finalDeepLearning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "mount_file_id": "1qMv20JYsgOBh5MR83zeJTpKZPf-jJukR",
      "authorship_tag": "ABX9TyMRP03FwrWLZw0qHZuI8X0U",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/RexGod/DataMining_Pgu_Nueral_Network/blob/main/finalDeepLearning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "qh3IUGzezLKD"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import glob\n",
        "import cv2"
      ],
      "metadata": {
        "id": "mmf_PdG8zbAa"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pathImages = '/content/drive/MyDrive/tits_pic'"
      ],
      "metadata": {
        "id": "xMAGTBKmzgwp"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pathManual = '/content/drive/MyDrive/Radiology_manual_annotations.xlsx'\n",
        "manualRadio = pd.read_excel(pathManual)"
      ],
      "metadata": {
        "id": "XukXTj8xzkAk"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manualRadio= manualRadio.rename(columns={'Pathology Classification/ Follow up':'classification'})"
      ],
      "metadata": {
        "id": "GVT0sX4Fzq2_"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manualRadio = manualRadio[manualRadio['Type']=='CESM']"
      ],
      "metadata": {
        "id": "RVe6t1nyzruJ"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "images = os.listdir(pathImages)\n",
        "loaded_images = []\n",
        "#filename_txt = []\n",
        "for filename in images:\n",
        "    if filename.endswith('.jpg') or filename.endswith('.png'):\n",
        "        image_list = os.path.join(pathImages, filename)\n",
        "        loaded_images.append(image_list)\n",
        "        #filename_txt.append(filename)"
      ],
      "metadata": {
        "id": "WdUTN7Rbzte8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_images = sorted(loaded_images, key=lambda x: x.lower())"
      ],
      "metadata": {
        "id": "diO5GtfGI8_D"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img1 = plt.imread(loaded_images[0])\n",
        "plt.imshow(img1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "ZFEqDcEgEVDz",
        "outputId": "92753803-b30d-4869-cfe2-2352205b2184"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f0f4cf333d0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_image = []\n",
        "for image_path in loaded_images:\n",
        "    image = plt.imread(image_path)\n",
        "    loaded_image.append(image)"
      ],
      "metadata": {
        "id": "a_KZqMkeLct_"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manualRadio = manualRadio.sort_values(by=['Image_name'])"
      ],
      "metadata": {
        "id": "eHHlacQGzyo-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manualRadio.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "tNvr3ytSElf-",
        "outputId": "02197ca1-bbb3-42ce-9080-7055b4d9f229"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Image_name  Patient_ID Side  Type  Age Breast density (ACR) BIRADS  \\\n",
              "556   P100_L_CM_CC         100    L  CESM   61                    _      2   \n",
              "557  P100_L_CM_MLO         100    L  CESM   61                    _      2   \n",
              "552   P100_R_CM_CC         100    R  CESM   61                    _      1   \n",
              "553  P100_R_CM_MLO         100    R  CESM   61                    _      1   \n",
              "564   P101_L_CM_CC         101    L  CESM   47                    _      1   \n",
              "\n",
              "                            Findings View           Tags  Machine  \\\n",
              "556                  Non enhancement   CC  postoperative        1   \n",
              "557                  Non enhancement  MLO  postoperative        1   \n",
              "552  No mass or non mass enhancement   CC         normal        1   \n",
              "553  No mass or non mass enhancement  MLO         normal        1   \n",
              "564  No mass or non mass enhancement   CC         normal        1   \n",
              "\n",
              "    classification  \n",
              "556         Benign  \n",
              "557         Benign  \n",
              "552         Normal  \n",
              "553         Normal  \n",
              "564         Normal  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-39c7d56b-275a-4cc0-8b02-f0886aee60c8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_name</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Side</th>\n",
              "      <th>Type</th>\n",
              "      <th>Age</th>\n",
              "      <th>Breast density (ACR)</th>\n",
              "      <th>BIRADS</th>\n",
              "      <th>Findings</th>\n",
              "      <th>View</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Machine</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>P100_L_CM_CC</td>\n",
              "      <td>100</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>2</td>\n",
              "      <td>Non enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>postoperative</td>\n",
              "      <td>1</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>P100_L_CM_MLO</td>\n",
              "      <td>100</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>2</td>\n",
              "      <td>Non enhancement</td>\n",
              "      <td>MLO</td>\n",
              "      <td>postoperative</td>\n",
              "      <td>1</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>552</th>\n",
              "      <td>P100_R_CM_CC</td>\n",
              "      <td>100</td>\n",
              "      <td>R</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>P100_R_CM_MLO</td>\n",
              "      <td>100</td>\n",
              "      <td>R</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>MLO</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>P101_L_CM_CC</td>\n",
              "      <td>101</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>47</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-39c7d56b-275a-4cc0-8b02-f0886aee60c8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-39c7d56b-275a-4cc0-8b02-f0886aee60c8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-39c7d56b-275a-4cc0-8b02-f0886aee60c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_data_size = int(len(loaded_image)*0.12)\n",
        "test_data_size = int(len(loaded_image)*0.13)"
      ],
      "metadata": {
        "id": "gStMqu1bz-SG"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_select = np.random.choice(len(loaded_image), size=validation_data_size, replace=False)"
      ],
      "metadata": {
        "id": "re9sdiHf0LkW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(random_select)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1_Ny-ZtNyu_",
        "outputId": "b2734b6f-dce0-4c0f-ee36-6246f227c18f"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 427   63   51  783   60  503  511  768  739   99  502  153  691   76\n",
            "  986  868   41  673  106  889  600   33  245  490    8  856  300   20\n",
            "  286  774  415  590    7  183  772  528  161  186  135  321  934  946\n",
            "  301    6   42  900  119  742  947  344  374  997  567  478   57  492\n",
            "   61  579  395  613  845  773  458  618  292   66  930  880  533 1002\n",
            "  677  922   91  943  727  243  379  389  861  909  863  173  488  832\n",
            "  812  291  883  686  726  635  332  512  857   45  348  167  817  166\n",
            "  472  658  497  840  737  902  585  336  807  564  298  212  416  867\n",
            "  532  973  894  897  367  892  422  647]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_data_x = np.array(loaded_image)[random_select]\n",
        "validation_data_y = manualRadio.iloc[random_select]\n",
        "loaded_image = np.delete(loaded_image, random_select, axis=0)\n",
        "manualRadio=manualRadio.drop(manualRadio.index[random_select])"
      ],
      "metadata": {
        "id": "Z00Czxz70PBZ"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random_select = np.random.choice(len(loaded_image), size=test_data_size, replace=False)"
      ],
      "metadata": {
        "id": "2wj17Av40Rkm"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_x = np.array(loaded_image)[random_select]\n",
        "test_data_y = manualRadio.iloc[random_select]\n",
        "loaded_image = np.delete(loaded_image, random_select, axis=0)\n",
        "manualRadio=manualRadio.drop(manualRadio.index[random_select])"
      ],
      "metadata": {
        "id": "OwrOHhip0Thr"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_x = np.array(loaded_image)\n",
        "train_data_y = manualRadio[[\"Image_name\" , \"classification\"]]"
      ],
      "metadata": {
        "id": "UKIn0P8v0Vhf"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "import keras"
      ],
      "metadata": {
        "id": "VK7s_-Kp0dt8"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "manualRadio.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "U26jERBoDMd_",
        "outputId": "c3e57310-4031-4452-9265-4e230f01af1f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Image_name  Patient_ID Side  Type  Age Breast density (ACR) BIRADS  \\\n",
              "556   P100_L_CM_CC         100    L  CESM   61                    _      2   \n",
              "557  P100_L_CM_MLO         100    L  CESM   61                    _      2   \n",
              "552   P100_R_CM_CC         100    R  CESM   61                    _      1   \n",
              "553  P100_R_CM_MLO         100    R  CESM   61                    _      1   \n",
              "564   P101_L_CM_CC         101    L  CESM   47                    _      1   \n",
              "\n",
              "                            Findings View           Tags  Machine  \\\n",
              "556                  Non enhancement   CC  postoperative        1   \n",
              "557                  Non enhancement  MLO  postoperative        1   \n",
              "552  No mass or non mass enhancement   CC         normal        1   \n",
              "553  No mass or non mass enhancement  MLO         normal        1   \n",
              "564  No mass or non mass enhancement   CC         normal        1   \n",
              "\n",
              "    classification  \n",
              "556         Benign  \n",
              "557         Benign  \n",
              "552         Normal  \n",
              "553         Normal  \n",
              "564         Normal  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0850824a-0df4-4d8c-91f4-e7b7e61ec3a3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Image_name</th>\n",
              "      <th>Patient_ID</th>\n",
              "      <th>Side</th>\n",
              "      <th>Type</th>\n",
              "      <th>Age</th>\n",
              "      <th>Breast density (ACR)</th>\n",
              "      <th>BIRADS</th>\n",
              "      <th>Findings</th>\n",
              "      <th>View</th>\n",
              "      <th>Tags</th>\n",
              "      <th>Machine</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>556</th>\n",
              "      <td>P100_L_CM_CC</td>\n",
              "      <td>100</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>2</td>\n",
              "      <td>Non enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>postoperative</td>\n",
              "      <td>1</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>557</th>\n",
              "      <td>P100_L_CM_MLO</td>\n",
              "      <td>100</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>2</td>\n",
              "      <td>Non enhancement</td>\n",
              "      <td>MLO</td>\n",
              "      <td>postoperative</td>\n",
              "      <td>1</td>\n",
              "      <td>Benign</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>552</th>\n",
              "      <td>P100_R_CM_CC</td>\n",
              "      <td>100</td>\n",
              "      <td>R</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>553</th>\n",
              "      <td>P100_R_CM_MLO</td>\n",
              "      <td>100</td>\n",
              "      <td>R</td>\n",
              "      <td>CESM</td>\n",
              "      <td>61</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>MLO</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>P101_L_CM_CC</td>\n",
              "      <td>101</td>\n",
              "      <td>L</td>\n",
              "      <td>CESM</td>\n",
              "      <td>47</td>\n",
              "      <td>_</td>\n",
              "      <td>1</td>\n",
              "      <td>No mass or non mass enhancement</td>\n",
              "      <td>CC</td>\n",
              "      <td>normal</td>\n",
