stroke_prediction.ipynb

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      "metadata": {
        "id": "R8w6GhxD0TCC"
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      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tN4q06ER09d3"
      },
      "outputs": [],
      "source": [
        "from sklearn.impute import SimpleImputer \n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.preprocessing import StandardScaler "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h_AdwPhC6npu"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "#from ModelTree import ModelTree\n",
        "import numpy as np\n",
        "# from sklearn.metrics import mean_squared_error"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "id": "tqhxrd7d6wwL",
        "outputId": "de0859ad-e682-4426-9ef5-2a3acf32eecc"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9046</td>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>51676</td>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>31112</td>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>60182</td>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1665</td>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "          document.querySelector('#df-ff401d78-059b-44f7-bd91-96a21f2e315b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ff401d78-059b-44f7-bd91-96a21f2e315b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            "text/plain": [
              "      id  gender   age  hypertension  heart_disease ever_married  \\\n",
              "0   9046    Male  67.0             0              1          Yes   \n",
              "1  51676  Female  61.0             0              0          Yes   \n",
              "2  31112    Male  80.0             0              1          Yes   \n",
              "3  60182  Female  49.0             0              0          Yes   \n",
              "4   1665  Female  79.0             1              0          Yes   \n",
              "\n",
              "       work_type Residence_type  avg_glucose_level   bmi   smoking_status  \\\n",
              "0        Private          Urban             228.69  36.6  formerly smoked   \n",
              "1  Self-employed          Rural             202.21   NaN     never smoked   \n",
              "2        Private          Rural             105.92  32.5     never smoked   \n",
              "3        Private          Urban             171.23  34.4           smokes   \n",
              "4  Self-employed          Rural             174.12  24.0     never smoked   \n",
              "\n",
              "   stroke  \n",
              "0       1  \n",
              "1       1  \n",
              "2       1  \n",
              "3       1  \n",
              "4       1  "
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#stroke_data=pd.read_csv('/content/drive/MyDrive/healthcare-dataset-stroke-data.csv')\n",
        "stroke_data=pd.read_csv('/healthcare-dataset-stroke-data.csv')\n",
        "stroke_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LfKqdcAp0NsR",
        "outputId": "87a5386d-36a6-4a55-9e03-cdab2f5a372a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                     int64\n",
              "gender                object\n",
              "age                  float64\n",
              "hypertension           int64\n",
              "heart_disease          int64\n",
              "ever_married          object\n",
              "work_type             object\n",
              "Residence_type        object\n",
              "avg_glucose_level    float64\n",
              "bmi                  float64\n",
              "smoking_status        object\n",
              "stroke                 int64\n",
              "dtype: object"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq8SmiGf0RpG",
        "outputId": "28c4e84c-a658-4c11-fc55-6310368e9c21"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Index(['gender', 'ever_married', 'work_type', 'Residence_type',\n",
            "       'smoking_status'],\n",
            "      dtype='object')\n",
            "Index(['id', 'age', 'hypertension', 'heart_disease', 'avg_glucose_level',\n",
            "       'bmi', 'stroke'],\n",
            "      dtype='object')\n"
          ]
        }
      ],
      "source": [
        "categorical_features = stroke_data.select_dtypes(include=['object']).columns\n",
        "numerical_features = stroke_data.select_dtypes(include=['int64','float64']).columns\n",
        "print(categorical_features)\n",
        "print(numerical_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m89Ot31A0aU1",
        "outputId": "fcb045dd-8a03-4f2f-a07e-e390ba1726e3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(5110, 12)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data .shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cPHPjNLU0dCV",
        "outputId": "6ea8a780-39f9-4805-80c9-ef94ece067fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5110 entries, 0 to 5109\n",
            "Data columns (total 12 columns):\n",
            " #   Column             Non-Null Count  Dtype  \n",
            "---  ------             --------------  -----  \n",
            " 0   id                 5110 non-null   int64  \n",
            " 1   gender             5110 non-null   object \n",
            " 2   age                5110 non-null   float64\n",
            " 3   hypertension       5110 non-null   int64  \n",
            " 4   heart_disease      5110 non-null   int64  \n",
            " 5   ever_married       5110 non-null   object \n",
            " 6   work_type          5110 non-null   object \n",
            " 7   Residence_type     5110 non-null   object \n",
            " 8   avg_glucose_level  5110 non-null   float64\n",
            " 9   bmi                4909 non-null   float64\n",
            " 10  smoking_status     5110 non-null   object \n",
            " 11  stroke             5110 non-null   int64  \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 479.2+ KB\n"
          ]
        }
      ],
      "source": [
        "stroke_data .info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "xFi70Lkx0hJ9",
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              "      <th>id</th>\n",
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              "      <th>count</th>\n",
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              "      <td>5110.000000</td>\n",
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              "      <td>4909.000000</td>\n",
              "      <td>5110.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>36517.829354</td>\n",
              "      <td>43.226614</td>\n",
              "      <td>0.097456</td>\n",
              "      <td>0.054012</td>\n",
              "      <td>106.147677</td>\n",
              "      <td>28.893237</td>\n",
              "      <td>0.048728</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>21161.721625</td>\n",
              "      <td>22.612647</td>\n",
              "      <td>0.296607</td>\n",
              "      <td>0.226063</td>\n",
              "      <td>45.283560</td>\n",
              "      <td>7.854067</td>\n",
              "      <td>0.215320</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>55.120000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <th>25%</th>\n",
              "      <td>17741.250000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>77.245000</td>\n",
              "      <td>23.500000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>36932.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>91.885000</td>\n",
              "      <td>28.100000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>54682.000000</td>\n",
              "      <td>61.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>114.090000</td>\n",
              "      <td>33.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>72940.000000</td>\n",
              "      <td>82.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>271.740000</td>\n",
              "      <td>97.600000</td>\n",
              "      <td>1.000000</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8860f0f6-7d50-4d18-baa2-64810ddf7806 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8860f0f6-7d50-4d18-baa2-64810ddf7806');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                 id          age  hypertension  heart_disease  \\\n",
              "count   5110.000000  5110.000000   5110.000000    5110.000000   \n",
              "mean   36517.829354    43.226614      0.097456       0.054012   \n",
              "std    21161.721625    22.612647      0.296607       0.226063   \n",
              "min       67.000000     0.080000      0.000000       0.000000   \n",
              "25%    17741.250000    25.000000      0.000000       0.000000   \n",
              "50%    36932.000000    45.000000      0.000000       0.000000   \n",
              "75%    54682.000000    61.000000      0.000000       0.000000   \n",
              "max    72940.000000    82.000000      1.000000       1.000000   \n",
              "\n",
              "       avg_glucose_level          bmi       stroke  \n",
              "count        5110.000000  4909.000000  5110.000000  \n",
              "mean          106.147677    28.893237     0.048728  \n",
              "std            45.283560     7.854067     0.215320  \n",
              "min            55.120000    10.300000     0.000000  \n",
              "25%            77.245000    23.500000     0.000000  \n",
              "50%            91.885000    28.100000     0.000000  \n",
              "75%           114.090000    33.100000     0.000000  \n",
              "max           271.740000    97.600000     1.000000  "
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 131
        },
        "id": "ri0uKNvs0lpq",
        "outputId": "4d3a2b75-2740-43f9-d0ab-f8f286867fef"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-79a96fae-9fe1-4c82-aa32-21198bd7a3da\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79a96fae-9fe1-4c82-aa32-21198bd7a3da')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-79a96fae-9fe1-4c82-aa32-21198bd7a3da button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-79a96fae-9fe1-4c82-aa32-21198bd7a3da');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [id, gender, age, hypertension, heart_disease, ever_married, work_type, Residence_type, avg_glucose_level, bmi, smoking_status, stroke]\n",
              "Index: []"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "duplicate_Values=stroke_data.duplicated()\n",
        "print(duplicate_Values.sum())\n",
        "stroke_data[duplicate_Values]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7hQIBVP_0oSk",
        "outputId": "7479f8d7-f166-4f4b-a209-ea9f435b8c74"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "id                     0\n",
              "gender                 0\n",
              "age                    0\n",
              "hypertension           0\n",
              "heart_disease          0\n",
              "ever_married           0\n",
              "work_type              0\n",
              "Residence_type         0\n",
              "avg_glucose_level      0\n",
              "bmi                  201\n",
              "smoking_status         0\n",
              "stroke                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dCb45H005Q0y"
      },
      "outputs": [],
      "source": [
        "stroke_data = stroke_data.drop('id',axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "id": "P4GRQlIo5WXg",
        "outputId": "e2646f00-d645-4fbd-ec07-8f614905e239"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-6749ea6a-bd8c-4b87-bcaa-c2ed5948b835\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Male</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>228.69</td>\n",
              "      <td>36.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Female</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>202.21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Male</td>\n",
              "      <td>80.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.5</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Female</td>\n",
              "      <td>49.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.4</td>\n",
              "      <td>smokes</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Female</td>\n",
              "      <td>79.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>174.12</td>\n",
              "      <td>24.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5105</th>\n",
              "      <td>Female</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Urban</td>\n",
              "      <td>83.75</td>\n",
              "      <td>NaN</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5106</th>\n",
              "      <td>Female</td>\n",
              "      <td>81.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Urban</td>\n",
              "      <td>125.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>Female</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Self-employed</td>\n",
              "      <td>Rural</td>\n",
              "      <td>82.99</td>\n",
              "      <td>30.6</td>\n",
              "      <td>never smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5108</th>\n",
              "      <td>Male</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Private</td>\n",
              "      <td>Rural</td>\n",
              "      <td>166.29</td>\n",
              "      <td>25.6</td>\n",
              "      <td>formerly smoked</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5109</th>\n",
              "      <td>Female</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Govt_job</td>\n",
              "      <td>Urban</td>\n",
              "      <td>85.28</td>\n",
              "      <td>26.2</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5110 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6749ea6a-bd8c-4b87-bcaa-c2ed5948b835')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6749ea6a-bd8c-4b87-bcaa-c2ed5948b835 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6749ea6a-bd8c-4b87-bcaa-c2ed5948b835');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      gender   age  hypertension  heart_disease ever_married      work_type  \\\n",
              "0       Male  67.0             0              1          Yes        Private   \n",
              "1     Female  61.0             0              0          Yes  Self-employed   \n",
              "2       Male  80.0             0              1          Yes        Private   \n",
              "3     Female  49.0             0              0          Yes        Private   \n",
              "4     Female  79.0             1              0          Yes  Self-employed   \n",
              "...      ...   ...           ...            ...          ...            ...   \n",
              "5105  Female  80.0             1              0          Yes        Private   \n",
              "5106  Female  81.0             0              0          Yes  Self-employed   \n",
              "5107  Female  35.0             0              0          Yes  Self-employed   \n",
              "5108    Male  51.0             0              0          Yes        Private   \n",
              "5109  Female  44.0             0              0          Yes       Govt_job   \n",
              "\n",
              "     Residence_type  avg_glucose_level   bmi   smoking_status  stroke  \n",
              "0             Urban             228.69  36.6  formerly smoked       1  \n",
              "1             Rural             202.21   NaN     never smoked       1  \n",
              "2             Rural             105.92  32.5     never smoked       1  \n",
              "3             Urban             171.23  34.4           smokes       1  \n",
              "4             Rural             174.12  24.0     never smoked       1  \n",
              "...             ...                ...   ...              ...     ...  \n",
              "5105          Urban              83.75   NaN     never smoked       0  \n",
              "5106          Urban             125.20  40.0     never smoked       0  \n",
              "5107          Rural              82.99  30.6     never smoked       0  \n",
              "5108          Rural             166.29  25.6  formerly smoked       0  \n",
              "5109          Urban              85.28  26.2          Unknown       0  \n",
              "\n",
              "[5110 rows x 11 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sos88zm401hF"
      },
      "source": [
        "Strategy Mean Missing Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZEEEwTcr006M"
      },
      "outputs": [],
      "source": [
        "imputer = SimpleImputer(missing_values=np.nan, strategy='mean')\n",
        "imputer.fit(stroke_data.iloc[:,8].array.reshape(-1, 1))\n",
        "stroke_data.iloc[:,8]= imputer.transform(stroke_data.iloc[:,8].array.reshape(-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "jDdRXc9F1Nqp",
        "outputId": "599f3ffb-7346-4693-c04a-8fc86038007f"
      },
      "outputs": [
        {
          "data": {
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0a996baf-f983-40ef-94a3-c4e3d2a2ad11 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0a996baf-f983-40ef-94a3-c4e3d2a2ad11');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "      gender   age  hypertension  heart_disease  ever_married  work_type  \\\n",
              "0          1  67.0             0              1             1          2   \n",
              "1          0  61.0             0              0             1          3   \n",
              "2          1  80.0             0              1             1          2   \n",
              "3          0  49.0             0              0             1          2   \n",
              "4          0  79.0             1              0             1          3   \n",
              "...      ...   ...           ...            ...           ...        ...   \n",
              "5105       0  80.0             1              0             1          2   \n",
              "5106       0  81.0             0              0             1          3   \n",
              "5107       0  35.0             0              0             1          3   \n",
              "5108       1  51.0             0              0             1          2   \n",
              "5109       0  44.0             0              0             1          0   \n",
              "\n",
              "      Residence_type  avg_glucose_level        bmi  smoking_status  stroke  \n",
              "0                  1             228.69  36.600000               1       1  \n",
              "1                  0             202.21  28.893237               2       1  \n",
              "2                  0             105.92  32.500000               2       1  \n",
              "3                  1             171.23  34.400000               3       1  \n",
              "4                  0             174.12  24.000000               2       1  \n",
              "...              ...                ...        ...             ...     ...  \n",
              "5105               1              83.75  28.893237               2       0  \n",
              "5106               1             125.20  40.000000               2       0  \n",
              "5107               0              82.99  30.600000               2       0  \n",
