Ensemble_Boosting_AdaBoost.ipynb

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      "cell_type": "code",
      "execution_count": 25,
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        "id": "pZJ8rX8Fr-Jd"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import sklearn"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Imported the dataset from kaggle"
      ],
      "metadata": {
        "id": "gNwByBUk073c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = \"https://www.kaggle.com/datasets/ikjotsingh221/obesity-risk-prediction-cleaned/data\""
      ],
      "metadata": {
        "id": "5H0R_uQoz0u8"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df  = pd.read_csv(\"/content/data.csv\")"
      ],
      "metadata": {
        "id": "bbVi3BuN14pm"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data preprocessing and EDA"
      ],
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        "id": "J1tn_M7M2Avv"
      }
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      "source": [
        "df.head()"
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        "outputId": "1be78e1b-6d16-4ff1-f8e6-a08055863e8d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Height  Weight  family_history_with_overweight  SCC  MTRANS_Walking  \\\n",
              "0    1.62    64.0                               1    0               0   \n",
              "1    1.52    56.0                               1    1               0   \n",
              "2    1.80    77.0                               1    0               0   \n",
              "3    1.80    87.0                               0    0               1   \n",
              "4    1.78    89.8                               0    0               0   \n",
              "\n",
              "     FAVC_z  FCVC_minmax     NCP_z  CAEC_minmax  CH2O_minmax  FAF_minmax  \\\n",
              "0  2.766876          0.5  0.404704     0.333333          0.5    0.000000   \n",
              "1  2.766876          1.0  0.404704     0.333333          1.0    1.000000   \n",
              "2  2.766876          0.5  0.404704     0.333333          0.5    0.666667   \n",
              "3  2.766876          1.0  0.404704     0.333333          0.5    0.666667   \n",
              "4  2.766876          0.5  2.164116     0.333333          0.5    0.000000   \n",
              "\n",
              "      TUE_z    CALC_z  Age_bin_minmax  NObeyesdad  \n",
              "0  0.550985  1.439033            0.25           1  \n",
              "1  1.092724  0.516552            0.25           1  \n",
              "2  0.550985  2.472136            0.50           1  \n",
              "3  1.092724  2.472136            0.75           2  \n",
              "4  1.092724  0.516552            0.50           3  "
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              "      <th>Height</th>\n",
              "      <th>Weight</th>\n",
              "      <th>family_history_with_overweight</th>\n",
              "      <th>SCC</th>\n",
              "      <th>MTRANS_Walking</th>\n",
              "      <th>FAVC_z</th>\n",
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              "      <th>NCP_z</th>\n",
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              "      <th>CH2O_minmax</th>\n",
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              "      <th>2</th>\n",
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       \"dtype\": \"number\",\n        \"std\": 0.7691201371849753,\n        \"min\": 0.3614184641562495,\n        \"max\": 2.766875794059256,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.3614184641562495,\n          2.766875794059256\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FCVC_minmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26749321542595694,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 802,\n        \"samples\": [\n          0.5430465,\n          0.1588644999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NCP_z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6457613564198882,\n        \"min\": 0.0023753474098138,\n        \"max\": 2.1641155666736696,\n        \"num_unique_values\": 632,\n        \"samples\": [\n          0.0725725393717614,\n          0.780979831652579\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CAEC_minmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15595559184951738,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.6666666666666666,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CH2O_minmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3065783300550289,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1258,\n        \"samples\": [\n          0.081632,\n          0.3758614999999999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"FAF_minmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2836868481579644,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 1183,\n        \"samples\": [\n          0.2347453333333333,\n          0.3111983333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"TUE_z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5377257862194899,\n        \"min\": 0.0001455502042623,\n        \"max\": 2.1946944415165657,\n        \"num_unique_values\": 1129,\n        \"samples\": [\n          1.2353485574254337,\n          0.3508769101797226\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CALC_z\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5180636249047574,\n        \"min\": 0.5165517806528838,\n        \"max\": 4.427720617102668,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.5165517806528838,\n          4.427720617102668\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age_bin_minmax\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35372283866942583,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NObeyesdad\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-6EbZX1f3Wmy",
        "outputId": "dd1b3f23-a276-49fc-c6c7-b4da1c30ee14"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2086, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETP2NRhlzuGj",
        "outputId": "fb503c22-2dea-4c68-d917-5efc00f48ede"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2086 entries, 0 to 2085\n",
            "Data columns (total 15 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   Height                          2086 non-null   float64\n",
            " 1   Weight                          2086 non-null   float64\n",
            " 2   family_history_with_overweight  2086 non-null   int64  \n",
            " 3   SCC                             2086 non-null   int64  \n",
            " 4   MTRANS_Walking                  2086 non-null   int64  \n",
            " 5   FAVC_z                          2086 non-null   float64\n",
            " 6   FCVC_minmax                     2086 non-null   float64\n",
            " 7   NCP_z                           2086 non-null   float64\n",
            " 8   CAEC_minmax                     2086 non-null   float64\n",
            " 9   CH2O_minmax                     2086 non-null   float64\n",
            " 10  FAF_minmax                      2086 non-null   float64\n",
            " 11  TUE_z                           2086 non-null   float64\n",
            " 12  CALC_z                          2086 non-null   float64\n",
            " 13  Age_bin_minmax                  2086 non-null   float64\n",
            " 14  NObeyesdad                      2086 non-null   int64  \n",
            "dtypes: float64(11), int64(4)\n",
            "memory usage: 244.6 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr() > 0.7, cmap = \"RdBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "KF5L8kOSz7MU",
        "outputId": "9a51e257-4443-4568-f720-54a33ba68d9c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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5zny7urpixowZhb7wJUuWwMzMDFWqVMHIkSORlZUlHXt7VlkURQQFBaFmzZqQyWSoUaMGxowZA+DNR/T379/H+PHjIQiC3NqfH374AXXr1oVMJoOlpSWWLl0qF9/S0hJz586Fr68vDA0N8eWXX6Jt27YYNWqUXLvHjx9DW1v7vbOfq1evlntf9u3bB0EQsG7dOqnOw8MD06dPByC/JCEoKAhbtmzBTz/9JF1HdHS0dN69e/fg7u4OXV1duLi4IDY29j3v7v8U9D58/fXXaNq0qcI5Li4umDNnjvR606ZNcHR0hI6ODhwcHPDtt99Kx3J/xnbv3o02bdpAR0cH3333HapVq4Y9e/ZI7VxdXWFmZia9PnXqFGQyGdLT0wEAz58/h7+/P6pVqwZDQ0O0bdsWV65ckdq/u4Tj9evXGDNmjPSzP2XKFAwcOFDh5zYnJweTJ09G5cqVYWpqiqCgIOmYpaUlAKB79+7SvykiIiJ1JWgIKinqoNAJ9MqVKzFnzhx88sknePDgAc6fP4+0tDRMmDABFy5cQGRkJDQ0NNC9e3fk5OTInTtr1ixMnz4dly5dQoUKFdCvXz9MnjwZK1euxMmTJxEfH4+ZM2cWahyDBw/GjRs3cP78eanu8uXL+P333zFo0KBC9REVFYW7d+8iKioKW7ZsQXh4eL5LRn744QcsX74c69evx507d7Bv3z44OTkBAPbu3YtPPvkEc+bMwYMHD/DgwQMAwMWLF9G7d2/07dsXV69eRVBQEGbMmKEQY8mSJXBxccHly5cxY8YM+Pv7Y8eOHcjIyJDafPfddzA3N0fbtgU/YKJNmza4fv269IfI8ePHUbVqVSkRzsrKQmxsLNzc3BTODQgIQO/evdG+fXvpOt7+lGHatGkICAhAXFwc7Ozs4O3tjdevXxc4nsK8D/3798e5c+fk/uj6448/8Pvvv6Nfv34AgO3bt2PmzJmYN28ebty4gfnz52PGjBnYsmWLXKypU6di7NixuHHjBtq3b4/WrVtL1/7s2TPcuHEDL1++xM2bN6X3p3HjxtDV1QUA9OrVC48ePcKhQ4dw8eJFNGjQAJ999hmePn2a57UtXLgQ27dvx+bNm3H69GmkpKTkuRRjy5Yt0NPTw9mzZ7Fo0SLMmTMHR48eBQDpZ3jz5s3SvykiIiIq+wq9hMPIyAgGBgbQ1NSEqakpAKBHjx5ybcLCwlCtWjVcv35dbjY0ICAAnp6eAICxY8fC29sbkZGRaNGiBQBgyJAhhV7z/Mknn8DT0xObN29G48aNAbxJQNq0aQMrK6tC9VGpUiWsXr0ampqacHBwQKdOnRAZGYmhQ4cqtE1MTISpqSk8PDygpaWFmjVrokmTJgCAypUrQ1NTEwYGBtJ7AgDLli3DZ599Js2I29nZ4fr161i8eDH8/Pykdm3btsXEiROl1+bm5hg1ahR++ukn9O7dGwAQHh4OPz+/997ZWq9ePVSuXBnHjx9Hz549ER0djYkTJ2LlypUAgHPnziErKyvP5Tf6+vqoWLEiMjIy5K4jV0BAADp16gQAmD17NurWrYv4+Hg4ODgUOKb3vQ9169aFi4sLduzYIbXZvn07mjZtChsbGwBv/vhaunQpvvjiCwBA7dq1cf36daxfvx4DBw6UYo0bN05qA7z5dGD9+vUAgBMnTqB+/fowNTVFdHQ0HBwcEB0djTZt2gB4Mxt97tw5PHr0CDKZDMCbP2727duHPXv25LkeftWqVQgMDET37t0BvPkE4ODBgwrtnJ2dMWvWLACAra0tVq9ejcjISLRr105aKmRsbJzn+05ERKRONLgLR+HcuXMH3t7esLKygqGhofQRdGJiolw7Z2dn6WsTExMAkGZxc+sePXpU6LhDhw7Fzp078erVK2RmZmLHjh0YPHhwoc+vW7cuNDU1pddmZmb5xu/VqxdevnwJKysrDB06FD/++ON7Z19v3Lgh/XGQq0WLFrhz5w6ys7OlukaNGsm10dHRwYABAxAWFgYAuHTpEq5duyaXdOdHEARp1vX58+e4fv06RowYgYyMDNy8eVNhxrUo3v7+5S6DKMz3qzDvQ//+/bFjxw4Ab5bL7Ny5E/379wcApKWl4e7duxgyZIi0BllfXx/ffPON3Kw1oPhevj0jf/z4cbi5ucHNzQ3R0dHIyspCTEyMNBt/5coVpKamokqVKnJx/vzzT4U4AJCcnIyHDx9Kf0gBgKamJho2bFjge5f7/hXlZz1XRkYGUlJS5Eo2xCL3Q0REVFzK0xIOpfaB9vLyQq1atbBx40bUqFEDOTk5qFevHjIzM+XavX1DV+5M6rt17y77eF9cmUyGH3/8Edra2sjKykLPnj0Lff67N5gVFN/CwgK3bt3Cb7/9hqNHj2LEiBFYvHgxjh8/rvSNanp6egp1/v7+cHV1xd9//43Nmzejbdu2qFWrVqH6c3Nzw4YNG3Dy5EnUr18fhoaGUlJ9/Phxaca1qPL6/hXl+1UQb29vTJkyBZcuXcLLly/x119/oU+fPgCA1NRUAMDGjRsV1kq//QcQoPheOjk5STPyx48fx7x582BqaoqFCxfi/PnzcrPxqampMDMzk1v3ncvY2Fip6yvKz1pBgoODMXv2bLm6hjBCIxgrMzwiIiL6AB+cQD958gS3bt3Cxo0b0apVKwBvPgovCRUqVMDAgQOxefNmaGtro2/fvqhYsWKxxatYsSK8vLzg5eWFkSNHwsHBAVevXkWDBg2gra0tN6sMAI6Ojjh9+rRc3enTp2FnZ6eQ+L3LyckJjRo1wsaNG7Fjxw6sXr260ONs06YNxo0bh++//16aXXVzc8Nvv/2G06dPyy0XeVde16GswrwPn3zyCdq0aYPt27fj5cuXaNeuHapXrw7gzScTNWrUwL1796RZ6cISBAGtWrXCTz/9hD/++AMtW7aErq4uMjIysH79ejRq1EhKuhs0aICkpCRUqFChUDfyGRkZwcTEBOfPn0fr1q0BANnZ2bh06VKR94rW0tIq1PseGBiICRMmyNVNNHLKpzUREVHJU5fZY1X44AS6UqVKqFKlCjZs2AAzMzMkJiZi6tSpqhxbgfz9/eHo6AgACkmaKoWHhyM7OxtNmzaFrq4uvvvuO1SsWFGaFba0tMSJEyfQt29fyGQyVK1aFRMnTkTjxo0xd+5c9OnTB7GxsVi9erXc7hHvu7ZRo0ZBT09PWmNbGM7OzqhUqRJ27NiB/fv3A3iTQAcEBEAQBIXlFG+ztLTEr7/+ilu3bqFKlSoq2UqwsO9D//79MWvWLGRmZmL58uVyx2bPno0xY8bAyMgI7du3R0ZGBi5cuIBnz54pJJTvcnNzw8SJE6XdXgCgdevW2L59OyZNmiS18/DwQLNmzdCtWzcsWrQIdnZ2+Pfff3HgwAF0795dYXkIAIwePRrBwcGwsbGBg4MDVq1ahWfPnhX5KUyWlpbS/QAymQyVKlXKs51MJpPWZ+fSRPn5RUVERGWfujwERRU+eA20hoYGdu3ahYsXL6JevXoYP348Fi9erMqxFcjW1hbNmzeHg4NDnluhqYqxsTE2btyIFi1awNnZGb/99ht++eUXVKlSBcCbbfUSEhJgbW0t3RTWoEEDREREYNeuXahXrx5mzpyJOXPmFGotM/BmWUOFChXg7e0NHR2dQo81d9ZVEAS0bNkSwJuk2tDQUG7GNS9Dhw6Fvb09GjVqhGrVqqnkj5LCvg89e/bEkydPkJ6errANnL+/PzZt2oTNmzfDyckJbdq0QXh4OGrXrv3e+G3atEF2drbcziNubm4KdYIg4ODBg2jdujUGDRoEOzs79O3bF/fv35fW7L9rypQp8Pb2hq+vL5o1awZ9fX14enoW6fsFAEuXLsXRo0dhYWGB+vXrF+lcIiKisiR3K1xlizoQRFFUyzuRRFGEra0tRowY8d6ZSHWTm5CfP38eDRo0KO3hUCHk5OTA0dERvXv3xty5c0sk5nDBskTilKQw14K3a1SVwXHHSiQOEVFZsU5MKPYYLRdGqaSfU1PcVdJPcVLqJsLS8vjxY+zatQtJSUmF3vtZHWRlZeHJkyeYPn06Pv30UybPZdj9+/dx5MgRtGnTBhkZGVi9ejX+/PNPaf9qIiKi8kZQam839aKWCXT16tVRtWpVbNiwQWHNaEGPWz506JB0w2NZdPr0abi7u8POzk7uKXoAcPLkSXTo0CHfc3N3rChJHTp0wMmTJ/M89vXXX+Prr78u4RGVHA0NDYSHhyMgIACiKKJevXr47bffpHX5RERE5U15WgOtlgl0QatO4uLi8j1mbm5eDKNRHTc3t3yvrVGjRgVeW2nYtGkTXr58meexypUrl/BoSpaFhUWx3rxKREREZZdaJtAFyX2C3cemYsWKZe7ayvofJERERFRySnMbuzVr1mDx4sVISkqCi4sLVq1aJffAs3etWLECa9euRWJiIqpWrYqePXsiODi40JsBfHQJNBERERGVvNLaQWP37t2YMGEC1q1bh6ZNm2LFihXw9PTErVu3pGdLvG3Hjh2YOnUqwsLC0Lx5c9y+fRt+fn4QBAHLli0rVMxytNybiIiIiD42y5Ytw9ChQzFo0CDUqVMH69atg66uLsLCwvJsHxMTgxYtWqBfv36wtLTE559/Dm9vb5w7d67QMZlAExEREZHSNDQElZSMjAykpKTIlYyMjDxjZmZm4uLFi/Dw8HhrHBrw8PBAbGxsnuc0b94cFy9elBLme/fu4eDBg+jYsWOhr5VLOIiozCip/ZlLar9pgHtOE1H5oao10MHBwZg9e7Zc3axZsxAUFKTQ9r///kN2drbCg89MTExw8+bNPPvv168f/vvvP7Rs2RKiKOL169cYPnx4kXYP4ww0EREREZUZgYGBSE5OliuBgYEq6z86Ohrz58/Ht99+i0uXLmHv3r04cOBAkR6ExhloIiIiIlKapopmoGUyGWQyWaHaVq1aFZqamnj48KFc/cOHD2FqaprnOTNmzMCAAQPg7+8PAHByckJaWhq+/PJLTJs2DRoa759f5gw0ERERESlNU0NQSSkKbW1tNGzYEJGRkVJdTk4OIiMj0axZszzPSU9PV0iSNTU1ART8rJG3cQaaiIiIiJSmqhnoopowYQIGDhyIRo0aoUmTJlixYgXS0tIwaNAgAICvry/Mzc0RHBwMAPDy8sKyZctQv359NG3aFPHx8ZgxYwa8vLykRPp9mEATERERkdrq06cPHj9+jJkzZyIpKQmurq44fPiwdGNhYmKi3Izz9OnTIQgCpk+fjn/++QfVqlWDl5cX5s2bV+iYgljYuWoiKlOGC5alPQS1xV04iKi8WScmFHuMbpvOqKSfff6fqqSf4sQ10ESF9PjxY3z11VeoWbMmZDIZTE1N4enpidOnT0ttLl++jF69esHExAQ6OjqwtbXF0KFDcfv2bbm+fvjhB7i5ucHIyAj6+vpwdnbGnDlz8PTp05K+LCIiIpUojTXQpYUJNFEh9ejRA5cvX8aWLVtw+/Zt/Pzzz3Bzc8OTJ08AAPv378enn36KjIwMbN++HTdu3MB3330HIyMjzJgxQ+pn2rRp6NOnDxo3boxDhw7h2rVrWLp0Ka5cuYJt27aV1uURERFRIXEJB1EhPH/+HJUqVUJ0dDTatGmjcDw9PR21atVCy5Yt8eOPP+Z5vrGxMc6dO4emTZtixYoVGDt2bL7tCoNLOD4cl3AQUXlTEks4+oQX/lHYBdnt10Ql/RQnzkATFYK+vj709fWxb9++PB8n+uuvv+K///7D5MmT8zw/Nynevn079PX1MWLEiALbERERqRsu4SAiORUqVEB4eDi2bNkCY2NjtGjRAl9//TV+//13AMCdO3cAAA4ODgX2c+fOHVhZWUFLS6vYx0xERETFgwk0USH16NED//77L37++We0b98e0dHRaNCgAcLDwwu98fqHrpjKyMhASkqKXMkGV18REVHZwRloIsqTjo4O2rVrhxkzZiAmJgZ+fn6YNWsW7OzsAAA3b94s8Hw7Ozvcu3cPWVlZRYobHBwMIyMjuXIZyR98HURERKqmqaGhkqIO1GOURGVUnTp1kJaWhs8//xxVq1bFokWL8mz3/PlzAEC/fv2QmpqKb7/9tsB27woMDERycrJcqQ8jVVwCERGRSpSnGWg+iZCoEJ48eYJevXph8ODBcHZ2hoGBAS5cuIBFixaha9eu0NPTw6ZNm9CrVy906dIFY8aMgY2NDf777z9EREQgMTERu3btQtOmTTF58mRMnDgR//zzD7p3744aNWogPj4e69atQ8uWLfPcnUMmk0Emk8nVaUI9fskQERF9bJhAExWCvr4+mjZtiuXLl+Pu3bvIysqChYUFhg4diq+//hoA0LVrV8TExCA4OBj9+vVDSkoKLCws0LZtW3zzzTdSXwsXLkTDhg2xZs0arFu3Djk5ObC2tkbPnj0xcODA0rpEIiIipajL7LEqcB9oIjXFfaA/HPeBJqLypiT2gf5qzxWV9LO2p4tK+ilOXANNRERERFQEXMJBRERERErTFMrPEg4m0ERERESktPK0BppLOIiIiIiIioAz0ERERESktPI0A80EmoiIiIiUVqEcJdBcwkFEREREVAScgSaicqck92YuqT2nud80EZU2LuEgIiIiIioCJtBEREREREVQnhJoroEmIiIiIioCzkATERERkdI4A01UTgmCgH379gEAEhISIAgC4uLiCtWeiIioPNPUEFRS1AETaDXl5+cHQRAwfPhwhWMjR46EIAhSm4JKUFCQlCjmlsqVK6NNmzY4efJknrGHDRsGTU1NfP/99wrHgoKC8hxXXFwcBEFAQkKCVPfjjz/i008/hZGREQwMDFC3bl2MGzfuvdd+8+ZNCIKAM2fOyNV/+umn0NHRwatXr6S6V69eQUdHB6Ghoe/t90M8ePAAHTp0KJa+iYiIqGxiAq3GLCwssGvXLrx8+VKqe/XqFXbs2IGaNWsCeJPg5ZYVK1bA0NBQri4gIEA697fffsODBw9w4sQJ1KhRA507d8bDhw/lYqanp2PXrl2YPHkywsLC8hxXbsJ6586dfMceGRmJPn36oEePHjh37hwuXryIefPmISsr673X7eDgAFNTU0RHR0t1L168wKVLl1CtWjW5xDo2NhYZGRlo27Z4thIzNTWFTCYrlr6JiIjUCWegSS00aNAAFhYW2Lt3r1S3d+9e1KxZE/Xr1wfwJsHLLUZGRhAEQa5OX19fOrdKlSowNTVFvXr18PXXXyMlJQVnz56Vi/n999+jTp06mDp1Kk6cOIG//vpLYVz29vZwd3fHtGnT8h37L7/8ghYtWmDSpEmwt7eHnZ0dunXrhjVr1hTq2t3d3eUS6FOnTsHOzg5eXl5y9dHR0ahVqxZq166N8+fPo127dqhatSqMjIzQpk0bXLp0qVDxACA7OxuDBw+Gg4MDEhMTAeS95GPv3r1wd3eHrq4uXFxcEBsbK9fPxo0bYWFhAV1dXXTv3h3Lli2DsbFxocdBRERUFjGBJrUxePBgbN68WXodFhaGQYMGKdXny5cvsXXrVgCAtra23LHQ0FD4+PjAyMgIHTp0QHh4eJ59LFiwAD/88AMuXLiQ53FTU1P88ccfuHbt2geN0d3dHadOncLr168BAFFRUXBzc0ObNm0QFRUltYuKioK7uzuAN7PUAwcOxKlTp3DmzBnY2tqiY8eOePHixXvjZWRkoFevXoiLi8PJkyelGf68TJs2DQEBAYiLi4OdnR28vb2lcZ4+fRrDhw/H2LFjERcXh3bt2mHevHkf9B4QERFR6WACreZ8fHxw6tQp3L9/H/fv38fp06fh4+PzQX01b94c+vr60NPTw5IlS9CwYUN89tln0vE7d+7gzJkz6NOnjxR78+bNEEVRoa8GDRqgd+/emDJlSp6xRo8ejcaNG8PJyQmWlpbo27cvwsLCkJGRUaixuru7Iy0tDefPnwfwZqa5TZs2aN26Nc6ePYtXr17h5cuXOHfunJRAt23bFj4+PnBwcICjoyM2bNiA9PR0HD9+vMBYqamp6NSpEx4/foyoqChUq1atwPYBAQHo1KkT7OzsMHv2bNy/fx/x8fEAgFWrVqFDhw4ICAiAnZ0dRowYwTXURET0UeAMNKmNatWqoVOnTggPD8fmzZvRqVMnVK1a9YP62r17Ny5fvowffvgBNjY2CA8Ph5aWlnQ8LCwMnp6eUv8dO3ZEcnIyjh3L+xHC33zzDU6ePIkjR44oHNPT08OBAwcQHx+P6dOnQ19fHxMnTkSTJk2Qnp7+3rHa2Njgk08+QXR0NFJSUnD58mW0adMGZmZmqFmzJmJjY6X1z7kJ9MOHDzF06FDY2trCyMgIhoaGSE1NlZZj5Mfb2xtpaWk4cuQIjIyM3js2Z2dn6WszMzMAwKNHjwAAt27dQpMmTeTav/s6LxkZGUhJSZEr2VD8w4WIiKi0MIEmtTJ48GCEh4djy5YtGDx48Af3Y2FhAVtbW3Tv3h3z589H9+7dpRnh7OxsbNmyBQcOHECFChVQoUIF6Orq4unTp/neTGhtbY2hQ4di6tSpec5S57bx9/fHpk2bcOnSJVy/fh27d+8u1Hjd3NwQFRWFkydPwtbWFtWrVwcAaRlHVFQUbGxsYGFhAQAYOHAg4uLisHLlSsTExCAuLg5VqlRBZmZmgXE6duyI33//XWEtc37e/qNDEN78IsjJySnUufkJDg6GkZGRXLmMZKX6JCIiog/DBPoj0L59e2RmZiIrKwuenp4q6bNnz56oUKECvv32WwDAwYMH8eLFC1y+fBlxcXFS2blzJ/bu3Yvnz5/n2c/MmTNx+/Zt7Nq1670xLS0toauri7S0tEKN0d3dHTExMTh69Cjc3Nyk+tatWyM6OhrR0dHS7DPwZv3xmDFj0LFjR9StWxcymQz//fffe+N89dVXWLBgAbp06fLe5R7vY29vLy07yfXu67wEBgYiOTlZrtTH+2fDiYiISkp5moHmkwg/Apqamrhx44b0tSoIgoAxY8YgKCgIw4YNQ2hoKDp16gQXFxe5dnXq1MH48eOxfft2jBw5UqEfExMTTJgwAYsXL5arDwoKQnp6Ojp27IhatWrh+fPnCAkJQVZWFtq1a1eoMeaugw4LC8PGjRul+jZt2sDf3x8AMGLECKne1tYW27ZtQ6NGjZCSkoJJkyahYsWKhYo1evRoZGdno3Pnzjh06BBatmxZqPPy6qd169ZYtmwZvLy8cOzYMRw6dEiaqc6PTCZT2C5PE+rxS4aIiMoHzff8X/Yx4Qz0R8LQ0BCGhoYq7XPgwIHIysrCqlWrcODAAfTo0UOhjYaGBrp3717gg0oCAgLktssD3iS59+7dg6+vLxwcHNChQwckJSXhyJEjsLe3L9T4ateujVq1auHFixdo06aNVF+zZk3UqFEDmZmZcjPToaGhePbsGRo0aIABAwZgzJgx0rKPwhg3bhxmz56Njh07IiYmptDnva1FixZYt24dli1bBhcXFxw+fBjjx4+Hjo7OB/VHRERUVmgIgkqKOhDE/BanElGJGDp0KG7evJnvkx/zM1ywLJ4BkUqFuRbPQ3zeNTgu75t5iYgAYJ2YUOwx1p+9r5J+hjWtpZJ+ihOXcBCVsCVLlqBdu3bQ09PDoUOHsGXLFmmtORERkbrSVI/JY5VgAk1lUt26dXH/ft5/ya5fvx79+/cv4RGpzrlz57Bo0SK8ePECVlZWCAkJkdZsExERqSsNNbkBUBWYQFOZdPDgQWRlZeV5zMTEpIRHo1oRERGlPQQiIiJSAhNoKpNq1Sr765+IiIjof8rTLhxMoImIiIhIaeqyg4YqcBs7IiIiIqIi4Aw0ERERESmNu3AQERERERUBd+EgIiKVKKkHnJTUA1sAPrSFiIgJNBEREREprTzdRMgEmoiIiIiUxjXQRERERERFUJ5moLmNHRERERFREXAGmoiIiIiUplmOduHgDDR9lPz8/CAIgkKJj48HAAQHB0NTUxOLFy+Wzvnhhx+gqamJf/75J88+bW1tMWHCBOl1fHw8Bg0ahE8++QQymQy1a9eGt7c3Lly4ULwXR0REVAZpCIJKijpgAk0frfbt2+PBgwdypXbt2gCAsLAwTJ48GWFhYVL7Ll26oEqVKtiyZYtCXydOnEB8fDyGDBkCALhw4QIaNmyI27dvY/369bh+/Tp+/PFHODg4YOLEiSVzgURERFQqmEDTR0smk8HU1FSuaGpq4vjx43j58iXmzJmDlJQUxMTEAAC0tLQwYMAAhIeHK/QVFhaGpk2bom7duhBFEX5+frC1tcXJkyfRqVMnWFtbw9XVFbNmzcJPP/303rEFBQXlOUOeV2wiIiJ1oCmopqgDJtBU7oSGhsLb2xtaWlrw9vZGaGiodGzIkCG4c+cOTpw4IdWlpqZiz5490uxzXFwc/vjjD0ycOBEaGor/hIyNjd87hoCAALmZ8SVLlkBXVxeNGjVS/gKJiIhKAZdwEH0E9u/fD319fan06tULKSkp2LNnD3x8fAAAPj4+iIiIQGpqKgCgTp06+PTTT+WWdkREREAURfTt2xcAcOfOHQCAg4PDB49NX19fmhVPSEjA9OnTsXnzZtSrV++D+yQiIqKSwQSaPlru7u6Ii4uTSkhICHbu3Alra2u4uLgAAFxdXVGrVi3s3r1bOm/w4MHYs2cPXrx4AeDN8o1evXrBwMAAACCKosrGmJiYiG7duiEgIAC9e/fOt11GRgZSUlLkSjZUNw4iIiJlaWoIKinqgAk0fbT09PRgY2MjFTMzM4SGhuKPP/5AhQoVpHL9+nW5GefcmeaIiAjcuXMHp0+flpZvAICdnR0A4ObNm0qNLy0tDV26dEGzZs0wZ86cAtsGBwfDyMhIrlxGslLxiYiIVElDUE1RB0ygqdy4evUqLly4gOjoaLmZ6ejoaMTGxkoJsYGBAXr16oWwsDBs3rwZdnZ2aNWqldSPq6sr6tSpg6VLlyInJ0chzvPnz987FlEU4ePjg5ycHGzbtg3Ce9Z8BQYGIjk5Wa7Uh1HR3gAiIiJSCT5IhcqN0NBQNGnSBK1bt1Y41rhxY4SGhkr7Qg8ZMgStWrXCjRs3MGXKFLm2giBg8+bN8PDwQKtWrTBt2jQ4ODggNTUVv/zyC44cOYLjx48XOJagoCD89ttvOHLkCFJTU6U12EZGRqhYsaJCe5lMBplMJlenCTX5M52IiMoFTTW5AVAVOANN5UJmZia+++479OjRI8/jPXr0wNatW5GVlQUAaNmyJezt7ZGSkgJfX1+F9k2aNMGFCxdgY2ODoUOHwtHREV26dMEff/yBFStWvHc8x48fR2pqKpo3bw4zMzOpvL0Wm4iISJ2Up104BFGVd0QRUYkZLliW9hCoDAlzbVtisQbHHSuxWESkGuvEhGKPEZPwRCX9NLesopJ+ihNnoImIiIiIioAJNFExmD9/vtwe1G+XDh06lPbwiIiIVK48LeHgTYRExWD48OH57uuc102CRERE6o43ERKRUipXriy3B/XbxdzcvLSHR0RE9FFZs2YNLC0toaOjg6ZNm+LcuXMFtn/+/DlGjhwJMzMzyGQy2NnZ4eDBg4WOxxloIiIiIlJaaS2/2L17NyZMmIB169ahadOmWLFiBTw9PXHr1i1Ur15doX1mZibatWuH6tWrY8+ePTA3N8f9+/dhbGxc6JhMoImIiIhIaZqltK5h2bJlGDp0KAYNGgQAWLduHQ4cOICwsDBMnTpVoX1YWBiePn2KmJgYaGlpAQAsLS2LFJNLOIiIiIiozMjIyEBKSopcycjIyLNtZmYmLl68CA8PD6lOQ0MDHh4eiI2NzfOcn3/+Gc2aNcPIkSNhYmKCevXqYf78+cjOzi70GDkDTUT0ESjJvZlLas9p7jdNpF5UtYQjODgYs2fPlqubNWsWgoKCFNr+999/yM7OhomJiVy9iYkJbt68mWf/9+7dw7Fjx9C/f38cPHgQ8fHxGDFiBLKysjBr1qxCjZEJNBEREREpTVVLoAMDAzFhwgS5OplMpprOAeTk5KB69erYsGEDNDU10bBhQ/zzzz9YvHgxE2giIiIiUj8ymazQCXPVqlWhqamJhw8fytU/fPgQpqameZ5jZmYGLS0taGpqSnWOjo5ISkpCZmYmtLW13xuXa6CJiIiISGkaEFRSikJbWxsNGzZEZGSkVJeTk4PIyEg0a9Ysz3NatGiB+Ph45OTkSHW3b9+GmZlZoZLnN9dKRERERKQkQVBNKaoJEyZg48aN2LJlC27cuIGvvvoKaWlp0q4cvr6+CAwMlNp/9dVXePr0KcaOHYvbt2/jwIEDmD9/PkaOHFnomFzCQURERERK0yilBxH26dMHjx8/xsyZM5GUlARXV1ccPnxYurEwMTERGhr/mzO2sLDAr7/+ivHjx8PZ2Rnm5uYYO3YspkyZUuiYgiiKosqvhKiMiY6Ohru7O549e1akjdLLsuGCZWkPgcop7sJBpH7WiQnFHuPmwxSV9ONgYqiSfooTl3AQAMDPzw+CICiU+Ph4AEBSUhJGjx4NKysryGQyWFhYwMvLC5GRkcjMzETVqlWxYMGCPPueO3cuTExMkJWVBeDNno2LFi2Ci4sLdHV1UbVqVbRo0QKbN2+W2qha8+bN8eDBAxgZGRVL/0REROVdaS3hKA1cwkGS9u3bY/PmzXJ11apVQ0JCAlq0aAFjY2MsXrwYTk5OyMrKwq+//oqRI0fi5s2b8PHxwebNmxWe+COKIsLDw+Hr6wstLS1kZmbC09MTV65cwdy5c9GiRQsYGhrizJkzWLJkCerXrw9XV1eVX5u2tna+d+MSERGR8op6A6A64ww0SWQyGUxNTeWKpqYmRowYAUEQcO7cOfTo0QN2dnaoW7cuJkyYgDNnzgAAhgwZgtu3b+PUqVNyfR4/fhz37t3DkCFDAAArVqzAiRMnEBkZiZEjR8LV1RVWVlbo168fzp49C1tb2/eO083NDaNHj8a4ceNQqVIlmJiYYOPGjdINAwYGBrCxscGhQ4ekc6KjoyEIAp4/fw4ACA8Ph7GxMX799Vc4OjpCX18f7du3x4MHD6Rz/Pz80K1bN8yfPx8mJiYwNjbGnDlz8Pr1a0yaNAmVK1fGJ598ovBHx5QpU2BnZwddXV1YWVlhxowZ0sy6KIrw8PCAp6cncldPPX36FJ988glmzpxZxO8YERERlQYm0FSgp0+f4vDhwxg5ciT09PQUjueuJ3ZyckLjxo0RFhYmd3zz5s1o3rw5HBwcAADbt2+Hh4cH6tevr9CXlpZWnjHysmXLFlStWhXnzp3D6NGj8dVXX6FXr15o3rw5Ll26hM8//xwDBgxAenp6vn2kp6djyZIl2LZtG06cOIHExEQEBATItTl27Bj+/fdfnDhxAsuWLcOsWbPQuXNnVKpUCWfPnsXw4cMxbNgw/P3339I5BgYGCA8Px/Xr17Fy5Ups3LgRy5cvBwAIgoAtW7bg/PnzCAkJAQAMHz4c5ubmTKCJiEitlaclHEygSbJ//37o6+tLpVevXoiPj4coilICXJAhQ4bg+++/R2pqKgDgxYsX2LNnDwYPHiy1uXPnTqH6eh8XFxdMnz4dtra2CAwMhI6ODqpWrYqhQ4fC1tYWM2fOxJMnT/D777/n20dWVhbWrVuHRo0aoUGDBhg1apTcPpIAULlyZYSEhMDe3h6DBw+Gvb090tPT8fXXX0uxtbW15Wbep0+fjubNm8PS0hJeXl4ICAhARESEdNzc3Bzr16/H1KlTERgYiIMHD+K7775DhQpcUUVEROpLQ1BNUQf8H5sk7u7uWLt2rfRaT08PiYmJhT7f29sb48ePR0REBAYPHozdu3dDQ0MDffr0kdqoatMXZ2dn6WtNTU1UqVIFTk5OUl3u1jWPHj3Ktw9dXV1YW1tLr83MzBTa161bV27rGxMTE9SrV08h9tvn7d69GyEhIbh79y5SU1Px+vVrGBrK31Hcq1cv/Pjjj1iwYAHWrl373qUrGRkZyMjIkKvLhgjNcrTejIiIqKzgDDRJ9PT0YGNjIxUzMzPY2tpCEATcvHnzvecbGhqiZ8+e0prgzZs3o3fv3tDX15fa2NnZFaqv99HS0pJ7LQiCXJ3w/58Bvf2UocL08W6C/744uXW5cWJjY9G/f3907NgR+/fvx+XLlzFt2jRkZmbKnZOeno6LFy9CU1MTd+7cKehSAQDBwcEwMjKSK5eR/N7ziIiISoqgoqIOmEBTgSpXrgxPT0+sWbMGaWlpCsdzb8rLNWTIEJw6dQr79+9HTEyMdPNgrn79+uG3337D5cuXFfrKysrKM4Y6iYmJQa1atTBt2jQ0atQItra2uH//vkK7iRMnQkNDA4cOHUJISAiOHSt4v9vAwEAkJyfLlfrglnxERFR2aAiCSoo6YAJN77VmzRpkZ2ejSZMm+OGHH3Dnzh3cuHEDISEhCs+Zb926NWxsbODr6wsHBwc0b95c7vi4cePQokULfPbZZ1izZg2uXLmCe/fuISIiAp9++mmhZmPLMltbWyQmJmLXrl24e/cuQkJC8OOPP8q1OXDgAMLCwrB9+3a0a9cOkyZNwsCBA/Hs2bN8+5XJZDA0NJQrXL5BRERUOphA03tZWVnh0qVLcHd3x8SJE1GvXj20a9cOkZGRcmumgTfLGQYPHoxnz57J3TyYSyaT4ejRo5g8eTLWr1+PTz/9FI0bN0ZISAjGjBkjt75YHXXp0gXjx4/HqFGj4OrqipiYGMyYMUM6/vjxYwwZMgRBQUFo0KABAGD27NkwMTHB8OHDS2vYRERESitPu3DwUd5EaoqP8qbSwkd5E6mfkniU999PU1XSzyeV9d/fqJRxFw4iIiIiUpqgLtPHKsAlHFSmJCYmyu1F/W4pyrZ6RERERMWBM9BUptSoUQNxcXEFHiciIqKyR10egqIKTKCpTKlQoQJsbGxKexhERERUROVoBQeXcBARERERFQVnoImIiIhIaeVpVpYJNBEREREprTztwsEEmoiIiqSk9mcuqf2mAe45TURFwwSaiIiIiJTGXTiIiIiIiIqgHOXP5Wq9NxERERGR0jgDTURERERK4xIOIiIiIqIiKE+7cHAJB5Urfn5+EAQBCxYskKvft2+f3D98URSxYcMGNG3aFPr6+jA2NkajRo2wYsUKpKenAwCCgoIgCAIEQUCFChVgaWmJ8ePHIzU1tUSviYiIqCzQEFRT1AETaCp3dHR0sHDhQjx79izfNgMGDMC4cePQtWtXREVFIS4uDjNmzMBPP/2EI0eOSO3q1q2LBw8eICEhAQsXLsSGDRswceLEkrgMIiIiKiVMoKnc8fDwgKmpKYKDg/M8HhERge3bt2Pnzp34+uuv0bhxY1haWqJr1644duwY3N3dpbYVKlSAqakpPvnkE/Tp0wf9+/fHzz///N4x5M6Ev1uio6NVdZlEREQlSlBRUQdMoKnc0dTUxPz587Fq1Sr8/fffCse3b98Oe3t7dO3aVeGYIAgwMjLKt++KFSsiMzPzvWNYuXIlHjx4IJWxY8eievXqcHBwKNrFEBERlREagqCSog6YQFO51L17d7i6umLWrFkKx+7cuQN7e/si93nx4kXs2LEDbdu+/+lpRkZGMDU1hampKWJiYrB+/Xrs3bsXpqamRY5LREREJYu7cFC5tXDhQrRt2xYBAQFy9aIoFrqPq1evQl9fH9nZ2cjMzESnTp2wevXqQp9/+fJlDBgwAKtXr0aLFi3ybZeRkYGMjAy5umyI0FSbD7uIiOhjpyaTxyrBGWgqt1q3bg1PT08EBgbK1dvZ2eHmzZuF6sPe3h5xcXG4ceMGXr58iZ9//hkmJiaFOjcpKQldunSBv78/hgwZUmDb4OBgGBkZyZXLSC5UHCIiopIgiKJKijpgAk3l2oIFC/DLL78gNjZWquvXrx9u376Nn376SaG9KIpITv5f4qqtrQ0bGxtYWlpCW1u70HFfvXqFrl27wsHBAcuWLXtv+8DAQCQnJ8uV+sh/LTYREREVHybQVK45OTmhf//+CAkJkep69+6NPn36wNvbG/Pnz8eFCxdw//597N+/Hx4eHoiKilI67rBhw/DXX38hJCQEjx8/RlJSEpKSkvK9AVEmk8HQ0FCucPkGERGVKWKOaooaYAJN5d6cOXOQk/O/f7CCIGDHjh1YtmwZ9u3bhzZt2sDZ2RlBQUHo2rUrPD09lY55/PhxPHjwAHXq1IGZmZlUYmJilO6biIioNAhijkqKOhDEotwxRURlxnDBsrSHQFSswlzfv6ONqgyOO1ZisYhKwzoxodhjZKS9UEk/Mj0DlfRTnLgLBxEREREpT01mj1WBSziIisHw4cOhr6+fZxk+fHhpD4+IiEj1RFE1RQ1wBpqoGMyZM0dhf+lchoaGJTwaIiKiElCOZqCZQBMVg+rVq6N69eqlPQwiIiIqBkygiYiIiEhp6rKDhiowgSYiIiIi5ZWjBJo3ERIRERERFQFnoImIiIhIeeVoBpoJNBERlUkl+XCTknpoCx/YQh+1cpRAcwkHEREREVERcAaaiIiIiJSXU35moJlAExEREZHSytM2dlzCQURERERUBEygqdwICgqCq6traQ+DiIjo4yTmqKaoASbQJElKSsLo0aNhZWUFmUwGCwsLeHl5ITIyUq5dcHAwNDU1sXjxYoU+wsPDIQiCQtHR0fmgWKoUEBBQrP0TERGVa6KomqIGuAaaAAAJCQlo0aIFjI2NsXjxYjg5OSErKwu//vorRo4ciZs3b0ptw8LCMHnyZISFhWHSpEkKfRkaGuLWrVtydYIgfFAsVdLX14e+vn6x9E1ERFTuqcnssSpwBpoAACNGjIAgCDh37hx69OgBOzs71K1bFxMmTMCZM2ekdsePH8fLly8xZ84cpKSkICYmRqEvQRBgamoqV0xMTIocqyCCIGD9+vXo3LkzdHV14ejoiNjYWMTHx8PNzQ16enpo3rw57t69K53z7hIOPz8/dOvWDUuWLIGZmRmqVKmCkSNHIisrS2pjaWmJb775Br6+vtDX10etWrXw888/4/Hjx+jatSv09fXh7OyMCxcuSOc8efIE3t7eMDc3h66uLpycnLBz507p+OPHj2Fqaor58+dLdTExMdDW1uYMORERkRpgAk14+vQpDh8+jJEjR0JPT0/huLGxsfR1aGgovL29oaWlBW9vb4SGhhZbrPeZO3cufH19ERcXBwcHB/Tr1w/Dhg1DYGAgLly4AFEUMWrUqAL7iIqKwt27dxEVFYUtW7YgPDwc4eHhcm2WL1+OFi1a4PLly+jUqRMGDBgAX19f+Pj44NKlS7C2toavry/E///Y6dWrV2jYsCEOHDiAa9eu4csvv8SAAQNw7tw5AEC1atUQFhaGoKAgXLhwAS9evMCAAQMwatQofPbZZ4W+fiIiorJEEHNUUtQBE2hCfHw8RFGEg4NDge1SUlKwZ88e+Pj4AAB8fHwQERGB1NRUuXbJycnSconc0qFDhyLFKoxBgwahd+/esLOzw5QpU5CQkID+/fvD09MTjo6OGDt2LKKjowvso1KlSli9ejUcHBzQuXNndOrUSWEWuGPHjhg2bBhsbW0xc+ZMpKSkoHHjxujVq5cU+8aNG3j48CEAwNzcHAEBAXB1dYWVlRVGjx6N9u3bIyIiQq7PoUOHon///hg+fDj09PQQHBys9HtCRERUasrRTYRcA03SzOn77Ny5E9bW1nBxcQEAuLq6olatWti9ezeGDBkitTMwMMClS5fkzq1YsWKRYhWGs7Oz9HXuEhEnJye5ulevXiElJQWGhoZ59lG3bl1oampKr83MzHD16tUixwGAR48ewdTUFNnZ2Zg/fz4iIiLwzz//IDMzExkZGdDV1ZXrd8mSJahXrx6+//57XLx4ETKZLN9rzcjIQEZGhlxdNkRoQsjnDCIiIiounIEm2NraQhCE9968Fxoaij/++AMVKlSQyvXr1xEWFibXTkNDAzY2NnLF3Ny8SLEKQ0tLS/o69ybFvOpyCngy0tvtc895t31R4yxevBgrV67ElClTEBUVhbi4OHh6eiIzM1Ou37t37+Lff/9FTk4OEhISCrzW4OBgGBkZyZXLSC7wHCIiohJVjmagmUATKleuDE9PT6xZswZpaWkKx58/f46rV6/iwoULiI6ORlxcnFSio6MRGxtb6IS4MLHU3enTp9G1a1f4+PjAxcUFVlZWuH37tlybzMxM+Pj4oE+fPpg7dy78/f3x6NGjfPsMDAxEcnKyXKkPo+K+FCIiosJjAk3lzZo1a5CdnY0mTZrghx9+wJ07d3Djxg2EhISgWbNmCA0NRZMmTdC6dWvUq1dPKq1bt0bjxo3lbiYURRFJSUkKJXeG9n2x1J2trS2OHj2KmJgY3LhxA8OGDZPWR+eaNm0akpOTERISgilTpsDOzg6DBw/Ot0+ZTAZDQ0O5wuUbREREpYMJNAEArKyscOnSJbi7u2PixImoV68e2rVrh8jISKxcuRLfffcdevTokee5PXr0wNatW6Xt31JSUmBmZqZQcmdYC4q1du3aErvm4jJ9+nQ0aNAAnp6ecHNzg6mpKbp16yYdj46OxooVK7Bt2zYYGhpCQ0MD27Ztw8mTJz+K6yciovKpPO3CIYiqvKuLiErMcMGytIdA9NEIc21bInEGxx0rkThE71onJhR7jOz7V1TSj2YtF5X0U5w4A01EREREVARMoKnM2b59u8I+0rmlbt26pT08IiIiyosoqqaoAe4DTWVOly5d0LRp0zyPvbvtHBEREZURarJ+WRU4A01ljoGBgcI+0rmlVq1apT08IiIiykNp3kS4Zs0aWFpaQkdHB02bNsW5c+cKdd6uXbsgCILczf6FwQSaiIiIiNTW7t27MWHCBMyaNQuXLl2Ci4sLPD09C3y+AgAkJCQgICAArVq1KnJMJtBEREREpLxSepDKsmXLMHToUAwaNAh16tTBunXroKurq/Ck5LdlZ2ejf//+mD17NqysrIockwk0ERERESlPRQl0RkYGUlJS5EpGRkaeITMzM3Hx4kV4eHhIdRoaGvDw8EBsbGy+Q50zZw6qV6+OIUOGfNCl8iZCIiIq90pqf+aS2m8a4J7TpL6Cg4Mxe/ZsubpZs2YhKChIoe1///2H7OxsmJiYyNWbmJjg5s2befZ/6tQphIaGIi4u7oPHyASaiIiIiJSXk62SbgIDAzFhwgS5OplMppK+X7x4gQEDBmDjxo2oWrXqB/fDBJqIiIiIlCbmqGYbO5lMVuiEuWrVqtDU1MTDhw/l6h8+fAhTU1OF9nfv3kVCQgK8vLykupz/H3eFChVw69YtWFtbvzcu10ATERERkVrS1tZGw4YNERkZKdXl5OQgMjISzZo1U2jv4OCAq1evIi4uTipdunSBu7s74uLiYGFhUai4nIEmIiIiIuWpaAlHUU2YMAEDBw5Eo0aN0KRJE6xYsQJpaWkYNGgQAMDX1xfm5uYIDg6Gjo4O6tWrJ3e+sbExACjUF4QJNBEREREpr5QS6D59+uDx48eYOXMmkpKS4OrqisOHD0s3FiYmJkJDQ7WLLgRRVJOHjhMpKSgoCPv27VPqrtuyZLhgWdpDIKIi4i4cVFrWiQnFHiP7WuT7GxWCZr3PVNJPceIaaEJSUhJGjx4NKysryGQyWFhYwMvLS1pPZGlpiRUrViicFxQUBFdXV+n1xo0b0apVK1SqVAmVKlWCh4dHno/S/OOPP9C7d29Uq1YNMpkMdnZ2mDlzJtLT04vrEgEAAQEBcmukiIiISHXE7GyVFHXABLqcS0hIQMOGDXHs2DEsXrwYV69exeHDh+Hu7o6RI0cWqa/o6Gh4e3sjKioKsbGxsLCwwOeff45//vlHanPmzBk0bdoUmZmZOHDgAG7fvo158+YhPDwc7dq1Q2ZmpqovUaKvr48qVaoUW/9ERETlWk6OaooaYAJdzo0YMQKCIODcuXPo0aMH7OzsULduXUyYMAFnzpwpUl/bt2/HiBEj4OrqCgcHB2zatEm6ExYARFHEkCFD4OjoiL1796JJkyaoVasWevXqhV9++QWxsbFYvnx5oWIJgoD169ejc+fO0NXVhaOjI2JjYxEfHw83Nzfo6emhefPmuHv3rnTOuzPmfn5+6NatG5YsWQIzMzNUqVIFI0eORFZWltTG0tIS33zzDXx9faGvr49atWrh559/xuPHj9G1a1fo6+vD2dkZFy5ckM558uQJvL29YW5uDl1dXTg5OWHnzp3S8cePH8PU1BTz58+X6mJiYqCtrc0ZciIiUl852aopaoAJdDn29OlTHD58GCNHjoSenp7C8dy7Uj9Ueno6srKyULlyZQBAXFwcrl+/jgkTJigs5ndxcYGHh4dcovk+c+fOha+vL+Li4uDg4IB+/fph2LBhCAwMxIULFyCKIkaNGlVgH1FRUbh79y6ioqKwZcsWhIeHIzw8XK7N8uXL0aJFC1y+fBmdOnXCgAED4OvrCx8fH1y6dAnW1tbw9fVF7u0Er169QsOGDXHgwAFcu3YNX375JQYMGCAtZ6lWrRrCwsIQFBSECxcuSJu6jxo1Cp99VvbXfREREZV3TKDLsfj4eIiiCAcHh/e2nTJlCvT19eXK2zOo+Z1To0YN6fn0t2/fBgA4Ojrm2d7R0VFqUxiDBg1C7969YWdnhylTpiAhIQH9+/eHp6cnHB0dMXbsWERHRxfYR6VKlbB69Wo4ODigc+fO6NSpk8IscMeOHTFs2DDY2tpi5syZSElJQePGjdGrVy8p9o0bN6RN3M3NzREQEABXV1dYWVlh9OjRaN++PSIiIuT6HDp0KPr374/hw4dDT08PwcHBhb52IiKiskbMyVZJUQfcxq4cK8oGLJMmTYKfn59cXUhICE6cOJFn+wULFmDXrl2Ijo6Gjo7OB8ctiLOzs/R17lY1Tk5OcnWvXr1CSkoKDA0N8+yjbt260NTUlF6bmZnh6tWrRY4DAI8ePYKpqSmys7Mxf/58RERE4J9//kFmZiYyMjKgq6sr1++SJUtQr149fP/997h48WKBT13KyMhARkaGXF02RGhCyPccIiKiEqUm65dVgTPQ5ZitrS0EQcDNmzff27Zq1aqwsbGRK7lLM961ZMkSLFiwAEeOHJFLPu3s7AAAN27cyPO8GzduSG0KQ0tLS/paEIR863IK+Af9dvvcc95tX9Q4ixcvxsqVKzFlyhRERUUhLi4Onp6eCjdI3r17F//++y9ycnKQkJBQ4LUGBwfDyMhIrlxGcoHnEBERUfFgAl2OVa5cGZ6enlizZg3S0tIUjj9//rzIfS5atAhz587F4cOH0ahRI7ljuTcXLl++XCFJvXLlCn777Td4e3sXOWZZc/r0aXTt2hU+Pj5wcXGBlZWVwtKUzMxM+Pj4oE+fPpg7dy78/f3x6NGjfPsMDAxEcnKyXKkPo+K+FCIiokIrT0s4mECXc2vWrEF2djaaNGmCH374AXfu3MGNGzcQEhKS5zPkC7Jw4ULMmDEDYWFhsLS0RFJSEpKSkpCamgrgzUxtaGgorl+/jh49euDcuXNITEzE999/Dy8vLzRr1gzjxo0rhqssWba2tjh69ChiYmJw48YNDBs2TFofnWvatGlITk5GSEgIpkyZAjs7OwwePDjfPmUyGQwNDeUKl28QEVGZwl04qLywsrLCpUuX4O7ujokTJ6JevXpo164dIiMjsXbt2iL1tXbtWmRmZqJnz54wMzOTypIlS6Q2zZs3x5kzZ6CpqYkOHTrAxsYGgYGBGDhwII4ePVrgOmB1MX36dDRo0ACenp5wc3ODqakpunXrJh2Pjo7GihUrsG3bNhgaGkJDQwPbtm3DyZMni/yeExERUcnjo7yJ1BQf5U2kfvgobyotJfEo78yTu1TSj3arvirppzhxFw4iIiIiUpq6PIZbFbiEg8qc7du3K+w5nVvq1q1b2sMjIiKico4z0FTmdOnSBU2bNs3z2LvbzhEREVEZoSY3AKoCE2gqcwwMDGBgYFDawyAiIqKiYAJNRERERFR4Ip9ESEREREREeeEMNBEREREpj0s4iIiISNVKcm/mktpzmvtNk6QcJdBcwkFEREREVAScgSYiIiIipZWnmwiZQBMRERGR8riEg4iIiIiI8sIEmsoNQRCwb9++0h4GERHRxyknWzVFDTCBpg/m5+cHQRAUSnx8PAAgODgYmpqaWLx4scK54eHheZ67adOmYhvvgwcP0KFDh2Lrn4iIqDwTs7NVUtQB10CTUtq3b4/NmzfL1VWrVg0AEBYWhsmTJyMsLAyTJk1SONfQ0BC3bt2SqzMyMiq2sZqamhZb30RERFR+cAaalCKTyWBqaipXNDU1cfz4cbx8+RJz5sxBSkoKYmJiFM4VBEHh3IoVK743ZlBQEFxdXREWFoaaNWtCX18fI0aMQHZ2NhYtWgRTU1NUr14d8+bNU4iXu4QjISEBgiBg7969cHd3h66uLlxcXBAbGyu1Dw8Ph7GxMfbv3w97e3vo6uqiZ8+eSE9Px5YtW2BpaYlKlSphzJgxyH7rL+Zt27ahUaNGMDAwgKmpKfr164dHjx5Jx+fMmYMaNWrgyZMnUl2nTp3g7u6OnHJ0BzMREX1kcnJUU9QAE2gqFqGhofD29oaWlha8vb0RGhqq0v7v3r2LQ4cO4fDhw9i5cydCQ0PRqVMn/P333zh+/DgWLlyI6dOn4+zZswX2M23aNAQEBCAuLg52dnbw9vbG69evpePp6ekICQnBrl27cPjwYURHR6N79+44ePAgDh48iG3btmH9+vXYs2ePdE5WVhbmzp2LK1euYN++fUhISICfn59cTEtLS/j7+wMA1qxZg5iYGGzZsgUaGvwnSUREaqocrYHmEg5Syv79+6Gvry+97tChA0JDQ7Fnzx5pNtfHxwetWrXCypUr5domJyfLvdbX10dSUlKh4ubk5CAsLAwGBgaoU6cO3N3dcevWLRw8eBAaGhqwt7fHwoULERUVhaZNm+bbT0BAADp16gQAmD17NurWrYv4+Hg4ODgAeJMMr127FtbW1gCAnj17Ytu2bXj48CH09fWl2FFRUejTpw8AYPDgwVL/VlZWCAkJQePGjZGamgp9fX1oamriu+++g6urK6ZOnYqQkBBs2rQJNWvWLNS1ExERlUWimiS/qsAEmpTi7u6OtWvXSq/19PSwc+dOWFtbw8XFBQDg6uqKWrVqYffu3RgyZIjU1sDAAJcuXZJeF2X21dLSEgYGBtJrExMTaGpqyvVhYmIit3QiL87OztLXZmZmAIBHjx5JCbSurq6UPOf2aWlpKZf4vxvn4sWLCAoKwpUrV/Ds2TNpWUZiYiLq1KkD4E1ivWTJEgwbNgx9+vRBv379ChxnRkYGMjIy5OqyIUITQoHnERERkeoxgSal6OnpwcbGRq4uNDQUf/zxBypU+N+PV+6M8dsJtIaGhsK5haWlpSX3WhCEPOvet6b47XMEQZDG+qFx0tLS4OnpCU9PT2zfvh3VqlVDYmIiPD09kZmZKXfeiRMnoKmpiYSEBLx+/Vru/XpXcHAwZs+eLVfXEEZoBOMCr4+IiKiklKcnEXLBJanU1atXceHCBURHRyMuLk4q0dHRiI2Nxc2bN0t7iMXq5s2bePLkCRYsWIBWrVrBwcEhz1nw3bt3Y+/evYiOjkZiYiLmzp1bYL+BgYFITk6WK/VRfDuWEBERFZWYnaOSog44A00qFRoaiiZNmqB169YKxxo3bozQ0NA894X+WNSsWRPa2tpYtWoVhg8fjmvXrikkx3///Te++uorLFy4EC1btsTmzZvRuXNndOjQAZ9++mme/cpkMshkMrk6Lt8gIiIqHZyBJpXJzMzEd999hx49euR5vEePHti6dSuysrJKeGQlp1q1aggPD8f333+POnXqYMGCBViyZIl0XBRF+Pn5oUmTJhg1ahQAwNPTE1999RV8fHyQmppaWkMnIiJSSnmagRZEURRLexBEVHTDBcvSHgIRlWFhrm1LJM7guGMlEoeUs05MKPYYyaHTVdKP0ZBvVNJPceIMNBERERFRETCBpjKnbt260NfXz7Ns3769tIdHREREeShPSzh4EyGVOQcPHsx3nbSJiUkJj4aIiIgKQ12SX1VgAk1lTq1atUp7CERERET5YgJNRERERErLyeajvImIiIiICq08PYmQCTQRERERKY1roImIiEitldT+zNxvmsojJtBEREREpDTOQBMRERERFUF5WgPNB6kQERERERUBZ6CJiIiISGk5XMJBRERERFR45WkNNJdwEBEREREVARNoKlcEQSiwBAUFITo6GoIg4Pnz5wrnW1paYsWKFe/tb9euXSV3UURERGWAmJ2jkqIOuISDypUHDx5IX+/evRszZ87ErVu3pDp9fX1cuHChSH1u3rwZ7du3l6szNjZWapxERETqpjztwsEEmsoVU1NT6WsjIyMIgiBX9yGMjY0/qA83NzccP35cof7PP/+EpaWlUmMiIiKi4sMEmqiU7N27F5mZmdLrkSNH4o8//oCJiUkpjoqIiOjDqMvyC1VgAk2kJG9vb2hqasrVXb9+HTVr1izwvMqVK0tfL1++HMeOHcPZs2dRsWLFYhknERFRcWICTUSFtnz5cnh4eMjV1ahRo9DnHzp0CFOnTsUvv/wCOzu7PNtkZGQgIyNDri4bIjQhFH3ARERExSCHa6CJyi9DQ0MAQHJyssLNgM+fP4eRkZFcnampKWxsbD4o1vXr19G3b18sWLAAn3/+eb7tgoODMXv2bLm6hjBCIxjnfQIREREVG25jR/QOW1tbaGho4OLFi3L19+7dQ3Jycr6zxEX133//wcvLCz169MD48eMLbBsYGIjk5GS5Uh9GBZ5DRERUkriNHVE5ZmBgAH9/f0ycOBEVKlSAk5MT/vrrL0yZMgWffvopmjdvLtf++fPnSEpKUuhDT0+vwDg9evSArq4ugoKC5M6vVq2awppqmUwGmUwmV8flG0REVJaI2dmlPYQSwwSaKA8rV67EggULMGXKFNy/fx+mpqZo164d5s2bB0GQT1wHDRqkcH5wcDCmTp1aYIwTJ04AAGrVqiVXz23siIiIyjZBFEWxtAdBREU3XLAs7SEQESHMtW2JxBkcd6xE4nys1okJxR7j7ti+KunHemXZf5ovZ6CJiIiISGnqsn5ZFXgTIVEx6dChA/T19fMs8+fPL+3hERER0QfiDDRRMdm0aRNevnyZ57G3H6JCRET0MeAMNBEpzdzcHDY2NnkWJtBERPSxycnOUUn5EGvWrIGlpSV0dHTQtGlTnDt3Lt+2GzduRKtWrVCpUiVUqlQJHh4eBbbPCxNoIiIiIlJbu3fvxoQJEzBr1ixcunQJLi4u8PT0xKNHj/JsHx0dDW9vb0RFRSE2NhYWFhb4/PPP8c8//xQ6JhNoIiIiIlKamJOjklJUy5Ytw9ChQzFo0CDUqVMH69atg66uLsLCwvJsv337dowYMQKurq5wcHDApk2bkJOTg8jIyELH5BpoIiIiIlKaqtZAZ2RkICMjQ64urweKAUBmZiYuXryIwMBAqU5DQwMeHh6IjY0tVLz09HRkZWUVaXklE2giIiL6YCW1P3NJ7TcNcM/pDyVmq+bRIsHBwZg9e7Zc3axZsxAUFKTQ9r///kN2djZMTEzk6k1MTHDz5s1CxZsyZQpq1KgBDw+PQo+RCTQRERERlRmBgYGYMGGCXF1es8+qsGDBAuzatQvR0dHQ0dEp9HlMoImIiIhIaR+6g8a78luukZeqVatCU1MTDx8+lKt/+PAhTE1NCzx3yZIlWLBgAX777Tc4OzsXaYy8iZCIiIiIlCbmiCopRaGtrY2GDRvK3QCYe0Ngs2bN8j1v0aJFmDt3Lg4fPoxGjRoV+Vo5A01EREREamvChAkYOHAgGjVqhCZNmmDFihVIS0vDoEGDAAC+vr4wNzdHcHAwAGDhwoWYOXMmduzYAUtLSyQlJQGA9LTgwmACTURERERKy1HRTYRF1adPHzx+/BgzZ85EUlISXF1dcfjwYenGwsTERGho/G/Rxdq1a5GZmYmePXvK9ZPfjYp54RIO+ugkJSVh9OjRsLKygkwmg4WFBby8vBT2dwwODoampiYWL16s0Ed4eDiMjY1VEoeIiKg8ELNzVFI+xKhRo3D//n1kZGTg7NmzaNq0qXQsOjoa4eHh0uuEhASIoqhQCps8A5yBpo9MQkICWrRoAWNjYyxevBhOTk7IysrCr7/+ipEjR8ptaRMWFobJkycjLCwMkyZNKrY4RERE9HFhAk0flREjRkAQBJw7dw56enpSfd26dTF48GDp9fHjx/Hy5UvMmTMHW7duRUxMDJo3b67yOPkJDw+X1ma9rSgfHxEREZUlqtoHWh1wCQd9NJ4+fYrDhw9j5MiRckltrreXZISGhsLb2xtaWlrw9vZGaGhoscTJT58+ffDgwQOp7Ny5ExUqVECLFi0KPQ4iIqKyJCdbVElRB0yg6aMRHx8PURTh4OBQYLuUlBTs2bMHPj4+AAAfHx9EREQgNTVVpXEKUrFiRZiamsLU1BRpaWkYOXIk5s+fj3bt2n1wn0RERFQymEDTR0MUC/dX686dO2FtbQ0XFxcAgKurK2rVqoXdu3erNE5hJCcno3PnzujUqVOB67AzMjKQkpIiV7KhHn+lExFR+VCaNxGWNCbQ9NGwtbWFIAjvvYEvNDQUf/zxBypUqCCV69evIywsTKVx3ic7Oxt9+vSBoaEhNmzYUGDb4OBgGBkZyZXLSFYqPhERkSrl5IgqKeqACTR9NCpXrgxPT0+sWbMGaWlpCsefP3+Oq1ev4sKFC4iOjkZcXJxUoqOjERsbW6ikuDBxCmP8+PG4evUq9u3bBx0dnQLbBgYGIjk5Wa7Uh1Gh4hAREZUEMVtUSVEH3IWDPipr1qxBixYt0KRJE8yZMwfOzs54/fo1jh49irVr18LT0xNNmjRB69atFc5t3LgxQkNDpX2hs7OzERcXJ9dGJpPB0dHxvXFu3LhR4Dg3b96Mb7/9Fj/++CMEQXjvU5BkMhlkMplcnSaEorw1REREpCJMoOmjYmVlhUuXLmHevHmYOHEiHjx4gGrVqqFhw4ZYuXIl+vXrhylTpuR5bo8ePbB06VLMnz8fAJCamor69evLtbG2tkZ8fHyBcdauXfvecR4/fhzZ2dno0qWLXD23sSMiInWVoybrl1VBEFV5RxQRlZjhgmVpD4GIqMSEubYtsViD446VWKySsk5MKPYYMW0UP939EM2Pn1BJP8WJa6CJiIiIiIqACTRRMdi+fbu0nvndUrdu3dIeHhERkcrxJkIiUkqXLl3QtGnTPI9paWmV8GiIiIiKX3laA80EmqgYGBgYwMDAoLSHQURERMWACTQRERERKU1Uk4egqAITaCIiIiJSWo6arF9WBSbQRERERKQ0sRytgeYuHERERERERcAZaCIiIirzSvLhJiX10JaP7YEt6rIFnSowgSYiIiIipZWnNdBcwkFEREREVAScgSYiIiIipYk55ecmQibQRERERKQ0LuEgKiRLS0usWLGiwDaCIGDfvn0lMp78FGacRERERIXBGeiPQGxsLFq2bIn27dvjwIEDpT0cBQ8ePEClSpVKdQznz5+Hnp5eqY6BiIjoY1aeduHgDPRHIDQ0FKNHj8aJEyfw77//lvZwFJiamkImk5XqGKpVqwZdXd1SHQMREdHHTMzOUUlRB0yg1Vxqaip2796Nr776Cp06dUJ4eLjc8Z9//hm2trbQ0dGBu7s7tmzZAkEQ8Pz5c6nNqVOn0KpVK1SsWBEWFhYYM2YM0tLSCj2GFy9ewNvbG3p6ejA3N8eaNWvkjr+9hCMhIQGCIGDv3r1wd3eHrq4uXFxcEBsbW6hY4eHhMDY2xv79+2Fvbw9dXV307NkT6enp2LJlCywtLVGpUiWMGTMG2dnZ0nnvLuEQBAGbNm1C9+7doaurC1tbW/z888/S8ejoaAiCgF9//RX169dHxYoV0bZtWzx69AiHDh2Co6MjDA0N0a9fP6Snp0vnHT58GC1btoSxsTGqVKmCzp074+7du9LxrVu3Ql9fH3fu3JHqRowYAQcHB7l+iIiIqOxiAq3mIiIi4ODgAHt7e/j4+CAsLAyi+OYjlD///BM9e/ZEt27dcOXKFQwbNgzTpk2TO//u3bto3749evTogd9//x27d+/GqVOnMGrUqEKPYfHixXBxccHly5cxdepUjB07FkePHi3wnGnTpiEgIABxcXGws7ODt7c3Xr9+Xah46enpCAkJwa5du3D48GFER0eje/fuOHjwIA4ePIht27Zh/fr12LNnT4H9zJ49G71798bvv/+Ojh07on///nj69Klcm6CgIKxevRoxMTH466+/0Lt3b6xYsQI7duzAgQMHcOTIEaxatUpqn5aWhgkTJuDChQuIjIyEhoYGunfvjpz/vzPZ19dXivX69WscOHAAmzZtwvbt2zlDTkREai0nW1RJUQdcA63mQkND4ePjAwBo3749kpOTcfz4cbi5uWH9+vWwt7fH4sWLAQD29va4du0a5s2bJ50fHByM/v37Y9y4cQAAW1tbhISEoE2bNli7di10dHTeO4YWLVpg6tSpAAA7OzucPn0ay5cvR7t27fI9JyAgAJ06dQLwJpGtW7cu4uPj4eDg8N54WVlZWLt2LaytrQEAPXv2xLZt2/Dw4UPo6+ujTp06cHd3R1RUFPr06ZNvP35+fvD29gYAzJ8/HyEhITh37hzat28vtfnmm2/QokULAMCQIUMQGBiIu3fvwsrKSoodFRWFKVOmAAB69OghFyMsLAzVqlXD9evXUa9ePQDA+vXr4ezsjDFjxmDv3r0ICgpCw4YN33vdREREZRnXQJNauHXrFs6dOyclgRUqVECfPn0QGhoqHW/cuLHcOU2aNJF7feXKFYSHh0NfX18qnp6eyMnJwZ9//lmocTRr1kzh9Y0bNwo8x9nZWfrazMwMAPDo0aNCxdPV1ZWSZwAwMTGBpaUl9PX15ere19/bY9DT04OhoaHCOW+3MTExga6urpQ85xXnzp078Pb2hpWVFQwNDWFpaQkASExMlNpUqlQJoaGh0h8BuX98FCQjIwMpKSlyJRvl5xcVERGVfTmiqJKiDjgDrcZCQ0Px+vVr1KhRQ6oTRREymQyrV68uVB+pqakYNmwYxowZo3CsZs2aKhvru7S0tKSvBUEAAGmZQ1HOzT0/r7r39VeYc94d5/vO8fLyQq1atbBx40bUqFEDOTk5qFevHjIzM+XOO3HiBDQ1NfHgwQOkpaXBwMCgwLEGBwdj9uzZcnUNYYRGMC7wPCIiIlI9zkCrqdevX2Pr1q1YunQp4uLipHLlyhXUqFEDO3fuhL29PS5cuCB33vnz5+VeN2jQANevX4eNjY1C0dbWLtRYzpw5o/Da0dFRuQtUQ0+ePMGtW7cwffp0fPbZZ3B0dMSzZ88U2sXExGDhwoX45ZdfoK+vX6j15oGBgUhOTpYr9WFUHJdBRET0QbJFUSVFHXAGWk3t378fz549w5AhQ2BkJJ9I9ejRA6GhoYiIiMCyZcswZcoUDBkyBHFxcdIuHbmzvlOmTMGnn36KUaNGwd/fH3p6erh+/TqOHj1a6Fns06dPY9GiRejWrRuOHj2K77//vkzuR13cKlWqhCpVqmDDhg0wMzNDYmKiwvKMFy9eYMCAARgzZgw6dOiATz75BI0bN4aXlxd69uyZb98ymUxhK0BNCMVyHURERB+iHC2B5gy0ugoNDYWHh4dC8gy8SaAvXLiAFy9eYM+ePdi7dy+cnZ2xdu1aaReO3GTM2dkZx48fx+3bt9GqVSvUr18fM2fOlFsW8j4TJ07EhQsXUL9+fXzzzTdYtmwZPD09VXOhakRDQwO7du3CxYsXUa9ePYwfP166gTPX2LFjoaenh/nz5wMAnJycMH/+fAwbNgz//PNPaQybiIiIikgQRTWZKyeVmDdvHtatW4e//vqrtIdCShouWJb2EIiIPkphrm1LJM7guGMlEgcA1okJxR5jd/U6Kumnz6PrKumnOHEJx0fu22+/RePGjVGlShWcPn0aixcvLtIez0RERESFwSUc9NG4c+cOunbtijp16mDu3LmYOHEigoKCCnXuyZMn5ba3e7cUhw4dOuQbL3fZAxEREVFp4gz0R2758uVYvnz5B53bqFEjxMXFqXZA77Fp0ya8fPkyz2OVK1cu0bEQERFR4anLDhqqwASa8lWxYkXY2NiUaExzc/MSjUdERESqwSUcRERERESUJ85AExEREZHSuISDiIiIiKgIytMSDibQRFSgktoPFSjZPVGJiPJTUr+LSvL367oSiFGeEmiugSYiIiIiKgLOQBMRERGR0rgGmoiIiIioCLiEg4iIiIiI8sQZaCIiIiJSWnlawsEZaCoTgoKC4OrqWtrDKLLo6GgIgoDnz58r1Y+bmxvGjRunkjERERGVhmxRNUUdMIGmD+Ln5wdBELBgwQK5+n379kEQBOl1dnY2li9fDicnJ+jo6KBSpUro0KEDTp8+XdJDJiIiIlIJJtD0wXR0dLBw4UI8e/Ysz+OiKKJv376YM2cOxo4dixs3biA6OhoWFhZwc3PDvn37SnbAREREVGyyRVElRR0wgaYP5uHhAVNTUwQHB+d5PCIiAnv27MHWrVvh7++P2rVrw8XFBRs2bECXLl3g7++PtLQ0uXPWr18PCwsL6Orqonfv3khOTpY7vmnTJjg6OkJHRwcODg749ttvpWNt27bFqFGj5No/fvwY2traiIyMBABkZGQgICAA5ubm0NPTQ9OmTREdHS21v3//Pry8vFCpUiXo6emhbt26OHjwoHT84MGDsLOzQ8WKFeHu7o6EhAS5eE+ePIG3tzfMzc2hq6sLJycn7Ny5U65NWloafH19oa+vDzMzMyxdurTgN5qIiEgNcAkHUSFoampi/vz5WLVqFf7++2+F4zt27ICdnR28vLwUjk2cOBFPnjzB0aNHpbr4+HhERETgl19+weHDh3H58mWMGDFCOr59+3bMnDkT8+bNw40bNzB//nzMmDEDW7ZsAQD4+/tjx44dyMjIkM757rvvYG5ujrZt3zztadSoUYiNjcWuXbvw+++/o1evXmjfvj3u3LkDABg5ciQyMjJw4sQJXL16FQsXLoS+vj4A4K+//sIXX3wBLy8vxMXFwd/fH1OnTpW7rlevXqFhw4Y4cOAArl27hi+//BIDBgzAuXPnpDaTJk3C8ePH8dNPP+HIkSOIjo7GpUuXivz+ExERUelgAk1K6d69O1xdXTFr1iyFY7dv34ajo2Oe5+XW3759W6p79eoVtm7dCldXV7Ru3RqrVq3Crl27kJSUBACYNWsWli5dii+++AK1a9fGF198gfHjx2P9+vUAgC+++AIA8NNPP0l9hoeHS+u1ExMTsXnzZnz//fdo1aoVrK2tERAQgJYtW2Lz5s0AgMTERLRo0QJOTk6wsrJC586d0bp1awDA2rVrYW1tjaVLl8Le3h79+/eHn5+f3HWZm5sjICAArq6usLKywujRo9G+fXtEREQAAFJTUxEaGoolS5bgs88+g5OTE7Zs2YLXr18X+b0nIiIqS8rTEg5uY0dKW7hwIdq2bYuAgACFY2IR/iHUrFkT5ubm0utmzZohJycHt27dgoGBAe7evYshQ4Zg6NChUpvXr1/DyMgIwJs12QMGDEBYWBh69+6NS5cu4dq1a/j5558BAFevXkV2djbs7Ozk4mZkZKBKlSoAgDFjxuCrr77CkSNH4OHhgR49esDZ2RkAcOPGDTRt2lTu3GbNmsm9zs7Oxvz58xEREYF//vkHmZmZyMjIgK6uLgDg7t27yMzMlOuncuXKsLe3L/C9ycjIkJtZB4BsiNCEkM8ZREREJSuntAdQgphAk9Jat24NT09PBAYGys3I2tnZ4caNG3mek1v/bjKbn9TUVADAxo0bFZJYTU1N6Wt/f3+4urri77//xubNm9G2bVvUqlVL6kNTUxMXL16UOweAtEzD398fnp6eOHDgAI4cOYLg4GAsXboUo0ePLtQ4Fy9ejJUrV2LFihVwcnKCnp4exo0bh8zMzEKdn5/g4GDMnj1brq4hjNAIxkr1S0REpCrqMnusClzCQSqxYMEC/PLLL4iNjZXq+vbtizt37uCXX35RaL906VJUqVIF7dq1k+oSExPx77//Sq/PnDkDDQ0N2Nvbw8TEBDVq1MC9e/dgY2MjV2rXri2d4+TkhEaNGmHjxo3YsWMHBg8eLB2rX78+srOz8ejRI4U+TE1NpXYWFhYYPnw49u7di4kTJ2Ljxo0A3iw7eXstc+4Y33b69Gl07doVPj4+cHFxgZWVldwyFWtra2hpaeHs2bNS3bNnz+Ta5CUwMBDJyclypT6MCjyHiIiIigdnoEklnJyc0L9/f4SEhEh1ffv2xffff4+BAwdi8eLF+Oyzz5CSkoI1a9bg559/xvfffw89PT2pvY6ODgYOHIglS5YgJSUFY8aMQe/evaXkdvbs2RgzZgyMjIzQvn17ZGRk4MKFC3j27BkmTJgg9ePv749Ro0ZBT08P3bt3l+rt7OzQv39/+Pr6YunSpahfvz4eP36MyMhIODs7o1OnThg3bhw6dOgAOzs7PHv2DFFRUdJ67eHDh2Pp0qWYNGkS/P39cfHiRYSHh8u9D7a2ttizZw9iYmJQqVIlLFu2DA8fPkSdOnUAvJnpHjJkCCZNmoQqVaqgevXqmDZtGjQ0Cv5bViaTQSaTydVx+QYREZUl6rKDhipwBppUZs6cOcjJ+d8KKEEQEBERga+//hrLly+Hvb09WrVqhfv37yM6OhrdunWTO9/GxgZffPEFOnbsiM8//xzOzs5y29T5+/tj06ZN2Lx5M5ycnNCmTRuEh4fLzUADgLe3NypUqABvb2/o6OjIHdu8eTN8fX0xceJE2Nvbo1u3bjh//jxq1qwJ4M0a5pEjR8LR0RHt27eHnZ2dNIaaNWvihx9+wL59++Di4oJ169Zh/vz5cv1Pnz4dDRo0gKenJ9zc3GBqaqpwnYsXL0arVq3g5eUFDw8PtGzZEg0bNvyg95yIiKisKE83EQpiUe7yIlIDCQkJsLa2xvnz59GgQYPSHk6xGS5YlkicMNe2JRIHAAbHHSuxWEREpa0kf79mXg4r9hhTtK1U0s/CzHsq6ac4cQkHfTSysrLw5MkTTJ8+HZ9++ulHnTwTERGVNeVpCQcTaPponD59Gu7u7rCzs8OePXtKezhERETlirosv1AFJtD00XBzcyvSvtNEREREH4I3ERIRERGR0rJF1ZQPsWbNGlhaWkJHRwdNmzZV2Hb2Xd9//z0cHBygo6MDJycnHDx4sEjxmEATERERkdJKaxeO3bt3Y8KECZg1axYuXboEFxcXeHp64tGjR3m2j4mJgbe3N4YMGYLLly+jW7du6NatG65du1bomEygiYiIiEhtLVu2DEOHDsWgQYNQp04drFu3Drq6uggLy3vnkZUrV6J9+/aYNGkSHB0dMXfuXDRo0ACrV68udEwm0ERERESkNFUt4cjIyEBKSopcycjIyDNmZmYmLl68CA8PD6lOQ0MDHh4eck9HfltsbKxcewDw9PTMt32eRCIqF169eiXOmjVLfPXq1UcTi9dU9uOUZCxek3rE4jWpT6zSMmvWLBGAXJk1a1aebf/55x8RgBgTEyNXP2nSJLFJkyZ5nqOlpSXu2LFDrm7NmjVi9erVCz1GPkiFqJxISUmBkZERkpOTYWho+FHE4jWV/TglGYvXpB6xeE3qE6u0ZGRkKMw4y2QyyGQyhbb//vsvzM3NERMTg2bNmkn1kydPxvHjx3H27FmFc7S1tbFlyxZ4e3tLdd9++y1mz56Nhw8fFmqM3MaOiIiIiMqM/JLlvFStWhWampoKie/Dhw9hamqa5zmmpqZFap8XroEmIiIiIrWkra2Nhg0bIjIyUqrLyclBZGSk3Iz025o1aybXHgCOHj2ab/u8cAaaiIiIiNTWhAkTMHDgQDRq1AhNmjTBihUrkJaWhkGDBgEAfH19YW5ujuDgYADA2LFj0aZNGyxduhSdOnXCrl27cOHCBWzYsKHQMZlAE5UTMpkMs2bNKvTHYuoQi9dU9uOUZCxek3rE4jWpTyx10adPHzx+/BgzZ85EUlISXF1dcfjwYZiYmAAAEhMToaHxv0UXzZs3x44dOzB9+nR8/fXXsLW1xb59+1CvXr1Cx+RNhERERERERcA10ERERERERcAEmoiIiIioCJhAExEREREVARNoIiIiIqIiYAJNREpLTExEXvcji6KIxMTEUhgREZUViYmJCk+VA97s1cvfD6SumEATkdJq166Nx48fK9Q/ffoUtWvXVmksKysrPHnyRKH++fPnsLKyUrr/Z8+eYdWqVUhJSVE4lpycnO8x+vhFRUXle2z9+vVqF6ekWFpaokGDBrh7965c/ePHj1X++2HOnDk4duyYQn1aWhrmzJmjsjivXr3K99iDBw9UFofKLibQRB+5OXPmID09XaH+5cuXKvsPRRRFCIKgUJ+amgodHR2VxMiVkJCA7OxshfqMjAz8888/Sve/evVqnDhxAoaGhgrHjIyMcPLkSaxatUrpOKVl69atuH79ukL9q1evsHXrVpXFCQoKQk5OjkJ9cnIyvL29VRYHAE6cOIFHjx4p1GdlZeHEiRMqi