Custom_Fire_Detection_TFOD2_0.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Custom Fire Detection TFOD2.0.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h15l5buQQFQB"
      },
      "source": [
        "# Fire Detection using TFOD 2.0\n",
        "**Author**: [Sayan Nath](https://github.com/sayannath)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o82GFs2QQSgJ"
      },
      "source": [
        "## Intial Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5MSIuQJfP-Bn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9e07354e-97db-420c-f3ea-926068da7c5d"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Thu May  6 16:07:23 2021       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 465.19.01    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   56C    P8    10W /  70W |      0MiB / 15109MiB |      0%      Default |\n",
            "|                               |                      |                  N/A |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-QGCIdAlQVhY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2fed5ea-5e8a-4f88-b64b-38fa9b7ab756"
      },
      "source": [
        "!pip install -q tensorflow-gpu"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 394.3MB 43kB/s \n",
            "\u001b[?25h"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o0Gu7FH8QcD4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48ff5c90-f938-4e4a-bd03-5486f2ce5268"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2QIrHPCUaFX",
        "outputId": "7e62e2ad-b8c4-4e59-8ba2-49cadb0ec5eb"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61Hbe_S8cOLi"
      },
      "source": [
        "cp -r '/content/gdrive/My Drive/fire-tfod' '/content/'"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OZnEK3dZXvMs"
      },
      "source": [
        "Rename train-tfod to `training_demo`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Xs487PTQex_"
      },
      "source": [
        "## Cloning the TFOD 2.0 Repository"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GI2K6MdlQd4F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "579187c5-d424-40c1-8bbb-09b51c2ae520"
      },
      "source": [
        "!git clone https://github.com/tensorflow/models.git"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'models'...\n",
            "remote: Enumerating objects: 56913, done.\u001b[K\n",
            "remote: Counting objects: 100% (54/54), done.\u001b[K\n",
            "remote: Compressing objects: 100% (51/51), done.\u001b[K\n",
            "remote: Total 56913 (delta 11), reused 32 (delta 0), pack-reused 56859\u001b[K\n",
            "Receiving objects: 100% (56913/56913), 572.42 MiB | 37.01 MiB/s, done.\n",
            "Resolving deltas: 100% (39329/39329), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hx5mZ_tMRTlk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "599dd0f8-c3f2-4730-9e58-d387d672d40c"
      },
      "source": [
        "cd /content/models/research"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DYL8iV6jRUM1"
      },
      "source": [
        "!protoc object_detection/protos/*.proto --python_out=."
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jC-X3iPlRXuS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "223e1ab8-fa70-43d4-c2d1-50199c0494aa"
      },
      "source": [
        "!git clone https://github.com/cocodataset/cocoapi.git"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'cocoapi'...\n",
            "remote: Enumerating objects: 975, done.\u001b[K\n",
            "remote: Total 975 (delta 0), reused 0 (delta 0), pack-reused 975\u001b[K\n",
            "Receiving objects: 100% (975/975), 11.72 MiB | 34.30 MiB/s, done.\n",
            "Resolving deltas: 100% (576/576), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gvVZCdNRYgu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6321513-1936-4160-f8e0-79c89d5866e4"
      },
      "source": [
        "cd cocoapi/PythonAPI"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research/cocoapi/PythonAPI\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oh0gcQMyRa1S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "89e2847e-ca9c-44b0-e544-e228a957e6df"
      },
      "source": [
        "!make"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "python setup.py build_ext --inplace\n",
            "running build_ext\n",
            "cythoning pycocotools/_mask.pyx to pycocotools/_mask.c\n",
            "/usr/local/lib/python3.7/dist-packages/Cython/Compiler/Main.py:369: FutureWarning: Cython directive 'language_level' not set, using 2 for now (Py2). This will change in a later release! File: /content/models/research/cocoapi/PythonAPI/pycocotools/_mask.pyx\n",
            "  tree = Parsing.p_module(s, pxd, full_module_name)\n",
            "building 'pycocotools._mask' extension\n",
            "creating build\n",
            "creating build/common\n",
            "creating build/temp.linux-x86_64-3.7\n",
            "creating build/temp.linux-x86_64-3.7/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fdebug-prefix-map=/build/python3.7-a56wZI/python3.7-3.7.10=. -fstack-protector-strong -Wformat -Werror=format-security -g -fdebug-prefix-map=/build/python3.7-a56wZI/python3.7-3.7.10=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c ../common/maskApi.c -o build/temp.linux-x86_64-3.7/../common/maskApi.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleDecode\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; v=!v; }}\n",
            "       \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:46:49:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "       for( k=0; k<R[i].cnts[j]; k++ ) *(M++)=v; \u001b[01;36m\u001b[Kv\u001b[m\u001b[K=!v; }}\n",
            "                                                 \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrPoly\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); x[k]=x[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:166:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) x[j]=(int)(scale*xy[j*2+0]+.5); \u001b[01;36m\u001b[Kx\u001b[m\u001b[K[k]=x[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); y[k]=y[0];\n",
            "   \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:167:54:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kfor\u001b[m\u001b[K’\n",
            "   for(j=0; j<k; j++) y[j]=(int)(scale*xy[j*2+1]+.5); \u001b[01;36m\u001b[Ky\u001b[m\u001b[K[k]=y[0];\n",
            "                                                      \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "       \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(more) c |= 0x20; c+=48; s[p++]=c;\n",
            "       \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:212:27:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "       if(more) c |= 0x20; \u001b[01;36m\u001b[Kc\u001b[m\u001b[K+=48; s[p++]=c;\n",
            "                           \u001b[01;36m\u001b[K^\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleFrString\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:3:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "   \u001b[01;35m\u001b[Kwhile\u001b[m\u001b[K( s[m] ) m++; cnts=malloc(sizeof(uint)*m); m=0;\n",
            "   \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:220:22:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kwhile\u001b[m\u001b[K’\n",
            "   while( s[m] ) m++; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K=malloc(sizeof(uint)*m); m=0;\n",
            "                      \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
            "     \u001b[01;35m\u001b[Kif\u001b[m\u001b[K(m>2) x+=(long) cnts[m-2]; cnts[m++]=(uint) x;\n",
            "     \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:228:34:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
            "     if(m>2) x+=(long) cnts[m-2]; \u001b[01;36m\u001b[Kcnts\u001b[m\u001b[K[m++]=(uint) x;\n",
            "                                  \u001b[01;36m\u001b[K^~~~\u001b[m\u001b[K\n",
            "\u001b[01m\u001b[K../common/maskApi.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KrleToBbox\u001b[m\u001b[K’:\n",
            "\u001b[01m\u001b[K../common/maskApi.c:141:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K‘\u001b[01m\u001b[Kxp\u001b[m\u001b[K’ may be used uninitialized in this function [\u001b[01;35m\u001b[K-Wmaybe-uninitialized\u001b[m\u001b[K]\n",
            "       if(j%2==0) xp=x; else if\u001b[01;35m\u001b[K(\u001b[m\u001b[Kxp<x) { ys=0; ye=h-1; }\n",
            "                               \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
            "x86_64-linux-gnu-gcc -pthread -Wno-unused-result -Wsign-compare -DNDEBUG -g -fwrapv -O2 -Wall -g -fdebug-prefix-map=/build/python3.7-a56wZI/python3.7-3.7.10=. -fstack-protector-strong -Wformat -Werror=format-security -g -fdebug-prefix-map=/build/python3.7-a56wZI/python3.7-3.7.10=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 -fPIC -I/usr/local/lib/python3.7/dist-packages/numpy/core/include -I../common -I/usr/include/python3.7m -c pycocotools/_mask.c -o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -Wno-cpp -Wno-unused-function -std=c99\n",
            "creating build/lib.linux-x86_64-3.7\n",
            "creating build/lib.linux-x86_64-3.7/pycocotools\n",
            "x86_64-linux-gnu-gcc -pthread -shared -Wl,-O1 -Wl,-Bsymbolic-functions -Wl,-Bsymbolic-functions -Wl,-z,relro -Wl,-Bsymbolic-functions -Wl,-z,relro -g -fdebug-prefix-map=/build/python3.7-a56wZI/python3.7-3.7.10=. -fstack-protector-strong -Wformat -Werror=format-security -Wdate-time -D_FORTIFY_SOURCE=2 build/temp.linux-x86_64-3.7/../common/maskApi.o build/temp.linux-x86_64-3.7/pycocotools/_mask.o -o build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so\n",
            "copying build/lib.linux-x86_64-3.7/pycocotools/_mask.cpython-37m-x86_64-linux-gnu.so -> pycocotools\n",
            "rm -rf build\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VQbigKSmRbpi"
      },
      "source": [
        "cp -r pycocotools /content/models/research"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OnC3OcVRgst"
      },
      "source": [
        "## Install the Object Detection API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kq4nxoGWRjna",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "198ff1cc-3c33-4b5d-813a-4ec00b695460"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research/cocoapi\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4yg2I0LSRk4-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8535f7cf-4b03-4c2e-93eb-212a5329a604"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models/research\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBimbaLtRlyf"
      },
      "source": [
        "cp object_detection/packages/tf2/setup.py ."
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1gpA28XfRpmj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fe482857-0bff-41f2-f4d4-ae43a99727c0"
      },
      "source": [
        "!python -m pip -q install ."
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 9.6MB 24.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 358kB 51.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.1MB 54.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 61kB 9.7MB/s \n",
            "\u001b[K     |████████████████████████████████| 829kB 34.3MB/s \n",
            "\u001b[K     |████████████████████████████████| 2.3MB 31.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 153kB 63.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 174kB 60.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.2MB 48.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 706kB 49.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 8.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 37.6MB 80kB/s \n",
            "\u001b[K     |████████████████████████████████| 645kB 55.8MB/s \n",
            "\u001b[K     |████████████████████████████████| 102kB 14.1MB/s \n",
            "\u001b[?25h  Building wheel for object-detection (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for avro-python3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for future (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for dill (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for py-cpuinfo (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[31mERROR: multiprocess 0.70.11.1 has requirement dill>=0.3.3, but you'll have dill 0.3.1.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement requests~=2.23.0, but you'll have requests 2.25.1 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: apache-beam 2.29.0 has requirement avro-python3!=1.9.2,<1.10.0,>=1.8.1, but you'll have avro-python3 1.10.2 which is incompatible.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kpgdY37oRujf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0dbf93f2-0425-4399-fd1a-b1f150acfc3e"
      },
      "source": [
        "# From within TensorFlow/models/research/\n",
        "!python object_detection/builders/model_builder_tf2_test.py"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-05-06 16:12:44.383507: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "Running tests under Python 3.7.10: /usr/bin/python3\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "2021-05-06 16:12:46.966500: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 16:12:46.967419: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
            "2021-05-06 16:12:47.029658: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.030268: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 16:12:47.030312: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 16:12:47.084733: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 16:12:47.084848: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 16:12:47.225823: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 16:12:47.265125: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 16:12:47.557317: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 16:12:47.579190: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 16:12:47.583295: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 16:12:47.583447: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.584071: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.584625: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 16:12:47.584943: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2021-05-06 16:12:47.585076: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 16:12:47.585188: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.585722: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 16:12:47.585760: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 16:12:47.585797: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 16:12:47.585817: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 16:12:47.585835: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 16:12:47.585855: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 16:12:47.585877: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 16:12:47.585896: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 16:12:47.585915: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 16:12:47.585979: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.586550: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:47.587131: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 16:12:47.587184: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 16:12:48.205539: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2021-05-06 16:12:48.205597: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
            "2021-05-06 16:12:48.205623: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
            "2021-05-06 16:12:48.205835: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:48.206519: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:48.207108: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 16:12:48.207624: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2021-05-06 16:12:48.207673: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13968 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 1.95s\n",
            "I0506 16:12:48.717882 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)): 1.95s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model0 (customize_head_params=True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.39s\n",
            "I0506 16:12:49.104372 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)): 0.39s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model1 (customize_head_params=False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.33s\n",
            "I0506 16:12:49.436362 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_from_keypoints): 0.33s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_from_keypoints\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "W0506 16:12:49.438380 139850320439168 mobilenet_v2.py:286] `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.92s\n",
            "I0506 16:12:51.352535 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_center_net_model_mobilenet): 1.92s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_center_net_model_mobilenet\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "I0506 16:12:51.353763 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_experimental_model): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_experimental_model\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n",
            "I0506 16:12:51.380250 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)): 0.03s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature0 (True)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
            "I0506 16:12:51.404628 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)): 0.02s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_from_config_with_crop_feature1 (False)\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.03s\n",
            "I0506 16:12:51.431112 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner): 0.03s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_model_from_config_with_example_miner\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n",
            "I0506 16:12:51.571625 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul): 0.14s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\n",
            "I0506 16:12:51.713178 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul): 0.14s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_faster_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.14s\n",
            "I0506 16:12:51.849345 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul): 0.14s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_with_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.14s\n",
            "I0506 16:12:51.993587 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul): 0.14s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_faster_rcnn_models_from_config_mask_rcnn_without_matmul\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.15s\n",
            "I0506 16:12:52.139560 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_rfcn_model_from_config): 0.15s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_rfcn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.04s\n",
            "I0506 16:12:52.181382 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config): 0.04s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_fpn_model_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "I0506 16:12:52.468945 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b0\n",
            "I0506 16:12:52.469124 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 64\n",
            "I0506 16:12:52.469199 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 3\n",
            "I0506 16:12:52.473532 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:52.489244 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:52.489367 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:52.543141 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:52.543271 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:52.691900 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:52.692076 139850320439168 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0506 16:12:52.837061 139850320439168 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0506 16:12:52.837220 139850320439168 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0506 16:12:53.052315 139850320439168 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0506 16:12:53.052485 139850320439168 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0506 16:12:53.261598 139850320439168 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0506 16:12:53.261781 139850320439168 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0506 16:12:53.537844 139850320439168 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0506 16:12:53.538020 139850320439168 efficientnet_model.py:147] round_filter input=320 output=320\n",
            "I0506 16:12:53.604022 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
            "I0506 16:12:53.635268 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.0, resolution=224, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:12:53.702367 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b1\n",
            "I0506 16:12:53.702515 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 88\n",
            "I0506 16:12:53.702588 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 4\n",
            "I0506 16:12:53.706812 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:53.720769 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:53.720870 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:53.838809 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:53.839004 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:54.181990 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:54.182195 139850320439168 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0506 16:12:54.392857 139850320439168 efficientnet_model.py:147] round_filter input=40 output=40\n",
            "I0506 16:12:54.393060 139850320439168 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0506 16:12:54.679616 139850320439168 efficientnet_model.py:147] round_filter input=80 output=80\n",
            "I0506 16:12:54.679799 139850320439168 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0506 16:12:54.961281 139850320439168 efficientnet_model.py:147] round_filter input=112 output=112\n",
            "I0506 16:12:54.961450 139850320439168 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0506 16:12:55.315932 139850320439168 efficientnet_model.py:147] round_filter input=192 output=192\n",
            "I0506 16:12:55.316122 139850320439168 efficientnet_model.py:147] round_filter input=320 output=320\n",
            "I0506 16:12:55.456708 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=1280\n",
            "I0506 16:12:55.482880 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.0, depth_coefficient=1.1, resolution=240, dropout_rate=0.2, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:12:55.562036 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b2\n",
            "I0506 16:12:55.562184 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 112\n",
            "I0506 16:12:55.562258 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 5\n",
            "I0506 16:12:55.566464 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:55.581832 139850320439168 efficientnet_model.py:147] round_filter input=32 output=32\n",
            "I0506 16:12:55.581938 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:55.697127 139850320439168 efficientnet_model.py:147] round_filter input=16 output=16\n",
            "I0506 16:12:55.697279 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:55.910030 139850320439168 efficientnet_model.py:147] round_filter input=24 output=24\n",
            "I0506 16:12:55.910217 139850320439168 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0506 16:12:56.127482 139850320439168 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0506 16:12:56.127698 139850320439168 efficientnet_model.py:147] round_filter input=80 output=88\n",
            "I0506 16:12:56.417664 139850320439168 efficientnet_model.py:147] round_filter input=80 output=88\n",
            "I0506 16:12:56.417854 139850320439168 efficientnet_model.py:147] round_filter input=112 output=120\n",
            "I0506 16:12:56.723037 139850320439168 efficientnet_model.py:147] round_filter input=112 output=120\n",
            "I0506 16:12:56.723209 139850320439168 efficientnet_model.py:147] round_filter input=192 output=208\n",
            "I0506 16:12:57.086097 139850320439168 efficientnet_model.py:147] round_filter input=192 output=208\n",
            "I0506 16:12:57.086266 139850320439168 efficientnet_model.py:147] round_filter input=320 output=352\n",
            "I0506 16:12:57.222478 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=1408\n",
            "I0506 16:12:57.250808 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.1, depth_coefficient=1.2, resolution=260, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:12:57.325086 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b3\n",
            "I0506 16:12:57.325239 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 160\n",
            "I0506 16:12:57.325310 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 6\n",
            "I0506 16:12:57.329674 139850320439168 efficientnet_model.py:147] round_filter input=32 output=40\n",
            "I0506 16:12:57.346712 139850320439168 efficientnet_model.py:147] round_filter input=32 output=40\n",
            "I0506 16:12:57.346823 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:12:57.613461 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:12:57.613630 139850320439168 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0506 16:12:57.826913 139850320439168 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0506 16:12:57.827101 139850320439168 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0506 16:12:58.033353 139850320439168 efficientnet_model.py:147] round_filter input=40 output=48\n",
            "I0506 16:12:58.033521 139850320439168 efficientnet_model.py:147] round_filter input=80 output=96\n",
            "I0506 16:12:58.391450 139850320439168 efficientnet_model.py:147] round_filter input=80 output=96\n",
            "I0506 16:12:58.391631 139850320439168 efficientnet_model.py:147] round_filter input=112 output=136\n",
            "I0506 16:12:58.761769 139850320439168 efficientnet_model.py:147] round_filter input=112 output=136\n",
            "I0506 16:12:58.761940 139850320439168 efficientnet_model.py:147] round_filter input=192 output=232\n",
            "I0506 16:12:59.189665 139850320439168 efficientnet_model.py:147] round_filter input=192 output=232\n",
            "I0506 16:12:59.189836 139850320439168 efficientnet_model.py:147] round_filter input=320 output=384\n",
            "I0506 16:12:59.335312 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=1536\n",
            "I0506 16:12:59.364424 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.2, depth_coefficient=1.4, resolution=300, dropout_rate=0.3, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:12:59.451363 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b4\n",
            "I0506 16:12:59.451524 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 224\n",
            "I0506 16:12:59.451600 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
            "I0506 16:12:59.455815 139850320439168 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0506 16:12:59.470548 139850320439168 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0506 16:12:59.470676 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:12:59.584213 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:12:59.584364 139850320439168 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0506 16:12:59.868767 139850320439168 efficientnet_model.py:147] round_filter input=24 output=32\n",
            "I0506 16:12:59.868949 139850320439168 efficientnet_model.py:147] round_filter input=40 output=56\n",
            "I0506 16:13:00.147398 139850320439168 efficientnet_model.py:147] round_filter input=40 output=56\n",
            "I0506 16:13:00.147579 139850320439168 efficientnet_model.py:147] round_filter input=80 output=112\n",
            "I0506 16:13:00.573331 139850320439168 efficientnet_model.py:147] round_filter input=80 output=112\n",
            "I0506 16:13:00.573508 139850320439168 efficientnet_model.py:147] round_filter input=112 output=160\n",
            "I0506 16:13:00.999965 139850320439168 efficientnet_model.py:147] round_filter input=112 output=160\n",
            "I0506 16:13:01.000146 139850320439168 efficientnet_model.py:147] round_filter input=192 output=272\n",
            "I0506 16:13:01.726651 139850320439168 efficientnet_model.py:147] round_filter input=192 output=272\n",
            "I0506 16:13:01.726824 139850320439168 efficientnet_model.py:147] round_filter input=320 output=448\n",
            "I0506 16:13:01.868580 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=1792\n",
            "I0506 16:13:01.894748 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.4, depth_coefficient=1.8, resolution=380, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:13:01.989281 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b5\n",
            "I0506 16:13:01.989447 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 288\n",
            "I0506 16:13:01.989554 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 7\n",
            "I0506 16:13:01.993932 139850320439168 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0506 16:13:02.008161 139850320439168 efficientnet_model.py:147] round_filter input=32 output=48\n",
            "I0506 16:13:02.008268 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:13:02.179572 139850320439168 efficientnet_model.py:147] round_filter input=16 output=24\n",
            "I0506 16:13:02.179764 139850320439168 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0506 16:13:02.534003 139850320439168 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0506 16:13:02.534188 139850320439168 efficientnet_model.py:147] round_filter input=40 output=64\n",
            "I0506 16:13:02.901991 139850320439168 efficientnet_model.py:147] round_filter input=40 output=64\n",
            "I0506 16:13:02.902172 139850320439168 efficientnet_model.py:147] round_filter input=80 output=128\n",
            "I0506 16:13:03.413784 139850320439168 efficientnet_model.py:147] round_filter input=80 output=128\n",
            "I0506 16:13:03.413960 139850320439168 efficientnet_model.py:147] round_filter input=112 output=176\n",
            "I0506 16:13:03.932652 139850320439168 efficientnet_model.py:147] round_filter input=112 output=176\n",
            "I0506 16:13:03.932839 139850320439168 efficientnet_model.py:147] round_filter input=192 output=304\n",
            "I0506 16:13:04.586175 139850320439168 efficientnet_model.py:147] round_filter input=192 output=304\n",
            "I0506 16:13:04.586346 139850320439168 efficientnet_model.py:147] round_filter input=320 output=512\n",
            "I0506 16:13:04.811570 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=2048\n",
            "I0506 16:13:04.836421 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.6, depth_coefficient=2.2, resolution=456, dropout_rate=0.4, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:13:04.941174 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b6\n",
            "I0506 16:13:04.941337 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
            "I0506 16:13:04.941417 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
            "I0506 16:13:04.945654 139850320439168 efficientnet_model.py:147] round_filter input=32 output=56\n",
            "I0506 16:13:04.959823 139850320439168 efficientnet_model.py:147] round_filter input=32 output=56\n",
            "I0506 16:13:04.959935 139850320439168 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0506 16:13:05.332621 139850320439168 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0506 16:13:05.332797 139850320439168 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0506 16:13:05.758328 139850320439168 efficientnet_model.py:147] round_filter input=24 output=40\n",
            "I0506 16:13:05.758503 139850320439168 efficientnet_model.py:147] round_filter input=40 output=72\n",
            "I0506 16:13:06.187407 139850320439168 efficientnet_model.py:147] round_filter input=40 output=72\n",
            "I0506 16:13:06.187589 139850320439168 efficientnet_model.py:147] round_filter input=80 output=144\n",
            "I0506 16:13:06.749847 139850320439168 efficientnet_model.py:147] round_filter input=80 output=144\n",
            "I0506 16:13:06.750055 139850320439168 efficientnet_model.py:147] round_filter input=112 output=200\n",
            "I0506 16:13:07.328334 139850320439168 efficientnet_model.py:147] round_filter input=112 output=200\n",
            "I0506 16:13:07.328547 139850320439168 efficientnet_model.py:147] round_filter input=192 output=344\n",
            "I0506 16:13:08.111563 139850320439168 efficientnet_model.py:147] round_filter input=192 output=344\n",
            "I0506 16:13:08.111731 139850320439168 efficientnet_model.py:147] round_filter input=320 output=576\n",
            "I0506 16:13:08.317325 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=2304\n",
            "I0506 16:13:08.341527 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=1.8, depth_coefficient=2.6, resolution=528, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "I0506 16:13:08.456601 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:143] EfficientDet EfficientNet backbone version: efficientnet-b7\n",
            "I0506 16:13:08.456767 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:144] EfficientDet BiFPN num filters: 384\n",
            "I0506 16:13:08.456843 139850320439168 ssd_efficientnet_bifpn_feature_extractor.py:146] EfficientDet BiFPN num iterations: 8\n",
            "I0506 16:13:08.460983 139850320439168 efficientnet_model.py:147] round_filter input=32 output=64\n",
            "I0506 16:13:08.475439 139850320439168 efficientnet_model.py:147] round_filter input=32 output=64\n",
            "I0506 16:13:08.475554 139850320439168 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0506 16:13:08.708420 139850320439168 efficientnet_model.py:147] round_filter input=16 output=32\n",
            "I0506 16:13:08.708644 139850320439168 efficientnet_model.py:147] round_filter input=24 output=48\n",
            "I0506 16:13:09.401088 139850320439168 efficientnet_model.py:147] round_filter input=24 output=48\n",
            "I0506 16:13:09.401265 139850320439168 efficientnet_model.py:147] round_filter input=40 output=80\n",
            "I0506 16:13:09.901350 139850320439168 efficientnet_model.py:147] round_filter input=40 output=80\n",
            "I0506 16:13:09.901525 139850320439168 efficientnet_model.py:147] round_filter input=80 output=160\n",
            "I0506 16:13:10.621192 139850320439168 efficientnet_model.py:147] round_filter input=80 output=160\n",
            "I0506 16:13:10.621362 139850320439168 efficientnet_model.py:147] round_filter input=112 output=224\n",
            "I0506 16:13:11.315682 139850320439168 efficientnet_model.py:147] round_filter input=112 output=224\n",
            "I0506 16:13:11.315855 139850320439168 efficientnet_model.py:147] round_filter input=192 output=384\n",
            "I0506 16:13:12.232838 139850320439168 efficientnet_model.py:147] round_filter input=192 output=384\n",
            "I0506 16:13:12.233029 139850320439168 efficientnet_model.py:147] round_filter input=320 output=640\n",
            "I0506 16:13:12.508738 139850320439168 efficientnet_model.py:147] round_filter input=1280 output=2560\n",
            "I0506 16:13:12.547942 139850320439168 efficientnet_model.py:458] Building model efficientnet with params ModelConfig(width_coefficient=2.0, depth_coefficient=3.1, resolution=600, dropout_rate=0.5, blocks=(BlockConfig(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise'), BlockConfig(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25, id_skip=True, fused_conv=False, conv_type='depthwise')), stem_base_filters=32, top_base_filters=1280, activation='simple_swish', batch_norm='default', bn_momentum=0.99, bn_epsilon=0.001, weight_decay=5e-06, drop_connect_rate=0.2, depth_divisor=8, min_depth=None, use_se=True, input_channels=3, num_classes=1000, model_name='efficientnet', rescale_input=False, data_format='channels_last', dtype='float32')\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 20.52s\n",
            "I0506 16:13:12.697036 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_create_ssd_models_from_config): 20.52s\n",
            "[       OK ] ModelBuilderTF2Test.test_create_ssd_models_from_config\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
            "I0506 16:13:12.703475 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_faster_rcnn_batchnorm_update\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "I0506 16:13:12.705151 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_first_stage_nms_iou_threshold\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "I0506 16:13:12.705631 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_model_config_proto): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_model_config_proto\n",
            "[ RUN      ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "I0506 16:13:12.707057 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_invalid_second_stage_batch_size): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_invalid_second_stage_batch_size\n",
            "[ RUN      ] ModelBuilderTF2Test.test_session\n",
            "[  SKIPPED ] ModelBuilderTF2Test.test_session\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "I0506 16:13:12.708412 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_faster_rcnn_feature_extractor\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "I0506 16:13:12.708846 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_meta_architecture): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_meta_architecture\n",
            "[ RUN      ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "INFO:tensorflow:time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "I0506 16:13:12.709792 139850320439168 test_util.py:2076] time(__main__.ModelBuilderTF2Test.test_unknown_ssd_feature_extractor): 0.0s\n",
            "[       OK ] ModelBuilderTF2Test.test_unknown_ssd_feature_extractor\n",
            "----------------------------------------------------------------------\n",
            "Ran 23 tests in 25.943s\n",
            "\n",
            "OK (skipped=1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifGDmjyidVbS",
        "outputId": "70cdaaf4-0457-469d-c2fa-05ad4144a0f0"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/models\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kp7ggOMtdeBs",
        "outputId": "286e134f-1dba-42fe-aaf0-731ad20dfe74"
      },
      "source": [
        "cd .."
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9NUVOUaqRvOp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36fde0c0-5ad8-4384-ae72-4355cdb206ea"
      },
      "source": [
        "cd /content/training_demo/pre-trained-models "
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/training_demo/pre-trained-models\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MVvTK8AKRzVm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4687e896-b553-4625-f069-b3a222814cee"
      },
      "source": [
        "!wget http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2021-05-06 16:22:11--  http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz\n",
            "Resolving download.tensorflow.org (download.tensorflow.org)... 142.251.33.208, 2607:f8b0:4004:837::2010\n",
            "Connecting to download.tensorflow.org (download.tensorflow.org)|142.251.33.208|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20518283 (20M) [application/x-tar]\n",
            "Saving to: ‘ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz’\n",
            "\n",
            "ssd_mobilenet_v2_fp 100%[===================>]  19.57M  --.-KB/s    in 0.1s    \n",
            "\n",
            "2021-05-06 16:22:11 (149 MB/s) - ‘ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz’ saved [20518283/20518283]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hmCuqZUiR1PE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4f804aef-0727-4e49-f195-d4f15c0a34d1"
      },
      "source": [
        "!tar -xvf ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8.tar.gz"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.data-00000-of-00001\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/checkpoint\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0.index\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.config\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/saved_model.pb\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/variables.data-00000-of-00001\n",
            "ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/saved_model/variables/variables.index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NGTqhfSzR4FR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cb6fced9-cda5-4c84-eb5b-044d6717d5f8"
      },
      "source": [
        "cd /content/training_demo"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/training_demo\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2eLS0AVdR874",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c819dcc4-f65f-4c42-e7ad-f6578db45d65"
      },
      "source": [
        "# Create train data:\n",
        "!python generate_tfrecord.py -x /content/training_demo/images/train -l /content/training_demo/annotations/label_map.pbtxt -o /content/training_demo/annotations/train.record\n",
        "\n",
        "# Create test data:\n",
        "!python generate_tfrecord.py -x /content/training_demo/images/test -l /content/training_demo/annotations/label_map.pbtxt -o /content/training_demo/annotations/test.record"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully created the TFRecord file: /content/training_demo/annotations/train.record\n",
            "Successfully created the TFRecord file: /content/training_demo/annotations/test.record\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PmMjZShuR__p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "765094a9-a7fc-4a1a-cae5-f22bd38f5515"
      },
      "source": [
        "!python model_main_tf2.py --model_dir=/content/training_demo/models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8 --pipeline_config_path=/content/training_demo/models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.config"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-05-06 17:00:44.218649: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:00:46.837790: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 17:00:46.840291: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
            "2021-05-06 17:00:46.878000: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.878583: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 17:00:46.878619: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:00:46.882261: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 17:00:46.882340: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 17:00:46.884340: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 17:00:46.884987: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 17:00:46.887291: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 17:00:46.888158: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 17:00:46.888407: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 17:00:46.888516: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.889165: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.889681: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 17:00:46.890075: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2021-05-06 17:00:46.890212: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 17:00:46.890334: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.890853: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 17:00:46.890880: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:00:46.890921: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 17:00:46.890943: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 17:00:46.890966: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 17:00:46.890988: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 17:00:46.891025: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 17:00:46.891047: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 17:00:46.891068: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 17:00:46.891136: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.891697: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:46.892221: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 17:00:46.892283: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:00:47.479134: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2021-05-06 17:00:47.479195: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
            "2021-05-06 17:00:47.479212: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
            "2021-05-06 17:00:47.479414: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:47.480051: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:47.480600: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:00:47.481118: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2021-05-06 17:00:47.481174: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13968 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "I0506 17:00:47.482874 139904780625792 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
            "INFO:tensorflow:Maybe overwriting train_steps: None\n",
            "I0506 17:00:47.487121 139904780625792 config_util.py:552] Maybe overwriting train_steps: None\n",
            "INFO:tensorflow:Maybe overwriting use_bfloat16: False\n",
            "I0506 17:00:47.487279 139904780625792 config_util.py:552] Maybe overwriting use_bfloat16: False\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:550: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "W0506 17:00:47.610135 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/model_lib_v2.py:550: StrategyBase.experimental_distribute_datasets_from_function (from tensorflow.python.distribute.distribute_lib) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "rename to distribute_datasets_from_function\n",
            "INFO:tensorflow:Reading unweighted datasets: ['annotations/train.record']\n",
            "I0506 17:00:47.613614 139904780625792 dataset_builder.py:163] Reading unweighted datasets: ['annotations/train.record']\n",
            "INFO:tensorflow:Reading record datasets for input file: ['annotations/train.record']\n",
            "I0506 17:00:47.613865 139904780625792 dataset_builder.py:80] Reading record datasets for input file: ['annotations/train.record']\n",
            "INFO:tensorflow:Number of filenames to read: 1\n",
            "I0506 17:00:47.613978 139904780625792 dataset_builder.py:81] Number of filenames to read: 1\n",
            "WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.\n",
            "W0506 17:00:47.614080 139904780625792 dataset_builder.py:88] num_readers has been reduced to 1 to match input file shards.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
            "W0506 17:00:47.616179 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:105: parallel_interleave (from tensorflow.python.data.experimental.ops.interleave_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.interleave(map_func, cycle_length, block_length, num_parallel_calls=tf.data.AUTOTUNE)` instead. If sloppy execution is desired, use `tf.data.Options.experimental_deterministic`.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "W0506 17:00:47.631498 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/builders/dataset_builder.py:237: DatasetV1.map_with_legacy_function (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.data.Dataset.map()\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "W0506 17:00:53.971459 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "W0506 17:00:56.807736 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:201: sample_distorted_bounding_box (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "`seed2` arg is deprecated.Use sample_distorted_bounding_box_v2 instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "W0506 17:00:58.313167 139904780625792 deprecation.py:339] From /usr/local/lib/python3.7/dist-packages/object_detection/inputs.py:282: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.cast` instead.\n",
            "2021-05-06 17:01:00.411562: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n",
            "2021-05-06 17:01:00.417927: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2199995000 Hz\n",
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.\n",
            "  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '\n",
            "2021-05-06 17:01:22.047415: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 17:01:22.434348: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 17:01:22.445292: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
            "W0506 17:01:26.220440 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._groundtruth_lists\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor\n",
            "W0506 17:01:26.220770 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
            "W0506 17:01:26.220858 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._batched_prediction_tensor_names\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
            "W0506 17:01:26.220932 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads\n",
            "W0506 17:01:26.221001 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names\n",
            "W0506 17:01:26.221078 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._sorted_head_names\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers\n",
            "W0506 17:01:26.221143 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads\n",
            "W0506 17:01:26.221214 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers\n",
            "W0506 17:01:26.221277 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers\n",
            "W0506 17:01:26.221341 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background\n",
            "W0506 17:01:26.221405 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.0\n",
            "W0506 17:01:26.221475 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.0\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.1\n",
            "W0506 17:01:26.221537 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.2\n",
            "W0506 17:01:26.221600 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.3\n",
            "W0506 17:01:26.221662 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.3\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.4\n",
            "W0506 17:01:26.221734 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._additional_projection_layers.4\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings\n",
            "W0506 17:01:26.221802 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background\n",
            "W0506 17:01:26.221866 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower\n",
            "W0506 17:01:26.221929 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower\n",
            "W0506 17:01:26.221992 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.ClassPredictionTower\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0\n",
            "W0506 17:01:26.222077 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers\n",
            "W0506 17:01:26.222141 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0\n",
            "W0506 17:01:26.222210 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1\n",
            "W0506 17:01:26.222297 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2\n",
            "W0506 17:01:26.222365 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3\n",
            "W0506 17:01:26.222432 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4\n",
            "W0506 17:01:26.222499 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0\n",
            "W0506 17:01:26.222565 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1\n",
            "W0506 17:01:26.222632 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2\n",
            "W0506 17:01:26.222702 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3\n",
            "W0506 17:01:26.222769 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4\n",
            "W0506 17:01:26.222836 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0\n",
            "W0506 17:01:26.222912 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.1\n",
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            "W0506 17:01:26.223467 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._box_prediction_head._box_encoder_layers.0.depthwise_kernel\n",
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            "W0506 17:01:26.223663 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0\n",
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            "W0506 17:01:26.223726 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1\n",
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            "W0506 17:01:26.223854 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4\n",
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            "W0506 17:01:26.223979 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7\n",
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            "W0506 17:01:26.224052 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.8\n",
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            "W0506 17:01:26.224369 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4\n",
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            "W0506 17:01:26.224430 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.5\n",
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            "W0506 17:01:26.224492 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7\n",
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            "W0506 17:01:26.224552 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.8\n",
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            "W0506 17:01:26.224858 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4\n",
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            "W0506 17:01:26.287769 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7\n",
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            "W0506 17:01:26.288126 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10\n",
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            "W0506 17:01:26.288484 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.2\n",
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            "W0506 17:01:26.288589 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4\n",
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            "W0506 17:01:26.288693 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.5\n",
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            "W0506 17:01:26.289263 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1\n",
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            "W0506 17:01:26.289367 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.2\n",
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            "W0506 17:01:26.289569 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.5\n",
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            "W0506 17:01:26.289669 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7\n",
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            "W0506 17:01:26.289771 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.8\n",
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            "W0506 17:01:26.289871 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10\n",
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            "W0506 17:01:26.289969 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.11\n",
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            "W0506 17:01:26.290093 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.1\n",
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            "W0506 17:01:26.290196 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.2\n",
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            "W0506 17:01:26.290313 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.4\n",
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            "W0506 17:01:26.290612 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.0.8\n",
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            "W0506 17:01:26.291910 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.4\n",
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            "W0506 17:01:26.291975 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.5\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7\n",
            "W0506 17:01:26.292063 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.7\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.8\n",
            "W0506 17:01:26.292147 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.8\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10\n",
            "W0506 17:01:26.292242 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.10\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.11\n",
            "W0506 17:01:26.292325 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.2.11\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1\n",
            "W0506 17:01:26.292411 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.2\n",
            "W0506 17:01:26.292495 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4\n",
            "W0506 17:01:26.292578 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.4\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.5\n",
            "W0506 17:01:26.292664 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.5\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7\n",
            "W0506 17:01:26.292754 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.7\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.8\n",
            "W0506 17:01:26.292845 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.8\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10\n",
            "W0506 17:01:26.292937 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.10\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.11\n",
            "W0506 17:01:26.293046 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.3.11\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1\n",
            "W0506 17:01:26.293147 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.2\n",
            "W0506 17:01:26.293256 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4\n",
            "W0506 17:01:26.293352 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.4\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.5\n",
            "W0506 17:01:26.293447 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.5\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7\n",
            "W0506 17:01:26.293542 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.7\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.8\n",
            "W0506 17:01:26.293791 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.8\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10\n",
            "W0506 17:01:26.293905 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.10\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.11\n",
            "W0506 17:01:26.294006 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.class_predictions_with_background.4.11\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0.depthwise_kernel\n",
            "W0506 17:01:26.294128 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0.depthwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0.pointwise_kernel\n",
            "W0506 17:01:26.294242 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.0.pointwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.1.depthwise_kernel\n",
            "W0506 17:01:26.294344 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.1.depthwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.1.pointwise_kernel\n",
            "W0506 17:01:26.294440 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.1.pointwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.2.depthwise_kernel\n",
            "W0506 17:01:26.294539 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.2.depthwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.2.pointwise_kernel\n",
            "W0506 17:01:26.294634 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.2.pointwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.3.depthwise_kernel\n",
            "W0506 17:01:26.294730 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.3.depthwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.3.pointwise_kernel\n",
            "W0506 17:01:26.294826 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._head_scope_conv_layers.PredictionTower.3.pointwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.depthwise_kernel\n",
            "W0506 17:01:26.294927 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.depthwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel\n",
            "W0506 17:01:26.295050 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.pointwise_kernel\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n",
            "W0506 17:01:26.295157 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._prediction_heads.class_predictions_with_background._class_predictor_layers.0.bias\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis\n",
            "W0506 17:01:26.295269 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n",
            "W0506 17:01:26.295369 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n",
            "W0506 17:01:26.295466 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean\n",
            "W0506 17:01:26.295563 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance\n",
            "W0506 17:01:26.295660 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis\n",
            "W0506 17:01:26.295759 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n",
            "W0506 17:01:26.295857 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta\n",
            "W0506 17:01:26.295952 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean\n",
            "W0506 17:01:26.296070 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance\n",
            "W0506 17:01:26.296170 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis\n",
            "W0506 17:01:26.296278 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma\n",
            "W0506 17:01:26.296375 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta\n",
            "W0506 17:01:26.296471 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean\n",
            "W0506 17:01:26.296566 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance\n",
            "W0506 17:01:26.296660 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.axis\n",
            "W0506 17:01:26.296757 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.gamma\n",
            "W0506 17:01:26.296852 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.beta\n",
            "W0506 17:01:26.296947 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_mean\n",
            "W0506 17:01:26.297060 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_variance\n",
            "W0506 17:01:26.297162 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.0.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis\n",
            "W0506 17:01:26.297271 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma\n",
            "W0506 17:01:26.297369 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta\n",
            "W0506 17:01:26.297754 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean\n",
            "W0506 17:01:26.297877 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance\n",
            "W0506 17:01:26.297982 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis\n",
            "W0506 17:01:26.298117 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma\n",
            "W0506 17:01:26.298231 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta\n",
            "W0506 17:01:26.298335 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean\n",
            "W0506 17:01:26.298433 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance\n",
            "W0506 17:01:26.298531 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis\n",
            "W0506 17:01:26.298628 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma\n",
            "W0506 17:01:26.298724 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta\n",
            "W0506 17:01:26.298822 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean\n",
            "W0506 17:01:26.298917 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance\n",
            "W0506 17:01:26.299026 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.axis\n",
            "W0506 17:01:26.299437 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.gamma\n",
            "W0506 17:01:26.299574 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.beta\n",
            "W0506 17:01:26.299680 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_mean\n",
            "W0506 17:01:26.299782 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_variance\n",
            "W0506 17:01:26.299882 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.1.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis\n",
            "W0506 17:01:26.299981 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma\n",
            "W0506 17:01:26.300106 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta\n",
            "W0506 17:01:26.300216 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean\n",
            "W0506 17:01:26.300319 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance\n",
            "W0506 17:01:26.300416 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis\n",
            "W0506 17:01:26.300510 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma\n",
            "W0506 17:01:26.300606 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta\n",
            "W0506 17:01:26.300702 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean\n",
            "W0506 17:01:26.300800 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance\n",
            "W0506 17:01:26.301268 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis\n",
            "W0506 17:01:26.301391 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma\n",
            "W0506 17:01:26.301497 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta\n",
            "W0506 17:01:26.301600 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean\n",
            "W0506 17:01:26.301698 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance\n",
            "W0506 17:01:26.301798 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.axis\n",
            "W0506 17:01:26.301894 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.gamma\n",
            "W0506 17:01:26.301993 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.beta\n",
            "W0506 17:01:26.302124 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_mean\n",
            "W0506 17:01:26.302240 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_variance\n",
            "W0506 17:01:26.302364 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.2.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis\n",
            "W0506 17:01:26.302483 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma\n",
            "W0506 17:01:26.302587 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta\n",
            "W0506 17:01:26.302687 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean\n",
            "W0506 17:01:26.302788 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance\n",
            "W0506 17:01:26.302890 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis\n",
            "W0506 17:01:26.302990 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma\n",
            "W0506 17:01:26.303113 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta\n",
            "W0506 17:01:26.303228 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean\n",
            "W0506 17:01:26.303333 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance\n",
            "W0506 17:01:26.303433 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis\n",
            "W0506 17:01:26.303533 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma\n",
            "W0506 17:01:26.303632 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta\n",
            "W0506 17:01:26.303733 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean\n",
            "W0506 17:01:26.303834 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance\n",
            "W0506 17:01:26.303934 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.axis\n",
            "W0506 17:01:26.304049 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.gamma\n",
            "W0506 17:01:26.304154 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.beta\n",
            "W0506 17:01:26.304268 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
            "W0506 17:01:26.304368 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
            "W0506 17:01:26.304467 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.3.10.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
            "W0506 17:01:26.304567 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
            "W0506 17:01:26.304665 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
            "W0506 17:01:26.304766 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
            "W0506 17:01:26.304867 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
            "W0506 17:01:26.304967 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.1.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
            "W0506 17:01:26.305088 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
            "W0506 17:01:26.305190 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
            "W0506 17:01:26.305305 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
            "W0506 17:01:26.305406 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
            "W0506 17:01:26.305506 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.4.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
            "W0506 17:01:26.305605 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
            "W0506 17:01:26.305704 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
            "W0506 17:01:26.305804 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
            "W0506 17:01:26.305903 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
            "W0506 17:01:26.306001 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.7.moving_variance\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
            "W0506 17:01:26.306122 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.axis\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
            "W0506 17:01:26.306233 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.gamma\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
            "W0506 17:01:26.306336 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.beta\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
            "W0506 17:01:26.306434 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_mean\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
            "W0506 17:01:26.306533 139904780625792 util.py:161] Unresolved object in checkpoint: (root).model._box_predictor._base_tower_layers_for_heads.box_encodings.4.10.moving_variance\n",
            "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
            "W0506 17:01:26.306638 139904780625792 util.py:169] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.696772 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.697812 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.699738 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.700546 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.702403 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.703209 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.705055 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.705843 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.707703 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "I0506 17:01:26.709256 139904780625792 cross_device_ops.py:565] Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:605: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "W0506 17:01:32.341963 139904032110336 deprecation.py:537] From /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/deprecation.py:605: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use fn_output_signature instead\n",
            "INFO:tensorflow:Step 100 per-step time 0.989s loss=0.804\n",
            "I0506 17:03:05.917893 139904780625792 model_lib_v2.py:679] Step 100 per-step time 0.989s loss=0.804\n",
            "INFO:tensorflow:Step 200 per-step time 0.645s loss=0.750\n",
            "I0506 17:04:10.373249 139904780625792 model_lib_v2.py:679] Step 200 per-step time 0.645s loss=0.750\n",
            "INFO:tensorflow:Step 300 per-step time 0.650s loss=0.541\n",
            "I0506 17:05:15.375789 139904780625792 model_lib_v2.py:679] Step 300 per-step time 0.650s loss=0.541\n",
            "INFO:tensorflow:Step 400 per-step time 0.649s loss=0.499\n",
            "I0506 17:06:20.294579 139904780625792 model_lib_v2.py:679] Step 400 per-step time 0.649s loss=0.499\n",
            "INFO:tensorflow:Step 500 per-step time 0.664s loss=0.347\n",
            "I0506 17:07:26.683392 139904780625792 model_lib_v2.py:679] Step 500 per-step time 0.664s loss=0.347\n",
            "INFO:tensorflow:Step 600 per-step time 0.655s loss=0.334\n",
            "I0506 17:08:32.138360 139904780625792 model_lib_v2.py:679] Step 600 per-step time 0.655s loss=0.334\n",
            "INFO:tensorflow:Step 700 per-step time 0.662s loss=0.384\n",
            "I0506 17:09:38.333630 139904780625792 model_lib_v2.py:679] Step 700 per-step time 0.662s loss=0.384\n",
            "INFO:tensorflow:Step 800 per-step time 0.653s loss=0.400\n",
            "I0506 17:10:43.616161 139904780625792 model_lib_v2.py:679] Step 800 per-step time 0.653s loss=0.400\n",
            "INFO:tensorflow:Step 900 per-step time 0.651s loss=0.348\n",
            "I0506 17:11:48.726927 139904780625792 model_lib_v2.py:679] Step 900 per-step time 0.651s loss=0.348\n",
            "INFO:tensorflow:Step 1000 per-step time 0.656s loss=0.366\n",
            "I0506 17:12:54.331622 139904780625792 model_lib_v2.py:679] Step 1000 per-step time 0.656s loss=0.366\n",
            "INFO:tensorflow:Step 1100 per-step time 0.662s loss=0.269\n",
            "I0506 17:14:00.551023 139904780625792 model_lib_v2.py:679] Step 1100 per-step time 0.662s loss=0.269\n",
            "INFO:tensorflow:Step 1200 per-step time 0.655s loss=0.324\n",
            "I0506 17:15:06.097184 139904780625792 model_lib_v2.py:679] Step 1200 per-step time 0.655s loss=0.324\n",
            "INFO:tensorflow:Step 1300 per-step time 0.666s loss=0.281\n",
            "I0506 17:16:12.690096 139904780625792 model_lib_v2.py:679] Step 1300 per-step time 0.666s loss=0.281\n",
            "INFO:tensorflow:Step 1400 per-step time 0.663s loss=0.238\n",
            "I0506 17:17:18.987120 139904780625792 model_lib_v2.py:679] Step 1400 per-step time 0.663s loss=0.238\n",
            "INFO:tensorflow:Step 1500 per-step time 0.656s loss=0.238\n",
            "I0506 17:18:24.582364 139904780625792 model_lib_v2.py:679] Step 1500 per-step time 0.656s loss=0.238\n",
            "INFO:tensorflow:Step 1600 per-step time 0.653s loss=0.243\n",
            "I0506 17:19:29.891000 139904780625792 model_lib_v2.py:679] Step 1600 per-step time 0.653s loss=0.243\n",
            "INFO:tensorflow:Step 1700 per-step time 0.660s loss=0.218\n",
            "I0506 17:20:35.884468 139904780625792 model_lib_v2.py:679] Step 1700 per-step time 0.660s loss=0.218\n",
            "INFO:tensorflow:Step 1800 per-step time 0.654s loss=0.219\n",
            "I0506 17:21:41.296246 139904780625792 model_lib_v2.py:679] Step 1800 per-step time 0.654s loss=0.219\n",
            "INFO:tensorflow:Step 1900 per-step time 0.661s loss=0.199\n",
            "I0506 17:22:47.387373 139904780625792 model_lib_v2.py:679] Step 1900 per-step time 0.661s loss=0.199\n",
            "INFO:tensorflow:Step 2000 per-step time 0.655s loss=0.201\n",
            "I0506 17:23:52.842182 139904780625792 model_lib_v2.py:679] Step 2000 per-step time 0.655s loss=0.201\n",
            "INFO:tensorflow:Step 2100 per-step time 0.661s loss=0.227\n",
            "I0506 17:24:58.988772 139904780625792 model_lib_v2.py:679] Step 2100 per-step time 0.661s loss=0.227\n",
            "INFO:tensorflow:Step 2200 per-step time 0.654s loss=0.205\n",
            "I0506 17:26:04.399042 139904780625792 model_lib_v2.py:679] Step 2200 per-step time 0.654s loss=0.205\n",
            "INFO:tensorflow:Step 2300 per-step time 0.655s loss=0.197\n",
            "I0506 17:27:09.944613 139904780625792 model_lib_v2.py:679] Step 2300 per-step time 0.655s loss=0.197\n",
            "INFO:tensorflow:Step 2400 per-step time 0.652s loss=0.190\n",
            "I0506 17:28:15.147732 139904780625792 model_lib_v2.py:679] Step 2400 per-step time 0.652s loss=0.190\n",
            "INFO:tensorflow:Step 2500 per-step time 0.664s loss=0.177\n",
            "I0506 17:29:21.581818 139904780625792 model_lib_v2.py:679] Step 2500 per-step time 0.664s loss=0.177\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EWqgqqQySDeE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2f2cd6fe-eaf4-418b-c29b-c9aaf3d10f36"
      },
      "source": [
        "!python exporter_main_v2.py --input_type image_tensor --pipeline_config_path /content/training_demo/models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/pipeline.config --trained_checkpoint_dir /content/training_demo/models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8 --output_directory /content/training_demo/exported_models/my_model"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-05-06 17:29:31.407371: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:29:33.457269: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 17:29:33.458095: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcuda.so.1\n",
            "2021-05-06 17:29:33.491140: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.491702: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 17:29:33.491734: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:29:33.500335: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 17:29:33.500411: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 17:29:33.501938: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 17:29:33.502308: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 17:29:33.504492: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 17:29:33.505076: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 17:29:33.505261: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 17:29:33.505361: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.505926: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.506446: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 17:29:33.506727: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2021-05-06 17:29:33.506868: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n",
            "2021-05-06 17:29:33.506976: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.507515: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties: \n",
            "pciBusID: 0000:00:04.0 name: Tesla T4 computeCapability: 7.5\n",
            "coreClock: 1.59GHz coreCount: 40 deviceMemorySize: 14.75GiB deviceMemoryBandwidth: 298.08GiB/s\n",
            "2021-05-06 17:29:33.507542: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:29:33.507586: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublas.so.11\n",
            "2021-05-06 17:29:33.507607: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcublasLt.so.11\n",
            "2021-05-06 17:29:33.507627: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcufft.so.10\n",
            "2021-05-06 17:29:33.507649: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcurand.so.10\n",
            "2021-05-06 17:29:33.507668: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusolver.so.10\n",
            "2021-05-06 17:29:33.507686: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcusparse.so.11\n",
            "2021-05-06 17:29:33.507705: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudnn.so.8\n",
            "2021-05-06 17:29:33.507769: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.508396: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:33.508890: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0\n",
            "2021-05-06 17:29:33.508933: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "2021-05-06 17:29:34.092073: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:\n",
            "2021-05-06 17:29:34.092126: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267]      0 \n",
            "2021-05-06 17:29:34.092143: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0:   N \n",
            "2021-05-06 17:29:34.092350: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:34.092981: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:34.093558: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:941] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
            "2021-05-06 17:29:34.094085: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:39] Overriding allow_growth setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.\n",
            "2021-05-06 17:29:34.094140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 13968 MB memory) -> physical GPU (device: 0, name: Tesla T4, pci bus id: 0000:00:04.0, compute capability: 7.5)\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.7/dist-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
            "Instead of:\n",
            "results = tf.map_fn(fn, elems, back_prop=False)\n",
            "Use:\n",
            "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
            "W0506 17:29:34.231415 139800745043840 deprecation.py:604] From /usr/local/lib/python3.7/dist-packages/object_detection/exporter_lib_v2.py:106: calling map_fn_v2 (from tensorflow.python.ops.map_fn) with back_prop=False is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "back_prop=False is deprecated. Consider using tf.stop_gradient instead.\n",
            "Instead of:\n",
            "results = tf.map_fn(fn, elems, back_prop=False)\n",
            "Use:\n",
            "results = tf.nest.map_structure(tf.stop_gradient, tf.map_fn(fn, elems))\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f25704333d0>, because it is not built.\n",
            "W0506 17:29:48.169721 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.meta_architectures.ssd_meta_arch.SSDMetaArch object at 0x7f25704333d0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f2525981510>, because it is not built.\n",
            "W0506 17:29:48.967658 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f2525981510>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25258bedd0>, because it is not built.\n",
            "W0506 17:29:48.967888 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25258bedd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25258be750>, because it is not built.\n",
            "W0506 17:29:48.968007 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25258be750>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f25258be7d0>, because it is not built.\n",
            "W0506 17:29:48.968112 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f25258be7d0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25259a1490>, because it is not built.\n",
            "W0506 17:29:48.968203 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25259a1490>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25259a1d10>, because it is not built.\n",
            "W0506 17:29:48.968308 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25259a1d10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f25259a1c10>, because it is not built.\n",
            "W0506 17:29:48.968401 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f25259a1c10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525954390>, because it is not built.\n",
            "W0506 17:29:48.968489 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525954390>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525924bd0>, because it is not built.\n",
            "W0506 17:29:48.968578 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525924bd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f2525b3fe10>, because it is not built.\n",
            "W0506 17:29:48.968660 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.convolutional.SeparableConv2D object at 0x7f2525b3fe10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525924c10>, because it is not built.\n",
            "W0506 17:29:48.968744 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525924c10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525894450>, because it is not built.\n",
            "W0506 17:29:48.968826 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525894450>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525949d90>, because it is not built.\n",
            "W0506 17:29:48.968905 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525949d90>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f252588d790>, because it is not built.\n",
            "W0506 17:29:48.968988 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f252588d790>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f252588dbd0>, because it is not built.\n",
            "W0506 17:29:48.969087 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f252588dbd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f252588d490>, because it is not built.\n",
            "W0506 17:29:48.969169 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f252588d490>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f252588d390>, because it is not built.\n",
            "W0506 17:29:48.969250 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f252588d390>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525887550>, because it is not built.\n",
            "W0506 17:29:48.969338 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525887550>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525b799d0>, because it is not built.\n",
            "W0506 17:29:48.969419 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525b799d0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525887110>, because it is not built.\n",
            "W0506 17:29:48.969502 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525887110>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25258fe650>, because it is not built.\n",
            "W0506 17:29:48.969582 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25258fe650>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25701cdd50>, because it is not built.\n",
            "W0506 17:29:48.969662 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25701cdd50>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257030eed0>, because it is not built.\n",
            "W0506 17:29:48.969745 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257030eed0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525e52e50>, because it is not built.\n",
            "W0506 17:29:48.969832 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525e52e50>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525dafbd0>, because it is not built.\n",
            "W0506 17:29:48.969913 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525dafbd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25380e9310>, because it is not built.\n",
            "W0506 17:29:48.969997 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f25380e9310>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25701c9c10>, because it is not built.\n",
            "W0506 17:29:48.970096 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25701c9c10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2538061410>, because it is not built.\n",
            "W0506 17:29:48.970190 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2538061410>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25701cea50>, because it is not built.\n",
            "W0506 17:29:48.970271 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f25701cea50>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525d9ff10>, because it is not built.\n",
            "W0506 17:29:48.970359 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525d9ff10>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2570392c90>, because it is not built.\n",
            "W0506 17:29:48.970439 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2570392c90>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f257022af90>, because it is not built.\n",
            "W0506 17:29:48.970519 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f257022af90>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257008fed0>, because it is not built.\n",
            "W0506 17:29:48.970598 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257008fed0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525fa7e90>, because it is not built.\n",
            "W0506 17:29:48.970678 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525fa7e90>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525fa7750>, because it is not built.\n",
            "W0506 17:29:48.970758 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525fa7750>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2570268f90>, because it is not built.\n",
            "W0506 17:29:48.970844 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2570268f90>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525c92810>, because it is not built.\n",
            "W0506 17:29:48.970929 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525c92810>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f257025f6d0>, because it is not built.\n",
            "W0506 17:29:48.971019 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f257025f6d0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525f9dad0>, because it is not built.\n",
            "W0506 17:29:48.971113 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525f9dad0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525e68cd0>, because it is not built.\n",
            "W0506 17:29:48.971196 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525e68cd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257025fcd0>, because it is not built.\n",
            "W0506 17:29:48.971276 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f257025fcd0>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2570303a50>, because it is not built.\n",
            "W0506 17:29:48.971363 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2570303a50>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525f59190>, because it is not built.\n",
            "W0506 17:29:48.975511 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <object_detection.core.freezable_batch_norm.FreezableBatchNorm object at 0x7f2525f59190>, because it is not built.\n",
            "WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525f59850>, because it is not built.\n",
            "W0506 17:29:48.975723 139800745043840 save_impl.py:78] Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Lambda object at 0x7f2525f59850>, because it is not built.\n",
            "2021-05-06 17:29:58.447341: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:06.572457 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:06.572784 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:06.573029 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:06.573215 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:11.268382 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:11.268697 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:11.268912 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:11.269105 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:11.269310 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:11.269502 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "W0506 17:30:14.970801 139800745043840 save.py:241] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 155). These functions will not be directly callable after loading.\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:15.167971 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:15.168347 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:15.168580 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:15.168758 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:15.441097 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:15.441417 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:15.441620 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:15.441786 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:15.441986 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:15.442190 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "W0506 17:30:15.940566 139800745043840 save.py:241] Found untraced functions such as WeightSharedConvolutionalBoxPredictor_layer_call_fn, WeightSharedConvolutionalBoxPredictor_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxHead_layer_call_fn, WeightSharedConvolutionalBoxHead_layer_call_and_return_conditional_losses, WeightSharedConvolutionalBoxPredictor_layer_call_fn while saving (showing 5 of 155). These functions will not be directly callable after loading.\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:19.635566 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f1bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3f5350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:19.635895 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2550>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e2610>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f3e27d0>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "I0506 17:30:19.636340 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417b50>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f417bd0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f41c350>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], False), {}).\n",
            "INFO:tensorflow:Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "I0506 17:30:19.636565 139800745043840 def_function.py:1170] Unsupported signature for serialization: (([(<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f4685d0>, TensorSpec(shape=(None, 80, 80, 32), dtype=tf.float32, name='image_features/0/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468ad0>, TensorSpec(shape=(None, 40, 40, 96), dtype=tf.float32, name='image_features/1/1')), (<tensorflow.python.framework.func_graph.UnknownArgument object at 0x7f251f468d90>, TensorSpec(shape=(None, 20, 20, 1280), dtype=tf.float32, name='image_features/2/1'))], True), {}).\n",
            "INFO:tensorflow:Assets written to: /content/training_demo/exported_models/my_model/saved_model/assets\n",
            "I0506 17:30:20.433587 139800745043840 builder_impl.py:775] Assets written to: /content/training_demo/exported_models/my_model/saved_model/assets\n",
            "INFO:tensorflow:Writing pipeline config file to /content/training_demo/exported_models/my_model/pipeline.config\n",
            "I0506 17:30:21.101568 139800745043840 config_util.py:254] Writing pipeline config file to /content/training_demo/exported_models/my_model/pipeline.config\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ztr533l6u-tH"
      },
      "source": [
        "cp -r '/content/training_demo/exported_models/my_model/saved_model' '/content/'"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90NnPHFVSKKN"
      },
      "source": [
        "## Inference"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V9Mr_Y2LSJ06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "outputId": "64a56997-0c15-4513-cf03-4767161940be"
      },
      "source": [
        "\"\"\"\n",
        "Object Detection (On Image) From TF2 Saved Model\n",
        "=====================================\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)\n",
        "import pathlib\n",
        "import tensorflow as tf\n",
        "import cv2\n",
        "import argparse\n",
        "from google.colab.patches import cv2_imshow\n",
        "\n",
        "# Enable GPU dynamic memory allocation\n",
        "gpus = tf.config.experimental.list_physical_devices('GPU')\n",
        "for gpu in gpus:\n",
        "    tf.config.experimental.set_memory_growth(gpu, True)\n",
        "\n",
        "# PROVIDE PATH TO IMAGE DIRECTORY\n",
        "IMAGE_PATHS = '/content/training_demo/images/train/fire.11.png'\n",
        "\n",
        "# PROVIDE PATH TO MODEL DIRECTORY\n",
        "PATH_TO_MODEL_DIR = '/content/training_demo/exported_models/my_model'\n",
        "\n",
        "# PROVIDE PATH TO LABEL MAP\n",
        "PATH_TO_LABELS = '/content/training_demo/annotations/label_map.pbtxt'\n",
        "\n",
        "# PROVIDE THE MINIMUM CONFIDENCE THRESHOLD\n",
        "MIN_CONF_THRESH = float(0.60)\n",
        "\n",
        "# LOAD THE MODEL\n",
        "import time\n",
        "from object_detection.utils import label_map_util\n",
        "from object_detection.utils import visualization_utils as viz_utils\n",
        "\n",
        "PATH_TO_SAVED_MODEL = PATH_TO_MODEL_DIR + \"/saved_model\"\n",
        "\n",
        "print('Loading model...', end='')\n",
        "start_time = time.time()\n",
        "\n",
        "# LOAD SAVED MODEL AND BUILD DETECTION FUNCTION\n",
        "detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)\n",
        "\n",
        "end_time = time.time()\n",
        "elapsed_time = end_time - start_time\n",
        "print('Done! Took {} seconds'.format(elapsed_time))\n",
        "\n",
        "# LOAD LABEL MAP DATA FOR PLOTTING\n",
        "\n",
        "category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS,\n",
        "                                                                    use_display_name=True)\n",
        "\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')   # Suppress Matplotlib warnings\n",
        "\n",
        "def load_image_into_numpy_array(path):\n",
        "    \"\"\"Load an image from file into a numpy array.\n",
        "    Puts image into numpy array to feed into tensorflow graph.\n",
        "    Note that by convention we put it into a numpy array with shape\n",
        "    (height, width, channels), where channels=3 for RGB.\n",
        "    Args:\n",
        "      path: the file path to the image\n",
        "    Returns:\n",
        "      uint8 numpy array with shape (img_height, img_width, 3)\n",
        "    \"\"\"\n",
        "    return np.array(Image.open(path))\n",
        "\n",
        "print('Running inference for {}... '.format(IMAGE_PATHS), end='')\n",
        "\n",
        "image = cv2.imread(IMAGE_PATHS)\n",
        "image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n",
        "image_expanded = np.expand_dims(image_rgb, axis=0)\n",
        "\n",
        "# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.\n",
        "input_tensor = tf.convert_to_tensor(image)\n",
        "# The model expects a batch of images, so add an axis with `tf.newaxis`.\n",
        "input_tensor = input_tensor[tf.newaxis, ...]\n",
        "\n",
        "# input_tensor = np.expand_dims(image_np, 0)\n",
        "detections = detect_fn(input_tensor)\n",
        "\n",
        "# All outputs are batches tensors.\n",
        "# Convert to numpy arrays, and take index [0] to remove the batch dimension.\n",
        "# We're only interested in the first num_detections.\n",
        "num_detections = int(detections.pop('num_detections'))\n",
        "detections = {key: value[0, :num_detections].numpy()\n",
        "               for key, value in detections.items()}\n",
        "detections['num_detections'] = num_detections\n",
        "\n",
        "# detection_classes should be ints.\n",
        "detections['detection_classes'] = detections['detection_classes'].astype(np.int64)\n",
        "\n",
        "image_with_detections = image.copy()\n",
        "\n",
        "# SET MIN_SCORE_THRESH BASED ON YOU MINIMUM THRESHOLD FOR DETECTIONS\n",
        "viz_utils.visualize_boxes_and_labels_on_image_array(\n",
        "      image_with_detections,\n",
        "      detections['detection_boxes'],\n",
        "      detections['detection_classes'],\n",
        "      detections['detection_scores'],\n",
        "      category_index,\n",
        "      use_normalized_coordinates=True,\n",
        "      max_boxes_to_draw=200,\n",
        "      min_score_thresh=0.5,\n",
        "      agnostic_mode=False)\n",
        "\n",
        "print('Done')\n",
        "# DISPLAYS OUTPUT IMAGE\n",
        "cv2_imshow(image_with_detections)\n",
        "# CLOSES WINDOW ONCE KEY IS PRESSED"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Loading model...Done! Took 12.083927392959595 seconds\n",