              "5108               0             166.29  25.600000               1       0  \n",
              "5109               1              85.28  26.200000               0       0  \n",
              "\n",
              "[5110 rows x 11 columns]"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "labelencodergen = LabelEncoder()\n",
        "stroke_data.gender = labelencodergen.fit_transform(stroke_data.gender)\n",
        "labelencodermar = LabelEncoder()\n",
        "stroke_data.ever_married = labelencodermar.fit_transform(stroke_data.ever_married)\n",
        "labelencoderwork= LabelEncoder()\n",
        "stroke_data.work_type = labelencoderwork.fit_transform(stroke_data.work_type)\n",
        "labelencoderres = LabelEncoder()\n",
        "stroke_data.iloc[:, 6]= labelencoderres.fit_transform(stroke_data.iloc[:, 6]) \n",
        "labelencodersmoke = LabelEncoder()\n",
        "stroke_data.iloc[:, 9] = labelencodersmoke.fit_transform(stroke_data.iloc[:, 9])\n",
        "stroke_data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "onmSKbId1UYI"
      },
      "source": [
        "EDA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Pj6T0Ej11TzM",
        "outputId": "3a9fd4ce-256a-4bfd-8a86-03d6868cd319"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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9fPrTn46pU6fGFVdcEdddd12sWLEiIgbWujVFIAwePDjOO++8WL9+feO2er0e69evj1mzZlU4GUcqpcS1114bd955Z2zYsCEmTpzYb/t5550Xp512Wr913LlzZ+zevds6Vujiiy+ORx55JLZt29a4zJgxI+bPn9+4bt2az4UXXpg+RvzYY4/F+PHjIyJi4sSJMXr06H7r1t3dHQ899JB1q9CLL74YLS39n1oHDRoU9Xo9IgbYulX9LslXrVmzprS1tZWf/vSn5a9//Wu5+uqry8iRI8u+ffuqHo1/+cIXvlBGjBhRfvvb35ZnnnmmcXnxxRcb+1xzzTVl3LhxZcOGDWXLli1l1qxZZdasWRVOzWs58lMMpVi3ZrRp06bS2tpali9fXnbt2lVWr15dhgwZUm677bbGPitXriwjR44sv/71r8uf//zn8vGPf7wpPy53KlmwYEE566yzGh9z/NWvflXOOOOM8pWvfKWxz0BZt6YJhFJK+f73v1/GjRtXBg8eXGbOnFn++Mc/Vj0SR4iI17zceuutjX1eeuml8sUvfrGcfvrpZciQIWXevHnlmWeeqW5oXtO/B4J1a0533313mTJlSmlrayuTJ08ut9xyS7/t9Xq93HjjjaWzs7O0tbWViy++uOzcubOiaSmllO7u7rJo0aIybty40t7eXiZNmlRuuOGG0tvb29hnoKybP/cMACRN8R4EAKC5CAQAIBEIAEAiEACARCAAAIlAAAASgQAAJAIBAEgEAgCQCAQAIBEIAEAiEOAUcu+998ZFF10UI0eOjFGjRsVHP/rReOKJJxrb//CHP8S0adOivb09ZsyYEXfddVfUarXYtm1bY58dO3bEnDlzYujQodHZ2RlXXHFFHDhwoIKfBjiRBAKcQv75z3/GkiVLYsuWLbF+/fpoaWmJefPmRb1ej+7u7pg7d25MnTo1Hn744bjpppti6dKl/e5/8ODB+OAHPxjTp0+PLVu2xL333hv79++Pyy67rKKfCDhR/DVHOIUdOHAg3vrWt8YjjzwSGzdujK9//evx9NNPR3t7e0RE/PjHP46rrroq/vSnP8W0adPim9/8Zvzud7+LdevWNR7j6aefjq6urti5c2e84x3vqOpHAY4zryDAKWTXrl1x+eWXx6RJk2L48OExYcKEiIjYvXt37Ny5M84555xGHEREzJw5s9/9t2/fHvfff38MHTq0cZk8eXJERL9TFcDA11r1AMCbZ+7cuTF+/PhYtWpVjB07Nur1ekyZMiUOHz78hu7/wgsvxNy5c+Pb3/522jZmzJjjPS5QIYEAp4h//OMfsXPnzli1alW8//3vj4iIjRs3Nra/853vjNtuuy16e3ujra0tIiI2b97c7zHOPffc+OUvfxkTJkyI1laHDziZOcUAp4jTTz89Ro0aFbfccks8/vjjsWHDhliyZElj+2c+85mo1+tx9dVXx6OPPhrr1q2L73znOxERUavVIiJi4cKF8dxzz8Xll18emzdvjieeeCLWrVsXV155ZfT19VXycwEnhkCAU0RLS0usWbMmtm7dGlOmTInrrrsubr755sb24cOHx9133x3btm2LadOmxQ033BDLli2LiGi8L2Hs2LHx+9//Pvr6+uJDH/pQTJ06NRYvXhwjR46MlhaHEziZ+BQDcFSrV6+OK6+8Mp5//vno6OioehzgTeQkItDws5/9LCZNmhRnnXVWbN++PZYuXRqXXXaZOIBTkEAAGvbt2xfLli2Lffv2xZgxY+LSSy+N5cuXVz0WUAGnGACAxLuKAIBEIAAAiUAAABKBAAAkAgEASAQCAJAIBAAgEQgAQPK/SSmJhe6yb8sAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "for col in ['age','avg_glucose_level','bmi']:\n",
        "    sns.boxplot(x=stroke_data[col])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PZaH3eYZ2i-6"
      },
      "outputs": [],
      "source": [
        "def remove_outlier(df_in, col_name):\n",
        "    q1 = df_in[col_name].quantile(0.25)\n",
        "    q3 = df_in[col_name].quantile(0.75)\n",
        "    iqr = q3-q1\n",
        "    low  = q1 - 1.5*iqr\n",
        "    high = q3 + 1.5*iqr\n",
        "    df_out = df_in.loc[(df_in[col_name] > low) & (df_in[col_name] < high)]\n",
        "    return df_out"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ucspox9q27TM"
      },
      "outputs": [],
      "source": [
        "for i in ['age','avg_glucose_level','bmi']:\n",
        "  stroke_data = remove_outlier(stroke_data,i)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwriPBpJ3eVF",
        "outputId": "8b9eae55-4697-4320-92b4-a41ef1f152c0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4383, 11)"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "n5E5cckg394T",
        "outputId": "da96a792-88cc-40f5-e06d-809aceff4da6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "for col in ['age','avg_glucose_level','bmi']:\n",
        "    sns.boxplot(x=stroke_data[col])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nG8Eohh467fO",
        "outputId": "5c0659c6-5e8c-4859-c4f3-dc4c89b68893"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0    4219\n",
              "1     164\n",
              "Name: stroke, dtype: int64"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "stroke_data.stroke.value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-vGYRzxNQ-I3"
      },
      "source": [
        "Correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "-BCasmEfREcZ",
        "outputId": "b47953d6-0fc2-46d3-d942-214f54e68c47"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0ed74fb2-4178-42d4-9e46-2ee813fecbad\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>gender</th>\n",
              "      <th>age</th>\n",
              "      <th>hypertension</th>\n",
              "      <th>heart_disease</th>\n",
              "      <th>ever_married</th>\n",
              "      <th>work_type</th>\n",
              "      <th>Residence_type</th>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <th>bmi</th>\n",
              "      <th>smoking_status</th>\n",
              "      <th>stroke</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>gender</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.047054</td>\n",
              "      <td>0.019768</td>\n",
              "      <td>0.089258</td>\n",
              "      <td>-0.046395</td>\n",
              "      <td>0.067069</td>\n",
              "      <td>-0.004866</td>\n",
              "      <td>0.032461</td>\n",
              "      <td>-0.000976</td>\n",
              "      <td>-0.054834</td>\n",
              "      <td>-0.003208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>-0.047054</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.252186</td>\n",
              "      <td>0.239679</td>\n",
              "      <td>0.685152</td>\n",
              "      <td>-0.390332</td>\n",
              "      <td>0.011288</td>\n",
              "      <td>-0.024772</td>\n",
              "      <td>0.381222</td>\n",
              "      <td>0.288246</td>\n",
              "      <td>0.227557</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hypertension</th>\n",
              "      <td>0.019768</td>\n",
              "      <td>0.252186</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.090491</td>\n",
              "      <td>0.150608</td>\n",
              "      <td>-0.058583</td>\n",
              "      <td>-0.009552</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>0.152933</td>\n",
              "      <td>0.116042</td>\n",
              "      <td>0.113637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>heart_disease</th>\n",
              "      <td>0.089258</td>\n",
              "      <td>0.239679</td>\n",
              "      <td>0.090491</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.106352</td>\n",
              "      <td>-0.028280</td>\n",
              "      <td>-0.012491</td>\n",
              "      <td>-0.000684</td>\n",
              "      <td>0.057383</td>\n",
              "      <td>0.056092</td>\n",
              "      <td>0.090173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ever_married</th>\n",
              "      <td>-0.046395</td>\n",
              "      <td>0.685152</td>\n",
              "      <td>0.150608</td>\n",
              "      <td>0.106352</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.373939</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>-0.005236</td>\n",
              "      <td>0.376383</td>\n",
              "      <td>0.274741</td>\n",
              "      <td>0.093886</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>work_type</th>\n",
              "      <td>0.067069</td>\n",
              "      <td>-0.390332</td>\n",
              "      <td>-0.058583</td>\n",
              "      <td>-0.028280</td>\n",
              "      <td>-0.373939</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.002985</td>\n",
              "      <td>0.013454</td>\n",
              "      <td>-0.354487</td>\n",
              "      <td>-0.334006</td>\n",
              "      <td>-0.025020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residence_type</th>\n",
              "      <td>-0.004866</td>\n",
              "      <td>0.011288</td>\n",
              "      <td>-0.009552</td>\n",
              "      <td>-0.012491</td>\n",
              "      <td>0.000126</td>\n",
              "      <td>-0.002985</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.013735</td>\n",
              "      <td>0.011529</td>\n",
              "      <td>0.015973</td>\n",
              "      <td>0.009009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>avg_glucose_level</th>\n",
              "      <td>0.032461</td>\n",
              "      <td>-0.024772</td>\n",
              "      <td>0.002224</td>\n",
              "      <td>-0.000684</td>\n",
              "      <td>-0.005236</td>\n",
              "      <td>0.013454</td>\n",
              "      <td>-0.013735</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>-0.002887</td>\n",
              "      <td>0.000119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bmi</th>\n",
              "      <td>-0.000976</td>\n",
              "      <td>0.381222</td>\n",
              "      <td>0.152933</td>\n",
              "      <td>0.057383</td>\n",
              "      <td>0.376383</td>\n",
              "      <td>-0.354487</td>\n",
              "      <td>0.011529</td>\n",
              "      <td>0.000483</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.261188</td>\n",
              "      <td>0.032416</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>smoking_status</th>\n",
              "      <td>-0.054834</td>\n",
              "      <td>0.288246</td>\n",
              "      <td>0.116042</td>\n",
              "      <td>0.056092</td>\n",
              "      <td>0.274741</td>\n",
              "      <td>-0.334006</td>\n",
              "      <td>0.015973</td>\n",
              "      <td>-0.002887</td>\n",
              "      <td>0.261188</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.022761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>stroke</th>\n",
              "      <td>-0.003208</td>\n",
              "      <td>0.227557</td>\n",
              "      <td>0.113637</td>\n",
              "      <td>0.090173</td>\n",
              "      <td>0.093886</td>\n",
              "      <td>-0.025020</td>\n",
              "      <td>0.009009</td>\n",
              "      <td>0.000119</td>\n",
              "      <td>0.032416</td>\n",
              "      <td>0.022761</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ed74fb2-4178-42d4-9e46-2ee813fecbad')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0ed74fb2-4178-42d4-9e46-2ee813fecbad button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ed74fb2-4178-42d4-9e46-2ee813fecbad');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                     gender       age  hypertension  heart_disease  \\\n",
              "gender             1.000000 -0.047054      0.019768       0.089258   \n",
              "age               -0.047054  1.000000      0.252186       0.239679   \n",
              "hypertension       0.019768  0.252186      1.000000       0.090491   \n",
              "heart_disease      0.089258  0.239679      0.090491       1.000000   \n",
              "ever_married      -0.046395  0.685152      0.150608       0.106352   \n",
              "work_type          0.067069 -0.390332     -0.058583      -0.028280   \n",
              "Residence_type    -0.004866  0.011288     -0.009552      -0.012491   \n",
              "avg_glucose_level  0.032461 -0.024772      0.002224      -0.000684   \n",
              "bmi               -0.000976  0.381222      0.152933       0.057383   \n",
              "smoking_status    -0.054834  0.288246      0.116042       0.056092   \n",
              "stroke            -0.003208  0.227557      0.113637       0.090173   \n",
              "\n",
              "                   ever_married  work_type  Residence_type  avg_glucose_level  \\\n",
              "gender                -0.046395   0.067069       -0.004866           0.032461   \n",
              "age                    0.685152  -0.390332        0.011288          -0.024772   \n",
              "hypertension           0.150608  -0.058583       -0.009552           0.002224   \n",
              "heart_disease          0.106352  -0.028280       -0.012491          -0.000684   \n",
              "ever_married           1.000000  -0.373939        0.000126          -0.005236   \n",
              "work_type             -0.373939   1.000000       -0.002985           0.013454   \n",
              "Residence_type         0.000126  -0.002985        1.000000          -0.013735   \n",
              "avg_glucose_level     -0.005236   0.013454       -0.013735           1.000000   \n",
              "bmi                    0.376383  -0.354487        0.011529           0.000483   \n",
              "smoking_status         0.274741  -0.334006        0.015973          -0.002887   \n",
              "stroke                 0.093886  -0.025020        0.009009           0.000119   \n",
              "\n",
              "                        bmi  smoking_status    stroke  \n",
              "gender            -0.000976       -0.054834 -0.003208  \n",
              "age                0.381222        0.288246  0.227557  \n",
              "hypertension       0.152933        0.116042  0.113637  \n",
              "heart_disease      0.057383        0.056092  0.090173  \n",
              "ever_married       0.376383        0.274741  0.093886  \n",
              "work_type         -0.354487       -0.334006 -0.025020  \n",
              "Residence_type     0.011529        0.015973  0.009009  \n",
              "avg_glucose_level  0.000483       -0.002887  0.000119  \n",
              "bmi                1.000000        0.261188  0.032416  \n",
              "smoking_status     0.261188        1.000000  0.022761  \n",
              "stroke             0.032416        0.022761  1.000000  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "corr = stroke_data.corr()\n",
        "corr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 948
        },
        "id": "fH52ZKWaROrc",
        "outputId": "63cd73fd-98a0-415d-ce4f-94ea7c7e5c5e"
      },
      "outputs": [
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        " plt.figure(figsize=(10, 10))\n",
        " sns.heatmap(corr, cmap=\"RdYlGn\", annot=True)\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lT_hIh0J0stT"
      },
      "outputs": [],
      "source": [
        "X=stroke_data.drop(['stroke'],axis=1)\n",
        "Y=stroke_data.iloc[:,-1].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXtMcCHt4WqY",
        "outputId": "8daebacf-6720-4c73-ec69-877c0f82b8d9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  0.  ,   1.  ,   0.  , ...,  80.  , 105.92,  32.5 ],\n",
              "       [  0.  ,   1.  ,   0.  , ...,  74.  ,  70.09,  27.4 ],\n",
              "       [  1.  ,   0.  ,   0.  , ...,  69.  ,  94.39,  22.8 ],\n",
              "       ...,\n",
              "       [  1.  ,   0.  ,   0.  , ...,  35.  ,  82.99,  30.6 ],\n",
              "       [  0.  ,   1.  ,   0.  , ...,  51.  , 166.29,  25.6 ],\n",
              "       [  1.  ,   0.  ,   0.  , ...,  44.  ,  85.28,  26.2 ]])"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(), [0,2,3,4,5,6,9])], remainder='passthrough')\n",
        "X = np.array(ct.fit_transform(X))\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JnAfVe9O63jY"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X = sc.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Awcdiu8n60xz"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.15, random_state =9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nmPdgt1d2-dX"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "clf = RandomForestClassifier(max_depth=50, random_state=0,n_estimators=50)\n",
        "clf.fit(X_train, y_train)\n",
        "y_pred = clf.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_fPI523I7Nxw",
        "outputId": "a3ff3151-c97f-4b8d-eaab-4f3b7a137b95"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9574468085106383"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "accuracy_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diPXdQYb7Q2P",
        "outputId": "465be7f1-f057-4c9f-9545-e5b286a620b0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[630,   0],\n",
              "       [ 28,   0]])"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xc2zZMJ57Qry",
        "outputId": "a3118b16-5488-4923-f6e1-811d45ae9aaf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98       630\n",
            "           1       0.00      0.00      0.00        28\n",
            "\n",
            "    accuracy                           0.96       658\n",
            "   macro avg       0.48      0.50      0.49       658\n",
            "weighted avg       0.92      0.96      0.94       658\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tlu40us64Giw"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logreg = LogisticRegression(random_state=0)\n",
        "logreg.fit(X_train,y_train)\n",
        "y_pred = logreg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UDl3HWj4x6z",
        "outputId": "629b87ca-1f14-402a-fdc1-e16f1b47d39f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9574468085106383"
            ]
          },
          "execution_count": 35,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qsJIew4140MF",
        "outputId": "0cce45f9-cf04-44f4-b126-625a5739a9ea"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[630,   0],\n",
              "       [ 28,   0]])"
            ]
          },
          "execution_count": 36,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, y_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCFN046l416b",
        "outputId": "f5a192b5-cfa7-403e-b077-bf8fb5b3cf6f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98       630\n",
            "           1       0.00      0.00      0.00        28\n",
            "\n",
            "    accuracy                           0.96       658\n",
            "   macro avg       0.48      0.50      0.49       658\n",
            "weighted avg       0.92      0.96      0.94       658\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.9/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PgGYJNZxZXvc"
      },
      "source": [
        "OverSampling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7ut9fx--6lC",
        "outputId": "7340c429-9a27-476b-f1ea-99e961cb0cac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Counter({0: 4219, 1: 164})\n",
            "Counter({1: 4219, 0: 4219})\n"
          ]
        }
      ],
      "source": [
        "from collections import Counter\n",
        "from sklearn.datasets import make_classification\n",
        "from imblearn.over_sampling import RandomOverSampler\n",
        "print(Counter(Y))\n",
        "oversample = RandomOverSampler(sampling_strategy='minority')\n",
        "X_over, Y_over = oversample.fit_resample(X, Y)\n",