              "      <td>1</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0850824a-0df4-4d8c-91f4-e7b7e61ec3a3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0850824a-0df4-4d8c-91f4-e7b7e61ec3a3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0850824a-0df4-4d8c-91f4-e7b7e61ec3a3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "label_mapping = {'Benign': 0, 'Normal': 1, 'Malignant': 2}"
      ],
      "metadata": {
        "id": "lR-PHU8g0s-L"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "integer_labels = train_data_y['classification'].map(label_mapping)\n",
        "integer_labels_valid = validation_data_y['classification'].map(label_mapping)\n",
        "y_train_to_categorical = keras.utils.to_categorical(integer_labels, num_classes=len(label_mapping), dtype='float32')\n",
        "y_validation_to_categorical = keras.utils.to_categorical(integer_labels_valid, num_classes=len(label_mapping), dtype='float32')"
      ],
      "metadata": {
        "id": "KQ3xmYDg0ux6"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.applications.resnet import ResNet101,ResNet152 , preprocess_input , decode_predictions"
      ],
      "metadata": {
        "id": "6r3B1Ehx00ox"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#train_images_rgb = np.repeat(train_data_x[..., np.newaxis], 3, -1)[..., 0]\n",
        "#test_images_rgb = np.repeat(test_data_x[..., np.newaxis], 3, -1)\n",
        "#validation_images_rgb = np.repeat(validation_data_x[..., np.newaxis], 3, -1)[..., 0]\n"
      ],
      "metadata": {
        "id": "HnX_v81W01mx"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data_x = train_data_x/255\n",
        "validation_data_x = validation_data_x/255"
      ],
      "metadata": {
        "id": "9nUq9VXj1BaU"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "base_model = ResNet152(weights='imagenet', include_top=False, input_shape=(224, 224, 3))"
      ],
      "metadata": {
        "id": "GDQd1cLp09Fs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "749b3814-ee61-4eb9-f058-ad5ee96ce5b8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet152_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "234698864/234698864 [==============================] - 13s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D , Conv2D , Flatten , Dropout , MaxPooling2D , BatchNormalization\n",
        "from keras import layers\n",
        "from tensorflow.keras.optimizers import Adam,Adamax,SGD\n"
      ],
      "metadata": {
        "id": "IkCoE1Gp1CXT"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()"
      ],
      "metadata": {
        "id": "VSKTpjdh1FBY"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(base_model)"
      ],
      "metadata": {
        "id": "Z6ESx_Ug1GhK"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in base_model.layers:\n",
        "    layer.trainable = True\n",
        "\n",
        "for layer in base_model.layers[:-7]:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "2cX7n2Hx1Idc"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.add(GlobalAveragePooling2D())\n",
        "model.add(Dense(512, activation='relu'))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(256, activation='relu'))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(3, activation='softmax'))"
      ],
      "metadata": {
        "id": "2ufHP4_EyCDS"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rotation_range=20,  # Rotate the image by a random angle between -20 and 20 degrees\n",
        "    width_shift_range=0.1,  # Shift the image horizontally by a fraction of its width\n",
        "    height_shift_range=0.1,  # Shift the image vertically by a fraction of its height\n",
        "    shear_range=0.2,  # Apply shear transformation\n",
        "    zoom_range=0.2,  # Apply zoom transformation\n",
        "    horizontal_flip=True,  # Flip the image horizontally\n",
        "    vertical_flip=False,  # Do not flip the image vertically\n",
        "    fill_mode='nearest'  # Fill any empty pixels created during the augmentation process\n",
        ")"
      ],
      "metadata": {
        "id": "8qeBgXcjyEJK"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = datagen.flow(train_data_x, y_train_to_categorical, batch_size=32)"
      ],
      "metadata": {
        "id": "CVXelJPmcN88"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adamax', loss='categorical_crossentropy', metrics=['accuracy' , 'AUC' ])"
      ],
      "metadata": {
        "id": "zmXWzLLl1MYO"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "9-wn8emz1Oyq"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True ,)"
      ],
      "metadata": {
        "id": "PNRM0yc71QsN"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nemudar = model.fit(train_generator,\n",
        "                    validation_data=(validation_data_x,y_validation_to_categorical ),\n",
        "                    epochs=50,\n",
        "                    callbacks = [early_stopping]\n",
        "                    )\n"
      ],
      "metadata": {
        "id": "OHViUbZk1Sdp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1d56c1a-bcd9-4b21-da32-778ccecdc391"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "24/24 [==============================] - 28s 654ms/step - loss: 1.4150 - accuracy: 0.3891 - auc: 0.5671 - val_loss: 1.7490 - val_accuracy: 0.2417 - val_auc: 0.4788\n",
            "Epoch 2/50\n",
            "24/24 [==============================] - 12s 489ms/step - loss: 1.3902 - accuracy: 0.4090 - auc: 0.5818 - val_loss: 1.1118 - val_accuracy: 0.4083 - val_auc: 0.5662\n",
            "Epoch 3/50\n",
            "24/24 [==============================] - 11s 476ms/step - loss: 1.3018 - accuracy: 0.4117 - auc: 0.5950 - val_loss: 1.0614 - val_accuracy: 0.4000 - val_auc: 0.5856\n",
            "Epoch 4/50\n",
            "24/24 [==============================] - 12s 482ms/step - loss: 1.2043 - accuracy: 0.4422 - auc: 0.6320 - val_loss: 1.0212 - val_accuracy: 0.4833 - val_auc: 0.6756\n",
            "Epoch 5/50\n",
            "24/24 [==============================] - 11s 465ms/step - loss: 1.2274 - accuracy: 0.4343 - auc: 0.6120 - val_loss: 1.0355 - val_accuracy: 0.4750 - val_auc: 0.6556\n",
            "Epoch 6/50\n",
            "24/24 [==============================] - 11s 461ms/step - loss: 1.2574 - accuracy: 0.4329 - auc: 0.6003 - val_loss: 1.3271 - val_accuracy: 0.3917 - val_auc: 0.6174\n",
            "Epoch 7/50\n",
            "24/24 [==============================] - 12s 503ms/step - loss: 1.2041 - accuracy: 0.4422 - auc: 0.6130 - val_loss: 1.1466 - val_accuracy: 0.4500 - val_auc: 0.6416\n",
            "Epoch 8/50\n",
            "24/24 [==============================] - 11s 471ms/step - loss: 1.1408 - accuracy: 0.4900 - auc: 0.6614 - val_loss: 1.0686 - val_accuracy: 0.4500 - val_auc: 0.6438\n",
            "Epoch 9/50\n",
            "24/24 [==============================] - ETA: 0s - loss: 1.1215 - accuracy: 0.4794 - auc: 0.6467Restoring model weights from the end of the best epoch: 4.\n",
            "24/24 [==============================] - 12s 510ms/step - loss: 1.1215 - accuracy: 0.4794 - auc: 0.6467 - val_loss: 1.1491 - val_accuracy: 0.4250 - val_auc: 0.6339\n",
            "Epoch 9: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12, 8))\n",
        "plt.subplot(2,2,1)\n",
        "plt.plot(nemudar.history['accuracy'])\n",
        "plt.title('accuracy')\n",
        "plt.subplot(2,2,2)\n",
        "plt.plot(nemudar.history['val_accuracy'])\n",
        "plt.title('val_acc')\n",
        "plt.subplot(2,2,3)\n",
        "plt.plot(nemudar.history['loss'])\n",
        "plt.title('loss')\n",
        "plt.subplot(2,2,4)\n",
        "plt.plot(nemudar.history['val_loss'])\n",
        "plt.title('val_loss')"
      ],
      "metadata": {
        "id": "a6E2u0HiNIbE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552
        },
        "outputId": "60faa126-1253-4873-c528-f4cdb90fb223"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'val_loss')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA94AAAKqCAYAAADMqYZIAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADrfElEQVR4nOzdeVhUZf8G8HtmYGbYkX2VVUFFIUHR3JVEM7cWsUwNfe1tsTJ+WWml2Uarr2WmZWlplrZZtpmG4hYuqbgCKsi+ozCsA8yc3x/IGLmBMhxm5v5c11zJmXMO95DyzHfOeb6PRBAEAURERERERESkF1KxAxAREREREREZMxbeRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRC28iIiIiIiIiPWLhTURERERERKRHLLyJiIiIiIiI9IiFNxEREREREZEesfAmIiIiIiIi0iMW3kRERERERuLzzz+HRCJBZmam2FGI6B9YeBMRERERERHpEQtvIiIiIiIiIj1i4U1EbVZdXS12BCIiIiIig8HCm6gTyMrKwmOPPYagoCBYWFjA0dER991331XnZ5WXl+Ppp5+Gr68vFAoFvLy8MGPGDJSWlur2qaurw8svv4zu3btDqVTC3d0dd999N9LT0wEAiYmJkEgkSExMbHHuzMxMSCQSfP7557ptDz30EKytrZGeno4777wTNjY2mDZtGgBgz549uO+++9C1a1coFAp4e3vj6aefRm1t7RW5U1NTMWXKFDg7O8PCwgJBQUF44YUXAAA7d+6ERCLB5s2brzjuq6++gkQiQVJSUlt/rERERJ3ed999B4lEgl27dl3x3McffwyJRIKTJ0/i+PHjeOihh+Dv7w+lUgk3NzfMmjULZWVlt5yho9+HEJkiM7EDEBFw6NAh/PXXX5g6dSq8vLyQmZmJlStXYvjw4Th9+jQsLS0BAFVVVRgyZAhSUlIwa9Ys9O3bF6WlpdiyZQtyc3Ph5OQEjUaDu+66CwkJCZg6dSqeeuopVFZWYvv27Th58iQCAgLanK+xsRHR0dEYPHgw3n33XV2eb7/9FjU1NXj00Ufh6OiIgwcPYvny5cjNzcW3336rO/748eMYMmQIzM3N8fDDD8PX1xfp6en4+eef8frrr2P48OHw9vbGhg0bMHny5Bbfe8OGDQgICMDAgQNv4SdMRETUOY0bNw7W1tb45ptvMGzYsBbPbdq0Cb169UJISAjee+89ZGRkIDY2Fm5ubjh16hQ++eQTnDp1Cvv374dEIrnpDJ39fQiRURCISHQ1NTVXbEtKShIACOvWrdNtW7RokQBA+OGHH67YX6vVCoIgCGvWrBEACEuXLr3mPjt37hQACDt37mzx/Pnz5wUAwtq1a3XbZs6cKQAQnn/++Vbljo+PFyQSiZCVlaXbNnToUMHGxqbFtn/mEQRBWLBggaBQKITy8nLdtuLiYsHMzExYvHjxFd+HiIjIWNx///2Ci4uL0NjYqNtWUFAgSKVS4ZVXXhEE4epj7tdffy0AEHbv3q3btnbtWgGAcP78+VZ//45+H0JkinirOVEnYGFhoftzQ0MDysrKEBgYCHt7exw5ckT33Pfff4/Q0NArrgoD0H3S/f3338PJyQlPPPHENfe5GY8++uh1c1dXV6O0tBS33347BEHA0aNHAQAlJSXYvXs3Zs2aha5du14zz4wZM6BWq/Hdd9/ptm3atAmNjY148MEHbzo3ERFRZxcTE4Pi4uIWU8C+++47aLVaxMTEAGg55tbV1aG0tBQDBgwAgBbvFW6GIbwPITJ0LLyJOoHa2losWrQI3t7eUCgUcHJygrOzM8rLy1FRUaHbLz09HSEhIdc9V3p6OoKCgmBm1n4zSczMzODl5XXF9uzsbDz00ENwcHCAtbU1nJ2ddbfJNefOyMgAgBvmDg4ORr9+/bBhwwbdtg0bNmDAgAEIDAxsr5dCRETU6YwZMwZ2dnbYtGmTbtumTZsQFhaG7t27AwAuXLiAp556Cq6urrCwsICzszP8/PwAoMV7hZvR2d+HEBkD/osg6gSeeOIJrF27FvPmzcPAgQNhZ2cHiUSCqVOnQqvVtvv3u9YnzhqN5qrbFQoFpFLpFfvecccduHDhAp577jkEBwfDysoKeXl5eOihh24q94wZM/DUU08hNzcXarUa+/fvx4cfftjm8xARERkShUKBSZMmYfPmzfjoo49QVFSEffv24Y033tDtM2XKFPz111+YP38+wsLCYG1tDa1WizFjxtzye4WOfh9CZIpYeBN1At999x1mzpyJ9957T7etrq4O5eXlLfYLCAjAyZMnr3uugIAAHDhwAA0NDTA3N7/qPl26dAGAK86flZXV6swnTpzAmTNn8MUXX2DGjBm67du3b2+xn7+/PwDcMDcATJ06FXFxcfj6669RW1sLc3Nz3S12RERExiwmJgZffPEFEhISkJKSAkEQdGPgxYsXkZCQgCVLlmDRokW6Y86ePdsu37uj34cQmSLeak7UCchkMgiC0GLb8uXLr7gCfc899+DYsWNXXXar+fh77rkHpaWlV71S3LyPj48PZDIZdu/e3eL5jz76qE2Z/3nO5j+///77LfZzdnbG0KFDsWbNGmRnZ181TzMnJyeMHTsWX375JTZs2IAxY8bAycmp1ZmIiIgMVVRUFBwcHLBp0yZs2rQJ/fv3191KfrUxFwCWLVvWLt+7o9+HEJkiXvEm6gTuuusurF+/HnZ2dujZsyeSkpLw559/wtHRscV+8+fPx3fffYf77rsPs2bNQnh4OC5cuIAtW7Zg1apVCA0NxYwZM7Bu3TrExcXh4MGDGDJkCKqrq/Hnn3/isccew8SJE2FnZ4f77rsPy5cvh0QiQUBAAH755RcUFxe3OnNwcDACAgLwzDPPIC8vD7a2tvj+++9x8eLFK/b94IMPMHjwYPTt2xcPP/ww/Pz8kJmZiV9//RXJyckt9p0xYwbuvfdeAMCrr77a9h8mERGRATI3N8fdd9+NjRs3orq6Gu+++67uOVtbWwwdOhRvv/02Ghoa4OnpiW3btuH8+fPt8r07+n0IkUkSp5k6Ef3TxYsXhdjYWMHJyUmwtrYWoqOjhdTUVMHHx0eYOXNmi33LysqEuXPnCp6enoJcLhe8vLyEmTNnCqWlpbp9ampqhBdeeEHw8/MTzM3NBTc3N+Hee+8V0tPTdfuUlJQI99xzj2BpaSl06dJF+O9//yucPHnyqsuJWVlZXTX36dOnhaioKMHa2lpwcnIS5syZIxw7duyKcwiCIJw8eVKYPHmyYG9vLyiVSiEoKEh46aWXrjinWq0WunTpItjZ2Qm1tbVt/2ESEREZqO3btwsABIlEIuTk5LR4Ljc3VzeO2tnZCffdd5+Qn58vAGix7ObNLCcmxvsQIlMjEQTe80FEnUdjYyM8PDwwfvx4fPbZZ2LHISIiIiK6ZZzjTUSdyo8//oiSkpIWDduIiIiIiAwZr3gTUadw4MABHD9+HK+++iqcnJxw5MgRsSMREREZtKqqKlRVVV13H2dnZ13zNiLSHzZXI6JOYeXKlfjyyy8RFhaGzz//XOw4REREBu/dd9/FkiVLrrvP+fPn4evr2zGBiEwYr3gTERERERmhjIwMZGRkXHefwYMHQ6lUdlAiItPFwpuIiIiIiIhIj9hcjYiIiIiIiEiPjGaOt1arRX5+PmxsbCCRSMSOQ0REJk4QBFRWVsLDwwNSKT/nbg8c64mIqLNp7XhvNIV3fn4+vL29xY5BRETUQk5ODry8vMSOYRQ41hMRUWd1o/HeaApvGxsbAE0v2NbWVuQ0RERk6lQqFby9vXXjE906jvVERNTZtHa8N5rCu/mWM1tbWw7GRETUafCW6PbDsZ6IiDqrG433nHRGREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIj1h4ExEREREREekRC28iIiIiIiIiPWLhTURERETURoIgiB2BiAyImdgBiIjIOAiCgIfWHsK54irMHRmIKRHekEklYsciImp3T208it9OFCDA2Ro93G3Rw93m0n9t4WStEDseEXVCLLyJiKhdHDh/AbvOlAAAFvxwAp/vy8TCcT0wrLuzyMmIiNrPnrMl+Ck5HwCQWliJ1MJKbD56+XknawV6uNug56VCPNjdBgHO1jCX8UZTIlPGwpuIiNrF1wezAQB9vOyQVVaDtKJKzFxzEEO7O2PhncEIdrMVOSER0a3RagXE/5YKAJjazxtRPVyRUqBCSqEKKQWVyCyrRmmVGnvOqrHnbKnuOLlMikCXK6+OO1jJxXopRNTBWHgTEdEtu1hdj99PFAIAXp/UG10dLLF8x1l8kZSJ3WdKsPdsCaZEeCNudHe42ChFTktEdHN+OpaH0wUq2CjM8OyYYDhYyRHV01X3fE19I9IKK5FSUImUAhVSC1VILahEpboRpwtUOF2ganE+V1tF01VxN1vdVXI/JyuY8eo4kdFh4U1ERLfs+yO5qNdoEeJpi95edgCAF+/qiekDffDW1lT8dqIQGw/lYMuxfDwyLABzhvjDQi4TOTURUevVNWjw7h9nAACPjgi46tVqS7kZbuvaBbd17aLbJggCci/W4nSBqqkYL6hESqEKWWU1KFKpUaQqQWJaiW5/hZkU3V1tEOx2+cp4D3cb2Fvy6jiRIWPhTUREt0QQBN1t5vf379riOR9HK3w0LRx/Z17Aa7+mIDmnHEu3n8GGA1l4ZnQQ7unrBSkbsBGRAViXlIm88lq42ykxa5Bfq4+TSCTwdrCEt4Mlonu56bZXqRuRdukW9ZTmorywEjX1GpzIq8CJvIoW53G3U7a4VT3YrenqOJtYEhkGFt5ERHRLDmVeRHpJNSzlMkwI9bjqPhG+Dtj82O345XgB3tqaityLtZj/3XGs3ZeJF8f1wO2BTh2cmoio9cpr6vHhjnMAgLg7ukNpfut37FgrzBDu44BwHwfdNq1WQPaFGqQWqnD6HwV57sVaFFTUoaCiDjtSi3X7K82lCHJtLsQv/dfdFnYW5recj4jaFwtvIiK6Jc1XuyeEesBGee03exKJBONDPXBHT1d88VcmPtx5DqcLVHjg0wMYFeyCBXcGI9DFpqNiExG12oc7zkFV14hgNxvc3ddLb99HKpXA18kKvk5WGBPirtuuqmu4NHe8qRA/XVCJtEIV6hq0OJZbgWO5La+Oe9pbXNHIzcfBkncYEYmIhTcREd208pp6/HqiAMCVt5lfi9Jchv8OC8B9Ed54/88z2HAgGwmpxUg8U4L7+3tjXlR3roNLRJ1GzoUarEvKAgA8PzZYlFu7bZXm6OfrgH6+l6+Oa7QCssqqr7hVPa+8Vvf4M6VIt7+FuQxBl66K93S3wcAAR37YSdSBWHgTEdFN++FIHuobtejpbos+l5qqtZaDlRxLJoZgxu2+ePP3VGw/XYQv92fjx6P5eGxEAGYN8muX2zmJiG7Fu9vSUK/RYlCgI4Z1dxY7jo5MKoG/szX8na0xrs/lq+MVNQ2Xlje73MgtrbAStQ0aJOeUIzmnHABgJpVg3az+nOpD1EFYeBMR0U1p0VQtsiskkpu7ChTgbI3VMyKQlF6G1387jZN5Kry9NQ0b9mdjfnQQJoR68PZIIhLFidwK/JScDwBYMLbHTf+e60h2luYY4O+IAf6Oum2NGi0yy6pxuqASqQUq7D1XiuO5FXjt1xT88sRg/o4l6gBcJJCIiG7K4ayLOFtcBQtzGSaGXb2pWlsMDHDElscH438xoXC3UyKvvBbzNiVj8kf7cPD8hXZITETUeoIg4I3fUgAAk8I8EOLZtrt6OhMzmRSBLjaYEOqBZ8cE4/PY/rBRmOF0gQo/HcsTOx6RSWDhTUREN+WrS1e7x4e6w/Y6TdXaQiqVYPJtXtj5zHDMjw6ClVyGY7kVmPJxEv67/m+cL61ul+9DRHQjiWdKkJRRBrlMimeig8SO064crOR4dEQAAODdP86grkEjciIi48fCm4iI2qyipgG/Hm9bU7W2UJrL8PiIQCTOH4EHIrtCKgH+OFWEO5buwstbTuFidX27f08iomYarYA3f0sFADw0yBdeXSxFTtT+Zg3y091d9MVfmWLHITJ6LLyJiKjNNh/NhbpRi2A3G4R52+vt+zjbKPDG5N74Y95QjAhyRqNWwOd/ZWLYOzuxencG1I28SkNE7e/7I7lIK6qEnYU5Hh8eKHYcvVCayxB3R3cAwIc7z/EDTSI9Y+FNRERt0tRULQcA8MAtNFVri26uNlgb2x9fzo5EsJsNVHWNeP23FNyxdDd+PV4AQRD0noGITENtvQZLt50BAMwdEQg7y/aZStMZ3d3XC8FuNqisa8SKnefEjkNk1Fh4ExFRmxzJLkdaUSWU5lJMDPPs0O89uJsTfn1yCN6+tw9cbBTIvlCDx786gntW/oXDWRc7NAsRGac1+86jUFUHT3sLTB/oI3YcvZJJJXh+bDAAYF1SFnIu1IiciMh4sfAmIqI2aV5C7K4+HrCz6PgrQTKpBFMivJE4fzjmRXWDhbkMR7LLcc/Kv/D4V0f4xpGIblpZlRorE9MBAPOjg6A0l4mcSP+GdXfGoEBH1Gu0eHdbmthxiIwWC28iImq1itoG/HK8aU1bfTRVawtLuRnmRXVH4vzhmBLhBYkE+PV4AUa9twtv/JaCitoGUfMRkeFZvuMcqtSN6OVhiwmht75MoiGQSCRYMLYHAOCn5HycyK0QORGRcWLhTURErfZTch7qGrQIcrVB3672YscBALjaKvH2vaH47ckhGNLNCfUaLT7ZnYFh7+zE2n3n0aDRih2RiAxAZmk1vtyfBQBYeGcPSKX671/RWYR42mHybU1Th974LYV9M4j0gIU3ERG1iiAI+OpA023m9/f37pCmam3Rw90W62b1x9rYfujmYo3ymgYs+fk0Rv9vN/44Vcg3kkR0Xe/8kYZGrXDp1msnseN0uP8b3R1ymRRJGWVIPFMidhwio8PCm4iIWiU5pxyphZVQmEkx+TYvseNclUQiwYggF/z+1BC8PjkETtZynC+txn/XH0bMJ/txPLdc7IhE1Akdzb6IX08UQCKBrtmYqfHqYomHBvkCAN78LRUaLT+sJGpPN1V4r1ixAr6+vlAqlYiMjMTBgwdbddzGjRshkUgwadKkFturqqowd+5ceHl5wcLCAj179sSqVatuJhoREelJc1O1cX3cO/3yOmYyKaZF+iBx/gjMHREIhZkUB89fwIQP92HexqPIK68VOyIRdRKCICD+t1QAwD19vdDD3VbkROJ5fHgg7CzMkVZUie8P54odh8iotLnw3rRpE+Li4rB48WIcOXIEoaGhiI6ORnFx8XWPy8zMxDPPPIMhQ4Zc8VxcXBy2bt2KL7/8EikpKZg3bx7mzp2LLVu2tDUeERHpgaquAT8fKwAAPCByU7W2sFaY4ZnoIOx8ZjjuvjR/8cfkfIx8NxFvb01FZR0bsBGZuj9TinEw8wIUZlL83+juYscRlZ2lOeaOCAQAvLc9DbX1GpETERmPNhfeS5cuxZw5cxAbG6u7Mm1paYk1a9Zc8xiNRoNp06ZhyZIl8Pf3v+L5v/76CzNnzsTw4cPh6+uLhx9+GKGhoa2+kk5ERPr1U3I+ahs06OZijXCfLmLHaTMPewssjQnDL08MxgB/B6gbtfgoMR3D30nE+v1ZaGQDNiKT1KjR4s3fUwAAswf7wd3OQuRE4ps+0Aee9hYoUqmxZt95seMQGY02Fd719fU4fPgwoqKiLp9AKkVUVBSSkpKuedwrr7wCFxcXzJ49+6rP33777diyZQvy8vIgCAJ27tyJM2fOYPTo0dc8p1qthkqlavEgIqL217KpWtdO11StLUI87fD1nAFYPSMC/k5WKKuux0s/nsSY9/dgR2oRG7ARmZhv/s5Fekk1ulia45HhAWLH6RSU5jLMjw4CAKxMTEdZlVrkRETGoU2Fd2lpKTQaDVxdXVtsd3V1RWFh4VWP2bt3Lz777DOsXr36muddvnw5evbsCS8vL8jlcowZMwYrVqzA0KFDr3lMfHw87OzsdA9vb++2vBQiImql47kVSClQQW4mxd19PcWOc8skEgnu6OmKP54eiiUTeqGLpTnOFVdh1ud/48HPDuBUPtewJTIF1epGLN1+BgDw5KhusFV27t4VHWlCqAd6ediiSt2I5TvOiR2HyCjotat5ZWUlpk+fjtWrV8PJ6drLMixfvhz79+/Hli1bcPjwYbz33nt4/PHH8eeff17zmAULFqCiokL3yMnJ0cdLICIyebqmar3dYW8pFzlN+zGXSTHzdl/senYE/jvMH3KZFPvOleGu5XuRkFIkdjwi0rPVezJQWqVGVwdLTIv0ETtOpyKVSrDwzh4AgC/3ZyGztFrkRESGz6wtOzs5OUEmk6GoqOUbkqKiIri5uV2xf3p6OjIzMzF+/HjdNq22aR6dmZkZ0tLS4OHhgYULF2Lz5s0YN24cAKBPnz5ITk7Gu+++2+K29n9SKBRQKBRtiU9ERG1UWdeALcfyATTdZm6MbJXmWDC2Bx6M9MHbf6ThSNZFk1zDl8iUFFfW4ZPdGQCAZ8cEQW7GFXb/bVCgE4YHOSMxrQTv/JGGFdP6ih2JyKC16beMXC5HeHg4EhISdNu0Wi0SEhIwcODAK/YPDg7GiRMnkJycrHtMmDABI0aMQHJyMry9vdHQ0ICGhgZIpS2jyGQyXZFORETi2HIsHzX1GgQ4W6Gfr+E1VWsLbwdLLL//NmydNwRKc5nYcYhIj97/8yxq6jUI9bLDuN7uYsfptJ4fGwyJBPj1RAGOZl8UOw6RQWvTFW+gaemvmTNnIiIiAv3798eyZctQXV2N2NhYAMCMGTPg6emJ+Ph4KJVKhISEtDje3t4eAHTb5XI5hg0bhvnz58PCwgI+Pj7YtWsX1q1bh6VLl97iyyMiolvRfJu5oTdVawsbzvMkMmrniquw8VDTFMUFd/Ywmd9tNyPYzRb39vXCt4dzEf9bKjb9dwB/XkQ3qc2Fd0xMDEpKSrBo0SIUFhYiLCwMW7du1TVcy87OvuLq9Y1s3LgRCxYswLRp03DhwgX4+Pjg9ddfxyOPPNLWeERE1E5O5FbgZJ4KcpkU9/T1EjsOEVG7eHtrKjRaAVE9XDDA31HsOJ1e3Oju2HIsHwczL+DPlGLc0dP1xgcR0RVuakLL3LlzkZWVBbVajQMHDiAyMlL3XGJiIj7//PNrHvv555/jxx9/bLHNzc0Na9euRV5eHmpra5Gamoq4uDh+okZEJKKvLl3tHtvbDV2sjKepGrXOihUr4OvrC6VSicjISBw8eLBVx23cuBESiQSTJk1qsf2hhx6CRCJp8RgzZowekhNd26HMC9h2ughSCfDcmGCx4xgEdzsLzB7sBwB48/cUNGo4FZToZrCTBBERXaFK3YgtyXkAjLepGl3bpk2bEBcXh8WLF+PIkSMIDQ1FdHQ0iouLr3tcZmYmnnnmGQwZMuSqz48ZMwYFBQW6x9dff62P+ERXJQgC3vgtBQAQ068rurnaiJzIcDwyPABdLM2RXlKNTX9zJSGim8HCm4iIrvDzsXxU12vg72SFSD8HseNQB1u6dCnmzJmD2NhY9OzZE6tWrYKlpSXWrFlzzWM0Gg2mTZuGJUuWwN/f/6r7KBQKuLm56R5duhh3wz7qXLaeLMTR7HJYmMvwdFQ3seMYFFulOZ4c1fQz+9/2s6hWN4qciMjwtHmONxERGT9TbKpGTerr63H48GEsWLBAt00qlSIqKgpJSUnXPO6VV16Bi4sLZs+ejT179lx1n8TERLi4uKBLly4YOXIkXnvtNTg6XnuOrVqthlqt1n2tUqlu4hURAQ0aLd7amgoAmDPUHy62SpETGZ5pkT5Yuy8T2RdqsHpPBuZFdRc7ksH6dE8GVu3KQKMBr+BkrTDDQ7f7YvpAHyjMuBJIa7DwJiKiFk7mVeB4bkVTU7VwNlUzNaWlpdBoNLqmqc1cXV2Rmpp61WP27t2Lzz77DMnJydc875gxY3D33XfDz88P6enpWLhwIcaOHYukpCTIZFd/0xYfH48lS5bc9GshavbVgWxkltXAyVqOh4de/Y4Muj65mRTPjgnC3K+O4pPdGXggsitcbPgBRlv9fqIAr/2aInaMW1Ze04DXfk3BF0mZmB8djLt6u0Mq5Qf118PCm4iIWmi+2h0d4gYHNlWjG6isrMT06dOxevVqODk5XXO/qVOn6v7cu3dv9OnTBwEBAUhMTMSoUaOuesyCBQsQFxen+1qlUsHb27v9wpNJqKxrwPsJZwEAT0V1h7WCb39v1rje7ljtfR7Hcsrx/p9n8frk3mJHMijniqsw/7vjAICHbvfFgwN8RE508/7OvICl288g50Itnvz6KD7dk4EFY3tgYABXCrgW/uYhIiKdanUjfkrOBwDc358FjilycnKCTCZDUVFRi+1FRUVwc3O7Yv/09HRkZmZi/Pjxum3aS7dPmpmZIS0tDQEBAVcc5+/vDycnJ5w7d+6ahbdCoYBCobiVl0OEj3dl4EJ1PfydrDC1H3+v3QqJRIKFY4MR88l+bDyUg9hBfgh0sRY7lkGoVjfikS8Po0rdiEg/B7w4rgfMZIbbbivQxRoTwjzw2Z7zWLUrHcdzK3D/6v0YFeyC58YGozubF17BcP9vExFRu/vleD6q1I3wdbTEQK5va5LkcjnCw8ORkJCg26bVapGQkICBAwdesX9wcDBOnDiB5ORk3WPChAkYMWIEkpOTr3mFOjc3F2VlZXB3d9fbayEqrKjDp3szAADPjgmGuQEXOp1FpL8jonq4QqMV8PbWq08/oZYEQcCz3x3HueIquNoq8OEDfQ266G5mKTfDE6O6YdezIzBjoA/MpBIkpBZjzLLdeP774yhS1YkdsVMx/P/jRETUbr462LRMDJuqmba4uDisXr0aX3zxBVJSUvDoo4+iuroasbGxAIAZM2bomq8plUqEhIS0eNjb28PGxgYhISGQy+WoqqrC/PnzsX//fmRmZiIhIQETJ05EYGAgoqOjxXypZOT+t/0M6hq0CPfpguherjc+gFrl+bFBkEqAbaeLcCjzgthxOr3P9p7HrycKYC6T4KNp4XC2Ma47eZysFXhlYgi2PT0UY3q5QSsAGw/lYPg7iVi6LQ1V7IIPgIU3ERFdciq/AsdyymEuk7CpmomLiYnBu+++i0WLFiEsLAzJycnYunWrruFadnY2CgoKWn0+mUyG48ePY8KECejevTtmz56N8PBw7Nmzh7eSk96kFVbi28NNHyYuvDOYHya2o0AXG8T06woAeOO3FAiCIHKizmt/Rhnif2+6M+Clu3oi3Md4l1H0d7bGqunh+P7Rgejb1R61DRp8sOMchr+zE+v3Z6FBY7hd3NuDRDCSfykqlQp2dnaoqKiAra2t2HGIiAzOSz+exPr9WRjXxx0rHugrdhyDx3Gp/fFnSm0x6/ND2JFajDG93LBqerjYcYxOsaoOw95JRG2DBh9N64s7e3PayL8VVtThruV7UFpVj8m3eWLplFCT+QBIEAT8caoQb21Nw/nSagCAv5MVnh0TjOherkb1c2jt2MQr3kREhJr6Rvx4NA8A8ED/riKnISK6NX+ll2JHajHMpBI8OyZI7DhGycVWiTmXlmZ7e2sq6htN+2rmv9U3avH4V0dQWlWPYDcbvDG5t1EVmzcikUgwJsQd254eilcm9oKjlRwZpdV45MvDuG9VEg5nXRQ7Yodj4U1ERPjleAEq1Y3wYVM1IjJwWq2ANy/d2vtAZFf4O7Prtr48PNQfTtZyZJbV6JaipCZv/JaCw1kXYaM0w6oHw2Ehl4kdSRTmMilmDPRF4vzheGJkIJTmUvyddRH3rPwLj355WHc13BSw8CYiIt0bpqn9ukIqNZ1P5InI+PxyogDHcytgJZfhyVHdxI5j1KwVZngqqjsA4P2Es6isaxA5Ueew+WguPv8rEwCwLCYMvk5W4gbqBGyU5vi/0UFIfGYEYiK8IZUAv58sxB1Ld2HxTydRVqUWO6LesfAmIjJxKQUqHM0uh5lUgnvZVI2IDJi6UaNb4uqRYQFwsmbzPn2b2s8b/k5WuFBdj493ZYgdR3QpBSos+OEEAODJkYEY1YPd9P/JzU6Jt+7tg9+fGooRQc5o1Ar4IikLw95JxIqd51BbrxE7ot6w8CYiMnEbL13tHt3L1eiWOCEi07I+KQu5F2vhYqPA7CF+YscxCeYyKZ4dEwwA+HRvBgorTHft5oraBjzy5WHUNWgxtLuz7m4AulKQmw3WxvbHV/+JRIinLarUjXjnjzQMf3cnvjmUA43WKPp/t8DCm4jIhNXWa/DDpaZq97OpGhEZsIqaBizfcQ4AEHdHd1jKzUROZDqie7kiwqcL6hq0+N/2M2LHEYVWK+D/vklGVlkNPO0t8H5MGGScunVDtwc6Ycvjg/H+1DB42lugSKXGs98fx53v78HOtGKjWqqOhTcRkQn79UQBKusa4e1ggUEBTmLHISK6aR/tOoeK2gZ0c7HmtJkOJpFIsODOHgCAbw/nIK2wUuREHe+jxHP4M6UYcjMpVj0Yji5WcrEjGQypVIKJYZ7Y8cwwvDiuB+wszJFWVInYtYcw7dMDOJlXIXbEdsHCm4jIhLGpGhEZg7zyWqzdlwkAeH5sMMxkfIvb0cJ9umBsiBu0AvDWpXn2pmL3mRK8d+lK/2sTQ9Dby07kRIZJYSbDf4b4Y/f8EXh4qD/kMin+Si/DXcv3Yt7Go8i5UCN2xFvC30pERCYqrbASh7MuwkwqwX0RvDpERIbrvW1pqG/UItLPASODXcSOY7LmRwfBTCrBjtRi/JVeKnacDpFzoQZPbjwKQQDu7++NKf28xY5k8OwszbHwzh5I+L9hmBTmAQD4MTkfo97bhTd+S0FFjWF2z2fhTURkopqvdkf1cIWLjVLkNEREN+dUfgU2X+pVsfDOHpBIePeOWPydrfFAZFO/kPjfUqE1wgZZ/1TXoMFjG46gvKYBoV52eHlCL7EjGRVvB0ssm3obfnliMG4PcES9RotPdmdg6Ds7sXp3BtSNhtUBnYU3EZEJqmvQ4IcjuQCA+yPZVI2IDNebv6dCEIDxoR4I9bYXO47Je3JUN1jJZTiRV4Gfj+eLHUevFv90CifyKtDF0hwfPRgOhZlM7EhGKcTTDhv+E4nPY/sh2M0GFbUNeP23FIx6bxd+Ss4zmA94WHgTEZmg304UQFXXCE97CwwJZFM1IjJMu8+UYM/ZUpjLJJg/OkjsOATAyVqBR4YFAADe+SPN4K5KttbGg9nY9HcOpBJg+f194WlvIXYkoyaRSDA8yAW/PjkEb9/bB262SuRerMVTG5MxccU+g5jawMKbiMgENd9mfn9/bzZVIyKDpNUKiP+9qYnX9AG+6OpoKXIiajZ7iB9cbBTIvViL9UlZYsdpd8dyyrHop1MAgP8bHYTB3fgBdkeRSSWYEuGNnc8Mx/zoIFgrzHAirwIPrD6A2LUHO3VHfRbeREQm5mxRJQ5lXoRMKsF9EWwCQ0SGafPRPKQUqGCjNMMTIwPFjkP/YCk3Q9wd3QEAy3ecM9hmWFdzoboej204gnqNFnf0dMWjl67uU8eykMvw+IhA7Jo/HA/d7gszqQQ700ow9v3deO674yisqBM74hVYeBMRmZivD+YAAEYFu8DVlk3ViMjw1DVo8N62NADAY8MDuWZyJ3RvuBe6uVijorYBH+06J3acdqHRCnhq41HkldfC19ES700J5V1jInO0VuDlCb3wZ9ww3Nm7aTm7TX/nYPi7O/HuH2morOs8H/qw8CYiMiF1DRp8z6ZqRGTgPv8rE/kVdXC3UyJ2kK/YcegqzGRSPD82GACwdl8m8sprRU506/63/Qz2nC2FhbkMH0+PgK3SXOxIdImvkxU+mhaOHx67HRE+XVDXoMWHO89h+DuJWJeUiQaNVuyILLyJiEzJ1pOFqKhtgKe9BYZ2cxY7DhFRm12srseKnU1XUP9vdBCU5uwk3VmNDHbBAH8H1DdqdXcoGKptpwrx4aW/d2/e0xtBbjYiJ6Kr6du1C759ZCA+nh4OfycrlFXXY9FPpzD6f7ux9WQBBEG8DugsvImITMhXl5qqxfTzhoy3xxGRAfpw5zlU1jUi2M0Gk2/zFDsOXYdEIsGCsT0ANM3JP5VfIXKim3O+tBr/980xAEDsIF9MDOPfu85MIpEgupcb/nh6KF6bFAInaznOl1bjkS+P4J6Vf+Fw1gVRcrHwJiIyEeeKq3Dw/AVIJcAUNlUjIgOUc6EG65IyAQAL7uzBDxANQKi3PcaHekAQmtZcNzQ19Y14ZP1hVKobEeHTBQvv7CF2JGolc5kUDw7wQeL8EXhyVDdYmMtwJLsc96xMwiPrDyOjpKpD87DwJiIyERsvXe0eGewKNzs2VSMiw/POH2lo0AgYHOiEoVzCyWDMHx0Ec5kEe86WYveZErHjtJogCFjwwwmkFVXC2UaBj6b1hbmM5ZOhsVY0ddnfNX940zKqEmDrqUJsPJTToTn4N4eIyAT8s6naA5G82k1Ehud4bjm2HMuHRAI8PzYYEgmvdhuKro6WmD7AFwAQ/3sqNFrx5tm2xRd/ZeKn5HzIpBKseKAvXLgSiEFzsVUi/u4++GPeUEwI9cDjwzt2GUIW3kREJuCPU4W4WNMAdzslhnV3ETsOEVGbCIKAN35LAQBMDvNEiKedyImorZ4YGQgbpRlSClT48Wie2HFu6O/MC3jt16a/cwvv7IH+fg4iJ6L20s3VBh/cfxvsLDu2Kz0LbyIiE/A1m6oRkQFLTCvB/owLkJtJETe6u9hx6CZ0sZLjsUtXGN/bloa6Bo3Iia6tuLIOj204gkatgPGhHpjFJeuoHbDwJiIychklVdifwaZqRGSYGjVaxP/edOUx9nZfeHWxFDkR3azYQb5wt1Miv6IOn/+VKXacq2rQaDF3w1EUV6rR3dUab97dm9MaqF2w8CYiMnLNzUNGBLnAw95C5DRERG3z/ZFcnCmqgp2Fue6KKRkmpbkM/zc6CACwYuc5XKyuFznRld78PRUHMy/AWmGGVQ+Gw0phJnYkMhIsvImIjJi6UYPvDjc1Vbu/f1eR0xARtU1NfSOWbj8DoGmOcEfPyaT2N/k2TwS72aCyrhEf7jwndpwWfj6Wj8/2ngcAvHtfKPydrUVORMaEhTcRkRHbdqoIF6rr4WarxPAgZ7HjEBG1yZq951GkUsOriwWmD/QROw61A5lUggWX1sJel5SJnAs1IidqcqaoEs99fxwA8OjwAIwJcRM5ERkbFt5EREasuanalH7eMOPao0RkQEqr1Fi1KwMAMD86CAozmciJqL0M6+6MId2c0KAR8M4faWLHQWVdAx5Zfxg19RoMCnTE/93BBn7U/vgujIjISJ0vrcZf6WWQSJq6mRMRGZLlCWdRpW5EiKctxvfxEDsOtbPnxgRDIgG2HMvH8dxy0XIIgoBnvj2GjNJqeNgp8cHU2/hBNenFTf2tWrFiBXx9faFUKhEZGYmDBw+26riNGzdCIpFg0qRJVzyXkpKCCRMmwM7ODlZWVujXrx+ys7NvJh4REQHYeKjpd+jw7s7wZFM1IjIg50urseFA0++whWN7QMplEI1OiKcdJod5AgBe/zUFgiCIkmPVrgz8caoIcpkUHz0YDkdrhSg5yPi1ufDetGkT4uLisHjxYhw5cgShoaGIjo5GcXHxdY/LzMzEM888gyFDhlzxXHp6OgYPHozg4GAkJibi+PHjeOmll6BUKtsaj4iIANQ3avHd32yqRkSG6Z0/UtGoFTAiyBm3BzqJHYf0JG50d8jNpDhw/gJ2pl2/ltCHfedK8c4fqQCAlyf0Qpi3fYdnINPR5sJ76dKlmDNnDmJjY9GzZ0+sWrUKlpaWWLNmzTWP0Wg0mDZtGpYsWQJ/f/8rnn/hhRdw55134u2338Ztt92GgIAATJgwAS4uLm2NR0REALafLkJZdT1cbBQYGczfpURkOI5kX8RvJwohlQDPj+0hdhzSI68uloi93RcAEP9bKho12g773vnltXji66PQCsB94V64vz+nZJF+tanwrq+vx+HDhxEVFXX5BFIpoqKikJSUdM3jXnnlFbi4uGD27NlXPKfVavHrr7+ie/fuiI6OhouLCyIjI/Hjjz+2JRoREf1Dc1O1GDZVIyIDIggC4n9LAQDcG+6FIDcbkRORvj02PBB2FuY4W1yF74/kdsj3VDdq8OiGI7hQXY9eHrZ4dVIIJBJOZyD9atO7sdLSUmg0Gri6urbY7urqisLCwqses3fvXnz22WdYvXr1VZ8vLi5GVVUV3nzzTYwZMwbbtm3D5MmTcffdd2PXrl3XzKJWq6FSqVo8iIgIyCqrxt5zpZBIgCkR/ASfiAzH9tNFOJR5EUpzKZ5mZ2mTYGdpjidGBgIAlm4/g5r6Rr1/z1d+Po1jOeWwszDHqgfDoTRnx3zSP71eBqmsrMT06dOxevVqODldfX6OVtt0S8nEiRPx9NNPIywsDM8//zzuuusurFq16prnjo+Ph52dne7h7c03l0REALDxUA4AYGg3Z3g7WIqchoiodRo0Wry5tWm+7ezBfnC3Y1NIUzF9oA+8uligSKXGmr3n9fq9vv07BxsOZEMiAd6fGsZxkjpMmwpvJycnyGQyFBUVtdheVFQEN7crF5lPT09HZmYmxo8fDzMzM5iZmWHdunXYsmULzMzMkJ6eDicnJ5iZmaFnz54tju3Ro8d1u5ovWLAAFRUVukdOTk5bXgoRkVGqb9Ti27+bfh+yqRqRcROrC7S+bDqUg4ySajhYyfHfYQFix6EOpDCTYX50EICmLuOlVWq9fJ+TeRV48ceTAICno7pjeBB7oFDHMWvLznK5HOHh4UhISNAtCabVapGQkIC5c+desX9wcDBOnDjRYtuLL76IyspKvP/++/D29oZcLke/fv2QlpbWYr8zZ87Ax8fnmlkUCgUUCrb7JyL6p4SUIpRW1cPZRoFRPfiGgshYPfrlYSSkFiPQ2Ro93G3Rw93m0n9t4WAlFztem1WpG7HszzMAgCdHBsJWaS5yIupo4/t4YPWeDJzMU2F5wlksmRjSrucvr6nHoxsOQ92oxchgF8wdEdiu5ye6kTYV3gAQFxeHmTNnIiIiAv3798eyZctQXV2N2NhYAMCMGTPg6emJ+Ph4KJVKhIS0/Edjb28PAC22z58/HzExMRg6dChGjBiBrVu34ueff0ZiYuLNvzIiIhP01aWmalMivGDOpmpERqlIVYffTzb11jldoMLpgpZ9blxtFejhbotgt6aCvKe7LfycrDp1o8XVuzNQWlUPH0dLPBB57QsvZLykUgkW3tkDD6w+gA0HsvHQID/4OVm1y7m1WgFPbUxGzoVadHWwxP+mhHFteOpwbS68Y2JiUFJSgkWLFqGwsBBhYWHYunWrruFadnY2pNK2/WKfPHkyVq1ahfj4eDz55JMICgrC999/j8GDB7c1HhGRycq5UIM9Z0sBADERvM2cyFg1/zvv4W6LeVHdkFKguvSoRPaFGhSp1ChSlSAxrUR3jMJMiu6uNgh2u3xlvKe7Lewsxb+yXKyqw+o9GQCAZ6ODITfrvB8QkH7dHuCEEUHO2JlWgre3pmLlg+Htct73E85i15kSKMykWPVgeKf4e0+mRyIYyQQhlUoFOzs7VFRUwNbWVuw4REQd7p0/UrFiZzqGdHPC+tmRYscxeRyX2h9/pk2e2ngUPyXnY+