cnn_image_classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os"
      ],
      "metadata": {
        "id": "cTeO_P7WxfTG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import to_categorical"
      ],
      "metadata": {
        "id": "6yD4WswBMpa2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "HOo80HAvJoEp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "yzT56U-aHF7Y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils.vis_utils import plot_model\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix, plot_confusion_matrix"
      ],
      "metadata": {
        "id": "yx1sLGNqRro0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Access to my Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDR2Y8A0uldC",
        "outputId": "7acf248d-3244-4e18-81b7-d3cdd650cd63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Extract images file\n",
        "!unzip gdrive/My\\ Drive/skin_cancer/HAM10000_images.zip"
      ],
      "metadata": {
        "id": "rmybd2OSur6o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Image path example : HAM10000_images/ISIC_0034013.jpg"
      ],
      "metadata": {
        "id": "Q1ATzqmHwHx8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing if images were properly extracted\n",
        "id = 'HAM10000_images/ISIC_0034013.jpg'\n",
        "img = cv2.imread(id)\n",
        "plt.imshow(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "-v17GTtvwNln",
        "outputId": "3de40572-127a-4b0f-d2dc-5d8b9dd115cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f8c5436c730>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Images metadata dataset preparation"
      ],
      "metadata": {
        "id": "T4gXU7_Z2bde"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#load images metadata file\n",
        "df = pd.read_csv('HAM10000_metadata.csv')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "r3zubCctyZXP",
        "outputId": "cb1a4b0d-5f2a-4456-ef01-99bd83b274fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     lesion_id      image_id   dx dx_type   age   sex localization\n",
              "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp\n",
              "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp\n",
              "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp\n",
              "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp\n",
              "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0137f45-77ae-45ae-bd12-e884e1841bf4\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lesion_id</th>\n",
              "      <th>image_id</th>\n",
              "      <th>dx</th>\n",
              "      <th>dx_type</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>localization</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0027419</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0025030</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0026769</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0025661</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HAM_0001466</td>\n",
              "      <td>ISIC_0031633</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>ear</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0137f45-77ae-45ae-bd12-e884e1841bf4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a0137f45-77ae-45ae-bd12-e884e1841bf4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a0137f45-77ae-45ae-bd12-e884e1841bf4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#build path for images\n",
        "df['path'] = 'HAM10000_images/'+df['image_id']+'.jpg'\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "vOqMrGW375JE",
        "outputId": "0d2c46d0-1bfa-4c70-e86e-c09ee8f86490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     lesion_id      image_id   dx dx_type   age   sex localization  \\\n",
              "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp   \n",
              "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp   \n",
              "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp   \n",
              "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp   \n",
              "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear   \n",
              "\n",
              "                               path  \n",
              "0  HAM10000_images/ISIC_0027419.jpg  \n",
              "1  HAM10000_images/ISIC_0025030.jpg  \n",
              "2  HAM10000_images/ISIC_0026769.jpg  \n",
              "3  HAM10000_images/ISIC_0025661.jpg  \n",
              "4  HAM10000_images/ISIC_0031633.jpg  "
            ],
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lesion_id</th>\n",
              "      <th>image_id</th>\n",
              "      <th>dx</th>\n",
              "      <th>dx_type</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>localization</th>\n",
              "      <th>path</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0027419</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0027419.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0025030</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0025030.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0026769</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0026769.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0025661</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0025661.jpg</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HAM_0001466</td>\n",
              "      <td>ISIC_0031633</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>ear</td>\n",
              "      <td>HAM10000_images/ISIC_0031633.jpg</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </style>\n",
              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7dcbc826-60d3-4dc1-850c-db99fba35658');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#coding skin cancer types\n",
        "coding_dict = {'akiec':0,'bcc':1,'bkl':2,'df':3,'mel':4,'nv':5,'vasc':6}\n",
        "df['cancer_type_id'] = df['dx'].replace(coding_dict)\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "BLl7b3Oi8Yu8",
        "outputId": "9b8e8d3d-fb4c-4370-8475-3878d71317ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     lesion_id      image_id   dx dx_type   age   sex localization  \\\n",
              "0  HAM_0000118  ISIC_0027419  bkl   histo  80.0  male        scalp   \n",
              "1  HAM_0000118  ISIC_0025030  bkl   histo  80.0  male        scalp   \n",
              "2  HAM_0002730  ISIC_0026769  bkl   histo  80.0  male        scalp   \n",
              "3  HAM_0002730  ISIC_0025661  bkl   histo  80.0  male        scalp   \n",
              "4  HAM_0001466  ISIC_0031633  bkl   histo  75.0  male          ear   \n",
              "\n",
              "                               path  cancer_type_id  \n",
              "0  HAM10000_images/ISIC_0027419.jpg               2  \n",
              "1  HAM10000_images/ISIC_0025030.jpg               2  \n",
              "2  HAM10000_images/ISIC_0026769.jpg               2  \n",
              "3  HAM10000_images/ISIC_0025661.jpg               2  \n",
              "4  HAM10000_images/ISIC_0031633.jpg               2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64f7be00-854f-456f-8a4f-75b49cf2b088\">\n",
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>lesion_id</th>\n",
              "      <th>image_id</th>\n",
              "      <th>dx</th>\n",
              "      <th>dx_type</th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>localization</th>\n",
              "      <th>path</th>\n",
              "      <th>cancer_type_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0027419</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0027419.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>HAM_0000118</td>\n",
              "      <td>ISIC_0025030</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0025030.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0026769</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0026769.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>HAM_0002730</td>\n",
              "      <td>ISIC_0025661</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>80.0</td>\n",
              "      <td>male</td>\n",
              "      <td>scalp</td>\n",
              "      <td>HAM10000_images/ISIC_0025661.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>HAM_0001466</td>\n",
              "      <td>ISIC_0031633</td>\n",
              "      <td>bkl</td>\n",
              "      <td>histo</td>\n",
              "      <td>75.0</td>\n",
              "      <td>male</td>\n",
              "      <td>ear</td>\n",
              "      <td>HAM10000_images/ISIC_0031633.jpg</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-64f7be00-854f-456f-8a4f-75b49cf2b088');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#loading images to dataset and resizing them in order to allow the smooth and less processing time for the size of this much image data.\n",
        "df['image'] = df['path'].map(lambda x: np.asarray(Image.open(x).resize((100,75))))"
      ],
      "metadata": {
        "id": "frV5Oxa_H62U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping unnecessary columns, and separating target variable from dataset inn order to create train and test datasets\n",
        "features = df.drop(columns=['lesion_id', 'image_id', 'dx', 'dx_type', 'age', 'sex', 'localization', 'path', 'cancer_type_id'],axis=1)\n",
        "target = df['cancer_type_id']\n",
        "print(features.shape)\n",
        "print(target.shape)\n",
        "#shape of images that have been resized\n",
        "df['image'].map(lambda x: x.shape).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rqspz42U_rsT",
        "outputId": "3d279302-f458-4645-86f7-9d14060c73ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(10015, 1)\n",
            "(10015,)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(75, 100, 3)    10015\n",
              "Name: image, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting dataset"
      ],
      "metadata": {
        "id": "00PqpiB2F0xn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#splitting data, 80% train and 20% test, with a seed = 2023 for reproducibility\n",
        "x_train_i, x_test_i, y_train_i, y_test_i = train_test_split(features, target, test_size=0.2,random_state=2023)"
      ],
      "metadata": {
        "id": "Mruz6aEWFwZr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalizing images"
      ],
      "metadata": {
        "id": "6MzepMcZHL0X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = np.asarray(x_train_i['image'].tolist())\n",
        "x_test = np.asarray(x_test_i['image'].tolist())\n",
        "\n",
        "x_train_mean = np.mean(x_train)\n",
        "x_train_std = np.std(x_train)\n",
        "\n",
        "x_test_mean = np.mean(x_test)\n",
        "x_test_std = np.std(x_test)\n",
        "\n",
        "x_train = (x_train - x_train_mean)/x_train_std\n",
        "x_test = (x_test - x_test_mean)/x_test_std"
      ],
      "metadata": {
        "id": "E3g_M1GyHKap"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data augmentation"
      ],
      "metadata": {
        "id": "eiguU612LFFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_gen = ImageDataGenerator(\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True\n",
        ")\n",
        "data_gen.fit(x_train)"
      ],
      "metadata": {
        "id": "AXI-sJdoLHtY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One hot encoding the labels"
      ],
      "metadata": {
        "id": "danprpOLMXOt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_train = to_categorical(y_train_i)\n",
        "y_test = to_categorical(y_test_i)"
      ],
      "metadata": {
        "id": "4YYMv0j-MZl-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Splitting into training and validation set"
      ],
      "metadata": {
        "id": "KI8VC5m2NRkt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, x_validate, y_train, y_validate = train_test_split(x_train, y_train, test_size = 0.2, random_state = 2023)"
      ],
      "metadata": {
        "id": "V6eAuMlGNSk_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Building the Model"
      ],
      "metadata": {
        "id": "HfSX_W94N2LS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(32, (3, 3), input_shape = (75, 100, 3), activation = 'relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "model.add(Conv2D(64, (3, 3), activation = 'relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units = 128, activation = 'relu'))\n",
        "model.add(Dense(units = 7, activation = 'softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9H9WBNkN4wW",
        "outputId": "be5d8c8b-1290-4295-822d-934ffe89adeb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_6 (Conv2D)           (None, 73, 98, 32)        896       \n",
            "                                                                 \n",