        "print(Counter(Y_over))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "93XlE0N3--Fo"
      },
      "outputs": [],
      "source": [
        "X_trainover, X_testover, y_trainover, y_testover = train_test_split(X_over, Y_over, test_size = .2, random_state = 42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Fw4u0uIUdecN"
      },
      "outputs": [],
      "source": [
        "accuracy = []\n",
        "preci=[]\n",
        "recall = []\n",
        "f1 = []\n",
        "\n",
        "def algodetails(rep):\n",
        "  accuracy.append(rep['accuracy'])\n",
        "  preci.append(rep['macro avg']['precision'])\n",
        "  recall.append(rep['macro avg']['recall'])\n",
        "  f1.append(rep['macro avg']['f1-score'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BprreM5gNU71"
      },
      "source": [
        "Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "qt3G36O0_Ai9",
        "outputId": "f08e3473-f187-4763-d665-6756efcbb821"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=50, n_estimators=50, random_state=0)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=50, n_estimators=50, random_state=0)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "RandomForestClassifier(max_depth=50, n_estimators=50, random_state=0)"
            ]
          },
          "execution_count": 41,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ranclfover= RandomForestClassifier(max_depth=50, random_state=0,n_estimators=50)\n",
        "ranclfover.fit(X_trainover, y_trainover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_cE_9GJk_DZ1"
      },
      "outputs": [],
      "source": [
        "y_predover = ranclfover.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHttUqS7_Fwj",
        "outputId": "dc27ee5b-3a65-43c7-9c11-6dcad3d6a757"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0.9946682464454977"
            ]
          },
          "execution_count": 43,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "accuracy_score(y_testover,y_predover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "syKuOw8felaK",
        "outputId": "84a08ed6-3fb4-4b2b-b626-7d06310ac95d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99       838\n",
            "           1       0.99      1.00      0.99       850\n",
            "\n",
            "    accuracy                           0.99      1688\n",
            "   macro avg       0.99      0.99      0.99      1688\n",
            "weighted avg       0.99      0.99      0.99      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mY1Z8cqk_IpK"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JAXVz4vw_M-H",
        "outputId": "05240fc1-8c99-4a54-b57b-5e082e86f12d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[829,   9],\n",
              "       [  0, 850]])"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YGhHbJQKNaRT"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "X2OUuiITNYM4",
        "outputId": "0d29afa7-49f0-4409-ebe3-09911c0beeba"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "Logreg = LogisticRegression()\n",
        "Logreg.fit(X_trainover, y_trainover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aojkROKkLqct"
      },
      "outputs": [],
      "source": [
        "y_predover = Logreg.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5AAs0SwP9bV",
        "outputId": "54c65478-d2c9-42e1-8f8a-da85f1f5afe0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.76      0.75      0.76       838\n",
            "           1       0.76      0.77      0.76       850\n",
            "\n",
            "    accuracy                           0.76      1688\n",
            "   macro avg       0.76      0.76      0.76      1688\n",
            "weighted avg       0.76      0.76      0.76      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OQcHIkyye6A8"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9_9Sz5wDQCj6",
        "outputId": "cba8be3b-499c-4b4d-af67-497b7ee11908"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[627, 211],\n",
              "       [195, 655]])"
            ]
          },
          "execution_count": 51,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB2d74HUXw44"
      },
      "source": [
        "Naive Bayes Classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KTZAiioWX4mU"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "gnb = GaussianNB()\n",
        "gnb.fit(X_trainover, y_trainover)\n",
        "y_predover = gnb.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QE8uFu4WYC8c",
        "outputId": "99c03061-9b69-4928-c42c-ef61e6476629"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.24      0.38       838\n",
            "           1       0.57      0.99      0.72       850\n",
            "\n",
            "    accuracy                           0.62      1688\n",
            "   macro avg       0.76      0.61      0.55      1688\n",
            "weighted avg       0.76      0.62      0.55      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TTFC1FiHe-1u"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uqT0K74uYFA2",
        "outputId": "023a7ca4-cd9b-4493-d316-b670a448daf4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[199, 639],\n",
              "       [  9, 841]])"
            ]
          },
          "execution_count": 55,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n0CMcEKLYSc1"
      },
      "source": [
        "Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1SPn4uGAYRgo"
      },
      "outputs": [],
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "dest = DecisionTreeClassifier(max_depth=50)\n",
        "dest.fit(X_trainover, y_trainover)\n",
        "y_predover = dest.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N6YPaUpvYk0s",
        "outputId": "b80c9bec-7c29-41ad-9bb4-ea44b9b53628"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.96      0.98       838\n",
            "           1       0.96      1.00      0.98       850\n",
            "\n",
            "    accuracy                           0.98      1688\n",
            "   macro avg       0.98      0.98      0.98      1688\n",
            "weighted avg       0.98      0.98      0.98      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CAiYwhZPfCY_"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oF1sHjoyYnnM",
        "outputId": "6e7dccd8-4c3f-43bf-9513-287e5b9fcfb5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[804,  34],\n",
              "       [  0, 850]])"
            ]
          },
          "execution_count": 59,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lKMaOlQCZi4i"
      },
      "source": [
        "Neural Network"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYquqtjNZh_0",
        "outputId": "139a90f6-7b5a-428b-90ad-628b2a70401a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:541: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "  self.n_iter_ = _check_optimize_result(\"lbfgs\", opt_res, self.max_iter)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "nn = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)\n",
        "nn.fit(X_trainover, y_trainover)\n",
        "y_predover = nn.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kR1PsVyZh2M",
        "outputId": "88d08fbd-890d-414f-ddd9-3177245fed3b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.71      0.83       838\n",
            "           1       0.78      0.99      0.87       850\n",
            "\n",
            "    accuracy                           0.85      1688\n",
            "   macro avg       0.88      0.85      0.85      1688\n",
            "weighted avg       0.88      0.85      0.85      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3YQH7NKJfDu9"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fTgXux5TaYx7",
        "outputId": "f365f8f6-0e53-4f84-88ae-f0c77aedf7d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[593, 245],\n",
              "       [  6, 844]])"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wyfdYS_5af_L"
      },
      "source": [
        "Support Vector Machine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3jDeqBPlafkE"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC\n",
        "svma = SVC()\n",
        "svma.fit(X_trainover, y_trainover)\n",
        "y_predover = svma.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mxVqosHwa6R_",
        "outputId": "bf570fab-6af5-4750-dee6-f3fc5b7fa082"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.80      0.85       838\n",
            "           1       0.82      0.93      0.87       850\n",
            "\n",
            "    accuracy                           0.86      1688\n",
            "   macro avg       0.87      0.86      0.86      1688\n",
            "weighted avg       0.87      0.86      0.86      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "A_7XwjD6fE4N"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwfcAclibCbF",
        "outputId": "c84be535-e958-42ca-bf47-75e5c1b2401d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[670, 168],\n",
              "       [ 62, 788]])"
            ]
          },
          "execution_count": 67,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JO-lcMNIbmGK"
      },
      "source": [
        "KNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gNApaQa3bnZ8"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(X_trainover, y_trainover)\n",
        "y_predover = knn.predict(X_testover)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XqNQD3Gob6Cg",
        "outputId": "8c5afdec-4699-49c2-cc9e-a47b9c3e1a7d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.91      0.95       838\n",
            "           1       0.92      1.00      0.96       850\n",
            "\n",
            "    accuracy                           0.96      1688\n",
            "   macro avg       0.96      0.96      0.96      1688\n",
            "weighted avg       0.96      0.96      0.96      1688\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_testover, y_predover))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gNlA9uSkfGWL"
      },
      "outputs": [],
      "source": [
        "report = classification_report(y_testover, y_predover,output_dict=True)\n",
        "algodetails(report)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ubT4mmNTb7QG",
        "outputId": "f22738d8-e936-4a5d-cde4-df93063078ae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[734, 104],\n",
              "       [  0, 850]])"
            ]
          },
          "execution_count": 81,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cm = confusion_matrix(y_testover, y_predover)\n",
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-j-1PjPjff2v"
      },
      "source": [
        "Algorithm comparison"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7pjmxDjcffOo"
      },
      "outputs": [],
      "source": [
        "algo_liste=[\"Random Forest\",\"Logistic Regression\",\"Naive Bayes\",\"Decision Tree\",\"Neural Networks\",\"Support Vector Machine\",\"KNN\"]\n",
        "score={\"algorithms\":algo_liste,\"Accuracy\":accuracy,\"precision\":preci,\"recall\":recall,\"f1_score\":f1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "_h8TeV-MgeJ2",
        "outputId": "a78df446-53d9-445c-c5cf-f26fac3667ee"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-54b2b4bc-41e3-4d22-a58d-405f38258547\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>algorithms</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>f1_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.994668</td>\n",
              "      <td>0.994761</td>\n",
              "      <td>0.994630</td>\n",
              "      <td>0.994667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.759479</td>\n",
              "      <td>0.759562</td>\n",
              "      <td>0.759399</td>\n",
              "      <td>0.759412</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Naive Bayes</td>\n",
              "      <td>0.616114</td>\n",
              "      <td>0.762487</td>\n",
              "      <td>0.613441</td>\n",
              "      <td>0.551193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.979858</td>\n",
              "      <td>0.980769</td>\n",
              "      <td>0.979714</td>\n",
              "      <td>0.979843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Neural Networks</td>\n",
              "      <td>0.851303</td>\n",
              "      <td>0.882503</td>\n",
              "      <td>0.850289</td>\n",
              "      <td>0.847941</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Support Vector Machine</td>\n",
              "      <td>0.863744</td>\n",
              "      <td>0.869784</td>\n",
              "      <td>0.863291</td>\n",
              "      <td>0.863075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>KNN</td>\n",
              "      <td>0.956754</td>\n",
              "      <td>0.960455</td>\n",
              "      <td>0.956444</td>\n",
              "      <td>0.956644</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ],
            "text/plain": [
              "               algorithms  Accuracy  precision    recall  f1_score\n",
              "0           Random Forest  0.994668   0.994761  0.994630  0.994667\n",
              "1     Logistic Regression  0.759479   0.759562  0.759399  0.759412\n",
              "2             Naive Bayes  0.616114   0.762487  0.613441  0.551193\n",
              "3           Decision Tree  0.979858   0.980769  0.979714  0.979843\n",
              "4         Neural Networks  0.851303   0.882503  0.850289  0.847941\n",
              "5  Support Vector Machine  0.863744   0.869784  0.863291  0.863075\n",
              "6                     KNN  0.956754   0.960455  0.956444  0.956644"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.DataFrame(score)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "rwu32X8Bgzv5",
        "outputId": "a077efee-1ec5-4bd0-9456-b95cd979dcca"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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7Ua5QbAX3XfG8KwC+rqzKVUhIiJ5++mnbtfHjx7vlvQHAm/z000/661//qkGDBumJJ57QV199pdzcXNOxjHG4WKdQSEZGhqKjo5Wenq6oqCjTcbxK48aNtXv3bklSaGioTp8+rXLlyhlOBQClk5CQoKSkJGtet26drrvuOre897lz51S/fn0dP37cuvbDDz+odevWbnl/ADDpyJEjeuihh/T9998X+rPY2FhNnz5dN954o4Fk7leSbsCdK5RI/qWBOTk5WrVqlcE0AHBlCt65at68udveu1y5cnr88cdt1yZMmOC29wcAU44ePaquXbsWWawk6eDBg7r11lv1r3/9y8PJzKNcoUTYdwXAX7hcLtsx7LGxsapYsaJbv8Zjjz1mu7s/b9487d27161fAwA87emnn7ZWMl1MXl6eHnroIWVmZnoolXegXKFEevToYZspVwB81bFjx3TixAlrdtd+q/yqVq2qIUOGWLPT6dQbb7zh9q8DAJ5y9OhRzZs3r1ivPXXqlObOnVvGibwL5QolUrNmTdsPIBs3blR6errBRABQOgWXBLrjGPaijBw5UiEhIdY8a9YsHT16tEy+FgCUtYULFyonJ6fYr//nP/9Zhmm8D+UKJZb/1ECn06kVK1YYTAMApVNWJwUWVK9ePd13333WfOHCBb3zzjtl8rUAoKydOnWqRK/P/yzBQEC5QokV3HfFkewAfJGnypUkjR492ja/9957Onv2bJl9PQAoK1WrVi3R66tUqVJGSbwT5Qol1r17dzkcDmtm3xUAX1SWJwUW1LJlS916663WfOrUKc2YMaPMvh4AlJU+ffooPDy82K/v27dv2YXxQpQrlFjVqlXVqlUra96yZYttUzgA+IL85ap27dqqVKlSmX69Z5991ja/8cYbys7OLtOvCQDuVrVqVd1///3Fem1MTIz69etXxom8C+UKpVJwaeCyZcsMJQGAkjtx4oTS0tKsuSyXBP6uc+fO6ty5szUfPnw44E7RAuAfBgwYcNnXhIaG6uOPP7Y9jiIQUK5QKvkPtZBYGgjAt3hyv1V+Y8aMsc0TJkyQ0+n0yNcGAHfIzc3VyJEjL/maJk2a6Pvvv9cf//hHD6XyHiGXfwlQWLdu3RQcHKy8vDxJlCsAvsVUubrlllvUokUL6+snJyfrm2++0e233+6Rrw8AV2ry5MnavHmzNVeuXFkJCQk6c+aMKlSooOuvv169evWy7c8PJJQrlEpUVJTatm2rdevWSfrtB4TU1FTVrFnTcDIAuDxPPeOqoKCgII0ZM0YPPfSQde3111/XbbfdFrA/iADwHSkpKXrhhRds1955551i78EKBMaXBb733nuKi4tTRESE2rdvr/Xr11/0tTk5OXr55ZfVqFEjRUREqFWrVlqwYIHtNS+++KIcDofto1mzZmX9bQSkgksDOZIdgK/w5EmBBfXv31+xsbHWvGbNGq1cudJjXx8ASsPpdGrIkCG6cOGCde3mm2+2PccPhsvVvHnzNHLkSL3wwgvavHmzWrVqpT59+lz0yfXPPfecpk2bpnfeeUe//PKLhg0bpjvvvFM//PCD7XUtW7bUkSNHrA/+0iobPO8KgK/KX65q1KhR4ue2XInQ0NBC+xXGjx/vsa8PAKUxY8YM2wFmFSpUUGJiInfdC3C4XC6XqS/evn17tWvXTu+++66k3xpxbGysHn/88UJH1kq/HZX717/+VY899ph17U9/+pMiIyP10UcfSfrtztVXX32lLVu2lDpXRkaGoqOjlZ6erqioqFK/j787d+6cKlWqpJycHElSo0aNtGvXLsOpAODSTp8+rcqVK1tzz549Pb5v9OzZs6pXr55OnTplXfvxxx911VVXeTQHABTHoUOH1KJFC505c8a69t5772n48OEGU3lOSbqBsTtX2dnZ2rRpk3r37v3fMEFB6t27t9asWVPk52RlZSkiIsJ2LTIystCdqZSUFNWuXVsNGzbU/fffrwMHDlwyS1ZWljIyMmwfuLxy5cqpQ4cO1rx79+7L/ncNAKaZOswivwoVKujxxx+3XZswYYLHcwDA5bhcLuvAit916dJFw4YNM5jKexkrV8ePH1deXp5q1Khhu16jRg2lpqYW+Tl9+vTR5MmTlZKSIqfTqe+//15ffvmljhw5Yr2mffv2+uCDD7RgwQIlJiZq79696tq1q+3/EAW99tprio6Otj7yr4XHpbE0EICv8YZyJUmPP/64IiMjrfmTTz7R/v37jWQBgIuZN2+evvnmG2sODw/XzJkzFRRk/OgGr+RT/6289dZbio+PV7NmzRQWFqa//OUvGjhwoO1/3Jtuukl33323rr76avXp00fffvutTp8+rc8+++yi7zt27Filp6dbHwcPHvTEt+MXeN4VAF/jLeUqJiZGgwYNsua8vDxNnjzZSBYAKMrx48cL3WV/4YUX1LRpU0OJvJ+xchUTE6Pg4GClpaXZrqelpV30OO9q1arpq6++UmZmpvbv36/t27erQoUKatiw4UW/TqVKldSkSZNL7gUKDw9XVFSU7QPF06FDB9tSzSVLlsjgNj4AuCxTx7AX5emnn1ZwcLA1z5w5U8ePHzeWBwDyGzFihO3fSa1bt9YzzzxjMJH3M1auwsLC1KZNGy1atMi65nQ6tWjRInXs2PGSnxsREaE6deooNzdXX3zxhe64446Lvvbs2bPavXu3atWq5bbs+K/w8HB16dLFmg8ePKjdu3cbTAQAl5a/XMXExKhatWrGssTFxenee++15nPnzlmHPAGASd9++611YJwkBQcHa9asWQoNDTWYyvsZXRY4cuRIzZgxQ3PmzFFycrISEhKUmZmpgQMHSpIGDBigsWPHWq9ft26dvvzyS+3Zs0crVqzQjTfeKKfTqdGjR1uveeaZZ7Rs2TLt27dPq1ev1p133qng4GD179/f499foGBpIABfkZGRYVv6bWpJYH75/w6TfnsgZ2ZmpqE0ACCdOXOm0IEVzzzzjK699lpDiXyH0XLVr18/TZo0SePGjVPr1q21ZcsWLViwwDrk4sCBA7bDKi5cuKDnnntOLVq00J133qk6depo5cqVqlSpkvWaQ4cOqX///mratKnuueceVa1aVWvXrjX6m0l/x6EWAHzF9u3bbbM3lKurr75aN910kzWfPHlSs2bNMpgIQKAbO3as7RdR8fHxeuGFFwwm8h1Gn3PlrXjOVcnk5OSoSpUqOnv2rCSpevXqSk1N5aFyALzOBx98YK2OkKS333670GZtE5YtW6YePXpYc7169bRr1y6W3wDwuBUrVqhbt262a8uWLSt0LZD4xHOu4D9CQ0Nt/8AdPXq00IZxAPAG27Zts83ecOdKkrp162Z7buCBAwf06aefGkwEIBBduHBBgwcPtl0bNmxYQBerkqJcwS1YGgjAF3jLMewFORwOjRkzxnZtwoQJnL4KwKNeeeUV7dy505rr1q2r8ePHG0zkeyhXcAsOtQDgC/KXq8qVK1/00R8m3H777WrWrJk1//zzz/r2228NJgIQSLZs2VKoSCUmJrJFpoQoV3CLVq1aqXLlyta8dOlSOZ1Og4kAwC4zM1P79u2z5hYtWnjV3tCgoCCNGjXKdo3fGAPwhNzcXD3yyCPKy8uzrvXv31+33nqrwVS+iXIFtwgODlb37t2t+dSpU9q6davBRABg540nBRZ0//33q06dOta8YsUKrV692mAiAIHgjTfe0A8//GDNVatW1VtvvWUwke+iXMFtCu67YmkgAG/irfut8gsPD9eIESNs17h7BaAs7dy5Uy+++KLt2ttvv81jjEqJcgW34VALAN7MF8qVJD366KO25zf+85//5ARWAGXC6XRqyJAhunDhgnXt5ptvVv/+/Q2m8m2UK7hNixYtVL16dWtevny5cnNzDSYCgP/y1mPYC6pYsaIee+wx27WJEycaSgPAn02fPl3Lly+35goVKigpKcmr9qP6GsoV3MbhcNhODTxz5ow2bdpkMBEA/Ff+uz9RUVG2vU3e5oknnlBERIQ1f/TRRzp48KDBRAD8zaFDhzR69GjbtQkTJig2NtZQIv9AuYJbse8KgDc6f/689uzZY83edlJgQdWrV9fAgQOtOTc3V2+++abBRAD8icvl0rBhw3TmzBnrWteuXTV06FCDqfwD5QpuxfOuAHijHTt22B7I661LAvN75plnFBT037+mp0+frpMnTxpMBMBffPrpp5o/f741h4eHa8aMGbZ/56B0+G8QbtW4cWPVrVvXmletWqWsrCyDiQDAdw6zyK9hw4a65557rDkzM1NTp041mAiAPzh+/LieeOIJ27UXX3xRTZs2NZTIv1Cu4FYF912dP39e69atM5gIAHyzXEkqtB/irbfe0rlz5wylAeAPnnrqKR0/ftyaW7duraefftpgIv9CuYLbse8KgLfx1XJ1zTXX6I9//KM1Hz9+XO+//77BRAB82fz58/Xxxx9bc3BwsGbNmqXQ0FCDqfwL5QpuV3DfFc+7AmBa/mPYy5cv71OnYY0ZM8Y2T5o0icdcACixjIwMJSQk2K6NGjVK1157raFE/olyBberX7++GjZsaM1r1qxhGQsAY7KysrRr1y5rbt68uU9t2u7Zs6fatWtnzfv27dM//vEPg4kA+KKxY8faHukQHx+vcePGGUzkn3znbxf4lPxLA3NycrR69WqDaQAEsp07d8rpdFqzrywJ/J3D4Sh092r8+PG20w8B4FJWrFhR6ECcmTNnKjIy0lAi/0W5QpngSHYA3qLgfquWLVsaSlJ6ffv2VXx8vDVv3bpV3333ncFEAHzFhQsXNHjwYNu1hIQEdevWzVAi/0a5QpmgXAHwFr56mEV+wcHBGjVqlO3a+PHjDaUB4Etefvll7dy505rr1q2r119/3WAi/0a5QpmoVauWmjdvbs0bN25URkaGwUQAApU/lCtJGjBggGrVqmXNS5cu5VEXAC7phx9+0IQJE2zXkpKSFBUVZSiR/6Ncoczkv3uVl5enFStWGEwDIFDlL1eRkZGqX7++wTSlFx4erqeeesp2jbtXAC4mNzdXgwYNUl5ennXtvvvu0y233GIwlf+jXKHMFHzeFUeyA/C07Oxs23KYZs2aKTg42GCiKzN06FDbb5y/+uor7dixw2AiAN7qjTfe0A8//GDNVatW1ZQpU8wFChCUK5SZHj162Gb2XQHwtF27dtmeCeWrSwJ/Fx0dbXtOjcvl0sSJEw0mAuCNdu7cqRdeeMF27e2331a1atUMJQoclCuUmapVq6pVq1bWvGXLFp08edJgIgCBxl/2W+X35JNPKjw83Jr//ve/6/DhwwYTAfAmTqdTgwcPVlZWlnXtlltuUf/+/Q2mChyUK5Sp/EsDXS6Xli1bZjANgEDjD8ewF1SrVi099NBD1pyTk8NSHwCW6dOn2/a5V6xYUYmJiXI4HAZTBQ7KFcoUR7IDMMkf71xJ0jPPPGP7QWnatGk6ffq0uUAAvMLBgwc1evRo27Xx48crNjbWUKLAQ7lCmerWrZuCgv77fzMOtQDgSfnLVXh4uBo0aGAwjfvEx8frT3/6kzWfOXNGiYmJBhMBMM3lcikhIUFnzpyxrnXt2lVDhw41mCrwUK5QpqKjo9