwKkm81yKUa8bjJkFY5VdSIBWzbggVUGi0HWeV487+qMtp60ndkKYYz5cY75QRqwhzCxASF9B6gxCMNlONkiUrrfJZNaYNiW/TVMTxOy1laoKEqLJDIFMue1Oai4/aDdg/lRxXQoMmUUteUJPwe4ZlejiX5Ir9DnZySBWmbgSIBdAsrHLdUlIkhgSpRjBZ6ZW0j2lN4ICdPEiYNZNhZG9yso1FYSSbLzAKEHnBbe/HbcD7HuaWJZZYqYZAInWxnrxyLHVBb/Oa3z87vvcryKVBmqJdLjUAn1ARPFq8/mAp0lgT+MG6ljRiWeI5gGl6vY+rlGN7VOl5iNDdGRLIQW69IsXq4h6iTRuytQy2WMWMo5dAvPxjEkU1NHFmkqmNuw2lr5jaDzrvgbxiFOWTUc3eLq+BhXOtgue1i33/BpNsbTCJB+aJvbA6AMGFNCsxxtoB3SpU2EuIClCiFVhXhUQTiVJD13pELkNNX305ZpyvCjeWBO5wvR9ztXuKS2faxZ8MAHCoCQL0yEKYRBWTSA2PbtB0OCsa2Gk9C3Z+6i5VDUPYvNwVMt0YgbZEtwXsoTPH65myfLC0fw2P6UTCYmBXGHm6U7soWrLxLph/ODtG79Pft40cqzJxzxcHOeqdT5EPs9SOZ8AR0Ubkc8lS911Im/J2+ZFg1w95guMvVfIDZf5srHjNtZ8U4zMoNwrLAY1tcjTgfu/6nkS7/G/uo9TvR356Owhd9mUtX+nntOwZOlamslOTAY4K/K8WgL3qzWXYcaTsKKPBrxc9DPmfjD/3qGhSx6+Q5oLs/l6G31Rj9Q+FB/genLMXazpJfkCB0/vvOVJxCJr32mNUyv6z13PLtbFNwumhXlJnf3Mg+u42nHaWx7QR8+76xMeuSO8i7RDxZPzFZtZz6Lq+f+ef4J/5vhTfHL16zwMJykvG4q/1U49NXEP0MrDp1wKrJt9lro7lItY8zguuYgLa9CFpIZbTWFnPR3Mq2vpOnoqu3YiZ3HFRZjTasWTzrYZes/Qeugc0jukM6WH60ztUW1TgV4zEyoxYsoaIv1MhQQJJAUHaQ095jp+GNf+EQpLWAYLWr6K9KtAd8eVbu2uM0BLoqZGJABJeaKTGFgYvClHyWuepqaYPU5k3er9CHwlby3XsVfY1joFRSdXC+XepesUhqWjvW/rq/pSSzddEruo1F9y3lPIDtfM0tleJZ/vTfEgv/bQHuawocp18SuOK6hy76aXd6hQSQw2iYq+CPkgv27vuq7bZrwWcXI1P8JiZszM7BROD43ep8yt226G/ziHqKHIQdG0yrMJwkzqJEknqk3k7m/Ak+o+/7N+J3/ko4EPNk8AYzkFkUKZtQ6CvnTi8xK5X13SasU6NJx3c3ZDXYCs2odCy+3C6DMyT8ysmKoi21BTSST6kWHVS0VPVV4/c721eScSceU7FnFGa42m456Lmclns16wivYdvyXMHe+dHPO4HpKnirBtG6SOOG87fNyuDITyBkLttCIQaIilSp4DRa6E584eZr5rrXI7rQoFehNmBmJNsoLzYV6kNDF1Cnm4O+LRdoUCtYupQKxjlTym6sVu4lWlhsC7xKrKVYVs9J4LtTFNcJBeF8ekpD9KCLSHMFO0VjSaB82sHvCzQKzN8F19Asy6kZWV9/VcLXHWWDOY4Tue0ko9VcrVC7HxNuEnWYhOgCOtPWHZsH6zoj9WVl8WM5tWJVYZ2U/o+WRCL0yja2i5QKL6ysRLJV4/QadPnWpqmZu04hIVTWDxjdX09LTmysWV/Zbdj1nc4b6+SulfBvquPj7dd/rnxtfcjtvx3CEge98XGUHRNFEL5kUxfwJbt+DXeI1vv/seJ34LDIU91GtFnf72RIZkxC6iLFxH8AbGtKFiSB0NTd1sTCtIC/bUgKRKEooMCvVYVb6WgHd5e/PWygWWnFS4BCzlQoYmMEmTF1VlK+oUS/N1KjMXOKp2HM8ati6WdWIfPDhjaQFshqb4bSnCgEN1CmiNskMtscdYzgFXPMdCjqVI8XSZymDaWBXAykAwYT005t+FsQ4kxRpS4pDn9QxqpZMszKNcOMkL1FJAgOKZVl6jY6KR40eeo63Kae+P9xGpFO2ni8bDOZxrpRVXhkJmBVvSkDKaAtbnJMDk3ZK9q8qcpyZ1rz39kRm/NxeMvpETIldelO8bv0/Bs4NTE8m2XumBZ1xMqt4X83is2j39qj33Pky3uRLTmLxph7HmOft+1mGve+2LPnY7vuGGpvyDCTNrBJKtG3Z9Ebj/D+E0POAn/ulv5w+87fj44r0CXmUAa6c1jRobKEveMnMoM32P/ZY71ZbTfmlWJwnQyj/zcJIM5YfavBkTY5Xc/TCM0r7opDB9Z66njTWoNzBHvXU2TBLIRgYDqzSUdb6TWArcbWJtmZ2Jef1ehAUz13PHb6klsB5mnLYL1m1DN3icS35EUQjR8bkn93nv8pjTV5d83/Hn+K75F1lNutDnEVM89geTpi9VBvAYqLZJ3ouncclpMJuUiDPmmYOdNmxiw6P+eMzzkhF/n/LCs7BgiI7LMOOsn+NS3KH11mm9TSBW8rfyuzSvpvnT6TQ2qLF33f6c4gaIbbI7GUbgQbAciJrRDqU3ppZ6i91HC9Mx7noHYoUTvzHCgN+NRQE3aIkzmryltPZIU6MhFnmh+QUH62QOUFcGUOUYUznrkOhSIaTvkU6K2gSxnAc/OUbKKzTFoDDz7O45umNl9lRoLmOZu3M8hZwjauku/7ymUqWbbwbWilQyX3hSdEXdu8fP7Gh45SBaCvwpgo2exhNCw0srVA5j27M2FUa1yuFzbsx19iWGGMAViyq1PP4y42VIyvzoj/4o3/3d383JyQknJyd88pOf5C//5b88nqwq/8F/8B/w1ltvsVgs+H2/7/fxy7/8y3v7aNuWP/Wn/hSvvPIKq9WKf+lf+pf40pe+9HJn/Ztp5Dag+YMUKYs78oc8LU6rdeT+pwL61+/x333me/iN3Wus44yojkYCK9cmWqlj6VqcKI+GY/PVwrF0Ha80l8z9wG6o2A0VbfBcdg2b3v5ddjMuuhnrfsZ5P+eyn7EZapMlRscuVLTRPD5K546kx97Fmssw51F/zEU02muuMufRa5W6cZiBfZf8vWbOqiKzRAt+dX7Jg8WGVd3RBU8IzmQgoqzmHa/OLgvoFFN1JYNWASltbbMEMQNZOZjm4GomiRVnw5J3uxM+t3nAl3d3edKveNgdczHM6dWxHmacDzMetyvOujldMHNI8xsD8Tqa0038S2QyAVi1XPGdUm+V2UVgfhqYnUWqrSYKrhqVt53sY0im6WnSH5ZKnKmZwGOMuspFnN+HyTOd1XdM/LnGb7hmKumUeptHmjSl7ZG2g643OWJ6znyt7E/pg3lo1R5tLECEoxnrt2ZcfkioL4WjLwX8bpzY8+ReqjFT6UdKcPYq8YdjUlUZv0u6/y+Bb5orM+WxFNyek5zsfSenjx+ex/N8VfYe55nHLZLQGzfgheLHN/O4jTM3DL3uX6K46yTBV5tvFo8U/fyCX3n6Bk+GFb0a2JITgQzUmK+W+Zd00fynagksfWfdDKNjiI4QpcybffB0oUp/V7SxoouePrritTVEl0AylxhWo3n6kJKSTWxoE+MpJHP5PGLyINEEHuU27pUEqvQTYFV1LOqexpksXqMtygWlqQKrqisAnAFkbg/EOuw+lYGsvE2fzjcmIKyNNZdhxmm/5GJYsQ0Nm2D3LmImxm2s2AwNbagIUQhR9kixZFBKxwJKmRemC+ZgPia+jVS7SNXae1vYXMP4eikSvPRxSWxgrfKHg5IYyeEk5dK0PjGynS4mR8PxSSEij3y+Q7TqeE5A8nN7c7y1V7f2565IO7pjT3fHYl1zbl0U2Tv+ZAE8AdzGWMMzxg3z+/Rfekz34lZ+riBn14/pOejB41dO5bpzecZ+nxMsXnbBfzu+sYeq2HcsMe4t0Y6lcVD2NK3XkXufCgz/2z1+6ssf40vdfTZJgufEjNR7rXh3uMNp6uKXbU/WSQ2RJYAz17OqDLjI80sGtPJjFgvs783Q7AFdgDX0CDZ/bmJjc2swlQWQPKhCYYV5iVzERckBsuRwk/Ips4WP3PHr0im+dBcUK6LUzozUX5ldcjLb0fYV2/WMzdo6xz+4a1qW9fsrHr17wt97+AF+ZfMWX+wfcBoXxdh9nnyFYZSrZzP3TazYqS9/n8aGCzWLlAxkreOs5FI7bTgNq5KHHbLV2lhzEeachUXKaRY87ZYWe1TwlW2XgSyXO+ROG4ukkZtSuZCtVHQsFuTO5KmAn8NFBqx8l2oyHsKckSE8GJN32FUs6p4Hx2uqk45wHAgzJTap+A+pkD7mEGBM4NA4YuMNqKo81JUVPCpfZIkyBGTXIbs+KUt0jDkR+30IxXYls6OKciUZze/5MYrQH3vWHxC0NlaWbw2IVBEjMKT1uwxjAeoma5UrRWp3kGtkVUpRpqTfQxhBLriai0y8Kw+H5PwkszOnedIU7Tlkbz1r7BXir77oxmL81BPzyjnm1149zlcb014KzPrgBz/If/wf/8f83M/9HD/3cz/H7//9v58//If/cEkk/syf+TP8p//pf8qf//N/np/92Z/ljTfe4Ad/8Ae5uLgo+/iRH/kRfvzHf5wf+7Ef42/8jb/B5eUlf/AP/kFCeFG+22+ysYcwaun6kz9U07adEpVqEzn5XED/zl3+h1//HfzK5i1Ow9KMcZO2OgM1c+mYuZ6LMKdXz5Hf8Up9yQeXp9yZ7QhR2HU1m7Zh29cGGqklHOu+4bydc9YtWPczNkNTJrxM981yvF49F2FORDgbFjwdllyGOU+GI9pE2Z0CWrmaP/X8yrRjgFfqC96cnXFUt3TR411kNuvxVWC+6Hh1dcmb8zOWri2A1tiu1rxSGsyEPoNa07a9fWJjZW39037Fl3b3eNKt2IWKi2GWuj7am7MeZlwMM97bnPB4t2Lb14SkI/aJmeV9JDYRraaL5LEKXL5gaoi8byP1RaA+D8yfDMyfBJrLmGi0Bnj5zmR5OWiYLESJjbGycIrzkdqHIhWdVtL3vugKvtexW2AcGYFlYksVcJwb0fwQkK63f1mDPgQzTOyGspiX3YCESGw84XjO5o0ZFx92hEZZfUmpL2My7Z2c0qT6v5dcq+5NSHvgTwbfDif1m1qY6wTEmv7LnUgmZvN7k+AULMvXmM8xxq+fT1XUF5p8r8hjGH9/YRPGb5JxG2deZKixtNKQg8+5JEBrdqrol+b86pM3eNgfs9Pa2DmJ5TTgC/vJS6RNxYvGDSxdx0m9Y+4HA4ESiDVEn4AnEkjlaYeKNtT0wXxTQpIlxsSuGpLsLiZQSzEmUy6YbGOTvLn2/bSyFDEzATKoldnAS9dx5NsEZFki5auAc5GqDizrjqNqV4zwIRN8xo6GWfaRQa1wAKhlWXuvnm1oOB9WbMOSIS5o4xJlgTIHPF20YtG6n7EdRnkhjMwscVbIuNa8/WCOyGsH39nCutpa4cR3OoJYU7lDXlynubocJx3Xy6RHY642u6uQia1XKCDNyAicxJpi6p4pphEJwf5NCxDBHiexqGSw5Fq9I86sc1R3x5KF+jyxkQ9iTQb/rnwHDs97Oq9n8O2QMXVjoeIwzqTHElhM5Pq4Ide8frLPr1useQ6mNt3uSky6Ljbejm+44VwszCwJIa2P2MtDXDBgq9pacX37d17hf/7cd/Jef4fzBBABxfz9NCyLF5VPnlZnCeACm4NfqS9ZVeaTtElWKJf9jKet+dLmjutd9OySfyCQGlrl+OA47cyMvY2VrdvDnE0wZuu00cZpsC4RThRPZKfJaiT5G8YEbO20YR1nrOOMizgvQFdU8zQM6nizOeMjq8ccLVrmy46jox13l1vm1cAwOKiMyXqxmfGF9X27R1pzEedsYs2udE/MuYorv2cJ4k49F7HhIs7L/TRlSe5YaPHxMgFVOd85Si7tOUd7Mqw4H+ZcDHPWoeGdzR2etkvWfWL/Wts+Y2Z1UpQcbjDQaloEkGBMrWqbnu8sx5ACgh6ANQfTRrWx52PKA3IsyfnAtq9Z1D2LZQuJLBBn9jMXxKdFdRI4ppVJErX2pct6mbPc5O8YkbYzSxXYL1rnbbxDZ7XZ/wxxfDx9P6RNOVDlCIua9eue7l6keSrMn8YiU8yMsXLeE2sAKTFymouNp1GWEznvEblqr3KlsBLRgzhSQLi8u7LeGx/b96LSq/ck518vOg6vY+9Ydj/GrobP2UdOyQ5YWSo5B9p/zQuDbWm8lMzwD/2hP7T393/4H/6H/OiP/ih/5+/8HT7xiU/w5/7cn+Pf//f/ff7IH/kjAPzFv/gXef311/mv/+v/mn/1X/1XOTs747/4L/4L/tJf+kv8gT/wBwD4L//L/5K3336bv/pX/yo//MM/fO1x27albdvy9/n5+Utd5D/SIVrYWIXOGyePu5GCmKl91S5y99Owvjjmf3jy/fyOb/88v++VT3HfX9KkpKJTTyOBu35DG2se9cfcqTYsXcfb8ye0saINFe+dHROC0FeeedNT+UgXBe80tTC3ard3kXk1UEm0YFJb4rAyd5Ni9m5eVY5NaKhdIIYFdTJjPPZb5t62byQUg8VeK2NVifGmPMrMDTRu4E5jnlxelLUPvLpa8z13v8S3L77MXbehllCklA5XwKxyThOAK2AGxjB2lzobljzsjnm4O2JQ6zDVpFbAMzdw2i847RZcdjP6ZCweVYjRUpoQk5RSFCotssEiy05so+jzFzfRS7uIbwPSB2NPVQ7XVUBFvxyBvzxRh4a9SrlWCnXEe/MnU1G8V/rE1rIkJHf7mE6eOnYLDLrPWIL9CbF8HtPfuZ0tjFrsytuEmKoBYVnT3qvZvOpQgePPweLJZFJPSUz2U8mGj3uylEkSVMZNoM8V48QccPaZZpon7TShW9dQq4NpBIKMtHpv1y17M3H6Dh52Mpzeq/IY11YUrh1TSu30dXug1fhE8VmZbpMD4e0A/snFGfjNHmtgZGvIJLkd/zZ5rf0tzqqr8yfQtw2/unmL11895SPLxyxchydi4m5X2pqHYJ5ac9dTS+BOtSUkdtV6NzOLPa9UPuCE0iVPRIjBPttO1JimOAZRIoP5TxAYMN/FDFoZk8kq7TtqvFrVvHFDYV7lqns2Xx/N5A2Qq8T8U2Z+SLfJ2r8vm4435ue8Wl2wkN66aKlZtI9W8zaMATY6TwQoBZyIAXK7WLMJC9bDksgRjgVePF46vGzZhYFdGOiCEvMCTylgViyyNS2L/ql0IQOR2eOjsLZCYlfkdYQTJFjmEOoJ8Jen+eTrUYoNTiGZ4+diqXMQE2KUvbjUbsQEeB+B0b05fRprJsDYHtiiWthwiKBDZnTlRbsn1o6w8PRLO4fZKdRb3Qf30wkX7zCm53dwzDxK4S2flAeGqyDWdXM/02k8SQ2ni3iFUp7PQNlhvMi7zSDY4TG/WjBJDo7zvHHdsabv3+34hhtOKMwsxSESTcGEM6lTVBgwk+mo+G3k3q/BabzHf6ffwx/+6C/yodljfLSOeSvXslMDXo791rroOWET4TQsWbmWlWuJ3nG/WbMNtQFYQ2XgPTB4hxNl5gfaUFG7wGU/w4lyVLeFfRQn0uzsrziLfaFcXIR5AtTMK2vp28LWgrHgbdYmlMcyc2unNUFcYmYN+MRAqyVwUu340MlTHtVHhV328HJF31VIE6ln1jQrF2UCwlICO63p8Kzo8Ci7nJ8geNWyLZiapNeqgG357+l5XoY5l8OMmRsKwHXaL4jqqN1YiGtDxXvbYx5tEmvOBypn+cThXOQ68FsdmVCS/Z6SYiTPr2mOVS8k/DABThTj9pKHJOaWOinMLHVKmEGYR3DKEIzRPasC63lAt360Zqmh6kYpfT6O7ybgWS7Ui+x7TxXVRYqH/WDsrcPnJnNfbDy+D0Z+ckKcV7id5bJaOeK8on1Qc/k2uE5YvqtU2zR/T/yBYyWlKctU4m8HYXIOh1/MfD/z2v8aexWw83bj75q5fhnEywy0XEg6tFI5HHoQnHIcv2nbZ8Sm/NnZfw3Xx6QXlNIf7k80rV++ivFSzKzpCCHwYz/2Y6zXaz75yU/y2c9+lnfffZcf+qEfKtvMZjN+7+/9vfytv/W3APj5n/95+r7f2+att97iO7/zO8s2143/6D/6j7hz50759/bbb3+1p/11H3vvWaZpTivkuSKiOnY9iFDtIsfvDLz2Nz2f+l+/hf/2C9/HZ9vXOI+LsU15osO+Xp/hJfJ+d5LMewNvzU/56Mlj7qy2iEDXVVxuZqx3DW1f0/b25c6Mn26o2PQ1l4mx9Xi34mm75LQ3umqbpBF5ZPPaXaw5GxZchHkCnbQEgEZCYY/NXI/HAuDMWZfDu/WW1xfnLKuOk9mOD5yc89tP3ue7l1/kA9XTRP9NASh9+7Ns8FD6AebDMtXub8KMJ/2Kx+2S7VDzaGPMrON6V657F+oCZPXBs+3s3uQ28xngAqBSYq3EevTBmjK0yiJ1MCDL7XpcOyBtj9sNVNtAcx5o1tEq6X0GXibra2zSp4r4ZkRVah/GtXUAiWKTaDUayo9drxJlfEInzSaJpX36dFI//LuAW2lCdc4MFJ0wLD3DQqh2cPK5yOq9JC+cfsxlAszJ5Jym8sD8/TisGjxrIT8FsrxLgNbkxk2Bu3zNIdq96EO6J/n7x4FkMQOBcQT0JkPy9s8Ye4BTTqheZExBuRLMuD4Ruh174x9nnIHf3LFmb+R56fC7ppoS/vw9sUVncxFZfdHx+LP3+eWzNzkdVrSpIuww4METWfkWh7IOxrZ1ohxXO+7NNsya3oqag6PrKvrBquohzaH5ox2iddDtknRkOzRshyQLT1X4KdM3IgUw28W6yA4NqDI2gCcW9liVfLaqbAKMNT05qltqF5hVA8ezlgezNa/XZ5y4HcJowpuPHHAMuCINmX4NA44eq7BnmeM2zNmEFUO8z6b/Fgb57cxeewu5/yHgWxn0bbpwh6gNIQp9cIToCt6yJ63J7Cy/HxvsfZz8keesIbGeBpvn3BDxrcUZl9gW5OqmHOzMKeLHnXoZe1OWAoTYjSkE6AmgRe5cmP8+ZLru/ZvGG/bALZvrJM3rEGtXvFhmpyZ9kmF/Ui2eXpldFjnY/2GsqYAGWAFHwAmwSI9PVslTIGv6b/q8phtxED+zbGs85sE9YPJd1Kuxpmz/jLEXEvSrDBHXAVm3seYbekyXMHtMESjWJ4XNkdZs9SZy79cC/M27/I+f/S6+0D4o8j2ApWuL11Svvqz/z4YlD4fjsu2x3/HK7NKM3wuL17Hta3ZDxWU3YzdUXHSJtRo8551ZoexCVeSHmcHVRp/mXfOH2sRmz6/wbFjyfn/CRZxb97/cdEO9NazCWLXZFiUbqmcALHtQmZ+ucFy33J+vWdaddVscvLF8vRKD42jR8vr8Ap8aiEztUQJjI65p7uIklmNn4CqDceukaOnVswkzs0ppT/ji9h7vtSec9guedCu2IbPOzPf4vJ/zsD1iO6THY+4OmQKJ1yItd8Pos1viyiQfMR9eY/3W68jsPDI7C8zOlWqnBbSqdiYvLCqTMLK5SCDHsIDYqPUBETvfykUqH/btmWICzryM5xcn8SaP4hWZ5mZ/ECi9K4/JtoW2g34YpYUuFfOHWH53bY/fDQnAatBZRZzXtA9mnH2koj9Rlu8Iy4fDVR8sZexkqBRywXQd9sycILOZrxt7eVmOKXHfZiWrUaZ53bPG5H5fsVu54fyeubvnxI69a5/GxcPzueY1h8qUkc327GNOx0sbwP/iL/4in/zkJ9ntdhwdHfHjP/7jfOITnyhJwuuvv763/euvv87nP/95AN59912apuHevXtXtnn33XdvPOa/++/+u/wb/8a/Uf4+Pz//TZVkFFDB2e8aI0j61Cd2FiEV1gZN3TQFp8r8aaD5B8LF49f5C991n+/79s/yu+59llcrk8yYceHAR2cP+VJ3n690dzjyLcd+x7csHwLwG/4VHp4f0bUVMZhRbtZOV8mHyYklGV00ucjG1Wzqmm7uudtsrRpRG6Oqyl0AccykI4pVTM6SHDJXbJxE5hJpNJSOh16U07AsunQcxKrjbrPlfr3mY/P3eKM6w0mkS6CUBR6hS6hNk4JMlh7auUjR6vda8SSsOAsLvry5w8OtVSc2Xc39xYa79ZbTfsHj1gC7DGQNwVhqIbo0H4ySlpgCgS2aldy5wjpqjKvJYk7XB6QNEz2yIn2gvrSXd8cGCkVvLWkTcW1c7FaKcxGXGHS1C0YRnnif5EV89kVxgzHCXGfJzZUFuZ1h+nHdzJG2mUyo6hw6s+gTK9ObNBd2DNerdSDp0+ehccRmZGXFOgfJJEmRtG+fWSPTwCTjJJ3OQ/LkXh53tp2bdB3JI1cscpIQor1HqialdA60SkaLiV5w8PoMAt5Ukb9xHFY3XmRcV8GAkZk1maxvJYZXxz+JOAO/+WNNARXKKgD2mCKGmliThZg2EHu82kZ8J7TbI/7ea0vefPUpH1w8Zek6A73EfEbuVhvOgxUwGjcwk4F79QaAJ7Jk084Ig2NQENRYq4yFEyv6W7fDqEIvjt45gg42pyKIM5ZwNlBXhArryKoIbaxRJ1QaS+fFCmtMMjCawVvc0WSya6zcuR9Y+I771Zpj1yZDdlf4WIaH2Hzv0eIZNl37deoLE2yrJsm/6JdshvvAR+g/dIfFP9ty/3c85nK34PTv32X3N46J7y0J8UsJ/xmMsawgogwpeOSYs7c4zPT/ayQDkmRB+QwzK9d1EdS621qMkdEwfQokORJ7zt4vkztOwCYdQaNps5Hsr7PHMDoEfMqH8GAUZtX+Y1k2khf1vlOqEIvR/dhefWRD52r+OIcy7jvHD3XYcnYBchf0Tvq9BZ4gvI/KOWiLLcYOAKzD65qYZkv6zumEaZZuqvlPTtYH5XZotpzIJ/wSsebrFQ8O93UbZr45xpQhEqU0p8pm15bYGps2x4dqG7nzWTiTu/x/2u/lh77lU3zn6kvMXc+x7EAGevGs44xazGvq2O9MBsgsmaxbkWF7VBPU8WSzSJJzsSZVTpOth9IFTxc8AlQuWW2gRV1RucAQPRfDnJXvaKN1JHzaL6mcMarOhkU5ZqsV96o1S9elRiP7wEedK8FYh8SLOGfm+gR2Rd5szpi5gV+Pr3HWLWiHitW8Y71rGICjZctrq0s+uHjK/cq8tKzz/DA2l0KpJVKDyQ8ZG20FxOSNE1AN2FOXnA8zk1xGx8Uw45XZmqgURnGbmomcdXNUhd1QMQS7ztoH2qEymWkdzcpkJ5NiAqU5SGY+mbyL5MGouDZaUQSbc4eFt85+Xojo2KQkx5eYfbWEuEwNrSor0ogo83oYG6SIzZFWwBmbJGW2Lek8XLDPrSS/rrJen6zZtfK2/i+fcftd+rTmd87sVpKVjOsGi/mVs6JQ2yOV5T2KZ/fKnPMPV2zeVOaPHMdfCrhBJ/EnHcoZySF3NBzlhYzF/Akrq+ACh6N8Dw/Bskgp3tsbb4/hx3ugiSDhdF+ZIn6vmbCoptwV9CA+F9Z1HCWKVwX7X9u4lsU1vVTZ35aUF5U1CFyxYXjeeGkw61u/9Vv5e3/v73F6esp//9//9/yJP/En+Omf/unxxA4RNtUrjx2O520zm82YzWYve6r/eEZJKiYLloj5YiiIjsm6YotKClvUZgXfKidfUBaPPb/yhd/Or3zPG/zQR/8h3zJ/yB2/Zu56lq7lo7OHOJT3+2O8RJau4yOLx5YUiPL++RFdWxMGR4xCDI6qDngfE0IeU2epYJNhV3MmSoiOVd0mv5HIzJlcY+aGYpTYFyqwVVVDMtqoJTB3LUtaeq1Yx6TdTp/CI9/yoF5b1aY657XqwjpyqKfHl2rFnJ51MqBcuZYQxSjNiWqbTRKDOs7Ckve7E76yMyDrbLPAucisCrziL/m9P/op3vy5U37+Ex/mv/pjv8s8v6IBV0GFvh8rtINT6nqwb1cCHtXJOPk7AxtUwKXZvyymHWhmUGUKd0isrYVDxRUQ69pOhKI01TB2K6kC2/SUVgptAn1iogQPqXtGMCaSsaomYNAUPDpIjPaflxEQCgHFE2sPXqi2ZgaPgjZmpux661SVq/vqjIYc5nbbQiOEGrwXYiW4XkZgdyKvy+daOnvkZGJKwU2A1nQ+KNrx/DOMNFtJrTlUdKTXR2zhNq2sxAQA5sd8Bp5lr2BfJC4K5PRXuJnOm4NOOdnJv8Mxies6eexWYnh1/JOIM/BbKNZMGH5TWVb5zpXiiqAFxjGAYnam1Bvh4dkDHr55xLfcfcT9as3M9VRi3avu+TWSGFrOmfnu3WqLmyuPBdZtQxg8Mdp3sI+CS5JpJ5pUzJp72zEETysG5Aze/Ktc8nACUgwb263bZabFZALpvCheAk2SRmbJeX5HGwksqz75fbUcJSArJOl8bjBSSaBLcaeRgagVlUSUFB9SFV2TlH0dZlwMSzbDkl3/KnL3DtUfUH74Yz/BPxP/Nl/hHn/1O34fv6jfR/zxV1A10CQyEMOIWkchNfmQgj2OEkNG5qpgzNzpElMoK39Nc3uOCzEqme00rRrvD8W7YJ8EiXgXc/d1i1Fh/IgVNnmuPMeMcDFJjvYCGVcmvGnBIO1TnU2i5utocaVUv70YxpTkIXmxa7IXimmwJpArg1+ak3J1qCyANxH/OnwMuKtwWcOnZ/DeEvgcyGk6wVjizyEOVc7/IIamb1iRHYpGA0VLvBhfa55h4wuvFDHY/97usaZuwr4OH5+CejeMvaTiJTC12/Fbc3g32jHY+ig1NUhrvtLdMGgBVzSBttUucuczsN4c8b+c/g6+/IkT/tkHv14AmWO3S90ETbZ3x6/p1fOoP2LpG+5Vts7/bYtHdLFi29c8vlgZk9dHZs3Aoun3rE/AGiDthpQHVEOSi9fUPnCESf5XdOAYTeCjnUNUM4yPKlyGOZswY+lbjt3OCu7pGpcu5ycjGwosn8l+jJmpNfMDx3XLLtg5hSi8dXLOt5+8y7fM3uPE7Vi61KlPazxKJyajz+zbsXt6ljnu26QAhZH1uF8VO5QcB50YK3lRdZwPCy6GGQ+3R9Z8JbrSMTHnNJkBHKPDVZFQa1FPJJ9+m9NJ+cyUgTsorjemb2b9qoDfedxQo84zzCfzFCOoNaRCvVYUIAunuNo6kZviRKiqQOvtc1gsVDJuUyXPx0kBZU99YW/UfsyZroFK4SEDtRM7FbF1j5vkDoLNvXHm6VcVm9c83Qk0p8LxF6KZvkeFKu8/7SfL93PRJcfZfNxDIIv8O+O5Pm9kQOuwkJ9zjGj3l2mH92AnolmKGNl7/XWMKctVXyDvyDFDJ7nQi1zG4XaT+2JdgzPItn+eB+nxnkfY88ZLg1lN0/Cxj30MgO///u/nZ3/2Z/nP/rP/jH/n3/l3AKuKv/nmm2X7999/v1TR33jjDbqu4+nTp3tV8/fff5/f/bt/98ueym+OMX3TopJZPbkqUthZ6Q0k6MGCIgElSQryyi9F2s8f8xMf+wHcd5zze97+LN999KWiV//w7BF3qg1f6e5yEeYc+x0fXjxmVbV8unmVL57eZb2ZEQdnEpDg8FVAG5tomyowr20Z69NC/7ybse4b1v2MB/OG43rHwvfF1L3WiqXrSqdCSCa5YiytnJDs1IyFH/hLC3bJ42vlWo7dlpVr8SibZMg4NY1/HI94MhwVaeWJ23LfWwUkg165ovK0X3HaL3m0MyBrt6vxPnJ/ueX/9Jd+gf/9//MXcQrf+wtfRFD+X//y77HJXhSPmSRmsE81IuJH3yVsYrb3c5y0rywinaBNZe9nrqoXnY0BlL63KkSsjMlk1QdwrSPOjZWVO4PdabbM6gFmkdg43GAL31gZ1mJdRA6qFVMQK4NT5RxvALay+WCu8u863BBwlS+To1ZmmhjT2tsmlJRgeqFfCu19oV9ZwjMMBmD5XpDozLA4oT+5WlHSMieIT9UV70fpYPaJuWnS14gGEAYzt1eZGJtNXhOsKqQxFvBJkrdYTpy+3kyoMjFnQ5pyztONuE0kXmLcxpnnjL1ENmesaXGQ12CaADwOAK1ojJjl+0o4m/Hp+x9AXmv50MlTXm/Oza8K82ucpwYkrVbMZOBOvaV2gSd+yfluTtdVaBQ0CBo90UV8Zd8/76DyqWqazqAN3roehopF1THzxtCKCE5dYfT6CRqTfawcsfgwGVanLKRDvDJzPknfjUlWy4CD0so9pNgE1rVxG5sEtjXMpDd2Glakyd0MO63YBjMz3gwNu37B0N/Fven5HR/6BX7o0U/z9L9ZUPmWf+5P/FW++J0f4unffg19Z4Gm6ynNjmMC3MUlsDvFmgyQ6wSrPHyfBQOApmu/PIdFk5LESpOfIWOH2QASBK3Uun6njpQzb2AWuYoezWxwX3rCnkR7D8TKMqUrH0auxhodn5chjKzB/JyzWFCwPc2tyW2bWIvJV2pK8pAN77Uwqx3CHPQeLF5H/zn4wPd9no/oZzkbTviVL38n8j8dw2feBu1A1sBw83SsaZldEgYSK/hgfo/pcz2Z2/dk/sDXfdJXcnZ0G09ux5Xhpt/FKSCbCrWieUKSlDen+aM2P79qFzn+kjJ/6viHX/o4X/yn7/HDH/xVPjp7SC++mMCbcsLxSnVOr5532rsEddypzAf3A/NT+hNjX51eLBgGTwyOtquo66QacZFZPVC5WACZddcQa2P0ViFa19y6pa88C98zc2Px1woUgota8oizsKDVCl+p5UzJ/gSgx4oXuTN8zj+si3zFV7o73G82HFUdbah42B5xNGtZ1R0fWT3hdx59hgf+ciJnrJhLXxpi7dTYYw2RhkgQLcfIksRNnJn0UI19/HQwq5fLfsZ5OyeocG++5V6zLUznQR3n3bx0Xx+CZxfGyqoXpQ1TCbVAHYmVN4ZWJ6XeIJo+BwMFZM9Alt8NViSP0VQTUakqSeCNJ2S7E7UYE50Vr8thszfj3Bqw5CZb82ogpqK/BOt2aNuneV3AbSeAVi6iZLBlKq/jmjlWdZQWFtAqffb9BOhy2b7EpIv9qqK944kVLN9T5qexeBJrUpcY6CblXEscCiPYcigtPCxOZwbcXhH/WeOwsQppHTcBsA6vWwb7W7239w72Yu+4o3SOIe2vrCMwkAxsnRhtzbDnXTkdmbCzx2jGPmeHBX4osaoQBeT6+yAK0U22v0mWec14aTDrcKgqbdvy0Y9+lDfeeIO/8lf+Ct/7vd8LQNd1/PRP/zT/yX/ynwDwfd/3fdR1zV/5K3+FP/pH/ygAX/nKV/ilX/ol/syf+TNf66n8kx+ThZ6xslIKofacGTGSUNzJ65wFktxBaH4aqP+BsPvCMT/18e/m5z/xQX73m5/jY8v3eLW64NXqnLn0PAmrwph6rb6gPjHj3C/Vdzm7XDAkBlIYPJ1a5XwIZni+qAe8i9TJmDGkNuoPd0dcDjOWVcedelu6Xl26nnv1hnvVOpknmj9WLQNzMZAriCuB467fFEni3HXMxVr9nqauIrljyrHfEVR4b7jD2bBMTK8eX6X2ualjohnBWwcS64olXHYzutaMJpvGruejf/dh/k7iFL7rl94hqqQkyKoYTT2wa2tiFDMp7k0Sg+aqgYFIY5V6fKtyC1sVsQ4ZeQJI+WRGnV0fU5KAJSOJnup6wXUQO0ffVoTB089bdqHGu4hfDOh5ZeeSJkHRROcdxk41xRTxuorFTaDW9O/JRCIhdc4opvAuMZnSS7wj1o5h5elWjt0Dob2fqjCDMCwU1wvDVpJZpEtWLWmiTYCgiwN/7Mt/je86/Ry/PP8A/+3R9xOprcPideeKzS/lGsOAUhng5f2VbS1RskTaDE4ZKxeH173HbtFrJ9avakwSzj2z9/x0Zh1cvdTb8YxxG2cOhuqYzCYp1BV2FsnAWg1AmAJaGXutdpHle8JwNuNz99/gy6+d8PbRKffrNSvXsnQtFZGt1oUxtfItfmYsqnM3p+1qYlpca3SEQW2hG80TqkqMLRFjYmXj99xht3aBue8T3muG7nPfs3Bdkj5qqVhXidasSK49MKdHxUCxSgIVIRnsVsV/C2DGYAbDSa7useq3c0qrGYDP4VnG7opYh+AwOEQcfhVZyZrdexWf/40lxzNh9iQwe3WHNpD9mSKC94FhsI6GAsSQTOAzKJEAqEL9n7ytkt4oFTPzv7Iuze93TEkqUGQRmREcDGiMgxlAx2pgiB4nDqkcuBlGU7X+jhCQmECncp75gIefQa7GnsPPKOzHmpiYIqX4Y8nGKC2w5CE2QmgMyAoLS5QkJml7hDik1wVJgbdGuA/fJrz9/Z/l/8J/w/LnO+T1yF9/411+6vt/P/KFuxBWQAs67J8rvjAAwTxKNLNcrosN+f1T+8wWwG9SnX8u0+prHXvvzTXnOWG43Y5vopHXTNkzKBobU7P9QoQ80eTvnQwRKlMSuEFpziMPfkW5fPoK/9X3/wC/79t/jU8cfZlXq4sCzngitYu8WT8lqOP9/pg2Vtypthz7HR9fvQ/AZ/19Hp0dMfRWXI/BziPWBnpkcCqztXK3cVWh8pE2VAyznflppSR55qwAkQGtyzCjdiZDD+rM41cr7vgKT+Su3xTLlqgOojGxPJG5mCokd0bMgODdZsOD2Zo3Z2d82+LLPPCXzKVnR52kjGYCbxJDW4/2WkEq2oMxtHxi+OauhbmToZm7L/nK5g5DtOs4XS94dbHm9dk5l2HGOix42i45b+cosOurQroZilel0g2epjLf3cwGVj8WOKI3ZpEMWkAaF5IKxQmuT82s+hRfBQO9OpNSSoR+5QiNlMZPsWIEyBQkCloHXBXxXksTstoHs78JMhqnWw0C9aZocUlWaFLDODa5ymOq1CiWLbpvXXJNvIEUc8CK9KUbov3wnbJ8qNTrdN1O8G0onzOtxgJRrG0+9ckzrBjVT8+xMI8m53bNOe2biMEeI+vQZiUXlbLNCilny8X8IQDJaqVO9wdnLIgMok3vZVTzEUuPlXtyMPaBqoP4cvCSLGvcO+/y2v1LLYDVNc/tH//6x28aLwVm/Xv/3r/Hv/Av/Au8/fbbXFxc8GM/9mP81E/9FD/xEz+BiPAjP/Ij/Ok//af5+Mc/zsc//nH+9J/+0yyXS/7YH/tjANy5c4d/5V/5V/g3/81/kwcPHnD//n3+rX/r3+K7vuu7Step32qjdOzJb3ZmZ2VW1uRxyaBqAMmd12BEMiUDGIpvYfk4MLtw7D5zn5/47fd45ROP+OEP/Cofah5z7I3pdB4XXIY5AeFeteETJ+9yp9nxhdk9nqyXtH1l7WU1M5GkyD0q71nWPa5SjmYmcBuidR6JKlz083KdTpRtbLgIc16pL3i9OmPl2xHIQpKhu/1uJpGJAYYWiu9pWHIR57Sp1W/AcTYseTosjaLrIUZhE2fMk5F8nvg3sSlA1swP1vZ10TEEO+fdUPEz3/YRvutn38Gl2/sL3/4hk7YEx66rjfpaDxZQ1cyMnTckSoLAkDoI1gY+ZYPEccJOFNnGGZtrUv0qEoksUwwJQKttEV78rxKYqVGQKppZZjSz5fm8Zz1r0MoW+n6rVBul2sXSUllCGCfyGI2RlasPQxgn9VyJsA+qacmnE82hZ0jRmxtLyyZ/ISwr+iPP9oEBWbsHSlxEpEsJWgPDCvo2TfzeUe0E34pRmFOg+pc/89f5v37x/4cDvvfiMxAj/83xD1DoxFNwiQRkhZAqMznbSRry6cjXnvZTErt8H/LC7rqk5HAeP6xIkHOWF8xIpvP/gfwkf7f3AK5pwnONMf0347iNM9ePKx/f6SJg6suTEuwsNxQnI0NLNRX8RlDLqVJvlKoVhqdLPv1gwfK1DR87fsgdv6VxPbUOtFrTaYUCc9fz6uySuR84qxZsu9oW5CFBUtFkdYPYsj6IUPtI5ZQmdR2MCeBRrDvT9Dp79XSuYumtW1Yjo5ejEZ1tUR6BOgFdYMyEiLGqspl8UIsPirCLZkSvKuACmmQglYbUXr1KP1PnRLW4VrtIXffEeIk8mvPp+C382sc+yMk/t4EF/OLxx/ni44/gnypRd7a+VIfzwQolWFyTzPBJsvbsi2hJ5Bgb7H1MscVbvN5jWuS3O88xeRniLG5N/a/KrpzJaqI2iMyp6hN6fw9cAwxIv8H1l7hhh2gCtLLvUwbbpmbw0/kqxw/74yqQMv3wZgariC2kc8wUzBS+cfRLA7KGJcYsy/WO1AkrNGl+FXCDR0KDxGPkQ8LH/a+z+Ks9T/4aPHhT+O7/2z/gpz72z8I9D4/nKRCnBVjy2lIS/UsFNGCAVw8MB1+0CVi1txDX8YfAtbHimu/vFIQeH78BQHvW/g5l13LNfqabXFM4uh2/9cdePhIZGRJ7RXYpz4/TiFiC6+27KGLd746/NDB/WvO/ffq7+Pnf+UF++EP/kI/MH3HsttZ5HWsI9eHZI+au59Pb12hjxYN6zdK3fGT5OMnH1Xx9d5UVBRy00RGCo65DYSFVaWlX+ci2qxMTaW7Mo6WxdCsXmMGoHHHKnWqLQzmqd/SxIiJF5pcbTWUf3nny98rDugqO2y18x8KbCfwdv+Wt5qmxlJPtScjG7zhjWqVOiV5ikWKSQK4cUy7iovj9GpPtHpdhxhfXdzndLugGT+UDTRU4SaysJ92Kd9Z3jIEK9Il91Q2JoTX48hUPwZXn87yUZXF5lA7kOTVNYWhslDSZz1NBxA2RCFQMxLpmWBywf9WKDFZEsZ1qFJwz9m8XPLULNLOenTbIYCAb3Ri3XJ9+5oJ9H5FuGFUb3u2dX86zjG01AXzKhV4DamWP3ZlPHRgrUGguAtV6sJwuscykz07O44heCjNLIoWcUJhb6XuWfbRKAaocX6+xU5nkJ7np1aRIL5I8gQ9JCxNAa7KxfZdDsBy2TlJ8KSu9dH5AziHzYyn3Oiy871muHMaOxLQrD2U7AbBu2ofMLDKIOcoM42HupfsA1iHj7XnjpcCs9957jz/+x/84X/nKV7hz5w7f/d3fzU/8xE/wgz/4gwD82//2v812u+VP/sk/ydOnT/ldv+t38ZM/+ZMcHx+XffzZP/tnqaqKP/pH/yjb7ZZ//p//5/kLf+Ev4K9jWvxWGQcfshI4kFIF2auIK4mllUEHgWBVbCnVQYVB8EQWj4XmF4T1l1/j//1dd/idH/8c33PyJV6pLzhxW07clvO4oJWa2g34ReS42vHu/ISH2yPOdzO2bUMIZnw+DA4RP05+aZzUO1ZNZ/IN39NGq2ob0BK5HMwPa57khjttWLm2MLTsuW5vnzbxV3iJrGPDJs5oEysrIDwdVlyEudGK3SgxqWUoIFabuqVkZsDC92xjY6bpqaIDcLmb8ef+0O/n8v0V3//Zz/Iz3/Fh/sIf+SQzHZJXmL1FbVtbhTyxsaKC9m70tUp01ywPNDDGrkcCJh3EpepFmsSyUW4asZb0D+KMsQW7pP13DpWK4JVdX9HWFQIm/5hFVLyxmwJUbTJEHBQ3TLw4Ju1q1SdJXy1l0te6GtlWPoFT3o/UW+dS0jOCrlYV92jtCI1DvTAsHO0dR3tPaO8qcZmCilqiEWeJlhsdsRbCTCw53poPA7uIU/jOs8+V+c8B37F9B44pQJvduHTu1wFZh9+3XK0QSV0jdZ+VlsezEoPIKEc8mEzL4XLSmKm1hxKP6eR9Heal9qBN4Fkvzrh9/uOGCsk327iNM88Ye3m1Tj5rk7hxJTlOiww0yQcYJSdpl4YFKdUWVl+B7mLF3399zlsPnvLG7Nxk5tIzk750QfQScZXSuIHLasamb2iHij54k9VpAnD2Foa25Ji5gcZbe49KQmFBBXUISpdYrVWaeAf1BmglScf0ufF2CB0eh9IlkKr4bwHbaEwwTUwAj7HFnEQ6tQRoSMWS3OGwcoE+eJwMCD3KGXz+FR7+9Ov8V5/8P/Ot3/dpeq35u/13E/6Gw53ucHIJ9CiRkFhZxsYy+r5pDxmr1JoBLdmTLWTDXlxaKRTmneyBEbkjonpbZO9h5UU+71CJDKFhcDXwACcfgrsrNDp4Ksgm4IbPI/EREiMSu/HzlpMeGOUSfpQ8ZFCq/HMZqMqvPygwpMfUT/4lWWGYWbv3MAetJ9fptAB1xqK2WOx6wQ2SPCWFrczhDtw/Bu7BlhXaNWkBH0fgVx3QoBxDfA30HuPk/AR4DHqOZWDj60qciNPJ++A6rxvXxYbrRk4M8tu8l0hM/n7G/kwKJPsxbXqqX2ep/e34zTHkMAPMuUgurjMCWYa5Jrlh+WxraSaRVQnNReSVX460X7zL//iJf5rXvuc9fs/rn+HN/z97fxpr23ae5aJPK3rvo5jVmqvYhXfhYttOXCVOHMepSMVJYgg5QETISSiFyJ+rG3FJdCWQkMIPhAQ/kHAEF3ESAqQwOiAO5HBugABJCCl87BSObZLYjr3tXe9VzWIUvfdW3B9fa633Meaca63t7QMXZzZprTnnKProoxfta9/7ve/71Xe5bk+koK0cj9W3aHTPp9fXOPJTdllzaBeYWWBqeipznZuncxaLiTArlcJFhXcGW3nxroViFD9vkvQ7SFfYlxY7TKzjrp1yY3rC9foUr7QsgTPTKoaUP4SBQaYDXTTU8jImusMoMYq/62fJU6tjqRr27JpKefatgFcHZsmhOU2FdTFuz96+fTTCLKPirp+hCSx0Q608B3qJVoGTMCmvXUfx9nqx3+fUN9xsdzjpGk6WjZALrOaR+THf82Mf4Ilfu80vvukN/H/++B9gNu8xKtIlWbXRAYLGO10WrX2wTKZyvGJQZPPzjYJ8rgcnP3Ed5XwX/NMYNppj5Gsqq4pclrQPgE4Go4qkXUWMCdTWl5wOED9gHct7jUpgW2JZmR50N/g0KhcGgGoE5JQca1tZcdHIBW4HrDp0CMSmQrmO6kg8wmJlCI2Ve6LPCG8qAhrJAf0E+h3wE/lcNxWFjXbCEtZ93hdV9nHDLxiGXAwSazLdi7lz+5ZXsNyeCdBCciKVlTe5eLR1ruSGiQKU6bMesXJchfkWlZLXJdnleWPD15GLc6Tt15VjP1YLpN8LiXw7NmWQNf8dR8fuAcYrArN+5Ed+5J7PK6X4oR/6IX7oh37owtdMJhPe97738b73ve+VfPT//45yMkY3V2ZnZf+efD5ypSTR6gvEFUmgiJxBoV6mv52S1ulr2H0m0Nyt+fBn3sxH3voIX/boM3zRzgtcsydMdM+eFVBrpjuu2AWvae7y2eaQ51Z73GlndN6wbGvaXk67UpG2t/TO0CbvpuvTU/aqNVZ7bKLt5u4hPkp3jplpaUPFEeI/UswW5UsRomai+5LCrKPQhm/7nQJkAfRBqucuiE+JjpFT3zAzHSdhQh8sR35akhedNOhmlBpV1qNUZN1VrJc1RPi7f+hbMEtFf8VTuZY6eowOWOuLx4sykeg1sU+eGF6hnPzLkzNQFsyqD6mrYCzAlSzAKec9VLpcB26qcRORSvg6IfrJtBagOlW4qAmVwU80nTf0XrNuK+zU4fYNymvMWlGdKkyt8V78TYriJEaUq+SqyVTS/E+Jp5dvBJiSNuhJvlENVOFsXC/U2ViSqgy+BSMV8n5HsT6MuKsO1XhirwneyNpo5vHzSLQVoGXtnyam3HJXxchH9l/Ll939ZGHNfXT2WLqH1LBy1yJxxId7A1kF0Er/5e+fJ8yLJvqLxnnV8IuShe3HMqAZ8/c+Ky/MicSGAeJGcvFgu/n7YVzGmfuMjYVcemzsYZAz4BxS0oWsSAuiDGghYLCKg5cWCMglrbkNLx5d46Ubuzyye8y1+lRkhyoUUKsyIgvfM2uOjBjVrp3IMHpnhnbhShqNZN+mYDSzqqPRLpnGS1eqbHwbUNJpUDtczBIOU/ywMjMrRkWlQikMuQSKrUJdgCyQooqLugBVKgFeFT55amnaUJXtKCWt18dVTa07rLqL88/h/r9P8sLHH+PFK0+iVhBejuiXHFXzWZQ6RWuPT75OpQwZ8gI2A1mqzI9AqZSX7rBpGVHOt2SfRNI8mc5rGM3nIQFb44Wi6RIz2FhhC8QDAq/HfZlFv9uzM73D4tmrhP9siP/hdUQPITxfZOL5mlMmllhTtJI5PzZmAKVKtZrSuarEyZg7VqX3pQIPyJwdrLCu3BQpmpgAQQngpoAqEGvwWgKUCkilObaoeBc+cY0PvvNdvObLn+WL3vI73Kmu8PPhD8BvKTgG6W6YD3QFXIHwWnitgbdFuAIcAR85hN+7AjwN3IHYkQWokJOq0eL8lYBDD8q8Yjg2239flFSc+zkPCqJdjv/hx8ZpznFgIzdhk9geNlcqUauhKUNmV0ZQDiZ3PA99QLH+3Yf4F1/8EI++4wW+6saneKy+w0y3wta1x+zOVnymu8aJn+CjAEWPT+6wY1p+xz7E82aX45OZNKrqhaESo8J7RW8sVeWojGeawK153RGiSL3bZIDeB8Np37Bfr5mbTtha2nHqG3aM7ItRrjB2QfwQK+VKl3SjQuqc3nOoTpnrlkVomKiOefL6nelW/K5CxTKKPL2LhonumeieZWh4we2z9KIk0Spw3Z6gk3wRKHFoGWpO/YRjN2HhGu52U+4uprjeEgPUtePPv/+XeO+PfQQV4S0feI7eGf7pn30PNjGdQBcbw7pxOCegltYB7xPAlcGs1DmwuJ2k5kumzZ3ucuBhkKyNGTUjdq0KEdMGsRBJDZ9EbSLzu+7BrDSu1vhK0/ZW2GY74ofZVA4mgbDW2MUwb2aGl0mdFEtTkAIAqXNZSZvX+LC/G39ndqJGAJyuR/WOuOqG3MAaotbCcgpKfiYme7Aia+1nivWhotsPKK/wk4jrk09wF5MPmJYO8GjJ+0eFjoiSey4oVGXBeVSwDxQDBpuVAD5N5Vqn+K8Hv2aGnyoBeEP7nXwOUxOs/HfOUy6aG87uzPn7vB1ftosoW2PjObX1b+OF99iXc8ar9sz6/T6UPufmyrKzmPwhgtowXyzMjvSc0ml2yo7bmaWVAS0vP7WD+iRy5XcUyzt7/Jc3v4ln3nDAmw9e5PXTm1yzJxhyZcIz0x078zUPN0c81x5wu5tx3Ey4s57iR3JCo0VGEVCc9k0yYbdMTc/MduyYtrSjBYqBIrBh1pvN4H0y8pVqu8ZHxUmYcuRmBBT7ZlW6fbRRPK908kI5dhMWvuFUN5z6Jvl7xLRP0rJ3pjvmpuWR6TEALy92aFcVcZmMrqwca91qXG9onaEyYjiZAXTJC4bjroIqjKySZHiGaoZS6DAYBKqUFI4rWVliFyqFm8gk2O+wYV6bKyUykUdUJabGPnV4Abg6P+XP/Ot/z5d8+Fk++Njr+dEv+Z9YOotZGaqF6LxzBV+l5lghUV6zz1dU0M80bga+GYFTufvIuJtJUGVbIVf2I5hOfrop+FnEHXjsXAK0S7p85RXRKbCktruDkSNxVEkHfuKJr4cQefvRp/no7HHev/du6LIx13YQ8hADOga+e/2bvNW9wEftw7x/+k6CNqWqWCZ14llD+M8TWlRkhtvU2fskCWeCRX6b4v6JyOW4HNvjQt71+N5RDAnvuFtaArSUXMsqvW7M0ooM17oKslCb3oR+1fDZa1c5PpxwbXLKldTYQxMLAFUpT107dmzLiZuw8hWtsaydTZ6FCatWMUn+SK3ZI0Zp6aKoxfNxOmL3RlSRhOjEpAKokhl8RKWCkdDkA4ouWtogIH+jU+fEzPpKjCyANlg6LF2wdElWqDJba/y9tHS3Alj2Gt/fhC7Axw6Jfh/dBlR1TJi+jAt3MapDK0fAlikiFl1HOkcZF0n/xqDWeP2wkWwGiRklycj+UnYkvRt7URQwM803GhQTYrxBfAfsv/cm31n97zzin+Xjr3sD//HGt3B7/Rj61x9G9SeYPi3U8z7mbekMug2fU6T0eb7PicrWXKcymBdH+0qKYzElSBXSICUZRecOYSqQugfnokuOMwHoieou6lPX6P7POf/8q/8Xrh8+x6Kfsf61HfgND7wMdIiMUFhZxGvwmIU/4vjag5/nMZ7lBR7m5173DfCvKuLTV1HhFNR666DmAzC6v/JF/mpZT2P5THls+KgL33PO547BwsvxhT9KJMjXQ2KNb/j4xlTAyIzyJI0aiK5K/IxSuX3wJQLdwfS2p/6AYvHxh/nf3voQj33Ri3zl9U/zRHOb6/aYue54qnmRW35HLFDSjX6jPoFd2KlaPlsdcOd0Rt8JUzYGCEqX2mXfy+PTuscHmbevTFbMbMfS1Sz6mqNuShcsd1TgSrMsc/bdfsbcttyoTrhWCbBUJy/eieoLQOVReK2Ln1WlHHs6pFxDiibrIM+dhKmwuJJ08dCesgTu+hk3+11CVLTJAH6sVAmJ9i/bH5jExafYGZSKTOY9k8rxlg8/V+ZZDbzzo5/lf+2/FpNYaiFKZ+AYFbV1OFeLuXq6yUOO/yGtxxWEWp6WGJOYWC6B8iNvKukymz48M4R1tk2RnEP7uBEL3DTta6sIleSz0WnadUUz6bnbTplVKVec9rilQZ2qMi9lI3Xdj7xts+WIHs2tcaR8ycXue43tuTCBV9uG4tGoZEyfvpRWBKvFWmXX0O5pVtcV6+uBUEfMSoo0fhJxTmHWOTaJsicWOxS5/0zwfM8zP8/bjp7moztP8P5rX02s7GC+nr9v2aE4+jUH3K3C/kXxJQNfmUgTR1LCMUCYthGT2mRDLrO9yVcqdwfGDb3ObCetGc5jC2/aEtz/Y7fHJZj1KkfWeouXEqMLFDL9oiS1md6bCTy5yhoSQ6sUW9OCKXmblLNvYqGG7jwXaO4YXnruUT7z1CFvfOwl3rz/Iq+d3GKmWzxSadYqcMUu2Dcr+qm00b3VzznpJxIUXF2q5yEqumBwwYCG3VTtADgJEwwZVPKl6gCImbsWI16iTOBLL/5Y61ix9AJMeTS7Zp0q7CF19ZBqeR8NJ25S9ie3UZ/YXpgA2hGUpg2WYBRT0zO3LbX2rHtL6E3qkqHKDaPXCtea1K7Wb3QiDblKrmOh5MIAYOke7JrSlTB7ZRGT9LAwt9IkmcChUClco+jnSoxrkwnfmOJbfmrQJhKCxnnNwWzF9fmC/8f7f47/+f2/gYrwNR/7BIePHPMj3/u1LLqa41VDu6yIrUG1GpW6HkYT0S1Uxxrdy0Lf7cT0+eLBZlbyPbVDuockyjDpMsstdt0sDu1ykzIjaiABb643qKXFLrVc58EQGwly0UZCpYa1fkRMFJ0iKMOPP/GNUn3pHGrdo3AlWKUTM0ziSvPdq1/nT61/TQK7ew6AnzTvSr4O5yQS+esENgNXliQCbL/vonFmkmYkSTxLqy1WXYVOO87iRr/myfwy0bgcr2CUAlwGV7d1SBGKB08Gr9gqnmSGVgLypXONbDT/rjLQnuRw9UnErDWLk12ODqdc3VtwdXLKgV1RKUdE5HmKyFR3TKoeX2mWvmYVKlpv6YP4AmZgohisp79rLSbvAF206dYQw/jslQXCvEIJmCXEIIlZ61jhkrSwC5aIok6dr4yKKKKYuUeFRwmAlfYnFxGsTi3RCUQiPgEulQoi31cVzgeibyHehHiEoPgK5RzRr4hxjdJuC5tXA/ozWrEV1qrP8vbMWoopjKkN01d5qyog0MBkUqUrVL4+gDJXlcRBQ4wTQthn8vaePzT7D3ztL/4W6sORN3zpba593Yqf/MPfi1sa+r6n7R2u9+C0MJdzDNOgHOKJmAozOWECeS4tGySW+jwf5y9OqcoXL8kM5uVDZOQYBK+g1yLjGMUTIslAOB/kDrhL1J9Gffhx+Kzi5cNHUH1EvRTgyAEnCciKiBHZhMghvC3wDQf/ia//2C/Cb8Kb3/pJzDs8/+Ed3wqf3ieqGumn6UcxI5ZzquKWP1hkONdq40EeeGy8/Ox7zxRDLpSJjF57GWu+4Eelz2Gyl7VVmk9G830urpciRkzPpyLpkIOky1nFZH8Smd0MNL+sOP7kI/zzt17jK976e3zFwae5bk/Y1WthaekVR37OOgordM+umc5fZm46nqkOuL2aid+i06WJiOsN2gTa3uKDxhrPvO5xQdN5y07VMrPdhoyt9WKJkjvjuqjpkrLj0C54pLrDgVpuePyOh0mxZqLXaAI9pnhjrWOVrFA0yyBWK+tQceRnHLkpp17M43eUFDxy53U0nPhpkhiKVUpufFVrR2Md+zsrFmvZpvOaX3/rE7zz1z4raQnwgadeV8zhfdB0zoqJfpAijnci6Vc64jqDroLM1UlpApIbBKOIOmI6Cig0FMIVNKbICcs1MpYgFjuVdCmluJPzhtKF0A/HVetIn/Zdq8hk2nFS1YRKp/gB1WmkWoaidtEuiAl9Bma2PYGzzK7kCKp0KBTJnknfOQe/jAyOXjOW6KXHgxVLlWg1bmbodsUjuNuH9logpPwmxy5fg5ohYJaWPDBUCtMpAeacRrvIn/rkf+LPfEZ8gr/s6JMQIj91+NXQ63sCcoWRlZqxMAbJzr54+G4F+AvlHG74iY28g1XMXRtjQhmG4yNWSCP/4ARsyvvS+hM1KtinXdnGECPnglTjHGj8fBxtR/49eNC6BLNe5Rgzs7L0DBgurAxqjdhZMRvYkYWGA6AlfhQJ4aaobhkWNLFUNevTyP4nFM2tCZ986XGefcM+b3voeZ6av8y16gSP4sTNAJiZlolyPFIfca06ZRlqlr7mrpux8hUnfcPS1dQJpamSN0k2Xm97uyH3yy1y22C528+w2vNwc8y+WTHTMqmfhAmnfuhcWCkvQSBMS+eQZfIxab3l5fUOK1eVBEMpSUBmtpO27USp+Acjfl7BYLVn3nQs64bQiWRQ95LwaAdqpVmvq0TRzadGFU8XyZqkgqG8TqblKcHoE5DlYwK50rG3Gm9SgmFGi+wsqdCJKZWALJO25xsBiuSNAvwoFZlNWq7PF7x+9xYHdsnbPvzsUJ2J8OUfe5ofVV/DybKhPW1QC4Nph6pAsIghoYV+L0gSmoJS8RmxknAoR0kMlKd4rJRJRMcSmNwsyD6mJ83UybFaG+ypxp4ObDanpJrua9GW61Zh1+JjUo6TZgAOx5WXPEI4M/m+1b2w6bPlXhhef97E/t9onDfJPpD0g/MSkc/PPl2OL+wx9kPZ9BuIKTSMAJPyHFvhYwRopQ1lVtYQjzL4kBazSlhak9vglpbbV/Y5PpxwY37CYb1gpjthWuV5XotZ+65ZMzMdvRUgax0qXNQF3MqM3twx16Po02tyR6lxB0Sf5B5aBXZMS6NdqYB3URhWWZKoVZDPGgFj2UPLR52KJiaREnIUFnaYR6EV9MHgosZr8V7RKlJZT286oo9Ah0pSaxUC9B7Xgx7PDaNcsoDXMaKiHpjAkVT1Ts+NJYbZL4vRnJOBHDX8y+c7S/iyUfrwellLVNYzm7RcOVjxpukz7N5Z8/xC8eidyOP1S+ztttyxU9pljW8t9DGBUykx0iQ/DvBNZpuTYkhMMYdklSDfDzvsM2yCKxnAClVMrIC04rHpIDiD7sSfREUpuAUVpWhiRMIv+5c8vrgtl/+th+CogXlae6kKOADWQJ/WZ1pAs2vwGp6F34LnfxcecfCadzwrksMacFm7ud2+6r/VuIw1l+PBhgt6mHC22Fkbj42e2yiuJ7m6rB/V4MmqUtSIABHVy7rTtLDznGdyy/DRT7+ZD7/zUb7uyd/jqdlL7JslB2bJo9Wd0sF8pi0nfsIT09scVEuen+xzaz3nuGs4XTd0nRWALUIImqAjbV+VggOIj+HMdhglBZCp6Vj5WubqmIzfg6H1Fqs8a13xfH+FZWiY6RajIrt6RUiywwO1LN0Iu2gwSrEONWvAqMBdP2eZOuCCxLcjP+Nmv0OIwhxutGOmOynUJ/nhSZgWACyz0yrlqaznxW6veD6GoDFGCsV/93/+RpZtzTt/7Vk++NrX8cPf9vWYzuGS5NLqQJ+61LdtJbLCCKHXKBPxrYGimhgApjjy/5VGIynXdCQ2EkjSlAzifRypPJICpdJJ0g4hF1DS2/L0qJwinlrC3NF1hpWtmFixBVCAmnhQtsQ47SJ2nXKsJDEsLKKRJyNKEa1JMkA9sJm0Fm/gHBttkrtX0sRqo6lBZMjL1Tn+wMnIvZ9r2n2F24FuPxImkagjei3nMFaRMI2y/Sgej6YFvwS71th1xKwjKgbefvfpjfzlbaefgatfw4YFijHD9xznQMEPnd7PGxuFzGRdpOIAko0Bv/HP9Ny4i2FhaZ1n3B5HBdLMCt9mdcFmbElzTpGpjov/FwBeBUjPi9M4AK8PMi7BrFc5znQzHF9AIZJb4mZJxIB8jtqnn8fQyol/CUyUBW6mfaJVai3qaY4V68/u83+9bpfffcN13vPI0zza3AXg5W4XreZcqZY0yjEzLU3ywKq1o9aO/WqFC4Y+ala+YuFrVr4S2YYXCUb+XavIxPTMbE+jHXcS3fdWO+ex2V1uVCfsJynK0jel+r4KVWmlG2JiWUWRhhx1E1auYtHV+CCmjUYHWmfLpG9VSNWZhi5Yau2YGMe12YJlV3HSGVjrIuFQXqE7hW8NrpGAAZmVhdxBfnyDA0FAMNNGTBfFlHALNJF8KS/uh8V8qAYaq3Zg1qAyWJSSi9BEQh1Aw+z6gscOjrg6WTC3XUngPvXOa7zhV18uGOgvvukpPvPCIfGoxqwFxFJeSVAqVXBFtxcJzRDESI8XSYaXpCR3D1ERKTar4XtFK3pxpdI1aCN2t+Vwf8HBZMXd9ZQXF3VpTauCVOhR4JQmNIF+LpX0aikdDSGmSnwGbOX+UNueVlmWm+8fDR+tHuGd7rnBZ8s+vHkDhrDRAeRBxkXyv2GbafGXFm5jhleekDe8rzY2fo8Pjlu/x9Fj//1wucvxP8oo11ZGD+JwDadrNcvWx4uPM11pRoAWyAJoMIMfRaW0sChyEw/VUlie7qjhuSs1tw5nPLZzxK5dA7DwNSpUTHVf5O5WefHqUCKBz4WQgKIPhi6ID4qwtVSRDPoo+2i1p9IBqwIrL/5WS1dLw5Lkj1IpT59kfQAuGLKTVgbCYvp77aUQ0iW5hkrSRx9i8ljRRESC7xMAZpR4R86qjr7RdKnzLTFrxlNxwIsvmErFk5KERTYYwMNijVI8Uf7sJBBHzIgxKJm7/8GwDZKsL8cbkX0L0FnNe/YmjqlV1HqJegZ++3VfxBd/8yfYf8Ry880V/2H1Vbz48R1Wt2viWg0gVt7PDE6h8A0D6zjFTcVIPpLfk9fio4Vqxl0zIFe+mwbdOKaTjontWbuKRW8onu3pGOteSYg3MRnGy2Pah7wjRDVBPRnhKYhTUM8r+M2r8FwNPA3xlIiXGHkLnn3da3jq7Z/mESJ8MTzLa+AO0JEQwrB5vz3wyDfdBe8b5SPbBKy49SK18djFm7zvrlyOL9iRmUqbsUESeBUj0bORi4wbU0Es81Tx8lUZyGK4X7Ps10NepNo2sv+pwPrODv/xTW/nY295iS+5+hyPNXe4Vp0wUR27RmJEthjZNWtu1Cd8thZf36NqytpZOmcLWyvPz703HK0mnLY1p3XDtemCq80iFTQ8lV2VYzA1wr7qo5GCiu448lOJG7oRaxQbUuMPWTuG5AdsVKDGS+EDAcWO/LQoRYwK9MGWruo+ZfN9NNxxM3ZMy6kX0/fbbk6f/LVAvLNyB0aQpiYxKiZ1j4+KVVvTLmr+zh/5g/CeBrtUhD6Ai/ggvlNKRTpncM4QvEbbQHAaek10SD4TQHcpR+hEZZKZscHK39qTvBkFONFe1rlBMahNYgQljaWCUqXLbKhIgNYI40/XiVlKV0BnDaGWfM0H8dBarSts4+j3LMobXK9wS0W9QJpT2UjwEWor628fUVYTQ1VAlugT6yiNaDWxqYRZZcUbWBi/mtCI59XgbSwx1q4TQGsSK02ln4gli5uAmyvaw4g7dKiJRwVF9AqvITYBNXW4mSZYm45R2n7JPQWU+62DJ/myO58o+ctHdp7YvGHPk4bHewNZMTOvjNlUnKR7fMM3ePw5W+SB0hjCbAFWW/v1IOyojW6Gik0W1/bbt/Oe7WXP+LFXEK8uwaxXOfTYMyujo2O5YQ4iMSEThpIAnwtowRBMMgDgY1kglhbdMb0vPaZWkZ3nPNWJZvnSVX7mzTt82Rue5g07N7Ha89xqn2eWBzw6PeZ6fTJMqMGw8DU+Kpau5rSXrlR90BgV8YneCqQFv6a2jnrqcVGz7ics+qZU3AEQMlgyYQyFheWVTGwuStVk4RNl11UsXM3aWfrUYtYozaQepIxWhQKKhahZOoO2kcY4ZrZjb9Kyntb0riZWET8Bu5TFuG9l8rcmSMtYE+h7C72i+JRpiFXy2koUWJ2TiyxVHt2AYna7+fe47a3p5IksAfENuGkkXumYzHp2pi1P7t/mxuRUqkjpXx81/+rPfil3+hlP/tpNPvCm1/HDf+jrCZ2mOtFUx2rouOhTYErdFs1aPms8gSif5YKJceVikX3kDidRJ8+TFKDcVCUtvMZPFOxFrs0WHDYLrA7c2Znh7whKVzxSgsgY3TwS6iidPxpGCZhotEUt6/me53+Btx0/zcfqR3j/zrsICdhK0Fc57++fvROAt/bPi2fW5EvO3oRxdJ7OqSycefm5Joajxd+ZSf+cCblsK79Inf+acxKVMwDYZZJxOR5gbHaqyoyqURFlfA1tSJy4ANCCIUkeJcwxfVa6povsMN3HOibpYafoFzM+cbXhkcO7HNZLtIqcuIbjOGHXtsxNV0x4s5w8xBGI5c2Gp1bxR1IS14wWACxExTpYei9dBz2aYybl+1kllW0VKYB5Lpr4qEt3RJfMg10QqYaPCo1CmWHhKB1y02GMij5qlI7SbVd7GutxVSAEIwviGNG9+C5GJ3IDq+UYKh2J2ZR3tECLRs7fIMMbFa3YjDVnQB81mKqryEjyzlA0sRCnHlsF6sqx36yYWwERXbyF+/BD/OpD72Hx1JTd99zht9vX8psffyf+Q5oYbqK7DtMmlthIDlkW6y4lMoy+Vhw9nwGuEfOsFH0yg9lQ/L5A2AM0kVnVMTU9WkXWdUUwCaUbzaXKKWIlYF2o03vbpJPnAJ4C3uv5ip1f4oZ/maefeoKPPfRW+D924OZVUC0qOiJ34bf3+cW3fy32rT2PveVZXuAh/nP3dfAxENf4zQ7NpSL+QHP2fV70Cub94e6/INact/nt2HVZNPmCHmMFwnZhncSOGfv4AoP1CYPccFwUyYk6Psr6KlluqBCLny8I62dyB+pfg6OXHubfvnWfNz/2Iq/fucWjzV0eqo6olBNvXS0dZAH250sebnZ5od3nhfUuXbCcdA1HK5nfrQklFuTCw9pbjvoJAVGHVNozNy0zI82vKiXNqzLYdC37LibFSRcNk2QAD+CJI5avxAofFYvQcOonaCL7dllArxDUhtcviN9vn6Tup77hpJ9gtQemxa6lUtKMam5arjYLeV874ebpnPVxA50m1gpVR1ikYvzKsKrrciyayhGCJsaQJM7CHCqMrGQAP5Zuq8KwS2t2LYV67WPKHyIkoCv6WBhaGRjKXf1co3BzRT+jyBG1Bye9n7ArRXcgH2wrh/eaPgFaIWh252u6hzzLyRTfWFTQRGvQHdi1oT4NKFcPbJ3kURytdF6PZmRcj+xTP5NmW1myHipFqIfYsl10yflTNCSAK1mv9EPzET+L9HuealfmftcbopU4HwNEr1EmEBtZa+jUH2Swk5Hj9xNPfAMqwNvufpqP7jzBT934WuhHMsnxfVrymHgxIyuBj+KL5Qs7rIBVMRbpZLHGGb93Ww1zvzFmdW6Pczy2yle66GPK+my8OGUzLo1zplcQry7BrFc5NsAsGBY6Y4pvNmQbLZILjXf0vlwrHweTQrvLlN8QwYq5ltzwyQA3rfea44BdK5o7Nb/54ht59h37fPm1Z7hSL/nE8XU+ffeQ/ema1+7e5jWTu1yvT9gLlmWomZqeiXEsbcdRN6X3SbJhvGi1U0DxQXNzNZfugkHTpfbjRkdWfaLizj1XrJgyZjmhC0YkjU405ktX03nDadew6i1Gi3+U9xqbaLUrV5UkLoySkhAVa2Wx2rNfrQlzSUzumBndqhK6rRoYO97rkjSFmECsmBIQFUtlYWAwicxQJqg0oSaUPzOwisFt+j2kyTFYNfiBZIlfBe7A89pHb/HI7BgXNRPToxMDoAuW077BBc3EOH70T30Nt79zxsmqwfeihfeTiF0oVDewx3QvhvC6l3NfuoskAEt7kYmIHl2un5JcIIlFrmLkCSZUin6qaA8U6+vSGjkksNOqwBPX7vAZoFUz6UwSk4F8D7qVCoufRNoDTXWa2tbmZFspvue5/8yffv7nxAdr8SmIkZ/cffdAH9YKgkTKoAw/ufMuef+9KLcb958MMdw/O6tueNidR8Eda8XPe+8FdNnhRVs/8+coCqB37usvx+W4xzh7OW6hoKPYcm5nw/PelllaisKKVAn0ijFKe2qGRVpCi4AE+DswK8OLi6ucPNTwyOyYqem53c65u54ysY6DZsWula5TLlentccGS689rZdutyjxt5JmGAIG5XlHZaZUEP5qlgECyeukRyXuVQbMXJQCgUJe66Oi8xbn9WAaHzQkIKsPusSMzObKh8tF6bY70Y5YtcQIK13jey1NP8bTR0g+XPnwl39p1ZZem4sAhfEUZX4pEvHsxTieM0ZxCigMrZiUcAUkmgQOdpfsVC0BJWAfnkiHj7font1j8c/3+ZU3vRu/r1m9XMHvArfuoPSLRNsSO18AquyfmIsitmWodud9D3Hj9/JV4ziRUmgzzLu5kOIn4GaqWAz0wUgCOVtxBDhVobt84NK+aEnIg424iSR+NsyI4QD1pZFv2v9Zvu5jv4z63cC7vvTXqd+w5Dfe+i74+eugXkYkhzfhU3Pc/zblZ9/2h0RaeBv4aIBPBNDPAy2lorV9/yVE8Yxv1pmb7YKKxXmL++FUD2v5+8Wac5+7jDW/34ZV51yn26qRXGDfKq6X53JhfauoDgkUMTmvUQNaamKJESrA3qcCze0JH3/Dkzz91BXefP0l3rBzk0fqI3bMGiJpntZoFbhRHXPFLnhyWnOnn/Nyt8udyZS1F8/FnFc0CZAHSlGkx9BoV1haAAYpohNDAZDy0ASMisx1Kz7Aic25jA2LJCfsoymeWG2wXK0EeMoWKjnGWB1og2XhGhau5qgXR/TOGybGga+wSaYOEBKoNTU9N5oTfFTcWs1YrytwqYv9eshbdKtESggYLQytGJXEmBQLYy7IRzWoUkZAgAoCtphWJIZj648Yt9bICTDKXWlVSOzXKje0EiZs8Y4dFfqVU/jkLaUmXuJrVHTOMq17quD5f/+rf8dbPvw8H33HI/yvf/JrufnGHRatyEi9M/ilRa3FC1h3KhXrR9sNUJ1IQT9qsTRxs/yFpaBu1hSQyiRmGgzFn5jipZ+KVD7YlO91CczVUQosTUCbIMfaaVSr0z4ZOQ9G/g6VHKMCHKacPVQaHyz/9MlvQj0mnQRVH9L1lmPZCMjKEsMobQO+e/XrQ+OryZcQlEY6JXiJJibnS+lkZExhm1113t+lcM99x7Y/8BjwvshDeNMGI11XxWbgnCC0tb65F5n5onEJZr3KsSEzlAc2zeDTcxI8giyWcwDJSXPh3stFtlEdISb/ibR9rYoOuASTtKCMWqETO0y7iOk0d1cP8Z/ePuE9r3maJ3bucNI2PHtrn1uLGS/u7/L4/A5GxZIYZIouwNpX+KA57hoBrIDcu9MlACsfA+81VeVxXnNT71Brz107ow2Gu92MlasK5bTIDnuLS7LFDJyFIBN1p+TS9Eaq8oX1BdTas1uvqbXHqMhBtSyVD6Mit/WMvrI4hVQr6kBVeUmOctah0wQYlNwFaoTUh5RU5AV6rlTkzh7pxszt0HPFwDdsdDb0jYBY/W6kP/DMbyyklbDtOHGNfF9EbrN0NXfXU+n8mBK6tpfvrHUUyvUk0F2RSdK08pm6k30xOk/cAlbpLmLWfvguDABWXpf7RhMaTT8T88OcqBAjJhnga6foWsOzR/sYLVWhK5MVb3zoZT5dHbL87C7VcWr/buXzY1TCztqNtPsa00WqpbS6VV7x1pPPbPpgrZ+FvWESVol5QBjNtHliy4uoe1UMxuNe0pCLHt+m5443d9F7xpT98f7e6+/PYcK+HL9/R7m6YnlgiBXluhy9ZszO2k6azwO0yrtH92GKUeRkPX+UIgHjiipEtFes+x0+/VDFY3t32a9XtG6H4+WEZV+x1zTs16v0NvngSvm0GOpxSuJJ69NCMX9BIIQRNUlJEiaNMxRLVWNUYK3Fa2vtq2KYG5JcEcRcNyTZYgGpooBWuXe5TvIbP5p3jIrUxhWG8cT0xetLKVipiqAjQZnEDI0YM4ot+Wsk4Kr4RmWyUcw7MgKyVJpnRqDXRmdApUpX2gJuJVaWrwXIqnZ6GuOotadNXT4E6nP0YcnaPU+8s6b7wD4hzvB+CeEUpe8Q3Aqsw08jsZPEAQdKyzWhPan9eFp++Ihy2Sw45tO0cTkGI/KTUA3s5fyc9pHokpm815y0DUoJE25iHYc7C+6aGf1RLZ6WnkHeElViZ4FvDNpX6AriY/AG/Un4ZXj+0+KD9dpve5rfeOgrZA0WNeBQHBHVp+BTN+BTB+KR1QHxFNSLiGl8O2Qo9xr3lCFeFIMufknkonEv8Owe4zLWfMEPo0PJQYAhDxmB5MXHdwxwjdhZsAVojYrqkUEpUszhs9edSgJtLXFjejtSLRTLF/f59TfNeebxA9505WXeMH+ZK3aBIbIMdfGZyh3Yd5s116pTjiZT1qFi4RpeandY+4qVk5xk7ZKRuvHY1OU8A1k3+105FmmenumOJiEahshtN2cZah6qjtk3SwDpXBhrjvy0dGBsg3Ra37NrAoqb/W6RIq6TZUrrKo5dw3E3Ze3l9StX0RjJR2a2pwuWKhnTL7oZU9PTxxYXDUtXpwYlw7mJNoqRei6srzXtoqaqHI31cipUROmA7yzRJ4qsiWU+zEqNXJg3uejtck4z5AVjMCvYlNOlPCAzjEpxPsUdKZDLv2Chn8u2i1pFUXLCxjreePAyf+JHP8gf/tHfQkV4+wef5TWTu/zM972dU9/w0nqXZ04PuLuasFxM8GtDaHXy+0poq42oTio2kuMIkCQNr9J+R9mHIm9PEvxoSbJCBpDPAbW8x09EUQKyXSIoG3DO4NcWdWKp76bmWinH87NImAZ0a3BTOd52ne6NyCD7h424EJXanIpHQFYuKH73+jfONr6avvPsDf9KRpkDRp8eSLnw/z3BIQNTpSnWhmULG+vTjYA3WuM+6LgEsz4f4xzKYDFjgyF4BCUeWglJFcPFEaAlGxiey7+nTtIxBamMe0UVC6glC8RBNqYCNEcB8wnF6WqfX/iSp3j7Y8/ypisv83yzx+3FjJcXOyz7WnTYXiQftfG0zopO3RnWXUXf2fLVQmuGGzUHx/Td/MTTWwGNTtrrnC4ndMtKKg35PToO9GYFuvHY1IJb62z8qEqikQEdkKpMpT179ZprzYJaO7q0UJ/bliu1Ze0rFm2Ncxo777GVZ9p0PLZ/hFWem6sdbsY5qzZ3vUhgoM+ovJyC3CpcpHiRkEEsm9hMVv4OdmiN7mait84Tvm8ibj+gr7TsTDuuzFYEFHe6KVaLb8BxP2HpKha9SD07Z3De0PWWdlURvULbIJdVHQhTT7urUWuNWcrkatZKgtVKJlPdSxXG1tJZI19WhZ6bqgeiD9f08xzAJPhlWWS3r/CTCCvD8XoXdEQ1gdP9hkf3jnnq6k2erXtuPrfP5LlqYBi4lC9Y6PegW4um3K4D0Sk+sv8kX3b0ycEHa/KaVFFQUmXwfgC0NoZhoIi8grGVZMQRyLw9iWeQme1JtzwvCe92i9/t1+XJOSefmx8y/HzFCcnl+H0+4uavY1ZIZmGloWIgFndfdT6gRXpLjlvp/RnUUkBpRpKSl4xZCKglC03TQnMbOtfwtDvkxv4xV6dLTm3DqquSdF06Hvqo8UGlSrNMuNLR1eC9LvsWvRpAt/wz/R5snpMjnZvT9Rbfm+RjNXptjsEKlIloMwBREVInRyAKs1d0CDp1NgwiY08AVgbBau2Zml5YZs7QKYWuPNpErHXsNS1aBZZ9zZIal79TKXylKq7afLgkECPWUmEAM/y+ISVMC+ZgIEwiauKoK8+k6okoVr4qIF0bLH1Q9CEQ4xE+tIR4G+8trlcQHUqv5QCbQEwdq7xTSUYpCYBORr9ZrqE96FJwU2cWpSomyUcCs8aPZx+XXAiiV7SuKclZN+nZbVoOpwtOjGd5PMGe6AKG5e5buXikXQSv0Mfw8v4NHn3qZR5xCl4Pt9RVOB1d70o66Sp6oloBz0OfM6EWYW4l/cjnjASN13XnbOOCh/M75Za+T6zZ/qgLNnYxOHY5zhu/8Au/wN/+23+bD33oQzz//PP8y3/5L/mjf/SPluf/3J/7c/zjf/yPN97zlV/5lfzKr/xK+bttW37wB3+Qn/qpn2K1WvHN3/zN/L2/9/d47LHHymvu3LnD93//9/Ov//W/BuA7vuM7eN/73sfBwcEr3mezrRQZj1y02PbOYrTuGQEb25LD8bwaCzuL1PU2DuBWTOsnBXYd2Xk20Nw1nD5znf/yhj0+8+gVvvTqM7x2cks6moeKl7o9KuXZGXkgPlLdZRkafKU4qJZFpn67m5cu8pA9uFSR+a2SfUn2ZfRRFcsUgNNeCsmfVNd4dHrE1WrBoV2gVWCiHL1ynAZ5TVaVZH9frSKV8iy8PN96UVQcdRPWzpb9WjubuvdqauPo0ySelSV91FQqMLMd16anLHYr7oY5IUh3XN3KDau9Qq/BrQ19bxkb4Xsn21I6sewSQKlSYaDYkSSViTSz4kxRO1gFqUNsBrZUTKCkoTS2Apl3Ssd3J3/HCYQ6EibyWX4WaCY9e/M1T+zd4cbklLlp+eLffH6IdRGuffCUT3zvdT57csCLt/bxJxV6rTFLhUm+X5lVFWogSoOr/iDvuCpdc0Mt158PwmbLfmFFoj8CmJSXWFOk833KeXY9VAFqqdJc2V3Se8NJawQYjInl1VM27Oee0EQcUvg3nfhYRyPbLdLIQkZhMKTPJ3IMZKXX3bPx1YOOeI/g8gDjInuVe+YsWx+5YXmTcJAN4PFexZwRCPYg4xLMepVDKrtxALS2DK3LTz0EkQLqsAVobXmanCs7jOeDWkqPugwkUEsWmpG9p+G0n/Hrqyd58+ue56HpCZX2HLVSNY9R4bzIBe/2hhh08kyJuM6WVVDsNfrUopxMHtFGsCmIeYVfWuIEjv0Uf1xjjwzNOtE+SQtvjdAya0lI/EzTTQx24oAgss1ETw1RTA9Napm+W7XsVC0H1Yqp6UqFvw2WRju0EpPgpnKs2gptHTf2Tnnbled5/fRlAD7THvLB+ATPLephcZeouQSSaa5sV6d2sSpEdB8IlSkV82weGExejAuQ5SeC8vtpIO545gcrDmYrrJbEyAVN6yYYLYbCnZdOX703tClY+aDpWkvstByv1kh7dBPBBiZX1ninca3FrQy61ZIcOAGjTKuoThXVaRQDSD9cC0CpjIfEJvP1oEOPOks+oN+JuN3EnoigWo1aGU5bzdO94fGrd3ly/zYAN90B1V0jczwSUFCiP28PFCrooln/idd+AypE3nb0NB+dvIZ/tvOudN2rUeIdBdAaywaNHu43EBkUbFJtP9/l58y42sajtmi1F1U2LgoI4+fDK9WxX47flyOWjHSMQuUn4+j+SY9l+YEaP78FaOXNqdF2ENkh8QJQq2xz8NTKErPmLnS+4gV3wNUrJ8ytADtt8kMEYd76oFn7hOYq+Yzcmh2Q9uKdTnMy5A56+YaKvRQR2lARW5kDTWmGMSxgM/gTdRQvRSvFAXRMh0O+U4QSZxRRGqMYL4mVFsmEh9L+XSnpwGhMQHmDUoF503FjcsKVSqr9R9WU59iXxiSFZjX8iyMwW6U1hMyRsZjw5tNcgKwkZxdwiNSlKgr7eOqYVOI1lT0mfbCDwX1MHmIh4oKHuCbEDu8qyCa+zif5gAIdsFPxhwlOiwzGyXEonixeWLm6QwpCY48shlgTtRo6/OahKIWgUCONUfJ16BSqV3S+5m7Q7E9X7Dcr2INlmEhnqXx4nFxDoQq4SQurDvW7Nb/w2q+j+YY1j3zdc/yeez0faN8jUkrugmoFCUs0L8VJusoNkIopypNbogtuPFrj3ZOF9TmOC3OP7Scu+Nz75C45n7scDzYWiwVf8iVfwp//83+e7/zO7zz3Nd/2bd/GP/pH/6j8XSdvozz+0l/6S/z0T/8073//+7l69So/8AM/wLd/+7fzoQ99CJMa13zP93wPzzzzDD/zMz8DwPd93/fxp//0n+anf/qnX/E+W+XxYzlhKm4D3NM7S41AjNGVMmZoQQJO2PLRygyUqHJCQgo0oMRLqzmO1KeK2fM1Lz35MD/zxj3e9cRneOPOSzTK0UfD8+t95rblen1KpTy7Zo1HsfQNL3c7RZLYGCcdcaMWj19Xp+KwSgCSgE7SLMowsY551bFXrTmoVnTBcrcVQ3itYpEAHtoFVdoXj94weXfZczEqbrtZ6aS+9hVLJ02rst2KNaHEkizxLjLDqDh1TWq85ZPnb8/hbMWqrVmtDWqV/aGAjlREUPS9oa5csUuRc4wAWZ0mI0XRxOEUhNTMKnWlzYqTcWEonegCXilPkbhnY3TZDjRHIumOqQARlRTtYxOJVubv3RunPHV4k6vNUgo+wXA3zPjEO2/w+l+9WUjL/+VNb+CTd66x6ir8wmJOpXGU7geQKucu9YnECbQmrCOmVQPxIOUVmWxAIMkT2WhUJcdG3lNijk1AWATVi2xzerDiDYe3mNmO5xb7LJdSXNGJoBtSd15Rx2jCJKB7Q78rTERfKUwxmVfg5XworSgWa9njysfBXmWU63y0eph39s9e3PjqolGaV8l2z/hmnZdrXBDHorrg9fcaOd6Pt3Fmw1u/XwCAjdcRDzIuwaxXObxPF2Jmc2x79mSab2qXqdKFG9EliIwlh1BSB8YBRZBb2ZYinmVqJRluNPlz5WpQASoX2PtMpDqt+PjJ41x/002uzRbMqp5FV7PqLW1ncb0l+KQPNgFThWReCyTNsNBLVaFx5gkFgABhbdGnhuktjVmnCSUMSYVMBDKLRpRMIBq8NsRKoWuHNgFrPbMmtd/Vgcp4auPYq9Y0ui9BRhM5cRMWRKz2WC36+KZ21NZzOFnw+OQ2h/YUHzVPNNBdtSy7ijsv7IkJbz8kDbnaXZIKpcqxFpZToNsxyWtKvpNvksSwAT+NuD1PfWXN4d6CvVo8SzLw1gdTQC2Q7jNrV7F2Fhc0CkZSSDmmdFoAKyA0is5EZvOWZtLTNhXei0FxVXm0DvSd5fSkxhxLRcG0Q2eqnBDlcxeaWBIRkGDgppHQRPyOR88dMV0TUWn0QqGcpj1ueM7s4fY0r9k9gifg5mQPtTLotUJ1SQ5iIt1+1t9r6gi9r/knr/tmdOfRyx61blEuUeGU2pjc1RZgpJQiEgqgdWbk7h73SDTGfhHb435MqcKauF8ikxcLo+1etO3tbpmX43KcN2KugJfgP0T9DTP4cg8g7Cz0xrWYAS0YXcdjfKx8htqKRCkyJQBKuiDK63LF1oRIcwSmM9xu95ldWzKrOiojwL3zCuelE1MMlGRK6/R7TD9TVyaVEqRSeR6BQtFpVKewS1XiTD42ZZ4rt5YYt+IhKI3KQJSO6BRfZH0Zpeuilq6LVon5cEhpXRcsPcIG0AkkssZjdGRqO/btiqnuBHS34Kea3mvWJxNgiJt5H0tCEdM+pu+YDXtDrTaBrARgFS/GJmCmjmnT0RifNiXHKETxuMyxRxI8VWSYisT4xRWgE6+GWGA0XkFVO7BerAXSeTAmFnuBrjWoVqFTMUUYuqoAivmSyYlSIaiV7xOJdURVaX9y/EsNWvzacKImhKZlt17DASxPG1mTuAFEQ3n8ZAXqWdSHX89L/lF+6ov/DHreo55VqP8rwKc7FC8hjKsMGKZYQzoWDNd+8VY8bwjCxebNdc6LxnLfVzBGK8lX9sZ7xJrL8eDjve99L+9973vv+ZqmaXj44fMTzaOjI37kR36Ef/pP/yl/8A/+QQB+/Md/nMcff5yf/dmf5Vu/9Vv5r//1v/IzP/Mz/Mqv/Apf+ZVfCcA//If/kK/6qq/id37nd3jzm9/8ivbZ6iD58kYeMhRoxXs0iC9F9vFN13i2SRi6PY82XOSIW16+4wJKZmblSVhJnMkqBhUik6OI+bime2nOr77wZp55ywFfevUZdkzL1PR84vg6z9oDXjO7y8ONolHig7Vn1xy7CStfFX/Z43aSFCWa2no6l61KNHrkkRWiYrdqWXvLp7tDVkmi6IPmhcVeWYtLp92+dB8EkcKHqDiNTWJniZQdRLr+8mqHGFXx9Oq9SU2yfNluFwwz27FOLFmL57Rv2KvX6CD+kXPbsTNtcU7TUxONlnVwTHNmp/Bri5/0pYmVrTxdV6d5Mp0LI0hRbgQlnrqDT1bpDJtPa5qjxTIlWeTYUfEhgRk5J1JO5IrRyZu7A3DzyOShBdNGGnFdn55ypV4yNT032x36tND+ie99N12wPPlrt/jgW57k7/6Rb2J1WhG8AQV2qYofVmlwlZpbaSeXVXWaYsZILkgCPbKJu05NrvLj4vmYVjBaTOyzUXzuXGhWCj+N9FckTtbGMTU9h5MFy/2Kl49r7DIplKzkNQB2oegrsVWJBvq5oj7anHxjyluGGzLlOCrHn/zwYK/y/tmXAfDW/gU+Wj3M++t3sD3OLeZvEQLO5EEPYgI/ft85r79fgb48d5G3cI5P29YssAl0vcJxCWa9yhEjKJ9Q1W09Kgz+WVCqIttBBCgMrczggvNBrdLpzadrZUxIGcWRsaeQCqBWkfmLEbsy3D26wa03Lrl6cErnDKtVTb+qZFLMn9gb3NKiV3ojMY+pOh5NFDAggvJaul15hTnVNHcVdpER8WESyRRPSToSoFUJ+h9bTdBRzA5VxDlDq8UU3tbCwsqdqKAuFZiVrzh1DSEq9qo1Vgf2mzW18figmdkePdK4exSH1YKnDm/yG4sJ/Z2G3FVq47zlyc8qOdYhSjIUh8nd1xSvLDcFNxEmU3O44tregr1mjQ8CuOUqTfZesTpQayct5NM2jYrld7lekAV7r5OJunxwmBj62jBteuysxUfFpHLsNmJOqVXk7u6UuzszeqfplhbVS5IS6gBNQFVBfGecInZGWBD5MyPEOmJ2pBVwSBdRDGoIIlHRd5aXjnc4bRp2mpZw/ZjlumF91BCPLabNAFnEeUW7LwaX2mnASsLgA6o3iR+spFJRTsI5iYTW6TK/j9zwfpXzMxLG9JH3majPMLLYfH3ckkeeC2JdYleX43MYEQqDh+1kAzb9s0YFhk0zeIbn1QCk3kt6KJscxYYcjzKoFYdCDFEq8dUiop1m3c5ZHdZMpx0+KPreEnrNpmO6ktDYb32nBJjFcZzLoFYQg1izprQeH+/7RgElP2zS9/Sy3ZDoZ0FpnIpCRjKemIoPISp6TPHe6oOmC5YIiQks3XRzt8UqddtyUSfGF0xNx+F0yQtdRVglhtbW/T8GdzJ4JxNHOaTi9TEqCIUMZM0cs6ajMY4YVQrVqTdlYmBkgE7M1WWbGzhfPu+5C5YfAYZWWHSV8cIKQBLmxvp0qCPrpmK9Tib+6dyqxHIW5nZingfA69J1q4B4JqLqkFNp6cyVAaAE8nmvWbQNnbXUxhHn0DuLWxvpYkgCpWgJ1V3c5Gn4yKOoj1qCrdB3A6xOUfqzEE8Ah9Dk80k4J9YUQOs+43541ucILJ372Vvb2pZpXIJY/23Hz/3cz3Hjxg0ODg74+q//ev7G3/gb3LhxA4APfehD9H3Pt3zLt5TXP/roo7ztbW/jl37pl/jWb/1WfvmXf5n9/f0CZAG85z3vYX9/n1/6pV+6EMxq25a2bcvfx8fHgBjAl0fPsT/ZSJ7HPr6ZnTVOXqP8VxQiOSZsyQ6LZMqPJuAMcKkRWzYV2qtlwHQKu1DcvPkI//5Ld/gDT36Cw3rBYlrz8TvX+eTL17i6u+C1e7d57ewWV+ySK3bJkZ/Kur+uWU0qFr7mbjulD4ZKSxFi7WzxPayMgFEvr+aJSCZWHr03VKWD7Y7M5brnSrUUACvFgLtuRkBxmjq9T4xj7Ss6b1i7ipO2Jje/yjL5uumwKf9Y9DXBClPstG8KawzEk9gqyQOuT06lyYiK3NWRvrOiyECUMLGS9/jUvASSzDCBiirF0zRdSo6mVLH+yE2gMttF+QQy6tSFPXcvzObvaoifMXk0ohKjqRKgy08gmEjYc3zRjRe52iw56idYJQb7Lhi0Cpz2DWtf0QfD3/+er4fvgdOugaV4mIEnNIp+x4gJe4rluosjE3dpYKWPKUUgFeTx7AFGjKXpVZ44Sx6XrGFCrcuxErmhwteKbk/RXpFOjTnXvNNNcdHw6M4x8THFTbtH/UJVijWZFabXWnwbrfgjd3uKaqkwxWpRul5+7zM/x9uPPs1HZ4/z/ivvSUQJtcHOUsj3C1h+cv6uzfv3QRpgfT7GvfKmV8JIzmDVKD5uNMBSo4LXBZs8z87lonEJZn0+RoxDsMg+UnmokSF8el0OIgpduhWVEz8yet8AtUZI5vhzN2JUXtNtv19FQKNDZHrLUS80py/PuPm6CX4eZOJIHSRAwAeVqua5hWmYhPS4SphKRPVKGEMK7FpTHSuqU0HuISUUKfeIQSbPnHhoBy6DIwh4FqKir4Sx5kykayu0DpzahmnTpa8VqbUEG4Clq4u5fIiKme3EU2tyytLVzK2E9pMwKcaOISp2bcv1Kyc8t6iITu6mUjVPk5R01UudDLVMPFGrgvyrqPBW6KpuHnH7HrPXsTtrR4CVZ+1FP++DBgMhDtVyFzUmMQOc15JkRCV+MYmdkI9ZPpZ0mr6zVJVnUjkqFTmcSucqrSJz23F9csrL0x1Ou5rTdUPXidxkNumY1j0T66SKFKXbiPNiON93FreykmA6hTKSDOfrSGSYkqCEKEnGneMZt/2cZtqzM22pK8eJnRFuVQKgWXl/3ws7KypFc5K+T6hQLohPVgjDRJnZWeOJM1cztJZ76EEZTRdNvq9UJpKlwJ9rwrABen2O27gcv89HRjgKGjWaGEbNQ2C43mIGd9Tw3jHgdRGoNb5GM1uLAdiKxAHoyjT+nPxEsMvAvFN0i4rlFVsW5MqpAczKSVEqKEQt8YcMaIB8N586PSlQThpgZAn1+LDk37NvJBFMJnJqRUCSsRg1IXm+BB/x2oh3pLZY68rhMyqUolIfTIkzMSqqIhMJ9MFQ69za3dIFS0Be12jPfNpy0k2JIUXwsLm/BbkoXcIoC3ZhXytCBrNqAbJU42kqh0rFEqUCLmpCinGGIHN2AoYCkkgZHQkxJuAsLTYTeJa9Isu59yIxDDpgTUATmVY9tRZvzFp7ZrZjaaUzceessNUVVNZRGZHTi6m/Si3aEzvMyT9iYs0puY5z0S6mboWo5KUZFat1xSrWmErANWM8ra5hJUlK1L3kd+FlfDyGdopZAKpH2xUqnBDDGhX9AGCdV8UuVeVkD8GrHa9wws+35+chTlzGmv97xnvf+17+xJ/4Ezz55JN86lOf4q/9tb/GN33TN/GhD32Ipml44YUXqOuaK1eubLzvoYce4oUXxAPnhRdeKODXeNy4caO85rzxN//m3+Sv//W/fuZxqz0btifpuj7DztpiWmXG5bm2J+Ni+gjQAgZQK8rrVe64vuXlK58r81BuUqVCRH9Ws1rt8u9O3so7n3qaJ+Z3cAea/9o/xAu39rmzmPLiwS5PzO8k43RdJH46Sb13qpYuWDotANO6q/Bpjm6VeF31/dBgqqqEZdopWUOHoFNzD8epb3DBcNRPOOoEOGudTVOzeG+1zrLurcxr6XPWnVhgeK9YqwrnjbDFkhfxoq9LUUCryJVmiU2F7akRj7BKe2ottip3Tmc46+l1hFbARl17qsoRRt1+MdsAfDoXWXWQYn9paOWSbHrUsTC/TwCf4VQFq3BTNpte1anJSCX+Vf6K4/DGcfGYzEV7HxUekbWf9BNOO5HfniLHwQcBjYwJeG+InSZOU4OrWqSGwQogFCqRFepeYfoozDASwNUFTDcKpnEAsFIyRaw0bmIIySM4qsT4yn1AlEgGVWKEdZ3lE7evUSXG9bXZgsf37rA3WfMpex37Qo1ZydojVMIIw4u/sJ+IEqVaaJoQUFHypO/55M/zZz77H8XQ/fj3wAd+avfd6dhvFlLu2wDrv+c4U8jfzIdyrDkv5oyL/GWdmpl/I2BrzOh+JeMSzHqVQzyeEmpqzObJ3q6GZIM3FZMuORawaAxoAQOoBecaxJfnGLY9Xo8VBolK23ah+GqZVWD3sxG7MKyvW/qdWKibKgiYUlpsJwsN5cUvw7QKlVi4KqjSnt0uRJudZQRRU/xUcs61od+NCaRxyeRPEDKh11oBTLzkMPQm4ndFi25VKBWVWjtmtqPWjtO+ofWWnarFqsCeFW+tEDWnfkKlPOtQFeovwNXpkpd3dnDtVJQW4+5TyP7LWjqOjj/kalSoVNFdRw1q5tjbWVFbJ+BUECNhCXoanapELkjgcyk5yh9ZJ/P8TJfOlOF8LAfvg7T4AKZVz9T27FQtM9vRaM/UdEyNBPzbZg6Aqxx7kzWNcSz7mtbZEohr67BGyWe3VTJRVsReMkJjgsyreQETBGzLSQ5AWFrWxzXtXsXO7prdgyXHYY4+tsLMMuKj5VI1BAxNRPxmfIXpvbCzwqj6kAGt/Hum5Sp1RmNOCHL/nTe2EpV7SQTPTMKjpEYO/VkKfmFznbPZsX5cIjCFsl1yyM+398rl+IIcKl80Gx5xaUQKoLXxWP5FXQBobbwOzjWIH54c3ptBrPTCgSyWkqF8PziojyO617iZEjarZqAGjRhIxYclF1JcZszKa7KUUOIGxQR8g5GyHSbT4l552e4G6yh1jELFQhIKacGeE6UsVzEqUGnpZNUFg4+aWiV2lnZMTE+Mii51wHJxYNwCTG3Poq7FF8whQNLWsVVxwLQ2nxuM33MsoAo0TY/RIYFTSphlSkB+k5jfIQFWmWmWh9ERRRg6N6pNEG18PPM0a7WnSgWaSg9dxKySZHClRWoTjKaxwljLAKBcMip5ycjntc4M0lKkC5coa+IQ7KISFWDIEkyIvca1Bt946qanmXa0sYZWJzlmnzrzdkS9SF8kQHCo4EoXxI34sc1c3P4ZI0WXXu61BxmvYG4vx1yVP+PWLX1PFth5z6khPv13ToO+4Maf/JN/svz+tre9jXe96108+eST/Jt/82/443/8j1/4vrhln6DOif/br9kef+Wv/BX+8l/+y+Xv4+NjHn/88Yt3dgxgZdsTo4fCutbD0+cBWuli3PDxTUWMHDNUYWMlAFhtgVrZXyuqIv9SISSfpJpf71/L8o01r5kd8SUPPcenjg85Wk65eTqn96ZIpk/bRtbYOnDS1kwqR+csi7U0fnKdxdhA8IqQQCxlkmUK4Gwg9hplI2HS421gYeryee26wi3t4FOrIyp1qI2JvWomHlsJG1YOqRQN8ikzOtA5g01TxkRFKiPz5sx2pYGV5ACGnaqlj4Yr9YpF33BqG1xvMI3HzHqUisynLU/u3+Gkn3B3NRXwrDNJMRHLva+c/F2kgSHF6yDspmiU+LRGSkOrDHRGJR66Gbhy0yHHAXm9m0XcYU+10zGf9OxNWjpvudXOmRjxLl4lKeZxP6FNxvirrkpAlhYmb2tQOgogagR1CzNHu2NQa4NphXkteafEfbsUT+AMJJlKEarBG0r5zVlO+yjewFNNP1W4meSbvhZSgjQlkeZd/a5c593tCR0TYQxPPKuu4sbuKdenp9jHA5+eHtI9N2PyskbpgaWoKnl/qKHdT01TokIDbz96etPQffkM7H1lmuvTvbTtnXWGlSR+jq9ojH3zXkmekck154FSGVPgbD40rq2e997SQGu0jTPbJudFD767cAlmveqRJ7MY48AsyeysEYhVLqicnCfNOobiGzKuEBbpgULYQXltt71aiXHjQhphWyPJaiwdKZRKIFpQzF+MTO8ouh3N6qqmu4JQ/DuFXSETSfHGko1lTbIkGXEAvcYJSQbBsrY5De1k+0ENsr5cCc6VdN2p8v1j2l6opIHEqpGqxazqCz1XI9Xha5MFS1cJaysqMYrUjmM34bn1AY9O7gKwCtI1cOUrNJGD3RUvL2tUZwv4FlIXJ6tVkrlI8PVWb3iYZHPiUEtb18lMWE9GSZeuoBUakZ1oRFrae2nbO7GdsMxQtKmlb228dF4xAV0lWUhqgxxtArYUZWWa6cbzquVqs6BSgTZYVr5O399zUK9YuYpgHbuVsNS8cbgg9OhVJ0FnWve0vcV3SQKUPkfp1AHMa3xkkAJFkZa6aNGpCYBeKVhOOO4Ne4cLdg8XnNqpgGIR+miJRszpu12F6fWgi+8rtEvsLD+6F8Za8EzLBTBmVPEbvT5d8eXxc34/A06eMwoQHEcAbLmvt197wfsvGPeTMl6Oy3HuyLEGRj5ragvEGvlnlZFjUl5p5Jdugrx542f8tEbPnftABrZi2cAAjCDShuo0YFcyr7qZNJnI90A2ax0AqHSfFkl6ejhufuzG33rznirA2AgdUmm+zo/pMpeNAC4jFH9nA62KVKjB6B0BiYSJpQUwSvOwVYE2Wk7chF0rHU/6KCKKPohH16TpWfYGvC6xZizlGI5nJGqdTODJivziOxVNxNaeKu1TBrLkd5Fhk2KQUWCNE5YZMfm8D2CX0jmRHZLWOGbFlXMiv1RGCkga6Urpginff2IkOYsxFJZaVIouGiKqdHW0uRqfu1XKB4gsUqc4l86/gGlysAJa1kEKKaj1ltYrmllHPevodFXAsRDFB0x3wiRQqZlLCKCDEUlH9JvX9DYzS4F00LXp61fDQgUPMRu15QN1zuLrnugT5RideWn+/Rxw6sy4pyzkvh9/OT5P45FHHuHJJ5/k4x//OAAPP/wwXddx586dDXbWSy+9xFd/9VeX17z44otntvXyyy/z0EMPXfhZTdPQNM2Zx0PUZ4GrMTtLb7yYcWF9fP3eC9CCBHjlm8eDKjjvxaCWivleHkAtE0QWpl8E01k+vnick7c0vOngZV63d5sXq11O2qY0qgoROmdZthUuFX1P0u3WrwdaUfCKsLLohZFcpI5QxWE+TT6wfVCEiYBS6+M99InFLhSTVkCPHC/E308aV0UTCRNDOzXoxlM3icmrIq43RBOKjLEq3XMj+/WKiXEc1ksa3WN1wAVDP2LSBhSVEb/g1Vo8tubTltdfucWbdl7iRn3MqZ/w9Ooqv3nzUVbHk9H5HE6h7lQpfgCDJC9GVCfSfT83ZW3rKwYGVmrU4ScjL+BJxNdi9M5uz+GVU+Z1X6T2XTAsXM1S+/S9NK0f4IVlWw8Nu9YVfmnL3B57BDisxcNyem1Ju67xvZYGV6tkswLJF0uhW6gWCruKJSctLLTiKzD87hNg5SsJptm7OVQKN4d+HvHTiJ8FKdabiF4YWBpOl5aus4RDxfXpKW9++CV+OzxE182SCbx8lm4VaPB1pN9V6F6aXlXLwIcPX8eX3f7EYOi+87iAb1GdtVeBgf0+ZmwZLefoIrnheE04uv/lvN4rRpyzDrzfeJD3KHVmTRYNmwV+SPjBVmdVRuu7BxyXYNarHFItHyXV58kN8+J8W24YAjFLprYBrfS+kvgmppYaB6M0NhPz9Lq8MM2Alkvd4UzygQgQfUQ7hW4jdhVxt1SqsISNDhDji2ojCU/bz1TNDPqU3yOlS5J8/zy5xqFFd9pOqOJGN6RcNSifbcWPxAfNSdswrztIWuyDeiUUXaNZJnPH3OEwRM1RP2Fup1RKwKIsAbHasz9Zc3syJ5waobZWMWnCZWJi1BodJd4l42MQLLidSJx5dGo1r1I130ctEsio6b1haqWSPjE9E+NYupqJ6emM4dQ3+KiK7l4BygaRQKbjURYB6UR4r1k7S22kVXyjHdPYsfJ1qYyEKN1U2vSd96o1tRHm2N31lNp6QhQzTK2jgGhZ4pguJ61FwtInuaBKXa1CgFhrfJVOmpLKiX6h4rjfYefGgvnumnYtbpS9Au/FSytacI20svWNRncW1Vaort8CpkYjT8p5EvUXvK5cV+r831/JOI9Wu71P5ckEMiTKftDqzP0i2zgLZl2ysy7HfUehIjEsJs7IDYeXbsgNIxBzCjK87jwvleFtZ++tM10Qz/ljHHsEz5L9Kx12HYQlZf/HX+vCWMOwzbK/Gwlb/qzR7uT7bYuwWcCaOIo1ejhMWdomEhNDbTx4i1ORienFf0qJhxbIPJ+N4tfBUoUqyRPluYjI+ybWsbKB2GnBFXUuMqkyf44qUAMLq+z3kJQpRQGxhJWthA2Gwgfx7xKpuzCo+iTN8VHhvU0kcS2di9N3Lp6QY1AynYgYNC4BeDZ1DY54YV6lAxwjUryR0o34iemADtLkxOiYlj4DcBX15omWxyHodAKz/1bUSJfHdMISUBW9oQ0N9byjblxKcKPYf0WN8ollYKWaH60ieo0yyadxlKBvX2sCZFWgJxCnEOfIkrkDlsBJ+t0NF1u+Hj+XsR0T7vM8DPd5TkTP2+aFbOPL8Xkdt27d4rOf/SyPPPIIAF/+5V9OVVX8+3//7/mu7/ouAJ5//nk+8pGP8Lf+1t8C4Ku+6qs4OjriAx/4AO9+t8iOfvVXf5Wjo6MCeL2S4WJidxS7EjbOd8lB8vyZr/3s42uG16vIOZJDyj0ztjyJqQCZm1NtgLEF1EqXqKLEBJTEBOUCU6/Qnebm4iFO3t7w+iu32a9XhKg4aRtCBOcNzhn63hCcliYgNjCZdXgrHrCoSFha9MoUr1kVgE4RJ17WjTlY9IqgDeu7NfUtMzQTGTe4UrLOV16+h6+loVJsNSEmq2EVqWuHSubsk8oxTZ1lXdBJjt1zWC9oEtDvgqbRPb1vuNXPabRjbjpupTynrqWh096k5dHpEU80t5jpll29plIed1XzgXXN8tZsUJX08v2jkZwqFt/kmL6PKssF00ZhJU1UIQ3E9F2jHYAsNxGgJ+46Zvsrru8uaIwrvmI+SqG7Spr/zhtRpKjAohf5uY8Kq0Pp2F6ujdy12Ck5F7WiNZHdnZV0o5xa+lUlfCQVCYklF3rN6rTCHmnsQmF6sYnRyew+d2YseGsj38+uRcYIiXk2k+8X6kjY9Zh5j882K03ALKXJTHfc8Gw4wB9qHt054k2PvMTvxIfobjWlc7vOqqUUp9srCtCoAP/ki74JYuQdtz7FR+dP8P7rXwOrvjSGK/foFjvr/AZYnJUbjtn6W6btGxLjBx3nxYi0LriIsQXnxJqyPTaA9MIWLuuc0VySro1LZtZ/42GtLye+sLPOkzttyDoiueOU8lK5ULAJaI3es8HkOEczuwFwxfGFEYfkYBy8NoIV6BCpT6St6IZcSjF0s0jV4wG42pwYQ+l6MXh1qVGlIH9ejnUhUTIzkBUmAbPKGx8FkUr8QbSVm77tLdZ45pV4aGXdOchCemJc+bx+S+pRKQF9QtTMTUcwitO+QeuAtzElF2mi7xM8nIJ/tBpfC7WtUG6NdC8MO45mr6W2ThhZUVEnSWFA0XvZjy6Y4qW19pYumGL6OK16Trs6mSQbXJskGL0syHUvE6QEflWSAqPFx8QQSheWPhp0sMVssrESeDSRg2pFnz6z9yIBcb0lIGaZxnqi1UXGGJzGOS3dq0w+ecLs073CVxG1NBLofdK1r0A7y8LtovY6jA1YG/C1J0wMvo74JnWD1BAqTWgMujHEdWJc+bg5oY4lhpmSawy44XyfGWc0Gunh+y3mdfZJuSBBuGhsvfa8CsTmfryCbV+O3/dDJG9pniUBp+dey1vXfQbCx7jX9rV9AUtre2wDXOdKEnOyEwFV4DOytMuEiOkG2Vh5T95UBnXGcShjPHGISWNAa/teGzN7CxtLIfILG1Eub3hz8RRNLB6BPnmpZFm7sGuH42BH+59Bqzw0sQBctZYug11iMUU9MrWPsgbIBQt5fCT7yIxsDbGCWAfMxGO0F5lJFJJ3Npwv3lRJ8iKSRy1ekcmvq9Li5RIieK9E+hhJrKahC215LMGiWsmnyHeTY+KVLnOlUiKviUl6NNF96gIJQWvWURW/F60DSgeiNkU5EYMwKnRmv5brSBUWNL0uBrzZe1MFTRcaVOMTkzgSc1eu1PUx9sOaJQNaOFUkOBtDpQ9XFbAD6mForsLDASbAsYIXFPAs8BKw4gygNWzsnMc2nz6vqcgrHZds38//OD095ROf+ET5+1Of+hS/8Ru/weHhIYeHh/zQD/0Q3/md38kjjzzCpz/9af7qX/2rXLt2jT/2x/4YAPv7+/yFv/AX+IEf+AGuXr3K4eEhP/iDP8jb3/720t3wi7/4i/m2b/s2/uJf/Iv8g3/wDwD4vu/7Pr7927/9FXcyBAFISkOqcR6RxzZDK+chQeaqohLJrx0BWnmcZ3mywdTa8tMqMkTF0HX9HAmiXQtLq/qI5nhxhY+8fcJj1+8khpOn7RsWiwmh18PX8zIjrY4nqKURoEpHdPKbjfm7ZNw6yddjkg0SFZxYJi8b6hN5nXjUxsKMFRbPYLeie2lcJdsRb1lbhyIzzGB95w0TK6BWNqIPUdOmBCJ3RzxxTbEkmZqeWg/NnAAmtmfHtOlraDyaSnmemN7mzrUZH1k/ij+qxLIlMbJUpBRxRGI/KhxE+X66FzJFsBpfC3gFEI14ZQUjLCM/C3DQc/3qCYfTJSDxDiT/kkslFn/g2oj3V+vFyqR3JhnC6yF+JqmngFkqgY5yLH0VWVcVVeXYma1ZpDhb1469SVuuutuzOetpI168vRYGV+qCKEDecJ2HSSjHR7ejPNYIoBd3HHbqZM1gIphIjHpYO3iF6w03T+Z03nBttuCJG7d5vt6jXVWEE0lmdD94f7p5ypO8gljxY1/8P2HWAd0FdOtkX13CDzT3b34Fg1/wRs5/0Tpw6zV5uw8yxoqUBx355bmIn/drvBRN9/u4oD9mYhW4InCmCHm/cQlmvYphSF19tN5EQx/04kqdDgbqbgoU24FoW3a4PbYArqGz1DjZiCWRGS669E/KmLLQVqRJPAWZHIg8KcilizNXX1J+pYlbyKt8v7F6grxQj0J5lS4a4Hc9qvF4bYlKF4ZUrtpEK0lEk1hElQm0fgBrAJE+qEgXDGsvbCjTLOijlmo6AY/GIEaFlY749P6q8vRJ7qF7qbqUfU4m8Hn/xwaJMQW5erdjb77GeanUTFKnp7WzpUNhBo9KYqRiqdr4xNyqdCBajzGRPgNZyYQ/V7O0Swv0tGAwiebbaJeYZ5qZFqBu4ZpkCDxKslRgRwugd6wnUi1BpB+dM7hOqvala1hU0jo3BqIXJlWRho4kO2apMGvRuJsW7FphVgY/meJmkfbhNbb2dI0nNLq0ZQ9WTDWDUYTaoo0B589c/xgBEqMZaHIq6NJJ/cz9MK5EbN2LDyIzPHecw4rcHhcmJec8fAbsuhyX44KhiNgtKfoDjw25LaBiurW3wasHiVtn96s8dR6wxbgbVjYdhxLQ9PZW8pQy9mcp04w8H+JmHBwBWpv7lhMlBt/BOoINxBwkQ+qepxikmIBNLKLMts225ABVkuv7KL6HAcXM9FJ9VlJciKl4oFRMf8tHGCOsISlkbXXRHX9JBiBrHG9N42mqvnQmtMkA2CUpY95PHyXW5berFIdiqqRnI/i8dBGWqxoVoHLc2WwyY3UoHXljVFTKEzX0qXiycR5VpCYQtRgxayUyRzmmCUTLJzj9LMb8UY5PBvkYrWlUL3FQO1Dpp3aKYC2hAr/j0CbgjSIaM1R/RwzyaDQqf/mSm+fFtwJliGoCPAQHN1BfE9h7x232OOKF9hHcf5nBf3kNxOQcXBZD22O84HoF4xKY+u8+PvjBD/KN3/iN5e/sUfVn/+yf5e///b/Pb/3Wb/FP/sk/4e7duzzyyCN84zd+I//sn/0zdnd3y3v+zt/5O1hr+a7v+i5WqxXf/M3fzI/92I9hRgXvn/iJn+D7v//7S9fD7/iO7+CHf/iHP6d9DqlgsKkQ2QK1xoBWSBISowZ21hjQIs2j2x6+92BqjeWHpXCu09x0jgRRQcoLZM6se8/hfw0sb0145s0Pox9bYm2gayv8wqI6KfCiYpE3qj4BOWldHpP1RYzyXWKdO5Socqsqp7ELTXMnF2BjWWsLG1TijE7r7uCkuO4bURYEC6rX4DRurvCJZSOHvxY5ddBY42m1ZWJ6sTdRkTYIwLPyFXfbqczJRJgsi0H8fr3ipJ8wMY5Ke/popStgNLShwkfN9ckpD1894tnlVUggXY6DGasrTVK0nEcdYpoDJZcTHy0IQWR4vpFOhVl6F3cdu3srJtaVvCVPdSI11zSJRCANupwYwiNy9sp62n6AGmKA2AnwmFUepdAWgE7jelPM7ufTFqMjN+angMS6narl+nTBzd05x+uGvre0qwrvBCAzM0fdSEOqyojv1aqtU/HGSPrd68Fcv/FS7BmrU/Qop9Vyb4SguHMy42gxZXe25srOkn5qOJ02rG5N4dgK8aARpVFooN9RSekkGwtWYWNE+UD0FWrdba69xs2vth9XaqOIfz9vvY1xYW5yXnKizhZILvqYEj+3ljBjttU59izludH77/k59xmXYNarHNb4TQRzLDm831nJckM/ovfm9dA2Qws2QC2gMHXz72WMgDXlQ/l9XFFRuXgD5C5tKsQhyJQu4ipVk1XpQDJIA2QDUQ+LzvGxUKRFaVogFyQ2yU3Q0O8G6v2WGBS904QJxEqROynqTkE0eBM5qZtyf2eTdKUifSO/T2xfwC1po9tTJZPau/2Mw3qB1YGresEy1LhgBOgymerJwExLoF6IwlwjRsxakqBuVxOteHG4eWR/2pZqzDTRi40KLHvRidtkXNmkYLB0NTPbSeJjIi4IqEV6rdZhmBXyOVKIKb6BUAeZaKNU4FtvWYa60HzlEtCl42MG/mx63urAQbUkTAVkW7TSaaTrLfHUCmU5QKxBWY82Ht8b1NJgT1PlvhIwS7UajNB0qxOFXYJdiSZd94qwhPpIsXYT+kfb8lo/VcSFwjUJQPRaKjUZrMogYv47A1njidcYtNF8991f5a3d83x08ijv33s3wdx7WrtvFXwLmL4XrXbYZjpXqcq+0YI2j61kVV7PcH9fjstxj6FVOAviPCi4lSf7CKUgcd7i4RVX8Ib3qxEwsAlspTigMxtLDclPYGAmj3ZEpX2RgooaAK2NxdXZuHsmHmYwRAmz1SR/lBCy5I0So8QcXgosrbXlEHmryj55myrSOpS97bwt7ci1CqxDxdRIcWWq+sIQttoXCXrZx7yRxK4SA1thjimlkkejVMlDHWkqYQAppABRpa6CvRdfKp3AJ9m/mFrWe+kglpYXIYH50gFxvKAYH9tY/LnygY9REoleG0wcfY8RiJWBv8KWTtLMvI7oU9z23kCXEUYGo2UViEFDr9EdBcgKWhGzh5aJqZovLIqoZT2he4hrcMHid50cUxOJVsk2bGJzpQYDY0lnop6Vv6MyoKbAw/AuePuX/zp/uP8/qJ/peeGx6/zv3/zHeOnoNfCx68ApIjc8b9zvvhy95AFxr/Nkg/dlZeWp4v6bvxxpfMM3fAP36pj8b//tv73vNiaTCe973/t43/ved+FrDg8P+fEf//HPaR+3h9WBLsahIRWMvD5H+cT23Bnjphn8tkpkPM5jaiVAC/LsNGw7KsRXiyigWb7O1bBPI8ccQNbcO885qoXh+PYO7WEgNDEVU5PMz6jEslUDEKKQrq+5c25u/NEm1kwvzCWzhvpYSeOqLpZ5Ju1MKYJgRvdMzKyt9NkK0NKtO9i0ftUigesXtcxnWvwNd3dW3FQ7dMEW4/fx0EROe1mH79Zrau3ZrdZcqVe0wYpHIYo7bk6f3qtTvLkxO+Gl3R1cN5VY2iX2WT4mUVhmKiZvKYbvGmwC6qr0FROTNVgIk0jc65ntrZnVfWkiUmuPNYGVq+i9sI1Fxj40FDntG1RiNbsE6BkdWba1sGJNLN3alR/F9oyDdobWVOzN19RWPB+tCtRGmq1MjXgTW+2ZVlPxVUuG/LV17DYdZhRrKx3Yn65ZO5E5rrqKtrN0qha56NKimkQ00HGYK20shI3gNCT2WruqWN+aoqaO/YMl+/MVSkWWcY46Sp0zm4iLksP2cyXXiFFUC/GMVF5yf3oniq6NC2IEaI1HAYc1cfs99xrj7WwX+88bF60px4+NQe17bE6wBTaZVme+FxtkmM+FZXwJZr3KYdTopOeLJEsNE4UXOHvy8sWSTBgzE0Zu6rMg1nljQ6WxFXPHlZTit0VK0vNkrVVqic0gDYyjrhAm719KmEZ+TSolA8GqodCeTOHHF3bZr5w0+DRRGEV7JWIeWbE3X3OybEQ3nRerSXccrUqAlsI7mTiVHlhOMYp3yUnfSIXcdkmjrVn7imm95KSf4KLmsF6wa0Rvrn3kOE5w0RQjyaij3BF5gQ1pQZx9TUD71Bo2eWmFaWBa9+zWLSed+F7lURkvJrw6ML6PM1NLJz7qzHb4oHGpel5bz7ryhE4XHbZ4mymplDhFbA2dtSyqmtvtjGvNaQlyyyCeWSd9w512xvN39wpgt1u1hcU1NakLYtNJMhQp1fBoIzQebYNU0qF0FTFdDvZSCfJzj59H1rrCtJr6OJ3vBO3rVcSuYNk3tNc8oQ50B6BbjVkn0LBSuInB1ha1ytdbTi7U5r98SRnNd59+kO89/QAa+NLus4DiJw+/6tz7pWzr8znSPS/yp62PimeThw22hUqXmhqkuZfjclw01GjxfwbIGt1vFy4stsCKc9lZn8uIZ38vFXjGyc72G2QVpMpvw/+j3SR7qxRfoILLbd4z2zFwLHV0E1C7jqZ2UiXOTS5Sll+6P2XWqU/JgIrSul2LB5bIxS0R6UyVO8K6qKl0L4UDFFMj3XYN0p47S0pCWuxL/I2bqzbFsEBUAg5qDyH9HW2kMrKg75LvVR7S0VASnM1cUhVmFlEW9YMcJmJ0wJlI9Ll6nJI1L+yDGAAHXms6Y1j5SuJVmsj6KJ5ZrRfWwMm6kX1ooDEOa7oiS6y1p7I+SSEZ4qyOYGPqbCWfWfzVskd7OlRxEog1OKVLPCrHDnm9dtB7i58FiU8TSgIs64sUg01i9m4sxtPErAwwgR3gTfAV6oPU/yLS/67i4a+7yTu+7sP87BOvgY/tIYsGzRlm1uc51BT2wtZlA+c/tl1VH26/y1jzhTr0eBLMPjrbwFWKG4OHLSUPKV3WYYOhteGPpRh5co0sGeSFZ66vsksqNTQi7ZKW2b4ku5qBEWZkcdTc9Rx8AtYHivZQSwF5JhJi5RgV2RnJBxWxM2gHZpXUDOl7mk4AKbOO6D6WxiMqSsEAZH5Q5buQyAZDUyp5LL3GgXEKFQX1ChUC1KTXoaCfapY2UJnAxIq1h1YiQ5/Zjp2qpRuBW9kqpdGOg2rFiZtwx83kMeVYxpqVr5maji5YJsZJI6vTWqSWJbaRGKmbE4OK0oQrH7tgwVdin+Kb5JmlRaZXTXv2ZutihQIil7dausq7KKysDGzJ88LCbb0VZpoWv2OtUnfgcYwybDa3Qq6BGKT4UVvHrOrZr1fUWnIWq33xHbvaLJjZDqN2CVHM869Ol9L1spPGAbXxLDvxbDRpX4wO9K0lrjO1LxKdxlTie6YQxl0phgXA6ZKDahPFCeVOzd3WMD1csTdbw3VYmhlqaYhNEMlmL7Sk7NWlgmxXYlzANPVmJ/eSb28nFOkA5QL/Nph1v47ueRvj6+EeapOLAKU4Xodu7N/wnhKPLlyLDvndSJyWnjs/d7rfuASzXuWojAedYe28UBwlGQ8yclUkLbyBoZqSnr9vBX7rufHiZqNaPTKSjyGv57eQ2sAGoBWJg88KFKZWAajisB6MpMXw6EIuOVYCznylWF1XuDeseOjKCb039J0ttFOiIlZBdMuK0vWoGKs7g9NC+aytRyGSPumoJF1CdIx03nLSTzh1DVb51EZdJuQQxTC3TUh9rkKp0XHPfhrFN0wPASBqCap6p2dW9Rw0K2rjOW4n4pGlh1boY3ZW742Y5AadfLVC0pJv3r1Z209I0r7UOMmsFVEb/CzSVYG+t/iRDh/ARemQ0gXLsq9YL2ui01TGc6VZsmvXVMazZ1fMbcvDk2O6YHlpvcPvVDc4fXEHvdIEEwkmoiqPsYG+kYNTnUji5ydK2A4zh9aB3iv6vZrm7iAhlHbAYJYx6ckt7XVPaAL9rmJyi4HFVWn6KxOqENDHy+GaVqOOHEYPCymleEv33GbL2/bZ8++PC+6TBxlF//2AVfZto0NGk/V4QTSm3l4awF+O+w2jAkPbqJQEvOKtjIKBGj20vaFXEGvu91GDa1YCl0aLqRg3Aa0Csm3v0PYi6aLEfvQ4QDCKfg7h0LEzbUXe5nSiKGUEJC2uzdmgGZLPR/YnBGEnaaQqrJVAaj5oWmxayIdUSZbFaQzileIT+7awEsaeknmeGLOFEkgZlSRJqhYm1sQ4TE4WkjxlTAiXfRI5oULijlEi6Qnx7EErsvI4ArQi4EArRawU3sQkz5DtZh5SiFpM5aOm9xrXSTKldWBihIVQK0k+au3ZsS0+ahau5pae053WkoRmdnjyLAtGTqRuSUBeSjIrSUZCA6ExxHVO2CgJgu5F4tFHjZuLR5qvhTUszQiTvcHUYmJEtf3owhkdmhJz0r0XNS5GKhfQhPvLzh8gZJz/vgePUdvXvoKzCUUe+ffLWPMFO0JU4o8U0wx6UYf18cgU1czQchCtPgNogbz/TD4x8sk9176hTO5DcYFRvpPLF7kR1uC7K1Yk9bGnOoXpLU27r1lfU3S7si3TJXZV6rxe9i3lJ6aP4oM1sqMohcccn4J8tgmpOD9KZlSaU2KM4v+XDpPK3kyWImHPFhy6V0PTUwWuV3S1p60tR2pCkzzArB3YnLX2PDQ7Fg9fFVj7SoofSXp5s93BBcOjzV1pTJIaOkkRwbI/WXO0O6XrJ9AKGKVIcsgKolWoVsAV1Yk3mWvE/D2fI+0SqFel91SRnVmbGMdCIMDAzK6FBBB08czKDa52qrbkPVOrOWon0owksbqsDpgqEJI/IgkACzYmqTjC2LJDk5MQhaRwUK0IUbPyFS75DwNUCRRcV5Uc14Q0zqpsUC/A2KqrqK2jqRzrriL0phTkCQpMTAb+qRkKiLl7UBIeq4jv5WSbWj5Dtwq9tqzcDP1wZNb0xCsrVka6TEYLfRA2oF0o9ETRzUn2MRrdW5QP8jMEuU/PKzbkQv4oD1L+HHZWee959+E5jwUoTAt9n9ee816lR36PcbjVN2p0MW6ua8q+shlvo+xD5va80th5CWa9ylFrD2bUInfMxrpXAjyusGfvrCArXKX1xcnt+ELPANf2c5mKeC9oM4yqoFqVbiRyMcUCaBEGTXtmacl6WPa3AFlbSOq5rBQDbqpYPqxYP7Xm2sEpJ+uG07szWFgxASz3piZqAbRUAHti8DuGZtrTJRZRTOBQlxbZCwRcnJgBsDruJxy3E27MTtg1a/bNiiM/5dhNeWm9w+3VTHTWJhkGJhq0Gs8J6Q6NuZNhFCCnP/BMJn35frV2hVYrh0w2lCf8LExxUXPaNxw0K6wKrKO0HQaKKXv0urASopHOTZnirIJMkF2n8U4qI5UKVMrThqp0Mqy1JD1KRVgZVq1IKxvteKQ+YtesqJSnj4YTP+V6vcNjs7v8vH6K5Ueu0DWqXFZKR5gEaU2c/MWCRTxokKSPmCnXcvx8A3ZFSVTsOtLcUvQ7Cr+T5IYTjevT4iBE+rlFhSn16XqoPCglVYosMzTDdf/R2eO8c/n00PK2ec3oIrzHbHiv585UsvLCDbig8LHx+nHFk63J+tw33H+bl+NybLCAYTPpfVCp4dZqQpLfVx9r7v25I0ArJ0CZfcQAaOXXyuPyywa2lO7D7ST9IlZKqBT9DrhDx2zS0jpDt6pTUw0GXC+jXynB0a0m1hpTebwbblsfNTpIhtIBOkjHpsx6aYOldZZ51VFrR6McbbS0wbJwNStXEXJyGIZjtxFrGL5n/k7RSmt0Ww0ycqPEuyonDiodM01OKmUEpPHIxEiPQVeCmHwfSX5HH5wAtJIYRgaj+DBuOCLG8i5NdEaFst6OTuOcLZ10d826sNQ8UmSZm5q9as3T6pD+pSneDPutVAQ7sjrIYFV+TaqUlccRVoV26fpQCu00ZqXwtSLWkrBIccVD9HIKKo2aVphudJIZJQ20sFDwqcivXfsy/vB3/Dumz2iOnprxsfgWeAZgwfnGjaNxr1vkouceEAjbvvbvK8+4jDVf0EOPaA4F0Mpz9bhAvsHOSnO7D6mbRBQz6sRQulendWBQgYxArY0R2fTYIpapZijmUaSIyqgSk3LXV6UV1anHtJH6RO7rDEIJiD18bimeRzZYSTHNJTmBlsJ0PMOq134U6lIuEJPFim/UcG8GiFYKu7odCgEgc7ZKEuhoI1XtCztp0dXM644FEhMa67jenNImltXdboZG2Lxz26JVoAuGu/2UPbvCo9EqcNxPpLGTiuxWa2aTjq6pYSFeu34ic554EysBKPtAqHQ6Bqocg3J8lBTp/TygptLQyiV1SUig0KJvSjOr1ll26rbkXXPbcaudY5Vnr16zcpUoZYIUOzJ7S2spMsReDr7Y18Qh3wK806y7iioZ42sV2bNL9qzi1DesfF3in05sq9ZZ5lazV6+SEb3nqJukZi596YhpTEBXnqCiyN1HrMCsYnHVcMOYlSa2ER8taJEP5pyxOtXYtWXV7uIfWzBtOtgD1xu0CXQ24kMlIOdKgEJfKXQtndyVs6i6QvVuYGeNR15jZSArP2bMxYqOB5ESfh5GZpEPP8fPsRnH0nriTBFIsXHeYwK0HoQ/MB6XYNarHJXxeK1RWpWuPBuL/ge9qDI7CzUkJsU/6xXu1AVMrjG4lasqG4AWkY1OJAHKolFBtAo30fQzXRaZPoEbOnlWDJ0zhsWmVEEV3S601wI8vGZ3vuZ4MaE9aVCntlROokYYScmAlwQM6BbCosJVXmKtMzIRqYhSgrhHBiS/CxYXNK233F1NaKxjpqUScqef89xqjxeXu+K9paRTX8weG2mfQzWYnZcbFgFyul2Ic5E5nnY1u9Wa2oj0RCNVnGyUmysIJgaClmpK6610OzGOWjuwEFwl97qKKJP4vzlZSZUg7QUkWj0caK6uuLK75JHpMdfrE/Hp8qK7z8ysedVx49oxzUOer7z+ab54+hwP2yMmqsej6KNlERo0AY+iUY6vefRT/J/P7EMdaKY9deXkuHpNe9WiO12M+TMl2JgA857uUNGeWpo7CeSyufujMLO0k58emdT7eTrf6UK364BvDLGyqLZjg5WltibzGKXFbYS3LT7DR+tHeP/eV9z73rhP8i0Ll3gG0HrQsd1iNo4n6lFiPl7XvWI+7eX4fTm0DmnRMEoqNgAtHjD4j9GTPLmNHnol40FYw+kjNwCt0UolS96KN11KNIIV+TGke8kMSUoGW2LKivK9JnFK5kg3i7Djaeqetq/wrfg0FdN1RSrIxMKWLElKpwk6SN7lc3IVUZjC0IpRPjeDQj5oYQhrKSwArHzNiWtY9A0+dQakfMfRPusBPCqsrLSPvgEqoTt33lAnZpbOciAEAFLpJ4CKCq2EMeajxmuFVVEYRhrxpcqv13lCSucjK0FSCOp3hH07rTt2bcvcdGSfFILIIH2U9vPz2RqzE3lsdpdr1Qm7eo0lCBEOTRetMK9SfHx89w4fP56AidgqYJJ1QLQKP9XFJL+wWKOY9FJ5/BRcp7HrdIkVcEujQo0KDSpMiSQwq16jQgu0gEc7aTyijU5dpfJ8rQAHag28DB+8zgd3v5pnv+g17H3xES+4hzj+xSvwEeR5Ws5IDIfDee/xChfsF25mFF8u3P5lnPmCHzPdsX6gAsPWfF0Ar1gK2gPJQ519z3nzfV43bXdc3wa5NsCtPF8NnxOTFUmuUI4TWxMDuh+9dlw0HHWAHSTpsdzXKsW5SNq2kvdESE1FVFGVjI3g8z4TgUbiEBFiJVYoALTyebnZiApJtqgp5vO9N/QrkejtNeuy37MRQyug2LFtYv0G2mBxyXMxAzeNTuvxJDdrg6FLygyVTctjAtoUmDYKaJdBK6uk4VIlgKSvh1gTKnC7HprAfLelttKF0TtbPH8Dqti7ABseXsKaEnCo6y1TK8X+hTeEoOk6K53aoypKHO2BKOAbAfE1S2InneSM+Xvnn8uwCWRZHYpnsFaBg2qFi4ZjNdnsKN9LrlhbR1dbYU3lg+8VrrOYVDRRJnv/xpKXKp+ul5MRdJKYf/WRpotzuuuWeuKo6pTUqkhogsS0RpQ2maQQGvFai40hrrWws7Y7ucNm/lPkhnGoo9xv/fWATKtXjDXAhTFso9CyHXsS+AUMSq7R6+5lGH/RuASzXuWYmJ7FGKzKUsOxfvWBZRkJTDJwpl3zedsYJzRwfnC64PmBbRIL02Wjva5HFC0RotZFYtfuaRaPKtprgTCVSKOcRnXSzc4uFKYdQB/RYUt7V7/j0XOHtZ7TkwnxuMasUkeLZBAXm9xSNS+yB1BMLzXsC3ASgkbpQPCGtk0Vqcqx7GtOu4ZVLxWPxoop4M3TOb+3d52p6fidk4d4+s4VutxhI8sMKzGfzccmKnl83HEqauhnin43omzAO82yrbljZ+zX6zLB9t6gTcQaX6rl+eZsvXxu1pbX2mO1yA2NstTWS0cNRolbln4A7WFk/7V3eWTvmMfnd3hycptds8YjlOQcVELUPHl4m3fMPst1c8yBWVKPFt0dmj4advUKQ6TjJn00/LZ9lP/08FMA7M3WpYvJemo5nnaczufoE0NsInrqmM46aiuz6qryLMMMPzHJwD8Bg7V0jAk1hCqgd3pCo3HHtTDytFCidQa2rIFuNHknQCsm9iAg9F/gJ699DWrv3dIF8V6jVDgufsmZyvYYPLvfRK/kvg3b4PUIsDq3cp7edzkux72GVYF+fA1mqeFGbHjA7HgEXp2JNee+/h7X54MytvKulZ+xPF6KKfkeV9I1qtsFP5MqbgGfPKnNNtL2mgEQEllFJNYRVUkzja6tiK1B9UhziwjZHBwVByAgzftKyYKaiQAn4jsViFHjHKhkLtwHYQW7IACX1bIqW3Y1d/oZlfbcbHc4Wk/E8Hw44MLwjAI6ZfDqTI9VJRVcXwNJVt97w9pVNMZtMMYs8l3L0jwFC58mLZEcUgosIUq0N8mkOOa3jP8BfgqTgzU7Tct+vWLfrmiUE8lN6vDkgiFqxcF0xUPVEXPdMlE9ZrSC9Sg8mloJQ8wbkSfe1Lt8eucqAE3VJ38VcJWmrTxdXaNaiQ2qCtjaY1LS4kykj5UYvMecgBqiqdH9AdHcIOoD1I4jrhWhXaD8S6DuEvVKWFqkBFzlc5PPRwA6UC/BrQr+zRWe//CTPF8H1K0AvxeA50HfBXrOgllq48cDjfza0UL/fuNeTKyLYs2lZ9YX7titWm5nK4ZckLuXl8722FaK5FwkpPXPNqBFAony27eBLNgEuYpPTgJWxhd6Ap5Urq/kj9pmkwExgQEqDPN/YfWOvXu1GuP0ZUiROo4kjRRFykYoTe8tBurdsL7zTSTuOAhKChWtEq/fKpZO59FGqCLWeuZNJ564Sb0RYi4COI7ChKvNglvtHK0ip11DZzsa7YoP724lAJhBiiUrBLjZsZ7bncWagK49obIC/K01ZhCODIc5HVNfS6FZ+5EtSPrOu1cXzOqeVVdhdKCpJN4s+4rG+AKuWR1YO8vESgdDOd2qEAl8slCZVT1rFVl3leyAG8CsPHUqJ/5mHohOle67UyuNvCa6Tx0SAzvJLPGkn9AGiYlWB+mumJzG56alD4bjXrq2r3pbuvj2zuA6U0gMRXURhVFlrJcivUMKKqlhGQGoI3qhMa0afIRbYCnH3J1M8U1kfeCpr6xpZj3rXuMnGrNUBCPH3q7kYAer0ZVJXQr9mfur2KukXCiWe0jLe/wFec+D4A6fC3h10bjX0lONnovIvqW1z7lS+PK6B//4SzDrVY5ae061Fm+J/GA2db9onLfgH1dEUhcNmbTVgJjeC9Da/n37hsi/nwNoDQaOZzu3RT387Wtp26oimFZau8ZakoI4EbN2t8MgWzBRvK+sMJ8UEDpDt7ToU0OVgawomul8IYdZSJ2l0k5a8ciIJqKNLHb9WoMBbTxKSRXgrtfFrFfrKGCWcexN1/Te8ImTa7TecmsxY7lsICq08Wgjmm7vhy8ujCLRcUvCowpbq99VuB2PTgi+D5rTtim+WLmjR5YV2lIxiKWykYOZ1dJtMQNbVQoUxgSCjUQn7DezVpCMLP0kcmPnlLcfPMcTzS3mWnxIPJqZ7uijYRlqrlYL3jx5njfWL2GSBKVHF8CtRjpwARgiMyWR76S6w7tf8xmOOmkLbLUvLei7mSEc3uLZk32c1zSVoza+JHP9pOUlFWkPKpmIXm7EvL4XM07lwV9xPHR4QttbjhYW7QyhAj1VmLXGLgOhtpjViJWV74UN34cRmJv/KXV/NmSACzv1nDNiTgDS4u7er91eNW0mJudJos593+W4HFvDqECndFbDybif3O/cmMEQn5KERK5L9cBY2IWfda/PZQScnfc5o3slGFn0qUjpzEQGn2wGyNlIOHJHvEJX9wrfWym0pEVzlpQUHK0a3ZBpkZ8r2lpLzPK5W1aS0nlvWAdN7mCbDWWNkkW/D4rb3QwfNMu+Ej9IlLw/H+vRJCCFoiheTmOmmUa8oeqhe1iIms7b4ouV5/J8GDILODO1Ir4kBVpL/MkeIkZHIqnBhyaZ/qqyaI9K4vK87nhocsK+XVIpYU4FFJXyUvXWhmnUXLOnXDULVFpNSAVf9kt6LUrWoolUSZ7ZxTWv2bvLOnWEzEBbRNrEx+mSk24iEkcTNtjO3noWKuInaVJeGFSoUGGfYJ9CvcEQv94zf0OHf0mz/uVd1Af3iebjqBDQrpWquzGo3m/NywpBP0+Az8LyCH57BkGDXwFHwAnEFYXGcd4YX58POjIN5VWGhItizYPGvcvxP96otRvO73gdtD0fP0Dx4gw76wGYG2fUH3lkkGsMdp0jS9zouJ7makLy1dIpQ8mAVhyBWqiy7exrC5RO8TEq8X8NyZ8xs5fO/e7D9yhF7fR7kRIqiIcdVe0JQeMBb42ANMhr7FLWr05BO6kwJpRmU10qbvTO0E5Eqgdi/p7n9LvttFiF7Fby/LGbMrctHs2eXdEn4MaqwLTqubORvA3nJCoBroyX46r6SLUI9Duafppzm8QCnnr2Ji2Ltqa2nnktnQEr7TntmsLIMkq8f6sEYi1dzY5t5Tug0KrntGtKPmR0oKoca5qSI0YTUVHhpqlglS+wSsziVfqMPmrWoWLXrMt3rpQXBluUZloudW2f2a7EwYNqCQj7rQ8HnHgpPC1cTTit0jUi13acBgG3gL61sNZUJ6JCCVWU9YYGvCJUAmBJ9/bUwd1IMd60cryrI8uSCdXBGlUHaWhWJZzYSrFOBYWqNMFqTAafR5Y+5e8xkFUUQhql9cVg1qscF8aPPO4zF5x5b17z5cfV8E/mGlVAafUAc832uASzXuVojJOAoRRFs5fZWQ/qabL1ujxRn1/dPgewut827/H+LKtSOgeSWCot44k+GiXyQqA+Roy7jaG9At1hGGiYqUIatVTUiQq11lIFB0kslhrTsVFViRaZ0CbC3tJZv2zlRu3WFbETM15rPWYeio67gERREXRMNFLPtOoLhbcPhturGaerBue0mMqDgIaCGqJtICDSCV/nqn9E+8wOU/RTRT+D2EhmpE3Ae81iXWN0oDa+6LfH1YvMznLJsDePzhvp0KHEf6Uxjt4brPV0VYDUbWNDYuOka8cTzS1eW7/MgV6yjhWL0DBRPT2GF/oDem04MAuqUdXYJEArV83NVjg3RCaq542zl3ip2sUgyUMOIF2w7No1b9x9ibv9jDvdFKAwy7pgcTsaP1fsN2s+rm7g79aETo6h7hQWx//rn/8sb/qNl/jPr3+KH/7KbyV0FqIsAOpTTaxGM9mYWjvu8KGjUHJjPLNIuufYAMTOe368IBgBvFuTa8mB7zfpw5BgK86VHn4+pCaX4wt7WJ06doylhpmdNX7hvQCpM7LEoUL2eR/nFlcgd7C6cD8VRV5o2uS7pxRuKt1cC0MrV+6ULDaJSAeinIl4JU0nxsbAGSSqpEiSDcUBVC5O9FqqxojMQdWyWC7gVfrYbAqfF/pZ5hC0YtVXdM6Kye0ILEupTmJDyTEIhiH5yl2dVGJlVUh7cBKDKii6KB2Vsk9VBrYiSaKe5vRANleWT/VRUelITIUNo0RaLmDW8LrMTpNFpZi579slB2bJRPW4qOmjxSqPR3PqJ3ilmKhuI7Ypovi7pMf0VqxRSJfDw2rBQjdoBBjMnRJ91DTacbVZsPaDF2SO9T5qQq2IVU9jHbfZI6xrlL+GeoNGfbvjmx//t7z78CN8+jWP869e/22s2ivoj92AeIruHabzm/O9Gk3KKiI6jhNQqxSEA8SewsbKLRIvGureT5+pRF8QD+J5r7nP2JAe3m8/LscXxKjUiNmxPTJI+iAFkDE7S8ehCDEuBN5HYn5RPb+slc5hcWXQvjSbCjLfZ3n6uNetykwvH9PaLPn55vekfRKLDolzovpQJWFWalT8SCznElfyd1DS5TGQFCQWVg8FdvZXxKhYnjbQph3Pvn5WLMh0r1Cdwq8sazVstE9d8QAWXTIu1wNQ74KmD4alq7naLDAqcrebYrVnbluu2CUT3XPbzWlT0xGtIiF3hNdJApkAgpiM1aPJ8UUafNlVIFiNr+R9bh5p5h2NdcnrStF6w07Ka6ZVX3yvstzeqoCLmpWrSmOULkjzj70msuhFElipyClNMg2PqE5j1hoiVKeyb/1uEB9eL93r111FO7EsXMNBtWIdKowK9NHQBstRPxWD/NUOa2epjGfppJt7ZT0z06WOu5pZ1XOybnBeOtjrVpdrMTYBPZIGtm2NXmt0K8w15YUJTkhNRZpAV4HyhvoY7DISKpXWIbJRu4yoaFk+PiVOPJhIvysojXLCuJamWOAnBlsZ1DrdT2Mm4zaQla5rHQP/y8kHeOvys2KxsvMuwpkk5Zx7M2/vPoD2uTnNfYqUxUIl/z5+6b3ginHRX43ypFcwLsGsVzlq7QaTQa2kcheDXIjjTiJZypcNF8fjvIsqBRPBWe4VnO4x7utjEoeLjuGC3PDsygWVNNlpD/SRaiUa7GqpWPWa1fUoQc6lQOHFNI8VRRMbtWiLTZsrzzFVAyJhEqCKwuZKAbSqHcaIZjpGhUudvPLflZWuIHX6O8Sh41TWUFstHQ77YFi2NV1bEbwS2UntZbKPsoiOKmm4ewoNV76/HA83UbhZ0nMrCgNMKUGVV12FmaSsJIgxew5MYwouUCi4K1clA8eWme2oo9Bzm8qxnjlCp9HdkOAQwK4Ui75mrluu6gW7ugNafAKuejS7es1dP5MuU2iqzMKKAliJX5Ycz4naNLDtomFmWh5JQQMkEemj4dhNmOieK3bBI/URL9Z7opOPhj4YFq6mq8WQ/7BZcrC/4EhHQq9xpxV4+H/+65/jT/6rD6IjvPPXPoPvNe/7lm9F9YpuT9EdayZTu3mfjGR+RVoVEN83HzjTpnYsD3w14x7vvyeIdb8EZrRguhyX40GGdDPcuq7HrER5Il17UvG8UDY7HnnBHu8Ra17tOMMK3gK0RvdD9j7JWILpZd7SvcJ56U44aMEpz6Wsh8xsUsnLIi+MokGSGRuFYali2S1tw9A6PFpCyqp0kg5qHaS1twoCHMWBBZUTEPHu8HTe0nuLd0a6InkNJkNLsfxPUKXKH4zaAHvEL4wy52UGWD5Uzmu0lfMeEKN1Bam4kw95BrF0kX4opABXaZGr+KgxxuMqQ/RGWOGjS0D3IpmvlGemOurUKUpKIvLZjXKsQ5W4V0nqnp7LPwtLeUuO56OmUj7J5JOPF5GQOvRa5Znonh3TsvANfdSly1cXDN7IyZ2ansnEsVaGGA6IXxr5Azd+jq/5rV/l2u+d8LovfYbuLZp/8e4/gf+9XZSf4pslvlXYSp9ddJcEN6Kig+ghBlQM8nv5HuNF+KuNNfd+6lWFi1cAgl2O/3FHo3tZN2kF3lO6q2ep4SvxA83srAyAja/vbc/GB7VSYRPkOhOfRt3WIYNaseQU8RwLBwVSkIcE5KQcJHsBpsdy06rBf4+NZj2KOISQcTj1OXeRrqjLhyI7Tx5xOFtx0tYs/FRYYzEON6mJ+B2IS5HYKBvQiZmlgc6ZxDSNzOuetbP03nA4iUys5CtWBbpgC4h/6hr2qjUT3TPRvczJuisewZkxBaRCrxr5GWd1iRw/sYARAEk7YQoRhYk7qTxT23N9esrLCSTKhfmh4chwErMZfKU9e/WKLlhCFPBLWFPDa6d1z6px9H0NUQpNZp0nWyDqxEiO+Ai9CdxezLjSLKWDY7rGFr5h5cVcfulqbi9mLI4mVNMeoyI7tmVqeolviIH8lUZYWi5onNfcrSfC+rYRbCAGAdBs5dE2EI3Evzp3b28UfgqxDpg9YX+1pka5CrOWtUaYJuBUgV2DfT5iVobFEwo/F3ZWdxBobuu0JhGA1U8Mft6gWidewRnQGgNZWm+s+777zq/wvXd+CQ18aftZAH5y/z3p/N8j/3kF92oeZb22ofhS51tUJDB5Q5UyDl7nrPkygDV84Dk4yX3GJZj1KkelAtFo0DpN/HmS32ppdtE4R2oIDOys/HvWrL/SsQ14XXQR///Y+9NYWbYsvw/7rb13RGTmGe74xhr6VVVP1dVT9VBUd1MtSlSTMEFRNmDLlCHAlmyJsGTCNPTFHwVYgAB9EGAbsGXBlmhBapUEWQNI2CLZlEg2yWaz2SO7uruqq7rqVb167753xzPkEBF78Ie1d0RknjznnvtutS1WnQXcm3kyIyIjI3fstdd//dd/FSc3DLTJxJzBgzIhSmRg1rt1Yv6BAj/tXQ2IYoUGR7kGWoKWUaSsZx5qpf7GKoNYuTWqObfYVjCtbt/lrhpD+UgUejN2m4iT0S9A43zWFHFYE5llHaeYhFVfsTyZISunLLEEyRjtopQn2xQF041149GCuNI1Bfwc/EKFhaUJVJWWOMYodJ0jBDPUl1tJNJOStC7YrTLDEA1tfu2D1RGruuZuo9mWo6plUzuWVaDLJS9TQTy3hHef3KL7qGZkgCGAqEVFi18xmkV/4G/xJCy4bdYsRM+rKqr6ErT8UNLA0Fomx7Ow4LZd0RlHnywxj+NN7sx17hsqCdxxS+5XZzwlZ4eo8NZwWLWcdHNWvuaVgyX3F+pEln3NvOr5Y3/xS0O3Z5Pgc1/+Kul/6FV0uTF0t4TQWCpjhkldSwzHLp8DGyXG8d+UtfUhJuwL98LUXuBY24LFe6KI/JIkXi5AubHvKDOkITs3iJbDCGY9b4xuBSOMgNDkUcekXBiy31LbvSXS9EUZkj4SJuwrNJhwy6w5MdfXFWvP5SNxcphSlldEeHP3pFKjKZ2o1mP2Y6FK+Cq/n69HEBCTmbERVOlVD+xMJEQFiIoALTgtUYiWfuOgn7DEpACL2dfk4GxYLpgMPqXRh5Z/OG0XXmLK4FXDUvLnliCuWEiS15KJNIjTa1C77Gv6qG3UHZHaeLw19DYSjE5aQ+Ik6WL+bD0jHJuBFaFvjz5jIT3OBM7jjHWsmIl2njKkUcsGMvOK4Th9MqxTxcxo0FGEhQG8aKfEUn4/Nx0L27IONYGEx+CMUKdAGxx9tCzqnkXVAku677b8wKu/z72/ec7XvmL5Hhf41A+/jdyLpCMhLYVkDaHJQOIAEuf/Jplr9TUZxMqg1sV7ae9If77t2/dFfM1VCZUb+46zxvhtfawQhoD4UlZFbth0wYpcyl521mS76wBal2wzdUEwGcsF1CJpOVUJkIfsQ9mOPN9LrjBRxpHOO/n+NWTx81LGlIaqEArelf3MAGxN/i7rND8Tzr8L3KfOuT3fsPGO07MFaWOzP8mf1+TyzCZoNUkvpN5gD7MQfOewUSVRwkSaRHL528xpV8CIgkebULHJwuol5jnM5XarWLMONed9w/lGWU/RpqFKprC0gKHkUgoWX6a8TDbojyDeVlmW2qi24UHVqXB9GMeUNTFXj+QOuRhar8n5J3LAcW6GBVp6WJnAqq8RSXRZq4peY8MCUkJmYWdgK9TCukqEhaZ4YjL4aOlwW0mf2gbOeo27aC19Es7mDatZzdI3LEzHsdtwT5Z87+KBXrPQcOpnfPHWa/zO19+g/npDf2RIi0ByWTbARfp5IFYG0+s6I9R5PTELOBe0M3FOjiUHeOgPheosDUCN7ROzJxBmhtWbCoSlKuHnhuoMQgU26//3t2owQvXes+3SwXL/Kg17GPefWb8z3AYG+Ez73rhPTKqzdZmVCrArpFOu41t2gayU78XxBYZlnR5030HGz2PymS+qI3wDZr2kNabfzhZMuhqmoDW4V070VzmAIko3/ft5gNY08bkjnLjXJiKO2+WGOrhKBwzx4No0TI6lYweA7RLVmdDdUURd0W6GyWr0VPp9QlPqo8G0yjxyS2H2BKrzhG0joRE2dw3tnSzyKDoR97cNy9ZCEHBREfQkuCpwsGh1gW/1tT5YahNY9jXvPznGfVBr4NKkwUdHkxfYOfNtMrOsfH8kT2RW2wHHGsIijiUpwdC1jthasqYkaS4q9sgoyF4YWqDgWmO9iiRm4O3JeoGRxCuzcw6rlk1wrGY1/tCSlrmcYqM/pd1A92TGbbtkJkH1R9AgJiCDDtYBnlftGQHhNDU883Mq8bmU0A9AVkzCKlU8CLf4Wnefb2zu8rHZE47MBgRibqk+w3OnWvKNzV1+4+SjfOLgMQurIpWVCZig5TiGpGUhvkIkDd1Ivu/gA15rTnn6uQPSr43rpN/4wY8p9bhKxCrRH4xdzPaZZtgiEoKyskK8fHyX8X8Zyr/vfhqyGvqDPi9DsDvhTxdE+86qvH4ThNzYi5jLOkODTeb1oQ27/sXeVcOVwcbOLtcJ0KeD+zpjeVreknKgciHbLwOjigKGTAGRkLBdzpC6EmjBDuFHty/6V8NiXks+TKeliyogr4K+WsI4zv0YCDOh96I3ahFKT2Bsoq485Hk9oSVvNiW6aDlfNZilOu7odu53UVBIhDGgYnSRhaUVLUPCZ0iGRlEgK8jga6gClQv55xrZWzHT0RI6bqLYQYx37fXxwHUqRpw8vbPE2pD6XIri8xrUQ1g7ZqbTboj5AhWp+RJYVBI5MC0RocWxiTL4AkccgKyUoMdyHmc8DQtOvbabbzI7OOXr6VJkbjtO/JwHm2Pu1KrX5UR1syTZQTvH50BH2dIdELl/uiHOj/jITz/jzrzm/U8c8QfpLdxJUD0UE3KAYkl2d/CMJgPzMepaKD4HOJ5moy+895zXrnPP7fqaYeF/iT95GaDtxv6Rs0rChbWOVh6USeSaA2ICPk3ZWYoRXbH/bmn5ro7i9Nhls+kuE3BrGtRuafoyzvsylBgyAFrA0FBk6Gp46fmy5V+Gl3N8U9b+3S3h/GOJ6q1zbh+uOFnPOHu2gKXDdDIcRyLa2bBhICC4lRBCRX/QY13cugQicLZpqF2gDzqbNrYZ5FFiErrghvjgrcMn3HfnHJiWh/6IMz/jUXvA082cTVspo60QB8KY4EmSS9tFBl3JJCO4F2ZCey8LyGcAbeF6ZrancdVW58CYZOhSqNpYuZugDax9xcz2zKzHJ0OImqSwWSg+RDOCjDbHklZjGklA7tiurDkhBaFxgTvNisZog5B1qFj6GX20A+jmbFAffW7pb2vH39eaUz7RPNSGJKanS5ZNrDiRAyLCZ269xxvfd8rf+voPZ9afMrNTSezVkTDPrKxK9HwrXQd4b1WqRlRnLDSCW2uSIzkFVGNFTrIpoNUfC91dXYf4BYSZ3iPem9zxV+huV7gnFbIuugOT8sKJXhZW+MLiY3x2+bVh+fOF2UeuGOh7hn4BhUvi5opYaMqiH+7NSO6eOSZ+pmWG+sLkPts12fP+5PHSc7rEbsCslzRn4iiymNkiqWhnwUXGyHVtqp0FoxP5EMJoz62RzyaJAdAC/Qrio6L8RrPYilIXdlYatKRsm5BeiEdxyGZEy6Bbr4t0tEVplTBrg1sKbiVUS3DLRH2eMF6z8rbTpbJtFUAi6mO0hrTWzomhgXCnpzrsaBpdDFujelWlvlsk0QVL7GzRDx7b7vaaXw5RkOxkpFdKrgQNqCRqsBMatA6+1nppYxIxGvreEs8r7X4l0JuKrgpUzlLZkCm4EZ/sVgcTPxFF7IOhdiqueLtec+BafGPY+IrOW1YLPX4BVcIMPvKJR7xVPWKW0xohyZaYu36ZyC3TYkVFf5/EGcvYALCRQCWePtfbxyywWInSg8/DjDZW9Mnm1wobzjC3PTEJv3v6OsfVhvvNOYc5a74MNSGpCONGKp5t5nTBMnOeN+anVCbwS//KJ/DJcPxLK37pu76b/+f/5I9Qx55uXSnF2JWy1ouLIQEFsnyE3uu/ksW4rGvPZAE3XYhdtSgb3nuJ0pHLwKoSoA/EzZtg48auYUbSFn18qywcrg6yr7KScJiys6YlgH9YlgEtfaqLvlSET0v2XMaMXaGiS2b7xiaN57srFWF0cYpVv2R6LR83nfow26WBcStBD24CY9t1mxf7vcF4Fc9Ns4CpAy77Cq3mUZCmMirMFaKQghkJWSUDHkUbeEzO1WSGs8S8XQbOBzaZRfWychl7CELqzCCeGwWCTZgYsaJ+RVIayvASDOX3oAmbGDWw6IJjZj218QrmVQp29ZUZFumgPu/ozoo7ZjWUCJYkDYzDw5JoJAxlhWvcUKLu0SRHzGBaSkKfTNb8SnTJDQ1MLKOGWUqizHfgYXtIY7yW4ktQ9ltuWV+ZgAc2Xggp4sxT5IsLfvkHfobZ93S88qn3+MWTH+a/fe+fxPwu2OqU0HuSuNyp2ajjTxOqHEU7JwNYpZT9effa1usv6msu3eS59rxy95vEyXeGzU2nVSKy05BqOj/uNMh5bjlPKuDRZGwX/PfDVIuUY0591x5wa8iB79H0HQCtQQeMzN5SMCFafewPVOC8HCs0uraUkLIfUMZvYWGV+bd0xu2OhO52orvvae6uWcxanp4taM8bZGUxrQz7KoVIVPMpPweIdS7XXlfIQQcJLUFHWbZIwh0E7SzLWL5+3jf0Uee41jtOlzO+59ZDGtPTJcfT/oAH6yPeXx2xamucC4Rg1Gf0Wr4WrV6eWI0VNQP5oswNRjSBvPA4ox0H184Ta92gMgpw9dFiJWJFGVFTgMtKpI+W1ju6ylFn/ciDqsUny7KvAbS7exWJ0VHUTYr/U20zXf8vPxqp3lyymHW8cXTK/fqc+9X5EI/EZMDCqW9onOd41nL01kNePzjlZ25/he+qH3JsNlT5QzapYpOqPMKWBIQTv+DjR4/5hdd+AGkNduaZz7uhScomCe19i1u5wS3o+kC/t62CMrjuCJtVhekEu9GxEK2uGSQmrUySIoWg65LQJPwij+coIAa3joiH1FTIuh3Rzt1/2T7/6h8FUM2s2Uf4Txc/vg0aPS/psrvdPmzhOmXJJTE5aRY33t9cBK0GAK2cCyOgPNlWptVh17AbMOslbVZq1HMZFCGM2llAijHT5eT6wcZ0u6ngYrGrAK1plvrDAF+wNYAlKsCUYVii01GnZQ75dI06hepc8Lcz4NDJ8F45j9goMNY8sdQnUJ0lbJuwfVLnksEj/VyozyO2FUKjA7+7lfWsfGZI3fKYmU6afW+RWieGzmsXjnVX0QfL8XzDq6+e8H57B3tmxzKw/LXo89I8MYi+k4OcIvgYatEyw4OkWltAt3Gkkxq7NqMWSxC8t7S9tquNSVj5etDMSlm7ZOMdPtjMXIqEaPCiIo/H1YZXm7OhPLI9rokbQ6i1U8b6tci/9vFf5iBP1AXIKlpYXTIjsDV5vGs2zMQzy0FHAbnKNq/Zc3oMPz77Gl/3d/nS5g3eXt9T1lUOSKoMnt2p1/zO09f48gf3mTc99w+X3KrXWw6uC5YHT47pz2tuv3oGt7QE5zw1/Ef/iz/CL37ue7n3SxXnbydmnzij7Y1O5oUKPRmPYhL4UuaqQJb0Hvp+O/NYTEbwb+899xIg1ZYNmYtL3i8T+tZr+aFkOF5gwr6x71xzMum4toftm3IWfWsR8VybbFjGoex/+4JNx+6HBb6m+6Vx/tf7tyRxJoGNZA2QDuIsbTGhpwuiZHNSZG2wLRMh19zNKYNHoMe2nb4frQI5oSmL7JyRbSKS2cQhGrBBg5JotdV37qTbVJ6Dww3LMEda2b4+CWUUD991eh3SMB+onomyssisoeCNlrOU7rrZr8aoJSrRKOjTZ/2ScrGKVlbMDs8YzaxLgk1wNMZz4FrdNxh8Y7UtutW25P1B4idvvTOwfwuQVS51mABb08e5eBxx0PIyJKLkbQUOjDKX33TPeBbnPPZHKuYraWCYlaTM3Hoerg940h1Q2cCi7mis3xquIQnn64rYGWYHT+FrlvBfGP7aZ/4Ey1s1X//9e8z/umFzAu7Oe4RwgMRbGRReowulFaQuZ+LG34UY1eeEsH+YC9v35QX7FvmalwCmbtzMd4bNpCM5c3HE7TLXp8yrfTq+xUqX9RgV9L2KnXWVrlaxfe/vJi13jj1NspfkzQBoAYTM+nRjoyZENaHaW8LyTaG7H0jzgNhIKmVuncGuBbc0Q6nXmEzVruHhIMChx9UqKfLs2QHxrMKuVcS7NBcJNSqZkk9YS9p1Hol1wrWCrC3mKGGsJhWq2mt3wySsNg1Hiw1GEmddw7M4Z9lV1C5wXLfUzpMSfOX0PrerFUYSv3/2Kl95ck9jn3IJkybmUxQlI0wlLybx2uB7BPqF0B+B1MpKanvHedvwxC6YWT+AWAXQOqj0YtXG45PFiXZ2L77HR8O5V3aZMrQiXbC0OCobcZWnNSo8LLDVoAWB/hgOPnnCG0dn3JsteWvxmFfrUyoJA5hlJbIKNX0yHC6e8en77/F9zbscmJaD3JU9oMmQTd6nSoF7Zjkk7zuUqXXntVPOlzOODtccNd1Qvr+eVZzNO84WCzipFGg7CFSN52DRqp4m0M16zjkgVm5IlpUEW6xExeFrFYeXecA1Hh9mhBOrCTOr6w3jBdMlUmWHe2CXlTVlU4Ug/PxrP6tJFh9yPJQRwuvqTe0AZFO7qLc6eX3KmpJxPij3KjuA6e4xhm3homP6kGvJGzDrJa2SMA64sjjOC3EKLT2EsX51cB75ANOg5LLgO6K//HUD8H1lF8VeVLAxgekVyEqFADNxGkWoVqJSRc3G6KSeNT1KhkMS1E8t84dQnyRsV0Ayhsy75GszfI0s/Gv7HETMDLEGf9tjj3pmVaDvVGjXR6FbK/KegsFUgRQFW0Xmdc/rh2ecv9Kw6g4xm1ErBTRQAaA3Y+ZC0ugAhKHbYlxETBapT6c17kwBmFgLfhaRatTzWveVspSqXoVzgShjQOSDDoKm8oNWyNpXPGoPeHN+wv1myfmiofeWZ+eOWBvVUHlzw/c3Wh8d8n6bZLFoy3MriT53TYx5Qgc4SxUP/C1u2xW3zQZL2AK0FBDTDFEtgZAMf3B2j7O24e58xccOnoHtWId6KJ1szxv8Nw54dnjE7dfPWDQqSHlvvlLx+WWFPbH09ywn/YyTfsbD9SGPlwsWX665/eUNEhseHS+QlaVaagDoVmFkXOWAgimQ1fakrhu0FdgVBt0d839YFJOdLOdgcvH5sACcBLhbXTxu7MauMFN8wNTXMC7wh38TdEhSXvRPy0y2xtzO4Esw1Fdfx77FUfKgjVI63ZaqBFF/U5IQxjOIt6qm0VhOCCoqWy1Vh6MkJShfDQbwaLCsqWKDvqjiuBqgSB1wNhGCIUVDDLnjIXqeYnP5odFyh8O6oztw9KFW8Cn7thQldybM3y+hvnqns3Zpk55KiWHShiWmy920rJBcHDtnJcFHS0JZStqhK4d6Rbg36kVUvUn90Xy0rELFkWtZ2J6ucsRo2HQmC9MCR55X7Lmech4THi2VtKguVshBSWJkBXfJcB61WchM/AVR+PJPJOFQttbTbkHrLfOq51a1ARPw0VLKMkNriZuKTd0wO2y1vDLBvOqVgdYZZBMJi3M2ccPmq6+w+tLrrEJF9XXL7BvPkDsdq+YY+ruYmYAD856HeAi8B5yg3Qqj/gsRCZEUwnbwfZm9jFbj8+xF3NjuOfwhusAb+++PNeIz01BgUiGSQtSE35S5/oJjtWguwcTvlIT5i+jbPO8+Snv81fDe+GToMl0CfxGSM4MPDLU2bZII0qpGXhGHx+jcnpzQ31L2TPliySWospRIFpEPncWvHebUUW00qS5BiFmH0XhNLMRFRErZYRBlatmkQJtNuNydfXNeE4xVoN9GgjecrWYsjXZHt1kqxUg36P/OZz19NHzl/BXO+oYnywXrdT1ke6oqaJmcNSSTkOwQiyxMkjHxULqjh1roDwV/EDHVGHy13nLeNZgm5YRILhHMGl9Ooiboo1YnOfFEBG+VFVwbZf1CblaS5UYqGziRuWpNmVytU6ufLgw3P0/cn2946+gx37d4n4VphwRNJYGZ9CxMR28tt9yaSgI/vfh9jX0mfqYsCGz2VVa6fIxIJHIkHc+IfPbVb/KwPcznrJUpPhliI3Rzh7n7iG+c3uF83TCre5rK63fOpZV90IZXq3qmZZqPG+xSM3F2pnIFxkN/K3L77jm1CzxKgn82x64KKCS4tbKzYm2xu6zFcg8Ytp9P1w7Te/AqRtVlgPL0tefNC5mtmWxe35DGstWySVkU7B5qd6qQyeMEdH3RdeUNmPWSVkmY0DazsyhB+NhW6PKg9yqbZkFK7fqL0nyn200H6TWcWBFgSxGkjxgRzEzwRh1FciPKXwAt6QWONEscKp3wzcYwf184eBCxbRpqonc/Byi65ApsBaUARyf4uWH5JthPnDOvPZt1TbdxxI3LTgMkiZaUBEjWkeqIryPPzJw7szX3D5e8XR1glvoZMYk2bIx615mNwa4ZJtXpOUUHsUGdnED0gl0a3Ko4VA0ch/bpnd5aPihLatAWsUHLIbJ4cOnKKJJY9w4fDXPXc9bPuFsveWN+qmLx9xzt0mJbw53jFUYifT7JDjNkLGaM9N+qtF7Pf7/r7/BL598NwEfrp/zI/G36ZHkcDjk2G+7ac2oiy0zHveuWHFUtT1dzNl5bo69DNXQv6bx+RwmCe+Y4aQ5YzmqqKnBntuZkM9OA680Ni6bj3fNbPDlfsH60YP5Nx73fC1SPVtzdeEJzQH8ouJUumqqTFvFhzEbEBDFnH9qO5MOwUBvqYqfdPrYGslyZgbiWXQaIXZUBkTG7sTebPn3tJm1+Y88xS9weR8YwdPGcLv4/TOA6BbEmi5PhPa5xzOtut8cGX4Pksj8FoErHpViyy2nyGGVgLinIpSK81blqL5pcwrd9jhNfI+P7ysLKC2yXM9V3sqZU75QZ5Q1jh18ZBdwFsCq8u5GKufMs6o4TU6lmRlLAiQx6AWPX3JTPY3JORUeEIvoewXR5+/GKZUxTtLQEDabEjdppxkQ8MpQaJgST9Av4oEGHM5YuOua246hqFRhbGEJXY7wwn2mpfvEpARnKAZOM36doh5XLfRbnfKO7C8CxXfN6dUJIwirVzMQzlw5LosuioHPTURvPOjl8LiHsgx3E7ENmuksUZCNsbE3nAtYmZs6z8U7XOEctlQ2cdZZ19wi/3ODOGhaPInYVmPtXiHfvEn9KMN+XcPM15u9Z5G8cwVdmJPkKpFNIm6zHGEjT8sLp+ulSxu9LJiguCyaeF19c19fc2LetzaQf44zCWi3rpOs0pLoMSNoqRdLnFzqZPYdhNRxn97MuA8Imr19kZ8EAaO1J1sRaS+dsl2ieCvP3heQcm3vQ3cpSJ52CTYWpmwxjwymvJebanAqkM7i1UW2nHa3DYJUlnGYBc+AH32Rcwhx1hGDoawdZXFwkMTvsqCtPiIaUNKHtsyi8CNQu4GykcZ7DSsGc14/OaIPjwfKIk+Vcy86Dzu+2HNsk8GTZFK1C8U3WoezTkDSPGZT0M8EfQDiMmATWaQfMvnc8y3PuoJNVqbxIGxyHsyUAi6yd1UU76AMDbELFUdUSkgyJ79qo7IpzAZkFWFntNpzjK5My69rrZ37X7AmvVSe8Yk+xEnkWDggIM+mJGB77QyoJ3LJLKonMJAzJfVBfRRoTLHayyJ52WPzeg/e5Wy9Zhoa56QhkDUZgHSru1ks+unjGOtS8vzkClCVfrLMODuGpiXzk1gkPDo94+s4tTbYZrdoxnfqEj946UbAtCQ/v1VTWEnPDM7sW6jNDaCzOlE7H4/dJe+MYxRYG7eCpXTfe2VPVAlzUYZyuK59z7EJQKSDqcP67a9OylisfUW5jwwv7rBsw6yWtkqClCUZG4EhkXJympOUfMV7dXWBqU3XAndfHRa9cBLKucgzXtFHcbUR2JSUIYNsAZ6UeWPCNDJNisii4ZZOi0ybBxmKXhvkHwuG7MetgMR7zwvfOQUUOyIqD6Q4N5x8zpO9e8uadU775+Bb9s0aBMi+DhkrRJ0mSAbFgiL2hN9pJr7IBXFZ1zInX0jUred3fbbJeVyJncdK2IGWCGIS0ckMwk5xOxnjVSzGNV78ftX34eas6UpWJyJChiPigrKjeWyqnrwG0wfG0mwNwu1rRLwxtcHzjlYq+nfHo3Vv8F2/8BP/40ZdU0F0iFYED01ITByZWAdBKRv22WfFTh19mEyu6ZHm3v0PAEJKoDklSEd0D6ancU0IyfOrwITPX0wWr3aVyK9xVX3NyPoNeCIuobYB7Q981NK8sOW1nPDubU817jg42bHrHyfmc7lnD7IHj8BuJ2aMO2bS45Zp7/9Bw+ok5aRb4V/7eL/DjX/4Sv+Ne5/O3PkcsY8UH8J7k/f6MwjC4JvfOt6rEcM8+04l+78S/Z7utzMP0/Ztg48aeY0amgrYy+IkRCCqY1Itk3PPq4sLmI3PpD509OFngFLFfCRHbaSY9ZHCnxGJJoHQdFJtF4r12wXVLbaVtLstYTmxkaTFQ3kMtdMdCuttzPNtwupoRN1aDnuIzkoz3t4zHSFGIogt7K5GhZWua/Ms6YAO7bCiRLEFZflvKMdGuiGVxWK5D1M8zTmf3lISQhC4oQ8tIwiXVMjHGEJMGMjEajFHNR1CQaB10GTgzPaESfDKcHlhCcPSnM37n8E2+q3k86FlZIgjY3H59vAxjh82Z9HysfkKftbBOwyyDXYInEUS/RE3AmjUR4W690i6RGXwLuaylD5ZN61Q3s0rDdYytwx10tMGxaSuMCzS1x0eh7RJh0+HOz6hPlrgV4GeYfs7iY56P/djv8zPVL3KnPeGrH3+Lv/ZzfwJOHfLBHXSRkyB6BbKutOdM3h/mvtnna6Zv7/qaF/i8G+2sb29rJMuelO7qu+yszObU5lIX44u9fmPodD6qVu3ddl8p44e1PfteALTydtOSQ0mq8OJnhsJ8t5uEW4PtI9WZsH7VsH4tbt0r0pevY4itxhVJGBpV2bVgWxl9kMnC4LNEnCdSE8AmrI00s57K5goH51l3Fadmrv4hGg5mHZ231C4g4jGigPyyq2lcO7CYQPWqfDTcrtc86+Y6N69mhKDHip0dftMCVIlBuxrmcnyM+rUBAMyxTbLgF2MMJ7kZ1iCCDqx7x/GsxcLQubcygS7YLd0sHw3OqLRL6cL4uD3gtdkZNoNh0QqPNwc4oyywMI9I0AZgmHFesxth4x0L2/KKPeV1d0ZF5BW7pE9GQasMaj0Oh4Mu1lQypYBYIf/IfbLMZOxuX+XmJAGDkcgdt6IxGksVZldIhsZUNMbzan3KTDzv1bd43B8MFS1ddKyDCuT7meFuoz7s5GRBXLusWSYYAv/b//oX+Nn/x+/x1R+7x7//P/0Znh4f0FNjVjqo+kOhXwi2NdBOIjcAAQAASURBVDlROSHFTMoLx5K+8l3Ttp7j7q10ZcL9inv5AsiVH1L29JNSwl2gKsnoa7ZA70tipLLPvtevazdg1ktaJWEHaZQtdpYuNJP+7ex+Z3EdU6B9/JhStrAPHyvU333srWsJl7KfcRITpg00z0Cilr3520KYZX2PJErXzVlye26ZfyAcvBdxm7gVFKUShE0TOUPWGxDoDwzre4b1a8LmlcDhvGPZ1fgP5lTnZhCal5y5RiDaNBnVgkmJUAKVaFT3RDS7YnrV8RADiNFzj2PNs17norUCQxeQzmBXOgkno90RY53AJFJniI3B5i6LhS4cJ6tIK5HaBlpxxFxjjocifHuSZjr5J3Ued+sV3YHj9FbD07Vj9k7Ffx1+jHd/6BafPnrAq9Up93IpyIHtBxDL5ghtqon1ilXxw5i0w+FZnHHbrDgyY/cUUEfwijvlrdljnvULnrW36KK2cu+j5cHTI/zDuXYWOwrgBWkNyakewMl6Rn9eYxeezjvWq4ZwVtG871i8m5g/DthVp0yrrqd++xF3Nrf555Z/l3/pd/4qBvgsX4MU+flb/9g4Np3TMt6sYXKlDXTc6bi+YrsXNEnPmXe3IpDJy7s+4uXw5xv7DjG7Wzu+M24VCEIXNuYlENKd7NnQTWrf4cq2u+DOlcff8TWXHFd8xG1AkqG3Ws4e3QhqDZSmhGbOlwxNRLaPfzHJUzKH5fNDpR0N+0PwC+1W2AVLXFaDbyn7FQCqlG+A+hPSCObENPHN2UcpyF50/2SSvJmcUwbMhjL3oGzjUhafrCaMynspZjYwOducZItEKlmcN+TSwJhEo72SQU+OlNlb4hJz2xMqQzuzbHqDOzV8Mb3B2WsNr9TnHNiWuXQsjAqxl6s8LSEE1cRa0OVjQ4ujTY6Z9DT5Ag75CODAtNx2Kzax4rRvCPm8YzKcrxviSjVLUp30+nhRf5s1XmJnkUq1J/vekVqDXUJ11lOtRYPVuIDjgP34GX/q4X/Dvf/2BPdO5PUf+iU++NRtfvOtn4QHBwwLrSKkXYKFq2zfYv1bCBxNb7EXsRtf851ltQTSNGk+sLPKnBHA5QXyVUmPq0oAJ2LwV8Yh02PtBtrT8sTLSgqnnzlli7ATIJePSUlxuyx2bgKqyduh8YcRXJuYfwCIYfmRiOmVNRPr8Th2M5lv/fi5Ov+mXBqXRhYX6I3WGvzK4ZmR6og0AeMi1kZiZ0F0bdz53Ok2wcwFahtorFcATCIbX7HqKxqr3VtjMnTR0ganwP6yVtaYkPV+o2onhhFBSAlMawbmcXRQJiT1j9Ad5q56c41dnAsYo4Bc3zv6XEq/NInaql7gYdXhTMxNRiJdVFDL5aYiXU4+APRBm17dbVYcuZZlqDmoOrpGNYXXzoFkvWFRcM30Klfz5OSAmMwgzwJQ5US9lYRJwsfcM163p3zd3+GBP+KeXbLAU+eKlEoiISnLt8NiiNTEIQETEc7ijFt2TTTCUXJskstd3iOr0NBGN8Rije1Vu8sEzn1DGyvI8dDM9rTOsQkOJ4FX758CGnM+PTngX/0v/zp//q/+d5gEn/21rxOT4f/wz/wpTlurMWvQbsrdseBao8SXXJkyAFgTIGvr3ij/PmyzuWnZ4u5rV+0TyWsPGZj8khgq1S5Nusglj0zv7xdPvNyAWS9pRuJF5LMAWhnEGsSpwwsOtunkPq1Lnw604hR27XkJxefZTs1tcR6SwHSR5lmP7Sx241i/IrT3wM8j8TBQu0B81HDwjrB4GHHtfscDFwEsUKCrPzQsXzNsXkn0x4HURNq2ouscdmWwmyyolzIwlQUcxQmBpHXsvaLSAZ1Ye0Ay+0nnSBl1unLAEHPDC9NrUJSMgGPQCtDgwmBX2rkiNkpNlomwbyn7kOwYjIk02WEVyu3c9fS1YdXWebIUQrCkBH0Y6cBGYqa6rvjkncd8KRpWp7eYve/4FfdJfvf+a3zs9jM+dvCUP377d3h1qm+SxscCaBlUVwuBJmn9eemIGJMMJYt90i6Hb1ZP+TU+zkk7o7GBwzrrfXWO+olelDCPWZgYMEk7PAaLOXdEl9hQE84q6seWxYPE/HGkOveYda8TdkqkTYv5xgf82MnvjZ2Wgc+07+b7yQwTO86qjknXPR/QusQuOIWdeu/rHWPn+Z5/WyKHk4D4JkN+Yy9qUgCO7RfH5xPm7guXtU8BrBI572pn7fy59frL2D7wrAQvIWE3AQkG7w39AsICokukWjvXhq6iPoVqqU1ELmMM7AJYxQbdkIUmJnCJ4C1BrHZBzE0pCoNrWF9PjxVARH1NyPpUYkYfNzQcKfunfMwe7XgUJ3NCmQCjaLnK5Bxiva0rmaKW9omgnRslYU0ahNdFEpWJxIm2RwJitOofym/s9FpXyTK3PXdmax4noW9nuHPDu+YujxaHHM823KrXfGL2kIOsQaIYnAyP006HA4uLHkfAZT+kZG8tWYxJuxwe2Q3vcYvWO2U1WG2gEoMKNQMEl0Z2m6ClNskinSa3PCiQtTZU5+DWCdMlpM/Xs07MFqfcf/J16n9gefIocfeW5/gTz2CRgAqMBewweW+BxB/C9vmaFz7G1vEYfcz0sHv8yo2v+c6yXdkTAWVnKZWTFECmUd+H0HgbkhvTfS+LQ8pnFCvb7SbYd59fxRTJsYnE4idyOD0JpE2f9DYunNESYEdwm0TzBLpjwR8k1Sc0+liALJPX8ykm7WRucidzq34nZS0tWVncWnDnWt5uNyr23R9a2juOZBNegAzCbwTSvCd4TXhvukoTCZKY1T2zXHrYB4MPlt4Z1r5i7nrWfcUHzw6RxzVC1nP0QsIQjTb3EEkYG/CbCtsPX3/wGamwewz4A+24F+YJmWmXXmsibdYhDp0lBjMInVc2EFH273G92WJlTcv2EtB5h7OBk3bG2le8sTjlIKvs98Fy7hr6hSdsLHYjI9tYtBkLby94//uPYZ41rzITKiLUg0/Rz7xtVzwLC57FORvpOZCehVHfMZMwNLwChuO0yfIgHPObq+/izfqpisLT0ySXu+QaFrZVGZb+gIfdEd978ICZeBrx4MDFSBsdlUQa41n2DSfdnMoEXlksh1LET95+zM+9/bsDUdsk+P7ffMCtf27D2ekcTCAFQ7KW/kjwZwLG7McVdu+JtFNiWDq6G7l4b4tcXxieS/xGSnvO63rHG7tv53Of7LhV/VTWny9wbLgBs17aymLtgpVBZO1IAdzHzrqGM9nKfkwm+2Fw7IowXsbKep5dcS5bpSapdIqKLD7omT0ztO8b2luG7rbBzx23vwnzxyEHF5PvMSBWlwBZFrojw+o1w+Z+IsyyOGNv6NcVqTXMz7SrX7IyaHbZtpT6gelHYKJkzs/XjQr45hKRsWX6eA6mZyhPKU4vVlpXDmipSW+w56qVZbw6guQSqUrDNU/ekLu943tLVzmMQDUPHDlt/XqeGqyoYHCMZqvWO0Zh3Smq1lgVG2yD416z5JN3LL/9xoz2UQO9cP7ogN87nfHo/gH/o7u/lsUN9Vg9qouiGfs4TOTls6wk6kxB75Nhk9wQXICO7QPTUht1sC0wi4Y+GtK5w6118qlOcgv2RcpU60SM6vBTAt9a7Jll9khoTiL1WcCd99p+NgRS0IUWPvHb8go/yteHIfyF2Uf0nimTe87yidGOPanrLg7W3Ql8OoafZ9fuAnK9zcokTWI75p/eEzfRxo1dw4Q9YBYM9wS529T4jxdaDKjlFcUOoLW1EJke80MuPJ7raybbSVIWbRUDbiP4pXa4DTMhVjWzU6jW2yDP7vH3AVnJbANZyeUSjCCE3kIQXKflgKk0DEkMOh8SJ68zLv4677RBSPm5MgiWyjmIAlrD+SaGxIkvK7Kkfr1oZal+GMpGKsAYZL1HDediMASrmo7WReosBt8lyevYlAGn6WWSIZNuJea25IaF64gz4YOjCr+yEKBb1TxqHatFxafn7w3HgwxmbeXelKlVfJFh7E4YkuBzT8ShNIZEnUvmY/7u0WRGc2dUL0wUqILMBDcpN5BOw7WPwShLb6X+27YR0wXEJ4gb0kli/bVDvvDWp/nsz/w2dx9B9zPCV+VT8NCAWennCGg9U9bCnAyfLdu6F3ZvjOfYde+XF/A1ZYq4UNp+Y98RVklQEfSr1jpTdtZldpWMA+zXzroK0Co2jU3K3+VzrvrMrbhHQSbIt2i+O8VHpFYNKokJ12qFNuj9YMIYLBuvpYP9najYuN/5uMk8K0GTCLHWtb4Ewa4NphXsRpg9hvp0bGqVrLC+q5NIrDRWARU63wCbtUU2lr6KMItDgj0uDH2wKi5uVVw8JuFWs6GPltNNg384p1qK6ve2ufzcQDKGICAuarmgjZjC6B2+lH533wih1r9Do8loIwnvDThUI3JjoTf4jWUNpLlgZ9MfjiExvwkVhqQsrSygHqKh89rpN0TDs27OfN5zYDu6yrJoGhJwdlor47qBlK8TUUHFT80+4BW7HHxH6ZwbkFH/SiL3zJpj0Z2XqeKb4RZVCEQMC9NSE4YOhptU8Swu+GZ/h/e627y3ucV5aHhr9og2VlTitWuiDgKO7IaI8KXTV3l/c8T3Hb2PkcTCKDs5JiGK0FjPK/NznrQL7S6chA5NHr0ye8x7P36bT//9B1oGK/A3Pvk9WBOZH7Ssl402LLMVodLfJ02kinZNCojlo5IBQv6X0naDh+va1HHv3ntXAWrTct+rDi9jee726+XJzqOwpbV1XbsBs17SKglEV4LsiRhvKfMobKxpZ7bLbMqG2rfYv46z+BbYlm4WXBrgDyLtXWT2JFGfR/xjzZDaLo2J/excyj7AILhbLKGijZtbhtXr2mEkzDQgM622mQ21wZ5bbaPugV6ZVMnpZ7gNiNfOIaHWzwgzwCTWz2bZo02CvCEw0a5Yphdd9DIJfiY/lwlgzgzViXbKCrPJDWkTErTjYyoeNDMpgrecrxucDRxVG2bWs3Adq77GmkSMaGlE/sljDoRSglVdc1i1rENFg/DK7JxPvf6Q9w6OOHt8AJ0hBcf5usEw1nvDWDI4tfLeoKuV69n7ZAl5cM2kH2rNZ9JTSeTR4yPEJDZHldb7P7bYtYKP8kyD31ipCLIxkdhXCvBlILI6E9wy4dYJu/KY0zWsN9v6V8Dn558F4DP9+/zO7E0+f+uPKJA1ZWYBSSKC04VUv6WMfDVAPKk3v95EzOQzJQc6Fyf4IVgddsxv5Xvpsnrwm26GN3Zds5Ss2I6vKWM0pu2g4Co0a+utPdt9KCDsJey5gJiuAoWsf9Il4lpGYGjr3ktbj7IzjysrSjtd9Ye6oC/aKOJFhdqDIJ0MIvKFXVUWZcYz+OOYSTzR6fn7tUOZPahzyiCgRD1YMmnQ39p7fjB04LKt6p4kN7k2htz0RH/vNBwEYjR0vbJqa+txWeOkjxaRrJsV7OgCB5DN0ltLbT19NDgDB67j7uGSs7qmW9X5Mw2dd8gQQm5f8n0/aQk8ytCMw94qpFt8ksv+a7VqEAHf9ISoLCvjdQ4dqnpMxlwlkeIo1Ewcr5nxYPqItD30kRTPYbmEX1nw/37jT/HBD7zKAUv+QD7FO7/xSfgDATkhmU6vTintSAkhaxDtsrOuuk9KfHBNXzNcpHLtZHKQyUEHtu+uG9r1NTf2HWeGHeZEiUXiGDikGPM6fIw1dH67InDN2w3JRDKQLjtrrRcBtC5jZ+2zK+6fQWcxJkwfScZifCLkssLoctmhV9A7OsH4hFtrCXesIyJG5/AqX5cMCkeXVBO3UdKCO7VUZ4JdKxPYbpKC5n0aGlcREs2pwbbKfiqJj86AXRnIjaNiI/S3BXPHM5931M4To6GpRm2nw1qTtes+d2qv9HymPkmFxoWUjPotF1VPsVOGmvisz5g7BSejwFqsITYJmoh1mgTpWkdYOqQzSCfEWcRvHKH2tL2jsQGsCryDsrJKc46I0Hoty7O562HvLUezltN2xv3ZOceuhRp8UqBnfdwR+gbxhkpk+E5HP/mQH599bYhTSjxSSggLoGUzM6F0MlzgsSQ2qQKCSrWYnk2qsESOzIaFaVlIyz17rl0Rk+U8zHjUH1KZQJWZXNoJXru6f+zgGV8+vc/vnr5ObTwfP3iKJXLq55z5BoN2QFy4jqftgqdL1Tw+bDqsJP7W/+p7sETe/LVn/MYPfoz/06f/B/gvGm597IQw6+nWlYabFbn5y57xHnNH0pS0SY4Pg0zLcP8UZla5Z67yO1Mgudhu7BTTdtny1vmwJX3EJb5HcmlydDJsV2KmaC7Z/0P4sBsw6yXNSswI/vYCWifryWAqDK2h1PA5v9aHoKJv2YuCXs9jiOUBOA3uYRv0MX2i9mGofy7OcSr2vlUiURZuFvqFYXPXsLkH/VEiNppZsBvBrhToSlazKabPC3yrlNTkoVrppK2ToTqW0Aj9YQJvqD9wRAf+th9LBkFF5EOCVsbsb25bGyv9zFJaYlei9fcrkLy4Tg6k14AilTKgBCnoby8uEr3Q4zhbz6hM5P78nNoGZq4fOhrqT6B0YxHNOBuT6KNh5Wtu12v9WSXy0YNnvLY45feq13j4zm3M2rJ+tOCL7Zu8as8Hyq+RRJ/MmCVPoxMo7KzyWpiMxw47sLO6ZJnbDvteQ30irA5mSICD93TMJJdp2R5CY2hnjlWcI+dW29AH7fpYn4Fbg1sF7MYjm/YCkAUQxfDzh59DrNF7xrmRkVUAraTpKNWeiYj345jM2166MMvjfG8DhZ1xuZuF2KcdNDAAL8YcQ2BxoQRxuu9N8HFj1zSRwkCZ3DMlKCnz8pShFVOO/p83wF5yAP5hjN9hUSNbLw1spwA2J0NK6YT+MfE1+wAWo/O6nwt+noEsm3Kwo9pKyaqQe8lsS2Dssls0FQujt8z3ucU4UbDLmmQccVaBmedP9khqIbUQopYGTO7/ZGVkcZbPCONnxRyEEAqENPqa7HyUtRUhYGh9pRnkShfTzhSx3pQ1dBg+32RQKCbJ+h8+X/rEcb3hoGp5ZA5Ync6RXvDLikf+iIOq20rohqS+JaHBR9HSKGBVAbLiZMAERv8XECoTMOcOu0E7gEWozvPXzcNaIiQneGfok9Mo0SiSI50MyS7TR2Vr+CLkvga+Cb/7Sfz5nL/3yX8c5oI8EviSwOYRyTxB6d2Fuq0XSvU5jTbyGb70+PvtHbslLr4iYbFXGFfGsb77OlziL6Z+Zt/nTLe5sW9bm0kgleR61u6VwowoyeO4m3l7CQtJA9oPE69cxtB6oWOkCQGAoet66UqbrAJX0arQ+aAhZYX6LLHZCPEgX49Ohd+HOR1AINYRd2apToX6RDvlKgsLTEhjp8BBP1FZocaD7RVcjE7F1o1X8MkfJvzdHjNTJlO7qQiVwbmAD5bWW2aV5/FyQds7ahd47eicxgUe+zuYjQxzv/qm/Bt7FAjpVS9rWsoO2ZdVEGaCn4FfRI1RoiH2QjipcWd2EL+XqEQC7w2dcWx8GBpCATgTwahWoY/aICpEQ4jCvNYk87qvmFc9jzaHzA96jqoNzgTOuob5ouWstcRNTai1wqY7hn/tk39n6E5YKkrsxKGHHLfU+Qe1JLqknePv2g2r6GkkYCVRkeiVJzXsf9t0fMSd8iPNN3k3HPF77Zuch4ZnmznHrqVPYxf6ue2oTOCobvm9914lRcNXFve5f7jkTrMCGLofzlzP6WbG0wfHEIX6408wJFpT8Zf+5R/hWb/gy6f3mf03FfOHiQ/cMbffOKU9a7CtSucYzzaekNLQhC0J6oP8pLN7TrDI7r1zSYXKC5nZ07V0eG98uu/9/WWK28/LGN0CtoblTdre/jl2A2a9pFUSJoK0aUeELa8Uh5K/SRnI8+xD1LIPtpWdv+TYZbtLPndgZ5k9onPDtvlfLHnWDHZZthdV5eOmAYBuTmiUjbW5L/RHSUvVRDMYbqVZVpulkZIV3Eq07Xqvk3jKI9itRgcTnZbN+Tn4g4R77Jh9UITq7ZjVHc6fsc16mp5vznIlMF2iPtVsh+1VBLJ0BJGYRWKLMG8Wih+VfSFF2GwqnrDASOK42ah2VtVrN8FKa7y914xFVakgZOcdj9cLfDS8sTjFR8tRtaGSgL2X+AddxfLdI6Q1/JWHP8APfOwdDqQbQCuTRRBhzJBHxha6X+jeZBkbbtslB6YlZCouqJhoSIZbbo0kmH+Q8HPNnlTLRKjzsZJg28TssWA3lugssUl0txL4RHVucMtEtY7YNiJ9UFC3aDnsmskLsVxeiDHbmlkiul+E5CxSgOLnsLEufW/f+zmakDKm2R+UbGmS7ABVuwLxU3DrJqi4sRe1CyXtk0z5wNBCtn3HdbCs/5/TsMrH7vi4Evjv3Xb7D4VJuPS0h7l9cm/GzMbyi5yVrnSeNr2M4FHI0kgyJk2GLrmFldWngbGliSwtD4x1wqxq3HJOdLdJ5j5JjvNJtJAeQnoMcQWU2sWL10KCLuoLeDYFwwcG6LDEGH3M9Fr53rCmRkRL1QXtjKWVCNp+PUYtUzFZ1zFEw7rXzkxHVasCvKXD0yLxbrB0Zw0E4curV3jl+JSaMOhkCQmfT7aczhRn/CAc0SXHLIvHpyT0+Yu4DHo1RlvbuyXETr+e7ZT9NuBvviSUhGQsyWYGdgTbTRkJuYt0LKvmDjgBvgzfuAvfuA2m1tJC8xTkGchG/TcCUrJeAhiSiYjp8u922frqOffRVeNbtv+8cKid8Ty8LFzc/zKg7ca+rc2gwM34wqQqxMgE0IoaPe5jgOz6ledJL+Rge5i7X5SdtbsEnMYnuz5iNz7ZMYkJEyJRjIJYlRBqyWWGWrHhZzI0dbIrwR9q4yKMaHONKNpoIicw5t90zB8m6rOI8WkoV5wyaoey84mP0PPRc04GulsWP0/EQ0911HHQeNpNRYxaGuk7zaKfJSAI53XOYpiEOdrgo+G7bj3h6d0F6YPZ2BgEvRip0nNOWQ4FMgvNaBwjk+RPtNqJMc0i1mkC3Z9XuBOrSX2jyYW4CEitrK3iIx6vDjioOxrrqY1nEyqsiZj85UMUBba8ozS1slkq5eHmkI8unnHsNry2OFPgKxg2zypiI8QNrN/wfG/9YCwrzH4ipkSPVpXUEtkkS5cMPQZLGlhcZ7HiQTjmnlmyKI4dBrCrxEGlK2JIKq1y5mf85vsfoak8rx6c8/GDpzgTeNYvMBJZuE59/bOazVfn/MEbB9y5f8a8VgbxvOqZuZ7WW8xS9TZjEp50C550Cx5uDjnvah588w4f/Wpg9kHL5u6Cs6M5srRUZ8rkq881RpIQSc7me1bjmy0ga9OqREux63Yn5Yp11l5Q6pJtIyoAX0qNSxZmqtea2GGKsv1835rvinjrKrsBs17SKvGj4GLI3QcKoFWyBnaSKS9Ia0wXM8qXIKnXoqhftwTxRQL+AmSJvl9YWWnSknM8x+1FNwlFk8umRU8qg0CxErpDob0jdMeqt5ScdhZxKxnLBLpc625UP0SRaaUMSwQ/16xHycCYXoGuWCnwQoLZY1Fx4F4Ic4OfpeEc9dz0ZjReSxXVYaWBygt6DvZEt5GUtNWtoM4PFMiqkgrJ11q3HnujpTG5jh2UxvuIg0whbgedksNZS0zC+aYhRsGZODiCzlsehwNm1nOnWVFJ4NC2VE3gh151/Lq3bB7N+eK7r/GfLT7Hdy8+GOi0t+2KWgKGSMRgiFiJVBI4izP+/W/8DH/wpde5/ZFTfuL1b3Ds1jij7x/ZTQbDDKHJWZrMQPD9+FtHVwaAdiIxFro6i+t3hupMAxLbJtUv6bzeK4X+niYTsmgHqew9dTKfAFmDMLQxY2RnDHvFeXfHdLnfrptFELk0GLhOkLAP0NpnNyK9N3YdMxLHcVLA2yHwYJinR8aOrrYl5dbll/maffSPq+wPA/uaLmqGgH1y/8nutjuv72FkTcVuQz0ysWIFGNU+KWtdiZNH0efl/pWYchmhDMeUqFT/wtqKlUCyuHWD6e4j6btI3wfxB/IE8E4Dv/1ROG2Q9C4SzzE+DOLv08BIEph2EghZFZZPg4Ksnj9Rsui7BjCkLDqfL0DwhhU1qYLahsz6hdopqNV5R0qqqVguowJaNU4ic9dr510JGNvy6sEZD6J27Hp8esgXqo9wt1pSZ52Ruej2Otokc7M0eGiT49dPP87TR4fMjlvePDyhMaUhSqQRP5RBqnaZZACSobPYACiWZoOZHR1yZpdcWqOgZC7FCHEy3iOkNdADS5AHCA5V7+9JJmYgK2YgqwLmIDOyQj7IGaSl7sOO0M7OeB7A5evYFZtdI/XJAHQ+D0u78TXf1lZL1PX5NIkuWfMtpQHQGksNd+y6SfRSajjxKVt/Dwn85xwnTh5LhcuHZJQM5YZ9VIwsN2KKNjOjLEMH9JRQJlCHguIHXjvKVWlo5uTODAffFOaP4hAP6HfNfiGNgXyZm4pGlcSEDQqmSYT22LB8MzF/62wQWW83FX7jtDNrHH24WZsBGItNhCqxMg1PJPHG4pQ7xyseP2qwQRCfK0hc0u8hCVzCLo3KrkziwiL7kpz6wDBPUMXylZC1xW30mBqnKZBkbCIGw2ZdExudp61RsfhN0ORHbQPnnc3i9TpBb3rH4ayl85ZnqzmzuqcygWf9nDdmp7w+O9WOwUl4736Nb2fYtVDf23BkNoMeVZ8MXdbyrTIAVSRUKhnX/YW99fvda/x3J99PJZHXmxN+6uD3uWdWPAjHLGPD6+4ZM/FUGRBbpYZbdsn9+hxnA8tNTT+znPYzjEQ6bQXJxpfuYPo7u0cVz+wB64UK+n/Xq0/ogmW9aoiHAXvYUdvAu8tbPFnNOXtygHtYcfttYfHuOfas5ZXfMISZsrfrM/2BmmdeS+OTirsPenQpIj5A16u0yrT5lRQUdc8N9yL3U4mnij2nkdAWNlGeDhVZ5X4bge4hmT/RsiuP01ipPL+W78t2A2a9pKmOyeSFUfgov5CzH6WsKZIHyM6BdgfZizKzXqT+/HnijuwECztAVrJCqI0+dxmccpLZSnkx7tHFJOOgTlaDivZY6G5pCWCYKYilQIjglmOAUT6/AFiYXMOemV8lG1KOHx2ZmSX0c6E/TtSnhupcHZFbJ5qzyL/6N3+BH3/7q/zqxz/B/+2P/tNErAY2ndKDga0si+3ThDqsNfil/jcJxFnMjjj/FC4XUuTa9RQFVwXtPJWErnU8YQFA4zxWtJzQSGJe96Qk+KCZjcrqhfBROOlmzJxSdysJVDbwkfkzNq85vlzdZ7Ou+fWHH+WL1WtUuXvi7XrNa80przcnLEzHTHoOjIol/mfv/wTf+Psf4dYDoX3vLn/91VukWcQd9Ny7fc79xZKF63AStYuLzWBWpYy68bmWwwxjx4I/jMQm4lo7CRazaGF/RRBQbMrCEiEZs01rpbRPzmBxvHrV9KJigte13aB6V7hwWvpxmdbJjd3YdczA9tiZZtDJb5YAeli5cnG8XbgXXhCd+sMcv7tAlhFlGpQFUAanpuzaaSORqW5QslnsdpaZWFlAnaQBTGFdbZe+Ty6dGQOUgf3CuBgrOjOhUkFe21pMd4CkjyI/nrj/p9/nJ+q/z4wNv/PpT/OlN38I/vIryHqNhA3GByBsAQwSEqb4z8wMDtN5z6Wt6y8mA1ET0re1GRRKCmitqQFtqS5oOaEIOKsoUYxCiGPXqhSFNjgtIbEKotYSOa42+EPDE7vA9473lsc8sodY0cBmZj2HtuXQKXPYEamysvIXlm9y8s4xzTn48zl/cDADlzBVYD7vWFRaymFIw/Ut/4oWWbJslQqV8VKEmU0rIzAY9foxzVwDCkIVICovJMoFHO4dC9IAt4FXgTva6XCTIJyBfBNleG3YD2j9Id0gu0vEvCbb+rtsegNafUeawJaMRkkKTruqD/FKCDvSDZcMmJ3O5sNxh/cZ4pkthtbukmxPKfhee9HYJ6Yhlp8CWi56wOUkhJaWx0rXqnExnl9sIraOBDz43EBiKRy+rU2sZDLvm7APAESnjTzVSMw+JibECJs7lpPvgcX3POONozPeeXaL9tlMO6P7/C/oB5SGWer7wAajAum9sHaBLlqsiVmIfvL5oCXoVnc2vSZETEhb7r2w9iShifjsJ+LaYTsZ1uphpvOwdPq+q8IwTFISVrlBVWscjfNjh/ZoMD53Oc96Wpo8UVZXSCqb8t7mWAGt+Rk+Wda3HU/WDrep6J7O+IXzz/CD82+ofm9pSCWRA1RDbCaBfmhUlQaZlD4Zjuyanzn+fSKGZWz4Zn+HB3KbPlk2sSIg3LPnHEhHJYHX7QlHZs077h6fvveBgljZT658Qxcda1+x6itlz6FSOIj6ys1Jw+xWSx8s77a3CL3h8P6S2nmWbU0fLKvTGfZxxcE3hcP3AmbVIeuW+p2O+//wPudvKhTjVlA9a1UHqyTui4sJEyDrqhh+SAruyK0YuXyf6b7T+++yMsOde3T3/aFsMOka6YIAfOLCa3vL9V9gGrgBs17SBs0sNPMh0wl7mj03hV2SF1gxknvH7rfJ4LiWgOh1mVmXOZLdRdFueWEG4wpwlZzg54Z+oVkPYMhQh4ZRWyRP/sloUOHn4I8SoY4Z4BDcqZ540awKDbluWMES22XQyCat927yZF2ovLm0MNS6mO0ODas39CasTrVkw2Sx+D/3i7/A//qX/woG+OmvfAkJ8H/52T+ZyxnScMxSzwvqNFwPbh0VkKkMcjAJfMr2LpLIIoy55FBsInYWLxZXByRk7apgOFnPWDQdVQ4iSrZBJOFyh5OUtPtgzBmPja849TMWVrtp3HErPnX4CCOJB8tjnIksu5pN7+g6/WHuHK34zN0HvDE74a5bchIW/JX3f4Cv/vLHWDzIFOxeM1FyqrWbDw9nfHD7Foe3tSa8OlNhf9Pr9w6Nlnb4AwUkTS/DNQvzRDwIQ9Zt0BIImmmQoY3sZCwO43L/QN7q6DlB+WW67w4A9odm5dBmPI/hrQKClkBDdibpPad1E3Tc2HWsBPqwAz9N5/QhwJ34oecGB1sIGXsHKS+2yZW2h1Z+wdfk75EMOYmhoNGwAJqCG2WujuN7MWefY10W7WhXujbPsWE8xnCpCguICZhi0fm8aI6UjLkFkuRuiHpNTOsw/gA5NqSf8vxp///hrb/xdeIGPvMnfpf/6PvnvP0Pvxv5wm1MeIjEzfiVE8pnSpoZN30u3ck+Ne3+RIMS+uiERPRcYzAYG4dAJcUszGvDUF4+DhnVZ9TMr+hD0qDDR5Nbj6so7sz03K1XCHDuGtVkDJZ1qAgZcZs1Pa/Ozzl0G+ZZePcr56/w9J1bqn0leo1Nm3+LZFgtHcvZjHqugYrtZNiu/C7R5d/SjcwJkv6d6jGlO4CCU62R7S+cLUAKaBRc1mFlcDlIRyBvwScb+FHgNYGTBL8xh1/9FMjbwAdQaBp/WCbbj88DrJ7nS258zbe3WUGZWUPyPM/9RYoBBpZWijGLgr8AeHSZL5mWSZd4ZXcpt7vvJSoTV37OxK4EbBNIiLhlRgJuW/oDTTqE+XYgbdaGdJSPuTHUzwzzB4n54zh2PSfP/YWJVZacjDFI+e75DfpDy+oVw+YVoX2j596s5byvWT84xC3NVjJGAtp8RFJOuoyJeeMTKRmsTZz3jVZsuDRUq9hYdAkTcSAclHhGz6X4KxXJlxEAA5I3yNINXXqjy/HUXGOYuLGkKgzMWWMS3luC09dCNESjer/TWCZGTcr7YHBWK1DO2obKBLromNue29Wajy+e0nrH5l7FenPI4m3Hv8s/yT/7k7/GW7NHHJkNR3bNgWmxJGaivXAXxg9sLDNhar3lnvIRezKwuTap4nE4ZCY9n6w/4FhaKomqJyxanjhLnu+ePeBxf4CRyGk3ZxNGeOTR+QFnp3M4rZSrcuShM8jK6bW8Bcuu5nQ5IwWDZN+42VT06wr7qGb+gbL86lOPtB56j8TI4e99wL/4O7/Jpzdf57fuv8V/tfkRUmFdxSnYnIeWc1peGKetKqdjPzGgu8NOV4BeH8bK/LL1uWzFO4VkAnl9JkkZiJPhp3rIF+/lUbrp+qd0A2a9pBni1UBTKS+UNNL3LusOAGMWZKdufQvQmgTvZfL8UGDXC9bUlolcJ96EW0Vsq8Lfodb3Q0yEmdDdUhaVnieZ2cNQKjGUE3qdyEs2SUsG86nljHh/mD/fQWgS3bFm1eMwaes9v7mrHUk2d3Uirk5kEGtUoEX48Xe+OlwGA/z4N76qz30GvXz+fiW7AkOXEuNTzgLpe0UovvAhUzDIJCteSgvFKBATvMU6nYCCN6yDivSapmPmPH2wGKs5BmcibbC0vSMGq0wrpx7zaaesrtvVmrtuyRv1CUYSd+o1J/2MB+mY5aYeaNbrruIrp/d5Z3mbygb+4NE90m8dc/BYA4TQjEFhaYvrVoI/rzk/dZCE2VoFI7XMEvwCutuRcBzAJqIk8EaDunnOcJ1WKuDfQrWKVOces+q0neyE8l7G8l6gdaIxJ5Bb0SdMivzz7/8in1l+nd9xb/D5o5+42L1RhK0mDM+znbLa69rIBtmNLHR2v9BlSiaAF5PHG7uxK0y4fL4erNxCJXHyvPolmTzZ/Xt4WsbxNX3NC4Jde31N+T8vfkyvZWMpC/kiolOHyyLuU/ZSvrdSAXlKOeGOZuOU7l60qVI9HiO5RGgklxJOABaj5e2SNDkTK2Vl2S4hwcEBvHL4kO9+9jbxbwsfJLj/Jrz52Xd5+/Z3I1G7Nw0dfUc3OQHN0vZ13L1EZVGYmPzW+lzzaMq0EiBGIUaLAJULOKOl40VQ10gkiOqcpGixEpWVBaxz16qZ8cxNx6FtkRrmtmcTHeepofM2z8vgg+FJu+C0m2FM5OlqAQ8a6vUIPiYzDjPx4HohdpaunQGaOIq5Y1cis39nidToOioJQ1mOVFlXpjUKAgYdK6aLqitSpB0u8y9bgy6BWB0Ech8+0sCfSvyTd/8a352+zKM7d/hbH/0ZnsTX4NdfBzlDmVlZXkL2/E6X2dRfvMC98jzG1V5f8yLndWP/SJuBiyyIgWWR3yii8CmB92B10hs0Yl/AropL9gJaxaZliLvbXOdendrOOQ9+CgVv3NojPmJ6xzoY1vdFE+qNlubFw8Bi3rFq51QnGch6ktf7oseZzllDHLSrAZwtWWFz27J6TWjvJRVZryLrrqLzDndqVOzbpuF7mz7HNLX6qljnuTkAXhRYAkLKDTMyQ1fL0/M6Oj+XjdFjZSKA6VNmlelnacl63k8SqTO4Vtf84qG7nYh17nDpgBy7SKXdFkUCTeWxJtJYrRBJSTsYLqqOPho6b+lwQ8lh513+aZXxdNS0PJRDuuiojefNgxOWfU3/isOfLli87fiv3Y/y5kef8AN33uejs6e8Vp3wZvWUu3al1Sy5uVVpZjXoYWWKXEPAmkRILQujGpBHpsvXQAXku2Toc/D5qj3jXrXk7dVdvnl2i+PZhqNKGVe9t8ijGrdUQNQvguqS2USyiRCE5aamezpDgtD3jhCE/rzGnDnm7wuLDxLNs4Bd++ybIoTAn33wS/wL57+CAT737peYHz7jP178uF4vm8A5vXeNAKqhJS5dLDUcbgDZfrzKZI8m9hbrMnczvM60MAWxdvzOULEi2/6rzDlbScoSU7+g3YBZL2n1VAB+13bZWVvCvFk3y04cQXlvH6232HTi3wWjrsvO2rU9FEF22WAp5exxbnMbkn6lAMYHklj6Rd5UIFaaYUi5tS0RjJfcjVAG5pXJHQiz5McAqJQykpSFCsNBJNURXKL1FbbVxazJC9/YoABapWBU80SoT7WN+1DmlhK/de8tPvfNLw0+9Fff+sSEHpz1stoilJWdRGmDbmTrd0l2BOGmgrxi0kQ7M0EVB9+c4kTXJAmdt0ANuYWrNZHS1XBR9TQ2sPEagLh8oitfq8BidFgi96tz7ldnLExHZY55vDlgVvc0ldcABThraz5oD1k/XDB7z+FW2jXEHyTVkEkMpZKmRxPXubxDojpGf8DgJPujRDgMmLnH2khV+yE7361q5Nzpb93l8s6nHnuyQU6X7NW3ugrIimnQUyhX/5//4Bf5Fx79bQzwWd4GEj9/+6e4QK19WbuGQ9ibUbhs3wx8DkHJh5y4b+w7z+xUM2vXdtlZ5Umubd0Kgrf80HT73WNO39vZ8AUBq/HcLu50gXmcGc6D5GJhF6S8+BEtFxk2H0rS0jCHSdTOVGXuhxHMmgLJuyWFqieSlPlkEj7aoTmIRLSjXgHQLBC1S6tti56ih3N4en6X9+av8cb3v8/rayF8Ch6aV5ATgBZJyooQP0Gy0s7jzrwyXfCVk5fJ9iLoeRdfk/1I2SBEo9iLA0GbgBRtq8omrEn4qK+bfJA+qqZJsAYhKSPYttq+PDSsTa3lirkVe0I1Hpehxi8r3JnB9KNWWbJsjR0JeUrMv0npbhQrhiRXrCHVadCetDYOiYvQWejM0HnS9OA2EbMZdUeuZ2UgCIiDdBc+Gfnc7b/Dz/7u34Ffirz55jdxP7vhP//h/xn83gy6BUlWaHj4LbAX9TXXuf/GW+fGvgPMwrj+mTLfd5mvU8bivqT2TjJ973F27bLk+FVxya5m1lXHuexz9sRLW+LREWwfaZ563Mowe2JYvWKHplMIrJcNzTs1x19VoXctwxtvnDKNTrsCblmeh6MT1vcUyOpvJfxh1GmlMyzXDf3GUecmFZK1vGyr+9qs96cVEKINRXoZki7GRE42M1ZtjVhF+ou+VQG1jB9jSk38JO3A3iqTKzQ7JV+dQVotq7StJhtik1RMflK+nyL43mFMxOcyQme1vNyKlpgbUQ2tmfO0/Sj+LqhMSkqCtYm2dxhJQzyzcMrQ+uF77/JbvMk3X6+QbyoI9O47d3nw6BaLg5bvufeQf+XNvwUwdmNn7MQeUyLkEzakIVlTS+SInjN00bDJQVtA2KSKPtksIO+55VaDb+mDxTuTh5xQnRnsBjwJWalQfjRQOunGKEiv467vHNEL5twxe2iYP0o0pwG3DthVr1IrIZBC5DPtu1sEix9ovwnzzyqb0mbN4FLZBfkmz2ukrhv1h3ftktLhqzq5j9uUE7rk3i/g1L59tl7MX0p0nRcvi82K7zfjWlVieqEl5g2Y9ZJmiJeDWcWKMPywGJ0AWOXxw1L+dplblzmOfa9dyspiqENPZZbe3TbmzACiA1TGRajxyu6RSsGu6LMA+Fq3K5pYBUTZovzmLoVlEvVNzo64xNGr2pr2SXXAyjQ6uazHBTDo51Zn2j63Wqas36UL/BThP/y+P04S+JFHX+NXP/YJ/t0/+k/rYjprOpHIYo8FMdb6+3KeKkBbuhgydpvKANWUjeVcyVgoHTcGox2kIoM+Sd9bpewm4aDpsDkoMOg2zmoGBMAnwyaoaG8ftIX6qVfxwDvVkiO7YeVqzSY0GoSs+/EW7zqHXerF7o80+xPrRMzXGC/YjejfKVOTcwY8VuoMrRX8QcLf8rijnqr2OBdY1D2dt5yvGjhzuKXBtEK1gvmTQHXaYc6WpLZFXMnSZGHqq6wAwlkMWXKJ7g+evb3lAD7Tvrt/Qr8q67Dv7+nnDhnNnYl76iOuE4Dk8twhQC23/zQz/y3E327s29OkUEWvslRWAuNeTACNlzM9xuBrLjvki35M0u926TETOremcSFeEh0SdUGPRd/PJRumlH3k8sNdEAUYhcXLx5Sp0iTqwxZnEmtb04vFtpq13tK3KmzeDmwfIEUkruBRIv1yzV/92T/JT//Jv0PtNvxG/UN8+Q8+Dd8A5BRSn0HGNAG2Zcywy7SLKllwOLtFSSN4lf82pZQyv5dy8DACWhCCMrQiKghvJA3MLkPEleOgQYLPGaWIwUmkFb1Ac9tRG0+TLLXxBKtS734i7hW8QboMOGXgr7CzlS6mya3kGNcAZTlkMpiVH+MsYuqAcdp1sbKREEXL6DuDyVovNksBmDYgXUfyXhuFMGKEV1rK90nKwOki8Fr/Lnyh54MvRl59Bq//7Htw18O8gu4CBebqvy/1NZNNd4GnrTF7TbBrC3x47i439m1kVnbAijGrCuT5pdwThdURInuFo/fZZSDXHjBqb4fDEvPsY2Tt+5yp7YJww/rseoNcUsL0kfo0UZ1HDh+oTMrmTkVyFYv3I7ZNY2lhnp9HnFs/Z0tjsWj4JWVVre4b1q+oXm9s0jAHJit0yxpz6qiWDBqzoc7TuZ/4rFDiqDwPBU0kx2h4/OQQY7XSY5qkkdydMWohBXYjmE47L2qCWkuuY2WHOUEi0BqqM6PdYSNDn4t4FMAL4g1EZc5KFUnJkDqhxw3J67vzFTPXY0h0UYGq2gVsTIQoGdhS3+S91cdgqG3gqN7QRU1+1Mbz1vETzu43nEZRYfzeEINw3huWxw2v21OqSW3qVAwe0TJDS6JLZgC8yvct2lp9smxSNXR8ryRQo8LyH6meAtDm85w7Sxsc7apisdJr49aCJC0TTU79mqsCzgU6NFaNQUidpT4V6meqt+xWUVlZ52voey0VTJEvVK/z2f6bwy3xhep1EKO+K/9L027upYRecmOHrrtkwMv4b/raPtutSixg7tRSZm4WnMHu2Uf2/H3F8nMq+j5d7+iaYNcZXm03YNZL2lQzq1B3rzW1TgGt5wXUL5qpKHYdplbeb1rnemnZVNklJQU5jCLT0QmmVxZUbwTbJmKdB2IvuDBO2JgRsBrE22Fg/ww6S7mk0C8SsYmYw57jWctB1bHuKpYHVV7EKi2XpJ303FKBrOZMHdMg1GshJSGK4T/49J/Az3JW5FwIt8YE0eCoIgqcOJNBroyEZ9F7PcmcQZnpc7EJ6+LWXW0yuLWbIC5lICkqFL3ZaNagMlpbHjNgZXM73FKvDtD6UXTRSOTUzzGSWJiOI7vhU4cPWbhbPOvmNM6x8Y5VWxNWDpuUsqzAo15bshCk6WUIJMhfYxB2L791lfC3A9WtltmsR4Cm8jgTOesb+pNGgawOqiXMP4g0j3stL2w7jVCKfkMpNZxmuXZqvVXE16vwYYykqKjkb9vX+FG+MTqA5iOXj/GppXRBTH6vTYLJSw+1L3bJ/9Lk33RC3np93zFu7MYuMQV8yvOC8VzH21/D11woNbzMLtngOljZFT7s8tLFDGINyQQZGLQx72OKPF/QnMJQaSBsdSUs5ylkKYrykmGYD5NNSB1pXKA2gb629J0uqE1imKtU12rSpTUkkvRIOifJO/B3Pso7Z2/xn/7gJ4gLSF8H+0UI509J5hngkdLFNTF0Ny6s6GEuKYFaAgkyNEvBlMTJeOGnZLutK1gArcyc8r2uQk1mdqUEATP4ndKNEFQ3SzvrajDSRs24VxKojeduveLMNGxChTO6fe8tqTea6Kr09FIuxxiYc6kI/Je/x2s7zJ8mA1mNVwFitMW7kUQXHLF1I5DVgVsm1bXsvZayi2gwMGWfbDEYt8ecssIjhJaUVqQHjrf7N/mxH/1VXu2Bjwt/wCfhfQfnoF0Rr7JEkmuUaFzHB1yFkxV/sht77IyHG1/z7W+GnIQsVsZ7ZKwsGJzIGBi/UEL9OjHLi9i+OGUvq/2ir5OErgmLTbV+t0gDMmxPAmLCrqNq9q7T+DqMQFbuNFviu5IUH3X48sYC/aFhfd+wfoUhZpEomE4wAbwYoovYVRZ7j2PZn5+ByYCYW6WslQVhJgMQEJtE1zp43OAPFfWa3vMSBdupprD6x3yKJflTEtPDeYNpdc1fnwpuqRq4pSKGION8EhmT2p3F1GGQTTlfNzgbOKhaDvM/n4XfQdlNfecQk1Rrq7eq5wicrGfcnq05dC19HrTH1YZPv/I+78xaHp0e0D6e5zJyy6PVgg7DQjx9MgMjq4BSkBlbAn0eULMJha7oZAWESjwzNH7qk81lh4aFtDxtF5y/c8zZLHB+u8G5gH2/oTrTuFQS2DZ3SZ5ryZ9IYrPOGgVVJK0tsrFU5/q7uE3ErgPmvEOWa5L3w3j+/PyzAHymf8AXqtf5/OLHkMLGshnImjKz8n2bJCHJIt6Q9lW7lLG/71667B7dwQMu3cewDVaXt8zYLG7v4SfglaRxrioN3oZK5cRen3aV3YBZL2m2rI6fN2AKwyQPSl2UvwDsyM7geRk21xW2RS/cPXxES+iYLnh1kk9GF5R+pje5ydRZkmavY5UBrKyRpV9oBLSGwMNqtqKUF8RjT3PUcnyw4bjZsPZV9k1pECsUn9f9uRthfR5xq7jV2py8oB87FOZM7kZrgv1ckeiB3lh+m4KCh5xVHgJDhkWkBCE12vHDukjwGpWIoCURUbtHxZhUE0sYNEZiqa8Eeps4Yca87hFJzLJGlooparDhc+15iMrSWocaI5En/QHnpqExnsZ43lo85qE74qSf8XB9yNNes9ipSvhZDixsgioNml4piLIYyOBiPQk+gjqzcBAxBz0H807BuCTMnNeWtMsGe6btfU0H9Uli/jhg172O/apCio6aMSOgBeNYNqJgV7n2XU9qW4Zyw6S1Pp+f/ygAn/EP+J3mTT5//JPbY3XIRpbxzMX75aqJe5/J5HGLfsvWvXLZ8aaAMXsyEjd2Y1fZuN6/hq8Z1u+yDeZc2ybo1HWAqpexfceeYg8T7S/JjGATcuORpH7DMD4fyghzgCBkdznBfkx+jKUzroXURGwTaKqexmqJ9vDdM7hSsvCF/VWahighypNkjcgjWEX45dfg1xtMI7ABIx+QDj8gujPIJaOmgFflG5bfbpcNXdxEFLDKoDImqf/Inngoby9fcwCr8jyXGUcgxAAtWioijCUfKUFESwp9dpYxqZ6WF81Wr0NNJ1r2biVyu1qzMoFNdKz6mnWsNBgyWcxYsh8x48mlVJgb+e+JEL/k5FSsFVisi/gwDHpffVcjbYXxFgkG2yaqlc8s6gTWTsSYp0HtdNzJyEhJQAwaZCCQ3offeZ3f+u7PYj/ueeufeZuncpdf6n8Gfg3wS5CWIQ22Ay699P0y9TW7x7qGr2F76IwA542v+ba3ocxwymKCzKYwufuz3iOEkAHcF2Bn7bI99r1/HTM7j1O7LL65btxTLsG+2CwmsKp5aLqsjQVD18Ot5GOZ8yfHTYWdbIRYC5vbhs09obutzZDiTBlZpTt7iXd6a7ViJWjcERpl+5Yy9WrJ4KtNBxISfp678R575HHD/ANDGxzhlh/kW0q5O7k8kaQxUWlUoskaGapXJANpbiX62ZvsV9HSRgSkM9nnpMGXpj6X3AWVSvGtHvBsPaMykZgMM9ezcB2bulKfEQytpJyMB+tCnnYj87rntJ3hJHBYtYSkza6OXMtn7r7H44MDfsN/lPTBDAnw5INjvtHfo64eDo1MLInI9LkytWKSAbyqJNJjtrofdljNnSRLnFAZeyy36g2LdyziLWFe0wscPUyYLuXK3Rz3dgk/N7S1YxXnyNoiXkhenXV1riCh2yhwavqAbFqS365RjWL4+ayRlYNGpPimoXFcjmfKWA4qX5QcSF0hbbcdQ+0CurvawVcxHyevX6cL/GXb7PqmJNtA10CYkQmICsPf+45xld2AWd8CSzuB8lZXw/HFUfsH9ostXnei3ieYuM8u803P+4yS3RgW8tvbF1BNQtJMkAFQHaxqneicotGSJIu+jx0JSxZ8yL5anVh3B2+oE/44YGeBGAwn53NOlzPaR3Ps2uB6oVrKkCGXqFkNt0q4zUSsPSedStAylAbm38z0ifkHifUrkrW3hLRRsI6UtruUJC2pTOU4ZU7KpSwxGlIvmsW3CVv31M5rt48MpoVglGqLAlnJZwfhhU40a952jrr2+GCpnUeALosQNpVHRLt3bELFOnjmtqcSj8/6JpUEDl3L3XpJZQKn3UyDhwNPWuSgEL0uxkWiNxBEHVddvo9ALg2RIKOAfx2xpRQlM8b6aDhdzUjnTks/c+lN8yzhznvE54m3qRAfLrZLFzOAVFImbmsgRAWyMh13alEMP3/4k2BE6bhDb+ZLxnahxe55/3miulMbJuStF6/YoQTCOwFIYSWMYtXX+/wb+w63neD2Upxp8sbe8X1dX3PhAy7Z58OO37KgueQ4AzCTs+kpU+tVB0UDAnIpRdFmMKEEMWxdr1J9ucuKVJ2QqKKqUWi7iravCEuHeMFMBOSLflbppmt82moWgvQkcw6pR+Qp0i6IQYAeE9ew7OkXnmSizqe+gFj6/WD0r4UdWnQkKdtlMCiGDAoJiIlYE8bSQpuTICGXIiQZEihEZWLFIHibsDYQkxnK2UMWjLcZ6EpoiXufLBUBI1oK32OwJGqjPshIog25zqWOA4g2/JY5aVIQrKJJI8NvpwM1SdK52qahRDLlCxGT0PY1dDW2O0D8ARIa7CZhuiUST3V72yIxZ7932VilDGrntSFbnjrgMTxawF++xa9/5nP8+mt/BJaCfFHga4B5D9hMfpiJTYDklzPZ71qeAy5fCCLKqUyChRv79jTL2F18BLEmQNUw76chcB4S7Zdo7FywXZB4apdVkuxjXl3FKr6MRTk9/lDOWNZT2w189q7T8leUkEhOhvgg7STvp91rh30n78dK6BfKxmpvK0PHH6jmiD1XDSrTayK/lGwlMbgN1Ke5Q/o64WfkBlMKekhQlm90QochNNAfJ+gMi3cN1ZlezHVtx1L7cpmCxmSF4Vv+Tb9PRuNwy1wev9GOh6XaJGZmV4pol9iSgMgJCcldckuyPiVhs6l4wkKrQ6KlNoHGekQszkaaRoMo7y3BG5pZjzORPhhiqujjESzgTr0mJsPcdsxtT2M8qzdqfnf5EWRjkKXlLz/5Ef7cq38DQ9wCoSoJdPlL1EQCgimgUxJWseJXN2/xujvBSqQST8idDkEBrpnpOAtz3jp8zFc3n6I6S4SNHsOt8qWLZEF9wXYwewxubYlOyzfbe1Gbu3SCOxfsRuNR20bER8QHHWuBi/cIKJBlcvWKsyMrq4BRZR9bGMdykbW1NeYvWeftsKz2vn4d24lrClC89d7uYzknmax1YOhcvX3+1z+VGzDrJc1IHAGf6QS8NVgmv9BVjuAq2zcoP2x54TCY8iCaHmKXcbK7Xy4vHBwDBdRRemyp8wbNNoRGnWtKYzAwONuywMoBQqzILbiLjlMinDvsI0fzREsWqpCZXm4Epso5uDXDxGF6fUNiAg/iDNHl7xwTxgvRaTDSnAVMMBqc9IlYZU2BEqgUx9YHLOrIisZHoUcy/PSCGA0ujIkKPOVFeAk8yJcun4pqb4hSeCMQnMH3Dt/0xGbyc0iiD0Y1Q3IZ4spXqo0lFXPba6eQajPQdmvjOaxa7h4tObHzoZwxeqP16BnIGpx1fkxVUkHliNauJyG5qC2BgU1XUVeeeeU53zSsT2e4c4tt1VG6daI+C5guXAwojEDYDTImE3apD/f+IpC1A1oNGYzpRL8vc3jF/bC1EHoB26LCDgHyxWPs1cYqL70AkHZj39lW9JB0FXCJr9nKvsFzOxrus327XHaY6x5+74LqqnMoUQfjgkYYRUJjQuI4b5VSw0FIvIA+Ztx3ON/EkGRJFi3dE0idwaxUhzEZJa6WDPe0DEV1GPXf4CPy6wKk1JNMhNSSZIlEIZmIxIhtA5L1TvS75Hkss4hHn6oIllizxegc5pvEJHBLiMTdr7j19/BHyD4/oHyumLUcXaCalieJsr5MJqfFaOglt5MXSyUKnInRoABUcqE2gXnTsTEVvmTyo0DMYFqcnFzxNQOQRcaGJIv5ZyAtWKwNOJPofI3fzDHdHcS/hgn3MEcJS0ROPPAI+Ca6qMgLjlTGU/luk8CgBLEhTvyUB85B3oGHG/ibr4JYHcLmCchj4AzIkeqFDDdXm+z3E1fa9AcdHp8TpOyez4eYCm7sHy0TKaW9wlajnRizvEMONkswPMVirwKXpiLrfwgVIVt2WWC++/7UpkDWzrbJjp14S1K6VGBMCQXaqbaAN2wH0nmeSkbwM2Fzx9Ddhv5Q2VjJokn2tWBa9UeStYJLMr/EDaWBlm21DDnWuYrP5JL1XpPozia6W0J/HKkfW+oTBZ6qc6E/Mwo87fxkpYSx6DlKyOcyJQwY9S/NkiGGSUG7xg+6wEmgztn6ziDzgHGJkNlYksBUpUxe6FrHIw64eyAcN5shCVE7T+OU5XyemvxTJSoXCDm5smpr3gm3qW+HAcRamI6Z6fnk4WNW31Xz9tfvY84df/sPPsVHZs/4aP2EhWmZmZ4D0zITLfm2JM4Yyw4r8RxIz2+1H+P/+Jv/FP2y4ie//6v88PE3qSQMvmtmehamJWC441aD9nPvtELI9AzEBsixar48JXnvF+gP6VUfuj7L7O1Wtdqky+XvMMYx+2KbXF44MIftGPMMum1lH2tI0SCFZblrO/fKrvj7ls72PjzgCpv6sL37JLbjrt2/J/uWpN21VDMusRsw6yXNTgXgB/aVXA4+7bxetDL2UmFfpivbLqBV7pmdwbRVVrjPctBU8pgJBbRKuWERgh9aqHsNMorgrni9RwcBXlDgJgcTptdzE/J2zdhq3awtsw8Mx1+NVOvA6p7VicVvL+pBj1OttTZ5BKEmXyMkbExEK+BMbrsriE85yx6wm4jdBL25KkOsDCZEpEsjyys/lhhRA6IcDEXBuKjlhlYz00Uba0yWiQrx5pa1khfsZRIkQeocwSQ2QQjBYEyiqvzQncOZiI+Gta8GpwHQRcuB6/DRsqImJmHp1YEcVh0rW9NbizGJIIm+N2Mdfe7elawoeGWAJijQ5hVcTC5BUDHIHnAu0AfDatlgnjmqU8GutfTGrRN2EzNrYQQELwThaRznQ0aiTKrxIiNra6xOqbP5+QW2o+wBqa4CeqXsM267lzmyc3/uDU6GSTrtf/8lJu4b+86zqWbWBR2gvYkO3WvYP+X5+8K2W5u9uO3uP5nn99pVr2+hMPpHIQkP4FyaAkDjZw7CvWYybchksVS6EsJQnjF0bO21JKR5psFEvzCjuO/03PLn2oGVtQ10AdlXeGWRGRkAGmLSWCnmfX0u7bNZdiCRQf7JdSy+hvy1JxqMJgu2S+6mlECTJ4zbS4Q0iHoWp8U47wed63xEmQoCxgbNV00SMX00IHZIZAURahMyQ0tDhz5nqWoT6MUSTS6vl1JOn0/MpPF3Lr9Z9i36u6gvIgopGAIRY7RbVN/VyOYI034U8+ox5scTs08ucSeJ+Ns1/OJd+GYCcpeZVIKH7ftkYACXdc1WwiUAHXCKlhI+1u9ehL1kAxJIw4CbjJF91MKrTLafp33vXfP+VMLbnvv7xr5jzHKJAHx5DgrclmDZyrg+CxEx5qLwM1waH+xf9+x5bTce2adhd9k5P2c8Xwiky/otA1nJCsGZyes652pZISOTKYxrtQJ+leRIqITu0NAdC/2hdgEvIBaAW6r/MLlSBEbfMXTRtYkwG3+fUOe5KHcOjzb7goIRGWjvgnhh9lBlaWwPrJICWodjGbeE8biml2ENniwDWws0PnMbSJ1QreMg7dLPx6RJqnMie+InbBURE0mVxjmxt1SLTmMJb4nR0G4qHsUDZWhVHc5EuhznOBNpqh4qTYysuwpn4sDyTUl4vDngldk5RpJ2SJRAqFZ86vgR8WPCu49vkZLw3z34Hg6rjsoG7jZLjl3LK/UZt9yKhVEx9Fo8C9NSS+BrseH//Ht/jPo3D1gs4Tcefi+/cveTyDxwcLTh3sGK282a2noOXMeB7SZNSyBU2gVSCtkiP+r4KuCfsvPCQUCCYDujsWXQjsUSItL6/eN9n4lkVpaMMcwUhEIdq0yki3bLhC/t6r5bpTLFL65Ihkxjokvtsnliynzc+YypjtYuiPwinuwGzPoW2AXBwatsUmq4fZCdgWSuP5kX2wqa933GVYNwOLedDMe07n7KeIlkpoAMi25SGsQH/VwwlMk0H2JSXljOZajjTjqhp6ypZHrBrYTFA6VpQm5dm4OHoYsgOplU68LISpgQGbrfDYGBnqtJkFIkiRkCCwkKwLmlCpUna0mN1c4fxamVDPmEWiVl4e2S0m8ZwSpQECuIQC4zNJLwUbIofNAyvVy6hzcj+CZ5Ie8NwWptd4yCtZHKBc42jU74JtJHyyYoO+u4VkbWmoq21y6H533D2lcs+3oodZSMxpmceYmtVQALIImWh5Kgtfk6jsFjMokUhOi1XDJGIW4sNiozrj5N2BT4l3/9F/jsN7/CFxYf4/N3fzqzA+TiRDa9d6ZtaPeZXPb6JffHyy7qS+CT9pQEG7mwiBq0eqbjpLy3E7R8y8/1xr6z7LJAYGrXBamGba6Pau0V/9x7zKvP7UKX0OlpTGms5AYcGYwqgbvEzERgXFSO+7PNSirVzHnbAePJZYTVOYNwrsmE0gImTfUcTJ99Rkhjt6vJb6GYkQwoVAmOJDMhVBMlIH0gGZOTP2b0NebidZUC5BlGQAtRHcbMYIu5/LyAWikqS5gYMytYy8WZAktl8Rg18UL+KtGof+lySbxxiZiMgl5AY5WRJaDaYmgpvI+WLtpJaZt+kOTS9OSFbSHifAITRne+aMMqN+VzS0lIvkbSMWZ2jP3HEj/9vX+Tn+7+HrZK/OoP/hh/1/ws/Fd3YPkM0hoFpXYu5xYr6zLfEVFVMY+WE8rkCFMU7sLRP5xNMu4Xg/OLh78ye/4tPK0b+0fPBjAqJYaGVCKTruo5SJ6uswy6/t+nK3eFXUjU7WEL703mPc9/XQVy7TJCpvPlLpBlFLSKtcHPFJAqMcfAzM1aubZL2Kz1OzB7rQq0+wOhvZ0BrMzklV5Ubypo90BEQY6ByQukbvQpyUIkDQwvjVUSITc1CbXgWt2xPzCsXjd0tyPNY6MJ95zstiHwv/lv/zo/+uCr/IO3PsH/9Y/9cYgux1k6j4rXhEtMssXeSlJYxVGbZcQM1DTqNIdtM0OtdDCMUbD5upsqEoF+VVMtOqwLQ5fCEAwn6xkb7wbN3y43sqpzd/ceLf0LSeOxEA2bXuhry8rXLH3Dkd1gJHLLrYiNwC0tmz9Zz4hJeLg8IETDV/r7AFSV55N3nvA9Rx9wx604shsAHsaGf/8rP0X65dtU5/rV3FJAHDxxtDS8feeQB3fX3D5ccyvrMhcmW+mSm6z+tn6O6qJVmYklDDIs/Z0Is0jaGI2T3CRGiGmbKbl3rBeUiatjmnIfmLTfEUzvkb0sxkuOe5ntLgv3VH7tVpgMZJPh/bQt0SIXT/0Ci37PNlfZDZj1kjYIwE9tWje6LzCfAFRFCH5vNmRqVw62SwCvMuj23UNb7BMZHi9QdXcDi+khUu6wl9lZScwA8hSqs+QaY8iZiLJGDWN9tvFZ9L04l8zYqjJN089ES0qEoQNIuexFCys6Pb4EoxnwkJB+dM6SA6KCpJleJ+nSoYSYkD4gmx6hh84htRuvn5XBQUrQzHp1Lvi50DeGJNrVIpmUtaSiAuZoR48CRgE4p6LwXWcQAdcEYhUVVCoL/byyj6UsJFlSFLw3w0LfR8PKRGZ1z61mw8rXrGDIpscktMERk9AFi5FEU3u63uE7N35MLC1480s58BAvKmTZSnbsgj/SgCoF/T59b5G1ClsWh/sv/dZf53/5hb+KAX7s2VcQH/n5+z9zcRwWEXjYArJKG1pxjtT1FD2tYayKMOhr7bMSqOyxFy7vuGpfuQhoPXe/nUBjZBheM2NzY9+xZvb5muncfxn7r8Q2Ze593pi98v1LAK/y8r5hvG/8yxX34r71GZNbZCoInyagVPY9kP1CuVyJgVFUOtsODC7RBavJiRIVlc/HnjJ7J58zCOrG7PpSGlhf4/VIQ55HAHLAMoBtMYHXHkoqymzH61f8rZTvlLQrViUEl6+G0c9I5A7K2W2HZAbWFoAxyuYLPvtPG0kmN/sY6hfzKZdrlwySUvY9elIxCb0knA00ztNHZeeWU05Jhs8OUXnczgZCsMSwVdewA6blJ1Fye3kZfi8VI87BmUnKZvY1xh8gH4PXv/sdfu7Z38D+fKJaB/7pf/Zv8OXv+xQfvPkG/P5B/tEtqjMwHYNygckrxuh4vHA/KfooY5Zi8q8cj732Mr7m4kGv52v23mvTv1/mlG7sv/dmMNvMrMssZsRmGvjmOWVvPLGvUuQaifatbrvTIPg6651dwGtPMhQm99nUNe0pLzce6mXCN2PlSDIKSoQ5dLdk8BFlYouN6vfGOg6aPnatXQozXjIm6R1Em3S9HPS1MINU4huBOEt0twz1iTri0OQStqSgWZcMZpbY3DGs3kjYlVCdQ32mMUcS+Bd/86/z5371r2CAn/rql0Dg3/2pPzkwcU0AV8oV4+ibpmwz28VBV9hikAO2q2dijutyd98UBKwuw4dfIELwFuzYgd1ElSDx0TDLer+1DSSg8w5rItYkZlXLqq3pgyUlqGzMnWojj7oDjESO3YYju+GWW1PlwO/kYMYmVDyTOc9Wc/pe46K+czyoj3Am8KQ64I3ZCV9v7/Jf/t6PsPgHC+p1ItSjCP4A+HWCWzrCw0MeHh3w8F5LPK847BXks63G5qHWcdIfReIiDt9fksZI8cgjdYTWYjZGhfij/g5uHTCtV73gKaC1r+pkeK/4xQRkxmRu9jKM7UGaYA8IPdW/uy7Z5gpW1pXHmLx0qY/aB3ax4yMzePxhZVduwKxvhU0vvpGxpOq63UE+jO1BRy8tp7rsNHac014ga9cGVFj/HOrOc/a5dDjcWrijE4cLaQCwYiVDsKBt0bU2Obo8Ga91cdsfQLWUXPqBLshz10LjoQo6WZcuV8mBP7CYLung7kup23i+0uvFi2aSXQct+4i6fYoR0+dFcAZXktXsthWhPrPESrIOlyHMhdgkoktQR6KLmhnP4ofWRmrniYMYvFDXXplNQYEZsZne6wvvOalAfIJkxjbq5buseg2ANk1F5y21CxhJ1DYMwUwRn585T0rCuqu0u2LpYBhFxRWjDPowqUqYg5641jSTrAXxQiQhvSHljowhGHxvsasseNnptfzRB3+wNfQ+s/wG3N8ZR+XLFIrsFMgqmcPK5ftpzwAeAFgzLnJeZLK+zvbZ9utgXbbxJR9dgujJMQfK+02AcWMfxqaL/T9MZt8e/OoisLvzuGvP23/v55Zga+fvnYPt6oYQweT7bQChBvCEoXxg0C/x5My4Lm4lJkKl22rXwsw4zkEHk6Ag1aLMLqLqXG0BWgx6WkUcdbhMWXBZdSICkrsTDd+3gFkiuQ14KSMRYmXy+WuwEUwaSt2BgbWbMksrIeDy3yVZIvnClQWyMCRSRoB9/FH7Tudg7ywhqli8iOpkDWTtpMw5ZyIeGQKV0W8JeBkF7QVtJV9HUm9IaJlO6air+pz5+mU/aXrB9BaxicN0jnwA3TvAJlF/AAcfP9cmJuTIE8YTGIL2DGRNr/dURHfXpgv5Yfw9b/zu3h/X9DXXONS1DnLhfrvi+Df2bWVXglkpaUA9vSd2xuZWcn163+yTUMnMry3tnV0ACi6XWpjavlLE3XOYvneJD1I5EBR8iSkzo6KWf22gWkK/MLp+t6JC7HPJrCu9V1JmwJb7xvQ6N0mvXQBBK0VCkT3JrF+TZEjKmx78Ig0gUawTqU50x8oC9gtU9kRUIys0QnsHkhO644TdQH0iVGdpBAMEPvvuV7fW1z/xta8S/wmNi5DspzKAZTcjOClRS96Sla1EDTkZU7rDJ5OlYoDkDeIiYlLu0q6fYauoUsdBCBjqxlMZTW73nUMkcbKecdBouWFMwmHTkpJg89+9C5jMup3VPU4iPlmiNzyRA9ahJtbCG/UJjekJM8Pdesn77RF9sJzIDOeCJmyS0AfD10/v4IPh77afoH33gMO3DXYDYSaDJtguK1lC/n07CE8aXKtsb4+uB/S3098yHgakUf0wYwO+cySgqgL9ukLarJuWY9T6NODOO8zZGnqfb4lc+VIaX11mBaSKOqjLaDc+8mcf/h0+s/w6X5h9lP/06CeIIXBBCH433rmGXSC4pMnzXRvWZRfnnKF08LJdJ0m9fUSeD5MIugGzXtJMbrH90ra72HruB+/Zb9f2AF4DI0zGAbOVPZmuduTiPgXIKi1sk5HRwaVM6bVQnaex2x9jwBFqbTVbjh9L0JAgOl3c25U6HT9X0Eu7RmkZG0BzGrEbFXk3YaKPlRJYIWax99BYpDKYTreVUFbJOZNtZRCK15ObiPnGRFH4S1EBFkmGZAzG6/Fsa6jPQJKKQioYl4iNJcwj/cziO4uYhK0CMQq1C1gTcaI3sY+Gtq2wRtuPx6ggR8rZ6FQy1d6oYOJ0Ihbdpg0VfW+Zz7uhe2JdMuhZ78RK0oy5pDHAQPcvQRuA8UJqDdG68bOSOshRG06DDBHVa0lGAcjCjPjNVz/B5x78/kBq+8LBxy6OQZOPM8mSb1HgRUh1hdQ1adOy1/ZmCS4uzi7dlskifxfIvcou6HLtPN8TMVy1wBzKkm7sxq6wLc2sl7Et9sm1Pjjvd8X2V73HJb7mqs+CAWhI5Z4a7lH1NQXgMd0OoFVioFwaMLw8ycqWRW1hk8YKlUUKYONYGuLaonmRcqlgPn7W+ivnkKyWFkj2IUP3qHJemYE9WBzPeQTIxsRQAbTKsSQkBdqA2BetDsmJIE2ixDB2NkxWMuCkuldaRSSEpOUgyuiadMsrlL0MOk2TJlOg0veGECqqSv1YNIKViMvgmSZqClk7M7eH36+AZvlwUVSI3m+Jgagv2hpzadg3SUTiGnkCX998F+986jXe+pPvYTfwjbde5+vL74InAOvJB+2On+I8x7+TVRHd5Ccqv1N7IV+z/xDDV3yRuX530xe5X1/srRv7NjArspU0u9SKTtYuO6u8Vx5fdF2yL+bYfR/Giofpts/L/V8SnA+Mo3yjD2zLaXJ9Op8FLSnsD8xQFjiwdZM+T5VuXBhYps+d04OyfiWhf5vRl0y1fxScQpPriwi3e6rGE4KhSw12oz+SbVGWr1NmVn+sB6lPlJFVLRPVKuVtNWb4rXtv8bn3vjRcwl9/4xNEmxAjW3pdxidMV+ZA7ZAoQRlbmtSZrL0ZATxgTI7sCOJbNwZ1YrU6I0ZD31uMaBLeWC0n7LxFpGJe91S5U27MMia1CbiZUtv60CE5AWNIdNHycH3IUb3BJ6OC8LblteqUVaxpo+M9bjHP3eKNwHJT51L0xHLd4N9dMHuk3SD9gZYGhplqjBXN5dKExbRCrBKhSaRKv09sFNAqSa/uOBGOPfbAY0ykbrx2/531hGhYnTWYE5fHiRIxmtNIddphTteksyViJwN8SnrZZymNetr573Jf/tkPfpF/4dHfxgCfXX4N6Tp+/uhze++NF7WBuV+Aa+HyeWCy5pNEbrSQ1yXCcD+Xslo9tkxiLsYkXTnOlDn/gnYDZn0LbPixStlUyhoVV+70nNX/vu2fs6Aash/PY2TtHvo59MKtAGqLvjh+92Qh1JrtsF3a0rUayzNE287WerMPWieSy0JgoNUi4A8TdiOEmW5fL8egIlkhYkg2f1YJIvqIa/vhZoyNJTSW6AS3RgEtyKWCcfgb0Gz5HpMQVSA2RsRZkrdDS3bxubzOkbVQhNSB3RjCzBArS3KJvo74JtLPemaznsoqeNW4wKzyhChsukqdQOeIvUFMQkwg9nYQh6eUh7iIuKhaJl6IvWFDTWyEziQ6G+ijIRStkRzYQM4iANE7zbygAZF4yV1QhJgsqUrYtcnXoGSnEmGRqGc9s7qn75wuDjYMncb+4g/9cVyb+NH3smbWvZ/eC/BsAVmFlVUYWiJIJTBroO9VtHRSYrjvGGnimMt9sSucuNe2Onhe457MZcSXAguTyXp4qcSK5X6Yrh1vIowbu65JwYBlWOAkeY6veR7SdN3NByBg4mtk+t41Dn3V/bX71g7oNmihlDI/k+fxEqjI5F4SGYOLSeZw6o9MP2azY52DE6cLbtulEbQqC/+8niyBUfEhw2c7Q8w6LcbHLUBrC+Aq323fJYiJVALMzEqd6kRKyNNfSUgEEC+qz2FHYC3aRHBBm5HkrK4zCVf3pCT4oLpWwWv5gghgc/KkuMECbmUGWEkkpyj43qpgfFTNl1LWDoIMISRYmwghkjAj8ysz4og5eZIDSNNPAs+Q/UmlLABng3bejR7xS/gG9H9zzv/rT/6P+aGf+AJmGfg9//2Ev9XA+wlYok6zBHMTIKv4isFfiPodlztCXVW6KzvjrLwo08crTC55fpntjuvrHHdy/+5qlzwHSr6xbwMbGkpNmbu7VthZMI736faXSpekveuly5vcpGG7C9vsA7T22WWg8SXntKtxWOZqyFOak6EyJBlNmFdLsii8TgWx0nNzK1EAy+XugBHIcUus2SqLMt3oW5T0mgGx1hCCcOdohTWRDzhmbRqqpyq2bzsGCQDbZvbYObgMZA1JjwiQ+A+//59CEvzQ46/yG298kr/4mT+OW+dkOgxsYj3mNNlvBo1HSVqZETOgVbrDm14IMIBaeuk1+Y9JGFO63CaqeUcIGl/EqIl5V4Vccp7ok1WmVrAczloWVU+fBJMEn0xmYpmBqbX2FcEYRBI+X0gnkad+QRsd96tzKgncqVa8Nj8DoPUqoxJqweWmW76zmJzwiU0aQKlYK4M5VCC9wXT6vaObajubHJuWxKHgF4lwFLBHPU2jaGHtPPO6Z91VrDcVnFbYjY4pu4HZk8TscY9d9cjZkhTD2HThuQk92b43U1KiRdDKoR88e3srxP+B9l04Ylwz7Fp+bVvuZP/9tF0KeMk9Zy6+d8E/5ftuqjWsSck0YApbovCpHHf7mC8SG71UHdy/9W/9W4gIf+Ev/IXxBFLi3/g3/g3efPNN5vM5f+yP/TG+8IUvbO3Xti1//s//ee7fv8/BwQF/5s/8Gd55552XOZX/v9lezazLhKp3BvF0cTt0fYOrEdutA1zyS1/GXHxOoD5kzScBgb6+fYxUQIJsxSGo1lXCtUkBrTAeL+UJM+agYEi2llJDp0BQda5Owy/GjiElwxFqFZbvFwa/yGBRbQi1ITQmU3bHzlDSB+xpR/20xfaRMFNR90GQt4+IH/9t1R/v/NPseITeY1Yt1UlL89Qr+n6WcEtwS6UwV2faPrc60w4ndmUwSwsnFf1Jw/npnNOzBatNQ+stITO2DmYd86bDuaCCuXkhKjZqt6ctdARSMESvHZ+SN/iNY7Ou2axrluuGp2cLzlYzztcNfTDEaAhhjOq2Sk6SYHIJiHj9VzpLJaPXXxkAgE0sZh1N5TXzn1v/FnZXMJb/4NM/x//+0/9z/uPXf5ZY7ocSRJSxNWk/m4ogaRlbuQtNmtVI01yYpKWwuJ5jUybIdTPiz51Azb77gq0AYmsxNQ0s4GJE8QIT9nei3fgZtb3D5JpjejpW97GYPtyHv8D+F85nZ8d9x5fCHNpeEKW86BlKBMMeHzVZGBVC0MiSYtDJMjmpEistMSmZ+phZXbHSwGcAi6wGAKVEbZhTYkLagN0ETEwkZ3LTkNwZKyT16+Ufl/8rOlmEiPQes/HYddQW310+7wzE2VbLXfR10cxwJ9AaYuvoNhVtW9F7h48ylHlUzlPZoN0Qp9fepBzIbPsaYu6yFQVibgDSW3xv6XvHuqvp+oqud8ouHsCtfICdSVWK78mgVimlH37f0iVKEpXzOBPVF6YeSadI/Dr8PXj2n9zn7/7tn+WX/+Yf5fQ/uQu/DPB14Iw09FKfgFYlOVf+RihtyZOziHMX7imBC6/ttxf3Nc+1yRjefa083+evbnzNd6YZzHbVBnueX/Xa84CjS45xnfF+ZXOq3XO49Bjb66tBaP2yfdIE0MkWnYIO9XnErTODKVd/FH9SnYnKm+QksukZwC+y7ymvA0OJHqg/6Q8S/WGivxUId3