9O+fXtMmjQJWVlZUt1///0HLy8vTJ06Ve3ivE9evzc+lKOjI5o0aaLw9DdV76QbFBSEDh06YNmyZXL1qampmD17tsriNGjQAHFxcQr1P/zwA5ydnVUWh8ou7gNN9JHT1NTEgwcPUL16dbn6J0+eoHr16nkmo4U1YcIEAMDKlSsxdOhQ6OrqSseys7Nx9uxZaGpq4vTp0x8cI9fPP/8MAOjWrRu2bNkCIyMjuViRkZE4evQobt26pVQcV1dXLF26FJ999lmexyMjIxEQEIDLly8rFedtlSpVyvMPEEEQoKOjAxsbG/j5+UlP1VKGhoYG9PT0EB4ejh49ekj1Dx8+RI0aNZT6eXibhYUFLCws8N1330mfDERHR8PX1xempqY4d+6cSuIAb67JxMQEP/74Iz799FOpXtXXFBMTA19fX+jr62PHjh34888/MWTIENjb22Pr1q2oVauWWsUBgM8++wxbt26Fubm5XP25c+fg4+OD27dvKx0j93fQ9u3bERgYiEWLFmHMmDEq//4Ab34Wdu7ciZEjR8LLywvr16+Htra2ymONGDECYWFhmD17NqZMmYK0tDSMHDkSERERmDdvHsaPH690jNzfr4Xx7h8MVAJEIvqoCYIgPnr0SKE+MjJSrFq1qlJ9u7m5iW5ubqIgCGLz5s2l125ubuLnn38ufvnll+Lt27eVipFLEARREARRQ0ND+jq3aGtri3Z2duIvv/yidBx9fX3x/v37+R6/f/++aGBgoHScty1btkysUqWK6OPjI4aEhIghISGij4+PWLVqVXHevHmiv7+/KJPJxA0bNigdSxAEcenSpWLFihXFWbNmSfVJSUmihoaG0v3nevr0qdirVy/RwMBA3LBhgxgQECBqaWmJX3/9tZiVlaWyOKL45prGjRsn6urqips3b5bqk5KSREEQVBrrxYsXYv/+/UWZTCZqaWmJCxYsEHNyclQaoyTjdOzYUaxcubK4a9cuURRFMTs7W5w1a5aopaUljh07ViUxBEEQHz58KIqiKB48eFA0MjIS/f39xcTERJX+zL0dKz4+XnR0dBSbNWsmPnz4UOU/36Ioivv37xdNTU3Fli1bitbW1qKLi4t49epVlfX/9u9TNzc30dDQUNTV1RXr168v1q9fX9TT0xMNDQ1Fd3d3lcWkwmMCTfSRMjY2FitVqiRqaGhIX+cWQ0NDUUNDQxwxYoRKYvn5+YnJyckq6et9LC0txcePHxdb/0ZGRmJsbGy+x2NjY0UjIyOVxvziiy/EtWvXKtSvW7dO/OKLL0RRFMWQkBCxXr16SsfKTTBiY2NFU1NTsUePHmJ6enqxJBiiKIqBgYGiIAiilpaW+Ntvv6m8f1EURQ0NDfHhw4fiDz/8IOrp6Ynjx48Xc3JyiuWaLl68KNrb24vW1tZixYoVxUGDBompqakqjVGScURRFFevXi3q6uqK3t7eYrNmzcQaNWqIv/76q8r6fzuBFkVR/OOPP0Rra2vR2dlZ5d+f3J8FURTF5ORk0dPTU/zkk0/E/fv3qzxWdna2OGLECOnn+/Dhwyrt/21Lly4Vvby8xKdPn0p1T58+Fbt27SouWbKk2OJS/phAE32kwsPDxc2bN4uCIIgrV64Uw8PDpbJjxw4xJiamtIdYJrm5uYlTpkzJ9/jkyZNFNzc3lcbU09MT79y5o1B/584dUU9PTxRFUYyPjxd1dXWVjvV2gnH//n3RxcVFdHV1Fc+cOaPyBCMkJETU1dUV+/XrJ9rb24t16tQR4+LiVBpDFOUTtEuXLokWFhaip6enePPmTZVeU3BwsKitrS2OGjVKfPnypXj16lXR1dVVtLKyUum/p5KK87apU6dKieDp06dV2rebm5v47Nkzubr//vtPbN26tco/IXg3Wc/OzhZHjx4tVqhQQaU/C/Hx8WKTJk3EmjVrikeOHBGnTZsmamtri5MmTRIzMzNVFidXjRo1xGvXrinUX716VTQzM1N5PHo/JtBEH7no6Ohi+YX+ttTUVHH69Olis2bNRGtra7F27dpyRdV+++03MTAwUBwyZIg4aNAguaKsPXv2iBUqVBBXrVolvn79Wqp//fq1GBISImppaYnff/+90nHeZmFhIS5btkyhftmyZaKFhYUoiqJ45coV0cTEROlY7yYYaWlpYrdu3UQDAwOVJhienp5ilSpVpPcqPT1dHD58uKijoyMuXLhQZXFEUfGaHjx4IDZt2lQ0NzdX6TWZmpqKBw8elKvLzMwUAwICRG1tbbWLI4pvZjG/+OIL0cjISNywYYPYv39/UU9PT1yzZo1K4xRGcHCwQqJdVOHh4eKrV68U6sPCwkQ/Pz+l+n6bvr6+2KdPH7nxnj59WrS2thZdXV1VFufteFFRUQr1x44dE/X19VUej96PCTRROZCdnS3eunVLPHnypHj8+HG5ogp9+/YVzczMxMmTJ4vLly8XV6xYIVdUKSgoSNTQ0BCbNGkidu3aVezWrZtcUYWvv/5aFARBNDQ0FF1dXUVXV1dp2UtBs9MfasOGDaKmpqbo5eUlzp07V5w7d67YpUsXsUKFCuKmTZtEURTFJUuWiL1791Y6VlBQkJiWlqZQP3PmTJXOrHt4eIj//POPQn3uulFV8vPzE1NSUuTqXr16Jfr6+oqWlpYqi1PQ0qHo6Gi1iyOKb2Y2W7RoId67d0+q27Vrl1i5cmWxY8eOKo31PgYGBuLdu3dLJFa9evXExMTEDz5/69atedanpKSIgwcP/uB+8zNgwADR0tJS/OGHH8S//vpL/Ouvv8Q9e/aItWvXFn19fVUej96PCTTRRy42NlasXbt2njffqWp2zsjISDx16pRK+nofU1PTfP/zUqWzZ8+KY8aMETt27Ch26NBBHDt2rHj27Nlii3fq1Cmxb9++0g1Cffv2VflH6UXRsWNH8d9//y2WvotzDXtBvvrqq1KLXVbNmTNHzM7OVqj/66+/RA8PjxIdi76+fokl0CUZSxXS0tLEr776SpTJZKKGhoaooaEhamtri1999VWxrY2ngnEbO6KPnKurK+zs7DB79myYmZkpbJf29nZwH6p27do4ePAgHB0dle7rfapUqYJz587B2tq62GOVZwYGBrhy5YpKHk5TVhgaGiIuLk6pa7pw4QIiIiKQmJiIzMxMuWN79+5VdoglHqcsKcmfOVXFun79usL3SBAEeHl5KTvEPKWlpUkPpLG2toaenl6xxKH3q1DaAyCi4nXnzh3s2bMHNjY2xRZj7ty5mDlzJrZs2SK3F3Rx8Pf3x44dOzBjxoxi6f/OnTuYOXMm1q9fr/AwleTkZHz11Vf45ptvVP6ffE5ODuLj4/Ho0SOFB5C0bt1apbFKQnZ2NpYvX55vEvj06dMSH5Oy80W7du2Cr68vPD09ceTIEXz++ee4ffs2Hj58iO7du6tolCUX523p6el5fp/4UJC83bt3D927d8fVq1chCIL0s5U7QaHKva3fpqenx+9JGcEEmugj17RpU8THx6s8ga5fv77cbHZ8fDxMTExgaWkJLS0tubaXLl1SKtbbDxTIycnBhg0b8Ntvv8HZ2VkhlrIPFFi8eDEsLCzyfRKhhYUFFi9ejLVr1yoV521nzpxBv379cP/+fYUkTxCEYvvPuDjNnj0bmzZtwsSJEzF9+nRMmzYNCQkJ2LdvH2bOnFnaw/sg8+fPx/LlyzFy5EgYGBhg5cqVqF27NoYNGwYzMzO1iwO8eZz2oEGDcOjQoTyPq+PPXkkYO3YsateujcjISNSuXRvnzp3DkydPMHHiRCxZsqRYYpbHTyXKMibQRB+h33//Xfp69OjRmDhxIpKSkuDk5KSQcH7obEa3bt2UGWKRvPvUP1dXVwDAtWvX5OrzeppfUR0/fhzfffddvsd79+6Nfv36KR3nbcOHD0ejRo1w4MCBPJfZqKPt27dj48aN6NSpE4KCguDt7Q1ra2s4OzvjzJkzGDNmTGkPscju3r2LTp06AQC0tbWRlpYGQRAwfvx4tG3bVmWPii6pOAAwbtw4PH/+HGfPnoWbmxt+/PFHPHz4EN988w2WLl2qsjgfm9jYWBw7dgxVq1aFhoYGNDQ00LJlSwQHB2PMmDEqfVIpUDqfSlDBmEATfYRcXV3lPlYEgMGDB0tf5x5TZnZz1qxZSo+zsKKiokosVmJiosJjz99WtWpV/PXXXyqNWRLLbEpa7h9sAKCvr4/k5GQAQOfOnYtt+U1xq1SpEl68eAEAMDc3x7Vr1+Dk5ITnz58jPT1d7eIAwLFjx/DTTz+hUaNG0NDQQK1atdCuXTsYGhoiODhYSuRLQqtWrVCxYsUSi6eM7OxsGBgYAHjzO+Hff/+Fvb09atWqhVu3bqk8Xkl+KkGFo1HaAyAi1fvzzz9x7949/Pnnn3mW3GP37t0r7aGWOUZGRtJNOnmJj4/Pc3mHMnKX2XxMPvnkEzx48ADAm5udjhw5AgA4f/48ZDJZaQ7tg7Vu3RpHjx4FAPTq1Qtjx47F0KFD4e3tjc8++0zt4gBvbkrL/YOxUqVKePz4MQDAyclJ6aVXERERcksN/v77b7n1/enp6Vi0aJH0+uDBgyWWDK5fvx4mJiYffH69evVw5coVAG/+/S5atAinT5/GnDlziuUmyII+ldiwYYPK49H7cQaa6CNUq1atEo1XqVKlPJcdCIIAHR0d2NjYwM/PD4MGDVI6Vvfu3d8bq1+/frC3t/+g/lu3bo1Vq1ahbdu2eR4PCQlBq1atPqjv/BTXMhtlfP3116hcufIHn9+9e3dERkaiadOmGD16NHx8fBAaGorExESMHz9ehSMtPB8fH6X++Fm9ejVevXoFAJg2bRq0tLQQExODHj16YPr06aoaZonFAQB7e3vcunULlpaWcHFxwfr162FpaYl169Ypncx6e3vjwYMHUoJep04duV1QXrx4gcDAQEyePFnp6zh27BhGjRqFM2fO5Hnzb/PmzbFu3Trp366yy7CmT5+OtLQ0AMCcOXPQuXNntGrVClWqVMHu3buV6jsvJfmpBBUOt7Ej+sj9/PPPeda/nXDWrl1bqRjLly/HvHnz0KFDBzRp0gQAcO7cORw+fBjjx4/Hn3/+iW3btmHVqlUYOnSoUrH8/Pywb98+GBsbo2HDhgDe3KT4/PlzfP7557hy5QoSEhIQGRmJFi1aFLn/y5cvo1mzZujcuTMmT54sJeI3b97EokWLcODAAcTExKBBgwZKXcfbNDQUPwxUxTKbt128eBEBAQH46aef8kwwunXrhhUrVsDFxUXpWHmJjY1FbGwsbG1tVbbFV1paGgICAvDzzz8jMzMTn332GVatWoVq1aqppP/y4LvvvsPr16/h5+eHixcvon379nj69Cm0tbURHh6OPn36fHDfGhoaSEpKkhLod7eOe/jwIWrUqKGSn+8uXbrA3d093z/OQkJCEBUVhR9//FHpWPl5+vRpvpMJyurXrx8aNWqECRMmYO7cuVi1ahW6du2Ko0ePokGDBryJsBQwgSb6yGloaCishwbkE7SWLVti3759qFSp0gfF6NGjB9q1a4fhw4fL1a9fvx5HjhzBDz/8gFWrVmHDhg24evXqB18LAEydOhUpKSlYvXq1lHjm5ORg7NixMDAwwLx58zB8+HD88ccfOHXq1AfF2L9/PwYPHownT55IdaIoomrVqti0aRO6dOmi1DW86/79+wUeV8UnCv369YOjo2O+64/nz5+P69evF3gDZVkzYcIEbNiwAf3790fFihWxY8cOtGjRoliTJAB49OhRntsNqvqTgpKK87b09HTcvHkTNWvWRNWqVZXqqyQT6Fq1auHw4cP57kV/8+ZNfP7550hMTFQ6Vml4+vQpXr16hRo1aiAnJweLFi1CTEwMbG1tMX369A/+3U0fjgk00UcuMjIS06ZNw7x58+Rmh2fMmIHp06fDyMgIw4YNQ9OmTREaGvpBMfT19REXF6dwE1x8fDxcXV2RmpqKu3fvwtnZWfrY80NVq1YNp0+fhp2dnVz97du30bx5c/z333+4evUqWrVqhefPn39wnJcvX+Lw4cOIj4+HKIqwt7fH559/rjY3Ob3L2toaP/74Y77J19WrV9G1a1eVrov/999/cerUqTyTQFXswlG7dm0sWrQIvXr1AvBmlv3TTz/Fy5cvUaGC6lcoXrx4EQMHDsSNGzeKdbvBkopT3EoygdbR0cG1a9fyvRE3Pj4eTk5OePnypdKxAODVq1dYter/2rvvqKjO7m3A9wyigAqCXWMIRIoUFTV2BRQbxBpL7CIaNUGN3SRYE+tre1H8qZGmEWKNSmwx0sReAUVFBMG8CpbYABGB8/3BYj5HQEHOzHCG+1ora8U5s86zFZQ9z9nP3usRFhZW5Pd3WevHqfxjDTSRlps6dSq2bNmC9u3bK17r2rUr9PT08M033+D69etYt26dUpeO0jIxMUFISEihx6chISGKOtqMjAzFqfWyyMnJwc2bNwsl0Ddv3lT8INbT0/vox6hnzpzBkydP8OWXXyraQwUGBuL7779HRkYG+vXrh/Xr15f5INzBgwfRq1cv6OrqFltmU0CMHe///e9/7/3zr1atmuLQnxgCAgIwYcIEVK5cGTVr1lT6eshkMlES6H/++UepTKdly5bQ1dXF/fv38emnn5b5/u8aO3YsLC0t4evri7p166qs3aC61gHyn6zs2bOn2ESwrKUBx44dU0w7zcvLw4kTJxTtJ8vyAfddBXXBxSXQMTExoh5Q9PDwwF9//YWBAweidevWKvkavXjxosTvFftgM30YE2giLXfnzp0i/3E1NDRU7DZaWFjg8ePHH73GvHnzMGnSJISFhSl2uS9cuIDDhw9j06ZNAIDjx4/D0dHxo9coMHLkSHh4eODHH3/EF198oVhr6dKlGDVqFID8Xs62trYfdf/FixfDyckJX375JYD8ndnx48dj9OjRaNKkCf7zn/+gQYMGWLhwYZl+H/369VPszr2vp7ZYO461a9fGrVu3iq13v3nzZpkf2b9t3rx5mD9/Pn744Ycia7zFkJeXV+jAZaVKlVS2Q5uYmIi9e/eqvN2gutYB8vtAb968Gc7OzipJ1kePHq306wkTJij9Wqz1XF1dMW/ePPTs2RN6enpK1169eoUFCxYo/k6L4c8//8Thw4c/6pxFSdWoUaPEfz5SeSqhTVjCQaTlOnbsiOrVq2Pbtm2Kw1WPHj3CqFGjkJGRgcjISPz999/47rvvytS/9NSpU9iwYYPiHlZWVpg8ebLSzrcYcnNzsXz5cmzYsAFpaWkAgLp162Ly5MmYM2cOdHR0kJKSArlcjk8++aTU969fvz5CQkLQqlUrAPldECIiIhT11Lt378aCBQsQFxcn3m9KDdzd3ZGQkICTJ08WuiYIAjp16gQLCwv4+/uLsl7NmjVx/vx5fP7556LcryhyuRx2dnZK5RoxMTGwtrZG5cqVFa+J9Ti9X79+GDlyJL766itR7qfpdYD8p0e//fYbXF1dVb6WKqWlpaFFixbQ0dGBp6en0uFfHx8f5Obm4vLly2VqXfc2Gxsb/P777yqtR4+IiFD8/927dzF37lyMGTMG7dq1A5D/tCwwMBDLli0r9EGFVI8JNJGWu3XrFvr27YukpCQ0atQIAHDv3j2Ym5vjwIEDsLS0xP79+/Hy5UuMHDlSw9GWTsEjTjEfX+rp6eH27duKP6uOHTuiV69e+OmnnwDk/yCzt7dXtJQSwz///FNssn/27Fm0bdu2zGvcuXMHLVu2hJWVFWbMmKGUYKxevRrx8fG4ePGiaLues2fPhomJCebOnSvK/YpS0ol8Yg39efz4MUaPHo3WrVvDzs6u0O63WIdL1bUOkF9HfuTIEVhbW4t2T01JTk7GpEmTcOzYMUXtuEwmQ48ePeDj41PmbkNvO3LkCLy9vbFp0ya1tA3t2rUrxo0bh6FDhyq9HhQUhC1btiA8PFzlMZAyJtBEFUBeXh7++usvxMfHA8jfHe7WrVuZHq2/ePFCkbh+qFZPSvV5pqam2L59Ozp37ozs7GzUqFEDISEhigEWsbGxcHR0xL///ivamjY2NoiKiirUd/nUqVNwc3MTrVb04sWLGDNmDOLi4hSPhgVBgI2NDfz9/RUlMWLIzc3Fl19+iVevXhXZ23rNmjWiraUuISEhGDlyZJHf72Ie7lPXOkB+ff/Ro0fh5+cn+gFZb2/vIl83MjKCpaWlYidVbE+fPlUc/rWwsFBJh4pHjx5h8ODBiIyMhIGBQaHvbzH/fQAAAwMDREdHw8LCQun1+Ph4NG/enL2gNYAJNBF9FB0dHcWQhIJWee8Sq49xixYtcOLECRgbG8PBweG9dYFlfVw/adIkREdHY8WKFdi/fz8CAwNx//59RUnAjh07sG7dOly4cKFM67xt7NixiImJQVhYmOKgX2RkJHr37o2FCxeKPnjk6tWruH37NgRBgKWlJZo3by7q/QHgl19+wfz582FlZVWotlYmkyE0NLTMa2RlZeGvv/6Cs7NzoQOSL168QHh4OHr06CHa5MPPPvtMMYpcrFIATa4D5NcH9+/fH6dOncJnn31WKBEsy9+n4nZ8nz17phhucvDgwTIN7Cmpmzdvok+fPopNhLJycXFBSkoKPDw8iqwdF7ukwsrKCn379lWa3AjkP+k5cOCASsaH0/vxECGRFvL29sY333wDPT29YneBCnxsN4TQ0FDFD76wsLCPukdJ9e3bV5EEve/AnRh+/vlnDBgwAI6OjqhWrRoCAwOV6mn9/PzQvXt3UdfcunUrBg4ciN69e+PYsWM4ffo0+vTpg19++QVTp04VZY2ZM2di3LhxsLa2RvPmzVWSNL9t9erV8PPzw5gxY1S2xubNm3Hw4MEiSxoMDQ3h7e2NlJQUeHp6irLekydPMG3aNJUntepaB8hP9C5duoQRI0aIfogwKSmp2GuJiYkYMWIEvLy8sHHjRtHWLM7r169x584d0e53+vRpnDlzRmWDh961du1afPXVVzhy5AjatGkDIL8d6e3bt7F37161xEDKuANNpIXMzMxw8eJF1KxZ8711fzKZTNS+v9rk+fPnqFatGnR0dJRe//fff1GtWjWlpFoM2dnZcHNzQ2ZmJmJiYrBs2TLREj8gv9NKYmIi2rRpg3HjxmHIkCGoWrWqaPd/V7169XDy5MlCj5zF1Lp1a8ybN6/YyYZ//vknFi9ejPPnz4uy3ujRo9GpUyeMGzdOlPtpeh0AqFq1Ko4dO4aOHTuqfK13RUZGYuzYsUhISFD5WtHR0WjRooVo5S8tWrTAxo0bRTmfUFL37t3Dpk2bcOPGDQBAkyZNMHHiRMV5DVIvJtBEJIqTJ09i8+bNSExMxO7du9GwYUNs374dZmZmov9wfvbsGfbs2YM7d+5g1qxZMDExUZywb9iwoahrqUpMTEyh116+fImhQ4fCzc0NkyZNUrwu1kn/yMhI+Pn5KXasBg0ahHHjxoneKQUAli1bhgcPHnzwCUhZGBsbIzo6utiezykpKWjWrBmePn0qynpLlizBunXr4ObmVmRdtxi9rdW5DgBYW1tj165dKu0mUZy7d+/Czs4O6enpKl9L7AT6r7/+wqJFi7BkyZIiv0ZSOvdBH4cJNFEFkZ2djaSkJHz++eeiT2nbu3cvRo4cieHDh2P79u2Ii4uDubk5NmzYgMOHD+Pw4cOirRUTEwMXFxcYGRnh7t27uHXrFszNzeHl5YWUlBRs27ZNtLVUqagR62//+u1R62L3eM3IyMDOnTvh7++PU6dOwcrKCh4eHhg5cqRoZQP9+/dHaGgoatasCVtb20IJRlkHdAD5k+3Cw8PRsmXLIq9funQJTk5OonVMUdfTHHU+NTp06BDWr1+PTZs24bPPPhPtviUREhKCuXPn4vr16ypfS+wEuuAA9rslL6r6O3v06FFUq1ZNsRnh4+ODX3/9FTY2NvDx8eEobw1gAk2k5TIzMzF58mQEBgYCyD+1bW5ujsmTJ6Nhw4aitBlzcHDAtGnTMGrUKKVxvVeuXEGvXr2Qmppa5jUKuLi4oEWLFli5cqXSWqdPn8awYcNw9+5d0dZSpeTk5BK/V5VtshISEuDv749NmzYhPT0dr1+/FuW+7u7u770uRr/ptm3bon///pgzZ06R15ctW4YDBw7g7NmzZV5LWxkbGyMzMxM5OTmid5MorjvP8+fPcenSJcyYMQOjR4/G/PnzP3qNAsbGxu+t387JyUFGRoZoie3bPZqLIsbQqLfZ29tjxYoVcHV1RWxsLFq1aoUZM2YgLCwM1tbWovVvp5LjIUIiLffDDz8gOjoa4eHh6Nmzp+J1FxcXLFy4UJQE+tatW+jcuXOh142MjEQd1wvkTx3cvHlzodcbNmwoaqKuauroHfshGRkZOHnyJCIiIvD06VNFb2gxqOMH+tixYzF9+nTY2toWmjIXEhKCJUuWSLJdnjqtW7dOZfd+3yQ9mUyGcePGidYnXJW/j6KInSB/SFJSEmxsbADkP/Hr3bs3li5disuXL0t+CI5UMYEm0nL79+/Hzp070bZtW6UfZra2tqKdSq9Xrx4SEhIKPQKOioqCubm5KGsUqFKlSpE7W/Hx8YpJi1Jw8ODBEr9XzMEZQP7Xxc/PD3v27IEgCBg0aBBWrFih0rHEqvDNN98gMjISffr0gbW1tdJwmPj4eAwePBjffPONaOvl5uYiICAAJ06cwMOHD5GXl6d0XYzWfOpcBxC/3drbiuvOY2hoCAsLC1SrVk20tUry+xC7rOLZs2c4f/58kV+jUaNGibpW5cqVFb2e//77b8X9TUxMPtiHn1SDCTSRlnv06BHq1KlT6PWMjAzRWlaNHz8eU6dOhZ+fH2QyGe7fv48zZ85g5syZmDdvnihrFOjTpw8WL16MXbt2AcjfyUpJScGcOXPUMvpYLCVtxydWPeWDBw8QGBiIgIAAxMfHo23btlizZg2+/vprUROZAmlpaZg5c6YiCXy3WlCsZOa3335Dnz59sGPHDsTHx0MQBFhZWWHRokUYPHiwKGsUmDp1KgICAuDm5gY7OztRW75pYp0CeXl5SEhIKDIRLOrJUklFRERg5syZMDAwKGuIZRIfHw9fX19s27YNDx48EOWeISEhGD58ONLT02FoaFioz7nYCXTHjh0xffp0dOjQAefPn8fOnTsB5P/eiptiSiomEJFW69Spk+Dt7S0IgiBUq1ZNSExMFARBEDw9PYUePXqU6d4F98rLyxN++eUXoWrVqoJMJhNkMpmgp6cneHl5lS34Ijx79kxwcXERatSoIejo6AiNGjUSdHV1hc6dOwvp6emir6ctdHR0hDp16ggzZswQ4uLiinxPTk6OaOv17NlTsLGxETZu3Cj88ccfwv79+5X+k6KaNWsKhw4d0pp1BEEQzpw5I5iZmQlyuVzxd7fgP7lcXqZ7y+VyIS0tTaRISycjI0Pw8/MTOnbsKOjo6Aht2rQRVq5cKdr9LSwshKlTpwoZGRmi3fN9kpOTBTc3N6Fp06bC1q1bFa9///33wuTJk9USAynjIUIiLRcVFYVevXphxIgRCAgIwIQJExAXF4fTp08jIiKi2A4GJSGXy2FqagpnZ2c4OzsrOh6kp6fDxsZGJTubBaKiohATE4P09HS0aNECLi4uKltLG+zbtw99+vQpsgNLfHw8tm7diu3bt4u2Q1e9enWcPHlSpQNbipuA+TaZTIacnBxR1mvQoAHCw8NhaWkpyv00vQ4ANG/eHJaWlli0aBHq169f6M/TyMjoo+8tl8uRmppa5BMwVTl79iy2bt2K3bt349NPP8WNGzcQFhaGTp06ibpO1apVERsbK3qJGkkHE2iiCuDOnTtYvnw5oqOjFQnnnDlzYG9vX6b7hoeHK/47d+4csrOzYW5uji5duqBLly5wcnISfZpaVlYW9PT0RL1neZCRkYGIiAikpKQgOztb6ZqYfX8LZGZmYufOnfDz88OZM2fQqlUrfPXVV5g1a5Yo97exscGOHTvg4OAgyv2KcuDAgWKvnTlzBt7e3sjLy0NWVpYo661evRqJiYnYsGGDSssq1LUOkJ8IRkdHo3HjxqLfWy6XIy0tTS1nEwomXz5//hxDhw7FiBEj0KxZM+jq6iI6OlpxAE8sAwYMwNdffy16mdD73LlzB/7+/rhz5w7++9//ok6dOjhy5Ag+/fRT2Nraqi0OyscEmkhLlfRgiVgN/7OysnD69GlFQn3+/Hm8efMG1tbWovZ51dPTQ+vWreHo6AhnZ2e0a9cO+vr6ot1fE65cuQJXV1dkZmYiIyMDJiYmePz4MQwMDFCnTh1R+/6qa4fur7/+wurVq7F582a19he+desW5s6dq6hRXbx4sWgdT/r374+wsDCYmJiorLe1OtcBgC5dumD27NlKHXrEIpfLYWRk9MEPAWVplVegUqVKmDNnDhYvXqw0PVRVCbSvry8WL14Md3f3IgepiH3wNyIiAr169UKHDh0QGRmJGzduwNzcHMuXL8fFixexZ88eUdejD2MCTaSlPvR4W1BRw//s7GycOnUKR44cwebNm5Geni7qGlFRUYiMjER4eDhOnz6NnJwctGrVCo6OjnByckK3bt1EW0tdnJycYGlpiU2bNsHIyAjR0dHQ1dXFiBEjMHXqVAwYMKDMa6h7h06V/YWLcv/+fSxYsACBgYHo0aMHli1bBjs7O1HXUEdva3WuAwB//PEHvLy8MGvWrCITwbJMKJTL5Vi3bt0Hy0DE6ASybNky+Pv7IysrC0OHDsXIkSNhZ2ensu/vgkEqRVHFv6vt2rXDoEGDMH36dKX+9+fPn8eAAQPwzz//iLoefRgTaCIt9Xajf0EQ4Orqiq1btxYadV3WfqbZ2dk4e/YswsLCFKUcjRo1QufOndG5c2c4OjoWO2q5rHJychR9oXfs2IG8vDzRf3CpQ40aNXDu3DlYWVmhRo0aOHPmDJo0aYJz585h9OjRuHnzZpnXUPcOXcHgnuKI1T7t+fPnWLp0KdavX4/mzZtjxYoVou+ma7OiEkGxpmBqogY6IiJC0aKxcePGuH79OiIiIiTXovFd1apVQ2xsLMzMzJQS6Lt378La2lq0MiUqObaxI9JS7ybGOjo6aNu2raiHXrp06YJz587BzMwMjo6OmDBhAoKCglC/fn3R1ihKfHy8Uv3169ev8eWXX8LJyUml66qKrq6uIpGpU6cOUlJS0KRJExgZGeHevXuirPHzzz/D398f27dvV9qhUxVV9hcusHLlSqxYsQL16tVDcHAw+vbtq/I1tU1SUpLK7q3q+u2iODo6wtHRERs2bEBQUBD8/Pzg6OiI1q1bY+DAgZg+fbraYxJDjRo18ODBg0Jj3q9cuVJoU4TUgzvQRBXE27sWYtHV1UX9+vXRr18/ODk5wdHRETVr1hTt/kVp2LAhXr16BScnJ8WaTZs21cgPa7F0794dY8aMwbBhwzB+/HjExMRgypQp2L59O54+fYpz586JtpYqd+hevHihqKn/UA2+GLX3crkc+vr6cHFxUdpVf1dZaoZbtGiBEydOwNjYGA4ODu/9Prt8+XK5X0edNLEDXZRr167B19cXO3bswMOHDz/6Pt7e3vjmm2+gp6cHb2/v975X7IO/M2fOxLlz57B7925YWlri8uXLSEtLw6hRozBq1CgsWLBA1PXow7gDTUQf7dmzZzh58iTCw8OxYsUKDB06FJaWlop6ZEdHR9FP4NeuXRs3b95EamoqUlNTkZaWhlevXml8WENZLF26FC9fvgQALFmyBKNGjcKkSZNgYWEBPz8/UdZITExUPClQ1Q6dsbExHjx4gDp16hQ7xlnM2vtRo0ap/INT3759UaVKFQAlH35TntcB8qdg9urVC7q6uh+ciFmWw3DvDmVRpdDQUHh6euLs2bOFPpw1atQIx44dQ1BQUJnWWLt2LYYPHw49PT2sXbu22PfJZDLRE+ilS5fiu+++Q6NGjZCbmwsbGxvk5uZi2LBh8PLyEnUtKhnuQBNVENWrV0dMTEyhR4BievnyJaKiohT10NHR0bCwsMC1a9dEXefZs2eIjIxEREQEIiIiEBcXh+bNm8PZ2RlLliwRdS1V8vf3R5cuXUTrEvE+Ojo6iuQWAIYMGQJvb2/UrVsXsbGx8PX1RVBQUJl26Ap2sitVqqRUg1+Ustbe08d7e2dY3YfhVKVPnz5wdnbGtGnTirzu7e2NsLAw/PHHH2qOTFwpKSm4du0a0tPT4eDgAAsLC02HVGExgSbSUu92bggJCUGXLl1QtWpVpdfFbImVl5eHCxcuICwsDGFhYYiKikJWVpbKfgg/efIE4eHhOHDgAIKDgyV3iFBfXx/Z2dlKw2icnZ1VUtP47uP0okp63rx5U6gLAxUtPT290A6rWC0hNbGO1JmamuLo0aNo0qRJkddv3ryJ7t27IyUlRc2RiSMsLAzOzs6aDoPewhIOIi31buuoESNGiL5GXl4eLl68iPDwcISFheHUqVPIyMhAw4YN4ezsDB8fH9H/0d+3b5/i8GBcXBxMTEzQsWNHrF69WnK7ms+ePVNMhAwLC0NQUBCys7PRuHFjpemOYg+jKY7YyXNWVhZiYmLw8OHDQkmg2H1y1SEpKQmenp4IDw9X6nogdktIda2jTdLS0t77/VupUiU8evRItPUEQcCePXsQFhZW5Pe3mBsTANCzZ0988skncHd3x+jRo9GoUSNR70+lxx1oIvpohoaGyMjIQL169ZQSvs8//1xla9apUwedO3dW1FiXdZpieZKVlYUzZ84oSmAuXLiAN2/eiDKKWkdHB6mpqYqadFWX9Bw9ehSjRo3C48ePC12TahLYoUMHCIKAqVOnom7duoXqr8X6AKeudQoUPDUqKhFcs2aNqGupyueff47Vq1cXWz++b98+zJw5U7ShRFOnTsXmzZvh7Oxc5NdIzF7dAPD48WNs374dgYGBuH79Orp06QIPDw/069cPlStXFnUtKhkm0ET00Qp+gFhaWqplvZycHPz222/o2bMn6tWrp5Y11Sk7OxtnzpxBaGiooqd2gwYNRPmhL5fL0atXL8VBNVWX9FhYWKB79+6YP3++2nbQVa1atWq4dOkSrKystGIdIP9wmpeXF6ysrAolgjKZDKGhoSqPQQyTJ09WfOjU09NTuvbq1Su0bt0azs7OH+yeUVImJib47bff4OrqKsr9SuPy5cvw9/dHcHAwAGDYsGHw8PBAs2bN1B5LRcYEmogkxcDAADdu3FDLwTtVKxhCEx4ejtDQUJw7dw6mpqZKQ2jEelT7oel2BcTaOTM0NMSVK1dU+jRC3ZydnfHTTz/BxcVFK9YBgLp162LFihUYM2aMytdSpbS0NLRo0QI6Ojrw9PRUfPi4efMmfHx8kJubi8uXL4v2Yc7MzAxHjhyBtbW1KPcrrfv372PLli1Yvnw5KlWqhKysLLRr1w6bNm2Cra2tRmKqaJhAE5GkODk54fvvv1d5qy910NfXR506ddC7d284OjqiU6dOWrOzPnbsWHTo0AEeHh6aDkU0d+7cwcSJEzFixAjFmOi3lWXstSbWAYD69esjMjJSK7o5JCcnY9KkSTh27BgKUhuZTIYePXrAx8dH1HKlwMBAHD16FH5+ftDX1xftvu/z5s0bHDhwAH5+fjh+/DhatWoFDw8PDB06FI8ePYKXlxcuX76MuLg4tcRT0TGBJiJJ2bVrF3744QdMmzYNLVu2LFSCIGZyoWpt27bFlStXYGVlpajpdnJyUvkwGnXIzMzEoEGDULt2bdjb2xdKAsXuk6sOZ8+exbBhw3D37l3Fa2KNvdbEOkD+NMf79+9j3bp1ot1T054+fYqEhAQIggALCwsYGxuLvsarV6/Qv39/nDp1Cp999lmh72+xh91MnjwZwcHBEAQBI0eOxLhx4wpNEk1NTUWDBg3U2n+7ImMCTUSSUlTfWlUlF+qQnp6u1Dv7ypUrsLS0VCTUjo6OGp/k9jF8fX0xceJE6OnpoWbNmoVqa8U6zKVONjY2aNKkCWbPnl3kwTGxyorUtQ6Q30nHzc0N8fHxsLGxKZQIit1NQlsMHjwYYWFhGDhwYJFfI7EnA3bt2hXjxo3DgAEDFOcY3pWTk4NTp05JrhuRVDGBJiJJSU5Ofu91qddGv3z5EidPnsTx48fh7++P9PR0UbpwqFu9evUwZcoUzJ07973DOqSkatWqiI6ORuPGjbViHQDw9PTE1q1b1dZNQltUrVoVx44dQ8eOHTUdCmkI+0ATkaRIPUEuTsEQmnd7akv195udnY0hQ4ZoTfIMAF26dFFLYquudYD8Wt69e/fCzc1N5Wtpk0aNGql9oM327duxadMmJCUl4cyZMzA1NcW6detgZmaGvn37qjUWYgJNRBKkLT9Izp8/rxgKExUVhfT0dHzyySdwcnKCt7c3nJ2d8dlnn2k6zI8yevRo7Ny5Ez/++KOmQxFN7969MW3aNMTGxhZZ1y3WcBh1rQPkt2PTpk4p6rJ69WrMnj0bmzZtUsvf0f/7v//D/Pnz8f3332PJkiWKUrUaNWpg3bp1kvp3T1uwhIOIJOXdHyTXrl2Dubk5AgICEBgYiLCwME2HWGJyuVxpCI2zs7PWJDNTpkzBtm3b0KxZMzRt2rRQEiiVAR1ve99uupj19+paB8gv0Th69Cj8/f1hYGAg2n21nbGxMTIzM5GTkwMDA4NC39///vuvqOvZ2Nhg6dKl6NevH6pXr47o6GiYm5vj2rVrcHJyKnJgEakWd6CJSFLWr1+PX3/9Ff369cPy5csVr7dq1QozZ87UYGSld+PGjVINywgODkafPn0KdR4pj2JjY+Hg4AAAuHbtmtK1d+tspUJd3Q3U2UXB29sbd+7cQd26ddXSTUJbqLtrSVJSkuLv09uqVKmCjIwMtcZC+ZhAE5GkaNMPktJOmpswYQLatGkDc3NzFUUknpI+Cfjnn3/QoEEDraqVtre3x+HDh0UbgqPKdbShn7omjB49ukTvW758OSZOnIgaNWqUaT0zMzNcvXq10JmIo0ePokmTJmW6N30cJtBEJCkV+QeJNlbc2djY4OrVq5L4UFBSd+/exZs3bySxTknbrUnp6Ud5snTpUgwePLjMCfT06dPx3XffISsrC4Ig4Pz58wgODsayZcuwdetWcYKlUmECTUSSwh8k2kUbPxRoIyk9/ShPxPr+HjduHPT19eHl5YXMzEwMGzYMDRo0wH//+198/fXXoqxBpcMEmogkhT9IiNSPH3Q0b/jw4Rg+fDgyMzORnp4uyQFL2oQJNBFJDn+QEFFF9PDhQ9y6dQtA/mHc2rVraziiikt7Tm0QUYXwyy+/ICkpCQBgYGDA5JmItN7Lly8xcuRINGjQAI6OjnB0dESDBg0wYsQIPH/+XNPhVUhMoIlIUnbv3o3GjRujffv22LhxY4Xqf2pqalqozZjUSbWlHZE6jRs3DufOncOhQ4fw7NkzPHv2DH/++ScuXryICRMmaDq8CokJNBFJSnR0NGJiYuDk5IRVq1ahQYMGcHNzQ1BQEDIzMzUdXpllZWUhMDAQGzduxO3bt5WuXbt2TeWt0dRNG2trN2/ejLp162rNOvTxOnXqBH19/TLf588//4Sfnx969OgBQ0NDGBoaokePHvj1118REhIiQqRUWpxESESSdurUKQQFBWH37t3IysrCixcvNB1SiU2fPh1v3rzB+vXrAQDZ2dlo06YNrl+/DgMDA+Tk5OD48eNo166dhiMtudzcXFy/fh0WFhaFEofMzEwkJCTAzs5O0ff53r17aNCgAXR0dDQRbomEhobC09MTZ8+ehaGhodK158+fo3379ti0aRM6depU5rVycnKwdu1aBAcHIz4+HgBgaWmJYcOGYerUqRp7AmFnZ4cjR45o3Qe4ssjLy0NCQgIePnxYaPhN586dRV3r008/xaFDh2Bvb6/0ekxMDFxdXfHPP/+Iuh59GHegiUjSqlatCn19fVSuXFktvXfF9Ndff6Fbt26KX+/YsQPJycm4ffs2nj59ikGDBuGXX37RYISlt337dowdOxaVK1cudK1y5coYO3YsgoKCFK81atSoXCfPQP7UufHjxxdKngHAyMgIEyZMEGU0+atXr+Dk5IS5c+eidu3aGDduHMaNG4fatWtjzpw56Nq1K7Kyssq8DgD4+fnh9evXJX6/Nj79KIuzZ8+icePGaNKkCTp37gwnJyfFf87OzqKv5+XlhenTpyM1NVXxWmpqKmbNmoV58+aJvh59GHegiUhykpKSEBQUhKCgINy6dQuOjo4YNmwYBg4cCCMjI02HV2KGhoa4fPkyGjduDAAYOnQoqlevji1btgAArl69CldXV9y/f1+TYZZKp06d8N133xXbUnDXrl3YsGEDIiMj1RzZxzM1NX3voJ6bN2+ie/fuSElJKdM6CxYsQEBAAEJCQtC0aVOla9HR0ejTpw/c3d2xcOHCMq0DADo6Onjw4IHiEG6DBg1w+vRpfPbZZ2W+d0XQvHlzWFpaYtGiRahfv36hWn4x/h1ycHBQuu/t27fx+vVrfPrppwCAlJQUVKlSBRYWFhy5rgFsY0dEktK2bVtcuHABTZs2hbu7O4YOHYqGDRtqOqyPIpfLlWqAz549q7SbVKNGDTx9+lQToX20W7duoW3btsVe/+KLL3Djxg01RlR2aWlp7y2dqFSpEh49elTmdX7//XesWbOmUPIMAM2aNcOqVavw008/iZJAv7t39vLly0JlCFS827dvY8+ePYoPv6rAMevlGxNoIpKUrl27ws/PDzY2NpoOpcyaNGmCkJAQTJ8+HdevX0dKSorS49/k5GTJHRLLyMh4bx36y5cvJXfYs2HDhrh27VqxyVJMTAzq169f5nWSk5PRunXrYq+3bdu2zLvcJI42bdogISFBpQl0Scesk2YwgSYiSVmyZIni/wt20aTaCm327Nn4+uuvcejQIVy/fh2urq4wMzNTXD98+PB7E6ryyMLCAqdPny5yFxUAoqKiYGFhoeaoysbV1RXz5s1Dz549oaenp3Tt1atXWLBgAb788ssyr2NoaIiHDx8WW2ucmpqK6tWrl3kdIP/vzNt/b979Nb3f5MmTMWPGDKSmpsLe3r7QE4rivv8/1qtXr3D8+HHFwVIrKyu4uLiI0uGDPg5roIlIcrZt24b//Oc/ijZvlpaWmDVrFkaOHKnhyErvxIkT+PPPP1GvXj1MnjwZBgYGimuLFi2Co6MjnJycNBdgKa1cuRIrV65EaGhokXW8Xbt2xezZszF79mwNRVh6aWlpaNGiBXR0dODp6QkrKysA+bXPPj4+yM3NxeXLl8v8tGDIkCHIycnB3r17i7z+1VdfQUdHB7t27SrTOkB++ZCRkZEiaX727BkMDQ0V3VEK/Pvvv2VeSxu9++cE5H8IEQQBMpkMubm5oq118OBBjBs3rlDP+1q1asHX1xe9e/cWbS0qOSbQRCQpa9aswbx58+Dp6YkOHToAyN/V9PHxwS+//IJp06ZpOMKK7c2bN+jevTuioqLg4uICa2trAPnJ5t9//40OHTrg+PHjkhsIk5ycjEmTJuHYsWNKTz569OgBHx8fpScHHysuLg5t2rSBra0tpk+fDmtrawiCgBs3bmDt2rWIi4vD2bNnYWtrW+a1AgMDS/S+0aNHl3ktbZScnPze66ampqKsc/r0aTg5OaFPnz6YMWOG4iBrXFwcVq9ejT///BMRERHvPXdAqsEEmogkxczMDIsWLcKoUaOUXg8MDMTChQsVY76loKT1rAWn7qXizZs3WLt2LYKCgnD79m0IgqDoZfz9998X2eJOKp4+fYqEhAQIggALCwsYGxuLev+zZ8/Cw8MDN27cUOwOC4IAa2tr+Pr6qrUneG5ubrlvMajtXF1d0ahRI2zevLnI6xMmTMC9e/dw+PBhNUdGTKCJSFL09PSKPNB1+/Zt2Nvbi9YnVx3kcnmRdacFj4GB/F3OnJwcdYf20Spi0iUIAh49eqRoCSeGq1evKg1Sad68uWj3/pD4+Hj4+vpi27ZtePDggdrWLe8OHjyIXr16QVdXFwcPHnzve/v06SPKmiYmJoiIiCg0QKVATEwMHB0dJdetRxvwECERSUrjxo2xa9cu/Pjjj0qv79y5U3KH065cuVLk64Ig4Pfff4e3tzeqVaum5qjKpmHDhhgzZgw8PDwk9/UojoGBAZKTk1G7dm0AgJubG7Zu3arovPHw4UM0aNBA1LrX5s2bqzVpzszMxM6dO+Hn54czZ86gVatWmD59utrWl4J+/fohNTUVderUeW+LOTFroF+9elXkAJ8CRkZGkto00CZMoIlIUhYtWoQhQ4YgMjJSUQN96tQpnDhxQpTDVerUrFmzQq/9/fffmDt3LuLj4zF79mzMmDFDA5F9vO+++w6BgYH4z3/+g/bt28PDwwODBw9WOhwpNVlZWUp9kyMjI/Hq1Sul94j1MPfBgwc4ceIETExM4OLiolTukpGRgdWrV2P+/PmirAXkl4xs3boVu3fvxqeffoobN24gLCxMlLHk2ubtPtnq6pltYWGB0NBQuLu7F3n9xIkTWvNBVWo4ypuIJOWrr77CuXPnUKtWLezfvx/79+9HrVq1cP78efTv31/T4X20y5cvo1u3bvjyyy/Rtm1bJCQkYOHChaK1LVOXefPmISEhASdOnIC5uTk8PT1Rv359jB8/HufOndN0eCojRgu4CxcuwMbGBt999x0GDhwIW1tbXL9+XXE9PT0dixYtKvM6ALB69WrY2tpi4MCBMDY2RmRkJGJjYyGTyVCzZk1R1qCyc3d3x8yZM4uscT506BBmz56NMWPGqD8wAgQiIi20bNky4enTp5oO44MSEhKEwYMHCzo6OsLQoUOFO3fuaDokUb18+VL49ddfhQ4dOggymUywsbERVq9eremwSkUmkwlpaWmKX1erVk3p65SamirI5fIyr+Pi4iK4u7sLubm5wosXL4RJkyYJNWvWFC5fvizqOoIgCDo6OsKPP/4o5OTkKL1eqVIl4fr166Ksoe3+/vtvwc3NTTA3NxfMzc0FNzc34fjx46KukZubKwwcOFCQyWSCtbW10L9/f6Ffv36ClZWVIJfLhQEDBgi5ubmirkklwx1oItJKS5cuLfc9bL/99lvY2Njg+fPnuHjxIoKCgmBubq7psERVrVo1jBs3DlFRUQgJCUFqaipmzZql6bBKRV1DRy5duoS5c+dCLpejevXq2LhxI2bOnImuXbviwoULoq71888/Y/fu3TAzM8OcOXNw7do1Ue+v7TZu3IiePXuievXqmDp1KqZOnQpDQ0O4urrCx8dHtHXkcjl2796N4OBgWFpa4ubNm7h16xasra2xY8cO7N27t8ie1KR67MJBRFqpevXqiI6OLtcJqVwuh56enqJXcnEuX76spojEl5mZiV27dsHf3x9RUVH4/PPPMXbsWMydO1fToZXYh4aOCIKAFy9elPngmImJCcLDwwsNoFm1ahWWLFkCPz8/DBw4UNTDihEREfDz88OePXvQuHFjXL9+HREREYrzBVS0Tz75BHPnzoWnp6fS6z4+Pli6dCn+97//aSgyUhceIiQi0pAFCxZoOgSVOX36NPz8/LB7927k5ORg4MCB+Pnnn9G5c2dNh1Zq/v7+alnHzs6uyDHoM2fORF5eHoYOHSraWomJiTAzM4OjoyMcHR2xYcMGBAUFwc/PD46OjmjdujUGDhzIThzFePbsGXr27Fno9e7du2POnDmirVNcq8u3Sa3VpbbgDjQRaSUp7EBro5UrV8Lf3x/x8fFo1aoVPDw8MHToUMkdhiwtMfpfb926FREREdi+fXuR11esWIFNmzaJMixIR0cHDx48UPSuHjJkCLy9vVG3bl3ExsbC19cXQUFBePjwYZnX0kbDhg2Dg4NDoXKkVatW4eLFi/j9999FWefAgQPFXjtz5gy8vb2Rl5fHVnYawASaiLSS1BPoFy9eYMeOHfD19cXFixc1HU6J1a5dGyNGjICHhwfs7Ow0HY7KSXXoiFwuV/Q0Bor++/LmzRvJjVxXJW9vb8X/v3jxAqtWrUKHDh0U0yHPnj2LU6dOYcaMGfDy8lJZHLdu3cLcuXMREhKC4cOHY/HixaKNDqeSYwkHEVE5EhYWBj8/P+zbtw9GRkaSa813//59rU+6KsrQEW3/OpbW2rVrlX5tbGyMuLg4xMXFKV6rUaMG/Pz8VJJA379/HwsWLEBgYCB69OiBq1evVogPqeUVE2gi0kqdOnWCvr6+psMokf/9738ICAiAv78/nj17hqdPnyIoKAiDBw9WSbcHVTp58iQ8PT1x9uzZQhPUnj9/jvbt22PTpk2SHNSh6qEjDg4OJfp6i3GotKhOIlL7XlM3MUpnPsbz58+xdOlSrF+/Hs2bN8eJEyck+fdH2zCBJiLJycvLQ0JCAh4+fFhoIljBIbWiBg+UN3v37oWvry8iIyPRq1cvrF69Gr169ULVqlVhb28vyYRm3bp1GD9+fJHjh42MjDBhwgSsWbNGUgnA6tWr4efnh+fPn2Po0KGIjIxEs2bNoKurK+rQkbfHQwuCgGXLlmHixIkwMTERbY237z9mzBhUqVIFQP60xYkTJ6Jq1apK79u3b5/oa1ckhoaGuHr16keXkq1cuRIrVqxAvXr1EBwcjL59+4ocIX0s1kATkaScPXsWw4YNQ3JycqHxyTKZTNQWX6pWqVIlzJkzB3PnzlU6ZKerq4vo6GjY2NhoMLqPY2pqiqNHj6JJkyZFXr958ya6d++OlJQUNUf28Qq+TosXL1Y6KKjqr5Mq6/iLGw39LnV1INFWZf0ayuVy6Ovrw8XF5b2HVPlBR/24A01EkjJx4kS0atUKhw4dQv369SW5S1vAw8MDPj4+CA8Px8iRIzFkyBAYGxtrOqwySUtLe2/tbKVKlfDo0SM1RlR2P//8M/z9/bF9+3YMHToUI0eOlHztKRNjaRg1apSk/43TZkygiUhSbt++rRj6IHWbN2/GunXrsGvXLvj5+eH7779Hjx49IAhCodIUqWjYsCGuXbtW7NcnJiYG9evXV3NUZfPDDz/ghx9+UAwdadOmDRo3bgxBEPD06VNNh0daLCAgQNMhUDE4/5GIJKVNmzZISEjQdBii0dfXx+jRoxEREYHY2FjY2tqibt266NChA4YNGya5R7Ourq6YN29ekX1pX716hQULFuDLL7/UQGRl5+joiMDAQKSmpuLbb79Fy5Yt4ejoiPbt22PNmjWaDo+I1Ig10ERU7sXExCj+/86dO/Dy8sKsWbNgb29fqFzg3SluUpSXl4dDhw7B19cXR44cwevXrzUdUomlpaWhRYsW0NHRgaenJ6ysrADk1z77+PggNzcXly9fRt26dTUcqTiuXbsGX19f7Nixo8xDR97uMwwAc+bMwaxZs1CrVi2l16dMmVKmdUh9ynqIkMovJtBEVO4VjLMt7p+rgmtSO0T4Ia9evcKGDRsKTTsr75KTkzFp0iQcO3ZM8TWTyWTo0aMHfHx8YGZmpuEISyc0NPS9rfnatWsHb29vuLi4lGmdkvy5yGQyJCYmlmkdUh+pD3Si4jGBJqJyLzk5ucTvldpErkePHuHcuXOoXLkyunbtCh0dHbx58wYbN27E8uXL8ebNGzx+/FjTYZZYYmIizMzMIJPJ8PTpUyQkJEAQBFhYWEj2gGSfPn3g7OyMadOmFXnd29sbYWFh+OOPP9QcGWladnY2kpKS8Pnnn6NSpcLHyqKiovDFF18o2gWS9mANNBGVe6ampor/kpOT0bBhQ6XXTE1N0bBhw1Il2uVBVFQULCws0KdPH/Tq1Qvt27dHXFwcbG1tsXnzZixYsAD37t3TdJilYmFhoeiyYWxsjFWrVsHU1FSyyTMAREdHo2fPnsVe7969Oy5dulTmdUJDQ2FjY4MXL14Uuvb8+XPY2tri5MmTZV6Hyi4zMxMeHh4wMDCAra2toi3j5MmTsXz5csX7OnbsyORZSzGBJiJJcXZ2xr///lvo9efPn8PZ2VkDEX08Ly8vuLq6IiYmBtOnT8eFCxfQv39/LF26FHFxcZg4caJkpikWePeh5uHDh5GRkaGhaMShrtZ8JR1CQ5r3ww8/IDo6GuHh4dDT01O87uLigp07d2owMlIXJtBEJCkFtc7vevLkSaEpauVdbGwsvLy8YGdnh8WLF0Mmk2HlypUYOHCgpkOjtxS05iuOWK351LXTTWW3f/9+bNiwAR07dlT698jW1hZ37tzRYGSkLuwDTUSSMGDAAAD5h6jeHkEMALm5uYiJiUH79u01Fd5Hefr0qaLDgr6+PgwMDCQ/oEMmkxX6gCP1QRAFrfl69uyptNsIiNuaTxuH0GirR48eoU6dOoVez8jIkPz3O5UME2gikgQjIyMA+TvQ1atXVyptqFy5Mtq2bYvx48drKryPFhcXh9TUVAD5v7dbt24VKnmQUms+QRCUPuBkZWVh4sSJhZ4OSKm/tZeXF/bt2wdLS8tiW/P99NNPZV5HG4fQaKuCaaiTJ08G8P8/JG7duhXt2rXTZGikJuzCQUSSsmjRIsycOVNy5RpFeV97Pqm25nN3dy/R+6Q2SlodrfkmT56M8PBwXLhwocid7tatW8PZ2blQv2hSv6ioKPTq1QsjRoxAQEAAJkyYgLi4OJw+fRoRERFo2bKlpkMkFWMCTUSkISXtGiK11nzaTJWt+SraEBqpu3PnDpYvX47o6Gikp6ejRYsWmDNnDuzt7TUdGqkBE2giKvdatGiBEydOwNjYGA4ODu+tMbx8+bIaIyubxYsXY+bMmTAwMNB0KFROaNsQGiJtxRpoIir3+vbtq6ip7du3r9Yc0lm0aBEmTpzIBJoUTE1NcfjwYa0ZQqOtiurVDeR/2KlSpQoqV66s5ohI3bgDTUSkIXK5HKmpqUWe5iei8qvg/EJxPvnkE4wZMwYLFiyAXM6OwdqIO9BEJCnz58+Hs7Mz2rVrV+iglRRpy246UUUSEBCAn376CWPGjEHr1q0BAOfPn0dgYCC8vLzw6NEjrFq1ClWqVMGPP/6o4WhJFbgDTUSS0q1bN5w5cwY5OTn44osv4OjoCCcnJ3To0EFyU/vkcjmMjIw+mEQXNXmRiDSna9eumDBhAgYPHqz0+q5du7B582acOHEC27dvx5IlS3Dz5k0NRUmqxASaiCQnJycH586dQ2RkJCIiInD69Gm8fv0aX3zxBaKiojQdXonJ5XKsW7dO0eO6OKNHj1ZTRERUEvr6+oiJiYGFhYXS67dv30azZs2QmZmJpKQk2NraIjMzU0NRkiqxhIOIJKdSpUro0KEDateuDRMTE1SvXh379++X5E7P119/zRpoIolp1KgRfH19sXz5cqXXfX190ahRIwDAkydPePhTizGBJiJJ2bJlC8LDwxEREYHXr1+jU6dOcHJygpeXl6Qm9gGsfyaSqlWrVmHQoEE4cuQIvvjiCwDAxYsXcePGDezduxcAcOHCBQwZMkSTYZIKsYSDiCRFLpejdu3amDFjBr799ltUq1ZN0yF9NLlcjrS0NNSuXVvToRBRKd29exebNm1CfHw8AMDKygoTJkxAeno67OzsNBwdqRoTaCKSlP379yMyMhLh4eG4ceMGHBwc4OTkBCcnJ3Ts2FFSPZXd3d1LtAvt5+enhmiI6GO9ePECwcHB8PPzw8WLF5Gbm6vpkEjFmEATkWQ9f/4cJ0+exO7duxEcHAy5XI6srCxNh1VicrkcpqamcHBwwPv+Kf7jjz/UGBURlVRkZCR8fX2xd+9eNGjQAAMGDMBXX32lKOsg7cUaaCKSnCdPniAiIgLh4eEIDw/H9evXYWxsjE6dOmk6tFKZNGkSgoODkZSUBHd3d4wYMQImJiaaDouI3iM1NRUBAQHw9fXFixcvMHjwYLx+/Rr79++HjY2NpsMjNeEONBFJir29PW7cuAFjY2N07twZTk5OcHR0lNwBwgKvX7/Gvn374Ofnh9OnT8PNzQ0eHh7o3r07DxkSlTO9e/dGZGQk3NzcMHz4cPTs2RM6OjrQ1dVFdHQ0E+gKhAk0EUmKj48PHB0dtfKQTnJyMgICArBt2zbk5OTg+vXrkj4kSaRtKlWqhClTpmDSpElKPaCZQFc8HNBORJLy3XfflSh5NjQ0RGJiohoiEo9cLodMJoMgCDyERFQORUVF4eXLl2jZsiXatGmDDRs24PHjx5oOizSACTQRaSWpPFx7/fo1goOD0a1bN1haWiI2NhYbNmxASkoKd5+Jypm2bdvi119/xYMHDzBhwgT8/vvvaNCgAfLy8nD8+HG8fPlS0yGSmrCEg4i0UvXq1REdHQ1zc3NNh1Ksb7/9Fr///jsaNWqEsWPHYvjw4ahVq5amwyKiUrh16xZ8fX2xfft2PHv2DN26dcPBgwc1HRapGBNoItJKUkig5XI5Pv30Uzg4OLz3wOC+ffvUGBURfYzc3FyEhITAz8+PCXQFwDZ2REQaMmrUKHbaINISOjo66NevH/r166fpUEgNmEATkVaSQmIaEBCg6RCIiOgj8BAhEWklVqcREZGqMIEmIkkJCwsr0fuOHDmChg0bqjgaIiKqiHiIkIgkpUqVKvjkk0/g7u6O0aNHo1GjRpoOiYiIKhjuQBORpPzvf/+Dp6cn9uzZA3Nzc/To0QO7du1Cdna2pkMjIqIKgjvQRCRZly9fhr+/P4KDgwEAw4YNg4eHB5o1a6bhyIiISJsxgSYiSbt//z62bNmC5cuXo1KlSsjKykK7du2wadMm2Nraajo8IiLSQizhICLJefPmDfbs2QNXV1eYmpri2LFj2LBhA9LS0pCQkABTU1MMGjRI02ESEZGW4g40EUnK5MmTERwcDEEQMHLkSIwbNw52dnZK70lNTUWDBg2Ql5enoSiJiEibcZAKEUlKXFwc1q9fjwEDBqBKlSpFvqdWrVolbndHRERUWizhICLJePPmDUxNTdG2bdtik2cAqFSpEhwdHdUYGRERVSRMoIlIMnR1dbF3715Nh0FERBUcE2gikpR+/fph//79mg6DiIgqMNZAE5GkWFhYYPHixTh16hRatmyJqlWrKl2fMmWKhiIjIqKKgl04iEhSzMzMir0mk8mQmJioxmiIiKgiYgJNRERERFQKrIEmIskSBAHcAyAiInVjAk1EkrNt2zbY29tDX18f+vr6aNq0KbZv367psIiIqILgIUIikpQ1a9Zg3rx58PT0RIcOHQAAUVFRmDhxIh4/foxp06ZpOEIiItJ2rIEmIkkxMzPDokWLMGrUKKXXAwMDsXDhQiQlJWkoMiIiqihYwkFEkvLgwQO0b9++0Ovt27fHgwcPNBARERFVNEygiUhSGjdujF27dhV6fefOnbCwsNBAREREVNGwBpqIJGXRokUYMmQIIiMjFTXQp06dwokTJ4pMrImIiMTGGmgikpxLly5h7dq1uHHjBgCgSZMmmDFjBhwcHDQcGRERVQRMoImIiIiISoE10EQkKTo6Onj48GGh1588eQIdHR0NRERERBUNE2gikpTiHpq9fv0alStXVnM0RERUEfEQIRFJgre3NwBAJpNh69atqFatmuJabm4uIiMjYW1tranwiIioAmENNBFJgpmZGQAgOTkZn3zyiVK5RuXKlfHZZ59h8eLFaNOmjaZCJCKiCoIJNBFJirOzM/bt2wdjY2NNh0JERBUUE2gikrTc3FzExsbC1NSUSTUREakFDxESkaR8//338PX1BZCfPHfu3BktWrRAo0aNEB4ertngiIioQmACTUSSsnv3bjRr1gwAEBISgrt37+LmzZuYNm0afvrpJw1HR0REFQETaCKSlCdPnqBevXoAgMOHD2PQoEGwtLTE2LFjERsbq+HoiIioImACTUSSUrduXcTFxSE3NxdHjx5Ft27dAACZmZkcpEJERGrBPtBEJCnu7u4YPHgw6tevD5lMBhcXFwDAuXPn2AeaiIjUggk0EUnKwoULYWdnh3v37mHQoEGoUqUKgPwR33PnztVwdEREVBGwjR0RERERUSlwB5qIyj1vb29888030NPTU4z0Ls6UKVPUFBUREVVU3IEmonLPzMwMFy9eRM2aNRUjvYsik8mQmJioxsiIiKgiYgJNRERERFQKbGNHRERERFQKrIEmIknJzc1FQEAATpw4gYcPHyIvL0/pemhoqIYiIyKiioIJNBFJytSpUxEQEAA3NzfY2dlBJpNpOiQiIqpgWANNRJJSq1YtbNu2Da6urpoOhYiIKijWQBORpFSuXBmNGzfWdBhERFSBMYEmIkmZMWMG/vvf/4IPz4iISFNYwkFE5d6AAQOUfh0aGgoTExPY2tpCV1dX6dq+ffvUGRoREVVAPERIROWekZGR0q/79++voUiIiIi4A01EWurUqVNo1aoVqlSpoulQiIhIyzCBJiKtZGhoiKtXr8Lc3FzToRARkZbhIUIi0krcGyAiIlVhAk1EREREVApMoImIiIiISoEJNBERERFRKTCBJiKtJJPJNB0CERFpKSbQRKSVeIiQiIhUhQk0EUnKggULkJyc/MH3vXz5ki3siIhIJZhAE5GkHDhwAJ9//jm6du2KoKAgvH79WtMhERFRBcMEmogk5erVq7hw4QJsbW0xdepU1KtXD5MmTcKFCxc0HRoREVUQnERIRJL15s0bhISEwN/fH8eOHYO1tTU8PDwwZswYGBkZaTo8IiLSUtyBJiLJEgQBb968QXZ2NgRBgLGxMTZs2IBGjRph586dmg6PiIi0FBNoIpKcS5cuwdPTE/Xr18e0adPg4OCAGzduICIiArdv38aSJUswZcoUTYdJRERaiiUcRCQp9vb2uHnzJrp3747x48ejd+/e0NHRUXrP48ePUadOHeTl5WkoSiIi0maVNB0AEVFpDB48GGPHjkXDhg2LfU+tWrWYPBMRkcpwB5qIiIiIqBS4A01E5d706dNL/N41a9aoMBIiIiIm0EQkAVeuXCnR+2QymYojISIiYgkHEREREVGpsI0dEREREVEpsISDiMq9AQMGICAgAIaGhhgwYMB737tv3z41RUVERBUVE2giKveMjIwU9c0c0U1ERJrGGmgiIiIiolJgDTQRERERUSmwhIOIJOXJkyeYP38+wsLC8PDhw0ITB//9918NRUZERBUFE2gikpSRI0ciISEBHh4eqFu3Lns/ExGR2rEGmogkpXr16oiKikKzZs00HQoREVVQrIEmIkmxtrbGq1evNB0GERFVYNyBJiJJuXDhAubOnYv58+fDzs4Ourq6StcNDQ01FBkREVUUrIEmIkmpUaMGXrx4gS5duii9LggCZDIZcnNzNRQZERFVFEygiUhShg8fDl1dXQQFBfEQIRERaQRLOIhIUgwMDHDlyhVYWVlpOhQiIqqgeIiQiCSlVatWuHfvnqbDICKiCow70EQkKbt378bChQsxa9Ys2NvbFzpE2LRpUw1FRkREFQUTaCKSFLm88IMzmUzGQ4RERKQ2PERIRJKSlJSk6RCIiKiC4w40EREREVEpcAeaiCQpLi4OKSkpyM7OVnq9T58+GoqIiIgqCibQRCQpiYmJ6N+/P2JjYxW1zwAU/aBZA01ERKrGNnZEJClTp06FmZkZHj58CAMDA1y/fh2RkZFo1aoVwsPDNR0eERFVAKyBJiJJqVWrFkJDQ9G0aVMYGRnh/PnzsLKyQmhoKGbMmIErV65oOkQiItJy3IEmIknJzc1F9erVAeQn0/fv3wcAmJqa4tatW5oMjYiIKgjWQBORpNjZ2SE6OhpmZmZo06YNVq5cicqVK2PLli0wNzfXdHhERFQBcAeaiMq9mJgY5OXlAQC8vLwUBwcXL16MpKQkdOrUCYcPH4a3t7cmwyQiogqCNdBEVO7p6OjgwYMHqFOnDszNzXHhwgXUrFlTcf3ff/+FsbGxohMHERGRKnEHmojKvRo1aigmEN69e1exG13AxMSEyTMREakNa6CJqNz76quv4OjoiPr160Mmk6FVq1bQ0dEp8r2JiYlqjo6IiCoaJtBEVO5t2bIFAwYMQEJCAqZMmYLx48crOnEQERGpG2ugiUhS3N3d4e3tzQSaiIg0hgk0EREREVEp8BAhEREREVEpMIEmIiIiIioFJtBERERERKXABJqIiIiIqBSYQBMRERERlQITaCIiIiKiUmACTURERERUCkygiYiIiIhK4f8B0BZ6nT32TekAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), cmap = \"RdBu\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "-Q6f-7SPz_S7",
        "outputId": "4b1fd94e-02f8-48ab-b1cb-3393944f95dc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XgqwsCFK2SXY",
        "outputId": "951bc19b-fa83-4cd2-a5db-29b0cf986d0f"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3weP8r-02jU",
        "outputId": "2a89dfc5-7ad2-4d5a-b1ce-8faadae0a5b4"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Height', 'Weight', 'family_history_with_overweight', 'SCC',\n",
              "       'MTRANS_Walking', 'FAVC_z', 'FCVC_minmax', 'NCP_z', 'CAEC_minmax',\n",
              "       'CH2O_minmax', 'FAF_minmax', 'TUE_z', 'CALC_z', 'Age_bin_minmax',\n",
              "       'NObeyesdad'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"NObeyesdad\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "HUlGdgSz2ow4",
        "outputId": "aae76f16-1b21-4ef3-f0c0-ada494c30031"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "NObeyesdad\n",
              "4    342\n",
              "6    323\n",
              "5    297\n",
              "2    289\n",
              "1    285\n",
              "3    278\n",
              "0    272\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NObeyesdad</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>285</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>272</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "CpWm1QFB2tJI",
        "outputId": "5cdfe45e-30db-4bb8-a8a0-c2238ef72c3b"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 count       mean        std        min  \\\n",
              "Height                          2086.0   1.702045   0.093419   1.450000   \n",