W2bVtr3rZtm9LS0gwmAhAocnNzbSfpNW3aVCEhIQYTudeYMWNs81tvvaXz588bSgPAtE8//VTz58+35vDwcM2cOdP2S26UPf7bRpkruDSQu1cAPGH37t3Kzs62Zn9ZEvi7tm3b6vrrr7fmtLQ0zZkzx2AiAKYcO3ZMTzzxhO3aiy++qCZNmhhKFLgoVyhzPO8KgAn+ut8qv4J3ryZNmmR7YCiAwPDUU0/p+PHj1nzNNdfo6aefNpgocFGuUOY6d+6s0NBQa+ZQCwCeEAjlqnfv3rrmmmuseffu3friiy8MJgLgafPnz9fcuXOtOTg4WLNmzbL97AXPoVyhzJUvX17t27e35l27dungwYMGEwEIBP54DHtBDodDzz77rO3a+PHj5XK5DCUC4EkZGRkaNmyY7dqoUaNsv3SBZ1Gu4BEsDQTgafnLVWhoqBo1amQwTdn505/+ZPveNm/erP/85z8GEwHwlGeffVaHDh2y5iZNmmjcuHEGE4FyBY/geVcAPCkvL0/bt2+35iZNmvjtEpng4GA988wztmvjx483lAaApyxfvrzQIxhmzpypyMhIQ4kgUa7gIR06dFBERIQ1L1myhGUrAMrMvn37dOHCBWv2x/1W+T300EOqXr26NS9atEibNm0ymAhAWTp//rwGDx5su5aQkKCuXbsaSoTfUa7gEREREerUqZM1HzhwQHv27DGYCIA/C4TDLPKLjIzUU089ZbvG3SvAf7388stKSUmx5rp16+r11183mAi/o1zBYwruu2JpIICysm3bNtvs7+VK+u231hUrVrTmzz//3PbDFwD/sHnzZk2cONF2LSkpSVFRUYYSIT/KFTyGQy0AeEqg3bmSpEqVKmno0KHW7HK5NGnSJIOJALhbTk6OBg0aZHue3X333adbbrnFYCrkR7mCx7Rt21bly5e35sWLF7PvCkCZyF+ugoOD1aRJE4NpPOepp56yHdwxZ84cpaamGkwEwJ3eeOMNbdmyxZpjYmI0ZcoUY3lQGOUKHhMaGqpu3bpZc1pamu00LwBwB6fTqeTkZGuOj49XWFiYwUSeU6dOHQ0YMMCas7Ky9NZbbxlMBMBdduzYoRdffNF27e2331a1atXMBEKRKFfwKPZdAShrBw4c0Llz56w5EJYE5jdq1Cg5HA5rnjp1qtLT0w0mAnClnE6nhgwZoqysLOvaLbfconvvvddgKhSFcgWP4nlXAMpaIO63yq9p06bq27evNWdkZGjatGnmAgG4YtOmTdOKFSusuWLFikpMTLT9IgXegXIFj2rdurUqVapkzUuXLpXT6TQXCIDfCfRyJUljxoyxzVOmTLH9xhuA7zh48KBGjx5tuzZhwgTFxsYaSoRLoVzBo4KDg9W9e3drPnnypH788UeDiQD4m0A8hr2g9u3bq0ePHtZ85MgRffjhh+YCASgVl8ulhIQEnT171rrWrVs3PfroowZT4VIoV/A49l0BKEv571wFBQWpadOmBtOYU/Du1YQJE2zHNwPwfp988onmz59vzeHh4ZoxY4aCgvgR3lvxvww8juddASgrLpfLVq4aNWqkiIgIg4nM6dOnj1q1amXNKSkp+uqrr8wFAlAix44d0xNPPGG79tJLLwXMoyV8FeUKHteyZUvbsaHLli1Tbm6uwUQA/MWhQ4dsy2cCcUng7xwOR6G7V+PHj+f5goCPeOqpp3TixAlrvvbaa/X0008bTITioFzB4xwOh+3UwDNnzmjz5s0GEwHwFxxmYXf33XerQYMG1rxhwwYtXbrUXCAAxfLNN99o7ty51hwcHKxZs2YpJCTEYCoUB+UKRnAkO4CyQLmyCwkJKfSb7tdff91QGgDFkZGRoWHDhtmujR49Wq1btzYTCCVCuYIRHGoBoCxQrgobOHCgYmJirHnhwoX64YcfDCYCcCljxozR4cOHrblJkyYaN26cwUQoCcoVjIiPj1edOnWseeXKlcrOzjaYCIA/yH8Mu8PhULNmzQym8Q7lypXTk08+abs2YcIEQ2kAXMry5cuVlJRkuzZz5syAPZjHF1GuYETBfVfnz5/XunXrDCYC4OsKnhQYFxencuXKGUzkPYYPH67y5ctb82effaY9e/YYTASgoPPnz2vw4MG2a8OHD1fXrl0NJUJpUK5gDEeyA3CnI0eOKD093ZpZEvhfVapUsT101Ol0atKkSQYTASjopZdeUkpKijXHxsbqtddeM5gIpUG5gjHsuwLgTgX3W7Vs2dJQEu80YsQI20lj77//vo4ePWowEYDfbd68udAvPJKSkhQVFWUoEUqLcgVj6tevbzsieM2aNTp//rzBRAB8GYdZXFpsbKweeOABa75w4YLefvttg4kASFJOTo4GDRqkvLw869r999+vm2++2WAqlBblCkblv3uVnZ2t1atXG0wDwJdRri5v9OjRtvm9997TmTNnDKUBIEmTJk3Sli1brDkmJkZTpkwxlgdXhnIFo3jeFQB3KViuOCmwsObNm+v222+35tOnT2v69OkGEwGBbceOHXrppZds195++23b4xPgWyhXMIpyBcAdXC6X7Rj2evXqqWLFigYTea8xY8bY5jfffJNHYQAGOJ1ODR48WFlZWda1W2+9Vffee6/BVLhSlCsYVbt2bdtvlzds2MASFQAldvToUZ08edKaWRJ4cZ06dbId7Xz48GF9/PHHBhMBgSkpKUkrV6605ooVKyoxMVEOh8NgKlwpyhWMy3/3Ki8vTytWrDCYBoAvYr9VyRS8ezVhwgQ5nU5DaYDAc+DAgSL/Oaxbt66hRHAXyhWM43lXAK4Ux7CXzM0336w//OEP1rx9+3b985//NJgICBwul0vDhg3T2bNnrWvdunWzPYsOvotyBeN69Ohhm9l3BaCkuHNVMg6Ho9DJgePHj5fL5TKUCAgcc+fO1b///W9rjoiI0IwZMxQUxI/l/oD/FWFcTEyMrr76amv+4YcfbHsnAOByCpar5s2bG0riO+69917Vq1fPmteuXcuybKCMHTt2TE8++aTt2osvvqgmTZoYSgR3o1zBK+RfGuhyubR8+XKDaQD4mvzlqk6dOoqOjjaYxjeEhobq6aeftl0bP368oTRAYHjyySd14sQJa7722msL/XMI30a5glfgSHYApXX8+HEdPXrUmlkSWHyDBg1S1apVrfnbb7/Vjz/+aDAR4L/+9a9/6ZNPPrHm4OBgzZo1SyEhIQZTwd0oV/AK3bp1s6015lALAMXFfqvSK1++vP7yl7/Yrk2YMMFQGsB/paenKyEhwXZtzJgxat26tZlAKDOUK3iFSpUqqU2bNtb8888/Ky0tzWAiAL6CcnVl/vKXv6hcuXLW/Omnn2rfvn3mAgF+6Nlnn9Xhw4etuWnTpnr++ecNJkJZoVzBaxRcGrh06VIzQQD4FI5hvzIxMTEaPHiwNefl5Wny5MkGEwH+ZdmyZUpKSrJdmzlzpiIiIgwlQlmiXMFr8LwrAKXBSYFXbuTIkQoODrbmmTNn6tixYwYTAf7h/Pnztl9eSNJjjz2mLl26GEqEska5gtfo3LmzbVMnh1oAKI785apmzZqqUqWKwTS+qX79+urfv781nz9/Xu+++67BRIB/eOmll7Rr1y5rjo2N1WuvvWYwEcoa5Qpeo0KFCmrfvr01p6Sk6NChQwYTAfB2p06d0pEjR6yZ/ValV/Chwu+++64yMzMNpQF836ZNmzRp0iTbtaSkJFWsWNFQIngC5QpehaWBAEoiOTnZNlOuSu+qq67SLbfcYs0nT57UzJkzDSYCfFdOTo4GDRqkvLw869oDDzygm2++2WAqeALlCl6F510BKIlt27bZZsrVlRkzZoxtfuONN5STk2MoDeC7Jk2apK1bt1pzTEyM3nzzTYOJ4CmUK3iVjh07Kjw83JoXL14sl8tlMBEAb8Yx7O7VpUsXdezY0ZoPHjxoe+gpgMvbvn27XnrpJdu1d955RzExMYYSwZMoV/AqERER6tSpkzUfOHBAe/fuNZgIgDfjGHb3cjgcevbZZ23XJkyYIKfTaSgR4FucTqeGDBmirKws69ptt92mfv36GUwFT6JcwesU3HfF0kAAF5O/XFWrVo3fDLvBrbfearsDuG3bNn377bcGEwG+IykpSStXrrTmihUraurUqXI4HAZTwZMoV/A6HGoBoDgyMjJsJ4qyJNA9goKCNGrUKNu1119/3VAawHccOHCg0L7FiRMnqm7duoYSwQTKFbxOu3btVL58eWtm3xWAonBSYNm57777bD8Qrlq1SqtWrTKYCPBuLpdLw4YN09mzZ61r3bt315AhQwymggmUK3id0NBQde3a1ZpTU1O1Y8cOg4kAeCMOsyg7YWFhGjlypO3a+PHjDaUBvN/HH3+sf//739YcERGhGTNmKCiIH7UDDf+LwytxJDuAy+EY9rI1ZMgQVa5c2Zr/9a9/6eeffzaYCPBOR48e1VNPPWW79tJLLyk+Pt5MIBhFuYJX4lALAJfDnauyVaFCBT322GO2axMnTjSUBvBeTz75pE6cOGHN1157baE7vwgcxsvVe++9p7i4OEVERKh9+/Zav379RV+bk5Ojl19+WY0aNVJERIRatWqlBQsWXNF7wjtdc801io6OtualS5dyFDAAm/zlqnLlyqpRo4bBNP7p8ccfV0REhDXPnTtXBw4cMJgI8C7/+te/9Omnn1pzcHCwZs2apZCQEIOpYJLRcjVv3jyNHDlSL7zwgjZv3qxWrVqpT58+Onr0aJGvf+655zRt2jS98847+uWXXzRs2DDdeeed+uGHH0r9nvBOwcHB6t69uzWfOHFCP/30k8FEALzJ2bNntX//fmtu2bIlRx2XgerVq2vQoEHWnJubqzfffNNgIsB7pKenKyEhwXZtzJgxat26tZlA8ApGy9XkyZM1ZMgQDRw4UC1atFBSUpLKlSun2bNnF/n6Dz/8UP/zP/+jm2++WQ0bNlRCQoJuvvlmvfHGG6V+T3gvjmQHcDHbt2+3zSwJLDtPP/20goODrXn69Om2JVBAoBozZowOHz5szU2bNtXzzz9vMBG8gbFylZ2drU2bNql3797/DRMUpN69e2vNmjVFfk5WVpZteYIkRUZGWg9rK817/v6+GRkZtg+Yx74rABfDfivPadCgge655x5rPnfunN577z2DiQDzli1bpmnTptmuzZw5s9DPqQg8xsrV8ePHlZeXV2iNfI0aNZSamlrk5/Tp00eTJ09WSkqKnE6nvv/+e3355Zc6cuRIqd9Tkl577TVFR0dbH7GxsVf43cEdWrZsqZiYGGtetmyZcnNzDSYC4C0oV541evRo2/zOO+/o3LlzhtIAZp0/f16DBw+2XXvsscfUpUsXQ4ngTYwfaFESb731luLj49WsWTOFhYXpL3/5iwYOHHjFzxAYO3as0tPTrY+DBw+6KTGuRFBQkO1I9oyMDNv+OgCBi2PYPat169a68cYbrfn48eMst0fAevHFF7Vr1y5rjo2N1WuvvWYwEbyJsXIVExOj4OBgpaWl2a6npaWpZs2aRX5OtWrV9NVXXykzM1P79+/X9u3bVaFCBTVs2LDU7ylJ4eHhioqKsn3AO/C8KwBFyX/nKioqSrVr1zaYJjCMGTPGNk+aNEk5OTmG0gBmbNq0SZMmTbJdmzZtmipWrGgoEbyNsXIVFhamNm3aaNGiRdY1p9OpRYsWqWPHjpf83IiICNWpU0e5ubn64osvdMcdd1zxe8I7se8KQEHnzp3T3r17rblFixacFOgB3bt313XXXWfN+/fv12effWYwEeBZOTk5GjRokO3RMA888IBuuukmg6ngbYwuCxw5cqRmzJihOXPmKDk5WQkJCcrMzNTAgQMlSQMGDNDYsWOt169bt05ffvml9uzZoxUrVujGG2+U0+m0rQW/3HvCtzRp0sT2G+mVK1cqOzvbYCIApu3YsUMul8uaW7ZsaTBN4HA4HHr22Wdt1yZMmGD73wLwZxMnTtTWrVutuVq1ajyaAIUYLVf9+vXTpEmTNG7cOLVu3VpbtmzRggULrAMpDhw4YB1WIUkXLlzQc889pxYtWujOO+9UnTp1tHLlSlWqVKnY7wnf4nA4bEsDz507x0OhgQDHYRbm3HHHHWratKk1//jjj1qwYIHBRIBnbN++XS+99JLt2jvvvGM7eAuQJIeLXzkVkpGRoejoaKWnp7P/ygvMnj3b9hDLl19+medIAAHsr3/9q1599VVr/ve//207bAFla9asWbaT0rp166Zly5YZTASULafTqW7dumnVqlXWtdtuu01ff/01S5IDREm6gU+dFojAxKEWAPLjzpVZDzzwgG259vLly7V27VqDiYCylZiYaCtWUVFRmjp1KsUKRaJcwes1aNBAcXFx1rx69WqdP3/eXCAARuU/hr1ChQo8m9DDwsPDNWLECNu18ePHG0oDlK39+/cX2ms4ceJE1a1b11AieDvKFXxC/lMDs7OztWbNGoNpAJhy4cIF7d6925qbN2/Ob48NePTRRxUdHW3NX331lZKTkw0mAtzP5XJp2LBhOnv2rHWte/fuhR4gDORHuYJPYGkgAEnauXOn7RhklgSaERUVpeHDh9uuTZw40VAaoGx8/PHHtgNbIiIiNGPGDAUF8eMzLo7/d8AnFCxXS5YsMZQEgEkF91txDLs5Tz75pMLDw635o48+0qFDhwwmAtzn6NGjevLJJ23XXn75ZcXHxxtKBF9BuYJPqFOnju343/Xr1+vMmTMGEwEwgcMsvEeNGjVsz5DMycnRlClTzAUC3OiJJ57QyZMnrblNmzaF9hoCRaFcwWfkv3uVm5urlStXGkwDwATKlXd55plnbEukpk2bplOnThlMBFy5f/7zn5o3b541h4SEaNasWQoJCTGYCr6CcgWfkf9QC4mlgUAgyl+uIiMjVb9+fYNp0KhRI/35z3+25rNnz2rq1KkGEwFXJj09XQkJCbZrY8aMUatWrQwlgq+hXMFn9OjRwzZzqAUQWLKzs5WSkmLNzZs3Z2O5FxgzZoxtfuutt3hcBnzW6NGj9euvv1pz06ZN9dxzzxlMBF/D30rwGdWqVdNVV11lzZs3b2b5CRBAUlJSlJuba80sCfQO1157rW644QZrPnbsmD744ANzgYBSWrp0qaZPn27NDodDs2bNUkREhMFU8DWUK/iU/EsDXS6Xli9fbjANAE9iv5X3Knj3atKkSbYiDHi7c+fOaciQIbZrjz32mDp37mwoEXwV5Qo+heddAYGLY9i9V69evdSmTRtr3rNnjz7//HODiYCSefHFF7Vr1y5rjo2N1auvvmowEXwV5Qo+pXv37rY9FhxqAQQO7lx5L4fDUeju1fjx4+VyuQwlAopv48aNeuONN2zXpk2bpooVKxpKBF9GuYJPqVSpkq655hpr/umnn3T06FGDiQB4Sv5yFR4ergYNGhhMg4LuuusuNW7c2Jq3bNmi77//3mAi4PJycnI0aNAgOZ1O69qDDz6om266yWAq+DLKFXxOwSPZly5daiYIAI/Jzc3Vjh07rLlZs2YKDg42mAgFBQcHa9SoUbZr48ePN5QGKJ4JEyboxx9/tOZq1arpzTffNJgIvo5yBZ/D866AwLN7927l5ORYM0sCvdOAAQNUo0YNa168eLE2bNhgMBFwccnJyXr55Zdt19555x1VrVrVUCL4A8oVfE6XLl1sT0nnUAvA/23bts02U668U0REhJ566inbNe5ewRs5nU4NHjxY2dnZ1rXbb79d99xzj8FU8AeUK/icChUq6LrrrrPmnTt36vDhwwYTAShrHGbhOxISEhQVFWXNX375pXbu3GkwEVDY1KlTtXr1amuOiorS1KlT5XA4DKaCP6BcwScVPJKdpYGAf6Nc+Y7o6GgNGzbMml0ulyZNmmQwEWC3f/9+Pfvss7ZrEydOVJ06dQwlgj+hXMEnFdx3xdJAwL/lL1ehoaG2U+ngfZ566imFhYVZ85w5c3TkyBGDiYDfuFwuDRs2TJmZmda1Hj16aPDgwQZTwZ9QruCTOnbsqPDwcGvmzhXgv/Ly8rR9+3Zrbtq0qW3fJbxPrVq1NGDAAGvOzs7WlClTzAUC/s9HH32kBQsWWHNERIRmzJhhe4YmcCX4fxJ8UmRkpDp27GjN+/bt0969ew0mAlBW9u7dq6ysLGtmSaBvGDVqlG3/SlJSktLT0w0mQqA7evRooQNXXn75Ze6Ew60oV/BZLA0EAgP7rXxTkyZNdNddd1lzRkaGkpKSDCZCoHviiSd08uRJa27Tpo1GjBhhMBH8EeUKPovnXQGBgWPYfdeYMWNs85tvvqkLFy4YSoNA9vXXX2vevHnWHBISolmzZrHEGG5HuYLPateuncqVK2fNixcvlsvlMpgIQFngzpXvateune1017S0NP397383mAiBKD09XcOHD7ddGzNmjFq1amUoEfwZ5Qo+KywsTF27drXmI0eO8CwVwA/lL1chISGKj483mAYlVdSR13l5eYbSIBCNHj1av/76qzU3a9ZMzz33nMFE8GeUK/i0gs+7Yt8V4F+cTqeSk5OtOT4+3nbEN7zfDTfcoGuuucaad+3apf/93/81mAiBZMmSJZo+fbo1OxwOzZw5UxEREQZTwZ9RruDTONQC8G/79+/X+fPnrZklgb7H4XBo9OjRtmuvv/46y7hR5s6dO6chQ4bYrj322GPq3LmzoUQIBJQr+LRrrrlG0dHR1rx06VI5nU6DiQC4E/ut/MOf//xnNWjQwJo3bdrEL8NQ5l588UXt3r3bmuvVq6dXX33VYCIEAsoVfFpISIi6detmzcePH9fPP/9sMBEAd6Jc+YeQkBCNGjXKdm38+PGG0iAQbNy4UW+88Ybt2rRp01SxYkVDiRAoKFfweRzJDvgvjmH3Hw8//LCqV69uzd9//702b95sMBH8VXZ2th555BHbSpYHH3xQN954o8FUCBSUK/g8DrUA/Ff+O1dBQUFq0qSJwTS4EpGRkXriiSds17h7hbIwYcIE/fTTT9ZcrVo1vfnmmwYTIZA4XOwoLSQjI0PR0dFKT09XVFSU6Ti4DKfTqerVq+vEiROSpOjoaJ04cULBwcGGkwG4Ei6XSxUrVlRmZqak304K5HELvu3UqVOqV6+ezp49K+m3wrxz5041atTIcDL4i+TkZLVu3VrZ2dnWtXnz5umee+4xmAq+riTdgDtX8HlBQUG2u1fp6en64YcfDCYC4A4HDx60ipUktWzZ0mAauEPlypU1dOhQa3Y6nZo0aZLBRPAneXl5GjRokK1Y3XHHHbr77rsNpkKgoVzBL7A0EPA/HGbhn0aMGKHQ0FBrfv/995WWlmYwEfzF1KlTtWbNGmuOiorSe++9J4fDYTAVAg3lCn6B510B/ody5Z/q1KmjBx54wJqzsrL01ltvGUwEf7B//36NHTvWdm3SpEmqU6eOoUQIVJQr+IWmTZuqVq1a1rxy5UrbsgAAvody5b9GjRplu5swdepUZWRkGEwEX+ZyuTR06FDbMuKePXtq8ODBBlMhUFGu4BccDodtaWBmZqY2bNhgMBGAK5W/XDkcDjVt2tRgGrhT8+bNdccdd1hzenq6pk+fbjARfNmHH36o7777zpojIiI0ffp0lgPCCMoV/AbPuwL8h8vlsj3jqkGDBipXrpzBRHC3MWPG2OY333xTWVlZhtLAV6WlpWnEiBG2a6+88ooaN25sKBECHeUKfoNDLQD/8euvv9qWibEk0P906NBB3bp1s+Zff/1VH330kcFE8EVPPPGETp48ac1t27bVU089ZS4QAh7lCn6jQYMGql+/vjWvXr1aFy5cMJgIQGkV3G/FMez+6dlnn7XNEydOlNPpNJQGvubrr7/WZ599Zs0hISGaNWuWQkJCDKZCoKNcwW84HA7b0sCsrCzbkawAfAeHWQSGG2+8UVdffbU179ixQ19//bXBRPAVp0+f1vDhw23Xnn32Wdv/nwATKFfwKywNBPwD5SowOBwOjR492nbt9ddfl8vlMpQIvmL06NH69ddfrblZs2Z67rnnDCYCfkO5gl8pWK441ALwTQXLVbNmzQwlQVnr16+fbUn3+vXrtWzZMoOJ4O0WL16sGTNmWLPD4dCsWbMUHh5uMBXwG8oV/ErdunUVHx9vzevWrdPZs2cNJgJQUgVPCqxfv74qVKhgMBHKUkhIiJ555hnbtfHjxxtKA2937tw5DRkyxHbtL3/5izp16mQoEWBHuYLfyb/vKjc3VytXrjSYBkBJpaWl6dSpU9bMkkD/98gjjygmJsaaFyxYoK1btxpMBG/1wgsvaM+ePdZcr149vfrqqwYTAXaUK/gdnncF+Db2WwWecuXK6fHHH7ddmzBhgqE08FYbNmzQ5MmTbdemT5/OnW14FcoV/E6PHj1sM4daAL6FchWYHnvsMduDoj/99FPt3bvXYCJ4k+zsbA0aNMh2VP+AAQPUp08fg6mAwihX8DvVq1fXH/7wB2vevHmzTp8+bS4QgBLhGVeBqWrVqnr00Uet2el06o033jCYCN5kwoQJ+umnn6y5evXqhe5iAd6AcgW/lP/UQKfTqeXLlxtMA6AkCpar5s2bG0oCTxs5cqTtAbCzZ8/WsWPHDCaCN/jll1/0yiuv2K698847qlq1qqFEwMVRruCXCu67Ymkg4Dvyl6u6desqKirKYBp4UmxsrO677z5rPn/+vN555x2DiWBaXl6eBg8erOzsbOvaHXfcobvvvttgKuDiKFfwS927d5fD4bBmDrUAfMOxY8dsdyrYbxV4Cj5U+N133+WRGgFs6tSpWrNmjTVHR0dr6tSptr/jAW9CuYJfqly5sq655hpr/vHHH1laAvgADrNAy5Ytddttt1nzqVOnbA+MReDYt2+fxo4da7s2ceJE1a5d21Ai4PIoV/BbBZcGLl261EwQAMVGuYIkjRkzxjZPnjzZtiwM/s/lcmno0KHKzMy0rvXs2VODBw82mAq4PMoV/BbPuwJ8D+UKktS5c2d17tzZmg8dOqRPPvnEYCKUtTNnzmj27NkaPXq0xowZo+HDh2vhwoXWn0dGRmr69OksB4TXC7n8SwDf1KVLFwUHBysvL08Sh1oAvoByhd+NGTNGt99+uzWPHz9eDz74oIKC+L2wP3E6nfrb3/6miRMn6syZMxd93SuvvKLGjRt7MBlQOvwbCn6rYsWKuu6666x5x44d+vXXXw0mAnA5+ctVrVq1VLlyZYNpYNItt9xie8ZZcnKyvvnmG4OJ4G4ul0vDhg3TuHHjLlmsatWqpSeffNKDyYDSo1zBr+V/3pXE0kDAm508eVKpqanWzF2rwBYUFFTo5MDx48cbSoOy8NVXXxXrsJIjR45o3bp1HkgEXDnKFfwaz7sCfEdycrJtplyhf//+io2NtebVq1dr5cqVBhPBnd5+++1iv5bnncFXUK7g1zp16qSwsDBr5s4V4L22bdtmmylXCA0N1ciRI23XXn/9dUNp4E5paWklOsX3q6++ktPpLLtAgJtQruDXIiMj1bFjR2veu3ev9u3bZy4QgIviMAsUZfDgwapSpYo1z58/Xz///LPBRCgpl8ulffv26dNPP9WIESPUqVMn1atXr0TvkZWVpXPnzpVRQsB9KFfwexzJDvgGyhWKUqFCBf3lL3+xXZswYYKhNCiO9PR0/ec//9Hf/vY33X777apZs6YaNGig/v37a8qUKVqzZk2Jn1sWGhqqcuXKlVFiwH0cLpfLZTqEt8nIyFB0dLTS09MVFRVlOg6u0IoVK9StWzdrfuCBB/Thhx8aTASgKHXr1tXhw4clSdWrV1daWprhRPAWx48fV7169XT+/HlJUkhIiHbt2qX69esbTobc3Fxt27ZNa9eu1bp167Ru3TolJyfL3T9e3n777fr666/d+p5AcZWkG/CcK/i99u3bKzIy0vpLefHixXK5XDyIEPAi6enpVrGSuGsFu5iYGA0aNEjvvvuupN9+oJ88ebLeeustw8kCz+HDh21FauPGjaVarhcfH6/q1atr1apVxXr9448/XuKvAZhAuYLfCwsLU9euXa0nvf/6669KSUlRkyZNDCcD8DtOCsTlPP3000pMTLQeDD9z5kyNGzdOVatWNZzMf2VmZmrjxo1WkVq3bp3tlyDFVblyZbVv317t27dXhw4ddN1116lKlSpyOp3q16+fPv/880t+/rBhw3T99deX9tsAPIpyhYDQs2dPq1xJv929olwB3oP9VricuLg43Xvvvfr4448lSefOndO7776rF154wXAy/+B0OpWcnGwrUj///LNVZosrJCRErVu3topU+/bt1bhx4yJXiwQFBWnu3LmqVauWEhMTlZuba/vziIgIjRo1Si+++CKrTeAz2HNVBPZc+Z/169erffv21nz33Xfrs88+M5gIQH5PP/20Jk+ebM2LFy8u9BBw4Mcff1SrVq2suWrVqtq/f7/Kly9vMJVvSktLsxWpDRs2KCMjo8TvExcXZytS11xzjSIiIkr8Pqmpqfr73/+u5ORkORwOtW7dWg888IDtpEjAlJJ0A8pVEShX/ic3N1dVq1a1/uKoVq2aUlNTFRTEgZmAN7jpppu0YMECa05NTVWNGjUMJoK3uvnmm/Xvf//bmt966y098cQTBhN5vwsXLmjz5s22MlWax5JERUWpXbt2tuV9/HOKQEC5ukKUK/9022236ZtvvrHmH3/8UVdddZXBRAB+V79+fR04cECSVKVKFR0/fpxlQCjS8uXL1b17d2uuV6+edu3apdDQUIOpvIfL5VJKSoqtSG3dulU5OTklep+goCBdddVVtrtSzZo145eSCEicFggUoVevXrZytWTJEsoV4AXOnDljFStJatmyJcUKF9W1a1d16NBBa9eulSQdOHBA8+bN0wMPPGA4mRknTpzQ+vXrrSK1fv16nTx5ssTvU6dOHVuRatOmDcstgVKgXCFgFNy/sXjxYpaSAF5g+/bttpnDLHApDodDY8aM0Z133mldGz9+vO6//36/L+XZ2dnaunWr7a5USkpKid+nXLlyatu2rVWk2rdvrzp16pRBYiDwUK4QMK6++mpVqVLF+o3e0qVLlZeXp+DgYMPJgMDGSYEoqdtvv13NmjWzivnPP/+sb7/9VrfccovhZO7jcrm0b98+W5HavHmzsrKySvQ+DodDzZs3txWpli1bKiSEHwGBssA/WQgYQUFB6tmzp7744gtJvz20dMuWLWrTpo3hZEBgo1yhpIKCgjR69Gg98sgj1rXx48f7dLlKT0/Xhg0bbGXq6NGjJX6f6tWr24pUu3bt2D8OeBDlCgElf7mSflsaSLkCzKJcoTTuv/9+Pf/889ZDbVesWKE1a9aoY8eOhpNdXm5urn7++WdbkUpOTlZJzxgLDw9XmzZtrCLVvn171a9f3++XRwLejHKFgNKrVy/bvGTJEo0aNcpQGgCStG3bNus/R0dHq1atWgbTwFeEhYVpxIgReuaZZ6xr48eP11dffWUu1EUcOnTIVqQ2btyoc+fOlfh94uPjbXelrr76aoWFhZVBYgClxVHsReAodv/lcrlUu3ZtpaamSpLKly+vU6dOcYQvYEhmZqYqVqxo/ca+Y8eOWr16teFU8BVnzpxRvXr1dPr0aevatm3bjN79zMzM1MaNG21l6ve7ayVRpUoV2x2p6667jgfqAoZwFDtwEQ6HQz179tQnn3wi6be/BDds2KBOnToZTgYEph07dtiWQrEkECVRsWJFPfbYY/rb3/5mXZs4caLef/99j3x9p9Op5ORkW5H66aef5HQ6S/Q+oaGhat26ta1MNW7cmOV9gA+iXCHg9OrVyypX0m9LAylXgBkF91u1bNnSUBL4qieeeEJvvPGGLly4IEn6+OOP9corr6hu3bpu/1ppaWm2IrV+/XqdOXOmxO/ToEEDW5G65pprFBER4fa8ADyPcoWAU9Tzrv76178aSgMENg6zwJWqXr26Bg4cqMTERElSTk6ObrjhBrVo0UL16tXTgw8+qGuvvbbE73vhwgVt3rzZVqb27dtX4veJiorSddddZytT1atXL/H7APAN7LkqAnuu/JvL5VJcXJwOHDgg6bfTlk6fPs1vDQED+vbtq6+//tqaDxw4oNjYWIOJ4Iv27Nmjxo0bX/S0vV69emnu3LmqUaNGkX/ucrmUkpJiK1JbtmxRbm5uiXIEBwfrqquushWpZs2aKSgoqMTfEwDvwZ4r4BJ+33c1Z84cSVJWVpbWrl2rHj16mA0GBKD8d64qVKhQJku54P+++eabSx5jvnjxYnXv3l2rV69WlSpVdOLECa1fv95Wpk6dOlXir1u3bl1bkWrTpo3Kly9/Jd8KAB9n/Fcp7733nuLi4hQREaH27dtr/fr1l3z9lClT1LRpU0VGRio2NlYjRoyw1llL0osvviiHw2H7aNasWVl/G/AxBY9kX7x4saEkQOC6cOGCdu/ebc0tWrRgAz9K7ODBg3r66acv+7odO3aoY8eOio+PV0xMjG6++Wa99NJLWrBgQbGKVfny5dW9e3eNHj1aX3zxhQ4dOqSDBw/q888/16hRo9StWzeKFQCzd67mzZunkSNHKikpSe3bt9eUKVPUp08f7dixo8j1yHPnztWzzz6r2bNnq1OnTtq5c6cefvhhORwOTZ482Xpdy5Yt9Z///MeaQ0K4QQe7gvuulixZYigJELh27NhhO1WN/VYojenTpxd7+d7OnTuL9TqHw6EWLVpYd6Q6dOigFi1a8PMEgMsy+m+JyZMna8iQIRo4cKAkKSkpSfPnz9fs2bP17LPPFnr96tWr1blzZ913332SpLi4OPXv31/r1q2zvS4kJEQ1a9Ys+28APis2NlaNGzfWrl27JElr165VZmYmv3UEPIjDLOAO//73v6/4PWrUqGFb3teuXTv2XAMoFWPlKjs7W5s2bdLYsWOta0FBQerdu7fWrFlT5Od06tRJH330kdavX6/rrrtOe/bs0bfffqsHH3zQ9rqUlBTVrl1bERER6tixo1577TXVq1fvolmysrKUlZVlzRkZGVf43cEX9OrVyypXubm5Wrlypfr06WM4FRA4OIYd7lDSv7NDQ0PVrl07212pevXqsSQVgFsYK1fHjx9XXl5eoZN7atSooe3btxf5Offdd5+OHz+uLl26yOVyKTc3V8OGDdP//M//WK9p3769PvjgAzVt2lRHjhzRSy+9pK5du+rnn39WxYoVi3zf1157TS+99JL7vjn4hF69emn69OnWvGTJEsoV4EHcuYI7VKtWTSkpKcV+/fz583XDDTeUYSIAgcz4gRYlsXTpUr366quaOnWqNm/erC+//FLz58/XK6+8Yr3mpptu0t13362rr75affr00bfffqvTp0/rs88+u+j7jh07Vunp6dbHwYMHPfHtwLCCpwNyqAXgWfnLVbly5S65wgC4mD//+c/Ffm3NmjXVvXv3MkwDINAZK1cxMTEKDg5WWlqa7XpaWtpF90s9//zzevDBBzV48GBdddVVuvPOO/Xqq6/qtddes22Kzq9SpUpq0qSJtfyrKOHh4YqKirJ9wP/VqFHDtgxp06ZNSk9PN5gICBzZ2dm2uw3NmzfnWUAolYcffviiK1MKSkhIUFhYWBknAhDIjP1NFhYWpjZt2mjRokXWNafTqUWLFqljx45Ffs65c+cK/eUbHBwsSRd9vsXZs2e1e/du1apVy03J4U/ynxrodDq1fPlyg2mAwLFz507l5eVZM0sCUVqVK1fWxx9/fNmT/K6//nqNGTPGQ6kABCqjvyYcOXKkZsyYoTlz5ig5OVkJCQnKzMy0Tg8cMGCA7cCL2267TYmJifr000+1d+9eff/993r++ed12223WSXrmWee0bJly7Rv3z6tXr1ad955p4KDg9W/f38j3yO8G8+7AsxgvxXc6bbbbtPChQv1hz/8odCfRUZG6vHHH9c333yj8PBwA+kABBKjR7H369dPx44d07hx45SamqrWrVtrwYIF1iEXBw4csN2peu655+RwOPTcc8/p8OHDqlatmm677Tb97W9/s15z6NAh9e/fXydOnFC1atXUpUsXrV27VtWqVfP49wfv1717dzkcDuvOJ8+7AjyDcgV369mzp3788UetWrVKK1as0IULFxQbG6s//elPqly5sul4AAKEw3Wx9XQBLCMjQ9HR0UpPT2f/VQC49tpr9cMPP1jzsWPHFBMTYzAR4P/uuece/eMf/7DmXbt2qVGjRgYTAQBQtJJ0A3YPI+AVXBq4dOlSM0GAAJL/zlVERITi4uLMhQEAwE0oVwh4+Q+1kFgaCJS1nJwc7dy505qbNWtm7ZsFAMCXUa4Q8Lp27Wr7wY5DLYCytXv3buXk5Fgz+60AAP6CcoWAFxUVpXbt2lnz9u3bdeTIEYOJAP+2bds220y5AgD4C8oVIJYGAp7ESYEAAH9FuQLE864AT6JcAQD8FeUKkNSpUyeFhYVZM3eugLKTv1yFhYVxBDsAwG9QrgBJ5cqVU4cOHax5z5492r9/v8FEgH/Kzc3Vjh07rLlp06YKCTH6PHsAANyGcgX8n4JLA7l7Bbjf3r17lZWVZc0sCQQA+BPKFfB/Ch5qwb4rwP3YbwUA8GeUK+D/tG/fXpGRkda8ePFiuVwug4kA/0O5AgD4M8oV8H/Cw8PVpUsXaz58+LB27dplMBHgf3jGFQDAn1GugHxYGgiUrfx3rkJCQtS4cWODaQAAcC/KFZAPz7sCyk5eXp6Sk5OtOT4+3vYIBAAAfB3lCsinTZs2qlixojUvWbKEfVeAm+zfv18XLlyw5pYtWxpMAwCA+1GugHxCQkLUrVs3az527FihPSIASofDLAAA/o5yBRTA866AskG5AgD4O8oVUACHWgBlg3IFAPB3lCuggFatWqly5crWvHTpUuXl5RlMBPiH/Etsg4KC1KRJE4NpAABwP8oVUEBQUJDt7tXp06e1detWg4kA3+d0Om0nBTZu3Fjh4eEGEwEA4H6UK6AILA0E3OvgwYPKzMy0ZpYEAgD8EeUKKAKHWgDuVXC/FcewAwD8EeUKKELz5s1Vo0YNa16+fLlycnIMJgJ8G4dZAAACAeUKKILD4bAtDTx79qw2btxoMBHg2yhXAIBAQLkCLoKlgYD75C9XDodDTZs2NZgGAICyQbkCLoJDLQD3cLlctnLVsGFDRUZGGkwEAEDZoFwBF9GoUSPFxsZa86pVq5SVlWUwEeCbDh8+rIyMDGtmSSAAwF9RroCLKLjv6sKFC1q7dq3BRIBvYr8VACBQUK6ASyi474qlgUDJcQw7ACBQUK6ASyi474pDLYCS484VACBQUK6AS6hXr54aNWpkzWvXrlVmZqbBRIDvKViumjVrZigJAABli3IFXEb+pYE5OTlatWqVwTSAbyl4UmBcXJzKly9vMBEAAGWHcgVcBksDgdJLTU3VqVOnrJklgQAAf0a5Ai6D510Bpcd+KwBAIKFcAZdRs2ZN2w+EGzduVHp6usFEgO+gXAEAAgnlCiiG/HevnE6nVqxYYTAN4Ds4hh0AEEgoV0Ax8LwroHQKlqvmzZsbSgIAQNmjXAHF0L17dzkcDmvmUAugePKXq9jYWFWsWNFgGgAAyhblCiiGqlWrqlWrVta8ZcsWnThxwmAiwPsdO3ZMx48ft2b2WwEA/B3lCiimgksDly1bZigJ4Bu2bdtmmylXAAB/R7kCiokj2YGS4aRAAECgoVwBxdStWzcFBwdbM+UKuDTKFQAg0FCugGKKiopS27ZtrTk5OVmpqakGEwHejXIFAAg0lCugBAouDeTUQODi8per2rVrq1KlSubCAADgAZQroAR43hVQPCdOnFBaWpo1c9cKABAIKFdACXTu3FmhoaHWzJ0roGjJycm2mXIFAAgElCugBMqVK6cOHTpY8+7du3XgwAGDiQDvxH4rAEAgolwBJVRwaSB3r4DCeMYVACAQUa6AEuJ5V8DlcecKABCIKFdACXXo0EERERHWvHjxYrlcLoOJAO+Tv1xVr15dVatWNZgGAADPoFwBJRQeHq4uXbpY86FDh7R7926DiQDvcvr0af3666/W3LJlS4NpAADwHMoVUAosDQQujpMCAQCBinIFlAKHWgAXx34rAECgolwBpdCmTRtVqFDBmtl3BfwX5QoAEKgoV0AphIaGqlu3btZ89OjRQj9QAoGKY9gBAIGKcgWUEksDgaLl/0VD1apVVa1aNYNpAADwHMoVUEocagEUlpGRoYMHD1pzixYt5HA4DCYCAMBzKFdAKbVq1UqVK1e25qVLl8rpdBpMBJi3fft228wx7ACAQEK5AkopODhY3bt3t+ZTp05p69atBhMB5nGYBQAgkFGugCtQcN8VSwMR6ChXAIBARrkCrgCHWgB2lCsAQCCjXAFXoEWLFqpevbo1L1u2TDk5OQYTAWblP4a9UqVKqlmzpsE0AAB4FuUKuAIOh8N2auDZs2e1adMmg4kAczIzM7Vv3z5r5qRAAECgCSnuC++6665iv+mXX35ZqjCAL+rZs6fmzZtnzUuWLFGHDh0MJgLMKHhSIEsCAQCBptjlKjo6uixzAD6rqEMtxo4daygNYE7B/VYcww4ACDTFLlfvv/9+WeYAfFbjxo1Vt25dHTp0SJK0atUqZWVlKTw83HAywLM4zAIAEOjYcwVcoYL7rs6fP69169YZTASYQbkCAAS6Yt+5uuaaa4q9MXnz5s2lDgT4ol69eunDDz+05sWLF6tbt24GEwGel79cVaxYUXXq1DGYBgAAzyt2uerbt28ZxgB8W/47V9Jvh1q8+OKLZsIABpw/f167d++2Zk4KBAAEomKXqxdeeKEscwA+rX79+mrYsKH27NkjSVqzZo3OnTuncuXKGU4GeMaOHTvkcrmsmSWBAIBAxJ4rwE3ynxqYk5OjVatWGUwDeBb7rQAAKGW5ysvL06RJk3TdddepZs2aqlKliu0DCERFLQ0EAgXHsAMAUMpy9dJLL2ny5Mnq16+f0tPTNXLkSN11110KCgpinwkCVsFytXjxYkNJAM/jzhUAAKUsVx9//LFmzJihp59+WiEhIerfv79mzpypcePGae3ate7OCPiEWrVqqXnz5ta8ceNGZWRkGEwEeE7+clW+fHnFxsYaTAMAgBmlKlepqam66qqrJEkVKlRQenq6JOnWW2/V/Pnz3ZcO8DH5717l5eVpxYoVBtMAnpGVlaVdu3ZZc/PmzRUUxJZeAEDgKdXffnXr1tWRI0ckSY0aNdLChQslSRs2bFB4eLj70gE+Jv+hFhJLAxEYUlJSlJeXZ80sCQQABKpSlas777xTixYtkiQ9/vjjev755xUfH68BAwbokUcecWtAwJd0797dNnOoBQLBtm3bbDPlCgAQqIr9nKv8Xn/9des/9+vXT/Xr19fq1asVHx+v2267zW3hAF8TExOjVq1aaevWrZKkLVu26OTJk5yiCb/GYRYAAPymVOWqoA4dOqhDhw7ueCvA5/Xq1csqVy6XS8uWLdOdd95pOBVQdihXAAD8plTLAl977TXNnj270PXZs2dr/PjxVxwK8GUcyY5Ak79cRUZGKi4uzlwYAAAMKlW5mjZtmpo1a1boesuWLZWUlHTFoQBf1q1bN9tJaZQr+LOcnBzt3LnTmps1a6bg4GCDiQAAMKfUR7HXqlWr0PVq1apZpwgW13vvvae4uDhFRESoffv2Wr9+/SVfP2XKFDVt2lSRkZGKjY3ViBEjdOHChSt6T8CdoqOj1bZtW2v+5ZdflJaWZjARUHZ27dql3Nxca2ZJIAAgkJWqXMXGxmrVqlWFrq9atUq1a9cu9vvMmzdPI0eO1AsvvKDNmzerVatW6tOnj44