            "Running inference for /content/training_demo/images/train/fire.11.png... WARNING:tensorflow:9 out of the last 9 calls to <function recreate_function.<locals>.restored_function_body at 0x7f562b715b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0506 19:47:03.947630 140011532117888 def_function.py:126] 9 out of the last 9 calls to <function recreate_function.<locals>.restored_function_body at 0x7f562b715b00> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Done\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cyLqyLMfjMecyjuMwSoAz60g7q5RqmiYNQ2e0f0N/tUQUhuHm5qb/7v6gMcaiKArDcH93PwiCPM+VUvMNicA5b7fb+/v7URQ0TfPII4+88847r7/++oc//GEEt7Kysr19K89zKcWXv/xlxtiP/diPDQZD/85N0yRJO4qiumoODg64QCEEYxocIUcPcGezmeBsc3MjarVVncdRmIRBWczGk9k7WzeYlP1er9vttlqtUEjBedM0aZqORqOdnZ0sy7y1ed/73vfNF75tAZxzL7/88kMP3s9l0DRNK0663a7fYIwxIOs3GOdMSkneJhMBzONwYOBvu9ZaIDDGpJT+T855WeRhGLZaLaVUURRKKSaCMAyNMUIIQGCMATE8ghBsEckjIKD/XORsNpsBgBBBEIaCzWEJY4wRNarRWsdxnCRJGIZKaWOM0nUURURUFAUA84CnKIpQckQwWiulkiRBxL29AyJ77ty5OI7DOCqqUikTJiEgDodDUFBERb+//NRTT73xxhtf/OKXGYMTJ04MB6MkSdbW1s6ePWutHQ6He3v7edncc88jdVP4Q+o/NIiTdrt9/wMPGmNms9n+/r4Md/f392ez2WQyyXv5W2+9laYp57woiu3t7Z3t+OTJk2kat9vtKIparVbV7/pDOhqOv/PKhU6vGwhZ5oWqy0DIUEgiGo8GAICcIWKl6rquRZYlg8GAIwvDMOCs1+tNxnTjxj4wDgApsDhNl5dXi8YMBoM8LwWnptFJIoUQ4/GUUKYpNlVtlZ7NZg/c9+BDDzz0h5/7fMiCGzdu/ea/+w9PPPkD//Y3/t1rr1xYW1sTKNaW10bDIUMcDA5ardby8vKVa1cEsqe/8bW93f0PfugDXMqm1sPhME7C45snO52eM3Ri88TW1nZVNTdvbnGOy8vL/d7SdDq9cfP6xupxDkzZvNE6FoGUsmma7e3tw8GoqqpOp8MYe+fqdWOMkOz0qVNXrl59/vnner3ez/7cXz515uyHP/IBrZs/+7M/4yh6vaUsTPd2D9vt9vuf+tDFk29+6UtfmUxGH/zQ+x+4//4vfvGLX/rSl86cOfXXf/6vnj171jlIkutl2dRFWZXqwfse3t3Zi8PIqerKlSvPPvvsRz/60SgKjm0un7vr/N7BPiJYaxnzCN6Ox+P9/f0kS4VAg2SMcuSEFEIwzjkRIYFH5UIIY2nhAolLDOMoa6eDQdHudvYOdmdFvrq+IUU029mbTKcnTm5Gcdzrt+67//xkMrl+7ZbWdjQpZnmhlCmKQgjR7/eLSgMALpyV97SA4BxIIbiQiOiMsdY4B0SIyIgIEYAIGXgOzOGct/Mn0zmntWaC+1MKAMhus27+BZ1Wm3HggOCIATLGPAepoS7LkiNZK3WjGGO6UePJuNPte5tb+RiBc44cg6DIS24550jIBOPGkc9dWGstubkxYkwIf0sRgXtPr7Vld4CDbrdrwSiliMBZ1zSN1hoAGWNKGWUsZ47E3ObWTQXIGCCCRdJAjtABY4BgrSVEfyOJyJHTRqM1jLPG6LKpHSkUPIx4FMeMsVaSxmnqIzojbJTE7XZHBtGla1tkDAIEnEVBaBw1tVbW1Fbv7+6JIOz3+wj88PCgruter9vtdqxRSSCzKMwCwRGs1TN0rSzz7klr3SilHXHOOEdEbLW7YRYxzitVNVqFMsripMwrg8Za0mgRgHPOpBSBVHWl66ZSDeO8bkrGmKq5EzZJkr3tnU57aX9rJxcyCUKOOHTjTpQUddXutYkoiIP+6srB4V6r11WaJkVZlLpotLJA4IjIWUucWWsNGlhAf4+TOKJzFgCklMZaf1c559euXU/iyFqLyFtZhoieMarrChGRe8RPXDIpRRQFZV0QAiLjnCHNca11xm98jyn9BjNGaGN5GBI5Rw79pgUgIEeOgJQyTaPJMSSnnTUOwJlWnPg97JxptDKAR9w5LXDfEV5ERIbMITpjq6oqCmkbDsAcazzthMSQ0DqLYMlYC2ScYswoFRtj6rpuqryoSgesu7S81F9uFEgpwbmVlZUiCkdgnTOOjIfyQgiPRQCgrmuP9T3Z74+qtbdDcechIJvTZvN77o/w4j/OeSCkJyC01oRAzjLGuAj2Dw929nY5F51+L05blTZFUTlC6xwT6Kn22yEWux0R/cWH99kA4NAdQUljDJOCc46MOetZbXYE/Y9Q/hHMvdPs3Al5AcABWefmGc85SPDZSQLyKJ8sOQdggRjgX7zCI8BKiw86ejACf/4BAIixBdZ0ziEBOEJH4NycHQf0dtIvrpSy3W6HoZxMJk3TBEGw1O202+18PDk4ONBl5fGNz3IQESx2mtb64OAAAKqqstYmSYSISqmqqu78+rS4bB8NevTvqWtE5JxrpQ8ODvxvnXNETCmdJJnWish4RDtnbSxZh8459GGSB9BAR7fF55qc31puDgf9O1hrrTWOLPMRFzIiZIxZcIhojOn3+9baRjU84FmSOLgdARIRB8Y4b5pK6ybL2svLy3meX7169bXXtkej0YMP3b+2sZGmaa2aqlFZ1gZg23u77VYXAInALZA/OQIg7TQWldIkGA+lRC4YE4wJ/xJrrXPWOqedNUaTMaW16KxHrn7phRBSyslk5KNfACzLajQaHRwcTKf5qROnvO84isA99B+Px1mWtdvZ1tZWEARPPPHExYsXDw8P2610MBh4vcAzzzwTRdFf+St/5cqVKx46q6ZhzFO9gb8n0+lUa2OtrevakoNFIi2O0vX19U6rff2dt69cuVLOppxBFKdLS0uzophOp8aYNI5lu4NC+B3Y7/clw2PHjm3dvDkaje69994Lb7w+yStwePXq1Sfe+55jq2vO6IDxVqtVVhWTwjlnrPF7mDHGGDPOE4WWiMEiFvJHwRijEYQQTAjPJkgpkyTxNhkRkyQLQ6v9/jAGEZGzo7N8lLp0AAAE1v8PfBao0+k4IHJz/9sYXWvVNE2SpoZcXddFUdR1jQsfXZblwcFBnueMsX5/2ZtBH7BFUTQZjeuqqaraOSs4X1rqLS0taa2lAx+lyDB0QMWs5EzsDw5n12ZK1XlViiAoiuL6ra0oCnCHpa1Wq9u5/6GH4yx94403dnf3gyDgjqVp2m63m6a5devW9t5+XdcXXn3N6xeSJEmydrvd7vf7HMlqw6UYjIaTySRN0wcffmg8Grzw4nfG43Erze6++7wMw2leIuLKUq/V7TzY6xtn67KKgrDfPbuxth5JUdf1pbculmWZl4XPNiCi+Cf/5J/8s3/2/7518+bx48eqPL948SJDOn/+/Hg6SeJMBuFwMJ7NqrKsAFA3anl9pWkaZ6jT6g4Go+nkRq/XY8A2N09Mx7M4jLMoYyRVpbau77xx4c3f/M3/cPL0Kavd229feeKJx9fW1q6/c+2hhx/44R/+5OXLl3/5l3/52LGNwWDw3iff+8R7njDOvevRd//Df/hLx45tnjx54s03Ly0v97/zwkvGqkapKIqCUBijDw+Hng5sZZ3BYJBGSRRFzkIYhpyLqjYHBwdp1h6Px4acP5lJkhirdnZ2Hnz4of/qv/qfn3/++f/81S+9730/uH7suFH1e97znr29g29989uPP/be/+4f/99bUWYtPP2nz377W99aXll5/PHHz549Ox4PXnrppU9/+ie/853v3LpxwzTKGsMAveNcWloajybtdidgrW4ne+nFVx5++OG33nrrC3/0h2EYCikdh7qutYUoijxXOptNlWpmdRGmkSf7RcCZFNZaZbTkgQcKzDOeYJGRQC+bEcZ4Dnj11q0bo9Go3W4Dcc+67e/vD4cHG9XK+sbK6dMn77n7/tdee2P32y9VVdXvL4dpu6lv7B0cJFkXAGgu9SEiQrLg6IhcAURCYIwxmjMcRISOgAE4QvAxN3he88jR3skdHrleR+7I9cqAIwIDBHRSSsaAnENHcRp7Oq9ptG4aIjJaKqWM0k3TlGXpRQJMCEQOAJwxrbUxZRAEaRwToqpUVTcWwMLcxzDOj8QA/uEvTzD/S+4P26xsqqqScRBGcRRFnHNt7ML0O2dNg6SU0lpYawHJWMecYdwJzjjnwBgxzgjIATinyQIQMAoRpZScs6qqlFKtdiyEGI+mmbVZls2pzEYFTLa7rSiMydFskgeMW4/EjDWgHRBnEKK0QdA0DZIzxlTVbDQeRFG0tn5MN4pxJKN1gyHnDqypK2fUcn/J4y1gaA01Rittwei6yK3gWmvBWZK1EnBNpUajUSBCRAyCgJhljmokpepG1wEXzjmwlnOUXBCCtVYppVWNiGkUW2uH09ny+fNcysPhYOwG6ChKk1Y7DcIQGDLOq6rhIrgT2CEyj621VsYYx7h37QDAOQ8CHjJhrNZaR1FknTvCdkIEUgScOe/ijDE+b9vtdhutvcPwyyIEm+sQAMDjJA+VyAESsHksSgv601okImcMAZFzzjoGYMkRogUnERqjy7pSGpCMUtpYYgy5DI1xk+kUGSGijDMRyCAIirJxXqSw4EMRER1wgcQ5giWHVlsDZIxTDppaOzfPJllnAa0z1iFFUYgOEH2myvnDJXiwdWsnSmLitL29vb+7OxgM0jAUQlRFjuA8m+uBmn9UVeVPcdM0PuHgw4MsjuaZEABPq3uD5v+5Z+a8XyQiIYTkXDOOjCMCggPknBMX2Ol0ZRROpzOciThOs0670qbOizRNHeM01w6hh+ZSSvd9yPq5xTjaJz4OZEhHl0R3ggkE59z3peEBwFMVd/x1Hg+IMEBEBuTQMQCHyJEBZ05pC4TOEUNGaOkowcnm7OJ3C36MsUefe3TNRAQISIBuzoj4wMGQCzw+9joT9BgZCCFJEi8X4ZyHYViWpbXSWyFPOm5tbU0GQyFEHMdefzWPVZyjef7DZ7qMT61wzrMsAQAf0Ho/cuelen7k6EBxzjmXPkp04Eajkdaac+6MReQ+gCyqQso5/FpY8rm158QXCGxx1uaOwPjV4ZwzQK3nZjyKIqWAtAGap4TBsXnKDsCf6DRNvZEJRRjFSaONh85+Q0om/Tciops3b37zm9+8fv16kiRP/sD7Hn300aIu9g72R5NxmqZ1rW5s3eKcb6xvFkXhgOgOEmoRokOpbKUKRi4QYSQDAkjT1C8BAdNaE1IoeSQDF4fFLCcHiCiEICKttY+dTpw4MRwOt7a2RqORZ5TOnTvX6XT2dw/jOPZpsaZphBAEVkqZZVnTNDdv3tzc3NRaJ0ly69at4XDY7bT6/f6LL754cHDw4Q9/+Nlnn/3a1752zz33BEFordXaNrU+PNzf2tpCYExwREzTNIjioigarYQQYRwJIY5vHnv11Vcvv3kxDOWjDz986j2PcQbGWQyT3f39yWRC1kVSxnGcpWkURaaprTGzfMYYm0wmUspTp069613v+ua3X5iVlarK/f39zbV1IURVVp47cFqjRWW196pE5JXARzaHIy12i0BB1mm/tRDAg3jGmOTMGGPJcc6jKHTO2bJsmgYAjwJkhuwIS0RRBABE6G6fbkYI2hqlVFU2lWo8qWGMs86VVSXCII5jH4BZa6UMoija3d2dTqdEFIbh4eGwqqrNzc37779/Z/vW8nLfqyfkBhuNRsPBqKqqd965XlXFXffcffzEia2trbIp77nv3rNnz1+/fH15aYUhv3HzutZ2Y2PTGDOZjIajw7Ist7d3Xn755QceeOD+B+79oR/66MHh8MaNW/sHg93d3dls5sUF/ZXVlZWVT3/60/5oCyG4DHGRl5McOUOf5dNaN2VFp08+/t73riwvj0YjABfHMRIQQlnMdvf2NjaPa62RABHrvDgcDiLO2+32+vr6wcFBUZVCiOX2chzH4t/8m19vVJVlyYULFwTi+973vjiKLl68yFCMx5OyOTCWpAzDMAxkpLXmwFtJa2N9I2t3rl69enA4dAaFkPmkDHh06Y3LVy5e7ba6CJBPi16nv75+7Or1a3efu7sxui6bKAjvvvvuL3zhC+vrq3/7b//tG7duzPJJXdf33XfPAw89wAQ/2B+srqzv7u4OBsMTx46XZbm1tbO+vpqmLc5kIMI4iqxzTaOtpSCIBAv8NoqiBDkbTybGQLfbLcsyy9rtdrtqGkemlWU+w3vu7ruAs7vvu/fl1168dOXy0899/Yc/9YmH3/3o5bcuv/rKazdu3Pj85z/PLI+idOfmjte0/Yt//v8Nw/Dv/+LfPXv27IMPPJDn02eeeWYynlV5EQrZ6nQZCAaolVJK1aoEdG+8+eabb72ttR4Mx+fO3/2NZ7+pVK2sAeLOOS4C55xRSkqptUuSpN/vB5G0TmtjGnDcIZFlTALdDna9y28ao1RdluWZMycb1d3b2zk4GBhjBoeHw+EwSRJtmtOnTxKoX/u1XwuC4D2PPR5FSafT4UJOp9O9wVgpdezYsdGk8Eb6CPozIPRiAGO1UoDIpRBCEENjjHfDYL0Dduhd1223ikcgmzEWBIJ8VIRegU3eiXKOWms+x0HEkXEpwTprtQcWHIkxJljsEx5xlE4mE8ZYlqRxHIdh7ICKvKqqyiE5BgxFIGQoAwdEwjVGK88pgxcL3CYMwGrn7NziMy/T4oh8Z2eHByyO47STyTiZVbUQAhorhEDkzJE1tTdnUsowCgSXjapNXQCRZIiMa+8AeEjAPNPrnHNAxAkQy7IOpReKcGV0WeZMYBgFaZzUda20ZgzBgW5Mnufj6cyh5EBCcIFIVlvjLBETMs3i7lK/LKud7b2qqbvdjozCw8P9VhhKwdFq0xhEUKquq9JpU8nAr4UIg0CGHIhZ06h6ud+zQHXTDKcTQpBSIoFVunI1EgIwh4wACcivtCFwzkjgC0W4JSKrDRkbx7Exbnl59dDtra2tBUJy4OUs77YzRRalMOSGB4fGmPHhIZdho6x1TBMCCxAJnUVHDNGT/c6B1lopFUshpUQ3Zzc9UpkzkRZanU4YiDzPi6Ly1+9JkiAOJpPJbDYzziAgEdWNoga4YERAzpEjJGCMcQTGmAPrcQkuJGGMSe7AOO2JfiTniLxsA8AFcSSEAGJGawJrHKAQQRhacgjEpEjTNEkSLoPKDBqtLPkIwyIjgHkOAYiapkFjWcCklGmapnGkjC2VresdawiRIaEzjnmBmiFnAZytqmo8HgtSSE5bC7aZ5DNkkjFsmmZra2tvb2+52wUvXSXr75uH/nVdK6X8r3xeWGvtMwBCCGXNgvV3nHGHQOSMNZxzxgTjggvBpCCjHQIROWMZASDzmXzrLAA5R7du3SqKcpaXed20O4ZLoRzVxkYhcwstonPgcRLn3Kd0vi/0P4KzfgP79bLWWiKtNTLGBGeMGWc99/+/8z5wx1vNHwiOiMA5smQdMHKMITG/4eGIt0ZwCP7P//0H3SH4QQBwiA7Bp23vSD04B0jgyDECmkMWAiIpgxDCdrudTSdlWY6nk16vs7Gx4UMvsXhIKQMZEFFRFGEYetLXYyl/QIRgC5g1v+K5uIsvVEqItODOrbWcS3/shBBMijmLDC7P86IohAhsqZDNqWXnnBCh/xmRhBDIwDpjreWM30b8ixjAZ0G9FYRFtL9gW4RzxjpO1gdGt6Omo8dRmOecK/NC+zItKQMZOudms0lZltNpvr+/3zS60+l85CMf2dzcFEK8+fab2rr+yrKM4p2dHa3s8tKqEGI0mQghABAIHTkicIQe+SOTwNBZ12hTlboUIuCCc+4Vns45IouWgANjgoiLDL0vjqLIOdc0jY/Z9vf3OecnTpw4d+7cUVatrussy7z03yc0hBD6SLpmrYfC+/v7nU6n1+tdvHhxdWXpzTffJLLLy8te3r2yulQ3ZZa2ldJCBGEQaz0aDAZCBEvLKwa0IwKkIJQyDBBRa13M8rcvvXXq1IlPfvKTS0u9gLHRaJDPJjIMgDdGqSxJIhkgYhiGRNZpxRlL2+2lXtcYk2UZY2w8HZ07d+7Ktev5jZv9fv/y5csnj22uLPUXoa8hh8jQgfPr66zfcg5gkeQ52g4L0a9z5JzjwI78cl6VcRwncQYAVVU1TWMcBUGotSYiZ3yW0qfxOaKtmhoRGc5TiIuCH9rd3c3LcjKZzYrc57KkCKWURVEsLS2tra212+04CL0dImOPHzu2x3lVVVJKsNCU1Ww82bpxs9VO61rl01m73b3rkUeDQOzv7pVl6Zx76eUXd3f2G6UODg7Gs+lwPHn1ldfWl9Y5Cs752ur6aDQajgZa6zCMl5dWw0g2TTMYHHzta3/+8ssvh2HYaL22utnrL58+fTqOY59hIMa11nlR4iLbOf/uzhFRGEWjwbAsyzRNe71umMR5nk9G4729vTAMkzQq66qpauSslWabJ06OR1NjjBTMVw5Yayd5Xpbl6uqqNq6sq8PDw8PDgTFGfOvb35zNZt12J8uyJAzLssxnMyDsdLpRpMNGK2Orsi7yinMZRVE+m2VJOjoc5bNqsD9wDsC5KBCzvFBOJ1GaT/K1fpZP8ziMZ5NZUdRxENV1c+bMmV/6pX9oyPyrf/Vre3t7n/3s7//Jn/2JkOzgYO/GjRtf/dPk7KnTw+E4iTqTycwYd8/dD/yjf/SPfvM3/+3Zu86/9tqrWZIp3Uwms3Y7S1uZc6YoqnE1Pn36NAM+Hk2Hw7GxToig1eq0sg5BYUwxGAyMMcdPHT9x4sT+/v7e3s7v//7v16oioiCMH3jgvr393T9/+pnPff7zayvrmyeOTwbTr371q5/42A9/8IMf2Lm5ffXq1S9+8cv/9X/9d06dOvXEe9+7vrHy6muvP/3018i648eP37hxazLOnTF+52mtEVEp0+mmP/qjP/qhD//QzVs3Ll+98tprr61urF++fJkzGYaRt7yeBYmCkAlMokgw0EpVVaGMduAcgrMUcMuAG2B1Xc39t2RKNUop51y73QZM1tfXr1+/ubu7+5Ef+vgrr7y6dWvnxs1rGxtrJ04e67S7S8v9wWCQ5zeneZ1mrbW1DRbEN29s7e3tyTD1FhdxrtjnSAyQM6asNcZ6nRwKzmAO3P1ZnicEPPx3zKvHGONHXJSHFBbcwh3AkfUXjDVlJRbKExCMIweyzrmyLBGRCyaE4Bho3fj0XBxGACCF9JyWtVZwjohELoniJG4DZ17r7xwlYeQrko+8necOnHO40FJ7wOfpTE92djqdOI41mclkMplMjDH+gwCYEIIcI2cAQEoZRgJBcIaGETktPD4gIAfWOGQoAw6OoQVmLVlrCSy5Tq9P4MAabZo0TeM4raoCCa01iIwc6ko7DpLJXqc/GI5FINM4jZLYOSrrutHGASRJmpfFdDphnJaX+2m7pZSq8wYCmQRRwIXgGIeBM5GKw1BwKWVVVXVd68qQ0Yjcae2Ucs6JQMaMNc7MimmlmiyM0zTVjSHnrPGsODGGgnNC1+SFV6mGOgQkIkIicK7X6xWzfDAYLPX6dVmMx+MoCLkUQRxFWSvPp7WxaFylVavViox1DqJIIgsscmtQW/D6AyklEJdSGlO7RQWIc64pa2Tg90BZVUdAdjabuSQh8qid+V0RRVFe5lVVlXUFQIvyAEcIltA5IkvkcB6NMubVXFyyvwA3ic2TWkT+AhEQiQHRgvo1DqyxShvGABl3hNo5uwiDHYAypmkaQAl3qEGIrCOLzpGznAiAITLkgstAgGYG61pZa1Fyr3OBBac1m83iAHQDo9HINXkQSEQEFP1+fzyrgZnNzc3D/f3LUpRlnkbCAdBCE3J0ZIjIGOOpowUKdx6Cz1N4DuZfkDN0BAyJyAGhI20NACijtdaMCQ0ajGVAjAEROOfIGXTsOy+/9NFPfoIHoSPKy8ISFVXJpahUQ8AWkT+/nVT8LzxwUdfhnENH6Fn5BZZ1ziER57dlhF7W+z1Y/Lbd+W5BDnlW3lcR+LwRkXWOwTyD6Z+ZI1IPaJwD+P5af79jjujzBegFdASOAJHNFTDM23wA8MJogDk6AoCQiSQRHqN4VXpd19evX/d/XV9ZPnbsWDtJ9/f3i6Lw/ySO43a7zRir6qIsy7qurbVN03jpmnOuqkKfd/Xw1J+dO+8GLuJtxuwdd5L5pNBwOOyvbiqlgjiRUv6FehtgjAFHxNs5h/mXOpJdLQQ/fu8hF4yxOI6zLJvNiqPbZa0lckicCGmRHeOc53nu6U8Ap7UDhqEMZBiQg+l0urW1dXBwEMfp+vr6iROn4jguy9KXQOR53l9e2dvbQ8Tl5WWj3XA4RM6XlpZmsxl9965wCAC8qKpAciEElwGB8+E2I+ZBIfPEDDpwYEytrQ1F6G+U9zJHCTFfFu/3rRfX+dYUURD74iXPa0gpPY9WVVUYhhsbGzs7O+fOnSOiD37wg88///xv/dZvPfXUU4899u5Lly6NRsNjx44lScIY81J4RBbHaa/X8aguDMNS6SKv6lqh4GEQ1arZ29vbP9g7f/78xtp6EAT7e7scKIqDNI21ddu7W85CmqYG53lPX0oORGWZhzIoiqKVpnEcT6fj5eXl2WzWlNVKrzsajQaDwfJyP4ijqqocgo9h/UqFQsJ3l5QQWJ/lO6rYQeDe1DLOoygBcAxZkmTOmbqufUpHCNFo46EUzGWTt7N2AKCV5QKRc8YYIThjtbba2jCOLVHVKF5XPrGp0PhNPplM6rputVqdTsdr/Yui2Ns78DCsKAoEnqZp0zSvv/76kz/4RJ7nnkWSyDqdDhB5PUW/t3Rr+9bBwSBJsuW1VWX0/u7BdJBXRUlEPsbTyhRFaZ2pqkII1u12z5w5Z4wZjUaTSS6DQFsjhIiiaJ5nc05pU5blXGrpi1IWtVWMsatXr54+fXrt2Mb29vbld6722p00TYMw7K2tzGaz8TQPQ7m2tppl2Ww2Gw6HMoxRcKsbrfXS8vLp06fHw9H2zRs3b95MkuT48eNZp314eFhVlcjz3BdVDIfDteWVbrfX7/bOnrnr8pV3Hnn4/ihNXnv9jYsX3xIi8HYkiVpg3eHeYW95JZJpo3Vd13E3Xjqx8s6Va73eUsDD8XAsmTy5efKtt94q6+rjn/zY8vLyt154/t//+3//cz/3s//4H//jf/CLf+8Tn/zk0krv9OnTn/jhT3z9618/debkxdff+Ds/+zOzofr1f/UbWZbdd8/9zkFRVB//oU8e7A+uXb/a63WzrGWtHRwMhWBRlKT9ZDKeJUmijJ4VOWei2+2vra8vLa3c/M5L/X4/SZI8zyWTs/GEA95//4NZq/W7n/2stfaee+9SVgEQcBbHKQBMp9O6rs+cOfPEE487Z6azyUMPPfD2m2/FQfjpT//oxTcvjcfjXqf7+OOP33PPPVGUbN/a+dbei3meg8WD3T0hZRiGabi8t7czmeZLS0tnz60eP3ny3/zbX8+6nZ3dfSJijFdFHUkpuWiqui7LuJWgcXVZ1aopm9KB45wDQ3Iw746A6CkEpRQQY1IAhzAJD4YHnW7KA4mCb+3s/OGXvlgWtRDBrKi+8dzzmzdXLVDWbp87fbYs68PhtFHaOecraY50inN/d4STAZ0FhkJK71zRGnIOCJiX1HrLj4gAvhoBPIpnbO4Cj3at9zdztglvFwNIgdILMgA4gEBGjDlEaw3n3BoyzCAXWlutTRAEzjmrdV2WRVEEQQQAda3KugqDhVcDAOvIWHDgmK/sAX9sfEaZnHPOMF+vwOfexV+Ac255ebnVyZRS4/F4VtXe6FhjVeP1RfLIUc3vEUEYhkkknFWmqWut0AFjSOCIGDlH1glHXLBIRKHkFLkwjozRIBiTjMD6/MloNAIAAUJK6ThjAIJLyUQgChRCChGHEec8CGTVKG2N0s329nZRVRsbG+1ub5rnWqulpT6rm5AzwSiSot/J0NmmFlEUcWR1FJZ1ZYwB5ABMCRZIPhqNZBxHSRTHsQNb1JXWWjHeSltkrNFUe7ETOIsEiGmaCs6kFJyhcyQ5l1xILpBYJKODfDcOoyTJtnb2lnt9n2nN64YzKFUjOWNIvG7COELgyDmXsbFQldrVmjHGkQHnvpTliOMEmNP/QSi916ybBhdaFOBCyJCLgAA84tnJ9/YODoJQaK2JnBACOQNwjCHnvFbGOULiNK/xm4O2xUa9LcX2SSFPfM+p4TlKA2LUNI1vFmGJlDZ1oxmHKLBcEDrr4VfV1DKMtdaMc3BzdbWPP+dIkUwYBKgJvFRJWR1YraluVKOMc4DzsmDLAR0gA0DGl5a6K51MMguopZRhGBHjeWWaplHKSil9HKuqkpHmjHwAhYhesCMlZwzyXBNZY5zWjbXaF80gkgxD4xM8jpAzJgUHJMHIWGJotbHOaK0r1ThnAwR0zldLMkCfFfHHo93qLi+vxq02IVTabO/tjvMiyVLGuG/M5eHmkQYJ/gsaen/KfHxCxjLGYJHuhwXO9t+OEI6EhUf4+4iDn2uBvlv0739LDJEhMo5uLvxFz+8y9CEJzlEsksPvf5V3fND3PMkA0RE69DUTBJaIyDGfPfDpBSI71/zAnCDx4hwhRBTETVPt7u5mWZbn+ZZqDg8Py+msqqo0CKMo8oLyJEl83gYAhBCMMWu5B+hHsMkrtXzCx98oX61xhP79DwtlteGc+4LI/f393sqxuq6DGIIgqKomSyKtG1/my5g/L3Ox0Lyq4S9o/Y/kUneuiJfxHCV8nHPkc7c415R6MD0ej4UQgCGRFmHggMBRVZRN0xR5zhnrdjr33nef1lrrRuvG00ZJknU6Ha11v7estR4cjrS1aZoi4mAwEDKcf6gXqSIwX/UBqByhAylEwAWCE4Qc5+pzRJo3i2MEAEiurPKjHSWlDMOQMW6sOiqbhqOq9zhjKCajKQA2TQMLQZ0rnFKq3W7fvHmz3+8uLy+fPXvWN+146qmn/vDzX/Bb98SJE+vra3VdX7hwYXNzM4wkYqa1cc5xgVmWDAaj69evraxuNEEAwFBwY0xVFoDU6/VOnTjZ6/WQLPh0HxezIh+OJqvLK1pbv2eUqjlQGIZSSq3q2axK42R1bVkp9eIrL37xC394OBo//Mi70zSdTCYA7sKFC1Ly48c2i6JI09TSbcpj/nCeSSRAB15SSY5oLr3jnDNyRjsONggC51Cpxmg1HA5nRR5F0erKWrvdJiLfm2SOSRA5l4wJbzfiNOEcpQz9VldKFVXVNA0RcSmWlpa63W5jtNbaaOeca6dZ0zSeOfXF1lVV+1KH06dP9/v90Wh06+a2rwtdW1sbj6ZBEJw4ccoYs7u78/rrr6u6UUr1+13G2PLySlEVu7u7xbUqiuN2qzUeTuIwQcTRZGyMSZKo0+tKKaIoGo4OlVKNcpzLJG11e0v9fl9pS0RKKX80rLUoZJIkRVkdYSfj5ufHOXf+7rsODw8PBoedTqfT6xazfFrkcRxvbe+2O1m333POlbVq9ITItjqdw8Nht9uNslZZFDs7e7u07YxV1vkWAlHaSoyTcpIXhfALH8qg11tKkmx7e/fGta0wDHv95SiKx6NJVTWf+MSnPvapT+7u7v/2b//24NbW6sra/Q8+8HM/91cd4B/90Zd+5zOfGY+nVaEYE+WsUHWzsrTaTrNW2j55/NRHP/6xD33kh06dWbt06eN/+rU/U0pJGZ48efIXf/EXh6MD7fRXvvKVS5ffEgHP2tmf/umfgg6DIDLGIfLNY8ffeuvtz3zm9+6669zK8hrjMJvMGt0sLy+fOnVCSjkej7MsOTw8NMacOHHCWTo8HCtN7XbXWnt4eMgYW1tbe/LJJ0+ePFkUs5WVlZdfe/XBBx+ezSZvv/02ADhnZrNpv9suy7KYFsv95SSNvvn8s+9cfufmOzevXbsxHc5+8zd/s7+yfPc9586dO9dqJ8BodXW11eqsrKz4UDVpt6y1MoqUUsB5b2n5xo1bL7/62nve88jps6utdndw89rS0tJ0Oi2KsqqqNGqHYVgX9eDg8PzyXZwjEDGkOAzAl3AhOEdhFHkFtm5MEEghGBcYxrGnOW/evAm4oVQTx3Ge55ubJ2fTqizzbrcbRcHa2saS7dy6tbXUXZ5TLwuLH4ahCIJaeXAzF+cBAIGvkWGMsYAHhKC8BprmUN47N0fEEBiyeUZ7ju/pCPr7HexZLsHnX8f7XWNsJ2tFgQyEcM6QrwogzyfNa7wAQMbC17+32+3RwT4ipmmapmkYp9ba6TR3zhFAVZRNbX3tRBiGVdWUZSkkn/tvpHk9HHxXzx9YFCR5jhmpKUtWlqXvchAkaVPvD4YHgsdE6BhHRMGFZ4CIKIqCJIojgVWdj5u6LHNlLHEJMnHWaqOttSFiHEStVtbOktlsNp1Oy7Lo9/ucy9FoqioF4LqdvtaaESRJlsYZAKvr2pf6GWvruhZCRHEshIgI0LK9rZ0wlGEYOqDpdNwYjVx4ChWMbqxxmqWhsEYVs2kVBGTnghbJGeeCgEnOTBhmrU6ltQMKAsHDQNaFrhtyVNe1b9kUSA4AHgtrZ4SUTQ2eHfH4II5jRjCajvr9vpSyLMtj6xtlWba6nevXry8vr2qte/1+MZtUjWpn6XA0abezqiq4DOKYOWKN0Zacp4s0kTHGV90umh6gVxj7kphF2zURBAFHprU+otCCQKDgwgm/NDIMEikYY8o0TWOILIA0xgIgI8YYZ3e0evQiCiJkQeChsy8Xc844Z8jCHKB6FnjBbgoREFnDBBE5i5Zc1ZhICBnGUSAYA2CInAVB0DRz9fwCFFpfhy6EIPIRBDkExqUAEQSgrTMOJUOwzF+JryZXTRUEK1krsU1lmsoYE8aIyGezSRAlyFxT1R43RFI4U4cBak1HqUWxqOHzP/iz6UGhEMJLbK0lC2St1YxJIuKMAwfOGWO1MVpp4w8meufNaF7oP6cMOOOM87quD0fDROms2xNhgEwQQhDFQoaehJtz9new5t/3MdfxExljnDZH0B8AUHDGmCPSHmBJIaVf3+/z8Ot1tOLegjkgpw04xgUuql7nL/bma24k4TY49tcz/6Z3Nvf8vhcP4Oj7PUfEUAA68lWxBOTrpoAVRcGzLE3Tfr9fVVXV1ADQ6XTyPI/juJXEHiV0u92Q8dlsFscxInoOeDQe+YZvUkpjlK9c8ujc22HGGAN+RMEwvC3LtPO9Pe/h5m2FMso5Nx6PnXM+ZhBCGFMEQVBNc58Z8GjYOH8chFPu6G7f+cPc/guBiAzmFzwej41xcx9DjBCJ5ikIJDxKVfm+PYyT1hY4mMb6pqXGmDhN7rrrrm63O5vNfCLLy7gnk4m1JMNQhpFvUhSGoSCq69pv+NvXhrdPJUNcWlpqlAJj54vtmCPjLGVZ5mtmFtXwQgSSMXZ4OPTpYmOM12z40jsfKnh6wseuTdNYQwznsZZfJi+sMsaUZbm8vBzHodfynTlzxhc53HXXXa+8/KpS6q67znW7nWPHju3ubRtjDg8PsywTgjeNklK2O5nWejiajMdjTxGqutk92B+Px61O+8yZM4eH+8bU7SSWgbDGHExGWtXtdobuSH+PHOYdorRpJBf9fj9L46tXr/7BH/zB5ctX77/n7k986kc2No9v7+x987lvWK0uvvXW2trK+bPnkDPnnLFkjTPWOmPIOrBEHJDd3glH0TgtVOxgjdaaAXBurbVlWb/y8ot1XTVaBUGwv3fQ7/fjLBVC+EbAeFsn5lNnlM/GnCPn0isFRqPRYDTxW6vd7ayvH1tZWVnurAZBQA6JqJjOfMDst3RZlkppAHjyyScPDg6uX7/ud9FsNtva2ppOp1mWIeLqykoURePDA6VUIGTTVFVVGGOCKNzY3Lj33ntH04kj2lhfH7XHk9G0KIooTHiKPufjXDAajeM46rRTY8xsOjVWpSnMZkW31/P3xzPpZVkS07572+0s2YLs8CA2TVMAKMsSAOI4DgJZFEWYxEwEvjdxFEVCSlUbpZTXuiuse73e+upaVVV1mTtjZ+OJd3lLS0vtbktrLdqtLiIu9fvW0PDgMJ+V7ayzs733yoXXX3v1YhBHhLCz87Ws2yGi4fAQgJ09e/782bukCA5HQ8bYztbuAw88NB6PffZgMpm10i66Ynh46W/8jb/xM3/1J27c2P/n//xfnTp1whhz6tSpNIm3d2/91E/91P/wy//P46eOl2XJmPfoRSCiva3Dqqxbrdbzzz//4osv3rpx8/z582VZqLHaPLlpDfX7fQB49yOPZu3WZz/7u/uHe61WK0riJEnzvFjf2Njd3X/55QsM+ZUrV977+OPddnd/d+/cmbNREL744ovPv/DtrNWKw/D4xrHDvf1Op9WUdXb8+Nf//Olzp8/4ypvvfPvFD7zvA/1291Of+pHf+PV/d3h4+Md//Mc7u7e+/cILP/tXfvrWrVu9Xu9f//r//I1nn/NZhVrZu++6fzKZFnmVLPWuXr1hjPnt3/7tC6+++tC7HjoYDqpGpe3W1u6OqptOp1OWZSDE6sra6HBQF5tBHCRZGoZSWa2tsWC5ENaS4Nwao7V22kQyiMMIGLfWRlEwm02TNBoMBlrrVqt1+fLlhx96bHl5+eBgUJbch3Od7kqSJL6lD8y9vvVYShljaa7dd855f+WhfxJFtIjWYdH5xwJxzmDRN3PuPxw5olAIrfWiU55L0zgMQ601ct/B0zHGkMMim8XSOAoEM0pXZUlESRr5HQnWMcZEEnoNdJIkgnMiq8rCJ8F3d3dFEPlMt3dk3v5WVeW1fT7PUKsaBD/ij33zPs45GA0LahkBqqrUWhtjSUCtGmstn4uQwJNqUiTWEuecMxRI3kQaC8ePnaiqYjweNVUOZNtZ1mhbO6qt8zlzwTGWIggEka3rSgiR57kDqpo6kFwEkgCTpMWFcIDOWO1IGwdAjdJlVVvr8qLI2m1ycHBwSAhZljlyk8kkaWdxHFsEf2F1U++NRstROBrV3U6r32lbo0aDw7qu4zCIw0hZI6WM0wyA6qZijMVx6kRUWZPnhckNCu6rCWMRoMOACyGkcZArZeqy0ZaItNZpGgvGjDHgjGqqpqwYYhiGk/GYAS9m5dX6OiK+c/1GlrWiNDX57GBwmEQhj4LRbBpIiUISagA2m80aZYEFBNxaixycc3Eco3UA3BiT5/lqvyclOqeLovDFdrM89/15rTYBFx6USCm9qCwIAkKwTqPvSuksEQVB4DNRxjHOBVhmzZx5ctY0dR3GommaIIim0+nBwYExhrFACFGUBZFF8ihEAAA6cmSEEE3T7O/v9/ttL5vpdrvW6laWBow558q6BoB6PDuczLTWnrUiAOd8myyUDFGwsixbcTQeDiW6frfbGI0grl2/OZnljIdKaQJ+VAXrQxSw7tSpU6rMb75zWSnNK3Xs5IYCkRfNrZs3Wq3Wzs6Oc6aYTjijulKIFASB92G+YxUtimv9lfsnGWNBGGRZNp5Nj4IrD9D9653zhfjMDwHwS4AWrbNg3aIXmVCmUVqFccK4tA6U0dPptKzqJGvlRRWGrq7rMAyjKPL9OTwwmuca4XYw5pkIrR0565tiSj+VggtvdrSblyb7tgFOa2NMFMVHgT0etb75btU7LCQuCMS5J+CPegfNAYqHbj7LCnQbuHjjNn+3RS9j/3CLqnQPcD3aNs6RBQZcMOFHB/iJE8gZY4LzOdFujCF06MiR8/DRh6/GGAA3m80452mahoH0UZNSinERx7G1djAYzFMYYI8UApzzo/oNIl+q5NPF5gg8LfgXL0jjSZIwxpumAc68YUxlGobh9evXH3ik7Pf7s9mMiSBJkqqq4jj2bhoXhVtW6aqqQh7CX1A9wULhSb7dM81jTmOMTwkDWn/rEIEsaa0DHtR1Pa3q/lInSZLpdJpmkZ8D4PdM1mmvrqynaVqW5fb2FmPcO4iimCFiq9VyCAyFc04wsAyJHCMMhPAN3xgSE9xPR/GSQhQcmfC3jiHzVahoSTDg6JtF+lQNMcEtgVMGEdvttlfyuEXpvK8JZvPCADqKGDnn5JzkcjbLx+Nx0zRRFOV53u/38zyvqiqKIo81lVLT6dS/1bve9S7G2Je+9KX3v/99Tzzx3rNnz97aunHr1q0TJzZ92NA0ZIwRwnKBYSTHk/HSyppzsLW1NxyOWq1Wv9tzxvrskLU2kXE1q0aj0fraSpIkjjgq5axB4FIIY4zRTRSG27e2HnvPu69du/Z7v/d7h4f7f/kv/+QTTzzBmACUVdlEQSiSWCn11uW3P/KRj5iqvHLlyuFoGCftc+fPLy8vq93dqq7iMAQOxmhE8DFq0zScQZIkWdbZ3t5mBHEcGmOaBm/evPn00187vrmhlDLWIpiyLJMk6fSXVlZWfKMCR6iUOjwcDgaDoiicM1o3q2vLS/2VleXlYDN8++23Dw+HvrlqXddXr1597bXXPLVhDRVFEUeRpx686bDWIjAhxJtvvKW1DoKg2+3GccoQz5w+3W63mcCmaeIw6vf7zN21t7c3Hg6Iuk3TpFlirK2qqtvtvvvdj0xm0zffeOv+ux8Iz897FgshwjAsimJ/fxcAVlZWLly4EATBmTNn9/f3OOezabG9s9Pv971NzrIsTVMLHindpop8rZo3WZwJpS0iChnCYh6Rl+tZa4UMpW+dpDUAIONl1QgRRDLQ2uYmJyLOJErkgQziJI5j5xwhyCgWYRjnee4sW11dESjLsuz1lh977L0/9uM//s477xDDOE3uueee7f29L3/5y0tLS6Pm4M0337x48c1vPPvcY+994tFHH/3ABz504cKFbrdfFMXps6fvvfu+8cFoPB4/+q7HPv7xj08mzcnTq7/4i3+HEJ599lu3bt26K7vr2PqxZ59/7saNG77a/cTmye3t7SxJTVMbZX2/px988n1nz52pquobzz1z6tSpLMtCEbrARWGCjH7t1/5lo5uNjY0Pf+RD58+f//zn/nAwGBJhFIrNzc1bt7aNtg8++CBj7ObNm7u7Oy+99NJ4PCayyFkchN3VJclFIOTx48ejIM6n+S//0//hlZdfGh4OJ8PRj//4j+9t72xuHHvve9/zp3/y57e2t15//fUoCY4fP/7000+/9tqFlbXVX/iFX/iB9z31//kf/9nmiROj0fQbzz370z/1002tZ9Px8vLKytqGkOHV6zdmdXk4GEZx5DrtJEutcVJKZg1ZKsvSGDWbjo+1N1RdTfKJsbbV7bS7rVlZ5NMCeUGWfN2RP/ZFMTMOhsNhFMtevxWGMo6jKIqrsvnSl76UZe0s7WatpN1ue41mWZZxEGutEUWWZVpbPRhXVcWOJvh4a7VoO4iIVVUtPDICIhMckHPPmTEEwjnZv2heYectfeYZ2yPSjs25unncPzeLQJPRiCP0er3Tp08T2P39/cFg4JzzvWK8YMMYM5vNrDHW2lCG4IyUMskyIizL0loiBIbIBGckvNf03rHRiuRRXZ1z3mcDwKKX+REdtQgDMAgCP75Ak3HOGTuvVfCnEa3jzAHzreK1dfTSSy8JBCkgS8I0SYIoBCYs8mtb+421HtwYYxxnIOdUE5eSMQQAbQwRkufkOLeERKiNK6Ehh3WjlXGzojTknIPRZHzz5lZR5Vm7K8Pg9NkzllxRV01VWQcouBSCx0EgA+EskS3L3FcYy0BIyYVkDtA5p3TDmeAcnaOyqU1tmkYToUMEIgTy0kNVKc9YKGMapa21HFkcSmfmnd39PWSAWltjDBLEYdTayABgNBlvbW0hYtZql01tiASXXAYAzkI1Go8Hw+FSbwkYdwQOgHltGQchhCREKZA552ihP2aMzdHWUUoKFzkr5wUnAABu7pnR+RIUIjKOFuDVIznGRFAURV0oBN5p9Tx9Mhge9pc7yIExweZzgubtL3HeyMqhY84ZRERHjqxn7JI45JyXulFKOTJREChlGqfBaAYUBIG349ZaxgVDAJqryxgHhoCe6URExjS4WjWzvKwbs3NwiCwAFAgCgTOvkQWO5KIsI7CDwUAidTodY1VV16PRJJ+VxmEYR0mSxHEsuQiCgDPiQmrdWGu9fBZ9A5AgGAwGR/DLu9UgCJDA57vAOuIMrHMAAhlwrusGBQ+F5EHACBCRESilQr9U4BggX3QSxDvqZ/JZeTgaKmfb7a6xpUfG/kFk2QKdL6Q13wX9vTBwrgQDEPPG3HSnetBbqjlni+jh1/z5O0RE35NZ8J/CAS19/yzBnaEC3fHwnai+58VHv4Xvk8FgRGZB4B2x4EwpxTl3jvt4wH8kcMzzKRIdHh7OZjPv14NAtNvt8Xic57mRwvMa1tpa125RoeEfyMgtutN6HQ4s5l556I+IuSr8cvsvAwC+/JTmd37emIF8A8rGNU1Tap3nOQZJGIZMBM6Bc5Zz6c2737/AFuh23jVr3rKIFlp/xvgR6YuAHo1FUTSbFXPoj457DRgXAAwdpmkacKG1vnbtWqcb9frtPB+vrx9bWlrqdDpKqcl4fHh4GEVRO2vVSnHOcZ6TIcbmA0waZfxwDofIkAiRkZ/95KGVo6PusY4IjHHkgz1rjNUNWScRGGOBFHyhWWLAkKNk0mcP/eiuOzfJ3Gl+914iIs7R/9ZDPR91+0Fs3W63qiqPI3u9Xrfb9YRdU9U/9mM/1um2Xnvtwn/6T5/74Ac/EEXR1taWnxVlLbXbbcGDPC8A3KlTJ5ZLfeWd69eu3XDObaxtdJf6TdPs7m7fd989rTQuZvl4cAhkl/rdbivLq7qqVZxkQDQejTjn3W4bAIbD4ZM/+MTTTz/9uc99bjQa/dAPfejxxx+31t64cYvx8MyZMxfX1998843Nzc3dve1vfvOb3aX+hddf297ZmU7Lbq934sTplbW1fn81CIKD4QFflGAJb0zBIeJRbNPpdKxu6rpeWVn52Mc+8Y2vP42MOt3uyZMn19fXAeBwOLxy5Uq/3y+Kom40YwyRl2U5mUyKYjYcHo4nG1Jcr6pKCFGrZjSaeM4IOU+SLG1lURDKMOh1+r1eL4oirXWe5+Px2GeTWlk7TdNTp874aLaqKudASukz26PJcHV1VXJhjIqj+O67z+/tZFeuXl5ZXd7Y2OBCbO9uzWaT4TDZPHH87Okzzz/7rZWl1fX1dR+WW2vX1tYeeOABpWovt8uy7Ny5c9PptK6rw8PDsi6iKErT1IdGxhhD4Jyba5G/217NrQQeMSPz/An8BWrjzocj8rOYDDhwRGae6W2aJs85EwicMSKxv3+4urpKDt68+FaVV+fOnXv3ux577LHHkiR74okn9weHw/Hg85///Nrx9dF4kKax67RefOGlXn/p+o0b/+kLf3jy5MlWu3/urvOjwRgRz507t7q6+tUv/ef19fVPf/rT3X4MEgihri0RnTt3TjutlBqOpp/5nd+5cuWK0vVgcLC6ujqb5p2k/dZbl3vttSgI+v3+B9///vN3n3nrrbdefPEFcJTneRRFCPQDj7/31YuvRlH08Y9/zJL78pe//BN/6S9ZoMboXrtXFvVkMmOMdTvZux555OWXX06S5L577n377UtJFJ85c8Y6vbW1VVfF6dOndd1Mx+MsSU6dOtHr9f7m3/hbhwcH//S//3989av/v26rHYXy//R//kdN7cqqCoIAOUPOvv7MM0U5W9s8llflv/vN3xxOxoNJvrqy8YNPPfXtF16YTnNwFIeyLKs4Tjv9TtM0THBtbNZqJUlS5RXnXIS8qeqmKq0WTVlVRTkrcgsubWVJFCRRfHh4yDjcd+99x45tDgajCy9d2NraqqpqbW3NEra7rZW15eMnN4ic1jqM4+2dg6q2IpAikEJKQtYozThJGQohiqJoNLXaHS+oCIJAhuE0rz3oP9pG3rc2jULkyBjnDPi8hg4RCSxDBswhzdPoyG9388SFrPMI+i+273zL+icdUKfTl+jaWdJKY4+rOlnLezLv7WazvCwrv9cFZ7Ld8tLeKImNcUVRaG25EATAkHEmmRSMMTIGOAM9d8yODBL5jDdDAgQpQ2utmzch9iMTiKPvHOKMMYaMc6TtvPOd813wgBAIFiHNXCwheJKINI7CQJCjsi5rY4nmRW8CWShkHMetVhLHUZ4XaZr6PpXOWQRwhFWjaqWdc2QBSCHWR/1tojiJBMZJpkwTxaljGCdZmMRKGW2NagwCjyIpwsBaaxmuLi0HAMgInEVHYdgLpJCSjwdDR0YrWzallKGQ0hhbNEZGLUXk4bNzzlrwKuder2MaU5d1aWqllCHrOJ+ToM4AgeBcBDFJ53XJ6Ehr7aexWEdbtG2JwjCMozSQkXXaETKEMAy1Vk1ZamvICgAGDIk5SwiciTCoas0WRs0DJmMMMuSM8cUY2qOQkjHmFcIAQGSB7ihbFGKx03yswBnnyNi8vDULnAW9mF0Vx7FSChhJ2RCfYylvSBmXPj+GhEBzw+uPSSBlO80IbFNXRjfO6CBJmqpyRoOzYSBEICUTsjbAa7cYf4c4R/seQRpjNGfEBSB3iJZhqXWptEUkYBbQAfMzpXx4HQUilEE+mTI0aRhYZWezGcqoaizgfK6T38YMSXJkAo0lZ40zxuOtIBRJEs1yISUHAK6RGRCSyYAHoSiLGqxjgAhIXpXvNSKA4MgZyxEF46GQZB0DdGR8RzDnjwYAIkcgxphSKuJy3rrEWMGDKAKi76ropUU56dw+LGLzOUSmxdhvxmEB/f28DlwIco4sEizs1W3GYfERAMB8FeN3+1EP8tj3Innwrwcv9SFfvkxgHYAD9l2FwrBQB/lQ8+j9j1ba96JyvhqemHMESAyxabQQ88mMC8xNiHj69OnBwYGngY0xdVPXtfNxgh+BFEURyqCqKl1W3/XRRFzMY2NjjJTzCfds8YBF1sjz3NZaS+DpyaZpjCXOuRCSc05s/j6WLBFpYyaTSdYLmIx81MG+q43p/Evf8QwQfC/0963AfKDJF1J+H5MQWQKChWSUI0fk+SQPg0hKyTg5E7Tb7ePHjyNuWGOkYEZpozRHCCVHRG9LAchP6jDkFr2CONJcisbmSRogxubVLNY6ImA+twNERBbBWRC+P6k1xlhtfA2J5MIy36GVI+PIkBgHpKapOfddm+epnoXahwGQ72SzaHFjAYEzeTTPC8B3RiYAGI1GUspWq5VlWVmWb7/9dlmW3W7XKD0cDqMouv/++7/61a/+h//wH9Isfs97Hp1MJkmSaK339vacc62sE4ZBXpRf//pzCGJ1dXl1dRWZmE6nBHTixInpeCw5MsAgCBiSFIwxFgq+cfrMcDyZlkUShWEY+mi5121/9rOfffrpp8sy/8hHPnzixImvfvWrSZJ89KMfLwu1srz83ve+94UXvjUYDKqquvzO1U/de8+nPvUpZKws1d7+/nRaAGJZllVTRmnIOHCB1mnnSJuGIyOiTqdjrd3b29vb2+u1O0qp69ev7+5tV1WVpJEx5saNG2+/faVpGhEGSZK8felKGIZpq+1ln4t7SHEUWW2aSpVl2Wq1VpdXzpw6myTJYDAo64oc8kCaRilrIhms9Je01hxQcSEZT6M4juM4ThBgf2c3yzLPkHoyazKZ7A8OT5w6TmSVMo2qkyDs93ucYV5MvNpieXVlabVbliUhOmfKsnzyySffeuutC6+9srq6ur6+joizYlqrqiiKY8eObWweE0I0WjVadfv99WMb0+mY7uhhZa2dA/rvi+IB5jDLmXnfMWIMfJ8VIAJ0vnsdEAFbtPCixe+QfNcBR0RJklmr87JIkkRyCYRCNXYyzmtpnGNp2mEsQORraxuXr165ePHi9VvXz5w7/fzzzz1s3nX+/Dkh+Nnjp7Os/Zd+8tOHo+Fv//ZnDgeDdodu3LgRyijP88//4eeypGW129zcvHHr5q3fv3kw3r+2dWM8GAJnQjAp+aOPPvruRx959NF3ffvbz+9s7yVp1DT6yqW3q2m5srRiGzo8PCyK4n/9X39VO3v58iUppZ8C2+m0rl279tLL37l6/Vqn066q6vjJE+95z3t+7/d+7/TpsysrK69feP30qfMnTmzs7e2tr64//PDDX//6140xjz322Cc+8YmLF9/4xtNfP3P65FNPPPnkk08srSw/+/w3tdbTfLK3t/fZ3/3f7r3nrr3tnV/5lV95/rlvbq5vvPH66z/8o59Kk/6f/fnXX3vlwjs3rq+trXWX+h/6yIee+sD7X3/94sr62vb2HqFsjN4/PGj3uiKMQNvDw8Mb22+tbR47efbMwWA/TluODONcSOmAtNYRD6UQYIgjVUXZTrPNzWNJlu7s71y/fqNfFGTd+toq4zCZTJqm6na7xzbXt25uj0YjLsPhcNjpZkLCeDyq67Ku9aVLb64sH3cWfFKMC4wi0YqTNI1to6y14/E0L8osa3v+zKf7FyHiog8d+jqkcN62giEBgSPfXNfjMFjYVAYLXE90p4T3KAaYP+mro44cJLiyzLtZWpblcDj0yrPl5WXvvY6YKu9vEJFxnpcFIkkpBYCUMslS1RjnXF03xFCAEBaJyBckAGe+cM5/uvMsItzR8Gfh/o8eWmvtJwRJP+b+9ssQkTMuJfoWEIwxR7bdbkdStNMgS0MpmDaGysrW2trc639x0ckOEZEgCAImRFW7uq6JQPhirKqKogiRSykYCgAGlhigZNwCWONGs9yRibJW1u2IMAJGo8nYzzrxVeCcc2QYClnUFUjJCHRTIUESSUA0xrS6Lc65UqaoSoYiThPniIpaOwSLfnkInLWuVkaUZcyltdpa45veMd+qzzpnlPfZQRBIHoCEgAufT/Qq1Xk6vt2q67qsVbcfaWuaWTPNJwwpTeNOp5tlmdbGASECMCRE5zwZyO5cGrjdfBA4csaYp6j9/SQizhh66o5oPiiXIVt0ecO56kAAu92TvmlqzgUjoZVVtfHClTRLJrOhT4uBkJ6TkzLgXBI6h8b3gPRYEIED4rwBLmN1VdR1ieSc1b6IUwQyCtMsjtIkUc4GtZGyrGtjYc5wz48JGLDGAdPWEENNTjmKkNXKGOeMAw3OABF4HRNxAgaklGq11hGhKArmbKONMq6pNYDwE3irqrHWKKX8JDBjtJ+q4+lhWgg85uJ+a+/cnx66cWS4aOHitAU+b12ilGp0w7T24TMXQspAqfl4YOc85epHbWJZluPxtM/DTqsTBnFZTb2R8Z1n/Ef7sg23aC75PaD8NrL/LqA5P1DOOzj6Xtju1dXsSIJ41Bfy9hvjnW87b95DQLj407PUvibbR11+2gkxR47jHKMvdtftHkRHH3DH5TDtlAVigjMhuC8SWWxsaxHA+vMCAIwBRxYEweHh4WQy4ZxnWdYGaprKt8D3VjdNU+bIGGNZI4RI09Q3NNNaO7rdNPNO83sESbXWgkl31B4Xb4tSgmA+0dkYQ2ze8MfHCSHnVVUlHUfWWtLWUhDKo4Wbl3XhPL3G4I65Lv9l5Y9b9HSKosg5Yx0aa2nev8UCUBRFCMw0igNwzud9D53iMlCNqvIKGMuyLIqiqlHTPAcAx5wvoHcL+T4iARIyNp/8Qoz5lBEwRK6sscpPqATkjIFvDEHc3zPrHOPIiLN55bQ//o6ArANghhnu96SdT704Wnp/0I7+6u+2c845iGPp8zk+6pNSOjKI6BvClGW+u7tbFEVRFNPpNI7jOIz+8A//sKzy97zn0fvvv//EyWPXrl178cUX2+32iRMnWq1Wu51FUZIkya2b269ceOXhBx8qysbHNlVZ5nlODIUQ66vLVVUx54JOKwqCusknEy2lfO3ChSjJhGCI6AdIHRzuXb169Stf+crq6uqjj77r/vvvX19fT9P08uXLf/RHf3T/fQ+//faVD3zgqaeeeur1119fXV3d3d29eeuWUqrf73MRWWtHo9FsNlMGnDMHg73eSu/MmTMnT570pJ4fX+i0qarq0qVLw+Hw8UcfO3bsmNb6nXfeOX3yRFUXfl5Vu931Rbp+kq5SamdnJ8/zpmmyLDt79uzpkyfWV5erqjDGRWkSyaDWihxGSXy4f9BUdVU1yJg/ZgOLR1xPXddaa0+0T6fT8XjcyjpElKZpr9cjwtFoNJ1OEamVJnk+00pFUWCMHo0HgvH77rtHawvgZrMJcoiiCDhoY2qlVKXOnz+3srJy6dJbe3t758+f6/eXiqLIsqyu67IsvfLKBzBVVQoxFyrfYZu+y9bdeXDuPEdHNufIboN1dlGkdNStxS3aGxgGAXolH+OAPsJ0YPmRWGhz88RoNLrvoQf+2l/7awL55z73uYsX3zx//q6nnnp/GIYbxzcGo8P3PvH4G5cufujDH7h8+dLzL70yPBgmafw3f+xvfPSjH/2//t/+u2eeeXZlfQOBPfDAA9tbO0u95Xc/8ujbly79N//NP7Jgl9eX4nZilN472K+q4vjx4xcuXPidz8iVlSXvjYoib0r18IOPlLNyNp52O6vr6+tSim984xuIWJZ5p9/r9/tBELz26qvvfvTRIAjqoiymU+dcb6l7OBxGSayMrmf5sePHi7p65zvf6XX755+8mzOpa8NBv/DCC+12u8pzIBruH3zjz/78s7/zmY99/ONLq0uPPfbY+vpGoYqmaV566aW33rj4L//lv/zZn/7pz3zmM6PBoN9bsW5vd39vf3AYZuEHPvyh+x+8L0mit69eeea5Z0UgR5PJydPnnMXheHT20XMvfedl0JSm6ZlzZ3/+53/+8Sfv/YPP//G3v/38eDprtVphkvi0iwg4LLLkuzs7rXZ6/OTx1dXVH/iBH3j3Y482ur5+6+b1G7cODg6MJillkrR7vd7wcLS3v99fSaI4jKLAAyMASNN0bW3NaCcFi6Ioy1rtVjeOE63NbDZDa7yKdzKdzWZFbagsS+NcknWJyCcfF7VpBDDv2OPt6dHgQwfEFk3x5ruVMSQAIrGYB3S0Nb3RbIxCRLZoLjF3UQCjwVCCi+PYGk3OVmVhlK7reprPPNfVbrfDMPSjKI3RBmwUBcBQGR1Fsp1161r5YW1kwTqljAYAS0RmPkOA0CEw/+2IwJcJGquPsAVHAt/q3VopQ+OnMwaBjBNQGrF0zvk+QIILL5H1+Mk6RdY1gplGqiYMA2GszctqXDZE5MdUATit1WymbTWTXGTttjKmLKq6roWUAQ/BWWV0NVa+W4uUIYK01jWLXhsyELqpG1VLEQQ8MI1y4LK0XTZ1URSzonSz3FdEZFE4m814lgaBsEAckRhactaopml8CrKoSmehcQaR1bUO4i4ZZa0lTozPBQClo3I8ZTRXpHDOJTJAtETOOc5x0UVEIxECC6QMRCiEmBVlkZdMyDCMq6rZ2dkJgkgIoeq6KEpyFpF4O2VCcABg6AdNAUfjSDlrvZpccGSOMefF6JxzxshLZfzwqUUneIcSkTyp5sg33AREX3PqI0AAT4EgonPAGGZZazabVXlBDqMgCcMQnLXWHAE4XBRUWWscIDBGwAhuw74jy9s0jdJ1UeZWqziUjMhp7QCkCAIZouDWOa3n2VUA/13nPW0ZzLu9iEBaZ5CzRquyqUUTjWf5tKwcBgSM5m0hYdEVHuu6SKJQcmhqHkVRlMQOWNEoJrmyFhaSHhlwsNzY2sPxI3kMLfrhOOfmdT5KebvhJ805NdffM4a+7B/AAXMAOG+cxWHOny4U8BbIgWHAEOhIfVXklZQSAKbTqaqbIAg48FleIL9NEPiEOM03lfyL/gwW4hOv2/IN7wTjficcmSOfAfck89E3vRP4Evna/tsFS7dfhoAAzAvPAJz/GW//aT0VQvOd5OUi/6XHbTZhIWs0xswrioIAiRDIGiLCQHC/HnA04pBzx+Cdd965ceMGEbVarSAIGq28VCDP80WI5ZzxjXGcbykDix3L+RxVc87ntbOMCSGOynyNMUxwX6iKiMDmqJSIwiCw1msqrZ8lxxYtR0QYNk0jpQQutCVjDIvDo2btRxrOo/UCAL8NEBYpYgQ/w4FLn7hBLzoyxiyK6Z1zjubpNQfAQhHOO0db8hVuWmtyGrmIwyiNmQ8UmqYhckEotLIEQPMOSXMWAAg4EJK/IYAAiOAADFEgBYBTCOQssnmXH4bI5ooUxjnKgDOcz820d1SCMIYAxIgcUiuOkezRfj7awHdq0u7c1c658Xhc17XHnVLKRt2etO3XdHl5eXl5+dq1a8PhcEZ5v9/fTDaqqnrzzTeFxFar1ev1nnvuG1/4wsE999zzIz/yI8aoL3/5z7S27370cVUzbW01njVNEyfZxsaGtdaTGlo37SSJ43il32N8aTaelGX5yCMP3by5VZblyspKGIZvvP76V776lUuXLp84sVGW+dbW1sbGRhxHxuhjxzY2No69ceHS3t5Br906d+bMlStXGGN5UbzwwgtCiKqpq1Jba8MwSdO03+kFcfDgI/eKUGRZO01Tv6OAzd1uq9VqtTqj0cQYR4TLy6uPPPzuqsw9Lq+qhmhirR1P86Io/G2UYdTv96MwYYzNpsXrr198+yI1qup2+qfPngmWlgWyxujh4eDE5ubq6mpVNbMi9/0Sq7IcDofdfs9vUe/COOdxLBhjxzdP+rZLQRBobaWUnU5HRvLqO1dGk6EzNo5DAVzrpt/tHT9x7Mzpc42qGqWUaapKE6MwjrrdtiC5s7MTRckHP/j+3d39F198IY7Txx9/nIgQod1uCSEYQyJXFHlVVT4757Gfj7T9RpJcfI9tmW8h78+cbzFtwefNEZ01hEDEHDmC+bQQ4GzeqR0YLmJy3+hQBkHoHIGdpwGdFVWplvqrcZy89NKFw73911+/WMzy69du7u3vP/Kuh1c3Vm/ceqeq8+deePY73/k24xDHYaff+9Vf+7U/+MLn/87f+T/+1M/+1N7hwc7OXjttl1X1sY997NOf/vSjjzyABJ/7T1/5wh99fjAdVKa8+9z5LMt2dnZms9n62sojjzzy27/9W7du3XLOnTp1Ch2ZxgwHo1Onzrxz9drGxnFE6Pf7f+kv/cSlS5f+85/85zyfhpH8+3//7973wP1RFJ46dernf/7njVFlWb7rXe/a2Dz25sW3x+NpK20DwOnTp3e2d31z3yRJTp06BYuZMqdPnDyxtva3/upfR8aeff6b//Zf//qFl19+/wc/OJqM67oEzh577LGDg4N/8S/+xZmzp06d2Pz9//SFu+56ZH19fTgcvvXWW7/1H//jxz71sXPnzly6/PaTP/iDW9t/sHni+OUrVwD4/fc9vL2zw4XgiHVdDwaDS5ffPn7q5Gg0UsZmWRuAut3uKMtGB4dKKYEMLFhtVFU/8tDDn/zhT86q8u0rl2qtwjjY2Ng42B+sbayHYTwZT/f2Dvb397XW3W43z3Mv1iwKplRtrUXkaZpOxvNauqMt3qhmNpvNpuPN9Y2lpSVAluclWhsEQcDYnIlZuEOaa3LmnI2bgyqvVCAvQ/MJfv7dXN1C53NHSn2xce98mX8wZFm77f2TH9k4m82Mtj4i8q/xgtc5yLA2igM/EqUxmhkT+ZwAuThJPOukvL7eOfLJsTsuDPG2V/Z1MAxwXls2hyOE4ujq5u0OfKedKJT+4q21jTZVhUmAgDbiUtWNbSqthIf+4+lsMCuzzpJFhkwe0ddGWYvGOKeM8c0fPU3KOZdRmA8nipwBFhDjHKxxylprbaNUP0uQydoo55EYkghD39UkiELkzAPisizB6KVWyoWQQeQTu4HgDAEZiWDe+4I4M8aFccSZCBOazJTRzloLDAEFQwIia62ua4FMMCkCKQQDQG2tdZqxxWgtY50x1loGnHNutEvTlHNRVJUDjOO4qKqqaiaTWZZlcZiIJWaN5pyMdgQ6CEPtyBIRMsGYbxKpnXXAv2cmEy0AHsN5I8gwirzBAgBHBhaUGvntd2TgAGAhZHIWDDkiTWS9V3YEXuoQShGEMrABMErTlIdRVRqcFMYYB0ica2utdZ6w5QhIjjlrgVRV60Y1Vc0R261MCIYLwq+sq7oBDmQIqqb2/Ys4Z5zNle6cI3PMOSSy2phQMOfIAeNSGiAQEoABMQThR48tJh4LNNw55xCyLOt2+yKQTET51o5W1lobBFEYym63m8aJA6hKHUSSNM2L5Jw7goN+Hqp//iiU8tAfFo0g74TI/vX+eZ+Fs9Y6pwj9jOR5B6QFFkcZhadOnaobc+3WVlWrlbX1NE7H43GcxQDzoI6x+QjP2wvtbkdWc1PkwZPH/ujb8nx34/yjdKPzaz23Mx5iHiU0nDZHAcDRrQCAI0Wtv4h5I2ACAvLiBx8V+IbkR7br+7jkv6C4XfC88zm+jDE/WcK/xhdrgZvzwf4fAOCbb75548YNB8AEPzw8LKqyLHO/Foiota6qCow9auVZFIVbzE7xWn+PkqMo8GCCcw6L8nchBNKc6eecA+MAcBQKoh8dyrkh55lRL+WKOJ/NZogYhCEp4wttfQTG5ip6nzny/dnmizJfwcX9mQcb1llrCeZ9GJumIULnjHXKkQHriJxvejSbzcIg4pwLiWQbxlgQBJyJ2XgCYeSLryxZgwgMOWDjnK/fJQRg3Mcb4MjngIgQCQmsHyIFDoAbhsQFguEA5INWZIzIGgP+3/v017z9qjXg0z2c+RCekAFCWZaCgb8Vnj31C+p5q9vpgvlR4uPxeDgc+pf5/gT+9aPRqNvtRlGUZVm/34/j2E8GeO3C62WZV2VT1+V4PC6r2crKCuf4Ez/xEwcHBxcuXPiVX/mVXm/pfe9738MPvytN2t958bUwStfWkrqulZ1nZoxVRWGW+j0k+LOv/Uk+Gd9117ljq6tpmr3++utCBMaYb33rWy+88MLly5fDJLzvvnuKYnb69Gki+pM/+RPG8MyZM/fdd5+U8sEHH7zvPvrCFz537vyZdrt9a/tmmqaz2eze+++r69oa5Jz7U5Wl3XavnbQCQ/MaaJKBlFKGgjG21O1NJhNfJjEej8fj8Ww2S9N4qnUQBMCwKIrd3d2maWQYZ1k2j5TCKAxDILZosQraGrCumM4uX3p72B9sbm4GQVTmuZ+J0ZT1bDKt69rX7/YQB8NhEIVCCC/lz/N8aWl5eXkZAHyPlsuXL4/HU4/FtdNByKMokL61gLFpGnd77TRNh6NDIVgQhXEaOecqVSuttNZxkC0tLdV1ffPmzSAInnjiif39/Weeebrd7vZ6vaWlJcaYb0knpez3+2WZ46LNscdgjOaNsO40L3dYvDvJkTvAFQEBHVkS58+gc4wxYvNXOiCwlhZpxiAICCwhWmeJSJw8cfr+++/P0vTPv/b15597bnl5+eTxEwcHB7/6q7967vzZu+87/5GPf/jkqY1PfOJjr7326pNPPsGMuHb1elU1L7/y4n/73/5firo+2B+cOnM2nxYr62t/9a//tXvuOrOzPZRCfOhDH3ryfT/w3IvP/r1/+HcvXHj5Pe95z1133TXJJydOnvzQhz/81FNPKaU+9/t/8Md//Mc7W9tBKDqt7u72zqmTJxH4eDTe3NxcW1tDxOe+9ZwQ4viJY8vLy+12+/577tva3SKwd5+/+4//81dzVf/NX/hbH/rQh/74j/90NslfeeXVUIQPPvDQSy+99MADD7RaLSnl2VOn3//+93/xi198+YUXfuSjH11qd5Ms/T/8zM9mcbK1uzM8PIzTdHNz49F3Papd8/LLL58/e/bFl1548+1Lv/RLv3RwUJw9f/doNPpn/9P/67d/9zPffvmF93/gfXEc/+mffO36zVuzIl/dWNfKVapZW+1NJjOVz8d5XLt2bTQaeSxe61oy9BP7JoOhJ2zAkTEGOdvZ2RkOh4fDwWw6TTvtyWTCQnn//fdn7ZZSxhpXlnWapoOD4Ww242EYJwmXAhimrayqKm0MMNze3el1TVSURDQr+mfDk2kadbtd1VRExBA7nU6Wtcd5NRnPLJG3E+h1tItthnMtBRDziHkuOqajvNJc+Yye4abbhWVzLgfnUtR5wxzGvrd+LhCSAVptSm0arZRSQCilTKLYq+Km0ykRJkkipZSSkHM175hsnAVltHNgrQ2i0JAjgxyBjPFpeYZojT6yvOyOPm6LjDN5emzhtmFeMo/onKvLcjab1XUNd4Qunp3yYWQYiUSGWtUcXZaG3U4LELN2Hk2L0bQkhuirhxnjjDgQItZ1bRwQApcCEY0xKESatYWMCIAxwbkEJoiAK2uMm8wKZZxpTK1MpxWHceqcI6DReOwBRKfd9W/bNI1xutYKC1trJRggOYYkGHJkxqhpnjsH2hoE1liLyLUBrYEQUHBkQETWWQYAjIdhyAHnMm9AIusrQgPOiEibBgkYMY6MAwNHPgzwtGsUxUEUGudEULFFh74kDYCcMbUxxjrLGHNGG+OY4Oi7JjF0QHMJipvjM1rMcqI71DIeoPjSJURw6AgdoVvokm7vMQYcGENwRIicEVBVVZzzLIu1srNJ0TQNJnGaJUzNlcds0fgPkQUyyJXyYwYQHUM2H1wD4KfwATjnjOSsnWaARoZSgLTWOqMcOeIIyI8u+8hkey2J34eN0droKEwYgghkEMXEGDLhlQrWHYHS+ZlMkkTpGhxPk6AoCj01XIZxHBfVvHmr13U0TRNyCEIB6HxOboGk5/8xhsZYAJJSeFDos+GMMSBieLu4AgC86/DP8LmsZ1F+gOgQLJAAcEBzDOWo0+lkWTad7TdVnSWZL5vzmW6080HCQpg7yVEAOCIdFtE3cM4EZ3O1Fc27T3qW9KgiBO9wkEex/cL9LdA/91Nm5zj7KLwHoPlqfLd0aAH1CYiQkAESAV+s2p0W7CjSWIQr8wughcrR3x9tDIGz1uKiCxAAkGVERjAi8joTJoSYzWZV01hyWusoicMw9L3yfKRqrZULrfy8EuboQUSL3k1HJfLOOd83eT61UDt/z6WUTEgvOjfGWHe7GtUbfWOMccb3ANnf35/NZh2ZecPo3LxN+9EdOIImtBD3e79BR1BlHto5YwyDeX0CImptfGTilwoAwTEA5qMdrbTPMFlrp9NpU+exCEpdeDmfDIMgjhyBMYZ8Ny8HDgiYY4z5fYiMCY4I81S2H/nsHBndIGOCcyE5ERly4Bw6YoCOrLOEyBmiQLTz6ch+vDEDQALmWzIA2KosQt+fQEo2L0DyO4QYA8bgu0l/PDw8zPPcYztvi3wY1ul0hBBNU43H40uXLnHOjx079uijj37w/R+aTse7u7tvX764t7dHYPI8393dbrezEydO/OiP/ijn/Dvfeenpp5/+9re/c/99D73r3U/MpuXe/uFgMPDjLMu6ms1mkrNH3vXwxupaVVVvvPHGzZvX/Xk4d/aufn/5xq2bL7744urq8j/4B3/v7rvvPjg4+LOn/+zNN98cDof33nvvI488LKUsqvLg4KAV95b7K373bm5uHg4PGGNlXb366qudTqfXXfFhW13XWVo3ptmM12QkkyRJkoQDn0wm129tHRwcrPSXyrLc3dotioKMreu6qqpWK732zhXOsdVpr6ysnD17HhFrpYwxiDyO4zhNAGAyng2Hw6ZpBEfpjLNa8sAPlJaMd1tt51x2IhsMBrrZl4wbZKZRTjs/64AaAAA/TC3Pc78uUpRZltEdwsiFOVXImQFnrQ2ZaHeyTqcVRUEQBMbosiycHxkpUEhmtJv9/xn77yDL0us+EDznc9c9k++99JVZvqu7uhvd6AbRQIMwJEiIoqjVSBxKQ4mUFFzKbKzWxVCxmtDuKEKzMRNaSbMhMzEaaSSOxJAhJRGABA4IIxgSIBquDRrtqqrLZVV689x9133m7B/fvS+zAHJiXnRkZGXnM3nv953vnN/5nd8vHSsZMsFkKBlnKlSbFy+srK8dHh6ORqM0T5MkWVjoLwz6zjnP/5mXyh6O9MHBno4OecX1eq9x8CRZHy39PJufruSIyL2yIgAAMEAANE28QkDG0BOzoRlTtMB8jAqCQEzTcV7M/tAnPvGJT3ziP3zyU1/5ylcOjw+0LqUSX/zyF2/fu/H/+/t/d2l1cG5zfTZLX3/9+2VqENgv//JfPBmP/tH/+I8551evXdne3m4nnZu33vr//vd/65mnnr5y6fL3v//mV7/05Q996IN/7f/9X33y3/+mEOq//q//X1/76teSJDo6Ovmrv/IrP/K+F1ZWl27fvjsYDD72kR9bX1/9M3/mF8Mw/OVf/ovD0SSIQgf06U9/OogjIlrodaIo+JVf+S+ffPqpZ559+lvfemllZQXRfvRjH7p1//5v/Ma/uXLlsftbdzbXL/zZP/sLR/tH+axYXFzKZlNdlrPx5LO/9duf+8xvv/vuu3/5L/2F//DJTzmjL1y6/Gd/+Zf+85/72ddf/97NO7dvvP3m/+Hx/+zh9v0gCCaj0SuvvBKEMsuyf/Ev/nm/vxHE0dWrV5599tl3b9/s9XpBEHQ7vTCI729t7+0d9AbLvfag0+kEIkjTTCIPkliG8u69e2/fvHEyHJdaMy5LXbbjJEpayKUji4wROkuOquqVV15ZO7duyWpyFbgbt24GScyk6HZ6AJCmszzN5xFfcBaFoeQikCoOA45sPJ4iQX+ht7S4xJgYjydFlpd5EQXKOXf10uU0TcfTaRBGnU5HGzgoj8bTaRS2iWpPcwIQ9TTsnPNKDsgB+RVJCAyE55QBApFDBw3bv3EP5YxZ531AjTEi8ArHp9i/Pxtmxay7vBiG4Xg4cqbsL3QYY2maTieTTqejpKwkArEgDqSURQmWXJkVPkcpq8JMU8GEUirLCh+aa6CDvHKM580xBF94cAAvP2EJ0OOEVEs9oG+QaV0BAJMSEa01WlvkIkmUNdDoGQHnmIRRr9frdhIBtsyF0WWoAiGEda7U2m9piwyInKPKWgtUkeGITAghuGC1uF5lrZIiCiPr0BhXagdaY80pcsaYwWAQx9GwHDPGFvr9Xq83nk68755PBbwgjwwDzjkCIfm2R8E94cWRklwppcvKGKOtY4wpFQpkzulKE/KQMS6ZBIZal8Zqjkwpro3TRIwMWMaFBIacQSD8ZbHWOcG4UkJwhQDOuW4SV6WZzVKH0ApaEng4E2UFQrCyzMmWQSAFr0XNOZOI3FltjJOcODLBLCIgOcECwQRjZC1xKYQQXDLB0RmDDYXHl5FekZp58fw6YWN1nsdYUZQeZnMAtYYQAUMMgqgoikrPEHgQCsbJWD0cHgOasioBGHI9mUyyLBc8VEqxqiLwyCLnDBj4NU9WVz5d8wI4UajA8SBQlkljrDXoJzItoLYwyUrnnPconaPjHjhHzv0Z48hVDvPSTKaz8XgigrYDBw7IkkMEBMNAgo2TiIy1ZMny7d2d0Wh0/uKlhXa7slNbgbU6nU2PhyfHw5Oldiy4muRTS5YhciF5PXVDWhvnPADqJUoCKYUHnIJQeW7GWa9fIgqCwDY2wD6p9bLkmdY+dbNAzDHr3YCRCxmMxunewQETvNfrpbM0K/Jut8s5J+CiRow9NwuAgIEn0riaIl4L9gBnjCNDgR58RQDtrDZaBIr5eIWAfuoXCACCwAuSknMWPJuGmBcKc866JpZ5TSD0Nk44N+46fTiqu58ArE7zmK9rJIMfpvzUySshenOy2jAaiDEGBGSsASLrrLWCcckbN3RfB/pQzhlj7Gd+5mdms9lbb701PDpkjHW73dJRkeVBEJB12Sxn5MIgUFxYW1tB+Rx3juVHUSSlLMvcUyuNMVqX85RCl4Vfit4xz69DRGT1mmzCJxFjTDDR6nRKrU9OTobHJzJoSRUzcAzIecMLaxGcA0LLrLVkocaN6jkMmg8CR0FIRIaqeZETxlGSJM55hR8ERI/6k0UiDINwlmZZkfejzsJgefPihXY7PtgruFT+Y2ut81IbBzIImVCCkIBZa5G8uzBjRM43/IgREGPWV36e1FRVVRiGnEvfQCBDFgAcCSG0MUQEYDlKYOiviVLKEmFtYN40uMC0210gTUTaETiLiBwQkQdBxBsFVb+JAAjRTqdTrXUSxYyz2kwTuJSi0WVii4s9IcStW7du3bq1t3fwEz/+8Tt37hwc7GlTdjqd2SwViXj88cdffvnV4XBYlnpjY+O555576qn3vPHGG6+8/MpnfusLvf7S2traQn/RSyG3Ou1z585d2Nwo8/z27dt3b98zxmyun1tZXh4MBt/9ziv37m0JJd///vdbq7/0pS+9+uqr165d+8Vf/MWdnZ0bN248fPjw9ddfn06nrVbrwoWLsYykDBYHvclkkuZpGATamHbSKkudz4oy33VEoYqWV5Y3z232Br23br1dmtJpI4SI41YgVVVVDNxXvvzljY2NOIxMydN0UpalNWYyrp566vrFzfNXH7/mJS+rykRJ3O/33711596DrVu3bh0cHFSVkVLGcRwH6sX3P6+rYnNzc+P8pp8YHI+maTrJ89mDBw8mk7TT7W1sbJRlORyOs7Lo9/vG2SRJPNJ/dHQ0Hk9OTk6soTiOl5eXL168uLpajUYjAGh1W5PZyWQyyvOcM5CB8hV1EARVVXpWRVGVRVWCAWCMIzfganVygDzPsyyTMpBSXrp06ejoqCiKk5OTvb2DJEl8K4M3fDmvD8s5D8NYBMrHwxpLBWeRvC8BocWmGQqepgxA4BiXvmwnAGLICLzoltMVEkOBXHDBGjViAmMIODACa62QPAwDEYfhubW1fr97YfPCr/w//8vn3v/MZz7zmTfffqMsy3Obq4UuLl29pJSoKgOAe3t74NAY87f+7n8rVZh0QiI6PjmIk2BWjJdXViezo8/89iePjo44x16v9/o7r/ylv/jLf+ZP/+L73vf+/+Jn/9TnPveFTqf1n/+Jn6uK7PDg+Na7NzrthYWFhZ/7k//FjZtv/9t/95sXLm7euP2uNyaobLV3eKCtuXDp4oc+9MHf+8bXNs5vdrrx7ds3oljFiRyNDyd7s6zSVx6//Ef/6E+/9/7Tv/vVr73z9qtlpleWVncf3F1dPtdN4tl4ooTcXN+8uH7+6ceefP97nu712sa46fGhisL3vOepZ5579sGDe5ZcKII8m33sIx/ZPHfu+Pj40vlL3/rOd6bpdHW5x8BcvXz+0vnNo5Oj11753sWLF1eW10cn4/7CIA6T8XCCoCQF5zc2d3YejqeTLm8/2Nt569YNR5TmZa/X09oCU+1ubzKdrS+v6LIapSfra2uTyaTf73POT4aj7vLgxu07i+vnjLPTWZo7bQqtta2MayWdNH2XITKCg53tzbXFtaXlbrf9ve99L5+mSRCWsyxT097C4qCz0O60Yhnl06zb7ZRlwRhbWlrSxvoaFwDiMHK1pwsyQoaEBOjzHOcYZ4jIECx5vJATQ4ENCmsdOOucrRFB5EIKhqStFZwRQ4cQxJH1GK2vQanOwxARBCcBTFDUkolj7RZ3zlGlW0GUldOscoIHjsF4OjQOmBAAgMCRC0cE5CTjHBEsBEIJIZhUiKiNyavSy/MzFJxxRgwMq8ghq2mszlkhPc/BZlp7JExwdGillNpqIBdG7R4ERXE0yXPJOTkHCAjG2TLPZ7PZTKENmBFggkBIAVmWTWbZyXBaFoYpZsmRVwMSAhkDVI4cYyCVsJZms1llTBAEBMKacjRKx+NJXuo4bnW6PcbYdDpL05ShlYq3ks7iUj8vZpOtEQEopWZFXlUVeTsoIC6FN6QMhVLMC0dUXLBWp9WKE99kzLKsmM2sA8YJOBljC+0Ua2QVvZgmhgCgjUXOGa/lEa2zWtfCTY685zISYq4d07WMnSMC5qJW4JyrihmBJVPofLqyuPjuu+/GUXThwgWyzjkIlCqKYnQ0maRTLqRkgc4LbY0AUgydE6Z0jDnG0VMXOEfGSLbUdDTWtgrjAMjqyiKhqayFCoA4IOd1b91Zb80GZB2BI4S6CET0PF6BwqLxV44zY3Q1yyrfemY8kEHc7fby0iHI0WgouOKMN2iuAyCGHIEm0/zy5cu6TKVgg8EyJ9eOotJo4kSmkgiBUpJTKIIsDF15zFFIGRgHVjsG3LP4A6WsNgvtjjds6vVXZrMiL2yUdKvKhIoXlbG2ElISgjGOS1hbWW0pQDCmLFYWe1cuX5BBUBq4+/BBFHcsQVkWt2/fEoKpODo+OkAmCLmQfDgeX9jcHI1G6Xi8tramp5lSgeDKkQEDaTY1xiRJwpH5iVIpOefMz5hCPY5pffMtz3NrQUqO4KrZTCnJhLIOtCNkHJjUjmTUvnN/G2XodJoWE2NNuxVwZjnxalq0ZMwBBSAhy/NcKWVdCV5pBcFZctZ6NUYVtZ011lmvKlujcUJqYz2gXmPLjHFExry4ftNJIH+0GeucsQjoGHBEwroEddZ67JlwfpxiTbG11s7JYs458ooGnNetT6yxAmTkj+DSGQBAQgfSU9stgbPgKi2llIwjkAXiDBkDsqbMs3pRofMFYVVV6OiNt0Z37t2/ffeeEGJ5eTkOozIvrHYi4LEKQiaIiIz1Qhdh2DkZnYhAhVLJQBHYGtiuHNRMekDEQCkAcNZmaW6tH95h1pLOSs8VBACp5DwFIQ1z0RHfRFLIqyxn2jFmuklcFRmGAkXgEBw5gcpoU2YGiQHSvJRCIPQuuQi6zGutcWTGGS54GIdcijAMq6rQpiIwknHmhQ208/aRg8VFa8o0mzHOLbne0nI+KyzjnPMobkXzsRxywJhAyRivB7oR/bGVZRlxLpiY9yYYl1wor9yVphljTInaXssBldoyLlAgAFSlKcvSIUglUSBpYy0xxiQXfkIaHFTWcADGvOIAOOe0dc5ZxhiXwlNNpAw4d0mSVIWeTWcMODoMVbjQWfBVgRJSiiCdTaqqWlpa2djc1Mbt7OycnJz8xr/+t9bayWQM4JZWlxZ6HQfWOlpdO2cJ8qLKi2o4miwuLv/4j/3E88994NXX3hhP0qIoFhf7y8urx8OTNE0Hg8Hh4fG5tZVAyl/403/mzrs3P//bnz1c2vvzf/7PP//cj3z9Gy8BwIc//OGbN9/59Kc/fefOuzdvvvO5z332ox/96GCxPzw+OjkyknPJudXV4vp6v7vA2Ob+0eHvfPVr4+nk2rUndnZ2QhVZa7NshpytLA5WVwa6mr3+vTvTPDPOVkWZ57m1NgqCXneh1YqvXr6UTkbD2WR4fFJV1eLi4ubF89evX99YP5emKSc3Ojp86523Dw+O/fBx0ukKzt/79FNx64VW0uGcz2az6WQURiqMZJqlb7315sHh3v0HD7IsC8MwDOOknRDC8eg4zbMoigw5XRlC3eq0O52ONy2Jomg2yyaTSRQmh4eH4/H43LlzSdLWWk+n0+PR8dH4KAilZNyXlH6yvKpKBmC1tgCMSDFRI54gQDAypK1GwlCGvuTmnOfpLAmjOAjbcZLn+Ww2y2bpZHgyHafdbncwGHQ6XSmldztFxEk6AV5z6gT3c+pIRIJxYwwhcc4MOe0MMlRKEYFpyDxKSOa7c9YIBMYcAwPOeashzjnjXLvCaI84oCOXF0Y8eHj/977xta0H9zqdjtblvXv33r1zazqdRq2YsVo3l9C7kDitrdYlojdS9rOS9Tv0et3FxYVut7uw0Eo6ajabGVMcj6udh3uf/s1P37t9LwiCq5cuv/766y9/+zsf/ehHf/RDH8nz/D/+x//ox1yGw9V//a9+/cHOdhyH5zbXt7d3F/q9Xq937969Bw8evPlm+/bt2x/+yIvtdnJ0vB8nQV5MiajdjjeXV1Dw3/qt/1hmeRgqx10rTibT0bnl9XQ8QmLLi8vPPPUMOCyKam9v78nrV1bObwLBbDK+detW3IoWFxeDIBiPxxcvXEhnsyzLHn/88d3d3ddee21xcam74P79v/+3H//4x3/u53725OTkn/4vvwrA1lbPbW5u9hcGb+/dmo6LpaXVK5cuOwff/e53iypfWVkqdNWO47LQ/cUBwE6aFVVRKhmoIFpaWc3LAskiZ7MsU0qdnIwmk8lkMsVQrqytanKlrpgUgEiMC4VCU1UZ542c8pwBFXnOEXRZzabpQqd7cjwm6+31nAdCJFeCo5sbqUBlHXl1v1OUHAAdOgCgmg2GiITO81w9XubBenTo0DEf7Rgx8oHe+zgyIvIKVfVZfbbfiadf/RGLHIWUrW47CmU2GU4mI1cVZVkyxqx1DIWQ4JBbJO4YIONcInJwRNYBc+DIqzaCQ7IAzFkE/2d6+MnL1c0J/c1pjSCAceHzBQeEzjHGOEN0lglkBI4hIrOWjHFa60BKY4y1jpH1PspWmyxLJ+lROwkX+otJkjARqrDFZBxm+WSSMSLbcAMsgCViBEWWBZoj8jAMQybG4/HB0VBKWVZWCNHt9oIgMsaWZWati+PYmkIIxrjHq0yWZdoYL/ThqaWMMW1ri2WJmOZ5JLkHHqSUUgQEzDowloIwVkHkKbY6K4UQURRpT0b2V6a5U44QnGHA5ppI8/TZAavNBWs9Quat39I8ZYx5hQ0FgiEEkrWS6Ohw9/yFjel4srV1b21lnXM+PhlHURSFsSPwqJvVRlel0WV91+oBRUK0vmtECM45C9YTi6FhOjLGOHnwl5gjanpKDFAoOV977gwnUleWarIKMYFcqFDx2AVZUWmtqXSWmKk0WGdBG60Drjgh45wxsIDOl8jOdXoLha6mo5Gr8kGnpYRCALLagfMMZKdBW2cFuVJbazkIBGSEXm8cwLdoiTNfVwsEXhmqDFniAIyc74EASs4Ec84Zzy52NonagtN0XJF14EyZ09FkstgfHByP0qxI4vjOnTtxwFcGvf7iYDLOjNEOmLWUZoUlVCp0DrS2WufW6iiKOv0OQGc0PqmqKoyTmhlibZZlHt13zVSAB4PnDXEi4gw4kvOAsf/TmGScbt97cHgyXFpZRM6KotCmUEG7FbeLWcWcBaMJiazjyJSQoQrSbMY5dz5lBxK+F4LgnANXa47VNxEBAAy5RnmTXMPC8ot3DsMDeknVJrQhAtWgfI3sExF6yLlmNdUnODRNB2Je3QCYF4n0nB8O5JrGORFzACCk1yznnNeyj2Q4MSuEkEIIjn5i23ln80ZonyP5zrxfF3789yd/4g899thj3/nOd06OjjmyUMl2koAjq43Rpb8FDCUiGmeDIEDBiaF21vl5fSIACLx8oz/pG0MMqsWX/Qw3+VAAAHPWATb8qBqxbhIRxhgSBFwgMoHcMR4EgVRKCG4tMOTOouIInBXVjE65U43qEUEQBIi1x4U37SrLMi/zudxaE35qgEhrXRodVJU2pUOWFblD45wx1qEFNI4xi3NtEwBEjgiMA/BT9pcjx2WAvt4CsGStJ6WRU8I77/mJ9po6ZZyVInBNFehErXQspSzL0hcO1AzKc855IEKl8mKWzjIiCqUKw5BL7kOQc64stOCKM5ZO073dg/3dg1arFUplra3yys/PeAaatrrd6hZllqaptfaZZ565cOHC6GR899ZtBthud6RkQRBlWTaZTiudv/e97zVOLy4unjt3rqrM4eHhRE64UC+88MLnPv/F2/fu3t263+sPnn/+RzY2Nm7durU6WCrLMg7DKOr+2Id/9BM/8fH/6X/4h3/zb/43f+X/+n+7du3aq6+++ju/8zsvvviBP/fn/ty3v/3NBw8ebG5uLi0tSSXiOPaJ8vr6+traSjsKe70eOZ7n2bPPPnv37t2HW9sLCwt5UURRPBgMgkAKIXZ3HmZZmhV5WTkuZafTuXz58tJggIiT4clwOMzSyeHhoTVVHMc/8vx7n332WQDSWr/55ptVUU6n0/F0AgDr6+vdbq+sqmKWlUb721SW2jm3sLCwuNgfVpnW1XgyTNM0TMIrVy+1Wi3n3GQ6A2B5VWpTlqWuqipQ8WBpsdvrikBFKgDOTFlxJYMgCOPYaquisJO0gjiSUoZJzBgDDqhYGIeRFJzjQqu9vNxfaHc4Z0VaTx47cuiAnCNkBgyC+IH+oV/T8zqcMRbHcRiGvV6PiA72Dquq8nQg3wdgghORlzzKq9L7Lc53pjaVtbZmWCAwhlwKGaiyLH3b0Kc91vk+oFMciSxZ55yXrLAE1jueUx0MkYgBOJGm6c2bN9988/u+/WStJXReWUlKyXydrR0AaFP684Bz7tMM58AzsBlju7v7ZamTJKqqqigzABCCMyZUO9zd21Zviw984AMf+tEPalN++zvf/Na3X7KG/spf+SuI2Ov1lpeXpZTXr19vL3TfeusNMR5fu3b153/+519++eU7d94NguD1119/+umn33rzne5C8uKHXnjw4P6td3fOnz8/TifT8aS/OLCVzmazKGxNp1Oy0E6677777vn1i+2ks729LYR66onrf/oXfr63mADCbDoZHp8cHO0bY7qL/ajV7ijVanX2Dw7iqNVK2lWpB0vLz7z3OaVUEMa/+i9+7ZP/4dNvvP3W2zdvWHLWueF4VOpqZW01nRVHh6ODo4N7W/fX1zfOba5v3b8LSPv7++Pp5PzFy7M8c85Jzj0bZGFh4fHHH3/91VcCwdvttnNmoT24c+fO+Yubi6srABCH0e7hQdiKwWggYgRCKVPRvOfugBGydDZLs9nJaHg8PLl06UpeaKEkEzyIgzwrmOAyEFxwa00QBM4ZQCRy1tqyLPM8L8sqUAkRsZqRCQBQC5v7OThEfwy4htrvl3WduLBTJT4/ENkQO51rxPWa+P4ooxaAI1dKLS+vtKNocnJ4dLhry9LPwKXZbDorZqUpdW4AhYplECDWpB2HRIYhAkOGjPnUgwz4GTXfPgMAcpbQU0IQiJwzUK97iwRATePCf2Z45DNbaz0BGhvlkDrL5Mw3nYuitFUVSJbnOQiBrEoLnaZpmuU1F3Y+cegvFBEIYa3JyyJJklYr9tyVdrvdYdI5B0wopQA5IqTprCgKwZtcBMC3GrkQiJiEkWS8svW0HzligkshlABf6wAAcm4IbKWdc5V1UkrGGSMAYx0Zh4wz7lXv68PyzPce94MaQcMGMgMvSnKah5H1/6tJF2Rt+AAQBGELcWd3f3l1zWozHo+Hw2Gv1/NmaioIHBDnkjjzF9kvkrlrQi3F53N3b3hAVGdktXAIY4xJJr1kOADAmdXIhZyvwFoqBGDe5vIypeQMADFEKWVAqK3VxmDd7j/VgWk+j6ecOLTkrAvioCgyIlJKzUtoIYSu20eMM+as06R97Q11WXXK/J3vC2stITigLMvSNPMCLPOcoGlSOQTHgG9tbTG3lMQqyzLOsNVJAMFa+/DhTm9xRagwHU8uXboUBzwMw+2dB2HQKo32iWZRFJzzMEk8gcE5543f57fYBwRE9AzvyWTi6WTevvfsZWFzvw52Sm33WRtjDBFmWTqbzdbYipKcMUSmJOOcY6hkKTnn6L2niMgYbUzNzavBA2D+hQnBmKoW3/V6MXNZITvfknRGdqmhsvoY9Yi0QPPD/30PVg8fNwgI/WDgojNzBQBQq3nR6f/1dxBrAtPpVeIeCDcGoC5Z6mqWERImrZaurFLKW7D58l0IURVlVVV+7sjTysmRtsZv1Pm7zm+lHzCtL5EzHry3hrS2XmKKiIqy9CCLh6hZ8/DJLuecS5GXJXJurQAApZTF+QY8DZKOQFfWGeBM/kHX009nzW+Nn2qQUpZV5a+2I3Se8EMMAP00PDujOWutdc7WLgnNJppfPV9azJeEa3Y+zjkOj46G+98UjM9ff77C519rSp7zsx7oL7tzztjTkbbDw8MoDrrdLmPMVtrrNxBRIJWUcmVlZXFx8ebNm9/4xjeiKLp6+bFW3DrYPZjNZgBgmy6NEEKROjk5cWAvXb58+fJlr/K0srLijDs6OMiPjvI8nRW5Clgcx8srg3a7LQRbWFjgnCMZJBBCxEny8OHW8vLilWtXqsp869vf+dKXvnj16tWFhYUwUsbopN8vy3wymfgL0um0X3vttYV+786d25PJ5P79u+12e21t5YUXXmi325xzFciLFy/6ceSiyI6Pj32eurS4trKycvnq488999xnP/uFo6OjKI4BIAzD1dXlKIqOTw6n03GWZSurG0KpXq+3tLTUiuPhcLi9vX3v3r1IBRsbG1evXGq324OFHiJ+//vfv3Xr1srSylNPPfXc+57Py8KfKYGKHEKe52VZam1loPw8zP17D958883ZdBiGATIqioJJlqZpkiQAMJ6kSoVFpf29LssSiIdxJALFOHcICCACtdDrtVsd4+zweMSlCKRighdVZZ1TUSgDMVhdlIFEa7IszfP8wYMHDx1VVfme60+e7twz+iVniYPYzHl4hO7sGmO19Ba7du2aF+k6OTl5+PDhw4cPjbNSyhs3boRh2Op2fJ+BMWadq6qqlST1tAqRtUZbo33R6hznXDDOED0tGwAYE/4Yd81IFQPmaXBAHqfDGtH1Ow6xnhQWQkjJCSEIgjSfAQBzYK11PuqhF0hSQohARYwJY7TRzjnHuYzj2BgzHI6LIrPWBqFEDBmHzc2N3Yd7P/mTH/8Tf/xPfPJTn3zttdcuXLjw7LPPtlvdv/N3/s7rr7/+p/7Un1paWrp+/frf/Jt/81/9+r95443XOecvvvji888//9nPfvbg4OBDH/rQ5ua5L37xi92FFgC888474/EQiOV5DoRVXty6cbPfXyzSmeJi89zGzXdu9br9n/4jf2R3e286ml6+dqW/0D+ejO5u3R2nC8aWaTqpqmp9fX1xcVEoXpb6xs03qso89Z4nlQxH6aiqql6/T4Rvv3Oz1V34qZ/+wwd7u+/eub12bv2JJ5548423Dw8Pz58//87b76ZpioitVssLidRypdO0311o9xd+/Md//Mtf/Yq/Mj4BCsNwc/Pcq9/9jkWIW1GRzvy85tHR0dUnHi9I+4LPGestk8iCUoojc85ZcsiZMy7NsjiOLeF0OjXkiqLIq5JzjoInSZKXpTEmK4u2CLQpa91rzt2ZmTnGahU7H1RryKwZvPP/8Kk/Ifixcb9CmPdBhHlmX8dQv4Tmy72ZKW+kONz8iHLD4UQwEoC9hY4pMq3tQndhfW01CMJ0Njs6mRwMhyejaV7aWjbIEWOMcebdZ51z5MhaC44cEBIYctoY58U05vvQEfDaqxict2d1BsBPBJB1/rlepM3P4YG/yNYKIVqtVh3razsy4MiEEByov7RCTs+yfJRmjrgmyPJqmhdhlAAANfhZLVJJZMhIrizl/oTwopzdbrfSpiiqUmsiYkz4+2KMUVLOIwhjLAiCoIll8/3smpk5znnIJfqhNc9ipgoJHThnXKUzL2EouORCkKWyLIGJU330M6cjr405H8l4iAiZ7wDVjN75SRkEQc23cczVmSsIruK4tb+/v9DprqysnBwNwzBcW14Zj8fz89UvJN6YvJoKEJFzhsxBfUMcMvAvOc8+AdB7gkKjrAcABD4dZD+cqD3yJ5xJJgDIt4WCgKMxQE5IyVjpo18cx44YAJBzxjkCZ8n4qzSbzbIsW2i1WqEUQmhdMs6SJLFlSZJiFSBiVVXWMc6Ac+4qTzwG55Csc4z8WKG1jvywsHGTyWQyTgvff/Aqb37Ay1rrNABIyTfPrw967UAxwcjqSmttySDiYDDQ1gLA+vr63/gbf+Nf/Oo/2X+wdfHC5YODI0T0zn1+vYWB9LCON0jRWnsVeedcHMejk6H3A55Dm54M7afi6NF6ns4Y6zLGPB7tr20riquqaMKLUEpYq9PpOOQBZyQ5084iGQdAYI2zQjBCjmcMqogICDQRQzibAtb/60yKj/NvHgUXzhSojeclnCbrv+/agGZZ05np1SbRrAlAdTg8UxUjopeXgXpR2RpFNoaR30qnMi+coxLSF4SeG+NpdX52RXLx9ttvv/zKd0ej0aULF8Mw1GXuFZk8M+fspUBArk6zbXxU2HT+aT2gcfaH83Dh09l5/3B+3bwAtAyUJQLGjOHOGSmFrgzU+njMOiPIlxaktbYVycb0ff6J5p/N798zCEJdQNbnCGPkattEIkDgdcUjhHUcG90J8qU7nWZR89XyyLjzmUTfWsvOLBU+16qurwyc/TASpKOzJ1UzqO2cCALRmF343zDGGOtWVlas01490+m6HxsEQZHl7Xb77p27X/ziF4no6aefvnDhwnScCiYYY3NtU/+9tbYociklE4qIPNHcGLM73NvZ2ZmOx36DG3JFmaZpWhSzZ9/zzMra8tLion+pbrdLBMPR8ebmOSb4/QfbAPDkk9dv37796quvBoG6t7zS7ba3Ot0bN94eHx/lxWx8dPz0M++ZZdN7W/e9WuDx8fG9e/d2d7cPDw/f9773HR8fc8GSJPF+OysrK0mSBJzt7u56U63D49Hm5ub73//+z33uc0qp0Wi0d3jg6wepOCJ0u900TfOyvHXrVp7n4FwYhp0kXlpa8vZ89+/fv3379uh41O8v9Pu91dVVKdX27s7RybEPZdNsVpXG2wKGcVSW2jPmZRCgw6Tdeu7590RKLS71B4OBITMej72zpLEkZVAZm+flaJg+ePBgNJwS0eHhITSVpy8+PVPUVxSV1icH+yfHI+dckiRRK7JHpt1udeKIc+x2u51OHAchADk7z2maSpJxQLT2bCQ5XUi+d0qN5+l8I5dN8ycMwytXrly5cmU4Ho1Go9XV1TzPh5Px7u5uURRExDgXQigp/eo11ha6qqrKkiOEq1evBlIxFSDn/mMxxgQyXVbWkXNezkR4VW1rtdYW63anLwNIGFNJGcVxzBh4G5c0m2X5rDnXnU/9xdw/HBkiJ+esNabS5BwSE0ySJUKQQqmWZIwxDtZaU1avvPLKxz78Ua3LWTb92T/xx7e2tj71qU+dO7dmjPnlv/BLv/WZzx4eHu7tHVy58pgxxjvVGVO9/vrrBwcHDx48WFxc3N/fb7eTxx57bJqONjbOj0ZHt27dbrVCFQhtbdLuCGRPPHbtoN194423C1Vubm4+88x7pZSV0Xe37osg7HYXPviBH3nm+aedg92dB+sLmwcHB0GcyDBCRrFQlaPP/Pbn/sW/+pdPPvn0L/zZPzNNswc7uy984MWnnn1vp9U9Gh//63/1r958650nnnhieWXlte99nwl+fHKSl8UknXCuBu1+q9USQsxmMymFtRoZF4wLxqIoAoZpmgIxP9UeKEUAaZoqyZ1zo9FoaWnJOddut6vJsJhlrTg+ODkOopATtxbsGfFgAFhZX4+S6Nr1J4G545NDQHzt9dcPj4aMQm2MUMoBTWczONin5X5V5YGU1uqAB4wxP6skpVQqyLLCK1mfOQo9Btecf3ULlZCxecCtT5TTPNC3U+nMGU1nPy3UuUIt/oPolpZWGNiDg6PxcCQ4JIEKgiiM4tFoIoQYDJZU3I7jydFonGZ5UZRMcA6cccE5gs+mvFwjCiRwzlqfnJDzALHkjHMmuefkIgNARw4ZGUtI3DfyGXN1l8NZz4QFxoBrXdfrYRhk6czvJac1NeN0gQi7ERZ5Op5M87ISMgqimMmQqaCsjKtT1PokZkR+fjeIYy6DLMtmeVZUZVEUe3t7eVEyxjiXGVFZenXkaNBfYOA4RyTQRWnPlFiFh+tkLUfg0wKNmGkHZAUKrrgUkhiBQ0ekrTNkWa1YCF6Q0TjgnM1v0NkUyq9GV6vo4Px2EztNpOaBDwDKUp+Wf6Y2KkLO2+32wdFhGYTdbtdUNs/z6XQ6ny+0logzAPAnvS8tsOkRNYkmoAM/J3AmmDpkCOCMMdbWrlLEfCGK4AdHmyxnLgeJiHleNG1R7xTLvFzDaJIyxqTkXAg/KRFwqZSqSuPAGm2tcwTOM44YotZmMpl0Y4UojDHcWO0sIgiOjIsgUIwAHZUW0HMRrCPmnCXH0der5JzP9mzjgzGZTidpWmntCLhUwDhZ7ZxjzqAjIUQSRYuLi+hqRpxDnM1mvlOkFJZZmWUFOuLWFnllLXnBH38veEMycY3+I9QzoNp7bM0V5Tw6MMdo/RhoGIaeHOKh0LOFfXPMoHeBsM55+lmZ5WCdrbQzpRIJAggEXRVAWgpghFJxwRA1csG1swSOgPkGyNygwTmNDF2jxTmPJ9jUG4joJTDgD34wxvylPis9+QO/Q4+i+HWe6mpBG+e8UBKzdeCqjU3mn8FaB2cKhvnDIXq9pHkV4ddnIBUDILI+fhIRA+eAjU+GD7a2ZtM0CsJ2u51lmTHGt2iEEKKVcM79BAMyEkIEcega02s/pevvjh/a8Q9ffDYTpcbP7UgpeaMjjoh5WVCNrDu/IAHAARlbcQyIyAPVWpdCCEeW89CvKCk5OGYNlaas+Xi/X+qvVJ2yWGu9q28j7kl45krWJAQ6NSIwxhCQ1hqZs1YHUmAjhghn3LLOZupNicU5Q0vGT5NDY/PnB3cqcyr2WldBXDDBwZvBIAcAKRyCRCaUUsYYT9qwjQueNras8ldfflkFotvtLi0tDRZ6nPM0TU9OTmbTdGdnZ3wyfPLJJ69du0ZE3nlNRtJPZyGr/wq0hojCOErT1GkDACfj0Xg8LnTVarVWVlastUVRVNYURYGM+t2FMJK/+9WvcsXXllcee+LxtdVzzrkoii9evPiP/sd/vLy64oAZ43qD/pWrl9rt9ngyPNjbU0qMT44Pj/ZtWUgpNjc3syxziO12O03Tg4ODOA6l5MfHx1LKf/g//IMoii5cuLCwsFCW+eLi4pNPPrm0tDQ8PlpYWFAyUkpt7x6MRqOLFy8+++yzX/ryl5eWli5dusQ5FkWRFzNP+AGUDqCmhzAWhuFgMBgMBr/3u1+TUq6vrVy4cOHCxmZRFIeHBycnJ1EUAa/Vhx977Fqldbvd7na7R0cnIlBx1Or2eutJEiVxHEZBKElXxwcHN27cAIC8yo+Ojrz4z0JvwLlELqIoUTLOsowxFkVR2EmMp6VbS0Sl0aS1jxHIORlTGVPoyjmHJXfoLl29mCRxrKRzRgDOoxBvKnDn3BwsgCb/OVuF+sXp0RxofI3mZ6V1zreMJpPJ3bt3q6pqddrLy8tFUYRheLHf8+2sqqqyPK+qam93V0qplAJEL3bsEx6yznFXx8lGlQCwgfDQMcYYigYXY4yJWpwAEJEDOS/poLEpX8qyrHTJGJNBYK11ZIwxvmwCC0SEXDlrNVgAtJaAmOfvzmYzYwznQkqhFPe5BiJ/6unr9+7fvnnrxt//B3/v8PDw/OaFS5cuIOLa2hoABEHwS7/0SxcvXv7Wt771rW996/P/6fOEMBqNHj58+P3vf382my0uLpZl+dJLLy2vLArBv/e97733vU9/+ctf3tl5+Nf/+n8Vh2Eg1d7e3qd/85O9fn82S8fj8aC3+JnP/Idep3/9iaeefe4ZKYPCVBsXzwOHk9Fo5dyGFNjuLQghvv6Nb3zuC5/3oNdj1574uT/5s9/59su/8e8+5dlj9+7dG02Gb73zdnuhvbq0/P73v78sy3a73el0ZrPZr/7qr7bbvSRJikIPh8MwiNbWzi10uscHe1KJnd39aTb7zGf+Q2+wZLQBACFEaaqiKMNWvLyyuHXnttYVY9jptKy1BwcHSZKoJHz75o2wnbSTlraGc06ulonwKIUhd+/evSAKdnZ3HWlAvHr1aq83vPa4/NY3XzPGCCWllMZUaZr2+x1CEIIRedkecI60LitdNCt4Lnk5X5l1xj8/zwAf+WcNxDrwER+bxOuHH2dL5DPIHEunmeJkEAsGSqIp1Ww6fvPNt5VSrVYrjDvGumle6LIi6zigNZYBOnSiVoCc1xbksOajeBcCQPSzKUhE1hE6L9vhrVsM6FrcgzzxGpx1AM4fq0DkGBIxMlaXlW9TCMYY+uwbhBBBEASCiiqzjohxb9sTBCGzVForHNoz9k/+wADnhJLWWgA0xoynKSIqpay1SZJ4AwFfVPDmwQAZa/rdzU72eQBjTHDhs//55eVSkTWcqyBUkqvKlJXxPUEnA6VEYJwuC61tJZiUQTBPqKlha9StcCF8dnj29jWX2jZg6ynKWFY1C1kIwbFWrWFSlLrq9/uzacoY89aPW1tbFy5cmBOC/RLyznH1yd1As26eZRL6b+aAnHPOUw4YYwBN197PdjbtrHmyRXha0Na0Zv9cr0ZO5BMRQgQSjKjmvXCJKInIWWessdY4IMaAc0SGSRh5spxwGgRvR4rIPwvJWatLC2AqbeuipSnXsbYMq+UaiBjj2hifORd5lWUFETTTKTjfigxBCh4EMs9nZTqypgoVRz8FJJRSwdHRUdLpxXFsKvNP/sk/ZU73+/2bN28icmON9831yL0uNRH5JoA//5RSQRBMpnmapl6GryzLyWTiXJ2ieUnieS/iEWAeAL0CLKGf1EAiP5ILAJ1ui88oL9IoVO12sjToH+3sKYFxJAttOHOawFptwHMMLYIkT2FljHMhGePOYWPMfPaCNGJOtcfq/POwM9jFDxeo8/Xz+8ao+eF9GgnpTGH8SA8BzlyAM12FmjDjvU3qjt9cK4mIyM/iAQOBAMgQOdSzNGQJyN65d+/w8DAMQ1+S+bs2HA69JlhNaPGlNZBPKOd3ZG6FDfVYtmtS+Ua5H3w1WLf0VRAAgEed4ziGJjUXWE83l2WZ5ZkKyFg2nU5d0wXljWyotVpKyQWfxyVg9MNcf/iDCT86N02J5I8P7hzOd8cP3j7ENE05q+eYzrYp2Jwd2CRe/nIFsm7QnD3gziILtpEzAkkcSFtjiRg71WzlQvjSizFmbO3QJ6VkHKzTP/qjP+rIZFl2cHBw46230zTlnIdhuLayeuXKlXItF0Ls7OzEcby4uKhUWKT5vDIBcMiZJOeca3VaJycnnOOFCxfyqjw6OsrzfHF9cXd71xgTx3HSCrMs44JacVLp/Cd/8iefeebp+/fv//qv//pwOL7+1FNVpV977bUfef8Hdnd3L1+9trGxkRWlv0QPHt631j54eH86GrbbSa89mM2mKhAyEA+29/qDpfe+973Oua2te0VR+IvzwgsvLC0tnTt3jnO+u7t9dHT00ksvGWPWlhY3NjZmabmysrK2tvbw4cPDw2EQBN1uV2t9//79yWSktQ4jJSV3QFmeCaWSJFlYWFBCGGN2dnbu3Lnz3ve+tyzL9bWVzc3NjbV1KeXe3m5Zlm+8/dbi4uJgMHj7xjtG2+3t7TAMtbNhGD98+JChePLpp86dO7ezt6u1biVRwFkopT9DV1ZWnn322Xa7DQDbO3uMCUugVBioZDab5VklAzWcTdzp5vUBEDx0KIQwxgRB0G53Z7OZ1tpaPR6PiRxGISIhF4hKCMEY8mbj+6horTVkCYAx5ZrxGNaIhv9ADKEz3apW3B6Px9PplIg6nU4cx8iZv+NVVR0dHXm2lZQyCEMpZRyEXtCPeeHYRhFba+13JaPTd3TOCSERmfXO4U3n3Bvq+H1AVIuqCe99bb3VCFljjacZaKdZ44ImpPQnR1VVQAyccZYYExw4q5nXuLa8WhRFnpWm1E4bLuoadzKZ7O5tSxmsr6+3F9qBiqbZLGpFFy5faLfb//bf//vvvvpdEajP/6fPv/rq96bTaasVx0l4+fLF4+P2/fv3d3YefvCDH+R885VXv/v88889cf1amo7+2l/7aycnR2maJskKWLe+umZKEyaxEsFoNG61Wmur586d27x79142K+K4tbi4/Du/9zuXtq+sr6/fvHmzLMvLly8fHh5+7evfOB6OV9bO/eIv/mJZVgTw/hfe99wL7zs8PN7Z2dk7ONDHx089856d3d00L2RY3Lp16/nnnz9/8fLbb789nqZrIGazmbWQxEG9t9HlxYxADQa9ME5GJ8Pl1fVsWlaFjrqhNQIRgyB44oknDna2rdOhkHEc7+8fzvJ0d3f36rWr59bXC6N5oKbTaRTFFfNIKfHG+219bSNuRUVRTaYnUgrva8bQA5YV1XLOaGyhAukcCiHyPLd5DowjCH8TjXZe2qJeN/XUG3s0TX/kMQ+dAFCn/rWPl0dVHsn1ce4NVDv5zWe7HEdYWOgmYYjgOq2k04onJyf37t1hrDDaqcKUlc6KygEKoRTzPAEHzlqyzst0IiLnjgAa3zFWa2wzxDrxQi+6R0SMOOOInANaIHREDNCLdjhwjmQg/OkcJFEct4vSTtLCVJqfyr1jIITnt1hbmarkjCmlKutKo02aakuzPFcymvNnsJb3RkCnlDw+GSql/DSPlLK7sFBVVZIkh4eHZVmGYbS8vBwEwXQ6nUwmDkk0pmzKsy8QtNaMMR9iOCBDhlw4BMGFkkHhsqLSlTOCaeO01Y7QMcYdoLbO+rFXxoHxeUiaB6Z5vuLP/h/gGACAc7YenKj5BYDAfAGjtQZgAIwIrHXGWLSGcy6UzNJZmqbtpNNqtXRRjkajME4ISAgOWGNCPkkyGhCpaWn61B+A0DkgV+O78w/mHErOEWsohWqRDXJurgfjc4HTFatUYK11DUZrrS3daZEA8+ql7qX47IsarWQAAAQgsipUAYdepyuZI517XNlfD23KXFfWWlNZQmGMJeutVdjcJxWxdulyUBskA0BWFllREDDOpG8aOefqKWtrwHIEGvR7VciqMkdyZA0wLCozHo+XlpaOhhOhoqtXr1prJ+MRJHEYxnmee9DIkwyrqiqyrN1ue5ZzVSnPTj47YMAYS9M0yzKfDlZV1ev15v9rvr+gRr9knY/Wpmf1ipKSW2s5AmcoGLaiOA6DQAoGvm/krCmrqjCA2hoAx7gkZDRvIfrBWl+qkWmWKGE9twMWHDTEWnJ2Xlyxhm4+TxabLPR/qy0wf9CZeYwfjmCMMVaTFz3fkJyrM12qHYVPz/gfLjzmr4+IWmuOTDJEzpm3GScCB0eHh3k6O3duLVIBB3TaJJ1kNBrFnbiqKl2V/rMZYwCddpab00Ggpjpg2ODiiGitdaf2YuQLZv955si6c06ompCAiJ6Maq01ru61EZEfM6jfiDEiW1WVM9o5F0i0TntsggmsFX4efVSV8VHLX0tPKFJK5Vj4a+jxFCJmDFmiOfYhhODCt6a5tVjmFTljDQFZv+vrdShErcXqPRrIWmMtQ2TCd7IRiLmma8cQCThnhGDQ4zJUGzcTIRE4A4jeMYYx42ydV3Fri7KsmjgZSJWmaRipOI4HgwEj8Da97XZ7abB4eHjIGLtw4YJSytO4wzCusnIymaRpyhjTtuKcV1VFCNPZdGVlJUzCQldFUTzxxBPb29s7D3YGg8FsNvOikLPZbHW53+suaBMOev07795++PDh5uZmFCXv3rwZqPDFF18cjkfjyaTTW+h0W9M0U0qtra384T/8h77+1d/Z2tpCRJ/gAkC32/3Aix88t3nxuy+/HIahX9VxHKtACiH6/T4RzWZTIjp//vzTTz9tTFVV1XQ42t7efuftd4losLSa53maFpzzbrc7mUyste12OwgCZFSWeVGV6+vrXqypqiqOGIZhO46IaGtryxiTTsdbW1sba+vXrl3r93tLS0va2ZPR0Djb7/dXV1evXLu6srI6mUyUUve27u9s7w3HJ3fv38mybH194/rj1y5evLi6tNRqx9Pp9PDkcDweHx0dlWUZhHEUSc6Yc64oiul0enI8Ns4aRiRqtg/nHDlD8M64TFvjyMWthAnJpRiNRqUu5mei9/9psnZQUsHcqM4LgdUlr5un/vMKExoC7fzwmqf+k8kkjuN+v5b511pzEGEYTiYTzrknj/g3qrT2WHMQBMBZczTWtUQQBPNSmTfi11RDYAjAfGuwUTiYz8k0PXIAYax2jkPj+jv/yy1YvxWVUmEU1SYjRWErqx0gAGMCgfv4bw3du7vlV1gQBEkrkh4klSzPZpcvX86y7OBgr6rMYDAYDoej0eiNN954+PCh1rrX6y0tDYwxzz337Be+8IWqqoRkr7zyylNPPfVX/+pf/bVf+7WdnR0hxDPPPHN0dFTp4v3vf9/e3s79+/eXl1f9hJ8uyl5vcHh4GMWtlZUV5+iP/JGf3tneG45HK6vhzs7e8fDoc1/4bSnl6sq6c25r6+GTT1zfevjg6uUr/+f/y1/57ndf/p//2T/7+Mc//rtf/9ra2tr1J56aTmaHB8cPH+xIpRiK8+fPX7x48cqVK0tLS08//fTtd+/u7e0NBosLC31TGgDUFR0eHayvrbVardWl5e3tB0urK9N0mrc7u9vbSbvb7/et1QwFEQkhNjY2lJK6KgIhD44OVRAEofzmN7/ZX+y/+OKL2/t7b779VqhUFIQMTJbn0ChGGXLbuzuXLl0YDodCYqfTOT4+FkJOxhkRZXlaFIVzhgs0tgbl03QyGg2BizBKkrgTRioIAnLeWoUBASJ6+dcfiN3zRXz2f9VnpFe++6HnnP3B75v6MwDntLOQZXlVFoFUvc1Bt90houl02uv1olZSlXqSzrKiqCqjTSWFcuCa2tQhMi87UVXmlK/SDOdxACEYAwfgvyJr7FQ1/GDT379eFAVVVVnCdrvd7a5wEc5yPZ2mvk/tU0Appe/Xl2UpkKtAcaGKyhVFZYgRMs5kYziF8xSEMYastnZeWlpaX18HYB60m0wm0+nUi/QHQYBIxlSMsTBUHoaZa6oAgDdv9j1oaOb5AMDb4M6KfDqapGkKUBsnhWHo54SKvKiq1FPYQxEZY4qyVJKfSVZOaa9W63rK7dGH11OBM6mVL7d86DHGcw9cnudVVTmktXPro8nQo5j7+/sbGxu9TveNN95YXOZCeZJPnSgIIcIomBmfBT0SIpucCXxcmsfQBnRpVNUJichaZ63lQp1dovNX8Jmus5oxkIJx76VHFARBZQw5JqWMoigMQ2CiqnyNgV7ZjYEDRgCOnLPWtrrd8+fPO52PDncBHeMYB6qwurJGV1pr7bRjKvSL3W8tx+ajw+iBTk9q4Fw68JQAw1AySZ7SA85IP7hlKsPAVPrg4ECiAbIMrJK8HbRZXpZpMZ1NPL3nzTff9Ll+wdlwOIyiiHu1BiKf0mmtlVLpZOpZvF5pjoiiKOp0OtsPHrZaLT/X227XIndCCK/2Qw2Y7RrethACOCNgxvelvA8qI0Y4ngwPDw+LcsbRGlNNRnmeTsejY+cclyKryqqqRBSEPGQqqKwDQiIG5MgBOmfBIlgRBH5K+zQdZ4iIxuiz97dObf/gBzao/w/s+h8OdGd/ARvoF5qGGJ8n8WDroEEOzqT+AACnLKHTBwAw8gZ/XAhhy4pxJgUXQghC54FHBxwwSZLr16+nabq9vf3gwYOFXmd1dZVzPp1OdVnMtyQBWGujKDyzf8mn8lVVef9df0x4BhciArHZLJ83IqiB/BExTVN/kYUQIlCsmZf1HSFAskRc1PmAN8pzzmjnuEDpNZcRfbH6+6b+tXtxDXnWmqF+ks1fRs78dUbnnIXT3Gj+yv6tu+3O2Rs4v32uofpAI8hqrbXkgjD2YZgaYk/dMHcaET1/eo6mcc6VEraZI/Y6nf57T+gyxsxms9KYqqriOEaEtfWVBw8ebG1taa2X+oPNzc0wDIno+Pg4DMMyy+/cuROGoQ8pR0cnSZT4NwKG6JAxxmsKE0vTNM1T32sVQnQ6HdrAPM02Njba7XYQiunJSCpEgodb+5/9zG998MUXVpaWDg4OOLJer1eV+sGDByoMHn/8cUPw7W9/W8ggSZJ79+4JIZIkiaKgFfWGw6N+p/u+970vCdTi4uJslj799NP7+/uj0einfuqn2u322++8df78+dFoNJ2Ob93a3t3dXV1dXVtbk5IrpaIg2N3dbbVaR0dHb7zxxqVLl1qt1nA4jOIYEZVSQrCyLCfTkRCs21sYDocyCHzbEBHLsjRAjLFnnnlmYWFhcdA7OTmZjsZ37tz5zneOZkUex/Hu/t7169eVUpM0ffDgwcJC79y5c1mRv/e9733qaecRsaOjo5s33/30pz+9vrTY73Yfu3blqaeeWlxaLMvSR6rReOr3SFVVXrEwiqJa5JKfjoNbW2vhSBH44jaO4yhK/LIvivYsmwaBiqTwyhNaYwmICAEXZ2OFh9vp0ePm0XPz9MxqaglERI7Cn0rYOPFpa7Is80ILxhg/CeDXOmuGVcg6rbV1TpCohX0QwZH3Hay5nY6co8lsemaPeCAPWD1jcyadQyc6nVZezBgKznlZ5ogYBMqYinHmuyppmgJip9ORUhZZyZkI4qiqjJLh4eFRFCXkHOP8PU898yu/8isrKyv/4B/+vZdeemk8HPf7fV260ui8LMI4Ojw+anU6QsnJ/uFL33rpf/3tzwLAoL/0yU//piXzp3/h52/evPnNb7+0u/fg/IWnLl++8uabb37qU5/yh1kcx/4AM8Z84QtfILKtVivLMi9fg4gnJ0d+Bz58+LCq9K/92q9lednrDZRSQvFWK+oNFsqyHE9HjImllcWT0RA5E0q+9K1vvv697xtj7vyz/8Xzi5Lky0EQpGm6t3ewsraMgl+7+vjBwcHNm5/PZtOHDx9+7GMf29ra2t/ZHZ+MkcTe7n6gkkuXrpB1ly5f2ntwV3Cs8sJofbi3f/Xa4yqMjo+OASBJIkQ0xiglLl268L3XXgmlSJLEWRiPx/fu3auq6t6du8PJWHIRxhHnvKoyIFJKpWk6K3I/USSEWl9fn2Vj7yTFuahKt7+/v7Z2jjHm68WNjdXxeNhqxTs7O+PxeFaUT7/nWWzaT5wLrQ3nKJkEAHTWWu1ZLsBrn0IvsEgNLQQazgcRsYbr5pxjXHp8jp95KKWqqhRCcFajTT7UMo6ez6oEi6NoOp2++uqrS4P++UsXTw6PsiybjsYyDDqthHOes9w5YZEBI61tZQwAMMatc2U+jaLEEjo/xItsPqvlnOMchOCe5Hl67FF9TM5TCv/NeDgiBC4DMjabzYbDYZZliCgZB2ROV37AN8uyA6uNzlqRqKoCmQAulAJLqK2DBg/wr+yFAH2ieZxOknZrPJ1kd/I4bqkw0KYMI3V0eEJEUoh0OnHWJEnCmUCCVpxYa632NvJQi+EYOykmvoYpjdGmppBpKZWUUasVJklRFFVVzeF0rwpqnNNVpZ2rp70BwjDMsqz2uI0jv8eLojBVLTAyj5L+4EQu7Jwr6WU4ERhjeV4IIcIwtNYaqz30xSQvyzIMQ6sNERln9/f323Hy+OOP33/wEBhaS2ErUUoRAlSlTzUq7TyQT06S83Gb53mhFFc1O9n4IcAmdlvnnBCCc8k598PB1to58HmW2COVcs45y5wzPmMUggkhtCXGmHWwt7c3Hs2CINCWtC5DpYxzxvhi0wA6xoCh0xr8hF8xm4UqaEUqFAIRyBoEVxVZGMZMcu0wicJAprOyqvKitE5K2W63Y8VnIz2bTYmT36RGOwSOnFWVdoBCBkaXgZRGF14Fy1QlGT2bTQPhGACSrUpCxrSt0x3GgyAK93d2h8Mh57IsdJK04jjKqzLLMs8oY4xFUeQTF39BjDHoyAP80+k0DMPRaORB2d3d3SRJer1elmXWWqUUY6woCtcYQMZxfDIca62tsz6N8Yw7xlhVVUrwdhJHIev32s4aXRXTIg+UunjxYqvd3d7fSyttnUPJyrJk0lsmAefcEaAjwXgUSGM0Z+SLjVq2i+qTe978oQYK8fuON+Qx1jQofFufiObaF/MS1zg9b2r5lNE3ifxws9eb8l0RACOE4KI58rE+8r22j63rT/+arixLY9wcd6+qOsh4tJExJoLAP10pJYGVZWm0M8YtLS3RG2/93te/DgAXLly4euXKaDJkjNW0eM6s0brSjDGlhO+9Y1P/O1O3eZMkmY5H/hZnWRYGstvtImI6zTjnALWWpX00vfZXQGvNpJgruhZFwYViLBiNpru7u72l5aLM41bEGAsCCVTXIUoprbSpja7mkAfO03EiiKKoKAohROVMLRh4ZszX2iqvfBlff0LOuZeFxcpOJpOl5b5zDusZIj7PqJp0qo749Z/m6VWIcwkj1jjJk3XWOimE/wOttb4k8wJeuiqIatMZBo559VbnGFKepVHc8h0wrfX+zs7y8vJLL70khFhZWVlaWlJcZFlWlmUQBK1Wazwe+3l6D+gAwMLCghIKGDLB8zxPkkhbE4igLEsmBXAWx2GSJLM8K3XlgKIoWuj2zp07Nx6OGKe1a9cWB11TVq4qHjx4wABvvP3O/bt3i8qcO3fOEWRlMfDpb1lprUfj6eHhoScw99qd69evb9291+12n3/u+eeff+/+9kOtNZfB3t4eY0xr/Xf/+78jpfzRH/3RIAjCUD148OD+/fvHx4dbW/eUUhsbG5ubm+dWVrXWd+88kFL2er08z6WEOI5H43G/39/e2y3L0oumfOITPyEDtfVgt9vr3b9//9atW61ud2FhIU+nZVkuLCxcuHBhfW1lNBq9+fr3Hz582Ov1nr303MbGunH2+Pj44ODAWvvUU08FgRqPR8DYdDrVxviOxOLi4vXr1wXj2/fu3n333a9//euf//znFwYLly5dunLlinfmOjkZ5VmuVMhZ4BmPeVnGnVbVzI3VPE8CPxUSRZFfkHmeA0Cv1xNiAOgACK1xzvhxdsVFGAZ+Xc1XoHPOs/2sfYTP43sL86Nq/nW+Jv2o9zx2zWuDWlMBaA49+Hahs86LsGEDCHphEqv9mDsDAK21rTQSEEEQBNbUo4Ccz8fcfRistwwiIgohFS8rRmQBUSouuHJAZVkGceQzyyiKsizb3d19z3ve8zM//Ue/+sWvam23t7ZJQL/bz0u92Ov/4T/yR//CL/0fHcK/+/XfGA0nutCXLl052j/YP9q/cHkzCILd3d0XXnhhNsu/9KUvvfjBH93a2oqikAgcmXv37/zrf/MvO52OFEFZzQaDflEU77777tHREUMxHA6llMfHx8gIAJaWFjc3L/T7C0WR3bl7ezabdTotzrnWOq/yoqjGoykii5K402lZqxeX1+KodXx8HIZq9dzq0eHJnVt3ut3etWvXWq3O22+//er3Xhv0F4UQs1nunJvN8jTNfGtybW3t8evXbt25devWrf39fWvtQqfb7iSIOOj13r1xyxnqdnvLS4u6cpOT40ub53/8Yx9xxfRgb1tJnuZE4NLJtIMiDEN/uoCj2WxWlbPRdKKUWFpaeri110o6rXY7T2e/97WvX7x8yXFUQdDpdKI4zqZ5lmVAwiN208lMCLW3t2cq+8wzz0jO37l5w1Tm0sXHbt+8PxqN7t55FxDjKMjz3Jqy046uXr1qjDk4PpFSalMFQSAlH41GUoRnk+D5YXA6sfvDSE7zqJ/IWCNwQU2YPYW//qDnMsauXr4YSLW98/D4+DiOQzGeDIdD7zo5zz61dc4YS2QZCBkwoBqAJss4F4Gq7Rutf9Pm8xIFERe+qd4UJz4KeD+s+b5t/j7qdrtFVZalPjk5AZYfHw2LLONSgXUeokN7ilJLkYSRQCTtyFhjqZaBcw3uNe+/+3dkjHk+n9+KiHweHVZWVra3t+Mo6vf7/vyIIpbOpu1WB5qeiffT8CfWXKDAX3lq5Ee90gUA+EXr70Ke53WS4TP+JtdB5zy+O5/s9HZOzjnBfaV0Clpggx5gLesEp7wt8M362orc8+nrfzLi3BsoYG4La61/xzAMS11VVUU5E0IwwRuYn1urCZAx8FfGd2zTNG21Esn5mRSNuMA4jLXW/m42lBW/jNk8NFPD7bHW+qlmaP6Q09WLKISQQhbGIWbOOca4UoKM9VOYQLXrs29zlHmRpbNpIEyRB4ystaW15Ko0myqlWnGSJAkAc8Q1qHgYjXOrmHLGgqNilrkKKx/BGXJsRpPRz7dy70tqyRmH1loDpDgoweMoMFUhmQjDUHJCcJW2uqiyrDKGlMA0TQeDpcXFpYf37vSSxFSVp4qGYZgk/iOBLvOqqlAqf0GEEEpIv0TnuxIbKUMP1znn0jT1SJgvDn02XA8gEQIyRIbgKdLICaIoaLfbiNiKk8XFRSDjnDnaPwAiclgZ5wAR0QGQMYU2ARd+htwRofcwBkuGIXOejDu/6bWQwFy35ZSjfzqe6/+cecjyrZvm9p1yb+YrYf4K1BBy/NvhKeKM8xUFAIgMat0hIkaIaKt6YvUHHvMnE5EF8vbBzjnOOUcmpWCMVdqURjtyKLgnZErFOecHBwcAkCSJJwTu7e3l+azX6y0uLmZZ5sUTL5zfrKpaEgpczad3zvl7jYhRFCnJdS0dxs7EPOfm0lhEYRx5VpjW2kJdExZlFgSSc7TOEpHP6sqyZAKZCIxBr69lxdz9F4xrjBvPXGQiYkycPUOay1hXCHTaSKy/rzMjBOs8H4qcc0AWoC698AyF+uxrziFV/xM3F6holrffzq6ZrUesleI8YcnHMa8h66sIhmAYU0pFkXetypaWlnq9XlEUN27cWN1Y7Xa7vpy2UMORk8kkVIFrhO38oYOI1tLJ0YmfB0iSJIwDl2W++9GK2/7eNXWmLy/x1u13N89t9BcHCDYdTaaTk0gqIcRoOFzsDzbWzz377LPG0uLK8ltvvf2fvvqVrMiNtTKM4jjmQs1ms7IsOeedTqeqqj/2x/7YlSuX7t++nWUZ53Jvb29n7yAIo16v1+l0Ll68ePfu3e985zv7+7vXrl3b2NjodFqvvPLKeDx+7LHH/Mjv299/48KFC5cvXRuNRsfDCSI6x4bDIefczz7FcRjH8XQ6feedd4IoHCyunpycdLvdj370oxyRMdZJ4jiO83R2+/btb3/rJSJaHiy+8MILS0uLcRwfDY+QsyRJNjY2hFBcSq31bDbz7SAP6ZlGUx6EvX79iWeeeqqs8gcPHnz/re/fv3//4cOHCwsLRanPndt87PEn+v3FsrBVVSmlFvq9g9FxYfV8MTDGgiAMw7AstFfQl1LGcatpYlfjyZBzFnDf8QMvXai1FtDgeo2IAqJPejg0I2qeRekXatCM1Z0NKNToc9Rr1S/CBow/DVVnHowxz09mRA7BU55do4HGkHHG/IAvECGiMbbpGSCrZTAAgCPaR4MeCK1LxgCRaV3OZjMhVBTHXiPYDwMtLS2ls3xnZ+fll1893D+ORBwHsTMWrGvFyWSyn05mK4srprKtTnzpwuXXXnut217YebDz0z/1hxeXB5/74m/fvXv/Yx/72F/+y3/ZO2i++eab3YW2MWY0nFhrjNGHhwcHB/udTkdKMRwdT9NxFCZXr15dW1vXpnz6qWeqqnrjzdex4bAior/KnU4nbsdlWZa5ZowtLi4uLi0JoRYWep1O550bt959993BYCCEGI2GaT6rKq2dHafjb3zrG4PBkrV2sDy4dP7ijRs3Op3WeDyVXHS7XSnldDq12iGx8XiqQrm2ssYYKo8AWfv0k0+98t1XdFlNRmMOPFCx5KK/0Hvv009982tf2d1+ePHqtU4StxYG/kANRMAky7JUIly+uLm62geqvjo5vr91l4wwxrQ6CSIeHh5ef+pJh4CcTUfjk5OTg8OTOI77i0ta6ziOyeVSBpLzMsu3H+zYqrx/955kwhqUHJMoXl1aduiydDIanpgqUwLOnz/vnIuyXAjh3CNScc45B8YvSQBABkDY6PacKmb/QIVADcPV1SLWtvn1GuD/30z9XVFk1hrL6/LXT6cNhydKCiEEki3LuXS9q7QGIRGRCc4YADkixhCVUojMOuflQXyCgMQInBBcMKiJudZaa0nXfy9jjHNBjcKp3xtlWRKQlJJJaakuGBiBcQ6AM+bnPBnnPAgCKVApZqx2nkbiAHkjB04MGGPsFKF0vtwXHDlnQllrnbVYA4EiiXD7oQ0C2e22i2ymyyIKlC4rGxj/99ZAF4Ft2uuCC9bIBfgAxBDJGEfEGFNB4KE7D+r70WEx95Zv2h2arGxGd4xXNqjb4j84s+EfXoyyPmiJEYGvc4SQVVUZoznnUim/N3WlkYPkTAoJAFa7oih84yJptSmr53EREfmpXrhP2hDrSjLPcx/e/LUiIuMlfcAC1MVPrQTSkNGFEB7Iq6/MGUsvD7pAzZ7y65aISFvy0k5QU6o0cglNh8qzO/wHZJ5cBK7SZVEUtiy5BFOhtsboXOuq3UoiFQRBYA1YFGBrBVJ/Ya21eV6QAfK+HMwBMudYM3CIAOiAWavJAnlYx2riXEnRioMolgyNsZWpjODIBA/D0DJVTTIpZZqVK0tLS0tL92/fDsOYwBqjqamA5hvWWouqnpxWSoUqYIwR1eIB/gbxRkRl3nR2Z0BiqnV1AZGfUcys03LnnC5LBpBOx8Gg22m1OFIYicnoZHQ8HY4ngbGzvHLAuJSAnBOzNR7vODAgQmsRGBhrufWzOv71ofaUcGdGdpsA1FDC5p9wHqOajwfszMB3fRI3JcQ8TGHj8dS84/zgrFE6/+teresHdA3o0Qc++mjeBay10jfuGVrnCl3lZYHkhOJLS0vdVtuR4ZyPRqMwDHu9Ljg6OjlmjMXtlgiUBQLOPC3HI5eeRi858/QVay0PVFVViBiGISL4Oyi4wjm990zePE87fBoqsK66pZR5kVsHxjKtq6LIGAMhmBCsHoho0FNjnPPHCWMO6OylaO4F/fCbzlP/RmdwDjlhVVbGWOmjEGMeA5ICipnmWHP7/BWH2qGvSacQGQDVWAl6mibU5b9/CiBAVWnGmEAmOAeGzep2jClyDafRq2oAgGNVBT4vXOi2hRD7+/tpmnY6nW636/kF3ozZNSSNOIyCIOBQozCsAVHqVK/x0KiqigkRJYm/OMbZyngLcw6cccYGy0tBFHLOkzgOuSzyKXMkhXjs8hUPjV24cCHLS+AsiqLVpeW9o+PKaC6jdquLXGhtZ7NZUVTZrFCBODw89M0iIcTt27fvP9g6OhldeezqcHRycHDAOe/3+8fHh9/61vYrr7zyzDNPf+ITn3jssceGo+PpdPruu+9Wt6rHLl7lnMdRp9vtjiZv3b59u6ocY6y7sOCtvsuybLVaBHZvb884ezKc5mX51FNPXb169XB/f29vbzoa5nleZvnS0tJTTz3V7XYFsqqqDg4OiqJodVvGVtYQEx7lMVVljDGdTscYY60zzpZl4de2ElIBtuOk1WpdunRJRercuXOeD3J/6+Ebb7zxld/53cFg6dpjT66urkoRjqeT9mAhbDQCtNZ5nuuyMpUGYkrIKAittVk6m4zGiMi5F2p35GWUHUmJqAIpJdbWjvV4DGO16DVjjJ1B/c/C//4bVg8izmPm7//AR2UJ5v/ktXmq302nAdk3Hxv5AR+FCRw06w0Y5/MpnTPv2+xKsAIa56A4jn1CwLicdyUmk8lsNhMyCIJgNpu98sorG4vn+guLZVlxVnFUgQyjMCHrpFTTcfae9zzzt//2356leXdhYX//8GM//tEXXnzhN/7db/T7/TfeeOOnfuqn/tJf+ks/+7M/OxgMer2eo8oZ6HRbfqBeSsaFuNS71G63T45HWusbN27s7Ow8ef3pe/fu+aDmnNve3j45OaqqwhtW7+/vn9vcWF1dPRkNH2xtTyaTc+c2L1269MabbwFAUWRV1T537txoNFpbWfqFP/vnV3rrv/ON3/n0p//jpUuXrl69+u1vf/eb3/xGkrSNtnEc5tns+Ph4c3Pzwub5w8PjL3/5y4PVxcP9/el4kqZpHAZKyJOj4zIvkiiWjDvLqkID0w7ZZHSyu73ziZ/8+K//xr/kCA93d9U4JeDnL11WQTQrZpxjv9/v9buIBOjCKNKVScJuMautW4wx/YXe0cnx/a2toio/8MEPdjv9e1v379y502q1iCjP8+loura21u10ep2uNdXSYKALa7WOw2hn5+5buZaBeOLxx37yD328lQSE7o03vz9Np3k+W+j1iPgsO9Fah2FYleb0pJxHZ3aqnnGW4U2Pjqs/yqc+KxN/+vj91zcBOLp3757gPAjk6uqqUsLqstPpMAQpOTrSphSMe6wxy7LajcxYIBLIHCNiBPWQ1uksX10B16N41uPrfisaXffU2BnWsn8wxrQulFJcKSYDbX1aLk7xY+MISQgRqSBUAePknDbWGuuMA2AckDu0zrkzuSycXg6ErCg8pc8YA02qCgBlXsRxHASBB2J9IhIE0pFFYmQBGkArEhEA+K7f2cvr3y4IQ/8XzWdVqcG0fOrpmja3Dyg+2cUzdZ2/MvX9PAOI+oxcaw3YVAVk58/iqi5IPBTq8xwAZy1RVfk5V9+F0EXp9QoDa7zLwPziSyl1aQEJvV+4LhFJMY7MLSwsxEkohTC2Yoz5xMsfn77s95/E31UAsO4sjtLodkvpx0HPVjPzq+dhRX/FrLVA6JViichLQyI4j0syAhVFSinOOTAG1hirwRoiSpKkFYVApKtCV45YWBlI09RW1oEwrplyQY5CSYmatNcerbSutDY+gXLOQ1xCCLAciRiBZBgEQacTl8UsmxXgKiWjKIqE5JYZmdeas1VpdGV9GycMYs1LQ1br0otHEZEnvPmxbL+ieC0AB5zzOI49WRaa8R6fXPr0xaeV9WHDGHJmtPMawR7tdUCOvF2XEUJUVUXOgXOlKcKglaUzSzCdFZmmtMi1Aca9cpYfOGHMO3kRMALmCBx5gc2zO9STMew8yjwaZ/ys6/ykZI0mtWt06+c/8V/n/UD/mL9LvSrOiB03YbABQ860IPDR8vjsU/BMkw0ALBA6i4jaa++QAQCjdeUsAwfWJkmyvLy89eCe1nqWZ2VZegb/ZDJZXl7mig+HwzRNe71ekkTT6XSWZ85Yv7YFg/nW9n0wANBaG6jHZox2nEufogMAnilOvPAla6b86/0CclbMKl1ozQHQxxzR6HozBo7IOaN1aTRorZFz4PMLVPdPziY6PxyvHkn9Ec8eHPXNQu7qjp0VHAHrtoI74xLgV/78IjNvgYcIiNzP/zUpGgCwZswa5r2FZklIry165nUE48gF5zwva+sxpZRnPSDihQsXeMCn06k/QzljvmuaJAkiKqUUF/N3AQCPp5yRVbXWWu+HmuUzY4xEMd96QgjOZJK40XRyXFTnz62uLS0nwRpnbKEV//OX/rkQbGNj4/rTTzHAMAifeOKJXr//tZe+mRU5MBFFSdxKBoOBkmHSikIugyA42t87Ojp54uoVpSRn8urVqxuVuXPv7vHxca/Xi6Lo+PiYMXblypUgkA8fPvzN3/zNPM/DSF28eHFjY+OZZ5452T9O0zRQQavVcsD7/b5zLAzDvf39yWQyGo36/YU4ju/e282y9NoTj6+urZVaD4fDL3zhC8Pj48FgcGHjXJIkS/1Bq9UKA6m1zrIMADqdztLyIM/zrDQOHQMk64xzjLF2kpR5XmptjGWMMcAgCCQXSqnZbJaOJ1yg3zjLy8t+C1y4eJkIp7Ps4cOdd95+95VXXhE86PYWRBxwJdvt9sLCQpIkQgilgiAInK0ncW0j4tRqtZIkGk2HAPPa8rSN73NU/8s1hETkiHhzx+cb3y9R2wwE/0BW4Lcbb0REAABOR9ceGa30TyNH86wL/bQkwHw7QN2IIHCnkgPQoGCI6P2mGpijHkxCBCAURZGXZUnU6vf7XmCo1JU2JYBLkkhrOxwOtZl0Op12ux2HMWOiLEspAimD4XCMwDc3Ny9dupym2dJS5x/9o396cHCAiLPZ7Dvf+Q4T+H//lf/HX/vrf/1rX/vaN7/50r/7zU8lSRJEEZeyNKWKFCI6dPtHe0QUhmEYRsPhcHPzg7qy3tfggx/84Cc+8Yk0Tb/3+q4nMOTFLM+dEAwADo6OeoudSTo+PDystA7jQKjeLE9ff+N7ACwMIiK7v79bZLOdnZ1apIVYURRE9qWXfu/u7VuBigCAyIZhtLmxkYQtrXWaZlt37zHGLl+8lOscHcZB2ElaVy5fiqKoyPLJcETGZlWRhO1uu42ERluOLArDGzcepJNpHHU67aS9sNjr9drtdl5UQqg4DvuD3sLCAmd2YWGh3UnGo5GHXozTSilwLoqiJ554YrC8lBV5nucHRztc8OXlXpZlWZ6HYThY6Zdlufdwt6oKBmY6npxb3XjiyadHB6PDw2PFlAOXjkeHB3tVOzJkl5eX4zi2BEKIsrTwSNrniBgRsTrtw7MZ4Vmln9/3tJufzlAnf48mvn/Ao91JinRCZKOoFUWBP7eiKKrKgoiIjGCYxGGrFTvnAiVKY7NKV6UmRMYlcmaJrDWIzBFaV6O8jDGOnCMaUxo0XqeZiBw4C86RI4PE0CESkbbGWstQALhWq8U5r4wt8rwowOtqCy4F5845Z3TAwWdOjDFjyrIsLBmvl9fsLjKWpDylGLEat+LAUCiFnDnnwFkABowZU2VZNTw+WV5eDqSajIZCMCLUZd6OEwvoLDljrTbAaqsdXxtQ3bmvUxYfRJRSCI3bCFkEF4UqUMKfjsxLpCMBA8bqDoBPFPx99Yc6ItaCgGfuOhERIDmcK/yc8S4FYyoAms8gEZEjwzlyJrQ1pSmFEEoEjDF0ZK1tXKUCENxTsABACGGdN3FEWwuWIec8ipJut4vMOmMa2mL9IbPMnoZIPM0J/PCW1rosS0LmLxpjzNnfvwEVhmGpNTmosyhhLSE1XkinK9yTvNEJEfiqEDiCBeec4lwqFkUhQ8xmWVVVzqKK0JF0XorUaHI+I1BhIK12RIaIoOGDVZW2loDQcXAAnDPu51SsZQwYB4b+JjIpeSBb3XZbKHUySqfpmAiLopBSei0RKVRZaqVYEAScLDVsJSEEhqHwpHUhqgrnbAQuUAgRh5Fnffgr7NeDb3/Pd/QPfJ1jtk3IIPQG1Z12p9MKAql1OZ0MGZnZbLYw2GA8KK2rDGjroLLOOOtACFEP8BJwQkSmGFOMY8BtM9vt74InzJwl/Jx9zLPGH/haZ/bs9E84m+7P7+/8a51KEjvzss5aGytFRETWs1C8luz8LX64BGBeqohq3y7/6ZxzDshoQ9r6/cYEB0CH0EmSq1euPHh4X2u90O5orU9OTuI49mmK73dJpaLYt3SM5MyTiTnnzmrfQwuCAMn5+Y08z53Vfso2z4owZHMNDz7n8zVdt7pmYPXuK3XR6/UIIc9JW/RltjEGOahIzMNdk1pbhsI6S2cuJsyZymeS/nmoZOyRyqpJVshzlI2xkgtjnS5tlmWz2UwKX4w5v+PPHixzkzXn6uEfACDk8MOin8gQMQ7C0w9Z46ZIiJWuAGrVYmiwDM55IF273U6zYjQaSSlXV5a01rNs2pKd0842rwunMAyLLEdEj/obM5d84JN0WlZVN4qEEIZMXpaer2icmReKxJAYetf5Ulc+xOdFcXx8nCu+0O4EQbCxfu7qlSuI+P3Xvre9f/DEk9c3L17sdDorKytplp+MJvv7+8E4XlxcjKM2EIuixJiq31+M4/DdW3dm2TTLsieevP7a91/nnA8GgyzLDg/3l5aWFhf729vb7XbinNOmNLYSIo7jGBEfPnxYzUo/PFBVVb/fv3DhAhHf39+/cvWqlHL15e+++eb3vTy/UmIymYwnNy5fvepHHS5fvNjv98GaeasqCpUQIncuTdM0xSgO/OZSgislEXmpNZEFoF53YZJOZ7MZkONMEEMytjS54EyGMgillNIbJdVbIIyJYGFhYXV1/fnnXjg6OnqwtXNwdLh/cjgbzo72D/weD8Ow213odDqtVseDbu12u99dAIDxePzw4VaYBErJWMkgCAQgovWzcCpOsPFZny9p9kOOdfMUyJeaP5A10aMg1P+eR8OtqDsC2Mw1uobWM39Nv+Y9EIZInl5ENcfBAECjueKXuhMeExoMBt1u9+RkmGXZYGnxySefvHv/AREJAZzzaZp5lV8lFEpmSmcthWGYzSoAGAwWr1+/PhyOd3fNu+/eOTkZfuQjH2EMn3nmmZ/8Qz8Rx61ee/Aj73tBSvnVr/5/jo+PVlaW8mJmnSSyQaD6g65UDMBlWZYX6fnzl0aj0e3bt1dWVpQKyrI8Pj5OksQPFHLO2+12FEWDQW84PD4aHh+fnMTt2KOCflNx7nnM9sHDLWscIhZZ3u12q6r6n//x/xQn3btbW1euXFFK3bpxY3l5eaHb73Q64+FkeXnp/e97/8a580f7R6+99lpRFHG7Rei+ab+VZena6uqHPvSjURDcufXuzSz/E//ZH//C5z53uHeyurISBolhptduDRa6zz/7zI/8yI9s7+zleT4r9lCo/tIy5zKOI0TSWmdF0etEi8tLrVbr5OSkUDYJ2zIQjLGiKA739z/4wQ+e21zZ2t5/7fXv7ezsd5Pe6vLK8WgopUTBdnZ2FtodhljMMoZWIC/z4v6du/fu3j3aP7j++NODpX6RT2++/c7yUu/8xfNxq7WwsFAZOxpPZ7M8CIJ2Ozk8PPYKPdTwZX3IJqqF/IGoPtfxkTH2H4jjyBg0qf/Z9e2c+wNwMbDWLi0NgiDwBoFcoOQcgIJAElkgYIJxjgDEEYJAyjAyk7QstAHnQSIiAn8OExB4M8jmCOfMOItgAU5XeXPenIJ/dd6MnDHI8zwMQwZMCK4UU0pxrIwxHuNyzs3Vs40xZVXO8gw4ADFCBsAskXWPcKIeSSwQrXMe0iYiwbmU0lSFD4WtVousm06nSZIQUZ7nSRwg48RqVez5GJC1lnE271d4pJNzLhDzLG36eqclQRAEPrmfsxrmcNccA0BP3ZgTEJ0FAIRTMXUAIAAhFDVQKGNY24IgWqNrcAtPlXkYBxUoKkGXlbWW+Kng8Ww2Q86kDLCJXGeTA9ZML0gpk6Td6SSI4CwYbSpdce/ZgPX6mb+mqwlm/lWYz2O01nXGdxrmAB5JGuqHc85ZmF9h48BaVxP9ETiQmycoQFZrU2myan4xheBhwMFRVZRFlgIxFJIxpphsx0lmciIGjhBRciGEMBUZo0HU69BzLZyr/YfOHhI+S/O5YxRFzla6Yr7j4U4TC4aIQso0TdM0DZtxiyLPUZwK1HikylrLwxrvL0t/uouQKwCoE5F6hp55AEwI4ZsGjDEvNud7R0YbLiPmLNTOVAjQuG2ALfNCcgEAuqyKophxFgSBFIF2zFgDjCEoh+gXLnmTOUfoffcAAyaU4EEcVlTOuw228ZxiUpy9PvNvrLHzuzm/gtZafiZPhQa8wDNH9Q+cynXC2rzGD5QQPt0lIgBXD/awHwS25xHSB6hH4iEQIRhw4BzjnDhjTDoyfmf1ej0AEEJ4qaVZMeOck7Na6yAOVlZWyqoajUaOjFKqzDMG6Jzz9Eg/ah/HcVXkSZLU68qZZi7W39lHxNj85zTaziPGWT5yEASEQOQUqGZCqSZE+QvcBFX05aSuKji9HafVFJ3CBHQ2PTp70ZrvHBArq9Jax9Wp+r5zjjFB9gexpHl2dfYeNYGdjAF6dCsROESMg5Ca+R/dGA4QgNPGM2wR0TlwznkPeE/iz7JMKeW1F/2GzaapT0XyPDfIWq2WD27edR4BH/2Twfdd/bwT8NN+hTO1dpxofPesteRQKSUYrwCNMdPptGTktJmNh2TtYKG3uLIMiPHNW2mavvrdl08m097ScndhcWnF5HmJjPvybzbLeu3uxsb5h/e3bt263QqDpaXFYjrL8zyO429959tEdPHiRSLr3XCJKAzDoii2t7eFEP4vvXz5cpIknU4HGlnYsqycc5wHiOiV+y9cuHDr1o3pdNrptlqtWEp5/sKV3mDgJ4X6/b53Vu31egvtzmw2m06nPrxEUaSUbHeS4fEJEFhtMmMYE4wxzpjzmBQyBogNfVQ7bY05G45cMxwopSwrzZiw1s5mM6MBEdfX11fX1yyjaZpOxuPReJxOp3lRVEU5pvF3v/2yULLTai8uL62trA8WFxe63cFiL69yQkJrjKmMsZwTVygErxXYGv6kcw6FFEIYU1eS80XY5D+PRJL5qrDu9+f6z6vis7mE/7dfqD5OUoOAzPsMHBAJCMkXvZ6fO/8MjUe8wRqiwaZmRrGyukREm5ubi4uLD7a2t7Z0kiTr6+t7B0dHR0dpmjHGAJgnLbTiVsJbvXb//r0Hw+OTVtKVUuaz7Ojg+PKVjTe+f/Nv/Xf/neTsZ3/2Z5NW/MlPfvI73/72YG1JBOK5557zuq33799LkigIZbvdTtOxkOzxxx8Lw1Dr8s0339zZ2dvaugfAugvdn//5n1cq+MpXvvK7v/u7d+/ejaJoMpm02y1fKXLOu91uVuYqFtMiPTo49hqoADAaTXZ3d4MgstYuDZaMMTsPt8ui0+v1kiRpdzpPPPF4p9PRWgdCXrx4cWVp9eR49JMf/4kPfOADe9u7v/Plrwx6iz/2Yz+2vj44Os7+m//2v9k4v/mxj/3Jp9/zJDj67Gd+a39/r9Pp/NGf/pn7t++MT16x1hazDBHHw9Frr7yKzDBwszRdW17RJJDAVY7HPMuyKApms9mbb75pq1lZpUEgL1zYHB5mnU6nrHJdVcOq+MIXvnD+4sVSX9/Z3/vwhz+8ef7SF7/0pZdffrm3OJBSPtzaXl1Y6XX7kovpeJLE4fmNzSuXrrba3eHh6GD/KJ1Moyhot+Jza+tLiwutJBmOh0EUz2W8peIeDa0PuUabe75cmJj3lx6hhdAjDjsN04MxX0vOOWQ+rbTWegfpH35MJuNOrDjneTErTRmrWATKaU3AnHPCO6dYM5uOJRdCBcClEIKL2mjTEiAXUkhjnSFA94PnLzb6tgjI0c8JMHDo+wAePvKMee9vV2allFJKEchACBnHZTottLFAxOr8CZ2xVVUh1HM83iQPfFd9rurjWTc1maUWnSQHaZbJQPlfaAiz5MdUyrLkDIJQMnCOnBDM2ErwmAvu24secqt7337OpHY/amKQc3meC4ZzkpKPAw4I3KntZZ3oOOuchaZsQEQ8wx30cwgIj2C9iIjAoeH5ekb+2Q6PI0PO02Nq7A+8pHEjTjzPJ8bTFBhKWQqsoQtspvScdUTMP9HDmeCJRjDvhHr+UnNxm+dSI7rs3xkb0JoahR/nnBSB8x0BR826RcaYqV1v6/cqisIBa6oh9Fz/OkY7cuisdVqXxgQMUUklBQI5Y4yzFUfgnIdBTIw7cF5dlyFyZIg1z8SdTpbD2Z3ik3zGGCGr73hVIVqQ4LeX1qVv2pZlbqpKhaFvuM9GeRS3KkOtVqvValXZLIzjokjLsuQkvOGxP9R9j0WwGs4vy9Ja67siXlPFi8n4HGV+ts2b12ePsfmh1ZTWDJH7xZFn+dHRkdYXyxKLMrPWpOkkCIK8rCrLNDlAzjgSAiJjUtjK+4WRJeIOOCJHpjy3hPjZZLpG/c+8O57pj1s4HV+brwqAR2LX2ec6Z+fd8LPBbf6GZxZYvU78lm2WhCPmj+rfP765Wk3/zE8AEKAyxhEJzoChl8xCBGTseDT0lB7O+fHxsY/S0+mUEIQQiUiklGVV5XmOjNpJK51oXxNKKQNVqwZ5gu6ZESbmodAwDKvKYIMXujMiB8ZZaEazGK+NRIDRdDrlUljLZRB5+KDRq/GpbU0RRHZaFZzyiE9TXvQzGPOfzOPJI9d6/l0txkCWC4J6CMEYQ8QZ4bwar1/Zx1tTEyp8AemvCTIGc2IDABKQc34WjQJVOykgIULTJWiOM6p1YIkInCVrfNOjqoper2uM29nZCcNwaWXZl8Q1gdPWdbXWOpCqORjPDpygHx0sy7IsS664c65uIDsDAMRwXrBiTUDVuqyyNBXgolan3+ssdhaOwc1ms+FwCJwtr6ycP3++NJoIku7Cpz7z2SCMgzgKw1AFoZSBcy4M4+HJGABu37798OHWpc2Nfn/h8PBwZ283bicvvviiMdXBwQERbWxsKCW2tram02mr1VpaHnz4wx9eXFzc3d31prO60NPpNArbCwsLQkVeDKfT6bzzzjvvvPPOrMj9LOW9+3e0XvjwRz+yvHKu1Nr3rAAgz3NdFFVV3Xrnxttvv53Npu95z3uef/a9SZIMhydbW1vrq2tkTVEUldGSsyAItLazdPrG669ra6UM+v1+vz8IwzAMAgCo8jzLMpvqKIqiVhTHsWkeSnnbojLPtNaaoWSC66JUUp5bXbtw4YJApp3lwJgQL/7IB/OqPDk8erCz/ebr3xeBOn9uY/XcatAKHTiPvaE9RTC11v6Q9R1s5xwHZJw/MvpzJrDPpS/m0en3DRdnnwhwSp6ebxvu8bYz9YM7s6HmAWv+E21q4t9Z9tGZUAQNfc6IPCtHk3EctS5eunr1amsyze7d3dq6/1AEinO+sNABAG2cUoIxoWT4f/oLf6XX7f/qr/7zb37zm8sglFJf/8bvdQe9J6493lnovvSdlxyjh3vbTz/95M//ws9/++Vvv/vuze6gq94SiPD3//7fu3v3djqbfOpTv+mcPThEKeXq6qoQYjQ6AQDOuQoTRD6dzb7xrW8Us+KNt9/ud/plWepZeXR01O/3klY0HA6NqYRgeZ7vnkyiJAzCWBuXphnnvNNZ6Pf7hwdHWZY9ePBAKbW4uCilDILgAx/4wNFovLi8Yox5/LFr3VbnrbfeQcQXP/hCEMr1ldVrly++733v+/a3v/uZ//W3rlx+7MLFzSeevP7yq69897vf/chHPvJjP/7R3uLg6tWry8vLX//d3zs8OomiSMmwmOW+2t7a3nrq6ccWBr3pbKLCZJZNi+FkZeXcklBKcVOUqa2SWAwGgyheNbp8840bjDHr9HQ6DYKg3Wrt7u7evHnzZDRUUbh/dLi9t2ecXV5ZLMsynU7aSUREe3t7x8crq+vLrXY4Go22dx9ejsLD40NEjJKQiEpdKaW4FKPxeJLN7DSdTqdJ0uYizLK8yDLhSf0+w6N62fkaGsD5qM2a3hTOG1VE4HuaZ1J/B27e2kYAsM7/x7hgAIwYAgExIssIEMiZajw6qcpMIAu5kEhgKnJ6Oh4ypCAIApCV004bjOMkigpNjAnBlQHtnLNAnAulFFUarLWADiwQATEkRHKcMQRGlohhLfbnELE2fnLOEQJDD5NrBFoaLDLGispmk1lpMcsybY1DdNZKwbkUhM44W1UVQzc/WYEBGHD1uVILAvpOvj8gnXPWGmstR5KCMcaqyjpTVc5URVlk+dLS0nQ8ieM4jlq6LAFYFCV5nnNjRSDCMOCca62zsijLUltTVgVjjHGBgvMGqq+s4b5/4f0mGw6rtqbmYQMxXif6dUkG5JWCoHHcdGdItEAAZ9qCyBhZ6whrT0BERI9aEWPMqxwBgGgOdeecqTQik7zmtqIDzpkQAaRTZ1xVFVwK5FwKZS0hec6PcWe4uaXWmDkpmJSccwVoTeWnThmiX4O1gy/hI9RqZELUpKYaX/QDAIhItfWY8UgcAgMgIYTgQdUMCTChpJS60ABAyM+EYERCIRmSdc4qDioQAtGWRVFpYqSUklEQtdumMuNZkemsLEvnDCEjsOB898an+IEBg8iATG0nVHcw0IJnUYPRpRDgrVo557s721JyxbjwsKV2lXHOQJ7nrW5/NB23O1GrHT842l/strMsY5IzxoxBn5QwxjiTjFljTBAEjMV5npOxUspARYxDVdRiiH5G3JIzxhhnkyTx+GjVtHcYZ4pL7Xxy60teP+pGQBTHsafXO+cqbavKTooZohAKGIEAREBDxpSVBeRCSCHAOvR8JweEDBgxBmQKohIM8aaPx5FbBG0JGp4rA/RO3gDMMZo32ufLlh7lK549/HweD57vAUQOARz45V2/OgP0kRAYqz0AsQmYvpMOANpZJFZXrwRey4cRwpmT/mzJZLW/jBwRParKOYpAhmHowLZarTiODw4OOMdBZ5DneWU0kU3HE0NOSrmyvFiW5Xg8DoIACYIgSJKEc17ms+FwWJbl8uKSn9lARHJY6lIIEYWJMZlfYgBADS7jnIvj2DtLzJMVAHQO8qKUlhBJSlMUmakK5Nw6GfFQCuHLWD8PZsii0z71P5viNJkHIQq/QBq+IDBgRNablaMj5xspzgJhkiTGWCWkNs4YnWXZdDq1pui1Fn4gefJXdV6dzn/CGAOGvC4vkYh829a3j+YEDMYYIptXlcAIgZAYIDIy81tWlmWv18vLcjgcArB2u93pdMi6drtdGu05M7bSaZoSUavV0mVFRAYIATmgQ0CH1ExS+TEhEQjvGluWpQxOU0M6FRxT3XaHIsuJjLHDyZgzCIOgtEScD1ZWgfH727vG2fZCdzqdPTw4vv7kk8ej8Wg8nqYpAEopu91uf6E3Hg4///lvPnb58s/8zM984+u/+8UvflGXOee8T4sf/bGP9nq9V1999fXXX797926SJMvLq0VRbG6eOzo6erC1rSvryAGxV1/53s72dm9hsL6yPppOhRCCq/39rXfeeef8hUtra2so+NHRQZZl3W4XgH33O69sXDq2zm1ubHS6i1rbQCkGeO/O3a2trfF43G132q1uVVWe7r+5fv7wYE9KjoiCcSWktbS9vX379u39wwMmVKvVMtYVZVVL1SGORkPOebedhHGMwLVxeV5WVSWDyBI444wlYIhMaGOMLoUQ1tjMlFjWonCMMaVCrXUU/f8J+9MY27IsPQxba+3hDHeI+cWbX06VlZVVWV1Tq2c1B5EgaYEsdFOkZdi0YViE/Ut/LKoBwzKsv9IPSwRhQRaoZos0KcMQJJrdIqmeq5rdzerqmjMr82Xmm4eYI+50hr33Wv6xzjlx38vq1qlCIl7cG/eeYe81fuv7ildef+3azRsHB0ePHj26+/FHf/y9b928c3O6Ob2yu7e9Oc29QxGOYVlVuc8sEpJhCSGENkUPSMaIoAY+OPDgdZ6TPrkpAMAOafaLWP/1bLiL8hU3YAhYVAtyKHR1q1eEkFj1zQE0Q2BmhqSSXsY4QAYSBBJIA/sFc2Rme3x6ygAf3L3//oePHFm0zphc5ZqcJ+d0LGPRtnFzY3dvd39Rh9t39s6Xq4j08eOH165dIwffeu8733//+wnSW2+9ee21/d/6g1////7qP/nJn/zJn//5n998WN6/f//73/3WlSu7H3/47u9+7Xf+0l/6Sztb07pefXT37NVXX93Z3Do8PPyD3/vXT548YaDaMgB4l7/3/g8N2qIsmxiystgstt9+53Onpydk7f/57/yds/OTX/mv//7t27f96fHR+fHFxTyGRACGsMgsixuPN50rMlfG2DahbWNo2+Z8PkNXzpvWGWuNWy2qcV4s0vz44PAzn/nsr/yD/zqE9Nrrr3/88b0vfPlLbRM/vH/vb/3v/re/8Dd/8b3v/+A3f+e3f/Vf/Esi+tKPfeGNtz7zlZ/8qYePnvw3/+Af2tjaIqva5gtf+rFf/Bu/kBVQ/LfFK6/dObtYHh8dvPnm585PT27duNk0EUn29vbGk6yq50Rhe2fvzmuvffz+vcPjw9xnZZ5fnJ1vbG3Gtr169erD509Xs2YZGlf6tg4iaVpmgexiviiLcRNasmZZrfI8I0uPnjyMmOrUHF+cjbc25s3q/Xv33rCvNE1FmWuaxnp3MZ9lfoSIKaVqtXK26OooAmiAuhWVUoqgVWESBB4ArsYo9AdFAJEAMDGgJEDuVF4FEZDAogCBQSGOLEkQVVQCQURSwBhGdnx1d2s6nhRFZgjmF+cnJ3PDLSJuT3e293bruj4/P6+q6uLxYz/abJMkwthCG8Vogd64KtWcEqdIzITgnc+9eOtHRZlSCHVoFd+GWKemWa1Gk3FKkgCSCCEQAhEahO3NTZXWWUU+OruYLVqTeWGhjJgTxwAkIbV1qI1xIsk5kyAxi0pUCqcQQwhtUYy0CwmpG/lV1eCUomUGZidinW2bWC1XHBNHKbOShNqqBTAi0kZGMBYk1st5uzLGGOMMiDHIDG0K0vkn7OkQU+SQe6OpRZIYUw+PFkEAQUEDSCraDIxKo44ELCkAAHXS3MzMaiY6aVVAFiBQRjzTRUKI2NE+sQgKM3Squh0tFCI6JGEmQEB9VYt8iTnu7e3cu/cgxliWY0pIhgxTNavIZqo50k1waA3DEBC2MYAkETadwIqGTiSMLJGZodcZQK1filikzHkRZbMBFGnrVhCYE0hCEeaYeh0GFjQGqqplBuecIDVtREMMIglFRLgbbzASF81c0q53ghyrVWMYLKBxGTtTo3FFUTFVbd2wJObxxuRgdmDyHNq2bWsCMQQiKlVr5/P5bLZKSZxzIaFwJOMEAmEKMRgCQTDOTjc3Y4zbWxsoIbYphAhCCNYiBpQ8z1arWVFaSOxyshldLM7ywk83N58+e6KU1XofZrPZ5uZm6OlfimKU2tQ0gai2lgANGYeIPiuMMbPloqpb410SJOutdSGEkBKySIop1IUvjKBEjsLGWGsppNg2jdY+EQ2Iqdsk5ItxXi2XmFoPNjHXMWEKDjG33pJhBiKLEiWxgAhJwhCiWIkkLWqpAQGESBhYDBqVEkvCKYpRdL41HCJzBEBLylDGImIREicAjJLWWwECknltrAv1Hlv/aYiSCJAOIFBKKXBKKTl00uWLiEqjLYiIVoCZgcgwiQAJkhgAIDSgSacIJFa7Cszeea1AJI4o4K0DSaGqt3Y3Nnc2Xe7Q4rJebm1tVNWSiCRF4WjIERDHUK8CM+feCUOMcVXVIaY8z62xxWhinLPeJeG2rWJUKRKHZOq2AVJrrQRNkmJMKlidpGmbNgYAQKIQkzHO+mynnFR1XRRF28aLs+NQzcvJOFTLce456jQ5R05owDhqQiMwjNsidlUY3fOABlDAIDEICJGQQ4IYk0TmIJJAkiGFL5q6qnXIFhNubm5672/cuEGYpGXArsnZB1XdEMuQtLBEEeEEksD4jEW6IUwQQVHwcx1qRLT9R6njE6TICIQElJgZEI0FxJA6ievc+7qu63ppCYBjVpZkMAOTUmpWSwBStp8YI6AZACGRmbnLGK3xKhNERMtlZYxDjNPpVFCqqjLkmjooEaQxJjSVt9mqWSFLDNEXRTYaz+twPF/SaOOsCTvbm7PTWdU2F1XHyno2P8snoxubN1ar1fnZDFnOL04e3P9oNV/8tb/2bzvC999//9VX77x65/aTp49CCPVy9bXf+drW1tbe3t7P/vTPHh8ff/TRR3ff//Dm7Vs//OEHxrvpfLG7D5ub21Ubbt68jWjeffeH3/3u96/sXy2K4vx8ZozZ39+vm1VRFIvF4vz01BJNt/fatn1+fBAxbl3ZnS3Ofe7KfFRV1exigcZWVbN/7RaH+LWv/d4fEL7y6u3r168dHRwWzpbb23meNzG0bRRBZ7PpZPP8Yh6Yz84vTmcL7Ts554zB1WJ+5cqV7e3tYjR2WcYhgE3A0KgaOmBkbFmiMKOAoSYx9arXAKCA16aNMaYQl7ZqrLXbOzubW1ufbtsmht/7/a8fHh999NHH43K0u7tz4+q1K3s749HWZFRkzp+dndx7+ODs7Gxra2tvz6eUnMuGGbiOSweBDITYDO1D0RYfIiJyiggG+sppjByDxBg1fF+fSxPEJEy2M1EM0KP+gbohKzTaw17nvXXYTcUgCHaADCREsKQ4225IgCwaQ4LCBgDbCJgiICOysYlqGY/LyWRizNZyUavay/PnT7/9re/+62/+0d6VnWu3rjlnltXigw9/8JnPfGZjY/IX/sqf+8xnPv0f/of/ITpu0vL/92v/3dbG9mhUvPXWG6+/8dp4PP4zf/bndna2nj59+PXf+13r6NmzZ7/8y798fHya5/nm5vbRyQkQQV9BVBwpozDI8+fPNzYnjx8/qeutf/JP/sndD9+DxIuPVvl07Gy+temMcRKlqqq6bo+PT9uqXiwWIDIel4R2VS1apJFxbaoXp6fVcvX4wUMLpsjyIiufPX7ym7/+W2+88cYXvvAlZhZC4+zN/Wt5Wdx7eM97//kvfuGL/8ZXzk4v/sv/8r/8z/7u39vd/W//7b/8bzfM127frBZLK7S9vV2MR1kOv/nbv3N4cvj9935wdf/W7du3l6vFhPxoNNrY8Ofnp87aW7fuCDQXs9OL2ZlxlozJ89wZi4iWqKnqp0+f7j67VoWmTiGmRMYIstJe1ava+1JRcETERELSplZiSMJAqPVZBmpTrJpQt2HkrZoggMjMKQkwgpDpae+QkEA577qJkvX2PimvGgB0HI8vNa6EAFCEkICg44UXAlYgLwIKEqIQSiJEFBiXxXhUFN6BhHrRcorVcs5tszkepRRSbOvlwmb5/v7+clUfHJ40ISURUAE2lG48jtmSARYkTAIGBCUoqi21TitMkBR9bjLn3cSSNVGTY2bsqpUMDOenZ8Y4ML4FapvYphiFk0jnGrBrCnf3CqWumwQsiIQWjYigIRrGcI1us6FkLqQAJgBwRAYpoeTeZ855YxGRSJGLSv7AkgS9Vg7VcwiiGJREoFU6Zo6xDYkQEY3J89wIK4JFBmaxYZhBJIHElCQmADDOeuckJQFM6QV4InRYQ6J1YU7B3hwhdL8fOBah/83LxVXl9OvfxNB16sV7u7ExiZFD0xoLiCa2UURim0IMxhityCIiGT0ZWbOE/XSgkLDCxHW8ZC2qE+kmvlFAUx7EZDAmEEn9aRP0NbbZxUKQsozbNimayFiLBCHF/sJIzbheYzkqnLcsMdQriMmjya0jn1ctJEpp1XgjKUQgSpDqpkEUgaSYg5QSR0WuSxJOSVR1haOAJEZMKXhjgQTEEYOz6L233jvnNjYmsa2qWFWRQx0BU0RsWAqftcihrsqyRBIAbkOTWbdaraxx1loFp04mk1A3A8s4QBgYewBA2yOmw7+RYg8YwSJqQtnNyYMAojADYU/bCCRgAEmAAAkQrSUBEYwxtsEzA0piwFFeIEDLQqHFFltJICCJCY0G0QIgyEAGCBDFGiSmbqkziIgFYgQGBciYJBStMDOBUaTHOspiwCzBjz5UQEq0UoaMwxoTRhGGBIIkSXFEiQE09AeRBGIFGLib7dP/MwoKKuE2I9KP+F41NtowVAlb0olqsADw9OnTBw8efPjhh1tbW9ZSlmXKNIA0QMIQhIFRw+hVW0uvD0pELBBCaJpwcnKiVse5jKDjwooxWp8xsyLRnbHOOYukVPQadAfgZtVETvmonBQTazxBUJVuTiHEhqMXSQCsXJYysNaaBIEBjHSPcc1wIKCgIWAdF0FImFBIRJwxzCkBxRiYRSAmgJS059B1JJAuZ4URTYdKvdzpAMNkmlZbL1M7SMz9PFhf41/DVccUB7gjxxRjFLQIZqA3GEq2PZcLGGMUouO999aGtEYzsGZ/+rPVEakucUUAnT5S/ivtq+uZoMHUi5EpdgwRAZFDBBaD1DA3TbNc1THLWpaL5erodOaLyWhjM0/p6Oioqqr9a1fF+zq2F7OZc+7NT7/hjf3gh++9++TRX/+FX6ib1axa+cwqT2M3tM2wulislrUwvvbaa5/77Ofv3LlzdHT0T//ZPy3LcjSZPLj/6MGDB8aYjfFkMplMJhvXrl0r8jKldHJyslgsjHEAoKKQGxtb+/v7Z2dnjx49AoDr168HbO7cuXX79isckiQoRqPlojo8OpnPl2dnF6M829resoRPnjx5+PBB7rObV/ebut7d20PEqg2z2eze/fv3HtyfbG4KAKLRZ1o3TdO2AmmcFcvl8u5HHz568rgYjcfTyXSymZflsgoIyIICwIiMoNZM0DAo/EwfDTGAkBjvtLYTOBGRgKA1nvBv/s2/eXh4+ODBg6dPn96//+Dhw0dllmVZ9uanXkcUbgOz3Lpze29vjwBni7kxTjrc2fqoz8vHsDZ0R6sRln5sYHi1Py6n4WNI8MLmAtJSBAt+kvkAGQhZQOdbCNVqorCgQOpZcbsu06paIhCiI3SmY9UFJDDGxNTooA8zz+cXdd1am33zX//xdLJ559Z+iM2z5w9ee+3VP/9TP//s2ZMm1GTir/7q//CrvyZPnz3KMvfd730rc373SztvvPHGW2+9WTfVt7/97fG4nE7HX/jCF37t134ttMmPbJ77PK9FJEX2PoOOmwVDCMwhtFH909bG5ocffuicOzg4ePbs2XiS53nuvZ/PF5EVj1iA5baNq2pVVfV0NB6NRoaoLHNEzLLMEOVFcbasbt+8ZQA/vHv38PTCktnd3rs4Pdve3v3GH/3x7//BN6q2ef21T21u7VS32iaGu3ffH08n29vb3nsE4/LsjTc/df3qtR/e/eDR48cp8mpVTUbjEOO/+v0/vHHrxt7Va/+r//X/5uN7j+ezqizzi3lF1l5cXJycnOxfu/KFL37xs5//zHe/90d/9K1/vVjM3vzUWx+997FxzpJRxEjTNA8ePNi6eiWbjCN0AhDWWvKitgOQ27atqqppGnQskubzZQhR97agURCItU5tVg8quIR8dPNzSpwuAABMgAgGkAiFP7GeXljQ8vKvO9W5zmYONTZd4kRdGYj6zuxisVqNs6oqVqvUVnWKwWBnSUejyXRrsxyNbJZb49twUtd1zUEQBYiVAb0XzhjSXMQXev16GGsxBG1Od9ya/XhNxDScO7Ocn58b48jlkay2DiUmAAQUHJTCLoGq4L1PwEkE5NLPY8/fbHpAah+8Gkte+YiMIW31AkCe57jGCaAoXOop6l4yA92dNYYQmDn1uiGOyJINVaOKAqg9wR78Jz3Dj+JHtf2NttM/BsDUt5qhp/nrDIteMrLmbsIv5oL9Ylg/x7WX13QhhrG/Pnfa2NiYz5dVVeUFKSbVOdc2sc9A+hLecM81/eqDJ+2HMkvHx9Hnrv38pUb1CkwnRFKQRkzdROpLC3c0GjGgc1mU1hiDGDvUAzCJMJJSrumXJhCyBhATA4OooHFCCknq2DIIxKbiSCJZlsUYl8slIvbZMrRt23pCFpGo8kkaDTADCwtySiIxKepHOPHaPNnJyYkzQEij0UgyiAlWITRNa4yBJG3bTiYTURrpkJzzKungvauqSjH9o9Ho/Px8VJaDy1F4WDfzI9q60aZOt9j6m7kWP2G/TqUHSL/IiKLi39hrY0E/6eHyDBJz4iBMnCiJ5kMIQpo+EiqZp/KcIKJx1gAp7kiEAcFa24Su480iCt5LHDpwGqDwsAC606Ze7rVH9F8u3aF+8aKP1kVIktZmM16cjmURLYEMt2i4T5px/8kpR3dzBkqQboUjAuFoMt7a2e5cDOKzg+dN05RlHlPS1Zs4rq3wLvLWUQ19oMwce/ZSZpYUsdcPqetVSglYvPI7Oo1UGGPPjUNABiwRojBHsNZYZI5qh7QajT1HMGqfzTgkTCS9kJrAC/QACNCJrfS/0REIiDEWRcFsYjIhQIyRBYW7RajUusxs6PKvEAi139xjSpVXyahj6VgoOollBAgxDPdX163pBygV7jQE9ww6rPnyHEu30gdzwKyGmpnbtuX+NHQHdMuN+9E4fUn68B9fko8ALXAws0LhITGykDP6QBkQQrKOPNumxRCbql6SAWPROlpViwcP7wHAtWvXJtNR3azqeoWIZV7Etnn+/Pm7P/jBar7YnE5+8id/crlc5nn+yiuvXFxcfOMP/rCqKu/906dPJ+MNNBQ5HZ0c+zyz3m3v7Ozu7QFBVVWPHz++f/9+SmF7e/u8jU+ePPE+J6IUO1nJK1eueJ8jovd+NpsdHBxcXFyoYPnFxcX9+/dfffNVYwyyNE0jCZzNJpPJ/v7eG6++9uGHHx4+e7pYLIDTqlqUWTYajU4vzouisM5lWQbGTiaT1157bXt358nz56y1EpDUa1DE1Maq2dicbm9vX79+fWNr23qXomjzatiJgxGAF/mm1h+x7p0hkKCe9fj8/DzLsk9/+tOf+tSnmnp1cXFxfnKyWCz+8A//UCQVzu/v76mu62BzeG1H/4g88MXfDAv7JWM1gHZesiHMDHi5jy43VEeU0CF/umu8FMp4Af2IWrh5cZFbnQcyZMgIkmjJDUmQxJFx3iBJnvud3W3vRqNR8eanX//www+b0DhnRmN/cPjo/OL2aOxPnzzPC3P3w3dns9n/8f/0t997772PPvpwYzoWkZs3b07Gk69//eu/+mv/7Od//ucWi8W7776r23IymVy5cnU8nj56+Hi1WjnjojCSRcSUhDuabRSR5XJ5fHL86quvGANt2+7s7J2eHq/qKpEJLMaYGMASgWCRjxCxbds8zy2ZlNg5u729mznPIK9/+u2Hjx5n3v+Zn/s39/euPrh3v6qa1Kbr125eLObT6UbdNjeu33r6/NkPP7p7cnpKBvI8jzHWbbuxsVWW5Z3XXr11/cb9+w8TyGgyrqoqK/KmTR/fvzdfLvb89sb2VlEUB0dnxtXKzygIn33ncx/d+/Bf/E//8nRxOirM/rVr4WFzen7Gyl7GzJysMRDjycnJ+cnpbpax7UTpEUDtuCC0bSscVqtVCMFZYpamaZqmxa4HiiklY6yueO995ATd/KIRuaS46WDrgIhd9qghOoN08ofDeJ/Q2s75kaH/C5H3+h6Qnhd72AlZluV5mWclcExNi9aO8qIc5QcHB1mmgsd4cXpRNc3FfL5arcSVAkZQuDe5IlFNsyj/Oir/vVPFK+MdAERhMKSBoHQqs92J0Zrek4aloDJG0nHGMzOQ0S9Tnj5ZS6+3trYCx5BSaFMbUwwDb66SUq8FxOqhySyXrcYhVVUtFgvnOoVUAFDQvMZeAMiECaQbqgWwzIKgxqltGhXBdc5B6LykIvVFtUKNMssBIRprlbNFB1+pVywKIVgkFhxMT++TLssM3c1hjF0OsNYi/1Eg5j/lWDd2VVV5nzvX1tUiK3LFgGVZ1iSIfU1ZenIPSQGNFQCN/ESSMtDq/4gESVnMkDlpQthXAXspKEx6M7TNC8gihMLYy05PN8acCI1NjNZagEZEOEUk6XXtEDT/4wQgdV2vmmaDvcuKUZZbpHbVVG0DZI0xiAzMzBEx1xtuiFqtm3S8Q8ZpHIA0VDf7CRoRgRijIdbQX/pwROE6mTMZeSJrjCVj2JiIdLGsjLeIGGOs65qInDfOuVVde++VJ5SZVdpzjaQ/cM8TBV01VAbM9LAkdKloH4B7qfnubnYKRYgvLnUB0eKFCFtrEaVpoyYYEWKKMXLSOFHzMv1RozpE1BiOQSInAiFS8gAQBB1WcYg6c5JSSqxSr7rBtUf3gnf/09fkjwz9+5HRF+IGBPPCX3WVZdUH6OmDmZl1y34yR748+j0uovgQALXM4/H4Ew20YgABAABJREFU1q1br7zyilIt6civqrhoVVhE2tAMU0YKKNKivtqQbhAZRHrxcumqP9Zai6F1zjjjsyzrpNwMEpHN/Gy+XK1WlLnxeETWhMR1Xbdtq1T4AzUNKAmvKmkIG2Mc2bWI6kdftS4G5czpFnxKbdsaYwDIgGE2IgLM2l4z1rdtXI9a9A44cvCJ56vr85PxHOMlVhrXRACwHwYge3nmncccVlp/qCUZGrnD7tDdRL3A4ks2UP8W16EX/XPX2SrnXIJL+Jnm/1oCANudLTNnzumI2qC1DAAqu3T79u0Y4+PHj5n52rVrxpjFchlF6lUTY9zZ3s6MfRrTdDr99Kc/vZzPU4gppd3d3Z/8yZ8UkefPnty7fz8JB+483XQ61Sl/AHj11VeLorh582bus5OTo62trdz7s4uLxarShae0PEVRhJDm8/lqtdLdrZ+Q5/mNGzeixPv37x+cHl7fv76/fzX3xZPwzKJVQr833njts5/5FBFI4qpeHh8cHB8f/8RXvrwxnuR5Hjgxg7F2c2f7Jty689prTdsul9VsuZjN57PZbD6fx9SWZUlEVVWdn58Lks8zThBStL5c2488PI71ZQBrYfGLBuHyWC6XSqjtvc+zjdFotL+7G0LI/JcODp49e/T48PDw+OTwypUrr73y6rUb11fLdgjBXzrkR0X/Q51lWLfGaMde1s7wskCAiIAvf+zlDwjDqvsRm/CTG2Fts9hRngEA6qiedMRlwqlt66J0bbU6nM9SQmcziHjUPHU2n07LxYLv3f94Z2dnPC5PTg+Pjg62djZPzw69dyDxW9/6I20AHR8f3rt375d+6ZdGo+Kzn/3s7du3Dw4Ofuu3fuvg4CCEtLGx1TThm9/85vnZRZ6XWZbVdQtExoAW7YxF75zCLNvYvPnpTx8cPvfehhTffe+9GzevrRYL60sShASxCWC993mRlXmeP3/yNEW0mZtMylGRO99JNZ0enYxHo/3dfe/9rVt3bl6/FSM/e/ZsY7p1cnIymkxOTk+L6TgxZ3lejEdZ5uq6TiKb21tE9uzi/Oj49OnTp+PRtChHV67fOLuYzRar3OWTre1XP/Wpew/v/t7v/97hyfHO3q53o/Pz1en5ifXm0ePHe1f3v/KVL33+C5+rq/nJ7Ozk5EQIy/Gomi0SY2qDzXNrbb2qZ7P57TdeF2/rJighNCJFhhiZwERICYSczXOPKGRdYtnZ3tvd3V3VUZ9x27aruvFe740AACvETCuxhlTnUhnxtcCjlIiwFvd3Bm5t56yH/utR/kurfLB+fWN88KhSlIX33rlMElnjgzRqVvI8Xy6rJhxY62eL+apukGyel+KKxBBZhFkbUgre1PIfCVBPyaznrJz9bduGFMkQEDahbdtWhda5p6tDrXGKjKfTxNAkaZqYUoKkEMHLbrLeMFEN7ctf4nq0hNih8PSfGmdbZc4mSADGGiGs29hG9rlBY5TbATvYDLFSinMHo08ipMqyeOk7iQi4i9q1hI+IZVkKR00zRCRowc+YTpO1YnXzl4ggY6W7nH4GlggR02B3sOuvd2Ta9CMqGX/K8WIIfrlU2rbxPtcOzHDThoxDtd6UDb1bpeuWTkQgiZCADm+vS5QLM+uiEIG+VN9VGaVDDvTsRIDYWTqsqkoYjcv1YRGRsv1EbnRuC4EYhgRJYuImtHUbTeFMllkydRVWTU1eDBibGRErDNaoDtd0UZ+mmHSxtG0bo/PexZjE4Fpzo1tfoLm9ERAmhizzZVlqV3Nz80oKVbOol8s5JHS+oCxT8aw891lWVFWjksmZzxXYo4GFanjN5/PpqNza2prPZsyc0mXHTPOl1IujiUjiS7acruSJXWTZedOY7Bo9TgKBvuWiNVGtZWbOG4uhra21LvNJOImEFNvEqQNEYBJhBAOichFJJAlHBgxRLFjbFauGDkOWZSHGto0sUZIgKL0mpt7Pr2/MP90XwppCha7Zl1/uDjN8zrAc/9SP/dNS4m4EEC5H+iAlEUZsOUGM8dGjR4vFYrlciiQlYlIZYEQUBk5iDFnjtImn/cMOuwJCRNVymWVZnuej0UhSN75f13WWuSz3ZVY650SHkVisIxQxVsF1gChKTKDtqKIoVE48xnY2O9/b28Oe/UlZxbkjBtC5r5cjYPiEobi0/iJ1VYkEFm1pJFYKHsA8cz1TOQL0IH5mQa30y0sBXCe6fXnnAVHxiEa69Ex7ESC9socxzjkjHdM5E5CzJgIqudblIheRNTVW7CsjXQLTWeyX+Z16zVTF7mjRBEAFZ4jIAJKYDg7EIsAtxxASh5haCpBYAJhjLHKP1iKi976ua/1SXT97e3uj0aiqquVy2Un45blDAoDQ1sv54uzs7Ozs7Mmjh3fff397c8sYs7uztbm5OTs7f/ToUYzt3t6eL0df/OIXb968ef/evd///d+/+y/++adee/Xtt99++uTxe++9F0L4N778lX/rz/2Z+Xx+8Oz59vb2b//u19GQNa4sS43+d3cnd+7cybLs8PDw7OxCl9n5+Xnbti53u7t7DDwajV999dWtje3zs1lq08bGRrOqyrLk2J6fnxpjbt++/frrr1WLpSMcjUZFUTBzXbdtCGrEdMsXxejKtatZlnnvjbVIcn50UtWrqqr0GTnnTOYEYVXHYY0Nq+VPOdIacGt4viIymUw0flitViDJWlt4X5Zlkfvp9FNvv/nppqlOz45ns1lVVQcHB6NyY52r/0duh/Udsb6M+x8Gg/ojqv7G2WED9X1tEB0ufum7oGM6/8TKfOEbh5OxqP4UBUGMIWutMUjGXL12vSx94nh+fh6DFPkE0VarpqqW9+498N6+8cZrk8nk+9//7mg6+it/5S/FGI0zi8XiydNHv/Ebv7G9s7kx3Uqh/drXvlZm48PD9OzZs2vXr/61v/bXRqPRb/zGbxwfH4tg2zbVqgYg5zLFBJKx1jrqCch1io+ZQwxtaPb390ejwlo6PT1dLGeIBoB8R45rAIjQW+u9z69evTmfX2RZdnX/2qjMZ7Pzuq6zLNuYTLd2ti25hw8fPn74ZGtj8/btV/b39+9+8NGbb336Z372535494MP7n707PBAS7MxYpZlDBJCiLHZ2NxMgduQHj15ogNqy6pKdSyK0cVi/uu/+Zvjjfzo5Hhre1fA1lUsJ+NqUV0s5qNywiIffHj3ybNHTbs8OT8Jzcpbv7e392i+xL7IYYxpm+bi7Hw6nboiXzbtslrVXCFaYxyhEUTgnnaSDEBXDcqyzOdlE5ZK464lYZ8XwpGFQTrzoVpsxtTOOOiVDhFR5R4AIHF6yWoPmSIAyAvkdy8sr66j3QfN6yt7LfTH5bKaXThvs8waAEIwKXFdtYZcaJcMMc9H08mGy4J2oysW9QQpCaIOoHYlHBGkjscSIkRJQERNiNLX1bx1WrsKIVgyfbwFjjpgjiBkWRYiR0jYA4GGq8MXiLOAmQX49OJcR5o5QUx9WQigh5pISkmFotCQ0gsAQJZlmu4750ajkXNOA00YYC5q4lMyZHiA4FNHpqGfIKKLMIbU0R475xxhaPumf4x1XQNhSsk6V9e1ik1mZVEUhTEG0STRBEPWrxQGdcAXCxJJm/FrVf+1x3qZCq4/cGtc70FT904BALDWiiTvbddDq+uUmIOSDLVGyXCAyYDVzkZXzIUXZp8k6cMHAFV+AQDohcg0UNTFqKuOOSK5Ho2wPrWhA0+Il/giNmAZBUGr/nz5PgQUMd4R2siyalpnamtMpdSeiwaQU56V3gKnEAIjemeIiDkAGADsOGEpg7XiYucM9H8DtbMASsfXpJRNWZaxEUoogqkVJJMStNyROaq8ZVEUJ+0hW1+HAIgppRBbNV9N09BkXJblarnEfkRSTCcH672v6xX0lSFZ69J0IAfpRA+6JdoGW2TDIuE1Hmv1l3AJASLlFNIUN0nH4sQIAoLK5Y6a/UISQeHAybJAih2awhAa0iAqcJImtSHUIabUYRX1oX+Sygd7rDb8qEMEB668wf8Oq3oIHS53x4sfP2yYPsF48aX/uWbY+hbT2xxjVLqY+XyuNHTdU+vbLNRLK3jvVWipbdu6rrVdj4jOWiIi6AQZiEgboSICwFEiERlLgNKR2IYIoIxJXBRZQqqqKnICoxTeWVmWIbKaLK06v7Q2uEfCSCeaRuubVN+sfUhOl8oeCGitXcYIkKTn7SUk7AWBh88fkBiIyBKRcf2hrN3tF4ujffto4KxbDwI7f7kGkUKkTqBY1jKNvuqvFw5wCYfT8n+UKCLCl13x9R29vpYQkUA4Be3eYA+3S71GoSoG5HlOPTOMnh6I6MZ3zqWU5vO5qmt/73vfSyk9evSormtdDNONjSrE7e3tjfHk9Oh4Pp+/+candrc3z87Onj15Wtf1g/sfG2NuXL1mjJnPq8CpeX5wcna6sbGBAjGlLMsUtDMelX/5L//lKzu7jx8/vvfRxxsbG977Bw8e7OzsJGFOEmM8OjpaLBY3b97e2tra3d1tmub4+HQ+n+d5fvPmzRjjsl7O6oVxdrVaPXzweLlVZ1mW+xxE2hiwWhZZtre3h8Dqo32Rr2YXek+sdyySpKtVpbqOKbXpcrIFiZBkWoxUm1VEgExKqa7aJrRZMfnEs9C9/cIKWV+iLzlBXQbz+Vyr/mVZdm1alTSJhCgqE7+1tTWdTp2xPivm8+VLu3t9AcAnon9dCcObpccL4aA+2EdZ6+idl878R/5TRJTF7pIhdK0cufYe0M1r1YMKdzpBzrksc9bBycnJauWct8aYzGdFUSA4a7xBu793pW3bi7PzlNJbb721v7//8ccfr1ar119/Pcvyz7z1WRBiiR/e/fjiYjbKtxyFul6dnTVPnz1pmubWrVs3btx6+vT5bDZz1r/22hvGuJPjs4uLiyzLRcSTF0LhqIUhvfidnZ1Hjx7dvHnz6dPHFxdnk8lktri4ceMWgymyMssKZmjbVhKkKCkKWTeabBjE1aq21o/Gm9ZX9aoKIb3/3gdvvfnpv/N3/s54PP2N3/iN3/2drzNznpd/8If/+ujsQt3OG2+8ycwPHz84uzjf29stigIAmnY13phy4LOzC0BsY0iBJxubMMEsy0/Pzn/tn/+Lz3/x7awox9ON+w+ftA1fvXZTBGNKTahNsvPlctUsmWPdNqGNALC1s33vo48zJDKQUiqzbLWqLi4uzk8vxtuYlKRA0FiX52U+KuuKL31tCImjCRxCUkIbZo4pZeQFOubyzkZDEhG0xniVEfE6YIpDLRQAlP2+B58Mu0I5L16yvP3PIggMHZU8AiApWRpoKQlJ4zJtlzMJk7UxphjjKM/ybJJS4BBT4tlsbq3b2NyaTCZNG7FqqqauFquWJSYJiZOoigANZdr1DcB9hhA5QP9SEiZh06uTwuVo/GUUWzV1ZEipN9ZE2HHUoM4pD0iABCIpRWFjiKwdPkNEYFCr0QhJ2BhjsKsbOZuVxVj9inr3wTMNdabOj673Ul4MI7qeO3cofzRGRDjGIBKaRhEIbYp1XWv/0RhDfSuABCRqJE3cDfiCOiYAQGFABE6ACMYiIqDRkyN4wakPtmPN6b4U+kNfQYn9nRFdDT6zMUZrfZZly/MLYfQ+B62WJWYCWcP6S4+2UgOmPQEAFiQBYWEBnTZVq9aFp4g6/otEqFgwfSDSszeqrdWHWvgsMpA1bYg68iKDMJAwgs6pK1IoIQizGGdt5iXFVd0QYAwJjTMprVZLE0O2MfHWcAyBU4ypuwlkOpfPCERasOFBsainTBKRFGPHk9XPF+pFnZ2d5Z4y773PIVGIPK/aFEKe56eLxWQyKcvylTuvPX/0zLmMIQqJtbbph0H1Sa1WqyECM8YkDLqidBaoi8x6h6SApfVtTgPW3woACKEOxHbPCiEJA4DOySRpY9sS2Ni0XBSz2axumqppIichg6a7D9wPUDMICgOASSkQE4IgCRKSMWoTUwohNk0bU4qiZ2gRUUeBjTW0vn0AOuKBfn/isEy7JqQMHlA67V4BAE4gHc0oiEBP+z4seOjtYQf+0+LU0IzH3lb+SUdXKYfLWp3aIUEcTcef+vRnrM8PDw8Xi1nbtk+ePBlPcuMcOU9EwIkTCCFaoxw1GkN0z4sQEafT6eXYKEgv9CFVWwFw29YAENsUQsshiggYA8BEIKTjNIIE1lrv8izzVTNj8V1q4Qx2kA9PzqJOXnEXoPQw5cv6jj6L3o+spQ39fG1fP1LeByXShbZtUxK9qwJpMPKcNDYfukCAAITYARH7KKd7EALrVc9uSQ9woAEh2ZvfGGNIrJ3VlJLIJSi/aRrsKllqWrpdYJFYmKF3a6DKzSwCyELUUaeLijoTamkjhUjQKaaTgCZjee7H5ajMiyQxpWSRgDCGRslbyzxr82y1Wp2fzs7OzgzCN/7wDy4uLoaS6Hg8Hk2mW3tXEGB7e3M6nrR1c33/qs/sD37wg6PDw1u3bo1HxeHh4fFZpw+dQIzP2hRny0XhMyJKzFUbEXFzY/r1r399lBfXr14DSe/94F0ieuWVV4JA3TbLxaquawDw3jvn9Aa++eab167deP/99x88eHB4eOicQ2NSmwDo5Oj05PB8Oh5fvXr16pX9tiyttSG1Toz3JXJaLBYxBWfszs6OmruQYtMGxS76PurVBdNB14h09l1x/yklIGOczbLM51kbX4j7h8h7qEiux/eyJmMy2L3u+fpCREIIq9WKU0DE3Dlr7Ww2yzJnstxaIuNEZDabLZYHOzt7gwT4EDXpUhTuhh2FudsjjMP5rBsHEelGkz7RrNCCyeVWGsouKIpEVqu3HtxzV6braHV7sAINu1E3ii2Kghk4KXVGSinEiIDivRdIiFiWpTUuhNQ2IbR8enp+cT7LskyYzs4uptPNuo6PHr1vM//ee+9vbm5PNyez2ayuV6PRtCw2rPgYWWFki8XsBz/4wb1797Re1dRtXS2ZqSzGIURmQTBt2xgSRxYdKg7EOSeQxtPxz/2bP/szP/NTr7zyyv/4P/7qP/+X/wLx5rNnR9PNcVmOs6xomia0CQAVXNc04dq166kNJ6fHk8nGO+/8mHPmo4/v1qvq9s1b29vby/liPJ7+u//Lf/fP/pk/P1ssnzx++pu/89urqiqK4mx2vqhWgBhj3N7eruvaWnf7lTuHB8cXFxchyHRzYzFfzhcrjlKMRvWyPp/PjLGj8TSktLOz9/TJ8f7+fky4XKyWy8pam49K3TMiYL3LuHDOTUaj5cUsxuiNIyJgUJPdrKqP7n64c23f5kWQxFGc8cZa7/MYYkpCZAEoJWlDa4jbxIonVJ4+EWmaZrFYAEkxGmlQJwKGnLUvu3YR0LVFgILcS9W8HPp3dei13/er/DKzRMD1XTe8Cr0HZRA0JiWpmzbPcxGqq3qxWFTVsllV21e2nc0uZotnB0chsbV21QYwXgQRjLVkjAVEDRK1LWigK2Si1kyRUcBYS73gtohYY4gotopyfiHLF5H5fC5oAG3nnqCTjdWf1fsPb04gZV4okT4n4DaEpExuYq0HABXSEk4AwIaYWWUvFX3BnXICtG1LZNfTceh4qRmoi5Uv7z9e6od3lPxCzJxiWrV1bu1Qbe1DAVJACBFNxuOhGtlF1gAAfccBlKQTkESzHeqlmkSEddEkGPSthic7PNAfFfqv9Rbx8uk7a6vV0hhHBkNoiKgwhS5FIgJl+WZWeKtrIXNKu8EA0HMSd51/wCRdyU303KRvOpm+VsrMZJAZhVlAtI4AwIigZH8hdLmQJFaaGjTgwDSxubwY5GGWq667+WxIkVvVGyJvzGhjkmJtCIrMF5lrI4dGC5Z9UII6xqvdoUs1ZWZG6scXeqt++c39U4sxtqqxIYRiuFcDyPNyeXCgel537tz57h9/qyxLialNbVEUVb0afIwWDhFAO7rSV/G1SAwvBvfUyyqrhI02SgadNXZOwuXt6X3s5YCvNhyapgFgzQROT0/bFIOyS1iDxqBeNgjjpexTP+5kjLfKxqG3K4HEGFTaGgC0GeJshohROQvWEEdDSAd/4kGDRXrp6GOCyzEY0fxYLm1dP7W+hizq2f6Hf/8ph4iwXJaKmRMAVVXjfa5g6JOTkxDCxsZkNBplPjcW+1hZVLQrhMB9G1CzemYOIYp0+MLu9hBq66BpqsnmRCRpIbwTjjWRmY33y9WqbVvK3Gg0EoDAym/JbdvOZjMA6AafABKHTi7PWlnXLwfTdy+7nvB6AsDMSqTbRatIup4BEiCDDjFBhzJLMYmgIuxZuuwXiZQa66XuygvL7/KhEwCjIXgxvxqGu5g5rAM+WWIIkbtxyaHkr+nxpUhiv7TUeuttX19mfRp5WeVdP1vtYHT7XbrBFWNMiKF3qV0sCxqsMRN0fb88z1er1WKxOD4+ds61bbu9vT2ZTObzuUIYmqY6OTw6PT3NMue9vzg9+6ANu3vbIrK7u3t8fEwoqvGcUtItTsYsqlVd121RUJIQgjVovKuq6itf+cruxtbjx49Xy/nnP//5renGbLn4zDufPzw+uvvBh/fv31ftv3v37n33u9/9q3/1rx4eHpbl+Ctf+cqdO3e+8Y1vPH36NCsK73Poxpl5uVw+f/48tmFzc6pT7DHGul45Mnmeh0gxxmW1EhHvfZZlkFETQ0wJQz9C19/bGKMAgCSJiUznUgUpcqrrOqTostH6qljfdGvRyGU7SHfW0Fwadm8Tal0D3nthAgB1Ub4oFC22WCwEkvd+MpkU5XiYnl3/lvVvf8kIYF+CuUQZ9Ti3wV/AGlw2xri+ntcv5Eeam/XFKS/EZi/fHLs53SCyMQCRM+RWq1VT1YaczRyioGosL1bzWZUiOpd7VzBT0yQR2tnad5Q9e3qYZXm9aDI/JsyOnp9pf+rkeJZSgiBbWztPnjxqmibP/dnpOQKVZblcrPK8jLFeLpfVquUEW5s7VdWMiimDHB2deG/H4/HtGzcfPnk8GhcbGxu/+Iu/+J3vffsf/eP/9/37H/+Nv/E3fuZnfua/++//6a//i98qt8utra35fBnrZIyZTDYmk0lZjL/7vW9vb2++9fbbz549+/Xf+s2rV6+Oi7yu69dffe311z+1WKyWH90/Oj7NsiKk9NM/97NXb924+8FHD588rkM8OzsjtKPxCCB65/Ki1Ozl8OR4PM6qqrp647oIzi8WTdXO5wtnbDmdHhyf3Hr1+nK59Hm2WFZFMcrzPDIkSMxcVVUxyoFxOVsQscv8fLksx9OsyFMbDdFqsbx27drh8YnGLrdu3XlycHh1/1pdNednZ9PpxnxWswQ0VNV107Yuy61xo+lkNlssF81oMj48OgshTP0kSQIkJQzWUTkkatt2uayaGIxzHF6Y1+tr/2b4xeU6lm6NdsGWrqSura+RcRccKGVhCEG1QhFRsTDMjMC62SILkjXWIxkwJstLsmY0Gc8vZmTcbLGMzKPJNDHEGDOkqk1IxhgSUggHCBAIEVpFeogI90UmIsudgpLW2CCEECWIiCUjIr1mLXT8biLe50kAyZGYEHGxbCCxRWqaxnmrEaRzDggZRIXb9BalFEMIbRuYBXtJL61PWEPWdvCPYlSGEI5Pz+bzed2Gzc1Nsi7VjXMvMA90d1VwcDCMkHr5zcGX6iMbqrmSeNksCXSwEj1ZKktFCOhks4iASBrGZwVZ7QhRlmWZzywhcEwpEhBDB6LGPvxnZiTiTny0M45qLr17wQUO/63rWk8WsOvMJD3/thWRpmkAqCzLto1nZ2d10zJznufLxcxaa71PbcAOHdS5WwAxRERoAIXFWDOYLz1NZu7BRZAYEAx2lAhkDAHo6IRGcoAAkphFyLiUkqSk3Ya2bYGSiHLsMEeQRKzixKBhT0eWhUSRmYiszUh4MiqJI6RWUkiJNibjcmTlYvHoySEBJoCUkiNi5qbpombNWp1zKQohWWObpho8QeH9dDoloqqqvMms8W27akJNaA26mGTZtFWM1Wyxs7PTNCGleUqS5+XR0dG4KJvY6AYEAKV3XC6XGxsbZ6enAN2VgDEiokGJ4oIUXdDMLpqm6WeCIzPbzDvn6rpWudmyLLu0k1XXC0QEBIwxGqY8ffzkU2++mlJq68b01FJK5GJyT8YGZpYIhpICEy+r6Z1HzPJMOAmioDHWOqYQYuJWL8p7S4QxtRqlOWdS2/UqsWfZgrV64eAOedhHHQRRz5xFpKP6xM4/EpGIov8E+oYnC6sAFHYsREpAxzowkxKEGAEdGfLes3TjuaBwEbmMANbjD/2hLMenp+eIpihGAFCWJTNcuXJluVyaTp9OEA2iYYa2jbGpuwi+p56kboA7aWXXGKNYf0S0VknDOtVeg9Y5B2TattUZTVWZBSDfgyiYuW3rENrZ7MI6d3p6fHx8vH/9WmejGDUwFxFOwMy2+/bIzCCJmWMMKaVRMdJ2DXTkxRxTq7OhMaYQI3PorIygCIQQrfU6cioiKk+WUjBgiEhZebnD9yMihFAXRQFAdV0jYpZlwriqFtYb9QXdwJW12O8sZkZ+IbrS683LQjFUtq8ZMbP3XqsnIqJrTzdC29bT6XS5bFer1dbWjvd+Pp/HmPIiWywW3uUpJS3QKHRkuVxsb20eHR0xJwXwKLZ3XJSz2Wx3d1u5d3yZKxbL28x7v1qt9vf327ZdLpda2bl69WpZlicnJ2dnZwr0r6pqPJ1c2d/f2toC4bt3737mrbe+9a1vrZbzz3/unaODw9lsNl9chBCuX79+dnZWFNnJ2emoKJfHyzzPt7a2ru/tr1ars9PjGzdu/NSP//jR0dG7777btu2d2zc3Nzc5JmPMfD6fTCZf/OIX33rrLe2EKJWIEvsg4nK5nE6nP/ETP3H//v0Hjx4tVtWzp8+m0+lbb73lMzufz50z0+l0Op2SAYOECFGS2hAACCk555LIfLlk7uhMIl/OQ6+XJATQOqePLsbI0LkzvbEikpIohFQfvTEGyK4H1kPMPSDZhrWx7oh1ew4VUhEhMiGEmBh6/eykA3fWC6wXUmVYV5fN0j9h9Kh/p9a8Eih5Nmpbq68qWCPC6eXV25XBoo47DsP0IIKCNGAQLu0eDb6sv7G2bVvvybnM2sxQpveuadr54uza9T1jzPHxcYqws7NnKLs4Xx0cHGxu7CDiycnJG69/+qtf/er7d9//1V/91c+89U5d14xQ1/Xbb7/19OnTo5Pjxw8fjUZFjHFnZ2d/f3+xmF25ckUBQqPRpG3j6en55sY2GbLWzufLi4v5uJzkef7Xf+Hfuffg43fffff8fPazP/uzf/3f+YV/8v/5J3/37/5n8+XiO9/5zmuvvbKoVn//H/zy3Q/uZb5gZp1104USA1er5uJ8/uabb96/f/9b3/r23/7b/95nP/vZh4/uP3/+/Hvf+uN79+599NE9BhHlv181q7r63OfeKUaji4v5/fv3meHq1avW+JPTo7ZdTTcmbd08evBwuaqbGLa29oTh6OhkVbfGOJ/lNvOSQB3PbDa/cePqeDTx3i8WiyIf1XV9fHy8ubk9LMEYY0ohslgjeZ5nWZYY2mU1Ho8Xs3nhsxR4cbEIdVNV1ZPHT51zo/F0drE4PDwcj7ZAUK1qUghIJ8ZurPXWWmMckdXykmLOOTIokZlBYxyhFWnXwv4XkLt9iL/2klxq1A0LeNgnrAR8gLr0PrGRuhU+bKkkUIWI80Vd1wgcY+AURNJ4PEVjBQ2DxMQJRAiNdRRV3gcF9EyGzSUgKMDQEz+KEKQXWb3XHG1nO/oyY2cpmC3ZBD3atHP1utWpF8frTA8AJ2GOKcYgiAo4ds4xC/cApK7GY5RWRgC0kZ106FbdrRZcBxNzeTkAAmAMKdJI1srAmqUMJkOfFBGhtepo9TdK2q048W5AsOc8760nWj8CAIvoLFlClJRSTLEVIUYAJGZmiCzd6AOJsPQRVW/K1k9+OENZr0PgC4W37vEYcM4Q+RATc71a1ovFIogh4RgjABOwCKYUUvJkVEyAAbtuqdr5lIL0GAwy2vJ/AQAm3Vg7apm/6zYBELDOXUNnUQ2zdIQ/At1s9eVUq876GjJAIMYYlAgsHCLH0NaNJeMz5ywtZucIaToeF4VXBlsdwbTWKt5cRBIOwaisVquuCtgBA3oOrB7hE5G7IU6Q2oqhFNtaWkCMlhKgAyBjrEfDhkJoY4xVVemi8t4zKt0T6Vy4yjZrAFEUhQK4m9TNDio+RL9On5eGkojKetSNmfJaZ7yfEtby1eDacLVaWWuqqtKAyVqjlbzcOlnismpSSmTUnlwOlrx0MMKqqQmEAJOwCSGkWK3quq6HXuXgLJmZmKGv2r7kXAco/EvR9p904Cdg/f/z7++f2pq96bfG2qbWn+nFMd+UEqTIDGQNAGh/e2NjAwDqul6taoUciIjPrBaA9UFPR2XXgRmMAEhKaWMyyvNcR3pi22hArMQdiGKQUkqSIISgbqUEAICiKEQjJESd8tJbPZ1Ox+Oxc95Yi4hN05QjDT7WuHcRNQBVvJgxhvvASNfMkM5pY0B5lmNf8tRES71GSmxMRwCAaFQei4gADIGhwT3JZSy4sbGxWq2YoSgKBKNwlMl4o2pWwzPSCCz1T0ODp2FJrD8jrcSra9Yx/ePjY43gdacURZFSWiwWZZlrAqMncHBwUJbljRs3Dg6OnHNFUWikDj2K7+GDB/P5XCc6WJmpOWhfGklSSnVdC6TCFN57m+Xzi4W3rm3bi4uLGON4PM7zPKV0/fr1nZ2dLMuePHmiz2g8Hm9ublarxXg89s7kWZbneVmWKYXj4+MPP/zw6tWry+Vye3v7/Px8tVqdnBzdfuXO4dFJWZY7Ozu587PZ7OzsrK6WqQ3f+MY3XnvttZ/6mZ+WxOdnJ48ePUopbWxtdvSo2KnFdwNjKSm558nJiX7IfD5HxLIc58XE+fz4+Pg73/nOF7/0Y++8805o6t297RijSiZLzyOiRoSsQUMI2LHjv4jXUjoHPVhlIGIcWNo6pBcOE1Ow3tXvnvCa7fqRP6x7Yehzg24jd1zGl4sF1/5WmKWnnF5fctLv96Fr9NIhnziG04AXWwfMrBXPH/khwwqHwd6ARK0n9V8PfXtwGHYfPtwaMMqJlTiQtZNyNMozlna6cePk9KCtquv7V7OsPDudHx8dCbtxMT49OiUiZ9wHH3z4y7/8K9Pp9Cd+/KeQ6M//+T9vjPnWt741mk7+F3/lq3Vd/1//o/9Latvzxenbb7/17//7/z4A/Mqv/MrR0dE77/zYH/3RH1+5cmVvd//WrdtPnxy2bQSg69ducoSbN2/+4i/89X/wK798fv6vrCX7rv1H/6h6+Pjh+++/95M//VN/9s/+/NUb17///e9/64+/88UvfvlMZgRYr6qmqrXyGpomheDz/OLiAhA/8/bbf+bP/dl33333+z/4wSt37rz++uv3798/fPZMAMj5EKMxbmNr+73374YQ8rzc3t515C7OLuq6ds5cvbIXOFzMzkc8KYuRi65pmrPTcxVFUpNBaFuOITISpSgxMoh464JNeZ6Xee6M0cKeMWgsImJKySbO8sJbmU7HZ1XDzOPJ6Pz8vByN2xiPjo6qqplON6q6XtWNMVlRjDZ3dkPFRBQ41aEt2ANwjJwYlJDKZZ6sQUQUoxztzBKFrYbjl71qYlKABwIo50CP3FXCBVzz6v1YLaz5tv6HjgMFUhIANcuK9U/ChCQsHSutCIoklCYEQ2KMYRGQBByBGQkADRjLSEGgSTGpCzEODWPPsdm7VI2ZsKNThg7rJl09T3TDIXQyZLKGB1B51gH+lkAkRUFC4DUsnIB0lWbuacvRGuwvX2tcIAQdvyTHlDp0e2hTSs4aEfHWiIjWeufzZdM0k8lIm57GGOZLFI1AWuvNuQTQI1zXcPZrAEE1Bc5aIpuNyqZaKRWjVjpJANILM7uEKjkuLGitQSSr6kISRRhSBI4AxpIBQ4klJomps14hBAXVGGMYLoupHTHHmmXrVgiBsPQYaG1TCCAPIyUaVnrvmzoAgMHOUhljCFCZBry1wKkTXgOgTscEAGCo7BINoGH1AjxQtQyd9E7JhaUfDge9P4ys7KKdEDX2GaMAglFxJkBDoqpPwCDekDM29xkQYGMMUm6NtcToyJgsc4SY2kBOvPMbk7Expx05uAACpb6JMbSkiHCNY4k4RNY8rA+vNbpVjnMWTikKAhJqBibSXa8xpkd8dXPkWZaRQUXsGGMkBg2MqNeB0rukMzAq6o49f5/3Xh+xJg+xD/X0D5lZiLTwKqw8wJ3IQpHl2NHsGotkEC2SNaacTAKnuo0arysynTn1E0SXDkkPFkBCIiNIIaYmxsCJEaw1YHqXLEmXH7MRoG7KqF8tyhOl9x2pW6FGp0RQUodeI0QA1qrCGu5WaAAFSd8agE8cgzNWoyNdSArYEd2vk5opbSn0fbPLi738XERATMxFOU4ptTEIgs8LsoaINGEDgBhbEKmXiyEH6z8KAGCxWLRtqylcbBvNwZhj4GCtNUiI6LQqUFrnfBO0m2qiYGhTEtaMWaniq7oF4CwrnM86XPVwvaJzEcAMzNC3iTqqU812tSyKPaxf4zMN41arFXMrkDqMJoIha4yEVvrLIQUUQQ+yGpCF3N1pJqSYJESOkQENorQxIaJJ2sYxIiKMnABEgHq6KlYpFulI4RgAaGt7ElNqmsp7W5aldFjZ2e7u9mw209ELnTfJ83wyGVljlDJ/Op1m3he5N+Sq5UpSzJwdlTkq1I0ltG0HGWfWTlqMbeIAANaYulmllAgAJYUAGhKQQFlkBIySDEqROZRUrxahqX7wve98+tOf/vEvf/FLX/j8arU6Ojp6/Pjx0dHRzZu3y7xAYGcsAWbOny3mTx8/+epXv3pwcDCelNeuXfvud787n89F0oMHD8rR5Oa162+++ea4HFVVhQAPzk4fPnzY1NXdu3c3J9NXX3311Vdu71+7enZ2tlwum9m8HI8m46nrK0p13TRNc3x8fO/evUePngy1Le/9ZCM9fvJ8d3f39s1bT589fvbk6Ruvvb63t6cYfSJUbZ7EwsKAQIAJug0Su3FBg4hoTIoJCA1cYmN01yRm7GEzw4oR6KZEANZSuyEy7i3M+g+0RuI3vEdETF/PuEwJ+mpm73cG8VxhVoXMdcug3lnbSn0YwtBX9mSohnIPSL40FC8CkzQCACRRdR3oRMqo44fAwftqVKMql93QvKjmPWhbTaMUxA79r3fILhYrxMpQLoxEVVmW3rumrRfLi6J0xpiTk9OyiBsbm9YUB89PQFCbkiJptawfP34sjB+8/+GdV1957733Z7PZV7/6VbTm5OTk5OTs//Yf/d//n3/vP/83bn/lgw9++J/+p//pl770JSI6OTl58uTJdDo15FahmU42210IbXry5MnZ2fnVnWuZzR4/fnp6cra9ubO9vfX8+fMfvPvdX/jrv/hX/spfOjh6/kff+uNVU4vIZ9/53MHBwbTcIqQQ0uAvY2yJrAcIIVy9duXRo0f/yX/yn3z44Qej0ejx48cHTx4DwMbGxu7+FSR7fHZeVw0aGk3GxpjQxIOnz2Lkjcl0f+9KnvtVdXHnlVvXrt24euP67GL5rW9/9+xiNp1OQx2CkXpVx9j54JTEGeO9n83moU2LxQLRnZ+fx9hWVQXMaFS3y+c+V5hqSgmd2dzeOnj8XOtzqQ1mbEHapmqPD4+muzt5Xoa4upjNCPHK3tVnj58TZSLQNI3wBFBiT/5ljXeu69vqkRJz4sRiCEQwhDQgzte3xHoiPmhvrYf+w6J8qerfbSG4/Cha2znMnTaRerduFwFp0OEzj5xiaoEToLSSkK0xklgCM4u2ghk7ibfB8SAz6L7o8xN1ur04sbpi3XRrkWkna6Vbva8NWKDIQUdKqUseLrXzjDEIztpuK5Ih62zmfEoxqlNNnKIW+tNyWQEAaUAMynJDIhJjQkQl4c7zXJvs3nteq5+9dHB/DPJqiKhxQG+nEBXXa6jIXNeAQCR7CeTgboQXB0EDRDRAsWmJCI3S9CXkBJJI2HtrrEWygSVJEhEkQmva+hLcTes0iH36t26wcO2Gv3TombdtS8SJ2Xs7npRtTOeLFSIag8pZZAgdGWMwvRR0YScq3McTQ9VZq/imi8FEZUAul6iKGGiW19ll7MayOvPc5w9q0a21IsCJEiAngSSMTJwAxROW3qERaR0lIWFI7J0DiIQAwIiqlpXlYvrYHVg6isymaUJohjgbDYEAGYPGiNhWEdsiiHSJHLO2KCxkjl1qmpYDSo8/aepgiIwxyounu75t2wSikl56At57sIaZpSxtr/bAzMrTV5alSBpIJ4ZyV0opc7nogLh0GlL6e2e9XpescVEDwKgoYwzDm3vBcQIAZ2zufR27YZt+uBYuJfH6p6DlYGtJMeUtxNRrGq4nLWveXTfRpfTEcGgCM7xTeodMDINoYf8h/Qjpj7Jva//sKgvywgkMxYiuIphS0oLKmkXtmD10312WAxG6yXprmXk2m6mVUGDVarUqilGWOURcrVZVVSFKlmU6Bwm9tgBiN+crwophIKLYttqu8T7PTA4oHLht29jGtm1JICUpx+OQuGmaOjQpJSBE48iAYhpZurGBLPdlWU4mk/5eksLnFKKwRlyTAED64X5m5qS9TewjpCSMQ3/ypRsIfdrAzIidOkGM0VpQOyZd2twx8xBRU8+yLDMGV6sVAGntf7lcKvIL1mY/BuMDLyKk9Qzu37+/s7vrvT86OvrOd75zfHy8tbX15ptvxhgXi0VRFFeuXAkhqGIm9tPVAKDtO+/9+fn5+++//9Zbb11cXDx+/PTJkycisr+/n2WFbjG9FjXLwKItjVW1RMTxuNzY2NDiR9M0HKK1NjQtAGi+d3Z2pjrNx8fHKaV33313sVhoQy/Lsp2tLWMMp6Rj3Nba69evj0YFAMzn89dee22+uPiN3/iNL33pS6+//noIzeHh4ZX9a8aY0LQHszn2rB6np6fe2fl8fnp6aowZT8rxeJxl2WRj+vjp86qqOMkAo9IoQlsco9FIwb26Pa3xf+HP/1sPHj0WSePR9NGjJ8fHh5/93Ke/+c0/2traAoCBUKHbcYZiDADQ1TsY7VrRHYe62xoO3hijLWWNTNToD6qRnWHpwxvutVPWXZX0pcBLs/Big3p94+si6X//sr9mZsRLjb81g3DpFoeFrf9UzrKh7clD66Cv+otIHyxJUu2SPg7AtYRkCLTWTZkAWGu5r4SuTS0J9FzJgxW1ik7LfEFkq1Ub2oQIdV2LxLLMEWW5nM9nTV3FzI9Ho/Hx0bPM59psLMtxUWRNTCcnJ//q9752/95H73z+8yE03/3md2/evPnWW2/+4b/+/f39/f/4P/6PAeAf/sN/+OTJo/F4fOXKlcePH6ckb7755ubGzu3br/z6r//ms2fPqqq+fv3mwcHR+fnsv/h7/8X9R/cF4vk5utx95StfmU6ni8Xit3/7t9Ga0KbZYj6dTieTiYSoVA+IaIiMCm57bx2xUFmWDx8+/MYff1Mkbe3ufHzvw+3Nrel0ur29mxdF1QafZSGmGGPbxrIst7e393f3VouqrWqMUjj/2be/7HL3+NHTu3fvks2891/6whd3dvf+5T//n1DSMi1TSkVROHIchSOP8tHela3x69PHDx+2DZ9enBd5uVjMiKjMcw1VsixLKS6Xy7oWb7Pt7W1mHuVFXdeaWOuKPzk5wcwzGUTjXBaa9nx2gdYYY1gajWVJxacQGTqbAgAJhEGiTt2lIIRiMYFICE3TRE5kDURFBAGAYFdEBw34BtPcra0X3OELER72tStdXjqlrmu9rwPB0FxFRADxeWZ9BoRtiim2KbQETEQCLRNZkVZSFIgswIk5OHLr4eSlo+3PjgipC+V78AZ2SXHfEkARiX18DH2jUNk2BrTAZdMMAAVCG5wOpaLEGNtWTGaJyFuH6CNzXbWhqofQRK2SQ4uImVdmxm5kUHsTznvncwFK3cBCVw0Eka7NiQICaKjP5y8BP4AoiTXo0PgWSelhcD6ft3WlQZ5B0hFjZjaoBeuOYmfw3CEEEUsiTEDAIIE4EgIJG4kAQIIEDJwEDXHHbw2f8NZ/Uozfm5o0WE79c+XWIE3oEJ11AOR9xVo8ZMY+gFovsawfnUZmxyii7IE4AHhSz9O8vk60K9t9BTAhIUPHzJ1SSskg6akOy4c7jamuJcAQu8xKmGNsmwpiG6vaKAkMYd0mMSl3k8Ln5DwZtwxxsVholkJECGgMIWJIsWrqJFqv1Zspeid7U37p7ajHjxF573O0YIwNNUeWGFPXFBLhBAhmsVhx6gEYq9VoVOiYStu2zGxAAEDJfAZaGCJSsIFW7DR5GOq7KSWXeUQ01Av9iiidvLHCINy3+4bYf7VatW1TLZaxaX1piciS8cY2qwoBvPdJpEmSIAEi9XzwQ2tusDYpRSRhnRpqWi1VDGtPKXQtoUEDAAzYhrT+0C/X52AghmCUmUEQ6ZN1fCICIFCnCwo7UCxW11HEy4/t/uSlyHJY+dD1ji73wvok8HClRIRgAFDYOKcKDAvVaPfeG3IK3dHeSwhBiczbtr2cdOkjIecsAKAMCgfdq9r/qZraEBnnEA3HhIjADMLW+pgCJwAga41xFsggQoiNMZgXfjQaWesBsWma+XzufJlSEhzY0I0xoLc0hBBji4iZt9Za7TajQWMM9KgeDexEZDweK69/jE3btiE2KSKzWJOvP74+WqKeY+BSVnL4wbkM0aQkMTAIIZEhx9xqQ36tPbgmSoiX9Snt+Vy/fv3s/PzRo0cnJycaoD9+/Pj58+dXrlwhoo2NjefPnwNAlmWjkWq4Jh2lPj4+/uCDuxqIHx0dnZ6eVlXFDAqCV8CV9z6EJrUh9VvMWivIMbbOdBj0EAJzLIqizLwti9n5XLl3tF2gf7WxsaENhOVyqaq6uqRHo8l0NPbWxaYufGYAJ+WobWuF8P39v//3izL76le/+mM/9mMfffQRAG9tbXmf62W2ifOi8Jtbx9Pp6empcXbkxkVRlJNxElmsVgbRFfne/pW6rlfLqm1bEbHWZlkxHo9TSq+88kqWFR999NHx8bExZjQaORtXqxpYzk5PpxsTa+3du3e/8uNfUlLaofWlihwA1An5qYNISXrA8PoWBgBkEWYSEAHjHFzmD31/bc3xwJqrEvkTK1NrTTMcTCIAhNQOP3e/x8GLrXnk9RTiRbDrcA4vW5m1V+USNyEiL6clXcUfRJShDru6hQZbvVsXUaR170gAOtd1aZjX7onWArD3v4hod3Z2zs7OAYBZ6rpFNDu7W1tbGz6j84uTGNqbN24X+WSxWB4fna9WDaKcnB6nlG7cuFHViydPH372nXf+wl/4c5/73Dv/1X/1Xz199vg//7v/jw8++ODtt9/+3/97/4fT01NE/Ht/7++VZfn06dNnz56ou27beOvmnel0I8syRLO3u79c1ACnq0W1u7O3s7NjLG2MJ750o0leFNnOzs4//sf/6M23Pm2tdXkWIt++ffv8/NwY01QNgYigtqmVK7csS+Pdcjm/uLi4c+f2zpWdjz66e3FxtrG1dfvOnRDCweHh0elJG1IxKpVRdDweP3v27ODZ4c39a6N8tL2/8bM/9bM/93NfbJKcnh8/f/LPHty7v7G5U4wn3/zGHz189PTq1esxphACCk3KSWOa5XzFzE+fPn3t9dtvvPGp3Z0rT58+HxflZDw9n8+W84XWTmKMjvLRaCwi3tuUwnhjOplMiKVe1Dp1Z4xLbfA+u3rl2uH5eWhTVnhnvKpNe+/rjs6oq2dgr8rZwUBTtyXiWqN2DRMGAJdLTUSwr96tQUrkcjX3L+kaG7aEHpxYgGEYxBENlFvnivUV330UYRIMiasQQZLEiMDeW1f4pqq5bUyKiSEyCIIGJcYaRfZwV++XIfTvT4P6eF6AxcDQDMNhn8AaiFk/OXV4FRWtXLvkPhDpijQogFpHhxghpTRvGkskiE3T1nXdthGRjLXT6dRaa0EQcRjzTcLC0MZWayT69OWyH/LygT21YteZSZfRTAqXAbEWuVNKJCypGwtWLGbqjzzPTX/5l6E/aOqCFgQhcQrEAUEQRYIETkgxMKYEiZOwpGR7WipBRIOdZeGe1nDd3q0bnaGqoY+LmSMzInjvEY2OZKSUFBduukND33UUuF7vYHC5zyUURaD/pCHgxw7nJUr1oX+r1Xx9TQl1sCf04AG7zAKciAwDtm0EIQBEMIjkCAkNojgSSSnUDYSK28Y6lxljjGmAQ4jL2UVqG2cpL0YgpnNxBCSEZJy9JAxpmybGyInQYEoMwgQxxmiNIQThLuiPMXIMkMA7NGXmyRNaxIBA2t/23gtRHeqmaU5PT1UnNcsy7YjqoSvBGyKiEKOIGIPWWmVx0VpyUWTcE0/hGgGoRhgvuEDlNdcFnC59s1bXLRkt4A1WSPdvGxI4Y8kQkerlDUYEBUitiwAJkDIn9gMzMcYUowys3muukXqHDYCELGuEGDIwtK5V3YbHnYR1APoFo7TGpjV8znAMrnrd8csAVu