6IQDxzaV5ssyp1I9IKVThdUImUAhVSC1RILaxETb3mqufysFMi+F+3qvs6WkHWgVcDF24+ga8OZCPM2x6bH7udyzqZuLTCSox9fze0AvD9o7cj3KfLLZ1vR2oRZn3+NwDgvftCcU+4V3vEJNJp7djU5iveRETU+TRotPjm76b1T9lUjch4abUC9l664j2k25UrxlgrzBDu44BwH4cWx2RfqGm6Kl5YqbtCnnuxFvkVdcivqMOO1GLd/kpzKYJcLxfiPdxtEeRmAzuL9r9KeK64EpsurcSw8M4eLLoJQW42uDfcC9/8nYv431Lw7SMDb/rvRXZZDeZtTAYATB/gw6KbRMXCm4jICCSkFKOkUg0nazmieriKHYeMwIoVK/DOO++gsLAQoaGhWL58Ofr373/D4zZu3Ij7778fEydOxI8//qjbLggCFi9ejNWrV6O8vByDBg3CypUr0a1bNz2+CuNzukCFsup6WMlluK1r664ESqUS+DpZwdfJCmN7u+u2q+oakFpQidTCpkL8dEEl0gpVqGvQ4lhuBY7lVrQ4j6e9xaVb1JuK8mB3W/g4WN7SXNm3tqZBoxVwR09X9PdzuPEBZBKevqM7thzLx99ZF7HtdBGie125etKN1NZr8N8vD0NV14jbutrjpbt63vggIj1i4U1EZAS+vtRU7d5wb86PpFu2adMmxMXFYdWqVYiMjMSyZcsQHR2NtLQ0uLhcu1t+ZmYmnnnmGQwZMuSK595++2188MEH+OKLL+Dn54eXXnoJ0dHROH36NJRKpT5fjlFpnt89MMDxlv+t2yrN0d/PoUXBq9EKyCqrRkpB5T/mjquQX1GHvPJa5JXX4s+Uy8vKWsplCPrHvPEebjYIdreFteLGbzEPnr+A7aeLIJNK8NyY4Ft6LWRc3O0sMHuwH1bsTMdbv6diZLBLmxqGCoKAF348gZQCFZys5fhoWl+OjSQ6Ft5ERAYu50INdp9taqI0tZ+3yGnIGCxduhRz5szRrViyatUq/Prrr1izZg2ef/75qx6j0Wgwbdo0LFmyBHv27EF5ebnuOUEQsGzZMrz44ouYOHEiAGDdunVwdXXFjz/+iKlTp+r9NRmLPZf+rQ/p5qyX88ukEvg7W8Pf2Rrj+ly+Ol5R04CUwsuFeGphJdIuzR0/ml2Oo9nlLc7T1cESPdxtLnVWb2rk5tXFQnd1XBAEvPFbCgAgpp83Al2s9fJ6yHD9d1gAvj6Yg4zSamw6lIMHB7S+2/2XB7Lxw5E8yKQSLL+/L9ztLPSYlKh1WHgTERm4b/7OgSAAgwId4dtOS6+Q6aqvr8fhw4exYMEC3TapVIqoqCgkJSVd87hXXnkFLi4umD17Nvbs2dPiufPnz6OwsBBRUVG6bXZ2doiMjERSUhIL71aqqW/E35kXAVx9frc+2VmaY4C/Iwb4O+q2NWq0yCyrbtHILaWgEoWqOmRfqEH2hRr8cery1XFrhRmC3WwQ7G4DhZkMyTnlsJTLMC+K0w3oSrZKczw5MhAv/3way/48g0m3ebbqTooj2Rfxys+nAADPjQnCwADHGxxB1DFYeBMRGbBGjVbXmIhN1ag9lJaWQqPR6JYJbebq6orU1NSrHrN371589tlnSE5OvurzhYWFunP8+5zNz12NWq2GWq3Wfa1Sqa65ryk4cP4C6jVaeNpbtNv6xrfCTCZFoIsNAl1sMCHUQ7f9QnV9UxH+j0ZuZ4uqUKVuxN9ZF/F31kXdvnOG+MPFhlMN6OoeiPTB2r8ykVVWg9W7M/D0Hd2vu39plRqPfXkEDRoBY0PcMGeIfwclJboxFt5ERAZsR2oxiivVcLSSY3TPtjefIbpVlZWVmD59OlavXg0np/a9ChsfH48lS5a06zkN2Z4zTfO7h3Z36tTdvx2s5Lg90Am3B17++9Cg0SKjpBqphSqcvnRl3Eouw8NDWRjRtcnNpHg2OhiPf3UEq/dkYFpkV7jYXv2DmkaNFk98dRSFqjoEOFvhnftCO/W/EzI9LLyJiAzY5aZqXmwcQ+3CyckJMpkMRUVFLbYXFRXBze3KD3fS09ORmZmJ8ePH67ZptVoAgJmZGdLS0nTHFRUVwd398rzhoqIihIWFXTPLggULEBcXp/tapVLB29t0+xjoe363PpnLpAhys0GQmw0mhnmKHYcMyJ293RDmbY/knHL878+ziL+791X3e2dbGpIyymAll+Hj6eGtui2dqCPxXRoRkYHKK69F4pmmN+IxbKpG7UQulyM8PBwJCQm6bVqtFgkJCRg4cOAV+wcHB+PEiRNITk7WPSZMmIARI0YgOTkZ3t7e8PPzg5ubW4tzqlQqHDhw4KrnbKZQKGBra9viYaoKKmpxtrgKUglwO+eskgmRSCRYeGcPAMCmQ9k4V1x5xT6/nyjAx7syAADv3BeKQBebDs1I1Br8KIiIyEBtOtTUVG2gvyP8ndkRmNpPXFwcZs6ciYiICPTv3x/Lli1DdXW1rsv5jBkz4Onpifj4eCiVSoSEhLQ43t7eHgBabJ83bx5ee+01dOvWTbecmIeHByZNmtRRL8ugNS8j1sfLHvaWcpHTEHWs/n4OuKOnK7afLsKbv6fh05kRuufOFVfhmW+PAQAeHuqPO/+xVj1RZ8LCm4jIADVqtPimualaJJuqUfuKiYlBSUkJFi1ahMLCQoSFhWHr1q265mjZ2dmQStt209yzzz6L6upqPPzwwygvL8fgwYOxdetWruHdSs2F99AO7mZO1Fk8NyYYO1KL8WdKEQ5klCHS3xFV6kY88uVhVNdrMMDfAc9GB4kdk+iaJIIgCGKHaA8qlQp2dnaoqKgw6VvRiMg0/Hm6CP9Z9ze6WJpj/8JRUJjJxI5E/8Jxqf2Z6s9UqxUQ/tp2XKxpwLePDEQ/XwexIxGJYuHmE/jqQDZCve2x+dHb8cTXR/HriQK42irwyxND4GyjEDsimaDWjk2c401EZID+2VSNRTeRcTuVr8LFmgZYK8wQ5m0vdhwi0cyL6gZLuQzHcsox+4tD+PVEAcxlEnw0LZxFN3V6LLyJiAxMfnktdqYVAwCmcu1uIqO3+1I384EBjjCX8a0bmS4XG6Vube6daU3/Ll66qyfCfbqIGYuoVfjbm4jIwHzzdw60AhDp54AANlUjMnq7L61ewPndRMCcof5wsm66uj35Nk9MH+AjciKi1mFzNSIiA6LRCth0qanaA2yqRmT0qtSNOJJ9EYBhrt9N1N6sFWb4ZEY49p0txX+G+EMikYgdiahVWHgTERmQXWeKUVBRB3tLc0T3chM7DhHp2YGMMjRoBHg7WMDH0VLsOESdQt+uXdC3K28vJ8PCW82JiAzIVwearnbf09cLSnM2VSMyds3LiA3p5swre0REBoyFNxGRgSisqMOO1CIAwP39vUVOQ0Qdobmx2lDeZk5EZNBYeBMRGYjmpmr9fR0Q6GIjdhwi0rPcizXIKKmGTCrBwABHseMQEdEtYOFNRGQA/tlU7f5IXu0mMgV7L91mHuZtDzsLc5HTEBHRrWDhTURkAHafLUFeeS3sLMwxNsRd7DhE1AEuz+/mMmJERIaOhTcRkQH4+kA2AODuvp5sqkZkAjRaAXvPXW6sRkREho2FNxFRJ1ekqkNCajEA4P7+XLubyBScyKtARW0DbJRmCPWyEzsOERHdIhbeRESd3Ld/50CjFRDh0wXdXdlUjcgU7DnT1M18UIATzGR8u0ZEZOj4m5yIqBPTagV8ffBSUzVe7SYyGbr53d05v5uIyBiw8CYi6sT2nCtFXnktbJVmGNeHTdWITEFlXQOOZF8EwPW7iYiMBQtvIqJO7HJTNS82VSMyEfszLqBRK8DX0RLeDpZixyEionbAwpuIqJMqVtXhz5QiAMDU/ly7m8hU7DnbNL+b3cyJiIwHC28iok7q28O5aNQK6NvVHsFutmLHIaIOwvW7iYiMDwtvIqJOSKsVsPFQ023mbKpGZDpyLtTgfGk1ZFIJBgY4ih2HiIjaCQtvIqJOaF96KXIu1MJGaYa7+niIHYeIOkjz1e6+Xe1hozQXOQ0REbUXFt5ERJ3Q1webrnZPvs0TFnI2VSMyFbvPcH43EZExYuFNRNTJlFSqse3UpaZq/XibOZGpaNRosS+d87uJiIwRC28iok7mu0tN1cK87dHTg03ViEzFsdwKVNY1wlZphj5e9mLHISKidsTCm4ioE/lnU7UH2FSNyKQ0LyM2uJsTZFKJyGmIiKg9sfAmIupEkjLKkFVWA2uFGe4KdRc7DhF1oMvLiHF+NxGRsTETOwCRsdl9pgSNWi2GdnOGmYyfbVHrabUCvvgrEwAw6TYPWMr5K5rIVFTUNiA5pxwA53cTERkjvqsjakf7zpVixpqDAAAPOyWmDfBBTD9vOFkrRE5GnZlGK+CX4/lYsfMczhRVAWBTNSJTk5ReBo1WgL+zFby6WIodh4iI2hkLb6J2UtegwYs/ngQAmEklyK+owzt/pOH9P8/izt5umD7QF3272kMi4bw9atKg0eKn5Hx8tPMcMkqrAQA2CjPMu6M7QjztRE5HRB2peX73UN5mTkRklG7qPtgVK1bA19cXSqUSkZGROHjwYKuO27hxIyQSCSZNmnTNfR555BFIJBIsW7bsZqIRieajxHScL62Gi40C+xeOwnv3hSLUyw71Gi1+TM7HPSv/wvgP9+KbQzmoa9CIHZdEVN+oxdcHszHyvUQ88+0xZJRWw97SHHF3dMfe50di9mA/sSMSUQe7PL+bt5kTERmjNl/x3rRpE+Li4rBq1SpERkZi2bJliI6ORlpaGlxcXK55XGZmJp555hkMGTLkmvts3rwZ+/fvh4eHR1tjEYnqXHEVViaeAwC8PKEXnKwVuCfcC/eEe+FYTjnWJWXh5+P5OJmnwrPfH8frv6VgSoQXHhzgAx9HK5HTU0epa9Dgm79zsCoxHfkVdQAARys55gz1x4MDfGCt4E1IRKYoq6wa2RdqYC6TYIC/o9hxiIhID9p8xXvp0qWYM2cOYmNj0bNnT6xatQqWlpZYs2bNNY/RaDSYNm0alixZAn9//6vuk5eXhyeeeAIbNmyAubl5W2MRiUYQBLyw+QQaNAJGBDljbIhbi+dDve3x3pRQ7F8wCs+PDYZXFwtU1DZg9Z7zGP5uIh5aexA7Uoug1QoivQLSt9p6DT7dk4Ghb+/Eop9OIb+iDi42Crx0V0/sfW4kHhkWwKKbyITtvnS1u2/XLrDi7wIiIqPUpt/u9fX1OHz4MBYsWKDbJpVKERUVhaSkpGse98orr8DFxQWzZ8/Gnj17rnheq9Vi+vTpmD9/Pnr16tWqLGq1Gmq1Wve1SqVqwyshaj/fHc7FgfMXoDSX4pWJIdecw+1gJccjwwIwZ4g/EtOKsS4pC7vOlCAxrenR1cESDw7oiikR3rC3lHfwqyB9qFI3Yn1SFj7dk4Gy6noATU33Hh0egPsivKE0l4mckIg6gz1nLs3v7s753URExqpNhXdpaSk0Gg1cXV1bbHd1dUVqaupVj9m7dy8+++wzJCcnX/O8b731FszMzPDkk0+2Okt8fDyWLFnS6v2J9OFCdT3e+C0FADAvqju8HW7ciVYmlWBUD1eM6uGKzNJqfLk/C9/8nYPsCzV447dUvLftDCaEemDGQF/09mKDLUNUUduAz/dlYs2+86iobQAAdHWwxGPDA3B3Xy/IzbjMHBE1adBokZReBoDzu4mIjJle72eqrKzE9OnTsXr1ajg5XX0wOXz4MN5//30cOXKkTd2eFyxYgLi4ON3XKpUK3t7et5yZqC1e/zUFF2saEOxmc1MNsXydrPDiXT3xf6OD8FNyHtYlZeF0gQrfHs7Ft4dzEeZtjxkDfXBnb3deHTUAF6vr8dne8/jir0xUqhsBAP5OVnh8RCAmhnlwXXciusKxnHJUqhvRxdIcvTz4YSsRkbFqU+Ht5OQEmUyGoqKiFtuLiorg5uZ2xf7p6enIzMzE+PHjddu0Wm3TNzYzQ1paGvbs2YPi4mJ07Xp5zVqNRoP/+7//w7Jly5CZmXnVLAqFAgoF10Ym8SSll+H7I7mQSIDXJ/eG+S0UVRZyGab274qYft44kn0R65Ky8NuJAiTnlCM5pxyv/ZqCmH7emBbZleu7dkIllWp8uicD6/dnoaa+qWN9d1drzB3ZDeN6u0Mm5RJyRHR1zfO7BwU68XcFEZERa1PhLZfLER4ejoSEBN2SYFqtFgkJCZg7d+4V+wcHB+PEiRMttr344ouorKzE+++/D29vb0yfPh1RUVEt9omOjsb06dMRGxvbxpdD1DHUjRq8sLnp7/YD/bsi3KdLu5xXIpEg3McB4T4OeHFcT2w6lI0NB7JRUFGHlYnp+HhXOkYGu2LGQB8MDnSClG/SRFVYUYePd6fj64PZqGto+lCxl4ctnhgZiNE93fj/h4huiOt3ExGZhjbfah4XF4eZM2ciIiIC/fv3x7Jly1BdXa0rkmfMmAFPT0/Ex8dDqVQiJCSkxfH29vYAoNvu6OgIR8eWS2eYm5vDzc0NQUFBN/OaiPRuVWIGMkqr4WStwLNjgvXyPZxtFJg7shseGRaAP1OKsX5/JvadK8OfKUX4M6UI/k5WeHCAD+4J94KdBVcC6Ei5F2uwalc6vjmUi3pNU8Ed6m2PJ0cGYmSwS5umzRCR6aqoacCxnHIAwGDO7yYiMmptLrxjYmJQUlKCRYsWobCwEGFhYdi6dauu4Vp2djakUs5jJOOVUVKFFTub1uxePL6n3oteM5kUY0LcMCbEDeeKq/Dl/ix8dzgXGaXVeOWX03jnjzRMus0TMwb6oIe7rV6zmLqssmp8tDMd3x/JReOl5d/6+XbBEyO7YUg3JxbcRNQmf6WXQisAgS7W8LC3EDsOERHpkUQQBKNYPFilUsHOzg4VFRWwtWXxQfohCAKmfXoAf6WXYWh3Z3wR20+UYqtK3YjNR/OwPikTZ4qqdNv7+XbB9IG+GNPLjZ2z29G54ip8tPMcfjqWD82lgntQoCOeGNkNA/wdb3A0mSqOS+3P2H6mC344ga8PZiN2kC8Wj2/dcqpERNS5tHZs0mtXcyJjs/loHv5KL4PCTIrXrrNmt75ZK8wwfYAPHozsigPnL2B9Uha2nirEocyLOJR5Ec42Ctzfvyse6N8VbnZKUTIag9RCFZbvOIffThSg+SPK4UHOeGJkt3ab109EpkkQBOw+w/ndRESmgoU3UStdrK7Ha782rdn9VFQ3dHUUv7u4RCLBAH9HDPB3RJGqDl8dyMZXB7NRUqnGBwlnsWLnOUT3csX0Ab4Y4O/AW6Fb6WReBT5IOIttpy+v4HBHT1c8MTIQfbzsxQtGREbjfGk18sprYS6TINLfQew4RESkZyy8iVop/vcUXKiuR3dXa8wZ4i92nCu42irx9B3d8fiIQPxxqhDrk7JwMPMCfjtRiN9OFKK7qzWmD/DB5L5esFbwn/7VHMm+iOUJZ7EzrekqlEQC3BnijrkjAzl/noja1Z5Ly4hF+DjAUs7fyURExo6TQIla4UBGGb75OxcA8MYtrtmtb3IzKcaHeuCbRwZi67wheCCyKyzlMpwpqsJLP53CgDcSsOinkzhXXCl21E7jQEYZHvz0AO7+6C/sTCuBVAJMCvPAtnlDsWJaXxbdZJJWrFgBX19fKJVKREZG4uDBg9fc94cffkBERATs7e1hZWWFsLAwrF+/vsU+Dz30ECQSSYvHmDFj9P0yOq3mZcSGdGc3cyIiU8CPWIluoL5Rixd+PAkAuL9/V0T4Gs4tgcFutnhjcm88PzYY3x/Oxfr9Wcgoqca6pCysS8rCQH9HzBjogzt6usKsE3+YoA+CIGDfuTJ8sOMsDp6/AAAwk0ow+TZPPDYiEH5OViInJBLPpk2bEBcXh1WrViEyMhLLli1DdHQ00tLS4OLicsX+Dg4OeOGFFxAcHAy5XI5ffvkFsbGxcHFxQXR0tG6/MWPGYO3atbqvFQpFh7yezqa+UYuk9DIAnN9NRGQqWHgT3cAnu9NxrrgKTtZyPK+nNbv1zVZpjthBfnjodl/sO1eGdUmZ+DOlCEkZZUjKKIO7nRIP9O+Kqf27wtnGuN8IC4KAxLQSfLDjLI5mlwMA5DIp7o3wwqPDAuDtIP7cfSKxLV26FHPmzEFsbCwAYNWqVfj111+xZs0aPP/881fsP3z48BZfP/XUU/jiiy+wd+/eFoW3QqGAm5ubXrMbgqPZF1Fdr4GDlRw9eUcNEZFJYOFNdB2ZpdX4YEfTmt0v3dUTdpb6XbNb3yQSCQZ3c8Lgbk7IK6/FVweysPFgDgoq6vDe9jP4YMdZjA1xxx09XeFgJYedhTnsLMxhb2kOa4WZQTdn02oFbE8pwoc7zuFEXgUAQGEmxf39u+K/w/zhbsc1dIkAoL6+HocPH8aCBQt026RSKaKiopCUlHTD4wVBwI4dO5CWloa33nqrxXOJiYlwcXFBly5dMHLkSLz22mtwdLz2knxqtRpqtVr3tUqluolX1Pk0z+8eHOgEqdRwf68SEVHrsfAmugZBEPDijydR36jF4EAnTAj1EDtSu/K0t8D86GA8OaobfjtRgHVJWTiaXY4tx/Kx5Vj+FfvLpBLYW5jDzvJSMW5hDntLua4wb37O3kJ+6b9Nz9sqzUS9jV2jFfD7yQJ8uOMcUgub5rVbmMswfaAP/jPEDy42XG6N6J9KS0uh0Wjg6uraYrurqytSU1OveVxFRQU8PT2hVqshk8nw0Ucf4Y477tA9P2bMGNx9993w8/NDeno6Fi5ciLFjxyIpKQkymeyq54yPj8eSJUva54V1Is3zu4d2523mRESmgoU30TVsOZaPvedKITeT4rVJ4q3ZrW8KMxkm3+aFybd54WReBb46mI1zRVWoqG1AeW09LtY0oL5RC41WQFl1Pcqq69v8PWyUZv8o0P9ZmDcX8S2L9ebtSvOrvxlvjUaNFj8fz8eHO84hvaQaQNP65zNv98Hswf5wsJLf9LmJ6Eo2NjZITk5GVVUVEhISEBcXB39/f91t6FOnTtXt27t3b/Tp0wcBAQFITEzEqFGjrnrOBQsWIC4uTve1SqWCt7e3Xl+Hvl2srsfxS3fdDOnGxmpERKaChTfRVZTX1OPVX04DAJ4cGQhfE2m0FeJphzcm975ie12DBuU1TYV4RU0DymsbLv23/tL2BlT8a1tFTQMq1Y0AgMq6RlTWNSL3Ym2b8ijMpFcU67oCvsXV9svFuo3SDNtOFWFF4jlkldUAAGyVZpg12A+xt/sZ/HQBIn1zcnKCTCZDUVFRi+1FRUXXnZ8tlUoRGBgIAAgLC0NKSgri4+OvmP/dzN/fH05OTjh37tw1C2+FQmF0Ddj2pZdCEIAgVxu42vKOGyIiU8HCm+gq3tqaitKqegS6WOPhoQFixxGd0lwGNzsZ3Oza9iaxUaOFqq4R5TX1VxbrNZeK9dqGfz3ftE2jFaBu1KJIpUaRSn3jb3YVDlZyzB7shxkDfWCjZMFN1BpyuRzh4eFISEjApEmTAABarRYJCQmYO3duq8+j1WpbzM/+t9zcXJSVlcHd3f1WIxuUPWea5nfzajcRkWlh4U30L39nXsDXB3MAAK9PCoHczLSW2WpPZjIpHKzkbb6tWxAEVKkbdcW57mp7bcM/tl2+2q76xz51DVo42yjw8BB/TBvQFZZy/pojaqu4uDjMnDkTERER6N+/P5YtW4bq6mpdl/MZM2bA09MT8fHxAJrmYkdERCAgIABqtRq//fYb1q9fj5UrVwIAqqqqsGTJEtxzzz1wc3NDeno6nn32WQQGBrboem7sBEH4x/rdnN9NRGRK+I6U6B/qG7VYuPkEACAmwhuR/tfutkv6I5FIYKM0h43SHG2dzVnXoIFcJmWnYKJbEBMTg5KSEixatAiFhYUICwvD1q1bdQ3XsrOzIZVe/lCyuroajz32GHJzc2FhYYHg4GB8+eWXiImJAQDIZDIcP34cX3zxBcrLy+Hh4YHRo0fj1VdfNbpbya8nvaQa+RV1kJtJ0d/XQew4RETUgSSCIAhih2gPKpUKdnZ2qKiogK0t18Skm7Ni5zm880caHKzkSIgbhi5swEVEN4njUvsz9J/p2n3nseTn0xgc6IQv/xMpdhwiImoHrR2beA8t0SXZZTX4IOEsAODFcT1YdBMRUbtqXr+b87uJiEwPC28iXFqz+6eTUDdqcXuAIybf5il2JCIiMiLqRg2S0ssAAEO6cX43EZGpYeFNBOCX4wXYfaYEcplxr9lNRETiOJJVjtoGDZysFQh2sxE7DhERdTAW3mTyKmobsOTnpjW7Hx8RCH9na5ETERGRsdF1M+/mxOaPREQmiIU3mby3t6aitEoNf2crPDLcX+w4RERkhDi/m4jItLHwJpN2OOsivjqYDQB4fVJvKMxkIiciIiJjU1alxsn8CgDA4EAW3kREpoiFN5msBo0WL2w+AUEA7g33wsAArtlNRETtb++5UggCEOxmAxdbpdhxiIhIBCy8yWR9tvc8Ugsr0cXSHAvv7CF2HCIiMlLNt5kP7c5u5kREpoqFN5mknAs1WPbnGQDAwjt7wIFrdhMRkR4IgtCisRoREZkmFt5kcgRBwKKfTqKuQYtIPwfcG+4ldiQiIjJSZ4urUKRSQ2EmRT9fB7HjEBGRSFh4k8n5/WQhdqaVwFwmweuTe3PNbiIi0pvdZ5qudvf3c4DSnA08iYhMFQtvMimquga8vOUUAODR4YEIdOGa3UREpD+6+d3dOL+biMiUsfAmk/LuH2korlTDz8kKjw0PEDsOEREZsboGDQ6cLwMADOnO+d1ERKaMhTeZjOSccqzfnwUAeH1SCG/5IyIivTqcdRF1DVq42CgQ5GojdhwiIhIRC28yCY0aLRb80LRm9923eeL2QF55ICIi/dqt62buzH4iREQmjoU3mYS1+zKRUqCCvaU5XhjHNbuJiEj/9pxpXr+bH/YSEZk6Ft5k9HIv1mDp9qY1uxeMDYajtULkREREZOxKKtU4XaACAAziXVZERCaPhTcZNUEQ8PKWU6ht0KC/rwPuC/cWOxIREZmAfeearnb38rCFEz/wJSIyeSy8yaj9caoIf6YUX1qzOwRSKefYERGR/v1zfjcRERELbzJalf9Ys/u/QwPQjR1liYioAwiC8I/1u3mbORERsfAmI/betjMoVNXBx9ESc0cGih2HiIhMRFpRJUoq1VCaSxHu20XsOERE1Amw8CajdDy3HOuSMgEAr3HNbiIi6kDN3cwH+DtCYcbxh4iIWHiTEWrUaLFw8wloBWBimAfn1xERUYfi/G4iIvo3Ft5kdL5IysLJPBVslWZ4cVxPseMQEZEJqWvQ4OD5CwA4v5uIiC5j4U1GJb+8Fku3pQEAFtzZA842XMKFiIg6zqHMC1A3auFmq0Sgi7XYcYiIqJO4qcJ7xYoV8PX1hVKpRGRkJA4ePNiq4zZu3AiJRIJJkybptjU0NOC5555D7969YWVlBQ8PD8yYMQP5+fk3E41M3MtbTqG6XoMIny6IieCa3URE1LF2n2m+zdwJEgmXsCQioiZtLrw3bdqEuLg4LF68GEeOHEFoaCiio6NRXFx83eMyMzPxzDPPYMiQIS2219TU4MiRI3jppZdw5MgR/PDDD0hLS8OECRPaGo1M3LZThdh2ughmUglen9yba3YTEVGHa15GbEh3zu8mIqLL2lx4L126FHPmzEFsbCx69uyJVatWwdLSEmvWrLnmMRqNBtOmTcOSJUvg7+/f4jk7Ozts374dU6ZMQVBQEAYMGIAPP/wQhw8fRnZ2dttfEZmkKnUjFl9as3vOUH8EuXHNbiIi6ljFqjqkFlZCIgEGB3J+NxERXdamwru+vh6HDx9GVFTU5RNIpYiKikJSUtI1j3vllVfg4uKC2bNnt+r7VFRUQCKRwN7evi3xyIT9b/sZFFTUwdvBAk+O7CZ2HCIiMkHNV7tDPOzgYCUXOQ0REXUmZm3ZubS0FBqNBq6uri22u7q6IjU19arH7N27F5999hmSk5Nb9T3q6urw3HPP4f7774etre0191Or1VCr1bqvVSpVq85PxudkXgXW7jsPAHh1Yggs5FwzlYiIOt6es5fndxMREf2TXruaV1ZWYvr06Vi9ejWcnG48CDU0NGDKlCkQBAErV6687r7x8fGws7PTPby92UjLFGm0gm7N7rv6uGN4kIvYkYiIyARptQL2nrs0v5vrdxMR0b+06Yq3k5MTZDIZioqKWmwvKiqCm5vbFfunp6cjMzMT48eP123TarVN39jMDGlpaQgICABwuejOysrCjh07rnu1GwAWLFiAuLg43dcqlYrFtwlan5SJ47kVsFGaYdFdXLObiIjEkVKoQmlVPSzlMvT1sRc7DhERdTJtKrzlcjnCw8ORkJCgWxJMq9UiISEBc+fOvWL/4OBgnDhxosW2F198EZWVlXj//fd1hXJz0X327Fns3LkTjo6ON8yiUCigUHCNZlNWWFGHd7edAQA8NyYYLrZKkRMREZGpap7fPcDfEQozTnkiIqKW2lR4A0BcXBxmzpyJiIgI9O/fH8uWLUN1dTViY2MBADNmzICnpyfi4+OhVCoREhLS4vjmhmnN2xsaGnDvvffiyJEj+OWXX6DRaFBYWAgAcHBwgFzO5iR0dUt+PoUqdSNu62qPB/p3FTsOERGZsOb53UM5v5uIiK6izYV3TEwMSkpKsGjRIhQWFiIsLAxbt27VNVzLzs6GVNr6qeN5eXnYsmULACAsLKzFczt37sTw4cPbGpFMQEJKEX4/WQiZVII3uGY3ERGJqLZeg0PnLwLg+t1ERHR1bS68AWDu3LlXvbUcABITE6977Oeff97ia19fXwiCcDMxyETV1Ddi0U9Na3b/Z4gferhfvx8AERGRPh04X4Z6