            " max_pooling2d_6 (MaxPooling  (None, 36, 49, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_7 (Conv2D)           (None, 34, 47, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_7 (MaxPooling  (None, 17, 23, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten_3 (Flatten)         (None, 25024)             0         \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               3203200   \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 7)                 903       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,223,495\n",
            "Trainable params: 3,223,495\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile the model\n"
      ],
      "metadata": {
        "id": "N7NdwnMzOHaS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "NpL23qhTOY0p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fitting the model"
      ],
      "metadata": {
        "id": "hVJEQm9COe3F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "epochs = 50\n",
        "batch_size = 10\n",
        "history = model.fit_generator(data_gen.flow(x_train,y_train, batch_size=batch_size),\n",
        "                              epochs = epochs, validation_data = (x_validate,y_validate),\n",
        "                              verbose = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o1KBJBGZOejo",
        "outputId": "be0e7dc2-de22-4345-eeec-529a5584e95c"
      },
      "execution_count": null,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n"
          ]
        },
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "<ipython-input-107-d1286238ca6e>:3: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  history = model.fit_generator(data_gen.flow(x_train,y_train, batch_size=batch_size),\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "641/641 [==============================] - 115s 177ms/step - loss: 0.9676 - accuracy: 0.6720 - val_loss: 0.9279 - val_accuracy: 0.6868\n",
            "Epoch 2/50\n",
            "641/641 [==============================] - 116s 181ms/step - loss: 0.8180 - accuracy: 0.7020 - val_loss: 0.8193 - val_accuracy: 0.7068\n",
            "Epoch 3/50\n",
            "641/641 [==============================] - 119s 186ms/step - loss: 0.7643 - accuracy: 0.7216 - val_loss: 0.7466 - val_accuracy: 0.7261\n",
            "Epoch 4/50\n",
            "641/641 [==============================] - 124s 193ms/step - loss: 0.7313 - accuracy: 0.7296 - val_loss: 0.7411 - val_accuracy: 0.7342\n",
            "Epoch 5/50\n",
            "641/641 [==============================] - 111s 174ms/step - loss: 0.6999 - accuracy: 0.7419 - val_loss: 0.7366 - val_accuracy: 0.7268\n",
            "Epoch 6/50\n",
            "641/641 [==============================] - 109s 169ms/step - loss: 0.6643 - accuracy: 0.7552 - val_loss: 0.7379 - val_accuracy: 0.7380\n",
            "Epoch 7/50\n",
            "641/641 [==============================] - 108s 168ms/step - loss: 0.6424 - accuracy: 0.7600 - val_loss: 0.7339 - val_accuracy: 0.7449\n",
            "Epoch 8/50\n",
            "641/641 [==============================] - 109s 169ms/step - loss: 0.6208 - accuracy: 0.7691 - val_loss: 0.7246 - val_accuracy: 0.7449\n",
            "Epoch 9/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.6036 - accuracy: 0.7772 - val_loss: 0.7481 - val_accuracy: 0.7386\n",
            "Epoch 10/50\n",
            "641/641 [==============================] - 110s 172ms/step - loss: 0.5873 - accuracy: 0.7844 - val_loss: 0.7509 - val_accuracy: 0.7386\n",
            "Epoch 11/50\n",
            "641/641 [==============================] - 114s 177ms/step - loss: 0.5563 - accuracy: 0.7925 - val_loss: 0.7529 - val_accuracy: 0.7299\n",
            "Epoch 12/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.5322 - accuracy: 0.7956 - val_loss: 0.7251 - val_accuracy: 0.7486\n",
            "Epoch 13/50\n",
            "641/641 [==============================] - 110s 172ms/step - loss: 0.5097 - accuracy: 0.8104 - val_loss: 0.8467 - val_accuracy: 0.7461\n",
            "Epoch 14/50\n",
            "641/641 [==============================] - 109s 171ms/step - loss: 0.4906 - accuracy: 0.8174 - val_loss: 0.8526 - val_accuracy: 0.7374\n",
            "Epoch 15/50\n",
            "641/641 [==============================] - 114s 177ms/step - loss: 0.4784 - accuracy: 0.8206 - val_loss: 0.8300 - val_accuracy: 0.7436\n",
            "Epoch 16/50\n",
            "641/641 [==============================] - 110s 171ms/step - loss: 0.4529 - accuracy: 0.8291 - val_loss: 0.8162 - val_accuracy: 0.7442\n",
            "Epoch 17/50\n",
            "641/641 [==============================] - 110s 171ms/step - loss: 0.4387 - accuracy: 0.8366 - val_loss: 0.8763 - val_accuracy: 0.7155\n",
            "Epoch 18/50\n",
            "641/641 [==============================] - 109s 169ms/step - loss: 0.4161 - accuracy: 0.8451 - val_loss: 0.8493 - val_accuracy: 0.7442\n",
            "Epoch 19/50\n",
            "641/641 [==============================] - 108s 168ms/step - loss: 0.3961 - accuracy: 0.8533 - val_loss: 0.8797 - val_accuracy: 0.7561\n",
            "Epoch 20/50\n",
            "641/641 [==============================] - 109s 170ms/step - loss: 0.3916 - accuracy: 0.8522 - val_loss: 0.8889 - val_accuracy: 0.7436\n",
            "Epoch 21/50\n",
            "641/641 [==============================] - 110s 172ms/step - loss: 0.3667 - accuracy: 0.8641 - val_loss: 1.0173 - val_accuracy: 0.7243\n",
            "Epoch 22/50\n",
            "641/641 [==============================] - 107s 168ms/step - loss: 0.3566 - accuracy: 0.8722 - val_loss: 0.9455 - val_accuracy: 0.7548\n",
            "Epoch 23/50\n",
            "641/641 [==============================] - 110s 171ms/step - loss: 0.3442 - accuracy: 0.8753 - val_loss: 0.9604 - val_accuracy: 0.7218\n",
            "Epoch 24/50\n",
            "641/641 [==============================] - 109s 171ms/step - loss: 0.3434 - accuracy: 0.8746 - val_loss: 1.0236 - val_accuracy: 0.7336\n",
            "Epoch 25/50\n",
            "641/641 [==============================] - 114s 178ms/step - loss: 0.3287 - accuracy: 0.8789 - val_loss: 0.9925 - val_accuracy: 0.7505\n",
            "Epoch 26/50\n",
            "641/641 [==============================] - 113s 177ms/step - loss: 0.3089 - accuracy: 0.8881 - val_loss: 1.0218 - val_accuracy: 0.7305\n",
            "Epoch 27/50\n",
            "641/641 [==============================] - 109s 170ms/step - loss: 0.2922 - accuracy: 0.8936 - val_loss: 1.0378 - val_accuracy: 0.7480\n",
            "Epoch 28/50\n",
            "641/641 [==============================] - 116s 180ms/step - loss: 0.2787 - accuracy: 0.8964 - val_loss: 1.1439 - val_accuracy: 0.7386\n",
            "Epoch 29/50\n",
            "641/641 [==============================] - 114s 177ms/step - loss: 0.2766 - accuracy: 0.9033 - val_loss: 1.0226 - val_accuracy: 0.7349\n",
            "Epoch 30/50\n",
            "641/641 [==============================] - 107s 168ms/step - loss: 0.2710 - accuracy: 0.9037 - val_loss: 1.3067 - val_accuracy: 0.7205\n",
            "Epoch 31/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.2658 - accuracy: 0.9048 - val_loss: 1.1527 - val_accuracy: 0.7405\n",
            "Epoch 32/50\n",
            "641/641 [==============================] - 109s 171ms/step - loss: 0.2497 - accuracy: 0.9098 - val_loss: 1.1505 - val_accuracy: 0.7417\n",
            "Epoch 33/50\n",
            "641/641 [==============================] - 109s 170ms/step - loss: 0.2333 - accuracy: 0.9164 - val_loss: 1.2780 - val_accuracy: 0.7280\n",
            "Epoch 34/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.2246 - accuracy: 0.9196 - val_loss: 1.2708 - val_accuracy: 0.7374\n",
            "Epoch 35/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.2104 - accuracy: 0.9260 - val_loss: 1.2589 - val_accuracy: 0.7149\n",
            "Epoch 36/50\n",
            "641/641 [==============================] - 111s 173ms/step - loss: 0.2150 - accuracy: 0.9196 - val_loss: 1.3368 - val_accuracy: 0.7274\n",
            "Epoch 37/50\n",
            "641/641 [==============================] - 110s 171ms/step - loss: 0.2173 - accuracy: 0.9232 - val_loss: 1.2213 - val_accuracy: 0.7386\n",
            "Epoch 38/50\n",
            "641/641 [==============================] - 111s 174ms/step - loss: 0.1963 - accuracy: 0.9303 - val_loss: 1.3794 - val_accuracy: 0.7318\n",
            "Epoch 39/50\n",
            "641/641 [==============================] - 111s 174ms/step - loss: 0.1963 - accuracy: 0.9345 - val_loss: 1.3736 - val_accuracy: 0.7455\n",
            "Epoch 40/50\n",
            "641/641 [==============================] - 113s 176ms/step - loss: 0.2002 - accuracy: 0.9282 - val_loss: 1.5883 - val_accuracy: 0.7374\n",
            "Epoch 41/50\n",
            "641/641 [==============================] - 111s 173ms/step - loss: 0.1796 - accuracy: 0.9396 - val_loss: 1.3486 - val_accuracy: 0.7367\n",
            "Epoch 42/50\n",
            "641/641 [==============================] - 110s 172ms/step - loss: 0.1910 - accuracy: 0.9332 - val_loss: 1.4671 - val_accuracy: 0.7380\n",
            "Epoch 43/50\n",
            "641/641 [==============================] - 107s 167ms/step - loss: 0.1790 - accuracy: 0.9365 - val_loss: 1.5165 - val_accuracy: 0.7505\n",
            "Epoch 44/50\n",
            "641/641 [==============================] - 107s 167ms/step - loss: 0.1701 - accuracy: 0.9395 - val_loss: 1.4432 - val_accuracy: 0.7305\n",
            "Epoch 45/50\n",
            "641/641 [==============================] - 109s 170ms/step - loss: 0.1623 - accuracy: 0.9451 - val_loss: 1.4957 - val_accuracy: 0.7349\n",
            "Epoch 46/50\n",
            "641/641 [==============================] - 109s 169ms/step - loss: 0.1690 - accuracy: 0.9407 - val_loss: 1.4346 - val_accuracy: 0.7318\n",
            "Epoch 47/50\n",
            "641/641 [==============================] - 107s 167ms/step - loss: 0.1409 - accuracy: 0.9479 - val_loss: 1.7026 - val_accuracy: 0.7392\n",
            "Epoch 48/50\n",
            "641/641 [==============================] - 107s 166ms/step - loss: 0.1467 - accuracy: 0.9499 - val_loss: 1.7122 - val_accuracy: 0.7473\n",
            "Epoch 49/50\n",
            "641/641 [==============================] - 108s 169ms/step - loss: 0.1692 - accuracy: 0.9457 - val_loss: 1.5862 - val_accuracy: 0.7268\n",
            "Epoch 50/50\n",
            "641/641 [==============================] - 106s 165ms/step - loss: 0.1448 - accuracy: 0.9469 - val_loss: 1.7726 - val_accuracy: 0.7080\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "pQtvrOMCQ94-",
        "outputId": "4d57f30e-d41a-4f51-c70b-42bd57c39598"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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f/NWrVzkcTnh4uPJBQlSeggBzBUKhEG6mpKTAdIGOjs6WLVvkfr5z505iCoJq3fXNmzcBAQEmJiZkMhleLAsLC3Ecd3d3BwAEBwf3FLmfn5+Xl1dXu7+/v0AgYLFYZDLZyMho/fr1xDRuxT5zcnJmzJhBpJP09fWdnJxu3boFS2/dujVt2jQajWZgYLBz586Ojg7ihz0VKW7Ozc3N3NyczWbTaDSBQODp6VlQUCBbwdXV1dDQUHZCT/+nILwFr8W8dXh7e2tpaQ2e/0F9LWawg1eMyiJVUlJCJpNVe7NkAJFIJDNnzjx16pSa+xyk5urq6uh0+qFDh2SN78RrMW8jvSZx1Zm3Ivj29vbr16+XlJTATK2lpWVoaGhoaGivL4oPHhKJJDU1taWlxdPTU519Dl5zISEhDg4O8Lk8juNVVVXZ2dkwK98fRqBIPXr0qOsHMQh6Ovqq/QoxXLx+/Xr+/PlWVlbEjN/AwMDly5d7enoqk0EfDLKysi5dupSRkaF4xtaw+xyk5qKjo/Py8q5evUqhUAAAly9fNjQ0nDlzpuwnKFSk680VGu71h8DAQDjrz8zM7MKFC4PRxOAN94YgeMX0v+9dv349ICBgoOJBKElqampERITsa20qAHoY7mF4l1cB4bJCXe0INWHEL2mF+t67CYZh58+fh99ikmUEDvcQCMRIAokUAoFQa5BIIRAItQaJFAKBUGuQSCEQCLWG3FOBkt8LRwwXI/gEjeBdQ6hAjyKl2mebEUMA/HBz15dURwA5OTmxsbGo772byH3kk6BHkeo6WwGhJsAZUiP1BMXGxo7UXUMopieRQjkpBAKh1iCRQiAQag0SKQQCodYgkUIgEGoNEikEAqHWDKlI3b17d+zYsSQSCcMwPT09uMbnoHLp0iULCwv4TSh9fX0vL6/BbhExBGzcuJH41JfcOb1x40ZgYKDsef/ss89kK7i4uHA4HA0NjXHjxqnw4fD+MGvWrK6fKoPLi4SHh8vZiXX9lEEqlcbExDg5OcnZs7OzZ8yYwWQyDQwMAgICZJdWUlCkgMjISBsbGwaDwWKxbGxsgoKC4NJYaWlpkZGRsp9LTE1NJfZFR0dH+X3phq5fbxns70nBhTEaGhoGrwk5BAIBj8cbsuYGm0H9fPDwovzng7W0tDIyMoqLi2W/2x0cHLxo0aLm5ma4KRAItLW1AQDp6emyP5ddZn0ocXZ27voHnDdvHo7jxCpbBOPGjVPS7ePHj2fMmAEAkF1mHcfxhw8fMhiMoKCg1tbWO3fu6OjorFmzptcixbi6uh46dKimpqalpSU5OZlCoXz00UewKDY21tnZmfhfS6XSioqK27dvL1y4EH0+WJ729vaulxSEkgzI0RuCU8BgMOCXOYnF6Q4ePJiUlJScnAwXuIYcPnyYRCJ5e3sP1+c6ZaHT6YSAQry9vXft2gVL5T7Q/vDhQ2V8/vHHH19++eWmTZscHBzkisLCwvT19fft28disRwdHQMCAr7//nu40rKCIsVQqdQvvvhCV1eXzWYvX758yZIl/+///T+4Us62bdsmTJiwcOFCuDYyhmHwy5xw4fX+MAJF6tSpU7LrPiP6xIAcvaE/BU+ePAkKCtq3b5/cssBOTk6+vr6VlZU7duwYyni65dq1a7ICWl5e/vDhQ2L1NtWYMGHCpUuXVq5cKbeSaGdn55UrV5ydnYl3jBYsWIDj+OXLlxUU9dpcSkqK7BE2NDQEABDflQ8JCcnLy4uNje3PHnVlmEXq6NGjLBaLyWRevnx5wYIFXC7XyMgoMTERAHD48GE6nT5q1KiNGzcaGBjQ6XQnJ6d79+4BAHx8fKhUKlymCQDwxRdfsFgsDMPq6up8fX39/f1LS0sxDLO0tFQmhv/85z+2trY8Ho9Op9vb21+/fh0AsG7dOjicFggEcC3JNWvWMJlMHo+XlpYmkUiCg4NNTEwYDMb48ePhIOXrr79mMpkcDqempsbf39/Q0LC4uHiQjpsy4DgeHR09duxYGo2mqam5ZMkSeKlU/ugN1Cm4du3aYC/Dd/jwYRzHu13eMjw83MrK6uTJkzdu3Oha2tNRUtAzAQDddoC+cvDgwW3btqnwQ2V4+vRpa2uriYkJYYFrfOXn5yso6msrJSUlfD7f1NQUbmpqajo7O8fGxuID+23VriPAIc5J7dmzBwCQmZnZ1NRUU1Mzc+ZMFoslEongzTCLxSoqKuro6CgsLJw6dSqHwykrK8NxfOXKlXp6eoTPqKgoAEBtbS2O40uXLhUIBLItKs5JXbhwISQk5PXr1/X19dOnTyfGz0uXLtXQ0KisrCRqrlixIi0tDcfxHTt20Gi0ixcvNjQ07N69m0QiwSVn4b5s27YtLi7u448//vPPPwfomP0FJXNSwcHBVCr17NmzjY2N+fn5kyZN0tHRgSv6KX/0BuQUpKenczic0NDQXmNWeUkrCwsLW1tbuWoCgeDZs2c4jt+5c4dEIpmZmbW2tuJ/zUkpOEoKemZPHUB5KioqbG1tJRIJ3AwLCzMyMuLz+RQKxczMbPHixb/++mufHL733nuyOSm4sjdcnpKAwWDMnTtXQZGSbYlEooqKiri4OBqNJjdKDQwMBADk5uYSlpGzpJWTkxOXy9XV1fX09GxraysrK4N2MpkMr3K2trZHjx5taWk5c+bMwDa9bNmyr776SlNTU0tLy83