6QA5e/pPXdtnbrtDqgiW33e2YVk9ILVPoULS0bY/IiVwCPtXZra2symcQYmWOzXGldfAht9dDpRuiq/o1C8EMI8+ViNBqNirIsS4Mkghyi8j20IcQY0aC1zhjLCMxJTd9isQghIDiyZjrZImcn020icuQAAHEQdsNQxWEjZ51uJqaUgMA5h51PUR4VohdVX9aWHKpUHwAo6Zc+NSJSYgZ9PkQaJBEiOps1TWOMy7Ishur4+Ng5v7m5UdW1PouhBzJ8i4ggrOWECCCyWCxGo9Ht27c3Njaqqjo7Ozs6Omqa5oc//OF4PAaA09PTLMu+/OUvv/nmmyml6cbGcrk8Pz+fz+ez2ezRo0dENBqNnj9/zswbG1uTyURELi4uzs7OAGCjnAKA2ufceRFp+0H8qqoODw/Pzs4Wi9loNJpOp+PRaFSM97Z3tPCn09j6UlEUSsDfj0rHtm2zrLi4uCBa1vVKRJbLZVVV8/k8hPC7v/u7f+tv/a3NrSkR/dZv/dbR0VGM7Wc+8xkOvL2xOZ1Oq6oyxoQQVqvFxsaGADdNw8zWu2JU5nm+Wizny0VZjr331O1yDTaM9ih0VTvnyrL03iPicrn8xje+8dZbbzuXicjVq1cfPXr09a9//cd+7PNVteSeoVUpj3Xmh8hSD2fnnrhPezuDEVtTulWQWP9PgAE/I8xdt7uPvPsl9HKxEv7UY22/X9oQXS89OEJwLQpSy4Avjbr1rw5WZXiJe06hS1/ZswsAXNZTAC5ZO19iBO5uy3rXYnhJQxfowzkB6CM3taUsg+w92Lpuzs9nzhbWutPTU2ttWebTjdHZ+dH2zgZi2bbt1mb+6qtvPLj/5F/9q9+PbZpOitWyDk093ZhevXrl1u0bxyeHDx7c//KXv/St73z7a1/73Xfeeef993/4S//Bf1CW5bPnTwBA2Vurann9+vXRaLK1tfXs2bM7d17Zv3JtuVweHh7ev38/z8vpeAMEUoxvf/adJ08eMLNyWn3729/+qZ/6qbsffWCtLcaj6cYkpbSoVtf3N7mJHEWkM6DWGu2x1HUoiqKqquPjo7PZmT4eZj49PQWhwCkviwKMyzyRaVMcjcc/8zM/IwmeP3q2Wq12trePDg7+2T/9n8abpcuMruPReKOJaXNzc2fnysNHT1KSpq7rujG5ASBhsM7fvfvRzvbm7GwZ23B+ejbZmM5ms5hiVVWZ80R01pd2Cp8Xpa+beVEU165de3z/QVftE2QR1WMqy3Ic43y2jDFmWbG9vf386YG1VmoJITAIQYcAkb5B9tJSzrKsiUELe9Z745mI2rYFJGaQJMxCcDmeBV2DtdsG0uMuXlrBg8lGRgZUzmlAEpHIjDEWCEQo1BfXSakxoG4bY9Ab78iwIZRknEGygCYmCTFEYUCD1ggakcTMQ+iv+bGsMWasQTsEQECA+jRdK+ca/qLCixFRkU8KEgIARHJWogCgjsMPV6eVlRADdKU1Jygxxqau1ZXFwEM9UkTyPDPGmA6JBNDjdkIIdVMXRTEajbWAR+YSbdUXL7hjeey3tH6s9g24Z53TH3AoDAskZZMwCEQdL15fMh/iGOmLl4Ag2FWXgVli4NSKsCMxpPFwVwoxQI5MZBRJn9Q67Z6+8mrLZXm++96+ICoiXSwv3J+1SupIjNFQZ5UMkoEeJ/ai/dWDLiOu7lu6Asdlw/SFen8XKPQBmUebUHp9AVSFQOgVHqCL2JTtGxhMDMwIRpiIu0kYERIBYGFGYO98NjaF9YXzCcS50XwZ26YObW3JsKDJRrnvynKJEwHqTJACKHhVrcepwgrLY7Smf+h97y6lAFzXdeZIVR1ijCxrA+Lc9ToUe6Dq0T2DXs09rg8RnXOr5dIYhQgZXkv1FVykA+hRmLlji6qa2hgDa6SHXfTf5aW4nv4hYlEUMQb9KCISSR1ris8BMDIDMwmANSwozOYTPrhLJIxTCZ2UhEHImo6pY21uZ3jYOl7PfQtqiPLhxcL/cMfWfTNipwLxyeU9eOXLruX6S33BRS4r+oCIrAzwhOtN0+E0+g+8JBM0/eewgHK5ONdN9FprQ+z6uohijPSNIOuQlDhVU4UYo1rsMs80VXPOtXVnChBxNJoUReF9JiIxphg5taFtW+lyZkyJUwxk2XhnjBmNs7atFyttJoP3fjqd7uzsXOKUAPoNSSLc2UZJA05DGRn7f16q5CKCMaaua8TUg7/JG28oM8bOZ5W+WRetpn8itisiXNr57tmljjET2iaenJxeXFwUxcgYYx3obenvXj8y3O2Fy89R2l8iqptqNr9gSbfv3Hrt9Vc/+uijjz/+OMTWWDLG7O7tjMfjyXSMBClENbPK3vPZz352d3f32bNnMcad7WnTNArvWS4rjdq3traW5wtzKXGQAIAADRIAi6S2bhaz+dPnTxAxy7I8yz7zxpuY4vb2duZt5m1d18Lx6v5e0zTXr+2PRwUzK+7r/Pw8Bn7v3XcJLUPa2dkpikJEvLVlnv/SL/3Scrm8d+/jra0tEXnrrbemmxODFFatc45jUl0m7/3uzs4rd+4Iive+UclITs1iTgJFUSh7ivdZWZZEVNd1XbchhI2NjaZp9vb2xuNxLw5YX5zPt3auTiYb3/72H//wh+9ubk22Njffe++9a9euTiajmFoWIbLO6eOQGIPBTkZdvXO3UxCk5wsWEbgcH9cq+2XVH/qytwKquh5vvxTXt+1g7bHLMC91J+ByPGytKQQ9wr6zfgSgVNiXxAyIKNzhFrErPA0gHxbpyUUEuJsvfyG1WL+Kwb9rs6uzKf3sEPaV0yEe63D/fdVjzY7hS5+MWiwDWj9JALAxsjFmd3dvc2Obkz09PT0+Pr2Ynd68dbWjWBF4/Ozp/UdPYoD9a9ceP3xYjPIQI0MCYJsRopydnbz33g8Wi9V0c+Pa9f1vfvObV65c2d7cPj8/f+edd05PT4+OjkIIV65cadt4fPxgOtm8fn1zOtn2vpzPlmU5Go+nmS9i4M9+7q0PP7p78+Z1l7vZ/KJ9Xm3ubG5ubqaUXnvldQb56N69PF9MNqZvvv7m4fPDopy2dUh1LYSEJIDKhGC8Oz8/G03Gt2/fXK1W5aQssuzo6Oj8bGaMA0PC2Ia2CaEsy0k5OTk5kQRFnhtnQwhPnj59+vRpSqGNVV5kQOYrP/4Tr7zy2je/9Z2PPvq4LKbGOE5RMf2dwB+yz7IJTB4/evro4ZPJZMM5Nx5NHj9+vLmzm+f59va2iJyen61Wqzq0kBgtWONAzO7+1bvvfzjO8thEZ4QlEvl7H3y4f/V6Od0Yj4rz+WK5nCdJAtpf0mx46H4SCiMlg8AgBkWkg3Nn2uCLCRJ7Y9GjQUwhWusRGESwzwVJWRE7bP2ao9Wl1pN7vuA7CcGQ6bQULKLRpvq6sxy8oNJ7sIi1dlROcm/btl4tF6tVraUXZm5TTCkZix4s0MBn36X3DCwCpOk3SxfeddOGCKqioTQifegPPUamQ+Zo0Erk+rOySIyXeLsEIghAmBU5c2pDcmC993meMQdmyH0WY+QYjTG5dR5IGX40uQqSmJNFso6AlT0nNE09GpV55lerRYqts144AahclD4+GbRpOMXL3ZskcVJm6zwv1dsRkXNaGQMWWS4XCggb4ptuuFMv50XhXkQGsQaQJUlKEpVHGh2RMS4ykxAgEXkCsEyBUwqRQYCFeglpYBFIaC32OSf0EKD+YvRf3D0UFgH2ziIY4Ng0bV01WVFaa8uyPDqbG6TQJkQUiQhch9q16DwBACAP07uq56XqyIAsETXYZemo3AA6cBcggtIkAZJBSIaFBUhVC3RhEJFJovVmEWGJzIa1zyCdinICTpwMhCihaVdtExBMWXoL6AkhpLppQAAFfVFaBBFMQiHIog4pSgwppcSWrTI+oXXOIRgCI0SEhkhY0AAKGGZWtSjNkMhaQemHtCiBBE5BtzGDcGxjNOAIxVkiYSRJLHWIzLxcLqki6wZKnC6r0l2ocRUilmVZlmXbtmAI0TBCjBxC0NS4sy0JYoqcwDohtIbAWsNsUkoJiJlJuq6j9hCatooxiiRA1uq3z2xMkiSklJDIkIkiEFjFjxT4igKEXTKWUqftEGMcJMYHh9rtkaFslgSNxU904fWdw98OUWkPAeRehYdfSCuRoccIailBfaauZhISYJW+AEBhFAFjEMGIXosiF1C7V4Q6hdjPInbqsL2qetdRMSaixBBWq9ViMd/e3i6KYjqd+sxq+RMAQmgWi4XWaPPcjzemkKLObetnasAAQkSiTW9LJoSgjaDlbMExNbbWQCHLsnIyHhO1bRtiXNVVWC7rUJtkJ94WRTGbnSKit64cTbzPVeKTiDJnvffWmCiSYkrCMbK2hsCA8drK46CIMgAioWjVZCkLGTkCALRowAgkxfCAGN3eqWfDIjIpJf3wlHxMMrQPuWvJggC1TZhON5nl4PDxweExADgv5xfznd2pACeB0BEoRRujMcZpVRUBAAyAMp6o1PGknLZNODg4OHh+eO361c9+5u233/rM3bt37969O5vNJpPpeDRezJfz2WJne/v584NxWU7HEwDc3d3d3d6plqu7dz969uT5eDy+dvXq9tbuYrF48uTJ4eFhCuy9N0QiUlUVS9TeBxAiU1EUe3v7WiNarVZN06wWy/fee+/uB++/+eabP/ZjX5hOpx999NFyubxy5cqjR4+2d/b2dveBcDqdisjBwcHp6bnw47pttGA/m81CCFW9LEaj3/u931uu5m3bVlX10z/90xfz85TSqloWvtAg23pHbKxzMbVkTFEUxlJkSSl5lxNRaJqqavI8V25fpRVSc2S8W9VtjAmN8zm2bVsvVnme37jzysX5qqrrL335x88uTg8PnuaFPT0/OTs7m0xGnCDGRBYMeQRghhQ5SQToZEOGzjwzt3UrIqxlXQEAtmy1rd3RxwkCAgoJCKJh1vICJwDS3a3kvWvj9IMZgT70F5EkLImTKFcYe+vAqD6QMpco+EbJrEmQWYC6+QEzEBOpc13/Fu2D9RZY+oQftFo7hP5qMvRP+LLoENEQgdXkGQGBeyPeT8B5Y9c972DhQemuWRJH1TmV1BNto1DHYYAibEObbt96xZpid/fKe+9/5LLsbHZx89b+k+fPRpNsOp2ORqPpxlYI8fDg9Pz4QhDnyyof5d77Zb2cPbl4+vRplmVN04jgYkYk5vrV65PJBBnPTs7u2XuIkmUuy7LVakVoDfm6Cp956/PW5Ff3b/wP//3/q65aELtYVNevXzs4eZ6P/Hsf/CArsv3xlfP5eZ7nLPHk5GxnZ2drY+PTb/jT0zMCOjk8W8zraI1zWV5MozAkBqPbW1LbTKdTRFzM58aYtq5j3Tjjc1/WTYh1S87mvgCAWMeLZmatPTg4QADtXoWUQggCYop81rahrf/lb/6Oc79vjQegqroQwRh5NJo0TdAUvxi7EFsAODk5u3HjhjH26rXri8WqKEYpyXy+XK7q8aTc2dvzma3q+uz0hKtaidW3dq/k42mzqokAUnIuayWE1eL973/3yz/xE/OzRTEeL5bL8bg8xNS2jfZ/Yxt2tvYWy9loVC6XS6mbPMfZfFWteGdnJ3cjStTUNQp45yy5erVkhu3NabVcAgdIQgr0NDkAcEyxjYDc57gKlO7SzG6rYN9MJ0kSY0JjjM7oRU6GyFjrjCciZCEEAx0DIPWMHgYphbBaLKK3qsBivQfw2q125Bw5EOImgQAhYcfjrpT+0g31AoTQbky3sizjEGNqm6pGosnG2CDVdd1UFQM454wxKUbdhymkerWMnBR+Y8ikFMlmzpjIGCKHlAKnJqXUNjFGRABhBmhj4HkLHHLv6sXSOWecY4AQYxQmMmRcHdqiKAyYJEkIjXOhaRar+Wq13NvbGo/ztplb4GKUE2AIQVUiBcA5B4ZC6vr1HDW+N9ZaYw1EQFV5q5sudmeObSLSEJ/KUaGlAl17SOiNQ8RVU/cFDBo0ayCBJ0dgCAU9mTwHiITMJGQ8iaAQJuBEVhmXyDSpZSMDnxogG4MAtq5q76y1NqWgJA/eWOMzEenkHiRRR5MKAEAMYLCpK28dFFmSlBhOzk/QuJikiQEtIgmBAInxNqQAnAAEUXc0dYIOBohIASSoyKJEzKz+lUFnwBP2beUQ6pS4560CUscL0LYxxmgcAoBenZazbZazUN9sSUQJFEeMPiQeT7Zyj81yLgaTROdJiCS0IUogQvIJc05m1fCqDUTkgGKKHFrNqFKUEBKCDTEBM4IF5hAjoljrQ2y88+PplJxtOaoA2aqJMFtmWURCKp2kFJoGMCDXk7IkiU7SZFxy2olNi4h1vcpzVQ9MPcElpSQIRhFBeZ5ziAoarqomK4uLw+dXr1zLimy+qK3PEEzisDHdShxiYEeOjAVkTkAAoWnUjVk05CxwB40LKY0nZR3qwKGOWNeVL7I2BW7qkGIIHVF327YigMAI3NM3ISJqC6ttKiKIqQ2xRUTvM2OMJG7acMlehV1+ppDYxEGABnfeRyfd+E1fh+7aZYZ0tKOvYiCsFQgEOCKCIAMyYABkQAuIBERiAIGQCNBAV3ALnGJkgTZyiikSGtNpyjELx9gmVZzgpLJNitU0vQRB28aQQkqCZOfz2db2xsnpUZ7nz54vb968nlLQ1o33djQqjMGqqhaLRdM0V/f3E+DF4ZEjs7u7Icyz2YVOblSLOoza3GfjctLalpkno3J7e/vZs2fn5+dXru6v6uXB0eHe3t5isQBF5iBbi9ZiDPX8oqmrerVa7ezsTCcTBDNfLhQWEmJDUbGIkDjqQwSMgaMgqLai9C0pEjDWtyG5zAPAclnVoanrynu3tbWxWMxCSAzYBCaiJFzPZ2QsAKSYLJEv8iSyrNq8HNWtVNVKgU/j0TTP8xh5tWrLYnR8enF6en52dhaZrLXns+XB0cmz4+e7e1tXrlzZ2ZiGJtb1qmpaY8zp+dl0PHHexBidsWVZiEBYRUA3XzZJzHg8TSk9ffw0hdg0zezsfHE2Y+FqWderZjrZ3pzuINjre9fqehWwjZFnZzNjzFufeuvKztXpdHr16lURQTBVVXnjNyebs4uLZTMPbRNjTCAxpcCJiKx347E/PD58550v/sEf/KsY441r158+fbq9udnUS1/4b3zjDz/++OP9a1cJTV4ULFiOJvNltbO5M18tF6vT119//bobLZZ3s6JQOD45e35+enp6ev361TzPj44P7n74obW2HBfv3/1hlnndHefn5yEkRekIwsVinjtvra/rFoBRsPC5CmUTkCC0bWsMGuMRkXtuPEmSBPJyxIKLxSK0MYHEJs2qU0QTKrbevfX2ZxaL+cXsZHO69bXf+dpf/It/YWdvF9E2bWsNNiHGyKPRCLhlTooV1EJ8F8WaTsVRNx0AoaACmYElCYsoO5ABQkDs2X0sASAaICQSIhtjJGAZYKJDsN4j9JIwAqBSTKBJKQB3Goqo5feOTYKUowoGvB+IgKA10EMOhtqEiIBBBhE1NQiIQ8dMSyVdlz62bUpBCCGRAAB1yJ8EkiBBksw6NVAiwokHnR/KrXSMt10aoVmTyilq6Y2wn+kTNsb0mYVm3ckul0si612cTlciov1fYbh2/XpMTRRe1tVsuVrMl9UqZWWhZIVJuAlt4phScsaSwRs3bpydna1WDQgVPrNoBXBrayulwBy1yw9A3uWj0dh7/0d/9Mdbmzvf/Oa33//h3b29fWPsdDI+PDw6OeWqWgqmg8Nnk80NBayG2EKE2WwGgm3btm00Sbwz3nhflNUq1PVCJ9OLotBe8+bm5tnZadu2k+lIx26YY56XPRsBGTDUydMIQHTWZt4jkQAslssYo8t8OSoZwRmrVVQij0TIBo2EIEplodEPEQgk5mgQU0pVVYcQmyYAkM3ysixjkrquxxvTnZ0dl/nj40NlYLfWAosR3t7Ze7q4bwUVe+3QREkQQ2xbiwCsjA0KptTklVPPzy2Srl+/+vHH92Nsitx7l3FMBu2VvSvPDh9GSY4sIhKAMeity5wVQUaNkVCb6brOlcgZ1nijBxy/hvDYMXh0WitJuB8v6dFsn5jR7Fh3OuxEJ2fdRbqSgAGAsVPlVEy5Rt0CIGCM9Bi4HlIiAoCSYlsDR0R0hjD3GoKQUcdM2P/JcCF65uqDDZKyAYYQkoAoRTiIooOA0DiLCBAFOikxhpgS4mQyMcYA2cgJIHKKKnJkjGGODMAAgly3zWo1ny1nu1tbZVl41dgCAVGN2kQ66qTle+5odzUIsgCE5MgAkEFMa9D5wXxo4q4kaUNwA2uAP1wfG1oDL0pSaksiQmPRWKOxMDODACQAQGYOQVIMSZjtOqq7HyhH7HiDUkSdr+jwPwnEAEJPPTlUdgFpmK8UAmCUvgufAA2gQUOEwBKZOXLA7jIBAJQuFgUSKOQrMRMA2CFoJwQ1xWqOu8tOPTK8i/CGdv/QJTDWJ47eWGtVQtYkpRtFTBAhiUgKkohj4U1iaJpgBEMbCVE4KnYuxshgWSCGQNImxtmqYYYQAosBAE1UUkqrutLnBULIIkigixyoR0l1W4YRCJBROEFi6JCdkBiYIQkEIqiWc4s2hXYymRwdHYmYtlpdKjX3clT6scysGkxt28amVepPX+R5nhf5iIxhVSbpyFEYsdunAAgGEIxqgxCgAHMSARbWnar5RQfK1+4/czIGGcQ5JwiJAVFMUotBBgwn1c4VETEiUYwhSEjG2B6g0UGMBoDf+v3RBUmIIqr6vIbS6X9eg6D0u6NnCRIZNIO6PZV0ZglAJaGNRRIiJFgzZsIoiAnRAKokkUjHD9VtKJ0713n9Lu/oPl9nDER1Sbs8vBMPJpSizHQWSERibI+OjhRoTtQ9suVy2bSdesPFbNbUdZZlhc8QjbG0u7vXVpUO+udZ6WynaQ2qBt22KhrgnGPmGNuTkyOyBhljjCE0CcRYJGOJMM9zIsqyDFmUjM0aYzv7kESkF/wGRQYmENG+HAJwt/MSSIwc06W0gjEGkWJsdZS8Mycd6MkY40Q6M8+iQhO2rtqqagSIjHMeASAklqYldMa6ug1NG0NkQGOcMdYKMJo2K0Yhyen5zGfWaA+QJaR2Ot1MKUgjWe6MMYvVMrYhiQRmJKv9DW6bi4uLw2fPnz9/XhbFtatXF8tKRPb2r7V1+PrX/mAyLl+9c6PIvUELlAgw95nfuTIqxmrozk4v6rr23l/Z2zNo73/8wBsLLAjGWiBjkBNZ45xr2lYzmb/4F//Sxdn5u+9+HxK0bZtl2WI529zctJbefffdPCtee+0NZh5NNg6PHyyXzfXr1/evXjs6Pv7mN781n8//zZ/9mdA03/7ed46ePS2K7FOf+tT161fPzs5W9fjLX/7yzu7WdDqtqtXjx48u5ud13R4fHlvjjTFFUVzZ27t27ZrZ2DibXexsbsUYFVRolPaVnOnUw7sWjbAoZZlIEjBBIMXUMicBICNk0UBmMwC2md3e3b1582Z8UAvH8Xj85MnTPC+2tncFqK5aMIRkQ0jUFedfGJMdNvKw8wBAdXz60joBCHRCVaRxRerqlNRhWwUE2BuLJC9EJD0ZiYgIAnWdVQUrJOiwoDCENOvh0NrpaaceaKBz6E3NgLVb89cw+OKuJ953np1z5CwRvTCW2fmpbgaxs3jqyfr/D6KBg2fXLzWXPl+pk4dGeI+M6P9rl6t5SsnarChGSkBGREo9Dmjz3JdleTFfrVYni3mTZUUfa2or5LJz8fHHHzvnEB2IzFfLpweHCGZre3Lv3j1jIc+K6XRzMpl6lzPLYrEQMdvb20+ePL9161ZVNUdHh6+++ppPLi9s01Rt2yoeADErioJacN4fHR7PZ4srV67kytlf0JUrV548OxbuCCtms1ld12VZFkWh3GRt2zZ10AariElRODWpj+ekfwyIeHR05L0vyjLPc43IjbNERBqcoaKKe1CaghMQO/G52DWPmNkqz5BxSEZnwAw5QruxsaXU+2WZX0aTgjFGSyYr8jt37hw+eYwpskASdi5rm1oxnVlZCoCmGXmep9jqOtNqq37U0dGRSocQKfluPZvNjCMRZO5B8gJD4N5x8A9EFgCIKpBpL5epQt+M0bE/jYmxH0VFo7Kp2PGgawFvyIm73fvC0kccHK2KGgpLRAEisMYoyo2ILFlEZJLIadhslwjyvguhTtFam3vXF9Ja8gaAjHGaHjMrOsCkJM5ZIlRLoWXIlBKkKEggJqWBe7vblogAwxAqoaKilKGZgbTFjtwRAhpjdJxA51UV6MzMeZ4jog7hqS6usIQQhnuo2y2lxKDxqFYhIEEHQo9qR+iy3aeAPRE2SdTdrof+8OK80UtGlEFI6U27KYiushJiAABgVBaPmDjEpOOowx651FIR0fJqWgOC62M1Sjm1buzWz6S36dhPLIjErhhPhKDehVNKBjtgF2jywJeoazW8rIpcfYijG6EjWOlh9L2TGPqhvW1l7qH1lwdAN3nGPURSsQV6zinxarW6uLiIGcW2StEQJ+tt3YaUEhorIjEmgDYwqMwCEYGQ1tp1DE5RYcO+GJwIdhIAEcAOPD/IotjWlFKMSEQMHUifRZxzVVWPR1MAGI1Gbds6WzRN44xdd5zDz8p7pucwMPwqLl+XtMZkejPXPdna/elYQZWuRZ8ykQUQIAwhausv6X0nHOiopeOCjMJGEF4qDKyf7eAvuxp/1zHsffPa+qFhzrRfFuvv6Za6JjBrVX9ZI7eWDsmPPdbWsrq2PqoQZqU6FoAEQgKAyCIEkBB0LkIAkHoYUb9Ju8V0CbJCRFQ+39STPIqIARS4FIvVR6Ou5+LioigK6ueb9bloH6Ou6xCCNWZzc7PM8tVqVTXV8GZvnfceoQtHAMD7PCUpy3GWFQxiDOd5WVVV6TwiAjCiIQBrfOYLay2FVpdKjJEsderkaxKE/VrQBpoYEeq3Vfc0WETAGSuJh2BIREKKdV1XVRVjJLKDiRoet4b++mB1wbRNJCICdMamlELTphCdBecckrHGeOuKLCcyRVFYa5OkNtXGIxpqQ+KYEgcDSARZll3MzklgZ2fLI9ZNS4jTja265cTAKRDZrCzKsswMKStJUZbG+tlsdnp6ulrVLDidjB49enT16pXpdLONYVmt2hi8z4Gwbdu0XCRm6xyLFFm2t3/lypUr8/MLEIcEaEkQkBOQGGNWi4X3/v3333v77be9t6tqce36PqLEUENNRLaqGmuc9353d5eITo+PX331VZ8Vp6en3/ve9+7fvz+bLb785S8/ffr04uJiNptlWQZAp6en27tbr73x+u1XbilybD6fn56eNE1zdHL88OHDvZ09rXqE2Dx9/iSk9lPZG7du3eIQ+4aYIAlgN34GCIjK1KTuWxNt0IgrhNjZNEQ7CF+m1LZtWZY3b948OT04PTnK8/zJkyebm5u7e/tFUVSrGRFm3oe2NphgIN8D7NHFl65jCJRFBAXoBRr9DgcGazUO6cpXXWyjrkLfrKUgpYxCs+4XBjDhpW96KRVZn6qXtQP6jTZ4Rn3DMEvwsonr7eGQHujOjS9O7a//7XCew7nBMFk37Mb+Wz45Q7V+DHcSAGxZljoutlotoIchMvO9ew+cx8lkPBo1s0V1cb68uFggzDOnAWhZFBkRJY7w/+fsT58sOZL8QFBVzcyvd8SVkZkAClcVqqpPLo8ZUtiyyw/7YXa5Mn8xRThc2RUZEW73kmxWd9eJAgoF5B3HO/0wU9X9oOb+PCKB4si6VGcHIt7z589MTc+f/tS4ZZb+2bNnREXbtnGQxWLvXPjRjz78X/79//X1mxcvvnu52+0AkCV27XA8ts+efvTm7atf//qXn332Y+caA3I4h8vlcrfbGJNUSqkoPIzIjevra1W9v79nhcvLS0fh5cuXKaFFsczcD611rni/2u/3TbMAgM1mAwAffPjMOXd/tx0GFsmAKFsOH3wI4fLy0uCPZgsRcUhxv99Xi8a8HkSjA1HSE6PqfCl1HN1SN8urqysiLwLb/ZG5H4Z0PB43m50rwv09Dine3t4cDm1KyXmqy4Ka6sOPP6iast8zzpzdGGN33C/W60SKpI58VRXtMU9LGYbB4J7GyXB2duZdcXe3FZazs9XZ+gwRy7JEtjSVAKhl8kQyX/4c7TpB0yZzlcWLCBFt7MCUYBMx4KJaa05+14Syn4mjwcFxlPvpUGWTAOKQHq1kfo0qjZnbaUHsrmZKrRN37ncys6cAmXbjlPKfzhiO1D3TQVUR8m4MkU8h/pR0tESmd6qoRRmM2UCAWMXGels4ZE40ETlHkgYD2p6vzhWAmUVZVY2tXVN2sJTQBohkN2I8syJiyW8Vo37L6YFJgUyqSlCcuikxMC1gXuuHehMAFAG9s+okq0gSSEKoAJJScuAQXea3ceTEGJkecCZMO0sjmxCOnakiIsI0Zmb0YQAgY9tWjkRnymhac5XEYpnFU5eWfdP8lUEd5WKCGuEx4gT1ztZjXIjv03qnNEmWDeaJT0YVGdwQFdCwIkAOEJ2iEipzbNv2cDgGrICtdjM6Q94FHxwSa+KRFjSpAKEDZ9/avDfba1WbPuUUWNVaRVVElYVGenuPoCiIVkt0WfKtrR4R0XlfIA6IrmkWJiSq6n0Gvus4PnY6NVVVTWeERwbP49GGKtjsuV5EQgh22Od+9mn9JZ90IlJBq8+NG3VCtaqiCghAZA3jybWyAxAqEo5t9NbA50asjsXzj6ydXfYnUJleMIG05FED0vj66YxPD4mOVHkmXKdeQO/9lK4wv5OZ0WnhSzzN01GbU4JTZJihSzBVJpHyRLDpyMzlfG7CYdzrvhsQ0bhTm6ZZLheW5nfOFYW3HNZyuWQpRSQVApCbIJm57/uubYkIRbz3jNS2rXCckushBGOVQMS+65xzNjPYHsbSSTIul0mF9ZR3XVfWtmI6r8PoTCHbn3RqXpzt1xThyCw7kFS6rmOORXEqSc10ixUPTtQuaaR4gpGjXURUBju7Xde13WG/P4po3/er1apqKvRlUQcbLL0/7o7HowMMhdvtDsf9zjnXp1j6YEtdVs3+2Kcoceicw7PFsmnqDz/+0dXV1c9//vO//du//fbbF0nFUXn97GlZ1L/+7W+un5yfna0Wi5WxbTJzUWT3d7fblUX94YcfmvdS1/VHH330+2OXKCQR9KCqDMoizLxcLhHxyy+/3O+3CHA8Hs+Wq1evXnz6ycd/9md/djx2L1++/NGPfnR7e/vq1auiqBaLVdu2L1+9+eUvf3l3d18UxfPnH3744Ye3796+fPnyeDyen59XVbXf73/72y9vbm6++OKLvu93+83xeFwul9fX1x/+6KOf/exnv/j7X/R9X1XVxcXFfr//6quvjvvDMAwfPH02yYMjkmm0QmE9VCQIiETOIaEoCpKmNKSYUuLJviMOwwAox+PeBzw7O7u+fnbY7UU4xvj69eunzz44v7wqSq/ovPcpIjNbpDodjSlWnGsemFx/pJH5MrsWRvjH00HT3J9jUajR3MyF1l42n0Q+/ytmUjicSZ3O3/h9ZuV7slqT+p1ubv8Gyipu+tdo+Gk2YmxM8J2SL+/rN3vyyfmZbgjzSGn2w6PHUFX/7PpJ31vKBssQEJFcACCOoiJvu3cvXrxMEVSoKhuLr8pQrJers7MVER2Px8NxH2Pc3G9FJCUIIfzFn//1n/3Znz179sHV1bkr5Ob2zXffvfzu2xdv397c3t5z2oaQbm7eAtDZ2fry8vy3v/3t/f227/v/0z//68PhYHZIVWOMVWWZ8vObu3evX78uy/qzzz5brs4M9vPkSXVoh/3uuN/vi6JYrVaWNWnbViS5jMfxROQoNHUD6u7vt8KZgM2MIo0JdWuNsoxsWZbkncEcicjDyaHEMRE2SWq26wTOOU7a1NXq/EIY4Pbe8u2AtLnfHo/Hpl0mHlJKZaguLpxIEmUAiDGWZVnX9XGzQ0RBihKNMmK32z35AB0gAYJqWRTt8WhOjh1OcmATv4m8MLx7d9ce+9XqbLlcnl+evb15w6oEoMoqbJOvcBpNolZXUqtXE9EQO1sNzJlIsccwb4DmpgvRspKIQEBIhpazdt5R5gAtX6uqIKrZ7s9uMvp+pi7zcgkSkbAmYQon4hqdsXZMDgrmhvo8cZaZEZ0VBDVzyyAimQVVVWa1+oZzDkCSRO89qCNO09Ea84UnT3o6M5l9DEhAWUBAbRooITrnHSBzag/Hoe+rwi8WC5ttZCXUwnnnXISBWFiBBDRHXladBEUg51iERZhFGFiYyKMjzdm0PIvAdkeZvfNorapTttv83pN8qk4TT5TAKYBpT9EkCtHKoSIilIs6RAgOSdHmLMsk8bNE3ZRBn9TQ9J+PfsDRFbDwAEVEUMZ8dn6jCEdVjqIJrKRgPP2auThVx0mc1iyYw4K8x3Dipc2ZGxEBPD3GuDI6f9TpGrWwSh7BaDeZq1xAF5JyEvFlUVCoPEGKRemBIioHXzBgzypAkFRVh2FQQEKXhJNwWVfmfI+2BGyXRBOAsyJO9saQgkNCYAUHmAAElBVUxYEj8kRClEzaTW/cvn0HLEpclmUaYrZhiPkIjyLNzIkHMz82P6Td9xZCTwrBlnFCzJ+Wbmb80AYVOBxJZoU5FUUxjfHKlg9VZ14jGZM9IZCfawAiwtm5jjGJynxrzKfOpnQsIzCz1USS0Em8H6bBpp/nv9ETwfHJVcUxI6CqAidaLWssNdfW0o1q3eOqSJRLQsbVAioqqOhGxNUjqz8V6K3cZzcUIEQgSj54ImqaxtqQiqI4HA5EFIKrqqqqKuecaGLm1WoNALvt9v7+PpBDxKqqiqLYbzbMnIbIzCn2U/LCuXA8HpsGvPdDn5qmqOt66NOhPTrnQAmUFDQl6boBEdu+raqq7/vj8Qjk0VHXdXXTFC4z5UwOvYgkTj5YLAKgefKGpWfMLbbcp4hBjcGMrEjyXsYDCyqogv3QIWJKETE7/RYLjYKAo8YGK6giZuhmKFzfxb7vQvAu4GK9iDLsdm3btm3fDylKYuggxrioS1+Uh2N3121SSt5vXr+9ubh6Quidc97bBMakkpKKR/roo48unlxvNpuvv/r21atXq+WZjtlcW3bvfVmWRN6M193d3dDvDoeDyVvbtpvN1rnAo6nN1VwQFgCUoiief/jBs2fXhQ+f/+QzjkmQi6r88EcfB19cXj1h1q4bnjx9/uTp03/4h3968eLF9dPnP//5z7fb3W6364b27/7L3w5d3w39Yrk6th0SffSjHy2WdVH4zWajyokHa891zi+bxdXPLgHg97///dvXb5hTWZbPnj29vnrSNDWDoFEPGZHe9D8BRGREBIRxxN+jIzMazYSogGK5467rFkX18ccfD137+s1L59zr128Xv//652VVVdWQUuIBHpYA5zoZHvqsMBZylZzpe7FYQEEktyA+ulBFc49ipqOwlnvOZQsFUEKyjVGVOTzv0TWZuccfMeZN5knGKUj+3pv80GXKyl5DU6oCUaPAmNAajS6AleoBYRwYDMbhgwI/8CHycHgwAPiiDKrqQ1BRABkGpkAxxrquAaQb2hQjgq+bpaKPXZ+GAdGpogiIpLZtDWZzfnZRlqVIv9lsfvWrX+52u8ViBcCC/dn5cr0+X64WwzDYlJm6rj/77PP/+l/+3tbkgw+eP3v27M2bN7e3t8+ePbu7u0PUAiodSZ0Xi8WzD54ul0vvi08//RTJW52rbfuqWU3fisexPtZQZeM2FotlWZb7/Z5Zlsvl8diZ629xLTOjdyLy8uXLs7OzJ0+e2Bi/YRhInPcejWARDB7tAAD4FJ/hjE/TOWcIWecCKMXY931kFedDVVXDMBC6qqpjdKB9UXpF2e+369VCUoyx91CcnZ29evGSnAMAI7hIabi7u/s4Do5KhxRjHudhn2zJBucdcwKArju2bVdVVRz47u5uubhZrJrJjE2iaZ592x4Q82Qc++tIpPu45qXj2yc7mk27I0Rt+84AG/Aw0Pw/IOj2AxnlIrNl5d3JldfM8PPQ7cgD4SfnZpJm51xRVEMXERnUyByJCAzEpwIppelfIHO7kcg75xA8Djz5HzIarZNcCXPfiyRr+xC1vO/pyxr/jbK0bWvha10GA/+IMfQBmEdoA94z7laYzCkdMWCuKJlZc4OsLXguxM1dusnznlD3Jy05c3nnP8yU0UhNjEjgAQRAwMjL0Kol1kZAAgyO3Am6ANPd9D2FNT2Djqn3+acbuxnM0iSch0kBIDIzIignzRTgmfwIxnraKAJjVco+bqbLJpNMY6I3k4pORWGwfrB8YHUcET+Jus3U8t4rOCsBMTMKqzCokIOhT8OQVNE5rwSsPYn4InCCObiFmS0+NDIp5WR+TFmWRCSZihQBRdge1QJpxRGM58kBqo71vyRCzALOuVPyOKWkglFiSun29jbGGEJpySyd4TDnZycLKpE5l33fx8NeVcuynBfQLPcBY+lgMmyTCGFeLJxqp2OZHuLAw5CKIqaUwMhw42AZChEBpbFYktURjVsruflHmFmnqo7VOuT0FeYiN74xc95NK5NF/DE21+QdOLPons6L/dD3vTVUmOtuS2E4LrDiizk+AGRsG0SaUUMqCMZrNn3UGGCM/zWmDGj2kKSAQIgQQgiFK4ri8vIyVxsmUCWiHRMi6vv+cDjc3tyvVitOKaUUCue95zQcDgdD5qDmGGN6uxlcBPLel2VVFIWlwyy9q3rKazKLqhg00Sw1M8eh3+12i+WyWpxOtG0NZ3pNETQ/SudYDHsA55xl0+y2xlNkbLw6K/BOG+GmdJ333hfmV8UYU+LptLKkoc+10KIobNKWTbooqhCq8PZ2b/O56rqu67o7Hs0dX59fXp6dD7E7HjqWOAzD4Xi8LipEBJaUuMeBCPKQ7LL64osv/vjiZfDlFz/988Ph8PLVmw/cs4vz5Xq9LsvSNtE5J5JF6PPPPz8eOtP/RVEURdXHQcAwt4AI4AidR1RC2G7vyyr8/Oc//Vf/6l8UIRDh/e3dT3/6xe27d0Mfq7L+5JNPfv3r3zZN88knnxwOh2+//Za8++ijj5xzr1+/sTTlN998Y1ivJ0+etG273W6++uor5/Hp06c///lP1+t137dlWa7X62EYNptNURR//ud//vTp069//9W3336rqtfX18+fPhv7c/Lu0whztbbP6Zhakymb0+1hiDHGKAgTHkxEJkREHBI7vri4eP78w/v7e2a+u7v7wx/+cHX95MMPPwSAruucWeiZUE1He7IUD/QYAlur2PR7zA0i04sRERU06wQVSfObZLNhtJjvVatyGgIQHvrK08PMHwlnFUW7cAYumKus+VsmRUojl5ESPgL8zN87nbjpnvN/Hy0RAExDKuGh/Z2+yPSzb9uDKlZlHQdxznXdYP7WUVrRCAB12YjS0Ke+61JKZeFTPxy2O01RSQ3Q7JyzwZBFVTfLxWa37b76sq4bEV4sw7sbVxQFkeckw5BsTb/88ktE/Pjjj6q6eP1mp4LX108+/PB5CCURdd0REbjjris2G/Xe//0v/vvPf/7zxWLx61//erU+/5u/+ZuiqP7u7/7Li1dvuVErB9szLBaLqqq8L1I6xpiKohKBw+HYdXEYUhyYiJz39pbpurq6ev78+cXl5TAMb9++vbu7a/supVQErxP9ZdbFOcagcaSFVcmJAJR8IEAXOfUxiYIjD04FaWC5224WmxUR7vd72AsR9f1hsawmXfnJZ5998/XXMsQ+Dt4HR46Zb25u2rZdhSI4Nwxp8noBRtbbcTQdEVVVBeAQXEpis35YOSUhBZumTmOhK6dksgfJE5bJjZNEJ2E9pWdGUZqsLqISnKSPFMBmZQHkbjc7sIbTRrBHdc5iJzukJzSO9976vSRl1mfyrovD/PgRkaWfgs8nh5kJxpE9zMKWsrWWGIf2IMIDJyJQRWYh8ggoAilFXz7grZ8c0+k/J1M9/Tw7e/YnZNCUEqLjIbZty8yLuq6KEkStcVKNbcug5CnP3WRmVMmslIhgEC9OVkkwo6IMqsAq1iAsKmC5GQJQlPQ9+mVcrhPmap7BfpAWyBAYMgTDONlMQGxcK6ieADaPlU72/eYwj8c9Bo8CABhBZaoAkv2G1Cf01uSHOrYPZf1oWV4EIsCxdmQy7E7FI83vG1nWdCx7ap6SCDDp8QwdJ0QUPaWox20FRMyOyRh5wIRgBowxHru263sHnkljNyhCAGdUYIwZGmFlQe+9TXg3KIWh6sf1fbA7oAzgiMgheofBIRGN6RxlBRBFZkJwigAk1geC2KfYlM1ut7Nlqapqu916fGAVpqx/PmLoaeTyn6AdRVla4n/yNadqDNFEdjSWViwaBzv+oJJjtT5FVW2H/th1vvQxReP+77oupZSSsDAoImTLNH7EvMoPKYmqWBQ0nUSrDBqyEcfH0DEstIIYwDjXY8pBmk+OqJCFChFRRMcEmipodtoALPds6Uy18NIhgrE+TCdlUkMw3nNaahyHHmS5mYU0I+OATupuErmpxdBIGEMIx+MRUS33771HzJrNBvq2bXt7cz8MQ1kUNEJzzblfNY1zDkSdc4TFZKoItSwqAEqRASglFhlS4uBLciAMiFHZyg8OEIqissDPjka7b7fb7fnFxXJmK0/qcQRS66hcQNRy/yb2VpiywG+cQlWaMAKQo5CzSADBl86jCFVVAahlGcx79gQ9USeDqmpiJUVRh3A87mOMjsJqtVoul84TSOo77mI39InQ11W5WNQmwH2MjoXICaIvqnVRO0cppcPhsN3u+r7rDkfhuFrUTy4vlouFc+7mfvPBBx98+snnh8PBhaIqm7Pzqw+fP7+9fUkEmlllZBh6Tln7XV1dFdWw2+0GTjElB3p9fd0fX4tTSJzRKaMKrRaNSNpsNtG8+KHvjm1d1x99+KPNZvf27c35+fmbd7fb7fZXv/rVixcv/s//7v9ye3v77bff/uMv/6nr+o8//tiwCaD01R/+eLe+++CDD37y058OQ//73//uj99+Wy+qTz75xCGoovUaGRPrdrt9/vz5xx9/fHd3t9vtnHOWrrJTYzFZQDRVhkDJ2mtzWdmUKqhCHIa+71NKVjIxKB4oICpzLKvaor6qLlarVV3X9/f3jsLxeLy9vb2+vgaAlAZflI4cCImICmTso2ZNDbPRKzClgRR5LGlbwsXOmiUjSI1TWHNJDpBNb9h9ptTVmD2U6UxPRUIR6/h6ZO/mLgHMrkyfPeorQDRTNL1+1BjZxMPo909ulX3Wg0+c16u/LwzQ9yKByUVhhCkCn96Zn2EcEGY/+BjZu1BVlSOpy2oYUlEUiOpLOrYHRGzqRRLc9gdEd7ZacmxZUtcNABCC896frc99cML65s0b6nobPXhxfnV+fpG4Fxnabq+qy+VyvTrr+/jNN9++fPnHpl6tz5ar1YI5t51dX193XbtYrJxzABSCd4Wv60UIzjn32Wef7ff7/X5vmYwvv/zyww9/9Gd/9mdVs/rmD9/atG2bF62qh8PBAlkDwZdlaSQ/9/f3i2ZVFEXlc1ArIuZL3t3dMfPN7a3p66Zp1udnRPT29iZ7aZgNgWX1RND6n+yDVBVQVJS8VwsdiYqiEITY9l3XlWU5DKnrusViQUQpSVUVi0V9OBy80yoUdV1++tkn/2292t7edYd2tSrIuSjc7jZ92y2XS+es82+CXOMjIaiqajcc3rx50x77y8snRHR7e392sYax3C/jqJS5+oYx4BZJFjxMYauOjHjmENhbptqWEgKIcw7xdESnCx5e029mxmPmm6qFBKeUPzM7yr040zWZWOecjdAygLKMNRwRQHXGbIVohBuWLycrFiLiOIbVpZRsQK+qTs8+9znsA0/wR5AQXOwHzh3amf3LaO1ijACShiH2Q3BusVjUdSUioxepDGJzR01mxGjFWMGRpaMBQFiHYVBlQgsmp8k18giDMSog84GmZRwdD1WaZSOmDL0BegQAlVXRhtqgWrnTfOQ5pNvQt7Ps6QwrT2OHxvsK6NG+T0UJc/vti6CKZRxjjC5PF8/qXkRABDUPeIeHOY8cdRu2JH/H04TCE8XaBDJ2s5UZ24KJiCPr4wtEJaU08SsjATjySA4pEKbOarsgAgyaFFSwG1LbdYhBnQf03vtCyRVBQJOICsgIoGfmY9+JWmg6yaT5m9ZxbVRH5JCmGrZlwhKqFxS1kfaigt4XKe3DsjgeuqpqADZNvbh9dxOqYn7cpsva9FU1xtgqTJgK730xpjAfGSQcqb14HCgxgVWYRQRg5MlBRFTKrQsAoORcMLD95Ow6BAQPhIpOVUfi0Rx1jQrnwUnH8TROr5/AhGZHFQBzku6BXZzfAWZ2MVvfh/VJ+8F7D5a1yHGFNfygjSk072NC8sCkM1XBUebzyWxdPHf9p0eicUItjOUUQpqyIcd2f39/v1wu27ZF1KZpAKCqKiLoukz20HVd3/dPnjwxCwgAthre+9Vqtbu/FxHL+tv0ABEZhhR8oZqbgxVhAnf5EDhBSqnvYhT24iEQklZV5RwafYLhc47Ho1UAmJnczO/PW3fKm863b7vdFUVRlqUZC7tb0zRHlpTSBKeeqhOqCmrTnQiBYET813WDiMIwcVoAkPf++vr65ubmsG9xHJacUupTPPRdUZXr5SqUhWiyRbOnslG43vu6rpumIXJ13bx8+bJt2/54dM41TQPkWCTGeHV19eLFi6psmuXqeOiqplaFX/76V59+8nzoW2vlsngGwRVFEWO8v7/fbvfn5+eff/757e1t1w2r5VlVbjkBsTAwmOoQZeC6aRD5/v5+v98vmnqxWKxXi+PBIP6f7HY774vr6+sYY9/3P/npF9fX14jIAv/8n//zqqpvbm6+/vrrJ0+frtfn18+fnZ+f7/dbdO7p02fb7fb165e/++3vnz17dr4+e/Pm1e3tbeLBe//8+fO//du/BQDrHlmv103TBOeLojCJMg0QRpiWMEc2i2GUyFYnR0HoD12MMQqX6Mk7HPfeB5f6oaqqsiyP260wlGW5XK7fvHlXVZUitG0bY/RFAdbhQ2A8gzNL9QC2Nzcrc7WWgZmTrp6O/Ph+U2NT9d7M3+lu9kLJtn+mix7kyE8f9wMuzdTtNj8O8hCgD5OhtFzpGIKY+YvGDuJP9Coyc/1pZv7mD0azVoT8SjtHot9b9n/fNPvu2C2acNgd+4FFoCqqmFJMQykOBc4uzr/44mdFaH75y9/84evveowgYnzeIYQnTy6fPb+uFwtmHni4enq13eycC0XVDDy8u7vxniR1omwj30IIw5As1tzv91dXV+fn523bVlWx2+1ubt/udrvt9rDf7805WywWIYRh6Ha7Q9sfu64DoLquY4Lf//7ru7vd1dVVVVU+EDlwzhWhsmUqigKUHIWyyHqWk3Iy5YKq+uLFC2Z+/vz5YrFoh94C4mEYyDmLFoqiWJ+fuYloQh+MZnDOdV0//dW2wdCfMaaixP3xWNbVYr1q370jIpt7YJ2yZm5DCAA6DAMQFEUBCrvjgUQ++eSTX9zfksfIsXQupXR+cdG27Wq1+voP3zx59vz+flvXdUqJSEVks9kgrU29mve8XC6Foeu6uoqrs/XhcKDgCZ1VltVK28zmjgAYpKqYZLcoKqv6TbYfxvQhwAkBjYjZPxQlhw7Qkcutw4KTtfNEqoKgDpAUJCZLclv4kHOuCgBaVZWOzHc04nr7vodxrruhs7z3RfATPbZR5vVdmwmIMkw5G/XpYNjzyGz4rr0+hKDEzJxiOhyOx2PfJQXNA3dERBGsTt3UIYQgklzw1qjKKmyumIqqNk1jDPci0iyX5+szSfFuc7dcr5QAjDlQwegoFZFViDwipiisIqCIpKST7hiGIXc+QA6HJhWTUtJ0Kvg4NPC0QUfAOXLOtX0PgITeeSegzJyiPa+NZsh8wya2CJRVHgEhklpzqqmwB0pw+nl0Pk61S7uh8OiDjxonfwxkRFNKubZpqDzvWWxQVdKh74uC6qJkZkmMuaF+rAshIqKM8ImUUtKxcjTldSRD/+2QAoBwzsGDgbzMWiOmJMwcuWfWsiydczEORmjCIz25AquKWH6YNAnf3N7/5PPPBh7WVaXCfYp1XdZ1zYw9Czis6low2hl3Hg/7XhAuztbo3Wa/G4YBSVHRqGS8994Ds8Z+cIBl5Qx4k9LgUEA5xSFUZd/37ZDKMrgiEHojQu26zmCE6/XZ06dPd7vD/d1dVdUp9SH4CcDTNA2q2vOMCCsJdGKf8N5b5sIq/s65s7OzruvsbI77xeaiiUjsjXTrZGOJPIB47/uhe/v2rQmJ4Tr6fijryg6wHRUdBdB2xOLi3KVhZHk0oYmmhB+oagY6zg2z3cyXD8A1M0GdoC8GtcrURmaYVRHRA004MWa2VqVo+RFlEUnCzhNABh3hCDLx3h8OBxFBIh8CIkZOQ4rDMDRFsCLV9PyZjcqyfZODocrCwkrkq6raH7Y/+clPvvrqKzvmZVnudjuLbc3pt04MM1LMXBbFer0O5I7HY9f1zFyMQ5G89yoZKM8sy0XZdYPYkGaXK8ZVVfGI7rDlMsEgB7vDrqoKADAavevr688++2y5XFrEziNBwnTEpgA+xmizI+w/F3WTUuqOrar6IrRtOwyDncS6roch45pUgVm8D8fjoSzLmKZhFAgARVF4VxQBUiGWHAeAoqjM1S7Loq7r1Wpl3X0oWtcla4pp2G16QbAvWFXV1cXl7e2tRQ5932/udybn5+fnvgh8PLgifPjs+QfPnxaejvudPS0zv313y6yE/uXrV/v94enT6/WqXC7qpqntBUTkXVBVM+5nZ2fMfDgc6rp2LvQwmICFEBy6rmsRsWmapEmEb+/fXZyvv3354uriHBGrItT1Yn1xuTu0T59/GCPf32/v77cf/ujjjz/+9NXLN+joyZMnf/M3f/PVN3/4p1//8sOPP/rpFz9zoTDBrhdNWQaOaX1xvjvulnVjpaS7u7uXL19anfxXv/pVKMP+ePTeV01T13VZljpmms0WT1KRUhqG6KhURXLBOsWj8fkQ3t3dp5QYVFUrQuccq8R+UPHrxUJBdvst5jZI/9lnn7169apt2/vt3atXrz788MOPP/2UPG7u75uyIjh55PPk9+TOnkxJzniax44qaANzAYA54eRhC09pI+89zvKHOhV0kzCclIZmvjssaDYufaZVJvsLD0MRGzWAYkWSKfpAfuh7ZI9IQUXcDFCEI+GBzCjR50EGEUGeaQlz7x1zCnh+e1QVMNLwE/r3AaXBlJUAAL9oVn0fj4eNqotJu3ZQVV/6q6tL0VRU1c3NzW777f39fV0vSh/2uxaCA8A08Ha7t+HPiFgv66urq/OzCwYVAWHwvigKL6lznhz57XZ72Hd5Mlw7XFxc3t/fd+2vTciM1Xi/39/dbZeLdbOo3r59vT3sP/7kQ0Ta7Xa+cOfn50R+v9+/fv0aEff742azudvsp46irh2MKG21Wh2PRzeyFth5u7i4Wq0W7969++ijj6qmNiW42Wze3d2aVWua5urJk6IobLTedr/77rvvzi4vRCRXwjSqKg/j6NYYITeDiUHzrY9NRCIPngMQkneUxA7/SCktACCaUFA1ATJrnqtLRNWiaRYLESEkUUZUTumw2/RtVxaFxFQUhe9tkNvJu7L8t3MyDHE8upQtvYpyrjbCCTOTG3kpk9J4i7UmOZukVvJEuNGSzYQyh9xwSnFNovl+yPsAJv7QSOPMvRvFFZ1z6AgID12rY61gfk3dZhM0FhERLQOnmqfJ0jTewnuvagk5tpy4TXVMKRFoSjrWQwAUNPPmCiKkZNScaMBBVQVCAGLVZBWMkZvy3eubOAxPnlxdrJZ934Pws2fP2r5TUESYkvOTVkIb4IeZtVgM+ften858rcb/nGALGSSDY082AACPcPZRZ5nX7L1XhL7v0Tnnpy1BUCSHyjqtqrn+JgkTVlJVCU45BmcZlFnifxSJx9mFP/1d3KhM6eFlsJn5Yoy67E/c/mG2Y1yi7/3o/KdR3sbQ5YEqHascqKpFUUBdE9Ht/d35slFydbMghGE4ImIoCx5SHPFIdV2/fvuOnAdHhffZasaYE5AooAhWfRkzQAqM6izlb3O4PZJZG+e9GvukSEpJ8oTUZBrD+8K5gEDWx+KIzOW1OD/GqNZgPcvrW7kSAIz+JXQdAKSRd9IibRuiZMsyGWBVpew+njB7iAjgACBFPh67vo9Nw6LKHKdg+8HxH5utdVbMmVx9IgLMiYz8HlEYRwrO2z6m/H0GIs+cBkQ0R9MOgnWL2gGxUiCOu6/zjFqm/pwpKCXD6NsYELvswablHYYhqbCK9z6EcRT69wkbPloLAMiQqqSqZVleXFx4743Xv6qqGKON8u26DhF9oKqq+i7aMzMzsFGmiqpmNgsrnVFWocxqDW+ILgSHzjNHZo2xD0Vlw5LJASYnmlRQhJumKctgoQIrhhDKsjQHUUTQeLo0Y01V1fuAiGicvElNyNu2rWueYKiJs2KZ2HsQEcHhyLAOAN4H5xwLEaGqQyAro1ja3g6OVaTNtNV17XK0Q0RUFEbhkM7WKwXwzvkQRKyIqhT8qlnYFAsRub/b7na729vbtm0//PiDq6srVKjrys6pMWR0bfv1V9/85ndfAsD5xdXxeOyG2HXdy5cvz89W6/X5lE62g2OHZZ51YuaB06E9xhjJu6IsyBNrUkKMXFV13S/KolbFw/44xL7woa47j6UILJdLEbm5ufnsx58/ffp0GIYkPHTts2fP/uN//I+//M2vnz17dn5+zirbzcZQipQlU6qyfvLk6e7+bnO/i/1gIGER2e+P2/3Oee/LsF6f1XVN6Jg5DilxXDZL78GTdw5UwVACw5DiEAHICoMueGY+dO0QY9d14GiKJ213Fk112O2mwv4M8ElnZxdd1xk7/P39/fXzaxshbFjjuZaefA+cOAZnNGJEp1d+T2/v7LipGtT4geKf+x5kPKDzd+loph++8tE1PeH8N3PffXrgB8YEQEWnVrTplQAPupnf+6zvcQnmTzhfsSk6eGAH33txPnFEvm33Q891tXLoARKRNlUzDGm1Wlw/vWqP/Yv9K+ZYlk27PyC6uqwWq6X3rqiC0QuYb+2cEzCSMo4p9X3cbrcXZ6vFoibyXTd471ers8394U16470Pvlytzph5v++6rlsu/fn5eRxybfrq6ipUpdGiLxYXh/bofTBVa7PoiOj29raP4r03DoQUxZruq6qK8dRLZFx4znki/2//7b+9u7t78+7tYrH46U9/ioj/7Rf//Ve/+lVOpTu3XC5NC5/15977u+1GREictWwiokHVjRAAVK2aYYFyKJxGZk19HHwRgMAFRykpCmsaUuz73sAVSOpAlZhMiQswk3NYFN4wuxgwJkakGOPNzc1+v182CxaxnBMzT1oGEb0PU43ppHeGoe9742sxGbAGWeeC90VKxm6U0fMh+BCCc77v4yMT9UgoT8YSTJgNkSeIiJmgl+zYo/UHjSJuvtUj8Z3c/kkmdSzeEVIevzCrt0xeSJoxZNNpLjdk8hc79irWcACoRKCAwioqKkIOjZtHWEBQZBrGkV1/MC99xOmKiBjuFozddXacQFW1Oxy7rnOzHkpUKUPRx0FBYSItkVM+Y/z6gKOXaof8xHypAApGlULmp6oqKJKCKiESonAeqjA5OkAPfLW5ClNV770LoQxOrec4xiRKDqqyEQBgUVEGnu5As06PKXuiUwuyzuBAajPHH2C05mLz6D/lxHGGIAonWhdFBYcEIAQwRYQ5BD01ouRW5fGG+Zc6uvPTUs8dz7ngjWI5g1/PulxUc+XU3jQMiXyhSK9ev100n/R9L4Qq8djuvSdfYB/jsYuRUV2YHCNbQEXgiZUSQU6rcQrgRQQJfCByhvkBJvJEURXIoRMW6Yc0RB5Be+P3P200qGpRFG17sB8QiZklJcvrO+cssT2BT6Z2XhibbWjse7YB2FNGYPoUm2abN8RKWBkY51mlH4bICR2lNMQUpyy3mTfRUzE6y3/eyGkjLImnEyjRhOGkjmakW9nI2TA3AB1L6rasAmC8LeZA8Ikd1QFkScYxiTg+4ZwkKicyUIBsoAVhhluJamIRiSmmlBLzYP05AEVwto8ndfZQ3h4dAVMdm83mm2+++eMf/9i2rfWtqqo5f8ejqMVvqoBmajI+3oQgpeQoM+2KiBGoWZhiRYNWWUFAQYFUEnNiFgBNPACYYkTnERhZBVTrujSgrHMuCRhj3hQE5o0Ym6QRMfUDERFg4Tw4EhFlTs4dj0cd2+Fk3MGRusRQkjJmIhGAjb+bmKxFBLJC8zz0nJKKFDaC3TmLLgi1roqsowAcwdB199u7zz77LKk4QHKI3gfvEJGc475nkeNufzgcttttSml5tl6umhRznLndbjvCoigCAjNvNpv/+d/863/xr/6n//yf//Nvf/d778P10+dPnlwUQX0RMtdEzryogEZOFpbkbVdIzCJJjf9pCv2MBgmhbVsiWq7Pri6vq8LvdhsVEQbwtN1u227YbDZ//PbFv/xX/7N3xYuXrwGgaZoXr1/94p/+ERE/++wz51xMLCIueEeFcmJV52l1tl6uFmc//1lZloT65MmTwz6HOuQCEJ5dnl1fP1utFq4oAYQINMbECijCrMAWlRF577TXQUS0H4gcxth13Xa/7+NQVRUFj4iJebPZiGoIoSwKAmCJzKemF6PryLNHQogxvnnz5vlHz8uqgtw6YGo+a/ox/Tdn1ocx2BdABaBRzz/2iQlmcBcEtfkEeVQfMkzPZIO91Hw4G6Fl9mDKxTy6bPd0zCxMR3ueZZv/+/55n96oD3mEsiV68LIHWuKHnmf+gvcf7Ht/mPsDfrc9xkEQAoAjLAqvQDkdjqhPnz0pSp+zQZgOh8PV+dlyub68OC+qoizLonKI2MWurmtVjSxlWa7WNSK+e3e72Wx+9ZvXF2dntvEffPDB+dnli+9eHQ5HBP/0aXN9fc3Md3eb7XZLROfn50OvL1++Hm43ZRWqRcNJD8ft8bhfn5/pWMNVBSP3jENaX1zaoBBV9a6wwu52u726ujaQYgihqmrvw2azefPmzd3d3fPnT589e3Y8Hv/u7/7ueDy6Ivz85z/f7/fG7WPTMZ48eRLKwhSfqmImGCATIAAwsDgCWF6k73tAcd6TggKnNDBXRBhCkMQAOAxDjEPfd87bmAIMzuZFJmvBHQZVF9ERete2bVWWLEMR6hj7zWZz2G2efPr5Zt/SaaCDMbEIYrYJAASax/SIiPn9i9Uy66KZKcJTdhNmetamPw7MI/+G+UOq4yd+T85VjLzAxgKjUbY9jonn8kqzav54W3sci8BP3fEsPPbhnXwFN3LMCUcrawDA1POpykikZsQV88i5zPJr0JzMpA6A5JAIPBB5D6rZy1EFpfGZgCiTThgo0ZaEjfQI88MkQWbebLbL5bIqy9j1O07BeQK5u7vD4MT6pCB/P9XsHSviFNLLGBU5PE0oNNryUSnN0JAjLQyOnu5cMZnrn0ZCPWa2dbBy0P121zTNctlMDO7BeYtdaWwcnLZLR/AVPHT94eR3P6QdsBcCPJQB+/lxmgRAwdLYajQmwszKKonZRnpN7t5MnH4o6X96gNzMND7z1Abw8JWTJcaxTEs2JRvzl8Ls1DoAEYBDezxb1qEsuq51zvVDajl2x20RIAl4wW6IbTuY639o21AUDCBDmkgzELGoynZEHmseeAxgESiLRwrOeyRS61InsklbgCLCqhmeYccBSRVSFMuJxhgd5tajrkOwSjcqEVnTkTFQIeW8gCnSPG1XVUcutZNh8LkSKCMvO2QQ6vcsu13GG+Oc875IKQlDsyh/aLPGvc1+9+T6wxRGPiTzHcUM4KGxPKmSh5d5qyOqZM7/g4g4zXmdq8T526f/DMGPkHxREWAxTXfY7dGRNXqFlPoU0Zr4cUx5TOdiLGk+stagKioxRmtCe/XqVdd1T548Wa2eLBYLI5UOIRjVprXD6Zhlz04nMyJ670IIkhJAptY8VSgA6rq2gdPDMMiI2HHOJRvhl/ciz1WYjsYU9eHYfFWMahBxwioDKfQpGszT5mrbAGDnXLs7TK6/Yh4YhwrzjwCwEaSgSrkIrYRIAMmkdNplU0oTqtPOxYTjEhiYuR/a+7ubu/WyKIqqqrxDAFZWUWXnv/vuOwDwLoSyuLi4sMU8HA43t/dJOA4dAV6dnz+5ulDUw26/Xq9/97vfMeuT62c+VG9vbs7Pz7/4+c/aw8Y7RXBTwZkoN0WYydARWWrfva7LlFISTX3b931Cdg6T4eiSEvpQVs2iCaEEUUSsy2UIJTn36tWrjz/++Pnz53eb++Px+OTJk+Vy+f/8f/+/FovFv/7X/9rCM4XYNA6diboHYUQsgnPOgXN933vnFovF2Wo9POmePXvW9X039Iv1qizqxAMndd6FYDqfmDVxEklEGoJzFMqSWIhZVLUb+mEYDm07pAgAF0+uAGBI8XA4xJRyFikO69XCzi+eaMDIu7BcrFTRO8cpHQ6HrutcIBANFOb+wGjHT4xt0zlSNaf/NOBvrn+mN87fhYjmV2TrOcvov5+FnK4/cf/5b/JHzCBA8z+9HyeoKilwSnMIxg89w3j94AumT5z02/jR+IiWY/5dHrj+wLBqVirU93yMRwBwgbp2qMr6eDi8efMGAA7bXdv2ywUWZc6v80hgp0jMvG/37dA659C5siyXaw4hsCZEXC6Xi2bV1I13Q1U2VVUvFsuL86u2bTeb3atXbwzPd7a+cBS22912cyyKoih81x/ruv7rv/6rIXY3Nzffvvhj0zTLxboqm9v7TYzx2dPrjz/+eHs43t/fb7dbAChCNSWoDofDMAwi6n2oq0VTNxNi9e3bt/fbTVmWTdMsFovtYf/q1StD6SGAqu73ewAo66rruuVyycwy0syklOJg3z3EGAmyaxhCiMka3pGTJk1Jk3eeArnkVbUogtWyiaBw5DwSETgVZUubWcxfluViUZuF1rGwO3T9brMtQ+FdB5KhKQAsMzyrYdydy2NizZEahqEZk+jz7ReRjF3JDhPb/xzlIbiPhOwkWw+50vMNWcGRMNsxsKJzNt6E1kiaE0WiI6nDbBSFKoC4kdwQEVFJMuL2McEWTojAmRDrjE8D8jgoHXN/+TUGP5hpJSIC5xz5knwglBCi9+xUchIA7FzTFGwQgCqSczEOKQoQOhcEwNY5xnhxcdbUdXc4dByL5QoBu67z1NhhJBidTAAwHgDFKdNvBQtANdczU/UrIlKmSrKRX+b66JiJRETyCE4VUmJDwOcJu8HbXGrjXbDqbdu2se+io8E7SWxcVZ6cc66ua1TyiOAAMIcKwszgZrt9qml668CeyYIt9A9ps7nY6BhM5h1hk3ZCmTZU31d6ZJTqP3xNuz798OBPk7SMm5v/3+wyR9KBoTLVKCGsgUIFQlFVVTkIA5IjAGVVrao68mDG3nkPjph56iwkIhsKrpnYBwzNMj5M3mDLWE+ulWgiAER1SCzCSAokKqhsW4+IIQRD97Vte9wfhr6vK1+E6uGXsuHYPoSQG6KFmVli7qih4E1gYOwYszKaqlrWwx6VR/ZuHe3neOIEABCcAkSI9hYZ7yYi3hWPqLunx/uhTRSREyEPIiJaR3fWALO34+n1D0za9BHm8gLA9F2Y2blAROPgM5kgSSIGk4VkjTEgWR6yJlYZR/AG56164sgVRUHBT8UTUlC2YB0fPc/7ooiihpxZr9cff/zxfr9/9eqVpbQNdKqqMY6i5VxZFk3TxIERcwRmAlME58d5ZDi6nvDAa1GL30VEgS314EaEcd7ETISM1vY2asicZuLHo0xP2rcsSwIQkT5Gw6G142WPMWEBRIQAS3/SJ+YWqp76B6ZnTuOlSYUhRYmxN/YOqw9YaGpnjQiKolivl8Nwtd9tLi4umrosyzKllI7d0PcxyeXZ+nA4pJSCr9frNREd9sdDe9zv9+gIVIhy5ENEoSxU9Xg8fvPH725u7tbrsy9+9rOrq+u3b982lU/KCCd4ZFHkchkRmV0SEbMa49OiALCySGIQAGTherk4Hg/DMOx3R5PwqiidC/v9YbFclWXZtt3nn//4cDhuNtuzs/Ozy4uvv/6667q/+qu/ury6urm5cc5R8ITAAqoMCEhoo+KZOQ7KQ0+ITVMVPqhqURTNYjGIuuBj5L5NymBDeBWdMDhPYy3aWaPXMMS27bwvfCiAOcVuGBI6Z00CRrrlvV8ulwDgihAcoTCiIiqiGy2U8x6ePn16eXl57A5gzeV9f3ax9uQ06Ujo9cB3n4RtfmQshcmzLJBapn8mUac75Knsp6NndF0TA+EE7qEx+wYnzfynrilbms9UrtlPxXtEm6D6oP6Q672TmZnrwD/xiTTD+s+vnN3M/sP0dlFQnkCks4UBgJHrLP/en51dee+7lo+Hu/bYee/JlZYo6vqJMJhUdRgGT05VJcrx2LkeU51WvnbOVVX18uWLZrmommZIcXc4AEBiJaL1+rwsKhG9u7u/ublFcC9evOj7/urq2o09tdYvDwD7/V4EvMemaZpFtVysvfdFsW6a5t3tW1PQbpS8i4uL58+fv/2nX+52O2sd7ikPqK+qKiUJITRNPZEMXFxc1M3zGAfmGMpiorfzZeGc2+12wzAY6WdKqeu6oipXq9X2sGdmS0kb6/yYHkMrdBp1AREd2/0QIyIpGitLEiGwydhoBMCJOYokBZ/xSAShQMuUKCmBLter6+vr5XrNyWwSqCqSGAuYScmYbxBQmVOnqeYhX9OBsSpw7hV9iNnN5mcUZVPujOqcn0ukWkFT8gSWKZk6/ny6A4DJNtjA0RE/g0bIN1mLPyHi0zVNzlQFiWnyEXEc8A4Abixq54ccc9XT4Z9fOs780hlqCMcEkv18+iUiIaaUphzkdBOBTG6ShEEgCYhI36e+761Mr4mLokDhYRg8QV3Xw7gZDOBn/Uw6JsdPQQyCFSwQRl6z76vfzZUGqakANGVqFrcfBmaulwtDxCLiBAHSMQs+Mm1LjNEAFeYbOTj1CZipCs7J9Lkzd3nuis0fTKcv9jjr/2D357s2d75pBJxkNZ8RHCMyCkc+0veuk5GY7j/7oOnjJsWb1/ZhKIsG9nzvA3TkrmZmcLTdbs/P1kVR1YtVKH1qYxIlohBIiCQmS+Wi9+i8kV30fZ9nb6NFVmPdAzLcy4EG551zIJpSckAOp33BnEe0RzfvP5NVWrOm5QKMRj1znjCzKouIt9ZwUxSaADJFpnPOh2COAoz1NB5pi6cYgEdeeQBIKt4Xs93E6VukxFbN3272FxetjGRiUxZDrRiHmRtq0j+jQc8byGw1/VMpxuVJ6jKXt0kFJU4yG7o5bVmeojCbRjJ9HQAgJBHhkUTfvq+5/lGFmRNmRTHOY9LcuUvOBfLOrdfrLJ2i5nyZ+jB3GmcQ4x/yY4CFFUVgsVg8ffrUxtpMIMbsuaZBxy4FIiqKQiWKzd1QdeSIyLvRBZnqGHricJv4DLz3SdgIGUXEj9kZZk6zCcqG56ZxoMp0MLP3jwwALMCZeUwLXxj80cBpJp/520kWIRl5qC2eGEmuMrhitEonAlmcMOgp4Vh/ZuaY+mEYmqYxTC9itnfOoQGlzs9Wh+2mqioSPmw3w5AEIThfFHR1efn23bv7+y2wAAt5XxQFq9QxlXXlHVrt5f7+vvIuFO6LH//k3bvbYUgfffRRN8Rf//rXzz/cf/Hjz7t2UxZUFkZB3rVtOwzJij8mJifNpqQIxpsXHBQ2Zl6SKrNKSsn7sFgsl8tlXS1ZkgimNOx2+xCKm5ubP/zhD2dnZ998803V1J9//vl/+8Xf/+3f/u2/+Tf/5sc//vHr16/N224PBwXPLIDinCMkRRVlVL28vBiGAVJExDyo1GMJlBSG1Fs3oPdEZGOCnLB4KELwiIWqppTa9nA8dHd32/V6fVaW3vsuDiEOLvi6ri212vYdMzvvi6Jw3js6qWcjiLMzTuTX6+qD5x/+9svfWhD49u3rJ0+vQlkNaZj7wZNxzPX8hz0ACsYM/SBKnN44yfD0A8xUxveGFu9f7xvcR/cfVRmqDfRg0ffKejiNTH14oYKbmVGYa7/5M/zQwz18Tn3PUCmQavreb/HoCVXVc5KuPbRHFplaqTQ4v9/vi5JQQRJXVdV1Q0qDp2C4Q+fclEhw3ocQrq+vXfAuBCAchtgPg3NhsVgs6pUMgohFqA6Hw2Zzt98fV6tVCOVqtarK5u7urut6730IhSpeXJzf32+3220o3M3NTfvf913XVXXx7NmzrutscG83xKIo7u/vf//73//1X//lL34hFqIsmqV5wFVV9/0QQvCuUEGb/1cUlfc+FMHSDH3fb7fbxWJxfnVZFMXZ2dlut/MheO+N8vZ4PNJIaz12puVsMREZuadl/YuirKoKSYf7e1VREAFOEi3AVWUiAhDRnCeAk1/OzoeMCxIh4cViYeyom9uNAVUBoHB+t9kctjuZuaowkkNN9VwAmjS7KogIisQhKRI5P+Xa7b1TORWzxcoDMtXG4z08IZPpPU3ByAyQ4uccnSI6Dn8FFuv9gqwCYDK9huacnDxjlZ/ESUSMT8k5R+j6FN/XC8w8+QcAeeiY5cAArIIss6z/vP/G/qcAeYlSYvKSYk6WiCgozY/UXDEBgHlvFjfaTJMYM8MP4unbpZTAU1FVNsoIcGby55pLZ3SUokigPA0IAsj7AaAKbC0iYHYSIfck2ZPROGJCRND8mLlLPSJ5YoybzZ2qEriyLJuqdIulcb1bM4JkyngERWclqofXfCnm2mf8/f9Aa81VkurkFD6uRWbFYmgmsn5ABAQiBJ3c/5wCzjHoGKCMd8CJitRqRCKS8afjmtgnz7+IibCYlEsesa6ap90Nw9CSrJry9va+rkpflTRirAEIKFctko5DGxCR8qzHNHroNo34wWqgQB4wBkQgmjgmRkcOAaAoKlVRo94zZJsCjOU+4+expCNApuw0cLaIWM7YmPhNUhV4SoFPSLYhRh0pdKYwO83IN0/ZX+GiIFE1Ibe1tPW347zf79++ffvk+rKuSwAYhqEo/WM5wRy0nLZ+5vpPSazp3M3f/sD6mv+ITkGBjNVuZv4RXPAlVvafPPbyjNOtQURSzIjBnM1hAOOml8yWpYCgQogOCRwQoMsD3bVwfmArnM1UYmLMnOKPT8fpmUfJt6x/SuLHvu0H7cgjYsTYzEYTVhShSimxtXiRIyJQHoZB7chnGtLTRWSoRbTc85iRtVyE2jwlQjBPXkWBcMoxDUMKZYHjyNLZMRkVtWgaBiBnaog0M19bX/LURpkBk86VRcHdYPApI1GYBWb5BzcSyIoIJ9GYm8W99woZviUiw9CBaWMQVTIOq7Isy4uLGId2v98e9oi0WC7rqkDv7u/uHMGzp0/KqiKi/X6/22y2+0O1XsM4V2Fzc7Pf7wNC0zSvXrxOKflQ7nY7AXz27FlZlv/1v/7X6yfrRVOsV+C9Px6P9/f3RhF2dnYWQjBP2o6/MFs87H3PoEBADhwiYgDCQ9uWZVlVDZFr275tD0aZd3V1TUSvXv0hhLJeNBcXF1VV7ff73/3ud9fX159++qkdNGsnU1UlUEKbFo9qjDpU+HB/fw8AAXNjrPc+OEeAIhyTAGDwJY2c3c4FVbZpdWY2VZDQE1FRlj6U3uc5Et5bkdj1XRyG2A99YnZGAsbJEy6q8tE5taOckjx79uzrb74uuFDVV69effzpJ54czDpr56bhkWUZ7ctDvrmH1+Sd5w5Bk8k5v897qPr5ZeO65QduP3lc01fDMXU4d4pgZuO+/1PoBMfLqmC0SqcVmL1ex9zKo2tapQclPou7T288/TC5edPX8a9evdrtdiK0XKyLshyGIaVBAY/HY1Gsu244HA5dH81ldcFHHtBjVdcKnFJ/v+kAZUjp8vpyfzzGGIuqtHS+Ffh2m327P5Zl6V1RVdVudzDuCPMvY8VW2TR+vb4/cMPLZeOc64f27Hz1V3/1l/vD9tWrF7e3t8vlcrlq2rYlihcXF59//vnz589/+md//tVXf7i/23qf6919PwzDsFwuU0op7UMINdUx9Xd3N7d3b8gTEawvzpnj29ubQ3fwpffeF0VYLOqLi4urq6vb29s//vEPfRwcwflqmZIMQ0qRJXGK3PVD3w02a1AUmBUxhMIR0RB7R8WkvlmZOdqoOUP4OOcsY6HKKUVBEGBQROdJIgB4wqL0ZXCg7J0jEAJ1zh2Px8PhSEXpRide1Skzs6YoEzpfTkTyeSxrTIPzpI5wzJ/Z2zNzxYw8i4hYOKXMnUwERs5N4BS47yKggJAAg6CiAJAqYwhT1JqpNlQQkUGTirO5TKiiBKoCmJ1j9uiIAAnJAZIjy28xJ1U11LLzGTisHG3svX0Hu5iTZQRF1VaYAD256Vg+Oo1TxWMePyBiP/QuSWKNsec0AKOSAyGHaK6vRXpkXqeiCLgQCheGFIfhkPoIqsE5IiiLEkXa7pCGWAavGtq2DXXNmruNYDzqsxOcf4mmIkXFmInUltf88RN4YGybzFhfAGBh5yAEF4KrqoKZDVhyt9lUVWFkHUTkPIH4WPif/OQnaK4MgAiklPrUi8iybkytsSZUh6TgyBP2wyAoDiymOi1g8HMg0KQfnYI+0q6aOaOER2ddQOE02imqOOsME0lGW8LMqDK2W58CPwLkjDRGGmMUHUFTExUqACoIKuqo60Y9aVtv8V8uxoybQSqIQCK9aHYvMr+yBW6EAkpEi+V6e/uWRfvEHBM5x+jJF945BiEBIvJF8D6wZlafrh26IYES+QJUVJPtNaIgOBBFEKBpDKSwhR3OAYB3mKKK2SQEA5TbdGlSpODLsixLm3vKiUUBfJGJB9iiGESLP00wyDkyLx9BRMqq4rHoZ9XOqcEXxu55yj0nQMyZmdq4aEFtMRUgBI+IbdtuNpu+i4vlEp1Pwl5DFnglRUAd45fT8bTJWyzWUCBjH8t0TkFVNRecZ14tKTAgluh0HKY2O1l939MY8aYkMXZjoy8BkDqniWNKKUb7miEEO4vWY6EIRI4IvQ+E6id2f1VVHlgkCjMbUz4heISBkwiE4Owov2+q5wZbVVlFBWOSQsQIhY1e/XBobQtoRBtaVDb06Xg81heNEggi5AHzkKIMw1DZWMlMpCtTCGeUxKqaUhpiRkDN+7ntqaKwxJRU+i5yhotyH7msK1vbMXOkAOQAAIlBFUFYGBEUYoxdO+x2uzc37+7u7parsynx6dE554qiqIry2FsqJ58+RJu9iMxi6i0XfscM+qFt8+IROl8AAAt0feyHwTA/SJ5F9oe27QZCLR0xx8RQ1Uvj7x+SHPbH88sLA7H0w3B3d2edvmVVKMt+13bdEZRDKOq6JsQQwmF3DCGszpeKcHNz+/rly6JqDrvN/c2r1WpxeXlZlmXb9kZEbtQgqloUp7EYMfIwDPVy0cUhdV0cYtf3ShjKonCF4Sj3++0fvvp6t9vFvjs7Oz9brc/PVtvt9s2bN8+fP33+/Pnz58+//vrr/+1/+48XT67+1//1//HmzbuXL7+7vr7e7jfK0jT1vu1Hr45TTDHGIjgoy8KVjsgBIggzc0zHbkjHdn15JdpbNGXoLACoqip4DwCWRkREazJxISxX5+bqpDQA58qS1RK1cEpV5AiKPrgyFKHwZGyChChoGRdFJKTYdeeXF8vlsqyLIXab/Wbo+r7sC1fYuZrO+2TB4VQRGvvNAKLo93nCkwE6gX3nBQiEk1nC/5F3/j+8prcTIAU/nescGFgqBL8HpQPWSIZAitmLwswPOj4vAljUogBIqoKzRsGHz/D9rj+BkgCjkqBY9E2AgugscgYUEAQU/3/7v/8v//RP//Tr3/7u0G8a1ziPSWW7b4lotzugI2bt2th1XdksQlU2ZeErV9TeudC22sXOObdaNMfjsSzLxbI2Q+JIYxzuj4emXC+XixDC4XDo+0EklWWo67qqGuvHTSkVRREjp9TZzLlhGBIPi0WdUnzx8lvzad7dvu26IyI+ffbk2fMPAeDu/vbNmzf/+//+/7m73SzrpXNuaAfNjDduv9mGwq1WK1UJDgqPbbtfrtf3x70Ax3u+uDz77MefAgh5bJZ1WQRAPrYbuokqsmg8tdGTkA9913XtAZSCKzE4kBCcF5GJbKGqixD8crlYHVdd3wfniZwyVHW1rJdt2+92O4fEymaMBSAJs4p3XgXLoubEaYgBdbe939/fr5vqjrjbb70vq3K5vd8tF2c37zZXz5+KDCFzTUAVmoKKFDEOKgpFQaH06/NV16fjYQhFvdvtqiqwByoCx+H+sK+qqiz8atncvDO6aQrBI6CwImAIfr/fm9GBTCcPAKgq3jmDp+JIf4EIAE5EnHMuN+u4KU1ILiiATfkGa64HIOe8J6MCKBwpsCZmABvBK2zWBJREEaOkQVOUJMDWM5A9AUACKKuajf1dwVOwYFqiFEXBqGN1hUYX4nRaIFdwzAnQ3BUN7Ai8pfsxJ+k5RRUmLE2fRGFQrMpF1w0UxJM3Ku+maRaLJnjnSRXBkrDe6OkEYz9M+get5Y9OsRZAznlMjIfkSERYBJGQEFRZUuJUhcJ0DVpiBoHQA4iQRu4jj4Aum4+l+vTJRUpp6A6ICM6JRmApC2dgXzb0gs3GRGFNXezsDs6jI6dgaAj2nsbirRu16OmS71NwI+0n2PCmHO04p4CJhYecZ2Xmtj0Q4SBDURBLKquQ0mCOTvBeISdUkwoCOiVEa21UIq+giE4z4YMAgYLNTGMAIhBEJ6i50QTA6C8hY8/FETJHJTfpVWaQnr0n6zcWTXmV0HyqzEaSEseoXZuCKwR8z9KzpqELoWTFtht2+9acHlX13iO4Q9cNkQWCcEo8KMcUjbwQkQRFQdV77x1Kiux1tVhUhROOgWi/2ysSCqMIQfCFTT5OBFSFqm8Hw0zWdRVcQIfd0MaUnPeL5dIXbrPZJOEq1ECVECJIqMoQAjNrYlG9ublT1bquAajrBma1Jt3FogGArhucc0VRdV0nwovFAgHEyhfgBFGEAZ0vQkqpqqrV2XpI8fLJ1aE7hKJIEg9dq6qghI7yFO3EPI2oQwCwgFqNBotsehASjqUGc2Ih567yoQDNczzTmKIGgDyuGRERgyvMvwZFAucpCIAAC0KEpCLOUVkXZRVA0CoANpFUHTnnXXCEHlDKQLHv+hSdc8E7kZzaABozHcBJJGlCVOcceSNEIucAnT06RJYJkCMnkBsBwWK13G63QxycL5wvKuecc8vl2nsfY68sgD64ogyV937ZLBwoq3gE5z2oxhSFuSiCd96aoxDRkydUBWYeIifnnHdORLyO0X4fGxe886b9mBlTTIyoAkGWizqy9nHgkcU/pVQ2Dk9OuZIoWocD5ZkAbd9v93tROT8/N5mxBryyKAGIkDDJ7rhxBA5VCYZk1J8IAClG5xyoeILjbo+I56vz1KdXr960fVc2JYLrhxYAmqap6woRmblXtcLr5CmiaEAEkMKXRVUeI+zv9kQQQtgfWiuUbQ/7u7sby45329YVtQoUPgyDHg7tMHRPLq8+/OiDuqi++frrm5sb71wgWjSNsHaHw/p8VdcL50Lfx76PzgXnXNv2KYlILwJjh5UADIjlbnMIoYyRE2td10lliH2Msa6r/WH7duj69TINw25zL9yfnzWbzf1yvbq5e/fZZ58uFs3Lly++/PJ3ZRn+p3/xz19+90dQujhbSRrqskjD0HZHUHDoEMF6gorSO6QYI/mgAoOwcnLOFWWN5LltD/ujdSBYwqmuGostu65zzpEPDomZh8RlCE1ZclJQjbHfHyxpS3VdVXVZFMXAcRiGyIMIWLELRDkKUUACAR0nZCdmUJVVs3z+4bNf/OIXPlCg8OVvvvx3/+7fMc9mt+f0jHPOmwo1x2A61AAgchojaP9aGTfFOBpQB+DAnBXQmFgQHKDMcvOa24jxxDI2udT0/YCZEfA6EuObJ6FAPE2TVIKspSDTMJwmC+GIZWU2bncUUFQSAIN2ZisGBCBu+hnBJg9bSDMCOk4RDY6zwPKjojDlzIOoKrKBUEDVkwcFdGOqTMEbvrCuqxgjkqD1fRClZNALAMAQSiJfVE1ZlkVdDindbe+qqgrB1b4eUn84HHxZiAgnTByTMQ8Ecq5Ejgr+2PY3t+/Koj47X3Vd17aHoqjM+TBHccTVkAQhBx49c9rtduSkKAqRtF6vM7rC+hNV27btu2HoVUTHth+XId3AzaIKwRWF3+/39/f98VgAwPJs+dGPPug5FaU/OztbVDUAlCHUdd3UZV2XHHvvvaQIul40BaADLUQERJkVwcVoksGqEOMAYNiPggj7vj0e9+QCWKJ7vKZNEjkxmYyYHE1JYmRCUUEKGAItFvX5xfqP30BZeASnbAG6HA7tBQPm/kK29w5D6vs4DNEHICIfyKcR2OPIBc8pcQQEKYrgHRJh1/V3d3c5XsRcXJ6e00yyiJWUZcr38DivPLt3NjoCEZ0jIkfGBXE6RpZsFkVno2oRgEDzXHqVmJISgiCpI+ccMkckZ6YLnQMizMScrACAQpmuzRfjFAIAIFZRMZrL6Xj/qbTAwwtHuII5qCCsSlNuAEAwTwZQVlYBEfAIwiAOUIEUHGAg8kh+TPdYRk/UIYCiOAinjxudfhyxgFl/WUgASEgWsygg5dm7eTTVSTdlO5d5LxEZyfYOkMZjrRpTr6qmf2JKyOicC+TT1G8KgKSO7GOdoiiAgqiQjNUbEXE2qWT25I914nvX4xQOIAAIEKJTdI5IkXBEWSBZMwyN/4549h+4LDMMYMyAeVDD7O9WmlD7vQKACuT4hE6rTZMZYNWxL4UBVJImVpZMCTfZmIzhyY0ngIqGGdYUE7N2QyJfGeTfyG2cc4qAFMA5BIeG0CIOGiwvBgAe8tqqCuiE0QIRiQKggqJ1WanqgGTT4tIQWTXG6MAnl7nDM1aEBUjJUVIZOGEcErMh1m3yNBEhoTGNjrpUlJWZbSCRrQmNVD8jeUA2OURUlSUPkREASVFJkFUhD4yLA5HlC10RaKAkBiIdwVmqKrlfYSojAKixy2cE22zo1SRv9nqr4kseoDebKPYDVb48XhBIhFNKbJyoItWyQiKP5JzzOp5yVU7CVm+x4pWSZIy6KDCSOo/o0IHTsV8IYh7b6QRNxpxznGmCeTL8PufLM/L4QfCMLsboi9Lots7Pz4/HI6eh7/vdboeInjLFQlEUnghYhmEQlRFAKJZ5CSEAywQ/i5whjoQ+xtxxbu1AKSWrPBvoy8Y7Tg0enpySU1UCaJrG+8KS2X2KjYVbkqNn1TxuWjTzHByPxxhj0zQWTsTIZo45JVCiEELhfeW649421XvCkSlbFWM00G8YDZAcj0eK3leh66NDRvIgejh2ceCyrlBhSJEAi7ImwMgJRF1wsW9BQCBpdE4UEb1zgtSslq9fv97tNgZ8J6LVavXkabNo1t+9fL3f75fLpSd6+/b1fr/fbnY//Rdf3N3dDn0fQrAx2ou6+vTTj9u2Xa/Pnzx5st1ub2/vmdlGLwPAer2uqqrrus1mQ0SLxeJsvXj53VsVBETyztwxDx4R9vs9EhaFR1JPWNWFyvDu3Zsf//iLdzd3u932L/7iL8qy/A//4T80TfXpp59arVQ0SYTc6INaBt+1vZrSG3dHjBpMgcrS5hiMaDNE5wwmZOfd9Ia9y1bekv2G2hpibPseBa2XI4SgwEkEAGI/hOBJgRw4tYZGQjQeXofoVHKZG6bMvaPNZjMMw8XFhQLf3Nx03bDbHRaLxdQlPybsVMcJg0ZEYfh1xNM5+hNmaPqNMTGCI2cFhPGvczfm/Wv+p3kAgN/Dx5//Nvn7UwAz/R4fvRIgt8OgsSMoghv3ZnrG+b95JgnMML32UmtJNby3jlRazrk+DjlYyv6GmxgDcz7OasdI/o9//KNRCoiIsDhH1uqKKMxs3OkpZQbuGGNVhTi1i2FpXukwDEroHHnvCb01ehIFIPDO7/fHYRjKsry6vAqhuLvdxBhz1gcyAxQoiYoxOjtvticdj0fAZPPMzy8vRKTruq7rjsejiBwOh67tU4KUosHliUg0qSoiOZfjJCJomma5XKrqarW4fnZ97NqUErB0x2OMcavqnLs4X4cQSmPzpNw3eWiPdeWssZjIq2DbdnFoR9SoMLPRbE3RCzmYxJeZp4p55nBMHPth8G4aep8SD0NHCMiJPQG4sizX67XBI4UxJSailIb7+9sP9aMpfIQ8mjTD/ZmtkzjvlAGLYZwLOwydc0hknXzR7IGZ0vnDmwyNmbYM8BhxCDpJPI69zpPrT4SgZCVae01KiSjHo2OlOFemsqPFSqg0tdONjdREBOA0ExrKLEmGRuxgOCBnAICZHbVpfOb6f+8Bnl+je3Eir8yOhG3TrBUBJ5Cfrck4DGu+RJPassZKs8f5I/h7PhrGxujZp49eDp4U9dybmeug2c+gCpmeGFEUMKdEISVr/EJRYU4AigAuIFgeHADHmgg59JQlZ/wGipgZErISeU/lja7b93+7+QPP8yZ5PUe5MsH4XldPR7Dmo7uNj/OgOjz3BR/8HmxBdHLiMXO0g8wY0CaZBEAWVsgodpx9kKrGGKPPbfTztyA65hhCGGJnNnXuvqoqC6c0sLCqjATp9ngCOZGkgBlahpjDDBA2gVfVJAoj0jSPBcDHD88KoFoWQWKSmBIgoBTeo4KygKizYyjKkniIydrB68Wj3Tlldokm6KaqZlRMYiWyZhQZR4uAqo0OwBNrsMYUQwhGHcicd8RWTKeWa1A49ZIC2jSP94yuXTq2pcKYpJiEU/gkahNP/3QfnF3MjKqGtzXRy9QelI/qeKzyd0lJOSkiqqCOLQpEKCxxGDlS89RSCgEwc9vn/A4i0owUHzAzE2S5AmDmEAqH0Pf99fX1fr+PQwcAVkX33olIWZZFUWA2KQyo9t0zqnYWltPYn23r45wbYjcdWDMKRVEYPYuOhL+ZOMFMDvkYmZmrIhRFYcKQz+O4wpKbiQEAiJwluZ1zVVUZ5J2Zj8c7HXtRhE+Kbrlc2pMbtsTMk3kg09kf+2eAiIJzx/4oCqEqlXl/PJBCtWiasupT9JirlMJRE4M4jiKZ9EKtDQbAkYH7dztmvrq6cu6pqpZl6Ytwe7Pph5YlAvrlshG5Ouz2b96+fvXq5RdffPHB02f/+I+/4CH+5V/+q6dPn37z3bdVVX351e+//fZbW/AnT54YQbn13fZ9j4gXFxfOuf1+//rV113XWdJskn/bmouLiyF2x/3u5vZtSf7Zs2efffrx02cf9H1/f3//9OnTtm2/+eablNKHH374/Pnzw7713huumJm7rh+GbkjiyiaDRETVMsozAzGkmFLimImb68WiYOs8zH6/xX52YC3gpBHTn60t+r7vj8dj13UK7IuiKIpQFtMeTRpjVAUnApzx2AIieu9TSuaJ3W9uDfHxD//wD//yX/7LqVUp320M/6cVG5XAY/vySEt87+8fm5D/0TW3IPNf6sySzW2NpB9k0nz/5pPKzysDOccGqlNy7dG1XCwy1ZXmjOyk8E2QyqIwvTD0fWrjctmw8ZqLCNs/qhY8PaQK8hahTvsUQvBFQESRSETAbEwgdobruu66rijz7HeD6QOh98UwDKYpENEa21VVBIKH7fbe++KDD55fXj558/pd2x2apqnrOg6cEiOSCiqxwUVEODfDjBJARM6hzda2EGez2TDz8XgU1pRANBF6JAUUk3yLK/q+ZY5VVT1//vzi8vz+bpNS2mw21pZeVVVdlhYwOOfOz8/7vn/37u3bt2/T0NsTFkXx7t07ViiKqqoqQi+iB+on1x9O9gyMdGi3PyIiZEgu8zjRUDlb9GEYQnDOZ7Y9VUwpkQhotCkTwom8d8ErgwowR6KCkxg4KoTccKPKwidxyckbUCJvjHQAYNRMKXHXdQBA5K3Yt1qtjscOZkHwXLLn9hJGJ28M1bLoT17CxKyHaM3QNJlYEVKwbVWkjLsAADQvx1llXyZRVlVFEhHzYpk5jdW96XnGgV35dHE+OzksVjgR5kzfDh7mBR9eRKSKgAkRjXyEdCTYyaZwDLQy1misek+6D8Z+64y3TsmSxHkd4okIbP4Yp0TguOZ2t6IqTwsi2YEQEeceZzuyM6TW3QFT7tzSc855Igv2BJFEGABTYhe8zKqQ0y7nFN3Yhm5P6EbM8vT6R0r2/et7l3p8znwTmX0E2KDsx5I4zmaafe4I/YdJHuY/y1i8erSqjx7WTAs5sP4QzG3fD3JIOK6jR+K8LyafKcaYRFgliqWJpSi8Qp6qSxSdKxAdgO0aAQowS6Z/ZUNIGcOayMj2qQAgRSjM1gKOFWdBpWkUXF4VMgQ6IsoJGG2XI4ekpmcsdg2eCh+YOQ3RmoYBwMZR2Qss93k8Hi2fZOYZx9LftGhuHIs+aby8KYQoCIAOsFks2r4DzfNczf31dR4ZoZnhIPdiz07l5LKfRHF+HOZOwPS5MD9QY158uqe9ljJN4Qkub1s4yACSuzgEkDSz+oloHruGqEgOnD0TcxIxGHpKSVIaRMD6oPq+G4YkkkRAJDkXmLlq6vnJmpyGucc/PyDDMHjAPsWbm5uf/OQn3nvhGGO08V6oYt5kCAFEui567yzLOwVmI2MPTarJXuDHa/o487FsN+2206NOpqppmjjEYRgweFUE7+yX8wOSFT4gZL5UZ1N9UhIbQrzf743cs2ma1WqVYo462rYtPNHYxWv+ZVmWNnwAZoMajbHn4uKsi51zwJFVlUUL55Q1dt2gCKiscthuHTp0yJFjjAYkVh3JhUSQgIju7u6GYaiqwgiCmLnv+3fv3gnD9fV113X7/b4oip/+9IsU47t37xaLRVmG+snlX/7ln29uN4fjbrMNl5fnv/vd71+8eOG9v7q6Oh6P7969q6rq6urq7Ozs7u7ueDwuFoumabque/HixR+/+a4qlga+TsIpxSgswABwPB5D4c7PLz9afHS2WK5Wi4vz87qud7v2m2++ef78+T/+4z++evXqr//6r588uTS/axInGYf5uEAKTjN3cG72s5dN4RwAGM0mq6jCZrPR0Q+JYwv16C/1NgO7qqq6rs01Wi/W9uI+RSLwkJklmJlBdUwwq465iTSOlRulzrjCuy5WVfXs2bPD4fD2XTRCvG+//fZnP/uZ7chJPxMRUdd12a0YRd0Sl1H+FIMNPDT6k8rCmTWZziN83zU/ofN3zVXf/ON+KAU2f/H0n4gIIDDyZf+AT/L4JpP60hMxhtrmlmUuGFqhhjne3d5Z9g5ygtU4Hk/PmRN/iOYmpsRRgb0PRVG4EFR1GA6UKUXJOVytVtfPry8uzr799o9TgQYNMG0jhNQi+CjCkdP03Lu2i2kwKi7riO/7fr06r6pKuBuGKIbzESCiEHxKOn5htNkcIQQAsX4UAEgp2fitYUhlUaUUAUQ0cU4tiHPOe3IOkUBBWFI/dPv99ub2XRK5Am37LqV4DMFubtMQr87WKca+7/f7fd92MfIwJAXa7faKxKzOBUeBRyI8azOdjg0irlarsq72X35F2ZqPrr9MI0azj8hckDOouwA4zhh3NfJ1TOp90dTL7XYveYyLA8CuP/Z9G5qJdHnqekNQCgU55wgcIRRFURSpLMui8H27J8pCHGOvys4Fm4iOiApiLSCTiZrwnZPRNUdhIvmeDo8JooDB5jxm+JYg2gTNpOonlwVmTlguXRUOREXjFMKaSE4nyvLseVSMkduzMHC0nl3N0Y6o0Gyyn9IDR+H9n6dDm78hIY8UQETeoUGvs6n23nubxwA5FT1+aE7zWyrUOTfnL7eXGVHGHPAzv6Z4ewp77FsHLaabTOf8kTsLJ9VDRF4kmRqxKMu2yPvC0CxEzrlCJMXIMQ5NCAgoplNFieauCaiCiJ3rnJWcq875A/yQ6pySNPBQ9zGzJbBnu3HKpuQRtQRTCsogDVPg8dA7PzWBvf+5+ugz3pOBSbaJjFJ/5qgRevQKogJsuf/R9Z+v/CQGSABAKUYR6LpORFLfp5SGIQKcJNnK1iKCqCIwktOMlR9QRCzHNMRpdi1m12rm7GZLiIgpnQgcJ3tJDmJKADb7WR2S866Xvh/aogiOaAomHVFVlia985QbjlH9pA0AwIgLreJqCFp7rZlkUZxKhc67lJJR3DJHww0zc47r87fIvmb+0Nk2IWLubH8PIztJ3VSUGOW2GOm8pl22jT6N2rEpGeScQCYpzh3KY59yfrhRZGYCpCBoAB9rxbcdQ0RER+Sn2h4REXnjhs9xmHM5xHp4Fiavwn7DzDIMse9MfpxzKfZt25pFj0M0H52IbP5dUVQxJREpisJKTH3bDcPginJaLh5rDsZaNqn0KbM+Lb6OWYZpwWOMhrZGxBgT9H2M0UzMNFDJtsI+LsUoI429UZyZnJhWtEbbFMV4MJm5WjYAMAwDj6SxMvI1y8xZMYvTNI0eU7MoeeB9uy+dv7hca9LdcQesoQwSZXvYFq5Yni0Lcv3QOYcpYUoKwCKdlcRb3xmtRdcN3377wntaLBbM/Pbd288+/bEvCwC9vb2RGC8u10+eXK5Wi7Y9JO4K8stVs1osYuxFUuGqf/bP/hk6evXq1TAMZ2dnFxcXZVne3d2tViszB33ff/nlly9fvmzbtiobYUEYk7DMiZO5/vbip0+ffv75p8uq3u02fd+LonfVd9+9sBzT2dnZcrkEoP3+aLAFlmh76r1vmnVVNm9v7/JBsYwVIAISYJ9iUimpNFs/DIOB9Y+7vdEBTWff9s62bEr2i8h2u+37flPtbL/NnUBHfd8fDodmtcjnC3N3maoiUdIBMUeYNh9Gc6TRhxAWi8XV1dVmszHgWRF2NsvVJDm7p8oWbNiZskfN2vkh27XOXPxHzj38/3s9qGDMTA+P5ODTKcb3zvX8PieX5nt+aVBSHG2ZqazvDyE2m7tphreITtgNk7S7u5vdbte2uY8FAJ5cXCKiQ+ds9p+laAGn6N2NGtcjYtd1+/YIAGVBJlnT1x4zjm4apbFarYbYWY5/saiLohhS7PveGkcsNy+gJkIA2h6P6/V60Sw3mzuRO9Fk7fC5kJ1EOC+KczYPMgCkaTvNN2I+8TDIyIMGgKHI7SDMUQSIxtIqgSibcuz79vXrl3d3xeFwWKxXXd8OQ+y63IoHqiJJRN69frNcLqu6WC6XTVVbQWp/aBfrM0nStq2IQScTjyUzGE2RLVQIblEW3jm1lkERU21GVTkt6eRp2fwXVlUQQPRFIGLVSN6VhVtfnL97dzumIDEEl9Jw2G+XFyvDSAAYMhvGHF4hxpBiPAkObL4WcyRy3vuqqgwubDDfueDiDH370LM8PfAku5Og57z16MGSEcXNavTj9CJWHbtLTL7HFgIAVkGVpKpVVYgIj2AdHE2qfez0MCJiiVhz/UeLlXMhAPA+TGSuLKZf5i9IKCCsmGRM1IN9pVw0JyKPRHZcURFp6IeUEpJONWUTMwV2zqmaJct2V0SaIkyL+eiH9/1pALC8C8x4xEw1azrNJZ2/xbmAiCoIKAgOySg/kZOysDA7b+2kaE1XOa8vqgQ0zjQVFWVJwsoioAQokIkCDSc/d1be12jvXw8VNMy/zlxCJjcOHqb98y/x+6VxZHF4nPiHh+I6aufTubMGzbk8Ty8bJXYsZIEBY6acu5CR04yuZ061OhyG4XDYDyx3t/dFVe6PXYzctl1dL3CsKdjnm9Ou8hgBZqe1qgofnPk/AI7IC2cQJ+bmF8IxSNYMZZwcaxERAURCYc6GcqxcBee6KVpTZXty7w1Gkpgt4QpwOlBT0xSMaSdb3n4YihDyiSMkJRGxVTkcDiLSFLW5Jsu66bojM5trKAKI6DKiKRfN5qIyP8Xv65zpvNj1SJdOP3+vHKrOiIFAirIUGnmH7XcKAGC4zWz7xtqZqhLgyF7gEMHYTmyCAZEPgRCLvPzjCGHEB/we87pZntdxii60LEsg5xAuLy9tN3e73WazMUyO5b/N+HKMzCWOyQJz0UKeFp9dFvvTxCSrqgbNpREoPAUklvmO4wXj/OYYY9XU3vtQliJA5Djp8XhcrwqVPLDF1tMYaa1SNJ04i0nOz8+JvFWT7IvSbDywOf3WeABjOsyWzsoUxtZvSeiUUlkGFyD0rgjh/OycU2KOKuJDiDqUPlRlsV4svXPe+yRKxNMiT8ZOxo4g61VdrVZlWS6Xy/v7+6TCzItl7QD3+z0q1HXZ9UcfmmPbicizJ9dXVxebzebtu9eOwk9/+tPr62vLyi8Wi08//fTs7Oy3v/2tmQP7iMPhwMyLxpErOKlITlU4cLb3FxcXiKiKbdtD0q4bCh9Wq9U//OJXRVG8ffv2Zz/72ZMnV4h4eXn5m9/85urqYvpSVj+5vb0VhvX63HS0ioDoxFZsXQf39/emBKyihQrr9brwwc4+jbS/JmYmNpNaNjm/vb2335RlqSgBgs7YtKaPy8cTHszMmZ/KEMLxeFQtz8/PLy8vDTG1XC5vb2+twYCITJCMn2oyEJNgZ76799rAJuXwvaqA3quuP1Ivj+/20H/A9xLz03/qDxTA59/9fRM//eZ73a33r+VyOVkcK9SY5txut5gn/ZXn5+dN0zRNU/pwd3cHJyZA84URAI7HY55cNt7ZE0GMvZ09EWnbVgAIvbdKcUoiUpbBXE+rFk2Pbi+w9ZXMB4/OBU/kPKpgTH0IoSi8D67rBIDKslwuoWmayQYjUo6B8uwpzRlHzVpMVZmTC6dRUyOoPcM9bfiQ9VPZKeA8uBcM+hJjNJbGqqr2m60ggKp3jgisZxQRY4zb7Xa/zzLtvfe+uLysB86Gh5kFYBgGs2U4Ut1b1t+qLVYAEVXEER0+TggGVVAlRVLwRIFsQlBOgznnQnAIxDEJKpC/vLr+1a9+49GR8wocfBljv9nc/+jHn4bCh8KJAHOci3IcZEg9kc+p0zxgC0WTaCKXzdtYD+WxS9KAUvav8cZ8T3n9kaSeXLfTSYfxjYZAzTG6rZXRchChjkNDiQHHsZ0W30/4HTFjbeoSABTIUhp0cgFz+4uddsoMg5NmeHSWfuicAwCzpizn2f+AMcaY5ZQVVSHnY00dnPJk2SOnrDFDCIinEOiU3Xzo97uxeXd6NltHmfUPyAzzoO8lv08qxogUjGaRVRUtCW3xsNV5vCfvC++KNDCAOEQAIgdWkAExQvvMyOAIVYEA+L3Hhplq+971/F6FOL04J6dH4vbJTkxvmETr/TtMgJ9JqB6peBlFa7qPlQFnndw2b2lkDbd2E53pX0RJyVgyM35uwpoDImYUrKrGlJJKcCEN/fHY+aLc7XZPF8u+300ZLAUa1ZhJCwJgNq6YJR0yyQnaCKqUEkvSkTBOkoI7Tbub5GpufqbvMpUWPRqVBCuzKwpwPjiPCp6cQ5KYVJkUrHkR1WaPqPWNpZHOf9qs6ZfTUoAjMy6qoAjWDz5t5fZ+c39/3zQVjTZCVU/0fKqqovrYmlq5iUbc+vdK3fs2e/ZLBCDIxb9TaIHwoERgvWQ2oWB2N4KRXyPfVmEKO1WtL151HFCFQOAxRRZWRHLk3Mhyo1MAo6fqPI6JFZOi021VRcV7L4CuKM7Pz+/u7hBxGgUtIiGE9XptKerjfm8N7olz6jeOzKTOUqwjmn+uHGjWszSFKI/O2rSMIsJJQUkFUxJQrKrKrIZI7noXEWUQEQekCr4oTDyKoiASy+BeXV2llB9mGAZOualgGIavvnozOY4Wf1pgczwezRCbHE4xbfAEygSwaKqqKIMnHhhBFk3d9h0Ir1eLRd2UhffkyquLly9vzDGYqiIiCfIEAAiFt/RnUVRN05ALL199F4W998tl05QVIipz2x2ePbu+u7vrDsfr6ysi+Pu//6/ffffd8uz81cs318+ebzabw+HwF3/xF59//vl2u/3uu+90nIzhnPvggw8uLi52u91h33JUkSQp99F5Qkta3d7eXVycWw6UiIqiQoW27b/88suLi4sQQtM0V1dPpvpP30cbPw9ACC5F2e/3x0O3WKxMLUxjMsBZhT+2bXtojzIOGm+aplk0TsAhiYhhp6e5QOfn57bg5jsZwLssy93haGLTDb0Aq2qoyuC95Qt01sqGiE7zMPIhjsVbY3fKhcrUthZRlMy63++Z+e6+K8uyaZqpZxLGkgg85NHPaXL32DpMLsr8pMN7PsD/wWv2cQ9s3BSBvP/iR8oKx8zd91qx4Ebmn1kBcfKv3r+WTTVOjhtsdyyavTxfT4gDVY19+26/TSmtlmcMijwauzGFUVUVzuwgAHgTPgu8vCv6fhBWX5ymwJD1qp+dhapkTvvDdppddzgcELGsq7Isd8eD/T6E4ENQVZv3tqyqtj0AkDEGHA5tSsmkOft86MGGkOUh5IwjxbWM7VNTQdkieCIqisIwQvOddp5EMg6kLEMah6SUZTAMYtseirIG5ykPJTnhxi4uLqYNti9u7mBMueRdVZUKpsTMcRgGQ3baORkTLUN+PETmk+oHeCxGpuAmYwATglwlsqJGImxWSxYlR8E5EEvhy/G4LwpfprIfWDilJJzESrEhFMx5dKzZAtNE3lPinEEJPsf6uatbpszXAxHXU6/9SMMyTdMcd23sCSMjO6cRYwqWCMyeCo5mnW2LIOu9mNhhcmbn3Vg50XH8sCgwqDAICI0nHR9eHJPmnNzJsMEPx+Lz4zc/osycRBNLFBYBnM3eQgDzjVDs/xgUiqJQQRawpL6FUszs/Gnc29SGz8zDcXjfjzGJmickpu81D5mmDCg8xEXMv+PYgm/+7qlYdzgcYOwAAWjLslytfFmW3b5DUiuQgblLiA4z6gsQ3ay5Gccp5fBQr8GfxDh+7/XAARrvOXf9H+0vZDDG4+87X4Rpx1W1LEucZfTzTTBzIo9mMStEGzErYvs68ykNRSZJkiRh1VzUmp7WBU/eyQkH5ZzzquC9T0mqskHc1FV96DpOKsiQM/TMbJRZlJIYNb4oA0vuThdynkbgIotYGy2JRDE02qz1DcbQcURZzEsi4GAi6codseMRBuecp0wNoaqSOGIMZW05fh3pAeyD7OaWBrbiu544f3JpMSPmRQFgdbaOMYry/f3d7buby8tzEXGI6twI9gHNZEFWIzUg3Pdk1B5J2iQ/k36YxInGXqPpfJgoAcCoiudBoH3eSd2NsuYAwCyrjMEYAVmiQUbwzPTpk/KEUZPDzEdBRFVhmbX5jvhY+7b4ID0Jw9CzAoGq6uvXr6uq8g7rujb14otyvV5bP0Z3PCJiCIVo3iwLEoLzRVFAxsxlNNek/23OK57QwGybaNVFyxoSURzHwBN6Zu77HqJDoPOiqOuaisDMYENaRCxFYytu4mFZZIB52nhM8RAxq835OhwOBjricfJX12VMzuXl5XSo7bxbpTq4wgbiWn3VvgIBlmW52+1ijJerVdM0wzAoyXLdWAzmnKuqqmmaEY6rzrntdmuzHLquM4Gv6rooClLZ7TY3N2/Xi+X19TWC3N/c/7f/73/58Y9/vKjK169fvxG1z7q5uanrerfbvXr16vz8/NmzZ4h4f39vy9g0jap2XVfX9QcffCAiN+/ufv3L32bJUZsSQ+PIHanrpmkaZh1SIoeEru/iYrFERz/+8Y/NK9tsNl999dX19bXLY92OIuK9Pz8/f3J9ieD22wMCAKEbMzV2ML578R0RlXVV1zUgppQsxvBEkti6cSZXqixLY2KlEVZufb0xRqCM+PDeE+Wk2zAMFsCjMoM6QCX0SIzoKUjKysc8gRC8jT9bLBYi0nUdERlh4zCkm5s7w01NJ9qRn/I4k/K3Q4yzPrG5lvgTFn86pzBz6P+EkzBPcc5/8DMOzbnL9Mi4T/8+MEOzd+EshaFjZP4nnv/Nmzf7/f54PIYQzs7Ozs7OENGUm93BTpxF0Yh47DqbkY2kVma2tIYCKAkIKrBRYPt+aOumVMK+j5y0KMoCHbMeDocQQl0vVNkKWBd1eX5+LpqJ+YuiUMUYYzf0McamXg7D0HdRBbs+mmY5P7v0oF3XdW2/Xp2p4tu3b1O0QfFSFMUwcNcaLywSYYzRe2c8kt5nB8g5V9fN/nhARBtvPgyDyaIVDb33dV0vFgskMHraqqpiHCbX3Jyzs7MzIlqu12JNZ8wizhw1IyWYIk4i7312JW2YIqLr+14451F0HDQNo40xMoGiKIbEh8Mhs7l7SCk5BEfELFVZMse+79MQuQh2892hC4UDR5EVVYi8MrdtV5bVs2fPdpt9H2PhCnQAILd3716++u7y6bOYdLe9Db6oSue8dy70XSQHqkhEofApDV13XCwWJhZEZE3SRVEYn1SMPYAajAvRqhaKiCnKAw9syp/NpFPG8WGICGQn0yHawC0l9KqaeEAKVVWWZQEoNrErGOv1wxrcxJJBRJCLIZIMEk2Yhog4RhdWbAFAxOBOvQeWlUNCQrLy9/TAMjbHTPSFJxXjHDpS52N7HFNlZLbZe89pIHLOYyic8xh7TnEAdEjoXGCJdk/LipVlmdhGkomImADbAjZNwzPODRw76qZoxz5uetQJiwVwimxTSrlMNIZVk41su8ECBMsBjBoKV6t113UifVmGxWJRVZWq9r3JajD9bjs7pQ8zkQjihAFAxMSnvM5cZ1l4Q++VdydEwRQbWIKVKDcqWYw71fR5nP1nX9OSGt5DXVaJRayhwlwZ8kTYtq15cVNQap8yoVamcoqIzc+OQI7IIZDNy5yWztxWS+UXRdG7IQkjQAgheHTCw5DGZ0ZQOB6Py7oxBPP9bnt1dZVS0qSXF0/Qu92u/fu//3v0Qfh2cbaydHFijpGN69MeNYQgkspQdH0LiCLa98P19Yfe+2EYCAQRba6WJ0KEwYbchcJ7n8aA046zKcAY42azse0DFFIw+GVVVUPsdrudnZSUUtu2RkHrvWfm4/EIhI3RDBCZBjMyR5NY6yyc1hMR+74risIFL2wdmUBERVUVRXE4HK6uru7ubmwyeoqRY+KCFU3yPQCoEAB4T0RkFBGqYvA/5zAEQ4TQZDXnYbCOmYgprWC/H+JAZB3tZHi2lKKIVGU9WfG+j5ZvqqqCEAEI8tj6sXinWlRl13VDNwBAUZBzTlhSSk1TWUQk4yCXSZ26Gc3OmOQzZzcHZlP0bsjmyWOYHj4mqaqmjykNvff++fPn3vv9bmOt0sal8eLFCztuq8Xio48+irG/ueW2bc1aDcPAMaWUHJzKMtPRM5EbhsFYKw6Hw8Stp6omJyZFAGCtupx0GIYYU+FqJBr6pIpNvWya5ZBiHFg4Dn3f9z2qeu9XZ2fe+9vb23fv3nXdYFwgzjmb3ZkXmcEACUS0qDPQyER3yhMdj0ez7FOM99vf/va3v/21pLaqCiJi5rqunzx5Yqj33/3ud0+fPv3kk09CCPv9vuu6lNL+2C6XSwZMKTErs1ZVAIC+70Xi9fV1SulwOKxWK0sjAuL5+XkUrqqibdvUD9vtdrVsnj9/XoViu91+980fVPV8tV4ul865zf6wXp13Q/z3//7f/+hHP3rz5s2rV6/Mr7BVtXmd7969+81vfgMAF+dXo/sbbPyDiJLz3hfMvN/vQZHQEeL52dn97f23337Xtu1ydeZdEXx48+aNucvMEkJo23YY4nK5rKpiGIb22JuL7Fz+9PZ43O/3KRcxlgAAhCklHAH91rZhUECaIcRMVMxEmstkTlHXdWAToyHrjaqqxlxnZGaVpAYzRbCGLVJwwU8WCjKAwrKQvm07AHz27HkIRdf19/d3z549e/funXPuiy++qOvaXLWZG52fcwrpJ4F5lCOTHyhnfR+wMEfsOvPvp5+nyNP21ORkcvbmH5HNzZxWf3a396v6lNPKgxlM76eMgDKnGON6vU4p7XY70xgG4jdSlo8//lFGJMaoGT5Hk9ox88ucGJQ8CQLARHUoZB8BPNa8wZMjdH4Miyf7jQCoKmVZA4AAE1FRVNZVEFOvyiLOrAUATRmR25sbs7sxRgHDY+DhcLhcL5p6EWO8u9uoondF8DQMQ9OshWEYIjNz0qltqKoKBScjq4ZZ9Bh7nagbDaMwbrl1QVntAhERe0sn0AyvFkJJYzJ7v9mCo8lZMT9sGAYcM9kAMLH3AECmrcmIW3LOg3pE3O0ONA5aH2FzacqpTPI6iYiFEEZ8pGMCCdkwAXmXEAgdAVhjKbkiAKEqA4iCGAH2BPYa1wGtqasoApFOTq3lDEwRD7Gb/M5JuA3bp2MMM2YlfxCqMQ8AJu8ZEadON7uICAEBJ5c7KdjEivFA2qKeOl0M5DPrvs3864yIChYW6/wj7GWPTuP0J3diBsfpvM2vaWdVFYUYKYkyoAoK6AT1m5xaNeQUChEiECLJDJguU+I/kxE9IM8hImOSmauV6Y3T8k4bqqrkZ5OzHu4Gjc+ks+t4POrYl2xoXZPhqqosn2dbbF3yqkIo3uAxAJaDRVUg4hj7GO1jrNXXCgIjzOnB4sPDgub8eUwz0DiClJljiiklX7jpzKbEpk/jSFo838Qptnm0a0kS5gbEE57hUZLm/QsRAU/TGwDA+Nrn3+WR2EtiAETVKhSCxhnFIDnnbeMyaDAqKiBHkBTRFUWxWKyU8HhoLY2SVMQ4i/WE11IRHTlwjVseBKq6rOsSIDNwo55E1L4jjk7k9JAWpFnOoizL/X4vIsYuKrO8+GQdTUcRnGykyfoE1D4pJUR8WOXTWS6WmQHlhJMfO9rtT0S0XCwWi0VZliIp3wcx90kLqqqdSAOxpBRZoj1PzugjTaYXJlrJ2dmZVmBUGw5Oxj5rXZnl4KffDMMAINZQMQmzjFM+Jzd0yo7bCywFNhHhW7waYxyrFie50hmTwbRxempuhvfVKyK2batIxhJjzjGhTgw5thum0k2fH497RJwIWPq+tzSTAX5yPI8njSp6coloVg3Y7Xb2nAb8MAjudrtt6uU0VNeO8zAMXdcZIhLBTXKFAERkUARDuegsjFwsVvv93tp5VbCua0S0NL/OYrlpuZqmmX5pX8RAsMFJWYbVamU59f1+f3d3Z32N+/3+m2++MS03DMN2u90f29Xq6uPPPv/oo48Q8e7u7rvvXgKI9/7jjz++v7/d7/fr9frqyRMrQYSiuL+/Bw9FUZVlCWxt95hSuri4+OCDD1B0s7nb3t1b6vDZhx8Vobq6fvrq1av/9J/+ExFVVdW27YcffliWpT3Dzc3Nixcv9vv95eXl1aVVgIUzUXV2XHRMINod+sPx5ctXb169vrm7LfJUN1dVVRBnt1XV+/t7EWmapqoqkdS2bYzR+2K5Wlkc1fa9LYsLvqoqNM8SRz0wGmzg71eVbrTjc/tlytNAWVkviVrRCVSdzVAEJESwhkWkIeZ5NRkUMHZm2v3tPqpqDOZ93x2P+67v7u/vX7x4sVwu67peLtb54yxKP+lnBDhh8adDN33co18+UhfTiZtkbH6f6QeaAUmm37zvRTy6Ht3kh/4Kj0366bJq0n6/NyjNcrl89uzZ+LkCQDH2AOQ9GZlHSiKSVFGViTyAqKIAq/OKAmBcM4gAKmA0xqrWBygMiKD+EdpWVQFUBFSQJSaJRZHbg/q+74dWJJm/671HdCIS2cp8zudZ0MkoUzhx4vhif7i6urBGdYMAIdIEk8gqUpPBRnKcYi0pKKSnnrAkPMWp5Lzt3BgjOispIiIbBgZSCAEgazrvPVF2RJg7S25NOJZkH5qSEdZMrpVk8jIbZ2MAfkfj+E+iB/HDqOpHPvixgdKIF5UymdS8i00MXY2OMvWSCgopkg+EBCx1s9zebScnGhEcaNseYoz2DCICwAAw9KmqCrOFto8uz811ZRW6/tD3XVkWNiSPmWPKKaVhGESS7SMiIrrvE938fbPszvwzW0AkxTwQAADUBlaInArQo8sKiAbLf+gyilGyPhhchejAyH1AHM6QIZMbPTNpdqIQxr7Vh2W4+U7ZNcEbQJBHUsvJCYCxbGrKy3Q1Kngktel/GS02ZY4lpaTZn9DpAbILyzqpVB3hWBNd0vyafffvAQjJSKNhz5/GiWB2q1GjmbxZmAcheOfIdjylCABEkJQFlFXtXzV1SuhCQEesCiqEiI5UdNp3eliXHAVynpI5yck8JQMTNeTkbI2CMX6Lx5AnHKFB8zWZzGZZlhPfznwX3nOrACZ8xfjMOQCwuMU9fv30tMxquHPnvCBETmnWSWmvEdAuDp4huEKSBIfeFU2DAtAeB6sQCoIo5TFzChlTrqQ6Tcc7naXj8Wg9GERgFBwIjhxI7EQAxtSyaqY9NetYlmXbtqvVyvJD/dCKCIMKgo3MEABCEFB0pACsyioMaukPIkpj27GMSHHLoUyR5IgZs3bzwkjwlXCKUGTsyLJpQXXd1FUVnB+Mx3Tm+ksmzs9LbT4BoGQNTQAA3bHVqcPe+0mQJpHAGUOOCCgQgBHEIYxjm+kUujhEMAy69cAYyEqsnWzsuBAEsWZ3BAFlK+Q675yj4AE1piHxODYY1QcHqCNHt6XYZMpvqIrOYhWcJxFmlx2IEPyQ2EqyFqHZGKax3f+krAig79u2PRj+aopGLNs6UZAholEuzlbpRJszfbTl+63W5Jxbr9f/P8r+rFeyJDsTxdZgtgf3M0ZGZORQxSSLQzXJe9lEo4X7cDVCf0DC/S96EKB/079B6AfhvgoNorsJkCU2ySKrsiorM+aIc46778FsraWHZWa+T0RWQ9oIBE54+HHfg9kav/V9ZnY6nYgImUMwIJcXNH9MKUkN/ckEEDky910/p9mrb45d8Wnv/X5/OJxcwgkRYyjsVSmlbNL2OJdRvbPBbPuaiPb7/eXlnkAAdBh2NzdPEDGEbhz3PgLx9u3b169fO2Xn7e3l7e1nWeztu4cXP7z69te/fXh4yLJeXFw8ffr06uriP/yH//DXf/3XP/nJVznnt2/e9X2PQPf39yGEVfIyz2o5Eg/DMI67vo+MGAJZlsvL6447K1UDu7q6+vWvf/3LX/7S+0jffvvt06dPf/7zn//iF784nU739/fekLm8vPRmSM45ZxVNOa8ikk09HA+hW9d0Os3X14DId3cP33//4uF4uLl+sq75cDhUb5dSXpl5GMaUkplWtBLG2Pusdgv6iWjYjU7NOa3LeaVVEZAth1VbCduwtf3cVgsBdiEO/aCqkhapk9nMBJWgvjFeWdEFPx8ikLPTlJGIeh19XXIM/WdPnh0Oh4eHO1V9eHj49ttvx3H84osvYujNddBrpb+F/ohIP4bFb/urXU77+0f9wkcXu/0oeuzjoOYV/53P+X0/fHSexb/U8mLNYYp3yml5/+4NIl5d7lV1N/YX+zHn3HXdPJ+maVbNIXQhkKllSQAE5hg8BUP3+wACgVz9DdAFeRVL/0HBpydNAdFMvQuvlQf3PKiUs6gaoNcbuOtjtpyFx7GnKvrgmk3ZXbmVlC6lhMRewFjX9cObN/d3p3nKT5486btR9V3lwjutKeecVDOAIhkxEAUiQvDS+7nM7FC5j26uqkpWZqcizWYnRMx5JSKmgCXvAQA3+upjAIGRnJgtOAelkoJAgUy1R+62HDeVMxEFMwDJCVJKbWTWm7kxRm9kay4Q8BLTAngtvkwHpsQN5V8QzuTsWCIGYEoaDA0JEPeXFxSCghmhSCI0QDscH1JaAOL5VKEgNLh0lrFUGACghggVAXm+e+1iN+sPzATgPE69XcQfhf5lJ6DmLN7u8/I8EVVTAC3C83nrBld2l2bm/lGxVMoNER1OClaG7sgRNvQjVf9tQ237X5Y3YKTN8XvKw5ST5KyioD7qXNN73/CMVNkMy2rIWdsEW7stZuXqNneyPgKjzRIS3LBY+K/LRh+gXUX5u77Y9qaqtsEsTyF2u51HSO0p46ab6V+3aW5qynJumCC4MhpkHffnqpvfeTHHDT/Kmtrh7nm7MPzrHDHi+0Ib/zTztKTt25rvt3XDqbLpDIlII1YsL0ozoLY9Ja1VNPjkKHWPzXr2byE8t3e2H+UiVexBiMvHZsk5ZcmISIwCJedR1dPpFIkzG2gxr9QksUqXmdqSMDPXY+mpAyUzlKwGQgxoqppTAkQ0NHLNC7+xWIvxsN2tpT6SUhpCj4iXl5fv37/f7XYpLwYglq00K8wjuUqub95kgwrqJaJlLTwNH91Jr6RgzdagJnVYFnzxXuW2qap6UJJsg8hvC7udvNWebaWWWZ0BEDEAfByFNNfbnPpHbtUMzreogYNrQmJmVSXNnTeHQEzFwfmjFJGsYGbMwWvmZuYNYEJCsq6Pqi4GnJznmjkSwbpmRAPgKiatAOR0uojnpud2wfv54+McIISQRHPK8zw7AnuapmmazGxZlrwuZuZPajoeRVLfxyylhupYHa+sRzoD7UzO3ws1NyOiZrX8KctG89E5FkUkpRw810JkpjZHSITrnFKaiagLfQghcqDa93aSjIeH48PDAxeS+Do84OzCXeczEof7D1hrtFjhUlDHcP3W+YRhCOHq6ur6ai+aTOBwOs6nZV6n3bDfXeyPx+ny+urzz7+IfQDFaTkdH07TvHz29Ms1aymUMByPx++///7bb7/94z/+41evXt3ff/jZz352fX394cOHeZ5vP3tyOh09CxIRy5JSOtkppTh2/fG4WJIQ6fb2M2Z8eHh48+7Di/zqw4cPf/AHf9D3/bt371xv6927d446btVMRLy7uzs8/PDl8y+9aoElPmYP/UVkGIYQwvF4/PDm7cuXL6dpurq6CiFMy+yoD8cWquUKGdB1XZpJF5Hj8Xj3cFhzQsRxHEcnK/fm5GN0O4gBogA45fn2ONtbe+S8ygwbR1SzLM4LXLTqsgyXewWljGCAZgV0RHjz5HaaZ29K2AbmqoWatjOzZVld22u3293c3KQ8tgEMT4A9Ha0m/JEBx8pG3Rwc1vz2U1Njm3n37X/B740HoEBlNqZmG3V8enxkkc7n+QnjUD3h3/s5KaXr6+vnz587z6+I3N3d7XZDztnMUViFkKZ1HRFbdcsDJ0AVQwf5uLabokJhcFFxZUgGBLLg1JbFblr1rEaIFgJRwDqCHwMFZtzWhELoEDGrqOqbdx9CCF3Xj+Nut790PdplWZ7dPvnNb37zww8/PDwcrq+vuzgQ5gZo5oCcHVngcBraX4wAsCzLsmitN4Fq7obCawYVPSZiGcVDfyqeoyYPyMxRJKlYTkKYLbJPzrkOAIByYkTXbXEzxK6KahWxrTWU8YqRmZlz1eCjNbFdXqqKBhvZKWt3lbH2NNSnSjOYAZeaqBbNIEETM4yABHh5eV1RTKbqnka9ixo3lJFa2CQB8iNqPDMzELe53ot3PIhIBAC1d433zXFjBYew3WabbbMF2LQQp+Xg3s/wypxTxTsVYa02bfeOJ51mmxjuvHsLH+jmHAjRjDxlgDNyg8I5um3PAhHFzgQXH5u/zVMrrQAiSUla0IBgSGAEJiVmJzBTkUyo3srx8KDWR6l1A2voeTYc/u2RC99LK9W37bY9Sah1r3Y32v9aBYdgHXx3n+pWVVS8u0XERNAiPC6dMTfKARFzzjmbISaVdamkHEwqkk2z6VZ1yxqviZ5fgU0tc9sg9kMrL2S7xoKaQPKwAyqQ1B6BKV1W5hztaR2pNBBuESciAnj27oCNjTH9vXYZtjuwwbQ+8gSb7aKqKskHXLUOJIgpAWq9NKsVqZQSsklSBuoGMwNTxMBdNzD087wqgvotbNP0jw+fe3cR9OvrawMppJPqxReFXBDk1VGdL6rUHQo28rx+kEgsq1kWEVOm4BKNyzr7HApqEQdQs5YGtOfoDwUAHDztGz9XeSBEC4G1cPo9WhUezYCqrGk6HDumQKQGSEWIAGoTxn92ogiRLFrEaNyuXu32LYhvyMl2gdst7KsIq3EWkUbiCQCILKI5O3bcXYPvLN3GE346hkCBG844xjjEDgHEckoLEcQY+54BCqozpQzQNqmfre9o8yFOrMvqo0C/nV5bvfM8GxQRJQ997u/vXXfWQ38fY+u6bp3nlFxNstB3lk6IlHGCtgdhsy9aZb31YP3vm5sbr/EPw+B8Na6itcxZVU0RgEKIXTe4vm+MIaW0rsmRhMwsa5rnuRu7/X6/ruv79++9IuZzBTH2bdqhyI0DOAy1tEMfF0Tb8mt2z88nq47j3sQO70539w+GFoMcT/PYj6Jy/3BY36+aFRn72F/f3vzuh9eVKrC47z/7sz/767/+68Phvuu677777m/+5j/vdsMf/uEfOrPk119/LVCEbOe0mGnYRea4LGkcx34XzUSyiUjfj8+f9x8+3O92u+fPn19fX9/c3Ph5vnv37unTp5eXlznn3/72t99//30BCH3+pSRRBRLmEERcBosA4HA43N5cEoXvvvv+d7/+zTRNtzc3f/5v/uLbb7+NGrtuiLFnxhhjSqACr179MI6jz1HknKZpur+/P0wnQBKwrutGJorBCFfJq+TgXIdnm1cLSb/HTlJl927FLP+tjoOprsuiqgBKgOYpuijWvQ/ocpJEzNN0nNdlWeeUksPDKr6RfF21p9N1w8X+KkYSTfM8O1vlxcXFMAybctgGCexlucdMX2215Eo12d6/XVrbV2BbmPvEtrSkvfnojzrenx6ffv5HP28d+kfJTDt8T1mjnqv7RSu3ftvItR9ydn/U2IcUQIRQ0QxQwcwRGQAKYCDZgy9GJBdfQDxrpoKRQblylzKFMgi4YECrXV23j11n3jJutYQQwn6/H3d7RPTqhczrV1/+ZBwuX7784d3be88ZfE63YL4xa9GtLS7f01Z31liESGLsi16xf0sIAdEk6LIsgbsq2Q0+f+lhqFlouIISV/nlSWmnFsJmCg7hVVUrKrxe9QdE9LlhomBF1qQ8uVwZzTwFBwCfUKwk0NiWC8LZUdEGQAkACBTDWVLIHTEAKBCA7i4uOJZEy7IgoGo+Ho9Z1g4vmNGUEdkjeA8E2TiEwN49R7U6GGq14RBjJGIX923rhsu8mg9Va1Ob21rnjzZJWzBEBKguvt02CZa6vsf92ay00VS1plfmhDlQHbmIw1qtZFke5Zek9tFmblt0+8r5ewnRfvzM2/W2N+sn/1u+CMEyUCXhBrVCoAYKwG1z4uNWYH2CZ3OjqhhKHWsp4jhlHqOtnOawy+fQxkxsQr0Ygplp7RsAQBnRO53a0/e/W+Vpaxr8W7KsIT5icIKalriqd4uz2/eS2pYkuDVbWkzWYhGpKkLNwfsnpyyImHJB6BGVYYnyCEpqCCKSKxoo52yqgOcwvTzbUqp8NGbw6dpohwGYKRQqzUf/VYw7FmPXAqN1XbvAFkDBNIuqEpNbOYfLG2HsSxGOIklSPNeYiUPous4IRSybghptyUPr96oylEkk7GLY7YZHixB9KxAAGJLXlc1L3O1KDaAONx9drOd0yiIxnFeUmPqoEsfA6hgzUazUwyXuLyPmUA24L6HmbLDCdhFR0TlPc6l0m6d5pqre7TExD153u2Hc9auPhDVLuEF3SKmvk2jJHLxw2J5Oe6zN4W1vY9tuzE5z7F3WR89Y6yC7d32JCFXY+ZURjZAALXA0KrqEVE4yEocQwMyyQB1m2O4mRDQTRC/zW43+DaCS8z5ebPb42LwIFSNkzsLuhjrGOI5j3/eMcHFxcX19zcynwyFGFklI4NMIWkF3iOjes9w6hLpl0Ms9H20QM+v7flkW36qn0+l0OvlDlAxEJCBQI3Uzm6apQWp9klVEDtN0OBxuw20bLXN2dp8/adMCULNHj2O2u6CBfqFms1ZrClb4D7r379+6Qfjw4W5a5svLy93FfhiGw+HgO2i/v3CZhfv7+9dv3vXjzvFFADBN0zzPcu+yaHY4HIjoT/7kTx4e7n7xi1/EGP/oj3/24sULDKVM7sKjQz90XUcRuq4DtWk6rdPs/xv6rus6ZPrhhx8Oh8Nnn3328uXLw+HwzTffPDw8eK369vbWuzT7/X7od7/6l1+JmCFo7U2JKgD0fX93d/dwf39/fz9w/Oqrr25vbj7//PNf/vKXnvOM44hoLu/gT2cYhv1+fzqd7u7ufGRit9uNl5euoNqGdP1+drFr621Lhf/RJmo/c5XyaC4JESNzbGrQakQYQmDEVTWviYgYkYkBAKXMAHw43CmVbrMTirg7IsLaPqLKxgbX19dv3p5ESxvKp1kaBcV2p/u5I54vpS0b/6G9/yP/9VEk8KNuYns35BNaC9hMHP3Ica7z/vc+//wVn+i6+HF/fy9V7wKqqey6LgRqW8atkPfQWvW8Xa+ZEVqbFCzXLWXMlwhV3UIas7JS6PqAiGu2lEQFrULtRQQRVNGnTrF04hY38YhlBpyIUAJCvryMAAVk6ov1dJpTSjKvX3/99X5/DQAvXrw6nQ6wwTmYuwsTMyEiQJ7nue/7UjU0JerHcYyRc0UIb60nADBF5kBUIxIMSGYgzihS4zx22I9LqCCJL0o9R4y1O+PITUQDIGQimqZjjJHZEJlKBa6lRiXY8jUtcl4uftCmiO5NTP+5mULesGd4ox/ROCAjai4NQUMkIrEMiGDmfTGP100ZgEgDlMmscjKt8ioiOa+utbWus1t8IvbyTM5Z8hmG4TT/Hy/sunO2p3oOoOlHN8SjAL0+Zah//F+u8wVthFcL1an3zV2cpDDufLqdzHwMtZ4eIpghgBeJcVPJbb/yaPs1REFLePFRK87MtI41WxWwMQM1f+gf43NUFf30a4zZ7pJHk87ZIvIIw9YGxOH32A7chHq6Kbdv6xBdXzJeg6L5pSaquqyTu2QDSLnE06oaKSITMs3zfJqneV08kXg4Htzy8oaKhJE6fjSD8enC+Og++zq3NmWoJR5D/BFADlXSXnBqFEDnOogRA7HV8kwJE81Lto/u0tm9/R6sv6oCeMMGtuPmZQGYQU0/2qYGV+wCA0Dyeeia5vkVRWJkciQeIuaU1zWLGAd24qPs+qnwaD0wAwDDIykO8AiJmY/HI5KRARExhnMOhqxqH83mmRkhhRCW4+zsOl4kJqacc1JhBEKq4h2qqkBVrc5FMaFMHjvNQK7M/a2AFJrL/4Th16ql4DL0WVYpMwNaSuvDw8Nnn93uaRQR+GSmwt/cGJmwwGOKLr2kXJfER1H++Ym3xwdlpqiUtAsPKpZTVVVnPDu7XVUEA1OsqslQl4RtOh5efSdEM2OGlPOyFv0pnxOLIaqD+x3QhbVdCeCsR1wHjqGu4XKZG0ydXwERA7HmdDgcnOoEwS4uLry8tRv63W4XY1yWZQ0hhOB9jG0G7gNOHjBBbXO1XRnsPDajmwbRmzdv5nn2SMvZdXzZ78ZL3xFcSTIAYFkWAGpewKWFXCnYzB4eHnzjXF9fn+1MDC3fcw60Avip3YlWLGhNAK06OW2HquqTZ09Pp9Pdw/E4LcuSDE+GPAzLs2fPjsfjw8PDh/uDU4pT6C6uw/v3dzk9MPNut3PCzZSXZVnmeXb8vYg8f/78T//0599995tf/epXZnh5ff38+fNnz55d3uxV83Q8vXv7/rPb24eHh/l4Wte149D3/ek0z3cf1nX9gz/8BgD+9V//9Xg8fv3119fX1869eDqdACDG+Pz5c1U9HA4v3r9wrqEsucxcSfZIdxzHN2/eSNKrq6s//aOfDcPwcHf/7t2HnHM39Mzs0JcYe1VdluXm+kkXh9Pp5FPOwzBcXFxQDDT0y7qKiK9yUyOksw3XQtvStpJsNtF2i9WqyqM42O1zFyISmomBgKorhFjO3PFuGHzh5TXlnCVlEUHiUtXS4tb9XEIIkp2wcfQO0sXFxQ8v0sPhwYsXu92uZYN1y39cOxd5RD9gG5jitm7VXm977aMfPvUX2G7ghmDgR/3yo2NTntv+sLVd/7+E/o6U8/a47xTfkmZubs8DfkUjtYGQsbALeOGxZ0ZwEk8zQ61+M3JUQlFQExIl0BC6TjBzyhTYHBUkoCZMiIhM1MduP17s92POq2UBITAzRq+CG5AHvcMwnKZlWmaOobMiDBFjvPniy9Pp5Jp5V7dXLmYX+q4NEYbQAWRE1yzsuZQNEgBkWVPqHb8OmwFBwJXIxZhS7DoEFMnrem4IMHUAgMiu5MWMRGAgpkIhEnXlCVWqDW2itE5fXdKpQER5iZEihYDICB5kZwd9QuXH8CyfESmEZU4iYpJMwXEMYEZEy7Iwsxka+EAGMXMXewUtzTLyW15C/yUtsStJFxFlK+KmklZVpTK5DwBQ9awUEAmQ/F+FWTAHpr7vc1YHkjqMFRG7OKycV3QIiq9XqvVU3cTBJUBuc6XNIhC58pOVmRLERp1RnZOH+AZOK2vUImhxrBAiAgERIIothKEW/cFMEblGdd6I0MJQa1jHgp0mSBHQyBgIERkIEEBUEKh1Fh5D0lvkJeB09kpQnjkiqrk6kgBqEcetN1/VQkfE0C5W0QRMLJvWbg8QYvRvZKQ1pSWtS1odL95qWkkyWzAEIERDICNkIDMFQyVjAFVkH6U0syTJzAi8S1Pis5zzbhzXvORVlrT4HfM9Ms+zF+EMsdlTz65D7Jh0XtI8nQxh6PrYd5ItREJgNdSsSTIBCsvgpKi+EDZwMthUQAHAiV3JjEMkZgMscP+yJeOSEwd0anhnFganY1IrTRVRRfOisgiHSGAooi7Jw2Zi6KBS70qV8SQoU0BMXNZHUY+qTQmo1Ab+GBEB8xmXVV4XMzHIYrob+pyzOKSKAgGKSkorM4Oan51FMlFBHxCFVTItk4KFjhExa5pPCxCqmqmAqHcvEZGAEzl1lVszyNmYMK8pxoi1uq+azVhAEC12hMhgjxyGlZpomMxCCGsuZYW+7w+nB8viAbnnzVpBuu2plezR2QUKUt8QW3rmCGxvDphI9mY9AK6SdruBLRjkxjjlu0ZEQghGtEqepuMqq5mtOXUhGmPRGTXxzQvgwFEQwZxzSnNKHpXS0PWeDmKdiiEiRJvntY6ibdNOSEXp5tw6IzT3oKqCyKFjRvL8h5DAcstCFcoEl1f9GdFisCxLXkQTMxNhmldguBh3wEQG2VRTnue54wDgFgbIvGJBdZbZp6itoP8Vgcwae5Od7ZiZTuuyG/f7/d5F7nM9lmW5vLz0zl7OuRSwCxndI4PmSU7fF2Bhe8UXOIczC7hXRtsW7vu+73sA2O/3+/0eAI7Ho5maYrIUrPcpEYdUIRGqWZlAKLqQXpy6u7tLKd1+9uTq6qZkTUQf3t8v6zpPk6oyRW/1Hx8emjJRGzzwMDGEIGBIxF0EJjPDwBh4WZb379/f3d0x88XFhVPZOjNsqx9vGgi4v7hISQCAY1CwNSevlN3c3hLRmmZTuD88rOu6v7z6n/6nP3jz7sPDw8P333//4sWLz25vP//886urq+vr65cvX6NrU3KgrjNmEU1Zh/3FP/3zP3dd9/yLL+Z5/nB3d3V1dXj/vus62whZ5qKF2r/XD26gkAyYGNhZ39K67sbx6U+f/sHXf3Bzdf3rX//622+/TZI5lk7L8XjMOTszkoehDw8Px+NxXk4+u8zM94eDLKuqgNVxKS3lxm07xUvlXHmxfzT0bFa9xdOef+e8doFDiBlUVhHJ6G11BO552I1eqJ1Pk05Tztr3PYSiPivZzIw5MnNKiTmsOhNDiDQvaVnm3f4akXPSeVpTXr2srOit8rK6HZXdlGj1PCcJsAnxt2TTtjna2z76YRuaP/pnuVekaKAeIeC29PDJ4f35H/mi9sntIINUf8fq3wBgAONux8w558PxOIj4hYzjOC8nACAwZg7MqhlEIa8RCZwVBopEZs4ZNO95QDBDU1Pw5Eezme2QMuRkyUQBVhILs2UMHMY+pJTzogrEvItRVjFERmIMgWLHA0PMQdclxxhDiICUBRAxDOPQxePxOIx7ABCFZc0hhNgxAKy6WsTuYph1zWuOQy85v3t/d7HfIzAqg5llE1XLqil99uwaEUTM4Stu9VQ1q7dcNQRCkpQn3+oi6sA4M16X7PBcZq5cfMwBOECM0A99112+v7sLoQshEBNB8TKmMOx77/kTUReKqIRkC8yaLedMaMBgiklUDUKIdceApmyI7IQ0ZpE4c0gqphqQEAmMri+ufdSfiNasvcEYB+YYA2TLqpI1q4tZzQZgHeE6z19/+dXbl6+OD3d97I7Hh8vrq0j84vvfPnvybF2OXX+5LMuSEochpSV2fdeFvK4g2odoS9rd9BhwWWBZJi8xno4zEcXQd50hTqrATMzRlcZjLHvsnMwieN2OOTSj4C+3pBvIaknVufUDEq15EZGsHDrmSGReiAPRlEqD2GFbZaJ33F14DS+bj6SAWTaQ2A0qWXICVMJADIQMCCYZUME8vCdTFQ+PjAGIsDCV8rlwfi4YF8JWJuawTpqT5SQ++O7EQUSw2/UxMoCpZdMESB1TF2JKWXNKaSHCfjcA2ZTnruu07G1SNfPRCxVETZDvjsdpmkIIY98RUwzBh8Ld5+Wck6xJVsbA3psiY587AEM/bYQQvWjs//KpUwDglGdCHLqgyJK9QA4A+NmzZ+ozwcvsrSk1ndf1ycXFcVrQrB/3zPFwOhkihW7Y7U/znJIMu13f9XlFQuy6sKiQx4tQqWo8WvLRjgDMDIhWOFJgXTOREgkxIDMxKcCS1yF2IhIZ8zqrQgjhdJzzPKNKUJuWUz/EJU39EFUzhUHNAA2ZDNGA1AB8AgmDqqpmNTVLhR4AIctiiOYP3Hzyx+F2oTRtoNBKqokKkOPIkdQsy5plAVxDx1OaAAiJoPLOoloklixpXq4+u0CDtKxD16eU1nXuQ4+Mx+nY9z2ArevcdSHnbAha5pyBrapfmS7LQkSQcwgBwUKIt7c37lvQvOMIhuDsYGAGyf0x1jC+0B4vefHAzlHyw9BlWXOiq8tLHxhFgMv9RYxxXddlnr2u4Bz/IrIsKyLG2BvS7uJSxMyk78euCylJ9igajDjGEBDZB1gjoiZFwEix+LzS7uKuC6fTqe/jze3tYTqJKYUgksw6zcY+Z1bm6A0MkAIAOPLNHOTGrELznCOxT6dINm9LIjkt0bkF5AlMCCElJ4/HOk1gWQ0UvIKYsyRNQIyBGdjMCILrtjmq7OzRYxRxmmlgRiMUNAMNjERAaAgGhmQAgAwYKGaRLAKEkWNkMgFRBa8E5EwEfT8ydznrskyntIZAXTcwBTMUSaoGQDEEEXl4mL3nICLjOK7r6pIsx+Pxw4cPp9NpmiZQdR6Fxt25zeVaER0RfZjSanG9qd15BBljvLi4cAyJVi4dq52fh+OBYz+M42mZ8/3d7bPPpnUBxHVNfT+KyIcP94Hw8vKS+3A8HsdxvHlyO03TktObd+9UC8YMmELfwTwj4m632w2Dg2ybVjERYThThQJRx72ZzcviTemU88PhkFJKWQEZifYXF05C6qkRs7M/oxVZajIwCkxgOeclLzHGbuyIBjO7u7vzTrvnV9OSbF7XrD5KcX19/cMPP/zyX3/9ux9e/uQnP3n+7LPrmycc8M2bN8u6juM4L8txmi6urqbpEMddylnWhMR3h+OccjeM87L0ISLxmgSSeCC05jTshsPhMK+zERCxQ34QmRE5hpvLCwD98OHd+/dvf/rNT16/fp0kT+vy8s1rZ5vFwET05s0bb5uM4/jk8oKZT/OKiEQhp9RSQKyjnWUwb0PAA9X/SSqafVbosMpEjYigA7wR05rWnAyhi2E3jICabcWAkaJUJusvvvpCJB3S9P50v66rek9DlWNnj6V1RJKIs5tI10VEnKYTEe72/TSdbq9v5tP06sVrCmRmh9PpGYKaKBNUYXKwQshFAE6QBHi+Nii9bgMz2SL4K98IbTA5Pxr6b38OCAKGaojAgEhsZprFSbfLnd5kT96IbqjORqIUiP2ri11y6w+AyA41VAQ00MoCub+6RsQ1pXmeT2vq+z4wH+YFECVl0BwIxxh6YhTVvOyHYT7cL6fjs6dPCfTVqxe7Ydjtdulw6rqOAOZ51pz62O2GjpHevfpN38Unu91u3/cxMmO4vL1RVXpYlnnlJWcVzJBUctYQAnLQlNd1TUvmGIb+4vTwNmelJRlT6GLcDQEIgcXQ0Bzz19WxwiUvtpqCIEPsw7IETYk4dpFj6AHAi8LYFUYL5sJLqMoxRqWiogKbMUoseC9DBA7oA5AheHemAK/dmogkVQQkZjOAeU6n5dT3O1/i7lpqHQkOh4MnCrjhtI4xWFIjMwxIZBg91AcrnSyu3UoEQBWHoxWCG1AyBCQEMBeFrUuwTSAkobws3jvxYA4xEAAjBkIUvb6+NtFhGPKaxr5X1RBI0gpq+3EwRAkEHBEN0KdwSEAR6jCOKFFwUAEAqDgkjhF5HMfT6TTPa13Dzs+DDAgNA28/MmPHriXu5oNsSal1Nry9TgXqx0TQ9/0wdOM4IoKBEFjEyFXyBhGlQllkWbaJNZbhQNacDIzYeziIiKpiauYV22rQwMBAUElNCdAR1nXbuWHccKUXIL04WBnMSu29XKN6F6YMYZMbYKuwt2xmWCgwC55OLBOEQBQoYkAgTClJlqxpkiWrcoz9MIxjz+6vED2SQUUi4kjI3qdCdQpeNChcDAil8O/MqW6vz8UF9A4NAhkYIyAZliSNiELn8J6yH1HytCTPq0PoACisPrYoCoTICghAYv43iGHwIUhEIAqOjuBSY3AHX5CFTlxtBoRA7HOV2Kh4QKb5qKoM0fxue06mRgbktlTFY0lFUPTAGwiADM1MEclAgZwnBwDAOf5JiBDJsikYGSoCKzqPGZkZlQqrP33nWffKqI+UNEWY7N0e4GCGKuZ8/F57RqzMP6XFClDqajLEoe9jCKQqh+mEiGvKKS1ADKoAyD7tapbBVNWndxjMjBggcgjEjMhInk+fV2ydM4Ha7MZa/2Nmq7K0boGAGb0dl8U78oGYAE3URL0W6HtWK+2Sb4pxGFTBLLmYSUqyLMuyJG8Z4ZlysQSawTMQP0MBAL9RjiLVNaeg9OH+blmWNS+OGuLSYnXHLIZupR0TRUTkkDPmEInRgArYozwaZw3q+96Nf4OT+W4d+17rvq5/k2Hp2jn6T0BRHCJoSmTgsQSRnrvzJZ0I1cdUBQ1XhTAzUAUjfxweffsgEiGpgu1D9qcAAQAASURBVKTkZDvTWdYqiohLMjC7OKZZ0a07A10UMOdMANfX1z/96U+XZVFJx+Px/v5+WRbGDcaXiAjWNW2hc2ZOJQpM3G4OFVpqQcS+66UOyLlol1e1nITRi/Sn06kMMxD2fW+IBsiMXddxjERBZEpJEVlEpmlC0xjj1cX+4vry9ds3ALBKXtc1ZY0xDrtxHMdlWncX+45Dzjli8LPqY+f0/GXLVdYyX8nqTTQAr/cL2LQu87HA3NsUCmwwVFDRR+CzT323rqsPbSOimKZldm+LhBSYY4h9R4GT5JSSIbx9+/bzzz//6quvvvzyy/fv37958+aHH374p3/6p9vb2z//8z9PYqd5vQZKYh8OR+47ihFNjVBWk5RRpEcMMU7TpAixzka2a0wqiuBNZdePKQS+FLhoAA95LUMOz7/84ocffvjtb3/rqCFE/M1vfuMyrt4CKuNGJYTIqrnrunNVC4FrYFqqzhUSULyqFfCex/remlBVZ5zzPXUe7PGJcBBGALBcqeE4cDcMWZOBASEF7qg3K/ynxBvuwc3hszq1H1VmY4hg7Mc+9vv9XixPp+X+8JDzCkyGTb5TyajUyM0qw5htg/gWnHx0GABv4v5H/7XZ+LZhNPGIv9ggIgIQ97lbBjn8+HPMDABxo+FTPvYTFYJs4D4erDQxym2qlX6KAdW88ZVNu9gxs2aytJ6OR5bcA3Rob373229+8nW43Kf5sEzz17c33/zBT0MXX7x6++H9fV7nz66vn35223FYl3mdp5/8+c/TMmteuxCur66uri62xH9n3yBiROzN1pzzw8NRFUJXULBA6ED/3X4/XO77vucQshVWQQ/9fSk7vAcAAvNuHGXJ6zwDYN/3Rfapdmlr9u9qbcXjQoCu62LkhtdEDGYiBaNLRAxWyPsREVEQExQ+GLWaioXQMYOIppR2YwCXxpXW1CKs2sghnJFFTDGEmnKQh/5BRJwwznsLvpG42mBVRZ8U2EzWeP1SQQzV/6WNoiGZc5gGdkGxchMIoAtsIj7gxUzJ1r4f5pzIeJqmZVn2+/1pFiLqQ1zXebv62zpW1caRAnXGwP+5ro4+TO39zBwjW5YN2uf8mbrNp+u3GHijzcpwM/iTYgAwUSSgikFywh8s+mhnPd22K3xuySq2mM7Dx+JootbLq0/tRw509g40kA3GroLOt6cOAGBkiq7b4JOUiKGmjt6qY0QkJAQmLJ30tqiaxTGHMFlWkTJVqcHtBiN3FB1S0XddUYY2AACnYDeUc2ZrpmbEATbsvx+bGDBogu0FhXNmIaAygMFmtmZpgx/+RNzQ39/fu613pRj/G2ulEB4jo1SVIiHC9mTqLSJENER1SIm6spwG7pwFC+vkg0fYTghNpWDZgJuPpo1VFeAsPFcvHAjc3Je/SlpI52zNR4zajfKCv2lhigWnjK2rq36jqarXm6tDKpM51bMW6iQvnm1HLJBKaJWzzDQ7OUPOeU0zVRB5KlJZwcNIc2hSw6SaqgohOLDZSklI8VzfKBvaacXKawBQmae1qbTU3/DTW1fn4iikjdDET8jP36va7LOY8+wkegXe2hzhp55y+/R9GL/dTC04cmHmJa2Iw/39fc45pdT3vWxQBLZhdnI9AS86Arv8YgieXHk8ex4XabPg2IYFzyuwjiv4zfF6IxpYfT+oOqkdEhBVvW5AQvJioh+Olm4c/OWeAyDo+fTLGDFhTb6JSqloWZbD4XA6ndYkl5eX4zi6j2gcvh76tBvry4mIsto0TYFwHMebm5uHh4f7u/dVz0uTblocRESwLOU0msHfWn7VMz13OxyV6mO4vuWnaaoq9ejgQC+0910PAFlVFJt9doqCeZXQdUPXjeM4n44PDw/zMqnqT376B13XLTm9evXK52jXnOZ5Pj6cxnEcQmRmFEBExxctqRAXAoDzWZWFQYUZyXNBZva7ejqdnP7IYTBaWWhc0k4rgwIR+fwxQJVraAlGzm7x2u3y6MVj367rDofD4XDwiOX29tallP7rf/2v/+k//aevv/762bNnb9++HYbhpz/96bt3b/ohhsCeGK82W9U9aJN7jRELABysBRUi62fl2uTTaXK4/8PDA1FYc/KkZZqmcRzdPmNNvJ3t0TUfmlDjR4++HQUbc3Z+aDUZKG+AGqGoi3IZqF3uL4o02LHkgcMw7PbjdHjoIm+H7F3h7v379x/ZCj+ZlI/bFMLXWC2QnosXzV69/N2r+/t7AOj7/uHhwZtUcegfjoePrqvtetvg+Nvn/L6QYHvhW7PWzNfZiRcD+CNxzu/7ZHichGxhP1jKU/jRO8UefeDZkBZrA9EHhfw10WWdIhX4oKoy8rAbr4buf/m//l++ePrZejrmNPcx9n1cl+mf/vGXu0v56ps/+frL50PXv3jx/Yd37//omz/8H/7yz8euf/HD99/+6l8/vHuTDB7mHBxStkxrpR8JyAZgjorhSq8mIrqKFxKIue/7cb/f7fcUY0ppmufYRfeXqrrq7Pc0RDLENRecsQfoWWRd192wh+rAWpgG4K3SElsECiEEZkJEMajBztkTIKKTFHFRO3KvUeIij3j8KhxlEc1EpHSqawTp67KCStt8mPmQCmiBjnhpsx0tDmDAAqv1GK5Et3W0EBQQTPEc5aAalAE8NB6GARg4YHPMquJQ3y4EEdlfXhw+vA8h+JLMa8p6fLi7//z519PpBGoB6ZhSW0PNdZmaiIQNEU0WcSPVws22M5th0sqwCXBG/QCAyHmHt91iYF2MQOfQHxEdZu2MPVYp3juOHt0yM22q/nW/mauQSqWf85MJiMrWBlibs//U4mw2tKiepZRbTLPd3u11q0U4x4NWW+DLo60QLbgPUBX1YA6JGImIkIirGmjOWVcDs4h913Vd6GOISGBoXppCtK7ruhCJyK9XH7OPl0I0/Yix8ZPXGgh+9HTMzBuI7Z7UYOXMjO4uyhSXnJy3yj/K10OrymwfCpohMUIZeMiq4KNDZswsYKCmZrluDCsEHYylOwFW5OoKKXBA15olX3LLshT8hpYqr0N2/NypWMQ6gm8AYFS0qFyBQc08eRMfYN0mAL5APPSH2n+oZ2Xt9p2XvXddROvoS8kBUI2olIGa+6E6DusaRl5E9xqE8xclZwkrk6c1uwuquSpiqjZqERHRaraaPSzn419aKh/nJ+4mCNQERGuHvRm04mYqv7sD6x3NbmYN9rCu2SrxSLPPIuL4XasQkc2mk03f+7zYAACRQqBpmUMIvrr8i9KazuaiuvwQQlq8e+bxCbjhdcyyS2w0p+4oF620CrgBugCApBJmma9/BNMyvt8Cjvp8saH8CdCcCgzB/07LSoEZiUpfrrKLVQk/35lQKwLbeKJ9haoOw+AAfSLyWMqLRD781gJQh3OYGYU4z3NkGsdRVadpclFP5/rUesfceJZp7PqdbsADMRExoHtwH7CBMxCogN0Lnl5ERBx10zo/Z99hICXpIkTMOa/zPMc5pdT3o8d8IuLaNW7T/v7v//7q6opimOc5xnhzc7O/vACAZVpjjH3sRSRLSimtlcGv+VDbhGLtyapqWhYfHWks9aEmA+26qCrFStVXISIy6Pt+Xpc62HaWZcyVWNnvRvP1wfUc6ps9vA4h/OVf/uV//s//+dWrV96mGMfRX1/Xlaj3weI+ROdvWNf15vqaiCSLM7n5R3mCAY/4DMqVeqLiSfLl5fXV1VXXdW/fv3t39+Hrr792Jh+/CSLiM6CN9dLTtrY7znZhYx9aVPXRbm23AjYBACK6sEOrFvmNPR6PfSiqr+0SfOzE08jt4Z95cblv28E2VZUG9LLHgbsr03VdFzr2K72/v49rT58amtI8fGR2tnvw0/fDxkluN+yn/4Tq8lTPJOZYiiwAANuMQB/b4fYhuGEYo0+0h8tt+bGKXvn2LHhmTindinWaLcaO0AwJAxNyCKHr/+Iv/+ryYm9pzWn59a//9f/9n//2zatXb969u75+cpK3d8cJ0R4+3C3rvBha7L/64vnLt+9evL9///7esoTAQSqjEzMbq4KT5TjLDxhVvs5xVLBlWQRWrLwlcEKZIaW0pjTsd1C9TpqXdV1DCOOu74ZhXibPGTywcHHAq4trMwMKmsV9LTtOY0Ol5HsbMSIqkNfy3E6hlukscO7gs/E6O06fPERTWNcMAH0fuy7M8+qhJNRB3jpmFF0rQVUJAxaZLXE5KSulTVebL5Wzc0Dp0SQzM2tWnyom8j6cAZghZMiAAAyIPtdqRMCBYsdYYGXobJi+vSFbF8K8Lk+ePHn5u++effZ0Pk3IlHM2g7u7uy++/GlzyVQVA3QzWWJVOxiqgK6BqBXu6hCo72POo3/CsqScTTU/BvxsVylujjP7cs4ZyNw910VNANB10aW4agE1MjMCRg6BgJD8/Jp16LpOUbOBuNo0OAACiIIJmJqKeZrFGJigql2et6i7afB0q0SKqKrIFazuIbxPVFeZ3sLdXsgNKrQGMfg28GqhGaAWvgJiJ8Mt/thDGaRsgGriOlbOBwmsgFlSW8yaxViV1Mz2w+ixuEeaWKuYaxLUOhbqF7KR/VMgMGDchIOtpVv3gJsOX5+FrssBhVlUte9GN+LnZ0lEBmlevDqILmRb/sORHoUvgFolA1GrxoJvNvJBafSUmCooz0p/wn+9hmLuo3LO8zznVYA8MRYpvZdSmoWNL6PaSGtJGjk0voVB1lqtZGag1UngJtDfWFW/vsJ51uphRCC2iS2grU+qLcG26ogIMVgufMRd12GIZjYva+ueqw/XFqk7ZOaclJhAFdA4YOxCCDVdAYUyn+ApD9XEqdyNjTBwXfZVParcEwPuuk1oJS36yZIQ0en5QhWk8MDCdUYRQx3El5KDnqnAsaVPfj5aTqCQBW23YQsgzMxr3lCTqAIYOCfVBY2sdYa+QLYBaqKCuKEVaqeEj9OwCgAF9L8Dm9PIWjl1/wPqaHBDAAKsQ+FQCn2iQMSE4HsdDA2QDB0bpr6GtUVTroCuKohoikyx70YwGvd7KrL05QnmLDkLuLwFJVVNlR9HVfeXlymlQJ1vB29+No67bYAiObtgpW1yvBBCF2IIgaEEZyICda8hoqbsRe5Cxi/idQeqPO4A4MvALSEyAVGI59gaEcdx3F/eMPO0LNOycAwX4zgOPSKKGhGdlnld1zWl169f/8uv/vXNmzff/PQPU0op9szcUXSizJxdTLwcqNYEE3NKZqaVStXBMIi4vxiZWS1nUUTkUNbzuuYs65pmtcwBQyTRdJoOhnw8Hl1Sd7fbxS54sDt0vRSRIEOEyAH6QUTm42kYhourax+oXdf19HDIOT9//vz//H/8P/3t3/7tP/+3f/zLv/xLEP32X391++Q6aQnPmLnvB0Q6nU7LsizzGmMkAETyDo0/elRjQAAUNaxEyVYb3Smli4sL3ykngJzzkyc3T57c7Pf76+vLnLNXFq6vr5dl8Yfo86wAJUht3IYA0DZCMZgAxZNUXRMwYGbn4WmRj1bFN/fmvjK9OtD1cT0d1zVZrRc0Sjrd1Aq3YbdLO2Nt7rUK6d3dnX8dVpPlG1kW7bpOHsRSyQxTSrvLC/G9tgXMeDdjIz7gR1vG/53j00D/9/3X9g1mZzpR3bz+aejvb26If38aiKiPe54AoFAMplVgHhUfiqbWCkD+1YFQiEEtq0BWUDHR93dyfDj93/7v/w8wQZWnnz1ZluWHH343juO4v7hf3kzLOs+zSR77YRz7l+/u//YX//Dk9jqvyzovoC4GSmEcR6KgURDopNOyJMumagiPuI2gBuLUBtiXxRDC2O92uz3Rw+noiynGyFBIRVJKS1687mvm8C1ERIdFBiRGBqKCxkMChL6L7HRROedUBuVCCFxiNcRKbZZSyikRdVaLdsWjVCJyt+q1qitm1tsZqwAAULSQynxVc07ERSy2do3Jw2EzA58eoEKSx5uAmAwANRquwUOHgqVxiWWfIiUqcymOgyGivCYgJHH4adlOVPuGInJzc5NEgHDNaYwjKsYQpsMRDboQV8wm2nUdiKqqOV6IiIiksuucVyIWoAIAeJdzXfO2+k7FkdZ9tcH6e2u7Hg2oo6tkgEfUn6Xa6s7czCwxMweKkcFEljkgtYoOVnZUqWSvuIGvEGGgws5xPsNNIb9dVPuhKBVtl66HCEzboguUcORcltjsTwWg0FXhDHXcLyGakSEhbARuqdK3uk1kjGZGGsBFkTRxjxTIu7RdYO9TSyW4KEuuhmjMbNLWLxJ+bKfqhZ8xhbThBtVNeOTRe+k11VsUqNRiaYOncrBvu2M5+xx/gQKu6wqgBbjl6S2eG6webzk4EsxLrOcw8Yz3AXApDyq1BvPC0jRNOSER5ZyRRPTMIoUNcfEYPbeuyTtG7JeOvp3hE5Outeq/qcps6/2Pq7abtQGIBXVSUGqO1snZrCwJXzOIGELsx94D/WEYKHYedizL0o+7TUHIVLXAWoJ3B9GAYoy7YRy7HknQGCp+qUWX56vx52rQiGYRKRAJEYgCABM50nRbVd2mhBVwAg3GqapdF3yPt1/x9eDwj7YNz6sdoHAYfwIv9DTbEQ5E5GVXEaEQm9nxEjjyGkIY9zuAEkwbOUmLWJYuxKKlWxuwUBPhtubbotquDaxsnd47b8/aZ/UyOo4SUM1NVMPrm/lMdc1sXVXNfM7Z9BOREP92rvTnAD51wn3fUygVUy9M2Mbx+xZrjfEWeJmZbzfXQzgej01DADbQRwBAZoBQA77SR2qmbCmEm0lE/HJ008Hzm+n1affF0zS1W+rRpJ9YyjmEQIgRGYiJaOz6YRxVi2JOCGHowjAMTNgsmIjsdrur2BNR7DsAuL+/PxwOESjGeDleDMMAah4X2id7tS2ktqp8MuHy8pLx3Gtv2SBUxJTfQz98GU/L4jg3ryW3ArY3VdpXt5s/xM7jmePx6KvdC52u1PtHf/RHf//3f//9999/+eWXu90upXRxdZVl9Xvl+g/DMDDz/f39OI5j33uA7pZznmeqfSfbtqpEljmllK6urv74j//4N7/57vXr15eXl8MwJE3Pnz8PIbx8+ZKZv/zySyLq+/50OjUIhlbE1HZZnk1FvZlu+c+LEMzUOo5Wsz6z8711rJQvIasYjUj8zZ/92f3d+zdv3tzf3799+9ZzAz+2X9qWYtePVFtMhW6RmYhubm64zhi0RYiIV1dXNzc3b969cWIo54X/7PNnmhN8cvhVbI3kR3vzR37l8a9/etM+WpAtqfjvfOb2aKexXb3+xS0GaQtAAAEfNQyLXfM8rVhyLOftrXUkf3SISBxJJInkJd8+eTrP89vXr5Y37/f7/c3zn4QQ7g4Pspyurq5urm+XeU7zchKISBC6b1+8CYEih8i95SxLCmbIzN0Q1yXNtDRzBqV/3QFQSsKcgYyZQQXJmBHIAJQI+j52Qz+nOaWUM8TI/RDVBs/dp2ny1tg0TTkpAcYQFoDj/cMwDIPLYIpaFmXgwMwcO1bFZQVbDaoVAC8wmyBZIBISyaa6gqFjM6yGmkRG7NVKN3/EHAIzGOesxKEA4MzMLEtSUkk16IcyAmw+1AVn7IWI5OxJHpNnrkTkvUhCdN1Ox9ESefJg3jkmL2ODA1krTaTX3xOAzxoU7diGN3Ux0b7vnYKgapAhI/axc5hWjBFxTjn3Qy8ioKyqbh6JKFN5jmSGqgwQkBgQNIvmdZ7TsuSUIASmSAaBqe/6lJftwm4/uW2tO7xpMbpzgsChIj3Qy66ugomIYJpTkpRBxSSppI45xmimHv37ZlvXZbuX2u7LOTvwtxmXbZlz69uKSygjJGe7UEL/DerAX3dR3rJNHx++J8FIBczE0JAqkgALzUg29Va+f1kXIhooZRERMBHJa05p2XcjATJSI5V3NIy7XlBzWktx+sVopojKRIauPoqFoaDWPwwAzCdty+Xj9pzbK16u8/iXAAHRQ/ZpmpgL/XxznB6utSA4V5Z3A3FfQ4iM3uTAKq17NmoK53TLOUyxMCz5IIB6/L19fF7jPB6PCJ3Hi0jnqr+nN+frgsrPWd0VIkJNapAQABmc4oxMC8bH54Md+I+beHFrj6E297ROcXhmYpXuFmsiKnVIBh4L3MYY53n2m0kheJzhxMzQeguAiCjn5mQpJLk4egjBVOn8eKFCyktV2++D/zIZ5PKDtbuhdVDBzNRnDMwCkcMIzcxEIseUkhEwBw/CAGAYBjPKREYUiNgXjCqakZejahmgnXTp23wUUpgxoxk4YzcAHI9HgGdYO71w7qcr1Karn7NbKzexfn+8LN1Wo6sdezzXXmwGykSpOWCA1vtxAWYHvAViRnYXAWaNxQzqp5kjsKmOlJmZqq938f4R+pxKIWECAIfjSmnJ1DQYcZqmJBJCQGYzW1JafSd6ccolERCBCJkpBPeSAHA6nR4eHuZ5Ns3runoo5k+94GwRQwin0+xYw5a6S8pmlpe1ZkrnKQhVBSxIPzdWnpaoqut5NZir/wwAsShalGfdlt+yLC7lE0IIkQzgNE3TNO12e89kYowh9oj42WefXV9fr3NalmU5nOZ5dgjTOi+n0+n29roZYf8iB0xSCCKCZkyErgPAgQHLLFwIvoRa+N4i+2b3/DOnJTXAVQsxzcwjS9jYAb9wB162r8g5n04nz36//fbbZ8+e/cVf/MXf/d3fXVxc/PznP3/95uW2TpTrnFXXdR9S8pvl3RVm9o4uI6l3TaUMQ6paznkYhq7rnjx7evv0s3/59bf7q8vnX365ruuL1y/cNr569er6+vr29lZEHI0Dj+ElvgAIzjXv0osrJQ8DKP172dTClmUJRJ0XAlRLFhrj/YcPu92uixG6bhI53N+/fvlymk7/8T/+P1ug79Mate1Qd2Ctx/lTePf+zj1IyxC8ygAAu93u+vraVZCfPHny5MmTy8vL0/309OnTV29ePRzvVfXlu1e//vWv+9148+QW8WNpPNiUuqwG0C2z/fitj4/2ftvkYO3F7TfU14uJcx8qGzDPtupfMwd3bbYNmfAcckD549JaeIYGFQ+PaKJOfd2F2MB7oCY5A5nTSzARcQhqEO27F6+fP3/+7OtvUloeplPO+ep6jPvrtMxvjzMd5hhj7EYxOU5rymuEEIENoyBSjBYsHA6H/e6yw1C722VXpDW73ofHB13XDbuemd+8eukBuiAsyzLl9XA4cAwUwuFwSCk5k1crtDPzbrcjg5RSXpfWBvI9WWAJ2cw7Sl1sD6ZEusyuXjFXBhgi4hhE0rpmgHX7OD9aDVJpg3e7Xa22JqjPQ89IwfJKk1gqJ68YiADJTBXERDRDViFEY2qQwUAcCBXUcxEqvXRCMjRUn0MzMI+GGEDBwAUKkuVY5Hw3y8UrXoyQ13V/sQei/eXF8eHgbO5mFomXZZGUiIOpyprCflBrcP9283B7c+CxS3DRQVch0WYbNjvKf3O7c/CcsLbxrEfQteonaozLREQcmgoViOo4jqGWElNK7ofMzIGn2+9SVQTNariphrZYpzmDx3E/glqLYj/d89uj9prwow9p+YKIZctoQgjIRD5nU9hoFQpdUsVcEXldc1kWUwYArBaiVVKZcRzHvs6ruSc1s5RJRBiJiZesHkmcn6ARbETEPvrhI0PjlgQAjseJmaFUB+tvGTgkyc8Zqxy1Q3jNrFQNS6Kb1XLfR49FfdgyiLi8LTN75z6bimmNaxGLJz6H/mZm4BcbA4acs6r5PZmmqe8YEU1UPcXaEGGhnWsnVkN/ZsaqJe+sMiAAqB0/kgxDMwAz+HgLWJ0HLU5UtwdYIeQGLQqUBQrYpo9acVEqxaTjubUCqHxlXlxcvL+7t4qZ9tOtv6WMZKqgCqbOzWVtKqCevgK4jlcXSocNqSQPWCekA7FrBUnOytzuj59qjNHBhM1zi3jYib5Qa9Zd7FVJQswaGsR+BOtvHjQUhh+vLptakZenYRzn5UhEXu9k5tO8wKZGCwBAoW12jxt8lTIziM7zzFhkwjxo8P5VDe/O4B/0Bk0zOm06AkqQhFq0zN3uZSgy7bC5opY4cZ273RpARLaNBE/7re1Ka67Ht8xxmqBiu32Re5B9c3Pj7/fgu8zOzjMA9H0PGt0S9n3P1FPFVeuGDsjHfHPOAbnNa+Wc07KmlJwGsXRTkdoWNihFVgBo81SenTZclt9nrDXvdV1FIfttFicIlmHYY0VriBS+oL7vfZ5ETVNKD4fTsiwcwziOFxcXrnzbdZ0Dfkx0XdcffvgdVmAYETW2vWwqVTUZ6yDQ4XDggA3arnVmtz0Ox6u0W9QQJh49ezvO3frpdGr4Fqn4e0ScDkd/BNfX18MwqKpvFuc/FZEnT5589dVXp9Pp1atXV1fXd4e7EMtUjEnRXfI7r3V4yT/Q7eoyTW3vlMkWMO/AuNX97rvvfvOb3/zJn/zJZ5999g//8A9ffvmlL4wvvvjiiy++SCkdj8erq6tp8zm2YUaxTUj6qMK9qWdvUJEwTdNuGJzUwdNpl46+vr5+eHj47W9/+6tf/erDhw9+Q8Zx+J//5/9dCEV7fht2P94m5WSgKh19ZHUB4M2bNymlw+Hw9u3bX/ziF4fDwR/Z//jnfxVjPBwOIYYnT544U/bV1dWnDs5q+Gf/f475fvoh2zO3R4HNOblqR+Vl/r2h//ncNoCfWKd6Pj3csVudMPaw3n+TkWKMwcrSKqPYVE5sTaXBGyBc3z6bFnl/fzcMQxguVWXFeFrTeOFLBWgYcpbDcQ5Ml7fPZF1U9Shqdfgq9N0oIlMdSBIREMPSRmcAFFFdFhE5ngKg5nW1pDHG4XLfD4MLfCHTaZqaq7AKKlAncp+X+/v7vu9vbm7evXuXc8GHzNM0xj6lZALDMHitcRgGCujWynepK731wy7Gfhg457zmlFI2Q+aIyGaYsxK54r2pakpNMYSYIwABUEp5WZa+L4wrZgaGDKV4TBwIyLIZGQH5BGNKSRyjyZ6HEBCaIqiNXU9VtbEtSgQ2KDaUmQ2QENWIEAjYzMCEmVyQy62nqhYqQy188/5paV2f3Ny8+P53T5482e/3h/uHq+vru7u7QJxSAonv37579sVPvVWXUuJYTCGAiaorWzIzERIzQJdzVl0ALIRIRNM0LcuUc/YqjsPTuz6IntvNbbXjptzicULbSFlygRlbDausVFLNhJld4nQYhhDI+tizo0mphUr+ycfjsXYJYwMgrilFdksb0KFTZkwcQ+lQY+FeLMgWC5DWFdDK+vWngyXNQET04p8YEZlCU6dv9BH+HkSbpqnvu9DFyAEsr+tKBoqAwY7z5Lfdv5cRUW2Zpvl0SklCCDlBSqnrhpvLq92+yzmVuTrGPkYPsNBIROZlAgBiiBzcHwzdPqWUlpQgxxi7LhATQpjz1MxrKbb6uo2dx+slcFGzrJ5XmFkXIxHlpMwMIT7kBwDwwXnJ2Xt8fj7Hw6E9a/fGHAJQWJYJA3ccoOZCgZSZnR1dVQ2RArcpYSvcSuUsy/MFcQ1pfVyo7rour+uyLOM4rpLHcfRKwTQfmV1cj0IIqCApm4gh9n2vUCjeERTRGNErrn4+zMwUDTVnEZVVhaM35tCyx+tARFm8ohyaC2TmeZ7L2i5LnenxfB5UueL7+/tnz551Xbh/f++27u7urt/t9/u9Grx//96wdcasArGAiMhQVcBk6GMrSahqF9ir/VoYXc3rBB/5UURkA0JStHVdx76/u7sru0AdPKpmFogjBzTLKZlq5BACYw8G4lRLQ9+LyPFwCNztx/E+JUYc+15zjsxpWQrVhtMD5KyOEffSLJ4LfaqFsmyaJmaclhlQ+r7/9ttv/+Iv/k1z0r7FindAZuYkLpqGIYRsuq6riMYQvDja7nnf98zoobmqamW8cTuZJYPalhWJiKyONpF3WSvEEYqcITlgAytyFep8tpk1sE3rRgIAFC5jZ4y1Sjpa/49I1XKWlBwnbUA4LeuSMjN3wxi6XkQejie3GMdpdq6F0IWcs+b04cOH58+eeqx5d3d3PB52u92yLMMwoJWpyrZrUkrEDa8onmp2XaepJAk5ZyfzrQFaARS5pK6/djweLy8vW3Lide6u63a7naQ8TRNxF4ceEP2rnzx5kpJyjKGLRKRuYtQwcCVNR2aO3TCO47wup9PJ29jjOHZdp6uklNAghPDs6dNm2z1vKWprRCISuujgz3EYri4vuxiRQTXPcy7ZbOScnTVh8WoCFgyFrOsKQBfjzi/wOC/ruo7juOuHNjvBSKHvUkpOJhNjDMzruuaUJGfPzj0zPB2PNzc3dx8+iMgffvPN3/zN37x6+TIUypBOVZd17ULY7y9TSvf398+ff3l3d3e8f9jvY9+NKjpPKwfuum6eJgTwNk4BHve9GO6vLodheP369dc/+YlPEB6Oxz948vXd3fuu625ublTzPJ8QbZ5PIZThclUT0fao+xgdB1geQ9XtIkBTy5UKmdyWMV1eXnJ1l1dXV8MwfPjw4Z//+Z//8R//0XfW5eXlv/t3/+7rr78ex9Fr9wBVSvXx0RKAZpqgNk8+Pb788kvPRX/2s5+5p3O1ig9v7v7lX/6lH/uH4+SD0W/evAkhTPN8eXn5cHdg5iGElFKgIOJExWdzapXAKlQOom1iYGbbWYiPjm3+0P7pHgGxwVZLud4/3++bVgxCCfPqsR3zBTvnBy3WB+9GV3bT4ozQEIGMYoiBgykaYAgdIovlEDp1flEEDhXwZohEBnb15KkDEBTgNItQOM4JMADCcc0EFnY7NDguebe71LTKulrl0Av39/cxxghOhtN1nQEiGC1LaucNrfeHEGM0VG/9hBgXzeu6Oo3A1dUVEaWUhmF49uxZ3/fLsszzybM9EcGI4zhaR7KmZSoIFv8WqcfpdOJI8pj1zxdNm05DxNKLR37UlN9k4XUepTLUZpf7GCoIotQRqWKsVR/hRtrfVGD56B7F+WY/XUYFcmrljhERUgAzcKSHmTkdCZBi423cqpejIRKD1bwTER3ij0wUg5guKXnljBABUESgjE1nVe3PTAWPbppq4MDehXSKgHUtkWhzJ83XruvaMKyqCmU0rZQFN+WWWldznANBK7eA11rxTPD/kZnQwt1cypNY2b64zgWKSAOiBGIG3EI5SyLu9CZtl7XdLmU7tSfYvjqXXyFwBPlmbODHDur7WNYPUAjEXU8GRjinbWp0xtz7sJdmgBiIgnMwe/ztTTNmjozeLc1VL7ZaqPJRgTtfkLXkl9sDjX3nY9Nbg2tmh8OhLdryRJhjjJWgffGM1NkG+xBds8+zRB/b8ttOFUC1/TQAG8c9cGHd0SwOKK9JpuN4CuJfEYywDj88xgxgQaI31FO70Y6NYWZWKjBrNTcIqgrEAWlrRlNKRdygzEJgVa5peZHj8k0rfOi8SfF8boV8o658RGQOSDav2/pOWz/q2J4YIzOBtkZWQUp4H1wBfAEj4vYziglFNFMqzWQgcvInMTFQAa0EU+AgwSJeXUYVsCBMbPOZfqijQQx8psiDYz+IQc4EI84jlBv6tnk+2BStW4/eC/NejoG6W8+NstLiw/ZzjJEIQKXwz9YKrhvtrYs1b13GM62CgPtvMKRhGPquY6bmEVIqUHJEJHxE2GVW+KHbPcl1vyCilWAdwNE4jsrBR5FK20ftSlteISLu+fETg4+1pVw/p65qIjFlYKmitm4BUkqXl5fmUri+OD2PEYm1FD1Nk5eZ/bbf3NwQkaTVT09ETERE26xk+3bvVqzT7K/knLUS+w7DoIViLXurh4gcauJlV798N1ChEGRjFyJSaMv+HPQgAiEzq4mZYYFNnkF6aoW+qTkUFcs5F27A6hd8QQZ2eCqjohJ5kZ6kIA996DYEOk6nEKh5B/+iEMLd3Z0D7fxoI7CvX73f7Xb7/Z6ZfXDi/fv3IvKTn/wEauNOKoc6AOz3+xCCBx4OvvLj6urKb5FX6H7605++f/9eRYZdD7X7Jynd39+HEC4vL92leiH/4eHBb/Iyn5ZpcRYgIqJGA24GyN999x0RPf382cXFhdcOlnUax9FtvI8MNZybt+maZ2xPfz1NREThTMbvvUXnb+3HwfsP/rJm6ULwD3x4ePj222+dXTTn/O///b9vACpfkI4xzjkD/EhBvYX4WxsCTU3sk8Nbo/6LbUBiGAZSfvv27ZIWZ4jq+/50Ovz617/+4qsv21qiypEAUJRttgawbZDt9mwbROXHuwFEH8+yQg0k2s+2MSzbH2hjOj7NiLafZp/cIrWCCmqnqFUzBVUTZFWVBkuxbJXmw6w2vgEBYZpXRVBDBadBQQAQ4I5oQ9PiNCHgFtw0QgAwBSREKLudvFziUXJydrQzjJiQCb1iQhhALBMDEXDAoCTidOoCqMM4XFzuxq5nRtVc2SoV1Iahv7m6jhxOD/OH5QBwLu2U22rA6JX7wl0QVABABfA8iGZmYOpzheQ0C4Slg1yg86Aez5NrbRmlVUwTUQBAVXAGcjvzpgXmAHAuJjnjOxiBGfwe0/8jr9QJVyfSIBIqgBhUIP2I1ckXhKKJovMVMjtfdmmgM7vdcfwAInqtiAA1iyrLmky0C3FJq1eDcuaG3WRmJQOAvuvMZF2naZoc0k3IfRdj6EyU8YiIzFE4qCohrTlpbaH6+BrWpth2t5xdV2REFyWrCDYrqCREIyQyAFEQRUZTU1TcNNranXTzZDWfbhEok7dJQLUSQFpRWvLSLJYsroX/iuBjFedRdWvQC+99KiiYKpz3LRo8DvgQ2ExTSqgE5goFqGqr5Iap8hycABzNH0LggoQJOWcoCvMaiToOwIEYiMhENYtXNwshrCmZs2xFQubAAYOPyLvpRxc6QizRfztHAJ8s9JqZo36VLWBARJ/qA4Au9F2Mno1giFopbvwXUyXGbmlw2wgKwsyKChs1A6otdRHJImLq5+Yc+T11AODADIRC4Y9E8JixAeo97LveE48Wb3mU4FTWSmxRCMgTXSBSE6s6D4zEzIVVyO8DaM65VPe9bF/ZqM5fWnCZbk6yJ0gppRBYqtBI8yz1nYLVgquqOlthSk6hMwyDa6+eljJGiYjbRny9WEAkNBf+o+0l12hAS/fMf0XNld0eL8vzGJ9HUT46qVoklkPg1i+1Uvfa4JRUuPL5+ivEgGRIBqiiSTRpU9FWFU1YqYGrBT571seJHGJV2Gy310+4ZHH18M5P4EKxhYjIFELwvH6aJjTrNshP2FS43au1k3cahrN/dQ44VUNAwI1UAygClv4fSP0t/+Nti+3atpJV+AIEBXBigwrcRQNIPpIOTtsAhggUmNHJPR0ybgUHVWBgp9PJET4tdAshdLHYvePxeDqd/HpdbSPnnJ11x+25mTfM2z3BSkHRbKb/VspCRF7u8Vy0MW14+T+EkKrgABF1XdcyH3+PAa1rMoTIwSPjTdEN0Jm/wKjWhooCkXdeHTnmltWEiICsVXw8IdQ6X4EbqlaqeJJUUUle/gih83P2fHIbnLX01d+cUoohxBD6rvPZ/4Zl8Ftgqj7I5yW1wIxqQ+wy5pRSEuVhCCE6xXCalxhjFztZ05Prm+++/Y3XJZeU/NlZnZzuuk6ziEjf9ymlu7s7ItrtdoeHZE5kVLFYauZJLYWu6zqO4auvvjoepmx6mE7DMLx8+XIYuv1+74/PH822QkfUmupmZp/d3vpt8Zw8xrjb7UIIudb+5tM0r0vB3GdJy/Lqxct3794R0cXFxWefffbXf/Vvv/nmG58q1opZymsCNU8rq9rIo0PgvIO2Vg7kx0P/s09R9VP15ffZZ58ty/JwfNAXEkIYduOyTHd3d8+ef962v/4eMM9Hu7Wtiu17Pvrn9vWPgnvbdDC2v9X2hdVKH+L511sYsz0ZqBMC29Df/0NrjNg+37cGIiYVNC2DSI6lRDEn2WsQDDMGQIRsAIAuL06EAGgIAR89EDzztcC8ZssiYoCFuyz0fc8cSMmhMpKd34DKyOa2/k2EZIFjafdVVUgi6sE6GR4eHtZ1ffbsGRG9ffuWiK6urqAWa2OMV1dXMUbUh+PxOB1n39hm1RGG0Pf9xc0FMogYESmek0gPblzrxM6wE63S6+6copkRlTgSEQlduF5VMyEgmaXV7NzVbWsRNoxGVid5sD05M1PvqxlanaHx5evyk+UVVjNEDkTM0dttCpUgFBihcCKaAqhjx5mIAI0qPo+QzCwEVlU3eVdXV274hnGUJaEaUPQ0ehxHMUWDlBY3l14jYWZlc9MPoI59LIyr477rOqZAZ26l3DZP00NpQZL/M1eMeFuYqiqSqTBl+F/lWUFhmPEqdbHj/Em75Lwlqo9sDsbtuKTM/WAbOoL2jKhSfbe9V6KrGoIQ0Zkq8eyDi5NQ1XXNHhnXjfIo+newMhBalrQYgjKaEWjYjjsDEfl46eXl5ZhzFjCzJVlWSSmdULtYKsNQxoBK95TrxaoqVFAEM6uYw8sAIFsBtiLS4XAAAA4YQggx+uX4rt72TxCRiM0eKYJ5cF+KRsQKpcTrIa+Dj1v0sI0U0XBeF0B1enn39H67xcUkVUTEELSJcxCSl1yxnqCZqXo+AdaC4Efsb9R6UDnbJv1r60R99tTUTUW583jOEqWSfqqq5IyIMQQkNChtunMYWA2umRmUZKOuZ/U+IdRwartQq3HXWo1OqujTPB85Ns8qAEALPasvMKzGRBkpFuisMhIyG0iph1tht4W6+wDqK5sLMOeSUz2dTuzyiKKqGmPfwnSo7S9mRmwx+nl0WDag5/b0PUZpK2rrVrH0wZvnc6JPhAKoMyCsEiXl+WolVDhvUSgAPLe6RBRK6ynLmrRMmZfzZGbE0B4BbGIIADAEZi5uEsz7qm5hpci+1ombM6OeqWkAch0MB/JmU/bZHFVFIH8YooqQi2aCtggAKkZ0Gzq0C/Ryr0eEiOgBHFTuFADw2qpHP33fB0JEDEwenQ/DoMzLstzd3ZmZpFUqwz0wA+g0HYlLCFi2hpm7V6s1SKtAJhFJBdZXymYtRnQf4e67TfiggWUJzGLO2lyOlDNz1yxwyU9NBQCZmBgDe2IJjVbY154reIAgoqTskeU2rnJCOkTsxwJAzznrPOWcj8djSsu4H1TVkyK/Cg+IW6mC6ljFsizTNI39zvcmIhZSclX/hHb/oXIEe6XM9bmabBYArOt6OBz8dWZ+9+6db+p3795RwHldHQ53sdsNw3A8Ht+/f//ss6cpJYcBHw6HynxfdkFsGVFdMr/8l3/58ssvL7rL0+n08s2b29vbdV0NcX+52w0jMzu4v7QTVb3FUd1BFcxC/PDhQ+swp5QO9w93D/fTNL18/VoqYatCyf8ZCc2Grv/zP//zP/zDP3TO0NPp5P2Hh4eH4/FoZuM47nY79+8x/jiARx9R7p6NEv8YJgJql9UtrS82LcKp4auvvhITsfzhwwcAcN3iaZouLi78W0rDi1RVgT4GGrX4oVmqttrNrPs9gB+txOK4OeFm05qp2f7X5sXz90oVzTCzZqCtFuh1Yx/AQxExoHNc7bGlmoUQAEF9jLCVjcyp68r1OvWnmhohYC07EAK4iCYAmkmGyszYHowB5LX0D8GAQdEwMJeyREUXUOwDYVhXb+zyRiXHdVC0tWyGYRAEERHTXX9xOp2Ox2PXdXhprRjWheg+qcEPLi4uROR3p+9RDbRI7xKgEXmYK5ZTElVdsrPscwghxi7nvMyrqsbeu5PuUWj7nNqDcRvBzIikCiqg4Gx3GanMfjFFAnKFHXKhUfPnoe2WFbRO9XUubuQn7LWBtj6KM1AEI6ZI/ib1KhERAZiRAaKhGACqgKqN42AmitIcZI05zDkZVPXi4iIO/XQ4mvM9iRDiuix5Wfv9xYQExNmwcbr59GKSvKR1vTteXOy8EjBNRWDBzLo+9kPRHFE9j+Y0Ek8iQjhvm1RVw0oIV92AqjoVNrTJrQ3rP2ywBCESIzABIxAYmrb5Iy3EbeCtFo8YvVDst7SeHrUsS7KZAQIYe1DDRV7LFNGoKBYiKOKjxB1MobFzqio4/TF8XNhYlqXve44dI0haVZIRAJERGqE3aso1moEaMxkzIOZKSaRq6ypMQWoUS1q8ZlulrhzmD4WIEHNehSm67C0DA4GREdFpPgKAAgMah7rQzNZ5YmYfFfKGKRqmlNKamHk/7gAgzUnWpDGzS2ExZcC8Ji8YM3MXoqoSIKh5R6KEC2CGJmqghlbCdq83Fb4BQETOKgjgxFkiAkgFmocGZSJcecs/XeNCrlN3LW7W2vnZ7/eq6nAvNJTkVSjEGJRKaaTwHgAjYk45BkJVy2KqIcZIrIQE6PsB4Lya3W6EELSocXi7jJ07vDU8W4gJAB5w9H2vtgrmFoDmlFta1dxhztkV8LzObJVWHsBAE4IBP2qwMKNkwVrPh00eapvGY/EfnguIGqKIOCmhiJj31pzTyCl6/C4ZkAETBSRAcpovU/NenGWxLCDqCR4DBg7UgYMfiLgVaAr2g0oyo1r2r1RGKSJAIK5Ttr7Ot2rT1SYQIqZ18VpsSolzYmZVk5x3u10g8rIvForVs3k/t/nrp5mau0wFQ3QOBTVCLf6S9Kx961B99YlqBgIwhTJhBUQGlkQ8hfCmvus4W+32VNIjAwCmoKreoQVwIn8fHMeskBUUnBKaEMiQswhy7GPvgHtdVy/M+wBToC2j8bltIlV1J8bo/aaHh4x0Lj1ALXh3FfCJdXLJK2WXV3vPOtpN8wfaljQA+ITxNqdCRFc0c4RJyioiKmI5AbnBBDUBtcjRiFG9WHCu36eUASB4kBpQRHIFFnodAQAESg/THExoEGLB+pNBXtac11dvXsYYLi8vb29vh2Gc5/lwOLx/v+73e0SKMbSp9JRSznK4PzKvfd9fXFyM42hm8zz7Dm2VDr9Mx176EDBuKO29JnI8Hm9vb32QxnGwz58/f/X2zcXlZRx65jjPMwhcXl4ShrRKCF3OBzPc7faHw2lZ0jQtACBVwEFTXpz6JouC/dEf/dF+vw9ddBXny8vLV29exxg/fPiAN3ZxcREiAwASLOs8z/M3f/iNPzhHAaWULBsZzNP86sXLl69fvXv3blmWruv2lxfjOH52extj7IYC+Gnr5Obq6vryarfbrev69u1bJ3xrNA/+uD1fdde8rut2qrUdoQKHWiBed+iPvLmtMduUADyMXE9pt9tRoIuLCz+fEMg7YO1JbWN9H7WBTbzXwpIWS9jj40fPZ1tGbE6h/N04zd3qlgobbN4JZlAwBtLKCtVy+3SA8+VY8yCtq4Co7ipLLV8BDCBw8IIqIOqZ1cP/vyApnO5EQdFlRtQKwtJQsZwnqwKqfnLRlSbwDBUL7mKTrmaWUs45AzBWxFsNZ5tXBkThSM442Q39Il4wtpTS7e1t03i7urpS1d/97neMBcbnmXRKKS96fDj0Ia5ay3tG2+fXlkirTlX0Timq8aYBtEUWSiHm99pAbCGsN61UDFAQC/MmMxPW0ul5KWxsnwcohVuwctsZtHpm+11w/kQDDySJiDmGAGyYTPyBUWBUQwBWQPOmdyneeNRVNGPrJ7tT7CMvafUs63j/kFK66IY1WeTgPADdbi8iEKAP0beriKiYM0sw8+VF3/yENb7eGL36wpVtqV3ylhMD4fw4PlpJ+Og43y5PqRBRNVshZpGW7tsmN2v7rT1ufxtUuFeMsesQM3nNeesXrQ3c1MCxpu++Zs672swA2M03Ipo6Y0k9Ueb8+7GA/oYusHEwzQymbIe1KFVrpYch1Wx2Os2GAMYJNCcwv9cIImL8KOp1qydpzU4WW6WXENEM05rJA4b6vIwMEX1nef3GLzxLke9pt5EK7EdOp5OPPXR9H0KYdKo3BFW1CI7Vz3cI+9m2VgOqqobAgcGwTO8CmpmoeFKKW8vo52YYNk38ev/ho5+xhv4xRpnVRCEAIlIFAQamFgF42dCIAFxU4fzJZ5C3gYooEW6ipbIqmFuGWYmOzMxElZm9r+iQADNJKXEMVtFN7mPaHfbTlnyGxQMo6pnkJ9fc20ct2yWX4AYBwMhMLWNkZuSACAagTCR1BOm8Pfxs1ZyuCAwIz+QSni2r6nQ8MbOkjAb6GEzi5tND9pTW7UNpmzpXDhk/2u7zrleLFx9593ZdhS8T6w0vyaEXehzcMq+FbNGakisUeigRmed1WRYM3Pd9CBHVZpsJIOfkw13DMMRYcN6qqpbb3mwPZevjy2b21Uq4pU1yy+OHgEItkJcGGaETLRRzVLg9waUwalL3yER8FFv4RxmV4atGlm813PEgzO1tqaaLdMTtM1oBrmwNEdy2mHJuvPgtym8n02wsEfk5+4ttulQqKJ8KmfLagHZS5+Nb9E8IIQSqvC7MLL5yEVolxXWQ53nOLjNK5IFFuZ0FyljKOCKSKRHRrh+a3TaVElXU0hLH4IQzXdddXl5iQDNZ0nI4HIhoHEcAGIZht9t5WN/cZSgjE0bA65pTSqfTyS8KKt9O3ebnMe0Qwtu3b7HQFolnAn4fvM3uVfDdbtf3/eXl5fv7u8Ph8MX1lZel3759m1JyOiOoIAJmdi3e0+kkeTUzL2uqaiqoIkOmy64/Ho9X8VoBVPVf/uVf3t99+OqL5z/56ovdODgia11Xz9+Ox+N//I//0dMbbyj50w9IBHR1cfns+ec///nPb25uxnF031GWygYK79e+niav7vs/d7udL9QXL170fX97e+sQIx9d4BiS/jiAZ7v4t0b+40mpx8fWmPg/u64TkSTJCytTntc1P3w4Xl5f5ZwZAyIS0Bbn5gduyHlakgyblMBXYF5/RB/A3/BRnFkux87O2jbQHaCznZHNtcO5hvj46ooH+fj+bHKARy7SHai5iiehlh615wlay5bW7DAiIxQNyFYkQtMs6VM1IDKQ0ks/G/+i6K4szWmpijIMw34TMqKIoQgimCVDEskprYaWQIlx7IdV8jD0MYbj8ZjSOk2n0+n0+vXrQIGZwYwQfd43LzpPE22q9a1xzz5ur47nKyU0DzXcTnl6ilSoSN2AWlM4N/NiRVsT4nx2lS7fPFUgQWDTIh5cW/zQHn87mRL6F8K42uk8E8pBfQzlSaNDjAgQLAOsamwu1ONEJQZqBAJamjgqICkbNom3c17r7t59sBuXu3fvtx56mebT6XRxc2tmoc4qtPNvket+v2e0dT7N06RJQwhkCpJf/O4HVV2XlCr5MQAogksRFatq0pYmc+E/bnfJ99vQR0fb1sKmeeZqNfP0KNVUTZQjqwrio/tMdeJwu53qjS2Qg7aftzmMn4MXtLTgdylEMBNP4cwFgwBbMFq+t1wGgdGPzTARAPZ9DwDLsoCGLkQOASTnLCklRSDHzCCYmVPAnE4zMoHxaqJChmDIAmqqHXMsMxi1nAaY6+iemTGXxQYOZBI1KRhoIvIawDDscs5ZEwDkIhG6ppT24+Dt7+Yn0pIOh0PAoKqB2MtanhgAkWRthrJNfEIFUOmGtpKZgTBp9tJmu/ltERIRVkpNzxMAEGshP6VUuAuIkKw+oHNd0Ks+irllAm1hQA1JnS6NDVXVaVtVVByKAY6SAwBBg4obRlBkhIAEKjlnrKA+M9MCevGla2aWDXy8clWJkXLOFEzVVPw8vXJznk1vS84tkpmM3bi1QqnyWMdIZlWRoB71NlK9XlRVn7qqNr0Uq+uKdSZKaFuADNpYGRFplT41aSQfvlsdy3Huy8/znHN2IlGp4HvaQH1g87HNeG4tYYsRoe0Z22prkFl5v6qqZq+Vegjiq8snFz30x5p/Erl2czkLx/o7b0SlFARoPBB6Rqj7r4cQPvLE6HPnVE6rPDk8Uyj7WISbgWwKCEiYXbzLe7XmJBzqPBAGpmCismk/Qp2oNHMlGSIfOA9cJAhcjNYTy60pcxx/m6JmhmVZhEoCKSII2jpI2+1mIo4IAjzHPUREAQEgL2u7J1BpAzxC9a/jDQ+mmbXZVqosC+CoLXapgWIBWljDzGCVOs0bw6YmutiCNeMlh9+Y5ZwDR1X12J60JIfMjPKJhQew2s32wQtE7Pv+6uoq9OHnP//T777/7rvvvnv58iUAXFxcfPnll19++aWvZ08IN7Ydbm9vT6fZB6Y9YRiGwXOGZuushsLruj58uHPL2XWdo4McY+OqXiEE11jwe/v8+fPvf/jBiYMuLi4cn4aIFxcX7W2NtfNwOEynwxhDCIERRGSVLCJI7GPWq2QfZr24uHj34f3V1dU333wjaXnx4sXLly9fv37dSvIxxmfPnnk5zLkZxnEchmGIXVpSwWGarTm9fv3akb3Xtzcikjb6cb6bnj9//uHDh+Px6KienPPd3V3Tm2uOuHgBJkJ+BJSsh7YGZN15DrX7eGauHm0suHkZ/+eu26/ramiXl5f7/f70YQIwqVPXFM8T7cV+ErY4BzZVjI+ChPaz2Y+fzzbesE0UTpvw3TbhQiuDaiVSaF/RVhRsK8JmVlUCt58PVMC051NBBIBUke0Chi4g5LcYjBQLz1BNMMyswpmpuAkA/9tE8ZOQxgxEzIgQRVVzUlUNp9OsAimZKQFyoMjEIXSMxFiA3CYKpJoJSVVUQR4Ox0UkdCH03cXV5f5yN/S79+/f3x3fz8fTJHZ8OPZ9//nTL5ZpZuZlWhFYxCLFi5sxPHn27bffOlKIiBHJWUdi3yXJ4vBRDkQkHjgikNNoDFFE1nyucPiNUAFiL2H29fGzqmZZiZgJuWKNiaM5kz0aiiATF9Z2Nh/6qs3+akkREKngHQDL7CqaQlHfxPIbigAG5LLdgdiYmdkAFJCQGYurBDJUKrKckmVF9D0GCChmllVMx35AsxjjMs3DMFzudjEGVEvLKilrSFkh5ckgIVoI5IpkiEgUGMPQd2i0G3oQNQbPAXzxTdN0/3AykxhL4pdT8egCBkDe8m4JJTtTjwFiKYyBqphqFpFcV7apqoGoKhYafsWSKNdIsVS2AEjReFtAokrs3XpN4DxoIl0c7JOsWlXddbVtvwn9gxWVNzM1ICQiLQENOzDAf0s0iUjBQ5ft49OAgIBdDPM8H9YlhbgfhxgoL+thmYRRCRUsgK7ZOlBRU0l+1WIqIlmyGBoKmhppDmSdqxCQmZiiSCqhtp0nwJjZJ3Y8XAAEIsclkGoG4FZuIAABkzVJSivxMAwxdCqWNQEgMsXQn06nnDWGvh+MYiAA4k5Mgc/m0le4u0Azf6KmmtspEfG6rmoZjCKjUWQElxoPyMgYQjTURARc53jVRERSFkmAGoKTsxJwMKNWs6/EJEzBMAUgYuCKjC+DN2gCXheAoiPGIQgIIQARqCEiAxIwlLaEh8/qGBEXxeuGwWd3UMWqWJ4ahsCKBElEIIkBBiygXM9SHYygiG52zYWlYsfi9KFUyItDpNhx7DgrqOqak4h4dUCsQp4VwNmZTSOTaSYwQuOymM2IGrEvnnspPntjPgO0NeUlZnJMimggBhRjBABX6vBKHhG0RM77Eh7/IbCqghEzhBAoBmb2OWlHInmOlE0hZwwcmRXBsiQVNYGiYY0AQtgI5rWAI7UgJ/0PU4yh72KfcwZbJJtABoDT4eRQbOee4i6SYUqpCxFU13XxWo+ZpSSqeRz3iKuzzROSA6YQcUsbUPwiFmUuU0QELK1aqhvQF4MZFTI+V1yzLEbIxkaIasnE9dHF/WY2KRXqsoCJXID80RNBRKvICu+keXE9VuVyj1ad7BgA1kmy5WVZhGhd0zzPjBQidSFKKiQZLW0gRzuyIwTLfANH4kpc695Qs6zr6hSuTHA4HHa73W6386h0mibf7MMwOCmZZ4ncRIiDrZKZlWIkkJTSus5KjMAOlDLEAMTEYGQm3nYWEUUoYaJoloxMKaW8JhAFRVDNklU1pRW11BkNW0bGUOe7zCxJdlgUM7948YKI/uRP/iSE8PDw8MMPP/zyl7/85S9/+W//7b9tiagfqiqix+MRAPb7PYDO8+oRpEuJt5gBatFXRLpxyKZG2I1DHHoFmNMqYMMwvHnz+ptvvrm7u5vWZVoXInr69Olvvv/dNB0vLy9vb693u53mFYE54P3d4epyD0DLMg3Dbhi6uzs5HA7dzbVzeSuCiGSRgERE+93+MJ1SSr/74Yfd7mKZ5levXr1/+8YkdTFcXFz86Z/+6fX1tSOyrMKT2ml7Ij1NU6SwrfI2/NK0zES07zpkJgDXUpnX1ecjn91ef/HFF4T24nffT2m+6uLN9ZNpmtZpSWoYKAROKa3rAiHoudV6PurcgoG2UlqhcPpRRqDWYoJNSR6R53URkb7vLi4uOOA6L90Q98M4n6a0rB1GJFLKVjEdXR/PY2SbCJ7IE8xSAGk5iANNG2Fa+9up0AhQHZddvAc2S9I+2UpqWkdrtMo9AhgiI0plu9JNPvBpUuElxZa3mJlPEBESEGZZAQCBy1XUdKbDaKagAqZUwT8KMK/JgBQIUBGNQIukJYB+gl726kYV41QgMYNwefGEiO7e3t3rgTkKiSQhECSTNWHAGOMQBmYuFJexU5AQut3uIvYhm6zzhCg//O57M4vEl7uL9bRqsq4bbve39/bw4sX3JtjvRhAg7FQgqewvLg8KD8fp8jKEMWRVJnqYTiPvvGOVRTl0l9c3JmYAh8NhTanrhhACc63kYAwcEckMcvLOjNeofI6Ku87JsJr+HC3LDIwBCZl9tMsMshiZAFMMMYTyuoqqKHP0wUY3F+azhmqBIxatHudqKVbafPQRkFADA+UsZgSB0TASiVlOGICBApuqxDggmpjmLMgUOm8TF5aS+TSZ5sPd3VdfPv/b//o3nz95tj7MTEEtcQjHw/vpdHN9c/Xu7m6/3x+P0zD2VxfXlkVTZpOIOB1P4zj2Q/RCLICOYz+O/c9+9rN379794h/+EQCur28tGRFB1mWZ0DuJTIygCGQoZppXYIoUsrNOqTAAd52qE0yV/VyQ/ogBWXICgMBEBus0z4jEI4IAkSE5e6vfU5GSMDBSztlAcs6asioslELViJhrBavruiSroSGDmbpAAyKI6v3DSSSFEPb7fQjdmnNK4kU+ZCVmUhDJSbKIEGMA9sEhVQNz8gcBg/U0R+ZujJplmU8SCAA4kgAjQozd0Ec2ScuU0wwAS1pIOMR+HIdklgWZYx+DLcex7yKTmaZlRTLCAKjTdPL6mZmtkgVs4CFGuj/cxxgx4LquaU7IVNcpd30YYw+Om1xXX9kOi+y6Yb/fF2VK5GG374ZxWRIwK7Pju+Z14UCq6mAwESEC0q1CiiIykCEhMiBDCLSnXoRyVgQFFWJkZgzBBMwkr7OAiIJo8gpoz6MKkAGHEAKGSIgCYCkt4zguy4zAZpamSRRP06JKYRiA1JQAdBzHu3fvvvzqeZJECEiKlswr74ZpXvshZlHVzIjEzMS1yALzfPJZFZEsILGLUTOCMpjWaAPJEJgipqyhiw/3xw+HibhTwGUW5iiqRMgcAMjr/WYCZi7uN58mokKj8f793eefP91f7b2pMk2nw+kkChg7FEtSHFEjRSAEJCHGnNY49Lu+jy5JoTo9HLuuQyTiytCHVgIi8oEhVbPae0AA2I3jPM+Y4fmzL371y18R8LLMSdb95W5aJ0nikw3Oi9B1HTIjERAZsiJaGZHg07zeHU997PbDDpEM1ruHIzIlMY4BiA1wSSKmBOhPvzo5QiRTzJVd/nRa9hcXzGwZclpev3p/dSUXV5dJbXo4HI9HVR3GHSKu63p1ccUOeuJARJ6RMxapxH7cxV5zzmsWRCXGNQsSc3fu+yc1AHOuYzNDLMGf+0yvUvvdatBbAzHTwERcdNTQDIxkEQQwMTExBJ8SQiBkylkADACZg8v5tc93kBgimkHOPjba5WX1iYt1mh12knNeiHxv+sph5ulw9HZlYCbq+q5f5nSxu0jrMnBAEzQFUwKIjOpFLJecS+nicrcL/bpMIkbo6lezrCsApGVN64pofYgAME9rvxsf7j5oTk+ePMEYNIdAKCI5rYGQhx4AnP6GiOLQianThy+aJK1AGEJIBvM8u7S8X/g6z5YF1ObTMcZIMajZlKesQhiY+X56YCREpsABmZHWdV1hYuo0r/O6ZsvuNcS0jg9hiFFV0asLVMJcVzl4+/rt5eXlv/0f/+2rV69+8Ytf/K//r//1yy+//Oabb25vb6G0Z4EDB2fPVQHUPjD03el0+nB42O12KgKiy1xy4L4bQuxCtHF34U/qw92DJ0LzkqZ5ffX67d/9/f/HzByAFGOMQ8x5/cV/+/vf/u67L778fBz2gZEpWhYM+P7uXYz95X53Wk7v3r1Zc/78i2eROTDnnJfDwQC8D3b3cE8YQt/93d/9XTf011e3KaU/+aOfffX1FyGQVxnc5bmtzjmvKXmT1iNgkcLfCpABAORjUG7XxXVd5zl1XUccGAGIuhCOdx+ur3Yy39+9WCytD69eXoVht7t6MvTv53RQZUU1W3UVWICjAZnx9rPd4BbmZPiI/Ue1dUgfH4X/79w0K2JAYqpgSTIRPH369HQ6vf3wlojmw/F0/3B7cXM6nfqexq6/u7vfXVwUhqjtoG2lVK/jPeC8eaV87pgnUXVeUEJGAkKmwAxoZZLWqmVNKo8Awx5fAEgqvS1vGiKCc1qLUyrXgsA52N6g6sspGQFqEUiJDOD0K1lS8tKkmAIIAmNgoiCmlmASHMIQYLGcCBIzZsQ1i3IvXvk3iLayrJ0uijThqGeakHIC3pz3jkUg4m6MiAGBVWxd8+k4L8eTZonU930fKRARI6GBc7CBmaIyd174CSHshjHpsua8Lss4dKfTKc0JADviBHb8cDjen7gL07Q4wAaZicwZSFJKYkocQhc5BstZTOe04srIUGr2DnTMCgCXl5chhBBclLeUe1Wt3/UOgvIt4YtBK/t4WXYmbfykGwc4D6RiQASAULNS8AoKIgKYgxmMgErf5vFifjQhauj8qTWEAmwkJ1gzPTQo5NImXn8CzSKJGAHBh7cClnNYpwlQzVsHRDFyH2PO6xA7r3RnWebltKxTyD2Yvn79ut+NfTf03CHb9eXV8XA/nQ6fP39ORFlWl0EBAEMTkd/97ncAoJqdRyzn47ymdV72Q3/ermYmmrQUS8gAEMghxeBseWfqD3xcmNTNTA8helPSd13hgAEC+rgb1/ZbLQychQs2pS90HLBtQH4NA9DHuK7sMGLDAmL2GMOsEKc20R8zMZdPAiBAA3W2dQCgQCGw94cIPE80VbCMq9Ofr3MXkNCG3djHsCyDmBoGDjFiUGMOfR+ov+ojWc55mmafN+BKkY6VNtGzskIJMg6uZkWGkUPta2GDDjcMKDPHEJ49e+7aKO/evXPq+XEch3FQgX4AM0hZfHpVwebT1MWwrqvjsLvOeYc0pTQMXSX1Yi8kGIgoEKIRBTY0Zh9nkiyqIQR0WK+hoRKio3JykkBEHA1SzjlLdlC7E2isaybsiCJTz8x9PyYpIBwEgcr/4K1/ICNDA4+93Kif68lqBmoCRR5h7IcCF2ZGgKwCgqqKomDGVe/PDFQlZ0UKOWeHBauCA+pFLOfVaNPxAwBUAliTy9Oie+K+GxDRm+8hcLkK5GxJFcRMshARe13ZsxREYkKZEYwJEYoOAwAYIXfRCr2AARW4ipppWnBzVLunaFTZA6Ctf12KgDpU8SCqMxVtyfk2MmNnyu3HgRwyZLbkdVmWlBMbu7hS+SgA0jLrkVMycwlXQAxmlpVU1RQR2AEYITJRQKRxv5/nJZqX6jl23TjsmLnvckP7uHGUXNhIfKZla1UQEYE8UGbXKKRS6gZ0thBtpuNsSRq1koEB1ZjDQijMY2iebAIgIaLkmlf5E7PyszqJZ7VtLSZoJHJYwVFe0b/7cCSiBrDxcYVxHA+HQ4lUKrdMgadnMy1EeaCW0mJpXWa4ubkqPJC13pnEVHXcDynruhzNbByGvu/X03Ge5z4EIorE0nWR0KknlxintYBGvL/nD3qtdB9WZ1pK5d7ntxkJMauRZnfBWW3sB628Z4EoUsTIZvZw98HMUEXNcpZVstoKANe3T4iIgAEgQuGTRdP5OJk5wLXCRAycjxsq7NDMnJJO1SdqwG/4NE1v3ryJMf7FX/xFCOG77777L//lvxDRV1999dVXXzmxZm1zKQISY9cHgF2M8dWrV+M4XlxcubzXNE2n03x/OHy4v3MhYaxAf58kvry8HHfjv/vf/Pvnz5/vdjs3sIfDPQV89/5NSun169en07frujbtZDMbu34YBjTIOXddl9JFmqYWxGtBXSIzP9wfvr7++q/+6q+ePXvGHN++fdsPw+Fw6PsIlWecKk+3L7aP8O7l07xVyG2Fly6WN51MlOCM02MCMHl4/26d7mU/jsw/+fzJ//Bv/oevnn/9i7//5zwv67rmtGYzBImRue+OkzhiFnygcePf/bsUzq8aOvHUj2BsGtSqnb+ZCUDkUPdFwZUNsUPEaZpkTeZUeDWEDUTrdrRms+XFqTUq9shTSjCLxqBQWXW8ZI6oKD+WnwA+Slu2n9++DhueB4owVFnGANvQ/5E1supKjDwCL911EyKMMTDztC6BCSEYQlYwy4BEHFXZwPnDHYeK5lz+RALsfU06Kw2c2wuK4BgNcl9uUrIYBQRFouBYTKkHVARCqEwv4EmQ8y2Qoaq3UgrjmOpaYK9lLRIxE6lIXmVdFjaRbDEWb9qefYO6iogKgBECEIacBQ0LZHFD+uEiHSFEACjwe1AsS+2sy/Op2/ixJ/w4MK0bbPvP9uB9MSA++nt7bD+2Xd3jN9UGUPuQNnwDtq5riAxe4XDqAyYRYefLQAqxDwAA+fLy0kRjjDmLGGjK8zyfTqdh3AMAM/chgujd3Z35dETO0zR9bpJTcuKzJc1Z0jTPrvcRQnRQnQjO89zFcRiGtm89s9oA6RgcjIF2fo5OjuHDio/vzCOB8c1DgQ3Qv+0oRDSnIfdbZHKenSDyprmvN+/bep7W6h+4QQ09CpLqmB0AxBA9690+ZVV1iZC2q7FOnoSCKDBmRifQRDMRNmAAU3EUewww9N0w9sRrSikbALJaG492CVvxU62xC5nZzc2Nd9jdJTBXUL4V6icVACAENrWcBSu9oGQzRcJAyITBM5/aDmYA8Ir+0O9ijCkVfnHv+Hvk4Rw+nokFYhMFtel4apmVWpn9cGgvABBgCKEPMRATVUTXY+OIPk6THXcX1Gxdk0h21HFOSphSyuwGAs0jkjwtxWoSqbpBqOPL5n0h8oaqQzXaA21nCz4bw5Qkg1rXdVxnEhDR010oAR4WLJgaE6SUCvpckZBMXFfbQAwMTK3AklEd7RaIGVFS0pz7GH2ioIV9ch6WBSsMMdVPbCYcZPWqT/QFjJVm0Sp/LhaZvEfTRFRgyj6PUWbimUiTnk6n0+mwG0ciMgSR5NwgHoX7liEiXxUAwEwAVmcVbNgNUPEPHnzbRqsSKwYJi8nSGKMIAJ6VB8t1icQYRXJK6aq/ACjRDxFRYbZV2PCvN1vqQTBtdDG3lraZZSc3bJhAPzcsAmmPG/SFivlHx/fVcmkwNs/iOMkff/tjyaqtNXOseTvD9n6qjJNm1qh1VPXy8tIz9hJkV8Td9eWNtU5mjLSQiUphmnpkUZ3TNqVEdQv4h7uUROeI2RhijJEQEX0W35sMwzCs6+pDq/6Lng36P0XOCJwQyB8KALpwZ4zR1GKMSSrO1u9MyikV5lBSJuYQ0AjXJCLiOpj+FWqCNcZotx3PlB7nB12z1qIEh4jOWUlEXdfN8/zhw4fdbufG84//+I9/8pOfvHnz5uXLly9evNjv99fX158/fdr2mi+VYQh93xOF0+n04sWL+/v7lKTruqurq2G3+4Obay/5+1AEE/MwRuLnz587Gk3WdJJDCOGLZ5/Ls8+Q4fPnT33RHh8ODSUva8LKuHU6HGET7Pq28rzL+28xxqvLm6urK2A6nU7H4xRCePr0aYh0ONxbRXu0GTyo0odYI5x2dWWzbKAm3GhsqZTWPMry2GwYhiGM9zI/PDxcf/nF//b/8L//sz/60//2D//8MJ3mZZnTqghJTVDMwCiLKKDX6oqqafPd7Shm8sc30MdHfehlpzM0LgHkStvqS/p4PDbRklrVreH+J1/W7gxiAUBRDTU+PbGP0obNpyhXFfb2Tv9BHxEcnz9ka6i3ob/UmRY/gfY5ucJA6+s1BFIDKrV/3ynEgZkAVYAJM6Jn2CjAYmTAGYIWagwMzIoBQU3OUmsG6uwzaGgqoEYKhD4MQOFwOAyxA7U+RoidiTCSE2tYLQkg6DYgtjp3teaUcslll2UZx3EIO1WbjjOAxBhj32eTxmxQb6V5F2+e53le53l2wKgbGkVxIGcJjkUcO1pZaM5xHhd2GmlPvXkFP3kqichWkr32cjaRZXN1W/sOG/fTfv3TVz5eTAhmUvhWndMYsQ13IiEDOUdkqUo1P1eDlZSSZhCR/TAgkWThQGpGROM4Ptzdm8d4YGAgkpdlSikhqEvkrOtKjFAlHM3sn/7pn3wkKATa7UeA0fmC9vu9md0fTvM8i5xSSjF0iGGZ582VlkrMdsWXNb6ZvoUfq/pv74yIpARuAYHMBwP+v4z9WY8tSZImiMmiamZncfe7xJaRS1V2dy1d1ct0T2OGD3zgA+eFDwTfCcyfI0FgSIAEweHbgAP0cKZBNrunp6q7qrIqs7IyMyIy4sbd3M9mpqoiwgdR1WPu92ZjDImbHu5nMdNFRPSTTz4JIVAVjwciSunaFY9WZEGfU21SrY6gQBMLW51MapTf0FkOIRjW9Gg3mh42ee/Dtj0eBa/YGHFYkwyu3F9RfyOCXAIxhzBGZDIzmWen844OjotYXpY5KdJSxgHywxiq7GbX9+g9Gn2Tu4fzaTqeLt049vOJiHizQy/VCq1pOSLOl1nBfO8gskvoXi4XU7xcLvO8XC4X/5xpmqZhzGmh1u4nxkqYJqKcq0IlIhpcoxPvJ0DelSwK1JoEoAr5V34hO6gH3gWOiQiBY4xsiGhEOI5jjKNZDjxgrRPEEKjGmnw9NquqC+GAgiGatUZXzdTaI5H46tgcVwM1b/va79/RJEQkBCK2WsRjCqY55+UiORFGZAMT8K6CWOvXtBpmMbObmxs/bPvSBYCSaksEVU1ZUiq+0gACEQRmRCIxM/MzvZp6Xn5oi8ERTWzyBo5ZdjgcqoDmlZGFrRMtEYmoIXiocTwehxiZgn/mZqxi7QDga2Z98vTB8f/0KsYW6kG3q16G2K2iXS+ZtlsRzEVV1eVS0VhVVWAcxzlpP5Z7D0FmxrZnRcRPIF6a7F/ngHSPY65rYGVV/A77f/bJBbQQwrXq+BGm+PHQH6oUxFX8u7bme0SQXaGJrda5f7t/S5fi6UPkd3V3d+cf3prxYX9qa7Aatxa5AOBajQTac1ZMDKBes1EfBYGZPWt/nk+bzcYD4vP5PJ/OhDZNk5P7gYwQrVTgPEl59uzWB/bh4eHh4aFLtofWQtGv0puwkrclZEAKYah72SqyVg8MqvM8X07neZ7328nzMCFGYBrMYq4dplQVgExco8rSkrzgpIf+UDG4evI0M2iRpS/CGKNqOZ+XUoqLCnpc+O7dO0R0AtKPf/zjTz/99P7+/nA4nM/nX/ziF32PVO+PAQC++uqbm5ubly9ffvHFFzGOLmJTSrm7ueUYtMi7+/f3796L6XbaxOfPPX5cLvO7+WKiwzRGDmLl7f1bI3327Nl+v/ejV6XjD+KFc0Q0hOhlyuM4zsejI3S+L0IIFZBSnOd53Fa50ufPn6vq4XAKodYR+cpxbKhXiuPqSFzFMX2ZNf/VHZkr+NGKvM7MwzBtiDTPhCGLvn+4/+rb386X9N/8N/8t0+Y0ZzGiITKHoiZpSVkAByW3oC3sWQFt6xoAT92afSgs+WhvPvl9FxFG5CFOXhzvSWnnsm6mXTEVSU1UrdJ6rh+I0O8HOk5BaK6yoB/kA9ux8+O3CU/vc33n6zjnd3zC73x2aNbAsQ7ECFJEc86l2kMFKbm44zPQIqqKHAQyWmZUV5bPBmauUkMCwcwEUZAKAJsQIqmrSlnVCUIAVKoQBrAhqoBhSJcZRH15QYtre4DlBpRavRExSROZTimJlVxmZJ423i00BhhSygCgCGJqRbmy7bl54Zo8DWEgCmDFGqzeooFAocbuKoZAWEkyjtd567Kr/sB6WP3qwWLf/N06A0BcZR7w2gWJRAQBUK23IIUWlK/fvl4B+EGwW4OSejvAPmhgpsZkjOYVxUo1oeyqjszsPVnF2wBh7V8Gquk8izKUgighhGVZJpxAjTgCoHdgMVFkPJ9Py7I8e/bs+bNPmPnh4X0paRxH3oz7/X67nRBRtIipi7YBMhHd7HbTMCCPLolwubwPK1V+Is9ou8arq/0QAFRRBrmu6TYsj0bDh8iTGaqSUkgpApmqeKgRrCrz9IMlrd7af4qrDlaldZ9w67D6lkZ2RPUIW0RsZRS6k3sya+v18+T3VosR6iIABALUXAQMmIRYpaS0gBYA3e8xhEAczFQkz/PMHCJiAEIgB+nBSAVK1mXO59PsnmC3vfGnk2LHwzmEwczQqPZ4RgJEAgEtJqDFT47kpSeGsN/v3YfNcnFUmJGGMXpQeDnPFfxe0m63c+hx4DDFgVwFVS0SD2M4lhTc5xCZAUNV3I23z+sxUrQURS2BDRGnYdQ26QzAVBOaybyYTzlYjANgrU4OAUMIqjDEyQw9+9JNDSIQsRqZokgtpWpZU4Sqxo5mAESmKh491FNlxaqXZfFaESWqztIsepmFNlEgBkRGQFKQUkrKqBYiAhiDVY2G/s1VogEBcTtOl/M5LZfNZjPE4aFImmfdTWSUJXfdQDMDM6xGRgGrCXUT4s8cwzDEkZDBCgAieG/U2utCzaRohgIunV68LaCvXao2DVGKDsPAjDG2Ht5gzh6FFf/NOQwedtS2D03odhiG7Xb77t27EIIXeBBg5AAAaCC56IpiV4fletC6drAXu4JruMoVuKtOKWHvENctvMdAzQJ7OOjH4I5G91DDP22aprXTrUONXt//hANwDdD7bu4/MNcyv7WFISKnlX54fRgE+OUqLv6zx5HeNeX2ZuhZICLybJu/jFoPKbdOdXCMlmVJQzgcDpKWIoU5EJG3a6grxwt+vPWVIwiICGBFilkM5EdHVVUUIkLwgxkws4j50UYBgXiYNpvdnkLUpuWlqqxGuWjt0mtgtZwLYDmfz8fjcS6y2eyR2evgy1LSUgDAnb47XSJCir7ToYlug5oCkvlxHcg7J4jYSqTYAJl5WbKqlpoWgA6Tp5SIPGsEwxBvboZ5nud5fvbs2fl8Ph7feJB9e/tsnufT6VTSfJ0su5LK/viP/6SU4oW/Dw8P0lqp/Pbrr2+fPbvd7+9ubqcQl5wdbDg+PJhZZDZRRkSDktJpPj179iyVTMaX4+wKPJthE2N8/fBaFUxUizHHzWY3jhtmdHWNnpqGxjgg4lLK2NbSfr8vpTjhh1vhNRGP4xRCVNVlWbBWRWOl0AKatV4KNesODUU0b0VnVfzHrSiC4Reff/nLv/25GH3+gy9ffffN//2//n/+6Ec/UYa5pMKIFDAGDuzyKkUNEBCUoHIVCAmd3Ozn9Ef8mI9uoHqtRwDWMXTLO/lMuZPyjKXDWJtp1wetrlj4iIyQNYtdfbZ6pa+pGn+QI9DWEe/ptbrDdWwJzSL1m1+fXX/HE38ECVVVRFIEbb3PHFgEgCEOS06lFEUgCpFYimbJxCqoqIIghGCiBszAYoRGYKiEAEENC0FQGSAT9LYwCqKACOSqEoigDIBmpBCklEX1dDicDkc2iByCy65Vg17bR9fTv5dAemQvkiUlWWKM4xTN7HQ6lfmgaoHidrudbX44noKJ72F38B2V96UZWmswaKmxMQ7rofclQo09yRxWg4hectB/s45E+xetsSt4zDlbR/D0AbZd/0aALRK92uLfjXYjE5oAAgEhFTQAFXSh5drHQAHN1AQKKs7zDGgDjf32kJCIrJS+OkspMeLNzc2b+LqUQoYhICGpWcm5SIo87be7VLJzGJyRLCIh0ueffjYMA4CeTqfT+Sgi3lyYgy/cYmaBcRxHFRdlC92ztmS3AYC3WuxPvZoj8SNRn7Iq4LjaMyqiWlF/ZCgl+4oCw5aIrB1kqw5TI+9ak+7pIGjfaU/ODNDZltvRg4llWYpCR7J74h4ew4ofghSeGYAq5tUfDtQkFyGiUrSUrLkQippE4hDj8XCOMYZRTbFkBanIYipJiznc5ccYvwdf87i6/HkdSbIr0FsT5dvt1hq3BBvq0xd5eyjphI1p2vbY2gHCZVkkF0YIE7kKRE0sErlcElwN3JUOt9lscs55SUBKREOMvQLB1Stb7rYes0TErCCietjtreysqIJIFbcHAD9JEpEXoLsCdbEg4iyJ4lksBHCmZwuqzTPsPnShRbdr9Brxep70HKaooAGogRUDsirVTFYyaEFQaiJ1BFay9s7EtdidAyIOMZZSStFxHAOSi8M4R6tY8WwkIpkqgGGvHYJuKNwiKUCdOFyhCdI6tvZl4EdcVY1M2kWyQH2nGJFLZw7j8OzF89vnz2KI2cnETPJYlHO9Z33Z94NK/6s9lva7omvdKPmavN7YI6foqP/lcnGyr7RWtesv7ZYcW/T/ZOX3L+p73Bdh9/pP9gtA5R2tAf6+Tn4XAaGU4l/YYGloQsDrV30kirHHYKHvnf4naK0kjsejtKozp1pZ057GFctIHTi/XDbjtpSS0Uop1k5HROj2tn7dysU4AFRyVlUGz9fly+US3QjAdRhdz9FJGl5ebGaeFzqfz93nrgefiMLARojIzhpNS5nndMk5hNFKAcgiYkUQ0YvErIiRNWUO8TNqn9zmOxGt2vZ65GjHSa3atuhMzob691xxRsT9fo+ILsLrYcAwDPf399vt9vb2VkQeHh5cw2ccR8YrP81bcPjTffXVVyGEGMfYuob5RMQY0zy/y9lDkd1ul3Oe59mHyzlaGCOaEfNms1F9FDb4a1JKNzc3AJBk8ZFX1ePxqCWNcXBoZr0XmHm33R8OB0/FDMNUg59xFKlYPzYQ09H9niH0ZeALqTOCuhfDfjhoVRN9d5uZiT4cL0UAMPKwnW7ujmk+S9lMO4xMhUuVmy5mCBaIvKD1cUDc+IFPNoiXzCp+JC5fbxN8xIQEDpWdWEomgv3uVj9RIjoej8fjeb4kuLsKAOoK5n+yYzsjCABqR093oBDsMd63vo2nV40HPgL59+levfY/AjHAk0GrPyBQCGaWUvFOQUOM0zQOIbrRyFacYkDM2aCYGYiUhGDkmkJmZIIUQHM0QDSvSlZUwNUXmQE4aVYAkNQIkNC4JpELmAVJWRzCF4mhwqum6kftakbNzEzA2G2f1e1NhFlTzvl0PvtBTQsyEI08xDAMwzDkZVnMgIhDcL05Yo4ixrzu9955hwRGXqxhWvGYGMf2dS3OM0Lw2tirvic8PoHhKkxfT3M/fa63Iqx0Z9cuCrGhqwhgVuVVodUieBu3Rud1ih55wQ1STwCYGUhRZQ3ovQTMzEOEDAaLciCOAapWrlccGnrv6xinaVhEpik8f/789f77dJy964SqZpFlWTQXCHZ3czOnlHN+9+atV98qiib47rvvhkB1bxCMYxw3E1U+AKaUzud5WAoiT+OGNlu1a6usBja6gqfrHmZE9I6/LuWprtHWkDb3vD4eAE83DLoYVltYqlZKIUBGCrTy686VFzWzonk9g92cOTq43lrUqCArHNG6kXIG/5MjBDOXVdMNq/GufwhDC2tMNOcsWlIuiBOaqagaAIHb5yEOy3yc58WWgshST0NWcpa0CFWGACJ6zZ8zTV29wZPjnQZwOl1C8FbzNQuvqiI6DFTnpNaGkgfoS57do8cYzdAV35ZlCWHYbDbjMLknc+t/f3//7PZGi2gRZgYzBFCRYrbf7lZxWOWgE1FKxUQZEJhjCEOIIYSAJCIFzERFRVSltbzrcadj/04GIA6mBkauCKvqzZiJQwXu/fzizsxjf8YRicBrqwGaZKSdz5WQFkKASDGGiISIrw+vAZnjIAaShZkRWTSfz2fAehLgEMBAQMzQVU3ajKOKmiq2sNUAkAgIiVwVjQYO2+1WtYDoMs+mOoQ4hkhUMxVPvILHw1VTEhTgCj2sF2EHgNcOqYfgAOAankQuQlcBJ1VFMpFs7AUwbCaqhRnjGA0k5QIAxOy6GkgWIonkMidr4TtlOJ1rDnod97uR5Nqa/npZOzCrajeTfQARYZ7nOAYichJCVe1kLi3etVbk6q/p1LWGg5IjRLBKEUALXHz7rMyyD7RZI/D0m/yP5+FFpGvlN6zRS8M/hgI+hoS6lVjfErS2uO46z6fkTZE6y9+/9P7+vh/5hmFwOxBjzEvN0oTgJ0oMxIjm1ah+k02PWBCxeH5e1EEibWw371/RPqdWv3CsnaGssWj8tud57s03/WqOmJnZCAldabgKyRu76gbkPKsqmSNB3pXCQOvOTaUoGDNzDCklIvI6HDIAs1JSkcrSBFzjcc6DYkSkWvSivjByzszoLBprpzWfkYeHh3me9/t9x4lPp9ObN288RVwpeVJKKb43p+2+86wk5VSSu85pM+YlnQ5HM+ucIi2yXFwIYXDDuCzLaZ6LZiUEBr6hYRimYcDGgCgpXy4XL4O+299shtHMkpbL5ZJzgtbwyxc2M79//z7nzEOMMT579iyE8PbtWw643++9xK5zQTvNtUcm3RVq05DFFY6gba77pvO/ppQKyH/4y7/ejNNpzpfv3w5j+OQHP8YQfvP9608/+VKJklopCgJEnh01wlXvDqhiJtZA60f24eNn7Uf7aLVJW5AK1p+ln3MAKMZxnt+eTqdSCnMkYqZYNYE++gVVBc0fVr1Dn6mS2VM2wv8Mrs76Bd0yr4NMbHjxh0egj31Umx0AaAZcRRDMiNkgEOYlowi70GDJYgamjK4rnM0MOaIBqaECg6lmQS2Ui5giKIGZIBia81ZQ1VCFQME8+wMBgExBFTSbaEjzwswEOA3jbpwisRZIKe22g6rWY14zEKpqaOIsIGYaKGi6LHM5Z985cZpKkZRyyWdUDCEsOTn84JQ4D6ZVtTWLTg4G+6zHIcIqanew38uwGsTiC+ga1j/xuLByrvA7sja4Qou77VvDY2sf8+HMdseDqyXVvkWRyBs1MxgaECiYGqCJGAF49wBRb3aLZptxcGsFAL2zrEeNcahtBTMRM0/TFEIoRGhgICYmuZSUpCS06fhwP213cZrmPCPFcRyXMh/uH/bbTXAB8sAYkJnzkrKUGGMINIS48GJmIikReWXe1e/io0d+tGNXFzyRwnW2nCrxdTwRO/mSSK6DLyIGKCJdroSIrPk8ERG9BvT9mCci0zRpu/yW/K/eWsUhHCrVT5RSoAmB+73plW9q/ebro8E1CAOPvEsxs1zSkrKSOTcgtDacOQtA2m63l8uyZHFFnCFEd43TNA2hFit7ROUP0kvuHAE1s8vlUoqGMPgLrPdVQfTXw6q6C1vxUwjBoD8OhRBcduZyudzc3MTNcLlcPNq4XC6nw7EPYPciDV27mmDV4jEEs0u1tCyyaNIkKc9m4zi6Vnd2/1oZAsRh8Bs0Q5HWmImHwI6QZVUo2SU1alFyo6OwiIiYigOxCOpaTLReXdw6E3kQsyyL800Ph0MIIXhwk7Jz3NN8KWmJjDxE55R7/lsAl2V2V8EtvaOqvPKs1vWjEN3F3t3dieTD4eCKmSEEopCW4jT9UrQ7CWZWBkREAzNRVUAFI1Xzmn1rQH4ffw+V1kCGT7ejfb2iurvz7TSJyJKTp8W9DnqYxhjDIkvLOVyZM5vNxkMTaxC1VtWRa5xBrTJqbVr7NhfxAmwxE6vFeY7Hm4iMw8bpxT6nZlZtWil51dGWWh3OGgjvLsAR2fX39kfmVtHYN6ZZxefaRr5eZoa/A/X/MJT3R/5dZb7rT8YVsdDzZvqBtsTNzY2jEl4H3NNQjk9fLpfj8Xg8Hp2vmHOehk0pRSM309fPfuIb3PcEACgQIqbsHR5vENGpLyGE7Xab57mPsNfquFM7nU5+yvIZd0fsOHq/qCVdQwgpZyBiFjNjisMwTNOG1JY5+3Y2M0IqpRxSUtUY2WuelpxTyYg4TOMIIwGrS9SKU/7BEYfa9GE1Ux76L0sys0hXgKbaf8n39/fM3Dv4+sH15cuX/nYvRvIpmKbJB7azfPtCOhwOvZ6klMrIMoTD4TDG4ebmxlYBtJ/NnAXtBycvjL7Z3mYrS1n89yUl766FiDf7GzdHvd9WSgkzi1TKpR/ypYpjgcc/wLUFmK+Tu9vn58uRqLbk64bCcdUPt2efQXvck/5qu9TWViWLigaKW5KMQRTl/nze73affP7lZS4IAXDgYIiEjGBZQQLiVQu/b66PlTv6pSYf/b2P/HpTA4AplJwbAzDGGAF0mfP9/b07rPP5nFIJQd0LqOrvOmBgq9uEzuxAICIrV34HPIlbPnI9WZkf/Lm9cb191nexeuXH71NEAI2RAgdUQZO8LFikLIuJeDciEQFCQGayYgLqJFIgI1Bg02gFVAzBhAqYEBQwQ9WK77M7c1fA8AVCWhl3ZgVEVCQwUVoWySUQicjAwbV7EVzXDwGAuDIUiVAkxzH4umRvFhtDKksIgcA1vqxGDEYjMcdwf3/vrUMcyJTip3k+n+bLZWGOt7fog7IJG2RQ6MBzg5/NXKmAMKiqqJfTXWuR1z/7W9w0X/dGx5bASWu1SxBVHQK4VnbX5L9WYh1dl+w6HOyrDR45JHSn62oMjgaBUZLiPKoQAhMWA5EMAJ7IK6UElzXUWpXvocbxeLzd7r0ObBz59vY2hEDMKWdMtXVOKaWkrEWYw3I5A2Gcxv3NTRz4/qCI+PDw8OnLl9vt5ng8zuezbaZpGkKomI0HBP69IrLZbDwhXk2kFVWpXeMoM3OMIzaY0KxqZdjqGKaqAAIAqFIJzuaGD0sph8Ph5cvnu93O5c8MdDNOASukAeBA9orgsEIL+uA7wt05Ceu/WsMsa4hs2MsZPYDzW+KmflhKcdLi2hZ4O0lnjVdasHWCJqlodN1cs6Kiqhl0TrWAshbPFXeQTIDn8xk30zTFcdxIFWOOzKGU4lEcQK87BABAtbIkf5xIrRMtoX8prWgSAKDBSe0AaiVlADFVAhTVaRhBzUSnYYwccs5apIzjPM8llKJefE9EFGLkEHJZepjiT11KOc8XNAIgBlyWxYpst9uypG+++aaU8sUXX/zgRz8MIq9evRLTMA6l5OcvPjmdTq9eff/27dvj6UEku8d1Su7z5y9vb8dnz56Z2XfffZ9zGseIWLc5Ig7DgMjzJQ1h9MCO2Mt7WFVLkfvD/bTdbLfbEAIapJQeHh4Oh8N8OruE4jzPMYRPP/10d7O/udmd7t9Lyp5nWGARtyoIIY5d1sNPgthCfDc4/vmqasU8VL2/v9/vt7vd7v79exGZphEA5nnOWXKWUgoSj+NohlKs7gVrdDUEU1MrIWzM8Hyeh2EgCqqac3IgZe1NtEocaiWqEiJ6jz1DUHe/l8slcv72u2+KJCuynTaXZQ6Bu7FyzCXn7FVAx+OxR4FdBkAWl/3hadr4ptAigbmIxs4Xamk8Mwjj4EVber0IAJz3jIillM12jDH+/Oc/f/npJ2u3V0FlM2crdVyTqDLQ/M7X0TmuareeuF4igqcs/yvq75SJNcTj8fQwxn4z7b4IAEqW1SdcNRgM9cm+699FKzJPj549SvO3rwlXfjTdbrfPnz/3Z3fD/urb7x0X+OlPf/rLn//NOI2WyzwviFeQe4gDImaxUgoZSMonPTHzGIfts41oXpbFUXxVPc8XA4kxTtvNZrNJuZ7TxmkCgGVZdMmbzZaIiljKWUQAkMMAUMvpp3EEoJTSMAz1wGwVQ2GOIQQy8DxVCEHUoMpSDWEYzayoHI/nITARMRIjRg4MyAGpFeGoqp9CmVkNzuezlxT7soxx9CXh/sgX8Pk8Xy6Ld0Mbx+gsGqkq6dEXnkgBwqJiS60jB2Q1y7mYmYqJh8uEYYhsAdSmm/3ldDazu7s7Zu56aO/fv+8YotslM5vnmQdezouZ3d7e7m53y7KkOW+3WxF7/frtdpye3z6f5yWlsp02ptpTQ7n1+VZfJJLHcbwsizdcc7YP1ybcV+zSW6iqQs5CZE6b7IvQnCgBjTBQ94Kvb2CKgFaK1mO7ghkCx4x08/xlLsfL/GDEKYtaNmBE9pFflny6nCPTdjst6eJbwcVX8MqV+HhKsOi1LL7/CT6IpOvuJhQTZrZWMOPLHoHnS4phzLnCIqoawjDnFAaW1pQT9eq1QwhONwMA7yHt34JQQTR4fPXS1kexO7qwbd3m0jiBuJYPsqu36iDdh4/px9v6ro5HE+a0IOA4jl72t5/GGPh8PLy8vXn99i2bjkM8zymlTCFCwHQ5/smf/PFA8Vd/8zeXy/LZ3V1Qvdw/bAIzGIA+nI67zQiR7x8O493zMtyVGExUsmgRJGCigICgaCo5l5RGDrv9Lvj+AVVQY7pm/SQ7t7IlWLna0BhjCIzNUVEIU6AoHsooEI5hDBBEsCTx3eJGhJnHcfB9JyKHwyHGSJTWUbV6C1bU9VA+ujqyfD3nPVpP/aPWL1tb7T619Dh33F+wXhDY8c4GCTx5fXctfTeqeORUkwAEtT+SiZqBkagnrcxa/4EKBgDUkh0n/DQ5vyumhYjDEI6q4xQdQYzIkVlSJsScsiHEcdwMY2D0pbnbbQLV6o0YWXUIjffiBVtxCMysLgBrDjLl9TMyM9jV4WlLx/td0XrbfIw/1yfN6wJFMOccMfgsmLlYRxGRaNVVUBMGpprlvKq7+AlBGit9Pbkd28Amj2ANnOhvBqj99gyvgYut1GPqPa9SkF4LUXtag6EBmgIREvrLsVEh23IiNz1mpiBQzI8QpZRpqvIF/Ya1JevXY9WVhfsTcVN+7AFHH2oycIUeq0qCok0Kyf1r1goXDcNwd3e33W7fvX+LYKqapKiCgFmRYroZo5gxQCrF27cFpGEYpHieroJPo0icxhcvXvzsZz97e/9+zukP/vAPv/jyB6/evA4hfPLJJ9+9+t4T9J9//vnvbX7sLlO1HA4nInJ+8zQePFLZbrfHy/nxknHbCio9/UIU2I/hwPTFzQ+csXA4HAhwHMdnz549e/ZsPp09rygigTmEcH9//+t37//kD/+gqmB1V0SKyKpQxExRESMG8EOWYd+MIlKF05oglcPYa2vUcVnzOt36PwRooVKNNMyz5YDB7efabvgS9Q/vG+3q3QEUwVG91shGAOByuYDDk9stPzxkVQVDhON8HMfR9bs8HeE+zGMaT4hZExxExCEO1uiUfU9BK9PsJhEcHWdvOK/a+PGrvXO1sb5WK3GrRdhrqwIr9MRWPhU+uNbWr79gbbfNbD0n0EKfniXob6EumvSxbqPd2PryWz/1andfv/fDYMKvng1whyitVUVP7vU3OjD0wx/+MKWUl4tXuYxT2G634xhVi4P63Y5BW5kdvfbJJYYYo7VQZhxH4kov8fNYNyAi4mc/VXU5ms5CqdCG0WazQSIRCGFw0bCU0pwLk+fNAjN752DCIcaYS3FJXA8JrAEiJS1oQN4fTU1NXQzUu6Jio2ubGdrqmNTstjaJJGgc3dDaxzoY34cXEX29+S93N1tzaRczW1Ww9FXXnQUzM9IyO7ScXNLKc7NE5GXlHZOqCwOMoa4rP+OVUgixyjQ1uapuqEMIu93O3+1WMecsdaE+KmOzBlr1mjpr3paa2s/jJVqnzEFYtOumcKdcT6R4vcxMjeNwgxRyvojKdhiZBwTNuWzjaIbz5ZJzocA3uy0RlOJdC8yRC9FirVxTPhC97Xaj3z+sQuGPXwYhBOZoRiEIEXp6Z7/fHw4HT+wfj8cYh8CD+0kRE2hlaSuAYE0IdCcYiCnw5Zw//s0rW3eNW1CRoAKaq5CgG5/1b548ncczHxo0ny5r0ct+sz0c7sVsmsY0n+/fnj59+fz3fvjlV7/+NUu6vb3ZP7u7zOn96TAvec7p8+fP0/F8XkrAsN0GLVLSMjHcDrwJ9k/+8T8emP7qb/76/nj45OXNEsIv798vcRxCCIHGYWMmknJKy24amUOMId7cPNvfvHh+FzQXzUVLPcQE4kDMxFkzIoLzNJyYT6TkwusVMcWC3qeGKCJiSomYx2Fk48vFk4MwDNMweP598ojEpTw3m13Ocj7P1eVWhWxvX1BXVG2l1fQ9FKqwtY+tp467C/noDPVZuS7QtkmeRO3X6V99GiI2YMmns68YgCoEVOWArGafEVpaBQAZMSApkqAHnEZVO6U2DoOW7ucciMga98Aa8/XJAt1ut19fvrm9uZFiqjqNk0Ppt8+flaIUmBhCY/aLZGTaDKPXOI5OTERlxBiCNOlCACBw4Vcx6c2zAMBakMlElFNRVbMMDZ8mombHsUMA/ef1eFrLxjizP1CtdLSSAUBMAECLANU4PFBldAERcVhbuh4o90/uwVO9c4Rr8RNWlqSZqXOv/d10PdPbSrqwfuaK8NO+EQF8bpEQwbTFcmjWStxUuIU5qkWcbI1mUFIqnerTQy7rvauak/BgK8+LtToEitHr2q2KVQBiLdzRRknPkjta76GeEyqGYco5L3Pt3uVgkofvYqqqpAVNxVzJQU4n2Oy2YxzEVKW45lQIIVb1mMrOcp3ZMA5//Kd/8vr16/uHh1/88m/3+70rBn736hVz7KXGTFEFTqfz8fQQeKgwXDvLXbcbPcqPqvcxQKqAlzojMpgZA6uAITPFGICIQhw8lPnVV1///u///peff5FSev/+vRr+3k9++uk/f/Hm1fceYPhTiyECI4WuP4jEzE6zFnDqC1y3vHoXFrOswlKwYCnFyT0KlsU1PR+JyD/xGY+sEHnbIFynv58Esn2112gsDJ6EFFWDa6l6Ljkyxxh3u12MAWoTYl7SEgJ5j1ttTRJKKURgJh5flSJmwowhBEch+hnGk2mhKT+utxsR+dQBmB9x6ynU8a1Vka4HYcsy9Jp1WBnh9Zj8Ry5bgTgfRv/dDteQbBX+Ow7qC2b1ynr9LkJCj6tUlehaxdhAmSvTuiML/Vo/Uf+cntPwD+lVDZ28gZ7QCEFEZhEP9IeRRw6IOE2TA5P9W/wDb3bbaRg9DeUn0jj4UR9ya/ZnaKlUHtp+U7njqtaIVQEAVZ3kZmagWsX3XOFnmWcRCNOmU7ZU9XR8oDgMEYRUcso5B2IEJg5WE+XgWOc6lnV3oFZMNOdFapIBZH2CrmG6U5WgP2xveAINB1n//tmzZ9AAGml11QDAFPsGdJ68v3uel754mGII5I3AvVVoa3E49L4xh8OhBwnr6fa+HJ6+KJ55JprnWXMZOEzDaKIlZQhXfe22Sq3vbr9BXwb9SOPrJK4OY30RYpMQXS9+uLqna7OttSVZhf2t8ZHrMWsBLawSqAwAKllT2o2b4+HMQrvdHogu8zmDDNOIQJ6UvubokALjJefroYKqfwQAhxSly521u1ob/OtTGGpRomwmpRTPfHqK+HA4bDabnOX+/v7m5pY21GAyU2zUyratAQDUkNCBrLo1iIAIMMLHLl1R9a7BIXkx0hWC6X/Fx9BD/5y+SX33959DYLjuiutQBCGVrAgojFrYStASrNxtRrrd3T67A8R3b75/ePsmxPFmHG0+n1JSASh2Timj/uM//IP/1X/2n+5Zf/Pzv/jP/pN/8pMvP//3f/mDt+/uv331/f/nL36+G4MASs6oRBEQjRCZOeeMMVKsHQY5DFccK2DFXNcHGiIP/IiYvejNwDzoBqYVIt5YyBiQKKVyuVwkawzDOA7O9rnuHxARGcfRIai+xNFFT5n9vIHXY3rdCU/mo1th+CBxs7bI63EHgB6Y2mMvsn7NE2v+aHE83lpPVgYiEF8/hog4IBUISBgqBYgZAUIcWASQUSS55Yoxturh+jg9PvAzACLu9/siCRFFiogSUUk5LTMZIMA0jFMcRMQKenR7Op1sSYF4nOIwDEiWcwUpUylmluYll4QQENGJ0KCPyqBhVTvRLVcXD3TZPvsgxAEAJkaqUjmeeFPVdTUktoxKDZhXLnk9/oFZ16TGNptrSyor9R7iaqSYOQzTFamt/rhSDPu32OM5vd5/ZYuaNVxKTQwMQAnd1CkDWjtK+kAFJlCAzBAEFF0oy9Mabo8ul0vVZmkKHrS6ACCGmohrHqKemmp3xtVV71lEr7qN7Em2eZ7NsJRyOc+uOchN0D1O0aRkyoBI6HW+Zghv3r3dzrv9dscxxEAEIKZlySXNDMjM5/lyOJ/O5zOoPTw8XM7nzXb72Ref7+9uVXVJCVfJUCfI+uGBiIY4vXr1apompxqvsh9Odmqn95WyBxGZsYmJWMlqllUlS7nMcxiHIYQun+KZhNvb28vl8pvf/Mb9k8uYvnv/cDyec15KKbW6t8YHmIsWNfFmG8heyO88LW8sR7XSDZ3/dpkdpCillDknaamhZVmKSxQCVT1UA2s8EABDMjVFAgIk89IOgBVr3wdiTWBbz7JzXfQDLIOZgaioEKOZDcPAgIAajUXkdDqFEDabjYc1y7LQqgWetoq6aRpP9xdr3RK6d7fGqLZVcwlV5eBAIgG6QSCsgmWmqz2ola1UcmsdRR9LsX5oVJ8447UNXz++dVq2fcR6r/+z7ZRH+/qRNzCDlRho+0bssIutjkB9hRNdU4VPtmSxAi1ctta5ElfaA33X+9ctOXV5eO9nVEopJYlUfWp/mQeRjOglRh7WVycooKr7/c4/POdcSrIWYp7Plz4aZdUialkWr0ZYDykRI6DbK1LNWc7n8xBNiYdhohiia3WAUd0eJGB+qDYz5OuoikggBoCAhKCitRFYNdpgfVSruIUVJ35YEwJ2poC0unBomSJfpQ7S+y87lwYa4aoBWO7XCBFLOV1XBbV5ZFCNFMI0TamUEMK03aLZZVkeHh78gGut0J7MFGBJl3GamHlZFhXxAEZEypJ6jYdPjYuN+iEc1s0o6lI0VfUy324HRESX63lvvUrXshb+dI8cxKPjcbUP1HZdHXNVFTid306bYReJgwYrQQVKnrTYuzc3PFqMaX5YRImAA2MxQJZVO5FuHJioZ/rsY5uu31t/15O96Zdbe0ceNPheA6d+xRjnOR2PJynmqJmZ38pHntpXhS88NAuuJp9SHONH8w70uFahfU7N2Pe/2gqaWT/U+lnsA+PcX1wtW3981LLMIxObHt6/x7K8vLkZmd59/70VkbzoNEw3u0+f36nmt/cP3755VU6n7XZ/8+zl7fMXKlvJy7jbPvv85Ztf/+1vv//2z/69/upvN3/xF39xuixIA7kEn7JqUYOSF23yADktUoqWwcys6Pv7h8BIjATYavz96C1K1tH3qxQPEGDkcYrb7SZOTAEBRcEEvBFgYAzueI6XS4AwjJOnfX29Bh4QMYTB3ZmHAfM899BNVc0I2vh6ISACEHalKpdeuSbC6INUbF9ST+bMfx85VJFawF4KZmawUnrxKMTpUbSK5WHlpdbOoK/s/nO1pFW7ho28uQIQGCMDQ3Me1ZbpKrUHrasAEbloF7cOBrvdLgxDPyEEIkWKxEOIqnkzTSHGlDOTTZvtsoTz+XzJZYzD/salHkGLKIhqQWTx7LMaMhKRm2D2qk64QrNWFUWg22wAr9tjIuiw4JMpcIwQABD9iGg+MMuyIEETBypEFJi9/xEANAne6yZzr9KNJqzCiyc7sM17xdXMjMJVVwT52uUOVrdKq+59df/DFWsJTZ26lCKliJaWkHCesZF3gGol6UOIoAXUOCsZcUAetp7qZeaUkvf/Yq4KWkREFIgCAJWiqmUTB0Sjugk6emLMCF6rD9YyTggAU5wAAM2YKE6bSHxBWpZlPp0RMTLTZuPYEgCkUrKVYiqixZRAgSgQAdIwjcX0cDoC02aIgwoDlqKMQYshYlmSFAMEEVly2d/c/ebrr97dP/zBH/3R/vbmNF/MbBiG4/17EXEffT7PrSgohjCUovN8FJHt9vT8+fNhCF3/cT2VUiwnacC6qVrO6ur2xTTGCIhZRVW9ZX0RMYD9fu+ZkwHRm+x+9/2rkvIQo1ZVDTSDlIuZUbAll6IGyIYkgLloLl5PSYaVbISIikaERlhM1SxLKVLEFAgVIJWcawtudGyvCsp16Wj0pnWKAG5cAcCsKgL1XU+EpVxZjh59AiARmnp3ZzXzijVAVEQcYzDR4/HgaPF+t9EmKKmNSCa1tqR2R7LWucnjFQBwTgU3vfmCCGZETvQq1upq/GRijl+udlzbo1cqhR8G2lBU8cHWjeCRN6UVzPHkh/6a9Q/w2IxXS/uxluHWTst2tRvWPIXR6izR3tItcb+Hq0MZwrAO2fuH96v/vt4wyNoF9CAMVhYS21FKVQPFYRiGQAB6c3MTB0LxFn5nNylmVlTMzIuEXJemKvLFqJvNcrFSyuVS28qW1uDc1SPu72tLPnDCGEJWmXMKIQiYETKziWjJfpQbhkERiIo5qYzDZgIep91uYy0ZQkghEKjmnOM4mQoQrALBK+LrRS5oTjGlENhrUcqqmquKNQJBLQmuRa6+aOdLQsSaXzVAYEIDotPxssL+MPBAkYhoWeZi6hprXp/m2k1Frz1qFEFVsgplTZSmaSTELGU5LOe0gNqcFo7RW3N66la72LkAGqGRCUgxQiCwkooPzDwnMwMg5uhtWMIUWvjXPI6vOS3WwJFuB1RVcle7XrVl7WlfX52AhNW96rUGBupSVwOAcRy9tM1tBhghAKEy5FDKEPj5ll/un91EKufz+Xj65jfffvr5l0Lw3bv3YYgvv/gio3317fcQN6I1xUexNszJWmojs7b5VueQ0vcFrM78ax993XoGwzD531UVa96Ax3HjlQlm0JMn/i5aWYO1xLBnYECViLbTxtspzGk5XfLvCv37Ku27st3YNfJ58q7165tfXhMLr3Pk9rwH0PXmARDKZhp344A5lxMFRjZRzcz0+ec/+PwHn7159/bf/90v37x/98Of/Pg/+0/+9J/+0T/8+c9/8Wc/+8Xbd28oDvM8/3f/9v/7N3/3F+nd94dXX/3w5Yvf+8EPtpvbC+jXX3130HAaNhrGGAIFzjlfJBPBMAwUBy1Sip5Pc5qzmVVpcEVlZ+4Q+TZjF/laJZIQEQnFpzMwMwNaVskiBlUd3ASq4gRiDF6wcj1g2SoydrcUY+wd5tEJNFbTXYhY9d31eg/rldTj477C4IPU0od+xaGIJ6eFtVl/NOXYZUb+ZyWpoW7gYqAAjE3Mm4h8KdQdAp5EroiLNs5iq0xqolF+xEV31cnMnC9epLb3yzk7jJ1S8kZoXphIa8ds5r17hsFjSlJwclcQkco9aOoupRSK7rqva93s2ssZPmjlXY9J1wFDuPIOnyTIbOWMr468e2UAwKsnrVhQzlnXv2lX/09cH00B1Ao0dUJcFes0tkkzw33qV0tlvbEbqPZoqVRzBgCgiEimRoBGUH15AUUR0ZJFBMkQqzoeNDiqf/j5fPbMpmsg0pXWL+0Hs2sT68pnXY+YX4a1ISgAMMX+FDnnEPxUxR7AqSoQRR6ySs4ZxU+AtR3PZz/4wvtKLvNcSpq0djDYDJu8FG/wq2AxRFcEf/bs2bvDw5s3b/79X/yH2+fPnr98sdls5uNhXuZI7HK9y7Kcz2efcW9XeT6fz+ezV3Q5yrwC/msNmeNADlQHRINKUEEyIw+pH+1cD3fOp9N+v/dwR3OpRPZpPB/PBF7nzSJlyUlELZUs7q+RkIpYzqWI60RKBdXAYLVWh2EI4xCoFpGbmZh6cgPWzqAtLyIiBgICVFBCghDI6QT9xSu3/XR591Xn9Aax4t8ITvoCiIwqMl8uKefzct5sRnFaP0jv2+XNHHz7A0AnlbnFqFVYOMLjFijdKOmKi+y/YSZUU+yncR+HqyZPD2LMzPXmb29vcfXJH/Wma+tqHwT9azf8ZKCw3drjYWy0pOsbDSoY5IeFRxXDFZTBa4IeVv6i83Sf2C5qgi32uC6Zm/fvnq6G+KEe9XGFXJhXykpltpVSDACKpJRKydM0Vay3OEuQI3NeDKTOS9XtseI16J43iDESQU/vUO+cZeZDWkq5XC5e1eof4uc6v895nhUBjKSYRYtx2G63NIyqqi5SrRqcjCqSSuY49ClZh0R9AXsuD5o6WVsnqwVQsy5XW91Le0sp3ot3bfpCuCq1+2f2NucU+PvvvwOVBhmZiICt0y+8nhcxi97DJHAIIV31Xqm2ePa7Ne1zjStJe6wEetNGTHI7XCtqRGPtfW7rxayVEBI6ObNiEyE4SETtKI4reRL5oLbkScDT17+H/u6/BK4hEzMj48042nyElG/u7v7BD17+6OUzOR7fff/6Zkmn+WHY3f3RP/vTzYvn3x/u/+7br3fRZAhZaxmSx4SISPpIqVPbQc6/f71JrRFxP7qLTcFhL6tK6Fe2c+3zAOAJlpQKIruCyPVjV3Btg03xyVfXPfXB1XlWfa1S7Yr48WBvbYv89f7ePi894HlivvoceWi0LMsm8u/96IdffvLJu1ffffN3v5pPx2kIh/v38MnziFhyIi1/+A9++r//L//L//X/7n8Lon/1L/9l/r/91//2r34mYdztN2k5vVuWu2c3n+///stnt3/vj//kf/Gf/y/Pl/L/+m//+2/fH/72u+9NauY8S4nMz14++/TTTy/H0+l0Oh2Oubo6CQzIgApX2TU29qWsK732Jw/cLIUULaLqIZsvYk/W39xsBxpULYTAFBHQFIsWAGC+qha6QfR1w+TKnjXrp2oAgogI10Kxnj9VdcmpqvxoKwfs/7lGldbXujB9vUqezHezWmhQSwvgcXRYa0pbFILo8no1hHV/Q0iIwETiKHF/byNW9dUjrfNI/3YVIXYf43rMpqoeR15OJ1WIMaaUHBE8HA4vX366LAuLUORSyuvXr4/HA4Dt9/tPXrzY3+3NrJTEjEBRVX2L1ps3APPGWxo60eU6PNfzq34wUOuN8cEmqaDayiVUOcXGP7Fu3aqUiDvXTp1un9pX3Ue/8cnV4xhaCQJ2t+3Tev3Y1RG//gBX0+k6a4Gqj0REzQURvPWqsx6QrDdtNTORkksqpQAxES7L4qG/m/XtdouI3szF4VjPVru3ANAiJQTXUnB5By/bpSbwWJ9Ctdr0LNo1EMAW9Kyxb07xOvKrNVRVGmMAouCSKSsu6TiCzy8RoCITIBLR+/fvVSCGAGrpMqeQIoeU0r/7l//yxz/+8T/8h//w/vBwfzxs97u7uzt/TA4YgpPaXZWo1hwz82az2e/3ZhZjLCXN87y2jwBmiq5iIcXCRDGwmixLynlBMgphTkucxmEYHBUTkVJkWdIPvvjy+++///a33w3D8OnLl9705+3bt5++/AwRFTCAE4WzFAPW03lOqZihKQpoLqoGgUkNjRAcH0YCM2RAJiAEb2SJ6JxoRRCp8h3WXKBVB1SNAyMbqFkl4nucV23LCp/uS7cvwu6ws1PUaknbdRcsyxI5uIitv9dXbLHiSL9bFWhwg9QG8rXGF1qFCUg9SXLrc9TX6rqSsodE3dP3hFTvcdTDFJ/uZVmOx+MXX3yBq/I7XdVN9nn/0Hp/6D5tdcFqvHxdr7ew1Q700Pd+p/SYKfkye/RpZGb0iJt7pVKIlSc3oK288qOoP1g9GfZ39Ynon7l+uhqjl+LO2gChSM55HIeWI3r0yDHGSDVe9Ga6oqiqAtoBSGRCIxFRUe8a2wsMPIejrR6jz3u/z2VZeIimVAzcXk3TlNQQMYYY2HLO6HB1HKKNc8nWhrq4dVLp6SZENDVTURNVRYNaBKzSFxtU5Xhej3DOGeYZEbeb/co+XMFa54S4AQkhOIsSCIkCARKV9RTYimHlkYNvMkSdU0qS+5Q5lwmreayMUEfMAYzUhqH2ZPTBBwAtJYQwxgERKxaglubFLV7OGfG6DFTVbcUQBy+zZmY3Iz4Rvbz+ydSvzXh/rid7p84+XK2K2iOcKJJGyKIzy8XOhufd5tm438UX+OxH+5tf/OrXOGx/+pMvlkC/+vVfHd58vdneXQwgBADIpbhUAA9xGIYlp2735PGGWt9nj4xppYRzfZmhlYTIqsVMcHWy2u/3LrIUQnCVz/3+1g2R9LRSnTLP1ds4DKB2uVxOp1POGQ2WnDCM8LHriRW6jh6hI6TrP1mjRNrqTOt/0sfsj/6ZiNfTUQcO0MRUlnk+HY7zbmcpg5SS5stiA9Hf/uxnf/M3P8umWtL58PB/+a/+q//H//X//DKG+8Pp775/d0pFJrmAlZJ2E7998+bZGC7vytf/7//hX/3536iN7+/nl59/GccNno85zd5fDyCYISJfUp7nlIqAIRiqQjv64HWJMCIUIlICFFBQMyIzL7911e4QIBARAAZQZkW0lAoquqrSMEw3+zsyPJ0uaZE5l4CURANe7bvTQLtLaONVufBSDFBMEaBGylIKOcm06so479oAwAjQHv0LWHvL1fgFkAK7qkyalz5z66kKraXXdREgIoKYASKs/sWWeOpoM1a+NHh4j/4/VCQmNcc1OaAZEDMxNME6NtGs4K1RzDnlWItdiyoSqQnVAzGpaozDZrM5n88ccdpuJAsAYuBlWTiGy7IE1Wnczeny/evvT5fTEGPcx2EzxBgvl9M8nx0aERGAilqJCCEYtmSrONYNCL09uLNbEACc5V7FYo1QUQkUgOtg+o+e30BAdXTcnD0NZKolpTIO2pSCVLUAmIlrNqOagfjpCQC8x7uaMqJasVYqYAhEsVscVAMTqd8L9YTISNBLllFErC0KAhf5AbN2xjGr6B9UcU8zMxM0MkBDIsaBB1Q75QcEQCNXcQoeVSBsxjEO7KVvhRFUAxMHy3mpve4RnexORMfjcRgGZ9aly2xFlvPFUx/TGMcxjuPGjYkhADEFJgoi2YSkJPfUTsiTYog8xK01dnXTnBFHWwAgBAxhiDEOEM9pRsTIRDEgXa3b6XgMgbabzTiE7lQkWMk6hnG/34PZ/bv3ZUkuQfUv/vk/f/Xq1W9+85vtdvvDL35w9+I5qF1O5/3+1mWRXPIihEBkRORQtCu9YmVBDGZ2uVwAiAzIwPnA1lC0GMZhCDkvqlq1GlU9ITZfkoEgMAckcuIQP3v2bL+/JQKikEsKPDx/9tKLENRQlFQ1FTVDBprnlFPxORcRLR7aBgRWVESMTn6sBffkWRQg7sA2GFht/ExeyFd7BjMQIpjhdUMrGAIQIruhYESt50zo1sP9g6giQBEhREDuEQ8AALo4GAFoyXkaOG62+/1+ihPTFQTx6g4A8HoqRFyWugjJIOdUlgxaNkMM293pdIkxjONIAc0slcUEgGwcRwVBI2QgII5EwNM05ryYssf/IgqI3hmOGDqg4JNYilzOi61kRqDVwTm+V/3i48RsB4a7c+3e1wkAj5y1x1l+xybt2w2gaje3771S8xHpiig0Yw+PtaFbXazz1wnRyY1+A4LoDdT8lWCmRF5STACQ8szMYKRWwMj7OQBqzlmtqACgMgZkIGC/D2aOPCIOu5t9YJCUFSRGNrMsK5kgFWfmEBEBKELOOaugSZIyTaNvfyIKAzvcoKqX0+wQoIj4adwDVpe1GUO0EEEURFENVcc4hBiXUiAVf+B5nt89HOIwDdPG15KWMgxxGiYnC9ZaLCJEMLBihCbB2SHAZllcUlcenZSu8R9eoTT3wt6ZhEPQJo7ZT8LdX+92u8vl0i2ex9wiEsch6xWhR/Q2R82gVS7iFUacNmNKqSxV00xBUc2YNBf/DTKRgTKPIVrFLAqoOTAvItnZnsMAan7ESiXLMpvouBkQCjISODeBQ0B1+hmStRWqWpADMSBZLwd/ggZ2mf91bF33CCqYr1ICUEJ3wWQmJqBWtBAxsEXU8j49TFgY7eF4+M2vv9qW+SefvHi+3dx9+uxHX9588/bdz372r3/+9Xdv58Rh1AzLfMEhAMDlfH44vC+m+/0Wb2+8bM6tGigaXg8q63NLT5a1ff3oEcwqsRoMASkAoCkBBeL9Znv/7j0BEtE8p/My727uAFFzBgRs+v6I6JpLl/kyxClwQEwpLWZLjJE5YiBDWO9E/5eJ1QoiGwgYEQMhE4OJIpJXeVQWoN9jE3BriX/H+G2KEVARrrQfRFSElNaQQSN1a7nd314Ohz/7s3//7/5//yaAfXJ7u4nj/eF+O40JKIRIRAh8uOTXv/i7h/t3W9Bpu7NpF7d74zGdz2KAYbO5/eR0OcoF02X+9vj9OOwPp/Lt6VdImvOCFDbbXVE4n8+/+eqb7169dnJNoLDb7aZpKKWEw/kUY2RGJPNEoVoZ4wSGgByYDAGQgMyhfaYAypKN0ICMgQ3MQEcezw/nZS7McbO5IYxFFHkUm29un4OKGghi4GBkIQZgCCFs8+Z4PGqRQHE77ZY8W7E4DttxMNCcSs7Zc7SRIgODmkgxETBDVGaWYhSYmWsNMlSSrOTiZwQPWk3U0+XjNLTVWKfWXZPao/LWbmS8f5YL/ZBT7KBWvVvtOqvQDhsAQApo3vyUmbkQiBbMppoBIJckSuM4jjGUxA+n026zTWnOc85DnobRq7hASgQg0xgjmqaUyGAz7ZDs5u72q2++HuOUVVJaYtiIKQV+eHjY3uw3260hBqK7m1tCOJ5PYQyLJC6BAg/TKCKSK3lABUQsp8KRQgxxOwSkPF8Qih99GBkA1FC1JbWBDND7ilJlaJghKNSQzkAd9BRT9IGoB7lKtTyfz8wtVS1e148xxuV8IfYQx9AAyZiZOCwqCsagCMKkUMVf0cgA0BQNhMgCQCBF4FffPxCPIdAwBIiRvcFU5EVzWmYzXHL57Tffvnnz7rNPv/jDP/xDNHr//n2IFGO8XC4xxnEcLpfLNA3MAyOqCpqCmNNU6lbWYmaB2duupVIm5jwvx5RyTmY2RIoROeCw3bjfJSKRfDhcQPSLTz8hb/0AlcsIAA7mAYBIXs4LMIQ4hiEihwKac3bqvzPGVA1UAUila4qrISBbYGJzaLkWmnvPIqPCiFv2rszqyp/XkMuUDZmudcZgFpBub5+9+v41Il1OZxOdj+cvPv0scXx4e5/Oy3w4zcezLLlc0jRNSJAsiSkBxhg5MAZWs1xSMWFmMqBWb8rM07RlPk7RsmZVADUn0ealnE6XcRyXkpe0DNN23G5UCzM7aijF1EAFcslSTE1P55kY4jBR6zzc66LneZ4v6bJkZg5xAy5OiTTw4CUcRLSZoohImuNm9DgjsickCxMPYShJMiXkQEaRIqCKiEuS1XppA8f9UTMQQc4q5JKiiEYBwVjFCiMCmJjTKQVMigpYQBIwVDOiSAxc5c2WJVWD02AmBDDQaRhTSptxfPv27cuXL8/HkzeR9fpIJ0vsps00TSJ5YMo5H96/e/niBROo5BCIGXfbYZ7n03wSER44zalYiSFmzQFDnCIZKaqpeQPJ87m2T0Jv0YomBmbZDAlYQUOMojkthTBA4PN5BiAxRS/WbPrFSOioTYvMXfEMEFH0qqXtVaeOehCxGxxrdBE/1assaiRFAciRpRq1a+lojp9FEZE5qCkiceBlWeZ5Gccxxng6nQyuNV0OCqihGURgMAVDNQVAM0AgBHDF5hBrvVNKs0gGMiISEKYYxhAwmEkpCqAISGAOnZCvEwAjFM2Bh5yyatnudw/3b7fb7QhyPD1st9shxpRSnkXNXITDaWNokEo2UQpsWhiMAg+Bx3G8v7/POU/T1Ov7f/KTHxPR4XDI81JKQdXtdns6ndLlEjd4Ph5FhBFzKZEDIWoRVtuNwxg5pxmZ9/vtu/cPqjqOYyDMCCnNADDRQBSI6/RJqVwdEXl2ewcqJRsaEZFJxcpaRkVUS+uzcm13JQouBrjd7oGoS5ZR62bVbFeV/vQ0mmexPKtWy3w7Eattm3LVnKlpOtECYFqI0EJkJ6CYWFHVZAhgBRQRvKatafmZ+fyioWYp8zx7qpwiWZFxM3hoQYgKcLlcbm92iIbejFxc4hsQIdm82YzMWDRP21ERsuYQSDSDXkt4+/HVGkOsh5P1pGqFCZkQAEVqTScxliyAyoGdTgOgorlAwoEx8gDb8zL/+ruDXJb9GH/vh8+3uzJY3rzc//iP/tkfvE7/6t/+7V/+8nXSyRIx4jAMAumXX7/69PMX5Xzm3TYGPC+zKoRx4DhkKVmNiHIrHHeDJWAIDhKimZsLAKgwMxKnYptpByUfHu6DyH4zbTguRTcYWYA4xBBnd0jBUkojkysdY+3RyTlJKWWM2zRLgozA47RtlgZiCFoD7+IYjU9jkYTo3Z18REXMith22lmRkkovHO9LlIgGHnwekXAzbEII//hP/9Hh4f2bN+8eHh6WlABATRUsIAFAqc34BFwbk/j+cNyEcXy+U8llSQ+GOUy7L1/kUnJKD8u8nBeRgERh2D/78hNQSWrZADIT8N32TlXLOacZAt8kDsNtIApLEZ34lGU7bYGDGp6LIYVhd+cba9zW7t2Xoud0UdVQVMgjNKxiPiDqdHAgM+jnHjNTj/GsWFkKGhopsfd1ImYaw2ADEQ0A6G5RigIyAmAgJ/1ToNAEgNulatwzxZVLYdYZJw6iu33x1KACGgATEzOq1tzKFUZrtS0GHqkjonOX8HFdfEPtr361HUavtE5mJ7O3vNIqyv/w335C9LtXVacLI0GIHkZXeqIjHB4BM8cu+oGIIN7a3cuJFADIwIAQDYEoMDIZ1mJP1xQRLRSr3AEKBOLdZoMEMXJRyVK7yiNijNGbq5eUAZCIwhCZBiISASO305Xcj0CG4IhFJclWsRuPKw2MimTpDIcKgSMAVIX+K2bpGSVDu2YAuTafB0T0uN+PXQDiYKiASDUehU0AFNEYAyCKu19iBGIrbMBqAHq7vwGMiOYgiknJVmkPJc1hmG73u9cxvn79+ng4T9P2xz/80WYcXf6WDMgURAMBAXDFothXuGfMpZHvGZEJAjGiIRMTQmCEyFSXTQgBmcRMTCtl30BVUc2PPUS1pL5xFlWsTNPkaXE/khY1yXMqGVZur4rh+ZuEK1u2QZlY4TMCcCaeFikgWosUrio0LlpaFSy0SFE1Nmd+iRQABIRCBRGzChAycErp/v17f5AxRNjujHAaxmkcb29uxmn67bvv3VIquFBabc2jqvM8g6ivea9PCCEQkGRxjIRwCK3ZrVevEtGSLszMAcyMWQC5A67+OOCwDRECtt9rv2IckQJHdbVSDzLMClOEIIhIImagWtAUUEE9t2dWUyL1hBZC8ESZuwTmerwHq/Go3zZVRh9O4+jeAtrbY4wxsmpB77Tuwa6BMbFbLwMBQ4NiSlotL2Nw+4KVS8pmgsD1PKk2n+fL5bIsC3ijw6Zwpauex4i4mSZTZUYADuRCMR6oLaWUzNmjfEQEBhQ0Mq+ARz9mOxwKQGJg3qfcDJHBirndMRHRElT74b96vm5afZ0TIAEisY+Ai0loyyjCB9wGN9c5iVnN0vg8AoCIqZgHxNaqPvz1/YfO9KjrapkRcZqmHkoi4jiOpVxrMNoeWdOxtNl1nwVkJrMO9nvVvlLAnDPWlQjFailICGE+X7AKRvmsm1i2ghiiy1b75cTxohKHARr5oWVsvM+Js1DcBAAAIFEIYVkWbd0nluXy/v37UsrNzQ0Gcp7P7e3tCQ5v374FgN1ux0iuXsC9fadqTmmKwY0eMJPj0wDAuNtuQ4xNtV0BeBhCJawDG4eV0iIBWCklEDGTiea5zPO5zLNo5hDUyip2Baje1s0Guo8WcQOP/uxm5pxGbSXsa9X//ntE5BjWuYU+sGsNTVuB0y5PXW/DrDFOrXNoujd3CpEXXkvlHVR9Nh92JWLh/qWqznISBtfNcGC+6dH6PZuKiGK9HwHE0tPPj7jKnTnzeGu42L+pXrl5xuIdSgGc/IMtuWgGyLupiBzP+XJWsDjuP/nsh7939w/+HsjbwGXa7wE2R3h/9+x+t4H5UNKy5HKxSS6nU87LGIf93WYa4ul8fH5zA0Rv3r+7f3sfx3G3u6EYMhuuRH5Fxc2BFKv4RWVPgQJYsXlZHg6HkejZbrcJrGlBkbvN9vTuPZuJaJnnZVmOp83tfAeo5+N5ZBqGKTAbBjOLPASKuY6Pc3Dd2aki5LworJJ4iETXusqmTuR5fkWk08PBSRnTtHUdTC+5+dnPftYzZo7WqeaS5Ne/+lW6zKfT6ZIWnzMzE9NaRv94Efo4LCACSBRoE2OMPI46DPfv3hWxbFQoGgZENGIBLiBYs4OQs0Btt8XbzZ2IZJFUDCDX72FKolYNBRKYc6hFLZUE6wspmKGZl2YyU2SKYmLI0PwTINS0DDgXq3I6c1ZgYzBmJOLLZVZVT9yLiJRUiro+twd2zBiIOZCTya0I06PCvpyzgoZVWY8H/T6a3Oxmj9qZOXjrzfb7/qe+t/uJDTui/0EDSK0CIFVAc20X1j/0n3F1Gx+7nEJtqobgYuQYiYsHwXq9K2YMgZprATOv36rhcmMM1y/2lwCAJ237/TBUsS2v7lIrnlsZhgEDxchOZsjLQkSMwMw1i2pVijuEwBQM3fQBEaH1dLl7Y1IAXYtngwKgApNr/1Uln4qbVcqrOjvHfAn14L4V6sBqzOusEdVGWbbOGKoauoBEJVtVB6Pm9C8mZjMUZxjZZjMZ+OOIgXgg6755GAaOcRiGZ7d3L5+/eHf//q//+q9ub3b7/V4V0iURgxEmKRxCUUVQA0Jy8lPttYUNANBr9VWVUmmpnrEvEgBGA5MmP9q0nBGrkwciDBx67NjauPoQiUhRSyktOfFK3JOqkXOvJGqIVfashgVtifohVsxMrXj756pY1VKfWLN6hsAA5o20VNp8IjLzzc3NOAxht9/EkRGD4e3NzWeffOop75zz8Xg8nk/zPJ+WOc8LD5GIvWFzd8PpMltTivSeU25Aj3R0P9GPxB6wNg1ydn9GlcuB3YjDyoV3o7Te2n0YsSX3e3lAmzggCmZXInukpqTJNTFs5pm/2qrJSpURRMQQCRElX+OJJyalGrF2e75UWsM6WBuobrV0pfRVP60HIADa6v7r6VFVVz3FAjMRJUmyUoYtreNpr3r3cIqZi+Q1DoKtgJIes3KhHaTqf9ajO1HrO+7BDDOvRX/dc9TYun+UVqpDf2rA1jy9gQtY9z4AdHjIb9tb4DmFrwLwZh7uXMXRuxn3pCu1hru97NV5J2Y2DIPUlrrZzFxxq97VSjO0L6oPvcCTxbYO1KBVodiqQ0t3RnA9m5qVQhj6X6F3euG6ZvyhmBnVa/3lyV35w4bI8zx7SXfOizOeh2GQrO4dxnHMw9KxTGfbQp/Ldlc+1MzBmtP02HazCYBI5GDNgCvtaVV1Hef+gB6GRie1gma4mrXHcbnr9rj995Ze15CAWyurfojtUb5/jh+nuz74ejH3vQ/tJEm0Jn2tY2uzRrB58gnd1K9jgNK6eJgZiPbZWdui62mTbb6c6Ep5u0YO64d6RG3L5pFOn52+kfGDUASq+p9ogyQQkdeyh3YdAQBA4HzJTIQ8xTGQyCWHb74/P/vbV3l59d2rX71+ePi73777879+fX/Z7G9/Spubu5vdOV94jDJbHMPd89sXn74QLfNyuX//PqcSY/zxZz/YbDaXy/Lu4VgIINYBMce5iIGp5OzYLzQCnqqaymfP70pJA1BEWE6n+eHhZhxvbzb46TNJx8PxeMrZyiWd7vP5bjOMFGgcxmEYEXHJmnMWpW7bHQ9bDROUkhSuSG5fGCGE9TD2N2w2G4eWXK7mcDjUJHCM3BpldjzFkH7729/6JFaesc+sCraAQds3eh3odr+rcraIgFhEyvl8Op9P53Mz+NUO1PBCa0cgbV0C1xmw+tWrB1kL+K5hsL4++yoNVRUEUJGQAwUGQg6DQPEVa+ZErOvGJmYiJAIFUfFQT5ZlIQzMkZmByNQlCFByqe9hYvabBkR00rNv3VSSg1jIsJZb7Vulm8W1oyIiRgKu8NiHu3r9IVcXy3XIuhHxq3bFa8/YXn+Nwz78WPjgIgNcKRD3zQkAUjJUn1KVof1PJdcdK02Qwb9d5WkXOr+lph4ACMC+RLTknGOTDvTbIERDKg12rKJ+hNLkUzabnamCKKgBgfrPxL5GAcD5ay3xcAVBVp5PAZC5UnmgJ0bqXT1FKVQrJVAbkRGbpo2ZuaGnBnTVXaBG5JkHQw95nVOEbFoDWzADE1JFQwVjwloljM7l916qZACbzSSi5/Px7tntP/tn//QXv/jFX//8F19//Zvf//2/NwwDUUN0JI/jdl4uWK7q/j5BWSW0CTKznKvumEjua5IYEbEF3GJYBRzQK1V6ogmqli4iUrgGGZfLBQiNULOYWYa6SmWl8NA/zbvSejq6IYaeEoOGimnzsFWEpOlloZdnmhiAnzlZRaCuTwBvTmd4Pp+BWC+XKQ6RQxa5zMvNzc0vfvm32+12t9sR0flyvn94OJ1OS8mb3YZaR3B/KLehkvLNzQ0RnU6nw+EwjuM8z+/fv99OW+i4ZgsapGkUcpMnoqYN0NmifRx8TJ5QY9fBbn/92hS6gpCztPtSRI6XZbl+S/s06/L2j70vEYlc0ce+qqlJvnTb1cJ0G4ZQI+A1HtEEfKHHUj30b+fgbj0cnYwhkgg0UQFHvhGxLFduNK1K03zje8mEf52amFkIgbWKDEKLeNzNwCquvcJmcLXGhCtu/YqH3b66DgsT1eFU6xJDdZtjbVHs/ZChorMrbgOAM++79YHar1CIGMBBqOqJ+yND60bcp08a3/2rb74SkefPn3/22WfuQWMcEPFyWa5uYnVoXC+q9TP2MHHtmKxQjFHxUQTpqFYMARH5cRyJAEmuXrlPekcow4rlAlJ7n/e7aq9XM9tMG/DuztMU403O+dWrV6UUP4ZJE0vZbredPe/csP68vgVUlYF9KPtjoh9NVxuhP1o3hqqVYuH7zquBLQRGCyGM45hVS+Ur9gVzDUS6T/S9CQBh8Hm52EoJo6/t2K5uZ6T2q7n2x1in3dZ2YD16TjDTD7j1tWqiKe3UaN7tDNYXgDzSs7Ke1+pK9lX4t1tsQ3vUnqjvrutdKSHqh8fvvtj67vMBRHS4DHB15KhQ1OMV6/95ueTNOIUwhQHKPH/3fv43/+GXv/rqV9/85t8PI/7k7//0ix/90Uw/nvOWwsuv3x5+/fa3D+X84u52zqfN7fazH3z245/86Hg87qbNr3/96xCHf/SP/snv/eSnD+8efvnLX5Uk79K5lGJFjJABCdFEVcSPdGhtc0PNHL765je77QaYUskT0Y+//ORusw3E33/7jSwXKwtrmQJCuZzv38o4vnj+mYs06vVy6OFRreb1IkR7pOL/eACvQ+Qj6X/sa9KjMkS8ubnxkhLPPvX4u6QFALztFRNbNdZe8n69CxclRYDgrQbViormkkqWXMSUkYC8KgTRwJnNspIXW+0aMDMvucZ2Ou1boPGI4Yml6i3h+nIKPIxIYKpghMDEEclCHKSob36qLBtiCsixRzBEjmKmnBUAxjgxc4gj0wiAKpTZVCx5n2BoxBvX6jcQT+dxGOOgRYmAGKJD2i6T04rTcb30bbWJ22bgxyk8L8eomw2RWlFGTdwjqRVEQAS9jhKQs1jaK+v/kNRpAHXRADqX/WmhWF9W8AjTgl7D5JW1IMHMzEfD3a3kxMyMBm2dMVH3Pb5gO5gJANtpM4S45KS9tFxqT5PAFAITMwfvvqYM2JIsYJ7fcaSuFFTr2KqB22SPvQQQFAgAG8qIAFCzTv5fjlCTIZqKOXSHiF1mFxG9NgTIoKk1I6IiMqnkUlIuxCESOJ3Jak/ctWkGNUMLIXj2BE0qBguKKoG4VAk3EdOAEICoxtygUG0MtpZbKsUw5pxLlv1u2n/y4jKf379//823v719dvfJJ5+EIXIInh90SpWCIXj5ryHUdrC1a33dgdiyxJBScTkgNkbEqoKiJqbFi1Vi4NaewsyxT69aJwSncqqqLrmEEMAwiyzLksEbdg7ei6qSUNCLyxGdkIaAaFoze2qPVH1stY6uO99Xk5maiZlXY5sXL3qJmONF5oW0gZfTGdWWy3w4HJbLZbfb5ZLL4XA6nz3K397s71485xiOx0P1u4BcSxdERIqZo5LORH94eBCRcRyPx6OIANS4P7d+n85Wd4CA6NoJgjj2x8FVbN2D+PYbcquYimTRXBw9VQAoYlmsmDI4jd1q+15DrhJsbYgaJg1N9EbBjNCXlwfH2iRrYOVL8JpQxm6dfStLi9fx8bVGLnEVTUb04EwRK2HYF3aVbcHaolVKYeQOpvj9NHoGdNDIiX9ahIhyzkgaQmC5JvS0KfxcTdpj8KU9qiI6X78eNXPOuRQzzFIgkJkVrec3CgxIqoJugZwNAw3ZavGAZ+qIqAogA7iv8GFJS1qdJ93Zq2thlZKkiSOvvWAfeSLyZKmqeus3rtpTdcmVUtZ75HcY90dYckfgejCqqqY68KDiQgjk67/lr6JPXY99nQjZjebax5tZKXkcx3EczcxL9FwMX5pC1Cp7UADAW1yVUt69e8eM0zSpakppv905zWyezwRwe7dflqVIEinEEAc2szSnlGbVEgJZucqn9JgYEBG9NdI1aPYVNU2TNYFLfxTH71PJOWfJeQgUCWOMlnOR1Fa+R8wtkYqYUgYAQE+OFRFhX6uPW6rxSojWVhBej42ISJuEGxLhqguV9wWwFosDIpkLB1vfdH0/eq5s5cqvG9x72Pv3XrcqVDInrLyYiWLzhb79AdQZ7/g47uz/9i/rK8GfrqPU6wtWkc+V0bTCFu3xujWDMU4MvCRb5hwWywyG5ViUps8WPX/7TvT+3a+/egC8HUZ68+7d5z/6NJzeDjEcXx1Pl8Pf/frv3rx99erVq+e3z/OSnt8+0yX/+ud/+/Wvv37z5h0M8fbZ7qxZcgHAyMEQkuSc0jhumn0xMPKDARJ9/vK55iR5iQi32+2G6eH+zcO79wEVZZ5PD8U0EgdQTAvGQKBzFlUXsx6GYXCWaivfNgDQPtpXnfGr1+7D/tHLG4f1Ue0XEXmSUFW7jRWRMNRsvKJPrRkC0pXN4Xwn9ykCcDifmDkSC5ioFFNiYmLUKh0DTKjWFVN7dpeaxmDfDr4IqX1Xn+X+dCtv+GgZ+y9DO8QrEAJTt7zIVEVwEQmBKDg5XVyKW4OzKVRNQA1kGreOoqGf7kBErJTCHtx0LnwNLLBjDzHGUqrG/zRN6sXbalcPuspWeG+vtWu0lX/VFdb+4fYwzyo3v9Lx9esZfXV1U8IBP+Te/cdXDzYqi3+On52qaJd59FhLM2OMWrwzolpLTHs7rdbUaQ1XGIBut9sYebmoSPYzgZlpkWVZQvD25OxAN6Ixs1pxdBkAIl8dhohIyiVlR9Xq2Q9dwbAOanvItZF6up4Q/GgP7YVAdqU02OqqE0ogIq7FhhTWqMz6Df5pZuZ8YABAA1SBBnEwhzrAgAzoa4O8w5yp99Btn1wXUimJmYZhANTz4WEzxD/9R3/yb/7t/3h//2673e7320498i3Xx6pyNAEAIKXUnY+ZJ2etTTeZVdlZc9Yo2LIsRSslFOnavg352smlurFcHNpCM/KW9VKKCiCyW0yA/o+q+lma2bHPitT2LdA8geezV6uoTtVV9tH/HUfyiKopstQQimIMxIUZA58Px3EzhRCGaYzjoKpaJJlQUdByWWYzu7nZp5SWZTEz19t2vCHG+PDwAACffvrp+Xx+8+aNmX355Zfv375X1Srtp9bhyZXxbTBDXR3t6LnCyM2uBBhouJcbCjV4AumVUptn+7LSlgowLUUFianWnT4yoC6S04/hnXyANDxxxj3Qv67kFabb4gR4tIlWCH1/cH9jXS2ufL3KiNYnQnC7DYKKQABNShx8GFsrrquVE5GSspnlkpCYOPrKSSn1ZPG6C28fW0S0JjjW77QvKqaAcn0WZnYlExEJDXxyAEJUcs7ezfpJlI3I5VH/CsDH1VnNtPYozYY4MGNpFTh9VXPrjNH9hf/74x//+HQ6+R364JjBPM/TtLWrC1jjdSswYvWD3yc1uUxrXPV1+O5j7shx3WuifYe2FdPKa7SK1cR2cnNn7969n2SGYeMf1Y9z/qfz6bTZbHLO7969S2m+vb3tfLmc8zzP5/N5dKJXKfM8M5I0ydFemwvXTMIVihYRNQuBtTrQVm+w2oDqTLxVpDVNk+QsOasqUK3TxVau2l2HD4SvVSLPmhMihxD84FRbRz32zn0ueu9eHxCgR5mu1bq6+nd8cpW+MR/hwWvH1Gg5aABequQL21a8ssCsnjlfHY2AvDdIvaUrZ2mVOuvfYmamdYv3RdLX0vreVtYG/dCFbcGYQ3val9mj+MecF4ogYqqgGAjxPuVjzpfTQ0mnr9+eh3GXS7i7HTa77Wcx/ObN18d8GsYwnx9ubrccUFWnaSqlvHjxcj/uvvvtq/PhLGI3uxvejG8ux2TFVIkZODDSECIAUL1VDw4MtdZhMuE8n1XyMIwP9+/eXM7P97d//+/9/r/4F//if/qf/t2/+tf/+tdf/WaRQjDc3uw++/KHKUPRIlLMDJgNwbwqGhrYClWeu/+8Hrcn62e9MMwMAL3aqtPM9FrzyZ5Id+4cES3LMqc0jEFUwQX1VtNjZoo9aqo4mkvFE4ARgjkFyNlKrN7vxUFvBDPQBjOv10CPcrfbrWdxu1Jz9zv9SddAUs/39n+DZm9+RMyBQjCinEuWgi5LTa5qg8wxcCAOatnMTBRMvRQtUkQKHjeoZt+/IqZZc8lD3PhiBnD+BiIhMSAooXHAEEIkRjUQRYDILGZ+bgsO4QCYmXcdrnGSK/EZqKphd2xA5CYYoNUp9Cihmw5t4fIT69DtwpPf0LWCF3rRA/xuZAispp4BwVODLVVtiOCSkEZmoITASAGdLG8eSJmoMZoZKiBYDfi0gvYAttlsuv4XGkKtShLRYlUoz3L2GFBUFUwCcXSRBAL3NRrisuTuvZhZa4GTEZEiIT4FPKDB1dDYuY6GMzkrejWG9eWKRLUA9ap03tKsbkyVmRDcL4r0hMJVsxWgltWaGBQwZY82ULRkBEYM/g4yHyRR0Mrtg+rJGIJnaZgZkcGw5CRFxiHsdrvPP/t0SfP9/btxCgbBVTtySeszkpcBSW03o0WMWPuaQmT0RE0LNE21d3GA5jxyzorXtPV6Z9bvykVETNWhWQOLMQaM6GcMepQmRqvV5qrFAw+9ZicZEaV1h3UvsDIf67whmHVwGxF8E1ULIiJgdjwcOASfhfv7+9v9zbDbipmLXW52W+fRZ5Gcs7nahlogVlUTLSkbKxHd3Nz4857P54eHh+PxOOckYPvbm3me01KKCCIhU10ohApWVNSyISCqtJbS3dmvHlB7yNhHqS5bwB6EEXkJYBGREAZVlWJgbuPcqmkMsbbo9G1pLmBVT96+K9SF/w3EjFbf1Rb5VSNfO9fr2liHAB81kuwvgBY09DDdzGSpEq11K5KfzgHBcSAahqHkQQC9mVT/or7RqjxG64jiv6RGOLQqgCNEsAr+Kr/ssT2sFBR3c+pH2/b4IYRgGpgMKQ7MzCgO65Rh5VxV1TUQ/XyiLcNireGK03vavriyI8Zx1GvrQzKzZZlTSkwwDMEPEl0sy2F+aQTovgdFpJwrSe90OqWU9vv93d2z7XZ7PJ67n0a4xrW0hplW1/F41KaU78F9CIG9iBzAzFJK55wBwE+/PqC0iq1rcEaDmeWcRaojH4fBp0ZbEsy/yGuHX7586SXyDqD0qHez2fhvxnHcbEbPp5nZ5XLpLIVA5O+tgvRt1/Rlo6qR2RDdevuCUdUsghhFtZRqDzvP3uMhIz9oedgpZsZTnS8tohkRTT1vlrM1Key+KvoNAGAIIcZxs9nQ4yZo3Rr3U32flz7FCFhK8VbBfSf6e3sia4339XtYB/p+P71vQz/LeWSmqgNTP2CsdkfNJMAq9jIgVTHTUkRVUy4uUGYI0MoszSqiVdOw+BTBXA/C+p7NbZI3DajaxESrWQPo+cvrAi4lQzQkhsDIsYC9SynP83/xX/xv3nz39ZvvvnGG5+FynufvCumL/bQxFJFI8OLuFlROpwMRq+rpdLq/P1yOFzS62d8xwuV8WsoCkSIxEKmIqSLQGIeUCljtegJAXpgHZrMkZn5+9+zT5882MW6H+Cd/9If//J/+k1//+tcvP/n09vbZOL0pS1IFKRbjkHJi5hC8EE5sKWDBEAJH6MDM40n5MPT/cO76eOZcEBgROcY4jo3xi6fz2T/EEFNl+QoAiKqAoQf6al4/V0N6X7RXHBQMIE7j9V2uhyuapLATlLV+jkCFEsxtELEBZBX01hkIipWmU4Mvohr4ulHxAKOpEyDidTV0i93LR2pUjTWVOU1bADAEQiQKvouYSLw5FYirJFFwvh1mdVyNEIBjQDQv9IyMYuYhdEvDmRdmhRDMMETyNF/OmVPaxAlXWWZoKGzfyWYGXPPsZFBUMax7KT/aM4/nFcwEYC3uefW+tOoKfN1CUJ0QqikCNZluDwv8N+t/fVL79mvlYg30QlS1Bux6eNqq8tvexI7/aaW/WsPWPOYOododkK5Ti00mBwFARJZ5zpIcIR+HtQH1NI2CmuSiRQggEAdiJdWioIbMVCFGQusxorXapprqQTRQUlQtut5yfUetK9ug4ShM8KjTcJ0mJKLiLbSwfkifPskJAAgUIPuCB6QAeE6LUXTKDJqqKUgxkxqorYrqmJiITvNls9mUog8PD4R8e3uLZu8fHr744rOvvvnt+XJK6Q4aR9xTKNXt5QxQgTfxcLj5mzVOFldluPWQj6gIIQwKuXJVG3Hfl3THZnztFSki4qVjiMhDHKeJWm8mqkC41zZA56KXXAB6lA99CTWu9rVWEqCzQq3PyMol1CCgV++VUorKOI4hBBsG94VhiNMwiqlHmkvO1riPMcZxmi6n0zAM++1WATosYWZLTpvNBhGd9vOnf/qnijDPMwO9f//+vhw6runz7u8FgCJzjJG5dmjuHrnt6Arwz/P8xND3qYGGKRKFztKBWjF8TQCqKqgQkZriKktbDWUIIQR8XPyDiDnLkzDC74pX+vTUih8A1Ll9fdjXYfo6iFlvpf5dvcOoz7t5QstTtYg9n7C+jR769xHzyw3cOA5Lkv7t131aWxE9Gkx/AQVWxW7Tug/t93/dAKvf9PvxXznMDADeqQy8ghlh9ZlPq405sK8lJ46LyP39/f3DO5Nyd3d3d3dHRPM8+871JhJ1v7bZRy+HHUIp5XK5vH79+s2bN9vt9tNPLyGE6u8qELjyDm2P6CpFBgCNqFYje5exisMgosgMAPM8H+7vVfXm5ub29pZabU/bqn5QB2wRrZcE9JkiwlLKeVUT5Uv0+fPnx+PxdDo5Ib5P2c1+/+rVq2VZXr58+cknL1T1l7/85fF4jFyb9W42m2kYvGzx9vZ2Pl+geaXei9PXrbSl101EvcNSliWnlMxkGIbdbuehPxFh8Polr1EWESlpcadukskU0QK0My+YHxKqHgsAAKSUicjzwCEMuDoMdzBCe4Lug5r+9iyQvOFwCxi6K/H11j+tr3YHa69urtkWP3pVu90BGsR5noEphFBK0dbYW10+1mqj5fWOtraY/FyaHa4m7N2j/e77E6ldTxrdhX00mPEfoR3mu83hlf2nazO7+j0c0EBSmqXgGGAaRg47DfD19/cPh/lhkTFuXt4904LH+7OKLg9nIysl2+WyjwMSZqPdzS0iL3NipmfbWzJKKZ0kAdPt7S1PA7n8w+W8LAvCtYu5mx0DwspokSHGDPLm7cPh/WE3jZHxL/7ir/4P/8f/U4zx/v7+9ds3WS1Om/Myf/3qu4w0bffMkTlikwzh6ELhNU8Iq/8DuBapdmvmo1frdFfsSv93GAZYZWm6W7SGyPT8mGe/FdWTdO5O6LHpbinq1W/Auth3jHHgwekxdZGYWadNEyKSpBw+VuZ7Pp/9ibxzizVFhzUM2+8cV81z+yIPkYNTP70F3Waz2ey2pRTypZ9cMVctBDTQoK5rQQTIRARE6EDdJgxm5rRmTyhr69VaBUmQ3P+aaCmZCI7HY8CwHacTnublcms3u2k0A9/92LJm3gqkbgMDaBLxbjTjEAG15kmwhURW8TmoVXKVPWLgrVmEV+3Nfd91VAwAvGSemZHIihADu3augZiaiBYVUWRPYVRGsMN8MUawlrAzQzPvJxCDJ20hBHJ3DQxjiPmcVSRGRq71bQOHZVmadKBIEUIYh4EIRPLh4eGLzz9/9c132922JCmlMDAQLMtyx89Op1Mch91uMycSsMvlVBYNSMkAEQMhM5vIsizb7VbtMk2TGKhxAHPDHZioJWedsQ4r/iICIGhgIsIxBAwMRlVbsJkY94LexTAQewztNnTJeQpIYCZSUprRRMRzEV674ymW6BiSSpGsBptpyKfzzX57vP9+v9umnOK42U3DUrCYgBG6xhHhNG2WPMcQROxyuUjxxisqIpvN5s2rN6fTeRiG29sbk2JqmzHGYfrVr36VwA73758/fy5mb19//8Mf/jDnnFMCoM04AcDlcsmpuP13mjiFiEilFPVgPcCyJFUdhoGJRU3FxUCFgBDR1LsegqpakZppRkREFecpUmDabMcaBBRFkoAUQqQYTw8HM/O5cKYUEyNjyVdaNtXAAs3MwRVEqlvAyBwtsNSLX8zgsZadWxDNuZVoA0IRzzk9HE+3t7eqmqUg4jAMVZodMPjRpRQtZTtNRFRS9pdtxhE6QqamaDFGAUtS3HpK0TAM43azLIspWSPRjuN4SUsYh/3trZmlfNGW1mMOMVaWtqq41tc6FLgaf7CiUK6HNMdKnd8LSCEMHng19VUkVQ0xcKhP5JPCzIZQVAjNcXczQ/N0/1VgpLtqd73ujJ311DryCBE42f2Jg3dX1Gtte8wBwJ7XV1VfJ8xIRMuy7LfbIYabm5t3r1+DqEre7Xbn86kHi8XAWhMiCeHFixc1+zo6Qgmn06kIDMNwPp/v7++32+0wDKrqsaA19RIR8Q3u4Lqqih9TAR3eMrOUZlFTsTBEABXJMQ4PD+9zliFOOWeiMDBfLhdCROScxbkc58sl54yBPcjebDaIVegTAVv3asxlQbI4MBGqFUCbNgPx82+/+fr169ci8uLFi1LKt99+uyzLfr//8Y9/PDbQLsY4z/PhcDCzN+/e7Ha758+f//7v//7t7e3bt2/P5/PLly8dR68yKbVjCQDA4eFhv997OTs03qaZ/eQnP/n+++/9M83s4eHhu+++E9Wf/vSnPCBz2O9vUfH+/v7b37769a+++uzTT7/88svLZTkcDpvN5nK5XC6XFy9elCVvNptxHJflst1uxzGkZTEz34zYcGtV9Zjgz//8z/25PICYpsm1gBBxt9t5i/e//Mu/vFwu2+32k08+KXk5HA5mSgwiGdGmaRjHeDkdiWCzGUspFHizGef5PM9nSTmMA3OUlkKJMYJZShmJNpvNMAylJA81PMkgIpd5TiktKTuwBQCSE5oFos3oIjxFRRCslAKoyM6aCCIyLymlNAwTACwpxxg3m02MMa8Ca1yBAn5M8iqIDp3U+fId9LhszFngwzR2j69gAAamDOQC29SUoKRR/Od57ju0p0RE1beAwxBONPVorIbaVmNEvyviaAwgRcSYeSBkZTNQsCVLKYXDoARG6IkuM/PjfdtlVRy5s/i0ycq1pGLhGHo73VpV8jjSrS6mxXy77SAi2QANkuWSJRDROPz53/xiYNmNEwV8/f4+zct2GO9u9/vt3Y9+70cp5/xwkssybHkIkZkNOO52IqaGYZjuxmciOp8vANaBTAZkQAUQEa/SFBOzKp4LAAp2ny6RMY4bIFzMlqWYAg5bJcLNfrq15fBwfzor4Ga6DcOWQkQiNUPEaRo5BFVKKYXRhfUcmHdo3EG0R/JN1C5cMdawJeFVgWNUhfL4yIeIm2njA0ihUo2hQaLOMvChNgDQujzMzOSRGAAiZi1AyBQAoKgUV0Mh9Kpgg54jAANDtZqgMzOA0MQwEJFDcHuVS/En4hA4VPaN1wfXRlSIiNjFcGssThwisQtO1vczg5ExqxbmOA1jXcdGhFTHk8CsVRwCetNKkSsFAq50B2203XpmQvKTAAPAOEYr5ovbyYjPK13wA0L/FSH2r8QQAiH6waPLbly/v7W179Pc94D3IulOen0WXJ//6hAZABp6HFW1UsBTGC7DCX6w9uwV4nU9XYcZyEBWvzCz9Rm+diZ4/NVE3pyy9/trjW9LNod+QQ1EraAhIBPo8fiwO+zENJdFZMpSMFSOl7etISJzc14Kmh3e35/mZV4yIFGYyKueQrDHt9p/HoZBJZciqGZWQDHrjBlVQNUGYuTKznI5XI8efHVCQ9OHMBKKqavXMeLAzP5wIXjoD91Ae1wKCg/vT7cDSzqTling/Zv328+n5XzKgkoxxBERwSQvWfKlgFmCUjSlpALLkpdlOZ8vTnINIWzGibFCr0Rkuey3kwrMp3PZ7p49e4ZqZUkxBArgfT5UFdXIQEWwkg0WaCiUP1opRYqamRQFFhE1Q1P1ertai1kzY0+5pwCVCGgIp8sFAJAoBkYmVXUcC2unayJAUC2lnC6zSNnuN3otxPT1wgiUs3+XAYBVmBbNHGmzJ9++3lmrleudmQUN7Fp5uYLNrkq5bUMBSC7FLISw22wBYMmpLIt356mPWfmNfc8+SgH1Egt3w6V4ee01Rz/PMzUxqx7jenhqq0tbwbJqnSCfhZxzKbJ+WGoilXXbdlm8xzbHFT8IK76oqi7W0RHEfpNtX/N6rqXyqsXX9BoL7yboo/uuveCaOPanCyEUVZvnlJKaIWFeyul0ur3d1wpgrQXfrqvmLsSxNy+eziWJyDCORcFDOrerdTGv+6F2YhJVdnU7liARWe1hjop+GOgpzaIIlZ3iCjwURMRB8Vqwa0ZE0zRRDGpWSjmfzx8dB7WrKqUbNO+N/ZOf/MRTAb4SNpuNv9FReWy6rv0otdvt3r9//+bNm08++eSTTz757LPPlsUTU3VScs45zfNcuyXc7PfM7DmfeieqIvLNN994NHY+n710ZLfbDeN4uVxYikfn2+22Z12Ox+PxeASpAL/LW6WUdjd3KaXNGLfb7TCG4/FeSpmmyXWf1Xl0TkGhSK20oE8xgDcgs1ySA/DTNO33e29coKqn0wkR/fSiuVwuF/9q32juDgIxUW2zGEPkEHDV6NtXwDRNgAhAOWfVmkcqpbx+/RoArAI3vlUZEffbDZoxYmQEKcuSpRRvhOeZagDwQ4t/tR99PWbyyfId14MqZ1WZVTWqXt3xOLS1pWSonMErgw4eBxIrE1HbVfaPsoagrxd/38j14NfN44pEt97L/ZcGJiIgtfdzkSKqIqpgxFUscm2L4APZgH5L7nHwcVCEVx0w++g9eORnLYdGRCnPomrG5kIRzMAABJubG5A0l2U+LwHlhz/47D//T//FP/+n//TlbkME/+7P/vy//+/+h8hxv785LPn1m3dh2mIcFLCIYM6cZzIsKQeAQIxMPdmoNfPgDCh4dLcISIMieU+NDEoYjAFAeRiCAmXBJWNSUAUMAkgYiKAKia+eVJsqGgCsQ3/XPetmeT2n66C8/caV8SoZElb+0avXOmRTJwthGML6N/2T1wRU8s7zgEpoqvbI2H/kWt8Vrahl6zXc8aa1vzMz5vjRz3ziYgAgILiACaCqE00QAUGlJAJEqh2NG8uDeJyM0JEnZq7cXLSUBZECIiJKjS1qokFVzd1kTVkbAJjIGGOSzEj77e5w/+BG39NqNV5XQyRBI6JINdttDX4m7xiCwNesFjQgszILjczIqsoNACKWmpIDp9g7+QwRRQXVnIlmjeQDfq43MNGrKQSoSu/+dgNoVSxUoyt/AZj5q5WcidIJu41H5NRGj9LMb71JjKtUAqDLt3vkLyIBIRJ7QxYQRahtKPO8qDrMxwAQAoVxICIhnsZxGkYAUDBFHDfb7Xb77u19MTD1JoWkAMaMzEULATtJ3wxMDaE9kajmYlqIKKA3uaTNOCkAIzr/O5Wclxlb7xIPdsnZ1AxMiEAiRXIRLibaey1nH7crrcicrH93e3t4WG62QzofP7nb/+SHPzi+e4N52Q4DCyQv9WGKFJVAwVJaFi2X85JSISMzm+flfJo//+yzTFlELqdzmhdmjkMYxhGZb/e7w+F0Oj7sttMnL59vN+PlcvLMjBfhAQAjKVIpmREVry3P0NcP+GGVEQGRG9cOEe14PLcQthjW1k7MnKU4RuEHb4XaO8qdHyKZoeeuq7YPkO9uYt5td/vd7na3j1P8zde/KVYLBEtREQWTJqoENfSvOUfXqtKur1bdAz3yXk8thaALh6IBATJS5x70dY7+5ABoMIQwz/OSZ3/BkhMiTjFmrOnX+ul9gFYUVWjl74joI7YsSwjQSnIBAEKIYNT0DxQAVEC5cnNFpYVwAACKoKrMgSmaFimpZBWxayjrzbOh1jvWW8KKvlS/Cwam4EaNwaxiOS7eYE1xpX1gBRp+V+hPBJ3wszb0/TDzocnGiq40w2FGhoSYc045LcuCiDGEPGNqJV8dcLFWw2NmKaWcFwDYjFOHOeM45rKM4wgQjsdjUBqnmHKJA7cTuAAqcU2kaF2SZmZA6p3PrFq6uswqzQzE7FrzYGaEpqrQ6NcOXSJijJFicKTNlZqaS+v53dq3tSEgCWoXHvN8d87Z3cft7a0ftk+nUzufgDfqfnh4MLP97d6X1uFwwFo2ZwBwd/dcpCYiVOByuRwOh/P5/Ob1a/firqrcM+8/+tGPENFfxszPnz/fbDYp59vb2yTF454Y44sXL+7u7nLOx8Nht9tJytbK390zvn37Nue8nYYXL54NY+2rsNlsYuRSSnHIwJ0CVs6SrarYm0s0ahWuzmxxUPx8Pvun3d7ejuPo3Xx9wUzT5NEMtsO8Fy/C0wUIfZVai0Hn+dKLCx2ZrieHEP1HAIhM7CoioOlyFhEV8R7taqa1vMGVZN0VWj9+8CoDvyZV0lV34SoT0m9PVSvYadBfdo3S2hFi/ThWu1Rr333aSD5VlmPF22Fmh1qtKas8wY8RryBrj88qE1cAAAMGK8UMzB59owH3nLrq1Z70O+8Euf6n9ly+vWsBfX8LtlAY4AppWz3dapNaAUTvJhbA7HzO0xB32/2438lyefdw/z/+2b978+rbmMsf/4M/OJ4vKHq5zHi6JEMOYzZgQGXMRqoCWQmQECSLkqKgmRVTrU19sKq2+3EFqoahARGiISlCqV1mBAkNTYkhRODBOCpwNpuLnS7p5uYmMAKymdWDjQdz1U76qAC4XrCZtZPzE+u6ntnVRIMnya95pBWjbD0pfhF5hXHNxvvKQzVUa4RbT244Qo6eEfnoFkNsXseg9tJCBLx2qepR/vpm+vqHdhjovuzJPX/oWQJkEc2ILLGUnGOOROQW2SQnk0AxuM0DEAATNYf/m7MHBDN0XLmOoLphAnCMsZJmveSgnnRTztM0AQAH3O023hcQ/LBFDY+nmj5TVa8Tt1Xupq7s1n/dVkS9J9ugP62ZrMOI7pJhVejz6DIhuBLxYbX3PhzT9kVNDRNrrF+72Yn1F9u1tByZ2cmRAGANIrJ2TFfrlKSKLDLHYYwVxa+XqNZkdAiBp2GaBkOIw5CQeNo4jKeqBBpC2E6bYRiO96eBRaKJGRiB+blntbhd6MkHx6AsKafZCWDDOMYQGEnBxhi8G4CIQIP5y7EgIgWOMfI4dN5zziVwi/maskRYCeG31l2OLjMByjxHwHQ+bQb++7/34xfP7759dvP24T6Mt6BgypeWyQoEzsJcFlWFUhRVmMM0bqdxv93sEycV8a7Srn+32U6H42mM8Wyal8v54XC8fwjMkjKa8DQGQvXCfDAULSkVFR6q58bW3jWl5KdtRDRD9VpjUwCY57nnZ/2c/CTI64U4HuKHoSb4muYgcIwhhOhY7JJLypaLioBoTLVcT8RyrsrlLtgfgitjOPGMuqtAfGoBP1zMaxPDVqM5UKOWeUBq8iAA1OJ5AiQAEYnMAlCcK0jk8UFlvHhRWlvPZhY4+nFIRKRc+dm+JFJKIYCZIVXXNfDoBTOlXJMh3Up0cNe3mHrhRynF1AO+DqrhKn1hqwfGldFvr1wPFABU7TbPsWi5KjzSB+/tS70bHERy9ST4wByvX/bEqvi/NXusaohFiolqyUUVmTgErza5XC6udx5CcPqB08hrdjjnHq4xsweIPjtE9PDwoE1OrqvXrYytzxfVRHcN0CWLqQJSKCq15L0NL9dOagyrYhIfpVp22ZxunzgP3LVytLi/fhjDam3Y9d6aiL7n9Ha7nXeLA4DKdBLp6uwAsL/dP3/+3AfhfD6fz+cYh91u96tf/cofM8YYw9j37B/8wR94O0+Pek+n0/l8nufZj5pePusl7NDOGHNOLscZiXsR8O3tbQjB7ScR3dzcuA158+7+crmUko/H4yRVCwugJnj7MVtENKuIxMj9uzxQdsXYm9u9Kw+WUlJaPLGjqp4bsSY8Pzkfr5SOtVOv6XcCtLrQX43VPFeWVI/HB0f9Vb3Vdz1hvnjxAhrqbyt8N6UlMlMIVdWunv0wDoMoZrGeEO5d69ZrpnvJXkMFTdVq7d872Nl/OQxDTS0+Dpj6Pnpq6KCqlMLqeNB3NPcOmIg+YtIl2lrs5RuhU8p7IKGqYEaECEi1VY7TOhDaqm15m9Vmb+eQbgp89Na2ZWUl3HtcCeL9NTXpAY/MEREZMGKokDt4saGowX6705SWJdPAkeOSl6++/ub1t989j+Or3776/t3bv/yrv/7JH/7Ri82NiBaROAxKbAgUEFx12KNtVTLwipEOkPUOvlDJE+1nIEQ2BDQlMARFIkIGUECiEOM4hDAQEWqlHbqV9aqRdW/EmpSjCkP0hSG59EnHVbi/PrP1DyHC0MSj+yv9o3wTrTF49y8i+XEPmOs7sXl2cjlZQIVaF/rhhc0S9g+Hx05k7argY76jmc2n/aCuq/HxFcAEpSAaSjEppkWRUCG6HGTg7TTtpg0R5SVdllRKQiOyYIAaXEwKAGAcByICQhFBLSAKIuat4A2wxsNg5icks5YuR0TCMI4xL2m+nG+HwQwIENu6ZagCHE8Hy/qa9qcyRCPyjWQhdM6bu4r6AeRayqsRrGMNjEjgq8HlJpzO41C01aaDPoWeC2nAfrsfAOkqkHWnoZn5zROIWT2QEYZ6/FVjQBD1Bqoue1dSJiKOdLWYTbCImBFrYVltywpeGSRuxMdx5HEIgYuKWsll4WGT5sWKIGIMxEiScgI4Ho9zyksWIEIA38OMZEj1iGoAJqg1ezMQupI/QBUmIDQCmOfZczlA6N6olLJ4gzY3l1gJ3CJiJVP4/7P3J02yLFl6IHYGVRvcPSLu8KYcK2vIQhaAAlEoEECDIhRQsCCkF/wHbKH0prf8ByXghqTwT5DCPXfkgg1hU8DmBDQaNTSqUJWZlePLN90hBnc3M1U953Bx1NQt4t6sLlIaQnRL2UuJ9Bvhbm6mpnr0DN/5PmZCDqvsCyJ0IYQQ2QtnamYIntAyMk3LtOspSPndv/3bv/3Xfv1P/qs/GCPtOj5NR+qGjgmkcjMLAiplQDMLIcRokoop9l0/DLvpNAFACLHrA4OlPN/f3r59a8PYg6qpBKL5fPryi8+e37wIMQzdCIgmJu5jgZVUTIGJnHkGpCb7i1oRVVMiIqQi1toEVXW3O1zq4yq29ntxDLD6/dVOAIBak7iu6xkARBCxpERruR9E7+/vb1+/KaVQz8VEyqXnmDk22+c0o60PGExb1tknc60srXXhJjlXjZdaoAhmJMZW6dj8OqHxkbuqw7qBn0+n3W7X9/2Ss5mFLuacH+7vu2EAqB3Yro3gCJYuggspUGBRa2XicRxNoYikkhUMUMyMkWZZVKqn4qV/9xVc+mfrBACAERpQkspd6OHo2gtB0GSosRYOcVNSr8YFsQI4EQArQ4tWxP8lcGp+f/u4rNWAi31AdBUq2Fj2Zn/asbXvPmJtz8dK3GFmJqV4F77PKBceZmaR4glLX3rMrBp9oTGzo3r8fMMwjLvd3cODDyOtIjWbogHAY4Y4Va3wnsoJ1dAU0phHfeK3G0kpqYIpeoGRKATuTLHS+zIB1WZ6QxdX9sqtT9HLAK63cAnYVEVVGXkcR1V1x7fdQvMGmHkYht1ud3V1FWMUqxFyCMFZcRyq+tFHH+na3hC48ytPKR0fHmD1LW5ubj744AN/Ct4UISLTNL158+aLL74YhuHm2bNpWiiGrutiRBBNqUzTYmYEeHw4l9pRoOM4DsOOmT/+eBCRZTqlNDPTOHYl59PpoWLNmyKKqoljiC8gGb9BZ49paftlWUpJHrx1XRcjl5JKSczsYtUpzdM0EZi3ZBBFA5iXOZdlntJ+2FtNzYLHNinLOaVlEWIOoYsxDkN3CS+JRCSrlVJKrYYRAEjOoK4fDu8Dj/lNGazB9rJkRHRCcMcy+Qzx/GDbCttSaoDSNj/d/VB+FHJX7rVNU812mSEjrZn+dpJWW3iS1Kem3gBPFyw0dkWkrQFZ12aNkxvDMGKjAAEAqr1D69W1eg6udQ/f0bZjuNqQ1bN5nNi2pgNg8Pj3lDIiIq2cKw6+QoOItIjlpFEZmJiGsB93HVvRV/f3r+4faBh3188zwpwLdB1xTKUk8aAIwaBIsiJBqRCorm7rmk9UrZeo2C4e0DAEMkMzQFOsMjcGgCEwBzLQaZrGacBliVSbD40Z6+boW+1qu1sSDZw3ExGRNvj4rXVtsQFszLgZYmAzFBEHLCCiE1p4c9rWMiM0VrIq5XZ59GZMxGtJnC70bCBo78sxQ3VxsZZ3L0+/0kpi3bFX6pdlnttdwLrjiIin/B5dTPvux0eICOhpMzBGYyd2DJZT6WIc++F6v98NOy0lT3Ne5tgdbCWswMrF25aS2SoUtdan1m3MsfF1YjsGKyBCjIzIpth1HfN8e3u7P1y57bgkR9fWFkSktZiOiJGDCw/Y1lXaEES0+4fNFYrm7e/xcdGwvdh4QtpCxXaq7bx59PwAxC5pxbYOn/zcfB1ut39ds+YhBM+v2JqkLKUgGgMiCiJyQDBDNCQDA/c/Ow593xMzrBZEckm6lFJQLYSgCqfT6SQPABBC6A2J1JDU2HPFRIQ1ZwJmRgYGgABkMA5DQJoBTATNoIiSGJJq9eM9D4hMzNyZ8Tj64lyWRddMs9vZwKFls3LO5Gu0ZiiLqkNWwF3/LmBHGAm/882vvfzg+Q//7E8/++wz7sd5KmxAPRMyE4poyWmZZAZUiogcQwcFc5aUClF2sICIikggMLMY+l3HZjoOw64fACDP6f727nq86rquLGnOSbMCESMqQEC6Ohz6q92yMnA7E6bvglIsxuiNa26sVX1zFa9lExFBBVG413vxj3AF06splEBMgUMItuJeUkqx+jTgss1D7IyC9vYwnYtVzqi22jdz78lEtdV5q+9phWDaFqDXnz65fHthQERiRFArdpFqdpIOdRi/W95c8pKc5j/2vaOi0jzraoAc6+8TbZqm5n7BxraKuNZYBf2r1ZIgCZVct2qHsNcAcmPv2k+1yqjgnQZr8ZJU1cFaUF3b9bNMtbdh4yt4IKpr88DF1nlrAF3ACU+szXafbv8UKbCRiWhb0WOGqEdWwh53B7WBQiJX+UopaZGSU2+RcGWxLCUS7/f7GBkR04ruwCYczv3Q914BaCl5d7meXAOuOaec0wpOtPZ7divGrCDtJGamAGJ4Op3meSbAUgpzaO4Ur6JahhXSAOTiG+XdEQAArZUKbLPUD1gjQFtZ3v3knnf3n/42H5ZpmaQRdKyBkEcCzbmUtUlgGIbnz541S9UKR26ivYTS9/3z5889uX59c3M6TV4mV9WyJC94EtHp4bjueugBA3gDSQguNxZCiJH7PjoDEjOnlNKySCW2iu7ru1wJrsUuB3Gp6pdffhlC8PCm8s+ohkCHw2FZJo+lOw7n81lEUkqlL/62ruug8b2i+s7u27rfqZosy/Ls2Qewcv154dQHyiOu5FCsqsKJbhWpeiTbpQ3LsiCZbbZ4Z/9wpqxGkNIy685H3nbzjZdWZ1qboh5JntLs76O119ZW1/zJ/uuLk8x0w5DT5qc/Av94WKXx6gzBC2fUZRVUUPFTIABzAC0NXOCOEK3MYB4y2Faww8jgUaTko+EwLVoPXLP4TiKyXSy6AqIAYEvRAQAAJOJpeCNWImSKhEBM03GK3O0OYxdoOj/M52UcwjgMUzkBQjgc9i9eWAy3x9MpyfPrGzEwyyBKzAHJKQAdbmm+m1xsljr7PqwIiEv6wyyG4AqcgIaq7gcAqGYlIgZzwUsyVck5WXWEGFVVPHwytBXnVkNKrIBMW3EEbQS2tms7kdrzkrUxQzeNfNR61TYfbxsuPLb89bl7FWtNXtQEH7w/5d8+1c7ZLq9IefLXNp91hbRtN6DttITNZkH0NOIIgdAMFJ1z05DM9XMKaAgUIwPqvExlSbkyD/hlMVHwmehwgou+WJHGWOj1MgDX9KjhtM9ORpLsmwEXkd04pjk7YcKlYvJ4x8N1SbsyMxERk5nA5gHgpqbz3sHVTRvN1qC8WxBZv3aVlHq8rz950d6gldefa0IRAACfXMwlSbZBlRERCV54/0RdC5CZ1/bcRAzM6w4H61NvMSUDEZiJp0XRwyxUEDW1LGl6mM/ns7tKz5+/9LygAoLS6hkLIoIBgetGeUoYANfcA5H3fASEGCLFbl5K1w8hhGmZnWCRQq3yi4g6hAmqekDggBelBWNCXPePXCeGU5rqOlssTctZCMrpX/+r/yKWCaRIWnLOAF06n9JpyYbeMpVSOk4z7HbZFJEJeW1pBRVgjkTGYIQYma+ubj755JMPP3r5Z3/2p90QI9Dt/d1duVtyMtHlPKHRkpYu9NdXV12I53nSrNx1/TBYSSqma6XPHzLVeNgdCM80mYgs88JgwJvGGCZqkkxQJbvNrJiiWkQqamjagmrfgHfD4B7DKRdTDUjeTUVEATtvZm0TTB/jXDcvBAmgislfEm9tLbQ316S5kaP8saYeqq8spXguxCehI74VHOsffRqM42hm53lWsG7oU0rwhExtzVh7SLZmxaqRSimh0wGJmFmRBR2MAdE2HIvNGrob16yMD76AqaGBrchdhpaJwZrbsxUN79uVVU5lIMSAlbKwvdMH0mevopFB2HSDbTeVrUXeZAqcMX2TXHjHWG1/g4giis7KjAArERNsXJnmJNXnvkofpJSMPM1/qaUgGhG5c+lcryKFA+eccs5EaKbzPPmMpFUspS3nekOoXpZbI5LHlhYVvEPGzVhzejaoEu/yL6VkKd43b+YpDK2ZYHQsci3qupFzp0FEPN9fP6XgjnhzRm0F07vb7T5cbbYx85nprrkHOcyh7/t5ri5mexp+hvu7u5budY/WXYGrq6vz+eyAn2EYhmFIKd3e3u52hyRlnuecM8hFg/NwOJiZw/bc7zcvLJzPIYQ+cowMAKfT6Xw6pZRCeAQt8IwyADgNIK04E69gMPOSZled80Znv+sYebfbOfPkOI4ByTkBh2FoJDlE5NuLM8lY9R0r9WYIgTga8+FwULPVKypNS8s/gt4C5H2+FBArjZuKrPQkdQLkUjggcvSZ1Cawl++2Aky0khh6XISPd/lmct/1fmD7ev1JG8x0O49j72C1Oc2hN7PWb+Amxa/Tp1Bt+g6B1pWIq3r3hra39vLFGL01yBegmoKhgbeTArEQEZDbagJQRnRpi+bGbe3D1mIgopnCmqlr9kf1kqqALfcKAFhAHBARwJnxlAwjEAGGfiQjVEqTaMIu7l+8ePGNb3zwK9/42p/+4E//y3/9Bz/94ovn3/z29Qcf5IfpNM3d0PtNB0I2kKKdAHKcJCuArjVPq3VlQ2BdLZitfj+CQmEERVBTQTBVIfAmsRJCkJItzyDJpJhpkpLTjNh7qqtorbTryuEGa981rDcv8pSkGNaqyBPb60Mt+jSP1spKFwu3nVeGCKgrQ0S1gA518W3Uz68GZgrGgd/r/W/z95fr3Pyzbjpr+ct3xnZt7T05P+oVaTf4HtdfsRgUM/DOTlZwzMxh6Meh75ksz+clO3J3t+/PaVbrzVDAggVEdNc/9t2aFKvrNkaOMZaiiEgAXNGDJKKgUEwAZBw7H/Gh3+125fT6TC4jRq37EBQQFAyJVqazFX3voVlVXFoH7pIPaE/Iw0xC1LrIvVptVn0gqsWUzWGmaBdyOQLwbleydpPsfbj+SwQlQyMEUUFCfFTuXxvflKrkAjY8GRICqFOk6iYzUdOu5NlxKkXmOQFo7Dgn1YrTceUhMIFSUilFcva0eVYJXRQxQihqzilxd/fWe936vj9mMUXmEEKHwGaILGRGXbCKR/MrdUyKns8LqKEpIATm0Hfjbtf34/GzL/eMfewkL+ciBhLj2Pf9aToDgJoSAqHXh5AJGGubGgFS4IBkYiUnAsfCQUBk7mLHkQOTcoFDx9Nx+q9+/w9+6zu/8o//0f/oxz/+8c8/+3xK5W4qt/fTlLXvwuFwELHdNN1nKHPO2XlgQmSOMfZDICBmZoRlmY7H+4eHu/N0vLt/+8EHH1xdXUm2VMrx7sxkCnh+OH3yyScxD9fX11/72tcQ6LPPPnv79m3JSeaQtDiiHtSKiuSliPWxA0IWm9NSUnExwJzTOHSe9hBTFANCUKjOnKNldPWQzER1N/aeNdcVK+mB1Nu3bwNxF2Lf906nCKICdjpORo2e65Hv234+ebFWYi+up26wpM1w+Km0qBGpghITkAKpWVGLXLFwpIBgYIIGbGp5wnn64KOPf+03vqsGf/L9H3zx1SszoRBbJLMlA2NkwyqgWLPRFBDRvcMQzIxEtGTjgKZ0LllVRYWIusjkMG4DcbgtoZG3JYsrUGQTjnHlRdFSYcoBEcTq9ehjpOza7m8YsLKSOZWQ+xWXiqx5GEdGDeZsVVXQhTas1g5BXX3Dv9HBRtt9aGN2jGrcCOApIhUjIGO4yFoTgCKySfEOoshh6HswCUiE5jhvZmb3PExLKTEEz+yGQMCY0qxiKWVm7obBgeAePvkLZxShlRikgmcAObApGZrDNlURRIooOZyNkDQgAREzceAQY+y7AEagxQxEJJU8p2UcxyWnLCWEQCEYglUNjUvuyjcvADAQKc4C4kGUrtk4RnNhXZ+6XMriILdSNEb28SEKzIZYE/Bel5a1pUrVi0IXP9I9PPdrQ4y0Cvbh6k2knE+ff74/HPq+P0/TNE3e/hRjfz5PxYFDFGJkM8s5T9PUx95MMca2c9d+IeZlWQh0HHvR/PDwcHx4MJOcVTxzj+SSl7mkLKWuXgUXM1EEil3Xx2G/O59P8Ngz7vve4T2+wM3s4eEhpdR13f3tnZnN88zMJedpmvwNIgKliAIQRcS+7/thN5oty6JmaNTaja3mHZkIA6IRgrmrpyZGMZaSbQVOMKIAVtLxFcLmTTtq/mQ9166+9lW1qDJzlmQYQC86DzU/SY8Ev6q5A+hCfELuWZOPIdTYffWb3UWsM3ytQbnTLCLd2hOItRdcPZOFiAGp48CVIWBNDAMbSAuDV3l7d94qEeqSpYgBICAvywJASAsiAtfUAKpx5O3FNyvtDehbo7GGTGQGgKxa1lQXAVkXBwNxC2KG1WIAoRP6ACBIXSmgDExgWQqbdl0X466UdH+eyi++mLVMClcffrT/4ivouuLKUyJ5cbZWIADJkpaFAWMMSFJhIABQlaS0KDgT6noLqy8OIJIJ0NBxjGrmrreoaaBLlYPJ00x1m3CBezUACI7frtku8cZD9/4JUQHZR6AlzVer5mEnmCcKyayS8NdmpXWQxVlY6HHVqK0y3rze/klNL75i81bNAl+4s7bHNvV88eaxsqOSQTEF0aICosU0YBV3UjAGNCZ2knsTM3f0L6ELrAHI9rsDd7lYtgKqfYQwxoEAk6SxC7pM3/jm177+jU9+9rOf/PmPfhJC2F1dl0WFegB0fgcm8ipuzqkqYoqoFymMTLVjCCtnj+PbkDhQFJGu68xwOReO8frZLovw7dtXX37x9a9/PQROKQ1jV4qkPB92e1WXLqqec0s3cSBtkaUaNFyRtxhi8LFcGU80pRQHDpFKSVa8iBwRIhp5NdPMVLNaVk0CKNgxMqNFRZDi2l5GXAjVQBG7EAIil4Q5a1agoCsGwEHeiBiZU85D3y2pqBTuuyKllDIMu+V+6rsouaQ8q4ii5ZSYqBsP8zyfTpNqISJABorLsiDztNjV9QcPd/cxOJct9kOMMd7evjkej2I27g9zKqr67MXzr96+WXISESMs/Qh7W+Z5ynJ6OL+4eYbA59O863d9jGVJy3kZXxxU1RQNpDYCqQIoh+CU/6CmVjLocZlSkU8++vCvffe7gbt/88d/9OrLr4ahY9O7168ON1eqyoZEFDr2lBJRALWSSgh82O0QcTodVXTsui7Ev/7Xv/cnf/zHd3e34/6ARX/jV7/z/GpH+ZROdz87n07Cf/pv/uzv/ff/zj/4u3/fzOK4lyJT1sPzD//zf/5/v7p5Nk/pWOw//1d/OMZ8PB7Py1zyXFQfjm9LKTc3NyJiouQywaivvjx98cXnz54974ddjL1h7MedEJ2mZbfbT0tmDtns9d3dMufXb2+naYYQNT8IAQO6iJu46l0gMXg4TyBqhAxoDAgYqct6yXoiNhfi4vv6XwgYEJhhPi3umROSC1j41hrG6HO+mJG7qkgA8Ozli0Yv6BX8aaqgZ5+ErYLv6TotyTmqaoLNAAHJ6ndV0wOtvxy6cQDDLJCLAmAURGZFPJ7mQCjzBGm5HmLHiCYD63Wnn3zrg3/yH/6HP/708//Tf/qfpbkMAfc3V6/e3rrwPYUwxA7Rs7+LZ0EYLRBDcFtsALam+lgFRc2gE8GzWQHwrlZFTABFNRAwkZiBCUtBRd8mABGDRQ4KJXkjBHipMiBCyUUUVEVBzYkUyBA0cthYdgOp6WVPEaByYK5ZJVEwIDUnVaj1hAplVWQ2k1qOZ9d2dHgKGIDC6t3jmo8R9bwFGjBd5H66HkVVSypVEK52WYxdL4Zv7281Za/AXu/2Dv/ANW05xE5VHTUOKs6JWSQtSxLxPb4X01IEkcZx50BNRCSyGC9quCLq7TRETK4bXVwtGtTQBFARDAI59wBqFkNGM8GSppnBAG3REmMvIqksCjKXDIE5kFb2OBeqgJTmwGiKBiClcsQFplCVmKnNfBPLOQExAJpYlmKKQAxGqQhTLKoAZGjzkgGVODrJuIF4lnrdaFvRpvaRcvCEtDj0KauRgQCSQS5CBsBh1w3FrCzZiEPkYmBZvFGwY/JEu2SHHHAfeysmUqYsMcaxH83MRHe73bQsFJiQZKWaYeZSBMz2w8hIy7LIkoEoxr7rusUBKqp5SaDGTDFGCiQiN8+fB6IlJWA63FwPQxdCmOZzUVmWJUuJMcahpxhUFQgp8P7qcHt7W1I+XF3FGA2AKfr88eGY59mAkJmMPQUmoro2rXmzk4ekDZTm8XFJSoAUo2TLBcDWvRiKiGW9NCO2LpF5nkU1xpjzgtgh4ZwmIVUsauYIdPSuYdNIzFQrYNW1EgCAnLPRJsVrFgzNLAKJmSigetspoqKhhyEcAtcsCLgca3AyH3cqedO5i6BM0JSzPQIpRQCMnOm7Ett7jj/3IXreLS2lmGt6WJEMQD68Kc+x74exiyGAGCGQcVjHhIk5dojYqmdrVFANDoiDzp01vRJ2EkIlYIO2XtBBVmpLDXoQkcCwcqMXyxixoCYTRMRogjBN8xff/xER3T6cod8LhHnJwzDmnNEkUB0TIIxdb2bZDGNPLTmtpg4GBVrZEz3VVdyZZiIP6YkI0RRxxf1ziN0sJqrU9dT15Tyb5Tj00zSFjpl7VdBSkFB5zXwDo1VJXas5AiFrvqI5lMGfJDMhUOTgfEf+09ChpE8yZYQIrVHTaiq4VZMYFNBIvRa5Yf/kVYimwYcQ3VJe8m6qF/BqOyeuFU9w2hsR8YqlGQMCESgUp9hyUqnqXAAZhNBoTOv/d6sqtpmBXqrEIWtWVObYd93Y78YwEGBU6In/zu/+7jd/5Zs//umPvvjiixBpHMbzNFEYXeuyEcvU/PpK1tuiZ2YOhOy7MBio+vUwMAAoslMCEtXKsm/259NDTnMMOyYwKaASnMHpvc0R6KgyWMGlaFV2dp2UsHJ4A4ARsXWhq+zCACEyevZdq38LZIjWESMFRFPGKVN12dRIBQAY2JCo66QUKXJaUpDSmQ1oTCTAPleIgCm4jUCED14+//kvPo392Hfhyy+/2l0dXrz84O3rNzfXz44PD6fjOefcdZ1jRhHxRz/6kWuwd1337Nmz/X4/jiGE7nw+lmzuMjRfTVUfHh72+/0wHoBomdLxdHw4ne8eHrrDDoiPp9Pt/d0w7G6ePx+v9PbNGxVcivhwEWAf4xBi0Vyciors0nsEAkCAJqYm3jtTSU2TpHyb//iP/9hEb+/v+sjMvMzTNJ/7IRhhCKHr4zB2Xj1H4JJESVupepqWUlJZwvNn15IrIDUG/uyzz/7wX/++pOXZEDuW48O95OlHP/v8NP0/Qcs8n3/re9/76c8+/cXnr77xrW//4rMvP/n6t3/+6S/+/Gef8+H5scicFlmbR2MgjpxKNqswRCasmUmAu/szTxLCJCJFAZCXnMvxmEoh5tuHo2N8KXQ7Zx/H2ohEtd7nqTAoju93FsjaS+t1m0vLB0JN97aVv7EvfjbAEFoCATeHrRAXRLxog2+BQ6opJadRb3iYdX9qhWOJjGtJ/EmZ6wJWab9UBFnAALNYFqWixYiEQLWk8vzlS2V+e7pPUK5vrso8v/nq88/vP/utX/0ff+dbH3/66c/ydLy5eTlid3s+7ff7RXRa0nw+n1WZqe9CF2PKxXl5TGo/hl/1yscLAEDIQL4rKHcr4S6AS3E5RWAdWFrJtgkRyNCE1s5UI3MFchMApsBopkSm1XDVDKKsJUQnP127o8yEkdaHvsFQro8CHckKYF7RcQpj74Wtv3H/ck1lP6kzQvtQ5b8wAAMtojVxoWpQ+6MBwNGAkTtmJsNSSgDTXMzMcSyIqKze51AjdlXncwQzooDIZrbMWUlbsQjW7a0VYR7HqF5RJ8UQUMUBUoYmAhQIag+aq+wZoaqqOP28mpmA+F4DSi6Nbu7ErElYRRuHjlaXwvtk/Pa3S2b11L2GQu0FgLW8splB7YhDfw5Wv+ky29db25Zf3qmVcWhd4VaplgHNklx4u0ulk0IwDWEwM117sNZHTdyRlctmXyQDACdWADGtEPoQ+r4HM7MBpRCR5BKQvB+g70bsAokU8Ia6xUTUpKhY0b7riFBMsyQphbl2de/Hfr/f1zy6M3sqxhhfvnxpZqfTKefsgB8XTOjCCB6HNhp71VIEqQOorJBrRR4MqWQxZEPFNrjeyiJmCCqllKK55Cwkpqjel7TFPKw9M7h9uL7C1QoFVlQFMVVymSxynfViuKoE1edQV3E7SV10ZoCoRUy1khRXVmYntkbH22BFZ9QVXUwNwcuovNZDiICBA/lsFDVy9EpFLqE3YxqBGgCBIuCyTDkvq59nauD8b0yRCB0bIppV2QAcu4J2meq68gttcX3NSntgvE7DSjPQrP1qSLY/NQTPIwNApQdyr/Zi9Fr3qn/McQfIiliN0GNu4jr8tVTpYwxmaL7CocZD2zfrqsrs3+6j7UR67URrNSZiiCEEisGK1LhvYwfaZoq4EmIarnk5RWQCdaKw9/18d3zAybvhYlLgXWvdYJ8tSG6j0SZeKaXh6+v8RkTE8j4y6Dp9N8fluypPFOGmPQ9W8GSNArE2UxoqmileyLsB1pZrRxypqjffAwTTAGbMfR+Gjr1apshhNw7f/Po3D+Ph9au703HZHQ4Yx5IWMEJUj0rAzLRYewZO1wOOywTXjkVbBXbNzBs6a3hgiKZgyODFcWbquiBS0KQLhIZSijPSTaq73QjvHArIRFWXoE6dCq4ZusHH08yc3x0JFDClIiYAGiIxB/BAs0CMPaqBKNT+XlBTESAKnhYF94DdKhFP82wx9ONIAGzKWdiKmUWMBaHCHKvsSSklPdy+3ff9eZ4U7OsffyQK92/e7oZhOp9bu1ij51uRmpGZx3Hc7XaIeDwe7+/vl2Wa59m1J9Uff238Whk8V6GWnLOezRhDF8ehJ7zpYr/vunmeg+rLFx90HLRIATGQYgXVppxi37epV+1Lndp1grepKSKmKpPc3t6iWT+O436vYMVKP453xweX3YldZ2ZFJJeiCiVJzgWgZthyyaBqZD/52U9V9e2bV9f7w83NdYjRSS1f3d9djR12Bwrd3Wy3P/n8eLwXyf/6T/48xEjU3SX6/T/4o/3hhwpocUQ9L+Z0MBQ4MkZDUJJU1AycQ5iRzUvMBgrg/C9EFLoOmZc5T3MyoMPNdRd6wzl0MO4PgXgu2Qtqq219z9HW7cUkvVMopLVH55EF8SwN1h2oGcr3vmj/9DZZWNthl8UbIQBaU9rmkZlJ5oubu7Uy7Z1P/qTevOdwfwJGIzQB7ft+mk896Ne+9rUONJ/vOfCvf/e73/zgd/7u3/8PYHd9+3DORh/evMjc36cvKQ4mkgWWZcnLgggI/Uo7a49dzJZjq/PZqdxUpZhGjsTGziJioGruY4dICMhGpIREqIZIAIrg3UxmXu40gMq4BQieM1kruYZgoKsj0fYSZ+YG5fV1Zfiwx4/7yet6O+vegBfqhrWc3D713mnUxqKqp3nZFFePFlSVGFs9x0zd/jfmGQ+2PQ4MIUi5iEM5dzoAZEk5Z0AFZo6RDBgqlsyKbGVGqkFY+4uotm3VO72YB2uj4kgDKqWIZiJvXiQA8y49JHMaMXO8I+L6Jwa4kCeuPpPixvXfDvV7lyFuerceLRzHVBraL11fBlBbEn0ztcfnae9vLsj29wiUpbYDVs+mHmoqYj6YDFBXWQihiBGwUx673RYRM4mIpZRlnqd5IgNidiHlZ8+eLVJSStNkeVlKKZKLisUQVFeZlBVllNJ8Ph7Jm26JlmU5n8+SCzNf7fbTNPnWg2uLc9/3VDnBDNeCISKaKV32BKy5g/VfF/6++hO23ouqlpJKyWw1D2HNJ1irn2a2bQlohlFV2RktDP0tVTSFyEQBq1YmrvOu2dvtSqzJ8TVrjohWdVSw0Z7UgMHgEuqXKv9BK8VnTWhSG5M2M6mldeuzrto+AADLsoiUdnnec65mxOQtMWYGAqUUJCMlyUarE7+d0roRC8fHx3bat9v/i7YnfI/JCauOODxeFJu9w9ote3vJkzP4p3z32X67n0FVa+5Dta4pfwqwcS/wwnyhrWyxNto2d6plu9qDRo/AyNoE9D/a+9pbf+mwtGtdLY//e90ZnzIy1+usEZNb+ZWb3ACZWlRpLfG39pq2ALVdnvd+vGcHuZQaYPvPd+9ru9djBSPV4YBNrwsAeKY8EI4IwhADBlKzkp1v7fnh+uc//dnDdPriiy/3h+cQ4rLYuLs6l4LoRZAL70R7VBdAnn83osrFRHphnTY0cIAAqIAKCMTa9eF8fzoej33ft0E3s2mahqF/9JDWF1o3fyQH2Hrgava//t+E9z1iBni/2hlcQl1cK2Xx8Z/i4886T9+y+U2DGvlJ/AK6X/J1sDlbD3Djr/7j/9lXumkrKaU8PDx4bub+/v54PO52Qx1trQ6KD61TnaQ0B+0MhYhuDlfUx9vjPUy4H3cfPnsBqvdv79Ky3PS7+f5+0WrXCoFhz12HkAUM7GJE2tNUNWZm5zJf2SoRQMFi33nGyMxKyaHrdtdXX37xmZoVkSUnBcFKVqN9P5YisOJJRITRiumw253naZpnYjbC0zwpwq4foN+djUqapNhDnlNaQG2/v0k2zaKyWO5KuPogXt3MSZIac+fbHxAahWxoAGKEzKas5jFvEDUyMpCrYaeqYI44IKRQxEzLnEpMJYZ+2O39oU7LfJ5n7qOH0U9WqRNubDZEXI31UyP4BI6/PQlAZYN+stqfvG1rg7wNjlYFXFu1ynkVxWx7xvpZaJe0vYvtXtIKBYgI5gU3CwRIwORUbjh23Xx8EElxPy6y3N/fffLBy9/83m/9D/7e33711Rf/2f/5//pHf/pDjP0icJpPKUuej4agCiEENCslewQbu7HaBsTVw6zOsdaGaWuiY6iqaaEQiJGIAgVbEfA9R8ezIhoqGXqjiXGIVlVmFS8uHchawfMN6SLesRn81sbnfSngwSKsaR8AA9BVUvBJ85ZtIzpcPR1477b7Fx3oqRJPNrvHZYiIUkpl1jJnvQBXQ/J+U3f3Y4zOhN11neAlS93SP+512/rQffLIhorxiQ9hZm5uqUq8w2UnezSRCB3mq6ZazGqyeD1b9ewNFBGwEomZl9Byzj4/t0tAV6i6bXwRRLfSlyvcLsltMLl5UXWOnuzi7fVaUrlsk2UjzbN9sV04l7+C5uI7AgFoK/EhYU5ZNDNwzV0jEgYOQbQ42kGk5PUwKRiocRO11S1abrqgxXI2A3HWTkIkwJwTkcUQmLEUS2mZppOIpHnu+36/3/d9D2sWWUTezIvf2n6/11L1ZIhCSUVVc1Fkjv3apkyM5K5hzSB4mKEgTmhRzUv1LAEcqICVmXZ9lIhkhFzzwpfnu+7jZm3AzExNTV39dLVYrgrinp/4LGoaodgc7l+mlto8rYulBURyCMPTnCsG1PWQrdUFcX4KrLfqrHG+mgwrilIRK1Owqk9+IwYyQq3AyqoVEKpFElFIgFryIsExbhsSIXun7bjZ5zY/q8natKS/dxA8xsCNdgFuNvp353ab9tZyxoiq+q7rX5+aFl/IbbW2M7Qn+2i068KBJ+urkvlunEmrvNhprUau+xeZyaNqz2rQERFtA7h9crVPXoAbfCKfZoi1i3X99u16f5QC2F55O1vTj2qzyO+9Sh28cyVtm8aN32tm3nPSKArawLbzb02TD6tn/RGrIrefn1bov9W+UwtsvVhBY0IMqB3Zrov90L24Pnz+809//uWXyuHq5YenZZmlPNtdyXL2JQ1YgU3txgANkXHtakUPEIsBgLSJZSCUa9KoVtrXzYAhRn54uP/sUyspPX/+PMYYiCAEEzHRNlT1AThwzssciGiAQIhkjtr9b+fR971DbF+/fp1ScjF535K9AlCWJYQQiJnZiJCMMYCaSZnnOcQuhM5nRgwdMO6vPj7NEwMOjtTvB4z90PXH4zGl3I9D6Dox9d4lEBIRQA/RLoCiZlZiiKrq6lH+G0UYx5GZFwcKM8UQispuvzes0h4mxU12KXq4foZctBQ1K6VIKaqFiZ5fXynA7vpmHLpS5DzNp/MUun4Yb0TknEAzBIaSMVAfaOTD7vhwephPS6fds4/5cFVu728fHq53UXytYi3tOhshurQzmoERBSDwPKkSGyCIioEWFUOg0PXjbndYcr57ODrUsqgE4t3hMC1Ttfp4MUkIwF67XV00r5xWzmHHcSBiVTxCl61oacbVHwNYy6DNcXliPWGz+Lfui5tjX4ZepudVGadtEnX3ZY+RN849ADyuUbTdhQBTXlABwdCMQMkU1UhEMe93Q57keLwfGK+urgBMo7/uAAEAAElEQVTg+z/6cUrnP/qjP/rq7T2Ert9ff/bV6zcP50W0qBoyETE74bot8zmldHUdzcwUVWFLmL0yGAAiUsMvgpmWoBrYGNeNv94eAFB1/c2b0w2AnYGfDAFqrsINH2jVF28EWY4RDUy6uoYEyMTsws6O7vWEcWvF9T7RrQe5Wctma32AaplgM95tWjw6fLpomxgIJWczUW9K86RJraI/2o+JsEWAbZK02M+fb9MBzCn5XoIIfd97NxEjgWsNgYJa5CAgXoH3OVrHSJUAgRTJUGHNt6AhIJC5M1G1KcxMSslgghiIkOqJ1ExBq5fmGB2rLN1CTjzkT7sywFyyD+sv68w12xaL6jSuT/P9WX8/W/uN//Rco6fvLsFJ/aBu3rp90ZyM9ZdmpggGpujNf85STkzelk+GCAZaGVwKWOksqAJaADSXhe66jhAlJ9Didr7rOgansQcp+eHhIZVlmqbpfHQjHGKIkQN7ntEQLRA4nYCaXV1dmWLJWvKUUjqdJkcJo5pvKF3X5SWVcjZDIuo69hij2meREB5T7f0FR1P3ADCQurLWLkxDQkBP1W0Dba2j99S+1dcFgHwgHQcGplasMpwStLwGkrnT449vk5dxwAUTrYLHlWvHDBTJgql3f9XVR17r48pSJSLq3C9eps4lBCYTIobaKqql5BACIpEp+PWiMSKAMEez2v7r67AUA9VcMmLoup6IXB7ezFQu/l8z0T4Ulaf/kcP8NFvxFzv9Tw57lJvAbdZ5+y28Snw0a4NrJ/T2VO3w95tVeSVb4dfOMdBOjI6IZqzpmNXktm2ofRDXwosgOlOzoxtaNgHWH5vrt3UCvJ+28d1j/SBg7YWtjilchtoRaOY7u7UU0gopf1LJtVX2xFGO5j0HCCE8Sj036/SkmNNGte97r9aWVY8SG53UeuVt6KBmYWo2yktYG/2rqkvjHwxawAp0Pe+6uO/j1Y6fHcbD4TD03EfaDWNCzqIFmHougMPYedgvhpItSZFcisrQDb4BG9YUmhZHzzwaXwPx5B1R5QNEUiQFNCLreu66cDwex3H0vky3Po2weXu3PkKiimhYYynkWv17f8b03/+j7/vXr19/+eWXXor13Xqd62uth8GAzLRIYvV9xfq+7wLfXF1fP7spXkHncJ7nw/XhcNiladZFIocXh8P5/uH1Z1/s9/ucC/XAiKdpzqCD6Wk+df24tR1tIlLN92vzJPwnBVKEtMzTPHcx7sfRQO+Px6ELAiZm1IoYhBQqe4NTKavVMpiavXr7BgD6EIuOoAaRGQN3/SkLh44HhJgBrejptEzz/TSdzuP+0F+/nAEfjme9nwyoPzw7zrMRVvIgiADGzLjSABMgM7MnSNFpDhOYKZgqqgEyBu4ZkUKwlIqBNzdjVgrc9f2c5idLFNeofQuwgbUURpfU33sEX7dW1V/QxgWHzUKVjZLldndsyj5PEvwtSwSrWamTJyCAbg1Ne9btnJffIMS6X2IhJSZHJgAaSMkmNzc3v/4rf+OTl88//8WnP/yzP/6z7//g+z/8wbQkpNDFLi9yWu6zWhxHF31VVUQOAWOMCANhcCYZR1XKZtmuLCsAAEUd2AZgOsYuMFYyR6sEowhYOcWtMrUQIRkSwmK6djBXHVpDYEBjbw8gdgQOmhEpAPIlb0CARMB19ITWDcXWdbF93OAbw2Ywnzzl9gg247sZ61/iXdU6qmdq3qEJhk0pyS/DiSPVu3uLzPPs2L80L94Ibla5ggGAGWPkUt5TfWrTo32je0Lse4sr2Ftd2kSkgO5ytYtRUzPwhiWqMB6u7oNJ3fJ9PA3NTEzQjLveLqwUZmsA0/65Te/ZGhhsB/zde3nnRYuZL/nOGga8R7BlzTRtHiKsWdvtF5mZgIUukmu+ESC67qEhuGwOEQJX8fLgCXVQdvojEUDEGIIVKWbLPMcYfXL6CJSiS8nzqyRWGkNRiBwjd30Yuo4ISsm5JFPZsHzKMmdPIfmzCMx938fosK8qxK6reM5+PKgqcU6l9o0QhZqsAFj9LSAih/mUogBrkQQvk8cZuqgKvKqIEJqRiRRDULxYG3+AVKsKF/rCFdl2oe7wKpMabg0dEUElo3YKv/cuI8AVv2SXlP86H56Utla3+HIl60dUK4AXORB5PbTOxkqBZQigZIZITAgAXdflvIgIGDG7UFopUvvX+75nZsVGbI0hRoYLFbptiIw3c/U9GXrYrFn8JcYEWjUGYDsIsMkx4eOj+frbTcc90e1n2xtiP9omQmgfeXJtl+RSTYA9ui83aQAV8ON7nHCVAS0lqRWq3MtUCc43F18x2043/T6z0Mbt0afqw39Ux9ve45Nh94+IPgqB2uvKk74OLK1aWM7M82QwEdGz+E8eAQBUi73SkbXLa7De7Y2AuRouvtf/RU91tTNjUTboA+x7vtrxYaSrHd9cdaflbJrGXYdKx2WB0PXDkCURGoKTVKoDfwFN0SIREgUkATP3yKukKAEQoyGikbYeNmK8tNk4ooCxj+HDD17cvr7tIx92Q875eD76RGFobRzgpTQEVGfqXrt2WvRomzkN+E/XJ/N7j37T/vnf4PHkzL/sq9tv3nnDV1999fr167dv3zZ9TViXlgcAz64O/RC1yPl8qkVGVjNj3hFR3/eHw2HJZmZAfF7Or199GQKRms0ioiSWphlSmko5nk+IqKDH00OwgcfOuLWHX5wJ5yMlch7uKjXQxBSRuJgaAHex63ruYikZmSqmx8xUObg0MxLReZ5w9YF80hsiqFHsptO5FD0vycy6LjDIXObbU76+ecEEKatInqdlnmde8tD3p1RKSYa8212ZwZJKyiKgqN7uDSSCRMbsjAwGZsCuHQLoDSCGFExMzcQwMgORGqjZ7evXiNjFIRdlRjU8T8vpPPd9rGmBvzATho+zF7wRi2nv2a7/9k8ET1xdcpPrR7a/tzVVaap15/bmTu8PaafdVofXEVegAGvZomUvqqTRGtp52oZAO+bagKderzDz9D9zOi942L148eL65vrTn/10SSWOh6LwwcubrDoveSkiRqHvQ9crFDUzkJVkwIhoGIaH0wmMqu3eDE7LKqmqVBeTCSyEjmkNt0AQEZAvJg2raHwdNASsPLiXzcRAwMzRRYjOuFP9O0OwGBqGh6DWc9wBWROKFdms4Dxwlwzcds9wfUfvd4VaPq673GUCAEAjXNvUz20NMMwgxmhG7u7AKmHj3+eL1KWpAKqJyJqHoQuBck5WskgBMNWCZEWSw1Fwg+jzXJbjNwDARN0X0yIqYlJVBd3wGkCMwTtkKhZCRGsSpppeAAMvB6ioyfF0fz4fQyBTBWYzITVUqZ2FtZYAtroPIuFSRl5Nn2d/mg+hGwU696L+gpX45Nhszy3wvuTYPJhsxX17FCc8+fijilz9p7ewmqLWtKf4dHH4h5MhrjUkUguARVRVU5pTmhEUUHLO5/NZSwaA4r5Oyj7/xRRRkMH1zz3AYCKskghaUipLMnNuWSYKiMQUu66b59lnS991+/3+dP+AiB5UmJl33dze3hJw9VEAbG2DIUJ9NHt9rqwVy1XZex03BajPqO4j5NKTisgi4q7/ZtwAHqdLdFXOcmelPqUiWmm4jQC9R9WtWE1V1ffDmr198i2Gay4fVanhM/3DWBMHrZSTlkUrvzZVdw3EDaSIgMsIEjPFDVVUncKOK3OXx6XfSs6qClwpBEWdR6vGh1A3XAbGwAGA8KKUCm50W1rE76lNPFo7mbym6+Em1GT6e+e/vtcx5LACpNd9oe4OG6zdeyf/098XAQAXcqZaynYXNjab43aCDFBrOqMOHV5WmatWt5P7RDKwImU7PepQoDh1L16S9ViN+l+uBtI+Ymtrvr2TGthezPZT2t6PF/ugDXfmGxGttePHY7913K11OGw6OlyUEDxm27xHNmrxm58OadOW78e1qh1XbUFE9NR8YFVCGdh20cZYIgNRABqW5fjmzevJCHc3RgZBQ4/znHLKDKuxR+uYBJgVOaBDDFCpoLKvZ1VEAgJGAgYAVlBQU1AGMzRGAAZPIDBBiDSO4wPfuRaJZ7C6rtvv99t4qL1gY2MDsoqg8OqhCDTX/71e/r87p//d39jvAf5TwH9aXzz5Tfunv99+7wc/+MFut/vwww/neZ6m6eHhQUSGYbi5ubm+vh7HfplOw9DnJaX5JMsMAIFRVT2vc3y47/t+yQUoMMflPHHHJeUhxN1hl+6nXJbvfOvb3/3131hyefXmDfb9uST9yZ+fJAtY1tzRUH2oi+tfm6RTSnlJIYS+H721QEyzCHOIQx8J0WBOC4ANw6BacF0nHLC5nggEQGv2pKiqlCQikfh0Pg3DwMyBOHbDPM8Px6PhQESAlkpOaRawbtyN43h/fx9jNGIzQyYTCyHQSrBeMzS5CIIAI1Pf96AKoCIFVaxkd2d2wyiqyyqEAaqpFCdW6oeBKLjPMQxDMXD4bGOD3toyZ47fpqNwLYy28Wz+yrtmtP5GzbMIFw9yUwRoFmfrZXr+z30j/wp3/Rsve2MNM+fk4nXjfnwBzhtta7pr/VLLIkxghiKCVNkkSilD19/c3JzP53/2z/6ZaYFSItP1s+dv7u/vl3J79wCEw25PXchq08MJ1vhHoeSUtBSqATzD6rmvWH8AqOJAfjGp5LoHE0sFCVotkSAwV7ALGNFa4hVTVBBQMEPQNTjg6sQjQO36Na9SwjrCtqYVnIaEDNQKrn+6XB5WZfG/uL643SAv297T9/zXnMGcG0R1dZEBVj5pD8JDCE4bk9JMMbROX9AKEQ4hXB0Oy7I4ejBy8CRTc6wbvr+lGFdaz0tzpF+/k5/KWoD2LCwiai2RexRh7crP07m1njNv+r7cGtQxuRTNnIQe1mxfG7GGdm2LyH+KvD8Dus2QPR5R2naUvrPB++8vn2oFhyfPri2ri2VDNC0oipXlrzbt+JQ87PZoJiK5FBDNOZsJhijCRS3nRbX0Xej6usHvdrsQQuNX9QpwH1AJKWCIjIjmbpCWUrSUhIig/iglJdefwhi7od/tdru+70sp8zwvy8LM/pNX6t8Qwvl8Pp1OJjCOY9ePuGYrQgghdrn47VNzPipfvAO7L0N9ERMkAq5F1lBKcaLn9zypx97PxRiu1UtVdf5yt7nsfbq1VfTpo3dD4i8IQFbcRTO5LRviJkU3Z9h6y03/m5kjhy1eRVWtFABANuqIiYliSslMUIqZkalziiDaMAy2Vq4IXSNZSim0ZnNVVVYJF0MWBcKL0MrW5rf5tt0OtvWxekcbkZZ3j1JKq/pua7/vbkn+IrzP79/O/ycruoXottnCAMD3R3iMZfXUcPuskzOubzMzA08uNGD5urFfTk7WxNE3niGYyXbrfPfYXt528vyyt71rt7efenfoWkB4uXj1TufLPMfN0TTy/PeNknteFliDAVql5eR9NEF+MHOby1tqpVXUsoYEqhoOAZhAl4eXN590PIcQf/M3v/flq1dzethddaixBBoAj7LcP5yJYBz6MiWqciFpOp9jjC+ePz8+nGMXiWiek5RERAigYmBKgSlEz3YIKIAW5wkyVVEH6CgqmXZM0EUwebi//dEPfzAMw9DFnJPk2I1RK988EDIxISAQTNMp9IGJRUozSeq0wc0d3/rcl0f0e+//05PXf8njSRb/L39s/H7YxHbevvnixYsYo1fqvXFzv9+DaiorpaNkM2Yis0IUTqcTh9iPO9WkqofD4fbh9sWz62989MnY9befv87z8te/91v/8B/+wz/6N38sZj/82c++uHsDRPthL2Z937slgrWiZ2ao4qtwHMe/9Td/+3A4/PCH37+7uyOi0+kUx737ns0oO5Airo1KZiaazczvYk4FAIJ6AEAppXlOpZT9uDtcP/PdLuf85vYuhHA4HJaMy3wiIlApKeeyhBhTWQ7X+1IKKMYwgIqKlpxBy7KcXr54fppOqtp1nSH0XZxzKlNWVRWgwN4RKc4ypF64RFVdsuMdqe/7ru9VtZQkYGQwpYU57nY7zclUSslUG+BqXbiBJp5YiMihDonV2ra7SJIvBVNY8zbWqMUfGyZcM51U6VzMzLyPU+TkoLjKzdd1AOD33s7QTljN4jvkP7jyRrctof7VFJVAVGFdWaUQcR87kXI+C4FxF0G4ACy5/OLLL+PupiSB0JVSHo4zBg7c9X1XSlEQyVmtgCq4sh7S4bB/eHjwueQaBbvdLuccY/SBJaLdbtcmErtil7croCcbTEFCYDBUkSISmEMMJiClDF2dhKUUBGTGnMtyng6HwyZhu7qJCCnNGBiJ0UBFyDQwB+KcqwkGQlg9cFPIKl3XOa2ql++tlnoNEXnLlLdxaLaDD+v4m5k6zT8CABQRzYmJANQ8WvbO/tomWlFPb968AZGc077vAHoM1dfvuk5S9jWYc+67Dh1KTsQrJ4mZLcvSxGjd3fH3uCSzf4VvNkTUdd3qBVgI3PeRCFKpcBREBDZQ1WyI6M3wV/vD/e3dRx98yDGcz3Pf97HrTqfTWhUBAHCqHzQAc1e7doKV4m2mXYzobX+rt1Q9e5GyzbnCZodul22bEB3qzJf1zet6APDrbyXx9XEZr7Lr9jirV9d1Kz6UIiqghiKRcEnl9PCgkq+urg6HAzMHxGykAPNyXqa56zoAfPP67SK4pCKSuz7gbmTqmPnq6gpQU0pWZBiGGOM8z0tOWCyOgxRBsL7vKcaccxYDgNPD/fX1dew6EZEijlLLWeaSmHpmK0XNcBz3u2G8urp6i+HiQhmYKVHo+7HZCjOjFVWcc67knmrWcpNrJ4utUDerPN2IaBgcp2pm4puXpqKl+NmrY+dPxAAAUipuPN3WOZ+jiDBg7IcvPvv8+tmNFun6Li9lGHqfD7h68ogIQGaikpCBqNKdE2AxBTCOLFbKnM1MWsspWM/9ZXX6M1+HRVWtiAEuRVJKkouBXl0dUprFZBx2oetVteryEpkRukwAGLjKoMo0Tabojm8qKUspkhEh5YX4ahxHRMxSx01EGNhHuLgUSeN/XC8S14kLmwz0kzgBKhD6UUwFAAYKhODqgLiqi6yZoJY2UlVA8FRLScm9Ru883O12n3/++ccff+xp2XfXXZUDAgCosB+nBwWs+1aLVVSg6YvVlrjNViWiDZ3lFklVYx95TXiP4+hrlkNQLW2/cF+lrfQ+dG0tt9HY/tzY5rWRqSZ/wLlUPPbgqj/tN+a0xao1Ca6X/XsN+GnttcNK5A8EBqApyZMn9XQ7WH+jK+lWu6+WjlkHlnxl1cls5gzUT6aEp9j8sybquUtEDDDdf/z1j29u4q9+86NPvn7z0ccvvve97/30519+/2f/aRIuhgqCIfaRmSgGkvNMDASa0mKqw9AR4Ol0KjklhkBRtTASh+DG0XXXpOQi7i0REQUOZkbmdJwIJqjmG0haJkRcluX+4Zb5pRciZV0GPsEuNgtt7Ie65HMBgGEY+iEGz21uE+11RN9x0FsO/pe9fhIh/Dd7tJOvZ97tdiml169fOyfDfr8fhoGIKmA3pYidmRoIGhCarrOEmT0TBKYmRQ2yCWTsONzd3S3Hc899Ok/T/fH1l1/9/h/+0e39/TnNpyVlNBw6tp76SMglFwPUy2Sqrv8w9iLy8PDg9WgRcahi6Dp9FHDX/lUpucEBXX2xklcoChiqmRkzezpKRNK8NG47/6ACGpaSMmhHBEUFsIRAMRARppRKKWigWhDZipghoQSSsjxoWojIsjIzY1em0248GKFVVns1sABEhP5xQCNkWxGobeGZ1USm1WibIgKsrbTbBNK27Wl7eFIUNnnf9roZTd0csGY+tm82s8439RXz54ZgWZaci4cBfuA2B+nGYEOFBgBWFKhCD7ff4mP+xCQ5N4Wpifh5iptyRFQVRGREaig7QKW4pJyNXChLAUmgoFAxZArIyGoWwcSJO3EFE5tBCCGt2wwzn89nv4sQQiAkBiJECrVZzlsoLtkOW5bJzELodmPvk01N+oj7sb+6ugoh3t3dvXnzJie7urr62ocfvH79urGIArissgoBEalZzpkBAwIzM6DJRdbHDMRMTAXMzJxl3Kl1/BkZQIxxnqdqeemSlAEAz1I/ebiwyRJtf68IaEabaWnmRXiVtQtTVXndBhBtzrM7RaUUWKecrkK5UI2nIqLnFE4ndf++TWOfyVsGCVv3P8TK8FCp0DeHX79Wiirx9YsEXRfcgoGady6pSAhhnhMAuPqSacuNAVGwdQm0W3avqHnzbeeDdz2P9YVnH31NPZrVqK2L15UN/G+r+3ixe35Jj0nJV/21lZGjfXt9iEhkpilryihqRab7Yz7PiNgPXR87VT3dPTDiy48/2Y9jCF/9+c8+M6QYY9dFImoQ4eZzL8viWrCqAESLHNUKM/d914UIqL6Ux2HvkL/dbmcyzMvZzMZxRIrM7HGdrlCBeZ69bFtXPbSJeumjKKUsWZLibmcc4sp6uaEIXAdczRzz4KUpz/94DOj8KqtlM6t0IOCBMTyyipcn2LIbZDBPkw3lG1//ehFZbO5jV1I20UDsdqxx3XrBIcZYsexWaxHrzFzPv0E8I2JKCd85YFM1ak8ZmxcLYGa5JEMww8qbDuDbZkPSw+qAttupExCRCPrYt8ftlRV/NJdNcx0fX49hBeS0ybzdc9vr7Zp98pgAYOUXfvTL7XxuO91l6QG4d9F1na8pd0bh8bEdpXbl24tpMNTH3yIxjOtjeQT49B4eX5UM2Pd96RNFWpz2QMuqnVxpFZ1027zIthag2jhsLwYf58K2w2XmSMLWpVDpQWkFduLjBD+CkoEiMKJnLKzWmmoLCK1VsAp8B4jD0L5xe221uvXOs3u3x2877PqUvk/aQmh3R5s3m3h3uwFA4Ol8E+D5LnxwM/zN3/qNj7/2gSD94qvPipkgFgMzEC2AApbEyKT03RiIUlFC4rHXXE7TLDkVAmIDU0YitCbiW8zMBNQYAyE6m7qpuvQl+YMyIINgSERX+93xeDw93L949ryLfD4594M3hdLj0Vk3RlUwY8Yu8tgPHQeAM/x/dbwLv3n3r/8uju3X2e/tdjvfkj/55BP/u650bD4zUkq4kk+tj1zMoOsGz/B5IAgGKmYALiV4kvN+2F/t9i/GfVnSq/v7GOPYd/EKFimzlaJWlkxEQxxw3QPBkwHmvQRcSvEmhOPx6N/edd22olbtNRgAxBhTSjkl3wzMzKOXw9VNcy+ILq7q/upKRFJKKuJMLLmo6gIixQoiGkIgrGbWpFTX38Q5xYowcxd56Ph0vIuh/+CD53dvbt98+er585c9QERAIkevFlUwIAJi0iLG5oARAFA1cQFjUyRkxLCWj21N9za3o5Zxf3l1FTYuQnMa/NgCCbZ7g9bOYC+b+Rs8fRIcy41VxgWWZZmmxdtaW0HAL8mNta0eG6wWmYhKUWYGRlzTA9tvf7KdIKigqZjruXqSg1bzjQYZgNeEDVAAgCSq3k0da4nRybNNCmDl0VPTIppz1iJk4iQGzPF8mq+urnw7dK7YGoReasKCHBHRyMH65lTBSNgP/bIsUhZBKyallC7Gw7ALIPevP5/npKq7bgghzPP08zevd7udggFUfh4xI4Mihl3QUrQkUcAQPT2lRYhoTbi5qLgJiAKqgabFqxM559PplPNydXUVV7CKmUm5RF8IF1aiC42rmQcS60h6kCBaigESGiC3v3rDfe8hkbsmqu4diuQilbyvSA5IxKAqakW1+JtFxERjjDFy38c3t8m5yld2q5qsAsLGfy+mYgoGWBBNmQBruv5yuL/uJXtzz4nAn1zfjwCUc4mxN4NlSSEEokuoI2uhyTfWrevQsjy1KrIqHNFjPM+TDb6Zo7YGL7+EC4RpdTfdyQgbIwaVAEYNwKlaHmFN6ztXEhNbvSU1ZcOSi4n2sYscJJeSMxG9eXV0e5FSGvp4vL/3pqKPPvoIOcaOiUC1iDr5EoBDU2IAVSYMCM6VpyAiUlLWItprjCF22HGwGIkjo8tJQRcHE0kpX13vS1FX+vNavafkPEtaiZ6IWqSHa3KtlJKKCoa+H/th1KrX6UZjzfojEJGAuea11MDADY55DOChpqpoyUaXMXzysLZPrQZsZgjQdyHN08vrZ1++vgNRsH439gaUc2ZgRlCzopdokAxh7Q6q85CJyAlkoWWpXBwADUTlMp3abAAISMCVjU1cCwyRuRZFQVREAAsiIrGLc9VeRJ9Yl1mBHh6sOuUAoEiGrouYFkSi4JEemaGqbOoYl9HYTNpHLy6G5fFawMeTFh7nm9o/nyyTR5NZlYgCoKf5hmFw5J5XZd/77MCpEbC2linUDiVQA63iS46UXIF+K2AJAVbrcTnVqmOFUHNtIuJ6rCvxySND0WyFmTtFF1/5yWR7d/Ta68rU5y6lIjXSgpV2bS2TALherDlLmReD3T2tssmIRq4ErEYILsyIa95hG1+ZWZNm0xXzU5/j5rIvW8BalreGvluj37YAAQA3916rl3oZk3DNSPOEk+wjf/0bH8MY/+j3/80//3/8vzE8B+xIQzJIKc3pYSmnIul6fBYAMUZGUtM8TyKCpjU3SBaJnf+6lIJWQgik2pFh4BAYEUuWLMnXJCKSOWUeILIy7ndjDKyq9/f3zOw1dMeB+DRu89Wn9cPDQz9UPAwzguhynhbUX87f/5c7/t35+tuj9QCsIUdK6XA4fPTRRx5ku8l2A21mZJpAI4NJje1846+XbMZYRRzNMGqlTOGu11xKLg/nUx8HYjbE2dQMxWzRIipG2IeatoRHezp6H6TDcU+nk+cqmLmi00C269VW118VZM3igxRdwf1nCkXEVIk5siFXhxWxVjbMjKmyEIIJM/nOgu71EBpIyhY7JgZVRbWSCoAycYy838Uu4t/4G7/9H/y9v/+n//b7/7d//s+ZQt+PS06IAUiLmgvAMnuY7u2erR2IVItnP8lBJfioloq4YtbXw1bS5V/2jLcWtr3NPRg/fONx7oU0iy/QbbaSiDz555GGO/TOddD3e8/3t1KAVBK63OwCrqBw3JB+6iqpuDU37YrbdTMYgpohM6+K4Qgr84BZbesCcwVoBsaAtUO9ZcgU5OHh3GaIaK4cbQKRbTfuM2RmXo7Hq6urqmfngCVPodWMoyIS+12syDLnpCRTRqBIVgwhBWaKXSAOli2lnih2cUrLMj0kAI79zdVhTosCKYAYegBgZobAguJM3WJWJAB61QvqJEFjNERlBDBAdKxLjbVEb29vRTIzH3bjZe+xDYjLIQFrycVpj8zM2zrrEOFq01UCElNNKNqlKcIhMSug5enOV4+V96zm7WB9G26o4rfJfvOGihAQsdmcdkI3AuYfr+6+Wc231dykwHp9RF6e868oRZclM0dVnaZpt9u3aj4YreqluP1GfAzRbtO1vfAx1I312754sqm3J+iu3hMn/snH20eJiN9xDvxnC/63I48GkRAIQxe7rgO1lBIzj2O/zPNnn30GgB9++GEIwZX4KHRdYCWP0lWk5JKWZcl5iR0DQBVBR8w5L3MupRgCzFAstzFfaV7x2c1zFfnyy89V9fnzZ1rKl68+N+Wil4aB1hyywtIuxsGPvCQ3I27WXGB+HMcl+Xbj1qPmDt3194WgqmoXMBUxAQDB5fE5UouZW5tv/VMd4YunW1MqFgit62NeztN8CgT9OOZlur6+vj+eVQqsClCO9UVkNFe/rmvZvxcFC1VdjvosqXrnsDLzNBPdpoqvF9p0jvlArXn6y0r0Kmh1SEgRCc28qqmq3tjmh6iIZn9hCqUUDqJaLHundTSzhsne+jnN1Wv2efvP9rbtLOUL4ORi1a0WWy5ZYdt4mfbeAyqL/zAMx+Nxnuerq6vj8diQyU+WT/uudvKWsmwhTRs9M8ulAFBlfLoUTIA2tsvV03POS1m6EYtk73Aj9HD9kixfE20Xnq4n1qCN6pNMXLtdVV1rI4+k6babeDsbGaiKd4upEyOvP2vRidAQyKC4DobZu/IvfnLvwtpaqro21xHbji2sKcXtJfnlb2cybsypd0SgXeZDuA6Rlmkf9i9vriF288Pxs69eJ2PGCGHH1rEKFyNEzWmZz6eCM07jOI7Dbp7n+/sjER0O1wRIAEzQdWEF+RigRgRjA0AmZAQ1JdAAGDlY7TYGEwMDAo+dsWPqAhOYlgRaImEfIrgCIpBbGDADZCQwESseZ6IVSLo4eR3AB/81WP8nef0nfbfvpeL5C+KBd8/8BD70y37z+OMO8Y8xPjw8wNqm6Rn3eZ6XZe4iEdDFpYPKTSYlAYCDamInsEbaqsqhg0DTks9polz6viIiRCx71ocpxujY6NP5DFDZxWoZwancCHBFJPuyd/gp96O27XCdzWY2L+cqdSSiUrlZ+65L06wIkZiJuhDMMcEGX37+hZtvl6wnYq0UXZeMKZG/3xR07EfPX4IqATrYPYRw+3CcT8cf/PBHhOHHP/zRLz77/ObqGfPU9SMRKoKoJRMDCoaEFAISGq36O4ZABAHQtIihGaE+sizDMDj1OAKggdNIMj6yI9ujsRbU5+w/1yWNG+PiBzOb1Z0S1qY03/thpXqc59kftKo6pc82SHAz4YAZXUkqmgmuhVdHKm4uBt6x4/7+Yhawyrn7fukA3hDYwNTU1tZYIgLEgBjQrxm8/igixYppscrdbmpIHDuOaEDAnoCkDZ+V3y8RsZ8r4IYiqYr4OLUnIhAaGZwf7sa+I0RV6Yl6xmU+v717q7mEPux2hzH2fRym5JKmR+JekBRAgbQKg5GA2FwYq2+gkotBJO44FMk18DGwlcrfnHG01XagOkPu2wFATRY6BIIIEad5IWedWhN7nqEs5s79Jl8IACaArGju5TiK1pumZhEkm89nEYkhuotmJjorM/kibVssAHhU7y5g5OAm5Xw+X11dAWEpRdNSVJxMxF3kNnlsdfaJuUbfmw3GvRoicHcLK0HIOrvUUBHErKgUz9iJSCWn03WHAk+fAWCdAz4PH3ljCAhGpm7ujAm2feHbqQtrVNM2Qt1oY8E6XbHSKnlRyS/4KW6B3ucV+Rua02+rf0aIBdA4EBHGKLlA4ND349V16PrD+YQGz54/V9XzdJqLgCwKIUtSLczY9aEPzvFnKc/tan1pxB6BENQKRUUjAjIC0UVSzjj2gwlIsZwFRDX7OMtXX31FITYSAjNhDEQgWU3Ra1CBuxgiAATuArHnaIAodtFjjza2WkkIxMyqEIaZInjWP+vaqgiC3aCq3CbzOnCqaqsKb33uFUpxaYt01z+YAUJJy27sTeT5zU3f9z/80Z8PQ5/SjIigYM5iTuCZZCLQYohGVEtn2hKsolgrt/V/aoBKAZiaBXao1SpZ7VOuWrBSYghENC0nJKtQxwtOrJ3gYtDNTEScgadmvrQULSIiVopmgAMzm0kuZVnyMETm6OtI1wmJq6e8QrOg/bzsF9Wtb0sAqhfo2ZtHMa2yk1r7BLaV2BSxrTXHuteFoObSKuSygCLH4/FwOLSUwZNFZ5Vb1VbnW61Ckr2BQt2zaFe6XblPXO06x8zMjIhbbssMxFsvRGgFuLYV2l6gI6/saYzUvqut4id/EpEmeWYb7J8HZutkutAhICKj4gqjag8IDYiRm2C0eW8MIrM9HjQ/vLfq6YCsUcqTOebpv3cHHx27DL5N1c4Zp8e9mFAAL2OGmAst5eX1syF2n//gh9//7NNPf/Hl1bMPX98adgF4YLZ9CH0HXZAx8nQuBZJIpyallGWZYuydSPvy9W4sGBipi7TWT9STt4Gpix1ChVYXU8tFwBgYyObzlMriW5Qn/ktWF8Vw2iURcXwzUQiBhmEIgQAs5wxmyj57fOt4x1P/S+by/3/44Hvf8Jc8z+aXXdedz2fXM25br2NkEV3YpaiCuSa8qW7Cdylpnud4PnrZNrv2goFhNjMg7PZ77iIiFtMQdygCpZhi8CRiyudZKOA2c8fMDisQLcxsWuuADjRPJWuuWX9c2ZexFphcYBlELecsufjE6PrRAQZY9X1LWns5PMUOAKqaUhJTKWkIFS+HHNTDaEMkOC2LSgFxNj5Q1ZSzoe72Bw7h088+//TTX8yn5XC4/vCjT968eSNiRUUBBcEwAJFxB4iOmqB1y2cEIlbmFVtJa6NPBc2fjydagfL+UNyZbljM9zze1SrBJh3S3r8xKGYmzChS2xlhzW66ufSv845S7y1j5lJSKZcUVAsntnzAtkFKbC1Oe8Tvyx/U16UUqXksBACRklcCEy9sVhc2sCEzIZn6Il23zORrue97AbNK4lnpCxhDXjIidl2HiFdXV7a2L7OT2wZEdKmolRrZGFx2BxTV0ARNCfTrLz9A0LRMyzljziAhiuwI9x/cfPn61U8//9wQ91c3+8P1MAw9h/uHsyAZkCAahDXdZCnnLjCHwCEQIRt0IfYhlsyeRyxghm6OVMFyEaJgK95gGAaTgoghRjOPfWqiWjZ0bGaVnBygAXAREWEL6wdgIlMzMHeHsvOiGhLREAJgRfDXEEKkmoVLg1edDNtSTymF11W25NTtBtiUfdp88MASNnnHag3YcI12YL1yIkJH7DRKbVXJGREDcqrclLhO2ugL3xQrMGOrlPlYKqh5jW0VtN/Xub3JFLZJC5ukia20J36dKW/oTarTX/349lncpksvz2PDO7n2xry7jiQXQyoq85S9HpTMCtp0PFHXdyEel6mUEmNE1bdv37784BNdsmrttGEOAqYaQtypqhd+iajv+xAiM6c5M8cQIATqY/SUtJkA4Nu3d4i4312ZyOk0icjhcH379j4ye6Okj5jXbKvztGYW1m9n8n2nFH8knnJSA2f4WRH7BTaAH8WKIxBwrayiqqd8AoDomg8XykKX9Kp1rTq6HtCuBMS6QS0iEwHuh2GapuMpFxkIbZqmm6vDnBOCg8o8c09OOERggaq0nNaumBVYBwbe4bpWGxC0FKN1etd5buDzvy0ZL1HW/IhqzTOamD1qMfe1jVgdLB8HT0R6hbY4Xa4WNa+jYt/3zKKwJWeTNu0vi2vjEz958aTg3Ex3u/7Htl14ZTV9Yu1xg4Dfeueq6pgLzzF54v/Jetm+0PVsq0W6GDdc2/Q3Wx4QRy+uIFbJRX9/zivLCKKLVOz3e0VVWEopzogFGAEUgNde8adVa6BHCk+4OeRxb946DEKbd8LG9W9ER27O17HSwBu/vFJfW7OZdQqBggISAjnx+COTYpsq07vPF9Yn0p5OmxXwjsUDABOt/HUtzUEAjSzbLpRQoTfqKHzywYcR45/92Q/+8Ic//HIyGD9YhEmCCw1GhrHfdZzmCJESc+ziQIRDF5/fXHddvx9Gj2Q1lwyLkCOMiZhQLQQk4jXwNTRjpJyKd8mIinrrIXMIeKc6TycmiIHub29NtOuGZZ5VPDFAIiJFEbHryJ3IQGQghK4MT5EYfnkW9t/z45/9X373/9+X8FfHXx3/vh//0X/0BkxADa2Qiqj84vPPZZkJbBw6Npsk7/rhw5tDYfsHf/d3fvVXfx0pfP9HP/43f/JvX79+ZRwgdIoEGOECiiVDIL2kWiNTAGSFhvU3E6x4Nt8CsJQyDJ0LlHjj+zLJNE3Pb65V1ZUHtnqKMV4IK6VmYsB/1vw6gKhavSQwL3Fu8t94IaS/7ASllMXjO0jRKIRopp6dZ6YYQymekZVSMiGo9Ry46+I8z8CkqkaIgSkEIFIzV2wBcGkCcJiqIQQOYOLhfgsyVTXlC4beqvQSAAAHms5LyQpG07SEEJhCXooXbsHQtOq5IqKBuTpm81ee7I7NO7wg/uk9qUd/0VBVze9nZpa49XebO0sb+Ow6zox08VTaARsHaxvL+bcsUnTVHGWkEGkxOd3f9yHGyNR1yyTJLEQO3HXLfpqmUtTrMUUSgCQppaTnL25alUbNutb4i/XGPUJGrDntlBIRjl0/7HY55+PR06K82+1C1zcof8MKekTBKwlb8xQD15MDkaouywJAamDgQi716xCRkZCpFolqN7RX5IyIsNRg8nKQPSmBttmyPexxiNUPUSQTwdu3b17+xq//o3/0P/z93//D/X60yVzShBCJCZGyFC2Cvjb9Tiu+39QviVYhoTXhbJXN5lIy9YsyM1BDQoPKPlJy9u6dvu85khg2tllv0GzhDa6hxTpRm+tfFKzF+Uw1eRQjKRBzvLDrbOaVH7oKWbwbAGwnPDx25Z+cx1qPFl2mcfsIbjid28f9DC5NEGPsuu7+/n6e5wYMfnfRwdp42oL2bc35nQteQwjXP6HLjVRU7dom13Vd13UhhSTLBuv/eCKhgnO5rlEYrHnu9p7mkZfHknzb7/USqzqf5Do4G79fN20bZvZYtta89AuVlWn9GFgVIsgl2/uybI2R76m12Zy85YKf2P8n49DYL7Z6N1rkElS7GmB3/Xy4Ojz/8Fu7m4/nV6/n0ifRci4GnYmaZtPCXDoxAlLpoO9d8HVZlshxeDbG0AMQM2mZUs5SitfKmRkCq/XjMPS7kSjOSeclFSFRSFMxYlNUFAANRLGz2Iexj9MJ+65Lw3D79h7g/nCwlOZpWggDUUA1M4wxMmofMOUjIocQmM0vw0PVdwflr46/Ov7q+O/GkeYFNZsksoKSSPPz3ThcD7/xq7/2t3/7b13t9m/fvBm7/pNvfuNrv/atDPpwPP/oJz/9yc+/QOoIOwyxAILrmjvuEAnAGx4DmoGadxKbQZIiKYfI3iOoIAKIYACEZl2Iu2EEABHpQkSDZTov03QKAcAYmZm6OHRd54C082nyLSSrkDpbOQkIaBWBMa0le0T0zhNPJDIzIlhEQg5Ep4cHAk0pgRoiBK7aBmk622BkwCv7ontyjrU1J+01E5G+70eiJWdgYmNhQbVSihVJUobYGWJDBsAqPWaEqAxrsywRIZoSnc9nQEZEI3apJTJQwCxymqckBQDmOXU9Dl23pELEvpPKps/En4N3cQNVXXYHI4hm9aZuVOeHRTE0Yrhgjrd7offGuMvrMHNeCwFb199LL21D3SBTwKCQYC6ycUgQwNAAAbsQPT7wN1d3xFv/rZQkamUYD4erXc75/v6euzidJ3e4l5TmZRnGLnS8HCdG6mPggEhqSD2wYj9NVfoQVpdUVdv+XUpCtECVw9RMpml6+fLl9eGQczZLw7Azm47HI4WgqnlJsMarAsCAOecuROLgaElVBy0gWEDkwGhEWUxLkhAABiRBA4CCoERIhJEZmVLOVZTHBwiRIBhKNw6gwogEmMQQUcwXTVXEbowd+DhP9yjEQs3Fyjy9fPky5/y7f/t3/8k/+Sd//v3/xTxN7o+amZoxAIJBUU2JFdSseH0DDMglrqjbjVY7Bd2Nq84csU88W0MG0NaIsOIeWxXCzPb7PRKJyNlQxRgpcjCyZVlqshVhpRiqMVUqRUrJLvtK3lBuzmtpqoTcx8gUzaCUAlzLL9uQ0h6LWG9flFIQjYwU3ZcSVFTUjjujy38CQsZuZ9zmPHk/A6spKippwGB+TlCnmE9L4Y67OJTyJi1lt9uVrIBq6rgaMnDqr1V2sIJ8dHXBcas+i5syy5IKYmVJsk17WyVKRlJ1dtaaUACjRggGAOp2AlxX1lVc3OU1MwA1xrJG6USGCEg1/vLFDAAuL+ZeNobAapVVxfyJVeCfl48ETNxGGwACiJgAEGBD/DsQLkIwBFATUxP1REKt6SIQMpARBEMl8NdsqKAoVkywWFm/5FEwoCtw8d3gAWrhjpxQkYh45SBERAsXYUS3A/iffOfvfPC1D3/je999+a2PXp0ffvjpTx+W1F89yyKn48IY9n0XVKGkh7evPv/yCxuH8er65uWNy2zlUohCjB1hyEsCLfNyWuaH5zfjbh9Pb++exz0VHcZxvN5Nuby+vS/W7a4+SCmkAnMRgxx6HXfU7zhGfrg7LUsOIXz6i1/cPhzNLCf5tV/7NTM4HA77Yb9Mc5qXq6urse+PD7d5fv3s2S7EfpoXorC/uiEK//P/1dVf7Dr81fFXx18d/60+/uP/6WcBk8z3Q9Rn++E3vv0rv/Pbf+vXv/2r8ym/ff3w8Pb0kx/97E9++P0ffPazGVQMABlCh30fuh3GeJoXIzRoaIe6kfRjJyKOyPX6JDMHJHfaquegIKDe/+2M+44t8TYCRNzvdjFG800BLu2VRGGekoN9KtbFt0aCNdvnm5KqFjMDU0m56uxiQ/GamXUhgJY3r17fvv0qMiHocj6p5JwXZLw+XDkjh+9h5/N5HEcXdVLV/X7vkA8A7PtxXpY0ZwUJzMSMoGKa5mVOCyMCYUk5dHHsB89f0pq7jcQAME3Tw3kKIUhRMUAK3PUGVIosOQ+78fr62W/+5m/23TjPKcQuzVnBYowu2nj7cO+SFCIZVPI87fpu7AeOwQMkAADCIjIMgyKcz+eiMgyD4xC6rmtFle0eeT6fG90tiBbTgGSEwzD4CYnI0PtrXd+jNmwwM6imlLQIIp5OEyKyY9Sq4mrNYpZSDC2EoGAppSVnNBv77jwd9+OBI92+vQdUh7FN03Rzc/Pw8ICIS0rLstzc3EzHhxe7PaGknKf5ZADDYRh2gyHe399zjGgwzynnzBS7bmBG0dz30YfdN/I0zefpdH24ah2okrLz5O72wzAMUhHYFX2eUiopI2JJ2bmV7+/vT6dTJA6hu7q6AsAl5yUnMQ3DOOwO3dATUSlaSnGdOG/WAmToYtKVRowjrLxnIEDei28gpSzTPM9zSXN9UoiNdVAN3UUGAOTaQB5jvHp28+LZ9TJNMVAA1FygOHMUFjDsQuOJRwAyYAUyHQAsp24YqAv3x/MsmUOnhvtnN4ZIFGpqX0BVi2nsyftY3DEqpbjDF2MspbgK8t3d3c3NzfPnz0+nh66LIVJayt3DQyml78bInTMTFEkEGCMTQErLMp+LSFZLOZe8OPrIqyvMTBhefPCy7wcAiKF3lzD0nUFthHgCxXQSUngK4DHQAqBo9MSJRKPta3crFazCtNbfixUTUBB/T/sUMhAwogUMOS+laNeFUvTLLz9XhU8++agU7fvIHFOaVSEEIgpm4pj15oxuvdL3GHHUEBix9iW4NVZv+QEspXTEzBwxMvPx7v7nn/5UowCDiIzD/mtf+8busC9ZwcynvUoOxEMXAqGqSl5iDGZGgESsRUrRgNT3Y0VfmRPqsdSoFA0152wgkQOolZRALQQuS1IEtVJnHdawIXCn4Ow9BmpA6NzHkgsQMlKVjCQMxBR4WRZDjBSBgYFdeNL/W8pCRnGIqLiUJWDod2NKpdV8XJORmcdxbIsdNtGpSA4heCa9EXs4Bqv2EmxYhcLP7756lY8/fvtl+IOuO+y6q33YDXk5YeBdX8ZIZPPd2zf5dO4ofPzBhy+++S3sIzNmKSklquLaJTIexkFK+uDAu/769u3nbz798Y75/quvxq6nPKDsQj988vxg4WC4vxUBI7VSDMzS+Xx+mB5MhWGczulwOOzGsRQXAprm8/H6+tl0Op7v7wMFVPvqswdEvNrxRzfj/e1X05zjMA7j/uFNKoIAf+X6/9XxV8d/l483b1999zuf/OP/yT/+nb/5m1e7+PlPf/ov/l//8n//v/vf3r5+GONN1+2kUFIbxqsYCCkK0iI6pXJ/up+LXN88VwWrbD1EAYkAmcq8yJppC0TgAE3CODgGWlQUPJGFBmC7w76l5Zioi9HMmPl8PsMKynQYvGoh0tbVioiXHnCF+TwBAKALtwEzBmbGkOWyieomzTPPM2PN3xQRQPfJcBiuck6qOs+zZ/od570sS4zRNtlxZibiUoqWFZmAmXnV5SPqQowxENGCZGa+8cQYRcGF8JAqBD8SahE1BSVwDA6RMQSvb0gqIiRFRJgVAAjw4e6eAMGsD0xgJmnX9ddX+3w+gxYRSeeTgFHgYRhC152mMzFY7WdQZgx96CzkpQDU/OP2GMfRPVQAUNboVfvakCampKqGfuMqIkyhddeAVoFcV/dzlWhERFP0IgRYHyOIJsnZsjsBXddFxunu7jCM8/EYY/y1b35zmqbXr1/3fR+BZE75PPd9/+zquv+gf/nyZTqfprevzqfz6f4+ax73Y8e8TOfj+YQcKTAYAigYqaqkrGSKxay4uIqZ9X0/7obYhdPD8fr6mohOpxOaffjhh8MwlFKIITZ/UVRIGElCnE5nAABRSRnVdv1QBWRSRryQKqqWXBZI5rAWRMLgnGgqYmoFEIopKBoCakZEUCRPAwMxuCMEzJEoEYZcFgAwZACFlQURoLb54sqNqaqlpDklUUFBtNrkXis3CBlr1tZb40ENDEjtahwezqf788kQkhp1fejYEDyQM/CHSc4ryAB5Scaw8o27rBV5nGMbmQtdKbbnWToLOasW0WIWFBl84RMigwUOgCpSmKIBLGVWK43GFwB8EsWhG7phHEZw+gozVe27mNQlRuq3w1qraWrr2yqEqu66CIAGigYGgoqG4g8ODcVp5Vf56poT93qgoZquGXQgRgQ2MFs9UQBBRG/dbkUPMywlH4/n3W6nCiIp56a5JCKy9u20prLLC1+YDYOEiAC1X4gBDdHVjhubdqDIxIHYnCpA0RS1GKBJsSpzlDsVQICAxJGZmdDYDEUCWCQ8398SKFMc9/txtwez48P5/qsvrq6fqSioATEEICAnIgSAJAVNTBSK5CWhmnh3ojoE0pvMwBAQOJW8uUfvIEEQMARCtLXChTV3Y7vdrj470QK1+4sBuV/hhSIGwIhqZZlOCqHRbbkx949s23ybVcfaeQiw9qiYmct41ULBGi2bWRiu5PkHsRspWxrGeHWDU3744vMv99dXQwzCfS5yuMFPfv1bz66eL1nfTAVDXySVtJDmPsaABpIOgS0fT68+v3559dd+5Ve+tLfhQb/x0Qfh46HvxmLlmGbucf9sb3x1fwaCbIoqKiaAYlCSSMnT88M+T+cZjFRRxVQI7fhwl5cUmKdpQsGrw0FSJqIX+5svPv3sxfP9N772LeL46vXdV69uXXewHf/L/+QNmg3DsEzzzz//7CGdpzLnnFHNFE01IHUhIiKYrTRSZVlmAKTI52VWtmr2nTwJEZC7bqDATkMJQFIxfAaYkNTZ9KVY6Lqh38UYQ+hO0zLPCRGHYYxdZ64wStD3UYuYSORgRUy17/vTcQohAGExJWbuIjOLaUqJCCIHk3y+v3u4v616e+poBKIQx3G/P1xTiNnIws4AEApTCQzMGDkQRSkMGACjIABohmIgCmWaTjlnxyeIVIW/ZVl4zeiUUhDIKSNEhCLrSkcDba2bLdM8jqOq3t3dSS6uTYZmjIAmRUHBijpzJChCzrkf+3HoQiDVIjmJCCiM3XXOOs2ziHAM3LGILMscY4yMAKCStQiIlrwsqYRhpBBrwg8qcaGjVH3NrA2W6ISwoevcLiOiExshsJnz9ELlscZ1hYJ2XVd5sh4fvjV6/2LDFhPRYRxijCIyz7O36y3Lcnd3t9/vtwhRX8CiShHnlEpKqur5RZOipSxT8mTt+TSnlGLsh2GggMUKAIhCztmQmJkpmFkuSXJS1cDWx0gAKc95npY0Pb955o9Scum64eXLD0MI05wQEYmBSZCAnJImzOelUX3Lqo1KRM456wkJpwHxoSaAlm9oA+J/ereEjYhLWZzCHxE9LZ1SijEeDgfYJLf8xf/x//DtNs0+/uATpO6P/+0P/tV/8S9//Od/1jHN89xx9+I735kn+eyrt4j87MUHFGKa54eH+9OSshqFPozjs8NViJ2Aiam34loxInB2/Sb2efG5VSMHn/y2yl76G5ZlAQC/zbVRz+Z51sdiK+1Bc9O6dKyIc3mYRa5+vMiiiEpoITg9J610Obhx/YmICRyA63SkIYTYxb7v5/kRTrfruhBCk0hrq9XMmAMRhkhqJGqliKrGyK0XsF28Q4RFMyurqkoxM4pKDEzQxZiykPO0bWD6ALAsc9fFZT6ZlrwUQlMxIrreD1aWnJeeeTcMc5oeXn91uv3qen8w0SzFzIxQwU6nk55Ose98hgCRgoUQQC3ljAIApKvL0dajIqhCro0ZbfzNuz8rGptAtfIOhSFeLts3TyJiBkWrGPVHyNrLZt9aVMkC8kcvXkpaFMFyun/9SlUHpp7JkgSRZ+N4fX292+1KKaevXr3+8suPX75Y4KgiJafSRwDY7Q7dMN49PFR5DyLE6ghqKRgUENUKoDLTMHZj16vq6eHem7wBlAPFjrs+EENrirDmgiEy87Nnz3LOZOALuSm2olM6EcYYyFtbfAIAIiJTcMiUmRWRIhqKIREjC5g3bQRkYsolAQCSAQAbkAGZQ6oUEbWyEPpwk10ALRXCpKouHxsdSkSIRLX2wgxgWBVpHYEGDWF/93B/dXX49sefcIyfv3p1e3ygGEIXxSFkTsKIVXqAgLKCd8V4FQ4RIdSGYwAoIkUEENUs5SymZI/470spkZVWwmWo6hYutpjFSjN37fDFqRtgva3Nr6oKj9tA2+pzO9OWZGtKRkQArv3SdQzW5hzXml2bRc088HmEFfGT20ZyazuxAbzlPLQH55zjx+NxGIaadygXwZBm5N89rOU71kmIiIghLzMgEjnXlF+wFz0Q1XVJEMRUjYEjhyILkknOs9k0TV3XITIDKmpg7LpIaKgCJkzYQ/zWyw++/fWvPX/+8vb29qc/+fnbt297DvsPPzqeZ+/UVlXTokQKlrU+C1RTEKuoeCMrLv4nTlKHpFAppzXlrRvQ7nQL0GoDDtCqu1WLvRlYZ3ZxeVMRaTK9ClVPvT1x3yK3M6SNp/PMAqBtZpeuz8Utm6503sEoUzAONs/n8ymNOx767sNn+9PpwWyY0qRSvvOtX/md3/lbMXZ//Cd/+vmf/6QfDoiW08yATAMDlml6+/rTm7H/zovhu7/60a99cv0Snn8Sv/Hxxx9//RvfLsBvbm9/8cUXd3Mynaak89lKtpygZBMQJuAY+zBGYhKDUs53D0CQp1lEGGCZ5mf7q29961unh/MXX3yx67v982cE/PL57rf+zvf++vd+48WLD/7sz3/0+3/4x7vh8PzFR9s59/qrr5Zpvr29LUtShhIpQwFPeiEic4gx9r0VUTUVLUWnXKYlm1kURsK4MkX4FsDMGMJ+vzcEUy8UJlUBdc0bBSygylaIkC2TZGSwrEFTBwUwsAmWZKqgeUkFqTdRKxLDyD2VReZ04oDdyLHvgVDADKCUkksJsbapiZkhAzJU/TkhIiYDUzRFKAEZoLJ0BwohQmBX7gwI4c3bI2A0AkUQheSGAcEbuTxkTKm4lkeMcZ4r37NnyHyqlVIiX4iuqAKDDdRCCA4PQINGQIngtCAmThDI5AFuQBzHAVCt5CxqIlJSzotljb0w8p6sgKW0pFkETE0pQE4qIibKUNnHQuwVCZBAK7G9VHpl32kIAIyQAYHZJWfmtFwsETAAOFPsqqPubM0X1VhVBXwPK/A0Td7l6TZxWZZpmvKS8jxdX18Pw+CZ4OPx6F/n3uHWTJiZqM6pCAgaAoKomOscp4UouFU4T8eU0t5ksbyUJVmJQx9CKGhIhNwVzTnnoesNrDiWYzYyNREs6Zq1K8eypGfXNzcfvmSKiPrZF59++PEnxME4KFKu5BRqUIY+um65uwiOIbFVggRW3CGubZTz+Vx3uNXKt/a1rR2ENUvR97UHERE9RvLn1Ua47YtP9qRXr968fV1+8uM/F0klT4erHYVxKuWLr75E6vYfPaPQPaTy6tPPI0XquvFqP1JQCmooplWBpW6EZpWOQpqmFa4kFZWnIlrOWUXcc6ogctXTNPlUb1lYAEgpXV9ft81g9elFtUQkxLpttK1XzPoYDZTdJ1EFIMvFta7Ek2PrRuXTZug60GJm7BeEQbQggvscHvr6NuM9nR6Pba6kYkZD6P33DQbq2r6lHtnMlmUJIfQxDsOQ06Wd1w0RkBFBjMzKRU3BVIuAiKJoqaIEjsYGDQgcIBA+e3Yzn8/HNJMW1IUk7zu6fnYzT7msexURE5KY5lKurq6YGAy8z7dAATUpGiGsLpIBAFTECJZl2WC2q+tvG+eDiDA45aX5I2txHUFFFSNwygusfQ4BydkkEdEXAhK6QU4l55JB02EYl3n+O7/933t+c/0v/sW/PB/PN9c3P//Zz/p+mN/enc/TVwh9P3Rd7PshqE3HE2N4dvXsOB2XaX4jrw/XV+NhP/Y9MssqmoKIrhiVUuopxnUX8GRwKeXm5sZJh66vrxlxWRZV9RhjG8K1/shht48xWpEGQfEH3VEsAMhkTAqgCKhqCExkyMAEHIxIxYzMVEWMgYCITEQUDYCNEQWQANAMXTxUDbKUlIkCkkEF2nlAUl1/s0bxAg5MIqLu6sDMXYjE5tw7hqCihk7caZvZiASQStldX33rV35lf3PV/exgv/j5eUlZrBuimZmiIjjdoX+KFZ18RRVUVMAZOSmEzsxyKara9KQQMTg1GQMz51x7gJsWr9NyFklVzhLENjSmdaOBunW2tQyr+DSHsEiVCfeH0hw+Z8eiDdsPbvIIT+zqu//c/n4bVLSl8Sh5V/1I3weTZ7VcocLvdJomZyFvPTNtmrXQooVP7QVsVl+7GFcw8zibkKxGi6pZQU08jClCwRhp7Pvl/IBADsXJ86K74vZKi6ixgsTAIUbGPhBE06s4XFG3h2D97tsff+3Dm+fnJT2cTznnGEIHeMp5LjkbKtZtgKw26Lo9ITVEPKfkZkAIDMAIwYDAVCyQbZ9L2xbbb7ZHM8U+ksuyHI9HB+99+OGH+/3eJQtgZSl4uH/AlRCvncRjg/Y0N6dXkWxmtEmCODV2S/e0I8yL3d1O+0yGrElfpTfE7FnYBRIiGurP8mdl+ZcplT/7wfdj33fDMRCaCCPgHCOoLVOZTmkixKuvvv/FL/7g9moX/+b3vvurv/YRRFC0ngPRgV/dfvn29njU45lA94SBibx2rqpZVHK5e/UKtIBRN3ZX4+hr73Q67/vu+dVhF7o8nbsQI0Ig2sX+e7/x3W988gldPftrEDve/fRnn/3wRz8B+LANxtvXb6xInpcQwrgf7vPsnSiqWqTyTBfReZ5XwTYSNfG6XxeGrlbAi2nLcYbYp3kWaNU3NYTIgQMGj7w5DMTqG4gVzJqLAuEQHU+rQGZogXC33/Xj6E3iV/sDAJxOp5zz9f7gLl2WMqeUSlZTLNaNo4rPL0EOQIFQatchITOFwH3EIWDfsQIjLKYWGMcYh7ErJd/d39/dn7vhxnkZTCxJXqQYqGHp+o4ZHSHw9u0dAOx2O2ZOqZhZyeL+TUtw5mVxXDIiOrGSGxUX/V2WZez6vu8BNC8TESmhQm0DWqtmSGBokudpmSYw6SN3gXcBkXC5/bKknKVw6LjvQuzQFAwsgykQQFjjClMks2VZBIoXRM0MXC4CgGMUKSKCTMABzdKcRSTG/hKyFyKiKihWc/1G1DS/yExNlQid/4F83L1DP0RElFy0iJkR4NgPfeyOD3el1KJH3/fTNBHR4XC4vb1t67b9FDAxoxCJWSSXklJOauopAgORIqo5MgQGlTmdT+OhYyigAi7QVhYHrS5nCwADchd5RzgE7jlE0jzf3TzbR7pWhenhbQZ49vzDv/6r3zxOixmoooComqqImhghdQa0NRktZ08rdcOyLPM8V+xHCB4itv2j5T9wJXdz17O+oQqjKDE7/KAsiQyW89Q+68Si/Djcunn2MjA8HN8qLVc3Lx4e7uIQJ9XbeekHtkgPp/tPf/zTX/3atyP3XdfHbjDiIpCyqMo0nZwfjJmdysOKCFBKqdV0aPNoAnHbj3kVkxKRGKMHe7bicb22c5lUT/hqLAMqrnSWVpvJ7PQwOR4UmQKtSFusW/IK9YFKFmEmOU/T6fb2Ns/nse+93G7gsr5L02r1r56myVexezBtw6aV8NEP3zYaG7v7MQAAqN46BgAZM6InZymEEDlkl9vbJMVV1ZDMABG7LnZOjxoiAHR9ALWOgyxzH4j34zSfTm9fI+Jf++6v/92/9w/+xX/5B/cPx9v7Ox/Mvu9DF4GImVPJ5/P5NJ3NLHTd2PeMQUrx0LReqphj+FXBYyozU7isL/ePyTynn2llzRPXZzW6MIjThQbRT67m8kNEROa6KZXhW0pJRYqJzEV0nlkUUkrH0/n2tgMq5zMnEc2D0bJMx7sjEYTQ7XbDpz+5G/fDeNjHjiF2iugFet/ac8WZGBEZoZm35CJiVeaYZ7Ui8zzvdztAYuauDwHJuz9VS6NF2rp6ZnZ/f4+IkZiZPWHh/kGZl6xihTCwAQoBhhpmKKAoGCiqAVBGMowmtZFgTao744kMXTBRH2oxCEhgBqpEYRWWgtUvfOQR4kri5DHJ1HXMYYx9jOwVJReJlKJKza9FIFfToo8++dppSX/6wx+O+/2cMlIA0nk+IbOYqXojpmdAyZNNHDByYERidtESE4WgiFYkiWaOERiyZq6VTJXy6FK95wTAWYixSpATIYIHz+1m3fUnInbxh3rjFwrUkrNe8kGeDwczcMGHZoJblWA3jtUy2CMPu71sq7LhQLaT4TK3N8wozVYDgPc4uYwMrvmdUopnqcdxbCzStKoBvuv0bz1ga3G4GQB5m6gbVWKwesWWUlIVzUVEoYgvYEZgJGIkNCkqpSCAN7MyEYiWUgJCF7sYIprgosev3py7Yc8Di0SzrGCppGl+fvOMxzGB4sPD8v9h689iLc2y80BsDXv/0xnuGFNG5JxVlTWyWKyBItkkZYlqi61Wu9WGbBiwgYYB+9VttPvJaPjFgGHYz+6GYcANQ2q3jJYoNkWIEsdicRBZc1VWVc4RmRnjnc/wD3vvtZYf9n9OnMjkQebNyBP3nnvO/++99hq+YXEpMVoudAUAEMw0yUbEE1VEMkIBQHPCgpgZ3URC9JSYsQ31vCO6uu30wyahz8s7z0/Oz8+Xy+V0Or26uhIR3jy6rlutVg8fn0yn09lslvF4ZkYERLzBImyahpuW2RbOT0/7lYg7AKHtTXHTZuLZVc5PJhMzWa1W7XIpIrdfeD7GOJ3NqqY+v7p872dvIbl50/RDK21EJM6HU+qdw8opVHbn+uyXvvqlV196DoYlaAKPsDpNjrhp9ie191PnQOGyHVaoA4MrGclzUuwlrru2bZepX7dnp6/cuQPIriyapumGfrFaha4/efRIY2zKuil8VZQhhMKX+3szR/6v//Kv33nv7unZxaLtl6u+G9Ju6p9tZCbzmXNuNbREI8UHAHJeKAQRVImTpqy2S64snCciz+RLVhNVBQEkoowpc+iMwNSSgSoiEBMzFQxspoLZA1rMZEi9BkRUAe89V1w4yn1lJTAEVxTZvdh7X5ZFSokIvXdGYKYhhRhjTJGImqauGxYrYjCTyMyuqMqyHlRNgqpm0JuqKKNFT957trrksvLeY5JhfXW67tqk1NTe+wjsjSGqaVAlUVAgIAKzpwXrZj1BbiNlWdWM9snnH6gwUo7mT52hDDwxGTikqiirsgohxBQ1uw3QhqFvZCCISKZduyKEgrBg4CipT+3QaRheuHY9BoyJNXvDsXBZuaI8u7wCJO/Lsiqd8yGEfgijRJ0KiOQThR0558j5ddfl/q1nYu+ASCFXLLYZk43GNFmZAXaK9q13aU55cGPoi89W+dugCTsPBM2CaPv7+3Vd54FpTpQ/2fVX04wQjZJCH4ZhABXPrvRs2cMrBFJxzA7ELFUgc6DYtaIZw0kI5tnNqlJTrHwxK4qauTTwpgUgU6qO9h4/+dBP92/cuPXCl75A7N/46duPP7o3P7g2SOpTGxQVUZAEUIyW3RrIbbP2baNxewzkFBMAcifDM2+T++3FAYDcJrcdLRfYDFXMDA2IyBHnr845R5wNjKKkFKKYfsw37d5H9yVERfXe8TqJQeM8liULnLfrxdn50bXDn//lX8J1xCgxSRsHNQRy5LikyiBsY6KmrK0Yogoy2KZicTvRk5kBILu92Gb8JSLofP6kufcvG4eKfGTunO75LhNmARBDQDUzICNANBDatDw1K3tbNDGzfN2ytwXsCDynTJ3sOk2xLsv8u5jRe59hids5/m7LRze2bvnjeO9DiDAqwlFON3PuizsO0MTjAZZbVgCACI5gKx4HACEENQNgRQIjojHBMTQDymVETpJALaoNqfeOLQmoeWaTtLxa3P/wXu6tZlpC/hShHzLsZ9RCRWBiE1kvVyI2KZqc9KRt7pJl1xFUNeaJDSEA5GbqWHbqjvC2moIVrrCn3TIGMCRCZO/JzCyJpnHokjs+vvCqmjIxFFVMCbFgZyL7s/ndd96OMZZMk7pOfffqiy9165aIrh0dmVnXdX3bXVxcsHOvvfTyol23XWcRqqb0dcFFwYBJVWzMD1IC5xyiAWpZeefGLux4xhsg4mKxmM1mRLRcLj3xFvfvXLHdjza2ulJKybMjIl83eU6VL4X33iNxCEkBHKtB5jGXvkiCBqZmAoLAhqIGBjYpajcWw2PWAZoAwLNTfHqdU0q5mSYiAKa79TCMDnS6cYoYc1PRlNJisUCzAoiLanT4M2PmEMUMRgazjR0YAetTXCyWTy4vyTtRcIV3Ve2r8nKxyGLiZmaKmwJAPELhGQrz3hMSOTIz3WjShxBijL4sITe2Czf0HQKojEMA3MhW7pbQuknNAWEnJ9v2aMfMLOd/qkqUhVMl/+8uPOjpVdqZo+7m6Lu/dze3++T3737P9pd87Ee27//pT4nkPjRudIeapqmqKhPWMy5l9/187Dd+7A3bsw9QA+czw8PINJMUzEDVMYoYgQEKsnkGNEEQT8jsIruUelBBgzyHKZ1PGkGSgAqraDTV1Pc39/YrLkPfPzk9/fD+R4tVy6Xnsto7PHRNVaisutaZkUYBA8kFhlPVHOABUAWiivOlZRwsEjIhOyBiNABx8LQRtr0CuxTt7cXJVyzvQdiY9uTO0Xq9Xq/XRVEcHh7euHGjqqq+75fLpe04rNsnCqrdFZiXmXNZN+6pVUvelZupGm43u/MINZE3SatlSqlhvn3r1sHBwaNHj1LfDbG3rrKhr0B84V1BLgKjVZVvqhpVUugqTLParfsVCp0+endKV/Pat1fnZ08eL9bLz/38F5rJNSjcBPTaQeOr2vmF2NmTiw4BUDSJDUOQYUCj0lcvf/r1X/jS5z968Ojh40ekgpLYtC5917WLS/QHx5Om9kyuKMrC9ev2X/72b5+fnZydX/YhDsmiGvIzWH9kHvpeAniJy9Wq2ZtQUslwq6IomAiZmZuJG4YYQ666/BjFUI0hm5RYJhg5RufEoGqmYuN2TSkoGBMB5abEiGxOIiHEnDD5sjazFOK2jaJJkuliucpnSVVVy6bu+z7r5paVL8uS3Ug+c2XhnSMuQyBDFSJflkwIKUochtDl4KsxBBOCVJcMlSu8W57fH7yxZ4PYhW4IKRopFm7/BkJArAAQMSBGBECiFClG8d575/b3ZojoXRljxBpSSpHEzBzTaIGo5kYXEkAE3hyuJjqkWDj2TASaQm8iaCIxhWzpsok7BGgAhrZXN1XhCgaLIXadappWZT2bQlg1RNi4QfRqtVy0HRVVNZ05djGFpIaIKaW+H/oupJSaukQwJiAgYGLPzjExa5sAzbFDR4rGDr3z7J1JVvsYNTAQ0bFj5ihpd3dtEoWnPYzdfW6j4R9vQ6FsIuZ0OhWR5XK5WCyyOnJOofLkZLcfYGasYCnlVp9GYSDioiTnANXEJICkErEgKMGKwns/30fX9yEBFk0jzvUpoHfT6fT4+JjUWKQAqIEqphIRKanrX37++D/6h//o+suvgvOnH9zv1v2tW889Ob0M67brhvUQlD0WBbFPoH3fw1YWYwMU3tY5+czOnzo/rykgGqLl2RE+7ThuDcU0fwMiEjGP5C5jAkdAaI7AO0ITVQDRGIbQ9ck0Uza3D19UTT0v6woRh9hHjUNIYDiZHTb7h0lTSOnJ2Sn3WnFRlnVZOlEYhrhq18MwTCdzVROxLKieUgLVZKmZ1roxadoN6JvsX7elTo6h/TBsjRi3rcqcPechIe0glxDRmAQYMyvPwLIvGmJVT8w2vWrRZNmhSB37zC3ZZv84al37URiHlJnRlIgKR67wudmd06yMtbPNsCVrmGzf3kZxTzNxNhd1zjEzZdUiYthi3DO/tshqJBIzgF41oQkDishmQABgkCeKiBjMokEyQLOoOohClAhYsuv7IfZ94Xk+nw9D9+DBgwePHvvJPCN3cgYfYwghhBSziEpRFEVZElFIceiHYYjVXg3yNKPKwnsAkBUYo2ZcJCFiTpEyBGU8j9WyupKBVYUXM4fMRQmYy3JkRgBDFTMxEFPlrOJHpONkMdlIxwBEQ4J+3c739xmomdYH+/vLy6uzk9OicLPp8eXlZQg9Ix0d7O+/8MIwDM1sXu/tvXP3/Xfff2fZLlOIRqYhCFozm+qzpRoRqYFzjkf9R0SksvSeOSW3WiQADSG27coh5d23XndlqTl/BYA8ucr8yGavNrOMUcwRKS/1sioQjQ2QKKqZCKqBGREbGAIbIQIrIICKGReeIE+bHQGaxBQk6+SYqgAQEhAAAzAZYiaW2g4IREfn15ysjAIqtpm2dV3nmR2QJqldkatTNUWmLLIiqqQmlvux9vDkKoTgy4IN+iFCP0wMybn1em0IRA4QMzcP1UCtmTSe2KEzRd0OiAhDCvm3p5SqSZ3EQozEdd5KKrmD62CHBGVmDAYAIQ5ZIYocykaEHjcjguwQnIu3XBh4P5YWopoMtj7ru/FzA4H7eFBK8ZkOC+JoXKCaWwlPiwcAMAPcmeDupqSfnAbkvy021pPjClTN9KEMeMtiBvksM7PMd9p9P3/jH7Y3FwGzYphaYuPxClkyk9KzaWLmovIlU10VsYfl0ta9MiETZPMmBmRARtoWVymlvlchJMTCgNj/7J13T05OhhjKui7rSs0Wi8uiqV3oB0n9YoEhVIBiFlJAgyxFACjARM6bYpAUTQwtC4caZeHMTAT0mI3YYQM726QL45XfGiPS2FjdPUZ54zmYs/wt/HUYBjObTqf1ZJZluPJ1w03jaXudt89n3CYz7j6/veLjjaannjBOw8WtF1995YUX0xAePrjfrVuvXKj74qeeu7w8f3Jx1q0vbx4fvfDq61HS22+92zTMwPP5bH++Pwz9+UkrfScIX/z8a1/5uU/fPJ4ARujaDz548uTs0fPP31otT5u5B5T1YgA3vXlwU7S6XIbHpx+JSYjUB4tJHbnpfG9a+b/99a9++Quf/eY3v3nv/Xf7tuuG3hBK7zQVTVWXjq/Oz/p28N6nEP/q5PF0UlSFi2KrEIpycrx/EO2Z0hM9g6P10NVluXewzwWLJQspc6gAM8ZL68JDFCPNtua5EDcT0SiKioSE5D17ztIeQ0pIYAjk2FFpJkSOIKfBUcWQEYCRvSdHzlVVE1KMUQ0SIANTGmIfgyuLFIIkNSeWVFNChaooNCkV4JiZSERQNPWDQXJ+j8EIufRszNb3LYKI7M3nIClFSzGRmQOtHDUF/dKvfWO5ODNKz7/0/GuffhUIf/jGm9//0Vtvvvshugm5SRDsJUZJCkDOs5umEEGtLH1dVjmBiCHs7+3FGLuuS1Ed8Wh2EhOKjuU7gGyUEEwkhDCbTJm5Xbc55SXAPgVBUhpLTyZyCIzEaJAELYiKxWFa+Nc+/eovf+MbX/r8Z67On/z5t775o5/+5PaLL/3ir/xq2Uy++ef/7g+++S3nfR9THzroB0KXVCVZHk2ON54UE6esk4Ch8oUSAlAy7cNgQ49EjpxEVdJxyo9PjTmTyk7Gv9UhHplYuHt0ZexsVeUAvbVBzds7xpjpzovFQkT29vaaptEN6BZ3krnxbSdBtUKRi8o5R4gSI6TonVdTdFB49YgF03RSH5X1Mbre18Y0OdgPZE+urqJKXVbDxSWoNEVxcHz0mZdf/tyrrz5/+w7sNT/8y3977/4HAPDg3Xevlu3F5dXZ2dmdlz+1DrZKmC67fkjC6NA5AEGqqmaLwNUd+nJRFLnJnWseszyi1Sw7gzs94w0o7inN13a8fhjN1JIIJYoGW13CRdfjBm2vqo4otye3D+dLdn4IYzpbTSrvSFEtJiaq61IgpSHsHc9TiH0f+9UypQxwd74sF4uFWVZTAIKRDovMZV3IRrSedwq/sRO2003JWXUfU2bglWVZFIXZ04+/e2efRmImGZMbBNU8L8uYXTM0UzEAYkclsycCGVpCyNI0CLQVki+KoqqqqqqiJRANYVAR73y+KbsD6Aybzr3/3EV2O6As53xet9sFmRency7EPkYVkb7vnXNVVWTGsEgUyRlPJPIAkDHw2fsJTRVMxchM0Pmy5KJwvnLeiyCSUyRATADITslFs2Tsy8mMHLJftF2ScXeQY+dc6XymG7V91/d9TlKBqPS+KRs0yLkOGSgCqeUEzjPL+CRmvizmpv326EUq8gbLg3jMiHV0CIqZVmFooJpURxtWsizUSgDQdZ1qQsLSF8CURKKElMQxPXryeD6fTxDeeu9dVKuqct21yLRYLmOMTVnN9ubk3fLifNF1y3v3Hp+dLZdrLnwznbjSCYKMdYptRjHE5ADIFIdhAGAiysuvLMs8m7p169ZqtQohlGVpSS4uLoioKKrtzSUiZJeb6zSaGDwzAho3dRxUNWuZiGgchlzeN9O5IwfIQAzESTUlAyWJvQGaMlj0xCoxxC72gxWFqioie0foElr+Z9wUY78Dt9kMbMaeuNM53q7GrCVonIqiAMIIymWhYGCaIf8ZnogGRrxoV7KwsqmLovDsVm3bdZ2Oo6hcBxIDGBmqzedzthEyMaSUUhJTABAQQwj9kFQsibClGJP3ZVHEEGzUAhrbw/nymtmu31/e8Lv4xhzNCHF7C/I6Z/a4GauGJLq59bud413390xKyY/SV7AzTd29brDTn9qGn422/DNBGBFzO2ObI26bO4S0LTxG8RKRzADMzr7bpZjf2BaD/rGMf7fJtX2TCAimBAmyOweogZglUBn6HlXQlWVRz+pyUpc9W1P586UwZJUpzRjf/JqhH8b+iGoY1BhLX5ArPnj0SPuem2avmAG5pFFEXVmcnJywd1Gl7TpEnNSVAKKYQfTskch5ZHJFVQK5IHpyfiFApGq5MDYiMwRjZtx4RDz9XPi07bVtt2/P+m2zzMyyHvTV1dV0Om2a5uDgYDqdDsOwXC4BYDKZhKR5aJaLrjwOtU/QsrdLReQpiHFThMDH3l7+6l55sfnVX/7017/6tTd+8MNu9e5v/p1vvP6Zz/y7P//zoV+4/eG1V+/8j37z34eDGSyXf/gHv39at9Py+LkbLx0dXXv3vbur5XK/aVrthn6RpDu/ejQMdvP6wd33337j3Z+9cOfFk+Vl2Z8H6Y6P7yiW627ZpYKsun64N60fSZc0Bo2GxogGKWrQb/7RH/7WP/unpydnRnhwdHhwcEDOhxRNpF0tjvYPjo6O3r987+zsZNrMuPCLtl0GZOeo8sJ43q0A3W6KsBg6rnxVeVATsG61yhsJHTvn2DtDAoAcu/O1W8ehcKX3PsU42Ztuz0UiArKYUgihqipNiYhikmEYisJ7pq5tHZgrGu+sC0NSQSrALCat0AEBcEpmpFoUfrY33XOcUirLEoC6bj0MQ+GKyjegysiLy6XEC+dcnhIws+NyFboQkogUhWvq2iFUzneGi4vLwvN0UhZNFYaucPQrv/SL/+Fv/j3pz771p3+4fzT7xt/5NTiYw3LVh/Tm23cdQkpp2V60QyLnq6ZBhpASQGyqMruBphRCSmZWMJEpgxVMqCZxWIUeEQkwagJUzEIlm4eJtm0rYRgT4hTC0DkkLrxzLuQkg0ZYbRr6kIIDQIZ5U05mU4vh/P6juz97+6iuvv3tb5Wl/y/+8//DnW98A5atqUaj3/uDP0DnnXMlUjTKgTgPHbYxS8Es04aDSsaYMjH7DCw0M8nUJRw5jkQETGamArlvlKuAXB/mM9g5MlUCQMCM6d+mwlmjjgDNYGMniCrap6Gu6729Pe9927bDMCBi1lvcBlnbAEjMrFuui6Joyjq/SRV1gM55Nl0tV4f782nBDz/6IJlOSK8ur1joi5/57MOTJ2//8MfXnn9uXldPrs570KqpoyTm+vqNG3uHB2+889Yf/MHviw4DDutu9cOfvF1WzXz/qKym+9duPXxycbHorlZDNC6aGRaVIGXCdFE9A9PcBpG8iXYD/RjUnkrU4zb67MYp28FBImaCnTJz5qfmwunq6mp/f99G1qnL3ehdIhoA9BIhCGqWoSxZHSX1o8UpWEwihkq5WM2NzyzYxyCI2NRZoQJM880tiqJgRgVB2kB0dmQZQoiIyDtzoC3+NUuAb/umOVLvHqvbmCtgUUCBQJF2aIpm4JnX3bos66IsV23vmJ0rV6ureVWCJdVsKGOImSYMq8VytVpkdmDXdSrBbRpI27ifO4u4GfLmrxmtl5t2ZVlOp9PMA85wi7wUU0pl5XOFQARmPsbY91oUhUgkAEAClJyt5NKuYCeQqZcAgGoYY4whEvHxbG+IYorOl1EUANi5tusJjH1FpH1SZnRFjURVjbnjlRvS7McqpRvWQwyOufRFjNEA0KBbt96XiAyE21yWNs5dl4vVZDJp6nq5XFa+qqrq9PTU+wKJmPMky5jAbc5HXxRRLIaQxIqqqSeTi4uzLGMw1lqFB4C+7dq2zQBfAEgpqMCo2q7iHLumWoV+PeQICaFvTfTq8aOiKsxzJ/Gtu+/1bTcMg4hV9RQd7+8dUOmENIlSydN6Us/qDz/6aLleHRwcNM10tVqtVx2gMvNyuc5T4qpyMQoylkXdNI1zRVX1fd/HfvC+ZGbvfYyy3Y9bHvN8Ps/Nmqurq+VyuU0pQggxDIaQWTBmEok0ybBqPReAnEydL8uy1BDiMNR1zajtaq0p1WVZ1QVIkhjUQlnVq1U3JCmgdgUaAjD5skghgqbNWwIAyNOjzVbJQJrMHAMRAeJuiBbXkX0sijIkV3hwvGivgkqUlBUyNCYSYzTyjn3pCkKmpDq0XU6+XVGKWJJgCZl9JlqyJ1UI3RAkOeeaZuqcixKGYVh1q77vi8JXrkopsXHlHaqEIfc78p7aqsbZqKCVbShQEREQc1TfBvndXZkP9Uyx6Pt+DKe5X7CNtqqwmRXwzqB1t7U5+iHsoChhAwTaDT7boL3jT/dMwbCblG/TxG3Mz+8/E6+JKIRQ13XuueToMdplAOSTdFtbbteebjRqtq+8aZ2PMEe1BKaAxqjgLIZ+Pp2AxMuLBet8Uh0Nw/Lq8gTRun4tomVZOuey+lyMUfMYxwDBwCCq6tBHMIwRUOfT2eG1Y19Wi+XV4uIyhRDjsL+/37ZtkDY7oddlGcJQlGwSELgpKyqcaGq7tg2xKViQAFmQgMwgghIikqcsR7S9BfkPfXjqlu14zNdBx2FsfuRvmM1mmbWf79p6vc5PbgE8RECEG03FqKqZxrN7HO/ed9jBhapqBrCMfszOb2+Nqyt848ffm9f+xRdvefi55fLkxz+4iMNFGFpP9uu//Otwe2/xvW//X//v/5X38Ld+8Rt/66t/9+Ks//CDB93lWVivVqsrX+pXv/bFb/zi59+/98bP3nvnJ28Pl2fni1UPJ4+O9/fmVXN0cOx9uVjEvf3rze2Xf/i9n/zTf/Lf9cI9OOSK6z1fzmIMV6fnQ9dWaHuT+vXXX7/z4gvXblwHoourxcXV5d27d6+urjITghzPZrOiqIbYQ+mASB2j80rOmPHZ1L+oSiLMl4CRrJVclDli3pDKBQxMCu/BAwOy4bRpCl8FCR88+Kho6rqu2WdYs6gl5yjGIRcAVVWKyDAMDNQ0TVj3+SjyDolHDFZKad0NRETsEVENYxCE4MF7LkoqiMjVVrlxiaQU6rqmZgIAaejXq1XfLz07N5lOippEo6ZJUd48OphM6ov55EHh1qvF0LXdah09gtrZ2dkf/Nt/885PfvTH//Z3v/a1n/v7f//vvfeDn/3wjR/+xbe/ff/hSRfNlwcJlIVrV5DzbB7VEKDrBmSJG/RCFtlDxNCutzjRrVAmIg5DRxus8DYvgcyMj6mXlpkdoM+NDTWUXDQDqpmoibEpAbaLpZ/VBXHj67Kqh7791je/+Tu//c9//dd/6c4Lzz98/OT3/s//l3/+r37n5Pzq2p07R8fXL1cdImQPGEUCM8w6dLKZaQIJmI3wAxYwVBWQPAPdgI6AODvPPBXBMBAA8GWxu6O2f97AWJ5OUbeJYP6LXaqTmWWUyhbxuaVDbVvm2z0fQkhDmDd1wYX3HnGDWFAVtb5rp82EDc4enxxM57/0jV+YN/V3//zPrx6dKdjf/w/+wS9cnv+bb/7J/Xv3ilmzXi7AsaF98OjBG2/9JPRtGvq96fT5O89VTdmFIQxJYU0Pz9W4i9JFeHJ2yfWknu5NpnuJqA1BDZ1zYeh2p8bbWLOteWhD9s1Xj3aAidvuFyLmzvFWCmPTGseQuYAAriyKojBCMjDCbrUm7yoqBMySJFPHzwB+1l1XF03lCwQw0RSizx6OikAimMwigHYaE6iJAqFzLgOjCTjGkKsVYthI9IICRhXYjmh36xXJU92PJ/TbMdF2JdiGA717EmwOWraMGEFT00wcyNKdlkYRvVFDKUZC532RUiQctfB1c1ojIjMX3ovzQxqY2XFRsmOHjON4JJ/98/nczC4uLmxnWAw7jcntObRNTQAAUPMon+jpRxhXL5pilsWzbV6SfzDr3yGyIRsQM5BBF4bQdkPZWVF5NlVDNRNQQwNUMFUUUwYUA0Tpu27ESQOEEDDGKCmlNJvNlsulb5rlcjmtmz6Gvl/7so4xIguZy5LakMUbTU5PL+7ceS5GCWm4fevWYnF5+uTJ8fFx9lwjIpchu2isBqaWJBmYAjvPBac4rGLUlIA9E2nKTlgDERFgXZfjFAg0TxQY0ecIF3qAnISMPWDIbEEGsdQPSURSiGJCnrngBAYGmgRBEqiiVlx5sP2Dg5hSvVqWZUnkiqKIhRjIgwePipLm8/29vVlZ1mbCwM5RGFKSpJIrcA8QRUw15qb+OK9D2rafxzUQU14quXFLREVVphBVIXNgyKAoismk0ZSKgoCcmUDfQwxewElq21VTlGVTASikHtBqp4K8Wpz5oizLalDph07UytI7N706P9vuifE/G1TCTrB9BiOeF20vqikNPAARMg0m2VYJETnDJyB7BosCOLRMWcs7Maf1Yio6QulEJJk6wDWuTUZ8Udd1zKyw0b8CpNFdGlRilgIft0zKpZQCgETN2E4zI1MiIoaRhQkj2XcbRVQ1n0/PnDjboApQsEubWmibK9Oz0NBtKIZPNETg2QJg9+Ta/Lq/AX8PADmCbV92Gy6yjcluIMw/ledLiJin2RmMF2PM08XN7nhak2xPCtzl/lJu1Od6acSCEiii1fNZ6tem6ehw786tm3VVxX41mdbDskNAQaPcbYT8U+OYwtTQGAkJDBAVdHI4NVWsKq0qcU4dW+EJwJmeX17EfiCDr3/lFw739t95552Xbt04XTxZ9+t12y+Hll3BZVUVRVlPluuBEQzBIRgiIOforwbbUmz3am8/qe4A8c2sG0alptz3zNcNd/A/2yg9zm83Snrbh+pTONzHNs721z2zSMxs025Tfcr2ce++8xDg4dmjixeev+0JUwyQhr5b3rx+rShovVzN7j9oXPEbv/Lzt2/ffu2zXwAr3nnjR3/xp986PVvcuHXnhdvXb945+rv/0d8fFh80M//1X/7qweF+Cqlb9mGwo+PrcG0ffvKzi0U8PLj15Hz9L/7Vf/Ojn72HamDp+tFBPTtcd+nJ2eOQ6Ojw5o2XX9LQHezv51YlIJ9dXjx+/Pj88sK7MiQ9vTgfUjSkZtowM/aucM7IEBnIAbE+IzUBAJBESDMLRwwpRmEkBjIGczY2/lSLoigcS0wpRDSQoWvbdTf0z926LqSqGkLXdyFneD53MSVpioLqAHzhC+9yJ1jFxsMFgIiSihkyUVEUuX8ZZSP9DqgpDLqxkgHIJkLMDHG4Wl6R2o1rx6+/8FkmOjs5PTk7XV+dEgInDak7t9BWzfn5+aP7H0ynU7UkMQBy6Yso6b2777/95ltf/dIvn59c/pf/x//b6WV347lmfngQFYGrvotJQQGZWCEFaVWTIXBdCo4rTGQLOwRE3iavpk+nWioJkZCIkJjJbZh/t65fj2Hk3RbsACCl1MeQfQ3NTM0kEiMyAgFcu3atctwPw9Cd3bx2+OILL4cbN+8/+OCP/+zffftHPz67vOhiunn7zp3Z0cnl5eV6qCZzATQiMCTEpBvHD5Usn2KUY+kYhMeYZyPzbEdGboPgwVGhKP/Ax/bbdi/lIPaxQImIWbpnF+uftygxbjGdZpaDI+yIoO1GRgCoy9IRE6EZGCABJgVS25vOutXV45PL/VnzP/mH/+Af/y//F4/ffvs7f/HnqfB3z0+PLk57TSuVntFVZej10fKqqmut/GR64858hmSrxfLJ0NFlNDMRS1FTkiSYgNWonh+hL6moA+AQpY8qhl4jPyuss/3I20bd9nzKh1+M0bnx+qiOtFdEnE6bGGPft7LhcWaNi3XXGUhO27L9DyMBYRe6kivnSFWG2EdJpX+Gw0OOzUQteWRmkpQYyYHDpGpRNQEoOWBmcI7cqPkKAKBGRsMACETkjJDQGYKogaVu6MamIwDvQBEyQJw2VOOny4PHG7cb7rdH++5RPX5FNkUwIQEEQDUyQ4L1alnXNaEZQtHUUTSGvvROUiQicjzumc2L5zMj+RAGBMTCFwU7xYSIRVGoap4vZaHoLSDBNhiD7XuLMc9D4vZOEQMiD8NABMwFc74MmT8AkoQ2mlcAmadjoJpNjs0AnkpPIICFIaW2lboDhCQOkcEwWcj7M+uYZhYIETGYgeV2NW7AHprERBeXV3EIWpQSYr1fici6H+q6bsOAwDzabhFksWK1F1+4s14v1+sOGdqVnzRV6YthGEpfmBmhOQRH4JnYRpqyaYSkZsK+SsPQ9amuyxh6ZmImM40hokHlnS8KHWedZAhIwEzkmIjaNIiRkakqmKGOLAQxQ7WNEBMAc/4ZjYBICqpJgkRjY/WGsFqtBCxPw0CGOAxoBma3bt2aTOu9+UFRuq4drhYXoQuIdrh/hMAZ6esRkoShj0PoshnIuFsB+VnhkZyz5qFcxhlnNDAbIYBq75J5xJpd00xSSl0fcuu3ZEopdWeXYOpJHTmVyEh1UzlXppQM/CC6aNchpXIyryb7/ZDOz8+JKAtjbJOVT2ao4z4CMzPvfe4TgW7IqYiWQB1lIddxexKCmIiAZ0BQyL1ZRCSHZPy0I5v5K9lq2AGvupaBiSiphZRFbAARUwgpBJEEBejGec8I2Bfb97ubDdszj3HPAwAz6wYFNObT+vT652CYD9l8vG40PxVgZI9uC2/cqLrkgy2/PvEnj6rx18FOo2JzZWGb+u++c9jpE+8eagAAz6b+u6Esm4pk1/Dlctk0zcjE2OEqbGPjxy4UjpAkRQAbyR6CiFkyk9EuL8+PD/c9jeOC55676RHu3X/gHG040rLNp8fc3zCH6VEnCNHQXfSDiLgYOlVG6tq2X7eQ4nw2kT7sTyYa03M3bt64dp0N/87f+3Uq4cHJg7fffffd9+4+ePTkYrVaLpdBragaIlLAzF0ZRxWI+gmo1XgsbBpVtlVwzlP9ts1z7O0FyTXA5eVlURRlWebdqhueWNqwRGxTjO2etvCJ0c24GHZOLtiREsldnjH1B2tm88nFVTq9ePfGteMXn79VzZ1vl1TWn/rMq20nj7/zox9859s3bxz7xP2T07fefev9998vXHvjxoRpcC4M/cUHP/v+bB+uXZ/vXZ+D2f33P3z86PL64e2jA4Z3Plp1cHUVf/rWj3/69kdv3X0w2T/8ype+dHJ1cdW2q241n85efvFLVT2/uuxOTy4O9veTyJMnTx48ftSGuO7atu/6EJh9lKRRAUeyF4CRY659QgOjlOXWssXgzkM2YhyACkCF85QzlhxQ0BABTft2XZUlqXrUaV1XzvdtFymF7rKXQVUJXemo8kyITBy6vnCAGoZlIMT9gwMiPLm8mlQzoixkPloDshg4ruua/Si2zdGCGJhoMhTtU8qjWOdcWXnvC+9weXX53LWjT730ykt3bhfE9z/84GJodXl1PJ80ezMiWrV9iMEGqxxePzw4OTvdhUZ470o/t9J+9ubd4/3DV175wu3YKlsfQgyBPBPXqOjQOyhVVUNUIyyg71rlURnKAW39RjFz8MEM0Wyb1hi7Ypu50mZ+hwBD30uIeRypxDkX6cPgqkoRUE3AVMARKaMjPjk7m9S1yRDa9XK1WK3X3tHVct1Ga4pmsudZovn6crla9vHweD+qCbAaKmBWQDEzVUgEMBLbN1r+RFlhGgBNTdFyiknoEDFGyWJszIyEzOz4qdfGx0Lndot/rKYHgC0Ib5vi57DODruuU9XcV9ueAbqRrt8OhZ1zXNdoahm2mgeYCIioCO3QF2Wxf/u5eVO+f+/Df/5P/8mTBw8enZ6J6NWH97577y4wlfUEfHW+WHNZEDP5wlTX/Wrd98mECOaTebvqyAiZDQEZHJEnh1SEJKs+9u0FFUU1nU0mE0NWSygBn41u+WOKPK1XtwHOQFIS1UxG5O1AQFWdc1vp9K2/bAhBN643u7VETnYp18E6tlUyBnf7aOoydKHrVlCUviwcu9KzZ6eWYiQ2RmRz7CdFytLiomrJJBee4ggN0VREMGkvBqYkIEmGjOVwzsEOpzm7gW7H5U9LIHuKkd19fOyZcW2MtqGIqqCCkMgEQEltXhV17dp1P8TUTJnY2HTWlMtFRxs2WLapyC+XFXTQAERNFYxERDSYmdiIMVDVvs/RJW37TNv1jM+2l2Qj+kmZ9r4BtOw+CFFMc5ttLJZzOsWcYlQ1UcuuqkCMwABWE3DsvYQCKpOIZMzeDILkqQskBCJAHen+DjMS0wHAVnKeiEIIn/70p9fr9Xw+r+saEWM/AAA7BIduFPhlNAUgRFsur0Ti9evHTVWslq3FULlSBnOlV1VLkVQkRVNRiapaliWjI3Srds1ODEAlejdZtV1ReOeLqiiiQS6cYozsxtOVNk5r47421AxcQCBgJVFFYIsp24Z6V3pQVE2qIArkPSIjmhqIxFwgKND9R48BNKXU972lnARm5oxJsmEIeQTtuPBNQYzM+W8zlhJInVlMUbcpgqrmAJbvO8HTSR1uhGIAoO17VXWuKNh54j6mJG0kbkMond8vKnUuhMDE2BTBAUiYVnSwP5/Pp5O6Simdn5+frK7OLq/KyfSgnihPBqVuuVi1Q8zws5F5sYkqOxtlszKfeQYBaKy5kRCBSHns6hsiqBqhQ/LoVFV5nOhqrrvMiICRQRRwdBNAxDw6QOAUVHbCOGX9RoAk41TWScoFMBGQYzFE53jcRywiURJuNal0dIOBLPxtW6fbp23+7cGhG/nd7XmaywB5Vh5tm+rhzmMbVZAYn21UfTIj/GRo2lkST+P5s4XLePFpexd2VG7NrK7rPCQ5ODho2/bs7CyldOPGDdmojn7syNiOuHc/FxEow6alIYjIBEqgBNP5Xkratu3+fPryy6/+rV/8+v0PH5xfrf/q+99LZkgmoGIpWVIEQgCjXOsAZLdEBDQBDGpJIQlgSJ5YELEssfBtTGDCypcX57//x3/iHUmIvvY/e+8n66Fdteu2DxEAnWum85rdquvV8sg2qxJqboYike1k/Nv7tVvzbD+1meFG1H+rvpCfz4Qx2qgtw8YZ0zaaqru3fnv+7myZp1H9E2/jadlgG2Fu7727XPkuIrFvmulapm99cEWYDvYmF9360dn3l+cnFMN6eTGvDuM+t6vuS1/7/PXnJstF+Ojh5b/+19/p0vlXb//cndsH1AjcmMLF49Xpyftvv/Xg3ln9uel7q/d++N3vHB3dODvr3nznw6TVSy+9Pjk4EEFJ6frx0Rdv3XzhxVcPjm6enq7+4s+//eCje8vFwoAUARCV2BCiipi2yzYpELkgkoZBEUvnDTmKGWVH6ixFgPgMFg5cJmYbgqF33EzqnAIAIoASohEykcXAllIMIIqOu/V6dXmRUMjYe2ymTVnWlmS1WoVuUKBrh8eqSuRSKtIQSkulK93BvhkheJGYkgEYojlHDEyadIh9NyK08gwwmpXszQKiEgGzkQlIIoO9xsXl+Vs/urj7k+9V7JuivHM4/+xLt9qh5cKv2u5+u2zXywSQFEAjgmWPsmEYeo2llk1Vl2WxNz/q1mtm4mKybq/EbDLbE8X1qg8JwZKnyMw+W3kzhTSYAY8ZGG96/woACFm62Tb/gpml+JQOCwBbk5H14oo2IuiG2URasmJGlvM3AyAUBCRWRj+fuLJ03PimimF4cH4BaKo6u/7c+bpr2w4dh1VQrveOp8V01l4ts38kUNZd2eiQIgIQ5t4j5E4+AYCnUdtDwBgQkAgdEcVNoWg7GMqPBdNnguamzYlEsMGEAEBTVRmnjgBZnowAHHNIYYv8zrlX5k1mz1ozy8lZVmtxjjRlfTcQQFMWETUDlb5vZ3XVOH+5Wv7eH/7+v/iti2lVTmZTqUo1FLFcD1sSVq6LpizLddsawCsvvfbKq68Osb97773zs0t1VUxgScxyo4QR2JIMcTzqNCVJQcSZhRhj4Rl2m0Cb67ONXDmbH78BbSuDQxuxyHxZzs/PnXNN0xBRSqlt267rQgjNrAHEnJZmzPG2RhrTSu/Z+21A3D7ItCyIPHkyoFg3ZdYrFIFEikJEhIUDZlVRCSlGjcE243U0EpUkGtVUwJAUGEB9wVudZrepXhBxo19j22N703B6RtPNnu3BbL9udj2hJVIkS6RCFskSgxDYenlFcQYiJbma0qrvF6cXizM+unYjp/j5wyOAqYoZWpZ7jZKBHVHEQHTAlEIK2UQihLBer3OtpTvca94I/G8/yAjBGrX4N9K9ZimFlCBrwuSPRwAEzhEpGGbzXmYASEmMDMZWnCHnja4lqA9tg+nAQzsEk8SEISbWMVQjEyAhUeYwi0juqamqpKQihEjM89ns61/72ve///2qqk5OTghxPpkuu9bVBXry3jETAGLOBwnSqt2fz85OHj/oups3b4VheHL2cH9/f7Fae++bqijKgo1QggqY6Hq1AsJmsmcxBrGqnkQAS9Fv+IuMmJ3LMIPToyJibvqDoohozOmUmebJM23UAAGNXJF7EqRjGEVTswQAbsywzABYQSNolGQARG70SI3ivWdUAFgv1n3fr1ZdWfqiqJwj7z2AEjpDM8UYEyQwUFNUta4d1NLIVQPcjisL5wGeihSPs0qwIaaQog/JilJTQEmMWBDePDhoqno+n7bL1b1790IIe3t7M+9eee2FMKw1Jlxeri/jer1q286rHVXF5dXl5ZNTKieunik5TlYR9UQKkM2BN6XIx/OVTUVgAJBSAiNTyIiQMW8GkAzrJrBkyJmr41R1PfRAiDaCc9woI+ZKXymYZMvKnHKpgEldVxJFRHKTHQA0JhHxjgUJ0RySJyYEBiKgqIJMm9TtaW91C/DbHhSbPHo7q9s8NhXXDg3gaR83Q4LHw/RZzyzbUMt2I2GKwycPKRh5n/nawm51tf3R7Tu3DXDok3n/JuR8fHqZvz9DBxExs/+z2H8uy//GZj/utKJtbN0jAOTxp1neShvTnxQLz7P5EQL88//+f/h//tf/r7psrt04Tvq0A6dbNw/CUZtp/IC5z4cARuiZFRFF0UCBHBTECCXT0K47Sc3R/nq5hk4K5//lv/k9V7CiCgIQK2ESkJgiRCSnQBnnk6V9MNcBCIqbNYxb0QHA7Sg4h00jdEwGmqTYOvXu+Gxcu3Yt3+u0MesdV1Q2s8DtLGPcGllBbed+2WbcijBqSG/v19NbAJtTFRFdWe9P5pO6qcq6AGeL7iqlIZAxJJZ+ry7PLy9evHW7appXX34Zy/7ee9/rwtV0fvO12Y0bP4AYh7fe/J6v1rduT19e31ytr+bT+Rc/85nj+nJeT9/40VuLZXj05N6jRwuB8vqtWzHxhx88KqdFsv4Xf/HrX//1fw9C+st/9/3vfufH5yerV55//tH51SAxiQgYsDNCMY0ifYhVXXv2SSXFmERCUfjChT4r+pAhITIR07NYf2+YDbaJqADPyACCCgkE8xyNIctAkxmIQIo2QFi3BdntW9dPVo9DGvqrZSCXRfbrop7NZml90fd9VTZlWYZucX5xOp1OZ9P9IQo7cgwoKiIYs6uOaYqmSVKO6eRylLFkKgTiPXvPAJZiJ4My2LWjg9RHFpsX1aTwkMLZgw8+enf95a988WJ1efn4pL280iEauJg0hDhr6iA6DENKKQ8B277rYs/Tql2va/QE2PZrIKtmtecCkVUoBtWUCgdV4Qm1t1CaJQbMwJh8ekTRjcVMbr7kfrQKGIJnNOS82xme5kCO2Tk3Gl1tdGA8YaeKCIZAAPkHgShXDxfrVYrBEXpGJijYF0URDAd11d6RK6p133XdgMxnyyd1XSuQmoLIqMQ/4g9o3HAbCMkmco2YS9oo9o/dZSp0Z2+oqoCYmSv8NgJuex4AG4G2HSHk/G05PdJnbc+ZGRW34gw5IqeUuq6zDXcqZ28Z4YAIggYmUU3MxNgMRVVNuawW7XqxvJh43r9+/dqNQ0gxoRX1/OxycePGzb3J9PFHjxzwjaPrfds1Wn7xc69/5nOfuX7z+vny4tvf/eurR5eihuwjaDJFYEeucM4UDLSeTiqgIYbler1YXPmhq+umKAraRLHtRcCdgeM2A95cz3yNR3hu/qvcV95WPm3bZp3TyWSyfzBPm2Z/zkq991sQ5LYVvTWo2t3XKnF/Nq8rnySEfgAnQpaABC2iJYYxYx6CSJIULIlKyuzG8W6aYlbQIzJ0jhwxOFbEp34ou0dXDqW7ubKZKT5F9m+Pot0ezG5hgAYsimqowpbIIps4FIR0cz4FlC4NceiGq0BIexNHXDKabMuMnVVKALEfMvoCEEQGTWoZmhhDluDIb8k5N5lMuq7LdOSPwWnynPBj5zoAbJyVUz7j843Y+URPvz8fY0VhkKIqqCkZmBogODAMvXfpsOQbe83FlayHRNb3w+CcV+TcQAAwRKDN8ak7OoOwoe2GEK6uri4vL4no0f0HGTwpIirRkJIpEWG+uQaEOJk0cQj787nWzfLyEsxm00mKQeLgCFIEia31fQwdSkLE48OjBw8fl0XtHcUk3hGartfryXwaYxzy4I7JOZcxjUMYiAgYENEUzCwr8RfOZ/42AAICjsx/8uXobq47MllUsgmpQgQDImMHkES1D8NsNokxZr8WM5OYMKki7O3tbQwKskaTj307DMNzNxsmTzQa0ubCuyiKtl0jbXquG0WdlJIjRkS3MdLeTreqpo7LNMTgkBxAU9V7k+lzx8ef+8xnPrx79+TDj7r1Ki0X8+n0xnzCZEcVJ3CLfn325OTi4qzve3Key0rEZr7aO9jXohnUrYOmpCElxqy7vLlEz6b+mwW2WZAIjN6y34IBGMhY1KCrS+d9Mo3ZrACTxRRjjKbkmJGYiAzyas9LBcbSEJEQzRQBkNh72Iy2mdAMiFwObgxASI4YTS1Z0GgDcFUhsx/pkrgN+Jlwj1kXjp6eCyJiH8P62zO5O25+YLzR+gwCZ7f9/7FNusl6n77O9utuCNr9ZjPLZcYnU/zd6LobtWSHLrwb8Pu+z+iUxWIBALPZbBiGs7Oz69ev0wantDvL3ZYx20uRd4ECQa6ekQFAQUFAUcuqWVxenNw/9wimyaQIvZ2dXiVNiVXMFEwR1AzIMoYDbOT7aL4qCACoAmZsAINEQzATQCWAyxT6dkUis6buU5hNmv3ja++9996UKkNUQkNIYNFAEaOBdxVusgsEHjMKMgRD1I9dzN0rDzsTgMycoc0dwY1Vgpll+c7dPtruZd/NNLZR95mDaafK+vh72ACYEUeX4vxXbsA4bxw1fN6dRxkm0wJKe7R6dDBv+uXpYp0qhM99+bVf+frXwPsf/uUfHT0Pz79wbbVIXSef+8JLTT199723rs4f3rn90uX545KBYH40m+EhLc576aOpv/nccwKXH3500nayf3CQjJMO8739k5Mnf/GHf3D/wYOfvPHuyZNLpqZsDmJMUTGqqhmacuFdUXgmXbdFXaNi6gWRDGmIScGIUEcxPkPMisPPAANQlBVMwRMVxJZELScrgCMszgxVY8TSFw48u3ldUVW++PwLv/q3/9bkyH/nR3/1V3/1Vw/uP4KYnJkOaZXaupwUqJxalTAv+bnD64h4fnFOblJ4VxRFJAghJFMAcQDs2ZDNijyT3exb18cOwBDULKmqpsEhlIV//503mrLYK5vzpTxatSR67eDwuetHT+7f68JQs3v1hVtczQaxdZA+6bt3P4rtakyVNogxY+niqpn5Pq1SCofXGi54ub7qlykEZSpCH2Mfm7Io5jO1tFgv/N6xmG4LeoCcUIuKEREYEFGO1rnM5qreLlOCpys+hQimEkZz2RzC1BNUVTJRQCJGJmBKiqrKRFaVZVPVZeHZJQlD16/avq6mwfjycuVL2TvYr/d9lJByy2HTIdgOJZk5Y34zAi/7biKOY95NnNzMzgDBkB0+7cvsLJtn/2/nyZ3dqBvMNBHlRj5u+UY737B18bCNRpuqZtBLBtp673M7fBg6tCQWo6EaIDlzDEKsJik2+/tsyVl0hUONQxqGKAZ9N8THj0/WxTKtQ+HKdNFa10+vz8/efvAHP3q7HVojE9Da42x/b0GkXnxKoDA6ZwGY2Xq9ZvZoymglU10Ws0ntymK5WP+Np0g+6rYff/xKpik6TxnYtkU9VXWRPUoWi6v1em1ge/uzg4OD2Wy2WK1sI9CGiEgoILrR11PM5omYxXV37wUDptgHjgCGTrq4SgaiFgXFwIAVEgNq7EmVMl7XhAwMxAAcFxt0lzNwhmSQKcZh2ybRnbw/Zwc0Ail321ojpPWTgfiZpH884QElpxSKps6UIbEJQZp4btuOY4jDsFyeT/cPXrp1u5odvP/RaRKI2drde9wAfwEg1/lVUTBh3yVQcGWZsiYlQNu2McaiKLz3udDKvf/tXTOzEMJsNhMRzNn6OMR6hjj4sTJmw1zN3reCZAzOMSeSjydwRGxSGsw93Dnee+H5G49LPr1c9lFbGByigApSntyZigEBMBGi4eigsbnIqtqt1n/6x3+CiFdXVwBjXVcUxVKCISRFMzNRVCNERro8O62r6jf//f/xbDL9rd/6rfVq9elPf/rycjGb75lZ6NarxeWwWpYFXz84ODg4+IWf//nf+u3fiaEvijqE0UrRCFMIIUbJkpeJVNUYCdBktArGPLsYySFEORrZzvzHwNTUgRnmOQBtu7lGXBYxRknRQCGjWRCSajLtui6TrauqKpz3xMBkSGlrBYWu8JUrqSxj2/bMnJKmpBrFeyJyjgvENpexIsL41I/PMrli42a43cKFL2HdqYYcrMioKXxV+JMHD+6+9WYKw7Xj49vXjvb39w8ODmK//PCnP711/fCFg/0Xj/aK4nPk6dHjk3fvffDGm4/+9t/7tc9/5esfnVx869s/ePTw4SBg3nNV6s62yHtld78g4m6XmkfdKgA13gJjGHPY5NxpSAKZS9b3rqqJnGNHgGjmiD2xJx5iss0wOItRZDNWETVi8hkzMMIhyrKUFJxz3rmmKp2nNIRh6ILo7gYc+ds7MM7NO3/a284RZHsc5P88DQgjs5O3KXWMIw10+z26cQ2HZ7vv+UFMGwDe03wRR6WXsQH+9J1kFbq/6XTTHWLuM6+TngGWbH/1FvCZlc2yra+qrtfrsiwzAzh/qC3RfPs629+uQAqgRg7dOFE1TEnQTBwdHz/XV+tH9z8K635vMvGuWK3XwaVdLuvT3INI8hxxdGRCQyPDyleEbGbJMl4cklkCLacNOCi961fLNSQGfbS4qA/2+17AKAP2lFCZkdkRq5kh5+RhbLAbIoHqAM/m+rsXavcPiGgGzrksrQA7Ux1V7fted1Sw80XDT4jjbU+ZXS7BM8mLPTvl2fmeERaxecYxDevFKUBTeSo8e6dIFB1cXZzcvLZ/3NTP3zx++PjBX/y7v1ycnT4+e/dmLP/e1/7DZpUePTz//Nc/9fzzL6bfXyIlqqmN6fDacwD14/PLJ6eLhw8uk6s+/wtfme3fKPc+SvS+LydKkFB8WZxeXjz464erdt22veN60uynyCen59DMfFWUVCpYTBo16RANcdLUZVF0/QCa2DkiTCmlKJNJzaMBnJmpSVJ85uhyxGBgDvNsNAk4AHbZDQKRwUDMpCmKeVP3q2W/Wl1252nob1+7ce1g//iXvxT60yd333vheP+zn/3sCy+8AGbr9fqH3/thXU36tr+8XD1/58Wvfe0bl2cXv/uv//DRo/Npwc20TlK0g08pmBmaOmLviAhiHNq+G2LQDGEsJEqvqprMJHmS2bQ5mFV/99d+4/LJ4ycPHvVXS2qKxpfzST1p4P2796tmcu36c3tHx+ugdx88enx6GQTKwpXBRx8BGdWSJedcPako6uuvv/LoyUc//dkPLJVI2rbt0dHx/Oi4KicqLiU5ODh47rlbAPrk4uI7P3nbksUoospc5EvtyPV9z2jssm0sAyEQqiET6ZhUK1qW/kA0MUCVmJKKRDRiJucKYOrERE1VzYFjjwayOcl8wUnt4mphZnWZbc2qxcX6+vWbdRhW7boPqb1chBSvX7+uRGaywR/BSL1Bg4wW2wQ0AFAkRFATAGDkDAgbx6A2SqvnncOjAykz83b3PJP25u+B0SEPzIBwFDiCp4L3sOHTmGUze1CzkfHsPTNvyb45BOQWZrZNKJyJqBggMTtkYvRsZp5rMOlXYRiCaLQQU0xFUbXD8MILL12ennVd96mXXpW2v3h8cuv4+sHBwf7+ftetHz663/Zr79gVDl0xm0zILMvna5KYxqtVFFWOoM45ZEqmTx49Prs4v3Xn9u5JsA1kMY047ExK3qTCpgxVVVZVLZLatkspVVU1mUxijFdXV/lguHnz5nx/X2JcrFZlWWZqLzJlDgYaGMLQ9QZARrm3Ys+2BgHAQB+fPFJLTVNPJnVUSWKDiigoOANUcGyQ2rYEYEJGcmAiappUtZgVQGYMDChoySRJNFACQxLEUTCEc9a7yfs3aQMYOiMDVIiRKRuS5kZUPiNGEQ9Gc5kEDwgAbHnfCFpyEBwGZ9FhYksHk+mXP/elX/m1X33l059erPuT86vTy+X9x2dv3/3dEDVGAaISkb3L3TdASimZSVGUjjFFZ2BVVUZNTsfhu6pm+/eu665du+acy7LcuqPfjyOv99lra5YVNjO7d7uYR8PabO6VgT2kzgFSRiGZmZiKAbMBgDJYw3BQFs8fzj5147iWwcdh2Q2B1EwCophFQ02QvdEFBTQTTNGQAGmbEjWz6RtvvPGLX/v65eXljeduxRjbdV/XTUioPPYjM2k678fj4+uPHjz85//9bznnurbd399fr7uuH/abqUkqva/2D2688uIvfOlzf+fXfvVTX/riW9/7wZ//8R9drFvvC40hezm5olxdrfIOz42PGKMEANTCeVMzNSUFZHREjhERdVwtBmIGWy8BDRGAxoSVGQBiHFKSpqoskZiiAaA6ZIfkACFa6KNEbZrmcG9/MqlzRnW5XIUQFler5eqqbQXJ6qImgouzi6JwIrZeLzVqWXoiF0IoioLdqBnvaJQTiTFKTJh74COSTBERCEOKIcUkScDULKVhsbbK0Xkf6rL4ua98+VMvv/zhB3fvvvf+uw8frC7Pru03fVOuRaqq+NyrL375y18+uzj/i7/+9m/+xn6zf/zu/fvf+pM/e/fDJ7Nrtw6a+cnVUhHRmGyEKn8MJ50fWamLjEwtQcrfR4AJwSEpoREuV6sGIUNNnPOVLyREIwQm59hnvnMUggSGpmnSVAAQTcU0mCSRbJ7l/UZVEyUFNVUi9M4ZABNOm2o+nZWeh65fr5dtiIsYTSVhVozbplKUPVktjStWLGZckwGZ5durYNnR6WnHZJPh8baiyBoDzOwcMTsABaCMgwfIYJdcokvWs9KUFMAR6bOI8J30elt1oJniSD9gACWj/PqIBqKIGZFg+Rn8xAN2GgFZ3BMA/OZEE5HpdNp1HeKoRGlmoJZpsSKKRBnRsx0UA6iIIpOhQzTLXFhJIhK8LBYn/XIdE6AvOtFo0ZwfpAdCMUsAueFvQEZsCoQ8Eu/Hz66APPQDISNi1JQ0ZtCXaur7vu/b68eHXdTZ4dFkMrn77ns3btwYcKQnS2aMAGX3CNyoCG5PQAQA0/EKGozadDgajKBa1hsd/4xABobsXZEkqAigEjrMBhM2uq2D5lODiMaFxMUIIsjLg8bYkrOWHPPE9CmQC7NXJGK2Shj3WJ4+yWY+zwgAjturWXPQWGyKuppM1+vV4vLioC59UR1U01ldLxe9NzxZ9tdvvXDzsy8+6d5ddgmdpandeu446OKz/95rBwdH1YuvwslSOvrg3unv/cUbb/z4o/msuXHtpfvv3Js0V0eHh7/2m3+XiL773e+899aDQuugsuwGUV8dHJZuFnobYjCu2FO0YVTPBSTkghnJadcvVytkmjWVcy6peIdM4NgEQCQHBDZTSc+GEgIFNINeU9+tfdmMzTvVrDWJCIQUQ/f4askiDgwBZ83s/OTs//v/+Se/+sFPbr949L/93/yv4c5NOH0AtYP1AtLBF7/xGiSANoBroJwv793/yx/99ZP777JU2i7O20U0MsCyLOdN8+ijD73E2eF+6fD86kxDN5k2VlIf+9ReEMU4tGXF00nVdpekxa2br9643t57543V+skvfPUrta+bsrn3/t2H9z/6D/7+P5hODs4vl+99cB99+Y2v/nwb7e33P/juj37CYAW7qIZZVIcopVRoevDBe/v79f/pv/jPjvebt978CWh6/OjUu8m7dx+2g3hXvXjnlWo6W3fdR4/ev379zjt376Gr9o+OLpdtL1oVvuvb6WQPIdGYEZMyZgm/5FjALJpoAkUARWQCiCk5Qi4rxxUophTamGSQIMDsCQkEQBIXRWZoxRS9c8wueVNVRs6Ql6aZrlZrAKh8BQqzagKE0sdx0Jtno+iYmIiU1JdOJEFWgEEUU8OxCU1EoCoxIaB3jgDFNIlkrxsiYvKZXYqYXcSxcK6oSu+LvDNNlJEsjW6p3nlgQrUkmoaYe1hDNxARIwNCNwxY+XU7ECJXlYQ4hIQGQx8tmYgURTGdTZPq5cWCiA7mBwjpqeAbgoqKRBVJudRRA+Ko5KuCizqE4aXn73zu0693XXd1cTFtJiePHkNfTF44ev1rX/7ggw/Ous7f2JvCvG1bQfCTZrXq1zEOXb872FHV3FXKKUJs+z4McUh1WX344fv1tJ5OZtlaKMf6rO8OYGTKxBm+y7ntxIVjJABD88yl967wKcU3336bmW/cunXnzh3vy6vlIokdHh7n0UcOjgyYTCXEKKnyVcpeX2b52LT0TC8jWSrqSlW7mBYnF4ZIjgFITBUSszOIKgBJfN14QmdYkUPSSVEdHx8/Pn3s6yqoXqyXK0muqXxdGZImU4XKV0lCu26ropxPpioCogSY9RFEJBNFANGkFwBDCyrBYkIDx+TGsOvBITsEVkVWAMR+aKdVCZAYY1yff+Fzn/rH//E/qBydnJx8/otfEuTf/Ve//bt/8KcnVyuhoh2kKKeqAGqIpilYtq7VtB6GqnYquFxd1HXJHoLKum8NIMNjyrLMyhvZIWG1WuXsJB/SmSzbdd16tShL75ly6zAOfd9KVVXr5aquazNgdibBkZ/P58vl0hxjVn13FEKIqhhjURRVU0M/5LoiaZ7Rm6R4NC9t2X7jM59Nob3p8OaLN96++yE27vHF6rKL1d5xUuslzQ4OVWHo155iURRC1IYUk7Fzjgs0E6JPf+Yzj0+fNLPJar02dlDVyyGxKywkSQmRnCuISVUtyXLRz/eO0UBE6mkZlc4WbVnWT04vUaVEmXmDoY2Ls3TxEJbX//z3/sV/+j/7h4su/uFffPfk4n1LaTrfW60HTEoI4AgAs+yNIIBiCIkAyTEjoi/AsbmcMhizOYiqqJbMIBmAkfe1SpbYYWIQiYbOEVprLnpUAQzOEbGZSFr3qjwtmuv7x/P9GTCs2sX52amqzieH6/Vy3a5Uk3NF37eLi8u+b+fTPWIIIS5XV7GLk2ld+CqmgQsnabRkapoyGwYtVktH6J0rSs8jPEWIiQu+WCwEoTex9RLqelIVIYZ7Dz64fXT8yqsvXL9xRChHe9NwvPck9X7aTCazH/34p6+++Pw//p/+w1vXjr75+//68ursldde+9rf/Y177969Wpx//Sufu37n9v2zxcW6nczrqzYYOUQe+RCIzOgKHgE5I3IbYZNCBUmKoIgKhggRcs4NrizyKDW7CPcxSIxGVlQeQM0CGThGBmMNFqJqoWZcFFXd1J7aELs+mEoX+hp8UddlWUJZaBhSiDpESOKcuz4/eOX5F/t29fbjt+JiPd+bnV0t1GdEiwMAUxMRUajrOtMpVSGqiaAoqZGZjpQPGL1UxzQOTEyTigmYxW1HdmMTC4CavX3NsryAZj9ptZTTTMxaaOQALLdLspO15KQUN9JAgAamAGpgpo4ITVHFQFIykShRVVNVNVkCEQzQTA0SZEufwjYk3W33GgCymdduAyuL06+6dtmuMxxo2kzquhaRLGuztSmArEXl2BPHITAbgZoJOaLCJ3EhBOdovrfnnFutVqgmIkbUzJrQYzu07NiSLtphL4Er6r4PPtPlHYKZpXyRGQDBbZrC2a8zt/+cB4CmrGMvVTmNQc/61Xz/ejuYFU4BVVVS5iVn3VvTKMzsSDKsCDKej5CIEXiUaLZsJgSGIEnIMaMjhHx8ESCiAyB0ngxy8SbZMFA3xIkNGiWjFpE3ciNqqEamiIAGSBj6DtFQETf6F4hIBF3fEoPLYRNZFVKUGKMkZUREQCJLQkRuPqkW5ydNcXN/etj3HUp68fad+XxaOE6pT3Eo6+kLd26//plP3XzuNszwavHm7LXnwbrp+gwOZ4B66+YN2L/2wTe/88Hds5NH3dlp+/Ak1LOjLsn3fvrO0bUX6jq8c/feH/3JHxEYMgRJ7aJP5IUcFkWv1C7XMlhB1exg+vD8ITp0XJSlZ/ZEDgwNwRw6JSAqcsovmswAUGMyVTRDoExsoWfkv8FXPi9N52rvfdeGfHdUMqzKABVN49ChiIFRZhcorNrWcfqX/+K3r92YfurVFw6P5geH0+s39pv9mbSrv/7rv/a+PNi//tKLr9EETh4+uDg5cSIa5YO33rnx4vN7h3un55fzafnh++986dOfjleLi8dPIulLz193jXu8fNK7ePv4wLvJ7TvXP/u5T8XUP3n84fXjA0n9n/3Zt9558/JrX30F9bXPvf75vc9+Eaj6lXv34bnX7v7rv3r44en3f/TGW++9L+z3Dt8elB5dXBm6saQjBERFAFVQrUq/XFzevrX30p3nSifPHR/cOD66em79//tnv71cBl/tPXxwcnp2tX/9+sVyee/+Ay6LqpkKFatlq8aGbFzcvHV4fvKQQLOJNTIAMnBhxFFVIOsmMoABMGWKKoJk8pqRgSkxMQAShMiM3pXoMJOlUkopxsIzZ0VARVQE+QQmMmN4IAuCAQEbojMFGHXZ88LWJJJ1ggkNMYEimABmM3kCNEIapbhpLMs3s1IAADUddYKZN8+Abrv7xICGpKYmGjRsR2mjG6Lb0B4ydtY4EQDTVrsax+JeRaQsy9IXqopmGZktIkyImhvMhIjGpghK2cUTAMC7oizLqqoQUULo+/7xk4erZbtcXVVlc3F5drVa4qMP3vhn77DDSTOb700dFy2ks5OLxft3J0fXRUlEN6DPcVQtEmDLolNAGztIN2/eRLfRBNw42KQUqqpgZs/siBkzz9oMNUe4rl/3fV9V1Xxvenm1fO+99w6Ojquqyk4lUVJRFFn2zoJsm0CCYGqqgIpGCAKqoKKyaTzv7ush9giMyGISkpZlGWMyU+e9Q0whmJkj10yq5dVVGsK1+f5sWjpyd27f/srPffmbf/ang8RkYTad1A4H0CENQ4oapaqqGFrP7vrx0dB1i6uLWTNZrZaO2LmCiBxk8gaZacEVQMqtRTNUNUMZ9YJdSYSadAgJojh0BbumKvtuFdqrGweTl155+Rtf+4VPf/Z16Nfvvv3mf/vf/pP3Pnjw+HIdI5ZVE8DtNaUppSz6kaENI4NCEC1oynsqxJ4RmTGl5HwJW4b0BkgqG0E3Zp5OpxnSoKqz+aTv+o32w2gxkTHidV1XVZUbe7Sxk8zfFlLWUlHJXA5TkORcgY6896aISbOyJQMsFoNjkKGvmGFouyH4FG4d7IeI58uzOISimkXV5flCTOezOg3tk4sTLCZHN265YnKxWC7atmQ0ATYBMlWQFEVMyYjIhlSW5byeJZW2bbswNh1DCC5LuxJnnIchiWUREDJLbdveO3/0wZs//NPf+539WbM3P/jJT3522af75yvnCqqrYJBSKokyIlBFRcFAMgnKFyUBoCkamyQlNXEqEIfcH0mAKQtCOSRFF/ug4BBQCYiy5pQicIqBMZvEO7Te0qAgBNC365Bi163buHYFDbENOiDi8uGHaJz1ELtuHWPUmFR1CF3Xr51zN2/e1JjW63WIfebPbDuIWxiJ997RSO1NKipxjGMEVV0nU5VIKoqqoMTEZSFoDx49ujo/qRBZhA2K0lfVcS/xuVc+de3mtXc/ePjt73zn8YN7n339tc9+/vP/3X/z/24FpJgeHR/T/FjLx+uPHi0XLZGjbKSzeeSB3qiQk1N/HcMlApS+EAIcCZSYZ5RMT1vmmXqRgxUpqSZH6B0VxAUAK5AaqKGOCgwahhg5SSTAsiwZjJmzBz2qqYimoDFJ25XNdHl++eaqTUOf+qF2hYU0reoeJKUUUptdFICZNuNQEU0pJclS0oTkQNRMNhjCp5+antVE3soYjM1yBtDNJBUVbFQSy6YgMLbkDQA4Y1lxvJL5BwBALDetYcz+kQwVAWOMaDoezJm46sjMowmYqSYFIkiKJNm7g5/hAu0CTnAjEWYbTioAzOfzy8tLCbGua0Jcr9cgWpZlhpYpjnpYG9ysOEZPeZQxkvQFFEGXi8v9w4OmqcQSo/OZkkSmDowdACDFYQjrtlsu12YWQmLmMRPIY1FEy0a2z4KeAMaBTZ5SbksaAzCEmKcjeTqD2UpFQJkRaBQkhg2BEDI2Tcf8BEa6ceZ0MTPljAYkw5BgHEdsJl1Z84QQFYFNwviipopgWVddhcFbLunyuQyKBlkGCQwADSxXHoBACFJ4MlSzJApgDDo6EvKIo0MzMTU1dML8yqc/85u/+ZvP3bn1rW996+333jYqzlftbNJ0665dLwtexiQXy7XnH4d0ZXT10hs3blyf+9qB3vvg/kfvvvn+xVX7tV/49Ruzl649X/3g7M2f3v+gXQ+Hh8fP7e+t2iDhUsKwvFqqpGvXjvamk9PFImkCnxWzzNCIlVxkV0wmNZBj9syMnAvrcbOgUZZVNyBUlGQGKevsEiGRY/KItKPUAgBgElMIqgnL0rOBDaAIqrZRxciluqREpkgsgGDag9rQGYTS6717DxdX66vF+bXr+7N5/eLzz63X6698+ecfPXz83pv379+9DIP97KdvP3rweFIf/NzPf+PuBw/ef/Du4/XVzedu/Kf/q3/8J3/wpx++9V4hjsWnvnvw4SMrh/KAv/C5l77wlU+9+oufgVLh4nx5dvraa59x8z3o2ldevFa/8DJ08tH33zg7vbAfv9138p1v/2B19bvnD4cHH509enISDctpcb5ohbwrmzYEQzDCUbs8p7+gZxeL473Jct395be/83NfeO0b/+g/gcXyv/zf/eeT+d6T048ul48BuFuclgV2y6tr8+Jy3d+69kIveLpYNXWzbvvl4pItOSIEYjAgQnRGTpERyUzyecLMgLnvYbaVMLOxmZHRAsxUNrTRdM/C1gTEDng22x8F711UVWYkcgDQ9wG2Qgo7pwV7QgWVrFTJOdUBECLO7WdmZ1n0i4i9izEiEBGMkICMclBlZqOt4QgQgWVWwGjloiaikIDJ4ajFmTe/bBoDkpEhhc9vUjcKvmqmYJIUcpVqBLqRLlYhpqrwpXdZTa9q6tysTXFAHX2Ct2dDPra36W8esKrq0HdhtVpeLUJIqqmuJyIR2S2W6+l0TgRtP1xcXTpXTCb1bG/fT2brIQKj31AORCTHhO08d5wGk5FDEvbeJ9AkysxF4b33BAwAmoTZO0cAEGJKKYEooAFDVnouimI6mT96+OTi6vK1115rprO6rjMpIsNDM6qE9RnX8S3eMZshPMVNfSL1l2SZD+xQGVhjGobezBgbdg5MTBWQ0KwsfOmpmpTAtlgs3rnbJw1Bw7pv29ChZwNmsIqx8eXksFkvlqpSEFm/ghgqJol9U1UE+UJBIpVcYAqYEEBhCAhcmHeEJJBbdjZoRs01ReM8xz4O63Vz2Mz25pPrh0fzhjX88Mdv3nv/7gf33p9MJm++9c6j04t6flhO92NMSaHguh+iYT5CMi1SmBmIJFpGAJtBu1oAc1OXeZ3kw2zrEJkL7Kwwm6H/AJAp6XlRbafJuwXAliQAAJnIaBv3+Dw7zveLiACyUZ3m36WSG1oKCOS49LDs5Ic/+9nx/qxv16KQAA1wCPHw8HCRoA/dtcNrV8vF/fsP9ya3Y7d6/bVXwNdvvXfv9Gp9dHzjcG8ehzZ0K0/EBLn+iZaACuc8qWqKCpAkaYomiZ0jMDRFUxydOsxMRTJnAwEAicp6UtbeQplid9bGi/70ctGuonKzX+ztJbAudIKCVGTghoIZKIAiKQESamaHAABCpisbZhl0ZSQANNwgBAgYgRgdApuaSspUJ0QM0LNX7wFQYhqS9AAKhKpJMCpohZ68y9AyU2zb3tHo25A7r/PJdDqdqkiWzBKRDFysqqooinbotpbbGcSV68BmOsntie0uU1UGLKdTVQVRGXoASGpM7As6vbxaAEyL8nhvfuPw8HC+V/rCEE4vl123frQOqw8fkaUl1X/0nZ/+27/+4eVqvQ46oINmL3HdCkRwriiTIBmqjnLjeQ09xZMYAADTmEsZQrTRBCCL/m0UdMARI4xiuwjASMyITMPQecMSyBsVkNk/SUQKXzETAhmgJMUkjOw8KaKKJElGxEiQRERSjMQ0xHBxdUmAbEqjaxj5shBNYyMczUxJcafAlhhjyvkf8Ij125GRwA0s5ylc/VmC5tYxd1feBz4BwYdnH/bxJ8aX3RqBjJcJoCgKk7Qx4VZVzensDnnpGaJCjgDb97D7KbZtr+3IIseNqqpiP/R9nxGSDJj5fkQETGPSrwBGgMBEsnmd/DZMxCQyWlOVaDYMnSc3mU0AIEoQDS2oqjKBdywpShzqujZFImRGM1XTTNwAARz7608JypkLmlLKZK7x+uWriqAp5PXHOCoQgKKCjiUW5JJrpHwBsqniWBGMJUB+/VyBIIiZgUoePSRVAJclpsy2anNqoIA83loyMLIM60qKnCFSZqagCia5Qh5tinL2lW8Niqk6n6GYICmAMSqCEKraZvOY5eIKnTl//c4d1zQ/e++9H7355sn5yWw2W3crMHUITEAEJxdLe+v9FKKk9Yu39378vZ+oQVlC3dDR0dH1ay/fuX70+F7/w0ff/+Ddx6cnV009e+nG7a7r3vvg3vUXnru8uNibTF984fnQ9Smlvg8ppHLWJCREKzz7srCkQ9+v1hfz+WEUS2k0cyEiQiYiQ81TrhwUEJERUwIqXb7QhI6IMdd6O49uvRpCn1LqWvTe10WZzwFTNc013yi+m6v3jKdjQDHtkvmy1BSHyETT05Pu8cOriye9arr79snVxUXXDvPpvKqmq8W6qWYv3X7x5tGN+fQgyuLmi9cm8+LuOz9aXz48e3J/QrOX77z06ksvFjM77z5Y6GPHFw8ff/fag8v58QTYZtcIwJb3f2JB5p/9MpydQ7V/+uTi8rJ/4fYXHj64980/+64GXl6oSVHNjg5mc0W3HIYgQIUzVjA1gGTJLI+LkACOr19fX5wA4eH169/94Rv/j//6v/LIILRcnZN33eXlZDq7OD9B7WazppyUztXD6nLRBklQlc20KmJwsetm04YgAYARGpCgM2BVw1FqmfJ9yceGqlZlqaqaNs5WRAxohJOqNgBNllQMgRz7svBlkVIKkrZBRIzcVnVuR5QgS/YiADMLGCkCoSPOenmoQASGiojomB2zGRCxd5BJeDAeKkQ0akw43oqQjhIEMAqrbTNvNEQlIULEEENuNeVy3QgdMTNnEYmMpsgU2CQioOhRYnDoBJJpFmAlRovD0DMKF1GCGXYgTF4teULdyCCMsNGNPlKeLehGNieE0HddQXS5WjP7oigEyddNNfUpBSXG3FZIMahhiE5Rwdg58OzJJ0sWTDPGl4GBFRUEBAQMgMGTR09RVEGy/MLYSyZGxLhhvGWJSRMFIFO9ee3G/Qcf3rx5czKZfO+7PxiG4fXPfXY6nRpSRpsMw5B/VkeOx9PdahvNu5zZPA3Wm7/a3dcpqciwhXKJRJ8Fz0TZ4bSszcw0edK9vWk/tJdXpz37sigE4v0nH/V9z559wQqqQyS2iuvC0XB5evHw/t7eXsl7l5eXk9l8Npvdf/To+Oh6jBKSmEAyTUlVAJHZfIbe0kgXMSJAsqaaDDYM3RCGFRZhb7o3m87d3uyll28N3aJrF2enF1fnT0CiIzg/PTk8PCRXH92cJaNukKggaqvVakjGvmBmRZBkAIBsTMjszcwXBSKtLsUYvPch9LBTK+acL4sm5duUn8x3IS+qLDK9mfyMJ/RGbwq30kz54ueyQVUNRkQyEYGJiOQ0FDc15OY0ImFEJ08uL5tZw0XFZrge1v3gvV+dXR3cuHO2WP3g239+7dq1l28fXZ4+PNxvPnj3LQE+PLpx+/adLurl5WW/XjWFN0h5hJiLVTNLKU69j12/DgsD8N43ZUVEipBp9GAgNq4oESFKmsxS7DX2zhqHJTnkhiB13dBzyd77yTSorLpVNCrLMsWU765CnoWBQ/YIiMKGo/MEASEIIYI67wEIDFTFIIGKKZqJdxMiMoUQo8pAKMimAOTZKCmqwWCUNlM3QmcF+2oy2T888BVjy8NiiDEeHh6CUpZmytzKSVVXVbVcLCaTyWq1Oj8/d0iHh4dFUWy1B7ZZfuYwDMMA08k2ibSnpgTGZYEApWMRDzkgIymxq+rC+aZpmv09ms6XCo/OzhfLdTQYUkxDDyaFYzBZrdLlYrF/fG3gNCgp+KQoxGU583UTF6sMoM4Uq21Ku10zOVnELBpBqDGMGNOsopiTLARUAzAxgAyIGQXGaOJLh+iJPYJTQEyArAil84ZAzMjeEZjZIGpDJB2LOiL2xFgwmoBo5Vy3WjPBzWvXHePi4jLFVE1qSIGZy7J0zkXJ8mWyG5k/lnwTM27surbVMhHl9sdWP2eb62+VoLdH3valtmXD7pPjtHkn9dd8kYgtj5d1I3iDTyWvbXO1nXNu85q2Ya/hjuhSfp9PKXDP/l7YqRny813X7e3txXI4Ozsb2u7atWt1UbZtm3MANB4rPR4rDnyW1b19IOLQtSml2HXgU+zIe186lsKvANddz8weEZOGvp/UDTkCQhgPkExnYkRUGBk3kvuQ2T4SwEzGETyOwqJZn18lMTBls96NpMGWSZ1fduQUAhpkT4bxco7ZvyGgZmrfeEdUcxUGIGIiZqgIuTmouTuq3vuMLADanv6gOLqejyY/lqkbAAAqlq0wATKSJ/+u5Cir1gLqWKsYmI0CShu8Vlb4ueraN+/de+ejjx6fPAkhVE05LNcpxaosoykmMBASZea6ms3Lg2XXtkssSnK+ubqSq6v1R/c+iuGjvkWmKg2VLzgKnJwty4pfeeXlZVqYdWDegURQQvRFcbDnkuOEZIREgCRAghBFYwh9lFHXL8Ozsui8ARDxhjLBjOAIDSmFSOSADDBjDoyeTf1T6NCSIzOV2PcFSh6+mCqBUbZlBnCOERkNTFSyUqVzhnB52U4n9Xzv5ksvPP/go/sX56erq3Yy2Ts/vbJUgdB6IbHvQhda6e6Gew/eOyGCxXBS1KvFFXz7Lz/E5F+4fdOlerW6PDn1X/nU61/9+d+A4wHKM2iWII/ev/dDTHD94EYzP579/Mtw2r7zb/7Vk0fL73/37cuLlKT6nd/+C8Pq4ODmEMn7QsGLQh9SG0OXRIktCHEJkExN0whacZ6Q8PT8rGT+/o9//O777xweTFMUTBEVl1fr470j1fSVn/vC66+/9oUvfuZgf/6X3/3ut3/83o/f/aDTWFRN3y3AlTf294YUGY2AETOSxwGwAQMYQ9aloFyvioiAIOJ0OhWRLJSxLegREbOxYs7fyYDBEA1xbETohlFHDEjkCHZIu2C4/T9mj2YAhEyMZAiqiupSCmZ50efbC0jM6Iq6JCJUSylZnn2LQkpGWRY6o5f9drs6Lp72S0RGUg0hgBluRIUYGTmnU2KSNCVNsnEZVFUxKx17hNyGBFXHiGBg4tmBSV0Vx7N9U1y1yxjFOyZ6KvQ2xh0i2VFJoo3+fW41zZpJCENV1d67YQiKgERBbWhbAPO+KJuJGQxhGGRg7wQVx/eWRFQsMTlyqKIKYgBiiYidY0feMyllgXEHmOsQ0xQQsanr8T0gkysKdszeTNq2fe21105Pzn/yxs/KsnzllVfM4OLiUkaiM+cYJyJg5JxLKTztRuy0+Xcfn+xyAUBIoikxs8tSHYhN3QBojJFVJnWFiH2b1hfnxd7MI3JTTpu6KssQQogtsFa1d46HYXAmjgklyGIRFxefu3PDOffk7CQuFpery2Ey35vOYr8OMYmxK0qP3jDLvpFjr8k0CYiBCKiARTR9cPeDg4ODw8lUCIdh6K/OrF+5wjXF8yePLh58dH95deGZDg73HbuDG82i7QoqYtLlamVI9WzOzEPXO18BeyACVdUYJUFUJCuc387TVFVkTC+YdjI5zT8TM6tkM9oaO5SQLSfRITvVqIBi+UNoSFIUBRAnNUSoywKSRFFEQhsZ7vBUgcTMrO8HZnbI+e5tzhh+vOpfvlZX+3vXbt8Oq9XqagHkVIOpzpv6o7tvJ8D/5Dd/4/kXbr/55k8uJ7C6ujyelvPD683BtdOr/sGjB1Hl+uHh0HcmyVQA0TEjc1A0EchCcJgjASCoiiQVZpaNMmnKI1Amb35WN0wNk5IZauhjiKGLSfrByropqklv0HV9MHVF4wsaPw5ZLmSIkEgZoPKOFRAMUMVA1RTEULL1MUICDKKDipiBKWfNdkuWhk4tssuIavPVfoQUhx4wOkpIKEaqRsgCqEaSQHtr13G9CqHrb9y5Y2JElBmuwzAsLi5PTk6Gvm+apiiKvb09h5RdHRaLhSt9TlXNTFPYwjP6vs81Yc6VRm1KorgJ1MxsCmoa1AD0cL7vAAfiszZc9ueqmobQh7gMCdgxsBlpJ0Su3Hvu8PrL55cX4hCQBXkQjUnb1PIQkR0R7qb+u9l/ZsPnpzKouvJFjvkZj7H9s+YZoBmIAiEyEKIzKH3pAJioIKzQFcSOkQFVoRuGKGqonl3NHjQl1ZKdgBC50vnCM6qhePCqqsDkyqKcNqUvBtGh7YRIDCH7gBJyzpYRQWQ0r0QDBjY2M7WNAt2mhw7Pcpq3T9qO5n1Gfn4y4m2LhG09sP1x+JtSf8zWcponE2o0jkvW6zWoZN+ebCZTFYVzbrFYWFb4ymk/IWykrj/5fnJTYFsP7H6Q/HxVVd77NIQYY+XHSWPu+m/pqFkSLv9rZiZCYMycDboK502TSpxPm6apENERTmZN4bFbLtrFlUoqikKGfrm4dMzT6XSE6IytfQBmJEwxqWXukY6C5GqKimCiRmBiRgaqQKaoyKakQBlkDxtSrW4o2gzbGRTbKBI1XnWALHYCAGCkmsY7M47Tbdu/yrWVYkbwbNSmmYwQgRVGSTAFAyNVlVzeZt8T0PxCSKSiGwvkEUaFpkPXIykCEzGRmmJOjeCpoTXkFrlbpqgnp6rqi+Lo1s126NdtO5nuAXPbrkPXZ7IjKw2SVl2aVD5IE5PTWAEAIcfkQ7Q+iBmGIZXeVbXv+qurq3WVKGL33I3DvWberVcI8NytW7O9o0Xb3n38xHsHiN0w9CEiaEE4ndTRwBMX7AREVaOkGIet4NHYfcyivSImEkIc0UGIW6/vZzdNKAvHjGZoIpA6BHAAmi9lVvkhRKA8a1EgQiJfIBdgWk98VXuwArBuWx0G7ns3nTRNVTjS9WLRdWt0OJ/PmKjyxdC1l4vTOy8ed8NZVddHB+U7b969dXQwnc++9PkvvfD8rdPlvT/+vT+k+eWrXzy8/ZnDHi5f/sxtmMyhtfThyYPv/fhn33vvx99vpxU8+hDWHZQ1vvTi68tWkrmqbrQo121ar5brPpIvpvsHgrgeerGNe7BjiTIi6gAns+n+3vT0RM8W5+BQUgjr4ejgmMumDXFvb++LX3j9b/3iz9clxX5Jw2J5/qhfXs6qWbk3f3h62a6jZ4eSgCCjrYgdYnagdDoCNUcnr90At+3gZlHwcecT9jGMpkKOkNjMhjAMMThXqKgZIjOyw9Gc52lPwRABkPOLIzA5QkDknNDYU0yIy1Hbe8/OjR0v7ypf0MY9N8urgWiQJBIp5/3O8cYRwjKW0UxlBAuiWh7lZR2PUTYBDGxssm7FubZhjZnJxFHuFWJKhioIDCaaEoIaF97z3mxmBjH1MQSRZ2SPN2t+7DfkjD+f3Pn1vfdRRREFzETy2AQxhhCOjo7atu1jiKP0ipVlWTfNaujMTFEQ0RVccrGVHNn9pbkZDESLdZ/5WGaWJIhI7uyWm4COiEQuK8mkFD6893i+N10sFiLy/PPPz+fzs4uLsiy7Ych7k5kBMISgAt57GcL2SMtLZdvdl43n1HYt7W5r54okAGBE7JlCsBxdQTR1Q0BwxJykW1xou2ia5vDa8Xw2Wa+XV1eXBHDt2jGoDG0nYfDENVcezQhKhy8f79376KNXrh/+z//3/9l6GP7F//C7i1XXRTXjypVFM1Gg5Xo9hLWEMGgPYpASmZagDtGhosns2n7pmWBIKFWD7J1I6LrFD77zV5fLhZkZcjQ8W7R9H3xRREWI0cwEvZnFVZdbyqYCadwGyOTQZWnqDN6IMaJGRDTR0LcpJe+fDklySoeIMcZdGvd2CRGRKGw3KW605GDrJWkGG8BPCME5l+UU7alsiOkGiyUiAGY6akjjaNELg8Db739w6+ZNr7Luh77vQ9cuzs+ef+W111575dOfff0//kf/8IP333v7u39K6/O5Z/S0vjx99PAJVpM7N671IZ2fn0+bKqlJSkCZ1YWMmADi0E/qsiz3VNN63a3XS1NEBnOFWEpJkyVTJI8MDKDnF6cFOeKxFcfMAKxUu9kMC5+AhxjAc+1rREwpNM0EDBOqYgRQBPFqzrQgIlRIYKoKSQUEswl3XqUJLAIKZcw3iMaeyJuAM3FsdV0wSC8iYFnUhNDQEwMisCCgFUmgX5tJZwiLrgs9IlXDEEI/mNl0Ot3b2ys3Os5E1LZtTv0ZsOu6YRiaplF8xkpJN7yaPPYZwXVDnzNXJLKYMo+fkQBREcV0EF70gZGYhPpgZozkfekm9SDLNkQimk7nZVn2fX+xXvdXV3t7R7kXLiKG5IockH03hEx2znnmqIhpltcnbJwZNtUlOGTaJFu6wfIqguMxVhMgEzkkRvJIKQT7/9P1n9G2Ztd5JjbnXOELO554z833VkblKhRQhUQkASAJiqSaQVRT3ZJsd7s9bMmhJbmt4W5Z9tCPHrZ/eLQ1POzRVotqsiU1A8QIgCQokACIVChUDreqbr735LDTF1aY0z/W3ucekPJGGPfknb615przfZ8XBUmJ0pRRnufdPDPGuMYzc93OJASxmSLMtQpAVWgQBISjBB8DM3vnfAhNXXXyAhTtHOwXNrPWGpBxNWOAON84YhJdIKExpq5agB/pUwGAQFyQr+QvvATG3GszHc+65YRo/mQZI4sQmEU/+96fOdl0/5GqJ4lOFw1p5LR/8r3QqcVvloXMb16Z4txZke7Ace6HLGT9suhJ4YkbL8QrxpjZbGZIDYdDVzdJ6z8cDudLhMxTSoAQOAYiy6QWoShISWpLRGAzHdoGYsjKPM9MNZ1MqqqupjbPLCoDxMwWVQCObRPaxvR7QigILBRj8l47Zkr5h2m/Tk7otCj5GAFlTiVLLwIiMSgEjQAQAYgT7xgEkCCdo1AJMQghYkyH/IVkaPGMznv/xxddSokBRJGk1mEFRApZKOl6SQmIChIgUpy/SeD4+UzSf5K5DnnuMwTQSoMsBgGkjqMjoneIUSkDkEwhBIBIQhgB52bYuRC0s7TkQzBZhpndOhq33pVlSXm5v3/gnEPGoigwy5glhthyrFtQpl+hjFsySisyEiBGQZsZrYOuQEXbB901xHZluQSvnnv6AwazN155ezpqjWLXzqaTKUSOnkOMITgEsMZYQmEFgDE9RpEAjMAIHiEKo6BiRmbWqJUxSpBj4AiIpFSyiswHMCdvRaaVnqcSgVbczAsIlSpJpROqMokaWRA1KKVtlhNqZI8Um8YdHBxNJ29Nx2NNZjhY05SNpzvd0uZZYYyyWoKvJ5PRGNzySs9PNm/vboXY3t2GXi9/5OHz/+nf/59DMYTJ+Mor337ptT/trPiPPfPo8sNLEPe0xunRwfarb+toNgbnMOoz65c+9j97FH3va3/y4mgsk9p4yWye9zrDN9+82h/mVQAPRheWrAlIs6adNs5kVgiUQYXCON/gQ4zbhwfOtcZmvcGyLfMiz0PjS5ttjjZXT60899QTd+/e+X/83//42rtXXANewGfoVVdpG9uqyKwoBg65nZ/alTKodCQdUXsgESEBgPlJgxf9dlxAx33rkvx0fshXFCVENBp1CtmVBLEOIQRugxdGazOt50h1IuqU+fzojMcdIgIA0oiIRAoUJX1zUmoZImYGImus1hrTTNMaFEhKFQI1L01EKDrvk4Ql2ZBFRCACiPhQA8vi3TSn+QohIEYQiPfEJ/PeautkkeRFRAlJLhCrqReIhkz0joNAKlokTiaTPGSHiHUzFcbG1c4FERkMl/69S3na41O1l9Q+qWJLqOnQtPNzglYAYHK1fzTSWitjj5FvPsadw/3V1dUI8+nBcXTu8Vnl2Bc43xUA2yARUFiIsDQF0ZxTGZ3XSEHmOgEf2Pt2Oh13u71v/Nm3HnnkkU996lNvvPFmVdWX7rt848YNbfMso8TeZkEASJm+GFkt4oSOBf24YEKfVKL/hdI/6VmIIEVEW9KhaVC4sBYlulmtsnxp0Pvkc18cHR3c3dqsm6oagzZ6fXWp2+26amYyvba+sTZcDlWzt7XlW9fvds48cO5zn//0uQuXcG0D+sPXv/b1fmaWhis372wG9pGD920EbJq6aWtm9r4FFhVjRpApNEpZFCXBKlKEdVtNZ1MvnJcFaOUCT6atc9HmWeNDGyJoHRnOnD61c+uOzbM8t4rAt672DTgGRZikxMqkA7NRmgFZgotRa80cJLossyBxHri7KBRokQmfmnDJfXFcEyz4kp5USu0N6WyZ6siTG/wxj/L49xORWiQbHLda5qEBYf7WAqAUdLW01Kvq6ZUr7z37xKNLZTEaHe4f7Ldt++Dlc1evXzl/8cIb39v+9f/uv8ktrQz7xrV3do6Ga6c2zl06e2Z4e/fo9rX3VVaeOrU+GR2BRAKOESN7xsCgCQA4cFTRu8iBg0dmnCNYJTX+ABQizZW0IsN+T5jbtm2dEySjjLYFg1Bu2xiSjIEUsfgYogQImEEihAMDRhCfTLEcGTiiEIMIoKAgIgkKKJwrUFDh/GIh0IioQAlEg5Qb3SsykKDqthGllCqUViogJi0jsqjWgzCKRwYThNGXHdMpStXUrqmr45i85PhfXl6eTiYpBLRtWwkx6XyyLGt8eyzl5+BOHvxkEQjt2jbGBDChGDwjcMLNJ8mEMANWMWZaEWAI3DSNd04YSSvWWlARqqpqwrhikaLo9ld6N2/dzowpc5vlmQYRjm3buskkK7vp5UnOMEhKjGOpd5L6zFf5Y/cRJLwPHVdYImqR8kHH+WiLEGgA9pElRuAAzDE4Y4yE2HoPSbCa9OACCBC9TxmOYa7fRgRRmoAwCM9aB1Uz1XWiY1VtC0qFuYQe5oU1CwAECQCQzr280I4joLAsrogf0cLhQr1zXOKni24u4LnHasSTP3jcFpn/eoDw71HLIMB8gzp5SBBmADbGMIfUYZrLU9s2Lf7zPkuaq2DS7XKC+Z5swRwvAsenhZP9VlTknCMNZVkqQOdc0zRVVc37bjKfiSdqNSKm30e46H0rpRAIpZ5OvG+bum4rFXwTnR8fHR2E3fW101phryycc9YobkN0Pvh2Mh0pY0xmVQIGscSYUGxmsZrJCcWLwALKs3CaUHq7qxiTI1aE56r4H+XGpCb7HKiCKMwoP6JETXJ/hWmsxSnwF2RurcDIoEyaFfBx0gMsMlX4OEsGSEhQOARBBlDzE0YyMQGnnLjAQgDpWZQYOfgkU+boUVKjlBLaRKlUAc+vIRHRdeDZrFpeWyObubodrqyX3c7NmzeXBssqjwSoEF2I0XlrbF4UkUBnmW/rWV3nZHPKwJDKKDjf7ZV+1u4fbo1cqwx0+2C69FOf/eyTjz0x3h4d7W1dr+9OjnZrh+Paoyl87VoXlFLD4bDbKUJVHRxOau/DXE7KSESGcquUypwLShEwsmciyQwAoEflvBhNRhORZsVBkH70Whh0ysht4tAohR4Xb20kBAIgQAIgEiHQqHUU0NpmNkdU7ESTAIu1enx42Ol0Uchau7l1JzMmsgfw2kiWi7Jii6Lo5Z2+/1//k38IIdbT6v33rna7wzs3t772B/9qNBrtH2w24fDC/SvPfezh5YfXoBOhsaPd/XffvyqOCuy+8+IPdm6OoTY//PPbo0Puds90e2fvbO1UHjb3dm05jmBu3Np0MSpt804Z2zBr6oiU5aULLWqFCs3ivS4cInOn229CyMoOcNw9HK0uKauzw0m1unb6yaef/fznPv2b//pfEvAv//Jf43Z8/e42dk/r7sbNnf0bWwfloFN5mFZ1p9dP5TuQBlJByAEJqyiBEpH7/88Njid78/keIGrAhbRDgUEgUsaYstdv2xZEWWuVtiEEZiCCGBwiCM4NHriQIc5Vj4qORflzTcQ8tzLNv9J0VijJ/BYqN7VwKRlRrNSxEAJZmCVEZmbXtIudBQkWjisSAeXZSxBEUcogpuGTV0qzBAASiBIlBMcMzKF1FQobYyQlCFhDClFgOOynzPPJ0SiCdDqdzlKHmfMy53gvxJEXTglexAOfXGqFsFN0GMHVDSOUZQcUsQ+gCGZV2euSQNU2ubFLqyuG1KSaGaNYoogkkMtxxNi9lXdxwHDOOR+yrGhb74InEF1YozQJRPaYysREpYs+BB5Nxof7eyT+mWeeadv2nXfeWV9fn06nL774Yq/Xa8bToihCCJPJBFD1+/0keE0QPbUINmc+Blvc22Dm2/+Pav3bxivEBNx0wS0Ph0dVbYiWen0AHo9GZZFfOHP2//hf/O+r2fTll1/+zovfPzjaF4TD8eFof08RXDp9+fOf+sxzTzxx++r1L//27958/1qW64M7N//r//M/7vWXz1y6vH80vbO7x7qErPCCk/Fs2ga0Bki3LjCDMmQLHdmRC8IcRJxniQHZNRy+8IXPfe4Lny+7xdvvvfva22/evHtnMm2bSm3vHDWudSymLEXZ3d29uL1DRSkKJ23rmrrI7WA48N4d7h/0ekYYOHoGJtCeIYTgfUsEubHBRwHI8zyGxreBFtE5J/v3SQcSQjgW/cMiVdo518mKeWWwEFbJwgk6v0aVSj+Y/gEcKVFaEOc2QiBENCbz3gf2LECk5m7/iK71hTKDXvnQQw8p31x545XxwU5RFLeuvbW+tPzeay8SyX/2H/+HG2srf/yV3x1N+K/+xC9deviJKtLX/vz7N27eWV9eEmX3tja1Ps5XjsGFiISarCYtqp5ORu4QgI3J8swoZQSZtGWIzCDIIBQxcgSQ4NqKUotRESkDxjSBq7oe103ragVS5BYh+rZG4TLrHu7uJPw2KSZkDZE5cPRaKZ0AiqSJlNE6cc9IFACkkaXikOoJECTRCMhBMMY8o0yAY0TXFiYoa40lIsvimFlIEWQHdTXXrajMsZAineuytLPZblmWSqmiKIqiOC5H6rru9XqIOBqNQuuSwufw8LC/NEgpwiEE4LnaR2ttjYbFYO34PJDGOwuBcNJkIohKYmEPqFCLQQUGckmDQVHKBd+6wEgmt0CqcTzaOxyubYBE5Bg4ILJRqlNkudVNnAci0ZwDkQzSks4qmlR6p6W0QURMNWwEEUwmpCTVnuMWku8yoS8RRRCyImdmiOxjYOedc7MF7F5EUJssyyNi03rvQ4CYcDfM86hsrXWeZWkEMZvNRKQoCg98tL/LzHmeh+DThWCMicJpnUx3hpljlIRaTE8mLczcx5X68S39iFrENsGimpcTzf6TJ4STMsjjLyFijH+h7kzPSgLE48lfiyyM4pw79oYmfgaeOOfHv1T6R7inXz15x46948fvn/SHQgzGGEXKOYcsZVn6pt3f319eXgaAKMzJ+Ke1zTJjTLKkp9LfapXlViNxbDUCQK4VCsdBt9frddbXVqpZM6vrfmdgSE0mkyDsnavqWim1s7NTdMr+cNjpda21SKznZB8GuBfKjsePPCVyzXkeczOMBhHHigUAoohKfi5BQQRhQoWYwsLuydQEFhmyJ3XmQkiQQlpxXmrHyD76gJFBIijFgjHwPF+WkrsABRaCLUFE1gCtd4iS7IuMJPOBK8YYA0sITMASEUmidxxcr1DJyRpCEMF0klJImoABKVkcWBhEV84XvV4ErNrWlKUTCVU9XF0TxtZ5BarX6ZiOdnUTffBCIVJbxeChaaVqqtyEIs81QlPPUDV1vZcV8YUXHv/0Z59bWy/b2dHFS/fDpNrcfA94emqtd/fu4d3b09MXzo9rt7GyVtUeSbezZlK7s6vrbub7vU7l/Gh0GCX0BwOTaRdakVg1o9WNjbZ2o2ra7fQm411f+zNnLrSKW+csEgKMJiPPsZMXJ6+DMtchiHONjy4GCa3bWN8YDpaLTtnp9KZVc/vu5v7hkQgWWdm6wCKZLcUzIp1aX+N6n32cjHaXhktN0+S2OBrtdntZ20xmVWMs6IwadqTcyqnBxrnis3/1CcjuQmdNb7rLD1/c2z68duutqp2unx7ub+0urcPTHzt/+umzgG73nXffevu902cfuXzm+dJ0ocFVW3Vhc//uVEkvNyBSNq1WNNjZ22k9cisMEAE63a6QEgBb5ExqPJvOZrPB0pCRmYNvXeoJEUFmjUQWwGlVW6u7vaVZG6pZUICd1aWiN/za17+hjf3Ixz5x8fxKqA/P3PfA3cP4te+8Nqrc6trZozpw22qtt7a2hsOh914YdVHknb62edVybDzHSJhCAhEAYYEiIUBNSmWU5zmqeXsjIb9BAWJSQyIiam3KskRtjDEgKi1bSilEYA7dbgdwzgNOToCEFXetV0qlJG9mBAClURvLnjJjAaD1LnpnrSUQ3zYAoBCQCIRDiKm/4r3Py4xjSPSP4EKyOVprjdaLyjs1KhARgdD5GkgQEpwbSIEipZVmz1rRrKmda4qsROSmqkN0eW5AxLU1svQ63SIzzGw19Xodk2Va606vZARjDCqKQZDomH1x3JpNrr5jPmPK5RGRIIzGIul+f8giHGOIkQyxiC1KAdKZXe72jNYs6CJba0XifE9a9NrTH0oEmFQKpLIvibsmk9l0Ol1fXXNtPd4/zAvb1k2eZ03TprltFJxMq6OjceNaIrqwsb6/f5Du82g0RkRFpqldBJlOpynnKjIcHo6UUkqZNLg/uckdHwBijM65pEKe150/ehMRRCqLMrP9bln2ijK6lqNXBKdW1/I8ryfT//L/8I9+4ed+7v23r4z2D7a37hbdzo1rVy9duNjOpntb2x1tSfCV77443t65uLa+vXO7t2w/8bGP1G187a13tg+n/dU11ubOzk6nv5JlBRhpYmicjxxAKISAhWpDsAg/8ZOfLwH+9CtfjjFmCgeDQVFk2gAomNbTm7dvXLn6ftVwWysBAyrLyqx2vm39yqm1wdKyAN3dvL08HGZl1lTjWVu7dtYblhbRt2mLIB91ethGYds2EkBrsta6pnFtq9KAS+um9alWSJXcaDRK/f501EwvdKfT0VrPZrOqbkUkMuRFJ8uyqqpC9EoTCx4cjmxWKKVG46nSNsvBtXW3LJxzLgQiEuEQAqGkln/TNKH1ioxSyfMM1trQ1PmgwxH2d3Znh1sv//CtBy4Pp+Odfrdz++qN0xvlf/H3/3Ml8e03Xvnpz3z0A08+M3bmX/zG77z69rWtUd2gDTrs7B/lZYf1HD/PgoIKSEXftl5cCKVVa6vLWWEJde3qqmqatgZoXHSuDa1vYpwf1Ikgto1SBpVhJC+NG49ZKIIoRZoACJq6gtBidAixappcD0MIxmBpM4ltNR1lwP2yo9p2qT+YjGeMPu/0PLNRtq5drzs4Ohpbq/PCtFVNSnU6XWbgILPZ7PL5c6Px/vToYK3fAeV1t+gMu5OmEokioaqmzAxkmkDkoZsPxrMRxSIvS41kc2sN6sEAMCR9XRIZ8kJteHR01LatUmrY74vIeDxm5qqqUlVdlmWRmRhj284FtG3b1nWttR4MBrPZrGkaRCxsBgAcQoxRa6uMRsDATDoX0kw6gjCQgETSQKqaTQUhCopACB6JSdmy0/HsiSOIAIcoEUEIIwgZZUUZVBRBmJlU8kwvJG1zXTXEGAnmykPmROSIc9kGACLmeb60tJRZO5lMZuMJIWU2I6JZUxujTJ5x8LFtm+gjgCEdYyAiAm5dw4AMQlZZ0hZM09ZN40XEWGutQaIQY9O2SmsixQIioo11zo1msyLvRI4+MB7DGABQqeiciABBgo2iSip2VGhP1vGw6MQfl/vHS5n8qPDmuLhPH54k/xx/MhXnf+n7gZm1sjLv+c4XVqWNIM9CEJEk+zCkadFoSV6yKAILAX5aPTjw8cT1+K+n70+HNFg0C9J9SOc6ZvbeW6WttcjSNE2i0mmjy05HROq2iTGmqEcWVklhq0hEEm47z/OyzFeWh8LxwtlzH/7wcw899FBZdr/yB195772rV959N8Y4moyn1QwQnXPONVUzc9GZTFurm6byPhpjfAhEWilFlCIIfOpoZFnGfE+/JBAjR2DIkJi9iBApZVSIEoQJiAmZwzwhgIhIp7FPCE6rhJ2cP0uKjNba+WaO8YkxxkQGA62V1jo6X7tWAFErrTUDcRRr9HQ6E5HM5CiUwkym07FVsLy0fDga7R3sD5dXrLVV3WaZHY/HWpMh1bRtG4KxyijS1gbfwGJukGJMtSKF1FR1XnTKvIjCs2ldNTV+/P6BtVZZAwACoLVmJGYuszLLMsXknLOkO50OAVSzOggQ6sg+uJpAytwWWa6VoISdzeuDgfqZn/3MF77wvC4aDke01KmvXPVVuHP17ss/ePNot77/vkdttvTSy28N18+9f+3One3dwWBppb/c63TXloYxyo3NOzsH+4w8XB6AktF0DCT9fldExkejfqff7w406U9+/JNG2S9/+au3bm8yaGYWxrwsu92uiPxfv/kzx/XBP/r8V2L0LrQhBGBZ6i6vLq9qbafT6WxaT6vatUFbg0itj3XjlM2Wlle0Nc4FlMbwpFNqTdTrDra3d9Mpf7jUPTzYXlrulh2lbex2cHmle/8Dlx58fH23evncQ2chDsB13/r2W1/5/T82SveG2c3tw0efzD/2qWc2Lp3ys8nh4YidhFgcHMDS4FyvGA6KJRychpba64f7u9Xbb9145dUr125uR7KUdZl0G2FWV/3lJRd83TrSCnQ2nc0YYbi0UreNkIiIC61ra+8dMyuUTlEG34bg0vKCIrmyuS06JlPC0jYfuO/cFz77wrnTQ9+MVJa/8u7Wt77/xu29w4DZ1uGsFex0By6EXqcDAAgKlBbSDpSLEkKA2CKxWQhvjhey4zoyzRCPOweCbPKsKApjDAhFkcQtybJCCEEo4Vl50fBum1mKqFBaE+n5qgfSKbtpDUq4lXnjSmAyGidbW7fTKcsSAGbjydHR0erqqnPOc0RE773z3hhTdvJ0z9LyGCOnofmxFHV+W6yrghxpPmtLD3MuTmWJMXbKMjif4gzTnbGZmdZTRLFKd4qy3+1aa9u6mU6nq+tr1lpBdMFHEGU0InIEILXIHoHjSX1qw6fOStoOE7pRCKdNC6gQIDKn5l5iSltjkptdABSR0tpoTQTMYY7QW+w36ZbOPKlkvFdta5sXg83NTWsUe0co/W5nMhqHlHKf5UA0q+u9g6O6cWW3NxwOKTjhcJJ3gUmbpdN0Raej3fFeqI+HsCfuT9paYCGmOv7kD777wvF1/fwnXsa5xVMUUqfMiRkiI7AmzIyyNs8Qpntb3U5xeHhoy2J5dbnX7/y1n/+5b/7p17du35rsH2zdvF0gnlvbMCCnllae+9BTjz398JWrb//gldfffu/q3YNxy2Q7g7w3nHl2gEGwDb51cxqVEE59Oxj23GR68fTpQnj/7p21XqeejJYHne3tza2tzZZD2e90hn1R5DwX+bJWRe3DZFqJzop+z7GMJ7PAoDR28mwyPSpyW+bZdHyw3B8006ZbdBGxahwA5GVHAGazWWSPKOmA7Fzr2loTaGuzomxqJyJZliFiKgqNMVVVFUXBzCnlNzWMZ7NZ0SnTK56ummONeCoFUjM4CX7Sq6AR2raW6BMaREQIRWsdozRNwz4qMkoZZuAoJNJByiS0VfVXPv7YhY3+y9/79tpKx7ezN96EX/6lJz/2keeH3f5kf++BBy7DbPrvvvmdf/4/frPWUA43JgGPAtnu0IuqnE8K2sRi9gJIVmW5UXq5WxCICDrXzOqmrmd16yN716ZBeRLfaQCOgMhRESAik2JRASgAJqzeZDLJrM6tsiQkQUG0Ggwabk1m8giubWaawlK3LBSC8ybGM2unB73+6TPnZyG8fuXdw+kMlRamLLO9bu58c7C/IyK9soNCCmhpaen2rZuDfjnsZ3v7mxcurF9+6L79w/H1O7dmk0neyazVPooP4oPaO2g6/bW720distWN02j07v7OhfsvMkgb2uNLVWuNLOPxeHdnBxGdc0qpQbeXXlxmjjDPbC7LMrc6HmNzvcvzPE0RXVuPRiOt9alTpxSDc671QURQGaWtMgaVQVRMCkQFjj6w5zjPfJWYSDuCJGgIF06tGJSIElbCBlhDVAQIyoMWZY9L/6SpPO52p2mDEiCiRE5LuqAIKSr7Hp9EKVWWpVaqqqrovFFaI3mO07ZGIg1CmFKQhIBRODMGAAJDlNR/QiKNKJEXK9XCWJVWXe99PKG3P65utcmOy/T0JUael87HS9k9WvTc93KyYYEnZph/4Usnl2VYTLaPPzw+ORzfARFRio5/1WJAisyc2WJ+72Vuv0knZ6V1hJhCcnRynJ7YBRgAUuADJaS9QKQ0Azx5h9M304lIr+OzByiy1ipAZrZKZ1kWnZ9OpyGE9fX1vFNOZ7MYY6fXJaObpkl7NMQQQojBpVLVKiKUTp4BQFPPQHguuJpMz565sLW1tbu73/jGe181dYiRjE69M5Nn/X6/2+9orefUEDBCSgEyMsR7Nt9uWboQJEZQkGkjhOxDdO1SXsbWeY5KKVLp2IlCKkSJgCmuK9lECRQAa2BSQKgBMMaYyG8iYmw62gkSAzCiADIBgGtRAAjTL4kCkYEBx6Npkefdsichjo/GEsRai8KZlrZtUassL0WZyDCpZqPxVKWHjOJ8A8zakCaF0hqKEH1cdCuThFcpk5qtEsGHmCY0WhhjkCgBAJTRzBBFQohBBSKKEZ1zQJzneeoTap+cw4rRaIgFqoLQoMpy/cizz2rtxnc23/3hKx949AzwdPu9d15/5Q32WB9V9126eN/HH1q7/ChU8ebN22++/pIp+s88+nDb+mo0aX3z/u3bo+loeWNdxdpo5HpU+SpGN5m2b7yys7QEwz78zI9/6gv/0d+B3QPoDm595/vt0XY/h0G/d/P2nbpqz67fP50c7B0enbyQfFNLgjtFFsHDw0PXOGaGCHmedzJr0zEXlFVqddAjpaN4Q3pp2CHUFGK3ozfv3N3bvrk0XPnC534sQrx959r6xkNPPfPoytllbo42d2++9/5b7999Yy+88amffQTKCHsjCGiMuu+++86cPVV01fnR1UsPn9p48CKsLZnrcPP995spXDz3xJOf+mmgPtzauvLale071/a2xzevbW1vHbU1d3vLlx58rAmytbvrWofaGKPaemKLvG3jwf6+tkXR7TCpqp7yvLAXFFBkIkWIEkLcr48yqzvdvMgyZvZti0Ek8lE1O3tqvewNdg4nf/qdlx/7wH1ah63tnTsHs6OmLYfLaLqNmdZOlLF+NnMhGGOsUQzomYVZoTFZBnGefJ4W6mN5xrSujmUDibCRDgPGWoPGktXaIlIIAZkTgA8iIKoIMU3o0rKVW80cJMQgorUQkXAKyeYUnxdDiItCUxCHS/2mqtumEu9i0xhjLOH68hKBBI6F1t1u18dwMDoSEU0qhMCujbKYvSZ4KGIIKeYmDU9TFJBKhO9jXDIzJ14vsHCIjohD4OCN0szsvI/Be9cSUVbYLCuMztrGTSazqqqHnoU9I/gYIgj6SEQMQqS1MiKSZPFNM4/dTZ3+dG65t9Br5QOnCPHkQBAQBQiKXF3N03BBFCAZnWmjtWYJx3eeFz6/4y0u/d30F7XWRmfTadNMZ15hNZv4ejbtdXKj8zxvXNM2VRti66NSenl52eZFAj0iSBIOJa1w8tm4yIgqheDGE2kPsuhR3NtmAeBEV4wWAOm/8A1aaxSRGBBYRGazmRJRgIQSkbz3qo0a4ulTZ/Z2t5fXT2tj6rad7eyDqOmkuXnj7tn1tSzL21mVlZ0cceP8hY/8Bz9fb1975/qtP//+Dx3jyvoZh3Qwabb3Rw6AUQuCj+K9DxyZOSLVdeiUWTWdXnnnlvLOAnezfu3o5Vevdrvl2sZ9UWRST3a2Z0GYtBnjjtGZzXNrjPPtwdaYAcnopW43z/PDo/3pwVb/zGnFvL9z53Drzko58KNRVuQ2y4PA/vZhGyIp1ev1AIVwYTMjTHt2VVUxCC1CuI7d50qpZL85dqUnTUJSXjDLotWbLDPILAkEISLehxjZGGOMdnXlvQcORGSMVUqBzI/T852GlFIK4FhPTUTKGrh169qjDz734MMbo9GWJvh7/5sPDvv9l374dTern3zkAyBrr7/yvbs3rz5w/+qR09Ogdafnpu5wNO2trA7zMlUoITrnHIYYRZQwAR8c7AFwjJKwsSE4RKUIjEroIVaAKegjRokcrbUMgCAAUXFUaEiDUrR6boOAJbSxrSUEJdEC5qR1Dgpd6yolVaawiy20bX0wPnVq/aFzKxfPXe4Pl967cRtnIxvD0tKAlLtx860713hlFc6cWtWkXLPjXQwuziZ3unm9urx8/vzaX/+ln3j66Ydfe/31P/vWO0udSd/6Ts8ItXUbTN4ZLJ/5wQ/f3t690un2i0GndTsKrQ97zvUYc+dDGtydvCKSBCUl5aUzXtr7a9ecVJUcX0op7zmt2By1tTbLsk6nExuXRotKKUEVWZjZGIITIIek/hAWJFEpxgUQRJB8KtEBMYWZJDK6YHJJASzeruknFv+Y97AxNY3/0i11yxHmXq/U9ffez2YzYfbeK0jCP1/5lkkFDkFYE1qtCRAkhgiIKXiR5wUhYkp6CzEmYwaARPZBOAZhZiRKslQGwdTWUkTzefVc5JJYdvORBc3NCYlXefzfY77PXyjuj2toOKHeOVlY/+UP/8LvmZttUOb0noXnAQCJKC7C2gnusTIE6XjrEpnTLlJ/ZS7gQURMZwlMTxWJPm7hnTwmHD+Q44eT3kspZseQAoBMmyzLRBtE3NnZOXlaUGqO2jZGAYAgOOca13KImTVkDS/ulbW5tdZmGgWyvDOeVW2IgkqbTJsMtWmaJgjHGBWh9348HqOC4XBojamrVhtAmvtFWAQhGq1Jm5TEzjEQYgpmjhy89041vmm892S0sTlAIuREmQOoUETmIbPiARgNCksidEvChkVhTikUgslnCyzgRSIKc9NoTSazpEgQYhRABhZhF7xU00hRCqPyTqYEom9Pr6+8+fqrtuz2+/2bd7e29w9W1k9fOH92d+9AKw3ICFY4IgoJCJB3QTiRryMAK6U0RSKfZUXTNK52IpDZIs9z/PjlIS5k09Zam2cimKY5TVWTQLfsGGU5OK11L++GJhok5OhDQxKKzGTWGICD/e1MC0lVduTSxaX7H1h/5OGz5x+4BIx3b9z+oy//0anlU2vDUy9+94fbO9XePpQ9aCOc2jh1dDBpJvXp1VMadNkt9iZHYLG31BtPRzv7e0998NGf/dmfXrvvEtQ1e//262+5Wfv1r319b2tv6447d65TdocCKs/LWdVs7eyTMnmn/1+/+LeOr5D//KO/QWpB2QJq60aT8d5brYf9gVU65aQeHBxYm21sbOjM7h8cMbPJs2Z61C+FfdXv9z/0/HOo1bQaH472hqu9T332Y7e3bvzpN//o1dfeFoKlNXjyyYc++pkn1i7EV1990R1mW9dmPdr42Ic/pVeWoADYyKDZhdEWRG6n8voP3rtz9aDfPXPrxlTp4c72wc2bd+oqKCoU5dZ06iYEhizvoKKqadK+3vh2Wk1W1laZ4XAyAdKg9HhWHU2nZ89dSAPRMM+WDdH7EFxZliIs7IQDChdZvrGyvrayMTmcig+Dfs9o2N65PZ0d9LuZzrOAZupYdMeJmrRBZ52i6LRtSxytVpkxDBAYXQRRVilF7Gle4BEiHrtUqrY57hbEOQUkphkfIBLQvONAiMn6qky6utLKkmz5gNLtlnU9m06r+YZkTbpQQ2J9AArM7WBaa0UELJrQaEsIrm7r2QwF8yIz2rrgiFRe5kCqaivvAhIYgKauW5dKGYNaaWWU0W3bMoLMsTGJ/kuCDMARTrDoj5EUMSok51ymzdrKKhEdHh5OZlPU6GPIjO33+1bpyWQyGY99CMPhMDXCBSGAAC38xDC326Yhb2rRiUhZlie78nNBDhFqk56B1OMHRK0UKTWbTuf8I6XUnKI234sEGRLDnzFKSP9WqNPno4ToWZAVaiQ62DvUWg+GPYihno2toiK3yR8hiC6wiwzaojJN62ez2aBTpsjn1OM/7tKRsemADQCB75039AlWzMldUGudHumxKFZr/c2vP3X8xH/0068gCyJmijQpa1SS/NIcgAYAoISjq7RWKysr3vubN6+XndwoUgSFtsg+Nk4za+FqNPGuLcu86Nv9w8P90ahhAFOIsi1Ty2jLEpWGY/RqjAAcUddeo9az0ahrLQXnpuPlQdeg5FolzqaPLnBUVne6vU6nnE4mcZH4VrVN612WZd1+Zzqdlr0uooxGh61r6npWT6vMmtnIlxp7w0GvP0RtPEtgIa1QqUW7lJ1zPrRG6SzLfOD0dCU1f9u2iJjUYqn0xwXck4iyLEOlkggkCclSLFRZljHGNFlKP5g+tNa4to7eKZQ8zzudUmudCL5V1cQYIeWhk05bILKE2Wyj3x32YFCG/93/6j9cWabtu+/2euahhx74t7/1pe2bdx+49MDGYLXU9vUf/JChc+VOs1Pp/Zlbu/hQDfmdg3FvZTVBfkJw0bcuprBUFFQAMKtGCQOvfrT1YK31znnvgSV5FYiIgSEluyKxoKR8GDJKY/RBIZAEDaxQSIJCUMLTvUMSsBlZA8EzOOhlsNpTX/zs5x+4/IB3sr2z/87VG29eu1b54KBpmslHPvbg8x9+9ty500vLAwC4fvXa229fef/Ku0dHR7/41/+DZ55+rG6O1jYGAO14PL5+7WBze+/atav7h3vamlNnzz/x9Icff+qFr3/zpX/8f/l/njp3brB29v2bt0xZmkxPKp/ny4I66bXmkzEfRqNR2zRJ0EVEubHOuaqqEDHOiQmCiJCKmxT7hZAmOVrrIrfHizO3vm1bQcqzkpHquo6M2hidlQLAoBgkMoggAwFy9DWkYj9lscNCxyICwMlIpxAVoCIAVMzJMIaMc3F5WvpSXZiqZyWAiEbNdYkiwiDJXBtPUI8RUZhjjFZpY4xEblzLWjOAJlAECiG9iAAgaUyX9hjSiVUHICG61DRJczxYZMPHyKlpxZxQiccD7XtN9+QPnp9PEgj+HotifltEL8lxET9fmhZWHFg0OIiO3Sz3Sn9ZTBuOh594AslARLVrjj+fHlU6bHAQIlJznMuCx4/svA8SUseKkoIqRmaei0gRSavUhAppbu/nVv7j4v4vTy3S/ZnzIRQZYwwpRMy0SYKftm0PDg5CCFlZ9AcDIqqaWgi73W4KhYQ4X8RExGqdWbu0tFTXM1c3zJxmfekMU2bl4f7Bzu5+VU/TSzB/fkAQRVnT6/WGy4N+v4+Irg1KGaWMQmKIElkQjNLaqrqqfAjBt8e5QBxi9C5DdNXMtUFZYzKrKJUloKwVgJhgI7Cwj6AoEuecc15YKZVpZYg0AC14FSISWXwILkQHMYhrNKG2RltDRguQ5xhjHA6WiaWazFzVlDbLjfW1980sNpPZeNxbWlndOOsEKxcrHw8n08HSMiPMK73oE3MZJIr4BC5KFFAkUYl5wOici5611p281NomoCGmhDISQAEEIZDoHcRAShEKSuTog0SvjXcBtAYJIg6EY8Qk9j5/5uLR4W50YLXZuj278sZLL59964UXnnv2Q88V9tRDl5/ul52t23f3d6oPPfPI8x/92Hg2+93f//JsOrp4dtg1Z8BLrNyFixt/+7O/dP3OjZ297fdvvmdUdf7UYG2tB0MDQ4PvX/3+N/9obWX9P/mbv9C5cPnPv/Q7BweHl+976KGHP3D16o0fvvLah595Qpn81TfePnHABg1CZNX8UMsxuLI0wDp679uJKNXvdtfW1i6fWx+NDwEaim5tqNfW1oZLS810dOWN1wpjLp4+df+FM7fuXH/7jRev333/7KXTX//Wl3RJ66eHf/1vffaZDz1pMmpDnQ0J8ODMxoOr952dng3UlPrCBYgRXANvb4GJr79x96UfvCqV0tI92qz2d1+tWmo8TGcNkO31VwBz55lZrZ0/+/7Va4cHe2vrq51+p65nLG2Ro1bZsJ8ZY/OCxrO2ifHs6bWn1p+6cecORklp5MioQAmK1iBzkL4C9sKMkYlFASqi3dGs9dgbdFssoVCNpaZ1eZFjriMaHwBtRnkZUHmBYa9P7JNPxmqlRXlB5sjMqdLyONf5hFTZLEQ+x4VI+tCajJnTBDdFWiYOT7fTD/eSATlKEqqxbxvfuuDaEIIog4hADADBhbQ6prEzaSXMgmgSNaxpOHrXeI4+N7lVZI1GCZPJdH9vO8vL/rBnitw39XQ8ic4HYWstKI2BWTzPCX1zJBcgMQgBi0TANEGGxTcAJVIEiEYAwszqTpkXRZFZ3W+6lfNVU3OIbevb2FZVExm0tqPRBLXS1gARgySuHANImKuPUhvvWPlzeHh4rK1cjHqVUio6xwgQ2XOUEIXQkEKtSEAIiSlKTLZDBchIEiLjPMDr+D8RWCSkYK/EvfKRI8coYXk4bJpmfLBPAJak3+v0ukVVVTeuX+uvLA2GKwZp1oQ21MYUp9bXVSKfnWhZsdybei9eXBEBIkWESuaqkuPS/3h3OSktTRqnH7muNaXSX6sEZzWJ7Ckcj6M9GJVni0V+bXe/3y1tf7C8tvrWG68tDfqt4p3tO1qwVxSdzJ66dBFieOedt/rYy/rLq4OVw9FsVHvUmc0LS9ZFTr09FJboWQAkub5VXbUqSNHRSlOsZ1VVZQqnruHoiagoirIslaLpeHaws9stCo0kEo3G1eVBkWdHRwebt64OBr39q7d7w/65paW7WwexqQaDYjapuz0a9odobDUbgbb9wVJEOpyOjc1ThsPxwYmTFTLOtb/pfMic+JWQnuFU7gNAkqUlRpIQGgStdZZlIaGvtQIQ1IqUUoQYA3P0HNGnZ15bTVmWpbaxh3l+DRHxXKIGRJqIkCDPS9HUcnMwqyGPD3382Ys7ZTa04627+RI/cfqhXta7+tY7z3zgyec+8sEr724+d+7JsS9fu3Lz9uHk9v7dwybuj6urd26dWlkNwQEHrZXJM2VMZGiD7/f73jvvvRAoTUopDjGEIFoRgEVCjVbrOcGWpA4NLlwu8zk8BgXIFAyC0VQYa4g4etfU3DQffvI+S0wEzA2wWxv0n37skaceeuTM5QemN+/+we//4UuvvnnUeEQ97Pa8yNqFfkmTjpo+cG5gLN66davU0+eevPDLv/A5ABDxt269sbSSQyfj0d3+6vDx4sLZM+vdDm7tds+cPXf/w4+un7sPlle+8Is/99/8f/5FE3w42i8HPY9Y9AeH1S4jGK3TmjlvAciPVGC0MMwk2Z7JLRGlblD07li0XeQZACRqU3py0oxRMYlgql2CxBBCZAREa4ERUCQRdQQBhETQkWEECwjIcyZPQuUQLZDnSSCBEedfpUWM0sme8fzDEwKYVNwzzwWWJ28MgoSkFIqiRc5U0ntMZ7W2JolevHcobCkF8QpCAoIplc4eDMziYhDkuSogUXFIECEKp4hutQC8xCjMLPNJJpDSKIqZA3tmtqQB5ogimeMGReCe2/4v/L9a3G04Trw5UfefXOiOBwvHXzr+jFoQeOeF+ByaBMKcmeze0GDu2I9Bgi3y4+CjOTZVLZB3ycB6vHYD4GL6DQtH8rEGiRYUuHQkOCYBKGuUUukQYkgREYIYY5aXlzc3N0ejUVGWaSqVnpzoXLLJakNZ3p0/FQKj8cS5FhHLTjctWcysiOrpLCJFgLr1MUZtldJWYYIEBqvUYDDodwcSxfmGSLum1TqSMUikiIREEyCAhAgSFWDrfOBGEK3WmbHEEZUiw/OxBDLO+2Zpop5y5ebWbUAOMbAEJCEirSC90USAUM/tvwAgMaUMpafLh9aFVgejM0vKJM/hbl0t9QcaWShCbNq2smjWVwbtOPz83/rlwfL6H//ZN155892ASinTyzOMIQb2MTAHliCJwgKQ512klDyYKGcMEkTE1U2eZWRBKWOUQQStkUDRPKs8xug8ptFvCJ2y1KTaeuYEyixHhNFon8hGMopYMGqFUaUUE7q1tbcyHEad7+zuWi2d7urRkf/yV1780pe+sb7av//8hdJOX/ruq4Oe/tBzz/YK/b3v/3BlYD/49AceeuBhaOLu5nas4vrGma1b7x0d7m3evFqP91744BOf/vm/Ct38/T/8Xde00YeHHzj/+MOPdiz4a++UKvZOLT/20OXJeO+V731rNKk++NSTlYsU3MkrJ3iv53BGEIhAHMSBiDFQGE3CEmtXH0oMuYret7PppGkaN9053C5jcLt3b60MB6/84Ac/fOnPdw6ngzUsO+j9wdlLq+tnlz74wjOXnnsc+gXUowwy6BrZq1cvPgZj6d6/AjfHm9/6ZlF0j6YzNLYK4ebV6uCO9hPMheKkZ1w2zI3LIM+8kAWdTytfBbaW3r99q7+6PBwMZrPx7s4WQeh1OyQcuP7ER5974MGH33n32jvvvTep3dG0vnv7uiYLClKScRDheZS0GY+nZa47ZZZ3cnYutM3+7l41aTvlcDBc1rYYV85jsX7xIlp6/9oVFm2yAlSmhTRlQNp7j6TqtoHQknCmjckKazQKxQCzqjleK47L/URLSNpHIkqNBAAQBoVaKSCTYnkWzh6YM4M5xgUyOTCDSEzS5NSd0tooJGGJwp1OZ74ii8TIIsIhArPRqpqOJ6OpxLC2vHb+3NnBYMkQXrt2A5AVAQETSmY1C1TOIUtmbT/LirLU2roYnPdRGFKuHgIBcAogEAERDTAfZM/HExxZksdHFUaQ2mm1Fe5aa7VSOs/SMxDY13Wd4EFZlhljGu/SXoSKhKNb0Po0quNpSdoh0odJoXFcFh9XyfMVGdGQokyTUjYFGgAsAo1iECGlQClN0DIgphFjggbPm0mBAyJGimm75Tg3+zZV1S2LvNO1GpvpaH9v9+iAd3a27r//QQFwTYPGZEYr0KR1lmWGEOGetYOZQxQRaeoKERMGIQVEaK0RVYItH7+FTp4Z0o5y/JD/wo6YZRlEjjGmronEtPlBqoRSQxcQoSxHrr27tzdou+zd9uHh6tqpW7duLvW6KxtngcN0dOTaNhwdGq37Z8/mxh6OR7OqJVt0l5dahspF75zJC0ZBlhg5usChQebk8jAgZLidjYymXjcD8cFV3V5uTNdo8t7PpochBGV0ZnLFUE9HMbheWYQYGiUDq07ff+HUqbV333+v3++du3jhbRV39/cGg8HtcHtteWPvYL8Zj8t+X1kaH+y1Mao8B4kpYg+ImFnEJKkNc3vcKTze0ecevsX2AAvFcOAYvWeENAQ45r2kN47MTS/zjX8+TPStJiSgpmkARCnlXVPXtdYW0xtrgftQpEAk65ROGl/XnQLu7t+B8lm29WS09eq7L33ox55YH25k5fLzH30eZvHKS29unDv9xAd/4qt/9sr+/st3NvfHLWLezbLi4rnLwbeIgkLG6MxmoMjHyIxkdZSICcpJZK1VGUnSPRrUKXg7XachegjaEipUKYoThTgSoAYxucIYODShjQySW7tcluWg+OnPfaKa7N26fW10VF84d/7HXvjwpfsvA9L2K9/dvrvdjDfPrXVWQR9UdXdlsLR2gcPhxQsbH37iOZ13fu/X/8dv/fmLTz7x8I//+I+/9fKbf/InX3v3vaO//Xc+df/jH917+62iQ53+KQ6y8uAjH7t8EZoa1tah7MLO9PD67VfeuqVsp2qoaeL62TM3tnbqg+ny2mkJGhPfI4TknsxsVpYliGRZli6WTBtZuIMY5bhfkOp+vUg7yfO8KIr0cidMTZZlqEiLdj5OJpOQuu+2MFprwiiLa1AEAZh9FCCdEwCSkDChgHAKKZqrwJMHADRD6tGAlkgIgHMVhixo7icKzvQXhIUBIHBETMmkmGY0IKIA0mqpkBCRY2yZNVJKedGkjCKRCKCYowClZgiyEKBCRQliFmKMznsPNK9f1eKM5L1P+JfEfIRFtyXGmMbvC2kNxBgZFqk1kGwjyDgP0BURQvoLa5ec0PH/aKU9753xCVstLlwQ1trjr8LCeyAixpjjmYCCexODuIgAS0eC47/OKYo7bR/JkXwiMUDmXEs5tuelJBn4UdgaLFoJsJjH6sVxNK3Yc68zIDPPZ7NZNhwOx7PpZDIBgG63K4TetxAZcB49nrIOmDlytJkNINH7adM0YR5gD5EH3U5nMFgSiQiuacgoBRijz2zhQ9vp9gaDQVEUyV1gFUXvJUbxQRltFKGiGEVCMJo0WKYoIsncnBmb5xaCVwhJIZmCdYHmOCNGnoPLUmdQgIEFojaY57miDEDFoNomhMBJm4CIAjEZSZK3xBqMAdOwS0RYgkBEiOPDEUavGQxQUXaF2SKvL/X+q3/6j2fT8e99+Q/fff01iphl5eFkEpVxbWhDDMJKpZRQQGZQ1LpICpQiJlYgAAwckaM1BiIDC3AMETSSBpbETfPp3EtRISgCpZVRRCASQwzBIxCRZ+9BlLAWBmQS8SAZI0VNRXe3Cs2k5Zj3rXFV9O2UiFY6K+0M6gkcVQehhcc+9OjqxUsQmwtn1p98/MGiKOpq7+aVa5P90fpgbevudMYI1vY7tm5pNtp74xt/cnS09+LLL6+vDI4ORoph7+aNUEfFeO70uUcee3K8e/u/+xe/qm3x4ec/unPnRnew+hOf/+zJEkFC9OCjoCDE6DuDwvu6dRWQBkvONePDydGebJxaP72+Zk1n7yAeHDST0ebBTktATz326OhwdDjaX1tb6g+yUxdXH336gac/8/zd21fKQTZ84Cwo3nnjh+N6qgxNJpP7LtyHO/WVl9+QiYeZjPemG6cuKtt59+rVK1c3J+NQ2OUwlru7ExNsWZajyT4UVLs4ro8iWTHW5P2sU2JmQNO0nc6qiZDvdsrlQRm9/+InP/3kEx84OJy8+vL33nr3atkfoi6bapZ3tdZGK6LIAJDgCoC8tLQk0bumcbGF4Cyqstcf9PtLw9WDUQXKDtZXxrPp+7f3xGLWX3Oubtswrqpx7dAUeVaWnbzfH4z3t1V0JAyRGUELC+gEtEWBdDLWiKznWvi0ZjH+5dLfkgAApfGkiIQYYoxtPU66oHkKjMS5zj6lBmpNRAgcQmQGZo7KASEAMTMvbL6Asnu4Z7XSZMqyszTsD3odX1Wb+zt3b9+xmUbSCgE4HB3uOx+r6Wx1uGJI5XluskxgzkNOA19ZZJbKQt0P84oy+Z8Sy41FBFmC94aU976t6qauAUABmiJvRUQpiYsmnDFEilm0MoFTKAx7H0KIIqCUSstxWsGTFC0t9HmeHy/rx0u/975TFoSoaZ5qrIy22iijfevSFiuEc4cksyAqpIUcNQ3ZjxtpDABzxF0S5woRsDbGaKqmE4/c7xTPfOT5v/YzXzx77vS/+h/+zXvXrt7e3Ikg2ijfhIPxgfd+eTBMewczSwoOToIpZYiIaJ5vctziOql0PXnZpm3gZN3PPwr3ZA7pdYf51pgrJKWUJgUAJMDMnmTi6gZguHGKQwQiFlg+fdZHaWfTKnD0QXV7vW4nRLdzdCQxagZr8uVT50zRGc2a0dEEtF1aGuzt7QGAcADvJbQcWyUCwDFOicAitm4mAUxpgQMqN5mNEMVqUxbFynKBiL51oW0Pt4/WlpZNkVWTUWbpx1748Gc+/fGHHrgkEL/0pd964523P/70E//g7/4vs5Xlu1ev//e/9qu7+0f9bh5FbZw7l3d7O/tHd3f2K+cmVQs6dSVJtAYAUoqU6ff7J0sEWBzDUoQTnPBPA0AIIUaOIMdZ0SfdvYn+ifc0xMAhRVRC+lLbNlpr4eCcS7mKGhVIYAatNaGKMY6nVWZDbqG/Are2rkMYZwPiWj7w7IMrly6723uQR8Bi585VO8wf+uLPvP/v3v/Od757+/btvLOULS0fVmFv7yDvdpRSRBlBYI5VPQ1z7Y5IdEJiMk2UkkKYiLTSoXVGm9wYXDxMRUBKsQFBRmGQgJFRRBNbwtHOZq8oyyIHRPZRc+xmZnWQXTizpFhfPF14V51aX7l4dhgPbm9du757Z/vShctP/u1fgPP3g4vXXn9rezRBMo8/8mRnaQmM/t6X/+itH2z9+Cd+9pM/+VPQtl/6xjfGO3qQZYeb+M3f/u6777/a6WqgF89efHRt/XD/aP/u7qbJizbg7lETqfOn336takh0v7+8MW016qXKt33sEQbvm7gIZyiKIjcWALIsSzCc4/Ir3WrXEFHS+cgJucixjo6Z0/KWvsSBtTUsWFWVZymKwmojqZSPCZYoiBJBQGLis0cEBaBIEAIllCJERcCCSIpBA2ogBagRgaBWwMnmO687F0yYJFABAIwsImmZgLmOfa4bTGtEBEl0hxQlCTwPoyKizFhCUoAgqLUBhQSoQLRgTI8UAEEhgBIgAGutYx/m8gwAhAgSYrDKiAjIj9ToSqkEdAUhEAISpJSyJ/NIAoSkWvr3du7hR2/Hr9HJUv7kWpdeYlzgj45/lSwkTyKSkNbHfw4XjJ3gw72+O6ZnFhVgG0KESHMHhSAinRi8MM9RmPOqF+DkvnN8V9MsMX0+QaKPezR/4fwmIqkXQ0S9Xi8IT2ezpmmSzXc2myhApeYg1DB/ICRAqAxpdi7EEJGg0+mmt7erZsrYvOyYbNy2bWQWRB9Dt+hmZdbvdhUo9kEBptdFk4oxBu9jVGQsmeT4l8JmQZiYrUnpMJRbbZRKdqAk8onRhxBJCFNQUoJi4r2nmoCVEkWaCEE4eIk+AIsCldKm0zlBJAoEEBZhQTZGWzWXVAmzJso6+dJgoERmB0cYAgTvqiovi9V+77WXvv/tb37jOy/+sHHMttw/PPKYLW+cORhPYwwphVobCsHFGATJRYYIJAGBCSIAK4gEkmcGIkuICtCQVkT4qUtr1lrUyMwArK0xitKcuqoqiV4hRe/rujZKF4PB3nSKVmtiDgFFLGlDGkUbsB3TaZsmNHWnKMpcR26V+I3lrsXYzc1svE+xffC+M088/vD9jz0IZ9d3vv/tr371q6FpFSMGOLVyajyd9YYrd3d210+v/cRP/Tg9cHnyxstvXXk772Qvfu/7vbJzsHMw2veXzq6cXt2giBHwlbfffPSJJzplLwRYGaxOK/fu1Tu//Nv3CD//8EP/CrVSZJjZc1O1M1LMoQXxhVHW4HK/e/70xtKwH1r3zttv3rlzeObM8OknHn/sscc2Hng43D787re/94d//FVdwuMffOyLP/8T9vIGDBSsdWHnDlgCiDyZUL8z3d7+8h/80cVTFzQobP1bL7+GHi0Vb791NzJ4yY6OXJEvl3Z5uj/DFpa7A6UgG4DXfudwNKmbpY0zyxun60jTyued7u07m5cuXPzExz4ym+x//7t/rsR/4NGHb928c3BweHA0VsZqmx+NxlXty/5SYDQ2J218hFnVzJo2xsgiUQJSzDRlWiyGHLFblP1yOJ02nf5KQLM3mjbCeb/vgPcP9wS40+8obQOAADWNC853MlvaDGLAwIkKH5mDMLNkebkY7ut7VRpCURTMQRBSml1qAQBjofPo2bsYOIoAcwwhBPZIknR7IUZO2gUhADQmY4AYo48h2StTaTon0ihK9oD5OghilOqWRQhhfDQhokGvF304ODhYXl5O+yJpJSIRotY6z3Nijam/AiqE0HonSNoaL8AKATEtgolHgRw1CXJMaRzA85RtEXFNa62NMSrAXq9HCmbT2sUwrau8003CXGFInblUfrXJkkNJvu9Iq9xmzjlNSiktmOBsaG2mrSZULFEYlCZFGlOCR/DzZUggcOQQGYQAIcUJ6fn3olCUwEGiiFZ52nTmeOJ7pbSko9f8KCApoDEKh05h69m03y37neLs6VMf+MDDTVPduXPn2s1be4dHpijJ5nXjAmBRlE3VpjYPACAqmOchoAiS1gt74jx5ngCROKGdjw8D6ZYlV7r3cdFFJqKv/v4Dx/f3E595KTgvIlZppVRwCwK9UvOito2NhP1mHEDWVlb3d3dzbddXl+/evDnoFgbhcG9v0C/XVofVdFLXM22Ig3gHWuWAOiJF1I65qttpVed5jsIgEWOEGAiCQSSC6eyIvcvzHDBy9MJegu92sunRRCswCBIAIpQ5LA16w+7y2Y3L/9O/8592i/J/+LVfOTrY+8LnPxt8/Vu/8W/29rZW15YHy8PnPvThDzz6+Mr6qVNnzlSTydvvvH7h4kUvcOXqYZg2UAABAABJREFUjSpEVvnXv/Xtf/3r/3a4egq0MYuCz4WglMpyG2MbJaT3JqISRudc27rknRARWkirkUSRATOnvqTLIfkBOp1OMgCkw+ec8BOi967IrUKQGNu2FYnWWkUYQjDaGmMUKO89MyhlhCGE4JpYlqTlcHWZlwbwj//Lv3l6QxvbDM+uvvu971+9cu2+cw+1B/yBy0+qwekf/tH3f+s3v/faOztQdrwuWsqg7I3rujPs+9BYRYogBN80LUewNrdZcTg7yPI8z7scZTar63oWQ6shMjeFUZkljQSgtTJl2c/K7KjeF3ASY/QOY9AgOelMqX6nvO/C+cvnLqHw3Vt3b928OR2NEWcfee7yU0898NyHnqXhYHbrxmsvvXzzveuzo/FzT31ofXWjaaUV1US8tXu4uXewubXfLdfeeetKt1uWneJwf/uxxx774DPP7u3tvfXWW4NB79Ll8489/uDGMx+49v0//ZV/+d+evXgJ8+GkCa+/+drWLi+vYOXlzjYwwekLF4Mqj2q59NCjb1+7cfby5aZ1W1ubq4Oybao037DWdjodZBmNRk1dp4KeiAbdXvq31tpzRIVt3ezv74/HYwKeWzicz3ObustlXgCyawOS+MZ3Oh0RGR1NXAydTk8r0zi/NFyJMTKTACBSmhSFKGAKVknQLwgMHGEek6wZQMgIKiaNpFFpBYJtZYFRaUm2tFSzzitUJCJIItJj+Z9AsrEmL1M4YTmNPIeeISarv5CQApN6QESgky2TI3PQpCR6iayJMltopYTRicdMTdvZbDYLgYFQKwsA6dTMLCAkdKxyQf5R0g6m+zc/YAdEBAIiYgSGKCLCmM6jJIQkiQmDSYfBTIRKKa0QWCByiI4jiNI+hjRzo0U4ABG1rUvU7LRsphEKEMYTCc16IWMTEaMWKXIiEtMoIwb2mKlkUVB4L3IOmNN6GxYJW8mAEQUNzbv+x40YZga+Nw88WfqnfQMAdNLUzV+UFL+onXNHk3FVVTbLBoOByaz3PkiwVidJT3rUhozWunYprTwmNEgaT9V13S8La21T19vbm5PJxBhtrWUJVVV1u91u2SGaK98AKIRgSLWuTi2MPM+NMYlFkUJmkrvg2DgBMD+oICIjOOedaxEpy+x0OgVgJEGUZJ0jBQrEWCUiwYNrpfUAoo0urM28iyIi0ccYhB2LA2FiH+pJr1PkZVcQmtZ7jtbqTl4cjQ4G3U6YTX0zMxJDWz375BP/0V//pd/5V7/x9utvHUwqyjoVo+4vqWKwO5oF0o5ZaVt2O4pM6+q2bQNzVpRBmL3j6JEZSTShIlCSIggxM7bMSq01fvzyGaVQKUQCItCa8iyxAnQMoW18cI4jIKLWFhSFGI1ViBhcKzESUUr5QSGYR4uhQjBKKYWEDOKshm5hNIqfjiU2G+srF86fvnj+9N07t69fvz46PFKAxtjo2Tf1ynDp4x/9yIMPPHQ0Gb/7/nu3bt/2HAeDpbaqR4dHTV13bF5mufgQWhcU/vT/5G+sPPLQW3/y9T/8vS+vD5YNZddvbP+DH/694xLhn/6V3yvyXGJspqO6bk2ne/7i+W5HoWqzzNftUT07KotsqT945YevWm0/95nPPfLo4yACzgGoK999+c6Nm/c9eIkpQo6XP/QEdBQYgFOrh2+/+a1v/vn25s5qb7Ugs3V7Z2dza211KCJVVR0etW0LDNA6mFVQlKrXG1bjtpn5UIcLp88//MDDd25dK7r5aLpP1ozb6eb+fm95dbB6fv+wItN3QX3m03/lQx/60NtvvfLd7/3pwf7WaDK2xTKhXV4aMPPWndve++XhkslslhV3NreDYNEZzFo/q72LcdLMlteWx9VhL1d7d29ePreOrv7QU4/PxqPHH3/86HD8/ZdeAZ3vj6u8M7hy/fqTzz539+DI5pkPM1JS5lkzq6Ln9dUz41EdGhZBrbVQjOC1YZ0XVcXD5XUR8TEaY6q69j4OBoMYfV5kiNK00xC8VogoELwfTXJlbd6PAerKa2uIYDQ7VJoDu729PRY4tX6eoznYnwjaJsQIyBwY05JEaFAIu91O0scTQVp3klLBtTHLMkWmcb6qqhhjkZVlWTrnSACAk/aLKGk6UZtCRDCAREEWRAWkWaEQiiJQBISQcsG9E455QcxRAosIJuwPCwBoorptvfdAaK1VhpjB+1bNMc9aGIPAQueuptMpEaVcc0VIBBDZe58WQREUEtKGDBmdoYIYBYDT0ND76NpaGLXWqCgV+toao7QgBNe64Nu6UUbnNjOZNsourL0pwYYAgFJG8jxYPUqMicrnAzvnfGQiMoq0RIURWDh6ZlYASdbJzGQ0agVKRQSYO8q0UVk6R83lxcLpACCCpJXRVgjTHVBKKYVVPWEOaXtLjlISsNYWRadpmlTQpz8XQvh3X/vA8XX9uc+/mkYucxpJ5NSxJoCkhQAWNJqJ2xhSPSqJxSQBY8g1rQy6k9FeoXi13/P1dDo52tsfD9YvM3WEMAj4KCGFIhJOJhOFlIiBEoNCtIq0ViLRh9Y5hxy1wsKowqIl+bmf/qn7Lpw+d2rljZd/cHpt5WPPP/d7v/vbv/ar/ybGwgctoLS2JusopRgkxvbd6zde+OCjSkumdVXVg17/F3/xl+67cGayf3003nv/1ubt7YODmbuxvXd9c3/ScBtI2TzLinmxkrQ60gQ189KAoKJMhNomescAJIKdTkdrqutZ09Z5bgeDXtHp7h2NWufTZD+9BBwjIlqlj3UgqQpJtsu2qVVSNTCn5l86ek2nUwBIKo4kJiYiQtPvLo329nIbLE0vnev83f/FL3z6Zz8GR9fA7wG7ravXwelBfvr2+/s//P67r7926/1ble4Mh2unRo27tr1tht180N082O70ciRp6wYZS9NnL80sICpd6FnTlJ01UsXhaGqtssbv7L5/+ULXuSkyjEZwZgPrhoTzuq0uXupnlvf3J5rgMz/28c/+2KeWh0vSeghxudstVje+9/u/98df/qP97a3bN2FtAz7xuQs//sXPDE6djpPqB9975Wt//A1XxTLvP//hT9y8ufX+tbuNE8zLrOi2EbyAyoqqqSejo5XBYGnY375ze39rp9ctC5shxK2t7YuXNn7qp74IGN555y3d7b1ze/fO3uF4NlVkXIij2YxU1h2ujGcu7y05UaosJ227vH7K2Lw/6NbVeDabjMfjEEKykejEsyLa3NwcDAZra2v1dJaad6PpRGk9ravpaDybzbz3kPiNApmxPrQS2VpbZjmS1FVbN7PcmpTE4lxovSfSgDpGWVle8zEED4yglEaaS8w9J4zJvEJNa5EgOBd4US8CwLyEl2gVKEwQ56QIQjmu31OFTQDIkZklAItiIsQQQnReIeTWwmI2RZQQnpBqVmZGxowKAgQCRmaIQULkwBystSSgBDSQBkUCKAgYwUiUUDVNXbeBo9I2yzJls6puiZSgSigjAJh7fUmcazkwIqaEVaO0MSa5JhgkeS84ldVKsweFpIQkeAkCwFprZcgFhyhaoSUR77lplLCyWYWqCSl4ay6zSeuta4MPrTCmdMjWOUK0RdG4ADgXIC2uPgAWozUAYJq7hhhj5CgBmQ0nkyoBJpda6mITUYxxOBwycxRGotlsVpblrGnT9ADn+yYRkSJayFPnnjRMvBwJOtOY3gWI6riVw6i1bppGKXN0dLS5vXv69OlLFy6Mq0mDwWQWWIioMDa0zjtnrTVKt23buJaZldag5g2+TBtrVAhhPB5NZlPmkLRGqRWY53lZdkFR8BFRWWur6Sjx9e+NTeDelCn5E45zoNNukiamLDBPH4rAEtL4PITGtzMAZ602ipB9YRREBjKksijaB0HSxmSIWM0m7FqjhTiwnymSXpYZx7PRlJTJy16IwoDWWkTRRtbXB7euv3V2Y/CFL3z84x959vr1d6rD6mtf+vN337pZDAaebCr99ysflN0fVxF1EBIkhsUkHyDLFEtgZogxHYGMpjQJR4mIymqTZVmmjQ4MQEhoSM1P1z4wg4vRp5KAgYQQEKMgxvT+V8QsoFKqmYSYaI8iAhwR0ZDyiSuC4EINEke1KjOrQRvTmUa1P/H7r12fTsfTKTvJQQgdeh9DyAqxL7994wdv3dzd3Z9MqxACIhVF65q2V3aKvDNp3N2tg+h8WRTFoPObv/fHm//8V/Zv7a+UtJR3Dkb7bV3DidtgKQ9trSmeu7y6vLz86c//pCpNM9t+78Zrt2690bXVRz/x+LnnPwTT+q/8/CeBCuAM9kajrZ2dra29zd2nHnrC19Nhv1t7dzA+uvvmjajQ9jtf+29/08dw9+7m1u1JFrYHGZSgC5NNbk61NcxaZu1sBm0EpSHPYHQQ22p/dXn5p7/4Ex/98POUl9PNnW9+k7/33Zc2zpz+9Oc/df6+iy+/+fqbV96LWK6vXRyN+dbtvW9+/duvvPTqeLJPyp86dfrMufNbe7Mi7x4e7Df17Nlnnnr6icd3d7Zfeuml2zfee/TRxyPqt9+9fnQ4jqhaLwLh7p0bzs9W7jt/7uyp3MjRwd57V17pZObuDXzqqWfWVz/yh1/702GOG2eHhb64v3kzz0pkKTLqlETSCI+8lzg1NmhDVptcEHxw/V6WlVhV1erGqcyaLMuOjsbK4Nml9YODgxirPLNbd6/7UPf7HQpue29TKTqzvlbkdLCz6dptk3VBzCxGwCgUmNzpc6f+0T/8u09//JPf+sqf/otf+fXZQeWi+KZlIEFGRUSaDGljEvM5ShARUqgNaa2jBwAIoWEQwhABlcnIApOqY0gNGyVALEnpqwSAMEAEpKSzoQiptRMjiyBg4oLNkSVgjAgBh+OWDyU4tAAidrv9rK0b1waOgggaUETrLCcEFh/ZceAoUZDQEIgmFGEJQaJXWnXLsrAZEdV162MMITgOwTEE9NorpfxCzZwOOd57EgjIVhcCMHfCIRCRyawyejAYtG1bVdPJbJwaHt1uN8+K6aRl9swMEilBqIEREUCAPZI2mrTJ52Y88bF2MThE7HQ6/X6/KApM3rjkySNMR7I451oo9kik7iEOYoTEiMR7AJYgICIxCHMw1rLM+0Pe+7quo/NKKWYgorLMicj7mKCNJ69rFFYKmSX4tqmDq5tkkCCidAxARBNV6x0oSGZABiStjcmMJkswGe9nivqd4vq7b9fj6rGHz1068+idEVSBatc6Hx1HH0MTfQjBmpxJFBJElsgRmJGUIlTgnHdtE2PMFGgqc8ysVd978QfiH/vwM0898dgjoCTs7+xubiqClZVlL8YHFSL5IOO6nVazuplevv/87uhwd3Oz2zGlze5cu/pP33jzYL+5sArCsD+BKkC2BFAUTdSIJjJKIFQmCiqYGyEJQcSLeESTYosWtRcJKxFhBqVUWXSK0pZlaTNzrzg7ITiGE0jB1L2by4Ln35xigxLpNilKJe1288jtYzMi4NbW1qDXtxSrab11Z/rHX/3+erfz2EcfgvHs/TfevvX+tejU5vUXd+40oc7v3KmW18/sTJtrdza7w8HlCxcOq9HscPdMv9zZ3+0NCmtU9KwgGGVs2dFat77uLS3vHVQHh7t53kcQRP/ofReOjm4iwEdeWFOAme2HmF2/tmuzQd3cfftlePZZ+if/+P906qMfhavXqsOj8rH74Nbm9/7sG6Fxb7382rtvba0vw8/+1P33P3T+qY89mD1wFvbH77135YevvHzzduwVAAp++Ma7jZNpJI84KIaqKLmu27apRvs++vOXzr/+8ivFHX3/xUs3b9weLA33Dg9WV1c//PwLWVl8489/OK1mTVMd1tf2nQRtmYvIYky5tLLOgnXr1tbPYla2Iei8mNbN/u62MNbV0Ga6aZrkzUg9pui8935tdTUVYcdwp7qup9MpoKpd2wZPRnezjIiEo4Tomjb1uI+HkDAnCANget1BKUWUZHSQQmpBEaWkWOYIKIltgBI5BAFijUqzQgTk9KYCAGQSBgYEn2AiEVBB0uITATISiKSmBiACAifbmACAFLmByMwUgYEhuDZGcW1dFEVwPnBEIjLaKPCIIozIICTCgHO8CaEG0Myh9V4DKm0jS920EMFoGnY6kZGiEEsCJQMLezdvo3CMLCGKUCJnYELGIoFGJCAU0ESaCLSOIoRAIDGh1JKMZ06FTBj4KDGxCGJZWIaoCHJNpCjE4Jva15XoIgqgACfJk0ByknbKzngcGtcoom5Rllle1009q0BnEZgCIEYCRQoYlEKKPIeiApG2pEQLg0Ju2TEJpZa/LEyQAGmIlAKMldEacTgcTiaTtGgrSqk9iIkglzCnST4qEBfiImMya1VK0WaOYX6mI1KQ2gSJJZBb29b1aDQCjZImGEmoRKiUikQKSUKcezOUVlojkZ+Pg2IIEkISKDIRIYlAdDGwZxc5CBhbiIgwOF+T1ghqfig6QVJqZhUAhCg+eJR7k5wEeooBZU7wnOugovNGozGU510ET+AUolXGpGgFoRCiMJMoEfbSKKRekdmOlehCdeS4jXVTjTzWvpcXp06t9vrLs1k7mc1YQvCt4tgc1g+cXbl839rP/Myn4b6NldPw5rdfOXtmOaNCFQWV3Yb1QePczv5B1VpDHgAAWZAEGIAEAcBqEtEiAsZgSgFKAF9miUoAAiAFjuw1L4hX87kZMzP7wCkJmuZe5rkmGwCy3KQthHlxforgXKB0ehdOz2xagACgMD3X1MFHr1CbjDkcjdq22ZsejQgSl5p4ri4gFrvn84M9V89m1azN844tbfC+aaXfW5/GOJl6TbZ76oImNRof3tncK6pxDNWFUyt9Y48ORh2bP/6BcydLhL3d91CCRu9bczg2O7/x2qw92jjTe+ZDDz/+/FPQRYgN7H0fBqvgGUIO+/U7r7575/pmr+ydPnOmXFu79e0fvPz2+00dIpiss3Vza3NUz8qVoTJIqnPxbLfQ0Bztt2OnOISGxgdOELqD4Zkzq1mnA6Si4PmLFy5dOL9+arXT7d69+v63vvln9WTc7Z1aXb84a9SV9w+wWMuKjY3Tqg3Ks5nWh6c2zjZN8C70e8tZAW0zGe/vld0+8+TMRreewf7OjaPDZRF37dpbzzz97I//xOcmddv4RhemcnE6a0zZ87FpGyuuvn3zxkoHP/b8071M+2b6xuuvFln++Z/8qccef/r0ufu/+9LLX/+z7+y++XaMoaSiLLKMoNft3n/+7NH+7N23r26cvn9760AT9vr9qm4Ko06tLUuMWDs/3etQr5ntHx4exk5XAwy75Xjn7gdOraydury83D9/7uzFS2d7vU5k+MpXvyEqy0wfxB4cjG7dvr29szmpDv+3f/8fPPdjL0Db/vN/9v/6nd/+6uFhi5xVTczKTgSJc85DZMYYoyCATw73KEJGSRSWIDFIygoAYdQmlSMh7XPztMgElwAkUopAEVoFhIaVEsAAMYqPjMKAKhInbToCkhIkEUEmiyBAyfXFwgAiADKp6hi9SwkxCLSIVmXXcvQxhOBDjIKklFZa0erygKP3rXO+sUSl1UVGiHh4MI0JoQBABJwKLZnrcBYymnk8vDImZZ2kEQSHKHOqABql0QIBZiZP83HfhhDqPM8T7AyBQ/Cuqdraed9aa6MAolcmszZDRT5w8GwyS5yiKXVVt9NZ3VR149r7778/dQbmHRVCEYB7trZ43GtJfz3dLZlLTEWYI0aIDBgXiw8lxU4yUt29cytJUPI8V6BCcMfAkHQL3msk59xsNpvNZnOuSxoCLGiPETgzVhtC0iGENkTgqFlloMQ7iqIARnsHBugX/+Zf/8Wf+9lrNzb/37/2O9BKkJi2XoogLkoEQiEEhYmYhemwBRxa75TCfqdMmY4EEgKPgw9h9+23f/MPfv8PZ5N9dO2gV0jwZ85cUFkZHTDEIOAJTGkHuRqI1SqMDrZRAFo/GftZA0PbPHweSyP9fveplTU7WB05efPa7cPNbady0jnoGMhFjJj0sQBInkWQf6SIJyJEpUwGCzN92oNjjG07pwDBQu+LJ4KNZMHxwHuRoPOUBjiB+OAFxSsdhvEk/Jul08sHy2U9OiKN2mbf+NZrt6+99sUvfOTTH3tyOqXZTKmYdfKN5WHIlpc7q/jueLq8kR/s7rcudsiaFuWo7drO5fse7Q97TfC7h6PaUdPwqPHNdNTJVDOZTPYPT6+dffgDj964/p5rx5/6yCcfuu/Hu3lQOPvEpz4JWQcmDsrlnRtXf/v3//VPfto+9thja8MevPvWzfff/Xdf+5Nvfv11N4Of/atPP3L/w4OV/NxFQIDecvHYkw9lhQk3b21v7bdNc/nShWFvrZnGq9fv7O7c9QFdENRFDOO7128GljNnT1uKh/uHN17feuTi2ScffUwr28/04eHhM08+sb6+fvPWnVdefzMigaK6alWRe3ap90Ywr4184BBj633w0wjQ63aJqGpqjjCEYbpGkso/NZvbqk7wq6S7wEXY6mw2G4/HSNQGzz4AgCTDhvfee02JykLM3Lo6VWbGGN82IQSShIoCZiYUgLltNMk8IkOMkZEARCmdIk5ZhNOQLOnjE+cHBBMM6/jtCCAJk4Lz6wjnh8S59iflsyNzKrLbthUO0TOSKHUsCdfOtVqT1br1bjqZCnLZ7fd6HV95EYwcJHLEKBJTGBwgL/X7Bk01mzFDf9gDxqqaeu8jB5FEgMXIHARiiNZmQZghcvDMIhFILAIhiRIkIEVKocZ0KgYFwgpAEBWJRmAAQYgiNi+VUhZARUHnOPgkum/ahiHqzGidZTb3qJnJtQ7RGEIiYITUa0/PnG9cbrLcZCJSz2rnnDAarUUTgXAUQE5ldJKjyGK5RFw814wGU0+ENJFC0vM5CwCARurkRdM0/X4/DTlTRsSxbySt24hICESgjDrZ70dErbWxqq6reeT2YvFPr34UVEoJoS3yTr9TVdX+4eHy+vKxJWm+kizMbHEREzZX7yCizO1JIuJc27atMKf9nZmbxjGz1qyUImXTjzBHbWj+9lvYptO/5wZ3H0IMcbGCAYAPIcYYAzJDYvKTkEhcGg6ZvQJPGF1VTaYjglhmirzrlR2lVdoxUKEPbdu2ELnbKdroJ4e7FJtTq4Pls+s5xKVcPf3440889fRkWn/zG3/+1pUdINUfmE63++47b8ZV89TgnLgZvv92qMcbGxuD5U3Ewc07d9uqkax7NKkAIMuyVloCTZIgxcgCmOLGmBP5Lb3ujBhZOKXnRRYRH6JDDwA6vTyRPYR0ymGRKBDnEU1pKo+KmWOYO1FijOxDjPNkxHRL1jIUTj9ywvahogaOXsQEBu8jez8eNYn4i4hzFgWCUQa02R83RVcp3Sl6RZFZFBHPzLGazRQIAUYMo1HNzJFDUWQq8CAfauZqPLl09vRnP/PJ3qn1kyXCdLrd6QIoqFqYOTisgRHO3RfWNwCWPHADs9Fk/2j3jTdmI0e+U4/5+pXbh7vV6VMbuV362p98/d1bmxgpevKeMx8KvdpZOe21CHplWJM/OtiaHLjHHx7+5Gc/P3zsebi7NzsYoclVVk7qdnv/YDSeEqm333r/G1//Vqe0K0uDMu/08nLj7AOdJX3l6uZ3X3r9Oy+/SpbKfg9VPpq0LmCvuwzCMbRFYTgE18xyDSiTMjf7O3emk9GptfUr7/yw0+n8ws998c0333719e8fTavdvbuT6exwUlW1K30HJGgVO6Z78fTK888++vf+yX8FhYXxwcHrb/zBl7/6h1/+ynD19Ln96e/+zu+8/e6N5bW1sxfOk4bTZ1Z73dy3szMbp5/7Gy8Mzz/0a//s//unh98xJJcvnImy1l/qvvCxDz1y32WaVr/967/+la/84Xgy6XX6p1aXH3zwQVDw0EMPPfGx5+HCBdjfgvG+m4y/+fU/+dLvfeUjn/7iZz73+Y2LD7763Ze+9Fu/89obrzOH3lL3n/+LX/nGd/58d3f7hy+/DpItLa9Ws6hyQtJeOMboIb0zgZk5zDHPgBgjNLVHDCQgIoXN2uBjEBRGiQKp2ckLvhsiiFKkNGqtwZDtFCJCUSAKgCDyvKwjoTQ6TRZYjsTMAj5RIpAEJDWoCFAIZ80s6SMZmQU5MhJpRWXHEiuO0AbvfYoTFKLo21ohKvA5BQQOdTutYuujQo2kCDUoEsLUxYqCeWZiXJAoQB03UUB+pF87L7kAqqpSSmVZlta4tm3ruva+ogHVoXJNG1wrEq1WWZaVnV5b1VFiDMDMEqMgBuYYQwTplkW321WITdNEH22edXrd8XgcOLoQGEQ0zStCoNwWKLhYVYA5tQgg8JwFQUov7m2IIMwxvRZEZEwmIgEpxhjzvG3bqqq0pl5/oHV53JtMN9+0TYxt206n03pWWWt5gblArVO3QyH6ugoNMmAIoXGBmWtS2lB0zVKvLPN8sDR48pM/9rf/47+pJPzqr/7fWmeDFxJQhIrIGJ1lthtj4JjA0qnFDhyRUcBrH601eZ4TkcSYhPUcfafodnqrtlMIaqtgdTCIwVFRbu5vhyjegfcxMhGgNmDIx+CahksLvRKGa3BmbfnJDzz2iY+8kNusv7oGFy5Db3D7B6/+s1/5728fHOSdzizEqEAoRAzpUKiQSAlFBaBBVGLJycJIneXGe+99m+Lr65rruo7CbYgLdeu8AZRoicGH9EmtNZ54j6Xij46rwBiR58eA4xsueOQxRiQ5ONqrJkdLnay/vHy0feetd0DDt8+tnf7gkx/pZ2cn+9X6B86/+9adl1+8MgrAeb5+/kLZX7577Ub0fP7U2Y3+sK0ny7ocZt2aQrC+azPXUaSmoxhXBuVjjz5y+872rZt3sRmdW+tff//m1373d79TeHYxIzg3XLn8xJPg2oPrV3/j1//1u7ev/Sf/2d9+9Pmn3/nB93//D377vfeOjIIzZ+CDzzz6yU+8sHVrM+vJL/zNz59ePZXbouwWvm2UVmdPnbGYjw9m0ldqUGa2vHTfIx964aO7+6N//i9/dXdvf3W1h6S2775/enWpv9oV6XzhC5967JHHrt+8vTrovvve9Q9+8IPKZjuj2dLpc5cfeIS0efvdK6OqPnvKHE2n3ntrcgBqWg9KnTp1OgC2VV3HMCAqisIDxyBFUSiFx821NChLhl1epDL59DK3bV3XTdMgURBO+3VShVGyb3Z7SiMizoV5c/gPJYMNRuZE8IGIxEppnZKDT3juGRP7eG7FgXmpFwAgxvReAgFGxEQMmL/HMOnIBXChKBcEQAYBEJDECGKCFEgQfXQijAA6xZISpPWm9aFuPABkeX5mbQ0VzJr68GCvY0uYh6Wy4iiYiEMsLDybBWUw+hDjdOYUGSSZzWYgTKSstUTaxYCBQQiSSB1QzyXsQIAIoEETAqKypBFxTkdIFwIiIClQSQYKQAGkrpvEmELvwbfEkQi0IU1CmgqbFUWhQUcvqIOK2tjcLoxPSY6ScuxGs5Fzzvuote50Ov1OP/owaxsgjIio5/9L2h9ElcpxmA+Ek3OXAdgYI4iaSJPSSIoWcv/IVVV18iLP8yTzq2azFIeyKPDmGWDplZxMxikGzhiTzNkhhLqJMQZSoJXFE4H0PkalDCKGEJTW3V6vbpppNSt9FxQkWzSHEAQg3osvQERZJLQQokYCUjFE733TNG3bsgQi8tE55xoXkjIqjS+01ggGgFkcLEB26U0+L/1tlk4dzHxc1iJinC9ciSqrEAkEQPj27VvCoZOplaXeoN8/tTo8tb5y/tSprtLDwcDYcla141lbO3dwONnf35+MxsN+ya6ZZGZjuf/C888+/siD3Uyd6me7m7fv3r21e7C/vt7fOPPC0WR88/aNw4PNwdDcf9+Fj3zkI1tbO66d7u7cNViub5zN8/jKu+/tjsZiyp3xjMoeK6uUEUEFqID4uCUoyAoVpXGXgJBADBFQQiLpCSBwZCCRqJNf3DlHBGkHR0QAMiajZBSkuZMyMfjSaSzZ7zJtlCbSoNH6pp4rpCAaCou3C1ZNgMhEJMzBgTBAJASdG50cKgwMSEAYQGOUTsfm2rBrY+swtBSdaVtLqCIgcGbIaOVcU7VNtygGq8t15RVCdbTXK/XZM2tNOzl8fwvguePX+Gd+7sNNO3LtdNjL19YHL/7wpQsXlz72qY/B/efh+nvvvP763Vtb+3uSo+l31rsmr0dtqdc7G7aeVn/01W9OmYSKteG6zUwRVWY7PgoTtNyM6/pwfwegKSysrsHqqaEtefza14/2D7f3xjv709ubR7fuHoynAITeY1F0UACQB90uSCSQjU0/4aJlKgZdx82snswO6yzvKJU1k0meiWvDrBrVFYRYDQbZw4/cJ+Jv3b7+8Y89+dHnXzg8OLh9+/ZkMrl+/a0bN9+9dvMq5R3PCiX0u3Yw6MUYOcTZeDThurC4eefO1W99SyN3y2L59Fmt7Ys/eFnoymDtyv7BEQBmxj756CMf+ehzZ559AqpxtbVpSN2+vflb/+ZLb71xdX9n84knn336ycem9XRz++6L3/v+K9/9zilbfvxDH332sadb744ORqhoeXn5j//kj3775Vd/79/+ls0zY5E5jGfjg929g0n9m7/5b//w69+fTdu7W7vW5r3+ELU+ms4mzfS7P/hBr9cbLq3s7033D5tBf00EogQBmS+oaEQTkkZFjXNam3n94b2IaFLKaCIwKEScRkmIYpQyKks1EAFqBKUwkRCFEDBE4cDMLooLMXIUjCiAimOKykIlgsLMkQUbB6iNUVoRKU2ImA7JxqgEMgoSWh9caAViCGFpZdWQgFDg2DRtVaUuew2+BaUsgSLJlDJaAZOLuru60QRuale3TRMiiiCQJlQqTR5TNSeI80OKcw7VPZ73iTMAeB+dC3KP90Ja43Q60QqNVZnJOHqIMbg6+mY4GPgQvA9t8G07i4AICklAayDFgMIQBZHmwZ+Hh4dBOCYMfKDFlsMNNyg4z7QHBQvGq0TxHJgDnbCDR2FrLQlJkBBcjNE779rGOdfJM4jBe+/bZjrBJEP/kSP9OO2FPoSgCYxC1BoANM1hcyjAzLF1aeyOiLk2aV0ikMxmxKKAPvVjn/4bP/+zg6XB7/zrX/vWt36wdP5SizoKHDve0gZs1JzlrJAUoizSHURH5tBU0xAWZjutrbV7R9PSWh81mS4qrMU2TdifHk6qChUA6CDiXet9SxKU+IMJfPaF1Z/7q597+OK55W7ZybLpwcF0NG19qHQOs3c2R+Nvv/zG9fevVrUztoqoIkBEL6iEABO7VRE6hQDCBKJSr+dkIy3GuS09iaMCR0ZCUqkEnO+4i7cQHBP9FkJZWSQwJDQkLdDyiBidlwRcTzbE9P0QSUHlJiYzqNXOwaFGWl9XCMWXv/K9XrZ2uDV9/eU3+/mWtEpR3u2Vh+C/973vlSq/ePqsVO1kf79jaW15yc8qGvz/mPvTIE2z7L4PO+fc5dneLfelsvbep3t6pgezYWYADEAS4IgACFAgQJASaZIibckhmhEOf7AjTFJSWArJoi06HLIdIQVtB3diIUgCxDoYDDD7TPf0vlR17VW557s+y13O8Yf7ZnXT8kcpwm90dHRVd2dmZb7PvWf5/3//qjK6p83ChRhjhYh59tTly5srw4ygMrK6Wo4G6xs9/+jerUGvRF789b/8F65euX7vO984Ozr45V/+jSbA53/s2d2d4bvf/eqv/+t/MZ+329twcAAHx/Ds8zueT64/s/P0x5+AWccBqOzDbG7QPLz/4OTo9P2bd2/fvpdng35v1QeZzSZ377zftJ7ADweFtpgXObhqaPlHPvvZH/jRP7Z/4+ab3//uytpmZujKlUt37t97dDRmZfauP3e6aCMGO9qqZ3cHtqh6JCLGZMycRSmr3nBl1ZbVo6Pjg5OTlJ9go699u1gstnc2ATjFciVPS/Q+LfTTM7JYLFzXpUcjVWyGFGTKOReEjbG9oiyKIgYHABwiAafOjQAlsjEG6Xw7B3I+9V+W+x/a6KRTKEE2AZG00sK45JY8Dq5KjAcCFNSKQCFqJYQEKd0vfWwCALVk84KAqKSWIRQhIGBOag2R2LVt8M6xd4vFYnNzc3t7O8vNfD4/m0zQ+35mRBwpNISKgEQhA0RmAWuz8XjMSq+srbchHh4dWZtfuHChmcyBxegsK3JBWLSNsCPCzsdEoQNtNKahERKSIX0+gdHp2xCThyhZnYCQEQVTrhaCaEGSCD6wa5XvNHKeaUu6GPRQK1WWStu2i20nnYcopEQR0HJOGyMqcM41TaNBl70SEZumcfM61K1SiiBKFKWByKTGLC38hKMstXe4LPmWfyODigjOYWiULAkEoEmdTcZro7WTsxMAuHBhZzwel0XWLtVfH6QKpAmItWsxRhRRGo3RRMQhhgBVnvN5DkMUJiIGNERaawaJHAiUtspkuuv8dDodro20QkYJLgRhldpDBUprRk72PERRCklrpbFrYvTh8ReTGteu66LnMsvLsszzXCFhsjFQStiNKPgYx50mzk3TMnP0IYQlVDDxe0irc8GPJBJGMontbu+4do7RS4iz08XYt/V43J5NC6TV4arJ86YNnYtR1HzRTMcT1zVQFINqsDnsP/vU1S985nNPPnEFxL/5zT98/92bJ2enuxf3Pv2ZzwyeeRqOj777nW99/7Xvv/SJjz7/wrN61Lv92itWryI1b717553XHwEUD45PPKqsUGQz1CqmVOZE6UNRCISaQAEpZQtWqEAhCjOE4EJgiWFJKUdFkFAgjJ+5vM3MAsnaRTr5c1HoQyEdS8FTBBExdgkJZmardJ7nRhMAROe974L3BJhplWoyFzl40spYrRARghcOCoGIFEq6R4kIlV5uFbhbWSk0cGgbzWFY5haY21ZB7BazXmlX1waj0VBIpvPZfD73jjM73N7cIunWV6snn7xwfLZ//+Dej/93f/VxiSDfOmiaU9fOhqt92NuA2QlwB3V9vP/o8PDI6Gp1sGVp+K2vvr6YMLcaOVNsLerZeHLr4cOQlQFIuuhrl6PJjGnbdlbPi0Fe9G1WqStXd37sRz/f+8gT0Mzg0b36eH8xmx8dTx4eTB/uL47PWh9ypXsPHp6UxaAoyq5pql4x7FWnJ0d1cKEoxvUcIPRHFaKcnZ0sFs4YXeS90WiUhMvaYF7oFz7y1Bd/9Asu+vXdbRiOIMazd9/95V/+1a985Xt1Cxf2BlHIlKN5649O5jortS1PTk5GpQ2uzq3qF/bi3vbP/5mfcd2ins2vXLnynZdfeeWVNw9PJ7PGj9Z2prO6qop7925eu3KhKDIfmueff/6HfuiHbr1/7//59/9hf7hxeHRaFoOs6h2fHAb2JjftbPHDL31qcTaZTxdf+MIXEPEPv/ZHiDieTtuuntczbfVobSQSD44Om2ah86pYvYC6qOtWQOVZOZ5Nve+2djZPz05sbowxrYuuY2MrhGwym5dlKQiSVqgiAQVkmYGljNHaikhMPkVlbaYBgqAwSxAWRiCltVVKtW1LIAqQFBhCpUgjsZKoxceY6v4ELQJARkCr0xtUEWkQLaJBGCmgRmOMUgAg5yNPInK+JaO11izSBe+CZ2Yl3CMujE7hR0mA27Wt995qRSDEETiW1vSqsrBZRFWubU+a7ux0fDad1M6TMjYvTFY451rnYhAiTaQBQIHSRoFOD7pmkOhDWEbTiNWGQSRyFP4wlOzuvdtFkQ17/arMrVKkQCERQa83SNOUpnWNcyxIRKB0URQpVFhCBADg5aBxOBxi2kskYbci1MqQWTIXlzelTnfP+TENIsLnU2HmJT4Z1XI+lDRCwXfOtRCZJTJz1zX1fNF2tVJqPv9fPn6u9zb+fio6rdap2n68qLTaKKXSH2dpI5C0LjGpSIrRs2+vXbpQT89mp8ezs6PTRbOR4ZWnnrl1PI1kBSGZ4wAgNRKP+dZpsgZCzAzsFvU0qZaVSlJXq0gTYls3hbYrw0GvyEPn5rOJBJ/luuMpkAgrYdLa9qpifbU/GmS/8LNfeuETz0EOzVuvzo4Oh0Vxenj0nW+/PJ7EvL/y8Pj05bfefnDqGgW1QA3AuWatQRvRREqlbbslHWsHQYSVCLouei+Eyto8xkTwDFqTMQZSyLyiedtpYx/PdFLFx8yPkXyISPBBxacUee+T4EQrtYyGVSp0Ll0KlKS6IiEEz201KGfzs54uNEI7ngyLbHtYSTN96urFH/rUD1TGvvqdl1/77pvDcvjMky+MnXvn7DgbDbc2thbj6cO799ZHg16V3bn97nPPPrG7t2OMOTg6OT2bj2dt3UQRsRmdHO/neXblyqWiNCfHR/P5OLdEyJf3Nv7iX/i5laF5+Vt/0MyPtOZPfeFTdGkH+gW0NZQG3PyPfvvX5/PZj//MT0NkmNWnh5PpWd3LhquDtbYO05PJV7/yh7dvPbKK9vYufeS5F/cuXT07nR0dnz14dPT6m2+trq3vXLy0ubn5re9+p6z6ezsbBzff//QnXsrz8qtf++Ybb97Y3N1b3bpw99FxhOxk0UawrM3ZvK1dzHv93qByfjEaDYzJ6rpGoH6/b/OidaG/snp8enp//8CWxXRRi6bFvLGFXVtfialgiXGJ+EwmxRgfj5/ZJ/RqF4SJKK1xvPfedYjYL6uqqpp6ziFycOcw5ZSWCjYzIiKAIuICh8CkjLUZkkVEICsiLMTMQgqAgQMRotKoCFFFBh+XGjBasoVFE9o0sVeUSv8l2FNIBIGX1BoCBoC0rcWlSIN9dDH6NPPgEBNyMNNmZ3t7dXWlKIrT09Pbt2+fnZ2UZbmytjqZTxBFiRBHjMzegY8xRqt1NRxkRdXE2MWoc2uKUpOhSKn0N8Z03p1Npk3dMULTOlCJBQHLZPmURoI6xsgIiRSXXE/42Nhw7rI9x79L3q9QPDlHrjWhKzT0itwWtgkukhJjA+pFw/NFFzwqpSQGJEnZAhpJa50Wm12zTGvuuk4h5nnGLIu27q32wKC2WYrnA4AoEJkREZQGTPhfWurpBdKFpZDS4J+WUQRotem67tq1a+/dvDEcDl/8+Mdffvnl8XSSZRlRisNK+m5QqEjh5sbWop7Xs0WMwdrMGA0MIfheUTrnFm2TWGFAiEorpQQhhBCixBi74FM/03Tt3t7uaDQCgK5pdYqpEdBaJ5tvMk9ra4wxggAsbd10XTev53Vdp41T29aLpgahlZWV4eqaMYYjigiCAWTmIPLBsB9x+edNZ3uCXKQuehlAk9LckoMtucsYJEajEDjmhnKNELqubbSSXp5Nj0+rvCJtQwRAZfOeUibGaIzKNLTNvJmNC4OrK30FsZ6eadeujXoXLl4cjAat6+qundezk/HJ9vb2aHW4s7P17rvvnhwfee97vfLGzUeLOWS5qrtYDPrVaC2gitp6hnnjIxIQpahpIq2QUGllK3msEwsxJZ3FGPH8PF++RZPg5/E4J23kk2rTd455uSI8v/AMIvrAxhhrrIhoShgHDcjK2vSBEJG0IaVijMTc6+WkDQFG5zth5rSKPgcwKS1Ektz4EiPi3eODLCcjUigxiIKsi5Bn9OwLzz711JWLF3cC+8lsOm/m4+lkfDJ7+3vvjc9Cf6g39rY2P3pxs6nWT8oPTwddOCl2BoUuQUWgGkoAlc8OH7357o3g8erlvf7gilWrF/bUGye3jg7PDKCvp82i1sR5r3/SutHqoDTKStTea4ybGxc2t1ZR44UrF4frq2AVlH04GcPJWT1blBur5UqR9QcBD6c1nk79ZDybzcZ5sdL5UFQ6ADiW/vpq3i8mzcmUp3rgkWRr3V66fHE4/HiR5b2yPxqtuMbdv3//zq3bR0dHSGJx1s4P7z98NJ8cXHnuBchzRfBTP/knf/Znf/bg6Pj9Ww/awHfuH71z87a1lgHatjXGtG27vbm+NhqOhoOLF7YC0OHJ7Otf//r9f/DPnI9lb2iLgZYAlEUI89a/8MILD+/fuXXrVq9XTiff+s3f/oowbWzuno1naGwb4uT0tHOx7PWLqiAq/80fvvKRZ583g9H/7R/9q67rLly4oDSOG9rYvDyUOK/r2yfjzjtlV8z6FgNMFkHrxvvQG1Sj1VUncTYLWVE+ubE+qxe3b91FpfcuXD45mx0eHm/v7rZtg4hICJjCCjh4H4WVthCBhRHR6ixV6QiMRAwBEDQQKEoZByzMwcO5J4hZmGMUiMSdxAhCLBTTehGFU+O7BMIIi4AwiDAjgMmQALWkL6TjGAVBiHzbgiJHJAhBltwcYX5welxaned5SkpnCRIZOSymDQpL8MiR80JDX0uFOpudnSyc79oa2GsSUqKVaGJPQgCRBZETnMekSz3lDXMIMUTnXQzLHN90FJBJS00XQvQ+Rn/pwm6IjkNsmjof9q9cuvjkE0/s7m7/83/+y23XNU3jQhRBJGIJ0UcfQtf5rusUUp7nVhtDCsjP68YYQ1qnM51BlDFsmIAez4+VCvghFEyIS615Gp8zQ2COUYhU2kxoTdZaQwqUjuIVECMEAo6d71r4t9N827pOJXkaXHGIaJQmpZW2KeuUl8PR1JuBcOxSoeMlMko4PZt09cLmxdUnn95aTNl1J5NpVuQeKIViEKX3VbozJMYYfGQRxvNACQ5t2wqhMSbLbK/saa2jj8H5yuZGkQSvxCJECl4JK/YGmZccNMoyu7IyunBhe3O9f/na1enh8b333+hbtbO9Q95TvvCYPzw7qCQfd+xUZnqINgNgYKhDQGWEUsGWzJmAjBJF4tKJuzy3lTJGOdckuojWKcEnKIXG6MfT/VT3K6UobQBCfDz7l3MtWbqGXfAJlpe6WUwyWUUCAinEgQApqTxotph33lHE0mZlf9Qvy0hYd7OXX73FLb343DOXrj7tW+jqrhhaaeQnn//Md77/mpwdf+GTn5TPvPid733n+OzRlRevjP0kjltiJtHbm/2t1d580nof2+D2nnsKKTTN+ORg3rQzBAEyWVUdTc7+9//5f/bEtc2djWI+ffjU9e3f+uovD69e+NQPfUZtbRy/870bN9/ORvLU808AjH1dG1uAcm+9/Wqh+quDrffeucUtvvn6IwXwxHNPf+ITn7z00iehP+zdP7j4hH1pc/NPPdzHy5egawHgqWefWV1fm56cHexemU+nv/YvfqM/GG1dvnYyXmxUw5k/nMzHqhxMF04irW1fpNlssqj7OiOIgNqH6ENUirxQt2jGk9nxeOKZZ/M5dI3zfmVzHREZ4ODgoCgyIkoQGGstCHjvCdEYs8z3TastIkMp04pRgBAMqRjjYj5dzKcm6e44xBg5MYKVWiJNmM+n/stXcniKSPAeEWFppEGAJW7/8SvRPeScEw8cCUWhUDKMAEqy/SAIJIOAoCCwMAAJKGGQpJPnFIzLwUkIaRBALFab1eFodW304gsfffDgwb1bt6ez8ciatd0LXdfNTo/LXAt4YtEgGsFakzrb3BaXr1/VZXl3f/+0Xth+PwKcHJ/2ixEJiRIR8T52Xee915nVSgks387AKXpRKaU4oggKgAhGYAFISNH0IKAQCBCotOowyNA1hMFGr8Vl6HqIFSmLALGZd9zMoRPVsYZgjMqNMZ4dojBElECkM6sMaYj6+qVrk8mkni8uXNz55EufuHL10sH+0fdf//7dg/ukFALqZLMBYCJWJIiJupYagBSxDoAQWZ3zmHQiMJy3BCkaYnNzM9Fd0+LUGk0E57QeBGAkAODZfNq2deO6GL33XikUQYlslfbep/DK8wOEWZZzH22oc01dL/r9fl5m83v3puNJbrMsy1TapJBa6kogBI5RGM/Vg3KuIVxaAtLFng5ngbwoEsSJfQhpgiGRJSTX2fJtnLypywYgdWvpPUykDZNKe0sDkPzPannPS4wx16rIVJXnuUaMIfg2uJZDvP7ks8AYmFsXnAs+cPAOEXNlffCA1Bv0tQLHTMI663ctPNhvj05vCcmsnjJAb1CZzLz65p3Zol5bXSXSvcHqZDppYmX7e9kI5m0z2uh75rPO6yLrXAyiAimGpU+b0nQGEQSc84DnWq+4TFePka3WSCTpKU687E9f2mJmQDbG5EZba4kIUNL/FpyP7NPoy+hMa71o6qIoMmNxmWiwRCNF3znnog+kILfZYx6fNkViRKQeDiUSaiSxJtcKiTREds4t1diGInRVlWnx3M0t+1Lzzkrv8t7Wj/3ID4K4g8MHh8eHK+srOxf3Hjy8962vf8sEAzFcvLz5sR94DnT7yhvfzXrmc//b//kHU//378DmKvgGJsfQzUAJZObB2+/eufPgiStPb158Zv+9o5e//a6VoavRzXF6Mhsfn3D0GMLZoqOqZzI7zM3Hn3niI9cvGQiZFiz0u2+9boqyNxz1Rquug5s3bp8cjcvK9gZ0+eql0eruo4Pxa6/dunFrfzqJXUQQe3xysr6xSgRr64Nnn3tifHb0/v13P/LStU999sXVjY0Ht28/vH/XKL27vb25tQuDEXiB2WJ8dDQeTxHU6upqf31zPFt8+7uv/NEf/dH4bPbxj3/sJ/7kT2499RQw3XzrxqPDk9/+3T/4/uvvZMWwdqFuw4ULF86OH2ZKWatXRoPQtXWzIILpdLqzc4FB9QarZ5P5ZNravKqbrm1mzXT/yesXrc3ni0WWZaj0vO4ig9FF60NkJGUYIQprZa0tymrtYP9ka2ur67qdra0sy771rW9tbG+cnp7mZUZKdaHzMSQKW/Sem+bKxT3n3NHJMTMro5988skf/uIXDw6OvvO97z58sC9E3glpU+SV9346nSqltDVkNKGKBBwhChubhxCYwWpTFKXVhpklOpbOJ/cvAgJFAe+9C8v0E6OUQiAEkQjMUcSRIKJFo4i0pKkJMgiYpfUFJKoYdYzIkURq32WZsZlWiceMYhWR0cnzGmL0LD5EF0OIIhy0j0aTSYqlNAkCIcTpyYkiMAhW07Dqr62MBr0+aHvz0akH7VNnwhAAUyKmNlndueBFkdFaE2lDSms9mU/SqZ+03Uopa60xmTEm5S55v/S/pvp1Pp8iMQoIRMWcF3bYH5RlOZlM6rpuWicIWVagMj6G1nmlcyCVxDNJP2qU0lpPp9M8z7W1IpK4RsaYLMs06Q87tB6Pk1OZmPScmgwqQCEGUZQJpXRSp7WuigxRfHBGkfOt915iCNEnof+9+3/xg6n/2n+ftBAKiZld2yVPsLU2EQCTDEAQbJ4lZGrrgvceJSLKoNdH9tPJSS/PrKFuPs+M8lHY5F1kgWi1LvPcasXMHD2iSirqGCRZfCODZ2+tabq0xjH9qirzAgUkRAy+yixGX1hTaQ0x+K49PHkolk1pM9tjUW0XvfdaobHSL5VGN+iZn//TP52R/Oo/+6WTw9lsBsWoKoarlOWO8WByduvBUSOwurXmCYEQFfG5Qzdt8NuFT/pqYQyBmcGarCiKyWQGS6ObON+1bUsEeVHNuzYvipQJCgAphVpE6tkcH/PLz2NcRaTqV9Pp1LVdsmfEGI3WyWvuvWcfkuQp/SvP3hZkjAIv7LwF0gwWoG/UalVWmtxsvD6sMgWHDx+sjAbPPPFkpoqTs9mN27dXd7d+6ud+ZmV37be+8ptf/upvb+2uTCdniwlsr5qPPPnCIBsc3D86PDiLqE2mvVtow3mhOt9ef/bpL/30T25ff+L+rRvvvfvaG69/C+Lp4f5kpQ/FCvzkX/rR1Z0VRPnGN7/24O7+n/jjX9h48ePx/du+jjfeuvPcEx+dHrdvv3p7kK8qzofV+mLC33/lrUePHong7oVLMcg7N272BwNlTNN1H//ES6enp/Nm0bruc5/7XNO0VhXf/Oa3x5PpYLSyaHk8r5kMK7No+d7BoS0GRX80q9u87I1W1ibzSb/KZ/NJ13XGmDzPQSiE4Dm6ELI8P5lMm+Catt2+eCEEbn1bVcVwONRaLxaLlMYQnZ/NZtaYLMuSyl9BCnPolFJn03HSqWutNZIPXVs3TdOUeaEUaqIYY/QOzhkvMS27kIjIRwmBtcmKolQ6DyH4iIiodKaUYiTmQMCMgkCo6HFJmUarEgNIRBYEpiSeURQNRQRMTE9BZOG0J/QBhZWwEtEghKKQCDjPjXet95G9Y+ayKPZ2L+zubt+5dSvPrdWZc+1iOmu7hlDpXI0XZ0ico6qMGeXVqFcNy0GR5S+++GLduai1V3jj/v3vvvH64WRS9YYHD08Mmbws8zyv2248HotgXhTMkPASLoaEhTDGaG19AM+RQZK0PkoAEq31YjYHAAKlURulrbJGaUWM3FjFOftcQi6uUlDmmc1NC/Fk1hzNmkVAJ7lgjqpUhoQ7hWlY64qsHAx7KFDXi+nZeGNjYzgYMLNVlOd527bHZ6ezxVxQERGqJHJSQMikBEgIY9paACURDovQebmvkMw51z8lqzRNU5YlKAohpK31aG0lmTfgXBO/5LYhJjlZmqqk/MR0vBOo9NGIKCZtIccgnBzQeZ6Pp5PJZLKzt1vk5dtvvy0x7u7uDnp9WeZdUsq+TGvDNLhZBn6F4L1P9La2a5qmiTHF47RN06ytbeRFYW0OAEDGGAOifeiUwjSIkzTze5x9Jues5387EC3LMkEg0mnrLSIxBPZBYujlWZ5ZS6KEQaJv265tjbUASNpqbX3ktm1jECICiQqkKu3qqG8Ut/U0BpeRXuutHe8fdV2jrXLR+egjRBdSR62mi0Wvv9LUrj9cGY+nnW+rUXE8Odm5sNd2frJohqubHUck27rAIInhBudJPQjEkjLgNACjEEuIQQRimZWIgqhi9Elai5++vJEaP6WUxqVomAi6to0xSmStVbJNsISU4oHn9JLlhh0FAKbjiTZklU7dBiJmVltrOe32QEESmwbnXWQJeVZycEoZq1Xa/lit+71hs3AXtrbn4yMDbmulaqeH/YI++uz1559/olmMv/6Nr5b98lOf+eSDg0evvvpq29Xrq9XTz17ZurC6vjt6/95br7134/mPXnv+L/x7j0uEd//lrypFxqjN7Q0Bd//hzQt7G8WwAoXg+e6N2++9dcc1WNDaWm9XOnP35qNHdw+jCxhx0cjCw3wCBcGzl0bPXL3Uy9Ts7Hg8OV3ZWK8Gw3Kw6qI6OpodHY6jB5vlO9cvmaJwXu7eO7h959Fk1mqTm6I8OjrQGYCOi/p0POWqB5/99KUf/Nwndy6s5mUOPjTzRZEXMBzByeTBzfeP9o9EqG1c2wajS2vKed0dnE4O5/XB6QQALlzY+8Vf/Atl1f9//4N/3Dm+dO2Jl199++69h2Sr1vOj/WMXZG19FTl614pEY4zSSaVn8qIMgV1gIKN0FkV1LgqjtVBQaw0S6s4HF1iQkFQilAWWKIikjMlIGREMjC7qLC9d102n0+i8SCyKYmV1tFgsWueKqjw8PuqvjGyRv3vjvSevXrGuPXr0iBRsbKwBQNO1WZblZQ9QHZ2eehezohTBtvPpOfSdIwWA6GIIgSOCMVYbA0BV1S/ySpgXi0VXN0TKGhJwIp4ZWDCCRBYfOcaYIktQmBRqUigxON/FkFVF60KhM2tMaHyMERX5GB07MrpXFO1iboSNSGyajfWV1bVq/+Hdk9Pj7e3Nuq57vTJEl2UZad26rna+86HzMbKYLC+KInZeIWbG+ujapjZGlWUOka9eufTX/vL/rMqyzNid7Z16Mn7jtdf++a/82nv3j2ZtnDe1k6i0MTZHY4UUC/nAIqiVJVJpiIKItjDpUGYWIkq+XmvylN/ZdV2MS01ksv+BEmO0InCubWeLEJ3VJstMqtUCi8ByHRcZfORqsNIFjyzJ3hDSOShJBMlBOA0YJUX8BNa0DDBPfffjktEY8xgmg4KColCRVkg2NS1pVmEUaUNaq7LIxuPTplmkhEHvOwB4+8YvPH6un7v6Tx9rTxExszapTYwxCjCpkiIIKy2aziVGzMtQIpbICjm3Js+MQvFNE1wXBbzIytpG1yxQRAlvb21c3rt48+Z79+7c77pOW1OWPRe46TpApaxpfBs4BuclRqP0oCwHZVEY7RfzQqOKoZ+bvY2Nwup6Opk3k8PZSR1CiITKElWBJUIUZB8WmUV2bjoBCrA2grWVgc3K0ybMOt90PgAKUdSajKXMLJruccBCdJ4lpCxnk5Xee+/SJU0iGAPHGOu67ff7SuFisfDBLW/ZEJrgbZYVRZHnefKJgggzh86l+5uZg1sK+onIZGa5H+flbCkpqjJrH/+s6dwWLCSBfZIbaEENaIEKpQtA7uqdlYEWr7gdlLS11v9zv/CzFPi3fvm3jg/OMDP3Dh+ZUfmnfu6nLj15cebHb994DTBYpb799W8cP+o+/vzTWrKjB2cro+2z4/HehfXT6cHZ+PDi9ct/5T/6j+CjH4HTEzACBcFsvx7f37//1rXrm5B1MZvXce5cNxoM1HAV5rOD2/cW43Zx1r3w/Gcg24C5Onr93r2b+5sre5nqE/TffvPWm2++eTKehCjMEhlA0XyxQK2U0UohEIhIZO9ZmWIVdB5jXHQuRBGlA2MXI5lCUKPSgEqUAQBBJRJ1ppiX/K6Uf4uICUMYhFnwdDbRxqDVk8lEZ3Y47Nd1PZ/PY4zGGGttcpG6rksVgFIKWZJIL8aYrEjp8k1gYg7R+84ojSJpyOe7LvUeZVnWXaMSBgExCqaDFADzok9EQDaE0LnIzKiNUqgNEoHWFvB8Bpn8ryGqhFYIkaMnEWuMyrUHBKPq2YKZB/1R27az6bTIcnEBgifmwugqz3Kt2Ifgu0xh8F1RVFWelWW5vr5ezxdvvf3GxQt729tbVpn9g0fNou71SgXqdHYiJu5d3L68fWFrZe0j157MCG+/d0uTevjoYFLXw83N4fbmW7du/d7X/uje0VFe9LQqtMoZIU1JlikBKVhXkTGZzTJrLSOEEJwPKitd8F3wvV7Z71e3br+/c2GbmSkN1JXhwO2iVWSM1r6ZZ9iuVubq1vp6r4Bmzs0MONZdfevhw0Xksy46zHS+EsQKZEVVNu2p0TidTkcrAwLlfTfs9ff3H66urPi2Syr6tm5ERCkMgUfDNSJtMisAQdjYnIzpgncsnkVIKWsAlecYYhRM/FZUSISoAJM2KSVeF0UBAG3XKWuqfi/GOB6Pe70yYXy89xGW2HvnnLVWPvSC8/y14KKEmCZQjetms5mI5GWhlGpc573nFFGsIGlU3n333d3d3SLPBaDf7yuluq5bdg7nesskc1omjjOgyGIxn0wmdVcrpbQmBsnzsqqqsuojIkhSvOgUeQ8fWlqKyNKPHqIs7SuocVlARxAi0HYpJE6fUZOySktkAlYAmQKjUCMgSxBunAfSymhjMqWUMKYWKPgut5RZRRglOo4dCROijjYj0+/3NrbWB8Ny3ixu3bl99/79PC984M5x66IPkHIjgCQbWCFBpZE0g0qjwMjAgEudfIyIaIzRhBCX1E1CrTQq1MzB+xijXxmshOCcCzF6YARg/OhmL+l8tNbmfP+TLtTIXiITkVKUtArMXPUGeO7Sw2WkBRJR4idqgmQtBQCb8E+ZxuVreQAth3+RKZl/YxRhBYgAFGlA/fnJpChwY7Vy9QmF+eqKJan3LqwVOR2fHFy7duXTn/3UZD5798Z7Z9PjaoXygbIDtXlpdbQ9MKWqRj28+qnHJcI/+k/+zt6Fne2drSzL9g/voQ1r67219d5gtYACoVnE1itdgQwO3tkfmO0Mhm+/evv4YLqYtG+8/hAJLu9eefGpp6/u7mLbHj96OBtPEHHRuQDQMvhA01l3eHCCaDZ3LyxEPKhF0y4WjQ8QWaaL+dnkbHN3/eDw4dEp5CU89xH8xCeff+L6pUE/mxwfW6N6WVWYvJnVhw8ePbz3cHY2nk26o0MQhtEKdY72DwIhrF3Ynoi0IHlWtG27s3cxMr36+huXLl8X0Ednk7NZLWICqM4H0qaqqrZeRA5pqIOKOCm7rBEB5yODTjrO4DlGJmTFM2sIQYeIjERKA+korAydMx/SdMcgKEHDANP5LAS/MhwVWT4dnzZN0+/3jVGnk3F/OHDBHxydlIN+b9APbdcjpBAApGlnrWuqqtKZnc1rQjudNxtbu/3ecP/wIMk/Dg4OVldWUnXhObKgUkpnmV7yoxQKhRAkRBBRZIySzs2VRq2sEApQBGEBIHQupEzeFGolEjnEwNELDAYD8HJ6ciKBB4MBMx+enqxurLVtneXGilgA8mGYlS++8NTf+dv/m69//ctf/vKXv/Wtb5ycnvV6dnN7CxEXXRsFIlIAdJ4bHwSV1laLDiEohQIpsUsVNutc8x/+9b/2M7/w58BaGE8Obt3+g9//yrtvv/nam+94VS5cbF0XmEVrUjoisWDnOQoQam0zRSYt8gBEaWTk1PYbY4qiKPLKWntwcJim1BzhvPSP3nfKEpEoohi9BC/Mms5pLcIxCotwmhWxigKojeeoQGWZyU0OwCREBMkmGqNEiIhKkDlCjJFII9L5EOUDXjKcC6hIAYIitdQKOs8my40xABx9AGStldZU5HY+nwbXaa1FYtu2kf2NW//+h0v/x/ObNIyQ85QWiJwQEF44XxnxudHtgzuAJcuMaxvvWuSIKFqpFGZ0cHS8srIyn04ghFGvyrUa9Kt+WV26dOmb3/zmfF6XvcHZeNpxrHqDumujQlLKaK2QMkUGUYMkn9K13S0D8dGtG6Ge7+1s5kbvHz5acBeVQl0J2rrDed15icqY+48OBiumXxXAkQCMssjUem4BvUAkQNIM4kKUJPFMSh6lgEUSUhoElQarfYyp3yPSMcaudc655EYQid57QEkXaoixCT7L87Is06We5CJKqZSusNRKsZy3l/wYafLBzA8wLZ0f1wF4zuUQQoaY/hsC1oIaqSDKkXrWcju/tLM6OX4wOZmuDuE/+dv/cTuZ/+6/+PLDOw8jQt6vWOOkO/vUFz75i//1fwr7t2BzAIs5nJ7+83/4T/7wq9/7gY+/VKr+t778cqkKo2Vre7Xsm/2zoxc/+6nP/viP5lur0LMcF9S30Bx1p/czw1B0YCdifHQeESXC0f7xo3sHoYadlUsXd5+GuT64ffa9r73+7uu3KzsgrKxai6xiFOfjoml9ZJvnea/MsiKxYyL7Ljjn2q7rXMAzZ9iUzNwFH1lQKwHyMaDKBICSo/o8f4dBAscIy6IkIQyX4grCwFHbvHVdiLHu2vF0MlpbLYqsruvFYhFCWAp0AZk5Oydcpel+jLFtW++955Cgn5D4+8y+a13bubYFAKNIKZWse1mWVVV1eHJEtHR+x6VnWyFSZNJak85Flq4f1CbxuoiAlElSipTVhYjAYo3KjUVg9j6GgChAWDvPhDGyUirLijQMzIzizhfWVlqzd2GxiK6zWudKL8ZnVy5d7Pf7o9Go6vfeeeeto8PDa9euMcfc2BjjdDJZLGYoYIxBzaON0ZVLuy+9+NH3Xntz/+79F55+9uDBw29/8zuHRyf5YMDWzkJwiFgWtuqbojwbL+rWpRRkZkjby7Kq9vb2FovF2XS2WCwis9ZaZ5a0WrigrNEEIkwo0+l41O8JxOtXn7h79+7J8dna2gYAWZNNJrNuPl6vKMyPebFY71cvPHH1o08/uXdxpzcc/NKv/ct7JydHs7YT4yFvPUU2SqPOmKXtV720eRNgiczBMXNwnSLql4VSqmtbIqrKPkQqi/6sXgjCytpG67rTsykaXfQHTXAhoigCVDFJZBRRgvAiKvgg1evxdCYN75XWWVkwc902SS6IqJiDkFIKAcj7LtFxRFAkPv47CVRF2dVNCvxiBKXUcDhcW1u7d+9e513btjH6xwHJiHh8cNx13dra2trmRoyRRay1aaXweMOQ5lYiopWCKADQts18Pm99mxYSMUZts6qqirxClWpsRDQpLPIxGns53U9zq7CMhQYAfX5hiQgZIiIAiolihWi1MUZxCBQZgQ2SQiBgCByA2ZgIAkvhzTLnLrKP3iGwQtZKNAlhBGBiEccphD4v7GDU6/V6SmEQPj6ZeObOsfPc+QS/w4AiWoCQUEna5ABFIQBQOke9zM1MDZIm4BCNWiavJT0On/O7OIQPRnIsIoIvbFTnIgFjyMC5l1+TgmTAgSWZFZBFpNcfwrmLi2NAgWSzkxgQkRKQNSw1QtoQQjonUOtl1HOCD0QfyiwH4eBaTdirKojBj+tts7I4OlkZ5isrWQzTfiVFwdPZdH0Vdi+sF7nevbC9ub2xWCzqRXvWjS+/cKk1dX+jyjYy2B5CJvX9+9XVP/64RJDbN8ALaA3C7WKeb4zqs0etPwKaCp0VZSxHA8grGPvT9ycDc0EPn4JxBlNanPib7905ORwPq/7uaHtUDuLMjY8mTe1I502M/ZXVG3fvvPLq92OMg0E/hDD3LmY2au19bNvOuxhBvPeti22AT3362hd+6JNPPnVpuNGDygC3UC+mjx4ppjs3bn/zD7/54JbfGMHlC5v9qnd2fBZCPJs006m3xcpotO05O5zOHvnptGuKohqPpwwEqE7OpnuXLtdNp/MyMk3rxjs2eWGsFZEUVU3KGGM4ZZQs17PaRwYhYzKlNEcIgQW8Rac0gqgoipTVJmMEH12WWSZObmwRQDBaFUpRWRAiu67p6kYRGKVd206n49XV1bZto2DRr8bTORD2R8M7t+9hJ6srK8Nhr+2mZ5MzbSgve52L3slgtPGZz34BAH/v93+v65qLly5MJpN63qRNJSiiZB9HBQBKKe9DdB6AjNJKKRIQDgJOa9LaCoJjcWHJaRGGFJTCHDgE5oACSOLS4pIxt1luiy4NwPKs7mqWUFqjWNZ6vVGWK467G8Of/skf8W66ujp69OjBe+/f/OpXv9J51wXvWUAbnVe26EXUdefr1sWIiqxrWgAW4AiS59oqvahn45P6+pWtydnYKIVRjg+a0ZB6/RVWWRfZsQiCKC1IXqKPIkpHBqV0lhWKTAghhAQxCgJReBneopTS2hJRmvvGGJNkg5lDYBddlMjIClAr1AhEhMDpqUz9uQDExIdGK4pQKRcCMmpDBo1IVKAUAaFeLhOJQUhS6AEIwtKPlU52IkoI4KUESNJ5kmLogZGc56IoiqJABNd2IToiMkYpQOdbFNFah+iapvHe37z7Qen/0Wd+9XGhKSLWWtd26YZIBb4xRmc2GO04QlwK2dONIswhOOEl2wARhUN6U1lr55NxDG59NCqUun3jvaeuXf07f/tvxRj/3n/zf3ntjTer/rBzoRwOev3hnQcPI0JKTc4zYwlRGH1H0U+ODgZWPXvtyvbq4PjRvdnpUWWtytTMtZ/6/A+tbux9/80b773/QHTRxfjgYD8rMlNabSjGqEDnJheh1rmafUAhItI2cHTdeWzZ+bxKJ26VICFGhIXvPAQindRQwtA0TfruJeOd1tpYndYggChaZXmeZZmILIXOiRwPyz0JnBN7UmWpzDL6UVI20fnAT5MSeYygOr9lAdL8GEFIGIE1YI7aEoamyVCu7W2+8OyVv/m/+qu+PoQ4/5V//ks7o4uT0/l7775748b7/X62tb2+tT389Oc+fv2nfnT++nePZ6fru9u9y9cAsJu7rNyAh+27X/vuO2+80XWL995/69KTVz72hU/fmeyvXtn55I98pqNOG5kePuwbQtfYMk7DPco4uNjVnffczIOK2Uq1sbLzFB81b3znxt139h/dPRkfLpTYehGVHuX5wBYlELJAFPSRuxiMyYBEEAUS8q7z3nesaj0MOmNmH0MUWVrYZSmWW3bCyc/KEpHqEBhhaaoGBGQEBchKqelsRkYro7VV08X84PBwNBoVvapt25RTvnS1AzJznmV8HkdqSKUnuuu6xrXGqESoTClZwXXee00gIilrHDgys1Ha5tl0MUdEFhCRwHA+6FGARmutTIGInNZ0SiuFkR0RLMN9U4gHKER0XWeUtkoTCsQIIpRmxkitd8poY0zyKqf8pn5ZdYuFdN3qoL+3uT7q9WLXNrP5SlXF1r325muTyeTSlcsAMFvMNzc3FotFU9cA3CurPM8Lm62vr2/vrLex/fLv/dbacLTaH6oouTaHDx4xw8/+2Z/XefHau+/94Xe/d1ovwNracydc9Vdmi5qZy7JExLbrsixbWVkpy7L1rmmaruuS4hwRmVQdnLIm14q9Y+/WhoOumSOw74K1Vuksy8tbd+4pY/u94db6qD65v9qzq0Xx3PWrX/zsZy7tbrEE0Orv/8N/+Np7N4+mTVTZdO7rJiLlIbi8r46OH25ubgLAymC4tbH+/o0bR0cHu9s7RlOzmEcfyrIMrvXeD/ujrbWLW1vbStsLF/de+NjH7zy4/9U//PqsqWdNW7u26SKDMKnUdiqrWaFITEfxBw3AkuYiIgKpLlckIlEwJqirUiKcJJxRIHIIPqaAHABJ3jlmkcgakH0gImttInZaa6uqcr51MaQjSAEqhWmy0PrwzjvvbGxsXH/iielizsy9Qb+ua0D8YLWYznOlrc27uo0xtm2zWCzSKpiRRcTavKjKPCsREUAhooBmDmnekRa+H94SL+v+yI9HGOk7YK31HFOgbZqmJ81L9B6YgZfJFSgRQvTCDUS/hKgtw6wMpWc8cHAcnVZirbKaAFjYF5a875xzznUIYIzK89xkFkGzoGcJUVwQHyCwBJYuAggBoKRcOFRJaubD0mHFzESQtrjAIScFj70N8sGoPYaQchiICCKHEPCl3RGdBxpr1ACJq8spvU0t7T3JNAJI5H1MNNhUBqauKDMm6QUpvYFiWH5MjW29kGWMpk5rFOec7xwAFzazRAghU1QWuQKktuu3MtDoQ62t++Snn3v6ud3Z4qDzp++8+561YC08ef3ClStXdJ6Hun371junYdLb7b/0hZdgvQDrQEs9n1Vbf+JxiRBffbmeNtN5e3R0VDs/rueguutPbTz57DpWHegFuFl9eIKxKFauuzvu7BFvrX4EXO/k3ozY3Lt79+jh4XR/rNiUqkLIQIxQ5gD2T48fHj3KqmJldXh0+HAxrTcurh8tpovgvAOFoAj7/f7uxb29vQuf/cFPr66vwmofIEI7g27h2tp3i8pk7Xj25vffevu1twbF4OMfeaFfFIf7j5TG2bxm0f3Rlrar+0eLt9+9f+vg8Fjqw9lprzcQAEbSJhvPZtpkLFj2RgxwNpm1bWuzYplurZfaL9QKEL0sSzQgnd6rGpVwEmQDQsiLxIvQLISq0MYGic7XZIRISAEISUQQm6lSG5xMHvR7WgmenRw1s/lo0B/2Bxz8fD6vqt7ZZMKAvdFwtqh9lO2dPZBs/9EhUDccVl1YzGYzZbKy7C8WcbboVkabStv9g4fKYFnZ4+PjCzt7IUqaH2hrAMCF4L1PLDxkMSlCEDA470OXGZ2mEQziBZIeLoKkNTpwDNElYZJWSikc9nvNYu4WnTWGBA8ODlwIu3u7qJXzrSUJdWMjW2Zw7vLu+snhzfXV6ktf+tKPf+nH9QvPwY0bv/N7v/Pr/+Y3xNjJvD6r2ygadBaEHKOIgoBd55PZyEvIMqM1tfU8ofQOHjWjAawMRuPTsQFFxqLWUZBJoSLQRhC8iBdmwMBApLUxRCqEtJSORZFHCBzh/HBLKbmcZYUkvCmo1HLHyJ5Dx4GIlEKjUJNSIIDL8o60QlCeY2AJDEoZUoq06oKHKCmSFziioFUKMUWloBAgUiJHB2ZSGQOm9JDHpX86WIkAeWnYAly2kaC0zQprNWKSuDqAJQGMmZP3K8bYdnUM8u7tP/f4uf7Yc78Wz18Q2VqbcNR4bm/NrLVF3hE679NUz5xr0EMIXVNrrcuqKMsSAOq67uomRl/2e8BBMbezyYWtzVGZnx0dX7l86eXvfu9wMt8YDte2tvcPjqZ1W/UHjiMZLQBaKWOUUagBFEiO0svM2eHDEmFj1Lt+ee+HPv/ZC1vr77///re//91O0BYjZfsPDqbv3Lobkda2dwJGH928qZumMWTKspfpgkEadp3vfAxEJEQxRmRBxK5pz5NEVa6NJkUCjDCu504CAmVZlue5iDRNU9d1CKGuaxGpqspYnUyixlpTFur827J0RSMqpYa9fvKKJAdXugWV0QACilInkGjx8CHbHHyo6AcABNLaAgASI0eQqEE0GYtqb3Nbgxzcf3+lp/7W/+5v7GxVgz5Nz8Y33r5/enRmyYp3vllcv7J75co2S/Mv/tU/+cTnXnryS3/i/ve//fatOz/84z9hLl6GWYS703j/5Gu//wff+fY3iyr/K/+Lv3pn8vD/8U///l/4j//SR3/4k6BCPZu+/PVvvPKNbxcBPvHSc09/fM9WCEAxKA0ZSIa6D4Ot/e+8fe/mwRuv3Dx5NEVvNOYQcV6HwWBb6SwwN50LDKB007nxYgFLqFPSIRMSAEtA05hBh4aZA0fPMXGtmFkpTGRnSnEgLCLCgosonJJBJGnhlkF4ZVkeHB+0rjPWbm1vCOHtO3eYuTccLIlbacCvdWrS8DyTSM6ZPzHGEPzyawih6zpg0ZpSk2Y1iSxxBBLDUrJCmOd5BPEhnlukkJQhUlneU0qRzgEgpvsCCYBZPKIAKhEJqZwSQoDcZiSQtP6KMDO6zPMsy1CZxI3Ji6Jp2tQ0Luq5ASqM7lmbK8oQcq1KawpjdYRuUQcJ4+nk/sOHglD1K+/9aDSaz+cCcdgfKKUWs3nXddpQf5CLxPlkKj6sDkaubbmL1554KoqINrce7h+eTbLBaOa64+m06A86H0KMRussz5OshYhskSenxDIlTS3jDroYsl7ZdLURUTHGtr66u/vg7u1cm1TYgLKdDzorq+GoHAwW8/FqaXbW+uhDQXRxe6Nr2qOjA1OUYswrb79VB8iKweHJrOtCWQyca9e2B8fH+3t7e9baG2+/fXp6+slPfPwv/nv//q//63+5OhwcHuwfHBwMqp7z7cGjfWYYVKsro/UsL7MiJ2MfHOzPF+369pYXWHRd03kWjASSMkmsQYURkgEDFCRU8dKRlYT1SeyeGh6jM89MRED4WDATz3UyaaYG5zaAJOXIlLZKJwFhCH4+n7dtCxKTQCh5GAgxBQIQEaB6/c03iOjCpYtIhFplRd60bfp+MrPW2hpDiUqkdbvo2radz2d1XYfgiIgMIWJZ9vI8Nzb/cOkfl9S4/x+lf/qzLHlHqYM+f4UQQnQMoJR6XC5jgmxwEBGVUHUhRuGpb4MwhyUsQZu0BZWqMBwDikcUo1HpBLrzTT3WGrTWqd2idNmRCUkkiSSkWDBE8RwjI0gWBSFCYBAhSMRBVCFE0ppIMwdEJK0AgGOoMi0c5Hynsaz7fUg9jLU2qQG991prejzRSWBTAIRlzi6cAx4+eHVdp7RJ8zOi5VnjQkgoYJHkp1S8pPWRUub8O66EUQRIlCJjEDCyVroqKg3CLiirR6Ny+uCOzbWuZBFbN5rvfmIbTG/RDlc+lrM0IbjhoOfzVo9yvXXt+Zc2YTyGjSE8sQeLh25xqpQiEz/8Bbdn4zs37r351q07d2C0CRef2K6q0bBYQ5eLc1hVUK2Vq7swiRC2wuxkcbyYda1v+ODeqZLsznv36+nM194ItLxwfh5YgbXjerF2YWdn5erx2cFxc4o9hQL3Do9NH7oONjbgj//Yj37+h38UdnYALQDA2aQ7OHr09ttnZ5OT47P79+8/ePBgPp2UBkprdjd2r196cWd1Q4tu53442AyxXVnZIltMF3xwMt0/mU1qX3uJOrPVWjVaCcwnZ+PoPKpCyLLIvG2jAINoa5QlpUmZAnjpVmcEQLJGISoBcpGzLMu04RB95wHEKgRlNCUSMIEYRIOoQFiQ6npqLObWKKWILErqGOTi7sb7N9/UgHvbW7LaPz48eHR7Pzh/9fLl2ex0tSjqpitE1rc2B6PVP/Nzv/juzYe/+Tu/+977b+SFKqsssus8e++HwxXB9ujoRGm9trbG4pnd9WtXsrzXtb7u2jSTSA260qgUGqs0klHaKAIARcqETKF0wYfAgmBtRtoi6QiyWCyQl4MNRIQEJ9akwO9ujbpF/ejBw/l0kRk9GBQSZ23rrSZkie0kL8qNfn9+3Jzt3/2Rz3+mbc5e/tbXH95//8q1Kx954flBmV+/euVzX/yxN9+7+d3X37xzb3/S+C5wF4Ejrg3WCRQqitGDa7Q21moRef/9oyqHrASdl22QiBpFKUYVCDEFuKdUxCU2xXvfxbTQt4iYSn8g6dz88eQ1QT+TdotThS2UUl5giaQQbbQ2xmhSy5CWiIRKqTLLhVAY2YfQ+hh9iIKKYhOZA4EyVhnQhGKVyaxuW0cEShkg4SieffAcRZTVUZbwX+CIiCoBIoWNMUlGiIjGaEFEiWiUsOtah4gKcPlVhWBtRoQkAFEkMDH92ygRaLomfZYYI7B0wSUleqbN0tgQvW9FZblEkBAZOQqAUhA9hNCvqhijb93MhXTcA6Ay+aKpXdv0i3wymxPHwdXLs9nsN37vD3oA2yuDqjcobPbCxz62aLuH+4e8mJdF7kMnEglQIvrITKiNvrd/aEj3euWdk7PXbh7dOjj6kS98oT8ofubP/8VvfPPbb793N3Z1J+BBzbqOFrVo8Bx8ZFEalPEioXOBo84ohOi811ori4jQBRdCsMoAAANGBA/IKQ+JBUlDiD7EEBZt2zJz13Vt25ZVoTRprateiUAxMDNbmwlAqvgfl+zLHXFRpjqAiBx0adWglJpOJ7Lc1S+lXMnVF8Py1E1ygtT1IRClEDQEWYoOkhxM3bm/vzIYjFYvGOr+0//8762umCev7RRlVlVrs0k9qobDojgaHz38+ivvvNcHWTBk77x5K6++sXph+9bbX33zjf/2L/0Hf91P3eLu/st/8E2/aHtZkevcqOzs0cHZI3jlm1+9cGVQDovvffs79f5sr9i4vHpp0OZv/O7N3ii3pkTKAIuzs+bwYNo28ujBqbBVMMhM1Ti3qF10EZWdNl05yNGapu1mi0aUNllerqzN53MmxUgMhIISIzJ4gI59JzEKM6QenAWBkXOTowAACgIkySGmEEGIgsuWmDkN4AEZdWLR+tlsVpRZfzTUWs/n86ZpHidwpcuXYfkk53mezPTOLS2YAGBIAaJn1lqjcJZlVhskmZyeoUDqNxRSAv4kY146Jc/lEATL5M3IzBDxcekfBESizRBRgIQjpHIDz2tEZgFJaPbzR5dj27TtdMpZYYBIwGitFGnB0HWCiCrEwLXzHiHmGRT5sOjP5/OiV12+dFUU3bl3t3FuZXX1eDrWWq+ubl7a21tfX40+HB4ejk+P//CPvvLxFz86q5v5fN50nhkG/eGr79+MgqDM6bwxVVWurTWTKS+czsrWz4teaUi1PhGTMkRpujYRJz+oERFNbizasl8aDEZA+W4+bprDAzg5G62vFUUxdn51deXobGx6xcHRYWaUYjk5m8TgZycnrl68v7qutVbKbO6t7R+dHS6izittKzEeJdqy0pmdz5qnnnz+5PTonXdurg0GP/tXfu6Tn/j4nVu3M1sxUwyg0Rhlhv3hR5998WMvfRwp/4OvfPXm7Ts5xBxxZ+9C6+L9g0d5UfkYGSQSCmCUROARUvB4L4qQstVEIXIIIGKtzfM8cGzblkOMGFmE45ILybDc6qXlFCJCSm8UTH8JoLZGBJJKHkGsMZnVmlTXNETAiB0zimRJiR84Iqyvru0fHty9e3dnd7fK+6kqZWbGZRL8Ehvgg2taiQIsCim3WTRKRBKYPvUSS7cSKCLiSIASYoAU77l8Y4IgAqBVmpmFBBFRKAlcRMT7jpkFGOk8l1AiACwjI5NASJZcIEYsq76TGF0XQ2DmrnESfYw+ukwp1MSA0rROIBIAUvShRR81GY2kyRCRIYooi8UCFCmjUSskAsLE8cuynCNFz4EjR16eHSDDXklaCWMXmJkFkqrHCZnI4bGTQZhBYsp/iJ4de4ncdV2MEX/w2qaIAJAwkqTnnABAkwIUFIaUUUG8bAIocfkpeYrTXQEAiXR+PrqQdPGLSGbyDx6hBFQSIOFMEUdfGNXLM4jOd22ema1haZtpmfGnPv/S8ez+w9P3//hPfW77k9dBphDn0DOQaeiVMGtgMgFQcDYFzGFYQQ+6xUMpON8YzY4eDTZ++nGJ8Nr//b+9ffNwfAajVfWnfuZPT8JsuDWCkZHjB48e3VIovbIPUT+4dfT9bz3kBVzYuGi4j0G1U3fn1sPppJEImTZV0ROGST1ftJ1DWbuwPe5qW2W9fn86Gc/Gk70LO88/9+xzLzwzvHgB9vZgPt//2tf/6I/+aDqejYYrdd2OT8Znp9O2jos5hADDodnc3Bz0815WlFkljseHx81s2q/s+sZwY3N46eplJv32zTuHZ8EUa4en3Ws37iyMroVRUdN14/GEQYqqMiYjrVzwSfBAtGxYlVLBRSISxigginSWkTIC5JzLssxoHZyPnUMWpRQoYCVIVljHqAWtMjpC9NwsFkfGQpaZTBsFBUaN0YjEhdvv2km/KMW76enJs08+8Yt/9hc+9/nPffm3fuc3fuM3etVgMps/3D8ser0gcOnqU20wdx4+nC9OTC4snfedgAqs2xqGo03XCSNUVTGendoMXnzxxbrxi3kzmc9SdpW1GvXSNJI0fSJxubtEUkr5tvPeOxcCM5ASRanVab0D4ORXgRhS3p1RrKHp5XbUH/i2Ozo4ZmZt1aJtVtdXHt6/t9LvXdrZsiF248lLz33kP/wP/goMCkAnJ8cP9h/943/6j9545/2DEyiGsHtl++HRyaMT3zHoIsesiEIcaZj1OEalNUtoXGNzk2XW+VYYOcZ2Uc8m87YGq2BQDYrMurYDElDEhFHAcWyj9xxtXgSOQqi1RlCJ4iciQJIiMxVprTUAhcDBs1IKAAEoQQCINBFFAspyZbRRWjiE4CBp/TV67wGVCIaloFsS8TkEJyKKyGpjkJLfN41wiEiTisIhhM47770XMHn/w6V/uvYRRULMC6tJAUgyFFqlXQyBo4shhqSq1GlBEULIjGXmNL2Iy8Rvef3mv/v4ub529R/Sh8gzifOzHOEAJvB80etNxjMfOXqXvmBETIpnpZRzLjkhhHHZVWYGiA8OH416lQWenR7nRKtFWZVFu6ifeOLJunUP9g+iNp4xAO7sbE3GR9F3ImiMCSxt20qI2qjN1fWDRw8ySxsrK81iXs+nigAlrK/1siyb1XH/aOrBjtY3ddFbuBYoMseUT2mU1cqQEDOzhNZ3ImKzTFvTuWa2WLRt2+8PYanlAhHheD5x5zRaCwBA5/sWZraZSXdqWZYg2DQNM2dF7iT6EJI2N63gE8ijzPKU3ZZ0sUslFaH3zvPyx7G8BVIhEZcM6NTspQ+FQCTnDRx4kICIhqyBAoPaXNlo55PcgG+Oq1zm84Mit1W1dvnitZPDI1cvLl3Y7RUkYdG2pxxn9+5PLl8r/8bf/BvVE0+/9ntf7lz8gR/+Ijx6cHzn/je+/I1779//5EufnPnp73/36z/zl/9E72LxxGeei27+tS9/rcdDu7CDsPLeW+9gznmVsyjHFMSenXV3HxxPJm1vsMFC/eFaDLBMM63bLM8770AbRPQCAgq0iUiBZV53j/sfAGAOyBgEROeOlwJFIIzCTKLUUiWV5vFwrlKLgkEZD8IhJgM9SIrF5BBdXhSkYP/ggCwNBoPZfA4AyprHGuVl95UWCDGmSOm2baPz55Cl0Hb1Y2C3JrTWKgKIPJtMUy0uIikFj5ld8CLLTjmEkFZ/2mRaG16u/cxjwU8EfFz6o9IiEiIwcwJJAktSFSZioEJJJisCVdc1KVP0KkIFhMKxbVslLM4pjoM8W+v3B2WhQThEJVSWJYtcefL6x1568ead27/+m/9m/+iwrApG0EhFUaysDMu8qOt6Mj7tV8WjBw8mk+n1K1eVUkF4c3P73v2HR2cTssW8cUKmv74egWaLRlujCcuqCCGMx2Nm7g8Gxqqu65L+B86jEkSEkRGFOSiIPaWN983xUQXUN/ba3l6WZd9/7XXMMsyytYuX3r1zN2g7qedVv8wy4+oGAg97Q+fC2XQiWtte73Q+NUWvKKrZdB5D6Oc9lOi6eW7NfD5fXRth4Nl8utLvPXjwYHdz4+zsZGdj48//+T//7/zJn7h79+6v/+t/efP921mvd39/f31ts6jKG7fvOB+rweD4bKxM1vgQWEQpQs0gSamvDZIsZ2CKAQDUUgAvxpiqqoqi6LyfzKbee6VMiBwFk5EsbT/Sg996h+eZLSxLLxCkOAWBdAJrlYLDUBNZrTJjYoxtvYg+JK+Rj8EjBoY79+5OZtPtC7uj9TVUZLPMp/bDB2ttZq1ETjd79Ml9G4lIkLuu61wTQtjdu2StJWVEJAUbc6RwvkZ+fE3I+Tw7N5bPc3zx/DsgIilvSkjSQmPJbz0PKWNmlCVInwQiSBLcL6MnInNwITiOXkIwFrVB4OhDwxyRRCns9UpABkaMKBGii87F6EOe56gINZImpRA1ogJELHQpkWOUEGJKa0mppMmHiYiBIyKSUYgUY/C+WxY556dxOp+ZOa13PjirP3NlfXlco6blyJ8AGFgiB+BICoxRSckZYizKvmeREInIGkVEwBxjPCdGQZJNJbNvjJFAaVRAJCLMgIiZNtpQphRJMMgGUUM0JJnRQ40f2dyCbv7FP/lD4/bge+998+lPXL76wh4METaK2Tuvfe8739AKdzd2Cp1r0OPxZLSyVm0MzIrWuz0o42J+Mj493rv+AeFn8iu/NFjZgYiP7u/vPHsJqhqy+OD991977Z4CeOr6Exe2rmkcvvfq7a/+7reVp4889XwvL0a9Srx78/Wbs2kxn3nn58YCqdhK7TAEA4cz6G/C5u52ZLKq/NynPn/tmedhvLh1495sMr17+9a9u7eDq6019WJ2dBABwCro9WB1uNbvr2hlRBRHGE9nZ6fj6enEotleXV8dVK6dnI2PLl/bDORXt7YvXHly0qrvfv/GW+89mHrdmqpD3XYdI2itgZC0Soo6BnC+bdtWhI0xWhMBiBdDmoF8DKK0zQpQKnLSVBABRB80iNVGIfgYJdOgLUjmPflISBrQBeg4zpCCIjakFBQStXTKR6crrN3MIGiQK7u7P/HH/tiFja0333jt9e+//sorrxR5afKic5wX5WzRzBp3Omv6q6PNrT5SOD57GKKzWRWiKbIVFhM85mVvfWMk4Bo3y/Pc2Mo76UInIqiAiIQQgH3bATIKIEKSwqUdeiKTiEDrQt12bfDnuVyamSN7Zk4qFxQgaQclL8bHe9s7a6OVg0cHANAf9qeL+d37dy7sbI96leLwo5/57I988tM333jja3/w+5nVs9mk61oX/MHpXGWwfWl3e+/SG+/enPlQe3Rkm0CTeTddtL7zm6PV2HXpOWXksldYa+uuybK8mS9iFNf40AWjDAf0XbPaq7Qmk1nR5KM0rlu4tg3eZDaAKGuKoiBSScjLHHuD6vw41lprEEwhvjFGEJJzGSKh1lqDMaz08uDnkErzlKTTdZ2IICogUsYqbYlUuvgSY5uIxIfoPAEarYuiSMef5+i9T/IjJiM653PlaPr4SXpIAlluFFII3ipd9QqttYsh7XaXX+SyNF+ODD+kxdREJJG/89a/80Hpf+0fwYe0JeYcCI2IBGitHQ6HK4PRwcGRRIjRp+UAACSUUF3XzIykjTECtOwuNDXcrK2tLqZnzXjcM3qlV2gfHjzc315bAcDpvBYyazsXTFVN6y642oDr2jkDGp1FgUXTRgZrbdu229vbRZ6Px6cSw8pwBCynxw/r+awqYWVlwxajWRPOpo3K7WhlJUgX2TEHAlGoibRGTYCurYOwUkppHUHqdtG6znMkZVArROQIIYQYk19NG0HgtJ1nREnKfq21iKQlgDFGnQNYgdCnOTMv1cyPh75VXkBaYWttlBYR770LfjDo112bPlSq75NyNP0UaHndJKEXKSBgRBRADxhFPKKyKlNQxIYubl88Ozpp59MLWwNF7cnJnSt7F072x1ubu+PZtNerBsPy+ORRWandnWHdHBZWTvf3x4fyl/7cTzx19Zmv/M5Xrj9x5dKVlUyp9tQ9unN48PDg1r2b1z526Ys/9dns+fXJ/nuT2enO6m77MH79X33LP4JBvrrwna2KrpNx7SJkTmwTSChjbQ9OTkAbF7zJdNEr63pe9UvPvunaGEXnhc5KLzKtm7p1iuyS/Z00+sApSTWhpRiBjGUEx1GIlDUu+MckPoDHQXOERdlFjs4H30FkWVoUZTodV1VVlNnJ6WmAkGWZC244HHb+Mah32XVgMmGHoLUOIbRtqwAT0Kxp6qZdGKM0KVwmPJhE3dGkmJmDDyGkoYnzbd00RDqB0Zg5CqbS3xirTQEADBoAkAwipqzzCF3ivgNAaglS6W+1SaW/QVRIKFFiwMhFVlqlZXkmq7prQ+f+zbd/BP7//vXFT/0WxBB8WxrKIpcAF3r9zbJ89vKVfpZ985vfLKp+f22tU+rtu/f2pxOxGVbl8WzGiCSQ6SyzBaJqfGw51CG4hOFngBApCglD8BL9cFD1q55S6Js2z+3GyijG6Jvm4OCRb+qqqoTDdDwZ9Kq9K5fvHR9O27rfG2ZFPl3UxuZl1ZvWjWdpffBRQGulDKAibYwipWIq/QEAUwB55DQ6Kcuy7FVKqabr6rr2MSAqAcURQFGaAsQYBSHhekRElnsnSR0jCWPwRulMG6UUJVyS66L3w6os80IhdF0HzFlmFFIbYlSq8WEymx6fntRd2xsNV9fXbJa5GEIIJJCcxdGHdBfU87Zt2xiD1hpIuq5zvo0xXrx8NTGvRQRQE1EMKWvcf/hk4/OXRlq2K+enVirujcI0A43CMQbPUQBSpnIK/ExLToWUOHXee5YgIgRoFBIgchQIHD1AAIwILBAAlrqp4MXowurMKgOsJAh3kig9Amk3E0gJalSGSEUtASRwFGYQVgCEoBHVfNEqbVM5pK3JykwrK8jzevEBSS/ZGM6vYzp3NeiUPF3XNWljtdGKgCj1b8gCIByjSDRKkVbGaJBIMRIRhy4GT0RG50olNg8E5yid90mSoVSgQEF3dQcaFSIv43vScM1KDFpbCs51rbJUVb3MKqnbw0fjgzs3jk4PslXavj6CDr/+O1+rBhqN9Mpsp7helXl7uvjeW+9OJ8AMJA/WL8KFZy8+O/yB+cn4D7/zrZ3dDbj+wRPrtH94eEtAHbXjDR5q7AARIW6sFE9f+Vjv4gvx7vTtN+6dHXTr1d74eLJ//+SF557Y3tsDcUU1rLIrbRPbbtK4yaI7aqGu1vLB1srG3k7HXG7sgC6hZljfgaPJN7725W9//ZUy67f1tG7mV69cfPbZJ09PTr797e898+STT1x9+uLFS7GTh/cf3b9z7969BwdHZ3l/WNdubWPnmSefLrS9f+9uw2rjyvW5uN5gVaqVW0fjG3cP37x9d9pFzEfHk/FwY2dY9dIUcN7UIQRUoXNtURSoyrbrOLJSmLwpGkkTAREAAaFBQGHHXOU9ZnbOIUqe5WVZAgt0LdkMjQbQAF2qvhgAxBd5ztIJtxwDSAeBvVPe+6JfVFney7NuMX33nbcX49PC6LfffIs9X716vV50TetsVhyPzxhpuLrSX9/qgm9bB+iBCYVC63xbQxONLtdXNknD2cM7xpLN1Xh8GqnQtshzmy8DcV3XdM63mhQipEwnQyoEV3eNaztxXVVVNisMqughBFGaTJaFEAJHCIE4GBTgKNFLrDVKETvdzqeP5rnAj//4j3/2B3/w4aMHv/avf21//6GOzVq/9+Zr3/nNX/3Hh/fh0h7MxrC2bqzVd+41P/jDT/38L/zC7tPPvPrdl2/dfxRc7CC0i+5s7msXlLXVoB+db0MHpBSZrLCm6ClN6EMa25dlWWT5Yt4RKQQ1oGpUFUVm8rLU1gSGum1mTdt59+BgnxRlSudWk9YAMUYMAZpFyywAoDWDhVQii8QEGhOB1G8TkVIGtY5I0XvnHAInnm+MENkP+qsuhlQOklJKmSAMMSKkOQeqJVZMWFgAQ7ecpniOMUZA1FobbRxHQFJJT6mW+m8AMESZVjHG4FvfxU6cR+yca1xHymitBSlKCgMiVBSTenJZvKpE8/jwTSyJuHyu4HQsCXCUABF1Xc9ms4f0cJgP0swjhMAhpJ0YKIoxeu+FvdOOiASS3wmjj1VVjE8Os9z0e9ViPrm8s/upT3/ijTfeODw8RqNQq/v79yErymooEpXBKOwDs2BEYkJjbV72VFaeLZqjySzP895gdFI3s9lMg13b3anKTEBNGq9Mvndl2wV/cnKSFVppXWTWGAMsrnFNW4cQiizTCALgnWvatnENaVXlxWQ+05xprZOjg0GICJUqTIkCzOB9F6LzMYgHFuiVVQhhOey3mLStPgYXfcrqTJPjBFsjotFgsFgs6rp2SGVeJLEWMUQfuPOxdQCgCm21AQ1K6VT9PzZapAWwAEokREFK2XMKEaIIUbSVeefOO1urmxDMg7MzjaHqb964d7LeWzuZ1MZWvZWtRVdno631jf6Nu+9cvLD6Mz/7k09funTvxo1v/O5Xb77y2+v9td/+1d9bvVD84p//+Z2PPHNy+vV37r1ve+on/t0vPTi9+fC3v7e5t2q09fP41itvh5qlU2eT2UKkDwMBG71rI3qxpHNbDE7nC4CyqPqxmS985xvvA/Szqp6dorV5VpA280V7MpkyUF5V83n9GNSThmUiiBw1UgxdYCCWANKFwEjkQzgHnj4e1QNAFERAH4NzLrg0lYDl7gVwtpgH9qCozErSFCVmWTZb1OeP81LIl2RV6nxdFmM0xhpj0u4iz3MA4Mg+JQ3ZaIwhgNY5kRi9T85vLexd7Dpf9CwBCDAjYFzOO6NwaXUQQEEBQgTBNBgA72G5/BAiwJSEl0iXkSV2wQNqRUahQiLS8/l8dXVVoUoVZAr3/J+mVv8f+SWMqHQ/72sJ9ckpEV14/iPXtnc/+tSTBw/ub+7tXb56dVzXNYs+PPzsi59/787tt2/dy1bWIug8y0RkPJ270KIynoFNlmVmXjdN1+TWgFZt3UgIq/1qUbf9wWjRLJxrbZndeXBfIRRZ3uv1VFV610Yvm6urJPD+u+8Uq/2ttSEA+a7pGcUSm+lYASnULBGZgVGRQoVaizFJooMACoABiYOPgksMnkYiShgMAjZIDKis9VHShBEAhGOq8hMULjWwSQ6iEu9fJCK1IUL0lIAwLJH5bDITkdwaRFSZrqrKapNFZq392Xhra8vm2atvvN7FMFpdSW5gIlJa01LdGo0xZZYDY4zBuS6yR6K0rLB5/ngFJ4AIIEAAKR9OEFiRQgRhBODkVmiThBiiwkQZJUIkAe86EIKoHLu28zH6pMMRkaTjiySyBB8ACITQ8HkAokeVQMYAMuxX3nfBdYJgdJ5phYgiqDKDoGPAtgnRRwRTZlWZ5W3bMscQW2HwEEmARbRC7zuEgMvE62SxCICqyhEopagJSJCgIiKLJOVe2ruLQMrnE4mccCdESilWhoTws9e3+bE9lzF9O9J4TEIUiVmWlVVhjAGOLnifUj3OAaCPTRJt3TBzZkxZlkSUDjIikuhl2YSAUioztrCZ1cZq6uoFN41G6eVZpoFDhKZbo6LSfDK/u3mxevPW5OqTMJnCz/2Zlz7ywkvHt+/funn7zq2bdeOvPLGyvtUbH5/M9uvt3bXBlc2Fcbcmd2vjn/voEx/943/+8RP78i/9N997Zfz088Wl609cvHYJ2vn49n3wxah/EXgNZurhe8ff/+6br79ya22l7JfVzsWdz372U+rZp6BdwIMjKNZhVgN17772jUend178gY+MnrgI/RJ8gC5C1oOiD7cf/sHvfeWV796aTsDmoBXkFWxtrT/99NPPPf8R2r4EWR9sf/7NV3/nN37vxtvv94vq8sVLw/7ARb796GBlda3sDdrWHR+dnZydzuZ1G30kptyK0nUI07ZtXERttK1Q95iWAm5UJEnhTTibzeJyde9AhFLCqDZGSFxw0YvIOcRVAyqTF3XTsJAuMhDduA5QFXlOgiF0Ps670DAzktWqUmQAwIeFtrHIMt+G+cxXdmt7c1PiNM9iXY/r6RlhUBiD9yA0Hs+dA9J52R+yMW3XRWGVFWubl+/cfei6xXBQWC2GWLyfTycWtSYzGo28jwCQZWaxWLC2rSmGG5sKCVkya+v5rFks+v2+JmWMtrkB5Hkzn83nUWKh8Wd/4k9sb2386r/6jdffekdMMZk11XAlCkbv6/msyo2K3kDIUEJXr/Wzlz765Be/8NnT07P7d++98MKLL37pS3B68n/6L/6Lg+ODg6ODopfNZtPjE/hrf/1LP/z5L/zdv/t3Hzw6qmcwGMKP/bEfef7553Z3d//Pf+//eng0qR1GymtWZ7WbdM4JZL2yLEtwoW26ed24iEWvNxwOrVIcfT05RWYEJiJSVlCxKJC4uzosjC7LXlEUWhkXuWt9593+8dHZZNwEVw17g1HfS5jN523r0WVFVuV5HmOo24Vzjoh0Zq3Np9Np03ZZVlS9kdEZIilFDx7ey8rMmCzGyBEggdtDiIKS6HwoiMJL7y9b0AYoJZ4bpdOetG1bABgMBv3RirXW+dh0redITBBFaw0xNq5Lg2Hg4L0PrrOaJqfHBHz96pVhv392crxYNI0Pvf7QVmXrZVo3Lgpog9r4EICQUAiSisVz9Lcf/JXHz/X1a/8AEVP7moJBkpqlqipjjIgoQKOsX7RWaaWU9z7FyzMzIGZZllJpRUQpBaRFJIgHTSG2CGwJLHCmcViVg161v7+vlCKVBSAnJFoZW2aZUTHE4LquazsfQgAia3JlsxiFI/i4nMcjoiJjCXOLzXzGDEVRGZ0xQpI/zedzY1Ruz40c5w7aospddOxDSq13rqvrumkWWVnkRYWInfeMYE0eWOpFG0Ioy7LMK5ZQ17VzrVU6y0wMQSlMuLfolyHHqJUQRRA4HxQpjZmxWmsOsaoqjbRYNNFHY4wIdl3DwTOzIm2LHAjb9EttgzARpSQjIg0sKMIRtKoQEdBFbiO3gE4b1FpLTBArREYASqNizdwjVWW2Vw3yvFRKCTuJLXKTZ7EyuNorV6tKeXaNK3S5MuivrvVfffN7h2f7V5+69Ghyf/v6xk///E+8+u7LTz1zbevZZw6++9p3fv87emFpbtxRmM/Zrm2dNWE2r10QUjnqPIByDI2Lkt4GCj37EGMIwUMUDT66GAWEhJBZOhc6F5TNlsRhEAEEhaiUAeLWoYBgWttkEbCL0YeQFXkqFgSiRAaJqVtoXAdKp/I3pjFjjCLSdrUyNBz227YF4OHqqCiKlZWVg4PD5N/oug6T9xowaS3Sbqfruug8nLt+m8Xi8ZYguW4AgDkYUqTAKE1EzOxc67vOhZAVWQRxzhtjqrLvI7suZEVB2nY+MDMqg6hC5OSCFeG0rBNJah/QWmfJGSnLDkch6ZS0jeSdkxA/PJgUgd/67hf/J6jV/0d+/ehnfl9rIvYQnUYYZFlldIZYGqqy7HOf+/xLn/jEy6++9k9++VccQNHvH5ycNhG6mEdYRp0gLTPCUsoVI0SQEKP3vguemQ1zzxgLqBRqQ1qTUWAQFEiuKUO0IBagZ/PSZLFzs3r68OxRhFjkVVFUbRNmi0W/t7Jz4eLp2bT23aJzQRi1EUWJGFE733oXApvcrK1tbG6ur6ys5bmdjWe3b920Onvy2lWJcP/u7bZ1ea8fVBbOnR4frJvOCTkf/jkqpVBREG67rmtba3WVF65riGVlOIy+M4oMgncuuq7f7z/15JPXrj/51W99a9F2zOw57h8cPHr0yOTZzs5Or9er2yZV/ESUHgqFpEAdHx7FGJ1vOcUDe9/v90ejVdJJ2gTBg4+iEIHIcxuC4xAVoiJDKMF5H1ymjSJQCglCjCFN7gFAIivUkaVtnfNRa6usQaKmaUilBJrA4oBEGzRKW1K+7VxgIIqCIQpplWWFJsUhSowSmTgSiAYk4PSsRCHnxUdQOi+qXlZUgSOH2NbTxXwaQ2ctaQLhWFWZQEQWjoE4KgSlUSkVPJM2qIyQjkgMCpR+rOcJ7JMMOJH0zjVrIknCjwoR8WN7Kx+eIiBiiuhKtTsSGGMKm8ZLMfm4IYkVmRO/Pw3yiywXkcdEjq7rAEBrwgSDTDgEpY0xFpdJH4awIEqpMwRBERWgTBPKDMjO+hv0iU8/sbu3YpU+PTi59+6D8fFUsWLxJ+NJfwVe/OS1J69dqffHL7/88luPZPNJ+MEv/fDmx5+DjHFr+/ETe/D1XypXyt6lC76bzcdn8XQytCOjN/wxnDxwOgzmJ+G9N265Rdd1bjIZV8Pq+Y9+ZG1r88GDB+/fuFWyuX/nzs7e6u6VzXJkymHWxVZndmd77+Dh0cHDo8nx9ODRYTPzF3fXn3nm2enCD1ZXh6OeskoQmtYdHpwc7J+Nz7o7t/bHp/Ptta3rV58oM3t48Ojho4P+xjYaGwVmi3o2b5s2tIET3hG0FlJOYuNdRDCZ1TYHMsKQ3GNAWilF2hDRbDaLMYbgYowgMe12rdLil14dpVRmjNE2XTOND60PjGSKikm1nWfAIsuUKOSGaSHQiHAI4lrjHeQ26w9Km0XfLbyPVvUKvWmUWhnSYnbYLMZVpYb9bDo5Ozg4cF6ee/bFsloLYI7G09PpzHGou3oydwtPWzsXK60XszHXU/RutV9c3t4uMzubT9iHw+PTxnc2LwNHW/Wx6g02NtiHZjZXQtF571xhs7IsTJ4Zo9rYnY5PTmcTIOjl2Rc//QPRud/58lfmTsrhytHZrKiGoHRuLMRgkOvJ2VqVP3V1r1BkyP+ZP/0T9WL65mtv/szP/Azk5W/+yq+srq6fnh7/s1/6Z+tb63fvH+xdXv2b/+u/sfPpH/jmr/7yf/Z/+O9sBp/8gY/82T/70wDxv/4//peLhRwewPaObryKVDjMOoFGxCsSBUopjOx9XLTRCxlbFEWFwuzajARiG1wXQogCntGxSIybw17P2qKotLIxsvcxeGYQNLZuF01waEhZDBJa1wWPOfSH1Wq/X5HCeTOfTqd1u4gxAmkR1MYWRUUqaxtX1433bjSslMYYZNG03kU4V4KS0QAgyS9KCfvDKIBeDCkCFA6IaJUWDs65oiiYWYCUMmS0AKWs29JkOk2U/dIbihK972JwigWBL+1uPf3UU+z9o4f3u85HFtCZY5k23azzXRAH5AUCR0bWSEhRJe0KyK37f/Xxc33l8v8LzzPeE4EuweAeK5uNMYXOwQXFKAiImPTWIUZmntcL772L5+wapRgkSCSCyB1yVCSKvUSnCXOrrbWCKgp0ntsIgFpnubXWEgAH79L9HYQxIVZ9AGaOAo+1WMZkhrBf5AQoj5VY6RL1Ie3Tk0iazxGZQLBoF0CiiHQKoovBtZ1zzmQ2xsgIaUcjQEKKSLdd1wWPjNbqPM9JQde0bVsrQBABYFqauQBZYpreUrrOk3xONCkislo75wiUtTZ2PJ/PAagqc991kT2gUkYLogcWQGU0kEZEjRoRgRE4DcnImh6RQogMjnkBKmjFpIUluSxIBBEsoSVSWrCvVG50llW5sZq0RlToFTkDAWKdExRKU2CI3M/7a6OV6NusUnWcvHn7+9c/dvnHfupHVrZ7W89cBU1wcPaP//t/9NrX37o4uDCklYwLZftT1DMX69q1LrBQFHQRXBCVlSFyFIgIoEgAQggdh9o3ggKSAqc5MgCQEPoQhTD9BYSScrgEMlYcltF4qA0ag6QFyHMUiQKRRBA4OW1CCKhIAACImYN8CI4ZOkTsDXvOtwBcFHlS7W9vXUBE51xd10uRHgsz2+TsCqFpGte0SZANAKnZS4q6pWwPWERMitBO0R8ctNZ5ntvcTKdT1JTyAW1WeO8jU1mWNq98DAyktQY0yRyitS6KEh4L4hOHgOjx2zh9bbgUAxMh9ssyhOCdS52tRkKk3/jWDy2favw7y3+Qv/Vv/c7jX/6P9foffqL/r0/3P/gPfvhTX1aKqsJE12EMilBJgOAVQGYUIm5vbx+fjo9OT3qjkYs8mc6r4dpkAUEIRYQAAJiDjyFGH4SBBFAF4SWABoCACyGFy7pfa7IaNYKGqEX6Wg2sHWbFWlWNylIzOF83XJ+OT46PTheLDgTzvNcbrJbVoHVhuqhPp9NpW7fRN941XTv38WhR9/qDsqq64Ou6zsviypUrFy9eDM4755QAh8jOFzbTpKZ155SNS6bLB/B7AEiH7WO5PKSWgPCsrgejIXD0bVeVeZnl3rXedRops7qyFoG7unG+zYyteoNp23qBsiiqXq9t2wcPHpycnMQY17c2y7K01qZPlPxpAKBRnxwdR/ZN0wT2ANB0Xa/XG62uIWKSJzFDOPfC2AJD8BIFEVNHyiFyDOwdSGRxEFOQYxRInlWtSQsr54IPgqAYcPnzwiDCEVpmD5qNIaupZ8vgYgQQshGwddFFJtTWZhoJGCFE8BE5ZsoaBZPxgc20zko0FtB6AccYBLIsU0ohx+gb4kjI4p1zrS0LQJFU90NUxIaWyj1GikxdkDaIizGAAiDFoDRaq63VxhgkiNF7jumu5IgxphE/6vRje9y9wbkOcanaBEwzBu99aiASsUc4TQeXxwozJ3KcOv/NNPDTOrPWikSApWYUETmG0EUFSIIdR+IAwRsENAaVQsOixfb07uXNlz7zEqhudnL6r37za1mEQaEzymNon35m5/M/+inY7fuHt3oXq4+Wz66fnZ7WzStf+w59+9W1tTX4mx9M/c/ePObdldnhW9koX7Tzs0cHoyc2QNkbN9/53tduYNeDrnhw+9DX8crlK1vbV2fz6Vd//+WOw+rq6sXd6zLlZ5+8uLO3Eqjdf3B//v6pC53J7C+//c2uA4iQkRbHmkzTH0xPihd/8MdhdwfEHb/68te+/UcHh4eojTb5vbsHbSfZcAXK4tbh/bqeZ4ZGVzYPJ03T1It5U9cti1LaBCQnSDYLwj6wCyGKACJ4iRBRp7pMQECYASiKR8TASbP7gagrWcpEQQRBJqU1WYvKMnOIAqABJUHpgAGAUSRGnM1qhU7pxtigDeVa6wwiosJAbhb8vKtnwGB7MOqvFIUue1zm2SnxdHw0Pq2jj0VuLuzs1Iup0YWy/dJaX+RdZE2K0RFoNHJ4tk9tc2E44umkfrB/PF9cvXLJdr6oqtWnRnY4yPr9s8VMFHXtPDNhfHo2nR1EwcJm/dKWpVlbyxdNvVgsnG8xtpX2nqPvul/91V81JjsZzzd2d7Mss7YFiZPTSb65TkqKPLdquL46WN3exBjY1X/7v/yv9i5svvbKO1/97rcH/x/2/jvY1uy6DwNX2Ht/33fizffl9zqigW4A3Wg0EgGJGSRFUaI0lmRZlq2xHMaamhmPx3+4yh7bMy6PPeUqy5ZcHsn2WCXJGuVACSRFghEgAAJodKNzv+6X37vvvptP+sIOa80f+9yLJmnaVpWrJpS+6urqfuHec8/Z4bfW+oXB+Ojg4PL5y9/+9iv9votRX/j4c8+/8Nyd9+999Ve+smjmP/0Tn3vv5p3r71z/D/+v//EPfP/n/8P/4D9afemln/3z/9WXvvRLXfAxaZQIbJ2xxJgwxZT8okFky9awy2ARJImmsmAJxEjIHSRNSSmKqs7ndXKhDaICjQ/BJ4Ac0m6QMSL4upFGmJEMG0LLKNrM2ygS27aNqSNSZk6gklQ0NM0coJEE1nHVG1hrECSpRxWEmJNeEMEAAUBuFQgoLmeaxI6tLQwjpAgpMqomZnSsqilJCkIBkyMyNoEKTCYTssYSAwBxhplZ8lVQktGwf/78eWY+3Nur67rXH5K1TZe6+aJt2ySQHaVCCFEUIDP3k2M2Bj7ovwYAZdk7PWc0d+5VW5FwSlA31lomBtYYffbsM4UjoiSx6zrvu5RSknTGAlWElNMflrJmZCRIKNkwSoNiVKQQNUUNEiFEZl4Z9BmAiI0BARbJMTcas8fCKff97Jku5v2yZOaUonNuNBoRUR6ZppRCDJDRJRESCsJgNARSEE3Rx9DFGFTVOh6NBrPZrA0+O991PgJLWZZNV0v0EtXZqipdUdjOGiJwbELsQgg5cgEAkgQfQtW3zGwou8OLZIWWBEadzWbRp+FwWJUDV5WhDU3TWIMgIpotXkgwe7oLQALAHCWGS3yACBL8HBENqVBETaJBRDWl3KJWVVAmUkYwzIYYmSKCSkwBLIOzVDlrDTErChkGVziTILYhqJ20bWrnpVCy8bEnnv7C933WUu/GO3e2P/wxOJkDrTxx6aPNFZaFOTxoTeiKqjyWRYsgCXIyGoFhQmdAAKOkuvVJJUN/732bQhNbNgaAvPd10wVJxhauKpMqIVsqyBrMPXtQFM15XKiSAFAVUkJFxRS9V1XVRAgEChKj9yEELp2oikCmzYFSdgvNSnpr7bL7a03dzGez2WhYO+ey7HI5qDmVDuf+vZ5JZxBxqUOAs8tddZnh1LZtBhDGmNwwatu2C6333pLLuyBnAiqYvCAtWSBjrQUwyZgkgjnkNS3L76zSybLjTP3iM3qxYUQmxqiSk2WX/D0+I0/8Lij/f3HQD78V3+N/8NvLjA9+6/wr+B+A/ntkDQHUnU8hYIoGlFQxJUYNMUmKJ/MbotAbjIre0M/n5AowDOQBMqFGVFUhgkbQRKAEy+BZUYSzNFXJc2BDzGBIOX+IwpBcUfR75agsB4UrGSyBQ66gXLtw4fL65mxah4TWVQLGByn6ZWanB++TKKMpXQUMW5eu2apnC9cGP51OZ4v57bv37z14OOj1Ntc3VkfjJImsG69vEOC03S2qUk/byTHGHBKfUiqK4ux9Omv/C6jGnByzPMSYGaxF0NwzaroWQYLEECXGuknJ9foxxEVdC6Fzbry22gZ/dHS0u7t7/vz5wtjkQwih4r61HGNEWk4YmFlgaQIRY8zFp6QkiojMyAiUQBAZMeWRqSQAWNYE1toQJYWliRASq8JS90SCBMYhMYto2/nO12VZQg65pgrAKgUiMIASA0rmzpNFq5Ck9SGlEAKyM2Qy9w2SCiEQbWxted81IcXUGcPonGGL+bREMoYNAcYIGpMCqiQiVUVkZFRcfiMkEDIJKYGKQSBALWwWN3chptQtOqxbY0xRWucMmwKWZzIBZm2/Gjj1rsqnQx4XAIAhznMA1Zzco0RAZAgBkZAA0Soy4pKbCKJ5MLHMGeEszoOurU9DvZazBYFEIKV1Xb2o65pS6jnLhYkxzLq6AkEjg7L8/h/8AmysgouP3n0TGaoSoo+jET7zzPNPvfgReGIb/JHtOfFz6OmQ+9RWNJ0f7J+8/+D4g/v3Q1/4qZM3v7nzaHew2b+5c/P8hW1jewA8nTd378aTvZO18qQww/VzK/t7k7ffuk2E6xubZAf37x4/uDGpwKnE7luzyaIFC+vn2Ram7cLFC9dmk6aeBRImhynEB3dO9na++42vvVn0ipC6eTMvBuXWhQuu15/X3eZ2bzbvQoLIFRF6lsli9rB51Ai1QXwboqg1pnBWhESTEicBAQE2DgwtCaAcUwIkWOohKUrKWXXfO9YQmQxzHuMgWRdCyEIRL5o0xRiTT0XVM6JJRVJMoprzrlNaH/dAOakXaSAIoekbY52NvmubE8vtep+iT/ODW/7gcLS6ItyM13u9Cq5cevxzn33pEx9/Pvq082D/S1/6lb3D+Wz/OIJtQjqZTZNKMRgE8DGcjHpaVQV2J6k7ubg9+PATj3/xR39kvLKyefni/vT4lXfffeP9dx8e3rdMYX4E3iefRob7Va9XWsvWORwaAYoRfZSui610i9B1IrR3MN/e6m9ubseQDu/fH62sPf/CC2j47q3bh0f7k24RfXN89Oj1N1873p8Tw2PXBj/7q+8aA1fLCqve4ax+7/1XBj2cTH1VpfW1rdlx/atf/vXBSu/Hf/SLyvSdV6/HQMdH4fXX3h2PV8/duP2VX//6fNFU5YoGDQHDsrRCRSFIMQRriB0CG2YGIkRFY5quJY3MVHBlgQoAHwVEpG0FsfVBAL1oIkJmQjOrF0XlELH1UTWVZclMIOKlCV0dJEm2HGa2hTOGkW3Xhab2TWwQTFFU/bJXVdXhwV7u3xhGYquCqoKgBih7GiCCLO0ZWZGALJnCGAIT1XtIHSIaJgTojwaldSlqXTddGwi5NHYObRJRQKSciBhBE0rqFUXb1vNp2NsrFvPZ9PikbWoFSoidl9mibrqQ2JI1TOQsUFKB7CuKAJlP8dugf3mGcpg5jxmzKU0m/GTfIUoSvO+6TgmdJCLyMXRdd5qBQmenn4CCJsimmSIiaAxaWyobkNg0DRlnrLGWyBBGDaJZMCAAiMuvQ0QAhMhV5fJsGE6j1ogIEdbW1gA0+RBTkq6D2cwaE2MsiiLmi0clk6UZQCQ2ixZIEUAkgkrpzGB13OuVSQVJuWkAKIkSq2rq/AIklc5SScZw281jMkTUH1QMyAHz6DyfEsYZp+VwOCQGVEgpeO9DiElEVZumWVtbYTRt67uuNcaChUU9JTaKkkmkifInk5PyAgAw8FIDh8iGSTGFQJgZMYCEjIwICERgRJfSWAPGMlnDxhgAEWQFTZryWMIAElu2ooFBI4kYNMKm9bFp47gqTurJIkz+N//7f/nCS88+ePvlT73wBZgAwADUkIyjr4xW49Vzfpp2Dg5TjxIrkYGcdoLsnBUw80WbO1pt8KGJXfDe+y7L64MklbYLi7ZJKlY0kJZFlQkboMp5EerSBputYUsWQJR9il3XRFGi7NwvKfvypphzu0TkVB+R7/bTjGSzzCLNtDxEYrKG9fj4OGcMLT/E0zTftmkyEI9LQcsyqyeF+FvQrC4Z2pnxlW3LAaqu6+q67poGTvPaRATJIKKIhhAqRIOEp3uQiM5eNsDSUSSzBpYen/H01j9F/5kQMm+alKKc8jcA1CwDQ09h9+/su8NpAfA/2JL/3fr3/6RPrgE++E0/gPsBILPbu86jAiIRAiORtQbQMJZlqZoEsIvh6HgCxriidzKZ2WqQDQ9VARVTIgDOChE+VZfKqQiEiEIeOjEJMSBGpVw3rK2sjXpurV+tOTtAsSma6FnDvJ5Yyz0u3KCfBJNy3aYuhIOTw3njSWFltOoGPS7KBFqn5LO4NqVeUQ4vDn0Mh8fHk8mkruvd9EhE1ldWS1cEFUIsB/1J3SqbvJbyIZZP19lshh/4cJd2nKC9Udn4zjd1TgmczGeWcX11zXdtaJOPopoIyJZFWRRFWYq12Zxk0dSZ3pNtVR8+fDidTnNoIDNDkq7rYggeQtd1bPB0cS1RpWUTIEoSJjSG2TjEpfUn5RG2iE9+OVdVNUxRIKWcdZadAjVKBEgQgZAR2VgCJQGJiUWDRawK5woiUlEfUydRQhM0gZBBZLK2sI7QhKgxiiqmlEjyOWMUOYleOn95Mjn0J7PooxK7omJTJFwymmIMMYgGTxAZ0dhKEAUTAjEKGQe8nDH6lARQ0SoDGVOQJeuYbGq60HVN03jf+uB9TNZaNGjQKCKpCBIDK6JZDuVPnd3OCD/ZgMlYVlUJOQQYiCjGSGQQEUA/uKuLysQYNSYicM4aSymqDy1k7wPRGBUxi6+RgJfSQGutY2ZS1S4G8HU1LNpUF4NhOazi0b65sHZ4fHDhsl0chE++8LFnP/Ix6JXQHofXXkUTTB+/9dqrV5987NJLL8L6Bjzce+M7r915/+5v2dJ780c3jx576slqY7WHo40f+CTsH4S95tOf/cKnP/vPHL9/9NVf+NZ3X36LtbKmt7lRikAMMD2edV0aVOXx/HBlpdcfONuPk0Xc2Umr6+ncudXZdDqbtfNJhxH7Zb9fDUq2SJqSV6lVQ1mZ0crQOXdyPLtzf3+6EMESsMCZZ7ZRy7oO02aCPYyYRDUpGA1BHAEG1LZdqCAiZy/FjFSUcRF9PO2aJI1AyGR1GYsNiAmRmNllgf3SpkojSkwgXSAQTZpSMj7DlgheBBKiMJNVK12wRizFpD6GBoCtoV6hTZiEdgIAhYPNFdu/sHVx64nzl689/dGnHnvmcRj2oJ6CZShKO2uvET/1+OXQ3dnf3Wl8TGgwJWe4MjIc8M7uvRBj6rq61S+89Nwf+IkvXrtw4ZVXXvnSL37z9bev7xzD9pXq6Y8++9jlcWnss1dfuPX22w/uP0SA0tpe6TbXNldX1h4dHM6b+vjhg/u7h7MGyj6cv3Rh6/zVP/yHX3z5O9+9fuP9tbW155772Llz53Z2dr70c1/aXF8/nhwzUYwREawlKGG4Ut16NMcBOAuvvHNTOkgdqIc26riPewfH33n5jR//4o/8oZ/+o7/2a7/yX/4X/3VANYPB4VG3vTUAdH/xL/4DEVhb4/OXHjs4nAFasuAEPSqqICogDMqC2CYyCVVEQghJQmrb5BuL4pitZTIMCiSQFI0rNSWfVIGoKIyxAiCAhoCdA1GjgCDWFkbQx8Y5URTDmj/33LNP4kmTgrCBgojQEGlTT+ezyVKOibrc7/kqJ6Ilyx8xUzcwp6JwmzhgnlerJpGQSD0g9MqqMMxIAgmTaMhsBegPBh4FRFEkRt+GDmKAFFGkqetIeHzsCGEwGBjG6WxR+5iQupjy1BtE8s8BmCVqmmPYRfTMRiY/kjK+WXYuDVtrfEopVwhd1y4WixRCkQm2CJRHlAjZ91OznlLlrGxWVQECIkkQQkg+BqbSEBOoalH1iQjJJGRCAsZsdZpSzGqq3AYHACJDpEXZU9VTfMRExEyIyNY0TR1jYGcRsfFdCME550MIMSYVhaVtRkwSJSyamhnz1QQqpOItGwMhBCJgg/WiUQTONdhsls3dETHG0MyDquZY3yZFAFDM81yyzlVVVRTOMoKkFKIPKUVImRkFQM5ub2+vjFaPjk4e3H84m00tmaqqVANYyoluTAhIWQmarwMEJQbO+BWZEB0LKiItDR4AbLaPAwUERFBENMSOTWENWRMkARtEw0DARo0NhglkOBw08zCfLU7q2pEtuShdj4nmfjEYrdy/tfud77z15nvv3tu59ZnPfPI7r3z7qSc/3K+Gt97ZPdoL0HFhir4d98dFqjqPISUJUVofQupCAlEMigoEZABjF9p508YYRZVYg/iQohcltmiIjE2IXpPEhEm4Q0ZjiBgJCG2/JCJQQlBJmDuCrIkJCU1K0cfY1o2m4JwxhRPN3hgGEZNmQEPZwCBGiTHOFzMRKUvXdV1G/FncgohuqQxJbdsG74kohJAtEZc5RKdj+SVOgqWNiYgMh0Pv/WKxICLnDDP3ej3nTNO1BqlLUUSsQ2OMBA0h5EWliNn6gYgMMyimXKAlUIWEigiQNCWpiirvKQVIUVMMHqOgKAmAEqEx1rFhyjkyHwDxZwj+g3D/7Fdyn/53++/fViH8bs8H/+4HUf5v+zMfwP0AkOsVW1VZsEmSQIFAQSWi1HEZrty03aJuhyvjqjdoEcgsiU8qgkqMOdkCId/ZmUy25FMwkGHDQpzhnSAoiCiqwNFkTr4YgAy0iJBM9Jo6C1JBwijOuGo8aD3s7O4/2Nk7nDWdENhyOBz219er8TghnSxmcT6vmwYARWQ2m/kU2ZrxeHz+/Hnv/eHh4WQ29TFYNmVZjgdDcs4KiuJSQ5KWQVF5IsTMebianWQRUTEnT2lsmxRj27Y5N7qwLsZAqLYqmVFT6rpmEboGkkmFsUV/MMjEHhVRBOPseDxu2zZ2vt/vj4ZDFWm9DyEgcNd1Tp2ePrSUzgsqgKpothmNADGpMKNK0qiSJGnC0zjtto0xBpXIjEqECClCCElAk0mETGSsJWu5REtY1fU8RW2bEDwQi0JIyWsCiwUgSExeOqNsXEW2dA6bOqSkKSYAtrbIfEufusPJtOuisYWr+rYcCXHThrprnHMpBQkxxZBiJBBn2DiOOZYUNHdOGCkCgIJi1vWiF0wCCRNKtIhGjXE8dL3cv/K+bZqmWzTOFJA79mwYAYmMtXY5acoXqsjZTAROo75Uz+in+axeXpaogKS5IMw5Z5BzfC0hFmyAEqysraSUkk85Qsx7j9kIIoaVwWB1fcUQhsWiWcyjRmZ1pem61Pruy7/8SxvnB1cnW3fv7965HaCBfm8IaHbfffdksr+6ORiuFG0In/7+Hzvaf/jet1+xo2q0sXb5Y1cuPHvxg1v4+nd+U9EVOpi9P924eDm88r5dG9q1C7Bwe2/e+Oovfef29d2VlRXtkohGHwvXH45XRoPVo8MTH1py6tN8by9un6cf+OGPbZ7bqNvm7p0H3/7W3dGQN9dWNZFGQUxABlF8mA+qfln05l3z6NHDuw922kCLVo8nqTcsifh4tpjP58bZ3rDqDdYXeiLgfZIYJS55sxUYosTLvAkCzTN2SKLQK1wTY4xNCCFKYOuQrTXmA86GZAiYkAAVxHufREAQJIoQADAQE6foScRqVI1EwKzs0FIq2GxujC5fuXbh/HhtdTgajEflWq/odfUMtO78vu+Oe73exXNX7MY16I3T4dH7b7/x9puvTU8OelUpMc1O5r7DusHFNDrWgFoY6vd6XfDTo52qjnrYbmzAlcfPPfnYld/34z867vf+wc/89V/6xW9dujL6qT/6e89dvnzl2pPnnvoQlBW4Kr3+/krCIdjFopEQh73RpY3t9fWNO+/dmh1OpJEnL1994ukPPfXhj5y/fKU/Wn/jvduDwcqwN5ydzL72q78eoy8sjS2t9YrttatFVR4cHSWV0cp40TbetwWGqnB3b9Wba0AMJx08eWEQmvbBcXzu2ua9nb2/+N/+9888/aRCUipB9WTSCkAXqeiNP/aJ6uTkZDatHx2exEgRFJSzOFUkSQoiiVVRKGkCYmJFItEl9RxzwHAGeinFmCQIs4mSQpRsswmGokBIsVOQmFCBjC3YltZZpMq6je1h3Uxn9cJ7r5CI2AAlxa5pRVEjggJyQmQVCSFUVd97v3TrZyYiY9kY0zYd5FkSamYbI5IiWuOiYJRoEVBJc0wYadu2i+kshETAhgt2FtRElSjJa0LV3P0mIsPEtpDoz29vXdjaNMyTyUlXN6Kx7toggKZkSwZSUBRNSUQFAGmZG6OYXcTPSAvfQ+qng8oQAgAs+Qmqqpqn0ksUa9iAEdAkoqcuBTElAFiijlPXZ0QIvlVNoJqiMgqwtdYSorXWh9CGEKIXZTTG2IKNSyEz9zPvMWOsJRcWABTprC/FzEQwmU+7rnNs+v2BtbZpGk1C1tR1ndulqpr77iqSNDrnjAVDLJJi6LrYpVlXNxxjZObO+0XbElGv309RmnbGJD6/M4SWDVlG0hC7JLIs53BpDmVTsiJt14KmGGMI3nsvEpkYEYfD4fHx0WI2d65cXRurauwCMiMqW0LEBIpEzKyYk11OveSICPjsggAWWDqBiIphNKoMOUQTINu+WEPOOMuG2ZA1ysaQRTQIDAxAmIiaEL2SmgIoATs1RacUozy4eefqhS1y/a/+xsujtbLfL955/Q6G3t/+qz9z/txlg73hcHsa/P2HR2sju7q10tBMJPmQ2hCbLrXet01sYzK2zDE7SSVmKZwxjm2MOaUUyJCtSlsVQpRUNePopKLKKJgj7pmtKSImSBBSilEQqSidIfY+QEoimkJMoRMRQ4hWySAxW+uISCAzduCUscMAJAkyLa3f7w8Gg8lkmhkOudWaV7ieZlbQqZBXP+BjCKd6vCwyya7e2fknl6y5kIgxhtC5smBmiHFJ2iEEkSCBAZFZIAeEL7+RpOUMAU45wyICp2aRyzWASKfuRoJCzhChNabIvoJIy7HHP9GT4fgHCTm/83f/53yF3/YXPzh5+G1fXP89zv44yEAECpoiajY6Ek2iBE2MFrEcjNVUCbATqXqDtqtRUw5Bzzc4KCkCag5BZVzyKcCyA8NsTcrvM6gAgIiKJISTkwksFqa2OOCAWmnbAykN9vt94yxbs5hNb959cPPOzqxN3B9duvS4JyPWkSu6JF0KXVRBWlnfAMSQZDo9CbO5996wCzYMh2NEXCyaxWKWkq4yVaLSdGwKiRJjzKzvM7no2toanj5n601V2xCH/YFhnEwmOUjEt/Xd+/e2t7eAGMkgYxQNCqrACjGmghMzB0ntoo6nyWKrq6vHx8e+aZehCiISomaBzVlmXMqKEoHcU8sMzqReOgFUQdHIjKoqAnLa4smvOaUEKogkOdcFNSkAmDY0LGwQiaLmEQJZ58DacQid9/OubQEiG2UmRjSY8a60MYXOKxpjDaAhMikmVRFEIAIkCRITPtzdRxS21oKJPoiGRRe7rqubBhGtMYYtIkgKApCUJV8tSZCJIgEySMpGeIokgEkhCikQKAGDApECohIZZDAFV+hMqJqmQUHBHEcgAJKpe8tPLreSQaKIVEWZUtIcEq5n+D8SWsRlmbXsT53+nrWWTWa7au4lVFUBokigqhq1iyF2HgEYqV8461xRVYXlgCCaEJWSzmYz5+Dg4Kjo4aVrL5TF+MlrH26PXp0+Cl/76je+9ZVvFYY3NseLxaIamq0LWy///a9IQSuX1h574tzKh8/DigGqP7idf+P6jRWmnfuHpVQf8c+sfHwbUnr02nvf+fpbt989mOzDoKgev3Tp1vUbxAalXUznEhZkytLCoFd++rOfvHhl/XCy5yp66tkn4eJ5QHrx3oM/9GeugRvCcfvotbevv319Pp05dsbCxuZzXeyAtOz1TFWdnNTv3Xxw5/5hWfC8Tl29GJTl2miUNM7q2dHB8cq5KiqRgjC5olf1BsxWEpkiqmBKCkmidEmWynpTWWcQe6UripBAABV5CfoVVIEQGIBFECRpFN+wYUdOkVAEFSwgI4HEvEYIECkSC0pEke5wD+zmKg0/dG79iSeumpV1wB4kgClDNQbq6WJwePTovfdfvvvln3/wcHq8gKYNs9lh9B2qtg04gs2NzbZRpN64Kiunddcu6nnTNuznqwwvfGz0Az/yA89+4rlqXKnV3/jNX//Zb3xr7TG48tFzH/v8h85vni/WzsGI4N6txb3Dn/17v9oupHLFajFupKXABw8Ob1+/HxZaYH97bbC6udUvNt99894/+ke/fuvug2js4fFRUbh+z9mYtOuM0vrKuJucWO1X/XJj1OeiHKysHp0cT+ZkLW+f21oZnmxvbFo0x/sHpSnv3rz1+U9/5PjwqBiubqyu7E3me/uPiqKIoODs5oUr8/nJ8bz7fT/5xarX+9rXvvHKd9+4cvWJ6bw5mc3bposQBVJO9yjYYFJkMMRkLTvT+ORVjXMEccmKThEADVHKBnjGASZhFIQgyYtGSUkl+WSIerYwxll2g7LqVbbqYYiNxhTajshwwZkuUlonQAG1a71osgarXtnr9UKSKEywpOUBgCiIgixvZwPACATKAIzAlisNXgWQmSBZsghIDIvZPKWkisYgGYNsRUkEmtj5FFDUWSYUZ6CwReFMbJphVY7H46Ze7O/v13Xd6/WSqBgHBKoATKhMJqeKRgQ1mHOIbLYJJf0t0B9VEUA/QPTMp//Z7xbWMrP6gKS4ZDirpCRJzqASLCNp5Kw5GmJwzjhboohhLJxz1gJIiDEliEFiWkIVBUpJNUned9niAhGZLDPn0gJoKVGgU/BVlWXVLxmJmIKEqLFrm8nspKoqZFDUM+IsAwJD57uEBAYMUa/Xs5arwljH8+nMe580VljS0lZRSmdBIyEaS2wNIwNi0pSSkDWgKiC5Humi97MwnylJMEtpgVpGUxTWFswMotPJpG260WhlOBwPh8MF1TEGWzjkbE8piGSMY2YRyJ2/5buqS9K5aBQfsxQQFJeGgmpAlcmCAiOxwUz1yQTFwhkhZGABUgEVTFFU9f6DR0TBMRdMINgtfFsvQl2PVzcnTRqNtjS1XW1809y/+fL29vbm2sX5xB8fnYxHG4Pe5nizIFPuzyeNTjpps2A3CQaFLsmi6zAkZKPIScUnATY5mCwKkiELlgp2/YqcTaQksqR4KbGCQ+uMLbkAi4EAl76KGRUpI+Skm+DbrusgSVEUhtiyQcPAmMmcRLTU4sKSgZZH8UVRKJh+v0+EVVWdnEzyCj9bJ1mh27XtkshnzBntNvNJcklgjMmxd9n4L3P9M7nC+3axWLRt27a1cZbIOjZNtvQgTElyuchMxKyqBoiREmhKqbSlGszzzNy4zeiqaZpM/TKGySx/QCAVK0oAREIojJkV9D+N1P/Hn39Sqs/vHAv8TlXxGfo/rQFcWajivO0McmatASqwUZWkSYlC0K4Nxlpl0/pu1rTM7CyCKEg+rggxI3/O1ClVUFIVFVKkzIeg5fweABQREBAQebyyUsQ2Re8X3lN0FNCgdY4gNbU/OH54b+fR/vEMXe/itSvDjXNYDhdJ5yFOvW/bLqhEQDHFYl6rqihaW6yvVUljCGk+r0XZOre2Pih71Wy6QDZtniZ109wSYuZeVVlrnXO8fPHfA43LRwRAGbW0LlU9YwgRUwoQI1snmrrgNamIKJvsEBUldT7G2Hrvu7ZNKRXGVkWJhvr9ISmFLtbTuqoqQs4upJqjxE6xJ6ISQQqZcIiSfWVFVBEAQgx5Synkmhckx4kzE4CiJFGRCCAgCGi8B2sJGDVCTFEgFFYAoCp6qhqDM5AAyeCyu5qCmIIcGwHxAho1akwAIgSASIzAUUBUkqgApKREGIN2sUm1B2QlZuNi1xEhobGWRTgoCIIhZxRSogQJFBBZBTTBkrTJAGQJyBImJCBHRCrgl5GOSkRs0NrKup4PAKdlT37fTD5i4PRyYmYCo6f+oNmomTJ213Ta9V8W/QTIBo1hY8x8NhERBq3rOsTOOdfv963lZlEby4QcY+w6nz9atma8sgKEddskMYXh/mBABLEOzbxbX1+fzA8vXXzisasf6Z1bf6pjqbnenD5x8erx7t4rL7/86FEzWkVT6Ftv3nnqqQ9vXtm+8PFr8KF16NfJ73a2+eA2/1P/xv8KjhmOAHy1f+/2a7/2LbHyzuu3rr8FKxVcvbjeTMO7b3+XFYajsWM6Pm5Dd7wxOvf4409fe/yqqWDeTR/s7Tz+xGVgam/eiDGlqGVzG9Tt3Dt49eXv3rpxmxRWRuOyLHcPDtvQEoEriwQ6mdYnk7rrANUZBVMY5Hpe79f1fDCqrl05d7Q4ikA9YmOr3mil1x9Goabrjk9CQlBIUZOAMKDJkqzQWFf2+0O2zgvUTTef100XnHOAgppyVCSQgiZK0SRxbApLAJSCaIxGExOpRMtKKAheJWj0MXUak/OAU2x36WgMZZgS8fFhfXIyfePN7zbtEZUyXgM0cDKFtgbiajormfuQHEpaGfSqtTL6JBGGVX9RR9Kusq5rg4T5pXNrzzz+zL/yz/0BaI7BKGBs50fHfr66Pf7X/o3f99JLL5m1TbBV/cb1+69+2yXz8Oa93fuH2umgGDjjdnd3792+Z4yztnj4aL8NKaGJSm9dvzWZNzOIBLYoel4XvaIY9XuQun5Vrp/bqApTVq6qqv3jIwihYiMiJwf7+4dHiDhfTGeHx9sbm7eu34hdXBmNTWk++7nPO2OBzKK7/+DweNHURTVYO39+0TatSEQrxr3yxnv3D/e/8IXPb1y8pG+999zzzz/Y2U03b9ddC9EzQsJgIQ5dKYoRUYxRgiQptN1isRhUhUgQTRaB2QKRZWMRDRowIAgewIu0ElUBScejQQjBgqmMs0ok4NAMe/29o526qUGwV/adKxHRd6Fr29XVdedKFazrpulaUEVJAhJTUo3GcFVVzCYsn1SWJSghWkImOAvoMylF8D6FDq06UhEl0ATgyqrs9aqqlwRmdTNddMFHYSxKh14kBdaIIhq8EIOmgune3TsPdx4Q0WKxKHtVUZWLrguSxEPKjuCZ/gcEESUBEVnHlg0zMf721mDXdUVRZApEvoTy9Bk+4D5BAEESnab85glnvq7yCZbhyxlqUVVnbFX1Cms0BYkxJW1CF5MnNIJgbWEdZx+3lFRiWkxnuVojotOYR8rE7kyQ+OBpqwp10xSlTSBN3TJSWZbOubquMz+estFqPtkNW0PDYT8mLyEE8QpoXWGtLUt36cL5o6OjyWyKyCIyr2vv46BfompVFUVR+CjT6XQ+n/mYAKmqqgSIiGycMYSgyYfGt0YSWCy5cEVRlmVZurIsrS3ev/7e2toKAE0ns0ePHhZFZYxDJiXAbEcTBQWsYTIOSR0XyJR1ugSYNANTIwZiChBUFQmspQLVoOjSJJCYSZmZiYkYUYyqKqkoaNBEgJgIQMGQAcSUQh0SgxDYpCTk2kSH06NIZjwY1F036g/HK/3Dg6lls7q65tyKimmTV0fJpOPjkw7aKF1UYLJIRhkC+iAKkqJPUVKeZpBhBlRVYwtWVUNoiQ0jUYKEkDU7xIgOrSPj2JWmAMOtn4NhYy0xI+ZBd0JNMaSua0Ib2GC/7JdlyUgRZBH9sm8qyyW+bJCftlEBAJS8j13XLBaLvMIztyeL1/U0cC176y3X2feacpoXf4b+KaUklPfgYDBYKl5AMlNuNBrUbYOIxhhzKhtICbM3LhHlwRFo1gSmEIJvYs7zWgZlnMoMchou47IazD9UIlFEUfEp5j3LRJRHb//jXP/f2Z7/YOP/t9H9/4kIP7/bH/sf1P4iOlsa50AkqsYoueUfQiyKgosytG3btYPBYNTree8lBpKUXdMAAIEVQRkFwVirCiJJYkoQSCFJZEwmiBIuJ6aAqkoilBIihrZbdFNLno2wkV6/TA4fHcybENuQLly9/IUvPrdx6eqDRyevXr9xf+e+FD0tq+ScCProGx9SSoWrMnNTRACUgJ1jdpYQJYEtbZ9H80VzdDypKu9csb65kWmXZ0PXXHC2bYsfePLZm6epOe9vOByWpZvNZrmFvyQKETAZJkihbbtQ+2CM8d6LCDMXZdm1bQg54ALLskTRk6PjHNNZ5YSKU8bRsqbF5cGev4Kx1qhG4iiaJeYphFMmkkZJManq8thPyx8maEqAktdqimzYClpVFa8ACRIhatfMUgoqQGCZLYIkn4KIhmBN6UrnAJJPopSiRogilJPFQCkm0dwQBwJy7NhYmwDFpxCzLDlWVQUATKzIqkkULRtjClSIwAYCERo0RKCSACIiqjICJ8UUVVQFPBDm3EBjizxlFoldCCH4TCn84JQGX7y6oR9IB0TEHBzQKytjDKB677u6SSkZQ0VRpbi0sIClXy9lwk/wrYhoDEuzIMwVhRhiVUXkM06YxpRi7FdlYc2wKktnLaiEkKLnFHCxMBhdRYNx2Rs5QxrrOs4XP/lDP/bkiy/BbPHgjdf3Dnev37g+nc+eefappmle+MwLm8+c83biLhI8vX609+b61u89262vfumvjPzo4N2DG6/cJ4X+drm+uf3g7u6t97p+CZUpVoarH/vws48/9WSsm/dvXD/YP7py8fL2+cs7O3tvvn195/jkcHJU9LjqcVlZn/z+XocIGuDgERiGtZV+VzeTYzEGBgNOUMxn9aKGsoK1NRdTaGo1Bsqqt7KyEmN8uLe3uma++GM/+PHnP1K33e5Bd/363a9/+9tRlF3FVf9oMueiPJkvjHVkXV6fOS0VkQe9fteFzoekTLZUohDBew85ZkGCSrIopUFnjUVwTMG3xrj5dCYhbqyvI2Jom8o5386Dr0E60IQARQnjsnr+qY9c2F578+3f3Nufft/nP7K2sX375kMf9Ob9G/2xqcYJi7B+bvPqE0+1jXnttZ3b7/n9nXnbTNZXBxtrQ2eI0Thb1nV3MlnUbXdwfHw0h4++cPFP/At//EOfeyl+62v10b432N8eVRsj2F6FjRVwDCG0u3vvvP7ur/zsL7332iOXAD1YAGf7de2nxwEABn3u2nQygQ7AILgeuXIY2UY0YBwbh0yLxSKEToI3KKXBfmFHw/5gUJ4/f/78xQsndf3q22/vHR5hWZB1AnCw+7Df6+WQ8H5/OB6ONjY2VtfX+sPxoqnv3L737o2bPobVjfXClYumrvq9tm3QprKy3teTyQQABoNRvzcKIYnI6ur49p0bPgQC2FovFwftyng4OnfhwcH0sA7jrXPztj08OiAJpaHCogFFYGstW2fILibzXm/ApeuSzNumjSECJNVRfxhCEi8QU0Fu3BsUtgixPZw9EvUZKPd6vbIsY5SmacqyzL3YwWAAAPt7B/P5vDfoD9dX2dmjw5PFoinLXvDR+zgerxLbnAFs2BrjRCTGqAlMQokppYCUyrIoSlvX096gKvu92ofpfIbWBYVF3bK1qFoZ9E1tECxi6tqSYaU/rKx9uHN/fX3dGBOigOUQ06xeeNHGJyFismQNcYFMQBnNLwkkjg0iqMQQwpu3/tmzff3sU3+nLMscKhxPeaUZfGSo7b0PwTvnRONZMXBGSll2JZmZ2Z5ZGhAupQXB+9BGH0QSExGBc+WSzwqYVFofvY8xxkE1oCz5QwQ+xVjM2YQCmfNwOXfEVBMZEo35S+UX7Izt9Xpra2vGmN0HO95751xKqXAuRt+F9tQWRlLwMXSWyBVmNOhnsfJgNGTmeVPHINbaQWWzKV7bhel0ejKbTifzRVNvbZ2rm9Z7r8TGGGI2zJZQuqZwNOoPqkEvG6R2XZeS1HWdM55CyAE+lskiM5DxIZVFkWV8WXLadd3aeKUNHiWZwjk2PsWUEpAmCAlSDJCSsFpDBQFBEk1AIJgESXuFK0qWFNt2XhhcGa+6ot+2IQqxMU27ODraH69UTEpEjAiCkhCESbF0zhAYlMLosCp6jiG1vm2YoOu69Y2tw+OTV197HUyxuXUO2QSNCTJTBRVMTBJEY9K29RkcZDWIcS4Xlr6JripdUQhKG9sEWhTOlZaIfBskxvPb5y2Y2WQKCUxZ1JgiaK5I68VidnKMyIN+dXhwjJo0QYbFzBw67yVGYqEcNgRLPQQaYkgqMQY0OBz2+/1+krBYzNq29UunIF0CdGNQNNtlZuzlvc/aPCI6I94QkbXWGauqIXrvPakgomF2zhlD2RW0aRbGGB+6EJMxxhgXRdouee+Ho9WNjQ1TlCEEJJf3FwBKQCIDANn+BQCMs2cs4u91CfPeIVWrAqKSOQrIRJbMP/6lT/2uMP3/a54f+qHfRDIhIRAxEmmOQkoSE4hYa3NkuJIaY5BJRDDFEhiSJEVFEmJR8KKZVKaqxlBRuJh8VzeqqXCmQEo+KJErKudKESFJViXMT0w3d+2sH5t1p+fH5ZWt9dXV8aPpZO3CxRc++dKFp5893Dv6+V/5ymvvvNeAaSJ44zyZJkqTUpdEiYkotcGxWdoZ4/JTCykWVdX6LgMJW7iiKJAZVUHQ8jJsJIei5MY/fKC2PKOWqWpRlmmJVTBnhEUVEWFGABBYDgqippSW5pACKjFlqhgqLIliURDRMqcQZyeTxWIxHA63t7cXbcPWeN+F0BljkoQYvXOOkXKa75J8SEaThBDaej4aDAWUbdGE0HS+KHuLxQKYVFUkgsTsfb+0tiAHpy+BFJbCJJTSWpWoEBASESAqgYBKnmwgsigEJVESMEKMYKLo0tk/pVNfNSktSnbYQUI2CtSFFELoDQZ57Kma5eJiiQ2xAXSGnTEiyxhQAiRGhCyyQoEl7UdAFUiIczQnIqJojB4A2GDoPBEZyppeAAD87FMXzqqo/AMbAkQMnRcR0SQitLyGgcjEkPDUXImXaQCIiIUzAIBLqmquilUhpbBkJWYzASJCQdXU1LVlqqytrDGQVBIpWNWeCsQQxQtGU1DhqGJTALzwoY88dvEyhKAS19ZWHuw9fP3N19puPhz1xmvV53/0s/0Pn79369t3Tm5tPLH24R/8w2c79s2f++tPrj/ufPH2N9/Yub0n5J568lnH5XTSoPB8ulicTPtVcXyyf7i/H1O4duXK+vrmg/u7d+/sJHI3dzuvsLIKxFBWUFZ2OgkpQgrw8Wc/euniY3u7+3dv34kxtvX8/k6tANvbF1LqHu4c1gsYDOH8VnFuazs3TpASUuwN8EPPXHvhk8/CuWtQ9xcPJ9/49stvvvP+nYe7nWpiO1pd25tMiC0QR1Efg4gooUGSLjhXsi2QrBeMCQUYESeTCYGgiIHkSCvHJbNBKFBRMjcUSAFQSufY6O79R2UB46E7f27jmacfv3B+azY92b1z7/LauXY+qbu9wbAYDocPHx3cvLE3a2S03u9gsX6p+uiLH372xY/D2ubdN2/+yi++9uWfuf7E1Y9sbYyPj3eP9nb6vfLxa1fPn7uQggDxZDFX0s9+4VOP/ejv1Xu3/s5//1fstD58+Gje1r2N0Whr1Y6qRuPJfMbOPtzZf++dSeVgXLlm6lMDkxNYH8FP/dRPSIj/+B//wt4ubG3BE0888aEPf+Q7333jxp37h5Mw3hivbl3oEhycTOq63hivY4qDsthaX1kdDkF87NqU0qKedzEE1cHKajkaHc3n+8dHRKydf+/du0SwutoHwKQyGo8vXb1yMl/4JLOmnTZNE4KoBjmTu2nVc67Epp7NF02vKra2tiToydFx1zTra2NJXdfUkmBrzT527vKD+3vBFAvlk047NHWIrjAqniQaEcZk2RRFZW1BaDBJSBpSrENXN01EHQxH49X1g4MjUipdUdgi1L6e1aGLCsn22BaG0eRmydLUkrnruqKwzDwY9kaj0Xw+PzzcTyqzdrZ14WJKcngwQTAApmuF2DLb0cqaMWY2m3nvq6ooy5JVoPEQg6q2wccYgTTGSIVF64SwiyGANMF70d6g33M2nkzUd+18Vhnz9GOPPXb5Umybvd2H/X6/3x8umvpoOlu0Ye/oeNHU4/WNgCTZXAAYCIlMDknN5pjZBINypGgK797758/29aee/zlrnXMWAGMMTdO2bdM0bQg+9zFENKWYVb6yBN/LxhUi1nVdFMVgMOj1eoVZOgKllOqu1jwhEQFVRLVs2JwKovPRDCQiCVAVCy4y0UJVY4ZTxhhj+sMhACT9HoEHkYnAla5u5m3bnjVcCuvKsuz3+0VR7O8+ijFmN2trTAhdUhGJCIIIIB4kVdZWvSJ0bdd1xDAYDGyGCMYNeuXk8KDXq8qyUsIYow+hbdu6803dbW5uCuDu7m4X/Hg8VoGjw4NhyQxgrXWFcc6xtdZaZjubzbwPXRtiFASTzXAUOCQE5KoomVlTKopiNBgaY46OjobDYVUVy6IRIaXUhlYdLr0vg0iAFERigiSGGVMsXbEy6vXKQkFQAyOs9KvJZCKJ+4OVKHg8OVGU8Xg0mx8y05JWBEaFQUgUY4xFaXvOWBSHWrCgRk1d6NokMUp4/+bNH/rij1y++thf/qt/ZTBaVTBJMCUVIEADkDOk0YcooJJAUIgo41dj3Hi0umi7plmoJlsa5yyAiMb5fD7sD1ZW1jZWNqKPh3uHs9msCXG0vTH3bUqpLIrCcAxhPp/7pra2aOumruuqqsbj1aZpQgjD8eikbhIuwzSXBvjAiJggqqot3Wg06PVK4uWVure3l7n+eaURESQJISBALsNSShpTZuCEEOjUtFVEKM/VCmuMIZUQgu86EcnTqZRSjL6qqs63PuS8i4KIfISYknPV2vq6Ma7xHVOxVIwgaSRjXDZ4ydMzoCW/6HsTsNMnoSSKyMhEBolgCV7+fwL6/9Pn/4PPlXN/adnGInTOxRhC6BCx803XNcw8Ho6MMYi8pB1lcXnorly88Gjn4aJtVjc2my7M23YwGp9MZsaYBKqaUBSzulUBgAyXmR9HBCg5p05AEoCgJlUhBsPKjKgAqFWBiCDAAhgTRAFAq8RNGxQoVxFnNRKAlJZAUnZ+A2JAVsWkGkJAPi2SFZg5B2AXyAUbazlP9rKFLhFFSbAMLSaBLJWnhOBhOVIjyi4UCQDYoKY85IGcZywi5qyLoGdmwKeinBijDx0iltZZawGyFjEysyFmQ9mnP382+dwiOiW0qaaU2bHJ2vwWUEyKIjlDbXVlnVAxBR+6xncSvGgyUbEaWrTATiG13ndeW9IC8de//t3fhNcXk7n3MB6TKVxInpyuSdg9Plh9dWV799Grb73aXymfeerZDy6adbvp6/hw534Liw99/OlLn/kp4BHsn9i3rz/aeWRNb227BAlPnn/645/8qHWMCotF8+j4MJAeTbvLT47J2fFKHylevLT9/PMfH2xswXgVDudQjcDryY1bKxtub3e3a+2Faxur61c3N88PBr2umd29c+v2++8z8IVzG23drays9AfVdHY4ne0vTua33r3hbu7u7aY7945u3rs3rZsr58658ehk0T7YOzhtKmTrkwyD2DCY0lpjXGmBCyvUdqHtYvDREEBSBnWElcGKsTRUkOpisbW2djQ56veLfuVm82MuwmI2+2N/7HPPf/y59SceB4gQI0gKuw/vVnD/3Vvz4/3zF1eGK8U777xx/z4MB2Z9fROcpd7q089d+cgnPwHnN6H2Zbn24Q+/8PB918zw4YNHTLI63lgZ9a5cvPLcR5/d2joHgwGsrYLRvTe+/V//2//Ht969MahYpuCw51w1fVBff3evaYAclD3a2ZWVNbg8WhOyk2lzNA2rKxuf+cTjX/jUsy/+9E/6t97sZPb0E0/2er2vfvWrL3/3q4OV1S88/onh6vrCp+s37+7evatMFy6tpfliPB6Mej3DqamPow+z6WQymcxms+c++tHV9fWDk+Oj3V2viaNv27adLD725OUf+pEfvnbt2u7eo1/7yle+8/rrxuLRYhFEwFXlYFDZcRAxVTXsD/Z39mazSdN2YEzZ6yMTaJpPp74N/aJc7/fWx4NPffKjTz9x5bmPPLX29FM/91/9t786//qNh/sR7KC/Ou4PWhFXFifHBxAFJTkkx2xAMHQgfm19fTabtTGUpV0pzbyuu/l0Ws+lC0XVP97fJzJXLl01KkfNUX8wEBJDziABaUgBkooGxbixOn748EFVWk/hwdFuv1+tjeytO3f6Q548eq9r1XDVH6yDGCjK0Xjt8HDSnJwQw7BfmdGg8/VicaJda0KDIRZlVbEl4xKAFtVkvoht51NsNUVIVBXDnkvQNouZ7eqt4Yj7blAWVzZXbGgm+4/SYv7qm+9fuLTpo57UtesPjbMlD4era022hxc4az1kn3I2RkRw6XqYmdDug/uaGFQlSZAEIXYxBtGICMTZDkFAFElDJqaAACnR8rBCxI2NNQBAhK5rku/O0AkDAi71xflQTAogy6BzjcvJPRE5ZiLju2iMZV72YTI5O5tei0iIMZdkRGQtGWt6/TJJF6PPoD+3hwE0+tYygiZCZQLBpe+HtTYIQBJCRbRsbK8q+oNq5cL5uq7brlbVJCoiJDGDwnqxqBeLbLsOTCklTHFlWG1vrltrWePJybS01sdYMDhmFe873zbfg7yZgZZrIVVAxsz5jjENx+sqbJCstbYyiNgu6hDC+uqYiDQlWKbLK4KW1k3qBmx2KWdmtYSudJZRUujZXopxPtmrJzoaDgpjO98+Otrrl/3hYEAAMYZ+4Yxzg15fxQPlyg1BWBREIKlOmmbATKxNjMk3mDrCZEA0dZPpITvzwz/54//mn/uz0Cx+5hf/4cHRhGggiUNSAEQSZot5ysQmCy8BBYgUOYGqyt2HDxXAGqoKV7KJvltMjubz+eb6BrZdfXj09v37EhCI+r3h2nD06PAwKCApiVajwajfQ0kT3+7v7RJRtknpD0fj1ZXZdHFwdCLGKEBe+wJ06tKGUQRQWCT3ZS0uk9irqlomUn/AiiMzZ5b7gggNZvFuCCETnE+HeOKcQ+rlwVTm88QYAeRs5p//nZXmOY0H0ZzxBHIdC5pyuZtHFJayFYVGVCLI5gWcE7zM0nZs+doA8tiGMP9BQP2e/vKfPv/0+d0eVxbe++l8VriKTvMrINPMOp+IfNkqWFZWVcqOaoYUzDtvvD7q99bHY4u4Pz2JgIU1RWFjjJmMD1lTJQlBBTAlWa5NBczsUFEQRcx5V0oIiiaTJwEiRCUWBFSFKCDKWW5pjAIykdGzTaoRkjADMC/LcUAkyNfHyUmXouYbiM2pDzgoigKmnALMSIA5j4syh0xzZrqe5qyhqiXF3LRCYshO/kyZCrj8B0DhjOvPp0qjTPgBACCG04ifzNVRTQDAHJiXmvRs3r88L+DMqwRSSjFqCJIk5YR2QgNKIpDNoBNBXU8sk0UBFQPkqh4zWoFm3gaybFnZKDiFJKJRpGccIw63h76pTxZzA7CycX44dl3YNwTv33j07pt3unpx6YUn4HD1g4smnpTfvXX99s07Wyu9jzz16ckr7+7t1Hfv3rt9+y4ifuYzn/rIZz4JgxJcgtAevf3Gm2++Wdre1Q99ZLB+6d2bt6hXXXrs8qc/9YK9sA6O4PDgjW99/fbNO8PhikYaD8br6xuDFfPuzYc796e9Cm4/uLu9de65jz77qU9/5mM/9UPw/s1vfv3lRw8O+v3h9tal7e3tWT15uHsnpub+vdnh8YPQOVX7+GNPNiHOQrd/eDL3oesCGIOIxAZIOefYGXaE/cIF71PorDFVv1+W5cHhZDqZOVdqFNZokRyRRXWkFeMnP/XSoHLf/ObXU5gP1objcbW2Ptx5uHjqyZX1TYSwM31w72j/oLClY1P1Nchk/dzg3OVV1dgf8GOPm82NJwV7Ccn07IXtKzDaho723r/z1a9899WXb3WTflioMbE0unv06MZ1mJ0cIKTDc3tlWb7y2ne+++ZrTz/35Bc+/9nHnzj/ta99M/X7saN20bHqxmDDjND7oKobV8o6iOHhjQcPsexfvvqRhwfHO4ezn/nZn//7f/ev/4Gf/Mk/+Mf/6Puvffft629+/MVnf/jHf2j7/EWsqvv3dn7x175ycHivcDrq9wqTCBm12d07nJ1MGGnUH2iC6Wz2xS9+8cPPPnt4fHz9l27s7j+6cOnihY21yWQCrtze3Di/vvr6K9/+0s//HFlzfns1xXZ7c7WJmgxz1Y9ImJQtk+G1tTVrqAtN1WPrqG2ons192wxcr2Dg6PukP/LpT1195jE53rv181/a7vde+NDTw/Ha23d3jrqmGIxCCid7j1QiarCopTGOCESyEWQzPcIQnSaInpnH434eCBxPpnsHhx957Ip15TvvvItsr1y+PJ0tyv4oJo0+GDBVr2KDvmnmi5M5xn7JG+t9NrCYHR3WaTAo18egkhRhPIDhaNAr+/O5HBxNHh4cj8Zr1pBoaGeH4bhOGKqeGW9Yl3R2WDdt7TuwBmI0q+vnMDYWrUoqCu5SZEylk2k9U+8rQOdbx7TVX1lx/PDe3fu37gwG/e/75Ic3z1269WDn5MYtRegNR+pDGyNbl0BJUURwKaDNPnFomOnUs5lOkc3ZE0JHlJKAKobQtV2HqK4wJRlEjtF3XfA+ZIFUnk6aU+4BAAx6/Ww4FkKIadmtP5M+E5EI5cErkyXOvhxJsor4FAnBqQcanLZ2zv63aRoRCacq5PzFRWSxWIhIURSlK3KCb9u2oeuCamasnrKDlgmsp3EGAhpASDT6lJxP0+k8paCCxGiNtYWrqqpXVqXhGHzTNF3XMbN1jhBDjG3b7t6/65yziKXB4/1dERlWZa+ySDalFEKXkqqmtg0p6Wg0EomAymyNNUQcUURgNptZUyQiScGUFTMSY+XKYVUeHx83XVf1CjLc1Yuu88pUDcY+ptiFIBE1kOTLkQhBgQdVtdYfkUjwbT077pr2o08/kyK0PgYfDJeuKL3K4eFhf1iG5L3vus4Hj8FLjBCSBtSIWnccuqZbzCEFxsSkw36xSHDycK//3vv/yb/5b717/e17e7uD/ups1qrYJIrITAiCoEmRrCkSACBk3wzRJJFAfDHsExFJaurZ5GjOKuN+tXnh/P7uI0Rc39o2IrbX73ysm3lq67rr+sORc0YlLqaz6AyC9qve2pOr77333uXLl9fX1997//bB0dH29nnoGu+9LgExwge4MZZJNC6D2xUlQQRRDUVRZHufvDTOZkcikk0/86wso6KUksSUb3bnXDZWijHWdc2wZG8753KVe7bw8vrPhR8RJU0pRnBLwy5LSxqbnjJ25FSg8sGp11Laa80Z9M9jNGZUFU0SM5tA4Rd/7XP/SwHEf/r8/+vz/u1/9vLFv+xjlNTgMmlWsgaViIxdNtfP/jwiEhAgba2MfdPu3L69vrV98dz2ZL5YzKZMFDUBMiwvmyzDSJjVrKophVMl/9Kw2Tq3hO8Akk1ZAVTJh85EIEoAJED5W6MiGwcAcoaKNYoIaoqSw9ORaAm0mcAY0yurlHckUZ7bA4CmGFPI3B4iSpKiJhVUDUSUQEU1pZQDYVKUiOK4TBJRgZbhrpCFScAIiikpaJKc3JV5h0t+arYBTkFE5tNZvrHgtGeQldTG5qZ+SgkiqoGlt4Mhq6qQsiWY5kxNADIMzhpblDm+UURAkRCaeWNKVrIISTUBsqp2KQGZOqbQdQLI1hWFLY1Bg1DapvNUGFv2i6InhLWpZrN6PBwaN57UamK/71auv3l05+YvwB/+HjFg7J544tLa9vjpvfv3fvHnX965V09PogI557a3t0ebl2G4CWnxzle+eefujfv37547d+HFz/1g76kPX2vii5OTv/o3/tLu0cGNe3eesMluDAHTYDy49sTlhzv7vg1vvfv67k60DMaAWogML332sR/6vT+C584dvvHW67/4947354tZDC1Xhdk/vHHuXNg6f0754u7+3RDUFVtq087Ozrzd5bLqgCJyHWRad/1xyegsF3iajkyKRiW2TQhtFAIy1kUD5AyXzmqMWXqECRRQBIikdOXA8eJwLzYnT3342kdfeMKUabTq7j3kzfO0d/DGyckJIW5sbq8MR9Pp7HhydP6JrdLgUbMXg//Qs0+vji+1bTWfwetvX7/21GOVWTn5zo2vffub3/7u27uPdDGHQdUOe0OEdHRy4ANcuEjj1d7uwzuFxZ1Hu2+/+3YK7cnR3pd/4ed29w+bGirnyLgEwRANhv3KmtAGRHzwcK83XN15tEcKrigOjg4VaTJvZvPjgsLf/5mf++pXv44q88Xk5OTkB3/4/MOHD/cODr/96qvfevX1aQ3j9QFI2N87uHrx3N7OQ2Z77elLJ8ezR7vHH//Yx//MH/jpjY2Nb7787S9/+cu7uw+3z20O+oViWhn2+uu9X/ql73zk2af+xJ/654q++Ydf+keL+TEXJcxBjEV04hslC6Lz+eLo0WHPlL2y2F4fI6WTyWEznVOSca832T+xpesZ8ocH77387fnd66OKhq545Y3XnO0/cWH74GR2cO9RNztBYyprgo8aE2pSDdnP29iyrNz+7sPN9dVqUC1m08XkJCFqUXg0zz3zzE2Jd26+z86dWx9P5+3J0d6Va4+3nXgfmyQxRpJkwIDFqrS+OfnQM48/eHBrOms/9PT2/v6j/YP2s5/Z+sTHnvi+z37uiY9+qt6rv/yLv/nV33jNBTxKC6nn/coF9Y2fVX0drVY+LI4eHR08gtUhbG3CsG/KYuXWjYO+8clRiKkkGfR6s0XXdhO34Kr1fYfY6IVzW5VzfnHy3nfv7z+cr67Q51765B/6I3/0zsPdxZd/7f37Oz7Gbl43KaorkNLZVs3UAFLIY8PcrjBs7CmZ+IOXgcnG6QAqgJJQEhEwMSGoSv6V04MbFSE7FJyl656cnGSgn8VkuV8eQjC0TK03xoJDZjbGGUMpN/8VM/vzlCSJg8GQ2WYubPQ+BmmkCz6hOfuhKO9f732SGGadMVQUBapqSqIqMUpMnLtKMUmMEVBEcvNlyV5FVaEYfYo+hZi8D761lkvngMjamIVAKUTvvUpipF5ZFUVR9ipU8d47ptlsFlXW1tb6zloQIlrfWj842LOWETEmJ7p8tT4kZstsoyCTNbYi4pQ0Bum62O/1gm/rehq7GSr0y2q8tfnuO2+WpSuKQnxidKsrPdV+52NUkhhZxFmypiAIDMFg3NpYOdrbnTx6QCopxtXR+Me///t/7Md+4u6tnV/+la/cfv0dVw76I3M0PT6ezZXUy7jxTdM0bReCB0mUEkZR07ettqAptF0KkQkMImh6cHx86fz59Uurd3dPvvHK3/YeLl4aH08a0SJ7HAGRIEuSlDQk7fU4u8hmhq+qkoIQOlsUxi4FrppIpAAYWbdy5crDhw+lbfd2HlTDcRtTQkPODUarZVEi6mIxnzWtNTn0STY217785S9/6+WX/51/99/tDVZ6g/67N95f21hXOFuSy8WZ87XZkMQlZ9da65xRSFnLuHTjyAZEMUK2hFc9I/lYWl7oRVEE8HkdMrOxDABJYghBY8DT6jpD/xhjSrkVChkMEBljLQqdMbkzkwfZAIAEUdAQAwEqAjEUyESn+XWZ72OImTJNDgRBOMvfiTjL03N29T99/unzP/m4slJV33YJcmhMJII8UC2KojCWmUB0ae8jEQQhhqcvX/rcZz717e+8+qtf/Y02+OHaehNj7f3Sjg0ZEVEFCUBYAdEZ0Yy4UrZyNZbpNLMs6RLc55ckwgoIDAoMy4wHRGQByoJi0U5EFEAh5W8EAYiZmAGRKHsEi0ocDfshxRhEEWgZpSeiGjWiUI7vkhx0KJBADRpVFYAIKeW5L4JAiqkViQCIkgCyzpZQtSxYBFJMEjVqEk3Lrv+ZM0CMMYUuxui9Z2Zjlwx+WMYyo7Hf81QCkJzVwswpJlWAtOxSIGYNgKqGpSxYkYl5aRakF69c6RUFoYZmHnyLIil6nyJbUsOkBRlrbEHGJJVG0qg38DCdiqBKC4BsxDhmtz8/GZa2PZxQihfXNufTydHJgw+umP/sv/gb59dWV4aD5ni+v3fciZnMG+dKl6B9cLT45a/Z3/jN3YP7O7uPRIAZdo7lxoOfmU7+xv7eSQJZWSvq9vj9m/cunt/Y3BgzycnhERP9yi/fKiz4Dp560v6Jf/6Prf7ezwNEmE4BGfaP3vvKr339q79Zz7wzw6aGk5OYQt00+PbNg6o/8BoEkikYMD68dwtIwdpykG7cPVzbWnG9wWhto26CVc3gRkJMIaIoYnDOG4ulMyJxcnLQeWkDWEZRYFEDbI0yKmPqF73VYa/n7Lt33teQnnrywlNPXZw0u2Dq0aooHVNZ91Yk+PTw8Oab70wO94/nM91aHVnW4+NZ6aDo92+/9dr1Nx5pGqysnn/lW6/9xte+Nqmni07JFuu9HrRzQ2l371EMcPWifeH55y5f2Nrb3X//3eu/8Atvra2BKcALvH/9UTWAlQ3z+LWtZqoro816Mjs+Omi743oRQXRtffPf/wt/FqrB3/kL/63pD46b9Df/3pe2L56rZ9MhVCujTZV4/Z17KcL29ujmjQff/tZ/joxHU21a4B70+7arQ0gBEV/+zoMnHseisO/evAsKz73w7BNPf+idOzePXvnOm2++ef/hztr66nh1NJkcN+1idXXl1s6DH/vxF994+7t/90/+LUG4dOnS+uYGueI3X341EiVgMYZcD4xl4IrZEVoETKLJi+8geIgiClc2NxZHBz1yLz799JNbG930YOfmA+fcpbXVW7uHe/P9ZnJkIZWWytGo6Pdmk+P5yVF9ctQmwASGgHsClq5d2LRMztKlqxevXrz05GOPr6ysgNLf+jt/d7s/wC2/fv7ihauPvfL6G9dv30VNoWkBiRF8181C5yyWlR31rbMrN99/+9LFtQvnygcPHn3m04//y//Kn7r01CXQORwewmyn5/qPn1u9Ne5NC7b9qgkymRwl8KtrhS1CN3tEBh67Cv/G/+7Fp5967MK5J2Jj798+/nP/+V+aHp1gF1d7q3uPjmPqKLbrPeg73ptBRbqxMbq8vtrVi7v7e5jkpY9dffHFF5//xCfeff31X//Nb37r1dfrkFLVn3atuGLoikXXIqI99SIkIgZEBbYmgw9mJqAz35uzJ1MKY1yS6Y0hEQmhOzOay2dUVVUCp82bTKs4hVNwSpZAWdIdc3P0jJ2sePofitYu81NVVVPMr0dET68QNMaURGcHqST5oLfJUgSJam1u4kvbtjmkiQCzbA4+QOGIMRpjFCmjIxVQUZ+ihKAJCWTQHxSFZcC6mS8WCwBICin4XumcNTkH0BjTNa33bdc0R0dHqMLMsVtYtAyJlKZHh5aROJchCRGNsUTOmBiTMlsDxOSQLBEbg87pYGCYeXJSD/vluc0tjeFw/+Dg0b0f/P7PXb58qW2ab738zfs7O6sbK1XZb+oZ4wCDOtZB4ZxNKQSNC4vp4OH+2qj/7OOPvfDcx37kh3948LHnYTY/efvGt7/59aPjAyJYLBaTRVPHGFMCi48O2s77LgYRAHSETomBpAt1tguEBMRknANmESmsu38wsZaPjw62NjaevHBu/+DRYDiq6xbBICKwAWVVjRKjpLrtllFq2YAeEUmF8OHJpKqKtcFgrVdtDweliE0Bum4xX4D35y9dXCxm482tTqRJGhRDjJOj4843oWsJNVnjW4kxHu0f/Kn/9b+QBHq9HqDs7u+VvWo0GslkhojZ4DK75uf146N474HBFbbfTwDmbIXkJlpu+Z/p2jM0yTd47twvJ/nwvYhfZPjecrU276JTJsDyig8hFIWz1oiIMc45l4QAvkfLISKkZXY1gEoMS5bYaasyb4ClDoGIaAn9EZERog+WjSucQRKRrm0/uKl/6HO/kRAEKfcOmYAgGc62VAZNNiPOM4qlJUuOHsxyxLw9EyyzR07zSRARQ4rfK7FOdZyogklUlgmZMcbTbAKV06hTwe9N9nLeoSAYJGCDQGfGKZAEUFBVNEKKqglJCQ1hJYmCJABgQCZAEJIoYTEwGhbHfnp4cXP8fS+98NGPPDUYDb/52lvf+M1vzY6Pvv/z3/eTP/qj4/U1f3wyP5585zuv7j7ca+q4srrpeoNHe0e37t57eDI5IdKqSEnnbReBbLZca1pETiK5t7A8MEUiQJeEjXPWVsZZYkIEURRtmmY5cTJMJrO1UAmZOcpStrs0gxZJKWUlN53Gxi3fvZSixhxWIyJL8S4QImYOpKrmwzafk1GFLAkCA+oZGzPzaogQ8a03f//3oH9R5Le6KAqJPqUokkRifhned/mmyKqqLEQlTZ/99Ev/0r/4L7z44otN196892A2Pak7HxRGq2s5V4EACRAViCCBCoFIFI05XIaVgQEZWt+kfI/k+jazBJHAOEUgxUy8VFFICXEZOJMDvpE0D5QJUNqlo9fZamRElcSuQEReUu0wnbl7GY6UdxWogqAKgqrG4AUBgIAAiFEhM4REg2gAIFFFFVAFJCFtW0GIBEKsBTESmbquz15EPnSyr39ZFMxsnUFElDyKEABhY5bSh1NJQO7lz+cLRDJoEBmUEHJ2oIBSShJ8RDDGUP7UY/S9qgIAYwyXvX7VqwpDiN63j/b3nHFcVMAuCoYQgveg4f7xYc/xoCqMNRzcfNFM5jNnK2l63pt6XreLptH5eG2IPfvB0ySZrVndP7gzlQnaYiOVIZE2kTpQ9X72YJ8ddUGmHVy6ct7Z6tHe0Xt378WAlgf9ft97bhfp/p1w58aNssCqNPXcOwNXLq7VdVNTs776dGEvwHuHh3dvLubT229f37n/YG9vv3K9frW5+/Do4W4gJFNUUPQWSe7cvdtJt7a9apX3Dx5iksevXhutrZ+7dNmuvffc859ou3RwePTKK99NSQwJJok+QEyoYDiVFhgUAZJ432nThS6AqlauAsOOsDDgxBdsBv1qa2P9w888vXP7ndWNYmVcTWePIs0A6qCzTsvRWq8nfPv2vTevPzo+gkvn6cWPPbMyXl8Z9YtCwZVwEn/lF761swehmb/3/nuugNGqMbawKQGUlobSM55PPv7C+U+++PFPfeL5Ytx/9P6NnQe3m3b+2DWYL6BpABnGI3jqw9ee/fgz59bX/aRezOpXXtl92M5GI3QrhQ/B92e//st/7ff8vj/4U//sj9hrT0zfvfP229+KMQ5W1z75sc/duXH3W9/4eohw7eqF473Dtuuee+7pqOL9/di1lesP+qtBMUQA033o+4vbd+7NZ/OnP3q+39u4f3/vzZ/9h4S2a/zm5uaTzz5bWfbtvOnqojArq4N//V//189/4Qv/6M/+2TffeffKtQttu3jttQe98fDK5fPTRdv4pGyaILPJhLgYDEar/XGKvpstkjQcdaXsQ0oFgp/NbABLKU4mL//6r9vU9pxevHTpsWvX9o5m9cluM5lpBA3BNzURrAz6fjHxjM7CuD+oirJfVVVV7D28b8riyuXzn/nkp564em16dPjO62/evn03TE6uXr32b/9b/yesev/pn/8vJ8cHV69cePW7r25vXnSuZMTCMbPJbq0pxuPZFBG++c2jc+fhz/xvf/yP/JGfhvUhaAPTY5jcn88e7Dyof/4f/Mav/PJtH2F9fbN0JfdLJaibk729NF6HH/+x5//AH/6BS9cK3FqBOdb357HrW6wt6PnVlbDwY4Zr5zbKiqoeWSf3zd21tfUXnvvs3sPjm3sPz62vfu6ll77vs59NUd99591/+PO/8OatR9ME5Wq/RjTOYdk7mS8QkQwzgCViYiLKNADmnAyLpEtuPfNv2dedbzLuyddM9jNRVdGkIMTZVJnV2qTZrUCWFQASIOYUvND5GGP2Qc/YpbDue/hbNKWUKZgZKWFmZckpQgBMSXMyJ7MpiiXKTyl1wSMgMJxRlUQEUB0bw0uOxPLWYc4kDUts2WRWRtKoSSKIUoopSUqQRAUJjXO2qqxzriwLSIINq6IxxhJFwqJwhpgQYoztoiaiELuuaXtlsTIaOOdi9KhkLccY5/UiSLTgVDWEbtnQQQ4hEtuM3FQ1xg6UcyZxF2aFsyBdvz+4dGFTU5RQt/ViPjs6OMBmUdeLE2tkPKq2t7fPb5+7e3MvCTAjhaaeT5v6gKkb9ujpx6/+i3/ij3/4898Hu49uXb/+1stfe+07r//KV75xe1cvXrk4GK42R7PjyQk4Z5zzKTZtGyAJgGHH1iHYFCklaULNlggNGARmNTYhJ03IJhkcD8fD0aYz/HBvsrszuXJ1CGQVIEv78thFQEUxieSrXREAJIvklGjrwvkYfYi+bXVlONhcXVntlT02n3j+hUVT20H/L/x3/939+/cjE1WVkkVwbeNT9M5Y6xhFgg8i0Rh+7733rCvH6+vOuQHxfNHcvH1rY30LADK3IJtmZmnhsvOo0rbtfD6P0Ru7VM1mVM3M3vsYY8pVouqZy6GEeFZC5IpuyRo7Df8iIpMzmJf2LHAKbSktk/446vceETHW5Voif0E8JQk7NHlLAQgAfo8yZwggY2xZGrkiAFCO8aL8nVOC35rnJSKCkP1VMyyqnGMSPjXPBYBMydAEAGAwx0QRnpXxSJwxl6pqQsgJwwiiovkH1VOiNhhVTIIpppTC0gx+eUhoknTqFXZWFwGhNQSUWdeUXU1BUUURMEcmIGTBAy2RUR4VLU1dFASsJpIwcpTqk8vj3u/54c998fs/U2ys1jfe/e4br735rW9d3d7+PX/o97/w/b8HYnj7y1/+9je+2SwWuw8Pqv6oqMazRb3w0HiIaEcb50JMkxDQ8NrmZkx6OJku2haJUxIAYDQiQSRBEkZFYjBWmc8OpUzZIsZ4epaSgkMGIkVIIuz4jFuf3WcJEIlD5/MyODM5IEBg9t7jacfktJXDRJR8AABEXoJhyKKqZJxVWGpc4IOuQb8j7EEJk4hzzjknBFl1EhNmoUxeeLj84pB9d1jN3/s7f/sf/K2/eXBysn3x0pXLl965eYMAH3/s2sHJBEFz3C4CEuTTGhJEwIiUMEfdkAoCIgT1SbK0lpgZJS1nWV5ykZklTii5WiTmjIEBEU+7WUSqZAyIxBgAwBBTtlECDV0jAKqYVFLee7mwYhKJsjydMBcECVUEFAAxp7giMoACEkkUACGF/MIlx8MrptghKREaQstEjCa/0Xhq7Z9penSaFMhISLnnFEOQlJLVpCC5ksyvIKIQUdu2xlh0XDASsaqmBCKAapE0b5L8vUIImd4tojFK8JFRkYmRgkA1HCWEmHQ+m83rJqVUWNNztj9cgeRP2pYDu7JI1jZN6zUOqpV550fnrqwZmU0P69pXo94HV8zBNE66eRmKvh0ni/N06LlHZJDZe39wuFAUU3I13uq0L1psXnjysQ9txABHh4u2bmJs+8NLvb6dzY4Rgy0d+2nn29ffOlpfq9oWv/r1N2/eeCDqu7beWl053j3pl2xtr0l2Ng8nNXFZ9UebszpNFgs0bri9UULqpGtCwKr/ez7/uT/9p/+0eerpeOf+n/5X/7Vf/Y1vNG1QZOACiHKCs0FCVstcGLDipW2k7dSWA9czxizqrm5Cih1JQkRSBlEmLdlWlf3FX//5R/X9z3z++cc++zisKZxzMH9Yv7rn1oxbG7nB6keuXvnICw2AhdUNGG/DSQvTOZzsdyezo0eLwcrqp75vmEL54P7ByWQS1CNjtKnuprP55HAOa4/B4y9tf/xz15imJw/vds10peeGlWkoTeZ69Snzwz/+Qx9/8Xm4eA7IwOEx3NuBJO10f3q8F0DLUp54+tpzz78QBX/5Z//mN77+8nvv+wvni76pjmezcxcv/eLP/9zj1z78H/0n//eLjz/2zivfunHjvaPj/bfeeqPu2tF4a+tiP6nM245iqkrHTtfW+/+Hf+v/PHrhs4df+eb/66/9XZZ2dVzFhIOVQRJ5ePTIIJYMZHg0Gm2tb5y/uPHNv/Lf/K2//rcGFVhs90+OVlfc0dGEUtu0Xa8/Xt/c6Lzce9AkaTfcSqxnBZvKoG9T7VtDaXNzZWttxSWAroOmPb+2UkDaHm/v3Lv567/69dH27YNZA2AKRlgoNnVpi15M2DWurkeo57fHj129tLqxWlbOWr75XpweHR8e3vv61+pf+rn5nRs74mFzs9/r9X7/j/3g/s7t/+av/LU3b9xzo3Ezrc+trlsUK945W632i3Kk4CeTw+PJ9LHHN1bXBn/hv/qTT3306XvvvwUWYT77S//pf/zeyzexBYlADI8/9rH/y7/zZx7sTv7m3/uHu3v7565u+dTOp+mFF8f/0r/6x1/4wksARxAeQFfDvO1V1bWnL50/N5jszGZHJyXAxy6N/+CP/2B/UN7bvX00OTg3Kj750mcff+Kjuw8PT57/qEFyprh1d+ebL7/8q1/5NhaMld0Yrc6STk7mg/WNcrRy58HOeDxmIQPMaCwZREUABV16iivkxj/CGXpZPt53MUYlJCIgWLS1yRGhrjw7agRx0fkkp7BAgYiAARH39/cNu7Ioqqq/9CIUVdXWd2fdesEc3IIGKaUEKPk6QF12VRWJjRVAAUBGJhaJPqaua6wtgE89WAAUM8VTF/UU4rI7a4jzNMB7vzoa51N3eSXnSiaJckohaooZ7Bjnil6vGvT6/aosrPcem44iWFc4Y1NKRBA1+bpuuxokFc5aRk1xdW08HA57vZ6q+i62bRtijAqzxbyEMnuvMbO1hap6HzBDRKSoIDElSURs2ayOV7Y3N+bT2fHRwXvvvz09OfZtc3576969G++//0bsIiI6a4529w4fPsJEPTOywAVrUSKW1m5sXNheuXpp+/f/5I/1i+LglVdee/XNb3/927fev9/UHqjaOF9MJdbzqRa2Go6ms0XdztkaiUlBDTOZbHzdtV1sujpxcMaycQAoip0XAJEoZVUMBqP9g6PReHB4fOy9/9BHnrtz59bmxrpIjCIqAkCqmlQFoderkp75hS9H2UJICqVzhXWOMAY5mc5T01WMf/mv/tWrjz32/Esv+ZCYeX1zc7i+3kat6yb2KmYunAkhTCYnKRDbigy7EkzhZk073TvsDYaD8UgNBUkMlK/w7EQuIsxoy9JZTpoMUmg7kViU1rk8yzEZwefI6szI9ymdfYgSlj6AufPqnCuKgog0iWgCACLo2o6ZrTHMKPI9/1m2lowFgJBUoxfFlFLbdhv9obWWGVMiRiU0xhIzm9JkBUYeYVVVVTlnChdCWEZlQx6iKCITia16EoL3IWbHsN8K/bMSABQAlQkMobP5XQFkWG68sxQE0SSqpMhECilKSHEwGEQVjUt7DBGFqJlVpaIxxSQQVTKCNYDadZT70EtyYJ7+ARt7CkazZhINO2BIEAgFkAkRFEQSpCiSDDOkkGJCUjSZRAUqQhAkRoyiuTIATSooYTafy+Lk6vjS+miwc+vmzi+9Ozl41B+P/uU/+ccuXLvaNf7X/vbfvv7WOxJkNpvfv3vvwsUrKxubQMXuweS46cj2fKLDeUtra9p2i7qNJ/MgyYeEZIig6zpiBICYYuh8SomBgMn2+ihCIQimRMzGADEgDYf9ruu896IxJWYGRCaGZVKQSq7U4LRdbYlVVZJ0IWRsqrrMm0IlZj4brQATUy4JkEBMDjDJ7y2wRjkNi8L8FfKXstbG3zrmlRC991VZEoEykzVKasSJREKzsbEBACnEruuiTzFKSklDu16VXbNwvb7XtLv3qNfrrfeHB492gY0sTeoxCOXTTkmZGZWYlkxROn20QEmaVJLiacsbRNX7jgwzKlFOukMmY4i6rjsb+oqISFQvKjKwVQwSgigkYTREoooIi2aRv1FSSTHvhQJybpyiCCwJRFmKoPndW1ZcCQAS5Kkh5shdAFADS2ISqWp/OEAQUkFIoj7GaFJKS10OLUPOiqKw1p6cnKgqATCjMY4IiQ0gxhSIgO0yKRCUMpXWFobZGEfWmaIosqZNRAtjs/woxpCnM4aRCvvw4cOyLFeGo36vakLYP96PPhgDgkkxWw671ZWetTaPpbzvfAjBx5Q6qltjTOFKADpZHFVFz1slx9XmRojtPPyWGWKEgksj/WomGELXiEtMIhC7mBKRG9rCGGtX11frrq0XyZg4byciEoIA6/HxzFnTpKTkYsTjowYEEVyx5uYpGluNqiomim1cH51/4vITH/rCEwcHR3fuPdifTHuj4cZjq/NO9o5Odo/u2mGvKnvkrAElKUWi6/UP6ub/9uf+/O7e3p17DzqvxhZsq7K0XFoiwCghdJoaA8JqJHZbq+XG6sadnT1T2g7FEDfqV0bVfD531pXWWcKC0KgcPjo4Pn4Ao+YHfvKlT/yRH4D6HlRT2B4CHT72E5+ERQAeQg0QAK5egxTj9XfrN14fbTwH0yAN1xPUWG1tXwj+wFYDt1187Zt7R1PY2w0BISF84QfPf/wzH617x6tr5tBcN9zrVclWUF675ILe2ts5bo9GF+X8cwU8TSAHcKSznb23v/bK9dfefP3N9pOfXvmxn/hRsAiEw60tKEobNTy7sPruo91u0nVdB6+/+uqVS0988tPPLJr9P/ef/bV7D+4fHB25shBUYGzmk0ur/Xv371VVsbGxHpP/gS9+3w/+4c9BH+Dw9riIT20OD9+TOqZYFAsIbjx8sHsyHo52D47OjcYXt68USn//r/w/x+PyBz772Cc+8cK719/+/EvPTKfTGON4bb1pw8ULVz/12d/zynfe+HN/7i9MJ2Cd7ZXj+cnJZHb42NULP/FTP/3ZH/shWO3LW2+cHB7dvXljf2fPL7rForsx60q3dfnJrbmf33nrYTG2sdatAV29fGlttLb78GFBachSrpcI0/nem4f78Jkf/PBoZeVzn/nC7r3D99/fsTR8cO/IPHw0XO+lFM9dXHv1ld+4effe0d69Uemc6b918+HHP/ZC8rPSycGjR8X64PDR3idefHbr488//qFrP/TjPwB9B0ahqc9vnwNyv/H3f+bd79y/MH4SS3P//s580bWb9uRwVjfza0+eG1+qbj/cHW2ZP/FHvv9P/PP/TO+Za+07r739+je//dUvrY+HP/r7ftpVK3/x//EXez3943/0hTe+9spiB/79P/PHBnbwla9+7anLl69+6tM7JycH0+kv/9w/BuLJtL6782jvcFrHNO9k0RuON86NbK9DslHOjwCMSQKXNi8yGstIlpgYAVSSQAKAsld0TR29JyUVilGya/jZQwygIJoAkjFmbWOcUsrnvQQJITASG6cxJFFELJw1aGKMKYiAjEerudHBzLnnmi2Yu5iMVWedsdYg5vZ8F0NRVLkaYWJUjCHGGAVJEpiiXzoniD7GGAMSF/0BkWFeIrmUkoAyorE8HI5POQsK+fWKMtsg2vjgysrP523bZm596LwBUzGAs0EgpOQRIIauru/s75euGA+G1WCj6FNoQowxqSZNgkKlHfRGzqpFAY0qcTafb587t/NofzqvR+O1GCFBRaXl1JJBZLBo88hFFQtykgkMrKwogElFUoxJFseL9x6dMKMruPONgmxtrW1try3mU1cLVGVpy66J88m8X/S2NtcXk+N+SYdHu09/9KlPfPLjTz159dnvewlUgOyNr37jq7/8jTdfu/HgznFoyPJGbandqu4d7y9mh09ceSIx7B+frPSGlDTOfX806Ff9hBJSQEj9Qq2lVPRXNja7Jp4czFBVggeRYb/XTGbRorUyme5X/b6z9u3bN1bHK3PfRZ89lwwipiysI8qYD5VPQQyCIgP7eZ1SalNqAGaSdmJgEYvw4Wee+YN//E/2+v1PvX/n5dde8z7du3l/3tSmsmyJyU7mEHwMomAKJTOpa7Z9ZJsIeEDz5LvZpCx6vm6dsSiaKcspJEmhLEtIgSRWlYsxHuzvV/3eyspoPjk5mU2LohgOh5ubm+vr6yGE6fGJcy54n1O3jDHoNKvMM/Lo9arcoe/3eqppPp93XYeIxlLwXYyxqqqisDEiIjetb7tERNaVqhpVooq1FlAlBonJGjcajb2PTd2WVdk2NYL2e5VxlpBTStGnlLqmaZD5zGsYUBEYAH1IgIBsEZR/R5Bv8Atr7WA4KntLI5ecS6AogIRMzEvE03V1r9cTgfmsQfX9/hDIzBdNq4uua0aDnjMmtrVhdGym0xNjjCChIgCSUgQVBREtuACIKgIqQEgGjUE2SARdDN57FS7coHA9jdz4hnuGjB4fHdaz+d6Dh49fvTLs91HFFcXB/l5v0Cei/zdX/x2m2XWdd6JrrR1O+FLlqu7qnBtANxqBAEgCzEkiRQVSwVa0JAfJ0pVsj8Pozjxj3RlrfMdzx5ZtWZZky5ZHsjhiECUGMQMgCRKh0QjdaDQ6d1d15aovne+Endb941Q1SX1/4OmnUV3hq7P3Xnut931/kW5tbGw0m00t0Qy2psc63WLEhHEc93q9sixEcDbvH9+/Z9+B/aM8vznqaqlP3Hf/7vnpHKs/++//6dlnX62s1MlUacEEjsb2Xsm8tBuAaExwHiXYSDcbk41FY7rO2hAYEIXigD5UzFy5kSYZEExwItHtdDyANKMiQYylJCLnnC1yK0XUbCVp7L0XKkk4tsE7z4AsFEVSIjARcQDnnK1qTCwhUQCuqsp7GyWxcY69T5qp9U7F6ebmJhG1mm2LXFWOhCIloxDVihcIzpvKOS8AUEqbe5KChGBEYCYALaWQwjmH3/t0oPUCkNkXVQEAxto8z60pFQkOptsv6h2VmZGRSChJFEXDYHycgAqZradVkGUDAR48CxRIGpFYqgDoQvCeQ7V9XQWA2gJT/zmOYwGgsOabbaMMEAJqiYIkKpJqWwUUgANKHROBQEBkhBAC+uAQRVVZdkikhVIgRBVcYCsEjPwI2dc7LqIiluyMt0SAAMSMVCvpavwd7vSGan4rbF/JApJHEiLeBkQySMFEIAmGWe6dIeBIIRJ477cZbPVnqcd5dQZFp9MxxpRlaY0jF+qQHxlJLdLta8yO70cLKYSYHJ+4K5jLi+yuPK4UkXPOG1tblOrPo7UeGxszxhRFkWVZPUNsNputVnN1c1lJmehER5JQ1sejC244HDlnvA3MXgilGCQC185lhSAxSMXgjSP7PW5A2NjqK1JS5kSSiStb1ql524JIRAgCgtjql3VYMgAxu/p7c9Y2mw0EcMzOBuMhsAIkgsDeEqNS8eT0/NED+5taRyTHOi3HYnJmb9Sea21urPSytf5ofTBcH2SQppVA48pg8vpdAh8Y3NXFGzpNvOfKhzhqkY5QatKRq0xVVj6vvMnRlamUaSSaSXr04AGTD1KtlldXD957bxkoq6p+NooiDQDOOSkIEIRShATk8tJMTk8CW2hKSGVx89LCwq2pyTlXSuH6w43yzvU7xVY/ETjeTuan9iy+8HLR9wCwsraeG3Ptxubxe/fJjji4f3elj16/fevn3v3O1a2NG7dv/djf+MjYyX0s19n3yVh3dWFta1nkibUJxby4sdWYgda8SCYc8AoUcP7511/+5oW1K9lUW51+KM7y3r/+7T8jAe9+37FHHnlEVMlUp3Foftdws9vRw8npXVNTs0ky9sg7vv/ls6/8h//421u9QWdyPGg/u2+mnw1vLSwdODDXL7eSjtAqjMza2sogL4+Gskfj4/ba1esXr/ZXFiYSOnPy+OLmYKkY3Vpfnt41+cbFxekGprFcX1rccPm+Pe0z9z44M54s3L6yf8/0I+942/nnntva2nrssQfXN7rTE7uhGixfvShLmEqgAWbh5tUTJ45uYRbyjY3Fyx//7ReWFq7v2T2zsbxaFIWWySgzW+t+967pvXsO5Fk21m4c3j8JcTK3W07t2nv95u1br79yYH5POeqfvv/eWwsXp/bOHnvw0MHT+2xkOuNjjekje07wxMTVqpTWXn3++Qtv3Bi+9YHxZjM5eeLw/J457/ni1TUh5In9x8yoHGuqItuM0b39kYdefuWFn/sbH51/8B7YPwflEFwOwHaUf+ZTf3H14qVymD/2wOPlJg42c65Wh1vV5dde39hYc8IXPjche/Sxg29/z1vf/jc/CqOuff3VeG5m+nLz/Q+/+6mvfuXm+RsHjtz7d376b2nVevWZF/Y1Ogc6081WPFpaOjA/M7Fn/tadpa+9cHZjmGX9gQuQNDvNyZn9M3tXe5kflBRo5MGRrBznLjBShCqKVRTxRKtTleWoyKq88BB2MCqeJLJAoRQSBOddMAB/3REopaxBMUyYVyX4UA9/gwveuoDIAdutVlGamnNkd+IylVKhHueSAMAaMOOcs8FGcUQEQBQAEIBrFTWR97b+t1EUSdxmqToQECdCaiIZgoNAKIVEKQQi07bGB1EIheDZQ1VVnXajllbXuZn1RzBzkiRCCGtdreLQWqtISykVkqi7rAzkwLCv1SkT41Pe+9KE4E1EGkA4z8aWrARJAgGBfVUay5WAICmMj42df/XV2bndnU6nP8gYZF7aznhL6piFDywY2JYu9xY8AJBWMXgMgMyBd8KXEAk9Rqo+1YJK03ankUaS2cdxlPUHVVZCys2opdvCVW642U2j0EqiIqL1lcXnv9VbW769e2Z6/NCBq9987t/99n8cbBVHD54aNuVmmbVb08YXo7xI2s3geG1thSue7IzNz+wqhsU9x08IIay3/bw3qIbWs2HrQ7DsB4MMAsU6kSwZAjIkSkUUVybz7CLB3paldUqpOE2UoNqN/R2NKwkhxKgsEBFB1Ido7VQVQqKQpiycs87Z3Fn0TgNqJV67ev0//Of/4r2/tbhQ+VAZU1nbaDdRgwu2NM654AICKpKChUxbHeNC6VzAoLROZOqCNcawDxOTYxB4c2vDVVWz2RxrdaJIjUajuNPqdjfzstg9NxMQultbJER9k7TWVlVVW1NarVar1SqLotp5eWO93272s/dKqVpaLQQKEVlrEXFkKyEESPbeh+CEUFEUxXHqw8AxWG+Ds/V5naaplNJazxCSOJVSB+fZB62U2A/V8TYAAQAASURBVLE/IiIEdlxPGxgRkVAgChACanMLhvqxZ2eDZ+cZPGG93L/zGh8bS5Kk5vRZa41l0rLeEJwPzpaeue4MJo24KEtrPKLgwN1uV6KM47S0JQCVhbFcOlt5YIvsnVNSEgdGkogeAQkZCJk0U7BgjLHeWGOYQ6CAyNZWSZI0m20hlMmrrcFIizRtJuvd9W53nb0fb7YmWs3++koYJdMTE7afS1fFIcmL/M7Kcrvdnmw3ehtre2emnCmkt0mUBm9tNkDEOFKnTjz4tkceeui+42Vv87Wzzy7fvr7aaW715l67fL6bZbumd5MeH+YiW+sV1lspkmbqBYTgHHggwezLvMjzYgTOBOawnanrnCMIpLDTaTF4x0EoaQ0PsgwpjpWW9XvpmIGlIhLkvRvlmY4iWweLIQghSAoiRGAJjBwAARGZgBGZA4SQJI3grLXBlAUSpIlOI2099Ie9yU5T67g/GIxGRdJoIoSlpcUjBw+xDzXcSouY2UMINcG6toTzXU9FvSV6/9cyXyOlNQUWuG0dEaS1FkIQoiIRPDDDjv4FmMGG4G1w4ICYkAhCLcdCIKWUAOEBfQjes2V2AQIAA3lvmXCbZFVrYQAAIGzHOgfmneFgCAABQ4DAgTCEsD04DlQXxhBqj+2O7p+hnh8JSSg0SWFdqKwNXIHYjnKubx3I9aCFgMnXkBnc1swJJCCWiMY72HHh7tADoOaEAUog4vpuGwAAAoQaZVnPvYkIgaRQkre/oe1JYlmWxtm6lx+ArXfgoXaaKSCJGNjVxhhmllJKpNoL4rwzZVVjNXknz87eleACA7AklJGOlR6NRsYY7xzvROP5bFiYstPpJEmUJA0h0JY2ywZ5XhpTahnVpgwGTywQ2TOCh0ajKbUCwgAMzEopEo2/Vh8wYGkNUYgidfeGdNdWWI9vRqPRdpaZ97WM0hgTvI87qbVVVZWVM957rukniFrFGETlwsrGFjJKgDIbsQ/5aJikTRaiO8y6ubEIQUaOKCBUztdz2BrtRvVYU0UhBGOsB2GtrYxDYQEAnDdlwZUVwJIUIJCUSaMp4wQgTO/aPXfk2BPveq9ujf33T/75wuJynufO2NIYRiItCJEhkA2Tnc6x8WPhtc1LN5+f3Z+ALgc3B28srZocbl8FMrB3BnSAXgZhejSWd8oB37h26+aCLyzETZiZhXe/6zGIVeWqGwvFE2+9//TDJ0EQ7D8ImxtgSwRl10d6vCVBbvW33EDkuXYNPX5QtObigydnWvvHoRqtXr723NMvXjkP73j7tFJ6fn5+z575zfVVZj54/ASQfPHJb1aFI4ze/MD98/uPQdSu1jZvL2/88e//3p313igfzO+bBcGDsrp07Q0dw4OPHmp3mouLt5pNunmrv3dvtPtgtDVcRnCr515SJbiyGG+3pjvTSWv8voce+KtvfP3azYVhWFQMeY9vbC25STh14sigv3Xu5RdPnjw5PjFx+vS9MDWxsb6aJs1sMJydnv7ql7/ivdjc7Ldb0Gm3z57vJzFcu3LlzP37ZqY6g97SqXuPv//dD9+4dnVt+aZ3gNKdPnVyenrf5sbg0qUrKwur4w24/8F7Hn37Ow3j+nC0vHgjz0ab/eWp8fa7vv+dKn5HJXJu+okD45W0Udr4b//qt0ebZmMt39iolOiMj6edqGqkcfB2ZWVlbGxMS7G4ULK8ffzeB1aW11yWg89PHDkEPuzfvefCS6/cuXP7xvL1Q8cO64ba2tp6+dy5l89emW4rNHx+8GIn7eSDIcnu7t3YaFBZrvRLCxH8zE/+4BPvfyfMjEGRbb507qUXnpufm+VR3rIY9eOzn3tx148e26w2vvCFP827WbHV/9kf/bGz515eW9voTM0uLi9+49XzZy9fSdvjE412lVdV5aqiYklD60Y+lCGAShgloxcIjAIjhVISoGFb2DIrR8YYkkS69jT5fp4hskJCxlpPgH/tKGAhpGBk732w3lrnvUdGARict9YTCJZeqkCAsY5q4Iu11lhfVVUcp4iIYjvZrN7JubbQgmdmF2phyHaPQ5FAREVS6VgrJZwjY2SAIGSA4J2xtnKVY/ZAChQFV9vqSClRT02d977+JreNilznKNY1NQcMAJ7rPRllrTGB4K2ruQbbsULBc/BgPUoran22BwAQpGQkpYpc8DoSWmkMZaiMd6yIlUD0Fr0ddjemZnZPdtqV5SSKy7KwTM4GiSCEDA6rykgSjaRJQiBJIum9B3Y19YmQBIAWBBiC80SoSZVl1d3a8tYM+31X2KowJq5inXTGxybGOs1E6kiUzIBhs1sur15eWvpj4vDyuVeaaaPZnLh660YxqjrTncD9QW91ZaWa2tWebzWc8SNTtGI9Odb2rdZg0A/gC1cOy6zypUPvwdvAoJvDraEWSTNuoAMklMShym6v3t4zOznVaW/1NouqOrT3QNocW9vYmJmaKcuyKu1dbyIzEm5n5tBOhv22pF4Kz4GVQg6kCL1gZwWgkKrw/qWLF7PRKEriianpoGSwvjM9WZRDsOyDI5JaChJRTamTUmkZKovGO0ki1lEIovRFe3Ky190yZdXptNI43tjY6G2sTk5O5nkegouiCAhHWVFjeupAqlqLW1tK6r9JkoRD8N7Xkf/bHB8htNbsPTNvc1glEVGe58aYKIriODZQ1TKPnasCIyIF9j7Ual2BUktSUpnC2LJqpx0tVZ6XwYNS2lXfAwXyge8KkJSKhRBCIgngHSeo9x6I2XkfLCLSd4nO65fSsVQRA1kXrAsuMDvPKJjQenAeAgMJElJmg6KqquBBEGEACpQkup2mbrOIhDKmtNZIISrv2FuhdGEd1zRZBEbwCAyEAYk0O2985Xzlg91J8UIiyvPSGx/HKXsoy9HIbg4zMSryYMokioveVsQcaTXZSH2eJVroRhRJ94Pf931nzpy5cPG1p5566tD89Ob6RlEUWT/L8zwvqqIYdTqdsjTe8eWr119+/tnzZ7/l8+qh00emx8eu31zKR2yGmOWl54ELURIkIYTSNpMIkUkIbKSEMQWRj7wrCu2KBJGDAJCmhtUhSRCBQ2UNIsZKCclsjJI00WqJYJ013jmk7dLZOVdawwKMszuoOC0CMmNwdixNCAnqLF5iT+x98MDd7mYUq4nxtve2KgqTj4rhgNn7YHv9rlARALiyKpxttDqzkxPAdfyYwRDqItQ754IXpOs60+3YsWr92V+b8QIAScFExpccahuFA6x19uDBe96WdCIgbXPunPG2JgqTlACBfUAIUmgppbeOPbvgjWPn2bMAIsBARKH2geO2sKeeAbjgERH4bh3PtTJV1BwsBg7bqKzg64YCBahVfDU0IyAx8I6iTAAREjKFwB6ImUgC1mQxqlvazJIZkRwLCMCIHLDmvRDUBBIAFrj9TwAAQGDt3UAiBET22waG+gQMHolBKyQiH1DWv+zvFtLWetNa8FO36uu3wFprbbDWEATcgQgwMzF47/M8Z2Zvt7MvtuVAUGtkEQm5xqSbChFDCEmz0aZ2vW0JorKq8tGoKHIEUaM9QwjGuKqyVWWtdWnckiABCJEJBGI92IG02fDeV5XNsoyDIyKt5Xc/MfXmXpYloggQay3rY+zuHYC3sSbbwRr1N39363TBWx+MC84HAPSAwfsQQm7LVhIbH7K1jdWVTXA+eBspmSSJy7KstFnpHIFKGwKEIzTWGx/qS5EWUghiRAYPtSedZBoncdK0xjOqOI57G5veWk2UxqmAgN5ZH8qqev2Nq1OTY0ln4gc+8pHx+8/A9Bz8+WdZSONrFBsZYGEZgrVAAl3ZHf3uH/xhe5JPP3Jw8sSp0fIbF14vXrsAxw7B+77/5L33vAkoCSsr5G0x3Dz77Lmy64zhuTmY2zu9uLb+9ve9BfaOQZVfeO7Fd/3ij0OaQH9449wL1blvjY1NrKwtn73wUmD42R/7QDQ2OT43t2DWbqwuymb7zT/8+O7D01O7FFDZX1vo9lbuv2/yLQ9PLGxu7D68/+TxI3DkSMuchG4X1nqvvfhyWQ3H2+Od5vT8zBRU1fqVC1ffuHH+ys3LK13dGTt8ZP+1W7e6A9h7SFfB/OLf/bkH3/0OUPi5P/mveZnP7Vt88cU7jRTe/s63YKulV+Sff/ITiVdjcUcpdfjgvqu3bly5+PpYApdvwYP3x1iU9x06/Oh9Z/bvm0+mx88998zJe052uxvnz59/0/69ExMTn/rUU85+6+DB8VP3nZken9v9tn1vOrPw3LNnf+SHf+Deh06/9tpLC7euV+Xg3hP3HHn4jLmzsLG+QAj33Dff3cguXblw7frNrc2s0576xb/3k1EoAlvC6vqNa69cfKPXW7nnvjkievd73t5oK7lrPF+6lpdV6GZTp4/2rt38mZ/+hV/+ud/q9WHXLnX82ImVpdXhxsb588v75qeK0fD61cvdzbXf+Gc/de3W5lefPttuN7ksK+OCt9UoO7x/31f/6osW7PyB3Qu3bi4uLWz2XCQh34Abi7YYwr1H+3K8bDbiualdsU6cc5vdrXagsenJI7vnoNnIXjn/W//i/5f14Ud/+MG9jfHm7qPPf+JL0BPDrPiVn/3d3ENnEoZduPeYvn57fWlpsz8YLlscrq7d6vfU9FRrZleMjSaIwahcH4yqYChqiIa0Wc4QmICkiqIoIDFz5WwIbjSsvHeMLGJV6y4cO8dcVRUAKBKCwHpr3bYL7TtVgowAoLK2Kq1xtUbC1a0bDiAQFSmppbXWAyuqQwyp3s79Tmo+fJfxUQhgFNYagJp7GAAIICAKAB8CIIaayu5Reu+Dh8DBuIIRvGPrKmcDsA/CB49laRBZSi1QAhIyCwAlsMjyuwNl+K6ch6Ioaq122mxAYJICAlvrmQCBoQ6hDA5dIGDwYEPeaDTiKGFmbzwzCCmTOArBxVrEkZCkwUq2GtgieGfMA6fuu3DxUn9zY2tzMKrcQw8+8vXn3pjeO2scOkERKB+oslZEWkcNLbftzg5djbpBkgIJnHHBAjIJwYx5XhbFaJRnSsiDh48f3ndAS1peWF5cXNzsrgeuzl9cjqSqqmpu18zk+Kw2dmvTrq2ujI3tEQjdft+UOREurWWBYWIqffv73v3i+QtLd1amp+cOHz8KLDc3Vm4t3mm2Wp7A07ZjA0WAOtm18rawUaSBHLjQbqSRxn6/97YH7rl1+3p3ZWvX/G4XMO9vaaCxtCHCtqWtrqQRse5SN5tNIhK0nftUC+WdDZvZMIQgEDTVtkCXG0PMsY7TRpKqDghyAkjEUvvKe6kiIpIKiKQUilE5F4z1AJg2my2AYZZZZ8hzrHTckN311bmZWefc1tYWuWrP3LS3bpANd81Ob/a6Bw4elJF+8aVzUqn5/fNr6+uJjlAKRKwXSFmWrjIAUJ9K9ZlFDHXOjzFmdnqambf50Eq5ncDQTqejtSbAsix9fWw7FwI4F2qDIREBhhp8Uc9DigKtq4ioKEZEkkjkeU5K1CZ7AEDa5o4JIXYQZJKIAtDd0l9sf+a7Ebrfc1gLIep52na1JwQL6Ri8A++DZ2QOzOidVbXGz7nuxmaZF2nc4KrcyBYQSaDmqnLexVLZwJXxzTipqiqA98ABvGPH7D0HZBK6BS44bwJXQA4RsfY6QmxLWxXOV8NYU6xc5TMzKscaHUsyUrTV6zcbcUvFE2k06A699a1GI9/qFut3Uj52Yn7yWTu8+drV/qBojo2344iiaGp84sjxYydOHN/aWLt9/corr7wibHHq/gekqxauX1m6tXBw7x5TVaby3lKayvG5mWZrvHJVlveHeVcoUrp2UaNzQRjjKmOkzwJbDiGwQmlIMQKyCAAgiQQopSIBqUi0jhtxVDnwxCRJCCG1BoDA3ls3HJnt0pmYgwtCIKII4IsCEFDIersk5IAAzHGiAMCZcjQaKoSmjrwzcZzaqugNBwq8jhIKflSVoYxEgwbdrg3eObcdc4lIHEIAJbFWeN69Q9a/+r+W5wYAVVUxMVPAmlsBwXvrPUsSd4Pjt03tSMxsvTPOKRUFBvYBOQhEAOkD7LSK2Hp2IXhAIK6Tn6SQAbfhtjtO7Z2IJKyJvzvkOwAAoaQADMDkASFACEw16N06hgDIAXGbggUEGFCg9YZcEBQJFKnUjATo1DY9mD0QgAZWAIoRQHOoSfHMDOyYwRMj1CHUO+8kAQAB1g33emUx1+mhXhAQoCR0EBCCIknIyCjrzkG9TdR4+boRXpbl3bz/u63x+tpMQmsh66LZe++YnfNEhEQqkira1gPVbSrrPAgSgPWYMoQwxGHtKNBS1aV/oiPHocqLPM/7Qx/HOopGiOyMt7ZiIKnjbFQIiUpoKQmEoB2amPdsnK2KsihGxpbMrMT3NBIYoX7MAYhQWusDhu8+dOunLY5jEMzgPWDwgZmlRCGol422/VJIQBgC2uCs9bZyOiEhIgPGMUshlE5lElXoApBQSdIIjsiBGJY2K6soTTwgSCWFECQQ0TvPnkLwcaybrbQ9NhmnjX5v2BvkddKWUkoLpbSg4D175/2oMiEEzrKZzgTrFPqZH9y4sbA4zIusLBUJLTQD21r0Jkho2drVHj+z52/8/A/DnHAbbwzG1aM/evpv/rOjFGI2CF4D4MVrt889/1IaQ5LgoenD1y5ePXby6PjcmGsH0RG33nhufbgxfWgaZH/llecuv3H1yKHjOlHZaP3G9SstFTdbjagzA5Hu7J0bP3L4EIvOrploOoEmQ+hu3V5uz7Tm7fxqufra61d4XO1/YBdMJrD6ui1tvjVYvb16e/mmClqN6PaV2y9l59CpUIKrYOvO5j0nT6yMBsNsa3xMHT0xjprPnV/v9hcgKkCED/74B2BybHjl2jtv3Dh+/Hg0OXX129/+1Mc+0ZHpQ6dOUwkXXn399TeuvOWdb/uhD75jcWP54P6VX/z5n8273TtXr5174SvPPF2pdguE/IvPfCXPYfcuvP/+U0UxeuyxYwLE+NjkrVs3nn/mBe9ASn3s6D3jnfRLn/t4b7j28EP3Hzr5BEiG7p0r1141rv/om+9RMq6qsuPiJx5/x+HH3wWyAcOt3tXzL5x7eeO1b3sh0knVskLEpff+E3/xJ+vr0GhBYwKSqdQpeMf3vWfv7r3XX78aRzC/m5RIvvn157yBA3sm3v3uU7dvXt9aj372Z3+2NbeHra6qZ6cnGkVlGs1o7uDxlaU7r6wunHNVp9NYXzeD3s0qAEpwDgAhCTC/V3fi1iOPnHn7Ox8WicjWNt648PrNmzfnp8ePnTh56oEzpa/+7a/8g8U72eED7f2n5qFrP/vfPr18a+AH0Gq3ZnYfOHVslZqt9W6vtzk889Dbzrzpiaj5+sXLbyz1uyv5aGmY3RlmwyurU2mr1RgTUpWOQWpFUSDplcqKktFCTbYC3Gm4esUgNWmpGaC0prJVrYyvdcM+eCmQUKBQHL5H8EMkjTHWegAZKZXGDUMmmMAhEAREjqIoTrQJtqjyMq+4qIgIpdA60lpb57ZFN25ngLudFCHrLruUEW0zTACAR8NRgGCtK4qqIldn2QEwIzEyACohBNYNGxIIsZZSCqW0QGT23noAlCS++0ijujlTt5vr84FIkEZmF4L3rvRWa2nY1w1VCKyQFCIRpHESKQ0AhbHWWUIZMGCwkSL0DC4ojTqKQLK34J1//PG3/vo//ae/9Zv/6xe++JW3PPKm58+99PKLzx8/emhgg+GIGbyXzhbWgAE2FSeREoDMDAgePDOj90TMwVem0lonjYSIRqNRCNRsdCKlJydmxiengrMiWmNyeVG6nlFJqlUMMhpmbthfA4DxdmesM18WWZEPd8/vBShfefX6xBj8+E986G2Pv+PKpZXR+hYO8/FWc2Z8zDG54PcQWA4WguPgIbhg60qXPQiktkpjqckZCr6VpM1Y+NwNt243temX3F25I6WamNw13kwu31iAwGXwAKSUkkIggKPtk15KSVLXTS7nnHGu8p6kCt4zsiMEDg6QAQg4BK8FRVESEBxKUlIRgSTBIAgUU10cM0iJoMgTSVlTgbS2iFIoRcTo0+np4KqG1tOH9pejfGNtbX7f3g9+/wcmpiYvvv66TlLr3Or6mrE2eNjc7E5MjCmM6pO3PrVNUVprI61rL0rt9K3/b33mSrntvgPYxn7djfGoq20pyfttXlqd/QPbdM7a+xuYud0eYx9sZQgE+0CE7ENZlhoj3sGKCUkSBaFQSnkOtMMfqBuidRXlCkMCEDFwqNj57831F1LXRUK9OkCQC2ysN8Y4ZwCgVhwEZ5ppmqIkZBUcs5c+B48hLxCFs7GvDDIHFW33uC17EEwI7BhQEDKAAMDASaSD9OSddWCD996ztS5I72ynOa6bypV58EOlbNIKTgfF5s7mEkdxR8N4LMrhplGhJbHMc9cfffRDH/zhH/nBL3z+c089/WRvbWVMN97x3ndHrU5ZVJv9wWZ/4PPRxp07C4u3+lub0+NjzXg6iTWUMDk1M+ptLtxZ2jM3f2jPnl279kY6qSpTFMUwK6QYpYlDFQCCsdY6oCBSZIhBB7E5qgbGO46VTLWMKqYA0Ol0hCLmYKoCBapEsudilBkCtxNCZJ1jZusNABhjtBJayu0aL0AURalWvp8hMAsJStTVMGynulOVFxycK/Nmq6UgbK6uJFo5U8RaS8Jq0AMfplptj3xn8Waz1WEpcVsTw0iMQiilSmfutnRwp9HOzPC9oQ4AUFZ5QE4aWsu6+PSmCuw9EAqBde++ntIKoPoBY0QXgjEOQiBCLZUgYOedc1qp7TQgIgHEULfSwdiqTjZDIk/b0htmlkh1vx12Uju3jwyuvckc2LOHUHuBmWu6NgIQIgHQNpeeUIK11hsTCZfINCZCJmSZ6ojZhwAucGAFrAKogMESA3oKELbt0MzAgLX7BZEIAaiWAzEjgEAgYoIAFLaRlIACEVygsOPdBSRUss6xhp3k6bpzUG8Wd/Uw9U5Ry/StKYUgidv/pN5f6lVd987q3lXYwYvULYodso4AE5z3xlqoYYT1RZJQkfKRl96V+aiqrHMBAGoIed2uIwJCAYghQAjWGAaoALFGG5KAOE6VUoFdzSq7+2o0WsBEWHnPAcEYy7WadWdjgm3bULXztbYp6MYYI9yOWUqgQA7kOHgWAUEl0rM01gbSsY68d2VhhmbUy7I4xajRRJJl5UwwgCpqNEkKLXUSxUmSaqmIwTvH3iQpKSVsqOnTSqlIKQsA7XZ7e1jqHbhKxGkkCCR0h5u3l9cnuqOV3/9PE7O7ltbWL16+JpSSQmsVWIAHsMEr5gCgHI/Wh9PrWW8jH91ZGdn1Y4/cv6sc5nmeju+CpG2uLV658PrLr7wRGN72tnfP7DsOPWHD10782IdhsDZ9375utTW1f/f+qZMQS19uNne34800Ho9uXr198eVLe/bsO7D/6NjYGLB446VXM1OefuzRmf37QDQ8rG8Wy8RZe34Wu+VKtnRtdXk9hx/9mXc2js/xRm+r2hz1Bos3FqnEe+6/5+bFW9eu3166Csf3j7cbY7dX7oCXE62mNeVo2Kt88au/9kv733z/i1/53LF7bzz66CnoLa1vrJ4998KuPXvPfOD7T5846heWzn/jW3/8h3/25gdPZ2vds2fPh9Ldd/yeN//Mz0JwkPWef+6Ze+897E0mwSTaV2WfpLh4aT0IOHxo4oEH7n/53Fm9a+6xX/+HcP361z/+qU9+8putFiwvQprA+9/z5o2N9b/8i0u79o697YlH544dvnHuuStvvJYNewThiSfe+rWvPR3p5NSZ+w8eOEFRE6iA9bUnn/rS4tr1k6dO7G4eRCUnpia/+OUvLN/ZmpvtPP7Ot+2d3/PlL3+5lw0ff8vbTr31zd98+sn/77/7nbEEd82MXbvWG5/Ee+458p53vmdjZWm83YjvP9HvrS8vL7147uVrt9cvXVvY2hrt3rXnwPxcf3PdlsOttXDvPWN/75f+zuTc1Ne+9uXX3rgklGRG9nzt0o2la6Zobo69rb28sNIZS10RWq3xffOwvr7+7DMvfOkrTy0sm+//0Js+8uF750/dO7p240uf/UwnnWwdnGi3Zr/05ecuLLwxOT9549LC5hDe/Ojx2d37P/flp69evby4vOIjzc0mUppEJGO1tta/s5FLpeNGE4Qs17ZQqWa7U5aVDRwYQZAAqoPztBQmGALpQLrgTVVVxgGhEIJr2mjgupsCAOJ7XV+jbOAdC6Xb7UaUNIpsJFiEECAAeAZECIBM3jlgFjuvOmmn7o4wc23lgp0JACKi2FZ7M0PgwDuAXillvd1Z63A7EMUjIlG976EQEuXOuVWvXxIA4I2tqoqtqxUOSmm/wxmot9Z6a4vj2Htvv6vYCiEwIGiN3jF4YJZIElgDSqRUxt6GvMoKY1BL3UhBkDUGS+vBmRKsxFiSBO99Ac5+86mvf/sbz7jAidK3bt48duhwUbm1/siBDF5gAB+Yg4xlEkstWYTK19NRtJacQ2sBmQliKRg9QSBk50JRVMa6NE2s5UtvXLt48SJyUBJiLVTUYB8G/REAxVpDAOt9JJXWerzdevnmlXYrunzlorXwvvff93d/6ec7c5NXz7323/7z72+u51HS2Fpfu7l4B3VMSaMxNpFnA+Od3zmJBYhURyKO2MeStCQwxYhC5Yut0jqyvY1V2L0LHnnbkbe9/V1Ld7Y+/8Wnyq214wf2bhRWbnPlAzNba50NATiO4xAC1LL1nV+NAFBCBOfBO0BQOk50pAXFSoYAZZkH6xrtlo6SAIxSaCld4QQTABljvTNEFEVJmiSRTvI8r/JcACRJQwhhbGnKisDt3bNLCLGyspJlWavZYGtfOvvi1RvXhZLWBcuBlDTejUajffv2DQa9u2rVOujQCFkrj+921oPbVgcJIdbX12vpfK2yqBWnRNTtdpVSVVEaY7TebvAxh/ra4JxzzvAOw6v+bx0TFELQWkupmFkK0kJ62J6b1Yfpzh/w7t/wd15+lA/rCqH+Kn8tt6s7GAB8J/rdBm8dO95mcchace4DBmeHw7XuhiKMBEbB2l5XIDfimAM6U+qAIDWGCjjUqFfvPQcgQUqAEERCCQJiilQEzBGiD2S9qI367FRwUSwSRVIph0IildZ2bTkYdH0niSbGFLogqOhMpp2GMKPR7Nzkow89+PCZe1785pPPf+1LbUWH7z2x3h3eunQ+oB4VeSDRH+W3r7ze6nSMLacnxlc2VjTRrqlxkw1vXb8FDvbtav3ET/wEBe5tbt28fmNlcdG6Ik2omYjGtGb2lStHVRmYdNyM4iaIdGFlEIEjY0bOBmYrEZgsijqv0TnjXJCMQhKzc8aGWO5EaaIHRmZJQiiOZUNJkkjOVmVZsg01qyrVEtg7YB8CEDn2HLzzrtvtRkpNdcYzU5l85Dl00uTRhx7wprh98/raZl8JIaVga4F8O00KU0iOUEsmDCEwgBTIgoIPzID8nUeoflD+eqBbvSSZObgavu688d77YNHVT9p2TG0IwYewvbVKASQDeOeCECglIKMNbKypc/Tr6hzr+Ncdn2f9tNfLp84YrZcX7gTL1pqXegOpDAME9nD3q9fXAZ0k24w+QXf5FkwsFJCBUBsPvBNAMrAETDhw8M57wfWAyxG4INj5CsmJ7QwkECiYkBGttYIlAAtBBCwAGBgBwBtgqituDAzAAgkRhEABUhJEOtJSMXjpPIOvBy5AQgn5nRVbl+yhDsRljyRRBBKCAbZDxeohIwmqYZbWWXbKh+0oMaQAqOPo7hAnwvjuu+a911GktAbE2k5QmqqoyiRtfOfmh6H+RTrv0iRBIkYMge/yfRAxz0cy0kkSxVorlMw+8PeU/nlZMtddxuBLmySRCzVaGbchnwDMYCt7d0s1xhnjhBAkmJECAjB45wOEgIGEVEo7G0ygABClcdJos3dCVyRCMtGRWjAJY11VlOSCipM4afQGfSlUmjaajXakNQYOzrM3jAUA5KOsKKooTrWOx8fHldKj/iCJIoRg8hGQiLRSUnAwzak5H/VAJ7eWVha6w2FeHDp2Agi7mz1i8D4E74SHABhsiDzKHp7/9PXfee33OzO6MyWXn1vftWf62D33wDo899QXvvnkt9779nf+1M/8fwARiGFr6zP/9b9uZr3TTP3RaBBGV5dvPXr0Ydi/79Zrz8/Ozt5ZXT7xpgeKQXFrfe1N73h8sjNtKhzbPe/ZtXbv2zMzqfbNczAbo7WNfDNqkRa6oRp/9fTTDROfetu7PvDE45DdADbdbOvO2tLlC5dWbsHJfZNxu/PI408Mjxbrx3u7J/fevry8+tyVuamZo4cPbEJ1Z+tOLOHmzcsX33j+q984+/jbjvQ2V775zFf/7BPnx6bgf/qf3+IXV167cOnJp7751FcX5ybg7NlLrShxWXX8wKGHH38HeIDB4OMf+9Pj9x07/fAZ8IUb6l2njqcKv/HNZ3/uF3/gxIMPQ6s9uHltq9cFBjj30te/9vSLL73sARbuwA99+ME0Sp9++hlBisHsPThx+dLrkeRr166trq199KMfTmYnly69tmvvlJKRF9WVW69dvHTt1s2tsfHGvuOHP/BzPzR9/0nobd68eX1Q9kdqcPDU5Dve+kRMGsry9L1H7n3sCWi2b33zuYWzr33/ow+874M//Oprl//4Tz4OPgyHw6985UuH9+99/wffC8KXK3ecsbt27X79jT+v8v6ZU/eMsmJr487CrYU3v+n+t7/1sQNHjiy8cam/udxJo4Pzc+/9wPsj3Tj73NmOTOcen5YsZ8Znu+u9q28s3rx2vd/ttRqNALC6lq137ePvfvB9P/f3IUmgt3VnLbu+sPXWRx577Od+Dq7c+Pyzz7ejxoWbm7O72//4N399eXl5czBcWdsqKvasMETeiLw7qkqbTowfOrR3azDM85wJrQsuBBFCcBbYo3eCSZBEZvYOQSA7Z3LjSTgNhBS8QmBgDJ4ZJSAiYwBwHoKT4nu0AWwrIaRWqKXQkkoIgoCVgEBAxCGI2jfmvCSVNnWSpkRUmqosS2PKKIkR6o7+NqOFiBCEZw4etje3nSocANI4ASAB23mdDp2gABhEYEQGAkEgxN3pKBBycMZa64z11gGAIIJAdUjlttwI69BxIMC681ILAuttnoiUEiBlIBIYBCnhfcQsQ6Dgs+4gaaRplIBQDphJoJICiauCPRpnzMhkbBVyJEAQzM5MX7lyZd/+A7t27fcsJqaml1ZW17qZRBmLegQchCSto1hKLchkmRIoBUEI5K1kX8ukOQQksLbq9XrOY5GXxvmqslJKBA/gIi3SNFEx5XnW3+p2WpPsrfOBvRNAEMzWen+4RQJzJTAEOHZk7EPf/34t8Uuf/MRLL1wQSpIUpGSwXkSxarW3snztzoJOG7WxT2uJwOytBIqiqLuVScmCQ6utFLAtu85VMxPw+CN7f/wnPnry3lPQHr/07PnVpYXN9Xy51411xwTvnAvOee9La2puAxE554Ac1G3PEBBJKdXvZ1VwGFgLgjoQW0lSmph8WXjP1oXAZWAMCEXwEfkk0krIELhyFQBEKtZSCAJnK7YmiqKYpDU2lA5DGOs0l+8sMPPk5OTUxNjtW4uXX1+IG02ldavVKq0zRe69Hw6HzoVdzTRN07TZrJ+xNE2FEEYq732/1ws7MjbaQW7dbaMSUZIkcaRqn7pSanN9tb5teu9rTPa2Aiow+MDOgg8IICVoJZSMBOBYq61V7FxQWtXz80ajIaS0we6UDAAADHW1UOOfHDPXSYv1n4N1LgSUMoRgXXDfa+bc3NyUUmghkdgZa5wty1KruG5QsmME1iS0kK00cnnwdiRDEMFoKCOimAAj2RuNCKWQjaIyEDBWDWCPYTusUhJKCUIGJVCA9IZJgJRSiyQGCgGcEd7JVExkg9wMqzSSUpKpRjLkU02cPbD/zL33j7fHbt9cuH712uzkDAZeKnqKqxef/ebTX/xsqtWZU6eyQe/6lTeGWdkYmxI6Lco+AqXejwa9UTWQkV7YWJqamlAUbl25nEbiPW9/7K1veezwwQOba6vXblx57ZWXV5cWW6k6cnDXoYO7xseSEPKAxoXUAZOOorSBQjsfjTWSieYo1cO1oR1YHEEIIiKpi9LkpvLesreeAdjHQrWbzUGwhoGDA0BBKEjU4fNKCAKm4AmwDtBNlEqVnGyl3tnSucLbgkPlvHPWWRNrRcgc7GjYFd7PTU6euu++f/jLf99V2Z9/6hPnXj6v04ZFXFhbzyrXbjbyrS2HiBRQChYYEEyw1ntNEQNzAEasx551gcg7xIC7L611CNZ7Uz9OdSOmVvWXlcPtZH+FWDPdoKZMMIAgBRKJaswKoBBSRXVxuXPJB6GEUopQ4g4igGo1IHAIdYin+c4l1gN7L3B7luWBauMmIhMhBATYzsAUQqCss2nrhoID8FEUxYJUELEn4TmWlEqKJAfvPDgXvGUILBilI0BwDjgA1+4UBgxMjLWeH5UguaOfq3NyvTF1UilsS5JYABOiFtKDF0galUQZGGS/36+3Ca11kiTba4x5NBrdxXP4Hcq9EKLdanhvrfXOVhxQKlJREuvtLogprXOOSBJ5a31ljW4kdUDE3djQuutW9x6cdcPhsP4zAlhrldjWKgkhUAjhQz1+HQ6HdeAAIhJv9+xRCjMcsLV12x65Pqq/RyVmrRVCJUkCQNY7gEBuG3lY/+C1PKn+qeujt5b+x3HcaLWLvPLA3nvrgvUeBcWx1lGsNBNRcCyRPAjjPWjdajfyrG9C5V1N+lQ++LIwVWmNtUooW7mSCpcba62rTGAXuFCxyLJ8mOVRZKIosT5sb7tISgrrGTxHSM6HojSjYpC2mv3SNjtjC0vLnfFJEcVZlu/avacsy2qUu7JE7wjYhQDWg0n2zk4Pt4beAlt58/LFylfN5jcEiGbaXrwKXx29NFqTE5329NjYand1YTm7vZ7//r/53Z7pHnvw0LEHjqaze9cXb83tP+ycm917cNjLL7x86fjJ03vmDjTak3cWlrpZubm52Z4Yb+w/Duj7W928yplxMp19/dLFFd+7crX3D/72r4loAoY5xG0wvfWNwcuvXiqH8MEPP37w1Fv6568n6WyixYVXnvrmM18q+/7AyXu//30/ELeS3/nj350Yb/3yr/xdfe+R3msvzO3uzMxPX798VaH8Z//kRw4cPo6kfus3//XiUr/bBQAoK+gi5lk1NT5+eWHtX/72f9i/fy8IXt9a65WjZ88+93d+45/IVhPA7z18+MO79gy9/spXvvbCCy/cup4fOpSET/3F88++WGRw6MDkb/3Lnz979uWqqGZnd+/et3cwyBrNaFS4w8cOb21lG5u9Rx57c7J7/vw3nlxeXjh27NiBtz1RXr/5zW89rxooGhCPyXf97EegCd945gvLiwv3nTw2tbszs3dydnwcI9tuNkdb5t4H7oPNtVe/9JVvPHnuoTOnjx079bE/+tMXL1zKciZRTc0037h8/a2PPWRHA0U+z/N//X/9zj33Hpmbm7qztra2fmdzsy88fPiDb981PVWVw3/zv//mhVezJIFf/fWPnH7Pu27fvPHSuVcQ1el7Trbi9rNPf+vG9etBx8Ms21hfL0eF1gMQEAS2Z1qf/Ny50PhPP/CDH7p989ZfPf3k7d5o82tfvXjrWmd8rDXXuXy9d++Du3/11//pxmb/s1/4/MH9B3JjA8nKsfWmmUy0G5rRSNVe3xgESYCyyEdKqenxcUQcjYaNKGISNR80eM5zZ0wFPjQjkoqEEkKooLE0tiiK0tg0TYWUkgQEZ3zwgKn6HqRXu5UgCgYs8kE27LfaY96L2vBUL9daMqeitveeCbEubpCiKKrz/DwH75z1PgQI4Gv2SJq0nGfvPe9Af6WUkkQIoY40qL1x211/CIK5jh4iKeodqS62eptbxhhTVhC4DoaXJBBhMMoYa/oKSqlUnWGOkA1yFxwEkFoqqYUgRHIEjjEgkSCEIIDQefYheB72s8nJ6clds4OyXNpcH+YFORVJlcap8NZVWJWVK43CQInWSbx8Z+PAwSNFXq0UGy7AjZsLaas9OTaRB4GkvXWmLNC7SAjJHMoSqoIloRISWbC34AQgA2dZL5DwnqtsRCJSUgOqqqryfNBuN5M49aHc6vfSSiSxnJmdoiDz/nAwyLpbAAxjTZjqQHtsfH5ubvHOypGDrQ9+6H0HD+wzRZkNq5u3l5HGBqanhR0YM7RBMeQ+NMfGPaBUqpE2Yq1CVY5GZVlkhsi7EiUpJWanxtqNRncjE2hPHNv7T//HfwCz06MrV0ZL14us31BcxtiO1cIwyy1Xpd1pJUKkFElZliUAeMY65ISIpNQkZLPRiD0jg5ZEwOyttZ59CQBp0kyShBGMcUorRBoO+kA+jeIkSZVywYMxpj5lTFGCD5HSsY68sdlgwMyNZrS8tBhFIooib42Mk/ndc81mMyCQUOtbmyh1u90uTDU1NUVKj/KsMzGeJMl2Rhxsl+y1wRd2RtkggjGmtq5xCFVV1XDoJNZ3U7zjOG40GvWllHl7hKWUqvIq7DRNkbj+4MCOuXYYK+cKITAEX8c6ueAJvnPj5e8y5NRlUgj1Dbr2y7u712PcIRB/96IOIbADkkIrGUkROVJgm02dJKkzPh/mVVECChmJbL3fjgRC4Go02Y4PHjg+Pd4JaF+88DJUGyQ0SVeNnIMoltIbTqMmbNcUKCVKwbWaPSsMAJKoeSIiOOeMZys4ArIQSd2OsRyVZX9rfnd8/+kjH/7QD+7btx9Y3L5283Ofsf2t3rCft1uRKUfB2PFWMx8Or7x2Ic+GJ4+fOHbsGEkxOT1z/fbC9Zu3esNsJaFhURZVPjXRslU2NTPzQ+//0ccfe7TV7myuLF+/eu0v//Iv89FQsNu7f9fpk4cPH5htN4m4VO1JoADEoAmiGICrQdbtmpOH5tpJLjGSa1kYcGXAEWKkPYvCVIFASMXGFFWpEtHpdPqbm8FZZ6xHiJVGWccVOm9M4CABtMCk2Ww2knajmWilAltTDsoyVFyZCoMPznhnWs3GYNCzpoy0bMnUVeb8S+e+8NnPPHDfCbBWC6EJm62WStLlza2F9XUtpcXaiUtKKo9gvAvO6ijGGlJbt67v5mbu3Fq/u/T3HjyGyuTGbOetEW3nPhtTRVEkpRZCMLsQAIEJsKpM2GZCoHMOa8ibELVWBYCJSCiptYrjOFIRwTZJq27wW+cMB2CuacTb81jHQgj2nll68PWcgnZyNmuJS53sjJJqL3tACMAIFIKNZBRFUcySCiusbQjqNGQimIOvzyPPwW9jgUXPWIvoAjsGB8GxCyACkhaKqI4UFYKgphEzcwEesB4MbOcUCUQiEZxn6zxiGVgQOefw9N7JsiyNMbXOp+5+iR0u4F35YJ0UVmf0GlsGD5GWSdJA5DwvR8MBoiCCYIN1FaGsozmtq+JGXG9J9WdARG+sMUZLhTvuYVczNRGJKFLx3d96PcKse/yx0ndrdC3VtlQRQ57nKJCZra2APQB4b5+88et3n5gfOfVfnQt5XkqhWu1mCI4Rav2StTbLsvo+F0VRHasnpUzTVGtdKymFihhBSa3iiJlHRV5VVWAkolarFam4LEtbmbrgsFUpMRCBinSSJCSltX6QDUejUac9VpYlMiVJQ7KoA3mEopHtt8daWuvRqCiKAkgSozFmfGysnjgDe/ZBEighpRYyFta5ytq8NMZ6X89VhWIfIqXSOGkkkQQy+ai/1R0NhhNJO42juCFZFCaMQBidyCSJXOWctaYw4GCqMz43MxVJYcp80N94+NEz9z9y38SesWQuhvkWaAbXB19ubWy0k5ZkBQXA/EFYz77yp3/6hSdfmp6VP/+3/vb0fachALi86K0nh/aAG926cnmj2xsfm90zc0B3ZiEIGK4BrZn+nZvXbve2hnvn9u46cgpCA+5sQTrz1J9+5vOf+jo4IK8m2jP3nTwzyvuD/M7Dbzlz7PjhWwvXk5aeP7Bb7t8Nve6TTz75zh/50aUrNz/9l19otKasp4sXr6KIjHFEJAHB2VBVbE1gC+hGpXvgwaMPPXDfWKextnx76faNw3v3PfKe9//b//PfHzlx7/d/5CNhmP3hH/7hhVcvKwGNWI2PjY21xjY3u6+9ukEEe3anszNzx44fnZ+fff3S+ZvX31jfyA8ciN/6+Jv27Z1rTrVBEUx0qps3gOQbl69eunJ1/74Dl+7cvNTtf+hH3/vWJ94MiG5r4/yLZx949NFbL72y/8hJ6BUwOf/8x/78S59+/eHT84cPHmWM95y8P52czYrip3/m/z27GwQCMnQacrwZ33Ps6NmzL91ZBhAQNWCtB+02KIQD863N1aEv4L3vPLVrdua+++7D/fvOff6vPvaJryQJHD10dHVxbXOlLwIwiJIVkJ4c71y+vDA7l+ambE21nrvY/6X/1/vf/M637H7g9D/5hb/12oXe/CxUGXzkw2/5vne/8+tPfeMbz5z9jf/5f9O7D/7GL//awu3l++9/YGNjq8jLlY2uTtob/ULE7X7hZaM1dJUDjmNtbDk10SHghcVbE51Oo5l0u93xzoRzDgJkWdZujQ1H2dhEGyV6BlNZD4gkq8pm2SiOY+dcEsXGVMN+d2ZmZmZq8s+f/b676/od9//f3qFxjmRMQpFUvE1ADRjqeEFGEM6FGgJqrS2ryntPUkgpS2uEkkII721VWeMNgkAhpYiIpFIqUlopVTc+ajyq1log1X2KumoxZR5sRQKY2YegIh3HMRMaY7bWN+rRsCCKlU7jpJmmUsfr3S0XfFXZEFwUJWkaM2NejjigSpRgARIIZVGOgCkdaxspR0XOlQ15cWLfgcH6uvQ+kfpXf+VXPvmpT126fjUdGysxWESMVJ6NZpuNvNtlZ5tpRMGOBj1wtplGSkpjDDCSFJ7RBZY6ThtNHTU2NrZsZVrNVDCMer3gTauRuLLYu3d+btcsEaytLl+5djkbjBqdZnNqMjfWGOcCWgfOegZJSrK3SRIpiYA20tRMFbA1+ejMPfdNtNvOFrtmJ97xxKNxDK+98tLiwg0Mfnx88pFHHht74BFY2vjzj338S1/82tpmhtGEhcgBVMwjB5YBlJJRjIxKSF9V/fX1TqTacZL1uo1mxNLu3jsDrlIi7J4dYzecnoze9a63HD64O0p0Nqp63dHLL1197vkLoxHkIV3KVa9i71go6b0vqhKAVBQVVYmI24ZUrrtCAknE6dh2a5BDLcuthQiBXQjBu1oSo1AKBMHgU00Mvr4iKiG996Nhlue5IpUkyfTU1PTEpLdhfX19OBwx28L0AxtvXZIk4+OTzNwfZJWzxvqomVbGD/IRRUpHEZMQilCKNI3b7fZgMEDE8fHxcpSXZZmPRkVR1ImfsuZUeA8Acpu2KScnJ1vN1DlXFIX3vhgNnXPIoLWWku7KfYnkDhOAiajRaCilRqNRmjYBqNXsJElalsbZUNPfGCFgEKS01lESK6UQBTMXZamUIhLGmKzI65uVEKIcjYgo1EbBAIh49pX3313UD5z4dPC23UoIgkBSIkjynVYjeGim7QuvXICA02MT461motV0p3Hr6sVGLI4f2hMrn2iZVf1Xr7566qEHfulX/tFWv/wnv/EvBjmTHu8OqubYjNKJcUFKGh8fVwK2uhv94UiQBgIhPaqgBAMQe81OzXTmLr926cSB/eh7C7fOPfLQgY/8yDtOPnC4GKyH4BudiS985gtf+OyXR5kZ9sNEs5nI9N6jJzSKV198cbQ1eNtbHnvgzJlgCoEuL7IsL0eV6Q6z9a2t/ih3HEiohx9++MyZM81GYzQYbqytX7ly5er1Gyub/fbY2NFDe+87sX//7olGzJIqkA5EgE4KyLC5McgHpJVQ0lWUwqRzzdtr+blLiy9fW1/M3AiTnGQZQuWd8UVwTjFLYAkIQMO8mpyc7PV6piwmxsbz0fDIgf23b1zXgtI4mmh1xsfarWYqBLLz3rnexoYxZWZMxd4gFN4Oi3JUFlmWEZEGUgiKQXhG78i56bHmeKclonRQFL2iqBhzHwbGsNKsBGsNgjyw894DA1BKEcG2m5S+S+uvhQwhXLz80bvPxoHdfyQkAlnSdb1ngw2ICIzee6UiIqpjGIQQWsUA4FxAoYUQ1rs8z621Wus0TZVSE1Pj9c2UmYlg29eKshhmjSQNgYejzHKI08R6zrJscmqmLkeZGTyEELx1LngPHmg72qeeCUONa1RKCOGBS1OZ4KVWQisBDMZqBBWEcpwwdWI9004mGqIYro035WCw5kOplJrfu+fE8Xs2+6OvP/f6sPLZKK88sJCgIo8yMDgGQhlFURJF4EKVF9aUnkPSTkPtOPC+vlYTEZG01tbGBah5eMz4wIGZPM/Lsqyr6joiIIqioijqC0AURTVCcCcPwd8dlCDAXVl8mRf1BwjA+v2VUnrvK1f5YLe1erU/o7ZZSHVXao87PjsAsNV24tD22IVIECGitdYb+x03AtUSKrS2CiH44BAxTeNWM1VKfezZH7/7xHzo5H+uURSNtDk2MZ5lQxusQCQpCcA4ByGgEFVRGOeQWWrdSBKhFDJb79NmC4UQorYnbrf/nXNxnNbCR2NM/fltZWxVCHZaySiOG41GlCb1HNlae/3KNa01kXSVd8YjYqJjimhu38zN2zfW1zcbjWRiaiaKohrd4KwlIkVUvwtaiiiKpBZJOy2qstsf9nq90ajggCrSURQpISEE9gEDE6Ik1EIKoryfSymEAlQhYAnoWDhENkXZSNNYxsF7dCFNkonO2FiajsfqgTP3nH74FEwn3m4N3FbQTmgc2z0NrQnobgFoiMZe+fhffPVzX9mzf8+P/eyP3VxcMAXcuHxjYmz6TW96YPHOjWs3L+89tHdxZU03xo4dOz0xOc8gUMeQhI0r3yDMO+0JMT4D/dFrL7720vMXVhY3F65tuQpMBolOBeu9uw8dOnj8zu0rOh6Zqlu5YjgcnDpzz72n7z1w5OCoqi5eujwcmZcvvLG02pua2uOCWF7Zssx5LVEF1ERKghIoITBY56pXz5eHDsCJY5NVOTp57PDRQwdfu3h5aW3r0be+7Ym3vPnChQtPP/kUEXlr7ywsRUpLlD//879w+D3vvfiZz33205+td6XV5bW0oY8cPnDk6L43P3YGj++HW2/cunqR0T39zacnpyaG+ajT6aCQFy/d+OBHv+/E2x+HdgLdLig6/61vfOz//tq/+M1fgPk9MMwhan7jD//07DOXIoDRFpw8vP/Nb3knJGNxe/xXf/1/KS2QhMUVuP8e6DTEwg1/7Kg6c+r0cFS8dP7izQWYmIF9B+cO7N116dVX9s7MnLrn5O6ZmReff2F55U5nfHLPgQNzu+fPvXT+5RcvNXTsDScUHz95352N7NvPvfLOd7zl4OF9n/+rz7QnW+lU68G3PPSOj34INPyjf/QrF17vHT4om2n8q7/8S3uPnnjp45/6yz/73P/yW/8CWlP/8n/6Fy+fv+0DCEWTUzMs1NpWvwqyPwpjk3NZxaXzupGUZX7k6MFhr3v50oVOK223kmBt8LbVamysbTJjM0mTJC0LMzkzPaiyrcEQQUzNzDbS5qgo81HJhKPByBgjBE6Md5QS3a1NW1bPr//q3XX94J7/U8hocnY3g7izvDozO+8ZdmTKznkbrHOBiSQJpaQMvB3eH4ARMUqTEJz33jjLPpAUWmut42Zrsh5N1DtVPXKEwHUb1VrrjKmlEUQEwbOrhEBfa2EBUFDlbFmW4AMRKSHjKEqklkTeOuOsjCOQghmtrQBICAQgayshlFKCGYVApaKyzKvKqjReGQ53792zZ3Jm6eatfGurt7J639Gj9528533vfs+f/fknz778ikWskHWzmXRapqpaglQIilAhUrBsKwpeELSbrbwcORfq0r8ojQ1BCNHf2GqlDUFkqyqN4/FOG4Ib9LvtZjNJoywbDAa9vfvmT506VRT52Vdf6Zcmd+ycd56dA+s8CqW13j03R8TelUWZWZMTWEEg2LWUftP9p37yJ3909yP3Q74Bww0wWX9ttdMZg70HoPAXvvrtz//Vk7durgAkHDVXBpUhxUIyURGgMi4wEFHwfvHGrYfuOzXbbl05/2oqxPzsjHXZ4dP7bi9fyYfDSLGEqtVU957cf/L4gXw0uP/MqSRtXb229M1vvvT664sCm3lobPnmZu6qylIdkRm8lDpK4v5w6EKtjahRrRgCBM/eCUSUQkhJSqAQ9f/EmmC6fbhuh14gsxcSkAMRxVoqpYLzZVlWRSmEAA9aqSiKgg2j0QhBxImMm8r7ypRlPR+QQqMUSFJoleV5VlaTU1P7Dh9kQYtLy93uZsAwMTExMzPT7/fLsmw0Gq4yRGSqajQaFUUBAAK2wzQBAJlr1FeapmkSAUDd4aqKUQihHoIJsX1nBgBjXFVVdTGEiLVPYDgcpmkzhJCmzThqWOsRRJI0SAoZSe/99hVIK621EAoArKvnCWCtLUxVTx5CCJGURMREIYR6vPbcuffcXdRvOfN5wS4f9gi4026CK5FHmxsre3ftB5AP3v9IkRULN27Pz87tnpn61tNfve/kwcceOnXj8oXu1tLRg3vHZprzx3Yf/4EPgoif/fhn/+i/fWKzZ/ojmJzZb7yUMs2NZ8ZOpxNFqjCVcy4rSlJSSmCw9QiQgiaQaHhucmKqmRS9JW9W+1vXElV98MNP/MBH343zs3/5e//lz/6f507dN3/44D397siVvGd27803bizfWuDKzk1MHj98aHZiyptRjEWR9wdZ1h/lvWw0LAxIKeNkembu6NHjU5PTq6urly9f3dzchMCs4m6BHilW0Iw4UpUCk2jfSKVWqLWcnJzYe2DP2K4ZiBVgAAuD25t5hgvr+Ru3u5dX8ztDv2Fx4KlCqMDXHFUKHgOTZ/CQRGlwPo5jCK7f7TlTAvvZ6SmFqIWMtIyU1pKAfd2QHQ4HpCiQMN6PTFmGwIJIyfGJKe+MLStflVxZCaBJasF+lMWxtoyb/UFvVHpBVqiKWTYbpDTECqUICAwQEIgJLG+Papnhu0Ij69p6efPv3H02Ds7+lyiRKpWoIIRQlqWrHHM9va3Do1Q9pK1DZYxx3jOhFEJGaVKnNQLT5Mz0rvndxpSMEEKtZ9kGYrDzVT/XSpEQxrvKe6FknDSiNNnc7DJArcUQYlu6wszGFShRIAbnAUCK75hFSQhGqLzzAPVPDc6PxylaD5XXgZtSTTfj2bFkLKWE8mB6q0vX5qbbb3nrI296+MGiKD75519+41qv8lEVQmm5DFx6KBmc587YFJCQUhJAVZSmKhWQTBTF0hOEAM45UznnvHMuMCpVt8ZqnCQCgJRRrAJ7wHqASFJGaaPZbKatdk2YH+YFj/K7Nt/amlNXtK4ydQ4XEUVxevfqJpUCIT2gB5ZSsvPBusBsnSciCAy1v8q5OnSvFtvUM3QGBgZnLTrcvgPUMlxEx84G670nprD9Y1Oz2cyr0uTWOye99CHQ984QUQghwXlfWZNlGQhSKo51lDRSgZSXxWiYlabK8hIIJQlkqKwnBgL0HKIklVohYlVVzKy1lgIqqLw1eVXVwWcEItRrjEirVCB7H7JslGXZ3QHWxNh4mqYAVOYVgmg2m0rpylZra+tSx0eOHZudnY2iuNvtbq6t1/cuIUQQQjODlJ6FZ0CG1c0eAzCQjhrWgTHWGceeQen6mgTgvfcAQkqKlGq0IsCAyDLSKo6RgvGltZVIhHehdKUUgoiMdf1hxqXLctu/03/myecKzvplj1LYd2z+8NEDJ++BzfXXF28s+DJEoNcX18dpV1K1/5///GevX7lMNg1G7Jmaz66NVu5cX1u/83r6uvE8Nr1v47wt/blBVclIJk1eW7sYqdBstl0VLly4dOG8SxQcOTy559C9+ciaIgDrrbX+1fVuoe9YaycT+eqlFWY4ee+u+f0n1raqledfdwF8UM8+e/720prUjRs3NwNGnbGZTrN5486CZcvOW0mp0BgpRAZ05Ko9h1fnds/2cqdE410f/LH54ycuXP+/rixeXvyLL3zlqW9vbGwUIzs52Wo1mnP7j993330f+umfefVrT/7jn/y5JGnMTM0IIYxxagTDMt996NRbfugHoOx947//RVF03/d97wZ0ujMzMTEWt1OIIuiMfZ+1kMQwOX3n6Sfnd82BYuhDA+CZJ19469uSwpT/y2/+q4N7Gz/8iz+xsbjlRubY/mMT+09A5j/5sU+0W7BnLF3eyD/84XkB/Gv/x//x+X//O9/6xre/+OUXl1dh3yE4dm+6//CRBx88s3t2atfs1KE9e1IpTZ574N6gEtHo0JHDr1+5/sprl0YVINm5uV2nj5+ujH/mC8/8yj/6hcd+7Eftlde/+u0vyCZDbE88dHSje+NPPv4nDz5+6uF3we3bt3/kBz+89/7jMCyWb1/7H375F0Dpv/y93x+ub4ABoWFsonVjaSUZb0IjaTXHVNAiao+6uR0MyOSS7cbiTUF85sRhQX5rfRnZ5XmetuSpQ/PB+VjFVWkKhKq/mSZxc2Z6Ynyy1RkbZPmw20Vn0kbLC5jfs6vX28r73X175sbmZ1ZXlr97Xf+Dv/9Lqxv9F185v7rR2zUzO8zz2pIVmOs4rFqqL1EhEiBKJQVLtGRM6ZylEpxzxhgHTiIRqABsXagohiRIAgAwZVUHogtADsHUoHu3LV8QQmilGkmMiNZ7hhpRgohCSM0U2HnngkUfEQSGEMB7r4mUlkKoquKiqExlAYg4AANgwICSZCKE0AqtB+/vO3xgZWXpxtpKquSuiXZsirzfvXLp/OWLF1bX1xpxHHU6/aKogg9VSdZVhY+TRKNg74CxmTYjIbwrwQdfOmsMSsEkvDPBBULaOzdFgSWS7KSCyJkizwYmz5f7mzqS4+PjnbHW0tLSYDicmBxvNJu31hYtAjB5DqYKxjspvZCQFUOBTOi393MPQqh23GhKUZX5k1/58sk7lw8fnN3aXHrl5eeX7yzOzu5auL1y69a6MVG7vWt615EsCyv9zEYNAxyQhNQioEArA0qB4822qlzV6y5trI1pNTPeidnGOpw7+40Tp/fxWLsqin27dgdfrK8utRJx4ODeZqNtbOh2e/nIxFGDqLHZNZU3znj2gSQgUXB2NBpl+UhFUX2WE1E93LYuOHYkSaKsY3BEHdKHABCAa2MdQ42gZvYueA4+d3U6eCmFVISBra2ctSIIb0JeoS40BAwhaC1R6f4oU5qiViuU5bDXF8EpIWxZyuClUjOdsem52SiON3vdqipUHNXu27pPt91K2/HO3e3KSSFrbh0RjYbDu8Ebzrk6zt9aC6HmYADsJHzUmh/jvAteS1lDnUjWMF0uTREC6CgBQiElkayPxZ0+HQghpFJSbmcOlpW/+6q/q3q5Ue0DJGJm57kmE919RQIUiQr8eKsx1tDr6+vNJPytn/xoGje/9FdfG2yugsNQlN3VNR6NyuFo/+497N3N61faDdluxrtmxuamOrBy66uf+eJTTz8bsT8wO9ntmUh5L7WMZRkJY4NgGyqL1ipBzWSbneeCr0rrwBIJLSlOKRHm5tXXhxuL+2eb0hhXMGfm8ivn57or1954/UMfOP0TP/P3BhuZEs1kz+GrT32rvzoYbvTbU/FE0iiy0ab37YhEZFsa4k461krm3FggmbTGGp0xIaPFpZVXz54tiqrVbO+ZmcuyfHUzm5g8XHp2Vdbrd6tyyD5vparVjA8dODgxu+vgoUPNibavsqVbi8srC8NeT5RFdytb7rluJQrfAK8wEDIgE0FACoQASIG99QGcbyhfjQZNLQaDQSTo3e959yvnXoqkLLLMAuQciOrfOXBw3nsXHHoBQthQCyeF0rGOI43KCyCFNjAjaSHSJEmVsmkaSVE6TkF6XQSpvVKOkIVkpVhQIHAhuOBh56moqYz1lRV3As8a7bYtq+/e8Z2phOC4FSERsCeURCGEgBCIOYoiY6yvvGAdAIP3Da3SpClQZlmGzkaRhsD9QY+D05LGJiY8BwgcnHfWmqoyVRGMnRwbs1VlvZcE1nM2HGT5KC5SYxwzY0CLKNASIAZgCDZUOhJIArwNvk6yZ2YGa4Mgj4TAUqoQgnfWFZULBMaRrUmCCkOoijx3VdLiarSxeyp5+5vvf/SBE0KZ0eLCOOTHZydKn+TG9ouqn5uRDRbJk2rEUWEqk1eOWQavCGMphRIUaxu8MRaD8+yYAyD6HbOyD+wYgg8AILe2tur1Wb+/9STFGIP4nc5BPQqI41hrXVVVrQvy3jshawls3d6427mnuxEY3jabqUC2UNPOUNQw5xo7HAL474i66i0s2tmC608VQqhD/eI4FkrGRHXLTUqphUQpWq1WYkwcx0WRk4DSulrAc/eltWaqE0uJCZXSgMwIdXpwURSDbJhlWW2ZujvcqL0cjDgcZZGLnHODwcA5F8cxM5d5AUB5nnnPURTVNyJikFKWZbn944AHYInbbnEpZWWNt06pqN3qJEkjz4tBNtzs9mZ2Tbda7a2t7nA4dM7VU5FaUVAHEKK1VJUqL4KAQZ5HSaRVxAxEAoJxzgXrgnVJHMsoQhLWue18OqOaUcLgkYiE0jIRGmP0ITglcDDom7JSSgkU3nFeAVrc355zZT5YH45MUYLHjK/mK4tXtp763ItX31hJFGgCX8Jb3vTI8UOPvPjytwd+9c5tmOzgeDqzeG342vNPmarcM9/yo9Bqj/uefv329aWtAcdEKWbFZrNJo7zb34KigEjD9KSQIrmzWiZptLjeG/QLrVIi3W6PQbPTGm8u3H4VUxof68jG5Pow9HqDqrKjonrtjcu9ftboTKVjk/1+URpv+/lofas5Pk7eGmMsu4FlzVZJIQmnxuemdJrbylrnrP3nv/Vvmf3GRq891rq1MlSb+dTU2Oxku9vtrvc277ln1qn0n//j33jllfONRtq7udFqrZ4+ffrci68Sa2f8Z7/6zWfOvpQX3dGo225G/YKLMvupn/2bn/38Z86++IL1Psv9Bz7wznvvvffc2Y+/+Nyzxw4eKvLB7Rur/SG8lF5d26huryyxgHd98KMHTt47Prk5XB98+jNfvPLa748nHW/8saN7SlemY/rDP/h9R+47BYPuAw8/9NTXv33gyNTcPru21T/z4MOPPvFWH2w/G555+E3tycml1167dOnKpTdupK3k2D33sdDPPPvCqITH3/XYhVcuXb212OnMPvX1F/ccGr/vzMnLT/7VF7/0+cnZzsUrt87sPTp331GI+Nd+43+AVGYbq2+88cbRM6chimB5400PP9rYd+gv/uAP//C/v75/L2iC+x86vbi18eCje0eecw+OtBna7mBzVNk4Vm40nGo3rcmzvO8yRl/t3T39/ne/o8yL//4nn3bxBgG+94c/evjw0f37Dv3BH/3x155/bveBA60kKbNsa229GA6qyg76vX379uV55s1IsL92+eJyz4zr717W8CMf/sH/+J/+y/Ltxe4wV3HLIwWimnPuOTBwwLonoZCEJ7hrGatrICIK7JSkRKZ1uLitzMhWW1uDSCdJkkopQmDvHQRAAu9C8A6RpBSA6IMPPgSAoTGenXPOBA+EpKSo5dRENoSyrExZsXXtRrPZbE7oidzltJ15Fpi9txUwAYFWKhIEiBh8MFWwFRiDge9cvkjgWmkzAfDZiFzOoSz6rtfrMVHUjMDkNh8yAAlQRBAsGAxBSeBYYkxCI4JQqytL1rkayB4YS2NsVdrAKTrwTiXpxPh0s9n03pd5y3s/HGWLi4sk5d79+67fvPH6lUV5azFupoUNKKPac+W5EsYLASRCf7BJHNIkbjXTRDWtkVrIKIqmxjpLd9Y21pbHx5qzkxNPP/XsN55+ac+e5Ny5VUCYmdk9f2Dv0kr24gsXjZez+w9ZsLmzIQTpARiD9xIpkvrO9WvTrUYMLAXu37c7FHl3fe3Qid0PvuPxH/vpj9y4dv1j//2Pu72NXdMTuw/c88Tjj42fOAYbm8++8O1z5y6ubRYeIs+qqiqQNVPKk3USUZJgiZ638W0E3zmM6tNKCb2tQ2DvnA/s635/Pa+unzcOeFfRjgg1etlAHSkXAodASAAqUUmUJkkCAY1x3rGFMCqrGLSKhNRJ1AhRFEmlBoOBR9i9d35iaqay5tbiwp3lJcdhamqq02l572vRTpIkaZrWAtq7ElkAuJteDTv86boTeVeIX4vWmBl2snR2TJMUQvDgmYUHhsCOgwAMCK6qUCgpZdJMMYBnRGDjLTuWUta6AACoqspa75wrK8s7mejubl+vhoshAGAArvM2vntRs6sqV4ylerKdsK1SGX7mb37kI7/+K5e+8OTNK9defem1ZtxJk9bGxsbtazceOnOq0WhdfuNKQIzSpNfvxz1xcuo+19tYvHVtfWlDIP3g+z7U6cx98tNfGAy3EskSZIlsrRlVpqwsasmCrFOCNID0Dup0FHRFXg5sGE2Ny9lGZ7h2R9ow0ZIXnn9lz55W0UiP7Dm0Z99RkEn7xCEYmM/+wX+9eeVmb32TvLdluTkaNpXSk+Ne6MqP0kiMt1s6baBSASQLxUK/8urFzW6vyEqtEy00BhHJaGwsXu+PQDekiDFuAXrvIpbSgGY5FWDy6vX+jS+/sLR8k4QXMjgz9P2tfh8GFUAa+xjZS7BCAIXAgRAJGIGYHLPnAN4TcvDWO1Nk/X379h0+uH9jbWVtZcV4t41aYxACazoDSBIUF6Yq8woAVJTEOiYQrnB31hfqpmxtei2QC2uHSnJpYh2xEKw0QT1pIOu8UhJQMCAEBg/gfQiBtsMP6uj6nUxMZgKcmZyqivK7nw0C8M6VpSG13SkOISCDRGICScKxEcCxJCVlCKIRJxNjHY1qzdvhqAglCJAKQtbrXSuKmblZ45zz1joX2CGyEEIDba2tuso5DqSV0JEikRXlYDAYH5vkEHwAZGBggQgeIHhBTktUJFwIzlTBe+LAzEqKEBgYEUEQAilm9s5mW1spSqViTQIhFEXBRT6CrLfa3z/bePSB0w/ee0yYYu3ajc2V1dlYSCuHJYOvjK0CcdKIKGmhjkeVKbLKZCMpZavZiHUEwVn2kp1zxlWVMw58UEJqrQBFFTx75DqDlAAA8Ohsu97j7mIR6hivusKul3S9cWzjrmr9eV2aB74rzhkNh9t1P5HcdvcH4KAE13Gr278/oprdHUVRPY3Yvg8Qbe8UBCDortoHdmDFtW+vbv8LvPsBAgBICgCw1gRvEREgfPrsT919Yj58/3+DgELIJG4kaZSk6agshsNhrT7EHaRXkiTb8X87TZT6e+4NeirSztjBYFBPyhCxKopWs12ZEhijWNczEEkiihPveWeCYRkCASq1faPIstz70Gy0G622d5xlo6oqdSpRgK1cUYyklO12WwtZVZUQYscbtZ30BwEdelIaBBCTc8YaE5yXSEpKRIikEkJwCKay279HFbOl+girb2g60VGkpJaRpmBdYMc+5HlZ5VWs0smxxnD1ZlMLnSipZdqKo0TnedbtbdXCZVtWVVEFxxgYAqIwe4+NV9Y0432+ik0W4khFUaVVGPS7OkoAmhuDfGCq5kRTtkMv28zzstVqGWNWltfy3LeajSRpVdZpEUsdI8jhcBTH6cz03HA4vHn9Mvrs+PH9s7OzCwsL7UZ7fHxcq/jazZsMtLy+sbgyMAEmpxtj45NCRSikC+wDGGfzKq9MSeziSDUi3Ygi70wxGHTGWjMz0ysrKxvdrV27ZssqR0QOaK3tdDrM3OsNdu3aNTc3N9Zqnz17tvalbGxsNJvNXm8YSui020rJlaWNVhNO3nOgzIcrq5v3nzk8GPQch6IoyqrygMNhqHJoKDh5eCbrdffMzRDy7NzkT/6v/xw2l65cfaM5MYaC5vYe6N2+8wf/9ndffGZJGPiJH3z4y188K1L4xV/68ek901u93ol77/3MJz8NAddXNman54ZZntnq+37ggxO7ZpPZ2SvPnv3YH//JC8+sz8/CdAfTSP/iL/7i1Pzu6zdvqqS5Z/8hPP3glT/7i3/7r38HAw2Hw85kc2kle997H2o34y9/7Zm4Af/uY38AMYEM0IhuvHFRahUJNXPkCAAVFy//6W//p5e+dXtinAbD8Nhbn0hbExvZ8NKtm5joEsCSHFU+G9lev+AgJanxRtzdWGumcvfM1OR4+vibH3rPe94OzXjxxXO/97v/gT0AiB/+8A+3W+P5yPyPv/mvXKpE2tA6DkAkNElVlbaWLigtBDGAu//0PX/7F37+9BNvxt2fvruuf/rRjy2sbJJKWca9vJRx6gCd9y54Gyxz3UiHOE45bIe4bUsyrXHOIQRmliSiWNc+ojzPi6Jk0rXOUSkhhKpDSkJwzgWBTCilIg7ovKnPx7Isa7liEOjrjGVB9eIlBhEAASKS7bTRabUbzaQ76ho2zjhjK1fV8iEQCHMzc2kcB+vLsgguFGXuKqs0SKp2z054Y9dX11xZjbU7wdjhcDi/Z98gy0eVDUgYx3GjEYCyLNNA6F2sdLvRSJRka6wpIfhBryulbDbTOE1GZbWxuZnlI0TMM7drutVqNIloampqz549LvDy8vKoLNY2N5JmI2qkq+sbZVUFhJWNQWd8XGgdx5FQ0piyrHJE1lrFUeRNJUmkSaRAmrJkHyJBZdYfayda4tKd9XwAJ44ns1OTCwuL95w6FZj6mVvbzLLcMyVMkQHKwee2QsRIaWIwVSU4NJTKe92Y/O7Jsel22tLkq9Hs1Nh7vv/tR77vQZhuwc2Fc8++EOvonkcfg7EOLC09/9TXLrx++exLFxAbQk0MBs5xnJUISbuXF6OsQCmSRhqnqY4ToeTt27cDIaJgxJ1CBJHR21ATcxh8CM4G571l8EmSfKdpFTCEUMeOpmkDBQokgAAYMHgkRkSlJJGIVKyUCh7K0lSVZfZxsxHY1Tu8Imw0GkKI0WiUNBsHDx7UcbR4587CylI/GwKEpJHunttV099qRFccx1l/wMxaqcFgMBgMrLUYtpMHETGNY601ABhjgL2U0hgzGo3SWNdafynldp8SQCiZV6V1TgrByMF5HUcCKctHAgUJNT05MzE5jYwuMDLV3mIhUAiFiNa5qqqqyjrntmOzUALAX0uz9d7XgrGAgIjnX/vQ3UX9poN/iLaYnWhTsMTu7W975Nf+t38G/c3/8h//6Mrl2zeurSCkqW71tvqxjt799icQyjLfJMjWV24JYWfn2u/+wOO9rHvn5h2J0a3ri7NT883GxDPfemGUOxW3cgeZdRbEyLmAlLRa/WLkggSOEBMlU0GAbCgMZyblwtWXmlQdmZ85NLf7/mMni1728U98Jp2AX/6Hvzh37N4v/vln/+MffDVO4KEH7r969aYIQIEnmulEK0kwtBPdSWIJ1XhDpRE1Wu2o0URSo8ptdAfdYb68ssEiEjJCUHlpy9IoGcWNia4hlBGDKU1/VPStqwgEoQBPUuqsN1hfvSME7N83PjXd0lhJ0w/OBdWidGYQ4tubo1tbWde4ofdWikCChQSk4MlaRmvnx8fyXm9qamqUZ51Oh4iyLKsfgHosc1ebLYQgAs+hqMqqtAQi1rHWGlGEEPrDQR1cE9gZb12wTCiABUOkYiGlZaics4ELDy54EcUkBUpBBFynXiIjoiQFfhulWjd5BSAyzMzMsA/nLn/k7rNxbOYPWVCIEFUdCVOx84QoAnMIVT4C5nYjnZ6aUoKKomDnkziuBkUIUFa29BaElnFsGUaVsRxqALAHJgKlhNY6IeqoOFEJI2RlYQBYUm7sYJjFSYOZIaAgUqQkCQFE7LUIzVhpqbytqiIPznpnvPdCSht86XxAlHEiorj0tspy6VxDRWnciKQS3pOvJIxiGEFZfOCJYx9612NTDemGfemqraXVy1cXuqaTWVk4rpAgagadWtIV0p21tawojfVKqSTWouarA5vgTXDGhRACoApEDNIjFc46j5UPxgUfmJHk1NTUd5f+NdO7rrDrxnxVVXcF/c65KE6+Y//d4WIBQKSU24kTru12zEwIVVEgf68CJzDsMAF45zlTqgbey9oZ993dd/Ch/opiJ3I7BF8rbYAwz4s0acaJZg4ckDFsUwR2XsYY9ghgTOWyXI6Pe+Pd3SZHPdutVbz1V/nu4WkArqrKBV8LuXYuHhgpbU0lECItdaS1lChRCyl1bFlYF6ytnDfBeeYAxA5YxzohQBAqikZVmWW5INmZmrC2Go2GCDA3tztJoqqqbPk9dX8IAYCY0QdvOeRFX2qVSK2EVLEAH4AZAxOhFKIeQUATd2a4bIIlCuSxsI6LSuYibqRxIiWC81X9c0HAgAoZN7JiZu/eRoze+36/v77Wr5VOWX/kvZ+bmS0MBlCz87MSSUp95Oj89cWXA/heKcoc8xGIyjrf6/WWd81O9laWgJMo7QRBRdYnXzn0Ihob5KQoPXr8wMTEFAdYWd9YWVnRaYtIJkkioiYRVcFlVdGamoLQ7FuqNkbLW+X6MOj1vNVqZSUNsmFeMWuYnh7fd+BAYYu1tTWlInYKSToOLnjnPZEAGck4XVtfF4DAuLTcXd/IOuOdVmf2+sJ6HEskzkfl6iqk6bDVIu8DqOT8pesTU+N5VqRp3B8VK30/rd3k7j2mCNlg2Irl3IE5U+Yvv3az34dOG779wrVGK2o22+sb1fj45HBUvHF99Pij+9ZXlkvfyIpiZd1pQUU1/MrvffzVy+dfOP/a+z/0rp/8qZ+68vL1f/O//8v12+69bz3+4D2npsdaX/vy2dOn5u89cw/s3TXj3fmvfW2ju9FM2leuXytNOHT4aBp4/tRp0OqVb3z9Lz752VsL63/zpx//iV/4hVtnz507+/zU3N5vPvPta7duT+2ez4M+PnNQNNoViM3NwZEjB6ammr/8ax889qYHv/WZv4yiZ4KHP/rt33vzOx+36D//1S899PDDALC10Z1svf78M88d2L3nvR/+8cvX/31haasYLax1w1r/3IWLE3NTV167jjFEjTgvTRK3oyAbURyc664tzkyPO1PcvvnG2h2fb908+82/Wrhx3RZQlVCVMDkGL5/91tTU7Ppa9/7T+1dGdqWfDYd9HafEUOUjpaLpuekf++hHv/a1L99euN5pp3Gizl94cWn5Bvx6enddrywsE+neoA/KRs12VQaD6JktA6MCAYIIiK3f9nKBD4CBYHu3MVVRIx7B+oBeEjUiLUmopLXtgkIkQmSyYEMIjSRSYnuHDN5pqSKpQJAQDUAUWpEUlXcjUxprfQiEmDTSTtqMpQLr0QdjjBtYE8qsykxpmT15ZPAQgIGDcwJZSBGobkDpIHWiWfmqA1W/6E8oP6rKols04+Rtb37g9uKd8ZZqtzSQBqFNCL3BwGdDESXBWwRPFtlTng1NPkIIE41m5api0B32NoqytN5PtFoTExPZYAgApswdB+fcVq+fV2V/kA2yTES6heRG5eZgIHSk41i3vEcRmKQHT86x8+CxjskMwXlTlcEWeSQ0gRAgPOPE1ByyV1Lfd+9ub92w31ta9mMTJwZZwwRe3egurQ5F0pjeNekCbayvgRQKIdZxK03Y+dxY8DYFMTU9/u4nHtXoz3776YWlVUWulR5odWS1fCvKI0D14NufgKk5GOZvfP5rX//61/O8bLbGfuiHfuqBhx7f6oVPfOqvvvnMSzeXVmfm95gQ6ka1EpK9zwZD6+92oKRnrmM2AiMBMYNjBwBIzBIlCIGIyLVStU6FCqFmHghEDMwSBIqdLHlkAUCCSMkQQvb/Z+9Pg2zLrvs+cK09numOOeeb69WruYACCiAmggAHECRBUaREmpZoyZZbUsvtD+qOdjjUckdY0e6w5Z6i3e5Qy5JlmZIpkSIpDmKYIMRBBAkQQFWhUHPVe/WmfDnnne89wx5Xfzj5Hgh/7+gIBfanFy8y82bee84+a6/1///+TUn1uahBZSkTHBnjIrV1wwV1Ot0QwmQxI6IEmKcYmmZRrox3UinA2JJ8Wt1pCGG5XC6Xy+h8r9drybBtZx3/VGBly/Bo/y3FOXe/DQQgouB8jJHz80GH914nCTgLFEOM1rtgSDBunO0VvUDROFvWFUQA5BxFiBEhhkAxtpD1CABCSKXUqiqJzgmjrbtGPFzWukiOkY90nqLw7dqAfKp4rkU1n+/ubP7Vf//nQMCv/NI/88HOlwuVpLaBs/G02x1e3NmdVfXk9JBz4+1kNV9sbvbvHZz88q/+1tbGkBPubub9LLv91uuj02maFJfXtiwRlo2zJgCy4D1iiKRkJBeDQ+RaIuOMU6gj1W++/taf/fxHP/XiE1c3hj2RQUV339q7ujV87ebkb//N/z4yQAGXN3SS9fbvHiUsJYhINs/SC1sbKfMsNJ0Ei6Szu7neejeIqQhckueyAWgGw43xdDk6m0XgedbrdjcY4x7YWj+trJmvxvPFqDYVcMa4BuC2Am8qzcXOhSsxlMvFQnB/5cLw4k4/1YnubHjeOZg1tT8ezZdTU2GM4DEyAVwQ14SyzWeaz6fdTEvFoKHaVJPJpNfvtylvIQaHBBwZY0Ey4Cwi1tbzJO9mnAKRi0Q8kTpJEozctILvQAERlGqnnVooJHARXPAWuUfgijPGfAwASBHiOQaqjd0l/zDaXArBGJOMC8YZ4Hw64995bQhkxFjgDLmASBx8IEKKQAQhCs4h+Ohss1qQVME20fvaNIr47vYucXH/4HA8myMFphLwjrVhXpxJRoAxxNiYKgS4dm1nozcIyA5Ojk4n0xhYojTv90LEEAIBMAIKkSIEikgh5TIl0IDARa5UQHAADgwSGgKI4TyyCZmMgWIskiQRUnFG3llvRXRSopRJrwAiNLWzFMO8jsbUowVvIrO1wKQQIlOJk3zhzHw1m1aNB56mutdThFCXVd3UgvEk08ZZjixPJOMCmGp8qIxrGmuCd5HZSD6AIyCMwofzerdteD/S/IWHcYCP5I9SykwnD2/nhwNEPA8uAMYCUTs2dQCtN05wRsExIP5wL2KMccFblKfgvJ0hAEFwnkeAyFgqCaML1rhI4dtVOCN4mK5Gj34rxoS1LkRoDIsxELWs/u84aVRVRZHFGKNf+RiOjo5kottzDmOM86a16kopH6mM4BGYDEJ7/kHEJEkQIiJyQMFktVoJwYAziF7LJMk0QzQhWBcdARc8S3KllFSCAUXyZ6ORTlKutLO+ssZQkMhN8DEGxgRALKtquZw3TZNI1e12y7J81LkBzhhnwATFgM5zJrMs6xadREvyoakrWzcIwJFxZFoqJXUIYVWVZV2Kjif0gbz35F0EAuPKCpn3DUGASIyxLMm1yo2v5021d3eWJDJNEoYcAnkXvfUgsiTX08DS3noiZSVlt9uNDL7x/p2ymo9Hs8bVeXGhcaxarboDWVy5YrqqAgKrWZE5MrVfsrgCLpgh2wB5O5qdwb2zJEkvXLjwqU8/d3x4dHBw0MyW1hnnXFWtjDGPP3Ej73Tu7d0/mk0bgwny6fGo3/e9Xq8MYJGxLLMQ758+qH2FjJI8cwvCSATEOVdpIqUs8ixNs/xCcrj3oMg6mxc35stFY0zRH1wbrC+Wk9Vq2Rl0ukMExMViNZ6WHicXLlyqTaXyXuXNaLoQuYhS7Z2eXdm9XBq7stY5kyaqv70jszkSIPLFyo8m0zQbnI1ikq1/8uNPn4xPt64+d298msrB3dPpIM83VfaP/8dft2Qu37j2xX/1R9/8k7ce3D7RAZ6/sp679MXv++EH3/zKhYvqtdcOfvNf/kpvc7B3sLe2vvlX/sZfh+H29/zxS1/6nd//vT/8Mgjx1p1bq2Y1mS15VN7jweH4D37ji6f7+3XZ3Htw/P7dw5deffPOb7y6ffFff/rTbwCI9d3LaVE3Powmy8WyPnj91vrahZ/48Z/+2te++lu/9nJTYxWcFJ2v/evXlovy2oUrr915xy6bz33sxbvH5QMTbNmoTvYnb76nhOJJdvfe8bXHtu/vH0PT7HZ6G/2N/XtHvHRKK1Wkw7XOycHsgx948q/+lb94+bknYDE5ufNuplQny2eTWaILIZJ7dx/8z3e/tFrOHCgUXABGoMV8PluWSaIio1/4xV/odvPuoPtg7+7b79x95Zt/kmYC/rf/2aP7OjTRklvvbdTE7j84zfq9gCxyQYyB5MgAGUOOgSwgAkGMQCEQoBBcCJHoLkRCioJxLTlHBpAEgrJxgIyAQvDGeAoPEQSCJ2kCAMEZgqgEExyt88hFbENYAKxzprEueCb4xtp6qpM8SSXjEa2t6qZuXDBRWOPr4KMQjAvGPABDzrBeLjRGJBac1UJLLYOLOhqoFpeubXz6hWe+8IUvdK5evfvyy7/0L35FCHjq+iVAMV/WR2ej0enpfLEixoedvuQgtWaR6vmkCT4RfNArlBZZki4Ws8WiAW8TFntZurG9sb2zczaa3nuwt1yWnU4nUDw+O3U+JkXRz/LxbH44mnSGaxsXrnjEsqp4gpFY9NH7GnkI0fhgGIZIgOAEcskZBQqepBJKaA547/7h1atXx9Py3r3R+mCzaTQjcfXa82+8+3ZpbOS0eempS9evZr18NJ96jZOTM06Qcl5IiciTRCciXe8WO5u9XiIXs7Pnn732k1/46/2N/qt/8pX3b791Kb98eetpAD2+c+/mb/+br33lG3fv3Bv0N27f3icE97uv9H71y43XZ+MmK9Y/9MJjtx7cEYorpVqEXbWq58uyrKvh+loMgQiBsbZNHiLESIlK4znzJwSIyKglBTrngAHgwy4XEDDGkLvGAkOkc60FEQBD5NjGQxACtYNxKaVUyNjJbDocDiNnnDOZZtPT07PxZH19aL0bz+bOmel8VtdlBOKcPyLxx4d5ZM656HyapnVVNU3TgvLaflYL7ageIuxadVD74CMiBhEAmodhuu1P8zFolnmKHFvOOgEHZICcRyAXg4/BeMcICUkgOO8QIsSHc3Jqz9VcKWW9C48IpJy11YX3PsZzm18IgRDidxrznn/2GV/OmSvTXtFJxHh0/Mavv3Tz9q3nnvpwcfd4tVqmeXexnGWd4aK00/l90yyqahTsbHOziDLxTn/4w5/8zKc+cXhv7/e/+MXFaPLktWvrWc5QJHnKpN7CoeW8JLh7fLJ3fLyYlyiV9ZJCF51AJxkDjhVC+ef//A/82R/52DCxfjY3VT09WJwdn/Ion7++PtjeDsDLxjniR6eT1WTe7/eJvOAiEVxgYGATGYe9pN/tMA6RcefBhjoSdxGZUFnePR4dAYq00wtRMpkIlUuVahb3jm5VflHWCxdMWuis22M8s4ayzeFssrRlbX29nIwgwNWr3Q89/7SOpkhz1L1ZFdOF6yrZT5Nl3aAPNZIF8AjIGDFBQnFJzHqheNWULlgBfOfSbgRanq2MDUREwJjgUnAUIrZcVy6EUpwLFjBiwIAMOETkIBi5EAiQK61ErrhWjDFXWR/Ix2iJBURiQFwIIbQ6RzPH6KN3bduXEEN07GFibCJVC28UjEOMrQHg28dCAAQk4ID4KN3xXBBBlOnEO+ONmftZqqWWggOAdzub61d2tpnS0dkQAnBBUhNiQBaQYjv8hTbYKggOBw/2l6djkHxZN846BCk1k5m2bdSzixAijwwJIRKLxJ2XXCacJJMiZZgkzjljm0jgYqidrEOITCIyA4CICUcOkaLjyIQQgjElJBcmy/Xx0ejowUl/d8svw/zgdHY86ReDWVOGUJVNXPjRwse5hbkNpae13YstDTRQtK5uXCUZZ5YoegBGkYIAYBR8MHXTNM4QeOKOIAALgBEYfvipK3VdW2uxRSw9FO2why19gHMfdHtCaMH/+FDNT0RSCM65c65pmhCCEAyRx+g5cCU5BS8YtPym9gNOlRYP2w8I4L2z1jrngAIyhkpEoOApREcBiAIRAkTFFSJBxBBcCAQQhVBCiBAJEWPwIQTGQEpJEL7+4D95dMW8sPZ3EZlSKpEJ57xsaik5AGsR/o+6/vAQ2vpIgNR+e20qHwJDFEJAjN57pCAYj963KFTOsdPpZVlirZ0sljLrmxApRNaGpyG1AKQsy4pOT0pZGWMaB8CcC01ZSYQiS4U8H56macKAGdMQQaDQZiIKIdoYY09xY2uTiBiQFlJJwQBD+wDwYblcVsuVp5gnmUoTInLBmFhFjMHH4NH7GCMQI0RqTLW1tTHs95z1TWM5Cq1yIdhsOV4u5yGEPC20TIINSCzTKQNcLkvGWFEU7bWhEq01vf/+azFSWlzaufAkyLSxlUys9wtvS28DOu5caMxKZahzb61D30dKJTvHS3vvW8CUNaZVr5blqiiK4bDfNM3axubpZNLtDba2tpzxeZ6Px+Px2eT9O7eG64MQnMqkR1c1ZZIplYlyXnfEgByY6D1F5JgonadaS1XNFqnSENvYZiaUjAxdsERhsZwpmSBikmdap8vlUkrpgm9xGS0cw5NfzuZZlimUTWMTJb33nEGqtGkarbUW2tkQQlRJRsSd9UxwYhhZ6PcLX9eaWEcp06yWy7nS3Ed37bHLs8nYrOYbeeHnKzNfKhYvXt3+5Gc+Xpryj1/+ikzVm+/Oih587Hs+dHI8fuutvdkUlIbhlj4Zm6IPVQn1CshBaCBP4LmnLqzmi8Vief8Q+utQ9JPe2qaxYdDfuHblsf39g1dffmVnY5Aq+f7Nw+vXNpJUvf/+wRNPXcx7XRciCM6Ax0hrnfV6Wa11B3tHe/em906X1eZat2msQLG7vVPOF91OGkx1673Tj3/osX/3Z3/2ys6Vf/Tf/fdf/+rLhkPsQ2c9+/AHPvAzP/2TO49fB8mg24HDfXdweHJ0CqTu3N27+f79N15/p27c/qiW60PPVQjkIwiZcKmbplmslvPprOgkPpgsEVuba021GI/LVyb/+aP7+j/85BdLG09G8+nKVNarNIuMk2AtOyKQjzFG8olgLfC4tTAFOA+UjTEyJI5McsYAkc6DAonJVkoYnfeNbYVzQslVVa5tbiRJslpW0fssSSHSfLka7Ow0zoYQ6fwnkJQyy7KNtfUYnPc+WBtM451zznhvhWYuOoGglWKA0VlOILmwpumkGYQYnNdSxRjrsgS7+JFPPvUf/Yc/xxi7e/fu/v7hfLk4Oxs//8EPvfKt1xvrx5PFeD431lfGIhODfp8R5Iluyup4/0Gw8fKFzcevXun1epPxmTGGCIWUAcg5h5zJNB8v6sm8mk7nxnqpM11kwJklR5zV3o7ns9KGtNNTaRGBS8ZNVTlT22ADOQLHoleMUoEX1tf6aZowVZdNXVmKGIEb5zu97tlo0thY5L1ub73XXQsBp5NFd9BHwY03K7MiRTJTja8Xs7lGDj5kSvayglEU0W8NBpd2Nq5e3r7z/jsf/vBzTz957fXXXv43f/h7d+898AjjCgLCtStMyfzB3aVW0OsM9g+mOzsbgPpsugSW6aSPImcsOx6NdVd7JO+9dS6Sb3tFHslY65EQeJIkWZpKoVuaW6plO/Q23jjvPUXCGDn6ENqHYwgUnMdIgnPOJDIlE50kCTIKwTECIVhLiW0Fpdju5N43ja1M40PY2tpazOfR+Y219ePjw6osP/ShD3LOq6Y8Pj2Zr5ZCCWAshCClvHjxUlmW7RmgVWhUy1WWZVKIdiNFxFb52TbalBCLxcJam+d5niXtIJ0xFr1dLBZ1WXHOtZaI6JxzwdsYAkWpBCJ675USUsq6NsFHIhoMNgb9NST0gaKnpmkSrR9NDIw9n6UzxjqdTl3Xdf2QjSEYEfkYGIr28AIAHBkivvLuTzy6qf/Jf8XffeObe3feSTksxqfrGz2UcTKfdbobWq/NZr7X23nv5v6gvxlCMFXZ6+kYV71CpAmtVqNrl3b+1n/xd0AxWC6WN99497VXzGp2crD33NNPV1Xd29wc7lzobe02jL/yzjt//PVv7B2ezmcVYk7URcwRU8FRiEbzxYVt/Mwnnry6WQxTJUyEmoGRb795G3m+c+EqSnU4Hv3hV7/+rTff2dha397evHHtqq1XaCvmKxlNv5MkAlfL5bLyXMgYoTaNNTEQc541hoQsQCjgWiit05wwjqeTk9O9xk4YB6Gh2+9s7l4YrK27wJbzplzU47PJ5PSoKSNH2NmED71w5YNP3RgmueJyWcP949ndw8X+ZLU/Xh0uyrGxNWEdyTJJXJFQjKdIcXetMx4dexfX19fLuuKcPzg86Pf7dWMBABhvsw/OySsUhRDeOggkQCRCMmK+8dbaLMsq2zTWkgCRJSKVbUyvqe35aQ55AApEhJyxttMKiIgUYgwQqRXCtXCUb/f7z1vExIE7Z964/5cfXRsf2P4feKZX0YHkAOBNE7zngBCJYkAf24jxhMs8SxIprTH1YqUDXNjZlWk6ns1XptZFj2tdulAaa2NwPnrwiNRmBWrJhad6VXobVKKlzh1BbY0DEiqJMcboBaBCrhiXiJJIe9PRSZEluVZaCsVY8LZpGu8tUbDe1dYEipGo8aFpmhAImOBSapVKrTgDoIbHWrjlesK+/yMfuNbvpt5mMS6OTjyIUqTHq+bwbHI4mc6sozTLBsOk2185uyrrurHAmRCKIrY3VQSwPlhrPSAw9ICNi40n5NyjiMACoAcWEYTxAbhAFq311taP5PupljH66EOkAHBO+eUcqrJ+NKRrT0s+cMaYMY4xYAIjeortGS1YYlJIYBy5RMYigSOI3rMIgrU01BDJI0VU1MYdG+sDYWvubaW5EIEgCo4hBHKRIDLkyFiIFJw9jyxG5JIxBhHCd/YRADkjAuOs9a4VGhLKGKlpGiJSQSGBtTbLitaqK6UMIRrvAYAryZnmot3iQ/DBhwAxWvQCGQAqIbkSPsKyqgFAZ3ltjHGhlQ8JzZMk0TrL8m5RFMY5H7E/WGNMtGrmJFHgjVZSCCG1NMYY1wAXIhUATEmGwBtnS2PafV8nunGl9x6JjJQpponSoBlKeXI4dt6h5sBl6f1oNiYiJXhdLTnnrZad6GEWaQgSk9WkVpAj4mJWa6mdpNHoTCqUHBnxelEZrBVXjLF53SilBGNCcAGkeEJE9aycNCuMHS1EMGbv9us6zQfDbqoSoZLpcsUiOGfJeA0SGipXvmych1PkLFFJnudapSgJNWHEbqfnvZdSD7bWiWhZ1cD4rCxVlpdNvX90qJS6f7h3fHzsnOut95tgOGe18z764LFa2eBZlnSlzCb1xDmXFTkAjKeT0Sh28yJL0/FyCeehgMGVTZ7n/bX149MjmXexPV8JYSOJJHXeR2DWR2NMmqYJMKmSbp87Fwwgz9LAGNMaEJtIlOYNgSXyGG30cTVrkwWFZ5GhzJJ7p2eC46DTXZhqPp8j0iDJ6nJ563A06HX7g01kKHoWahOtPXDxt77y3qpaLmqNHLxQ+2d29PtvARfTFeieNFzcHNVr62vZxpqZzgLMIaIeCpWm9+oQUZuEda4EkaVLH8HxrOjfm0xfvf37wRPLcpcPI8G1D38w73QQaYN33niwf/at/etPXFCJ7ncGnU7nLNDMmrduvmeqmXf1ekcH8qojpFaHi31qaufE4sD/p3/9z//gD36+Ojn9N7/xK6f3b+YFbK4rU9jPfv6ZXKlepwK9Agurr7/x+mvvfu2rr+49OC2bMFvaOweN1LCxuVUnSdlIR4TIhNRA2jRQGWy87G9cYgwGhe4VRQzORpTpd0R63Z4v0qyj1te6HZd7aOx5K7EVQDMG1lpX18NUjs9OUYpOlo6bChDzPEel5vN5mqaeaFWV5EMMTiJL01SmkiOzi6VZlrnUqdIouJBC9DrGNQ6DKhJFOYbII9vczObR1OCapiEb+rrYGawXKkEfadqE4IKrnVlVZmVDEyAwxnp6qFQ3zxIpebVarGyVSTnsFhzzYbe7WiyPDg7R1Z0k5dGtVsuTu7cPb9987IMfGuj0tQf7jXE7/eEXf/U3sqIwIaJxKYRE8c3BWqc/6ObZrTdf507O9scXBsknPvo9RwcPHrz3rYsf+7hH19noAbHauhhjHcNitaxmqxDkTm+bN8nhcsR4Zha0MjNRYOCGKUy0TwtNjC1mMy76aa77vfz0bCESLpNiuZxLL4ZaamO6o/KZS+sZ8pPl+HQyzYbDtF88GI3L8Yg5jyHOq/nE1DA+6fY2BhvrNZEUCEyAF1W1pLoSqez3h91ut6nqKxd2zXJ58uD+he2Not89mo5OpsfG1Le/+PvH/+To+PQ473V6g4uz+Vh3PBKcjSTnPO1uC649T3evXDKNj8Sl7vpIEVMtM2u8ULIBNppO8jwfbmzMF9PJfCISmRY5SyBTigOL1gVrRCBqglvNGTWmKbvd7mav33CKQkcpZ9VqXK1EkiqlXGOQwrDoKCam81lIyBKQ9UCMc6mEjMBqE4N1MUadSM55bWuigIjRVwlgNTrwZbW9uaVCVZ4dFkUxSGSMkXEYaGnL2JQrlWitZIihrivj7LJctd25thkXYyQApTVjzFprg5cMszRL0zRYqxIdKPoYCCFJk1bEW9V1m1CJnFEboc2ZFjpaw4ixiEJwrSURoaVCJitTaq0TFAkTqU6tC9Wq9sAxove+sdZaG0IbAcQ5F0cHx0qpVMv22AMxEBFHXFaLNE27WmOIprbefAeTIyt6B6dTkQz29/e2hztKppcubTG/V65iNEAxX6xg58qT82XjglP9vEEfAtlVXXiWJBsHZ+Ybv/etG1cugp+/+s033n/nlWGHPfnsbn8Yn7z0OKgkOrd//1v3T8ezqnn8Yv+xnc1b37o7m5hZOSI+l2nBIvhVWdv59rWne5DudrYAKrBzyDxo9/hzmYhri9Xk3t7Brfu3XTh75vm06LFuUcV4p99JpA+LyWI+m07PrBCCcZ0WG4eTyWI2RYJe0cl0VzApOHNGYFC1NyUuUR/N67OjcdlUsD6EH/zs8xvDrcWqTnQePE1X00zHb+2/sVbwx59LN9aLixfWL13a6g46gAzGpZ9P3MKo4HNphV/GZonG+qVxTETkkVNUJCQJxaSUs2oROAfGqmBVltd1neWdxbIsy7ooCimFbSwittbwaC3PGRMsMqAQSu8oRGAYE/JUYcJEpo31s3LpF4FzLmSrO+YtCR0iMMaAccbQGBsCcEDOoJXoMAIuEVPhvPXGETKhtGgDEyEEikF+B/2JK6rLie7lHkKMEVkkDCac5+9lncQ7Z63znpqlEdwjgWApgZjMnDYYkQETy+Ucasl16q1DLiTnHDhgTKTodPK0yPZPD33GQJB1VpXAmZJcCY7A+MpWITiZSskd91aEoLgsukWwpnJeJt2ESxdc9FZi0CJyCpGc5zWCzxKRpBr4eulSTDrExWy1PJ5MJssZ8KgVSKJhp/9g3tgAA856HJP+mo98UbEVqBVzIYV8qGWRefLT5ZkPgbzniBR5dCKAdF7ZEMtgK+Pr2thgkTMUHJCHSMQ4YEAukQsJBIhiVVdERD740M4kGUOGiGVZAgADQgaIBEAUXAjExTlqEwBi69BnAEhpps4JaJzHh+MYDlzxROBDBpmnFhdFgUwMLQMHkSQnhkCIgbyPQMTOoUPn/B9gKIhauzTiwwAIQCCIQnDvrbUuhPidtIDz5YIXXLYlb6tOAoYcMUk0EXDO29NOa2kQ2B5BGTwkJDgX2hDmSD62c30EBFYbp4CYFJJxJs5PyZwwhKpt8AghhJSCK4YCiC1WVTs4ni9X+uHyaIRKBEJE8sEHCBFBCORSzBYLFZRQkgnUQnPO0zRNkiQ6q0i0GiRPvrShNZmB4AzPw9g9D8GbpjFVEyQxJEJgSujzd4RQctnOu50JSqlu0c/zPMa4XC5NvWBKKJXolCsupJQQMYTAUQAABzTGmNo+pKcHrQoUGEKIvvEmrObGNRIRnHNE55RoIME5p6gEUHeYuGCttbPFjLGFkjrJ8kHWjzECpFxpIURV1ib4CAghuKpuh0Wc8yTTWZHO53ZZLpjkAgUicy4YYznniRZKZlVTEwIT5/4Nay2FWImGMdZYwznPkk6WptbapmnORiMmFGMP06PZeWvKBe+9Z4JnMk+SBDnzPvpIrWU/RIIY2m/hQrS9DOc9KiUYr6qqrpeI2MnzLCsCgkyzSH60WkVvPYXow/LkjCGVxq4akyVJqpWWUkoppC7Hc25NY8k5CZx5FKid5yLvdQtZBgQfQ4hYRY6VXzngnV5w0SFaqR2B8dETRy5OT6ZMKseTpV+uKoNZvrO5Oej0fWV3tnZ3dnaI6MGD+2Zedrcurl/RxtSNp+Pp4mgyb5xpD8Ypx7X+2uZGv/LN0ehYJeIzn/3Ep178yI0Luwfv3Woms3/5C//wpa+/dHoGF67IJ57aOl6c/OjnPvDjP/P5vZt3jw9uLt54LVhuGvjyH740Hte1xffvLjzCE09fX1bu8GzcHa5xqYJ31viqqSM0rRIiK3oPSQPSk/KBXBAufAfiZ2ZsDRUR+kAArGoqxhgyNKZu6hVjTGtd5Hq1mErBmOSmaRhj62trOs8ms7mP8XQ06hZFp9MJzmjZE5zNxiOVclM1q+koF+rZ609cuXS5rKvD0eisXEybejlbcSEKlWdSa1QIsDPsH52eBeu7WWe9N8xVIpFJIe/ded+6GpnLMt0vhA8sECmGKVbC134eAsREsE7BEJowXyADlGvDVEAP9u8d3StBA1zpw7femLzz2muPXbzcz5NBXuxN9lcREoa+qge9vtXKntV1UzoMB9Oz189WHQbzGi5fSHqd7st/8mXF+cWtrb277//Yj/0YZ2Iymb138/2D/YNA0O8P17trR4t6ulopJXZ3Npd17SgMBymomPWKs/mpLRuVBa21QvL10kQDZT3IhCq6NsR+un3j0uXtNKn399eBfuDFD7/98kskZH97o7ez9cKnP/21N9768jdfpwiNs5WxNXCH/KiuxOgII+sWnURpaw1B6PSyXtHNOhnncme4JoSqYHXl+vVcindvvXtydJjnaZ6np5Opg3jhiQ+Uxt4+O+kWG1ol0XnTWNuEGAA5KB6YDDGgjy7GCJzxaFa1JQqkWWXt7qWL49OTm7fe3d3d3t7dmsynxtvI4nQ2DtYlXCcgyspQ7SW5RbNc7+vl5PTw4Gh3c+2Z5z802NlyiHePD0azeVXXJBXLSQueC9XrpGerJdOSMUYECEyINv9BbF+8vLe3Z+pad/NMybq2nLNut+PrqpMkslPMZtM3b9/PE/nEpYunD+5vbm66sqxXSxHjsMhVlhrv5quyLkuRJFmWhRBM3ZxPpYR4pAJqm/3nppQYbV0/8gCUZfnIMnf+KDhnWpw/7M5df4iMIPjgogM411ZwZNEHW9UrnDtlvI+mCda7+XShkyQt8m63D/BICxCfe/oZ541tjHMGYowRY/Quhn6/zznXwEJ0KRNRfoee++79vbK2qRJ5dy1y5aIKTuTZ0IR6Wfl5Y0DEJGc8VTyVQCEGxxkvdKffUUnCRPT/9J/+/LCj89QKnPW7buNqf+NyMlzjAY5t6aeLZjavNIPLO92iO0x57+nuxuHeeP/sZF5VJ7Px6WisJV7eHKynstMKSxAAGwgrYFbn7ODu0dmoPJicsNQ9/aH1tfV1KZhrXE8X3CEz+Nj2VicbpiptGjddVC+/9pZ3LDhGMa7mjZOQ6TxV+e7O4Oj0tPHLLGXH86N378LmDvzoFy7++E98Hjif7Z9OxtTLu4vFsi6nVTP7G3/9h5KMslSgiMAiQAzViVk0umbWIbKoE1nk0O/l646RhmWcKqGDziiRTqANfmXKsrLexOiiEIIAtCYXfFuu9Ho9KSUFYoBSyFQniMgAnLFtO7W1HfpzRyU8zMPFCASILfILgXGBnCPjnAFQQB8DRO8ja6PQW30NB6YSroRkAhdmgQITTDgQAnpnnIMAxJRw3+kRjUAoOJBHBMZRACOmeETGOAdOxEAwFhiwAMiAcYZIjKxjHESgSACeQWCMcRY5dvs9F8DYVszimspYU8cZhoQ5DojEkNkA6F0gH4BkJ+FCMEHWlW5RqmAGWqu8KJdjxgRjiXVNBaQYZpkuko6vV51EpAIwNAJMIiBNFBO56l8bLez948OT1axZTp2pQXJCRCVGy4b86SjVm3k60IJZ19Tu4Mx4kTrEmGRRcBfJBheCl4IxZFKwACqCqr3wxCoPlSdDPArFpWICiWEIIWLgLXqHiLwD4EQk5pMJY+3tx5AAgBFwQt6aNiRHxhAhxujbZC55fqjjjLH40KLRbhxCCKVEW/q3Fwoj5owBFoUQgIAcEYATBwBnfKRIFBkDYiwG8t4FT0IVIZwHibOHhThjbDlftP/ZVoGtdpAgtvqf8zgdzqSUQnxHiYDArPVNY0MIMZJSPD7EjELrxArAGEuT5PxswBnQ+Z7oKdYmEEP28Mpv3ybGUGolpDzfZIOHABgpEAohkM6HqvgwRrT9GiKqmrosS6XUcDiUUtqmThXnQBHhUZ4iWvPoHW4PIY9S7s7N04y1TKGmti19FRHzPG91U48cC0QUQ0ik9s4AxUfRpFKqNE37/T4AKJUAnKfNt/KnXmdTSCaYpIfSL4yI3kuuiMgb3zSNrc/zHIAzJRRFBAQSDDnzDIACEHiKjLEoGEVOkXEplEg0S2aLkU51p9PjTHjvnQt1XVexAS6EUAnjMVBtbFk3FJEidorMOTeZTE5OTgJFAMjzHBErU2dZlncKIqqqVVuq1nWdJJlWySOHOhAigJSSM651IqWUUjHkMZBprHFWJppxLgRyhggsxkARKEIMxFsxHnJr3HngjhBCiFZi234QohV1PcTYhYdsWaVUURR5p1MZqzupc24+nzPGBoMBxVguFhQ9F4IJDoz5GFzlvPfgA9lo6oYQ2vtIapXkhfEOGM87PULwMWSdLhcCEYu8a4xxtgnWg4cQgm2cECLPsxe/56n7e/vT6bTT615/7PGdixeMs/t7Dw7vH7z+5tsQY7fb7ff7Psb5fDZ9MN/e3j7HUhERRa11v99fH3QurPWX09PJwfLS+oWf+Xd+8vt/+iegmr/25T/40h/8lmvKvbsjE+HCc8CY83LxqU8984XPfQ6G25evazua/c5Lv/+7X/pWU0Onm0RStnGDniibcPOd23OAoZTDopiWpeRMFqkP5GJo9XsA0FgTY/TBWmusax4huR6tsizr2kRCQsY5d0AM2lAQF8gxYiH6SLoKJsaILvpIkYONwZar+XwqhLi4vVUu59Ozk24nny/m5WqxNuj5elYkurvZ68pkvV/kCa9WxlaLbpaAxLYPyoiCqQ1YZXlYLbPlEp0TEeoYZxAba3x0IdhITYKgmVUxcl9zilpCrOs84/1+9+LuhUuXL1Lwr3/rm698fbK1Ab0LfGO4dqHf/d4Xdnd3d/vdXiLVWqf36ksv/9//L/91muaMiel8WdfN0XH5zHNXgbxtDMaQaJloRdErBpvD9G/+x/8R5/xf/sqvMoRnnnlmd3e30+l+89XXDo9PDo5Ol6uKiWRtfXNBbP/klGf5tJkqLYjicvYAkbp5d7kq6xKkVFtFVhof7KInU2KRwjLWSwzKOVcZK1ViukVtkuVievXChdqVKpPPvfgcK/J7pyfv3rt16/A+FAno4FlcVY1FxrQCxutIm+trjPEIKFSa5Unez0CplXNxWWuhXV2Xq7qJ9N7xwbCT/63/w38eovvKV/5o9eZblW0ODs9MiMiT2vFFE31gwQkfEQAYoMTAggnBefKME+cYyTpvOMdEFRjldLQ/6HW3167MZrPlotFaGGs73YIF0h3ZL7oygib29NXrH3zh2dfeeeXjH//o808++5Xf//Jv/vKvZVx++PpTR2ene++8x5cVNRUTyJX01p4tZk1TFYkms/KRfAQutBPSGl/W9exkr9PtDoukrhbWNhvDodZyNp1e2NxczOf79/c4ss988uOf//znL+zufvmP/ujtt98OFFOpQgjWWO89ISomAEBLpZX23reCGQgPexxECNA2uRhjjABCNMa0z82WYWWM0Vq3z4J21yIigG8/EBFRKdX+TOcc51xw3s4WHkmMEFjr+FNK5XmnLRVijDa4dpOUUiWpgiZ67yVTHBnjEEKwLgTOYwDrWpAjikT/6Zv61dfemMzmvTxRQpVVE5ydvTdGpNJES5j11phWjmoAY62F6Dg4KUFI5nzZjGuzGm0WfO/+vvfw9HNw44Unn3lqJ03JhNMi76RcpWK4q6+B7oAFOBsvD8+Sgcqc4MDuHswnfrzVhWvXdp954vrTj1/NJPnJA9tM62oUYsVlRM4724kYim1xqbexVly8DGkBx5OTvZPf/63f2yy2NvIdA/G0XC3mzWRWrsq69LVxrp9tXrxwYXttQwoAdEK5B4e3ti+5ji3nTblTwLVnsg++8KHnPv1hX96KWPmiLDIGsJwu71fpSdKHE/OyiMBLFEKkKkuTXIhEdBIuc07kXVMu7XTVzMqm9jFwKXWKOoUkh0RaJGfKUIWmtuQCEiPGnLVtU4/OaemKMUYMmOBaa50m3vtoiDEGQAAQEQCBRSDAtvCLMYZAwFAI1VIWHxlB4aHRnDEWIzGIkmshRJsUBnAOhCU8DwsIABQoBAeRgCFxFox/5ABu18pGZMwZT4yAIbR2FUIgjIhIEGOAhz5PgkCEwDFwVrGIFAiD562Emlj0YJ1z0dkW4U4M0TrX1DZaGZCpwCQIZAKZbAHzNkQUiAAxkOaikyX9JC2U4oWsluV8PDo9OEKAVOlu0ekU6Von8+RDIgZFsTXc2Vrr5VkCKF9+/d7B6WRvf/9sOvZEnSITScoEQgzWxTNbL2fVWC4KKVgMzniKSmgMnLc+Uu5QKVGkWYiOIwZCBGECBgpNcI11tW18W6AKwSVrIVrRk9Y6AAX/baG+6PVzzjnnEoExwHOdfxu8BcAgElEMrcaECM+peRygDcA6r34Ya9sJrYj/YYxVBIIQAgRorcMU8ZxuwJhp6UMM2+YptqwLPO9btJDg1hzcVuRJknDOpZRKKcl4W9g5F4jO6aLtxaRU0n7voyWESBL1p9NPrG3apiAA1HVNAYQQi8WirbDbHTDGiJEiAgpJBATfJhC370+/3yciCtEY0xYljICQF72Bj+fuJcJzU3Lb7287zW2RXVUVIgZnvUHJkEnxaINut9duv88eAk9d8G0XR3Ih+Plv2Jb+rT9bSpllRfsAMMbESK3CEoVUjHtnQgiPjjpJwvM8F0KkaSqlLsuy7Qa1BOj19T5BcMbXdX3+maJob2bvvXHtZNcHihyQgAJC65QXXErF23MZIj1x7RoiGuNms8V8tnQuRAIidD6Gsm5q0yYctd8shIoYBSeKwJgwxjjnvIvOuel4kqTn+NR2tue9bZqm0++2hXj7cpzz9qdZ4/60YV0phQ9x121E62w2q1Zl0zSMMZXoerHgQrRDmPb9b11o7aGr/ezaCUcrn82yrPXGtKgNOD9KU8t5dMZxzoqiSNO0naKUjclDCCHUqzIGh4EEgxACAjkXQqjruqYQnTe2Ns7A9sZgUVXt/htjRK2V0iGEVWNa8B8iJFJ4740xKIROlY8scCcYxxAYU2maDgaDB/uHIVKn27Pev3/33vF47EI4PT3d6K9HQNuY0jRmPEJEAur2er5F7gXflv4xxvl8Hr1dnE20gGF//eLGup+W733xd99451u//aXfrj0cncJHP4E/81N/9uL2xnqvK3t9WFWQFzCrweA7r7wJJv7o5z719W9869b75dVrvYODxeQMIsLuUH/iyhNJXhyejUXrNBdcaxFiND44H1opwvkdxBgTSmYZMP6n7+vovSHvQyDGmeCI6GwgoiRVWSeVUnjvS7vq7Azn82XwIc1yAKjr0nufSpEoffzg/qDb2V7rT8enWaIuXb+yWkyCrxMALghceXLw/vzsoLYueO9CzRAVYojk6to7L3Seybw+mQy07CVJ7U09r71AwhDJMhE3t7r9TNaL0WKyygVcvti/tD0UcXFhe+2pJ248/vjjg8FwNpns5tV29upsXP/ED7zw/Mc+Bj6ADSAYVGYxXezdux9jHK4NrHGnx8f7h41WsLOdHe4/EEpnRXdzba2xZr5YQgxXLqxDtfzlX/oXzz///I//+I/3ej1k/I233vz1X/ol62hZ1zrrbq7tlMZNQ1w21lF05YQSE0Xt7CIpYpFDri0j3y3WJrMqgoAYy6ZhzgUIqVKf/vQnTk5Ho8Wyn2UmxNODvVWkAkin4puvv7YYn9S33xG9bgX0YDqxSRbzvk7TXIqScyJW9IdZpy+5gsDQRyVlkaVSiwjBRgvEEpmdnU7r1VIiDPLcOygr35hYltXZaEEg0yxp5rMQQAg9WdQGBQophGBCMiYixDp6it5RkIohxhBNiBZF5AyCr/q6v1otDkZHWVZ0Op1ukqAS24P+eLVQjAuA6G1VVi5CuVqr6uVkMf+N3/7tn//H//Rs74BbspV97aVXDqanaZKj5I4RU7LbLaTiyEgiVYcnm2tZXnQXlTk5O7MBuv3B2sZwOpuf7I27/d7GxsbSlHfeeStJks219bOj40TpTl588pOf/Ev/3r+ntf7d3/3dt958szHm4uVL29vby7q6fe/u6ehMKDno9VfWt7S9GGPr3G3Kqg3nAQDOmFKq5by1kajWnmvxnXPBh9YHHGPER89oAKKH8DrOz48KBO02+Mj21hp5BVL7mCBCkpwxVvT6zvv64QIORVEkqTg4OACIrZFAaYmI4BwDrEzwgdAFTpDIROnvUPHVxnT6vVSIPFF2uUwTVVWrbr8njK+s01lhYqwWcwAoioxFlMAUj5J58DbaGkN1djK/dhmf//BjTz2329/kXi6tjp1CLeZn3eEu9Dowq+G990dHY0G8q9ZWFV3f3txev7LZZ49d6iVFURQ5g+DLs/3xSb0aZxnrD9KiSFSKqPionHV3e8VwDdIU/Mjs758dni2Olh9/8YMX+pdVvg1T/+DOUbOc9HMxHKynPa0SvdbfGHQHdVmdjh5Ev9J53LqghjvdeQUHo3J958LzL3wUB9vgjmt+1FnD9Ys5RKhGI5idFADra9DrO8GkFInEhEEenagMBRPt2TITg+7WZrqZxN7q1N6fHu/dPxtbkUSSPtbRGgOh8YYiaq2FbAtdJCLTNOcZ5IhN02itldLnMU1EjTFVU+d5CkDIkBNjMYYYiRCAGHIpOI8AAMjOJRWISNHHGDEGYEiADM4boETEGUBA412MXnAC71x0MYTgovcBAwlAziVnHEAQELLvmAi5iAIEREsAjHhLbYghEkVEj8QgRIgEFAgwMogIhEymMkQfwEKkiBERY3QQIoQYPEAAzphgQjASQAz5wnlihCg5F1wmQJzFgBR8jOQd+SZlbHtz68bO1lqSoHPz0cRDFa0NxgidgOBN9G5Vnk5GWuBakV3YHIKQ86qenh4fHp3uH01mq7jwQAB5V+RScs5DRKlTwbFl3B3NlhB8IlWmVDdPIueByFpXe4MWYtRpImNsMTxkKNYhlg5KE0vnm+AI21oUYts+FlwxVGkSQnDY5ndRjITP3thgjLXh20isnbFg60KO0Yc2Mcq1LX+iIAQDgQJb4y57lPIbnW8/fnpI4GkvBSVkCLGNHXEuAIBgknPeNA0iSS6UFlorzs/r8ro27Jza/224JxF1u10hhBKyLfKqqioXy6qqVKbxIZyUcw6EIYR/887/Cr67vru+u/7tXU/u/gMTog8hMIgIzvsQHSJkWdLtFVrLGIO1NiCvaxOs0zIRgGZVRWfzJLXlqpNnq8WUM7h8caeuy9FoPhwoDDYRICPmOhnkXfBQWktMHk4nUQidF2lWILJqVdeLiqp6K++R81wIkWoSYKNnmqeFuH33/sXLvTwTq3I87CXf+73f87kf/Mzlqxeqd1/JUgF5F6wt948ePHgQjM2T9OrV66Ex5+EhKmmq8uxstKrDg8O6qmkymZyejiVjWafjbLDWFt0eE0onaWBsOlusqmZja/P6Y1c/9ZGPlIt50elorQ8ODvb2HxyPxoenZ7fev19H6K8Nh9u7q9refXAwnpYRIR+AVhACZBq214F5sCVkCuYjuHMI6zn0BoP5clZ7Wt9OPvKhF8pJfXIymlcm6Xa51qvFUsVwYdAvRye5QM3Zg6MzyNnw4sXD+WJcm5opkQ8Ng/GiXFqXdQdrw82i6EcTMFCrdquaejQdRQjdbj82zJbGNJVZrobdTj9L6uUiS5PxeJwkajJfBAYb21uVMeP5rLPet5Ici4wgAHoXrXUtn5oDFp0ckeq6BIC0SDmCWS5zoFg2u9sXGLEHe4drG1trm1vv3b8js7yxtQ82ETxhPDZNWNXGG0rwaGzXM7i+e8mVxlV2fbjWHw62dy6UtprMJ8YYb5vT4+Ng7IW13hNbw3I+PZ1Mp41DJWS3CzIxhAHx+OTMBdrY2Nha3+r1elqpxth7D/ZigAcPHnS73e3NzclkIpS6fPnyaDK+cOnixYsXA8Lewf6Dg/26aVCqGghE65QNeZJ2Oh1jzHg8bgffjDGlVJ6kbZuDiOq6ig9jaij6Rw0spGiMCc61BRsAtI/y6WKutW6FnTEGpZRsqd/GxRg7WW99fTNLc++jd8AYczG0ZwMX2qn1eQewaazWUqVJ6yFuJwYuYuBpiMADaeQZ11qKX/nyC49u6p/+gT/WkqG1qRJgTK/T5zwvuoNlU42nE0exsbUNvtcvmnJF0aGveXAiesUgVbKjzcUN9/xzm088f6UYQo1jpupeP2GdFIabcO9ocfvYz2wXc8EyaDwsIvgEki44ezqbBSE7g36IMBqfTs5OqtVM8bizu7a51eMaCH3QwHtpstmHNIVFeXp8Vs5KFUSOWRd6fh7qmbUrCE4QJsQSEKwY5EKIGPjZyejmzZvz6emFK+vPfuCS7tnIV/PmLPCwtrWd9detwxIng+sO8hqEAs4BBfgAHCEtYLYAx8BKsBpM5mtha4km16YLLm08G8+bt28fvvT6+996d+9wvHJCk85QawuxiT5AEIorIdERIwghNM6SJ2Ln9j/nY5qmOknP8w1jaJrGmEZI1kaUB6DWx0lEIUYhWoqUiETtsbOFQHrnYmxj4SIxjsARWWsZT6QiiqapgvNaciRwwWLCYwwsokSmuEJER+AIRJaAkDdv/syja+PS9j/hjJT0wIkxAYA+hBDaeTszdcUIEABiQIoMiQMBF5TkDmIghyy0blIkQZFlqg+BYYDoQ1vcIwTPohU8cC6ASyYRpQvRetd4ywQSOPRNzuHxrY3nH792bX0t4/xs/7BpmnlZzVblvK4WVV1WTWWafreI3rHgUo7DIutoCc7UZZVlWWOtsT4yrpJM6DQy7iNDzpExF6mu62q5cs5JKTMtVaiVQCYUcuZjiNFzBoJDIoXxrnG+DFDHZBXYykLlQwTW8i0YYy3KTCnJOedShUDWhVb9BwAiRBsJQ3QxQPR07pElTHUWQvDBed+mAXMpOTGhtWQcHzl9H+n+W7bA+cnvOzk5MYaHGJ+AiCjOCQCMMY6MMUDWtiCICPK8OBfKCwEAMcboWnrP+WqPBEopr7Wn2KqCzhvATDzqZHx3fXd9d/1bvLiUkkWUggN58kpp5BohhmiX07OSg5ZCJfr0bN7pdIn8cnLS0WkvSbliLPjdjcFyNr22ORQSDh4cCgFPXhumCfv8D3x6MTq7/e77B/ePz+a1FJAX3d6w//iNa3f29/cOD+vZpNvtZ1LylNmAXPj5fJrm+urOjTTVByf7ZT1JdPIjn352PDs6OhnrFD72PY9/4Sc+tX11G+Yn2UDByfHi9jtV1ZSLxqwqJVSikntvvrW7tT3IOvViefbg4OjoqK7rJBskQuyND08O57MZAES7NweA69eT1WzqAlU2GAtcQtHtmbK6+c7NL/2rLxWFfOGFF7iSp6enP/hDP/Qj/+nfAs5AJ9OXXv7lf/mbf/LKK5PFylLYuLh28crmrdvvgIR+Hz7xkd3v/+RHyJhqYi5tXX9wZ/Fbv/kHe3uj2dH08acv/dTP/uRTzz42XFv7//w3/93aYH1aVvPK2Bi9NMv55O7+gjVV7OYroO3HLu6NT7/22t5gJ1/VbmlqZRCV4oES4Gi8X9U+cAG8WVWVdVoJlegcmXFueTou62BKW+RpiHR8fAzra500FVJzIQFFkufzcnV4dMqUTPMO5zzP0MTGGOcbZ63zDlgAJEAE7pkQopVLpqCBANELqjz6yelxnnV3trcAxXg0ylXmAuVp0YZwaQZKa4sUpk2eFB/7gQ+7xt69dZsR6w67XuDMVLO9m03TMKDL29sZ66Vl1d9IP/e9n/jsi8+++forf/yNlx+cnWJeQJYdTZeHx2OHUBQdG0K1mF949vk/+2d+cj6f/8tf+/Xo4tl0IrXyMbz0ztsA8NmPf3zn0oWAtH94+Oa77xBDpVSgWFXVwkx1p6PzvBX3r1arFkud5/lysQCAdmhZRWqb9wDgvTsX3zLGH04yQwjRu0diUQBqZ5sAkCQJACBgmqaIQETwUD5K36b/sRi9tZ4x1go7GWNIUTAMiAjRB59mLaUR2rSfCIQMhVRJmgEJEUESamD/i8wf7ywLFE3tqpgwqJnK0uRk/3RVLUnGJE8FF8tqVS6WkoNUkKTAicfGYQhKsjRnH/mep/s9W5uRn4fhxSy5tA3Mw2wyf+lbzDCowS7sg5OpamCrtymG2wAEo5NFVepEC62DKeum5tEmCkQnESx6bw6OD5tQC42yrwfdbVNNuJH1qlyZBZFDUBDx9GS8OiubqdOsk+VDxmxdz0pjv/bNe/NluZqTd9DpyKvXLj7+1NP9axfAT0DUkGWVXyzqembv5Z1scDmDKxp4ABehMRAZJBpIwGoBnR5YCT4DX0CZgkfXYCQNvHNwOHn7vTvv3tq7tXeyf1auGohK1zYySRwQmGi7qt5HpBBWdesOt94hcqVUW6dJlXApAMC0oRZAyFma53VdETvP4CKGjFgk4ojWWqUUIG8vjFazIITASAGCi5EDI0Bi1Oa5YSRnDSIJjgI4UEACzRkASa0zpSVXSGCM9XVjnROp/l8UcjEyCl5LyZGQcwSOLD6C6zLGOCBCZMgoRoyRMYjgTbNwDBADFwwZYwgcIyALvuFRYGCcCGOAGBkEAeh9hIiAEBlDjoQIDLhAoiAZcQ7gmvnZyankW5JvbG597Id/OIRwMp2++f6d1957d9bYqFWS6EXwSil0WNV12cx3N4bXLz22tTZYzMfGNE1VNdYBCSYklwnjcraqy7IujXUETKVcaBuDNzYBw6wnxtsOuxYyT1We6rpaQojWusZRRVBH3hB3RJzzAACBhRgxwCMRjWvDV84NvQAAIk3bgSCGNrPeExEiw8bWj/r3gMA5Z4wjByaAi/OT4vl2ELHVsjPGFBet2wMAgDDGWJnVefUOrYNAaJUopbRMuGiz/Zwz1vlz8XSW5219zxhrlUOeMQyhPVo0nLfq6hACcKaUMrbxITgfOPdt9rDUyQ9/6J996dW/+P+zquO767vru+v/n+uZK//YehcoAmdtpnqeJoCegvUmOm/IBBQYXbOeqm6WmBh9ojd6nW6SxbrhIZhm+fjWQApUCX/2+rZK+Nb2xnPPPrXd7xzc1+O9exMBT794/Qs/9mdf/MT3wu5F6A9PvvrVX/jFX/rKn3x9NBl7xqTUeSE6g6xYU0WapF3EUPUKHHTzoqNnx7eq0vYzeP5Da9//fc9vPzaAMCnH9+evv3t2897x0agouk8+/tT1Z56GKP2qFjsDcOH0zoN7d+5baxkIu8LTo5O7Z4t5A48/vvvEE08Ixl9++ZvvvjvN82xjI+8N1nr9dWJsulienI2n0/lisXj2A89de/z6aHT26qtvHBwuf+0P33/2H/2jv/Y3/toL3/vpwcWdZ5+78a23Xl3sN9MlpMtxvx+evNEXyly9uPGZT37kgx99EVQKR1M/MqP78/VuuvHBJze2L3/mcz946fs+/uqXfuv/9H/+L4/3z7Z2LjcBxouFTnKttVApBqG1hlR+9rPf98M/9qO/98df/Xv/+B+P5iVydXG4u6qa5WyFyDUX4BvTTIgvF5NZP+9JZJUzst/f2dlqmNqbHuVpZ+nmxuBg0Mn1BgR/Nh5Lzr33J+PJcH1tuL5xcHjIkXSeHR8eDNfTEG20ngVSgWkUHAVjkiLKmpJE9lXmratGFQAUWlu3eOqpJ95+7Z1nnn7+//Zf/z++/Mdf/zv/xX956fr1/dNTnShiVFaz2WImIeaK97qdJPC/+Zf/2ltvv/v337uzfXG3dObNO+9/6Hs+cufO+90sywDnkzMWGR/N0swnoxmfzV+8/vjlza1v3XrvG2++effgAXF57cLm8Wzugl8frO3uXJZc/e6Xfvfo6OT2rTur4KfzhQXqZrlE1uv1QMi9w6P1za3Rcnk6mRpne71eWuQiSQutAjIIEQAFMkveGMMQkyQRDxOR4KG8p022rE19XpojEkFb4ocQfGteak0BRETU1ug6S40xgJhlGeesaRpnbVs9tOOUVo/a8nwQsdvtOuesbUJ0iCg5EwwJhVIqAsUYiCHnnDPOOQ/IOQCyKAhYiM666L+D4iLBedNICr5pQKnV5ISUG08WwENnmIdqXjUTY5bGL5EFhl5yxBiDARag2xEE/Ksvv//CCxc/cPmpwU4XOgDWgmlg7gooJqPZ6O7ETm1ihJYFNQnMAxAAyG5/A3oFCLGYT6tV2di62yuqiowtV76WnKf9vLfZzzcLHApQiMg6ad7palZTXDRxapO+WtOJ2E4RUtPQ6Wg+XsyWpUXBA2DgNFzPnn3+Q88+81zaWwu129tf6KI/Wq72Tu4yPd+6ku3mUfkm7s9Iuzzro+xCUMAL4AUwAaIDkALmYMVqXL/zxv7Ndw9GR8vTo8lyWU0XTdnE2mMNPGgJPElSRa0EX3KJ2gZrrA3WcmRKCAAQQnAuVaKBsxij0kmgGEIIQHDOeIfW7oqM/6lLqD0MUiuzD9EBACAhY5G88xBcICKGDDkXQgFr6+oIjBlTswhKCMTgrOOcZWkSoy1SlacpI6zLxlojKXaUit5TCH/62kACjsiYYNh2/Vn0Hs5HWUFxwTggMQw+BgQEBGiNelJgy9TFSEgcIQAF39hIXKFOZJrmqRQMovUxjlbL0IZ3CCKMjDMlQQAL3nOKEoED2eX84F6lm6XZ2p7sH3gfj6bT9/bu3zk5WbrI0pSn6XQxXx8M1/trvBtjWY1KW966n96/r5kH8oyAI1dcqKAhEKDtIEQIAUggOIyBHBFFjDJLvWtiBEcUnW+jVIu86631gYQILJ4rmUKAwICAGOdMCMaAc2QPhe4QzzEAbVI5AIhIHs67BcG7GAJRREQeA51X9kiPevwIwVVeaQEArVUoxkgBYoytvlALyRijeG5IJYh4Hp4lpIwUGedcSy2EqOu6zVUhQADGmRRCCHW+c50Tfs75A4GIkiRp/22MORcjeX9u3Pbe+wDnsv5E6/RPQwy+u767vrv+LVuNNY01AUgpKZBRdK4O0TUsulSwjbyQnLyxxpikm9rZFGZlT/N1Bm561swW3VTv9jqTsyNe6Mcff+bJ554crA+sN/PR4fKwGR0fT88Oixw+8uEPvviRF8rp6Btf+uKrr711dHZ2cHTkGtPLFGMMOSJS0eU7O7vONu+9/fZ4Eq4/lj///JNbm933b5lnnrv+me/72OD5xwEMHN2bz04TD4O847trp/vTk/2RiA8E9fvdobd69v7Jndt79+/utWrs0WgWPfS2utbC93/2U5/74R+GREFZPvb49ZOj06uPXet0h7B7Eba24d6D3/jnv3jz5s21jc0f+KHPRa6+9fobD+7fWyxXkcPzj+dc0N/+P/695z74z598+qk333rrW68unnlWffYHfvDZ55979oNPOJoAq/M8ByKoHaya9195+xd+/lcfu3Kj6KR/6T/4q+lHPg4HRy//z1/8V7/9RRPF5aee6Q02xvPlzAcLwhnvXciUzofd+fh04/L1SWlv3n3AZE7OPnnjme21rdu37yzu3bfWgFKeamsiBLp24fIPfPozFza233rrzfv37/uyZEBgXenHW5t9BjiZnlLR7RUdFBAhRozDjUFZl6Ferm2urUw9mY4GvaFZzDmAYoqdWz44EIdIQirXmFSGXlGsTD0ZnRDFzu7w09//A++8+0be6d66dfvHfvzPIOii6B4enAZGq1UFHABYmuYseoCAkXzV/Pzf/4d/83//n/y5n/7z/+0/+PtffuXrz374gzfvvb+9u33t4m59fLb/5juzhR8CaBCL/cMvHd0drndVr+OXq0zqTpoFH6y3SogbN5554ulnnaW33njr8OCUAU+z7uHx/nBnazKZOIjrO1vO+8lq8eTTTx0cHJDkO5cvWuds8LWzyJlUCQDUdR1COBe4AhhjWqeWlFKLc5aDc45CbP/9qJUWgnsIZgvBe0SEh6U/PDREWWurqpKMp2kqpWjHCIyxNtyAUV2WZQxkrafI2zyfEBwySoVUSgnOiIKPTkq0IcQYEQUXDJB7761vOLMcRQwEPlBVO/sdcM96OW3KRaZ4s1gEIVaTmYhMILt0bbfQ7s7h+5ULN55du/7k0yej/cVyuph5UwPLYH0AN65fuX5tY61XX7pYDHZS6HDgFhoDpYEmVifL1cGiGVnldaYGAtVs5uPZSS9PAeLcjM9uzqMWW5d2HnvmcRgUzdGDyRzLKuhCr+8Mss0BdBToCP0I6MhY34BMJGQclAQVoJEwDTCPsQ5l42arcjZfTSt/OnGRa9Bh2tCr7969dTCtGxhPFqUxlVmcLQzL4JkXQG6wXpQ6suHujpmPbMM1ZoB9MAWEHBr5xivvHDyY3Xv/dH9vcnpMixmQAyn0qjS1AeOBKWAyRyERBCEvOv3Gex+IMS4UF6QAaxtiv59pLoBhCAGY4FKEECpr2kvFR+JSSCldCC01JMTIHnJcAL5NgkrT9LxIeyj9iD4Y553xjDEptRJCK80ED4G8t4gYfAMuSC4ogotRcMwUjx4KiTmnpjahXMTGJDpVRVqGaL6T2JhrwZhKtWAC23FTS9aIMYbgFRccI2MtYYQI2pFD7CIJQM4oUozBBwKKnFAkssCIioturvu9npTM2ao2jQ/OEflADlplDAagSD5TMtRWBJsLSCLasnpw5+7s8JBFdJ4W1q4isUR2O50y4rSqdq9ejQFGVe2qktugCRQ4jHWuY56wQdHtFd1cJyyQb4xrmjxJVKa6qVwZM1lVZV0KIZNO6sDyRKUyFcic8Y1pprNFdD5Lk0AkIgkMIjAMSIAtgFIyISWXinNkgkFL6UECwdqjGxBRJMTnn+y0YinGBKAInryPMcai6LY7SDsrlJJrraXC5XIWontYmjPOeRvp5W1gjAkuEZHDOSsAkZSWi8XM+5gkCQdZ17X3kXOZJ5n3DiKd5/lSeGhXTZblCiIVRcEYq6qKYmwTTGKM5MOjMVA7x1wuFyh4u/EJIWOMDwPCWEvyEUI88v4S0cbWpo+uXK6m80ldVufCJAQhRAguxigezRwi+eh0mp+jHoELITmTbTajc67b7XY7ufd2vpgtl0uAKKW2JrT4UcYYnoe9IwAY7zjnPoYQQp7nw+EQAJbLpambNNVap1mWJElWVavlsuQcvY9ScvJUVssYQCdScOWDTbRYrVYhhCzLWi9p8LHdvluXMiIKrhhj1lpjay2ZlDxJsizLpJQhhHJV1XVdFAXnPARqk97p/JTlvLPtMbG1eQkhzl/IB50miZYhxnMQqhBaa855XdfOhda7vFwuW9xQjJQkun02tFeRtY0xrtPtdbv9otdFxLIsy1UdHno6EdE9zHpkjLUwU1c1rQlptVq5YLXWSZIwDi4E51wgr7VO07T9TRhjbYhMe7W0l0oL35xNJu1LxBibqjYmJgkvuh3rvfXOOffI4e29dy4miWyvH3yYbJ0kSVEUnW7/Ubxd66vmrOVKCaUUErQmYM654gIA4jkzhxhjaZ5prWtbr1ar9g/Ji0Jp2TTNbD63xhCAzjtp3hGMCSF6vR4Rzaez1WrVdvVMVZ+dnTHGrl+/3u/1RuPxe+/fLjqdPMsSqXqdXq/XC87P5/MWxyaUXC2r49MTlSaDwaBqSiZZaxFmjBVpao2pliuItL42gBDTNO128uj8fD4PIXR0OlBZMA1TOF+cObPqdGQ3lbvr/UzGXpFEV3f7xdXrl0/OTr7yjVdnh/Az3/fxxy896bz/5ltvjVaLpz7w3Ac+/GJvbf3lV179pV/5laP9WSeX1dxlCc9k+u7RavjY9oKo8dH5WBpbOydkmuaFaxtLhKFFbdF5fAdy1nYfoQVE+wAUGJBdVc8/dS1WVSbw8tbGajYxZTUc9mWi5/P5X/yZn+2k2T/4f/+9Xp4pZFW5nM3mj924uL614bnfurj16e//1NUPPAOMQVrAeAI7u6DS937pV/9f/81/++ZbDSAggk5g52K+rMqLVy6/d2dvY7M7XBv08uytt975xPd+9M/81I8/+dEPAnfQURBqyAWcPAAeQND+7/7OWidN+z04m37zD189uHd6euife/bqY9ee5iwdrl8C4L//23+gdXrn9t1bt44GA3njxg3O8HQ22Z+e3Xj26R/+oc8RhX6/f/PmTSQ6Pj1N0sKG+K3X3vj6N+5v72Yf+PCLhOiJf+O1N+dldXBYZgouX+pU5VJI+OiLzx8f70vFlZKf+tQn1rc2n/70p8zR4XRxtn1lAFcvwN39L//Ol9946c03Xj3s5/DjP/qj3/uDP2Tr5rW3b/3rP/zKO3ceMNVJOwMLUJoKlVrMKy70aLJYHwwTKda7xeTsWGJ0jVnVFRdJ3huCUMaYSzvb1rlFVZ9Mp6PZnEn52JXHrl9+bDmdb69vKBIP9u/f27vf2FpqTVpMXK3ylAOvy4p8yHRSpBnnfDZbMMbOJmOupA1+59LFpmmsdSmlgikhBGOIkXyw0QeIdn2tLyXOZ+Ob9+/tDvPPfPZTEMN7926/v3+YFb26tAzE2dm4X6x54jrvOiTkMNwYFpmC6Nxq0ZTLDhcDpsdHJzsXLzz+/DOns8mbd28ZRp1h95133nnswq42/vpg+InHnn5m64Ja1l/7w9+/fG17bXv41Aef80pir59t77z89lt/+NI31y9dvXdw8gd/9FXT+CIfEoFk8u6Dvc72cFYvW+gGAAzX1y5eudzpdhljd+/fWywWSikXQ1t/A0BsyRmt8OZh0jznHCMJISQXLeSg7ZcRUWObb++ESI82di1FCIHOXXktyAWgBbkAuMYgYpLoEEIMIU3T4KMxBiMvim6v25dSWxPrul7fWk+SZGNzjby7f+9uXa0Gg06WZcaYlgmBTCzqsj9cPz4+ZYzlic6UTJgo54tYmQvbO7/4zS88Ku/+3Y/9smTx0tbm1QvbW2vDrV7x5MV15+Z3927duv8OJO76M1e3rwyjdKt61lvvbm1tJv0+SAXAgQlgDuIEzBzMEuoKmhLmq3q0cnN/cn8GjaImjQ1bzsxiXnoPieAJo/6gSAfd7tZa/8oWJLhaTsp6bl21s7suNrvAHFAFzDfkqrhIN6UuJGMCmgg1gWXxtKmOa3Nqtc2nJ8Y3cjy2+4fj6bI+OPNrF6+uTFhWq1VZVo01DkAIqTRPZNnMUMHl6/jhT+x+4KM7V66nYuCNPdKdAmJRTlk5T+Zn9PYbh6+/evelr9sWPccRBAcESUG4iFEwDxAJmUiY1MQSR+iIgRDWBeOsDT5GzzkTQiiGsWk4ASEIIXSa6zSJMZamcS74GBrrrHft4+280GcIj9gvD12YLXYlhNBiIc4BKiF477VKJOecSeAMgIXQNpSDqWuOIBhQ9OA8YFSCJxI081cubnfTzuh0PBvPAmJgcmntynnL2Z3R33x0bXzv87+dJMmqXslUt2bRVV21517vLfkgODEEJMeABMPgLdTV81evZQxUImpXjucz46x1MJn7Ii+4TDPV2draunBxJ0Y/Gp/UdU2RLct6tJgtm8YiohRCc46wmk27WmnycdVwCx0BCQdwkGgGTDrOamQLIMNFUGlQUkjlvY/WiUiaWIIoKPJguxkq7guVDJK0nya5ECICeAeRjLOrxtTBOSEiw1VjZk0JWUqCCVQCRfQEPmRC5FqWq4WQErWuAEaNHzd+GdERIgnJuOSMcaAQIXqIEQC05D6C996FdjBD+NyTGRADYERIwDgXSiZKJS1DIEZow0eAg2QcBDVNRRRijBRCDN/WAkquGGOt1AcjtjU359zYKk1TAKirBonleYdz6ZwzVdPajIRsGUHQysUIoxAi1Umbpm6tbXNe26INHmqV2u0shCC0BEYM2ggJIgTBBOO8qWsCxpExwSUXhBA9uWBVooEDRvLeNY1xzgIAP48uJobYhi8KIThgRAgUg2+nEIiEQIwIiWitvyaE4ByDt01TOW8RiXMZAwKdF7JtUHlbOBrvlFKE0G7iLbbcGJPqpNvtImJbMrZQmvZgQHTeuWxVmCEEH2xwFpCUUmmaSimJKAZqi+OmaRgTm5ubly9dHQwGMca6KW+99zbnyB6m5xIREALAYDDw3rdHhaqqZrOZMYZzXuRpa8xoq9t2ehOBtFRM8POJHxExbKn/iovW1tOe3GKMy+VquVy2ofEAYO05X4hxiAE8kVKJ0CrG2NS2aRofAwA459I0zTrd9u9SSiVJIqWslqv2w2maZjqfVFXVEmwG6wMAaOvC9h1uLSKLxap9z1tYqnPOWxdjNHX96EQhuWibW9Y7cT6Yjg//sHPyRZqm7YmFP4w2PEdI6bTVRxpjJpNJWVZZmvR6vZaYpIR8OA0P5IP3fjwa93rFxsZG0e200cWefEvKs9aGdlymZAi+aYwNXkjdH64rIdqXbj93JeRwOLx582a1XO3u7l68eFEpNTo7u7+3t6zqbq/X7/Uk46Y21loGKKU0xh0fHwPyoiisd22Prm4a443SotPpdDodjNEa440NIZSrRb/bW+sPiGg8Oq1X5XA4vHzhohlNyuUSJJpQq4xvrfVTjBtZ+tTFS6P9vf07d69ev/RTP/PTnc3BV175xnvv3Dx6/f5mf/10Mr37YHT96cs/9bM/s3Xh4t379/6H//GfXLx4cWt96/DgwNb285/74WduPPXNd9/74suv3D48OTw9A8aL/kAoXRlbGZsX3QjoY2gJWu192vYpUfDWxWitcabmhKlkV3Z3r25tdiXLgArB79967+zo8Mbj1z/72e/7ob/wF0Dpf/0P/uFXv/xHO1ubs8nk2WefHm4M37/z/vrO2vd8+uNrj1+B9Q7Mpm+/+s23X/oWEnS6/UVZffOV1+7dO+IMtNDO+aYJTEIgaAJ0BlKkMivS4bD3Uz/9Ex/5zPeRr/HiJlADzQLsCnxZj07effXlP/7S793Y7ScxltNFM4cikZzlG+u7zz7zguhsA8uW+6N/9gu/QpE5S6tVmWXZc88998QTN5qm2j8+fO7FDzbBRWfv3bt3eHRwcHBQFEXR62Z5sVxVx6cTEMrY6AEo8lfeuje3sLZVKCnK+Ywz2FpP13odKaipZ5/5vk/9xM/++fG9u2++9cYnPvGx3/iNXxOp+MEvfObB3sHJ4VyxvKM26tKcnZwuFouyWd47uL9/smwAmOo40i6mgXFIaNWUCCpGplXSLXr9Ig+mXkzPmtWCQiyKQqnUBwQmdZKcnh1UrtZ5UQx6xofauMevPPbhF178k3/zZVPV5WzRNI3USmi1KFen07P+zppKNBKYylAgrZRuw1DrurF+uVwSQ0+x0yuSPCvyATXa1r6u6xicECxNVKK4YFRXs8n4BMj+yA9///MfePLk+ODe/Ttv37pDWX80XQkSrnZb6zuLxZKYWpZVNugZ1ySZ7nayItEYrK9qBTBQupwtVKKLXrds6rPFDLUsurkxBqNbS7LPvvjitsz233o3aZxGsGaW9zuXH3/spTdfh7zItrZu7u9XxN4/PJlXjY1c6UyqoizNall1Bp2zcmzISylbY+VgOLx+4/H1rU3v/d379+bzOROiffyccy/OwWjAOW/ZZW3pTz603jl4CKNra7VVtXqkxm6Z2+1uKRi2D/G29D9XBQAAZ4jojWWMPSr9kyQJPtZ1zUGurW2sDdeFUE3tK9McHu5v72718izRqlNkq/ls78Hd5XymEq1UsrW1NS/LZbmqjQPkg34XveXkM6F4BEnYKzr/06t/7lF59/lr/0/J4cbVS888fuPGtavXd9dGD14/vP9OaebXn7n67ItP6HVd2em0Gl147nHACBAgEgTwBEAMyS0Wo2BWVFdgat54KF1YuLhENFk1DbNJWMzsfNGUlQcps4QrqNY3evl6kQ86+UZhoDmbHi2r6fd+5hPWLGq38GCiIlIAEkFTYC7JtNYpjxwMQIVubMM0FqHDQ6c6aZYzOjhc3L59MJqXh+N61vCkO0iyLICbrZbzau4jkIRVDcMtePFj65/70Y8896Fd6DYAM+AV+GVswuQMTg/dfJwcHfhXX7r/2quzXke6IILHEAlABE/OeRspSB2REWOAPKDwARofrI95txtj2wCntoRjDAWgJBLtKY8xpRKpladorS2rxgXvrG/xffiQmxLh24/FR5cWInaLTisJeej9iNF5733TGMYYZ5IxBlwAABBjECnE6G3wlkJQDLXkUnLNYi/FjDMllDN+Np6NZnVDQApYmnvJ3zv73z26Nj589Z8RkQVMslRq1eo+XAze+2CNt44LEgAIXnJUkkEk1lQ//vFPclNHdIH8eDHdOzycLWuUmiAxNqwWhknR63djtGW1BIBO1iHkIGQUzHFsvGua2jtTTmtNkAAMc7hx5cIHnrqxvbEhkN+7e3jrzv1b+wcL542UFYDhAqRmUjnnonHMew0sYVwzFOD7hcwlDrO0n+qelBkDHUlQWC3nWmshZRNjHYMhsjGUBCPvVy54R4JJwaRElkqRttZ7DibGpQsTEybWlZF74BSAc64ZY0gYiaJHikgQo49AMUIgAsYIuUDgXHBo4femAUAng/ctJRmQcUQEiBGiCS6GABgBiTEEzsGT974FNHkXOecMWAiBPCFiolKlBGM8BmoJ7pyzSAghmMZKra330VkfAjIWKEIkgFg1dVEUAFA1dbthOWOrquIP6VFCCMm4EIKIUDAfXZLoIuswwZ2xjTUUIELUqQZkHBlyhgQ+Bh+djy7YCAwSpZM8QcGpisgoT3Sn00FGD7fOSETtZKuqai4YgCZCjByRC6E4k0qoSCFEzwQvuh1EIAgxwHJetvcGYwzYOZk0hBCtsda2pb/3frVatZOKJM27vQFjTMjyPHslRudCr+hMp9PFYsEYS5SOMVZ1XZbL/qBIsyRJklZYBQARW/pkROR51un3hkKIs7Oz4+Pj6Wy8vbkmBEPkrVbKe99Oedt0dyl1lmXtUUoIURTFcjELIQBimmZFUSilQvTGu2q5sMG3vzxRAIYNIAckT9284FwuZjNnvdaac6mFhgDtdMg5B0hKiEQp5HJZNdZH4+oQggs+PJSg2eBFDACRMUAOEYJxTQguTdMH9/em02m/319b39jeloxBS5fTWrZbQAsDBWjHUzJGYCwAMO9jCNYY55zTQjW1bZqqKpssSaWUWVpkDBflqn1IPpqEIyJjvOXGKpV0Op2WqtnugGXVLJdlOycBYEppZ8N4NN3Y2KCIjIkkEZxLa62LBsBfvLzb7Xa11lXTrFarVhrHUOg8Q1YbY2IE71tVG3LgWiaCMQrRNE0rwA0hpEpXyxUHfOrJJ2/cuBFjvH379p07d07Pzi5eueq9X87meZ63ojuIpLiYjWecc2TCWlvWFXDW6XR6vV7dlESUJ6lkfDqdhxB6nQIRJRdCCONdqvTW9q63zth6f3+vK5RxTeQsCNR5BlnuGruq6c3X73aF7CRbk0P3z/+n3z6rFjcP95rGxiUwmhoLkQE7nf78v/j16Xz2/t3xjSe2/87f/b/CjRu/8V/93d/90u+JotNI/tad29PlKunkl7PMR6i9b4wNQEmaNs4iYiSKMRBFZCS5EEoKqdM0S/IsxriazxuGuUqGvfzC5ubWcLCeZWf3741mM6ptweTV9c0f+r5PQ7X61q/+6lsvfePTH/toURS/+Iu/OJ9P3797WvTh5/6Dn0vS/Eu/9ptfe+VrjTXz6ezK9qVqWRofAjKK4rEbN5wL09PRRne4t3f0Yz/2wz/3l/8Cy9Tewf2zxfTGczeyzULkEtYGuGJQT2E1X45OvvHVL995+/W7N48vbzMVwDUu0frilUudvLN399DWsb8zEIV+6Wt/dPO9BxTU3skIgb/7IFxah+955ullsLcO99bXh6pX/Pwv/OJ0PpvNGq2BcRACzuZzfTbeuXzx6OSstv7CpWs3374Vkf3cz/37H/mh+EevvPng6LhI5fWnn/T1Itqq09GpwoO9yUtf+xMBLlNCJ3I5Prv//s3eev93fvN3vvbSe88//fyf+8m/NB2Zr3/jD954442js7OkAyZC5QE1rA+H28OryxXtH5+4aBvg2xtbq3kFTM3mpVLJfLLw1ttIgguSKiCrjTVNDbKsII7qkkW70dUqzRZ1+dU3Xnnl3TdjY0NjvXOScQkJC7wJzqU8RBNdiIFiCByFYBSCt9Z5bzudznPPPdMZ9FwId+7dnq+Wi9WMscJi8MpzRCZlYLRwq+DMajm78dSNT33iI//x/+Z/vXfv5n/2t//W2dlp1h+AzNOE5Srzic2yrKqqJFF5oT2QYBxZ4BTJWW8NjwQMY6bybMM3ZlWWmdLXN3eFEGmqa2sgOgz+/p37txbL0737HSmHWe6r1fTd99Rbb43LOlvr6+OTZYi3Do4Mco+8t75T1j6ijQinqxHLNDLBiBjjAplKUWYpSumIamuN88Z5RkQhIiJnnAFGjAwRANijEC5ADhgIKERHoR3uM8ZQCGCs1Y+xc070OXmPcx6Dbxt78NDC2+7D7cj60SOs7eY+MgMAnrd+269njD3zzDOE8e6tm9ZUT9y4LhgNOsUzT1y/8/7t6XxWZ6lv7KXNnXlVhQgcAkSrJSsUqxar/Qdn9yIAfLv073c708np22++eXT/3skTT7gPPnnn9a9I3ly4tL3eX7OlaXzZ2+5kTz8JqwmwCEKDEhDJr6qzs/F8tggusojCqyRK5YhXRBVFw1ZnfjZxZyfleIbLShjiKJVq/MWdjthcE2udsVvdO9hjSdy+uPHcYx88OnlwdLZnXbl7efPSlSvYzx/i7PNmaWzthc5UL4dC6CxAP9b3x2l3MwO3iovx3eM745PZEmY19LbWbt49qS0MN1hp42gBTz4PP/Bjz/7cX/mCp1PRi7AuoZmVD24vlnMt1bAYVhN/tLd4763xnffKw31/durB42KhQsRABHjOWPfgHGDEFLjmUgEiBQgxBCQQEEIghpwhY5yReNj1i5yxNpU1AhFCCKH18GqtWeBAGBE4EXImpUTOgXGittn7qDsWAKBcLh6RFRkDIgreB08MECIFCjFGFttHKzBAEywFxwDSLOmkSZ4mUnAO1i3OQkBkmCVKbQzSTC8b1yCsfIjxOwQ/5WJqrEetXbDapshZi5SBENueGuPxUenvJINI0tS+rHfXh0mhkMMlf6FbdO7uH0amiCWrsolh0TTNajUjCIFCqlWWKkDmGLMQEWIiIVUZh/Tqc898/IUP/dBnvvfpp56ENIHp2d3bt+7f23//4HQVqfQ+CiHTTEViQqkiNz5w4C4CIDBg7UlKM8U4JWmWd4oiVSmHDKHDMZNsd6PHAIxzy6ZKPEbOA7KaCXQxLsvpvHTOayEiA3LReTvodoxrVo2b12bRhDpQQ8EDByLOOSEyJAwR21Z0O9YjiiFEgkAQgfCZxwulFGPMuVA3NoTY9vJbSQbn7cSHCFoFhW/jP9oPgwK0hX6MESIKITjyGGOwAQC0TJRSw/XBaDRqGtvpdPKs430wddM2HuqmCtYxBlwgIrUd+rb2erRDtaWM9x7+lIu8NRPHGD2FZTlL86Qouq2apYX9hxDSNEU4x4PGSM5Z57wNTiWaKCilkkRZa8vVSikxGPSKLGsPFyE4Z633loiQCe8jIgNiRBwCE0IlutBaN1VDRMhAKaElRzwXGi3n5bmvBaAdlrXVW22NtbYNz0NEKWXbec2zTp7nrXSkKAohxHg0mkwmiVQtWV8IgXQ+K0gStbE5UFq2eIemNu25whiTZUVVVdb4dpjQNI2UstPNN9cHSaKyrBBCeO+bpqmrxjmX57n3HoARUV3Xq9WKMZZlWZZq732IseVDA2fGtvMECHRuEYvRRwQOyAAliCxNgdhyuSzLijHRFqBV2QBGIorRI4LWWidSSF3VNgI7t3NQfNSdKnpdIZhSCWMQAYkCESJBqlJnbPtljTVN0ySJ6vf7x2enSimVyEeJDcbUTWPbmUz7oGrp+7YxLVmslQZJKSnE81NWa/mh2I4I2kFHe8JsX1FK2Z6yrLWr1aqua6XTsixDoE4nHwwGWuvow6MMAf2QqRdjm1ERbLBJkhhjlstlr9e7du1aURTL5XJv/wE+THJorGlHLkmSXLt6vY0wK8uyzbIgIqVUuVolSZIkSVmWi8WiKIr19XUp5cHh8aJcmaqWXHS7vSxJo/NN02RZMRwOmVTHJyfL5TLvFFrrZVU2TVVV1WDQ45zv7e0xgM3NDSLiyNbX1wf9bghhOZuXZem8ARc6SlJwSb9ryQGjRPC4qnpcm+PRxeFGKpKmaeamOZiNJo1NCumNsxYYA50yIUQkklKmadod9Isi6xU9IcR0Mnnw4IExpugN5WBtvFhN5jPnI9epVElk3FMsy5pzTnAeLsSAkiTRWnsPUmkppXF2sZhbU/fzzuag+/yTTzy4+W4zmXQ4+2s/93Of+dhHf/1f/ItXvv4nH3nxg0fHBx/72Me4UL/0S798595Z3uG9fr8z7B6cHkstIqMHxyup4cLloUAmiQcXZJoJncyXpXPusSuPPfPkEz/10//O/jtvX3zxA5AI2B5APYdoYSMHbldne0WaAqLZP5weH25fugiTMTTV2YO7wRn2/yXrP6MtS9OzQPD9/LbHXh8RGSZdpCuTWVZlZZApqYSEkIRpYBpo3JrFohuYGRh6NQzQA9M90xpmVmvWDA3doGlBt0BCQJckVKpSeZOZVVnpMyMz7PX32G0/+86P70ZIveb8uisyV8Q5d+/z7ed93sdQXK1n0+m46dtL738/tP0Lv/GF+ay6cOHR+Vn31ht3jw5X+3fbtoP3vfcxYLSqqu3tzSSVi8UMAF599R0pIQRYrYAxSHMgBJYV5AMADteffOqp97zvG99+6d7Ryff/wA8FoV5666ZDcLarlzPbrifDbHOUK+r+kz/9J+rl/J23XvnIBz6QJeIrX/3SFz7/BQcQJOsM2Zhc0pq/9spNT+TG1lbTr28fzAdTIIJULVKRjUYXrU2O52fZWNW62Rxt9p3rq15rXabpyekBJaEokjxVjNAsKYu0cDos6nUvbOW61hmWyMFwTBg9Ozo5OlgXAlLB8zRjjHfWaG9FmgzKzKzmHDw6GgJmKiuKASDtut57n+flQ1euBMAA+NY7b1nneu95miFlnAnBGEFwRrd103fNY1evPnn9sfns5GD/7vH+XUrhmfc8pR04yFbr9uzgaGsylpRtbk6rauUwGHQyUZRCmqZeG71uhnmRFvn+6eFgMIDOMI8XJpsZl9YYIcTp7EQkcrmcn52dCIbb02kieDOfJwEFZ5tbW+/50HNf+/a3X7txY7x74Ttv3NUAuxe3VDm8fe+YikSlxfHJGU9VVqR1VwdAJkSSpZPtzeFoFInYg4ODtm0jycUYS4QkhIT7AusH/TMMCGMsOA/3W7piPz3nnAm+XC4elM0Hfy7fZYw5oxHxvp0z3Bd8eiYFYwx84JynaeKcs8YopbwLVVVxIjc3twfl0FpvDRJOFqvlZDqgAK7vdLOu1otnnrj+8z/3h5958qnf/M3f/NYLL7751o3BcJwWBeMyUfzS7ujo3u1RVnzyox/7vg99NE+zjT/w4gN49zNP/WLX1rpp0cKVvekHnnrssYvjrWmxubc52RrJjQwKDlR73yEPVVcv1quqbppO121XrZu27cpiKgnPGM2pUIGQ1puVdTVZnem2JlUdtAMHzCJaDAHWmxumHKEq1WR3eu3Jy9uXtqBUkJGwf4tmBIoEUgamcX0FgvB02h/xb3z9peeff77Xendn++LO7vZoY5wMd8vNbtXdeOWde3eO7949ns0rkRYW5Rvvnk22p0zIVbucbpWf+uEP/MBnniufGED9hscTQtcE+rZuqnXHiBqmm+2Ju3Vj/t0Xbn7vu/2td6GpQXKW5Zt1YzwFwghST1ggFAIJAUTvEypSwpjH0FujjXPBA1D/AC+Rc0U1IkIIghGlBGPMYQB/vuSJlJb33sa8TERglHNOGO20/T2xJSEMCGUQxWAUCGNxjoAHQgygHIAEJABwTp8iUAicU8FZkagiS8s04YwS9OD6YUr3tjaG5YgipCpPs2LRNLeOj37rS19unPvuwV9+cG+8d+8fuxA8ZURISikAjb1JWuu+b4eDglPCCGBwjHgKgNaQrr2UFp/8wHP5IFs3C1UknXV39g/WjbHIfSBNZ0MIQvEQDELIsmRnuq2drXRf6a4PjkkxzAZlmrznseujJAFnTw8Pbt165+j0qO0bG4jk+brptfcszQ3QxvrARFKUxtngPDoP3glCFKMJZ4pCKdg4z6ZFkjOQ3mTgx4kYZjIYrbXuui4goWkCjK+a9qjuF1wdruvZorI+cKYEk5wCBxiXZafbqu3WfVc50gXaI3PnV5wyApQgDUiDY4QygqlKPAaHwXnvY+HZ9WuFEIJSGjwQRhnjwYO1lkkWo3oIxeiOjk1pERMjRvkNQEASSAT6nIvYAhtsgIAxclPbvmoaQlhZDiTjXadjVk9TrRECJ5RxpBQoJUmSpGkqpVpWa29sZFsREQLGClU8tyefe3yNMc5ZLolDHTw8wGqMibgbjbg2xALxYL1DD54w6sGnaZrlibW2qysp5XgyauuKnUeNBjw3DTPGuBSZQ7Am9L3xGgnhUmRSqjwtILZGECQEEVzUeCiR6t5G1B4t81EaTgWP4halVFTPZ1mWJAllwvtwcnJS1/XGZDoajfq2q6pqfnpWFMVwMAghLBYL59zli5ceeeThtls7Z5bL5cnJSdd1hJAQQGsd9SHR4ty1+hzK50lSiCSRZTmMNV59369XVaSfsyxjTPR9H0NkIwzNVPLACubQOeesM9a5NFNMCKlihRYhlApOOZdgsa1qY1yapoLLtu0Xi1XTNHlWUgaMEUohOsUpBcaE9SGW/8WLiIhMiNh9yzihTBCCIUAsaYaAnMjN6eZwONRaH58er9frEIIQgnBijOl0i4hZlkkpo2yJ0t8rdMPz8w4RcVQO4mf03ltt4iOQMAqMdrqPvwS4bwaIToYYdP1AdRN/LeVgFCe3aHN3zhEExlhVVXFjg4jxPkylEolIMmWt1tpSSofDYZZlTdOcnp7WdQ0A8d3GZ3mWZUVR5GnWdd3JyUnbttn912A0rNfVaDQajUYP1FBHR0ffeem7p2dnw+FQcmWtTZSajsaM8rZpmqYZjcdSJoHAeDoZjcdN052cnCwWs67rNjc3syw7OLwXnM/zvG3bPEtjqVnf98b0ECMgCIK1g2G5t7fXdc3Z2YluO6L1RjlKCUu51J1ZLNertu0hpMPBZHtjc2+Lc1rX9cnhUd91SZIIyrTWT15/wnlzfHS6XC65FCGE1WrVGttaz5NcpgllwiOauJsLPs9zoAR9MKa31hL0QgglJAAnlMdHV9u2WneJkGUm6/kCTL9VFma5+tAzTz739FPfe/75+ekRBffxj3/f9u6F2WLJk3T/4PCFl75nvOt6s7m9xRVv+y6eaUmqJqPxlb2Ls7N5531SlK3p793ZR2subW/vbWzoen39sStpkfz4H/kpSABEgJHY748vXL0CWh9875Wj199+/rc/PwQ+Vsk4T3Z3dy4+9fjJ8b2vvfTtz/7cH2Lj4RsvPn/vjbeuX3tMicErr7z73RffvH2rm81ACiiK8vCwmmykDz/8cN2t9/fvOR8UJ4tTLDIYjJO+73/mZ//QRz7yobzMv/zVr/373/qt7706qzRMNkGmGZdZZ+zh6Wq8u33l2lXBqe3qhNMy4c9cf/gPfP+npk88DHdv/+3//G8FpyUXd++dlRk0GnoPaVF45G3nuSqG062m7+/s31OpssFyoYRM+y6sa61YNpyMT5fHi9XSda4sxt26zfPCed339c7FbSbAO933veLJIB9z5LXuKugxYb2zq7ZGAkVZFmlGEVani+A8AyJlgoxaCI6CIJBD4MEFCxigzAeDwQgDbdu+qhohZTEczBZzqVTVNnmet751zDrivUfbu2ADJSTlSgl5YWuPU6q7vqnXwzzf2Niolot379yT+ShNM9u0ZSr7ptrb2UBwSgkiqVC8rlvBeLtqqtlqa7whsqSlQSSK9mHA1c5oM7R6tVxyTm/t3+10i4wkmRpvDgd51rWtrtb12dnmeLS5vf2xT3z8i1/6yndf+d7R3FEJxaS8/Ojjs3V9NF92DjoTCOd120imtDWUM5kmaZaNtzbSLGt1770/nZ1ZbTjn6HxUHTvvkWHUfzLGYmlXRPnoA6WU4H19RlT4MFrXVSy8CyH8fuhP4mb7HNUh3vdHEc4i9BdCPID+UkrvQt/3nMjBYCSFci4QECpT462Ng4M7ru/HgyyTHJwFb4j3H/7AB/f3909Pz4wL2oRiNKrbbjjIrz403RiXzz71nicfezw4vHfn7of+/PrBif3XfvLzeZqMBiU4S5wdplkzO5WcWW86p9NBUozTxXpx895NlSuPiAQojVY/KYSiRC7PKka4pCgI0IDBBN8jal6mG8HLgJwyyYQASnqrtZ2PRj3QqkddjPNr169s7owRtLPtsFCL2eFiMc/K7Opj1zYvbq/b5uhuxZq9l15464WXXly2C8oBEGmAlEHooZQwzouPfvijH/7QR4udXQA+Ozx58+1b733uQ/lDl6BbgTDwUAlyGc5eoYMWkgZ8jdXCdkGyHCCFPj9+q3rhmze+/pVX9u8FDCmhQ214Z5FxGRjx1EbtiocQED1yISeBSkTsbd/p3liLiEhZJB9JQIDIpZ0/EqXiSSKFEC744JDc9yI6Fyldci5YIOcvZBzg3AdyXgZG4mDJIuCH+9w8ZUCAySRFQsn90E/vfSSNu7ZWQuRKSk7B2+CMt4agSyRsjIaZyoLzqSryYrDq2ltHRyvdV9Z+8Xt/9MG98alnfhURLQQkzAVvtHPBR8ed1vrihd2YLgXoKQZAb7vOte3y7vHTj11zXh+e7KeDLMnSqusJlS4wKTIgnHOeJtI57XyfpknX9UjAEtIHp70jhCRMpZxBo4m1aK33FgRjCQ8cTMBq2ai04EJ11jXa9g59QOcxdolSQEaAg+eMSEolwM5gOJAil4Q5TXSbgJvkyShPdNumSVLkZT4cSZVXnb69f/DO8cm7y/Vc+0aDA6CMcs6VkInkgoC1ujO6t6gDNcAMcnu+lQMKyDDQgAyCiAIUCACx/pgGSoASHil/79AYw6VMEsGU0E5rrZEgASSIsf3tvO5b8HiGICKG+5pCBO8Dog0EAYB6EkJwLmhj1uv1xvZ2URR11c7nyzhTOueKoqCMCEoIReeMD4YyYJwYb51zjNI0TdM07brOefeASX1wsJ3/u5wFsBDLmSkVsbyY0si6RjLDe3TentueSHDWMsWEZFJyzimAZ4R4bwnBgNG9EBgQxgllQCkx3nhPjLHOne9DMGjnvBKJtVp3vbGdc4bQELU9mch0b6NTIu6k4jRMECmlnBIpZYT+iLherymTkbm31kZvpWC8KIr4hWmaBgDSNI3OYMbOa4z7vj9X4Z87uGB3d3e9Xi+XK2ttlha7u7vD4TBJ5dnymNLz2YBSqrWuqrppmoh0hVBt20acF2VIc30G9yV9gQRKKRNMStl0XQogJANC4rPCBY/ehIC9ty4E4lxr3GpZtV1PGCeSAyGEUyYoorfWBO9owEQmQogI/eOhI5RSiTC2Z4ExHuIEEpuPnfFOh9ViRSm11iLBCE+7rptsTowxVVX1fV8UxWAwiJc4jkMPPEmc81h3H0JYLpfr9brv+0Sq0WhUFAWXwgOKIBAxGoUh2kuMiaNmBOVJkkwmk+heqJsuLqbiO9Fad03b972Usu97G4eZ+8UUSqmqqjrd5nl54eLFVCVVVQkhHn744dVqtV6vm6YjhEgpz3NpGX/37RtlXpRJlssEEXXbOW0owuOPP268Q8SqqlarVbQxKCEfe/TRza2tLMmN1omQo9Gob/vDw8M8z6MbIfqw33jjjbbpsyy7eGmvqWpBWfA+Fcoxz9i5dH4+n2utlVKj0SAGOPRGt9bkgguVeOt44PGjizSbbG0xIbm2s/17pzeXnbFbZGSkuFutokpUbmySrnfGCi5Gw+kLL78qhCCEOMpnq+r4dEE47F3YWxyfbmxOt7d2tbX3Do7W9SoAlVJa79BhcNYHC8EjYPDOBkwEJ4icESmULHkrubW60/rC5Yd0vZKA9Xr1ze+99uZbb9gm7Gxnu3sX/vV/+N3P/PiP/IW/8leAi1/4hf+7z4tEJaE3WqjZui6KwWOPXW2b5uzkGCD/6te+89gT169du9KHsL5zu9aeOtJbsAYp4S++8N18mNw+fne8O7n+7FPv/+GPXti9Cv3qxndefuObz5+8cWN+7/TR69dLLt594839d+++9Morz3zwuZ/6K/+HL/yL/+/nvvj81cvlH/3sHxztXpq/c/DWO/cWlS5GuXFaJaON6e5HP3FdpskXv/g7r765/773XtzYnHzlS9/7wDNXnNFS8dlitrG5Xe7swqDMRqPX3p5lE7i4tblqddNaMNYRcu2px27evffGjbcp2q2N6c50/O6dO86Z9773vcX+6be/9d27x4ub79rNTZiOVGV9Zz1PhycrXbf1xs7mxqXd48XqZD4b7O0cny6cZdB6gm1cYbnQrueWOjNNs9r00zSbN5Y5SwAvP/aoyujx/Hi5miNiD/1qtcpkUZSlJLzXhgGO0rzt++p0bkQ1KMpxnqPzwSGSmBbBTfDBWwpIAwkhAEKwwWjvnG2azlmUgnkTOJEEWZmV3vmmbj13gSEJ6I0HD0KqMsnKonB9d3x2OsyLgZL1cnb77de7zl966GLrg+n13uaG6aq9i9tdsxyOsgDd0cFhkiSL9WpUTogBalqJjmh99drVTvcOTcKE7XVVrauqAkb74BrvE6U0wKKq67ZZzhc0+CJJVtas9+/d+Vf/yns/GI/enJ996NreI08+uX9yduvGW5CmwFNtmiIfh9pb1+UqK4cDZNRjQONEwQZZrrUuk8wxQQjx1CEiJzSgByCB0gePXbivzIlnHiP0AX8fH5RJkkRyNGr945GLiFKISANHgPdgY9BbEym2B266+EIEzjkD5r331HMuBE8YZ2+//SaiH5clIjZVPR0WisuTg/0vfuHzBCEE4CKZzZcApK5bTvCll+6RoL/z7ReG2SAYC0Dhz//0A3gXCF21vXFeN02zWgugiUwEZQ5D3Tbrtw+RhLzMsvJitdRCSaUSgsR0ftV1xvTetpvjHeuxs531BjEQwhlNKJd3TmsABsgJIUpyzqm3Rtv1vPNpLiptzt6+/R++/jrjUGR0Mh6sF8tJmQjOqqbp3UtJKVSZi1Cs71IwkuXbuxtXHXHrZmZNZxnNxwwQjqrud7/15ouvHSESwtlqXacq/daLr1959OoT7318uj3qT287qFWB4ShkWaq1Xc0ED3xztCWY6lf0xivLeraxMXzfIJFcjT0ky6pfVjWTLDAEjo5YC85jAKRIxXplAaUnPglZiQ4BKKXA6HnZvAcICIEQQhkQACSSqkTEXgjvMIKWEEKelxHeP7jucQfOVR69cN77mKOJ6ClCrzuCgOijHgwABCWEi8PjE6CMUv7gTgMfAL0SEq12LaJ3XmsMRjDCOTOSn81WYL1gUjLhCNpALcV0MhX3refnL0aD95RQJECQINxfUABwytbLlRBCUkIBKSCgd7rXvUk2Ng57Y632xcAmSW20AzYajM4OZkIwDIZRqhpmbGtdn6aKEBIYcYR6Cg7il8aBCXrdppRzKlAwDaHrdIvOYoCArO/MuqobiwSytEQkuu102zFGBKOMAgdPCVhKHCEVoifQUORoFbqQsCxhnWfj6XQwGOXlwHqYLbujs/nxad22wdkAAJwDIHEIvQ0ePFJam56gD4Gcj/iBcSoIIR4CAEIgAQMNQAkwQjghWmsACIABwBGCAOR91zeFEM6HrusCYJqmhLFIcEbKHykCBEQfFQxSqAiJ0MdeZEaQMiDBnQd9AgDHSDOAD2EwGG5s7ZRlWdfterH03hOK3vsiSxECQADigrXG9oSglLJqdAgYYzopQtu2fd8jomAcAAjAg+JeQkiAUDVznrAkSSJ88e5cyyGEoucNJhhxkvceAaikQvEYhAwh+BD7BCBPlbUmRpVRgtHcSSnXvcPAMFBKJSWSAKfIo78laieM7SB4ys6/RSywuIJQSmVFLuW5H6UzGhGNs5Fuz/Pce980DWUyjgHW2nhwc8qKoijSbDabnZ6ceO8nk8nm5qbiwhjtfB/CuQFX6/P1AqV0Pl9GDDccDnd3Lkyn077vZ/PTul8BhAcLEGOM0dY5t7e3h4ht269Wq3hMGGO6LuxtjxHRYTT4I+ecCcY59+A550LxyJ0jgfg2RqOJlAklvG27s7N52/RJkpVlGel5QpAyCMFp3QMJgvFBOiCExQ1SZMdFos5jXjHESkhtTHQtO+OqeauEopS2bbuuK0Jga2tra2uLSWatXa5XVVUxRmIelNa6aTpKeLxD4hWJ42K1XEWFVZIkkgtCiI5F5YOS3C+lZoxFTZQxZrFYxJHMex9lP4QQ51zTdVprIcTm5ubm5ialtF5XVVXF2Bz0IbLyQohMJSpPeEIJJ4nKGGPVajWfzwGgLMtBMWyapm+7uCWIZhjwznZ9WeZRZeS9X9VVCGE4HD7yyCO90XG2mc1mQohLly9vbW112iilnLHL5dL2OoSwmC2PDw7n84XDYKxN0zTJi7pt0jR99NFHzw6P27btus5E8wsBKbmUstddmqZ5URBC6mbd9/1wONza3r5zfJgqtTec+qrulysI3nubjwaVN2fNunWOcsaZSJTinla627x6edXW1KNivFms1rPF7nTziUcfu3v71mKxYIJvbG8ho6ezs063aZq/773PzmaLe/f2z+ZzykWSFZRSbU3XdSEE9I5QFJRyRhgQCIQ4hh4IpVSJwLB31jkDJCScS4q55AkGai32HcUwLPOua4y3SV7WvdYeTxf22sMXt3Yu3L27r7VGEybD0dZ0Q9dtXzd5lkxHwyRPzxaLe6fHFr3u+n5dpQSxw2efvpgKUveLw0X9sR966s/9p38xHysYyu5oP00yGI39y6+/9vyLzzz25PLoOFdS7O2evH3jq9/8xryrF03183/8j1361KcAg335tS9/6Vu/+Vtfm5+BUqnpxGTjguDZn/iTf/rLX/3KP/sXv7y9MbKuu7PUzz08PTuYPfuexz/5qY9zxZkgL7/28ptvv/Xi904vXFWrRs/WQBTk5SArhsDV6Xw23ZzkebpczGYnJylnUhA0jgOcnngS4MknxmVZcib7tn333QOZsNnCX7yyRxUsmhkRVBQppQkVxXKhBcm7db+azRNOtjbKREBbrfN0wAgLhm5ubqUqu3P37mw9v/zopbduv7lsWoOQ55CplALLRF6kg6qqCOUs4SyR1rmqqY3uCIJivMxyRnjdNo02KBiVihDs5zNJaeR2EpELmerer1brJBuEEKqm9oAGdCqzc1mCFJRzJQRjTBDglCacSUH6el3kanF2vF72F/bSDzz33ot7uyfz5t6pe/OduxLd9jh/6rErq/lh16+retHb9sq1q0Konc0L1dn69o07O+NtBF45v1ivnfEUiLXeOmfQt0bLIutNxyRbzM4kwO50GnrDBb10+UJRZNb0b772et81SqmNjY0PfugjL7362ndfe/2sgmTIeyAgkmIy6VsDPaSyyIrceNdrXU5G4+nEEzDGxAkfAJyxnFJOWe+1o8Ggp/h7gp+ou4hnXWT978syAxJIEuXC+cYS8Dz3LISgBA8hwHkHMND7MKvVPaUUnWeMJYl6wPpHXxkJjHNZ5GVRDAB5b3sQBMGhNRx8ymjQbbWY6aYFB2kiR6OJtU5bVGkxmy207y9envRdpZgskyzlSVkOf/Grn32A7v7Yx/510zTgrLOaBSjzYl112lkhRDkcCCG6ruu6DhFj5ag3zjmHgUgh8iRNZGoa7TC4oDUYS20QjAoJXAJyRjhFAO8UCZIR9Nr0tlo5IVODjgosRwVlrqmWpqtFAMUZJxSAOiCBMk+BOlpi5q1vva5ct+ya1oJQUOakXuL2SCgmEybbSq9WbZIl6IPijHGs+/60AwdQDoBL6HrAAGUBCQO0wAkkAkwL6yVMclAi4yJ3gRlHLIJBb9HxXBAJRBJkxBNASgmVjIosGdBAAw2MMcoJZRFEYIR6BGlUejMgFCgSDAyZoPHhcp6VGNB7H3n64MLvxWM4F0LojHchUqieYJwnPQEoy4ICIfRcCQ4AjADlgqkEgWCsfSWE3m/aCs5T8MQ7QMcwcEokp4wx4501Hq0jhFBgQAhwCYmorHcUvvna77H+zz7+L8EHD15IHhC01sY4RMRAEJFAYIxxQIKBEeCMoPO9dbPF2kKQigwGORU0phFKkTIQjIhgHQSUgjnTOdulaWLB9cH1zkeJuaBMEaGQMuNTJiTjre1mXb8GQAUipcSGYEEbIACCQp7mlHJ0qPuWUhCMUuIpCYISIUlK2IQl0lkGThAsFBvmclikeaKGwyGjQht/cro6Ol6tms460JTwUVY52xinPXQu9JF744wGxygQwrwL2lMbGDCFjGtvgAUagKJlDiUDRTln93lwDB6D9sEGTx65lJVlKaXqjY7KcsaYcQ4hAEXGGFKPiD4YF7z3lhAGeL4MosA4oYQICkRxZYy12kFAQQVjDAMJIQiVWe+USofDoeQi4vhE8r5vg3eEoJCEUwzBxXzPqu3SJKeUNk3XVnXfa2OMMW5QlABwfoFjdS+lSEJvKyZ5zLqJAhLOOZeCUR6DqEIAY0zfGWutJz7Lc8oJhmCM8d5xThljgBil/6ZvQwiMUSEEp4wQ6l2sHYi68hSQeeOt9d56rbUxGkhQglN2nqY8yAeAlHOe53lRFNGBYIyZr5aRsl2v10KI0WjEGGvanlJGKTXGxcknOE8pLctyPBgaY5aLxXw+A4DBYHC+KFieccUHg1GWZX3fLxaLiEQxkGj0FEJMhpM8z9fr9f7B3ZOTA56ISGnH4zsy4tPpdLFYLBYrYwyllAmeSCWl7OomBgJwJdM0TfNUSgkM2rYNEJ8HiUqTmIVqrR2NJuVwLJlcN01btdpa29tW9xShtwadDySgt9oZwYiSqeTSOxcDK9I0LYpCJQljjAvWdV1VVXVdt11nrQ3oIJBMFYuzhXNuY2NjMB7FGelcUZOmLvjVatU0TdwLGWN0bx8Mh1GQE597SZJcuHBhe3ubEFKv1lVVGWOMd1VVpVk2GAyi3TluVIwxcXsQDSfRvVDX9XK5FErFixt58bqu27qJW6wQguSiKIo0TRljgjKeiNni5OrDV4picHx8vFwuOechhOViTQmJMU2U0rbpV6uV0RoAHrl8uW/bs7Mza22SZWmaDgaDra0trfVsNtPWFEURhx/v/f7R4XK9GgwGnPP52ezg4KBv2izJi6KYTCac87ppCSHFcFDX9dHRESGkXTXn6mHBlVLee0SvslQlcrVa1U3jnOt1a60dDocbO9ueUN12OeG+aYX3Ssimq0nCy+3N4/Wi8YYJIaiQjGPnzlaLBkM2KHOVEB/ABQ4EjHPaTMcjKSVhdLFe1W2TDcqiKDrdD8tRCKHt+r43SIhQSdxrEUIezOGKC0oJhoDOpzTR2jrnAiXIKWFAOZeK67adDEqrG9926Gyu5HQ4sLoP6FerFRXCBFTZ4Hg+H403Om2lTJp1pajIk5QFWqZJypO2Wdf1mku2bKrW2LwsnLGmbUaJGqeJa1bXH31osjmozZrn7G/87b/x7s3XV7N7ipHVbH5l9+LDjz4+v3UnFfLg7r2vfvkrnPMXv3vvI993/fqTTzz6xHX1+HWYz2+98sq779yuW/fK9955/fW7Poi+BymKqu6SJIt+nkGZAXFS0Ol03Om+betcJZcuXaCCH50cHZydaPTHy7ko8866jc3ts5NZXdfXH7u+v39IBFkul4LSXErTtgQgyUQIIS8zG3xZlrOT074JO9PyysVL21t7VYsn89miPlnpaq0r4DwrxkINFJ+cHa1Wi4YjkSxwFoZDtTUZp4Gf7B91nX7k4cd+5Ed+7PmXXvj6899ofeO5T8qEKe7RMcIFFU3VrU+XW+XkysVLIs3u3Lt77+gIKE2SBBigD2maBo913dqASV5Qxk2vtwbDlEvGGDAqpCJcNG2/WK6XVaWtqZpusjFtO62y1Fo7GI4x0MhSMwBOQDIviOfEJNJJ7k2/+pmf+exP/eRn1GgIAV/+1sv/13/0yzaIXCWPXn3o1jtvHB/dlQo/9NH3f/qHPvG+D30AyhI8/c6///z/9Eu/4nrQjelsaJqGMQYUO9Olg0yk8mw1S8vi+Pg0y5JUyJ3x1qXt3RTY9t72u8d3O90cHx2ZtqEI49HgH/yX//C3P//5f/pL/wy5+OinfmCl9Ve+/bwmjCjFaIJOeQc+QGt1ABxPp1mRx9KbarWO0B+jj0gpE2zvtQtR8MPvQ392X+AaYdx5mDIiAoXBYGC8ttrEh2MILuasM0JDCHieExorO2kAypgghDjfc0KSVHrrjHFKZsFTAILOM0ono9FkNHBGzxdnyCih0Df1uMz2Nqd9vbr80IWf+0M/fXHv0ny+ODud//q//c3bdw+quhcyBUFO5odC0FFRKsZd0yvBfuWtP/0A3v3o5f9G912eqkQqhoFzLpNMWxe1LAEooTIAQSTGehb3uoRSIMQ7cNFcCpQxmhAniGXOUTCEBEKNsUokggDxXvjAiUfd9p1nalNlI21NXa99sBRQMBimKcPQrNdeGyFEoNxY7wnNpKLaOOcMwSAJShYocehCCIIyhtCsmkykkinvUYqs65pEsa5fB+JVIYgEYz0SpWRBIAHH0CLxTlAvaSBEk4DBUsaVC1RbF4DwRHpOjbeNXYOgRFBg6JEgUMIYoyKVqXeI4IEQoCQEp52NCl5CCKWcE0qBUSAABBG5PBf/R2QPANGrds6RBQI+DgSEAgFKkHH/++Q/UbkQf+aUcc4JQMxdpICEUe1DABJCgADx6Rlvrb7vQnAYHKcoKaEYqLXWWhNwPJ4C5avV2nlSlENPYNlUhNFA8eVbf+bBvfGeK/+cM4bOi0QxzntrOt1H9zEgWuMJBPAheMsp4ZwSCCZ4G5jHQAV48AF8muSM8LZqbeckEeCQMaKUsK53zshEeO8dBo8BCWEEg/PBQEAYMmI9BgBKGc1SQ6H2VlutJKAHRkjCM7SgO00AJAgCgRDPSAAIlAAXkKSsYPxiMSRtb01Hgk04ySQTnFIKkknrse5d21kbBJUpYdIQ0mJXaV13VmOwSG0IQIBRSKXgjDEgzgWjg0UAkjJJXdBAPAQE9CwEQUAKIRlLkhQAAoGAJCDxgHy6s+G9N94xRimV4RwTeyG4dc4ZS1hgUgghKDKPNPhY5kWcsT4mDxASEC0GoRRnsu/7Thvqo02EBQq6d1W9aDu9Od0Yjcac8+CtMabvO2taLogShFDPGGWcJxIx1G3nmqq1FqWUqcq8x2q1llIKwbz3tu8JxSSRKlNJkiAFF2w8JaXklKENWiTCeavjIUgYTTmXjAYnlGz6JoaXIZCutwBWcD5fnDHGBOOMyYDUeUaBMyayXAUP8XhljIWACD6g8Ri01c5apRSXilJKwAFyF4Bz2hndGR0ZlLgBQOe995LxjfHkwdZimBdGe+0sBcYZhUBIIBh8s676tplOpxcuXRiOy6Ojo9nijCyJECI4T4NjvFdJxpO0GMFYydFoxAi11krGB4OBErJvWx9sWZbObRjTe+MJg3isA6GM8eVy7VwYjkfD4XBjYyPNc0aI9/7w9u3lclFVFXoHBLmgxbAoBrlzbr5cVFXVmw7BPzg7mqolB0dFVpaDgRLcmN6aHr2tm9Z6BwGjYjugJ0QxxrU1HgISdIjedP3asjVBxLIs44YnhBDVMlIIpVTTVNlIAiQiZ61e11WDiFlWEJYZx5WQo8GYU7Fer/tWO+ekkBH0W33eS1AOhnmeb21tzWazN9962zm3qtbL5TJJku3NLZUk1Xrd1PV0Os2yrGvbWJvgvR+Px9ba9Xrdd50Qom1brbVKEyF4lqVpmq6Xq3q9JoRsbEwF523b9rpz3npr0jQNnGvTT8cTq91JfbyYz631slCMizwvJ5NJzEJt29aFtmoaF8L25uZsva7runfOeY/aDMcTLtX+4dF8Po8Dz2pVzU7n33nhu4hIOSOS3njnHe99miSEEJkmxajc2trSbae73gEGH9qjw67rKCAByPKEMxm90ZSBkHn0nCzrFaWUC5qkmUzEer123jttptNNw1RT1ZYQopKkKCYbG4EEIqSpz5p1PRiP0lw2VdNUNUFkznpXVVApLtI01dq0TRN3LEqptMgJYWU+GA8no9HIOEtTNZsvRJolRQkA082NNM1ns9n+/r7iLARper2oWu+9UipPk8roJJEJlX3f110DEPI8F0puX3yoqdac58OdqWTcGUsSuXNh3KyWyBZVXQOFVaO1I3cODgnllFL04eFr165cuHRw+55M0zt3949OTnYubV59+KGHB6M7d+689cbbzoSH9nYe2tvdmYznp4f3Fmdnxm5tTcpk8Iu/8M8ocb4+feTqxTt37