oOb685alpuNRuaWa8MoIqhiyFkf9ZC8yRRnSdmzyLVUoXaq2XICRFIUdfX/7t/4s/xf/+RP0EKltmjhGkVsJlKwOgJ6XOTJVdMHxUc8WlIyg+XPoB0Zpg3xEVsFbTD7USH0WRt4BhLUykwNuF7OyTNyy/Sto5lWxOi0XkcBaEKYBWGxiNCRDhpZ0NypDGemITz0PDEH7CJFZUEPj5/wiePHnHcbDhuNtyab6hsUBmVlf4QYZG0uqdOhIOobLs6Dr/fAHCV65UbwTApFVUgK1Ld3rBYtDgbaSoFsgTVB+tPGuxSRfrtWjsiH7zvscseWXckH8BY1cW8anzv0QEWH5B1C2dL0vmKtFzx2/bVIcSPwBfqN/Yfr1ipNppKUpTnl92L16kO22MDK6sMqR1ftcWonMj/TPVIpwnSCxISV9iHZmb9yq/8Cv/ev/fv8cM//MNbr//b//a/zb/z7/w7/MW/+Bf53u/9Xv7Nf/Pf5Od+7uf44he/yNHREQB/4S/8Bf7SX/pLfP7zn+fevXv86//6v86f/tN/ml/91V/F2utwZP/7Y5YJFRANshNl0RqvFXAXEfi99gKO5YU7hgzb5f2nmcVCPd0ajGOHkGTy9mYUeS9gxtDy1soAcg3/rGY0yOtMCWjH714RbbdK+IXQ3omqCcK4v58pAi9JF/W20+4fOkGjmYcINsRMedS7Stoe1/bERU1srGbZh+zzOOETLkMBJ5cgJpJEzLqnKt89WGxfwLQC7mk73zDTskotmYQYLKk1+DoRZgFfW/oq4PMEWVUBI9DWnq5zBG+1O2LpZFWy5fmcU19as+gPFXtR3a6MdIvRTosiCWujttLN2RSSKNMrT+52k5llLu/rZWhPvz3JJKxJLKqeZy5qh6qt7BEjhbnYdHzvZAiSkUEAPhkziO0jQqqAeQPea9YcLmYGdxdgW9mAb+HqfW/Avf0Z00VWmry2BW7tsRt21n678TOjmZ0yw2HNcJlo7x4rGeD99hK+5gXt0qYIw2uTrlSSNyrnHseFWVk8DZT1jE9MQZEphR1BWT+5hXqstARhF3yObizXkaCgVwls1FfnzopZq3GY/3xAfCBVluRUYyTtfsHEtRJWqqeVEB+wbU40RCGGMSmQRL+jKXonbkwspQjJW6LVMkRjRZ/bgMuCuyIeb4UQLCnIWBY4iLRMzrmUA5YFaBSGFu4FKzIJRJuNiOg2A04XR18jOWhUwpYgmdUw0fYffg8jKhBsjGpZkjrgEZJa+OJd+MIBnHnwp8BjSM/QToRhZ2yV8gcz+gwzji2MITmr67bC0r6QzS7/xvO75I+Xs2scasvXyOS1G1/zHW1jVzBd/2uDCBlfm1qImlQEZcbDwCLdq5s1fMj2a3u7Gu4G5mWb57GxrohZLox1GNd7u+M66ny0G9cYn3T5L+QGUDkRnzsZ4sGtxnV8aUhlPEPDKZlMLTarYGhSRN9PDuIsUt3Z0DSeV4/OmbteO+WVkmkzqYjI38vlCof6PGLbmEvhtHRy8P9lX7JP8ppElwSr11XmpbDzZAesSiJIjOqy3PT3G7+DBAWWpApYF7SSoxuhAudiXnIIzkX6zinYZRLRaEWJz3FJ8g7jIn3veLqas2g61XTM5e8+GvpoMnM4suorKhPZeEdXOZahps41/0/6A85NQyWBSgKvN6dUEnnWzzk1Mx6vF2w2qltV2FapyvP4XGMH46L2H4kyVA7hNIaPs4j4/y97fx9sS3LVB6K/lVlVe+9z7ld3S92tllpfjAYzagxCRgLxIXkkJMeDIBy8CP0hm2c8PEwEIFsBBA6Cf0SEQwT8YYgQbxwOHgMYSZYnXgRhPztGoxYY8RjxIYvRgASWZNToA9Rqob5977nn7L2rKnO9P9bKrKysrH32ufcKQfdeEefsvauysrKyqnKt9VtfImSYE/HQ6q948MrheNWislLdt7Iey6rHzc0Sm7MG9sTCbikal5ZPMuobneZmBmjRjN8F7wecIN4ASiJPKLbDZgvuukFeYI93L74WYOCl/eP4aPMQ3n38cuQ0chpI9aV93JcmaVSmDGNSzbqUeiXj48PYpm3C859604/a7kG3BWbdunUL/+Af/AP8wi/8Av7Fv/gXwyCZ8XM/93P4iZ/4CXzXd30XAOBXfuVX8MADD+Bd73oXvv/7vx83btzAL/7iL+JXf/VX8brXvQ4A8I53vAMPP/ww3ve+9+ENb3jD7Qzpy0aGeHDrHTaiWOY2ULLQ7ww92MMtcG8674EEEJHTVFEIAA2FTwy5QtIHlgFynLxEatlOYtKDgiEVo9Rtl2Vf1QLVWhbr7jLACwb64RzeEvqrwPYeCT0U7yeZx+D+yxXEBbYTSZ6IQLFEE2ButTAbA3ckfsJGPbPiNWtS3uLc5ai2lxBF03mYzognWqI4cQvUCnT2C0J3iSTh44LgFgy/BfzWoF9Y9AuHbiFhH5VW+1hUDotKLA0bbsBe849YrUQFZCgJZCH2BERrkSyXbCzWvYFdOFSV5O5yoXphyMkVLi+EDCqQ5RY8VJjigdFZ47GwfTwmUPCYkPtsYNR7gQyJd0LyLMZnRfNkpUBWzLFjDLBoQF0PPtuM70kJvJ2j1CqYHmsoWZTPf9/2UgSIpovwHkrGgcZ04DNjGnTp7DndCWYN6MDYIytvP6eg3x0ql2fHgPhSyk+U14Q2EdBSCutVJiBFbywzHBvzmaj3VqqYMACvXr8x6TikD78SA4TpVclppUMJcyfAaGiCeiwRYQhTZ4BaB+o9fC1CvSiIAU3T+1USDyhcYLgAjn/SB2ly38T67ZJxVxpuUiOGzbAjsGN4K4YOZwnc9JJLizT80PTojBXjCPOQTYCTgaXjDSBVXDsHD43eE8h6GMtS8TEFtFJewwC1iMptqECZGkbkMWVYtZLIRweAwSReWkwV2DgQNrKPNgAnXllI1m2VY1IgK/IaAKgsyHtwN2N82bVt2Dn6GOYIYwXgnL72NUrv5EkHXvOMowDsx7QnOxsPT1ksVrHLK+N2PDaiYfwCx+4jf5XCp3Z16YbrI6d6x1aq3PUryUlFTDHXIjnNaRX0lnroy9eIeRVz8jWjv+Jhr7UgAvre4rRt8PiNyzh74hjUSRREdUYxWbvdyvpXn0hooTgHDNEtpNcZgLCQ74k13YXpGcsnGa62aK9qbuIFSeibS9ZODscLyCkh6RT7C4U5yImewd6g3Rp4RzBa5by2wnAcS36pZtHBOQPXiw5DTtKekIatu20NOgacI2y2NRaLDsu6R+tsrHh4pilWFnUPT4zaeGxdha2U78XKdqjIofcWMMDWV7jPnOJafYZKKx0+tVmJbnSlHzIEWAZ3BkYLcvneiF5pGH6hz8WWwJU4C3Aj4KKEJAJcMUwt/YtTtRh8tn2Fm6dL8FmF+paBUR20OWEsbjiY1gm/Nxpd0mv4emmxJsJQrd0M79i2BW9b5EniPRm86/jl8TgyBsUCJqX3wkMK/KZe5ekhDHCpcmlGkxzDeXGIlHnlw+Igqw28Ocp+DLkPt1HV8LbCDH/wB38Q3/7t3x6VhECPPfYYHn/8cbz+9a+P2xaLBV796lfjAx/4AADgQx/6ELquG7V56KGH8Mgjj8Q2OW23W9y8eXP099eFLHi/hIv70j6eVCmdt5DPuA0PyWez8wUhOwjQHDyfPMzWoVo7VKc9qlsd6rNeErH3cgwb8eoJyWnJS6JFq4h1tQZWf+lx/DmWUrOduLrazQBktdcI2/scmMTtly3QXmZs7wFOn+fQPbtDe49He43RXiG0l2XRFvCE4BsjFgcKL8lw/RJa6GBvbmFvbUHrblAWQvL3HaGGYX6GEBCpQhXcdcOf5FIRbzW7ZSxOGEdPyHUvnwCapwj1LQ1BPLGgmzXcSY3TkyVunS2wbqU6oWeJzT4+3mB51KJe9LCNg2k0DNFyjG2P99URzJaEYW4MzNbArA3opIK72WB7c4HuZAE+q0BbC7M1oJYmYUimRyxXb1q5d9WaYnUPayTpb1P3Yl3SEsBp+CjXRrwT0ueUgmef3hdjYp6sGO4RFA317uPagpcLUGXFEzAcB+xlRZijsdchpu9eakEAEMJjg5VksIzzaHFO+yzFlkdGQMk4DkrHhP6q+Qzw15vXUOYFfMd0UV5zhw/plNcknyz+SyHEjnofC22Y1sF2fhwSQQl4RYh5E03wuurFy7c+kTA8ctAcTQOQJR6zgp6QAipuISBWe4Xhj5wmjpXtbjEoD8HQk96PsYAnBg+z7WHaHtQ5RJOjD/w3R3gYKZgDDTlECHEPyeA13GSUS8sjlmSvT+W6q1MxTFgNQaTWAFsDbi26bY22rdA5CXNnBqxxaJpO8mxVHsb6JNydi0IpaW4rSXoryW+xNeCthdtY+LYCOgM4ExPjTp6LkAtM+zPqNUdqtDFqxbfWDeXjuQfxGuAzEJ+A7QnYnGGc+J0SQTnwGxoArQBkUXieSL2zKlEQSHPRlQCodPvtUP7qUfYZpjuIHcmphjDJ3eO56Nt9oKcJ5SAqZn4HSuTbEZC1L38IwHoa5bDv8XP8LJW5i8fNv3sjuS6ORf/C2plUQa+2HBNXm1YqC8YwZ6g87LJ3MNkvRl/E3ExuAQFLOoP2xgLuv13Cjd+/H/zhqzj+VIXLn7Q4etyguQk0N9Ubq1cdaSM6g9142FYKXJmeYbcO1caJrhXyDXaqbygPsFvG6kmP1RcYVsMifWPgG5voQIgLg2mdFMdyPCS1VyMGh+JOmg8rFAsxhlFXDsaId5fXcHGi4Xv4za2Jldn7rUW3ruGcwfpsgZunS7S9FQ8sJ6GJninm+HVMMRl85y1udks81a3gQTjpljDE2PoqhiIeV1sc1S2uXFljeWkLGIappQojAEnF4owaYRRs0VBRvxQgC8rHzFZ4mbiLCQ9cb2v0zmJZ91hUPU42C7SnDawCWdWZ6EiLpxjVuh90yTxXZu70Qkb+Ur0meFR23QTImqQwSvMEh/5neMM++YOjs0pBbym3p3jMeFxI9JvpwakH/Wh7SY/aky7smfXud78bf/AHf4APfvCDk32PP/44AOCBBx4YbX/ggQfwqU99KrZpmgb33HPPpE04Pqef+qmfwk/+5E9edKh/JWRSBWPkZhvMjfuFGgIYVy8o0CSB2516a0U0NfQ5/iQG0A2JbNPzEwA4RsVa8S94a9UGvjaxTLko+gTXSJ4rAKC1VNZwtYQOenWf7Y8Jm2d58NKDOiMA1ZFHf4WBxqO51KI9bVDfFIsD1MurXwKA0dh3gJyFJYLdhEUlQL7huhjoBUFmLU1OJeY5QZuT/d5rZRAP00mSwcFzAqPzAQzrANsxqjWhPhNPre6ShCC6DvBtBb816BYe/dLB1B5N06PW6iFVEiLY95L4npngyABbG72nhipdNMj0wYKzheRxsXJzqRcLEYJVJvpvJ7kAKs1j1jK6YwHgumcDy6pH742Ek3gSBqyhpkYtSt4SfGNh214tPslcQn+HhO9WFYmw4CbPGlsAiwq0XIDWmXcWcOfvQd5fTsrgU8/Ec63tpW6MzjMxfOKJEgWMu3UdTwP6cvAZ4K83r6H4L6N0fdrXSj1jJMzOds6JL0DhHdpFWtwDKAg0jmFUsItewnbIjxiBIpKcVr4OwjuDu5ADiwadRktto+IY/uZrBhZiJLCNg2stzFbzphCSvCihyiAB3sCAgd4rDxnzmgBeqWl9GGcONcT5CdqEbg9+95wYDFJBsTCN5AAbDSriqeUaaH4Wtbj3JAnrnQcZA1tJ2CEAUVSI4A1phURJ6c4OQD9IoCEsIHruhltgIBZyGuQZAdxoeumqiAFyXAAefa1hoEAs7R6mxyiPkWPF4s2UJPENa2n+PqRhHJQAWelwDEnVqcqCusT1Ys81fm+a6yflNbHdbZybhnc8GktovP9AT09i9bxIc/CyCY9S/sCLwg0NKQwhbeGzyFP28Nwo0cg7N+joqb5T2sascuF5nafnwQDOEWI4cwz3Zgap1yvzkAg+pivRAhy+VqdRN/QbokwktYoOV6e5r8U4wrV60F5vcPlTBlc+5eArwtmz5cJsy/AbXWuYg6OpGB06AaWCg0AKEBI071Uf0rdIpXc2iIb05qZDtSZUpwp8KZ901oq+0rrBU4shkSnexGuLxgZNacKexbDR+JimpOvFe6OyTnJbealqWDc9+q6SJazy4K6SyA+BvQBHcK0BLMMvCBvDaPsK1gjvaSqH3hksVLe41TXo2eByvUVlHCrjcbMzMOTRe4uObMzLuHY1LtVbdMcWX7h1jHrRw2i4o3c0qG4EoFI5woghHwB44cXbujXwFUno4ULiEH1v0DOpuuJx1ta4ef0I5qkqeteZTu5rfeolvNBxfJ7lPocHNOPYIf8ukaRaCTzqPDAXwCjH1hxwnYNdwTNr0tce77PqZsUUEfmasEPei04AIZS50G5UfXRPuhCY9ZnPfAb/7J/9M7z3ve/FcrmcbZcnMRvFbM/QrjY//uM/jh/+4R+Ov2/evImHH374AiP/0pENFsu7oIgW3XtL/RYAr9lKIjsseMGlkCAP4ii2PAjQgdkBgKXR78FKLuEXTAB6D7OZntNuCHZr0V22WklEPLZ8TeiPCG4BbJ4lVSNCCINfeNBxjytX13jwslSv+MTmftQ3ST271IKreUtcQ+iOxVOrPjOoTgnVmVFQywuANUfp4jEqoR7MGMncqWLBXryzTOdBzgjuZcfvZkwAHA73wnjqM0J3oqDWZbl+3xq4luRz4bE9IvhGeqtrqX5YVyFUUJK5E1mJFNzIiU2vi7MRhmoSjwTZLsqNKGS65rDsD9b9oFwAYt0P26ozdbX2hNo4HFWSBP4sJIknUaKCtR5GwCxT26QYQLL4hiqEAchKcmVNbo+14q7bdkXX+VnX+DwEeB9KgbSgOI+25e3DDj0fT1fyuZxZo9w+BwLw5eMzwF9vXmPuoq9FUTnYc9tth4sE4ZwKfYR9idA5+p2EeYf1VPIX5mNh2B4wvVGPXYoASMyBYqXACC8CUAQpRd54LJYdLjUtAOCL/XFMsB7XND23twRuIHlduyDQkoJablDMSpQKahmwIxeWTIKKF8zinUVeFlq59qRpmOuU12g724lnVgpqsSV4B7CzUuGJKeZQsUZcviyxlJ5nqXjoIRWEY77LUJ2QJDwnCqD9oDjHdS0Z58ijLJmL4AlMakwRLyyCIY/aOFjr0dEA0JjEEziCm9bI/OfzEkChAGSlOUVG94Yl3L2yovQVFIpZxToHjPahlK+EPiZAMuZ/F2iW14RtByDraU1F4/ochTYFz6xR3qxAqdKa6RfzSdhn9JB9KD9uVz7UHARIZUnNnxUMCpLXmLRKN8NuRXcIxncAUgSJEBO0IwGHTa+hiLp++QrwCwXUe4Ac4egvDC79uYfdePDKoD4VozdYwxetyMrByNmcMuzWSxGR3qvnEA+GJ69GFCPrrbcEb4zK7uLNDEuoekZ9qwNtHVAZuIUF14JmSn7a4FLGCKB3kP+95vpCLQsxa1hd+HPqLRtCDSvj4YnQOwPvDIyR0EPSCrgIRvLeCOrnCNC8j1vSuagdrGVUlUOjQJbkpiIsqx4ehNZXuNVZXKq3uFxv4UG42S/h2aBng7O+wcbV6JyNl8YMHB1tccuL0wKnqVUqGR9rQavoeaxfudJnyBF4a8HLPl575yzgJEqlPiHUt4biAbYV549pgRsa9BZjYv7fKItaTXypVd2pL7kwJ9EoRVwg420lKjnLzL2bu3CNROchlHki0+C5nmXEGdrk0S9xxw4+O0MXArM+9KEP4YknnsDLXz4kHHPO4bd+67fw8z//8/jYxz4GQKziz3nOkGH/iSeeiFb0Bx98EG3b4vr16yOr+RNPPIFXvepVxfMuFgssFouLDPWvjCy0wtsI/VTGoKETE+8sH16e/e/WrkogdxKLPrHQBz6QglgYb0stH8MY9IvXEt7BU6uSioNDcnRCtwrmWqC9TNjeC3SXBiCLOophZqZiVNahtg4VeelD49mrtVRNNE5cg30tDIkNob0kyeLd0qA6NahvEcy6G1n/xxdQALJSpljy0tJSqdSLS7Ajo0kFKc5X/kJSsFb3AubVZ4T2zEhOrWMJ0/A9w3Vi8eiOZbHfbiRgv9L4bWNk8SfSvFjWw/fiokutrBwC9CUaDw/MO+QnIV1kfAOphmJlzTRa0dA4Ga9rKFZfAQNbV+FKs8Gy6jXpMEBniEmW2YrCh4rgF5WEZHpKrG9h4TUDoBUt5tmcgQALcG1BTQ203RgUy60TqeX9HCq60zJLbrI5hhHaJQv62OqdtckUiIknlzLRAwl9ufgM8Neb1xAwzc8YlBBgWNcyACkefKEzlY+5G3lPJs0K+aOKQNZkMACBB8A5hv4N60Dw0AI0VHClgE6jSH4IJyBd94hhjI/AIWv4gYSPY6goZQFXyzlcA3hrYCqGaUnS/vUB0CqNnUcfo2skStaT8EV5sZFP4xgeVJjeySTGe1g5BndS7dBpTi3Uku+LrQBWHgAZsXoDgLE+DskYSQzGgBi+vBi/xBOKEKp7Te5PSLKbbrYA3DC3MUxSZZGQwDh4GTtvQFYqGobtEcQCBm9ZhigCLqBk6XMc+Avmgay4jRHD30dFYbKFPGwL/Z9DswZHQ+Xln5Lri+3Lp7qo4H+gpycZFPJ1ThrNNGBd21R2LxaluqhH1i4g6zyvrDlKAbXRuzHmfyF/4hChEBLQs+RwTXUfFhmYHMVQZtsKmOcWcp7YH6nnaIsIZKW5GUESwbC4LuPpVyaOUyro8ug99pWu+czRCBznQvMjxrB0GpwOjGNwzzA8rKFSuIlh1h1o0wGVBfoKvLCje8oB9FB521diQJDK5AQsWadSQw2dQSgiJVNNaPsKUslQ2tRNH4tLeU+wyx6+kkrr6EicFCoeeC6E57I36IMXExPONgbWehB12PYSSmiIcVxL8vjTvsHWVWiVT7W+wroXIOu0rUEAmsaBGTg9XYJ7A9M4SWPtjZw/FL5iAjrJKUmdQSww04oe5Y+8eJL1RvSwTirGm1uVeMS1wOpmj3/0x7+Br/3cJ/Enqxfg3z3rVaL7j55HHj51G4V9IeeVFSArGvpTysMLkT4nMy/NBQHkSdqVfHt2mlEe7dDudjCNlI8n558UgzmHLgRmvfa1r8Uf/dEfjbb943/8j/G3/tbfwj//5/8cL37xi/Hggw/i0Ucfxcte9jIAQNu2eP/734+f/umfBgC8/OUvR13XePTRR/HGN74RAPC5z30OH/nIR/AzP/MzFxnOXwsy8V+BEvfdv7rB3M5xMsYRyBNAK2RAVQpkFT1oDPzSojuu0F4Wl01fqbDqNF6dGa4Wr6T1/YzumgdXDNqamCvDNwyuGG5rcePkCD6Ubz2rtGS6CuFWE/AqRCzunkl+FV0k/cKCiWA6B7T9kFiRS9b95BrD9xzQ0u2SH8vDa2y7MIjhWIqTGDZgqPhHhMoxbOfQnBpsLwug1R0T6AjwnYXbGnTHPczCgT2hby3IhlK4UkGkrh24AnqrlQ+dlVCNhVrw5bQwLQ+AVjqmUB64kZZhETFq3ZCqlOLN0B1z9KsO1UiiIKILGyWCCVcGfmFhulrmPJmbkPA9AFoRyMoWcgbkHLUFN/WQTLEkWM0IW5MQ3ZR2vaKlfQXPrbRtsEKkldaG9nv0/wynA58p006d+U6s37c9mNs/bsRrduA9O3mNEW8iXw9eWFGpSHgVWwFw+mOpXBgsxJKniSRk2wDcEzaQRLQAAA11Zwtwr0NIBNKBp6lyEpb6SsKlyfnMu2cOiSjwmhTQitJi8AjmOJadIEb2SJDyK9sS+oWG0TRiBOJQJbEmUCUJ4L2zeh6AyEsYoGUwWNp6UQ4I6sGRXEYMV/Eqh4e5CSAVNIQnnZJgCFEPOqkMpnMNSEhJOlUpq1W+wRWBvQV12UNFpCESZvgdjpuQeHjBWg2NzKsiJucs0E5es4tmxrLr52AwyQryoPBaHXjNM4PmytUGxblEcwb2C4WuZ2GEu7ziwzBCuwR0yscs1ccV9CUSACTIXak3ara8SuTJ+HwxfBIDAGV6jjmxBNASIEuKHQ3rMTlZ53wTjkOsIBsALq4YZkuo1tLGqdEgeAabTqqcEyceXxBe4mtCr/PlLYke4zMrgQ95jGWtC5UZKRjp1UtLwtw8sHUwXQ+01WAwhsreBoAxsIYkRO4mCy+wBh1ZoGZw5WFqWZi9k0msbA/PhL43qCofAS0iwFovubO8VLQ1TQ9HFh42ifCBAESdeHCxNzC1h+sN+s6CmdAB6DqLMzXaX1pusao63GyXcGxQGydGDuKYJF5AL/EsNsTonJVHw0giem6t8v2Ej1iAl07yOhqGOTWwW0kQLxUpIUCcEeNRUzmsPcFuxSvLdMD3fOTX8X3/13thALwCn4DZ9njX/d8yfZZL3lpB9wl5g3Ub15W4feeAcgyVz/nYRYGrufcyAZjyJgEsLenMpWqGQDT8hTGOvIaVf6cyQRyfKW/fRRcCsy5fvoxHHnlktO34+Bj33Xdf3P6Wt7wFb3vb2/CSl7wEL3nJS/C2t70NR0dHeNOb3gQAuHr1Kr73e78XP/IjP4L77rsP9957L370R38UX/3VXz1J9Ps3gSwB3qZSXHJTS4p17pqbxqcX9gOFBy/xRpl9KC8gUMUqHxkjKH3fFcfLlUF/VGF7b43NNUJ7RRbt6oylfK2WNje9WMfbK0B3j1jIq5tWvIFiokUFOVqC2xg8tZFHlbYGvmF0l9KqTlp5r6ZYoYR6cdk1mqA9JtElAi9qYNsNoW+TCylsLwBa5LyUClfvLDJSbSS4Zs+HI8hLHYAuSaDrYVugPzVoLwug1R8BbkPw6xr9qgIf9yKQdwC8xsHXykyMDtsAvPDw1ug1k3oWhJAPAfiMJnKHATzEuiOKm+QzE+YKTYorDNstGe7Yw64kfr33Bts+xI1zpmgECyHDLazcg97DtH3cDzMAWimQVayyYQCwLPKUWsz3WcwLXo0j0CxfuEvvH5Xv5ywYmgtVqdKVNg+66kHRiHTgM2UylDH3VJCfA+RHwCsulisr/jyH14zan9Nmnn0U2u7gNUZBrJVBv1TrORArFcbhBC+qhQJZgBS98IjgCaBrj+aS2vQ6yWql9w00pI41lQgN1XY0Ia8k4hU+E8A0EIErO4Dv+15jAdASGYEjoCUyAA+3a27aOREgw2GeUTvNW9lTBI6oInBnJXdYrV7QrH0YAkwA0TCMrRLZhYNXAetcmeScLmkfbwwicBUE2rg2Kr/xDYNqSf4ekgMbTWw8Ip0LVsuiVKe0IOeGk5ECWUEB1s1FXhMGYRYAaUIbeMQqAjufcRp95JunzQsN6WKvyYjSeS7xmts1eB7obyRJvjtdl+buPfMQena36HYiSPJjEh0n6u75epkCWZPzjc9N6i1DTDHklpijwd1o8QlyAupUnayvds1wy0EmDrmyvPIGXwH9saw/dkOwZ5JnsbtM4LXoP2yAzb2E6kxyYtW3NKqkGwp6MEGM8xXgKxPnQzzAksqGIcS8B4z3YDJiVOk8yBKol8q5ITcWwwPbFjHBuFa6lPvhYJlRn9ihgJchkLNq7GD4xgMVw1UM0zjxwqodKs2zuKh7OEvw3gigZBjcOLStjU4IZOT541bWYLIe3Ev4ujyeFrQUD6igF3XbCvCErWGpCLltsNBolGNNBQBIqOO6k8iVS4stbm4WaPsKbWvjs8EQPcieSv410xK4ZrhK9BlnWcazldyQpiN4wzCtgV96UO3gnMG2q4CTGkaLmpme8TWffyw+ugbAIyefBu4PGygx9itf5ATECl5YpAaUWM1QolC4nymZmVKqA52nD6Vt8l3BJjfrsXzOO1xwEgiAscgMM8A4I+atizyLk8896cIJ4M+jH/uxH8N6vcYP/MAP4Pr163jlK1+J9773vbh8+XJs87M/+7OoqgpvfOMbsV6v8drXvha//Mu/DGv/5iWOMVDkMU3cFih4ZuULsBlvG7l/znly3SVeM8mzEXfwrEHnPGCMCdEDxzeSiH1xI4AjhPoUMUF4EKiYCO5IFIHmKSOJdlX2dEsFWHoA3sCHcD0SRuIrrTp1pBYOVWB8PneEJO488SqLL/SON6WEoqeAFiBvYO9iCd2Quyu8iFGHSIcVQxWHfpkAgjC0pnOoNoTuloJalyRvlj0juHWN/kgS5AMAWiOJfA3UjUpPZlkYmCY2pk5KspN6IxhHkg9Ah2p6ScKfLh4h3DAoI9GKZIC66bGqOjTGSSWssAhB9SudB8mNJlPmVhbUV5K7zHG0CsUKhiGsb+bxZ4XxqdIFP1kYR2Ej0cOLypbBOa+s87wcSpQoQ6GqRwpMETM4uaDALOJ5R8LWBc/9DKdnGp8B9PUMzw0RRrnj0vDn0XoNpBpuuv7vzP3zpaQdINU+RpjgkcVaUtxuOb7vkt+KIy+JXpIhV8mGhvxXQKy+GwpgMCChHUDMZ+ItQDUAUEwUPOSlCLwACNWRosIVeQ1wEaFsdGw8Tg0ynhVgUsWGx/2PTqP3PlmBBgdhB1gv3sS+1pD8RubCdARfK6iV5A6BD94/YSHTh0n5BCMkUxbEdFTuPZw3JsVNLlX5ByMRarV7a6VqriUvbDd9PJJ54ESm8hXBeANiPwCWqSdHsm5PqQJTBeJjwBzpSXoApwA2YNrI7+iyhuFz3/fmdt6v9JiCF9a5HjQHXvOMoVnAcm7d3fHcCHCyw2By7mCG57LouXXOeIYQpiFXYh6eJMdMj40U15fw7mvTXgzP/UIAjuYGDx6lUbNXMd8CfRXkTfUaDWPQcRgn/KXaSEVckZ1JqoFvWKsMMhY3nPCg3g+evW4wUgi/MupxbOAaA9N52K0CVE7zHCtzIzYCYDnWKAYehUcP4aKyXUATBlkjxuQYYcKwG0J9U8CeWL23MnALhlsy/MqgXRh0pgIZRtVI0vZGC1Ut6h7OE3pnUdeAU2829h7OieWCnZFxV168pQCgB9xawCv2kDxWgHgJ9wbb0wZdY4GjLWrrcNo2sMbjSrNFzwa9N6iMR+sEQJMiJlbzpEG8rSuGrzkCWdQRLAycqZFa+UxHMeUKG9GbmBET4PPKaTi7DP0Pn/0ivOLxT0Tnwo9cfj5i0q68kmG6TudAVvIblsCrBbDdZqHu4ZnW6ofJ7wmolRY82UUl3WfmkDnjfUzPku6LwkY4NpNPwpSnf5x9vwDdMZj1m7/5m6PfRIS3vvWteOtb3zp7zHK5xNvf/na8/e1vv9PTf9nJYgfzSD2z0rxZfmwJiUBWjIGdeQB3oa6lMcRFn8sPbKltaJ/SDkEpAFlcG6mu0TPozKNiRrU16FYG1WawQACAWxq4lQAs9Q2D6kzAFdNBrRIYdATGANSoNhcsJG6hHkZguIWMz25VmM3DDNLrvJ2QnJKyGLyzeq8hJZLEPCQ5lvmhMUioQE9UhUjj95P5tD3DbBnVxqA70/xfC4JrCfbMwh0ZAbVqBqkXAWtVwaFjmS82DKhbtHekHlhJzgDlJeQxuGSTbs8VD11g+s4q83BoKoeTS2K5kkowcg8ARGUTVpQuv7QwnQWpRsOJR9Y4XK90b1gqMVbySUGR38cz6zxK340Ago32I/EaO78vAPGaiAE1Tg2CmRmU7KAPHmg3PdP5DDDozeWd2fqdCvzJvgmQNdcpTb4U9hXOv8/aepu8BlAeqesGeQZ10BLlkh8rWNaHEOpgbQbslqJnTwBNOIniiPJsBE3k/WUrPCkIryFROvWYzy0TrpMj49o9J8WLTeciCTEMITe6PTnh+NZkp2bweGpJ50I9EkwnuRFDTkoBtUhALctiHIl90/DsRAE0CTfk4fzBbhT5SKIApocDAz8K4/aOYs4UaxhtqMoYqhA7StoL2Ggg94idiTPCZpinYjUmACLNLQC+F2weBo4roOlAZx54EgA+B+AJAGeIWfAvSqP5J0zeJZ2Pi4BjIVcJpXM82jf0eeA1T3+K1QxH6+wO9IiTNzD5vnNtK3Uzp7OM+M9geADmhxSPzfov5rtN2se8PcwpPqH7McqryJbQr2TNqLYc18IIrtOgk5lOdAzTqadujxhuGEMTt1L5GxBPLXjZRg6wHdCcek2yLiFrZIzyHzWGM4YK6ese1S0F52uD/lKN7lKFau0Em9MkU1LhVpK/iyeWmS1cIZOoOguzRJRUFnBmqCjrtU9WvcXJtQkoR3BbUl0P8AuPtq7QLRzcssOi6cFMsEYOXDXiwrbtKmzaGlXt0LVVsBXBVAzPHtwlD0FYz8GxqrB3spb73mCzbuCaXvAe4yVHMDFc8ArTnJd9X6GqpGjIdl0L72oNyBP8glVv1OtaGyk21plhu9471xFc41EtHI5X25hWRQqayXPyS3/7tWADfO3jj+FPFg/j3fd90wBipZ5Z2bMa9fyRl1bQ5whUV6DFAny2HvoCMMqflYUNzyV/H71z+1L+Pu84XnjPbv1oNLYgGwTMOLxvaVTTBVW6u+6Z9UwjSySVDyYeHzsYRyr4Y0eyxRLl7n67HtCRF1KyObwTc6fMPcx2KRchaR0Qc4cY9oMATTTkr4I8rN2K0F2SBaG+pWEhPOTJCNU9GCKEsmWgYphVD8eA3VoBvgxiZT6uVL72PPbQMoiW9sncIHnJS20ATJL3h2ODhxZ5kHMgZ5VRYLBUzzHfRMEgGoNIKpNHplatDeozg/bYwC0hITUbBbVWDBcqqBBAPQ3CPSCx4mFxqBh+5cXLay0x4aaX1SR4awRlzdciBQQPhpjcV/8Y0HxZonn0VxzayxVcI7Hk1ZoHpVKt9RrPCNNWMNzFixehYhz+kS+IFBXy7HnOP/dMTrornFEmADuthnOJEvPFl5jhzVhhyZO/B2DrQAc6jyTSNlX9lfYCcpXXXEihzXjNrtPM8JrzT3EBXmMoCQ0WgRsQRmH02Ji/CgCM5CxxqnzYdljLYgXRFOwJWcUNQLWXzb1aYGkAWoKyE0Kzh7HPjnxokJxv2qwgR4w8tFiYBas1PelvCmxNT0+BuYSxRhuRWuY7glVAy6tRyfSAV1Ar5BaLa3qelyCs3wZqdBBALIBm6XkjqBrmz4+3p55mpGGGgChQbmHgrXgYx7DD6KkGeBDAjdy3PumQvL4/vnCvLEQcvgbQVwBfw8DXM65cexKnJyv4Dy2A33sI2DCAxxEzBe9DNKPoxwucP3CW10z64VlFZjyO3U0O9DefiveYDGJBKmCQawtrTvA8jTrJ7eZj3HVMKmOlPOCc80w8tELz6P0FcFgNCfpemLEsThLt4CsBbGzHQIfhGFDkDcSs6yENy6sHeIEIaLFhzaEE2A1jew+huyReTr4BeCvrabgvxpF6QgXdCNEIYLc+yqekea/smYNZ9/BHAmixIZg2VDxU8Kv3yhP94LGd89Xkt9xbRqiSW1cqg3MFwChIRmNgr5IoDrdQ44434C3Aa4PtyqJb9bCVhB8awwA6GAKOFi0q6+E8wTkDHw0QDAoJ5dXzV4qcBB5PcK0VPcYT4AidowhgVbVD21WwVry+jPG4crSRcEftg4jhWyvhkSSVe1M9yXRyHq7k3GwIrhGvLbfQ4m6G0Sw6XFq0uLGW6toC8DFMC/S2wv/7a94A+1WMS5/dwJx1IDiZ8xDqGe5n0DeDrEQhhN9IAvhkP8MCq4UUvPLBXTzluYn+c4H3c1dOx9G+lKew3JfZyLE5ouSTWQE8+Z06SQRQK1RQDalZvmQ5sw5UpmgJGW30gDfxZsS9JQu2KuqTUrh34m2yB83lyDrv3PE4kzzcAdE3gxuilHwdYr19LUyhu0QaX54AJUBMpChJ5gB4gl950JFDc9TieLXFTXsEd7YESMAwtwwMRfryW6glnQZsg4ZQvnhttAPMCIu+99PtEUEnzY/CQNeDOgtqKvVoIoREeZHSroI/agjjA48VxuQ7dR71TQ+7MeiPxNPNbQC7IvTbwJQVrLKqUwSjMdGQSM+Kay0T4FYsOFzIL6MWrFD9UCxSorgYzakSksJTrxlFiNHrw02rHtv7xKphN1Ixy24hQKUqG2yEibullcqGPIBKwWIW701+O3QhJBDIeLzpi/8HHjn9ND66fB7+7X3fmIXWJPc1vc+p4BTblm//TspDh3hQckfD0OvLS8+OQg8p+TvQgfahJKQ3UhZ+QRPlI9GYd1itv2y0D59L35MAehAP7z5jVPCDrawpvpF3NaxzkdekghQAZjVK1WINbqoeW6rhO4IhAkIpds2VRQywlm5nk8wwQcLtRsrZHte3KwQorDUaJkJGQ0WieTM7Q95VGJz2wUgPTdYiLx7B1AePLC3eop5a4tEAyX0SDot8LQl907B3JjGMBF4eeb1RZSA8g4QY6hmL75L8Dl36MM7ao1+JMmJ6gFSptK2GOLIBUQXyS3B1JEYmWABbgE4BrIXxBWVjRAsAzwa+Alh95wm+A/8Rf+vmR/GXR/fi0W95HT7ZfyXwfzwXwA0AARlN5g9ZqPwdy2/Je5s/twml8zhLBx7zzKHsGSFrNPdOIqjMJYIHcFtGdmDqdVXqemL0p/m1b3KCpI8UBJ57z8JYdM0ZwhN5JJeGvsWwqt6pdrw2gUQ/4bB+6doPFsC+PpHq6v2KsL3Pa3g2xWJV3gLcDGuD6SR6xLZycvGMZU3NYUCtEz4TdBfvYU82MGcW7koDv7Ca0kPawWtItUUEwcYTz+PvQVbwkpLEnLWoIcfarUV3yaJfDnPgrVyHbTQkfQH4heYMs+I9y1uDrvbolw5VIzmm6lqUEWs8FpVHZT3Ots2QT4uAGKoOAD0N/ItYcmwFUoCqdwRYRrcRhNAuHHxnUC07nG2bAXP0Bq43IMvwWzswFqjuGcC6imHXRh0nxOgV5SzNS2kMY1V1wAq4ScdayEzamJBz2iR6TDrn+TMe3r0UyIpeWsl+C6CpQUdL8OkZijQHbuX7SpRMbTGXat73zvd63O8AgiX7Kbm3ACJIqjLUyDP+NugAZt0hGUA8h5Q4PsCFO+89MJOvpcg47tQiciG0FolVdA8riSFJUpgoR2Ap0SoCvy6WjiPTiBU7loTqdGAmxEMMunglAQaEfuXBjcfiqMW1S2scNy22XY2zegHqCVgy3FJCDSWJeniJWIXqsFhgSJoYXy4V4/PbVFp00nklknK3pNK2WjbQOZjOwZtK1IVRSMZgNWbC4AXmhjmOQFuurCr/o42HaT2qMwO3lKTH3YrgNPmx15wnAKK7dEh+zFaUCu/0LEZ/H3k4kpK0A5DFIwbujVonSAAuv/QwAG5ul8AC6JwwJL8QS5NbMrorYuV3C0JzUwBN74RJm45hN1Zi91Xw53QhnyUCO8abPvtb+Id/+dswAL52/WkwEd717G+ePqu7wm7PowyAmhWcsu44bR+apIoGpQIVbnvRPtAzkwhjoSE+PqXn+pzQ8InxIn3Gb2twt3nwPrwmXSNiPirZTjHcDmJQ4eH6vEEsOZ4COtHDKrAKB1E+NKxh2XRorEPvLTo1BkCrVsVk5gmOEceoykeunAmv4en8nueRFowmCXJOzGBN7suViYpnsc8wT3G+Aq9Jd2fzCeGjwVOLNTmyqzUxsR14SjiOeDg+NZ5QmBfLYkhRvhirSBaAxQAMMkk1YwKGalXKU1mrI/tK+pbKXuKxTb4C+8sgfhCEe0DP8mIsug5gcwrQnwP0FEAtphYm9cz6CuAV5vfxP/zex2F+HXjwuV/At37X/w+ffOlXAv8XAacrACfJfZreur2Jss/R7ZznNcVzhmOpsP9O3+8D/Y2hVB8BEp3EcznSIOwL1e6YR7qIeGpl7ff1upIBnLO+T9fM0e40T8/oGR+OY0KsHJ/KlTGkkMa8QdZ9Fp3BqucpYQDsGUOVwlBApNGic50AHm4hfTU3gfpUxtGvAHfsJT+t8hYxBADtVSnqZFqgPpVPAJLfUZPOA2aoim41759RI40HjOtB1x14UcMdVWCycc6JGewg4YPB0F6a63BPst/Ue/XykjHY7bA2W4Q1XyJffAN0lwjdkQJavTgQcEvgzqBrLPqlhhUuWxwtOgG8mHC0aLGoCW1fYX22kCqJxHKdNRL9iESPYiCkmiFPgLOal1Huj2vl+M41Ul3XeDSNgzEeHSuI1dvIC01HIC8Gd/LBYM8xAT71FHUe6imZKilGgs7AtgnfSp5J3xjJIJTodqO5Tt6DmCdLE7+zJflM9fcawHIBajtw1wEkwFdM5g+MI1Iyz6rRu56ePw82oqyfEilYKu1p4CeGBo/Iwnu602ia8L0IBuY8cU86gFl3SBYzSvieIU+RURBNGUZKHvJC5x5Fu0KilHaFlkyVmt0adnjoS0DWaEyJsCv+gspoDNCcsIQTjh5aGiVV9JUIwLRwqGuHtrc43V7C+rQZ+oXm14LEQNtOkgEHhSPmLUpQgzTK4raoslI2FSL0Q6sZoncSt155cRdNX+yERgwVGM1bALriYVm4IjFg1g52K6BWdTSAWr4B3CJZhJWJAtBqXqJEyKINGCbB2yqAaw9vaQgLURAsJuzThUas5VLCfdNXWNhKyt86EpfpFtESBTC29wL9EVCdkVg7GLCdQXVmYDY8UlJz99UgwORhmo+cfHqUbuGlm88mB+1/V3MLYW6ZmFj/5vpInuG4oJskDxjG/U48s8L+g6JxoHMohpVNduzJaxKBI/FpmlLYRdl7crcV4n0s8skaMQKyZseUbCDJ38c2OSS8c4kgGsEZrc7kPOG0b9An1ZDSY8LaGoGt2C9lg7tDMiQJeoEk1JsREsEjJKLf1+hVALpiElkduqqGCmox4MTTNpShDxWDxfNNjwtWaiCGo4QQAfGIls5DRUQmqRQVSqRHq6yOKfaryk/vDSoycGrGTXPbBAWrX2nOtG4Fcs8BHrwX9LWMoxefouYtbn3yHuD3LgF/+nww9RAwapteOigILQ1whDPgOvDUTeDaMeESbgErD5g0qdd+dF4Vt3Dd53aZ7k/aR6t3+L7r2AOfeVqTJTOOFFGjLVsLuJAQaob2DPWbUHz2yopykfYBx2KuXxoALSq0T9qxCUU6EPNJAYjbuTaJMUP6iwZfK15HIdIj9sOSHL4/lnOGarmhcq7diKG8P1KvLIJUwiWgPxqMAqfP8/ArD3tLkrvXJ3L+EKFiOlbPMCNhg04N8MmcsddwtXUH23vAEqhzcQ5IQamYgDzMVZrzN50/VmcDz0DvJWrCWRlLACNUl2WdCwsGb6RKY70k9EfyF6NkWonA8a2Bqz3WGhpoDKOpesFuCFg1HYgYXVPB9QbMBO9pqHJILKkJQ75DAtCrMYQk7F1SxOi1WEbfE9B4uE5ust+qi50XYApQHUg93iIo5QlcA85o4bFejCfUyL7KeBxVrSSSr72GWSLKD8F5wNeSdkdymokuIEwxCwMPwFIKZKW6fYjeIQIvKtBqOfa228cLi8J5zV7G/V3hh+PjMAaYNYdnTlFeUt4eeNOcbiQbZr7vQQcw6w7JkOQfior3jFtujFefEzz3dbXVfnYKSNk5UgDl3JwJ6cJXouDtBIxj6kvDKDGetKt+YBqAKgcqJEerKwFntxbwJ7WUVO9JKlSoEC25mURpaU5Yks27pCoJMHg7pS+cpTF6nlNYOFJk2xoBsqyJigTpRZLzQNuD1BIkENowLyngtxNYDAsQIY5v5LUFmXfTe5jWoGoM7LGBWxD6xO03tmfEJI6+E884rgC3kIeBK6hFSObIVxwXUgbG1ad0TMyEbVdhbWtYYlQLh/4a0PsapqVRuKJbMXyjVigmNBvGP/z0b+Bv/+Vj+MiVF+CdL3gNnK0memAUNJSVh9Cpj1x6Pl5240/jWv/R1fP0WrP7SAlzMMPvUVsevpfzTGSAFTC8G6lSnO4P3g88XE76PRf4DjlMDrQvESEsEmNhNSPG8L6UG+wrJYRwxC/RQ3oer0mEniKQlbXNvox3qyfV5J2k8A7Lnq6twFsjQIuX3BwRsFHreMi/FfKdkE9PVBgGJX8lKimRAcgKhTooyA/iwc3OD6EJhOk93ecWp8cp75kMUb00yBGMpehl4KskFCe53tAluwHwYivjTnM4AhDruq77o/CCMFeqkzln0JGRCAzr4ZeAZxOr8xITmAx8VYHtVVB9L/AGwjc+/Nt47a3/jNVJi//yFS/He65+B/j/cwR88RrAa4im5IbngHsAa9DjK3wMX4mX/d0/wLUGwEPAH9JXA5+2wE0M2mw6j5gJMcyfg133YgJW82jbHK9I5bpyuMju4w/09KKiZ5ZnxGiRUTGqLFokBT0KgNFeFAzsu5TlXUb4LAwughnp+lDgByHP7JwOFry5mBALVvnawINARqq7Vls/VLGtCa6WKuvkxLvTLWjwUlVPXWKgOyKsH2C4y04MAKrbuCNGa4B+xfDXOgFNjEF/JGuqbwgNWM8xAEe+NbA9gzV/FYikCrjODwGgth8BeQKeYCobMI8/c1CLWb25xOPX9B7UGxgbxpOASRhEENuGxPYsRvVL8ucWAJYQD6eFgFPr1sAsHNqqQlVLYvam6kEAjpZbtH0F5wz6zko0SChstXCSuL03MXcyQ5LRC4Ck8rth0NaIfrM18A1Hx1vhEwTqgq6mF0EUw9utU8dFC1Rncn1Ojfr+iFBZj9ZX4p3ViHJpOsm3FlIYhOgf31iYNstxlc95ALLCvQt/0UuZRBdmgGDAq8E7i9I+i885jfShvKq6PGBIvKySdqk8QaGoyDCu0CY39qcGlci2vMgJ5TGG/jCE8apXVnDIOHhmfTkoXZRDLqV8QTWUJWBkDKjGsHDPJljLI95SZHSO9mBAE6+tHYpOcNkND3mJhgSMCupw+bqipZkUYAnl0QHJ/7RgwDJ4bVE9WeHoi6EvoDsWZmLU24cYsGtEt9HBxVGBRi9umXKtIa6fQHN5A1IEPGX8+aLDZsibxSzeWZ0B1fbiifLiJCvgFVcJHoTQDHwz8DBrAbV8Izm1+oWWWA8goSprZNSdVitGUi+WFHY6jzE8hOEti3urB2irC8xK8wCoq23vDQwxlo24D69Nje7YgnrRcNxiWORDKeN+Bfyj//rr+J7/9j4YAF/31J+CDeFXX/zauIimyT1Tr6zgYfdvH/oW0KbDS88+g4+unod33/eq/YUsZAL9nQj5yUKe9xHiwMPc89x59HkObu0HOtC5NHp+qQxqEY0fzxTBieDwjuc+2363eE3xmDkiTIHkHeeNVWNL40jYLFsMID8Qk5XDQJK9rw1qTU9BLJ5IIcdT6Mv0kFC8EfvWEO2gC2LwJqBU0Ns1B6OxK58BRcF7cOFR5cMRdhayOI/G4kc2lvGYCazl30nDctQL15Leq6QvL/wm5qrXkEBikjCYuDbyYAxgxDw0XOv9gBizPMscVtaBG6DrrOaWkY58xSBvAF6A6RrwQuA5j/wZvu2JX8fif/Ew14FX/N8/hD99+MX4+PNeCnzxCkBPJBMRFLYOwJPgjz0Pn3zuV+KdL/2HeP4rP4mn+Co++vjXAB8CQE8BWGO+YsyXgHbwmnzuI68p9LGXMfNAf/PJQPSQYHQmNazuqmiYU/CcCOlP5tbqXQp1QpM1KnrGFtbrzEOEXEgKrbJhipyHr5rQPHqshqJUCe8SUF4TcivYIAWsSI3bPNJr2BDcSvJHAeI5RSzRH92REeCilvWvvQp09zjhFb16gS09eOmApcOVS2s4Jpx8/hJWT5gY+k4sa2h7WXJ1mR6wLUcgoDpzAkZyqLaYzNfcPUnvV/CaSavrcdYHkYata1XEzsM4BruQr2uY7uG+BP4n/ZqeUa0J1ZkAWv1KQC3nIQnwWwJvLbrGo184VLUTIK9y6JxFU/XoyMZk7r4ieGfgrYHzqjtrPi1iaCX3YQ7JkfBkL4sgb0SXCVXZTQCylM8Er+CgL4aw9epMCliFiBir83RpsYUhSaMj14tRZWRy6kTBAmb5xgoomIPDKRBFarCywXCVPfvajis9ftmA+uAWmIUPppSnHihRAprF/L4Fz6zIL0pLh/KbvAJhAKXS4gG5d5YcgHgcecCAY1GGqENdgA5g1h2SBUnZ6lGMuEpzJQrgSIqcqpfPzmo0MWF4gu7OUWHfLkGmnENlfJ4IZBX2xe8p+BCArN7Lp+Y2EXfaBOvrAWMYTpMvcg10lxn+kgM8sPh8hUufESDE14DZAk0/5NgSryxN6OiGhTdeFwJDG5SM1EsrgiYjzx0zBbSs5skySTsKXlhGmE3vgM6AOif9JrH6OxW3fBeN24/s5ekcq8JhnIPpvFSHbAzcwsQKLME67q0I/daI9SHkGYvIuQFcw7pw6vwaBRWD0FB52MajbnrU1qG2Dgvbw5JUm+qPerhOLSBWmKFR67mvALdkfO3nHxuFCX71jU+NFtFREus4F8MFe0d4132vAu7N7ll6/C55LSh/tyHUj6wPSnEOd/WX7kvCm2IBgAOQdaA9yIAGL9NA53lgZc/6XoJCkIVYD7idl+UilPOTVLDKAZfwPX1nApDlg/fSgPuM+IFXHhDWMwP4BYMbOcDeMmhuBq9gEXptkiQ4CsIhrC5VfAKvSS8r884ZAPrknmR8c3TPwp92Lt5ZcvHkJXcWvJnc4wsD46O5TLdPwS3yHuRFcTBWc2qFPDNBuA2hPlHYlePZIDogRW8t5lGC5UiGQZZhrYcxHpZ8TFXJTGhrCUkhpxuxAPEVgK6BvwJ4YfUY+OOETz1OqDbAw18gHD98KjneERDNcMEMcV/eAvwkcGsBvOdZ+PQfvwifufQwcMbAnxHwxVOAPgcBs/z4PpYoVxAuQhT/DZsYu2XEYh8Y8ZqD0eTpT7ln1k7alQge2J0AvvRsn4eVBZf683C1vO8EjJkYVziRF7XdCMRyfiyDp8Z4p55Y1kyNp8yoTgXE765Y9AujwIkAF11t4BZAdxnY3u9ED9RQNm4YOO6xurTFc++5gXsWZ7jZLvGxxy9LzqxbjGojwJXpOa6b/Yqwvk/Sh9RnEo5YrQ3sxsB4gFy/W5cApvvTexjnMZnn0N5JaKPpvBguKo42+xGFZW8kGhCsZ9inGM0tQntM6C5TdDzwLeCWAmr5ntA1Elve91YcwxoDIkZlPAwxnDeg2qFtK9CK0VOl3kCiX0SxhCHGE/WQo23wJmJJrRK9pqBGfAiQRepgq6o6ecljBog8L+Cc5jFzQOcsLlVbGDDqRa95iBGfh1GYvQH8ohLvrK6gK4dcWURTIGuGX7AxoEUDbFoJFw59JoWzYjqGTC8v6VcXJnPOsYm8NvbwCseWjhm2R6N/Lovsib0DBzDr7lCVVY4IQFYJ1AqW9IIr7l2pZnjBY3Yq0urNFAGAXK/JFakAfFVSxdF0agUJD6zFWDEIeS/UrbA/Arpjhr8i6HPzeI3Lj0mb9jJQrQGYYAUY+krdPCdKTzq2sAJTntNI4aLIELP7mdGQPF5WLoYfyoP3TuLOrQGTGQNRxc52bIvCaKFRQNRDKJBazq1jSRRvCa4xomzUBG8F9Q7Jeb2VBI++lhwB4jKt1Vy0QhSrUsd1YHbCiHwleUw6Z3FlsQGR+OJVxuO0adCdLDR5I4COImDDFfChh1+EV37m41Gm+aNrLywqYjmoRTHcJbOIZwv6aBozK0hpDqXvc5T7EqmiGZ/nTOmNgEHC5PLjWRfrg7X8QHuTydaCzMI6oui1RchBqckz/+V+BktAVko574lCULAec2wGJICVboyXb1T+rSWhLy9krbe3LBZPyXFuIQYXEKLxZfAACH2ep+RxHDNjWH8GXrNHP+FCA5NWDwsKC0vwNIiC7Dld7UOl4eR8iIXnEAPsGMYIoBWqGAWPqwD+s9OUAkmurei1ZUm/c/QOBiAhhkExYIJjg4Xt0RADqGCI0NoGvj0GvAHzJZB/CHgJYP/2Fs8znwZ74OF7gOoS4ezrGnyKXgB8EQCdAeiTa01Rn1MQPgusT0F/dBnoG4A7SNL465CKiIpoJhN+br6RO7k3gdeEZzB9d3m8b/b4pJ8DPc2p8sMzaEIlQ6EYHTKXCD7jI/tUNDw3J1y6P+g9u0XsCRVltLAshjDKlDwLSOEYpTU27yuGT6mXlqsFSApKvLcCNMn6L4nPt/cA/TGju6pAVkcwrYTDccUgXbd7L/NcGS+h1pVcj+lYq71LuBp5D9sa2FbO1x0R+oVFvSTYrUXdGFS3DGjbISR8n1A6r+mcpG3F3XXw0ortxZOIvSSC54oEsldv2jRaYpJzNlEkyTFWLdDcImyvGPTHklOLvPBc8kYALTSojyUL/nZTgwE0TQ9mEiMGMRaLDm1biSfukuFbydELDTM0TryuhFcwlEMCLNUiuUK0yTGLIZ8sw2y1Srvyd3JyPwDhUf1SvY81RXPvDVpv0dgeRotiSY7hgT2HapjsCb4x8MtKvLNCERc/vgchT1YOZE2fTQjoVVtQUwPbHe9j7piRUcnLPuj4k65mAKihQfZ7jr9w0n/ehpJ3O+Vzc3rTDjqAWXdIBgYmMA+iwaUXmAJZoWJISvtY1uVE2ucF7m6+4FzkAcmBrFKfBQ+tVOAK1tgR8RjU8rXEoXeXgO6qh7vaA56w/GyNK4/JYn/yfAO/YNCZACNQICymrmCMQLko+/vx9jRvGKliA6IhFn0f8iyxzHqvZTEaAC1yXhIpupBzK5m/TACN2+ZOn4Nao33ybMRrTZQj8gx0gNk6wEqyfl+L667E+g/WdDasZdjlPvRLSCy/LuS+Argnddk1QMVoNxbdqkLfW/SXDK4sNrhUt7DEqCuHs8ah3Vbozmo4Ei8wJsA6wi9+3euwfNLjaz//GP7o2gvxjhe9Zs95h7pAu+JzN+uqHrwZw/NcmkrKjtE5n01SOKdwJ/1N7nmyUKfvRJ5z5kAHmiNCki+QaLx0lICsyTbMP2f5moQdbYuDu4MHeA7IygWnmTEFcKj0fkfXd0CT/IpA7ZYsQBYTqpsGi+sAecb2qoa8d8N5xesrOV+J1/A505Wu/ftSvCfjtSQAWuKdxQghKHfkCbTXOPLtHD3ibA+EfJox2XLyN/LaCgmXKwW1rIJaYZ61HRzgegNXGXhv4BvCwgKNaUDVfTDmWeiqe+B6A19b+OcS6DWMv3v8G/jvP/cY6BaB/g7Qfg3hP9H/DU9+8AHgkwBwAxJSmGvUTv7YgfwasZJW3LfVyciPy9b6fWjynFOc6gsRYfrupUpBKq4deMwzgqgqyEeBgg6S50yaU37PAbLOo9F6NErHsuOg89YwBapGvwu6zQjI2iWvWQO/sOhXFu0VK7Kwhg8KYBJC/EKEAbC5V5K9+6UHCDBnVvL5OsAvIdVbe4PNWYPH+QrWXY2ztga1Bq4OfIhieBW0EntIOt+sPWyr64wPegvBLSvJXdhrjl43GHvnL3BmX5a4O3j7khMjCfVGPbPUYBT12uR8KV+OPE7mrnIM2zn0pwatemm5pVR09J14aXVbAzpyMLWHaw02nQFprmRjPGzlJXH8shdfAQCuJ4Bt1FNST0QPTTfDEIN6mvMy5kIbDE1guTZxiBh0Btb73B9zzC1VGS+GFWdQ8SDLp6GYYT32FeAXFrS1Mlec6P7BWypJ/J7q25PbFy+MwIsa1GXeebnR3mTnSdqVdPk0p+/FeFh2/mhYSYrZ0bh90I3S6IBRpED4vA0j/wHMukOyROWIwpJXVsibFT2AEBnL8DLcRYkjt7LQWBAvttcx7ERls8Vx1FZf2LDIyosdhFvZF8p7h5wk/ZFU/XDHHugMjj5d4fgvGK4GTp5vsL3XozqTMfkGEjutbyBb0oTogtATs6DvHpKEdgJE6Aufio0XBQpjqJguHF7nzCjzdwq6hPur+QsmSfji+Rg7pdh838x9yPuWXAeA7R3M1onHnCWtuAENQ5Q8Wt4C1ZpRVxLr3muVEt/o4g8Bwtjo/WwN1rcq/PlTSzx5bYP7r9yCIUZtnSR2VNN9Rw2clfCdN/9vv45XfOIxfPTSC/DPv/Z74DXx6Oi5TG6NMAEMXlmdF1fo0dyVr33n77x5qggn7rQjK0bKZArTPsqLNXnmMoAL2XP5pVJCD/S0ohTXGNFM3pGIQYxC24Jnz12mi+bM2idcHti9LoZ3MoSW6BqchgOnBRpC7j5fi7IBT6hvGNQnsv51VwlupdWnABFiSTCNUViwh5Ze5yHfXQnPCOMYCZ4oNNxBk3YB9BiqCJPzEqoQWFtpPUlYzW3RruPC/GiVRYLy+gzUEh7JkS8bMyRSFqVE2yEJWzQAO4u+NTixK6xXFY4X94Dq58H+dwT/HA9aAL0B+LkOf3fxm/im//r7MO8hmFMGfQPjA9Wr8MePfQ3wQYDxeRBuQrSb9LqU1wAAdxoKpUm8ohdWqrFMv16YKJ3WuYdoSiNes7PPoedxmwOveToT2ZB8dualzQ3S4bcCXXmBpwuFtob+i0AaxiBWyRg5N2bWEGut8JfmgsqNC4PX4jxgF73nK4P+Uo31fRW21yQ0DiyASLXmpIqsRISwFVBmc7/kw6KtVOcOxajC+ZiMhByuLTZri3ZTwa8rmE5k6u7KoLcABGie25AapFoz4AHbhoTsCojoOsuGwMsaZtMNFRuD3JrNW2ku47ykHlosugu8F8Ox56Ga4D45gFVvjF5aunTWzsNuNZ/WMcFuxWjuliQeUmcG7pIHrXqwJ3Ar5YcdAf3CgUi97EjyHwvYxPBLIDQMkT7UDZ7BoYKhCZ5lGtLuoQYVN4QKslfd0g28qD9i9Edynyvj0XsDz4RuU2G5lgT4w7XLUIYKu+LdR0e16C9JxUkmkvcsvCdzjiPxWQ33yIh3ljVyn8zMe7YvlYz8KRtK3qVJqKLqNBPwbc7wFS9m+AvecqN+edh+0QwXBzDrLpAxMz6zAdBizW1RYgo5wjqLomPkZbIXzSnP6elHQELWqPRy5eNLx5OAAAAios+W4JYmWiJCvisw0B8D3SWGWzLsLYPjzxgcf96jXxJOHya0V2VuQ2x6tQaq7RBayCzhc/0SwErcPu2GUK8ZFby4tHrAwIvRNSoTJG7Wuxb7dB7DYh/2BW+s6DMtebZkEfWgbQ/UAMOKklXyygunC8KDS4DOUrsUAQd2v+iB8YUwPQKodQDRbmDLSiLHxjKchiD2S/l0DTRWXD21DACyaJ+q8OlnN7h8dY2mcmC1XngvMT3UGbz5f3sUP/T+/x0GwCvwcZie8W9e/FoZ23kKlgeoczDbTtzjEyYwVPTSydhl/UtdXUfziosxhUQ5DgpXagkvvWvR+kGY3rfbVTAP9IwjmntZwjq8EyTiZMHf8byH3RcRJi4qVBXAlimvmRlXcnzUW1SJZwp5nBCFy9DON/LHFYNaQnOTUN8S/tFdEW8tQEOuNdTQhIgyHUuotIsK8J5iIlijICEzxIKdjj2sL/vymnCtxXs5TIIkdRYFRKoCmfPvGw3d7MVr9iHOPiG8DPsAW72wRW9p8NYK4YgqO8izYQFq4DbPwo3LL8Ti2zs89E2fwVfxH8M4h6fcVSz7Db7pi/8F5j8SPvJJxt/eMujbgD/FVwAfBvCXaxD+EuJhlVUkTK6FHEuiXXZj2YZ0AuMzWJjXud+jfRdXQCYAVv47O/WBpTxzydjxujGKFgHmdQwFNdIcvgHQkm3BKJIptvvmtZkDsjDeXjS6J+unsC+SiIrCtcTUHjsAMiaAawu3tHALA9Oz5kuUwkimlTQcdiv5scKa6hotnLTwsCcW9YkZ5eqVsDTJ4Ys+AAUGfl1JcSgH+IbhN4T2iqyJdjsAI2mamVJo5Sj/V9Bh8tQbJSp58oQ+ovedAllOjRK9ByoRbsf5xJJ+02rgwVOXkm0k66l1YiSqzwidglrdJYJrRO/oWwnNc5cdUCtA1RpRY/R5JstyGQyg8ZKepjWDFxZkbslTDIMPSdrF6KTVeBccZXFSnZArMVqFKpTe6r6KUa16HNdt9MzizgweWYzh+QDG1fvYwHmAOgvjQ242nZskTxbbTM+eiEEJumQtuLJJTmFSXpo98yll6YzS93ACoiW6UNSLznu/Y2jjMPY0H/MoOivcv+Ra0xQtI4/iCzKxA5h1h2RgYNMY9Tlij+ivuKscbkqlPuesHqV2pWHsI0elCDIweKiUQBQaQt1GKC4pmGV1sVrKwmW3yiir8MCL4FqdEY4/C6yeFCDr5AWE7bMdzIZQnRLsmgTIWjPqUxYm0w/jYSOMpl+I62lbSbx5Yz1qZsid8sP40nmaA69K88Is+TysVENk70HB0ysFvbyX/E6WwFAgcwJiyFz4RqozmY5hN/2sa3eufEw8vWZoVJqVeQxstRIbHz2u9DtbwOq9a044Wo2CV10EwAzAFaF/con15QXOVh1+4Dfeh6//xJ/hv7zoxfjXr3odqpsGr/hElvj9+p/JcILgkj1b0SvLCVM12x7odzPtUY4sYPCMK7WZIw+QScrPZs96Gts96S9nSClfS5TIAx3odohAMEGh2EWpgWFi/JhBqkpd0tyOvN3FlfNhODO8pniewuFhexC6FGzylkTIpCDIDkCK6Qj1TaBeC5DVXpNwAuolJDoCVL0oNaZQWCRd/9xC1k7bMmyrgrgZxhcDQi/CayJ6Bi0ygmTtKCimrIAWbPmW6e2W6l0iqCNUWypN9cT7+gK8JjO6RGCLaAxkhapHqlSMDAIB/CIMubioBsw98M+tcPUlT+Dbn/r/4t7fvSFr9v9AuHF0GfVnCVXH+IpTxuoFjD+/9AA+e+MFwJ8DjCdBWAOsgkO8JOU1DM1D5nBe3swJolV4Lc+vAKq3P/Qzaj7lNfu+ZwcW88ymqnajZ4XTdzk1qIe8WXH/ba7jqcfVnH4ySTfC47bnpIKIfed97EPZcQHI8o1UJDQdo/aM+hSozxjbqwb1GcO2XqvWyvrgGzXuHjPMmUFzQ6oSUi+5m6KhA/KdnKQF8IYBT2BimN6AeumjuRFkbUa/koWAKzHYhyTkpEnKpdPy5eWVv6cN9l8RqHfg2g+VHVXfSeXdES/iuHrFuQUYUJ6VOg+IJxrDtohJ7btLoq+JxxrBbQj9EcMfeXDjQa0BNpCwTZI55ZA3VNQ64SVWQaw+kf15vKZKoREeOdqyquHOyPrvjNzL0J4cwTtCzwbHtsVJvwA1XgBNNb64xkilS83ZHCryeUCcKVaVRJXEspBBVjFDxEamZ4zuXRDXDAm4GHLdBdkh13tmdf9MVykYFM99B0O7Sd+IskrKGkehhGr0J5aqkFEus0Of45QEmLU5legAZt0FssGtt+T2x3tmOizmOCls+6ugPYSwATmVhSUiwwEMgCxeIuB7ABW2VwzqtczH9irBreSlr28SVl9gLK97tJcNTp5P2DzUyRPtDcxWXUVZLSe1Cr/gWBrVbgXgam5Ck5gbdEeE9rJYq6tTD9rwCCgKKDnBjK85LBYlgVbDRNkImEQw4F7fRefHC01AzSm85BlTDWCfCutuRWBToVq7IefWDiolYyxVZJlsT4Etz7EkulWEP1aoqoxayFXpSL8HpSMsgKoo/k8f/c/4vg+/DwbAN//xf8PxZwn/5r97HT569ALxyAI08fsLEMM6RpOSAVnOw2x60CbzyooXvN/CXWQaJTKYKiEhD0y85qiB6FjHMeDjcYR2w5zJhuEvDYU60IF2EQVBTt+7sZ/2nkJrCQ/bAyP7ktDteADR8HuYAkmkKyEXBrwQJaUC0C8IXEt7uwWqU6DaMFxDaK8C/WUvffPYyh4TlEfACIMlVqvohjauFmMNYIbEvpkFMn7JBdedXlsc1xwCpuma4hwOi0txnaNhjWKS+SAyEsKyR16c2+Y1KbDFIZ0C65ADYEUYJX8ngGmQfThcOBGAJdgyXvr5T+LhD5/gifcYWEO457WM9uuBp1b34aHXP4Fnfx3j5vOO8Jv8auBDBviiB9FNiFeWH8aUA1m95Lwsy157Kgn5tjka2Mj4FLlCEeUtkTjO5TXI9id855A36+lPdd0j5nTNaLSttOaUjvFclF8j5Z4fyJ7/kmdHydgy83zPeu6nfexJIbTQ1ya+99R5oJPrNK0H2wqmZ5hW1qsAqPcLIzkVidE8ZSQ3kxPDRwgvS/P0jnIRAUDNUlG8k1A7XwNuJUYT31AEBXkzLgQWUqnEuZhzfgjC9bmTwNO5I9KQQ0gRK+/lt9c8nTxe41MePKJoSOYRq+MwKUaic5rOoVp7dGcG3bFBv5Q5cEuC3RDcGcEtGX6lyfS1AiG1mpfaCHgFxlCgCoBfeLAhmK0R/swU7x9X4h1GHvBh4EaXV/W+ZjOdQt8bnHU1DDFaV6FZduiuLNDclDH1C6DaAMZRXKvJA6ik+q/zFrSsYNfd4MkY1vgA/swa8IZFnZVHxWqIo/1UPu482hVCnPF6Tjy4Bo+uwrniHPCQEyxsR3iOEB0Z4t0Lxiu93otWeT+AWXeBrPFgMoWbmtyNYHnOFxGP4YHatSiPXPUyQSu1jCT7zs2RFQ4ptAuL5u5ynBiui6HhDjzky0qbdh7gRhiG5mCCB+oT8bZaPuXRXlIg63mdVAfZEqT6B6GvABwBdq0MxA3JEk0v10xJosb6lkN1Jmi/t4BbiTuq3booWE8qoOSKhckWDWMUTZd2AvYwyJJA+uqiG61MNvxNn42cWRsnQYtcGfQrwPQGpnWDopGUJp7M7Z7W9ZhbRi0nk+s36obbS/BkBHESz62Sd1JKX/fZT448sF7+6T/F/3r1W/HuB74ZtvV45Oaf4Y+uvhDvfMFrRh5jaUJJUmsQOQ/TanjhPlUMJ2GFxWkZUVyM9wWOdXxpJcw0H9y0bwzCWa6EzCglBzrQHBkSXnNulVRkrycHCSP83vvA8YYvEei10yOrNC7PA+idN3MsmgUDFBygWUI5TJcBWVe85AQJeRgNwRsANaJVnNSqPiSCJ1W8VHlxLJWTgmGiIjBrXovAa0qoRcprSlbTCFJhCAUwapCJYe8YABBdr3fdH9K5C6F/vjJaJZZHa/BkvsOmfXkNJ9sJ0+snbeRJ+FG8huQTGIzrImDI3w2P9eYY9WWP5zYAFgA9G/jgzZfik+7FoMbDPuTwBN+Pk9+9F/gQAPwFwKdQk30cYxhGqAxJfcErKwUH80/M/J6ji/CawrHFnIzZ+bmwrfj7QE9LqkziSgPROUKYYeqlRUQYVTUcJYcP688Moyg9v6VtczJY/g7seB9S/WSkq5Teo0I/I4OiNeDagJys1+TG6xMxog4hMp0USAKAfiX5ZKszQnU2hLBJ8SQFQhoGdQBVAFuGO/JA42GXDlXdY+tWsFsruksvY+tXYqzvjwXYcgtJpVKUKS2pIT9cW/CASmTgOQNXWNeCN97MnJPzQOdgOgdnSZZdomhIG3IbJb+VTLJ2DoZvGt8zvScSXcOo1hwBLbfRQlSdglobrRxYcdT3qCcBssJjGzypDOT5rRju2IGtiWCj6eW4mPydha97yzFgKgBd4XkwPQREM4xLTYuepaJhXTucHntsr1mwkYghdybPUrVOnyNBbMgT/MLCdMmNU/0l8uxzKM6foUEvDXOZHp98T4Gz2fcrKwIwohiGKs/dnOw3BqA4jqeYtzOo/jbTfXjcT/B+v0jV0wOYdYdkQGgqN37A2O8GpkqUu93eBbpQ9cIdNFsqHRChPo3jLgB4gAj8duOkql4tAEl9xjFssL1kcPPFhM1zOykt3BpZSBjoLntJDm8Y1fUKpg9eXxQt6SEnVWplNp2HVSsHVwLGkDcwXRYWGoX4bOFPF/2oPAxt2BBgDdiKhRs9AXAafA2wtQJkBQQ7n5fgxBUTGCPGeruFVB+0Wz9eBIGdwFaRkjmJi2Ip30Bg6Mj2Jc9mROSL+d+AP14+Dy/HJ6KR6KPHD4N6DzYWv/r8v4vgYhsZ4ihkRhmkxuyb1sGsO6ALYSG7BB7a3ea8uTrPoh7moOS5lewPwNZE6QjD85BE+oTBI+ugZBxoDyIQbKxmiOl6uzd9iRCpu0A7AS0e1sqd180E6gdwCUYF2o5hOwlH396TAFkuSE+AaxjcyBybtYmW9sCLgts/RVOpkHi48hASF3J0+KA05OMurK/p74xpR8E0aHNeSrnLyaEJ4Gl6WxPLbug7jj3ocWqsMOoJm8/56PjzqHRbwryNByb/s2c4nia1SMf56wBcBz6+xMc//VL8zrM/i6/7ng+hRYPfwyvxe49/C/jDAE5Imp4A9DkG43MA/hKgDUQzwwBkaZGUCZC1Uw7L5nRm9/zhFIYwQwFMOK8fDDy5dH5GlDEORpNnDlXWA8aqIurHYYbn0S6gNYBcs/uwn/5yGzpO8fk9T+bLyFcGXKnrDet6DcRUFEEmNa2ALENOVIKvCdurAsqYkN+qguTh1WJW0fChnkTuyINWDtWyw71XzlAZjyc8odsewa4BU0E8tBrElCuxKq16eonnjgAEwXgzun6T3dt0+T7vvqc6p03CTZ0T76zeR9AvpVEBM2Y1RkzvQfDoIiSGqghY6PrfMyrnYFsv0TTHBv1CPKj71eDBFlLTuCY4LAwAmYWAVEGm5nrgl77x8Jqr2W4RvbBCihryMg5fM5hZir2ENTPkzSKgc4J4dd6CmYDLPba9JrCvgf6YAE9obgD1qaTA8TpvxhlQzzBbC9P7xKuJps9wUJwCCBy89TxgncObPvt+fPX1x/DH9XPw7muvhE+LzCX3YFaOSrwox+MotM2dBPI2aR7olOfo3E2OCcuH/gm4PLQJubPSd/0ifOsAZt0FqqwDqAGYdzMOr+hyad95QFb6gOe0hwcKgGJCwUATi0c4pyZgzZF4Us+mCGLNnlSsv35p4VYW/ZLQXpbcWXYji8r6WQYnzwfa57Yg68HrKrqV+obBRw6raxsQMc7MEbqzWtB5ZoQUMiH+NsSaB0GPmGG2HtgKQMRGQuhs7rYb/rwvhxcqoEVa7YPjXACoDLwhuX2pe7Dmw5okfk+6jwyKhS+FiojBddktjAjauYdW+n0XUJNeJ5J7NQLu4oiHMeVtQghJ4RrS5+/d970KcIxHbn0aH7n8fLzroW/RGGlRADlY44OVJ1VqvCqFWg3SbHqg7aYAafDK2lOIKYZslCi5jlE4ZYmSRflcigpZslibfP8e/RzoGU+GNPdiJtdOSD2daC5catfztusdudvPaXy3x2Oa8Jp8/SqOLfEkrcV44RZi8AiJ3Lsj8chyl50suN0gPbEFUHtUqx4EoKMavjNaPh2A0yGSDidJkAxWD6NehHtOvFgnSfs5/M2AkQreDEmXE4pl0iHe4Om1h8q5+bmGGZbnhjVZPUF5mnz3lmB8EkrChX7Oe25GvwvXlt3HwaOLp23CKeNvD9BfAo9fAv7TEf7zK16H33nJN8A5i/ZPjoH/k0GfITDdAqgDsAboDBJeuAZrrqwwDyHMXsILnXrS5Sen4abfTaLsB+14ny9yahp/59K2A695WlNjHcQ1qPBEeQZZA1ZdZfIoBJBBQ4TEu+UC7hGT82Gqn6Q8aU8ZblZ32RNEY6tAVnJMrNYIDICWwQCw8yCbukbe0eYmQI6HRN/6EfJlUQ9YR3ArD64Ztva4fLzBqu5QGS/LSfD+ISl+FSux91JkKdTHAoCYosTKD/I8H3Gzy5MOGDzw0vkiAupqSPavERDUO/HOqi28sVFuH0/qEErPk0WbxGCu36PR2iQ8Pcyf8sPKOdith2sM+iMDd8aSa3khHnGhkr3kIwueU9JnLCDSaISLdh0q5fqVh1/K8WZjYlh5CG9nq7fcQL23ZJLdsYcxjJubBU5WC2z7Ct4TuKfoxQcDtJclv5pfENxNQn0il+drKPhmUG1MojfS4BQQDQ46L2b8Pczbm/7s/fh/fOY3YAC8DI8BAN5176uK2EH0dJ/zXLxNp5lJhfeRd+UACIcbEGUMDEYX8qxecRgdGwCumKPtHCw2pwOYdRdoYZOEiyEEIHwPVBI0AUysHSWE+7wQjF1AFwZGcJ4QExfJANwwRMDL+4ugBzCnXHBt4RcW3VEVKxm6BaE71j56edHXzza49QIPuncLcga8rmDWWi1i6UArh2tXz1BXDtuuAi0c+mMpcWtb6cfVJJ6imo/DE8G0ALGEecTqHI7hG42V75Nxq4UBzgPOCZMnkoWdaPDQgvq5Oh8Tk6O2A8OpLdgP1u1SeCGAcWx7sG4QRlYQ0/uYu+rcheecnCcTr7lgfE5BqyK4VQC28vudAWpMhHc965uBB0i8BZg0fj5RVpmj91oISQ1KhekFyKK2F4+sVFgpzQOllTho+G2G/eP2M9tLbbLz7GMpTz2y0u/E8lyGRTskTDzQgS5C4pkVHrAp2Axg/l3Jga3iO7ALyTp/94UpeL1iUC5Gu+euMSUj4dmuNrGSIVuCa7QPtfh1R4T2GoNW6j3bGRGOCUDFQOWxXHYwxsN5A1gP35gYdkLB3d7xIGsZDVVRdDGstdQpOBaE5ngN+k8NJ3E6Uyt5lAl4AFwosZATad8DmDab0yZdZ8LYwjmStSl6nJ53b/dZszi/XoxBqxJIlv7g4k5I4vYbAD0GfPy5wCevYnPPJdl3nQF6EqDggeUB9JCcpYJkjgw6jGg4CeXoz1WMU1BLv4/mbOdxM1Tct98LNrFg57+1qwN49cyi2qr3Yf48atGpoFDHlCdpQaqUR0Rv8/PkT0wNgbuosD8NBZw05+FzbJg8fzGK3ichP+6cZ6+HGlhVGScanY+JsLjOMZzQthzfOV+T6hnS1i1Z1ufGo6oFQP/i6RH63qI9bUA2YaIUAA85WXOCaOQHI4Y6xnnQsDDWz1iYJA8v3IcCkFUL8MnwmodKeBN6J/kDKwMyaoguyR4Y7lHUITO+He9bCCsPAE5qzCZED2e7cfALg+7IoF8aOK1E7Brx0AqAVphz63W/J5gOcAudMxCk7rwBrCSK90ceXAt4SL140gGJJ1Wn4YgAqCP41qL3Bq2v4JjQtRJaYULus2rI6+UWDHc/oz+SnGqmJfUEM3ALK+l2dO5jLi9DA+tTECvo/OnnIzc/NUrh8tLNZ8dVDJPvuQ7E2Tsdb00Rb0jes5lIlEk+uIRG3lZB5Uy8u4qRK0GESvQj3oFplOgAZt0hWTKorZsKDLsAhnxBCIn35lDUud970m0JM8FCky5M552fGTDy0rb3NDh7th3iXsPDy1Ktwy2Bs2cTNs9xwOUO/qyCObOwnSaAbOTJrmqHRd3jZL3A2ZNHoDM7lMGtBaU3PcN3Uk2KnFYBsQA7XVkVPKLew/Z+LOADRSBwSPqb5BLxHvBG3kFrJFcWMABaBF30tcMEdS/G/gYgR+eYCRIeoxYk6tXzbIZJ7Z0gj1lD9wqW5zngKn0254CsUj9Ewqw9ieeDJ8CYIYloKB8PjhEfAWhEALK6fpwjK3fHDYpb+jyWFr7EglBS1EbJC/NFu6QYBBlEF9q0fVygQzszLPgR7EwWcFGMMYDMB4XjQOeQIYKlwkJyjrfSuC104ch5S37cbQzwdikd4768RtuwJbiVRXdkoneu7JPvpge4AtojoL/MQOPAnQF1FD2SYOVFNZZhrUfbVejWNdAlQqZJrKwKYEUror7kRDzwFs9FcK40rwN2k/AaXd8krwoh5jPMrJmDMJv0XXocRoDawHfC8eItsENC3ZcCUKSGpPG+wg/Ovu9UyByktBUDaOWmfmEBgEC0AcwaTBs5N4XcHeNxRC+/AGTlntg52BsArPS+nQNOzVfjHO7V6Cp39R35Q6qATNtFg2XaB2X7DjzmGUGSMwuDHBfTWCTFLEKUyG3qFBPDiMcQ8raPnpC1SxXoHLRKPZEmEQal86VjM5qzFpiuxwU2CJO/nOG8LN4/GrIeKucyIYaih3ePLcSjlhjeGTz5hSugUwt4gm2lemHgVaYT/mRayb1U32LVY7SgiSr5YR5gjRofRKYlYxDL75nUSp5Qmisru1auK7lmj6lM7RxM20vkSS15OiceWGG+wzyBimt46iTBhCEPV+KxFQ0fqhuYXqJqKuXvriHYmuEaDDmckksKCfnFQ05AL4mW0qTvTTjPsA67lXjGMQFQAIs9xXWXetFjnDO4uV3CeYO66cEAesPg65VUQN4SqKMYmthf8vALcaqoO49/9MH34eWf/SQ+evQw3vXwq+FIIna48LzxiE8M9+MjV1+Ir3vqT4cULsvnabsw57T73SthDKObRHr+/N4l1d3jGMMXjnyyaAxjDEa45FkGZJ7kHqEcynhBWeQAZt0Fqo1DVL1NkBqSO2FoYCqm/LID2L0o3y2KVQd5un0OgKP9xuzVG6s/tuiOjSwqDrAdIuOoFMBZXzFor4mgaR9fwG4gVgEvbp7GEZw36JcV1m2Ndluj+XyF6kxe/uZk6NM1YoV3jeZG0fKtFYmlgdyQZJY8jxYIGfjwtRQmys7H+xYrNq23oCoBraxBCmgB+hjs8JorJt2XQcRP6sdK0Wy4YEkh1GsjtbSUEvPH9rk3Ye6tlVtiCs9KVKx68Wgj8lKmPnhMWQKMGcXgS9l2HVvvAOfG3gajk1I5vHAUB07Db6KyAJ/PVclVFohWvXTf5Jp3vJ47gapEuUgFlgMdaBcZSsTJ0rqcW+BmeUjOa6ab7pjmwBUqbIv79uM1oRKsbwxcTYNykKYX1H76hVRGAgPmVqXl1qGClgivzIDvDXpn4HoDe0vKrgMiJAfyFoCW7A6VdMVmIaHU8AmWUPKo5uLXZMjDfYu8pu+jUiZeZjNr0m3cw9hcPbGLCd73tfiH43PekbUZA1mF/mes/kICaDF1AJ0OJl9igHtQuAEUql4lvCZNnO+V76SK4uikQTDPedCw6cJeWQVeExpw3rZA5/GayfnmxnGgpzUtbA9X8n5KgC0A5bQn6gUKIHq0DvLYzAmz7aXE4BOa8RSZo0lo3R58IhZfQnZslJkR9zEYTEYjITBEEwAAD147aT++JnjVoMkBqEV38SHvIhP6J5dYPmHR3AhzCbTXJPG4bSlWEiefeGTF61AZsiJxLg1jTnmIVUCrcP0ySD/+nocaBnlaQZ4YVsoC9nMvURJk60LurMnM7+YVgZ/FNS9pG/h2CnCCYVnyl9k1wS2tJuGXivbBqJx68hhPkq/KCa8mJznJfM0aoqjnrkQfhOV4D6GglF8w/MpHcJaI0bUVPBMq47FaSNK0nhhda0C9lfMs5H2y26Eip2uA7/2D9+GffPh/l4JY+AQA4Fdf+NoIEFEmCYzCMJNt73z+q0HO45Hrj+Gji+fi31175TBfO96hkZeWtp3oNrHtzPGYkWcCqWfj2MCPgVeG7/k9C9uSd43N7elEBzDrLlBjHNbpgxGBBg+EvBbhgci9TC7qGpr0tZPmmMSu6gW7+trRJxPgGwuu5VqrM6cuoBKbHKtv6KFnzzboLok7Z3VqJA7di+VbLB0Ez8oMTiuslzWYKVovwIBpWZD4YITSmHW3JHRaXcSfGjQsCD+rAlN8GUtMOgdrgnsvM8g5oCPQdkhMzkSaX6wAoOTGkpKnU27lys49DpHgggFGF96SYpC2zwGqktt1YXxTq9ZUiSYVjkbkODJNUnSP0ueeOYZ5lD0ZaPgr0HjxNKNj5hbsi1Cwiqfu5nKO4S8IWgPIUOhIF/H8b7C839EwD/QMIUte6kzkVFibRfi90wfrDnjN7QBk5/AaICgpss90HuRNFH5yAbs7FkMHeYJZq/VWBaZoc7IAQODOoKssGFJdKoR6hHCGFPiQqoWCpXBPYE0KbD3imlrkNXP3Lv9NmvcjKCPOqx5jBqBlbs5LfY8UwV3tzzk27WYiu+giyMPPyb6039I4UVKS8g0exB6MDoIs+uG8iVAMcCy5HjcqoDWfcwY73pmE15QS796VV23GAJO02ZX7NG03Ctk48JdnDDWmxxqYPsczxupR7qySgluSoSZeWbGz6f496bznupgrasZg4ytF6Uoy9kj+xPidIsC0PnqqukYuzLZDVUMAkj+x5yEfrvIUt4QUq6o8aG1x/JjF4ilGe0WTkG8YzXWCWw7Fq0wv240a/UcGmXw+Uv2CMDLMswL4IzJmHtDKZeuSrO0cqDOgSvINU74+XcDQMRi3gq6CyX2beH5pWKLtPWznUa0N3MLALaVAlrfKi0MlYcMxd5kxwpf7FaIXNrEAW+woejl7DUnkWhLAxzyVtYdtPIx1qCoPazxWxqG2DrX1uH5yBCw8uDIx8X/IK2Z6kmeiYrzszx8bhQc+cvNTIr/kOkWJgowCwJPFOx96DczxK1DM61w8XvsuOFWMdZkLvq9JJMrovWXV5Ssa3e+Yo1PbhHeNWO1Rk7x6KEcz7aADmHUX6KhqcRYfmgTI2pc4fdMLu/e2tmNYjOYWmcCYdij5swkGC+24klK3TATqfHywDRGqdZK8Vy+xvWyxvSYLe3Uqea+sljNFcAVlUT4kISEk5MMRGu3HdPKy2Fas1+QhOUq0VK5biiVF8nMJU6sB2I3DSDA2NCQpTGnOQy0DtKjt4jyK66eNrscjyu7H2EqE6a0PCQD7ZNFPQKmo4OzDSNL8WJnb+aznVQnEOu9cgUFlyeTPpQIoNooDT5/z1CtrZOFOtpdCRvO2SO5R6tEV26AMhAXlwE77zN3iR5bybLFPz59aLw50oPOoNg5dyg/Sz33oXEt4AgbQ6MtM8x28hrLPOyQJU1aQx40X0eBFNTL2Lgj9UtZPakVJiO3Uw4qBCITBA763CfCPwTvBIYIgIUrTs1jmxZgzXKSFkXyKOYBUmqZZD6YE0FJ+w6l8aNOYyvQ8O+5HiSiZg3xMDAnZw8zYJ2NOvnD6id3XGYaSHr/HiSjEq+Sg2fkDDGec8IFkF+Js0x7bwxkmnlfpsTQdhc7/tC0SQCpT+DJeM3rPVKmOJ8r3H3jN056Wthd9pGRUT7+XDPCFBySmiNiXzlOM79jAklDB+OFjoneML6eoPymQoflpTevj+h4AB1+F8DEBr4xWuLOtyIK+BvojoF8x+nt60MKBzyocf8ri8mc8zu436I8kebyvJadTteHoBWY65Sse0SA8GOnlpSaWc7NjifzIvMtmK2zPUSIzi3cQYlQRJ7m4yLN4Z/UWVAdPvj3vX3760iOWe+FmfY+9txjGOZjeo1oTXCPhj76W0M2YBD7cNw3nNJ1URmQFvqiXCoQxXk9TovgKEgLaDO7VzEBVedSVg/MGZHsc1y2cNzhebWEMo20c3M1a+dHgnQUAzhL+y4tehFf96cfj6f7o2gun3tXZXKXeUOGTPEsOsxQwDn+lfFkTnjbWp0aeVjtAaOLC+x8qGQbdhxA9laNOE64pOTZeV5JmRXYgglsMDNU8LwCjXDDF1oFKtLJdImX6MhjCDHBWJjd7gIphVbdD5yksO/J5TYCswoLIutD7xopHFpFayHkChKReWaG8bQg/rE/FtTbEn4cXIDALXwPccKx85xZhoWeAgO4SoV8IE/KVMh0jfVZrRnUmL42rCf3Kwi3sJFHuhS2WKbjTawnbTv6MLjRR4cnmcHZeefjNuigExS221YTz5FzynXUxxuj76A8ZkBUqNuV/4Ty6aIa/Ytv8L+Qd8V5yXc39pe1K+bsKoFP8bqb7Ju6zuyh/tS6g8xSTFYbt+jl4W9GwmCfb4nElpfagYBxoT6pNwt3TdzcnLma3mIK5cfttDug8XnMBXGUXKBa8sRi6/gcAKjlPmk6MrYBZrBKk7RBDOgZerUJVEMDssHBHQSsAV03gLzS85yRWdalGpYKYJVV+zJ2DB+m67MOfBwUv1n3BsTmlInxN11c1PpGeB8HTLDVKjb4juxdhXJwcNycPDbySwjGY6Xt07vDnd/zl/G3HojsxjJSa0ZdmnS7xmlKzhNeMQK4EqJrwqRKvOdBe9FM/9VP4+q//ely+fBn3338//v7f//v42Mc+NmrDzHjrW9+Khx56CKvVCq95zWvw0Y9+dNRmu93izW9+M571rGfh+PgY3/md34nPfvazozbXr1/Hd3/3d+Pq1au4evUqvvu7vxtPPfXUbY99ZbuxMmuTRHspqJWS90P72XV4l06xR5uUZpTniex8zvZ0bEEvybeXPVgLuleaUH3Uh6zxoYKh6Qce4Suguwz0lxj9NQez7IGTGpc/UeHKYw5sgO09iOsXOcA4HnSUtUSYmC6AWRj4UtB15tb6GIFAgw6V03kePFHvSIERk8isUumVei9/uU4Q+8HOtYb2bZut2yl/iPffMajzqE57NCcdmhs96hOH+tSjPpO/asOo1yyft4Dlk4zlFxnNDdELJTk7YDcS7WPXkszdbAlmbUGtAbYW/qzC5rTB2dkCT62X6LzF0na4tlzjynKL+y6f4srlNep7N8BxD1458diuVYawjP/5f3wt/udvegN+//6X4Fde9Dq884Wv0UnBaP2e6ijJfVc+TJ27vfczeTaCoX5i5M899GJ7yj7z807HjnC/UrBK2xYN+EGHssPvi9IBzLoLtLA9RhZRQ8I0yMx7QO3rJvjlpuwBZ0uxUiHXBuQYpsvQYkBkv1iaFXALg/aSgVsSqg1Q3wqutZr8m/VBDrzWSOUJ1B71sodZuGgB8fUgxPlGXXsbtZJrJQXbybkDg5CyrQa+GRj76GVOkmTuE5rGCuKEkt4B0CqGymFYiCdeWen3bJGS2GnI4h1ArDSRO8v3EXqf/0WQKQFZCwBWCmLNgl1p+1AFSgG9yV96PpnY8ffCbw6lain7K3hqlbytxnOH6YI9R4Xkg/E+ZYt6aSGeMFs9f+qdFa19lCzY+XgPdKBzyFKwGibrbfpXoot46nw5KRemAohVSZ49sV4nAt7o/Rp4DVcU+YHpEavepiFno/eYhN/AMmzlQRVHi3saNh2UlxDSEB10Ap8LWEwIe7DDwaN1aBjqXsqf4DxhLWflAx7F/Fb7Ug6iJF8iiBXXfyT8iZMjsr8Rzyidc6qkjI/Px5X1G66/9Dc6jsZfKd835DSZB7Ky76W+s2734jWUfY6+jo8v8YRSOC1nwxvtK0n4B15zLr3//e/HD/7gD+J3f/d38eijj6Lve7z+9a/H6elpbPMzP/Mz+Jf/8l/i53/+5/HBD34QDz74IL7t274NJycnsc1b3vIW/Nqv/Rre/e5347d/+7dx69YtfMd3fAdcUuDmTW96Ez784Q/jPe95D97znvfgwx/+ML77u7/7tse+st3wY7TuFHSOXCZMQPO9KQdS8nWpCBDtfgjPjQxJzxG8QyYKetI2leMSg7YYMhSk0kpzI+CYh+++AriSqri+Uc/f4JF1xYGOevCTC1z+uMWlz3r0S4OTFxh0lznx9kJMiQIMukkwmkzeV0aS5zi51jB2wihH7G1RdCagJIeWGRv+E0DLdCmwNdNnspbO5lpK2Qcw7WvkXRj0OdVTgszdM+zWRWCrvulQ33KoTx2qtUd9xqj1s1ozmluM5XXG6guM5gYE2NpoXuVewgPNlmBPDeyJhTmzwFMN+pMat546wme+cA8+ef0+tM5iYXtUxuNo0eLS0RZHVzaoL7Xwl3v0lxz6Fcs9hcUvfN0b8MPf9H34Ny9+LZyt4tyMQzZnplLfTan0niRV20d2yNuk4CVQ5gURxyiMo9g+NMie30RuYINR3rlolEnzat0hXzqEGd4FqigJcg4MAZgHRZjFWhJe1lSB38edN31Az4EjRw/Inq7CuXdWTJpnBZyLFZA0ObmEP0wFsZBYERAXz36p5VBD2AbkM7wA4btbBEs4QJbRND2qyuHsWgV3bOBri/pEE/NagjNaKtcI0GVbGbzpBfwxTpIISky7gXFerPTQuXe4GIW8Y6mXUS9AGKkSM/tixsUZU2VEXYi9MgJyDNM6AbJ8oX3eZ/49HW/YlzEHAGMArrA/fh8l0C1IKHks/ly4YNiWXkIphHAGsIoLcWJ9zBO/j8817mP8ToybRlfZHQtsTPxLg0AxyUuSffdJudqoYIWcWUZzjeWJPA90oIxMbjqesXCP9o+Svuq/3ANllopa94UOuxCllnIaflMAVwp9R0xENQZRPNTKnigkwQsLNCgRXA0KBgiw1sMYRrcM4QsUqyQRMLIccsrHgoDtefD2qgiWaQBbkrHsTUERyz1hCRoesWOiU30v5wthPERg0rFHQ0l28EyfxTZRMSnzJCodP7etxItiRxlviD9Lz/VUNtnNjwrfafh64cTvs9uU16TjmjQZv38jz6wZmrN8p/zqroZ6PQ3pPe95z+j3L/3SL+H+++/Hhz70IXzrt34rmBk/93M/h5/4iZ/Ad33XdwEAfuVXfgUPPPAA3vWud+H7v//7cePGDfziL/4ifvVXfxWve93rAADveMc78PDDD+N973sf3vCGN+BP/uRP8J73vAe/+7u/i1e+8pUAgF/4hV/AN37jN+JjH/sYvvIrv/LCY6+MG+7vLsA7kfuYs0qqgBhr5xSMFCAq5OOZpZJyPZLttNm+62QC7syOM/9Mj1EDfZDHCQwPA9+YaJTwakgJhnLyYjzvjoH+iOGOWPIr/cUCl/8MWH3RYXvF4OSFQHdVGQfsYAzpJI8jgDhvAqSwhqdJjiEDAIYHDDLEqYV5DHxh17t8nnzgh/1MUGM+InAGaIiZ99EriCsDMgbsGWSAUUGtwFfCORMgK+VBs4VREmCkeO/S60rALqkszLA9w7QALMFXRr2kw98QvcNGQz1PJJl8fyx5zFytqW4A+JoBp3m5vAVOLZypcb1Z4Na9C9x/7RYMMSwxrGHU1oFrgFc92BF+6D+9H6/4r3+G//M5L8I7X/hamG4GDAo0Jx9o+Cm1/aB3pTrSnAyQ6knZtp3J3PehfRw+QrV2k5wT4TtCejFxEijpWSGv8J50ALPuAi1MP869MHcDSgvL7TxQ5y1godlck7kHMalomOYMmqC4zAoGjc8RAaw6saQz4I2EA8KIi62nNCY7eWhVQfAVgysGrxzqZYdV06GxDl1bobuxQH+kccmtlD6V5PEiGEYLPBCT+FIPXZwxyMp0AYZZIs+66GvVDyKxXmwJaKohRBAXOw85hvE9uDcwwdvL8XQhP29sKRWYQhHAKrVPASznkIbJ0mhBLQBZu8ApDItcsU1ciGfelVJMeOg3eXZ3Uul4Go4f5b3Kee8u3Sf5SxdqVgtYEfw60IH2oCoHs87jA3estKbS6W3Q3KG54JYCBaPT804ASCqkal86VF/JZ7AAxinT/aPE2Jo0FhXDVA6VdbBaittv7MBXkiTwFMACP6ztwQN4nHD8LlEU3BMh1EuoeQxnvK1+Ibm9DImnV/T4zRqd08dkrNnvIoA1+cljAMv70uMx8JfR76QFTY6Q3kdrdDZhJSt1aU2fo33mf47XlPrfxWvmzh/fn+Qzec4PdGd048YNAMC9994LAHjsscfw+OOP4/Wvf31ss1gs8OpXvxof+MAH8P3f//340Ic+hK7rRm0eeughPPLII/jABz6AN7zhDfid3/kdXL16NQJZAPAN3/ANuHr1Kj7wgQ/Mglnb7Rbb7VBm9ebNm8M4TD+Wo0ayY5IzC6GJ5IGdyFxB7iMt7mNHB+2esJRyr61ZI6Vu4ukzOwG58vPv64Vf6H9UfTF9lyyhX5mYw8otKOa38o1GiTTS1+KzDa58ktHc8theMbj1Agk9pF70oO09DN8Q6hOgvsXiLewlz5OvxQvYLQhuCVBPGoIok2fh4dmIRxR4bHxPQa30d75Ops9ACClNjdtEmkPSaE5eFu+scCwz0PUgfQi41mTwIXcSUF63KiM8IMtNXDLMl7yXJ2trKo6UZANmuewAbBGiZ7evDYwRcI4r8RQynQCI9ZrhKwGz3CLck9BOrtdbPZmx8CfH+ItNjeOra1Sa+mHbVejaCq4zePN/+M94y398FAbAq/7bx7H6S8Y7nv8/Tq5hTjcMIYbRK6vzAmZlHomj7wUj/3SOcb5udFGZkcb3TipNIwM2x5+j9zkBudL30rj5+SnRAcy6CzRiHsB4cYlePOmntgsLu2d56Xc9RAV0dUIXeAjnyn8iizkvLToxITmpxcIQGLLIS4iHiXHlANTiC1RnDNcQcISYmI+8PLRhQQ/WcrfysMc9lssOhhjbvkLfWZiNlievgb5iWEuiVCwkV1a9FqDGtmLpMH1icWLAtn6wOlCi7NxOyEYaNqGAFnkP0zl4U40Ey9JLWbJYANBcY70oGucBWTvyn43al4CsEoiVAVjc9/rbD88rGZBNwKs5IKsAYs3mhSsJOOeFF+q+Ue6skXC0433I24UFmad6dtpmqngkCRDN1MIQrREj4WnoP1ZVOygcB9qDJMxw5mFJrZVzVlvG+VZ0mv1xWzTLawrvSrmDZBgpbzVicR3lylKAyXQsQJdWyI2CUcBMEuMJVwxqPKragQhw3sA7iopIMLbEvLtW82RpUnijVanyEHOj4fPRwg2KwunFJzGsyUnV2MB3qvNu6DyvCYrGEFo42pm13WOMed8pWDXpawxgcRI+H4mSCl27gKy4eaocj9rnNAGyyn2F33NeWXvzmtit8hqamda57pJz7wVSJe/Ogc/cHjEzfviHfxjf/M3fjEceeQQA8PjjjwMAHnjggVHbBx54AJ/61Kdim6ZpcM8990zahOMff/xx3H///ZNz3n///bFNiX7qp34KP/mTP1ncd2TaUe4oIsIkgyIZwDvAJAhVqo+kgJZ0MjuWCeVtU8+tGcNmSnPPdVGxjZ5W4/WN0xcrk9dCX/E8XvQWQGQ4X8mfWxC2V7RIlUZ7uJVUyBNdRdav1ecNrvyZR7VmtJcMTl4EbO93oJZQnxgpdKU5skwrHkHVVnST9LaEfIvdEcEtgPYKoV8RmluE5sQBXtZCA688TK4xRsaksn74bu04P20OcOn6721IAq8dBl2Vsn6dVCNn1VeH3JBJu9B9JR5u3hLs1sNu+oFHps9YvL8FGaF4z6ebRl5fGJ5dIoBa8VQ0rY9eWr4iGKupTawCXIZRKRAkYXFIqiSqV11MIQD01xfwTYV/9HuP4u98+jF88L9/Af5fr38tzLrGN3z0U6MKhl/zxCfxrue8Zj7cEijLQ07DC9edpG+JOzMdKZ2zEinvjAnWZ/jY6HuQq3SJyHW3cpXT/JooemEFw/4EqDYUZTOovCXbcaGoqQOYdRdoaTqtfEEYrB6aUyivHpgq6Onn7VLJxbfU587qhXuMIT6kJQVJGYihWEmEHA+hIhhyWLmGhjLnhBjiwZWEF/bHjP6SB5byJrVthbat0HcV+KyK5dKDjNavOFrNqzNSBUPHlC56AaEIypyGglD6Qu/jzZNbthJAK4Ybdg5o7CB8M48BrX10GQXYZhW8uSIDxTHPgFfptpAY3jlRKpzmvUqp9AzvArICAHVeYYP8XZh5RybhhZRZ44rKynjfrhDD0J6zYyJvNMmCHJgeZYJRtlgPlRkx75F1UDAOtCdV5PX5SRavxIIOYJ633CmvSUGlXX3uOM2FXNtL50uE8vhuBaE7XLZGdjithhN5TfCoUsHU14BvGKiUNzkL5yy8M0BnJuCOr5P+uhnlqjT+vF0ByCsfX+C1aRie91ISnOcVir0o8MW9QguTse3ss8Rrkn3BWBL4Zt6+9CzvBLIK63t6fE4lEGvut/a/97NbajazjfMG4ZXW+5kCUGlBgkBFD+FRH/sN+UDz9EM/9EP4wz/8Q/z2b//2ZB9lzwQzT7bllLcptT+vnx//8R/HD//wD8ffN2/exMMPPwwAqDX2mQ2BjBFZbugYUUtVeW1SlGoO0LpdKnlmFde2C1LSRxo2NRvClv8Ismvw7NX3y7Yetg0V82opVnXmcQRgc43gGwk9q04Jpgcuf9rDdoztVYNbDxO2D7UCPHkD01FSuU1AKuMER5SqgVqN3TFMK3/1qehO/ZLQHRu0lwneWixuEuzagT2JpxMp4KGpSQL4Qflaacw0LUh+Tw3AZABHABwoKEkx3BDD86L9+8oUIyeYIJUXg3HXAG5lAKpgNw6U6sU7aARMZtuDgaxkKCsCmmrED0VgrMoPoYolV2Yo1kScGLuGc0eQJVmb/6ePPor/5x+9FwbAN//xJ7D6C4tf/OrX46PHL8Q3Yqhg+JHLL4AYynkYV4G1p44XIUey2fSgthvJPuML3v89Ev2jLH8If0neyzkbWZJqaGLc1+uKRkBKfo/GnOhNDKSVDUd5tA5hhn+1tDBJNcMkVG/0wpY8s4Bxuztd3PekdAEoPtjJwl7en6DgCcMLqLfkp5LjfEVwS4ldJsdwS2lOTgAerqRsan8EtNc83LEHKg9sDfxJhY4XMC2BK3EZFcu0KCx2S6KMQKtRbFSRScI/xIItYwmurkwkFRJ9ommknkYTC3POCATQYlYbgGeAQv4wqThhOg+3KGT6niOfh2NgGGs25+MGOwSNXceolQXei/eV0yoZwQsrpRS8s3ZglhcBsopKb2Hfed5YpbxYKbYYBfmCgJhv2ifXQ8431DtCAFUehyMCypBmpj5tl+TLKp3nQAcqURWQGmC8VuUW1zmFIT32r4AmvOacttONyfcEvAugMKVetgZgzYlBPCTZDcaPFMRySwbXLJZoR+DWwLMFOQKMhioE7xkGqEcMswkVDCOfCbxGBbQUwOI47MK9KvKawm+iQfZNlgsmiMBreVrFaxfNsoyS1LljbPuejDGAVyHHJIZt4yGMlTAK2y4CZO16zkb9JF9osjER1EvHJFe4z7tUUl7OPYZBoMSba9pHvF1535R9T5WOA6/Zm9785jfjP/yH/4Df+q3fwvOe97y4/cEHHwQgnlXPec5z4vYnnngiems9+OCDaNsW169fH3lnPfHEE3jVq14V23z+85+fnPcLX/jCxOsrpcVigcViUdy3NF0EHEAJWFXy8Fe9hIjkvdw3b6euS6PcO3PvwQ456yLpPuZCDfMlKwc7ZOP4GOEdrHI3T1N5KFixIqA/tuK96hjkpbBIc1N4yfHjHtXWY3PV4uaLCNvntlIVtzeaC1W8nlgLVdm1egl7Us8sMcAbIKleKHynPvWoTz3c0sDVAmyxAapTN0xnqHJ3noXcZJE/1sRtEQSsjCACPYGDt274DHNiaehrbh1JAEZiAlQfdI2E+5nWw2zdANyck2O6HB3E030pABvmJNfB43nECQIdYAJIZyQUMXpfmeThpHydFzDxaz/32MgD6+s/+Un8u3s7/K/3fQuaFzg8cuPP8JGrL8S7nv/qydhTsC0FsYgZiABnD9p241xZu3SpxANylEc4kKFxtEguJ4bfdqx7zfK4nK+F6beYjHN8XgzphZLcWHnaoovQAcy6C3Rk2nEC6xCSlXvPBIUfGBZOa6YP6N1WNAqLxM5qBrtOT5k3SnwIzUig5koT8TZSxZA0ublb6MPcyoLhlsD2XkZ7jxNvrI5gn6qw/IKB6QHXyIPuF2pJj1VABNQyLcF0QHNzcOG1HY9ycgUPMIl3T+6JujHGbSVGXqoAk1IqACQgEbU9qLERxd9bEQiuv3GBKRw3F1pYUgwSQYZcSFbvwG0n5qESeFUiQ4A5B8hKfhdBrJ2LcK6sFBbROY+sbBxzz/ZQRWNY+OesFCmNE77v+Z5SAqzFsOJkDGG7PpuHvCYH2odqcmNBcpfXZRHISrVa4NyH/6JU6O62E43OKOicrjdI3yOpYhi8ckPCXjhEIMutALf04o3lCbQxqM5S6zliGEH+3kINJSEhvBQYSeRdFeyYoGH2qXIU2hTAx3gh5/CIETg5gETkHNgGxnhBXhPGNMNq9vIiLvQbwyk9g4OhpARelUjXckq+j/eF7/JvLxAr3T9qlq7/NNq80yMrAKgXOGcRdCoekx27R3tOvgMIulZi3NHPC1i6n8nEzHjzm9+MX/u1X8Nv/uZv4kUvetFo/4te9CI8+OCDePTRR/Gyl70MANC2Ld7//vfjp3/6pwEAL3/5y1HXNR599FG88Y1vBAB87nOfw0c+8hH8zM/8DADgG7/xG3Hjxg38/u//Pl7xilcAAH7v934PN27ciIDXRenIbKcyfyrjpWvQeR4y53hQlQ0Qu48p93Oh5rPnmDWeRCODrJvG+fm8sbqGEiQ8jisT+UK/knxWpgOOvsCo1h7rey1OXkTYPq8FVR68saBOwtR9I9EjfuVBHaG+qd5aIXKFAai+khYsYYu4rb7Zo9KcT74muKUFnQkYtJcRIwAhKaDl1ZCTXHNMt1FbGPJis4cHw4z4VnBamOiCgZI1hnoGKoHaTOdBLJXtfWNg1w6mS7wGU4cPYL9Imcm1Bp6mv13hHudqUrLBbrQJYQilNDToCulxzPjjS8/H3/nLT0QPrD9ePg/NdXn/3vG81wAPB/6UgEjycwxkhe8KqgYgy6w7IK1gGE9Og/NAuLw5UDkYxFL5KZcfc5oBoDk9bofuNNJzKABX2fuY/JkeoyJZ/gJ+IIEOYNZdoCV1cVEhtaLuzGUU3D4T8GR8owsL9UUqhiB56G5nQQiUCrjhZUgsG+kLznZ4WUIFiZCYvb0kFaEAoF+JMuAb8cba3sPo7+2BysN+scbqCYPmKYbpGNt7BOyyG6C+Jcn5uJIE8THJeyflVU2n1nKH8SIR5mMG0EtdudlKlQ65N7T7Ho464njPJJ8Ja+J2L667SBbXMJ136rodzlv6DgxKDTOod1MvLOf3A7FSr6yweOZAVnpOYApk7QtiZW1HYYWl86X7kDHU0Xea7s/epVEVQ5OWbpc/LjCztE2wMqR/6ThGOXooGQsBeyk4BzoQgAo+Pjtxed61luSWN+SKQ25aK2/ai+7Wc1xS5lOF3QziZ3gvQ+5G10C8qyDGD+oBWPXGWgF+5cXz6syg1lwm5IF+KWCX6QHTqjCl73Ga5N10iN6/I6E4V4YyiZmzy4m8J/Wi24cC8AS95hBaEvJoFeiu85odbSjNtxi8sC4CYqXfdxr5sjV9p2EheWHGO6bf03V5Yg2ZP8XONqXzhuc58A/Kv095zQDmJnynwD9GBQ7meOKBdtIP/uAP4l3vehf+/b//97h8+XLMX3X16lWsVisQEd7ylrfgbW97G17ykpfgJS95Cd72trfh6OgIb3rTm2Lb7/3e78WP/MiP4L777sO9996LH/3RH8VXf/VXx+qGX/VVX4W/9/f+Hr7v+74P//pf/2sAwD/5J/8E3/Ed33FblQwB9cwKslrJqB4o7JsAX1HoH7bdTQM7c5Qhp3LxIIPtlL+j44C28Zh4k0wOCZ5Yu4ofJTJr1HFUdpP8WZKYvT6Tfs6eZSVH1sNbmNrDn1aIkSAsHsD+Uo/VtY2kTLEL2I2F6eTe2A5AvGbhYYY55hzWriRRedtLwviFgFriHTwAQFJwq7DWznnb6fpM3kfvPdYIFWcJpg/yfpJxzQC+qQToCelaNJx/6FfakUYhUccgp3mqWg/Ti77oVoJYmG0C1qT3Js83vYuS40bP1C6vxHS8SPgkK+S0KTx/2bP1765+A8yDPV56+hl85PLz8W+f8y3JefRfBBABMkOYYcqXozeWFhSjrYPZZkBWyg/nCmPlFN6LCcCFkZw15hM0Bu9mdJQhlDDofQWgLDP6xNBD7VdA0UQ/Ss8Vtu9JBzDrLlBN/SCAjCTW5A238uLSjHA2qRYyR7ssu/scPgcszAmbe5xmiI3WOGRdlNkA/VKs3MYJkOUbWfzcgtFfUovFmcXq8zVWnxcG0R8TNvcB3WWWioUOMFtRMtgIEEZOQC67FWDLtup5NWJU+pFY2ePL5qFuyEEZ0mNVyYiu2WRmQR9KgZ3RhDDgPMymBx/VcX4m7tRzzDo9nXqPXZi8FxCrd0MerH3BuUAByEq9suK+bGHNlY+digiG4/J9EXiiMYiV7wPGLrRqqSh6bM3QKPF7Tvm+XJlIPUMoea7SdvoOhAqGJeWDba72HuhA82TID4r2iNckT1FQFGb62BVJNiLWxrepBZffK9zZAx+MRcpv0/cqJGg1HYZiIgQxgDRSIRcdob5lUJ3KpflaqlK5BkMidwdYp8daxDDDkK9R2s2FhacAgq4hzIN8RxI4lkQvJF4183yYwv4SsVY2rO38rZqb83T77d6bwDsVuOJ9wauUMr4xuYwRIEXDZ7ovbzvaNvky3kbldkUv3zm+seM1ic/sfmjXcFzOL4KcmR9a4C1FkOtAe9G/+lf/CgDwmte8ZrT9l37pl/A93/M9AIAf+7Efw3q9xg/8wA/g+vXreOUrX4n3vve9uHz5cmz/sz/7s6iqCm984xuxXq/x2te+Fr/8y78Mawdh/53vfCf+6T/9p7Hq4Xd+53fi53/+52977EvqBtkpz5UEFD1f2HlQlSggRNJOwY1isufbpcL7GUCsISl7AmRkVJKlxXPWRzAm9c4iBe1i4ap8LNk2rgz8wsItLLpjK7mrLhHcklCfiDeua4DNPRYnL/KgB7YwDPizCtQaAacahqsYtOpx5eoai7pH33S4vrXobxqQl5dVxikXLcWqFOgxBNv6JMJE1njb6fTURvSq3o+vLfz1DugzIMSYAWDA0C8HICV40hoCLMHDgIhAxg9pW1TPm6bKCfdt/D0CWp6jEmZ6D/bB00w81eZ1of2BrCEncQZgJa/ABOwbhSmWux8Zg1JdUGWRdz77W4AHhW+xE/7OUNUxGNoSUGtiXAr5kUOy985J5cKunz6vet5Z2mHsB+aB3mJXLMDbODpm4Lm5oYT8DPiUXMIkLF4f/9yYEytRX0CMOIBZd4GaEPqRUg6AhBKmuxT8ZGGbpRwkSB+e23EJnjTKhpA/tHGcABDiinVzALVYFy99MENVQTYSj+wWDHckykXzRYvV5wnVGcPXQHeF0B1rThMD1LeAkHSXnHw3uqDblgcQizGUR2fsFMrlhRNXX2IMoEgKFO70dsje2JLHEDPQe1Eymgv6TN4ugBWG4zSBewCy5hbjvTuksVdWvrglQBanv8OxKc15YiXtJkBWvg8YP/eFhTWnkrVbdkCYTwrIjhTSxOIQ3o28m2RbHs4xeEgmCz3p+EmECFQXs0Ac6JlLFiEWIaF8rYoWsXN4zXn8IFe+L6DP3FZoYemQdE1OrX7pO58IRLGqoO7nGuBaeIldG1S3hH8IH9Iw9mBMb7UvVn7Dw3JptMhIBLGSdrvnARhVmopzui+vKa2XhXupHgcXyp2VDeV2iEKYuoJZo65u1yMsN4zE7fpPP6fATql9YQNl2yjdXeA1mTy09zXkFMDhESCb8Zo5PlWgPIRwAmwl54jnN5jKcweaEO/x7BIR3vrWt+Ktb33rbJvlcom3v/3tePvb3z7b5t5778U73vGO2xlm+ZzBMwtQcCMpRlUADSbVDnM5ODVaDgfJRwCN5oDlEu1rkN/H+JoCMwypAJ7u3qkIpNdm4BuL9kqN7pKRdCiQglVsJK9vdSYOB30DbO4zOHt+j/rqFv22Aq8tqDXCW6yAF7Tqcd99t1BZh01b4/RsAThJAl+dQd5FC7CTCJZ4DU4q5AbDx+h6PKM66wfQLtxXN73OPLH/JMQ9GOI7Alkr+qln0Ve8yvMWou+oVxtbMwY2U7nVZ5/hfC40lYrv0Or2tC9SscdzMALzPDIAah7ckmN5pt05RpnwDhgCPIGNEQALNrJVgsgaEeJyw3FyT+Q8AkoqkNW56JFFAVws8cSgc6U6kXo9jnQoDCDkKI/cHOWOHymlMkamIxXnh8a6UG5wIWZ4fZ6Yxv2Ed2NfOoBZd4EM+fMFhFEiOh89tUJC+FHOrfMoXYRTJHwHcLAzgVtAjWMIw/hcwU4qQJUKY3YMZBkn1oDgLpkuHJS4n5hevLPIAYsvWiyelMW7u0zYXhOgK4yLfHgJxavL9BrmocnxAAkHCeGF6doYqoSkFeiCk0F0j2QCqbbCxgyeR0ZixNlA7k8Ar5JKhpTmOssBnDgGD+q8tB15IWmZdu1/NvF7mMtwXJjTXCjJF+6uF6tMsJLfCRkCSMMvUyArvW79PbvoliwrGYh1bpL3tE0GdEVrUal/FIT3zIU2PB+xbarEhK/RSpGeYzg+PU8Ev4JXVhhvpkykOecOdKB9iIjP5zXpe5PyClWmufBuzVNi3dhlaxkp5Dv6jhLe7lPJ7wGUSoEsYgZ6llcnCLEAwDbjASIQgaXyVLUG4CX/Yr/E4AmtbI91WnyN6KE1SvBuEMML81CGUT6+RJIN/cq4w8DShSURMEf3avg+qk4VD83mOA0ZCrt0HFEpmmEFkdfw+LjJ98mBHENVwDzbbG/S65xU44r3friw8Tqdti12nO3L2hfmcyeQlZ9j16tEyfTpefLf43Fg/NxP+svGkH7SDqAq7f9AT2uSSJF0zTAQ16HsDQ1yC7PKqKkmmS4Au2mUq+cOI0cizYUZ7pP+44Jj4NqiP66xflaN9bMlP5ZtOa71knSdUW0Y3THh9LnA9v4etOrR3VjAngbvIuEpvmZQLy+jZ2DT1rhx4wj28QUsIRaqcgvAWwKORa+xG8BqGDtrCKEHwXoaMxwGTOeGeU/nY8c1p4UAiChWuQzGH1QGqIx4TlmTyLPJ/U27n7OL63kCwBSPdwxUJKCZY5jOT8ef0EW8cmJKFRd40nzTEXjFyXuxT0jipDOKgoPoa5oPjQgU8opVRiuLBj0h8Rb0rFULnQCxvZOIGkAjO6isT010/WQ8pTGGyykBVHPtMproWUFem+kvBaZG+cKC/k2I6YlicZ0O4kmvIawXoQOYdReoSStMBUrD0wIYQqb8cswCTbxzcSqirHNC0ORgTIXUlBnlQ6GkTbgcdX0V62x5bMYxbMvwFcFuGaYlWEuoTwjNDRlHe5XQ3sNwDcNuCHYj5+iPGG7JsJ0keZfEuwxfE/xSrtNuw6InSRlJvg5Kh5fwk2JsflpiFJCcWclcyBSljEJAqShsWzssNDTuSzqURcr0Rqtk0O4FuhSecUEvLerdONwjUM4wDO3uV68VRCLojPbRfkDWDoBpFsQK+0r7Q5sZ0HafZO5F8CgdVlRKdUPgb6FvCs8NoqUhvoeUXsMYtEpza8lnFlZ8UDQOtAdVJcigpETMKhZzvGZ+FzDPa26bSvxn0iZ5n4OgWrp8HZvkm9BqhmrgMATQdsjZ6Jbyx1YUDqOlur3mY4R6YBn1AmYLybkF2cbB9Th4AAMR8CKoEJbzmngtww42EiofrpNyMCgAO8n3AQQr8XitOuXHRqZZKsoh07HvolF+rF1975Ar4v4UuJvsC4PDsK7mQNbkeqnwtbAt6XuvsMG4pu/x7O84XjqZ+Z2Bc6MQ9VQmpKxN6CbhO2HfIXfWM4dqOHBFu/UHE95JD8AKoOXcYGTP6SJr/W3whaLOsgO4OjcEaW4MqcJeGfjKwB1V6FcGxIBda9qSTsIJfSVrs+lEj9jcR3BLBnUEe3OB6pauWzqVvhGF3C0IrrJYbxssmw68tVg8OZy7uTEYQNiQVHU/JjgnOg1Y9BFJo0IgF/QOFHQEmnpmldZbn/If/dL3IGNALQF1BaYaFDyoogfOUCF46AvADOBQDAEN5Aagi5GlhUnbloClOQqeWKzAUOmYtL+SI8A5AFY+ztg0eEMRyVwEPmZN9G6D9yASzy1Al/hoCILw7QBiBUNGoap71LvioMKzF6oEJm0TPWmIOCnLb4OHe7ZeUKZTZalVJjqSyh2p8T7oOzLOqR4UDYNO5K9SvuF96QBm3QUy8BEYOS/ZKjOLl0tSGnWWzmEKAzKPi3t4cPa95JUV92uJaNKX2kMtvdPFKC3lGdwRTT9U7rAbyW9lN+KuuL0KbO/z8CsPszaob4niQT2weJJAvTAW02pYYSfnc7VUFumOCe1lgukIzS2OHlnTuRquddbVMtwXRcbj4pGWLyYa7lvYb8zUsy4sNI6BzsnPasf9HgFP+diT56qkGOg2CS/0MbQw5v6ayz+wIx9Y7DcAdvk1pwts/rcrOeHtgFhzLqxIBPfCOSdJDZXSBTqPBx8rBRR0x5FSkIdwsDL74bhkf8ojc0zQ47aqdhzomUukqEkAUXcRQ8GB+L7u4jW7zxvfk3NAr71oJ6Cf9K/Kn1gbAACUJ0lEQVTeQqTb8+sdlY0Oy1GwMkM8duGEl4DEG8utGFyrMtIiJnO3awxh6k48jQPYxFaUGteINZ2cHpt6M6U0EvJmJizlLeF3uOYcxIrf5XM2vC7wAJjdgNZIcC8Nngvfs1YByNLQwtgyCMQXBbSAMY8Jx1AYB4bvk/3p2Es/abot6XdcobYwroyHXaQ97xpHfuyI9yC+53nfIx41Ghiy60sOC4rFgZ721JDLCkr58fc8TQYAyt+7897VElC2CzzLI0jCOYpyOO2XK+k82jFGX0tYIRtJSr7YOrjagJyoxORFb/EVxZQpJ/dV6I+FL9Q3DJqnRFeJhhAAIMEw7BaAMdisGnhPojM5TZFCYhix22EdDe9nvyL0R4HXSOVEEDSfks4jYfBymbvulPbxZOsd0HYwRFrNkOCNHTkxTNaiNAJnbgyF+8s2vfdZZEr0ltLD9wGzkmMmIYPp91LY4AxoNgGvSvxM27FJrid8Oi+6JKs+SgQK3gOhLxcS8HN5vvJ3Mjv34MGb6TZ5u/Az13sn4NX0XuXH5A4buQNBGlIY8mOJnqM6kvKoHNiKFd2DjnWoZvjloZFn1j5WCWsBa+ODEQVw3qEpZDmCbpvm5dOiDDskUoxrz9BVSbkYycKal6oH/JFsqtasigGhvQZsn+XAlmE2Bs1NA7vVUMJeH3ALOENgUgsFy8Iu+bIYzS1CvyT0K6A7Brw1WJxIWVnTq8WeEUPEiIYXPs67MYD1YlY3GkuSJlEOc18CdIgkfr3koh3BRi8VtgztFij3Wbh3HRfQ/rg5sUJcJPl79MrKwgtLQFW4/nThDfsK32dBrORzLi9W/n3k9hpendK7MQHUkrHQ0E9QEkfJCA2mDCIsuLmcRBgdOzpfeC9Cn4nFgg2Pw1wPdKAZsjEBPPbjAyTv8SjUbZciAWQs6K/umZzkXynsC1SyMoa8ib6WTSE3FlvxxuqPxAxIPcFuxTMqgFmTd5IpKhmSEF7yM/qKpDJiI4Kd3XIUzmSc+RowFjxlDcWYoQZ+pNeB/DP0Q5gaTThl2oiCvQiSX8p7N1YMIpA1GtMelBhBJgp1voYis1iPd06+FrfTeEMxnDAfX7HvmWNmeE28f2FbuJ6U18zyrzKvOXdMBX7EFJ63A695upJUV082kAFcP4BaLN5YEwqVvOe8s4B5WS3QDhCjSLsArfRz7vBk7R2dP+k3bcOVga8tWEEqs9UoBkMwhjW8ULVwBqpO0o1sr1q0VyEV/lrhLdUGoF70Eq9eWRINous6A9xatACoM7HP4LlFPaPq9R3V7RWLvmQWAFcCbMU8VUSoTnsxkKfTojynUDZjnhKwkJk1JUoPtjb2YgCNKMmOnbm3E0+5YMwprImS6B6gjsf3LPWyOo+PpKBrapOP4FYB0Mq/Z78jiLUPmKtjCMcMHkn6DPU7Ql+KntF6P+ae+UKkyyjEN9WbSiBiQWYqDi2CZRhyCafjTnkmJXJTeOxNGFt6rtKJEj6mj7Qf6UUzx83QAcy6C1RTPzAPY6DJkOR3agVhDzK1uHOa9CVI7lhqkdgDOZVzJqc47+aneFkONsdFJRlL2AfeyVgm1koaqlW4BaFfqBKwIPgFsL2X0d3bA55QP2lhNzQoAgD6S6IwhNLpYILvBOU2uvpLqXTGovUCai0E2NpeMajPGNUm5MMa+o0AiOcIVPnKwLAdKxWh+l81bB+/yAmIFdqkVoVsroQBeaAy84sVcDGLxOgGcMxfEsMLSx5ZwRsrKFX5epuHF5aArEQBGYUXllxj0yGGc8ZzJXOZt98BZA1J1ROBKmkyEnAyYGzkzZFT6FsBrLCYhiSFI6YeFRAaPV9hW9oudZuVPhPmd9ArDnQBkiSq+iO8ZyUFIvEAHgnzmRA84gXnCR+7tl+QJutbbiTZcZ5SOFjwjPaV/LGBuK1braK78qIwrI1YuBPyzZB7kVmxJg1TR8AOdfm2Ww/bEnyl3loLCYFPS6lHXqNji95lOk6wgaSDrUDQkzJ0PSvckLCmBMMLhc61TT4dflxNaX4ik/m+CCXKBqfb0mcRmD6bJet24LmzQBbFyRs/9/n+nLLtSfu9qxAWgKyd85kBY6n0VBpfGqqRhnrMetXR+JgSkDWnQMyCXwd62tGS+nECeO8wrsKWGV5DcapgCA2yZApq7QtOld7zEu0ypgCDzFrSjVSmPTfUEGO5n6vEG6vz6sWK0TjIDZEdIWeuW1ps7jERrKrOlFcQwHWSOiQYU0h5yooBE5EmuGXijQWgvULAiXprKZMIvMtuAbQS3micAGrdsQFThWrthnxT6jkTM/FdxCsr1zVD4Sgoi6Eh71M6lzvvb65DhjFGcFJ5daXrcPTwhYTbJV5Z51Oywqbgl0ue43xchbHSvqBXcQjJGApJ+GVHWdco/R4Zq9LPgm410afycSXnmBht9gS2ACBU2MzD1+VH3lja+9TTyiQRKPqbzYB7pLlN499t6EUHMOsukMWQswNAsuCa4TcgTETDtkauhefcNWIGexJjyjnKeIl2C2CID2TajrgAhMy4/g7hWMML6WuD7rJFvzTojgVkcuo91V3xcNd6efDXNlrIQ8JstwS21xhuxTBbQrUhuG0YLABIHq2Uk5FjNLcYtiV0Rwb9gsBkYDuObotsSLy1fIhXl3GTjp0rA6YahkhQ9dw6kAvqCmRxpV5coZJKaYFhjvtjCfXS2pfGoZcEg9ziEJp6ZUZp+9RdO1oc8lIeSahhKuSo9+BeQNaoP5p/RnOLQd5ul/fhpC+aLtChaYr4JzTJqRW+T6wPEOHAk7q/0qTfSahhakUYLdhD+7zNXuDBgQ6UkAEQvJCg34sGj7g9tN2P12Aw88a2d43XzLSL4NVIAC4PtSSUsRFQSbymFGiqocYTBi8VkWrNkNA9vLeVJoOvGV5zaIUKOkYHRTngzxxDFOV8ykdc0i8RDBgDLifwFcUxAyDlA7vCH7R9XIPDOuzDfSoxES1+Agx5T0o0mvMSQ+IijwoVmIrAVcqzSuBVCXgN3r/xWpMx6XNY9lrK2ubXRvmGwvNTohJYuq8yrxSBzHx4+bgCHhl4SzScDe1mvbLi2JJPZN/zcRzoaU+Ghnyc0bBZCC0sH2zGn8CUx5RoxtnrtmguLca+YYejtUX4A1sjecQAmNaViy5pKhT5Lms7LwzaYwO31AqEDNSnWehVNXhlmU5zMgJgy7BLB2M8uo0VfrQC6HRYg/oVwRrJ9Qhg8FhiSaviLUmUSVJ53S2kQBX1e4At5yXM17kOOdOod5LbSSvrpcXJSnL1LjBxyOWEIRwvzFkGIAqYNpPvah8q6Wq7eCpQ9sLaAXpFSnJFjjtMZK+SB/E5ctS5QFaJPITHz4FjhIuF65byVQV+H4q+hc0Uql1ikBMYsVBO6h0ai65pqGGoDk8sMAkTAJvoSCmwtScdwKy7QCO33pjszSK6vRABZECVFS8eoPwizDy4JUEqxrLmoVqB5h7gOQFrn4emFC+r293Soj+ycAutTmAkp5VrZMFmK/lG2ns9/LEDegN7y0iydwBcA76SPFpuweiuOdTXJLv75qmleG+1BLvBAB6EgXuKrsHV2sNuGb4htMcG/UoUFF8BiyAv5/oaaX4vDxARfNUAvZfF3SULVwrmkSb0q4yAWSG8Iyg+c4uyY5DxCpxpRUOXKQH7UKow7HLLPY+ZldobK14d+wBZaZs5IOu8nFh5m5RG1oVkjHH/jnYly0RCo4We8pCP4HFFY0AqNGeIt1Z4FvUzKLZhbBNvrpgkUrwaYp+3ycMP9MyiitzIQkooPNtEQ3iwNJx2lAO7w47plsBr5mSiL4WyPFHgBwVAQkVIKkHp++Mr0kpSiMYLt2Jwo/yhJQkRARAqjHoSvuCXHmbpADD6TQWzNmJg6ZXD0MAiCbpOs3gFm155ViMgWghZtCrlx+NSXhsYDwFsKvESDlbpADxyOt/DGhutmcF1bNfiwYMSMapoeLu8Jn6fOfai/So/iOHV+wBZRKP5i23zrzT8uC1PrF1ti9sSXlPYPQa3Ui8z3R/kkJR3JLwmhnvs4i25bJcBwud67R/oaUG15vAdUSoHTrydBPQiosEjyzmguoB6OGeI3ANDm30u91DCJ3h+osSzNSLXq3wfK8fxuG3al1HvGnFOkEiS7lg9sTSnYjB2klMFnSW3L1ei47gFgysGLMNYh9WyQ99Z9McG/TGw/IJBdQYJUQfglgTTan/6295UHtMzjFbVjZE8ltCvKlS3OoQyVcUk8PtSqtc4L4AWdOnoALJGDPAZlcM7w76Q4mVsEOHEuEJOczyGROhzRpDzKI1ECb93AFQTT6xdAFbepy+4jZWqvIfv6WccQPbczbWfM1Ii1b0L21Iga3Re7N4f+pg7Lvwc39J4TOBPAaAadzzsH5wIxsdEPCy0MZD3aE86gFl3geo84SKgoEAVX4ZRyFb60u5KAr8vUwgAQ3xIbkNqmZOJZ/qKD7Ah+IVFd9lic83CLQDTahtV8k0nl7o9UtfgtYFpCc1TBraVcELfMNwC6O5x4KWDXfVYrlpUxuNGb+HWBq4RC3xc2MOKSzJOCgqEY9gtw9aE7pKEN9otRu6rFBIp0iAsi5OS9lEZFUAH11cyPF5EjAJZcZsBw6uFfl6wD+7NrFUCmbIFNk7yOS9yANA8l0MMiycPyEwSapiuPETTMs26/UJAVi7c7AK5QpvSd2SL7kxo4fQ6k/sUt4X+KD4zo/71WRqYy7A9ALRBiZiEbaT8MbRJQC62wza2PFggDE8VkwMdaIZMjFlLKLxfwW0+vptAfDAZo/dwQrkivItG7+reQz+fZvrKgSzXEPqlAVcYkrQHoUg9fJ2CWugkN5bdEIxT93crIBbXDFQeVHlUdQ9DjK03///23j3YtqO8D/x93WvvfR733CNdva4uSBohCz+QIIOw9ajYEAOyVQZMyIyxoTTyhHKgDExUoHIc8wdKSiUckmBiE0hIpQI48ciTSUQyZaIgj23ZRCYRilUWgthiwEgyugiJq/s45+zHWv3NH/36utda++xz7xFXR6d/Vfucvdbq1atX7+7+un/9PcA1gadOK5NjpEEvIknOvthPyoFm6GRTjWQ8oLAysDkkxBJgSSooS1B5hjvehLDhosTvTHLgoV5+ycoHpPOTTrmywKSR3UKwy8SwC76MXdpYIUlXm6RQXwsRWa222yFr+tppX38Q92yrlSXaZ/t5lE7g8zRdWVMqX+aRUL1aWR3/beMspNYLHZq4PZ/o0nbKLS2Yo2mhUjtfR8zp5723dCU9jfVLPkdkSSrAjdHONDE3uyeG1Tpxm9L+mpUzVm7QhFGvENTMbl7oKSdzQqula9cxXLH121gZVJXB8nAGHAROGgJPFCYNoVkiVFuWBLOargRSNlI7gDBmq9pG8VUz+7EX4aLsErjhQGjZd4m/bwgAtR2kqaEnbAz5R0EphWY79ygCvgyUrUVUw547BRqGHtegWRPN8/rKusg7bKNltbApobR8YQ5ml53KJ1KbOJyj9Fj+l4/JfQ/n38P6ZU6d55Yu/lyW50JR3t29Upsu9ykcy46OOWg8l2y6yOtI+2lYJ+n4PazB/FxuB7xmIbN2AQMyLeERI9+5E963kr0YJqSLDhDWD9POhMXCO5I9985V+SRLxjQjBTO0O9mDTbYO3Gs/mNlG2YwIG4cVVE1Y+g7BDBlqRhhs2Aasatidk4bBmjFam2AwaDCdVjh5amTNQwbW7LCe+dbuw+dSMCVshlbwqJkln4anGuiZdWCoxxx2XcIrGK9xYCWai9kYdxwUwP4H1GgNaCwGeIIVhlQDDBN8rcT8U18dZDj+nkB74r8TMKcmhn1q2snLC/NCD0Xd5oVdRJac7Mj320VTwtZER1FHm97mHshBmVrpwo64EmmBYJbaZeIRnMLLPKR6bJY2lhXO2Xu60GDdQVAUFHRAE7faJAGxj/rvQa64RrmTRaxvw5JQ2e6WHSxguu7d1rxAWU0s1pYw0jMGO+ftNhNbVKOB2QG7q15tWELKhlp3EyQXVYsMwMTQowZaGTSNxnSigcaOYaYCaOAfTk5D106wbJ+1myd+p15P7Q46k5V/3dGQKMj+1juTkzX+J8xlTTZJ9qr88d19/jazPGpRS3vhdM05wKmJYW5a2IWu60GG5OaF8h38eTkZz9IlzS6TNfOa5LYE1enNnziko/ScON9pNkjt85GIyuSW/2Tds/f+/FkFL2gMwO3FcliHdDQA04D0IJ2Dkps/+uA+uw05F2bRLjusPzplg2r3rxZ5zY7o8SSNHD/BIQCU1ex1RJZbI5AjqZohAGcy1QwBaiyhRQ1sBEOysqK1abnSYLgyw+rSFKtDu7t/ShtgPLD3DBnTod1koRrAMqCm9ncanHSyxljNX1UjmLB7bTA1NUHLn2Hfw7rGQBLRdyEIB+YEV2+GwWTHepo1oFqDhz12pLmscr9Z+O3EWkTNGmBmXbhQbaLly5z88nJuWwahbdVLYsljuW5yBBZ7n8k++qfKNIg9uoisno35zrV+njbZ7M+eJ9eQGd+Qy6qWeaGUFcz9ZczXa7IcIX126Akw+DVP1CLOj0PZwr2u+qURgUtrvB+tBVHIrF2ARrYTIllbf+zPyYld305530Ak0p/J4qEXMsuwcxGFSUIGaEtisba7B5jaEKOzFetglwxHNd0Vqx1VnYKd8G/afNTMCgJd2+9gQrNKIAJmM43JxhCDpwYYHCfM1tg6hB8CZmY1allZ8oqVzZcM0DibXDWzAkFvmajJJR3aO/VXAjsVZDs7ZBdOFcY5UPa9jSh02OT389o9xlFhmkCW4nbrF+oehJzg5Hk+TeYhH7ybpn8Xpo/Y8oEKfJmqqm1e2EVkdQzcCZHVMTguSmLN26nbbpHS8qvSZf/dcW/L5KNDm6sFOTB7jauO58jdByNJK3LElkZUty0o2AaUa2Z1yZBwzo5p/s5uudIna+K150TWdKBP1kA5M3DlxvHG7gOYATkyiZ3TUeszi7XVDiYGeOZex2lskUFw7m6Gdk+7MQrNVENtaOiJi1ToNLg4rBnsszyB5Z2iwvvUYoBqtvsTmWkFgnmf2z13PwsrYYZB8rfqIlJITEaBhNDK7OY7x8nFecn5cO/XO8fsI7b8+SAPRPTWeUQWkF0T30UbBXaPxErymptWzsqz/z3l6DQvXBQUP5yfd/+lPJLX2WvriEVFwQsT2i8EJWS/lBpZuXaWTOc1MLfbcJ+nVW7E9Yxgj3PH+dnnhFZf3+yUHXka4zZ2vEYVA82SRjNUqMZNvLc2oEZheMrO02erCvWKHW/tRoizOFH2PZqh08zSgFltoFdnGA5tpJET4yWc3BzBbFbQUwKMI8AY4BWGHlOYIw5OWdmlvDZWHZUDYgV0nJNVq3YU1zDL262TyMTNeGNAMwMa6Dguc4cDfi9jOPvNcq2pxpnVd5lF5rJjJy5StjMnzL/7d/Xrp7oBTBOfKzaQWkRWl2mhnIvtlMTKz+WuXbrQIr/aSfo2/9uKAt2P8P6yknUqx/7j+1KfZUmuEJCYKBKieyY//XHr9055ug0KmbULUI7MYkUgr9XSStRu6Dyvweak1ZmaIS3SKPyzMqGQTBLJRqIwlQq70N7pm43QaIUKa7uoqJesqV+1aTW3wiBexck4OV/regpUGwrTSQWtraqrmgLD44CeEGYHgGrL5gPYjtBoq5EFskKgGjtNLxWFW2vmLY69dpYvO+B9kZEltJzJYGC6DYO4YxdV1CFrBDXdThInGUwRv4tja4oi0iblj/eHELDzdsa7BELYaVCBBLPRz3R7cM6JLK2SNpsTWb3+svJ68K/TGljblbtdNI6+3yNvw3IHMF8whYAAxvngEQuOMGAnkwZ/H2IebmfOmzPZZ7nLLO4JZog7ENYF+x52gum4i97NkPAn/E+J1lzWZF9Oeya8A/hniE0T+T+k8Q58gaAFFXb5PHFEfnfdElFq5hcaCE5zvWsp7wCelN0Jb2oFUm5jo3HRc2vr0Ff5TRZXFktwuTGi8RpYYkbf1ZWlrPETucBmiTHHa27Jd19gaLBOi+PEsj+hyD+Ti3bh0fMCUtYwrAnKPFnTQ2SR+x+oVSFb5hJZisQ1kS6kzeqs49lz0XH5dDXa45tTeizKm+RHXiZ0+MrKntmpdSXkj4yYGx4u5dP3ok8XPC8wQJx7kO9zpABuUvKqy0k10N6Il2m74Amrrk1MiUw7qoWOZ7RILJlku/ExH9MyYs6PM6ay0Q1ztyWs7Ka4jVqLoJULRtzsqFzAkREwWzNoVg0wNDAzhYmqMJtpNLVGvVUBDSXTPTWzfh09OU0Nxfm/q0921huBqPOLfSDOrY2oly7Ts9766Qj8ZEz8/b3JIWCdwZsqBhSRpGdCgCLILkloEWfpFkGndnPfu/QQV/I4rLHse3FdWzPCQGoZtAIlkLJuV/x7AvOJrC4Sax4ZlX/PieWuedG8vtb7rO7T/fPH7J6O95LBscJaKfj0dNMSYbaYbLoIrasQrEJqZ/lMdoBCZu0ChmScM1qyC/0uCAHR28g7VGzt+Y60/nwPencnFh3oWpMnF07VLy4oTcs6hkRvRDj4ZmhJosEGB0fsIIRdbCbEKCMzQG8SZmMNtWIHeeNaqJpZIssvOsDRSs9GHgFma2RD4laEamygp15Di8N8P/yHHGjZLngY8HsaveZqOg7IMcqdnSB47SwASeTJNBOOgzoLfyb+nFOWWjiqh989k+llO5I7cOIcEQX/CHGdQJ2Dcx+R1aWN1ekvS/6XRZen5hFYIa8szzyPkJe43z9eTmQEkZVM9MUCIew6EAKRJcmsxC+WL4v0taXS615zy3+8/xKZvqBgO2hvQuL7aRe6+se2C3TRdruynSM2+mVN/z1zQUj7aOuaHzMRQkZ7TSlip5VlRH81ccMvEMvGypS6JtBALBT8Nef3KjHlc8/3JiesKZjJe79Z8JpzjrgiL3RcBuT/ZNyVN3EPxwnJlcmacD95odUva/JjP1aLfBaPIMVJVdgCUft7Yj7pJKKb/ySl3BGRRaJtClnT2Va3aXh9sn3BtF3XOUkrF+1I303c129WmF7rOof8u7ge0sccUllzuv2yYE9AkwuOAbg+JlxKeC19qa2vXTQ77y/LExrbLcZzCLIqBA2R2C4fQbRxVz4d/SUnT1r9wj9Xjn2+rADM0I1LYR7u1jID6y9LT23faUbkZAaHTZJ6idAsAbMDjNkhAx7Z6H8Ya9CWQoMhMLa+eHXFYQGv3PpFjwl6agNe6Sm5TXvn8L1BsG6xi/040KiZcf27xwQz3+TKLTNy9yI5iSPMMm0AEYCaBmrWoNEpZdDSznJl3BG6SK4uH1hdmKcFDMTNl8YAdQ1ujNW+8uTVdsj7wAJEVq8/rHnHXffMI7Lya3JdOo94zjUtc5PDPDnlZRN9kgEfo9n7IyWnxRVlUWy31CD0gbCx7675PmU0kjVTMTP8HkMBQgtDxcEASBrmtkytv5Y3XiBpdLtl9uEbXGswyhdDmoL2UkjiiShK7wmRDCgu+NXMD85ikQG76GAFzEaWjGLfGg2BlEG1VKM+aJ3LWzNEBOIqHPuxYGbriBrb2czAlrciY3fbM5Z/kQl8QjKJd0xuzXYgwmQ8H1C8Y3kIwSoIrWSREfJeoCc7FdnWs1RWBs+YB8JKdbQ1OQGi2GYXILJaGoQdi5wdaWCFe+X3rF3m8NelarvIMxmYKdsNF5pXnqz1RFZYLPjJjj+mWD4/eZQODWPecOro7ruP0EFwC/OdSv+C/QoKf+DGCyBK/NjoekmsPLeuyb8gWp5zE8OOMcESA5QkYch5TZwg5ZFGvQmg/wQZ53yOmMr3R8QbiaEqAzNSdtHiFhvWNxZihFqRl/IFYu8Dj6DAoLpjYr7IjIwQJojpK6YV1Pa1Re06ZHGtj9BqYRFZg3Y0p3yR6J/rX0FeS9KIL65RL0pkzTUn3AEpNbdtb9fsxSQ9Y+jCafmOiXlhNm/qJqtSmdnpc8TLk7wPJ9c5u9YqcMELDAPYTd1WX8jNCz1yIgPo1TY5I8xbOGdzQb9gzq1C2r735jxP9CHyfdBtFoc1jfPVa2WDS68J0wMaZkDQM6AZWNJKzQCaWJlQjwj1KjA5ZNCcW1uBUCvQSY2VJxX02EY2ZGW1fGng1zhsg5I4OYXGaggPjwPVmO2m/Zid43f3fs4PlhlY+UBjTodrt+RU7N9NpfU5j8iS8N7ZpSYTUdTOGtegoV7YNQp5jafwbE5/P2Xff0do/f6UnvfWKsYAdQOezYC6Xpy8CmUjQOl0vSSfl6/xJJHVtcGTo2vdleXfkk9ibdUiuDI5zFKpJpMrYV6Urdm6fAsHeBKMYrpEzohb8rVZiJXj02VyjFy7TZQFnNziHTBUhczaBWj5Y8rOFkI3ZNhO3bZr0D8ToSImTkC7sfWaoHRouCQdQ+yeNyM3gW0AItvwm5E1udMT52fEMNQ02oebIYKAsRpVQLPCQMUYDBqMBhMcbwjjrSUMTllHidWWmMC58pkBEoe4YTedLMmgvBkJIKKYpBGlmMjtonPS0dlPppMKQ4zqREAgtGR9ySp1mlutgR1IHfv3kFdJyFrp98u4HYdt4KOadBJZfTsCixBZboBPtLGygXye9tXp7IbPJbFyAksMzHLRmz9HalQlzguFLbg3W2VQ0LqShK3d0bNlyLW9wnP8dbc7YZ3BCxK0oGAbeKsyAOmElhEHN9mBttl56/Sl1DXm7RSL3N5BbiQkQN5XXbm8tq43GbSkk+2XIZIg2+AggTzQMe9gCjwAoBhaM7SuMWFCXVfQU1htrlnsy6HKHVEdzBXlkK4ITI5+Yll8ijM6Vzzq2CTprjdO/gFCdqFH1lCaPsnKybh+cPwv17te3m0D/2j7U4k5S4t0I/FvESLLy+M8H59dPl/K3mq3FuQdRFpss9n8qrOcaV69ZFUyRxTP8u2Z0M5fpvMLA7e7HbSB0d00Cl5YCGS91nZh70mDoPESHVsnkGZmz3UZ587zOi4uqkEq5+7+OzgQWPAKaIK8gvNf6x3ATw8SGueTsV6yxJSaWD+NZgBMzgWmhxrQOVOQIdAzQyx/W2HpGRtJfbYKTM8hgIFq0z7D+9WyvhtdcBK3pqnG7EzXETb9XcGdjy4KY1+ufMBEIG9W6uvIuwvpWnv0ISfAnLaPD5ZitbMMGulLKueW8t/IP1ucX1wTWKBPA8tdo8a4dm5SE8LGO8ncAfo037uILEkg7ZTImpd/Uh75fX7fTDfsu5+7Xb/zViw+r9CPSKyrkrWWm95Qul4Kmy3+oxEmCHGN5edvIk8h33ayLipk1i5A+T/SJjchK+KgAIjFs0dXx+lB5yAvSQPVs3PZRY6Fwm8z2WtNDJE0clPZj566yZKyA34zgB3Mx35AtFGoTEUwyqoUNkOb1n4YZsCAAepa45zVLUxXp9g4pwJrjeEJZccmRagUghNEVk4wkTXDaYYUdjZYUyKkfMcLjncBu/OhOOxwwJMz242B2U5Rp/+OzkUFp3lLQit/pvSp5f/LjzFoOX7PSaN5RFbX4LsdkaXFdyBqaIk8ZHtMyrLNoiO9lt/bf62LyGo9w6uwZ2qz3geCvYgwiKZmhQQ5uAYzQ+HAUO48hAFaI2hlhZ1yduX1H29yuOAYULB/Qf4PeyYHcWwIBID16wd0TAb6yIAO9MkaWZjuND33+L4z76G5rPHn3It734wqjOd2rPaRb1TN0V9DYyfyIfSzjn3W/rf1ZwxhaWjQDBvMlhQaIugJgSvrjkQRhC9Ddy+ciNDWFCMovIjd5sgTClnjOQryCSh+365eMkJrYbRIz47z8jj8l5ssVt60ipmP0T77TiKrQz5uR2Qpn2aROU32Ojupox3IoW4iK7sm5Ya8xulCIJzveK+W2aBK75F9o9s8keMP0vUpeEFCkTAz7EKHbyBK2qlveIJQzR3F7yaEaeH8COqEXkLLrbG8pnKLWNDpGBLmgIaBSsEM/ZzXriXqZUK1xTAaqFcIzZChlgizAVCvMmbrDbDcgE8NsPJ4hdVvWY2gZgRMziVMDgHNkO0mvLFWKFTbd2hGlrRSU0BPYDf5nZ9H7d2iuM35YFHSGiNcfTWcWoX4d2IGlLLExOmQR10wDJrMQAPVHsOydAC6SbRcsbfrN9+JiSEzqG6iGaHXHN6J4/gukGqvmYD2einf+M/TdB0DbfNFkXahoFkhH6TPlev/7cogINdMQUOcEEjNTpN46X9OagjPkYc2LbVkmb3g5m3GnjMaoBkWRiGzdgEaiNFD6qatiu9t0QObTVENcNEJV9/iXZ7fRtiExiYEXddkqzXREWslSWQFU8MKUI0L5yoEAms7QPvdBqPtzkYw4XLOdI0GzNA6h7daK4TZuMLm0hDTqQaNFfTEambpqd0lmSwDeougpwhmIORszYO5ibHEFjXWkXxaF3Z3m0GWyDJwkQj7hCWi+Zpxd/rFSGL20VP5fsHTN0AvsnmQD9Bdbc0Xo2tA7YtU6K879BJZOp5vaWPJ+10bsd97KqTrdEfauZpYHjkhS/l3pDsMSf4Udgyk+qyNNOa4AqnVJRYVUX1W+N4S1/MBnl0mdoIBBNup3FSkoKAHhI5Jq4dffGQa5iHFwov7fOzYPkkv5IQnv387WePTibR2ks/JvSEyldvRDk7fvYkwZNpIZnnCpKkVZrVGUxNQU3DE63081M6s3Zqwu+wYdsx2ctD7ewATdCtKk00U1oZ+0tZXZ0ndCPmS/JhzkJNRfdcXycMjb2sCrffoI7KofUcvkSXua29OdLfPXSOvknQ9z0qOhazxh/K8AHfM21i2bxLnesqZyJSe7/HLLi1kC/YMdCCBEdsZMxINFU9o+bmgX6N0QZJa22n6LoKe+3eksSPkhD225fK+d+zcjOP8vHFfPanl+1ulEj+pTGQ32CtHNs0YU1fe2QFGvcowywZkCMPHRlh+ChietGuXyTk2SNXsAMMMGcPjKgQtIeOJK3vsySxrUuhexXAqw7LqCBrBcHPOimCgoBoXGdARGZS7uUnWJwotTaWW0/OuQccTRwZqZmCGOqzjev10e9cq8voipoVdJFgHkUl1Y9dAdY3gN1hqHu4UXlNRro/6giF0EVldhHD+Xn3XwwZGOn9rKwn4Mojr2eaHPd/9iq11RljLcKdWVnwGYh/xjt855hGIrWRNE+Wi/xinQZ/m5fkAl4X3qwXnsmhBFDJrF6DdQt/b6rIgGCjvhG7QtY7SegYRyc46+24ga8h9LHDIK803TBplI+tMi5Qt5+ya18pxWkys7S6QnnIgBnyHUTNgsCU10qKDN7ng904UeWQTquUaZqpx4uQySDF4wCHqhyemZmsAr9gyqZn7P45h072PLiD+71W39elyQS21s+Q8QFu/KoHQCh05lz7iu+pwEN8lvPvG4Hwg92q1Un2XKN2FSd6lh8jyA2G+UyCILFYqmBX2tcFtNQNzbLtTsED6XBsrf6af4Etn0hTTsNPa8PdL0irx/aZSB/DBNFaLBTMhOCz090pbcaNjeaPaLVuNPFGmgoJ58FrAnhiX/Z2ArJ/YGbuPmJZC9hcxEekghuMtCzRSn8RNgFojXGvCRQipMlkTtXIg+pjUyhKv4Zzzxsk+ogo7xXM24iED2k20KwNuCJPJwFaqYquR5eqNXBAnM3DDvPdjUosFh1h4JJaeXZN8wUvlWm4tzSh40kvMIaidpgWfl5979P1svXlkskZqAncU157omKi3iKxs0t3aiKN4D/XIkazOFiKwdjq2duUpTnHHuS4iK19cSCJLEk+JM9xcRvl2n8kIT4AFOSWfI9OGYyFniqx5wSP6BNyGIErcopg4T9ypJpb0Uyrn0V2L0R5CbG5fZvdnmzFN+kmMJ92444JU+e9ehqoaCfFCjcLwhArWJPWSe8UhwAOGGissfceaFFIDzFaB8SFCs8QwI4Q5vJoBVCP4vqLaag4Pa+f+pPbO1SOJJd/By59eLSjnnJ3ZWZ5400C5rvTaWSq+Y0JoeSJLuaBQ/vfv2+xmtpENK5UoRdg0cMoBmEtGbauB14WMnKO6AWYxGmGYB+VrPEWL+eWShJ7Xysr9ZPn85EZEn6mgTN96FnWm7XTXkmx6zOlT856Va+715evmiakyAsI1OfcgcFwzuX4kSa/UyTxCXjK6oZybxXIhua+YGX6P4ebcccInOpPdSTZuxhEFRG/EAdewQsSAecxkRiIAHQLBM8fzFinIrkEMOBSPJSHgiQAvFPyAa7Td2ZitWC0qv/AHor15+D4E6mWgXmHw0A2uA4PllSmakcJ0XKGZaGBowLUlzZplhqotocXaLjR8SPV89xxkyTQ9Nt27B4x0UA2kV3d1WwJDJff0DRDB7MT/jgwRccIPuCJUb7ZYSMvJaRpnXpibGPaqE88hsjpJLJGuRWT1mRTm7a9vAdKDhcir5J06zom22sqbskWx3G0Asu8dvkyy43zBkZgbCtKLnfkqwY3p3pw0ccy7Q8FesG8hRujWmBF3ZV1rk212bmZuTJrX5XLiBV2yBnFCg55Ff8ebhN5B8Zj9/zA5snlH56VWBhltzdmVWzCE4VT6u9NRVpgBApEFzaiGDdhY7SxuFKAZbOw4ZypAm7gYYbe5ofwChdOuq2cA1dxNEuXrL0l69VSPrd+e/Lru43T8DZsz+f3zFhKcpREyhzuStcsl5YgviP3fSWIl6fyYvI02Vl/7E9d2hO1kTddl6rnYetfty9XSxkLHsUyj4rlWmuQ4I0BtDv0FKXhBQCE1AyPyQTW6tHJc4/Ab8H7ulziIp/Z3vynftU6RaxhD7blax7olpO+SKXmTFQv0BKFM4hQswcNea8lZbNDE2A3h5H5bBjXVWHpWWb+/A+fzSpGds20qjL5LGB63BFG9QpicA7vZDrsG8fO/eplRwTqPt76DAapt+fQ4rpmaIUH7jRjfZY0lvrxWl9cmpjrWizfHYkUgGb1SmsX11ZckbzyRlZNXfn4vN+uNJbNUpWBIB/Ij2XDxj5gnd/p+w3nwRFZjLJFV1+00XcoKXe2+C27NQ1ql9eD/56aE+fdANs0Z7GU6ZDKsh8jqNRvsWIem/q17ytEjN8O9vv8n79tTDiAZa3LyK3Hr4Db+wyajkGOtdZP3r7UDFDJrF6ARndKyV+cNk0GKDY8obYRZw241chXTtDRi+oisrCEHcz6gxYb7PFtCRAiQPo2XEIJTkFj+eLpGmJ5DWHqGY+OkaIrZuEggzdDan3PFqI5rDE4S6lXGRk1QKzXY3zijxOwDAEbH7Y4JGaDaZOiJq4rGCgDWADOh2mqi3Xlrt8C9ZwcxEupGSFKjFbgiqIlJf0MJv4DKQroHjYGwiHETAcP9XAZHe/jE8XtjI3X4zFomhb5cua13NkgnRFbHoN1LZPWRWHMG0W0Z9nkLivz3kVpyCxBl7bIQ8oGytUtA4uMnGUFTK00XBmv33WgxGIt8g7aIinl5v1lzfRAUFDgQBEkDpBNC5ycj7nxTmFTYm7MxXh74to4OjZjtiATxlal1KkvE6VUha8L5/GYhSgEITUu7EGiWANqKWciJkiewfMARKLuzrqb2/MwQaGCi2a+hsIDw4041tvmA7WJFebP2zJ8J1WK8bi3A2q+evV66KUn2d7Zh3HtkjazSjoeyqM/ER2QXWNyf8OycmBh2jrZd85hkjD0DImsHbW9hzJMZPW0v+7rD52VZUvvcwuWR8kTKEp939oxkMyaTRwUvTFhLEXdAmba+hyAzWsEo5EJ20QVxFkEaOL25WfezEOdgXjvLlzkfPL25YTZ+kGEQGDQzrcjmObii4PRcNdbqpDYEPSYMt5xZ4YBQLwPj8xjNqgHVhOGzympiifkpGWB0zGp/+c32ZgTMDhCaA9bCY7DhfFwyQ3dxLiwIpc5roq69Fo5Wye9KSoFhWlpL2/nSTaPiuWvO3JAUAZXqL1soY099kxD+i5BbfmOlaUIE913xByYDIXjH+fnaqYPISswLfZrW++XPcvXas25M11fUvi7zDQG4EC1iun6KLo5BKrfIuQkjbSMcXQdJJMoAbq4iNx4Dp+D7bdckhxA0i6M1DIcNm53+soXM2gUo92Mmuxa+QUhVzHk2s/6+nQqAnExICDH3KBepo08rq9WxciILssO4Y6ey61VxCbZR1iPCbI1CCHRTEdSMwc6HlqmsRpYZIJgODk6ooFkFRaCnKzTDCmbNDlhqS0HNrH8s/zBqGNWYQDWj2nIdobKDvpqxjRRiONXU6oA1jSQ0QwU9lSyJW6y43XhPOFh1XpdGDgy5EPKDj3G/D3p8bHUgmHXkzt/9d6eVZYvQP2C2hFNOZHWlAyJ55U1KdZquZVKYTRx2rGXlsQ2hEzRPci1DAMnOArL2SnHxm+Sn0B6UxYCcTBLkc8TzW8SWEnlLx+8Ea1ZIiFpZhPmNs6AgA5HvX6JddpEdlN2Uo4dYmf9wd2uPHAEQ/Sh2laPrREdCdodxnEH0YecnXE62GBf+POxQG7/YQdwV1AgawWpCUbOKANpUMFoBTjOYnM8skqYJDGeK4rSyGEHek+EwEfP+UPrAAKCshrGSfrUYCKYzfkfT5RkngaLOu4gy+UWaJG4zvJBn8boIscy8sLO59JJTsUzBxUErnT9PQb2d++ZIhNZ43/1CCzbqbZKF7kHxOL2X0msUz3eRRa06gGjf1H09J576tLC6CKuilbW/kcxLtiNvfP+uqsRHqs0nH6971ihzrBkC0TWPHEPHXAtoN1k5VgKBXAkknBwiGEgiOTJ6N/Rjee0YpCcGGCnUTm5UW3Z812NLZE3XgMl5Bs0BKzeqDYVq08mHGhg9C7f2YGdeCOgpWzPDDWDpWSu/pquE6bolxvSEMDzBGBgOwa3CuzhH9SAnbwyinyP3PWxCE4E8GZO7Ick09jqJLKUsiSV/r/x3r03UBqtU+3f1blUCu+6+7DCoVn4+RHAXG/mB0PIkbNMgaGNJwmw7KI3EvNA/N5dJ2xFZi5JYOUG1CNHVar+I98yZ83XKF0DIYfFdZWSwlFMqpmXxkfMWXx4ZQCuXt+SnFsJ/qTVZdBMfA6BCQgxvh0Jm7RK4Qrao8GSWThuR3z2XUHnDo8W0slqmXbERJmtk9hGexAQsnxhKZtafC+VBmIRzRdaJu0IgssLk2/mrkmE2vbZKM3DptN2ZaIb2QYOTduHgCS4zYqipswEnBTPk4P+KDEFNrLBgRfA75eSIJzKWRPOhbGmO09qkvnVUTe6sPwfVsDMj6brYkfFp+CCMheoudxjMt0OX7TuJxULXQCzbmxaaWX0kFgBJoHbuvidl2r7YEsEhYUKqdufdRbymx1n7l4NyTkiFD8VjFc/7hXOqSpHmESJy+Psqv2PGzlcWEP1lcdHMKlgcCuicoOXmBX4XW6LVfUhMZOZoZeVyo6eLg23kVE7S7EDWuGNvGh/MGFiOLy4hx/5oy2a/Gx/QQZBZAGxEKWGywc5EXRnrI4s17JjNCP5KvJN3IMo3OFPO4C/Ck+wLTJoDSdX9+rGa2Pma7JzYd2W87aPnFKr7tF0MLpBxkCVAaE+gtjxIJtwivQKYVLvNuctzSaw+WdPXPnuQN0lgXpVSmiafK2UT+F5CyqWRC41Eo8rnkd+TEdGtQCVus8TKuLihknwKXtAImlnKze/klFH4SQIpazqmFVBpS2S4udN2GkxA/3wsQD4315rqdbXi0897sPjixuRgNujfWaSbHyUxpgsW+s6MfLZM0GOETYt6mTBbA8aHG/DAgKYKelNhcDxukqjaRmxnF7WQl6z2lX8x7+hdTxlLNWN0kjBbJtQrwHSdYCqF0UkDxZbUihouceMkGRd92ZX33QxLSlYamIn1pjFx87tP84hccDKto7lZq77ce9SOLDJi/koAY45PrI6N/4V8aEn3Kl77LHERIwgtaULpSb4+v1nC6Tv59/bv2LE2AtBPZLXmWkJOzCOxxP9Ov1itjSAhK4C0rROy53bkkZWJZX4ZKZeQZV3vkpFdaf5yvST+I01PRlyvOJFvZrD4xKaQWbsADYrRDCWBojNHckAcLLoY3Rw5kRXOYa5Glj0f0+fIWVyWh3ICJpzAcUUuQiFF4sox76ExgqEnhHpJoV6lkJ/XxpqtEpploBlFIsvandvBsNqwC4raaWzpid0lZ82JjxKvhVWNGXqKYFqoXfTCZhA7d05KdZJUXiPL16PrdMnOuA+Tm2tBLEpCEFntLNMxAPcRV2EA9zS2+PQ8I3FauCiRJUksBXSZFc5rc622tguklbzWi/xSx2AaD2K5om22GKDdojmYKPlBODtO+o1fLKvudC1bcn/dE1nOATypxQfsgv0NBbG7mvmgIPdfnEw2OuYvYneByOrsq5mskcnEHCzd/YuazlFDiWOUKpelaqJsCfk7X45mEM0LAeuvhMmdI7sJYuWafaZqyHFUcjfZrXOUXZSoJi7wqOFgWp+8XPrm7dpoOGRMrm6C/JXP9aSZrNe5v598CCExwUny7htr/KJQppm3AKRsku3+uP/9RFb238udnba5ndaJeCNC95vNHYW3kTWtjQ2xmWevp3XVIq0Qz3XlGTdk8ny3KY/PU2ycFLyw0fKZFQL3ZL+9cZN3cot4raN26+lgu03M7YgvD0nW9nTWPnNuYrhAS+5yD1HS6ZsLsNEN3VqnGdo5cDMAzJDQDIHpuYzpeQ0wNNDfrTA4pUJ5mezGyGyV0IycD0W/eSLmh3Yebsdb6xvLQE8Bs0GoR4R6mTA+R2F4klGN7dqEncYYOULL1ivBR0lnP5dXCjxwdW2c2Xsg+SJx0/JfJYks5YiwUKHcSUpakz8GwYAHOmjJteo708pa2Pl7Vs6w/mqaVBtL1EV8pknz2M5vllQ66SOy/Bqqj8jqad9zCSq5xveYt36ScyL/3q0HctBkTzYBg7VQd/v3myLyObG9xrkUMcMIn6T2HX0bp2TtQ+w8N3gC1ER3Q4nPLFG24M5FvucCKGTWLkBBBTOGeJKCgAgIqqIdDXDeQN8jDCSp0EtedZlKdUBqJLUYWsCZWLAdDxjBBwc1bD9etmjC8ncJM6eWayrbCabrhOk5VuVWjymYefgwtnps/WXVq8YuLiYEGisowzDDaPZhNEBVVOf1uxyAm0POGPUSgavY+Vq7C8miQQzUyn1YvL/YHW85liRq+QpINZbEwJ0IA4CazCm9K8dOTAzDb50LBym0ugZhf29OZFUd0QoFkZUvOvpIrNP2YwXE/OWEZs49gQSTCwNhXhjy9FWlIEi9eJwvJMJuhYy+6dNrYSOOeF2mNZojyVXF38fvmksii1TRzCpYDNJnVjxJrs/LgQixD3fkMucBnVgosANtQwiIovmsEiJLJLCajRmx5MbFwOURY6AUmjoGIWFymr/OnyL5qIOw2r+sGVTbQCRm6IkpBMe80BTGXh+xlwyiHBCFJ2N34CXJ0GkW48ufXWc3ewy75QmRJPPwi4e0ruLEM/nXXuB1LU59fS5oYtgrazpIqn6zQvE/1+6VzxCT3/TF0jQdr7oQ8npbRNbE1kppOw9yo13evB66CCybXr53zJOz/3maxCQ+yCK5SPULvUhkUdfzC15waM19Sdl20KWhogioBJEl/C71rkvcmNT2t+vmTfJchz+tULbcuiTkj6zfIxs7M5LKvWdo2y7vbTWyfH6KAE2oVzTqFYV6RJiu2c2K6UGAK2C2xqgPzaBGDczJgZUZtS2Xr+96CZiew2hGdvOj2ogdV2/ZOlMzhp5Ra12iJ2w/M4XpKmG2asmlaux/Q+ubGLAyybsuCYG6yGlmQYEHbm6vVeJf195MVkvL15lwLwKtwFrbdaoxTokg/k45aUPGgKGgyMAMVPxtpPWIJ5ScVlby2ym4DR7/Q/D8TX6fr287vdpjgrzqInIT7URnlpmbW8r3zYmuHiKrc+2Tr98TmUDtdFmaruNkTQa0ZGSqye6++H4otejkmk7mDcSNwx7rGDt98Ynar53PU+U6ynRYb3m3LHM1meegkFm7AE0UJsZg52TPa2VJLKqe2HUudBakxEJOKuQNKCz004V/vJ6nFwcm7oYDbsEdSJ42wROKahjV2H+3DuHNwA7sessKBusEkdGsNlb7qibUaw2w1EAdH4BmFDMnS4opBlAB7AQIyBILBhy0w4gt0VUPrf+usGmfDXpBAITFhqOQHZllCboo2GW43FhZfpXkhQHCIM2ykvMAMjmRlYPFYC6d7/aZGAYyScV2tVMiy/tUkwvgLiJLtsOOHYdeIkvW9SIDVNfgKIRgIJukfTd8WUXZXZp8p7prFzyYE4rdhWRHjeLzvJYHwkAs/1vB7c+FZ1ZOoAe7EAIpY+UqAYl6dEFBBwhioeLHJ6J2d6HwB6lWVpesmf/AXiIrlx3Jc+PCP7mepxcHBFhCH5z2zygG2mBE/yJsd9BZW80rH1mKKxtshAfWLp4MYEYGqBg0VqAmmyi6SXdwuxHGLQIrTmSB32k0FUIQkrY2QYes8XXjCCwK53vA2eApqja5bREiKyta+C/ItF4Tw3xC7n9v938xIqtjEt1FZPVcz4veLqO4eJqypjMTovSZoY3KOVg7v84JeZBBUZ4mRFaSJpVBXcfyXP499B9/DvG5BS9MBPMc35+1ArNJzagAgJT1laXUaWlkLbRxKeaU+Vxt8QehNa7GRTUn8/tkId9XVkUwQ4VmpMOczzp1J7sZroF6lZxLE+sfy6zVAAP87BDVhrLPcYFBWHk/wMDsoAHWZ2DFmG1VqL5bAUxQM/sCNgovQ8GV2RdWWbPCwakGeuLKs0SYHFTQE+uryxJnFKxiQnAv59/XjqsMAoEHGlwpqz01a0DeRM+w08LzA4N7vlZWM01ru5aBAhtjiR6/gS43N0T9szG2ripBEi3SnhZxxSJNDJ3j9yTiXqd2kknvV+R8frQf6Nv/dtpWneRuliaB6k4zl/Dy6bu+J+m3CdQzpzy9BJZ4bynPcvdEnJdVyj5kaybtCFi3NgfbuRIIiXmhJU0pEloAsEOLlR2tnm6//XYQUfI5fPhwuM7MuP3223HkyBEsLy/jNa95DR555JEkj8lkgve+9704//zzsbq6ije96U144okndlTo5xsUlB3UgPaENNOkaTlUzBpu4ng7J6fmsb8iLbt72T0vEFp+kS4+9gWo/Unydv8MbMSmWpJcLi8fzdA1TO87qxkSZgcIamZD3JKxC4vZQYPmYO38YFlTQiw1IM0wQ4NmxaBeZevgN0zQGKZiNMvRjMT7RwGiEFM1Q81sxBBTUf/EfE5fSTS2wrn0e/gE9Vf3MbDRPmpjj5mdBhmnCxr5ybSyJJEW0nth1PK5lu0q9BFZcrCWRFalrH8snd5nf1PVIk7DJNyx/LK9hQl0/gGCqnTefpP2Oucj0yaLBtdm2Ydgdiqwvk36yb2pfF4IRJVxWlY+AqHsn3HHwA/QlAzS1gl11pd83wrpOPEhB8BpZflGJP4XAChyZh46NbM8kjEjI3ndufSrlxmZ1iWwvazxfVp87D1xrPCTknDdP7djbMiL6IkiMmkyxwEHmRDkkJNBZmjlj5oBfse8GTF45Iis2soRVPZG1gwzsNrB3hxEEgJewzfvw4HQckFGzNCZgfhC5pgna7a5nubjZYT7OPlD4dh/Ou4RpjmpVlbyC8V7gPmTd/l750RWkDdI0thJuBIT4JiOuyboIQ3a7WmOrInl7Gpgi3xE2o5TfoHu5Ym8NZpbULs9if/p3E/ci/gdkPOf9DxkOiFH0uuclj+wxgUvVCjE6OpeK4a9qVXSf5X1E+Tnk9u5sZDYZuEsNW8WuSfRoMq7bd7/5iHf3HSQ4wpXCvVqhfGhAU6+qMLJSypsXKQxPldhtkKoV6xWFBk7/k/PYZiVBpgq0KbG4ITC8DhZtyhkicN6hbF1scH4cA0+0GD14BgXnHcSK+dtoj6nQbPEaIYIv0uwJnFm8dF1gP2oKUM7NyqzVcJknTBbEfXkX6dhJw/FuCnqgBXZ+b0jtlhr8KCyZoSVBtx3HlSWiFIqWA+xJrHJnbUdILQTJgpuWE7bRLUPHNdPvW0zyD6DTlPCOYQQqkrIIOrWyJLrKH8tUxAI6xKv+dRBZMl1jH2++FCaX3ofwv+5Glmy2qQcTM5T53fpUijAt6uuvhjktbjHkbpBvrFo5+TXYCIfiPReCcBfc0olO5FVO1YFeNnLXoYnn3wyfB5++OFw7cMf/jA+8pGP4GMf+xgeeOABHD58GK9//etx8uTJkObWW2/F3Xffjbvuugtf+MIXcOrUKbzhDW9A0/R5aNsbsKRKKigApAKii1BAvK+XyArXITob0oYqWVd3PWikUOwIrCku/AUBlWudxE6TTvCT/FxEQzNSmK5pTM6pMFvTmK0QxucQts5T2DxMqA8gDNLNyJp3NMsGVFvniWCCGTBoowJvVHahwQS9ZZ0vVlvWv5aeUDBRNJUlq+pl58vLvb81N4lRFmcHdKdw60XHWNmrRSV+Wz+Q28WNif7EkkUFovpt38DcN5GQtu8S0kdWTlTlRBbQr5Gl4z2hjfiBLJmYu7SCxErbxXwyKrxOz3n7Lgt8ENtgQrBl90fTPzdYhx1rXw9ikRBIsFQN1t6X9qXkXcL91rxQqvHaMtjFcxisAZA2IM1QiqG1KWaGGYqc6Qc7AirAt/vELAxJG4+zX8TjbYis1iQm+54SVKJ/uGtBbiSTtnb6/L7O865/cUVoRgrNkkIzVGgGQL1EmK0QZgcQ/WcxIhFVMWD8zriTFVMFzFToq2pmiS41sxpW5MzYw667W4hITVAfndYrWpohLcxJpRW4YNquj9wgybCwVlbrfI9sSsgX8fuhg8hCvCbbW6LhK9tMPnb7fPwGHDraxrZkVChsz/ltspCyBtk8KOl/CON/rm3V+Tz3bnHe1V5E9C3ek00TlV335xTSXW1lJyJEVt4UMuuFDxucQsw3yDq4pqpyUQs1qHJuUPLIdfYGtOaOu4Vsw7x3HtiHvqTBXC7NX97HWln5MVTWJxbZ8b4aWxO/aswYPWuw9F3G8LgzF6yB4XcqVCc1qg2FwUlCNYZ1DO/cpXDFMKsNBusTrJ6zBQA4NR5hNq2AoXFm7UCzRJitAs2Iwkaoh/X/SFEm1YzBRoPRswaqBqoxMDyZWnUENysk5rjkN6Fl3SCaD1ZOA6tSdv5c2Y1s1o7w8nkRhU1u7moPHeQW1UYQmVl78l8z+dL523dt5rv1D/vNnFz7ixTCult+l/DnFQFKW62zPEhb9p7sfJH1KQMkJFayxldpGvFbzCWwOuojEFpa9ayZqJW29Tx/PSPDvHZfYtGSiUuvCBCOvauVQEQhBFKLmuxpWSLBGj9BIcJv+gtbwZZc3AY7NjOsqirZJQ8PZsZHP/pRfOADH8Bb3vIWAMCnP/1pXHTRRfit3/otvPOd78Tx48fxL//lv8Rv/uZv4nWvex0A4F//63+NSy65BL/7u7+Ln/iJn9hpcZ434EytN5xnbtvjAq3/ixJZLfPCnGml9LskwCRb29dICOKaYUDHyXp8jlhUDK0q7PQgYXKubYCq9mnsjoVdKFB0zDsE9KaCHltHiWbZDlJ6U4Fqm0Y1toPUK5YcUBPC6FkAbK8H7a8KmKwTyJATRrYMzdBer5cIQ02JXXbw9yW1r1w99jki74VfO0pb7zCIi91tPzjIAdggnXS2NLFimyIRjtY+Q6oJiEHWD6DbOCrkQF4p0e6QtjGXPmlnfkECpG1NYhfnPrKdttTHM99YLdVY+d33Bfdf7nZLItfnk0TkVLY/GqeNFa5TXFDYwZyDQ2njj8VgHXyXKDhfWa6IJZphC0XOzEGP+rXlVryMkR0l7a9YkMiap8bO8rwjLJJz4lqvrGFBDAlTvOTtfN/U5Mz5rMxolm3/izvcdoecDEH5yb3TqKIZQdVu8eD816kZAcbKJK/uztqWgRoKZvI+YqH3ydUsAWAbucrLOaNt0U1F0AqZRq+TNaHSYj0SsDPyK9SbqKRM1LR8TbG4nvwOnH6Va4J8IdEa3+McJPy+yYRcFIDiuaCR1dXG5LvI6/m1XLjspqzJss3boSSxpKxJy9Mli7Lvcv4lZJF8TpArQJQ5Po3Q8AJ8WmGam0UwpCz/HW3uFexJBD+duYalMxkLgalkVDux6d6yIJnnEmURyPvm+cpaBHLsW2AADfO/gTUrNJU101s61kBP7Zjk5YiNog40Q4XNCxTMEBgep7DuUDVQbSAGDplZOWYq+1qjpRmYCdNphekzS6hOadCSQbNsMJspUE2oxoRmYNcZyqkVK6dh1SgbNV5P2Fl1AEvfbcK7qoYTAie43mD7Pfo9c+/j5sz292awd6UCapv4OcuGsEYhAjRgDSKNbUrSv3AXCcnWhzJrSommjLyi7Nw8dDp+nwd2PgKA+F8624Qte9BKDH2DwvooIbJyEsvdHyCJKVkMsWZKX6ijzc8jo3zZWvdsc2/ODch7MrSUAdw9waWFOx/mQ4aDJnr4RdwcI6yt3BqJnAsf7wYo2Rhyc4+w9gLieolgrVgWxI6XUI8++iiOHDmCyy+/HD/7sz+Lr3/96wCAb3zjGzh69ChuvPHGkHY0GuHVr3417r//fgDAgw8+iNlslqQ5cuQIrrrqqpCmC5PJBCdOnEg+zycoEMzQxIYvdwYMwzvkZu0pd99RlGsw1CayRKfalsiSO57IFu+JdlU0wfKL6mS3HKKRUSyDN90yjsCaHdCYHNTWtpwANWNUG4zBKQDEqJcZXFm1WlYIdufNErvIhQy9ZQVAs9YEwUvGam2ZJYN6zWC23sCsGJiVBpPzDWarABgYnLI7KHrMGJ5gDDYYZgCMzyOMzyXUS3YQaoaEetlqZyWRGbsGk1zNeRFkC0Fvchic4jtTQ1ULZ4tEMY0n07xZYcgnEwKJxoV8YPdA20lkyfbUQ2SxVlGYybbn2iTr2AY9u99ajFCceMsJeK4qu+gn+Ylcew5mhZRdc206lhFR28qXLZgjIj3vBl+rfQFxf/zuCa3E7MgP2tLhOyH4zYJisNyu0Gy1stwChJSxu+UFCc6GnAGe/7KGQLatyYmMXIyI/uluiOn8yvZMiCzCfCIryBJhgiWvCaT9W5TPy5yK7A6628W2iw5rQq6m9i7fX71vOr9LbSq4yIUMVbv+PjSRCGTXZyuGGTHMyIAHDB4wmhUru6y5B0KwET2xx6yAegWol6P/B+M2apqhirI0qUD54qcjazrOeVKLEUwtyEewgvsNWZxzaVOnyF2yZl45KPy+9hlIJ/jhGok5BFpEFifzGfF+Yu4S21SeKB7615d8nPy/k09SteIZ+aNbRBbFekjkVjgvvgNCFmXyh9J7OMgz8az8mIDUlJDFNY4bKI6RpRLN8AUPTcqNh3E8TYJReW0U5/C7d065U3QtnrPz2+exYJqkj/mxiFrfEyJrYLWO1NRAjw3UzAQLDmoYaup975J1wH7AElfVpv2MnnVauxzlwfA4MDgFVJsEmilHZGnMJhX0psLyUcLqY1ajy88VjQaaZUKzZE0I62XCbFlZn1iUrc/cO/ro8d6PcaiKfFwPv5+bz3srC7/R7V2JeHNCp5UVz7uKleSlgl0rKJFWZcGiJJMgNadCQWkx/1gyD/k/d2Kfwz9LEln+vHLtX+lQP147MSgFzCOyZEAt9y75uj1v+4m2ln//rn4lnxfGfGp/kM3JpLzM0dd/KfsuZFJwGaTStCR+s3QuR8m59jpL3CetWpLzAFfOv1/GRNlrjIXGA4cdaWZde+21+MxnPoOXvvSl+Pa3v4077rgDN9xwAx555BEcPXoUAHDRRRcl91x00UX45je/CQA4evQohsMhzj333FYaf38XPvShD+Hv/b2/t5Oifu8xNEljJyLLIofBIahhpI1Nk2gYFIksl3YukeV35sIEkGIDldpY4R6X3jcuj8Dic3Iu+miwzgiboV1UeEdu0oGbqoFq01KzzRAhcpQe213xepXRLFn/InpM4AFQn1sDAwZqV2gDmBGDVxtQZYCGgFqBpgpg6zBezQh6Cgw2/ITdPhsEzIwVEPUqBWe8zTJBTRWqsYGaCEFAAINAyhFKeX0qgMDd0V+Azkm/JUlU72Ig+DUBrGqvHxAdy+0XI6gNaFqn9/XtlHeQVf0kl3IDrAqEli9D6ldN5N/Vxlz9tarEty9NaJZUMAGMZe2ry466YkuSqmmqVp0s4uW5MFj6d0VQn/XHfqIgd7SDaa0fiMWut9fICmq0YkBOVGApprHp2Z3jKNAcsUXaQFUMpRsoFc0Mm0JoBZwtOQPsEVmjOWn/BL/4jnIj/hdjvd+RRezPMZPYrzuJLN+3w3k3HiTp7J9Wn99uQsJI5ZPTwvI7eeHBYoKlZj6tWyAo2OhSxpsEsvPxaPu8WXZElokvwhWAgTvPZK819qW87y3dAHoaZQ05WWHYEVlOSxgAmgqgxso7qkUIEELgGZ36HORFJu8Avr9+WiA3nicVLY6EfIqbG76u3Q/nfZEI83Uf7KUTkqwK/6l9TR6T0PLokiH5ZgiyNtZ+tZgGsBsMFcU5zmlAbkK11LOyAnSRs9z3Hh1pcsIqSefPq1gHSXqRf5ybcZpf+Fgiy7pJMu4/QxGjOc16Ktg7CC4N/EI+lw0eHcdh0z1HlkdCZmxHWIUBcA520i59XpK4948K5YpuM0xlz6magywxA0swKWcq2IxsGlMRpuvWz+9gw0ZgN0M39jOFjUxVuzkfW3lUnVSYnDuwQX2c4LKBSDj4cxxs+HVSnFM2IzhtMUuQDTadFtgUSAzXPbHkHd3LgIFeO8s5hw9jbRhD7TjvQ8XINSI1+ZrRBwtDMKWHYpfXnPYjCCjizOR+QS2sTkjH78l5bn/PTQsNWw0ssi/DbNfmSZ84DSKrvU4X9SAJrBz5uiVHZn6bB7zqI7GSvJhjVGZBTnH+7C4/WRzv8VrrLVnn/getLTevgDwns5RrKB39XPu+Qw2Bq6hZbKpI0O5EdX1HZNZNN90Uvl999dW4/vrrccUVV+DTn/40rrvuOvteeSUzt87l2C7N3/27fxfve9/7wvGJEydwySWX7KTozznIk1kkGn9jQkcKkRAky+oX2D2NvpPICuRYuiDp0sbKSSy/2M/Z19BgTHrNRt1wttyVzd9P+Kx/oFjuyTmEE1dPoVdqKGUwGDRYqxoYJiwPZzi0vIlJU+HkZAQAWB1Ocd7SBqamgmGrbrsxG+I7p1YxmQywujzBeDrA1okl6FMKegLnzNf6yqIGGGyyXZ9ooNryAynZXfMlQE9hd1D8YkTbRcY8hHp0E3a7AJFsSraQ9NcMY3zhMh77KWD1oo12vgxwNnoRmdZ4xwxM/uIgXvJ/b6H67kY86QXUPFLLt4+cyJIDcB+R5duM7miPWRuLddVRgYowPajx7b8+wY9c9k0oYigndTUxlGtsxmXUZJkYQd0/8PiluPCuZVSbQpDlixY50Iq2nZBa5IWTGEgpOw6LA5Gn1MTKtLXCcyrrJytxFF8hEloiUgBp+/GVp5xWVqUMpgrpTtY+xtmSM8DekDVB9VpOwLzpCDomPoJ8mOtvgbJ75fmO/MK5kLfME7E/ITsPtE2GBdEcZFyYyCHpG80SMLmwAQ3sokFrxkgZMIBKGyxXMzSsMKntzGqom3DOc2Izo7ExHaCpNQaDGnWjUY8rqKk1dYffNWSA2IZTV27zRs0is2YGltRSDayGrTdnV9h2JzrUHxMYrm1mG0pyvA2yhoF6tcLxlwKDA7POjHMpF8w4s3T1syOc+5Uaamua3jxX1rgcSVxz55L/fUSWv09Re67aarOxKlogoBkpnPqBGi8651n3CLeIpEgmhulN1hhlxOFvHV/H6pcHUDP5o3UQZHIuJd8fbeKJs0VNkBnZnCz0Fal1Re38IGWPP59oanHyP/5kFOYagczajlgo2NMIbk/yYEESlDSSdrS2njbSuXAHwty407yqI6+5JoY7aZ+tPhpJrLBxn/lXihuWSLREyURt22qLUW3aNYff9FQ1g6b2flMRUCFsrlQbhK2JRjWCNeerrAyB98PoIt8G5YHabpYAQDNgNCPCdN3KFD0mVGNGNTFO3gAtQojsGBbXILBmgcqO3TJAFylycqaj7nXHOdg6Y8OBAPN1G/LsGpRDWQwIKk3nZCJl75EfhzVW5vg9MTFMLJ/6vwfiKlaZu38BjaxsDRXqyBNZ+bmsjlrf/evlp7J5fyeJJQvfR4h5mS36n7w/5JnXl1xTBdnl3k3bPKQsk30nKbcgwcI5nZZVyjQyVisraqQhbGB6R/G8g03+HfvMklhdXcXVV1+NRx99FG9+85sB2F3xiy++OKR56qmnwi764cOHMZ1OcezYsWTX/KmnnsINN9zQ+5zRaITRaHQmRX3OoSsDaBVZYCDuiuQOFnMiK2dyJZElG5tL2+sPqIvEoiy9XyyIhgUg3pdppkjH6pbxRwy3KSZapy4z+N3XfxQvrtq/kxI93cAkxzn+4TM/hH/95z8c7M7tKt8WkBq2ThSNC5trrONG1cBqULHV/Bodh/OpxVA1MDhlrNbXQIGaJtHOCky06ReurYE708ryC5hmifC/XPvfcOdFX4La0TZTir//fVfjvt+9wZJZ0sQw392SWn+SyPLX+ogspVKiVBJZirrrQbYTiWQnwf5vRoTXft+f4eMv+i/QXU4YF8RH1/8n/PbST6La2m5RQaFdBjVzMeEP7b2r7cuBmSJB682WIK6FXQWhqRXMCxWCnTcrjiqyBGBgNbJAgFLW1IPcf60MlCoO4OfheyVngL0ha5QSE7VEpqjW5CtZLGdkdLgPHdcDqY+Yz3YkVjKxijIFyCddSPwmJDILbqwVRFZuMjldZ/xvV3wRB1V7G1BOvO1ecv84/F+2LsCfPvMigAlNrZCorBjX3xlhR17VHKIrAtY/lx7H8pJxGyeMqGkr64/j/4WnaHlCv1aoCD/04ifw2tVvzX3H7XDfoQvxF1+/FGore14ua2S7ACW/7Vwiq699BTPxObKm57z8bjTwkvOexk+tPQ51BgTNH4/OwSPV90HN5mh4JTIH7bZN6feutt8iqyAWBdm1GKwk7ytAopElnborhjelJfKkFrv1vCV/T1eDrWAPQfsNXCVIcLEJ4v4nvrHmbXTMS7NTbLdply3Wg9+nrkEzW8CHAEZyvGGIqH9wWivO549xfcmNRwQbgZ1qBM0nVXsfvHaup6aAMt76xJmz+z7cEIgYg6Ua0xWN2QEVfDMCsBudFWz+QSnAWZcoG72QjCXNWAOzZQVdMapNEzSAk9dPCB53rGz+EvZ3VmgTYhRPyX2UhFARv4mc7wOBIIxrKkrWK0E25ZsjfRB+gkP6xrR9ZXkiRpSHtIrp5PxIzpE8kk0WStpPOBbr9m4/1r5O0M4/O07G3DntX5rJ2jwWyC+cFDJbamUh/p7B9YPQypJ9LLHayvpQsLQhcQ0IGlbJhgzEWspbrfh8GY50TeWlCaSqr6f0HbbDGS2hJpMJvvrVr+Liiy/G5ZdfjsOHD+Pee+8N16fTKe67776wgLjmmmswGAySNE8++SS+/OUvb7vIeL5DabGj51jf4Pg9c9bNsgNlLK8cNDh0GoS0uc+iMPHROfHl0ihyA641E2yGzmRwEP/7880QSZoQRjYb5Hwj9X5C6mUCDxgjAkY0aH0GpMMnP84/Lx4+gwPLE9RGgRsCTVQwGWQN1MuM2UHGbNVqg00PEuqR1SAjhgvLzqgmjOFJg8Ep4xYhImwsdQwafuIIpLPofOeJ7XVyTtmpYasGWxuAgQE1GJCGJnXan2emB1CNG/HMDiGg0vbS8pMlB1xPYOVElhMGrCgSWd72WU6kVWxjuS23HwyTXWoGpuaMeHIAgIYQpojlieXytuuIRJYvryYXcUNqfIjM5cBLflLh7vfqsLn/LD8fCNpfnPjP8loa0qwQlYmRC7XzkaWNI7VEeRbfgNh3KHImBcndKteIWhM3exaBXAj/0bp3LpHlZY8kslQ8l/fNpO8533TeZFCqmRvd7qNAtmARiy3ff42LUFgRUJFufeQ4mh/nn4N6C8Oqtho7THYh4hcB7llmZBcyzZKPQEXR/NFt8Kiaoaf2Q37nWW6YBPorrav0JNLfR/7EYYcakIFBFBiaFBTRaX82myFULSLsdtp8p19SIitrY36Tro/IkpsnFOVHQuIgPdf5EeVpdmEnQOWyJn9/dz7KDFkXqSxJSC2RhySk5H0tWdT1rj5tkMWpjLZslSWyiCB8MnL4iQKKrHnhwxOakhwA7NjRN6fsIxsy1xMtTZousqADvX2rKy+RXv5vfZS1euGKwBWFeXwqz8R7eHlSKZghufEbzieVG/OHdlPCm7L78V3NEM3YnU9Fu2ayc7/ZAQYNDQbDGitLUwwOTjC+sMH03JhGTYWfx1Au638x9Gd2mr6eICAkGi72N/B1Qsl6xp9L6i4h+lTwjRv+SzP0/PeQc30XSS/xlZsTM7JtNNlGfJ6uyzdwjj4TwzkgIrv2Jgob/UHLShJgOyWyRH6JT6x8/U5eKUCM9SrWV/u3EnUq2+z87pS/NCDLFM7HfBLLlUxGcXiXeOw1sjpllSNOo7ZXWpxA3sp1dZZHeFbwe8rR8bviHWllATvUzLrtttvwxje+EZdeeimeeuop3HHHHThx4gRuueUWEBFuvfVW3Hnnnbjyyitx5ZVX4s4778TKygre9ra3AQDW19fxjne8A+9///tx3nnn4dChQ7jttttw9dVXh6hTexWj0SyaggGxE3WFvQVih2idk8fxPELjonAtDFhJNDo/aCGGz9RCNVD4Mera6fANN9jKQhABTDHyk7KN0FTWTxUfqHfk368PQ7f9UNcaetCg1gy9pazq4YBAA+vsvV5xRNoKYXDKOmEMZTKEpgGGTBhsmsA8U17Avr5C4ppBoinQgjhfLxMuHh4/g7e3eHa2DJrZ6IXSz1YYkP13/z//BFKTHEml2hELfZpMHTvxc5WQmD2F7Zp8KMJQ1T037BD5oB6EdNztkpOXxAl7rp3l0wpyK0Yl9O8fF8wQ+YS0wrabXWjlcM75yGIXCh3afsj5xvLO3oOvLGUwrBpUuimaWQJFzsyHrhoEUzDk/7s6ares6URCZIk+468rnx+ShXuu8dipbZVBXmN5jsRDw2QQgczC0OzKelw7iWWMAikDJm13yBnR/53YefeLET0VspbtOGR31KOfkdb7LiJrOnbI0zziBVMBa3p8Gm+dYmwGznUAB4LOlktMrEHiXz5xF+cV0BuxULYHSttK9mpzJvFtWQMi6JZg3yXkk/1wnmIZ8veQ756nURAEsZBRqiOtkF/h+YQ4wQ/9j+P/MNW0DdD7ybJTAgOtnMl/b/0WvGDg26skCTyRJSLShahk3m/rdsjMmAJCn1iscfW5suiFX6iL+72miSTX/DvIqOSJJor7X684hYMGlgBuAHYb+6xcsI8ZwsZENWbrbgVw7k4okFJmYOeAZsDghmCMwupoC4OqwdOTCmPNqE5oJyMIeotcxD9EjRjnJxIA9JjRND5QlH13M4DVGk2Mo5FunADB1BBk0wbfSX316dc3/hxzkEm9FhrJ83KNqSx9h6+0/DdrpfePmke8drTV4EZCklgyfdccaCdEVkLitfOw79ZfvtwfVixHls7LkKzuUm6A+vsi0CI/O0GUyK5wr5A7vn0a7fxlex0LId/jMwVXII69rEp8Zfn38bJOc9BUDDLRKwQsiB2RWU888QR+7ud+Dk8//TQuuOACXHfddfjiF7+Iyy67DADwS7/0S9ja2sIv/uIv4tixY7j22mvx+c9/HmtrayGPX/u1X0NVVfiZn/kZbG1t4bWvfS0+9alPQQu71r0GTQoXHNiAObAGvSkmmIIwkMdBwypjdVuDbsKwC/tvkTZZSCirUdVaoGe7fqwQQ5onHURc75hYkTe98BGcXJj0ehVYXpvsSl1umBEUMQ4s2/ye3hiiWVZQUzsoqwmh2kQk1QiYrcHuzJAlGKoNuAWJsqaIW8Y6ewSg3WLFvy+AVDw4wWKdscu6oTCoUviPZHLQDAjruu0va6d4erya7ZYjHRw7dgHC8TZElvWLgKiN5QkthWTHHHC/fZcgaQknf94da+BgNT4jE8Mkb2oPnpGokvbcqflrl1mh39VOiC9l+2LuH8uI3QW/uPXvF9IpzrTYOJBaINidcXefd/autYEmRqUNKt1gpJtOx4n7FUXO9EMRYXU4Aw9HoJkgjOV4LY7bu9TtSVTLF55PR/IYrX4o1c7lc9NdYZ9BJmtk2vy6P0dyEe9kzxCoRrtDlM+4AhEwHNj8NmcaptJ2wsZwWr5xQgdyO/KelFD2uh9nKrZawWFBZeK79ZE2nWSfqJO2PxH3TxNGqsNf1g6xWQ+i9kZ4qEwhx12piUfhHHqIrMQ8VFFoV4nmUZJ/18S1R9aI45Gqz8jEMMk7mw8lz8w2G6XcacnN5Lz7DvFdpc9r+dOSzw5pOSsfx7TZdeu61W2gwMoeT2iVjZMXPqgS88c69Tkq/RqBCMRxvAptIycDdgpHlLRsf7rcWHTIptCfMgJLgvNxJlxA2k+8CaGQM5aw8tYadiyzFilwa4Y4/2+GURBKDWFLYsFuZvrFd60w2RpgslxhWmuwIegtZX1qbRLUFJgdtIGpqg0hI9zHa4j59ZZyP109sibtfo/Yvw8TgTK7dW9qiEb8Bl2ElrsWrE/9BlmHb67WcfI8oF+zylVcXoau5+Qmhq7ttkwMQxYUriW+UGWwNSCuu7uUR3ItrS4iy+XX5xLIXvPPppgWSEjV9Nmtl4H0FRbK19UF803MDG3fxkKbPHdFlN+XfZdagapGIjvyzRZqvFxLxxMmhMBxiXKAI6oYABmyx24jcBGL1Bw7IrPuuuuuudeJCLfffjtuv/323jRLS0v4jd/4DfzGb/zGTh79vMeNF30V/8/lP471YxtWLTJElEg/7FnjLiLLIZgXKj9JpI4JjiCylA0JHs3DpKlhnGz7hXy4BsTJsRuMo7aXT0shHz+4m0H8X68C03MMDg5qNKfRAHPMWIOIsTyYQSuDY8s1ZgcrDE5YYeDLq7fijjkZWB8nQ/ufjI0eAgZmKyqEtfWOFH3HDs4TQ71SmICSEtKvC2JHwi84ZmuEFw2OndH7N2zwnY1VXDBp4oCeE1liMOtyVugJrPif4iTCD2bSrFCLtgTEgdAJt85ogqE82X/Y+j2gz5zc9LvI0tTIPyuQWGHBEFVic6IrHIu2H0gvcc37w/I7CP6ZccfBtZWwe+Adv9vz1k+WE9yVcUQWQykDXVl/JUqxdYSvLJG1VNWolNse24UF2QsBRc7MxxWr38GfnfMSLG1NATbpKjmMD/LY3djRvhLzL0qzAtAmstzkpqWNAqR9keIxAZl8i/f645wYyENFew2pZsk6e883hE8HDexCQCsDRYytagAzUlATguSJvEP4MIln2N1v/92dNwOK/rPCDqZ7Vc/ahfdOZXq+QEiQL1RgzVYOBkdXpwfDjM3pECtecOeLQF9G9z3ROiJ/3c9RBJEl2gqA9gTaX29hG1kjiiW/D7ucyewQPvpYq03bi+0NG9G+kzRCbvj0rbRyczFZrKfX0vs5ySv4yQrfOWhlqXDMbpppiazKtfP+hlbwQoGqTJw/GtNeHcpNnTDPVG4B2tPx+uYnfl0THu6fIeapO5ja+DUN0DNO9OXF6XepHBA0lZhhllQgkbwVi9xU8X4QlQ92pS2ZFDdWKNzfLDGaEcMMbX+kgQFPNE5sLEFrA0y0zWNgTdX1xGr3Ttetw/dqy65l/BxUORKNCdbvb2PTNQOK76eorREl0UkiItHIS4glp73FSKO9z6vjXs0q/9w5yrLyXpLEVQ5j4APbzCO03Bf3/EhG5VEIk/9AunYC2kSWprRtSyKrg8TqImk5kaGtwiPRruojyGT6Oegje+Nxlh3Hc74fhO+e/MrlEOKaCdkxU/oMzyF49z/ekiWx+PEa8PI53sTQReRdFGfu2OYswDfsGrOzJpf1dBN/8uT5+B3X+09uGpw4Z4YVnoJmUydElO0gjSMWlAIqDW6itgyAjPEFPEnRqZ4vyK1kcTGLGjZysWE0hcaS2Mz6Z7k24/2aAOlCwjOq4R4APLD5mxqoKwDHGd/9S417XnQpLq2eOaN6/S9PvwhPf9sya2wIs+MG1ckJ+AQBp+LOhN5kwDlgVFPbUUhbJ73sOqk6YcNtNzO2EfEaDmFt2Q+2QsvKEzs0ZrABjDPxo8bF3mNREQYgY4CGo2O9jRHu/86LMV76i9N+/ylXePprSzhv8zh4NgVmdTrQ+0Haf5eDb022jWkV2hg3njj17axtYpi0pa4Bb055k/S+XKcqPHT0PPzO6dDrAvc/fQS8MYZx0QzTxUC/maFP698tmugipgkTLpe+iveTEvk4LUS/bvODL/lFhLiOypFbAKDZhpglRu2IL9KW6FKK7KdS0HoApQzo2QnqZoKGz1zb4nRRwz67b+KwH+HrwmC6TcrnDtRs4cmTQ/w57OA3qWtMliYYYALy/iT8WMB+EuPGAuNli5A1ss+6pC218ZzcQhwfuBFjhViotMhlf14+jhG1UiBki+97SO9hZb+zseIUE2DrhMGjB1dxDp0ZmfP41go2N6wgYAOY8Qxq2oAnAJwjXgCgGYLDUmuW4qrDxHKqCdsgI37TRJh/RAe76TgeiS7bzpghfNyIfwwQm7DgAAg8Ax7fXEGtj5/2+zcgbD4DLE83gaYB54vepK1QaAdMsO3Ktznj5xjiB5TtysT3TdpSVp5snttCcs23ranCk6cG+HM+M229x7eWgekY7De88ufIxWG+2Ahtvt32cxLL55WSxNkzsv7ku3PoH27Cn/RPFc3fG2F6GEwNlfg+nsDwBHwWxzQ/nhZZ89xAKa9WZGy/9uNK0Bp1A2quaZJ/7zLn8te7SBOZR774dmuaoK1CaXq/ydp6TkjTXY62Dy+RxhPpfnyCtaDwEc5DGr+Yd0GlbEQ1O2ecLfvn2Crz66JmGahXGGatDs8drUwxHQ8w3RxAjxrQco0GFXhsvfKpCaEa22iFfuOUhlaWUINAknkNLX9+ODVtR/jc8e6AG8PT+g8aXEBCNIU6FD4D5wcU4TDHILfRzk5xg8JAxmFenZZpG0iSqzFWo5DTKIae2Ar/pT/qeUSWH7O7FANk+i4iyyueIP7v08RqVZ2cT3WYXKa/UU+9dJFYnpDM+5//CfLyKCmTYp8Iab1sZplHVl9AVIyRa6RMXiE/7/KWSgK+EVv3RQjyLbxbmAjOa4vZq/MelCZPPPHE8y9cekFBQcELAI8//jhe/OIXn+1iPC/w9a9/HVdcccXZLkZBQUHBCw5F1uwOTpw4gfX1dRz785fg4JrCD/6Xm3H5BzZBm2PwRJCWbABSoEpb7ax8Ia9VaqbVY6I115dQj+ZKuA9IF/lAvzZWx1q2dV3y79JXVhWDVXlfws3QLtylMqeprAXH5FxCtckYnnQb3M6tymzZmR8yUC+Rjag+BKbnMMyyASsG1QQ+0ODAuZtWy3driNnGwJn7EdRJDTWzGr/D4xTMFNXMmhJWpwA9Y1Adzbmqid2MJ7YR2YP/XKeZFYKQuI0T8i5PPGmSaH1SiLoezmkb0EQ1DKo7tKNyAka6VxHH+Q4EiSAliQmiEdcBoDZRM6sRmoTM1oXCdJaQWbmGVuIfy5d3OyIrJ21b7T8jtTIia6H23EVayXrfjp/puzfXrmvlGwni9Lv7X6l47D8uLVfpfXLDUbpqsZYomRWMKw8roTyjogUXEM1yvQaiqaJ7Fuvr2kUbrUQkeM0wsy088Yu34/jx4zh48ODcatuTmllHjhzBV77yFfzQD/0QHn/88W1f8oWMEydO4JJLLtn39QCUupAodRFR6iJiXl0wM06ePIkjR46cpdI9/3Do0CEAwGOPPYb19fWzXJqzi9KPIkpdWJR6iCh1EbFdXRRZ89yiqgRb480QAvngndtki+5cK7MHvURWK13HfUCy8M+17fvM1xKNrg4E64guE0lqf7dBfqKGyWyNMFuzZoBS07IZOPKLrdP3EHhq1UZeU2PC8LiG3gKm5ypsbK4BB2qriV8xeKaAmmIAKrL/l77LmK3avPUYGGxGzUrVMNDYMiomVJtG+DVEIIoSLS0Pp4GWOMWXRKMgQ0xliRwfbMr/JmndCVLMCIKSvTaWq1P5PFgtsBbpJWGkelhKpAWtLAC5TyzKyyd9u3WRrzmRlZNZHUQWezdAPk0HMdtHyibP7OtCkhyc08/iO/Z8xzbEL2fnEmIyklQyLTHDeOud0CYEkUUuWEQYAwShxSKt8s/w5JW9FoLcU/rJtbq8tvFOsCfJLKUUXvSiFwEADh48uO8nDkCpB4lSFxGlLiJKXUT01cV+J2xyKLfrt76+XtqOQ+lHEaUuLEo9RJS6iJhXF0XWPHewfjgpXTgbtsRW7i+LBLG1zeJ6brTCRBNIns8W3CKP4LenYaBqkwRdTqnTAnUc+wW1XyyzJa6aEUXTcVhuxmirbTU7EBfgzdCZIbLTKHHEVzMEuLJ+srhiVFuE6hRBNfY91Iww/K6COTnEbL2xLiXGympbzQh6TGDNgUDTE4AMWz9aNYeIcaysdpapbOG9r98cedAQBqFe1pYgm5jElD9oZTlCy7sYUTNGcHYsSS8gMWNjIpAkFzyx5Ukrr5GEqHnVRbZRRly1iC7v482ZvEvyiuWx1MgKx4Kwys0Kc4JLyTSOvFJqcW2sjMSa66i9D/J6n7muQG5W29UfWhpZXeaF2W3Sx7A0fSdQjEBIHYSyJ8skoZUTVC5/YtdkiDOtL8AM3Ptojm0u+IRcnNDak2RWQUFBQUFBQUFBQUFBQcTAa2YFssr7VlSA0i3NFQCpzyGg5S+ry5RwnnNseX/L/y86iI6Go59Tf04unqUWSQapkRUCGmkb3d37vlJNZhoVbvCEFQfyy2i7cDcDe99sBDQjp51SAdUphWrLkWED+/7NEqM6ZQkzs6lgRpYQA1Pwf6WmFE0cCSFyrtV4sc+qlwiDTW86iJTw6XP+7okFBWsyKNMG7SlEzSwG9NTEtF0QfpmCptc8P2kZWmWXMDJPTsiwPuf2La0se7KTyJpLYonzvf6xdkpiLULyzqm7XCtxXjTEPg3GFrnl/eOxIHdVRnhJAlr4qgpB1ADbblw6b7abO30PQbpEH1WN8wnH1tccMQBDYIoRdVnbgAdmwK5sDPh+SPOrNUchswoKCgoKCgoKCgoKCvY4htpFqmCnjeWhFUj6xfLmROT8ZfUh98PUpVWSkwBASkS1FuXiQC7efYCIFpmGNMKazEekNdpF6NY2wiyA6FidnRkfnAbUzDp8nx3UqBsfxc1eb4aWDGuWgNkq0CwzTAUMThBUbf1mNcsMNSXoCVCvuDTaOnhXNdlFOjHI5a1ncIt3S2p5rSxfRnZaU/UoRnbtUk5Jo75acoMMo9rwpCWilpqypJl0Ak9sTRlb5oV5E+hqEjJSoSehevxnJWlCgTu+e19ZjUlMEBNn74G46vGRtQiRJUmsnJTNiaxFNbFkv5iHbdL0alrNS7sN2RP7DMU2IZy3+zxsG5IajWlE6hBACymR5e8P5BchaBf6d06j/DK8qa03QQxlUBw1srQnZPeBZtZoNMIHP/hBjEajs12Us4pSDxGlLiJKXUSUuogodbEzlPqKKHURUerCotRDRKmLiFIXZxergymglu2BcMgNraOZIQktnypbaUtCwIGYwSYu3NMo7G2tkJwE6NVi6XL8DkSNIgizJF+sLuUkT4AJDRNvomc1o6wZk2pifZAzQVx+ilAvWe2oxjl7b4bAdB2YrVlTOr1FqDYtATU9aIkrH3mwXmE0I0azYp3Bq5MENbXEmp4CVNsCeafYxMBgzFANQzXiXdkSbM3IarVUY/dyWTRKCelPy5sTBg23YHZJUDJabRPrUPrA6nIwnvwuGqlWkBGECSM4f09+H+f8XZoYtswNQ34mOez1lSWJLOE3K3HyPo/IyrWxgECSRfO8NhHbasO5k/05ft0Wgv/N5Hf5vwetaPTCvDCN4OjITRfcwPrGiudZpWS0v8f+Xs4MliIJxU7Ti4XlMjllwCSCoSC5wqdiR7LCRr8k2PrUjgAmdHf0vqrbi9EMCwoKCgoKCgoKCgoK9jPyaIZv+drrMX7XuaDjp8DjcUhHw2GnZgsPqtSZdlfkN6CfCMg0WrYlsrocWVN2LCEX6hJu8Z34hYJbcFcqLNxlJD5qOCEMWBMm51TW4XptF+DNkDA+nzA+zy64B6coLM7NEJitG5iRgdpS0GPC7FADVAa0qaHHCmoC6Clhtmo1TfTYkTINUG3Z/8OT0V+W1xrz79iMCLNVwvAkY/XozJpgem0hT8TJ6ICBSHLvqikhKhDeWxBfiXaXrOv0N+oiZxJCq7FaYT7aYuL4XZgnUvCHJb9HzSyqGxvBsGnQgnRerjWgRRtWKtXG8v/ztiyILA6aiWnavvYbrs1rv12Q5FTX+R3wXn2miJ19w5NSou8Fs0FPXOmUyAIimeW1r4ww+2UVHbn79MHs0BNV2pbFk7Y+SqK9j+117UguxTGSoYhg6MksVRnwZAvffMcdL9xohgUFBQUFBQUFBQUFBQURBwYTjIGUMNBKkAsm1dBKzAazYwdpipVGUetZkTOSRbXNuyOZok7yKj63O3v7HtzmAxwxRFPh7d2Xxx0nvoIIADP0NGqxmIHVzBqcIuix1ZaaHrRRDOsDBrxkQFMFaghmyBisT1BPNaixS2oeALW2foCsA3pPphHMAKhmjhzQtkBqxlHjha1GmZoB0zXC8ncIuukmoXItqRZIaG5lRFbiID7X0gKCk3fKTb0EORjKw4hEVq4fk2ll5WUJpJfXHFPKfu/6n5sOdrXDRYiszMl7ePftiNg+jTWgn5ha8HwvWQX0klgBiX86ZARc/Ei/WIDQsBKElXTQLjXNWOQhTQw9gRXILsS8bNtHILKMI7M8kSWdyMObGRpETa25nT9FIbMKCgoKCgoKCgoKCgr2OEaqCVorAeTIAE8aBD9YUsulg8iSWlg+4huQElO5VhbQXsSrbgKgHcHQp+9eyIaFdsOJZhYL00GZf/qsNG9/rBqgHlln8c0QmJxDTqPKXm+WADNi1KsMXm2sBtamAmuGWWaYmQYbAq80MA2BZlZji8lpUBGgZgRTsVvw2xX8oGGgRohgGCLJsTVnrJeB2ZqGfsYxVYajWV8O77A9qSz3X5zv9cXFDALFdiG0quQzE1PPLIJh0q5ySHND9y6Jz6wmK7w0OexqoyT8vOUkV2Zu2ElkSaKqw6xwWxKrg4zydbGIqWGfc/cuRAfrsd1KQjIcy3ZN7piQErkij8QUkSIplUdFDFpXwgdWS5tS+NRKohsi3kewZTHak4dsfWUZAE47i7xmlmKYEs2woKCgoKCgoKCgoKBg/2BZT62mhzzpnL0HZ9rGAJVO/QxJJNpaSDWyMs2WxAl7rhnShYzE6iKw2r57soW/Fs8Bg5wJnl9Iy8V+IK+Udeo+W1GYrRLAgJ4x6iXC7ID1/TM7YP1hVafsMRlHZlUAj5wp4YYCCDBDBs0IeHoIVAxeMlBjheGzykZxGzD02DqIb4ZAtUkhwlszso7gBycZemIZB68lZbTV4BpsMibrhOFxCuRdK9qfNDH0cATajtGhxQWfVfi5UkLFmxdCkSWkDBLH3Z3+sTLNLDIdaUIGFDW2Mmfv4XqujSXunUtkdZGvPUTWdiRWciwJpB600u3w94rlz8rs85JEU2Wf401PEw2tXIvL3Z84fA8ZwTmQt2X3bZn9f5eHP2e1tdgFVrBRDVk5k1nNMYKoLwAZUGVcsRh60GFy2oNCZhUUFBQUFBQUFBQUFOxxLOtZ1CZhY7WyPAxbh+9ad/vJCotbd15nZFcXkeUflfsfEuRXJxlASIiw3LdWlzNqe8GSKlJLyC6QYxRAI/IyFaFZIjRD9xkB40OE6Tn2Rj2FW+SzdexOAFeExplBmcpqZlXHNQanCLMDjGbFgAcMTAjVlECbBLNpSax6xebDAwM1VVh9XGF0zBasGVlH82rGYEXWP9YBQDWEaoNRbcFFYnTvaYBmSdlIhR1Eln1Zf+y17UR9BbKEugkjfw+5xJ4cA1LH8+FWr9pFlkzRBIIzZyX5QKDT9HEBx++JRqF/lv/vyaguYms7jawuJ++dpE5GYuVtPClbxzvOOz8nXa51J9t/Qs4Ctt45ljtJTx3lDoSu1JyiTNsq+sEiI7SyPPnlCTHyfq9sP/O+sby2F7trrDh8N2SJLQBRI4vQimAYmilxL7/ZhUUCSj7v8PGPfxyXX345lpaWcM011+CP/uiPznaRdh1/+Id/iDe+8Y04cuQIiAif/exnk+vMjNtvvx1HjhzB8vIyXvOa1+CRRx5J0kwmE7z3ve/F+eefj9XVVbzpTW/CE0888T18izPHhz70IfzwD/8w1tbWcOGFF+LNb34z/uzP/ixJs1/q4hOf+ARe/vKX4+DBgzh48CCuv/56/Kf/9J/C9f1SDzk+9KEPgYhw6623hnP7pS5uv/12EFHyOXz4cLi+X+rhuUCRM/un/RQ5E1HkTDf2s5wBiqzZS1jTY7D3iQUAWqE3IpwkA2AXwV2mhNJnFoAY+UxRixjoJLLCgjmmldolksgKTqjlR2qOQCyw/bMEyWG0JVmaJYXpQY3JOQq1I5GqLUsYLT/NGH2XrNP3ASxboCxppSaWvGENNEuM2ZqxWlxjQr3MqNcb8JIlYFQNgG3Uw2bNYLbeoD6nAY8MUDHMSoPx+YzZmi388CSjGtsohoNNxtIxxug4o16CdTp/iFAv23oyQ0t2TQ5qmJFKTf3mkSXSnC9UWJsZSH4nZN+dDywyDKoNqHH/3UfVBtRYk1Umium8llgXkSXyDiaJ0sQwL4v89Dl57yJjyf7+uUaWJVqseSK7dis1tQJBpvJ2Smm/AJL2GEgc8Qn1i/a5vk9yXYtn+bx8mRW1yS3XH1in5bS+qihoSgUzSm21BBM/We42U9mont6Re3Qa7657gmqA0I8D6eU+7P1iqfa12G+jzStpAzUw9ruy0Qy1XpzN2nNk1m//9m/j1ltvxQc+8AH8yZ/8CX70R38UN910Ex577LGzXbRdxcbGBl7xilfgYx/7WOf1D3/4w/jIRz6Cj33sY3jggQdw+PBhvP71r8fJkydDmltvvRV333037rrrLnzhC1/AqVOn8IY3vAFNV7SI5ynuu+8+vPvd78YXv/hF3HvvvajrGjfeeCM2NjZCmv1SFy9+8Yvxq7/6q/jSl76EL33pS/jxH/9x/PRP/3SYMO6XepB44IEH8MlPfhIvf/nLk/P7qS5e9rKX4cknnwyfhx9+OFzbT/WwmyhyxmK/tJ8iZyKKnGmjyBmLImv2Btb1VtSoIgXvLysEr3cmW8HnUKaVBUcEeM2tnMiSBIJc9AdNrQ4iKyEHlFusSwfxjogKi3Kft3IfLZ6TkVvBp4+G1cJaVpisa0zXVIhSqGqneeK0SKi2kQWHzxIGJwjUEJoRQ28pVFv2ObODBrODxvqwGhO4YszObYBRE51VG4KaEnhgwKMGtNIAQ+NUaQCaKjQjRjOyWllMltAanGKomkGNjWo4PGnLU68Ak3MJZmTLOztgj6cHNExlX9hGaIzvn0SVFMe+7vxvl7sgSiIPenNBeOJS/DbSQbv4UG2gtmrorRloWsc0AlJbrwuU+8rqSp9rDebtNXP0zq7tJkRWRrrGyH2UkFhJm5Ukbf5BSkJJoslrrLG2WoFd3337z+8Jxz5PWQf+mbLMSDWypKmgqWw676Q/Ic+C5pnNz1SURC+UBJb3o2XfAbG9mXjdeO5cPkfbZsqCuGIttbJgNbIUQNr6ydLaoKoaVFUDrecxoimIeSeKXGcf1157LV75ylfiE5/4RDj3gz/4g3jzm9+MD33oQ2exZM8diAh333033vzmNwOwO2BHjhzBrbfeir/zd/4OALvjddFFF+Ef/IN/gHe+8504fvw4LrjgAvzmb/4m3vrWtwIAvvWtb+GSSy7B5z73OfzET/zE2XqdM8J3vvMdXHjhhbjvvvvwYz/2Y/u6LgDg0KFD+If/8B/ib/7Nv7nv6uHUqVN45StfiY9//OO444478Ff+yl/BRz/60X3VJm6//XZ89rOfxUMPPdS6tp/qYbdR5Mz+bj9FzqQocmZ/yxmgyJrnM06cOIH19XUc+/OX4OCawqdOXIj/8+d/EtUTz4C3tkCDgV3Q1w1IOxPD4QBcabsg1o60EuZYiRaK8DUESI2qbUgsIBBZidZWFuUwEgI+DQUtEJ+HN7mzCZAQWdbsyRJhzdAuzIkBaqwGlF1sU/DdAwBbFxC2LmCwczxtRmy1qRoCKoPBgSmWl2bY2Byh2aoAxdCjButrm6gbjcYoKGVAADY2R9ZSqmqwujzBrNHY2hyheWaE0TMazYhBDTA4SZZAO8Gotqzmk6miJs5sFaiXrdkhV4xq05NfwIFvNdBbBmpmAjlh/VGh7TNLkA/w2lWNI6Dkil86ew/5RN9ck/OX8K0fq2AGnuRCoh1GTQdJRZz+1uL04KTCZf/PcahTk0iIzeqomZXTEV4jK9O8C8eZA/hWoAJNsW369irbXu4Py7c1pO03f5cEjlh68oYKa698xvp6Uk5rj2y0TeUqzTCBAWhiTBvtiu7qVjxgWmuY3z0P5/7ZtPU4by4YNLOCObHTylK2n9gog5Q5Znf9Tcf3Cc7fVSSg/HsHQooomBKaCiF9MDf0pB5ZbS3WnGiCmYHrZ4airyzFlsjSDFUZgABSDKUMBoPG/rzTDXz5rf8Ix48fx8GDB3t+AIs95TNrOp3iwQcfxC//8i8n52+88Ubcf//9Z6lU33t84xvfwNGjR3HjjTeGc6PRCK9+9atx//33453vfCcefPBBzGazJM2RI0dw1VVX4f7779+zE4fjx48DsJNrYP/WRdM0+Lf/9t9iY2MD119//b6sh3e/+934qZ/6Kbzuda/DHXfcEc7vt7p49NFHceTIEYxGI1x77bW488478ZKXvGTf1cNuocgZi/3cfoqcsShypsgZiSJr9gaWaCa0TXSq6aJ1Qg60tK26zAwXJbIyP0MtJ++eSIC8D+20Os3HI12EuzxUJKv8czyR5cmiROOErTP2l7zp/8MnX/LvO82TBqSg3Q0z9sQEhXMSCgozNPjfv/4mfOWoNbttGoV6XLkIc9YJPGsbybBZAmY1QdXO3HAW381o6+h9GJQZLUk1OsnQW8ZqxpCCnsjwhDZZiGYoiUR/He56lwKkJ5AIsPEMo4bWbFXhE//rJ/Ha5d3RnHxkuoV3/9f/AyunJvHZksiap5GVE1mZuWFCZGlxTxeRJbT8mCiYZHaTWh0vkgUqYALwA6fwwCv/rzOsoYjLv/W3cO6fpw9P/dPJMlLab3TUPItEk+u/weyPknw8KRVMDTUCGRr6tc6fkWpvWcfvHJ6TBHcg2CAJnuysLKFFim0EQwelrIYWEaPagWbWniKznn76aTRNg4suuig5f9FFF+Ho0aNnqVTfe/h37aqHb37zmyHNcDjEueee20qzV+uKmfG+970Pf/Wv/lVcddVVAPZfXTz88MO4/vrrMR6PceDAAdx99934oR/6obDI3i/1cNddd+G///f/jgceeKB1bT+1iWuvvRaf+cxn8NKXvhTf/va3cccdd+CGG27AI488sq/qYTdR5IzFfm0/Rc4UOeNR5ExEkTV7B0tqlpoQKgUYY7WynEYLSxMmTwZIZ++Zzx4JTwzY7whEVqqBRSkh4E2jgITEkmnjAjg3N4RYlEOYWvny+4IhRthDJLFshMC4+DYV4cBgggv16ulUbycuXT2Gb4zOAzNhNq1AGxpqomxZK3YmkAxsEdQMmKwrNMOooUUG0FOGaiyhpWeAnjoibcZQDcMA0ZeSJ5/YOpKn3Dm8hzxNZCM/MiKJlTl8D5EFyfoTm3GFbhZs5xizhppFgqK3zEDLmXv47z+ZaeFcIkuJYx3NV5P2J7X98qa/XZRNAqbjAWbcYEAauwKhQpcQQvI/UiIrIYQFYQVEM0AWZKfsT5Z49f3EVwTAVdQAY0KrnoImmPCTFTSyhuxMEbPIhZpBlQG5qJcEQGkDIk74TO5kEruxp8gsj9yRYQg1u89wOvWwl+vqPe95D/70T/8UX/jCF1rX9ktdfP/3fz8eeughPPvss/h3/+7f4ZZbbsF9990Xru+Henj88cfxt//238bnP/95LC0t9abbD3Vx0003he9XX301rr/+elxxxRX49Kc/jeuuuw7A/qiH5wJFzljst/ZT5EyRM0CRMzmKrNk7WCIXzbCuQT4ynCO0oBU6oxgGJ9nU9qacaWV5dBFZXeZZLM29/Hlv3qSjCaD1jZVqnUi0NL3CBQTSyi/WvUliMLPyvn0q62Bd0eJaH4vgRaNjGFQNTmzYsYIHjMEzBDWxhIDVymJMDhmwVtBja1I4PAHoKaAnbH1ihTpk6KklsrwfIjLc8nvlySomq1UVSESO12LaNpEVkOfdMJoR2ba0S3i8PoTBKa+KxmkUQzkGdBBZCfnqz29DZLFWUXvQ+Q6TfrASJ+sycmMHOkms8B0gYqi5XvlPD6Ev+N9GEnCUEsD+euhLWT8KDuCz4yRioX8GRUfv0kG89znHyhFdypWNGGYYCS6QI7BcHgAQIhc6Ios0u58namMFrSxloHfQR/eUA/jzzz8fWuvWLs5TTz3V2hF6IcNHkJlXD4cPH8Z0OsWxY8d60+wlvPe978V//I//Eb//+7+PF7/4xeH8fquL4XCI7/u+78OrXvUqfOhDH8IrXvEK/JN/8k/2VT08+OCDeOqpp3DNNdegqipUVYX77rsPv/7rv46qqsK77Ie6yLG6uoqrr74ajz766L5qE7uJImcs9mP7KXLGosiZIme2Q5E1z1+sqS00I6eK4aMaEqUmhv5cH6R5lyePsqiFAKJ/psS5NoQT6JTIYk1oRgrNiFAvK9RLyvq5GlAgtkwVP8EBfKJJg/AcyvxnsbJ+e8zAmRhW1qyQlXXAbobA5DzGst49kgYANpsRGkOoKoPhqAYt12BlfXZRDVADVBuE4bMK1Njy1SvWd9d0jVAvE6YHCbMDhGYJmK4RxuvR6bt9P/FbZOSG/y0kAkmRQ/zuSdRD79zdcQjTNcIl1YkzqZYEU9aRNPLPk5ARBn05u8wLg6YVhUAF0em7d7CeElnWn1QMMmADCgjSJziIR+dHlkf2CXZEkh7schALQVQFZ+nuWd6nXPSHFfudESaGtj/5fiDrIPV1FZ28I/jd8tEHw/0D4S9LOyLLaVw1Q7Z9DJ7kYutjy5kdBtNChtXK8qQWrEaW0gZKGWdiaDDQDSptgu+xRbCnyKzhcIhrrrkG9957b3L+3nvvxQ033HCWSvW9x+WXX47Dhw8n9TCdTnHfffeFerjmmmswGAySNE8++SS+/OUv76m6Yma85z3vwb//9/8ev/d7v4fLL788ub6f6qILzIzJZLKv6uG1r30tHn74YTz00EPh86pXvQpvf/vb8dBDD+ElL3nJvqmLHJPJBF/96ldx8cUX76s2sZsocsZiP7WfImfmo8iZImdyFFnz/MURvYmNwwNgNAzO3UEEOIfvnBEGVrtFRS0XQXbIaG1JlDdJKAiNjoQY8ESWArgimCFZQmlAUSsmd+Yu8xHOxr3Gh6niIt0f+8V2M7SfkGaIQJTVS4RmBNTLQH3Q4ICe7Gqdn2pGGFYNDixNcMHaKQyWaswOMOoVWzY1sx89BganAD0Bqk1Lxs0OWOJodsB+NwNbv7M1wvSAJWq4IjRL1kG/JPB8fdl6yn6LSjniB6mDeIHETxTSdM2AMNpFZaM/3bwUatpE0sxDRvCTpNUCRBYr5d5Ttf1jqQ4iS2fHkvDqIK36CCxkn9XlKTTtIqUS+gRFAstrLnoiSnnCF+LjyTrfZwSBR94XVtQWNAOhoaXjvZ688s+yZUHiI8uDQ54xoIL9XRGJuMpYh++O2CLFUE4Ty0cxHOgGS4MaA/d9VNULV9eeMzN83/veh5tvvhmvetWrcP311+OTn/wkHnvsMbzrXe8620XbVZw6dQpf+9rXwvE3vvENPPTQQzh06BAuvfRS3Hrrrbjzzjtx5ZVX4sorr8Sdd96JlZUVvO1tbwMArK+v4x3veAfe//7347zzzsOhQ4dw22234eqrr8brXve6s/VaO8a73/1u/NZv/Rb+w3/4D1hbWws7f+vr61heXgYR7Zu6+JVf+RXcdNNNuOSSS3Dy5Encdddd+IM/+APcc889+6oe1tbWgi8bj9XVVZx33nnh/H6pi9tuuw1vfOMbcemll+Kpp57CHXfcgRMnTuCWW27ZV21it1HkTJEzQJEzRc4UOeNRZM3ewZFqhKf/Z8Z5D4xA42mqDZObcwHRTNATWdKs0KWLZkyeVBCmTpm2Vh4pzQwomtB5v1leE6TDT1bQGvHB6pxPKSNMlrx5k88jcYLNccFtBgiaUM0SMF034FGDZpd1OYx76aFuMNI1BoMG0xWDuiYMTlCIQAgC1BSAAczIklvUIJhF6rE1LWxGBD1mNEPCbEVhsGmgahsVMfGhZJwDc1n/nphhDppDpDi6vhJEko9gKE0MvbbW+EJgTe2SDygA35keAGr7otRl1pdpDfYRWRBEVohYKJy6J/6xJIklr+XtlTFfUxHiniwZK+zIWfmiSLQRu8wJKf4P2lmiT8l0koiKTt5F/3fdwXjTQa+9BXuPj5AI4RsrMVFE1MryTuMtscWB8KPK+YDTVgOrcsda26igShkwE7RusFTVwPQF6gAeAN761rfimWeewd//+38fTz75JK666ip87nOfw2WXXXa2i7ar+NKXvoS/9tf+Wjh+3/veBwC45ZZb8KlPfQq/9Eu/hK2tLfziL/4ijh07hmuvvRaf//znsba2Fu75tV/7NVRVhZ/5mZ/B1tYWXvva1+JTn/oUtN69wem5xic+8QkAwGte85rk/L/6V/8KP//zPw8A+6Yuvv3tb+Pmm2/Gk08+ifX1dbz85S/HPffcg9e//vUA9k89LIL9UhdPPPEEfu7nfg5PP/00LrjgAlx33XX44he/GMbD/VIPu40iZ4qcAYqcKXJmPvZTXRRZ8/wFO/Lh1//yIiwdqMCsUA9OYTpg6BOb1jdRY4JmVnAKT44QCBpb3r+Wy1hqaCkVNS2IrNJUbnYo/WVpspZFRMBQgRyJQN4xtbMCgycZFFy50my8c2q4YHeelAjnnAN078uHgEAaEQAeuvMVgRuAGkZ1DLh35VL8o+Yvd6n+Ff7fv7gEz37X+cviFdBmherkDDSuoZ+1zt2ptu85OMUwA0IztP6yfITF4XGE6IZUAYPjBnpincfzZmOjGDryyVYGWyUnX/0+Kp8jcAJhZBhs2P3P0grTQvJ+rAwDiqC/vYRff+IHsKymu1JH9zz4Enz/xjGQMcCsjpEMpVaW/J9HM5TElvP9lvrJorSN5lEMKba7xLebD+iYmz3K8ncRXZ5cUsBT3xziH1148ZlVkoB+vAZOTCNpFS5YZkgScgREDTz/fuJY9kdPLNm+KIhl7/tqgHCOfB7kupTrY0YjRAq16SLZlUQcHXCqbVnZ41rZDjolDR44R3cVA6iAykBpDVIjmLGVCzznd/EgXiRVQUFBQUFBQUFBQUFBwfMGX//613HFFVec7WIUFBQU7Doef/zxxIdpF/acZlZBQUFBQUFBQUFBQcF+x6FDhwAAjz32GNbX189yafYeTpw4gUsuuQSPP/44Dh48eLaLs+dQ6u/MUOqvG8yMkydP4siRI9umLWRWQUFBQUFBQUFBQUHBHoNy5oHr6+tlMXwGOHjwYKm/M0CpvzNDqb82FiXn91Q0w4KCgoKCgoKCgoKCgoKCgoKC/Y1CZhUUFBQUFBQUFBQUFBQUFBQU7BkUMqugoKCgoKCgoKCgoGCPYTQa4YMf/CBGo9HZLsqeRKm/M0OpvzNDqb8zR4lmWFBQUFBQUFBQUFBQUFBQUFCwZ1A0swoKCgoKCgoKCgoKCgoKCgoK9gwKmVVQUFBQUFBQUFBQUFBQUFBQsGdQyKyCgoKCgoKCgoKCgoKCgoKCgj2DQmYVFBQUFBQUFBQUFBQUFBQUFOwZFDKroKCgoKCgoKCgoKCgoKCgoGDPoJBZBQUFBQUFBQUFBQUFewgf//jHcfnll2NpaQnXXHMN/uiP/uhsF+l5gdtvvx1ElHwOHz4crjMzbr/9dhw5cgTLy8t4zWteg0ceeSTJYzKZ4L3vfS/OP/98rK6u4k1vehOeeOKJ7/WrfE/wh3/4h3jjG9+II0eOgIjw2c9+Nrm+W/V17Ngx3HzzzVhfX8f6+jpuvvlmPPvss8/x2z332K7+fv7nf77VHq+77rokzX6uvzNFIbMKCgoKCgoKCgoKCgr2CH77t38bt956Kz7wgQ/gT/7kT/CjP/qjuOmmm/DYY4+d7aI9L/Cyl70MTz75ZPg8/PDD4dqHP/xhfOQjH8HHPvYxPPDAAzh8+DBe//rX4+TJkyHNrbfeirvvvht33XUXvvCFL+DUqVN4wxvegKZpzsbrPKfY2NjAK17xCnzsYx/rvL5b9fW2t70NDz30EO655x7cc889eOihh3DzzTc/5+/3XGO7+gOAn/zJn0za4+c+97nk+n6uvzMGFxQUFBQUFBQUFBQUFOwJ/MiP/Ai/613vSs79wA/8AP/yL//yWSrR8wcf/OAH+RWveEXnNWMMHz58mH/1V381nBuPx7y+vs7/7J/9M2ZmfvbZZ3kwGPBdd90V0vzlX/4lK6X4nnvueU7LfrYBgO++++5wvFv19ZWvfIUB8Be/+MWQ5o//+I8ZAP+P//E/nuO3+t4hrz9m5ltuuYV/+qd/uveeUn9nhqKZVVBQUFBQUFBQUFBQsAcwnU7x4IMP4sYbb0zO33jjjbj//vvPUqmeX3j00Udx5MgRXH755fjZn/1ZfP3rXwcAfOMb38DRo0eTuhuNRnj1q18d6u7BBx/EbDZL0hw5cgRXXXXVvqvf3aqvP/7jP8b6+jquvfbakOa6667D+vr6vqjTP/iDP8CFF16Il770pfiFX/gFPPXUU+Faqb8zQyGzCgoKCgoKCgoKCgoK9gCefvppNE2Diy66KDl/0UUX4ejRo2epVM8fXHvttfjMZz6D//yf/zP+xb/4Fzh69ChuuOEGPPPMM6F+5tXd0aNHMRwOce655/am2S/Yrfo6evQoLrzwwlb+F1544Qu+Tm+66Sb8m3/zb/B7v/d7+Mf/+B/jgQcewI//+I9jMpkAKPV3pqjOdgEKCgoKCgoKCgoKCgoKFgcRJcfM3Dq3H3HTTTeF71dffTWuv/56XHHFFfj0pz8dHG+fTt3t5/rdjfrqSr8f6vStb31r+H7VVVfhVa96FS677DL8zu/8Dt7ylrf03lfqbzEUzayCgoKCgoKCgoKCgoI9gPPPPx9a65ZGxlNPPdXSoCkAVldXcfXVV+PRRx8NUQ3n1d3hw4cxnU5x7Nix3jT7BbtVX4cPH8a3v/3tVv7f+c539l2dXnzxxbjsssvw6KOPAij1d6YoZFZBQUFBQUFBQUFBQcEewHA4xDXXXIN77703OX/vvffihhtuOEulev5iMpngq1/9Ki6++GJcfvnlOHz4cFJ30+kU9913X6i7a665BoPBIEnz5JNP4stf/vK+q9/dqq/rr78ex48fx3/7b/8tpPmv//W/4vjx4/uuTp955hk8/vjjuPjiiwGU+jtTFDPDgoKCgoKCgoKCgoKCPYL3ve99uPnmm/GqV70K119/PT75yU/isccew7ve9a6zXbSzjttuuw1vfOMbcemll+Kpp57CHXfcgRMnTuCWW24BEeHWW2/FnXfeiSuvvBJXXnkl7rzzTqysrOBtb3sbAGB9fR3veMc78P73vx/nnXceDh06hNtuuw1XX301Xve6153lt9t9nDp1Cl/72tfC8Te+8Q089NBDOHToEC699NJdqa8f/MEfxE/+5E/iF37hF/DP//k/BwD8rb/1t/CGN7wB3//93/+9f+ldxLz6O3ToEG6//Xb8jb/xN3DxxRfjL/7iL/Arv/IrOP/88/HX//pfB1Dq74xx9gIpFhQUFBQUFBQUFBQUFOwU//Sf/lO+7LLLeDgc8itf+Uq+7777znaRnhd461vfyhdffDEPBgM+cuQIv+Utb+FHHnkkXDfG8Ac/+EE+fPgwj0Yj/rEf+zF++OGHkzy2trb4Pe95Dx86dIiXl5f5DW94Az/22GPf61f5nuD3f//3GUDrc8sttzDz7tXXM888w29/+9t5bW2N19bW+O1vfzsfO3bse/SWzx3m1d/m5ibfeOONfMEFF/BgMOBLL72Ub7nlllbd7Of6O1MQM/PZItIKCgoKCgoKCgoKCgoKCgoKCgp2guIzq6CgoKCgoKCgoKCgoKCgoKBgz6CQWQUFBQUFBQUFBQUFBQUFBQUFewaFzCooKCgoKCgoKCgoKCgoKCgo2DMoZFZBQUFBQUFBQUFBQUFBQUFBwZ5BIbMKCgoKCgoKCgoKCgoKCgoKCvYMCplVUFBQUFBQUFBQUFBQUFBQULBnUMisgoKCgoKCgoKCgoKCgoKCgoI9g0JmFRQUFBQUFBQUFBQUFBQUFBTsGRQyq6CgoKCgoKCgoKCgoKCgoKBgz6CQWQUFBQUFBQUFBQUFBQUFBQUFewaFzCooKCgoKCgoKCgoKCgoKCgo2DP4/wGd10Tayo7osgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the first 3 images from dataset without transforms\n",
+    "heatmap_generator.device = \"cpu\" # because dataset tensors are still on cpu\n",
+    "inspection_plot(ds_test1, range(3), heatmap_generator=heatmap_generator)\n",
+    "heatmap_generator.device = device # set the desired device back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and initializing the SpatialConfiguration model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initializing the model, optimizer and loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of learnable parameters: 6181030\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
+      "  warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.models.spatial_configuration_net import OriginalSpatialConfigurationNet\n",
+    "from landmarker.losses import GaussianHeatmapL2Loss\n",
+    "\n",
+    "model = OriginalSpatialConfigurationNet(in_channels=1, out_channels=19).to(device)\n",
+    "print(\"Number of learnable parameters: {}\".format(\n",
+    "    sum(p.numel() for p in model.parameters() if p.requires_grad)))\n",
+    "lr = 1e-6\n",
+    "batch_size = 1\n",
+    "epochs = 200\n",
+    "\n",
+    "optimizer = torch.optim.SGD([\n",
+    "    {'params': model.parameters(), \"weight_decay\":1e-3},\n",
+    "    {'params': heatmap_generator.sigmas},\n",
+    "    {'params': heatmap_generator.rotation}]\n",
+    "    , lr=lr, momentum=0.99, nesterov=True)\n",
+    "\n",
+    "\n",
+    "criterion = GaussianHeatmapL2Loss(\n",
+    "    alpha=5\n",
+    ")\n",
+    "\n",
+    "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5,\n",
+    "                                                          patience=10, verbose=True, cooldown=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting the data loaders"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_loader = DataLoader(ds_train, batch_size=batch_size, shuffle=True, num_workers=0)\n",
+    "val_loader = DataLoader(ds_test1, batch_size=batch_size, shuffle=False, num_workers=0)\n",
+    "test_loader = DataLoader(ds_test2, batch_size=batch_size, shuffle=False, num_workers=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.heatmap.decoder import heatmap_to_coord\n",
+    "from landmarker.metrics import point_error\n",
+    "\n",
+    "def train_epoch(model, heatmap_generator, train_loader, criterion, optimizer, device):\n",
+    "    running_loss = 0\n",
+    "    model.train()\n",
+    "    for i, batch in enumerate(tqdm(train_loader)):\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        landmarks = batch[\"landmark\"].to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(images)\n",
+    "        heatmaps = heatmap_generator(landmarks)\n",
+    "        loss = criterion(outputs, heatmap_generator.sigmas, heatmaps)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        running_loss += loss.item()\n",
+    "    return running_loss / len(train_loader)\n",
+    "\n",
+    "def val_epoch(model, heatmap_generator, val_loader, criterion, device, method=\"local_soft_argmax\"):\n",
+    "    eval_loss = 0\n",
+    "    eval_mpe = 0\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for i, batch in enumerate(tqdm(val_loader)):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            landmarks = batch[\"landmark\"].to(device)\n",
+    "            outputs = model(images)\n",
+    "            dim_orig = batch[\"dim_original\"].to(device)\n",
+    "            pixel_spacing = batch[\"pixel_spacing\"].to(device)\n",
+    "            padding = batch[\"padding\"].to(device)\n",
+    "            heatmaps = heatmap_generator(landmarks)\n",
+    "            loss = criterion(outputs, heatmap_generator.sigmas, heatmaps)\n",
+    "            pred_landmarks = heatmap_to_coord(outputs, method=method)\n",
+    "            eval_loss += loss.item()\n",
+    "            eval_mpe += point_error(landmarks, pred_landmarks, images.shape[-2:], dim_orig,\n",
+    "                                    pixel_spacing, padding, reduction=\"mean\")\n",
+    "    return eval_loss / len(val_loader), eval_mpe / len(val_loader)\n",
+    "\n",
+    "def train(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs=1000):\n",
+    "    for epoch in tqdm(range(epochs)):\n",
+    "        train_loss = train_epoch(model, heatmap_generator, train_loader, criterion, optimizer, device)\n",
+    "        val_loss, val_mpe = val_epoch(model, heatmap_generator, val_loader, criterion, device)\n",
+    "        print(f\"Epoch {epoch+1}/{epochs} - Train loss: {train_loss:.4f} - Val loss: {val_loss:.4f} - Val mpe: {val_mpe:.4f}\")\n",
+    "        lr_scheduler.step(val_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1cb48970e6e245c2b2d9f619e42ac71b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/200 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0460a93fc99642a6a1af5f7a0f71ef0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/150 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheatmap_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m      \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[11], line 41\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mtrain\u001b[39m(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1000\u001b[39m):\n\u001b[1;32m     40\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(epochs)):\n\u001b[0;32m---> 41\u001b[0m         train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheatmap_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m         val_loss, val_mpe \u001b[38;5;241m=\u001b[39m val_epoch(model, heatmap_generator, val_loader, criterion, device)\n\u001b[1;32m     43\u001b[0m         \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mEpoch \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepoch\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mepochs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - Train loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtrain_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - Val loss: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_loss\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - Val mpe: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mval_mpe\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.4f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "Cell \u001b[0;32mIn[11], line 11\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(model, heatmap_generator, train_loader, criterion, optimizer, device)\u001b[0m\n\u001b[1;32m      9\u001b[0m landmarks \u001b[38;5;241m=\u001b[39m batch[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlandmark\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m     10\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mzero_grad()\n\u001b[0;32m---> 11\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m heatmaps \u001b[38;5;241m=\u001b[39m heatmap_generator(landmarks)\n\u001b[1;32m     13\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, heatmap_generator\u001b[38;5;241m.\u001b[39msigmas, heatmaps)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/Code/landmarker/examples/../src/landmarker/models/spatial_configuration_net.py:280\u001b[0m, in \u001b[0;36mOriginalSpatialConfigurationNet.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    278\u001b[0m skips \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m down_layer \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mla_downlayers:\n\u001b[0;32m--> 280\u001b[0m     out_la, skip \u001b[38;5;241m=\u001b[39m \u001b[43mdown_layer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout_la\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    281\u001b[0m     skips\u001b[38;5;241m.\u001b[39mappend(skip)\n\u001b[1;32m    282\u001b[0m out_la \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbottleneck_layer(out_la)\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/Code/landmarker/examples/../src/landmarker/models/spatial_configuration_net.py:352\u001b[0m, in \u001b[0;36mDownLayer.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    350\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv1(x)\n\u001b[1;32m    351\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(out)\n\u001b[0;32m--> 352\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    353\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpool(out), out\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/container.py:217\u001b[0m, in \u001b[0;36mSequential.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    215\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[1;32m    216\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[0;32m--> 217\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    218\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    453\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    454\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    455\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    457\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdilation\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/monai/data/meta_tensor.py:282\u001b[0m, in \u001b[0;36mMetaTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    281\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 282\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;66;03m# if `out` has been used as argument, metadata is not copied, nothing to do.\u001b[39;00m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;66;03m# if \"out\" in kwargs:\u001b[39;00m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;66;03m#     return ret\u001b[39;00m\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _not_requiring_metadata(ret):\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:1443\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m   1442\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunctionSubclass():\n\u001b[0;32m-> 1443\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1444\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m   1445\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device,\n",