Nrb6+vra2FgCwadMmiURCNNfc3Hz//v2FCxd2dHQcPXrU3d196dKlfD5/7969FApFNqqDBw9u2bLl0qVLNjY2Axuq8rS3t0dHR3/88cdeXl48Hs/e3v7bb7+tq6tTcpFnWfp/ClxdXZubm4OCgvratJK0tbU9e/YM3g50i6Oj4/bt258/fy63TrUyR6lrz+y1AyjDwYMHt27dSiL93x9w1apVaWlp5eXlra2tiYmJZWVlzs7OhYWFffIpC3xaR6zVDKFQKO3t7QqKlHRubGxsZGQUEhLy9ddfy71wBzNQPa3pqxrqIlIEcLWSbtcZnzJlCpPJVCa9pzJwNR74JHXOnDlWVlanT5+GGp+UlOTp6amhoVFcXCwUCoknxAwGQ19ff1CjUoHCwsLW1tYpU6YQlqlTp1KpVDhYU5khOAUqUFNTg+O44lWYwsPDra2t4+Pjs7OzCWOfjhLRM/vfAaqqqtLS0uDi9RBjY+OJEyey2WwqlTp9+vQzZ860t7fHx8cr71MOmDmCOWwCkUjEYDAUFCnpvLy8vKam5ty5cz/88MPEiRNl84/wLLx69UrlyLuidiKlGBqNBm9zBpArV67MmjVLV1eXRqMRj1oAABiGbdy48enTp5mZmQCAH3/88e9//zsAoK2tDQCwd+9eYhrIixcvhELhwEbVTxobGwEAcA4OAZ/Pb2lp6afnwTgF/aSjowMAIJc5loNOp585cwbDsLVr1xK3DKodpf53gMjIyPXr18vl+GWxt7fX0NB4/Pix8j7lgOlCOIkJIhQKOzo6DAwMFBQp6ZxCoejq6rq4uCQlJRUWFkZERBBFUOngGRko3iaREovFjY2NRkZGA+Lt9u3bMTExZWVl7u7u+vr69+7da2pqioyMlK2zevVqOp1+8uTJ4uJiLpcLE4S6uroAgJiYGNlhc05OzoBENVDw+XwAgNyfrf9Hb2BPwUAB/xi9Lrzs6Ojo5+dXUlJCTEpS7Sj1swNUV1efO3du8+bNCupIpVKpVKpYdhVjbm7O4XBevHhBWOBKwuPHj1dQ1NdWLC0tNTQ0ZIelcEFp5W/KlOFtEqmsrCwcx6dPnw4AIJPJ3Q4Jlef3339nsVgFBQVisXjz5s0WFhZ0Ol3um5CampoeHh6pqamHDh1av349NBobG9Pp9Ly8vP60PtjY2dmx2ezffvuNsNy7d08kEk2ePBn04+gN7CkYKEaNGoVhmDIzocLCwmxsbODjWtDbUeqJfnaAyMhILy8vLS0tWSN8mkQA0/COjo6qNQEAIJPJCxcuvH37tlQqhZaMjAwMw9zc3BQUKfZZX1+/YsUKWUtJSYlEIjE2NiYs8Czo6empHHlX1F2kpFJpQ0NDZ2dnfn6+r6+viYkJHMlbWlq+fv06NTVVLBbX1tbKXha0tLSqqqqeP3/e0tLS7b9ILBa/evUqKyuLxWLBB7E3btzo6OgoKSnpmozYtGnTmzdv0tPTFy1aBC10On3NmjWJiYlHjx5tbm6WSCQVFRVwPpv6QKfT/f39U1JSEhISmpubCwoKNm3aZGBg4O3tDfp49Pp/CjIyMgZ1CgKTybSwsKioqOi1Jhz0ETljxUdJgZOeOoCnp6eenp6Ct21evXp1+vTprl9VraysTEpKamxsFIvFOTk569atMzEx2bRpkzI+eyIoKOjVq1dfffVVW1tbTk5OVFTU6tWrra2tFRcpaI7FYv388883b95sbm4Wi8W5ubmrVq1isVh+fn5EHXgW7O3t+xqtIro+8Bu8KQh3794dN24cfKKhr6+/f//++Ph4mGkbM2ZMaWnp8ePHuVwuAMDU1PTx48fe3t4UCsXQ0JBMJnO53CVLlpSWlkJX9fX1s2fPptPp5ubmW7du3blzJwDA0tKyrKzswYMHpqamDAbj/fffP3bsmIKHPikpKTiOBwQEaGlp8fn85cuXHzlyBAAgEAjgU3bIxIkTAwMDZXfkzZs3AQEBJiYmZDJZV1d36dKlhYWFkZGR8C7X2NhY7rnswKLkFASpVBoVFTVmzBgKhaKpqenu7l5cXAyLlDx61dXV/T8F1dXVV69e5XA44eHhvcas8hQEHx8fCoUiFArhZkpKCjzvOjo6W7Zskfv5zp07iSkIPR0lxT2z2w6A47i7uzsAIDg4uKfI/fz8vLy8utr9/f0FAgGLxSKTyUZGRuvXr6+qqoJFin3m5OTMmDGDSCfp6+s7OTndunULlt66dWvatGk0Gs3AwGDnzp2yrxD1VKS4OTc3N3NzczabTaPRBAKBp6dnQUGBbAVXV1dDQ0OpVEpY+j8FYRjmSSkPfEVruKPAFy5c+PTp0+GO4n8M5bt7Q3wKVBapkpISMpk8qNcGZZBIJDNnzjx16pSa+xyk5urq6uh0+qFDh2SNI2eeVE/0mg0dJIhxYn5+PrxZGJYw1IHhOgWKaW9vv379eklJCczUWlpahoaGhoaGEq9oDD0SiSQ1NbWlpcXT01OdfQ5ecyEhIQ4ODj4+PgAAHMerqqqys7NhVr4/qLtIDRcBAQElJSWPHz9es2ZN1zfUEcPO69ev4QvGa9euhZbAwMDly5d7enoO17vEWVlZly5dysjIUDxja9h9DlJz0dHReXl5V69ehZMNL1++DF8wvnLlSn/D6npzpSbDvcDAQDh9zszM7MKFC0Pc+p49e0gkkrGxMXwPRq0YsuHe0J+C/ve969evBwQEDFQ8CCVJTU2NiIjo7OzsjxPQw3APw7u8CpicnOzh4dHVjlATli9fDv67sNUIA/W9dxkMw86fP991QTM03EMgEGoNEikEAqHWIJFCIBBqDRIpBAKh1pB7KkhOTh7KOBDKA988GJEnCL6mOyJ3DaE6XR/4qfYtVAQCgegnyk5BQCBUBj4/RrdCiAEE5aQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWkMe7gAQbze3b9/OyckhNh89egQAiIyMJCyOjo4ffPDBMESGGClgOI4PdwyIt5jMzMwPP/yQQqGQSPJ35VKpVCwW37hxY+7cucMSG2JkgEQK0S+kUqm+vn5tbW23pTo6OtXV1RoaGkMcFWIkgXJSiH5BIgzJJ3AAACAASURBVJFWrlxJpVK7FlGpVC8vL6RQiH6CRArRXz799FORSNTVLhKJPv3006GPBzHCQMM9xABgZmb24sULOaOxsfGLFy8wDBuWkBAjBnQnhRgAPvvsMwqFImuhUCirV69GCoXoP+hOCjEAPHr0aOzYsXLGhw8fjhs3bljiQYwk0J0UYgCwsbEZN26c7H2Tra0tUijEgIBECjEwfP7558SDPAqFsmrVquGNBzFiQMM9xMBQXl5uamoKuxOGYU+fPjUzMxvuoBAjAXQnhRgYjI2N33vvPRKJRCKR3nvvPaRQiIECiRRiwPjss88wDCORSJ999tlwx4IYOaDhHmLAqKur09fXBwBUVVWNGjVquMNBjBTwoWLZsmXDva8IBGJgWLZs2ZBJx5B+qmX69Onbt28fyhbVn5ycnNjY2PPnzw93IAPD7du3MQybOXMm3PTw8PD19XV0dBzeqBADS0xMzFA2N6QiZWRk9Mknnwxli28FsbGxI+awLFiwAADA4XDgpoeHh6Oj44jZOwTkwoULQ9kc+ugdYiAh5AmBGCjQ0z0EAqHWIJFCIBBqDRIpBAKh1iCRQiAQas27K1KhoaG2trZcLpdGo1laWu7atau1tbVrtXXr1nE4HAzD8vLyevUpFosjIiIsLS2pVCqfz7ezs3v+/PnAhw7A1atXeTzev/71r8FwPlzcuHEjMDDw0qVLFhYWGIZhGCY3c93FxYXD4WhoaIwbN+7BgwdDGdusWbOwLrDZbABAeHi4nN3Ozk55z1KpNCYmxsnJSc6enZ09Y8YMJpNpYGAQEBDw5s0bZYoUEBkZaWNjw2AwWCyWjY1NUFBQc3MzACAtLS0yMlIikSgf81AzZDOyli1bNpQTwHrF2dk5Pj6+vr6+ubn5/PnzFApl/vz53dZMTEwEAOTm5vbq093d3dra+u7du2KxuKqqys3NraCgQPFP4Aypvgafnp7O5XLT0tL6+sMhBgBw/vx5ZWoGBwcvWrSoubkZbgoEAm1tbQBAenq6bLWMjIzFixcPfKC94ezs3PW/M2/ePBzHw8LC5Ozjxo1T0u3jx49nzJgBAJgwYYKs/eHDhwwGIygoqLW19c6dOzo6OmvWrOm1SDGurq6HDh2qqalpaWlJTk6mUCgfffQRLIqNjXV2dm5oaFAy7CH+L7+7IuXq6trZ2Ulswrk8ZWVlXWsqKVKJiYkYhuXn5/cpDNVEarARCoWOjo7996OkSB04cMDKyqq9vZ2wCASCn376iUQiGRoaNjY2EvbhEql58+YRAgrx9vbOzMzEcTwsLOzs2bMq+MzLy/v4448TEhIcHBzkRMrDw8Pc3FwqlcLNqKgoDMP+/PNPxUWKcXd3lz3Cy5cvBwBUVVXBTR8fH0dHR7FYrEzkQ/xffneHe+np6bILmejo6AAAhEJh15pKfgP32LFjkyZNsre3H6gIh5FTp07V1NQMTVtPnjwJCgrat28fnU6XtTs5Ofn6+lZWVu7YsWNoIlHAtWvXZKeAlZeXP3z4cM6cOf3xOWHChEuXLq1cuZJGo8naOzs7r1y54uzsTHS8BQsW4Dh++fJlBUW9NpeSkiJ7hA0NDQEARIojJCQkLy8vNja2P3s0SKijSJ09e3bKlCl0Op3FYpmZmcHbaRzHo6Ojx44dS6PRNDU1lyxZAhfLPXr0KIvFYjKZly9fXrBgAZfLNTIygvc+Y8eOhS/lT548GarPrl27eDwenU7//vvv5RqtrKxkMBjm5uawraioKGtraxqNxuPxdu7c2WvMIpHo7t27Dg4OA30wuiE7O9vExATDsCNHjgCFR+Dw4cN0On3UqFEbN240MDCg0+lOTk737t0DAPj4+FCpVPg+MADgiy++YLFYGIbV1dX5+vr6+/uXlpZiGGZpaQkAuHbtGpfL3b9//2DszuHDh3Ecd3Nz61oUHh5uZWV18uTJGzdudC1VoUsAACQSSXBwsImJCYPBGD9+vGovJB08eHDbtm0q/FAZnj592traamJiQlgEAgEAID8/X0FRX1spKSnh8/mmpqZwU1NT09nZOTY2FlfDLw4M2T2bkreI8LWgAwcO1NfXv379+rvvvlu5ciWO48HBwVQq9ezZs42Njfn5+ZMmTYILT+I4vmfPHgBAZmZmU1NTTU3NzJkzWSyWSCTq7Ow0MzMzMTGRHdZt3749JiZGrtG2tjYOh+Pj4wM39+zZg2HYN99809DQIBQK4+PjQW/DvWfPngEAHBwcZs2apa+vT6PRbGxsjhw5QtyW94Rqw73y8nIAQFxcHBFwt0cAx3Fvb28Wi1VUVNTR0VFYWDh16lQOhwNHtStXrtTT0yN8RkVFAQBqa2txHF+6dKlAICCK0tPTORxOaGhoX+MESgz3LCwsbG1t5YwCgeDZs2c4jt+5c4dEIpmZmbW2tuJ/He6p0CVwHN+xYweNRrt48WJDQ8Pu3btJJNL9+/f7tFMVFRW2trYSiQRuhoWFGRkZ8fl8CoViZma2ePHiX3/9tU8O33vvPdnh3q1btwAAUVFRsnUYDMbcuXMVFCnZlkgkqqioiIuLo9FocqPUwMDAXvs55J3OSYlEIj6fP3v2bMLS2dkZGxsrFArZbLanpydh//XXXwEA8G8DeyQx3oaa8uTJE/y/kpecnAyL2traTExMmpqa5Nrds2ePlZUVTDoIhUImk0nkFHHlclIFBQUAgI8++uiXX36pr69vbGz88ssvAQAJCQmKd3kARarbI+Dt7c3j8Ygf3r9/HwCwb98+vC8ipTK9ilRrayuGYYsWLZKzEyKF47i/vz8AYMuWLbiMSKnWJdrb25lMJvEroVBIo9E2b97cp53asmXLsWPHiM2ysrIHDx60tLS8efMmJydn4sSJDAbj4cOHyjuUE6mff/4ZABAdHS1bh8vlOjk5KShSsi09PT0AgLa29j/+8Q+o2gSnT58GAPz444+9Onmnc1L5+fmNjY3z5s0jLBoaGtu2bSssLGxtbZ0yZQphnzp1KpVKhSMXOeBqumKxGACwbt06Ho9HjLQTEhKWLFnC5XJl66ekpCQnJ1+/fh0mHZ48eSIUCufOndunyGFaYdy4cU5OTlpaWjweb9++fTwe7/jx433yMyDIHgE5pkyZwmQy4bBIHaipqcFxnMlkKqgTHh5ubW0dHx+fnZ1NGFXrEsXFxUKhkJgiwGAw9PX1+3Q0qqqq0tLSVq9eTViMjY0nTpzIZrOpVOr06dPPnDnT3t4OZVE1YOaos7NT1igSiRgMhoIiJZ2Xl5fX1NScO3fuhx9+mDhxomzmEZ6FV69eqRz5IKFeIgUnbvD5fDl7Y2MjAABOSyHg8/ktLS2KHbLZ7A0bNty5cwdeZo8dO+bj4yNbISkp6eDBg1lZWcTnbisqKgAAurq6fYrcwMAAAFBXV0dYqFSqqalpaWlpn/wMATQarba2drij+D86OjrAfyW+J+h0+pkzZzAMW7t2bXt7OzSq1iXa2toAAHv37iXmNL148aLbpyU9ERkZuX79erkcvyz29vYaGhqPHz9W3qccMFEI/wsQoVDY0dFhYGCgoEhJ5xQKRVdX18XFJSkpqbCwMCIigiiCSgfPiFqhXiI1evRo8Ne/OgTKllz/a2xsNDIy6tWnj48PhUKJiYm5ffu2sbExTDRC4uLiEhISbt68CduFwP6n5AQ5AjabPWbMmKKiIlljZ2cnj8frk5/BRiwWK3nchgb4x+h1JqGjo6Ofn19JSQkxKUm1LgGvPXJJyZycHCWjra6uPnfu3ObNmxXUkUqlUqlUsewqxtzcnMPhyK4I/eTJEwDA+PHjFRT1tRVLS0sNDY3CwkLCIhKJwH/PiFqhXiJlZmampaUFB96y2NnZsdns3377jbDcu3dPJBJNnjy5V5/wI1YXL14MCgry9fWFRhzHAwICCgoKUlNT5a7GdnZ2JBIJZij7hIeHR25u7tOnT+GmUCh88eKFus1IyMrKwnF8+vTpAAAymdztkHAoGTVqFIZhTU1NvdYMCwuzsbHJzc2Fm6p1CWNjYzqdrszLA90SGRnp5eWlpaUla5TNTgAAYBq+P9/5I5PJCxcuvH37tlQqhZaMjAwMw9zc3BQUKfZZX1+/YsUKWUtJSYlEIjE2NiYs8CzApJVaoV4iRaPRdu/effv2bR8fn8rKSqlU2tLSUlRURKfT/f39U1JSEhISmpubCwoKNm3aZGBg4O3trYxbf3//zs7OhoYGYmJLUVHR119/feLECQqFIvtCw6FDh3R1dZcuXXrx4sVTp041Nzfn5+crmVfy8/MzNTVdvXp1WVlZfX19QEBAe3s7TJ8PL1KptKGhobOzMz8/39fX18TEBKZULC0tX79+nZqaKhaLa2trZa/PWlpaVVVVz58/b2lpEYvFGRkZgzQFgclkWlhYwCG2YuCgj5japlqXoNPpa9asSUxMPHr0aHNzs0QiqaioePnyJQDA09NTT09Pwds2r169On36dNdPy1ZWViYlJTU2NorF4pycnHXr1pmYmGzatEkZnz0RFBT06tWrr776qq2tLScnJyoqavXq1dbW1oqLFDTHYrF+/vnnmzdvNjc3i8Xi3NzcVatWsVgsPz8/og48C+p2WQVA/aYg4Dh+5MgRe3t7Op1Op9MnTpwYHx+P47hUKo2KihozZgyFQtHU1HR3dy8uLsZxPD4+Hib8xowZU1paevz4cZgXNzU1ffz4MeFz9uzZJ0+eJDbhw7iuwIe7LS0t69at09bWZrPZ77//fnBwMADAyMjojz/+UBx5eXn5p59+qqmpSaPRpk2blpGR0evOqvB0Ly4uDuYmmEymm5ub4iPg7e1NoVAMDQ3JZDKXy12yZElpaSn0U19fP3v2bDqdbm5uvnXrVjgdzNLSEj6uMjU1ZTAY77//fnV19dWrVzkcTnh4eJ/ixJWbggDH40KhEG6mpKTAIbmOjg58oifLzp07iSkIqnWJN2/eBAQEmJiYkMlkeEEqLCzEcdzd3R0AEBwc3FOcfn5+Xl5eXe3+/v4CgYDFYpHJZCMjo/Xr1xPTuBX7zMnJmTFjBpFO0tfXd3JyunXrFiy9devWtGnTaDSagYHBzp07Ozo6iB/2VKS4OTc3N3NzczabTaPRBAKBp6en3Dtbrq6uhoaGvU6awd/xKQjvIIP9Woy3t7eWltbg+VeMMiJVUlJCJpNVe7NkAJFIJDNnzjx16pSa+xyk5urq6uh0+qFDh5Sp/E5PQUAMBmr9gjsAlpaWoaGhoaGh3X6FYmiQSCSpqaktLS2enp7q7HPwmgsJCXFwcJB79q0mIJFSlkePHnX9WAfB0HTEkUpgYODy5cs9PT2VyaAPBllZWZcuXcrIyFA8Y2vYfQ5Sc9HR0Xl5eVevXqVQKIMRWz9BCzEoi42NDa6GrzUpZPfu3WfOnBGJRObm5lFRUeq89OH+/ft//vnnAwcOHDx4cOhbnzt3bl+n7w6Lz8Fo7vLly2/evMnKypJ9316tQCI1komIiJCdrafmuLi4uLi4DHcU7xyLFy9evHjxcEehCDTcQyAQag0SKQQCodYgkUIgEGoNEikEAqHWDGnivKKiIjk5eShbVH/g260j+LAo//ou4m2hoqJiSN9RH7Jpo+r8/BuBQPSJoZxxPqR3UsuWLbtw4cJQtqj+JCcne3h44G/bDCwlwTDs/PnzcCUexIgBrjQzZKCcFAKBUGuQSCEQCLUGiRQCgVBrkEghEAi1BokUAoFQa5BIIRAItebtEKni4uKtW7eOGzeOw+GQyWQej2dlZeXq6jpIEwVDQ0NtbW25XC6NRrO0tNy1axf8HtulS5csLCxkPyNFpVJHjRo1a9asqKiohoaGwQjm3eHGjRuBgYGyB/mzzz6TreDi4sLhcDQ0NMaNG6fCh8P7w6xZs7p+RAwu4REZGWljY8NgMFgslo2NTVBQkOySUwoQi8XBwcEWFhZUKtXQ0HDHjh3Egl0AgHPnzsHlpk1NTdesWVNdXa2MTwXB9NSr09LSIiMj1frLiEM2I0vlT46ePHmSQqF88MEH165da2ho6OjoKC0tTUpKcnJy+u677wY8ThzHnZ2d4+Pj6+vrm5ubz58/T6FQ5s+fT5QKBAK4JjBc4ODf//736tWrMQwzMDDo64Ld+OB/Pnh4AUp8PhgSHBy8aNEiuIg0juMCgUBbWxsAkJ6eLltNdpn1ocTZ2bnrf2fevHk4jru6uh46dKimpqalpSU5OZlCocguf62AzZs30+n0xMTE5ubmf//731wud8WKFbAoKSkJABAZGdnY2Jibm2thYeHg4CAWi3v1qSAYBb06NjbW2dm5oaFByaOBvnH+F3JycjQ0NObMmdP1DF27do1YZ3xgcXV17ezsJDbhXMSysjK4SYiULBcuXCCRSKNGjWpsbOxTW4MqUkKh0NHRcRidKClSBw4csLKyIlZFx3FcIBD89NNPJBLJ0NBQ9pAOl0jNmzePEFCIt7d3ZmYmjuPu7u6ykcOJjsRaDD1RWlpKIpE2bNhAWPbu3QsAKCoqwnF89uzZo0ePJtZEOHLkCAAgOzu71zgVBKO4V/v4+Dg6Oiqjgzj6xrkc4eHhEonkwIEDZLL85Ph58+Zt2bJlMBpNT0+X/Uqhjo4OAEDxOrfLli1bvXp1TU3Nt99+OxghqcapU6dk19EeRicKePLkSVBQ0L59++SWBXZycvL19a2srNyxY8fgta4k165d43A4xGZ5efnDhw/hCmkpKSmykRsaGgIAev1e+/3796VS6XvvvUdY5s+fDwC4fv069G9gYIBhGCyCq+PJrjnWEwqCUdyrQ0JC8vLyYmNje21i6FFrkRKJRJmZmdra2tOmTVNQDcfx6OjosWPH0mg0TU3NJUuWPHr0CABw9OhRFovFZDIvX768YMECLpdrZGSUmJgIABg7diyGYSQSafLkyfA87dq1i8fj0en077//Xs5/ZWUlg8EwNzdXHC1czC4jI0P1He6ZnvbRx8eHSqXCFa4AAF988QWLxcIwrK6uztfX19/fv7S0FMMwS0vLw4cP0+n0UaNGbdy40cDAgE6nOzk53bt3r09OAADXrl0b2DX4Dh8+jON4t8tbhoeHW1lZnTx58saNG8ofEwXnHQAgkUiCg4NNTEwYDMb48ePhnWxfOXjw4LZt27otKikp4fP5pqamij2QSCTw1+WCx4wZAwD4888/AQAWFhayFwaYkLKwsOhrnAqCkevVmpqazs7OsbGxuBq+oTVk92wq3CI+fvwYADB9+nTF1YKDg6lU6tmzZxsbG/Pz8ydNmqSjo1NdXY3j+J49ewAAmZmZTU1NNTU1M2fOZLFYIpGos7PTzMzMxMRE9gZ4+/btcgtw4zje1tbG4XB8fHwIS7fDPRzHYYbS2Ni4T/uo5HBPwT6uXLlST0+PqBkVFQUAqK2txXF86dKlAoGAKPL29maxWEVFRR0dHYWFhTAvC2/4lXeSnp7O4XBCQ0OV2TugxHDPwsLC1tZWzigQCJ49e4bj+J07d0gkkpmZWWtrK/7X4Z4K5x3H8R07dtBotIsXLzY0NOzevZtEIvU1k1hRUWFrayuRSGSNIpGooqIiLi6ORqMpszxXfn4+ACAoKIiwdHZ2AgDc3d1xHM/KyqJQKIcPH25ubn748OHYsWNh/ktJeg2ma6/GcTwwMBAAkJub26t/lJP6H3AR7Q8//FBBHaFQyGazPT09Ccuvv/4KAID/IthZiVF6fHw8AODJkyc4jsfExAAAkpOTYVFbW5uJiUlTU5Oc/z179lhZWcnmI3oSKRzHMQzj8/l92kdlRErxPvZJpGQjv3//PgBg3759fXLSJ3oVqdbWVgzDFi1aJGcnRArHcX9/fwAAXCiUECnVznt7ezuTySR+JRQKaTTa5s2b+7RTW7ZsOXbsmJwRrk6ura39j3/8A6phr8yfP19LSyszM7O9vf3ly5fJyckYhv3tb3+DpTBFBTEyMiovL1c+wl6D6dqrcRw/ffo0AODHH3/s1T/KSf0P+IhXcTKosLCwtbV1ypQphGXq1KlUKhUOZOSgUqkAALFYDABYt24dj8cjBuEJCQlLliyBS90SpKSkJCcnX79+XTYf0RNtbW04jst5GBD6tI/KM2XKFCaTCYdIw0VNTQ2O44pXYQoPD7e2to6Pj8/OziaMqp334uJioVBoZ2cH7QwGQ19fv09HoKqqKi0tDQ7tZSkvL6+pqTl37twPP/wwceJEZbJ4SUlJy5cv//zzz7W0tGbMmPHPf/4Tx3H4THPPnj3Hjx/PzMxsbW19+vSpk5OTo6NjeXm5kkEqDqanXg3PwqtXr5RsZchQa5EyMzOj0+lw0NcTjY2N4L9yRsDn81taWhQ7Z7PZGzZsuHPnDrwCHzt2TG5lxKSkpIMHD2ZlZZmZmSkTLYzTxsZGmcp9QuV97BUajVZbW9tPJ/2ho6MDhqGgDp1OP3PmDIZha9euJWYSqXZM2traAAB79+4l5jq9ePFC8VVQjsjIyPXr18vl+AEAFApFV1fXxcUlKSmpsLBQmUV6eDzet99+W1FRIRQKS0tLv/nmGwDA6NGjX758GRkZuWHDhjlz5rBYLHNz8xMnTlRVVcHbW2VQEIyCXg0TZPCMqBVqLVI0Gm3evHl1dXW//PJL19LXr1+vW7eOz+cDAOS6ZmNjozJfDvTx8aFQKDExMbdv3zY2NhYIBERRXFxcQkLCzZs3R48erWS0165dAwAsWLBAyfrK0599VIBYLO6/k34C/xi9ziR0dHT08/MrKSkJCwuDFtWOia6uLgBALvOo/JTg6urqc+fObd68WUEdS0tLDQ2NwsJCJX0SwNH37NmzS0pKJBKJbMfjcrlaWloq+JQLRnGvFolE4K+5fDVBrUUKABASEkKj0fz8/GQn40IePnxIJpPt7OzYbDbMXkHu3bsnEokmT57cq3MjI6NPPvnk4sWLQUFBvr6+0IjjeEBAQEFBQWpqqtyFWgHV1dUxMTFGRkZr165V8ifKo3gfyWQyHMD2laysLBzHp0+f3h8n/WTUqFEYhimzcHFYWJiNjU1ubi7cVO28Gxsb0+n0vLw81aKNjIz08vLS0tIiLPX19StWrJCtAyUGThroEydOnDA3N3d2doY6+/LlS6KopaXl9evXvfpUEIwyvRqeBZjPUi+GLPulcrLt4sWLTCZz8uTJV65caWxsFIlET58+PX78uKWlJUymfvXVVxQK5ezZs01NTfn5+RMnTjQwMIAPg+QSqCdOnAAA/Pnnn4Rz+HaFvb09YXn48GG3ByoqKgpWEAgEXC63paVFIpFIpdKampqkpCQLCwt9ff3ffvutr3un5NM9BfsIby7++c9/ikSimpoaOHcM5rzXr1/PYDCePXvW3NwsEom8vb05HM7r16/FYvEff/xha2trYmLS0dHRJydXr17lcDjh4eHK7B1Q4umeQCBwcHDoaiQS5wRwZi/xdE+1875p0yYqlRofH9/U1NTZ2VleXg7nOnp4eIwaNer333/vKc7q6moul/vixQtZY3t7u7a2NnyMKBKJHjx4MH36dBaLVVBQ0KvPqVOnPn/+XCwWP3v2zN/fn06n37x5E8dxqVQ6e/ZsfX39W7duCYXCsrKyTz/9lEQi3b59W7FPBcH02qtxHA8JCQEA5OXl9XQECNDTvW4oKyvbsWOHvb09m83W0NDg8/kTJ078+9///ssvv+A4LpVKo6KixowZQ6FQNDU13d3di4uLcRyPj4+HucAxY8aUlpYeP34cZrVNTU0fP35MOJ89e/bJkyeJzYKCgp5OZ1pa2vjx45lMJpVKhfNc4OO8adOmhYaG1tfXq7BrSopUT/uI43h9ff3s2bPpdLq5ufnWrVt37twJALC0tCwrK3vw4IGpqSmDwXj//ferq6u9vb0pFIqhoSGZTOZyuUuWLCktLe2rkwEXKTjoFgqFcDMlJQWOu3V0dOBFSJadO3cSIqXaeX/z5k1AQICJiQmZTNbV1V26dGlhYSGO4+7u7gCA4ODgnuL08/Pz8vLqandzczM3N2ez2TQaTSAQeHp6QoXq1edHH33E5/PJZLKmpqarq6vsTAg4Q83S0pJGo7HZbCKt3qvPnoJR0KuJ37q6uhoaGhLT3BWAROrdYijf3fP29tbS0hqatiDKiFRJSQmZTFZmbtGgIpFIZs6ceerUqXfQJ47jdXV1dDr90KFDylRGUxAQg4gavuxuaWkZGhoaGhra66skg4dEIklNTW1pafH09HzXfEJCQkIcHBzkHnCrCUikEMNPYGDg8uXLPT09lcmgDwZZWVmXLl3KyMhQPGNrRPoEAERHR+fl5V29epVCoQyg24ECidS7wu7du8+cOdPU1GRubn7x4sXhDkee/fv3+/j4HDhwYFhanzt37k8//US8vfhO+bx8+fKbN2+ysrI0NTUH0O0AMqTr7iGGkYiICGVmGA4jLi4uLi4uwx3FO8fixYsXL1483FEoAt1JIRAItQaJFAKBUGuQSCEQCLUGiRQCgVBrhjRxfvfuXfjRZQRBRUUFAGAEH5aYmJgLFy4MdxSIgeTu3bvwlc+hAcOH6muh0dHRg7QCFUJ9gK9f2NvbD3cgiMEFfpdiaNoaOpFCvAvANUiSk5OHOxDEyAHlpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag0QKgUCoNUikEAiEWoNECoFAqDVIpBAIhFqDRAqBQKg1SKQQCIRag+E4PtwxIN5ifvzxx+joaIlEAjfr6uoAADo6OnBTQ0PDz8/v888/H7b4EG8/SKQQ/eLx48fW1tYKKhQXF1tZWQ1ZPIiRDG4b7gAAIABJREFUBxruIfqFlZXVhAkTMAzrWoRh2IQJE5BCIfoJEilEf/n88881NDS62slk8qpVq4Y+HsQIAw33EP2lqqrK2NhYKpXK2TEMKy8vNzQ0HJaoECMGdCeF6C+jR492cnIikf7Sl0gk0owZM5BCIfoPEinEAPDZZ5/JWTAMQw/1EAMCGu4hBoCGhgY9PT2xWExYyGRydXW1trb2MEaFGBmgOynEAKCpqfnRRx8R6XMNDY158+YhhUIMCEikEAODl5cXkTvHcdzLy2t440GMGNBwDzEwCIVCbW3tjo4OAACdTq+rq2OxWMMdFGIkgO6kEAMDk8l0d3enUCgUCsXd3R0pFGKgQCKFGDBWrFghFovFYvGKFSuGOxbEyIEsu1FRUXHnzp3hCgXxtiORSJhMJo7jzc3NycnJwx0O4m3FycnJyMjof9u4DOfPnx++wBAIBAIAAM6fPy+rS+SuNVAqfcSwfPlyAMCFCxeGrMVbt25hGPbBBx8MdkPJyckeHh6or448ur6s3o1IIRAqM3PmzOEOATHSQCKFGEjk3uBDIPoP6lIIBEKtQSKFQCDUGiRSCARCrUEihUAg1Jp3WqQOHTo0atQoDMO+/fZbaLl69SqPx/vXv/7VH7ehoaG2trZcLpdGo1laWu7atau1tbVrtXXr1nE4HAzD8vLyevUpFosjIiIsLS2pVCqfz7ezs3v+/Hl/guyJATkCasiNGzcCAwMvXbpkYWGBYRiGYXLfwHJxceFwOBoaGuPGjXvw4MFQxjZr1iysC2w2GwAQGRlpY2PDYDBYLJaNjU1QUFBzc7MyPsVicXBwsIWFBZVKNTQ03LFjR3t7O1F67ty5qVOncjgcU1PTNWvWVFdXK+NTQTA99fm0tLTIyEhiMSEV6TqZE3+XKCkpAQAcO3YMbqanp3O53LS0tP74dHZ2jo+Pr6+vb25uPn/+PIVCmT9/frc1ExMTAQC5ubm9+nR3d7e2tr57965YLK6qqnJzcysoKOj1V8uWLVu2bFmfgh+QIzAE9KmvBgcHL1q0qLm5GW4KBAL4GZn09HTZahkZGYsXLx7gQJXA2dm56x9z3rx5OI67uroeOnSopqampaUlOTmZQqF89NFHyvjcvHkznU5PTExsbm7+97//zeVyV6xYAYuSkpKg4jQ2Nubm5lpYWDg4OIjF4l59KghGQZ+PjY11dnZuaGhQ8miALpM5kUj9RaQGBFdX187OTmLzk08+AQCUlZV1ramkSCUmJmIYlp+f39dIVBCpwUYoFDo6Ovbfj/J99cCBA1ZWVu3t7YRFIBD89NNPJBLJ0NCwsbGRsA+XSM2bN48QUIi3t3dmZiaO4+7u7rKRw9m5VVVVih2WlpaSSKQNGzYQlr179wIAioqKcByfPXv26NGjpVIpLDpy5AgAIDs7u9c4FQSjuM/7+Pg4Ojoqo4N4dyL1Tg/3BhAcxy9cuHD8+HEAQHp6uuzqKXClTKFQ2PVX3a4E1ZVjx45NmjTJ3t5+gIIdTk6dOlVTUzNkzT158iQoKGjfvn10Ol3W7uTk5OvrW1lZuWPHjiELpieuXbvG4XCIzfLy8ocPH86ZMwcAkJKSIhs5/GZ8t9kDWe7fvy+VSt977z3CMn/+fADA9evXoX8DAwOi7xkbGwMAXrx40WucCoJR3OdDQkLy8vJiY2N7baJb+ixSsbGxLBaLRCJNnjxZT0+PQqGwWKxJkybNnDnT2NiYTqfz+fxdu3YR9f/zn//Y2tryeDw6nW5vbw8P0/fff89mszEM09TUTE1N/e2330xNTTU0NBS/PX/48GE6nT5q1KiNGzcaGBjQ6XQnJ6d79+7BUhzHo6Ojx44dS6PRNDU1lyxZ8ujRo16LZMnOzjYxMcEwDF5bjh49ymKxmEzm5cuXFyxYwOVyjYyM4L0PAEAikURERFhbWzMYDB0dHXNz84iICHgBkaOyspLBYJibm8NIoqKirK2taTQaj8fbuXNnrwdcJBLdvXvXwcGh15r9R/kjoOBc+Pj4UKlUfX196POLL75gsVgYhtXV1fn6+vr7+5eWlmIYZmlpCQC4du0al8vdv3//IO3R4cOHcRx3c3PrWhQeHm5lZXXy5MkbN250Le2pz/TaK4KDg01MTBgMxvjx41V7GfbgwYPbtm3rtqikpITP55uamir2AKfUMhgMwjJmzBgAwJ9//gkAsLCwkL1OwISUhYVFX+NUEIxsnwcAaGpqOjs7x8bG4qq9xiR7W6XkLfRXX30FALh3715bW1tdXR0U6StXrtTW1ra1tfn4+AAA8vLyYOULFy6EhIS8fv26vr5++vTp2tra0F5UVMRkMletWgU3AwMDT5482WvT3t7eLBarqKioo6OjsLAQJv/gXWVwcDCVSj179mxjY2N+fv6kSZN0dHSqq6sVF8kN98rLywEAcXFxcHPPnj0AgMzMzKamppqampkzZ7JYLJFIhOP4/v37NTQ0Ll++LBQKf//9dz09vVmzZnUNuK2tjcPh+Pj4EA4xDPvmm28aGhqEQmF8fDzobbj37NkzAICDg8OsWbP09fVpNJqNjc2RI0eI23UFqDDcU/4IKDgXK1eu1NPTI3xGRUUBAGpra3EcX7p0qUAgIIrS09M5HE5oaGifgsSV7qsWFha2trZyRoFA8OzZMxzH79y5QyKRzMzMWltb8b8O9xT0GQXHZMeOHTQa7eLFiw0NDbt37yaRSPfv3+/TflVUVNja2kokElmjSCSqqKiIi4uj0Whnz57t1Ul+fj4AICgoiLB0dnYCANzd3XEcz8rKolAohw8fbm5ufvjw4dixY2H+S0l6DUauz0MCAwN77eoQMCA5KShSLS0tcPOHH34AABB53F9//RUAkJSU1PWHERERAICamhq4+d133wEAEhISzp075+fn12u7OI57e3vzeDxi8/79+wCAffv2CYVCNpvt6elJFMEwQkNDFRThyokUMQ6HmvLkyRMcx6dOnTpt2jTC54YNG0gk0ps3b+QC3rNnj5WVFcw4CIVCJpMpm/hUJidVUFAAAPjoo49++eWX+vr6xsbGL7/8Eh63Xg/XQIlUt0egp3OB90WkVEaZvtra2oph2KJFi+TshEjhOO7v7w8A2LJlCy4jUor7TE/HpL29nclkEr8SCoU0Gm3z5s192q8tW7Z0zZDq6ekBALS1tf/xj39ANeyV+fPna2lpZWZmtre3v3z5Mjk5GcOwv/3tb7AUpqggRkZG5eXlykfYazCyfZ7g9OnTAIAff/yxV/9dRWoAclJUKhUAAKUaAEChUAAAsguHEMAi4nnkhg0bli1btnHjxuTk5K+//lqFpqdMmcJkMh89elRYWNja2jplyhSiaOrUqVQq9d69ewqK+toc3FO4ax0dHbjMvatEIqFQKHIL+aakpCQnJ1+/fh1mHJ48eSIUCufOndunRmk0GgBg3LhxTk5OWlpaPB5v3759PB4P5r+GGNkjIAdxLoY8qB6Bl0Mmk6mgTnh4uLW1dXx8fHZ2NmHsU58hjklxcbFQKLSzs4N2BoOhr6/fpwNSVVWVlpa2evVqOXt5eXlNTc25c+d++OGHiRMnKpPUS0pKWr58+eeff66lpTVjxox//vOfOI7DZ5p79uw5fvx4ZmZma2vr06dPnZycHB0d4cVJGRQHI9fnCeBZePXqlZKtyDLoifMrV67MmjVLV1eXRqPJ5qog+/fvb21t7U8mlUaj1dbWNjY2AgDg1BICPp/f0tKioEjlRgEACxcu/P333y9fvtze3v7bb7+lpqb+7W9/kxWppKSkgwcPZmVlmZmZQUtFRQUAQFdXt08NGRgYAADq6uoIC5VKNTU1LS0t7U/8gwE8F8Mdxf+AH1yHKt8TdDr9zJkzGIatXbuWmEmkWp9pa2sDAOzdu5eY6/TixYtuH5j0RGRk5Pr16+Vy/AAACoWiq6vr4uKSlJRUWFgIRySK4fF43377bUVFhVAoLC0t/eabbwAAo0ePfvnyZWRk5IYNG+bMmcNisczNzU+cOFFVVQXvdpVBQTBd+zwBTJDBM9JXBlekysrK3N3d9fX1792719TUFBkZKVsqFou3bdsWHR2dk5MTHh6ugn+xWNzY2GhkZMTn8wEAcn2o1yIVWiQICQmZM2fO6tWruVzuxx9//Mknn5w4cYIojYuLS0hIuHnz5ujRowkj7Hxv3rzpU0NsNnvMmDFFRUWyxs7OTh6P15/4BxziXAx3IP8D/jF6nUno6Ojo5+dXUlISFhYGLar1GXj5iYmJkR2q5OTkKBltdXX1uXPnNm/erKCOpaWlhoZGYWGhkj4J4GB89uzZJSUlEolEtltyuVwtLS0VfMoF022fJxCJROCvuXzlGVyRKigoEIvFmzdvtrCwoNPpck/ct27dun79+u3bt/v5+YWFhSl/OgmysrJwHJ8+fbqdnR2bzf7tt9+Ionv37olEosmTJyso6s+uFRYWlpaW1tbWisXisrKyo0ePampqAgBwHA8ICCgoKEhNTZW7FNvZ2ZFIpFu3bvW1LQ8Pj9zc3KdPn8JNoVD44sULdZuRQJwLAACZTO52SDjEwNcJmpqaeq0ZFhZmY2OTm5sLN1XrM/DptjLvD3RLZGSkl5eXlpYWYamvr5d73g0lBk4a6BMnTpwwNzd3dnaGOvvy5UuiqKWl5fXr1736VBCMgj5P8P/Ze9OAKI6t8bt6YPYdWSTAsMygiJpLjBoh+hc1ooaIohIx6pUkRowLAoI8iHAREUUIEgnqjaJJREEQLhoi6gPGq7glJhAQoiLKpkEWQZYBGYZ+P9SbfjoDDs2wTbB+n+iq6lOnqoszXdWn6sCnANez+srgGimJRAIAyM7Obm9vLykpIU/p4+PjTUxMlixZAgCIiIiwtbVduXIlFZf/rq6uhoaGzs7OgoICb29viUTi4eHBYrG2bt2anp6emJjY1NRUWFj4+eefGxsbe3p6qsnqT9M2bdokkUi6e6wUFxfv27fvyJEjdDqdvMshOjrawMBg6dKlZ86cSUhIaGpqKigooLiu5Ovra25u7uHhUVFRUV9fHxAQ0NbWBpfPh5cenwUAQCaTPX/+PCMjQ6FQ1NbWkn1w9PT0nj59WlZW1tzcrFAosrKyBs8FgcPhWFlZwVm2euCkj5itazZmWCzWxx9/nJSUdPDgwaamJqVSWVVVBc2Bu7u7kZGRmt02z549O3bsmI+PDzmRy+VeunTp8uXLTU1NCoUiLy9vzZo1XC7X19e3V5lTp04tLy/v7OwsKyvz8/PLzs5OSEhgMBiWlpazZs06cuTI1atX29raKisrYaM+/fRT9TLVKKNmzBO3w6eg4S8r+dWUyheT2NhYuAZmYWFx7dq1vXv3wnmHkZHRyZMnk5OTobEUi8VJSUnQxOrp6YlEIjc3N+h9I5VK7ezsMAzT09O7ceMGjuM+Pj7Qs0MoFN65c0dN7Z6ennQ63cTERFdXVyAQLF68uLS0FGZ1dXVFRUVZW1vT6XSxWOzq6nr//n31WV988QXUlsvlLlmyJC4uDnr3cDgcFxeX+Ph42FJra+vS0tKvv/5aIBAAAMzNzR88eHD58mVyhF46nT5u3Li0tDT4Ma47UVFROI43NzevXbt21KhRPB5v+vTpISEhAABTU9PffvtNfbdXVlauWLFCLBYzmcypU6dmZWWpLw/p69e9PvWAmmdRX18/a9YsFotlaWm5efNm6A4mk8kqKip+/fVXc3NzNps9ffr06urq8+fP8/n88PBw6kpCKH6J9vLyotPpcrkcXqanp0ulUgCAvr4+/KJHxt/fn3BBeNWYUd8nL1++DAgIkEgkurq68DepqKgIx3FXV1cAQEhIyKv09PX1XbVqVfd0FxcXS0tLHo/HZDKlUqm7uzvxGV29zLlz54pEIl1dXbFY7OzsTPaEgA5rMpmMyWTyeDxiWb1Xma9SRv2Yhzg7O5uYmFDxmwF/920xnp6eenp6w60FjuN4fHy8t7c3cfny5UsfHx8mk0n8P2gDg7otZnifBcWxWlJSoqurS8W3aFBRKpUzZsxISEh4DWXiOF5XV8disaKjo6kU7m6k/n7bYvq7o3ogqK6u9vLygm/IEAaDIZFIYNS5YVRsiNGGZ6EemUwWFhYWFhbW61aSwUOpVGZkZDQ3N7u7u79uMiGhoaF2dnbQzVsDtMtI3bt3r/uZFQQD3ncaw2az6XR6QkLCs2fP4LEER48eDQkJcXd3hy//feLv0uq/KYGBgW5ubu7u7lRW0AeDK1eupKWlZWVlqffYGpEyAQAxMTH5+fnnz5+HbpKaQH6t0vLpXmBgIHScs7CwSE1NHV5lrl69+t577wkEAh0dHaFQ6ODgEB8fT3Gf95AxeNO9YX8WfR2rFy9eDAgIGDx9ED2SkZERERFBPiChV0C36R6Gk9ymUSyzEcbQx90bMtBYHalgGHb69GnyXn3tmu4hEAiECshIIRAIrQYZKQQCodUgI4VAILSaHsKsw9VWxAjg1q1bYIQ+ULjNYkQ2DaECepNCIBBaTQ9vUiPyi/XryYh3QRiRTXvN6R6dBL1JIRAIrQYZKQQCodUgI4VAILQaZKQQCIRWg4wUAoHQagbYSKWlpVlZWcEzRoKDg3ssExMTg2EYjUazsbG5evVqP2vBMAyeD7ly5UoYoFUzoqOj4ZHYhw8fhinnz58XCoXff/+9xjIBAGFhYba2tgKBgMlkymSybdu2wYONVJqAYRiDwTA0NHR0dIyKimpoaOhPpYjuZGdnBwYGkrt99erV5AJOTk58Pl9HR2f8+PFqzvkdDCIjI21sbNhsNpfLtbGxCQ4OJp+jrVAoIiIiZDIZg8EQiUQTJkwoKyvrk/z29nYbGxsi1p5CoQgJCbGysmIwGCYmJn5+fkSYHI31fNU4P3fuXGRkZH/PHSMfiTBQR7XAE1pHjx7dPXZgZ2cnjMs8Z86c/tcCg1O2tLScO3dOIpHweLx79+5pLFAlUGhmZqZAIDh37lx/lJw5c2Z8fHx9fX1TU9Pp06fpdPr8+fO7NwEeFv7jjz96eHhgGGZsbNzXyLc9Mqgncw4vfRqrISEhCxcuJMJVSqVSePRzZmYmuRg5gvFQ4uzsHB0dXVNT09zcnJKSQqfTyRFkXV1dx44de+vWLXhymYuLC3GCMEXgmehBQUHwcsOGDSwWKykpqamp6ccffxQIBB999FE/9VQzzmNjY2fOnNnQ0EBRWzA0xwdLpVIYVyMlJUUl6/Tp0w4ODgNrpCD/+c9/AAAbN27UWKCKkRoQnJ2dyYfpwAMoYCxyvFsTIKmpqTQazdDQsLGxsZ+1D6qRksvl9vb2wyWE+ljds2fPmDFjiIDDOI5LpdKTJ0/SaDQTExNyJw+XkXJ1dSWrB73bnj59iuN4UlIShmEFBQUaC79+/bqTkxNhpEpLS2k02rp164gC8A2ruLi4P3qqH+deXl729vYUT1vrbqQGa00Khg87dOiQSnpMTAwMbD3gTJ06FQBw9+7dwRDeJ3AcT01NhZFgMjMzyRFD9fX1AQDqA0YuW7bMw8OjpqaGmHhqJwkJCf2J6jqAQtTw8OHD4ODgnTt3qkTcdHBw8Pb2fvLkiZ+f3+DVTpH09HSyeiYmJgAAOF06dOjQpEmTNA5f1tbW5u/vHxsbS6T8/PPPXV1d77zzDpEyf/58AMDFixf7o6f6cR4aGpqfn09Wo08MlpGaPXv2uHHjfvzxx/v37xOJ169fl8vl0K6TuXbtmq2trVAoZLFYEydOhP31zTff8Hg8DMPEYnFGRsadO3fMzc11dHRUgn8RwDjvMFwtjuMxMTHjxo1jMplisXjx4sVEtGs1WWRyc3MlEgmGYTDCzcGDB7lcLofDOXv27IIFCwQCgampaVJSEiysVCojIiLGjh3LZrP19fUtLS0jIiLIp3YRPHnyhM1mW1paqu89GBgqKytLfbGB4lV94uXlxWAwYPwYAMDGjRu5XC6GYTDcyNatW0tLSzEMk8lkBw4cYLFYhoaG69evNzY2ZrFYDg4OMIIZdSEAgAsXLgxshKsDBw7gOO7i4tI9Kzw8fMyYMUePHs3OzqbeJ72OhJCQEIlEwmaz33zzTfi611dKSkpEIpG5uXlHR8etW7fs7Ow0EAIJCgrauHEjOWg2DMtEDtJpbW0NANBgPZfQs3uWyjgXi8UzZ86MjY3FNTukkPxaNYDTvcePH3/55ZcAAHJIFVdX1+PHj8PAsOTpXmpqamho6PPnz+vr66dNmzZq1CiYXlxczOFw1qxZAy8DAwOPHj1KroU8Vzpx4gQAwN/fH8fxkJAQBoNx4sSJxsbGgoKCSZMm6evrV1dXq89Sme5VVlYCAOLi4uBlUFAQACAnJ+fFixc1NTUzZszgcrlw0W337t06Ojpnz56Vy+W//PKLkZGRo6Nj925pbW3l8/leXl6vagIBXI+EYRf7A8Xpnpo+WblypZGREVESBuOura3FcXzp0qVSqZTI8vT05HK5xcXF7e3tRUVFU6ZM4fP58IWfupDMzEw+nx8WFtarzhTHqpWVla2trUoiHJ84jt+4cYNGo1lYWLS0tOB/ne6p6RM1I8HPz4/JZJ45c6ahoWH79u00Go362mJHR0dVVVVcXByTyYThbR4/fgwAsLOzc3R0HD16NJPJtLGx+eqrr6gEhsJxPDc318XFBcfx2tpa8Od0r6CgAAAQHBxMFIO/7q6urhrrqUL3cY7jeGBgIAAgLy+vV/lgyNakHj9+3NjYyOVyxWIxjPJUWlpqamr68uXL7kaKDAwtX1NTAy///e9/AwASExNPnTrl6+urUguxcH7mzBkjIyNDQ8Oqqiq5XM7j8dzd3YmSP/30EwAgLCxMTRZOzUgRc/L4+HgAwMOHD3EcnzJlytSpUwmZ69ato9FoL1++VGlaUFDQmDFjiOVb/NVGCsdxDMNEIlGPWdShYqTU90mfjBS5LTCu986dO/skhDpUxmpLSwuGYQsXLlRJJ4wUjuNw8QHG4COMlPo+edVIaGtr43A4xF1yuZzJZG7YsIFii2AIyFGjRn355ZfQ5MF4dnPnzr1+/Xp9fX1jYyOMCJuYmNirNLlcPnny5KqqKvyvRgrH8fnz5+vp6eXk5LS1tf3xxx8pKSkYhn3wwQca66lC93GO4/ixY8cAAN99912v8rsbqUH0kxIKhR999FFDQ0NycjIAYP/+/Rs2bICn96sBhpQgvlmuW7du2bJl69evT0lJ2bdvn0rhFy9eYBgmFAq3bNny/vvv//TTTyYmJkVFRS0tLZMnTyaKTZkyhcFg3L59W01WX1sHGwIDWLW3t+Ok91ilUkmn08lTdABAenp6SkrKxYsX+Xx+r8JbW1txHNcg8IwGDGCfkJk8eTKHw+lxKj1kwJ869bFPwsPDx44dGx8fn5ubSyT2qU+IkXD//n25XD5hwgSYzmazR48eTb0HKisra2pqTp069e2337711ls1NTVw7WL8+PEODg56enpCoXDnzp1CoZBK4Ovt27evW7cOLhupkJyc7Obm9s9//lNPT4+IDEqOdNtXPcm5rxrn8Ck8e/aMYi1kBteZEy6fHz58uLGxMTU1df369T0W++GHHxwdHQ0MDJhM5rZt21Ryd+/e3dLS0uPyKvzp7uzsrKqqOnbsGJweNzY2AgBUYtKLRKLm5mY1WZo3EoD333//l19+OXv2bFtb2507dzIyMj744AOykUpOTt67d++VK1csLCyoCHzw4AEAwMbGpj9aUWSQ+gQAwGQy4W/4cNHe3g7+XKZ8FTDAOoZhn3zyCeEupFmftLa2AgB27NhB+L6Vl5er/0hChk6nGxgYODk5JScnFxUVRUREGBsbAwDq6uqIMgwGw9zcvLS0VL2o3NzcwsLCtWvX9pgrFAoPHz4M5xylpaVffPEFAOCNN97QWE8iS804h6tg8In0lcE1UnZ2dtOmTfvpp588PT3d3NzEYnH3MhUVFa6urqNHj759+/aLFy8iIyPJuQqFYsuWLTExMTdv3gwPD6dSqUgkAgCojKfGxkZTU1M1WX1tGpnQ0NDZs2d7eHgIBIIlS5Z8+OGHR44cIXLj4uISExMvX75MfRxcuHABALBgwYL+aEWRQeoThULRfyH9BP5j9OpJaG9v7+vrW1JSsmvXLpiiWZ/A9en9+/eTpyo3b97sq9oymUxHR6eoqIjH41lbWxcXF5NzOzs7hUKhegkJCQk5OTk0Gg3aSqjY7t27MQy7c+eOSmE4MZ81a5bGesJL9eO8o6MD/HXBnjqDvi0GvkydOXPGx8enxwKFhYUKhWLDhg1WVlYsFkvlNJnNmzd/9tlnPj4+vr6+u3btovLIJ0yYwOPxyA/j9u3bHR0db7/9tposDZsHAACgqKiotLS0trZWoVBUVFQcPHgQmmMcxwMCAgoLCzMyMlR+ltVQXV29f/9+U1PTTz75pD9aUUR9n+jq6moWk/nKlSs4jk+bNq0/QvoJ3EJAJSborl27bGxs8vLy4KVm48TMzIzFYuXn5/dJyfr6epUP1iUlJUql0szMDACwfPnyvLy8R48ewSy5XF5eXt6rR8Lx48fJhpK8JkWew0KOHDliaWk5c+ZMjfWkMs7hU4DrWX1l0I3Uhx9+qK+v7+rqamVl1WMBiUQCAMjOzm5vby8pKSFP++Pj401MTJYsWQIAiIiIsLW1XblyJXnHQI+wWKytW7emp6cnJiY2NTUVFhZ+/vnnxsbGnp6earL608ZNmzZJJJLugbyLi4v37dt35MgROp1O3gETHR1NlMFxvKWlBX6vqa2tPX369Lvvvqujo5ORkTE0a1Lq+0Qmkz1//jwjI0OhUNTW1paXlxM36unpPX36tKysrLm5Gdog6Dff2dlZUFDg7e0tkUigLwV1IVlZWQPogsDhcKysrOBBw712wvHjx4kZumbjhMViffzxx0lJSQcPHmxqalIqlVVVVX/88QcAwN3d3cjIqMfdNlwu99KlS5cvX25qalIoFHl5eWvWrOFyudAOSerLAAAgAElEQVRN3NfX19zc3MPDo6Kior6+PiAgoK2tDS6fq5GpnqlTp5aXl3d2dpaVlfn5+WVnZyckJMCVNc30pDLO4VPQ0OGLbHH7/3UvPT0d7onR19eHX0xwHN+2bduNGzfg3zt27IAuMzQazdbW9tq1a9AM6+npiUQiNzc36JcklUrt7OwwDNPT04P3+vj4QBcPoVB44MCBMWPGQP2NjY3d3NxU1Ojq6oqKirK2tqbT6WKx2NXV9f79++qzvvjiC2jmuVzukiVL4uLioJ4cDsfFxSU+Ph6u/FlbW5eWln799dfQgpibmz948ODy5cvkdUc6nT5u3Li0tDT4daY7UVFR586de/PNNzkcDoPBgO2Cn/OmTp0aFhZWX1/fn6dAQNEFQU131dfXz5o1i8ViWVpabt682d/fHwAgk8kqKip+/fVXc3NzNps9ffr06upqT09PuIlSV1dXIBAsXry4tLS0r0LOnz/P5/PDw8N71ZniWPXy8qLT6fD7Mv6K8Ung7+9PuCC8qk/Uj4SXL18GBARIJBJdXV0DA4OlS5cWFRXhOO7q6goACAkJ6VFJFxcXS0tLHo/HZDKlUqm7uzt540tlZeWKFSvEYjGTyZw6dWpWVhZMVy+TjMrXvblz54pEIl1dXbFY7OzsTHaS0ExPNeOcuNfZ2dnExISK8wQYGheE1434+HiyO9jLly99fHyYTCbxvzFcDOXePU9PTz09vaGpC6c8VktKSnR1dXt05xlKlErljBkzEhISXkOZOI7X1dWxWKzo6GgqhbsbKXRUS3+prq728vL69NNPiRQGgyGRSBQKxbAsxAwj/d3sPgjIZLKwsLCwsLDuk/EhQ6lUZmRkNDc3u7u7v24yIaGhoXZ2dl5eXprdjoxUf2Gz2XQ6PSEh4dmzZ3Cf+tGjR0NCQtzd3YdmUQmhnsDAQDc3N3d3dyor6IPBlStX0tLSsrKy1HtsjUiZAICYmJj8/Pzz589DF0hNIL9WoemeZly9evW9994TCAQ6OjpCodDBwSE+Pp7inu9BZcime4GBgXDl1cLCIjU1dQhq7OtYvXjxYkBAwODpg+iRjIyMiIgI8gEJvQK6Tfd6CGmF6CszZsz43//93+HWYjiJiIggO/VpIU5OTt13tiMGm0WLFi1atKifQtB0D4FAaDXISCEQCK0GGSkEAqHVICOFQCC0GmSkEAiEVtPD1z2VLb6Ivzsj+IGO4KYhCDCcdFpbVVXVjRs3hlEbxN+d/fv3AwBedeIFAkEFBwcH8qk4fzFSCEQ/geEnUlJShlsRxMgBrUkhEAitBhkpBAKh1SAjhUAgtBpkpBAIhFaDjBQCgdBqkJFCIBBaDTJSCARCq0FGCoFAaDXISCEQCK0GGSkEAqHVICOFQCC0GmSkEAiEVoOMFAKB0GqQkUIgEFoNMlIIBEKrQUYKgUBoNchIIRAIrQYZKQQCodUgI4VAILQaZKQQCIRWg4wUAoHQapCRQiAQWg0yUggEQqtBRgqBQGg1yEghEAitBhkpBAKh1SAjhUAgtBpkpBAIhFaDjBQCgdBqkJFCIBBaDTJSCARCq0FGCoFAaDW6w60A4u9NXV1dU1MTcdna2goAePToEZEiEAj09fWHQTPESAHDcXy4dUD8jTl+/Pgnn3yipsCxY8c+/vjjIdMHMfJARgrRL168eGFgYKBQKHrMpdPptbW1QqFwiLVCjCTQmhSiXwiFwvfff19Xt4d1A11dXWdnZ2ShEP0EGSlEf1m1apVSqeye3tXVtWrVqqHXBzHCQNM9RH9pb2/X19eHS+ZkOBxOXV0dm80eFq0QIwb0JoXoLywWa8mSJXQ6nZxIp9OXLVuGLBSi/yAjhRgAPvroI5W1c4VC8dFHHw2XPoiRBJruIQaAzs5OIyOj58+fEykikai2trbHBXUEok+gNynEAKCrq7tixQpixken01etWoUsFGJAQEYKMTCsWLGCmPEpFIoVK1YMrz6IEQOa7iEGBhzHzczMnjx5AgAwNjZ+8uQJhmHDrRRiJIDepBADA4Zhq1evZjAYDAZjzZo1yEIhBgr0JoUYMAoKCv7xj3/APyZOnDjc6iBGCEO3tBkTE3Pz5s0hqw4xLPB4PABAWFjYcCuCGFzs7e19fX2Hpq6hm+7dvHnz1q1bQ1bd34WqqqozZ84MtxYDhrm5uYWFBXF55syZqqqq4VMHMSjcunVrKF84hvQj8bRp01JTU4eyRu0nJSVl+fLlI6Zb4ElSVlZW8BLDMB8fnw8//HBYlUIMMG5ubkNZHfJkQQwkhHlCIAYK9HUPgUBoNchIIRAIrQYZKQQCodUgI4VAILSa19dIhYWF2draCgQCJpMpk8m2bdvW0tLSvdjatWv5fD6GYfn5+eoFOjo6Yt2AfkMDzvnz54VC4ffffz8YwoeL7OzswMDAtLQ0Kysr2HurV68mF3BycuLz+To6OuPHj//111+HUrfIyEgbGxs2m83lcm1sbIKDg8kxchQKRUREhEwmYzAYIpFowoQJZWVlfZLf3t5uY2OzY8cOQmBISIiVlRWDwTAxMfHz82tra+unnq8a8OfOnYuMjOzxbFVtAR8qli1btmzZsiGrrldmzpwZHx9fX1/f1NR0+vRpOp0+f/78HksmJSUBAPLy8noV2L17582bp/6u06dPa/AUMjMzBQLBuXPn+nrjEAMAOH36NJWSISEhCxcubGpqgpdSqXTUqFEAgMzMTHKxrKysRYsWDbyiveHs7BwdHV1TU9Pc3JySkkKn0+fOnUvkurq6jh079tatWwqF4unTpy4uLoWFhX2SDx0jg4KC4OWGDRtYLFZSUlJTU9OPP/4oEAg++uijfuqpZsDHxsbOnDmzoaGBorZD/L/8+hopZ2fnzs5O4hL68lRUVHQvSdFIzZs3j/gfg3h6eubk5Ki/SzMjNdjI5XJ7e/v+y6FopPbs2TNmzJi2tjYiRSqVnjx5kkajmZiYNDY2EunDZaRcXV3J6kFHoadPn+I4npSUhGFYQUGBxsKvX7/u5OREGKnS0lIajbZu3TqiAHzDKi4u7o+e6ge8l5eXvb29QqGgovAQ/y+/vtO9zMxMHR0d4hIGsJTL5d1LUtwre+HCBT6fT1xWVlbevXt39uzZ/dZ0GEhISKipqRmauh4+fBgcHLxz504Wi0VOd3Bw8Pb2fvLkiZ+f39Booob09HSyeiYmJgAAOF06dOjQpEmTNN6r2NbW5u/vHxsbS6T8/PPPXV1d77zzDpEyf/58AMDFixf7o6f6AR8aGpqfn09WQ3vQRiN14sSJyZMns1gsLpdrYWGxa9cuAACO4zExMePGjWMymWKxePHixffu3QMAHDx4kMvlcjics2fPLliwQCAQmJqawnefcePGYRhGo9Hefvtt+DC2bdsmFApZLNY333yjUumTJ0/YbLalpSWsKyoqauzYsUwmUygU+vv7a9CKvXv3btmypX890TO5ubkSiQTDsK+++gqo7YEDBw6wWCxDQ8P169cbGxuzWCwHB4fbt28DALy8vBgMxujRo6HMjRs3crlcDMPq6uq8vb23bt1aWlqKYZhMJgMAXLhwQSAQ7N69ezCac+DAARzHXVxcumeFh4ePGTPm6NGj2dnZ3XM1GBIAAKVSGRISIpFI2Gz2m2++Cd9k+0pJSYlIJDI3N+/o6Lh165adnZ0GQiBBQUEbN240MDAgUmg0GgCAfDy8tbU1AOD333/XWM/uWeQBDwAQi8UzZ86MjY3FtfDEgSF7Z6P4irh//34AwJ49e+rr658/f/7vf/975cqVOI6HhIQwGIwTJ040NjYWFBRMmjRJX1+/uroax/GgoCAAQE5OzosXL2pqambMmMHlcjs6Ojo7Oy0sLCQSCfkt18fHZ//+/SqVtra28vl8Ly8veBkUFIRh2BdffNHQ0CCXy+Pj4wGF6R6ZqqoqW1tbpVLZa0nNpnuVlZUAgLi4OELhHnsAx3FPT08ul1tcXNze3l5UVDRlyhQ+nw9f8leuXGlkZETIjIqKAgDU1tbiOL506VKpVEpkZWZm8vn8sLCwvuoJKEz3rKysbG1tVRKlUunjx49xHL9x4waNRrOwsGhpacH/Ot3TYEjgOO7n58dkMs+cOdPQ0LB9+3Yajfbzzz9TbE5HR0dVVVVcXByTyTxx4gSO448fPwYA2NnZOTo6jh49mslk2tjYfPXVV11dXVQE5ubmuri44DheW1sL/pzuFRQUAACCg4OJYp2dnQAAV1dXjfVUQWXAQwIDAymO89d6Taqjo0MkEs2aNYtI6ezsjI2NlcvlPB7P3d2dSP/pp58AAPDfBo5IYioObcrDhw/xP01eSkoKzGptbZVIJC9evFCpNygoaMyYMXBFSS6Xczgc8rIoxTUpMps2bTp06BCVkgNopHrsAU9PT6FQSNz4888/AwB27tyJ98VIaUyvRqqlpQXDsIULF6qkE0YKx/GtW7cCADZt2oSTjJRmQ6KtrY3D4RB3yeVyJpO5YcMGis0xMjICAIwaNerLL7+EJq+wsBAAMHfu3OvXr9fX1zc2Nv7P//wPACAxMbFXaXK5fPLkyVVVVfhfjRSO4/Pnz9fT08vJyWlra/vjjz9SUlIwDPvggw801lMF8oAnOHbsGADgu+++61X+a70mVVBQ0NjYOG/ePCJFR0dny5YtRUVFLS0tkydPJtKnTJnCYDDgzEUFBoMBAIBH2a5du1YoFBIz7cTExMWLFwsEAnL59PT0lJSUixcvwhWlhw8fyuXyOXPmaNyKp0+fnjt3zsPDQ2MJ/YTcAypMnjyZw+HAaZE2UFNTg+M4h8NRUyY8PHzs2LHx8fG5ublEomZD4v79+3K5fMKECTCdzWaPHj2aem9UVlbW1NScOnXq22+/feutt2pqaphMJgBg/PjxDg4Oenp6QqFw586dQqHw66+/7lXa9u3b161bB5eNVEhOTnZzc/vnP/+pp6f37rvv/uc//8FxHH7u1ExPcq7KgCeAT+HZs2cUaxkytMtIQZ8OkUikkt7Y2Aj+PKuIQCQSNTc3qxfI4/HWrVt348YN+DN76NAhLy8vcoHk5OS9e/deuXKFOGAEHi1CXiPoK5GRkZ999pnKMrD2wGQy4e+2NtDe3g4AgP/qr4LFYh0/fhzDsE8++YRwF9JsSMAIpjt27CAc2crLy3v8WtIjdDrdwMDAyckpOTm5qKgoIiLC2NgYAFBXV0eUYTAY5ubmpaWl6kXl5uYWFhauXbu2x1yhUHj48OGqqiq5XF5aWvrFF18AAN544w2N9SSyug94ArgKBp+IVqFdRgo+BvIjh0CzpTL+GhsbTU1Ne5Xp5eVFp9P3799/9epVMzMzqVRKZMXFxSUmJl6+fJn8+KFxefnypWZNqK6uPnXq1IYNGzS7fbBRKBQU+21ogP8YvXoSwiPWSkpK4FcUoOmQgL89KouSGhyNJJPJdHR0ioqKeDyetbV1cXExObezs1MoFKqXkJCQkJOTQ6PRoK2Eiu3evRvDsDt37qgUhpP0WbNmaawnvOxxwBN0dHSAvy7YawnaZaQsLCz09PQuXbqkkj5hwgQej0d+eLdv3+7o6Hj77bd7lWlqavrhhx+eOXMmODjY29sbJuI4HhAQUFhYmJGRofJrPGHCBBqN9t///lezJkRGRq5atUpPT0+z2webK1eu4Dg+bdo0AICurm6PU8KhxNDQEMOwFy9e9Fpy165dNjY2eXl58FKzIWFmZsZisXrdPKBCfX29SqDTkpISpVJpZmYGAFi+fHleXh48SAsAIJfLy8vLe/VIOH78ONlQktekyHNYyJEjRywtLXv0Fqaop5oBTwCfAlzP0iq0y0gxmczt27dfvXrVy8vryZMnXV1dzc3NxcXFLBZr69at6enpiYmJTU1NhYWFn3/+ubGxsaenJxWxW7du7ezsbGhoILyWiouL9+3bd+TIETqdTt7FEh0dbWBgsHTp0jNnziQkJDQ1NRUUFFBZX4A8e/bs2LFjPj4+GrZ/cOjq6mpoaOjs7CwoKPD29pZIJHC9TCaTPX/+PCMjQ6FQ1NbWlpeXE7fo6ek9ffq0rKysublZoVBkZWUNkgsCh8OxsrKicnonnPQRnj6aDQkWi/Xxxx8nJSUdPHiwqalJqVRWVVX98ccfAAB3d3cjI6Med9twudxLly5dvny5qalJoVDk5eWtWbOGy+VCN3FfX19zc3MPD4+Kior6+vqAgIC2tja4fK5GpnqmTp1aXl7e2dlZVlbm5+eXnZ2dkJAAV9Y001PNgCduh09BGw+nH6IF+r58Efjqq68mTpzIYrFYLNZbb70VHx+P43hXV1dUVJS1tTWdTheLxa6urvfv38dxPD4+Hi74WVtbl5aWfv3113Bd3Nzc/MGDB4TMWbNmHT16lLiEH2W6ExUVheN4c3Pz2rVrR40axePxpk+fHhISAgAwNTX97bff1Gvu6+u7atWqPnWLBl/34uLioH8Th8NxcXFR3wOenp50Ot3ExERXV1cgECxevLi0tBTKqa+vnzVrFovFsrS03Lx5M3QHk8lkFRUVv/76q7m5OZvNnj59enV19fnz5/l8fnh4eJ/0xKm5IMD5uFwuh5fp6elwSq6vrw+/6JHx9/cnXBA0GxIvX74MCAiQSCS6urrwB6moqAjHcVdXVwBASEhIj0q6uLhYWlryeDwmkymVSt3d3ckbXyorK1esWCEWi5lM5tSpU7OysmC6eplkVL7uzZ07VyQS6erqisViZ2dnspOEZnqqH/AQZ2dnExMTKs4Tr7ULwmvIYG+L8fT01NPTGzz56qFipEpKSnR1dXt05xlKlErljBkzEhISXkOZOI7X1dWxWKzo6GgqhV9rFwTEYKDVG9wBkMlkYWFhYWFhPZ5CMTQolcqMjIzm5mZ3d/fXTSYkNDTUzs5O5du3loCMFFXu3bvX/SQWggEfNK8VgYGBbm5u7u7uVFbQB4MrV66kpaVlZWWp99gakTIBADExMfn5+efPn6fT6QModqBAgRioYmNjg2vhtia1bN++/fjx4x0dHZaWllFRUcuWLRtujV7J7t27L126tGfPnr179w597XPmzOmP++7fWubZs2dfvnx55coV8vZjrQIZqZFMREQE2ZFPy3FycoInliCGkkWLFi1atGi4tVAHmu4hEAitBhkpBAKh1SAjhUAgtBpkpBAIhFaDjBQCgdBuhsxtVJu/fyMQiD4xlB7nQ+qCMG3aNG3bfDvs3Lx5MzY2VrOTtrWf5cuXe3t729vbD7ciiIEEHng7ZAypkYKnpgxljX8LYmNjR2q3LF++3N7efqS27rUlNTV1KKtDa1IIBEKrQUYKgUBoNchIIRAIrQYZKQQCodUgI4VAILSav4eRun///ubNm8ePH8/n83V1dYVC4ZgxY5ydnTWI80GFsLAwW1tbgUDAZDJlMtm2bdvgeWxpaWlWVlbkY6QYDIahoaGjo2NUVFRDQ8NgKPP6kJ2dHRgYSO7k1atXkws4OTnx+XwdHZ3x48drcHB4f4iMjLSxsWGz2Vwu18bGJjg4GIZfU6G9vd3GxmbHjh19la9yo6OjY/czy14VQEEFhUIREREhk8kYDIZIJJowYUJZWZma6s6dOxcZGanNJyP+DYxUQkLCxIkTCwoKYmJiKisrW1tb8/Lydu3a1djY+KqTm/vJ5cuXN23aVFZWVldXFxERERsb6+bmBgBYunTpo0ePpFIpjAnc1dVVU1OTkpJiaWkZEBAwfvz47sGIEBT517/+deDAge3btxOdPGrUqMTExB9++IEoc+nSpdTU1IULFxYVFU2aNGko1bt27dpnn31WUVHx7NmzXbt2RUZG9uicHBQUdP/+fQ3kU7lx+vTpVEQtX778u+++O3nypFwu//3336VSafcjT8nVubi4sFisOXPmwFCG2siQuY1qdi7yzZs3dXR0Zs+erVAoVLIuXLhAxBkfWJydnTs7O4lL6OZTUVEBLwkjRSY1NZVGoxkaGjY2NvaprkE941wul9vb2w+jEEDhjHMcx/fs2TNmzBgiKjqO41Kp9OTJkzQazcTEhNylRJj1IcbV1ZWsHvzRevr0KbnM9evX4XlYRDwFinS/cd68eSox0D09PXNycnoVlZSUhGFYQUFBn6rDcdzLy8ve3r77f1mPoDPO/0J4eLhSqdyzZ4+urqrf6bx58zZt2jQYlWZmZpJPKdTX1wcAqI9zu2zZMg8Pj5qamsOHDw+GSpqRkJCgEmJ7uISo4eHDh8HBwTt37lSJ+ezg4ODt7f3kyRM/P7/Bq50i6enpZPVgbHTyG0pbW5u/v39sbGxfJfd444ULF8gx0CsrK+/evUsEZFPDoUOHJk2apCYs1av0DA0Nzc/P10D/IUCrjVRHR0dOTs6oUaOmTp2qphiO4zExMePGjWMymWKxePHixffu3QMAHDx4kMvlcjics2fPLliwQCAQmJqaJiUlAQDGjRuHYRiNRnv77beh9dm2bZtQKGSxWN98842K/CdPnrDZbEtLS/XawmB2WVlZmjf41byqjV5eXgwGA0a4AgBs3LiRy+ViGFZXV+ft7b1169bS0lIMw2Qy2YEDB1gslqGh4fr1642NjVksloODw+3bt/skBABw4cKFgY3Bd+DAARzHXVxcumeFh4ePGTPm6NGj2dnZ1PtEzXMHACiVypCQEIlEwmaz33zzTc02JJWUlIhEInNzcyIlKCho48aNMBBxn6By4969e7ds2dKrqI6Ojlu3btnZ2WlQnVgsnjlzZmxsLK6FZ2QP2TubBq+IDx48AABMmzZNfbGQkBAGg3HixInGxsaCgoJJkybp6+tXV1fjOB4UFAQAyMnJefHiRU1NzYwZM7hcbkdHR2dnp4WFhUQiIU/rfHx8VAJw4zje2trK5/O9vLyIlB6neziOw5VUGC2WOhSne2rauHLlSiMjI6JkVFQUAKC2thbH8aVLl0qlUiLL09OTy+UWFxe3t7cXFRVNmTKFz+fDaSx1IZmZmXw+PywsjErrAIXpnpWVla2trUqiVCp9/PgxjuM3btyg0WgWFhYtLS34X6d7Gjx3HMf9/PyYTOaZM2caGhq2b99Oo9HIUe3U09HRUVVVFRcXx2QyyTG4cnNzXVxc8G7h83qFyo1VVVW2trZKpbJXaY8fPwYA2NnZOTo6jh49mslk2tjYfPXVV0QoPfXVBQYGAgDy8vJ6rQhN9/4P+G+v/qNGW1tbTEzMkiVLVq1aJRQKJ06cePjw4bq6OnLYYQcHB4FAYGBg4O7u3traWlFRoaOjs2XLloqKivT0dFhGLpenpaV98sknKvIjIiKMjY3Dw8N71ZbP52MY1tzc3Od29gaVNlJEV1cXvnrY2toePHiwubn5+PHjfZLg7Ozc1NQUHBzc16p7pLW19fHjxzAaaI/Y29v7+PiUlZXBmMAEmj339vb2gwcPurq6Ll26VCQS7dixg06nU+8BMzMzU1PT0NDQffv2LV++nNDE29v74MGDfW07xRv37t27efNmGq33f1U4/TQwMNi9e3dRUdGzZ88WL168adOmU6dOUanO2toaADBIH6P6g1YbKWie1C8GFRUVtbS0TJ48mUiZMmUKg8GAExkVYKBqhUIBAFi7dq1QKCQm4YmJiYsXL4ahbgnS09NTUlIuXrxIXiB4Fa2trTiOq0gYEPrURupMnjyZw+HAKdJwUVNTg+O4+gBN4eHhY8eOjY+Pz83NJRI1e+7379+Xy+UTJkyA6Ww2e/To0dR7oLKysqam5tSpU99+++1bb70Fl+q2b9++bt06uErVJ6jc+PTp03PnzsGVhF5hMpkAgPHjxzs4OOjp6QmFwp07dwqFQmi4e60OPoVnz571qRVDgFYbKQsLCxaLBSd9rwJ+N1V52xKJRL2+0fB4vHXr1t24ceOnn34CABw6dEglMmJycvLevXuvXLliYWFBRVuop42NDZXCfULjNvYKk8mEb/7DRXt7O/jzv+tVsFis48ePYxj2ySeftLW1wUTN+qS1tRUAsGPHDsL5qLy8XP2vIBk6nW5gYODk5JScnFxUVBQREZGbm1tYWLh27VqKEggo3hgZGfnZZ5+pfFJ4FcbGxgCAuro6IoXBYJibm5eWllKpjs1mgz+fiFah1UaKyWTOmzevrq7u+vXr3XOfP3++du1akUgEAFAZmo2Njaampr3K9/LyotPp+/fvv3r1qpmZGXnSERcXl5iYePny5TfeeIOithcuXAAALFiwgGJ56vSnjWpQKBT9F9JP4D9Gr56E9vb2vr6+JSUlu3btgima9QlcMFZZedTAJVgmk+no6BQVFSUkJOTk5NBoNGjyoPzdu3djGKbeaY7KjdXV1adOndqwYQNFrXg8nrW1dXFxMTmxs7NTKBRSqa6jowP8+US0Cq02UgCA0NBQJpPp6+tL/IQS3L17V1dXd8KECTwejzwgbt++3dHR8fbbb/cqHJ5vdebMmeDgYG9vb5iI43hAQEBhYWFGRgZFH18AQHV19f79+01NTbuvavUf9W3U1dWFE9i+cuXKFRzHp02b1h8h/cTQ0BDDMCqBi3ft2mVjY5OXlwcvNXvuZmZmLBYrPz+/T0rW19d/9NFH5JSSkhKlUmlmZnb8+HGyvSMvSJOnot2hcmNkZOSqVav09PSoq7p8+fK8vLxHjx7BS7lcXl5ePnHiRCrVwadgZGREvbqhQduNlJ2d3cmTJ+/evTtjxozz58+/ePFCoVA8fvz4yJEjn376KZ1OZ7FYW7duTU9PT0xMbGpqKiws/Pzzz42NjT09PanI37p1a2dnZ0NDA+GEUlxcvG/fviNHjtDpdPKmhOjoaOIuHMdbWlrgR5Pa2trTp0+/++67Ojo6GRkZg7Empb6NMpns+fPnGRkZCoWitra2vLycuFFPT+/p06dlZWXNzc3QBnV1dTU0NHR2dhYUFHh7e0skErjeQV1IVlbWALogcDgcKyurqqoqKp1w/Phxwn9Ns+fOYrE+/vjjpKSkgwcPNjU1KUg9IR4AACAASURBVJXKqqqqP/74AwDg7u5uZGTU424bLpd76dKly5cvNzU1KRSKvLy8NWvWcLlcX19f9Tqrkdkrz549O3bsWPeTbNXL9PX1NTc39/DwqKioqK+vDwgIaGtrU/nm8CrgU1DjYzVsDOzHQjX057NlRUWFn5/fxIkTeTyejo6OSCR66623Pv300+vXr+M43tXVFRUVZW1tTafTxWKxq6vr/fv3cRyPj4+Ha4HW1talpaVff/01tCDm5uYPHjwghM+aNevo0aPE5au+bkRFRZ07d+7NN9/kcDgMBgN+bcEwTCQSTZ06NSwsrL6+XoOmUXRBeFUbcRyvr6+fNWsWi8WytLTcvHmzv78/AEAmk1VUVPz666/m5uZsNnv69OnV1dWenp50Ot3ExERXV1cgECxevLi0tLSvQs6fP8/n88PDw6m0DlBwQYCTbrlcDi/T09PhvFtfX3/Tpk0qhf39/QkXBM2e+8uXLwMCAiQSia6uroGBwdKlS4uKinAcd3V1BQCEhIT0qKSLi4ulpSWPx2MymVKp1N3dvbCwsHsxlU/76mWquRHHcV9f31WrVnUv2avMysrKFStWiMViJpM5derUrKwsKtXhOO7s7GxiYkL4K6hhiF0Q/h5GagQzqNtiVPD09NTT0xuauiBUjFRJSYmuri7Z7WhYUCqVM2bMSEhIeA1l4jheV1fHYrGio6OpFEZ+UohBRAs3u8tksrCwsLCwsO77YIcMpVKZkZHR3Nzs7u7+usmEhIaG2tnZqXzg1hKQkUIMP4GBgW5ubu7u7lRW0AeDK1eupKWlZWVlqffYGpEyAQAxMTH5+fnnz5+n0+kDKHagQEbqdWH79u3Hjx9/8eKFpaXlmTNnhlsdVXbv3u3l5bVnz55hqX3OnDknT54kdi++VjLPnj378uXLK1euiMXiARQ7gAxpSCvEMBIRERERETHcWqjDyckJHiGCGEoWLVq0aNGi4dZCHehNCoFAaDXISCEQCK0GGSkEAqHVICOFQCC0miFdOK+qqkpJSRnKGrUfuLt1BHfLIEX0QQwjVVVVQ7ovfcjcRnuMroFAIP6ODKXHOYZr4ZHGiL8tMLLOCH4xRAw9aE0KgUBoNchIIRAIrQYZKQQCodUgI4VAILQaZKQQCIRWg4wUAoHQapCRQiAQWg0yUggEQqtBRgqBQGg1yEghEAitBhkpBAKh1SAjhUAgtBpkpBAIhFaDjBQCgdBqkJFCIBBaDTJSCARCq0FGCoFAaDXISCEQCK0GGSkEAqHVICOFQCC0GmSkEAiEVoOMFAKB0GqQkUIgEFoNMlIIBEKrQUYKgUBoNchIIRAIrQYZKQQCodUgI4VAILQaZKQQCIRWg4wUAoHQapCRQiAQWg0yUggEQqtBRgqBQGg1usOtAOLvzdWrV2/evElc3rt3DwAQGRlJpNjb2/+///f/hkEzxEgBw3F8uHVA/I3Jycl577336HQ6jab6Vt7V1aVQKLKzs+fMmTMsuiFGBshIIfpFV1fX6NGja2tre8zV19evrq7W0dEZYq0QIwm0JoXoFzQabeXKlQwGo3sWg8FYtWoVslCIfoKMFKK/rFixoqOjo3t6R0fHihUrhl4fxAgDTfcQA4CFhUV5eblKopmZWXl5OYZhw6ISYsSA3qQQA8Dq1avpdDo5hU6ne3h4IAuF6D/oTQoxANy7d2/cuHEqiXfv3h0/fvyw6IMYSaA3KcQAYGNjM378ePJ7k62tLbJQiAEBGSnEwPDPf/6T+JBHp9PXrFkzvPogRgxouocYGCorK83NzeFwwjDs0aNHFhYWw60UYiSA3qQQA4OZmdk777xDo9FoNNo777yDLBRioEBGCjFgrF69GsMwGo22evXq4dYFMXJA0z3EgFFXVzd69GgAwNOnTw0NDYdbHcRIASdx+vTp4VYHgUC87pw+fZpsl3o4qgWZqhHD/v37AQA+Pj5DVuPVq1cxDJsxY8ZgV3Tz5s3Y2Fg0Vkcey5cvV0npwUh9+OGHQ6IMYtBJTU0FQ/tAFyxYAADg8/lDUFdsbCwaqyMPSkYKgdCYoTFPiNcK9HUPgUBoNchIIRAIrQYZKQQCodUgI4VAILSa19pIRUdHGxoaYhh2+PBhmHL+/HmhUPj999/3R2xYWJitra1AIGAymTKZbNu2bS0tLd2LrV27ls/nYxiWn5+vXqCjoyPWDR6P1x8lX8WA9IAWkp2dHRgYmJaWZmVlBTtQxS3eycmJz+fr6OiMHz/+119/HUrdIiMjbWxs2Gw2l8u1sbEJDg5uamrqXqy9vd3GxmbHjh19la9yY3+Gk0KhiIiIkMlkDAZDJBJNmDChrKxMTXXnzp2LjIxUKpV91ZnMa22k/Pz8bty4QU4ZEP/7y5cvb9q0qaysrK6uLiIiIjY21s3NrXuxo0ePHjlyRONapk+f3g8dX8mI3IHwr3/968CBA9u3b1+6dOmjR4+kUumoUaMSExN/+OEHosylS5dSU1MXLlxYVFQ0adKkoVTv2rVrn332WUVFxbNnz3bt2hUZGbls2bLuxYKCgu7fv6+BfCo3UhxOy5cv/+67706ePCmXy3///XepVNr9B5hcnYuLC4vFmjNnTmNjowaaQ5ALwl9wdnZ+8eJFP4XweDxPT094bsmHH36YlpaWkpJSWVlpZmammUAWi9XU1ET+ur9+/fpBchEakB5QQ1tb25w5c1R+GwaVvXv3Jicn//bbbywWi0g8cODA6tWrPT09i4qKhELhkCnTIwwGY+PGjVA9Nze31NTU1NTUP/74w9jYmChz48aNu3fvaiC8+40aD6fk5OSMjIzffvtt4sSJAABjY+OzZ8/2Wt2WLVsePXr0/vvvX716VVdXE4PzWr9JDSA4jqempn799dcAgMzMTHKIFH19fQCAXC7vfhfF03UvXLhAHlKVlZV3796dPXt2f5UeDhISEmpqaoasuocPHwYHB+/cuZNsoQAADg4O3t7eT5488fPzGzJlXkV6ejpZPRMTEwAA+Q2lra3N398/Nja2r5J7vFHj4XTo0KFJkyZBC0W9OgBAaGhofn6+BvpD+mykYmNjuVwujUZ7++23jYyM6HQ6l8udNGnSjBkzzMzMWCyWSCTatm0bUf7atWu2trZCoZDFYk2cOPHixYsAgG+++YbH42EYJhaLMzIy7ty5Y25urqOj89FHH6mp+sCBAywWy9DQcP369cbGxiwWy8HB4fbt2zAXx/GYmJhx48YxmUyxWLx48WIYTVd9Fpnc3FyJRIJh2FdffQUAOHjwIJfL5XA4Z8+eXbBggUAgMDU1TUpKgoWVSmVERMTYsWPZbLa+vr6lpWVERESPP0dPnjxhs9mWlpZQk6ioqLFjxzKZTKFQ6O/v39f+BwDs3bt3y5YtGtzYK9R7QM2z8PLyYjAYcKcxAGDjxo1cLhfDsLq6Om9v761bt5aWlmIYJpPJAAAXLlwQCAS7d+8ejOZAPXEcd3Fx6Z4VHh4+ZsyYo0ePZmdnd8991ZjpdVSEhIRIJBI2m/3mm29qtmunpKREJBKZm5sTKUFBQRs3bjQwMOirKCo3UhxOHR0dt27dsrOz06A6sVg8c+bM2NhYDRcTum8wxnvjX//6FwDg9u3bra2tdXV18+fPBwD88MMPtbW1ra2tXl5eAID8/HxYODU1NTQ09Pnz5/X19dOmTRs1ahRMLy4u5nA4a9asgZeBgYFHjx7ttWpPT08ul1tcXNze3l5UVDRlyhQ+n19RUYHjeEhICIPBOHHiRGNjY0FBwaRJk2BkSvVZJSUlAIBDhw5B+ZWVlQCAuLg4eBkUFAQAyMnJefHiRU1NzYwZM7hcbkdHB47ju3fv1tHROXv2rFwu/+WXX4yMjBwdHbsr3Nrayufzvby8CIEYhn3xxRcNDQ1yuTw+Ph4AkJeX12vDCaqqqmxtbZVKJZXCy5YtW7ZsGXXheF96QM2zWLlypZGRESEzKioKAFBbW4vj+NKlS6VSKZGVmZnJ5/PDwsL6pCROeaxaWVnZ2tqqJEql0sePH+M4fuPGDRqNZmFh0dLSguN4VlbWokWLYBk1Y0ZNn/j5+TGZzDNnzjQ0NGzfvp1Go/38888UW9TR0VFVVRUXF8dkMk+cOEGk5+bmuri44DgOI7AGBQVRFEjlRurD6fHjxwAAOzs7R0fH0aNHM5lMGxubr776qquri0p1gYGBFIc66LbBWHMj1dzcDC+//fZbAEBhYSG8/OmnnwAAycnJ3W+MiIgAANTU1MDLf//73wCAxMTEU6dO+fr69lovjuOenp5CoZC4/PnnnwEAO3fulMvlPB7P3d2dyIJqhIWFqcnCqRmptrY2eAltysOHD3EcnzJlytSpUwmZ69ato9FoL1++VFE4KChozJgxTU1NOI7L5XIOhzN37lwiF/4C98lIbdq0idC2VwbKSPXYA696FnhfjJTGUBmrLS0tGIYtXLhQJZ0wUjiOb926FQCwadMmnGSk1I+ZV/VJW1sbh8Mh7pLL5Uwmc8OGDRRbZGRkBAAYNWrUl19+CU0eFDJ58uSqqiq8j0aK4o3Uh1NhYSEAYO7cudevX6+vr29sbPyf//kf+P9Lpbpjx44BAL777rteK+pupAZgTQpGr+3s7ISXMLSRQqHoXhJmEd8j161bt2zZsvXr16ekpOzbt0+DqidPnszhcO7du1dUVNTS0jJ58mQia8qUKQwG4/bt22qy+lodbClsWnt7O056d1UqlXQ6XSVab3p6ekpKysWLF+ESwMOHD+Vy+Zw5c/re0P+fp0+fnjt3zsPDQ2MJ/YTcAyoQz2LIlXol8OeQw+GoKRMeHj527Nj4+Pjc3FwisU9jhuiT+/fvy+XyCRMmwHQ2mz169GjqHVJZWVlTU3Pq1Klvv/32rbfegit327dvX7duHVyl6hNUbuzTcGIymQCA8ePHOzg46OnpCYXCnTt3CoVCuA7ba3XwKTx79qxPrYAM+sL5Dz/84OjoaGBgwGQyyWtVkN27d7e0tPRnJZXJZNbW1sIPnCq+HiKRqLm5WU2WxpUCAN5///1ffvnl7NmzbW1td+7cycjI+OCDD8hGKjk5ee/evVeuXCEO0q2qqgIAaLCyQBAZGfnZZ5+prAFrD/BZDLcW/0d7ezv487/rVbBYrOPHj2MY9sknn7S1tcFEzcZMa2srAGDHjh2E81F5eXmPH0x6hE6nGxgYODk5JScnFxUVRURE5ObmFhYWrl27lqIEAoo39mk4wU+NdXV1RAqDwTA3Ny8tLaVSHZvNBn8+kb4yuEaqoqLC1dV19OjRt2/ffvHiRWRkJDlXoVBs2bIlJibm5s2b4eHhGshXKBSNjY2mpqYikQgAoDKGes3SoEaC0NDQ2bNne3h4CASCJUuWfPjhh2Snp7i4uMTExMuXL7/xxhtEIhwNL1++1KzG6urqU6dObdiwoT9qDx7EsxhuRf4P+I/Rqyehvb29r69vSUnJrl27YIpmYwb+/Ozfv588Vbl582Zf1ZbJZDo6OkVFRQkJCTk5OTQaDZo8KH/37t0Yht25c0eNBCo39nU48Xg8a2vr4uJicmJnZ6dQKKRSXUdHB/jzifSVwTVShYWFCoViw4YNVlZWLBZL5Yv75s2bP/vsMx8fH19f3127dmnwOK9cuYLj+LRp0yZMmMDj8chP7vbt2x0dHW+//baarP40raioqLS0tLa2VqFQVFRUHDx4UCwWAwBwHA8ICCgsLMzIyFD5KZ4wYQKNRvvvf/+rWY2RkZGrVq3S09Prj9qDB/EsAAC6uro9TgmHGLidgIrb165du2xsbPLy8uClZmMGft3udf+ACvX19SoftUtKSpRKpZmZ2fHjx8n2jrzWQ56KdofKjRoMp+XLl+fl5T169AheyuXy8vLyiRMnUqkOPgW47tZXBtdISSQSAEB2dnZ7e3tJSQl5Sh8fH29iYrJkyRIAQEREhK2t7cqVK3vcDaBCV1dXQ0NDZ2dnQUGBt7e3RCLx8PBgsVhbt25NT09PTExsamoqLCz8/PPPjY2NPT091WT1p2mbNm2SSCTd3W2Li4v37dt35MgROp1O3nYQHR1tYGCwdOnSM2fOJCQkNDU1FRQUwPk8FZ49e3bs2LGhPGOTCj0+CwCATCZ7/vx5RkaGQqGora0tLy8nbtHT03v69GlZWVlzc7NCocjKyho8FwQOh2NlZQVn2eqBkz5itq7ZmGGxWB9//HFSUtLBgwebmpqUSmVVVdUff/wBAHB3dzcyMupxtw2Xy7106dLly5ebmpoUCkVeXt6aNWu4XK6vr696ndXI7JVXDSf1Mn19fc3NzT08PCoqKurr6wMCAtra2uDyea/Ap6DGx0odZBNI5YtJbGwsXAOzsLC4du3a3r17ocOukZHRyZMnk5OTobEUi8VJSUnwtUJPT08kErm5uUHvG6lUamdnh2GYnp7ejRs3cBz38fGh0WgAAKFQeOfOHTW1e3p60ul0ExMTXV1dgUCwePHi0tJSmNXV1RUVFWVtbU2n08Visaur6/3799VnffHFF1BbLpe7ZMmSuLg46N3D4XBcXFzi4+NhS62trUtLS7/++muBQAAAMDc3f/DgweXLl0eNGkV0I51OHzduXFpaGvwI0p2oqCgcx5ubm9euXTtq1Cgejzd9+vSQkBAAgKmp6W+//aa+2319fVetWqW+THf6+nWvTz2g5lnU19fPmjWLxWJZWlpu3rwZuoPJZLKKiopff/3V3NyczWZPnz69urr6/PnzfD4/PDy8r02j+CXay8uLTqfL5XJ4mZ6eLpVKAQD6+vrwix4Zf39/wgXhVWNGfZ+8fPkyICBAIpHo6urC36SioiIcx11dXQEAISEhPSrp4uJiaWnJ4/GYTKZUKnV3dye+lZNR+WqmXqaaG/FXD6deZVZWVq5YsUIsFjOZzKlTp2ZlZVGpDsdxZ2dnExMTwl9BDWBAXBCGEU9PTz09veHWAsdxPD4+3tvbm7h8+fKlj48Pk8kk/h+0AQ1cEKgzvM+C4lgtKSnR1dUlux0NC0qlcsaMGQkJCa+hTBzH6+rqWCxWdHQ0lcLdjdTfb1tMP3dUDwjV1dVeXl6ffvopkcJgMCQSiUKh0Ia1mCFDG56FemQyWVhYWFhYWI8HUQwNSqUyIyOjubnZ3d39dZMJCQ0NtbOzg27eGqBdRurevXvdD5EgGPC+0xg2m02n0xMSEp49e6ZQKJ4+fXr06NGQkBB3d3f48t8n/i6t/psSGBjo5ubm7u4+qBun1XDlypW0tLSsrCz1HlsjUiYAICYmJj8///z589BNUhPIr1VaPt0LDAyEjnMWFhapqanDq8zVq1ffe+89gUCgo6MjFAodHBzi4+MVCsXwaqXC4E33hv1Z9HWsXrx4MSAgYPD0QfRIRkZGREREZ2cn9VtAt+neXyIYp6SkLF++HB+JJwq9nsBzrGBgqxEGGqsjFQzDTp8+Td6rr13TPQQCgVABGSkEAqHVICOFQCC0GmSkEAiEVtPDkcMpKSlDrwdiMIB7EUbkA4U7PUdk0xCqkD/1aXbaKQKBQAwgKi4IPbxJoc+6IwbkgoD429E9Oglak0IgEFoNMlIIBEKrQUYKgUBoNchIIRAIrQYZKQQCodUgI4VAILSaATZSaWlpVlZW8CCk4ODgHsvExMRgGEaj0WxsbK5evdrPWjAMg4fYrly58vfff9dY8+joaHhu/+HDh2HK+fPnhULh999/r7FMAEBYWJitra1AIGAymTKZbNu2bfD0NZUmYBjGYDAMDQ0dHR2joqIaGhr6UymiO9nZ2YGBgeRuX716NbmAk5MTn8/X0dEZP368ZgeH95Ourq79+/c7ODiQEx0dHbsfMaYS4KNHXjXwAACRkZE2NjZsNpvL5drY2AQHB1OJLQBRKBQREREymYzBYIhEogkTJpSVlamUaW9vt7Gx2bFjBwDg3LlzkZGR/T0csbszZ5/Oi+kReIz06NGjiUCsBJ2dnTDI/Zw5c/pfC4yg29LScu7cOYlEwuPx7t27p7FAlWjGmZmZAoHg3Llz/VFy5syZ8fHx9fX1TU1Np0+fptPp8+fP794EGNHgxx9/9PDwwDDM2NiYenhuNQzq8cHDS5/GakhIyMKFC2EcaRzHpVIpPJ8+MzOTXIwcZn2IefDgwbvvvgsA+Mc//kFOnzlzZvf/2Xnz5vUqUM3Ac3Z2jo6OrqmpaW5uTklJodPp5Kja6nF1dR07duytW7fgWY8uLi7dj2OHISSIM85jY2NnzpzZ0NBAsQowNGecS6VSGPwnJSVFJev06dPwt2IAjRTkP//5DwBg48aNGgtUMVIDgrOzM/nEL3hKTkVFBbxUaQIkNTWVRqMZGho2Njb2s/ZBNVJyudze3n64hFAfq3v27BkzZgwRFR3HcalUevLkSRqNZmJiQu7k4TJS+fn5S5YsSUxMtLOzUzFS8+bNI2wrxNPTMycnp1eZagaeq6sruTegx+/Tp097lZmUlIRhWEFBgZoy169fd3JyAn8NxODl5WVvb0/xSMjuRmqw1qRg0MFDhw6ppMfExGzdunUwapw6dSoA4O7du4MhvE/gOJ6amgrDVWVmZpLDGuvr6wMA1Ee1XbZsmYeHR01NDTHx1E4SEhL6E3p6AIWo4eHDh8HBwTt37lSJ0+vg4ODt7f3kyRM/P7/Bq50i//jHP9LS0lauXNk92PKFCxf4fD5xWVlZeffu3dmzZ/cqU83AS09PJ/cGjI1O5Qz4Q4cOTZo0SU1Yqra2Nn9//9jYWJX00NDQ/Pz87ukUGSwjNXv27HHjxv3444/3798nEq9fvy6Xy6GhJXPt2jVbW1uhUMhisSZOnHjx4kUAwDfffMPj8TAME4vFGRkZd+7cMTc319HRUYmkSNDZ2Qn+jKmN43hMTMy4ceOYTKZYLF68ePG9e/dgMTVZZHJzcyUSCYZhMAzXwYMHuVwuh8M5e/bsggULBAKBqalpUlISLKxUKiMiIsaOHctms/X19S0tLSMiIshHCxI8efKEzWZbWlqq7z0YvS4rK0t9sYHiVX3i5eXFYDBgkCsAwMaNG7lcLoZhdXV13t7eW7duLS0txTBMJpMdOHCAxWIZGhquX7/e2NiYxWI5ODjAMIvUhQAALly4MLBh+A4cOIDjuIuLS/es8PDwMWPGHD16NDs7m3qf9DoSQkJCJBIJm81+8803B3wz7N69e7ds2aLBjWoGXklJiUgkgoswaujo6Lh165adnZ2aMkFBQRs3boQRjMmIxeKZM2fGxsbimm1jIr9WDeB07/Hjx19++SUAgBz3ydXV9fjx4zB6NXm6l5qaGhoa+vz58/r6+mnTpo0aNQqmFxcXczicNWvWwMvAwMCjR4+SayHPlU6cOAEA8Pf3x3E8JCSEwWCcOHGisbGxoKBg0qRJ+vr61dXV6rNUpnuVlZUAgLi4OHgZFBQEAMjJyXnx4kVNTc2MGTO4XC5cdNu9e7eOjs7Zs2flcvkvv/xiZGTk6OjYvVtaW1v5fL6Xl9ermkAAFzLNzMyo93mPUJzuqemTlStXGhkZESWjoqIAALW1tTiOL126VCqVElmenp5cLre4uLi9vb2oqGjKlCl8Ph/OL6gLyczM5PP5YWFhvepMcaxaWVnZ2tr+f+zdaUAUV7o4/FNN7003i6yyNy0ibsSoEdTRxAkZ4xXFFSOZqGPELQRRZBAlyKYEg1wUklGRO5GooHLRGNFEHcwQiZq/MCCMiqgsIrLI3g00Tb0fzpu6PQ00zdoFPr9P1Knq008t/VDn1KkqlUJ8fJIkefv2bQaDYWtr29zcTP5nc0/NNlFzJOzatYvD4Zw/f76urm7Pnj0MBqNPfYvvvPOOSnNPWXl5uZOTk0Kh0LxCrOuBR5Jke3t7eXn5kSNHOByOJq/8evbsGULI2dl5/vz5ZmZmHA7H0dHx6NGj1Kv0srKy3N3dyR7euxcYGIgQysnJ6fWL0LD1ST179qy+vl4gEBgYGOBX0RUXF1taWra1tXVNUsoiIyMRQlVVVXjyb3/7G0IoOTn59OnTfn5+Kt9CdZyfP3/e1NTUxMSkvLxcKpXq6up6enpSS969exchFBoaqmYWqVmSohrz8fHxCKEnT56QJDljxoyZM2dSdW7atInBYLS1tamsWlBQkIODg3IXQ09JiiRJgiD09fW7naU5TZKU+m3SpySlvC737t1DCO3fv79PlWhOk2O1ubmZIIjFixerlFNJiiRJ3PmAXxRKJSn126SnI0Emk/H5fOpTUqmUw+Fs3bpV85VSn6S2b9/evw7TrgceSZL4tbhjxoz57//+764XuLrCb719//33f/nll9ra2vr6evzu4uTkZJIkpVLp9OnTy8vLyR6S1MmTJxFC3377ba9f1DVJDeE4KT09vY8++qiuru7s2bMIocOHD2/duhW/YkQN/N4b6prlpk2bVqxYsXnz5tTU1C+//FJl4YaGBoIg9PT0Pv/88w8//PDu3bsWFhYFBQXNzc3US+gRQjNmzGCz2Xfu3FEzq69rh1cEv2WvtbWVVDqPVSgULBZLuUcAIZSWlpaamnrt2jXlLoaetLS0kCTZj7dj9cMgbhNl06dP5/P53Talhw3+V6f+BU3h4eHjx4+Pj4/PysqiCvu0Tagj4dGjR1KpdNKkSbicx+OZmZkN1haoqKi4dOkS7gfok54OvLKysqqqqtOnT//9739/6623eu0ZxB0pEydOdHV1NTQ01NPT279/v56eHu573bNnz6ZNm3D3VrfwXnj16lVf40dDPZgTd59/88039fX1586d27x5c7eL/fDDD/Pnzzc2NuZwOLt371aZGxER0dzc3O1GxP+6Ozo6ysvLT548idvV9fX1CCGVsST6+vpNTU1qZvV/JRH68MMP/9//+38XL16UyWS//fZbenr6f/3XfyknqbNnzx48eDAzM9PW1laTCh8/fowQcnR0HEhUGhqibYIQ4nA4+J+qtrS2tqLff1094XK5SUlJBEFs2LBBJpPhwv5tiRBduwAAIABJREFUk5aWFoTQ3r17qQFNJSUl6i+SaC4qKurTTz9V6f7vlZoDj8ViGRsbu7m5nT17tqCgALdg1DA3N0cI1dTUUCVsNtvGxqa4uDgrKys/P3/jxo1qPs7j8dDve6SvhjZJOTs7z5o16+7du97e3itXrjQwMOi6TGlpqYeHh5mZ2Z07dxoaGqKiopTnyuXyzz//PCYmJjs7Ozw8XJMv1dfXRwipHE/19fWWlpZqZvV11ZSFhIS8995769atE4lEy5YtW7Vq1fHjx6m5R44cSU5Ovnnz5tixYzWs8OrVqwihhQsXDiQqDQ3RNpHL5QOvZIDwD6PXkYQuLi5+fn5FRUVhYWG4pH/bBHcYHz58WLmpgp8gOkCVlZWnT5/G//I1p+GBJ5FIdHR0CgoK1Nemq6s7bty4wsJC5cKOjg49Pb3ExMQbN24wGAycmvF2iIiIIAjit99+w0u2t7ej3/dIXw35bTF4y54/f37Hjh3dLpCfny+Xy7du3SoWi7lcrsojrz777LNPP/10x44dfn5+YWFhmuzySZMm6erqUlsHIXTnzp329va3335bzax+rh5CCKGCgoLi4uLq6mq5XF5aWpqQkIDTMUmSAQEB+fn56enpmowSxiorKw8fPmxpablhw4aBRKUh9duEyWT278XxmZmZJEnOmjVrIJUMEL6FQJMXF4eFhTk6Oubk5ODJ/h0nVlZWXC43Nzd3gGF3FRUV5eXlZWhoqOHyag682tpalevjRUVFCoXCysqq12pXr16dk5Pz9OlTPCmVSktKSiZPnpyUlKScl5X7pKgmM94LuCOsr4Y8Sa1atcrIyMjDw0MsFne7gLW1NULo+vXrra2tRUVFys3++Ph4CwuLZcuWIYQiIyOdnJzWrl3b6xB+Lpe7c+fOtLS05OTkxsbG/Pz8LVu2mJube3t7q5k1kHXcvn27tbV115EmhYWFX3755fHjx1kslvJtDYcOHaKWIUmyubkZXyKprq5OSUmZPXu2jo5Oenr68PRJqd8mEonk9evX6enpcrm8urq6pKSE+qChoWFFRcXz58+bmppwDsLj5js6OvLy8nx9fa2trXEfiuaVZGRkDOIQBD6fLxaL8YPee90ISUlJVAu9f8cJl8tdv379mTNnEhISGhsbFQpFeXn5y5cvEUKenp6mpqb9u9vm1atXJ0+e7Po/Xk2dag48gUDw448/3rx5s7GxUS6X5+TkfPLJJwKBAA8TVx+nn5+fjY3NunXrSktLa2trAwICZDIZ7j7vFd4LasZYqaOcAgd+dS8tLQ3fE2NkZISvmJAkuXv37tu3b+O/9+7di4fMMBgMJyenf/7znzjrGxoa6uvrr1y5Eo9Lsre3d3Z2JgjC0NAQf3bHjh0MBgMhpKenFxcX5+DggOM3NzdfuXKlShidnZ3R0dHjxo1jsVgGBgYeHh6PHj1SP+urr77CaV4gECxbtuzIkSM4Tj6f7+7uHh8fj3v+xo0bV1xcfOzYMZxBbGxsHj9+fPPmTXybBcZisSZMmHDhwgV8QaSr6OjoS5cuTZkyhc/ns9lsvF74ct7MmTNDQ0Nra2sHshcoGg5BULO5amtr3333XS6Xa2dn99lnn/n7+yOEJBJJaWnp/fv3bWxseDzenDlzKisrvb298U2UTCZTJBItXbq0uLi4r5VcuXJFKBSGh4f3GrOGx6qPjw+LxcLXl8kejk+Kv78/NQShp22i/khoa2sLCAiwtrZmMpnGxsbLly8vKCggSdLDwwMhFBwc3G2Q2dnZs2fPxp0+CCEzMzNXV9dbt27huX5+fl5eXl0/paZONQceSZLu7u52dna6urocDsfe3t7T05O6tUV9nCRJlpWVrVmzxsDAgMPhzJw5MyMjo+sy3V7dW7RokYWFBTVeQQ00PEMQ3jTx8fHKw8Ha2tp27NjB4XCo34a2DOe9e97e3oaGhsPzXaTGx2pRURGTydRkHNCQUigUc+fOTUxMfAPrJEmypqaGy+UeOnRIk4W7Jil4VMtAVVZW+vj4/OUvf6FK2Gy2tbW1XC7XSkeMFg30ZvchIJFIQkNDQ0NDNbntY4goFIr09PSmpiZPT883rU4sJCTE2dnZx8enfx+HJDVQPB6PxWIlJia+evUK3xp+4sSJ4OBgT0/P4elUAuoFBgauXLnS09NTkx70oZCZmXnhwoWMjAz1I7ZGZZ0IoZiYmNzc3CtXruAhkP2hfFoFzb3++fnnn//4xz+KRCIdHR09PT1XV9f4+HgN7/keUsPW3AsMDMRjGm1tbc+dOzcM39jXY/XatWsBAQFDFw/oVnp6emRkpPLzGHqFNHnvHuiruXPn/vTTT9qOQpsiIyN7HQ2oXW5ubl3vbAdDbcmSJUuWLBlgJdDcAwDQGiQpAACtQZICANAaJCkAAK1103GOn3kMRoFff/0VjdIdim+zGJWrBlQQpNKDkLKzs2NiYrQYDRjp8A0Z/bxFCwCEEEJ+fn4uLi7U5H8kKQAGCD/ZPTU1VduBgNED+qQAALQGSQoAQGuQpAAAtAZJCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGuQpAAAtAZJCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGuQpAAAtAZJCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGuQpAAAtAZJCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGuQpAAAtAZJCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGuQpAAAtEaQJKntGMAI9u2338bExCgUCjxZU1ODEDIyMsKTOjo6fn5+f/7zn7UWHxj5IEmBAXn8+PH48ePVLPDo0SMHB4dhiweMPtDcAwPi4OAwdepUgiC6ziIIYurUqZChwABBkgID9ec//1lHR6drOZPJ/OSTT4Y/HjDKQHMPDFRFRYWVlVVnZ6dKOUEQZWVlFhYWWokKjBpwJgUGauzYsa6urgzGfxxLDAZj9uzZkKHAwEGSAoPg448/VikhCAIu6oFBAc09MAjq6upMTU3lcjlVwmQyKysrx4wZo8WowOgAZ1JgEBgYGLz//vtU97mOjs4HH3wAGQoMCkhSYHB4eXlRfeckSXp5eWk3HjBqQHMPDA6pVDpmzJjW1laEEJfLrampEQgE2g4KjAZwJgUGB5/P9/DwYLFYLBbLw8MDMhQYLJCkwKD56KOP5HK5XC7/6KOPtB0LGD2Yw/+V5eXlt2/fHv7vBUNNoVDw+XySJBsbG1NTU7UdDhh8rq6ulpaWw/2t5LBLSUkZ7pUEAAyGlJSU4c8YWjiTwsg3u8N+5cqVCKFz585pO5BBduvWrczMzJCQkDd8/45K3d5GPgy0lqTAqDR37tzKykptRwFGFeg4B4OJwWBo6/8tGK0gSQEAaA2SFACA1iBJAQBoDZIUAIDWaJ2k2traPv/8czMzMz6f/8c//tHExIQgiG+++Ubbcf2fqKgoR0dHHo8nEAgcHR337dvX2Ng4RN915coVPT2977//fojq15br168HBgZeuHBBLBYTBEEQhMrTqdzc3IRCoY6OzsSJE+/fvz/8EXZ2dh4+fNjV1VW5cP78+UQXurq6vdYWGhrq5OQkEok4HI5EItm9e3dzczOeNZDDSS6XR0ZGSiQSNputr68/adKk58+fqyzT2trq6Oi4d+9ehNClS5eioqKo1/zQ2vAPzcKDOTVZMiIiwsHBoa6u7m9/+9u5c+eKiooQQl9//fVQR6i5RYsWHTp0qKqqqqmpKTU1lcVivf/++5p8cMWKFStWrOjTd12+fFkkEl26dKlfkQ4fzfcvSZLBwcGLFy9ubGzEk/b29vgBL5cvX1ZeLCMjY8mSJYMcqGYeP348e/ZshNDUqVOVy+fNm9f11/TBBx/0WuG8efPi4+Nra2sbGxtTUlJYLNaf/vQnPKvfhxNJkh4eHuPHj//111/lcnlFRYW7u3t+fr7KMn5+fgihoKAgPBkbGztv3ry6ujoNvwJpaTAnrZPUjBkzPvroI2pSwyQllUpdXFx6mhxcHh4eMpmMmsRDNCsqKnr9YD+S1FAbrA2l+f49cOCAg4OD8ga0t7f/7rvvGAyGhYVFfX09Va6tJJWbm7ts2bLk5GRnZ2eVJPXBBx9QuRXz9va+ceNGr3UuWrSoo6ODmly1ahVCqLS0lBzA4XTmzBmCIPLy8tQs88svv7i5uSknKZIkfXx8XFxc5HJ5r19Bai9J0bq5V15ezmKx+vqpxMTEqqqqniYHV1paGpfLpSbxI72ps/eRZUg3VFdPnjzZt2/f/v37lTcgQsjV1dXX1/fFixe7du0atmB6MnXq1AsXLqxdu5bD4ajMunr1qlAopCbLysoePHjw3nvv9Vrn5cuXlV+ug1+kKpVK0QAOp6+//nratGmTJ0/uaQGZTObv7x8bG6tSHhISkpub27WcVmiapH766SeJRPLy5cu///3vPTX1//nPfzo5Oenp6XG53MmTJ1+7dg0h5Ovru3PnzuLiYoIgJBKJyiRCSKFQBAcHW1tb83i8KVOm4H/7CQkJAoGAz+dfvHhx4cKFIpHI0tLyzJkzfQ27qKhIX1/fxsZmwBtAVVZWlrW1NUEQR48eVR9wXFwcl8s1MTHZvHmzubk5l8t1dXW9c+cOQsjHx4fNZpuZmeE6t23bJhAICIKoqanpuqGuXr0qEokiIiIGfV2wuLg4kiTd3d27zgoPD3dwcDhx4sT169e7ziVJMiYmZsKECRwOx8DAYOnSpQ8fPlS/TVAP+30QHTx48PPPP+/HB1+8eMHj8ezs7LrO0vBwam9v//XXX52dndUsExQUtG3bNmNjY5VyAwODefPmxcbGknS+jWn4T940bw6Ympp+8skn1KRKc+/cuXMhISGvX7+ura2dNWvWmDFjcPny5cvt7e2pT6lM7tq1i8PhnD9/vq6ubs+ePQwG4969eyRJBgUFIYRu3LjR0NBQVVU1d+5cgUDQ3t6uSZzt7e3l5eVHjhzhcDinTp3S5CP9aO6VlZUhhI4cOYIn1QTs7e0tEAgKCwtbW1sLCgpmzJghFApxg2Lt2rWmpqZUndHR0Qih6upqssuGunz5slAoDA0N7VOQpMb7VywWOzk5qRTa29s/e/aMJMnbt28zGAxbW9vm5mbyP5t7wcHBbDb71KlT9fX1eXl506ZNMzIyqqysVL9NetrvGnrnnXdUmnvKysvLnZycFAqF5hViLS0tQqHQx8dHubCvh9OzZ88QQs7OzvPnzzczM+NwOI6OjkePHu3s7MQLZGVlubu7kyRZXV2N/rO5R5JkYGAgQignJ6fXL0LQ3OurFStWfPHFFwYGBoaGhu7u7rW1tXgfqNHa2pqQkODh4bF8+XJ9ff29e/eyWKykpCRqAVdXV5FIZGxs7Onp2dLSUlpaqkkkVlZWlpaWISEhX3755erVqwe0Vn3UU8BMJhOfazg5OSUkJDQ1NSmvpiYWLVrU2Ni4b9++IYgatbS0PHv2zN7evqcFXFxcduzY8fz587/+9a/K5TKZLCYmZtmyZV5eXnp6epMnT/7mm29qamqOHTtGLdN1m/S63wfo4MGDn332mcpLvTQRGRlpbm4eHh6uXNjXwwm3B42NjSMiIgoKCl69erV06dLt27efPn0aISSTyXx9fRMSEnr6+Lhx4xBC+fn5fQ1+2IzgJKUMd131ej310aNHUql00qRJeJLH45mZmeHGggo2m40QUn79iRplZWVVVVWnT5/++9///tZbbw1nzw5FTcDTp0/n8/ndrqa2VFVVkSTJ5/PVLBMeHj5+/Pj4+PisrCyqsKCgoLm5efr06VTJjBkz2Gw2bs+qoLaJ5vu9HyoqKi5durRu3bq+fjAtLS01NfXatWvKfVuo74cT7i+bOHGiq6uroaGhnp7e/v379fT0cOLes2fPpk2b1LwAEe+FV69e9TX+YTOCk9QPP/wwf/58Y2NjDoeze/duTT7S0tKCENq7dy81sKWkpAT3WQ4Ei8UyNjZ2c3M7e/ZsQUFBZGTkACscdBwOp9fTzOGEH4XetTdaGZfLTUpKIghiw4YNMpkMF9bX1yOEVPoo9fX1m5qa1FQ1RPsdi4qK+vTTT1W6/3t19uzZgwcPZmZm2traqszq6+Fkbm6OEKqpqaFK2Gy2jY1NcXFxVlZWfn7+xo0b1Xycx+Oh3/cIPY3UJFVaWurh4WFmZnbnzp2GhoaoqChNPoU7Dg8fPqzc4s3Ozh6sqCQSiY6OTkFBwWBVOCjkcnl9fb0WHqjYM/zD6PXM18XFxc/Pr6ioKCwsDJfo6+sjhFRSUq9rN3T7vbKy8vTp01u3bu3Tp44cOZKcnHzz5s2xY8eqWUzDw0lXV3fcuHGFhYXKhR0dHXp6eomJiTdu3MCPpiAIAm+HiIgIgiB+++03vGR7ezv6fY/Q00hNUvn5+XK5fOvWrWKxmMvlavh4ECsrKy6Xm5ubOygx1NbWqjzMu6ioSKFQWFlZDUr9gyUzM5MkyVmzZiGEmEymhm3YIYVvHmhoaOh1ybCwMEdHx5ycHDw5adIkXV1d6geGELpz5057e/vbb7+tppLB3e/KoqKivLy8DA0NNVyeJMmAgID8/Pz09HSV88GBHE6rV6/Oycl5+vQpnpRKpSUlJZMnT05KSlLOy8od51STGe8FU1NTDVdh+I3UJGVtbY0Qun79emtra1FRkXKXhKGhYUVFxfPnz5uamuRyufKkjo7O+vXrz5w5k5CQ0NjYqFAoysvLX7582b8YBALBjz/+ePPmzcbGRrlcnpOT88knnwgEAjyuV7s6Ozvr6uo6Ojry8vJ8fX2tra1xp4lEInn9+nV6erpcLq+uri4pKaE+orLdMjIyhm4IAp/PF4vF5eXlvS6JG33UwCIul7tz5860tLTk5OTGxsb8/PwtW7aYm5t7e3urr6Sn/e7p6Wlqatq/u21evXp18uTJHTt2qJSrqbOwsPDLL788fvw4i8VSvp/m0KFD6g8n9XH6+fnZ2NisW7eutLS0trY2ICBAJpOpXHPoCd4LasZYad8QXz3shiaXqJ8/f/7WW28hhJhM5rRp086fP//VV1/hZC8QCJYtW0aSZEBAgKGhob6+/sqVK/HoIXt7+9LS0vv379vY2PB4vDlz5lRWVqpMtrW1BQQEWFtbM5lMY2Pj5cuXFxQUxMfH4+7DcePGFRcXHzt2TCQSIYRsbGweP36sJk53d3c7OztdXV0Oh2Nvb+/p6dn1XoRu9XUIwpEjR/D4Jj6f7+7urj5gb29vFotlYWHBZDJFItHSpUuLi4txPbW1te+++y6Xy7Wzs/vss8/8/f0RQhKJpOt2u3LlilAoDA8P1zxITMMhCD4+PiwWSyqV4sm0tDR8sc/IyGj79u0qC/v7+1NDEDo7O6Ojo8eNG8disQwMDDw8PB49ekSSpPpt0u1+J0nSw8MDIRQcHNxtkNnZ2bNnz8adPgghMzMzV1fXW7du4bl+fn5eXl5dP6Wmzp4uokVHR5NqDyf1cZIkWVZWtmbNGgMDAw6HM3PmzIyMjK7LdDsEYdGiRRYWFtR4BTUQ3BbzRhnS22K8vb0NDQ2HqPJeabh/i4qKmEymhsPKho5CoZg7d25iYuIbWCdJkjU1NVwu99ChQ5osrK0kNVKbe0A9+t/dLpFIQkNDQ0NDtXgXkUKhSE9Pb2pq8vT0fNPqxEJCQpydnX18fAa32sEFSUqdhw8fdn0cB2XQj5g3TWBg4MqVKz09PTXpQR8KmZmZFy5cyMjIUD9ia1TWiRCKiYnJzc29cuVKP+6QHVbDf/IGzT1yKJt7gYGBeBCjra3tuXPnhuIr1Ovr/r127VpAQMDQxQO6lZ6eHhkZqfw8hl6hN+29e2CIREZG0nA0qRpubm74ESJgOC1ZsmTJkiXajkIj0NwDANAaJCkAAK1BkgIA0BokKQAArWmt4xw/v/mN9euvv6JRuhHwbRajctWAVsCZFACA1rR2JnXu3DltfTUd4BONUbkRUlNTV69ePSpX7Q2n4bNGBh2cSQEAaA2SFACA1iBJAQBoDZIUAIDWIEkBAGhtJCWpCxcuiMXibp+aYmtre+jQIfzk7G+++UbbkYKhcv369cDAQOUj4eOPP1ZewM3NTSgU6ujoTJw4sX8PBe630NBQJycnkUjE4XAkEsnu3bupR2WFh4erHLHU+7UQQllZWbNnz+bz+ebm5gEBAW1tbb1WqKy1tdXR0XHv3r0IoUuXLkVFRdH/aWJ9M/wPXhjgo1rs7e319PTw3x0dHVKp9NWrVxMmTCC7vOKYzob0yZzaNXSP4gkODl68eHFjYyOetLe3HzNmDELo8uXLyospv+54OM2bNy8+Pr62traxsTElJYXFYv3pT3/Cs6i33VAmTpyIZz148IDH4+3bt6+5ufn27dtGRkbr16/vtUJl+CHo1EOBY2Nj582bV1dXN+griODJnP2go6PD4/FMTEwcHBw0/5RMJnN1de1pchQYlDWi22Y5ePDg2bNnU1NTlV+lGRcXx2AwvL29tfXYPGW6urr4wc1CoXDVqlUeHh5Xr14tKyvDc1UelPzgwQNcHhYWZmZmtn//foFA4OLiEhAQ8D//8z/4xaXqK8Ru375NVYV9/vnnU6dO/fDDDzs6OoZlvYfcyE5SlPT0dM0XTkxMVH4rrMrkKDAoa0SrzfLkyZN9+/bt379f5R2crq6uvr6+L1682LVrl7Zio1y+fJl6qw1CyMjICCGk/hWkHR0dP/zww7x586hxkgsXLiRJ8uLFi5pUKJPJ/P39Y2NjVaoNCQnJzc3tWj5CjZIk1a1//vOfTk5Oenp6XC538uTJ165dQwj5+vru3LmzuLiYIAiJRKIyiRBSKBTBwcHW1tY8Hm/KlCm48ZKQkCAQCPh8/sWLFxcuXCgSiSwtLc+cOTPUq0CSZExMzIQJEzgcjoGBwdKlS/H/WB8fHzabjd8fgxDatm2bQCAgCKKmpkZljeLi4rhcromJyebNm83NzblcrqurK34DmOaVIISuXr06dG+46lVcXBxJku7u7l1nhYeHOzg4nDhx4vr1613n9rQB1e/Qbo+Bvnrx4gWPx7Ozs1OzzNOnT5ubm/H72TD8ypy8vDxNKgwKCtq2bRt+5acyAwODefPmxcbG4jbaiDf8LcxB7JMiSfLGjRv4dUBklz6pc+fOhYSEvH79ura2dtasWWPGjMHly5cvt7e3p2pQmdy1axeHwzl//nxdXd2ePXsYDMa9e/dIkgwKCkII3bhxo6Ghoaqqau7cuQKBoL29vX9roWGfVHBwMJvNPnXqVH19fV5e3rRp04yMjCorK0mSXLt2rampKbVkdHQ0Qqi6urrrGnl7ewsEgsLCwtbW1oKCghkzZgiFwtLS0j5VcvnyZaFQGBoa2mvMQ9EnJRaLnZycVArt7e2fPXtGkuTt27cZDIatrW1zczP5n31Sajagmh3a0zGguZaWFqFQ6OPjgyfDwsIsLS319fVZLJatre2SJUvu3r1LkuStW7fQ7++zovB4vAULFqivkCTJrKwsd3d3socXVQUGBiKEcnJy+hS2egj6pDTX0NBAXSVZsGBBT4utWLHiiy++MDAwMDQ0dHd3r62txbtTjdbW1oSEBA8Pj+XLl+vr6+/du5fFYiUlJVELuLq6ikQiY2NjT0/PlpaW0tLSQVurLmQyWUxMzLJly7y8vPT09CZPnvzNN9/U1NQcO3asr1UxmUx8NuHk5JSQkNDU1KS8UppYtGhRY2Pjvn37+vrVA9fS0vLs2TN8itEtFxeXHTt2PH/+XOV1mJpswK47tNdjQBORkZHm5ubh4eF48pNPPrl06VJZWVlzc/OZM2dKS0vnzZtXUFCAL+Qpt+kQQiwWSyaTqa9QJpP5+vomJCT0FMC4ceMQQj295m9kGZFJSvlM6h//+IcmH8Hvw+j10uyjR4+kUil1eZjH45mZmeEGggr8soMhfWV5QUFBc3Mz9TpshNCMGTPYbLby65r7Yfr06Xw+v9uVoqeqqiqSJNW/KCU8PHz8+PHx8fFZWVlUYZ82ILVDNT8GepKWlpaamnrt2jWqj9/Kyuqtt97S1dVls9mzZs1KSkqSyWTx8fG4i02lh7u9vZ3H46mvcM+ePZs2bbKwsOgpBry5Xr16pXnYtDUik5Sy+fPn99Rp+sMPP8yfP9/Y2JjD4ezevVuT2lpaWhBCe/fupc7USkpK1Pd9Dp36+nqEkK6urnKhvr5+U1PTAGvmcDi9nlTSR2trK0KIw+GoWQa/jZ0giA0bNlCnIf3bgAM8Bs6ePXvw4MHMzExbW9uelpk8ebKOjs7jx49xh2BjYyM1SyqVtra2Uu9M7rbCrKys/Pz8jRs3qgkDpzm86Ua6EZ+kelJaWurh4WFmZnbnzp2GhoaoqChNPoX7IA8fPqzcJM7Ozh7iYLunr6+PEFL5RdXX11taWg6kWrlcPvBKhhP+vfV6Fuzi4uLn51dUVEQNSurfBhzIMXDkyJHk5OSbN2+OHTtWzWKdnZ2dnZ0cDsfOzk4oFJaUlFCznjx5ghCaMmWKmgoTExNv3LjBYDBwDsUBR0REEATx22+/4WXa29vR75tupBu1SSo/P18ul2/dulUsFnO5XA0fhWNlZcXlcnNzc4c6PE1MmjRJV1eXOuwQQnfu3Glvb3/77bcRQkwms3+NzczMTJIkZ82aNZBKhhO+kUCTkVBhYWGOjo45OTl4Uv0G7En/jgGSJAMCAvLz89PT01XO3RBCH3zwgfIk7oZ3cXFhMpkffvjhzz//3NnZiWdlZGQQBIF7xHuqMCkpSTmBKnecU21bvLlMTU37tBb0NGqTFL6se/369dbW1qKiIuVuCENDw4qKiufPnzc1NcnlcuVJHR2d9evXnzlzJiEhobGxUaFQlJeXv3z5UiurwOVyd+7cmZaWlpyc3NjYmJ+fv2XLFnNzc29vb4SQRCJ5/fp1enq6XC6vrq5W/m+ssoIIoc7Ozrq6uo6Ojry8PF9fX2tr63UK6kBXAAAgAElEQVTr1vWpkoyMDG0NQeDz+WKxGD+VWD3c6KP6odVvQDWV9HQMeHp6mpqadnu3TWFh4Zdffnn8+HEWi6V8+8uhQ4cQQi9evDh79mx9fb1cLs/Ozt64caO1tfWWLVsQQvv27Xv16tUXX3zR0tKSnZ0dHR29bt268ePHq6+wV3hzTZ48WZOF6W5oLhqq0+9L1L/88gs1stzMzEzlMu1XX32F/28IBIJly5aRJBkQEGBoaKivr79y5cqjR48ihOzt7UtLS+/fv29jY8Pj8ebMmVNZWaky2dbWFhAQYG1tzWQyjY2Nly9fXlBQEB8fj3six40bV1xcfOzYMZFIhBCysbF5/PhxP9ZFwyEInZ2d0dHR48aNY7FYBgYGHh4ejx49wrNqa2vfffddLpdrZ2f32Wef+fv7I4QkEknXFfT29maxWBYWFkwmUyQSLV26tLi4uK+VXLlyRSgUhoeH9xrzUAxB8PHxYbFYUqkUT6alpeGLfUZGRtu3b1dZ2N/fnxqC0NMGVL9Duz0GSJL08PBACAUHB3eNsKfraHh4wc6dO+3t7QUCAZPJtLS0/PTTTysqKqjP3rp1a+bMmRwOx9zc3N/fv7W1tdcKlXU7BGHRokUWFhadnZ0D2OqqkJaGIIykJDWaDOe9e/jWiuH5LnJo9m9RURGTyVS5s2T4KRSKuXPnJiYmajeMXtXU1HC53EOHDg1utdpKUqO2uQeUjfTb4iUSSWhoaGhoaLePARgeCoUiPT29qanJ09NTWzFoKCQkxNnZ2cfHR9uBDA5IUmBkCAwMXLlypaenp7buJc7MzLxw4UJGRob6EVtaFxMTk5ube+XKFTw2cBSAJDXK7dmzJykpqaGhwc7O7vz589oOZ0AiIiJ8fHwOHDiglW9fsGDBd999R93qSE8XL15sa2vLzMw0MDDQdiyDRmuvtALDIzIyMjIyUttRDBo3Nzc3NzdtR0FfS5YsWbJkibajGGRwJgUAoDVIUgAAWoMkBQCgNUhSAABagyQFAKA1rV3d0/CO39FtFG+EUbxqYJgR5LA/Bbm8vPz27dvD/KVgeBw+fBghtGPHDm0HAoaEq6vr8D/kRwtJCoxiq1atQgilpqZqOxAwekCfFACA1iBJAQBoDZIUAIDWIEkBAGgNkhQAgNYgSQEAaA2SFACA1iBJAQBoDZIUAIDWIEkBAGgNkhQAgNYgSQEAaA2SFACA1iBJAQBoDZIUAIDWIEkBAGgNkhQAgNYgSQEAaA2SFACA1iBJAQBoDZIUAIDWIEkBAGgNkhQAgNYgSQEAaA2SFACA1iBJAQBoDZIUAIDWIEkBAGgNkhQAgNYgSQEAaA2SFACA1iBJAQBojantAMDIVlNT09jYSE22tLQghJ4+fUqViEQiIyMjLUQGRguCJEltxwBGsKSkpA0bNqhZ4OTJk+vXrx+2eMDoA0kKDEhDQ4OxsbFcLu92LovFqq6u1tPTG+aowGgCfVJgQPT09D788EMms5t+AyaTuWjRIshQYIAgSYGB8vLyUigUXcs7Ozu9vLyGPx4wykBzDwxUa2urkZER7jJXxufza2pqeDyeVqICowacSYGB4nK5y5YtY7FYyoUsFmvFihWQocDAQZICg+Cjjz5S6TuXy+UfffSRtuIBowk098Ag6OjoMDU1ff36NVWir69fXV3dbYc6AH0CZ1JgEDCZzDVr1lAtPhaL5eXlBRkKDApIUmBwrFmzhmrxyeXyNWvWaDceMGpAcw8MDpIkraysXrx4gRAyNzd/8eIFQRDaDgqMBnAmBQYHQRAff/wxm81ms9mffPIJZCgwWOBMCgyavLy8qVOn4j8mT56s7XDAKDFiujZXrlyp7RBA73R1dRFCoaGh2g4E9O7cuXPaDkEjI6a5d/78+fLycm1HMax+/fXXX3/9VdtR9I2NjY2trW2vi5WXl58/f37owwHdG1nbf8Q09wiCSElJWbVqlbYDGT745HGk/LvD8JOkxGKx+sVSU1NXr149Uo690Wdkbf8R09wDI0Kv6QmAvhoxzT0AwJsJkhQAgNYgSQEAaA2SFACA1kZtktq4caNQKCQIIjc3V9uxILlcHhkZKZFI2Gy2vr7+pEmTnj9/PhRfdOXKFT09ve+//34oKtei69evBwYGXrhwQSwWEwSBR7crL+Dm5iYUCnV0dCZOnHj//v3hjC00NNTJyUkkEnE4HIlEsnv37ubmZjwrPDyc+E+TJk2iPpiVlTV79mw+n29ubh4QENDW1tZrhcpaW1sdHR337t2LELp06VJUVFS3z0cdBUZtkjpx4sTx48e1HcX/b/Xq1d9+++13330nlUr//e9/29vbd3vYDdxIuajcJ1988UVcXNyePXuWL1/+9OlTe3v7MWPGJCcn//DDD9QyP/7447lz5xYvXlxQUDBt2rThDO/mzZvbt29//vx5TU1NZGRkbGysJgOPCwoK3NzcFixYUF1dnZaWdvLkyS1btvSpwqCgoEePHuG/3d3duVzuggUL6uvrB3HV6IIcIRBCKSkpffrImTNnEEI5OTlDFJLmYRAEkZeX19cPrlixYsWKFUMRUr9JpVIXF5eB15OSkqLhsXfgwAEHBweZTEaV2Nvbf/fddwwGw8LCor6+nirPyMhYsmTJwGPrq0WLFnV0dFCTeChfaWkpSZJhYWGnTp3q9lOrV6+2s7Pr7OzEk9HR0QRB/Pvf/1ZfIeWXX35xc3NDCAUFBVGFPj4+Li4ucrm815g13/50MGrPpBBCNLnH9euvv542bdrouJctMTGxqqpq2L7uyZMn+/bt279/P5fLVS53dXX19fV98eLFrl27hi2Ynly+fFlHR4eaxG9ClUqlaj7S0dHxww8/zJs3jzpEFy5cSJLkxYsXNalQJpP5+/vHxsaqVBsSEpKbm9u1fKQbVUmKJMno6Ojx48dzOBw9PT1/f39qlkKhCA4Otra25vF4U6ZMwf9JEhISBAIBn8+/ePHiwoULRSKRpaUlPv9CCN26dWvmzJl8Pl8kEk2ePBm/p7fbetRob2//9ddfnZ2dh2yl/09WVpa1tTVBEEePHkVq1y4uLo7L5ZqYmGzevNnc3JzL5bq6ut65cwch5OPjw2azzczMcJ3btm0TCAQEQdTU1Pj6+u7cubO4uJggCIlEghC6evWqSCSKiIgYojWKi4sjSdLd3b3rrPDwcAcHhxMnTly/fr3rXJIkY2JiJkyYwOFwDAwMli5d+vDhQ/XbBPV953brxYsXPB7Pzs5OzTJPnz5tbm62tramSuzt7RFCeXl5mlQYFBS0bds2Y2NjlSUNDAzmzZsXGxtLjrJWv1bP4/oAadDcCwoKIgjiq6++qqurk0ql8fHx6Pfm3q5duzgczvnz5+vq6vbs2cNgMO7du4c/ghC6ceNGQ0NDVVXV3LlzBQJBe3t7c3OzSCSKioqSyWSVlZXLli2rrq5WU09Pnj17hhBydnaeP3++mZkZh8NxdHQ8evQodZ6vRj+ae2VlZQihI0eOUBuk27UjSdLb21sgEBQWFra2thYUFMyYMUMoFOI2xdq1a01NTak6o6OjEUJ49ZcvX25vb0/Nunz5slAoDA0N7VOQpMbNDbFY7OTkpFJob2//7NkzkiRv377NYDBsbW2bm5vJ/2zuBQcHs9nsU6dO1dfX5+XlTZs2zcjIqLKyUv026evO7aqlpUUoFPr4+ODJsLAwS0tLfX19Fotla2u7ZMmSu3fvkiR569YthFB0dLTyZ3k83oIFC9RXSJJkVlaWu7s7SZLV1dXoP5t7JEkGBgYiDbo4RlZzb+QE2luSkkqlfD7//fffp0qoPimZTMbn8z09PaklORzO1q1byd8PWarLA+e1J0+ePHjwACF0+fJl5a9QU09P8vPzEULvv//+L7/8UltbW19f/9e//hUhlJyc3OsqD1aS6rp2JEl6e3vr6elRH7x37x5CaP/+/WRfklS/afIjaW5uJghi8eLFKuVUkiJJcufOnQih7du3k0pJSiqV6urqUruJJMm7d+8ihHAy7Wmb9GPndhUUFOTg4NDY2IgnS0tL79+/39TU1NbWlp2d/dZbb/F4vAcPHvz4448IoZiYGOXPikQiV1dX9RVKpdLp06eXl5eTPSSpkydPIoS+/fZb9XGOrCQ1epp7T548kUqlCxYs6Drr0aNHUqmUuvrL4/HMzMzw+b8KNpuNEJLL5WKx2MTExMvLKyQkhBouoHk9FA6HgxCaOHGiq6uroaGhnp7e/v379fT0jh071v9V7S9q7brOmj59Op/PV78uw6yqqookST6fr2aZ8PDw8ePHx8fHZ2VlUYUFBQXNzc3Tp0+nSmbMmMFms3F7VgW1Tfqxc1WkpaWlpqZeu3ZNKBTiEisrq7feektXV5fNZs+aNSspKUkmk8XHx+Muto6ODuWPt7e3q7wBrGuFe/bs2bRpk4WFRU8x4M316tUrzcOmv9GTpPCDXLo21BFC+L2Ve/fupYarlJSUqO/a5PF4N2/enDNnTkREhFgs9vT0lMlk/ajH3NwcIVRTU0OVsNlsGxub4uLifq3lEOJwOPifM020trai37N8T7hcblJSEkEQGzZskMlkuBBfhsdPtqLo6+s3NTWpqaofO1fZ2bNnDx48mJmZqeZJNZMnT9bR0Xn8+DHu8sO9nJhUKm1tbcVHS08VZmVl5efnb9y4UU0YOM3hTTdqjJ4khf87USPilOHMdfjwYeVzyOzsbPUVTpw48fvvv6+oqAgICEhJSTl06FA/6tHV1R03blxhYaFyYUdHh56eXl9XcEjJ5fL6+npLS0ttB/J/8O+t1wGKLi4ufn5+RUVFYWFhuERfXx8hpJKSel27/h0k2JEjR5KTk2/evDl27Fg1i3V2dnZ2dnI4HDs7O6FQWFJSQs168uQJQmjKlClqKkxMTLxx4waDwcA5FAccERFBEMRvv/2Gl2lvb0e/b7pRY/QkqUmTJjEYDNwlqcLKyorL5fZp6HlFRQXOLMbGxgcOHJg2bVphYWE/6kEIrV69OicnBz9oCSEklUpLSkroNiIhMzOTJMlZs2YhhJhMZrdNwmFmYmJCEERDQ0OvS4aFhTk6Oubk5ODJSZMm6erqUr9bhNCdO3fa29vffvttNZX0b+eSJBkQEJCfn5+enq5y7oYQ+uCDD5QncTe8i4sLk8n88MMPf/75587OTjwrIyODIAjcI95ThUlJScoJVLlPimrb4s1lamrap7WgudGTpIyNjZcvX37+/PnExMTGxsa8vDyq34fL5a5fv/7MmTMJCQmNjY0KhaK8vPzly5dqaquoqNi8efPDhw/b29tzcnJKSkpmzZrVj3oQQn5+fjY2NuvWrSstLa2trQ0ICJDJZLj7XLs6Ozvr6uo6Ojry8vJ8fX2tra3XrVuHEJJIJK9fv05PT5fL5dXV1cr/8A0NDSsqKp4/f97U1CSXyzMyMoZuCAKfzxeLxZo8jhU3+qixRVwud+fOnWlpacnJyY2Njfn5+Vu2bDE3N/f29lZfSU8719PT09TUtNu7bQoLC7/88svjx4+zWCzl218OHTqEEHrx4sXZs2fr6+vlcnl2dvbGjRutra3xyPJ9+/a9evXqiy++aGlpyc7Ojo6OXrdu3fjx49VX2Cu8uej2L3CghqAzfkggDYYgNDU1bdy4ccyYMbq6unPmzAkODkYIWVpa/utf/2prawsICLC2tmYymTidFRQUxMfH447GcePGFRcXHzt2TCQSIYRsbGx++uknV1dXAwMDHR2dsWPHBgUF4UHA3dbTa/BlZWVr1qwxMDDgcDgzZ87MyMjQZJX7enXvyJEjuLODz+e7u7urWbvHjx97e3uzWCwLCwsmkykSiZYuXVpcXIzrqa2tfffdd7lcrp2d3WeffYaHm0kkEnytysbGhsfjzZkzp7Ky8sqVK0KhMDw8XPMgMQ2vLvn4+LBYLKlUiifT0tLweCIjIyN8RU+Zv78/NQShs7MzOjp63LhxLBbLwMDAw8Pj0aNHJEmq3yY97VwPDw+EUHBwcNcI8dXbrvDwgp07d9rb2wsEAiaTaWlp+emnn1ZUVFCfxQPxOByOubm5v79/a2trrxUq6/bq3qJFiywsLHod4DKyru6NnED7flvMSDekt8V4e3sbGhoOUeW90vBHUlRUxGQye7qzZNgoFIq5c+cmJiZqN4xe1dTUcLncQ4cO9brkyEpSo6e5B/qK/jfNSySS0NDQ0NDQIbofWxMKhSI9Pb2pqcnT01NbMWgoJCTE2dnZx8dH24EMMkhSA/Xw4UOiZ/Q/smkuMDBw5cqVnp6emvSgD4XMzMwLFy5kZGSoH7GldTExMbm5uVeuXGGxWNqOZZBBkhooR0dHNWeqZ8+e1XaA3dizZ09SUlJDQ4OdnR39X20UERHh4+Nz4MABrXz7ggULvvvuO+pmRnq6ePFiW1tbZmamgYGBtmMZfPC2mDdRZGRkZGSktqPoAzc3N/xkEtCtJUuWLFmyRNtRDBU4kwIA0BokKQAArUGSAgDQGiQpAACtQZICANAaQY6QJ43S5IHlAIwaI+W3P5KGIPj6+rq4uGg7iuFz+PBhhNCOHTu0Hcjgy87Ojo2N7d9DxMHA4e2v7Sg0NZKSlIuLC369zxvi3LlzCKHRusqxsbGjddVGhBGUpKBPCgBAa5CkAAC0BkkKAEBrkKQAALQGSQoAQGujP0lduHBBLBYrP+OJzWabmJjMnz8/Ojq6rq5O2wGCAbl+/XpgYKDyXv7444+VF3BzcxMKhTo6OhMnTuz2OeVDrbOz8/Dhw66ursqFoaGhTk5OIpGIw+FIJJLdu3crP9jv9OnT+J3SNjY269evr6ysRAhdunQpKiqK/o8qHHzD9ATQAUMDe3ywvb09fmEvfvvAP/7xj3Xr1hEEYW5u3tdXaQ+bIX18sHYN1uNrg4ODFy9eTL3g197efsyYMajLq6eV38A+zB4/fjx79myE0NSpU5XL582bFx8fX1tb29jYmJKSwmKx/vSnP+FZ+BlkUVFR9fX1OTk5YrHY2dlZLpeTJBkbGztv3ry6uroBRjWyHh88cgIdpCSl7Ny5cwwGw8TEpL6+fmDRDYkhTVJSqdTFxUVblQzKj+TAgQMODg7UC9NJkrS3t//uu+8YDIaFhYXyPtVWksrNzV22bFlycrKzs7NKklq0aBF+tQeGh4yVlpaSJPnuu++OHTuWepnC0aNHEUJZWVl40sfHx8XFBeesfhtZSWr0N/fUWLFixbp166qqqr755httxzLcEhMTq6qq6FBJ/zx58mTfvn379+/HL4WluLq6+vr6vnjxYteuXVoJTNnUqVMvXLiwdu3aru9hvnz5MvUOLoSQkZERQgi/MLmsrMzc3Jy6D8zKygohRL1YLCQkJDc3dwQNxRy4NzpJIYTwm+YyMjIQQgqFIjg42NramsfjTZkyBf+3SUhIEAgEfD7/4sWLCxcuFIlElpaWZ86cwR/HbyXi8/kikWjy5Mn4xdnd1jN0SJKMiYmZMGECh8MxMDBYunTpw4cPEUI+Pj5sNpt67u22bdsEAgFBEDU1Nb6+vjt37iwuLiYIQiKRxMXFcblcExOTzZs3m5ubc7lcV1fXO3fu9KkShNDVq1eH7jV8KuLi4kiSdHd37zorPDzcwcHhxIkT169f7zq3p82lfkcP9T598eIFj8ezs7NDCInFYuXUjzukxGIxnjQwMJg3b15sbCw5Qu68GwTaPZHTHBqC5h5JkjitWFlZkSS5a9cuDodz/vz5urq6PXv2MBgM3F0VFBSEELpx40ZDQ0NVVdXcuXMFAkF7e3tzc7NIJIqKipLJZJWVlcuWLauurlZTT19p2NwLDg5ms9mnTp2qr6/Py8ubNm2akZFRZWUlSZJr1641NTWlloyOjkYI4SCXL19ub29PzfL29hYIBIWFha2trQUFBbjXFrc+NK/k8uXLQqEwNDS015gH3twQi8VOTk4qhfb29s+ePSNJ8vbt2wwGw9bWtrm5mfzP5p6azdXTjiYHvE/feecdleaespaWFqFQ6OPjgyczMzNZLFZcXFxjY+ODBw8mTJjwwQcfKC8fGBiIEMrJydE8ABUjq7k3cgIdmiRFkiRBEPr6+jKZjM/ne3p64kKpVMrhcLZu3Ur+fuxSfR/x8fEIoSdPnjx48AB16aNVU09faZKkpFKprq4u9XUkSd69exchhDNFn5KU8va5d+8eQmj//v19qkRzA/yRNDc3EwSxePFilXIqSZEkuXPnToQQfocolaTUb66edvTA96n6JBUUFOTg4EB1/5MkuXfvXuo0wtLSsqysTHn5kydPIoS+/fZbzQNQMbKS1Jve3GtpaSFJUiQSPXr0SCqVTpo0CZfzeDwzMzPcEFDBZrMRQnK5XCwWm5iYeHl5hYSEPH/+HM/VvJ5BUVBQ0NzcPH36dKpkxowZbDYbN9b6bfr06Xw+f+jCHqCqqiqSJNW/Yyo8PHz8+PHx8fFZWVlUYZ82F7Wjh3SfpqWlpaamXrt2TSgU4pKgoKBjx47duHGjubn56dOnrq6uLi4uZWVl1Efwir969WpQAqC/Nz1JPX78GCHk6OjY0tKCENq7dy81nKqkpAR3ZPaEx+PdvHlzzpw5ERERYrHY09NTJpP1o56BqK+vRwjp6uoqF+rr6zc1NQ2wZg6Hg1/kTUOtra0