              "Weight                          2086.0  86.622985  26.256245  39.000000   \n",
              "family_history_with_overweight  2086.0   0.817354   0.386469   0.000000   \n",
              "SCC                             2086.0   0.045062   0.207491   0.000000   \n",
              "MTRANS_Walking                  2086.0   0.026366   0.160260   0.000000   \n",
              "FAVC_z                          2086.0   0.639326   0.769120   0.361418   \n",
              "FCVC_minmax                     2086.0   0.709818   0.267493   0.000000   \n",
              "NCP_z                           2086.0   0.763670   0.645761   0.002375   \n",
              "CAEC_minmax                     2086.0   0.379834   0.155956   0.000000   \n",
              "CH2O_minmax                     2086.0   0.504361   0.306578   0.000000   \n",
              "FAF_minmax                      2086.0   0.337873   0.283687   0.000000   \n",
              "TUE_z                           2086.0   0.843202   0.537726   0.000146   \n",
              "CALC_z                          2086.0   0.855417   0.518064   0.516552   \n",
              "Age_bin_minmax                  2086.0   0.499760   0.353723   0.000000   \n",
              "NObeyesdad                      2086.0   3.110259   1.993832   0.000000   \n",
              "\n",
              "                                      25%        50%         75%         max  \n",
              "Height                           1.630000   1.701383    1.768770    1.980000  \n",
              "Weight                          65.130595  83.000000  108.009452  165.057269  \n",
              "family_history_with_overweight   1.000000   1.000000    1.000000    1.000000  \n",
              "SCC                              0.000000   0.000000    0.000000    1.000000  \n",
              "MTRANS_Walking                   0.000000   0.000000    0.000000    1.000000  \n",
              "FAVC_z                           0.361418   0.361418    0.361418    2.766876  \n",
              "FCVC_minmax                      0.500000   0.695087    1.000000    1.000000  \n",
              "NCP_z                            0.404704   0.404704    1.031717    2.164116  \n",
              "CAEC_minmax                      0.333333   0.333333    0.333333    1.000000  \n",
              "CH2O_minmax                      0.291005   0.500000    0.740243    1.000000  \n",
              "FAF_minmax                       0.042901   0.333333    0.557356    1.000000  \n",
              "TUE_z                            0.466622   0.813973    1.092724    2.194694  \n",
              "CALC_z                           0.516552   0.516552    1.439033    4.427721  \n",
              "Age_bin_minmax                   0.250000   0.500000    0.750000    1.000000  \n",
              "NObeyesdad                       1.000000   3.000000    5.000000    6.000000  "
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
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              "      <th>min</th>\n",
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              "      <th>Height</th>\n",
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              "      <td>1.980000</td>\n",
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              "      <th>Weight</th>\n",
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              "      <td>108.009452</td>\n",
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              "      <th>family_history_with_overweight</th>\n",
              "      <td>2086.0</td>\n",
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              "      <th>SCC</th>\n",
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              "      <th>MTRANS_Walking</th>\n",
              "      <td>2086.0</td>\n",
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              "      <td>2.766876</td>\n",
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              "    <tr>\n",
              "      <th>FCVC_minmax</th>\n",
              "      <td>2086.0</td>\n",
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              "    <tr>\n",
              "      <th>NCP_z</th>\n",
              "      <td>2086.0</td>\n",
              "      <td>0.763670</td>\n",
              "      <td>0.645761</td>\n",
              "      <td>0.002375</td>\n",
              "      <td>0.404704</td>\n",
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              "      <td>1.031717</td>\n",
              "      <td>2.164116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CAEC_minmax</th>\n",
              "      <td>2086.0</td>\n",
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              "      <th>FAF_minmax</th>\n",
              "      <td>2086.0</td>\n",
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              "      <th>TUE_z</th>\n",
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              "      <th>CALC_z</th>\n",
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              "      <th>Age_bin_minmax</th>\n",
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              "      <th>NObeyesdad</th>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 2086.0,\n        \"max\": 2086.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2086.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.171539428168767,\n        \"min\": 0.026366251198465963,\n        \"max\": 86.62298507190796,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.5043605009587727\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.672096609306977,\n        \"min\": 0.0934185650158527,\n        \"max\": 26.256244513570227,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          0.3065783300550289\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.034295909197974,\n        \"min\": 0.0,\n        \"max\": 39.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.45\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.696962101970552,\n        \"min\": 0.0,\n        \"max\": 65.130595,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.5165517806528838\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.256496220220352,\n        \"min\": 0.0,\n        \"max\": 83.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.5165517806528838\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.6356183486952,\n        \"min\": 0.0,\n        \"max\": 108.00945225,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          0.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.135697701061446,\n        \"min\": 1.0,\n        \"max\": 165.057269,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          165.057269\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "Ntn8zz0a27We"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns = [\"NObeyesdad\"])\n",
        "y = df[\"NObeyesdad\"]"
      ],
      "metadata": {
        "id": "IRU_jWAL3Z0Q"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 42)"
      ],
      "metadata": {
        "id": "5V4udI-b3eF3"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train), len(X_test), len(y_train), len(y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WhPayh__3rBq",
        "outputId": "34672daf-e9bd-409c-9856-d50a4dc261b7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1668, 418, 1668, 418)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Experimenting with single weak classifier"
      ],
      "metadata": {
        "id": "bygNhFpA33zj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "HGALY6ah3xWS"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weak_classifier = DecisionTreeClassifier(max_depth=5)"
      ],
      "metadata": {
        "id": "yCkfD3yl4KgL"
      },
      "execution_count": 122,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "weak_classifier.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "gWa-Gmxe4QYq",
        "outputId": "6cb10e2c-4ac3-4db4-b727-010b4b13e831"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(max_depth=5)"
            ],
            "text/html": [
              "<style>#sk-container-id-18 {color: black;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" checked><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Training accuracy :: {accuracy_score(y_train, weak_classifier.predict(X_train))}\")\n",
        "print(f\"Test accuracy     :: {accuracy_score(y_test, weak_classifier.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mpnWdFI04Wab",
        "outputId": "8357b7a8-c10f-461b-dd4c-492f8affa1be"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy :: 0.8501199040767387\n",
            "Test accuracy     :: 0.8181818181818182\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Experimenting the weak classifier with AdaBoost endemble model"
      ],
      "metadata": {
        "id": "szvPFT1X5Dm7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier"
      ],
      "metadata": {
        "id": "Im5weaVL5CSE"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble = AdaBoostClassifier(base_estimator=weak_classifier, n_estimators=50, random_state=42)"
      ],
      "metadata": {
        "id": "xzGPvSWO5CWr"
      },
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "id": "eJR3Smnv5ecM",
        "outputId": "5aa4d6f6-7b78-47a3-db78-ea2c3807bc7d"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/ensemble/_base.py:156: FutureWarning: `base_estimator` was renamed to `estimator` in version 1.2 and will be removed in 1.4.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=5),\n",
              "                   random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-20 {color: black;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=5),\n",
              "                   random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=5),\n",
              "                   random_state=42)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">base_estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Training accuracy :: {accuracy_score(y_train, ensemble.predict(X_train))}\")\n",
        "print(f\"Test accuracy     :: {accuracy_score(y_test, ensemble.predict(X_test))}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXtxFarK5j2j",
        "outputId": "cbb8d7a1-c7b1-4777-e4e1-f42a5158ff02"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training accuracy :: 0.9988009592326139\n",
            "Test accuracy     :: 0.9593301435406698\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can see that esemble model performs far better than weak classifier"
      ],
      "metadata": {
        "id": "DeXDteHnNg5F"
      }
    }
  ]
}