+    "      epochs=epochs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:06<00:00, 15.84it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Mean PE: 1.0885\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "pred_landmarks = []\n",
+    "true_landmarks = []\n",
+    "dim_origs = []\n",
+    "pixel_spacings = []\n",
+    "paddings = []\n",
+    "test_mpe = 0\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    for i, (images, landmarks, affine_matrixs, _, _ ,\n",
+    "        dim_orig, pixel_spacing, padding) in enumerate(tqdm(test_loader)):\n",
+    "        images = images.to(device)\n",
+    "        landmarks = landmarks.to(device)\n",
+    "        dim_orig = dim_orig.to(device)\n",
+    "        pixel_spacing = pixel_spacing.to(device)\n",
+    "        padding = padding.to(device)\n",
+    "        outputs = model(images)\n",
+    "        # heatmap = heatmap_generator(landmarks)\n",
+    "        offset_coords = outputs.shape[1]-landmarks.shape[1]\n",
+    "        pred_landmark = heatmap_to_coord(outputs, offset_coords=offset_coords,\n",
+    "                                        method=\"local_soft_argmax\")\n",
+    "        test_mpe += point_error(landmarks, pred_landmark, images.shape[-2:], dim_orig,\n",
+    "                                pixel_spacing, padding, reduction=\"mean\")\n",
+    "        pred_landmarks.append(pred_landmark.cpu())\n",
+    "        true_landmarks.append(landmarks.cpu())\n",
+    "        dim_origs.append(dim_orig.cpu())\n",
+    "        pixel_spacings.append(pixel_spacing.cpu())\n",
+    "        paddings.append(padding.cpu())\n",
+    "\n",
+    "pred_landmarks = torch.cat(pred_landmarks)\n",
+    "true_landmarks = torch.cat(true_landmarks)\n",
+    "dim_origs = torch.cat(dim_origs)\n",
+    "pixel_spacings = torch.cat(pixel_spacings)\n",
+    "paddings = torch.cat(paddings)\n",
+    "\n",
+    "test_mpe /= len(test_loader)\n",
+    "\n",
+    "print(f\"Test Mean PE: {test_mpe:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SDR for 2.0mm: 88.4211\n",
+      "SDR for 2.5mm: 92.1579\n",
+      "SDR for 3.0mm: 94.5789\n",
+      "SDR for 4.0mm: 97.6316\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.metrics import sdr\n",
+    "\n",
+    "sdr_test = sdr([2.0, 2.5, 3.0, 4.0], true_landmarks=true_landmarks, pred_landmarks=pred_landmarks,\n",
+    "               dim=(512, 512), dim_orig=dim_origs.int(), pixel_spacing=pixel_spacings, padding=paddings)\n",
+    "for key in sdr_test:\n",
+    "    print(f\"SDR for {key}mm: {sdr_test[key]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "SDR for 2.0mm: 88.4211\n",
+      "SDR for 2.5mm: 92.1579\n",
+      "SDR for 3.0mm: 94.5789\n",
+      "SDR for 4.0mm: 97.6316\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.metrics import sdr\n",
+    "\n",
+    "sdr_test = sdr([2.0, 2.5, 3.0, 4.0], true_landmarks=true_landmarks, pred_landmarks=pred_landmarks,\n",
+    "               dim=(512, 512), dim_orig=dim_origs.int(), pixel_spacing=pixel_spacings, padding=paddings)\n",
+    "for key in sdr_test:\n",
+    "    print(f\"SDR for {key}mm: {sdr_test[key]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from landmarker.visualize.utils import prediction_inspect_plot\n",
+    "\n",
+    "model.eval()\n",
+    "model.to(\"cpu\")\n",
+    "prediction_inspect_plot(ds_test2, model, ds_test2.indices[:3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Detection report:\n",
+      "1# Point-to-point error (PE) statistics:\n",
+      "======================================================================\n",
+      "Class               Mean PE   Median PE Std PE    Min       Max       \n",
+      "----------------------------------------------------------------------\n",
+      "landmark_0          0.71      0.61      0.43      0.01      2.33      \n",
+      "landmark_1          1.09      0.68      1.33      0.06      8.42      \n",
+      "landmark_2          1.22      0.86      1.17      0.08      5.13      \n",
+      "landmark_3          1.50      1.02      1.58      0.18      8.13      \n",
+      "landmark_4          1.36      1.08      0.96      0.14      4.64      \n",
+      "landmark_5          1.04      0.80      0.76      0.05      3.28      \n",
+      "landmark_6          0.89      0.86      0.58      0.05      2.77      \n",
+      "landmark_7          0.78      0.60      0.56      0.03      2.69      \n",
+      "landmark_8          0.76      0.61      0.51      0.02      2.24      \n",
+      "landmark_9          2.16      1.82      1.47      0.10      6.26      \n",
+      "landmark_10         0.80      0.63      0.68      0.08      4.46      \n",
+      "landmark_11         0.59      0.35      1.00      0.03      8.29      \n",
+      "landmark_12         1.11      0.94      0.70      0.05      3.34      \n",
+      "landmark_13         0.91      0.79      0.63      0.04      3.11      \n",
+      "landmark_14         1.13      0.90      1.02      0.09      6.60      \n",
+      "landmark_15         1.13      0.81      1.04      0.10      6.36      \n",
+      "landmark_16         1.00      0.81      0.89      0.12      7.53      \n",
+      "landmark_17         1.27      1.00      1.08      0.05      6.18      \n",
+      "landmark_18         1.23      0.87      1.23      0.09      7.43      \n",
+      "======================================================================\n",
+      "\n",
+      "2# Success detection rate (SDR):\n",
+      "================================================================================\n",
+      "Class               SDR (PE≤2.0mm) SDR (PE≤2.5mm) SDR (PE≤3.0mm) SDR (PE≤4.0mm) \n",
+      "--------------------------------------------------------------------------------\n",
+      "landmark_0          98.00          100.00         100.00         100.00         \n",
+      "landmark_1          86.00          88.00          91.00          96.00          \n",
+      "landmark_2          84.00          89.00          91.00          94.00          \n",
+      "landmark_3          83.00          87.00          90.00          93.00          \n",
+      "landmark_4          82.00          88.00          90.00          98.00          \n",
+      "landmark_5          91.00          95.00          98.00          100.00         \n",
+      "landmark_6          93.00          97.00          100.00         100.00         \n",
+      "landmark_7          96.00          99.00          100.00         100.00         \n",
+      "landmark_8          97.00          100.00         100.00         100.00         \n",
+      "landmark_9          54.00          63.00          73.00          87.00          \n",
+      "landmark_10         95.00          96.00          98.00          99.00          \n",
+      "landmark_11         94.00          95.00          97.00          99.00          \n",
+      "landmark_12         89.00          95.00          98.00          100.00         \n",
+      "landmark_13         94.00          97.00          99.00          100.00         \n",
+      "landmark_14         89.00          92.00          96.00          98.00          \n",
+      "landmark_15         91.00          93.00          96.00          97.00          \n",
+      "landmark_16         94.00          97.00          97.00          99.00          \n",
+      "landmark_17         85.00          90.00          91.00          98.00          \n",
+      "landmark_18         85.00          90.00          92.00          97.00          \n",
+      "================================================================================\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.visualize import detection_report\n",
+    "\n",
+    "detection_report(true_landmarks, pred_landmarks, dim=(512, 512), dim_orig=dim_origs.int(),\n",
+    "                    pixel_spacing=pixel_spacings, padding=paddings, class_names=ds.class_names,\n",
+    "                    radius=[2.0, 2.5, 3.0, 4.0], digits=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from landmarker.visualize import plot_cpe\n",
+    "\n",
+    "plot_cpe(true_landmarks, pred_landmarks, dim=(512, 512), dim_orig=dim_origs.int(),\n",
+    "                    pixel_spacing=pixel_spacings, padding=paddings, class_names=ds.class_names,\n",
+    "                    group=False, title=\"CPE curve\", save_path=None,\n",
+    "                    stat='proportion', unit='mm', kind='ecdf')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/_collections/examples/README.md.txt b/_sources/_collections/examples/README.md.txt
new file mode 100644
index 0000000..e69de29
diff --git a/_sources/_collections/examples/endovis2015.ipynb.txt b/_sources/_collections/examples/endovis2015.ipynb.txt
new file mode 100644
index 0000000..6fbd784
--- /dev/null
+++ b/_sources/_collections/examples/endovis2015.ipynb.txt
@@ -0,0 +1,444 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p align=\"center\">\n",
+    "    <a href=\"https://predict-idlab.github.io/landmarker\">\n",
+    "        <img alt=\"landmarker\" src=\"https://raw.githubusercontent.com/predict-idlab/landmarker/main/docs/_static/images/logo.svg\" width=\"66%\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "# Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Multi-Class Landmark Detetection (EndoVis 2015 Challenge)\n",
+    "\n",
+    "In this tutorial, we will train and evaluate an direct static heatmap regression model for landmark \n",
+    "detection with EndoVis 2015 Challenge. We will use part of the EndoVis 2015 challenge dataset to \n",
+    "construct a multi-instance and multi-class landmark detection task. The dataset contains 4 training \n",
+    "and 6 testing videos of robotic surgery. The goal is to predict the location of instruments in the video, \n",
+    "more specifically the tip of the clasper. We only consider the clasper points and ignore the other points,\n",
+    "since they are way more ambiguous. One of the difficulties \n",
+    "\n",
+    "The videos are transformed into images and the annotations are \n",
+    "given as 2D points. The dataset is split into a training and testing set. The training set contains 4 videos and \n",
+    "the testing set contains 6 videos, such as specified in the challenge. \n",
+    "\n",
+    "We will go through the following steps:\n",
+    "* [Loading the dataset](#Loading-the-dataset)\n",
+    "* [Inspecting the dataset](#Inspecting-the-dataset)\n",
+    "* [Training and initializing the UNet model](#Training-the-model)\n",
+    "* [Evaluating the model](#Evaluating-the-model)\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/predict-idlab/landmarker/examples/static_unet_endovis2015.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !python -c \"import landmarker\" || pip install landmarker\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(\"../src/\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup imports and variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from monai.transforms import (Compose, RandAffined, RandGaussianNoised, ScaleIntensityd,\n",
+    "                              RandScaleIntensityd, RandAdjustContrastd, RandHistogramShiftd)\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fn_keys = ('image', 'mask')\n",
+    "spatial_transformd = [RandAffined(fn_keys, prob=1,\n",
+    "                        rotate_range=(-np.pi/12, np.pi/12),\n",
+    "                        translate_range=(-10, 10),\n",
+    "                        scale_range=(-0.1, 0.1),\n",
+    "                        shear_range=(-0.1, 0.1)\n",
+    "                        )]\n",
+    "\n",
+    "train_transformd = Compose([\n",
+    "                            RandGaussianNoised(('image', ), prob=0.2, mean=0, std=0.1),  # Add gaussian noise\n",
+    "                            RandScaleIntensityd(('image', ), factors=0.25, prob=0.2),  # Add random intensity scaling\n",
+    "                            RandAdjustContrastd(('image', ), prob=0.2, gamma=(0.5,4.5)),  # Randomly adjust contrast\n",
+    "                            RandHistogramShiftd(('image', ), prob=0.2),  # Randomly shift histogram\n",
+    "                            ScaleIntensityd(('image', )),  # Scale intensity\n",
+    "                        ] + spatial_transformd)\n",
+    "\n",
+    "inference_transformd = Compose([\n",
+    "    ScaleIntensityd(('image', )),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.datasets import get_endovis2015_heatmap_datasets\n",
+    "\n",
+    "data_dir = \"/Users/jefjonkers/Data/landmark-datasets\"\n",
+    "ds_train, ds_test = get_endovis2015_heatmap_datasets(data_dir, train_transform = train_transformd,\n",
+    "                                                     inference_transform= inference_transformd,\n",
+    "                                                     dim_img = (512, 512), sigma=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize import inspection_plot\n",
+    "\n",
+    "# Plot the first 3 images from the training set\n",
+    "inspection_plot(ds_train, np.random.randint(0, len(ds_train), 3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Plot the first 3 images from the test1 set\n",
+    "inspection_plot(ds_test, range(3))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and initializing the SpatialConfiguration model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initializing the model, optimizer and loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "from monai.networks.nets import UNet\n",
+    "\n",
+    "\n",
+    "model = UNet(\n",
+    "    spatial_dims=2,\n",
+    "    in_channels=3,\n",
+    "    out_channels=2,\n",
+    "    channels=(16, 32, 64, 128, 256),\n",
+    "    strides=(2, 2, 2, 2),\n",
+    "    num_res_units=2,\n",
+    ").to(device)\n",
+    "\n",
+    "lr = 1e-4\n",
+    "batch_size = 4\n",
+    "epochs = 5\n",
+    "\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-3)\n",
+    "\n",
+    "criterion = nn.BCEWithLogitsLoss()\n",
+    "\n",
+    "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5,\n",
+    "                                                          patience=20, verbose=True, cooldown=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting the data loaders and split training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_lengths = [0.8, 0.2]\n",
+    "ds_train_train, ds_train_val = torch.utils.data.random_split(ds_train, split_lengths)\n",
+    "train_loader = DataLoader(ds_train_train, batch_size=batch_size, shuffle=True, num_workers=0)\n",
+    "val_loader = DataLoader(ds_train_val, batch_size=batch_size, shuffle=False, num_workers=0)\n",
+    "test_loader = DataLoader(ds_test, batch_size=batch_size, shuffle=False, num_workers=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.heatmap.decoder import heatmap_to_coord, heatmap_to_multiple_coord\n",
+    "from landmarker.metrics import point_error\n",
+    "\n",
+    "from torch.nn.functional import sigmoid\n",
+    "\n",
+    "from landmarker.metrics.metrics import multi_instance_point_error\n",
+    "\n",
+    "def train_epoch(model, train_loader, criterion, optimizer, device):\n",
+    "    running_loss = 0\n",
+    "    model.train()\n",
+    "    for i, batch in enumerate(tqdm(train_loader)):\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        heatmaps = batch[\"mask\"].to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(images)\n",
+    "        loss = criterion(outputs, heatmaps)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        running_loss += loss.item()\n",
+    "    return running_loss / len(train_loader)\n",
+    "\n",
+    "def val_epoch(model, val_loader, criterion, device):\n",
+    "    eval_loss = 0\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for _, batch in enumerate(tqdm(val_loader)):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            heatmaps = batch[\"mask\"].to(device)\n",
+    "            landmarks = batch[\"landmark\"].to(device)\n",
+    "            outputs = model(images)\n",
+    "            loss = criterion(outputs, heatmaps)\n",
+    "            eval_loss += loss.item()\n",
+    "    return eval_loss / len(val_loader)\n",
+    "\n",
+    "def train(model, train_loader, val_loader, criterion, optimizer, device, epochs=1000):\n",
+    "    for epoch in tqdm(range(epochs)):\n",
+    "        train_loss = train_epoch(model, train_loader, criterion, optimizer, device)\n",
+    "        ds_train.transform = None\n",
+    "        val_loss = val_epoch(model, val_loader, criterion, device)\n",
+    "        print(f\"Epoch {epoch+1}/{epochs} - Train loss: {train_loss:.4f} - Val loss: {val_loss:.4f}\")\n",
+    "        lr_scheduler.step(val_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train(model, train_loader, val_loader, criterion, optimizer, device,\n",
+    "      epochs=epochs)\n",
+    "# model.load_state_dict(torch.load(\"best_weights_unet_endovis_static.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_landmarks = []\n",
+    "true_landmarks = []\n",
+    "dim_origs = []\n",
+    "pixel_spacings = []\n",
+    "paddings = []\n",
+    "tp = []\n",
+    "fp = []\n",
+    "fn = []\n",
+    "test_mpe = 0\n",
+    "test_tp = 0\n",
+    "test_fp = 0\n",
+    "test_fn = 0\n",
+    "model.eval()\n",
+    "with torch.no_grad():\n",
+    "    for i, batch in enumerate(tqdm(test_loader)):\n",
+    "        images = batch[\"image\"]\n",
+    "        heatmaps = batch[\"mask\"]\n",
+    "        landmarks = batch[\"landmark\"]\n",
+    "        outputs = model(images.to(device)).detach().cpu()\n",
+    "        offset_coords = outputs.shape[1]-landmarks.shape[1]\n",
+    "        pred_landmarks_list, _ = heatmap_to_multiple_coord(sigmoid(outputs), window=5,\n",
+    "                                                           threshold=0.5,\n",
+    "                                                           method=\"argmax\")\n",
+    "        pe_batch, tp_batch, fp_batch, fn_batch, pred_landmarks_torch = multi_instance_point_error(\n",
+    "            true_landmarks=landmarks, pred_landmarks=pred_landmarks_list, dim=(512, 512),\n",
+    "            dim_orig=batch[\"dim_original\"], pixel_spacing=batch[\"spacing\"],\n",
+    "            padding=batch[\"padding\"], reduction=\"none\")\n",
+    "        test_mpe += torch.nanmean(pe_batch).item()\n",
+    "        test_tp += torch.nansum(tp_batch).item()\n",
+    "        test_fp += torch.nansum(fp_batch).item()\n",
+    "        test_fn += torch.nansum(fn_batch).item()\n",
+    "        pred_landmarks.append(pred_landmarks_torch)\n",
+    "        true_landmarks.append(landmarks)\n",
+    "        dim_origs.append(batch[\"dim_original\"])\n",
+    "        pixel_spacings.append(batch[\"spacing\"])\n",
+    "        paddings.append(batch[\"padding\"])\n",
+    "        tp.append(tp_batch)\n",
+    "        fp.append(fp_batch)\n",
+    "        fn.append(fn_batch)\n",
+    "\n",
+    "\n",
+    "test_mpe /= len(test_loader)\n",
+    "\n",
+    "print(f\"Test Mean PE: {test_mpe:.4f}\")\n",
+    "print(f\"Test TP: {test_tp:.4f}\")\n",
+    "print(f\"Test FP: {test_fp:.4f}\")\n",
+    "print(f\"Test FN: {test_fn:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.metrics import sdr\n",
+    "\n",
+    "sdr_test = sdr([4, 5, 10, 20], true_landmarks=torch.cat(true_landmarks, axis=0), pred_landmarks=torch.cat(pred_landmarks, axis=0),\n",
+    "               dim=(512, 512), dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "               padding=torch.cat(paddings, axis=0))\n",
+    "for key in sdr_test:\n",
+    "    print(f\"SDR for {key}mm: {sdr_test[key]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize.utils import prediction_inspect_plot_multi_instance\n",
+    "\n",
+    "model.to(\"cpu\")\n",
+    "prediction_inspect_plot_multi_instance(ds_test, model, range(3))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize import plot_cpe\n",
+    "\n",
+    "plot_cpe(torch.cat(true_landmarks, axis=0), torch.cat(pred_landmarks, axis=0), dim=(512, 512),\n",
+    "            dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "            padding=torch.cat(paddings, axis=0), class_names=ds_test.class_names,\n",
+    "            group=False, title=\"CPE curve\", save_path=None,\n",
+    "            stat='proportion', unit='pixels', kind='ecdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize.evaluation import multi_instance_detection_report\n",
+    "\n",
+    "multi_instance_detection_report(torch.cat(true_landmarks, axis=0), torch.cat(pred_landmarks, axis=0),\n",
+    "                                torch.cat(tp, axis=0), torch.cat(fp, axis=0), torch.cat(fn, axis=0), dim=(512, 512),\n",
+    "                                dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "                                padding=torch.cat(paddings, axis=0), class_names=ds_test.class_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/_collections/examples/plant_centroids.ipynb.txt b/_sources/_collections/examples/plant_centroids.ipynb.txt
new file mode 100644
index 0000000..d9cf3d9
--- /dev/null
+++ b/_sources/_collections/examples/plant_centroids.ipynb.txt
@@ -0,0 +1,648 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<p align=\"center\">\n",
+    "    <a href=\"https://predict-idlab.github.io/landmarker\">\n",
+    "        <img alt=\"landmarker\" src=\"https://raw.githubusercontent.com/predict-idlab/landmarker/main/docs/_static/images/logo.svg\" width=\"66%\">\n",
+    "    </a>\n",
+    "</p>\n",
+    "\n",
+    "# Training and Evaluating Static Heatmap Regression Model for Multi-Instance and Single-Class Landmark Detetection (Plant Centroids)\n",
+    "\n",
+    "We will go through the following steps:\n",
+    "* [Loading the dataset](#Loading-the-dataset)\n",
+    "* [Inspecting the dataset](#Inspecting-the-dataset)\n",
+    "* [Training and initializing the UNet model](#Training-the-model)\n",
+    "* [Evaluating the model](#Evaluating-the-model)\n",
+    "\n",
+    "<a target=\"_blank\" href=\"https://colab.research.google.com/github/predict-idlab/landmarker/examples/static_unet_plant_centroids.ipynb\">\n",
+    "  <img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/>\n",
+    "</a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# !python -c \"import landmarker\" || pip install landmarker\n",
+    "\n",
+    "import sys\n",
+    "import os\n",
+    "\n",
+    "sys.path.append(\"../src/\")\n",
+    "import landmarker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup imports and variables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from monai.transforms import (Compose, RandAffined, RandGaussianNoised, RandStdShiftIntensityd,\n",
+    "                              RandScaleIntensityd, RandAdjustContrastd, RandHistogramShiftd,\n",
+    "                              ScaleIntensityd, Lambdad)\n",
+    "from tqdm.notebook import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loading the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.transforms.images import UseOnlyFirstChannel\n",
+    "fn_keys = ('image',)\n",
+    "spatial_transformd = [RandAffined(fn_keys, prob=1,\n",
+    "                        rotate_range=(-np.pi/12, np.pi/12),\n",
+    "                        translate_range=(-10, 10),\n",
+    "                        scale_range=(-0.1, 0.1),\n",
+    "                        shear_range=(-0.1, 0.1)\n",
+    "                        )]\n",
+    "\n",
+    "train_transformd = Compose([\n",
+    "                            ScaleIntensityd(('image', )),  # Scale intensity\n",
+    "                        ] + spatial_transformd)\n",
+    "\n",
+    "inference_transformd = Compose([\n",
+    "    ScaleIntensityd(('image', )),\n",
+    "])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1398/1398 [00:00<00:00, 62890.94it/s]\n",
+      "100%|██████████| 100/100 [00:00<00:00, 32668.46it/s]\n",
+      "100%|██████████| 275/275 [00:00<00:00, 58336.72it/s]\n",
+      "100%|██████████| 234/234 [00:00<00:00, 57365.55it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading and extracting masks from 1398 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1398/1398 [00:05<00:00, 253.85it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading and extracting masks from 100 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 100/100 [00:00<00:00, 246.31it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading and extracting masks from 275 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 275/275 [00:01<00:00, 263.84it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading and extracting masks from 234 images...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 234/234 [00:00<00:00, 267.46it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from landmarker.datasets import get_plant_centroids_landmark_datasets\n",
+    "\n",
+    "data_dir = \"/Users/jefjonkers/Data/landmark-datasets\"\n",
+    "ds_train, ds_test_A, ds_test_B, ds_test_C = get_plant_centroids_landmark_datasets(data_dir, train_transform = train_transformd,\n",
+    "                                                                                  inference_transform=inference_transformd,\n",
+    "                                                                                  dim_img=(512,512),\n",
+    "                                                                                  store_imgs=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Constructing a heatmap generator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.heatmap.generator import GaussianHeatmapGenerator\n",
+    "\n",
+    "heatmap_generator = GaussianHeatmapGenerator(\n",
+    "    nb_landmarks=1,\n",
+    "    sigmas=3,\n",
+    "    gamma=None,\n",
+    "    heatmap_size=(512, 512),\n",
+    "    learnable=False, # If True, the heatmap generator will be trainable\n",
+    ").to(device)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inspecting the dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from landmarker.visualize import inspection_plot\n",
+    "\n",
+    "# Plot the first 3 images from the training set\n",
+    "heatmap_generator.device = \"cpu\" # because dataset tensors are still on cpu\n",
+    "inspection_plot(ds_train, np.random.randint(0, len(ds_train), 3),\n",
+    "               heatmap_generator)\n",
+    "heatmap_generator.device = device # set the desired device back"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1500 with 9 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the first 3 images from the test1 set\n",
+    "heatmap_generator.device = \"cpu\" # because dataset tensors are still on cpu\n",
+    "inspection_plot(ds_test_A, range(3), heatmap_generator)\n",
+    "heatmap_generator.device = device # set the desired device back"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Training and initializing the SpatialConfiguration model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Initializing the model, optimizer and loss function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/optim/lr_scheduler.py:28: UserWarning: The verbose parameter is deprecated. Please use get_last_lr() to access the learning rate.\n",
+      "  warnings.warn(\"The verbose parameter is deprecated. Please use get_last_lr() \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "from torch import nn\n",
+    "import segmentation_models_pytorch as smp\n",
+    "\n",
+    "model = smp.Unet(\n",
+    "        encoder_name=\"resnet34\",\n",
+    "        encoder_weights=\"imagenet\",\n",
+    "        in_channels=3,\n",
+    "        classes=1,\n",
+    "        decoder_channels=(256, 128, 64, 32, 32),\n",
+    ")\n",
+    "\n",
+    "lr = 1e-4\n",
+    "batch_size = 4\n",
+    "epochs = 100\n",
+    "\n",
+    "optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-3)\n",
+    "\n",
+    "criterion = nn.BCEWithLogitsLoss()\n",
+    "\n",
+    "lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode='min', factor=0.5,\n",
+    "                                                          patience=10, verbose=True, cooldown=10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting the data loaders and split training set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "split_lengths = [0.8, 0.2]\n",
+    "ds_train_train, ds_train_val = torch.utils.data.random_split(ds_train, split_lengths)\n",
+    "train_loader = DataLoader(ds_train_train, batch_size=batch_size, shuffle=True, num_workers=0)\n",
+    "val_loader = DataLoader(ds_train_val, batch_size=batch_size, shuffle=False, num_workers=0)\n",
+    "test_A_loader = DataLoader(ds_test_A, batch_size=batch_size, shuffle=False, num_workers=0)\n",
+    "test_B_loader = DataLoader(ds_test_B, batch_size=batch_size, shuffle=False, num_workers=0)\n",
+    "test_C_loader = DataLoader(ds_test_C, batch_size=batch_size, shuffle=False, num_workers=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Training the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.heatmap.decoder import heatmap_to_coord, heatmap_to_multiple_coord\n",
+    "from landmarker.metrics import point_error\n",
+    "\n",
+    "from torch.nn.functional import sigmoid\n",
+    "\n",
+    "from landmarker.metrics.metrics import multi_instance_point_error\n",
+    "\n",
+    "def train_epoch(model, heatmap_generator, train_loader, criterion, optimizer, device):\n",
+    "    running_loss = 0\n",
+    "    model.train()\n",
+    "    for i, batch in enumerate(tqdm(train_loader)):\n",
+    "        images = batch[\"image\"].to(device)\n",
+    "        landmarks = batch[\"landmark\"].to(device)\n",
+    "        optimizer.zero_grad()\n",
+    "        outputs = model(images)\n",
+    "        heatmaps = heatmap_generator(landmarks)\n",
+    "        loss = criterion(outputs, heatmaps)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "        running_loss += loss.item()\n",
+    "    return running_loss / len(train_loader)\n",
+    "\n",
+    "def val_epoch(model, heatmap_generator, val_loader, criterion, device):\n",
+    "    eval_loss = 0\n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        for _, batch in enumerate(tqdm(val_loader)):\n",
+    "            images = batch[\"image\"].to(device)\n",
+    "            landmarks = batch[\"landmark\"].to(device)\n",
+    "            outputs = model(images)\n",
+    "            heatmaps = heatmap_generator(landmarks)\n",
+    "            loss = criterion(outputs, heatmaps)\n",
+    "            eval_loss += loss.item()\n",
+    "    return eval_loss / len(val_loader)\n",
+    "\n",
+    "def train(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs=1000):\n",
+    "    for epoch in tqdm(range(epochs)):\n",
+    "        ds_train.transform = train_transformd\n",
+    "        train_loss = train_epoch(model, heatmap_generator, train_loader, criterion, optimizer, device)\n",
+    "        ds_train.transform = None\n",
+    "        val_loss = val_epoch(model, heatmap_generator, val_loader, criterion, device)\n",
+    "        print(f\"Epoch {epoch+1}/{epochs} - Train loss: {train_loss:.4f} - Val loss: {val_loss:.4f}\")\n",
+    "        lr_scheduler.step(val_loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ae0d4c5d8efe4b6ea72d7badab4adf4e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f1f0d40ba16e4e8cac1d58bcb03448e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/280 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4728585bcec469e9a491a5d787b7442",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/70 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/100 - Train loss: 0.0938 - Val loss: 0.0283\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5648a8372c35425bb9d735856179eddf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/280 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheatmap_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mval_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m      \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# model.load_state_dict(torch.load(\"model_static_unet_plant_centroids.pt\"))\u001b[39;00m\n",
+      "Cell \u001b[0;32mIn[13], line 39\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device, epochs)\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mrange\u001b[39m(epochs)):\n\u001b[1;32m     38\u001b[0m     ds_train\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;241m=\u001b[39m train_transformd\n\u001b[0;32m---> 39\u001b[0m     train_loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_epoch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mheatmap_generator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrain_loader\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     40\u001b[0m     ds_train\u001b[38;5;241m.\u001b[39mtransform \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     41\u001b[0m     val_loss \u001b[38;5;241m=\u001b[39m val_epoch(model, heatmap_generator, val_loader, criterion, device)\n",
+      "Cell \u001b[0;32mIn[13], line 18\u001b[0m, in \u001b[0;36mtrain_epoch\u001b[0;34m(model, heatmap_generator, train_loader, criterion, optimizer, device)\u001b[0m\n\u001b[1;32m     16\u001b[0m heatmaps \u001b[38;5;241m=\u001b[39m heatmap_generator(landmarks)\n\u001b[1;32m     17\u001b[0m loss \u001b[38;5;241m=\u001b[39m criterion(outputs, heatmaps)\n\u001b[0;32m---> 18\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     19\u001b[0m optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m     20\u001b[0m running_loss \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\u001b[38;5;241m.\u001b[39mitem()\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:516\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    469\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Computes the gradient of current tensor wrt graph leaves.\u001b[39;00m\n\u001b[1;32m    470\u001b[0m \n\u001b[1;32m    471\u001b[0m \u001b[38;5;124;03mThe graph is differentiated using the chain rule. If the tensor is\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    513\u001b[0m \u001b[38;5;124;03m        used to compute the attr::tensors.\u001b[39;00m\n\u001b[1;32m    514\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mhandle_torch_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m        \u001b[49m\u001b[43mTensor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[43m        \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgradient\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m        \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    523\u001b[0m \u001b[43m        \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    524\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    525\u001b[0m torch\u001b[38;5;241m.\u001b[39mautograd\u001b[38;5;241m.\u001b[39mbackward(\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[38;5;241m=\u001b[39minputs\n\u001b[1;32m    527\u001b[0m )\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/overrides.py:1636\u001b[0m, in \u001b[0;36mhandle_torch_function\u001b[0;34m(public_api, relevant_args, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1630\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDefining your `__torch_function__ as a plain method is deprecated and \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1631\u001b[0m                   \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwill be an error in future, please define it as a classmethod.\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1632\u001b[0m                   \u001b[38;5;167;01mDeprecationWarning\u001b[39;00m)\n\u001b[1;32m   1634\u001b[0m \u001b[38;5;66;03m# Use `public_api` instead of `implementation` so __torch_function__\u001b[39;00m\n\u001b[1;32m   1635\u001b[0m \u001b[38;5;66;03m# implementations can do equality/identity comparisons.\u001b[39;00m\n\u001b[0;32m-> 1636\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43mtorch_func_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpublic_api\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1638\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m:\n\u001b[1;32m   1639\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/monai/data/meta_tensor.py:282\u001b[0m, in \u001b[0;36mMetaTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m    280\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    281\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m {}\n\u001b[0;32m--> 282\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__torch_function__\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtypes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    283\u001b[0m \u001b[38;5;66;03m# if `out` has been used as argument, metadata is not copied, nothing to do.\u001b[39;00m\n\u001b[1;32m    284\u001b[0m \u001b[38;5;66;03m# if \"out\" in kwargs:\u001b[39;00m\n\u001b[1;32m    285\u001b[0m \u001b[38;5;66;03m#     return ret\u001b[39;00m\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _not_requiring_metadata(ret):\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:1443\u001b[0m, in \u001b[0;36mTensor.__torch_function__\u001b[0;34m(cls, func, types, args, kwargs)\u001b[0m\n\u001b[1;32m   1440\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n\u001b[1;32m   1442\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _C\u001b[38;5;241m.\u001b[39mDisableTorchFunctionSubclass():\n\u001b[0;32m-> 1443\u001b[0m     ret \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1444\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m func \u001b[38;5;129;01min\u001b[39;00m get_default_nowrap_functions():\n\u001b[1;32m   1445\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m ret\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/_tensor.py:525\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    516\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m    517\u001b[0m         Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m    518\u001b[0m         (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    523\u001b[0m         inputs\u001b[38;5;241m=\u001b[39minputs,\n\u001b[1;32m    524\u001b[0m     )\n\u001b[0;32m--> 525\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    526\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\n\u001b[1;32m    527\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/autograd/__init__.py:267\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    262\u001b[0m     retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m    264\u001b[0m \u001b[38;5;66;03m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[1;32m    265\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m    266\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 267\u001b[0m \u001b[43m_engine_run_backward\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    268\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    269\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    270\u001b[0m \u001b[43m    \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    271\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    272\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    273\u001b[0m \u001b[43m    \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    274\u001b[0m \u001b[43m    \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    275\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/anaconda3/envs/vision/lib/python3.11/site-packages/torch/autograd/graph.py:744\u001b[0m, in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    742\u001b[0m     unregister_hooks \u001b[38;5;241m=\u001b[39m _register_logging_hooks_on_whole_graph(t_outputs)\n\u001b[1;32m    743\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 744\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m    745\u001b[0m \u001b[43m        \u001b[49m\u001b[43mt_outputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m    746\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[1;32m    747\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    748\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m attach_logging_hooks:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "train(model, heatmap_generator, train_loader, val_loader, criterion, optimizer, device,\n",
+    "      epochs=epochs)\n",
+    "\n",
+    "# model.load_state_dict(torch.load(\"model_static_unet_plant_centroids.pt\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Evaluating the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pred_landmarks = []\n",
+    "true_landmarks = []\n",
+    "dim_origs = []\n",
+    "pixel_spacings = []\n",
+    "paddings = []\n",
+    "tp = []\n",
+    "fp = []\n",
+    "fn = []\n",
+    "test_mpe = 0\n",
+    "test_tp = 0\n",
+    "test_fp = 0\n",
+    "test_fn = 0\n",
+    "threshold = 0.05\n",
+    "window = 9\n",
+    "model.eval()\n",
+    "model.to(device)\n",
+    "with torch.no_grad():\n",
+    "    for i, batch in enumerate(tqdm(test_A_loader)):\n",
+    "        images = batch[\"image\"]\n",
+    "        landmarks = batch[\"landmark\"]\n",
+    "        outputs = model(images.to(device)).detach().cpu()\n",
+    "        offset_coords = outputs.shape[1]-landmarks.shape[1]\n",
+    "        pred_landmarks_list, _ = heatmap_to_multiple_coord(sigmoid(outputs), window=window,\n",
+    "                                                           threshold=threshold,\n",
+    "                                                           method=\"argmax\")\n",
+    "        pe_batch, tp_batch, fp_batch, fn_batch, pred_landmarks_torch = multi_instance_point_error(\n",
+    "            true_landmarks=landmarks, pred_landmarks=pred_landmarks_list, dim=(512, 512),\n",
+    "            dim_orig=batch[\"dim_original\"], pixel_spacing=batch[\"spacing\"],\n",
+    "            padding=batch[\"padding\"], reduction=\"none\")\n",
+    "        test_mpe += torch.nanmean(pe_batch).item()\n",
+    "        test_tp += torch.nansum(tp_batch).item()\n",
+    "        test_fp += torch.nansum(fp_batch).item()\n",
+    "        test_fn += torch.nansum(fn_batch).item()\n",
+    "        pred_landmarks.append(pred_landmarks_torch)\n",
+    "        true_landmarks.append(landmarks)\n",
+    "        dim_origs.append(batch[\"dim_original\"])\n",
+    "        pixel_spacings.append(batch[\"spacing\"])\n",
+    "        paddings.append(batch[\"padding\"])\n",
+    "        tp.append(tp_batch)\n",
+    "        fp.append(fp_batch)\n",
+    "        fn.append(fn_batch)\n",
+    "\n",
+    "\n",
+    "test_mpe /= len(test_A_loader)\n",
+    "\n",
+    "print(f\"Test Mean PE: {test_mpe:.4f}\")\n",
+    "print(f\"Test TP: {test_tp:.4f}\")\n",
+    "print(f\"Test FP: {test_fp:.4f}\")\n",
+    "print(f\"Test FN: {test_fn:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.metrics import sdr\n",
+    "\n",
+    "sdr_test = sdr([4, 5, 10, 20], true_landmarks=torch.cat(true_landmarks, axis=0), pred_landmarks=torch.cat(pred_landmarks, axis=0),\n",
+    "               dim=(512, 512), dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "               padding=torch.cat(paddings, axis=0))\n",
+    "for key in sdr_test:\n",
+    "    print(f\"SDR for {key}mm: {sdr_test[key]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize.utils import prediction_inspect_plot_multi_instance\n",
+    "model.to(\"cpu\")\n",
+    "prediction_inspect_plot_multi_instance(ds_test_A, model, range(3), threshold=threshold, activation=nn.Sigmoid())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize import plot_cpe\n",
+    "\n",
+    "plot_cpe(torch.cat(true_landmarks, axis=0), torch.cat(pred_landmarks, axis=0), dim=(512, 512),\n",
+    "            dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "            padding=torch.cat(paddings, axis=0), class_names=ds_test_A.class_names,\n",
+    "            group=False, title=\"CPE curve\", save_path=None,\n",
+    "            stat='proportion', unit='pixels', kind='ecdf')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from landmarker.visualize.evaluation import multi_instance_detection_report\n",
+    "\n",
+    "multi_instance_detection_report(torch.cat(true_landmarks, axis=0), torch.cat(pred_landmarks, axis=0),\n",
+    "                                torch.cat(tp, axis=0), torch.cat(fp, axis=0), torch.cat(fn, axis=0), dim=(512, 512),\n",
+    "                                dim_orig=torch.cat(dim_origs, axis=0).int(), pixel_spacing=torch.cat(pixel_spacings, axis=0),\n",
+    "                                padding=torch.cat(paddings, axis=0), class_names=ds_test_A.class_names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/_sources/dev/changelog.md.txt b/_sources/dev/changelog.md.txt
new file mode 100644
index 0000000..ff772da
--- /dev/null
+++ b/_sources/dev/changelog.md.txt
@@ -0,0 +1,2 @@
+```{include} ../../CHANGELOG.md
+```
\ No newline at end of file
diff --git a/_sources/dev/code_of_conduct.md.txt b/_sources/dev/code_of_conduct.md.txt
new file mode 100644
index 0000000..8bd2c1c
--- /dev/null
+++ b/_sources/dev/code_of_conduct.md.txt
@@ -0,0 +1,2 @@
+```{include} ../../CODE_OF_CONDUCT.md
+```
\ No newline at end of file
diff --git a/_sources/dev/contributing.md.txt b/_sources/dev/contributing.md.txt
new file mode 100644
index 0000000..e75758c
--- /dev/null
+++ b/_sources/dev/contributing.md.txt
@@ -0,0 +1,2 @@
+```{include} ../../CONTRIBUTING.md
+```
\ No newline at end of file
diff --git a/_sources/dev/license.md.txt b/_sources/dev/license.md.txt
new file mode 100644
index 0000000..1c0e048
--- /dev/null
+++ b/_sources/dev/license.md.txt
@@ -0,0 +1,6 @@
+# 📝 License
+
+## IMEC License
+
+```{include} ../../LICENSE
+```
diff --git a/_sources/examples/index.md.txt b/_sources/examples/index.md.txt
new file mode 100644
index 0000000..aec7ed7
--- /dev/null
+++ b/_sources/examples/index.md.txt
@@ -0,0 +1,15 @@
+# 🔦 Examples
+
+```{toctree}
+:maxdepth: 1
+
+../_collections/examples/adaptive_scn_isbi2015.ipynb
+
+../_collections/examples/static_unet_endovis2015.ipynb
+
+../_collections/examples/static_unet_plant_centroids.ipynb
+
+../_collections/examples/pipeline_scn_isbi2015.ipynb
+
+```
+
diff --git a/_sources/index.md.txt b/_sources/index.md.txt
new file mode 100644
index 0000000..1cce5b2
--- /dev/null
+++ b/_sources/index.md.txt
@@ -0,0 +1,107 @@
+```{toctree}
+:hidden:
+:titlesonly:
+
+user/index
+examples/index
+reference/index
+```
+
+
+
+```{toctree}
+:caption: Development
+:hidden:
+:titlesonly:
+:maxdepth: 1
+
+
+dev/contributing
+dev/changelog
+dev/code_of_conduct
+dev/license
+```
+
+```{toctree}
+:caption: API reference
+:hidden:
+
+reference/data
+reference/datasets
+reference/heatmap
+reference/losses
+reference/metrics
+reference/models
+reference/schedulers
+reference/train
+reference/transforms
+reference/utils
+reference/visualize
+```
+
+(main-page)=
+<p align="center">
+    <a href="https://predict-idlab.github.io/landmarker">
+        <img alt="landmarker" src="_static/images/logo.svg" width="66%">
+    </a>
+</p>
+
+# Landmarker
+
+:::::{caution}
+🚧 Still under construction. 🚧
+:::::
+
+**Version**: {{ version }}
+
+**Useful links**:
+[Instalation instructions](installation) |
+[Source repository](https://github.com/predict-idlab/landmarker) |
+[Issue tracker](https://github.com/predict-idlab/landmarker/issues) |
+
+
+Landmarker is a [PyTorch](https://pytorch.org/)-based toolkit for (anatomical) landmark detection in
+images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art
+landmark detection algorithms for small and large datasets. Landmarker was developed for landmark
+detection in medical images. However, it can be used for any type of landmark detection problem.
+
+
+:::::{grid} 2
+
+::::{grid-item-card} 🚀 Getting started
+:link: user/index
+:link-type: doc
+:text-align: center
+
+New to Landmarker? Start here to learn how to install Landmarker and how to use it.
+
+::::
+
+::::{grid-item-card} 🔦 Examples
+:link: examples/index
+:link-type: doc
+:text-align: center
+
+The examples section contains a collection of examples that demonstrate how to use Landmarker
+
+::::
+
+::::{grid-item-card} 📖 API reference
+:link-type: doc
+:link: reference/index
+:text-align: center
+
+The API reference contains a detailed description of all Landmarker's methods, modules, and
+classes.
+
+::::
+
+::::{grid-item-card} 🚧 Development
+:link: dev/contributing
+:link-type: doc
+:text-align: center
+
+The development section contains information for developers who want to contribute to Landmarker.
+
+::::
+:::::
\ No newline at end of file
diff --git a/_sources/reference/data.md.txt b/_sources/reference/data.md.txt
new file mode 100644
index 0000000..e88cf68
--- /dev/null
+++ b/_sources/reference/data.md.txt
@@ -0,0 +1,6 @@
+# Data Module
+
+```{eval-rst}
+.. automodule:: landmarker.data
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/datasets.md.txt b/_sources/reference/datasets.md.txt
new file mode 100644
index 0000000..e7f7166
--- /dev/null
+++ b/_sources/reference/datasets.md.txt
@@ -0,0 +1,6 @@
+# Datasets Module
+
+```{eval-rst}
+.. automodule:: landmarker.datasets
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/heatmap.md.txt b/_sources/reference/heatmap.md.txt
new file mode 100644
index 0000000..2daa3a0
--- /dev/null
+++ b/_sources/reference/heatmap.md.txt
@@ -0,0 +1,6 @@
+# Heatmap Module
+
+```{eval-rst}
+.. automodule:: landmarker.heatmap
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/index.md.txt b/_sources/reference/index.md.txt
new file mode 100644
index 0000000..cf884e1
--- /dev/null
+++ b/_sources/reference/index.md.txt
@@ -0,0 +1,18 @@
+# 📖 API Reference
+
+```{toctree}
+:maxdepth: 1
+
+data.md
+datasets.md
+heatmap.md
+losses.md
+models.md
+metrics.md
+schedulers.md
+train.md
+transforms.md
+uncertainty.md
+utils.md
+visualize.md
+```
\ No newline at end of file
diff --git a/_sources/reference/losses.md.txt b/_sources/reference/losses.md.txt
new file mode 100644
index 0000000..d246b96
--- /dev/null
+++ b/_sources/reference/losses.md.txt
@@ -0,0 +1,6 @@
+# Losses Module
+
+```{eval-rst}
+.. automodule:: landmarker.losses
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/metrics.md.txt b/_sources/reference/metrics.md.txt
new file mode 100644
index 0000000..1076022
--- /dev/null
+++ b/_sources/reference/metrics.md.txt
@@ -0,0 +1,6 @@
+# Metrics Module
+
+```{eval-rst}
+.. automodule:: landmarker.metrics
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/models.md.txt b/_sources/reference/models.md.txt
new file mode 100644
index 0000000..4e979a6
--- /dev/null
+++ b/_sources/reference/models.md.txt
@@ -0,0 +1,6 @@
+# Models Module
+
+```{eval-rst}
+.. automodule:: landmarker.models
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/schedulers.md.txt b/_sources/reference/schedulers.md.txt
new file mode 100644
index 0000000..497e544
--- /dev/null
+++ b/_sources/reference/schedulers.md.txt
@@ -0,0 +1,6 @@
+# Schedulers Module
+
+```{eval-rst}
+.. automodule:: landmarker.schedulers
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/train.md.txt b/_sources/reference/train.md.txt
new file mode 100644
index 0000000..812befe
--- /dev/null
+++ b/_sources/reference/train.md.txt
@@ -0,0 +1,6 @@
+# Train Module
+
+```{eval-rst}
+.. automodule:: landmarker.train
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/transforms.md.txt b/_sources/reference/transforms.md.txt
new file mode 100644
index 0000000..4c7363d
--- /dev/null
+++ b/_sources/reference/transforms.md.txt
@@ -0,0 +1,5 @@
+# Transforms Module
+
+```{eval-rst}
+.. automodule:: landmarker.transforms
+    :members:
\ No newline at end of file
diff --git a/_sources/reference/uncertainty.md.txt b/_sources/reference/uncertainty.md.txt
new file mode 100644
index 0000000..a8bd5b3
--- /dev/null
+++ b/_sources/reference/uncertainty.md.txt
@@ -0,0 +1,6 @@
+# Uncertainty Module
+
+```{eval-rst}
+.. automodule:: landmarker.uncertainty
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/utils.md.txt b/_sources/reference/utils.md.txt
new file mode 100644
index 0000000..7137f7e
--- /dev/null
+++ b/_sources/reference/utils.md.txt
@@ -0,0 +1,6 @@
+# Utils Module
+
+```{eval-rst}
+.. automodule:: landmarker.utils
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/reference/visualize.md.txt b/_sources/reference/visualize.md.txt
new file mode 100644
index 0000000..c69a89a
--- /dev/null
+++ b/_sources/reference/visualize.md.txt
@@ -0,0 +1,6 @@
+# Visualize Module
+
+```{eval-rst}
+.. automodule:: landmarker.visualize
+    :members:
+```
\ No newline at end of file
diff --git a/_sources/user/index.md.txt b/_sources/user/index.md.txt
new file mode 100644
index 0000000..0f008f3
--- /dev/null
+++ b/_sources/user/index.md.txt
@@ -0,0 +1,82 @@
+# 🚀 Getting Started
+
+(installation)=
+## ⚙️ Installation
+`````{tab-set}
+````{tab-item} pip
+```bash
+pip install landmarker
+```
+````
+`````
+
+## Overview
+Landmarker provides a simple API for training and evaluating landmark detection models. The API is designed to be easy to use and to be flexible such that it can be used intertwined with other libraries or custom code. Landmarker can also act as utility library for certain components of landmark detection algorithms. For example, it provides a set of loss functions and heatmap decoding operations that can be used in combination with other PyTorch-based libraries.