Ioa690cq4XG5SUhJBEBs2bJDJZLiwf5tr6Pbp2bNnDx48mJmZaWtri0tevnwZFRW1adOm9957TyAQ2NnZHT9+vKKiAp/AYjweD/2+Ed4Eb3qSunr1KkJo4cKFxsbGCKHDhw8rn2dmZ2er//jEiRO///77ioqKgICAlJSUQ4cO9a+eftPX10cIqfzG6uvrLS0tB1KtXC4feCVDB/9Kex3W6OLi4ufnV1RUFBYWhkv6t7mGaJ8eOXIkOTn55s2bY8eOpQqLiooUCoVyiUgkMjQ0LCgooEra29vR7xvhTfBGJ6nKysrDhw9bWlpu2LDBysqKy+Xm5uZq/vGKiorCwkKEkLGx8YEDB6ZNm1ZYWNiPegZi0qRJurq6v/32G1Vy586d9vb2t99+GyHEZDLlcnk/qs3MzCRJctasWQOpZOiYmJgQBKHJO43DwsIcHR1zcnLwpPrN1ZNB36ckSQYEBOTn56enp6uc1uF0+fLlS6qkqanp9evXeCAChlfc1NR0sOKhuTcoSZEk2dzcjMfIVVdXp6SkzJ49W0dHJz09XSQScbnc9evXnzlzJiEhobGxUaFQlJeXKx8rXVVUVGzevPnhw4ft7e05OTklJSWzZs3qRz0DweVyd+7cmZaWlpyc3NjYmJ+fv2XLFnNzc29vb4SQRCJ5/fp1enq6XC6vrq6mxtoghAwNDSsqKp4/f97U1IRzEB6L39HRkZeX5+vra21tjcdnaF5JRkbG8AxB4PP5YrG4vLy81yVxo48akaR+c6mppKd96unpaWpq2te7bQoLC7/88svjx4+zWCzlG7YOHTpkZ2f37rvvHj9+/Oeff5bJZGVlZTi2v/zlL9TH8YpPnjy5T186gg1tv/zgQf29unfp0qUpU6bw+Xw2m81gMBBC+HLezJkzQ0NDa2trqSXb2toCAgKsra2ZTKaxsfHy5csLCgri4+NxP+W4ceOKi4uPHTsmEokQQjY2Nj/99JOrq6uBgYGOjs7YsWODgoLwGOJu6+lH5BoOQejs7IyOjh43bhyLxTIwMPDw8Hj06BGeVVtb++6773K5XDs7u88++8zf3x8hJJFISktL79+/b2Njw+Px5syZU1lZ6e3tzWKxLCwsmEymSCRaunRpcXFxXyu5cuWKUCgMDw/vNeaBX13y8fFhsVhSqRRPpqWl2dvbI4SMjIzwFT1l/v7+1BCEnjaXmh39+PHjnvaph4cHQig4OLjbILOzs2fPnm1ubo5/a2ZmZq6urrdu3crPz+/2xxgdHU2SJB6DJpFIOByOrq7u7Nmz//d//1e52kWLFllYWFBD0vthZF3dGzmBDmwIwkg0nPfueXt7GxoaDs93kYPxIykqKmIymadOnRqskPpHoVDMnTs3MTFx2L6xpqaGy+UeOnRoIJWMrCT1BjX3gHoj6/Z6iUQSGhoaGhqq/PCAYaZQKNLT05uamjw9PYftS0NCQpydnX18fIbtG7UOkhQYqQIDA1euXOnp6alJD/pQyMzMvHDhQkZGhvoRW4MoJiYmNzf3ypUrLBZreL6RDiBJAbRnz56kpKSGhgY7O7vz589rO5w+iIiI8PHxOXDggFa+fcGCBd999x11Y+NQu3jxYltbW2ZmpoGBwfB8I02MpFdagSESGRkZGRmp7Sj6yc3Nzc3NTdtRDIclS5YsWbJE21FoAZxJAQBoDZIUAIDWIEkBAGgNkhQAgNZGUsf50N2mS0/47ofU1FRtBzL48K4clas2IoysnxJBjpCHkFKPfAYADIoR89sfKYGCEQG/9QROkcAggj4pAACtQZICANAaJCkAAK1BkgIA0BokKQAArUGSAgDQGiQpAACtQZICANAaJCkAAK1BkgIA0BokKQAArUGSAgDQGiQpAACtQZICANAaJCkAAK1BkgIA0BokKQAArUGSAgDQGiQpAACtQZICANAaJCkAAK1BkgIA0BokKQAArUGSAgDQGiQpAACtQZICANAaJCkAAK1BkgIA0BokKQAArUGSAgDQGiQpAACtQZICANAaJCkAAK0xtR0AGNl+/vnn7OxsavLhw4cIoaioKKrExcXlD3/4gxYiA6MFQZKktmMAI9iNGzf++Mc/slgsBkP1rLyzs1Mul1+/fn3BggVaiQ2MDpCkwIB0dnaamZlVV1d3O9fIyKiyslJHR2eYowKjCfRJgQFhMBhr165ls9ldZ7HZbC8vL8hQYIAgSYGBWrNmTXt7e9fy9vb2NWvWDH88YJSB5h4YBLa2tiUlJSqFVlZWJSUlBEFoJSQwasCZFBgEH3/8MYvFUi5hsVjr1q2DDAUGDs6kwCB4+PDhhAkTVFVI10IAABdOSURBVAofPHgwceJErcQDRhM4kwKDwNHRceLEicrnTU5OTpChwKCAJAUGx5///GfqQh6Lxfrkk0+0Gw8YNaC5BwZHWVmZjY0NPpwIgnj69Kmtra22gwKjAZxJgcFhZWX1zjvvMBgMBoPxzjvvQIYCgwWSFBg0H3/8MUEQDAbj448/1nYsYPSA5h4YNDU1NWZmZgihiooKExMTbYcDRgtyhEtJSdH2JgSAvlJSUrT9Gx2oUfKoljctVa1evdrX19fFxUXbgaj6+eefCYKYO3duv2s4fPgwQmjHjh2DF9Sba/Xq1doOYRCMkiS1atUqbYcwrFavXu3i4kLDtV64cCFCSCgU9ruGc+fOoTdvhw4RSFIAqBpIegKgW3B1DwBAa5CkAAC0BkkKAEBrkKQAALT2JiapjRs3CoVCgiByc3O1G8n8+fOJLnR1dYfiu65cuaKnp/f9998PReVadP369cDAwAsXLojFYrwBVca7u7m5CYVCHR2diRMn3r9/f/gj7OzsPHz4sKurq3JhaGiok5OTSCTicDgSiWT37t3Nzc3U3NOnT8+YMUMoFNrY2Kxfv76yshIhdOnSpaioKIVCMdwroG1vYpI6ceLE8ePHtR1Fj+bMmTMU1ZKj8daCL774Ii4ubs+ePcuXL3/69Km9vf2YMWOSk5N/+OEHapkff/zx3LlzixcvLigomDZt2jBHWFRU9Ic//MHPz08qlSqX37x5c/v27c+fP6+pqYmMjIyNjV25ciWelZKSsnbt2pUrV5aXl1+8ePHnn39euHBhR0eHu7s7l8tdsGBBfX39MK+Fdr2JSYo+uFxuY2Oj8uBab2/v3bt3D8V3LVq0qKGhYfHixUNROUJIJpOpnCwMtYMHD549ezY1NVV53ENcXByDwfD29m5oaBjOYLr1r3/9669//euWLVucnZ1VZunq6np7exsaGgqFwlWrVnl4eFy9erWsrAwh9Le//W3s2LH+/v56enrOzs5+fn65ubl37txBCH3++edTp0798MMPOzo6tLA+WvKGJimaPNb26tWryj+wsrKyBw8evPfee1oMqd8SExOrqqqG7euePHmyb9++/fv3c7lc5XJXV1dfX98XL17s2rVr2ILpydSpUy9cuLB27VoOh6My6/Lly8rv0TEyMkII4bOtsrIyc3Nz6hC1srJCCFGPkA8JCcnNzY2NjR2G+GniTUlSJElGR0ePHz+ew+Ho6en5+/tTsxQKRXBwsLW1NY/HmzJlCr7DJiEhQSAQ8Pn8ixcvLly4UCQSWVpanjlzBn/k1q1bM2fO5PP5IpFo8uTJjY2NPdXTJwcPHvz8888HaY3/Q1ZWlrW1NUEQR48eRWrXLi4ujsvlmpiYbN682dzcnMvlurq64n/jPj4+bDYb30KMENq2bZtAICAIoqamxtfXd+fOncXFxQRBSCQShNDVq1dFIlFERMRQrA6OkyRJd3f3rrPCw8MdHBxOnDhx/fr1rnNJkoyJiZkwYQKHwzEwMFi6dCl+67L6PT7wnaveixcveDyenZ0dQkgsFiune9whJRaL8aSBgcG8efNiY2NHZfu9e1q4X3BQ4cOl18WCgoIIgvjqq6/q6uqkUml8fDxCKCcnhyTJXbt2cTic8+fP19XV7dmzh8Fg3Lt3D38EIXTjxo2Ghoaqqqq5c+cKBIL29vbm5maRSBQVFSWTySorK5ctW1ZdXa2mHg2Vl5c7OTkpFApNFkZ9v3EUNyWOHDlCbZBu144kSW9vb4FAUFhY2NraWlBQgHtwS0tLSZJcu3atqakpVWd0dDRCCK/+8uXL7e3tqVmXL18WCoWhoaF9CpIkyRUrVqxYsaLXxcRisZOTk0qhvb39s2fPSJK8ffs2g8GwtbVtbm4mSTIjI2PJkiV4meDgYDabferUqfr6+ry8vGnTpuE3mKrfJgPcue+8887UqVN7mtvS0iIUCn18fPBkZmYmi8WKi4trbGx88ODBhAkTPvjgA+XlAwMDqaNXvX4cJzT0RiQpqVTK5/Pff/99qgT/h8zJyZHJZHw+39PTk1qSw+Fs3bqV/P2QlclkeBbOa0+ePHnw4AFC6PLly8pfoaYeDW3fvv3rr7/WcOHBSlJd144kSW9vbz09PeqD9+7dQwjt37+f7EuS6jdNklRzczNBEIsXL1Ypp5IUSZI7d+5ECG3fvp1USlJSqVRXV5faTSRJ3r17FyGEk2lP22TgO1d9kgoKCnJwcFDundy7dy91GmFpaVlWVqa8/MmTJxFC3377ba/fOzqS1BvR3Hvy5IlUKl2wYEHXWY8ePZJKpZMmTcKTPB7PzMwMn/+rwC/plcvlYrHYxMTEy8srJCTk+fPnfa2nWxUVFZcuXVq3bl3fVmzwUGvXddb06dP5fL7m6zIMqqqqSJLk8/lqlgkPDx8/fnx8fHxWVhZVWFBQ0NzcPH36dKpkxowZbDYbt2dVUNtkgDtXvbS0tNTU1GvXrlG9k0FBQceOHbtx40Zzc/PTp09dXV1dXFzw/xgMr/irV68GJQD6eyOSVHl5OULI2Ni466yWlhaE0N69e6lhSiUlJSpXi1XweLybN2/OmTMnIiJCLBZ7enrKZLJ+1KMsKirq008/VekDpg8Oh1NdXa3tKP5Pa2srQqhrb7QyLpeblJREEMSGDRtkMhkuxBfvVUai6evrNzU1qalqgDtXjbNnzx48eDAzM5N62vLLly+joqI2bdr03nvvCQQCOzu748ePV1RU4JNWjMfjod83wpvgjUhS+Mff1tbWdRbOXIcPH1Y+vczOzlZf4cSJE7///vuKioqAgICUlJRDhw71rx6ssrLy9OnTW7du7fOKDQu5XF5fX29paantQP4P/pX2OqzRxcXFz8+vqKgoLCwMl+jr6yOEVFJSr2s3kJ2rxpEjR5KTk2/evDl27FiqsKioSKFQKJeIRCJDQ8OCggKqBL/UHm+EN8EbkaQmTZrEYDBu3brVdZaVlRWXy+3T0POKiorCwkKEkLGx8YEDB6ZNm1ZYWNiPeihRUVFeXl6Ghob9+OwwyMzMJEly1qxZCCEmk9ltk3CYmZiYEAShyUiosLAwR0fHnJwcPDlp0iRdXd3ffvuNWuDOnTvt7e1vv/22mkoGsnO7RZJkQEBAfn5+enq6ymkdTpcvX76kSpqaml6/fo0HImB4xU1NTQcrHpp7I5KUsbHx8uXLz58/n5iY2NjYmJeXd+zYMTyLy+WuX7/+zJkzCQkJjY2NCoWivLxc+RDpqqKiYvPmzQ8fPmxvb8/JySkpKZk1a1Y/6sFevXp18uRJuj2IsrOzs66urqOjIy8vz9fX19raGveXSSSS169fp6eny+Xy6upqavAOQsjQ0LCiouL58+dNTU1yuTwjI2PohiDw+XyxWIxb8erhRt//197dx7RR/gEAfwptuRZaKQM6BgNbiiKMueC2jAIRJGkyibwMMU3ERKemm0q3wRCBDbHjVQgsGMiyifwBc+NVQAXipumMcS4mA4cQXsQBm1gKyGiB8tbe749Lmv4YLX2hvaM8n/96d/3e9+4pX9q757lH2yMJQZD09PTW1tb6+nqFQtHX13f69GkvLy+RSGQ4iL7GFQqFbDbb1NE2AwMDn3/++bVr1ygUiu6IqLKyMg6HEx0dfe3atZ9//lmlUj169AjL7d1339W+HTvwkJAQk3a6g1n90ryVGdkFQalUvvfee3v27HFxcYmIiMjNzQUA+Pj4/PHHHysrK5mZmb6+vmQyGStn/f39VVVV2OXJgICA0dHRq1evMplMAICfn9+tW7f4fD6LxXJ0dNy3b19OTs76+jqKopvG2TKxtLS0lJQUU48amHjX5osvvsD6N9Hp9Li4OANHNzw8LBKJKBSKt7c3mUxmMpkJCQmjo6NYnNnZ2ejoaARBOBxOamoq1t2Mx+NNTEzcv3/fz8+PRqNFRETIZLLOzk4Gg5Gfn2/qoRnZBUEsFlMolKWlJexla2urv78/AMDd3R27o6crIyND2wVBo9GUlpYGBARQKBQWi5WYmDg0NISiqOFzoq9xExMTAQC5ubmbJnn37t3w8HAvLy/sb23v3r18Pv/OnTt9fX2b/jGWlpaiKIr1O+PxeE5OTi4uLuHh4d98841u2NjYWG9vb41Gs+VZMvVzQky7pUjZGat++LDhGlYKviUji9TIyAiZTK6rq7NBSgao1erIyMiamhqb7XFmZgZBkLKyMmM2to8itSt+7kGmIv5Qex6PJ5FIJBKJ7sMDbEytVre1tSmVSqFQaLOd5uXlHTp0SCwW22yPuINFyooGBweffhKLli0/2XYpKysrOTlZKBTiNZZYKpW2tLR0dXUZ7rG1jcrLy3t7ezs7OykUim32SASwSFlRYGCggS+xN2/exDvBTWRnZ9fW1s7Pz3M4nObmZrzT2UJBQYFYLC4qKsJl7zExMdevX9cOZrS29vb2lZUVqVTKYrFss0eCgLPFQP+nsLCwsLAQ7yxMIBAIBAIB3lnYQnx8fHx8PN5Z4AB+k4IgiNBgkYIgiNBgkYIgiNBgkYIgiNDs5MJ5Y2Mj3inYmuUDXIkJG/OxCxsU0stm3UatZNsf5ApB9sQOepzbyTcpdPc87xkAAACJRGpoaHjjjTfwTmT7YTM7NTU14Z2IPSDIhCMWgtekIAgiNFikIAgiNFikIAgiNFikIAgiNFikIAgiNFikIAgitN1bpFpaWrhcru4DnqhUqqenZ1RUVGlp6dzcHN4JQpa6fft2VlaWbkO/9dZbuhsIBAIGg+Ho6BgcHGzqc8otFBUV9fQjxlxcXDo6OkpKSoj/0EFb2r1FKikp6e+///b398dm69VoNHK5vLGxkcPhZGZmBgcH684pAu04n376aWVlZXZ2trah9+zZU19f//3332u3+eGHH5qaml577bX+/v7Q0FAcs8VERETExcUhCBITE4NNEQiB3VykNiCRSK6urlFRUbW1tY2NjVNTU7GxsXg98hFHKpWKz+cTIYgliouLb9682djYqJ0WGABQWVnp4OAgEomI0KwIgujOq46iqEgk+vjjjwEAZ86cefHFF1999dX19XW80yQEWKQ28frrr7/99ttyufzKlSt452JrNTU1crmcCEHM9tdff128ePGzzz7bMCM0n88/e/bsP//8c/78ebxy0+ru7tYtoI8ePfrzzz9feeUV7GVeXl5vb+/ly5dxyo5YYJHaHDbNXFdXFwBArVbn5ub6+vrSaLSDBw9iowWrq6udnZ3pdHp7e/vx48eZTKaPj8+NGzewt9+5c+fo0aN0Op3JZIaEhCgUCn1xrAdF0fLy8hdeeMHJyYnFYiUkJAwODgIAxGIxlUrVPvT2ww8/dHZ2JpFI2ExK6enpo6OjJBKJx+NVVlYiCOLp6Xnq1CkvLy8EQfh8/r1790wKAgDo7u623hx8T6usrERRNC4u7ulV+fn5zz333Jdffnn79u2n1+o7Y4bbeluatbi4+MyZM9qXLBbr5Zdfvnz58m4b77U52w8X3F4WTmmlvSa1AVZW9u/fj6Lo+fPnnZycmpub5+bmsrOzHRwcfv/9dxRFc3JyAAA//vjj/Py8XC6PjIx0dnZeXV1dWFhgMpklJSUqlUomk504cWJ6etpAHDMAIwaO5ubmUqnUurq6J0+ePHjwIDQ01N3dXSaToSj65ptvstls7ZalpaUAACzJpKQkf39/7SqRSOTs7DwwMLC8vNzf33/kyBEGgzExMWFSkO+++47BYEgkEmMOzcgprQzgcrlBQUEbFvr7+z98+BBF0V9//dXBweHZZ59dWFhAUbSrq0s7JZ+BM6avrdHtaNbHjx8HBQWp1WrdhVlZWQCAnp4eM88CiqJwSiv7xmAwSCSSUqlcXl6urq5OTExMSkpydXW9cOEChUKpra3Vbsnn85lMpoeHh1AoXFxcnJiYGBsbUygUwcHBCIKw2eyWlhZ3d/ct42wvlUpVXl5+4sSJlJSUZ555JiQk5MqVKzMzM9qpm41HJpOxLxdBQUHV1dVKpdLUtGNjYxUKxcWLF03dtRkWFxcfPnyITRS6qbCwsHPnzo2NjX3yySe6y405Y0+39bY0a3FxcWpqqoPD//0xBgQEAAD0TSO6q8AitbnFxUUURZlM5tDQ0NLS0oEDB7DlNBpt79692K+ADahUKgBgbW2Ny+V6enqmpKTk5eWNjY1ha42Psy36+/sXFhYOHz6sXXLkyBEqlYr9WDPb4cOH6XS69dK2nFwuR1HU8BxT+fn5zz//fFVV1S+//KJdaNIZ07a15c06OTnZ0dGBXV7QhR3C1NSU8aHsFSxSmxseHgYABAYGLi4uAgAuXLig7cwyPj6+tLRk4L00Gu2nn36KiIgoKCjgcrlCoVClUpkRxxLYDWwXFxfdha6urkql0sLITk5O09PTFgaxnuXlZQCAk5OTgW0QBKmtrSWRSCdPnlSpVNhC886Y5c1aUlLy/vvvb7jGDwCg0Wjaw9nlYJHaXHd3NwDg+PHjHh4eAICKigrdH8lbPhUzODj422+/nZyczMzMbGhoKCsrMy+O2VxdXQEAG/7Anjx54uPjY0nYtbU1y4NYFfa3vWVnyLCwsLS0tJGRkUuXLmFLzDtjFjarTCb7+uuvP/jgg6dXra6uag9nl4NFahMymayiosLHx+fkyZP79+9HEKS3t9f4t09OTg4MDAAAPDw8ioqKQkNDBwYGzIhjiQMHDri4uOj2R713797q6upLL70EACCTyWtra2aElUqlKIoeO3bMkiBW5enpSSKRjOkJdenSpcDAwJ6eHuyl4TOmj4XNWlJSkpKS4ubm9vQq7BDYbLZ5ke0JLFIARdGFhQWNRoOi6PT0dENDQ3h4uKOjY1tbG5PJRBDknXfeuXHjRnV1tUKhUKvVjx8//vfffw0EnJycPHXq1ODg4Orqak9Pz/j4+LFjx8yIYwkEQdLT01tbW+vr6xUKRV9f3+nTp728vEQiEQCAx+P9999/bW1ta2tr09PT4+Pj2je6ublNTk6OjY0plUqsBmk0mrm5ufX19QcPHpw9e9bX1xe7gGJ8kK6uLpt1QaDT6VwuF3tQumHYjz5HR0ftSwNnzEAQfc0qFArZbLaB0TZTU1NfffXVuXPnNl2LHUJISMiWB2L/rHz30OrM7oLQ0dFx8OBBOp1OpVKxGytYp/OjR49KJJLZ2VntlisrK5mZmb6+vmQy2cPDIykpqb+/v6qqCru0GRAQMDo6evXqVSaTCQDw8/O7desWn89nsViOjo779u3LyclZX1/XF8e8owZG3FrWaDSlpaUBAQEUCoXFYiUmJg4NDWGrZmdno6OjEQThcDipqakZGRkAAB6PNzExcf/+fT8/PxqNFhERIZPJRCIRhULx9vYmk8lMJjMhIWF0dNTUIJ2dnQwGIz8/35hDs7wLglgsplAoS0tL2MvW1lbsZp+7u/tHH320YeOMjAxtFwR9Z8xAWw8PD+tr1sTERABAbm6uvjzT0tJSUlL0rY2NjfX29sb+d5rNmM8J8e3eIrWj2ezDJxKJ3NzcbLAjLcuL1MjICJlMrqur266UzKNWqyMjI2tqasx478zMDIIgZWVlFuZgH0UK/tyDtrDjRuTzeDyJRCKRSBYWFvDKQa1Wt7W1KZVKoVBoxtvz8vIOHTokFou3PbGdCBYpyA5lZWUlJycLhUK8xhJLpdKWlpauri7DPbY2VV5e3tvb29nZSaFQrJHbjgOLFKRXdnZ2bW3t/Pw8h8Npbm7GOx3TFBQUiMXioqIiXPYeExNz/fp17dhG47W3t6+srEilUhaLZY3EdiI7mXcPsobCwsLCwkK8szCfQCAQCAR4Z2Ga+Pj4+Ph4vLMgFvhNCoIgQoNFCoIgQoNFCoIgQoNFCoIgQrOTC+fJycl4p2BrFRUVTU1NeGex/X777TewKxsU0oeE7vDnk969e7e8vBzvLCCIoNLS0sLCwvDOwiI7vkhBEGTf4DUpCIIIDRYpCIIIDRYpCIIIDRYpCIII7X9sEMnxpxT1uAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Accuracy of the model\n",
        "loss, accuracy = model.evaluate(x_test, y_test, verbose=1)\n",
        "loss_v, accuracy_v = model.evaluate(x_validate, y_validate, verbose=1)\n",
        "print(\"Validation: accuracy = %f  ;  loss_v = %f\" % (accuracy_v, loss_v))\n",
        "print(\"Test: accuracy = %f  ;  loss = %f\" % (accuracy, loss))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLP2U9OtQ_YI",
        "outputId": "192629b1-12d5-419a-83c8-5756dc66cbdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 6s 86ms/step - loss: 1.4597 - accuracy: 0.7284\n",
            "51/51 [==============================] - 5s 90ms/step - loss: 1.7726 - accuracy: 0.7080\n",
            "Validation: accuracy = 0.708047  ;  loss_v = 1.772576\n",
            "Test: accuracy = 0.728407  ;  loss = 1.459675\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#confusion matrix\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')\n",
        "\n"
      ],
      "metadata": {
        "id": "mERzhTNeRMhJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict the values from the validation dataset\n",
        "Y_pred = model.predict(x_validate)\n",
        "# Convert predictions classes to one hot vectors \n",
        "Y_pred_classes = np.argmax(Y_pred,axis = 1) \n",
        "# Convert validation observations to one hot vectors\n",
        "Y_true = np.argmax(y_validate,axis = 1) \n",
        "# compute the confusion matrix\n",
        "confusion_mtx = confusion_matrix(Y_true, Y_pred_classes)\n",
        "\n",
        "\n",
        "# plot the confusion matrix\n",
        "plot_confusion_matrix(confusion_mtx, classes = range(7)) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "AqT-Igi4SJYU",
        "outputId": "49f4ecab-d50b-4e62-ecab-245fa39fb4eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51/51 [==============================] - 5s 91ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Note:"
      ],
      "metadata": {
        "id": "Tiikd1Fjv-eX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The aim of this project was to gain familiarity with the techniques necessary to perform image classification and to develop a better understanding of the issue of skin cancer. However, there is significant room for improvement in this work, and future efforts should consider implementing the following steps:\n",
        "\n",
        "- Incorporate a stage for optimizing hyperparameters to improve model performance.\n",
        "- Enhance data augmentation techniques to increase the diversity and quality of the dataset.\n",
        "- Due to class imbalance in the dataset, utilize F1 score instead of accuracy to evaluate model performance.\n",
        "- Apply various cross-validation techniques to ensure that the model is robust and generalizes well.\n",
        "- Experiment other CNN architectures"
      ],
      "metadata": {
        "id": "1P53YNDgwCyu"
      }
    }
  ]
}