jRae9hbwd7ISOw4REXVCeu1qTqQPy/48i7zyWnjaW+CpUVyzm4iIxNU8v3tINydIJLwDi4iIrsTCmwzK6XwVPtvbtGb3a5NCYCm/qZs2iIiI2s3l9bt5mzkREV0dC28yGM1rdmu0Asb1dseIYK7ZTURE4iqsqMOZoipIJMCgwBuvykJERKaJhTcZjK8OZCE5pxw2CjMsGs81u4mISHzNV7v7eNnD3pIrsRAR0dWx8CaDUKyqw9tb0wAA88cEwZVrdhMRUSfQPL+by4gREdH1sPAmg7Dkl9OoVDci1MsO0yJ9xI5DREQErVbA3nPNjdU4v5uIiK6NhTd1ejvTivHr8YKmNbvv7g0Z1+wmItK7FStWwNfXF0qlEpGRkTh48OA19/3hhx8QEREBe3t7WFlZISwsDOvXr2+xjyAIWLRoEdzd3WFhYYGoqCicPXtW3y9Dr04XqHChuh5Wchlu62ovdhwiIurEWHhTp1Zbr8FLP54EAMwa5IteHnYiJyIiMn6bNm1CXFwcFi9ejCNHjiA0NBTR0dEoLi6+6v4ODg544YUXkJSUhOPHjyM2NhaxsbH4448/dPu8/fbb+OCDD7Bq1SocOHAAVlZWiI6ORl1dXUe9rHa3+9L87oEBTjCX8S0VERFdG0cJ6tTeTziL3Iu18LBTYl5Ud7HjEBGZhKVLl2LOnDmIjY1Fz549sWrVKlhaWmLNmjVX3X/48OGYPHkyevTogYCAADz11FPo06cP9u7dC6DpaveyZcvw4osvYuLEiejTpw/WrVuH/Px8/Pjjjx34ytrX7jNNhffQ7pzfTURE18fCmzqt1EIVPt2TAQB4ZWIIrBRcs5uISN/q6+tx+PBhREVF6bZJpVJERUUhKSnphscLgoCEhASkpaVh6NChAIDz58+jsLCwxTnt7OwQGRl53XOq1WqoVKoWj86iWt2Iw1kXAXB+NxER3RgLb+qUtFoBC384gUatgDG93BDV01XsSEREJqG0tBQajQauri1/77q6uqKwsPCax1VUVMDa2hpyuRzjxo3D8uXLcccddwCA7ri2njM+Ph52dna6h7e3982+rHZ34HwZGjQCvLpYwNfRUuw4RETUybHwpk7p60PZOJJdDiu5DIsncM1uIqLOzsbGBsnJyTh06BBef/11xMXFITEx8ZbOuWDBAlRUVOgeOTk57RO2Hew+c7mbuUTCpp9ERHR9vHeXOp3iyjq89XsqAOCZ6CC421mInIiIyHQ4OTlBJpOhqKioxfaioiK4ubld8zipVIrAwEAAQFhYGFJSUhAfH4/hw4frjisqKoK7u3uLc4aFhV3znAqFAgqF4hZejf7sudRYjet3ExFRa/CKN3UKWq2Agopa7M8ow8IfTkBV14jennaYMdBX7GhERCZFLpcjPDwcCQkJum1arRYJCQkYOHBgq8+j1WqhVqsBAH5+fnBzc2txTpVKhQMHDrTpnJ1FXnkt0kuqIZUAtwew8CYiohvjFW/qMI0aLfLL65BZVo2sCzXIKq1GZlkNsi9UI6usBupGrW5fqQSI55rdRESiiIuLw8yZMxEREYH+/ftj2bJlqK6uRmxsLABgxowZ8PT0RHx8PICmudgREREICAiAWq3Gb7/9hvXr12PlypUAAIlEgnnz5uG1115Dt27d4Ofnh5deegkeHh6YNGmSWC/zpu29dLU71NsedpbmIqchIiJDwMKb2pW6UYOcC7XIKmsqprPKmovrGuRcqEGjVrjmsTKpBN5dLNDV0QpT+3kjxJNrdhMRiSEmJgYlJSVYtGgRCgsLERYWhq1bt+qao2VnZ0MqvXzTXHV1NR577DHk5ubCwsICwcHB+PLLLxETE6Pb59lnn0V1dTUefvhhlJeXY/Dgwdi6dSuUSmWHv75btfvs5fndRERErSERBOHalZABUalUsLOzQ0VFBWxtbcWOY9Rq6ht1RXVWWQ0y//Hn/IpaXO9vlNxMCh8HS/g4WsHH0RK+jpf/7GFvAXMZZz8QkXHguNT+OsPPVKMVEP7adpTXNOC7RwYiwtdBlBxERNQ5tHZs4hVvuqqK2obLV6sv/bf565JK9XWPtZLL4ONoBV8nS3R1sGpRXLvZKiHl7eNERGSgTuZVoLymATYKM4R624sdh4iIDAQLbxMlCALKquubiunSmqY51/8osMtrGq57vL2leVNx3VxUO1jC16npz45Wci6tQkRERqm5m/ntgY68S4uIiFqNhbcR02oFFFXWNRXWZZcbmTV/XV2vue7xLjYK+FwqrH0dLdG1udB2sGIzGSIiMkmc301ERDeDhbeRqVY34rVfU/B35gVkX2jZKfzfJBLAw86iRXHd/OeuDpawUvCvBxERUbMqdSOOZF0EAAxl4U1ERG3AysqI1DVo8J8v/kZSRplum5lUAm+HSwX1paZmzXOvvR0soDCTiZiYiIjIcOxPL0OjVoCPoyW6OlqKHYeIiAwIC28jUd+oxSNfHkZSRhmsFWZ4+94+CPGwg4e9Emacg0ZERHTLmud3D+nmJHISIiIyNCy8jUCjRounNh5FYloJlOZSrHmoH/r7cXkTIiKi9rSH87uJiOgm8VKogdNqBcz/7jh+P1kIuUyK1TMiWHQTERG1s5wLNcgorYZMKsHAAEex4xARkYFh4W3ABEHACz+exOajeTCTSrBiWl9+Ck9ERKQHe881Xe2+zdsetkqu7EFERG3DwttACYKAV39JwdcHsyGVAP+LCcMdPV3FjkVERGSULs/v5gfcRETUdiy8DdTS7WewZt95AMCb9/TB+FAPkRMREREZp0aNFnub53d3Z2M1IiJqOxbeBuijxHNYvuMcAOCVib0wJcJb5ERERETG63heBVR1jbBVmqGPp53YcYiIyACx8DYwa/edx9tb0wAAz48NxoyBvuIGIiIiMnJ7zjRd7R4U6MQlOomI6KZw9DAgmw5lY8nPpwEAT47qhkeGBYiciIiIyPhxfjcREd0qFt4G4qfkPDz/wwkAwJwhfng6qpvIiYiIiIyfqq4BR3PKAQBDunF+NxER3RwW3gbgj1OFiPvmGAQBmBbZFQvv7AGJRCJ2LCIiIqOXlF4GjVaAn5MVvB0sxY5DREQGioV3J7frTAme+OooNFoBd/f1xKsTQ1h0ExERdZDLt5nzajcREd08Ft6d2P6MMjy87m/Ua7QY19sdb9/TB1Ipi24iIqKOsqd5GTHO7yYiolvAwruTOpJ9EbM/PwR1oxYjg13wv5gwdlIlIiLqQFll1cgqq4GZVIIB/g5ixyEiIgPGSq4TOpVfgYfWHER1vQaDAh3x0bS+kJvxfxUREVFHar7a3bdrF9gozUVOQ0REhozVXCdzrrgS0z87CFVdIyJ8umD1jAgozWVixyIiIjI5nN9NRETthYV3J5JVVo0HVh/Ahep69Pa0w5rYfrCUm4kdi4iIyOQ0arT461wZAGBod87vJiKiW8PCu5PIK6/FA6sPoLhSjSBXG6yb1R+2vK2NiIhIFMdyy1GpboS9pTlCPO3EjkNERAbupgrvFStWwNfXF0qlEpGRkTh48GCrjtu4cSMkEgkmTZrUYrsgCFi0aBHc3d1hYWGBqKgonD179maiGaTiyjo8+OkB5JXXwt/JCuv/0x9drORixyIiIjJZu880ze8eFOgEGVcUISKiW9TmwnvTpk2Ii4vD4sWLceTIEYSGhiI6OhrFxcXXPS4zMxPPPPMMhgwZcsVzb7/9Nj744AOsWrUKBw4cgJWVFaKjo1FXV9fWeAbnYnU9Hvz0AM6XVsPT3gJf/icSLjZKsWMRERGZtOb53UM5v5uIiNpBmwvvpUuXYs6cOYiNjUXPnj2xatUqWFpaYs2aNdc8RqPRYNq0aViyZAn8/f1bPCcIApYtW4YXX3wREydORJ8+fbBu3Trk5+fjxx9/bPMLMiSqugbMWHMQZ4qq4GqrwFdzIuFhbyF2LCIiIpNWUduA5JxyAMBgrt9NRETtoE2Fd319PQ4fPoyoqKjLJ5BKERUVhaSkpGse98orr8DFxQWzZ8++4rnz58+jsLCwxTnt7OwQGRl53XOq1WqoVKoWD0NSrW5E7NpDOJFXAUcrOTb8JxI+jlZixyIiIjJ5Seml0ApAgLMVPPmBOBERtYM2Fd6lpaXQaDRwdXVtsd3V1RWFhYVXPWbv3r347LPPsHr16qs+33xcW84JAPHx8bCzs9M9vL292/JSRFXXoMGcdX/jcNZF2CrNsH52JAJdbMSORURERAB2X1q/ewivdhMRUTvRa1fzyspKTJ8+HatXr4aTU/vOkVqwYAEqKip0j5ycnHY9v77UN2rx2IYj+Cu9DFZyGb6Y1R89PWzFjkVERERomgK3+8yl+d3dOb+biIjaR5sWiXZycoJMJkNRUVGL7UVFRXBzc7ti//T0dGRmZmL8+PG6bVqttukbm5khLS1Nd1xRURHc3d1bnDMsLOyaWRQKBRQKRVvii65Ro8W8TUexI7UYSnMp1jzUD7d17SJ2LCIiIrokq6wGuRdrYS6TINLPUew4RERkJNp0xVsulyM8PBwJCQm6bVqtFgkJCRg4cOAV+wcHB+PEiRNITk7WPSZMmIARI0YgOTkZ3t7e8PPzg5ubW4tzqlQqHDhw4KrnNFRarYBnvzuO304UQi6T4uPpEYj054BORETUmey+1M083KcLrBRtuj5BRER0TW0eUeLi4jBz5kxERESgf//+WLZsGaqrqxEbGwsAmDFjBjw9PREfHw+lUomQkJAWx9vb2wNAi+3z5s3Da6+9hm7dusHPzw8vvfQSPDw8rljv21AJgoCXfjqJH47mQSaV4MMHbsOw7pw3RkRE1Nk0r9/N+d1ERNSe2lx4x8TEoKSkBIsWLUJhYSHCwsKwdetWXXO07OxsSKVtmzr+7LPPorq6Gg8//DDKy8sxePBgbN26FUql4a9nLQgCXv81BRsOZEMiAZZOCcXoXlfelk9ERETiatBokZTeVHgPZeFNRETtSCIIgiB2iPagUqlgZ2eHiooK2Np2nmZlS7efwQcJZwEAb9/TB1P6GU73dSIiunmddVwyZPr+mR48fwFTPk5CF0tzHH7xDkilknb/HkREZFxaOzbptau5qVu1K11XdL88vieLbiIiok5sz6X53YO7ObPoJiKidsXCW0+++CsTb/6eCgB4dkwQHhrkJ3IiIiIiup7L63dzGTEiImpfLLz14JtDOVi85RQA4ImRgXhseKDIiYiIiOh6ymvqcTy3HAALbyIian8svNvZlmP5eO6H4wCA2YP9EHdHd5ETERER0Y3sO1cGQQC6uVjD3c5C7DhERGRkWHi3o22nCvH0pmQIAvBAZFe8OK4HJBLOESMiIursmud3cxkxIiLSBxbe7WT3mRLM/eooNFoBk2/zxGsTQ1h0ExERGQBBELCneX53d95mTkRE7Y+Fdzs4kFGGh9f/jXqNFmND3PDOvX3YDZWIiMhAZJRWI6+8FnKZFJF+DmLHISIiI8TC+xYl55Rj1ueHUNegxYggZ7w/9TaYyfhjJSIiw7ZixQr4+vpCqVQiMjISBw8evOa+q1evxpAhQ9ClSxd06dIFUVFRV+z/0EMPQSKRtHiMGTNG3y+jVfacabrNvJ9fF1jKzUROQ0RExogV4i04na/CjM8OoLpeg9sDHLHywXDIzfgjJSIiw7Zp0ybExcVh8eLFOHLkCEJDQxEdHY3i4uKr7p+YmIj7778fO3fuRFJSEry9vTF69Gjk5eW12G/MmDEoKCjQPb7++uuOeDk3pLvNnPO7iYhIT1gl3qRzxVWY/tkBqOoa0berPVbPiIDSXCZ2LCIiolu2dOlSzJkzB7GxsejZsydWrVoFS0tLrFmz5qr7b9iwAY899hjCwsIQHByMTz/9FFqtFgkJCS32UygUcHNz0z26dOnSES/nuuobtUjKKAPAZcSIiEh/WHjfhOyyGkz7dD/KqusR4mmLtbH9YaXgrWlERGT46uvrcfjwYURFRem2SaVSREVFISkpqVXnqKmpQUNDAxwcWs6XTkxMhIuLC4KCgvDoo4+irKysXbPfjCPZF1FTr4GTtRw93GzFjkNEREaK1WIb5ZfX4oFP96NIpUZ3V2usmxUJOwtzsWMRERG1i9LSUmg0Gri6urbY7urqitTU1Fad47nnnoOHh0eL4n3MmDG4++674efnh/T0dCxcuBBjx45FUlISZLKr3zGmVquhVqt1X6tUqpt4RdfXvIzY4EAnNkYlIiK9YeHdBiWVajz46QHkXqyFr6MlvpwdCQcrudixiIiIOo0333wTGzduRGJiIpRKpW771KlTdX/u3bs3+vTpg4CAACQmJmLUqFFXPVd8fDyWLFmi17yc301ERB2Bt5q30sXqejz46QFklFbD094CG+YMgIut8sYHEhERGRAnJyfIZDIUFRW12F5UVAQ3N7frHvvuu+/izTffxLZt29CnT5/r7uvv7w8nJyecO3fumvssWLAAFRUVukdOTk7rX0grXKiux4m8CgCc301ERPrFwrsVVHUNmLn2INKKKuFio8CG/0TC095C7FhERETtTi6XIzw8vEVjtOZGaQMHDrzmcW+//TZeffVVbN26FRERETf8Prm5uSgrK4O7u/s191EoFLC1tW3xaE/7zpVCEIBgNxt+mE5ERHrFwvsGauobMWvtIRzPrYCDlRwb/hMJXycrsWMRERHpTVxcHFavXo0vvvgCKSkpePTRR1FdXY3Y2FgAwIwZM7BgwQLd/m+99RZeeuklrFmzBr6+vigsLERhYSGqqqoAAFVVVZg/fz7279+PzMxMJCQkYOLEiQgMDER0dLQorxEAdl9av5tXu4mISN84x/s66ho0eHjdYfyddRG2SjOsm9Uf3VxtxI5FRESkVzExMSgpKcGiRYtQWFiIsLAwbN26VddwLTs7G1Lp5c/uV65cifr6etx7770tzrN48WK8/PLLkMlkOH78OL744guUl5fDw8MDo0ePxquvvgqFQtGhr62ZIAic301ERB1GIgiCIHaI9qBSqWBnZ4eKiop2uRWtQaPFI+sPIyG1GFZyGdb/JxJ9u4q/3igRERmG9h6XqH1/pmeLKnHH/3ZDbibF8cWjoTS/emd1IiKi62nt2MRbza9CoxUwb1MyElKLoTCT4tOZ/Vh0ExERGZHdl652R/o5sOgmIiK9Y+H9L1qtgGe/O45fjxfAXCbBx9PDMTDAUexYRERE1I6a1+/m/G4iIuoILLz/ZcPBbHx/JBcyqQTL7++L4UEuYkciIiKidqRu1GB/RhkAzu8mIqKOweZq/zIlwgt/nSvFmBA3jAm5/nqlREREZHgaNAKeHNUNx3LKEezGpqlERKR/LLz/RWEmw0fT+kIikYgdhYiIiPTAWmGGx4YHih2DiIhMCG81vwoW3URERERERNReWHgTERERERER6RELbyIiIiIiIiI9YuFNREREREREpEcsvImIiIiIiIj0iIU3ERERERERkR6x8CYiIiIiIiLSIxbeRERERERERHrEwpuIiIiIiIhIj8zEDtBeBEEAAKhUKpGTEBERXR6PmscnunUc64mIqLNp7XhvNIV3ZWUlAMDb21vkJERERJdVVlbCzs5O7BhGgWM9ERF1Vjca7yWCkXwUr9VqkZ+fDxsbG0gkkls6l0qlgre3N3JycmBra9tOCTsO84uL+cXF/OJi/ssEQUBlZSU8PDwglXJmV3vgWH8Z84uL+cXF/OJi/pZaO94bzRVvqVQKLy+vdj2nra2tQf5lasb84mJ+cTG/uJi/Ca90ty+O9VdifnExv7iYX1zMf1lrxnt+BE9ERERERESkRyy8iYiIiIiIiPSIhfdVKBQKLF68GAqFQuwoN4X5xcX84mJ+cTE/GQpD/3/N/OJifnExv7iY/+YYTXM1IiIiIiIios6IV7yJiIiIiIiI9IiFNxEREREREZEesfAmIiIiIiIi0iMW3kRERERERER6xML7X1asWAFfX18olUpERkbi4MGDYkdqtd27d2P8+PHw8PCARCLBjz/+KHakNomPj0e/fv1gY2MDFxcXTJo0CWlpaWLHarWVK1eiT58+sLW1ha2tLQYOHIjff/9d7Fg35c0334REIsG8efPEjtJqL7/8MiQSSYtHcHCw2LHaJC8vDw8++CAcHR1hYWGB3r174++//xY7Vqv4+vpe8fOXSCR4/PHHxY7WKhqNBi+99BL8/PxgYWGBgIAAvPrqq2D/UeNlqOM9x3pxGdNYDxjeeM+xXlwc628NC+9/2LRpE+Li4rB48WIcOXIEoaGhiI6ORnFxsdjRWqW6uhqhoaFYsWKF2FFuyq5du/D4449j//792L59OxoaGjB69GhUV1eLHa1VvLy88Oabb+Lw4cP4+++/MXLkSEycOBGnTp0SO1qbHDp0CB9//DH69OkjdpQ269WrFwoKCnSPvXv3ih2p1S5evIhBgwbB3Nwcv//+O06fPo333nsPXbp0ETtaqxw6dKjFz3779u0AgPvuu0/kZK3z1ltvYeXKlfjwww+RkpKCt956C2+//TaWL18udjTSA0Me7znWi8tYxnrAcMd7jvXi4Vh/iwTS6d+/v/D444/rvtZoNIKHh4cQHx8vYqqbA0DYvHmz2DFuSXFxsQBA2LVrl9hRblqXLl2ETz/9VOwYrVZZWSl069ZN2L59uzBs2DDhqaeeEjtSqy1evFgIDQ0VO8ZNe+6554TBgweLHaPdPPXUU0JAQICg1WrFjtIq48aNE2bNmtVi29133y1MmzZNpESkT8Yy3nOs7xwMbawXBMMd7znWdy4c69uGV7wvqa+vx+HDhxEVFaXbJpVKERUVhaSkJBGTma6KigoAgIODg8hJ2k6j0WDjxo2orq7GwIEDxY7Tao8//jjGjRvX4t+BITl79iw8PDzg7++PadOmITs7W+xIrbZlyxZERETgvvvug4uLC2677TasXr1a7Fg3pb6+Hl9++SVmzZoFiUQidpxWuf3225GQkIAzZ84AAI4dO4a9e/di7NixIiej9sbxvnPhWC8OQx7vOdZ3Dhzr286sQ76LASgtLYVGo4Grq2uL7a6urkhNTRUplenSarWYN28eBg0ahJCQELHjtNqJEycwcOBA1NXVwdraGps3b0bPnj3FjtUqGzduxJEjR3Do0CGxo9yUyMhIfP755wgKCkJBQQGWLFmCIUOG4OTJk7CxsRE73g1lZGRg5cqViIuLw8KFC3Ho0CE8+eSTkMvlmDlzptjx2uTHH39EeXk5HnroIbGjtNrzzz8PlUqF4OBgyGQyaDQavP7665g2bZrY0aidcbzvPDjWi8OQx3uO9Z0Hx/q2Y+FNndLjjz+OkydPGtS8HQAICgpCcnIyKioq8N1332HmzJnYtWtXpx+Qc3Jy8NRTT2H79u1QKpVix7kp//y0sk+fPoiMjISPjw+++eYbzJ49W8RkraPVahEREYE33ngDAHDbbbfh5MmTWLVqlcENxp999hnGjh0LDw8PsaO02jfffIMNGzbgq6++Qq9evZCcnIx58+bBw8PD4H7+RIaCY33HM/TxnmN958Gxvu1YeF/i5OQEmUyGoqKiFtuLiorg5uYmUirTNHfuXPzyyy/YvXs3vLy8xI7TJnK5HIGBgQCA8PBwHDp0CO+//z4+/vhjkZNd3+HDh1FcXIy+ffvqtmk0GuzevRsffvgh1Go1ZDKZiAnbzt7eHt27d8e5c+fEjtIq7u7uV7xp69GjB77//nuREt2crKws/Pnnn/jhhx/EjtIm8+fPx/PPP4+pU6cCAHr37o2srCzEx8cb3Jshuj6O950Dx3pxGNt4z7FeHBzrbw7neF8il8sRHh6OhIQE3TatVouEhASDm7djqARBwNy5c7F582bs2LEDfn5+Yke6ZVqtFmq1WuwYNzRq1CicOHECycnJukdERASmTZuG5ORkgxqEm1VVVSE9PR3u7u5iR2mVQYMGXbGkzpkzZ+Dj4yNSopuzdu1auLi4YNy4cWJHaZOamhpIpS2HRJlMBq1WK1Ii0heO9+LiWC8uYxvvOdaLg2P9zeEV73+Ii4vDzJkzERERgf79+2PZsmWorq5GbGys2NFapaqqqsUnfufPn0dycjIcHBzQtWtXEZO1zuOPP46vvvoKP/30E2xsbFBYWAgAsLOzg4WFhcjpbmzBggUYO3YsunbtisrKSnz11VdITEzEH3/8IXa0G7Kxsblifp2VlRUcHR0NZt7dM888g/Hjx8PHxwf5+flYvHgxZDIZ7r//frGjtcrTTz+N22+/HW+88QamTJmCgwcP4pNPPsEnn3widrRW02q1WLt2LWbOnAkzM8MaXsaPH4/XX38dXbt2Ra9evXD06FEsXboUs2bNEjsa6YEhj/cc68VlyGM9YPjjPcd68XGsvwUd0jvdgCxfvlzo2rWrIJfLhf79+wv79+8XO1Kr7dy5UwBwxWPmzJliR2uVq2UHIKxdu1bsaK0ya9YswcfHR5DL5YKzs7MwatQoYdu2bWLHummGtLyIIAhCTEyM4O7uLsjlcsHT01OIiYkRzp07J3asNvn555+FkJAQQaFQCMHBwcInn3widqQ2+eOPPwQAQlpamthR2kylUglPPfWU0LVrV0GpVAr+/v7CCy+8IKjVarGjkZ4Y6njPsV5cxjbWC4Jhjfcc68XHsf7mSQRBEDqmxCciIiIiIiIyPZzjTURERERERKRHLLyJiIiIiIiI9IiFNxEREREREZEesfAmIiIiIiIi0iMW3kRERERERER6xMKbiIiIiIiISI9YeBMRERERERHpEQtvIiIiIiIiIj1i4U1ERERERESkRyy8iYiIiIiIiPSIhTcRERERERGRHrHwJiIiIiIiItIjFt5ERurzzz+HRCJBZmam2FGIiIhIz25m3Od7BaKOw8KbiIiIiIiISI9YeBMRERERERHpEQtvIiIiIiIiIj1i4U1kQj766CP06tULCoUCHh4eePzxx1FeXt5in7Nnz+Kee+6Bm5sblEolvLy8MHXqVFRUVOj22b59OwYPHgx7e3tYW1sjKCgICxcu7OBXQ0REZLi+++47SCQS7Nq164rnPv74Y0gkEpw8eRLHjx/HQw89BH9/fyiVSri5uWHWrFkoKyvTWza+XyBqf2ZiByCijvHyyy9jyZIliIqKwqOPPoq0tDSsXLkShw4dwr59+2Bubo76+npER0dDrVbjiSeegJubG/Ly8vDLL7+gvLwcdnZ2OHXqFO666y706dMHr7zyChQKBc6dO4d9+/aJ/RKJiIgMxrhx42BtbY1vvvkGw4YNa/Hcpk2b0KtXL4SEhOC9995DRkYGYmNj4ebmhlOnTuGTTz7BqVOnsH//fkgkknbNxfcLRHoiEJFRWrt2rQBAOH/+vFBcXCzI5XJh9OjRgkaj0e3z4YcfCgCENWvWCIIgCEePHhUACN9+++01z/u///1PACCUlJTo/TUQEREZs/vvv19wcXERGhsbddsKCgoEqVQqvPLKK4IgCEJNTc0Vx3399dcCAGH37t26bf8c91vr38fw/QKR/vBWcyIT8Oeff6K+vh7z5s2DVHr5n/2cOXNga2uLX3/9FQBgZ2cHAPjjjz9QU1Nz1XPZ29sDAH766SdotVr9BiciIjJiMTExKC4uRmJiom7bd999B61Wi5iYGACAhYWF7rm6ujqUlpZiwIABAIAjR460ax6+XyDSHxbeRCYgKysLABAUFNRiu1wuh7+/v+55Pz8/xMXF4dNPP4WTkxOio6OxYsWKFvO1YmJiMGjQIPznP/+Bq6srpk6dim+++YaDKhERURuNGTMGdnZ22LRpk27bpk2bEBYWhu7duwMALly4gKeeegqurq6wsLCAs7Mz/Pz8AKDF+Nwe+H6BSH9YeBNRC++99x6OHz+OhQsXora2Fk8++SR69eqF3NxcAE2fvO/evRt//vknpk+fjuPHjyMmJgZ33HEHNBqNyOmJiIgMh0KhwKRJk7B582Y0NjYiLy8P+/bt013tBoApU6Zg9erVeOSRR/DDDz9g27Zt2Lp1KwCIWsTy/QJR27DwJjIBPj4+AIC0tLQW2+vr63H+/Hnd88169+6NF198Ebt378aePXuQl5eHVatW6Z6XSqUYNWoUli5ditOnT+P111/Hjh07sHPnTv2/GCIiIiMSExOD0tJSJCQk4Ntvv4UgCLrC++LFi0hISMDzzz+PJUuWYPLkybjjjjvg7++vlyx8v0CkPyy8iUxAVFQU5HI5PvjgAwiCoNv+2WefoaKiAuPGjQMAqFQqNDY2tji2d+/ekEqlUKvVAJpuefu3sLAwANDtQ0RERK0TFRUFBwcHbNq0CZs2bUL//v11t5LLZDIAaDF2A8CyZcv0loXvF4j0g8uJEZkAZ2dnLFiwAEuWLMGYMWMwYcIEpKWl4aOPPkK/fv3w4IMPAgB27NiBuXPn4r777kP37t3R2NiI9evXQyaT4Z577gEAvPLKK9i9ezfGjRsHHx8fFBcX46OPPoKXlxcGDx4s5sskIiIyOObm5rj77ruxceNGVFdX491339U9Z2tri6FDh+Ltt99GQ0MDPD09sW3bNpw/f14vWfh+gUh/WHgTmYiXX34Zzs7O+PDDD/H000/DwcEBDz/8MN544w2Ym5sDAEJDQxEdHY2ff/4ZeXl5sLS0RGhoKH7//XddB9UJEyYgMzMTa9asQWlpKZycnDBs2DAsWbJE1+WUiIiIWi8mJgaffvopJBIJpkyZ0uK5r776Ck888QRWrFgBQRAwevRo/P777/Dw8NBLFr5fINIPifDve1eIiIiIiIiIqN1wjjcRERERERGRHvFWcyIiIiIiI1JVVYWqqqrr7uPs7Kxr3kZE+sfCm4iIiIjIiLz77rtYsmTJdfc5f/48fH19OyYQEXGONxERERGRMcnIyEBGRsZ19xk8eDCUSmUHJSIiFt5EREREREREesTmakRERERERER6ZDRzvLVaLfLz82FjYwOJRCJ2HCIiMnGCIKCyshIeHh6QSvk5d3vgWE9ERJ1Na8d7oym88/Pz4e3tLXYMIiKiFnJycuDl5SV2DKPAsZ6IiDqrG433RlN429jYAGh6wba2tiKnISIiU6dSqeDt7a0bn+jWcawnIqLOprXjvdEU3s23nNna2nIwJiKiToO3RLcfjvVERNRZ3Wi856QzIiIiIiIiIj1i4U1ERERERESkRyy8iYiIiIiIiPSIhTcRERERERGRHrHwJiIiIiIiItIjFt5EREREREREesTCm4iIiIiIiEiPWHgTERERERER6REL76s4na+CIAhixyAiIiI90WoFZJfViB2DiIhMBAvvf8m9WIPxH+5F9LLd+OZQDtSNGrEjERERUTtS1TUg7JVtGPbuTlSpG8WOQ0REJoCF97+czldBaSbFmaIqPPv9cQx6cyc+SDiLC9X1YkcjIiKidmCrNIeN0hyCABzPKRc7DhERmQAW3v8yupcb/lowCgvGBsPdTonSKjWWbj+DgfEJWLj5BNJLqsSOSERERLcorKs9AOAoC28iIuoALLyvws7CHP8dFoDdz47A+1PDEOJpC3WjFl8dyMao93bhP18cwv6MMs4DJyIiMlC3edsDAJJZeBMRUQcwEztAZ2Yuk2JimCcmhHrgwPkL+HRPBv5MKdY9QjxtMWeIP/6/vTuPi7Lc2wB+PTMDwz7IvoOoiAvCuCFuuGZklllpammarXqOxWmjPFrnWLRY2lvmUqZ5yu2Ua7nkhrgryiguqCjKDqIy7APMzPsHgnHUBGV4Zrm+n8/zeV+GZ4aLOcY9v+d+7vv3SJg3rKS8hkFERGQqIv5UeOv1egiCIG4gIiIya6wWG0EQBPQKdsX3E3tg5z+iMT4yAHKZBKeyizF9lQr9P9uNRXsuQl1RLXZUIiIiaoTOvgrIJAKulmiQo64UOw4REZk5Ft5N1MbdAR89EYaDcYPxj6EhcHOQI1ddifgtqegdvxMfbjqNzOtsT0JERGTMbKykCPV2BAAkZ9wQOQ0REZk7Ft73ycXeGn8b3A773hmIz57sghBPB5RVabF0/2VEf74bU38+juMcyImIiIyW0r8VAECVUSRuECIiMnssvB+QjZUUo3v4Y9vr/fHj5J7o184NOj3we0ouRn17AE8uOICtp3Kh1XEjNiIiImMSwQ3WiIiohXBztWYiCAKiQ9wRHeKO1LxifL83HRtU2Th25QaOXbmBABc7TO4ThKe7+8NezrediIhIbHUtxVKy1ajW6rhRKhERGUyTR5jExESMGDECPj4+EAQB69evb/Rz9+/fD5lMhoiIiNu+N3/+fAQFBcHGxgaRkZE4cuRIU6MZjVAvJ8x5Ohz73xmEaQPbwtnOChnXy/HBpjOIit+JT7akIo8buRAREYmqtas9nGxk0NTocC6vROw4RERkxppceJeVlSE8PBzz589v0vOKioowYcIEDB48+LbvrV69GrGxsZg1axaOHz+O8PBwDBs2DAUFBU2NZ1Q8nGzw5rD2OPDuIPx7ZGe0drNHcWUNFu65iL6f7kLsahVO56jFjklERGSRJBIB4TdvN0/m7eZERGRATS68Y2JiMHv2bDzxxBNNet4rr7yCcePGISoq6rbvffnll3jxxRcxadIkdOzYEQsXLoSdnR1++OGHpsYzSnbWMjzXKxA7Y6Ox+Llu6NnaBTU6PdYmZ2P4/+3DuO8OYVdqPnRcB05ERNSilHWFNzdEJSIiA2qRxUxLly7FpUuXMGvWrNu+V1VVhWPHjmHIkCG3QkkkGDJkCA4ePHjX19RoNCguLm5wGDuJRMBDnbyw5uUobJzWByPCfSCVCDhw8RomL0vC0Ll7sPJIBiqrtWJHJSIisgjKgJs7m3PGm4iIDMjghfeFCxfw7rvv4qeffoJMdvumYoWFhdBqtfD09GzwuKenJ/Ly8u76uvHx8VAoFPWHv79/s2c3pC5+zvh6rBKJbw/Ei/1aw1Euw8WrZYhbm4I+n+zC3O3nUViqETsmERHRHTV1z5fnn38egiDcdnTq1KllAt9F3a3ml66WQV1eLWoWIiIyXwYtvLVaLcaNG4cPP/wQISEhzfracXFxUKvV9UdmZmazvn5L8XW2xfvDO+JA3CDMGN4Bvs62uFZWha92XkDvT3bh3V9PIq2AG74QEZFxaeqeL1999RVyc3Prj8zMTLi4uODpp582cNK/5mJvjUBXOwDAiawiUbMQEZH5Mmhfq5KSEiQlJSE5ORnTpk0DAOh0Ouj1eshkMvzxxx/o27cvpFIp8vPzGzw3Pz8fXl5ed31tuVwOuVxuyPgtytHGClP6BeP53kHYejoP3+1Nx4nMIqw6molVRzMxoL07XuwXjN5tXCEIgthxiYjIwsXExCAmJqbR59fdoVZn/fr1uHHjBiZNmmSIeE0S4e+MK9fKocosQv8Qd7HjEBGRGTJo4e3k5ISUlJQGj3377bfYtWsXfvnlF7Ru3RrW1tbo1q0bdu7ciZEjRwKoLc537txZX6xbEplUgke7+GB4mDeSrtzA93sv4Y8z+Ug4dxUJ566io7cTpvRrjUe7+MBaxn6jRERkmpYsWYIhQ4YgMDDwrudoNBpoNLeWXRlqP5cIf2dsUOVwnTcRERlMkwvv0tJSpKWl1X+dnp4OlUoFFxcXBAQEIC4uDtnZ2Vi+fDkkEgk6d+7c4PkeHh6wsbFp8HhsbCwmTpyI7t27o2fPnpg3bx7KysqM4iq4WARBQI8gF/QIcsHlwjIs3Z+ONUlZOJNbjNg1J/Dp1lRM7B2E8T0DobCzEjsuERFRo+Xk5GDLli1YsWLFX54XHx+PDz/80OB5Iv60s7ler+edZURE1OyaPGWalJQEpVIJpVIJoLZoViqVmDlzJgAgNzcXGRkZTXrNMWPGYM6cOZg5cyYiIiKgUqmwdevW2zZcs1RBbvb48PHOOBg3CG8Naw8PRznyizX4bOs59IrfiVkbTuHKtTKxYxIRETXKjz/+CGdn5/o73e6mpfZz6ejjBGupBDfKq5FxvdwgP4OIiCyboNfrzaJ5dHFxMRQKBdRqNZycnMSOY1BVNTpsOpGD7/ZeQmpe7cZrggA81NETL/YLRrfAVrxaT0QkMksalwRBwLp16+5ZSAOAXq9HSEgIHn30UcydO7dJP8eQ7+nI+fuhyizCV89E4PEI32Z9bSIiMl+NHZu4SNgEWcskeLKbH7ZM74efp0RiQHt36PXAttP5eGrhQTzx7QEcunRN7JhERES32bNnD9LS0vDCCy+IHaWBW7ebF4mag4iIzBMLbxMmCAL6tHXDskk9sf2N/nimhz+sZRKoMoswedlRZBdViB2RiIjMVGlpKVQqFVQqFYBbe77ULTeLi4vDhAkTbnvekiVLEBkZedseMGJTBjgDADdYIyIig2DhbSbaeTrikye7YP87g9A1wBnlVVrM2nAKZrKSgIiIjMz97PmiVqvx66+/Gt1sN3BrxvtMTjE0NVpxwxARkdkxaDsxannujnJ8+mQXPPJ/e7HjbAG2nc7Dw529xY5FRERmZsCAAX95cXfZsmW3PaZQKFBebpyblwW42MHF3hrXy6pwJqcYyoBWYkciIiIzwhlvM9TO0xGvRLcBAMzaeBolldUiJyIiIjJugiDUz3rzdnMiImpuLLzN1NSBbRHkaof8Yg3mbDsndhwiIiKjx8KbiIgMhYW3mbKxkuKjJ8IAAMsPXeGHCCIiontg4U1ERIbCwtuM9WnrhlFKX+j1QNzaFFRrdWJHIiIiMlrhNwvvK9fKcb2sStwwRERkVlh4m7n3h3eAs50VzuYWY+n+dLHjEBERGS2FrRWC3e0BAKrMGyKnISIic8LC28y5Osjx3iMdAABzt19A5nXj3E2WiIjIGCj9a3czV2UUiRuEiIjMCgtvC/B0Nz9EtnZBRbUWM9nbm4iI6K4iApwBAMlc501ERM2IhbcFEAQBH48Kg7VUgt3nruL3lFyxIxERERkl5c113icyi6DT8UI1ERE1DxbeFqKNuwNeG1jb2/vDTWegrmBvbyIiov/V3ssRcpkExZU1SL9WJnYcIiIyEyy8LcirA9og2N0eV0s0+GxrqthxiIiIjI6VVIIwXwUAIJnrvImIqJmw8LYgcpkUH9/s7f3z4Qwcu8IdW4mIiP6X8uY6b+5sTkREzYWFt4XpFeyKp7v5AQDeY29vIiKi20TU7WzODdaIiKiZsPC2QO890gEu9tY4l1+C7/ZeEjsOERGRUanb2Tw1twSV1VpxwxARkVlg4W2BWtlbY8bw2t7eX+24gCvcPIaIiKiej8IG7o5y1Oj0OJWtFjsOERGZARbeFuoJpS/6tHWFpkaHGevZ25uIiKiOIAiIuNlWjLebExFRc2DhbaEEQcDskWGwlkmw90IhNp7IETsSERGR0agrvLmzORERNQcW3hastZs9/j6oLQDg37+dQVF5lciJiIiIjMOtnc2LRM1BRETmgYW3hXupfxu083BAYWkVPtnC3t5EREQA0MXPGYIAZBdVoKCkUuw4RERk4lh4WzhrmQQfj6rt7b3qaCaOpF8XOREREZH4HOQyhHg4AgBUvN2ciIgeEAtvQo8gF4zt6Q8AeG9dCjQ1bJ1CRETEDdaIiKi5sPAmAMC7D3eAm4M10gpKsXgPe3sTERFFcJ03ERE1ExbeBABQ2Fnhn492BAB8vTsN6YXs7U1ERJatboO1E5lF0OrYdpOIiO4fC2+q91i4D/q1c0NVjQ7vr0thb28iIrJo7TwcYW8tRVmVFmkFpWLHISIiE8bCm+oJgoCPRoZBLpPgwMVrWJecLXYkIiIi0UglAsL8FAAAVeYNkdMQEZEpY+FNDQS42mH6kHYAgNm/n8X1Mvb2JiKi2yUmJmLEiBHw8fGBIAhYv379PZ+j0Wjw/vvvIzAwEHK5HEFBQfjhhx8MH/YBRPi3AsB13kRE9GBYeNNtXuwXjPaejrheVoX4zWfFjkNEREaorKwM4eHhmD9/fqOfM3r0aOzcuRNLlizBuXPnsHLlSrRv396AKR9c3c7myWwpRkRED0AmdgAyPlbS2t7eTy08gP8ey8Korn6IauMqdiwiIjIiMTExiImJafT5W7duxZ49e3Dp0iW4uLgAAIKCggyUrvnUbbB2Pr8EZZoa2Mv50YmIiJqOM950R90CW2F8ZAAA4H329iYioge0ceNGdO/eHZ999hl8fX0REhKCN998ExUVFXd9jkajQXFxcYOjpXk62cBHYQOdHjiZpW7xn09EROahyYV3U9d07du3D3369IGrqytsbW0RGhqKuXPnNjjngw8+gCAIDY7Q0NCmRqNm9tawULg7ynGpsAzf7r4odhwiIjJhly5dwr59+3Dq1CmsW7cO8+bNwy+//ILXXnvtrs+Jj4+HQqGoP/z9/Vsw8S3s501ERA+qyYV3U9d02dvbY9q0aUhMTMTZs2cxY8YMzJgxA4sXL25wXqdOnZCbm1t/7Nu3r6nRqJkpbK3wwYhOAIAFCRfZSoWIiO6bTqeDIAj4+eef0bNnTzzyyCP48ssv8eOPP9511jsuLg5qtbr+yMzMbOHUterWeXNncyIiul9NXqjU1DVdSqUSSqWy/uugoCCsXbsWe/fuxUsvvXQriEwGLy+vpsYhA3skzAsD27tj97mreH9dCla91AuCIIgdi4iITIy3tzd8fX2hUCjqH+vQoQP0ej2ysrLQrl27254jl8shl8tbMuYdcWdzIiJ6UC2+xjs5ORkHDhxAdHR0g8cvXLgAHx8fBAcHY/z48cjIyPjL1zGGdV+WQBAE/OvxzrC1kuJw+nX891iW2JGIiMgE9enTBzk5OSgtvXX31Pnz5yGRSODn5ydisnsL81VAKhGQX6xBrvrua9KJiIjupsUKbz8/P8jlcnTv3h1Tp07FlClT6r8XGRmJZcuWYevWrViwYAHS09PRr18/lJSU3PX1jGXdlyXwd7HDG0NrZyI+3nwW10o1IiciIiKxlZaWQqVSQaVSAQDS09OhUqnqL5zHxcVhwoQJ9eePGzcOrq6umDRpEs6cOYPExES89dZbmDx5MmxtbcX4FRrN1lqK9p6OANhWjIiI7k+LFd579+5FUlISFi5ciHnz5mHlypX134uJicHTTz+NLl26YNiwYdi8eTOKioqwZs2au76esaz7shST+7RGB28nFJVX46Pf2dubiMjSJSUlNVhOFhsbC6VSiZkzZwIAcnNzG9y95uDggO3bt6OoqAjdu3fH+PHjMWLECPzf//2fKPmbSskN1oiI6AG0WDPK1q1bAwDCwsKQn5+PDz74AGPHjr3juc7OzggJCUFaWtpdX89Y1n1ZCplUgvhRYXji2/1Ym5yNUV390Ledm9ixiIhIJAMGDIBer7/r95ctW3bbY6Ghodi+fbsBUxlOhL8zfj6cARVnvImI6D6I0sdbp9NBo7n77cqlpaW4ePEivL29WzAV3UuEvzMmRgUBAGasT0FlNXt7ExGRZaib8U7JVqNGqxM3DBERmZwmF95NXdM1f/58bNq0CRcuXMCFCxewZMkSzJkzB88++2z9OW+++Sb27NmDy5cv48CBA3jiiScglUrvOiNO4vnHQyHwcrLB5Wvl+GbX3e9IICIiMifBbg5wtJGholqLc/l334OGiIjoTpp8q3lSUhIGDhxY/3VsbCwAYOLEiVi2bNlta7p0Oh3i4uKQnp4OmUyGNm3a4NNPP8XLL79cf05WVhbGjh2La9euwd3dHX379sWhQ4fg7u7+IL8bGYCjjRU+eKwTXvnpGBYlXsRjET4IubnhDBERkbmSSASE+zljX1ohkjOK0MlHce8nERER3STo/2qBlgkpLi6GQqGAWq2Gk5OT2HHMml6vx4vLj2HH2Xx0D2yFNS9HQSJhb28ioj/juNT8xH5Pv/jjHL7elYanuvlhztPhLf7ziYjI+DR2bBJljTeZttre3p1gZy1F0pUbWJ3EHeWJiMj8Rfg7A+DO5kRE1HQsvOm++Djb4h8PtQcAxG8+i4KSSpETERERGVZd4X3xaimKK6vFDUNERCaFhTfdt+d7ByHMV4HiyhrM/o29vYmIyLy5Osjh72ILvR44makWOw4REZkQFt5036QSAfGjwiARgI0ncpBwrkDsSERERAYV4d8KAKDKvCFyEiIiMiUsvOmBdPZVYFKf1gCAf244hYoq9vYmIiLzVXe7eXJGkag5iIjItLDwpgcWOzQEPgobZF6vwFc7L4gdh4iIyGCUAc4AajdYM5PGMERE1AJYeNMDs5fL8K/HOwMAvt97Cal5xSInIiIiMoyO3k6wkgq4VlaFrBsVYschIiITwcKbmsWQjp54uJMXanR6xK1NgU7HWQAiIjI/NlZSdPSu7dOazLZiRETUSCy8qdl88FgnOMhlSM4ows9HMsSOQ0REZBD1/by5zpuIiBqJhTc1Gy+FDd4aVtvb+7MtqcgvZm9vIiIyPxH167y5szkRETUOC29qVs/2CkS4vzNKNDX416YzYschIiJqdsqbLcVO5RSjqkYnchoiIjIFLLypWUklAj5+ojOkEgG/p+RiV2q+2JGIiIiaVaCrHVrZWaGqRoezudxQlIiI7o2FNzW7Tj4KvND3Zm/v9adRXlUjciIiIqLmIwgCwuvWeXODNSIiagQW3mQQrw9pB19nW2QXVWDu9vNixyEiImpWESy8iYioCVh4k0HYWcswe2Rtb+8f9l/GqWy1yImIiIiaDwtvIiJqChbeZDADQz0wvIs3tDo93luXAi17exMRkZmoK7zTC8two6xK3DBERGT0WHiTQc16tCMcbWQ4maXGfw5eFjsOERFRs3C2s0awmz0AQJVVJG4YIiIyeiy8yaA8nGzwzsOhAIDPt51DrrpC5ERERETNo/5284wiUXMQEZHxY+FNBjeuZwC6BjijrEqLDzaeFjsOERFRs4gIcAbAdd5ERHRvLLzJ4CQSAR+PCoNMImDb6Xz8cTpP7EhEREQPrG7G+0RWEfR67mNCRER3x8KbWkSolxNe7B8MAJi18TRKNeztTURkyhITEzFixAj4+PhAEASsX7/+L89PSEiAIAi3HXl5pnsxNtTLCdYyCYrKq3H5WrnYcYiIyIix8KYW8/dB7RDgYodcdSW++OOc2HGIiOgBlJWVITw8HPPnz2/S886dO4fc3Nz6w8PDw0AJDc9aJkGYrwIAkJxxQ+Q0RERkzFh4U4uxtZbW9/b+8cBlnOQusEREJismJgazZ8/GE0880aTneXh4wMvLq/6QSEz7owj7eRMRUWOY9mhHJqd/iDsej/CBTg/ErU1BjVYndiQiImpBERER8Pb2xtChQ7F///6/PFej0aC4uLjBYWxYeBMRUWOw8KYWN2N4RzjZyHA6pxjLDlwWOw4REbUAb29vLFy4EL/++it+/fVX+Pv7Y8CAATh+/PhdnxMfHw+FQlF/+Pv7t2DixqkrvM/mFqOyWituGCIiMlosvKnFuTvK8d4jHQAAX24/j+wi9vYmIjJ37du3x8svv4xu3bqhd+/e+OGHH9C7d2/MnTv3rs+Ji4uDWq2uPzIzM1swceP4tbKFm4M1qrV6nM4xvhl5IiIyDiy8SRSju/ujR1ArlFdpMXP9KbZhISKyQD179kRaWtpdvy+Xy+Hk5NTgMDaCINTPenODNSIiuhsW3iQKiUTAx0+EwUoqYGdqAbaeMt12MkREdH9UKhW8vb3FjvHAlAGtAHCdNxER3Z1M7ABkudp5OuKV6Db4elcaZm08jT7t3OBkYyV2LCIiaoTS0tIGs9Xp6elQqVRwcXFBQEAA4uLikJ2djeXLlwMA5s2bh9atW6NTp06orKzE999/j127duGPP/4Q61doNtxgjYiI7oUz3iSqqQPbIsjVDgUlGszZxt7eRESmIikpCUqlEkqlEgAQGxsLpVKJmTNnAgByc3ORkZFRf35VVRX+8Y9/ICwsDNHR0Thx4gR27NiBwYMHi5K/OXXxU0AQgKwbFSgs1Ygdh4iIjJCgN5PFtcXFxVAoFFCr1Ua5Bozubn9aIcZ/fxiCAKx9tXf9LXtERKaM41LzM+b3dOiXe3ChoBTfT+iOIR09xY5DREQtpLFjE2e8SXR92rphVFdf6G/29q5mb28iIjIxvN2ciIj+SpML78TERIwYMQI+Pj4QBAHr16//y/P37duHPn36wNXVFba2tggNDb1j65D58+cjKCgINjY2iIyMxJEjR5oajUzY+490gLOdFVLzSvDDvnSx4xARETVJ3d1ayZnc2ZyIiG7X5MK7rKwM4eHhmD9/fqPOt7e3x7Rp05CYmIizZ89ixowZmDFjBhYvXlx/zurVqxEbG4tZs2bh+PHjCA8Px7Bhw1BQUNDUeGSiXB1u9faeu+M8Mq+Xi5yIiIio8epmvE9mqqHTmcUqPiIiakYPtMZbEASsW7cOI0eObNLzRo0aBXt7e/znP/8BAERGRqJHjx745ptvAAA6nQ7+/v7429/+hnfffbdRr2nM676ocfR6PZ5ZfAiH069jQHt3LJvUU+xIRET3jeNS8zPm97RGq0PYB3+golqL7W/0RztPR7EjERFRCzDaNd7Jyck4cOAAoqOjAdTucnrs2DEMGTLkViiJBEOGDMHBgwdbOh6JSBAEfDyqtrd3wrmr2H2OdzwQEZFpkEklCPNTAACSuc6biIj+R4sV3n5+fpDL5ejevTumTp2KKVOmAAAKCwuh1Wrh6dlwB1BPT0/k5eXd9fU0Gg2Ki4sbHGT62rg74PneQQCA2b+d4UZrRERkMpTcYI2IiO6ixQrvvXv3IikpCQsXLsS8efOwcuXKB3q9+Ph4KBSK+sPf37+ZkpLYpg1qBxd7a1y8WoYVhzPu/QQiIiIjULfOOzmjSNQcRERkfFqs8G7dujXCwsLw4osv4o033sAHH3wAAHBzc4NUKkV+fn6D8/Pz8+Hl5XXX14uLi4Nara4/MjMzDRmfWpDC1gpvDA0BULvRmrq8WuRERERE91a3s/m5vGKUV9WInIaIiIyJKH28dTodNBoNAMDa2hrdunXDzp07G3x/586diIqKuutryOVyODk5NTjIfIzt4Y8QTwcUlVfjq50XxI5DRER0T14KG3g52UCnB1Ky1GLHISIiI9Lkwru0tBQqlQoqlQoAkJ6eDpVKhYyM2luC4+LiMGHChPrz58+fj02bNuHChQu4cOEClixZgjlz5uDZZ5+tPyc2NhbfffcdfvzxR5w9exavvvoqysrKMGnSpAf89chUyaQSzBjeEQCw/OBlXLpaKnIiIiKie4vgOm8iIroDWVOfkJSUhIEDB9Z/HRsbCwCYOHEili1bhtzc3PoiHKidvY6Li0N6ejpkMhnatGmDTz/9FC+//HL9OWPGjMHVq1cxc+ZM5OXlISIiAlu3br1twzWyLP1D3DGwvTt2n7uKjzefxfcTe4gdiYiI6C9FBDhj6+k8Ft5ERNTAA/XxNibG3NuT7l9aQSmGzUuEVqfHz1Mi0aetm9iRiIgaheNS8zOF9/TQpWt4ZvEheCtscDBusNhxiIjIwIy2jzdRU7T1cMBzvQIBAP/+7Qy0OrO4TkRERGYqzFcBiQDkqiuRp64UOw4RERkJFt5k9KYPbgeFrRVS80qw+ih3ryciIuNlL5ehvVftjIcq84bIaYiIyFiw8Caj18reGtMHtwMAfPHHORRXsr0YEREZr/p+3lznTUREN7HwJpPwXFQggt3sca2sCvN3p4kdh4iI6K6UdTubZxSJmoOIiIwHC28yCVZSCd4f3gEAsHTfZWRcKxc5ERER0Z1FBDgDAFKy1dybhIiIALDwJhMyKNQDfdu6oUqrwydbz4odh4iI6I7auDvAQS5DeZUW5/NLxI5DRERGgIU3mQxBEDDj0Q6QCMDmlDwcvnRN7EhERES3kUoEhPsrAADJvN2ciIjAwptMTKiXE57pGQAAmP37Weh4Cx8RERmhug3WuLM5EREBLLzJBMUODYGjXIaUbDXWJmeLHYeIiOg2Ef6tAAAq7mxORERg4U0myM1BjmmD2gIAPtuaijJNjciJiIiIGqqb8b5QUIoStsEkIrJ4LLzJJD3fJwgBLnYoKNFg0Z6LYschIiJqwN1RDl9nW+j1QEqWWuw4REQkMhbeZJLkMiniYkIBAIsSLyG7qELkRERERA3VtRVL5u3mREQWj4U3mayHO3uhZ2sXaGp0+GxrqthxiIgsSmJiIkaMGAEfHx8IgoD169c3+rn79++HTCZDRESEwfIZA+XN2825szkREbHwJpMlCAJmPtoRggBsUOXgeAZ3jiUiaillZWUIDw/H/Pnzm/S8oqIiTJgwAYMHDzZQMuOhvDnjrcosgl7PLhxERJaMhTeZtM6+CjzV1Q8A8O/fzvCDDRFRC4mJicHs2bPxxBNPNOl5r7zyCsaNG4eoqCgDJTMenXwUkEkEFJZquCSKiMjCsfAmk/fWsPaws5YiOaMIG0/kiB2HiIjuYunSpbh06RJmzZrVqPM1Gg2Ki4sbHKbExkqKDt5OANhWjIjI0rHwJpPn4WSDV6PbAAA+3ZKKiiqtyImIiOh/XbhwAe+++y5++uknyGSyRj0nPj4eCoWi/vD39zdwyuZX11ZMxXXeREQWjYU3mYUX+wfDR2GDHHUlvt97Sew4RET0J1qtFuPGjcOHH36IkJCQRj8vLi4OarW6/sjMzDRgSsNQcmdzIiICC28yEzZWUrxzs73Ygj0XkV9cKXIiIiKqU1JSgqSkJEybNg0ymQwymQz/+te/cOLECchkMuzateuOz5PL5XBycmpwmJq6Ge9T2WpUa3XihiEiItGw8Caz8Vi4D5QBziiv0uLzbefEjkNERDc5OTkhJSUFKpWq/njllVfQvn17qFQqREZGih3RYFq72UNhawVNjQ6puSVixyEiIpGw8CazUddeDAB+PZ6FU9lqkRMREZmv0tLS+iIaANLT06FSqZCRkQGg9jbxCRMmAAAkEgk6d+7c4PDw8ICNjQ06d+4Me3t7sX4NgxMEAeF167wz2faSiMhSsfAms6IMaIXHI3yg1wP/YnsxIiKDSUpKglKphFKpBADExsZCqVRi5syZAIDc3Nz6ItzS1d1uznXeRESWS9CbSWVSXFwMhUIBtVptkmvAqPnkFFVg0BcJqKzWYcH4rogJ8xY7EhFZII5Lzc9U39PdqQWYtOwogt3ssevNAWLHISKiZtTYsYkz3mR2fJxt8VK/YADAx1vOQlPD9mJERCSeuhnvS4VlUJdXixuGiIhEwcKbzNLL0W3g6SRH5vUKLN1/Wew4RERkwVrZWyPI1Q4AoMoqEjcMERGJgoU3mSV7uQxvDattL/bNrjQUlmpETkRERJasbtZblVEkag4iIhIHC28yW6OUvgjzVaBUU4Mvt58XOw4REVmwCO5sTkRk0Vh4k9mSSAT882Z7sVVHMpCaVyxyIiIislQRAa0AAKrMInbcICKyQCy8yaz1bO2CR8K8oNMD/2Z7MSIiEklHbydYyyS4UV6NK9fKxY5DREQtjIU3mb13H+4Aa6kE+9OuYefZArHjkIGlFZQivbBM7BhERA1YyyTo5FPbZkbFft5ERBaHhTeZvQBXO0zu2xoA8PHms6iq0YmciAzhyrUyTFtxHEO+3INhcxOx40y+2JGIiBq4tc67SNQcRETU8lh4k0WYOrAN3ByscamwDD8duiJ2HGpGhaUazNpwCoO/2IPfTuYCAKq0Orzy0zH8djJH5HRERLfUFd7JLLyJiCwOC2+yCI42VvjHQ+0BAPN2nMeNsiqRE9GDKtPU4P92XkD0Z7vx48ErqNHpER3ijk3T+uLxCB/U6PT4+8pk/HosS+yoREQAAKV/7QZrZ3OKoanRipyGiIhaUpML78TERIwYMQI+Pj4QBAHr16//y/PXrl2LoUOHwt3dHU5OToiKisK2bdsanPPBBx9AEIQGR2hoaFOjEf2l0d39EerliOLKGny184LYceg+VWt1+OnQFUR/noAvt59HWZUWYb4KrJgSiR8n90SYnwJfjo7AmO7+0OmBf/z3BH4+zLsciEh8/i62cLG3RpVWhzM57LRBRGRJmlx4l5WVITw8HPPnz2/U+YmJiRg6dCg2b96MY8eOYeDAgRgxYgSSk5MbnNepUyfk5ubWH/v27WtqNKK/JP1Te7H/HLqCtIISkRNRU+j1emxJycWwuYmYsf4UCks1CHCxw9djldgwtQ96t3WrP1cqERA/KgzP9w4CALy/7hSW7EsXKTkRUS1BEKCsu908o0jULERE1LJkTX1CTEwMYmJiGn3+vHnzGnz98ccfY8OGDdi0aROUSuWtIDIZvLy8mhqHqEn6tHXDkA6e2HE2Hx/9fhZLJ/UUOxI1wuFL1xC/JbV+QyJXe2v8fXA7jO0ZAGvZna8fSiQCZo3oCLmVBIv2XMK/fzuDymotpg5s24LJiYgaivB3xs7UAm6wRkRkYZpceD8onU6HkpISuLi4NHj8woUL8PHxgY2NDaKiohAfH4+AgIC7vo5Go4FGo6n/uriYt2xR47z3SCgSzhVg97mr2HP+KqJD3MWORHdxLq8En21Nxc7U2jZwtlZSvNivNV7sHwxHG6t7Pl8QBLz7cChsraSYt+MCPt92DhVVWvzjoRAIgmDo+EREt4kIcAbAnc2JiCxNi2+uNmfOHJSWlmL06NH1j0VGRmLZsmXYunUrFixYgPT0dPTr1w8lJXe/FTg+Ph4KhaL+8Pf3b4n4ZAaC3R0w8eYtyLN/O4MaLduLGZtcdQXe+u8JxHyViJ2pBZBKBIyPDMCetwcg9qH2jSq66wiCgNeHhCAupnbfiG92p2H272eh1+sNFZ+I6K66+DkDADKul+NaqeavTyYiIrPRooX3ihUr8OGHH2LNmjXw8PCofzwmJgZPP/00unTpgmHDhmHz5s0oKirCmjVr7vpacXFxUKvV9UdmZmZL/ApkJv4+qB1a2VnhQkEpVh7lvx1joS6vRvyWsxjweQL+eywLOj0Q09kL29/oj4+eCIOHo819v/bL0W3w4WOdAABL9qVjxvpT0OlYfBNRy1LYWqGNuz0A4ERWkbhhiIioxbRY4b1q1SpMmTIFa9aswZAhQ/7yXGdnZ4SEhCAtLe2u58jlcjg5OTU4iBpLYWeF14eEAADmbj8PdUW1yIksW2W1FosTL6L/57uxaM8laGp06NnaBWtf640Fz3ZDsLtDs/ycib2D8NmTXSAIwM+HM/DWLyd5xwMRtbiIm23FuMEaEZHlaJHCe+XKlZg0aRJWrlyJ4cOH3/P80tJSXLx4Ed7e3i2QjizVuMgAtPVwwPWyKnyzi+3FxKDV6fHrsSwM/mIPPt6cCnVFNUI8HbBkYnesfqkXuga0avafObqHP+aNiYBUIuDX41mYvlqFahbfRNSClFznTfRArlwrwwZVNrS8c41MSJM3VystLW0wE52eng6VSgUXFxcEBAQgLi4O2dnZWL58OYDa28snTpyIr776CpGRkcjLywMA2NraQqFQAADefPNNjBgxAoGBgcjJycGsWbMglUoxduzY5vgdie7ISirB+8M7YNLSo1h24DLGRwYiyM1e7FgWQa/XI+H8VXy6JRWpebV7OXgrbPDG0BA82dUPUolhNz57PMIXcpkEf1uZjN9P5kJTrcM345SwsZIa9OcSEQG1O5sDtYW3TqeHxMB/84jMiVanx+RlR3HxahlS80rwzsOhYkciapQmz3gnJSVBqVTWtwKLjY2FUqnEzJkzAQC5ubnIyMioP3/x4sWoqanB1KlT4e3tXX9Mnz69/pysrCyMHTsW7du3x+jRo+Hq6opDhw7B3Z27TZNhDWzvgegQd1Rr9fh481mx41iEE5lFGPvdIUxaehSpeSVwtJHh3ZhQ7H5zAEZ39zd40V3n4c7eWPxcd8hlEuw4m48XlyehokrbIj+biCxbqJcjbKwkKKmswaXCMrHjEJmU7WfycPFq7X83CxIuYufZfJETETWOoDeTrX2Li4uhUCigVqu53pua5EJ+CR7+ai+0Oj1WvBiJ3m3cxI5kli4XluHzbefwe0ouAMBaJsHzvYPw2oA2cLazFi3XgbRCvPBjEiqqtYhs7YIlz/eAg7zFOy2SGeK41PzM6T19euEBHL18A3OeDsdT3fzEjkNkEvR6PR6fvx8ns9TwVtggV10Jha0Vfv97X/i1shM7Hlmoxo5NLd5OjMjYtPN0xLietT3jZ/92luuFmllhqQYzN5zCkC/34PeUXAgCMKqrL3b9IxrvPdJB1KIbAHq3dcN/XugJR7kMh9Ov47klh7nZHhEZ3K3bzW+IG4TIhBy4eA0ns9SwsZJg7Wu9Ee6ngLqiGlNXJKOqhvu1kHFj4U0E4I2hIXC0keFMbjF+Ocb2Ys2hTFODeTvOI/qz3Vh+8ApqdHoMaO+OzX/vhy9HRxjVlenuQS74+cVIKGytkJxRhHHfHcL1siqxYxGRGVMGcGdzoqb6NqF2n6lnegTAW2GLb8Z1hcLWCicyi7hkkIweC28iAC721pg+uB0A4PNt51GqqRE5kemq1urwn4OXEf35bszbcQFlVVp08VNgxYuRWDapJzp4G+ftoV38nLHqpV5wtbfG6ZxiPLP4IApKKsWORURmqm7GOzWvhPtLEDXCicwi7E+7BplEwJR+rQEA/i52+HJ0OABg2YHL+P1krpgRif4SC2+imyZEBSHI1Q6FpRp8u/vuPeTpzvR6PX4/mYuhX+7BPzecRmFpFQJd7fDNOCU2TO1jEmvnO3g7YfXLUfB0kuN8finGLDqEnKIKsWMRGaXExESMGDECPj4+EAQB69ev/8vz9+3bhz59+sDV1RW2trYIDQ3F3LlzWyasEfJW2MDDUQ6tTo9TOWqx4xAZvQUJFwEAj0X4NLhrbnAHT7wS3QYA8M6vJ3Hpaqko+YjuhYU30U3WMgnee6QDAOD7fenIvF4uciLTcejSNYz89gCmrjiOy9fK4WpvjX893gnb34jGo11qP5SbirYeDljzchR8nW2RXliG0YsOIuMa/y0Q/a+ysjKEh4dj/vz5jTrf3t4e06ZNQ2JiIs6ePYsZM2ZgxowZWLx4sYGTGidBEG6t8+bt5kR/Ka2gFNvO1LYkfvVmkf1nbz4Ugp5BLijV1OC1n4/zLhIySiy8if5kaEdPRAW7oqpGh0+3poodx+il5hVj8rKjeGbxIZzILIKdtRTTB7fDnrcHYkJUEKxlpvknJtDVHmteiUKQqx2yblRg9KKDSCvgFXSiP4uJicHs2bPxxBNPNOp8pVKJsWPHolOnTggKCsKzzz6LYcOGYe/evQZOarwiApwB1PbzJqK7W7TnIvT62s9p7Twdb/u+TCrB1+OUcHOwRmpeCWZtPCVCSqK/ZpqfiokMRBAE/PPRjhAE4LeTuUi6fF3sSEYpu6gCb/73BGK+2otdqQWQSgQ82ysACW8NwBtDQ8yiHZevsy3WvByFdh4OyCuuxDOLDyI1r1jsWERmIzk5GQcOHEB0dPRdz9FoNCguLm5wmJO6Ge/kDO5sTnQ3OUUVWJecDQB4dcDts911PJ1s8NUzSggCsCYpC/9N4ma5ZFxYeBP9j44+ThjT3R8A8O/fzkDH9mL11OXViN98FgPnJOCXY1nQ64FHwryw/Y3+mD0yDB6ONmJHbFYeTjZY9VIvdPR2QmFpFZ5ZfAgpWVyLSfQg/Pz8IJfL0b17d0ydOhVTpky567nx8fFQKBT1h7+/fwsmNbwufs6QCECOuhIFxdzMkehOvt+bjhqdHr2CXdD1ZjeAu+nT1g1vDAkBAPxzwyleMCejwsKb6A5iHwqBvbUUJ7LU2HAiW+w4oqus1mLRnovo99kuLEq8hKoaHSJbu2Dda73x7fhuCHZ3EDuiwbg6yLHyxV6I8HdGUXk1xn13CMeu8E4Iovu1d+9eJCUlYeHChZg3bx5Wrlx513Pj4uKgVqvrj8xM85rBcpDLEHLzttlk3m5OdJvrZVVYeSQDAPDagLaNes60gW3RP8QdldU6vPbTcZRUVhsyIlGjsfAmugMPRxtMHVT7B/7TLedQXmWZ7cW0Oj3+m5SJQXMSEL8lFcWVNWjv6Yilz/fAqpd61fehNXcKOyv8NCUSPVu7oERTg+eWHMGBi4VixyIySa1bt0ZYWBhefPFFvPHGG/jggw/ueq5cLoeTk1ODw9zUb7DGwpvoNj8euIyKai06+TihX7vGdUeRSATMGxMBb4UNLhWW4d21KdDrefciiY+FN9FdTO7TGn6tbJFXXInFiZfEjtOi9Ho9dqXm45Gv9uKtX04iR10Jb4UNPn+qCzZP74eBoR4mtVN5c3CQy/DjpJ7o184N5VVaTFp6FAnnCsSORWTSdDodNBqN2DFExZ3Nie6sTFODZQcuA6id7W7K5w4Xe2t8M04JmUTA7ydz8Z9DVwyUkqjxWHgT3YWNlRRxMbXtxRbuuYhctWX0cz525QaeWXwIk5cl4Vx+CZxsZIiLCcXuNwfg6e7+kEosq+D+M1trKb6b0B1DOnhAU6PDi8uTsPVUntixiERRWloKlUoFlUoFAEhPT4dKpUJGRu1toXFxcZgwYUL9+fPnz8emTZtw4cIFXLhwAUuWLMGcOXPw7LPPihHfaNTtbH4yqwha7ilCVG/lkQyoK6rR2s0eD3f2avLzuwW64N2YUAC1e/ac4F0lJDIW3kR/4ZEwL/QIaoXKah0+33pO7DgGU1mtxZqkTDz2zT48ueAADqdfh7VMgpf7ByPx7YF4OboNbKykYsc0CjZWUnw7vhuGh3mjWqvH1BXHsfFEjtixiFpcUlISlEollEolACA2NhZKpRIzZ84EAOTm5tYX4UDt7HZcXBwiIiLQvXt3zJ8/H59++in+9a9/iZLfWLTzcIS9tRRlVVpcKCgROw6RUdDUaPH93nQAwMv9g+/7ov8LfVtjWCdPVGv1eO3n4ygqr2rOmERNIujNZNFDcXExFAoF1Gq1Wa4BI/GcyCzC4/P3AwA2TO2D8Ju3BZqDK9fK8PPhDKxJykRRee3mI9ZSCR6P8MHrQ0Pg62wrckLjVaPV4e1fTmJtcjYEAfj0yS4Y3d28dlymB8NxqfmZ63s6dvEhHLx0DZ+MCsMzPQPEjkMkutVHM/DOrynwdJIj8e2BkMvu/+K/uqIaI77eh4zr5Rgc6oHvJnSHxILv3qPm19ixiTPeRPcQ7u+MUV19AdTeqmTq16q0Oj12ns3H80uPYMCcBCxOvISi8mr4Otvi7Yfb42DcIHz+dDiL7nuQSSWY83Q4xkUGQK8H3v7lJJYfvCx2LCIyQXW3m3ODNaLazymL9tTurTOlb/ADFd0AoLC1wrfju8JaJsHO1AIssrB9e8h4yMQOQGQK3h4Wii0peUi6cgO/p+Ti0S4+YkdqsutlVVh9NBM/H76CrBu31qtHh7jjuV6BGBjqYdHrt++HRCLgo5GdYSOT4of96Zi54TQqq7V4qX8bsaMRkQnhzuZEt2w7nYdLhWVQ2FphbGTz3AHS2VeBD0Z0wnvrUjDnj3PoGuCMyGDXZnltosZi4U3UCF4KG7wS3QZzd5xH/OZUDOngaRJrnvV6PVSZRfjPoSv47WQuqmp0AAAnGxlGd/fHs70CEeRmL3JK0yYIAv75aAfYWkswf/dFfLw5FRVVOvx9cNN2YCUiy6W8WXifzy9BmaYG9nJ+PCPLpNfrsSDhIgBgYlQgHJrxv4WxPf1x9PJ1rEvOxt9WJuP3v/eDu6O82V6f6F74l52okV7qH4xVRzOQXVSBJfvSMXVgW7Ej3VVltRYbVTn4z6ErSMlW1z/e2dcJE3oFYUS4D2ytjf/CgakQBAFvDQuFrZUUc/44j7k7zqOyRou3h7Vn8U1E9+ThZAMfhQ1y1JU4maVGVBvOxJFl2pdWiJRsNWytpHi+T+tmfW1BEDB7ZGekZKuRVlCK6auS8Z8XInm3H7UYrvEmaiRbaynefrg9AODb3WkoKKkUOdHtLheW4aPfzyDy4514+9eTSMlWw1oqwSilL9a91hubpvXF6B7+LLoNZNqgdpgxvLYF3YKEi/hw0xno2B6IiBpBGdAKAJCceUPkJETiqZvtfqanP1zsrZv99e3lMiwY3xW2VlIcuHgNX+043+w/g+huWHgTNcHj4b4I93dGWZUWX2wzjj/WWp0eO87kY+IPtZulfbc3HeqK2s3S3nk4FAfjBuHLMRFQBrTi7GsLmNIvGLNHdgYALDtwGe+tS2FvXiK6p/p13hlFouYgEosqswgHLl6DTCJgSr9gg/2cdp6OiB8VBgD4enca9py/arCfRfRnvNWcqAkkEgEzH+2AJxccxJpjmZjQOxCdfBSiZLlWqsGapKw7bpY2ISoQA9pzszSxPNsrEDZWUrz9ywmsOpoJTY0Onz/VBTIpr3US0Z39eWdzvV7PC6VkcRYkpAEARip9Dd5ZZaTSF0cuX8eKwxl4fVXtem8fdnMhA2PhTdRE3QJd8GgXb/x2Mhf//u0MVr7Yq8U+IOn1eiRnFuGngzc3S9PWbpamsLXC6O5+eLZXIAJduVmaMXiqmx/kMgneWK3CuuRsVFZr8dUzSljLWHwT0e06+ygglQgoKNEgV13JIoAsSlpBCbadzocgAK9EG262+89mPtoRJ7OKcCq7GNNWHMfql6NgxQvkZED810V0H96NCYW1TIJDl67jjzP5Bv95FVVarD6agRHf7MOobw9gbXI2qrQ6hPkq8NlTXXD4vcF4f3hHFt1GZkS4T23vUKkEW07l4ZWfjqGyWit2LCIyQrbWUoR6OQJgWzGyPAsSantrP9TRE209HFvkZ9pYSfHtuG5wtJHheEYRPt2S2iI/lywXC2+i++DXyg4v9qvdbfPjzWehqTFMMZVeWIbZv51Br/ideOfXFJzKLoa1TIJRXX2xfmofbJzWB6O7+5tEazNL9VAnL3w3sTvkMgl2pRZgyo9JKK+qETsWERkh5c3bzZMzuMEaWY7sogpsUGUDAF4d0LIdYwJc7fD5U+EAgO/3pWPrqbwW/flkWVh4E92nVwe0hbujHFeulWP5gSvN9rpanR7bz+Rjwg9HMHBOAr7fV7tZml8rW7wbE4pDcYPx5egIRPg7cw2giYgOccePk3vCzlqKfWmFmPjDEZRUVosdi4iMTIR/7c7mnPEmS/Jd4iXU6PTo3ca1fpPBlvRwZy9M6Vs7mfLWf0/gyrWyFs9AloGFN9F9cpDL8NZDte3F/m/XBVwr1TzQ610r1WD+7jT0/2w3XlyehMTzVyEIwID27vjh+e7Y89ZAvBLdxiDtNcjwegW74j8vRMLRRoajl2/g2e8Po6i8SuxYRGRE6oqOlGw1qm/u4UFkzq6XVWHV0QwAwGstPNv9Z+/EhKJbYCuUaGrw2s/HuSyMDIKFN9EDeLKbHzp6O6Gksgbzdlxo8vP1ej2OXbmBN1arEBW/C59vO4fsogo421nhpf7BSHhzAJZN6olBoZ7codwMdAtshZUv9kIrOyucyFJj7HeHUfiAF2yIyHwEu9nD0UaGymodzuWViB2HyOCW7U9HZXXtnjV92rqKlsNKKsE345RwsbfG6ZxifLjpjGhZyHyx8CZ6AFKJgH8+2hEA8PPhKzif37gPSnWbpT369T48ueAA1t3cLK2LnwKfP9UFh+IG471HOnCzNDPU2VeBVS9Fwc1BjrO5xXhm8SHkF1eKHYuIjIBEItzq583bzcnMlWpq8OPB2qV6rw1oI/ryOW+FLeaNiYAgACuPZGBdcpaoecj8sPAmekBRbVwxrJMndHrg37+dgV6vv+u56YVl+PdvZxD58Q6882sKTufUbpb2ZFc/bJjaBxun9cXT3CzN7LX3csSal3vBW2GDtIJSjF50EFk3ysWORURGgIU3WYqVhzOgrqhGsJs9HurkJXYcAED/EHf8bVA7AMB7a0/hQiMnVIgag4U3UTN475EOsJIK2HuhEAnnrjb4nlanxx+n8/DcksMYOCcBS/alo7iyBv4utoiLCcXhuMH4YnQ4wkXYUITEE+zugDUvR8HfxRZXrpVjzKJDuFzIDV2ILB13NidLoKnR4vt9tS3EXoluY1TL6aYPboc+bV1RUa3Fqz8fR5mGnUioebDwJmoGga72mNSndkfM2b+fQbVWh8I/bZb20n+OYe+FQggCMLC9O5Y+3wMJbw7Ey9Ft0IqbpVksfxc7rHk5CsFu9sguqsDoRQeRVsCr60SWLNzPGQBw8WoZ1BXsfkDmad3xbOQXa+DlZIORSl+x4zQglQiYN0YJD0c50gpK8f66lL+8m5GosVh4EzWTaYPawsXeGhevlmH0ooPo/T+bpb3cPxh73hyIpZN6YmCoh1Fd3SXxeCtssfrlKLT3dERBiQZjFh3CmZxisWMRkUhcHeQIcLEDAJzMKhI3DJEBaHV6LEqsne2e0q81rGXGV464O8rxzbiukEoErFflYMWRDLEjkRlo8r/0xMREjBgxAj4+PhAEAevXr//L89euXYuhQ4fC3d0dTk5OiIqKwrZt2247b/78+QgKCoKNjQ0iIyNx5MiRpkYjEpWTjRVih4YAAJIzilCl1SHcT4E5T4fjUNxgxD3SAQGudiKnJGPk7ijHqpd6obOvE66VVWHsd4eQVlAqdiwiEkn9Ou+MIlFzEBnC1lN5SC8sg7OdFcb2DBA7zl31bO2Ct4bVto39cOMZnMpWi5yITF2TC++ysjKEh4dj/vz5jTo/MTERQ4cOxebNm3Hs2DEMHDgQI0aMQHJycv05q1evRmxsLGbNmoXjx48jPDwcw4YNQ0FBQVPjEYnqmR7+mBgViLE9/bFhah9smNYXT3Xz42ZpdE+t7K3x85ReCPd3hrqiGs8vPYKrJWw1RmSJuMEamSu9Xo9vE9IAABOjgmAvl4mc6K+91C8YQzp4oEqrw2s/H+fyD3oggv4BFi0IgoB169Zh5MiRTXpep06dMGbMGMycORMAEBkZiR49euCbb74BAOh0Ovj7++Nvf/sb3n333Ua9ZnFxMRQKBdRqNZycnJqUh4jIWFwr1WDUggO4cq0cXfwUWPVSL9hZG/cHE7ozjkvNz1Le0+MZNzDq2wNwsbfGsRlDRG+zRNRcEs9fxYQfjsDWSooD7w4yiX1u1OXVGP71XmTdqMBDHT2x6Llu/G+SGmjs2NTiiyp0Oh1KSkrg4uICAKiqqsKxY8cwZMiQW6EkEgwZMgQHDx686+toNBoUFxc3OIiITJ2rgxzLJvVEKzsrnMxS428rklGj1Ykdi4haUCcfJ1hLJbheVoXM6xVixyFqNnWz3WN7BphE0Q0ACjsrfDu+K6ylEvxxJh9L9qWLHYlMVIsX3nPmzEFpaSlGjx4NACgsLIRWq4Wnp2eD8zw9PZGXl3fX14mPj4dCoag//P39DZqbiKiltHazx/cTe0Auk2BnagE+2HSaO6oSWRC5TIoOPrWzJsmZbCtG5uF4xg0cunQdVlIBL/ZvLXacJuni54wZj3YAAHyyJRXHrlwXORGZohYtvFesWIEPP/wQa9asgYeHxwO9VlxcHNRqdf2RmZnZTCmJiMTXLbAV5o2JgCAAPx3KwOKbO8ASkWVQcp03mZkFCRcBACMjfOGtsBU5TdM91ysQI8J9UKPTY+rPybhWyn1YqGlarPBetWoVpkyZgjVr1jS4rdzNzQ1SqRT5+fkNzs/Pz4eXl9ddX08ul8PJyanBQURkTmLCvDFjeEcAQPyWVGw6kSNyIqJbDNXlhGpxgzUyJ+fzS7D9TD4EAXg5uo3Yce6LIAiIHxWGYHd75BVX4vXVKmh1vBuNGq9FCu+VK1di0qRJWLlyJYYPH97ge9bW1ujWrRt27txZ/5hOp8POnTsRFRXVEvGIiIzWC31bY1KfIADAP9acwJF03t5GxsEQXU7olrrC+3ROMTQ1WnHDED2ghXtqZ7uHdfRCWw8HkdPcPwe5DAvGd4ONlQR7LxRi/u40sSORCWnyVrmlpaVIS7v1jyw9PR0qlQouLi4ICAhAXFwcsrOzsXz5cgC1t5dPnDgRX331FSIjI+vXbdva2kKhUAAAYmNjMXHiRHTv3h09e/bEvHnzUFZWhkmTJjXH70hEZNJmDO+InKIKbDudjxeXJ+HXV3ub9AcXMg8xMTGIiYlp9Pnz5s1r8PXHH3+MDRs2YNOmTVAqlc2czvQFutqhlZ0VbpRX42xuSX0hTmRqsm6UY6Oq9o6t1waa5mz3n7X3csTskWF4878nMHfHeXQLbIU+bd3EjkUmoMkz3klJSVAqlfWDZGxsLJRKZX1rsNzcXGRkZNSfv3jxYtTU1GDq1Knw9vauP6ZPn15/zpgxYzBnzhzMnDkTERERUKlU2Lp1620brhERWSKpRMC8MUooA9jjm8zH/3Y5uRNL7mAiCMKt280zuMEama7v96ajRqdH37Zu6OLnLHacZvFUNz+M6e4PvR6YvioZ+cWVYkciE9DkwnvAgAHQ6/W3HcuWLQMALFu2DAkJCfXnJyQk/OX5daZNm4YrV65Ao9Hg8OHDiIyMfJDfi4jIrNhaS/H9hO4IdLVD1o0KvPDjUZRX1Ygdi+i+/W+Xkzux9A4mEf6tAHCdN5mua6UarDpaOyH36gDTn+3+sw8f74RQL0cUllax9Sc1Sou3EyMiovvDHt9kLhrb5cTSO5hEBDgDYOFNpmvZgcuorNYh3E+B3m1cxY7TrGyspFjwbDc4yGU4cvk6Pv/jnNiRyMix8CYiMiHs8U2m7m5dTu7E0juYRNy8LffytXLcKKsSNwxRE5VUVuPHA5cB1M52C4IgbiADaO1mj8+e6gIAWLTnEnacyb/HM8iSsfAmIjIx7PFNpuqvupzQ7RR2Vgh2swcAqLKKxA1D1EQrDmeguLIGwe72eKjj3VsEm7pHwrzxfO8gAMA//nsCmdfLxQ1ERouFNxGRCWKPbxJbaWkpVCoVVCoVgFtdTuo2WI2Li8OECRPqz1+xYgUmTJiAL774or7LSV5eHtRqtRjxTUbd7ebJGUWi5iBqispqLb7flw4AeCW6DSQS85vt/rP3HumACP/aDVCnrjjOFoB0Ryy8iYhMFHt8k5gM0eWEbqes29mc67zJhKw9no2rJRp4K2wwMsJX7DgGZy2TYP74rnC+uQfLR7+fFTsSGaEm9/EmIiLjwR7fJJa6Lid387/dS/7c8YQar25n8xOZRdDr9Wa5TpbMi1anx6LEiwCAF/sFw1pmGfN8vs62+HJ0OCYvS8Lyg1fQI8gFI8J9xI5FRsQy/ksgIjJT7PFNZN5CvR0hl0mgrqhGemGZ2HGI7mlzSi6uXCtHKzsrPNPTsloADgr1xGs326a9++tJXLxaKnIiMiYsvImITBx7fBOZLyupBJ19FQB4uzkZP71ejwUJtbPdz/duDTtry7u5NnZoCCJbu6CsSovXfjqOiiqu96ZaLLyJiMwAe3wTma+6dd7cYI2M3Z7zV3Emtxh21lJM7B0odhxRyKQSfD1WCTcHOc7ll+CfG06JHYmMBAtvIiIzwR7fROapbmdzzniTsfv25mz3uJ4BcLazFjmNeDycbPD1WCUkAvDLsSysOZopdiQyAiy8iYjMCHt8E5mfiJsz3mdzi1FZzdtWyTgdu3IdR9Kvw0oq4IV+rcWOI7qoNq74x0PtAQD/3HAKZ3KKRU5EYmPhTURkZtjjm8i8+Drbws1BjhqdHqdz2PecjFPd2u5RSj94K2xFTmMcXo1ugwHt3aGp0WHqiuMoqawWOxKJiIU3EZEZYo9vIvMhCEL9rDfXeZMxOpdXgh1nCyAIwMvRwWLHMRoSiYC5oyPgo7BBemEZ3vn1JJeAWTAW3kREZmrG8I4Y1skTVVodXlyehLQCtjUhMlVKrvMmI7ZwT+1sd0xnLwS7O4icxri0srfGN+O7wkoqYHNKHn48cFnsSCQSFt5ERGaKPb6JzAd3NidjlXm9HBtvLml6NbqtyGmMU9eAVoiL6QAA+GjzWSRn3BA5EYmBhTcRkRljj28i8xDmp4AgANlFFbyARkblu72XoNXp0a+dG8L8FGLHMVqT+gQhprMXqrV6TFuRjBtlVWJHslhVNTqsOZoJra5lb/tn4U1EZObY45vI9DnaWKGdR+0tvLzdnIxFYakGq2+2ynp1QBuR0xg3QRDw6VNdEORqh+yiCsSuUbV44UdAwrkCPDwvEW//erL+325LYeFNRGQB2OObyPTVbbCmyuRtqmQclu5Ph6ZGh3B/Z0QFu4odx+g52Vhh/viusJZJsPvcVQz/v73Yd6FQ7FgW4XJhGab8eBTPLz2KS4VlcHOwhoONrEUzsPAmIrIQ7PFNZNoi/FsB4Iw3GYeSymosP3gFAPDagDYQBEHkRKahk48CX42JgMLWCql5JXh2yWFM+fEoLl3lBqiGUKapwWdbU/HQ3ETsOFsAmUTAlL6tsevNAXgs3KdFs7DwJiKyIOzxTWS66nY2P5Gp5i2qJLqfD2egpLIGbT0cMLSDp9hxTEpMmDf2vDUAz/cOgkwiYMfZAjw0NxH/2nQG6nL2+m4Oer0eG1TZGPRFAr5NuIgqrQ792rlh6+v9MOPRjnCysWrxTCy8iYgsDHt8E5mmEE9H2FlLUaqpwUXOjpGIKqu1WLIvHQDwSnQbSCSc7W4qZztrfPBYJ2x9vT8GhXqgRqfHD/vTET1nN5btT0c192K5b6ey1Ri96CCmr1Ihv1gDfxdbLH6uG5ZP7om2Ho6i5WLhTURkgdjjm8j0SCUCwnxrd41Wsa0YiejX41m4WqKBj8IGj0e07O265qathwN+eL4Hlk/uiRBPBxSVV+ODTWfw8LxE7E4t4H4sTXC9rArvrUvBiG/24ejlG7C1kuLNh0Kw/Y1oPNTJS/TlECy8iYgsEHt8E5mmiJu3mydznTeJpEarw6I9tXuEvNg/GFZSlhPNoX+IOzb/vR9mj+wMF3trXLxahknLjmLi0qM4n18idjyjVqPV4ccDlzHg891YcTgDej3wWLgPdr0ZjWmD2sHGSip2RAAsvImILBZ7fBOZHmX9zuZFouYgy7X5VB4yrpfDxd4az/QIEDuOWZFJJXi2VyB2vzkAL/UPhpVUQOL5q4j5ai9mrE/BtVJeIP9fBy4WYvj/7cOsjadRXFmDDt5OWP1SL/zfWCW8FbZix2uAhTcRkQVjj28i01K3s/m5vGJeKKMWp9frsSDhIgDg+d5BsLU2jplEc6OwtcJ7j3TA9jeiMayTJ7Q6PX46lIEBcxLwXeIlVNVwnM4uqsDUn49j3HeHcS6/BM52Vpg9sjN++1tfRBppazsW3kREFo49volMh5fCBt4KG+j0wMkstdhxyMIknLuKs7nFsLeWYmJUkNhxzF6Qmz0WPdcdK1/shU4+TiiprMFHm89i6Nw92HY6zyLH6spqLb7acQGDv0jA7ym5kAjAhKhAJLw5AM/2CoTUiDf6Y+FNRETs8U1kQiJ4uzmJpG62e1xkABR2Ld+OyVJFtXHFxml98dlTXeDuKMeVa+V4+T/HMPa7QzidYxkX4PR6PbaeysXgL/Zg7o7zqKzWIbK1C37/ez/86/HOcLazFjviPbHwJiIiAOzxTWQq6gtv7mxOLSjp8nUcuXwd1lIJpvQLFjuOxZFKBIzu7o/dbw7AtIFtIZdJcOjSdTz69T6888tJFJRUih3RYM7nl+DZJYfxyk/HkV1UAW+FDb4Zp8Sql3qhg7eT2PEaTSZ2ACIiMh4v9G2NrBvlWLr/Mv6x5gQ8nWzQs7WL2LGI6E84401iqJvtfrKbLzydbEROY7kc5DK8Oaw9nunpj0+3nsOmEzlYnZSJ307m4LWBbfFC39ZGs4v3g1JXVGPejvNYfvAKtDo9rGUSvNI/GK8MaAM7a9MrYznjTUREDbDHN5FxC/NTQCoRkFdcifHfH8La41ko03CjNTKc1Lxi7EwtgEQAXurfRuw4BMCvlR2+HqvEr69GIdzfGWVVWny+7RwGf7EHm07kmPT6b61Oj1VHMjBwTgKW7r8MrU6PYZ08sTM2GrEPtTfJohtg4U1ERP+DPb6pMRITEzFixAj4+PhAEASsX7/+L8/Pzc3FuHHjEBISAolEgtdff71FcpojO2sZnunhDwDYn3YNsWtOoMdHOxC7WoV9Fwqh1ZnuB24yTgtvznbHhHmjtZu9yGnoz7oFumDdq70xb0wEvBU2yC6qwN9WJuPphQdxwgTvijl25TpGzt+Pd9em4HpZFdp6OOA/L/TEoue6w9/FTux4D4SFNxER3YY9vuleysrKEB4ejvnz5zfqfI1GA3d3d8yYMQPh4eEGTmf+PnoiDHvfHojYoSEIcrVDeZUWa5Oz8eySw+jzyS58siUVF/JLxI5JZiDzejk2ncwFALwazdluYySRCBip9MWufwzAG0NCYGslRdKVG3h8/n7ErlYhV10hdsR7yi+uROxqFZ5ccBAp2Wo4ymX456MdsWV6P/Rr5y52vGbR5MLbEFe4ly1bBkEQGhw2Nlw7QkQkJvb4pr8SExOD2bNn44knnmjU+UFBQfjqq68wYcIEKBQKA6ezDP4udvj74HbY/eYA/Ppqb4yPDICTjQx5xZVYuOcihs5NxIiv92Hp/nQUlvKuFbo/ixIvQqvTo3+IOzr78r9dY2ZrLcX0IbV/E57s6gcAWJucjYFzEjBvx3mjvICuqdFi4Z6LGDQnAWuTsyEIwJju/tj91gC80Lc1rKTmM0/c5N/EUFe4nZyckJubW39cuXKlqdGIiKiZscc3iUmj0aC4uLjBQbcTBAHdAlvhoyfCcHTGECx8tiuGdvSETCIgJVuNDzedQa+Pd2LKj0fx+8lcVFZrxY5MJuJqiQZrkrIAcLbblHgpbPDF6HBsnNYHPYJaobJah3k7LmDQnD1YezwLOiNZjrI7tQAPz9uLT7akoqxKiwh/Z6x/rQ8+faoL3BzkYsdrdk1emR4TE4OYmJhGn193hRsAfvjhh7ueJwgCvLy8mhqHiIgMrK7H92srjuOnQxnwb2WHl/kBjFpAfHw8PvzwQ7FjmBS5TIqHO3vj4c7euFaqwW8nc7H2eBZOZKmx42wBdpwtgKONDI928cGTXX3RLbAVBEEQOzYZqR/2p6OqRgdlgDN6BbPDhanp4ueMNS9HYcupPHy8+SyyblQgds0J/HjgMv75aEd0DxLnf9P0wjL8+7cz2JVaAABwc5AjLiYUTyh9IZGY798jo5m7Ly0tRWBgIPz9/fH444/j9OnTYkciIqKb2OObxBAXFwe1Wl1/ZGZmih3JpLg6yDGxdxA2TOuLHbH98dqANvBW2KCksgYrj2TgqYUHEf157S2oV66ViR2XjExxZTV+Olh7B+prA9ryAo2JEgQBj4R5Y0dsNN55OBQOchlOZKnx1MKDmLriODKvl7dYllJNDT7ZkoqH5u7BrtQCWEkFvNw/GLvfjMaT3fzMuugGjKSPd/v27fHDDz+gS5cuUKvVmDNnDnr37o3Tp0/Dz8/vjs/RaDTQaG6tV+LtZ0REhsUe39TS5HI55HLzu91QDG09HPH2w6F486H2OHTpGtYmZ2NLSi4yrpdj3o4LmLfjAroHtsKorn4Y3sUbClsrsSOTyH46dAUlmhq083DA4FAPsePQA7KxkuLVAW3wVDc/fLn9HFYdzcTvJ3Ox/Uw+pvRtjdcGtoWD3DCloV6vx3pVNuI3p6LgZpeU6BB3zBzREW3cHQzyM42RUcx4R0VFYcKECYiIiEB0dDTWrl0Ld3d3LFq06K7PiY+Ph0KhqD/8/f1bMDERkWVij28i0yaRCOjd1g1zng7H0RlDMG9MBPq1c4NEAJKu3MB761LQ46MdmPrzcew8m49qbqhokSqrtfhh32UAwKsD2pj9TKQlcXeUI35UF/z+t37o3cYVVTU6fJtwEQM+T8DqoxnN3o4wJUuNJxccwBurT6CgRINAVzssmdgdyyb1sKiiGzCSGe//ZWVlBaVSibS0tLueExcXh9jY2Pqvi4uLWXwTERlYXY/vcd8fQnJGEZ5fegTrXusDd0fOSlqa0tLSBuN0eno6VCoVXFxcEBAQgLi4OGRnZ2P58uX156hUqvrnXr16FSqVCtbW1ujYsWNLxyfU9gMfqfTFSKUv8tSV2KDKxtrj2TiXX4LfU3Lxe0ouXO2t8ViED57s6odOPk683dhC/PdYFgpLNfB1tsWIcB+x45ABdPRxws9TIrHjbAE+3nwW6YVleOfXFPx44ApmPNoBvdu4PdDrXyvVYM4ftTPrej1gZy3FtEFt8ULf1pDLpM30W5gWoyy8tVotUlJS8Mgjj9z1HN5+RkQkjroe36MWHMCVa+V44cejWPVSL9hZG+WQQgaSlJSEgQMH1n9ddzF84sSJWLZsGXJzc5GRkdHgOUqlsv7/P3bsGFasWIHAwEBcvny5RTLT3XkpbPBydBu81D8YZ3KLsfZ4NjaoslFYWoWl+y9j6f7LaOfhgFFd/TBS6QNvha3YkclAarQ6LE68CAB4qX+wWbVzooYEQcDQjp6IDnHH8oOX8X87L+BMbjHGfXcYD3X0xHuPdECQm32TXrNaq8NPh67gy+3nUVJZ275sZIQP3o3pAC+FZbeLFvRN7Avz5yvcSqUSX375JQYOHNioK9xTpkxB+/bt8dZbbzW4wv2vf/0LvXr1Qtu2bVFUVITPP/8c69evx7Fjxxp9Fby4uBgKhQJqtRpOTk5N+ZWIiOg+pBeWYdS3+3GjvBqDQz2w6LlukPEDWj2OS82P72nLqtHqsPdCIX49noU/zuSjqqb2tnNBAPq0ccOorr4Y1skL9gZaF0ri2KDKxvRVKrjaW2PfO4Nga22Zs5OW6HpZFb7acR4/Ha695dxKKuD53kGYNqhdo/Z92J9WiA83ncb5/NplaJ18nPDBY53QQ6Td01tKY8emJhfeCQkJDa5w16m7wv3888/j8uXLSEhIuPVD7nBb0p+vcL/xxhtYu3Yt8vLy0KpVK3Tr1g2zZ89ucGX8XjgYExG1vGNXbmDcd4egqdHh2V4B+PfjnXkr6k0cl5of31PxqCuqsSUlF2uPZ+PI5ev1j9tZS/FwZy882dUPvYJdIeVaYJOm1+sR89VepOaV4M2HQjBtUDuxI5EILuSX4KPNZ5Fw7ioAwMXeGm8MDcHYHv53vMCeeb0cH28+iy2n8gAAreys8NawUIzp4W8RfxMMVngbKw7GRETi2JKSi9dWHIdeD8TFhLLH900cl5of31PjkHm9HOuSs7H2eBYuX7vVishbYYPHI3zxZFdftPN0FDEh3a9dqfmYvCwJDnIZ9r87iLvbW7iEcwWY/fvZ+o1UQzwd8P7wjogOcQcAVFRpsXDPRSzccxGaGh2kEgHP9QrEG0NCoLCznH87LLyJiKjFLNmXjn//dgYAMG9MBEYqfUVOJD6OS82P76lx0ev1OJ5RhLXHs7DpRA6Kb67nBIAwXwVGdfXFY+E+cHXgnjym4qkFB5B05QZe7h+MuEc6iB2HjECNVocVRzIwd/t53CivBgAMbO+Ohzp54ZtdacguqgAARAW7YtZjHRHqZXl/m1l4ExFRi/pw02ks3X8ZANDe0xGPRfjgsXAf+LvYiRtMJByXmh/fU+OlqdFi19kC/Ho8GwnnClBzsyWRTCJgQHt3jOrqh0GhHrCx4nphY3X08nU8vfAgrKUS7HtnIDycLHsjLGpIXV6Nr3ddwI8HL6Nae6t89HW2xfvDOyCms5fFLjVj4U1ERC1Kq9Pjg42nsfpoJqr+1PtXGeCMx8J9MLyLNzwcLeeDHMel5sf31DRcK9Xgt5O5WHs8Cyey1PWPO9nI8Gi4D0YpfdEtsJXFfkg3VpOWHsHuc1cxtmcA4keFiR2HjFR6YRniN5/FwUvXMLlPa7wS3cbiN+Bj4U1ERKJQV1Rj26k8bDyRgwMXC3Fz4gsSAejdxg2PhftgWGcvs187yHGp+fE9NT1pBSVYezwb65KzkauurH880NUOj4f7YHAHT4T5KiCxgA2YjNnZ3GLEfLUXEgHY9Y8BTW4hRZZHr9fz4tlNLLyJiEh0BSWV+P1kLjaocqDKLKp/3FoqwYD27ngswgeDQz3N8mo5x6Xmx/fUdOl0ehy6dA2/Hs/GllO5KK/S1n/PzUGOAe3dMSjUA33bucHJxrwvyhmjv69MxsYTOXi0ize+GddV7DhEJoWFNxERGZWMa+XYdDIHG1TZ9T0+AcDeWoqHOnnhsQgf9G3rBisz6QXOcan58T01D+VVNdh2Og/bTuVj74WrKPtTES6TCOgR5IJBoR4YGOqBNu72nFUzsCvXyjBwTgJ0euD3v/dFJx+F2JGITAoLbyIiMlqpecXYqMrBxhM5yLpRUf94KzsrPBLmjccjfNE9sJVJ337Kcan58T01P1U1Ohy9fB27UguwO7UAlwrLGnw/wMWuvgiPbO3CzdkM4P11Kfj5cAYGtHfHskk9xY5DZHJYeBMRkdGra0e06UQOfjuZg8LSqvrv+Shs8Gh47c7onXycTG7Wi+NS8+N7av4uF5bVFuHnCnD40vUGGzXaWknRp60rBoZ6YFCoB7wVtiImNQ8FJZXo++luVNXosPqlXogMdhU7EpHJYeFNREQmpUarw8FL17BBlYNtp/JQornVEzjY3R6P3SzCg90dREzZeByXmh/fU8tSpqnBvrRCJJwrwK7UAuQXaxp8P9TLEYNuFuHKgFaQmvAdMmL5ZEsqFu65iG6BrfDLK1Emd4GTyBiw8CYiIpNVWa1FwrkCbDyRg51nC6CpuTXrFearwGPhPng03NuoZ7w4LjU/vqeWS6/X40xuMXan1hbhyZlF+PMnWGc7K0SH1G7QFh3iDmc7a/HCmgh1RTX6fLILpZoafD+hO4Z09BQ7EpFJYuFNRERmoaSyGn+czsfGEznYl1YI7c3+ZIIA9AxyweMRvojp7IVW9sb1QZvjUvPje0p1rpdVYc/5AuxKvYo95wpQXHnrDhmJAHQNaIWBoR4Y2N4DHbwdOZN7B/N3p+HzbefQ3tMRW6b3M+k9NYjExMKbiIjMzrVSDTan5GLjiRwcvXyj/nGZRED/EHc8HuGDIR08YS+XiZiyFsel5sf3lO6kRqvD8Yyi+g3azuWXNPi+t8IGA9rX3pLep60r7KzF//sgtspqLfp+uguFpVWYOyYcTyj9xI5EZLJYeBMRkVnLLqrAphM52KjKwZnc4vrHbawkGNLBE49H+KJ/iBvkMnF2Qea41Pz4nlJjZBdV1BfhBy4WorL61lIVa5kEvYJdMai9OwaFeiLA1U7EpC1Hq9PjWpkG+WoN8osrsef8Vfzn0BX4tbJFwpsDIDOTNo5EYmDhTUREFiOtoKS+Pdnla+X1jzvZyBDT2RuPR/ggMti1RTdf4rjU/PieUlNVVmtx8NK1+rXhf25fCABt3O3r25X1CHKBlYkVoHq9HiWaGhQUVyLvZlGdV1xZ+3VxJfKLax8rKNHUL9P5s38/3gnPRQW1fHAiM8LCm4iILI5er8fJLDU23mxP9uddkD0c5RjepbZHeLifwuBrPjkuNT++p/Qg9Ho90gpKsetmEZ505UaDYtRRLkPfdm71a8PdHeUipgU0NVoUFGtQUHKrqK47/lxUl1dpG/V6EgFwc5DD08kGnk42aO/lgOmDQ2AtM62LDUTGhoU3ERFZNK1Oj8Pp17DpRA42p+RBXVFd/71AV7v69mTtPB0N8vM5LjU/vqfUnNQV1dh74Sp2pRZgz7mruFZW1eD7XfwUGHhzbXiYr6LZNh/T6fS4Xl6FPHVlfVF9p1nq6/+T56842cjg6WQDL4UNPBxt4KW4VWB7OtnAy8kGbg7WvKWcyABYeBMREd1UVaND4vmr2HgiB9vP5KOi+tYMUaiXIx6P8MWIcG/4tWq+9Z4cl5of31MyFJ1OjxNZRdidWoDd564iJVvd4PtuDnIMaF/brqxvOzc42Vjd8XVKNTW1BfX/FNG3bgGvncGu1jbu47e1VAJPhRyejjbwVNjA8w5FtaeTnBvGEYmIhTcREdEdlFfVYPuZfGw6kYM95682+ADcLbBV/Uz4g7Yn47jU/PieUkspKK5Ewrna2fC9F66i7E+3c8skAnoEuaCzrxMKS6saFNWlmpq/eNVbBAFwtZfXFtH/U1R73Jyh9nKygbOdFVuhERk5Ft5ERET3UFRehS2n8rBRlYND6ddQNyL+8koUuge5PNBrc1xqfnxPSQxVNTocvXy9fqf0S4Vlf3m+o1wGDyc5vG4W07VF9c2vb85SuzvKTW4jNyK6MxbeRERETZCnrsRvJ3Nw6NJ1LH6u2wOv5+S41Pz4npIxuFxYVr9DuoeTHF5ONvX/19PJBvZy3vZNZEkaOzbxLwMREREAL4UNpvQLxpR+wWJHISIjFuRmj8l9W4sdg4hMDO9xISIiIiIiIjIgFt5ERETUZImJiRgxYgR8fHwgCALWr19/z+ckJCSga9eukMvlaNu2LZYtW2bwnERERMaAhTcRERE1WVlZGcLDwzF//vxGnZ+eno7hw4dj4MCBUKlUeP311zFlyhRs27bNwEmJiIjExzXeRERE1GQxMTGIiYlp9PkLFy5E69at8cUXXwAAOnTogH379mHu3LkYNmyYoWISEREZBc54ExERkcEdPHgQQ4YMafDYsGHDcPDgwbs+R6PRoLi4uMFBRERkilh4ExERkcHl5eXB09OzwWOenp4oLi5GRUXFHZ8THx8PhUJRf/j7+7dEVCIiombHwpuIiIiMUlxcHNRqdf2RmZkpdiQiIqL7wjXeREREZHBeXl7Iz89v8Fh+fj6cnJxga2t7x+fI5XLI5fKWiEdERGRQnPEmIiIig4uKisLOnTsbPLZ9+3ZERUWJlIiIiKjlmM2Mt16vBwBuvEJEREahbjyqG5/MTWlpKdLS0uq/Tk9Ph0qlgouLCwICAhAXF4fs7GwsX74cAPDKK6/gm2++wdtvv43Jkydj165dWLNmDX7//fdG/0yO9UREZGwaPd7rzURmZqYeAA8ePHjw4GFUR2ZmpthDpEHs3r37jr/vxIkT9Xq9Xj9x4kR9dHT0bc+JiIjQW1tb64ODg/VLly5t0s/kWM+DBw8ePIz1uNd4L+j15nEpXqfTIScnB46OjhAE4YFeq7i4GP7+/sjMzISTk1MzJWw5zC8u5hcX84uL+W/R6/UoKSmBj48PJBKu7GoOHOtvYX5xMb+4mF9czN9QY8d7s7nVXCKRwM/Pr1lf08nJyST/MdVhfnExv7iYX1zMX0uhUDRDGqrDsf52zC8u5hcX84uL+W9pzHjPS/BEREREREREBsTCm4iIiIiIiMiAWHjfgVwux6xZs0y2dyjzi4v5xcX84mJ+MhWm/r8184uL+cXF/OJi/vtjNpurERERERERERkjzngTERERERERGRALbyIiIiIiIiIDYuFNREREREREZEAsvImIiIiIiIgMiIX3/5g/fz6CgoJgY2ODyMhIHDlyROxIjZaYmIgRI0bAx8cHgiBg/fr1Ykdqkvj4ePTo0QOOjo7w8PDAyJEjce7cObFjNdqCBQvQpUsXODk5wcnJCVFRUdiyZYvYse7LJ598AkEQ8Prrr4sdpdE++OADCILQ4AgNDRU7VpNkZ2fj2WefhaurK2xtbREWFoakpCSxYzVKUFDQbe+/IAiYOnWq2NEaRavV4p///Cdat24NW1tbtGnTBv/+97/B/UfNl6mO9xzrxWVOYz1geuM9x3pxcax/MCy8/2T16tWIjY3FrFmzcPz4cYSHh2PYsGEoKCgQO1qjlJWVITw8HPPnzxc7yn3Zs2cPpk6dikOHDmH79u2orq7GQw89hLKyMrGjNYqfnx8++eQTHDt2DElJSRg0aBAef/xxnD59WuxoTXL06FEsWrQIXbp0ETtKk3Xq1Am5ubn1x759+8SO1Gg3btxAnz59YGVlhS1btuDMmTP44osv0KpVK7GjNcrRo0cbvPfbt28HADz99NMiJ2ucTz/9FAsWLMA333yDs2fP4tNPP8Vnn32Gr7/+WuxoZACmPN5zrBeXuYz1gOmO9xzrxcOx/gHpqV7Pnj31U6dOrf9aq9XqfXx89PHx8SKmuj8A9OvWrRM7xgMpKCjQA9Dv2bNH7Cj3rVWrVvrvv/9e7BiNVlJSom/Xrp1++/bt+ujoaP306dPFjtRos2bN0oeHh4sd47698847+r59+4odo9lMnz5d36ZNG71OpxM7SqMMHz5cP3ny5AaPjRo1Sj9+/HiREpEhmct4z7HeOJjaWK/Xm+54z7HeuHCsbxrOeN9UVVWFY8eOYciQIfWPSSQSDBkyBAcPHhQxmeVSq9UAABcXF5GTNJ1Wq8WqVatQVlaGqKgoseM02tSpUzF8+PAG/x2YkgsXLsDHxwfBwcEYP348MjIyxI7UaBs3bkT37t3x9NNPw8PDA0qlEt99953Yse5LVVUVfvrpJ0yePBmCIIgdp1F69+6NnTt34vz58wCAEydOYN++fYiJiRE5GTU3jvfGhWO9OEx5vOdYbxw41jedrEV+igkoLCyEVquFp6dng8c9PT2RmpoqUirLpdPp8Prrr6NPnz7o3Lmz2HEaLSUlBVFRUaisrISDgwPWrVuHjh07ih2rUVatWoXjx4/j6NGjYke5L5GRkVi2bBnat2+P3NxcfPjhh+jXrx9OnToFR0dHsePd06VLl7BgwQLExsbivffew9GjR/H3v/8d1tbWmDhxotjxmmT9+vUoKirC888/L3aURnv33XdRXFyM0NBQSKVSaLVafPTRRxg/frzY0aiZcbw3HhzrxWHK4z3HeuPBsb7pWHiTUZo6dSpOnTplUut2AKB9+/ZQqVRQq9X45ZdfMHHiROzZs8foB+TMzExMnz4d27dvh42Njdhx7sufr1Z26dIFkZGRCAwMxJo1a/DCCy+ImKxxdDodunfvjo8//hgAoFQqcerUKSxcuNDkBuMlS5YgJiYGPj4+YkdptDVr1uDnn3/GihUr0KlTJ6hUKrz++uvw8fExufefyFRwrG95pj7ec6w3Hhzrm46F901ubm6QSqXIz89v8Hh+fj68vLxESmWZpk2bht9++w2JiYnw8/MTO06TWFtbo23btgCAbt264ejRo/jqq6+waNEikZP9tWPHjqGgoABdu3atf0yr1SIxMRHffPMNNBoNpFKpiAmbztnZGSEhIUhLSxM7SqN4e3vf9qGtQ4cO+PXXX0VKdH+uXLmCHTt2YO3atWJHaZK33noL7777Lp555hkAQFhYGK5cuYL4+HiT+zBEf43jvXHgWC8OcxvvOdaLg2P9/eEa75usra3RrVs37Ny5s/4xnU6HnTt3mty6HVOl1+sxbdo0rFu3Drt27ULr1q3FjvTAdDodNBqN2DHuafDgwUhJSYFKpao/unfvjvHjx0OlUpnUIFyntLQUFy9ehLe3t9hRGqVPnz63tdQ5f/48AgMDRUp0f5YuXQoPDw8MHz5c7ChNUl5eDomk4ZAolUqh0+lESkSGwvFeXBzrxWVu4z3HenFwrL8/nPH+k9jYWEycOBHdu3dHz549MW/ePJSVlWHSpEliR2uU0tLSBlf80tPToVKp4OLigoCAABGTNc7UqVOxYsUKbNiwAY6OjsjLywMAKBQK2Nraipzu3uLi4hATE4OAgACUlJRgxYoVSEhIwLZt28SOdk+Ojo63ra+zt7eHq6uryay7e/PNNzFixAgEBgYiJycHs2bNglQqxdixY8WO1ihvvPEGevfujY8//hijR4/GkSNHsHjxYixevFjsaI2m0+mwdOlSTJw4ETKZaQ0vI0aMwEcffYSAgAB06tQJycnJ+PLLLzF58mSxo5EBmPJ4z7FeXKY81gOmP95zrBcfx/oH0CJ7p5uQr7/+Wh8QEKC3trbW9+zZU3/o0CGxIzXa7t279QBuOyZOnCh2tEa5U3YA+qVLl4odrVEmT56sDwwM1FtbW+vd3d31gwcP1v/xxx9ix7pvptReRK/X68eMGaP39vbWW1tb6319ffVjxozRp6WliR2rSTZt2qTv3LmzXi6X60NDQ/WLFy8WO1KTbNu2TQ9Af+7cObGjNFlxcbF++vTp+oCAAL2NjY0+ODhY//777+s1Go3Y0chATHW851gvLnMb6/V60xrvOdaLj2P9/RP0er2+ZUp8IiIiIiIiIsvDNd5EREREREREBsTCm4iIiIiIiMiAWHgTERERERERGRALbyIiIiIiIiIDYuFNREREREREZEAsvImIiIiIiIgMiIU3ERERERERkQGx8CYiIiIiIiIyIBbeRERERERERAbEwpuIiIiIiIjIgFh4ExERERERERkQC28iIiIiIiIiA/p/Xa9411p6qAkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#model.save('/content/drive/MyDrive/ResNet152new.h5')"
      ],
      "metadata": {
        "id": "VzVDDrQZyN7V"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Resmodel = tf.keras.models.load_model('/content/drive/MyDrive/ResNet152new.h5')"
      ],
      "metadata": {
        "id": "TQnuc1GpyaLu"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data_x = test_data_x/255"
      ],
      "metadata": {
        "id": "eQW9CvmXUdUk"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "integer_labels_test = test_data_y['classification'].map(label_mapping)\n",
        "y_test_to_categorical = keras.utils.to_categorical(integer_labels_test, num_classes=len(label_mapping), dtype='float32')"
      ],
      "metadata": {
        "id": "IFNEFa-R39n1"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Resmodel.evaluate(test_data_x,y_test_to_categorical )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BJ5OyucgzbDa",
        "outputId": "5ef4aaf1-f822-4a12-8ef9-7887dd8c0556"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5/5 [==============================] - 1s 176ms/step - loss: 1.1475 - accuracy: 0.3846 - auc: 0.5192\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.1475260257720947, 0.38461539149284363, 0.5192307829856873]"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    }
  ]
}