+
+In the following sections, we provide a brief overview of the library and the API. For more specific details, we refer to the [API reference](../reference/index).
+
+### 📦 Modules
+
+#### Data Loading, Preprocessing, and Augmentation
+The [**landmarker.data**](../reference/data) module contains classes for loading and preprocessing
+data. All classes inherit from the
+[`Dataset`](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) class such that they
+can be used with PyTorch's
+[`DataLoader`](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) class. There
+are three types of datasets:
+* [`LandmarkDataset`](../reference/data/#landmark_dataset): A dataset of images and corresponding
+    landmarks (landmark coordinates). Is constructed from images and landmarks pairs.
+* [`HeatmapDataset`](../reference/data/#heatmap_dataset): A dataset of images and corresponding
+    heatmaps representing the associated landmark. Is constructed from images and landmarks pairs.
+* [`MaskDataset`](../reference/data/#mask_dataset): A dataset of images and corresponding masks
+    (i.e., binary segmentation masks indiciating the location of the landmarks). Can be
+    constructed from specified image and landmarks pairs, or from images and masks pairs, because
+    often that is how the data is distributed.
+
+Both the `HeatmapDataset` and `MaskDataset` inherit from the `LandmarkDataset` class, and thus also
+contain information about the landmarks. The `MaskDataset` can be constructed from specified image
+and landmarks pairs, or from images and masks pairs, because often that is how the data is
+distributed. The `HeatmapDataset` can be constructed from images and landmarks pairs.
+
+For all three types of datasets images can be provided as a list of paths to stored images, or as a a numpy
+arary, torch tensor, list of numpy  arrays or list of torch tensors. Landmarks can be as numpy arrays or torch tensors.
+The landmarks are assumed to be in the range of the dimensions of the image. For example, if the image is 256x256, the values of the landmarks are assumed to
+be in the range [0, 256].
+These landmarks can be provided in three different shapes:
+1) (N, D) where N is the number of samples and D is the number of dimensions
+2) (N, C, D) where C is the number of landmark classes
+3) (N, C, I, D) where I is the number of instances per landmark class, if less than I instances are
+    provided, the remaining instances are filled with NaNs.
+
+##### Preprocessing
+If the images are provided as a list of paths, the images loaded from these paths and their values
+are normalized to the range [0, 1]. If the images are provided as numpy arrays or torch tensors,
+they are assumed to be normalized to the range [0, 1]. When a dim argument is provided, the images
+and landmarks are rescaled to the specified dimensions. The original dimensions and original
+landmarksare stored in the `original_dims` and `original_landmarks` attributes of the dataset. By
+default the rescalling is done such that the aspect ratio is preserved, i.e., padding is added to
+the image such that the aspect ratio is preserved. If `resize_pad` is set to `False`, the image is
+rescaled without preserving the aspect ratio.
+
+##### Augmentation
+All datasets can be augmented using the `transforms` argument. Currently the transforms needs to
+follow the [monai](https://docs.monai.io/en/latest/transforms.html), and more specifically they need
+to be in a compose function.
+
+#### Datasets
+Comming soon...
+
+#### Heatmap Generation and Decoding
+Comming soon...
+
+#### Loss Functions
+Comming soon...
+
+#### Model Architectures
+Comming soon...
+
+#### Training and Evaluation
+Comming soon...
+
+#### Utilities
+Comming soon...
diff --git a/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
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diff --git a/_sphinx_design_static/design-tabs.js b/_sphinx_design_static/design-tabs.js
new file mode 100644
index 0000000..36b38cf
--- /dev/null
+++ b/_sphinx_design_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
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+function ready() {
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+}
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+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
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+    label.previousElementSibling.checked = true;
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+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
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diff --git a/_static/basic.css b/_static/basic.css
new file mode 100644
index 0000000..30fee9d
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,925 @@
+/*
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+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
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+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
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+    font-size: 90%;
+}
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+div.related ul {
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+    padding: 0 0 0 10px;
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+div.related li {
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+}
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+div.sphinxsidebar {
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+    width: 230px;
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+    font-size: 90%;
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+    overflow-wrap : break-word;
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+img {
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+    max-width: 100%;
+}
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+    margin: 10px 0 0 20px;
+    padding: 0;
+}
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+    margin: 2px 0 0 30px;
+    text-align: left;
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+    margin-left: auto;
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+}
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+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/check-solid.svg b/_static/check-solid.svg
new file mode 100644
index 0000000..92fad4b
--- /dev/null
+++ b/_static/check-solid.svg
@@ -0,0 +1,4 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>
diff --git a/_static/clipboard.min.js b/_static/clipboard.min.js
new file mode 100644
index 0000000..54b3c46
--- /dev/null
+++ b/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body},i="";return"string"==typeof t?(e=t,n="rtl"===document.documentElement.getAttribute("dir"),(o=document.createElement("textarea")).style.fontSize="12pt",o.style.border="0",o.style.padding="0",o.style.margin="0",o.style.position="absolute",o.style[n?"right":"left"]="-9999px",n=window.pageYOffset||document.documentElement.scrollTop,o.style.top="".concat(n,"px"),o.setAttribute("readonly",""),o.value=e,o=o,r.container.appendChild(o),i=c()(o),a("copy"),o.remove()):(i=c()(t),a("copy")),i};function r(t){return(r="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}var s=function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{},e=t.action,n=void 0===e?"copy":e,o=t.container,e=t.target,t=t.text;if("copy"!==n&&"cut"!==n)throw new Error('Invalid "action" value, use either "copy" or "cut"');if(void 0!==e){if(!e||"object"!==r(e)||1!==e.nodeType)throw new Error('Invalid "target" value, use a valid Element');if("copy"===n&&e.hasAttribute("disabled"))throw new Error('Invalid "target" attribute. 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You can\'t cut text from elements with "readonly" or "disabled" attributes')}return t?l(t,{container:o}):e?"cut"===n?f(e):l(e,{container:o}):void 0};function d(t){return(d="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}function p(t,e){for(var n=0;n<e.length;n++){var o=e[n];o.enumerable=o.enumerable||!1,o.configurable=!0,"value"in o&&(o.writable=!0),Object.defineProperty(t,o.key,o)}}function y(t,e){return(y=Object.setPrototypeOf||function(t,e){return t.__proto__=e,t})(t,e)}function h(n){var o=function(){if("undefined"==typeof Reflect||!Reflect.construct)return!1;if(Reflect.construct.sham)return!1;if("function"==typeof Proxy)return!0;try{return Date.prototype.toString.call(Reflect.construct(Date,[],function(){})),!0}catch(t){return!1}}();return function(){var t,e=m(n);return t=o?(t=m(this).constructor,Reflect.construct(e,arguments,t)):e.apply(this,arguments),e=this,!(t=t)||"object"!==d(t)&&"function"!=typeof t?function(t){if(void 0!==t)return t;throw new ReferenceError("this hasn't been initialised - super() hasn't been called")}(e):t}}function m(t){return(m=Object.setPrototypeOf?Object.getPrototypeOf:function(t){return t.__proto__||Object.getPrototypeOf(t)})(t)}function v(t,e){t="data-clipboard-".concat(t);if(e.hasAttribute(t))return e.getAttribute(t)}var o=function(){!function(t,e){if("function"!=typeof e&&null!==e)throw new TypeError("Super expression must either be null or a function");t.prototype=Object.create(e&&e.prototype,{constructor:{value:t,writable:!0,configurable:!0}}),e&&y(t,e)}(r,i());var t,e,n,o=h(r);function r(t,e){var n;return function(t){if(!(t instanceof r))throw new TypeError("Cannot call a class as a function")}(this),(n=o.call(this)).resolveOptions(e),n.listenClick(t),n}return t=r,n=[{key:"copy",value:function(t){var e=1<arguments.length&&void 0!==arguments[1]?arguments[1]:{container:document.body};return l(t,e)}},{key:"cut",value:function(t){return f(t)}},{key:"isSupported",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:["copy","cut"],t="string"==typeof t?[t]:t,e=!!document.queryCommandSupported;return t.forEach(function(t){e=e&&!!document.queryCommandSupported(t)}),e}}],(e=[{key:"resolveOptions",value:function(){var t=0<arguments.length&&void 0!==arguments[0]?arguments[0]:{};this.action="function"==typeof t.action?t.action:this.defaultAction,this.target="function"==typeof t.target?t.target:this.defaultTarget,this.text="function"==typeof t.text?t.text:this.defaultText,this.container="object"===d(t.container)?t.container:document.body}},{key:"listenClick",value:function(t){var e=this;this.listener=u()(t,"click",function(t){return e.onClick(t)})}},{key:"onClick",value:function(t){var e=t.delegateTarget||t.currentTarget,t=s({action:this.action(e),container:this.container,target:this.target(e),text:this.text(e)});this.emit(t?"success":"error",{action:this.action,text:t,trigger:e,clearSelection:function(){e&&e.focus(),document.activeElement.blur(),window.getSelection().removeAllRanges()}})}},{key:"defaultAction",value:function(t){return v("action",t)}},{key:"defaultTarget",value:function(t){t=v("target",t);if(t)return document.querySelector(t)}},{key:"defaultText",value:function(t){return v("text",t)}},{key:"destroy",value:function(){this.listener.destroy()}}])&&p(t.prototype,e),n&&p(t,n),r}()},828:function(t){var e;"undefined"==typeof Element||Element.prototype.matches||((e=Element.prototype).matches=e.matchesSelector||e.mozMatchesSelector||e.msMatchesSelector||e.oMatchesSelector||e.webkitMatchesSelector),t.exports=function(t,e){for(;t&&9!==t.nodeType;){if("function"==typeof t.matches&&t.matches(e))return t;t=t.parentNode}}},438:function(t,e,n){var u=n(828);function i(t,e,n,o,r){var i=function(e,n,t,o){return function(t){t.delegateTarget=u(t.target,n),t.delegateTarget&&o.call(e,t)}}.apply(this,arguments);return t.addEventListener(n,i,r),{destroy:function(){t.removeEventListener(n,i,r)}}}t.exports=function(t,e,n,o,r){return"function"==typeof t.addEventListener?i.apply(null,arguments):"function"==typeof n?i.bind(null,document).apply(null,arguments):("string"==typeof t&&(t=document.querySelectorAll(t)),Array.prototype.map.call(t,function(t){return i(t,e,n,o,r)}))}},879:function(t,n){n.node=function(t){return void 0!==t&&t instanceof HTMLElement&&1===t.nodeType},n.nodeList=function(t){var e=Object.prototype.toString.call(t);return void 0!==t&&("[object NodeList]"===e||"[object HTMLCollection]"===e)&&"length"in t&&(0===t.length||n.node(t[0]))},n.string=function(t){return"string"==typeof t||t instanceof String},n.fn=function(t){return"[object Function]"===Object.prototype.toString.call(t)}},370:function(t,e,n){var f=n(879),l=n(438);t.exports=function(t,e,n){if(!t&&!e&&!n)throw new Error("Missing required arguments");if(!f.string(e))throw new TypeError("Second argument must be a String");if(!f.fn(n))throw new TypeError("Third argument must be a Function");if(f.node(t))return c=e,a=n,(u=t).addEventListener(c,a),{destroy:function(){u.removeEventListener(c,a)}};if(f.nodeList(t))return o=t,r=e,i=n,Array.prototype.forEach.call(o,function(t){t.addEventListener(r,i)}),{destroy:function(){Array.prototype.forEach.call(o,function(t){t.removeEventListener(r,i)})}};if(f.string(t))return t=t,e=e,n=n,l(document.body,t,e,n);throw new TypeError("First argument must be a String, HTMLElement, HTMLCollection, or NodeList");var o,r,i,u,c,a}},817:function(t){t.exports=function(t){var e,n="SELECT"===t.nodeName?(t.focus(),t.value):"INPUT"===t.nodeName||"TEXTAREA"===t.nodeName?((e=t.hasAttribute("readonly"))||t.setAttribute("readonly",""),t.select(),t.setSelectionRange(0,t.value.length),e||t.removeAttribute("readonly"),t.value):(t.hasAttribute("contenteditable")&&t.focus(),n=window.getSelection(),(e=document.createRange()).selectNodeContents(t),n.removeAllRanges(),n.addRange(e),n.toString());return n}},279:function(t){function e(){}e.prototype={on:function(t,e,n){var o=this.e||(this.e={});return(o[t]||(o[t]=[])).push({fn:e,ctx:n}),this},once:function(t,e,n){var o=this;function r(){o.off(t,r),e.apply(n,arguments)}return r._=e,this.on(t,r,n)},emit:function(t){for(var e=[].slice.call(arguments,1),n=((this.e||(this.e={}))[t]||[]).slice(),o=0,r=n.length;o<r;o++)n[o].fn.apply(n[o].ctx,e);return this},off:function(t,e){var n=this.e||(this.e={}),o=n[t],r=[];if(o&&e)for(var i=0,u=o.length;i<u;i++)o[i].fn!==e&&o[i].fn._!==e&&r.push(o[i]);return r.length?n[t]=r:delete n[t],this}},t.exports=e,t.exports.TinyEmitter=e}},r={},o.n=function(t){var e=t&&t.__esModule?function(){return t.default}:function(){return t};return o.d(e,{a:e}),e},o.d=function(t,e){for(var n in e)o.o(e,n)&&!o.o(t,n)&&Object.defineProperty(t,n,{enumerable:!0,get:e[n]})},o.o=function(t,e){return Object.prototype.hasOwnProperty.call(t,e)},o(686).default;function o(t){if(r[t])return r[t].exports;var e=r[t]={exports:{}};return n[t](e,e.exports,o),e.exports}var n,r});
\ No newline at end of file
diff --git a/_static/copy-button.svg b/_static/copy-button.svg
new file mode 100644
index 0000000..9c074da
--- /dev/null
+++ b/_static/copy-button.svg
@@ -0,0 +1,5 @@
+<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>
diff --git a/_static/copybutton.css b/_static/copybutton.css
new file mode 100644
index 0000000..f1916ec
--- /dev/null
+++ b/_static/copybutton.css
@@ -0,0 +1,94 @@
+/* Copy buttons */
+button.copybtn {
+    position: absolute;
+    display: flex;
+    top: .3em;
+    right: .3em;
+    width: 1.7em;
+    height: 1.7em;
+	opacity: 0;
+    transition: opacity 0.3s, border .3s, background-color .3s;
+    user-select: none;
+    padding: 0;
+    border: none;
+    outline: none;
+    border-radius: 0.4em;
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
+}
+
+button.copybtn.success {
+    border-color: #22863a;
+    color: #22863a;
+}
+
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
+}
+
+div.highlight  {
+    position: relative;
+}
+
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
+	opacity: 1;
+}
+
+.highlight button.copybtn:hover {
+    background-color: rgb(235, 235, 235);
+}
+
+.highlight button.copybtn:active {
+    background-color: rgb(187, 187, 187);
+}
+
+/**
+ * A minimal CSS-only tooltip copied from:
+ *   https://codepen.io/mildrenben/pen/rVBrpK
+ *
+ * To use, write HTML like the following:
+ *
+ * <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
+ */
+ .o-tooltip--left {
+  position: relative;
+ }
+
+ .o-tooltip--left:after {
+    opacity: 0;
+    visibility: hidden;
+    position: absolute;
+    content: attr(data-tooltip);
+    padding: .2em;
+    font-size: .8em;
+    left: -.2em;
+    background: grey;
+    color: white;
+    white-space: nowrap;
+    z-index: 2;
+    border-radius: 2px;
+    transform: translateX(-102%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+}
+
+.o-tooltip--left:hover:after {
+    display: block;
+    opacity: 1;
+    visibility: visible;
+    transform: translateX(-100%) translateY(0);
+    transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
+    transition-delay: .5s;
+}
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/_static/copybutton.js b/_static/copybutton.js
new file mode 100644
index 0000000..2ea7ff3
--- /dev/null
+++ b/_static/copybutton.js
@@ -0,0 +1,248 @@
+// Localization support
+const messages = {
+  'en': {
+    'copy': 'Copy',
+    'copy_to_clipboard': 'Copy to clipboard',
+    'copy_success': 'Copied!',
+    'copy_failure': 'Failed to copy',
+  },
+  'es' : {
+    'copy': 'Copiar',
+    'copy_to_clipboard': 'Copiar al portapapeles',
+    'copy_success': '¡Copiado!',
+    'copy_failure': 'Error al copiar',
+  },
+  'de' : {
+    'copy': 'Kopieren',
+    'copy_to_clipboard': 'In die Zwischenablage kopieren',
+    'copy_success': 'Kopiert!',
+    'copy_failure': 'Fehler beim Kopieren',
+  },
+  'fr' : {
+    'copy': 'Copier',
+    'copy_to_clipboard': 'Copier dans le presse-papier',
+    'copy_success': 'Copié !',
+    'copy_failure': 'Échec de la copie',
+  },
+  'ru': {
+    'copy': 'Скопировать',
+    'copy_to_clipboard': 'Скопировать в буфер',
+    'copy_success': 'Скопировано!',
+    'copy_failure': 'Не удалось скопировать',
+  },
+  'zh-CN': {
+    'copy': '复制',
+    'copy_to_clipboard': '复制到剪贴板',
+    'copy_success': '复制成功!',
+    'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
+  }
+}
+
+let locale = 'en'
+if( document.documentElement.lang !== undefined
+    && messages[document.documentElement.lang] !== undefined ) {
+  locale = document.documentElement.lang
+}
+
+let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT;
+if (doc_url_root == '#') {
+    doc_url_root = '';
+}
+
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-check" width="44" height="44" viewBox="0 0 24 24" stroke-width="2" stroke="#22863a" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_success']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <path d="M5 12l5 5l10 -10" />
+</svg>`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `<svg xmlns="http://www.w3.org/2000/svg" class="icon icon-tabler icon-tabler-copy" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+  <title>${messages[locale]['copy_to_clipboard']}</title>
+  <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+  <rect x="8" y="8" width="12" height="12" rx="2" />
+  <path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
+</svg>`
+}
+
+/**
+ * Set up copy/paste for code blocks
+ */
+
+const runWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+
+const codeCellId = index => `codecell${index}`
+
+// Clears selected text since ClipboardJS will select the text when copying
+const clearSelection = () => {
+  if (window.getSelection) {
+    window.getSelection().removeAllRanges()
+  } else if (document.selection) {
+    document.selection.empty()
+  }
+}
+
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
+const temporarilyChangeTooltip = (el, oldText, newText) => {
+  el.setAttribute('data-tooltip', newText)
+  el.classList.add('success')
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
+}
+
+// Changes the copy button icon for two seconds, then changes it back
+const temporarilyChangeIcon = (el) => {
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
+}
+
+const addCopyButtonToCodeCells = () => {
+  // If ClipboardJS hasn't loaded, wait a bit and try again. This
+  // happens because we load ClipboardJS asynchronously.
+  if (window.ClipboardJS === undefined) {
+    setTimeout(addCopyButtonToCodeCells, 250)
+    return
+  }
+
+  // Add copybuttons to all of our code cells
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
+  codeCells.forEach((codeCell, index) => {
+    const id = codeCellId(index)
+    codeCell.setAttribute('id', id)
+
+    const clipboardButton = id =>
+    `<button class="copybtn o-tooltip--left" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
+      ${iconCopy}
+    </button>`
+    codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
+  })
+
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
+
+
+var copyTargetText = (trigger) => {
+  var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
+
+  // get filtered text
+  let exclude = '.linenos';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '', false, true, true, true, '', '')
+}
+
+  // Initialize with a callback so we can modify the text before copy
+  const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
+
+  // Update UI with error/success messages
+  clipboard.on('success', event => {
+    clearSelection()
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success'])
+    temporarilyChangeIcon(event.trigger)
+  })
+
+  clipboard.on('error', event => {
+    temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure'])
+  })
+}
+
+runWhenDOMLoaded(addCopyButtonToCodeCells)
\ No newline at end of file
diff --git a/_static/copybutton_funcs.js b/_static/copybutton_funcs.js
new file mode 100644
index 0000000..dbe1aaa
--- /dev/null
+++ b/_static/copybutton_funcs.js
@@ -0,0 +1,73 @@
+function escapeRegExp(string) {
+    return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+}
+
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
+// Callback when a copy button is clicked. Will be passed the node that was clicked
+// should then grab the text and replace pieces of text that shouldn't be used in output
+export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
+    var regexp;
+    var match;
+
+    // Do we check for line continuation characters and "HERE-documents"?
+    var useLineCont = !!lineContinuationChar
+    var useHereDoc = !!hereDocDelim
+
+    // create regexp to capture prompt and remaining line
+    if (isRegexp) {
+        regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
+    } else {
+        regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
+    }
+
+    const outputLines = [];
+    var promptFound = false;
+    var gotLineCont = false;
+    var gotHereDoc = false;
+    const lineGotPrompt = [];
+    for (const line of textContent.split('\n')) {
+        match = line.match(regexp)
+        if (match || gotLineCont || gotHereDoc) {
+            promptFound = regexp.test(line)
+            lineGotPrompt.push(promptFound)
+            if (removePrompts && promptFound) {
+                outputLines.push(match[2])
+            } else {
+                outputLines.push(line)
+            }
+            gotLineCont = line.endsWith(lineContinuationChar) & useLineCont
+            if (line.includes(hereDocDelim) & useHereDoc)
+                gotHereDoc = !gotHereDoc
+        } else if (!onlyCopyPromptLines) {
+            outputLines.push(line)
+        } else if (copyEmptyLines && line.trim() === '') {
+            outputLines.push(line)
+        }
+    }
+
+    // If no lines with the prompt were found then just use original lines
+    if (lineGotPrompt.some(v => v === true)) {
+        textContent = outputLines.join('\n');
+    }
+
+    // Remove a trailing newline to avoid auto-running when pasting
+    if (textContent.endsWith("\n")) {
+        textContent = textContent.slice(0, -1)
+    }
+    return textContent
+}
diff --git a/_static/debug.css b/_static/debug.css
new file mode 100644
index 0000000..74d4aec
--- /dev/null
+++ b/_static/debug.css
@@ -0,0 +1,69 @@
+/*
+  This CSS file should be overridden by the theme authors. It's
+  meant for debugging and developing the skeleton that this theme provides.
+*/
+body {
+  font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif,
+    "Apple Color Emoji", "Segoe UI Emoji";
+  background: lavender;
+}
+.sb-announcement {
+  background: rgb(131, 131, 131);
+}
+.sb-announcement__inner {
+  background: black;
+  color: white;
+}
+.sb-header {
+  background: lightskyblue;
+}
+.sb-header__inner {
+  background: royalblue;
+  color: white;
+}
+.sb-header-secondary {
+  background: lightcyan;
+}
+.sb-header-secondary__inner {
+  background: cornflowerblue;
+  color: white;
+}
+.sb-sidebar-primary {
+  background: lightgreen;
+}
+.sb-main {
+  background: blanchedalmond;
+}
+.sb-main__inner {
+  background: antiquewhite;
+}
+.sb-header-article {
+  background: lightsteelblue;
+}
+.sb-article-container {
+  background: snow;
+}
+.sb-article-main {
+  background: white;
+}
+.sb-footer-article {
+  background: lightpink;
+}
+.sb-sidebar-secondary {
+  background: lightgoldenrodyellow;
+}
+.sb-footer-content {
+  background: plum;
+}
+.sb-footer-content__inner {
+  background: palevioletred;
+}
+.sb-footer {
+  background: pink;
+}
+.sb-footer__inner {
+  background: salmon;
+}
+.sb-article {
+  background: white;
+}
diff --git a/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
index 0000000..eb19f69
--- /dev/null
+++ b/_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
@@ -0,0 +1 @@
+.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) !important}.sd-bg-text-secondary{color:var(--sd-color-secondary-text) !important}button.sd-bg-secondary:focus,button.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}a.sd-bg-secondary:focus,a.sd-bg-secondary:hover{background-color:var(--sd-color-secondary-highlight) !important}.sd-bg-success{background-color:var(--sd-color-success) !important}.sd-bg-text-success{color:var(--sd-color-success-text) !important}button.sd-bg-success:focus,button.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}a.sd-bg-success:focus,a.sd-bg-success:hover{background-color:var(--sd-color-success-highlight) !important}.sd-bg-info{background-color:var(--sd-color-info) !important}.sd-bg-text-info{color:var(--sd-color-info-text) !important}button.sd-bg-info:focus,button.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}a.sd-bg-info:focus,a.sd-bg-info:hover{background-color:var(--sd-color-info-highlight) !important}.sd-bg-warning{background-color:var(--sd-color-warning) !important}.sd-bg-text-warning{color:var(--sd-color-warning-text) !important}button.sd-bg-warning:focus,button.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}a.sd-bg-warning:focus,a.sd-bg-warning:hover{background-color:var(--sd-color-warning-highlight) !important}.sd-bg-danger{background-color:var(--sd-color-danger) !important}.sd-bg-text-danger{color:var(--sd-color-danger-text) !important}button.sd-bg-danger:focus,button.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}a.sd-bg-danger:focus,a.sd-bg-danger:hover{background-color:var(--sd-color-danger-highlight) !important}.sd-bg-light{background-color:var(--sd-color-light) !important}.sd-bg-text-light{color:var(--sd-color-light-text) !important}button.sd-bg-light:focus,button.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}a.sd-bg-light:focus,a.sd-bg-light:hover{background-color:var(--sd-color-light-highlight) !important}.sd-bg-muted{background-color:var(--sd-color-muted) !important}.sd-bg-text-muted{color:var(--sd-color-muted-text) !important}button.sd-bg-muted:focus,button.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}a.sd-bg-muted:focus,a.sd-bg-muted:hover{background-color:var(--sd-color-muted-highlight) !important}.sd-bg-dark{background-color:var(--sd-color-dark) !important}.sd-bg-text-dark{color:var(--sd-color-dark-text) !important}button.sd-bg-dark:focus,button.sd-bg-dark:hover{background-color:var(--sd-color-dark-highlight) !important}a.sd-bg-dark:focus,a.sd-bg-dark:hover{background-color:var(--sd-color-dark-highlight) !important}.sd-bg-black{background-color:var(--sd-color-black) !important}.sd-bg-text-black{color:var(--sd-color-black-text) !important}button.sd-bg-black:focus,button.sd-bg-black:hover{background-color:var(--sd-color-black-highlight) !important}a.sd-bg-black:focus,a.sd-bg-black:hover{background-color:var(--sd-color-black-highlight) !important}.sd-bg-white{background-color:var(--sd-color-white) !important}.sd-bg-text-white{color:var(--sd-color-white-text) !important}button.sd-bg-white:focus,button.sd-bg-white:hover{background-color:var(--sd-color-white-highlight) !important}a.sd-bg-white:focus,a.sd-bg-white:hover{background-color:var(--sd-color-white-highlight) !important}.sd-text-primary,.sd-text-primary>p{color:var(--sd-color-primary) !important}a.sd-text-primary:focus,a.sd-text-primary:hover{color:var(--sd-color-primary-highlight) !important}.sd-text-secondary,.sd-text-secondary>p{color:var(--sd-color-secondary) !important}a.sd-text-secondary:focus,a.sd-text-secondary:hover{color:var(--sd-color-secondary-highlight) !important}.sd-text-success,.sd-text-success>p{color:var(--sd-color-success) !important}a.sd-text-success:focus,a.sd-text-success:hover{color:var(--sd-color-success-highlight) !important}.sd-text-info,.sd-text-info>p{color:var(--sd-color-info) !important}a.sd-text-info:focus,a.sd-text-info:hover{color:var(--sd-color-info-highlight) !important}.sd-text-warning,.sd-text-warning>p{color:var(--sd-color-warning) !important}a.sd-text-warning:focus,a.sd-text-warning:hover{color:var(--sd-color-warning-highlight) !important}.sd-text-danger,.sd-text-danger>p{color:var(--sd-color-danger) !important}a.sd-text-danger:focus,a.sd-text-danger:hover{color:var(--sd-color-danger-highlight) !important}.sd-text-light,.sd-text-light>p{color:var(--sd-color-light) !important}a.sd-text-light:focus,a.sd-text-light:hover{color:var(--sd-color-light-highlight) !important}.sd-text-muted,.sd-text-muted>p{color:var(--sd-color-muted) !important}a.sd-text-muted:focus,a.sd-text-muted:hover{color:var(--sd-color-muted-highlight) !important}.sd-text-dark,.sd-text-dark>p{color:var(--sd-color-dark) !important}a.sd-text-dark:focus,a.sd-text-dark:hover{color:var(--sd-color-dark-highlight) !important}.sd-text-black,.sd-text-black>p{color:var(--sd-color-black) !important}a.sd-text-black:focus,a.sd-text-black:hover{color:var(--sd-color-black-highlight) !important}.sd-text-white,.sd-text-white>p{color:var(--sd-color-white) !important}a.sd-text-white:focus,a.sd-text-white:hover{color:var(--sd-color-white-highlight) !important}.sd-outline-primary{border-color:var(--sd-color-primary) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-primary:focus,a.sd-outline-primary:hover{border-color:var(--sd-color-primary-highlight) !important}.sd-outline-secondary{border-color:var(--sd-color-secondary) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-secondary:focus,a.sd-outline-secondary:hover{border-color:var(--sd-color-secondary-highlight) !important}.sd-outline-success{border-color:var(--sd-color-success) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-success:focus,a.sd-outline-success:hover{border-color:var(--sd-color-success-highlight) !important}.sd-outline-info{border-color:var(--sd-color-info) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-info:focus,a.sd-outline-info:hover{border-color:var(--sd-color-info-highlight) !important}.sd-outline-warning{border-color:var(--sd-color-warning) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-warning:focus,a.sd-outline-warning:hover{border-color:var(--sd-color-warning-highlight) !important}.sd-outline-danger{border-color:var(--sd-color-danger) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-danger:focus,a.sd-outline-danger:hover{border-color:var(--sd-color-danger-highlight) !important}.sd-outline-light{border-color:var(--sd-color-light) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-light:focus,a.sd-outline-light:hover{border-color:var(--sd-color-light-highlight) !important}.sd-outline-muted{border-color:var(--sd-color-muted) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-muted:focus,a.sd-outline-muted:hover{border-color:var(--sd-color-muted-highlight) !important}.sd-outline-dark{border-color:var(--sd-color-dark) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-dark:focus,a.sd-outline-dark:hover{border-color:var(--sd-color-dark-highlight) !important}.sd-outline-black{border-color:var(--sd-color-black) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-black:focus,a.sd-outline-black:hover{border-color:var(--sd-color-black-highlight) !important}.sd-outline-white{border-color:var(--sd-color-white) !important;border-style:solid !important;border-width:1px !important}a.sd-outline-white:focus,a.sd-outline-white:hover{border-color:var(--sd-color-white-highlight) !important}.sd-bg-transparent{background-color:transparent !important}.sd-outline-transparent{border-color:transparent !important}.sd-text-transparent{color:transparent !important}.sd-p-0{padding:0 !important}.sd-pt-0,.sd-py-0{padding-top:0 !important}.sd-pr-0,.sd-px-0{padding-right:0 !important}.sd-pb-0,.sd-py-0{padding-bottom:0 !important}.sd-pl-0,.sd-px-0{padding-left:0 !important}.sd-p-1{padding:.25rem !important}.sd-pt-1,.sd-py-1{padding-top:.25rem !important}.sd-pr-1,.sd-px-1{padding-right:.25rem !important}.sd-pb-1,.sd-py-1{padding-bottom:.25rem !important}.sd-pl-1,.sd-px-1{padding-left:.25rem !important}.sd-p-2{padding:.5rem !important}.sd-pt-2,.sd-py-2{padding-top:.5rem !important}.sd-pr-2,.sd-px-2{padding-right:.5rem !important}.sd-pb-2,.sd-py-2{padding-bottom:.5rem !important}.sd-pl-2,.sd-px-2{padding-left:.5rem !important}.sd-p-3{padding:1rem !important}.sd-pt-3,.sd-py-3{padding-top:1rem !important}.sd-pr-3,.sd-px-3{padding-right:1rem !important}.sd-pb-3,.sd-py-3{padding-bottom:1rem !important}.sd-pl-3,.sd-px-3{padding-left:1rem !important}.sd-p-4{padding:1.5rem !important}.sd-pt-4,.sd-py-4{padding-top:1.5rem !important}.sd-pr-4,.sd-px-4{padding-right:1.5rem !important}.sd-pb-4,.sd-py-4{padding-bottom:1.5rem !important}.sd-pl-4,.sd-px-4{padding-left:1.5rem !important}.sd-p-5{padding:3rem !important}.sd-pt-5,.sd-py-5{padding-top:3rem !important}.sd-pr-5,.sd-px-5{padding-right:3rem !important}.sd-pb-5,.sd-py-5{padding-bottom:3rem !important}.sd-pl-5,.sd-px-5{padding-left:3rem !important}.sd-m-auto{margin:auto !important}.sd-mt-auto,.sd-my-auto{margin-top:auto !important}.sd-mr-auto,.sd-mx-auto{margin-right:auto !important}.sd-mb-auto,.sd-my-auto{margin-bottom:auto !important}.sd-ml-auto,.sd-mx-auto{margin-left:auto !important}.sd-m-0{margin:0 !important}.sd-mt-0,.sd-my-0{margin-top:0 !important}.sd-mr-0,.sd-mx-0{margin-right:0 !important}.sd-mb-0,.sd-my-0{margin-bottom:0 !important}.sd-ml-0,.sd-mx-0{margin-left:0 !important}.sd-m-1{margin:.25rem !important}.sd-mt-1,.sd-my-1{margin-top:.25rem !important}.sd-mr-1,.sd-mx-1{margin-right:.25rem !important}.sd-mb-1,.sd-my-1{margin-bottom:.25rem !important}.sd-ml-1,.sd-mx-1{margin-left:.25rem !important}.sd-m-2{margin:.5rem !important}.sd-mt-2,.sd-my-2{margin-top:.5rem !important}.sd-mr-2,.sd-mx-2{margin-right:.5rem !important}.sd-mb-2,.sd-my-2{margin-bottom:.5rem !important}.sd-ml-2,.sd-mx-2{margin-left:.5rem !important}.sd-m-3{margin:1rem !important}.sd-mt-3,.sd-my-3{margin-top:1rem !important}.sd-mr-3,.sd-mx-3{margin-right:1rem !important}.sd-mb-3,.sd-my-3{margin-bottom:1rem !important}.sd-ml-3,.sd-mx-3{margin-left:1rem !important}.sd-m-4{margin:1.5rem !important}.sd-mt-4,.sd-my-4{margin-top:1.5rem !important}.sd-mr-4,.sd-mx-4{margin-right:1.5rem !important}.sd-mb-4,.sd-my-4{margin-bottom:1.5rem !important}.sd-ml-4,.sd-mx-4{margin-left:1.5rem !important}.sd-m-5{margin:3rem !important}.sd-mt-5,.sd-my-5{margin-top:3rem !important}.sd-mr-5,.sd-mx-5{margin-right:3rem !important}.sd-mb-5,.sd-my-5{margin-bottom:3rem !important}.sd-ml-5,.sd-mx-5{margin-left:3rem !important}.sd-w-25{width:25% !important}.sd-w-50{width:50% !important}.sd-w-75{width:75% !important}.sd-w-100{width:100% !important}.sd-w-auto{width:auto !important}.sd-h-25{height:25% !important}.sd-h-50{height:50% !important}.sd-h-75{height:75% !important}.sd-h-100{height:100% !important}.sd-h-auto{height:auto !important}.sd-d-none{display:none !important}.sd-d-inline{display:inline !important}.sd-d-inline-block{display:inline-block !important}.sd-d-block{display:block !important}.sd-d-grid{display:grid !important}.sd-d-flex-row{display:-ms-flexbox !important;display:flex !important;flex-direction:row !important}.sd-d-flex-column{display:-ms-flexbox !important;display:flex !important;flex-direction:column !important}.sd-d-inline-flex{display:-ms-inline-flexbox !important;display:inline-flex !important}@media(min-width: 576px){.sd-d-sm-none{display:none !important}.sd-d-sm-inline{display:inline !important}.sd-d-sm-inline-block{display:inline-block !important}.sd-d-sm-block{display:block !important}.sd-d-sm-grid{display:grid !important}.sd-d-sm-flex{display:-ms-flexbox !important;display:flex !important}.sd-d-sm-inline-flex{display:-ms-inline-flexbox !important;display:inline-flex !important}}@media(min-width: 768px){.sd-d-md-none{display:none 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diff --git a/_static/design-tabs.js b/_static/design-tabs.js
new file mode 100644
index 0000000..36b38cf
--- /dev/null
+++ b/_static/design-tabs.js
@@ -0,0 +1,27 @@
+var sd_labels_by_text = {};
+
+function ready() {
+  const li = document.getElementsByClassName("sd-tab-label");
+  for (const label of li) {
+    syncId = label.getAttribute("data-sync-id");
+    if (syncId) {
+      label.onclick = onLabelClick;
+      if (!sd_labels_by_text[syncId]) {
+        sd_labels_by_text[syncId] = [];
+      }
+      sd_labels_by_text[syncId].push(label);
+    }
+  }
+}
+
+function onLabelClick() {
+  // Activate other inputs with the same sync id.
+  syncId = this.getAttribute("data-sync-id");
+  for (label of sd_labels_by_text[syncId]) {
+    if (label === this) continue;
+    label.previousElementSibling.checked = true;
+  }
+  window.localStorage.setItem("sphinx-design-last-tab", syncId);
+}
+
+document.addEventListener("DOMContentLoaded", ready, false);
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 0000000..d06a71d
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 0000000..7e4c114
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/_static/favicon_landmarker.svg b/_static/favicon_landmarker.svg
new file mode 100644
index 0000000..62698f1
--- /dev/null
+++ b/_static/favicon_landmarker.svg
@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="500" zoomAndPan="magnify" viewBox="0 0 375 374.999991" height="500" preserveAspectRatio="xMidYMid meet" version="1.0"><defs><clipPath id="6d05e60ad9"><path d="M 37.5 123 L 246 123 L 246 331.890625 L 37.5 331.890625 Z M 37.5 123 " clip-rule="nonzero"/></clipPath><clipPath id="8ff37bc66d"><path d="M 241 43.140625 L 293 43.140625 L 293 129 L 241 129 Z M 241 43.140625 " clip-rule="nonzero"/></clipPath><clipPath id="718461105f"><path d="M 248 85 L 334.5 85 L 334.5 137 L 248 137 Z M 248 85 " clip-rule="nonzero"/></clipPath></defs><g clip-path="url(#6d05e60ad9)"><path fill="#4b9ddc" d="M 245.515625 227.804688 C 245.515625 285.238281 198.953125 331.796875 141.519531 331.796875 C 84.089844 331.796875 37.53125 285.238281 37.53125 227.804688 C 37.53125 170.371094 84.089844 123.8125 141.519531 123.8125 C 198.953125 123.8125 245.515625 170.371094 245.515625 227.804688 " fill-opacity="1" fill-rule="nonzero"/></g><path fill="#ffffff" d="M 232.4375 227.804688 C 232.4375 278.015625 191.734375 318.722656 141.519531 318.722656 C 91.308594 318.722656 50.605469 278.015625 50.605469 227.804688 C 50.605469 177.59375 91.308594 136.890625 141.519531 136.890625 C 191.734375 136.890625 232.4375 177.59375 232.4375 227.804688 " fill-opacity="1" fill-rule="nonzero"/><path fill="#4b9ddc" d="M 213.382812 227.804688 C 213.382812 267.492188 181.207031 299.664062 141.519531 299.664062 C 101.832031 299.664062 69.660156 267.492188 69.660156 227.804688 C 69.660156 188.117188 101.832031 155.941406 141.519531 155.941406 C 181.207031 155.941406 213.382812 188.117188 213.382812 227.804688 " fill-opacity="1" fill-rule="nonzero"/><path fill="#ffffff" d="M 192.878906 227.804688 C 192.878906 256.164062 169.882812 279.160156 141.519531 279.160156 C 113.160156 279.160156 90.164062 256.164062 90.164062 227.804688 C 90.164062 199.441406 113.160156 176.449219 141.519531 176.449219 C 169.882812 176.449219 192.878906 199.441406 192.878906 227.804688 " fill-opacity="1" fill-rule="nonzero"/><path fill="#4b9ddc" d="M 171.410156 227.804688 C 171.410156 244.3125 158.027344 257.695312 141.519531 257.695312 C 125.011719 257.695312 111.632812 244.3125 111.632812 227.804688 C 111.632812 211.296875 125.011719 197.914062 141.519531 197.914062 C 158.027344 197.914062 171.410156 211.296875 171.410156 227.804688 " fill-opacity="1" fill-rule="nonzero"/><path fill="#050419" d="M 142.914062 227.742188 C 141.09375 229.566406 141.09375 232.523438 142.914062 234.347656 C 144.742188 236.171875 147.699219 236.171875 149.519531 234.347656 L 280.128906 104.320312 C 281.949219 102.496094 281.949219 99.539062 280.128906 97.714844 C 278.304688 95.894531 275.347656 95.894531 273.523438 97.714844 L 142.914062 227.742188 " fill-opacity="1" fill-rule="nonzero"/><g clip-path="url(#8ff37bc66d)"><path fill="#ffd034" d="M 241.820312 128.980469 L 242.261719 93.3125 L 292.457031 43.117188 L 292.019531 78.785156 L 241.820312 128.980469 " fill-opacity="1" fill-rule="nonzero"/></g><g clip-path="url(#718461105f)"><path fill="#ffd034" d="M 248.867188 136.023438 L 284.53125 135.585938 L 334.726562 85.386719 L 299.0625 85.824219 L 248.867188 136.023438 " fill-opacity="1" fill-rule="nonzero"/></g></svg>
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diff --git a/_static/images/categorization_detection_approaches.svg b/_static/images/categorization_detection_approaches.svg
new file mode 100644
index 0000000..80de467
--- /dev/null
+++ b/_static/images/categorization_detection_approaches.svg
@@ -0,0 +1,3 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.1//EN" "http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd">
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" version="1.1" width="621px" height="421px" viewBox="-0.5 -0.5 621 421"><defs/><g><path d="M 120 280 L 140 280 L 140 190 L 153.63 190" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 158.88 190 L 151.88 193.5 L 153.63 190 L 151.88 186.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 120 280 L 140 280 L 140 390 L 153.63 390" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 158.88 390 L 151.88 393.5 L 153.63 390 L 151.88 386.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><rect x="0" y="250" width="120" height="60" rx="9" ry="9" fill="#ffd034" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 280px; margin-left: 1px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Landmark detection</div></div></div></foreignObject><text x="60" y="284" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Landmark detection</text></switch></g><path d="M 280 190 L 300 190 L 300 90 L 313.63 90" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 318.88 90 L 311.88 93.5 L 313.63 90 L 311.88 86.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 280 190 L 300 190 L 300 270 L 313.63 270" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 318.88 270 L 311.88 273.5 L 313.63 270 L 311.88 266.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><rect x="160" y="160" width="120" height="60" rx="9" ry="9" fill="#ffd034" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 190px; margin-left: 161px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Heatmap regression</div></div></div></foreignObject><text x="220" y="194" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Heatmap regression</text></switch></g><rect x="160" y="360" width="120" height="60" rx="9" ry="9" fill="#ffd034" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 390px; margin-left: 161px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Coordinate regression</div></div></div></foreignObject><text x="220" y="394" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Coordinate regression</text></switch></g><path d="M 440 90 L 470 90 L 470 30 L 493.63 30" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 498.88 30 L 491.88 33.5 L 493.63 30 L 491.88 26.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><path d="M 440 90 L 470 90 L 470 150 L 493.63 150" fill="none" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="stroke"/><path d="M 498.88 150 L 491.88 153.5 L 493.63 150 L 491.88 146.5 Z" fill="rgb(0, 0, 0)" stroke="rgb(0, 0, 0)" stroke-miterlimit="10" pointer-events="all"/><rect x="320" y="60" width="120" height="60" rx="9" ry="9" fill="#4b9ddc" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; 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align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 30px; margin-left: 501px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Static</div></div></div></foreignObject><text x="560" y="34" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Static</text></switch></g><rect x="500" y="120" width="120" height="60" rx="9" ry="9" fill="#4b9ddc" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 150px; margin-left: 501px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Adaptive</div></div></div></foreignObject><text x="560" y="154" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Adaptive</text></switch></g><rect x="500" y="190" width="120" height="60" rx="9" ry="9" fill="#4b9ddc" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 220px; margin-left: 501px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Uncertainty aware</div></div></div></foreignObject><text x="560" y="224" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Uncertainty aware</text></switch></g><rect x="500" y="310" width="120" height="60" rx="9" ry="9" fill="#4b9ddc" stroke="#000000" pointer-events="all"/><g transform="translate(-0.5 -0.5)"><switch><foreignObject pointer-events="none" width="100%" height="100%" requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility" style="overflow: visible; text-align: left;"><div xmlns="http://www.w3.org/1999/xhtml" style="display: flex; align-items: unsafe center; justify-content: unsafe center; width: 118px; height: 1px; padding-top: 340px; margin-left: 501px;"><div data-drawio-colors="color: #ffffff; " style="box-sizing: border-box; font-size: 0px; text-align: center;"><div style="display: inline-block; font-size: 12px; font-family: Helvetica; color: rgb(255, 255, 255); line-height: 1.2; pointer-events: all; white-space: normal; overflow-wrap: normal;">Uncertainty unaware</div></div></div></foreignObject><text x="560" y="344" fill="#ffffff" font-family="Helvetica" font-size="12px" text-anchor="middle">Uncertainty unaware</text></switch></g></g><switch><g requiredFeatures="http://www.w3.org/TR/SVG11/feature#Extensibility"/><a transform="translate(0,-5)" xlink:href="https://www.drawio.com/doc/faq/svg-export-text-problems" target="_blank"><text text-anchor="middle" font-size="10px" x="50%" y="100%">Text is not SVG - cannot display</text></a></switch></svg>
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diff --git a/_static/images/favicon_landmarker.svg b/_static/images/favicon_landmarker.svg
new file mode 100644
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\ No newline at end of file
diff --git a/_static/language_data.js b/_static/language_data.js
new file mode 100644
index 0000000..250f566
--- /dev/null
+++ b/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, is available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
GIT binary patch
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literal 0
HcmV?d00001

diff --git a/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
new file mode 100644
index 0000000..3356631
--- /dev/null
+++ b/_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css
@@ -0,0 +1,2342 @@
+/* Variables */
+:root {
+  --mystnb-source-bg-color: #f7f7f7;
+  --mystnb-stdout-bg-color: #fcfcfc;
+  --mystnb-stderr-bg-color: #fdd;
+  --mystnb-traceback-bg-color: #fcfcfc;
+  --mystnb-source-border-color: #ccc;
+  --mystnb-source-margin-color: green;
+  --mystnb-stdout-border-color: #f7f7f7;
+  --mystnb-stderr-border-color: #f7f7f7;
+  --mystnb-traceback-border-color: #ffd6d6;
+  --mystnb-hide-prompt-opacity: 70%;
+  --mystnb-source-border-radius: .4em;
+  --mystnb-source-border-width: 1px;
+}
+
+/* Whole cell */
+div.container.cell {
+  padding-left: 0;
+  margin-bottom: 1em;
+}
+
+/* Removing all background formatting so we can control at the div level */
+.cell_input div.highlight,
+.cell_output pre,
+.cell_input pre,
+.cell_output .output {
+  border: none;
+  box-shadow: none;
+}
+
+.cell_output .output pre,
+.cell_input pre {
+  margin: 0px;
+}
+
+/* Input cells */
+div.cell div.cell_input,
+div.cell details.above-input>summary {
+  padding-left: 0em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  background-color: var(--mystnb-source-bg-color);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+  border-radius: var(--mystnb-source-border-radius);
+}
+
+div.cell_input>div,
+div.cell_output div.output>div.highlight {
+  margin: 0em !important;
+  border: none !important;
+}
+
+/* All cell outputs */
+.cell_output {
+  padding-left: 1em;
+  padding-right: 0em;
+  margin-top: 1em;
+}
+
+/* Text outputs from cells */
+.cell_output .output.text_plain,
+.cell_output .output.traceback,
+.cell_output .output.stream,
+.cell_output .output.stderr {
+  margin-top: 1em;
+  margin-bottom: 0em;
+  box-shadow: none;
+}
+
+.cell_output .output.text_plain,
+.cell_output .output.stream {
+  background: var(--mystnb-stdout-bg-color);
+  border: 1px solid var(--mystnb-stdout-border-color);
+}
+
+.cell_output .output.stderr {
+  background: var(--mystnb-stderr-bg-color);
+  border: 1px solid var(--mystnb-stderr-border-color);
+}
+
+.cell_output .output.traceback {
+  background: var(--mystnb-traceback-bg-color);
+  border: 1px solid var(--mystnb-traceback-border-color);
+}
+
+/* Collapsible cell content */
+div.cell details.above-input div.cell_input {
+  border-top-left-radius: 0;
+  border-top-right-radius: 0;
+  border-top: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+}
+
+div.cell div.cell_input.above-output-prompt {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+div.cell details.above-input>summary {
+  border-bottom-left-radius: 0;
+  border-bottom-right-radius: 0;
+  border-bottom: var(--mystnb-source-border-width) var(--mystnb-source-border-color) dashed;
+  padding-left: 1em;
+  margin-bottom: 0;
+}
+
+div.cell details.above-output>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.below-input>summary {
+  background-color: var(--mystnb-source-bg-color);
+  padding-left: 1em;
+  padding-right: 0em;
+  border: var(--mystnb-source-border-width) var(--mystnb-source-border-color) solid;
+  border-top: none;
+  border-bottom-left-radius: var(--mystnb-source-border-radius);
+  border-bottom-right-radius: var(--mystnb-source-border-radius);
+  border-left-color: var(--mystnb-source-margin-color);
+  border-left-width: medium;
+}
+
+div.cell details.hide>summary>span {
+  opacity: var(--mystnb-hide-prompt-opacity);
+}
+
+div.cell details.hide[open]>summary>span.collapsed {
+  display: none;
+}
+
+div.cell details.hide:not([open])>summary>span.expanded {
+  display: none;
+}
+
+@keyframes collapsed-fade-in {
+  0% {
+    opacity: 0;
+  }
+
+  100% {
+    opacity: 1;
+  }
+}
+div.cell details.hide[open]>summary~* {
+  -moz-animation: collapsed-fade-in 0.3s ease-in-out;
+  -webkit-animation: collapsed-fade-in 0.3s ease-in-out;
+  animation: collapsed-fade-in 0.3s ease-in-out;
+}
+
+/* Math align to the left */
+.cell_output .MathJax_Display {
+  text-align: left !important;
+}
+
+/* Pandas tables. Pulled from the Jupyter / nbsphinx CSS */
+div.cell_output table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 1em;
+  table-layout: fixed;
+}
+
+div.cell_output thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+
+div.cell_output tr,
+div.cell_output th,
+div.cell_output td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+
+div.cell_output th {
+  font-weight: bold;
+}
+
+div.cell_output tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+
+div.cell_output tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
+/** source code line numbers **/
+span.linenos {
+  opacity: 0.5;
+}
+
+/* Inline text from `paste` operation */
+
+span.pasted-text {
+  font-weight: bold;
+}
+
+span.pasted-inline img {
+  max-height: 2em;
+}
+
+tbody span.pasted-inline img {
+  max-height: none;
+}
+
+/* Font colors for translated ANSI escape sequences
+Color values are copied from Jupyter Notebook
+https://github.com/jupyter/notebook/blob/52581f8eda9b319eb0390ac77fe5903c38f81e3e/notebook/static/notebook/less/ansicolors.less#L14-L21
+Background colors from
+https://nbsphinx.readthedocs.io/en/latest/code-cells.html#ANSI-Colors
+*/
+div.highlight .-Color-Bold {
+  font-weight: bold;
+}
+
+div.highlight .-Color[class*=-Black] {
+  color: #3E424D
+}
+
+div.highlight .-Color[class*=-Red] {
+  color: #E75C58
+}
+
+div.highlight .-Color[class*=-Green] {
+  color: #00A250
+}
+
+div.highlight .-Color[class*=-Yellow] {
+  color: #DDB62B
+}
+
+div.highlight .-Color[class*=-Blue] {
+  color: #208FFB
+}
+
+div.highlight .-Color[class*=-Magenta] {
+  color: #D160C4
+}
+
+div.highlight .-Color[class*=-Cyan] {
+  color: #60C6C8
+}
+
+div.highlight .-Color[class*=-White] {
+  color: #C5C1B4
+}
+
+div.highlight .-Color[class*=-BGBlack] {
+  background-color: #3E424D
+}
+
+div.highlight .-Color[class*=-BGRed] {
+  background-color: #E75C58
+}
+
+div.highlight .-Color[class*=-BGGreen] {
+  background-color: #00A250
+}
+
+div.highlight .-Color[class*=-BGYellow] {
+  background-color: #DDB62B
+}
+
+div.highlight .-Color[class*=-BGBlue] {
+  background-color: #208FFB
+}
+
+div.highlight .-Color[class*=-BGMagenta] {
+  background-color: #D160C4
+}
+
+div.highlight .-Color[class*=-BGCyan] {
+  background-color: #60C6C8
+}
+
+div.highlight .-Color[class*=-BGWhite] {
+  background-color: #C5C1B4
+}
+
+/* Font colors for 8-bit ANSI */
+
+div.highlight .-Color[class*=-C0] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC0] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C1] {
+  color: #800000
+}
+
+div.highlight .-Color[class*=-BGC1] {
+  background-color: #800000
+}
+
+div.highlight .-Color[class*=-C2] {
+  color: #008000
+}
+
+div.highlight .-Color[class*=-BGC2] {
+  background-color: #008000
+}
+
+div.highlight .-Color[class*=-C3] {
+  color: #808000
+}
+
+div.highlight .-Color[class*=-BGC3] {
+  background-color: #808000
+}
+
+div.highlight .-Color[class*=-C4] {
+  color: #000080
+}
+
+div.highlight .-Color[class*=-BGC4] {
+  background-color: #000080
+}
+
+div.highlight .-Color[class*=-C5] {
+  color: #800080
+}
+
+div.highlight .-Color[class*=-BGC5] {
+  background-color: #800080
+}
+
+div.highlight .-Color[class*=-C6] {
+  color: #008080
+}
+
+div.highlight .-Color[class*=-BGC6] {
+  background-color: #008080
+}
+
+div.highlight .-Color[class*=-C7] {
+  color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-BGC7] {
+  background-color: #C0C0C0
+}
+
+div.highlight .-Color[class*=-C8] {
+  color: #808080
+}
+
+div.highlight .-Color[class*=-BGC8] {
+  background-color: #808080
+}
+
+div.highlight .-Color[class*=-C9] {
+  color: #FF0000
+}
+
+div.highlight .-Color[class*=-BGC9] {
+  background-color: #FF0000
+}
+
+div.highlight .-Color[class*=-C10] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC10] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C11] {
+  color: #FFFF00
+}
+
+div.highlight .-Color[class*=-BGC11] {
+  background-color: #FFFF00
+}
+
+div.highlight .-Color[class*=-C12] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC12] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C13] {
+  color: #FF00FF
+}
+
+div.highlight .-Color[class*=-BGC13] {
+  background-color: #FF00FF
+}
+
+div.highlight .-Color[class*=-C14] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC14] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C15] {
+  color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-BGC15] {
+  background-color: #FFFFFF
+}
+
+div.highlight .-Color[class*=-C16] {
+  color: #000000
+}
+
+div.highlight .-Color[class*=-BGC16] {
+  background-color: #000000
+}
+
+div.highlight .-Color[class*=-C17] {
+  color: #00005F
+}
+
+div.highlight .-Color[class*=-BGC17] {
+  background-color: #00005F
+}
+
+div.highlight .-Color[class*=-C18] {
+  color: #000087
+}
+
+div.highlight .-Color[class*=-BGC18] {
+  background-color: #000087
+}
+
+div.highlight .-Color[class*=-C19] {
+  color: #0000AF
+}
+
+div.highlight .-Color[class*=-BGC19] {
+  background-color: #0000AF
+}
+
+div.highlight .-Color[class*=-C20] {
+  color: #0000D7
+}
+
+div.highlight .-Color[class*=-BGC20] {
+  background-color: #0000D7
+}
+
+div.highlight .-Color[class*=-C21] {
+  color: #0000FF
+}
+
+div.highlight .-Color[class*=-BGC21] {
+  background-color: #0000FF
+}
+
+div.highlight .-Color[class*=-C22] {
+  color: #005F00
+}
+
+div.highlight .-Color[class*=-BGC22] {
+  background-color: #005F00
+}
+
+div.highlight .-Color[class*=-C23] {
+  color: #005F5F
+}
+
+div.highlight .-Color[class*=-BGC23] {
+  background-color: #005F5F
+}
+
+div.highlight .-Color[class*=-C24] {
+  color: #005F87
+}
+
+div.highlight .-Color[class*=-BGC24] {
+  background-color: #005F87
+}
+
+div.highlight .-Color[class*=-C25] {
+  color: #005FAF
+}
+
+div.highlight .-Color[class*=-BGC25] {
+  background-color: #005FAF
+}
+
+div.highlight .-Color[class*=-C26] {
+  color: #005FD7
+}
+
+div.highlight .-Color[class*=-BGC26] {
+  background-color: #005FD7
+}
+
+div.highlight .-Color[class*=-C27] {
+  color: #005FFF
+}
+
+div.highlight .-Color[class*=-BGC27] {
+  background-color: #005FFF
+}
+
+div.highlight .-Color[class*=-C28] {
+  color: #008700
+}
+
+div.highlight .-Color[class*=-BGC28] {
+  background-color: #008700
+}
+
+div.highlight .-Color[class*=-C29] {
+  color: #00875F
+}
+
+div.highlight .-Color[class*=-BGC29] {
+  background-color: #00875F
+}
+
+div.highlight .-Color[class*=-C30] {
+  color: #008787
+}
+
+div.highlight .-Color[class*=-BGC30] {
+  background-color: #008787
+}
+
+div.highlight .-Color[class*=-C31] {
+  color: #0087AF
+}
+
+div.highlight .-Color[class*=-BGC31] {
+  background-color: #0087AF
+}
+
+div.highlight .-Color[class*=-C32] {
+  color: #0087D7
+}
+
+div.highlight .-Color[class*=-BGC32] {
+  background-color: #0087D7
+}
+
+div.highlight .-Color[class*=-C33] {
+  color: #0087FF
+}
+
+div.highlight .-Color[class*=-BGC33] {
+  background-color: #0087FF
+}
+
+div.highlight .-Color[class*=-C34] {
+  color: #00AF00
+}
+
+div.highlight .-Color[class*=-BGC34] {
+  background-color: #00AF00
+}
+
+div.highlight .-Color[class*=-C35] {
+  color: #00AF5F
+}
+
+div.highlight .-Color[class*=-BGC35] {
+  background-color: #00AF5F
+}
+
+div.highlight .-Color[class*=-C36] {
+  color: #00AF87
+}
+
+div.highlight .-Color[class*=-BGC36] {
+  background-color: #00AF87
+}
+
+div.highlight .-Color[class*=-C37] {
+  color: #00AFAF
+}
+
+div.highlight .-Color[class*=-BGC37] {
+  background-color: #00AFAF
+}
+
+div.highlight .-Color[class*=-C38] {
+  color: #00AFD7
+}
+
+div.highlight .-Color[class*=-BGC38] {
+  background-color: #00AFD7
+}
+
+div.highlight .-Color[class*=-C39] {
+  color: #00AFFF
+}
+
+div.highlight .-Color[class*=-BGC39] {
+  background-color: #00AFFF
+}
+
+div.highlight .-Color[class*=-C40] {
+  color: #00D700
+}
+
+div.highlight .-Color[class*=-BGC40] {
+  background-color: #00D700
+}
+
+div.highlight .-Color[class*=-C41] {
+  color: #00D75F
+}
+
+div.highlight .-Color[class*=-BGC41] {
+  background-color: #00D75F
+}
+
+div.highlight .-Color[class*=-C42] {
+  color: #00D787
+}
+
+div.highlight .-Color[class*=-BGC42] {
+  background-color: #00D787
+}
+
+div.highlight .-Color[class*=-C43] {
+  color: #00D7AF
+}
+
+div.highlight .-Color[class*=-BGC43] {
+  background-color: #00D7AF
+}
+
+div.highlight .-Color[class*=-C44] {
+  color: #00D7D7
+}
+
+div.highlight .-Color[class*=-BGC44] {
+  background-color: #00D7D7
+}
+
+div.highlight .-Color[class*=-C45] {
+  color: #00D7FF
+}
+
+div.highlight .-Color[class*=-BGC45] {
+  background-color: #00D7FF
+}
+
+div.highlight .-Color[class*=-C46] {
+  color: #00FF00
+}
+
+div.highlight .-Color[class*=-BGC46] {
+  background-color: #00FF00
+}
+
+div.highlight .-Color[class*=-C47] {
+  color: #00FF5F
+}
+
+div.highlight .-Color[class*=-BGC47] {
+  background-color: #00FF5F
+}
+
+div.highlight .-Color[class*=-C48] {
+  color: #00FF87
+}
+
+div.highlight .-Color[class*=-BGC48] {
+  background-color: #00FF87
+}
+
+div.highlight .-Color[class*=-C49] {
+  color: #00FFAF
+}
+
+div.highlight .-Color[class*=-BGC49] {
+  background-color: #00FFAF
+}
+
+div.highlight .-Color[class*=-C50] {
+  color: #00FFD7
+}
+
+div.highlight .-Color[class*=-BGC50] {
+  background-color: #00FFD7
+}
+
+div.highlight .-Color[class*=-C51] {
+  color: #00FFFF
+}
+
+div.highlight .-Color[class*=-BGC51] {
+  background-color: #00FFFF
+}
+
+div.highlight .-Color[class*=-C52] {
+  color: #5F0000
+}
+
+div.highlight .-Color[class*=-BGC52] {
+  background-color: #5F0000
+}
+
+div.highlight .-Color[class*=-C53] {
+  color: #5F005F
+}
+
+div.highlight .-Color[class*=-BGC53] {
+  background-color: #5F005F
+}
+
+div.highlight .-Color[class*=-C54] {
+  color: #5F0087
+}
+
+div.highlight .-Color[class*=-BGC54] {
+  background-color: #5F0087
+}
+
+div.highlight .-Color[class*=-C55] {
+  color: #5F00AF
+}
+
+div.highlight .-Color[class*=-BGC55] {
+  background-color: #5F00AF
+}
+
+div.highlight .-Color[class*=-C56] {
+  color: #5F00D7
+}
+
+div.highlight .-Color[class*=-BGC56] {
+  background-color: #5F00D7
+}
+
+div.highlight .-Color[class*=-C57] {
+  color: #5F00FF
+}
+
+div.highlight .-Color[class*=-BGC57] {
+  background-color: #5F00FF
+}
+
+div.highlight .-Color[class*=-C58] {
+  color: #5F5F00
+}
+
+div.highlight .-Color[class*=-BGC58] {
+  background-color: #5F5F00
+}
+
+div.highlight .-Color[class*=-C59] {
+  color: #5F5F5F
+}
+
+div.highlight .-Color[class*=-BGC59] {
+  background-color: #5F5F5F
+}
+
+div.highlight .-Color[class*=-C60] {
+  color: #5F5F87
+}
+
+div.highlight .-Color[class*=-BGC60] {
+  background-color: #5F5F87
+}
+
+div.highlight .-Color[class*=-C61] {
+  color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-BGC61] {
+  background-color: #5F5FAF
+}
+
+div.highlight .-Color[class*=-C62] {
+  color: #5F5FD7
+}
+
+div.highlight .-Color[class*=-BGC62] {
+  background-color: #5F5FD7
+}
+
+div.highlight .-Color[class*=-C63] {
+  color: #5F5FFF
+}
+
+div.highlight .-Color[class*=-BGC63] {
+  background-color: #5F5FFF
+}
+
+div.highlight .-Color[class*=-C64] {
+  color: #5F8700
+}
+
+div.highlight .-Color[class*=-BGC64] {
+  background-color: #5F8700
+}
+
+div.highlight .-Color[class*=-C65] {
+  color: #5F875F
+}
+
+div.highlight .-Color[class*=-BGC65] {
+  background-color: #5F875F
+}
+
+div.highlight .-Color[class*=-C66] {
+  color: #5F8787
+}
+
+div.highlight .-Color[class*=-BGC66] {
+  background-color: #5F8787
+}
+
+div.highlight .-Color[class*=-C67] {
+  color: #5F87AF
+}
+
+div.highlight .-Color[class*=-BGC67] {
+  background-color: #5F87AF
+}
+
+div.highlight .-Color[class*=-C68] {
+  color: #5F87D7
+}
+
+div.highlight .-Color[class*=-BGC68] {
+  background-color: #5F87D7
+}
+
+div.highlight .-Color[class*=-C69] {
+  color: #5F87FF
+}
+
+div.highlight .-Color[class*=-BGC69] {
+  background-color: #5F87FF
+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+body[data-theme="dark"] .highlight .s2 { color: #e6db74 } /* Literal.String.Double */
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+body[data-theme="dark"] .highlight .sh { color: #e6db74 } /* Literal.String.Heredoc */
+body[data-theme="dark"] .highlight .si { color: #e6db74 } /* Literal.String.Interpol */
+body[data-theme="dark"] .highlight .sx { color: #e6db74 } /* Literal.String.Other */
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+body[data-theme="dark"] .highlight .ss { color: #e6db74 } /* Literal.String.Symbol */
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+body[data-theme="dark"] .highlight .fm { color: #a6e22e } /* Name.Function.Magic */
+body[data-theme="dark"] .highlight .vc { color: #f8f8f2 } /* Name.Variable.Class */
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+body[data-theme="dark"] .highlight .vi { color: #f8f8f2 } /* Name.Variable.Instance */
+body[data-theme="dark"] .highlight .vm { color: #f8f8f2 } /* Name.Variable.Magic */
+body[data-theme="dark"] .highlight .il { color: #ae81ff } /* Literal.Number.Integer.Long */
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+body:not([data-theme="light"]) .highlight .k { color: #66d9ef } /* Keyword */
+body:not([data-theme="light"]) .highlight .l { color: #ae81ff } /* Literal */
+body:not([data-theme="light"]) .highlight .n { color: #f8f8f2 } /* Name */
+body:not([data-theme="light"]) .highlight .o { color: #ff4689 } /* Operator */
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+body:not([data-theme="light"]) .highlight .c1 { color: #959077 } /* Comment.Single */
+body:not([data-theme="light"]) .highlight .cs { color: #959077 } /* Comment.Special */
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\ No newline at end of file
diff --git a/_static/scripts/furo-extensions.js b/_static/scripts/furo-extensions.js
new file mode 100644
index 0000000..e69de29
diff --git a/_static/scripts/furo.js b/_static/scripts/furo.js
new file mode 100644
index 0000000..32e7c05
--- /dev/null
+++ b/_static/scripts/furo.js
@@ -0,0 +1,3 @@
+/*! For license information please see furo.js.LICENSE.txt */
+(()=>{var t={212:function(t,e,n){var o,r;r=void 0!==n.g?n.g:"undefined"!=typeof window?window:this,o=function(){return function(t){"use strict";var e={navClass:"active",contentClass:"active",nested:!1,nestedClass:"active",offset:0,reflow:!1,events:!0},n=function(t,e,n){if(n.settings.events){var o=new CustomEvent(t,{bubbles:!0,cancelable:!0,detail:n});e.dispatchEvent(o)}},o=function(t){var e=0;if(t.offsetParent)for(;t;)e+=t.offsetTop,t=t.offsetParent;return e>=0?e:0},r=function(t){t&&t.sort((function(t,e){return o(t.content)<o(e.content)?-1:1}))},c=function(e,n,o){var r=e.getBoundingClientRect(),c=function(t){return"function"==typeof t.offset?parseFloat(t.offset()):parseFloat(t.offset)}(n);return o?parseInt(r.bottom,10)<(t.innerHeight||document.documentElement.clientHeight):parseInt(r.top,10)<=c},s=function(){return Math.ceil(t.innerHeight+t.pageYOffset)>=Math.max(document.body.scrollHeight,document.documentElement.scrollHeight,document.body.offsetHeight,document.documentElement.offsetHeight,document.body.clientHeight,document.documentElement.clientHeight)},l=function(t,e){var n=t[t.length-1];if(function(t,e){return!(!s()||!c(t.content,e,!0))}(n,e))return n;for(var o=t.length-1;o>=0;o--)if(c(t[o].content,e))return t[o]},a=function(t,e){if(e.nested&&t.parentNode){var n=t.parentNode.closest("li");n&&(n.classList.remove(e.nestedClass),a(n,e))}},i=function(t,e){if(t){var o=t.nav.closest("li");o&&(o.classList.remove(e.navClass),t.content.classList.remove(e.contentClass),a(o,e),n("gumshoeDeactivate",o,{link:t.nav,content:t.content,settings:e}))}},u=function(t,e){if(e.nested){var n=t.parentNode.closest("li");n&&(n.classList.add(e.nestedClass),u(n,e))}};return function(o,c){var s,a,d,f,m,v={setup:function(){s=document.querySelectorAll(o),a=[],Array.prototype.forEach.call(s,(function(t){var e=document.getElementById(decodeURIComponent(t.hash.substr(1)));e&&a.push({nav:t,content:e})})),r(a)},detect:function(){var t=l(a,m);t?d&&t.content===d.content||(i(d,m),function(t,e){if(t){var o=t.nav.closest("li");o&&(o.classList.add(e.navClass),t.content.classList.add(e.contentClass),u(o,e),n("gumshoeActivate",o,{link:t.nav,content:t.content,settings:e}))}}(t,m),d=t):d&&(i(d,m),d=null)}},h=function(e){f&&t.cancelAnimationFrame(f),f=t.requestAnimationFrame(v.detect)},g=function(e){f&&t.cancelAnimationFrame(f),f=t.requestAnimationFrame((function(){r(a),v.detect()}))};return v.destroy=function(){d&&i(d,m),t.removeEventListener("scroll",h,!1),m.reflow&&t.removeEventListener("resize",g,!1),a=null,s=null,d=null,f=null,m=null},m=function(){var t={};return Array.prototype.forEach.call(arguments,(function(e){for(var n in e){if(!e.hasOwnProperty(n))return;t[n]=e[n]}})),t}(e,c||{}),v.setup(),v.detect(),t.addEventListener("scroll",h,!1),m.reflow&&t.addEventListener("resize",g,!1),v}}(r)}.apply(e,[]),void 0===o||(t.exports=o)}},e={};function n(o){var r=e[o];if(void 0!==r)return r.exports;var c=e[o]={exports:{}};return t[o].call(c.exports,c,c.exports,n),c.exports}n.n=t=>{var e=t&&t.__esModule?()=>t.default:()=>t;return n.d(e,{a:e}),e},n.d=(t,e)=>{for(var o in e)n.o(e,o)&&!n.o(t,o)&&Object.defineProperty(t,o,{enumerable:!0,get:e[o]})},n.g=function(){if("object"==typeof globalThis)return globalThis;try{return this||new Function("return this")()}catch(t){if("object"==typeof window)return window}}(),n.o=(t,e)=>Object.prototype.hasOwnProperty.call(t,e),(()=>{"use strict";var t=n(212),e=n.n(t),o=null,r=null,c=window.pageYOffset||document.documentElement.scrollTop;const s=64;function l(){const t=localStorage.getItem("theme")||"auto";var e;"light"!==(e=window.matchMedia("(prefers-color-scheme: dark)").matches?"auto"===t?"light":"light"==t?"dark":"auto":"auto"===t?"dark":"dark"==t?"light":"auto")&&"dark"!==e&&"auto"!==e&&(console.error(`Got invalid theme mode: ${e}. 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+//# sourceMappingURL=furo.js.map
\ No newline at end of file
diff --git a/_static/scripts/furo.js.LICENSE.txt b/_static/scripts/furo.js.LICENSE.txt
new file mode 100644
index 0000000..1632189
--- /dev/null
+++ b/_static/scripts/furo.js.LICENSE.txt
@@ -0,0 +1,7 @@
+/*!
+ * gumshoejs v5.1.2 (patched by @pradyunsg)
+ * A simple, framework-agnostic scrollspy script.
+ * (c) 2019 Chris Ferdinandi
+ * MIT License
+ * http://github.com/cferdinandi/gumshoe
+ */
diff --git a/_static/scripts/furo.js.map b/_static/scripts/furo.js.map
new file mode 100644
index 0000000..4705302
--- /dev/null
+++ b/_static/scripts/furo.js.map
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get default export","webpack:///webpack/runtime/define property getters","webpack:///webpack/runtime/global","webpack:///webpack/runtime/hasOwnProperty shorthand","webpack:///./src/furo/assets/scripts/furo.js"],"sourcesContent":["/*!\n * gumshoejs v5.1.2 (patched by @pradyunsg)\n * A simple, framework-agnostic scrollspy script.\n * (c) 2019 Chris Ferdinandi\n * MIT License\n * http://github.com/cferdinandi/gumshoe\n */\n\n(function (root, factory) {\n  if (typeof define === \"function\" && define.amd) {\n    define([], function () {\n      return factory(root);\n    });\n  } else if (typeof exports === \"object\") {\n    module.exports = factory(root);\n  } else {\n    root.Gumshoe = factory(root);\n  }\n})(\n  typeof global !== \"undefined\"\n    ? global\n    : typeof window !== \"undefined\"\n      ? window\n      : this,\n  function (window) {\n    \"use strict\";\n\n    //\n    // Defaults\n    //\n\n    var defaults = {\n      // Active classes\n      navClass: \"active\",\n      contentClass: \"active\",\n\n      // Nested navigation\n      nested: false,\n      nestedClass: \"active\",\n\n      // Offset & reflow\n      offset: 0,\n      reflow: false,\n\n      // Event support\n      events: true,\n    };\n\n    //\n    // Methods\n    //\n\n    /**\n     * Merge two or more objects together.\n     * @param   {Object}   objects  The objects to merge together\n     * @returns {Object}            Merged values of defaults and options\n     */\n    var extend = function () {\n      var merged = {};\n      Array.prototype.forEach.call(arguments, function (obj) {\n        for (var key in obj) {\n          if (!obj.hasOwnProperty(key)) return;\n          merged[key] = obj[key];\n        }\n      });\n      return merged;\n    };\n\n    /**\n     * Emit a custom event\n     * @param  {String} type   The event type\n     * @param  {Node}   elem   The element to attach the event to\n     * @param  {Object} detail Any details to pass along with the event\n     */\n    var emitEvent = function (type, elem, detail) {\n      // Make sure events are enabled\n      if (!detail.settings.events) return;\n\n      // Create a new event\n      var event = new CustomEvent(type, {\n        bubbles: true,\n        cancelable: true,\n        detail: detail,\n      });\n\n      // Dispatch the event\n      elem.dispatchEvent(event);\n    };\n\n    /**\n     * Get an element's distance from the top of the Document.\n     * @param  {Node} elem The element\n     * @return {Number}    Distance from the top in pixels\n     */\n    var getOffsetTop = function (elem) {\n      var location = 0;\n      if (elem.offsetParent) {\n        while (elem) {\n          location += elem.offsetTop;\n          elem = elem.offsetParent;\n        }\n      }\n      return location >= 0 ? location : 0;\n    };\n\n    /**\n     * Sort content from first to last in the DOM\n     * @param  {Array} contents The content areas\n     */\n    var sortContents = function (contents) {\n      if (contents) {\n        contents.sort(function (item1, item2) {\n          var offset1 = getOffsetTop(item1.content);\n          var offset2 = getOffsetTop(item2.content);\n          if (offset1 < offset2) return -1;\n          return 1;\n        });\n      }\n    };\n\n    /**\n     * Get the offset to use for calculating position\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Float}           The number of pixels to offset the calculations\n     */\n    var getOffset = function (settings) {\n      // if the offset is a function run it\n      if (typeof settings.offset === \"function\") {\n        return parseFloat(settings.offset());\n      }\n\n      // Otherwise, return it as-is\n      return parseFloat(settings.offset);\n    };\n\n    /**\n     * Get the document element's height\n     * @private\n     * @returns {Number}\n     */\n    var getDocumentHeight = function () {\n      return Math.max(\n        document.body.scrollHeight,\n        document.documentElement.scrollHeight,\n        document.body.offsetHeight,\n        document.documentElement.offsetHeight,\n        document.body.clientHeight,\n        document.documentElement.clientHeight,\n      );\n    };\n\n    /**\n     * Determine if an element is in view\n     * @param  {Node}    elem     The element\n     * @param  {Object}  settings The settings for this instantiation\n     * @param  {Boolean} bottom   If true, check if element is above bottom of viewport instead\n     * @return {Boolean}          Returns true if element is in the viewport\n     */\n    var isInView = function (elem, settings, bottom) {\n      var bounds = elem.getBoundingClientRect();\n      var offset = getOffset(settings);\n      if (bottom) {\n        return (\n          parseInt(bounds.bottom, 10) <\n          (window.innerHeight || document.documentElement.clientHeight)\n        );\n      }\n      return parseInt(bounds.top, 10) <= offset;\n    };\n\n    /**\n     * Check if at the bottom of the viewport\n     * @return {Boolean} If true, page is at the bottom of the viewport\n     */\n    var isAtBottom = function () {\n      if (\n        Math.ceil(window.innerHeight + window.pageYOffset) >=\n        getDocumentHeight()\n      )\n        return true;\n      return false;\n    };\n\n    /**\n     * Check if the last item should be used (even if not at the top of the page)\n     * @param  {Object} item     The last item\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Boolean}         If true, use the last item\n     */\n    var useLastItem = function (item, settings) {\n      if (isAtBottom() && isInView(item.content, settings, true)) return true;\n      return false;\n    };\n\n    /**\n     * Get the active content\n     * @param  {Array}  contents The content areas\n     * @param  {Object} settings The settings for this instantiation\n     * @return {Object}          The content area and matching navigation link\n     */\n    var getActive = function (contents, settings) {\n      var last = contents[contents.length - 1];\n      if (useLastItem(last, settings)) return last;\n      for (var i = contents.length - 1; i >= 0; i--) {\n        if (isInView(contents[i].content, settings)) return contents[i];\n      }\n    };\n\n    /**\n     * Deactivate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested || !nav.parentNode) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class\n      li.classList.remove(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      deactivateNested(li, settings);\n    };\n\n    /**\n     * Deactivate a nav and content area\n     * @param  {Object} items    The nav item and content to deactivate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var deactivate = function (items, settings) {\n      // Make sure there are items to deactivate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Remove the active class from the nav and content\n      li.classList.remove(settings.navClass);\n      items.content.classList.remove(settings.contentClass);\n\n      // Deactivate any parent navs in a nested navigation\n      deactivateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeDeactivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Activate parent navs in a nested navigation\n     * @param  {Node}   nav      The starting navigation element\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activateNested = function (nav, settings) {\n      // If nesting isn't activated, bail\n      if (!settings.nested) return;\n\n      // Get the parent navigation\n      var li = nav.parentNode.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class\n      li.classList.add(settings.nestedClass);\n\n      // Apply recursively to any parent navigation elements\n      activateNested(li, settings);\n    };\n\n    /**\n     * Activate a nav and content area\n     * @param  {Object} items    The nav item and content to activate\n     * @param  {Object} settings The settings for this instantiation\n     */\n    var activate = function (items, settings) {\n      // Make sure there are items to activate\n      if (!items) return;\n\n      // Get the parent list item\n      var li = items.nav.closest(\"li\");\n      if (!li) return;\n\n      // Add the active class to the nav and content\n      li.classList.add(settings.navClass);\n      items.content.classList.add(settings.contentClass);\n\n      // Activate any parent navs in a nested navigation\n      activateNested(li, settings);\n\n      // Emit a custom event\n      emitEvent(\"gumshoeActivate\", li, {\n        link: items.nav,\n        content: items.content,\n        settings: settings,\n      });\n    };\n\n    /**\n     * Create the Constructor object\n     * @param {String} selector The selector to use for navigation items\n     * @param {Object} options  User options and settings\n     */\n    var Constructor = function (selector, options) {\n      //\n      // Variables\n      //\n\n      var publicAPIs = {};\n      var navItems, contents, current, timeout, settings;\n\n      //\n      // Methods\n      //\n\n      /**\n       * Set variables from DOM elements\n       */\n      publicAPIs.setup = function () {\n        // Get all nav items\n        navItems = document.querySelectorAll(selector);\n\n        // Create contents array\n        contents = [];\n\n        // Loop through each item, get it's matching content, and push to the array\n        Array.prototype.forEach.call(navItems, function (item) {\n          // Get the content for the nav item\n          var content = document.getElementById(\n            decodeURIComponent(item.hash.substr(1)),\n          );\n          if (!content) return;\n\n          // Push to the contents array\n          contents.push({\n            nav: item,\n            content: content,\n          });\n        });\n\n        // Sort contents by the order they appear in the DOM\n        sortContents(contents);\n      };\n\n      /**\n       * Detect which content is currently active\n       */\n      publicAPIs.detect = function () {\n        // Get the active content\n        var active = getActive(contents, settings);\n\n        // if there's no active content, deactivate and bail\n        if (!active) {\n          if (current) {\n            deactivate(current, settings);\n            current = null;\n          }\n          return;\n        }\n\n        // If the active content is the one currently active, do nothing\n        if (current && active.content === current.content) return;\n\n        // Deactivate the current content and activate the new content\n        deactivate(current, settings);\n        activate(active, settings);\n\n        // Update the currently active content\n        current = active;\n      };\n\n      /**\n       * Detect the active content on scroll\n       * Debounced for performance\n       */\n      var scrollHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(publicAPIs.detect);\n      };\n\n      /**\n       * Update content sorting on resize\n       * Debounced for performance\n       */\n      var resizeHandler = function (event) {\n        // If there's a timer, cancel it\n        if (timeout) {\n          window.cancelAnimationFrame(timeout);\n        }\n\n        // Setup debounce callback\n        timeout = window.requestAnimationFrame(function () {\n          sortContents(contents);\n          publicAPIs.detect();\n        });\n      };\n\n      /**\n       * Destroy the current instantiation\n       */\n      publicAPIs.destroy = function () {\n        // Undo DOM changes\n        if (current) {\n          deactivate(current, settings);\n        }\n\n        // Remove event listeners\n        window.removeEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.removeEventListener(\"resize\", resizeHandler, false);\n        }\n\n        // Reset variables\n        contents = null;\n        navItems = null;\n        current = null;\n        timeout = null;\n        settings = null;\n      };\n\n      /**\n       * Initialize the current instantiation\n       */\n      var init = function () {\n        // Merge user options into defaults\n        settings = extend(defaults, options || {});\n\n        // Setup variables based on the current DOM\n        publicAPIs.setup();\n\n        // Find the currently active content\n        publicAPIs.detect();\n\n        // Setup event listeners\n        window.addEventListener(\"scroll\", scrollHandler, false);\n        if (settings.reflow) {\n          window.addEventListener(\"resize\", resizeHandler, false);\n        }\n      };\n\n      //\n      // Initialize and return the public APIs\n      //\n\n      init();\n      return publicAPIs;\n    };\n\n    //\n    // Return the Constructor\n    //\n\n    return Constructor;\n  },\n);\n","// The module cache\nvar __webpack_module_cache__ = {};\n\n// The require function\nfunction __webpack_require__(moduleId) {\n\t// Check if module is in cache\n\tvar cachedModule = __webpack_module_cache__[moduleId];\n\tif (cachedModule !== undefined) {\n\t\treturn cachedModule.exports;\n\t}\n\t// Create a new module (and put it into the cache)\n\tvar module = __webpack_module_cache__[moduleId] = {\n\t\t// no module.id needed\n\t\t// no module.loaded needed\n\t\texports: {}\n\t};\n\n\t// Execute the module function\n\t__webpack_modules__[moduleId].call(module.exports, module, module.exports, __webpack_require__);\n\n\t// Return the exports of the module\n\treturn module.exports;\n}\n\n","// getDefaultExport function for compatibility with non-harmony modules\n__webpack_require__.n = (module) => {\n\tvar getter = module && module.__esModule ?\n\t\t() => (module['default']) :\n\t\t() => (module);\n\t__webpack_require__.d(getter, { a: getter });\n\treturn getter;\n};","// define getter functions for harmony exports\n__webpack_require__.d = (exports, definition) => {\n\tfor(var key in definition) {\n\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n\t\t}\n\t}\n};","__webpack_require__.g = (function() {\n\tif (typeof globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n  if (Math.floor(header.getBoundingClientRect().top) == 0) {\n    header.classList.add(\"scrolled\");\n  } else {\n    header.classList.remove(\"scrolled\");\n  }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n  if (positionY < GO_TO_TOP_OFFSET) {\n    document.documentElement.classList.remove(\"show-back-to-top\");\n  } else {\n    if (positionY < lastScrollTop) {\n      document.documentElement.classList.add(\"show-back-to-top\");\n    } else if (positionY > lastScrollTop) {\n      document.documentElement.classList.remove(\"show-back-to-top\");\n    }\n  }\n  lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // top of page.\n  if (positionY == 0) {\n    tocScroll.scrollTo(0, 0);\n  } else if (\n    // bottom of page.\n    Math.ceil(positionY) >=\n    Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n  ) {\n    tocScroll.scrollTo(0, tocScroll.scrollHeight);\n  } else {\n    // somewhere in the middle.\n    const current = document.querySelector(\".scroll-current\");\n    if (current == null) {\n      return;\n    }\n\n    // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n    // // scroll the currently \"active\" heading in toc, into view.\n    // const rect = current.getBoundingClientRect();\n    // if (0 > rect.top) {\n    //   current.scrollIntoView(true); // the argument is \"alignTop\"\n    // } else if (rect.bottom > window.innerHeight) {\n    //   current.scrollIntoView(false);\n    // }\n  }\n}\n\nfunction scrollHandler(positionY) {\n  scrollHandlerForHeader();\n  scrollHandlerForBackToTop(positionY);\n  scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n  if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n    console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n    mode = \"auto\";\n  }\n\n  document.body.dataset.theme = mode;\n  localStorage.setItem(\"theme\", mode);\n  console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n  const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n  const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n  if (prefersDark) {\n    // Auto (dark) -> Light -> Dark\n    if (currentTheme === \"auto\") {\n      setTheme(\"light\");\n    } else if (currentTheme == \"light\") {\n      setTheme(\"dark\");\n    } else {\n      setTheme(\"auto\");\n    }\n  } else {\n    // Auto (light) -> Dark -> Light\n    if (currentTheme === \"auto\") {\n      setTheme(\"dark\");\n    } else if (currentTheme == \"dark\") {\n      setTheme(\"light\");\n    } else {\n      setTheme(\"auto\");\n    }\n  }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n  // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n  let last_known_scroll_position = 0;\n  let ticking = false;\n\n  window.addEventListener(\"scroll\", function (e) {\n    last_known_scroll_position = window.scrollY;\n\n    if (!ticking) {\n      window.requestAnimationFrame(function () {\n        scrollHandler(last_known_scroll_position);\n        ticking = false;\n      });\n\n      ticking = true;\n    }\n  });\n  window.scroll();\n}\n\nfunction setupScrollSpy() {\n  if (tocScroll === null) {\n    return;\n  }\n\n  // Scrollspy -- highlight table on contents, based on scroll\n  new Gumshoe(\".toc-tree a\", {\n    reflow: true,\n    recursive: true,\n    navClass: \"scroll-current\",\n    offset: () => {\n      let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n      return header.getBoundingClientRect().height + 0.5 * rem + 1;\n    },\n  });\n}\n\nfunction setupTheme() {\n  // Attach event handlers for toggling themes\n  const buttons = document.getElementsByClassName(\"theme-toggle\");\n  Array.from(buttons).forEach((btn) => {\n    btn.addEventListener(\"click\", cycleThemeOnce);\n  });\n}\n\nfunction setup() {\n  setupTheme();\n  setupScrollHandler();\n  setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n  document.body.parentNode.classList.remove(\"no-js\");\n\n  header = document.querySelector(\"header\");\n  tocScroll = document.querySelector(\".toc-scroll\");\n\n  setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 0000000..7918c3f
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,574 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      `Search finished, found ${resultCount} page(s) matching the search query.`
+    );
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent !== undefined) return docContent.textContent;
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    // array of [docname, title, anchor, descr, score, filename]
+    let results = [];
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      results.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
+
+    // now sort the results by score (in opposite order of appearance, since the
+    // display function below uses pop() to retrieve items) and then
+    // alphabetically
+    results.sort((a, b) => {
+      const leftScore = a[4];
+      const rightScore = b[4];
+      if (leftScore === rightScore) {
+        // same score: sort alphabetically
+        const leftTitle = a[1].toLowerCase();
+        const rightTitle = b[1].toLowerCase();
+        if (leftTitle === rightTitle) return 0;
+        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+      }
+      return leftScore > rightScore ? 1 : -1;
+    });
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    results = results.reverse();
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        Object.keys(terms).forEach((term) => {
+          if (term.match(escapedWord) && !terms[word])
+            arr.push({ files: terms[term], score: Scorer.partialTerm });
+        });
+        Object.keys(titleTerms).forEach((term) => {
+          if (term.match(escapedWord) && !titleTerms[word])
+            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
+        });
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
+          fileMap.get(file).push(word);
+        else fileMap.set(file, [word]);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/_static/skeleton.css b/_static/skeleton.css
new file mode 100644
index 0000000..467c878
--- /dev/null
+++ b/_static/skeleton.css
@@ -0,0 +1,296 @@
+/* Some sane resets. */
+html {
+  height: 100%;
+}
+
+body {
+  margin: 0;
+  min-height: 100%;
+}
+
+/* All the flexbox magic! */
+body,
+.sb-announcement,
+.sb-content,
+.sb-main,
+.sb-container,
+.sb-container__inner,
+.sb-article-container,
+.sb-footer-content,
+.sb-header,
+.sb-header-secondary,
+.sb-footer {
+  display: flex;
+}
+
+/* These order things vertically */
+body,
+.sb-main,
+.sb-article-container {
+  flex-direction: column;
+}
+
+/* Put elements in the center */
+.sb-header,
+.sb-header-secondary,
+.sb-container,
+.sb-content,
+.sb-footer,
+.sb-footer-content {
+  justify-content: center;
+}
+/* Put elements at the ends */
+.sb-article-container {
+  justify-content: space-between;
+}
+
+/* These elements grow. */
+.sb-main,
+.sb-content,
+.sb-container,
+article {
+  flex-grow: 1;
+}
+
+/* Because padding making this wider is not fun */
+article {
+  box-sizing: border-box;
+}
+
+/* The announcements element should never be wider than the page. */
+.sb-announcement {
+  max-width: 100%;
+}
+
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+  flex-shrink: 0;
+  width: 17rem;
+}
+
+.sb-announcement__inner {
+  justify-content: center;
+
+  box-sizing: border-box;
+  height: 3rem;
+
+  overflow-x: auto;
+  white-space: nowrap;
+}
+
+/* Sidebars, with checkbox-based toggle */
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+  position: fixed;
+  height: 100%;
+  top: 0;
+}
+
+.sb-sidebar-primary {
+  left: -17rem;
+  transition: left 250ms ease-in-out;
+}
+.sb-sidebar-secondary {
+  right: -17rem;
+  transition: right 250ms ease-in-out;
+}
+
+.sb-sidebar-toggle {
+  display: none;
+}
+.sb-sidebar-overlay {
+  position: fixed;
+  top: 0;
+  width: 0;
+  height: 0;
+
+  transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease;
+
+  opacity: 0;
+  background-color: rgba(0, 0, 0, 0.54);
+}
+
+#sb-sidebar-toggle--primary:checked
+  ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"],
+#sb-sidebar-toggle--secondary:checked
+  ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] {
+  width: 100%;
+  height: 100%;
+  opacity: 1;
+  transition: width 0ms ease, height 0ms ease, opacity 250ms ease;
+}
+
+#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary {
+  left: 0;
+}
+#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary {
+  right: 0;
+}
+
+/* Full-width mode */
+.drop-secondary-sidebar-for-full-width-content
+  .hide-when-secondary-sidebar-shown {
+  display: none !important;
+}
+.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary {
+  display: none !important;
+}
+
+/* Mobile views */
+.sb-page-width {
+  width: 100%;
+}
+
+.sb-article-container,
+.sb-footer-content__inner,
+.drop-secondary-sidebar-for-full-width-content .sb-article,
+.drop-secondary-sidebar-for-full-width-content .match-content-width {
+  width: 100vw;
+}
+
+.sb-article,
+.match-content-width {
+  padding: 0 1rem;
+  box-sizing: border-box;
+}
+
+@media (min-width: 32rem) {
+  .sb-article,
+  .match-content-width {
+    padding: 0 2rem;
+  }
+}
+
+/* Tablet views */
+@media (min-width: 42rem) {
+  .sb-article-container {
+    width: auto;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 42rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+@media (min-width: 46rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 46rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+}
+@media (min-width: 50rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 50rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+}
+
+/* Tablet views */
+@media (min-width: 59rem) {
+  .sb-sidebar-secondary {
+    position: static;
+  }
+  .hide-when-secondary-sidebar-shown {
+    display: none !important;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 59rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+@media (min-width: 63rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 63rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+}
+@media (min-width: 67rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+}
+
+/* Desktop views */
+@media (min-width: 76rem) {
+  .sb-sidebar-primary {
+    position: static;
+  }
+  .hide-when-primary-sidebar-shown {
+    display: none !important;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 59rem;
+  }
+  .sb-article,
+  .match-content-width {
+    width: 42rem;
+  }
+}
+
+/* Full desktop views */
+@media (min-width: 80rem) {
+  .sb-article,
+  .match-content-width {
+    width: 46rem;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 63rem;
+  }
+}
+
+@media (min-width: 84rem) {
+  .sb-article,
+  .match-content-width {
+    width: 50rem;
+  }
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+}
+
+@media (min-width: 88rem) {
+  .sb-footer-content__inner,
+  .drop-secondary-sidebar-for-full-width-content .sb-article,
+  .drop-secondary-sidebar-for-full-width-content .match-content-width {
+    width: 67rem;
+  }
+  .sb-page-width {
+    width: 88rem;
+  }
+}
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/_static/styles/furo-extensions.css b/_static/styles/furo-extensions.css
new file mode 100644
index 0000000..bc447f2
--- /dev/null
+++ b/_static/styles/furo-extensions.css
@@ -0,0 +1,2 @@
+#furo-sidebar-ad-placement{padding:var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)}#furo-sidebar-ad-placement .ethical-sidebar{background:var(--color-background-secondary);border:none;box-shadow:none}#furo-sidebar-ad-placement .ethical-sidebar:hover{background:var(--color-background-hover)}#furo-sidebar-ad-placement .ethical-sidebar a{color:var(--color-foreground-primary)}#furo-sidebar-ad-placement .ethical-callout a{color:var(--color-foreground-secondary)!important}#furo-readthedocs-versions{background:transparent;display:block;position:static;width:100%}#furo-readthedocs-versions .rst-versions{background:#1a1c1e}#furo-readthedocs-versions .rst-current-version{background:var(--color-sidebar-item-background);cursor:unset}#furo-readthedocs-versions .rst-current-version:hover{background:var(--color-sidebar-item-background)}#furo-readthedocs-versions .rst-current-version .fa-book{color:var(--color-foreground-primary)}#furo-readthedocs-versions>.rst-other-versions{padding:0}#furo-readthedocs-versions>.rst-other-versions small{opacity:1}#furo-readthedocs-versions .injected .rst-versions{position:unset}#furo-readthedocs-versions:focus-within,#furo-readthedocs-versions:hover{box-shadow:0 0 0 1px var(--color-sidebar-background-border)}#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:hover .rst-current-version{background:#1a1c1e;font-size:inherit;height:auto;line-height:inherit;padding:12px;text-align:right}#furo-readthedocs-versions:focus-within .rst-current-version .fa-book,#furo-readthedocs-versions:hover .rst-current-version .fa-book{color:#fff;float:left}#furo-readthedocs-versions:focus-within .fa-caret-down,#furo-readthedocs-versions:hover .fa-caret-down{display:none}#furo-readthedocs-versions:focus-within .injected,#furo-readthedocs-versions:focus-within .rst-current-version,#furo-readthedocs-versions:focus-within .rst-other-versions,#furo-readthedocs-versions:hover .injected,#furo-readthedocs-versions:hover .rst-current-version,#furo-readthedocs-versions:hover .rst-other-versions{display:block}#furo-readthedocs-versions:focus-within>.rst-current-version,#furo-readthedocs-versions:hover>.rst-current-version{display:none}.highlight:hover button.copybtn{color:var(--color-code-foreground)}.highlight button.copybtn{align-items:center;background-color:var(--color-code-background);border:none;color:var(--color-background-item);cursor:pointer;height:1.25em;opacity:1;right:.5rem;top:.625rem;transition:color .3s,opacity .3s;width:1.25em}.highlight button.copybtn:hover{background-color:var(--color-code-background);color:var(--color-brand-content)}.highlight button.copybtn:after{background-color:transparent;color:var(--color-code-foreground);display:none}.highlight button.copybtn.success{color:#22863a;transition:color 0ms}.highlight button.copybtn.success:after{display:block}.highlight button.copybtn svg{padding:0}body{--sd-color-primary:var(--color-brand-primary);--sd-color-primary-highlight:var(--color-brand-content);--sd-color-primary-text:var(--color-background-primary);--sd-color-shadow:rgba(0,0,0,.05);--sd-color-card-border:var(--color-card-border);--sd-color-card-border-hover:var(--color-brand-content);--sd-color-card-background:var(--color-card-background);--sd-color-card-text:var(--color-foreground-primary);--sd-color-card-header:var(--color-card-marginals-background);--sd-color-card-footer:var(--color-card-marginals-background);--sd-color-tabs-label-active:var(--color-brand-content);--sd-color-tabs-label-hover:var(--color-foreground-muted);--sd-color-tabs-label-inactive:var(--color-foreground-muted);--sd-color-tabs-underline-active:var(--color-brand-content);--sd-color-tabs-underline-hover:var(--color-foreground-border);--sd-color-tabs-underline-inactive:var(--color-background-border);--sd-color-tabs-overline:var(--color-background-border);--sd-color-tabs-underline:var(--color-background-border)}.sd-tab-content{box-shadow:0 -2px var(--sd-color-tabs-overline),0 1px var(--sd-color-tabs-underline)}.sd-card{box-shadow:0 .1rem .25rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)}.sd-shadow-sm{box-shadow:0 .1rem .25rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)!important}.sd-shadow-md{box-shadow:0 .3rem .75rem var(--sd-color-shadow),0 0 .0625rem rgba(0,0,0,.1)!important}.sd-shadow-lg{box-shadow:0 .6rem 1.5rem var(--sd-color-shadow),0 0 .0625rem 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diff --git a/_static/styles/furo-extensions.css.map b/_static/styles/furo-extensions.css.map
new file mode 100644
index 0000000..9ba5637
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diff --git a/_static/styles/furo.css b/_static/styles/furo.css
new file mode 100644
index 0000000..3d29a21