qj3jV594Tf6pn/u/c96uvHO3fqtN2tKYbkSTz/7Y7qqPvf/+J+//vVvXtq7sq7as9nqZL6sa2pcSPNhNhrVvRmNBmkiUkGLVPbN+uqFnVsH+4dNnw6mR7dP33zrZGd7+/a9/cO+evSJx10mty5fbI/vNsGv1+vdYVL2Kzhe9AjXr+ye3DvMNHLGT2dOEtsx0NiKYXbr5PjCbqnm1aNXt/7OX/tP17PV/+ef/HM/uyuDc4sKAljqTo5Pn3p6+/DefqEKlGy+XJM8EWmxv6puvPPqJue7w9GoyN99581f+7dVa5tbB4fbF1KgYbo5CiQkWXJ0dHRwdnx0BJc3gNnF/huzP/yHf+6v/Md/4j/763/jZDYfDQeN1YFDZxugPBsXElJGREKzrEjMorp66aHp5qTqq4Ozo9Vq3gdDuZUp0oyzLO9dn5RJXg6VSoGwwEiwniFJhSTWuHYpmRvkMB3y48ODyRD+wKefUNdG+O7d2Wz1O7/xq6k7e/zSo3/sj/3pV1576+jW3Yceug7SP/HeDz7+1DMnx/vVO8sbr7/9tS9+a9ksFc1BsPn+fpGnkgekushCXnRMNdPcrxengzHMjvo87Z967KFxBrPjs4PXjhrX0JTNDo6//1PPrWbzVIqvfuFzn/u3v7Y7yf6Pf/vvbH3sk9XNd9/+y6+1ziWDclGb27MTkQ+Bs3lbewyJmgyGqZSkWa2N0+cMrHMKUsvROC0kZS4A0Hq9llIplRrtsmFxrvNBTxhSDOiREso4sa6zxnjrCICL23VgSnFrrZDcOQQAIYS11jqbpQNtwXtPAblEa1rvMHjadz7LRof7x+NikKacaRNWx83ibqnofGZUKkhrvZ+d9Yfb29Pr16a3b7704vNfFCr//Oe/fPvuyWiyu+4a6HVWjkW2uVwuy8Gg7bujg6OdUfn7NR3XUnz06euDIh+NRqlUs/mp1doY8/Zbt+6cVipToOiqNZaJRIq6bRhjRLLgNAtOEeLBU86IEE3XK5FRhG7dUKliVLVQxngg1vsQfKc5YDYs95f3pFsIpoCiIFwxHkw/Oz7h1NMQUkmzTAGEBow1CNYFTgIHSiTlwgZoW61dCOClJIBBpAky3jlvrKXai0StwPaCS6kcJc2qCQCDgnFG6vWCE84IR/Shc+gtBUIZcM6oDwGhC84jgGbEi0AAuQRCgg/eBBceyEpdV3cAgECBEiQEKMY8l66LnkykhJyXuwZCAhKCnDFKCSJ6bxGRMmCMCSYoEqQECUjGOZfog7bGU1BKAsFOa+MM59wRggGllK3uQuMZkOC9t04yKVPpiUcMDKkATjwYFwIlVAnDQBaq61xTrzNCFUXpAiM0eHsyO/EkAZVBNjijtO+7vrejXFH/v6p7w6ZhqjSNT6kSMg2kalyjvQZGGRVN0wBi/ICEYjA+gAXETChJad32gZFsMHSBVlUFOpQ8RRPQWUaE1WamGwcwThLfOUE4+i4ljKLXAGMOjMOqR0qBc2qA9G3rAqdMFIJb1zAK4KFtOw4qg4ICetABdIJMMAgIxoMHoElIlACng+0ZhjLLleTemKrqrIWmN0kOwKVhgmUZTfPOhvm6OmsqD+AQAgLSIBRTQjLGBnnRt53uOoK0yNNEpgQoQWdcH9CWebG5uZkksl6v5/O57u2oHDKh9vePu14Px1OGlLz//btRBOic88bFBWLUThBGOKfAIIB36AIJAZFSFhlxGml1wp0NxpiY5R9cMMahi12DAhG1DbGsN0mS/H6+inOO0dA3je5rBMepowyUkkkine8ppcFT55CAYFQFB8Y475BGXRP1AAjEUYRAnMrTCP2NMQCBc0543DYkIYSAhBAGlAFQDATBt23rvY1JQd573XZaBwDIkiQKIiUXSZI9qEanCJTG3QCLQK1arpuma5qGEh4ZVillXIx672UiAKBt2xBCnmZxsxbz740xwfksy4bDoZRSa922/cnpPM7GMSQrhKB1Z4wJ6Mfj8WBQIGJkYePbm4w3ptMpV7Lvey7FeDxGgMVi0bWttRYCUkrB+qZpTNuFENb16gGlDee1Wb7v+729vTRNmeBVVZ3OZn3fl2W5MRnpqm6bqmnbAAiMBkq4oFyIGINDCFFKSK58sFHcpZIsJgVF26vW2mgLAH3fN03jXEjTtCzL+F+RgHY6wO95YZVSSsiYRmp77ZyLXgjBedd1TVMVg7zvW0p5URRCKKOd6Q0iWuut9boz1loMwBiTTDLGmGRx2Q0AUso8z/M8V2lyeHjYG13XtdZaSpkVBWPMGYPel0XBGDPGxP+/bduTk5PJZLJardq2LYoi/iEAbG5tueDvy5Owb7s4qzDG3P1dhJSSAYneI2utSMRwWCZZGmtShBCEcufcwcEBIra9ZoxFF8eqWuuun47HztjI5yFilqQhhPl8vre3FzNtQwh1XddV5b1ngiGjhN3f/p/XZxLOeVPVkXShlII/Ny4rlfreUsop51JKIRnnPDq9JlvT09PTxWruvTfWdl3HGMuyzGnHmYjLlvhJH+ScxskqFiDUdc0YK8tyY3PqnDs+Pl4sFjGSKBonolMibq4QcTQa5XneGW0IVG2DHnqjq9VaKJmqLJb6IQESqMeAPgAlgnEhmbeGoI9n1H2RA8ZdH2OsyPJ4q3vvU5WMB8Ot8fj48Gi+XCCjQMl8vTqanVZVNR6PCSEf//BHE6Ve/vaLhwcHVx66/Mx7n1YDdXhyfOudW4eHhyTQyWg0KgaK8SJLlmdnq/kpeFfkajwqx8NykMthQt549bvWhl/4hf/bYLzxS//0n37pd79y86ZNEkglYAAhgFOS5+XW1taVK9fefev2yclZb0NaDhrtjs7OuEouXrwI6Pfv3f2r/9u/tD0ZfuurvxtMnwv2lRdevNeZ02X/8N6loH3Ck2I4KranX/3ei6NLG7Nu2fULsObpC1sjAp/9xCcP7h2+ezh//oXXcwrDIkNrLl65dOn6NTpO3zy69aVvv1JO4G/+1b/2wUevJ9Otr/3j/+HbX/nmnYOT3sNKG1EWJ+uap/n2zqVvv/DG5YcuNk0fkFEulm27rJt8PL66s9sfHgprxtORTOTtg1ud6UVC78zDex4fqlQdnZ4cHsOlS2Rre6Nt+vVJZWdwaQPQ073dS2ezarK58/bdO/OmHu9ONfpAqKSJgEwGqSBJmbi4tXm4f3fdVcW4ZKlaNOtl1wCnk63Nw9NT68NwPGVMrKuaMbGxMUEwuuubZeu1HWfl5qgQpNf1UdceSu7/oz/+k889+x5AejZb/c7nv/rOjTv1sut78uSTzx6eLo7PVsPpRKO+8vAekO7k7HZXL1gA3Zi+MqYG3cH1Ry5a3TDuphvF1k6ZJOBcbbq6LApO+MHdk/2bvdNAPCQiGW9vVkzfOjh56ulrX//qux96/4W/8Tf/5q//m3/zy//yt37ipz79wz/xE7/5xa988ZvfOqsqnuXv3DpClbB8Y9Eai0iVkEptbW3tbG+nXN5++2azWvreIaLxTqRJVhaBBtNX3jpKudGuLAd5XnatLoqBORdJts6bEBwAMsa4oASDdTo+O86jlgGEEDFS7MGePPJ9aTagNHPOUNBKBkHQORe8QEglHzpLTds1zdHD24PNARzePZUMrl/Pt3d2hOTvf/97izz9pV/6pee/4zenMBzA/iH88I88+3f/4S/8F3/r//TCd15dV/3h6WLn8qXT01lCScJgrMQ4Fb988y89gHen/20VXPfKq9+98c7bRZE9+thVyflwOLpz9/jbL756eFy1nnYgMMmP11XrTN15r0FRUASEB0agsZDk4BCYgs7CooJ0AJSCFMAp2BbQQCGBI0GDIABybjHoNrQNeICC0kGR56m0tpaC9n03XwECDAcklbn2wVLsTDA2BCoJSTwllHMueasr7w14RwEIUhqIIIoI1nrdmlZKmaaJtRbBU0qDsxQYIYwBgQAUaVy9UApduxKKM8FtQOu8RXAee+dFouJxFxU2McmaUqpUSggBwpCAA3QYfAgeg5QyMveEkBhZHt9YsI4CARIQAyGEUuCCMkaC83mSSq76vu+bHkMQXDHBF23NBEcStLMBkUlGGA3BLZfLsigUF946DixVCQmk6RsiwHvLHJVUcOAANDCGih8uZyipDVoxulHk3HmzXBILggFwbmjWIKsc8YRnmRwWqj87ylh4ffG/e3BvPJL818Ez72A83MjGuWOuMrVFh5RQJLPjBQlIA0ZpegDvCADCQMJkMjIQThdrHyDPs4RIYQPWvQhgwCCAklITv7SeEeAeSsYyKS/ubBPvDg7utQ5qAM6hC6ARAgBngjFBAw9oLPQuQAjAARQUkipGKIXgfY1gKCAFkArKko1Go0maqc4yqykQyUVAp9uu6zoXgAgh0sxTWVnrCSNKaQ+rttU+mJiUylkEHpRSCGhiIIbzFEFQxiBuxfXu9gSIL4vBxuaUUnpydnp4eFit63I42NnZmy/XBwdHgIQwynvbC8oIUmOs6bRzTjApueBUME4Zox6dD0gJl4xSwQEAKCEI6AEBgARnjO46RjghxFnvnGOEAKGxawmRAqCxtuur5TxqdKWUXHAe/HlNj0Mg3jPnnKPaeUqRMc4EZ1QBsBBcQFRp4p2JyEZIgUisMb3ueSIRIcS8AgL3NRrsPHmdMEpnYSIDAAEAAElEQVQJJ4xSGhgFQrY3Nq09DwC1aKkiWcKEEASjv9OhQ3Q+nDdBBSEEJcEzFolSxniel0KoWPBUZKVSyhjTa2OtRsTlsiGEGGOSJMnzcyDCOffGxisXQuiadrlcGmO8RyklFTLiFyaE4pwK6qoQPPbWQFNTSgEIUG7j2d6Z+XyelUVZljkrlstl07Z1Xa+WS0qp5IIxBtZHDB0bqMqyRMTFYmGDH41G02IEAHlZ3Llz5/bt233vhuNyOp167/f39zPOrNWEIgUIEAKicxQBXIjFMr5pmuCQEMzTIsuy4Lzpdd+bcL9lOQqypBAkzwmwB57mvu+NNU1bUcGVUpxygkR32rYaEROhWCCNbep1bbUdDgaCCaXS1apC9IwhpX2WnheTdV23XtfeIwSSpmmRl0mSkHBfIsIwtj5xzl1wxydHUUEUBYuM0TzPxqMhi42A64oyFhC7vnfeJ2kqpMzyHAgpypILAQCUsaIso0b2gYsXAMRQJFm6nC/Ozs4IQJqm8c8ppVmWlWXJGPPg+74/O5uH+7GhzqP3fmNjQyk1WyyPjo4ODw/LskyyfDSajAalMyYGFXddZ73z3rvgtTUGIfoN2rbVfU8ppYq3bZvmSXQLRP+xszZamen98kUEH83ESZIs1o0QJAD0fW+dppSmWZbn+b1799br9bpeee/hfp1z3/eJSIiKKuKAEKRKhsNhnufR2UwpjYFFxmpJZED/7s1bFy9e3N27IKTq+55xMcqLJEniVev68346ynjb9atqvayr3lnBORDijPfegwtW94JJQgklhDHB43GKiOhTpUJw6H9fpXdMXipLQkjTNKvVKk5T29vbF3Z2iXNLpbQxVd/a4HtrACDNstV6ffmhh8rh4NWXX5ktF2mWpWn6xhtv3Dy4va7r4LDI89FgiIEcHZ3ortmYTCE4KhUD2TtzcHx6cnaqJHVtMxoq5PA3/+4/ODw4CR62NjeefC5xDmyv67rZ29sTTF65cmW9qm8dnB2uVjzPFWW1sS24ZDoMSO7NTi5e2N25cunO6cFrb71899a7t956p+9gOKW7lx6CZFb3s0k+/Pmf+cmDw8O379zcmQgPNWlPpQ+ggTQrycVDo+GjW7vZWzdfevH1yw/tbo1GHDAfFS+98G2bEDbJ/+yf+qkr16584vu/H3oHt+6e7B/evXlPpPm66RKZPPX0exdt9/LrbzXr6uql7Ys7ezdv3lpXNeUiAXJhOrIE33zjlccvX2nXq5unx6Nhuax6TuDxhy9TdzOzALZ/eDDegMVGPj66cUopDAh8+Cef/Pmf//mt6Z5uwuf+3X/4nS98dbWsP/UDH7l7fLhq6nXTNqZjoU5pxuTQZWyJfdioSNPPreamSMajCxu7xqGQyaW9iW67dTWvmhmhmOYZ605CmPWnx9yqnfGeIm519x7DMB2VguGzzz71g9//H81PD77z4nePjmff+OpblCuVbxDqZl0nynwqhXb29Pjk3VtvX7q4LQUqUlJvre7tCnIJ158YHS/uUQrTIR1v5uMp5JkYD/cm49EgL3Xrzi6v712erc76vkNvuYVw7/Dg4x95z3/yF//i4n9zOn7sMX969o0XX+gQIMn+8v/+b945MhsXhvunlSXV9t7Fu8czt659QKmSqNpJhBiX5TArFvtH9dlp3zQqTYo0AcGael011aDMvfdRthkbwb3D++WJBs69cBTPu5zQGuN9FPQTAozRmO9OMRCglACjlHAmQNDAQpIkPiACUADKkICnLMQGYec7JVM0vpCcoD47W+9uw8/89Mcee2zrwx//EIw2IcDyjRsvP74H3d3pJLt3t/3Uc1s/+OEPQSHn925gc5QFfHibrxd3dzLgAbiDx3cvX9nZ/uXfx+y+8JX/cHBww9iOcbi0vfvBD1+6e/fu9q6U+eDwTMzXbrWAzpiuae6dwbWH4erVLATH0ZoWQwdlApnMncOqaSfbxWRnxBNIB7nWndG+yIbgmGkDD4nrQj1vjNaz00PBZDkZjYZTLtKqqk/OThbrBSf+zhwYwOOPDlU6uLN/tGia0XSjWpw6BMoE4zxg0DZY02MXkDjKkFGE4AE4EKpNZ3o73dygxBICHN15epwxusXhgCN6ihQIZTS25iKi5xSId73R2kMA4Iksi2zIxKquCAAlFACAPSheIOgsoRwpAiEUELwLzsUYR0I5EsKAuIAkYiOgSqWICL8X+hOcs8YEgsEYAx7QB0q5oKI3pq9rgw4MCYAOHaGUIgdKvLcX9y4ZY5qqapZNQEgIKKEIA++ZtZqjCIwJGrzz2mtLUaYJSMo8QWvmZwvShXFCrlza2xqWdw4Ob5+sPIg8HQROQt+t20UpH0iPzl/G+RBwc7qZppJwH8ACC+gsWnA+5IpzTxkCogeCAWwgAQhyRNN0HnAoKGOMo4GmDRpygEsDNUiGHr1loTbmtAJtoWBw5YLKsjxRxASVqoe6gPfO5itraPACSJIkZZoIoLbpuq4fDWTfm4BCZQPGi96h9Y5S0E3vvUYHiYCNUm6MhqNyUAhpXS2VpJQGb5tGd0Z7JEwwlZeBM+2I9qF3LjivA3TWeQqx85RTyjlnnHvvnbNt25GABIFiDFOllBBG08XcGWNOyPzwoKWMNE1Tt50FrPsVBJUX2dZ00nZ12/fk+lMjAAAPVhvTWfSBUxHVzN57a7VxNkCQUqZFniQyxgU4Yx+ExkRERSmPP3jvBeVCcETQ1jImEEnM4gw2EEKUkFJKCMg5TRXjjCJYRM/IeUUXY4wADwGCp8GBd8R7n8rUOeecIdQLzpAEq/vedPkgp4wgAe8tYyxJZJxPXPAY4peFMSZoDIJFTBIZ4x3PW3IRBVeRxPXmnBRhhAJlwXnnQ8TEznjrdPBACBGMMcaGwyEi4nmdRN+2NXrgnPdGR3ymlNqcbnDO27b13hdplmVZnBNWi2WM12RCjoZTYDRy5JzzsswRsa7rGIjpvX3g2dK66/ueIiWEJHk2Go1UmiBiQKSUGq2FEJILAAja9n1PfAAALlncS1hrbTivhHTOHZ0cx+Agzrn1vq7r+XxerfrLF0YYXGx3AEqYFCpJlFJtr2OQVt/3zlhKeZ5meZ63bW2tjYab+zYMwRjTWnPOBVfxnzuPy5Si0z3hhBKO4IMNzpkQgGBIkmxzc1qWQ6M73VsfbN+Zql71plWpFEIhIqNCShlc6DptjKOUJjIty7LISwDo6q5pqqLMo0U4CodiXpANPk1TbUxkskMITdcyxoqi8NYtFgtjTJ7nSZJEDizP87g0iK6yKECK5gQXfFVVy+WybVshRJ7nkov4iI28mnOOE5rn+WAwSJIkyZOmabQ1UbzeNA0CjWaAruuOTk6NMdvb25PJxAWMxWQxoltrbfo+dlqv12sIGK1XABCBr5RSJdJhiHNj/DJGE0xM2yD3s4MYkBhVxLkcDUYU6f2iZod4nuy9bisA8OgAIF5F773R2hkrhACgxvTeY5qqwWCUJFKplHMKEM3uvdadMc5aO55M45sXQuzt7Q2Hw8j637x5czwej8fj1Wp148aN1apWilPOxpsbxjtOqEhUKlPCmDNmXdd5kjMpBBUOAzpvg/fGGtMLyWL393kKcghZkqRpGmsQOGVxFzEYDAZFKRnfv32bAFApmOC9s+uu0cYAoxRhvV7v7uwEbRmC6XVfN8tGD4YqECizvCyHELDrOgGsLPJESKO7vl1bo4PTieSj0WA0LLpm5Z3lnMff42g4aer69GS2XOo851vTzel0OpstNjY2NidbSZIcHx4Ox2Pj/P7xSaO1ygvr3dnZmem7R69ePdm/dXa8uH55s0zkY9cud878xteeHwwz6C33+MzjT7z88st313DhInvjnt/dgyKHj3/g/bkNH3jk0d1seDZb/e6rb77w8ivbgzFaMx1PmGAv3bj53k8886f/8l+YPPMUpBJWq/b1G//9P/p/zm7e4yi+98ZSFpCNx+lw8ORzz1ad+erXvn5h73K9bm7duuO0QUJr60bjspyM5sulUuri3u7B/t1gulGRz47nmYJHH77EaOjbigtaVau2xRDgZ3/2Mz/7p/4Y7A3d/v6//bXf/NZXXjy4e/bE9fes6v6dW7eSUbHq2rrprEGwQIPMWcoTsvTHg81CiqKqtfNqUE7TdOw9OuOtblbLmdHryTidTrOuWR8dzEYpbI5BUaH48JknPvjkE+89uHvwpd/9QttVP/7ZH929tLN/cPD6G2/fvL2PwJDxwPm6qZuu5ZwTgkmWlsXQaJ1wllDSV3Pi2oFC3Rp0MN6EwTaf7oyuXrm0tz1JFJEMhkVeDkdvvvyG4NnG5KHB5BLwgVu711595zvfe+nG7bf+6l//z6SUTdtTJf763/jPPYH/4Vd+FUYbMJr8z//t//sX//F/P9rcu3t4vK6ajZ3d9boRaYIE1m0j8/Txxx/fGE4XJ2fNat3VDUeCQGfLRWd1NiizslhVyxACo8I5PxiM8qzsOl2Wpfe+qipjeqk459yYvuta5xyj4L2NvAy5X8gTf/j/Z/2zrCCUW9dR7AVHTrz3HoMAkG2DeTbIOCeuRX3KMXz6I1f+7J/57N6uV0MBln7nmy998be/YQ3dHF9aLrrFvB2NN46OZ/cOjg+PIM/BeGhbSHKQEh7aLSbFKGOK9PZvPf8fP4B3/+an/4nuz2RiiPTFAHYv7g43d7PBmHheNY5glo+20sEGqEJduQKjESxOTV/LnU3o2sPvffds/2Sr2JuONgJ3DuujxZ1lc3bhoe3p1pSPJmDgzuu3vvut147uLL3mKSvLNH/isasnB/t953Z3L+ztXmi69o033nj71tsHh/c+/UPf/32f+lRnwu9+5Rtf+vK3D49mHriXgnDORI5U9TpUXe+CJ5x0upGKUoDgHAtE8UwwCSF0XdP3LaHoEbs+EAJJAiGAlCKEEDwBAAIihGCt8+hzyYRghBDtg3XBAjgfdEDBeYReMWiRELifRCKQMKAEKTlv7sXg79sxIeZ7BkIICiYZY03XUSaUElmeJklMRjHWWmc0ACASipQzSQjR2tRdyxhz4OM75ZwDg4DEeVOva/SwszV+/JFHMy4P7u2v5iuuZEiFDyFlqsiKlCfGuKrrW2ca3SPzAS0nIQXMCDx57dr3ve99H//g+7/whS/8u89/+c7xnKQFTzMAwGAE9dSbb53+tQf3xl/6qde5FLdv3nLBNKZd9VXdN9ZaDIEFmtJUIqEILACS4METikCBUnp0dAYUtjYLDqFbtkmATQXPPnb54d2d7fGIcVJ19c2DO2/fu1M3sDmEvb3NJB0FyO6erN6+e9LSZO2w9kEjBvCp4qNUDBKeYKDeVrOFtsBUkk22HVdn627VN4EERhz6Lgl+lIjtvNjIypIrRmgIjgsahSHL5bLvey6UzHIToHVu1dq10ZYwR6nx0HtLGEUARml8KANA9GEHh+T84qIgNFVJkWZZkqOlXaeNMYwxxgiCB/CUgUqosa3zXduZJIXhMKZceQwYIlVJgVBgDJjiSnttHAFLGJMMORo03iRZKgQPVFKkWmsChAtOFY2g2VLKGKGUU069R+IDgqOc5YksSQoe+r7vW71aV2VeECoI4QEQAgFCAxBwARlzARCd7n3fO7RBiDRRGRWcUYIUvQdjnQdHAJkU5P7AfP9eP8+pFUwGiiEAIbFeLtYVur7tYkC4yGQIIVhnrdetppQywrNExm+LDYFRQikeHZ0wFoOTEIASEpDSSLH0fW96HcunOKcyUUII6ZSQkgBIKcuytNZi0zjnjs9OnXMkYBT8XLh0kRHqEShhgdAQQtvW0aEVBwqlVEzUiiiWnlcWsFSmGxsbHvDw8FCfnW5sbBRlGa26UecQbfVJkgRjrbUxILXVvVIKAKqqMs4yxi5durSzs5OX5WKxuHPnjnNua2vr0Yfz1ewYgYFDa60zHp3utaacOReEEIKrIpeQIXr03q/XVb1aEQqUxA7IYJ3uXOucp5RIKb308UkTZT9pni/XK21N35let94GSkHKRAqmRKJEIrmiAYlnATkN3DnHJc/LjFFR13XX9dY6yZWUMk3zEEJwWNd1XTUAEN0UjJIQAiMUAE2vjTsPd2p4ZYOP9DPnnFPGGeeUURF3BT6uaKqqipcghHB0dKS1jt1np6eng8Hg0qVLV65dOzw+Pjs5aZqmbdu6rq11SZIolcQ7DWK4U5Yxxpy3b731lpRyOB4lSVKtm/17h9qasixDQEopF2J3d3d7e9daO1vMrbWr1SK2HKR5Frd7fd9ra8395oGY9B8vNxLcGG8s1gvb6/PoTOeAsTRNR6MRIiZSRZI+Dh7ocDlfRst7kiRZMbDWnp6enp6eAidCCKAYZV2R0Y9pVFJyzmUIqbXeWn16ehzHoSSRSZIpJdI0z7KhMa5pGqWUcyHP5XQ6LcvBbLY4OTkBAK3t6enMGJdl2bVrj1hrq6o6PTuz2qhEpSpJ80wJab3rnR8UxaAYplkmOG+7rlrVNqarOKdNJwQTjD8Q+eRZlmVZ/OVEkZVSquu6arWuV+vtrU0hhExUIOC6lltuMRBGnbGEs9PTU0FZW9WjclD3WkmCSNqu7zpdt1pQhiEM8oGQykHwBHiSJnkmpUiVyLIkSeSlRx89ODhIkqTvzHq9Plz3FMTOw0/8wPUnXn319bpqbp6swNPTm4e3jpZVvZpMxvrgwPqAjJngm7Mzay1lAN791je/c3HKkyF/9/D0R3/oYx/5/k//vf/z368bqOr26YfLXIjf+erLP/HD13/ukcu//r/85p/8ucf/3F/4c8PHHwOkv/H3/sHqcP/mzW+ezNdvL9eSE23aetFXq/ri5Z0/+vN/8DN/5k/A1T1YzKBhv/HLvzy/vX+4f/TqK83uABIF8xWs+oU9Wrxx69585R97/NI//Pv/5cnR8T/5x//dyy+9NB6PqWTG2dZ2zFf1vNIjTGh9unYXJqTYY2Wu/sKf/9miyL7x7W+8c+ud93/wBz/26U/mTz8Fs7P5wb1f/b/8N1/+7Re6FXzw/U/tDLcETxRX6eOPv/TmG0Ahz3MyUM6i6VyvffAknV7YX8y8W46HG0lezpaNOZoxQhezI0BXKigyqGbrkzuQJnBxCtcvw2d/5Eef+8DHIci21s7CIMnQXfvmC986Pn39c5//l3WDDmGxhEsP7Rn0Mh8ITiWDNFUYXN/3bj4/2T974pFHGacSZZGQx65MtqY8kV06EVfed2WyNymnE6AI1kJTrw4O33z59ceffBacDEacHt29d7A6Oq1v3Tq5ceOG0e03vvb1H/2jPy+r+r/6hX+UTUY/8tk/CHsX2/3jg7duHy4bA3L/aOG9eOjK46enp2WSBILGukLKvb29x69eZcBWR8eh701VaR+EUCmlSZ6rJAEkEAgBzhg3xnddB0i9x9/bgP0+iI8IhBAphfcsIvsYABolcvELjqhjtljfG2ttCFAOi9hzCCEgeAgBAxJChoMC0fZ93a4WuYAf/dH3/8mf+/ELF+R89fzzX/jifNYoOnr/s099/Pt+hD/yHNQIlfudX/33X11/TdfH21MgBK48fHEyLZ2tX37p7jAlVy5szg8Xq+Xy9zO7ly7vUTEQaX/x8nBwYQwkwLXrcLwK80YlvlrZ+fFtf3pMksGoXW5cvw6ZlCoDNM3+zZe+9+177956zyNPh7CdDwRRJpA7Ho4dWXsyr486DHxZzzp3e9Eu9m/D6QH0PTz86BeuXssv7F584eZLv/jLZ7duwqVdeOYZ+It/8ccuXLhg9I3vfeVbb3ztFbqAiwo6dGsKhrBge+1M07uAUJRFOSwODhoGAYNDa0KgQqrxICvS7Oz4KJuOyjI33i0WM8bYdHMjy7K79+7Z4L0jABCQeu+Ncc5qtJqhDx7Ae0FYnuUqzYBzrbUN3jqHECjgAzbKBxKAeH9ezgwAgbCofMD7sfrAKKdEcUUFl1IFJJQBQXDGIqKxvTFmMBhMp9OyGPZ9P58v27ZTeVGMxnVdu2C998gpY8xD8AEppXt7w1SKJx97/APPPjcpBvdu3zk+2LcBn3/9VUeQMs6ZlCoXErnKC7Qg2Hw1b6pFwumAU1uvb711A2fzb/4vv6b7Ngvs+u7EMtk7HwA5l7ZrPP1ftfm+9tprxunJdKh9q12L6LhglAAJyANJEESgAgli8B4oEkIgMCITMciE5CwJQa/aTQkfevLi+x++8uzVqxkJPDgfzNmqdaedT4Ck8NBD+WSSbuzsjbYf/tbLd26+e6eqWzXeXHddmiVZLjKBwjV+eVa3QDTkAEkAJnvhm6Xt2npRa0sVZLlSAjJCC04ywMSaxAFjjGYKGXHOkRAYY1leZkWpysHdg6Nl2y4b7SgNUnjKbUBLXKEUk0xyHgBcjK/EQCllilEgDAECskAZE4wJwujZ/CzSfILwgNQHbV0f0Gxng2W1VgouPgSf+YmP/9RPf4a877kt7z06JEglk4JIBgwDGQ4GUb0DAJTSAGiM1k4PBoM0U5GzrOta2z4mCUQdMKKPuZCMEe/RWk15tHsTQggJxFrft70xbm9nm1AAH4wxcQMghEASHDgXrHdoLRrtKPAsLcpymEqFiAFdNJJ672INMJeMEAwhOH9u8yX8/KYPHnw0rQMFgBAghJCqhFJgwABCFPF776P+R3HBOQc431F46zxCp20xKEeDYZIk3nuttTc2RoJq3QUXi4ETxhglhFDqPWZ5yRmLiqAIQUIIy+UyhAA+xJCcLMtSlXCpmJCUUmPMg6x960zbtnmehxCigDNSvEIwACCBFEUhEmWtbbrWOQeEUErHoxHEU4TSXCZJkjAEay0T9PT0tGqbKBPvuo4wGlNoBoNBmudwv6+qbduqXqYSAIL32PV9Z7T1ThuntR4MRpTETAkkAQFjHqVFb6TiUiTkvkqkrpum8VJCliWDwWg4HMaW4r7vq6ap2y4GclEGjArKgCANwe3tXTSm75te6y5JssGgIIT1uj6enahUAUBd123bEUIEk4QQziVE8WIIGIAQImJWku4jHYL3S2qAkgcZ/w4DIo7H443NzaihXywWcSPxYCfTtm2M9k/TNK7UAWC1WiHidHPj+PTMe1/m+WQyCSHMZrO+7WKof1EUo8GwLMuiKABgNpudnJwkqWrbmkuxubkpRbJYLObLhXMuSVIpJWGMUupcqOtaJurChQuHJ4cR1DrndNsRQrqum52exTGgKIrtzc3RaBTvGeecypLZ7FS3nZTSe79cLnXfSymn06lzTnIxGo2Gw2H8e/rezE9m59o853pz3lHAGDPBUkqjXT6OanmeSykHRc7uv+IWpeu6aMiOrGF0P8dBN03zjc2dNM1jGd/du3dfe+21EMIjjzwymUxu3bo1m81iSUKMf71954423XRzc1iWTAiCuKqqYEM+KPMkzwel4qJqm3pVt33vjNFOa9uH4CiQeGkYY8G5qO4LIXjrGGPj8XgymVAgXddNx6OmaZbVumlbHRwCROPbycnJ9uZWsG4+m61W5trFzdV8EULQNsT5ajAYcEK991mSjkYjZ3pCIEvVaDRIE6m1Xi4XTdNMNyfz5UJytVgt8zTPsowAW8znjIrZbDYeTwVlg8Hw6OhICNF1bVzLOQQfwBNAAlprrXVT95vjdFyos8Plc09fRq2b9eKjH/3wsx9+bmdj+k/+X7/47pv7P/PZT/yRf/j3oF/B6REUEooC7t77ym/89td+4/PbyVAZ8vqNQ7IzvX0ySxmXnK8W/fuefeKP/9k/tfHep4EBKPH6N7/5P/3S//jil98YCoAKGgt5DtlkY17XBkLv/e7FS08+8fS9O3dGxeDgzm30VkpxMjupW52PpCqFLMC6ZlSWwfbXLu49evXKoMw+9rGPsoTfO97vvH3iQx8ARhvTP//d75zcPdzluT6r775zPD+p+zU0rT04Xqy6lpWFYWAotYQZG5wFJdIkzQcbk8V6tV6vKQJnDJzuu9bpda7oqGAJN22FzsBDl+AHP/3kxz/0gYsXLuuTU2stF+Lg6PClV15q+mq8OXn0yet3Dg6/9rXn370DnIO20DQAMum8cADBeUTs6o4D2RxvskBd2wtviW8mBTxyOX3s0ekTj+889Pim3EtgmoFHd3Jcz5ehN/PjxeHdk43JpbrB23eX796cL2oAUbiQaG0T6v/+3/97YjT6V7/yK//y1/9dkLy2fuvCQ+V0+/a9o9miSZLBxnT71VffpEQQ8N40SJxMMlVkWVns7OwUWdnXzZ0b71Kg08EoLwvjQmf1olqfLJcizZFxIUTTNPw8GJpsbW0JIWaz07qupeJCsOjM4Zxj8N7buHmLW0EAOF9uMxZpjgeHSZZlTFBnexI6QcJ91p8BlYwmXacLpYY5F676yLOP/dAnnluv3wny1tZOcfXK9e3Nh2HvCdBJ97271cr/23/zW1//2gsHBxUh4BGKAp770Ht/7ud/+qXvfu1g/+7j167vbe7pxu5t7u7+pd8H7/7u1y9fHbO0U3s5TBXMThd35qfHa+KF1/Day+9+54X16RICg5/545++/p5nVJndPrh3fLwfvH3o8oVnn3oK0IMCgA6mElQL+hS4BV3DpYtQ6e7ucnbcoy66hrRVQPRPvfchmQBQFurm5PBoNTthFId5lifi1ru3jo9m48Hmxb3H67X/8pee/+2vHB3WYGJcJbDeYO8C4VSlEsEJSfI0SaUKDk1n0BJOaX223JwONra2rNOHR0c+2Ml0muQZE9x6tD44jwFoFG0wDPV6oTgjXABhVMosL2WWUy72jw6d9845BM85lVxwzgnjLhDrgrXWOHs/0ZUQQkIIeF6L6RkTnJHoWxxv7XbO6q7TprPWhuAQMQBGhWqWFta7rjXa2WhFiPl1SAJyioRY70IAQjA4TwFpQIohoyIRnAIY5zDNurgrpkJwRSkHRilndVt5a4I3o0yNUkl0lzNxdXO0k3NJQufweLk6mi1758vhYDgeHRzc6637rfnfenBv/MgT/zowEkhvUCNQ5NTFMlPtqAtDmUugzJHgnNbauIAEgGJgSCmmElyzTMF9/wee+YEPvndDkJ1MKm+b9eJg//Ybb73+7h1bTuCpZy5duDS5fO0RUOPj4/63v/bqb3/95f21OzVggKAkggUpYMhgIGCooKSwkSZMpJAO1khvnC3ePV0uPbAMECHlMOEwZGRK1JDIIU045y4VloE1vu97hyhkItIMpHrt7XfP6rY2AIlgSWYAWudD8GWRSHm+hO+00VpDQMYYg2hs5BQBTQjOxz42oP6BLFnGRQ9Hxmxe8NOT+cc+/tCP/Nj3ffBDT27sTTmSEEUg6CGEYLxhKChQtMAJV0kaUZR2FllM4SfBAFIXK1QIUgwuBAzBs/MdA3pEQpFRCpRTBtZ67bS1FgJhVKS5yoqEcEQPztoY5EeYoh4DBE9Bm+BsYEykueJUJCqL1UuEEkq4ZIkQDCEQgpRCQEcIeu8jsw9ASSCA4B3G3CcASgAAKEGgQAQVxvRVXRvTS5kMi1IIwYAKwSkQ9HheO0t4wIA+XL36sEoSJWXkKZumsb0O6IqiYIxxypJUSSkQMTiP6CkRiKiNWa5WRuvYOSWl3JhMo8yjqqrF2ezk5CTCC5lmLIbQc0qRGmOc91wIJIEw4FLkgscpIuqsvLGnp6eD8ejatWuUs1u3bs3mc6VUURSEEPTBe0/OpxfbdZ1HFwgMh0MhhLbGBd+27Ww229zeXq7Xs8WibVvCaFmWWZZNJhN0LaIzxnJBU5YmBKRxnMs0LxHR9qbvO3BBCHXeisYzlYgI/SPzlOdF0zRd10V+OqLYtm0Xi8V8tkyLEgA4E1IlaZJRRrx22uD8ZOaDI0iyNE/TzGq3XM6W6zlyqNsmPsbyvGSMmV53XUeIFUKkKkuShFGOiCRgCGFQbjjTd13ng8+ydHNzc29vbzKZ9EZ77xeLxel8RinF4NqmWSxndd0ORkMpZdt3ddtwzj2GTvec890Le0VRxPLpwWg4n8+Pj48nG5taa2PM/uFh/EEwxgR/3/vel6aplDLeFfEjCyHm8/np6anz0HfmypUrGxsbMkmiKbaqqsPj09jbkOc5oyIOGE3baq3btrXGxOVPpyHJuBAiyVLKuQuBIAKlQimtNSGMcOEQAhBg3LpQNw1nMoL4ttOLxSqEEENLJRV5XkqlXOvjV2+6tbm5uTkcDYwxzpk46nPOlVJSyvn8DEi0ZZM0U0VRMLYReQVCSN/3JycnBwcHbdsqKwaDkZTy6Ph0f/+76/U6JqWORiMEOhxNnniy6Lpuf3//jTffappmOByWZX7h4m5R5MF54yx61F1Xrytj+4oui7pMZBob0SWnkipmCefUehO7S6PV+LwDou8Hg0GUTmVZhohd0xJCnv/Oi0JKmSgmhSLcBt8bbXW/ubl5eHzEKZtMJhsbtFquVq3b255MZbyTqTGmaqsQAiEs925rd69pKmPMompbYxHRM87SbN3bed3nCUsGE8b4rf3DPC+dQwCbjyeOkoChsTYZDEIIZZZU1VokwvbdyWJOCBkMh8h4rVePXr965+bNZ56+/sTjXFfrrMz/0M/+zGd/8ifa9fzf/5tfOz48IgS++fzXfujzn9t43xPt2d1seFm/81qzrE9np5OtbWLJ1ceuTa48+q2bd2oza5wbj7gspaHs5TduqP3DnoTZYv6lL33pyoVLH/noM7Zqf/YzP9k0+l9/7rfX2qSUD4vs4OT08PRMZrffffPNSTFIBOEQqmolBW5sSOvN2alRAHuX4WMfe/R9Tz/52JUr/NFHwFu8ceMf/Nf/1XBz+rEf/AHY4pAVOaSfuPppahjM6O1f/93Xv/pWfbxWfLI+XrnGXnno2sF6iRD6ECrTrlvtkZYDMRFq/968zItSwsnxbaeryURtjRn4kMkA3ukaRjl87A/sfOaHf/Dizna9Wn/va99YLeZ3D965d7zsHWxdhPc8Wz78RKKKwyW/9Qwj6i3sWnAWDg4hzcMTT3+o1ZQGfnI4v/XO7fW8bpY1w3Bxe+Ps8F6RwJWH84uX8nwYiKw6B5IPbXXWLter0xn0PhfZ5rgcqsH3Xr7hsTw7my9r4CptPbt9cLZYwKVt9sIrb791691vffe7tYf1yk12p9+7cfvs+ds7e2XXutPTVddoQUAQQB96q2XCBAndcj4/O2lXy43xJkMwbfPs+577wHvf33Ttt1948Wg+k0pe2rtwuq48MAIstpdQGjMSdZ6nMWPgflUlEEKUUsvFHCA2l5wnLcbwMO895xACUMqkVDGTI8/Tvm8ZEMDzAihAQggFQsbjcbCnq8WCe5mCWc6Xu9sXP/nZ7wd2DHtjqO2Xf+Xff+Mr/3J5pu/cOO5a0A0kKnUA08no4x//5IULu99+8YVf+def//HPfPKZ93xke2NnOV/15uidw0OA3Qfw7qCuq3tNYG1+CpPNIk+zxqaMySIbWIrUnRG3ThB8gP/xv/tikF80AK0BT6AoYGcn+dzW7165tHHxoeloIx+ME4NrR5qrj17Mti+5exWnaTp56OKFLVCbUCNoAqkAewTtmZ0vuq4pJ3Ln0iMgA6BdHd178qPXrvtHekOyjUsbYjgj1Yt3jpbvgEQQSiTZkLB03Xarat01fTGUkpLNsriwu8eAHe4fn+wfd02fU1AI3LlgrAwekTDnsetNB9q5VvetsS4gF0qlearUqCwmo/F4usFl0hkzW64ODu7N5kuVJpFU5ZwzwoG64EJwjossEIqMhEAgACGAsVcodtIHEJxLIRkBa21n9cnxYaCMIlAKqVScp0opKujZ2dnp6ak1R0CJUJlQ0ui+a9uUyxDLSYEFRO8RkDLBsiKXnKI2ru8E5UWWsQB113JOaeAuACKAsxYM55IQtjseJVKQYKkz1HaU0q1heWV3i61PxqkgQm5k7OFpqYNrjamb2Sffe31Rtb/1+1h/06xRMJkCJUgoI1yi9xSddxY8ICIQAIpISKAsEETCCAPgGNB47wni1nT49PXHrl66sLhzw3IvKFrbVdXKe7+3B9efeep93/chEAby8vDlG7/1O9/+2vOrlYaCQwPwxEPbq3bd63aQwaOXJk9du3hldzxOsze+88pkusPKycGq8mDbbqUMgoCug1LBZs6388GYJ4kjtPPW2L7VmkQ1CsvSVCRp73C9XHfWBQCecFEUnkvX6wDApLDeoXHW0SgJQ/RAaAAMwRNCPAEEQghFEqxzIVgq0QXrbQCA1CuSp0qpRDGpwuUrxU/+wc/8wGc/AYkJs33y9PvHjDGOwmrXN73rPUPBqUSHQgjJFQDEwmQAoBQIP3fcOW+BkCSVXHFCcLFaqEQIKQM6D54QQihF8G1bRc6VEEYQAAgnnBC2Xq4AaHAYrGOUK6UIUm37dJA3XW2tz7I8z0rOJYWYC9tTSgWLXgdCKVBAgNC2NaEYQrBWE0KE5OcJJISEgACUAGNMEHJeex5hmdbae5+p9BwldB0F5r2PhGsi0+ibNNZ31gIlwZ83PXHOx4OyHOQnJyfeW0ogor0QAgRPGG9anWdl/DudtVH0Px6PnbHRKGyM0e15DxcVfF1XjDEl01j+FeGaUgrIAwu/KopCKRXhYLeutdbA6Hg8LgalMeZsNlsul4CYZVmeZpxzRTml1GtTt81gPIgjY13XTRdbCxil9OTsLD4qovpoe3vbObdYnulmaWxX103VNECZVGmEQb1xaZpJJr33xBPOuaAC0RPqVCI4kxEvSim9D7FPRErpPcYO3fihBFeEi2pd13WNiHmel2WZCEkpjY4IAhB/83Vdm663weWTvG6rEMLm5vbu7q6UcjlfzOdzjDXTwCilNNaGO++Dm4yGqVJRuA+UWmu7ruuNHgwGbds2TaOdjZAdEbkUxvq4lIjjSkyhidqqqN2PMURt27ZtyzmfLZbxihNCsiybTqeT0SiGcMeL3nWd5KIsSwDouu707IQxlhdFNFuvVquqaeIW6MKFC1FyE7cxy1XV6lalifeWcy6EEEIURRG8n81m6M7peUJirq4XQmRZxvh5+XG8owghbVWvVl2RifuJTEoKEd2wGIATHvU8RVEURcY5184aY577wLNt2xrTx7u07/uu60Jwg8GAcRL1RVFTdy51M2YwGEwmE0rp6enp0dGRtVaq9GD/tBxMlFJxBdR1XZIk29vbVVXF31hctrRtSykNwUnBnLMxunRjY7PrurOzM8ZY1dgyV7EKLUmSsiwppb3pOqMpJxR+L65ECRFNF0mSxECk1Wq1XC6dsYyxwWiQ5hkVfLVeH54cr9frAMg5H41GbdvmabZcLi/tXYjp1JJLYxx6eBDWxO93w4XgjDGEYEx8AoKRSa2aNQBgIOv1mjHWNM2lC5dms1ma5pcvX66Wq7gdHY1G3obpdAwUD48OTmdzJEAY9d5LKcoyR2cfe+RqXy2P7t3dmYwfuXLpx/7AD6Mz/+KX/vnBnZt5Ij798Q//+I/9wMaTjxy99p1/9eu/YtAW5fAnPvPZ3QtXYKFf/PoL996+c/PO4Re/8zZJ2M7m1mgwTNP0dHa2f3y0ubdTdd1yvXr66aeJC6XImPE/9ukfvH377r/63OfaAMjom/dOEeDC3riru2GRM+eYtwxsojgw21tXDtWFx6dPfuKhz/zUpwcPXQIEWCzBI/jwzte+rq2/+vhj6Qc/DHUDQICyQPhr3/jeu//hpVsvvHt459R1RNDSebru7cFyEXJJypTkWYN+tqoXVUeJLJOsIDT0LQWrlOdMJwp2ttNLe2PB3LBQTz728Pd99MPyyjU4Pf7eV77+4osvvvnmcVXDpavw2Z9535M/+hyMPaxv+eaEpRxUBiQHk2GfgS/I4CKUl+Ge/t3ffuGbX335W994dX7UjMupoCIRvm9ONzf4H/zJ7/v+T7/37PTtt996fnd39PD7r8GELo5uzQ9Oc5HsbF2CZARn3fpoTem47cWXvvrK1164Z5kIvFg1gct0Mii3d3fuHR2/8s5bNEmOln05zQ5O2tE46TrNqZiUo3q+Xi/7aZ5b048nBeWkd1YHd+Xqw5/41Cc3Jhv7t+69+K1vT0cbRZ6fnc7vHu4HSlSWNc41gVmEuHWPRZZN0xRlNp1OY3REliWMsa5rQ0Cl5OnJSYz/ijR/dGE1TRMPsbjVj1r/qO5jjDjTgzOcBAoBgw8IlPLj0+ryxW3btWCb0ISMw8c+cPGhy5uPvu/aGzdep0Bfeen1G6/764+UguZvvnZUZmIy3M6zgRDqD/30z1279vCv/tqvf+GLv5VkeHh0anpQAoocmho+p/+LB/Duj1z8Oxcvwx/++U9+6PveT1IGTMGa1LfP5kerG6++85XPP3/rXRASAgee87PaWQosBUKAcGAMnAeloByAA+ASfACZwuNPDXb2ttveAGXWQNdhtTbrym5u7D7z5COPXx1DaK3uAP14mE03SsKd12u2O4WqAhugmALki3cOv/Kl51968fbNN7uuAQSRDzYGww1P6LqqVu16Nj/rehiW8MjVy8NiuJwtz47nwVg0Lk1knufW265rkjwbTyeEUW19p/tV0yzrWntQqRoMx2VeMAycMo/Q9kZb64D4EEzA6BaLjEySnksAQoDFugtAo3ErhBBVrxAwy9MQAgOSqiRVCZDQNE3d9Y4LKpWUkhH03jtnojBCSOkw9MY2ba+tR0ICUHR+lBUAwASnUjgEbQxQkkiVJtL1HUPMhZAArml023nviGABCBOSy4TENFFGJRfLxUwxSqzJOBlnKhN0lKYXx8XFAvZGRZZl2lkX/P+Pqj8LtizNzsOwtf5xT2e8c86VWWNXVc9Ad6OBRgMNkATQGE0SMCfIhKygSNOWrQgrbDkU5IM8KOQXO+wIUwyadFiiJVGcJJGYSBBjd6Oruqqra8rKrJwz73jmPf3j8sN/M9G8D/VQN/Pmufv8Z+9vfesbgOG6rk8XS5YVGxf/F7/+S8/Oxk9f//t99CxnJngPEpi0Hvq+pxAyxmxday4kQIxgHNkIKDQK3Nqu+mbJo6FmvVOwX/iJH/mxz33ab87yYG2zevTw/mq+mO5sX7127eLlS7izRYujN9/6zu/+6+/OZrBag3Hw0qcuP//aJ49nZ7rUB5d3X335ucsHW7aef/D2H9967731YvncjRe3D64cb7o33/v4vbsPz2oIHIDB1hiu7O9f3juYqMrVZnm8WCxWK99HBEKusrwajkHp2bI+nM3nm7YjYPlAVWUbYb7e2BizXJNrkSIRBSAEBgwROQB4GxCRA+fIFArGGAMEBptuwSUXTHAuFUrBAGIgMt6tXn758q/92p/d2yvywj732nV84RMFEaE/D5UHj8FQ9OB6x9MbmGVSnQeBA9JyM0+935FCiJFxEFpIyX30qVeCC2SSIaKPLgQf0QMAR+RMipQVGigEcsaHQMFF7z25CMAQOTAmMoEiJfeJTCqlstTJJRhHRCGYEEJwJAreex9cvVomsRAX+KwjNkVqEhERJsFP6gNNwCVYl6ycierIdTYYDLyPxhhn0gdMxRi7uqm71gEAw0yrZwWrGAMyYox1XdN3LQAkMYYzfWdsUQ6tj8mRyQiklHmWJTEGEUl+vrtJMokYo/WuLPOqGqZUoq7rEqBJZU8J6qXPfAoIalebvu9t8FrrVHC4qevlctm1LSIOq0FVVaZu5/P5IC8OLl7YtBsfQ3haeU1E1vskbS/LcjgepXVB27bL9apt1rOzJ1Jyxhgw1Dovq6FSWSCKEfremNZ4F3kyggJHFgfDLESnZJYE5V3XEUGWZTs7O23brtd1yjlNDVxKZnfuP3DWpyfNeT5jPO8J11pnieTuur7vyQfgKEpVVHlZlgi873tjDBBxzp0NiCjFeeaPc871xlo7GlR5nieBkHNOKcUET+5z491TWRq54BFRSimkTpNAusiJSEvipfTKkxDomcX28Hixs7dVVZX3vsjynZ0dzvlqtTo9OhZCSCHS/MYYGwwGw2E1GY8AgHFOROv15uzsbNPURHTh4BIi2hDTQBJj1FmmCz1bzF2wRCSlLLI8nRbnnOn7pNAdDAZayL7v67q21j6bUvq+Xy9X1sJ4XOzu7hZZ1nVd33bPok7TOGE6m5hCpdR0Ok7OZillluvj42PTNsPhMB0JrfVoPCiqChmlfw4AqqoqigIR33333ZTdmT5TdV2v1+um7a0hZCLh/vSTk8Ygpdk+KxtKRvnJZHL45FHXtUmgnzwMfW/6vt/Z2Qnnx5aSfQWBA6N1U+dlpqVK8akhhFRDur+/37btarFcrVbOkdZ8MhpXw4GP3njnvUfGZKallIQQY0z6vST84IDJMccIGUnnwmAwODg4UErM5/PVepHWC33fp4zX8Xi8vT3VWnvvz+Znfd9aYzjnRV4ppWII1lqtsrSJerZF4Vxynu4t3hMEisZa5w1jmGkpGPq+w+AweNs2wfXTweji3u7FnZ3bH37wyovP/bu/9leKGxcffPsPfu+b/+bo5HFj+5P5bLP2169eu7hz5e3vvHPz3eXWtugi57rY2dpWSj15cnhyMicGKpdZXqo8E8h8b8BEjTwHHhF6CD2Aid5GgvMqQ8FDHJdZ7BrbrCaTshqo2eL06vXLP/+rP/XaT1x7/95bn3zpJbducp27o5ksBs29x6YN050DGG7DlecAlfng9je+8/Ybf/Sd2f2FXbpCDMHxo8dn67oDptauZwPdY2RVqaqqMW4+21Dk00oJexo7uHARXn752s7WcHd3OB0X1nXr+fy11z9xsHvAAPvOv/3mW9/4xrdna/doAV/7evaLf+4nD17bBzrp2gdMNXrA+77NRmPgQ1gG8BXk+6e3D3/3t958eGv94GO/PAW7Yb4bjMptBDo8vPPZH7zwK3/hqy9/9sLxo++dze9fu3JQ7u1Bvzg6u8MFDFSRFROQJdQEc9OvYlYe/Jvf/c7Nu7N5DZhvPznrDKoPPrp98cLl6c72x/fvbWwni7LxRmQ67Vqn4wlYHzpTMu6aztZ9WeZCq8hR5tlwazLe3vn6179uO3v34zurs8XHH97uuw6ZmK9XqISQ+uHpGeWDyFV6DnKBT2X9IR1RRMzSIYcQPAFAEtOnD92f3CqdS3tgIQQRpcSL9Cnr+971BoPNlcyUhEjWu+CjLnLTteBdIbEABzZADzGAKMB6mI7h8sWtP/9Lf/bHfvnPPfnjb//n/9n/dbNslssuelYWY9PDbL1ioLfGk+X6UBdMZwzBOU8xwj9d/Qn0/9/80N/+qZ/9oa/86I+qnWmsN7Oj2R/8+h+9/+YHt77Xb9ZQAmxPQenRunOtjx1FQyFw1Jkqh+V4UOWFAnLIQqDQ931dr1tjGQOpRdv75Rp6A0yCkEAAQkCWw/a0QOirMt8eDwSPXXMKgcoMfvbrX75+7VJWDtZn9aOHZx99eP+jmw9nJ/3ZCWhdSlV54L2JvQ/IGZPMe3N6NtcCtqbjjOtM5eTg7PRUa11oZYw5WyykwitXruzsbQNjd+8/6H1YNc2q7jzAYDyc7uyOBsOTJ4cQKQI5gkjgAQOkAi8Wz03aMb3jAECEIJQPBJEQUaRFqDXBuhg8IuaZmkwmW+MJY2y9Xi/WK4vMBg8AKnFbQADABC/K0obQWte0fd0bFzwhE4xVKuu6LkRQRa7yggAiEAOk6Ad5JiIx5zIkjWxSFrvbW9/69h+PphPG+WJTt9arTANnrjcK+TDXlRZk+tjWmuPu1vTSpDjI4k4htrcm0/FQCtzU9Xy1bI1dGb928D/7jT9p8/1zL/w9w9AA1db2DkDkOisBWNfUbbMpJVZFfnx0mGW5UMXZspZ51dtuMs4ZWepr1tvtHF65dvC5T7zw/OULT+58dHx06Pou+sCE/PSnP/3yS680pr3/8N47b7/VLWu0xAL84Oe/8Pzzz9+9f29rf/vq81erve1mdvjB996+9dEHs5NZ18IXvnTt5U+81ht4/6P7R6f1vUdHjw47puHSle0LFy/u7W4zJmzn67o9OTx7cnTsggfkTCguFQhpAtWdq63zqJa9iUKowbCLcLZYttZkmSLfCw4pg5sIXYg+UoxRqxyIYgDykQJAJCSKADKXAZARAxICuGQytVgrGbUOg5KqEvLK5xmKrusgEkae3L0sckSGGPM8Z0wILhnnAJBgUwieMSEUV0ogonO2NX3b9J5clilCQiRBDCNHRj4EHwyTKW5MIEKM0UcPERkh5wjAIKaVYpJlYQBiTHS9QcSq0pxL586Z+CTK77ouhADnrl0fYwzOISopGRASUOorTjAOkSPwtFvwPhjj0t4nhMhY2nWSd7GL5jxZnytdZYyx4KN1RnCZZRkj8tEnJygROWcwEuNQ1zVjoIQUkifkGiNwztu2RS6TAYARJHtAEkYDQOTh2ctLNOokV0QUgjOmSzoKrXVCpem2nqCbtTZl5pi+98amt0NqJaVMYvq2adq2TYFdVVVlWZZLVRQFcAiUXl5M645orbW2qqreGnt2dnR0lLhq612M/uKFXUBPhFyIqhoWg4qhsNYfHh4555xxCfojYp7rLFPOdS46IMak4IA2eIE8AK3X66bvurYzziGRcW6z2VgfvT9/CLGn1fHp6gGAtdb0fbIQMMayPFOZamzb1F3f2acPOWKIIQSKmATrz4RGiR6eTCZJw6oyrZTKy0IIYYM/Ozt7Jv0XQhRaaa2zPGdMpJb7ZHhwziVLXOoiSEKjxP62bdv0XTnIE9KVUvZ9/+jRo4RBd6ZbRJT0dglPA0Df25sffhSiZ5yXZZnnxXA4rIYDAJidzUIIniAtdhhjkahpurquXbAhBMZYp9pE7CUSOtHMbdtGqWKMKb0nz/PUZwwAo9EosdTGmCR5dMY+e+qnPz+sRqkHer1et21LRKPRaGtr68nhY631sCzyPE/vCOc808V8Po/kn/nGVqvVfD5HxOFwmNT21tr1er3ZbDjnFy9eXCxr57x3cTAsB9VIZ7JrzWq96Lt+PBlKoX2weVaOxgPTu5PTI85Zsi6EEJ4Jjre2ttIZeLpsZJxzziRykPa8XiPtx8bj8e72dlVV9+/fT2Q/AIzHVZZlzthHjx7lZY6Cy5SIzDkRAQEApM1J+mIEnHMpBGfCm25YjUIIN2/eRKQsV23bHh8fX7l6Nctzxnnf9857Yz0yFcL5tU3uneFgLKW0xrRtm6zn329USwd1MBhtNpsQwnRn+7lr15QS6/VqtVy6vsvzEoPru6YYDMflRa1Eb6yQ1Vd+9CevXLnwxnc/+Obf/X+98703AoPRtAhI1Xg79Os/+u69qjwWvBhdn+jBZFpNlov1OqD0AOUw32XW+M47pYrIdURGQkBI9URgvSkmAyVRMm6i75xzPgQixcGGOJ6MfMY2zbKx6/Gk+vJXvvz5X/yl3/7n//d/9i//+//wb/zVg/HWt377d29/9z23bqEPAz2ajPdGw92Dg6t1Fz+4de90sfKrqLKtB83DxcmDy/u7e89fi4+enM5m2xfGLBeN61pbh1WXo9jPROgBVqelhB/+0eGv/pW/tPelL4LtoFlBnoPksFpCVUHfffhH3/jt3/7ts9P6wqXd169Odl8efO7HXsuu7sDmQdPOyosj4Nnp2ZNhNbhz5xE5uTXcazerhx+/cXI4Zwhf/PyN7vjjzWOYZHy18bMnh889d/kHv/7jf+pnXs3Hq83yo2o77F67joMKTGh9yLOxAsxUCVDAad8u+iLfyV5+YfHO3XJwcOPFS+xwfffx4uHhKc9HQg9nGxOzvoucqUFWDTn4LM8DhfHQRmtWq4WIYXv3IB9X3WoVIkwPDh4cHiHE/Z3tnYMDCN5bsz0dr08X3hkIMS/UCIaeosjy3d3dee9j6u7FSMQSBAQgZETgvQ/Ue+cFQHyaeBGJEL4v3id9ms6zwug8HCzdJRBRCgUyAjLGBWcqQuSMRxGI0LugmCiLUnnb98syg93ptGt9COHi9t6Ll69c37sCQpdIB+P80c2z3W1kUKyW67GevvzqZ/su3L1379LWQcB+U8+aDiZTuPZi/k//5CMCv/znfnz/4GLoAyzcd7793j/6//0T5viXPvvlv/5rX55ML0BQftn/wTfe+PXf+tfdas2JUqTaemM2TT87nnOGVy7sCY6SSR0VUim8Mca5VZCIFzKlp5oJbF3f9k2MEQM7ObbzZZSsmQ4b00K9gOtX4Id+YH+vvHH00dlifl+zbFBsXR89L7bLmeiGr1/c9LRad2fLzXLZMnQRCBGLosz2ueQouXDGu95Uurp66fKq3jCOEmF7e5oWyNET4zAoq1g3kvFhVYoszwaDTOkYIS8HMcZAxEKwIUYCiuABXExUJhFFAGAcGGPEeAxEwAgihOCcYwAceZ5xJCCKSojofLOpGWPeGcm4kqrpgunawLnWOsuElJJL6WPQnLOMcc6Fkr0PxFAg86053xgbFpEhY4TAkQVvDVGgIHzIlSwKPR0Nd6ajH3ztNeCw7nvGQRuPSjAUQcucSw1QaTmejsdlphjYvmu79eTSwcWtgeLQ10smcSiZKMSaxb5vx1n5fUcDyPcRoLceZVZkObLcBWaNt9ZjJK2V8/1gnFnnScTJ7qTu/LAatWZVKlYNByzr+rb94O7hcrn83nQ0OzlqWoAAwQFjcNy9/f6jmXNuVW80HwXJXnn+6te++AXXro/u33rhYHzwiUvQLte3HpycHR/d+WB9PLu6P3r906/uXJqWW9Pjh6fM19LVOwqnF8ud3f0XPvFyMRwEBofHZ4/PTmaL9WLVtNEVeQ4heko2WiRgQsmci3Xncy09CgpBMpyOqtJmIbpIQmuptQ5AvXHe+1STnJg4JE5EHDgApPZlRxARGXEGGFEEFECcYoy9cy52nV2uvBA9F1FET0QAITAgAmKRMGIkzIssPW4TZk1Im5Clc2m8Q0QCUpnWuQJGm3qFCJxjTCGaMVKMRGBtVIp5DohA3sUYGXDggoBCpHDuwlSIHCMgxUJXxjjvPATwPnZd561L6Mc550wfQkBGQoiU8odacSW5kpwjRpEqMzjnpus5Z1IiQ44COTCM6IkRQ2ABuYjAvIt9b0MIddMVWa51liT1prN938YIwIhL5mxw1qb7o3MuQf/t7W2AyACf8ZreOeuc9YEJldapCfebtntGdgYeIlAaYxKc6rouPn3NeZYhnlPUBHDO0wMQpC4PFp9KEVBw9lTNorMsLQQAoGkaa+2oqKSUKZem6RsmeFKPIOdS6xyAc84EPycy+w4Rh6Ph/v7+1tYEyLfdut60xlnn3Oxssdk0i9Vye7pdVZUe6xCCba0xpm1rY9FFa71hwMVGQiQXPEee5flwMAgxchTD8WhQVlwKb11n7Gq5IYJ0NzmXjgSfKp8YYxxQxqjSJYix78zu/n7bd13XheDPpRchhcme73bS+4KIyaU9Xy4IgrW27XslhCXHOfcxEMbzcwmBkHEpVSa11vP5MgAld29yVCelh4uBGEYCF0PGUCrNnPW1f/75F+fzeTK2Js8rA9za2mqSWEhpIUQaBtKIcuPGjdVqtdqs+95Y60IIvXGpQSLGGBGklFrlyWPgouOcC5Elnlg8zXF4tt8gHzBScD49sQHg7OwsLVWeCc/SJENK+RiRMykF51xKmequtM6zLONSEEJZ5oPBAAm6rpOMl1lelrlSKjlGzj8LxiTVfsoIqus6VUbUda21TuGwz/Q/eZ4jU03ftU0HSJ48ubhpN8vNCgjvPphzJkbjYW/t2fKUIS8ynSkVoktlIKUs0wYjxpDmLoCn3jWigAEDSz10FGLXddbazWbjrU0bMyHEZDJJQw5jTDDOBNe5ZlLwp9shKWWWZSrPZieniCzVoFnvASBIKYUaVtOirJq2ni3mzpnJZOSca7rm1u2bN27cuHHjRlEU6UM6my1OT2ezsyPvHQCUZWk6m5Ybfd9zwOTFEiJZ7BIOQ9P16dfZbDb0OBRFkRfZ3t7e8ZPHiIhM5sVgWBSDsvK2XzX2D771nWaz7vqNdZ4JmOyoTIk7h+1kOmxXYdZA0IXeusRl1rS259livjk9PQuBiqJAwTErQAL2fe0i904QIoDiWnNBEiCIRd+QAxAShSAuCFlwZKO79+jxc5f2Kq3KaqhzEhK/89Zb7j/f5GX81PDGgd3X9eDk7ZMH355jB7mESy9urz5u3/jo92aLYBwAx8l0d83w3eVRLLKt5/Z65O88vNlt7P6l4spz2zv700ePHhw+PuoaCBuIHYyLYu/ShInFVz733N4Y62/86/l8uX/holLlx3duVYPizXfevH3n5smSdAY/9BNXXnn1peP28f6nsoftm+13Np787t6W5HvGsmU7HIyfm2xfUCQlCHSby5f0wfZ+bLzq1I/90KVrW+7eLWdnbVZk2wOdCXdycmf14EMnH0/3s90LewO3UxTbxXTv7d+9vT+aOmkpuFG1XVx9CVZh9t07f/iN9+4+nJEar3tcrI0NjJkQQai8soFFkCHGvvO97Tbrxtvemtb3Pkf4gc+++pd+6Rd/6PM/gJkGgrt3H/w3/+SffevNN05PjoaTcV3XJ0en85PTs5PT09PTYMMWQ+NDayy3LjDOGEQAIvI+IFIC/QAxV9r76L3z3hNxAEh8VtKRJm4lIUeAyDmG4IhC8vY8GwCIKMtL8AwiAXCKjCIAcQTYbJq9vX1y9uTk+PLW9Ktf/YkM4P7NO4o7hnEEqn5y+s3f/K31w4+W88NtTV98vSyzYZFvaTnkrOoaenD/ZCLg5PTxGcAXro3/xt/8q8+9dHDvzlv/x++Ddz/41a9ANYH7h9/8jd+9fevBte1rf+2v/XXIBu2Ts9//17/z6O4hMrVuLS/Vld1ry7Y9Xizb1dy5IBGUzkrJpTfYpWh1UYpimk9YIQDY0cmxYJw2vrF18I3AwAQ4z1leqgxLrQaDKmfrIjaXxuPLo+eX99t7H957+4/fb5fw/JXRKy+89vm9V/KXt989OiLWbtZ9sMG54H2MQBSp7frhsByUuZaKAe/qzveeS71/6fKmWZuuz6pScu58XG8apdRm0zR1470TSkvOXW+CJ6GcdY6IPIGN5DzZGANBIALGE3N6zoZEEAyQAyEDREBGFCgQ46iUKpREAqQIkShA13VppaOlYgIdxAgkGZWKZ0pzzomx1houQSolpZBa5ZGIM8m4z2zf972xARkKmbyTgvEi1woi9F30dtM3zDQ6Oug3Lz//wmw5a/uGYWQ8EkRkpJSq8lIEp4TIsiwvqhjsZrXczGf/4nfufOVzr3zypev7ly+OMlydHp8ezVrjMGKu5PdD/1FRKMAuNsg45wqE5KQEes0haM6w7/pVUaimbSPGbDBt5ytdTKViERyBlnkBRJt1t37U3T/pQoQ8hzznLI9A+HDZHi4/stZ3xl062Lqxt1cMR7fvfLR4fEcze/1K0T14d7Y4nW9WddPYZjYq4Oqlg2svP2/MzPaLrpkrtGMBXDIh9YVBpTaN4jxoJRAYY5EjK7JC8thbjiiFIC4AhffR9qaxLhITUgOA6ToSotRKSd40ndI6y5TKchdCdNFS8oMzkahkYgiAwJP4hxgRj4xHDsgY54AMiQEBkOA6hhiIGx8iKg4k8lyHEMgjBcCI6RxDRB8dARBAhEAMAQAVU8jH0+2UQWmttc5472P0AKBkxjigSOGeDCAGFiTIxrQhkDEOkRidq3HS2JBuNwBccBRCcuACIARCYEDc9JYixEAxUAq4pBCJAmNMSpHJ82IzYoQMYgREEIicKWQEACkBJngiRhxQCCVFDgBMiqQTQOQ+hgR/icD2VogGkTvnkkxCCCG1cBB8NAy4lDyZqDhiCsfcbDZt3SCC1rlSwvYO+05p5ikmCjMRyYkXTMqNNDx4cgmtcsk606bvPmXB41N5A34fH5O6QEIApOgVF0pliGi9W61WvGm01m3T9H1v+t5aK4EVRUHON11rg+VSJEYHvo9GPcci7Hx0SRZVxljXOusoAgITyBhARM6eEc+Zzpyxra/beu1NAE7FqEimk+gAAQiAC8EEl0oxigw4IfTWRNs7Y431k60pEaTrY7peSmmUCs6nn18URZWfVykjYkRYbVbIWRKQJOgPkZKuPaUqnQcNJUM3g/l8xSQDAGDoQqC+Q0Qfg2AcOWMIESDG2FsbiDpjNm2X53ni6ZMGKUX0PHnyJAHvZNXY2dlBxKZuj0/P0huaWPAYoxDnZoCUKZSqDxBxd3//woUL29PJarU6nZ2tVquEVtN7kdQ7KLj3frPZAIDOszzXWTbmkmVZllYBzrmubc87gwGF1kqpNDomjjyJZ9J87r2PMfJ0a1SKiKL33z8VdF13ejpLhVzGGMSJ1rpr2tVqVRV53/fr9TLLsqooOefr9bq53+zs7QohnAshOMZEURQxAkAcDEaI5H0E8HleVlUVAj15coSMeYrOh5QmXpb5eDIajauuM/P5GQAriqzr+vn8bDyeXrl6sd3UxkCMERDLvBJK9m23aWoGjJ5SlkQUYkxphU3fZEWeysyfmVjSKJiuAxElX1Cm9KgYERJwxp42EIcQvHMAEENAxpSUSVD37DIS4tHpcbPZeG+dM4dHjwFAynNdRNodEaHWuTPJXyGQRwqx702MK6UURwaEnbWMo0QhpOdMcMGS4BAA0pTb9M2jR4+Y4JcuXrh06dLBpYvr9RojTceTUVn0Xdcbr8rRctXK0WR4sOejOTk5Olx226Lc2d9brheceDnYn+7s7xxcWjXd+tFR3XXeA8tLKRRTsus6aywiRoBKZhCiJ9JcSJ1ppRFRxrxfm863wRuhcqE0MgE8AMmrz13PBTjTKMEn40lZKUR/79bjv/zzP/cTv/DvwuNH8HC+Jy81k03M7d2Pz767+vBP/6mf/uEv/KmP7zx553sfLmujs3K5WbgIrelPT3tOcGEHRkOYN+3sg9u7c4geAoBAqHKoKhgwz237wov6yf1bv99uBtXUBbz53ocPHj5++HjjGTw6hs/+IPyH/9ufnrx4MTRHxMzlnaureFRVmOUHwMSy7h8enVXFzo1rX6zXdrK1A5hBs1SwHl26AWDgyROI1YUXtz71Qf+b//y7dnnMoJLM3Ln1zh++sXr5c/CJz2Yyk7XpmrPTpjmrz8yTt++5i1Rmg8Viw+JROThczNqPPno0X7nvfVCDPpXlcNND33vTromnW2jDiHSWlWUxGQ2IQgxuuTjLJ+y5Sxd+7MtfevGlF3yw99959/ade//9b/5O5+JoNOJaAQAXOBgM+rq5dOlS9MG0RmXFuu2w2ZjgvfeMy5Rol0Q+jAMiIEKShxB4RAap+AlDjKSU9j6mE/6UUTl/xKSHCzz18JwvmaOL0UMkD8EQxAAxxohxNB5vmjr2XVVVo8m0N54CVOXI9it0Hoxv+vWH9dHJw5sxtkrAy9evz2drgX4yzJqNvfvxx48eHhPjA4a/8nM/+eJLV/7o9/7N3/073xlNAP7mnwh+4Gxz9s7d3/iN3/32H9/zFsqB+tv/yX9qrTe1YSSrcsJFZiNgpo4Xs43tO9eLXE62Rwc7e5f29/ZGg3e/9YeSQUDPIuQsDDUvsoJzcXlSGWcW9XLZGGJDmXMT+nnTLho75kpYYU7baBy38PDW8n+8/wfX/r1ffunGZ2Qofv933vjmG6t33v5DJf+wJ2gK1gL2lkzACELoQmWZlHJQZoHc/YePkWh3e08yyZTOB0OSfGVMW9dFpgTjtu8YY1VVSaGqiucIKFXv/LrrCL0mXDVtRIiEIZJH9BE8QQBSUgRCAuExIgUk9DFCBKk5ACABIZNSSMnzvMiUzKTCGCjG5AdlDBhjyGizmGugosjzPNdaEsMQfAykgACBgICzgJwDohJKSEPIGBBCa60xXQCEVCkWgwLIgDKMmWLjUbW7PR5ozSl263oxm9em6xkLITARtcyavtPIKVp7Zp4cHodoiIJAgUy+/fHDs+Xi0y9eeeXKzny5WG7WeTWaVgOvq++H/kwKDMQ4tK7vGgOYKVkxxqLtnF0GaHm0oTVoAagP7FSRj/2yGmT1plm1HS/KKh8g8r7t+hh4jmxYERcUAycwfV83FiL0Hqqqev6F68xs3njzm7Htr1/St+98rzebCGFV29UadAYvvXDp6sV9Ws9J9ba1jOzWoKhI5x4h8JxhwRGsNda06029Wp/OFsuudy7sVgNikMhrLjRTCFxLF1ZNj0J4AKRgg4PeM6KcQZmwEGEMJAgl48BjRJZVBUUMCSIGICICFtFHNAwRWXxaZIWBGBF1jXG+D74XjLQCJRBf+9T4GfSHANFDsDEEYoynMEdCSCstluhDLYQQSkjGMMZojOn6xhiTZYox4JwzjhzOZfcBAkjsbWd7A0hSSi0kAxZjlExaE01nQwCBUggtuELk63rDGIRAMfo8zweDEoi6rnPOckAuMEkakCCEEMjLTKYJRAhW5ZnWOrGto2rkvXc2hBA48iwr8ryUKnMxNG3bdZ33Icbobcp9p7SajxGcc0kmwRhjAnvfMEFlXpVVrmQGybfAz53HzlgASPVGSMwFT5wZZ/uUwh4pMdnpee+ciyE8uwszxoRgWaFD8El/GSM9S1ZJcN/Z4L33PiZFCoWYaZkrnZUFY6zp2q7rGOdFUQTvETFln2dcVlWVSxWB9i7tP2WJfPJudqZPkxsiCnXuZUxo2ForOQIQY4xJgYiRIITgKUbnBecQYls3TV1DiGVelYOisZ0jL1Cg4IoLrmSZFSrPMFIAggDWewohEAnGCPnW1k58+nXeQUKEBAcHB+dd9F2XnADpmVQ3bcrmfwbO2PlQFBIjnlRMzjkOyDkuN+uiyvM8DxT7tjPOeu8DkRIiJWkiZ0iQAGWM0Vva2toaDAZJxpMsBwk3A8AzeXfyrXZtr7KcCXVeUexD13UAkGVZVZZ1XbebOr340Wj0wgsvvPD89ZsfvO+9r9tmsVhsNo1zjp3r37hSigmVVkNJvpVlar6ah+ieGSFCCLbr+96UZZEpncoHcq3Tt4xzKsuX6/VisXDOFVmW+gTSwW7bNliXtCiI+Exqv729HUJYLpdK8uFwmDLyD/Z2m6ZxptdaJ+FQvV7PFgvjrC5yziUiFUU1GJTOhdVq8ejRkyxTQigh2O7u/t7eToxwNj+dzecBgveRKCiVDQZllhWIdOfOvdFoUBSVtf16XbdtHQIh4sWLF9M2ozU9+YCCCxSe4uGjx1zJTGp8GhrGiEUE6w3nHM7rz0BrXWRZ2rSk7P8koNJaC8YJoW5r4OfmjfOj0hvj3f7OLlcykyoAJTkfY4whj5GdzRbO2LLKY/Tr9ZIjjEajC/sHSqmu68/O5jHAeDwtssIFt1wvUCAHPD/JiFopKaVzjihQgBh96j8XKBApDflSSiaFj8F6ywCFkuPxuGkaJBgNh1rrrm6aTZ2KDObzORc4mo6IQts1lFL8fLhy7ep4uhMIHKAHtqyb9XqdMRai11nOpEjjaK4UAogI4AIn0FJpIWNKfHK9kjGQdwQmxrazpne5yIZZcWE8apbzxclj23ZZBsOxunhl76XLV9hp88VXP/Xee98tctV3G8aprZe3bh2dLeBnvv6ZK1ev33909PY7H5zM1pH4vTMz17BzY8yi864RAuZL2L8AP/+Lr3/xhz5VKPHR+x/8xj/91uIe/MAr21/+5I9MxsOPn3xva2/85PHx7/zOew/uw9YWDEbQOGAZ/Dv/8y+88OOfA1xAf9SHRWNmXVxPr0ytCCEiimIw3pdwGWDgIwq2ExvLUIBiUJ9BdwrQQohwuFk8aN7/zuzd75zcu92sFpBlMNlVP/Gzn37+tenO8xXoHpSActyvzdm9BXsS3/r9t95/77Sq4MaNF4bTvRB479i7H9y7++hkvH1ZlqPHJ0sHUmflhauXv/nNb67rmktRFIXOs1TJxxh6ZzbrOQY/HpbDMtMcgzNtbztHeTkSuT5dLK6/+NLnf/AHosOTo6OP3rvVt623gYDZSAHRRL82piH0gDF6oogscI5cMMbA+fPoAiklZzLd3BA5xj9J8Ey3mnQjTU6V9C18+kRDxEDeWosBJFcSZJoWgAFjkQvMGIoYuSXu4khmk1zXJ0ejQn3y9Vdef+35a1f3g23e/d6bs/nZ8ZNDhlyKcjjYunjh2v6Fa2UxDuQvXbnw8NHHdx/eCbG9dv3glU88V/5K9gze/db/+jfffuu91bwdDrasobv3TjxBIGAAW9PBaLh1fLZ4eLyKCk2kyBmTLMuyyWCwPRhvjYYjLR58+N1xprRUAhAjCmAQonfu2rVrXIjAoA8mxNi67ujs9Ph43nWAoBA4AnBknCNDR2DaDj79mQvVqHp8eLSpW6krBGkCresehAKmbEDrWGRMFZnKtTGN82Z+dgyRDrb3BJPexLwcrEx7eHbkWjMeDYdFHr2TnA/KSmvdG+cJQEjnw6ppu956xpxgAYGQBcIA6CMl1j8QAEPGADjjkOQAkSIwqcgHiKQYL5TMtc6ElAjgYyQPwQOeezxCcOAdWiMZFnlqnsFEZBBnIKVDMhGaGPoAgTOUQnIuUYYQmt5sur4xJsSng6J3YM1A8oHASogXr1x68erVQsn58ezeo4d3Tk7WFLxSjjFgUkrpN30hhCS0bd23NQqcTifb0+FrL14/vPXB8tHtFw/GP/LJ54eamaYWWZlPD06b8IP/t889Oxs/f+X/sXG9z6UF5p2MQVAUFCMLnaC2lP3WRK+WbSQABnUHKLCxFAVYCwpgVBaVLKy1rekDxcGo4ByjsyL6oWBZDDnBsMzzcf78tSsi9KcPbulo9qf5eCAyTctN3Rs4OYH1Cp67Aj/wuRd2t/ebbrNzeTxfzedH624VfI3t2jDQw9F078KVw+X848Oj2yenD+erhQHHuJRyf2uMPhAil3mWD2RecqEi8I8+vheQAWcErLem7XvOeVbkjHHvfee89c5GcgSWyCMyLn0EH6MLlAK7KGJE41jNWEwpLOfJOhGJaFiU1vbe9hCD4ICMBCJKJkEgCMaIBQ9BxOgJAAGRiKx3qdCXE2dSHM82THCtZFIhKy50lkmlrOmIkMhjQE+ECIwx4HxQVdix6ClGz5CnvVUIQaBK2C4E75xHdJxJzqUQIjnwkskSkTNGAJAW6Il6T/ke1lprDRH1zoToiVTUqd2aJQb0KcPBtM6rajgcjLMiPzw5jqk5LwQpZTbMGJznwxDROch26e9CBDfMcy5ZpnUSFbRtCwFSYGWWZWU5SGRqXTvJhdQKOUtSfs7T/oOlZKEEyJLjGFJEF+dS8kgh3XaFEN6fN4+laYToPKcr0c9p7zEZDzlg6i5IgCYpHAbTKQDY3mw2G9f2bdvKkuVlsVwuCTG5BVIGonE2hLC/vx9CIDzXtwAAS6ntTQMcBCL6kAwGSZRS6KRCQY6syHOBrMjyrMjC4kSBTnLSdGCMd7GHlIfDmUxLhkLrsizzPF8u186F9Mg5v0qARPT2O98VjCe9BwCwtPpgrG4bOi+cPe+xg0hE5L1LtloiSjH80fkAgQmOnAklOZExpm+stQQAIVippWSSESFjTPAYI3ly3m02m6T2CSFkT3FkYo6JSGud1hFKKUB2eHyahqX0BJ1MJskHXG826Qqnd79t25s3bx4ePr50cCGJefq+dy4opVKWa+qg41IOBoN0/Y2zxnQXL15s2s16ve66Lm0GxuOxEML25lkohwFIVpCkOEp5NdZaOPecxBDCernq+55RaoSQ6ikeFU+LujnneaaUUn3bGWOePHliraVwLuaJSTkU47putPPp4zYej6W8yBiLEba2tvI8996v1+tHjx4lkf2mrifbQ+MIMXAuskwR0Wx2ul6vq6pKs+5ksndwcLBerxeLRdM0aSTetG1afeR5ThydczwNpVIxxrz35H1Su6aaBW+fKugSNAHQWg8GA8nFM3mDlkpqtbWz1Zq+ruumabyxEGL6NKWrmvBQD9iEru/74COBUEpoLaXiHGVeKA4khHjy5ImUkgiccQBstVgu/byxfT4otchElnHOg0siLuaJS8VjjJFHFs7fKc4542D7VMDOFUPGmLMh+RaOZ2d5XnBkp4sV5zyTyhi3Wi2Go0pMhkKItXdt23JgAvPetMN8cHpctzXovDQQVm3XGBuiM4zatgHOZa6kUNWgFLkGa23fsUBSqkwJIXjfu971valjhCxTWV5w41fLul63eqgG5WBrss29F86Hqum7xWphnX9o5o1c0x/89ttXr023tsd37t7Z3skA7atfev7hw4c3H374rXffkpJlw+GLu/uT8c4nPZ268O7t21z6rBIo/V/51S/+/F/+GRgjxCW0y/HBjS9++Xk/D49vHr/7jT/66DePr1yb/pP/cX56Cp//HPz5X/mKUPzw9JGL9Y//xV+AIYI5BliD6I6PH0Zhn/vcJ0CHAiygABJd1zT9Han2y/ISAHO9kcExzaHrYl0z3oMQgOici2Sn23n0+WM2854GlXz3ne9aOPhU9cL4hQOYDiBG8j0Gzn32tS/99M98fQcmO7Da3P7ozoPDExcF06UshibEoyfHH99/OJzutN3h4dEjQV6D3R6NGefLxWLWtcaYLMu2tiY8QqYz7+Ltu/faJjAGHCEvsnsPTgIDmcvPf/FLWZY9Pjm6e+f+rVu3OHDnQmccE0IUmQeqgw9CBwSiCBhZekoTAIGUHFEgsnS/ihG89xRxOV8HT2nafKbtSZR/QvzJw3ZOtXAI4Ig5hpJxRCIERMCIvndmezDiQPVsAa2d5sMQ4Xh29pM/8eXV/OjB7MmHv/7hqCwkw/nsLM+1EhUQ2Nqd9vNeDjtZWHd4fHzIvhUuXd6/8dILBwf7yMLN+4+/P9zzt//1u9vbO0z2t+4eSZHvX7wUgrt48SIAW61b46JnmcWV86CGIxO98bZft/2mW4n5idI555OyAqFBCu8tgkd0gCFA/857b4yn08nWNghV92bdds6yMp90mwUDPxqOJlu7McJmsxGKT7YHTLOe+aOT1cbKoKZNazZdg4iDXAnkPtKmt+vGmBBFrXSuEGNe6O2LF7SQPLLVYr2aN2E+p0z2MaJWUYjIU7s8brreOuqsCRF0XlTDUT4Yr9b1bLOOQiTCFuBcdEExBIKuM1wyzqXgDIQAiJyAiFykmEQgCOHc8BF8jNE6Ch6iJwqIGKL33pPrt4tMAkgiQZ5FJjlTWqu8cEi1ta410RqKGAOj4D1jxSjTWgqtmJLC6AhAyDmSAmyWM+acMR20oe8mwfW9s5vNxvvIhBJAXgpiLIAglCxjxsfeOu8xcMWVaJCR8b/+R2+FesFqWi0Wy9m3P/387oW9Pew9i+xkuf5+1n/Z9G3oe1+rwSAfDsjzvg4UfZnLcVa99vyNC7v54aOPnTV1bR8emghkOlhFqAYwHVSa62hicIFFDpy1jVUadsbD3VE5hJA7OxK8ysTe5Z3Do/uuWUlmBwN+/ZWr167urzczPHrYdpvhAe1v7Tx/9flssgU9FZu4PjupVwvfBsW0zmXGsxhExPj+zfcfnp7eP1ktPDDJtsYDXU3zsmhXZ1wKrTOdD4TMfYBmU3e9gxgQokAhlFApxUqIoirazsVAZFzwjnMBgnHkFtFFQkRCjinIFAEjIipPOlXFRmQAxAAiBCCarxfeewoBIgkGiCAgAAGdtx6d82PBgw+BuOSMMYwcHPPkgSHj7MKVi0mq0fV9Z1oG54tCLTlDRgGIPCbDkJSMgzU+mcUToosxQjwne4UQPBeMeQshhFT6hYNymPCNFy54t1mtOSaBBxKl3amPiCEECJGIuq4jds4HhxCMcYIjIs43c8aY5EoKhQTB+bZpjDF109Zd1/QdImqdl+U58GLAnXNda5qmiRE445mUQkA11iiII+v7vmnqvu8E8nPFsMwEVxGjViC4Oi9bziTz58IDjk+LpejcR0UAgMifon+lRN2uGQcppc4yIGat7brENDNEZCiUUlrnCeohQVlkwTpPMRHDiPjUZBwTMk6X0RizieSCF7lMSv9nshZCQMTBaJjKaNu23TTNucoIUSiVZgmhpNZe5S4NY970QohMpCglIh9iCD6E8XgcGT41XCUlIjrvk/ALWHrV0FtjnMXV0lqfJhAAOMfQIYHMWOWFzhVjzBjTda0xJgZgQqR9FgCk6LIktAjBJ+jPnua7B+dsoPFWgZxHAGRMal0OqiyEpFNnUkQA5x1jTLBz1VNVVSGErmlijCmtEhHbtiUA61yyDmutQ4xN2xJBWZYhUN/3UspMqmdE2mKx6PseBVdKRefX6/V8Pp/PVXS+79t13bRtyznP89wHCqFPuFPQeV4nERlrjDHOmxi8ZNwQ9L1xvaEsL4piOh6nQ2Wt7dvOYE9ELvjWunIwSHlQbV33ff9M2YKITHDgLF09rXWe52nzY61N7D4iNpu6ruthVXrvJWepJ8s5p/M8L8usaNreNPWybVuGvCrXRVFwJvpu3bU9Pe1NW9ilUkprff/hQy5Zsrn76KIPve162/m1Wy6XnPPd3d0syzabTXoNPjoXrHG99YZz6WMw1icjwTNjbqAYKBIQ42y5XEopBeNJ7/esdbhpGiJCBX3fL5dL51xVlIPRcP/CviKVSWVZHxlXQhZFkWWZlgoAKJ6/fvLBWuusT14dybh1PUcQkhOCdX1R5jFA9DFXGoAxFFLLwXjUxYhcEApCLvW5rIiITs7mAIAIkvOkTeKRWIBIkQtOHBrbtW3btC1wlpdFiIRSIJfAAnBOUgEwLPLHsxkw4pyHQIyx7fG0FFkMvMgHx4cn7dpduFhwIVxn+7YFTnmVAUbrg6l7H2GxmQtCW9dbgxH3seHMFGXyUYQYpBRdvXCGaRdQZsOyEqBynTGC1Xx1dnjarhaSByGw1Ax8fHw8v3b1+qpedrvDm/XZnRbO6j6T8NKB/pu/9jdkpgEY5AUczz/4zjtHT45ns/buzcf0uJMjgB6qPfjcq5fhYu4evRl0F4XJ9gpX+/dv37x5//beixf+/T/7P429+OW/tgfow2a+WB7fe3BzaR4IHc3Z97TVoKP1XefbstxTuYI583Z2fPKgCXbvwsXRweW85E0zXzbdxx/+fpVPh7oi04JdToYyz2N/Ume4t3v1BSUujsfLo+HGu+7evebhw+ZXvvqFi89tjccXIBsBcSCbqWxrKE9vrhZtf3bzew+ePHl0eHq8WD087o5msHXATpcRxeGmg+F08PWv//Sjx4e3PvpwM19OcvGZV56XWf7RzVsnJ9QLNtna2draOp2duWA729d9QA0XLl/YGk+GxeDsdL42/aZrmOBt3xdVeePFF5xxtnez2YLWtfOh7loXCbQOIQQEAEIWYiSM4H1kDKuqYpwQuFJSCBEjMQZEyDkDiEJwpYT3Hi3FEABjb4wQgnGGjHnvfbAQAAIAtwQxKYU5YYweMSDS/sHO0fEjcnF/OhlOd/ymZ0zs71359odvcR058I3pN4BaFHUspnK67F1nDedyWAzvrvt3D9+aLeZtt5mOim/d+sD8y19vGwoBpAT4j/5E8LPx29hWrUHDJqu6e3z2+OXnnhtWB52xy9XSAx9MLjw/2edF8fDkBKwJbR2DJQLB81wVlS5NwJXV3PnoKWNQZAyZscQezWlmZoPGM1m2BnoPWm+X4+LC1iUpxXCwlWfD5bLuzXFvTb3ihN4401iS+aQajIGsaVfk+01zJiVxngPL9HCYCamyTGoRyYVoguAdUb1a9r0RRY4MDcRiPMaIgaIlKpWGAKZtpMhHw4nOcy51lpcoldJFFPLxZuMJCQEYpwjEIAIjIJSRGCMmIrBInDGeAn84I2QCQ0SKxgUWKHAmAMo8B4gUvffWey+A6TzPWFUEK8lzipxIMy6U1ForrZZt7fuua1vnfeQCUYZAwbnFcq5VRoyf82tAEREjuUjr9TqLUbreedusV5vVMlp3cjyvjfMBDKPWU89iwIABhrII0UUkkhlq7RnMbZzbNlhf8HJvT8d6du8YLu2b1z59aTjeevP9j28/nn8/9M+qIRcl8jpytL5zPTiPCkggCfJ/+se+tFVh8/LB6eGTs9n6wmh2eLjSzMrtLVEMeODtquuslXkRkJlgT+cLBrC/demFC7s0P8N1t81RUd0dz8Nqc/3K1rXnLmktrt64LHcn9gF180dMD/e2pi9cfU6qHM7m3XzFgErNMMvHKhOsjF5bg4t1d7pY3z16cLayJsJwqFg59qgDCHKphpknOGdtv6m7s/l6tW6UypgUkWKIQQqRVzljLHrvms57T9ZDDM5HIk4MkHOOjBA5IhACAlFkxAIITZoYIhLDJL0JyAghIkelkCFynsK6mSAIiAgMEDghxUgRQqC4ajda60wXTKBggkUAhkxgljORZXkhAYCCd871vXXGohCAABEpshg9T+IyLtaLTYDICDkTmBoXCBGYFFoJzbnwPtouOOcZCim1cyHxr/g0YJELTGEsMYZkJH1mEvXeuxCUFlJqIDS9N71nHAXjIUQpmeCcQPTWNe3C2mMXQjkaGmO8sUkenfJbAEAJ2bawCZu6WXd1k3jBrKystwKAAGNABJFn1bCqJuOto6OT1WpjujMp5XA4HAyGDNB6B3C+V0389FPbZZyOxilgkzFMhXtAjIDt7h70fdt1XWdt+rvWemPcueYEYzJmpatGQKvNMuF7JnggH8g777nhdbMOIUguyrzIxpX3HiJFjE3XyRgoDV/EAnkABhBPjs8IAkWUUmKE1G2MiFLqGJMjls4F5caSDwDRGtMQCWSSC84QI7jol+2aS8GYSIgHABAYPS2KT0aChLzbtu/7PsXnW+uFYFVVaalCCN5YxlgTyTuXVGQhBMG4kLpu27TMYowJydK/kuIpQoiMEbLzFYpjDL1t2k1v6q7JmBAAQE87z7XWEcEYY42PQDqCEAIiMQaIWOR5NRgc7O/rLDt88uThw0eXL1001mZaT6bTqiyNtV3bWueratgZo6VMuTSz2ezJ4aO+77cmU60loiKitrNS8itXLl2/fr3vuvl83nR9CukfjUbOxxSJk2VZBJbKXJN7QSo+HBajybjIS+vMYr5MXHjaMqXZDBEDRYrknOuMuXj5StN1x8fHpuuEEKmnwnR9URTn3j4flsvl+ZZfiaqqpECGEjH2fccYG44GO7vbjx8/7vveMiiqssgzJiRHprK8ny83TdsZK5QUSvbWpJaPajiYz+fW2rIsKMb5YuGdy4uirMpqVFVVlbZhremsdYJJ05mdrV0iOj0+896nquC27k5OjqbT6f7+bpap2Wyx2awYE0Kyrm+810IIRAZJCCeVUHI8HKX9Tzok9NSeOJ1O27Y9PT1Nww9jrDN9f2oOjw9Tjm36mFtrO9P7GDrTn+/NGAKA0KrgLGTRmphOl1IaKTpvkaHOVLLdp4zOYKjvjUUnKFdF0XvXrWtEVJKndVCMkQuVIoAZQ4YsIMQQIEZEwugDnBswsjxnjAmt2rZr25YxMRiMGGN13fV9j1zsXbzUmTZlOdve1J3tgrVNzyLfOTio8qpt22Wz0lVxYX/U9rXp61xnwzwjjpumizFygqg0Cmls07a2bXsppZQ8L4tBnpcc22bjOjvOhzsXdpwL3bq1Xffg6LBbrTA4gkiM5ZkEBrVpf/fbd248t3Xr8JgL+8v/3k/8yJc/UxW4c/0qnB6DjzSfwex4s1xtNoePHn7wnffC9mXxH//1vyC1N3H++ldfhzHZ9/5AvXhRuhoyDTZqHj77I3/msz+5AwsDD08AM8Do1vPZ5ujJyd17R/d0Bq9+5gXUIWDHyzFvwunhcV4Ms2x6dO+MfHvx2iswUraZdYvTfDjp2/bDDw/feWtxsDW9vHOtZJKHThlPObVrt/HNMJ8ORwevv3b9uX1XiPHW+GE5zD/x+ufLSyPYUdBu+pPDj+5+9Bu/+a1v/EvY7iEuAQGKCqQCkcGrL+z/qT/1/Ed371+8GDsPdx8+2dkdffZTn2BI773xjSGL++PhZ1+8pnTu5mduteqAdgalBADvYiAEjjxZ27QJcOvjj50xXOWFKq4dXNkb771z581vfeOb4N1mvVws1p6AZYoIs7ycXNg+mq8DMsCIyJFF5IAsIEIg70MACOAokEguMYqsKIrgIRn9LeuTBizGYExPqDOpUj5aksYCei4RkCKyyHwkiiyEaAHg6PjRdDQSyOrVuu03u6PtajgOHGQxBHSbdTNbe7Yl8mIQLW48m62aoiqzLDvt3OZk3vQdY0yXoyebVZbpvBwPxxpIeB+/H97N29i4Zj47bpo6z3Q1HLTWzJeL+XJ9eHTCdRVV3wVSA3/x6nPWOWN727VoOm59jLGxfWtiHiGXIJngkjXeLRfr06N2MICVhdWilRkPoFsTyTTahTzDqpKbebNZn3adI2C9i83haWQ02Z3yYbFxvu8j8kwPRfANWcuEZyJnvCDgLmBtbegaJdH23Wg0GFSVKxC4DYGWyzUolTPtg9/UXa+i3C6V0jFgj2p758JkMm26dr5a9KtF27aLuvY+hKfeLS4YEnIAIqZBRQSiZFYLgSEwJIAsyxEIgyXbgW19CDmJXCi33nBkBMEZY0zHBKuqoqgyHYAHFByU4IVWKDgCWGvrdbNp27rpDCLPJTKJiDE4ioA8aVQDd5YCsBgpeqWklqLgjDsMG99Ye7xYLueL9dpZzpsYWu87jF4IQARgG2sEIGcSFRJ6QkqQdJwP108ekxDTrW0SZyvD7zxZhsfrk1U/W7fffzaKcsBz9pXPfenx2fH9B8fr2OWZKAXLomGmfvX5SxXrs+emhwN2Ol88f3Hy3Q9uXY3y2ic+P1uae7fu3z1cakPlYOKQLxpzYXusNExG00wX87aFthXTaVnojx/fe/7lPWe6Bw8efPWrPyKnU3t6Oh2PvvojX0EGyDgY0xydNWeLaVGJ3V1AN1iufONN75q6q3t/vKgfHJ710RsAUKCzQihtPfPBsxDHo6Expqlra1Y+shBRI58MBl3Xa5YRQArsHg2GPob5YlU3rQcMBD6QicEFz2REqZjK8ekWL0aClHeJwJkCwBR1iQQQCYgQIDqPnDPBIYInAIiid4nxFRwsRRYjixEi0mBUZVmWZyWTLITgvPHeRQjILQuBBDHGEDgK5iJZ51xgRJTpXHJlLQPOUWvnaTzccr3x1jEGKYM8ofblvBZonv6fhPYi55xFkdjTqqqyTKVokazQg8Gg69tNUyfLo0s+Wkejqsx0VhYlAK3X6/V6zRgbnDNbEIIXAp5hBedcPqgk4/lonKp5iGi9XBhj7t+/HwIppabT6cWLB6lFNYSw2rQWse+b9XodQiiKwlg4Pl1u1u357OGjtfP5fJmmiMACIiEiRxaAGGNFkUmhnHNcKkTuve86T0SSBW79fNl471OSPSJlWSaVEKrIy7zrukABOKDA3vXGdt775WolM12WZa5yNciHuXS9cc4NR4O+7wWyrMo4Mtd4TwE5ZyAZlwSsN7ZvjfUmhVU6F7SWQiiIkVFCzxIgRgREQmTpVxDIOGNKSGtt8BRCiBwQycdgjDOmCxCBhXSFU1ySVpnWWoiUvN4m7bUQItMFZ7LvbJ4xLVPjIEYXOeNcZiEECtA1fcK4eVYIZDb4IstWmzVjPEQ3O+kFgzzXzhMilFmWZVkk75wnioxzJbT3PQA500dDiRWRQhPRaDSpqqq39ujoxBhT5pXOM9sb39myKIdVhZwfPzmaL5fe2jIvGGGRFdF7BowDo0BaqLbpKHhnum6zRopVVVHwk9Fw6/pzKfVysVgk1VA1rCLGw6PHgqu66ZCJ7Z294XAIAK6u02AJKTiLYqYESR5CCM46wxZnnR/YLFNVkZd5BgB930dAJoTWWijlVhvT96Px9MbebtvWRZENyrytGyFEiut2xpyengJAksWncUtJPqhKIqsUn4y3Oeez2Wy9qg8OLr700kuDQTmZTLTOl8vl2WzmofcxLuqWZUVs+72Ll3Z2ts6zp4JbLdZKyen2tK7rptlopfIyswaLMivzajrY0lovFot60Zxr6CO/sHOxKAprbbNsZ/NuM19Op2U5KJDBsp6pTiHieDLo+75ve2ttmZcxguA8z3MtlEvxt01b5jnnXOd5rvV5/wNinufr9VoIMdmaWmubpqmqan9/vyiKjz682Vvz5MmhEGI4HALjm6aBFGVdFFxJa4wxhlL6IZHkGogYY1pJIVikLAQXiYSQtm+UYjGGZVMDYVFUm66PXceVlJILwZ+65ymJ+yMFRExMhA2+75qua5RkWssSgAMpKXOVfBRYDNQa+WbTzE+OOeeEPHhvu9BZAwCInjHGgROCh8C0bKJrFmcv7kzXm9m6b3cG5dHRUZkr03XDYUmeFvNlZ22WFcC5R7loTK5LLkpjegCRySJ4dna6GirJSeeqqGSuARlF4Bg0sGGuNDrTN/X6bG20gaLSiNknLu8yj6O9nYcnt/7rf/Tbb7/9zWkpB5xdKEesN7HtGPjTxfr9j2C0Bz/9q9df+NoX9m7s5TslLB6Cewg+qFdf9O99bE323bdufvT+I4xaOvbw7oPVsSmHcOmFYufi5OTo8PQsfurTOz/1s3+mPJjCVgHcw+yImn656PZ3b5zN1sdPTNf4G1dvfO/b3xntqyufvBLi5uGT95cb76P/ya99Guyw8FPltZ0vV/fOnILh5DJUe1LtsjAAnlWwGcvdZgAxY3/vv/pnh92TB8sYJJQD4AxeuA5/+//ypcvycgXDZlb3G0sO1/NNRHbn4ePnJmretid1uzOASvp//A///uMnp9Kb3HY3xhdf2hr3Lt7YnnzwxhtlVn7qhRcWdTufzx8/uodaX7303Ls3b0236NbN98oMcsEmWaUxp7Un1uzz4ovXrixnj/ZffTkf6nIymXXmrQ8/fnhSrzYnmcwa50bD0WRr7KKru9r7PkSPSgjBnOk71wgSuZJCYPRQr0yhhhTDerE23iilhBLW9jaY6KzQZVUV2bAwBp1zRNJbK4UGorbbCCGqolBB1nUtAOrVWgvJmbBknizOmNY3Ll3TYXt3NGKSnZ2dnM2O+6YvS40c9i5NZ4vVydGMkJXVeGu6k+lSCHYyPwzkawB0jDGApxHP55JO6YHZNtZRgMhDH9Y9he98cIgoh9sT4NnGOAQ03n3w4YdZlTMGHEMu0Hpjba+lxCKs7aIPfLNaL9dxb4Dj4ajBlqLwgDGi7w2TBKVmiiIGDNnmcI3IN5tNNRzOFifZsBpcGM2b9Vmso0PvfbRrhTxXuaqkbXkTHAanuGMIEVAInutsMhiapu265nSzNMGa6D0DPqhyNcIIrd/wYjzYGjcASxOq8f7puj59cEr3j0I0EA2g4yLyjEPtmAMuNGPMWquEyLQOLohMxMCkVE3vI5MWaLlpqunUWWLowbcZmTIzOnYjKQ/Gk4EcPX7wuGvNp27cePn1V2TGb9758Pbt2zEyAJ5nZVlpJVXb9vW6dgSzdVM770GBkJ0X/bonLoSUgnjbOc4jAOVSOTDeB4Bw8vhIAltYk3O+u71vUd8/XcusWMmmca62xsYQOU+UMUUIRJFICWFdpzMQgupmvlPlbHayR/bK6OLB1tRWFSr5YGY3dXv30eGqc99/Nij6oR7f/uZ7AcJQimxcrNp13S2V5rt7+fZANY8fgAwHQ86ceemlrcfLP/6pL32tW+tbm+WsPZvY1nnADTCZTTh/dDoLw+zWrfv19nhLj6vdodeq06y4xH7jm3e2R/BTP/bFfPsKCKF4DVrAegHkm02zmC3IxulgSwynEOXso1uL+SxX2dbO3mCkDLhiIPbFTjir9Xa+6fy6sej87mS7yDLvrS4yrTXjquv9ydni5HS16BpytLe1I7Ksc77uG13kelCy4KE3YsgogLPGxti54HygAOCBuSCUllILIbyPzrkIkQAQPCJwxhggJ2DEOSAH1KVKyfhSCkAwxgidCUQGxIiQAkSKASgCIKILgXmjmBKSMa4ZQx96b03SoYUQYjTOBaIglCyrwhqPnAWKLnhCUMA4B9c78IDEyIOPFDAgMskyVeV0nsrCAHyMkbHAuRCpWEmrqqpCcNbapmubrk1hlMh5XpZMcMZYTNHGfcyULvKCcy5FlmcV57woisVi4SN463kgIYRUWVZUjDGODLhIU3UycW42m67rhsMhIhZFMZ1Oq6pK3yUKQiggxjBypin64LGue2PMoCiLosrzvCgK8XQd5qNbbxZc8acELRERw5RXCIjA+blcz1rbGBObWBSFkoWSRVnaGKMQTAiGDOhpA4sQ4tzXAxQo7F++AOw8IMk4E2MEgVKo3vbAgEsRIDpr27611nIjBM+wM4nvTNKUlO++vb0N5/mhjChYe77YRcGfDWmImNBeis9PFtJkeEhOieFk7KMHiClq5tkWJcuy8wwo64mobfpngRLJYZaqr6LzRCQYF0K0TSM45/xcEMkZh0gYyUabLiPnfDwsEBGACRGNcTGeY3tUGGOIMUbyjDEEIgZIEAmQIMYIzm9vbxtjjHEAECM1TYuMV9VgtFuZruu6LplDMqVQ6xSNn8Tx6+VyvVy2bZssKI+fPBqNRuPJqKwKpaXOVD3b3H9wL52l3nQAQH00tl+uFoLLdtOOx9OdnZ2iKNq2XS6Xz3IzEzRPnpnE6ztnNutF29Xr1aosy5RG731cLpcqyxN1R0RN17Vt25l+XW/W62XXdYLx7e3tFIffbDbW2vQpyJROxmVrrbeuruv93elqOT989FgpNZyMR+PBarX4vd/7N4PRuG6bTBcAIJTSAF3X9cYMhqN127Rd9+TosOu6EBwD7Lpua3tasGoyGSkljO0kcKkKpfR8PtdSU0ld09XrOsmohtUwU9lyvlwsFqYzw0Jyzjly7xyict70TUtEQqiiKHa2trXOYoCmaTbLpl6tNwRJ6WSMiaPRaDRKop1E5yd50tnZ2Xw+DyEMBoPhcJi8DTHGz37+c8vl8uTkxHo/HA4ZY8ZZxs5juNq27VOhG2cMMEbgIlJESEJG5EhprRq998BZBCaEHIxGRECE6AICDIfDLFPpmmeZquv68OjJwcEBBIrRh4AAERFUnupHAiJF8gSBMy2FSLEHq8XSWgshSp6StkWgzMfgfATOBTKA80hcIgpAtu+Lotj0ra4KWq/uP3oIIfYtloVsmgaAcUAtsxTiQiiNc4AxE5yYrJvWtN10PNnd3ot93zabzbqp2z7Xcjgc6ky0bS81H06meZ5b27dti0hN05w8OvEFQgz3HzzsKexeVL/8K3/h2oXtSsDv/8vfvPnd79ravfKJK1/9yqd/bncgBzqOqB2uvcaj+0fC2e3RGDCDd+48eP/kv/4vf//uHVgvYVCCAKAIly7A9RdHz33mwmivuPTSdpkV1199FaQ4/ej94zce3rl799Ll6cufeCmX4uMP3n//wyfbO6OXX37p299587kXdi+/sOu72cPTO3qYv/jSVXM1G6rL0E3iqrBzZw0f8CpnEWQW1Xixatsnx3/82986u3tKgd38eL7wUD0Hr37lytd+4IVLz+08//wlvjOB+by/e5hVB+bWUWB10y1+51/84bgaKFUsN/3GhY21xkT051U20ZDr7MsvXf93/tJfrtvug3e/995bb03Kspps/3f/8B+feSg1cIRrly8sNptPv/R8a/prF3YJ21sf1pnaXHn+moq8ni/B2j/3Cz+3/6XXYP0ESklnx3/w1nuPjk/uPT6djnaPVr0z3XzhetsFCJ68UBwgeuM4R6EkBuAsUR4chax5QEQAVFroQme5jBC6XuhCMEZCC11wYMiVipEhcrLFZtM466VmEMNsNZcAVZ47YykAESFwJiRF7ENYdd3V0QFB1jbNat2vmo4JKHLBM3H1+nOjxfL0bNkZH4lbAtP3wNBEhlwjgPfWGZPSRZ99zdZnfWeaNTCASD7XcPNus7MNo6Ey4CgYVZQx4KIzEaG1tu+b6LpKyUphoZku2PHJyWoJSPCJF/d+8uVX+8Z89OHHmPG1A2Q8CgFMEGNp0yEoRgIKTnKRlVKoMJrmkIGFVg5Y4EQIggQjEEwKRGARhAQGkXHHGASyzoKLigSP3Pa9M56QopBMKiFBimx+Vu9M9yZFdXJydO/x4f7+vi6rJ/MFoXCud84hWS68UIFDZM5rrcEjIMYYOIu5xkxEY7phXnVNg8ZK53lWVpnWvCiHuXHR9FZLJcGGZlVIurY/uTgebOXDayNd5cNr158b724/mR0/YqGQ5CxluS5HAyZFa/t5Xa/WTedp1XRtBIsiqixw4RCJA4+0WtbIYorQ4JwDRogxkr9y8YJgfLNYblbr2lqZ5TaCr/s6RsMYSsGJSSYYF4wJQGn64JPhwBoOPnrnuiZit1/m40pd3pqMBqOTzj05WTw8XLedM4F49m8n/JB97603n790FTF603dgW7vqGkeN06b/+NatHR0fP7pLUF/6+o++/6/+8ek6DLdhULAHt2eCzS4fwLDYzrKJqCZ6MH3n9u13bt5eHPVmdVxcv/rSKy/289mjs9NVX1+4tvO1L33hBz7zKvT1wzf++PDxrf296WxxgoITsBgZglgtIqcFxvDk4ceXLuztX7rKlXpyeLTYrE3gnnE9GIXAwfuAwTgfQpiOR9vTSVFky836+Ojs9MnJ2WJjep8LnWey73tCBoAArO5ae0bWm/m6iVgQQxAcpULvvQ02GLJOamWcZ9wKIYRQWshEpQrFu67tu1Yhz7JcKwU+eGN9iKXWFOOmrq21QknBWGr5RgCOSJwzeGrSjU97SRkXSdBPMfWIJOhP1lrvYhK1d03vrJdSci4ZcMGk5AqI+qYGHxMQPFc1yPNUmWdez2d2VcYY/VspN7woUnZkTKyeyrXWWmqVFN4QyUufsGMyLaWFu3MuNRMl99IzVUAip9MrSVKKZx7BpKJJcS6r1SpB+RACIUtGQ8BYlJnW2lvnnLHBKq6EYFpLRLSefLDWG3qaEQkA6fdKD+w8P6+CSigTEQVXCfllWcY4WCuttYyBEIwLFkIAiABRnLtCKb0FznlgCPFPDI6pIZgoaK3zLEPE6ALngvMYI3jyqX2ZMVYUhRAiZfyvVqsULKOfxsUwxpQS3lnGwHv/bD+ToH+6wml5YowBeOofRRlCYOAgIkd57vFATCVTDDASheDtUydASl5PbxaXinOuhOScN3WdbK+SMQ6Ypg6GRL0RQrStCSFkuiCivu+IqKqKoijyXDPGQnTnKUneC3Y+dwFASAmyiIh4enrati0QmwxHRVGu1+u2aRTjKxcYgNY6aVRSs++z9y6tmAAg1c0KIQZ6cPHixYTjUxXa2dnZ8VkzHZ3XbAkh0p/v+x7Bbm9tI/LVarVYLFInV3pDnx3LpOFJuNbaviyKZxVanDHBOSLPsmw0GgEAco6IQunkJN5sNpcuXQohIEGe51VVaa0pBGvtue92s6nrmnO+tbU1rAaAtFnNsiwPAfq+36wbIIbI02d5NpvVTZcMCUmvBYAf3Xq/qIYXLx0AxNu3bx8er7SAslSp2OvC3u6z+i3n3GKxiDEenRwnb8lzN64LIRaLxZOjQ855/7SQGDwaY4BhPhjUdRvRn/d3J9Nt3zvnKWLi6dNCc2tr6/Lly9PpdLPZMMZSeXDXdVVVpZ986dIlpVR6JavV6uzsLPWdGWOKqrp05UpytqzrTaqaNsbUXWuM0UopKbkQCOBswIiEMYnKYowhuBA9InZdxzk3vRfCFXkVQqzrxjmX5Xnf1H0DIYS80FryTKmt8YQRMIbAJCICJlWDAi6c7UJw6ROUAgYgoveeI1NKScWIIeccOQuEEog688zIFJzz1sYQgIgjIMX52dlkMhmNhiH4qiiM6TjHzrREKDKtmeyc8d5xzquibJvGd77SeVVV4F0Ioes6ir5xJnrvusBr4rkel5nt4uzshGahzIssUzFGgti73st4Fpdlnq1Ns7WTX7584Qtf/CIMFKxPrv/Qq989+eC999x06n78Jz9bXD4A10GF0D/44Fu/e//m7QuTne1PfG7x8PE7bz34nd963HRwcZJfO8ienC1sgK/8WP5zv/CV4bUh7Bfrs8O7Nx+QyqFi0PuT5ebR0dlz16699PzlzfLk7e/cPD2Bl1+G0QgO7//xjVeujC8MIA8cxd7lXZSMmAd0JtQchZhU2WQvO2vObt37+N7d1Wrz7kf33vgmlR7MHAYMdnfh4jX4xa+99CO/+OO+jHKvgFLBZtV9+KhbrM2y3e+otUuU9u6j96++OBExu3Hj9fuPZnceLXwXW7L9fNkfu2zVtDVTcnrx2otP5qu33n7n4ZMnb7572AJc6932dnl9POmcHU3Gdd8NmO+Xx75v82H+3u362lW4eu1iXsg7j+5cObi4d/liNhz87//iX9QZ/Mwv/DTP83v3j51XzggNmdbI2j5lF0SMwIFQAERvY5ZpIRVjSDEEfy4JUkpyRgFISZllWV5IHx0yn4EI0RAjAg8REDzjkXOe6TLG2KNhDChGApdxPajKTSRPIYaYUqpiAGvtYrVcHM0KpYlC028AwnA4LLcmuix+4md+9ltvvPnkG99am01VjYu8bJp2tVoSRRYBiZx3PvgUT/7sa+/Cwe725PWXX7hx/erWeFJVRZHnNz+89Xu/94dvfee9pmmrMWO6Ai6FlDb6QMCV1GXubH88m8/OaDKCr//Kl55//oXtyd5ouP3eezdPZmdM68eHp8AVFzlIGRlzFEOAEFxwMwUOuRqPB0jtcFxsbDPbrOSgAiICjkxwVEghEEUPSmbBQN/ZJljngQIplMQ46ByJIfHIImAIEAMRRdza3VtveqVZNa780tVtLZTMMkHEQ/AxhggRmQAuI/oAkYFElh5eUSo1nU4Gedav12azjmbNEYX36GrBc+76sFltj/aezGYBjBBuqNUXP/v6a1cvzR89HiBMxuVkOBwpnD15+OHND2/fujVbmq2d3XK4VQzGNvhF18ybdtm1jfGNcQ544BEZDxAjJhwYkGOMFGLASDwiYLL5Oba1rbXsWxEheAyRk7dhvWkcEXGBHIQQwBAZMM4YMlEqZy1HkCgziYwRlzDJswzCpz7xyo3rL65ru1nbPCMiVQ7EyXye1rPPviS2Vy+OjVsLqVKb3UBVOTTcW2TQRO423Wh7/+D67vLmnX/4z+/ceB3Gl/at8S/h/pVXxsyI5aP1o3vHOlPPPX/lJ7/2i//FP/gHf/itk709ULj85h/+lgiwt1ugt9IVb37jD37zv/sfphl87tM3dqYXEdlgeGndNstNs66bpun72iUO/OXrW9lwm+cjH4LIR2M5mK37+eOzR6ebWW07y4tytD3d0oPRxrj+5OzD979njOl74yPjItdFpbMSRTZf18iYErJgaIJp69qFwAiM6QIKBBIMpGAyYPBkoxeicCEmRA2cSa0RMUYfISAyBBYBfIicR4GMS1UVxXQ6dcZu2rbpugxRpJ7UZD1FEIyxFIJCBEDneeoESdHhQvBC8hgDheh8jJ5iBIhACPW6jgRljixXDDAG6LvOG2vrNvk4vfdEIARP2KhpmhQ1mAjmxGkhMg6coeid7bqOMQjnyje+adaJdVZKccZd8CnLbz1b8rQoEOfzCT7NeufnKYqYZpjEeZdaJYlwQucpWkcplWBZQmDp1aYpxTifQMl56nxVeesQMXUbBevW63Xi1ClGYKTyLMn7Y4zJ1pzSdUcjlaTzCforpYq8SpxlstxZa2P0Uuos00IIH1zK3gKA4BNGZAjs9PRUaqWEToXERCG4yJiVTDLmKECIDmNybcsQ7Gq9Sb9jQvwp6if9mk3TJIip1HmJFuccGJzrOp7OS+kypjCZVA6S53nylQZPPtgY/6Si+DzvtevqTau1TgZl585bmWKMdV2nUHkp5agalGUZfUiDx58EoRA8e2u2trYA8fR0Xtf1U7KcVVWV2nYBonMOfMovpRhljB7x3PadsoMS7NpsNt77shgMBoOciIjS5LNYLHa2tpL3NEXvJ/iY4msAIKFYRBwOhzHG4WS0s7OTfs3kAEbEwWC+XC7TRU4vMv1dwWXbtCnHsyiKBN/TSUs/hL4vdC/tQxAxRSGlC26tlSory/Ls7EwIobIsXXyl1GAwSLPZ/v4+R3Z0dDSbzcbjcaZUXdep+xkJ0jC8WCzWy1WkcLC7MxlvAcBsfnpyNp/NFtPpdH9//3R2ltTz54uFZhMCAWeDQRUxWtvt7u5+8YtfWK0Xi9lys9kkeO36LtmCR6ORc+7w8JgIxuPxaDTKy4JCXKyWi8Wit4ZCBIYcWWf6tnYWYEB9bvVkb8QkJJUbAHgfnXHGtBcOLs3ny9npDAAuX75y6dIl7/1HH320vb292WxOT09Xq1We5yGE8XjMOU+iprZtrbXz+TwFHyFiXpZN1y7nC2C4tbWV6odTmqf33sWQJljOOYToRfQ2OAghuhDIexuCAwAuWJ7ng2rofeSc7+7ucyabphVCAeJsNuv7fjAsOedt20opLly4sFjMhDg3uvhwfm+J0XMG6V5nrUVqrbUQMYQguASiEL3z3njngqeIEaEoBxGRQkz9fdZafLos6rrOGSuEGAwG+/v7grHlcl5v5lwyAEac+YgaiEFI6cxCMM5VWeWV1uRc33Wn81NgaEw3GAxKIep6vWg3POOO/GR3uljMQEA1HhCRNd1gVF26crBarYpcC1ZNi2w7H4JlMN2GkXxxP/sKzH/614rXf/SHQAho6nrl77/51oPv/ZsvffLlF77ww7e+9zFs1PGt5dXJy3/rb/3V0LG3PvjgOx++0dDi4Br8xC99dvjJ7OTeG2984+7Lr13/1OdfBTZY3L//x3/wxvtvz1wHN372QA3Lrede/doPfRJOHoNm8fBhsYG9Fy8Dj65tmFLF4DIwDoFn4ynAlI6tXa+Ytw9vP/7G7/zh+++smwZ++MsXfu2v7P/AS69R59enp8jCyqz4iH90+40LL1+SdnL35t2P37+1N9m9vn+NIXv77Xc/8/qnYF+Pb989fXjyqc9+/p//k3/16c9+FYWthhWrMgOzxdpsGrNam2EpvvX2u7fv3d3a2oo8u3hlmOmCS7G/fzBfLXhH41xNKjUcXrx1+2YvFMj4n/4nv/yz/8F/ALP+1//bf/H//nv/1VvILu5t//3/74P/5b//q9VAtab7O3/v779/e/WFr3z5tVc/c+vRkZRKKQWBSaUCBEKMIRrjAgQgBoES4UJE5CPEHgAAwnkTOjjnoo8+kkceKQQfXIgEcF6aw9GBwDzXgmPXdZnWe3s7EljXdVygc+BjIGQCJWNonJ2vF753mdKco48eEU3TLp9YYPQf/R/+lqdICEyofrOAZqlUVm0NunUdQrDWOee4wGI4+H5454LPyuKFV15+5YUbzlpj7f2T2Xc/uvvBvUeNI8zL1kXvm8h5a60nHxGk4E3XuXazP528/uKln/upL33uS5+H8RasO+jD0cmxje6ju/e39w6Ia2A6oAiEIRC5AFDnQ1kozLi8dHFKzu/s7ByenvjQrNqlJQxcEpMAJgbkwDkKJhAiIHJkjAkkyRhKYhK0ogghBh9DH6n33toAGNlwcLKYWVdvb43LSvd9vVr6yWTLOyJiiFkgASKel6dhdK0TjAvixCjjajweX9qdoJ32yzPbjifDMpJv2nZt6sfHtenXZ6ePJ8XwytWL6/V8OTsaVeVnXn9tMx27xbxCORoMQehNvfRtq5ne2qpQ5JGpTW/nm81ssaq7vg2xjdEJHpAB54Sp/pnAU4gktOSKA3CIIVKIzjrnYnAPHtyrisIZi5zpLCOGvXc2+K43QmmhBSAQQYjECRjG6DpnrAKIduN9VNxmBENJL1688Ponbui8vHPn3tnZmQvCebdqNkwImeX/1tloZ1u7U+e4zMpQd+2mlUozzJz1bYz/xf/nH339T//wFz73CgzL//If/N3/ya9+7dO/9HU6uZNvdRdGO7T2m4fLkyenhGfXLlx4/XPP96fLv/GXf6Fb/J27D+Hya8UP/PQPH+ztfvet7/ouvP3t7y7mcGkHP/uJVy8f7Hd94z1v+3bdilUne1+YKA0aWaphmYEuWy/WPWRZWY0z5sOD2aM7j44d5qvW20CyZLUJp/fuL09OutaXGnSGVTmaVEOlqgDc2NCZFhFs31noHEHv+9aZCMCkQiCGAYFpxTkvhFbaO0eg8tzHVNktWGJ5ASLBpmmk4nlZBOc7a4wxVV6UWc6lBMYiw/TfQFH0vUcEzokhcQ6MCYbsnHNlwDiP5I0hImIMOZeCc+v6YEOMIFAgxxCc9SFX2gXiwKMnb1wgxwBM13MijizFiTyrakqAMjHKCYI/ZdljnpWIBN7VdQ0QGWMpgp0wWmtZ0yR6PgnxkagoCgSIMYboErhMyBURlVK5yIWUAMnDEIkICIqiKMsyyzLv/Wq1SsxoURTP4GkCfOeJKNakjJSnAp6QzLWco3Ou7Qy1KbYHU4B6Vp6HqHgf4emewTl3enoqhJCSD4eV95ExprQQkhnbnQuDiJSSqWgjpXE9G1oQERmlMYCjYMA5k4wDBwzRxQCBYnSubVtnfIxeqUwpkbqQMWII0TlvTNoNicQ37+/vK6WJqCxLxlgI5yOByhVj52VV/Gk4Sdp7pCyUdGWSF8IY07RNfFoCnzBN6iIIgUI4p069j2m9I6VUXIiclVmeRg44V47FsqqSUDo9AJxzECIx9NHpLEv/aAp1KUu5tbVVlqVzrq7Xbdv2piVKTK113kopFKBEhsAAEBDS45Aj1HW92WwQGWNMasU5f+6557SUCRzPZrPlcplwf3LbDAaDNGMk2UxaEKX3t+/7JDVJQ2ZyRaf/PjsPeVZIJkejSVmWiYm31uZ5npIlv/+ipT+vlAjeMKQ0E6Y3SwodQgghCiEwVVa1XTrkMca63YxGo6oo0/yWzpv3/rx2V8h0zpumkVwMBoPJ9s56ve77Xup8d3e/bVvrwnK1YYxlWSakBIC+b7u+QS7LMq/bTd21x4ePHz8Z7ezspODLqtq7fPni2dlZVzez2cwY1zSdUsp7P5lMkfO27zZd29b1YrUynQ8EuUYA5qP3kQajbLK1NR4OgZPMMEAIITpnEJEhT+kCDx48mkwmN27caNt2Pp8/efLEu5jQf9d1McYUipoKDQaDwWKxODw8PD09Tc3Q29vbMcbD4+PTxVJqXVSD0WQymUycc5vVquuWaVRLuN8ym1aIiMi5DCGEmGQSMYUIpQNflmVdt0m1BcT6vldK123Tti0icpFiCZo0SKRlWrqRJt4hQf+yLARnEAmefuIYcEREYDFGF7xxtjPOx4CIhBi8DhEpRGut6fsYo+JCMsGQYnDjrUnb1Uqwa1cunZ2dpTaV4XDImFg1tbeOc47I+75fLedb48m4GnCg6B1DVEoRhcb0IGU2qqqiNOROFzMX7GBY6izb3d8bDcuiyLqu4yIwxojs/PSx1yJ3EE76o435b/6z/+cLr172eXfxkwdf+vmfAB36diaj+uD2rUd3nrx27dJP/fm/CNRDUJfm7IO3PjrYeQ7j5Lf/8f/weLZ+8/2Pqz346tc/8dmvHGy/kkF+Wk7an/4znwI5hH7TnR5tTppRBs9dgaaBKGxnF3nTAJe9nWUqY3vV3o0L7dm6mO5IXvp1FzeSqYqcCl586w++defDhx+8221msDWBz3/q+f/4f/cX8s98HtoANsIHH9366K03v/WNzppPf/GlYljsHVwoioIsLQ7XH7796D6ddM/hsBp97+27fctuv/fuk7vh6z/5ye++9/4P/vAPMzEqxzDfYNNC06MjzqTKB3o0Hexe2X3y8J5B0XWdyMvFeiWQdV0nOF67evHOrY+2pqMPb78rOV7f3929fHF9evTf/p//T3fuH9+9d8wVvf76a5v1wp3GP3rrzY9v31yuF5Pp7muf3Lv54e11z0gXWKWiAIaIDDgxjIAxggtE5LyJQjCtFOcyBPLWOWOdcIwxgtC7yBgCxhg9E+S8BYhSchQcCTBGROi6RgiFBFmmh9VgMBhE69IJT4+pSDFiBGAhBmeD1NJK4JwRyRhj65xrW+sdIuZlPqhKrnhv2rZvu9gXkKtMhT4QiyiAS8HEv8X6Pz4+Wq3nxjb/qsgOHz0+my9ndWcjq3Q52t3jKNebrut7AOlCQAFKSSC7Wa9yjj/6oz/yv/prfwXyFuqz7qPDVW1PF5vb9++KTG5d2raBAlH0FIACsRhYjBxB2rjRLESGUuFoONrfHjnTtPWgb9sQKQQkQPP/p+q/oy3L0rtA8Nv+2Oveu8/EC5sZGenLZjlVlcpIJQsSEqJAVDMwTdMgoOk1NDM9TK/pGaaZmW6gFzM9zYJmaWiMMELTNIVEyVRJKpelNJWVPjMyI8PHs9cfv/38se97VbyVK1bEy/fuvWeffc75fd/3M8YaAwTzWBCkLOWMp7mn1GCqjFNKK2WPy0p30ijrkTPeBQt2jOHByUk2yKjynaqTOOEMpFzIBrQCB5Rg7AEp67U1BjnskTdeEPDEOwNUK6214CRP4kXtNzbFU9eu5Bk7OTncPzlg9mg5tS0F167skgjnhkmUMdGP0/GFc2/fv8WSKHLESGnrwintHXGYI8SXtSqbxfF8UbQ1EGIIURgAB1NR5Jx1WnuHwDrjHbRABY2iiGKwRhstrVHeQdnZtu4Ign6WMME7KVvZYUogqI8BkPMaWevBWouxQQ45qzAGbA0DKbwVFHZ6yQceu8hQc3jvZHr8QCrpfDQv1WS+5Ekc59EP7o2f+7mPR2mWbVzUVrz6yvVXXn27a61X1CqiPZnW5le+/Nt/95e//EOf2LIIf+BTPwUyQoMdBXdZykk/jZOkx3h1uRQ2hek9rlH00NVPf+iqad/75Ec/9Pj/5kvg3Bceu/bbv/zPHrl4rv9Yttkb9ER6clzevntXWgucG4xJlLE0csJq0njvLWO39idA+bmLA761RZp6cnjSGZLkm7NSE04Y0FbD8vi4rqoY4/Hu6MrFXYYRxtQ4rJTW0lpwGON+P1+UZdM0yhrrATnMKMFBPupBeyDeauu9A0qp9tBpgygjGHsHoZvsHfLISd154MA5ILAIHHhpNNJ4WRYni5lzrjOKJRFgTCGcJus98t47jA0ChJALbpuBM7M2CsSUE+qtB4ORpxQBo9R50BashSiOm7b1FoxTWmrCKKMMhCDeR1yctWkDCglBSOT0K0AlQggAYlSEqFpjDIAjhDjwUspWNl3XNe3ahpIwxinFGI+3x3Aa1hOIsOHP8HYBLIZ7ZXivtqoxwqHrdhqk5bz3q9XqDPGHXwlj9zhLKSXOeaWkMaZpaq2N1kpKaZ0B68OkgRBMCAm2TW5tie6M/n47XEodcN5ZP5uurf3ZKUHFBThY17WUknFq19kFKpDvAQFCRPAYcFAOIEoY5xxjCuCssnVTWqsxJoQwgEDT9wyTUA0FYkmwJQ1vdOZGYk6/rDWtahn7/kkJ+D4sb/j8xpj5PBgXogCFAdbuogghBOR0ArPmtGitAdYpS0IIcequGDhXbdtiQGERnHNwSrTQWiPnMaNlUcTGMMb7/T5nEaU0ipLAAwl7abVaSdUGbYW1lgt8tq8QhOeKB4C6bslp/hdjfDgcJnlGEU7TtFytFotF0zSnsxoIDv2j0SisVdM0RVForaMo6rQMpzj85HQ6PTg4aFubZfwM9IfNHNrnOzs7cZxaaxeLRSDhhDiwQEwPPx+GMISQOBZbm8Oua5RSgctOKfUOVVW1t7cHAF3XLYuirpsQHOac01a99dZb4LwQghAynU4xQHjrrusM1qFyE0IM+4O9vb1iVR4eHkkpt7bHW+NtpeWqKMKrZXmeJAnCvqqqqhLGOSao1N042/DGaq1ns2NC2Lmd3YcfvvLgwcFoNDJpJoSYz5d1XRtjsizzCBVFsXRure2OImtrrz2PovF4PBgMwvqEsYbUclFOnLfe2DCdIYRQvL4iptPpiT7x3hNCOedJzBFCTVuFzUYplVIGw9A8z4fDYZ7n1trlchlOTRRFWZZNl6uLe+cu7J0nhEwmkzt37tRlGRY2qFm01rLtpJQB5TMaO+cArYl2lGIh4jgWod5oW1mWZV23YQ/UdZ318sGgb61bLhdKqZC5BgCBfRRKRGPXEXVhwoOwRx6sd14b5xwGQgiRnQpFoAPPMAnWWJgSa5y13htrjMEeyOn/8s71er3Lly/fuXOnKIrJZLK/v69UZ41knGDi2rptO0k5B8BGacYYeNu2NXI24TxJY6+t9SYTDAvChPAUKKeNbP1K9wfpZHq8ORwqI6f3j4qi6KVpJMRqeXL10taAiwFkqDLN8eyFr71w863XG9Y+dnThS+/bhbGQqjQu+ea3nn3um+/94i/8yPmHnlndv9XnJP/UZ299/X8oKvTuG1//7vfaCw+RD3986zN/6Icu/8jjEB1BdwPMIt0aQO2grsyiO3kwPdxfdp3eGSf8shhupvEgAe5107DeBuydg7qa3D0Ybz9kTyriYzp+HGz24IU3v/xvv/L8s92wD088vvmTH39mZ2fnwt5etL0NTTf93e+qxu/f3b/+6qsbwx7mozS2H/z4F+DxC8AN2BYQe99jg+I++4Pfef7fPfdSHKMrTz1yeGTeeNv+pT/3MxHGJ1O9bMy9/ZtUnFs27WTZNcogRggyTrbS+emStFbfe/MtSnESx9bZn/+Fn8fOvv7qK49cuazr1c0bd0Z9eunShT/31/4qfPTDcOOdg/2DP/an3vfCt1/89a989WR29PLLLz/15NVf+/I3tsbQlHA0uY8orSVO8i0UcYkQIcQ5CCicMi54RDCfr5beYesAPOKUYsQwxo6gui0oXrO0HViEgFCEMahOOq8xxoQgijgG8Mh7B5zTpmkYoePxOM+yMMKljBCGmWHOIKO9dwYDAU88csCJAk28J4RgFlGMKSIxAOd8sZzc3T+MYtYf5cPNYTBn7urWOY8pJYJzyv4Dfx+AJEs72bx9/TqjRLZ1IyEaDPvZiGKmWuWt5EmcMFJUJaHYemuNYhSnqaBGHxw8+M6z33zm8R1O9OHBg9/82je+/dw7kyUYBBoA0dSBMl46YOsML8QoAm0lxuCQrSt5eft8JtJBlLrNbW9hUctlpxuHLYB1gAknRMSUAvbSQKO6DjBQ6gnxnLSBvoMAAFuEFHgLCHmPkCMC9ZMEOWRUtZitrIQIaaMxIbFnMcbMg3PgnQcAzCPhnTfgvXdlrU5OToYxsilFTvZ49NilnfOXz5nmQlnNHxw/vJisrl183+/93nPPv/b2SknK2IvPv7D/5us5dD/6waciYrGu60rKciUYH462BYpmVbdqm+mymJWV9B4T6gl1FGFKESbOWCuNdw6FPGFrKGUMgUCIEMyAdhYjA9ZDnhKtrbWAAIzSIS8ojbMkQhhjQOAwEMAOBb62iWkkBE0JBt8J71MCGzl/eHdjI2OmnhKnNzejoin3p3OL2OZeuqhXhV784N74j/7KF4HFkJyDEghF9+/cnauGUYYFJEl078HBlUc2ty/Trz17Ygzs/L1//PCli5/+sQ/HD+1YNVtMJ4ubxzBRIzHIxQbgBA/6gPhTjz79b3/jvbfefOfxWXnr29+89c6NPM9/6EMfyXcunrz5zp0bd7T2nWYHk/LCw7uIYI1J2/miakJsDrEm4dZ7L6Lexc4oaxZFg1i8s3txIfeRVNoAJmS0Od7b2xsPB1uj/vTk0DortVHKyE45hzHmEaOYU9oR54zW2uFAWUDeAmcYrFNGaa07qztjpfbKuVopQjkizHrfSq219Q4hAnEaNbLruo5zHouIIKyUqldL5HxgmcZxzOLIWkspIgCAgEAIhgCPwAN4ipDT2rjvsz6sh856bx0AMEwRIYwQ6xFxGCGGPaDQgiCYE0oZj3mssXTWhDCRtm0D2aPrOmNMQCeErG0xTnEwV9LEcRrk7cHxxqPg1Y2stcbqgKtCJqsz9v69e2eUoUCrOKP7n7F9wnM39FARQk3XLotVAOUY4zhNhBABh4UOH0IodOm890YrSgWlVGvfKQXagQXrtDLKg0WAMYK1lZDsCMHGO0qY4JF3oFUdjjSO4zzH1lpj1lJXzjljNDRdfpAdbsyaG5DlaeiIe+8RwgHAYWL9OsUdMBDOBWOUAPbeWeyMMZSIOI44F13XalMzypuqWfusEhJKmjBhCFKHgCbPlih8DO990PeegZWwFKeRUrZtW63DsAiNBoMwS8GAwQOC7wdDhhdEFIUVSNNUCLFaFLLtuqYNRp9BRRBeeV0NUho4JAGwDkZ96xxjEcZYK8sYi+M00HVC8RZqGEoJQsgYlfX6Z5OKkDaAnAslCuc8JwQhxIUI4M85d3R01NZ127ah7e1PU6ICeT0Mptq2DXwhhJDHEGYOATgmSRLI7kqpsN/CsYS/MMZu3LhBKQ8l5d7eXpqmYc0D6AzygMARCjV227ZNUwfZSTg0QKRpmuVyFWrgpmmMg1BGIYQoz2ez2dkcoG07RnAYU8Rx3Mvy4XAIAGG6dePGDYRIqOJWy8JYoBRjTJIk2T98sJOmm+NRkmXGmOVqPp1Ol6vVYNiXRldtE8hXWpnJ9Liu66KooiiiiDoHURQVRaG16ff7ZVUlWToYDDjnq9VKarVzbnd7ezuO4zzPoyiq6/r4+Pju/XtSSgCf9mNCkOARxjSUfG0rjTGBJIYcaK2bpsEYn9+7ePny5ensBABCHkXYYJTSMJOJ43hjY2M6nR4fHwdZ9rlz5z77o19oVDefzuplDRjt7p3TUgkhjFIIoZC80WJaeQAATlmSZaf7cG17GDYSpRwA9fv98Xicpul4PI6iqGm6MHxYLcvbd26WZZkkCWOs67q2aYLy2xgT6NGUUhQu3rUGxRMgQdHvtGnb1iEAj1C4IyLijDMGECIevHeOYkA8KJoQAgfIJ0nsjU4Eb8ri8HC/6xqtNXhTrCpESNAPYACMcCKiNIlk0xZ1k0dR1OtFUaRch5Dv9fMoS2XbhXi14XBglaSU7uxsY3BGabCOIUQJytNkEO1c3Bmyzhy/dxIbUS5lwvuDpN/Pa9rBV//1v33mCx/hG/l3X/ju1UvXvvh3/pPNixdmi1sbT/2IXT4ghHzhz/zRN55/6fWvtT/5J7d+4ud+DjZyiA2Ut0GuYDwETmGxhI7P3rxzcjyPk/zqpfNcxB57CRqovn90LxsM0nyDJINyXxG6Md6+BJXVE1aW9OZvvPmv/8Xvvf0afPyZi3/1j//UU089aWwzmx8c3X3w2u0bPL4rZTudrx7cnQueGzVYrDjnF9//zPvh8qegqmAUQ0JWb1z/rV9/7r3Xbvl6C1tzdH+11NNFMf9//52/kV7ehWJ55373r/7Vb124/OjJ/KZGadzf3OlnnS1PZtN5vdJu9c67x09cHcZZ7IwWgv/YT//0T//pP20P779z/Y3bt28NBgNC4JM/9Okb77395rPPsldeuT+fldr9EM6aqo4jPltM017UquqxJ8cY3JGZzWaQ5wY5ONp/cOHqFUtSzjn2znnvjEWIYEQJQVGUYIyxx845o0Fh4IQgxDBixmltNUIeY4wJgAGPkdFgrMXYE2K9t2vM4VyWYoKBcQLISdkGAiRBVAiBkQeQzkklDfaOYIQIigep9s5aL423nfQOE8IIIVEi9s4/FCfUgymr2aqYOq8EFUZ5jAkhFDDS1mhrfhDeYS6wt7VseiwZjrcS4/enq0Ja4jCyPovTjDGCLJjghoyslRK7hBGt7O9+7aVnv/bSp56mn/7kM8fz4htff6cyMN4VlYRpqaQ1FmEfrHxDGpJ3Btl+b8QjQpyzVpzbeijC3vTs9vBcFvXvHk/MZOaVQ4QSAR6x4D/unDFaNa1qwSEhCOUAmDPqPQdtEULgHfLeW+89RIwcHdwd9EQ/RtWyGGWw9/AAOXQyWQXAZbwTlCPMEHjvESdc1o33FmNktZ3P5w+IljFhXaE5HF/e3U4wwWaYRsMLO2W/vzo+3snFMBFV2zqw9+7MFxSeudqfTA9pL2kB758s7x1U8xoVNi68X9TNqm3rzmAec0oMw8AYYdS4MNYLNEPHMQDxyPuEc3CAleaIMcrSOO3Ata0i1nKKjXdea9XUzgNBGDzK09Q5p73RyGGEKMbee+SgqVfMuoQTULW3jgroUdjpx6MEt9Yi8ItaKrdwBGisJWqyHHYvbnzlBzfHsAOrIB0BEoQbrWunJLacWkQxe/Ta1RdeeS8dQJrAMIIv/9rrSL/+e1/92viJYTrgqWNm0fIStpP64ggNMwDXEboa7z76Yz/xoX/3le/92q//+cefHv1v/9SXLl48d/OFF1548TcO7x5NT5Z1paOod/WJD03LsjGq6JqqbcBpSjHn3Bto2u5evWi7N0+mi94gZzyyBilpdrbPIVaQVsdpNt4YCUaNbh8cHmCwVhutlFY2sEy89k4pVVRNJ7VUSipLCObCI9BSOe+1DRRQ55zz1jlrjbFWG22cA+UArEcAHmHsPTgExnhwnpIAyoj1SFoXMQ7WOecMIG+9MY4iYGuIFmgQmCIECMipplOFhnHg54DzDDMhBKGMURY6qogiSkEpI5hFgDHGGmkMSCnVNQ3BqLUhylQHBBmErQHqBUgaYCLnPI4TBGtnlWCwg4KBvbXj8dh7jzCEnw+S2YAdA2DVWoe2/VoSZ21Ae4FMH0jVlNKI8fDDoQV+xs/e2Nj4waZvkBtKowLAIgQBgFKdMSZkpkYxd87BOp3sVBFobb0sAsQJH0NrHRrtURQ55xAiAbRZa8uyLMsyfIxQGnVdA6cmOcEwJEwMgmcOxthaHwmsnQtdz1A+qU6eQknDGEvTjBASbtkOfJqnsRBnnf7APlJK5XkeViagk3WbHKE4SwOBPgxPQvc6AFl0qsYOsDWUJbPpgnES+vHee+eUPzVuCnLt0L2O4zi0pRFCs9lsMpl0nY4ilud5MLcJBIw0TbMsE0kSwHqSpg7sfD43xnVdt1wUhJAkydq23dra8t4zxtI0xQQYo+EEhaFNyBmw1gRKd9h1Z+wpbUxVVQCAEWKYhsokkJpC9/1sjy2Xy5DjyxhTShVF0xum4RYZcqPCQoWzFmqVde5veJIhkmVZ16ngOetP4w7OJhvOuUA/C6G2CNyZGltrvVqtnHNxklFKKWVa6zZ4vDjkeXDudg7seDwO7qthO8HpRAshtDQ2gOBQbRrjqqomhBijF4v7DnyWZb1exgTv94dN07z86utNU1FKB8NeECdIq4Z5MsgzQkgcp9PptOs6jGie5865YlmEGiNNU4zJ1s7Oo8PhfLkInPskSYI8Jrhm3b9//8GDB3W9ZogJIZIkbhuJsJeIBu2KtdYZa62P43gymehOD4eDIGXuuu69994TEQu/HuQiWZaFwqlpmnDugjiYEJJlWZymv/+NrzPBw74NqxSSgJMowhhHXDDGsjiJokgrhQm7dPGiOyVUSNWGq8N7v7u7K6UMFy8hJIo4Qmi1WrzyyvcA8JkwJgQh/+BNCWMsBDuba1GKjdGh/o84Qwh5bZRS4f4Gfp0z4MAbY4yzIYYiiIYZIcHWzDhDABmp7ty5E/ZqXdd5ntdNyYgIt9k0jjmLLHiMKaW4rWqCIUqTQS9nBJWrpXcuTzNEaZpEsq5U225ujWOGV/MFJWhrtHH37k0wdjjsJ5StigWxfnujVy5X2Phb+yeXt6+0NDl34QJKdK06vT//43/ui5CR3/7tr9J88wMf/MRwdB5gtDFkC3UwHFw8uvE9ZIor77vw3/zdC9hZuIgh0aAr8BqSCJpudW+6PJkeXT/SFVw4v3PpyacB4eL4yFgdp/0oS1mSAUuPj5bbbCvf2AUXnbz13re//Nsvf+vO/i3gCM6NLvzlP/v5x68+WZfVi998/e0bL9+9d92ixiK7KqFVQAVsbKb379ZpBHGczhb11194/ZFvPp+P+71xzyN3+/btO+/c8bXzjccdjrOh88kTTz/6T37l1z/8gcdH/eT1d+53Ht09nlOx5XlkKZ3Mjw4nd5pm0RvCh99/7Quf+vGv/ebvX79+3WjzvZvz5fJXv/Xtb1zc3Ts6PnnvneOtDfTotasf/NjHO2f/51/51enSiwyiXv/Z514djHcqq+8/uHfpyvm7926qBhIBwwF/+Or4T/7in9ncPPf3/v4vX79/6C1FCDHKnPfaOq2ssbLrVBQnlDKwruuUVsZZ4xnGGCdxr5OVlBJjhAnDCIF31lrGIu+R99YarLw1xiplrPWqqwbDJKjzwywRA67bJo1SgpG3IDvjnLHeA/YU46qtjHfeE6fBGuwsJgRxig4PJtZJQJZTR6jBxDKOMAbCGQHsHMjTJuAPoruqqQkCTFma9/u9bF6U+SDXBjhinFDbdEf7dzDGvTwzHm9ujpRu5yfHSKuNPkep9q2/+Z755Cei3Z3RJz9JRX/j/mT+/Gtv58ORawwG7oGBJ84ib6231nrTKi8oNtos5g140lSlamWvl5/f2Vk11eHMYa8iRpBD1hlvcbmSUZKmaU7TrPGmc66q26JuBmlujMEWEUJCWiQBgrATFFpss5gaVUQE/vQv/vQPf/JT3/i9r//Wb/5ep11rWuQ9pRgIc9Yp64lAxmnwNo4o8tgYUxYV79ClUQZdWc5W0/1jivXGMAVqcSdBtxd2xqP87r2j5WDUj0RbHDf7R6shbeje9mbeV05LZ1oFJ0t5sJLKQ2OMpZjlKRFUe+cwoYzLpiYhMcBqYiwQQhFCCLXLlXFAAPoZS4eDiFOhOSVqsoJ+5AQB1WqHtRAxwwg5LyKujFZag7OIIIZw0DBQcBz7mBHiSGTcIIGdYf/izjjjWIGU7bKp7m+M4YmP7+qIPvf6fUPhkz/58N/9D0ZCDaDYru4Rv4GIYtxlEQMraqME4D/+C3/swx956+0brxw+uJtRsBG0DSzvygcnR5gDUmBriB1c3u66a73LF8bjjS0g9KU3b0qXKA/JAP2J/+iXWif/8T/6Z2++/FZXwfYw3tm6kIzwwfHi1Rt3PaOtkWVdaN3mWdzr9Rj2srbO8jwW29u7W+Od/iCnIiLLar5qyqJAAGkcUUpk23StNV2tpcQATdO0deO9j0UcxzHBGGPktY0TgYWIlHWUEpE48J1Si9XceuMAEUII5xQ8tcCs800jjZXaAwoCWg6ArXNKKUwJYxRR0mmFFQAAj6O26xzyDnmjuoD20ZVL0SnoXzvsBugWRQmcytGU1N57xgTn3BsfcRF4z1prKdcYMZh2y04FCH6G6SnBAQFba7+PVk9NVEKXHQCCrDaKYkzYalU2TcM572Xp9xuiTjPGCFmHOoW+frDqD22/M5FiQLSBU4RObX9CB90YE8fJWf87wKx+v59lWVBMwmnEbHAHl7JDCFVNaYwRQoTpSejUolMmDwA0VS2lHAwGOzvnLpy/WFVNEBoKIYbDYZrGGGMpZRAVBMQcPnx40wBMMcZd12GM4yQKGDEcVNc14Z+hQqBMcC6MMUVRKKUCItda13VNKeVxFFY4YI6I8X4+YDiECQRXVxYwetA2NE0TYlADPcODdchL2YY7cijJgsQ5lChnYtwAZLXWRirvvVJGKRV+8pTn0GxtbYV36ff7586d01ofHBwcH54gOLUuRShUI0FhPJ0uMYa9vd1erxd63nmeS93N53OlbBRF3qG2bZMkG4/HFy9eNMYUxXI+nztvokgghLquSfIewhghHIgc4enivR9vbZ5JkEOpcyruXBPrw/fDuKmu6zDrCDvkbDbCOR/vbGVZJqU8PDw0xgQGy2KxyPP8oYce2t7eXi6Xd+7cWSwWhJBIxDGPV6tSa723t3f+/PmiKO7cuRNGLmHZB4NBMJZt27ZtKq3aLF5LYhwCpVQUJePx+GQyPTg4qKpqMBpubmxZayeTyXy1tN5sbW31814YCHjvMUA4+9baiIvhcBiMbqqi7DqV5f1Q6nDOtZF1XUvdAcBwOOh01zSNlC2PxHA47PV6QrB5sajrWlDGGCuLylq7s7W7vb39kY987Pr166+/8vr9+/e7TlJK0ySP0mRjY8gjERrecOpHGUQRgSfW7/eVUpPJhHO+u7t7MpswRrDH2kjOojRNvXNlUYX1iTgXQjgLRVFUVWWMFRENqusAykPhFKSuSZIcHx/PZrMwW6gbOxylOIouXLp85cqVKIpWi+VkMmmqGgCMVGmaEozrulZtxxiLuEAED4cbhLEwaggM/tVqdXR0NBiEJJDYOVeWq6JcBYpOlvXCnSeIAcKAZblcnqk1Atv+9KZlecS89xQTQohTum1bhlCSJIJxZbS1VjsbinOMMeM8imImOEFYW+OMtd6pTtZtsz3eMs62bRv03EmShPsAWg/BQqUBxlpjDHhfFEUaR8NeX3BmVCO7hmISJTHCeL5aDvJeksbL2RRjGPX6Uta6bfNePMx6SRrptn3w4F5dVr1+UtVVEnGr4670/Wzj4csXNgbM2eO037z/I1c++MmnUSJQb5PuPgQ+AYQVlEcn7+7kgiMJy0Nolu+8+K1vff3OdAF/+a88U2u5/dSTs7u3b927e3x8hBCMh5sf/dBH67JpGjnIN1jag14P5kvQFoY7t154fTZpqorcvTtdLtX3vns34QAOEiJ60Sa1WVOpZlUa00xnjQMgAmgE0kLVggeIEgAEXQvbW7BagdSwvZMs6mZeAo8BEWhqwA42UxAeUgTSghhttFptb+azk4O6gkev7fzQp3/0u6++sygdSwaLor7z4Dag9pFHti9d3kgoJh288+rbH/3oR//oH/1jTd3Vdft3/tbfXq3Kxazd3R0886EPXLt2bT6dvPDCC0fHB1mcDQcbFy8/fLyaT8syGqRv3Xzn1t3u2lXSzwXx9hf/+B/77A9/hmQ96I/+61/6z+5N65KNprVpug5T4gC1UipjMSL90dAYVy5XjAmjdFnWg7wXRVHbltpI733InbBOG6OcMxEX1hmMgTLsve26zhiNCYgIEQKh2SQ4BwAjjdY6i7OybKtV1bXaW4yAYMwQgWwzs95YQ7xB4Jl3zHuMnPfeEwqYGO+ksTUiijHEGPNO1FXXNF2YXQPAUf39NN+PPvR3vTPUe0aJYARTqpyTneqlWTVbbA82VsuloHS4uWG9OZmdzObKtHBuCziG1Qx2R3Dt/O6nP/3pDzzzkdrYV66/9703r79+8+adg4nFHKjwDpQyGPGYC0YpWAcA26M+VKXQ8pGdzSvnNgV2SjdRL33j3esHy2Vt/aLSNE62dy/EUa9cKONIB1YiL5FTzkujjfWqaXu9HkesLMtOG8yZR6BknTHLkRRENcvu//bX/+zP/PSP3n7nxmwyu3f36LkXX9s/KY6XzbJVva0d4Ozw6ARjCs5nvbRtKt3WCYEE/IVBdqEXk7r4/Mc+cPXS7mp+OOgncULu3btXzJtVa1uaGpE3DiklVTFrZncf3oyffGhvsz/8zd95QUfDN+6vjlo+rYwjKOnl87aRACiJNMaWM2kNE5wR6rquKVauURGGjAuB6ajX/+gzHxn109deeeXOrffyLMqTuG3qk2kFBAgFIiLCE0+YCdxvZUfjERZ0US4MsmmeSymnJyf9KI0RxGBi1/WZOzeMr+yNL2yKzUhvbETv3L4x1+oTP/6Jyz/26a//5r957s33PvdTzzz5oafznYtne0M1h8jGBLYPb9e/8au//53feZl2PSTFclFRhsfnByyBrXH6h3/qJzJCX3/ue7/7G7/TGc/GG8tOybazVhMPFGHOmGD8iSeeuHbtmnH23Rs3jqezoqmN9XnOVTuTTR1jfn7r4mZ/q23swf5kslgm/dQ4LSK8uZFv9CKwXb2ct0W1OdzI4wQ5U62W2khMuTR+3rQQ5Z5GGojxjhDECDJdV9elUkZqg5xP4rif51EUe++VtghzwEwj0mpbdrbqZKut9UZrlfVSkWbK26rrqq4tG1l1Msqy+WJVKkcRJFmOMdbaKqPTfi/oZwJrg+L139eNQr12oPHeo0evDj2EpvgarwfMV1VNoCIghLQKJo8eAHppT7A1RSHg/jOgH6B/4EUEnj0hZLmYw6mQ8YzbEPqd4ZuhCRoedUJEXMTBRiPP8yQS1tq6rruu00YKIUJzN9CFkfPKmlMhqQl9YkppoGcIIUINE97iDPqHh+LZP8OgILj3BGTc7/eDTiA8q0+mJ2W5klJzThnhCHkh4iSJVKs8WMHjKOZG2bJcOQdCCEajPM/TNA1jE2OM1jJ8yFCouFO7oVAjBWuUUEKEp3gUizN9RSi9rNWhXPEetZ0MNUCYJwS0EcxqAMBjtJ5XBEAgImd8nqTBHCng7KZpQlO21+sFPWvXdeuah2GRRFVVlGUZahJKacguCCLms0rsFDqzrm7qup7P50VReO/jOA5Emn4/D41YznlQeHddV5Xl9njHnHqGAkJh5BKQd5ZlQQ0SaqqmaRaLBRNUax3H2XA4xIiuVquqagAgMCus1V3XIeyTJCaEKGuyvBfmLUVRLGbz4MOTJEmWr7v14XjDl1LqiUcfD1WiECIMH5qmIYQEByRrbZIkoToNTX3jbRjRFEURoGc4un6/H+qxwDUPwb2ciWpV9fvDsNRVVd29e7coil6vF0XR2YUT+sHWWu8MZxj79XSCRUJKqZQRQuye2yuKYjabtW1L2ZqwZLzL8/RkOqnLKhSHXdeprgvbu+s6Z2wcx+G9nLHOwd75i8EWk3OeZjEmJPjPNLJN07TX68WxIIxaq+u6raoiSqOu65DzWuugl+jnPUq5EKKqGtm0GGPGOKWUYMYYc8htjDcppXVdh50WdsV4PA42SvP5/OTkJFij1nXdHw2l7ATlg0E/imIpZRCnhkpGdZ0xxhp/5rjVySZc2iFc4sxsSggxHo/Lslwul4G4hRAab28PxztxngrGq6o6PDyczWZWG0ppnqRpmsZRpJRqyirsVWNM1XRbW1tCiOl0Op1Ow2QJEzidd63tUAAgnDiCqTHG+7WKJuw9c2qGGy5hY9zZRFHqDghQjwAgooxSip3TWkdchBtj3TZt2yprwlsgwHm/l6cZIhicd7DuGoSpGiKn0R8Yh5JbMLbuOQIERRAhhBEaRTyN4khwq2VZzItiJdvOOVfW1e7ubl1WdVNevnBxOOjVxcoadfHCeeTsfDZpq7Lf66Vpqrqu6WoWYUSEiDeZGFiFjW63++zidhLT5dHR9UWxePipS3/iz/5S9OQH6+MVyyKpjvPNHKrJ8sZrfawXx7dfePYbXQt/5E9+zht9vFhEveG9/f0XX7r9gQ9c/dAzH0Y7Q8AUKg0kgQ7L/alstDfo7q39G+/eeuP1eydHUK4AYcizqOrkovGAQHXQVUAcDPqwMYA4gv4Q9i6Mts8NkyzOeoPhxiZg1jayres06jvH3/+BH8LnrkJvBAcn/9X/8b986Xtv7+5GP/dH/pBV7Zd/7d8jCUbCwSE0Dja24c4RXNuBSxe2b989JkxE+RbiPeXJoqyaphoNo729ISOynB3v9of/z//ub+uyZNcehTv7z/3B89/85rP/7je+gzEIAYwBACAgcSL6/X4eJZd2LjZNczA7WTalj0ici4euXXrmQ09fPL/z+I9+Fu7fg+1NOD7+g289+7/8r1+uPL9+5ApNtLWEccKo9chY570PGitvfJ7nRtnJZMIQc84RioxR3vswuLZOO2cRQhh558ypisyHpwwmru06TIBSiCISi4hSigGc9XXZWOut9MY4awAcIoRiivNRZsF6x7zD2DPw3FkEzgMAoUCoR1556Ah1IiJCCGtw05nQDsCEIey/+/afPYN3n3z8HzLiiXfeGmeNtRZTBM6D1B944qkbb7650R/MZhNrXaPh6rXzn/jEx372Z39266knv/Er//xX/ud/8uSjj13YujCfL+4dHh0vFrWxCpHa+krJZdl6Sp1zShvkgTFGEfbOpUk/oXQriVw5W94/+sDjW6peMA4XH7703v1bs9qNz49/6LM/+qM//tPG4W9966V/8o++rA1rvZHeauw9Qd6B954SQjFx2nRd5wCzWDjARjXENsQ3Vy6O+xE89eiln/rC5/ppsn3uAlj2S3/hP793uNRAUZTxvHfr/sEjTzy2d+78u+/dmEwmbdsMsnQzT11VZWDPZxlpimcef/ixh85j20YJqYrF62+8WqyaxgDtbyQbO5ZGmEDKXYTaFLVPPXKJU/Y7v/vtN2/JowJWBs8b1wJggCiLLGcdRopgCcgi6JQkCGNnkTbM+5gS5gCk+bHPfv6v/xf/+95HPuRff+0f/oP/8eUXn2+bSmuJKUGEOcq0w50D5cBiginb2d6dLRd1W3vqjNNFXTkHqeCgbT8SGfHctkNuL+0MH7qwud2nsV1pveA5/8gXfwrO94DUxeTW2w/evfr0Y/2dDZY8cbY3muZOnOyapZjvm6/+r89++7dfNkuuK7yaLQFcqzVg+JHPXnv8kYfG+chV+vrL1x8cLN96cCQGm55C3VYW2zRPnFerVYExhPhLIUScZU3bKuuGw97Fy+NU8JjG7Uod3T85OZpZAyJKqraKYkKJ9baOiR2lPBOMgt/bvvjuW2/fu10QAMFBG7AYSAStA8c5ZgIo4Yxh7HXb1HUDiDgEFGHBeSzEKZkCtAFPuPKoaPWyVq22nnAm+HhnAxHUdHK6nM9WRS27zljpoNHAGFAmuk51zgMABZz1cu0dYWsrnYDDEULGO+R8aMEbpa212ANFiDjrnPMACGOMEAk28GGivZZ/auucCxy+siwljzjXoS1HCImiaC3akxLTOvTFMcGIIoxRwKYB6IducWh7nzX+w9MxaOyCZaHWNsDuQBMKEHnUH3DOEYJ1v9xaF/gcYJMk4XzdttdaMkZGo0F40/A64aMi5APUPGtan2pbQ8/DhLlEgLahDY8xRoAJ48wiQgjCBJAHQM5BUdfWmpgbgJxShgmr6nKxXGVJ7j3yHgFA29ahLWeMIeT7bBljtbEakKeUIgzWGactQsiDQ3jtKB8wR6hbtF53Da21sYgCsBOMZ1k2GAwIo0op490pVbpVWoeKh3CWJRFFOHQxwzGe9RRDGSClDGY1GGPK+Gq10lojRDiPAogpy7ppuuFwSAijlHsPxjgAHMc8iiLskWyl6lRVtMHpNU0TxpiWRrBIcO6ca5vGGpMk0eD8+TztYQ8eo+8XZoQEAe5wONTWHB4eNk3DPdfW1G3TY1nYCcvlklERDiEUTkmScE7DsCIcVKdV3UgmBEGoLKuybkI7VghRV03XdV1Th40XRVEvzaKN6IyCkqZpkGYGtBSwddgSAWbleT4YDM5funB8fHxycpLneVVVVVUtFos0TYuiCJ9KCLGxsZFlmbV2tVotpovFYhVgX6DyJ0mS53ld10EqGtZ/DW0pJkmE8FooTAB5YwOGzrMeIzSO49lsVlXTLMsGwyEVfJ2uwHkoOKWUxXIZpAsIIQwoOJB678M1f3D4AGMsoiiOI0Co6zqlOwcgO8UYU1ozzikjlCJGHedRVXbOG4qw90iIgLPJalVqPSeYJlEU6iKlVKul936wMWiqOlSDxhjO+WAw2BiOiuWKIBzkznmeX33qaWvtvQf3948O27btQFprKeVt2wZoHkTb2GPOIk/9mfJEgAi7JdSHoQw2xvR6vSRJvPdhGhYWsGma4+vXMSXgfKiHMcaDvBdOpXNOad207bIsQuoCQggjGthZUsosy8PStW0DAMYq5wwhKE7iKOLhQ8pGJpEghK5vOFYThCinVdmcluvemHUlDwQjhBgTYGxd18q3SZIwwFKqzdFGU1bLcrUsKkKQELzr5KI2CQVCMfEeCAbrHAKMMcFIG+2cXd+EGQ1tEUozZywhhCDivXcICCFCiEREWZZ4Z5uqWM5nzpvRYDh6eBBF0Xy2xBjXSRmLC+fOnZNt7Y3eGJ2POJNtIzvbtrqf0zhKMRClrGrMub1z2eZe1ZrStoKliKDFvF418yu7V6Pu9t3n7958/J0nd96Xsg3gjC+OoLhvDu6/8+yLswe3iOuqEiiH+TvL0YVzO+//IJycPFDLJ65effjCE77FcOfkwYP9cmVsx3vJXkxHb795+Ppr19vG3Lt30Cno9bPLly4WVfvuO7e9IBWxV5/cffzxK+d3Bxcu9h95ZHs0jgA34FtARlZVXckoHiS9bdPZ2dF0flAOe5DnQzzuYNQWr/7eP/2Vf1k0t/53f+3nf+wLn0N7Yzh8cHjwvSuXL/3kj//Ejffuv/TanavXPvAP/se/XyyaWwfHxzMYjsnmxs7t+yeecA0Eibi15HiiKLKuoRmR//1/99/evX+fEt515rHHn1pJjWNIsgRTZo233vV6g82tTRYJWavvvHy9KVYf/dQnhlh/8/lvfv6pz33q05/+nd/+9Ycvnh9mva/+9ld2d7aapnr51VeUkUXddh0DnCMERjsPDlES2kBJFBPMQtJO2GyhBWO8cwgDAgsIbFCPYMqYVp33CCiyFmEA7CkAxuCTGJxX4J2W1uo63IUAcCsNAgCELYCygJz3yFIHVhuPAMAgz7x14A14EpwGMAaMHXjmwWHkkUPWICUdWADA3iPn1tLBs68s7UecgNFtUxXNsmsbxuHxRx7ev333eP/epXM7ppXEubaGLINf/OIf+8JP/MSDe/d+6x/9s+8+9/yqaN69+eDV1++0jTaARJoBT5qmWRRVY8xgY+QRGO+U6qy1IXEHrNWmsZjP5vMYySiDo9nJ4RH8lV/6+Obu8Nr7z21duvjE+z/It3ddpZ57/qVXv/c8QtYDC7pcBh4QxhgwgCDYKKmlSjFQzphgjjHkyd74PHJtFuNyfvTWG29/6Yu/ML72CKzKl1942Rg3Go0qaVvtQduIgABIOE0Fa9MIvLHWN63mHqe9QdbPeZwpJyYr6WXtpq3sqkrBooGyBWYWnidR5hhlmIDxJhpl8c54OV90gioi9y4Nkpbqg2liQWp48tErNM0fzGeLttufzDyClHPvPUZExLyXxLkQYKytm+e++c2/+ObbH3j/Uw9duNDNF8Vk4rR6+OqV8fa2w6QxbrIsjhelaWVAV6Fl5sBtDIeEIHICVVVzwIRg1dSlkaOEbJwfn7u4E2Vk1a2uXrvy3Rfvf/YTnwIM1RsvZ5d6lKqPfeaj4Dvg/8He4ISC8TRKB0L34jRivABPGcnzlGB7OafHh7NreztUynsPbuzf3L/33lHVwtNPfshFUaXr+yf378+mR0WT5pD0qHdmIVe+gV4vg5RJYmZVMemWh9UsFgn1VFaqLVrkaBQlmmAbx5Xr2mqhm2q7x7Y3B3ke20699dZbqpNXrmTj0dAadXIybZQVWW/eSGAJMAGMMEw8GLCSa6aNJ4QyggMAA2cIIYCpMQ4h7IE656TRrXYYMfBQdVpbVdbVZLFYlZ3xgDiijBBtwvMlTeNxkvX7/ThOjIPj6QRR5pxTRjvnrEfOOuMsI9SBtx55hAG8x4iGrMrwOAnjaedM6H9ba42xWmutDEIIY4oQkbIOAUABtwVcFXpOobsA4BBC3ltjwCIbJXEwpQ5BWqEGCE/uMzlm+BJCcC6iOOk6FQgDWnbhjSilLQZrLUIgpQwmdwENR4lApw6JgdoRmNOh3ehO/TpDdUGII4Seva87Nbc5Y4QH5kyYBoR6iAnunDtr6Z2WCjKwPjAgFzochGKMAbAQceDxG2Os1WHWQSmt6zJUSpSRUC+Fmix4+JzJQ8MPnwkV4NQkJ4xsrLUegzEmgMXQvBdxhBAinMEpwz70zsM0o5f3VNsVRRGOKxx7gIOnggofOugIISVN3TQIQ1AlhoMN/jNo7WxqAyZbTx6Uccp2nXIOQpeTcy5ExDlTSm1tbYmIBfvFjY2Nfj8HgGJWIoT8KbrlnLPTWdDBwcGyWFVVFarHQP0Pc4m2VdPplBIePnlYt3BHIIQEiltd12VTK+2YiDmh1q2paG0rA9UkiHfDQMY5F7BgVdUIISnlfL5kjOR5jhCaz+eBErP22PE+SZLz58+fP3/+5ddeOTw8XCwWZ/5RVVWtVnWSrAPawsgonDKM8cc+9rHpdH7v3r2yLM9YRoEHHyr+MNQKO9BoT7wd9ntJsi6YMcahbT+dTuM4dt4JIeq6LYqiaVtK6eHhftbLYxGFKi4Ee2GM14Q3v+bsAYCz1jm3XM7zvN/r95MkCRZJTdeFOtNoO58vlstVkGcwxuIoW5W19xDFUZZGAIgQYrWhROVZDwCQ96dWtoZRIYSAwPBBqN/vp3EMGBulZrPZhb29yWx2dHCwLIosSQ4PD50x8+WSMI4I0bqbzWbh7BCEV6tV6PEnIiKnxqmhhBYRO2OIBQQfmHJKqdVqtVqtJpNJYFI5544nM+eRxwh7sODDTaYhjfc+iBbOSPld17FAmwESSrgoisbjrTRN5/P5YrHAGCHsKaUiokIwhLxSndaaIhp2MQAopc8IZtYGq9Ag2qXh2gSCsUKUc8S9tbapiqoo0yiOo0h3cjgc5r30+Ph4vlxSTMYbG1sEG+MAQHVS6nUkIueccBb2MPoB07BwaVBMOOeCBUKm9cZijBmhk8lxFseM0P4gj4XYHI8CeenKxUu3bt3a293eHm8dnxx2Wu3s7GRpqjqprUOYEBYdz+a379zT2mVJnCV93aGu6FZlaa1Pe7m3dj4vYqnfm+8vJ1Pr4PCN/YujV0prETO7l3pu9kAulrmMnd+k1A8GWmQc5AiiC/7t6b//3a892J/9hb/0H8P5i6BbWE2f/a1/e/e2IwBIZ7Jl86lM0o3r7xxceGjXmPbwuDye3bxzT2Y9+IUv/uGf/TO/mI+jKHOAZgAnQAuvJk0zdaCQ8zRPRptDcHG5OpnPKilbJGyckfSZJ6D2b/zmr/7yP/zX0yn8jb/xFy9f3EM91Fx/6Z//yj997Y3jP/LzPwGRe+SHPvDIT/w42PhLTfHtr3/v2W+9vHmud+HS4/ePpvO6dqjV4IyS3toI016c5xF/6+b+S6/uf+bz7yuL5vnX7s0ae3wyu/rUU/NV5QDFPEp7uRCikXIxWXSrZpgNNoeb7z440KCG2+e++dzz3/jON3tZ/Op3X/vKr/9mVZhzO0nVNv3NXiu7OwfKJCOMKfbOaA3WCsExoes+EZDQBHFBnkcRcsiYdWggwFqYhvGa74oQCtE9HhAARgh5b5I4C1aTxihnrbPeeg/gOKMIEXAIQFOCggCNUkrWXSnkLDgEgDxBgBCJI4ExRtg7p7QB66TpHHSGMQGAsEfeYfDgAP0gvPMeeY8BUUwEZ4m3Dnx3cP/+Zn/wn//Fvxwj9A//3t9/6MKFn/rDf2hRrr7y67/xL/7lry6Xq7Joz+3s9Ue7h9OFssQ65BEuy8Y48BiPNnYu9fuzxdQRxDHEnFmwCPsADJDGiaBSr4zp0gw8hg99ED7/459OtoZw/hxkmblx641nv9l07sZb773y8ktEPEQR5RaINw45DA68w9b6uqHOMISo4B4ZayzGXAh2971bvZS7QdS2cu/ybj4Yw7L6ym/8zpf/7VeoyGJgnkE9XVRFcW575+TwQLbNqiwSEbMeka2SUhOERZxZLBTSR6sCE4dcq02TZnzz/MX92XWFADmHvdwcbG5uDZUu9w8ODR33z++ctNVJU+9cyc9fuKatOP9g+vrrby9XcGl7lGxsImxF0axmc4Nx10qllVFeEQDZ2iim4JE0qpPv7j+4s/9gg6I0YhHD2+fP7Yw2dra2WmPmVbcgtff2DI8dTU4wQWkUMcBZkvQuXFrOl8v5IosjoHiQDK9d2X380b0Lu4NEOAZSmupn/sSfRLv99uRO9tDDzeLuV77+nV/4cz8LgEBQoN/fG4T0oFBH19+Z7nf337u3nBddzSjCDpzX3exI7m2iGIFt2sXRyfHxzAAdbI72Dw8MwwpbzNCjj5/HEZycHNy9Z2IBQgBYKKtq2VT5cAScrap63ipnFqpWVkHKk17am6/K4uCw30+dkbqrKEBm8LI1hDjmwAB5+JFr436vWi5m05MkzeMeNUATzxQQab3SurKNM52SrVLABcMOeQAPziMwzhtjLHTOYwBwRHiMMCVO2a5rKyUP5jOPvPFOGQ0EE4IRp5gwnmBrrTYuiqK9vb1z584ppY6PT+IoAkQCXNRBv+9sUHg655wJ5hkOACjDjGGCMaYUE4qcs9oaa11Vl5RwSjmLYse90c5ar9ouH/ThlC1jrPHa4w5bsGVZOmfWDzjCMMEI4bWjkPMBPq6n5xgTQuI4DrzzAHHC2dVaV/XUufWUQJ/mcBFCtGw554ySUP+JRHDOPUZSd1Vdd10XXNjDI7+qqoBlzwYOYS2893UjKaX2tAsbvg/r/jNGCHmAteun1s45wCRQaQnjCBGECGDsAcVRSgl3znlEtLZaW+8RxnixWORZP06EtQETO0JQeGArpc6a/RgDxghjCFicntrahDoqPOOttUbbum4CTX/NtEHSe+9MsArv2q7hQhBCgGDGGI8EQgiQxwQBAef9bDazymqtCWGMrb3GEUJ13SKEhIgD7g81T9vWXHAP31+cUN0FoB/gFzqNKNZae7cShCulhBDj8bYQIo7Fqa85j+PYOh3H8XA47Pf7XdecnJyARl3XBXxPBd/Y2Nje3uaMLRaLyWRSVCVjDDMKFjxGLBKhBRu2R6jTMKYIoVA7reXOPNIah1WKo0xZU6kGIcQwQ9h3SpZ1lSWxMQaDD/DIWuu8QUCcc0KIoMzmnPd6vTOlBwAES8dQy7Vte3JycvPOrYAsoyjq9Xq7u7th0jKZTMKRhqSt4BbqHTz77LOcR0HOEbrjYR/u7+8H6B/62WsZNIaYkSSJw1jMo6B9T7MsW5WV1hqcHw2GUZSUZam1xoR47MMJChQahNDGcDgcDkM8WbkqiqKw1nLOkyhmjG1vb9dtczI5quoky7I4EYgipRRgcia419aXdet9o7XmLG2aRikrBA4oP0zzF4sVxjiY03uwaZpubGwEA5yqqu7fvXcyORGMp3kWiyhOk7fffMs4C873sswoPTk+ZoRmcdYoTRjFFEfCU0qzNGeURpEKpB0jVV23UrbuNPY4GKSGFeOcZ1kWrvEA5cOy9Hq9zc1N731Z1Mo6Dxic9wgQQsijpunaVobxFxAshCAcY+uMsU0rwflQviKEV6uyqpoz1RDnLI4F4xjAaS2V7owxDgnn1qEE1npjdOBVhq0VRBfB6NY5Z41P07zTimA0Ho/dYNiUFXI2juPgJBtW8lFG4iwNpLXloqjrurGVBs8IRgQDuMAAZJSJOAosRwDw3iqlVnUTRXESxQAgG1mWZVs3VmuMYdDvJUnE6PqeEy6fN6+/2batNHI2mxwfH4dVnS1XFGHnHGKRxVpq52jU66fnts+51pdLKbt5EkcWdDs9oYJuJn0uxC/+/M+MH3/shX/9L7/61d8+eu9o9+L5The7F3pRBA9u358eHD106bJWtpHlhz7yw+yJqzDb/+73biwO1QevfRDa5I1/9dXlYv7ct9++cD774o987t7dxUsvvGdqWBwsr8/uVw5EUkyKmufAKX30g7u/9Jf+4jNf/DmgFlAD9gi8BW+0rRQoL7ggPYqF13i+aBaTB1a78cbW8PJV6O1Mn33pn/z1v/nG68cvvwqf/Bj5f/33fxM4A0HtwYO/9Tf/9sEBEAYHB4tz1z7cTgtUmV/7X37j8G41XcrBxhVtxGxl7hxMe+O8lGUrK499RCKEaCuNlg4I6+3F/+a3XmMMtnYHtec+7R+VLRGJVHpZVvdnM2OMx4gxFtOo8tDbGDfNUhq3uX0R5uJ4/0GOE49xUXXD4eaqUYfHYCg0Fmgalx0gr41z2hqGMaWMCxHHcd1JzhFjJIq5M1ZrBuCU6gAjwB68B3AOGYcNQSGhHmGCMCDvvAMP4DB441xZ1IBCSYkQDo05QAgRQJRyBDhJMedRHKcIIaPWudTWehMcbbzDxBFKjTfYYwLEY4II9w60lc6Bs2bdjEP+bGp39jU5mUecMYIpBs5jwenP/cyP/9J/+mchzaCo/qs//xd+9md+5rOf/ezdew/2X3j+xrt3Oufy/qjumrfevZ/nvSzvF12NOKeEUUpzkXiMqqo5PHg3ShNEMOWEcMIZ89h75DAg46xz/srlh+XquJkdvf8D1/6L/9N/BgKBab/xL3/1vdv3MYlWy+7kaFVXepBsFgY5BJgABoy8Q85io7G1yOmUR0kaASVL2SzqUtWgRUQRm5ws65pY2zI2/aW/8n+oqmo2XT105dFW+tmyipI0SnumbRAinFDTya5ukMdJmgz7IwqoXZW1VO1yhbqC2c7B7mjA416y+9B5SunWxSvYY4wc8SZKSJLSuweHh8eTJz78cHT5nL53694MPv+ZCw9f3Nsa7KrWPXx+88Z7d67sDFvraNfSromdITQukWOYGGq8B9Uq3ShAwC3kAOM02Rlvbm8MeknUS8VwkPd6vYOT47JpTxbFdDavWqnCCMm7rJdhhLSWq8XCa7XR72/kPaYtdn60MXrq8atPPXV1kGOAcms7Hz+0B9hBU3z13/wL4zv/XHt3//gP/9FPgdsDbOGghKvf3xuv/Nrv647YIj243xzdnjpFMI3BUW0bVcnHLmV/5hd/vp4dv3L93fduH9UdWrVwVB0VABdGHHF880HX3IGL23D5fLI7hOMHDSGgFcxKONqHeTkXw9hiHqU5QVyJbrUoFrUq2oX1rlV6v6w5gTzGMfW3juXhyf6lrfyhc1tPfeCZ8SAj1noP/dHGcDisGnn7/uHhzXu1VmWnG6WMcc4DAiAAGANCFigEZ3aCwFjlje2UNtp0IFuDOg3K2UY5acFhBBgBIkAIpmDAOwcWeatVnCRcYKXU/Xt3JidHWutVUfEoxYQ58Moa6x1g7MEHJBNagaEN4L2ngRQewFUg9wQDe4yxB2uMwpgihBljnBM4ZZO7U8fPgFC7rlm78WB0amOHvPcIAGMC7vtqzv+QgbNm+wTwHXQ/TdOF+bj33lsTnvFxHIMzOGSLr/v3BCHkvK/rWmkdGq6BoB9+92yecDZkOEPPoVV81jMOvdjQpg3f8adOiwBgnAv99a7rCML4NB+gWK7CLwY2ZGgfEswIoWmaxokItO+u65IkCj4qGGNtVGAMew/ho4bXC/MWOBVkrNu0zrVN1zRNYKGEQ0giEZrlcRxrt1ZveO+dBe89piQopwkhiGBGmezaswU5hQg+QOdAdAuN56qqtNYhPhmT9fwn0BXCgp+pEc5OnDHGSN3PB0qa8IIBN4RHhbV2Pp9jAsGDpSxLpbo0TjwLXknKe48wllIuFou2bTsp0zTlkbDWYkYRQoGcDdYQQhCi1lrwOGwWSunm5qbWOsxVzgZHYRt0ZRU0mr00EywiRIfBUdd1YY7EKcMYM06iiJd17Zzr9/s7OzthWhLi55RSi8ViPp+HBrD3PrjCiyQKzpVN04ROfKjWNjY2zirYwFCazWZK+kGenRlxGmMCNi3L8tKlS+QHPKCCipRgmB0fyrY5OTkpyzJOkyzLGBPeeyGiIBOnlCJC1+ZUnMdZrIw2xgSpetM0J1qHyQPGmCAcePOnmN5478J/xpigxAiTCqnDOiPvvUdrFgGAt9Y3Tac7bTQ0TdM0VRxFSZI0TccYoQgjhChloYxRSr3x2utxHANAGifOuaooC7cSQoxGozDbqaqqQU0gO7Vtiyhrmk5QEkxI67p21oYP7703SnVdp9Q66phSisna4ytcL3VdB2ujcAkjhNI0zfM8JD84C8XhsYN1TAelNPAgEUJZlnVanWmeMCEeIYeAUnK28wPVMExd6rrkghCCnbNaS+skQhBFghNhjQ8TCbeOJqCU0q5V4bojhIRM63AKRqPRZD7D4DY2NhIRzU4ms5PjqqrmIfKPkyRJhsO+c25ZFpPJZNgbUozDHAYHCocxxlkhhHbWatOdBpMjjIOcGiEcyrOu7qqiLJbLVpt+wo3uKEEALkniopzneYoxnk7nlLP9wwNjzHg8brR899bN4XA4n0x3ds6ROEbKDHvD0eZGxAVCeLI/72U9zknbll1VUOIzninMeNz71d/8nWceHInheO/KtaKYpceT8d7o7oOZRY66JBpeblwfiOe9Yet67Lh++fVbq6XL+NZHn/7Mg9fuvfHsO0VRxjg62oevr946Plp+94XZoYVdDh/72BOf/OynXvzeiy++8vLGeOQJfd+TTz/zzMfhcAIR9rhAQkMiwEVtgaoKeRsPe9t84xyIZCQXo90WBIZmNbtx9MYLz/3e7/7BrZsKY3h4Dz78vk8BG0AsVm+8+g/+/t+//w5EKVgJ92+3D+7/nudof373a1/7rtN8OaV7O0/rFg4nq/5ouzHLKKPpRg+cUbXzrTLaa+cmKz0mRFG4+PClXm8wXxbxaHu+KBx0WdbTlEymhdZ6a2d7tDmmlOvaHrdNkvdO9idvv3Trs5/65Kc+/blXX3zppD7Y3j1/fHC4LGtg7N5RSRK+sbtXThtzOs0OVFVAKHSOMMatqZum8tY5ZyKRWLBKd85aFLi1wWYdjHVoje+9d856sAgh5LwzWlmLMQ7DKvAePIDHmGDBuBCCUU4IoZQTQpqmK6oyFLcYiLHOGOcdwpYia7pOhfQafGr07BwDpzhhyAcfL+W0s8b/IPSvqqYCxxDinEaUMOYPD4/fe/fdi1u7d9+9sVisvvXNZ19/7a1vP/cHHjOR5JSKstMXrjwhonQ2meMkSuOk0XKxLLumTXnS7/XAY8GimEYeASBEgVnruk5qb8AB1pgKITtnLdnY3FKWgo8nd+9cv3H9zu3p8XEzn06ayvfSzUGc1kWBgAD2YB0BzyiJMUmiiHs7jONeluV5rrE/Ws4fTNHBYlou6vH4imLWWOWBeZpev3V7NBp95JOfXy6a6fyIRr1ZUXNON8c7y+ViMBw6ozMRU8aRxxQRIYRupHYOgRv0c69QaVtd1dd2Lz724Se9R//T/+f/uzvaPr81yLjDCqX97MLuoFTjJz/wBDD03uH9FuD9zzz18M7lKOnDsuvHH87jqG6r48Pj4vhQStNDSMTR1mhDe+wBHPjg96CNpBY2WZxQ2u/3d3d3BIXVbLJcTONY0Cgu6mpZLMq6lBosY4C8tSblWVksrZZZEkeUmk4i6wdJ1tUNGFuX9cnJyWTWLIvDvEcvH+3Oj49vvPv2/oPDXp8US7uzjXfzJ2985c2trc0b770Nf+2Rs71x49tvjTcvPnLh6lwdoBZjL6wjyniPsYjg2tWHdzaHv/UH39jfPzIWLOUmotC0F7ajg2lTGzi3Cx/58LX3P/Xo7sZGSihz+PDBycm8OJgtX7p5585krixNBiOMiFG2kbbpTKsMgAFMgFLjbGN9W7mUAdGAAcxBCRBVxWspccMsGw97o0HveDK/e//w1v3DVaMK5RrllQOPABNIBcRCcMIpQRFnvSxKk5hQr7WUyviirJUzSksNxhIglHFsPUKUSG1CuIvz3jgLGBPstTHCuYDKAn7z3lvjOXMObLg7hIo/3ChC5zGkrWHnvfe0qVsuGAC3FoxVxmjnDAD0Bz2ttVbWWosxMMo4F5TS+Wqp181XzDmhlHhwyjiPAMBZ77x11q+TaL2FlCchhuZMVhs+ivsP++5n2DeKIin16SiAYIxjIZIoauvKKG2UPDMMQR60NYjjLMsCfSLQioJU152G0QYkfUb+CYFKcbxurIaGaBRFgbQQmotnpH8AiAgFwGFi0ugOIcTIOgyIUuochNgsSjlnEUIoidKwygSzLMvSNI1jkSSJMYoQgolACAUbULS2TO2Ukt4Hc2UI6xDqEGNM23SnAmgRkCJG4Jw5FfAhjLFHAABZmjrnAu8JAEKup/M2z3PZyOB7GFos4X0D3yPMVQJxCGMcRaJVLUIUAcaIIHDegbPegkuTCJ+qUTHClFJGuaMGIWRDnpqyRmlKaUgqUFoCgBCscqW1OlDzB4NBW8mIC9Tvw2lkWFVVs9lsOBqlaaqtWa1WquuEEM57KeWwlzvnEKJJkggeM8YCkDoDZ0op6JwxOvCmtLMIYyFiSmmcpXmeR5wihDB4pRTFKI5jZ+x8Pl/Mp6vV6uGrjwZuCQAEaocQYnNzc2NjI5Bter3ecDhMkiR4NB1NjgP/O5ygUB8Gl8mTk5P9/f2wSsHfpt9Pi2XVtjIs+Gg0Ch6OoboI44XlchlC7rz3WnUxI0bJ1WpVlso5hwFRbrTWOV635KWUhK11IB6gruug0Ao69aIouqZBCA0Gg+AbEzCBMcYoLbXSWvGIpWnCeeQQSKPiEGorImut7HTXSYcwpRQhzChfLgutHGHUGAuAoijhjFtre70exhh7Z60lFIWqqSzLKIowoK5p67IKMvSgeK7rWksV5gaUkDTvee/rttOAsHWCMiFiY4y1vmukIqYoiiiK+HoklpwRC5N0M7CwwkuFmjxoxM/kMUqp+XyOMW7bbjQaBc9QbS2ES8Jb730ju3UjIIQzDAcRFwDQFKVRa1POkPntvQ8yetm5DrSx0hiFsGWMcs4xw0Cd9zQwGcO8K0gvmqZpGxmq4jRNKeX2tOrG4M5KC4yxds4Ys729vbM9llIeHh/Udd3r9S6dvzA9mYCz4R5CKe20klIG6K/KMthzYUoopTwSAsVbW1ver2/xiUh6Wa42R1qqyckxxURQgok3qrt3+46xzjh47ImHtTUeuSSLRRTNZrOqbnrDEU0SHEVa69aYzlSNNUrKslEbu+ejfuK9PpnMbFduj0edMNPFYZz1q6q69XxBjMycOrfZn9fzW6/ca3q9WSvf9+hTm+Ph4WIeMdI2yxd+7d8U5clgmH7ofU8PsricyPndsu+35it3t2wlwQcv36QYPvr59/3o5z7/oU98BEYD++CO94ticW+2WKrOV3ePD1+8vvvUY7AVo8EmsBZmdx/cnR4dL2ULGMV7P/w5OMLl/eM7N+ZttY9hebD/xptv3v3eC3D+4rnLO9mrr77741/4xM/8wl+B6eT43rtvvX59J71KLi0fvvb0rLSvv1i0vnswubdUtwmjjG4N+n1OdzpmlJXY6UY3eQ+IsE1T1xKQgYwJTNLPffJzb9648cRD21GU3L+/3zStX4WI26avDOeUx5FIYsqJ1F0ju+msHW9vLcsl7eVXd8dv37rz3RdfGab5qL9jnKgk70xMGSvbmiLmloqJ1GtnvacYAUJSSh9CS+LIWtM0lTEGwjZjHGHvnLUuyHwJQg5h66xTSoPzCFHwzliDnCcUOe+dc4yKkBHpvbXaIuQJdd57S8EjDGuvsw4AQryd0Y5FIubC4zBndtZJACO18h45jxBCBAtCCKKEAt1MMvBOa93KzllPAP8g9CeEOWecs0oZMF4r89Xf+dqLz34r5xH3/rErV19+6aW6suOdDUMojbPagu26UsOqa8rOX9jZPJncUabzHEc49Q7VTZNE6cZghDE23nmCKWPKG++1VgYcGsYbMeXHxzOsTTxKnn/x+v/l//y3NsfDwcbglVf3V8uOkWxyVN8uDi7uXtm9+Phq9sC61lgD3mVcjPvZTpYNhRglSRpHIuEWwd758c5s9Pqtd9+5fTibzZPe0HtuQS4KReJ+qfArb982FjGakThVq1Z3LrbgMFbS2LZLWBynvaJpAu/UWSM4i1jaH8RtaTqQSRpdffrhzat73/n6NwvZ8mI5SGEYpf2YbeTs3ODc1sXR4PJ50xYGu8//5ENPvu9xqDt9fOveewfHE3Wyf//O3f1F0SLPMirS4QCLVAHGHizCQAmhCGNsneAel5P5hz/+0St7e8f7948W0+2N/uZGv6yLVjadapU1FgPilAhOopRQ1ippjEni+NzO7jBNm7LoypYCzuKka6q33377zv13mDDSLjrTeIDpFK5e4VGc7R9XP/dTPzGI0y//i++kEf3qu99VTQ1/7ft748c++Pn+xt6DG8Vqf7U6rmTlC9m1WqccRnH0vve9bzKZtG2LCJCYHx7IldaQilePmieusf/053/m53/mp/uXL0KxgukctK9u3LOswxET42GpEpoXrYgcJaptF6tpuayUspQwC0haq5zp9weqKmtrnYaccO6hceTOUQEbTEDTNE3dNO/dvH1yUpY1IAqlgtqC8YAYIIoRwYhSzEXERCxEL0/zVHAG4IzGiBDtMBEGsLJIOt+B1d57LBzWCDvrjXOAsUeACCKEUMF4nAQESwgJTRzkvAVkLYFwxSIA7zzBzjkCJHgtWm2MMeAsQgid20h5zJNIOOScVsoqihFmOEsiB4A99hhhT4xzTjvtLKE8cGExxlxQjHEg89R1e3r1QkjaJYgCgO2MYGtz/fCcC7CJUhq47J2SzljCKCPUI4yAAkaMrAnrbVPJTgehoXPOGh04vr1eT1CmnaWctFIGOn6ogQghlAur17z20Bpp2zYAfcZ4nCRCxFK2weokAOtAbT+jz55NAOypJjVQgBBCgR8VWnpWm0DpFkKEw5Sn9AAhRJAeWmubpinLFaWUc4awV0op1YVWTV1WSilKeVBlcc6FiAP5WGstO+WcC7mkQWkQcXYWh0wF5Zxjuo7QarouYBTvkXMuLOMgHVilMcZZlgVv+/U0w5gg/A3q6jPXHcywNursK3Q9sywZDEY/OARYN/gRWcxm8/l8tViG+o1zHsdRFEWDwcB7X5TL+XzuvQ9W613X9bL+ev8QEkcRwjiUH71BHyHUKlmWpfc+zbKAsIvVynufxOnm5laWZc45KXXAuFJKrU+tjbz14BDgKOuHVCTGWMxZmMBEXEwmk7ourdZCCOShaRrZtQihy1euZL1cKXXnzt0HDx6Ax/1+P8mzkH/hvccEoigK5qFhStM0TXBAYoxRzpH3nVKBYEMp7fV61trFYqU1xDFORBK8ewmlkRBKa6M143y1XA6GwzRJtDFGa4Sxs7brGitlliXeulZ2nDIeCc6jOI6jNKGUamWapglEIGNM1dTKWRYJb9byEsE4AGgprbVZkgohnLHe+7BpnbeVrJu2DW2APO9TwTEiCCEexVrrumqrqur0WutijcMYc0oD9d8YjRAyWi4Wi/F4DOCcNgihLE82NzeDiGJ6MjHGLJeFMWo43EjT2GrnvV0uC0IQ5xHGIKWWsmVMJFl6Ml8kaR4sngKD32pTN2UwVqIIay0xxlmeZGmPcbJ//8F4PM6yrKqq6XRaVVUowMLELxhMSSkJo2mcAEbO4vWxgHcWnHPG2SCBDXsYTiltXdcVRUG8QwBhhgaAAmWx67pze7vOGWu1ctJ7i5CnDBNCmrLhnDMqwsAkOGLFcYwxXSwWs9lMax3HSZIklHDjHSKYci4Y89aoVlmjKCac0J3traIoqmIppVTGWKfXA1SM1xKaOMYAzakivFOybVtpdLh+HaxF89PJXClttUEIJSKJuKAMYw+MMa1araV1GsAyTnq9Xq/fP5lNCePD4bBpmoODgzzrDwaD6XT65JNPAcBqVc5ms3AvEjwebo7H5y/UZWGbgoPNOSLYW2u1MXHex4xyRFSxyrGLrbLFvLe91SSj0vq2aKrl4sK53Qt7O9PJfl3OOYeLF7YvbG13RXV054FvjSzkO7ePlxg0AXDwpV/84i/8+f8ExiNYzuDurX/+j39ZSvnuO++0jX302lP7+7P3bux/6OMfevSjj199+qHtvc3jkwdvXX/96GSiJfU2LuZmmI57SaplsZjdmhy/O1+oqoYLF883DY8i/rnPfKrfS27fvM6pZ8i9+PwfnNs9fzJdvXPrOO5vVxKdLLudy9s0W7WmPbrfXdh7n2ojbWmrm84tdx/OpZstyoOykDGFrcF2QntGk0nZxPmgruuTk5M0ydc37SgKNw1jVZIkhCBjjBCs198QaV9EifayqVam61LBx/3heDiShbxx/b0L5y9VbXPzzm0SURwzRAjlvJUawDHBEcEWfKC9zRcLQkixWoURkFVaCNE0DYuoNtJZYJxgjEMst/ceeccYA78WyDHGkPPKWu8IQiS0kJw3hGDOOWNreRLFuGkaa4IZdKj2izhLsyzjXDjnpDLOeAfeGtDayrUxIKaEU0opQREBir21VqkOADPG3j76C2fw7undf0AREOwpBoQ8AYdACoJcp8b9fi9KvNF3bu9vbQ+Xbds5n462Zst2uqhGo3OMpo1uow22LGZeuV6accRULTFCvTQPvRgmRJzGgFGnO2MMQiTnmarbTBAG0rSrXoy2NjLO/fb25muvvXFwUHjgGOdpuo1Jfrya5zt5ocquqSjAZq/38O7O1b2dnV4mnIoophRoRFmSztvmhVdf/c4rb0gY8HS4LOc8ppP5ZGM84iI2DiiJ87x/99a9NE0FYdPZZLzRN21LkItYFGf9ZVEqr3v92LqOUYOcjBlZnBzubA4+/5mPf+k//hLg7pe+9Odmh+BbuLAJH37i0hOP7F1+aHd4YROyGMYjZ/SNW3cefexpiNL3fvcb99++8eD2wdbuo4ez+sa7t4zHPBuVnSpaUzbaM1FLqZwnghHGEAHCWcb4bn8wznt5HDndqarQbdM2dVlViNOyU8umK7XxNBZ5L8mHIonruvRWJ7HYGo1SzruqLJelaeWw13feNE1R1QuLOx6D87Lt9O52zyl1cbz7yWc+8cRDj77wrWcPH9y/9d47J8fuykPov/zef322N/yvvdMt5G/9zqsvvflgf2Vpb7fxvKjqhLqN2P/pn/9RVC/K+clzz798uIL9OUjWr2zzh7/46T/1Z/7o9kc/2l1/67UXvyvLWq6qk7v7h3cOp5OyNViRaIUwGmx0lN4/Pi6KqliulFEUMEHYeusBKKad0zGljFBQinmXAkkoj0EOiBsmsD2M8jhRTbOYd8YDjWnZmQ6Bp5xGMSHMe0sxSaLIGZtlyWjQFxHxTnVdrZQyztIosZi1ni5adVx0s1JW0irneZp0UhtjECUIeRPsuRgJ+VTGGGOU7DqlFCeUR4lUFjOOMLbeAcGUM8A4YKQ1qpTSauPBows7o/5wQAmaLSaya5JM5FkK2EecObDegvXgrUOYMsIIo8Y4e0r1Dg9F1XVae06Rc54QHMcxYwLAESAYA1gXUujD0/eMm2H92tkTYUwwDiJWbQxCjIko5mvpcNvUXSu1Ub0sP5sPpNHabpJSmmVZ27YhC5Mxhgg2xihtuqYN0wBrTFnUTVWtb3mEbGxs9PtDa/WZ+NV7H8V8bYRkDEIQuv7KaItQ07VBDnvmduq9D+hfShnYFHEcC8YBPFhptUKIxHGcxL04joWIKKXHx8daa2M0IYgLHMg/SimnLMGY4GDJhymlQT5VlmXwnwyHHMJKBY+iKNFad13TdZ0JlF9GAnBx4BEQSiljnBDiPXLOxIxrrc4CEEIPWGsdlJRrTrn3Z+SoTqswN2CMJEmSZkEMQNFppoHgcZZlQc0spby/f6/uWm/WiQrGmCAsy5LUWhtkBmHpkiRhkVjMl2EjIoIxIExJmqZpmiqlzgIWuq5rVVhaaZXlnDvnlDQhEVZKOZvNKKWhnAulmgcbBBUIyJn5KSUk7BMC6PDwcDjsD/r9UIUihDhlhKCNzd5sMb9/f//+vf2qNowREaeUcwxICN7PUyGYs0rpDiEvGF8tm729C5iSYFh5eHT0xuuvl1XlMQoCAMYYYdR7H3rSSOssS0fDYZwkztqqrtumCf5UaZb1ez3KmNG67TqtlPeeMRLHcSIiC1530iEI5qSBZwWwBrKhhDPeRcPewcnxYjbz1lFKMSBBaMzFYjaPhIgYx4AYJiTkFTRNZ6WnQAizznWdMsaIOMnzvgc8HA6jNKnrejabNU2DKeGUeWPTJAqwPpyRsFv6/X5VFXVdCyG2t7dDX38ymSCHCCHOQdvWxjiMAQOxTqdJLiJmjQfkNoabUcS1tsbak8XsTFMe0r4JIVHENzc3KaVa66aphBD9Qc87qIqVrtuqKIOK3XoXgjKEEIh+n59GCGGcE4yt8yFq1HtPOUuTPHTxlVLnzp1r29YYF3ZaUNV3XXP/7m0hRNBFUIQxQmE4FmYO1moHnscijuO261bFguJ1WHWQW9R1HdxsuaDhF8NdK8hw4yhtO2Ots9ogDFma9/MMnA8+pxhjtA5LkUp13ngAoJiGawcT0rVtUZbmNJUi/CVY4rZK1nWttdZWSymtdRhjZ2zXdVp7ANAeMAAFiGLgHHFBoyiKksR5TNi679C2rbPQy/PhcBjkLtOTyWq1EkLs7Oxsbe0kSTSfzwnyHCFOiCBYEIwBEeR5HDnnnDZGSytbrTqvpfZ+f9l87FOfqZaLo6OjrmoGw97Vhy7dvPFuxGlVrhgh9WopG9nPcuQ8AvelL/3iF37kM//0n/zjB/funtvZ/MwPf3I5nVx/+03dyeVyuZiXCNHRcCdOe6tlfXB81CLZ2+jvXdjb3d2NkqRqmoP96clknsSD7a1zGNNbt27duvmO1l1/kGRZ1nbWWi8Y7eUp8na5mFbLhbWu66CqQSSwtXfRs2S6ahGNN3c2N/fyd2++i3GSZ6PJSVHVrUOwWE13L2wBdh4rAB+4MW0lm7LRWnu/dtZDhIaprAfc7/eXZaW0ZpEAgKqqlNGMivMXL7JIYO/LalXMl1Z2iYgSEWEP29u7vV4v7E/KCWVMeziZLlZlFfLsKKXO++BGEO7w4U9rLayJtQ4R5HxQ2RjvPV77+K1VUmtFWXg6a2OcZVSsSa2nadahvRUopkkYgnXaex/xOEpE3aw4Z4RQ55w1SDurOy2lopQa7UK/LJTEQcTFI9F1jeAkjYXqWqPUjclfPYN379/6ewg7ToFQR5ljhI43tgQVWZouJifVfAZONXXNORptDudlWbUGSAIksy4yVlgMTlhEgQJy2oAB5Lw1RmsZTNs4p95aAj5mjHpktDw5OTh/YW85mwuOU06uXtxJqPaqury3+dxzrygDKwUHC8h3xqy/vazatpiPRoN+Nlgui5hGMUGRVz/8kfclSLtuaZoVJTAYbWAqbt7Zf/Xdu1PDfJRbsLVsW90hSoBg7xFjQvAYAzaddtJSwBTjgAWzrJdFI4yxdZ3BpRdLx9tVq40BAfDIxUf+1v/1vzm+ffNr//7Xvvn7rwiR9nub5XKeJ/SJRy937VKa8qn3P/b0h566df/2D3/us/DwYzf//dd++ze+dnD3QCogSb+1wFgcZxnmcdmpsu064z3gRsumk53qAuWTEEIwjAejLBIRpl4rqzTx3hontbIIaBRHcU4IcxYZ40B7g2wNXdnV3lvOiJVdXZQIoBeng17fai3bxmhNQvQqBuL1MIXZwclnnvnQT33mCyd3D77z7W/d279zvIRoE1AC/8M73898sL/y7bffvvvvf/+9lRKtTU+WBmi/Lup6Mf/4+6/8qZ/9UVMeTw/3X335rcnCH870Qecfu/ro3/h//IWyO7p598Err7359q07q6rBJME8oizGJGo6M19VVdcp56umm5dNC9wTigGw09gbCp4AEIAMgfcgAHIGEQZsIGbQi/Gj5zdGWZrxyCltpI44T/opS+NFXfqIKYI7rZeL5uhwYpTvDzYcFTTiIhEAdlks5qs5CiFLQDEVhrDa+HlrFp2WHlmEV8v52XMErfnpBBEc7PKC+hFO/V0QISxOQkpwiIYEAOPWzJo1UrIuNMtonGYIEaU1IVREEWPUA3jntDUIeYQJ9t4B9t5qC8YZ4z1CCCPKxToG1QXfHhNMchBjjOHTHj9g6633FsA55MAHb1HnwMdxjCkmBIcbEEcIIQQeO4QxOmXUMIIJAoyQRKuyCEdIrAkyOiA4IthZC95jjD1455wxumm7ruvKsi7rJuKCUootIpghiq1zIUm3KAqluoBjQsAqIBeYPwCglOq6tqrqsi6H22OPwXirlJJGUbX+bFEUUUZjlvBYhH8KIRhGYDvZNU3dFdWqauo4SqMoIZgulkvrjPeWcQIkiiIRJSKORVt2xhhtpAcQLCKMem+N84wx4yzR1lirlFJaN01DMB0ONwLphXOuTXAbJKEEIoSE2DXwSClT123b1vP5LI5FliVntcpyWVZVJYSgVBBCEQr5X4hSgjHLBzkhhHMabP4DKbxta8aYlB1C65ELxngymR1PjlbVyoGL43gwHKRxaoyp61Yp1dQtYyxKk5QQZ71SqqhqvVgmWaq10lqDBUqpQARRghm9dH6v67r5ahlo+sGz0lpLsLDW1k3XNE3Z1HXXAoA0WjurnfUYoTUNCZD3YBwn2BjjrA3VmtbaKk0IuXbtGuecU3Y2fcIIOadu3ry5XM0n06XWJk0FE6nUejVdbG5uKmNWZS06BN4AMpFgGOP3PfnUzs557WzTNFpZ2apIJFne77ruLE/NWOu9w5THsWeRyLMkjmOMvNTaaIkQ4oysmsZa3TYNxoAxZYxEUSIES5KkruvpYh6mPQGqVlUVBnbOaXeqWbfWWvDVVAHAYDRihHjrtFROG6VUSOTBgIzS1qrAdQlXinXWgg0cJO89pgwhtLOzG6hxGOPRaJTnuTLaWyMY45QwRpxzlGLv12Y1JydHSZJsbm4GLcHR0VEoda5ceaTrusViUbUdcj5w3iili9k8iqIsiwLELMtSSum8Nza4hwWLsDVXzVq7WvcvHQB0sjUzTU/lOxRhbWwbnGrlWjZ95oSLGQ2kPgCQSuVJginxHoUpVtOaOEp7vV74DG0rw7BL0lZrXRTLzfEIwGFAAM47Z7xHHgSjqmtHoxFh6aosymUhO8UjkWd9a5RzJkhl2raVqqWUNm3lvY/jKEkSIRghEULeOVs3Rd3oOErjJDLGlFWxWM6NVEqp4XCIPaaYMM4JpSKKrDHOueV0JZVR2gZjqK5T1lpC3Hy+DCINY5xSrVKKYZYOUsDQtm3TtNbaqB8lSYIxtdbmeUoINlbVdWmMwgRZqxup0mRAmBiORufOnRsMBlrrYrEsy/KVV/8gyMqFiEUUKW2lUnme5mlslMTOCxGNer00isEaKxUhaLlcFotlpztKqUe4kb5o2ygfNZ2elW3RyNliuajb3sZOPNh2xh7NT8A55HEn0dJUm8PRY488/KnPfs57WyxXxqp3rr/94M6tpi4JBgTEWk+J2Bht7p47H8U5JtNV29bL6XSy7Fo3m1TOoVVZY8KGG5t7586fzOb3HxzNlwWIfpxuVNqeHFT9Xtx2RTupvdO9JOnn2XBr13t/PJlyagxiK6mTiA62NrVHtere/s67VVOON7eV85Pl1Dk32hwn+Y5xzlsktVeq894DBq2lVjLn3CitrHbOESaiLI2znItIG7e5tYER1c4qbbmIHXiEkEgihBDyNhYRGQ6t0uCtc7436COOK12tilVRFMY5xhgilPHsrH2GEELeg/fWGIwQRohg7DA+xf2wds/2AGC9w2GSFehglHLwYI13DpwF54BQLigNk+F1dwZ7QgjF7LTTp50G770x1hjTgMJLH2fce4mxcQ68Q94ja5211qg1H9KtRXrgrPdgWu+YYIQgKVtjFAb7g4SfjX7POtV2hVYNY0xEvKxWGxcuZ0lyciBb00UUqAARo4OjeaMg60Hd1rWqlSHn9h5lSbbqCgteSWWl9tZRTByyFkPVldIpQQlHKCbEKaa1ll19dW9L6TJPMCVYcMo4Xk6n28OYeXtxN0l7o/1ldVQvj1cTBOA8Hg5zpdR8WTSNgpgZjSaL2bdefOmhnXwQY+6lXBXT+SzPRgkXj1595O6Lr2GECGMADrx3zlFKMCWUYgQhukBh8IQKRhgAYj1CsPCIg6MenNFIg7PIsQTfuuFGGZq9cvv//t/+T/XJwf677zxydffG3UVhy4ilcZLfXYE2lOD0jTvz77z2/1OmrTqys3P7xedfefPOyWKmHKJbaSKR0x5bi5DxrUcSEYMdcT3+aQABAABJREFUYMxolEdRnwwopYSEzWU554gQpa2WAIAZpTRhESKIEWXcqpPdqrKdQdYxT4CiCplVW8mu8d6Bt9hDEgtLSNF15apQTcswowhj7wQVWZrcvX+wkeWj0XnZweHBydHRtChBAcznsJH+4NaAw8ODe/fudBZIEqW4R+pVXVcI/O724MLe7rm9HblwoNtIsMW0aCU8NNj73Mc+/vw3vrmsju4eHN/cP5rVneUJ4rFHfFlIQkFqs6qqrmkxgDOWASgwHkAQzAhFRjOAzSza7KfcGmQUcVogEASngg/6+SBLejHzyjRNixzqJXmv1wPqWym398bHi1nXFNN5cXQ4oyTuxb3VbPHkh545nM1u3rp3ND9ZtuAA0gznChtjAXOEqUa4cr6UulCyU4YR5J1DyGMMAN5544wBA5FIMMaMccYCdkYIIcDIhvm2d7Au/33QlRL0/TDcUD9QgmnTNEp13jtCqXPQddKD8Z6RtecHOmXna/DYY0QIwRRTSkLYM/YAAMeHJ1rr4Pu5dodAiCDsrA63IYwxIRQw8t5b5zxgjChnAjN61vAOkjijTxNwTqNAEULG+BD/aq3VoM9UucErRiplwSOErHdSrWnfUkqrTRRFmUijKALvrXMiYkmexFGKsA9P66qqmqYJuMcaH6jYvX62Mdpigh7NTrz3TpuQiUAACbpOoQpjh4DDGGOMMkoQZsAodha6TiqlwhPLObc53gjTGQ82ePwHQsHm9lbTNF3dGe8iLlgkKKIOAFlJGBMRACXYAxBMAAGgVVmEjC2MwQN4BIhgwrBxWupOl4VWVmujtVbKaC05ox57C2t7xACAtDVgMOGMUo7Bk1P/b8ZYWPV1xxoHlYgPTKQoirIs55wvFouqqpbLomrqjY2xtkGUvI54C28xHo+VUl0jg9FNeP0kSbQ1BBCmLHStCCFOm7aq351fD28auChxHDvnpJR5Nuy6rqsb1XXOubaqw8YNtAdKKUXYY4ScxxgzTILLjT/dOfjU7XQ+n1trZdsFSwqMsTVGqabtCsowIBbHMcIUExJTyllECIG1BB0RjBGmQWS8KFZ1p4uicM71BwNrLeUMAAIV23hX13XdNOsN7O3Vhy4nUcS5aNumabu66bwHSkmW96w1znnwiFGWZlme9+I4uv/gblmWZVkGKWqSJIGpzwp2JpLxZ492hHrbW0kUOefauqmqqqnqrm5068cbOQJIREQpNWCwB0IxFTziSBkNAEIIxiMA0DbIQkxVV12rAik2cDwQ8gsp0emAbn3LOBXQ53keoH9d10VRAABj7K233jr7nKEbHcJux+MxYLSOudDre4LzvjfoE86C5lhrGU4WQmi1WpztSXUa4zDqD5wzHnsLVhmNMU6yGK3luTTk6aZ5FrJElstl2dST/f0kS/M855whhLTWTVtJ1c5ny4Bw/KklAKU0WLtqbZAHzjkAMmYdABR6+SKOrHda61MyoSGce8+klNYqAByJBACUNKeGrZZSEkVxksSE0FCvSSm7tg15Z845hsn3rQX8af8GIRukOEZLozut1rIlrZxzxDseR5hSZY1p6rDZ4vj/z9WfxuqWneeB2LvmPX7jme9Ut0YWi1Uki5RISpZkSdZgS57dbcdqt+12IwaCoDsNJ0Hi/pE0EgRpBAnaToJGB0EHRtoOHMexZXlQW5Yly7LEqchizXVv1Z3P/M17XOObH+s7h6V8PwqXl+eec/bea6/1vO/7DGlC6Wqzmu6Mj46O+r5v264oitu3nzs6OlqtFlIKY/vVauGcUYnUulss11k69EDicAydvzg9e++99x4+PN7bG207I4lSSkX5BAY3HuZKZhTBanNydsopG+ZZmRcESfRN19Z12minm6apO/3KzeefHZ8+fvx4NCzvvvAiBL9cLo+fPrbWmr7Lssw7L5O0LHMf8Mmz4z/6x35lPID1HH7mZ17Z2S+reh24LIrSGkcRAfiqM83jp9aT1Wo9W6564pDT0+Pj7pNno2Fx9/kXxzs7PsAP7t0/OT2fLVZMCCml1wSRMCE+OTmejsvdW7d3p6P9nd08VbOL8ydPnqjhaDoYjSb7492DYrxbdfrb3/vBd976blkkhBOkpNN9b/S1xLxpuhCC1l3bN957IXhUvCRJohGsD+RKfVsURZrlhPLAiDW+7lrGYTweU86894QzrbXVjhKeZSwI17Vtp7uLy8vFckkodl23qetoBiCTdHdvG1cX37LrDQGubfeu7DQi6hdCIF41/K6+LOphrkmtcT+MiX6EUa21Nh0ASC4iAc9am+c5IpJAAIDzEDuR1jnoPCEIQBGRUcG5hIAQEABj/h2lhFICJHjrnAbt6iTNKBr0Nkt5Kj5j3whw8vQRQJhM8539Hcp817TBtscP7qOzGNxX3/jccJDcu/der6vlBv7Un37jT/6Zf/+je4//X//wn7373onFZbVaqbwM1ntnvLeCySRNo3eF0JxTlnCWcVFykRLiuy4Bn1HarbvpdIcyQjEwZN6GWzfvvvHqC6+98frRrec+enyy9r/54bO5YzItx9j363YTMARPhICsSMTOFDh9+4N7L9/Zv3O0MywGjIqA/PRi9cmz07uvfg5V4jGsqg1ZV8Y7zoVQMsuKEILpLSJyxaSQiivL7KP5J9qhsGtFBmhJr60FQpQ6W7Uv3D26efDyxeNnv/f79yeJBwIPTy52X3hh1ZokzTBJjuuNEHQ0nFx6ffTCq+988IP/2//nn6/WEDwMRspzGkyA1So4JISKRjOhHBAb0CFa5zwgAAUaoSEFiK1aTwDQONdrCJgIyZUkXCRlXvd9Vdddp9GiYCylijlea+dQ0kBDcAgeEPsWXV9DQIKBIMOAgSABr223bKt0ONkk4tOq5ZcXM9dVAtcUvIAA0Ns/AP3Pz1YX525/b7Q0xar2WptWB/DBdvrdD95d/PSX5qdPHj346NnFJgi4e2M02b9Vh/qbv/etxq43ndHIeDbyRMxWm8tVA0wyIZ3x9aZxCApAACgCu5Os79vgAwewAQIA6ftkwO7euZEKnlAA9BwgS9IiS5Tk5+enWmvdakmEF0nXdr3Wi3bz/m99f/coqbt+s4EyY8ABGfni5994cO/hxWp5sVq2AYgggfGlIbNZR4CF0HgAQilh1ATvbO8MoLiC9VcqWe8hABDQEVFHkBM3hCjXieLPuDlcVf/kWtsZwQMhhFtrm7q1zijFGFBrPBDHGNXaMuauvHqufXg8pRwRQgDvA6InhFEEANK2tTHBWqAUOAEAiB19Fo31cAtSadw6txb124wb770LkcwjdV9f9zXxahbJOR+PB9d7nGA8gu+IfkKMaLXGY6CRRcJ5JMZAQM55JAMwSgVFrnhZFpPJNHrDr9drcuWQ7b0PHiJbN0kSQpjWtihKoSSnrObcahM5xEmW6q4nhESrZAgY5x42ICOeU5HnhfegexsrkHjGR2FutOVOEqWU5JxbbQkhVHAeggcEa01wP4R3jDJk0dKHMQZAmiZKm1oPgaAnnCgqCCdFWnRGY9N2WjtnbfCUgpSy61rrDaxRWxNcYIJtGeRccCkYY5QzhYpLETf6qmqs9cboOE+Isx3Gtn6jIYT1er1abuq6BaBc8q7rAnhKqdba2617UpIkUYAYARmj/PqgitYu2xGz9977WKddXl7Kqw+9sjdVSkULF0RMkiTKNiJ9Ikos6FU02/VpF9lBSEisVdI0FXybeBB/n1j1cs4xBGt5024IMCElAuNMqrRUacqZXCwWhGIiZZJyDmhdH5zV2n747EPvSdSV3rhKOYh6TcYYMMo5n06nMVyCM8KASMUjdx8AgFLwwXqPxsYxPOUMgCCBtu0IIavVSioe6S7kKvsiz/Mo4SdXVvHx7wmlbdNa76qqqtYbACjSrMhzzXVvdJIkXEnJeCzGQkCgJEky6vTV2Ep579G6LdnjKsmuarduuaPRgAPRuouVwBVAt9ENpqqq6wcaBT9930fQGeUokeUyKrdJxoSQJE0jIxkA8jwXUg7HI+1s9EL13sVy+lqCEic/sWQFAE6o7trgXQBESqSUWVkwxqx36+WKMKqCN9aStrXObTabut5QutXwFEWR52W0V2rbNkb2Wusj5S+KQ8bjoQ/WOSG5yLKMBqzr2moTRSyxDomyB6FU23eXl2tnTbwD1voQIBr8a60BqDXe6Dp6Ro3GAyGENV6lmbNO931/FaeQShVl6977+E5EZNbUddM0znoXEALGNN+4ZiJZDihRQmZFXhaFTBQjFEk4ODjodRvDDULAy8vL5XL98ccfDwZFzEO0rhOCxchwa8w3f/DNl1/9/MHBQdd1P/jw+0+fPs3z/Md+7KtZlp2dncX8iixJ49nT6b4IiTGubZrF5WyzWEnO93d39nd3BeMUiMzyRpvL+cVm0yEBysnxydnu/t4LL72Yp9lqtTo5PlFcOCBAeTYYCsa0rnKVFoPJer28XKzLqdqdjm/c4nuHd6rNSmRIedJ7M1vVUirOqTfWWeM8AaDJZPrk6ePRziTfGSrniqIMSfJ0tnh2crxeV8b6tCgG090QwmazSdPs6Ojoy7tfdd72bWODW1sbBOeD0d5zjHMx3dnzhH/6+Okn3/pOVXcW6XRvb3d37INhTPTGxgowzouA0ViwCSYlR5WqRCnGiAiAiEAZUKrShKuk63XddV2rO2e8Q5Wlg+E4PmJjTDEcWGu7yESnxDmnu67rOm9isrvkUg4GA+MsY0yleSTKsmszru0g3eKVK138L/4wfBrIVfjDdcsgooHPQv/twSoEUHI9DC+LkjHW1k18AUMI6D4blYMhBGM8ROtcACli+M/21yMECIFoHxSCc9bbmCBgreB0OBntTYYu7opXn6988QtCAqMemGnbpQPNCHSLVnewt8OGKelWF9W8mu7zn/tLP3rjzo3xSL72+nOfe++WB6w7WJ0u0jSlDlkAQpmUkktGEHywhERkTwUB4p11lhonCYSm380G03IYAE8vTs+faW96QPLaF98ERuDw5gvso53pe+TJsl3VzrLpcDQcCCHTEEBr2ztfCOXQ9B6fnF6s18ujg8PXXn/j5q27k9nalvfXSFpn282m7TpjTDR1zZJ0NBhaa3tmfJJQss17AcZGyU6rtXCDgdxPILe6M75jMjx3l9w4unP88PwbX/3J6uJ4ff7g5TfebLr1hiHNuOO09q4JKJERAwjk7OMngRejo1HIVrrHxviT8xY02LFnHoEwLnoqFRUSKUXCmJBobad7HywDwjlljAEjTdcxQgWhLFCCoG3QXjvS+7bWPhhnA4AUIlCqgwXjdRcEV5RJhyQgoRh8MI3ue92PBrlgtGkaymA8KBHDpqps487O2/XF+fGtG8qbi0WzrkDloFJo1n8A+itW3Dra/e6n6/OqW2yCMSBUhj4IAE/x/U/uyVCjoJAAz+Hgzv6Nu7cN5aQcNQtzUdW1RZYgVZkNnImk7SwxvbcOEDKAPJGZFIw4MDU3QAlkCoBDcDAZwME4BVunxajMcu+M7XXbN+tq1el+sdoQxgRw4vG8nlltmq5tjcuGyfc/6YsE9qaqrTUw46n57jd/P0nLbrNpDVQAnUNNrAZiEPJEOBfQOUpQCMY4ZJxLga328XWllMZGFWIgCLH9BFexGPTKAH3LntkO/bYh9OwqfSN+ZdwQeOzUEkIYE5SCc4FSppTqujYEuLIrDD/8N8QhEB9CQAcAGGK5QZRSABbAEUI42b7/Qogkkcb0ztqA6Ly/2n8wvoou+NhFM85RAEr4ZlUJIaNgF/w25TdL81QlUa2LiFIIIaT3oWnaQZ7HH2SDDz5adgBSgsEjIgGIzWNDTCJkkkrOmfe2qjaRUx5CUErt7e1dRfn42HdExKbp2r5RKXfBQQgEI88aKQBB4JQG50Ow2lpGCAAwBIAQwJGrmQAB5pwzxoUQ6rpmjCklCCGUAUCI2aIAQClXaRKfU4i5yoQCEkqRoA0O0QeOQBC8d8PRyHnrrQNERoVIOOUUCT56+igAQiDGetPr3lirjXPAOUjgSgiVpoIxyjmEoK0dTydxvXhEyTkTghHinIt+9uQqiYyymCnmQgiLxdLoyxACIUwIQSkHisaaNM/yPGeMeRO01jFOEjwIIaSSEfpfQ3wkW+/LcJXuHG/47u4u38oaIwbaVhFd9KwAiEsirtq4KuJ2+dnWFyLuH+waY65F3tbqZrOOqFRKKa7wtJSSEQoAOzs7bVtXVSVVNt6bDse7LuZCSPbDIzYE55zVOgRQSnEux9ORUiqgs8YKwYoii0jOAzLGBoNiNBrJRAHAerkKBtB5F0AlGZUqWKedFZR5QIpAOIt/dtoY26VpWpRZWZbR6Cnq6eOCjBd+jfsZY4RSgdC2XVM13uBoVO7t7BJCqqqSnGdJKqQKPmjr6qb1xiIDqXskwDl3HgE655zH7fEfQpBJyqjghArKtniXccbItUDCORelNYSQuq5j5RzvUvyC45OTqHnI8jx4r7U+vTgHAMXFeDze29vjnB8fHzdNk+V5VGUYY/q+BbgmOHnvbcwmA4KLxaLv+1gNrlarRHCkhHBGAwQCxllw1lhrnCWGkaZpdC8ZZ1JIpaZ7u5TJOP9BRIAgBMvzXCmFgRBC2nbeNBXnMuJ1SgHBh+A9oc454sMV895E+CiUjGZZ/MoXXwolpfTeG22DR0LiVIQjtoRACB6RWOs369o51zTm8OhQCBG77FfGAyKWtc45b118xNelFKHMBQcBAQj6YD1CAIqghJBKZVmaFalUAihq0xtjlkuTZmoy3mGMnZycnp+f7+3tvfTSC/v7+4vFfL1eM4t9384XsziOe/HFF8eDYVc38/n85OTk/GKTZ3XsEsURzWaz2QhRlmWapsSj9XZ2eX55ft63PlM8KXKLcD5fMEKVUhSh7vqq67WHLBfFcLS7f0A5X22qi4tZ27Za24DEO3u4tz+fz6uqcs5mAXvrjm499+YX31ifn6aC13V1tqoePXgIgIkSWuvzizrN7GQyHU93x/nAI+s63Rl9Oysa0yJjiUwuFovv3f92kiQ3bt28fXijaXul1GiyE0Igi3kIYDi7qDvvreA8TdKlbj95+nR2edm27f7e4fI7P5iv15Sp6f7hzeefD1T0fet8Hyx2vanbJiAyQN21q02tlEpSmSnJ00wIJjOluAAIXdMKlQoF0TWr1f1sNluslnXrLcAwT+5MxlmWGWOMc3FYj1ceEuCDtTYYHbeyEIJxQVK4Lvi3Zzlj5ErWQq6OdIQYLAsUkcb/i1LKwGPwwXsMHkPYunUCARCcxw4dUBJ88MF3ul9XG5WIrm+iuEWqlDFWV23XG0pICGHrTRdHrEIpxZ030Y0vbD+OAiMEnLMhBEaQEEIoIiUMgTmwHtCbsiz3xuNMsqqpPwvviGutcxZ7ZL3VNTGOO/jSC7t7e3vordnMzo6fHE7hT/+ZP/bTf/HPge0hzQ6IgL/fnJ6dLlawvz+2ukHLafBAIIDXzjptuq6hhABgCggQrPO6rWUgheRFlt06umGCN+hB62XdSUE+vP/onXc+RAKTZ4v7T46NCWVaOIJSqEcPHkuVZbmXMum6pjZdx8H3m2Eq5pv6Yt7NG02KiU1GnfFOqicPHiBjUiY3btyKjSdrvXMOPaCHOEWlgjPGkABhbHe621sj/WCsRjlJnebeMUIdpZRC1S8eQzOhui65AAurxepZt1LDIRBBQKZpWbXm5HxBGBkME+vB+W62aAAIpTzJ1c3njtrZnEAIhFqk1AOjQIEFAmiDswE8QAAgBEKMQQGCNNZrnPMtvxyCD+g5RXaVBYPBed8bHbpAPSfWRZKwB51woRRTHIs0A2KNMcaD7aHuVgBAPBDbCgPemAv9sGS8AMgGABzaAEf78rNr47XXfsTa9z7+6BILCCCHo3Hn2enpqQAcDcgX3vzS/OSTy/kpL6FIwCdkadeXm37lfA3KiNKhdSjBUYdcCM50DYiMsiLhwyydDMoiSST21F5OCj4YjABgvV5rbQaDwXR3X2udJwKIa/tuvV53rW7bdt0YSwCYEISho6YzXeNMAA/Qt/1uCesKFsf67gCCd7PT+XSiunatSCgo6AAdgKNgCbdI1yZQoIxwTgILQQTCGEomNPFRiAkAhMD11N3ZcF38x5M3dreBULLNsNmWA5RSSmi93myRzxXK4l3XeY+MkRDA2RAibx4JpQyiWzzEqcFWCeqDBQBCXMypAaBxCBhZ8lHhBAEQMXj05Kqza30gQTLOCaGUe3QEGCJa4x1GbrYFoIR4mSglVCTSeNw2U2MLMBrsxA3mWtJUr9eMMSo455ww6oK3PmAIfd8gouKCXZkLERmzZhUhpOuayPSllKZpppRq6iZCf8YYYyJCVe/tfH4ODCgQJMApU0mSSKWk5EmKBEyvQwim1845R1n0/fDecy6FEIyKEEKWFdPpNDreRImm8+aK7tyV5TA2kz5TmRFKafROjv1URnmaphH9GKsJwLZOF4KwmLRlZCI9BmcceKSCJFzleR7Z0pHjThjllFHOvHXaGi6lC94Ya5w1hjLB0QetLQI4a+O1E0KiUydlEOFXfGqI3lrPuWecjydTkfA43OgbHa5CAESsz67amdcdpuV6FXWZETxF4lasgmJnN/bLu67bbDZt21Iir7Vi1wyK+NkSfjiPEsyIX+MEIPa9In7SbRc7r1LKRKk4c+j7niCE4HwwAUiiMi6l8a5uK+dC07RCCO9d53TbefTWW8soCCFGo6G27rr2kFJSxoQQcYjU9F3Mc91sNtbapmu3VkhAXPCMUKEkY8wb0L0OgASBQnDgPAarjTH97Zs3CIS+7+OLHQlU8RvGa6RXGwEiItCsKNH5TKikHO7s7GRZFnnnu9Opc65qamNMTPtCHxhjq6pijCqVCMHjrkG5iHFmXdetq1oIQQkPIVRV1VTrPM0QPb0KFWaMpWkaf734if6qESzmeV4OJteem4zSsiwnk8loNDp5+qxpmrOzsxiaNhwOCSGL1TJJkqbvQgiDwSCKZbXuvPdd10kpgWyDw68YKbbI87ge4m1puy4uoSgsGQwGXAqCAJRAQBL85cWcMR6tP69IDlQpRQlvmmaxWFRVdXh4I0mS9Xq92WzarmZs6zAmyLaV4r2/vLw0xqg0Mc4SQlSaAiVpmo4G47AVTAdKTVyNnNPdnb2mratNbZlhjFEKnPM854vFIs/z/CpKGQDKnXxvb+/x48eIyMh2Sce2q1QquVJ4x7or3g3GWDQeZYx1Xbder6O11HA8fOXlF8/Pz6tNg4jRB+ny8vLb3/72aDRar9feu3KQC8G0NoLLoig458+ePYtGSYg4HMi6NvcenADAIKNSyq7Tq7Xuum5nZ6csc601YbQYDMoSU5WlUsVEc21dV7feWGtdNhiNdmSaFSpNjs8v0jT13i9ms/W6kZLu58VkZ/dyvhhPd3Y4Pz09XTddoFykuQ1wdOdFa/rhzj6QYIF/8un9Z0/PtQ5ZIRet3dhFA+mUZAH85eXicjnv0W/qtQ+Q53la5M9/7tWXP/fKq6+++s57H2RN0xu97pvNup4t5tbai9XCO+h1CwETxdHbvm0U42U5enJ2vjPd+9KLrzChzufL+w8fGeeFUmUhKCUmRKUdOo8QSBxGYSCUcqVUksi4Vp3DuDMwxpjgHsBY23Vd0/qdnWHbd0mWee+jjH4wGt28eXNdb49kb51xrus6GrwQwlhvvCPWIAFCkAoupXRAHDLAK9OqK/XedS//un+0nZAT4v0P56uf/bL4Cl/1nmikXzrn9KaLlMg44osbu7V2NBw656wFQghFEHJrYts0nlKBSJxzGAhEhQAnzgEXlBPKOGGMMgqUUgqk3XSm7cDqanGxMu2wTD8L70yzdK6dTPODw93R+E4uyI3B6Cd/5OvDl1+F5XI1O5stZ3mpDn/mJ8LjT+iwdHX16PRidnYyHIDWMDtbMpQUcmSUyG2XByVTQQVrOIGEsyJJmO4NQSVIkcqM80TwZrnhSu5PdwkHF0zd6X/2L3/bYxhPd9etXS7WUso8oFTyYGdKmVyu19a0Skmh1CARvoeEu2xSKs4opU/PLh+d/05jfNX32WAwm80uL+fO+DRVk9FoOtrJy6KpNjZ4ZFQoGUII4JESoBg6zxGY1bo7h0DAW2/W3nZNXSmRvnw41vMHerN++blb88tnCQ1f/NwLHYbL8422VihqLPcd4VJ0FdlUjUdPgooR6ct6s5qvUyFj79H54AB8wKgt8bAdHJFAHAanjbXWBivz1DnXWhSURSI3UoKcUpQe0KANIRDvgvNoHdVOesEYV5RJhZ0G56xxVkroe+gNDEfw6iuSSXJxqa2FcQ6lh8SDsgAtFJTfuHPbo/3k2dMvvTEt99QfWBse7j06+Zv/+V99utD/5Nf/zQefHgeWBgq7RzuvvfHiza98NWDvPvxg99ZRYPn5vP7gBz84mdW0GDjC1Xg3ZaI3blM3Tds4FwgA8chCSDnPGS0ZGSle8uSlwxe+8Mqdnb3d49Pzj+89qNtWZYO0yH1R9t4v19VsvlptKmu8Dd5xVntsa+uMFQCCUpAcA1AIR+OB0c10Elzr2wZeeWVyMBo+uPcQFIACnUBvoAvgCBhng4eAIAjhhDEgBNFa7y0E6n0AINHDEL3H2AOllF6RxjE27kMIjDHCKHB6/fr/gX7BVY/1mlzKIyaDLQWQMioIQWs855IQBEAEErb+iSSEECAQCgAEMM4Boo8PDRjlxxgH1t4FJAjg2h6ts94jF0xwJRMBQGPur0PXWxf/zKXiTArOU5lQ2KY+WSDxGqy1seK8vlp/9TFdlyRJkmdCCEooiZwfACFIZBunacqpwKsE1hCs3fIUNaIHAO9d32O05hiPB0Kopm7Xq9lms3Hejid5JMkIJZWQMlFKSCa47nqhZFApItpOG22okEJxAHAuMErSLIv7JtmKgOVnCRvW6UiKWK3XvdbXgcKxmgKA6MXJmeBCSinTPAMA7PuuXgcSOKFASQAfAkIIDl2aJ52xxhjtdHCBSZFlaZZlIUDscFvv0AfKmZRJQqDeVBFuamsi7YQgeO+ng3HbttqYCHEIA8GkkLJuujxJsqwAgK7VxhgEQimrqspvQhxepyqbTqfj8ThJknsffjQcDmNKbt/3q9Wqbqq+7zEApVRJFZk5wXnnLSK2dcM5T6SSUdyNQBCiNoXE/FLrQgDGgFEavBecU0L4FfU2lknW2rprMRLMELemhFyUZTkej5VSgnNjTGxnWm28922vh6OyyAcBoDduvV4zJpjkLngXPDobvA3OAKBUSZaX/nqAkyQiDkaiM1Lkb1DGBCGEBMTIu12vqiTPBGW9NQyIsI4Tqp3N05xwJhmnggvKHIZgnfNGCOm9897Hwp1QCGiN9UmaR1B4XeFE4s3l+blHLLJsb2dnOp3GrkDsKNdtGw1wQggeMfb8XECPPoB1VxM9yShCKAdFQO+rhmDIy3Q8Geretl09HU/ia7Jdt9ZuM30BrklW1zdfa12fnkb+TFmWEVlGkthgPFqsV5fzmXEWAsYw5qZptO46s31HAELft4iYZRnjlHEKAGmaEoKMkSSRSTKgGAtfEIr3fa+9oYKqTBljkASPDgOQgMY43XZdpyPpjnNGyDj6FMU98eDgKL6bq9Vqf38/tqm09sPhMM/TKAcXhHLOnbFd161Wq7qurXeUM0KIb1vCKABdLFZRI8QFzbIiPiMh2HA49N73wsaak1AgQIXkl7OZtXa5XMbtK1Y70R2Ic85VEnfnqy0XhRBAafxLay3r+7j7xxkQUIqEUM5VmhaDwbAsfuu3fitNVQxbbZueEMjzPPqSpWnqnHXOtW3dNJW1yNjl/v5horLxcFTX9dnZ2br1gsLeJIv0bpWl+cBF8h5SwqQIiGVZluXQW6e16YxFHyLt0wcEztMsUzKN47hOW6Ds5OzcGJOnyYsvvZjlabXenF/OOaG37jyXJel8sTpfna+b9unx+cXF7N/7U3/m8mKuBC8HRVJOTBDrPhRFvtGm6YLz/dKelJueMGGss0jHO7vTo5vO+q7rZJrIdPD+R5/863/z76a7W4eo2XKxXKysM0qpztqutZRSzphHwwhJimGZZFmWjaYHdV1//OmDuu0QmVRpkZcikZT7qFAljArGQoCAKBMVmz7WWs6pFAxDCMEbrZEApRQJbbXp+77XOsuym2k+2d27nM1WVf306XFAECKSQn20ciWECiFDtF22AdFQwZVShKD1jnOeZBkT3PqgdU8pi3SdeNRSQhHi86EAgOB9sJEPCZ5F6H89FKVXae7W2hixF8l1UVNkjKmahlBkVLR9790cAGJaC2UsXNXblEJw1AaLLVJKJJOMM0YZCYRztg1MpDpCEcQQ0FFAwUFxvjc8uDg9YSQE21CE115+7rc/A+8mBUEQ3q6WF6tBfnC0fyPMz5598BauLubL5en55Xw9r/t2/523fvxnfjK9eZM33W//97/17ncXjYHJDt943ywNUgacITpglComlFSCNUtDMQhChplSinVoiPdZwrNE6q7hlDBOh6OyGA6enjxD4I/OZkBY7XnT2WXVVI3dtH1qzB/7Y7/MGH/3ww+qepOmAr3JJSNepJzQ4Akw48O67RutWZrvHh4+fvyIBrx9dFhkuSSibdvVxez86fHh4SGFQIAR7wJ6FywIJj0b8yxhFBy4vmFepwwpM8a2d48GTx+fHEz4qBw1OZVw9uKd/E/82b/4wacPv/vOBxpX2hO/rHKeD6c3TcC6rQ7SvXKQLZfzxWxhQxDOSZb0VlMlCDDr0aOjGFv2WBRFCA4BEBA8RlsY54MkzHkbnA3oPPAQAqFIOKvrjScAFBECeGQBBIGEAPOm5GZQ5lKmTeuqKiCDvICihHLEX3jpzquvvzjeKTvbONcPaBh0VfN0Tvqxq9NmAcVgTFPcOSJvPXjy4sFnkT/8H//u37v/Mbz2E+Hg4KAYDrg8G0yns9X6yfGsWs++/M9+Y2+Y3rj76mZTPz1dPjg53mhGi4EoR96idrbvurpu+r7nVEwGeVdVjAEjRDhtVroxVWaHtGRDNt1LRAHkWbXhzkyLYeDy8nJ5uapWbbes2kZbbcA68ADIwFHRmkAC8ETxJItUAkaACJYyRL35yhfv/Plf/iMv39h98vF79/bFk/OL482mugigoUihLGGPs8ZC23hr0HcOPCABQsEHcAiMU8J5xOuEhOtXmHP2mdf6KinLEwrEuq3jPLuK3HXORdvreIhE/LPdXwCAMiIEF4IheOcsZ5JQjMYUHnwIAQME8MDidDFeI1wx+rhMOCIhxAYP4GMBwBhjHjwAMBqEEFyKaDfhXFCZ8gZDcN4jFzRRWZqmSspmU0PY8sLJ1tAQYg8+VjZxwnGtYJhOp0wKznkAdNa4EJnepCxLa61kXKWJYDI4zxgDCFVVWRevXUuRDAaDSC/Os5IQQojoo0mqlHfvvjDdGSnFKMPrVut1r+Xi7Hw4HCqloolQBOtlWbZ9EyVZSinnXEzLikyG2BGPFQi5MjI6ODho2za64my7FEhCCCcnJ3meK5kAgBAitnKbpknylDIINJCAOsSnxlKWRrpCkmdMCmcRAAhnDkPf9pzzNE95EFF6GBtFbd9RSj1uG0KCgBBCcjFfrgkipSzPSkopZRACxJ5fbBplabEz3aOUdZ1uum7TrEJw1npCtsOZEELTNLu7u0IIa21d15GvH3nS3oWIHVer1WdyA4q2bSPiWS6XsShSSo1Go82mjU39a+pLZO1HRBVXdgTBdV3XdXN4Yz+2l/q+11pb68osn06nsUMfS4X4FKw2WndJlqhERMJ9KiSl1AXwHqPIlQmKXAZGGIEsLYqiqKp6NB5PJpPoEtP3/Q89Pbd69K3Q2RvrjM3yMk5stLOd1sa7iIriIILmeUIpUgIePGAI4fj0NFUyTkXizCcKCWIEWBw1XA80rLVlOSCE5Hk+Go3i5IExNh6PT09P421BApQzCN5a1xuXJUmE7N4jIUi2c3wf4V2EoUVRTKdTzqR1enZxeU0TvG4feu/jXC4u+xheERNtjQlxrV77yTprI8allN44PMrz/NmTpzGyqiiK2XIWVQEAgOi11mmaTqfTu3fvRlyilJrPQ9u28al1fRu7klLKqODknJfDQdQqrKpNXCdKKcaZylSWFpeXl6enpwAghIzmVIh4cnISi5M4aOr7viiK8WSolBqNBpKLtm1t11trm6qezWZR5hhCSFWWpqn1vm6bvjd9Y51zSZJMp4c7OzuR908onp6ertcVAIzH0zzPu67pui54PDg4WK1WTV3HIUkkCx0fH0fu5nWfJm7QTdN6RC5lmqZs6/DTx2s/PDwsiiKq4fu+r6rq6dOn76wWN/b3RqPhcDhO03Q4GBdFsdlUZ2dnDx48iOoJay1IiC9U3Kmi+CFCwIOUqTSJW2L8++jqGH1LGSOT20dNU3dtHTxSSgOBpuubpivSLE3zsiyzNEfEpmmWy3Vdb5gU5XBwdHAopVyv12cXl9banfFks9k8ePTEGHt6OfNIEDFP0+defKm2btMZZq0BqoFpwnsPCVeE8GEumUqAsLrv+6ZXWT6Y7CyrJkYcIvpQd6fnF9ZaF+zFbBYH0U3XSSkP9g/TLLHO70zzoig5pV3fuL6D4Luuny2Ovds6UYok3d/fHU92tLGz5Xw8yZuuads2BBgPSgDoW42IBrT33mhHKZVccL4NVieMIiXWuaqum6YhjE3GO8PJuNc2vrmEkJ3pdDqdDgaD4LxuO9vreDzFc8Q4SyxIxQmlUkp0mlJKGNXWVFVT5APOt8mMcSuL3Ycr7u9WCwhbXw1mjL4GCNekf0JIDFn33kcdWuwm1M3GORcTAIwxXd0hYszxzfM8hKB7SmmMCCDW2r73nAMheLVyiZQ8S5KYEkAZQAjamK6pffBC0FRIgaFvdZ5AlkCW0JuHo8/Cu+XFjDJQKTCAzfx4xvRzaYHVJbZFqFdP7n349HS+d2Pn1t6Nft195+/8/Q8ePP7Vf/HBpICwATByedqOygQI7zH0fR+ck94mUnHA6XAgnd0ty6O93YKTplTWdLlMhmne9/3ueHfTNU1Te0Y+uPdgOC6qri8GwyFPs2GxL9OxAx9gOp0+/vS9mzePUtZgovOcVOul7bwgYHTYrCprfZKXIDKA0DSVNmZnNDS9Ddb5Tgfmcy4nB8M0Tbuu8xg8DYigvUFjIFAOib9smZRB2349595lhZIcUfvFk5O7B8MyQwyXhGwswiuvv3njlnrv3YuTj96vLkKS53XdlkM1GRQXs+V+MVlulu28tpt2QOV4OqxW1fl8YRLCMGNchmi1ggEIscFv2iY445zD4CkQwQgTPFGs65pO99QhoYJicMY6RALgCDABXAAhQD3QABIgIcARhAVlmlTKRGIxhPFEHt7c+Y//+l/dub0PYwVuA9zCUIHTcPls/d63ny3rZDChw8G/+/TD77339tFL+8NbyS/9yPN3Xp/8ga7/mP43/+B/jxWRN19549On737yuDVaBweSJuXw//53/v5PfeNHu3o1u1x1jqKcSKFai/P5JlDCuUBKKeXowQfjNRnnOUdLDbG1cxbatdM4B5aNkptgwsnjp/feu3++qQbT/dPl5tvvr4SE2oJnoHIOUnSubzp0AIQ7IZJykI/LQhDwzlEAJVjXbkaD9Od+8qf+5C/8oUT68x986/LkXUY25/NVC7C7Dy98Sd58+XOD/aN1584vN9/65tvzhZkZaDwgAmWMSAUERSKYFHHriFSxba/Nx/nelvCzrQAIRDgUD+j4psdjlF5JgWPr0zhLnr99I80Sxljf901Tee8pBcYJ5xwxRAYL55wQDCH4YAj3QHxMTWKUE8JIIMFTwQQhDJE4G7q67fueMZGmKeEk+mlWVYUE4g4S3cqu3TyQQOTrSy5MZ1gMJfE+WHfNCI9d/62AKVYcAEBCUeRxvwuASCB2z4GS4MFaKzgv0gwCaZoGfUhS7r3pdR1joWKKeJYVZVmWxUhr7RxIKYeD0Wg0StOccdBdVdfr9Xod07Ii/o5dT3rlShSVwXmeyzTpjb4WUgAExK1pd7wJ8ckNBqOyLEMIbVuvq018alLKNM0RcblczmazIi+v8Vbfm77XjNEkkcbbcliMxgPGSN/3xmiViDzPvffR1b7rOmM8Y0yKhHM+GoylVJTStm2Xy2UkohBCIkaJYC52xxljqVSDbLCdFnES6fTXQ2HGGOfRSYrG22uMWVcrlUhKGCJmaRoFvrPZTMYHKkT8h3GgTCk9P7to23az2RgTOIcsy7Isi7g5gsWr+mc7tpYyc1ehufH70KvlcV2MIWKExZxzLtnFxZkzNlruCCHyNI3nkjEGfYhI3Xu/nC+Wy2V0JuVSekSgTEpJuYw/3TmH3gKAYFQJHodRab61HItfE+3Pr2Xc1ydrrP0EV511hLOIzyIqjfchFoTx0XPOI6oTjHBKsiTJsgwAIpyNGcDRjzUe1bGI6rru4uLCGh8HGhGA5nk+Wy6ePHnCBF8ul33fyytaiDGBMZBMdL1FgCxlSvAQAkLgnGptR6NBlhXeBc5lkiR9bxaLWQiBENzd3T06OnLOnZ6eGmMGg0EsI2P3EREHg0EEkctVPdndGQ6H1tpox1SWZZ7nuu+7roty3iLNrvMZqKB930YeUbw6lcjJZMIYGwwGhOBqteq6Ls/zwaCIA43ZbBYdluIKzIo8qqKrtomeodqaSHcZloPZ2eJ6LTHGiqIYjydlWTZNkyRJnpcAEPM9pEgGw6LrOkphvVxprcs0a5pmvVzFMowQkpfFdHcnTVPjnMfAuRwPdrzfCh/btl0s5jHuDQAog5hEkSSJEEwppZRYbVbWWgKAiJvNZrlcovORFVaWZZ5mWuvZbLZcLoP3jPMkS6ngeZ5HlB+XRJwtRLwOAPFZ7O7u3rp1Y28yzrJkMtlRKr04n927d+/ycnY9/zVGI2KSSmv1er221jLCR6NJnNRBPCeCd86dnZ0JIVSWKqWc97EwKPJ0OT+PPzdJsthJimlNezv7VVVfnJ1XVRMJdZTy0WhQDPKDgwOl1Hw+n8/nACCEQh8Gg8H84ryqGkRvjOOcvvbqF776I2+eHZ9tNqvNZoM+CMm995vVerVZc86d950xxnpkXKgkltleW04BA7FOG+0CDUoplcq9vb0Qwng83NRVVN5vNhspkr53s8VSd22WqEFZgndVVXVt3Xcmbs7lcCS42jS1EPLwxuFsfenRNU1XVRVBmqgMPDZNUxaFtcZbl6RyPChHo0GWpoQRD3hxeRmnOkVR7u7vMSou5jNjvXNuurePATrd53keKyWPbr1eL+eLpml01xtj4uAaESeTUZ7nvTW90UggBNdpOxiM4jlyTQOL206k68S/jzVk3IviO3VVBmwjXOIrHP/ntt6IeY4UhBCUQaSqam0RcZCXw+HQGG2MaepN1/VZyobDklLa9U1clhhICCHYAABFng/ywhnXtg2jNM9Tb/ViMfPeD3P1H/4P/kJXLX/3d35zMWsyCf+7/+3f+NpfK67h3V9/7b9YreFXfuXrf/xP/SLcmDz87//FTaZENnrv33379GJVtbbp7cHt5zxjv/Ybb1UWPAfPoOqZC9IG1fbO9popaQDWpjOAPFGSc2LdTpY2l8ujcVoKsTctFcfPfe7F0XD4zls/4JwTyh8+eTwYT27ceW7TtqOd3cWm0c5dzpdJlhIE59xgWMzOz+8c7TbtygarTVvV89s3Dx9/epIImOYFI6Jp3OWyuv3CK5dV0xjHpHK1JgB9q01nBvng9s2bw8E4hPDgwYPRdCIUf/D0UTrITPBC8X5T51bkTHDKVov5jf2D4IygpG+Xo4l6dnIy3IH9W2zd+8EUfupnv/Dl13/027/x0a/9w98DSFcbf3xuxtOjpkcHDCg6Z3rdONdxCooLb90GQxhIJ8W6apyDclh473VviqKIHEvvfCK4khyDQ+cB0AKkqUwoJw4EkjJN8jyPwOAH797b3ZEEcJQqsIZYtzcujkaD8+NnaSIpYwQgK7Llan5+CXdegK//5Ju/9Jf/PV+fsWkKzJ0+ume6xSQ13/rtb0s8uv/ewuvx6eVy3fd/9M//aHbQff3n3+Avv3i9NhBegyWHpw0kk3/6//z7v/avfufjJ2dMZZvNhnl3c2eScc4JdQ5qAxYlyhyU6mx1Mb9cLDacwM5kcnhwcDiZllmagO+Wc+yboWK+Xl8en97cH//0j3/tx9987Z23v//2ex+eL1eLxp4t3bwFQ0ETaD30AAiAjHlCvEePQXAlBcsF35sOdbPeLNaJIqbFIoe/8it/4o/+4a+7+qw6/iiFiujF2eXZUvLh7Vs37t4Z7u56SlvjtSMI6vy0/vDDZ9966+P7Dy9XLTOQtoF11lHmsyyhlMYUmtjr7LqOEk4IueoAkHgQW+8CUI/hehPYbpLex4DUa5SCiFyb1gcTzw/nHOdcylQI5r0nlACgs+isoZRyQaVILHaEACWMM86YiOwUDGiM895GGmKEwpxLpZRIRIQ4EX+4q+xAsPYPsBdCuOZp4JUqWdAf0jkYoZHkEEIgeMWNIQERKCWUM0aJC8Fbq63x3lPC0zQFxMvFHDykKmGMNXWHYJz3EYEFDnG7bJqWM4WIAMRaO5vNZrOZ9z6gh9Abo+PNiajuCpLKCPq3qgvn6romfRcdGOIlRCwaL2Gz2cTriNVOJE7UdROTWP7/pgRZlk2n0zjrMMYIoSaTSZZlaao60y/Xi8ePngKBPFdZllrj5/2cMaZkqpSSUgmxdW+IVq1am/idY2RS/H1Wq1VkQ12FGcULpHXbRaIzINirMJeYGhav0VqHgVBKo5lDkmSI3nkHAHVdx0zZuEDjoRR7+fHuXS8+KSWAua4KIvTJsiy6IV33ICPYJYRcl7DkyvQmLoBr9rNSajKZlGX+7vvvtW2bJWlsqUopyzwXQkQjF61NPDjjAhgMBpTTTvceMc9z68NyvSaEjUYjAKAUCOGEEAponA/Oeu+rrtNal3l+cHAQ67TVasUYGw6H1y3buEIgoHW67TrKJblSwZIrR6AYXhsxWWS/SCmzRNabNc2yeLcjhIqmNLG6iyympmnW63XbtqvVSsk0lppRepuVBSFEKbVYLZ1zQOnVKgpJwgeDUVd3UqD3PnjovQmIQgDn8saNG+fn50+enCjFRsNxCMF7zPO8bduYMhZFMvTKUklKeXJyopR6/vnno8Cg6zqh5O27z202m8ePHzPG9qY7o9FIa31yckIApJR7e3sxsIwQUtd1ksgP7308Gg0ikCWEjEYjH9yzZ892d3cj0UhrLaUcDAbDYRnxTSz/Qghx1uExrNdrxtiq2lRV5RwQBoyRrusg4Gg88A7j8OSzvz8ANE2z2WwHFIhYFtRae/v2zUePHl1cXDjnbNZf60z6vo9ZAXEqiITIRHHOl8vVYrFqmipi9zTNELFt2xg5zAWNlWHbBoC197Zq6sGgzLPsuoV/LXrZbDZt3cSdejgcJkpJpZjgMk2ijD4aGcffZ7lcxpg/ehXUPZ1Op6Ph7u4updA0zSeffHL//qcnx2eUxiKKNE3T910cKBnjEEmel3s7+/Hq2rZVSgkptdabukqSZLq3OxqN4luntX706NHbP/g4YTCepAf7R0VR2OABYDSa7Ozs3L/3idZ6XTer9Qo9xKC3u3fvnJ6fHB8fE0KyLNvb2+v7fj5frlarzWYjhLh55/bOzk6UsjiCDx4+JoRQqWSWL5fLxfnptVFYV1WMS2A8UHA+tFXV9r3xviAM0TOgWZ4eHR6Ww0JKCRDSNLXWeuuCcdb6erVB743rrXEHOztFniLiar64uLjsuo4x5pxrtWFAksRQYKlUUirTa0FZ17VaawzEWW9NxQmLXP9ocdO1OkKT+CBmi8umabTWxmGAKhuUk0m+v79vrA8BxtOdpml6EwdiFhGj2iTuzN66EAKnhDGmlNLO+6ZmglPOOq2N7b27muxfKf7plbMHfIblH7fKuC1EUXt8j/Aq7jrO5cKVOjBusIhICBKKSZJGJUxkzF4R83QIPgTHOVBKCcU0U+UgPb84Y4xxJoAE541zQRDoGadAAL33HoJQSuxOdwiFXPF//s/+iWLhydPm9VcH3/jRL/3oL/0iwO9eX8LB/uDVz+V//M/+CRgpyJK7L9xZvP2RuFj2bds3bde4VvtnT8//9e+eygFoCyQBKUaZFL0TJCgQztNGZNILCp1cd20IgQMt83J9cfmll59npl3PzzcrM9opfudb/7Zt8cd+5EtfeO211XK9f3tfa9sZn2XZ2++8y1VGZaqtg773wRAM4Gkm7fzifttusoIb7e7cTD7/yuTFm0nC1EANH31yslGWATl5/KkmnKfF+ZOzcT7Qdc+5PJhOCdJHnzwAgLIs8zw7ffrEesskI7rr67UVrJSZrzY9ZWkmKNfHlw+DM2VZCo73n8wP78Af/rkvjA8Fyuro+Z3BtETe7h6NgMB83jUaTIDZerYyhoAgwCroOcBUijJNrOkZwk6afbBpRztiNB7UVWutzvMybg7W2jRLIHiru3rTWw/DDCaTMZOsb9pcqEkxMFW/uDjHtr1560i39R/6ystvv33vYE+cPK1zgLs32Ot3b33lC69dPH1qrY/hjFLKk/NspM6XM/PO73+foTXQXq6e5eN0tFP8kV/4ic3qyd0vvfGv/uk7uy/cef7Om1VjfuJP/zK0Z3BIYfgHMh/0szOohNLy49//7fuffrJab6a7O49PZ8Vg4nqNrDCBsCRJ0sR0frPqWu2458enzwJxea6GRbk73Z0Oh4yAbjYnx89EMHuD/GBntzyY7BZiUhS708l3v//ep58+e3ax3vShcVQDBAFUJuiBJzIh1AEz6IPznniCwLlou/Wmg/VmnQEoAhKxQ8gYfO/3fxuqJ6/cmoxVh2HNSTXZ5Z974w2xMyZKGjNHH0YqI0pqHW587Qt7eRl67x1/cNpetuAMsQg8QHzlnXOR8B5f8CtKD79u58d3P83zANuvgSuWf2wjXrNz44cDcQFD8BCjN2LrlBAKgJzJKCaI0l5KGGNRxxN9FTkg9R6jtMsZ71wgwISQkkkpYywAy1SmpIqgF4DWdR1CkCLxDhkwBiwQICR2WdGCB791MLpqM/PYMO7b7rpLTa9qHYAfDjGvP/F0x7CVD/d9Dx4E40xISulovBuH7xFTCsE4F1LKeAYzFp0KtyaGIbiur0LYOqbFbq5MVOxSX9dViOgwWOvBAqFXc3mCRZENBgMhGRBlbM8540xGupXuzTb7UwkIhBEuuQohWG1IIMNiWK034/H4YO8wwuLouN/p9vTi9PDgYG93V+uu6zqjex+21VRTzQlQpVJKKSLhTKZpuqi2LZ8QgmScSOWcQx/yJBVCpEkqpTTGBOu8sdbbQZFlSaqUQvS9buO1EyKNcdZarY21FpByLpNECcFiOmM8SIw2xhguaJ7nXd2EgNfdqdij9d6DRwKQp2mRZRH0Z1mmlKrrmhKSKqWEmIxGkWaglPJuC5fruq7rGgAGg8FgMLj2R4rUlxCCN7Zt25s3bzZNFUMrq6qy1q6kZIxNp1P6GZereE4XRcEEb/vOIzLGfN8xTgQXSaa6rgsBvXMEfRRfKpVQSpkQddtQxrW1IQTKeQzAivFYMT81zoVMr13wLE2dM/GHbpUnnEd6HmO07yNvR2/vkkuVUkmWySRBrYVSSEgIoWqa5Xodb8h2poTIhCiHwyTJnHNd3/dGA4ANnlIaYCvrIYhsa7QFWZYVxcBoz5EKdNFlFIJTSuZ5uljMvLdFypVKCaLTRsokzbM333yzaSqt9XK5jI1bIURZlicnJzHEd7FYPHr0aDgcJknS1V1vQtO1kTJnvGv6Trdb1W+WZdFxlXLetm2k2r/wwguXl+da64ODA8rIfD733h8dHRnTb3vnFJJESsnjsHK1mBljIDgIjnGVZsoY07a1c84bE19HQoAxmkiRpmmapCEEqbj3HpByzgHQGD2bzcKVi1R0JW/ayti+qtbn5+eEkMlkgtZprePD3doEEYgupUKpAKi1ffroNAZtI+JsNkPEwaC8efNmHAzGz1VOhauqNWGUcxYvP8/z6XTqtFmtVsPh0DnnzDb6mnOupKSMUUbAu77Z8v2s1plSeZ4/f+d2HBo4xKOjozt37uR5rk1nre769vJidnJy1jRNWZbxeFitVnmeZ1kegtdaBwyxcnjttdfGg6Fz7v6DTz/55JNNVXHOuRRlWQLAfD5/+OjR2UXFBezujl568ea4HAjGi0E5HA6ZFFVdL5er07Pzi4tZVVV1bQaFeunFF8uy7Nvu2clxliXeO8ZYkiitzWw2s9YeHOzleZll2Xg0Lcuy73sfoG3bk8vzGLbYt53WOti4ezDnrXUBiAcgPgRCWZqmeVnQgKuL2edeevn5O881ffPg/oMHn9zf3d29c+dO13UIvq4qSoiUHK3N09QYm6Sp97ZadMaYzWaju053HSIKriTQwWCQZlnXaSllkeUeME3T3vapShKZBhustXlWTCaTk5OT3ckkTdRms7m4vNg0rfHmSIm2bZuutR4pJ0lWDIfjvb0Dytjx8Wnc8Jum8TYowSLLTvfWGh8CUMoJj6xLLqVM8rSqKq1tygQXHLT2AQj7oeDtsy1/vFIWxT02tqKuZUha6ysJioArAYn3PrYqIriHLVPIWucSgABonA0hcC48kqapwQfEQAmRigNF6x0TZDQZNP2aMcapsNZC8JSCSkSaKsE5Z8RpE5wHShIhKSOM4Ghn0laLoyP2ta99bTLO3/mN34C/9EOl73g04sTDeAztAu6f/+C73599+Mk4KfPxJDOUZmyvmLz82pd7+ttvvf9R24AxQLqm6mjVo4cUINw6HHZ91RlI87ycTgAAtSFal5NRxnlXm9u3jlRCRM586r722ud/5id+5oP3Pvz2D74luaKUV5s2H+ymXBoPiZCEUURLjEfUwUIi+jwNt24OVMrafnnj5uDnf/YNW+uEJXu7z3/41ke//3vvbpbrbuXyCRWC7I+LlCWOibbtLs6eBQ9JkiVJZp2xa2NN1/dtEuSq6rM8sU0zv6gOxoPlarkykI3gcg0uwMly7T289hr83L//pV/8q78EqoHmBAYJWkfsZPBEsBQWGoBASKAPZgNAwVKwk1GZSOZ0yzJ2484d8OF0Ph8asH1HGCXeOQugbMzhRkDdt9b06DFTIBjJizQVcHExkxR0257NlqBBcFDUN6sZgLt/cjLMIfT2889Bt4G//tf+wpe/+Gbz9HxXFU8ePXnn/pPFYiWEEFI+f/Nleit0vnn/O/fySbJs9Pu/qR1Z/diP8cmrPzNJsxd+7q/BsgcrABRw+p3ffAuIe+XVl+AXf8j5Ubp4+P5H80fLDz96+OHHn85X62w3M9b31guetpZkXDIr2s6tWlN1rrbBVBvrQzlIxsNJmeVFloUQVstlu5y7pr5YVG0Jy5PHzTz8/E9/7ud+4efml7MHp4tn637WQaWxMrjW0CENnncBCZcgeEBwOmgfQkCKoHIpSdp1nQIIABmDF24nKeenz+qTh5s8vF3gLT/AjDZHu+rmrcN5czGYcCGHUnCpFBQj6CC9qJ/8u9/59NH64ulxs1z2nXNOAZFSKRI0XHnvxpI+HuiRj83YljgT+4nGWW09veqKXrcJYjfwmqweu5OcUqQUKaWUyegdZK13LmaXIOdSSgaQeu+dM31nqQR0SEggxJOowvWASDiXjBIWHfeBXU8Wuq4zxighKaOCboEXZwxDiHgxOlsjJeBDCME6F2IqJ6VAt783IjZN88OGB2y3qjhMCBC8DdvtkNGoaQse2rb1ziVJEmxYbdaM0CJLF4uF1p0xLp76sY/ovW+bnhAC8EN9NKU8WuNH+Btrg3heCiEi3HRuK6IiW/0ucy4QwtI0TVI1HA7TVBnbx9Gq9z54QwjBYLz31npEIoWKNPT4PBhjlDDG2NHR0Xg8FkJFC0XGWAjQ921dbQ5vHu3v72vTPXv2bDa7oJ5KJXRvjLaIhDHFOdW9ATDOeXSeXOUxxf9GID4cDvEzqq84qGGEBw/OBUKctbppGm06SqkQPGrCohCccXH13QQCCKG8R62beN4kKhFCOCHizYlPMHZPQwicbJ154rQhUsa3FjSIlNLBYLC7uxsNkfq+VzK/vLzcbDZSypdffjk2mKPdeHwHhsPhdXXLJDs+fmqM6duu7/uqqhFhUGRSyqjN5XSbf7H9M2dSyiRNtLXz5aLpuzRN0yy/5hcxCoQITigjlFMaABaLBeVMd/35+XlkoqdKRUlDCAH91lRbCFFkOZeiD6E3Jg5zACAuHkrp7u4upTTWM35rJ+W9943RjLHrr4+zoOiyfz1uiiv86hUgm7qilI4mYyVkb3RUVkROjnEuFlFxyl/Xdd8bRFSCp6kkFK3tOSNAwsVlfXhYjsqRNR6R5lkphDLGfPe73+WcxqojSZL9/f3Yaz86Ojo5OZnNZkVR3Lx5EwAicbxu+zRNdybTyBePt2I4HEbK2ePHj8/Ozrz36/Va95YR2D/aiRR5Y0zX9ZH2s9lskkQ656KtcLyiuMFBcLEnGv1t4gayM53WdS3GY8ZYb028pUmSZFnSt/31jknJ1jrWXyWORWsjQtj1RNFaWxTFzmSapunl6Vl8c9fr9XZDoyTJ0t3d3WIwaPtuNlvEjjhAGAwGcfIDgNba8Xjsg63rNs7ZrLVVtZ7PL0eTMWJglOZ5vrOzU5Zl37RKqXgtwW1/N2ttnH11ukUAAuBDAMTYCPHet01zenp6croGAGettZZR2rZ1Va3TLMEAsfZI0sRZrOt6OBzu7u72ff/s2bMQ/NHR0a3bN/Z2dhUXTdNUVWV7PZ1Op9OpNiZaLq7X69F4/NM//dOD8SjuIalUx4+fJjLlnFd1/ejh09Pzs7ptI+Nr7+BAxBR2Kpqm6duOUBwO96tqU9d1kiTxvY43pG37tm036zoOHKKUizLY1GvnHEFI0yRypdB5a33cfgMB5xEJiESlaSoZ//qbXzp9+uzb3/k959x4PP7cyy8CwPzy3BgjBK/Xm8lknCdplPubrhcSuqa55j3meSoE67RVSpWj4c2bNxHx0wePrHNCSQrhYnHWWwM++liTyOAqy/Lzn//86enpo8dPfHDj0TDJUqO79z54fzgc9tb5AMMyK8uSUrqpKq11VVWnp6dIKCKW5XA0GiEBay29OssJIYFs9zF6ZcfhvNfWUKCUM0kTQkgAvB7zwxXd8brJF7F+PGuuuT3rdZ2mcjqdxlfpetuP/yTWtLFBGwK13linCQghhOSMc06AMCryXFlrMBjGIaDx3kZbz4PDHe+9t0H3lAEJAQZpUZQZ8cgINQSMMbrVjhHOmCOhrRZ5yhKVffOb39oZFZPBAODuNbz79reeKAHf/Ae/Ohyrk9PH73//bVgCdfOdvTVh5dd+/CeObr8in3/5S3V4uGorcmka66jyDHjCi2ynzNVoQFiLCWfD/f1iPLbWdou1W1fTRN3aG5/bNkvEcL88nj+98fLt//Hf+i/Dw+Pf/t1/IzIpGUtYksv0crbZG006B1kxMmita0lCwIOgWqTujS/f/aU//ofzgfzwg+89evjx4XMFuCEcvAAfPnvlxVvf+/YPbNvuDiBQ+PiDy+EIZlWdJ0AYRwyMM6A4X83n2isARQAQBO+XNXLsnAFjwdlNMYYvfePoz/3lXxZF0L753g/efv/dD3/+57/y5ldeAfEUQgOZBwwoCtvgxXztOSU8BAaMAvVcBUelSFN1Pq8SDi/cmk6HQ611JtUX3/jCQdd98+3vX65dLiBlgLqD6Ottet93tgPJIU1AKoa62zTt7oCXSoHFbt1yCnkqEXG9WFMBeQF37+6cncwaA//V3/4bu2++CZ8+mc1OPn7/w3d/8N7sUt842pUy+fTB02dP5lwBkXDwXNmuLQL8jf/Jz3zxK6/TG/ubT07+q7/9v/nKl784LIbNunn84Mnb37rMJSxnkIhfh+P/1fXa+F//5f/kpZc/P7uoNSTB09F4+vZHz4pxzoTarGtVqiB51fSX80WrA88KD2HZLvd3y/GwyPMcEeqmbb1vV6t6Pk8Y3t7Nzy4b3YS/9h/84uufe/GtH7z96cMnm86fzzeXm7Zx0CO0SAww673xAagl6F3wxjkAJDSAd5frSw7w8oH8lT/3y7/wkz+yNy3Ah9mTJ//1/+X/ulr0gkC7ri57PR0AlQNQyfRwDyYj4BLWm/mTZ8+evvXxBw8/+aQ/fgJ1D4sGVhZqACsRJKOcB+9jJmxku8PVu9+2LSJGvB1LfWutx+CRACURmsbhdkS5Usrr1sAWzQY0GAgAxRBhIaGUEKCcieBR9wYwQjQWAlhtO6MRbHTFjv14TjgTigLhXERYYzpzzSPUnQkhJFJlRY6IDBggoEN0aLxhjAkmsyQXSjpj+753xmNA6zyA99Q7F4xxhJDrYQUhJHrReO+cM25jEUI0MgVKKWeMcaCUMREIMM6lEDbEQ90gemt6pWSeF9c6pziR3zJ5PEQcLAQnhBGClCfWmW0DkjPCqPXOxc7ZVXhKPBcDIPHBGJfn+Xg8me5MyrK0Vp+e1YvFIhJ+BFdSSkYlAERJJSAqKTHgZrUOATnnAcJmvbbaxbn5crlcLtexPtHW7OxNq9XGGxfQgw+jchBCcN5YdJnK8jzf2dnjXC4Xq9VqZTqjhLTGWtvHx3HFuWd9qz/T9QTBZCJTIUS16WK16IMljLIoNrUOgSAi55IyCIDeegQL0dpcccZY35vgfCRk11XLCHjvg3dxCGOMQfSMbXlNEekaY67lhlGTF4fLcZkaY6qqevTw2Z07d5577rk0TauqWiwWSZLcvXs3ustHtne8KCmlSASllBAI6IGgSkSappPhiHM+m80YozGkMATvvQuMMRTRg1UpnmUF4SxJEiGjtWsfoSHnHDzoru/qprd9lmV5mUerWWctIQSd7/u+bZvIWAMAzpjgTCVSSMkBGCPO9FZ7q7u2hhiJbq1VSjljCHoKgUJA9CE4AlRra4xDxCzLkiTTWofQRMiutQWgWVZE8xwAePe9D2zweZJmxYAT2hrrkRAmGBVFLulVBG88gNfrCokkhAqVlqNBqljX1V1faWPu3JkWRSGZZNQrkWZZ0bX6irbOoxdtbN7HF0RezVKEEJeXl3VdZ1mWFds7U4WaS7E73o2FmXNOUPbkyZOTk5P46AkhUvHIiBtPRqPRqOu6xWLRtq1zJgRXFBMfMUUIEQqrRBRZVqSZ1YYxFqMwoudsWuTRogQRldYxxSyE0FY14g91I7HHGTUwN24cKaU4F1HrQinEiYqUPPJwYnf/4OAAAl6phIVMVFbkUSuyWq1OT08TkVMa5ypJlqVsGyAA2nSRly8lhyva0s2bN613jNFrVczZ2ZnTZjuBjUYqVyrwqNKu1i0TEL3FJBdASd92m80mVQln7JUXjsrhgAJp6tpbJxRHCEdHBwcHR32n1+uKUp4mBWNstdpkWaZ1l+e59y7WM1ab1XzVNe1qszbGqCwty9KH0Pf9wcFBb7flVtu26/V6tpi7zhQqDw67rluslqtq47xnUkipsqLMy4EzejmfO2MzlezsTPf2dupqMyjzPEsSJTB472wMq14ulyEEa721NvgrGQ+DoshFIvIklUJQYIQQyThnou86LgQhpDM2TjuXdeWc+fSj94osH05GRVFQSo21ptv6NAR03jsgwQfb9Q1jrOsb79vBoNjdu9V1er5atk1fpOVEJbHTn5S5916mCSHEQ+iN7rtOSEkE1VoTIHmaS5n0vTk/Pw/BZVlmg3PB99bkWTaYDJ1z4+j6OhoB0Lpt5otV0zQyzVrdYwDGBGOsKApCWNf328PYewAwzhujnTHWWn9lxFnXNVKS5llsvkjGP8PZjePt4NHHxUMYUL6V3yCEACikGowKKaXH4ILPipxSuiWtIUZ2Pr9y/WOMc8mM7SlhWZYR4MYYCD5JknxQtG3jLQLxDBmAt1a33SZJRAjWOUcZqIR5h87ZqlorpgghgqvgwXkTXCAxNW8yHhTq1tH+/XvvnZwu3vreB5+F/m+8fvfRg4f/j//217/+jecIdQdHz7389Re8Y7NZ/Vu/8613nv3qbN09/9Ln+wAVko5Rmwiu0nKYynS0u3tjVBbHD94vhwNW5FSp5XJZLVbM+CFnrz73/Nnje1/43OcsVm99/Fa6V/zMn/h5OzsWZfrKF15JJbv3zvvnx0/Gg73EC+WDNeg2lUcXsM+zQFkgvh0O2ZfefCk/zKBffHD/W3kmgFVh0yx+90O7EpJMqtWiWUNgkKR8kJm9ySAUfr1qrHdpLqvOnCwbArA7Tbumq3sAgB1JM/CmgSKDL706tlCrkX3+C+roJceHhu8NXvzaVxbz28NUsIPcPHzbWktF4WBY3npFIvvut369awKX0FnQFjxFlclAyMm8npYgCDw7noe2+eKrr+2Nd5u+v7s/fDIYhWqZCUq5FEomnHvvFefDYYlFMLb1BrF1lIAgALWrly6XsFcqNG61MkigHMJgmjw9759ezp57ufxD33hz96svw+WDp8/uvX/vu/fv31+s/d6hfP2Nz4VA1uv1pq5ai5mE+axaafilP/PlL3/1a1Cop997/923P9yRO6TyZak269PTjy6/cCuVIXu6mW8u/wCbo3tUv3/5zs1XXv8zf+Ev/Xf/33/8m9/61utvvHy2Xr9//+Tzz91Qiep0p5s2ECdTGrimFPNMIAHjPOn11jHHO6edA4KMdw4Pd7OvfvHzw/3Df/m7v/ft3/teVohnF7Z1oGMoX5YyxomH4FyghFDvvPXBSkETJQnBYNy6hr0xfO1rr/2Rn/3q3t19OH3m6lpB+2NffeOd7//Aau0ds5wbZJ0VYuXS3G1mz+bLzenF+pOHZ/fvdyfPYFNBXQFSsAQCBYLUO+dwEyxzzjlLr0gTPyza86zccqqvOHtREqvb2MLeGiTSKxfyH1q8XKV089g9DMEFHyhlUkqlUgIUgHZdp7XV2kbExlOllKr7pbXaBut98N4xhpx4Qgh6jFPFEEJbtTF8VAlJgXnvtZSxYUkQ0AcdtPceQiCUxhJCCYl+2zuPv2IIAQmJlwcAqUriCoi7XRxwWGtMqxmnQknKOQnBamdc673nXJZlmQgZXcxu3LgBAfu+L/NMJULJlLJtzyPCo9h4kyKa8SSMk+DBBwsUoiNNhCzuKtCUXv3m18JT55x3MfYo1HUbJxWIPuKnWJwpqdI0Z5Q7F5wLiL7ve8TgjV3XFUWSlQX4UNf1ZrlBxL2dnTRNwUNntIupolyEAF2nvbdAUAhlrXV9fyU4logkeOScp2nuve+allIam83Xg6FoGhPPj63SOmY7cylFEhtJjAqVK0rL2IqMUtQQAJFY4733hNAkSQjBRG2tx+N0PoTQdV0iebQLiAWo1poQTBJ63awKV/LcWM5FQkj8KRcXF5HmhIgHBwd1Xb/77rvRCIgxNp/P7927t7e3F1lb11TpEALhJM9TpRQgKqWub7tz7vDwkDFGELTWsWyTUqokETLpjOZS7Ozs9LaPik/OeTQIamKGbiCUUk6ZUgooWa1WnPPI8Fmv197Y2FmP71sIwTvX933f90BINixDCEqpOLUTIkZBea11nKVQSmJ6tzGWMVZkJfuMDRG7yiyLgDJ27uVVzK31rjM6y7JiOBBCrNfrSJjJ89w5N5lMiqLo+369Xkfjds75aLJLCCmKbLozShVrms2m4sb0ZZELIYq0VDJt6+7i4iJ6w1OE65FRNKWJM4TVaoWI1tr1ep0kyauvvmqt/fThg/F0v1m1VVWtVqv1cjUcDqM7TSLkarWKemjOWFmWUso8T2/dutHrrmma+BJtNhvO6c2bNyMkslcxpdF3aG+6k6fZYrHQWgvGKaXboN8aELGxnTGGcz4aDilj8W6Mx1PnwjXWIYQUuYzGPlmWSamuUwicixOGqiiKRKq+72nALMuiTCjO5ax3dducnJxY713wZVmSwLMsL4qCUrJYLKKNWjnIy7KMrDnn3Pn5+fn5uRBib2/ntdc+f3l5OZ/NZrNZbDpIxqPPGCI6tFEFdN0PPjrauxpC0jijiLWK954RkmXZsBwYY+K9TZyMo6SLiwsMwLkcj6ej4URK9eqrg81m473d399P0+TJkyfHx8eC01Sm1XojE2WuokaTND04OoyEutlsdu/ePePdaDSa7EyLLHv8yTPTG+Msl3wy3lFp0vTdZrNarVYAQDAApTHJoUhSADg7O0tTRQg5OztbLtdd1wWESGq6bkbEWBYhBJcMmQMIAV0AmkglpeSEE4S+Q2u01qbpGt0bZEAJowReeukFbbquaU/OlwQhTmWLtPDWNl0X0EXXXWNMAw1lgOgI84QG7fumqdpeJyrjqaKK9c5eLi6lTMrxkFKqvV2s5kKIRCkM8STin5E8WaQkK4vJZDyZTIBitVmtNmskkCTJeDzO83y12iwWC++2e2OW5nGSFh9xkmSJUjAcJkmSZVnf901VE0Jia2C5XCZZ6oJro2GoFKlkAVGmCTp/vX+GzyiIEK+7g/56euC9H41GhJD5fE4pff7558uyPDs7i35csYGitY40IUSfZYm1lkCItVPTNBBITBPXunemBeIZdVwQa2ldV9Yx7y1BKmVCKe+DaarKare/uy+oEEJKzntC+773DlFQIGK+rHenu6PBbrOqv/2t7wP80jW8+/Ef/1nA3zo/e/Qrf/E/ki8/B30LTC0/+vTJb39zoRFCOF3rs/c+EEniOa8ghIRRxTxBItzKrOv5yqKlxvUL3QfX9c73JgOimHj68MHdg4Nnjx52uPmf/c3/OZtKnSAmDAJ+9Rs/kjP24uGNy0dnDz96EKx8+vhYZKMWWgfOuk3IPYRKAty5OX75618Ef7lqzz9+0P6V/+BNOMgo2se/9/Hh6CWjmxfu3prNuo8/bSy0w4I09aatYLKbHd68k5WD49kCHj5cVq42XT5IZdqvlniy8GMB+2PYGw1v39xNBvmLb0xe/6k7yWDz8OIDWGA5nOR5pttNtsBqvhyWE14cgRvDMf7+r791/91HYFnKfduD1wAchWLGuxdvTuaXi97A3ogowj5+98OL7Nn+4eHy+Hg3K7IjUredC6FQUgpZ2apIkhAcEktEkU5kliWScQiW2SbhnAWyWa8XtacISQ55Sear/u5Lct0ZksOf+A//LCS2Csvvf/Kd8+WnnfUHt+D2rSMqzcXpJeH29vOHFkNnm7/4V/7C4ZufA71YLi/GRy/eevGFftn+sf/ofwjr+Xf++T9dnV2+dGf8hRe//M53PuQeXrp79Fno/5M/8bNv3/v4yeOLv/m//C9aBk1rju/dt4z/oW988ezpqQ12vZyTgNOdUeD0fDkPxE52Btr43joEzhkjGKwJxloXQDd61cB//Of/1B/+iW/8zr/6F99790MxUKdL3TPgGU3TlIjEU9G70LRt12uVJkAQvQVnleBlRhhFJ8hzt3eWi9mnD9/95JMXDyevA2tpqNpqlnBCvOeEUZYs103bN9OdnXJvAF5WF8tHDy8fHi/uPeoePoWqAcFhcjR1QWiPxBijNThNwSMwTln4zEAvNrAIIev12iEyINcjfWstYRSQIv1h0Md1qXDdoY6du8h6AEopBhIY4YwxRjjnjEprPaWcQJweIiKRUkrCA3FWiAh/vQvehYA+hECBxcZkCMF0vTFACDDiEik4oZyy7UHuQ3DeOEuBUM6VUkoICMFq7YwJzuE2uclF6H8tYoigKlypgcPVRwjBBRNSMiHi2ezC1t3smhOppCrLUgnpnJNcEILW+s26igieUqpkGtt+ZVlEw5kI1BCVTJQL3l7ZmV+hBBc3XHaVqxrZlta4gqq4pVbVWiUyTRXnfDwexzMjsuQBSSQKoUcS0GnjrCUBKaHoPKeizMoXn38BCaCHru8JQCKkzHIuRGP6IsvTNO37ttcdBs8ITWRqifXe685cdOcEKGOCMyG56qpGShEDgMiVvNgwEzvBzrngtugcERkzVkX5mqeUCskYI4g+hFCW0XFoO2qOy0gIYUxAREaFFInVpm1bzmPyHOGcRx361YOAqHOIgkh25UERv+N6vY6k/2sX1/jojYnUnaptWxsgkh8IwL1PHnAKg8FgPB5zziNxXKbSWt3pDhD9lXMROu+vFe7Ox+cYAbrzPgOmnQ0tUkqNN03TaKuj5RS6EFwIIUgm8zwfDYZ5nnoMi/Wy2VRt2wbvrbWc85SxpmkY2ULkuCSiZ78NnnLKWSIFRYwB6QIg7O7slGXOmHDOWOurat00HWMsYIzcxBC8abYiVBf8rVu3Yl3Utm3dNtY751xVVUc3b1wFVgSghDAK0Y+F8euWbV1VzrlEKaXSJEkQKOcSAxjnPKBSKk3VerUcDocR4DZNU9c1AVIOBjIRO/s7MSwscmyqqprP56vV6uLighAyHA67rnvvvfeKojg8PHSBDgaDKCCZLeZPj59RINHs77qAAYyZx4M0VcvlMklVFAyEEG7dupVlyWazcc5cv2g+2BDQOu20ydLE9Np733ZNCGFrNEQpXnkaqixN04RzRgAjqxAAhGCIwlp/TfeKBZ5zvq7rWO9dOyEURcEpa9u2b7snT55QIIyxJEmcc8ZZ46z3XibJ/v7+dLpbrevlYj2bXUop8zyXiq83y5PTZwDAOY33s+9bKfnOznR/f3+xWJyfn58cH1trh8PhrVu3BnkRVc4hBKujcMpdzR7Fznh0xdTqDUIqVVkOyGBIKY3iy8XlLFJxBGWBBK7UarVar6vRcHzr1p3xeOysPzs7A7ho21br7uLiYjqdUkpu3bq1M52+/867J8+Oz05Ox9PJrVu38zzvtdbW7O7uVm3DOC+KQiQKCZyfn3/49EMR5MHu4a2dqUe3aeq6rglno/HUeXP79u3d6WS1Wi0vL+bzy0tjKSWL+WW13lQ9AMAgp8PhEJForTkT3nugjHOOgcSrFkgJD9ZpEjBNUw4UIHTG667nlPZ9v1nXddMySspRORgMhOK9rrUzHh2NAVKCBvDa9d65qt0oIR064lgA2DSbwTAL3szmZ1rbptMegUlhwc2WsyQvjLW68oMBJsPCObdarbSzxLuuaZ0LjDGpsq7rrAt933Mlb9+6devWDcrpbDZbrVdKsFu3bj179iR6Uiml6rptmkaoNE3T7YmG6L03xmltKTUAIEVCoxs+k9HV2hIkhDRd1/c9YSRNE6CEUhoCMCYopYFsQT9efciWevvDozNOUaSUxri4nOiVaew1kzAihuvNnxASgvNgvQ+cQMz6dM5RYFrrql4zRim4gJ4LkCnhkhFGQ7AIjlFJKRAARB/V+cYYG2wqIU9TKZOm6XTXee8VV4Oy/P7bH5wdnwoC+zvJZ+Hdr/6jf3ly8kRyeOutD29fLL/51rf+7be/S0U2HO0Xh7er1pWHuSpLmaWzzaocZERwh9D23kDftGfdsj5MM6/1xlhHUMl8sjNIXGBtv5rN768Xm/XZK19+6ejGjcnrdxpSy509AD5semO71eLS6ZZYyy0dpWmrHUPgAq02nWmtgcMpfO7lO9Wze9m+GO2VL74Mvd2sPny7W3TDSV5X6+eP7v7Y12/XNa/bj+49rhqPFuCnfuo1ylnVmnsP78/qlhfJ/li4EFaL9aRIRN07C1/72ouvv/Qidc76s9e/NL37epmO8fH97102l+lwfHPv86Lc35w8+s1//G83l5uX7x689tpNYPBb/+I3/99/91slZSkm2tvEmSyA85QiY4C2qo5GGbGuX5lVaAoC83n74On5MBOjvZ2RVNhpR4BpozvDgg3OAwmUQJaq3WE5Ho8IIb5vlqeL3VFRZCXsTqZf233uxReSQbbo17/xe/+6wTpV5j/7m/8Z3NnRj+6/++TDF958IbNtNXsiFOweKsWDPl4u66423U/9ka+Vk+L+4/cX9vi1n/uxsbx5/M63Hj98JHv8+B//dwHY+cnx4cHNB/cf/Z2/+6+phzs3xl/68hufXRttnpHRdIDpUl9ob5MUc8Bl01RVVXd16FtGfZElXAWDWihDGCQFEb40GoEyJhWS0G/arnfgYZAUZcL/4T/79X/0q786LJSk6KzGlBQqTYdjIVXd60XdVr3urUESKEcpGSB31krmJO8FBRPcbDHLU9jZS0OoZif3sK18ZWZny0/ufbReuTxTy1V/Oev3djmoCWGjpx8fn17Mz0+bxSV1YVhOlByWnuTaiuDBgfa0YrRKuJMcAycmCAfb6G7GfmhqL0XivVcqiVAqngVIwGjnMFwTOuIkPFIMImCGK68aHoLjXDLBgycY0NjeuYBInI3B30gCscYTMMED4xjnk5ERxNn2C0IIiVLbDpwPssgAQHIhpbR6SyVERBesDxiRdNR/RDCxFYma7bEXPyEE2MYXE0TsdO+9D27rWMw5p5xRzrJsy2ogjAYCAqVSARETlXVdZ7RWSoXglvOZUmpYDtbrtVKKE8qY4HFUL0Sc7zPGAKnWOuoKKKWUMbdai0QILhljUijIYsYQ0Vp767z3EBAIUkIE4zxhaZKFAM4lAT3nNEmvvOeNr+u610ZrS4B7j4BUCBYIOmcoYUVRUiBAWSLVKEs3m43HwKmQSikpjbUxZFdyFkJo27rrOoQQCxXv/fn5uWCSU7DWYiAUGAR0zo6HQ0R0xlRaRwSzO53GyKcoO7umf3HOmRDeE+e999sZk7WA6AHCfL703guhsowRwq4KADDGAAkYCJBgre26Jo5NYmc6hs8SQoy1lKCUiXehLMtI744un9E74uDgIOKkayXuer1ezFfW2sjiyvM8Lo9wZUwREXYcKTjnimIwHoxvPXcjtnjn83l0ooy5lrGnTgihnDMhwPtAiAtB5mnCy6bpLi/PvfflsMhJsa42nDIVLdyl4lQQQgRlQCHP8yRXS6nOzk+cC4NBwbns+3Y4HAJ6Z0NAh4ixkuSK54IHAsaYpolmROyqiR4j8EgIlJCYuGkBYDgaxxPXXn1ivkE0wo90KUJI7FsTxnanO9uwNiCjwZACWa/Xuu1IQuLtXS6XRuu4TjCQ1XzmAVvJmypB6n0wieR5nhLKQsDj09P1Yh0CTCfTYTlCAuPdnbzMOBVVU6/XVQC/WqxPz09mF/MAqLiy3pXl8OjmjeDw/PLicj5XSk2nu8/dvpVlWfQyQu+TJIlGNH3fLueLqEaYTqfPTp6dXJzXdT3d34sWk86ZdFgSirLvQwicxsDa5bKqjbXNuh+PsyLPu76Po8UkSbiSfd8nWZqqxGOYLxcOQ1c3ddM0bZ9lWZw8IJIreg+N+b7L5fLy8rLeVEzwLElFIQ8PDxHx7OR0sVhIzrXWTaWlJCGg95AkfDQZl2WJhDhj18vVarUJIWRZxjgxttdae2MVZ1JKILEz4oZlOZmMh8ORSOS3v/1tlSQ7OzsAkCTJYDDghPZ9H0VH1y0ZdhWw2hkdQgBGAwHtbN21IabqxGEp2SZ+pGk6GBZcccIIAiRJurOzMxnvWReeHR8/e3oCAK+++ur+wcF6vZ7NFmmqGGOr1ZowPpxO0yzf2d8rhwNrLZNyN8uqpjHWEUK5Uqv15nJ24V3YOzgYJgOCZLleNE3jMCRZmghFGBsNh23dvX/24fzyHAAo4GazqTdNpjhQMiqZUopSro0TTBTloN5USGiWJEVRWOMXi0XTNKSDwSQfDMdlXkAI1bo+O7vwxlMgR/sHlFIuqFLb86zrmqb3SSKttb3RUbfKnKVIETFLC2v8aFjE8ULwuFwuCfV1s5SKSynzspBJRrloG71aV1pryjhjjDAWlSfWWpUkoTebem2dH48n5WBACGHWyzR5/vnngZDL+Wy+XK43Ky7E4cFeXg6/8Mab77//7unxcdsbBizJCqVSzrk1rmmatu1DCEmyraL7vu/6/prSZpw1xmhjAEAmyjjDmCjLQggRe2nReS/42LXYFtJxt4xmaHGgGqf817EPq9WKUj6ZjPrePHv2DAACQHTW0Fr3fdRRRLtnojubpJQzEZzHAGmSMCoIxbazUiZCSuc7Lkia5/koE5x4r6lnBIl1wRmrnQcuEimWq423rswMl4Jw4kkwwRBPLtfLfFzOm3Z0tCcp2Ww2n4V3Hzw8HY9GaaH+27/3T370R77oAF/7kT9yNl8W5ZR1bvbpwyTPA+ePZ5fIKJFSSuFdQGE5k2iDB//Jk0fDouQq8TY0tgHjkDLhTLtaN8zfuLlfDMr/89/+Wz/1J3/2R3/2G1DNQHvYzX/yF37yN/7eP2rRvvzqy/feeyQE900HgqdJ5oLStiUUJnvpmz/yDSc6lmX95uwrX3k9EaFIsvc/fq/g025uXn5t3Lfm2cmz0d7gDoeDO3d+5hd+djTMf+/3v/Wd779DqZlOitYYB1AWOUcvSDjYg9deuvOf/y/+EzkqzZNHbffs09PfNUhTpJylX3zta2q4C3y3e1xXM/GDtzYHA/rwI/3xd3/L9PLdd86PptN6422grdYOKeVAMHAAQkBQQRwUKju6uyM8MXVrGp0EFzgQ11trU+qZ4lW10S3kJV0tw+6enEwmiFjNF+vLGeVMQnhufx9cn3B47UuvvfDC3c7b9+9//P6jT1SRfPet8//R//TPHbz6RQgWVfLlH/t66vRLw8LX89C4RNrLp5+uTpc3p/C511770S996Xw9+7V/8JurDp77nd/YO5wsVpf7OzukteNiEpB2HXpn3/uo3rsh/9P/9G8Inv6X/4f/E8DXrtfG3/p7v5blzLX8i29+7f0P3mm8KXYmTCUPPnlw68bB2ZOzF24dTAblarVounowKlWWNsHzTPXY9d5xntJAPaEITHHqrNVowWtJoTd6Y9BZkAUa1xYi4ZYuFu1ivnYQspwVQ8mIKVIqmLSdA++SAIJ6S+D2jZSxPoF+cflkWeqhENY252ePZ6drXQNovZpr7+HGF57bmzy3mq1nzzbrZb847c4u9MoPQjJ0tKha5EIAoxSIIh5ZAAeOaEfBYABAIASAAGEBAwSklGirEVFIRhgCDY64LvSIWJRlxM9bChC55rs6RIy+OIwAIvKIt7z3wUMEe4T0lAjvScwBpJRfFwpAQu86IQiLVBxgaZIqkQBAqlTTVC1pCQaVSCk5BLTeDSfDttnSXkMIzoWAgUumTW+cjofWdQ5U13Vtb+jVBwAIo0qqmGjQdV0gLurheq2FEHmREsJ118VhRzwSYm0A2kUFa9DaI0Js+TozGU6Mdq21MTCFAO2b3hhTlqUzZt2tVSKKwaDIlTGmbpv5fM4EFUIliSyyssxkZL4ibuF+5PwQQpwzzjnjGkqpTDhjwlpfVRUhRMm0qhoplZKZ1jYO02NHvDetEKIsM5WmnFJtfVc3l/MlOr93eJAmabTSRErqpqm7mkuWZklsAENAZywhxBjbt32mEko58cxZG6xBRGs1gRCCI4RIKXmaems2K7P0Pk4hOCVJnsVpgPceEbIyn8/nESxSKtq2pZQNBsPVapMlOQA4YxkL3jlnKYHw3J1bp6fHi8WSUtjb24mDZql4bCG3bXNyclKvN4QzQsl6s5E8oVzm5fDaTd+ht705vTiLJzQihnAVnYYBKbHBMsZUqkQi2rbV2gZEIRRlzAXojRNCpHlKudhs6mcPj4VkXdddXl5648usJJwZY5pOB8DYmi3Hky3+5jIfltZawlU2KKSUnLLNZuM9ZkmaKpUlKWPMdH3btrXpA2Bmst5rY2ySJ31n6raSMsnTZLacpUoIrpAEQgiS0NseHRDBkywVSgpnO913bU/7Lk48jLPT6ZRytllUs8UcAMoiOTk5Ozq8mQ/KxWIBSIeDcaxLi6I4P7uMC3U0GgFS55xk8sH9T2IhHT3aTNcH66QQfdexqPm74rgTQrTtSTB7k4kNVvc1l8w71wNMdvLFaiMlWusIZ6MsL4e5VFTIpOla4CzY7nJxSQIZjAdJmg/HU2eBCqq4CiSYzpyeX0omR6PRL/7iLx4e7ocAH330wcOHDymlg0EpOGeMJAkHcFLAc3dvZFnWNM3ZxRlyKKaDwe5ISOmcm7cbREzy1Hmfl0UIYb1cbZzVjFrr1su+SGDetMu6jcZvnAOA58GV0zESCIQyEbW8jockZaSvddN3kQQVi3aCoPtecN40zWZdBe+PDg6yovDWrqvq0/ufdF2nraGUuuAJJcPdQZIkq9Xqxt5+URR92yLiZDgqiiKEoNvOORcCmL4DgIPdncl47Jzx3vd9533I82xnOhWCrVab8+NnjJE0U0KIaIu2Xi9tr6uq2tvdJYSURZYkCaVUMt513dnlxcnZqXMgBAwGJReibqumqaTiIYTVau09JAkblvnO7sFzzz2Xl+W7H99bLFa8s0kxVNq2bY9EHN26HcL/j73/jLG1y9LDsLXzm06sqls33y/HDtO5e0JPkzPNoWc4M5wZMWdTNAXShgnRgGzQgAFLtkTRgC1AEGhBoiyDNkiRoilKI6bmBHb3dE5fzjdXrpPeuOPyj3Wq5uMv/7IAAjzoH7e7q06d87773XutZz0htYO9/+jNruumo/Fms1oul7PZjHPpIwrGjs439RB1ZhjDuludr84559aF5XrV932WF2U50lIpqQ4Pj10/TCaz67Od6XSquKIojwfvP9hsVlJLY1SECCyNZqPQ+zyvGGMhhFybndmMM1Y3jdSGC4XAV+vaOSIQj5VSe/t7q836wf1jP1hrLaZUVcVkMp1fuZLlxvbD48ePl8tzRBRCSKGUUj5FKVBJEssKO7h+6Jt2GSPef3QoJR+NyqIoilF+crYQAooqz/LCRxwGZzJRFIWQOkZs+2HoeqM0L0YCGEceMHXOq6KcVdX+/v6VK/vVeKS1ZkLEiA8fPjw5W/gYsrzq+v7td+6+/sbbt27dWiybwSKymGkphLTO94MNIQmlETHLDBNwfHo0Ho/zPB9qS45w3vsQndQqQfQx2KFHzjiitT1iVEIkBt4Nrvtdhx++DcJLKUXOc0LQ+IV9bdN0zjklpJI8hti2dUoQIVprrYtTsgHwUWumpGYAkbTUnEEUCRnExBnjiNEPIbrpuACBXCHnWkj03PfRgTGz6a733nbW2gBKGw6Ohb53520/HZWQ6z76UVU88fxT3g2buj5dLd46vJfPR56ruuk37sOVP2Q3nyl3JspIHDbfeW/JpdAlDAGHg+PBBxcKa4ONvUsmuMS9kFZIaTiYoXNDbwGVGU9B6cQ5E4z5OAyDD0EEtzcttAE+MstmvXt18tv/n1//5j/7p3/lr/6lbG4gz2Asj88errvmsF68+eiDvfk1J50Ng+tdH13nQeXwnff7f/LN1z/x07fHiQte3L72lJqN6zffWR7ah2cHn/jIp9774Sv//F98d9H2y8G3Af69//T/AKGHG1erkb338LWmbQyDEJ3v4snhkgOcbuDlp+Ejz135xlf/Qb18jKF94eNPvPyFj4UsvPnug9HsqilfsCtrrt/Mq9ZH31p+uCjubVrmVd/YpoXeh+Munm+6rk9FoZXUzboBCE89cXs+nVVFMa5GVVH6fvjg3feOD4+M5vNZ9u679/fmkkO4ee3681/8xI9++P2m7v7EL3/5hZdefvj44B//s39xttiA5FKZYly9++79P/enf+Wll56a7ZRvvPPKg8PH905OHp8fnA/p6Rev/v7/2R+D/Dp0Z2fn8dGju596/pbenTz1wu2Ry+zp0PfhZ196rm3xUz/+5RhQp/IjL3zsWz965f47PuPpyRsvQcI+hJPTsGnWvQ+Pjh4ereGP/8U/XHzsY9/6ym/e98OH18b+83ttBzdfev7dVRurGQzdalUD4NXJTAb803/sDx0+fLg4P8/zkRQ5B6FTwXR6b/NITkeVnuIALkI1npU8x2bNBw/RlRru3LoymU++96N3dQFDAivAdt41Q/JFZab9cJ6azSxLLOEU25mZq3yXgxxXoxvXdvf2q+lcvv3Od5aLw6tjNRsb22wSG5zbsAD9OZwh3JnAT3/x45/+9CeDDwd3F5sT2/WJuVILHQY8a04bf+YS4cxiVJQk0J3uXBdZ8fjkMde1QOcCdwFTYlzmEFMMjiFyFvu+XXdtAEgKpGFG6cRcmSsmtXOu7TrnAg0At17lTLCEkJAzxq5du+ADIQdgAFxwxbnKTNF1Q9N03gUpdZYVWmVCYudqKYEDeBcRmVJKcoGIDDEEhzFJxYos11oCSyHFwfqwtR8A70Pf997/rueMIdmrENv2IyXg8hLyRETSzxGlYUvqINKt91LKKi+GvoW03Qdp1Eh0z5h8CMFaClO3aZsgpqflhHwqCTZDxK7viTUuhCB5FjGnASBiSik0XUuRXgyBc05MdSkEAMgLlA4RQ3QuOs5BakksbXIWYSA4520zEO5Icg3GWIoQY+QML4lMlz0PY+zWrVvk9JJSIr7Q+fn5cnkOIpVlTqLG6FPf9+v1pmmaMiuNMZKrEALJ5hhjCUO7WcfkGWPkMAgABPbT4UF2k/RlyVeCcU0eKX3fk+0PAAzDsLOzQ/0G0YTIRX46nT799JMhBIohI/gTWCLHnqqqssxcovJ933bdMB/Np9M5JcI4N/iwtYY9OzvjnHOmAIBKf/Kvs7afz+daa/KZHo/HWmf0hhRoxy/cbRERUvR2UFKShoQJrpTiSgohdnZ3nXM+RXIWYoxFTMigbnsmOFHIOEKMsW029Xpz7cp+Sgm9G4Zh6HuK2gHJUAJKMEoTY6rrumAdY6wqyhACQ5RSphCdc5KLrCizqgTO7ODrZh18KspsNt0pq9zoPMt1vWnvP7g79G6+M9Uqa9u2LMf1pgWA8XgspWzblgySY4y3bt0ajUaLxeLk5IRaNWutvGDh53lOfoXEWum6jmx5tpw3Y6j6n42qLNN123TDMBqNEuCqXscYZ7PZeDwuc5NlWaHVMAy2H6Qy3/vR64MLRJQnSQyRx37sx37MWnt6enpwcNB13Xg8vnLlyrgqDh8ftG292Wy6rvPBCSGKIsvz/NatGyklqTh5+KzXq81m43wY7+4ypRljNnjrnXXOWmuDl1ISRY1qa0NDJEy+79IF5EnUHar126H/Xc3TtiRCjFjo3A02WEfTzhACwy0vK8uycTkaj8ej0Ugp1bbtarVK5NwfvBACBE8X5l2UfbE+W9R1nSs9Ho8ZQtO11vosz2mJOmuFEFpL732RZ3SPlFK5VojYNM26qavJeN01XdOmC7MmSi/p2jbLslFZee/r1ZpmAgHTZDa1ziUMnPPgfN/3QrCiKDAlxmA6nU4mIw4AkGKMddujzDZdj8iyLONMhRByXVLgGuf89Pik7/vpbHx+fv7WW290Q5xNJ4PzDNHk+WQyKYoCGHrvIwZ6ipVSu7vz2c4cY9o09aNHD27cuPHME89IqU8PTk5Pz1bnq/PlmeYZE5CVWVEViLF1HWOY53m/bgUIUol475fLJSlwaAe7lPqkC+O58+U6ATIEIYTkIIQocpPn+Y1rV6WUbujPzs7W6yUAGKW5FOtmTdRLAAAUAEBxk8TIDyFIxaxFh7A/N08/82QIwXtHuLjzoesGzuVoNFpvWsYYA1GWpc6L5XJ5frZERGoCi6IYj8fGZCklR6xUhM1m45wry3I0nuZ5Tj7F9+6+TxO58/Pzs7OzLcrDeYzxktBISQJ7e3t7e3tdO1z6crpgh2Hoh9ZaC4IxxsSWL6+VUkScjMmnlFIE2rHZh5y+0kWTT+djjMg55Ep67wfvfAgppQgYE6SUnAPOwSiZ57liwjkXrENEleksy4zUIYTgHCIixJSCj15mPC91MSnKysicMnmSGwaMKTr0PvohDr2LPnLkYss2EJKDUkJJQTYV4915ZwfX2+ABIi/z8avv/7nL8u5Xv/DPlMAYbNeuU/BcquWqASZQCK40V5oJhUIgg4Rsy26KPgQfvPdDH4PLJAeWMALEpJlQXBAwVhgmEu7vZreuz/vm/BM/9vKXf+ZL3/7BV3/x3/o9d++/vzpvfA9nj9b/7X/zzflYHj4KnIHJWDWboIQkYjEuTs6Pf/z3fPyv/LU/CaZ55ev/4iu//rXbu/Brv/BLzInf+MdfPT91zz37qW9/760fvn6oRuyTn//0n/5Lf2GzOPrtr/yT1157/a231rN5EYI6PFp/+rOf/7Vf/UPPfeoT7vgBC+u//h/9tc9/6rmbN6ab5cHXv/vWGuDmM7mS5slnnv3i7/t5yCvg2Rtf/e7//T/7r10HN67cWBxtFkf1egGZhnoAl6liNu+adrlsxpXZ2dlJLtV1bZtuOp1qKVOIWVZkWgFwAe7hvYfjEkalUAwyxf7in//zN69fOz89Oz4+3mzad959//7jo2Zwi1U/ONjbqf7j/+B/P7k2Are6+8GPfvj69zyPn/7i7/nBO/f//v/4G3/0z/zFX/7Tfw4YQlgGv/5H/93fngp/lcWpT8290+7h8na5L5zivBg98+LJ2flbB4+/+eYbj84XV/evPP3008z7x48PO+uZ0qeL884Oh2eYJPzbf/kPf+YnfuLuo0f/4B/9+v/1H/+hy7XxF3/xK0yWr79z0A0huAEwCAwpOoFJK/HpT/xY1zanp2ebdZPn5c7OnpK6Bfv+5p4eV6WaJcv9xmPjsOl4v5nn/Cc/94mDx/c+uPd+a1MXwCawAPlMu8DmxZMain5Rjwsc5fXy9PjlF6o4DJkc7Y6vaF1giGWldq8Ur73+dY/pZ7788mc+99HsygiazbvffxUH6I79i0981EzvwKo7OTtfNfWiXq4265PT0/Ouf7y2B21aRN5xHZhC4GVmXD/kSkqjN51dtX3iiinc1A0CIAcpOZMlAylkprRYLU+9twFAGSjKXGTa+WD7YZTnSgspZUQWQkiAgkspZV23GBMGBERO6VspAiBceDwKwZUQSohteqUxxuhMSs059962Xcc1IgrGpBCQEsQYifdi+14IpqVSWiXAiInhpQ5pG3XknE8pASDnTAATwBCjc8NF2Sm11si289x04Z9D/7h0JiK1IpmLz8aTsri+Xq66rsuybDyuCE6z1p6crlNK3juS51IlkWX5dpMlr8CUiAx96fBDc97FekXS2KIql8tlbweaqCrBY4wASWvNSCa7HWVs39NFV1ZGahlj8j4wJrRmgND3/dnZknOOuPXJkVJSIO6oKi6lt/RHCZ4kT26a2BK/iH4gQSIqCwBwIGNmPZvN/ODx0tNNMKm4FJpxPQxd8gkRgXOpNefchSCcK8uSrjnZa1prIUaRUlHmCrbONkVRUOowqTCJeRUvgqgAIIRw9+59MnWx1lPUQMLQdV1VVX3fe+/oaBdC5HlpTDGtpmSNGmNEjELyPM8RNTUVfeestYyJoijG42me54wh5+C9F6IajUbj8bjrhq5rOGcAEGNCwJQYYiLh+Gg0Iuo2AEitqqq6Mr165cqV88Xisp+RRhMMGTGFiBRZCgAhRPLDyrKs7eoYY7I+hMAZVlVhjBFKOPQuhBCCHby3LtgQIwrBAbhzgSNolXEphsF5TAZgsVhxIRABE+NcpAh13TRNc/XqtfV63TQtZ7IsNSaWEpZlBQDEwlJKGWPquu77Pssy7/3R0dHJyclyudyS0wCGwUWE2bjc2dkZjUZEgCGKPOmbqcaif9N4qtDKGBMxtX1f1zWXglAB8nEn4elxNxwdHTV1XVbj2zfv9IOjMD7iEFMZ9+1vfLMoihij7XqOwBE2y9XZyRFD2Gw2JALOi0wpxRgMw0ARTlKWF3Ve4lwYI4L3McS6aU4X5977vCy4ENbaoig8MES0wwAAwmSZNpqzxycnGBMy4FvvAq0MZWUERPQxuu3SQs65EmLV2egSXKijGGNSSOLIXTo4EVNCcDWqJk3fKKWY4EIIrmS8yEVPKS2Xy8XZuRC8mEyLoog+ZCkuFuum7UIIo9EIIQohEDNqsGlYGkLogqdHpiiK1WoTAcusZIy1bdsO7fWr15566ikq1zhjzrl21hLzZxiGvMzc1tCA2m7PAxPCcsYQU9d1iJEhXsweRVlVeVmlCCGErrPBuS6wlFLTtEVRnJ+fnZ2dmUMFAEVVzneKddNOZtPJaKwzQ2woZ4cYYwiOvO2dc8eHR+fn51VRKqOvXLnCOb//4O7ifLVZrLXKFJdGUfxz13VtgiAVJ5VMjHFvvpOpbDweky0v7ZZlWZIQhape2n/atu17u3/tmtZaacE5V1xorZXkALBarWKM1g3k9ErVgGaa2ldEFkKIAWKMgCnGVDe1MSYrjBD85q3Z1WtXskxb26+WS+89YxwRrAtt2yLwEGPX2SzLpBYuhtj31lpCgvLkqen13pIPKVGwpNK0iQ1Dd3xyckmgv3Xz5qUhBOH6IYTNZkNGEe4iT5d68qZpAPnlCeKDj5ex5ZkGAGTpAtpHUlhJoQOGBOHDdF7GGPngXZr3AwBj4WKc6y/7ZM6ZBEpntwAgODGIfIxRSqGUKscjck3t+74OwVobkwdIQBGaF+5eXJKfoI3eA4CQSgvpfReiAyZMboxSQ9d3duCAKkotuJRS53q1WghtptNpWUwk0yn8K7E8q7ZWHBlEBORGmSybCI2MR4SQsPfBWedjiphImL7Nf4iBJQRIgJEnEbwNFgUDnucmUyg4MkDg/RAfPBykWrRre/Yb33/iyad/+Vf/yJvvfudTP/PTi/cea1n85//Jf1GNgfHkE+ztAjLedqveAxMgFHM9PLr/+PxxvXO7+tiX//ATV55+/63XQM5g7/pzLw/f+/bbB6d13SdQEFG//8Hj/+1f/WtcxN35SGXj6W6/XHXrDUymo8/++Bee+7mfefyNr3/n219Hv/rWd5s/+CtPv/DzX4Ru+cxLb/x3//Q3zh407767eu9H353IZ67u3xgsfvs33qqPIQVxb7UGNFzsnm3OxrkaUujRR9MCJmM457zrOghJSr5/5yaRDzlnkOLQ2bbth765cWPnox95cbk4Oz189N4HTd0O5c3b9Xpz9/337919cO/hmXUgNcwrOd/Zu3r1yntvvS7fc51bPD5676vfvftTX37mqZ/87Ozpp394/+HNZ2+CiikNfFZJkJ/5yc/0p6eL1+92y+7k0WYKZlDi7rtvvvDyRxaLt4/b9rQ7rd1m08Hw8LRrTSmrIZS7N6433p7erz3IqPv53u6b7y5ff//XT1argxP74bXx7ttnzfB4423ExBhIwRKEBClgDCG+fff9nfkcpQItx7vzajZdnC9PzxdzvVv6cQZF9KHzzvmBM8uzJKW/8/T1xPvTetWct+VotDlrylHhunPm0njc376+1xZDvTx4+ube537xc6cHDyBE18Zm3ZwdHfVtbV1yCcZz+OLPPv9Tv/8XgA+r+2+fHx8uFosnrj1ZMR4AF3ffOzpatTacrdfHi9MB2PuHi6WH0w5qBCtSkgwYQMJ+tciF4NFCaAsBHuyyGxTAyzNollAnwJAc1B1AYiUfj6eTK95bN/TedtHaksvr5TwbmyO38ClFnxICJeQ6b+0wRB8xxhQiR2BMcs6lMRljjFg9gJcmQXzoiT3Jtc60MogYY5swyou8XqUUoWjBkZBOSSmNkvIioJczRAZZVvgL7RGVDvS7fvCXFSSBd7TB9V2DnMGFFTe9Fcl56ai+DKAty3JUlM72JF2lUjKlROGIk+lIKaWUpGN465RXN9PRlPQEwMiJEQjSoz2XcG5pNCJqrXOT1VLmPM/zfDwep+DJfzClNPQ951xdUHKJW5lYatqt84m1VgillGIAIaQLYSvhiIJ0BSEEwCgufJ1pkksTifPz89lsdmlQQ+hOnuftUF92ZYJJGvLS/xJCgLRFQwEAExOSVVXlnNy2DSkR2Mk5J6t4cZGMu/36WgmtQj0IIciLfbVa0YFNP0wHGN0XWicEDRZFcf36dWv75XLpPbty5UpRFH3fO7cNq8Ktm4feNLVRnnO6rUFImnJgWZYA4F0CAGPy8Xg8Gk2MUYzherMKIZCd5eHh4fHxcdMMOzuzC9BLRPTUuHIt1vUmxUjnX0Js2/bhw4cnJyf2wlyfSn/SnzDBEfjgHTnQJx8wJsAIAMV0rIQEEiNipEO07bvZ3k7sG2t9CAETU0plptBGdk0fQpBMWmshMRpKxFT7mCgDTmtDVe9qteq64fDwiDH2YTRUSjkeT0ej0Y0bNxC3AbSUJ0UzrqOjI+dcVVX7+/sElyqlRlUVQnDOLZdLilIejUbUdxE1jh46uunE7u373nq71f0Akj3RvXv3ENENnfdeJPDeZ8ZQ50my3ZOTE6rI27Y9PT3N85xaqa7rLr0+pZSnx0dlWdKPMQ5lWYbgVqvVZtPs7e1wzjfrBoDg6knT9vlobL1LF5F5xajSmSGlh9YaY2IJMUSOEK0bEu7t7WMM3sfB9db6YVgxJrhkShqEmHxy3jkXEKOUGgVKrpQSRiliuPV9T+ZLe3t71ABQt3NJwIsxrlar49OTYQjFKNvf37969epkMrl37x5jbGdnvrOzszudIWLXtELJ6zfEer2mrUObTEqphIwx+ouVn1KKKdL6z7O8rMbL9So4P5vPn3vmWaUoQ9AR118KQVeSBhNSqTfffguAJpaKMzkej6nO7pqWC9DaAHBnrXeRc9cOPXLpEdzgB+tCSIA8xuHs7CyEeOPGjZ2dHWl0SgEAvLdCqBt3noiI3jqKmLDWMkAhBAASIJLn+Xw+NcakEHrXhxTOTk+Gbkgp5TqvRgVEwE3a1EtL0e4i5iov8kwZbXR2Y+9arvMY42KxoHiElNJisbgc81I1SWNJdRHsjRCttQFpnMG895Iza22IXill5AQxKqV0njnnaCWkCIxxxpj3gSTURVEwhkKI27dvX79x9dGjR9///g+Dx+k0z7K8ruuYQErpfDw+PpVSpZRgoIhx5ZxjILTWPLHBdl1n2daaDOnTni8WQsiiKIqiYII752JAzvnrb72ZKU1DsKtXrxpjjo6OlsslRZV770m2VFUVLT+jNfU/1lofHe0JnPPEMKUUcesncVH9b/+R/lVfabpNdBbQbHy730qOARCFSGR2EH2KIWxDLajpAgAJTEpppDLG0HzbSLUFQXIthOEcBmeF4VqrC2AOAYAJLplmiFoawSV1KYByVJSCc8QEHIzS2kjJmTGmqMqm7UFIxbdx7MguKAEAAOCTj4gpOuc7lhLr28yUwBQycCn66G0IPiREsN557711wTuq+8mhPHHAgIhAlhRKKamkEVwr/rGP3hwVZnl68PTT083q/P/yf/t7v/H135hfy4vx5MmbT/5X/+X/88s///v/zJ++5fr0ja9/62tf/SYii4BcSRd84qxp4IN3z/7KX/4/fuzHbvyJP/or1++8sHPmHzzu7uT65hf/wDe+9/gHr7x9eFRHFIZldx8cO4zXr+99+wf3n3v+hinn2C/NKCXJ//4/+of/r7/3d1MYnO2iHWQFA3JI+u1v/OD73/tRu9LH94eR4F/63C9+/JO/cPT2e7/zm1/9zX/2rbKYnpyve9tKideu3tndEynxXIJWzqZhcDbLTF6UkJIwcjaZRu8zJT2mtm423cIYXZVlkc9CEjYIa7Hr4s3bpdJFOlstFivG2DNPPzmqildfe7BYwO07vMzEwf27/2JxNBprjxtZpIcH8MSzT4K3s9vX/5f/m//VeechB54EQP/43uu3XnwZd496c+3Nr37PjPdGWfHaW2+3fffR2fjN995//3Sx9CiKvJy6ZoOHp/XeuBjt7J30uOjdYROqcekEa5P62ndevffoaG9/x5TVh9fGjetPoJbf+uG3k0AOycdkY4AYGEeJYtG2+XTqBECeZeOxTel4uVwv6mdvPlulUkVpXRtsYjEogVkun33mye//4Fu/8423Fh4qI3Ix4ZDzqEKbPvHitfOjR9+4f//JHcgl3H97VYZzW7cKpGCKYcrB5RUvc52NzQufePmnfv73Ql2fnd5fbZZd3c8ns7sf3HdrXJ29de+DcybL/dtPPjg5++Ebp3WE3kAL0DNIBjDPkMngUgqRxVBoLXgQMkoZyxxu3YbP/Nidz778/N233rt7/7y3etXxh8ftWRti7I/Xq0yXs6oqJlPuohiC6kNwVpZgfYwxpguWvvXOuai2LBXOOZOcS8alEluJfUophoSIgSXOPefSx+hcb/tBay2l5gyMUdb2mskktDGGMOzEEZHJTHLOuWARU/CBMRQcuBARA2mwOOdSS6EEJOCcR05YGts2DFIishAjQmS4NRxFMjGIjHPet5BlWVkVBO3HGLtm0zWbIssnk8nOzs4wDGdnJ4vFghD6vu9HoxHFmBtj6Lx3ztdtG0JKPiBnIgYhBHCmMqNiom13Mpkopfq+7+pmvV5fvX6ttwNhckPXEyLLmKFiGhmPMXG+1SeAAERGH5ysYAVXShnGtrRUskwlKyQqF9JFTAM1OcQhYYxVVbVer+u6Vkrdvn17l1w+Ie2p3a5vCT1yg6OdOoQwLsc0N0ZEOoJTSiKJclQoI6l9ipiCs1TwhRQhMu4dZRSQw4kQAtmSc06NCnFGqa2iN3TOWWuNMVJuOTZkFE32L0zwzA4q6qIoVqsF3XSlNDB0zg19x7qeJSaYFIJ5763tgaFSSghGEJoxZjabjUYTKWXf29VqIRVjDPMiTxiPjs/Ozs6EEDdu7KttQAEPIdEVYJzRGKOcTDjnBFRzzl0Mq9WKcw4X4KjgQkml8ywrcudjCMFbR+hXSomyotu2VUIKhjFGCuoahqHtu/P1CjkUJr8gAMTgQkxBTzIuWLC+aeoQgjY5IlhrTZYhcOcCQCCCR1VJpQzn/Pr163meL5fLrutu3dqhYQgRpTAlClMjX3yiFsx3pkVREMW873ut9WQyuX3z1mq1Ojs7C8EVRSYlR8Rh6Lx3ITBre+cGIVhRZIix69ow2MViIaS8cfvWeFw1XXu6OD8/X5ycHGdZ5vqhaVOpYTqtqqoyxnAODBJnmBklt++DKfo802dnZ+SzmRKcnhxTBlPft9RpaK25YPIi1Ysx1vd2GFzft1LK6XTKOXfOnT94OATHGMvzjHPeN+3qfJEYpJQwT1pIyThIxhP6brDWjspqq5JOg+2cDYMUWhvp+ggcATgkUFxxrqTJMqUlF4LxTGvOuR1831lyP1wuVlmWpTKWZcmZHKyztg4h9K4XQty6dcsYw5X03j98+PCtt94qy9I5V+psPp/v7ez2fR99AM7yoprMZ7PJaD6fhxDatrV9PwxDlufWWu+GdJGuIgTTNkNkZTUa7VbAWbNpgDOSxYcQpVS0cR0eHnVDX2S51HI8HvfOYogpAYEMSkoAnhUl56Ck9sE2fU9VjnP+4ODARxzaYdO0ALyqxkU5EkLcfuJJAFgsz5erVTUqrly5kucmAVsu1pu2bdYbIj16723fu4iT0mRZNh2NKY/cOWf7YRg6D5ExvHp1f2dnN/lQ121bN1xxwcStndnu7i4TsG7qYei11vPZlKYlq9WKYHvS5ROHjV/ktVMfTvyu1eKMhLnOOSFYCE5w7r1v2xoRJRdKSwAYhiE6HwFDACaAM0oG0EqpoijoDfu+j9Hnef7w4cO7997fbDYh4GChHywwGmsLREZZzhHRhUBSe6WzGGOMLkFMGLqusdYT8CS4opJ1OhsT62YY+pBi3/ddZxPA1d29YRiapum67tGjR2VZXrt27fOf//x77733YV4TYfakBecChGRScWSSMSYVl1J2tifVEwACxAtmPwPK0KT/IABnkBgAYzwBxuD9ZamklEKhx+NxiL4brO+d84MLlAsEzoeUQEumjSl1llKKLnZdJwSL0XfOO+cIluIcEGPbh6xiQgG3PKWAKjEGyJISkiA8rTKheF5mkJhU3BiTUqFznRullErBM8YiBp3rEKAbhhh7xU2m/hWHn873gCklF5PniBATw4gphYjeB+ujo8w7ZNEHABCcyUxzBEwh+ZBikkzoTKYIDJEzhohCaS2kVuz4ZNGP8uOj084On/jkR0aznaOzh0Hj3/l7//3zT73w+uvv/tyXf2nnM5+CevjVG9cPj4/Wq7puG5OXq03d9n2RwbQqorff+trjr/2L/xQRRhW8+NzoS18Kn/7EZ5/76E+0cYbZ0Vtvv7d2rTTqZ7/8c73rnnohLZv1O/ff2nQ2M7kNvtuchxCGvtuZj7NS/IU//muf/r1fag+Ofvs7rz94/3BYwmaJ165c8418+6uv/NN/8s9/+INXl2dwFFZcwN6NG+/dexzZ+WRnb1P3KGJIIS+qPCsl50Vm3DCcnRw/enhW5TDKs0wba4d2gDxLRvFN02mTv/PuvW6zPD3pf+6nP/H9H776D/7u3zt4dCYAnn5qzjnXCj718fkv/vIvFdX4m9/85uuvvHp80kcBoz345Gfh07/4S5ApuLo/15ldNwDWJ7teHi8W5zeeuMY4FPs7uzeuhLY9enx8/3T505//ohxdu3/2epeqNgY01c61bDxj9ao/b+qDvn7YrQfOuthyVZ43XVtHJsV0fzK/uluWow+vje9+/+vCaAZJS6mUTin5gFSYRMaiYIumaZpGCFXbwbs4pDDb2Z0U4xwURAw+ceeZdUx5wXiRmcG2n/3Mc3fvPj45H+qzs2vzW7lmN/NJ5msd4o8/D3/sj/xCxtjXfutr9enyynhnebpQOjxx58bebAwszqbV1ds3d5962i7qf/m1f6pM+Ikf/5T62Kfg5PTk8e+sNt1s72ZS80fHzVsHx28/OL1vQWWwcjBw8BIYCJZSBBe8jz5VSjW+9wkLDWhBSPjCT03+3b/6hyDHj76RHdxdgh0tz/B737/3/VfuHixXAaB17XrR8nF2dX6lYFl/3pxslk5KhwkRhZJZZrgQRlmvvPdeciG5YOQhCIndvj4hI8sQfAiRijxCXml7og0aAIJPIbqEqI2synFZlgDcWusGT4pSAIAUECNCIpNQKWXnbIxIw1MCGr31JI9LKQFwcyGmDCF576VWNP8l5RNcpJFteb3id+cDtBuO8pIOj2EYFouz1apGhCyT2si9vT3yqLq0Uh4G2zY9VcaXYin69Uk1ou+utSb6h7W2d9bkmQuefiz60Pc9AU7eDYwRZwkRtxeKSbG7N2WCE76IieV5rpRJKW3Wtdb6svSn0a0QAjDSNJ+g/csBLgU2XVLqCbU1Ro0mFfHjvfdDN1yqFApTDMPgLY3CaaTLpeRCcYKHqZSnuodQn3ThB0dmjhSwNZ1f2d/fn81ml8ASmTmSnTzNsi+KQq61bvuOwnRoqA0AZMuYUrg81xnf9paQYDae5aZgDPu+7/uWcciyTCnRdV2MkTNVlmVVjRljm01T1+u225RlLoRomsa5sOWUj8eCk0F+6rpus2m2v855pjLi2hL7ZTweMyn6vn/w4AEACEkUVWCM6TwriiIhG5z13gvGpeKcc8mF5MAQtNYMo3MOY9qKy51d1hsmxXQ03tnZ2VbhbXdJKrN9T4gmNZBt34eEF7Q3Rl0oANDtvsxwJTbOds0gktM2uYNvtaTWxhhHo5GUcrPZbDYbqqc55/Pp7HLd0lNAzpuj0YhWFMW40n/tui5at1wuy6q6cfuWlHxdb9ZN3TTN7du3OOenR8cHBweas9lsNh6N8qy03i9Wm67r6P3pPbXWNO2h6RAAbMFCY/b2dmhMNBqNijKnhcoYi9GTFw3pdqTkSimdGR+xG3oAMHkWY1xu1n3fCymD92VZaq1JlgQAth/6tiUzHM6ki85azxhmWZEVxvsIkAA4ssSYUEooZaTkgnFIyAFijF07NE0TQ+CcYwLGWKZ1nueCK3oGY4zPvfgcAIQUnXMuhkTWK86llNbrteHyzp0716/shxA2q3VvB2DCFPm1/T2SArdtS+5X9FjZoaP+mUr/PC+vXr2+Xm0onfratWu7u7shxc1mY4yh9ru3w2KxsN6XeQ6cP/nUE5vNZrNc0VYghOAMEZGSYiUXTbNZLtfkKOWcO1ssTVYwxtu2rVunlJzOdiaTyeB8OaqqqhJKMMYSSxTTdn6+1NqQcGVouxhjbkye55NxVRRFYbK+709Pjii9YTabFJNqtVlm2kyn8+RD3w+SaDlKGWO4FF3Xnq+Wfd8qpYqi4pEfPDyo65ruJtF1tNbUt19u5unCqnJr/rYdSHIhRIrROae1NMbkJuOc++Dquq5X66Z3PsJ4LEfVRAgRY1JKzee7s9ns/v371K4PQ4eYlFKMb50uhsExBnmeAfCu3+oiusESNKO1LqtxjLFpupQCsOTcgIhZlhmTA4D1PiUgn7rgE+dcZ4bGs4jYbBohVFVVRVGQxVZZllevXiXOnrWW6EOXX5zAdeoPrXPee7IIG7wlsIa2cbpcjDGCIUIgG7HfFUvE5C8H1+wiD0cItrczd87Vbdf2XQiBCZEVJXWYfd8HF4UQmVDEW40xznd3ETHYMAxDip5zTpahyqh8lI0mhcxlSmGI1gebIBWZAZYEkJWbBGCQGEX0UJC80VpKmVIg8d5m3UmphVAMuBBGcfWdH/5J+Devf/P6//X69JX/pHPDwNJoPC7HJcB28uNiSBHqupZSKy52d/f29vbruu6a/ko1v1bMhYdgh7ZZr89PhnYlhC9yXo7kSy8886UvfenrX/vG73ztO0MHT9x5pm3Or++prjn5/Bee+lN/8g/CpHjrt7723d95pT7v+9pBjJmC/SvTZ5954vr165LJTeveuP/4G99/dV2DyeBTnyyffebJerWejudXdm6PR9dff/vRP/rnX331g806gJMAebZokwfhuY0iJQXIQSTgEQzIWaEV9krindvwuR+/+nt/30df+OyzENb+dMmbXOBeey/85ld+8JXf+tHbj4KYypMunHUQEUYFTPI8BxWRH3V9H2JI26GoUooevZTI9pFBQogJMEo6MokrSawc2IaqNlpvx68A4JxLGEII2hjBlZRa64xQE0RiXccYPUUOEysROYs0OxaolBJaANV/gIxBURQAIBiXUkquAAA4ouSUlEkg+odLfzIvI40UVa6ELJIUkpi1Sqn5fELc3MlkUpallLLvexpkO+f6fijKkdYZFTFEXyFGhItBSikZJ8L9fD4XQvTOvvPu+8AZY+BiIE7kMHTee4aglMq0NMYYU5DGS2VaSp4AaV7c97auW8COAE5yArHWxoh0FAFLgbKCnSMCaJ7nVPSfn5/TzJqalsViEWOsqoKfsOl0Op1OM5NnOqMibBgG761zQ/B06ZCw/5Sk7QYmhY6a1NLEZULES1tPxpjKzDzPSE92/drtsixTSlTuE4FquVyGD2WfXbpMIuLQOymcUkpwlecAAG0bqMJTSsXo67oGgLIsy7KUQsQYrXOMY8TEpWAMYkrRBWU0eB98qttmcJ4xFgMiA+d8CLQMYDabTqdTzmG1Wuzt7dOU3HmLGBnDGL216cHBaZUpsomUUvXOumY7SEHEFCMgpgvX/7ZtB2epjK6KkqGGhIg+gbh6ZV8pEb1frVbD0CdIjIGUcn/vio+kvvAMuy092jnK+QIAZEJrDVwm4EKopm2UUkoR54dY6T74pJTZrBsp5d7eXlmW1ChOJznCluxLwgZiLlEDQLoUKgFpRQ3D0LcdlQJUkROEfO3aNQCo6zrP82vXro1GI0r1klJeu3Ltyt7V0aRSmWnbdmdnb+/qftM0VHcSDSxTOiXo2gGQI2PBuuSD0CbXxgHz3kfnN8tVSklr7fpBaz0uKzmZqswwhhGj805amSBdsvX63g7Wxxi5DCKGBCIBAy6UNJnOnXP1uqEejCOHxMqszHWuhEwYGUPGGIjkuZtNihCj94EHLpmIkJzzIYSqGoUUYkjAQEohuVJcCc4LkvcM1llL0CxGkDJOp1NrrR28HbZMaK2zoiju3bs3nU4ns+lsNmNS0NZHVgFZlvGIiHh2dtb3vbeOCa4zY21/eHzsva+bjdZ6Np5wzq3rQ3TIgAshITEm8jwfj6dvvPpaDIke9qZpPvjgg6IqR6PR2+++s1gsUkp5WWZZVlSl0SaEcHBwQL0HciaVMsZoIQEghliMislkUlRlSrxpNqvVZrHYKMO9dUpprXUWGW6d3fDmzdvAUSjZDt3Z2VlvO6o+hZKM85iSDwE5G5fja/v7u7u7nOHp0fHdxwfe+8Lo6/tXi6Iwmep8F4MLgMENSqnxeEQIPURYn62bponJa63LKuecp+DatXWDFYwbpTnjKUSGoKUqisI55+w29F0rRSVsP7QAIDgUuaGHxXubUmRMCSFCirbvKNiBS6V12JuNTZZpnQGxcYTmQoWI48lMCLbZbKwfAMHkmbX96Vl3Za9kjGRRiECRNejjFruhQBQ6a4ZhcD4hRqUoLVHG6J1zEZAzmVJAjAljDIAukcwDkM9mM2vtMHQhOMZYlmnGcL1eCjG/7JbzPJeSE8JFFCDGWEoxYaAKHhEZ44wBA7LvZDHiFokDhvjh/E5+iWFRR0GbTEoJIFFHF6MXghujtVZcybwojDFaSyFYW3chBJci5zzLtNbaut4YU1Z5WeXU1GktGUOpFddMaoESQ3B96KzrXXAheq01IFjvGUuFyYAzF4J3XnIhuPDRIUtCcGRonSMRYHDOOQ/QKqH/J6oc/83rX/MXj43vgspAs6gZJESfIkOQjAcOCVhCGE2nOzt7jLEUouIicL9wZ8n75H1ItoO+iwMP6BAiuO9+95VH9985PxmUhzs3Qcb3JMDqFH7/zz//a3/2l2F/1H7/+1L7z3/hk/X5MCpHUiQlei2RC+jqxf33Tz64e7q2UC9hMgbr4fvfbevVOy89/9zHPvlF4OXJ48XD01UTOSskJLGo7doNMxjnnGeKJTlEnoCDkExyY0Q2ropr+zeefWb641+89dM/+5za7VfHr+eVELtCTApYhEV7vFqfJgzVCI76wEuY78ghpaFLvetz1ufKFEUhXHLeAwALCTEkDBhiplWMkSXUSuRlqY1kV3dLurKEB9Mmwhgj1PZS3Hm5swyWnASno9GIMUEIJQCkQEpcixA5B6GE4ACMgdgabgJAsME5JxjXWk+qMedcMJ5Sin5bIgAAcMHEh40LtmadwzBcGB1sOfEpJYwp+kAHM9IUg+yujRGSTSYTrRVtuFS2xpiKcpTnpTHGWktEdkgopUwpzefzcVnBhXZqvV4fnhwnZMAZoTJSMDKHCcFtQ5KlZAwJtTXGCK2UkhFDjOi9J9VBDIlklCGErqM8qS02k1Lane9Q7UhHAvmcEiRMAHBKiY4i0m6OJhWRLtbrdds0BPwAgOKi7/voEw1b6KJJrUAAcTpJ8XnJ+b709d9ysaTM8zzLMkxis9mQOQb1WuHCbpyMUKSUJAOgQcGmbjnns9ns2rVr2sjlcnl+fu7cEGMsiiKltF4vaVBA8bduiEYqpSmwFgDQWjvYjghUgpPVLHrvvYsx+WtXdwDSfD6/fv065/Lk5GSz2QghUoJL7ybBZdp6VA8UMUZzf1otVBCTkI4gdgS4tJDyKUopiX3LOXdDR01O3zZFUShB44iQ57lROgEu1431DgCyLFNCUtxY37vpdEQs3slkcunj4b1/fHCkLl50hUmVsV6v8zxHxK7rlFLz+Zx+C8gb9CKgippepVTbtiS2Ho1GzrnDw8PVasM5BAdKkZwUUwJjOPl5F0Vxeno6Ho9feuklAHjttdfOzs5ms9lHnn+Zcz6aVFxJxth0PuuG4Y03XyPYlSOWZVlos1gsFmfnjLHeWgaCoErSnW82m9PTU+89uZpcMjcAwCe/2qyIcUFKevKPIqo3yTnathVCjMYlIG+ahnpCEJzaMODskmOWaSO5wBAF51pI6tW1Nr21wzAkACkFEYp8iqSnjDFyIcqypI+qlDo6PGCMKS7ERS6yljrLMmp1KKeZHpaqGk8mowg4Hld5WaSUemeJ4TYMw+npaVEUs2qslHJd75ybjMa7V/Y2Tb1pGrIKSBjH47EWcrVaZUbRAmMMKairqqqdnZ2hdZvNRgo1v7IjhVqsFtY6kxsp1eBtZnKpZdf1g7dGasbAe6+kpO1iu+mFGEJwg6WHcbPZrFerLMsmk1FKSelss9l0Xc+41HkxqiaznZ3JbPb+3XvrzQYk5HkeMcYYe9c3dTeZTFNK47K6devWrZs3EfH+e+/fvXvX9521NniPiBBBCJiMR+PpiGmWWMo1zUUJFca6Xvf91sqGMUZOaz7F5NNmHQvBSX9SVdVkMiFEAxHJHoqUEgTiMMaEZLQZkrbHOReCo7+itbbWLhaL6APphpGBj77rmmFwQsiyGBljYkRCUtbrtXODMQYh9v3AOYzHlaXUcKFd8DS3dCHSOpRSuhABIM/KGONms/HBhuD4NlErDUPyEbSBsswR6MsKRES2pdWmlBTXQsjLQR9V4SEE6snpG9HaI0eHS1QixujCdoOiAXIkriGRfi6OPKP0xcQbLk/nlFJRZukinV1KyTlIKbVWzWbFGIuAPiTnnA0+psQYH4YBAJTQWmsjpBAi1ybLMhC8KIpc57TGUkqcA2MYUnTJumADC0KwJBAxhuR9cFVVcGBN06QEZVlKxvu+z7NMKYW4tZ+mL97VzXIxVFVWliMGAhF8inbwd9//X/9PUDv+m9e/vq8vTP+DPM8Pj5tswsrJWJu8874ZhgiMCcW43LlyJUWYjsdFlterNRmmlXnWtecxWB4Zi8E1XbdZogelACI0LcwL+OTLMM+lTmF1BJ/7/LXp1Ss/+eWfghtj995b779z7/ykRleOq9ni9OjR47dv3si/8PlPLhf1P/i7P3znTdjfh9n82uDC46PTvIBqIj72kRe//OUvI7L33r3/9W/+4EfvPDwf4LiNJ32KJsuLab/sKqWKMeNZQN5HiIwpAM1QnZ+fffYzL/7Z//kv/NinZ7I4EeoAVCel5KmEjTl89fxrv/7Kv/zNew+PIeVso+QS49qnIYBEGBkxN6NSlzHowQXvfQIUgnEpyJESMTnbI+JkXO3v7k1GY7ZT6S1PHUApQVuVtVYpgYgINHDcvpCxBBB8oiFpUVRSKIKlEYjS6jgHpQUIhsHb4Pu+ZYID8Bhj9IEjaGmUUkWWMcYkk5xzQRGbwBKDthsAgKSHhM1fMmFob40Xbj/buHsQ8uJoJPnaliDEMaVExgkXpHMlle4GK+XWThQAlFJFlud5fnp8cmkzqoU0xjDGbPBZXvZ2oCLe9kNM2wDgcTUCSJzcGNTWwihgEkIgQ0C2xaSdI75NSvC7pSduZbWcczdYmt1ba2naEGM8Pz/PsgwRifzNOadxOSIqI6mIcc5xYEVRZLlmjGkhl8ul7Qci4hNxPy8LoZUPgSJsLwPbL+/p5WiYUOS+7zlsmyuqeNLWFFXR7SCAKly4r8YYi3IktSrLcjwel2UJLNHfUkoxhjHGvu+bZkPgllF6f+9613Wbzco5ZzJVFDkAODdQ1JpSmbV2uVgPwzAaTXbnU61gf3+PIpA2m2a9XpODTYxIPQAAbNY1cYXbts2L8eUX1EoRvURrfZksQSdx3/dt2/bDYK2dzWbznSn5FKUQlVLGKLr+pE6TUnrvN5tNXdcRt3nadNGIEEJFJ32GsiylVmTr0TQNZ1sqs5SSPgk9XwBAQyeSN5RlSYQc63oiHAMA3QJ6HC4PdXo36hmappFcbDZNjDCfj2azGa1Duk3L5fLmzZs3btw4Ozvz3l+7dq2qxr5zZ2dnQkkmRdd10/mkHI2Ojo5efe1HeZ6XWW6MKY3hnPdtt9lslut1RBRCbBWNjNGQipTH9OcufUVCCE3X6Hxb5VADD1tGB9KYhfgek8l4Z2e3qqrl2TkiuhistWSzS6s0xsgpuI1xcWFWAADdEMjCxAWQErIsA4DeDlpr5ExKWRTFbDYjFfL5+flyuZpOx3vznTzPU0LnXAqRMbZe14jIkfML7yMqmHpnOQcuBec8MYgX8UmUCSCRKaXGRVmWpWDcx7Bs1ueLxWaz4ZxPJ5OyLFMKZJWolCqLjCheMXrOuVHZq6++zhjL83IyG1flWCjOmAABfW9NrrXKIsSUIGLACCG4ZrMJIQDyGGO92bRtW5bl3t6ekWqxWGxWa+KiUAtEFeT+/v7ulStS6AhMCq2zTCgTUlyuVsdnp+vN0gUHnFNL+cYb7z755K2XX3gxz/PlYnF4eLg6Pev73gjedX1MkGkxLouiKKqyzMtMGtXbztqehElt23bWxghacyFElRej0Yia2OVyvVnVzz31/M5sV0pZ1zWR1og3yBhbLBbk9Um8l+0QElKeG9ptIibaoon/07ZtCIk+tve+67oQXMQYQuBcjMfj8WgKAMvlerFYxRgpWZZzro3UeisNatu2qiopFaFF5gJtEUrTttn3fQJOwpvNZuVtX5YFiX+cA2O4NDqlVJQj731KwBgDfmljoCCy3z2bOCdWHiKSeHc2m/V9f3h4OJvN9vf3yQjLWrt1PGMAAIkyfVNUSnEhaFtgjO3u7u7v77dNgyFcTqIYY9pICuSOMVKbdLnpCcm9t97bTds0bS+lzKsSGfSdJV6rlpoxZoS8nCa1Q4+IySfGWJHlFP7YNBtkgCINrm+GVkiejYyU3AXr7AAXNqPUjcit49BFPg8wWirW2ujT+RmORjCbzYq8AgAfk3fhzVf/nf8/lo3/5vWv/+sni7/e9cN8ZyxLLjKZ5eUQcNE0HllAUbftjVt3hFDjsspNNjT1ZrUu83z/6txjt96cJ2vPTw7D4GcTuanDagG37sDP/eyLP/W5jxsYjt977fjeeyMOt25d/8RPfTEgDrZ9/927r/7og74FdNnh4ZJx+PlfeObTX3oJSnX/m6//9lfeKtWtm/sfOTmuf/D9V88W69lu/tJHX/jSl76Y5/k3vvGNV19583TdHJx3ywFargee1Si8j1fLkUQ/nUtlgnWbth+Ch8l4zkRxdnYynahf+tWf+MJP3ppf6fb2PQPHfbU6dD/4xtu/9U9ffedtCAhWj1ZBHlorJiOuVfA+czjmukgcXchn085Z51zAAABMcCGYEKLrW8nZZDTanc+MUs4NkvYmgO3JSpA/v3CV4YJdQgveW0TGBLUKkjHhXfBAzgHbeGEpJZNMKCUEA60lBqnFMAzWenbhXUByor5tsywbl+OyLPMsE0JATCmlIq9C2nIe6K5TAXFJ+KGikz6eUoqTATJjWus8H223PCFi8s65YeipwKLtOLSdMllEZACS80u0MoRw7do1Ok5ijNLI6XRKYOTxyRm59zDGJBfee4RIWxtjjGSgMbJtvyH4aDRCACrvaJRBBXSWFQCXOA1Q7cgYo7OQSEd0DFCpcX5+flnC0j2i6+yCJYQJAIosv1QmDG2nlFJCAoWoe39JK48p8Qvr98uqnYp+4v3TpyLur+SGLgtjjEYEVBWRgwQ1M3hRC0qt1qsanKWqjhzfSabWtq2UkiQfxCBSSmmd0RUmpUGMsa5r7/0wdHQR0oUH0bYbCWE+myLiarVyzvW9veSADUNPbFd24RBCHCcCobed2EWmHZVElz9JddKWcK91lmUkBmVSgVTGKGPM/v4+Y0xyRo1W13VEcFrXzWX9ffknaInSmw/DEPuto5QQQkl96al/CZALIeq6puGMc+7o6Ij6Q3JFpE9I952QM6XUer2mP0QVHtW4SqnF2fl0Oi7Lcj6fX716dTweD8OwXC7J/tU5t9lsaDEg4nK53JyvT05OkAFXEgCU1sQw4UzSWrXWDpwrpaIPIUUmhBE099f0MJLUmFopOtEJ3tZaS60MUG4uUtNIlzqEsF6vLgU8F+aMrO/7erUOmOjRHqx1ztH8T0lgjEnGOTAOEFNMIaYEklO2BmiZAIAkFmVeGGOocrrUtFzsCYaa1e0nlNL2A31sznmmto5hdC9SSs89d6fv23W9advWxUDAsLgIHhkG2zRNXzdaaw7Mx/Dg8NGNmzfv3LnT9z0DyLJMCNZ1nciLEB11mFmmaQrBOX/x5RdOT0+PT85OFmcJQEqQUgvFX375oxFi0zcRQp6VVVXEgF0f87KElOq6Xm+WGGF3d5dCG7qYqC28BGtoA9/f31fGdO3gUwsoldHSeibaBw8fcs47O2zWTcQwnk2oRv+jf/TfCtbVdX1wcHByfDy03agsr1y5cn52Ms+yzJiyLBVnIQTnfaj9vTceIgNjxGQyIjO03A2EakspOfL1er1c1l0HQkBumNF6s9lsBzJ8m5NAH1hKubu7S01yXdda6yzX0btLgDwBXqLgAKCU4hwv56WIGBPsXbnCOZNSCUHOuW1dt33fRwAGIDkIsXXmIVa9MRljnCZLIaWISC0HMn6JmiODpmkIYqDLS+NnKYE4SEMflfYhBNpw6Jyi7atregqHodOEpAs0r6A3VErRbDDPc+reCTLo+77p2hCClEprPR2P6rre1LUQYjqd7u/vX7t2bWdn59G9+7SoaEsXQkjFCfaitUo7TMI42B5synODKIuiyIrc6DwrCy5FjFFJwzkHRGc9+hBj7J0PIXBy+i8VIThd19F+1XRtAO9DiCkyDt5HIgeTD3gIMW3N5ZDOYzJnu0QipJQxRsZwPN2aWblgNbGOBLz8yb+JiEwKar+RkZYPASAFtNb7wWJiWupMGsmVABF9QMTog7cEFngA4Ax5RMG5kcYYo1XGmAg+Xg6u6SxjF44ayafpaIcBcAiMJaWS5Ohs3XetACzLUgk99N7ayEAAiOj8dDp20VvvbQxMy2Jc7ezNq3nZD5th6GzfppRylUsm63WzWG6C1DIzIXkf7DalPXmpt7wsrbUQKiVIEY3KTJXdP3ucl9m8Gldac+ftqm6XS9fYndlEMOYjOh8SF0zLmGCwwXZJcpUbg4i27xhG9LFrbJGDFkIKJjhATCmk6zf2P/HyR/6dP/Gnvvm1r37ta187OjmJCL2zbTc4nzoHiQMKFplMyIALZJwL7Nvu6v4I0N+5dZWzKDT7pV/5A5/89Kd+67d+67XXXnv4wQPuYpYYtLY779bn8OTNYrPpruxXv/ZHfunKjV0wuPPxl6Dd/M3/+v/xypvvV9Nrq028++C82cTlyu3sXMGct123M501641AMEw0q+WLzz4/Ksqz01M/9E3TAEsI4ADAuedffuH47GS5WPQRrYsgFBNMMOlaawxE5X2CMFgeAw8+9haTd+tmUprprb3HDw8ePgw3n4I/+Rc++cu/+gd29orQrx+/9+ZOmt6689Gb41F5ZQ9GUq77xx88/tGP3nlw12spY8DzNfy+n7/56V/5Gchr6Os7Lz6x8+bRV3/j4b37w+akT5FZD+Px1c994WddEv/kv/+Nu3fv9n2/rPvBgg9go9uAs6Akl9Ztdq5Msiwtz05ShL2ZSpFvlosYVgUgNO79H7wyEac/9mO7+6rq6v6Vb7/9o+/ee/1HdrWGoYfTFjZQe2HkeBRaCL3LpJqqfMQ1a23b22U4TwqUUpmSANxF5120KXIOmTZGyRRCO9im2cgLCk1MaUv1oRUZghMXMk1ghKCnGKOPTkmjNYfkPQTYRqiw3lnGgHEU5GwIjHMGDMqyFEpKbRkCFaYePGAUILIs00YpKQQwiIlKjaIak3rjEuOnvuJyMErVMNV8QghvAxWLo9FoPK4InEspUaSX947egQoO63xjLVDwLzAACCklOwzOHh4e0nxVKcUScs7Xq5XzPnGy6NR5jilE772zARHL4sPWBJwxIYTiUgLyhMn7GEJiTBiTS6lTSiSBpWIoxhgjXToEqQCgLMvd3V2CMMk8lArWS+7mJV9FKEntjxCiyIBzTtFgBP4ZpZVSQm4bBiFl7/1yvSJP0stCIcSwaeottso56U2ts94HfwHzU1NBC4BzTrz/S7T7shgajUYuhGEYhmG92WyIakL2fB/uW+ACGw4YqBZs2zZEp7Uin1ZxYf9PIk5jDAAndHa9XtMIBZFd0plSRMGlkpoxpnVGShWq0bmSkm2jITjneZaRKJmIH9smISbJuBZyPK6EVhwhWBeTR0SMKoXwve98W0pJF4H8iPq+t9Yjbvu3dJECYb1PKZFZJJfCe0+WpqYwWZbVqxohxrS9R3QxEXG1Xuzt7T1144n5fJ5SWiwWZ2dndV2TsIF6M7rp1PVdvXqVaGnE/KE6BgCqqiIK0Hvvvffw4cNbt25VVUVULlIgEI5eFMV6vV4sVpUpV6sNcMalTBgY5+3Q1/WalhmDFGMMCTkXWhuT5+VoHHGbN7c9OBlLKamkY4wRE49bdIEiAjrbxw/lbXHOu64np/8QgneQEngeUmy8i1o3F9O5sLXcBRCCCQEZhZElTDFBQkgoGU8SGptESMZs8/hiCAlAak1TGiJfJB8SF4qLUVFSl75eb/p+ICzZ9q7v+93dK4jIERhjnEulDMnNd3Z2rC3ysmjbtum7CyHNtuHUTJRlOSmr0WhklI6Y9m9ec96fn5+v1+vxpDK5JvKDJ16490oJZXQCPDs7W65XN+/cvnr7+p1nn5aSd91wdHp0cnLWts3x2XFeZsbkUgiphVA8RO+9TRiVEONxNRqVeV5qrdfL1dHRUQjBBae0mk6n4/E4pVSvN23bdsMwhCCl5lJoVWAMQ9O44ENIbVv3rs/zfPfKzu7ubpYbxtjR44Ojw8PT09OUUpnlOzs78+l0Op3u7c6tta4fiNJGrS/nMBqVXdd2XfR+lZVdnudZpouq9N4jA+t972yMMBqxG9duXt2/dvTocHG2bNuWCIeXTDYCwikaot5shmEoy7LI8mIy4YIhaZl9hIQhBcaYcwERhZRKayGUkFqbHDFuNmuVaSVjCKFt+7puh94lAEUyYsmk5FwwJgQTDAAwonfRxeAjSqm0zpTZ7s8hBKGUyXPGmPc2YlBKRW8v3S+0NsYYFoLjfdcNMUYGQkpJqD9jLPitRy0J+jnnl8ocykY0xlRVRegJNTwmU4gYQuKCehvKt1F1XXPOb9y4tb+/v7u7S4OO8/Pltas3zhenwzAgAudMKSUkQ8SyHIkLYTTjW93w9sGKjjHMjFZKphTItGC1XnDOGUKMKBIopQRjXDBjtPeuG5oQguRKblNfeiklbU1KKYTUti1AAs6Mkhij9z4ElxIyDpwD5+DcQIeXUkpKrYVROkNEkxHk4gcPXDGjcs4BOV7sjQxZ2spTEBljzrsEILRQXBmVaSYZcoigcxNj9IDOY8CQIGqtM6UhRMmFEloqBZwnTCiAMS64FFJKnSmlBDAZI0U4dy6whAwSF2iACZbaPtUbWzdeQAsJGAMpoCiqyXhW7lZJJteF1g8BIOMFF7q3rj8ZJtMCWerd0DftIi0w8K4Z1hsvM5lVo6LKjc6tH1wMIaAPYeij0CrLmNYipRQD+ugHLorp9SHa+6dNaFscBhWhkDIrxoerniWIMSbgxhgDEoFjYlVlOOe2H7y1SgkGLCIWE51pk7yzIRgmlTYofB/wbF3/zb/1X5wcHpyengITo8l0lGUupMGng5Ozzvm2972LCRkoIaQQAHtPXIvJr87qatXvzsb1avMP/84/+lv/2X91dtbvzMwoK5ONTesM8ueeeO7FLz8zm1cf3P9gsTo737TeKAehuFrn+/u3br/8zt3lwePT5cZKEPtXxlo2OnOd6yrDN2f3XQvjnI/K0d7VGdh175vQnu/uzHjC00X/kY889dnPf+aFF15YtfXXv/XN195823qrGLcBYwyQmNG6zKvCZBC8iDFjXMQYN818PLt586XT0/fefevgY58o//1//w989PPPVc9e9d2ZymKo/ZzN7zy9a3xIp4t2s8bNorr+1PMf+0wYZv36jbfePhqGUExgNB/D7gQWRxA9jCa3n3mu+cffPnhwqgMoASBgZ/8mk6Mfvv769159V2t5fNa0PbgAAIAADKAAX5VmspOLMkqFN6/vSPQiYt9aABBaCMGVCqOAeuX4SeBFTJt4RVzj9VF3aqOFQsNcy7Ess9nuuu83XZcgTkZqXmoJrGMJWLLOSqk5V5yzlBJLCWKElEhEOgyu2WzcYBkCsT4wJbhk5H8Y9b88v4EhufvXXU/gTYwxJgQAIRQRE7fTPcCInkXknDEBCTghxFvuYIiSybKqxsUIEVnEpmla+usJkYGPSGagl8UlbbvEEyDoEbZyYSD4M8uy8Xg8mUyybLuJD8Pg/MA5v8Bnt2EC2kSRZSFuPTcSOYURW4PxLMsybcjXAhGHvm+7Lh9NsiybTCZCiKHrLk/Brq0RUQCTil8aVkTEuq7J/CHGaIyZTqdFUUgpqX6FCzJGuojvbTYtVdKXB+Flh+Av8ozp5+lPCLXltVNpSH2CtTYFJ4TItJlMx+ToYq3d1PXJYnG2OD89PbU2GcM/xDW3dMOlBJIWMMbyMvc2CbZFpqnGpcapaZrLop++QowxxTCezJlzF80VTVoGrfVkOkopxYhCAP0KHQNJImtZ2zKagRARn5A5RKRUTiGAaFEAUI1GgNG5wLmkUh4AyIWWzldEZkxMKTkXEHgIgTNgXNAS5ZzLD5HRI/rL9UMsAiKg44X3EZWPNK4hyxdENEYJIegS0ZOSEsYYUkqMQZ7nQnIAoFkOAFmIynI8rqpqOhqT/Sh1a5fGhbdv376ctNDCLssSIHmv6ZNT9U//lzFmuVxSh0bC9EsNwKis9vf36UE4Pz8/OTnZhphKSb9ON4sYBTSmyPPc5BkTomk3q9VquVk3TcMYKqWMEowxwIiIDEEypYwOzvrBMSmyLFOZ8YOtu5YlJIBQGs0RhFaS8QhpPB5fspvKskTEEE6cc9ZSrwtSAmOAiNZ672NVFQljQtpJxCWGSvCqdw49MAQlQCstpdQ6AWeUrso574aegH/GWCDnxBDdYKlGMcZMd+ar1Wp1vqBUB6UUJIaIJDkQQM9CTTOuLNO/9dWvas2KqqyqSucZLQbnHFk2ZWIb4pFS6rrOBZ8ExJRms9mVK1cYxxCCUfrmzZubzWq9XoOW8/mcjHrPzs4Oz1ZHy/P57vzq1Wt5nm3qel2vI4vFuGhtW07Iqsht6lXdAGM8pGCMZgmn0ylx3o6OTmhEZq0ty3J3Np/Pd6WUTdMMXa8zs9pssiIfj01ejrTOrPPrelPXdT+4ruuKUXH79u3dvXnTtseHRymlk5PTuq6TD+QEpZTqmubk5GS5OLM2JAQGUBVyPp/vzneqqrK2X29Wi8Wi7bu2cTFGqSelpkivYQhWcDWdqjwrsyyL3l8OhC+3DtobvffL5fLk5AQuBC00sM3zPGF08XcDmymJjYykaKBKaLS1NgSHHH0KnFnnQt/blBIFadHuygU16khKcc758mwJ4HyKtEK01lwIuKAVSdqjLp4vABCMASTal/I8H1UTJkUIoe274Ld7OKHUnAspBZoEANZaIklyzknKdRlGPhqNaFExxmbzCTXnBAyZrNjy40PY399XSuVFRbl+tF/lJpNSAnJr7WazAQBjNOmmYvRaa2OUkPxiN/OIMUTnvQfOhSKsPQBnxpjd3d2U0tD1XdeRvQcjK5+U2ra13cA5H5VjwlkAeAjBuxgxMsExpWFwMXkpuUgFYoLIOJeSIxNMCsYEWOtIgXx56xlQvD1P4AK4iMRr8owxLpLg6oI6xS/GLNt+QAhgyCXjnAMlGmBAIKM2CUJxLhlGxiVnWkgpydOOcQ7AGQDRcb31IcUw9MwOtCAZcgaCcQaMxZRCiugZ5xiYYCaTMdo+RQQlARS3GPtkNdOt70XGptWeycqymuZ5DjLFNDjXFFmZXzVt2/b14D1mZjSeskW9pnZL6gyAS5lNOCQGTdsO3rWNj5lQRifE9aYZVutBKRBcMq5lYUZlIWSuTSHE6eExpMQEGGOKojLGILDogxt6KWUX4+CclFlCiMjG1XizWme5BmCe8/l8Xpaltfa9B4/erJeZUlyoBNi1fZZ4Vpa6yHlruQpSWBOZ0Npkhc6MlGy9PpzNijnf8wiPHh42qyZT0Lfw/PO7Usrjx0fdBl5++vZTN58c1t13fvSGLtxomg8CX79/zz2Ux+eLv/+Vb3Xet91gg9dmFoeDxSIqddb3wOUgNJzUIAFeum3Oj229XF9/er9enI1nsz/+v/hTH/noy1/9na++8tqP/sSf+lMv/qFfhXsP3n3lzXfff68sHkQmwSffOx+Acw7IpZSCcR8iYMoUN4Jx79cHJy36wZ5/8sXJn/+zv/rSH/hMbO89fPtb2d4IgojBF+PSiBxO12erbn14uj+bhIMzCVdffuHHn73xpa9+7Ttf+8ZvrrumrRu4/wh4gp2rcDg8+cRzn/rU0fd+50GeYOjhx7/00z/xpZ99+4P7v/073znfdOQNYCAUAkyu+4irxkcm8rE0JefM+W4YF/lYF7GzuWJKh6bxAWHvuv7E888+dXvG2rZ9bBXPoLVXZteu7a8PTqAcTz/59Ms3bj9TTWZf+cpX2iH3mJRRAcKybdswJMMm2UhqlRg4N1gXaGejpppzji7Um9YPNsu0NEoyhiGE6FMMEWOgQrNvfdQBEoNEJGOutBFKCpXHGL0n7k1ICRLzAJDnBjhjgjOGwBITjHHGObRtixxZYiGE4DxDAAmc8xBcCCEMwXvPEnLGtJBMitXizMVAwImUkqj9KSWyDvIu0ayWgBMGYj7fpRbFWtv3LZ0Kzrnbd25qrbVWcMGZTilJycIw2LAlDmGIkBC2yWKYUgrON03DEYDIJJwL42jTr6oqjSdVNeq61nv/4L4NwQGC4OqyJuuHwbmADC4OPGFMXpYjKSXZehIWQvspnW15nhOu5r2nkoJ+d7lc4oWB4yWrh3NuXSBsCQCcCyl1zg3DMEgOSikGaK0hFmxd1+eL1f3DgwiYlcW1mzOyLarrerPZBFywD2mpfYrGmJEprQwc2GXpT+dQjLGqqktSOwHAWmsm+Hq9TgyAM51nIqqUUsAUnU1rIANHpZQ2MssyxY0Qom9bH4PUajydSMmlEt77tm/zPI8pYsIYo7XeOSeEApCj0RgwOesZcFJ/0ooQQmidSalTAq0z72OKXGktpQTB6fIKHwCAcQ6MkXhXUoQYbL84ACiy5cEouJSRxRiRIQDs7e11XTcMXd+3zgkAaIfedn1RFFIIBkIK4sIwleVay7PzpYsouOIChDIqU0pJxmA+n282G2t7IdhoVNJlrOs6y7L1er1cnj9+/JB6abjgtglBLCbBGHDOlZKcM8TEOdNaaa3o6aAIpMePHiWMiGjdwDhYNyTclvgAkKGRyqgorbVSib29nX4zZFmRFbmPcRgGHwYu2GQyidEbY8o8F0LgRQZ2crhenjMtBeOAYd0k6frow+BskeVCCSWkwIQxAWeIgAhb0BHQesetAICu7+umQQDOmbpokhljgBwhWu9C2hLPLhdkSmkYHCRkCAJASq6kUkoLwUZV6byVUkgpsiyTUjjnkIFzDlLEGHyKPjgfnDFGGyOcomsbQrDWC+EkF5zzzeZ8NpuNpzs00SIIFiA99dSdlEICpJtCTwcAHBwcZFlWmbwoCjkiWDfLGZSzsU+e6jyGSF7yUsos08Mw2D5wzp2zq9VycMNoYgIHC37RrJVrnA2o2SgbG6Om07nW0kW/rldt22stR6NJWeaZyhhj2ph1u1menR8dHocQqmocgpvNpru7cwbs5OSIhNcqy8fTicqMyXMmeNf36/VmuVyTgOrGjRt37typqmK5On/w+BF5NI3H4735DiT03p8cHaeUvLN9349GpRDWWosIEbFu22EYEJEDqXiZ0Wbwtu8jLpfWu6qq3GCpXxVMeusPHx3ct8ENngHEmNpuq2xJKfX9MJmM6ZE0xlAGXJ7nSsmmrTkHIUSmJBqBiCkiMSEZYzGBtbYd+mEYnA0x+SzTMbqUiOUYaUTmfaANqigzrTVjGIKjNVaMqpRAEcKiVEJmO2ut3faxkvd972NgghtjvPe9cwCQIqSUUgTBTTUZV1UBTND0FRETIAAyEEJIpYV1vfNDwkBHj6UolZScc+vNsqzysiy5gBgDcZm0lkJUxphyVOV5zkCklJiUKSXnw9nZ2TA4IcTOzs6oHD9+/LjebNzgIWFKyaFNkXPOtRQBGMOEQNEBgYYetF8KoRgDIZhknHGutUSMIXjnBucGCCilFMAA0vn5OWOMC5ZSXK0WdS3yvMzz/PHhiYueS6aZoT5KCKGUTgE5l1IAl0wKZJJxQOAs0+aCGMwwgg8BU0AGiYHUggudECNECFZKyTlLGDjjDBkgcoZMMKrPJZlQR4AIKSYMKYUEKaXItu5DouRSWCIpCKlRcBAAkAAoD0EIwQTTjM7WFELAxBCRgwAIRmvBOERwLgQflZYyy6vcRCFAdsCQwDLXD+fNYu03TIadnZ1ykktuBu99SloIxiNjLFMmy+WknPaTaG1wFpz1TIrlemW3oRmRS2F0FmLQsvAOBu8wuRCBqL2G6RRQGlOZPDOKw1ZBubah2t0JzicfmBAoZeRcMKk454BVUSql8q4TjHddl3yMjLuE43yklArRdSENdQsAWuvbL388xdD1wzAMCaQVct26ultxJnkmq6KS2kihEqD33vkhHxc2unq95H3YG1Wf/eTHP/vJT169Mq8mVT+03/idb339X379nYcPlxs7H83Hu9NFc5dptXT2jfc/6LywXio9Bp6Nip316WPv1inwDKLm8MXfc+fXfu0PPjw6mM1mP/jeD/7Zr3/n3/3Lf/CZp579G//h39jdmfzar/38cn3yP/6TN4pRwdTwD/+H//f0ihaq+NFrP3z06EHb9QF5QkzIkhCMiU3TcM6T08l2zNnMSCVAxKQFa9bnm7qJCX7nq1/Z+A+qW+Wzn3lJTqskJAMhnUiHiw9effTolbtQ1/Iq3l+dLBb3d3eev3Pn4zd2b1+dX2k2zcMPHi0eLibTTGizPliuzoPEsLsDzSncenLn5Y++cHB69Lf+9t/+YD28dGtPSbZenBgFOzvZ/t4uMn50fHpy1nfrVT3AfF52Z+2BbfncPHnj5qQa3bv3gWYbF6Ew0fvm3gfL9eLomZvXbty40XUD8MgU2AQ5b/IyVtrq4fzZnZxhHgBXQ/v2yfHJYtEKKMaT6WQqmfTe9733wQkhGBdcABFcJZdaZ+jR+yi3xnkXHsN0zqWUtN5ayxM1nHMupRNKMiZCSjFSNQ4ppQjIGLPescA4Z2QkL7f6gYScDUPvh8A5y02mlGIBnLVtTBhTCoghAmJiDGLCwIRUBKWIi4xbgmCpDKXq85LkLS4CYi5K4eHyhw8ODqSUUgpiMG9HATHKLPcI9AU4ghBCSamEwJgI6QQACJHK7iwvgfMYIxmue+s2m01dby4Y+YgXNGVE7Pu+6ToALrVijBFSHmNcLpeEcn249L+82kpoQnSoWiXjnQt5QHZJsCZ+5DAMTdvT96U3obuTUuJS5nk+nU7KsozR13XvnCvK7ObNm9Lo0WhEWlIannDOyfyeEGgyP6XWwhjDYWt7Qj9APcAl02ZLJaLAr7gNJqO5B32eYei9923bskvhl/pd5Rm6rRxcCJFlRVkVKaW+NwRL0zSGmiLqnR49OoC0zXkgbJtzSf8gk1bEoKTRKnIW8rIY/JBS2qqQnacvyDkXnBtjZMallBwuzFJwy0aljouY7j56asnYh5x2CLuSUi4XaykvFLdSpiQCJmt5lmu6gEopvbWd5SmFvu+sHUIInBOdSSCiUvKNN95gDLIszzITQuy6NsaktXLOEfucPiFdyRDCzs7OpXc4FbLkzjSbzWgSpbW+c+dOnuf0RT744AO6g/TIbA1wkRmZEQ+h6bq6rn0MIzPa2dnJcyOlNEoh4tDU6/V66PoU0MWwM56Nyqrtu8XZuV05yQWXYjKZCCVTQutdcB44k1wwxoahQ8RhcNZaGlls1nXTRK23sdmXPl1U+vd2AIiccya3rWZ0PqXkHBrFqrIosjxTWgrBIyLGvCpYB9bapq5Jb7NdMAxo8WBKAbfaax/CZlOD4KSTdi5cDnZ2d3fH4/FkPNmqLKR0ziHG9XqdUkAGSilpyM4r0PXnnIsEwzAsfBiGIdOGCfbWB++CADpW9/f2rly5gognJydVUYqtjaO0diDpy2w+nVzfDRC1zkajMsuK4G3b9MENALxta9s7zmE+n2otpdRK8vPF2e58J6VwfHx8enSKiFU50lreuHHDWnt8fBxjDD7JbQeLeVX6hHXbufXadr63A6CsquqZZ57Z29uLMX7wwb2TkyOpeJ7nzaY+OzmdTqdlXnS+Ozs7A4BrV/efeeaZxeKMWHYhBKWEyTPJRYwRQlRcCK0SxGHoO99LxZVSwzBwJvNMA3B0cRicdzH6JDgnnhVtkrTRMcaIAjSZTMhZi/S11kJwvTHq0uPBWmsHF0JgQnLOYwKqtvM8LwuJEA8OjmiaRK/gU+2bummKvCDjASr9cRuPHXd3r9CDL4RgW9+eQSllg6cAO/qcNFILIUgUl+y+dEHbk1JeTCNImctSjMBTCGGr/brQM7RtS+82Go3qum7bdrFYEIvywnoO5vN5VY1JY8A5p4HJ/UePnHM+JCllWaoQwnK5XC9XfVMTWlOWI4BEkTLAUlmWUnIA8MHFuB0XZ5kGh4xBBHTOxZgSAwRwzq3XjxGRDPirrMyyLFMaEa2PwzAM1iIiS4ye5bqu6VtLTc59iSidgqtgneAIggNLAQFCQkiIUUqODChhZhukkyABCq1oBE9ba4IEgFIqcrOgq0qzAsk4CE4NG/oUnE8BIQIkxhJQA8+l4EpoZiAAIgLjjEuq7AGAIyAAIQshJACQTEgpGVCoAgNIdbvSSmIC51zEaJjJlZFKT3d3mkYNto8cBUdZKc5BGSlUMaTw6OiQJTPKp6NyPKSha1fzndLbDReojNbZSKmsa7vVuu37fuja0Wg0m0zsEKyP3obz5UpI6WLwMbSpjwjGiOl8tjebFtXYBW+tHfrGe8c4GiVVJhImn5yPgSOLIRiWlVmZ6cwURaYNDfkpYcYy1luvi3JZN1QnAEDXdaOyvHXnzjsHp2Rfm0/3qjxPCexqE6yfTCZUpVjnatc4a621PnYWBilgJzMvPPXMZ17+2HO3b/KUjg4P2Ul694P3X3/7rSBYPhu3AH7o2x42ISwXi86xTcRmCIt1C2kYV9PHh+sbezuNS4tl86mP3/rlX/7yjWu7q3r5K3/8j4P382I0LDfL07PVeP7xl17+9rdf/+t//W/Nd8FGmO+W6661CQ7/xt/44SunH3vpiaOTZWeDY6oZwhAROfcplEajYCHFFKPA4H2QIAHiew/u37w2uTZVm8a3Q33txvVbLz7Jx7v90OflDKAEV5/dv3fvlfvHrz8oU1q/f5Kpyfm5/u7jB93wm5Gnzq86B4tHqTmzwyqoR+HkYPnO23cffnDwxK2dl37m44PlX//mb//L77y17OAzH3lWKfXGm29MK9jbn3zsxSdfev6pSVkdHx+/+877Dw5Pf/D2monWdPD0jd2Xbj81n4xnO5NZru6d3N245bKJ//JbPxQIN66qT37qM/tPvrj/lHLuuz9Ud5MKZy784L1X377/gYp8zESpcuCy3aw2i2Xbga8AlbR+YFIhUAnNhGBUVXLOu65Dl/q+971FRNl2zQX/28cUAIABB4D8IioLEUNIMSbEmIBJIZQ2ea5CCG03UPA759xahxCRofRca+0TJ68CIREAuGJGqq14i3lMxOgAZAkuzToxITAjZWLbHLHLWhMRKcqH0DWASy5juhStAgDpigixpmIdAKlm3dZSzg3DgEJSJcfSVtkshGi7HiktUil5EXcVQtjd3VU6I6nA0PV1XTdNba2Fi1FmCMHXnlgBNnhEljEBAKRJrWXLOJK133b3vKj7qUVZrTbEJ6EylEb5KSUCw4jiiYiUCNM0TYjIGMtMkee5FOwSOQvedl0nBW21W0aQybO9vb2AKaV0enp6eHhIlkHW2vF4fPkXKUuBPhU5/MCFag0uRFqXRT8dijR2d86NRiPgko5/ang4Z9S6EJpOL2dD1w6c81xRibydsNPZnNLkwYMHVGfQoSWlpCH7ZrPBmFJKQojRqBqNRpxLqsaKvEopBZ8YQ8a4ELIoin7V+xiIYEPHBsnHM2NijBgijQWpGRBCkJYML3KLh2FIKTLGjo6OEDGlkFIClghG4hzyXEJCxAjAlRZ5nusso0ORMcbJiJEUMpyxxKP3s/lkZ2eHC1DSKC0wsbLKTk9PB9vFEJ0fMDEugHzliZdP4Ch9fmL8N01DGKFSiihk1GjRU022ifP5HBGPjo4ODw+n0+lFJzYQm8g5t16vRxlyLl3wvbVULdEEbDQaeUpyda5b1+v12rshcXbl1rX57m5VFGnJVF0PwZP4srcDjxJSavs+OAecayk555JvcQRShJMpUJYBeaFQ9W90ThQdYIkJnlhiF8rmEKMdBj/YFprcZFVVjcqqzHIlpUiAGPNREeNWbk4Pr/Ws70Pfdd77RKjBNjYVQhBZlus8y3VOzXnXddEnqh1DCOfn55fzHyklIjNgGNNCSaUUCN51XV3Xm42jUpUIPzLLL5kMWuubd27u7OxsNpvDxw8fPrxPa/jpp54YTyqik3VHLQAYY3Sue9c3rsOA5ythdC4Vl0xJwWKMgx+AwXgyynTe9U1dryWHYRiahijXvixzrbXWGSICS3WzXi03nPNxVRVlFgO6mNqu60NIPgHnUqppPi2LCRlwnZ6ePnrw8PDwkAvY2dmRkmutjdKQUEt549r1Mi+Wy2We53t7ezpTMca2bdfrtfeWS1FVo0ybnck8WDf4ISSfUow8mUxlec4YG4ahXbd13botQMOIFmi7XiklOE+Irh+klGWWC2BKKcUFxOSdJydcgKQkw7iNf7LWe+9DQsbYRz728RBC0/br9Tq5wIAlwBBCXmrGUAh1QSnhMW6zCBPg4Cx0iBC99947csQn0y0pJQleaU1u2gYAEqBQMmIiylye55NizPkWArOWuhcjuJRSA3ClDM0i6ANzDk298sFKKWmOQVdASjkMA/WBlDFPXa5SarE4I/cLGhF47/vOGmMmk0nTNLHtY4zbvOEYQ0zeR4bA+Bb8Ymyb4dI0DdEmrRuGoWOMVaOCC5BSAqB3lpTBXMkQExHYGGNEfjNCAqS2bYdh4IJ1fVOvHOeQa/Ih6JyDvf2xyrTUKjCfEnLOETDGyABYwpQCRkSMwEJKmBg970BdGefAgAMXyCB5RxbYnHOGKBgR/LYWohxxCysiJuDAWWf7BMgjhYUCR85BCGCMyZSScxY5I1kAMIaQYgKGjHLEJRe0B0bnT8/PEJEhZ8CJgkp8MGcbr6UQCjhIobgRiTOf4v7+nszVer0YbOcxcokM0KaohLZDHBoU0WdmUpaj0mg3Krv2PMQhoDdZLuS4rEaMj4TKxLGNvt+b7+ztX+1aGyMyrqtqeXxyCiAASOhgqT7r21ZGJwQbKTmaGGQaWUosAce27UB7rbiRUjDJWeQmGGO6Zbc8X9AxsV6vu65jjHfD4H1oO3AAswKuXt0fTSZZnueTqZrvDc73ztWdc6smOAcJmVQH5+fJOzfY6C0k1EpUWS6LSVKF0XymKx/sG6++cv7w/t5sOptWDw7uv/L6jx4ee11A5DHL9Gi6m01Hp8f9weY8II9C2xyE0BxlURViCO988OY8y/7Sv/1nvvi5T7/2yvf/26/8OvD43/yd/+HZ55956omn/3f/3v/5ze+/9h/9n/7DzWZoBqgK6Gqu8qxupcp2nB9ef/vU5PDOB/e4UKByZ0Nnh6RzoU2wNqtGTLCYopCcM44QGUeu062Xd9br80rCMx8vP/WFz9155mNQjcBmub4CMMHz5TvffvfRd15v762us9HeeLTa1CdH3epxvzxypw44wM4OTOZwc8csHq4xScDu+OD08fsHT92svvSlL+TFri7mXpn3jk7Exh+eHW02NRfw+Aym1ZozW+g4H4kcqrQZQV8303V0PGL67J2XX3jqmb5tZqPRZCTeffxabeNpD/N9+NjHbv7k537imY98CprYvfFwc9DYFpKERsEqOgVQcaMaN4KBB9g01ltZGeF1iVbVQ5uMAiEQopRcShEjem/LctQ0Tb/pXEIOwAHkZt0qowXjPkZMnEuhhKQBOjKgmsYYAZxpqYSSJs+JatYN/fnZ4ny1RO+ZACFZCBADZaMEgSKlEKJDTMaYIs+EECkm6wMAkypnXCCGBNFjZMRuJ4YPAkaekCVkCJxxyYUSyAZbGy4yqU2Rc2BN1zZ90zT9bDIhqoDW2mS5UgKApxTc4K21KYU8z02WZ0oxLiJAZIErLaWEmLz3RPcPjFGsUvCBMSakEZIrZVRmhmHoBwcsxRijD85Z+nOISJqqEJyzQ28tw21RRTsXYTacc62kEIIw76LIlFJko4mIWuvc1IjofVytVkSPrqqKzCgJ9yK9FyFMiJgZA/RljcFE/YPkHHJj2rZeLFcxJaO19QPG5EOyASOmEJL31jlKqueM4enRKUCiSp4jV1ykBCkBogchAOF36eZMcylIneasI8I0MciNMQkgJo+eMwZaG6UkTUjTVhC8JbFcslmctaOyGI/HVVWVJZm+h2HoY0TnwjbyxigmefTBh2g0pUrzsswpQNe5sNlstMmLqrTW9dYixogpYULGtckT48EnBC4VAEt0i6XkCWM7tCklyXiW61FZmdxE3EaVKaNLLLMil5LMWBM53w9DV9d13/cRExF9nHPeYQDMeDmeTnev7JVleb5aSqFJ6cuBkdU353y1WI8ms+loHDC5fgghCMmNMT/7sz97cHBwcnJCZUrTNHVdr9drY7KUMASSfGyjAMi2kpoBrfWomuSFCT61bRucoxKtbrquP9Jahpi0yRCAcd4PjbXDzs7OeDKp63q93rg+ZFkhlKqqSmo1DIMNlmY+zrm+rYdh6Jp28I4DV0rmWYEIbT+EEPOyMnlB86vleqO3CWXBhwikQAOEFLTWPkYfAyVmhBBihO3RzkFKrrQwNDhiKUFEzhQXKjOkKRzablBD9EHJ7SiMmmSTGSFYQl8UWZUX7dBTqRScp6rF+8A4y7JCCMk5kqsq0UBpMXdd12xq50JKyUhF7fdWMcKhH/qua5QSXHKpZVHkxagS4sr169f6fthsNlmWkZ89JATEdVsPzulMv/fee3fff19K6dzgvZ/P5zdu3Hj48P7Vq1cT4tHR0XvvvrNaubJkliVohS7yoiillNbavg1Zlo1H5e7OXDLufZRcLJfLo4MDig549unn1ovlelMrpUal8T4EF4zO33/vfaVMUVQhpNPztXOuLKrxdNJ7HyNqk4/H49ForFVWmCLLsldfffX09DT6cOPGjSzXdb12zo3H4+D8ycnJMAxPP/303t5eN/R1056enRuj8yyTQscY6xppKMo5f/jwPk0ddaaqqjSFMTKTXGVZ1qya4+PTerVmTGY6V0aDSlVR9n0vpZyMKxK3FEbv7Oy0bUuOycPQXXCrIiL21lvLEqBzPgQQGkbVuBxVWkvgyDpEjlxQOq8MEZSe0HwM0jZckswkaI+KwW02Q4gOAKTkWmdt23LcJq/T9Mx7H5IH5E2zIbq5tbbrOmPMbDLtApMUCoHJWmetDTSCZpyc06SUEciZLQEkSKFpes5ZXlS7u3uIWNf1MAza5Ldu3drd3eWcO+cWi0XT9gAwm18xmRJcCSkzJhhs3dKK0QgAqqrgnDtHmq4ADEejcrBd3w/BecaIX0dOOPH/S9WfBtuaZulh0Frv+E17OufcIW8OlVlZQ6tnWmrLbVmSZYEICYEshcMOgUNh/lgBInAQQJjADv4RAUQQBAIpECYQEGBZICRLYJClbmtoqQepq7q6urqqusbMvHnvufeeYU/f9E5r8WPtfbI4P27cPPfsPHt/3/e+71rPegZtFSKmFGMoyuhCClAzMaMBzqVErU1V1dLoliS6KYYCc045zsfj8XDonVcxEiFwgd3x5CVgPeyOhxZawylTAsXGW9SKS6orjwREJXNGQABtDAFQ0QAgBwqUAkwEQIyQGFTMxZ0AEgWKDRCBczUAcC6lUDrn/CDilKJSoEBrRKW1RiOG4MY4As5MhYiRlFagUKEKU9SonHamsrX3RIQTTEyL9SLNaeineZo4g0Kw1lqnnGVUZJ1y1vu68t5nghjj737nW0qhMUrU/wx5PB72h1T28+Xl+uKi273pv/vd7968fPP+u+88frR5/PStXMI8j1OYD4d+nLLGSgE/efbWnOZhnsqrV4f90C1X77z3/ubqsqqbYRrHMDESKw5pnuf5/u7m8Rc+h5wzxzDNQ5ynMMacC3NdV1JFoDMpURim/aHfateq7vr19TRNOcE4gTVwcVEjYub47FmHoK21FxePFotFonIcU7Nu+7nf7/clJcplmoYUApdCJVmlvVZagdbKOmUsWq3YeA3gXFUr76xj63bjFIG+9u3vjqQ/+LEni8urYaYp8c08feN3XhDk/ZhtbdBgAdbWjv2w++RWJ9osWwD1D371V7/+jW/cXL/UiM/efVZ1zX/2S/+M8q//jf/kP08h75KJuop63mUTYl5W5pOP9u+8u3n7cx9+9Obrl5fr6xe79QVUtRvHcSJqK191TRpgnAcE9kCVxtpoZ0xdVV0D0zx5DZeb6q2n7y5XT6Beg11BIRg1GDPeDh9/6+NPvv0DvzuaTHfD/tmzd1589G2t8ItffGu93RfMv/cXfuL3/8s/98+++mvf/K1Par+uqu6rv/HdcYQ/8od+8umXfwzA/sov/erf+3u/fLsrrt7c3m73Mzyq4dkS3n2re/vRYr1wq65euGqeOOfu5afBLtrr4eXHLz611i5XzVVzEfvitDEF3rmCP/bH//C/+qf/dXj8zuFr3/o7f+1v3vzOx/vd+GbOI8IANqHXbGPGZ+vHecp5nubMdd1UdXUg2u23ynNCp5TiQkpZY5xSkCIDayaF2nRWI0AKETujjHe186yAUkmUNSAa7bRxtVu0ra/r2ltb+cZX2tlpmqzxjHAY+vu73e6wF28H55ywdq21zllkmKZpHsc5JuecqzwA55iYuWmrtq0Ph8NisWjbmpljCqUU5kIEXbWYxyCgtXgvSNToer3OTJRyoqIYQJ9sHKfD2Fa1GB0SUaFUMou7vP6RSLIzgQS9Pxk8C5/kRLtPGUGLOKzv+zhHXzmjbWES+xThCzETIsYYp3moqkrOKuYisKFCZABjqhPoCyA9gPzner0+eRufufLWWq0xxjgMx83m0jl3c3OjtW6q+u7uzvuamXOIzLxcri8vL621UwxvXt8gorFaShZEgVfneZ7rxi8WC2P01A/7/qiAqqajgqnkYZjGsVfKdF3TNB0iI2oZnJSSlDKInFKZpsHXTrjyUmgys0GltZX0UxFQel/bs4+7r5oxzKUUoSikFB7UyXIRhI8kzUDOOUyDc6cIrcr5kyw7TDmeAu0RsfGVrTwSz5GYFBFobbuuWa1WMhWZpkmu/zBMd3c34kBnjCFg17QEnFNIKTltvHdMOcZ5HI45JygESFrruvaXm4vN5rKylVJ6mqbtfk85+7q21mYqwkvWWh+Ph9vtvQRvNU314Rc+EC6ycVZ64BSL+HjImIIRFCvUyhhjtZmm0HUdMry5vbm/vXPOnSw7tE4p3d/f932vUUk9DQqHYZhjFIxcClaljD2rD+WmhJSUwtVqvdlsat8AwDAMt7e349gjAhHFNB8Oh6dPH7/77rvLZRdT2G632+12GufWN/N0ykga5klsTBeLxQ9+8APx61RKpTlIQI+t9Puf/+DRk8fyKyQFDxGfPXt2e3s7zzMiiuvrfr8noqqtEEESSUspUB648qdU3ZKyVEtiIk5EoRRXeaM0nSO6iWgcBu+9+PP0h2MIoWua5XJZORfGA1GmzNqaruuMc30/3t3d3d3vlFKFyfuqahop6aqmjTFmgvHY9/3krLraXDRNRURhnhlAKVVVlW+8AMA5p48/+WR92V5ePqrrWisr2DCAko6XCstQ7uTKV9LdmzdERCWBwq7rNptN01SI+PHHH4NWiHxm+sVCGUH5uiFm59xyufRNLZzaUtLl5aPX1y+HYUphKik/efLk8dWjNId+dzRKS1FrrEXUt7e3L65fffDBB48fP724umTC3W53d3e33x/7aTyO49O3n737zudWq5VS5ngcXnzy/NNPP5UF4oxu2/byalPXfrfbvXp1vd/vLy4ubm5ulDE//dM/nWL55MWnm83m4uLi0cVlyXG/3/fHw5s3r0pMbz17ggamaSqlNL7ylcWTT7G+vb3t+34cEgvKq633dV3XTlvn3Hq9vtxsFNDd7ZvX1y/3+/3VxSad08QLQwghxkQEy7beHycG8AZAK2ut8zblvFgshLmBRrdta72Zp9gPBy6nBEaDyvuqaRpvHSLeb/cnLwcUc2utrfXWYiathBiT4znKHQ0eDr2vnbVWZqQAEFMKU7xcXlXWI2LdtY8ePQKFr9682R32qeTMtD8cUkrtctE0jbG+qeu7Nzeb1cXl5aXYGeVzKI1zTlp0Mds9k4hmY8xyuWxrX06qK+77frvdNk39oLaSllLGj9Mc9UlPQynEaZok27irm5ALMWutM5U5ReNs29bW6vVmWVXVOA77/SmUHQ2WWLTWzBhjYkJjHDOmFEKcQwSloK6gFJhnaBb46NGjEIKvnatsznGYx1yisbauHEcS6RQoRvGsA5AgYeIiE9eHERmiihlkU3igUGqtUatSWGutnVVKMZ3y0RjBWh1ygkJitKDRyK4oKY3y/ChzOmhSnBdNrfEkUYtzopw1GoPaascZoAARJdGSgbZOIUSFZK31vn6w0BCy4jyfjAE2m1XbtnMYD/v+5m6PAHXdLNo1Z5z6AQh9ZRdN897nnj1+fHXsDx9//LGUN+vLi1e3N2JbopSiwoywXl88evSIAMZxHOdJKdUuW5kzP3/5Sd3gatMZrQ/HY0pFTP+GYUYE66u6bSSvI6VUMitiGBJmtpKy2nbWeq2tAe197YwngnEcwzhLN3sYjof+TntzskeLQTRdCLxeLzSgs6Zpqq5tq+qz+FSh+SmGyjivTYzzNE2rzbpqm6zU9jDujscplHEO/XFQ1jBQPwzjNAKAMeC9rasqT6GESBm6yj2+vNqs1qWUsZ+uNhdUyjTPh2HshyFQVtpqZ11dGe+Ms5kp5iDr1BmLMYYQ9sPYxwjGaF8Vpjnm9955xqloKJbZ5GiZFt5f1O79y5bi/vXrl1Oefurnv/xf/Tf+1Pu/9yfAAhgEbUAZyFR+85v/z//DX/3qLz1fO/iFn32qQO/6YYzp81/80s/8Cz/vFuubm7t/8Eu/ev36/u7NvTP24x8O77wDf+Ev/NfbRf1b3/zG3/n//vrtAZ6+/Y6pLt+8uX39+sWmga6CP/Ff+rF/+ff/3uriEmB583L4J7/6za/91nc/+eijuvbHeRoTNB38V/7EH/35n/2Jb/3mV69/+H0D6urq6q13Pjew+sYPfvjb3//h/fawqpvE8Gk/3megpj7GzAne2jym/fHt1cWHz546Rb/1ja9Wi2oqIRs21g5xXq4uf+c7t++9e6Vt88nzlxdXT1Mq/f5grW1qiyVXlTeu6rTWoBQCgEIFLFZEBTilchyGOcbRaWvt3iit9SnBEWBOsR+mlAIBayWSuwwESimrDRKjY03a6JwThTFrrY31ADSHPE47peA49MdxKJQAWCkFWqQ8mjMXBoUYM2mt28WqbheFSZ2BxUSFU8FMiLi5vKBcphj6aZT6Xv601imZ6p6jzoUR5IzV+jSJRgHwAOBk2j3nXEKIqBCVzlSEX85nqetJmKXBe2+tTolinHMGBrAWjHfWuHOW1AmwPL+w3N/fPyD3wg6StoGZS0lE2dp6uVwaY0Ryl1LJOZeYAEAsYrTWpZQpxJxzoSzVw8XFxaNHS+89c9ntdvv9XiAurbXWTmgMWluNpvaVUkqGziduj7KgTqJeY4wzxRjVh0Gh0loro1AjImo8OeQIYfph6KG1dpU3VtEksL2tqnNsc85C/pEv+MxUNHIp85yF2uuMfeBqh3mms71g0zQxxilMRMikiBEZYyrDOIeYU0ohzsbZlNIU5sIECgsTlwwKY04FOIU0TYNCbKlpa181NQClHHMMhRIxJUqpxJjDNAVnT5ER0zTNMRpnmVlZU8JMRMM4xiQMVoi53NzfrTfL9aJ1tiLmcZpypkyl65Zaa62Eco149rWw9uTywYzaOtQmhLTd7m9evRZ+eUrJaP2Q3XZ7d5eZlFLWeiHFITIRHw7HdtG5qooh6wKscH8c7ncHKACgiEjS9Lz31lvr3fsffLhcddbqYZpSCr6uNurCuuHFxy/GcRZGgcQz931/e3u73W6lqJUdX1lTOdd07TzH169vxNWq67qqauZ5vrm5a5rWGJdSypmUMpeXj7TWoMFaNUz92E+IUVmjNHDmnOM8z8wFCHOOJepilTXeGAMSTNu2i8UizPOr62siatt2e3vX2wMQT9MUQtm67eJw6Jq6sqqkEKY55uKcs96XwsM0bjabmFPfD8dhmGMW1LAwpHLy+F8sGq1USmka6cEoDBUXKCEFjjzFEML09O3LtmsWXau1jjHv9ocQMxGhNtZahSebXalegMrc90PfE9Fy2Rljbm5umAsiVm0DAIxknK2aWvoKylQil8I66xI4Qc5UYpwTFaMOMdL7777/wefen6fhzZs3nLmp2ue3L9ar1eXFFSh8+fLl65s3i271Mz/zX7i8fGSsZdLzPE8hx8ygbFV3V4/fWl1sqqoZhun+fnd7e3vcHlMsfuFF1eC8kbWZJc7WG0DKVNJU7rb75XK52lxY741xc8qQS13X627htLl++ent7e3mahnLFMY4jr1FBQAGjbWWUrHKtK1BrYBVZkKlGMBU3nqfSvnk0+d3t2/6w67x9urRpsQEQIhcgHIhRm66pq7rsZ+ayjlnCGG/H0MIS6Oaprm9v7NW28o3zhunEVmSW6w23ldd3ViZ0npvz+zEGOM4T3OMqVAqJVIJIazrFlHQXMdcixtbKqVta+ud1hYFb9bK+bqp+NWLV++89fbl5aXxbpqm49Bvt9uYUz8O4kC6uth8+OGHl4+uUkoxZGcrgyYX7odThLxWGlDv9sdTWRmzzPRkH6t9lVKJ9mTqJdwkpfD+/l6Cuqy1xipALeKylKdYUDMQAYV0MtxUehimAgyosrBlWOXEx2FGJpknV7VjRlH/W7ZyAk5THHpihsUC16tNdXHx8vqHuQARxAQIgAaIuB+G5aozThtjMic4Oe/lnMKy7eQMJc5MyFCED7lYLBQrhqLUqfTXqBBVThkIAZABjbLOOGu9Mvr+/l5psgVF8wMKlXbCayJKJRU01vtaOxuneUyhczYBpRgSFZu18a6u67atp/GAihG0sapzrVLGsEYATlBy5gycGQhKKUgIAJvNhkokAqKcpxzjPE0hhKlpuhjnlArlMvvG6gRktfZd280plsJznErGIYwhRD2ou7252d1XlVOatEJtcJiPh0/3xynmUoyxtnIEHEMIt6+P8/Hy4moOU38cMpRhHnzlmHmx6Kbcb4+9hJx6XwMgEHjrUqZ5ykO/z8DGuKZru8Wyrep8HB2oqqraqtVa50QpZ0Le3twqUjGm4TjO81wKl5jmNC5WtfdGnGcpC6U/5BzDMHrvXNssl8v1aqU1hhDmOWplnfGqUgpQG4PW1k2nF6Wfpvvb/na3f3V7l3KpFq2vGr9YpFT6w66q2sViMQxD5XVd1/vDNuRY1752ThuzI9rf389znIfx+vbQ+MrUvoCejR1CiSkwpXXju8rXq5VVCNM09kPMOSfu6k6hVRmBIJTMcSaimBIiK4eWrQNyVhvmnPNuf/h0PObpcOynRPDtr//A+1/82dc373z+2bOf+XEYehhGaJf6Z3723/h3/vy7z/7e829/r716+vhq/SSPx2n88Mc+dF/63PVvf+f/9h//v+5vZyq277FrbbuBy6dPSLWHIX7ve59QgbbB+7u7EPeLxeInPny7q+OTFf3YB+9VF6t4v//K1772a1/56OvfubndTZuu/u799OzdtvL80Ztx+ePPlr/n2btqeLV95TIe9uMnz//5R6/vPt3HaMF11fevx8fvtFfPnnI/7TO0WAXKb262j5vuOE3/9Dd+zSO8+7lNP21zguUCbdXYGfvd/buPHaU4DLMCNKg+vf300eqRs1goNpV2HsTZM6eEwh89TQMLMXNKYZoAEcUiTarqU79IFEuOqZRSQKFI6BDRKGOtBcNWSPPa2VyIqJyqISbKU5hzzsaolIiYAclaY60twCXnfhisdlprAsghwIPlH7MCdMYapeEcPYaI1tqQi4z+5TgXwED40PIzwk5RSiFynGatT70snSQHnHPWyhKRbMoPJHvh2xhjRAgtKIWYqU3TAOcvhYBwlnz9SCya7O+SfppzFo2jOodzEREi13VNRLvdbhgGIri8vKyberVanXiuJ6cdM8Wg4SFyIYc5hjkiisthJ/x76ewBTzag8luG8aj1KU9Kvv8A59M5GpnO5ioCgmp7ynOQb2o8WXnKRyulTPMs8LAyep5nUErEFbvdTq68ODw+QDsy5QCAlANnSdk8iculHAGAi81mu92Kw5Jk1uScra+BLWrDzDERTKfpQSmp708SZzHSEVhFGZ0oA3MpKZfEhRDJKDrZ8xtVtCpkpMCVUGdkPOetltNbEl1UmE+ypxBOUBkiAFxfv0bERbdS3sjj1zRdVVUxJGOMMa6UIi8BUEop8YMSMs88TkqpMM1KqTfXrwS4ijEifCZedN54RGutd7XWOp2CcrR39vLqqq5rqR1A4TiO93e74zQQgVIg3pTWWuu0tabv+5TDNA13d3dKweXlpbFaRhkhpB+9++M4brdbpdQcCgHkHJwz0husNmut9ck/B6BpmqqqHmTo5mxU5b0XjLOf+pTyNIXjMBCV2tdeO0QGUHVTowLFirggo7Wmadqqqt569uz+/v7u7m673R72+2HkykNd17vdrPVsNWitvNf6bBi6WS5CmBAP0/643R9B9VpZAo5pB0qlwiiPK9M0pZSPtvJEVDtf13VO4Xg8hqC6rpPcibMySJ/uvsLFaq01gkJlNMU0TOM4zIi4Wm2AWFt0dZW0GYYhhplKEWZ/VbnlcumrKoQgi/pEYil05pZrUY7c3t7mRIjo6so4W5iIMjPf39xeXl5Wzh+Px939djyOXHIIoes6Bjj0RwJmhEePHr337vuf/+IXnj9/8e1vf7vvx/XlBRO8evVqjmG9Xr//wXvTNN28ur67u7u5uevHXqMVpVApxWolzfY8x3meZb2fd/WUUvLeX1gnC3C/3+d58t5eLFciy5njNI5zLqftohSWKlkpogJMyExUCjCjVtZpwfu32+2r++scJ62gaSrFfDgcNssVIGUSG4kAAOLR3NbNfr8vpSjExaLS1oi46+rqkdYKtDLGGO2cN9bUbduWlNu27epOdAtVVXnrEdE4OwzD7d325v5uPuyHcQwpl5RjN0mYg2icbOWVcc5CJo6Z8jzNMchK7LrOtRVnWF1cKusOx6Hv+/v9bhxHbQ0DbK4unXMEPB5HolvZb602IcyhD3iyi600aY7cD/0JQqIEyMxMnEsp4zgao0qR2JnpjDQlpRQAydNVToq2lCnN80zIqnApBImYQaMCrZx1AJALx5wQsaocIibKVeVCmJRSm83GWnN3d7Pd7kLMRj8ci1DK6ZwyFt9+9jTGcZ6jqDUEoQ/zOFttsjbGMBZvrFFw8vlRAAqJSDEQMBMBMCCLkwLiycIDEQAJAI3GguIeoaxRWkL7kFerlRwVrJBPaRsysafaedbWOIsMJaYUIqV82O0ZQQE6bRQg5zKnCZCsdURZgzba1r7RWmMGKmW32zMRkjJKKY3OWw3KGJ0zETMVVlpZZ613dcO5pFfXrxmYCZTBlEtIGUExIBidC6RSqCSlHDgDTIWYFd0dD2UHdQOPH122yy7nOPRHIesTMiILvynmSH3WWocc5hBTCtM0ucoiaG0VFhwGVlC6zhprSylM4GxV1TYTMSMptNZa76yzzJRS1MYycshh7ud+389TBEJEg6xyojRHOYxkHhNCSCn2fQ9C+Daqrmvvl0ZrEbkNw5BTki0x57xabgpDziWHKKoVawwqw4yZAFBXdeMVtoulcr6Ukvu+HwZjbbuox3lyla/b5ubujTZmuVlfXl5qrWMgzmWN2jDOhwkZIpU5zMM0RS7WO1P5t956yzgncv+mqltfHY/HMM1idCtzOQCFoK2x2pjD4eCsyYyZi/G+qmtFJQ35Ng0KSnO5eORMGA+/+Y++enhx/ZM/8+Mvv/7djz/9+Nvf+d1Fu/xTf/JPfe4P/MFf+PNf+IVPXvzTv/633/vZfwnefat8/MPnr17e/+a3P3p+k21zOxy0Mtf3ge/CsoZnbK73g1aJtY8Zhpn34zSOYCy3Vb3f7/+dP/dvL59sYNj/+q/95t/7pa//4FNgC5tlQ5qMhQOH97/0+f/lf/zvPf3JLwDHxQfrv/cbv/hb3/z41fdBI7RLsFcQMgxx9haG2yHbYcwwEVSLZnnRpRhViYf+2C1NW6tsIyr4+V/48vX19f2rg2YXBnrr7c3LV3c5FoOwv7/+8gfv/s4Pn19V8IUP37u9uX55ncwJHisF8JQvCyAmP0UuLyJoI1Au5Ax1DcxQCACkez/x+cbxqDU4Y6023liBvqVsXSwW1vtc4jiOIZD3zlojQk/jtDqRAsswTymlOBXvuXL+oW6WMrFpGik99bmwlq3huD/Ic/AAtD88GOf6nh5GAYjMKRujpDIWhyLxkXBWIoFATEUF4fbeD8MgF0GKXfE5FdjbWuvcZ1OFQjJ/PEUc/GgbIO9f6ukH2oZSSmtV17W1mpnHcRTGiJyX+/1RRITWWiIIIRCAczangueEWqkpEVGshKRNLyXHkFGxQlEzGzh7BJ3lsyjDU0GhxCZFXEQtGUiTuDLQOW+YgQGAiaU30Fpbe3K64ELee+v92SCFpRCUwFHRBAvCKseMsUoDnggzxlh9ygsDgKaurbX7+61kXp6Ky5gZrfOVqhURpMTnbov7/ijnd904KdAR0dfVzd2WgAG5rr1G5SvrrNNalxSBUbqgRCnFMo4zom6qVspuOocGyJd8nIebLpfIWP3es3dFatz3fUrFOZcz5XTioxONYtU/TZOQdrZ398wsXh/zOPFZxOy0kQdvGAYWmlPTLBZtzhk1WO1k+swpjcM8xbBarY6HXU7p4dE3SteNt9aL0lc0oMRZa11VnvgUYLlcLq3Vy+XSOtP4xmvv3KkQ3G63MkiVpKGmaa6uri4vL733ggYx8/1uq7R2zsUY7+7uJFRbehtp24RrJ/Fhu90ucZ7maZ7BGNAYRdDnrFdnJyUiFocwZggx3tzeAkDf98MwVd6+9+5K3lhVgdZ62S3W67XoHJxzy65DKJVuGVUGZFAxJ9RKVAHWWu8BtXLOVTF6F1ihePXUvtKAMc3MzMBEpI1CrY1zrvLeO9CqppKzRI3Gh9wMIVrIMuRCCrBy3hnHKadpTjGtVyt9ceGckWU4jqOkQQuV/NypZkkWTyHVdR0xpZRyiEKVRGRl9GazeXz1CBGff/zJ6+vrEAKXPE3xSx9+0RiTc5lj7LquWy6HefqH//gfhTkRgqurvu/3u0NI8e233/7www/7vn/z5s3t7W2KxRjTVI2s1JSS1sjsEbGUJAlW4v0n7kMSK55zzsR930t8IeRU114zIMpAydzcv6kav1wvjDGQOcaIBEqp42EgIlBorXW2QqMZIZf4/PknzCyp8DnPqFXd1ou2qZwrzEQQUiTGkCIjZCrL1VIWBcApejHnHHOSZBVGBEZm0MrVnQPo1qsVc0lzmud5GkalFDKmkler1ThPt/e7N7c3++MxlQKAQPzyZlcbWC4XVvlEZTwe5nmeY26aBlAzc8lMxD3NIRarh3eevRun+Qc//Pj67pUCveja9eZyvVl2i9Xl5WZO+cWL5zevbwtnAZLEYks6GXngT25jpTxs+w9HBjOnMMsmHNOcTzl9sota0eiXUoRBx8wAJIa/CKwB4KSTVwqNtZZRQQiJWFuzXC+1tTnn4bgFJIYCwHXtnzx5slx1RHR/t5Muw/kSA5SS++EYw/jO25e1X3Q1j2EmOsVrjNMUpzklNFYZZ6y1lbfGKKUFsyhKKWZBD5UMAcQvC4kBCVgBkgKNwNZ6Q0hnHA0ZUgiZqOs6AiylxDnFGAWDI4TGe+1s7TwoVWJKVJC4qWooJLRkQuBcQk6UcsGSc5Q+BBF7GLhQiSXnXEJiQgPGWmvQsNCnSCttSilcgAsloMI5xxJzWK02qEGBBsVWO2LgQrHkscQMrKxRVeVsVS9aBGW0ElffXCIyRYUTsKl8Z5QwWnNKIirz3iqllDXHcc/MzKQMoqKYAxfKVIyurJmAgQhTYmZUgIh6nmcCUMooffIrPw59nqMHFbSe06yUCeO02+6HY8kZutpoZY2ySintZBYNppi6Nlor5hJCyDEy66oCa71ROpd4Qg+JrdPeCfeVYgkxhKGfSs6yurXWjIoB0Jim6RJQyjTPB3GQZIXbw35KI+eCqmQqoPDDDz9crVZVVfV9v9tux3HU2tbGVd6GOYUUC1O76B4vF5vLi7brEpXj0N/f3sopz4WOh8Nxt0cgYwzgCcyF02DNLJdrbw3mTPOstV0sFq2r8mbx3e/8jqGsne2ULXO5fw18uMYxxxKnGF7fz6R2Dv/zf2WAR48ez/vx9Uh/5T/8ax99+lEfyvKirhfrm+3x+fM+JVh0nAxYg2ZVvTyMf/9Xfg0p5/H4B//wL/zpf+vfBrb/p//j//kb3/j6T//0j//X/sQfNXn4+Le/+7Xf+Mo//8rHn7wCUMAEbw7j5Vr/xE+917v8v/pbfw3KAUwATdX7m//hf/SXj1/52vNvfjcchpcvXv36V7/67e8NwPC5d+D734cZ4NEzWBs3ljSHu+FY6mWtW1Aqs4PdHP/AH/rp/9a/++d/9xu/+3f+o//3m0/vsaDOEVJeNBaM3R/H60+f/9g73fa+/43f+eS9K/jShzVerTpBN8s5ZFH8uYUIoxUYA8YqAIiRUhK5HigN1irjK6W1OH7Mc0REBQrolJFpjdFa182y6zpXu7NVXMyUU0pV5eq6tt7I3Hae534cpykoVoha40OhQPJWxHaQz+/sXMqfmCfmHDJ63iJhuVzKj0ldeI5ngkeblXPm3Cew8HymaQJWSiljrAQDC+d4nmfR9cr10QYlkimlZB4Sc7Vm/lF42Ej87wPdX8iO4qmPIhWYJiFXKAWi/ZUDgwjEEeV4PL5+fZNSkrqzZJbMl7qqwhxLYbFt9t7XTVXX9dmdcJa62RhT176qGoHepX1/AP71KWN1/NHmSqrbwnmIo0Sd0VnEZrXVWteufij9jbJwFgMIujaOo1JK2ptpmsQttOs6kU8IdiVH13K5dM6IelUcGE5OTTEqpShlYZwT0fF43O0OjLau67bpnLcP910pKKW0bd11HSKKKTszu8rf77ZzzERU1W7VLYTgBHAipCIAURHlBiJW1i275cMlkhZFYhn6aZQLVUqJkqOstTHmg8+/L0j5OI5MIEacSpnVakVEISQxiZ/nmQpog/M8V5Xz1oUQ+r5PKS3adrFYaK2bpuFCD2EFVunVatUPBzTKKkMA8osOh74fB3kwuna5uboUd5qcCRBDOCX+VlWllBLX1Lqu+uGQUiISBUXOOTOQQq1AHw69fNhTv+S93AWxIOy67kFXgIiZirCz5EMJ0i+PGRF3Xfv06enXQugAAEqlSURBVNOqqna73c3NzTBFMEAITqP3XqKdvXMi9s05U8p0NssX+TUzO3dq3rqm3Ww2KaW7uztvrbV2vVxtNhtZEfM8I3J/ODhn5R2O4zSJXbHzXdcR0WHfH8dBlh4zOu8ZStd1SsFw7KdhUErV3okBi1LKOuO8N94poxGRkRBZnkBjTJjn7XZLiWWJcSlaWbnOQ9/f3d73/VR7Xdc1q9OrlsvlarOsqkqEIqChlCLOQnOcKVOl/TjO4zjGXEqBwqAMOKdl7Szabtl18zy/enkdxrlZtFpbZ33OeZynpqo3lxdEcLfdWutXq9UwDNevXy0X65/8yZ9s2/b1zZvnH38SY9TarFYrY9zhcNje76dpkkdusVgsVx1z6fs+pWisOhwOd3d7X7knT54o40spmTjGqFGFECqjN5vVqu0QIczjMPWxxG7ZLdoOEUM/jeOYY2Tm/jgqpWzlm66tqzZR2R8Ph8PhVPKmjFyc0cuuXS7aqnKUC3EGVqAwZ5pDYEatde1reSxjjNvtthRqu4XYnVVtI+ZX1p+yHUspbVullKZhCiFoRGst5VMIAAOMIR77PjM1XbtYLLz327tbjZhzPg7Dfn+UbVBp27atGBgrpajAFEOMkTMv2q6uKmBlvbm6etw0FZF0a1ZrvL29f/36GkChwRhzSkHce2TMKG9SSv+HQ0RODTzzIUsMxFkmzAAgGZwppZhmOKUcojjXISIhaatBg0GFACC4FjGCijFKbHymYiu/WK61szHOcR6ZZXpQJOjgYch5PAy73WGagAmsBeecsxpoWi6aqqoL00Oq+hRDzpGAmQsrmcPjCbDXwEDnyoEBTud1CIG5KIBzmCkrUADK2Vpi2uUkyqWIEfDl5WVhTCnNMYQQUirMDIgKURtTeV/VdVPX5mwVVXmfS8kpzSFQKQzgrNXG9MNBnZMo45xSiJwLMxtQRKRZG2MUq5RkJowSpKhBK6sMmsw5zWlOszeekEosU5ygAGhQrGZIbtmyVgIYGO2sMd772vnb21tEjmmex6GU4ivXtnXtrTe67/vx2IeczsCZ1caIYIaIxDOsAJeYQk7Ot1pZJprnmXOy1jprASBE6YlKQfCV8lVlnNWoHCguRQEa4yiXYZim4xgj5wgAYBAQAVlRAWZG4Mv1ctG1TdOklI777fF4BABrtTNWBHjWaSBmJmOstmYYR1ZaTJQEJJLciZAzg5pTHMZ5CnNhRK0IwTmDXO7u7lJK61VXSkgprTfLn/w9Py7N8Ha7vXl9PwwDE2qlNvUi55yZtDGurqx3ko3NiHKSCrClAGKMcQ5N0znvQaspxZByYjGD4qdXl94ajnHYbnXOq65pfQUUGWKaB19oicpncgQXnbp6dPne++9mjdvD/jsff/ziVVIWlgttlS19CVNSDpaXa+X1EKJSddttPvn0tUJ32O6sUW1jczp2rb1atU+XmzRPf/JP/6mf/TN/BroWvvPtr/yjf/jRd7/d32+/+s++f9zB08cAFrcDB4m00sosqzdp/Ju//v/JVaa6OGcBCsAE+x1MCVhDKHB7fzweVHS403/37/zir3ztt26mcD9TstCtL621h+1uvWq//927J0/g/Q8f/Zl/81/7w3/qv5xfbX/5P/kn//f/y98ap/DqNgwBTAPKKVP5uq12++2Xf+yDd95+9NEPfjfG2Ww2m1JySjnGkM6ZTcwMwEqdoGKJCjcml1IEHpZRuYwDcikZi9YaiMWHUcy/xJXGGBXjPKdZppBd1wzTIBwYAIhZCaqhlFosFm270NrmkE/FcTlHcuDJqRcZFCAiKq00KtSqwMmaXWoLQc1lz+WzdzuerQMBSBB9SWYV7uNpsEjybq3808N1uLi4EG+QEIIQokS31zQVn03fAejM4YGcs7UI50wlOQbkl8qX/M+FAkSUY4x1XUlHUYqYTye5zlLBICIrlh8gZsmvFaDFGHMmlz/wl0zTOKm5JUJSCjXpNB44NmI290B2AgBzykNNyusHsPME1SvDzGezVFBKGWU/a7TwNAGQLlz4YPLauq4lpExOBUQEEEOVk0OoghP3VN5G0zSASohbUmJOUyiMWiMqEp9N+eCIummq5aprmmaaxlxiLiHnPKexqlzIoYQ5YQnWAJwSIUTqYIQ5or1W4teppYoVvseJKHU20nko/RlA3hURffc738s5x5iVgq5bOleFkFIahaUQQpK8OSJC0LJwBLeQajWEID5OTdMsFgurjcgM7u/vD9vdze1rAFYKZlDyQgayVlfOKuBCrDQs2rppmn4cEYtzzpjC3ABAKSnGOedkrc1FLRaLvu9DKMJZn+e5UPaumsf+eByUUrLR8znY4fLyEgCkixO5bdu2VVVZ7169eiU6BHGpkpZYOCRyiWR0Zq3dVH4Io1gCyEaBn2kebClFkjTk2sqSefb0LbE0OR6Ph93+9evXWuu2bXOM8nLpG0MI+/1eZkHWm6ZqtVWkdGFIKUfiGCNqPc7zPAdjNADEVNQ0KXvSgez3EzGsmpPEX4wXQUEBLiliRlAIQMqaRIXzaSOr61rXpqoqBWIlbpC4lOKNXdQNp0xAKaU5hZTg8nLx/vvvd8tWYiiUUtpqGfSVUkABENy/votTihEygyhiNYIxZhzHtm7Gvk8hdE376NGj8dgj6iGEMR6dNsvNetG0xjut7Pur1d3d9vb+bp7nJ0+evP/++3Xjf/jxR7/7nW8tu4WvfFN3WutpGuZ5tk57vxZfAet0jDGEaRj6lMN5dz3NSCVb0Di/WCxSiMfjsS8ZgCyqxaKr63qO01uP3/K1V6DEn6fve8qZmS8vH+WcQ07DMMSQlTUPu0qYRsqlrs2Ty8vFop2n4fXr13XlCpHW1teVdY61ijEXoinMcQ7OOanyjbNduzTGLDdrmSEzMyolT1ehNM+BWWSavpHnM/M0zzFG492ysNvvhmkU4oH3fr3+AlEehgFvb+eYMoMzVsT94zAP/QRa4uEv5Gmf+uHp06cXFxeSoDf2w+3tbQhhfzyIwL1yJz0uVugqnzkzF2YsJY1jLiURAbOEYM5EILgvAOVcxAQvhDnGxEzGGD67UQmkZa11zml96gdCDiklZFTayDyWSiYiAOUrZ5zVyhACI8Q0zkOc5/nR5UVVOWYehmGah+12KqXEyJeXy2mamKGqJAtSuuUCfBbLaYPKGGNk0J61KVCIsgQIYiFgYA3GGoKHIUYRqI65ILJSWgHBmQqLDACYUgL4LBJenYWkgm1JJ2NRKYt4YpLROE/zMLaLVFlnqqqkPM7TcDgSMBATMDKAwpALIxitjbNaay7klMvOa0CtlFgPaDTOWMk8KikDwG530MZUXlfaO+8dYrLRpPrEgVZRaW+cbtq2qWsyMFBSTunTCQiZSeWMGppVW0riiTNnzeSrylUOFWZKiKSsMkKBghwzQZ59VY1zKiUzs0KlFaI3ypqUwFkHyAixEBJhyczM1npCYA6nsLJThoyOIZacEMAhWKN941EpX0ijmec5jCFFyJEyAAJ4gOv7w2F/aJpKKZVSwLNb4GrRtW27WCyUUjGEaRpizJDK9e2t9b7xjXOVNjYRihzCNy3IYM5YVYiZRLzVNNXl5aZdLPphf7Fa32/f9H3/5MmTfhoJQWym7dt1zrkUplwMq3jS+JcpzIf+OEzjNE1t2xrnau8VQN/3KUThHGptlFKkTuRqBZDPhrCIaK3nrs3jGELMcygUll1ljFNQCLDq3KqqkPOru+NN/73LJ4/f/fz7f+CdL37/hx99/PzF1E/HaR56AA2VqRx0XbOoatjuji+ev7m8eHK32x9yhkQjR6XB26q9euvy8sk3vvab/9u/9Feu/sZf//l/4ee+8Pn3Xrx+/vzFp9/4zVeLGv67/+6f/dk/86+/+Y1//pf+yv/u+m73xS9/EOfwyeuXX/7w6f/1f/MX/+x/58/FOE0YrS4uZ9PUUDs4DKAMvP/OYo5wfYAYf+anfpyN+s6nL57f3h1SDuHYH9Nq0faHu6sr+MIXn95uX12//AQOW1PZL3/weZWTptQ6WK/NMeQ508Wq3h12/6N/77/3x/+1PwmWeX87DEezXC4AiAhCmD5LyUVkCUIXij8UOsdOtVVNCESUS4lCQpQgMOJSSkmFCLT09XTiEwNAARaOjBB2QwjH4z6EQFCYWbjCxov2TiFo4YNniJRLoSKoPxArQKWVuO9755TWcwyS4CP3Xp/I8Uq2koeiU2pNY5S4jpzZLFoKEWZmQqn/8OxqLxUwImqD1ulcUAJb5LXzfM4DP79EvlLKD9ftzOrRYtYpcVpS058oBFy8dSmH25ubnDMqI5eraZrNZgMAYjVTMjvypZQckzPu4TfKB5mngBj7vnfeWmsB7DjO0xTEkryqnLQZstWWczclkuJTacsspXwqOmM5d22fXQpmTvOJFUNEXOChm2oXnQhzy8mORi2XS2vtfr+Hs3feqYi0FlE0lyckDPkzr/e6qmKMx74XpFBKzKpyIRUAinGWy6sNauOMVcJoJyrTNJSS69rnrOd5dtY0lbeI1lqtUPQozDwcR0QUoYgU6NIlKiqiy3zwZSpMzCwccTkFhWZ0+jtLi+iYiwh2ZZwlKE7JJ4d4a61SXIiN0tMwUi6ImGMM05RCGMfx8+9/MI/TIAMZ4jBOwzAUSgqJKIvI21pbN43VlmsvGtOm8qWkaRpSmFMqKYWqXljrmPlwCNM0AZzUOMfjcRiOZ9MeIKKSqY+9ghNpXsIc5FI3TTNNU9d16/VahC7SjIUQQOF2uz0cDg8tsVTz4gsucjHpARaLhbam7NhaZ4wj4nPUXXhQ3cjKzQSnfljb6+trmVl1XVdSFvg/pVQ5J2SPvu8fhnvMvFgtM1EunDiHmEXRK4aq7aJr2kXTLoTMJjOWRHEc+xyZGKwGrU8NKipABFSKEQuJJpiYGZMGAEAFBEY739XOWGNM7f1qsbRK73e742EQWmPb1N1iUZjEg8V4R0SH3X6eZyFA26JlN7DWEheKpamqyjkAUMZo65RSxjkhXC0WizfXbz755JOSaLNcZeN3h/3Td95pV8vHjx51i0WY5xcvX754cb3dboVW94UvfP7q8aPtdvvbv/71eY6PH185Y52xpZTd9jgMg0Lddcu6rvtxMFYx8ziOw9SHMKcccs5t3dS1M9anlOZYUkq+blarlRDVgHme57u7O6LStbXRrmQOc8yxbLf7w3YfQsDzGFZ6wn4cmNA3tfXOGHN/u3v0aH2xXqYUh2O/291bo6zV6eR/wKCwaE5UciYmyCX1Q++zr5r66vKiWy69r2OMACrGPE9jjFEmZt57RrLOsmLOPE2TjLNKLCdpyqIrBC9fv3p5fT3HaIxGoy83q4uLi6urq6vHT7rF5n6/6/fHKaSq6QhjyKUyvmm61fpiuVzWdc2UtdY393e3t7c5ZxFcNU2TY8Kq7pq29XVK6USaapvdYRtLCHNKOVABobswFCoQ04ygfaURmYFKSSkHQS5KYaUBkBgYFTtvvG/VOdwG8TNIqHDWABkIEYGYJPIaoGo8ISilNWJIktIYieDu7qbt6uVyud4s21SLT5e1NPSTCKW891qb0/i3lKePLhQwMxKBQZVjAWBJ0lAGjVWIp+gYpQE05JLPigBB/QHgFNMBSBoMninECACMuUi7UgihKAXCLNC43+5ki2AEIGQEpQoo1TQNAMV5LimO/SGlgMwhpf12C0p5ewqaKYVDjHOMxvkHuPCUTYmaC0vGqEZtnDVKgwatUSgFpRSMqSCmlMWCeYohjBMabVCh0dY6VirlkjO7zuHZAU+ImjGVmKBpGq219a5wppIRMWbKkEsYmRIjowYQMAeAgFAjaGAFDCXnRFAk7jyEHOZBK9DaVd6VklLKSmGckyCACgsRzWGc4oiIzmgu5IxhJFZKWVUbp9C0dTeO4zRMKZUcsmDnlbZhDJzLMM1aY9NU6+VqtVrVde0kvzXnME7TME7TkFIpTMdh8sSFFM5BCNclc0rJxczMBECCPDJQKrmUJ289brr2qbPD2IZpJoRutVwslwBwsvo2WrMmhFhiTDHjyZBKfJDEK9J7f339quvaRdspQKsNGnbWVc6z0qBVyVQKAyujjbYoUYxTrxxqDQWJxjCXQCVBHod1Z5qqVgATqDTH4/Fwew8zzPydw+NvfO9ys8asls3m8x9+2frm177+O/tx/ORu/MbdpxVA11lGNc7h9hhijJX3bVfVXitdJko/fPn67/2Dbz1roF3Adz/aBf7lT18+u72+OdyNrgZtm7sjHb/1yTe/8/Hm6tEf/mN/5F/9438s327//f/Jv//Vf/zq+9//u3/r//F3/5t/4U88fffxi49/sH/96ulms9R+3h/DENaL9ePHT1bd1affu/34o+tvf+t3P73ZzggpQSRwHl5+3H/wQfM//5/9Tx///E///b/6Hy6WHtZXsBufffj+H/oDv/+3v/HN5TIUbRYFxhCdMRfrzT/4xV+6WLY/+VO/5ytf+fVf/ZV/YoxRQoC2VhtjTsWu1nLGC8id8mdVtRRSUm2nVBKd1J9NU8cYAwUoBQByLlAIE/Z47JYL442wF5hZW13X9TQNOeeYIp0M4FWZpxASgzLGGKWdcwogceRCyIwMxJxzAeKMmGJMMaJSxtkfRW3NOYFY3jz9iKQVEQGUUyceDoiM4ZxXFUPOOZeSH8oUACAq292dlCA/miIkRa289sFEiE4hkRI06x6oPnhWLMm4gM8MJa01IlfOT9O03++Z2VeNAMZKqa5bolYlUkopJ2KFOcZxHDXOzlihE5xGwzGWUqqqaurWeTtN0/39vdBvHj9+zFzkrUqemlwN+UTShJyYGEQ5ZygQY4w5SvQjIoQQkJGZxUDg9AAgeO9P3vyLTigQ4zjKVRX6ltD9j8ejwPlSXyLyzc0N0WnAjQxCr5Li72SfXwoRjeMoNzTklEtJmeVt17oyRnvvSpHZifwkL5YdAByPx2kMRmGzEuKKjzHGIJloDREJGUBSXeXNGgVWKxF4PMxAlFK73S7GmDNoDVVdybimFFbKeO+k3hVmpFw6ucvKKgE2pCZmKF1TC4ZnjOG6ds7JnOHNmzc5Z3H6WzStXIG2qo2GGOdhGIiyMaZtmrpppOlxzhltc0zCwmemEvN2um+7DgD7/tj3R19ZVLVS6rS+Yowxypgl57zfHy83V23r2rZFxP1+j4iSays958NnEQSoqqrvfO+74nOltZb6Q7KKAEACKOZ5HsdRFqAcMDJJkGnGw5WR0NYCeZ7n/X4v0zDn3NXF2js/DMOrV6+4kBif7/d7Z4wESz8sOq01agXKxDjFeZREJ/lXQ2i9V0rlVB7AC0Q0Vi3a9eFwQE6LRVVXTgN659u2TSkZY/SpS9eIyASCKMuGwLloVN46rTUQaVTLtrPGHA+HGAIAWKXJ+9evX/u6attaBBI3NzdKQV3XzMKIU9ZakfkCQC7p0aNLGZ0rYxhVKkUGcTHG58+fX396HWNcLhZiqfT4radXTx5/8PnPt237gx/84Hvf+17f96WwMaZt2/V6PYX5N37jN4ZhaNv28nJTUlTWMBQuBYDEyddom/OJvIeIpaSTU5lSzLzdDctFZYwJIcR06nUFb3706NFm0XlvD/fb4/FYcowp3W5vrXdAfDwewzASgUFghk8//VTgm6ZpYsillDxNRPmtt66Uhu32/nDoSwBroa6wFFwsFoxAxHPMzCllEnHnftsrBW3bFua77fbQ94vFylp7d7eVbdPZSiZmWutMad/vjTFcSt/3Yy/RubWrq91uV3UL461QI6DkAqCIr1/f7I/H2/td13Xee2Ps+uLqQqntdm+tffz46dXV1XojE7Bxv9/f399KJ6m1vrq6cs6lORBR0zTjON7f3mmtnTH94TBN0/32tlk0UzrxOWXDlGARudragJgs5QyomJkYitbaWvVw7sijLtvgmQgkhwkZVMZaZU6lLfKZEaTVME+yd1lrxbfUGGzaSlQ6fd83TSNHsFJquVinlOY5nv2mp1LIGC2a72GcQopGu6qqjHFS0+dcvLJGWa1R3jYAIFDIxKoAqJMIARHZIHLOGZVSoM92n4yMgMoYyHySPeDZWQERZQp3PrXhAdgex9EY47ou53w8HvU4tm27aFs4X5wco0Z0QpytqjnmwiRghLHOaSPnlxxzyPInzPMcp7kUnmPRyhrtauPbM9dR+J9VdSLTyu0YhuHQ70ukhAkKsUJvrDIGiUMO97c3dV0rDTml4XAQNoHRaHVBKA8QpLVWGTTKJUoAYJ1WSuVS5jmVAgpjnO04Ds6Yq6vLyplhoJSSc8Y5x0p6bGmutHHWGAXARuvK17WvEBEJtdbeVsMwaKvqRdOhKoVSiMzslfOP3HDsh2FC5LZptTX9OOx2O9H5EBFKWnyhOYYpBAIm5jmGkHLO5FxVV23VLRFxmMZxHAuzMUYGdNYaOYWFVHb9+tX+sHvy6DKW3NXNCVkjOsNnhrzjTKZyjVY0Qt/3MUQBSjarlfx8VVXr5UprrQELcCwUuaRxLkyFWTMiKgC96NocA5dsjDNoQqEcZ0QoBIAajUtUYspEFLWBZV54td/RfYDp/gAjreo0HcucSh+KqhdP15fPjM5E4zwNU19QTTmGlJVlmlLMZrVuE6lx268728/pcAs//VOr//7/4L/9zs/+1K/8jb/9n/2nv/RiuPv45fjJX/3rf/M//UXieXlRp9/62q/+2j9+8b27L3/p6s/+uT/ys3/w9/3w+uO7w+3vWT37fb/w4Sff+d6v//Iv//JXfsAJuhasNe1ieXX5LAzwg49evHyzffLs8aN33nn+8voHn1w7Yz98r/qjf+QPP/7pnzn85te/+iv/PJf5C8/evfqxn4HPvfNf/KP/yvd+8N3nr968eANXz6x29avrN2jtRx/ffe1rX//gg881td9u70zKoZBSSqHCqna+srLqSjlVlopAk0XR/ZZSUlDq5LSgtLVw6vWZkAshsbXaipZUHGlql3PMnLXWSmGMgRM8zIKF1wUA0xRCTjlnrWzCiKeUFmZmjUohngbogESUCxEnAzMaNWcqAFqB91bQSu+9pEo91OhSjM7zTJQ5laqyVVVpLSFWp67GGl/Xdc5FhgZS0GitcpahLQGwc0YWsNauaa7kQj2wos8bPT0MAeRtyBN8PB7FIEX2oHN1AsfjsVCRfVnqJGHjRMGJcx6maRxnAq59VXdtGmel0VgtZkGfIfEsKq9QSmnqVlAxQH6YD8rdhLNjkpwBcioAQNu2xpgp0APkr9TpE8nWiYgS/GSMkbktM9d1XTk/DWPf91LRwhkgV+pECVBKcSGjdJwDIstdOG39DGJaSkTAfHNzc9juAEA2DpnkEBHzSYp6YiWFOFFu23Z7d5NScs5tLtZ17adpmq2ekDSCs7ptqrZZMCMRIyKCNsYcDv2LFy/mkOqms9YxlZImyfA6qR1KSSXnnJfL5Xa7LaU0TW2dkxKzqhpnPDMCYeXqtu4A4OFoF7jiVDjyiZ6b4+ytLqWkOBtjLi83QBxCeP36dQiBS2mapmtr6X8QIaYZkZumWa1WIpxgUG3bJirH43E7brXWvqoAYBynYZjaxeqw3+ZMIUwKWQFSLjkGq41yKISiQinnrBCXixYRZewWY2yaM3naWqXUmzdvBGKXB1VK/+HYa1RxTlyOdV2jwhyTMaY/HHNM8ogKdR4ApjBfrNfjfNKcUC4y7lAG+8NRDlEiYoSu61ab9Xq5Gvt+e/d6miZErY25v99JEzKm0XtfeS99oMhSGeDVfa8VOGNQK0R0vjZWGWM+/ODzxpgf/vCH83Hc7Y8/8RNfIqLj8RhzWK+XEvOEAFZphTCHcwduFSPkTCklUKy11UrnWADAaiNEC6OUsy7G+O1vf3t7fz+P0+V68/TpUwW4O+y1UlabFON+mqQDR43TND179oyICmcAYOISUxincRzHff/kyZO6bg5Df+xHQtDKZipa2+12a7x7++23kXG/3y+77t133gNjU6H73f769ZvnLz4NIW5Wq81mhYivX1+nlLz3Tx5dEVEMcynFImaAFCMV6tr1ZrliVOM4VtUy5zyFWcDjlBIgV1WFPPf9vNTWWqsUiHNAVVXd22//1m/91mbRvfvuu5+kfHd3a0wLiFMMNzd385iMgdo6RIRySlRcLFax5GF3AKTHT58uVsuU0nDsQ5yolK5rTMcnlNeYq8dPjsfj8TCElAB1Zh6GcZyhdeArZ70fpvH29jZnEP261WaxWGjdEOeY5mlWzDyH0E/jy1fX1y9eFYKnV5t333338upxVVWr9QUzoNaf+9z764uL69c3t/d30zSg0to6pU0hZsC66ZqmUcocjlOIp9HocOxDCMfj8X63/eCDzxGCGNZppWTrJiI5UySEWIJjSsr7+/HVmxdVUy0Wi6au4URQnmWbyikZRHvmWTqtF5uLNzGUkohKSeSc884JHU4kQCmlnJMxrq6bpuKQ5jkPIUyBozGfHTcabUhzVVXzPA+HtFz6J08vuVApnx1nIQSFxmjh2vF+fxQunlLKudNZicyH44AMCg0zznNUWE6hwUop0CVSPw/TNCiNVVXZWvuuzhypsDbq5F+XckpBa4xxZkBfibAhaNRV1RQDoFIIITNoITUZD6z2+/1nhC5iY0xdt652h/6Yc0xJTlhEZPFd8946Z37kCM5aa+eMr+t5nuV4KjENFBBAa71sFzKvVkohKe/qzfpys7nc3h+qqtlsNqvVSkiJcgpLmyQtBDMPw7TbHaZpVAbHMKYQUSujMFMpORKztXabXpVC1pq6ruu6zTnHOLFKSrTv7mSTD6AQ9TSOckwUKNqaxcLHGKcxTlMUtFvOkaZpstUxztZaNMjMwEikUIO2wl1kbRA1FigKRZMZhZ9JSKyImAlYGWWMqa2rdOWcaRcdALV1I/t2CmHRrbqu2+123/n27x4Oh81m4+v6OI3KoXYWUUMp1rvlctV1K1Bmv9+DMt1y7SqfUhiGgYGqakVErvLjPL24fnk4HLqufvz0yebyIs4hllxiKaVAUYiojVmcDUL299t5nmIMgsSkEMQkxlljjTYKFQIhE/Hm6vLN/ZY5KDREQZ/1k6ptLi8va2tKCGkavdXQNjmn4Xi8G8NhDBZBIwBAYcgM856MBTZK6/rJ2xdP1o86VxUCn6YIMM7zOE6ZqKqbum0KlGE4totScsoUNPEwjUZpNO6m79+7Wu8Pu0MI22O/ef7i01c3N/vh+Q1vFs080Q9e33Wt+XQ74Ee3X/zA/eW/+r+At96CNL3+9m8fb+/Xlc8vB9j49zZffO9Pfvhz73/zK7/52/f7w/2h/+G37udwv1kvfvDxsfLwU2+9/2Nf/smLq7cZvvHi+tMxTf/kH/zqH/h9v/r0yaN8KF/9yov/9au/+Ll33v0Dv/CH6qr9H/8H/8G3vv/Dv/iX/vff/v7u8bPaVYtYinGl7+lb33rhrQYA/LEvvA1AEhUufbbMGQV1EEBd2GanclYyMo0GrQpBzjmWTERMGEIoUbhraJWufVU1ta08MYcsc/yolPJ1VVVVSiHnLJoeaTdZIQBoZVGY67kwswK0Wgt1TwMiQymlCD0dFRqtnY3lxFYXKP2hWZfaS7ZpASyJskUle0VKKYRTpoxSSqFxzmlt6rpu21Zor7KuSjlFFP3oJTpFkJy5zg/o+wlvOFE+PhMrq7PBpXxYddInRGcsQzkTS05lNyjU2tZt23Wdtmaeo+Sa5ZgeX2yMOsmahU6TM0l5JCQKRLTGnXobLjHOIriUTBmx7ZOC+0GFJmVrKWWchwQ502kOcOpzUCulGn+aSDBzClnQ381ms1wuBfyWU/Ch5BUalZTsIiQdx3GaBgI+YRjMOSaik/qi5DxNU4nJ/IgPklKqqvzDzZUn0DpdVdVy2fE5MUck4/JZXry4Bq0WTbu62HTtUikjaOI4zPM873aHvu9RiWMghnnSWFKYxebo9OR4p5S6223FYWOe5xCjFPfGOMq8XK5Xq5UcqDlHcQlUZ3cg5iIItbXeGHV/+0Zuuug3BMlOKZ0isZi7rru6uBQUk6jc3d9M08DMi8WirmsCBlbKGvHf1NoSsxDzvK+6rkNthQggjyUR5RylHD8xZcfx2O+F2FNXbc4kfrJSjjw8AIh4OByEt/D+++//3M/93Gq1urm5+cVf/MXdbhcyeANyH+W+XF5eyk0USaWoVHPOL9+8ErxTnvYphDjPmXi5XMj3F4vFxcXFYrXUWlMuN69fI4MslmkY7+7uDodDytBVVtbzef1mMR4gBZmAABTActm+/c5b77333tXV1be/+S2xl12vl/MclYJhmLquqeuzJVFKBlXtK2M1M4dx8o2v6xq1ijkXJmOVNZ4ZT1p5pa3VVmsh/FDKd7e3XOjp06eb5arv+8Nun6l0q/VytdIG7+/vb+/uSinWaWWM6M4Jynl+SCnHnPPn3v2czJdSzgWwMPXHcbfbxZg3m82yWzCzBlX7ShhWqqr2x8Pr1693u51zpus6yuV4PK7X68PhEEJo23a9WllvuXDOKU2jFCsAqmtXy9UGUQ/TvN1uU0pTmGOMqSQAUBoQUVJECkFIhIir1UoZ+/jx4y98/sNf+7Vfy/N0cbE+3G9jDKtlJ57OwzSmyEZD45zAMULzU0pNMUzz3Hb11ePHxtlpmmKY5GoPQ28Am6Zy1paSxnmqq9bVVcq0Ox4Px0H8u5ZdzSVJETYMsar01dXjqqo2q7U4L+WctbanR7fkHz7/VPCORds9evTocn0pE9e33no2hXDoj4f+2I9zKrkAA0BlxVah1WdvsZSKRDg9f/48hSy5gVprUUxtd7sCRbECJCAsJVEi5mK1yzlSZlTstENkLpApvbx5wfjAfzkdmjLylTHs48ePlVJ3d3e3t7dzGL01OcecSXY5IYLKhpzOYXCiUco5T2G0Tk1xyAW0AaVUPpsxvPfee4i4324fUuE1AiLGXPq+jzFWvqnruhTu+34cpxM6Blqc/WRr8tbFeSopz1MQM3hjTFPVgsVogwCcc0TFVeW898qoQzjYxllrc84yafTWCeokbWFKCZFFroqgh2kq5xGuNcZoJwtkvz8KclRVVXdSqkz9NBIUKetlav2w/zwM8+lH5HzWWkI4eXIoa5SiAlL6j8dhtdw0dc2EVpu2XSilYkwKjfhoy9ktHZdcEDlfhmHY7XZTpLYy6/Vyd39PBFpD29Z148XuTymou1Y2eTpr20ophbIyxVgl8N9D8QCIIQTJhZATUFtTSomBUjDWVlYbADYKlAImSimAAtAAQIGihCkpo5VC7azW6I2X3RIRoQAAWGWFlUFEnJmIrDGV84t6WXKOkyChUEqZhnEYhsVisdlceut2u51gXkopgsIIpvJKmZDIaLdaXbZNB0qXzETEUGKM+/2274+V96vVan2xcpU9HA6vXr/w1r3/wTur1WocewFf5ApY5Y0xWlmlVIqx7/tXr15dX1+HMTmDVVVppVar1TRNkpXZtu1qtWrb1nifGG53+6Gf5himadJag1ZpDhfrjXfGAHMKmqnytnEWFPi2iWnOw8whKUnnKZSZQi4ZOYaUM3mwtXWqYEzzbp6VAgBViLW2TdPYyirNxiFgoZJSOlEcU0pxhkY1bz+9moZXnOOHH6yQ6ZOPjilA7evN5u0wU86xXZhxfENlfPvt9S/8iz//aLMMYX7z8no+DpATFDJofuxLP6ZN9cnL6+/84KObQ/96u3+zgwywD/Dld7zVbrc7/sRP/J4//sf/eCr5H/3yL//gh99/9erw4QeXbz95HKdBpXTY3t/ehn6Gp++Yf/O/8W/9vn/xX/q1r3z97/79f/i7P/jkxevDxeXqOMySymWtVQyGKJeSyzmnXdYPAMrmCyeRzmlcBQDOGq21cVZZQ4w5Z51izjnMSWvN6jQsA6Tay1ol513V1iKWPbXjpVRVk1IqDCkJsV/YN1pkBDklygQAqJAQFaAVO3/xHdJaKSWKfutdFK3Muc5+0EhJvYhnbSsAMCsDKDOAaZpSIgDQWmmtS045Z6X0j7JWlMLGOq1RKW2tliNHyDbb7f6Bl0ynPF8AwNVqJVNUKa/PxJ7Tl/BepCabpiml0Pe9UaBOdY7WWoOkqykNYjavldbaVVVhLjY0TaXgYYIZYwwpnSICiHIp58MmglQAbds+9CcPRAjpiMTlRkCoYRjEb2LKszq7l9JJOmOdc2K0Lx8ziS0YDzKo5R+RhZzWtj1hW7JC5IUnKcVZZyx9i1yllJIXnszKyixC1rxSauh7a601pq4qMU5ZLNrFYhHjLHXn8Xjcbbf73U6u87tvv1MYNCoFejyO8zyHEEspt/c7RCSCGGPKtN/vq6qqK2etDdP4IGJ2MtlhBoDFYtG27eFwMPMsHiPGuK5ZaNA5x91ud9xtU0pVVXFJoqsOIRBlY1xdewWg0TRNI8VrKcVqY7WRa1U5pwAol4fvyO0uRIwQU9od9tMctbPCnAFQqNRiUS+7Lue8Ox5KKZmpQuynQdTDVVWVFI+Hg+yJTdO0bbteLleLBTwoEIxzzj08nzHG4/GYUhJ/2NVq9ejRo/fee897v91uP/nkk4vV2mkj6PJDEpY8WieNsph1xmSVNsas2jakSEQaZZ362hpJBmBmY7WvbN14rSCGSSTFkgjb9/327n67H+QJjjGllPDMrzNGREeA1lnvqvoUkTGO47d+55vMRXp7ILZXa11X293dcOy71s/TdOIFFXJ1VXsndUxtjXVOa50ocUm5ZCqKNDdNp1EzktPGW48C3E6zQiSi3fbu9vbN5Wbz+PHj9eUKWI0h7na7lEPf9zklBuj7aZqmbrkEAC1R4gDGmKatpbs7Hsc5RELQWheGcZxiToyorUm59Mcjgn76uNLaxpzG3e7m7laY97aunLHaee+cUvjk8SMhnvX7fT/1Vtmqdm8/eSyLPaSCwNM8pFiGYQoxyJ5GREor5xwa1IhWm+vrm8xgFFRNO88zQXj69KloDz66fbPvjw7AGj0MQ8rZ+ApBG0VaK2AFfIpGEL/tRIUBpglevXoFCk+kPmektGrq5unTp+v1upRyf3+fMw3z1PfDNCYiUKiUUv04lQTel6qqlpt2sVgsu46ZD/0+hJmJnHOAZZr7NE+hkLU2ppJC3u6OMeb9rhdTr6/85tdFbIBKNYvl+mLj61psJo1VVtmHVjnOIcfw5tV1fzw+vnz8hQ/e11rf3d2Nx0PfH062Now5x5KIuWjQAESqEGcgROZELELekAMzMRKIkz3zGZ3JTdP4ylqnCyVitE4vV50Pdh6PPzKbJYk5k5gOeXtaa2OcMLIIirHIinNhpYAVakGjjH715rWMlK33bVvXtRfsjDkvFguJz0upTNNc+bapF33fe181TSeJ8uI/1hN7Y0spmZgAUWvjvK8bGUoTEeWklK0q37a1MyZBXFdXCTIzG21Wy0rAD2aepsE5l1M5HsZC1DTe25KpMGoABYjEQDEHoBP3DJQ2DgFK4eNwSmdfLpeg+Hjc9/0wjpP3TlojPMfmwFmC9XDUaiBQqPg0Hwgh5ViYuatbrTUxziH2adztZbAzVK6WQ1yKH+GuOOe2263IBZfL5Ze+9CWBSyjHy0UHxFor773SQJQJWCnIOVulM2KMcQ4xxkgFQLFvjVEWAc8boGXFpWQEpVEDQhKWP4iqQ0WlmDHnVCiR0s4ZhYwIMSVkFKmSUoqZAFhrQ0SIOpZMeDp8EVAqBGYmznTqjwiJU9GJYwhhd9jtdrsUWNJcWfHddv/mdu89rlar1WYpY2GFKlES9FIp9N4775XRhUBb47TOKRwOh8NuP4dZM4RqAlht7+5vbm76YWge11VVVc4jkxwZikEppcACAJWSUxrnYZyHnKPWWFWqrqqmqp02Viu2BsCLutd6k0qcjmEI6TCMOREDa22UNU4bxZBS0kxolFHKObdom66u0OleFQTddW1V0BbOx9AfDn3fN96HQgkpUpkhHISjmKi2oDUwYYg0lzgdoz4CKrh6tLAOrdXeN1pbpUxOEOaMpCdm3y2tTvtj3u+GOcFqXe8OxCkyal1V3FbOlZJt9s3f/sf/eLFsMNO0P+aeph0MPWiA9He/13kgDQNBADhEGAmaBlaPqk/3M5XgFXztmz/4+jf/cuHMzG9/7p3NY/Xi1fbmze73/tTPvP/h228+/mS93H/xZ3/8tt//2m/+zt//p1+92Q+g7Xtf+vG3vgCfvnxjqJ/yQKAMOtbKMJ9Y70oprU/W7w8w8AmE5lMjezrJmM93UaNFREzGlHyyu2EiKJSBpmkChYmyq7w9G4oxMxMJ0fahsjyhlURyTipAjUrrU67Hg9GnlP6yRAVLttZmKqBPdigPgkv54RMF7czFl72VU04pxBiJQGslTA+lVFN3AHAO0XowLDeoCqI+14VV27YSMyFe++M4aq2pnGmOiNqcrG8AQIpm2aRETCMGc6dCZ7tF5D5lY6Qec9o4pRQjAEBIZY4h5oRGKzRKwrZqn3NEEDZeJi6o2FgANnVd53yab8QYY3TywbuukZGFbJQP3Ou+74WPKB9HEH1jjAFj3AlkEjitqNP8R1AZPDn5FIHhBY2ms331A+ovqMnD/FS0Ad7bTAXxpAgsPsv19953bVtVVVvVACCWL/LCkj8jKck5Os+jTIcBSIjjwzAQZ2OMQvPyxRs+G78CKGIWFEYZh4haW6UUYI4pKqWc1fv9EOdJrs+JpgJcSgGtRJYt+53klDVNNx+nw+Gw3++FMCPZqMx8e3sbo0wPoKpq5oaZYjTWuvPgPhc8cboeSGLSdJVTso8hKuM4okFrLYFCo8XTc5rjw8iFFYJWcpHrurZKSxsp/yofxHv/ICzZbDaXl5dC7i+lFEb5+RijoGjy3D5+/JiZnz179rnPfQ4Rb29vp2mqqkps9R8kDXwOzJY1JQ+D3GWRlDx7+lSODTq72gOAJNE676qq4lxuXr2WSZ3T5nJzJWDb4XA4HgcGcBqaptaAp/mhtQ/nPTG3q3WBLJc0xphSECHINE2LrimlvHnzuvG1YkbOu7s7gJO9knG2sk7jKdrGWweIVFJOMYWYcgKApNKiWXhlqsqIYCPNYTeNwzAc9vuHakNu3zRNx2Go65ZByUhHBCHjrB6GMKg4nyanRVq849DnnGMuMacQQkhFK+Wb1hs/DMOx9M5YbfTddjsMg6/rTz59nko+qxk1Ea2Wy4uLC+/dqlu0bZtL3G930zRZa9u23t/dMnOM8TiM85T7w36awjDOvmll335o5tEgAMQ5LBb1Yrl2zs2x3N3dlVwAQGTfja+mMDd1bYwO85gzgSqISmtk4pAiAOcT0TFba7uuc97nIoJLu1wuzakKmTSGnMt2uw8hAcCTJ0/6cSqIUyy+aFs1UnjtD/eBgzSui2W7Wq1q50OYAcBYBCrW2hhjP9wPh2NidXegAlBbEVLDbre7vb0tRO9/8Pnlctm0ra38anOxvrhghL7v72/vYozDNMj+LF9N0+x2u6ZpmtYxpDlMMY1hHuYYWCEqxYwlx5JZaTBgULFCJMpAyFwSJ8oxhhxLnMPI+FkiVSklpZwShHhaQbe3t4h4hmNOC0pyD5x1YhYsI9mH5sRaDycLJjX0Q+bCiBJwRlCIiFJ88vhx01QyLW8qh4hzTLLNamWLYUSdUi6Zva+bpiNSXdd1XYfIIiyJEXMu1ipl7MJZY4y1Tp/1skxKIREgcwFAEY9OeZ45hhKJyHvftLUxJsc8z/OjR5eIWExCtCFMiMikFeoxBoAHTqlSgKWwykXUw8iAgEoZa41zlbX6bncDAOv1arlctm0rjP9xHKU1elCv6bMe2jqnDeSsgFWMkbmkFEoBznTzZgugKl8rwBizUqZtW6EvipuTbMgCkbz11luidxL+rZxHRunl+gpKJi4FJAB+KJRQo6jPUcYRuUAhICCmNCOiRuJ+monIe1JKxZyMMdaC1poJCSExW6UIQNThJUcAMNYBKiIqlGKckdBX1lhrjGIE44z3forh4WE7NUKiiuRTUqdSCvWJgUxQtvv7nPOU5wQMFlxt23ZhrYVC/XEgIlP5qqpN5atSlIb7+ztAzpQVOtSKgFPJJTNR0lqHeTwe9yHOGkBrROJ5PAkXHz++qqrq008/3TXter1kFrMYQkQFNucspq4C4THzo4tLZ623zqASUK9pmoVbicscM4/TFEu+3+2mlLSyWmtW56dI6XmabFM3ta+tMVoVgjllpfhuvFdKdcpaU3m0xhRRjKWQE5cCrJxFo4yzSimgMm0PbABBqUJY+NSUM9zeH6sKqlpVVdXaumlX1jRMOMUwj71VddMuIY2uhsJpN5dtyOPxUFhpoyZVGV0UK5joCI7ROmOixzAdeyzcwHrl2+XGNQvbdlA1M8NummIh6/33v/0t0lx717QLimm33SLiYrX4+nc/fvToor18kobhejfU9dhdPPvSz/y+r3/0uzPQm9v+5v7e1l3TLLZjuNv32tZkU9YRGbM0uWLxprQyRho5zSzGlPGzkiWfailE1JIaKGpIg1KnKgZnTElJKM/Knir1OIcEZZyHWE58aO993TRaexF0MoJQU0op4zzHea6cByJ2TuT5D3USidITQYI2tTXaGmU0EnA4mVFKuTPPiVgc3ElWMp61RIisGbTWUu+e8lx/ZCxQyomK89DhIBAqpMxnos5JHiCH/cXmSp3TtWJMKcVu0UifKgYRRCSGibI5SlzAMAz39/f7/T6EyVqrz8GK8nulzco5i+OvdlbySkspiQpT1J+VX+icwFda3oYkI+achNavlAphMmdrUZk7S2Um9Zbsng+AR9X4Dosyp7UHcBIPhBCWy2V68H49WTtrZn7gcIv/EoiLizYlZedcWzcPBZM1VilVeyfRldZat1rJIWeMscZUVdX4Spoc2eWJaBpGQWXmHHJJQisiyvLYIGLOERFr38phmUOWVo0QEJS1Vp/mVNVut5umqa7rzeVjpdQ0hTCNOc7q5CMB8knnGFJKddcKh2qxWCitxdW+6+btm3upwrtFs16vlVKHw2G7vYsxEhWl0FhlrVEacomF0mG/F6vHh9mIBsSTkSgC8DiO0ziEearrWmn9+K2nvq6AeBjnnDMxUiHRXKMxMdOr1ze73a6U8t577733uc9977vfLSUR5XkuKQU6ac31er2U0VaMcRzH9Xothv0xUylFKEPyfoTe8PbbbyPixcWFUmq/34/jWNf15eXlD4dRmlVp7c66wPLs2TO57+bsDXBiuBE7o5tqIYK5KczzPGemzXopm3QK8zAMMae2bdv1Rc4ZWDlbLRdrq51cWwBYtB1LgPdZQUFEmQpxDjGM4xjjDMTW2q5prdO0Wqy6bhiGYTyuV8sQpmnswxwuLjbC3EDiknKKkSkrpS7XG0JgLm3bbhAIAJCYME6ZS0FWSGBQESoFGggv1pvj8dgsl0+fPjVWvX79er/fa2UvL9XFxdVicRVCuL2/G4YBFV5eXh6PR2WMFDksZgNUSim2ssprxyqkSMBzngiBAQrzOM/G2Kv1qqqa4/5wfzjisc8555Qq59fL1eXVpmma9WK5XC6HoR+HYbe9U0p1dbN8dAkAOSYGEpxPFrUDZEZUZk6ZzvJJhlIoUQQiynP4whe+cPXoyfPnz0Oa33nnneMw3t3dbVZrY8xqtVJHcM6J3y9IrrPSyFDoJOyhnDFnKS5apZbLpTY4haCtWSwWh7t9PxzCNEvr/vLltQw/b+7uLy6uLjaPl+vHu/1hGGdlXdNUl5cXu/19jsE4C4jaGN9UymKYBmMAQQGUQrPWcHm1rtrlpi9zhhBCiclqs16vHz16sl6vpzFYawk4UcmJSmHUihlXq8147I+Jcs6EJTMgFShkNXpbAdHr6+sQJ6WU0cCUDv1kjGGGEiMRG6MRVSm5chUznQ3AEMTlAQgASoopAREggjHgjKq91lpvNpvNZiPjdNmHQ8p4Gp2g1spZ46yRPBxk0KiM0oVB4tX6w3G3O2irUCtjnLEKtQYFmUos8X67PfY2hqmk3DWN914jeO8fP368vd8fj0NV1evVlfd1TiWltFlfGGOYIJVMBG272Gwuvfcn6pESUwEn88CxH5RS1nprXc6JSprGWEqJOSRF9WK1WLYyyC2l1JVfdOvj4RhC0MjGGCaz2++BuG7roqCQsDSrumm99cxMpaRYxnHkUtbr9ZPHbzlvtne7m5vrKY6ZowefKGXOJEaZ3oQcHtRuzAwaChQo2bBHICEJe2e8c7EmIpiHKQaKkSJGpRyjMtZXvokwNZUTWzPB8rgkLvjFL31BjsvD4bDdbsXJQAFOqDkn4hOAlShpg+KphchaOeec9xURpMJEeZz6EgiYKTIRZy5KcS5EKUNRbJCAJfOYgQsTE6FCa7XS6K0xBnOiUhJx5sKmKKOscxVqZb2tqsrmih9SlwuVUricKUd84iSDoXyy8Fa7qa+q6qq9vHp8KtK4QM4ZtXr07IlW6njoP339Yp5Te7osOlNhJuJcSoppTinHTMY4iHGeRhE41c42tTdGSXj8s6dvPX5ymVJ68fLjT28+eXNtBBQ+Uct0zczjOA/zlHMyRnd1s1ouq6pChhiiiJJRK7GTUZPWxmhnF1X98mYLyihrUSkWo4+UQwhIVMopc2no5xxma5RtnGltCtMQ+0TYKm9Baavb1eL2sE+cM2c+sUWyUsZYvHy6tEoDGT9RDEwkEoMwTmFOEDIdh/FwzMsFNLXS1qBT4AyBut/Pw26vAKt6BUSLKxu1msOcUpiOwWlwig+J7KLm4DQppRZq2VozaW2qi6vu8ioUis6jdSEWAlVijrk8++KXNQGFMO2P05ig6QD5yPTBT/7EFGeoqon4q9//4W/97vdNorZtm6vLwzyN87zYbOrF1W5OYyjLx2+9fPHqmNNEBQCAdQE4wX6nxtdWMmovpeR8ShoqpQjMaIwxYtxeSGK8WMx8yol6ks5m8FZpIAZiRvbOG9YW+NyfKVQq5yymE9qe5gyllJgzGWNQMYOYfklWn2CK2tqHsuYzdgSw1ASi7JFH2fsTg+JhLPgAdyHysltIAjkACNdNSsx9OFprEdVDoTMMAyI4D9YZJkjpxEAVmEEq5rMdsiT7eoH+nTcPxMSHaukBPJjnebfbHY/HUooxyjuHyPCj/QYCITRNI4IKZpZseUbQUOZ51OcoA2ZWaJgVAAxDT8QCZSHiNE0xZrEZUUo98GdkEiLhGg+Jng8sINk9Mp2kbLK3CvNKPrhs7go+uwVwzi6AszxXajXhk8iPidMLM6MGRGQkfTb0lN8u11O6r3z+kuFFfzwKW+ChQ3vgUAnPshSLyOfXUtsuZHozTdMUTwasBLxeX8hYQzBRYwyck2jkAH+obsVMVnQRQojH85Ckru8tGgRtrCql7Ha7EKbj8TgMfV3XqFgkp4AUwiQ3M8ci5giyCyOiQiWUd0SczSRzsKZpuq7T55C4TCSCy5BTKZRSWiwWxAwQ5b64yvu6mqYQQqgqL/4M8rDlU3beCSpOKd3c3Nzf3y+Xy8ViYdypZRIKk1LqRCOeptVqVUq5vr6e57nrupzzd7/73RKTVC3Sa8UYRRJwAgLOhv2noRxQChMzlaTjPOVcYkniSVo7fxiO4zhlyohYV76p6rr2VIjpNByQz/4wt2JmZEjnqOZSSqayHw+olQTqaa1zjrtpyDl96cMvMBSj8cmjx1/84hc++eSj4367aOumrrq6cc7FOYSUENEb7+2JncyI2lhXedQqxjmEdLc/TNNMuQxt37UNn8KJeLWSUOE8DIPQe2KMWvHz58/3+2Pb1qWU49CP4wjqFNhMRBpOHF95ojhBLJEVam1Bq+V6XXXtMEzjPFYWjLOIanc8NIWMNdba4/EosLQI8uwpQJBzTghQShoOx8PhAEhd0zZNY4yiXHKOuRSNUFfOMzqXfS7z61tZp4ioLCKiZJG8/fbbQgBTSi2XS+fcFOJpmSvVtm3OkdLJSclamwppaxUgkdFMWmsNyEDzPE+pwG5X1fVqvbDWjvM0DMOw6/u+10qtVgvrTwktTdPc3G1DpGnOtq4LARqbCx+HcdVWdV2Dr7RGonzsD/PUhzjdvXndtPVq0fnK+soa29TOV90qY7xq12LXg8SUi+zqKaWYU6KCWpmcpjCjuHJNcRznGDNzEevSEkOhdHNz03WtNSi6/bqriSjHoJGRS0kU0wyslGYuGOKEDAyFCRlKyVwoUQHiLNExdW0ftmhZKfIYyyCunFPeYpzbphZs5AHnEoRlzPNnOC6ohwn5oZ+0A22KysZ6Z71hVIj69n7ouui0aRfdarlyxgAV56qmXSh0i0Xyrq7rBTAGTAAqpZIzidsyE2hjrfXWeq2YhOREClD+rgmVFdGWM6WkFIPMKzzUQ5gRVcmgEBVaUCrnklJaLi9CmGrnV6tVCOHNmzc5pcWqe729oZJB4ksACTinEkKgXHImzjT00626tdbOYygMi9Xq2G8Ph3m3e6U1OIcyGHm4sLK45OplAqIsOYYK3KlmQQAwXd0+urLjEO7utmEuWpthGG/e3DkDXVOtVitBf+Qi13X9ySefHI/H4/GIiJvNRoaicZjSHAoxZWKxHTOotUKtEE9KPVYIoFGDVQhgcyYEBlRNpYiAoChlGuNyJgBVMjKQeCMRq0KFFWmtNBpB8QqllEJKwVodS04pWHLn6oUepAV4slbPKaWScinFGw8AWny9iNPZ89BV1nqjrWJCxQoAciIAjHPyFRGqxJkRrNcZeT8c1osO4lwKE1DOeZ5nRkMFmDFOY5hHrbBuamuURs45hmlcbTbiK2Wtffvtt7e+evPmFZUibGTvMzot2GIYEyKkXGaYnLVAzLlMw8nAVxmNRhemQgRaWe+Mq8Z5IlDG2M9WB4JSqvJeKRVCmvI8HA7znGoPXajXsLA5qUxMPHAGAGLMyMHAnDkwMWVVsERWSrPVy7evADUlBZptpRA1UMw5+HZkyCmFeeYSCurIQLoUBVkhZNJcjHaLru60tWGaVGWIM4OZaZ5zadGC9rnguM94KFyorutHlxdtdzmE+abwYRr7OWjrtPVQ2ChrrZ1iyoW32y3GuKoqu1gedvcE0HXd63H86NOPuuVy1Xb64qLRrvTTbgxjX3S9cn4dld1OXEDPTOE4ZKNU5SoEq9EZq5CNUqA1mlPQlZWhJAADnGYdSikE0QB4q1HaSkZQSktxIwXHNM05ZwkEISIg1qiMNabyvnZV21hrhe4/jGOMEVEbY6xzSqkUhQihal9ziEBMhZCZGFErxaAYlKDywKAVaMUKCzCUInwD2RmlsHvYCB6m83xmgIhrjbCWUkoxZuGo4FkkpJQWzjScUpw4xKNMY5XGB6IRESlliCjFEkIohZxzy+Wy69q2bYVpKIe9lJtt20pImSDuUghaa723IjsGAEKQ3gi1YoWVbzKVKYZxHEPMqFXbtlVlx91kEH7kXBHyR4oxSO66tU4DKg1KATOI6C2fMwokrlXotnIUSWEtZX0aYuQkpb9U51VV5ZhTSrvdjpkFynJGCdxSSpEGHQAeLr7w2oWp8tD5yBQCNcQYrTcydXkQkwCAbhprrTVWyj5BykMIXbc8qwImgV5EjLtcLrXWpaRxHIdhmOdJCvfqSe2M1d4pNN4JA5IylcNht1qtfLMMIUzzqLX23i+W7e3rQewyH+hMBAwAD7kN8zzDZ4rqbECrE71Zy5ktdfw0TdogAMhOLfcUUbd1h6ilXq/saYhvjOmaVpZSSskas16vq6oqRN/7/g8lN6OUwnxW/jk3hwQKjdHtYrler6umiTF+/PyTuq6XbSvV2xnjn47H4/X1dr2u1utaujKRwE7TZFzlvX/o2ZRSwv8u57Q+ob0qpW5vb7/5zW8+urj03gtdbbVaAUDXdfv9/vr6WqZxD9hbzhm4YMmFUk4U0xzmhBrW64um9vMcgTLnjEhaG9SgjVKAsRQqIAnQOaWHGZS8Ky7//wIVYGDOJaQAxuim9k3T1Ku11irFGOOMxPVq8ehy8/r6uVHYtG2KOcaIDOINYJV2xnrr+uN+nKeUknGuaSsG2O53+/1RFTtNgVIMIaRxZoW5pFLK87FfLpdV43POKWdtTKbyZlsaA8fjGwAwBvQpy4/7klwFxmjnDCJmJiJiJkZIQMZqra2ypm5aV1eZYIyhADvvUszb3WHow2q1UkYbY6ZpaKoaEY/H42F3r7Xu2maxWBy2u6pyJee72zfb7VB79e57bz958ljoSEDSS/tcOJ7jybTRztfGGNBcSoGclVLPnj179erV4TjUde3rbrfbzfP86NEjsW0VdDbSZIzRCpQ2/7+ezmU5khqIovmQVI+uapfbYBawmA3Bhv//GSaIIQgwzNg9bXeXqvTITBby8A+Sbuhm3ntc8OyCYzQzjxSCI4MqpYm969z/psC6Xm+3eBzmeZ69c8MwtH62lFLay7Kc1hg/ffoDnD9MxzCMapjzXrYbsU3T5Byt1z1uq9ayp8io3nM/BGYEzaWmr/Ganl9++vCrYt+aynLMItJo6mpYSilqoeuQ17hvcdu2GA/DlOJWUmaHbWaoJQPqNI4q5XJ+MRCH1AeepokfHz6fL2YmpZpU77uhCwCEUOdlQrSWAUipbFstUqQqMwzD0K5zrbWxIGutjdMC3yI3TbCmaaxlByT4lpZq7WcAkFJuUchm2YQQ7u8fxunwAwI5R46BoR+Hwzyy9wZaSrm7m+fx0HedI3y7XJ4/f8k5r+tK5MZxNMXL5RLXTZWcc6aI1Nb2xExEa1NnIE/omJnEAAjViP1hOpoA+UAt98ZMhI5RCX6cDlvJIhI61978tO0x3m63W5vZNEQAIpvWfaved8ZOVUXhtu1yW3PMOWcmYvJgFi+v/3x5DiHcH5fjcUlyO8zTOFk7Ofuermvc0r4sC3sHAFIUGlrKOSJAqu8JW1Uw1/rhiDAVc8yNeuk9M3nTDACBndayx7Vlk9p/L+/bx09PY4Bh6InopZaS9mVZ5nmOgJuZaTEwM6siqSTIERENsfEB6zsqq28um+RC5Nw4NnoSAHVDKFnUaslSVZkZBIkYoagqqiKCiCgiEZgJM3d9X+K1fts+AEAtamYpZcT3vnKtknNpKBsMqKoI0DYnRYTRucDd1FeTtOVaxBE1vqbvHB4OAKBq8zI/PDwQUUppizcSMXVVxNTUpNYMBIiU4hpjlJrHoQ/eqbyjcvrQodn19TXeYFmW+9PcPTrH+Pb10ia6wYcuBFVNlNtYTOTdgS1UasrNtSQC9s51oXn/eU/5dq1quQCQ67qemRGYib3ziDgOg5a877vWvUmnCFgq9nwZmNk7Q0wmseYVNIPyOO7JajEnwNlchhHFAfz+29/VAxl4no7jaZ4n7ybVnGsAlJwz+13F+zChG9A51++mSTZj381hlqJPT89Pb1+m75bEqg4KmoF27MEPUCGY69kLydCNh/nRDcH2eK3b3vliKsRWs2zprj906GTb/Lx8+PkXrvV2/kpaTo+ny/r2579/oXQro5Gp1Xkaguu2nMS681t8CEdCdz6/wms8fX9S9i/n57u7afDjAYbgiMlE5D/bgXcG09yrZwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=1022x319 at 0x7F563A239190>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RrQY2nqQLcYy",
        "outputId": "d2dbde3e-4897-4f0d-988c-89cf85d4a05d"
      },
      "source": [
        "!du -lh saved_model"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11M\tsaved_model/variables\n",
            "4.0K\tsaved_model/assets\n",
            "21M\tsaved_model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GsVoNfXQK01k",
        "outputId": "dcf6ee71-8c86-447f-a241-d2dea8ea7b58"
      },
      "source": [
        "!tar cvf mobilenet_ssd.tar.gz saved_model "
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "saved_model/\n",
            "saved_model/saved_model.pb\n",
            "saved_model/variables/\n",
            "saved_model/variables/variables.data-00000-of-00001\n",
            "saved_model/variables/variables.index\n",
            "saved_model/assets/\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}