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Correct the line height in all browsers.\n * 2. Prevent adjustments of font size after orientation changes in iOS.\n */\n\nhtml {\n  line-height: 1.15; /* 1 */\n  -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/* Sections\n   ========================================================================== */\n\n/**\n * Remove the margin in all browsers.\n */\n\nbody {\n  margin: 0;\n}\n\n/**\n * Render the `main` element consistently in IE.\n */\n\nmain {\n  display: block;\n}\n\n/**\n * Correct the font size and margin on `h1` elements within `section` and\n * `article` contexts in Chrome, Firefox, and Safari.\n */\n\nh1 {\n  font-size: 2em;\n  margin: 0.67em 0;\n}\n\n/* Grouping content\n   ========================================================================== */\n\n/**\n * 1. Add the correct box sizing in Firefox.\n * 2. Show the overflow in Edge and IE.\n */\n\nhr {\n  box-sizing: content-box; /* 1 */\n  height: 0; /* 1 */\n  overflow: visible; /* 2 */\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\npre {\n  font-family: monospace, monospace; /* 1 */\n  font-size: 1em; /* 2 */\n}\n\n/* Text-level semantics\n   ========================================================================== */\n\n/**\n * Remove the gray background on active links in IE 10.\n */\n\na {\n  background-color: transparent;\n}\n\n/**\n * 1. Remove the bottom border in Chrome 57-\n * 2. Add the correct text decoration in Chrome, Edge, IE, Opera, and Safari.\n */\n\nabbr[title] {\n  border-bottom: none; /* 1 */\n  text-decoration: underline; /* 2 */\n  text-decoration: underline dotted; /* 2 */\n}\n\n/**\n * Add the correct font weight in Chrome, Edge, and Safari.\n */\n\nb,\nstrong {\n  font-weight: bolder;\n}\n\n/**\n * 1. Correct the inheritance and scaling of font size in all browsers.\n * 2. Correct the odd `em` font sizing in all browsers.\n */\n\ncode,\nkbd,\nsamp {\n  font-family: monospace, monospace; /* 1 */\n  font-size: 1em; /* 2 */\n}\n\n/**\n * Add the correct font size in all browsers.\n */\n\nsmall {\n  font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` elements from affecting the line height in\n * all browsers.\n */\n\nsub,\nsup {\n  font-size: 75%;\n  line-height: 0;\n  position: relative;\n  vertical-align: baseline;\n}\n\nsub {\n  bottom: -0.25em;\n}\n\nsup {\n  top: -0.5em;\n}\n\n/* Embedded content\n   ========================================================================== */\n\n/**\n * Remove the border on images inside links in IE 10.\n */\n\nimg {\n  border-style: none;\n}\n\n/* Forms\n   ========================================================================== */\n\n/**\n * 1. Change the font styles in all browsers.\n * 2. Remove the margin in Firefox and Safari.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n  font-family: inherit; /* 1 */\n  font-size: 100%; /* 1 */\n  line-height: 1.15; /* 1 */\n  margin: 0; /* 2 */\n}\n\n/**\n * Show the overflow in IE.\n * 1. Show the overflow in Edge.\n */\n\nbutton,\ninput { /* 1 */\n  overflow: visible;\n}\n\n/**\n * Remove the inheritance of text transform in Edge, Firefox, and IE.\n * 1. Remove the inheritance of text transform in Firefox.\n */\n\nbutton,\nselect { /* 1 */\n  text-transform: none;\n}\n\n/**\n * Correct the inability to style clickable types in iOS and Safari.\n */\n\nbutton,\n[type=\"button\"],\n[type=\"reset\"],\n[type=\"submit\"] {\n  -webkit-appearance: button;\n}\n\n/**\n * Remove the inner border and padding in Firefox.\n */\n\nbutton::-moz-focus-inner,\n[type=\"button\"]::-moz-focus-inner,\n[type=\"reset\"]::-moz-focus-inner,\n[type=\"submit\"]::-moz-focus-inner {\n  border-style: none;\n  padding: 0;\n}\n\n/**\n * Restore the focus styles unset by the previous rule.\n */\n\nbutton:-moz-focusring,\n[type=\"button\"]:-moz-focusring,\n[type=\"reset\"]:-moz-focusring,\n[type=\"submit\"]:-moz-focusring {\n  outline: 1px dotted ButtonText;\n}\n\n/**\n * Correct the padding in Firefox.\n */\n\nfieldset {\n  padding: 0.35em 0.75em 0.625em;\n}\n\n/**\n * 1. Correct the text wrapping in Edge and IE.\n * 2. Correct the color inheritance from `fieldset` elements in IE.\n * 3. Remove the padding so developers are not caught out when they zero out\n *    `fieldset` elements in all browsers.\n */\n\nlegend {\n  box-sizing: border-box; /* 1 */\n  color: inherit; /* 2 */\n  display: table; /* 1 */\n  max-width: 100%; /* 1 */\n  padding: 0; /* 3 */\n  white-space: normal; /* 1 */\n}\n\n/**\n * Add the correct vertical alignment in Chrome, Firefox, and Opera.\n */\n\nprogress {\n  vertical-align: baseline;\n}\n\n/**\n * Remove the default vertical scrollbar in IE 10+.\n */\n\ntextarea {\n  overflow: auto;\n}\n\n/**\n * 1. Add the correct box sizing in IE 10.\n * 2. Remove the padding in IE 10.\n */\n\n[type=\"checkbox\"],\n[type=\"radio\"] {\n  box-sizing: border-box; /* 1 */\n  padding: 0; /* 2 */\n}\n\n/**\n * Correct the cursor style of increment and decrement buttons in Chrome.\n */\n\n[type=\"number\"]::-webkit-inner-spin-button,\n[type=\"number\"]::-webkit-outer-spin-button {\n  height: auto;\n}\n\n/**\n * 1. Correct the odd appearance in Chrome and Safari.\n * 2. Correct the outline style in Safari.\n */\n\n[type=\"search\"] {\n  -webkit-appearance: textfield; /* 1 */\n  outline-offset: -2px; /* 2 */\n}\n\n/**\n * Remove the inner padding in Chrome and Safari on macOS.\n */\n\n[type=\"search\"]::-webkit-search-decoration {\n  -webkit-appearance: none;\n}\n\n/**\n * 1. Correct the inability to style clickable types in iOS and Safari.\n * 2. Change font properties to `inherit` in Safari.\n */\n\n::-webkit-file-upload-button {\n  -webkit-appearance: button; /* 1 */\n  font: inherit; /* 2 */\n}\n\n/* Interactive\n   ========================================================================== */\n\n/*\n * Add the correct display in Edge, IE 10+, and Firefox.\n */\n\ndetails {\n  display: block;\n}\n\n/*\n * Add the correct display in all browsers.\n */\n\nsummary {\n  display: list-item;\n}\n\n/* Misc\n   ========================================================================== */\n\n/**\n * Add the correct display in IE 10+.\n */\n\ntemplate {\n  display: none;\n}\n\n/**\n * Add the correct display in IE 10.\n */\n\n[hidden] {\n  display: none;\n}\n","// This file contains styles for managing print media.\n\n////////////////////////////////////////////////////////////////////////////////\n// Hide elements not relevant to print media.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Hide icon container.\n  .content-icon-container\n    display: none !important\n\n  // Hide showing header links if hovering over when printing.\n  .headerlink\n    display: none !important\n\n  // Hide mobile header.\n  .mobile-header\n    display: none !important\n\n  // Hide navigation links.\n  .related-pages\n    display: none !important\n\n////////////////////////////////////////////////////////////////////////////////\n// Tweaks related to decolorization.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  // Apply a border around code which no longer have a color background.\n  .highlight\n    border: 0.1pt solid var(--color-foreground-border)\n\n////////////////////////////////////////////////////////////////////////////////\n// Avoid page break in some relevant cases.\n////////////////////////////////////////////////////////////////////////////////\n@media print\n  ul, ol, dl, a, table, pre, blockquote\n    page-break-inside: avoid\n\n  h1, h2, h3, h4, h5, h6, img, figure, caption\n    page-break-inside: avoid\n    page-break-after: avoid\n\n  ul, ol, dl\n    page-break-before: avoid\n",".visually-hidden\n  position: absolute !important\n  width: 1px !important\n  height: 1px !important\n  padding: 0 !important\n  margin: -1px !important\n  overflow: hidden !important\n  clip: rect(0,0,0,0) !important\n  white-space: nowrap !important\n  border: 0 !important\n\n:-moz-focusring\n  outline: auto\n","// This file serves as the \"skeleton\" of the theming logic.\n//\n// This contains the bulk of the logic for handling dark mode, color scheme\n// toggling and the handling of color-scheme-specific hiding of elements.\n\nbody\n  @include fonts\n  @include spacing\n  @include icons\n  @include admonitions\n  @include default-admonition(#651fff, \"abstract\")\n  @include default-topic(#14B8A6, \"pencil\")\n\n  @include colors\n\n.only-light\n  display: block !important\nhtml body .only-dark\n  display: none !important\n\n// Ignore dark-mode hints if print media.\n@media not print\n  // Enable dark-mode, if requested.\n  body[data-theme=\"dark\"]\n    @include colors-dark\n\n    html & .only-light\n      display: none !important\n    .only-dark\n      display: block !important\n\n  // Enable dark mode, unless explicitly told to avoid.\n  @media (prefers-color-scheme: dark)\n    body:not([data-theme=\"light\"])\n      @include colors-dark\n\n      html & .only-light\n        display: none !important\n      .only-dark\n        display: block !important\n\n//\n// Theme toggle presentation\n//\nbody[data-theme=\"auto\"]\n  .theme-toggle svg.theme-icon-when-auto\n    display: block\n\nbody[data-theme=\"dark\"]\n  .theme-toggle svg.theme-icon-when-dark\n    display: block\n\nbody[data-theme=\"light\"]\n  .theme-toggle svg.theme-icon-when-light\n    display: block\n","// Fonts used by this theme.\n//\n// There are basically two things here -- using the system font stack and\n// defining sizes for various elements in %ages. We could have also used `em`\n// but %age is easier to reason about for me.\n\n@mixin fonts {\n  // These are adapted from https://systemfontstack.com/\n  --font-stack: -apple-system, BlinkMacSystemFont, Segoe UI, Helvetica, Arial,\n    sans-serif, Apple Color Emoji, Segoe UI Emoji;\n  --font-stack--monospace: \"SFMono-Regular\", Menlo, Consolas, Monaco,\n    Liberation Mono, Lucida Console, monospace;\n\n  --font-size--normal: 100%;\n  --font-size--small: 87.5%;\n  --font-size--small--2: 81.25%;\n  --font-size--small--3: 75%;\n  --font-size--small--4: 62.5%;\n\n  // Sidebar\n  --sidebar-caption-font-size: var(--font-size--small--2);\n  --sidebar-item-font-size: var(--font-size--small);\n  --sidebar-search-input-font-size: var(--font-size--small);\n\n  // Table of Contents\n  --toc-font-size: var(--font-size--small--3);\n  --toc-font-size--mobile: var(--font-size--normal);\n  --toc-title-font-size: var(--font-size--small--4);\n\n  // Admonitions\n  //\n  // These aren't defined in terms of %ages, since nesting these is permitted.\n  --admonition-font-size: 0.8125rem;\n  --admonition-title-font-size: 0.8125rem;\n\n  // Code\n  --code-font-size: var(--font-size--small--2);\n\n  // API\n  --api-font-size: var(--font-size--small);\n}\n","// Spacing for various elements on the page\n//\n// If the user wants to tweak things in a certain way, they are permitted to.\n// They also have to deal with the consequences though!\n\n@mixin spacing {\n  // Header!\n  --header-height: calc(\n    var(--sidebar-item-line-height) + 4 * #{var(--sidebar-item-spacing-vertical)}\n  );\n  --header-padding: 0.5rem;\n\n  // Sidebar\n  --sidebar-tree-space-above: 1.5rem;\n  --sidebar-caption-space-above: 1rem;\n\n  --sidebar-item-line-height: 1rem;\n  --sidebar-item-spacing-vertical: 0.5rem;\n  --sidebar-item-spacing-horizontal: 1rem;\n  --sidebar-item-height: calc(\n    var(--sidebar-item-line-height) + 2 *#{var(--sidebar-item-spacing-vertical)}\n  );\n\n  --sidebar-expander-width: var(--sidebar-item-height); // be square\n\n  --sidebar-search-space-above: 0.5rem;\n  --sidebar-search-input-spacing-vertical: 0.5rem;\n  --sidebar-search-input-spacing-horizontal: 0.5rem;\n  --sidebar-search-input-height: 1rem;\n  --sidebar-search-icon-size: var(--sidebar-search-input-height);\n\n  // Table of Contents\n  --toc-title-padding: 0.25rem 0;\n  --toc-spacing-vertical: 1.5rem;\n  --toc-spacing-horizontal: 1.5rem;\n  --toc-item-spacing-vertical: 0.4rem;\n  --toc-item-spacing-horizontal: 1rem;\n}\n","// Expose theme icons as CSS variables.\n\n$icons: (\n  // Adapted from tabler-icons\n  //    url: https://tablericons.com/\n  \"search\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"currentColor\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><circle cx=\"10\" cy=\"10\" r=\"7\" /><line x1=\"21\" y1=\"21\" x2=\"15\" y2=\"15\" /></svg>'),\n  // Factored out from mkdocs-material on 24-Aug-2020.\n  //    url: https://squidfunk.github.io/mkdocs-material/reference/admonitions/\n  \"pencil\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z\"/></svg>'),\n  \"abstract\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M4 5h16v2H4V5m0 4h16v2H4V9m0 4h16v2H4v-2m0 4h10v2H4v-2z\"/></svg>'),\n  \"info\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 9h-2V7h2m0 10h-2v-6h2m-1-9A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10A10 10 0 0012 2z\"/></svg>'),\n  \"flame\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M17.55 11.2c-.23-.3-.5-.56-.76-.82-.65-.6-1.4-1.03-2.03-1.66C13.3 7.26 13 4.85 13.91 3c-.91.23-1.75.75-2.45 1.32-2.54 2.08-3.54 5.75-2.34 8.9.04.1.08.2.08.33 0 .22-.15.42-.35.5-.22.1-.46.04-.64-.12a.83.83 0 01-.15-.17c-1.1-1.43-1.28-3.48-.53-5.12C5.89 10 5 12.3 5.14 14.47c.04.5.1 1 .27 1.5.14.6.4 1.2.72 1.73 1.04 1.73 2.87 2.97 4.84 3.22 2.1.27 4.35-.12 5.96-1.6 1.8-1.66 2.45-4.32 1.5-6.6l-.13-.26c-.2-.46-.47-.87-.8-1.25l.05-.01m-3.1 6.3c-.28.24-.73.5-1.08.6-1.1.4-2.2-.16-2.87-.82 1.19-.28 1.89-1.16 2.09-2.05.17-.8-.14-1.46-.27-2.23-.12-.74-.1-1.37.18-2.06.17.38.37.76.6 1.06.76 1 1.95 1.44 2.2 2.8.04.14.06.28.06.43.03.82-.32 1.72-.92 2.27h.01z\"/></svg>'),\n  \"question\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M15.07 11.25l-.9.92C13.45 12.89 13 13.5 13 15h-2v-.5c0-1.11.45-2.11 1.17-2.83l1.24-1.26c.37-.36.59-.86.59-1.41a2 2 0 00-2-2 2 2 0 00-2 2H8a4 4 0 014-4 4 4 0 014 4 3.2 3.2 0 01-.93 2.25M13 19h-2v-2h2M12 2A10 10 0 002 12a10 10 0 0010 10 10 10 0 0010-10c0-5.53-4.5-10-10-10z\"/></svg>'),\n  \"warning\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M13 14h-2v-4h2m0 8h-2v-2h2M1 21h22L12 2 1 21z\"/></svg>'),\n  \"failure\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M12 2c5.53 0 10 4.47 10 10s-4.47 10-10 10S2 17.53 2 12 6.47 2 12 2m3.59 5L12 10.59 8.41 7 7 8.41 10.59 12 7 15.59 8.41 17 12 13.41 15.59 17 17 15.59 13.41 12 17 8.41 15.59 7z\"/></svg>'),\n  \"spark\":\n    url('data:image/svg+xml;charset=utf-8,<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\"><path d=\"M11.5 20l4.86-9.73H13V4l-5 9.73h3.5V20M12 2c2.75 0 5.1 1 7.05 2.95C21 6.9 22 9.25 22 12s-1 5.1-2.95 7.05C17.1 21 14.75 22 12 22s-5.1-1-7.05-2.95C3 17.1 2 14.75 2 12s1-5.1 2.95-7.05C6.9 3 9.25 2 12 2z\"/></svg>')\n);\n\n@mixin icons {\n  @each $name, $glyph in $icons {\n    --icon-#{$name}: #{$glyph};\n  }\n}\n","// Admonitions\n\n// Structure of these is:\n//    admonition-class: color \"icon-name\";\n//\n// The colors are translated into CSS variables below. The icons are\n// used directly in the main declarations to set the `mask-image` in\n// the title.\n\n// prettier-ignore\n$admonitions: (\n  // Each of these has an reST directives for it.\n  \"caution\":         #ff9100 \"spark\",\n  \"warning\":         #ff9100 \"warning\",\n  \"danger\":          #ff5252 \"spark\",\n  \"attention\":       #ff5252 \"warning\",\n  \"error\":           #ff5252 \"failure\",\n  \"hint\":            #00c852 \"question\",\n  \"tip\":             #00c852 \"info\",\n  \"important\":       #00bfa5 \"flame\",\n  \"note\":            #00b0ff \"pencil\",\n  \"seealso\":         #448aff \"info\",\n  \"admonition-todo\": #808080 \"pencil\"\n);\n\n@mixin default-admonition($color, $icon-name) {\n  --color-admonition-title: #{$color};\n  --color-admonition-title-background: #{rgba($color, 0.2)};\n\n  --icon-admonition-default: var(--icon-#{$icon-name});\n}\n\n@mixin default-topic($color, $icon-name) {\n  --color-topic-title: #{$color};\n  --color-topic-title-background: #{rgba($color, 0.2)};\n\n  --icon-topic-default: var(--icon-#{$icon-name});\n}\n\n@mixin admonitions {\n  @each $name, $values in $admonitions {\n    --color-admonition-title--#{$name}: #{nth($values, 1)};\n    --color-admonition-title-background--#{$name}: #{rgba(\n        nth($values, 1),\n        0.2\n      )};\n  }\n}\n","// Colors used throughout this theme.\n//\n// The aim is to give the user more control. Thus, instead of hard-coding colors\n// in various parts of the stylesheet, the approach taken is to define all\n// colors as CSS variables and reusing them in all the places.\n//\n// `colors-dark` depends on `colors` being included at a lower specificity.\n\n@mixin colors {\n  --color-problematic: #b30000;\n\n  // Base Colors\n  --color-foreground-primary: black; // for main text and headings\n  --color-foreground-secondary: #5a5c63; // for secondary text\n  --color-foreground-muted: #646776; // for muted text\n  --color-foreground-border: #878787; // for content borders\n\n  --color-background-primary: white; // for content\n  --color-background-secondary: #f8f9fb; // for navigation + ToC\n  --color-background-hover: #efeff4ff; // for navigation-item hover\n  --color-background-hover--transparent: #efeff400;\n  --color-background-border: #eeebee; // for UI borders\n  --color-background-item: #ccc; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2962ff;\n  --color-brand-content: #2a5adf;\n\n  // API documentation\n  --color-api-background: var(--color-background-hover--transparent);\n  --color-api-background-hover: var(--color-background-hover);\n  --color-api-overall: var(--color-foreground-secondary);\n  --color-api-name: var(--color-problematic);\n  --color-api-pre-name: var(--color-problematic);\n  --color-api-paren: var(--color-foreground-secondary);\n  --color-api-keyword: var(--color-foreground-primary);\n  --color-highlight-on-target: #ffffcc;\n\n  // Inline code background\n  --color-inline-code-background: var(--color-background-secondary);\n\n  // Highlighted text (search)\n  --color-highlighted-background: #ddeeff;\n  --color-highlighted-text: var(--color-foreground-primary);\n\n  // GUI Labels\n  --color-guilabel-background: #ddeeff80;\n  --color-guilabel-border: #bedaf580;\n  --color-guilabel-text: var(--color-foreground-primary);\n\n  // Admonitions!\n  --color-admonition-background: transparent;\n\n  //////////////////////////////////////////////////////////////////////////////\n  // Everything below this should be one of:\n  // - var(...)\n  // - *-gradient(...)\n  // - special literal values (eg: transparent, none)\n  //////////////////////////////////////////////////////////////////////////////\n\n  // Tables\n  --color-table-header-background: var(--color-background-secondary);\n  --color-table-border: var(--color-background-border);\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: transparent;\n  --color-card-marginals-background: var(--color-background-secondary);\n\n  // Header\n  --color-header-background: var(--color-background-primary);\n  --color-header-border: var(--color-background-border);\n  --color-header-text: var(--color-foreground-primary);\n\n  // Sidebar (left)\n  --color-sidebar-background: var(--color-background-secondary);\n  --color-sidebar-background-border: var(--color-background-border);\n\n  --color-sidebar-brand-text: var(--color-foreground-primary);\n  --color-sidebar-caption-text: var(--color-foreground-muted);\n  --color-sidebar-link-text: var(--color-foreground-secondary);\n  --color-sidebar-link-text--top-level: var(--color-brand-primary);\n\n  --color-sidebar-item-background: var(--color-sidebar-background);\n  --color-sidebar-item-background--current: var(\n    --color-sidebar-item-background\n  );\n  --color-sidebar-item-background--hover: linear-gradient(\n    90deg,\n    var(--color-background-hover--transparent) 0%,\n    var(--color-background-hover) var(--sidebar-item-spacing-horizontal),\n    var(--color-background-hover) 100%\n  );\n\n  --color-sidebar-item-expander-background: transparent;\n  --color-sidebar-item-expander-background--hover: var(\n    --color-background-hover\n  );\n\n  --color-sidebar-search-text: var(--color-foreground-primary);\n  --color-sidebar-search-background: var(--color-background-secondary);\n  --color-sidebar-search-background--focus: var(--color-background-primary);\n  --color-sidebar-search-border: var(--color-background-border);\n  --color-sidebar-search-icon: var(--color-foreground-muted);\n\n  // Table of Contents (right)\n  --color-toc-background: var(--color-background-primary);\n  --color-toc-title-text: var(--color-foreground-muted);\n  --color-toc-item-text: var(--color-foreground-secondary);\n  --color-toc-item-text--hover: var(--color-foreground-primary);\n  --color-toc-item-text--active: var(--color-brand-primary);\n\n  // Actual page contents\n  --color-content-foreground: var(--color-foreground-primary);\n  --color-content-background: transparent;\n\n  // Links\n  --color-link: var(--color-brand-content);\n  --color-link--hover: var(--color-brand-content);\n  --color-link-underline: var(--color-background-border);\n  --color-link-underline--hover: var(--color-foreground-border);\n}\n\n@mixin colors-dark {\n  --color-problematic: #ee5151;\n\n  // Base Colors\n  --color-foreground-primary: #ffffffcc; // for main text and headings\n  --color-foreground-secondary: #9ca0a5; // for secondary text\n  --color-foreground-muted: #81868d; // for muted text\n  --color-foreground-border: #666666; // for content borders\n\n  --color-background-primary: #131416; // for content\n  --color-background-secondary: #1a1c1e; // for navigation + ToC\n  --color-background-hover: #1e2124ff; // for navigation-item hover\n  --color-background-hover--transparent: #1e212400;\n  --color-background-border: #303335; // for UI borders\n  --color-background-item: #444; // for \"background\" items (eg: copybutton)\n\n  // Announcements\n  --color-announcement-background: #000000dd;\n  --color-announcement-text: #eeebee;\n\n  // Brand colors\n  --color-brand-primary: #2b8cee;\n  --color-brand-content: #368ce2;\n\n  // Highlighted text (search)\n  --color-highlighted-background: #083563;\n\n  // GUI Labels\n  --color-guilabel-background: #08356380;\n  --color-guilabel-border: #13395f80;\n\n  // API documentation\n  --color-api-keyword: var(--color-foreground-secondary);\n  --color-highlight-on-target: #333300;\n\n  // Admonitions\n  --color-admonition-background: #18181a;\n\n  // Cards\n  --color-card-border: var(--color-background-secondary);\n  --color-card-background: #18181a;\n  --color-card-marginals-background: var(--color-background-hover);\n}\n","// This file contains the styling for making the content throughout the page,\n// including fonts, paragraphs, headings and spacing among these elements.\n\nbody\n  font-family: var(--font-stack)\npre,\ncode,\nkbd,\nsamp\n  font-family: var(--font-stack--monospace)\n\n// Make fonts look slightly nicer.\nbody\n  -webkit-font-smoothing: antialiased\n  -moz-osx-font-smoothing: grayscale\n\n// Line height from Bootstrap 4.1\narticle\n  line-height: 1.5\n\n//\n// Headings\n//\nh1,\nh2,\nh3,\nh4,\nh5,\nh6\n  line-height: 1.25\n  font-weight: bold\n\n  border-radius: 0.5rem\n  margin-top: 0.5rem\n  margin-bottom: 0.5rem\n  margin-left: -0.5rem\n  margin-right: -0.5rem\n  padding-left: 0.5rem\n  padding-right: 0.5rem\n\n  + p\n    margin-top: 0\n\nh1\n  font-size: 2.5em\n  margin-top: 1.75rem\n  margin-bottom: 1rem\nh2\n  font-size: 2em\n  margin-top: 1.75rem\nh3\n  font-size: 1.5em\nh4\n  font-size: 1.25em\nh5\n  font-size: 1.125em\nh6\n  font-size: 1em\n\nsmall\n  opacity: 75%\n  font-size: 80%\n\n// Paragraph\np\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n\n// Horizontal rules\nhr.docutils\n  height: 1px\n  padding: 0\n  margin: 2rem 0\n  background-color: var(--color-background-border)\n  border: 0\n\n.centered\n  text-align: center\n\n// Links\na\n  text-decoration: underline\n\n  color: var(--color-link)\n  text-decoration-color: var(--color-link-underline)\n\n  &:hover\n    color: var(--color-link--hover)\n    text-decoration-color: var(--color-link-underline--hover)\n  &.muted-link\n    color: inherit\n    &:hover\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline--hover)\n","// This file contains the styles for the overall layouting of the documentation\n// skeleton, including the responsive changes as well as sidebar toggles.\n//\n// This is implemented as a mobile-last design, which isn't ideal, but it is\n// reasonably good-enough and I got pretty tired by the time I'd finished this\n// to move the rules around to fix this. Shouldn't take more than 3-4 hours,\n// if you know what you're doing tho.\n\n// HACK: Not all browsers account for the scrollbar width in media queries.\n// This results in horizontal scrollbars in the breakpoint where we go\n// from displaying everything to hiding the ToC. We accomodate for this by\n// adding a bit of padding to the TOC drawer, disabling the horizontal\n// scrollbar and allowing the scrollbars to cover the padding.\n// https://www.456bereastreet.com/archive/201301/media_query_width_and_vertical_scrollbars/\n\n// HACK: Always having the scrollbar visible, prevents certain browsers from\n// causing the content to stutter horizontally between taller-than-viewport and\n// not-taller-than-viewport pages.\n\nhtml\n  overflow-x: hidden\n  overflow-y: scroll\n  scroll-behavior: smooth\n\n.sidebar-scroll, .toc-scroll, article[role=main] *\n  // Override Firefox scrollbar style\n  scrollbar-width: thin\n  scrollbar-color: var(--color-foreground-border) transparent\n\n  // Override Chrome scrollbar styles\n  &::-webkit-scrollbar\n    width: 0.25rem\n    height: 0.25rem\n  &::-webkit-scrollbar-thumb\n    background-color: var(--color-foreground-border)\n    border-radius: 0.125rem\n\n//\n// Overalls\n//\nhtml,\nbody\n  height: 100%\n  color: var(--color-foreground-primary)\n  background: var(--color-background-primary)\n\narticle\n  color: var(--color-content-foreground)\n  background: var(--color-content-background)\n  overflow-wrap: break-word\n\n.page\n  display: flex\n  // fill the viewport for pages with little content.\n  min-height: 100%\n\n.mobile-header\n  width: 100%\n  height: var(--header-height)\n  background-color: var(--color-header-background)\n  color: var(--color-header-text)\n  border-bottom: 1px solid var(--color-header-border)\n\n  // Looks like sub-script/super-script have this, and we need this to\n  // be \"on top\" of those.\n  z-index: 10\n\n  // We don't show the header on large screens.\n  display: none\n\n  // Add shadow when scrolled\n  &.scrolled\n    border-bottom: none\n    box-shadow: 0 0 0.2rem rgba(0, 0, 0, 0.1), 0 0.2rem 0.4rem rgba(0, 0, 0, 0.2)\n\n  .header-center\n    a\n      color: var(--color-header-text)\n      text-decoration: none\n\n.main\n  display: flex\n  flex: 1\n\n// Sidebar (left) also covers the entire left portion of screen.\n.sidebar-drawer\n  box-sizing: border-box\n\n  border-right: 1px solid var(--color-sidebar-background-border)\n  background: var(--color-sidebar-background)\n\n  display: flex\n  justify-content: flex-end\n  // These next two lines took me two days to figure out.\n  width: calc((100% - #{$full-width}) / 2 + #{$sidebar-width})\n  min-width: $sidebar-width\n\n// Scroll-along sidebars\n.sidebar-container,\n.toc-drawer\n  box-sizing: border-box\n  width: $sidebar-width\n\n.toc-drawer\n  background: var(--color-toc-background)\n  // See HACK described on top of this document\n  padding-right: 1rem\n\n.sidebar-sticky,\n.toc-sticky\n  position: sticky\n  top: 0\n  height: min(100%, 100vh)\n  height: 100vh\n\n  display: flex\n  flex-direction: column\n\n.sidebar-scroll,\n.toc-scroll\n  flex-grow: 1\n  flex-shrink: 1\n\n  overflow: auto\n  scroll-behavior: smooth\n\n// Central items.\n.content\n  padding: 0 $content-padding\n  width: $content-width\n\n  display: flex\n  flex-direction: column\n  justify-content: space-between\n\n.icon\n  display: inline-block\n  height: 1rem\n  width: 1rem\n  svg\n    width: 100%\n    height: 100%\n\n//\n// Accommodate announcement banner\n//\n.announcement\n  background-color: var(--color-announcement-background)\n  color: var(--color-announcement-text)\n\n  height: var(--header-height)\n  display: flex\n  align-items: center\n  overflow-x: auto\n  & + .page\n    min-height: calc(100% - var(--header-height))\n\n.announcement-content\n  box-sizing: border-box\n  padding: 0.5rem\n  min-width: 100%\n  white-space: nowrap\n  text-align: center\n\n  a\n    color: var(--color-announcement-text)\n    text-decoration-color: var(--color-announcement-text)\n\n    &:hover\n      color: var(--color-announcement-text)\n      text-decoration-color: var(--color-link--hover)\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for theme\n////////////////////////////////////////////////////////////////////////////////\n.no-js .theme-toggle-container  // don't show theme toggle if there's no JS\n  display: none\n\n.theme-toggle-container\n  vertical-align: middle\n\n.theme-toggle\n  cursor: pointer\n  border: none\n  padding: 0\n  background: transparent\n\n.theme-toggle svg\n  vertical-align: middle\n  height: 1rem\n  width: 1rem\n  color: var(--color-foreground-primary)\n  display: none\n\n.theme-toggle-header\n  float: left\n  padding: 1rem 0.5rem\n\n////////////////////////////////////////////////////////////////////////////////\n// Toggles for elements\n////////////////////////////////////////////////////////////////////////////////\n.toc-overlay-icon, .nav-overlay-icon\n  display: none\n  cursor: pointer\n\n  .icon\n    color: var(--color-foreground-secondary)\n    height: 1rem\n    width: 1rem\n\n.toc-header-icon, .nav-overlay-icon\n  // for when we set display: flex\n  justify-content: center\n  align-items: center\n\n.toc-content-icon\n  height: 1.5rem\n  width: 1.5rem\n\n.content-icon-container\n  float: right\n  display: flex\n  margin-top: 1.5rem\n  margin-left: 1rem\n  margin-bottom: 1rem\n  gap: 0.5rem\n\n  .edit-this-page svg\n    color: inherit\n    height: 1rem\n    width: 1rem\n\n.sidebar-toggle\n  position: absolute\n  display: none\n// <debugging things>\n.sidebar-toggle[name=\"__toc\"]\n  left: 20px\n.sidebar-toggle:checked\n  left: 40px\n// </debugging things>\n\n.overlay\n  position: fixed\n  top: 0\n  width: 0\n  height: 0\n\n  transition: width 0ms, height 0ms, opacity 250ms ease-out\n\n  opacity: 0\n  background-color: rgba(0, 0, 0, 0.54)\n.sidebar-overlay\n  z-index: 20\n.toc-overlay\n  z-index: 40\n\n// Keep things on top and smooth.\n.sidebar-drawer\n  z-index: 30\n  transition: left 250ms ease-in-out\n.toc-drawer\n  z-index: 50\n  transition: right 250ms ease-in-out\n\n// Show the Sidebar\n#__navigation:checked\n  & ~ .sidebar-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .sidebar-drawer\n      top: 0\n      left: 0\n      // Show the toc sidebar\n#__toc:checked\n  & ~ .toc-overlay\n    width: 100%\n    height: 100%\n    opacity: 1\n  & ~ .page\n    .toc-drawer\n      top: 0\n      right: 0\n\n////////////////////////////////////////////////////////////////////////////////\n// Back to top\n////////////////////////////////////////////////////////////////////////////////\n.back-to-top\n  text-decoration: none\n\n  display: none\n  position: fixed\n  left: 0\n  top: 1rem\n  padding: 0.5rem\n  padding-right: 0.75rem\n  border-radius: 1rem\n  font-size: 0.8125rem\n\n  background: var(--color-background-primary)\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), #6b728080 0px 0px 1px 0px\n\n  z-index: 10\n\n  margin-left: 50%\n  transform: translateX(-50%)\n  svg\n    height: 1rem\n    width: 1rem\n    fill: currentColor\n    display: inline-block\n\n  span\n    margin-left: 0.25rem\n\n  .show-back-to-top &\n    display: flex\n    align-items: center\n\n////////////////////////////////////////////////////////////////////////////////\n// Responsive layouting\n////////////////////////////////////////////////////////////////////////////////\n// Make things a bit bigger on bigger screens.\n@media (min-width: $full-width + $sidebar-width)\n  html\n    font-size: 110%\n\n@media (max-width: $full-width)\n  // Collapse \"toc\" into the icon.\n  .toc-content-icon\n    display: flex\n  .toc-drawer\n    position: fixed\n    height: 100vh\n    top: 0\n    right: -$sidebar-width\n    border-left: 1px solid var(--color-background-muted)\n  .toc-tree\n    border-left: none\n    font-size: var(--toc-font-size--mobile)\n\n  // Accomodate for a changed content width.\n  .sidebar-drawer\n    width: calc((100% - #{$full-width - $sidebar-width}) / 2 + #{$sidebar-width})\n\n@media (max-width: $full-width - $sidebar-width)\n  // Collapse \"navigation\".\n  .nav-overlay-icon\n    display: flex\n  .sidebar-drawer\n    position: fixed\n    height: 100vh\n    width: $sidebar-width\n\n    top: 0\n    left: -$sidebar-width\n\n  // Swap which icon is visible.\n  .toc-header-icon\n    display: flex\n  .toc-content-icon, .theme-toggle-content\n    display: none\n  .theme-toggle-header\n    display: block\n\n  // Show the header.\n  .mobile-header\n    position: sticky\n    top: 0\n    display: flex\n    justify-content: space-between\n    align-items: center\n\n    .header-left,\n    .header-right\n      display: flex\n      height: var(--header-height)\n      padding: 0 var(--header-padding)\n      label\n        height: 100%\n        width: 100%\n        user-select: none\n\n  .nav-overlay-icon .icon,\n  .theme-toggle svg\n    height: 1.25rem\n    width: 1.25rem\n\n  // Add a scroll margin for the content\n  :target\n    scroll-margin-top: var(--header-height)\n\n  // Show back-to-top below the header\n  .back-to-top\n    top: calc(var(--header-height) + 0.5rem)\n\n  // Center the page, and accommodate for the header.\n  .page\n    flex-direction: column\n    justify-content: center\n  .content\n    margin-left: auto\n    margin-right: auto\n\n@media (max-width: $content-width + 2* $content-padding)\n  // Content should respect window limits.\n  .content\n    width: 100%\n    overflow-x: auto\n\n@media (max-width: $content-width)\n  .content\n    padding: 0 $content-padding--small\n    // Don't float sidebars to the right.\n  article aside.sidebar\n    float: none\n    width: 100%\n    margin: 1rem 0\n","//\n// The design here is strongly inspired by mkdocs-material.\n.admonition, .topic\n  margin: 1rem auto\n  padding: 0 0.5rem 0.5rem 0.5rem\n\n  background: var(--color-admonition-background)\n\n  border-radius: 0.2rem\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  font-size: var(--admonition-font-size)\n\n  overflow: hidden\n  page-break-inside: avoid\n\n  // First element should have no margin, since the title has it.\n  > :nth-child(2)\n    margin-top: 0\n\n  // Last item should have no margin, since we'll control that w/ padding\n  > :last-child\n    margin-bottom: 0\n\n.admonition p.admonition-title,\np.topic-title\n  position: relative\n  margin: 0 -0.5rem 0.5rem\n  padding-left: 2rem\n  padding-right: .5rem\n  padding-top: .4rem\n  padding-bottom: .4rem\n\n  font-weight: 500\n  font-size: var(--admonition-title-font-size)\n  line-height: 1.3\n\n    // Our fancy icon\n  &::before\n    content: \"\"\n    position: absolute\n    left: 0.5rem\n    width: 1rem\n    height: 1rem\n\n// Default styles\np.admonition-title\n  background-color: var(--color-admonition-title-background)\n  &::before\n    background-color: var(--color-admonition-title)\n    mask-image: var(--icon-admonition-default)\n    mask-repeat: no-repeat\n\np.topic-title\n  background-color: var(--color-topic-title-background)\n  &::before\n    background-color: var(--color-topic-title)\n    mask-image: var(--icon-topic-default)\n    mask-repeat: no-repeat\n\n//\n// Variants\n//\n.admonition\n  border-left: 0.2rem solid var(--color-admonition-title)\n\n  @each $type, $value in $admonitions\n    &.#{$type}\n      border-left-color: var(--color-admonition-title--#{$type})\n      > .admonition-title\n        background-color: var(--color-admonition-title-background--#{$type})\n        &::before\n          background-color: var(--color-admonition-title--#{$type})\n          mask-image: var(--icon-#{nth($value, 2)})\n\n.admonition-todo > .admonition-title\n  text-transform: uppercase\n","// This file stylizes the API documentation (stuff generated by autodoc). It's\n// deeply nested due to how autodoc structures the HTML without enough classes\n// to select the relevant items.\n\n// API docs!\ndl[class]:not(.option-list):not(.field-list):not(.footnote):not(.glossary):not(.simple)\n  // Tweak the spacing of all the things!\n  dd\n    margin-left: 2rem\n    > :first-child\n      margin-top: 0.125rem\n    > :last-child\n      margin-bottom: 0.75rem\n\n  // This is used for the arguments\n  .field-list\n    margin-bottom: 0.75rem\n\n    // \"Headings\" (like \"Parameters\" and \"Return\")\n    > dt\n      text-transform: uppercase\n      font-size: var(--font-size--small)\n\n    dd:empty\n      margin-bottom: 0.5rem\n    dd > ul\n      margin-left: -1.2rem\n      > li\n        > p:nth-child(2)\n          margin-top: 0\n        // When the last-empty-paragraph follows a paragraph, it doesn't need\n        // to augument the existing spacing.\n        > p + p:last-child:empty\n          margin-top: 0\n          margin-bottom: 0\n\n  // Colorize the elements\n  > dt\n    color: var(--color-api-overall)\n\n.sig:not(.sig-inline)\n  font-weight: bold\n\n  font-size: var(--api-font-size)\n  font-family: var(--font-stack--monospace)\n\n  margin-left: -0.25rem\n  margin-right: -0.25rem\n  padding-top: 0.25rem\n  padding-bottom: 0.25rem\n  padding-right: 0.5rem\n\n  // These are intentionally em, to properly match the font size.\n  padding-left: 3em\n  text-indent: -2.5em\n\n  border-radius: 0.25rem\n\n  background: var(--color-api-background)\n  transition: background 100ms ease-out\n\n  &:hover\n    background: var(--color-api-background-hover)\n\n  // adjust the size of the [source] link on the right.\n  a.reference\n    .viewcode-link\n      font-weight: normal\n      width: 3.5rem\n\nem.property\n  font-style: normal\n  &:first-child\n    color: var(--color-api-keyword)\n.sig-name\n  color: var(--color-api-name)\n.sig-prename\n  font-weight: normal\n  color: var(--color-api-pre-name)\n.sig-paren\n  color: var(--color-api-paren)\n.sig-param\n  font-style: normal\n\n.versionmodified\n  font-style: italic\ndiv.versionadded, div.versionchanged, div.deprecated\n  p\n    margin-top: 0.125rem\n    margin-bottom: 0.125rem\n\n// Align the [docs] and [source] to the right.\n.viewcode-link, .viewcode-back\n  float: right\n  text-align: right\n",".line-block\n  margin-top: 0.5rem\n  margin-bottom: 0.75rem\n  .line-block\n    margin-top: 0rem\n    margin-bottom: 0rem\n    padding-left: 1rem\n","// Captions\narticle p.caption,\ntable > caption,\n.code-block-caption\n  font-size: var(--font-size--small)\n  text-align: center\n\n// Caption above a TOCTree\n.toctree-wrapper.compound\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n\n    text-align: initial\n    margin-bottom: 0\n\n  > ul\n    margin-top: 0\n    margin-bottom: 0\n","// Inline code\ncode.literal, .sig-inline\n  background: var(--color-inline-code-background)\n  border-radius: 0.2em\n  // Make the font smaller, and use padding to recover.\n  font-size: var(--font-size--small--2)\n  padding: 0.1em 0.2em\n\n  pre.literal-block &\n    font-size: inherit\n    padding: 0\n\n  p &\n    border: 1px solid var(--color-background-border)\n\n.sig-inline\n  font-family: var(--font-stack--monospace)\n\n// Code and Literal Blocks\n$code-spacing-vertical: 0.625rem\n$code-spacing-horizontal: 0.875rem\n\n// Wraps every literal block + line numbers.\ndiv[class*=\" highlight-\"],\ndiv[class^=\"highlight-\"]\n  margin: 1em 0\n  display: flex\n\n  .table-wrapper\n    margin: 0\n    padding: 0\n\npre\n  margin: 0\n  padding: 0\n  overflow: auto\n\n  // Needed to have more specificity than pygments' \"pre\" selector. :(\n  article[role=\"main\"] .highlight &\n    line-height: 1.5\n\n  &.literal-block,\n  .highlight &\n    font-size: var(--code-font-size)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n\n  // Make it look like all the other blocks.\n  &.literal-block\n    margin-top: 1rem\n    margin-bottom: 1rem\n\n    border-radius: 0.2rem\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n\n// All code is always contained in this.\n.highlight\n  width: 100%\n  border-radius: 0.2rem\n\n  // Make line numbers and prompts un-selectable.\n  .gp, span.linenos\n    user-select: none\n    pointer-events: none\n\n  // Expand the line-highlighting.\n  .hll\n    display: block\n    margin-left: -$code-spacing-horizontal\n    margin-right: -$code-spacing-horizontal\n    padding-left: $code-spacing-horizontal\n    padding-right: $code-spacing-horizontal\n\n/* Make code block captions be nicely integrated */\n.code-block-caption\n  display: flex\n  padding: $code-spacing-vertical $code-spacing-horizontal\n\n  border-radius: 0.25rem\n  border-bottom-left-radius: 0\n  border-bottom-right-radius: 0\n  font-weight: 300\n  border-bottom: 1px solid\n\n  background-color: var(--color-code-background)\n  color: var(--color-code-foreground)\n  border-color: var(--color-background-border)\n\n  + div[class]\n    margin-top: 0\n    pre\n      border-top-left-radius: 0\n      border-top-right-radius: 0\n\n// When `html_codeblock_linenos_style` is table.\n.highlighttable\n  width: 100%\n  display: block\n  tbody\n    display: block\n\n  tr\n    display: flex\n\n  // Line numbers\n  td.linenos\n    background-color: var(--color-code-background)\n    color: var(--color-code-foreground)\n    padding: $code-spacing-vertical $code-spacing-horizontal\n    padding-right: 0\n    border-top-left-radius: 0.2rem\n    border-bottom-left-radius: 0.2rem\n\n  .linenodiv\n    padding-right: $code-spacing-horizontal\n    font-size: var(--code-font-size)\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n\n  // Actual code\n  td.code\n    padding: 0\n    display: block\n    flex: 1\n    overflow: hidden\n\n    .highlight\n      border-top-left-radius: 0\n      border-bottom-left-radius: 0\n\n// When `html_codeblock_linenos_style` is inline.\n.highlight\n  span.linenos\n    display: inline-block\n    padding-left: 0\n    padding-right: $code-spacing-horizontal\n    margin-right: $code-spacing-horizontal\n    box-shadow: -0.0625rem 0 var(--color-foreground-border) inset\n","// Inline Footnote Reference\n.footnote-reference\n  font-size: var(--font-size--small--4)\n  vertical-align: super\n\n// Definition list, listing the content of each note.\n// docutils <= 0.17\ndl.footnote.brackets\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\n  display: grid\n  grid-template-columns: max-content auto\n  dt\n    margin: 0\n    > .fn-backref\n      margin-left: 0.25rem\n\n    &:after\n      content: \":\"\n\n    .brackets\n      &:before\n        content: \"[\"\n      &:after\n        content: \"]\"\n\n  dd\n    margin: 0\n    padding: 0 1rem\n\n// docutils >= 0.18\naside.footnote\n  font-size: var(--font-size--small)\n  color: var(--color-foreground-secondary)\n\naside.footnote > span,\ndiv.citation > span\n  float: left\n  font-weight: 500\n  padding-right: 0.25rem\n\naside.footnote > p,\ndiv.citation > p\n  margin-left: 2rem\n","//\n// Figures\n//\nimg\n  box-sizing: border-box\n  max-width: 100%\n  height: auto\n\narticle\n  figure, .figure\n    border-radius: 0.2rem\n\n    margin: 0\n    :last-child\n      margin-bottom: 0\n\n  .align-left\n    float: left\n    clear: left\n    margin: 0 1rem 1rem\n\n  .align-right\n    float: right\n    clear: right\n    margin: 0 1rem 1rem\n\n  .align-default,\n  .align-center\n    display: block\n    text-align: center\n    margin-left: auto\n    margin-right: auto\n\n  // WELL, table needs to be stylised like a table.\n  table.align-default\n    display: table\n    text-align: initial\n",".genindex-jumpbox, .domainindex-jumpbox\n  border-top: 1px solid var(--color-background-border)\n  border-bottom: 1px solid var(--color-background-border)\n  padding: 0.25rem\n\n.genindex-section, .domainindex-section\n  h2\n    margin-top: 0.75rem\n    margin-bottom: 0.5rem\n  ul\n    margin-top: 0\n    margin-bottom: 0\n","ul,\nol\n  padding-left: 1.2rem\n\n  // Space lists out like paragraphs\n  margin-top: 1rem\n  margin-bottom: 1rem\n  // reduce margins within li.\n  li\n    > p:first-child\n      margin-top: 0.25rem\n      margin-bottom: 0.25rem\n\n    > p:last-child\n      margin-top: 0.25rem\n\n    > ul,\n    > ol\n      margin-top: 0.5rem\n      margin-bottom: 0.5rem\n\nol\n  &.arabic\n    list-style: decimal\n  &.loweralpha\n    list-style: lower-alpha\n  &.upperalpha\n    list-style: upper-alpha\n  &.lowerroman\n    list-style: lower-roman\n  &.upperroman\n    list-style: upper-roman\n\n// Don't space lists out when they're \"simple\" or in a `.. toctree::`\n.simple,\n.toctree-wrapper\n  li\n    > ul,\n    > ol\n      margin-top: 0\n      margin-bottom: 0\n\n// Definition Lists\n.field-list,\n.option-list,\ndl:not([class]),\ndl.simple,\ndl.footnote,\ndl.glossary\n  dt\n    font-weight: 500\n    margin-top: 0.25rem\n    + dt\n      margin-top: 0\n\n    .classifier::before\n      content: \":\"\n      margin-left: 0.2rem\n      margin-right: 0.2rem\n\n  dd\n    > p:first-child,\n    ul\n      margin-top: 0.125rem\n\n    ul\n      margin-bottom: 0.125rem\n",".math-wrapper\n  width: 100%\n  overflow-x: auto\n\ndiv.math\n  position: relative\n  text-align: center\n\n  .headerlink,\n  &:focus .headerlink\n    display: none\n\n  &:hover .headerlink\n    display: inline-block\n\n  span.eqno\n    position: absolute\n    right: 0.5rem\n    top: 50%\n    transform: translate(0, -50%)\n    z-index: 1\n","// Abbreviations\nabbr[title]\n  cursor: help\n\n// \"Problematic\" content, as identified by Sphinx\n.problematic\n  color: var(--color-problematic)\n\n// Keyboard / Mouse \"instructions\"\nkbd:not(.compound)\n  margin: 0 0.2rem\n  padding: 0 0.2rem\n  border-radius: 0.2rem\n  border: 1px solid var(--color-foreground-border)\n  color: var(--color-foreground-primary)\n  vertical-align: text-bottom\n\n  font-size: var(--font-size--small--3)\n  display: inline-block\n\n  box-shadow: 0 0.0625rem 0 rgba(0, 0, 0, 0.2), inset 0 0 0 0.125rem var(--color-background-primary)\n\n  background-color: var(--color-background-secondary)\n\n// Blockquote\nblockquote\n  border-left: 4px solid var(--color-background-border)\n  background: var(--color-background-secondary)\n\n  margin-left: 0\n  margin-right: 0\n  padding: 0.5rem 1rem\n\n  .attribution\n    font-weight: 600\n    text-align: right\n\n  &.pull-quote,\n  &.highlights\n    font-size: 1.25em\n\n  &.epigraph,\n  &.pull-quote\n    border-left-width: 0\n    border-radius: 0.5rem\n\n  &.highlights\n    border-left-width: 0\n    background: transparent\n\n// Center align embedded-in-text images\np .reference img\n  vertical-align: middle\n","p.rubric\n  line-height: 1.25\n  font-weight: bold\n  font-size: 1.125em\n\n  // For Numpy-style documentation that's got rubrics within it.\n  // https://github.com/pradyunsg/furo/discussions/505\n  dd &\n    line-height: inherit\n    font-weight: inherit\n\n    font-size: var(--font-size--small)\n    text-transform: uppercase\n","article .sidebar\n  float: right\n  clear: right\n  width: 30%\n\n  margin-left: 1rem\n  margin-right: 0\n\n  border-radius: 0.2rem\n  background-color: var(--color-background-secondary)\n  border: var(--color-background-border) 1px solid\n\n  > *\n    padding-left: 1rem\n    padding-right: 1rem\n\n  > ul, > ol  // lists need additional padding, because bullets.\n    padding-left: 2.2rem\n\n  .sidebar-title\n    margin: 0\n    padding: 0.5rem 1rem\n    border-bottom: var(--color-background-border) 1px solid\n\n    font-weight: 500\n\n// TODO: subtitle\n// TODO: dedicated variables?\n",".table-wrapper\n  width: 100%\n  overflow-x: auto\n  margin-top: 1rem\n  margin-bottom: 0.5rem\n  padding: 0.2rem 0.2rem 0.75rem\n\ntable.docutils\n  border-radius: 0.2rem\n  border-spacing: 0\n  border-collapse: collapse\n\n  box-shadow: 0 0.2rem 0.5rem rgba(0, 0, 0, 0.05), 0 0 0.0625rem rgba(0, 0, 0, 0.1)\n\n  th\n    background: var(--color-table-header-background)\n\n  td,\n  th\n    // Space things out properly\n    padding: 0 0.25rem\n\n    // Get the borders looking just-right.\n    border-left: 1px solid var(--color-table-border)\n    border-right: 1px solid var(--color-table-border)\n    border-bottom: 1px solid var(--color-table-border)\n\n    p\n      margin: 0.25rem\n\n    &:first-child\n      border-left: none\n    &:last-child\n      border-right: none\n\n    // MyST-parser tables set these classes for control of column alignment\n    &.text-left\n      text-align: left\n    &.text-right\n      text-align: right\n    &.text-center\n      text-align: center\n",":target\n  scroll-margin-top: 0.5rem\n\n@media (max-width: $full-width - $sidebar-width)\n  :target\n    scroll-margin-top: calc(0.5rem + var(--header-height))\n\n  // When a heading is selected\n  section > span:target\n    scroll-margin-top: calc(0.8rem + var(--header-height))\n\n// Permalinks\n.headerlink\n  font-weight: 100\n  user-select: none\n\nh1,\nh2,\nh3,\nh4,\nh5,\nh6,\ndl dt,\np.caption,\nfigcaption p,\ntable > caption,\n.code-block-caption\n  > .headerlink\n    margin-left: 0.5rem\n    visibility: hidden\n  &:hover > .headerlink\n    visibility: visible\n\n  // Don't change to link-like, if someone adds the contents directive.\n  > .toc-backref\n    color: inherit\n    text-decoration-line: none\n\n// Figure and table captions are special.\nfigure:hover > figcaption > p > .headerlink,\ntable:hover > caption > .headerlink\n  visibility: visible\n\n:target >,  // Regular section[id] style anchors\nspan:target ~ // Non-regular span[id] style \"extra\" anchors\n  h1,\n  h2,\n  h3,\n  h4,\n  h5,\n  h6\n    &:nth-of-type(1)\n      background-color: var(--color-highlight-on-target)\n      // .headerlink\n      //   visibility: visible\n      code.literal\n        background-color: transparent\n\ntable:target > caption,\nfigure:target\n  background-color: var(--color-highlight-on-target)\n\n// Inline page contents\n.this-will-duplicate-information-and-it-is-still-useful-here li :target\n  background-color: var(--color-highlight-on-target)\n\n// Code block permalinks\n.literal-block-wrapper:target .code-block-caption\n  background-color: var(--color-highlight-on-target)\n\n// When a definition list item is selected\n//\n//   There isn't really an alternative to !important here, due to the\n//   high-specificity of API documentation's selector.\ndt:target\n  background-color: var(--color-highlight-on-target) !important\n\n// When a footnote reference is selected\n.footnote > dt:target + dd,\n.footnote-reference:target\n  background-color: var(--color-highlight-on-target)\n",".guilabel\n  background-color: var(--color-guilabel-background)\n  border: 1px solid var(--color-guilabel-border)\n  color: var(--color-guilabel-text)\n\n  padding: 0 0.3em\n  border-radius: 0.5em\n  font-size: 0.9em\n","// This file contains the styles used for stylizing the footer that's shown\n// below the content.\n\nfooter\n  font-size: var(--font-size--small)\n  display: flex\n  flex-direction: column\n\n  margin-top: 2rem\n\n// Bottom of page information\n.bottom-of-page\n  display: flex\n  align-items: center\n  justify-content: space-between\n\n  margin-top: 1rem\n  padding-top: 1rem\n  padding-bottom: 1rem\n\n  color: var(--color-foreground-secondary)\n  border-top: 1px solid var(--color-background-border)\n\n  line-height: 1.5\n\n  @media (max-width: $content-width)\n    text-align: center\n    flex-direction: column-reverse\n    gap: 0.25rem\n\n  .left-details\n    font-size: var(--font-size--small)\n\n  .right-details\n    display: flex\n    flex-direction: column\n    gap: 0.25rem\n    text-align: right\n\n  .icons\n    display: flex\n    justify-content: flex-end\n    gap: 0.25rem\n    font-size: 1rem\n\n    a\n      text-decoration: none\n\n    svg,\n    img\n      font-size: 1.125rem\n      height: 1em\n      width: 1em\n\n// Next/Prev page information\n.related-pages\n  a\n    display: flex\n    align-items: center\n\n    text-decoration: none\n    &:hover .page-info .title\n      text-decoration: underline\n      color: var(--color-link)\n      text-decoration-color: var(--color-link-underline)\n\n    svg.furo-related-icon,\n    svg.furo-related-icon > use\n      flex-shrink: 0\n\n      color: var(--color-foreground-border)\n\n      width: 0.75rem\n      height: 0.75rem\n      margin: 0 0.5rem\n\n    &.next-page\n      max-width: 50%\n\n      float: right\n      clear: right\n      text-align: right\n\n    &.prev-page\n      max-width: 50%\n\n      float: left\n      clear: left\n\n      svg\n        transform: rotate(180deg)\n\n.page-info\n  display: flex\n  flex-direction: column\n  overflow-wrap: anywhere\n\n  .next-page &\n    align-items: flex-end\n\n  .context\n    display: flex\n    align-items: center\n\n    padding-bottom: 0.1rem\n\n    color: var(--color-foreground-muted)\n    font-size: var(--font-size--small)\n    text-decoration: none\n","// This file contains the styles for the contents of the left sidebar, which\n// contains the navigation tree, logo, search etc.\n\n////////////////////////////////////////////////////////////////////////////////\n// Brand on top of the scrollable tree.\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-brand\n  display: flex\n  flex-direction: column\n  flex-shrink: 0\n\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n  text-decoration: none\n\n.sidebar-brand-text\n  color: var(--color-sidebar-brand-text)\n  overflow-wrap: break-word\n  margin: var(--sidebar-item-spacing-vertical) 0\n  font-size: 1.5rem\n\n.sidebar-logo-container\n  margin: var(--sidebar-item-spacing-vertical) 0\n\n.sidebar-logo\n  margin: 0 auto\n  display: block\n  max-width: 100%\n\n////////////////////////////////////////////////////////////////////////////////\n// Search\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-search-container\n  display: flex\n  align-items: center\n  margin-top: var(--sidebar-search-space-above)\n\n  position: relative\n\n  background: var(--color-sidebar-search-background)\n  &:hover,\n  &:focus-within\n    background: var(--color-sidebar-search-background--focus)\n\n  &::before\n    content: \"\"\n    position: absolute\n    left: var(--sidebar-item-spacing-horizontal)\n    width: var(--sidebar-search-icon-size)\n    height: var(--sidebar-search-icon-size)\n\n    background-color: var(--color-sidebar-search-icon)\n    mask-image: var(--icon-search)\n\n.sidebar-search\n  box-sizing: border-box\n\n  border: none\n  border-top: 1px solid var(--color-sidebar-search-border)\n  border-bottom: 1px solid var(--color-sidebar-search-border)\n\n  padding-top: var(--sidebar-search-input-spacing-vertical)\n  padding-bottom: var(--sidebar-search-input-spacing-vertical)\n  padding-right: var(--sidebar-search-input-spacing-horizontal)\n  padding-left: calc(var(--sidebar-item-spacing-horizontal) + var(--sidebar-search-input-spacing-horizontal) + var(--sidebar-search-icon-size))\n\n  width: 100%\n\n  color: var(--color-sidebar-search-foreground)\n  background: transparent\n  z-index: 10\n\n  &:focus\n    outline: none\n\n  &::placeholder\n    font-size: var(--sidebar-search-input-font-size)\n\n//\n// Hide Search Matches link\n//\n#searchbox .highlight-link\n  padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal) 0\n  margin: 0\n  text-align: center\n\n  a\n    color: var(--color-sidebar-search-icon)\n    font-size: var(--font-size--small--2)\n\n////////////////////////////////////////////////////////////////////////////////\n// Structure/Skeleton of the navigation tree (left)\n////////////////////////////////////////////////////////////////////////////////\n.sidebar-tree\n  font-size: var(--sidebar-item-font-size)\n  margin-top: var(--sidebar-tree-space-above)\n  margin-bottom: var(--sidebar-item-spacing-vertical)\n\n  ul\n    padding: 0\n    margin-top: 0\n    margin-bottom: 0\n\n    display: flex\n    flex-direction: column\n\n    list-style: none\n\n  li\n    position: relative\n    margin: 0\n\n    > ul\n      margin-left: var(--sidebar-item-spacing-horizontal)\n\n  .icon\n    color: var(--color-sidebar-link-text)\n\n  .reference\n    box-sizing: border-box\n    color: var(--color-sidebar-link-text)\n\n    // Fill the parent.\n    display: inline-block\n    line-height: var(--sidebar-item-line-height)\n    text-decoration: none\n\n    // Don't allow long words to cause wrapping.\n    overflow-wrap: anywhere\n\n    height: 100%\n    width: 100%\n\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n    // Add a nice little \"external-link\" arrow here.\n    &.external::after\n      content: url('data:image/svg+xml,<svg width=\"12\" height=\"12\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\" stroke-width=\"1.5\" stroke=\"%23607D8B\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path stroke=\"none\" d=\"M0 0h24v24H0z\"/><path d=\"M11 7h-5a2 2 0 0 0 -2 2v9a2 2 0 0 0 2 2h9a2 2 0 0 0 2 -2v-5\" /><line x1=\"10\" y1=\"14\" x2=\"20\" y2=\"4\" /><polyline points=\"15 4 20 4 20 9\" /></svg>')\n      margin: 0 0.25rem\n      vertical-align: middle\n      color: var(--color-sidebar-link-text)\n\n  // Make the current page reference bold.\n  .current-page > .reference\n    font-weight: bold\n\n  label\n    position: absolute\n    top: 0\n    right: 0\n    height: var(--sidebar-item-height)\n    width: var(--sidebar-expander-width)\n\n    cursor: pointer\n    user-select: none\n\n    display: flex\n    justify-content: center\n    align-items: center\n\n  .caption, :not(.caption) > .caption-text\n    font-size: var(--sidebar-caption-font-size)\n    color: var(--color-sidebar-caption-text)\n\n    font-weight: bold\n    text-transform: uppercase\n\n    margin: var(--sidebar-caption-space-above) 0 0 0\n    padding: var(--sidebar-item-spacing-vertical) var(--sidebar-item-spacing-horizontal)\n\n  // If it has children, add a bit more padding to wrap the content to avoid\n  // overlapping with the <label>\n  li.has-children\n    > .reference\n      padding-right: var(--sidebar-expander-width)\n\n  // Colorize the top-level list items and icon.\n  .toctree-l1\n    & > .reference,\n    & > label .icon\n      color: var(--color-sidebar-link-text--top-level)\n\n  // Color changes on hover\n  label\n    background: var(--color-sidebar-item-expander-background)\n    &:hover\n      background: var(--color-sidebar-item-expander-background--hover)\n\n  .current > .reference\n    background: var(--color-sidebar-item-background--current)\n    &:hover\n      background: var(--color-sidebar-item-background--hover)\n\n.toctree-checkbox\n  position: absolute\n  display: none\n\n////////////////////////////////////////////////////////////////////////////////\n// Togglable expand/collapse\n////////////////////////////////////////////////////////////////////////////////\n.toctree-checkbox\n  ~ ul\n    display: none\n\n  ~ label .icon svg\n    transform: rotate(90deg)\n\n.toctree-checkbox:checked\n  ~ ul\n    display: block\n\n  ~ label .icon svg\n    transform: rotate(-90deg)\n","// This file contains the styles for the contents of the right sidebar, which\n// contains the table of contents for the current page.\n.toc-title-container\n  padding: var(--toc-title-padding)\n  padding-top: var(--toc-spacing-vertical)\n\n.toc-title\n  color: var(--color-toc-title-text)\n  font-size: var(--toc-title-font-size)\n  padding-left: var(--toc-spacing-horizontal)\n  text-transform: uppercase\n\n// If the ToC is not present, hide these elements coz they're not relevant.\n.no-toc\n  display: none\n\n.toc-tree-container\n  padding-bottom: var(--toc-spacing-vertical)\n\n.toc-tree\n  font-size: var(--toc-font-size)\n  line-height: 1.3\n  border-left: 1px solid var(--color-background-border)\n\n  padding-left: calc(var(--toc-spacing-horizontal) - var(--toc-item-spacing-horizontal))\n\n  // Hide the first \"top level\" bullet.\n  > ul > li:first-child\n    padding-top: 0\n    & > ul\n      padding-left: 0\n    & > a\n      display: none\n\n  ul\n    list-style-type: none\n    margin-top: 0\n    margin-bottom: 0\n    padding-left: var(--toc-item-spacing-horizontal)\n  li\n    padding-top: var(--toc-item-spacing-vertical)\n\n    &.scroll-current >.reference\n      color: var(--color-toc-item-text--active)\n      font-weight: bold\n\n  .reference\n    color: var(--color-toc-item-text)\n    text-decoration: none\n    overflow-wrap: anywhere\n\n.toc-scroll\n  max-height: 100vh\n  overflow-y: scroll\n\n// Be very annoying when someone includes the table of contents\n.contents:not(.this-will-duplicate-information-and-it-is-still-useful-here)\n  color: var(--color-problematic)\n  background: rgba(255, 0, 0, 0.25)\n  &::before\n    content: \"ERROR: Adding a table of contents in Furo-based documentation is unnecessary, and does not work well with existing styling.Add a 'this-will-duplicate-information-and-it-is-still-useful-here' class, if you want an escape hatch.\"\n","// Shameful hacks, to work around bugs.\n\n// MyST parser doesn't correctly generate classes, to align table contents.\n// https://github.com/executablebooks/MyST-Parser/issues/412\n.text-align\\:left > p\n  text-align: left\n\n.text-align\\:center > p\n  text-align: center\n\n.text-align\\:right > p\n  text-align: right\n"],"names":[],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/togglebutton.css b/_static/togglebutton.css
new file mode 100644
index 0000000..54a6787
--- /dev/null
+++ b/_static/togglebutton.css
@@ -0,0 +1,160 @@
+/**
+ * Admonition-based toggles
+ */
+
+/* Visibility of the target */
+.admonition.toggle .admonition-title ~ * {
+    transition: opacity .3s, height .3s;
+}
+
+/* Toggle buttons inside admonitions so we see the title */
+.admonition.toggle {
+    position: relative;
+}
+
+/* Titles should cut off earlier to avoid overlapping w/ button */
+.admonition.toggle .admonition-title {
+    padding-right: 25%;
+    cursor: pointer;
+}
+
+/* Hovering will cause a slight shift in color to make it feel interactive */
+.admonition.toggle .admonition-title:hover {
+    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 1%);
+}
+
+/* Hovering will cause a slight shift in color to make it feel interactive */
+.admonition.toggle .admonition-title:active {
+    box-shadow: inset 0 0 0px 20px rgb(0 0 0 / 3%);
+}
+
+/* Remove extra whitespace below the admonition title when hidden */
+.admonition.toggle-hidden {
+    padding-bottom: 0;
+}
+
+.admonition.toggle-hidden .admonition-title {
+    margin-bottom: 0;
+}
+
+/* hides all the content of a page until de-toggled */
+.admonition.toggle-hidden .admonition-title ~ * {
+    height: 0;
+    margin: 0;
+    opacity: 0;
+    visibility: hidden;
+}
+
+/* General button style and position*/
+button.toggle-button {
+    /**
+     * Background and shape. By default there's no background
+     * but users can style as they wish
+     */  
+    background: none;
+    border: none;
+    outline: none;
+
+    /* Positioning just inside the admonition title */
+    position: absolute;
+    right: 0.5em;
+    padding: 0px;
+    border: none;
+    outline: none;
+}
+
+/* Display the toggle hint on wide screens */
+@media (min-width: 768px) {
+    button.toggle-button.toggle-button-hidden:before {
+        content: attr(data-toggle-hint);  /* This will be filled in by JS */
+        font-size: .8em;
+        align-self: center;
+    }
+}
+
+/* Icon behavior */
+.tb-icon {
+    transition: transform .2s ease-out;
+    height: 1.5em;
+    width: 1.5em;
+    stroke: currentColor;  /* So that we inherit the color of other text */
+}
+
+/* The icon should point right when closed, down when open. */
+/* Open */
+.admonition.toggle button .tb-icon {
+    transform: rotate(90deg);
+}
+
+/* Closed */
+.admonition.toggle button.toggle-button-hidden .tb-icon {
+    transform: rotate(0deg);
+}
+
+/* With details toggles, we don't rotate the icon so it points right */
+details.toggle-details .tb-icon {
+    height: 1.4em;
+    width: 1.4em;
+    margin-top: 0.1em;  /* To center the button vertically */
+}
+
+
+/**
+ * Details-based toggles.
+ * In this case, we wrap elements with `.toggle` in a details block.
+ */
+
+/* Details blocks */
+details.toggle-details {
+    margin: 1em 0;
+}
+
+
+details.toggle-details summary {
+    display: flex;
+    align-items: center;
+    cursor: pointer;
+    list-style: none;
+    border-radius: .2em;
+    border-left: 3px solid #1976d2;
+    background-color: rgb(204 204 204 / 10%);
+    padding: 0.2em 0.7em 0.3em 0.5em; /* Less padding on left because the SVG has left margin */
+    font-size: 0.9em;
+}
+
+details.toggle-details summary:hover {
+    background-color: rgb(204 204 204 / 20%);
+}
+
+details.toggle-details summary:active {
+    background: rgb(204 204 204 / 28%);
+}
+
+.toggle-details__summary-text {
+    margin-left: 0.2em;
+}
+
+details.toggle-details[open] summary {
+    margin-bottom: .5em;
+}
+
+details.toggle-details[open] summary .tb-icon {
+    transform: rotate(90deg);
+}
+
+details.toggle-details[open] summary ~ * {
+    animation: toggle-fade-in .3s ease-out;
+}
+
+@keyframes toggle-fade-in {
+  from {opacity: 0%;}
+  to {opacity: 100%;}
+}
+
+/* Print rules - we hide all toggle button elements at print */
+@media print {
+    /* Always hide the summary so the button doesn't show up */
+    details.toggle-details summary {
+        display: none;
+    }
+}
\ No newline at end of file
diff --git a/_static/togglebutton.js b/_static/togglebutton.js
new file mode 100644
index 0000000..215a7ee
--- /dev/null
+++ b/_static/togglebutton.js
@@ -0,0 +1,187 @@
+/**
+ * Add Toggle Buttons to elements
+ */
+
+let toggleChevron = `
+<svg xmlns="http://www.w3.org/2000/svg" class="tb-icon toggle-chevron-right" width="44" height="44" viewBox="0 0 24 24" stroke-width="1.5" stroke="#000000" fill="none" stroke-linecap="round" stroke-linejoin="round">
+<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+<polyline points="9 6 15 12 9 18" />
+</svg>`;
+
+var initToggleItems = () => {
+  var itemsToToggle = document.querySelectorAll(togglebuttonSelector);
+  console.log(`[togglebutton]: Adding toggle buttons to ${itemsToToggle.length} items`)
+  // Add the button to each admonition and hook up a callback to toggle visibility
+  itemsToToggle.forEach((item, index) => {
+    if (item.classList.contains("admonition")) {
+      // If it's an admonition block, then we'll add a button inside
+      // Generate unique IDs for this item
+      var toggleID = `toggle-${index}`;
+      var buttonID = `button-${toggleID}`;
+
+      item.setAttribute('id', toggleID);
+      if (!item.classList.contains("toggle")){
+        item.classList.add("toggle");
+      }
+      // This is the button that will be added to each item to trigger the toggle
+      var collapseButton = `
+        <button type="button" id="${buttonID}" class="toggle-button" data-target="${toggleID}" data-button="${buttonID}" data-toggle-hint="${toggleHintShow}" aria-label="Toggle hidden content">
+            ${toggleChevron}
+        </button>`;
+
+      title = item.querySelector(".admonition-title")
+      title.insertAdjacentHTML("beforeend", collapseButton);
+      thisButton = document.getElementById(buttonID);
+
+      // Add click handlers for the button + admonition title (if admonition)
+      admonitionTitle = document.querySelector(`#${toggleID} > .admonition-title`)
+      if (admonitionTitle) {
+        // If an admonition, then make the whole title block clickable
+        admonitionTitle.addEventListener('click', toggleClickHandler);
+        admonitionTitle.dataset.target = toggleID
+        admonitionTitle.dataset.button = buttonID
+      } else {
+        // If not an admonition then we'll listen for the button click
+        thisButton.addEventListener('click', toggleClickHandler);
+      }
+
+      // Now hide the item for this toggle button unless explicitly noted to show
+      if (!item.classList.contains("toggle-shown")) {
+        toggleHidden(thisButton);
+      }
+    } else {
+      // If not an admonition, wrap the block in a <details> block
+      // Define the structure of the details block and insert it as a sibling
+      var detailsBlock = `
+        <details class="toggle-details">
+            <summary class="toggle-details__summary">
+              ${toggleChevron}
+              <span class="toggle-details__summary-text">${toggleHintShow}</span>
+            </summary>
+        </details>`;
+      item.insertAdjacentHTML("beforebegin", detailsBlock);
+
+      // Now move the toggle-able content inside of the details block
+      details = item.previousElementSibling
+      details.appendChild(item)
+      item.classList.add("toggle-details__container")
+
+      // Set up a click trigger to change the text as needed
+      details.addEventListener('click', (click) => {
+        let parent = click.target.parentElement;
+        if (parent.tagName.toLowerCase() == "details") {
+          summary = parent.querySelector("summary");
+          details = parent;
+        } else {
+          summary = parent;
+          details = parent.parentElement;
+        }
+        // Update the inner text for the proper hint
+        if (details.open) {
+          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintShow;
+        } else {
+          summary.querySelector("span.toggle-details__summary-text").innerText = toggleHintHide;
+        }
+        
+      });
+
+      // If we have a toggle-shown class, open details block should be open
+      if (item.classList.contains("toggle-shown")) {
+        details.click();
+      }
+    }
+  })
+};
+
+// This should simply add / remove the collapsed class and change the button text
+var toggleHidden = (button) => {
+  target = button.dataset['target']
+  var itemToToggle = document.getElementById(target);
+  if (itemToToggle.classList.contains("toggle-hidden")) {
+    itemToToggle.classList.remove("toggle-hidden");
+    button.classList.remove("toggle-button-hidden");
+  } else {
+    itemToToggle.classList.add("toggle-hidden");
+    button.classList.add("toggle-button-hidden");
+  }
+}
+
+var toggleClickHandler = (click) => {
+  // Be cause the admonition title is clickable and extends to the whole admonition
+  // We only look for a click event on this title to trigger the toggle.
+
+  if (click.target.classList.contains("admonition-title")) {
+    button = click.target.querySelector(".toggle-button");
+  } else if (click.target.classList.contains("tb-icon")) {
+    // We've clicked the icon and need to search up one parent for the button
+    button = click.target.parentElement;
+  } else if (click.target.tagName == "polyline") {
+    // We've clicked the SVG elements inside the button, need to up 2 layers
+    button = click.target.parentElement.parentElement;
+  } else if (click.target.classList.contains("toggle-button")) {
+    // We've clicked the button itself and so don't need to do anything
+    button = click.target;
+  } else {
+    console.log(`[togglebutton]: Couldn't find button for ${click.target}`)
+  }
+  target = document.getElementById(button.dataset['button']);
+  toggleHidden(target);
+}
+
+// If we want to blanket-add toggle classes to certain cells
+var addToggleToSelector = () => {
+  const selector = "";
+  if (selector.length > 0) {
+    document.querySelectorAll(selector).forEach((item) => {
+      item.classList.add("toggle");
+    })
+  }
+}
+
+// Helper function to run when the DOM is finished
+const sphinxToggleRunWhenDOMLoaded = cb => {
+  if (document.readyState != 'loading') {
+    cb()
+  } else if (document.addEventListener) {
+    document.addEventListener('DOMContentLoaded', cb)
+  } else {
+    document.attachEvent('onreadystatechange', function() {
+      if (document.readyState == 'complete') cb()
+    })
+  }
+}
+sphinxToggleRunWhenDOMLoaded(addToggleToSelector)
+sphinxToggleRunWhenDOMLoaded(initToggleItems)
+
+/** Toggle details blocks to be open when printing */
+if (toggleOpenOnPrint == "true") {
+  window.addEventListener("beforeprint", () => {
+    // Open the details
+    document.querySelectorAll("details.toggle-details").forEach((el) => {
+      el.dataset["togglestatus"] = el.open;
+      el.open = true;
+    });
+  
+    // Open the admonitions
+    document.querySelectorAll(".admonition.toggle.toggle-hidden").forEach((el) => {
+      console.log(el);
+      el.querySelector("button.toggle-button").click();
+      el.dataset["toggle_after_print"] = "true";
+    });
+  });
+  window.addEventListener("afterprint", () => {
+    // Re-close the details that were closed
+    document.querySelectorAll("details.toggle-details").forEach((el) => {
+      el.open = el.dataset["togglestatus"] == "true";
+      delete el.dataset["togglestatus"];
+    });
+  
+    // Re-close the admonition toggle buttons
+    document.querySelectorAll(".admonition.toggle").forEach((el) => {
+      if (el.dataset["toggle_after_print"] == "true") {
+        el.querySelector("button.toggle-button").click();
+        delete el.dataset["toggle_after_print"];
+      }
+    });
+  });
+}
diff --git a/dev/changelog.html b/dev/changelog.html
new file mode 100644
index 0000000..ba88184
--- /dev/null
+++ b/dev/changelog.html
@@ -0,0 +1,355 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="../">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><meta name="viewport" content="width=device-width, initial-scale=1" />
+<meta property="og:title" content="📈 Changelog" />
+<meta property="og:type" content="website" />
+<meta property="og:url" content="dev/changelog.html" />
+<meta property="og:site_name" content="Landmarker" />
+<meta property="og:description" content="v0.1.0:" />
+<meta property="og:image:width" content="1146" />
+<meta property="og:image:height" content="600" />
+<meta property="og:image" content="/_images/social_previews/summary_dev_changelog_72616ca6.png" />
+<meta property="og:image:alt" content="v0.1.0:" />
+<meta name="description" content="v0.1.0:" />
+<meta name="twitter:card" content="summary_large_image" />
+<link rel="index" title="Index" href="../genindex.html" /><link rel="search" title="Search" href="../search.html" /><link rel="next" title="🫶 Code of Conduct" href="code_of_conduct.html" /><link rel="prev" title="🤝 Contributing to landmarker" href="contributing.html" />
+
+    <link rel="shortcut icon" href="../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
+        <title>📈 Changelog - Landmarker Documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo.css?v=135e06be" />
+    <link rel="stylesheet" type="text/css" href="../_static/copybutton.css?v=76b2166b" />
+    <link rel="stylesheet" type="text/css" href="../_static/togglebutton.css?v=13237357" />
+    <link rel="stylesheet" type="text/css" href="../_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css" />
+    <link rel="stylesheet" type="text/css" href="../_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css?v=0a3b3ea7" />
+    <link rel="stylesheet" type="text/css" href="../_static/styles/furo-extensions.css?v=36a5483c" />
+    
+    
+
+
+<style>
+  body {
+    --color-code-background: #eeffcc;
+  --color-code-foreground: black;
+  
+  }
+  @media not print {
+    body[data-theme="dark"] {
+      --color-code-background: #272822;
+  --color-code-foreground: #f8f8f2;
+  
+    }
+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
+        --color-code-background: #272822;
+  --color-code-foreground: #f8f8f2;
+  
+      }
+    }
+  }
+</style></head>
+  <body>
+    
+    <script>
+      document.body.dataset.theme = localStorage.getItem("theme") || "auto";
+    </script>
+    
+
+<svg xmlns="http://www.w3.org/2000/svg" style="display: none;">
+  <symbol id="svg-toc" viewBox="0 0 24 24">
+    <title>Contents</title>
+    <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 1024 1024">
+      <path d="M408 442h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8zm-8 204c0 4.4 3.6 8 8 8h480c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8H408c-4.4 0-8 3.6-8 8v56zm504-486H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zm0 632H120c-4.4 0-8 3.6-8 8v56c0 4.4 3.6 8 8 8h784c4.4 0 8-3.6 8-8v-56c0-4.4-3.6-8-8-8zM115.4 518.9L271.7 642c5.8 4.6 14.4.5 14.4-6.9V388.9c0-7.4-8.5-11.5-14.4-6.9L115.4 505.1a8.74 8.74 0 0 0 0 13.8z"/>
+    </svg>
+  </symbol>
+  <symbol id="svg-menu" viewBox="0 0 24 24">
+    <title>Menu</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-menu">
+      <line x1="3" y1="12" x2="21" y2="12"></line>
+      <line x1="3" y1="6" x2="21" y2="6"></line>
+      <line x1="3" y1="18" x2="21" y2="18"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-arrow-right" viewBox="0 0 24 24">
+    <title>Expand</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="feather-chevron-right">
+      <polyline points="9 18 15 12 9 6"></polyline>
+    </svg>
+  </symbol>
+  <symbol id="svg-sun" viewBox="0 0 24 24">
+    <title>Light mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="feather-sun">
+      <circle cx="12" cy="12" r="5"></circle>
+      <line x1="12" y1="1" x2="12" y2="3"></line>
+      <line x1="12" y1="21" x2="12" y2="23"></line>
+      <line x1="4.22" y1="4.22" x2="5.64" y2="5.64"></line>
+      <line x1="18.36" y1="18.36" x2="19.78" y2="19.78"></line>
+      <line x1="1" y1="12" x2="3" y2="12"></line>
+      <line x1="21" y1="12" x2="23" y2="12"></line>
+      <line x1="4.22" y1="19.78" x2="5.64" y2="18.36"></line>
+      <line x1="18.36" y1="5.64" x2="19.78" y2="4.22"></line>
+    </svg>
+  </symbol>
+  <symbol id="svg-moon" viewBox="0 0 24 24">
+    <title>Dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-moon">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none" />
+      <path d="M12 3c.132 0 .263 0 .393 0a7.5 7.5 0 0 0 7.92 12.446a9 9 0 1 1 -8.313 -12.454z" />
+    </svg>
+  </symbol>
+  <symbol id="svg-sun-half" viewBox="0 0 24 24">
+    <title>Auto light/dark mode</title>
+    <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24" fill="none" stroke="currentColor"
+      stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round" class="icon-tabler-shadow">
+      <path stroke="none" d="M0 0h24v24H0z" fill="none"/>
+      <circle cx="12" cy="12" r="9" />
+      <path d="M13 12h5" />
+      <path d="M13 15h4" />
+      <path d="M13 18h1" />
+      <path d="M13 9h4" />
+      <path d="M13 6h1" />
+    </svg>
+  </symbol>
+</svg>
+
+<input type="checkbox" class="sidebar-toggle" name="__navigation" id="__navigation">
+<input type="checkbox" class="sidebar-toggle" name="__toc" id="__toc">
+<label class="overlay sidebar-overlay" for="__navigation">
+  <div class="visually-hidden">Hide navigation sidebar</div>
+</label>
+<label class="overlay toc-overlay" for="__toc">
+  <div class="visually-hidden">Hide table of contents sidebar</div>
+</label>
+
+
+
+<div class="page">
+  <header class="mobile-header">
+    <div class="header-left">
+      <label class="nav-overlay-icon" for="__navigation">
+        <div class="visually-hidden">Toggle site navigation sidebar</div>
+        <i class="icon"><svg><use href="#svg-menu"></use></svg></i>
+      </label>
+    </div>
+    <div class="header-center">
+      <a href="../index.html"><div class="brand">Landmarker Documentation</div></a>
+    </div>
+    <div class="header-right">
+      <div class="theme-toggle-container theme-toggle-header">
+        <button class="theme-toggle">
+          <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+          <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+          <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+          <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+        </button>
+      </div>
+      <label class="toc-overlay-icon toc-header-icon" for="__toc">
+        <div class="visually-hidden">Toggle table of contents sidebar</div>
+        <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+      </label>
+    </div>
+  </header>
+  <aside class="sidebar-drawer">
+    <div class="sidebar-container">
+      
+      <div class="sidebar-sticky"><a class="sidebar-brand" href="../index.html">
+  
+  <div class="sidebar-logo-container">
+    <img class="sidebar-logo only-light" src="../_static//images/logo.svg" alt="Light Logo"/>
+    <img class="sidebar-logo only-dark" src="../_static//images/logo-dark-mode.svg" alt="Dark Logo"/>
+  </div>
+  
+  <span class="sidebar-brand-text">Landmarker Documentation</span>
+  
+</a><form class="sidebar-search-container" method="get" action="../search.html" role="search">
+  <input class="sidebar-search" placeholder="Search" name="q" aria-label="Search">
+  <input type="hidden" name="check_keywords" value="yes">
+  <input type="hidden" name="area" value="default">
+</form>
+<div id="searchbox"></div><div class="sidebar-scroll"><div class="sidebar-tree">
+  <ul>
+<li class="toctree-l1"><a class="reference internal" href="../user/index.html">🚀 Getting Started</a></li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../examples/index.html">🔦 Examples</a><input class="toctree-checkbox" id="toctree-checkbox-1" name="toctree-checkbox-1" role="switch" type="checkbox"/><label for="toctree-checkbox-1"><div class="visually-hidden">Toggle navigation of 🔦 Examples</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul class="simple">
+</ul>
+</li>
+<li class="toctree-l1 has-children"><a class="reference internal" href="../reference/index.html">📖 API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of 📖 API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="../reference/data.html">Data Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/losses.html">Losses Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/models.html">Models Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/train.html">Train Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/uncertainty.html">Uncertainty Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/utils.html">Utils Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../reference/visualize.html">Visualize Module</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
+<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">📈 Changelog</a></li>
+<li class="toctree-l1"><a class="reference internal" href="code_of_conduct.html">🫶 Code of Conduct</a></li>
+<li class="toctree-l1"><a class="reference internal" href="license.html">📝 License</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
+<ul>
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+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          <section id="code-of-conduct">
+<h1>🫶 Code of Conduct<a class="headerlink" href="#code-of-conduct" title="Link to this heading">#</a></h1>
+<p>We follow the code of cunduct defined by the <a class="reference external" href="https://www.contributor-covenant.org/">Contributor Covenant</a>. This document is used across many open source communities, and we think it articulates our values well.</p>
+<section id="contributor-covenant-code-of-conduct">
+<h2>Contributor Covenant Code of Conduct<a class="headerlink" href="#contributor-covenant-code-of-conduct" title="Link to this heading">#</a></h2>
+<section id="our-pledge">
+<h3>Our Pledge<a class="headerlink" href="#our-pledge" title="Link to this heading">#</a></h3>
+<p>We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.</p>
+<p>We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.</p>
+</section>
+<section id="our-standards">
+<h3>Our Standards<a class="headerlink" href="#our-standards" title="Link to this heading">#</a></h3>
+<p>Examples of behavior that contributes to a positive environment for our
+community include:</p>
+<ul class="simple">
+<li><p>Demonstrating empathy and kindness toward other people</p></li>
+<li><p>Being respectful of differing opinions, viewpoints, and experiences</p></li>
+<li><p>Giving and gracefully accepting constructive feedback</p></li>
+<li><p>Accepting responsibility and apologizing to those affected by our mistakes,
+and learning from the experience</p></li>
+<li><p>Focusing on what is best not just for us as individuals, but for the overall
+community</p></li>
+</ul>
+<p>Examples of unacceptable behavior include:</p>
+<ul class="simple">
+<li><p>The use of sexualized language or imagery, and sexual attention or advances of
+any kind</p></li>
+<li><p>Trolling, insulting or derogatory comments, and personal or political attacks</p></li>
+<li><p>Public or private harassment</p></li>
+<li><p>Publishing others’ private information, such as a physical or email address,
+without their explicit permission</p></li>
+<li><p>Other conduct which could reasonably be considered inappropriate in a
+professional setting</p></li>
+</ul>
+</section>
+<section id="enforcement-responsibilities">
+<h3>Enforcement Responsibilities<a class="headerlink" href="#enforcement-responsibilities" title="Link to this heading">#</a></h3>
+<p>Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.</p>
+<p>Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.</p>
+</section>
+<section id="scope">
+<h3>Scope<a class="headerlink" href="#scope" title="Link to this heading">#</a></h3>
+<p>This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official email address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.</p>
+</section>
+<section id="enforcement">
+<h3>Enforcement<a class="headerlink" href="#enforcement" title="Link to this heading">#</a></h3>
+<p>Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement.
+All complaints will be reviewed and investigated promptly and fairly.</p>
+<p>All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.</p>
+</section>
+<section id="enforcement-guidelines">
+<h3>Enforcement Guidelines<a class="headerlink" href="#enforcement-guidelines" title="Link to this heading">#</a></h3>
+<p>Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:</p>
+<section id="correction">
+<h4>1. Correction<a class="headerlink" href="#correction" title="Link to this heading">#</a></h4>
+<p><strong>Community Impact</strong>: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.</p>
+<p><strong>Consequence</strong>: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.</p>
+</section>
+<section id="warning">
+<h4>2. Warning<a class="headerlink" href="#warning" title="Link to this heading">#</a></h4>
+<p><strong>Community Impact</strong>: A violation through a single incident or series of
+actions.</p>
+<p><strong>Consequence</strong>: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.</p>
+</section>
+<section id="temporary-ban">
+<h4>3. Temporary Ban<a class="headerlink" href="#temporary-ban" title="Link to this heading">#</a></h4>
+<p><strong>Community Impact</strong>: A serious violation of community standards, including
+sustained inappropriate behavior.</p>
+<p><strong>Consequence</strong>: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.</p>
+</section>
+<section id="permanent-ban">
+<h4>4. Permanent Ban<a class="headerlink" href="#permanent-ban" title="Link to this heading">#</a></h4>
+<p><strong>Community Impact</strong>: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.</p>
+<p><strong>Consequence</strong>: A permanent ban from any sort of public interaction within the
+community.</p>
+</section>
+</section>
+<section id="attribution">
+<h3>Attribution<a class="headerlink" href="#attribution" title="Link to this heading">#</a></h3>
+<p>This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+https://www.contributor-covenant.org/version/2/1/code_of_conduct.html</p>
+<p>For answers to common questions about this code of conduct, see the FAQ at
+https://www.contributor-covenant.org/faq. Translations are available at
+https://www.contributor-covenant.org/translations.</p>
+</section>
+</section>
+</section>
+
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+            <ul>
+<li><a class="reference internal" href="#">🫶 Code of Conduct</a><ul>
+<li><a class="reference internal" href="#contributor-covenant-code-of-conduct">Contributor Covenant Code of Conduct</a><ul>
+<li><a class="reference internal" href="#our-pledge">Our Pledge</a></li>
+<li><a class="reference internal" href="#our-standards">Our Standards</a></li>
+<li><a class="reference internal" href="#enforcement-responsibilities">Enforcement Responsibilities</a></li>
+<li><a class="reference internal" href="#scope">Scope</a></li>
+<li><a class="reference internal" href="#enforcement">Enforcement</a></li>
+<li><a class="reference internal" href="#enforcement-guidelines">Enforcement Guidelines</a><ul>
+<li><a class="reference internal" href="#correction">1. Correction</a></li>
+<li><a class="reference internal" href="#warning">2. Warning</a></li>
+<li><a class="reference internal" href="#temporary-ban">3. Temporary Ban</a></li>
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+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul class="current">
+<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
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+          <section id="contributing-to-landmarker">
+<h1>🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code><a class="headerlink" href="#contributing-to-landmarker" title="Link to this heading">#</a></h1>
+<p>🎉 <strong>First off all, thank you for considering contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code>!</strong> 🎉</p>
+<p>It’s people like you that make <code class="docutils literal notranslate"><span class="pre">landmarker</span></code> useful and successful. There are many ways to contribute, here’s a few examples:</p>
+<ul class="simple">
+<li><p>🐛 <a class="reference external" href="https://github.com/predict-idlab/landmarker/issues/new">Report bugs</a>: Something not working as expected? Please report bugs and we’ll try to fix them as soon as possible.</p></li>
+<li><p>🔨 <a class="reference external" href="https://github.com/predict-idlab/landmarker/issues">Fix bugs</a>: We try to fix bugs as soon as possible. If you want to help, please check the <a class="reference external" href="https://github.com/predict-idlab/landmarker/issues">issues</a>.</p></li>
+<li><p>🔍 <a class="reference external" href="https://github.com/predict-idlab/landmarker/pulls">Improve documentation</a>: Did you find a typo in the documentation? Or do you think something is missing? Please help us improve the documentation.</p></li>
+<li><p>✨ <a class="reference external" href="https://github.com/predict-idlab/landmarker/issues/new">Request/add new features</a>: Do you think <code class="docutils literal notranslate"><span class="pre">landmarker</span></code> is missing a feature? Please let us know by creating an issue, however check first if the feature is already requested. Or even better, add the feature yourself and create a pull request.</p></li>
+<li><p>📝 <a class="reference external" href="https://github.com/predict-idlab/landmarker/pulls">Write tutorials</a>: We are always looking for new tutorials, for example how to use <code class="docutils literal notranslate"><span class="pre">landmarker</span></code> for a specific task. Please help us by writing a tutorial and create a pull request.</p></li>
+</ul>
+<p>For more information on contributing to open source projects, <a class="reference external" href="https://opensource.guide/how-to-contribute">GitHub’s own guide</a> is a great starting point if you are new to version control.</p>
+<section id="setting-up-your-development-environment">
+<h2>Setting up your development environment<a class="headerlink" href="#setting-up-your-development-environment" title="Link to this heading">#</a></h2>
+<p>To get started, fork the <code class="docutils literal notranslate"><span class="pre">landmarker</span></code> repository and clone it to your local machine. Then, install the required dependencies using <a href="https://pdm.fming.dev/" target="_blank">pdm</a> (see <a href="https://pdm-project.org/latest/#installation" target="_blank">installation instructions</a>):</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pdm<span class="w"> </span>install<span class="w"> </span>--dev
+</pre></div>
+</div>
+</section>
+<section id="running-tests">
+<h2>Running tests<a class="headerlink" href="#running-tests" title="Link to this heading">#</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">landmarker</span></code> uses <a class="reference external" href="https://docs.pytest.org/en/stable/">pytest</a> for testing. You can run test by running a pdm script, by using the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pdm<span class="w"> </span>run<span class="w"> </span><span class="nb">test</span>
+</pre></div>
+</div>
+<p>If you want to run pytest with coverage, you can use the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pdm<span class="w"> </span>run<span class="w"> </span>coverage
+</pre></div>
+</div>
+</section>
+<section id="building-documentation">
+<h2>Building documentation<a class="headerlink" href="#building-documentation" title="Link to this heading">#</a></h2>
+<p><code class="docutils literal notranslate"><span class="pre">landmarker</span></code> uses <a class="reference external" href="https://www.sphinx-doc.org/en/master/">sphinx</a> for documentation, and use MyST markdown for documentation pages. You can build the documentation locally by running the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pdm<span class="w"> </span>run<span class="w"> </span>doc
+</pre></div>
+</div>
+<p>We also support the use of <a class="reference external" href="https://github.com/executablebooks/sphinx-autobuild">sphinx-autobuild</a>, which will automatically rebuild the documentation when a change is detected and live-reload the page in your browser. You can run it using the following command:</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pdm<span class="w"> </span>run<span class="w"> </span>docauto
+</pre></div>
+</div>
+</section>
+<section id="ground-rules">
+<h2>Ground Rules<a class="headerlink" href="#ground-rules" title="Link to this heading">#</a></h2>
+<p>The goal is to maintain a diverse community that’s pleasant for everyone.
+<strong>Please be considerate and respectful of others</strong>.
+Everyone must abide by our <a class="reference external" href="https://github.com/GenericMappingTools/.github/blob/main/CODE_OF_CONDUCT.md">Code of Conduct</a> and we encourage all to read it carefully.</p>
+</section>
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+
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+<li><a class="reference internal" href="#setting-up-your-development-environment">Setting up your development environment</a></li>
+<li><a class="reference internal" href="#running-tests">Running tests</a></li>
+<li><a class="reference internal" href="#building-documentation">Building documentation</a></li>
+<li><a class="reference internal" href="#ground-rules">Ground Rules</a></li>
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+<meta property="og:site_name" content="Landmarker" />
+<meta property="og:description" content="IMEC License: MIT License Copyright (c) 2024 Jef Jonkers Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Softw..." />
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+
+    <link rel="shortcut icon" href="../_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 -->
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+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
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+<p>MIT License</p>
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diff --git a/examples/index.html b/examples/index.html
new file mode 100644
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diff --git a/genindex.html b/genindex.html
new file mode 100644
index 0000000..21cff85
--- /dev/null
+++ b/genindex.html
@@ -0,0 +1,768 @@
+<!doctype html>
+<html class="no-js" lang="en" data-content_root="./">
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+
+    <link rel="shortcut icon" href="_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 --><title>Index - Landmarker Documentation</title>
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+      --color-code-background: #272822;
+  --color-code-foreground: #f8f8f2;
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+    @media (prefers-color-scheme: dark) {
+      body:not([data-theme="light"]) {
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+<li class="toctree-l1 has-children"><a class="reference internal" href="reference/index.html">📖 API Reference</a><input class="toctree-checkbox" id="toctree-checkbox-2" name="toctree-checkbox-2" role="switch" type="checkbox"/><label for="toctree-checkbox-2"><div class="visually-hidden">Toggle navigation of 📖 API Reference</div><i class="icon"><svg><use href="#svg-arrow-right"></use></svg></i></label><ul>
+<li class="toctree-l2"><a class="reference internal" href="reference/data.html">Data Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/losses.html">Losses Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/models.html">Models Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/train.html">Train Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/uncertainty.html">Uncertainty Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/utils.html">Utils Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/visualize.html">Visualize Module</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="dev/contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/changelog.html">📈 Changelog</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/code_of_conduct.html">🫶 Code of Conduct</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/license.html">📝 License</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="reference/data.html">Data Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/losses.html">Losses Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/models.html">Models Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/train.html">Train Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/utils.html">Utils Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/visualize.html">Visualize Module</a></li>
+</ul>
+
+</div>
+</div>
+
+      </div>
+      
+    </div>
+  </aside>
+  <div class="main">
+    <div class="content">
+      <div class="article-container">
+        <a href="#" class="back-to-top muted-link">
+          <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24">
+            <path d="M13 20h-2V8l-5.5 5.5-1.42-1.42L12 4.16l7.92 7.92-1.42 1.42L13 8v12z"></path>
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+          <span>Back to top</span>
+        </a>
+        <div class="content-icon-container">
+          <div class="theme-toggle-container theme-toggle-content">
+            <button class="theme-toggle">
+              <div class="visually-hidden">Toggle Light / Dark / Auto color theme</div>
+              <svg class="theme-icon-when-auto"><use href="#svg-sun-half"></use></svg>
+              <svg class="theme-icon-when-dark"><use href="#svg-moon"></use></svg>
+              <svg class="theme-icon-when-light"><use href="#svg-sun"></use></svg>
+            </button>
+          </div>
+          <label class="toc-overlay-icon toc-content-icon no-toc" for="__toc">
+            <div class="visually-hidden">Toggle table of contents sidebar</div>
+            <i class="icon"><svg><use href="#svg-toc"></use></svg></i>
+          </label>
+        </div>
+        <article role="main">
+          
+<section class="genindex-section">
+  <h1 id="index">Index</h1>
+  <div class="genindex-jumpbox"><a href="#_"><strong>_</strong></a> | <a href="#A"><strong>A</strong></a> | <a href="#C"><strong>C</strong></a> | <a href="#D"><strong>D</strong></a> | <a href="#E"><strong>E</strong></a> | <a href="#F"><strong>F</strong></a> | <a href="#G"><strong>G</strong></a> | <a href="#H"><strong>H</strong></a> | <a href="#I"><strong>I</strong></a> | <a href="#L"><strong>L</strong></a> | <a href="#M"><strong>M</strong></a> | <a href="#N"><strong>N</strong></a> | <a href="#O"><strong>O</strong></a> | <a href="#P"><strong>P</strong></a> | <a href="#S"><strong>S</strong></a> | <a href="#W"><strong>W</strong></a></div>
+</section>
+<section id="_" class="genindex-section">
+  <h2>_</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/data.html#landmarker.data.HeatmapDataset.__init__">__init__() (landmarker.data.HeatmapDataset method)</a>
+
+        <ul>
+          <li><a href="reference/data.html#landmarker.data.LandmarkDataset.__init__">(landmarker.data.LandmarkDataset method)</a>
+</li>
+          <li><a href="reference/data.html#landmarker.data.MaskDataset.__init__">(landmarker.data.MaskDataset method)</a>
+</li>
+          <li><a href="reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator.__init__">(landmarker.heatmap.GaussianHeatmapGenerator method)</a>
+</li>
+          <li><a href="reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator.__init__">(landmarker.heatmap.LaplacianHeatmapGenerator method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.AdaptiveWingLoss.__init__">(landmarker.losses.AdaptiveWingLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg.__init__">(landmarker.losses.EuclideanDistanceJSDivergenceReg method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg.__init__">(landmarker.losses.EuclideanDistanceVarianceReg method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss.__init__">(landmarker.losses.GaussianHeatmapL2Loss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss.__init__">(landmarker.losses.GeneralizedNormalHeatmapLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss.__init__">(landmarker.losses.MultivariateGaussianNLLLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.NLLLoss.__init__">(landmarker.losses.NLLLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.StackedLoss.__init__">(landmarker.losses.StackedLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.StarLoss.__init__">(landmarker.losses.StarLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.WingLoss.__init__">(landmarker.losses.WingLoss method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.AddCoordChannels.__init__">(landmarker.models.AddCoordChannels method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.CholeskyHourglass.__init__">(landmarker.models.CholeskyHourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.CoordConvLayer.__init__">(landmarker.models.CoordConvLayer method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.Hourglass.__init__">(landmarker.models.Hourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.OriginalSpatialConfigurationNet.__init__">(landmarker.models.OriginalSpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.OriginalSpatialConfigurationNet3d.__init__">(landmarker.models.OriginalSpatialConfigurationNet3d method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.ProbSpatialConfigurationNet.__init__">(landmarker.models.ProbSpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.SpatialConfigurationNet.__init__">(landmarker.models.SpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.StackedCholeskyHourglass.__init__">(landmarker.models.StackedCholeskyHourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.StackedHourglass.__init__">(landmarker.models.StackedHourglass method)</a>
+</li>
+          <li><a href="reference/schedulers.html#landmarker.schedulers.AdalossScheduler.__init__">(landmarker.schedulers.AdalossScheduler method)</a>
+</li>
+        </ul></li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="A" class="genindex-section">
+  <h2>A</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/schedulers.html#landmarker.schedulers.AdalossScheduler">AdalossScheduler (class in landmarker.schedulers)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.AdaptiveWingLoss">AdaptiveWingLoss (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.AddCoordChannels">AddCoordChannels (class in landmarker.models)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/utils.html#landmarker.utils.all_annotations_to_landmarks">all_annotations_to_landmarks() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.all_annotations_to_landmarks_numpy">all_annotations_to_landmarks_numpy() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.annotation_to_landmark">annotation_to_landmark() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.annotation_to_landmark_numpy">annotation_to_landmark_numpy() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="C" class="genindex-section">
+  <h2>C</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/models.html#landmarker.models.CholeskyHourglass">CholeskyHourglass (class in landmarker.models)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.coord_argmax">coord_argmax() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.coord_cov_from_gaussian_ls">coord_cov_from_gaussian_ls() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.coord_local_soft_argmax">coord_local_soft_argmax() (in module landmarker.heatmap)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.coord_weighted_spatial_mean">coord_weighted_spatial_mean() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.coord_weighted_spatial_mean_cov">coord_weighted_spatial_mean_cov() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.CoordConvLayer">CoordConvLayer (class in landmarker.models)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.cov_from_gaussian_ls">cov_from_gaussian_ls() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.covert_video_to_frames">covert_video_to_frames() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="D" class="genindex-section">
+  <h2>D</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/visualize.html#landmarker.visualize.detection_report">detection_report() (in module landmarker.visualize)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="E" class="genindex-section">
+  <h2>E</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg">EuclideanDistanceJSDivergenceReg (class in landmarker.losses)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg">EuclideanDistanceVarianceReg (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.extract_roi">extract_roi() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="F" class="genindex-section">
+  <h2>F</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/losses.html#landmarker.losses.AdaptiveWingLoss.forward">forward() (landmarker.losses.AdaptiveWingLoss method)</a>
+
+        <ul>
+          <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceJSDivergenceReg.forward">(landmarker.losses.EuclideanDistanceJSDivergenceReg method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.EuclideanDistanceVarianceReg.forward">(landmarker.losses.EuclideanDistanceVarianceReg method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss.forward">(landmarker.losses.GaussianHeatmapL2Loss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss.forward">(landmarker.losses.GeneralizedNormalHeatmapLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss.forward">(landmarker.losses.MultivariateGaussianNLLLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.NLLLoss.forward">(landmarker.losses.NLLLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.StackedLoss.forward">(landmarker.losses.StackedLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.StarLoss.forward">(landmarker.losses.StarLoss method)</a>
+</li>
+          <li><a href="reference/losses.html#landmarker.losses.WingLoss.forward">(landmarker.losses.WingLoss method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.AddCoordChannels.forward">(landmarker.models.AddCoordChannels method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.CholeskyHourglass.forward">(landmarker.models.CholeskyHourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.CoordConvLayer.forward">(landmarker.models.CoordConvLayer method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.Hourglass.forward">(landmarker.models.Hourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.OriginalSpatialConfigurationNet.forward">(landmarker.models.OriginalSpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.ProbSpatialConfigurationNet.forward">(landmarker.models.ProbSpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.SpatialConfigurationNet.forward">(landmarker.models.SpatialConfigurationNet method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.StackedCholeskyHourglass.forward">(landmarker.models.StackedCholeskyHourglass method)</a>
+</li>
+          <li><a href="reference/models.html#landmarker.models.StackedHourglass.forward">(landmarker.models.StackedHourglass method)</a>
+</li>
+        </ul></li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="G" class="genindex-section">
+  <h2>G</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator">GaussianHeatmapGenerator (class in landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.GaussianHeatmapL2Loss">GaussianHeatmapL2Loss (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.GeneralizedNormalHeatmapLoss">GeneralizedNormalHeatmapLoss (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.get_angle">get_angle() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.get_angle_numpy">get_angle_numpy() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/datasets.html#landmarker.datasets.get_cepha_heatmap_datasets">get_cepha_heatmap_datasets() (in module landmarker.datasets)</a>
+</li>
+        <li><a href="reference/datasets.html#landmarker.datasets.get_cepha_landmark_datasets">get_cepha_landmark_datasets() (in module landmarker.datasets)</a>
+</li>
+        <li><a href="reference/datasets.html#landmarker.datasets.get_cepha_mask_datasets">get_cepha_mask_datasets() (in module landmarker.datasets)</a>
+</li>
+        <li><a href="reference/datasets.html#landmarker.datasets.get_cepha_patch_datasets">get_cepha_patch_datasets() (in module landmarker.datasets)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.get_paths">get_paths() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="H" class="genindex-section">
+  <h2>H</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.GaussianHeatmapGenerator.heatmap_fun">heatmap_fun() (landmarker.heatmap.GaussianHeatmapGenerator method)</a>
+
+        <ul>
+          <li><a href="reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator.heatmap_fun">(landmarker.heatmap.LaplacianHeatmapGenerator method)</a>
+</li>
+        </ul></li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.heatmap_to_coord">heatmap_to_coord() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.heatmap_to_coord_cov">heatmap_to_coord_cov() (in module landmarker.heatmap)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.heatmap_to_coord_enlarge">heatmap_to_coord_enlarge() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.heatmap_to_multiple_coord">heatmap_to_multiple_coord() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/data.html#landmarker.data.HeatmapDataset">HeatmapDataset (class in landmarker.data)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.Hourglass">Hourglass (class in landmarker.models)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="I" class="genindex-section">
+  <h2>I</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/visualize.html#landmarker.visualize.inspection_plot">inspection_plot() (in module landmarker.visualize)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="L" class="genindex-section">
+  <h2>L</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/data.html#landmarker.data.LandmarkDataset">LandmarkDataset (class in landmarker.data)</a>
+</li>
+        <li>
+    landmarker.data
+
+        <ul>
+          <li><a href="reference/data.html#module-landmarker.data">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.datasets
+
+        <ul>
+          <li><a href="reference/datasets.html#module-landmarker.datasets">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.heatmap
+
+        <ul>
+          <li><a href="reference/heatmap.html#module-landmarker.heatmap">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.losses
+
+        <ul>
+          <li><a href="reference/losses.html#module-landmarker.losses">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.metrics
+
+        <ul>
+          <li><a href="reference/metrics.html#module-landmarker.metrics">module</a>
+</li>
+        </ul></li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li>
+    landmarker.models
+
+        <ul>
+          <li><a href="reference/models.html#module-landmarker.models">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.schedulers
+
+        <ul>
+          <li><a href="reference/schedulers.html#module-landmarker.schedulers">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.transforms
+
+        <ul>
+          <li><a href="reference/transforms.html#module-landmarker.transforms">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.utils
+
+        <ul>
+          <li><a href="reference/utils.html#module-landmarker.utils">module</a>
+</li>
+        </ul></li>
+        <li>
+    landmarker.visualize
+
+        <ul>
+          <li><a href="reference/visualize.html#module-landmarker.visualize">module</a>
+</li>
+        </ul></li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.LaplacianHeatmapGenerator">LaplacianHeatmapGenerator (class in landmarker.heatmap)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="M" class="genindex-section">
+  <h2>M</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/data.html#landmarker.data.MaskDataset">MaskDataset (class in landmarker.data)</a>
+</li>
+        <li>
+    module
+
+        <ul>
+          <li><a href="reference/data.html#module-landmarker.data">landmarker.data</a>
+</li>
+          <li><a href="reference/datasets.html#module-landmarker.datasets">landmarker.datasets</a>
+</li>
+          <li><a href="reference/heatmap.html#module-landmarker.heatmap">landmarker.heatmap</a>
+</li>
+          <li><a href="reference/losses.html#module-landmarker.losses">landmarker.losses</a>
+</li>
+          <li><a href="reference/metrics.html#module-landmarker.metrics">landmarker.metrics</a>
+</li>
+          <li><a href="reference/models.html#module-landmarker.models">landmarker.models</a>
+</li>
+          <li><a href="reference/schedulers.html#module-landmarker.schedulers">landmarker.schedulers</a>
+</li>
+          <li><a href="reference/transforms.html#module-landmarker.transforms">landmarker.transforms</a>
+</li>
+          <li><a href="reference/utils.html#module-landmarker.utils">landmarker.utils</a>
+</li>
+          <li><a href="reference/visualize.html#module-landmarker.visualize">landmarker.visualize</a>
+</li>
+        </ul></li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/losses.html#landmarker.losses.MultivariateGaussianNLLLoss">MultivariateGaussianNLLLoss (class in landmarker.losses)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="N" class="genindex-section">
+  <h2>N</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/losses.html#landmarker.losses.NLLLoss">NLLLoss (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.non_maximum_surpression">non_maximum_surpression() (in module landmarker.heatmap)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.non_maximum_surpression_local_soft_argmax">non_maximum_surpression_local_soft_argmax() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.normalize">normalize() (in module landmarker.utils)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="O" class="genindex-section">
+  <h2>O</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/models.html#landmarker.models.OriginalSpatialConfigurationNet">OriginalSpatialConfigurationNet (class in landmarker.models)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/models.html#landmarker.models.OriginalSpatialConfigurationNet3d">OriginalSpatialConfigurationNet3d (class in landmarker.models)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="P" class="genindex-section">
+  <h2>P</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/utils.html#landmarker.utils.pixel_to_unit">pixel_to_unit() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.pixel_to_unit_numpy">pixel_to_unit_numpy() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/visualize.html#landmarker.visualize.plot_cpe">plot_cpe() (in module landmarker.visualize)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/metrics.html#landmarker.metrics.point_error">point_error() (in module landmarker.metrics)</a>
+</li>
+        <li><a href="reference/visualize.html#landmarker.visualize.prediction_inspect_plot">prediction_inspect_plot() (in module landmarker.visualize)</a>
+</li>
+        <li><a href="reference/utils.html#landmarker.utils.preprocess_all">preprocess_all() (in module landmarker.utils)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.ProbSpatialConfigurationNet">ProbSpatialConfigurationNet (class in landmarker.models)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="S" class="genindex-section">
+  <h2>S</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/metrics.html#landmarker.metrics.sdr">sdr() (in module landmarker.metrics)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.SpatialConfigurationNet">SpatialConfigurationNet (class in landmarker.models)</a>
+</li>
+        <li><a href="reference/models.html#landmarker.models.StackedCholeskyHourglass">StackedCholeskyHourglass (class in landmarker.models)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/models.html#landmarker.models.StackedHourglass">StackedHourglass (class in landmarker.models)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.StackedLoss">StackedLoss (class in landmarker.losses)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.StarLoss">StarLoss (class in landmarker.losses)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+<section id="W" class="genindex-section">
+  <h2>W</h2>
+  <table style="width: 100%" class="indextable genindextable"><tr>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.weighted_sample_cov">weighted_sample_cov() (in module landmarker.heatmap)</a>
+</li>
+    </ul></td>
+    <td style="width: 33%; vertical-align: top;"><ul>
+        <li><a href="reference/heatmap.html#landmarker.heatmap.windowed_weigthed_sample_cov">windowed_weigthed_sample_cov() (in module landmarker.heatmap)</a>
+</li>
+        <li><a href="reference/losses.html#landmarker.losses.WingLoss">WingLoss (class in landmarker.losses)</a>
+</li>
+    </ul></td>
+  </tr></table>
+</section>
+
+
+        </article>
+      </div>
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+<li class="toctree-l2"><a class="reference internal" href="reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/train.html">Train Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/uncertainty.html">Uncertainty Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/utils.html">Utils Module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="reference/visualize.html">Visualize Module</a></li>
+</ul>
+</li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">Development</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="dev/contributing.html">🤝 Contributing to <code class="docutils literal notranslate"><span class="pre">landmarker</span></code></a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/changelog.html">📈 Changelog</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/code_of_conduct.html">🫶 Code of Conduct</a></li>
+<li class="toctree-l1"><a class="reference internal" href="dev/license.html">📝 License</a></li>
+</ul>
+<p class="caption" role="heading"><span class="caption-text">API reference</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="reference/data.html">Data Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/datasets.html">Datasets Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/heatmap.html">Heatmap Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/losses.html">Losses Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/metrics.html">Metrics Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/models.html">Models Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/schedulers.html">Schedulers Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/train.html">Train Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/transforms.html">Transforms Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/utils.html">Utils Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="reference/visualize.html">Visualize Module</a></li>
+</ul>
+
+</div>
+</div>
+
+      </div>
+      
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+        <div class="content-icon-container">
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+<p align="center">
+    <a href="https://predict-idlab.github.io/landmarker">
+        <img alt="landmarker" src="_static/images/logo.svg" width="66%">
+    </a>
+</p>
+<section id="landmarker">
+<h1>Landmarker<a class="headerlink" href="#landmarker" title="Link to this heading">#</a></h1>
+<div class="admonition caution">
+<p class="admonition-title">Caution</p>
+<p>🚧 Still under construction. 🚧</p>
+</div>
+<p><strong>Version</strong>: 0.1.0</p>
+<p><strong>Useful links</strong>:
+<a class="reference internal" href="user/index.html#installation"><span class="std std-ref">Instalation instructions</span></a> |
+<a class="reference external" href="https://github.com/predict-idlab/landmarker">Source repository</a> |
+<a class="reference external" href="https://github.com/predict-idlab/landmarker/issues">Issue tracker</a> |</p>
+<p>Landmarker is a <a class="reference external" href="https://pytorch.org/">PyTorch</a>-based toolkit for (anatomical) landmark detection in
+images. It is designed to be easy to use and to provide a flexible framework for state-of-the-art
+landmark detection algorithms for small and large datasets. Landmarker was developed for landmark
+detection in medical images. However, it can be used for any type of landmark detection problem.</p>
+<div class="sd-container-fluid sd-sphinx-override sd-mb-4 docutils">
+<div class="sd-row sd-row-cols-2 sd-row-cols-xs-2 sd-row-cols-sm-2 sd-row-cols-md-2 sd-row-cols-lg-2 docutils">
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+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+🚀 Getting started</div>
+<p class="sd-card-text">New to Landmarker? Start here to learn how to install Landmarker and how to use it.</p>
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+<a class="sd-stretched-link reference internal" href="user/index.html"><span class="doc"></span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row docutils">
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+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+🔦 Examples</div>
+<p class="sd-card-text">The examples section contains a collection of examples that demonstrate how to use Landmarker</p>
+</div>
+<a class="sd-stretched-link reference internal" href="examples/index.html"><span class="doc"></span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row docutils">
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+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+📖 API reference</div>
+<p class="sd-card-text">The API reference contains a detailed description of all Landmarker’s methods, modules, and
+classes.</p>
+</div>
+<a class="sd-stretched-link reference internal" href="reference/index.html"><span class="doc"></span></a></div>
+</div>
+<div class="sd-col sd-d-flex-row docutils">
+<div class="sd-card sd-sphinx-override sd-w-100 sd-shadow-sm sd-card-hover sd-text-center docutils">
+<div class="sd-card-body docutils">
+<div class="sd-card-title sd-font-weight-bold docutils">
+🚧 Development</div>
+<p class="sd-card-text">The development section contains information for developers who want to contribute to Landmarker.</p>
+</div>
+<a class="sd-stretched-link reference internal" href="dev/contributing.html"><span class="doc"></span></a></div>
+</div>
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+            </a>
+          
+        </div>
+        <div class="bottom-of-page">
+          <div class="left-details">
+            <div class="copyright">
+                Copyright &#169; 2023, Jef Jonkers
+            </div>
+            Made with <a href="https://www.sphinx-doc.org/">Sphinx</a> and <a class="muted-link" href="https://pradyunsg.me">@pradyunsg</a>'s
+            
+            <a href="https://github.com/pradyunsg/furo">Furo</a>
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+          <div class="right-details">
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+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
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+</html>
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+<html class="no-js" lang="en" data-content_root="./">
+  <head><meta charset="utf-8"/>
+    <meta name="viewport" content="width=device-width,initial-scale=1"/>
+    <meta name="color-scheme" content="light dark"><link rel="index" title="Index" href="genindex.html" /><link rel="search" title="Search" href="search.html" />
+
+    <link rel="shortcut icon" href="_static/favicon_landmarker.svg"/><!-- Generated with Sphinx 7.2.6 and Furo 2024.01.29 --><title>Python Module Index - Landmarker Documentation</title>
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+  }
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+      body:not([data-theme="light"]) {
+        --color-code-background: #272822;
+  --color-code-foreground: #f8f8f2;
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+</style></head>
+  <body>
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+    <script>
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+    <td><img src="_static/minus.png" class="toggler"
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+        <code class="xref">landmarker</code></td><td>
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+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/data.html#module-landmarker.data"><code class="xref">landmarker.data</code></a></td><td>
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+    <td></td>
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+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/heatmap.html#module-landmarker.heatmap"><code class="xref">landmarker.heatmap</code></a></td><td>
+    <em></em></td>
+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/losses.html#module-landmarker.losses"><code class="xref">landmarker.losses</code></a></td><td>
+    <em></em></td>
+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/metrics.html#module-landmarker.metrics"><code class="xref">landmarker.metrics</code></a></td><td>
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+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
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+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/schedulers.html#module-landmarker.schedulers"><code class="xref">landmarker.schedulers</code></a></td><td>
+    <em></em></td>
+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/transforms.html#module-landmarker.transforms"><code class="xref">landmarker.transforms</code></a></td><td>
+    <em></em></td>
+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/utils.html#module-landmarker.utils"><code class="xref">landmarker.utils</code></a></td><td>
+    <em></em></td>
+  </tr>
+  <tr class="cg-1">
+    <td></td>
+    <td>&#160;&#160;&#160;
+        <a href="reference/visualize.html#module-landmarker.visualize"><code class="xref">landmarker.visualize</code></a></td><td>
+    <em></em></td>
+  </tr>
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+          <section id="module-landmarker.data">
+<span id="data-module"></span><h1>Data Module<a class="headerlink" href="#module-landmarker.data" title="Link to this heading">#</a></h1>
+<p>Data module.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.data.HeatmapDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.data.</span></span><span class="sig-name descname"><span class="pre">HeatmapDataset</span></span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#HeatmapDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.HeatmapDataset" title="Link to this definition">#</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code> is a subclass of <code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code>. It represents a dataset of images and
+landmarks. The images can be provided as a list of paths to the images or as a list of numpy
+arrays or as a numpy array/torch.Tensor. Th landmarks must be provided. The heatmaps are created
+from the landmarks using a Gaussian or Laplacian function. When using the <code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code>,
+images are always stored in memory.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>list</em><em>[</em><em>np.array</em><em>] </em><em>| </em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – list of paths to the images
+or list of numpy arrays or numpy array/torch.Tensor.</p></li>
+<li><p><strong>landmarks</strong> (<em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – landmarks of the images. If the landmarks are 2D: y, x
+or 3D: z, y, x.</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the images. (defaults: 2)</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em><em>]</em>) – pixel spacing of the images.
+(defaults: None)</p></li>
+<li><p><strong>class_names</strong> (<em>Optional</em><em>[</em><em>list</em><em>]</em>) – names of the landmarks. (defaults: None)</p></li>
+<li><p><strong>transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – transformation to apply to the images and landmarks.
+(defaults: None)</p></li>
+<li><p><strong>sigma</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>]</em>) – sigma of the Gaussian or Laplacian function. (defaults: 5)</p></li>
+<li><p><strong>dim_img</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>]</em>) – dimension of the images. (defaults: None)</p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>) – batch size to use when creating the heatmaps. (defaults: 32)</p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – whether to add a background channel or not. (defaults: False)</p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – whether to add a channel for all points or not. (defaults: False)</p></li>
+<li><p><strong>gamma</strong> (<em>Optional</em><em>[</em><em>float</em><em>]</em>) – gamma of the Gaussian or Laplacian function. (defaults: None)</p></li>
+<li><p><strong>heatmap_fun</strong> (<em>str</em>) – function to use to create the heatmaps. (defaults: “gaussian”)</p></li>
+<li><p><strong>heatmap_size</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>]</em>) – size of the heatmaps. (defaults: None)</p></li>
+<li><p><strong>img_paths</strong> (<em>Optional</em><em>[</em><em>list</em><em>[</em><em>str</em><em>]</em><em>]</em>) – list of paths to the images. (defaults: None)</p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – whether to resize the images and landmarks or not. (defaults: True)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.data.HeatmapDataset.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imgs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_imgs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">32</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">background</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#HeatmapDataset.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.HeatmapDataset.__init__" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>list</em><em>[</em><em>ndarray</em><em>] </em><em>| </em><em>ndarray</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>landmarks</strong> (<em>ndarray</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>pixel_spacing</strong> (<em>ndarray</em><em> | </em><em>Tensor</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>transform</strong> (<em>Callable</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>store_imgs</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>sigma</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>] </em><em>| </em><em>ndarray</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>dim_img</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>) – </p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>heatmap_fun</strong> (<em>str</em>) – </p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>img_paths</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.data.LandmarkDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.data.</span></span><span class="sig-name descname"><span class="pre">LandmarkDataset</span></span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#LandmarkDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.LandmarkDataset" title="Link to this definition">#</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code> is a subclass of <code class="docutils literal notranslate"><span class="pre">torch.utils.data.Dataset</span></code>. It represents a dataset of
+images and landmarks. The images can be provided as a list of paths to the images or as a list
+of numpy arrays or as a numpy array/torch.Tensor.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>list</em><em>[</em><em>np.array</em><em>] </em><em>| </em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – list of paths to the images
+or list of numpy arrays or numpy array/torch.Tensor.</p></li>
+<li><p><strong>landmarks</strong> (<em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – landmarks of the images. If the landmarks are 2D: y, x
+or 3D: z, y, x.</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the images. (defaults: 2)</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em><em>]</em>) – pixel spacing of the images.
+(defaults: None)</p></li>
+<li><p><strong>class_names</strong> (<em>Optional</em><em>[</em><em>list</em><em>]</em>) – names of the landmarks. (defaults: None)</p></li>
+<li><p><strong>transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – transformation to apply to the images and landmarks.
+(defaults: None)</p></li>
+<li><p><strong>store_imgs</strong> (<em>bool</em>) – whether to store the images in memory or not. (defaults: True)</p></li>
+<li><p><strong>dim_img</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>]</em>) – dimension of the images. (defaults: None)</p></li>
+<li><p><strong>img_paths</strong> (<em>Optional</em><em>[</em><em>list</em><em>[</em><em>str</em><em>]</em><em>]</em>) – list of paths to the images. (defaults: None)</p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – whether to resize the images and landmarks or not. (defaults: True)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.data.LandmarkDataset.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imgs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_imgs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#LandmarkDataset.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.LandmarkDataset.__init__" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>ndarray</em><em> | </em><em>Tensor</em><em> | </em><em>list</em><em>[</em><em>ndarray</em><em>] </em><em>| </em><em>list</em><em>[</em><em>Tensor</em><em>]</em>) – </p></li>
+<li><p><strong>landmarks</strong> (<em>ndarray</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>pixel_spacing</strong> (<em>list</em><em>[</em><em>int</em><em>] </em><em>| </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>ndarray</em><em> | </em><em>Tensor</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>transform</strong> (<em>Callable</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>store_imgs</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>dim_img</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>img_paths</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.data.MaskDataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.data.</span></span><span class="sig-name descname"><span class="pre">MaskDataset</span></span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#MaskDataset"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.MaskDataset" title="Link to this definition">#</a></dt>
+<dd><p><code class="docutils literal notranslate"><span class="pre">MaskDataset</span></code> is a subclass of <code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code>. It represents a dataset of images
+and landmarks. The images can be provided as a list of paths to the images or as a list of
+numpy arrays or as a numpy array/torch.Tensor. The landmarks can be provided as a list of paths
+to the masks or as a list of numpy arrays or as a numpy array/torch.Tensor. The masks must be
+grayscale images with the landmarks represented by different values. The landmarks can be
+extracted from the masks and stored as a torch.Tensor or they can be genererated from provided
+landmarks. Note that if mask paths are provide this can be only be grayscale images and thus
+the dataset can only contain one class of landmark per image.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>list</em><em>[</em><em>np.array</em><em>] </em><em>| </em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – list of paths to the images
+or list of numpy arrays or numpy array/torch.Tensor.</p></li>
+<li><p><strong>landmarks</strong> (<em>np.ndarray</em><em> | </em><em>torch.Tensor</em>) – landmarks of the images. If the landmarks are 2D: y, x
+or 3D: z, y, x.</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the images. (defaults: 2)</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em> | </em><em>torch.Tensor</em><em>]</em>) – pixel spacing of the images.
+(defaults: None)</p></li>
+<li><p><strong>class_names</strong> (<em>Optional</em><em>[</em><em>list</em><em>]</em>) – names of the landmarks. (defaults: None)</p></li>
+<li><p><strong>transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – transformation to apply to the images and landmarks.
+(defaults: None)</p></li>
+<li><p><strong>store_imgs</strong> (<em>bool</em>) – whether to store the images in memory or not. (defaults: True)</p></li>
+<li><p><strong>store_masks</strong> (<em>bool</em>) – whether to store the masks in memory or not.
+(defaults: False)</p></li>
+<li><p><strong>dim_img</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>]</em>) – dimension of the images. (defaults: None)</p></li>
+<li><p><strong>mask_paths</strong> (<em>Optional</em><em>[</em><em>list</em><em>[</em><em>str</em><em>]</em><em>]</em>) – list of paths to the masks. (defaults: None)</p></li>
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – number of landmarks in the masks. (defaults: 1)</p></li>
+<li><p><strong>img_paths</strong> (<em>Optional</em><em>[</em><em>list</em><em>[</em><em>str</em><em>]</em><em>]</em>) – list of paths to the images. (defaults: None)</p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – whether to resize the images and landmarks or not. (defaults: True)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.data.MaskDataset.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imgs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">landmarks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_imgs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">mask_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nb_landmarks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">img_paths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">resize_pad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/data/landmark_dataset.html#MaskDataset.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.data.MaskDataset.__init__" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>imgs</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>list</em><em>[</em><em>ndarray</em><em>] </em><em>| </em><em>ndarray</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>landmarks</strong> (<em>ndarray</em><em> | </em><em>Tensor</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Tensor</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>transform</strong> (<em>Callable</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>store_imgs</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>dim_img</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>mask_paths</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>img_paths</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>resize_pad</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
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+<li><a class="reference internal" href="#">Data Module</a><ul>
+<li><a class="reference internal" href="#landmarker.data.HeatmapDataset"><code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.data.HeatmapDataset.__init__"><code class="docutils literal notranslate"><span class="pre">HeatmapDataset.__init__()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.data.LandmarkDataset"><code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.data.LandmarkDataset.__init__"><code class="docutils literal notranslate"><span class="pre">LandmarkDataset.__init__()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.data.MaskDataset"><code class="docutils literal notranslate"><span class="pre">MaskDataset</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.data.MaskDataset.__init__"><code class="docutils literal notranslate"><span class="pre">MaskDataset.__init__()</span></code></a></li>
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+          <section id="module-landmarker.datasets">
+<span id="datasets-module"></span><h1>Datasets Module<a class="headerlink" href="#module-landmarker.datasets" title="Link to this heading">#</a></h1>
+<p>Datasets module.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.datasets.get_cepha_heatmap_datasets">
+<span class="sig-prename descclassname"><span class="pre">landmarker.datasets.</span></span><span class="sig-name descname"><span class="pre">get_cepha_heatmap_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">junior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/datasets/cepha400.html#get_cepha_heatmap_datasets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.datasets.get_cepha_heatmap_datasets" title="Link to this definition">#</a></dt>
+<dd><p>Returns a HeatmapDataset with the ISBI 2015 cephalogram challenge dataset. The dataset is
+split into train, test1 and test2. The same approach as in “CephaNN: A Multi-Head Attention
+Network for Cephalometric Landmark Detection” - JIAHOONG QIAN et al. is used.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path_dir</strong> (<em>str</em>) – The path to the directory where the dataset should be stored.</p></li>
+<li><p><strong>train_transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – A transformation to apply to the images and
+landmarks during training. Defaults to None.</p></li>
+<li><p><strong>inference_transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – A transformation to apply to the images
+and landmarks during inference. Defaults to None.</p></li>
+<li><p><strong>sigma</strong> (<em>int</em><em>, </em><em>optional</em>) – The sigma value for the gaussian kernel. Defaults to 1.</p></li>
+<li><p><strong>junior</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to use the junior or senior annotator. Defaults to False.</p></li>
+<li><p><strong>single_dataset</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to return a single dataset with all images and
+landmarks. Defaults to False.</p></li>
+<li><p><strong>**kwargs</strong> – Additional keyword arguments for the HeatmapDataset.</p></li>
+<li><p><strong>cv</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a> | tuple[<a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>] | tuple[<a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.HeatmapDataset" title="landmarker.data.landmark_dataset.HeatmapDataset"><em>HeatmapDataset</em></a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.datasets.get_cepha_landmark_datasets">
+<span class="sig-prename descclassname"><span class="pre">landmarker.datasets.</span></span><span class="sig-name descname"><span class="pre">get_cepha_landmark_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_imgs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_img</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">junior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/datasets/cepha400.html#get_cepha_landmark_datasets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.datasets.get_cepha_landmark_datasets" title="Link to this definition">#</a></dt>
+<dd><p>Returns a LandmarkDataset objects with the CEPH dataset, a combination of the ISBI 2014 &amp;
+2015 challenges. The dataset is split into train, test1 and test2. The same approach as in
+“CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection” - JIAHOONG QIAN</p>
+<blockquote>
+<div><p>et al. is used.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>path_dir</strong> (<em>str</em>) – </p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a> | tuple[<a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>] | tuple[<a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.landmark_dataset.LandmarkDataset"><em>LandmarkDataset</em></a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.datasets.get_cepha_mask_datasets">
+<span class="sig-prename descclassname"><span class="pre">landmarker.datasets.</span></span><span class="sig-name descname"><span class="pre">get_cepha_mask_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">junior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/datasets/cepha400.html#get_cepha_mask_datasets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.datasets.get_cepha_mask_datasets" title="Link to this definition">#</a></dt>
+<dd><p>Returns a MaskDataset with the ISBI 2015 cephalogram challenge dataset. The dataset is
+split into train, test1 and test2. The same approach as in “CephaNN: A Multi-Head Attention
+Network for Cephalometric Landmark Detection” - JIAHOONG QIAN et al. is used.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path_dir</strong> (<em>str</em>) – The path to the directory where the dataset should be stored.</p></li>
+<li><p><strong>train_transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – A transformation to apply to the images and
+masks during training. Defaults to None.</p></li>
+<li><p><strong>inference_transform</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em><em>, </em><em>optional</em>) – A transformation to apply to the images
+and masks during inference. Defaults to None.</p></li>
+<li><p><strong>junior</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to use the junior or senior annotator. Defaults to False.</p></li>
+<li><p><strong>single_dataset</strong> (<em>bool</em><em>, </em><em>optional</em>) – Whether to return a single dataset with all images and
+landmarks. Defaults to False.</p></li>
+<li><p><strong>**kwargs</strong> – Additional keyword arguments for the MaskDataset.</p></li>
+<li><p><strong>cv</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a> | tuple[<a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>] | tuple[<a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>, <a class="reference internal" href="data.html#landmarker.data.MaskDataset" title="landmarker.data.landmark_dataset.MaskDataset"><em>MaskDataset</em></a>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.datasets.get_cepha_patch_datasets">
+<span class="sig-prename descclassname"><span class="pre">landmarker.datasets.</span></span><span class="sig-name descname"><span class="pre">get_cepha_patch_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_dir</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">index_landmark</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inference_transform</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">store_imgs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">junior</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">single_dataset</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/datasets/cepha400.html#get_cepha_patch_datasets"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.datasets.get_cepha_patch_datasets" title="Link to this definition">#</a></dt>
+<dd><p>Returns a PatchDataset objects with the CEPH dataset, a combination of the ISBI 2014 &amp;
+2015 challenges. The dataset is split into train, test1 and test2. The same approach as in
+“CephaNN: A Multi-Head Attention Network for Cephalometric Landmark Detection” - JIAHOONG QIAN</p>
+<blockquote>
+<div><p>et al. is used.</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path_dir</strong> (<em>str</em>) – </p></li>
+<li><p><strong>index_landmark</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+<li><a class="reference internal" href="#landmarker.datasets.get_cepha_heatmap_datasets"><code class="docutils literal notranslate"><span class="pre">get_cepha_heatmap_datasets()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.datasets.get_cepha_landmark_datasets"><code class="docutils literal notranslate"><span class="pre">get_cepha_landmark_datasets()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.datasets.get_cepha_mask_datasets"><code class="docutils literal notranslate"><span class="pre">get_cepha_mask_datasets()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.datasets.get_cepha_patch_datasets"><code class="docutils literal notranslate"><span class="pre">get_cepha_patch_datasets()</span></code></a></li>
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+<li class="toctree-l1"><a class="reference internal" href="datasets.html">Datasets Module</a></li>
+<li class="toctree-l1 current current-page"><a class="current reference internal" href="#">Heatmap Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="losses.html">Losses Module</a></li>
+<li class="toctree-l1"><a class="reference internal" href="metrics.html">Metrics Module</a></li>
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+        <article role="main">
+          <section id="module-landmarker.heatmap">
+<span id="heatmap-module"></span><h1>Heatmap Module<a class="headerlink" href="#module-landmarker.heatmap" title="Link to this heading">#</a></h1>
+<p>Heatmap module.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.heatmap.GaussianHeatmapGenerator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">GaussianHeatmapGenerator</span></span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#GaussianHeatmapGenerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.GaussianHeatmapGenerator" title="Link to this definition">#</a></dt>
+<dd><p>Gaussian heatmap generator for generating heatmaps from landmarks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – number of landmarks</p></li>
+<li><p><strong>sigmas</strong> (<em>float</em><em> or </em><em>list</em><em>[</em><em>float</em><em>] or </em><em>torch.Tensor</em><em> or </em><em>np.ndarray</em>) – sigmas of the gaussian heatmap
+function</p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> or </em><em>None</em>) – scaling factor of the gaussian heatmap function</p></li>
+<li><p><strong>rotation</strong> (<em>float</em><em> or </em><em>list</em><em>[</em><em>float</em><em>] or </em><em>torch.Tensor</em><em> or </em><em>np.ndarray</em>) – rotation of the gaussian
+heatmap function</p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – size of the heatmap</p></li>
+<li><p><strong>learnable</strong> (<em>bool</em>) – whether the sigmas and rotation are learnable</p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – whether to add a background channel to the heatmap</p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – whether to add a channel with the sum of all the landmarks</p></li>
+<li><p><strong>continuous</strong> (<em>bool</em>) – whether to use continuous or discrete landmarks</p></li>
+<li><p><strong>na_zero</strong> (<em>bool</em>) – whether to set the value of the landmarks to zero if they are not available</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.heatmap.GaussianHeatmapGenerator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nb_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(512,</span> <span class="pre">512)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learnable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">background</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">continuous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">na_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#GaussianHeatmapGenerator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.GaussianHeatmapGenerator.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sigmas</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>] </em><em>| </em><em>Tensor</em><em> | </em><em>ndarray</em>) – </p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>rotation</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>] </em><em>| </em><em>Tensor</em><em> | </em><em>ndarray</em>) – </p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>]</em>) – </p></li>
+<li><p><strong>learnable</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>continuous</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>na_zero</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.heatmap.GaussianHeatmapGenerator.heatmap_fun">
+<span class="sig-name descname"><span class="pre">heatmap_fun</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">landmark_t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#GaussianHeatmapGenerator.heatmap_fun"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.GaussianHeatmapGenerator.heatmap_fun" title="Link to this definition">#</a></dt>
+<dd><p>Gaussian heatmap function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>landmark_t</strong> (<em>torch.Tensor</em>) – coordinates of the landmark (y, x) or (z, y, x)</p></li>
+<li><p><strong>coords</strong> (<em>torch.Tensor</em>) – coordinates of the pixel (y, x) or (z, y, x)</p></li>
+<li><p><strong>covariance</strong> (<em>torch.Tensor</em>) – covariance matrix (y, x) or (z, y, x)</p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> or </em><em>None</em>) – scaling factor of the heatmap function</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>value of the gaussian heatmap function for the given pixel</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.heatmap.LaplacianHeatmapGenerator">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">LaplacianHeatmapGenerator</span></span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#LaplacianHeatmapGenerator"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.LaplacianHeatmapGenerator" title="Link to this definition">#</a></dt>
+<dd><p>Laplacian heatmap generator for generating heatmaps from landmarks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – number of landmarks</p></li>
+<li><p><strong>sigmas</strong> (<em>float</em><em> or </em><em>list</em><em>[</em><em>float</em><em>] or </em><em>torch.Tensor</em><em> or </em><em>np.ndarray</em>) – sigmas of the Laplacian heatmap
+function</p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> or </em><em>None</em>) – scaling factor of the Laplacian heatmap function</p></li>
+<li><p><strong>rotation</strong> (<em>float</em><em> or </em><em>list</em><em>[</em><em>float</em><em>] or </em><em>torch.Tensor</em><em> or </em><em>np.ndarray</em>) – rotation of the laplacian
+heatmap function</p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – size of the heatmap</p></li>
+<li><p><strong>learnable</strong> (<em>bool</em>) – whether the sigmas and rotation are learnable</p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – whether to add a background channel to the heatmap</p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – whether to add a channel with the sum of all the landmarks</p></li>
+<li><p><strong>continuous</strong> (<em>bool</em>) – whether to use continuous or discrete landmarks</p></li>
+<li><p><strong>na_zero</strong> (<em>bool</em>) – whether to set the value of the landmarks to zero if they are not available</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.heatmap.LaplacianHeatmapGenerator.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nb_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(512,</span> <span class="pre">512)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learnable</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">background</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">all_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">continuous</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">na_zero</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#LaplacianHeatmapGenerator.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.LaplacianHeatmapGenerator.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sigmas</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>] </em><em>| </em><em>Tensor</em><em> | </em><em>ndarray</em>) – </p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>rotation</strong> (<em>float</em><em> | </em><em>list</em><em>[</em><em>float</em><em>] </em><em>| </em><em>Tensor</em><em> | </em><em>ndarray</em>) – </p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>]</em>) – </p></li>
+<li><p><strong>learnable</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>background</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>all_points</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>continuous</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>na_zero</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.heatmap.LaplacianHeatmapGenerator.heatmap_fun">
+<span class="sig-name descname"><span class="pre">heatmap_fun</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">landmark_t</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">covariance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/generator.html#LaplacianHeatmapGenerator.heatmap_fun"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.LaplacianHeatmapGenerator.heatmap_fun" title="Link to this definition">#</a></dt>
+<dd><p>Laplacian heatmap function</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>landmark_t</strong> (<em>torch.Tensor</em>) – coordinates of the landmark (y, x)</p></li>
+<li><p><strong>coords</strong> (<em>torch.Tensor</em>) – coordinates of the pixel (y, x)</p></li>
+<li><p><strong>covariance</strong> (<em>torch.Tensor</em>) – covariance matrix (y, x)</p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> or </em><em>None</em>) – scaling factor of the heatmap function</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>value of the gaussian heatmap function for the given pixel</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.coord_argmax">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">coord_argmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#coord_argmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.coord_argmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the coordinates of the maximum value of the heatmap
+for each batch and channel (landmark).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2) or (B, C, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.coord_cov_from_gaussian_ls">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">coord_cov_from_gaussian_ls</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ls_library</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scipy'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#coord_cov_from_gaussian_ls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.coord_cov_from_gaussian_ls" title="Link to this definition">#</a></dt>
+<dd><p>Returns the modal coordinates and covariance matrix from a heatmap through fitting the heatmap
+on Gaussian distribution with a specicic scaling factor gamma with help of least squares
+optimization.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W)</p></li>
+<li><p><strong>gamma</strong> (<em>float</em>) – gamma parameter of the gaussian heatmap generator</p></li>
+<li><p><strong>ls_library</strong> (<em>str</em>) – library to use for least squares optimization. (scipy or pytorch)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2)
+(torch.Tensor): covariance matrix of shape (B, C, 2, 2)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.coord_local_soft_argmax">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">coord_local_soft_argmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">10.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#coord_local_soft_argmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.coord_local_soft_argmax" title="Link to this definition">#</a></dt>
+<dd><dl class="simple">
+<dt>Returns coordiantes through applying the local soft-argmax function on the heatmaps.</dt><dd><p>Source: “Subpixel Heatmap Regression for Facial Landmark Localization” - Bulat et al. (2021)</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>window</strong> (<em>int</em>) – local window size</p></li>
+<li><p><strong>t</strong> (<em>float</em>) – temperature that controls the resulting probability map</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.coord_weighted_spatial_mean">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">coord_weighted_spatial_mean</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#coord_weighted_spatial_mean"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.coord_weighted_spatial_mean" title="Link to this definition">#</a></dt>
+<dd><p>Returns the spatial weighted mean of the heatmap.
+Source: “UGLLI Face Alignment: Estimating Uncertainty with</p>
+<blockquote>
+<div><p>Gaussian Log-Likelihood Loss” - Kumar et al. (2019)</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+<li><p><strong>activation</strong> (<em>str</em>) – activation function to apply to the heatmap</p></li>
+<li><p><strong>require_grad</strong> (<em>bool</em>) – whether to require gradient for the coordinates</p></li>
+<li><p><strong>t</strong> (<em>float</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2) or (B, C, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.coord_weighted_spatial_mean_cov">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">coord_weighted_spatial_mean_cov</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#coord_weighted_spatial_mean_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.coord_weighted_spatial_mean_cov" title="Link to this definition">#</a></dt>
+<dd><p>Returns the spatial weighted mean and the weighted sample covariance of the possitive elements
+of the heatmap by the heatmap values.</p>
+<blockquote>
+<div><p>source: <a class="reference external" href="https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance">https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance</a></p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+<li><p><strong>require_grad</strong> (<em>bool</em>) – whether to require gradient for the coordinates</p></li>
+<li><p><strong>activation</strong> (<em>str</em>) – activation function to apply to the heatmap</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2) or (B, C, 3)
+(torch.Tensor): covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.cov_from_gaussian_ls">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">cov_from_gaussian_ls</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ls_library</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'scipy'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#cov_from_gaussian_ls"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.cov_from_gaussian_ls" title="Link to this definition">#</a></dt>
+<dd><p>Returns covariance matrix from a heatmap through fitting the heatmap on Gaussian distribution
+with a specicic scaling factor gamma and specified coordinates
+with help of least squares optimization.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W)</p></li>
+<li><p><strong>coords</strong> (<em>torch.Tensor</em>) – coordinates of shape (B, C, 2)</p></li>
+<li><p><strong>gamma</strong> (<em>float</em>) – gamma parameter of the gaussian heatmap generator</p></li>
+<li><p><strong>ls_library</strong> (<em>str</em>) – library to use for least squares optimization. (scipy or pytorch)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>covariance matrix of shape (B, C, 2, 2)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.heatmap_to_coord">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">heatmap_to_coord</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_coords</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'argmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#heatmap_to_coord"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.heatmap_to_coord" title="Link to this definition">#</a></dt>
+<dd><p>Returns the retrieved coordinates via specified method from a heatmap. The offset_coords is used
+to remove the first offset_coords coordinates from the heatmap. This is used to remove the
+background class (if present).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>offset_coords</strong> (<em>int</em>) – number of coordinates to remove</p></li>
+<li><p><strong>method</strong> (<em>str</em>) – method to retrieve the coordinates</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+<li><p><strong>require_grad</strong> (<em>bool</em>) – whether to require gradient for the coordinates</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2) or (B, C, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.heatmap_to_coord_cov">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">heatmap_to_coord_cov</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'soft_argmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">require_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#heatmap_to_coord_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.heatmap_to_coord_cov" title="Link to this definition">#</a></dt>
+<dd><p>Returns the modal coordinates and covariance matrix from a heatmap.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>method</strong> (<em>str</em>) – method to retrieve the coordinates</p></li>
+<li><p><strong>require_grad</strong> (<em>bool</em>) – whether to require gradient for the coordinates</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2) or (B, C, 3)
+(torch.Tensor): covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.heatmap_to_coord_enlarge">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">heatmap_to_coord_enlarge</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">offset_coords</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'argmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enlarge_factor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enlarge_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enlarge_mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'bilinear'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#heatmap_to_coord_enlarge"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.heatmap_to_coord_enlarge" title="Link to this definition">#</a></dt>
+<dd><p>Returns the retrieved coordinates via specified method from an enlarged heatmap.
+The offset_coords is used to remove the first offset_coords coordinates from the heatmap.
+This is used to remove the background class (if present).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>offset_coords</strong> (<em>int</em>) – number of coordinates to remove</p></li>
+<li><p><strong>method</strong> (<em>str</em>) – method to retrieve the coordinates</p></li>
+<li><p><strong>enlarge_factor</strong> (<em>int</em>) – factor to enlarge the heatmap</p></li>
+<li><p><strong>enlarge_dim</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] or </em><em>None</em>) – dimensions to enlarge the heatmap to</p></li>
+<li><p><strong>enlarge_mode</strong> (<em>str</em>) – interpolation mode to enlarge the heatmap</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>coordinates of shape (B, C, 2)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.heatmap_to_multiple_coord">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">heatmap_to_multiple_coord</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmaps</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'argmax'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#heatmap_to_multiple_coord"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.heatmap_to_multiple_coord" title="Link to this definition">#</a></dt>
+<dd><p>Returns the multiple coordinates of the maximum value of the heatmap
+for each batch and channel. Additionally, the scores of the maxima are returned.
+If threshold is not None, only the maxima with a score higher than the threshold are returned.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W)</p></li>
+<li><p><strong>window</strong> (<em>tuple</em>) – window size</p></li>
+<li><p><strong>threshold</strong> (<em>float</em>) – threshold to remove points. If None, no threshold is applied. Default
+None.</p></li>
+<li><p><strong>method</strong> (<em>str</em>) – method to retrieve the coordinates</p></li>
+<li><p><strong>heatmaps</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><dl class="simple">
+<dt>list of coordinates of the maxima for each batch and</dt><dd><p>channel, with the first list the batch, the second list the channel and the third list
+the coordinates</p>
+</dd>
+<dt>(list(list(list(float)))): list of scores of the maxima for each batch and channel, with</dt><dd><p>the first list the batch, the second list the channel and the third list the scores.</p>
+</dd>
+</dl>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(list(list(list(torch.Tensor))))</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.non_maximum_surpression">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">non_maximum_surpression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#non_maximum_surpression"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.non_maximum_surpression" title="Link to this definition">#</a></dt>
+<dd><p>Non-Maximum Surpression (NMS)</p>
+<p>source: Efficient Non-Maximum Suppression - Neubeck and Van Gool (2006)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (H, W)</p></li>
+<li><p><strong>window</strong> (<em>int</em>) – window size</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>list of coordinates of the maxima</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(list(tuple(int, int)))</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.non_maximum_surpression_local_soft_argmax">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">non_maximum_surpression_local_soft_argmax</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#non_maximum_surpression_local_soft_argmax"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.non_maximum_surpression_local_soft_argmax" title="Link to this definition">#</a></dt>
+<dd><p>Returns the coordinates of the maximum value of the heatmap for each batch and channel
+(landmark), with the soft-argmax function applied on the heatmaps.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (H, W)</p></li>
+<li><p><strong>window</strong> (<em>int</em>) – window size</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>list of coordinates of the maxima</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(list(tuple(int, int)))</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.weighted_sample_cov">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">weighted_sample_cov</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#weighted_sample_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.weighted_sample_cov" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the covariance matrix from a heatmap by calculating the mean of the
+heatmap values weighted by the heatmap values.
+source: <a class="reference external" href="https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance">https://en.wikipedia.org/wiki/Weighted_arithmetic_mean#Weighted_sample_covariance</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W) or (B, C, D, H, W)</p></li>
+<li><p><strong>coords</strong> (<em>torch.Tensor</em>) – coordinates of shape (B, C, 2) or (B, C, 3)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+<li><p><strong>activation</strong> (<em>str</em>) – activation function to apply to the heatmap</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>covariance matrix of shape (B, C, 2, 2) or (B, C, 3, 3)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.heatmap.windowed_weigthed_sample_cov">
+<span class="sig-prename descclassname"><span class="pre">landmarker.heatmap.</span></span><span class="sig-name descname"><span class="pre">windowed_weigthed_sample_cov</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">coords</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/heatmap/decoder.html#windowed_weigthed_sample_cov"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.heatmap.windowed_weigthed_sample_cov" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the covariance matrix from a heatmap by calculating the mean of the
+heatmap values weighted by the heatmap values. The window is determined by the
+distance to the closest edge.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – heatmap of shape (B, C, H, W)</p></li>
+<li><p><strong>coords</strong> (<em>torch.Tensor</em>) – coordinates of shape (B, C, 2)</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions (2 or 3)</p></li>
+<li><p><strong>activation</strong> (<em>str</em>) – activation function to apply to the heatmap</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>covariance matrix of shape (B, C, 2, 2)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>(torch.Tensor)</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Heatmap Module</a><ul>
+<li><a class="reference internal" href="#landmarker.heatmap.GaussianHeatmapGenerator"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapGenerator</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.heatmap.GaussianHeatmapGenerator.__init__"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapGenerator.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.GaussianHeatmapGenerator.heatmap_fun"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapGenerator.heatmap_fun()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.heatmap.LaplacianHeatmapGenerator"><code class="docutils literal notranslate"><span class="pre">LaplacianHeatmapGenerator</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.heatmap.LaplacianHeatmapGenerator.__init__"><code class="docutils literal notranslate"><span class="pre">LaplacianHeatmapGenerator.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.LaplacianHeatmapGenerator.heatmap_fun"><code class="docutils literal notranslate"><span class="pre">LaplacianHeatmapGenerator.heatmap_fun()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.heatmap.coord_argmax"><code class="docutils literal notranslate"><span class="pre">coord_argmax()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.coord_cov_from_gaussian_ls"><code class="docutils literal notranslate"><span class="pre">coord_cov_from_gaussian_ls()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.coord_local_soft_argmax"><code class="docutils literal notranslate"><span class="pre">coord_local_soft_argmax()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.coord_weighted_spatial_mean"><code class="docutils literal notranslate"><span class="pre">coord_weighted_spatial_mean()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.coord_weighted_spatial_mean_cov"><code class="docutils literal notranslate"><span class="pre">coord_weighted_spatial_mean_cov()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.cov_from_gaussian_ls"><code class="docutils literal notranslate"><span class="pre">cov_from_gaussian_ls()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.heatmap_to_coord"><code class="docutils literal notranslate"><span class="pre">heatmap_to_coord()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.heatmap_to_coord_cov"><code class="docutils literal notranslate"><span class="pre">heatmap_to_coord_cov()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.heatmap_to_coord_enlarge"><code class="docutils literal notranslate"><span class="pre">heatmap_to_coord_enlarge()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.heatmap_to_multiple_coord"><code class="docutils literal notranslate"><span class="pre">heatmap_to_multiple_coord()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.non_maximum_surpression"><code class="docutils literal notranslate"><span class="pre">non_maximum_surpression()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.non_maximum_surpression_local_soft_argmax"><code class="docutils literal notranslate"><span class="pre">non_maximum_surpression_local_soft_argmax()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.weighted_sample_cov"><code class="docutils literal notranslate"><span class="pre">weighted_sample_cov()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.heatmap.windowed_weigthed_sample_cov"><code class="docutils literal notranslate"><span class="pre">windowed_weigthed_sample_cov()</span></code></a></li>
+</ul>
+</li>
+</ul>
+
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diff --git a/reference/losses.html b/reference/losses.html
new file mode 100644
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+          <section id="module-landmarker.losses">
+<span id="losses-module"></span><h1>Losses Module<a class="headerlink" href="#module-landmarker.losses" title="Link to this heading">#</a></h1>
+<p>Losses module for training landmark localization models.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.AdaptiveWingLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">AdaptiveWingLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#AdaptiveWingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.AdaptiveWingLoss" title="Link to this definition">#</a></dt>
+<dd><p>Adaptive wing loss is a loss function that behaves like a smoothed Wing loss when the target is
+close to 1 and like the MSE loss when the target is close to 0.</p>
+<p>The loss function is defined as:</p>
+<div class="math-wrapper docutils container">
+<div class="math notranslate nohighlight">
+\[\begin{split}AWing(x, y) =
+\begin{cases}
+    \omega \log(1 + |\frac{x-y}{\epsilon}|^{\alpha-y} &amp;
+        \text{if } |x - y| &lt; \theta \\
+    A|x - y| - C &amp; \text{otherwise}
+\end{cases}})\end{split}\]</div>
+</div>
+<dl>
+<dt>where :math:<a href="#id1"><span class="problematic" id="id2">`</span></a>A = omega (1/(1+(theta/epsilon)^{alpha-y}))(alpha - y)</dt><dd><blockquote>
+<div><blockquote>
+<div><p>((theta/epsilon)^(alpha-y-1))(1/epsilon)` and</p>
+</div></blockquote>
+<p><span class="math notranslate nohighlight">\(C = (\theta * A - \omega * log(1 + (\theta/\epsilon)^{\alpha-y})))\)</span></p>
+</div></blockquote>
+<dl class="simple">
+<dt>source: “Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression” - Wang et al.</dt><dd><ol class="arabic simple" start="2019">
+<li></li>
+</ol>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em><em>, </em><em>optional</em>) – Adaptive wing loss parameter. Defaults to 5.0.</p></li>
+<li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Adaptive wing loss parameter. Defaults to 0.5.</p></li>
+<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – Adaptive wing loss parameter. Defaults to 2.1.</p></li>
+<li><p><strong>theta</strong> (<em>float</em><em>, </em><em>optional</em>) – Adaptive wing loss parameter. Defaults to 0.5.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.AdaptiveWingLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">omega</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2.1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#AdaptiveWingLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.AdaptiveWingLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em>) – </p></li>
+<li><p><strong>epsilon</strong> (<em>float</em>) – </p></li>
+<li><p><strong>alpha</strong> (<em>float</em>) – </p></li>
+<li><p><strong>theta</strong> (<em>float</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.AdaptiveWingLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#AdaptiveWingLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.AdaptiveWingLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceJSDivergenceReg">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">EuclideanDistanceJSDivergenceReg</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceJSDivergenceReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg" title="Link to this definition">#</a></dt>
+<dd><p>Euclidean distance loss with Jensen-Shannon divergence regularization. The regularization term
+imposes a Gaussian distribution on the predicted heatmaps and calculates the Jensen-Shannon
+divergence between the predicted and the target heatmap. The Jensen-Shannon divergence is a
+method to measure the similarity between two probability distributions. It is a symmetrized
+and smoothed version of the Kullback-Leibler divergence, and is defined as the average of the
+Kullback-Leibler divergence between the two distributions and a mixture M of the two
+distributions:</p>
+<blockquote>
+<div><p><span class="math notranslate nohighlight">\(JSD(P||Q) = 0.5 * KL(P||M) + 0.5 * KL(Q||M)\)</span> where <span class="math notranslate nohighlight">\(M = 0.5 * (P + Q)\)</span></p>
+</div></blockquote>
+<p>Generalization of regularization term proposed by Nibali et al. (2018), which only considers
+Multivariate Gaussian distributions, to a generalized Gaussian distribution. (However, now
+we only consider the Gaussian and the Laplace distribution.)</p>
+<blockquote>
+<div><dl class="simple">
+<dt>source: Numerical Coordinate Regression with Convolutional Neural Networks - Nibali et al.</dt><dd><ol class="arabic simple" start="2018">
+<li></li>
+</ol>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – Weight of the regularization term. Defaults to 1.0.</p></li>
+<li><p><strong>sigma_t</strong> (<em>float</em><em>, </em><em>optional</em>) – Target sigma value. Defaults to 1.0.</p></li>
+<li><p><strong>rotation_t</strong> (<em>float</em><em>, </em><em>optional</em>) – Target rotation value. Defaults to 0.0.</p></li>
+<li><p><strong>nb_landmarks</strong> (<em>int</em><em>, </em><em>optional</em>) – Number of landmarks. Defaults to 1.</p></li>
+<li><p><strong>heatmap_fun</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the type of heatmap function to use. Defaults to
+‘gaussian’. Possible values are ‘gaussian’ and ‘laplacian’.</p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>, </em><em>optional</em>) – Size of the heatmap. Defaults to (512, 512).</p></li>
+<li><p><strong>gamma</strong> (<em>Optional</em><em>[</em><em>float</em><em>]</em><em>, </em><em>optional</em>) – Gamma value for the Laplace distribution. Defaults to
+1.0.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+<li><p><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Epsilon value to avoid division by zero. Defaults to 1e-6.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceJSDivergenceReg.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigma_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rotation_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nb_landmarks</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_fun</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'gaussian'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(512,</span> <span class="pre">512)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">gamma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceJSDivergenceReg.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) – </p></li>
+<li><p><strong>sigma_t</strong> (<em>float</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>rotation_t</strong> (<em>float</em><em> | </em><em>Tensor</em>) – </p></li>
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>heatmap_fun</strong> (<em>str</em>) – </p></li>
+<li><p><strong>heatmap_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>]</em>) – </p></li>
+<li><p><strong>gamma</strong> (<em>float</em><em> | </em><em>None</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+<li><p><strong>eps</strong> (<em>float</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceJSDivergenceReg.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceJSDivergenceReg.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>pred_heatmap</strong> (<em>torch.Tensor</em>) – Predicted heatmap.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceVarianceReg">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">EuclideanDistanceVarianceReg</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceVarianceReg"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceVarianceReg" title="Link to this definition">#</a></dt>
+<dd><p>Euclidean distance loss with variance regularization. The regularization term is defined as the
+squared difference between the fitted/predicted variance and a predefined target variance, as
+proposed by Nibali et al. (2018). The authors point out that this regularization term does not
+directly constrain the specif shape of the learned heatmaps.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>source: Numerical Coordinate Regression with Convolutional Neural Networks - Nibali et al.</dt><dd><ol class="arabic simple" start="2018">
+<li></li>
+</ol>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – Weight of the regularization term. Defaults to 1.0.</p></li>
+<li><p><strong>var_t</strong> (<em>float</em><em>, </em><em>optional</em>) – Target variance. Defaults to 1.0.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+<li><p><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Epsilon value to avoid division by zero. Defaults to 1e-6.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceVarianceReg.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">var_t</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceVarianceReg.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceVarianceReg.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) – </p></li>
+<li><p><strong>var_t</strong> (<em>float</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+<li><p><strong>eps</strong> (<em>float</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.EuclideanDistanceVarianceReg.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#EuclideanDistanceVarianceReg.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.EuclideanDistanceVarianceReg.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>cov</strong> (<em>torch.Tensor</em>) – Related covariance matrix of the predicted coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.GaussianHeatmapL2Loss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">GaussianHeatmapL2Loss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GaussianHeatmapL2Loss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GaussianHeatmapL2Loss" title="Link to this definition">#</a></dt>
+<dd><p>Loss function for Gaussian heatmap regression.
+source: <a class="reference external" href="http://arxiv.org/abs/2109.09533">http://arxiv.org/abs/2109.09533</a></p>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.GaussianHeatmapL2Loss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GaussianHeatmapL2Loss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GaussianHeatmapL2Loss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.GaussianHeatmapL2Loss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GaussianHeatmapL2Loss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GaussianHeatmapL2Loss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.GeneralizedNormalHeatmapLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">GeneralizedNormalHeatmapLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GeneralizedNormalHeatmapLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GeneralizedNormalHeatmapLoss" title="Link to this definition">#</a></dt>
+<dd><p>Loss function for adaptive generalized normal direct heatmap regression. The loss function is an
+extension of the loss function proposed by Thaler et al. (2021) for adaptive heatmap regression,
+where they used a anistropic Gaussian distribution for adaptive heatmap regression. The loss
+function is defined as the sum of a specified distance function between the predicted heatmap
+and the target heatmap, additionaly a determinant of the supplied covariance matrix is added as
+regularization term to penalize the loss of the fitted covariance matrix.</p>
+<dl class="simple">
+<dt># TODO: add formula.</dt><dd><p>source: Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization</p>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em><em>, </em><em>optional</em>) – Weight of the regularization term. Defaults to 5.0.</p></li>
+<li><p><strong>distance</strong> (<em>str</em><em>, </em><em>optional</em>) – Distance function to use for the loss calculation. Defaults to
+‘l2’. Possible values are ‘l2’, ‘l1’, ‘smooth-l1’,
+‘bce-with-logits’ and ‘bce’.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+<li><p><strong>**kwargs</strong> – Additional keyword arguments for the distance function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.GeneralizedNormalHeatmapLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GeneralizedNormalHeatmapLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GeneralizedNormalHeatmapLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>alpha</strong> (<em>float</em>) – </p></li>
+<li><p><strong>distance</strong> (<em>str</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.GeneralizedNormalHeatmapLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">heatmap</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#GeneralizedNormalHeatmapLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.GeneralizedNormalHeatmapLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>heatmap</strong> (<em>torch.Tensor</em>) – Predicted heatmap.</p></li>
+<li><p><strong>cov</strong> (<em>torch.Tensor</em>) – Covariance matrix of the fitted heatmap.</p></li>
+<li><p><strong>heatmap_target</strong> (<em>torch.Tensor</em>) – Target heatmap.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Loss value.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.MultivariateGaussianNLLLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">MultivariateGaussianNLLLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#MultivariateGaussianNLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.MultivariateGaussianNLLLoss" title="Link to this definition">#</a></dt>
+<dd><p>Negative log-likelihood loss for multivariate Gaussian distributions. The loss function is
+defined as the negative log-likelihood of the predicted coordinates given the predicted
+covariance matrix. The loss function is defined as:</p>
+<blockquote>
+<div><p><span class="math notranslate nohighlight">\(NLL = 0.5 * (log(det(Cov)) + (x - mu)^T * Cov^{-1} * (x - mu))\)</span></p>
+<dl class="simple">
+<dt>As proposed in: “UGLLI Face Alignment: Estimating Uncertainty with Gaussian Log-Likelihood</dt><dd><p>Loss” - Kumar et al. (2019)</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+<li><p><strong>eps</strong> (<em>float</em><em>, </em><em>optional</em>) – Epsilon value to avoid division by zero. Defaults to 1e-6.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.MultivariateGaussianNLLLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-06</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#MultivariateGaussianNLLLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.MultivariateGaussianNLLLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+<li><p><strong>eps</strong> (<em>float</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.MultivariateGaussianNLLLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#MultivariateGaussianNLLLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.MultivariateGaussianNLLLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>cov</strong> (<em>torch.Tensor</em>) – Related covariance matrix of the predicted coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.NLLLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">NLLLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#NLLLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.NLLLoss" title="Link to this definition">#</a></dt>
+<dd><p>Negative log-likelihood loss for 2D/3D heatmaps. Assumes that the input is a probability
+distribution and calculates the negative log-likelihood of the predicted heatmap given the
+target heatmap.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em><em>, </em><em>optional</em>) – Spatial dimension of the heatmaps. Defaults to 2.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.NLLLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_softmax</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#NLLLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.NLLLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>apply_softmax</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.NLLLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">output</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#NLLLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.NLLLoss.forward" title="Link to this definition">#</a></dt>
+<dd><p>Define the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.StackedLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">StackedLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StackedLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StackedLoss" title="Link to this definition">#</a></dt>
+<dd><p>Stacked loss function. Applies a specified loss function to each list of predictions. This
+loss function is used to calculate the loss for each heatmap in the stacked heatmap regression,
+suchs as stacked hourglass and U-Net networks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>loss_fn</strong> (<em>nn.Module</em>) – Loss function to use.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.StackedLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">loss_fn</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StackedLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StackedLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>loss_fn</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.StackedLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StackedLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StackedLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>preds</strong> (<em>list</em><em>[</em><em>torch.Tensor</em><em>]</em>) – List of predicted heatmaps or coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target heatmap.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.StarLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">StarLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StarLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StarLoss" title="Link to this definition">#</a></dt>
+<dd><p>Self-adapTive Ambiguity Reduction (STAR) loss. Star loss takes into account the ambuigity
+(uncertainty) of the intermediate heatmap predictions by using the covariance matrix of the
+heatmap predictions, and extracting the eigenvectors and eigenvalues of the covariance matrix.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>source: “STAR Loss: Reducing Semantic Ambiguity in Facial Landmark Detection” - Zhou et al.</dt><dd><ol class="arabic simple" start="2023">
+<li></li>
+</ol>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em><em>, </em><em>optional</em>) – Weight of the regularization term. Defaults to 1.0.</p></li>
+<li><p><strong>distance</strong> (<em>str</em><em>, </em><em>optional</em>) – Distance function to use for the loss calculation. Defaults to
+‘l2’. Possible values are ‘l2’, ‘l1’, ‘smooth-l1’.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+<li><p><strong>**kwargs</strong> – Additional keyword arguments for the distance function.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.StarLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">omega</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'l2'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StarLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StarLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em>) – </p></li>
+<li><p><strong>distance</strong> (<em>str</em>) – </p></li>
+<li><p><strong>epsilon</strong> (<em>float</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.StarLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cov</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#StarLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.StarLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>cov</strong> (<em>torch.Tensor</em>) – Related covariance matrix of the predicted coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.losses.WingLoss">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.losses.</span></span><span class="sig-name descname"><span class="pre">WingLoss</span></span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#WingLoss"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.WingLoss" title="Link to this definition">#</a></dt>
+<dd><p>Wing loss, proposed for facial landmark detection by Feng et al. (2018), is a piece-wise loss
+function that focusses more attention on small and medium range erros compared to L2, L1, and
+smooth L1. It has large gradient when the error is small and a constant gradient when the error
+is large.
+The loss function is defined as:</p>
+<div class="math-wrapper docutils container">
+<div class="math notranslate nohighlight">
+\[\begin{split}Wing(x, y) =
+\begin{cases}
+    \omega * log(1 + \frac{|x - y|}{\epsilon}) &amp; \text{if } |x - y| &lt; \omega \\
+    |x - y| - C &amp; \text{otherwise}
+\end{cases}\end{split}\]</div>
+</div>
+<dl class="simple">
+<dt>where <span class="math notranslate nohighlight">\(C = \omega - \omega * log(1 + \frac{\omega}{\epsilon})\)</span></dt><dd><dl class="simple">
+<dt>source: “Wing Loss for Robust Facial Landmark Localisation With Convolutional Neural</dt><dd><p>Networks” - Feng et al. (2018)</p>
+</dd>
+</dl>
+</dd>
+</dl>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em><em>, </em><em>optional</em>) – Wing loss parameter. Defaults to 5.0.</p></li>
+<li><p><strong>epsilon</strong> (<em>float</em><em>, </em><em>optional</em>) – Wing loss parameter. Defaults to 0.5.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – Specifies the reduction to apply to the output. Defaults to
+‘mean’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.WingLoss.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">omega</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">epsilon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#WingLoss.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.WingLoss.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>omega</strong> (<em>float</em>) – </p></li>
+<li><p><strong>epsilon</strong> (<em>float</em>) – </p></li>
+<li><p><strong>reduction</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.losses.WingLoss.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/losses/losses.html#WingLoss.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.losses.WingLoss.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>pred</strong> (<em>torch.Tensor</em>) – Predicted coordinates.</p></li>
+<li><p><strong>target</strong> (<em>torch.Tensor</em>) – Target coordinates.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
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+                <div class="title">Heatmap Module</div>
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+          <div class="left-details">
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+            
+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/predict-idlab/landmarker" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
+                </svg>
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+              
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+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
+      
+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Losses Module</a><ul>
+<li><a class="reference internal" href="#landmarker.losses.AdaptiveWingLoss"><code class="docutils literal notranslate"><span class="pre">AdaptiveWingLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.AdaptiveWingLoss.__init__"><code class="docutils literal notranslate"><span class="pre">AdaptiveWingLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.AdaptiveWingLoss.forward"><code class="docutils literal notranslate"><span class="pre">AdaptiveWingLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceJSDivergenceReg</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg.__init__"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceJSDivergenceReg.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceJSDivergenceReg.forward"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceJSDivergenceReg.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceVarianceReg"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceVarianceReg</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceVarianceReg.__init__"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceVarianceReg.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.EuclideanDistanceVarianceReg.forward"><code class="docutils literal notranslate"><span class="pre">EuclideanDistanceVarianceReg.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.GaussianHeatmapL2Loss"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapL2Loss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.GaussianHeatmapL2Loss.__init__"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapL2Loss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.GaussianHeatmapL2Loss.forward"><code class="docutils literal notranslate"><span class="pre">GaussianHeatmapL2Loss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.GeneralizedNormalHeatmapLoss"><code class="docutils literal notranslate"><span class="pre">GeneralizedNormalHeatmapLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.GeneralizedNormalHeatmapLoss.__init__"><code class="docutils literal notranslate"><span class="pre">GeneralizedNormalHeatmapLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.GeneralizedNormalHeatmapLoss.forward"><code class="docutils literal notranslate"><span class="pre">GeneralizedNormalHeatmapLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.MultivariateGaussianNLLLoss"><code class="docutils literal notranslate"><span class="pre">MultivariateGaussianNLLLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.MultivariateGaussianNLLLoss.__init__"><code class="docutils literal notranslate"><span class="pre">MultivariateGaussianNLLLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.MultivariateGaussianNLLLoss.forward"><code class="docutils literal notranslate"><span class="pre">MultivariateGaussianNLLLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.NLLLoss"><code class="docutils literal notranslate"><span class="pre">NLLLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.NLLLoss.__init__"><code class="docutils literal notranslate"><span class="pre">NLLLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.NLLLoss.forward"><code class="docutils literal notranslate"><span class="pre">NLLLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.StackedLoss"><code class="docutils literal notranslate"><span class="pre">StackedLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.StackedLoss.__init__"><code class="docutils literal notranslate"><span class="pre">StackedLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.StackedLoss.forward"><code class="docutils literal notranslate"><span class="pre">StackedLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.StarLoss"><code class="docutils literal notranslate"><span class="pre">StarLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.StarLoss.__init__"><code class="docutils literal notranslate"><span class="pre">StarLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.StarLoss.forward"><code class="docutils literal notranslate"><span class="pre">StarLoss.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.losses.WingLoss"><code class="docutils literal notranslate"><span class="pre">WingLoss</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.losses.WingLoss.__init__"><code class="docutils literal notranslate"><span class="pre">WingLoss.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.losses.WingLoss.forward"><code class="docutils literal notranslate"><span class="pre">WingLoss.forward()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
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+          <section id="module-landmarker.metrics">
+<span id="metrics-module"></span><h1>Metrics Module<a class="headerlink" href="#module-landmarker.metrics" title="Link to this heading">#</a></h1>
+<p>Metrics module.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.metrics.point_error">
+<span class="sig-prename descclassname"><span class="pre">landmarker.metrics.</span></span><span class="sig-name descname"><span class="pre">point_error</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">reduction</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mean'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/metrics/metrics.html#point_error"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.metrics.point_error" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the point error between true and predicted landmarks. The point error is the mean
+Euclidean distance between the true and predicted landmarks. If the pixel spacing is given, the
+point error is calculated in mm. If the pixel spacing is not given, the point error is
+calculated in pixels. If the dim and dim_orig are given, the landmarks are rescaled to the
+original image size.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>true_landmarks</strong> (<em>torch.Tensor</em>) – true landmarks</p></li>
+<li><p><strong>pred_landmarks</strong> (<em>torch.Tensor</em>) – predicted landmarks</p></li>
+<li><p><strong>dim</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – image size. Defaults to None.</p></li>
+<li><p><strong>dim_orig</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – original image size. Defaults to None.</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – pixel spacing. Defaults to None.</p></li>
+<li><p><strong>padding</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – padding. Defaults to None.</p></li>
+<li><p><strong>reduction</strong> (<em>str</em><em>, </em><em>optional</em>) – reduction method. Defaults to “mean”. Can be “mean”, “nanmean”,
+or “none”.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.metrics.sdr">
+<span class="sig-prename descclassname"><span class="pre">landmarker.metrics.</span></span><span class="sig-name descname"><span class="pre">sdr</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radius</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">true_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nanmean</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/metrics/metrics.html#sdr"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.metrics.sdr" title="Link to this definition">#</a></dt>
+<dd><p>Calculates the success detection rate (SDR), which is the percentage of
+predicted landmarks with a point error smaller or equal than the radius.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>radius</strong> (<em>Iterable</em><em> | </em><em>float</em>) – radius or list of radii</p></li>
+<li><p><strong>true_landmarks</strong> (<em>torch.Tensor</em>) – true landmarks</p></li>
+<li><p><strong>pred_landmarks</strong> (<em>torch.Tensor</em>) – predicted landmarks</p></li>
+<li><p><strong>dim</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – image size. Defaults to None.</p></li>
+<li><p><strong>dim_orig</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – original image size. Defaults to None.</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – pixel spacing. Defaults to None.</p></li>
+<li><p><strong>padding</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – padding. Defaults to None.</p></li>
+<li><p><strong>nanmean</strong> (<em>bool</em><em>, </em><em>optional</em>) – use nanmean instead of mean. Defaults to False.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>SDR or dictionary of SDRs for each radius</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>float | dict[float, float]</p>
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+<span id="models-module"></span><h1>Models Module<a class="headerlink" href="#module-landmarker.models" title="Link to this heading">#</a></h1>
+<p>Module for defining models for landmark detection.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.AddCoordChannels">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">AddCoordChannels</span></span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#AddCoordChannels"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.AddCoordChannels" title="Link to this definition">#</a></dt>
+<dd><p>Adds the x and y coordinates of each pixel as additional channels to the input tensor.
+Optionally, it can also add the radial distance of each pixel to the center of the image as an
+additional channel. This is done to provide the network with spatial information.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>radial_channel</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to add the radial distance of each pixel to the
+center of the image as an additional channel. Defaults to False.</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.AddCoordChannels.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">radial_channel</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#AddCoordChannels.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.AddCoordChannels.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>radial_channel</strong> (<em>bool</em>) – </p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.AddCoordChannels.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#AddCoordChannels.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.AddCoordChannels.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id1"><span class="problematic" id="id2">*</span></a>spatial_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, in_channels + 2 + radial_channel, <a href="#id3"><span class="problematic" id="id4">*</span></a>spatial_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.CholeskyHourglass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">CholeskyHourglass</span></span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#CholeskyHourglass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CholeskyHourglass" title="Link to this definition">#</a></dt>
+<dd><p>Proposed in “UGLLI Face Alignment: Estimating Uncertainty with
+Gaussian Log-Likelihood Loss” - Kumar et al. (2019)
+# TODO: Note that the implementation of Kumar et al. use DU-Net as the backbone.
+# We use the residual hourglass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>img_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – size of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional layer.</p></li>
+<li><p><strong>subunits</strong> (<em>int</em><em>, </em><em>optional</em>) – number of subunits in each convolutional layer.</p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em><em>, </em><em>optional</em>) – upsampling mode. Defaults to ‘nearest’.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p>predicted heatmaps of shape (batch_size, out_channels, <a href="#id5"><span class="problematic" id="id6">*</span></a>img_dims)
+cen: covariance matrices of the predicted heatmaps of shape (batch_size,</p>
+<blockquote>
+<div><p>out_channels, 2, 2)</p>
+</div></blockquote>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pred</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.CholeskyHourglass.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">img_size,</span> <span class="pre">in_channels,</span> <span class="pre">out_channels,</span> <span class="pre">channels=[64,</span> <span class="pre">128,</span> <span class="pre">256,</span> <span class="pre">512],</span> <span class="pre">conv_block=&lt;class</span> <span class="pre">'monai.networks.blocks.convolutions.ResidualUnit'&gt;,</span> <span class="pre">up_sample_mode='nearest'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#CholeskyHourglass.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CholeskyHourglass.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>img_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>conv_block</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.CholeskyHourglass.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#CholeskyHourglass.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CholeskyHourglass.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id7"><span class="problematic" id="id8">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p>predicted heatmaps of shape (batch_size, out_channels, <a href="#id9"><span class="problematic" id="id10">*</span></a>img_dims)
+cen: covariance matrices of the predicted heatmaps of shape (batch_size,</p>
+<blockquote>
+<div><p>out_channels, 2, 2)</p>
+</div></blockquote>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pred</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.CoordConvLayer">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">CoordConvLayer</span></span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#CoordConvLayer"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CoordConvLayer" title="Link to this definition">#</a></dt>
+<dd><p>CoordConv is a convolutional layer that adds the x and y coordinates of each pixel as additional
+channels to the input tensor. Optionally, it can also add the radial distance of each pixel to
+the center of the image as an additional channel. This is done to provide the network with
+spatial information.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>source: “An intriguing failing of convolutional neural networks and the CoordConv</dt><dd><p>solution” - Liu et al.</p>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>radial_channel</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to add the radial distance of each pixel to the
+center of the image as an additional channel. Defaults to False.</p></li>
+<li><p><strong>conv_block</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – convolutional block to use. Defaults to ResidualUnit.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.CoordConvLayer.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radial_channel=False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">conv_block=&lt;class</span> <span class="pre">'monai.networks.blocks.convolutions.ResidualUnit'&gt;</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#CoordConvLayer.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CoordConvLayer.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>radial_channel</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>conv_block</strong> (<em>Module</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.CoordConvLayer.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/coord_conv.html#CoordConvLayer.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.CoordConvLayer.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id11"><span class="problematic" id="id12">*</span></a>spatial_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, out_channels, <a href="#id13"><span class="problematic" id="id14">*</span></a>spatial_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.Hourglass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">Hourglass</span></span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#Hourglass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.Hourglass" title="Link to this definition">#</a></dt>
+<dd><p>Hourglass network is a network with symmetrical encoder and decoder paths. The encoder path
+downsamples the input image while the decoder path upsamples the image. Skip connections are
+added between the encoder and decoder paths to preserve spatial information. This network is
+used for pose estimation.</p>
+<blockquote>
+<div><p>Proposed in: “Stacked Hourglass Networks for Human Pose Estimation” - Newell et al. (2016)</p>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional layer.</p></li>
+<li><p><strong>conv_block</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – convolutional block to use. Defaults to ResidualUnit.</p></li>
+<li><p><strong>pooling</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – pooling layer to use. Defaults to nn.MaxPool2d.</p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em><em>, </em><em>optional</em>) – upsampling mode. Defaults to ‘nearest’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.Hourglass.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">spatial_dims,</span> <span class="pre">in_channels,</span> <span class="pre">out_channels,</span> <span class="pre">channels=[64,</span> <span class="pre">128,</span> <span class="pre">256,</span> <span class="pre">512],</span> <span class="pre">conv_block=&lt;class</span> <span class="pre">'monai.networks.blocks.convolutions.ResidualUnit'&gt;,</span> <span class="pre">pooling=&lt;class</span> <span class="pre">'torch.nn.modules.pooling.MaxPool2d'&gt;,</span> <span class="pre">up_sample_mode='nearest'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#Hourglass.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.Hourglass.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>conv_block</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>pooling</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.Hourglass.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#Hourglass.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.Hourglass.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id15"><span class="problematic" id="id16">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, out_channels, <a href="#id17"><span class="problematic" id="id18">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Tensor</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.OriginalSpatialConfigurationNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">OriginalSpatialConfigurationNet</span></span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#OriginalSpatialConfigurationNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.OriginalSpatialConfigurationNet" title="Link to this definition">#</a></dt>
+<dd><p>Implementation of the Spatial Configuration Network (SCN) from the paper
+“Integrating spatial configuration into heatmap regression based CNNs for landmark localization”
+by Payer et al. (2019).
+<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1361841518305784">https://www.sciencedirect.com/science/article/pii/S1361841518305784</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of input channels. Defaults to 1.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of output channels. Defaults to 4.</p></li>
+<li><p><strong>la_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of output channels for each convolutional layer.
+Defaults to 128.</p></li>
+<li><p><strong>la_depth</strong> (<em>int</em><em>, </em><em>optional</em>) – number of convolutional layers. Defaults to 3.</p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em>, </em><em>optional</em>) – kernel size for the convolutional layers. Defaults to 3.</p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em><em>, </em><em>optional</em>) – dropout probability. Defaults to 0.5.</p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of channels for the convolutional layers. Defaults to
+128.</p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em><em>, </em><em>optional</em>) – kernel size for the convolutional layers. Defaults to 11.</p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em><em>, </em><em>optional</em>) – factor by which the image is downsampled. Defaults to 16.</p></li>
+<li><p><strong>init_weights</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to initialize the weights of the convolutional
+layers.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.OriginalSpatialConfigurationNet.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">11</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_downsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">init_weigths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#OriginalSpatialConfigurationNet.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.OriginalSpatialConfigurationNet.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_depth</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>]</em>) – </p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em>) – </p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em>) – </p></li>
+<li><p><strong>init_weigths</strong> (<em>bool</em>) – </p></li>
+<li><p><strong>spatial_dim</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.OriginalSpatialConfigurationNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#OriginalSpatialConfigurationNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.OriginalSpatialConfigurationNet.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id19"><span class="problematic" id="id20">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, out_channels, <a href="#id21"><span class="problematic" id="id22">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.OriginalSpatialConfigurationNet3d">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">OriginalSpatialConfigurationNet3d</span></span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#OriginalSpatialConfigurationNet3d"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.OriginalSpatialConfigurationNet3d" title="Link to this definition">#</a></dt>
+<dd><p>Implementation of the Spatial Configuration Network (SCN) from the paper
+“Integrating spatial configuration into heatmap regression based CNNs for landmark localization”
+by Payer et al. (2019).
+<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1361841518305784">https://www.sciencedirect.com/science/article/pii/S1361841518305784</a></p>
+<p>This is the 3D version of the original SCN.</p>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.OriginalSpatialConfigurationNet3d.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_depth</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">64</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_downsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#OriginalSpatialConfigurationNet3d.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.OriginalSpatialConfigurationNet3d.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_depth</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>]</em>) – </p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em>) – </p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.ProbSpatialConfigurationNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">ProbSpatialConfigurationNet</span></span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#ProbSpatialConfigurationNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.ProbSpatialConfigurationNet" title="Link to this definition">#</a></dt>
+<dd><p>Probabilistic Spatial Configuration Network (PSCN)</p>
+<p>Adapted implementation of the Probabilistic Spatial Configuration Network (PSCN) from the paper
+“Integrating spatial configuration into heatmap regression based CNNs for landmark localization”
+by Payer et al. (2019). This is the same as the Spatial Configuration Network (SCN), but with
+a different last layer. Instead of multiplying the output of the SCN with the output of the
+spatial configuration network, we add them together, since the output of the spatial
+configuration network is a probability distribution in the logit space.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em><em>, </em><em>optional</em>) – number of spatial dimensions of the input image. Defaults to
+2.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of input channels. Defaults to 1.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of output channels. Defaults to 4.</p></li>
+<li><p><strong>la_channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional
+layer. Defaults to (128, 128, 128, 128).</p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em><em>, </em><em>optional</em>) – kernel size for the convolutional
+layers. Defaults to 3.</p></li>
+<li><p><strong>la_strides</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – strides for the convolutional layers. Defaults to
+(2, 2, 2).</p></li>
+<li><p><strong>la_num_res_units</strong> (<em>int</em><em>, </em><em>optional</em>) – number of residual units in the convolutional layers.
+Defaults to 2.</p></li>
+<li><p><strong>la_norm</strong> (<em>str</em><em>, </em><em>optional</em>) – type of normalization to use. Defaults to “instance”.</p></li>
+<li><p><strong>la_activation</strong> (<em>str</em><em>, </em><em>optional</em>) – type of activation to use. Defaults to “PRELU”.</p></li>
+<li><p><strong>la_adn_ordering</strong> (<em>str</em><em>, </em><em>optional</em>) – ordering of the layers in the residual units. Defaults to
+“NDA”.</p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em><em>, </em><em>optional</em>) – dropout probability. Defaults to 0.0.</p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of channels for the convolutional layers. Defaults to
+128.</p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em><em>, </em><em>optional</em>) – kernel size for the convolutional layers. Defaults to 11.</p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em><em>, </em><em>optional</em>) – factor by which the image is downsampled. Defaults to 16.</p></li>
+<li><p><strong>sp_image_input</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use the input image as input for the spatial</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.ProbSpatialConfigurationNet.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(128,</span> <span class="pre">128,</span> <span class="pre">128,</span> <span class="pre">128,</span> <span class="pre">128)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_num_res_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'instance'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'PRELU'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_adn_ordering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'NDA'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">11</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_downsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_image_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#ProbSpatialConfigurationNet.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.ProbSpatialConfigurationNet.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_strides</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_num_res_units</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_norm</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_activation</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_adn_ordering</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em>) – </p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_image_input</strong> (<em>int</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.ProbSpatialConfigurationNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#ProbSpatialConfigurationNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.ProbSpatialConfigurationNet.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id23"><span class="problematic" id="id24">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, out_channels, <a href="#id25"><span class="problematic" id="id26">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.SpatialConfigurationNet">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">SpatialConfigurationNet</span></span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#SpatialConfigurationNet"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.SpatialConfigurationNet" title="Link to this definition">#</a></dt>
+<dd><p>Adapted implementation of the Spatial Configuration Network (SCN) from the paper
+“Integrating spatial configuration into heatmap regression based CNNs for landmark localization”
+by Payer et al. (2019).
+<a class="reference external" href="https://www.sciencedirect.com/science/article/pii/S1361841518305784">https://www.sciencedirect.com/science/article/pii/S1361841518305784</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>la_channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional
+layer.</p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em>, </em><em>optional</em>) – kernel size for the convolutional layers.</p></li>
+<li><p><strong>la_strides</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – strides for the convolutional layers.</p></li>
+<li><p><strong>la_num_res_units</strong> (<em>int</em><em>, </em><em>optional</em>) – number of residual units in the convolutional layers.</p></li>
+<li><p><strong>la_norm</strong> (<em>str</em><em>, </em><em>optional</em>) – type of normalization to use. Defaults to “INSTANCE”.</p></li>
+<li><p><strong>la_activation</strong> (<em>str</em><em>, </em><em>optional</em>) – type of activation to use. Defaults to “PRELU”.</p></li>
+<li><p><strong>la_adn_ordering</strong> (<em>str</em><em>, </em><em>optional</em>) – ordering of the layers in the residual units. Defaults to
+“ADN”.</p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em><em>, </em><em>optional</em>) – dropout probability. Defaults to 0.0.</p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em><em>, </em><em>optional</em>) – number of channels for the convolutional layers.</p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em><em>, </em><em>optional</em>) – kernel size for the convolutional layers.</p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em><em>, </em><em>optional</em>) – factor by which the image is downsampled.</p></li>
+<li><p><strong>sp_image_input</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to use the input image as input for the spatial
+configuration network.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.SpatialConfigurationNet.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">in_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">4</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(128,</span> <span class="pre">128,</span> <span class="pre">128,</span> <span class="pre">128)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_strides</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(2,</span> <span class="pre">2,</span> <span class="pre">2)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_num_res_units</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'INSTANCE'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'PRELU'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_adn_ordering</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ADN'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">la_dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_channels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_kernel_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">11</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_downsample</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">16</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sp_image_input</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#SpatialConfigurationNet.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.SpatialConfigurationNet.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_kernel_size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_strides</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>la_num_res_units</strong> (<em>int</em>) – </p></li>
+<li><p><strong>la_norm</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_activation</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_adn_ordering</strong> (<em>str</em>) – </p></li>
+<li><p><strong>la_dropout</strong> (<em>float</em>) – </p></li>
+<li><p><strong>sp_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_kernel_size</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_downsample</strong> (<em>int</em>) – </p></li>
+<li><p><strong>sp_image_input</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.SpatialConfigurationNet.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/spatial_configuration_net.html#SpatialConfigurationNet.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.SpatialConfigurationNet.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id27"><span class="problematic" id="id28">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>output tensor of shape (batch_size, out_channels, <a href="#id29"><span class="problematic" id="id30">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.StackedCholeskyHourglass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">StackedCholeskyHourglass</span></span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#StackedCholeskyHourglass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedCholeskyHourglass" title="Link to this definition">#</a></dt>
+<dd><p>Stacked Cholesky Hourglass Network as proposed in “UGLLI Face Alignment: Estimating Uncertainty
+with Gaussian Log-Likelihood Loss” - Kumar et al. (2019).  It is a stack of hourglass networks
+with a Cholesky Estimator Network at the bottleneck of each hourglass. The output of the
+Cholesky Estimator Network is a lower triangular matrix that is used to estimate the covariance
+matrix of the Gaussian distribution of the predicted heatmaps. The covariance matrix is then
+used to compute the Gaussian Log-Likelihood Loss.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_stacks</strong> (<em>int</em>) – number of hourglass networks to stack.</p></li>
+<li><p><strong>img_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – size of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional layer.</p></li>
+<li><p><strong>conv_block</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – convolutional block to use. Defaults to ResidualUnit.</p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em><em>, </em><em>optional</em>) – upsampling mode. Defaults to ‘nearest’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.StackedCholeskyHourglass.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">nb_stacks,</span> <span class="pre">img_size,</span> <span class="pre">in_channels,</span> <span class="pre">out_channels,</span> <span class="pre">channels=[64,</span> <span class="pre">128,</span> <span class="pre">256,</span> <span class="pre">512],</span> <span class="pre">conv_block=&lt;class</span> <span class="pre">'monai.networks.blocks.convolutions.ResidualUnit'&gt;,</span> <span class="pre">up_sample_mode='nearest'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#StackedCholeskyHourglass.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedCholeskyHourglass.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_stacks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>img_size</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>conv_block</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.StackedCholeskyHourglass.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/cholesky_hourglass.html#StackedCholeskyHourglass.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedCholeskyHourglass.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id31"><span class="problematic" id="id32">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><p>list of predicted heatmaps of shape (batch_size, out_channels, <a href="#id33"><span class="problematic" id="id34">*</span></a>img_dims)
+cens: list of covariance matrices of the predicted heatmaps of shape (batch_size,</p>
+<blockquote>
+<div><p>out_channels, 2, 2)</p>
+</div></blockquote>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>heatmaps</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.models.StackedHourglass">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.models.</span></span><span class="sig-name descname"><span class="pre">StackedHourglass</span></span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#StackedHourglass"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedHourglass" title="Link to this definition">#</a></dt>
+<dd><p>Stacked hourglass.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_stacks</strong> (<em>int</em>) – number of hourglass modules to stack.</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – number of spatial dimensions of the input image.</p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – number of input channels.</p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – number of output channels.</p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>optional</em>) – number of output channels for each convolutional layer.</p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em><em>, </em><em>optional</em>) – upsampling mode. Defaults to ‘nearest’.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.StackedHourglass.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="pre">nb_stacks,</span> <span class="pre">spatial_dims,</span> <span class="pre">in_channels,</span> <span class="pre">out_channels,</span> <span class="pre">channels=[64,</span> <span class="pre">128,</span> <span class="pre">256,</span> <span class="pre">512],</span> <span class="pre">conv_block=&lt;class</span> <span class="pre">'monai.networks.blocks.convolutions.ResidualUnit'&gt;,</span> <span class="pre">pooling=&lt;class</span> <span class="pre">'torch.nn.modules.pooling.MaxPool2d'&gt;,</span> <span class="pre">up_sample_mode='nearest'</span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#StackedHourglass.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedHourglass.__init__" title="Link to this definition">#</a></dt>
+<dd><p>Initialize internal Module state, shared by both nn.Module and ScriptModule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_stacks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – </p></li>
+<li><p><strong>in_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>out_channels</strong> (<em>int</em>) – </p></li>
+<li><p><strong>channels</strong> (<em>Sequence</em><em>[</em><em>int</em><em>]</em>) – </p></li>
+<li><p><strong>conv_block</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>pooling</strong> (<em>Module</em>) – </p></li>
+<li><p><strong>up_sample_mode</strong> (<em>str</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.models.StackedHourglass.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">x</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/models/hourglass.html#StackedHourglass.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.models.StackedHourglass.forward" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>x</strong> (<em>Tensor</em>) – input tensor of shape (batch_size, in_channels, <a href="#id35"><span class="problematic" id="id36">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>list of output tensors of shape (batch_size, out_channels, <a href="#id37"><span class="problematic" id="id38">*</span></a>img_dims)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list[<em>Tensor</em>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
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+                <div class="title">Losses Module</div>
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+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Models Module</a><ul>
+<li><a class="reference internal" href="#landmarker.models.AddCoordChannels"><code class="docutils literal notranslate"><span class="pre">AddCoordChannels</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.models.AddCoordChannels.__init__"><code class="docutils literal notranslate"><span class="pre">AddCoordChannels.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.models.AddCoordChannels.forward"><code class="docutils literal notranslate"><span class="pre">AddCoordChannels.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.models.CholeskyHourglass"><code class="docutils literal notranslate"><span class="pre">CholeskyHourglass</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.models.CholeskyHourglass.__init__"><code class="docutils literal notranslate"><span class="pre">CholeskyHourglass.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.models.CholeskyHourglass.forward"><code class="docutils literal notranslate"><span class="pre">CholeskyHourglass.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.models.CoordConvLayer"><code class="docutils literal notranslate"><span class="pre">CoordConvLayer</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.models.CoordConvLayer.__init__"><code class="docutils literal notranslate"><span class="pre">CoordConvLayer.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.models.CoordConvLayer.forward"><code class="docutils literal notranslate"><span class="pre">CoordConvLayer.forward()</span></code></a></li>
+</ul>
+</li>
+<li><a class="reference internal" href="#landmarker.models.Hourglass"><code class="docutils literal notranslate"><span class="pre">Hourglass</span></code></a><ul>
+<li><a class="reference internal" href="#landmarker.models.Hourglass.__init__"><code class="docutils literal notranslate"><span class="pre">Hourglass.__init__()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.models.Hourglass.forward"><code class="docutils literal notranslate"><span class="pre">Hourglass.forward()</span></code></a></li>
+</ul>
+</li>
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+          <section id="module-landmarker.schedulers">
+<span id="schedulers-module"></span><h1>Schedulers Module<a class="headerlink" href="#module-landmarker.schedulers" title="Link to this heading">#</a></h1>
+<p>Schedulers module for training.</p>
+<dl class="py class">
+<dt class="sig sig-object py" id="landmarker.schedulers.AdalossScheduler">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">landmarker.schedulers.</span></span><span class="sig-name descname"><span class="pre">AdalossScheduler</span></span><a class="reference internal" href="../_modules/landmarker/schedulers/adaloss.html#AdalossScheduler"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.schedulers.AdalossScheduler" title="Link to this definition">#</a></dt>
+<dd><p>Scheduler for sigma parameter of gaussian heatmaps. In essence the scheduler increases the
+problem difficulty by decreasing the sigma parameter. Implicitly by doing this, it creates a
+training curriculum.</p>
+<blockquote>
+<div><dl class="simple">
+<dt>Proposed in “Adaloss: Adaptive Loss Function for Landmark Localization” - Teixeira et al.</dt><dd><ol class="arabic simple" start="2019">
+<li></li>
+</ol>
+</dd>
+</dl>
+</div></blockquote>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – number of landmarks</p></li>
+<li><p><strong>rho</strong> (<em>float</em><em>, </em><em>optional</em>) – [description]. Defaults to 0.9.</p></li>
+<li><p><strong>window</strong> (<em>int</em><em>, </em><em>optional</em>) – [description]. Defaults to 3.</p></li>
+<li><p><strong>non_increasing</strong> (<em>bool</em><em>, </em><em>optional</em>) – [description]. Defaults to False.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="landmarker.schedulers.AdalossScheduler.__init__">
+<span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">nb_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rho</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_increasing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/schedulers/adaloss.html#AdalossScheduler.__init__"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.schedulers.AdalossScheduler.__init__" title="Link to this definition">#</a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>nb_landmarks</strong> (<em>int</em>) – </p></li>
+<li><p><strong>rho</strong> (<em>float</em>) – </p></li>
+<li><p><strong>window</strong> (<em>int</em>) – </p></li>
+<li><p><strong>non_increasing</strong> (<em>bool</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
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+
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+
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+          <section id="module-landmarker.utils">
+<span id="utils-module"></span><h1>Utils Module<a class="headerlink" href="#module-landmarker.utils" title="Link to this heading">#</a></h1>
+<p>Utils module.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.all_annotations_to_landmarks">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">all_annotations_to_landmarks</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#all_annotations_to_landmarks"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.all_annotations_to_landmarks" title="Link to this definition">#</a></dt>
+<dd><p>Convert all annotations of LabelMe to landmarks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paths</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – list of paths to the annotations</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – class names</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.all_annotations_to_landmarks_numpy">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">all_annotations_to_landmarks_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">paths</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#all_annotations_to_landmarks_numpy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.all_annotations_to_landmarks_numpy" title="Link to this definition">#</a></dt>
+<dd><p>Convert all annotations of LabelMe to landmarks (Numpy version).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>paths</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – list of paths to the annotations</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – class names</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.annotation_to_landmark">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">annotation_to_landmark</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">json_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#annotation_to_landmark"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.annotation_to_landmark" title="Link to this definition">#</a></dt>
+<dd><p>Convert the annotation of LabelMe to landmarks.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>json_obj</strong> (<em>dict</em>) – annotation in json format</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – class names</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.annotation_to_landmark_numpy">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">annotation_to_landmark_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">json_obj</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#annotation_to_landmark_numpy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.annotation_to_landmark_numpy" title="Link to this definition">#</a></dt>
+<dd><p>Convert the annotation of LabelMe to landmarks (Numpy version).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>json_obj</strong> (<em>dict</em>) – annotation in json format</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>]</em>) – class names</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.covert_video_to_frames">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">covert_video_to_frames</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">video_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">frames_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">zero_fill</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#covert_video_to_frames"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.covert_video_to_frames" title="Link to this definition">#</a></dt>
+<dd><p>Convert a video to frames.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>video_path</strong> (<em>str</em>) – path to the video</p></li>
+<li><p><strong>frames_path</strong> (<em>str</em>) – path to the folder where the frames are saved</p></li>
+<li><p><strong>zero_fill</strong> (<em>int</em><em>, </em><em>optional</em>) – number of digits in the frame name. Defaults to 6.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.extract_roi">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">extract_roi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">imgs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">roi_middle</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">spatial_dims</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ensure_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/preprocessing.html#extract_roi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.extract_roi" title="Link to this definition">#</a></dt>
+<dd><p>Extract a region of interest around the landmarks in the image.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>img</strong> (<em>torch.Tensor</em>) – Image to extract the region of interest from.</p></li>
+<li><p><strong>roi_middle</strong> (<em>torch.Tensor</em>) – Middle of the region of interest.</p></li>
+<li><p><strong>landmark</strong> (<em>torch.Tensor</em>) – Landmark to transform to the region of interest.</p></li>
+<li><p><strong>size</strong> (<em>int</em><em> | </em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>int</em><em>]</em>) – Size of the region of interest.</p></li>
+<li><p><strong>spatial_dims</strong> (<em>int</em>) – Number of spatial dimensions of the image.</p></li>
+<li><p><strong>ensure_dim</strong> (<em>bool</em>) – Ensure the output has the same number of dimensions as the input.</p></li>
+<li><p><strong>imgs</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>landmarks</strong> (<em>Tensor</em>) – </p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Region of interest around the landmarks.
+torch.Tensor: Landmarks in the region of interest.
+torch.Tensor: Upper left corner of the region of interest.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.get_angle">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">get_angle</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#get_angle"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.get_angle" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the angle between three points. The angle is calculated in degrees or radians. The
+angle is calculated in the direction of p2 -&gt; p3. If the angle is radial, the angle is between
+0 and 2pi. If the angle is not radial, the angle is between 0 and 360.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>p1</strong> (<em>torch.Tensor</em>) – first point</p></li>
+<li><p><strong>p2</strong> (<em>torch.Tensor</em>) – second point</p></li>
+<li><p><strong>p3</strong> (<em>torch.Tensor</em>) – third point</p></li>
+<li><p><strong>radial</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether the angle is radial. Defaults to True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>angle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.get_angle_numpy">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">get_angle_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">p1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">p3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radial</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#get_angle_numpy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.get_angle_numpy" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the angle between three points (Numpy version). See documentation of get_angle.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>p1</strong> (<em>np.ndarray</em>) – first point</p></li>
+<li><p><strong>p2</strong> (<em>np.ndarray</em>) – second point</p></li>
+<li><p><strong>p3</strong> (<em>np.ndarray</em>) – third point</p></li>
+<li><p><strong>radial</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether the angle is radial. Defaults to True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>angle</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.get_paths">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">get_paths</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">extension</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#get_paths"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.get_paths" title="Link to this definition">#</a></dt>
+<dd><p>Retrieve all paths with a defined extension files recursively from the folder path.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>folder_path</strong> (<em>str</em>) – path to the folder</p></li>
+<li><p><strong>extension</strong> (<em>str</em>) – file extension</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>list of paths</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>list[str]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.normalize">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">normalize</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">img</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/preprocessing.html#normalize"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.normalize" title="Link to this definition">#</a></dt>
+<dd><p>Normalize image to 0-1 range</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>img</strong> (<em>np.ndarray</em>) – Image to normalize</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Normalized image</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.pixel_to_unit">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">pixel_to_unit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#pixel_to_unit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.pixel_to_unit" title="Link to this definition">#</a></dt>
+<dd><p>Convert the landmarks from pixel to unit.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>landmarks</strong> (<em>torch.Tensor</em>) – landmarks</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – pixel spacing. Defaults to None.</p></li>
+<li><p><strong>dim</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – image size. Defaults to None.</p></li>
+<li><p><strong>dim_orig</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – original image size. Defaults to None.</p></li>
+<li><p><strong>padding</strong> (<em>Optional</em><em>[</em><em>torch.Tensor</em><em>]</em><em>, </em><em>optional</em>) – padding. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks in units</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.pixel_to_unit_numpy">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">pixel_to_unit_numpy</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/utils.html#pixel_to_unit_numpy"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.pixel_to_unit_numpy" title="Link to this definition">#</a></dt>
+<dd><p>Convert the landmarks from pixel to unit (Numpy version).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>landmarks</strong> (<em>np.ndarray</em>) – landmarks</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em>]</em><em>, </em><em>optional</em>) – pixel spacing. Defaults to None.</p></li>
+<li><p><strong>dim</strong> (<em>Optional</em><em>[</em><em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>np.ndarray</em><em>]</em><em>, </em><em>optional</em>) – image size. Defaults to None.</p></li>
+<li><p><strong>dim_orig</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em>]</em><em>, </em><em>optional</em>) – original image size. Defaults to None.</p></li>
+<li><p><strong>padding</strong> (<em>Optional</em><em>[</em><em>np.ndarray</em><em>]</em><em>, </em><em>optional</em>) – padding. Defaults to None.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>landmarks in units</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.utils.preprocess_all">
+<span class="sig-prename descclassname"><span class="pre">landmarker.utils.</span></span><span class="sig-name descname"><span class="pre">preprocess_all</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_folder</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fun_name</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/utils/preprocessing.html#preprocess_all"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.utils.preprocess_all" title="Link to this definition">#</a></dt>
+<dd><p>Apply a preprocessing function to all images in a folder.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>folder</strong> (<em>str</em>) – Folder containing images to preprocess</p></li>
+<li><p><strong>output_folder</strong> (<em>str</em>) – Folder to store the preprocessed images</p></li>
+<li><p><strong>fun_name</strong> (<em>str</em>) – Name of the function to apply. (Only normalize supported for now)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
+        </article>
+      </div>
+      <footer>
+        
+        <div class="related-pages">
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+          <div class="left-details">
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+          </div>
+          <div class="right-details">
+            <div class="icons">
+              <a class="muted-link " href="https://github.com/predict-idlab/landmarker" aria-label="GitHub">
+                <svg stroke="currentColor" fill="currentColor" stroke-width="0" viewBox="0 0 16 16">
+                    <path fill-rule="evenodd" d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.013 8.013 0 0 0 16 8c0-4.42-3.58-8-8-8z"></path>
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+              
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+      </footer>
+    </div>
+    <aside class="toc-drawer">
+      
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+      <div class="toc-sticky toc-scroll">
+        <div class="toc-title-container">
+          <span class="toc-title">
+            On this page
+          </span>
+        </div>
+        <div class="toc-tree-container">
+          <div class="toc-tree">
+            <ul>
+<li><a class="reference internal" href="#">Utils Module</a><ul>
+<li><a class="reference internal" href="#landmarker.utils.all_annotations_to_landmarks"><code class="docutils literal notranslate"><span class="pre">all_annotations_to_landmarks()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.all_annotations_to_landmarks_numpy"><code class="docutils literal notranslate"><span class="pre">all_annotations_to_landmarks_numpy()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.annotation_to_landmark"><code class="docutils literal notranslate"><span class="pre">annotation_to_landmark()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.annotation_to_landmark_numpy"><code class="docutils literal notranslate"><span class="pre">annotation_to_landmark_numpy()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.covert_video_to_frames"><code class="docutils literal notranslate"><span class="pre">covert_video_to_frames()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.extract_roi"><code class="docutils literal notranslate"><span class="pre">extract_roi()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.get_angle"><code class="docutils literal notranslate"><span class="pre">get_angle()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.get_angle_numpy"><code class="docutils literal notranslate"><span class="pre">get_angle_numpy()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.get_paths"><code class="docutils literal notranslate"><span class="pre">get_paths()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.normalize"><code class="docutils literal notranslate"><span class="pre">normalize()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.pixel_to_unit"><code class="docutils literal notranslate"><span class="pre">pixel_to_unit()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.pixel_to_unit_numpy"><code class="docutils literal notranslate"><span class="pre">pixel_to_unit_numpy()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.utils.preprocess_all"><code class="docutils literal notranslate"><span class="pre">preprocess_all()</span></code></a></li>
+</ul>
+</li>
+</ul>
+
+          </div>
+        </div>
+      </div>
+      
+      
+    </aside>
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+          <section id="module-landmarker.visualize">
+<span id="visualize-module"></span><h1>Visualize Module<a class="headerlink" href="#module-landmarker.visualize" title="Link to this heading">#</a></h1>
+<p>Vizualization module.</p>
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.visualize.detection_report">
+<span class="sig-prename descclassname"><span class="pre">landmarker.visualize.</span></span><span class="sig-name descname"><span class="pre">detection_report</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">radius</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[2,</span> <span class="pre">2.5,</span> <span class="pre">3,</span> <span class="pre">4]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">digits</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mm'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">output_dict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/visualize/evaluation.html#detection_report"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.visualize.detection_report" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the detection report.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>true_landmarks</strong> (<em>Tensor</em>) – torch.Tensor
+The true landmarks, with shape (n, p, 2).
+n is the number of samples, p is the number of landmarks,
+and 2 is respectively the y and x coordinates of the landmarks.</p></li>
+<li><p><strong>pred_landmarks</strong> (<em>Tensor</em>) – torch.Tensor
+The predicted landmarks, with shape (n, p, 2).
+n is the number of samples, p is the number of landmarks,
+and 2 is respectively the y and x coordinates of the landmarks.</p></li>
+<li><p><strong>dim</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Tensor</em>) – tuple[int, …] | torch.Tensor
+The dimension of the image, with shape (2,).
+2 is respectively the height and width of the image.</p></li>
+<li><p><strong>dim_orig</strong> (<em>Tensor</em>) – torch.Tensor
+The original dimension of the image, with shape (2,).
+2 is respectively the height and width of the image.</p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Tensor</em>) – torch.Tensor
+The pixel spacing of the image, with shape (2,).
+2 is respectively the height and width of the image.</p></li>
+<li><p><strong>padding</strong> (<em>Tensor</em>) – torch.Tensor
+The padding of the image, with shape (2,).
+2 is respectively the height and width of the image.</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – list
+The names of the landmarks. The default is None.</p></li>
+<li><p><strong>radius</strong> (<em>list</em><em>[</em><em>float</em><em>]</em>) – list[float], optional
+The radius of the success detection rate (SDR). The default is [2, 2.5, 3, 4].</p></li>
+<li><p><strong>digits</strong> (<em>int</em>) – int, optional
+The number of digits to round the statistics. The default is 2.</p></li>
+<li><p><strong>unit</strong> (<em>str</em>) – str, optional
+The unit of distance. The default is ‘mm’.</p></li>
+<li><p><strong>output_dict</strong> (<em>bool</em>) – bool, optional
+Whether to output the detection report as a dictionary. The default is False.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><dl class="simple">
+<dt>dict</dt><dd><p>The detection report. Only output when output_dict is True.</p>
+</dd>
+</dl>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>report</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.visualize.inspection_plot">
+<span class="sig-prename descclassname"><span class="pre">landmarker.visualize.</span></span><span class="sig-name descname"><span class="pre">inspection_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">heatmap_generator</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Landmark</span> <span class="pre">Dataset</span> <span class="pre">Inspection</span> <span class="pre">Plot'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/visualize/utils.html#inspection_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.visualize.inspection_plot" title="Link to this definition">#</a></dt>
+<dd><p>Plots the transformed image, heatmap, and original image with landmarks for the given dataset
+indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ds</strong> (<a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.LandmarkDataset"><em>LandmarkDataset</em></a>) – Dataset to inspect.</p></li>
+<li><p><strong>idx</strong> (<em>int</em><em> | </em><em>Iterable</em><em>[</em><em>int</em><em>]</em>) – Indices of the dataset to inspect.</p></li>
+<li><p><strong>heatmap_generator</strong> (<em>HeatmapGenerator</em><em>, </em><em>optional</em>) – Heatmap generator to use. Defaults to None.</p></li>
+<li><p><strong>save_path</strong> (<em>str</em><em>, </em><em>optional</em>) – Path to save the plot to. If None, the plot is not saved.
+Defaults to None.</p></li>
+<li><p><strong>fig_title</strong> (<em>str</em><em>, </em><em>optional</em>) – Title of the figure. Defaults to “Landmark Dataset Inspection
+Plot”.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.visualize.plot_cpe">
+<span class="sig-prename descclassname"><span class="pre">landmarker.visualize.</span></span><span class="sig-name descname"><span class="pre">plot_cpe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pred_landmarks</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dim_orig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pixel_spacing</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">padding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">class_names</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">group</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stat</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'proportion'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">unit</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'mm'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kind</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ecdf'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/visualize/evaluation.html#plot_cpe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.visualize.plot_cpe" title="Link to this definition">#</a></dt>
+<dd><p>Calculate the cumulative point-to-point error (CPE) and plot the CPE curve.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>y_true</strong> – numpy.ndarray
+The true values of the target variable, with shape (n, p, 2).
+n is the number of samples, p is the number of landmarks,
+and 2 is respectively the y and x coordinates of the landmarks.</p></li>
+<li><p><strong>y_pred</strong> – numpy.ndarray
+The predicted values of the target variable, with shape (n, p, 2).
+n is the number of samples, p is the number of landmarks,
+and 2 is respectively the y and x coordinates of the landmarks.</p></li>
+<li><p><strong>class_names</strong> (<em>list</em><em>[</em><em>str</em><em>] </em><em>| </em><em>None</em>) – list
+The names of the landmarks. The default is None.</p></li>
+<li><p><strong>group</strong> (<em>bool</em>) – bool, optional
+Whether to group the CPE curves by landmarks. The default is True.</p></li>
+<li><p><strong>title</strong> (<em>str</em><em> | </em><em>None</em>) – str, optional
+The title of the plot. The default is None.</p></li>
+<li><p><strong>save_path</strong> (<em>str</em><em> | </em><em>None</em>) – str, optional
+The path to save the plot. The default is None.</p></li>
+<li><p><strong>stat</strong> (<em>str</em>) – str, optional
+The type of statistic to plot. The default is ‘proportion’.</p></li>
+<li><p><strong>unit</strong> (<em>str</em>) – str, optional
+The unit of distance. The default is ‘mm’.</p></li>
+<li><p><strong>kind</strong> (<em>str</em>) – str, optional
+The type of plot. The default is ‘ecdf’.
+Possible values are ‘ecdf’, ‘kde’, and ‘hist’.</p></li>
+<li><p><strong>true_landmarks</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>pred_landmarks</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>dim</strong> (<em>tuple</em><em>[</em><em>int</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Tensor</em>) – </p></li>
+<li><p><strong>dim_orig</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>pixel_spacing</strong> (<em>Tensor</em>) – </p></li>
+<li><p><strong>padding</strong> (<em>Tensor</em>) – </p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="landmarker.visualize.prediction_inspect_plot">
+<span class="sig-prename descclassname"><span class="pre">landmarker.visualize.</span></span><span class="sig-name descname"><span class="pre">prediction_inspect_plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">activation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">Identity()</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">save_path</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fig_title</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Landmark</span> <span class="pre">Prediction</span> <span class="pre">Model</span> <span class="pre">Inspection</span> <span class="pre">Plot'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/landmarker/visualize/utils.html#prediction_inspect_plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#landmarker.visualize.prediction_inspect_plot" title="Link to this definition">#</a></dt>
+<dd><p>Plots the transformed image, predicted heatmap, and original image with predicted and true
+landmarks for the given dataset indices.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ds</strong> (<a class="reference internal" href="data.html#landmarker.data.LandmarkDataset" title="landmarker.data.LandmarkDataset"><em>LandmarkDataset</em></a>) – Dataset to inspect.</p></li>
+<li><p><strong>model</strong> (<em>nn.Module</em>) – Model to use for prediction.</p></li>
+<li><p><strong>idx</strong> (<em>int</em><em> | </em><em>Sequence</em><em>[</em><em>int</em><em>]</em>) – Indices of the dataset to inspect.</p></li>
+<li><p><strong>activation</strong> (<em>nn.Module</em><em>, </em><em>optional</em>) – Activation function to use. Defaults to nn.Identity().</p></li>
+<li><p><strong>save_path</strong> (<em>str</em><em>, </em><em>optional</em>) – Path to save the plot to. If None, the plot is not saved.
+Defaults to None.</p></li>
+<li><p><strong>fig_title</strong> (<em>str</em><em>, </em><em>optional</em>) – Title of the figure. Defaults to “Landmark Prediction Model
+Inspection Plot”.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+
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+            <ul>
+<li><a class="reference internal" href="#">Visualize Module</a><ul>
+<li><a class="reference internal" href="#landmarker.visualize.detection_report"><code class="docutils literal notranslate"><span class="pre">detection_report()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.visualize.inspection_plot"><code class="docutils literal notranslate"><span class="pre">inspection_plot()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.visualize.plot_cpe"><code class="docutils literal notranslate"><span class="pre">plot_cpe()</span></code></a></li>
+<li><a class="reference internal" href="#landmarker.visualize.prediction_inspect_plot"><code class="docutils literal notranslate"><span class="pre">prediction_inspect_plot()</span></code></a></li>
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Permanent Ban": [[5, "permanent-ban"]], "Attribution": [[5, "attribution"]], "\ud83e\udd1d Contributing to landmarker": [[6, "contributing-to-landmarker"]], "Setting up your development environment": [[6, "setting-up-your-development-environment"]], "Running tests": [[6, "running-tests"]], "Building documentation": [[6, "building-documentation"]], "Ground Rules": [[6, "ground-rules"]], "\ud83d\udcdd License": [[7, "license"]], "IMEC License": [[7, "imec-license"]], "\ud83d\udd26 Examples": [[8, "examples"]], "Landmarker": [[9, "landmarker"]], "Data Module": [[10, "module-landmarker.data"]], "Datasets Module": [[11, "module-landmarker.datasets"]], "Heatmap Module": [[12, "module-landmarker.heatmap"]], "\ud83d\udcd6 API Reference": [[13, "api-reference"]], "Losses Module": [[14, "module-landmarker.losses"]], "Metrics Module": [[15, "module-landmarker.metrics"]], "Models Module": [[16, "module-landmarker.models"]], "Schedulers Module": [[17, "module-landmarker.schedulers"]], "Train 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[[16, "landmarker.models.AddCoordChannels.forward"]], "forward() (landmarker.models.choleskyhourglass method)": [[16, "landmarker.models.CholeskyHourglass.forward"]], "forward() (landmarker.models.coordconvlayer method)": [[16, "landmarker.models.CoordConvLayer.forward"]], "forward() (landmarker.models.hourglass method)": [[16, "landmarker.models.Hourglass.forward"]], "forward() (landmarker.models.originalspatialconfigurationnet method)": [[16, "landmarker.models.OriginalSpatialConfigurationNet.forward"]], "forward() (landmarker.models.probspatialconfigurationnet method)": [[16, "landmarker.models.ProbSpatialConfigurationNet.forward"]], "forward() (landmarker.models.spatialconfigurationnet method)": [[16, "landmarker.models.SpatialConfigurationNet.forward"]], "forward() (landmarker.models.stackedcholeskyhourglass method)": [[16, "landmarker.models.StackedCholeskyHourglass.forward"]], "forward() (landmarker.models.stackedhourglass method)": [[16, "landmarker.models.StackedHourglass.forward"]], "landmarker.models": [[16, "module-landmarker.models"]], "adalossscheduler (class in landmarker.schedulers)": [[17, "landmarker.schedulers.AdalossScheduler"]], "__init__() (landmarker.schedulers.adalossscheduler method)": [[17, "landmarker.schedulers.AdalossScheduler.__init__"]], "landmarker.schedulers": [[17, "module-landmarker.schedulers"]], "landmarker.transforms": [[19, "module-landmarker.transforms"]], "all_annotations_to_landmarks() (in module landmarker.utils)": [[21, "landmarker.utils.all_annotations_to_landmarks"]], "all_annotations_to_landmarks_numpy() (in module landmarker.utils)": [[21, "landmarker.utils.all_annotations_to_landmarks_numpy"]], "annotation_to_landmark() (in module landmarker.utils)": [[21, "landmarker.utils.annotation_to_landmark"]], "annotation_to_landmark_numpy() (in module landmarker.utils)": [[21, "landmarker.utils.annotation_to_landmark_numpy"]], "covert_video_to_frames() (in module landmarker.utils)": [[21, "landmarker.utils.covert_video_to_frames"]], "extract_roi() (in module landmarker.utils)": [[21, "landmarker.utils.extract_roi"]], "get_angle() (in module landmarker.utils)": [[21, "landmarker.utils.get_angle"]], "get_angle_numpy() (in module landmarker.utils)": [[21, "landmarker.utils.get_angle_numpy"]], "get_paths() (in module landmarker.utils)": [[21, "landmarker.utils.get_paths"]], "landmarker.utils": [[21, "module-landmarker.utils"]], "normalize() (in module landmarker.utils)": [[21, "landmarker.utils.normalize"]], "pixel_to_unit() (in module landmarker.utils)": [[21, "landmarker.utils.pixel_to_unit"]], "pixel_to_unit_numpy() (in module landmarker.utils)": [[21, "landmarker.utils.pixel_to_unit_numpy"]], "preprocess_all() (in module landmarker.utils)": [[21, "landmarker.utils.preprocess_all"]], "detection_report() (in module landmarker.visualize)": [[22, "landmarker.visualize.detection_report"]], "inspection_plot() (in module landmarker.visualize)": [[22, "landmarker.visualize.inspection_plot"]], "landmarker.visualize": [[22, "module-landmarker.visualize"]], "plot_cpe() (in module landmarker.visualize)": [[22, "landmarker.visualize.plot_cpe"]], "prediction_inspect_plot() (in module landmarker.visualize)": [[22, "landmarker.visualize.prediction_inspect_plot"]]}})
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+        <article role="main">
+          <section id="getting-started">
+<h1>🚀 Getting Started<a class="headerlink" href="#getting-started" title="Link to this heading">#</a></h1>
+<section id="installation">
+<span id="id1"></span><h2>⚙️ Installation<a class="headerlink" href="#installation" title="Link to this heading">#</a></h2>
+<div class="sd-tab-set docutils">
+<input checked="checked" id="sd-tab-item-0" name="sd-tab-set-0" type="radio">
+</input><label class="sd-tab-label" for="sd-tab-item-0">
+pip</label><div class="sd-tab-content docutils">
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>pip<span class="w"> </span>install<span class="w"> </span>landmarker
+</pre></div>
+</div>
+</div>
+</div>
+</section>
+<section id="overview">
+<h2>Overview<a class="headerlink" href="#overview" title="Link to this heading">#</a></h2>
+<p>Landmarker provides a simple API for training and evaluating landmark detection models. The API is designed to be easy to use and to be flexible such that it can be used intertwined with other libraries or custom code. Landmarker can also act as utility library for certain components of landmark detection algorithms. For example, it provides a set of loss functions and heatmap decoding operations that can be used in combination with other PyTorch-based libraries.</p>
+<p>In the following sections, we provide a brief overview of the library and the API. For more specific details, we refer to the <a class="reference internal" href="../reference/index.html"><span class="doc std std-doc">API reference</span></a>.</p>
+<section id="modules">
+<h3>📦 Modules<a class="headerlink" href="#modules" title="Link to this heading">#</a></h3>
+<section id="data-loading-preprocessing-and-augmentation">
+<h4>Data Loading, Preprocessing, and Augmentation<a class="headerlink" href="#data-loading-preprocessing-and-augmentation" title="Link to this heading">#</a></h4>
+<p>The <a class="reference internal" href="../reference/data.html"><span class="doc std std-doc"><strong>landmarker.data</strong></span></a> module contains classes for loading and preprocessing
+data. All classes inherit from the
+<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset"><code class="docutils literal notranslate"><span class="pre">Dataset</span></code></a> class such that they
+can be used with PyTorch’s
+<a class="reference external" href="https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader"><code class="docutils literal notranslate"><span class="pre">DataLoader</span></code></a> class. There
+are three types of datasets:</p>
+<ul class="simple">
+<li><p><a class="reference internal" href="#../reference/data/#landmark_dataset"><span class="xref myst"><code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code></span></a>: A dataset of images and corresponding
+landmarks (landmark coordinates). Is constructed from images and landmarks pairs.</p></li>
+<li><p><a class="reference internal" href="#../reference/data/#heatmap_dataset"><span class="xref myst"><code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code></span></a>: A dataset of images and corresponding
+heatmaps representing the associated landmark. Is constructed from images and landmarks pairs.</p></li>
+<li><p><a class="reference internal" href="#../reference/data/#mask_dataset"><span class="xref myst"><code class="docutils literal notranslate"><span class="pre">MaskDataset</span></code></span></a>: A dataset of images and corresponding masks
+(i.e., binary segmentation masks indiciating the location of the landmarks). Can be
+constructed from specified image and landmarks pairs, or from images and masks pairs, because
+often that is how the data is distributed.</p></li>
+</ul>
+<p>Both the <code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code> and <code class="docutils literal notranslate"><span class="pre">MaskDataset</span></code> inherit from the <code class="docutils literal notranslate"><span class="pre">LandmarkDataset</span></code> class, and thus also
+contain information about the landmarks. The <code class="docutils literal notranslate"><span class="pre">MaskDataset</span></code> can be constructed from specified image
+and landmarks pairs, or from images and masks pairs, because often that is how the data is
+distributed. The <code class="docutils literal notranslate"><span class="pre">HeatmapDataset</span></code> can be constructed from images and landmarks pairs.</p>
+<p>For all three types of datasets images can be provided as a list of paths to stored images, or as a a numpy
+arary, torch tensor, list of numpy  arrays or list of torch tensors. Landmarks can be as numpy arrays or torch tensors.
+The landmarks are assumed to be in the range of the dimensions of the image. For example, if the image is 256x256, the values of the landmarks are assumed to
+be in the range [0, 256].
+These landmarks can be provided in three different shapes:</p>
+<ol class="arabic simple">
+<li><p>(N, D) where N is the number of samples and D is the number of dimensions</p></li>
+<li><p>(N, C, D) where C is the number of landmark classes</p></li>
+<li><p>(N, C, I, D) where I is the number of instances per landmark class, if less than I instances are
+provided, the remaining instances are filled with NaNs.</p></li>
+</ol>
+<section id="preprocessing">
+<h5>Preprocessing<a class="headerlink" href="#preprocessing" title="Link to this heading">#</a></h5>
+<p>If the images are provided as a list of paths, the images loaded from these paths and their values
+are normalized to the range [0, 1]. If the images are provided as numpy arrays or torch tensors,
+they are assumed to be normalized to the range [0, 1]. When a dim argument is provided, the images
+and landmarks are rescaled to the specified dimensions. The original dimensions and original
+landmarksare stored in the <code class="docutils literal notranslate"><span class="pre">original_dims</span></code> and <code class="docutils literal notranslate"><span class="pre">original_landmarks</span></code> attributes of the dataset. By
+default the rescalling is done such that the aspect ratio is preserved, i.e., padding is added to
+the image such that the aspect ratio is preserved. If <code class="docutils literal notranslate"><span class="pre">resize_pad</span></code> is set to <code class="docutils literal notranslate"><span class="pre">False</span></code>, the image is
+rescaled without preserving the aspect ratio.</p>
+</section>
+<section id="augmentation">
+<h5>Augmentation<a class="headerlink" href="#augmentation" title="Link to this heading">#</a></h5>
+<p>All datasets can be augmented using the <code class="docutils literal notranslate"><span class="pre">transforms</span></code> argument. Currently the transforms needs to
+follow the <a class="reference external" href="https://docs.monai.io/en/latest/transforms.html">monai</a>, and more specifically they need
+to be in a compose function.</p>
+</section>
+</section>
+<section id="datasets">
+<h4>Datasets<a class="headerlink" href="#datasets" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+<section id="heatmap-generation-and-decoding">
+<h4>Heatmap Generation and Decoding<a class="headerlink" href="#heatmap-generation-and-decoding" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+<section id="loss-functions">
+<h4>Loss Functions<a class="headerlink" href="#loss-functions" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+<section id="model-architectures">
+<h4>Model Architectures<a class="headerlink" href="#model-architectures" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+<section id="training-and-evaluation">
+<h4>Training and Evaluation<a class="headerlink" href="#training-and-evaluation" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+<section id="utilities">
+<h4>Utilities<a class="headerlink" href="#utilities" title="Link to this heading">#</a></h4>
+<p>Comming soon…</p>
+</section>
+</section>
+</section>
+</section>
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+<li><a class="reference internal" href="#">🚀 Getting Started</a><ul>
+<li><a class="reference internal" href="#installation">⚙️ Installation</a></li>
+<li><a class="reference internal" href="#overview">Overview</a><ul>
+<li><a class="reference internal" href="#modules">📦 Modules</a><ul>
+<li><a class="reference internal" href="#data-loading-preprocessing-and-augmentation">Data Loading, Preprocessing, and Augmentation</a><ul>
+<li><a class="reference internal" href="#preprocessing">Preprocessing</a></li>
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+</ul>
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