ePVrk6+fOnatnn31WL7zwgpKTkzVr1izNmzdP//M//1Pq9wTKQsGlgZwaCH/FfisAAP6rVOVqyJAheuqpp/T+++9r//792r9/v2bPnq0RI0ZoyJAhxX6fyZMna8iQIRo4cKBatGihpKQklStXrsj9XJK0evVqde7cWffdd5/i4uL0xz/+Uf3797fdmSrpewJlgeddIVBwDDsAAP9VqtMCR40apRMnTmj48OHKzs6WJEVERGjMmDEaO3Zssd4jOztbmzZtsr0+KChIvXv31po1a4r8nE6dOumjjz7S+vXrdd1112nPnj369ttv9eCDD5b6PSUpKytLWVlZ1pyRkVGs7wG4mM6dOys0NFQ5OTmSuHMF/8WdKwAA/qtUd64cDofGjx+vY8eOae3atdq6datOnjypcePGFfs9jh8/rry8PNWoUcN2vUaNGkpNTS3yc+677z69/PLL6tKli0JDQ9WoUSP16NHDWhZYmveUfjv9MDo62vqIjY0t9vcBFKV8+fJq3769Ne/atUsHDx40mAgoG/nLVVhYmBo2bGgwDQAAZpWqXP0uNTVVJ0+eVKNGjRQeHi6Xy+WuXEVaunSpXn31VU2dOlWbN2/Wl19+qfnz5+uVV165ovcdO3as0tPTrQ9+CIY7FFwayN0r+Jvc3Fzt2LHDmps1a6aQELc8PhEAAJ9UqnJ14sQJXX/99WrSpIluvvlm64TAQYMG6emnny7We8TExCg4OLjQKWppaWmqWbNmkZ/z/PPP68EHH9TgwYN11VVX6c4779Srr76q1157TU6ns1TvKUnh4eGKioqyfQBXiuddwd/t2bPHWhousSQQAIBSlasRI0YoNDRUBw4cULly5azr/fr104IFC4r1HmFhYWrTpo0WLVpkXXM6nVq0aJE6duxY5OecO3fO9vwgSdbzVFwuV6neEygrHTp0UEREhDUvXry4zO/uAp7EfisAAOxKtX5j4cKF+u6771S3bl3b9fj4eO3fv7/Y7zNy5Eg99NBDatu2ra677jpNmTJFmZmZGjhwoCRpwIABqlOnjl577TVJ0m233abJkyfrmmuuUfv27bVr1y49//zzuu2226ySdbn3BDwlIiJCnTp1su5YHTx4UHv27FGjRo0MJwPcg3IFAIBdqcpVZmam7Y7V706ePKnw8PBiv0+/fv107NgxjRs3TqmpqWrdurUWLFhgHUhx4MAB252q5557Tg6HQ88995wOHz6satWq6bbbbtPf/va3Yr8n4Em9evWyLQdcvHgx5Qp+g2PYAQCwc7hKsU7p5ptvVps2bfTKK6+oYsWK+vHHH1W/fn3de++9cjqd+vzzz8siq8dkZGQoOjpa6enp7L/CFVmzZo06depkzf3799fcuXMNJgLc55prrtGWLVskSSEhITp37pxCQ0PNhgIAwM1K0g1Kdedq4sSJ6tWrlzZu3Kjs7GyNHj1a27Zt08mTJ7Vq1apShQb8Udu2bVW+fHllZmZK+u++K4fDYTgZcGXy8vK0fft2a27SpAnFCgAQ8Ep8oEVOTo6eeOIJ/etf/1KXLl10xx13KDMzU3fddZd++OEHljwB+YSGhqpbt27WnJaWpuTkZIOJAPfYt2+fLly4YM0tW7Y0mAYAAO9Q4jtXoaGh+vHHH1W5cmX99a9/LYtMgF/p2bOn/v3vf1vzkiVL2JsCn8dhFgAAFFaqo9gfeOABzZo1y91ZAL9U8GHCPO8K/oByBQBAYaXac5Wbm6vZs2frP//5j9q0aaPy5cvb/nzy5MluCQf4g9atW6tSpUo6ffq0JGnp0qVyOp2FntkG+BLKFQAAhZWqXP3888+69tprJUk7d+60/Rkb9QG74OBgde/eXV9//bWk3x5Z8OOPP6p169ZmgwFXIP8x7MHBwYqPjzeYBgAA71CqcrVkyRJ35wD8Wq9evaxyJf22NJByBV/ldDptB7M0bty4RM84BADAX7EuCfCAgvuu+AUFfNmBAwd07tw5a2ZJIAAAv6FcAR7QsmVLVatWzZqXLVum3Nxcg4mA0iu434pj2AEA+A3lCvAAh8Ohnj17WvOZM2e0adMmg4mA0uMwCwAAika5Ajwkf7mSWBoI30W5AgCgaJQrwEN43hX8Rf5yFRQUpCZNmhhMAwCA96BcAR4SHx+vOnXqWPPKlSuVnZ1tMBFQci6Xy1auGjZsqMjISIOJAADwHpQrwEMK7rs6f/681q1bZzARUHKHDh3SmTNnrJklgQAA/BflCvAglgbC17HfCgCAi6NcAR7EoRbwdZQrAAAujnIFeFBcXJwaNGhgzWvWrNH58+cNJgJKhmdcAQBwcZQrwMPyLw3Mzs7W6tWrDaYBSiZ/uXI4HGrWrJnBNAAAeBfKFeBhBZcGsu8KvqLgSYFxcXEqV66cwUQAAHgXyhXgYZQr+KrU1FSdPn3amtlvBQCAHeUK8LDatWvbllJt2LDBdrQ14K22bdtmmylXAADYUa4AA/LfvcrLy9OKFSsMpgGKh5MCAQC4NMoVYADPu4IvolwBAHBplCvAgB49ethmnncFX1CwXDVv3txQEgAAvBPlCjAgJiZGV199tTX/8MMPOnnypMFEwKW5XC7bnqt69eqpYsWKBhMBAOB9KFeAIfmXBrpcLi1fvtxgGuDSjh07ZvsFAEsCAQAojHIFGMKR7PAl7LcCAODyKFeAId26dVNQ0H//EaRcwZtxDDsAAJdHuQIMqVSpkq699lpr3rZtm9LS0gwmAi6OO1cAAFwe5QowqOCR7EuXLjUTBLgMTgoEAODyKFeAQQXLFUeyw1vlL1d16tRRpUqVzIUBAMBLUa4Agzp37qyQkBBrZt8VvNHx48d19OhRa2ZJIAAARaNcAQZVqFBB7du3t+aUlBQdOnTIYCKgsOTkZNtMuQIAoGiUK8CwgkeyszQQ3obDLAAAKB7KFWBYwX1XLA2Et+EYdgAAiodyBRjWsWNHhYeHW/PixYvlcrkMJgLsOCkQAIDioVwBhkVERKhTp07WfODAAe3du9dgIsAuf7mqUaOGqlatajANAADei3IFeAGWBsJbnTp1SkeOHLHmli1bGkwDAIB3o1wBXoDnXcFbcVIgAADFR7kCvEC7du1Uvnx5a2bfFbwFJwUCAFB8lCvAC4SGhqpr167WnJqaqu3btxtMBPyGcgUAQPFRrgAvwfOu4I04hh0AgOKjXAFegkMt4I3y37mKiYlRtWrVDKYBAMC7Ua4AL3HNNdcoOjrampcuXSqn02kwEQJdRkaGDh06ZM3ctQIA4NIoV4CXCA4OVvfu3a35xIkT+umnnwwmQqDjpEAAAEqGcgV4EZYGwpsUPMyCZ1wBAHBplCvAi3CoBbwJJwUCAFAylCvAi/zhD39QTEyMNS9btky5ubkGEyGQUa4AACgZyhXgRYKCgmx3rzIyMrR582aDiRDI8perypUrq0aNGgbTAADg/ShXgJdhaSC8wdmzZ7Vv3z5rbtGihRwOh7lAAAD4AMoV4GU41ALeYPv27baZJYEAAFwe5QrwMk2aNFHt2rWteeXKlcrOzjaYCIGI/VYAAJQc5QrwMg6Hw7Y08Ny5c1q/fr3BRAhEHMMOAEDJUa4AL8TSQJjGnSsAAEqOcgV4IQ61gGn5y1VUVJRtqSoAACga5QrwQg0aNFBcXJw1r169WufPnzcXCAHl/Pnz2rNnjzVzUiAAAMVDuQK8VP6lgdnZ2VqzZo3BNAgk27dvl8vlsmaWBAIAUDyUK8BLFVwayL4reAr7rQAAKB3KFeClKFcwhXIFAEDpUK4AL1WnTh01adLEmjds2KAzZ84YTIRAwTHsAACUDuUK8GL5913l5uZq5cqVBtMgUOQvVxUqVFBsbKzBNAAA+A7KFeDFCj7vasiQIRo0aJBWrVplO3AAcJesrCzt2rXLmps3b85JgQAAFBPlCvBS2dnZ+vrrr23XDh8+rNmzZ6tLly66/vrrdfz4cUPp4K927twpp9Npzey3AgCg+ChXgBdyuVx6+OGH9fHHH1/0NUuWLNENN9ygzMxMDyaDv9u2bZttplwBAFB8lCvACy1cuFCffPLJZV+3ZcsWvf322x5IhEDBSYEAAJQe5QrwQu+9916xX5uUlKS8vLwyTINAQrkCAKD0KFeAF1qyZEmxX3vgwAHt3r27DNMgkOQvV5GRkYqLizMXBgAAH0O5AryMy+Uq8T4q9l3BHbKzs5WSkmLNzZs3V1AQf00AAFBc/K0JeBmHw6FatWqV6HNK+nqgKLt27VJubq41syQQAICSoVwBXqh///7Ffm3Pnj1Vs2bNMkyDQMF+KwAArgzlCvBCw4cPV1hYWLFeO2LEiDJOg0DBMewAAFwZyhXghRo2bKg5c+YoODj4kq8bM2aMbrvtNg+lgr/jzhUAAFeGcgV4qXvvvVcLFixQ27ZtL/qa66+/3oOJ4O/yl6vw8HA1aNDAYBoAAHwP5QrwYr1799aGDRu0adMmzZgxQw8++KDtz5OSkgwlg7/Jzc3Vjh07rLlp06YKCQkxmAgAAN9DuQJ8wLXXXqvBgwcrKSlJlStXtq5//fXXOnTokMFk8Be7d+9WTk6ONbds2dJgGgAAfBPlCvAh5cqV08MPP2zNeXl5mjFjhrlA8BvstwIA4MpRrgAfM2zYMNs8Y8YM2x0HoDQoVwAAXDnKFeBjmjRpohtuuMGajxw5oq+//tpgIvgDjmEHAODKUa4AHzR8+HDbPHXqVENJ4C/y37kKDQ1Vo0aNDKYBAMA3Ua4AH3Trrbeqbt261rxkyRIlJycbTARflpeXp+3bt1tzkyZNFBoaajARAAC+iXIF+KCQkBA9+uijtmuJiYmG0sDX7d27V1lZWdbMkkAAAErHK8rVe++9p7i4OEVERKh9+/Zav379RV/bo0cPORyOQh+33HKL9ZqHH3640J/feOONnvhWAI8ZPHiw7TlEc+bMUWZmpsFE8FUFD7PgGHYAAErHeLmaN2+eRo4cqRdeeEGbN29Wq1at1KdPHx09erTI13/55Zc6cuSI9fHzzz8rODhYd999t+11N954o+11n3zyiSe+HcBjatWqpbvuusuaMzIyNHfuXIOJ4Ks4KRAAAPcwXq4mT56sIUOGaODAgWrRooWSkpJUrlw5zZ49u8jXV6lSRTVr1rQ+vv/+e5UrV65QuQoPD7e9Lv+DVwF/kZCQYJunTp0ql8tlKA18FeUKAAD3MFqusrOztWnTJvXu3du6FhQUpN69e2vNmjXFeo9Zs2bp3nvvVfny5W3Xly5dqurVq6tp06ZKSEjQiRMnLvoeWVlZysjIsH0AvqB79+5q3ry5NW/ZskVr1641mAi+KH+5Cg4OVnx8vME0AAD4LqPl6vjx48rLy1ONGjVs12vUqKHU1NTLfv769ev1888/a/DgwbbrN954o/7+979r0aJFGj9+vJYtW6abbrpJeXl5Rb7Pa6+9pujoaOsjNja29N8U4EEOh6PQsewcbIGScDqdtpMm4+PjFRYWZjARAAC+y/iywCsxa9YsXXXVVbruuuts1++9917dfvvtuuqqq9S3b19988032rBhg5YuXVrk+4wdO1bp6enWx8GDBz2QHnCPBx980Hbndt68eTp+/LjBRPAl+/fv17lz56yZJYEAAJSe0XIVExOj4OBgpaWl2a6npaWpZs2al/zczMxMffrppxo0aNBlv07Dhg0VExOjXbt2Ffnn4eHhioqKsn0AviI6Olr333+/NWdnZ190zyJQEPutAABwH6PlKiwsTG3atNGiRYusa06nU4sWLVLHjh0v+bn/+Mc/lJWVpQceeOCyX+fQoUM6ceKEatWqdcWZAW9U8GCLpKQkOZ1OQ2ngSziGHQAA9zG+LHDkyJGaMWOG5syZo+TkZCUkJCgzM1MDBw6UJA0YMEBjx44t9HmzZs1S3759VbVqVdv1s2fPatSoUVq7dq327dunRYsW6Y477lDjxo3Vp08fj3xPgKe1bt1anTp1sua9e/fqu+++M5gIvoI7VwAAuE/I5V9Stvr166djx45p3LhxSk1NVevWrbVgwQLrkIsDBw4oKMjeAXfs2KGVK1dq4cKFhd4vODhYP/74o+bMmaPTp0+rdu3a+uMf/6hXXnlF4eHhHvmeABOGDx+u1atXW/PUqVN10003GUwEX5C/XAUFBalJkyYG0wAA4NscLh6KU0hGRoaio6OVnp7O/iv4jKysLNWtW9c6zMLhcGjPnj2Ki4szGwxey+VyKSoqSmfPnpX020mBO3fuNJwKAADvUpJuYHxZIAD3CA8Ptx3w4nK5NG3aNIOJ4O0OHjxoFSuJJYEAAFwpyhXgR4YOHSqHw2HNs2bNUlZWlsFE8GbstwIAwL0oV4AfadCggW2f1bFjx/TFF18YTARvRrkCAMC9KFeAnxk+fLhtnjp1qqEk8HYcww4AgHtRrgA/c+ONN9oOsVi1apV+/PFHc4HgtfKXK4fDoaZNmxpMAwCA76NcAX4mODhYw4YNs11LTEw0lAbeyuVy2cpVgwYNVK5cOYOJAADwfZQrwA898sgjCgsLs+YPP/xQGRkZBhPB2xw5ckTp6enWzH4rAACuHOUK8EPVqlXT3Xffbc2ZmZn68MMPDSaCt9m2bZttplwBAHDlKFeAnyrqYAueGY7fcVIgAADuR7kC/FTHjh3VqlUra/7ll1+0YsUKg4ngTShXAAC4H+UK8FMOh0MJCQm2axzLjt8VLFfNmzc3lAQAAP9BuQL82P3336+KFSta8xdffKHU1FSDieANXC6Xbc9V/fr1VaFCBYOJAADwD5QrwI9VqFBBDz30kDXn5uZq1qxZBhPBGxw9elSnTp2yZpYEAgDgHpQrwM8VXBo4bdo05ebmGkoDb8B+KwAAygblCvBzLVq0UPfu3a354MGDmj9/vsFEMI1j2AEAKBuUKyAAFHUsOwIXd64AACgblCsgAPTt21c1a9a05oULF2rXrl0GE8EkTgoEAKBsUK6AABAWFqYhQ4bYriUlJRlKA9Pyl6s6deooOjraYBoAAPwH5QoIEEOGDFFQ0H//kZ89e7bOnz9vMBFMOHbsmI4dO2bNLVu2NJgGAAD/QrkCAkRsbKxuv/12az516pTmzZtnMBFMSE5Ots3stwIAwH0oV0AAKXiwRWJioqEkMIXDLAAAKDuUKyCAXH/99YqPj7fm9evXa+PGjQYTwdMoVwAAlB3KFRBAgoKCNGzYMNs17l4FloLPuOKkQAAA3IdyBQSYhx9+WBEREdb8ySef6NSpUwYTwZPy37mqWbOmqlSpYjANAAD+hXIFBJgqVaqof//+1nz+/HnNmTPHYCJ4ysmTJ5WammrNLAkEAMC9KFdAACp4sMXUqVPldDoNpYGnFDwpkGPYAQBwL8oVEIDatm2rtm3bWnNKSooWL15sMBE8gcMsAAAoW5QrIEAVdfcK/o1yBQBA2aJcAQGqX79+qly5sjX/85//1KFDhwwmQlmjXAEAULYoV0CAKleunAYOHGjNeXl5mjFjhsFEKGv5j2GvVq2aYmJiDKYBAMD/UK6AAFbwmVfTp09XTk6OoTQoS+np6Tp8+LA1c9cKAAD3o1wBASw+Pl433HCDNaempuqrr74yFwhlpuBJgZQrAADcj3IFBLiCB1skJiYaSoKyVHC/FcewAwDgfpQrIMDdeuutqlu3rjUvWbKk0F0O+D4OswAAoOxRroAAFxISokcffdR2jbtX/odyBQBA2aNcAdDgwYMVEhJizXPmzNHZs2cNJoK75S9XVapUUfXq1Q2mAQDAP1GuAKhWrVq66667rDkjI0OffPKJwURwp7Nnz2r//v3W3KJFCzkcDoOJAADwT5QrAJIKH2wxdepUuVwuQ2ngTpwUCACAZ1CuAEiSunXrZvuhe8uWLVq7dq3BRHAX9lsBAOAZlCsAkiSHw6GEhATbtalTpxpKA3fiGHYAADyDcgXA8uCDD6p8+fLW/Nlnn+n48eMGE8EduHMFAIBnUK4AWKKjo/XAAw9Yc3Z2tmbPnm0wEdwhf7mKjo5WrVq1DKYBAMB/Ua4A2BRcGpiUlKS8vDxDaXClzp07p71791ozJwUCAFB2KFcAbFq1aqVOnTpZ8969e/Xdd98ZTIQrsX37dtupjywJBACg7FCuABRS8Fj2xMREQ0lwpdhvBQCA51CuABTy5z//WTExMdY8f/587du3z1wglBrlCgAAz6FcASgkPDxcgwYNsmaXy6Vp06YZTITSolwBAOA5lCsARRo6dKjt4INZs2YpKyvLYCKURv5yVaFCBcXGxhpMAwCAf6NcAShSgwYNdPPNN1vzsWPH9MUXXxhMhJK6cOGCdu/ebc2cFAgAQNmiXAG4qIIHW0ydOtVQEpTGzp075XQ6rZklgQAAlC3KFYCL6tOnj+Li4qx51apV+vHHH80FQols27bNNlOuAAAoW5QrABcVHBysYcOG2a5xLLvv4DALAAA8i3IF4JIeeeQRhYWFWfOHH36ojIwMg4lQXJQrAAA8i3IF4JKqVaumu+++25ozMzP14YcfGkyE4spfrsqVK6f69esbTAMAgP+jXAG4rKIOtnC5XIbSoDiys7OVkpJizc2bN1dQEP/KBwCgLPE3LYDL6tixo1q1amXNv/zyi1asWGEwES4nJSVFeXl51sySQAAAyh7lCsBlORwOjmX3Mey3AgDA8yhXAIrlvvvuU8WKFa35iy++UGpqqsFEuBTKFQAAnke5AlAsFSpU0EMPPWTNubm5mjlzpsFEuBSecQUAgOdRrgAUW0JCgm2ePn26cnNzDaXBpeS/cxUeHq4GDRoYTAMAQGCgXAEothYtWqhHjx7WfPDgQc2fP99cIBQpJydHO3futOZmzZopODjYYCIAAAID5QpAiRS8e8XBFt5n9+7dysnJseaWLVsaTAMAQOCgXAEokb59+6pmzZrWvHDhQtvzlGAeh1kAAGAG5QpAiYSFhWnIkCG2a9OmTTOUBkWhXAEAYAblCkCJPfroo7Y9PLNnz9b58+cNJkJ+lCsAAMygXAEosbp16+q2226z5lOnTmnevHkGEyG//Mewh4aGqlGjRgbTAAAQOChXAEpl+PDhtpmDLbxDbm6uduzYYc1NmzZVSEiIwUQAAAQOyhWAUrn++usVHx9vzRs2bNDGjRsNJoIk7d27V1lZWdbMkkAAADyHcgWgVIKCggody56YmGgoDX5XcL8Vx7ADAOA5lCsApfbQQw8pIiLCmufOnatTp04ZTAQOswAAwBzKFYBSq1Klivr372/NFy5c0AcffGAuEChXAAAYRLkCcEUKHmyRmJgop9NpKA3yl6uQkBA1btzYYBoAAAIL5QrAFWnbtq3atWtnzSkpKVq8eLHBRIHL6XQqOTnZmuPj4xUWFmYwEQAAgYVyBeCKFTzYgmPZzdi3b5/tYc4sCQQAwLMoVwCuWL9+/VS5cmVr/vrrr3Xo0CGDiQIT+60AADCLcgXgipUrV04DBw60ZqfTqRkzZhhMFJgoVwAAmEW5AuAWw4YNs83Tp09XTk6OoTSBiWdcAQBgFuUKgFvEx8frhhtusObU1FR99dVX5gIFoPzlKigoSE2aNDGYBgCAwEO5AuA2RR3LDs9wuVy2ctW4cWOFh4cbTAQAQOChXAFwm1tvvVV169a15iVLltiOBkfZOXDggDIzM62Z/VYAAHge5QqA24SEhGjo0KG2a9y98gwOswAAwDyvKFfvvfee4uLiFBERofbt22v9+vUXfW2PHj3kcDgKfdxyyy3Wa1wul8aNG6datWopMjJSvXv3VkpKiie+FSDgDR48WCEhIdY8Z84cnT171mCiwEC5AgDAPOPlat68eRo5cqReeOEFbd68Wa1atVKfPn109OjRIl//5Zdf6siRI9bHzz//rODgYN19993WayZMmKC3335bSUlJWrduncqXL68+ffrowoULnvq2gIBVs2ZN3XXXXdackZGhTz75xGCiwEC5AgDAPIfL5XKZDNC+fXu1a9dO7777rqTfno8TGxurxx9/XM8+++xlP3/KlCkaN26cjhw5ovLly8vlcql27dp6+umn9cwzz0iS0tPTVaNGDX3wwQe69957C71HVlaWsrKyrDkjI0OxsbFKT09XVFSUm75TIHAsW7ZMPXr0sObWrVtr8+bNcjgc5kL5uY4dO2rt2rWSJIfDoczMTEVGRhpOBQCA78vIyFB0dHSxuoHRO1fZ2dnatGmTevfubV0LCgpS7969tWbNmmK9x6xZs3TvvfeqfPnykqS9e/cqNTXV9p7R0dFq3779Rd/ztddeU3R0tPURGxt7Bd8VgG7dutnunGzZssX6wR/uV/CkwIYNG1KsAAAwwGi5On78uPLy8lSjRg3b9Ro1aig1NfWyn79+/Xr9/PPPGjx4sHXt988ryXuOHTtW6enp1sfBgwdL+q0AyMfhcCghIcF2berUqYbS+L9ff/1VGRkZ1sySQAAAzDC+5+pKzJo1S1dddZWuu+66K3qf8PBwRUVF2T4AXJkHH3zQuqMsSZ999pmOHz9uMJH/Yr8VAADewWi5iomJUXBwsNLS0mzX09LSVLNmzUt+bmZmpj799FMNGjTIdv33zyvNewJwn+joaD3wwAPWnJ2drdmzZxtM5L+2bdtmmylXAACYYbRchYWFqU2bNlq0aJF1zel0atGiRerYseMlP/cf//iHsrKybD+8SVKDBg1Us2ZN23tmZGRo3bp1l31PAO5VcGlgUlKS8vLyDKXxX9y5AgDAOxhfFjhy5EjNmDFDc+bMUXJyshISEpSZmamBAwdKkgYMGKCxY8cW+rxZs2apb9++qlq1qu26w+HQU089pf/3//6f/vnPf+qnn37SgAEDVLt2bfXt29cT3xKA/9OqVSt16tTJmvfu3avvvvvOYCL/VLBcNWvWzFASAAACW8jlX1K2+vXrp2PHjmncuHFKTU1V69attWDBAutAigMHDigoyN4Bd+zYoZUrV2rhwoVFvufo0aOVmZmpRx99VKdPn1aXLl20YMECRURElPn3A8Bu+PDhWr16tTUnJibq5ptvNpjIvxQ8KTAuLk4VKlQwmAgAgMBl/DlX3qgkZ9kDuLSsrCzVrVvXOszC4XBoz549iouLMxvMT6SmpqpWrVrWfPPNN2v+/PkGEwEA4F985jlXAPxfeHi47XEJLpdL06ZNM5jIv7DfCgAA70G5AlDmhg4dKofDYc0zZ85UVlaWwUT+g3IFAID3oFwBKHNxcXG2fVbHjx/XF198YTCR/+AYdgAAvAflCoBHDB8+3DZPnTrVUBL/UvDOVfPmzQ0lAQAAlCsAHtGnTx81aNDAmletWqWtW7caTOQf8perunXrcggPAAAGUa4AeERwcLCGDh1qu5aYmGgojX84duyYdQqjJLVs2dJgGgAAQLkC4DGPPPKIwsLCrPmjjz5SRkaGwUS+jcMsAADwLpQrAB5TrVo13XPPPdacmZmpDz/80GAi30a5AgDAu1CuAHhUUQdb8Czz0qFcAQDgXShXADyqQ4cOatWqlTX/8ssvWr58ucFEvqvgMeycFAgAgFmUKwAe5XA4Ct294mCL0sl/56pWrVqqXLmywTQAAIByBcDj7rvvPtuR4V988YVSU1MNJvI9J06cUFpamjWzJBAAAPMoVwA8rkKFChowYIA15+bmaubMmQYT+Z7k5GTbTLkCAMA8yhUAIxISEmzztGnTlJubayiN7yl4mAXPuAIAwDzKFQAjWrRooR49eljzoUOHNH/+fHOBfAwnBQIA4H0oVwCMKepYdhQP5QoAAO9DuQJgTN++fVWzZk1rXrhwoVJSUgwm8h35j2GvXr26qlatajANAACQKFcADAoNDdWQIUNs15KSkgyl8R2nT5/Wr7/+as3ctQIAwDtQrgAY9eijjyo4ONia33//fZ0/f95gIu/HSYEAAHgnyhUAo+rWravbb7/dmk+dOqV58+YZTOT92G8FAIB3olwBMK7gsewcbHFpHMMOAIB3olwBMO76669XfHy8NW/YsEEbN240mMi7cecKAADvRLkCYFxQUFChu1eJiYmG0ni//OWqatWqqlatmsE0AADgd5QrAF7h4YcfVmRkpDXPnTtXp06dMpjIO505c0YHDhyw5hYtWsjhcBhMBAAAfke5AuAVKleurHvvvdeaL1y4oA8++MBcIC/FSYEAAHgvyhUArzF8+HDbnJiYKKfTaSiNd2K/FQAA3otyBcBrtG3bVu3atbPmlJQULV682GAi70O5AgDAe1GuAHiVgnevOJbdjmPYAQDwXpQrAF6lX79+qly5sjV//fXXOnTokMFE3iV/uapUqZJq1qxpMA0AAMiPcgXAq0RGRmrgwIHW7HQ6NWPGDIOJvEdmZqb27dtnzZwUCACAd6FcAfA6w4YNs83Tp09XTk6OoTTeY8eOHXK5XNbMfisAALwL5QqA14mPj9cf//hHa05NTdVXX31lLpCX2LZtm22mXAEA4F0oVwC8UkJCgm3mYAtOCgQAwNtRrgB4pVtvvVV169a15qVLlxZ6gG6goVwBAODdKFcAvFJISIiGDh1qu5aYmGgojXfIX64qVqxoK58AAMA8yhUArzV48GCFhIRY85w5c3T27FmDicw5f/689uzZY82cFAgAgPehXAHwWjVr1tRdd91lzRkZGZo7d67BRObs3LlTTqfTmlkSCACA96FcAfBqw4cPt82JiYm248gDBfutAADwfpQrAF6tW7dutiKxZcsWrV271mAiMziGHQAA70e5AuDVHA5HobtXgXgsO3euAADwfpQrAF7vwQcfVPny5a35s88+07Fjxwwm8rz85apcuXKqV6+ewTQAAKAolCsAXi8qKkoPPPCANWdnZ+v99983mMizsrKytGvXLmtu3ry5goL41zcAAN6Gv50B+ISEhATbnJSUpLy8PENpPCslJcX2vbZs2dJgGgAAcDGUKwA+oVWrVurcubM17927V999953BRJ7DfisAAHwD5QqAzyh49ypQDragXAEA4BsoVwB8xp///GfFxMRY87fffqt9+/aZC+QhHMMOAIBvoFwB8Bnh4eEaPHiwNbtcLk2bNs1gIs/If+cqIiJCcXFx5sIAAICLolwB8ClDhw6Vw+Gw5pkzZyorK8tgorKVk5OjnTt3WnOzZs0UHBxsMBEAALgYyhUAnxIXF6ebb77Zmo8fP67PP//cYKKytWvXLuXm5lozSwIBAPBelCsAPmf48OG2OTEx0VCSslfwMAuOYQcAwHtRrgD4nD59+qhBgwbWvGrVKm3dutVgorLDSYEAAPgOyhUAnxMcHKxhw4bZrvnr3SvKFQAAvoNyBcAnDRw4UGFhYdb80UcfKSMjw2CispH/GPawsDA1bNjQYBoAAHAplCsAPqlatWq65557rDkzM1MffvihwUTul5ubqx07dlhz06ZNFRISYjARAAC4FMoVAJ9V8GCLqVOnyuVyGUrjfnv27FF2drY1syQQAADvRrkC4LM6dOig1q1bW/Mvv/yi5cuXmwvkZuy3AgDAt1CuAPgsh8OhhIQE2zV/OtiCY9gBAPAtlCsAPu2+++5TVFSUNX/xxRdKTU01mMh9uHMFAIBvoVwB8GkVKlTQQw89ZM25ubmaOXOmwUTuk79chYSEqHHjxgbTAACAy6FcAfB5BZcGTps2Tbm5uYbSuEdeXp6Sk5OtuUmTJgoNDTWYCAAAXA7lCoDPa968uXr06GHNhw4d0vz5880FcoN9+/bpwoUL1sySQAAAvB/lCoBfKOpYdl/GfisAAHwP5QqAX+jbt69q1qxpzQsXLlRKSorBRFeGcgUAgO+hXAHwC6GhoRoyZIjtWlJSkqE0V45j2AEA8D2UKwB+49FHH1VwcLA1v//++zp//rzBRKWXv1wFBwcrPj7eYBoAAFAclCsAfqNu3bq6/fbbrfnUqVOaN2+ewUSl43Q6bScFNm7cWOHh4QYTAQCA4qBcAfAr/nCwxcGDB5WZmWnN7LcCAMA3UK4A+JVevXrZltBt2LBBGzduNJio5LZt22abKVcAAPgGyhUAvxIUFFToocKJiYmG0pQOJwUCAOCbKFcA/M7DDz+syMhIa547d65OnTplMFHJUK4AAPBNlCsAfqdy5crq37+/NV+4cEEffPCBuUAllL9cBQUFqWnTpgbTAACA4qJcAfBLRS0NdDqdhtIUn8vlspWrhg0b2u7CAQAA70W5AuCX2rZtq3bt2llzSkqKFi9ebDBR8Rw+fFhnzpyxZpYEAgDgOyhXAPyWLx7Lzn4rAAB8F+UKgN/q16+fKleubM1ff/21Dh06ZDDR5XEMOwAAvotyBcBvRUZGauDAgdbsdDo1ffp0g4kujztXAAD4LsoVAL82bNgw2zxjxgzl5OQYSnN5BctVs2bNDCUBAAAlRbkC4Nfi4+P1xz/+0ZpTU1P11VdfmQt0CQVPCoyLi1P58uUNJgIAACVBuQLg93zlYIvU1FSdPn3amlu2bGkuDAAAKDHKFQC/d8stt6hu3brWvHTp0kLL77wB+60AAPBtxsvVe++9p7i4OEVERKh9+/Zav379JV9/+vRpPfbYY6pVq5bCw8PVpEkTffvtt9afv/jii3I4HLYP9iwAgS0kJERDhw61XUtKSjKU5uIoVwAA+Daj5WrevHkaOXKkXnjhBW3evFmtWrVSnz59dPTo0SJfn52drRtuuEH79u3T559/rh07dmjGjBmqU6eO7XUtW7bUkSNHrI+VK1d64tsB4MUGDx6skJAQa54zZ47Onj1rMFFhHMMOAIBvM1quJk+erCFDhmjgwIFq0aKFkpKSVK5cOc2ePbvI18+ePVsnT57UV199pc6dOysuLk7du3dXq1atbK8LCQlRzZo1rY+YmBhPfDsAvFjNmjX1pz/9yZozMjI0d+5cg4kKK3jnqnnz5oaSAACA0jBWrrKzs7Vp0yb17t37v2GCgtS7d2+tWbOmyM/55z//qY4dO+qxxx5TjRo19Ic//EGvvvqq8vLybK9LSUlR7dq11bBhQ91///06cODAJbNkZWUpIyPD9gHA/yQkJNjmqVOnyuVyGUpj53K5bHeuYmNjVbFiRYOJAABASRkrV8ePH1deXp5q1Khhu16jRg2lpqYW+Tl79uzR559/rry8PH377bd6/vnn9cYbb+j//b//Z72mffv2+uCDD7RgwQIlJiZq79696tq1q86cOXPRLK+99pqio6Otj9jYWPd8kwC8Srdu3WxL7bZu3aq1a9caTPRfx44d08mTJ62ZJYEAAPge4wdalITT6VT16tU1ffp0tWnTRv369dNf//pX28b0m266SXfffbeuvvpq9enTR99++61Onz6tzz777KLvO3bsWKWnp1sfBw8e9MS3A8DDHA6H1x7LXnBJIMewAwDge4yVq5iYGAUHBystLc12PS0tTTVr1izyc2rVqqUmTZooODjYuta8eXOlpqYqOzu7yM+pVKmSmjRpol27dl00S3h4uKKiomwfAPzTgw8+aHsw72effaZjx44ZTPQbTgoEAMD3GStXYWFhatOmjRYtWmRdczqdWrRokTp27Fjk53Tu3Fm7du2S0+m0ru3cuVO1atVSWFhYkZ9z9uxZ7d69W7Vq1XLvNwDAJ0VFRemBBx6w5uzs7IseouNJlCsAAHyf0WWBI0eO1IwZMzRnzhwlJycrISFBmZmZGjhwoCRpwIABGjt2rPX6hIQEnTx5Uk8++aR27typ+fPn69VXX9Vjjz1mveaZZ57RsmXLtG/fPq1evVp33nmngoOD1b9/f49/fwC8U8GDLaZNm1boYBxPK3gMOycFAgDge0Iu/5Ky069fPx07dkzjxo1TamqqWrdurQULFliHXBw4cEBBQf/tf7Gxsfruu+80YsQIXX311apTp46efPJJjRkzxnrNoUOH1L9/f504cULVqlVTly5dtHbtWlWrVs3j3x8A79SqVSt17txZq1atkiTt3btX3333nW6++WZjmfLfuapdu7YqVapkLAsAACgdh8tbziH2IhkZGYqOjlZ6ejr7rwA/NXfuXN1///3WfMstt+ibb74xkuX48eO2XwD17t1b33//vZEsAADAriTdwKdOCwQAd/nTn/5kKzTffvut9u3bZyRLcnKybWa/FQAAvolyBSAghYeHa9CgQdbscrk0bdo0I1k4hh0AAP9AuQIQsIYOHSqHw2HNM2fOVFZWlsdzcFIgAAD+gXIFIGDFxcXplltusebjx4/r888/93iOguWKkwIBAPBNlCsAAa3gseyJiYkez5C/XNWoUUNVq1b1eAYAAHDlKFcAAlqfPn3UoEEDa161apW2bt3qsa9/+vRp/frrr9bMkkAAAHwX5QpAQAsODtawYcNs1zx594r9VgAA+A/KFYCA98gjjyg8PNyaP/roI2VkZHjka1OuAADwH5QrAAEvJiZGd999tzVnZmbqww8/9MjXplwBAOA/KFcAIGn48OG2eerUqXK5XGX+dXnGFQAA/oNyBQCSOnTooNatW1vzL7/8ouXLl5f5181frmJiYlStWrUy/5oAAKBsUK4AQJLD4Sjy7lVZysjI0MGDB62ZJYEAAPg2yhUA/J/77rtPUVFR1vzll18qNTW1zL5ecnKybaZcAQDg2yhXAPB/ypcvr4ceesiac3NzNXPmzDL7ehxmAQCAf6FcAUA+CQkJtnnatGnKzc0tk69FuQIAwL9QrgAgn+bNm6tnz57WfOjQIX3zzTdl8rUoVwAA+BfKFQAUUPDuVWJiYpl8nfzlqnLlyqpZs2aZfB0AAOAZlCsAKKBv3762orNw4UKlpKS49WtkZmZq37591tyiRQs5HA63fg0AAOBZlCsAKCA0NFSPPvqo7VpSUpJbv8b27dttM0sCAQDwfZQrACjCkCFDFBwcbM3vv/++zp8/77b337Ztm22mXAEA4PsoVwBQhLp16+r222+35lOnTmnevHlue38OswAAwP9QrgDgIoYPH26bp06d6rb3plwBAOB/KFcAcBG9evVSkyZNrHnDhg3asGGDW947f7mKiopSnTp13PK+AADAHMoVAFxEUFCQhg0bZrvmjmPZz58/rz179lgzJwUCAOAfKFcAcAkPP/ywIiMjrfmTTz7RqVOnrug9d+zYIZfLZc0sCQQAwD9QrgDgEipXrqz+/ftb84ULF/TBBx9c0Xuy3woAAP9EuQKAyyh4sEViYqKcTmep349j2AEA8E+UKwC4jDZt2qhdu3bWnJKSokWLFpX6/bhzBQCAf6JcAUAxFHX3qrTyl6vy5csrNja21O8FAAC8B+UKAIqhX79+qly5sjV//fXXOnToUInfJysrS7t27bLm5s2bKyiIfxUDAOAP+BsdAIohMjJSjzzy/9u777Aorq8P4N+lCCpgV7AXROwFFXswsUVAxS7G3mLvFNHYsbdoomLF3rBgN2rsgr0iYhdRxIY0peye9w/fnbCW/DQiC/j9PE+eZGfubs7uMOXM3Htud+W1RqOBj4/PF39OSEiIznitsmXLpkh8REREpH9MroiIPlOfPn10Xi9ZsgSJiYlf9Bkcb0VERJRxMbkiIvpMJUuWRKNGjZTX4eHh2L59+xd9BpMrIiKijIvJFRHRF3i/sMWff/75Re9nckVERJRxMbkiIvoCjo6OOtX9jhw58kHC9G+Sz3GVOXNmFClSJEXjIyIiIv1hckVE9AWMjIzQu3dvnWWLFi36rPcmJCTg1q1bymtbW1sYGhqmaHxERESkP0yuiIi+UM+ePWFkZKS89vX1RUxMzP983+3bt5GUlKS8ZpdAIiKijIXJFRHRF7K0tESrVq2U11FRUVi3bt3/fN/73QdZhp2IiChjYXJFRPQffKywhYj863tYzIKIiChjY3JFRPQf1K1bV+fJ0+XLlxEQEPCv72FyRURElLExuSIi+g9UKhX69u2rs+x/lWVPnlyZmJigWLFi3yQ2IiIi0g8mV0RE/1GnTp2QNWtW5fWmTZvw7Nmzj7ZNSkrCzZs3ldelSpXSKYpBRERE6R+TKyKi/8jCwgKdOnVSXickJGD58uUfbXvnzh0kJCQor9klkIiIKONhckVE9BXe7xq4ePFiqNXqD9pxvBUREVHGx+SKiOgrVKhQAbVr11Ze37t3D/v37/+gHZMrIiKijI/JFRHRV/pYWfb3cY4rIiKijI/JFRHRV2rVqhXy5MmjvN6zZw/u3bun0yZ5cmVsbIwSJUqkWnxERESUOphcERF9JRMTE/To0UN5LSLw8fFRXqvVagQHByuvbWxsYGxsnKoxEhER0bfH5IqIKAX06dMHKpVKeb106VLEx8cDeDcO6+3bt8o6jrciIiLKmJhcERGlgKJFi8LR0VF5/fz5c2zZsgUAi1kQERF9L5hcERGlkE8VtmByRURE9H1gckVElEIaN26MYsWKKa9PnTqFy5cvM7kiIiL6TjC5IiJKIQYGBvj11191li1cuFAnuTI0NISNjU1qh0ZERESpQCUiou8g0pqoqChky5YNr1+/hoWFhb7DIaJ05Pnz5yhYsKBSzCJr1qzQaDR48+YNAMDW1hY3btzQZ4hERET0Bb4kN+CTKyKiFJQ7d260bdtWeR0bG6skVgBQvHhxfYRFREREqYDJFRFRCmvTps0n1+3duxe//fYbNBpNKkZEREREqYHJFRFRCnr16hVGjBjxyfUigokTJ8LDwyMVoyIiIqLUwOSKiCgFTZ48GSEhIf+z3YwZM3D16tVUiIiIiIhSC5MrIqIU8ubNGyxfvvyz22vnwSIiIqKMgckVEVEKuXDhAl69evXZ7f/6669vGA0RERGlNiZXREQpJCYm5pu2JyIiorSNyRURUQrJly/fF7W3tLT8RpEQERGRPjC5IiJKIRUrVkSpUqU+u32HDh2+YTRERESU2phcERGlEJVKhSFDhnxWWzMzM3Tv3v3bBkRERESpiskVEVEK6t27Nzp27PivbYyNjbF+/XrkyZMnlaIiIiKi1MDkiogoBRkYGMDX1xfe3t4fTZ6qVauGQ4cOwcnJSQ/RERER0bekEhHRdxBpTVRUFLJly4bXr1/DwsJC3+EQUToVHx+PPXv24P79+8iUKRNq1qyJKlWq6DssIiIi+gJfkhsYpVJMRETfHRMTE7i4uOg7DCIiIkol7BZIRERERESUAphcERERERERpQAmV0RERERERCmAyRUREREREVEKYHJFRERERESUAphcERERERERpQAmV0RERERERCmAyRUREREREVEKYHJFRERERESUAphcERERERERpQAmV0RERERERCmAyRUREREREVEKYHJFRERERESUAvSeXP3xxx8oWrQoTE1NYW9vjzNnzvxr+8jISPTv3x9WVlYwMTGBjY0N9uzZ81WfSURERERE9LX0mlxt3LgRw4YNw9ixY3HhwgVUrFgRjRs3RkRExEfbJyQkoGHDhrh//z62bNmCmzdvYsmSJShQoMB//kwiIiIiIqKUoBIR0df/3N7eHtWqVcOCBQsAABqNBoUKFcLAgQPh4eHxQftFixZhxowZCA4OhrGxcYp85sdERUUhW7ZseP36NSwsLP7jtyMiIiIiovTuS3IDvT25SkhIwPnz59GgQYN/gjEwQIMGDXD69OmPvsff3x81a9ZE//79kS9fPpQrVw7e3t5Qq9X/+TMBID4+HlFRUTr/EBERERERfQm9JVfPnz+HWq1Gvnz5dJbny5cP4eHhH33P3bt3sWXLFqjVauzZswdjxozBrFmzMGnSpP/8mQAwZcoUZMuWTfmnUKFCX/ntiIiIiIjoe6P3ghZfQqPRIG/evPDx8YGdnR3atWsHLy8vLFq06Ks+19PTE69fv1b+CQ0NTaGIiYiIiIjoe2Gkr/9x7ty5YWhoiKdPn+osf/r0KSwtLT/6HisrKxgbG8PQ0FBZVrp0aYSHhyMhIeE/fSYAmJiYwMTE5Cu+DRERERERfe/09uQqU6ZMsLOzw6FDh5RlGo0Ghw4dQs2aNT/6ntq1a+P27dvQaDTKspCQEFhZWSFTpkz/6TOJiIiIiIhSgl67BQ4bNgxLliyBr68vbty4gb59+yI2NhbdunUDAHTu3Bmenp5K+759++Lly5cYPHgwQkJCsHv3bnh7e6N///6f/ZlERERERETfgt66BQJAu3bt8OzZM/z2228IDw9HpUqVsG/fPqUgxcOHD2Fg8E/+V6hQIezfvx9Dhw5FhQoVUKBAAQwePBju7u6f/ZlERERERETfgl7nuUqrOM8VEREREREB6WSeKyIiIiIiooyEyRUREREREVEK0OuYq7RK21MyKipKz5EQEREREZE+aXOCzxlNxeTqI6KjowG8K6BBREREREQUHR2NbNmy/WsbFrT4CI1Gg8ePH8Pc3BwqlUrf4aSoqKgoFCpUCKGhoSzWkc5w26Vf3HbpF7dd+sTtln5x26VfGXnbiQiio6ORP39+nUrmH8MnVx9hYGCAggUL6juMb8rCwiLD/eF/L7jt0i9uu/SL2y594nZLv7jt0q+Muu3+1xMrLRa0ICIiIiIiSgFMroiIiIiIiFIAk6vvjImJCcaOHQsTExN9h0JfiNsu/eK2S7+47dInbrf0i9su/eK2e4cFLYiIiIiIiFIAn1wRERERERGlACZXREREREREKYDJFRERERERUQpgckVERERERJQCmFwRERERERGlACZX9M1pNJoPlkVHR+shEiIiIiKib4fJFX1zBgYGePDgAebOnQsA2Lx5Mzp37ozXr1/rNzD6pjjLQ+p5/wYGf3uitEO7P7569Qrx8fF6joZS28eOx9/jMfp7+s5MruibS0pKwsKFC7FixQp06dIF7dq1Q/PmzZEtWzZ9h0Yp6P0LfJVKpadIvi8ajQYGBu8O5cePH0dSUhJ/+3RIe+GhVqvx9u1bPUdDKUVEoFKpsGvXLvTp0wfHjh1DQkKCvsOiVKLRaJTjcWhoKO7evYuEhITv5hj95MkT3Lp1C8D3dU3A5Iq+OSMjI4wdOxZFihTB6tWr0bZtW3Tt2hXAuwsJSv9ERLnAX7JkCYYMGYKZM2ciODhYz5FlbMl/9zFjxqBz587YtGnTR7viUtqlvQDfs2cPunTpgqpVq2L06NHYuXOnvkOjr6RSqbBt2za0b98e5cuXR4kSJZApUyZ9h0WpIPnxedy4cXByckKDBg1Qrlw5LF++HC9evNBzhN/W27dv4eDggGHDhuHmzZv6DidVMbmib0p7NzZTpkzInj07GjZsiEePHmHKlCkAAENDQyZY6VzyO3Oenp4YNWoUrl69ijVr1qBDhw4ICAjQc4QZl/Z3HzNmDHx8fODr64sGDRooJ3RKH1QqFfz9/dGmTRsULVoUw4YNw7Fjx+Dm5oZLly7pOzz6CiEhIRg2bBjmzp2LMWPGoFixYlCr1bh27RoiIiL0HR59Q9rj8+TJk7Fw4UJ4e3sjJCQEBQoUwMSJEzP89jc1NYWPjw/Onz+PCRMmfFc3W3kGpm9Gezf2/PnzCAsLg6+vLzZu3IjKlStjx44dOgkWADx//lyf4dJ/pL2Qv3XrFqKiorB//34cOnQIf/zxB2xsbPDLL78wwfqGHjx4gL1792L58uWoV68eDA0Nce3aNUycOBHHjx9HVFSUvkOk/+H58+eYOXMmvL29MWnSJLi6uuLGjRto2rQpKlWqpO/w6CvEx8cjZ86cqFatGmJjYzFv3jz8+OOPaNq0KZydnb+7O/rfExFBVFQUDh06hFmzZsHR0RF//fUXLly4AHd3d5QuXTrD9jLQaDTQaDT44YcfsGXLFhw4cAATJ078bhIsJlf0TWgTq23btqFp06aYP38+Xrx4gezZs8PLywvVqlWDv78/vL29AQC//fYb+vbty8G+6dTmzZvRsGFDnD17FgULFgQA1K5dGyNHjkSVKlXQqVMnJljfyNu3bxESEgIjIyMEBgbC09MTHTt2xOLFi/HLL7/g5MmTAL6vwcTpjampKeLi4uDo6Ih79+7B2toaLi4umDVrFgDg4MGDuHfvnp6jpE/52L51/vx53Lt3DyqVCi9fvsTUqVNRunRpHD16FD/++CPmz5+P2NhYHDt2TA8RU2p5+/YtQkND0bhxYxw+fBht27bFtGnT8OuvvyIuLg7z589HWFiYvsNMMaGhoQgKCkJSUpJy47VWrVrw8/PDgQMHMH78+O8jwRKib2TPnj2SOXNmWbZsmTx79kxn3dOnT2XEiBFSokQJKV26tOTMmVMCAgL0FCl9rS1btkijRo3E3Nxcbt68qbPu7Nmz0r59ezEzM5Nr167pKcKMQa1Wf3R5ly5dJGvWrJI1a1YZPHiw7Nq1S0REKlasKGPGjEnNEOkzaTQa5d+PHj2SMmXKyMqVK8Xa2lp69uwpSUlJIiJy584d6dChg+zfv1+f4dK/ePr0qYi82z/VarXcunVLrKys5MqVKyIi4u/vL25ubjJx4kR5+PCh8r46derIihUr9BEyfQPaffp9jRo1kh9//FHMzMxk2bJlyvKHDx9KnTp1ZOPGjakV4jcVGhoqhoaGolKppHXr1tKvXz85ffq0REREiIjI+fPnJU+ePNKxY8cMfy3A5Iq+ifj4eOnSpYuMHDlSRERiYmIkKChIRo0aJUuWLJHHjx9LdHS0HDhwQObPny+3bt3Sc8T0uT51gb9//36pWbOmVKtWTW7cuKGz7uTJkzJmzBjlgpG+XPLf3c/PTxYuXCgTJkyQ58+fi1qtlqNHj8rZs2d13uPg4CC///57aodK/0J7AfbmzRsREWWf8PLyEpVKJc2aNdNpP2rUKClfvrzORTmlHZs2bZISJUrIhQsXlGURERFSunRpCQsLU7Z3YmKisl6tVouXl5cUKFBA7t69m+oxU8pLfnwODQ2V8PBw5fWqVaukWLFi0rhxY2VZTEyMNG3aVOrXr5/uz4vav/GrV69KzZo1RaVSiaenp9SuXVtKliwp+fLlk8GDB8uBAwdk165dkiNHDhk4cKBcunRJz5F/OyoR9hWhlJeYmIiGDRsib968mD9/PsaMGYNbt27h8ePHeP36Ndq3b6/Me0XpR/Ky30ePHkV8fDySkpLQtGlTAO+6L82YMQPR0dFYsWIFSpUq9cFnqNVqZZwdfTk3Nzds2rQJtra2iImJQVBQENatW4cmTZoAAGJjY/HgwQO4u7vj4cOHOH/+PIyMjPQcNQH/dJc+cOAAli1bhujoaGTOnBl//PEHjIyM4ObmhrVr12LmzJlITEzEnTt3sHr1ahw/fhwVK1bUd/j0EXv27MGCBQvw6tUr/Pnnn6hcuTJCQkLg7OyMwMBAZM+eHcA/x85ly5bh2LFjOHDgAPbs2YPKlSvr9wtQivLy8sKePXvw8OFD9OvXD127dkXBggUxYcIEbN68GTly5ECJEiVw//59xMbG4ty5czA2Nk7X58W3b9/C1NQUiYmJuHHjBvr06QONRoO///4bL1++xObNmxEQEIA9e/bAwcEB+/fvR1JSEgYMGICZM2dmzOqZek7uKIP42OPwXbt2Sfbs2cXMzExatmwp69atExGRKVOmiL29vXLnltKfESNGSP78+aV48eKSOXNmady4sXLndv/+/dK4cWOpU6dOhn/0n9pWr14tlpaWyh2/AwcOiEqlkh07dojIu/1w69at8sMPP4iDg4MkJCSIiKT7O6MZyfbt2yVLlizi5eUlK1asEDs7OylQoIA8evRIQkNDZezYsVK6dGmpXr26tG3bVq5evarvkOl/OHjwoDRr1kyqVq0qFy5ckNu3b4ulpeUH3eFFRI4ePSr9+vWT4OBgPURKKUmj0eg8sfL19ZUCBQrIqlWrZNKkSVK4cGHp2LGj3LhxQ+Lj4+XQoUPSpUsXGTBggEybNk15mpn8qWZ68+TJE7GyspIjR46IyLvvcuXKFSldurRUqVJFoqKilOVPnz6VLVu2yKBBg6Ry5cpy/fp1fYb+TfHJFX01+f+7sSdPnsTx48fx7NkzNGjQAD///DMeP36Mu3fvok6dOkq7wYMH48mTJ1i1ahVMTU31HT59oSVLlsDLywt79+5F3rx5ERsbCxcXF+TJkwcrVqxAiRIlsHv3bowbNw52dnZYtGiRvkPOMKZMmYKnT59i7ty52LhxI3r16oXp06fj119/RXR0NLJmzYqoqCicOnUKjRs3hqGhIZKSkvjkKo2IjIxEs2bN4OzsjJEjRyIsLAx16tRBgwYNsGTJEqVdREQE8ubNq9wRprRJu289ffoU586dg4+PDyIiItCnTx+sWbMGTZs2RbFixaDRaJCYmIi3b9+iSpUqKFu2bLp9SkEfFxAQAD8/P1SqVAkdO3YEAOzduxdubm4oX7483N3dP/r0OT0/sQKAx48fo2/fvjh8+DD279+PWrVqQa1WIygoSPkdTp48CXNzc533xcbGImvWrPoIOXXoN7ejjMLPz09y5colzs7O0r17d1GpVOLh4SFv375V2ly+fFk8PT0lW7ZscvnyZT1GS59rx44dEhMTo7Ns0KBB0q5dOxH554nI06dPpVChQtK5c2el3enTpz85Pov+mx49esgvv/wihw8fFnNzc/nzzz+VdVOmTBEvLy+d9nxipV8ajUZ5qp+UlCQxMTFStGhRCQsLk6dPn0qBAgWkd+/eSvu1a9dKfHy8zvspbVu3bp2UKlVK7ty5I/v27ZPmzZtLrly5RKVSiZOTk9jY2EipUqWkfPnyUrZsWT6xygD69u0r27dvF5F3Y60uXrwopqamYmJi8sEY1z179ki5cuWkc+fOcuzYMX2E+82FhobKL7/8IiYmJnLy5EkReXe8u3r1qlSoUEEqVqyoPMHS9qbI6Mc2Jlf01YKDg6VIkSKyePFiEXk3UNPY2Fg8PDyUNpcuXZLOnTtLuXLlMvQgxozE29tbnJycdA6CarVaWrduLT///LOyTNu9c/369VKgQIEPBt4zwfo6s2bNksmTJ4uIyKFDh6Ry5cpiZGSkk1hFR0eLs7OzDBkyRF9h0v9L/veu3Xd27Nghv/32m8THx0ujRo1k2rRpUrhwYfn111+Vi40nT55I8+bNZdu2bfoIm75A8qIkrq6uMmvWLGXdgQMHpF27dlK+fHml25P2byI6Ojr1g6UU9fz5cxk6dKiy32qtW7dO8uTJI61bt/6gYu6+ffskT548Mn78+NQM9ZuJjY2VuLg4nWX3798XV1dXMTExkRMnTojIPwlWlSpVpHDhwt/V3z+TK/pqgYGBUq9ePRERuX379gd3Y7UX22fPnpVHjx7pJUb6b7R9wS9cuCCvXr0SkXcXipkzZxZfX1+dtuvWrZNKlSpJZGRkaoeZYb1580b69u0rLi4uIvLuArxbt25Srlw5mTt3rrx+/VouXLggTZs2lSpVqijbK6PfFUyrtBfRV65ckd27d4uIyMWLF8XS0lJWrFghcXFx0q9fPzE3N5emTZvqvNfDw0PKly8voaGhqR43fbkjR45I9erVpWnTph+MLd2/f784OjpKzZo1lTv5Itwv07v3t5+vr6/Mnz9f53X+/PllyJAhH1RAPn36dIboSRASEiLVq1cXR0dH2bFjh5JIiYi8evVKOnToIJkyZZLjx4+LyLsE6+LFi1K7dm25c+eOvsJOdUyu6ItpDzD79++XwMBAOXXqlBQrVkwCAgKkWLFi0rt3b+UgcuTIEXF0dGRSlc4kPwn4+/tLzpw5ZeHChRIVFSWxsbEyZMgQKVasmPj4+EhsbKw8fvxYHB0dP3jSRV9PO1+ctkvJ3bt3pVu3blKyZEnJmjWrVK5cWerXr8/iFXqmTawuXbokRkZGsmTJErl586bMmDFDhg4dqrQLDw+XevXqib29vXh6esry5culZ8+eki1bNj7VT0dOnTol1tbWYmxsrGy35E8zDh06JPXq1ZMff/xRp3s8ZQwxMTHy008/Sc2aNXXmrlq2bJkUKFBAhg4dKrdv3/7gfen5+PzixQsZNGiQqFQqMTIykvLly0vBggXlp59+Eg8PDwkJCZHz58/LoEGDxMTERM6dOyci775z8u7O3wMmV/SfHD9+XLJmzSqrVq2SZ8+eiZOTk2TJkkU6dOggIv8kYB4eHlK/fv2PVk2itOlj3fg6deoktra24uPjIwkJCRIaGiqenp5iYmIiBQsWlJIlS0qVKlWUiwt2Bfxy/5aU/vLLL+Li4qI8FYyKipKwsDDZvXu3BAUFKb93eq46lZ4lf2KVOXNmGTVqlGg0GrGxsRGVSqU8edR69OiRDBgwQKpWrSpVqlSRVq1asSpgOpOQkCABAQFSokQJqVOnzkcrvx05coTzk2UQHzunPXr0SFq3bi1169aVpUuXKsuXL18uhQsXlm7dumWYG8s3btwQFxcXOXbsmPTu3VuaNWsmHh4ecvXqVRkyZIhUq1ZN8ufPL2XLlpV27dpJ9uzZRaVSfbfj65lc0Re7f/++eHp6KuNAREQWL14sZcqUkS5dusi1a9fk7NmzMnLkSMmePbsySz2lfclPIJs2bZJ9+/Ypr3v06CElSpSQJUuWKHdib9y4IevXr5fdu3crd+R4gf91vL29xcfHR2dS0jVr1kipUqXk3r17IvLxEz0TWv3Q/u43btyQXLlyKcVeRN5NqmlnZyfW1tayf/9+nfclJSVJYmKixMXFfXd3ddMb7Y2PBw8eSFBQkE7XzcDAQClUqJD8+OOPSrv3x+NQ+pZ8/wwKCpInT54oN4xDQ0PFxcXlgwRr/vz50rx58wxzXF6+fLnY29uLyLtjXffu3cXe3l7Wr1+vtDl48KAsW7ZM6tSpI8WKFROVSvXdFnBhckVf5MaNG1KzZk0pUqSIzoB6EZGZM2eKg4ODGBgYSMWKFaVKlSpy8eJF/QRKXyz5kxM3NzcpUaKEzJgxQ2em+a5du0qJEiXEx8dHXr58+cFnpOcuD2mBRqORvn37SoUKFcTGxkaGDRsmQUFBIiJSr1496dKli34DJB3aC6eLFy9K5syZxczMTGxsbOTIkSNKoZegoCApU6aMODo66oxPyCgXXRmd9rjo5+cnxYoVkxIlSoipqan06NFD2Z7aBKtRo0bcrhmIl5eXTnLg6ekpRYoUkeLFi4uLi4ucP39eRN4lWC1atJB69erpdBHU/u1khL8Jb29vsbOzU87xt2/fVhKs5OPORETi4uIkMjJSHj9+rI9Q0wQmV/TFBg8eLDly5JDmzZt/ULwgKipKAgIC5MGDB/L8+XM9RUhfY8qUKZI7d24JCAj46PqePXtKqVKlZM6cORIbG5vK0WUsnzrp3rhxQzZt2iS2trZib28vzs7OMmrUKKlWrZqEhISkcpT0by5fviyGhoYyadIkERGpXbu2FC1aVI4cOaLc8b569aqULl1anJycdAocUPqg7Qb/+++/y7Vr12TNmjVSr149cXJyklOnTonIuwTLzMxMmjVrpudoKSWcPXtWKlWqJPXq1ZOHDx/KiRMnJH/+/LJv3z6ZNWuWtGjRQkqVKiWBgYEi8i7BatWqlZQuXVp27twpIrpTMaRH2htEIiITJkyQBg0aiMg/5y1tglWzZk35448/lLbsvcLkiv6HTx0Y3NzcpEyZMjJ+/HilihylbxqNRl68eCENGzaUFStWiIjIvXv3ZPfu3dKmTRsZNmyYctBs0aKFtGvXLl2fOPQteWJ16tQp2b9/v1JhSSsyMlIOHDggLVu2VPqwL1iwILVDpU+IjY2VFi1ayJgxY3SWfyrBqlChgtStW/eTNy4obdEe37y8vHSmnxB51wWqZs2a0r9/fxF599T+7NmzH1SJo/TL399fGjZsKD/88IOMHTtW5s2bp6w7ffq0tGzZUkqWLKkkWNohExmhB8ejR4+kTZs2cuDAARERGTt2rM78ltrz182bN6V79+5Sq1YtmTlzpt7iTWtUIiL6nsiY0iYRgUqlQmBgIE6ePIlMmTKhWLFicHR0BAAMHz4cR44cQYsWLTBw4EBkz55deQ+lDxqNBgYGBjrLfvzxR5ibm6NPnz5YuHAhXr16hfz582Pfvn1o164dlixZovNebvMvl/w3GzVqFLZu3YqoqCgULVoUJUuWhK+v7wfvOX36NDZs2ICDBw9i7969KFy4cGqHTR/x8OFDZVskJibC2NgYAFCnTh2EhYVh5cqVqFmzJjJlyoRLly7h119/xebNm1GoUCF9hk3/Qrt/ao9xo0ePxtGjR3H48GEYGRkp++6SJUswdOhQ3L17F3nz5tVz1JRS1Go1DA0NAQD+/v5YtGgRTp06hdGjR2PEiBFKu4CAAMyYMQNBQUFYsmQJ6tSp89HPSI/u3r2LX375BdmzZ8ekSZOwZcsWPHr0CKtWrfqgbWxsLDp27AiNRgNfX1/kyJFDDxGnLUyu6KO0Jxc/Pz9069YNlSpVQmRkJG7cuIGBAwdi9uzZAIChQ4fi9OnTcHBwgKenJ7Jly6bnyOlzJU+sdu7cCQsLC/zwww9YvXo1fHx8cOHCBQwZMgRNmjRB3bp1MWHCBFy/fh2rVq2CiYnJB59BX27q1KmYO3cu/Pz8ULVqVYwbNw7Tpk2Ds7MzduzYAQCIj49Xfu+zZ8+iY8eOWLFiBWrXrq3P0L97n7qpkJSUBCMjIwD/JFirVq1C9erVYWJigoSEBGTKlCm1w6UvdPz4cRgbG6NGjRpYtmwZ+vXrhyNHjqBmzZo6bfr06YO//voLBQoU0GO0lFKSn9O0+/LevXsxYcIERERE4MCBAyhRooTS/syZM3Bzc4OlpSU2bNiQoc6Jt2/fxoABA5A1a1Y8ePAAIoJy5crBwMAABgYGiI+Ph0qlQubMmfHkyRMsXLgQBQsW1HfYaYN+HphRWvOxsR+3bt0SKysrpXDFy5cvZcOGDZIlSxYZPny40q53797i4ODAcuvpyPvFK0qWLCl//PGHxMbGytu3byUmJuaDCf8cHBxk0KBBqR1qhnXz5k1p2LCh7NmzR0RE9u7dK2ZmZtK3b18pWLCgtGzZUmmbvA+7ra0tuwamccm3l4ODg1hYWChjrdiVNn1o2LChlClTRnndtm1byZs3rxw/flyioqJERGT48OFSoUKFjxb3ofQn+XXQzJkzZezYscp5cPfu3fLjjz9K3bp1Pzg3Xr9+PUMUrfiY4OBg+fnnn8XMzExy5colv/76qzRq1EgaN24srVq1kmbNmkmTJk3k+vXr+g41TWFyRTpztCQvvR0QECA2NjY6ZWdFRNauXSuZM2eWQ4cOKcuePn2aOsFSivL29pY8efLIiRMnPnpyiIqKkqNHj0rjxo2lQoUKHKj6ld7/jVesWCHh4eFy8uRJKVCggCxevFhERPr06SMqlUpq166t037Dhg2SPXt2uXnzZqrFTP9N8n2lSZMmHIuTxr2f9D58+FDKlCmjjKeLjIyUDh06iKmpqVSsWFHq1KkjOXLkYEXcDGjkyJGSN29eWbp0qYSFhSnL/f39pUGDBlKvXr0PEiyRjFEV8GNu3boljo6O0rBhQ06t85kyxrNL+s+0j7CvXLmCihUr4syZM8q6LFmy4M6dOwgJCQHwrhsMADg4OMDKygpPnjxR2rK/efrz/Plz7N27FzNnzkTt2rXx6NEj/PXXX+jevTtGjx4N4F2Xh6VLl8LY2Bjnzp2DkZERkpKS9Bx5+rNnzx64u7ujffv2uHDhgrK8a9euyJcvH3bv3o2GDRuic+fOAIASJUqgefPmKFWqFNRqtdK+aNGiOHv2LGxsbFL9O9DHySd61iffV/bu3Qtra+vUDIu+kEqlwqFDh+Dv749nz56hUKFC6NSpEwIDA3HlyhVky5YN69atw+rVq9GlSxe4uLjgzJkzqFSpkr5DpxS0e/durF+/Hrt370aPHj2QP39+ZR93dnbG4MGDYWpqCkdHRzx+/FjnvRmlO+D7rK2tMXv2bBgYGGDkyJE4fvy4zvpPHQO/Z0b6DoD0R5tYXbp0CbVq1cKoUaMwZswYZb2trS1+/vln/PHHH8iePTuqVKkCAMidOzdy5syJxMREfYVOKSBbtmwwNjbG4cOHkSNHDixfvhwRERHIkSMHNm/ejDdv3mDWrFnImzcvypYtCwMDA53xJPR5lixZAk9PTzg4OCAsLAx169bFpUuXULJkSaVNSEgIQkNDYWpqisTERAQEBKB+/foYNGgQgH/6/tvb2+vra3z35P/HWN26dQtqtRqZMmVC8eLFdQofvI/7Svrx7Nkz9OrVC6Ghoejbty8aNGiAwYMHY82aNVizZg2mT58OAGjdurWeI6Vv6cmTJyhQoABsbW0/WpTCyckJCQkJOHbsGPLly6enKFOfjY0N5s+fj2HDhsHNzQ1z585VzkcsaPWhjJlm02cxMDDAzZs3UaNGDYwePRqTJk1S1u3atQvx8fHo2bMnnj9/jnHjxmHXrl24fv06xowZgwcPHsDBwUF/wdMX0Wg0HywzNjZGs2bNcPv2bbRp0wa2traYMmUKdu3ahT59+uD58+cAgPLly8PAwAAajYYXi1/Ix8cH/fr1w5IlS7Bx40bs3bsXJUuWxO3btxEfH6+069SpE549ewY7OzvUrl0bwcHB6NevH4B3F/X83fVPpVJhy5Yt+PHHH1G/fn107NgRv//+OwAo+welX3ny5EGHDh1gZmYGGxsbjBo1CnPmzMGIESMwc+ZMHDx4UKc979ZnTI8ePcKjR49gZmYGQ0NDJCUlKTdQDh8+jAcPHqBly5aYO3cuDA0NdXoWZHQlS5bEjBkzULBgQVhZWek7nDSN1QK/Y2/fvkW3bt3w119/YfPmzahfvz4AYPLkyVi0aBH++usv2NraYtu2bVi/fj22bt0KGxsbJCUlYePGjahcubKevwF9juR31VeuXIlLly5BrVajbt26aNu2LWJiYhAeHq7TbcnBwQF2dnaYNWuWvsJO93bv3g1nZ2esWrUKv/zyi7K8VKlSKFOmDK5evYpmzZqhU6dOKFeuHPbs2YMDBw7A3NwckyZNgpGRUbov55sRaJ9YhYeHw8HBAW5ubsibNy+OHTuGTZs2oWfPnko32oxUKex7ERQUhFevXinVN6tVq4aaNWti2LBhaNOmDYoXL459+/ahSpUqWL16NauhZRCf2lcvX76MFi1aoG3btpg2bZqyPDIyEu3atUOHDh3QtWvXVIw07WHF0/+Nt0O/Y6ampujduzcSEhIwceJEmJmZISAgALNnz8batWtha2sLAHBxcYGTkxPu378PtVqNXLlyIU+ePHqOnj6X9gTi5uaG1atXo3379khKSkKfPn1w8uRJzJs3D9bW1oiNjVWeTL569UrnxEJf7sqVK7C1tcXFixfRrl07GBsbo1WrVnj79i1q166NkiVLYv78+Xj8+DFWrlyJ5s2bo3nz5sr72QUzbVCpVDh9+jS2bt2KH3/8EZ07d4aRkRHs7OyQLVs2LFq0CAAwevRo5QkWE6y0J/l20f73o0ePMHToUISHh6NLly4YNmwYpk+fjkWLFiE6Ohp///031qxZgzt37uDKlSvKlAiUviX/W7h48SKSkpKQO3duFCtWDNbW1nB1dcVff/2F3r17w9PTEw8ePMDMmTPx7NkznRtl3ysmVv8bz9zfufr168PQ0BCzZ8/GL7/8ggcPHuDIkSOoUaOG0u1BpVLByMhIZ4wIpS8HDx7Eli1bsG3bNtSoUQObNm3CqlWrUKFCBaXNoUOHsGbNGhgZGSnFK/jk5L8bOXIkDA0NsX37dri5ueH27dsICwvDkSNHUKxYMQDvuiK5u7tj3Lhxys0MLSZWaUNcXBzWrVuHtWvXonz58sp2sbKyQvfu3QEAy5YtQ1xcHLy9vZlYpVHaZOrRo0eoUaMGtmzZgjdv3mDs2LG4cOECRowYgfPnzyuTqO/btw8jR45Ez5490aZNG8THx/OmYgYgIso+OmbMGOWc9+jRI8ycORO9e/dW5q1atGgRypYti6JFi8LKygqBgYE8L9Ln0U+RQkoLkpeePX78uDg6OkrlypXl4MGDH21D6cf7JWFXr14ttWrVEhERPz8/MTc3l0WLFomISHR0tJw6dUo0Go2cO3dOeS/Lrv93yX9Db29vKVu2rFhYWChlbN+8eSMiItu2bZMKFSrI7du39RYrfVzyY9+VK1dk0KBBYmJiopTL13ry5ImMGjVKypYtK8+ePeMxM42KiYmRNm3aSP369WXy5MmiUqlk1apVyvobN25IgwYNxMXFRYoXLy7Zs2fXmW6EMpYJEyaIlZWVso179eolmTNnlnHjxkl8fLyIvDsGnDp1Sm7fvs3zIn0RJlffueQXAseOHZPmzZtL/fr1lYlN329D6cuKFSvk8uXLsmPHDnF1dZUNGzaImZmZkliJvJu8tn///hIREaEsy6jzdaQm7W+YlJQk06dPl+rVq8vAgQOVCUeTkpLk559/FmdnZ+5jaYh2W8TGxkpCQoKy/O7du9KvXz8pVaqULF26VOc94eHh8vz581SNk77c4cOHpVKlSqJSqZT5qzQajbKdnz9/Lr6+vtK6dWtRqVTSuHFj5UKbMgaNRqNMjLtjxw4ReXeTK0eOHNK2bVtRqVQyfvx4CQ8P/+C9PC/S52JyRToXdkePHpXmzZtLw4YNZfv27XqMiv6L5Af/6dOni4WFhdy8eVPOnz8v2bJlE5VKJQsWLFDaxMXFSZMmTaRbt268wP8G3n+CVaNGDRk4cKBERkZKixYtxNbWVrmw44lb/7T7wK5du6Rhw4ZSo0YN+emnn+TEiRMiInL//n3p37+/lCpVSpYvX67PUOkLaLfrkydPxM7OTsqUKSNOTk5y/PhxZb32iYRarZaEhAQZPXq0BAUF6S1mSjm7du2S+fPnK6/DwsLE19dX3r59K8ePH5cCBQoo6zt37ixmZmbi5uYmUVFR+gqZ0jl2Dv9OSbIikSqVSnldr149jBgxAklJSVi+fDliY2P1FSL9B9q+5NevX8ebN2+wfPly2NjYoEqVKvD19QUA3L9/Hzt37sShQ4fQrFkzPH78GD4+Pjp/B5QykpewHzlyJJo3b44LFy6gYMGCCAoKwpUrV2BsbIykpCSO1UkDVCoVdu/eDRcXF9jZ2cHFxQVGRkZo1aoVli1bhiJFimDQoEFo0qQJ3N3dsWbNGn2HTJ9BOw9Prly5sG/fPsybNw8JCQmYNGkSTpw4oYwrBoD4+HgYGxtj4sSJKF26tD7DphRw7tw5ODs7Y9GiRZg3bx4AIH/+/GjWrBlMTEywfv16/Pjjj+jduzeAd38j5cqVw4kTJ2BmZqbP0CkdYyn274D8fynhe/fu4eXLl6hQoQKMjY0/2Q4ATp8+jUKFCrHsbDp04sQJ1KtXDyYmJvD19UXbtm2VdevWrcOECRPw6tUrFCtWDPny5cOWLVtgbGzMQbr/wb9Vhkv+e2rbJSUlYcKECQgODsa6detgZGTEqoB69OzZM50iBW/evEGLFi1QoUIFzJgxQ1ner18/+Pn5Yffu3ahatSquXLmCtWvXonfv3ihRooQ+QqfPoD2nXbx4EaGhoYiIiICrqyuyZMmCgwcPYsaMGTAyMoKnpyfq1KmDSZMmIUeOHPj11195LMwgTpw4gfr168POzg758uXDDz/8gGHDhgF4l0g7OzujaNGiWLx4MVQqFVxcXODl5YWqVasC0L0uIvpcTK6+E1u3bkW/fv1gYGCAbNmyYcqUKWjYsCGyZs2q044HkvTnYxf4c+bMwfDhw+Hh4YHx48frJNPPnj1DbGwsTExMYGlpCZVKxQv8/yD57+7r64vLly8DACpVqoTOnTt/sr1Go4FKpeLvrmdjx45FXFwcJk+erJQWjo+PR926ddGuXTsMHz4c8fHxSvltbRW5HTt2AAASExM/epOK0hY/Pz8MHDgQJUqUwNOnT6FWqzFp0iR06NABu3fvxqJFixASEgJbW1vs3LkT586dQ5UqVfQdNqUgNzc3BAYGokCBAnj48CHatGmDwYMHAwCmT58OT09PuLi44Pbt20hMTMTly5dhZGTE6yH6z9gPJYMTETx+/BiTJ0/G6NGjsW/fPpQpUwbu7u7YsGEDYmJidNrzQJK+SLKysqtXr8alS5cAAEOHDsXkyZMxbdo0LF++XOc9efLkUUrLamee5wX+l0s+f5iHhwcSExMRExODoUOHYvjw4R9tr91e2i6Y/N31p2zZsujSpQsyZcqEuLg4AICJiQly5syJXbt2Ka/j4+MBAFWrVkVCQoLyfiZWad/Zs2fx66+/YsqUKTh+/DhOnjyJe/fuITw8HADg6OgIDw8PdO3aFTlz5sS1a9eYWGUA2mcG2v21Tp06KFSoENzd3VG2bFmsX78ec+bMAfDu+D1z5kxkyZIF9erVUxIrtVrN6yH671J7kBelDu0AXrVaLXFxcTJ48GCJiYlR1nfp0kVsbGxk6dKlEh0dra8w6SskL4AQEREhKpVKmjdvLlevXlWWT5w4UQwNDcXHx0cfIWZ4Bw4ckGLFisnp06dFRGTDhg2SJUuWD6rJsVhI2nXo0CHp37+/XLt2TUREjhw5ItbW1tKrVy+ddl26dJG2bdtKQkICt2ca86liMBs2bBBHR0cReVdqvVixYtKzZ09lffJzYlJS0rcNklLF/v37ZerUqTrbNj4+XsqWLStjxoyRuLg46dGjh9SsWVPmzJmjtEleGZTl1ulr8bZpBqUdmL1y5Uo8fPgQpqamSEpKUtavXLkSXbp0wZw5c/D27Vt07dr1gy6ClLZpn5x4enrizZs3KF26NPbu3Yvo6GjMnz8fZcqUwejRowEAAwYMUJ6q0H8n/99NRPvvBw8eoFChQqhRowa2bt2KXr16Yfbs2ejRowdiYmJw7tw5ODg48A5oGvbo0SOsWrUKRkZGGDx4MOrUqQM3NzdMmzYNtWvXRr169fDo0SNs27YNAQEBfGKVBhkYGODWrVsIDg5G06ZNlfFSN2/ehFqtRnx8PBo3bowmTZpg4cKFAIBNmzYhKCgIo0ePhpGREcdYZQAnT55EkyZNALzbvu3bt0eVKlXw008/YcGCBZg5cyYiIyMxadIkjBo1Clu3bkVMTAxGjx6ts1+zRwF9LXYLzKACAgLQvHlz5MiRA4aGhrhy5QqmT5+OV69eKW18fX1hY2MDX19fncSL0o958+bBx8cH7du3x8aNG3Ho0CFcv34d/fr1w/Xr1wEAo0ePxpAhQ7B161ZWA/xK2iTp+fPnAICcOXOicOHC2LRpE7p06YIZM2agT58+AIDjx49j+/btePLkid7ipQ9p94HQ0FCICDp37ozFixdjy5YtmDVrFp48eYIePXpgzZo1sLS0xMWLF5GYmIiAgACUK1dOz9HTpyxZsgTNmzfHjh07oFarAQBt2rTBvXv3kD17djg6OipFCwDg1KlTuHr1Kt68eaPPsCkFmZiY4Oeff0aDBg2QM2dOPH/+HB06dICbmxsuXLiAyMhInDp1CpaWlpgyZQpy586NsLAwnhcp5enzsRl9G8HBweLt7S2zZs1Slg0dOlSqVasmEydOlMjISJ32YWFhqR0ipZCuXbtK586ddZbdvXtX8uTJI02aNJErV64oy7VdZ9il6essWbJERowYISIiAQEBYm5uLiqVSv744w+lTVxcnDRu3Fh69OjB3zsN0W4Lf39/qVu3rk532bVr10qBAgWkf//+cufOHZ33sZtQ+jBw4EDJkiWLbN68WZKSkiQyMlLc3NykRIkSMmPGDBERefDggYwaNUpy5col169f13PElNJOnz4t7du3lyZNmsjmzZvl2rVr0rlzZ2nZsqWoVCqpW7euvHnzRkREXr58yfMifRNMrjKYO3fuyA8//CCWlpY6k8WKvEuw7OzsZPLkyfLy5Us9RUgpQa1Wi0ajEScnJ3FyclKWv337VkREfv/9d1GpVOLs7CwPHz7UeQ99nfHjx4u5ublERESIiMjGjRtFpVKJp6en7N69W/7++29p0KCBVKhQQbko5++uX8l//61bt4qpqanMnTtXbty4odNu1apVkj9/fhk8eLDO2EVKW94fY5U8+e3Xr59kzpxZNm7cKCLvbjYNGDBA8uXLJ/ny5ZNKlSqJtbW1XLhwIVVjptRz6tQpadWqldSuXVuOHDkiIiLh4eEyatQouXz5sojo/g1xAndKaUyuMpjExEQZP368FC1aVBo2bKgzqFNEZMSIEVK8eHGZMWMGL/jSkU8d/Ldt2yZZsmSR5cuX6yxftWqV9OzZU/LkySNdu3ZNjRAzHI1Go7OPaLdBQkKCODg4yNChQ5VB0CtWrJCSJUtK7ty5xd7eXpydnZV1HCivP1evXtX5/UNDQ6VixYry559/isi742VcXJzs2rVLnj9/LiLvnmCZmpqKu7u7ziB3Shu0++Tt27dlypQpcufOHXn27JlOmz59+oiJiYls2LBBRESioqLk1q1bsnjxYjly5Ig8evQo1eOm1BUQECAtW7aUunXrip+fn846JlP0rXHUXjon783DYGRkhFGjRiFz5sxYv3493N3d4e3tDQsLCwDAjBkzkClTJrRq1YqD7NOJ5PMp7dy5E/fu3YOxsTEcHBzQokUL9O7dGxMnTkRiYiK6d++Oly9fYuPGjWjRogWcnJzQsWNHDB06FBUqVNDzN0lf3t8/kpdSr127Ng4fPoyEhAQYGxuja9eu+PnnnxEbGwtTU1OlzD3nsdKfBQsWwM/PDzt27FCOf/Hx8Xj9+jXKli0LjUaD6dOnY/fu3bh27RrMzMxw9OhRuLq6wtjYGJUqVWLxijRIpVLh1atXaNy4Me7evYv169fDyMgIDRs2RLVq1dCqVSssWrQIlpaW6NKlCwwNDeHs7Axra2tYW1vrO3xKIf82gTsA2Nvbw83NDTNmzMDvv/+uTBAM4F/fR5QSOIlwOqZNrE6dOoUjR44gKSkJ5cuXh4uLC9RqNWbOnIlt27bBzs4OU6ZMUS4wKH1yc3PDli1bUKRIEWTPnh3+/v44ffo0LC0tsWTJEsyYMQOWlpYQEWTLlg0XL17E0aNH0bt3bxw7dgz58+fX91dIF0aOHInmzZujTp06AIBly5Zhy5YtWLBgAfLmzQtzc3O8evUKNjY26NOnDyZNmvTRz/lfJ3/6tmJiYhAeHg5ra2tEREQgZ86cSExMRPv27REcHIzo6GhUr14dNWrUQK9evVCzZk04Ojoq899Q2vX8+XMsWrQIy5cvR6FChTB48GBMmzYNjx8/homJCezt7dGjRw+MHz8eDx8+xLRp09CiRQtlsmjKOObOnQtra2s4OTl9dH1gYCBmzZqF69evY+HChahXr14qR0jfI5750zGVSgU/Pz80btwYBw8exN69e9GqVSv07dsX8fHxGDFiBJo1a4YrV65g4MCBiI6O1nfI9B+tW7cOq1evxoYNG/D333/DxcUFIoLbt2+jcOHCGDduHC5cuIDffvsNs2fPxoULF2BoaIg9e/Ygb968MDU11fdXSBeCg4Px8uVL1KhRA8A/leWio6Ph4OCAgQMHYvfu3ciRIwfGjh2L8+fPIzg4+KOfxcRKf9RqNczMzGBtbY3AwEA4OjrC398fmTNnhre3NwYPHgxPT08sXboU7u7uyJUrF8qUKYOiRYvqO3T6DLlz50avXr3Qr18/XLlyBS9fvkRgYCCuXr2KHj16wNDQEL/88guio6Px4MEDTJw4UWcCaEq/NBqN8t8+Pj6YPn06LC0tP9ne3t4eAwcOhIuLC2rXrp0aIRKxWmB6dvfuXSlcuLAsXLhQRN71I967d69kyZJF+vXrJyLvxoeMHj1aGjZsKE+ePNFnuPQfaPuGjxs3TgYOHCgiIn5+fmJmZqZUOouKilKKVmgFBwdL3759JVu2bMoAXvoy69atk/379yuvlyxZIt26dRNDQ0Pp37+/DBw4UEqVKvVBf35KWyIjI8XOzk5q1qwpu3bt+mAMXGRkpIwZM0by5MkjN2/e1FOU9F9ERETIlClTxNzcXCZPnqyzLjg4WI4fPy6//vqrMkE0ZRznzp2TgQMHyqpVq77ofRwDS6mByVU64ePjI6dOndIZYH/16lUpUaKEBAUFicg/F+K7du0SAwMD2bNnj4i8O5hoB2tT2qdWqz84AXh5eUm/fv1k69atYmZmpiTUIiK+vr4yevRoiY2NFZF3s9GvX79efvnlF51S7PR5NBqNPHnyRCpWrCiNGjUSf39/ZZ1arZbDhw9Lq1at5KeffhKVSiUtWrTQY7T0Pu0x8uzZs3LmzBkReXcDwsHBQapXry7bt29X9q+dO3dK586dpWDBgqwel0ZpizJ9qgDTs2fPZOrUqZItWzadBIsX0RnXiRMnxNTUVLJmzSpLly7VdzhEH2BylQ5oNBopUKCA2NraytmzZ5WTzLVr10SlUil315OSkkSj0UhMTIyUK1fug1LslPb5+/tL165dpVGjRjJ79mxl+cqVK6VUqVKSNWtWmT9/vrI8MjJSfv75Z/Hw8ND5nPj4+A8qRdKXCQwMlJ9++kmaNm2qk2CJiLx48UKCg4NlzJgxrCiXhmiPjX5+fpI/f37p3r27Mo+fNsGyt7eXHTt2iMi7BGz27Nly69YtvcVMn7Zs2TIZOHCgPH78WET+d4JlYWEh06ZN01nHqrgZ09y5cyVr1qzSoUMHuX//vr7DIdLB5CqN054Y4uPjpVKlSlKuXDkJDAxU5vVwdXWVWrVqSWBgoPIetVot1atXV8oNU/qwePFiyZEjh3Tr1k2cnJxEpVLp3Int2LGjmJmZyfr16+XWrVty7do1adKkiVSpUoXzKX2Ff5vvJCAgQBwcHKRp06aya9euT7ZjgpV2HD58WLJkySIrVqxQnthrt5c2wapVq5Zs3bpVNBoNn3CkYSNHjpTy5cvLqFGjlG7t/5ZgTZ8+XVQqlcyZMycVo6Rv6d/Kpk+dOlWsrKxk3Lhxyk0UorSA1QLTgfj4eJiYmCAmJgaVKlVC4cKFMWXKFNjb2+Pvv//GrFmzEBERAS8vL+TNmxc7duzA0qVLcebMGRQvXlzf4dNnWLp0KQYMGID169fDxcUFT58+haOjIyIjI3Uq/Tk7O+PevXsICQmBnZ0dTExM8Ndff8HY2BhqtRqGhoZ6/ibpS/KKfosWLcKlS5cQFRWF1q1bo2HDhjA3N0dgYCA8PDyQNWtW9OvXD02bNtVz1PRvPD098fTpUyxfvlzZJ9RqNQwMDKBSqRAdHY26desid+7c2L59O8zMzPQdMv2LiRMnYtu2bWjcuDEGDx6sVET92FQiERERWL9+PZo0aYJSpUrpIVpKScmPz8uWLcPZs2dhbGwMW1tb9O/fHwAwadIkLF68GL169ULPnj1ZFZfSBCZXaZz2JLJp0yb8/fffCA4OxtGjR1GpUiUsW7YMlStXxtGjR7Fy5UqsWbMG1tbWMDAwwJo1a1C5cmV9h0+fISgoCOXLl0e3bt2wdOlSZXmlSpXw9OlTHD9+HImJiShdujQA4OHDhwgKCkLBggVRpkwZGBgYcD6lr+Th4YFly5ahe/fuuHnzJh4/fowffvgBo0ePRrZs2RAYGIhRo0YhNjYWc+bMQc2aNfUdMn1C06ZNYWhoiJ07dwLQnQvwwYMHKFKkCKKjo/Hy5UsUKVJEn6HSv0h+YT1+/Hjs2LHjsxIsToGQ8bi7u2P58uVo3rw57t+/j5CQEJQrVw579uwBAHh7e8PHxwetW7eGh4cHcufOreeI6bunv4dm9LmOHTsmmTNnlqVLl0pAQIAcP35cSpUqJWXKlNEZhH3nzh158OABi1ekM/fv35cRI0ZIzpw5ZfXq1SIi0rJlS8mdO7e0bNlSWrRoIYULF5aWLVvK/Pnz5caNGzrv52zzX+b932vFihVSvHhxOX/+vIi8G/dmYGAgZcuWlYEDB8rr169F5N1+OGDAAP7eaZharZYxY8ZIvXr1JCQkRGd5WFiYtG/fnoUr0pHkXQB/++03qVy5snh4ePzPLoKUcZw+fVoKFiwoR44cEZF3QyT2798vBQsWlJYtWyrtRo8eLS1atODfBKUJTK7SgVmzZkmNGjV0xnW8fv1abGxspEqVKhIQEKCMuaH0KSwsTNzd3cXc3FzKli0r1apVUy4OX7x4IRcuXJCOHTtK0aJFpUmTJnqONn3T9s1Xq9WiVqvFx8dHxo0bJyIi27Ztkxw5csi8efNk5MiRkjNnThk2bJi8fPlS5zOYYOmf9iLq8ePHcv/+fXn69KmIiFy8eFHMzMykb9++SiXVhIQEGTdunFhbW8uDBw/0FjP9b9rt+ubNG4mLi9NZN2bMGCZYGZz22Kr9t5+fn1hZWcmrV6+UNgkJCbJ582YpVaqUHD9+XFmu/Vvg3wTpG5OrNEx7gPjtt9/E1tZWWa494ezbt09UKpVUqFBBuetO6VdYWJiMGTNGsmbNKt7e3spybVKdmJgor1+/5oX9V7h48aKoVCrZsmWLsiwyMlKePHkiYWFhUrFiRZk5c6aIiDx69EisrKykUKFCMn36dBHhSTut0G6Hbdu2SZkyZaRs2bKSP39+cXd3l8jISDl48KBYWVlJnTp1pHbt2uLs7CzZs2fnU6s0Trtdd+/eLW3atJHSpUvLb7/9JgcPHlTaaBMsLy8vFjHIYJInUKdPnxYRkevXr0vBggVl27ZtOm3v3Lkj2bNn/2CeQR6jKS1gx+Q0TNufvG3btggLC8OUKVMAAJkzZwYAZMqUCc7OzjAxMUH27Nn1FSalkPz586NXr14YNGgQpkyZgmXLlgEAjI2NlTFVFhYWMDAwgFqt1nO06ZOVlRV69+4NV1dX7NixAwBgbm4OS0tL3LlzB1FRUfj5558BvBscX6dOHYwZMwbDhw8HgI+O8aDUp1KpcOjQIXTq1Al9+vTBuXPn0LdvX0yfPh379u3DTz/9hJ07d8LV1RXFixdHjRo1EBAQwHGoaZxKpcKOHTvQpk0bWFtbo0ePHjh+/DgmTJiAbdu2AQAmTJiA5s2bY+3atVi6dCmPhRnEzp07MWzYMERERGDQoEGoXbs2Xr58iZw5c8LW1hbr1q3D6dOnlfbZsmVD0aJFPxhrzGM0pQn6zu7oH9o7LhcvXpQ1a9bIuXPnlPFTkyZNkuLFi8ukSZNERCQ6OlpGjRolgwcPZpfAdORz7qqFhYWJp6enWFhYyLJly1Ihqu9LeHi4DBo0SFQqlWzfvl1ZfuzYMSlTpoxMnTpVbty4IU5OTtK1a1dlm7Fkd9qg3R79+vWTX3/9VUREQkNDxdraWvr06aPP0Ogr3bhxQ2xtbWXx4sUiIhIbGyu5c+cWGxsbqVWrls7+6u3tLXfv3tVXqJTCtm3bJnny5JGKFStKrly55Pr168q6EydOSMWKFaVJkyYyceJE8ff3lwYNGkilSpV4XKY0iclVGuPn5ycWFhZSokQJyZkzpwwYMEDu3Lkj0dHRMmXKFDE3N5eiRYtKuXLl2M0lnUnenU/btfNTyVZYWJiMGjVKVCqV7Ny5M1Xiy6hCQ0M/KPLy5MkTGTBggE6CFRUVJb179xZra2uxsrKS6tWrK10y2dVEf94fg6HdFm3atJE1a9ZIXFyc5M+fX3r37q2s27hxoxw+fFg/AdMX0W6z6OhopbhPZGSkPHz4UIoXLy79+vWTkydPSqFChcTe3l7Wrl2r54gpJSU/tnbo0EEMDAykVatWH4yNDAwMlF9//VUKFSokVatWlaZNmyrHZyZYlNawFHsaIP9fUjY0NBT9+/eHs7MzOnbsqJRXL168OMaPH48SJUrgzp078Pf3R7Zs2VCvXj1YW1vrO3z6DMnLA0+fPh2XL1/G77//jly5cn3yPaGhodizZw969OjBMuv/kZ+fnzL3Sa9evZAvXz506NABAJCQkICRI0di/vz52LRpE1q3bo2YmBile2CtWrVgaGjIMvd6ot1ntMfH169fI1u2bMr6QYMG4a+//kJsbCxatGiBWbNmwdjYGImJiejcuTNsbGwwZswYbrt0YOvWrThx4gQ8PDwgIsiXLx969OiBhIQELFy4EGZmZmjVqhUCAwNRtWpVrFq1Cubm5uwCls69XzZ/6dKliI2NxezZs9G4cWMMGTIEZcqUUY4BarUab968QWxsLPLmzQuVSsXjM6VN+szs6B9nzpyRAQMGiIuLizx79kxZ7uvrKzVr1hRXV1e5fPmyHiOklODm5iZWVlayYMECuXXr1me/j10/v1x8fLz069dPzMzMJG/evFKvXj0pXLiwlCtXTlq2bCkHDx6Uw4cPi5eXlxgYGMi+ffs++AzeEdUP7VOqe/fuycSJE6VOnTpSpEgRcXV1lTVr1ojIuykMatasKQULFpTY2FgRebe9Ro0aJYUKFdIpxU5pj/aJxd27dyVfvnyyZMkSZZ1arZZatWqJl5eXsqxnz54ye/ZspUogpW/Je3LMmDFDPDw8lCdRW7dulYIFC0qvXr10ph55/xjNHgWUVvHJVRrh7e2NuXPnwsjICMeOHdN5IrVq1SosX74cFhYWmDp1KsqUKaPHSOlLJL8zd/jwYXTt2hVr1qxBvXr19BzZ9+Hp06eYMmUK7t27h7Jly2Lo0KHYtm0b9u3bh8uXL+Pt27ewtrbGqVOnoFarcfbsWdjZ2ek77O+adp+5evUqWrVqhapVq8Lc3ByFCxfGsmXLEB8fjx49emDChAnw8/PDuHHjEBMTg2rVqiEuLg5nzpzB/v37WbwiHThy5Aju3LmDS5cuYd68eTAwMIBGo0FcXBx69uyJhIQEuLi4ICgoCGvWrMGZM2dgZWWl77ApBbm5uWHdunUYOHAgWrdujRIlSgAAtm/fjiFDhqB+/fpo1qwZli1bhgsXLiAsLAwAC1dQGqfv7I7+sWDBAilevLj07dtX7t+/r7POx8dHmjRpwtKz6YS7u/sHy5YvXy6VK1eWt2/fKsvev/PGMuspLywsTPr37y9Vq1aVBQsWKMtv3Lghf//9t3Tp0kXs7e3F2tqaTwj1TPv3f+nSJTEzMxM3Nzed8sw3b96UTp06SZ48eWTu3LnKMnd3d+nZs6dMnjz5i54Ik3516NBBVCqVVK5cWZmsW2vXrl3y008/SbFixcTW1pbTjWRAO3fuFCsrKzl16pSyLPk5cefOnWJnZycVKlSQevXqcQwspRt8cqUH8v/9h+Pi4qDRaGBmZqasmzZtGjZu3AgHBwcMGTIEhQsXVta9P+aA0qajR49i2rRp8Pf31+kL7uvri7Fjx+LIkSMoWrQogHd/CxqNBuvXr0fDhg2RL18+PUWdsT158gTe3t44c+YMmjdvjlGjRinrtPuj9t/sw69ft2/fRvny5TFixAhMnDgRarVaZ+zbnTt3MGDAAISGhmLbtm0oWbKkvkOmz6Tdx2JjY5E1a1aICIYMGYKFCxdi06ZNaNGihU77J0+eQERgbGyMPHny6Cdo+mbmzZuHHTt24PDhw8rfxvvjsO7fv4+kpCQUL14cBgYGPD5TusB5rlKZ9gCye/dudOzYEZUrV4a7uzv27NkDAHB3d0ebNm1w5MgRLFiwAPfv31fey8QqfahZsyZ2794NIyMjbN68WVlepEgRxMfHY8OGDXjx4gUAKBfzS5YswcqVK/UUccZnZWUFLy8vVK9eHf7+/pg2bZqyTjtPjvbEzhO3/mg0Gixfvhzm5ubKxbShoSHUajWMjIwgIihRogRGjRqFGzdu4Nq1azrv573CtE2lUuHvv/9Gjx49cO3aNahUKsybNw9t27ZF165dcfjwYZ32lpaWyJ8/PxOrDOrt27cICwtDZGSkcoPLwMAAiYmJ2LRpE+Lj41G0aFFYW1srXUZ5fKb0gMlVKlOpVPD390fbtm1Rrlw5jBgxAhcuXMDEiROxbt06AICnpyfat2+PzZs3Y+nSpUhKStJz1PS51Go1MmXKBJVKhZCQEHTt2hVOTk4AAAcHB/Tu3Rve3t6YPn06du7ciaNHj8LZ2RnR0dHKRLX0bVhaWuokWKNHjwYAnZN18jumlPoMDAwwYMAAuLq6Yt26dZg6dSqAdwmWRqNR2tnZ2SFXrlx48uSJzvs5DiPty5QpE7Zt24Zp06bhxo0bAIA1a9bA0dERrVq1wt9//6205fbMGJLvu8mVLVsWr1+/xvbt2xEdHa1s74SEBPz+++/w9fXVac/jM6Ub+uiL+D0LDg6WcuXKyaJFi0Tk3XxHefLkkVKlSom9vb1s2LBBaTt79mxOkpiOJK/yeOjQIRER2bJli5QsWVKcnJyUdTNnzpQ6deqIiYmJVK5cWRo0aMD5OlLRkydPpFOnTtKrVy/23U+jtPOQ2dvby9SpU5Xl2v1DO6kox+Gkbe/vX9rXp06dkty5c0uHDh0kKChIWd+pUydRqVRy9OjRVI2Tvp3k44jXr18vs2fPFg8PD7l9+7aIiPTv31/y5s0rM2bMkICAADl37pw0btxYqlatyjGwlG4xufpGPnXR9uDBA3F3d5cXL15IaGioMkni+fPnpVixYlK5cmVZunRpKkdLX2vXrl3i4uIid+/elcGDB4tKpZJXr15JXFyc+Pn5SbFixXQSrKdPn0pISIjcu3dP+VvhiST1vHjx4oNJaSlt+VSCJSIydOhQadSokbx48UJP0dGXuHHjhjx9+lRE/tnfTp48KTlz5pS2bdvqlNvu2bOnBAcH6yVO+nZGjhwpVlZW0qlTJ6lWrZqUKFFCudYZOnSo2NnZiUqlkkqVKukUr+ANR0qPmFx9A9qLtufPn8v169flypUryrqkpCSJiIgQEZHevXuLq6urREVFici7ykkFCxaUZs2aSWRkJC/60pFTp05JgQIFpHTp0pIzZ065evWqsu7NmzdKgtWsWbOPvp9VAvWDv3va9rEEa+LEiZIjRw6dfYzSJo1GIxEREaJSqaRnz54fTbCMjIykZ8+ecvHiRT1GSint4sWL8vLlSxER2bhxoxQsWFAuXbokIiJ79uwRlUolW7ZsUdqHh4dLQECAXLt2TTku84YjpVfswJrCtJVurl27hp9//hmOjo5wdnZG7969AbwbO6AdnHvz5k1YWVnB3NwcAGBubo7hw4fDx8cH2bJlY3/zdED+v9pfzZo14ejoiJCQEFSrVg2GhoZKG1NTUzg6OmLmzJkICgr66BxX7EuuH/zd0zbtOLlq1aph9+7dsLe3x+TJk3Hw4EGUK1dO3+HRv5D/L96UJ08ebNy4EatWrcLEiRMRERGhnNtq1aoFe3t7LFu2DAsXLkRCQoKeo6aUcOnSJTRp0gQhISEAgNDQUNSqVQsVK1bE+vXr0b59e/zxxx9o1aoVoqKicP/+feTNmxf29vYoW7Ysi1dQuscrixSkTawuX76MGjVqoF69elixYgWcnJzg6+uLhQsXAnhX9CAuLg6FCxfGzZs34ePjA3d3d+zcuRNt2rRhOe50QqPRQKVSKRfojRo1gq+vL+7cuYNx48bh3LlzSlsTExM0bdoUEyZMQK5cuT45wJeIdGkTLGtra7x8+RKnT59GlSpV9B0WfYL8f8XGN2/eQKPR4M2bN2jTpg02bNiAP/74AxMnTsTTp0+V9jVr1sT69esxfPhwZMqUSV9hUwqqVKkSsmXLhnnz5gEAXrx4gcyZM+P8+fPo06cPpk6dir59+wIANmzYgDVr1iA+Pl7nM3jji9IzznOVwt6fowUA7t27B1tbWwwcOBAzZ85U2h44cABz5szBrVu3YGpqitWrV6Ny5cr6Cp2+QPK5OObPn4/IyEgMHToUZmZmOHnyJDp37oyqVavC3d1duRDcsWMHmjdv/tHPIKJ/9+zZM2g0Gt58SsO0T6v27dsHHx8fvH79GsbGxpgzZw5Kly6NnTt3olWrVnB1dUW1atXw6NEjrF69GteuXUP27Nn1HT6lAO28dEuWLMHcuXOxY8cOvH79Gg0aNMDr16+xevVqdOzYEcC7BLxVq1YoUaIE5s+fr+fIiVIOr+xSUPI5WnLlyqUs37BhAxITE3Hr1i3MnTsXq1atwtu3b9GoUSP4+/vj5MmTOHLkCBOrdEL+fy4OABg5ciSmTp2KPHnyICIiAgBQu3ZtrFy5EhcuXMCkSZOwcuVKODs7o3v37jpPrJhYEX2+PHnyMLFK47RTjbRs2RKVKlVC7969kZCQgOrVq+PWrVtwdnbG7t27cfPmTfz555/YtWsXdu7cycQqA9F2iXdwcMCzZ8+wZcsW2NnZYcSIEcifPz8ePnyIp0+fIjAwEK1atcLjx48xZ84cAJynjjIOPrlKYY8fP8b06dMREBCALl26IDo6GlOnTkX//v1RqVIlrF27FqGhoXjy5AlKlSqFIUOGwNnZWd9h02d4+/YtTE1NldcrVqzAqFGj4O/vj2rVqgF4d3KIjo6GhYUFzpw5gxEjRuDNmzewsLDAvn37YGxsrNzdJSJKz94/lsXExKBFixZo2LAh3N3d8ejRI9SrVw8NGzbE4sWLlfZPnz5FpkyZICLImTOnHr8BpTTtDUQDAwNMnToVf/75J44fPw4jIyP4+vpi5syZMDQ0RP78+WFpaYldu3bB2NhYeeJFlBEwufoGwsPDMXnyZPz111+4c+cO9u/fjx9//BEAkJSUBCMjIyxYsAAXLlzAiBEjUKZMGT1HTP9Lhw4d0L59ezRv3ly5QBgyZAhevXoFX19fBAUF4fjx40pXmKlTp6J169aIiIhAQkIC8ufPDwMDA2X7ExGlV9pjYGxsLLJmzap0cX7+/DmqV6+O/fv3I0eOHKhUqRIcHR2xePFiAMCqVavQokULWFhY6PkbUErZuXMn3N3dMWrUKNSpUwdFixZV1h0/fhw9e/bE2LFj4erqisTERERGRiI4OBh58+ZFyZIleV6kDIn9kr4BS0tLjB49Go0bN0aZMmVw8eJFZZ32rs6AAQPg4+PDxCqdKFasGH7++WcAQGJiIgCgUKFC2LdvH0aMGIFffvkFBw4cgJOTExo0aIAePXrgxYsXyJs3LwoWLMjqR0SUYahUKkRERKBo0aLYtGkTDAwMICLInTs3ypcvj/Xr18POzg7Ozs5YsGABgHdj5rZv3469e/fqOXpKKSICCwsLFCpUCBMnTkSdOnUwc+ZMpZhT3bp1YW9vj3HjxgEAjI2NkSdPHtStWxelSpXieZEyLP5FfyP58uWDp6cnNBoNNm/ejKSkJLi7uyNTpkzKXRoeUNI+7R1Zb29vAMDChQshIujevTtatmyJyMhI+Pv7o0ePHmjUqBFsbW1x7Ngx3Lhx44OKgBxjRUQZhYGBAZo1a4ZOnTrBxMQEzZs3R2JiIkqUKIFZs2ahVq1aSoVcAJg9ezZCQkJQq1YtPUZNKUmlUuGHH37ADz/8gFOnTuHgwYOYPXs2li9fDjs7O3h5eaF37964d+8eVq1ahc6dO3/wGTwvUkbEboHfmLaL4MWLF/HTTz9h/Pjx+g6JvoC2+4v2305OTrhx4wbGjh2L9u3bI1OmTIiJiYGZmRmAd90+nZ2dYWRkBH9/f46tIqIM4WNjRSMiIjB58mTMnz8ffn5+cHFxwatXr9ChQwc8e/YMderUgY2NDc6fP4+tW7fiyJEjqFSpkn6+AH0T74+VCgoKwokTJzBjxgwYGxvDwsICd+/ehbOzM5YtW6bHSIlSD5OrVBAeHg5PT088evQIGzZs0KkkSGlX8ouJ0NBQFCpUCADQqVMnnDlzBh4eHmjTpg3MzMwQExODAwcOYMGCBXj58iXOnj0LY2NjllsnonRPexyLjY2FWq3WGTP15MkTeHt7448//sDmzZvRqlUrvHjxAlOnTsXZs2cRGxsLGxsbeHp6cuLnDOBzCzJpNBosW7YMZ86cwbJly1CpUiWcP3+eNxzpu8DkKpVoJ01kKeH0IXlStG7dOmzcuBFubm6oXbs2AMDV1RUXLlyAu7s72rVrh2fPnmHlypV4/Pgx/vjjDxgZGXGQLhFlGLdu3ULbtm1hZmaGXr16wdLSEo0aNQIAxMfHY/jw4fjzzz+xceNGtGnTBklJSTAwMEBiYiIMDQ15LMxgwsPDYWlp+dF17z/NOnXqFOzt7WFoaMhqufRd4NEulTCpSj+SJ1YnT57Evn37cOLECZiamsLY2BjVq1fHunXr4OrqihkzZsDQ0BAdOnTAiBEjkCVLFqhUKqjVal5MEFGGoNFosHLlSly+fBmmpqaIjIxEXFwccubMierVq6N79+7o1q0bcuXKhXbt2sHCwgKNGzeGiMDExETf4VMK2Lp1KwoWLIjq1avDzc0NL168wJ9//vnR7atNrLSJlHacHW840veCT66IPmHYsGHw9/dH8+bNERERgR07dqBp06YYOHCg8gSrc+fO8Pf3x7p169C0aVMAn99tgogovQgPD8e0adNw584dWFtbo3///li7di2OHz+OK1euIGfOnChevDjOnz+PiIgIHDlyBPXq1dN32JQC3rx5g86dO2Pr1q1o3749duzYgVOnTqFChQr6Do0oTWJyRfQRJ0+eRMuWLbFt2zblrtvmzZsxadIk2NjYYOTIkahevToAYPz48Rg9ejQnQCSiDO3x48fw9vbGmTNn0K1bN/Tt2xcAEBgYiMePH8PHxwdhYWG4du0arl27xqlGMgDtzUK1Wg1bW1vcv38fK1euRMeOHfkkiugTuFcQfYSRkREMDAx0ujy0adMGarUaHTt2hKGhofIEa+zYsQA+7GdORJSR5M+fH15eXvD29saKFSvw6tUrjBo1Cvb29gAAJycnaDQavH79Gnnz5tVztPS1kvfC2LBhA0xMTNCgQQP07dsXxYsXR82aNVm0iegjuEfQd0/78Pb9h7hJSUkICwsD8M/Ewe3atYOtrS2uXbuGVatWKesBMLEiogzPysoKXl5esLe3h7+/P6ZOnaqsU6lUMDExYWKVAWg0GiWxGjNmDKZNm4YNGzZg06ZNcHR0RKNGjRAQEKCTWD18+FBf4RKlKUyu6LuW/ASSlJSkLLe3t0ezZs3QtWtXXLx4EcbGxgCAFy9eoGrVqujatSs2btyI8+fP6yVuIiJ9sbS0hJeXF6pVq4Zdu3YpT+/ZRSzj0CZN9+/fR0hICGbOnIly5crB3Nwc8+bNg7OzM5o0aYJjx47hzZs3aNeuHebMmaPnqInSBo65ou9W8u4Mv//+O44ePQoRQdGiRTF79mwkJCTA1dUVe/fuhaenJywsLODv74/ExEQcPXoUdnZ2qF69OhYuXKjnb0JElPo4h2PGtnDhQvz2228oWLAg1q9fD1tbW2VdREQE3NzcsGrVKlSsWBGxsbG4fv26ciOS6HvG5Iq+e56enli6dCl69uyJZ8+e4cCBA8iXLx92796NvHnzYtSoUfj7778RFxeHIkWKYNOmTTA1NUWtWrXQtm1bDBkyRN9fgYhILziHY8YVFxeHOnXq4NKlS9i8eTNcXFw+GF+1bds2vH79Gp06dYKhoSGLXBCByRV954KCguDk5ISFCxeicePGAIC7d++iZcuWyJw5M06fPg0AiIyMhKmpKUxNTQG864O+fPlyHD16FNbW1nqLn4iI6Gt9qjDF27dvYWdnBwBYtWqV8t8fm3KERZ2I3uGYK/quRUZG4vXr1yhdujSAdyeM4sWLw9fXFw8fPsS6desAAObm5jA1NUVISAj69OmDJUuWYNeuXUysiIgoXUueWB06dAgrV67E4cOHERQUBFNTU5w9exaJiYno2bMnLly4AOBd8ZL3780zsSJ6h8kVfddKly6NzJkzY+vWrQCg3IkrWLAgMmfOjKioKAD/nDTy5s2LNm3a4NSpU6hcubJ+giYiIkoh2sRq5MiR6NSpE6ZOnYpu3brB1dUVmzZtQpYsWXDp0iW8efMGvXv3RmBgIAB88OSKiN5hckXfFY1Go/y3iMDExATOzs7YuXMnNm7cqKzLkiULsmfPrgzO1d6hy549Oxo0aIDixYunbuBERETfyNq1a7Fy5Ups2rQJV65cwcaNG1GrVi0MHz4c27ZtQ5YsWXDx4kXcvXsXixYt0ne4RGkax1xRhnfo0CGcPn0ao0ePBvBh3/IbN27Ay8sLDx8+ROXKlWFnZ4dNmzbh+fPnuHjxIrs6EBFRhubh4YHg4GBs375dWRYSEoIJEyYgNjYWK1asQPbs2ZGQkABDQ0OeF4n+BZ9cUYYWHx+PTZs2YdOmTZgxYwaAd10gtE+wRASlS5fG9OnT0b59e5w9exbr169H3rx5cf78eRgaGkKtVuvzKxAREX1T5ubmePDgAV6+fKkss7GxQf369ZW5rAAgU6ZMPC8S/Q9MrihDMzExwdixY/Hjjz/Cz88P06ZNA6CbYAGAtbU1hgwZgosXL+LIkSPYsGEDjI2NkZSUxDt0RESUISQ/7yVXpkwZvHz5Ejt27EB0dLSy3NbWFoUKFUJ8fLxOe54XiT6N3QLpuxAeHo7Jkyfj7NmzcHFxgbu7O4B/ugg+ffoUw4YNg5OTEzp06ADg46VmiYiI0qPkXeK3b9+OmJgYqNVqdOrUCQYGBhg6dCjWrVsHT09P1KtXD7lz50avXr2QlJSEgwcP8nxI9JmYXNF3I3mC1aJFC3h4eAAAnjx5gjZt2iAiIgJBQUGcAJGIiDKU5DcLhw4dipUrVyJ//vwICwtDsWLF8Pvvv6Nu3brw8PDAvn37EBwcjFKlSsHExAQnT56EsbHxJ+fCIiJdTK7ou5I8wWrVqhW6d++ONm3a4OnTp7h06RKMjY05ESIREWVIjx49QqtWrbB48WIUKVIEANCsWTO8evUKa9euRcWKFREcHIzw8HAYGhqiVq1aMDQ0RFJSEm88En0mJlf03QkPD4e3tzfOnDmD4OBg5M+fH5cvX1bGWPEEQkREGc2cOXOwa9cuZMuWDWvXroWJiQkMDAygVqtRvXp1ZM+eHYcOHfrgfbzhSPRl+HyXvjuWlpYYNWoUSpUqhVq1ajGxIiKiDO3t27dISEhAUFAQQkJCkDlzZhgYGCAuLg6GhoaYPXs2Ll26hODgYLx/z52JFdGXYXJF3yVLS0vMnTsXu3btYmJFREQZyvtVAU1NTdGlSxd4eXkhJCQEI0aMAABkyZIFAJCYmAgLCwuYmJiwcAXRV+LVJH23cuTIAeDdSYiJFRERZQTJC0/cvHkTiYmJKFWqFCwtLdGzZ08kJibCw8MDCQkJ6NWrFwwMDDB79mwUKFBAGYdFRP8dx1wRERERZTCenp7w9fVFUlISjI2NMXLkSLi6uiJnzpyYP38+Ro8ejcTERPz6668IDw+Hr68vMmfOzKqARF+Jt+uJiIiI0rnkSZG/vz9WrFgBHx8fFC5cGOvWrcPixYsRHh4ODw8P9OzZE8bGxpgyZQoMDQ2xadMmAO/GZpmamurzaxCle0yuiIiIiNI5bWK1YsUKvH37FiNGjECzZs0AAJUqVYKlpSXmzJmD6tWro2XLlmjbti00Gg3GjRsHc3NzTJgwgYkVUQpgt0AiIiKiDOD58+ewt7fHvXv30K9fPyxYsECnYFObNm0QFhaGU6dOAYAyv9WgQYMwYcIEjB49Wp/hE2UIfHJFRERElM5pNBrkzp0b27Ztw5AhQ7Bnzx6EhYWhQIECEBGoVCpUqFABr1+/VroQ5siRA66urjA2NoaDg4O+vwJRhsAnV0RERETpmLe3N+Lj4+Hl5YVMmTLh+vXrcHV1hUajgZ+fH/LkyYPMmTOjUaNGsLS0VMZYabGIBVHK4ZMrIiIionTM2NgYo0ePhpmZGQYPHoyyZcti3bp1+OWXX1CjRg0UK1YM5cuXR2RkJA4dOgQAytMsAEysiFIQn1wRERERpROfesr0559/YsCAAZgyZQqGDh2KTJky4dq1axg6dCgCAwNx8uRJlC9fHgB0xmERUcpickVERESUzgQFBaFMmTI6yxYsWIBBgwZhypQpGDJkCExMTHDt2jW4urrCwMAAp06dQpYsWdgNkOgb4p5FRERElMbFx8cr/3348GGUK1cOa9eu1WkzYMAATJs2DWPGjMHSpUvx5s0blCtXDuvXr4ehoSFKly6NmJgYJlZE3xD3LiIiIqI07MCBA/j9999x5swZAMCPP/6I4cOHo1evXli3bp1OW2dnZ2TOnBkDBw6En58fAKBs2bJYvnw5ChUqhIiIiFSPn+h7wg63RERERGnUihUrMGbMGDRr1kynXPqMGTNgYGCArl27AgBcXV0BACYmJhgwYAAqVaoEFxcXpX3FihVx+PBhZMqUKTXDJ/ruMLkiIiIiSoM2bNiAAQMGYMWKFWjSpAksLCx01k+bNg1qtRqdOnXCrVu3ULZsWaxatQoigsmTJwPQLV7BxIro22NBCyIiIqI05tmzZ2jbti1at26N/v37K8tjYmIQFBQEtVqNmjVrAgCmT5+O+fPnw8zMDHnz5sXBgwdhbGysr9CJvmt8ckVERESUBkVERKBAgQLK64ULF+Lw4cPw8/ODlZUVihcvjmPHjsHNzQ3t2rWDsbExLC0tYWBgwHLrRHrCghZEREREaVBUVBR2796Nw4cPo3Xr1li4cCHy5MmD/fv3Y968eXj8+DEmTpwIAChcuDDy588PAwMDaDQaJlZEesI9j4iIiCiNyZMnD1auXIlWrVrh8OHDMDc3x9y5c1GxYkXkypULr169goWFBTQaDQBApVIp72WpdSL9YXJFRERElAb99NNPuHXrFmJiYlCsWLEP1pubmyN//vx6iIyIPoUFLYiIiIjSkWfPnqFbt254/vw5Tp48CUNDQ32HRET/j0+uiIiIiNKB58+fY+nSpThx4gQiIiKUxEqtVjPBIkoj2CmXiIiIKB149OgRTp48CWtra5w6dQrGxsZISkpiYkWUhrBbIBEREVE6ERkZiWzZskGlUvGJFVEaxOSKiIiIKJ0REZ0KgUSUNrBbIBEREVE6w8SKKG1ickVERERERJQCmFwRERERERGlACZXREREREREKYDJFRERERERUQpgckVERERERJQCmFwRERERERGlACZXRESUbty/fx8qlQqXLl1KM/8vBwcHDBky5JvHQ0REaR+TKyIioo8oVKgQnjx5gnLlygEAjhw5ApVKhcjISP0GRkREaZaRvgMgIiJKaxISEpApUyZYWlrqOxQiIkpH+OSKiIjSlH379qFOnTrInj07cuXKBScnJ9y5c+eT7f39/VGyZEmYmpqifv368PX1/eAJk5+fH8qWLQsTExMULVoUs2bN0vmMokWLYuLEiejcuTMsLCzQu3dvnW6B9+/fR/369QEAOXLkgEqlQteuXZX3azQauLm5IWfOnLC0tMS4ceN0Pl+lUmHx4sVwcnJClixZULp0aZw+fRq3b9+Gg4MDsmbNilq1aul8z8uXL6N+/fowNzeHhYUF7OzscO7cuf/+wxIR0TfH5IqIiNKU2NhYDBs2DOfOncOhQ4dgYGAAFxcXaDSaD9reu3cPrVu3RosWLXD58mX06dMHXl5eOm3Onz+Ptm3bon379rh69SrGjRuHMWPGYOXKlTrtZs6ciYoVK+LixYsYM2aMzrpChQrBz88PAHDz5k08efIE8+bNU9b7+voia9asCAwMxPTp0zFhwgT89ddfOp+hTd4uXboEW1tbuLq6ok+fPvD09MS5c+cgIhgwYIDSvmPHjihYsCDOnj2L8+fPw8PDA8bGxv/pNyUiolQiREREadizZ88EgFy9elXu3bsnAOTixYsiIuLu7i7lypXTae/l5SUA5NWrVyIi4urqKg0bNtRpM3LkSClTpozyukiRItKiRQudNu//v/7++2+dz9X64YcfpE6dOjrLqlWrJu7u7sprADJ69Gjl9enTpwWALFu2TFm2fv16MTU1VV6bm5vLypUr/+WXISKitIZProiIKE25desWOnTogOLFi8PCwgJFixYFADx8+PCDtjdv3kS1atV0llWvXl3n9Y0bN1C7dm2dZbVr18atW7egVquVZVWrVv3PMVeoUEHntZWVFSIiIj7ZJl++fACA8uXL6yx7+/YtoqKiAADDhg1Dz5490aBBA0ydOvVfu0YSEVHawOSKiIjSFGdnZ7x8+RJLlixBYGAgAgMDAbwrMvEtZc2a9T+/9/3ueiqV6oNujMnbqFSqTy7Tvm/cuHG4fv06HB0dcfjwYZQpUwbbtm37zzESEdG3x+SKiIjSjBcvXuDmzZsYPXo0fvrpJ5QuXRqvXr36ZPtSpUp9UOTh7NmzOq9Lly6NkydP6iw7efIkbGxsYGho+NmxZcqUCQB0nnZ9azY2Nhg6dCgOHDiAli1bYsWKFan2/yYioi/H5IqIiNKMHDlyIFeuXPDx8cHt27dx+PBhDBs27JPt+/Tpg+DgYLi7uyMkJASbNm1SClVonwQNHz4chw4dwsSJExESEgJfX18sWLAAI0aM+KLYihQpApVKhV27duHZs2eIiYn5z9/zf3nz5g0GDBiAI0eO4MGDBzh58iTOnj2L0qVLf7P/JxERfT0mV0RElGYYGBhgw4YNOH/+PMqVK4ehQ4dixowZn2xfrFgxbNmyBVu3bkWFChWwcOFCpVqgiYkJAKBKlSrYtGkTNmzYgHLlyuG3337DhAkTdEqpf44CBQpg/Pjx8PDwQL58+XQq+6U0Q0NDvHjxAp07d4aNjQ3atm2Ln3/+GePHj/9m/08iIvp6KhERfQdBRESUUiZPnoxFixYhNDRU36EQEdF3xkjfARAREX2NP//8E9WqVUOuXLlw8uRJzJgx45s+VSIiIvoUJldERJSu3bp1C5MmTcLLly9RuHBhDB8+HJ6envoOi4iIvkPsFkhERERERJQCWNCCiIiIiIgoBTC5IiIiIiIiSgFMroiIiIiIiFIAkysiIiIiIqIUwOSKiIiIiIgoBTC5IiIiIiIiSgFMroiIiIiIiFIAkysiIiIiIqIU8H9az+aezE+V2wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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JJ9vBCgCA8hCcfI7gBADS7NnS8OEHf83+/dKQIdKaNdHpCQDgLQQnn+vSRWrVyqlnz5Z+/91cPwBgwj//WfaaZUnBoPvavn3Sc89FpycAgLcQnHwuEHA/dSostEdSACBe7NhR/tP2vLwkXXbZABUVBVzX33gjSo0BADyF4BQHGNcDEM82bLCfLpUVUG5uigoKEsu8vqgoKq0BADyE4BQHTjlFyshw6smT+aYAQPxITz/42y3L/cQpJUVK4F9HAEAp/NMQB9LSpL59nXrbNrbdBRA/2rSRmjev/Ot79nSfgQcAgERwihuM6wGIV4mJ0rXXVv71N9wQuV4AAN5FcIoTZ5zhrjmrBEA8GTZM6tSp4tf16iWdfXbE2wEAeBDBKU40bSode6xT//yztHq1sXYAIKrq1pU+/VTq3v3grzviCPsJFQAApRGc4kjpcb1Jk8z0AQAmNG8uzZsnZWY61zIyChQIOFvu/ec//FAJAFA+glMcGTzYXTOuByDebN8u7dnj1J067dBllznBaf9+afRoA40BAGIewSmOdOsmNWvm1J9/7v4GAgD87tdf3XXLlrs1alSRUlKca2++aY8zAwAQiuAURwIB9yYR+/fbM/8AEC9KB6cWLXardWvpxhuda5YljRwZ3b4AALGP4BRnGNcDEM+ys911y5a7JUkjRki1azvXP/lE+uqrKDYGAIh5BKc4c9pp9oG4xSZNkoJBc/0AQDSV98RJkho2lP72N/fb7rzTfvoEAIBEcIo7GRnSqac69ebN0oIF5voBgGgKDU7NmlnKzCwsqW+7zQ5Qxb74Qpo6NYrNAQBiGsEpDpXelnziRDN9AEA0bd9u/7CoWFaW+3FS7drS3Xe732fECJ7KAwBsBKc4VDo4sc4JQDwovb6pdHCSpL/8RWrd2ql/+EF6990INwYA8ASCUxxq2VLq2tWpv/9eWr/eXD8AEA2l1zdlZZV9TWqqdP/97mujRtm7kAIA4hvBKU6Vfuo0aZKZPgAgWsoGp/J3frjkEunII5165UrpP/+JYGMAAE8gOMUptiUHEG8qG5wSE6WHH3Zfu/9+KTc3Qo0BADyB4BSnjjvOvXvUp59Ke/ea6wcAIi00ODVsKB166IFfO2iQdOKJTr15s/Tkk5HrDQAQ+whOcSohQTrjDKfOy5M+/9xcPwAQSTk57rWcnTsf/PWBgPTII+5rjz5q78wHAIhPBKc4xrgegHhReke9ioKTJP3xj9LAgU6dk2OHJwBAfCI4xbG+faXkZKeeOFGyyh/5BwBPK72+qTLBSSq71umpp6R168LTEwDAWwhOcax2bal3b6dev94+swQA/Ka6walrV+lPf3LqvDzpvvvC1xcAwDsITnGOcT0A8aC6wUmyd9RLSnLqV16RFi8OT18AAO8gOMW50A0iJHtcDwD8JjQ4HXKI1Lhx5d+3XTvp2mudOhi0D8UFAMQXglOca9vW/ZPX+fPtbXcBwC9yc6XVq526c2d717yqGDVKyshw6vHjpW+/DUt7AACPIDihzLjepElm+gCASCg9VleVMb1iTZpIt93mvnbXXdXvCQDgPQQnaNAgd824HgA/Kb2+KSureh/n73+X6td36k8/tX8BAOIDwQn6wx/c3wxMny7l55vrBwDCqTpnOJWnbl1pxAj3tREjOMYBAOIFwQlKSnIf8pibK82aZawdAAirmuyoV9qNN0rNmzv1d99JH3xQ/Y8HAPAOghMkMa4HwL9Cg1NmptSiRfU/Vnq6dO+97msjR0qFhdX/mAAAbyA4QZLUv7/7nJKJExk/AeB9eXnSihVOXZ0d9Ur785+ljh2deskS6bXXavYxASAWrV5t7yo6ZIhdjxwp/fKL0ZaMIjhBklSvnvTHPzr16tXx/QcDgD8sXWqfu1SsJmN6xZKSpIcecl+7915p376af2wAiAUFBfZoctu20oMPSlOn2tefflo68kjpggukPXvM9mgCwQklGNcD4DfhXN8U6txzpWOPder166VnngnPxwYAkyzLfrL+7LMHnj4aP94+zmb//qi2ZhzBCSUITgD8JlLBKRCQHnnEfe3hh6Vdu8Lz8QHAlMmTpXfecV+zrLIhatYs6T//iVpbMYHghBIdOti/in39tbRtm7l+AKCmIhWcJOm006Q+fZx6507pscfC9/EBwITynp7n5yfpX/86pkx4euaZ+FoTT3CCS+hTp2BQmjLFXC8AUFOhwSk9XWrdOrwf/+GH3fW//iVt2hTezwEA0RIMln+wt2UFNGdOS+XnJ7mu//qrtHFjlJqLAQQnuDCuB8AvCgqkZcuculMnKSHM/+odd5x0/vlOvXev9MAD4f0cABAt+fn2350HYllltyXdvTuCDcUYghNcevaU6tZ16qlT42/hHwB/WL7cfb5SOMf0Qj34oJSY6NQvvujeAh0AvCItTapT58BvT0wsKnOtYcMINhRjCE5wSU6WTj/dqXNypC+/NNcPAFRXJNc3herYUbrySqcuLJTuuScynwsAIikQkC68sPy3tW+/U8nJQde1fv2k+vWj0FiMIDihDMb1APhBtIKTJI0ebf+kttg770iLFkXu8wFApNx8c3lXLf3tb9+VOUB82LBodBQ7CE4oY8AA9zqACRPia8cUAP4QzeDUokXZbzZGjozc5wOASFm4sOy1lJQiNW6813Xt9tulgQOj1FSMIDihjEMPlU480amXL5eWLjXXDwBUR2hwSkmR2raN7Oe78073GtHJk6U5cyL7OQEgnH75RbrhhrLXExOdn6A3a2ZvQ176LLt4QHBCuRjXA+BlhYXSkiVO3bGjlJR04NeHQ/369k9gQ40YwRN7AN6QmytdcIG0b59z7bjj7I3CHnrIrseOlX77zQ5Xpcf24gHBCeUaPNhdT5hgpg8AqI5Vq+xtdYtFckwv1LBhUuPGTv3VV/zgCYA33HijlJ3t1HXr2kGpf3/pppvsa6efHvkfQsUyghPKlZUlHXaYU3/5pbRzp7l+AKAqorm+KVStWmV31BsxQioqu4MvAMSM116TXn/dfe3VV93fC4LghAMIBNzjekVF0rRp5voBgKoI/ampZP8wKFquuca9nuqXX6S3347e5weAqihvXdOwYdI555jpJ5YRnHBAjOsB8CpTT5wkeyOKBx5wX7vnHvfoIADEgtxc+9ym0uua/vEPcz3FMoITDujkk6XMTKeeMsVecA0AsS40OCUmSu3bR/fzX3SR1LWrU//2m/TCC9HtAQAqctNN7r8vi9c1paSY6ymWEZxwQKmp9onQxXbulL7+2lw/AFAZwaB7VK99++h/E5CQII0Z47724IPS7t3R7QMADuS11+xfoVjXdHAEJxwU43oAvGbNGmlvyDmN0RzTC3X66faT+2Jbt0r/+peZXgAgFOuaqofghIMaMMC9Tz/b6gKIdSbXN4UKBMo+dXrsMTtAAYAprGuqPoITDqpxY+n44506O1tascJcPwBQkVgJTpJ04onSmWc69e7d0sMPm+sHAFjXVH0EJ1So9LgeT50AxLJYCk6S9NBD7if3zz5rbxYBANHGuqaaITihQqHnOUkEJwCxLTQ4JSRIHTqY60WSjjxSuuwyp96/X7r3XmPtAIhTrGuqOYITKnTUUVLLlk49e7aUk2OuHwA4EMty76h32GFSerq5fordd597DOaNN+xvYgAgGspb13TssaxrqiqCEyoUCLifOhUUSNOnm+sHAA5kwwb3D3ZMj+kVa9NGuv56pw4GpbvvNtYOgDjDuqbwIDihUhjXA+AFsba+KdRdd7kPFf/oI2nePGPtAIgTr79edl3TK69IbdsaacfTCE6olFNOcY+7TJ4sFRWZ6wcAyhPLwalRI+mvf3Vfu/NOe7wQACLh11/Lrmu65Rbp3HPN9ON1BCdUSnq61LevU2/dKs2fb64fAChPLAcnSRo+XGrQwKlnz2b0GUBk5OZKF1zgPhCcdU01Q3BCpTGuByDWlQ5OnTqZ6eNA6tSRRo50X7vzTnvNEwCE0803l7+uKTXVXE9eR3BCpZ1xhrsmOAGIJZbl3qmudWv3mqJYcd11UqtWTr1okfTee8baAeBDr79un88UinVNNUdwQqU1ayZ17+7UP/7IIY4AYseWLdLOnU4da2N6xdLS7O3JQ919t71jKQDUFOuaIofghCopPa43aZKZPgCgtFhf3xTqssvc/a1YIb38srl+APgD65oii+CEKmGdE4BYFXrwrSRlZZnpozISE6WHH3Zfu+8+9zc7AFBVrGuKLIITquSYY6SmTZ36s8/sn24AgGleeuIkSWeeKf3hD069aZP073+b6weAt7GuKfIITqiShAT3JhH5+dKnn5rrBwCKlQ5OsfzESZICAemRR9zXHn3UvU4LACqjvHVNN9/MuqZwIzihyhjXAxCLQoNTs2ZSvXrGWqm0Xr2k00936l27yoYpADiYvXulCy90j/p27y793/+Z68mvCE6osj593LOyEydyBgkAs7ZvlzZvdupYH9MLVXqt07//La1fb6YXAN5z883uoxjq1LGPOGBdU/gRnFBltWpJp57q1Js2SQsXmusHAEpvDOGl4NStm3TRRU6dlyfdf7+5fgB4xxtv2OuYQrGuKXIITqgWxvUAxBKvbQxR2gMPSElJTv3yy9LSpeb6ARD7srOl6693X7v5Zum888z0Ew8ITqiW0sFpwgQzfQCA5P3gdPjh0tChTl1UJI0aZa4fALFt796y5zWxrinyCE6ollatpKOOcuqFC5nJB2CO14OTZAel9HSnfu89acECc/0AiF2sazKD4IRqK/3UafJkM30AQGhwathQOvRQc71UV9Om0q23uq/ddZeRVgDEMNY1mUNwQrUNHuyuWecEwIScHPcTby8+bSp2++3SIYc49fTp9kHjACCxrsk0ghOq7bjj7J/sFpsxQ9q3z1w/AOKTl3fUK61ePenOO93XRoyQLMtIOwBiCOuazCM4odoSE6WBA5163z7p88/N9QMgPvlhfVOom26yD/AtNn++9OGH5voBEBvKW9c0dizrmqKJ4IQaYVwPgGl+C04ZGdLo0e5rI0dKhYVm+gFgXnnrml5+WWrXzkw/8YrghBrp21dKTnbqiRMZKQEQXX4LTpJ01VVShw5OvXix/Y0TgPhT3rqmm26Szj/fTD/xjOCEGqlTR+rVy6nXrpV+/NFcPwDiT2hwOuQQqXFjc72ES1KS9OCD7mujR0t5eWb6AWDG3r3ShRe61zUdc4z02GPmeopnBCfUWOltyRnXAxAtubnS6tVO3bmzFAgYayeszjvPXvhdbN066dlnzfUDIPpuuUX6+Wen5rwmswhOqLHSwWnCBDN9AIg/ixe7az+M6RVLSJDGjHFfe/hh6fffzfQDILrefNNexxSKdU1mEZxQY+3aSVlZTj1/vrR5s7l+AMSP0uubQv8u8oM+faRTT3Xq7dsZ0QHiQXa2dN117musazKP4ISwCH3qZFnSlCnmegEQP/x0hlN5AoGyT50ef5wfTgF+xrqm2EVwQliU3paccT0A0eDHHfVKO/546dxznXrv3rIbRwDwD9Y1xS6CE8KiRw97N6ti06dL+fnm+gEQH0KDU2am1KKFuV4i6cEH7TVPxV54QVq50lw/ACKDdU2xjeCEsEhKkgYMcOo9e6Q5c8z1A8D/8vKkFSuc2k876pWWlSVdeaVTFxSUPSQXgLeVt67pxhtZ1xRLCE4IG8b1AETT0qVSMOjUfhzTCzV6tHtU5+23OTcP8AvWNXkDwQlh07+/lJjo1BMn2htFAEAkxMP6plAtW9q7ahWzLGnkSHP9AAifA61rSksz1xPKIjghbA45ROrZ06lXrSq74xUAhEu8BSdJGjHC/oaq2MSJ0pdfmusHQM299VbZdU3/+Q/rmmIRwQlhxbgegGiJx+B06KHS3//uvnbnnTzdB7xq8eLy1zVdcIGZfnBwBCeEVeh5TpL901AAiITQ4JSeLrVuba6XaLr1VqlRI6eeO1eaNMlYOwCqae9eOyDl5jrXWNcU2whOCKsOHaTDD3fqr76yT7oHgHAqKJCWLXPqTp3c23X7WWamNGqU+9qIEVJRkZl+AFTPsGGsa/KaOPlnBtESCLjH9YJBacoUc/0A8Kfly6XCQqeOhzG9UNdeKx12mFP//LP03/+a6wdA1bz1lr2OKRTrmmIfwQlhx7gegEiLx/VNoVJSpAcecF8bNUrav99MPwAqj3VN3kVwQtj17One9WnqVHusBgDCJd6DkyRdfLF01FFOvXq19OKLxtoBUAmsa/I2ghPCLiVFOv10p/79d7bLBRBeBCd7TdfDD7uvPfCAtGePmX4AVKz0uqbataWxY1nX5BUEJ0QE43oAIik0OKWkSG3bmuvFpIED3efnbdki/etf5voBcGAHWtcUuqkWYhvBCRExYIB7hyuCE4BwKSyUlixx6o4dpaQkc/2YFAhIY8a4r/3f/0nbtpnpB0D5ylvXdMMN0oUXmukH1UNwQkQ0aCD16OHUS5favwCgplatkvLznToex/RC9ezpfsq/e3fZMAXAnPLWNXXrJv3zn+Z6QvUQnBAxjOsBiITS65uyssz0EUsefth++lTsmWekNWvM9QPAUd66Js5r8iaCEyKG4AQgErKz3XW8P3GSpC5dpEsvder8fOm++8z1A8D29tusa/ITghMi5ogjpDZtnPqLL6Rdu0x1A8Av2FGvfPfdJyUnO/Vrr5UNmQCiZ8kS6S9/cV9jXZO3EZwQMYGA+6lTYaE0bZq5fgD4Q2hwSkyU2rc310ssOeww9+LzYFC6+25z/QDxbN8+1jX5EcEJEcW4HoBwCgbdT1Hat7e3I4dt5EipVi2n/uADaf58c/0A8WrYMOmnn5yadU3+QHBCRPXu7f5HfPJk+8kTAFTHmjX2DlXFGNNza9xYGj7cfe3OOyXLMtMPEI/eflt66SX3NdY1+QPBCRGVmir16+fUO3ZI8+aZ6weAt7G+qWJ//at06KFO/fnn0owZ5voB4kl565quv551TX5BcELEMa4HIFwIThWrW1e66y73tREj7DFHAJFT3rqmo4+WHn/cWEsIM4ITIu6MM9z1hAlm+gDgfQSnyrnhBqllS6deuFAaP95cP0A8YF2T/xGcEHGNG0vHH+/Uv/4qrVxprh8A3hUanAIBqUMHc73EsrS0suc43X23VFBgph/A78pb1/TSS+z66TcEJ0RF6XG9SZPM9AHAuyzLvaNe27ZSerq5fmLdZZdJWVlOvWyZ9Oqr5voB/OpA65qGDDHTDyKH4ISoGDzYXTOuB6CqNmyQcnKcmjG9g0tKkh56yH3tvvvcuxICqBnWNcUXghOiomtXqXlzp541S9q921g7ADyI9U1Vd/bZ7lHpDRukp5821g7gO7feyrqmeEJwQlQEAu5xvYICtscFUDUEp6oLBKRHHnFfGzNG2rnTTD+An7zzjvTii+5rrGvyN4ITooZxPQA1QXCqnlNOcZ+nt2uX9I9/GGsH8AXWNcUnghOi5tRT3Qu5J03iXBEAlVc6OHXqZKYPLxozxl0/+aQ9tgeg6vbtsw+03bPHuca6pvhAcELUpKdLp53m1Fu3St9+a64fAN5hWdIvvzh169ZSZqa5frzmmGPcPwnft0964AFz/QBeduut0o8/OnVmJuua4gXBCVHFuB6A6tiyxb0uhzG9qnvgASkx0alfesneohxA5bGuKb4RnBBVZ5zhridONNMHAG8pPaYXej4RKqd9e+maa5y6qEi65x5z/QBeU966puuuky66yEw/iD6CE6KqeXOpWzen/uEHae1ac/0A8IbQg28lnjhV1z33uMeJ3n1X+v57c/0AXlHeuqauXaV//ctcT4g+ghOirvS4Hk+dAFSEHfXCo1kzadgw97URI8z0AnhJeeuaxo1jXVO8ITgh6kLPc5IITgAqxqhe+Nxxh1SvnlNPmyZ9/rmxdoCYx7omFCM4Ieq6d5eaNHHqmTOl3Fxz/QCIfaHBqVkz9zf+qJpDDrHDU6gRI+ydCwG4LV3KuiY4YiI4PfPMM2rTpo3S0tJ0wgknaP78+Qd8bUFBge6//361a9dOaWlp6tq1q6ZOnRrFblFTCQnuTSLy8+3wBADl2b5d2rzZqRnTq7lbbpGaNnXqb76RPv7YXD9ALGJdE0ozHpzGjh2r4cOHa/To0Vq4cKG6du2q/v37a8uWLeW+/u6779YLL7ygp556Sr/++quuu+46nXPOOfqe1a2ewrgegMpiY4jwy8iQRo92X7vrLnunPQC2226zN7EqxromGA9Ojz/+uIYOHaorr7xSnTt31vPPP6+MjAy98sor5b7+zTff1F133aWBAweqbdu2uv766zVw4ED985//jHLnqIk+faSUFKeeOJExEQDlY2OIyLjqKunww506O1t6801z/QCx5L//lV54wX2NdU1IMvnJ9+/frwULFmhEyJY+CQkJ6tOnj77++uty3yc/P19ppaJ+enq6vvzyywO+Pj8/v6TOycmRZI/8FRQU1PRLiDnFX1Osf22pqdIppyRq2jQ7u2/cKM2fX6BjjjHcmCFeuW8oi3sXeT//nCDJObm1Q4dCFRTU/Cct3Dvp3nsDuvRS51uB0aMtnXdeYcz/RJ17501euW9Ll0rXXpskKVBy7dpri3TeeUHFeOsR45V7Vx1V+ZqMBqdt27apqKhIjRs3dl1v3LixFi9eXO779O/fX48//rhOPvlktWvXTjNnztQHH3ygogPMF4wZM0b33XdfmevTp09XRkZGzb+IGDVjxgzTLVSodevDJB1VUj/xxApddNEScw3FAC/cN5SPexc5c+b0kNSopF63boYmT94fto8fz/cuI0Nq27aXVq6sJ0lasyagW29drDPPXGm2sUqK53vnZbF83/LzE3THHSdrz566JdfatPldffrM0eTJQYOdxYZYvnfVtXfv3kq/NmBZ5gakNmzYoObNm+urr75Sjx49Sq7ffvvtmj17tr755psy77N161YNHTpUEyZMUCAQULt27dSnTx+98sor2rdvX5nXl/fEqWXLltq2bZvq1KkTmS/MoIKCAs2YMUN9+/ZVcnKy6XYO6rffpPbtnR67dw/q66/jc8DeS/cNbty7yGvbNknr1tk/+W3Y0NL69YVh+bjcO9v06QENGuT8HLVBA0uLFxcqlv+J5N55kxfu2403Juill5wn3JmZlubNK1SHDgabigFeuHfVlZOTowYNGuj333+vMBsYfeLUoEEDJSYmanPodkmSNm/erCah+1WHaNiwoT766CPl5eVp+/btatasme688061bdu23NenpqYqNTW1zPXk5GTf3fhQXvj6Dj9c6tJF+uknu16wIEFbtyaoWTOzfZnkhfuG8nHvIiMnR1q3zqk7dw6E/fc53u/dwIFS797SrFl2vW1bQP/+d7LKGdaIOfF+77wqVu/bf/9rr2MK9eKLAR1xROz1akqs3ruaqMrXY3RziJSUFHXv3l0zQ/aiDgaDmjlzpusJVHnS0tLUvHlzFRYW6v3339dZZ50V6XYRAaV315s82UwfAGITO+pFXiAgjRnjvvbPf0oH2NwW8CV7XZP72l/+Il18sZl+EJuM76o3fPhwvfTSS3r99deVnZ2t66+/Xrm5ubryyislSZdffrlr84hvvvlGH3zwgVauXKkvvvhCp59+uoLBoG6//XZTXwJqgG3JARwMO+pFxx/+IJ19tlPn5koPPWSsHSCqOK8JlWU8OA0ZMkSPPfaY7rnnHh199NFatGiRpk6dWrJhxJo1a7Rx48aS1+fl5enuu+9W586ddc4556h58+b68ssvVY9j5D3phBOkBg2cesYMKS/PXD8AYgvBKXoeesg+oLzYc89Jq1cbaweImvLOa3rvPSk93VxPiE1G1zgVu+mmm3TTTTeV+7ZZxUPX/9OrVy/9WvpfUnhWYqI9X//GG3a9d6/0+efSgAFm+wIQGwhO0dO5s3TFFdKrr9p1QYF9SO7rr5vtC4ikd98te17Tiy8q7jeDQPmMP3ECGNcDcCChwemQQ6RSp1cgzO691304+ZtvSj//bKwdIKKWLZOGDnVfY10TDobgBOP695eSQp59TpwomdskH0CsyM11j4p17mxvZIDIadVKuvFGp7Ys6a67zPUDREpennTBBaxrQtUQnGBcnTpSr15OvWaNs0U5gPhV+hz0rCwzfcSbESOk2rWdesIEae5cc/0AkcC6JlQHwQkxgXE9AKWxFbkZDRtKf/ub+9qddzIJAP94913p+efd11jXhMogOCEmDB7srglOANgYwpzbbrMDVLEvv5SmTDHXDxAu5a1ruvZa1jWhcghOiAnt2kmdOjn1vHkcvgjEO4KTObVrS6NGua+NGCEFg2b6AcKhvHVNRx0lPfGEsZbgMQQnxIzQcT3L4qebQLwLDU6ZmVKLFuZ6iUfXXiu1aePUP/5ojzgBXsW6JtQUwQkxg3E9AMXy8qQVK5yaHfWiLzVVuv9+97VRo6T9+830A9TEgdY1dexoph94E8EJMePEE6V69Zx62jT+gQbi1dKl7rEwxvTM+NOfpCOPdOqVK6X//MdcP0B1sK4J4UJwQsxISpIGDHDq3bulOXPM9QPAHNY3xYbEROnhh93X7r/fvUYEiGWsa0I4EZwQUxjXAyARnGLJoEH2RECxzZulJ5801w9QFaxrQjgRnBBT+ve3f8JZbMIEzg4B4lHp4MTht+YEAtIjj7iv/eMf0vbtZvoBKmvsWNY1IbwITogp9etLJ53k1CtXSosXm+sHgBmhwSk9XWrd2lwvkP74R2ngQKfOySkbpoBYwromRALBCTEndFtyiXE9IN4UFNjf9BTr1Mn9JBpmjBnj3tnwqaekdevM9QMcSF6edOGF9lrpYqxrQjgQnBBzSq9zmjDBTB8AzFi+XCosdGrWN8WGo46yd9krlp8v3XefuX6AAxk+XFq0yKlr1WJdE8KD4ISY07Gj1K6dU8+dK+3YYa4fANHFxhCx6/777R1Qi73yCuPUiC1jx0rPPee+9sILrGtCeBCcEHMCAfe4XjAoTZ1qrh8A0UVwil1t20p/+YtTB4PS3Xeb6wcIVd66pqFDpUsuMdMP/IfghJjEuB4QvwhOse3uu6WMDKd+/33p22/N9QNI5a9r6tKFrfMRXgQnxKQ//lGqXdupp061F4wD8L/Q4JSSYj/lQOxo0sQ+GyfUiBFmegGKlbeuadw41jUhvAhOiEkpKfaZTsV27ZK++spYOwCipLBQWrLEqTt2dK+pQWz4+9/t4yOKzZwpffqpuX4Q31jXhGghOCFmMa4HxJ9Vq+zd2ooxpheb6tYt+5Tpzjs5sBzRx7omRBPBCTFrwAD3mSGc5wT4X+n1TVlZZvpAxW68UWrRwqkXLLDXOwHRwromRBvBCTGrYUPpD39w6iVL3IdiAvCf7Gx3zROn2JWeLt17r/vayJHuM7iASPrrX1nXhOgiOCGmlR7X46kT4G/sqOctV1zhXkeydKn02mvG2kEcee896dln3ddY14RIIzghpoWe5yQRnAC/Cw1OiYlS+/bmekHFkpKkhx5yX7v3XmnfPiPtIE4sXy5dc4372jXXsK4JkUdwQkw78kipVSunnjNH+v13c/0AiJxg0D2q1769vcMmYtu550rHHuvU69dLTz9trh/424HWNf373+Z6QvwgOCGmBQLucb3CQmnaNHP9AIicNWukvXudmjE9bwgEpEcecV8bM8Y+RgIIt7/+Vfr+e6euVcse22NdE6KB4ISYx7geEB9Y3+Rdp50m9enj1Dt3Sv/3f+b6gT8daF1Tp05m+kH8ITgh5vXubf9EqdjkyVJRkbF2AEQIwcnbxoxx1088IW3caKQV+BDrmhALCE6IeWlpUt++Tr19uzRvnrl+AEQGwcnbjj1WuuACp967V3rwQXP9wD9Y14RYQXCCJzCuB/hfaHAKBKQOHcz1gup54AF7N8RiL74orVhhrh/4A+uaECsITvCEgQPdNcEJ8BfLcu+o17Yt3xR5UceO0lVXOXVhoTRqlLl+4H2sa0IsITjBE5o2lY47zql//llavdpYOwDCbMMGKSfHqRnT86577rFHrIv997/SokXG2oGHsa4JsYbgBM9gXA/wL9Y3+UeLFtLNN7uv3XWXmV7gXaxrQiwiOMEzCE6AfxGc/OXOO6W6dZ16yhRp9mxz/cB7/vY31jUh9hCc4BnduknNmjn155+7fxIFwLsITv5Sv750++3uayNG2GvZgIqMGyc984z72vPPs64J5hGc4BmBgPup0/790qefmusHQPiUDk58g+R9w4ZJTZo49ddfSxMmmOsH3rB8uXT11e5rV18tXXqpmX6AUAQneArjeoD/WJb0yy9O3bq1lJlprh+ER61a9kYRoe66iwPMcWDlrWs68kjWNSF2EJzgKaed5t6tadIkKRg01w+AmtuyRdq506kZ0/OPa66R2rVz6l9+kd5+21w/iG3lrWsaN07KyDDXExCK4ARPyciww1OxzZul774z1w+Amis9ppeVZaYPhF9ysn0obqh77pHy8830g9jFuiZ4AcEJnsO4HuAvoQffSjxx8pshQ6SuXZ36t9/sb4iBYqxrglcQnOA5pYMTi40Bb2NHPX9LSJDGjHFfe/BBdkWFjXVN8BKCEzynRQvp6KOdetEiad06U90AqClG9fzv9NOlk0926m3bpMcfN9cPYgfrmuAlBCd4UumnTpMmmekDQM2FBqdmzaR69Yy1gggJBKRHHnFfe+wxaetWM/0gNowfz7omeAvBCZ40eLC7ZlwP8Kbt2+1NXooxpudfPXpIZ53l1Hv2SA8/bK4fRF/oAcgrVpRd13TVVaxrQmwjOMGTjj1WatTIqWfOlPbuNdcPgOphY4j48tBD9tOnYs8+a28WAf+yLHsTpwEDpIYN7WstW0p/+IOUk+O87ogjpKeeMtMjUFkEJ3hSQoJ0xhlOnZcnffaZuX4AVA8bQ8SXI46QLr/cqffvl0aPNtcPImv/fntXxcGDpalTpYIC+/q2bQnats15XUYG65rgDQQneBbjeoD3EZziz733SikpTv3GG9LPPxtrBxF04412IAr11VdNVVSU6Lo2dCibwsAbCE7wrD593P/4Tpzonp8GEPvYUS/+tGkjXX+9U1uWdPfdxtpBhCxbJv3nP+5rwaD09NPdyrz200/59xveQHCCZ9WuLfXu7dQbNthbkwPwjtA1Tg0bSg0amOsF0XPXXVJmplN//LH09dfm+kH4vfRS2Wv79ydp797kMtd/+UX66qsoNAXUEMEJnsa4HuBdOTnuM9gY04sfjRpJf/2r+9qdd/LUweuCQfuHIa++Kr3zTtm3W1ag7MX/4Qef8AKCEzwtdIMIyR7XA+AN7KgX3/76V/cTxjlzpGnTzPWDqtu61f539+67pb59pfr17T/HV10lrV9ftY8VDEamRyCckkw3ANTEYYfZuzT98otdf/uttGmT1KSJ2b4AVIyNIeJb7dr2N9y33upcGzFC6tfP3jkVsSUvz34qNG+e9M039q9Vq6r+cTIyCrR/f4IKC90bRHTsGJ4+gUjiryZ4XulxvUmTzPQBoGoITrjuOqlVK6detEh67z1j7eB/LEtavlx6+23p5pul44+X6tSxDzG+7Tbp3XerF5pSUwv01luTlZzsfrzUurV02mlhah6IIIITPG/QIHfNuB7gDQQnpKZK99/vvnb33fb5P4ienTvtMcn775cGDrQ3amnfXrr0Uunpp+1pjuIzmA6meXPp3HOlRx+VZs+2D70NlZBQ/tPEu++WEhPLXgdiDaN68Lw//EE69FBp+3a7njHDHilISzPbF4CDCw1OhxwiNW5srheYc+ml0j/+4fz3sGKF9PLL7i3LET4FBdKPP9qjdsVjd0uXVv3jZGRIxx4rnXCC86tFC/druna1g9jBdsy76y7p6qur/vkBEwhO8LzERPsv5jfftOvcXGnWLOn00422BeAgcnOl1audunNnKXDgDbfgY4mJ0sMPS2ef7Vy7/37p8sulWrWMteULliWtWeMOSQsX2j9crIpAwD5jLTQkHXmklFTBd5F160qffSa98IL07LN2L8VOP91e39a/f5W/LMAYghN8YdAgJzhJ9rgewQmIXYsXu2sOvo1vZ55pTw/Mm2fXmzZJ//63vVkEKm/3bnusLjQobd5c9Y/TqJE7JB13nB2CqiM1VbrlFnut1Jo19jq21avtzwF4DcEJvtC/v/2Tr8JCu544UXrqKX6CDcQqtiJHqEBAeuQR96Hmjz4q/eUv9hbXKKuoyN5RtniHu3nz7HHHqp6FlZoqHXOMOyi1aRP+fz8DAalZMzs4HXJIeD82EC0EJ/hC3brSySfbIwGS9Ntv9j8oRx5pti8A5WNjCJTWq5e9mcCUKXb9++92eHr0UbN9xYoNG9wh6bvv7JHXqjr8cPvpXnFI6tpVSkkJf7+AHxGc4BuDBjnBSZImTCA4AbGK4ITyPPywE5wke1zvllvs3driyd690oIF7qC0bl3VP84hh9hbiRcHpeOPtzdTAlA9BCf4xqBB0vDhTj1xIvPxQKwKDU6ZmWV340J8Ovpo6eKLpf/+167z8qT77pNefNFoWxEVDEpLljgh6Ztv7F3vioqq9nGSkuynR6FPk9q3Z2QdCCeCE3yjfXv75PElS+z666+lbdukBg3M9gXALS/P3nK6GDvqIdT990vjxjlrVl95RfrrX+2/3/1g61Z3SJo/3x5LrKrWre1wVByUunWT0tPD3y8AB8EJvjJokBOcLEuaPNne0hZA7Fi61P4pezHG9BDq8MOloUOl556z66IiadQo6b33zPZVHfn50vffu0fuVq2q+sepXdve2S70aRLnngHRR3CCrwwaJP3zn049cSLBCYg1rG9CRUaNkl57Tdq3z67HjbM3Qzj2WKNtHZRl2U9SQ58mff+9feBsVSQk2OtzQ58mdepkn3cFwCyCE3zlpJOkevWkXbvseto0af9+dgwCYgnBCRVp2tQ+HHXMGOfaXXdJ06cba6mMnTvtMbvQoLR9e9U/TrNmzlOkP/xB6t7dXvcHIPYQnOArycn2wbfvvmvXOTnSF19Ip51mti8AjtLBicNvUZ7bb5eef94OKJI0Y4Y0c6aZv88LCuwNG0JDUvFYeFWkp9tPzUKDEhujAN5BcILvDBrkBCfJHtcjOAGxI/Tw2/R0e5E7UFq9evbOqLff7lwbMcIOLZFkWdLatfZ6pOKQtGCBvalJVWVluUPSkUfau98B8Cb++MJ3BgywZ8SLF59PmCA9/ji7dgGxoKDA3hyiGGs3cDA33SQ98YR9+Kskffut9NZb9nlEkh1yamr3bvvjhj5N2rSp6h+nYUMnJJ1wgr2ZQ716Ne8PQOwgOMF36te31zp98YVdr1hhj1R06mS2LwDS8uXONtMS65twcOnp0r33Stde61y7/HIpLc2eLDjpJOmaa6SrrrJHtStSVCT98os7JP3yS9UDWEqKdMwx7qB02GH8gA7wO4ITfGnQICc4Sfa4HsEJMI+NIVBVf/6zdOed0o4dzrWiIjuh/PKLdN110tix0ieflN1UYeNG98jdd99Je/ZUvYfDD3eHpK5dpdTU6n9NALyJ4ARfGjxYuuMOp544Ufrb38z1A8BGcEJVvfqqOzRJUmFhovLzE0rqzz+3n0QNH+4OSmvXVv3z1avnDknHH89B6gBsBCf4UqdOUtu20sqVdv3ll/bOTIccYrYvIN4RnFAVhYXSAw+UvW5ZAb33XkcVFjqzcR9+aP+qiqQk++lRaFBq395eJwsApRGc4EuBgD2u9+9/23VRkTR1qnTxxWb7AuJdaHBKSbF/wAEcyPTp0rp15b/t/fc7VPnjtWrlHCp7wgn2OqX09Bo2CSBuEJzgW4MHO8FJssf1CE6AOYWF7rNvOnZka2YcXOjW9VWVmWnvbBcalJo0CV9vAOIP/2TBt04+2f6Hs3gh8JQp9jdufKMGmLFqlZSf79QcfIuKVHVkrmVLafRoOyRlZbHVPYDwYooXvpWSIvXv79Q7d0pffWWuHyDelX56wPomVKRr1/KvJyQEdcIJG5WUVOS6fuml0tVX2wfNEpoAhBvBCb42eLC7njjRTB8A2BgCVde7t70VeGmpqUUaMWK+kpODJdcCAftMJwCIFIITfG3AAPeBhBMmmOsFiHcEJ1RVQoL00EOVe+3QoWw2AiCyCE7wtUaN7Fn3YosXS8uXm+sHiGehwSkx0d72GajIhRdKTz7p/iFYaRdcID31VPR6AhCfCE7wvUGD3PWkSWb6AOJZMOhe49S+vb0OEaiMW26RFi6UrrpKqlXLuX7qqdLHH0tjx/LfE4DIIzjB90qvc2JcD4i+NWukvXudmjE9VNXRR0svvyzt3i1t2GBf+/BD6cwzD/40CgDCheAE3+vSxd6ittjs2VJOjrl+gHjE+iaESyDgfuoEANFCcILvBQLucb3CQvs0egDRQ3ACAHgdwQlxgXE9wKzSwYnDbwEAXkNwQlw45RQpI8OpJ0+WiooO/HoA4RW6MUQgIHXsaK4XAACqg+CEuJCWJvXp49Tbtknz55vrB4gnluV+4tS2rZSebq4fAACqg+CEuMG4HmDGhg3uDVlY3wQA8CKCE+LGwIHueuJEM30A8YaNIQAAfkBwQtxo1kzq3t2pf/pJ+u03c/0A8YLgBADwA4IT4krpcT2eOgGRR3ACAPgBwQlxJfQ8J4ngBERD6eDUqZOZPgAAqAmCE+JKt25S06ZO/dln0p495voB/M6ypF9+cerWraXMTHP9AABQXQQnxJWEBPdTp/37pU8/NdcP4Hdbtkg7dzo1B98CALyK4IS4w7geED2sbwIA+AXBCXHntNOk1FSnnjRJCgbN9QP4WXa2uyY4AQC8iuCEuFOrlh2eim3aJC1YYK4fwM944gQA8AuCE+IS43pAdJQOTqxxAgB4FcEJcemMM9w1wQmIjNDg1KyZVK+esVYAAKgRghPiUqtWUteuTr1wobR+vbl+AD/avl3avNmpGdMDAHgZwQlxq/S43qRJZvoA/IqNIQAAfkJwQtxinRMQWWwMAQDwE4IT4tbxx0sNGzr1p59K+/aZ6wfwGzaGAAD4CcEJcSshwb1JxL590mefmesH8BueOAEA/ITghLjGuB4QOaFrnBo2lBo0MNcLAAA1RXBCXOvXT0pOduqJEyXLMtcP4Bc5OdK6dU7N0yYAgNcRnBDXateWevd26nXrpB9+MNYO4BvsqAcA8BuCE+Ie43pA+LG+CQDgNwQnxL3SwWnCBDN9AH5CcAIA+A3BCXGvbVv3N3Xz50ubN5vrB/ADghMAwG8IToDKPnWaPNlMH4BfhAanQw6RGjc21wsAAOFAcAIkDR7srhnXA6ovN1davdqps7KkQMBYOwAAhAXBCZD0hz9I9es79fTpUn6+uX4AL1u82F0zpgcA8AOCEyApKUkaMMCpc3Ol2bPN9QN4GVuRAwD8iOAE/A/jekB4sDEEAMCPCE7A//TvLyUmOvXEiZJlmesH8CqCEwDAjwhOwP/Uqyf98Y9OvXp12W8AAVQs9M9NZqbUooW5XgAACBeCExCi9LjexIlm+gC8Ki9PWrHCqTt3Zkc9AIA/EJyAEKXPc2KdE1A1S5dKwaBTM6YHAPALghMQokMHqX17p/76a2nbNnP9AF7D+iYAgF8RnIBSQsf1gkFp6lRzvQBeUzo4ZWWZ6QMAgHAjOAGlMK4HVB9PnAAAfkVwAkrp2VOqW9epp06VCgrM9QN4Sejht+npUuvW5noBACCcCE5AKcnJ0umnO3VOjvTll+b6AbyioMDeHKJYp07us9EAAPAyghNQDsb1gKpbvlwqLHRqxvQAAH5CcALKMWCAlBDyp4PznICKsb4JAOBnBCegHIceKp14olMvW+YeQQJQFsEJAOBnBCfgABjXA6qG4AQA8DOCE3AApYMT43rAwYUGp5QUqW1bc70AABBuBCfgADp3lg47zKm/+ELatctYO0BMKyyUlixx6o4dpaQkc/0AABBuBCfgAAIB91OnoiL7TCcAZa1aJeXnO3VWlrleAACIBIITcBCM6wGVE3rwrcT6JgCA/xCcgIPo1UvKzHTqKVPc59QAsLExBADA7whOwEGkpkr9+jn1jh3S11+b6weIVQQnAIDfEZyACjCuB1QsNDglJkrt25vrBQCASCA4ARUYONDeKKIYwQlwCwbda5zat7e3IwcAwE8ITkAFGjeWjj/eqX/9VVq50lw/QKxZs0bau9epGdMDAPgRwQmoBMb1gANjfRMAIB4QnIBKGDzYXROcAAfBCQAQDwhOQCUcdZTUooVTz5ol5eQYaweIKaWDE4ffAgD8iOAEVEIg4B7XKyiQZsww1w8QS0I3hggEpI4dzfUCAECkEJyASmJcDyjLstxPnNq2ldLTzfUDAECkEJyASjrlFPc3hJMmSUVF5voBYsGGDe6xVdY3AQD8iuAEVFJ6utSnj1Nv3Sp9+625foBYwMYQAIB4QXACqoBxPcCN4AQAiBcEJ6AKBg501xMmmOkDiBUEJwBAvCA4AVXQvLl0zDFO/eOP0po15voBTCsdnDp1MtMHAACRFhPB6ZlnnlGbNm2UlpamE044QfPnzz/o65944gl17NhR6enpatmypW677Tbl5eVFqVvEu9BtySV7kwggHlmW9MsvTt26tZSZaa4fAAAiyXhwGjt2rIYPH67Ro0dr4cKF6tq1q/r3768tW7aU+/p33nlHd955p0aPHq3s7Gy9/PLLGjt2rO66664od454VXqdE+N6iFdbtkg7dzo1B98CAPzMeHB6/PHHNXToUF155ZXq3Lmznn/+eWVkZOiVV14p9/VfffWVTjrpJP3pT39SmzZt1K9fP1188cUVPqUCwuWYY6QmTZz6s8+k3Fxz/QCmhB58K7G+CQDgb0kmP/n+/fu1YMECjRgxouRaQkKC+vTpo6+//rrc9znxxBP11ltvaf78+Tr++OO1cuVKTZ48WZdddlm5r8/Pz1d+fn5JnfO/A0cKCgpUUFAQxq8mNhR/TX782mLJgAGJevVV++cO+fnStGmFGjzYqvbH4755Vzzfu59+SpCUWFJ37FiogoLq/zmItni+d17HvfMm7pt3+fneVeVrMhqctm3bpqKiIjVu3Nh1vXHjxlq8eHG57/OnP/1J27ZtU8+ePWVZlgoLC3XdddcdcFRvzJgxuu+++8pcnz59ujIyMmr+RcSoGTNmmG7B15o0aSLphJL6+efXKTHxhxp/XO6bd8XjvZs6tYuktiX1zp1fafLknQd+hxgVj/fOL7h33sR98y4/3ru9e/dW+rUBy7KM/Xhww4YNat68ub766iv16NGj5Prtt9+u2bNn65tvvinzPrNmzdJFF12kBx98UCeccIKWL1+uYcOGaejQoRo1alSZ15f3xKlly5batm2b6tSpE5kvzKCCggLNmDFDffv2VXJysul2fGvPHqlp0yTl5wckSU2bWlq1qlAJ1Rx+5b55Vzzfu379EjVrlvMf/ZYtBapXz1w/VRXP987ruHfexH3zLj/fu5ycHDVo0EC///57hdnA6BOnBg0aKDExUZs3b3Zd37x58/9+ol/WqFGjdNlll+maa66RJHXp0kW5ubm69tprNXLkSCWU+s41NTVVqampZT5OcnKy7258KL9/faYdcoh0yinS1Kl2vXFjQD//nKzu3Wv2cblv3hWP9y50jVOzZlLDht78+uPx3vkF986buG/e5cd7V5Wvx+jmECkpKerevbtmzpxZci0YDGrmzJmuJ1Ch9u7dWyYcJSbaM/YGH54hDrG7HuLZ9u1S6M+82BgCAOB3xnfVGz58uF566SW9/vrrys7O1vXXX6/c3FxdeeWVkqTLL7/ctXnE4MGD9dxzz+ndd9/VqlWrNGPGDI0aNUqDBw8uCVBANJxxhrueONFMH4AJ7KgHAIg3Rkf1JGnIkCHaunWr7rnnHm3atElHH320pk6dWrJhxJo1a1xPmO6++24FAgHdfffdWr9+vRo2bKjBgwfroYceMvUlIE61bi116SL99JNdL1ggbdhgjywBfvfrr+6a4AQA8DvjwUmSbrrpJt10003lvm3WrFmuOikpSaNHj9bo0aOj0BlwcIMHO8FJkiZNkoYONdcPEC2lgxOH3wIA/M74qB7gZYMGuWvG9RAvGNUDAMQbghNQA8cfLzVo4NSffirt22euHyBaQp84NWzo/nMAAIAfEZyAGkhMdG8SsXev9Pnn5voBoiEnR1q3zql52gQAiAcEJ6CGGNdDvGFMDwAQjwhOQA316yeFnp02caLEkWLwM3bUAwDEI4ITUEN16ki9ejn12rXSjz+a6weINIITACAeEZyAMGBcD/GE4AQAiEcEJyAMCE6IJ6HB6ZBDpP+dVw4AgK8RnIAwaNfOfQDoN99IW7aY6weIlNxcafVqp87KkgIBY+0AABA11Q5OhYWF+vTTT/XCCy9o9+7dkqQNGzZoz549YWsO8JLQp06WJU2ebK4XIFIWL3bXjOkBAOJFtYLTb7/9pi5duuiss87SjTfeqK1bt0qSHn30Uf3tb38La4OAVzCuh3jAVuQAgHhVreA0bNgwHXvssdq5c6fS09NLrp9zzjmaOXNm2JoDvOTEE+31HsWmTZPy8831A0QCG0MAAOJVtYLTF198obvvvlspKSmu623atNH69evD0hjgNUlJ0oABTr1njzRnjrl+gEggOAEA4lW1glMwGFRRUVGZ6+vWrVPt2rVr3BTgVYzrwe9Cg1NmptSihbleAACIpmoFp379+umJJ54oqQOBgPbs2aPRo0dr4MCB4eoN8JzTT5cSE516wgR7owjAD/LypBUrnLpzZ3bUAwDEj2oFp8cee0xz585V586dlZeXpz/96U8lY3qPPvpouHsEPOOQQ6SePZ161aqyi+kBr1q6VAoGnZoxPQBAPEmqzju1bNlSP/zwg8aOHasffvhBe/bs0dVXX61LLrnEtVkEEI8GDZJmz3bqiRP5BhP+wPomAEA8q3JwKigoUKdOnTRx4kRdcskluuSSSyLRF+BZgwdLf/+7U0+YIN1+u7l+gHApHZxCD30GAMDvqjyql5ycrLy8vEj0AvhChw7S4Yc79VdfSdu3m+sHCBeeOAEA4lm11jjdeOONevTRR1VYWBjufgDPCwTcu+sFg9LUqeb6AcIldL1eerrUurW5XgAAiLZqrXH69ttvNXPmTE2fPl1dunRRrVq1XG//4IMPwtIc4FWDB0shG09q4kSJqVZ4WUGBvTlEsU6d3DtIAgDgd9UKTvXq1dN5550X7l4A3+jZU6pTR8rJsespU+xvPJOTzfYFVNfy5VLokAFjegCAeFOt4PTqq6+Guw/AV1JSpP79pXHj7Pr336W5c6XevY22BVQb65sAAPGuWmucim3dulVffvmlvvzyS23dujVcPQG+MHiwu5440UwfQDgQnAAA8a5awSk3N1dXXXWVmjZtqpNPPlknn3yymjVrpquvvlp79+4Nd4+AJw0YYG8UUWzCBHO9ADVFcAIAxLtqBafhw4dr9uzZmjBhgnbt2qVdu3bp448/1uzZs/XXv/413D0CntSggdSjh1MvXepeXA94SWhwSkmR2rY11wsAACZUKzi9//77evnllzVgwADVqVNHderU0cCBA/XSSy9p/Pjx4e4R8KzS43qTJpnpA6iJwkJpyRKn7tBBSqrWClkAALyrWsFp7969aty4cZnrjRo1YlQPCBF6npPEuB68adUqKT/fqRnTAwDEo2oFpx49emj06NHKy8srubZv3z7dd9996hE6mwTEuSOOcB8S+sUX0q5dxtoBqiX04FuJ4AQAiE/VGrZ48skn1b9/f7Vo0UJdu3aVJP3www9KS0vTtGnTwtog4GWBgD2u9/TTdl1YKE2fLl14odm+gKpgYwgAAKr5xOnII4/UsmXLNGbMGB199NE6+uij9cgjj2jZsmU64ogjwt0j4GmM68HrCE4AAFTziZMkZWRkaOjQoeHsBfClXr2kWrWk3Fy7njxZKiqSEhPN9gVUVmhwSkyU2rc31wsAAKZU64nTmDFj9Morr5S5/sorr+jRRx+tcVOAn6SlSf36OfWOHdK8eeb6AaoiGHSvcWrf3t6OHACAeFOt4PTCCy+oU6dOZa4fccQRev7552vcFOA3jOvBq9askUI3S2VMDwAQr6oVnDZt2qSmTZuWud6wYUNt3Lixxk0BfjNwoLueONFMH0BVsb4JAABbtYJTy5YtNXfu3DLX586dq2bNmtW4KcBvmjSRjj/eqX/5xT4bB4h1pYNTVpaZPgAAMK1awWno0KG69dZb9eqrr+q3337Tb7/9pldeeUW33XYbG0YAB1B6XI+nTvACnjgBAGCr1q56f//737V9+3bdcMMN2r9/vyQpLS1Nd9xxh0aMGBHWBgG/GDRIuucep544Ubr5ZnP9AJURujFEICB17GiuFwAATKpWcAoEAnr00Uc1atQoZWdnKz09Xe3bt1dqamq4+wN84+ijpebNpfXr7XrWLGn3bql2bZNdAQdmWe4nTm3bSunp5voBAMCkao3qFcvMzNRxxx2nVq1aacqUKcoO/dEkAJdAwD2ut3+/NGOGuX6AimzYIOXkODVjegCAeFat4HThhRfq6aefliTt27dPxx57rC688EIdddRRev/998PaIOAnrHOCl7C+CQAAR7WC05w5c/THP/5RkvThhx/Ksizt2rVL//73v/Xggw+GtUHAT047zT3qNGmSfcAoEIsITgAAOKoVnH7//XfVr19fkjR16lSdd955ysjI0BlnnKFly5aFtUHAT9LT7fBUbMsW6dtvzfUDHAzBCQAAR7XPcfr666+Vm5urqVOnql+/fpKknTt3Ki0tLawNAn7DuB68onRw6tTJTB8AAMSCagWnW2+9VZdccolatGihZs2aqXfv3pLsEb4uXbqEsz/AdwhO8ALLsg9qLtaqlZSZaa4fAABMq9Z25DfccINOOOEErVmzRn379lVCgp2/2rZtyxonoALNm0vduknff2/XixZJa9cabQkoY8sWaedOp2ZMDwAQ76oVnCSpe/fu6t69u+vaGWec4arr1KmjRYsWqW3bttX9NIAvDRrkBCdJmjIlQS1amOsHKK306RIEJwBAvKvROU4VsSwrkh8e8KzBg9315MkBM40AB8DGEAAAuEU0OAEoX/fuUuPGTv3ZZwHl5yeaawgoheAEAIAbwQkwICFBCp1szcsL6IcfGphrCCildHDKyjLTBwAAsYLgBBhSelzvu++a6MMP3QvyAVNCg1OzZlK9esZaAQAgJkQ0OAUCrNsADqRbN/vJU7HvvmusK66wd937y18IUDBn+3Zp82anZkwPAAA2hwCM2LJF6t9fCgadazt2pMuypH37pBdflHr2lLZtM9cj4hc76gEAUFZEg9OUKVPUvHnzSH4KwJOuukpasqTs9aIi54/kr79KQ4dGsSngf1jfBABAWWENTmvXrtVVV11VUvfs2VOpqanh/BSA5y1eLE2aVP7biorc460ffyytWBGFpoAQ7KgHAEBZYQ1OO3bs0Ouvvx7ODwn4zrvvHvhtlpVQqj7464FIYFQPAICykqry4k8++eSgb1+5cmWNmgHiwYYNkX09UFOhT5waNpQasFM+AABVC05nn322AoHAQTd9YCc94OAyMqr2+lq1ItMHUJ6cHGndOqfmaRMAALYqjeo1bdpUH3zwgYLBYLm/Fi5cGKk+Ad845ZSy1wKBA/8w4tRTI9gMUApjegAAlK9Kwal79+5asGDBAd9e0dMoANIZZ0itWrmvJSYGy31t27ZSv35RaAr4HzaGAACgfJUOTj/++KP+/ve/68QTTzzgaw4//HB9/vnnYWkM8KukJOm559yH3yYmlv2BQ2Ki9Pzz7tcBkUZwAgCgfJX+lqxbt27q2LGjTj/9dLVt21bbt28v85patWqpV69eYW0Q8KOBA6X335fq1rXrQMBSo0a5rte88orUt6+B5hDXCE4AAJSv0sGpXr16WrVqlSRp9erVCgbLHy0CUDlnny2tXSs9+6zUp4900knu7fNycsz0hfgWGpzq1ZMaNzbWCgAAMaXSwem8885Tr169dNhhhykQCOjYY49V27Zty/0FoHJq15auv1764AOpZ8/1rreNHWuoKcSt3Fxp9Wqn7txZYqNUAABsld6O/MUXX9S5556r5cuX65ZbbtHQoUNVu3btSPYGxJW2bX9Xu3aWVqywv1P98ktp/XqpeXPDjSFuLFnirhnTAwDAUaVznE4//XRJ0oIFCzRs2DCCExBGgYB03nlB/eMfiSXXxo+Xhg0z2BTiCuubAAA4sGrt1/Xqq68SmoAIOP9899rB994z1AjiEsEJAIADY6NjIIZ07Sp16ODUX31lbyABRAPBCQCAAyM4ATEkEJAuvNB9bdw4M70g/oQGp8xMqUULc70AABBrCE5AjCkdnBjXQzTk5UkrVjg1O+oBAOBGcAJizJFHSp06OfU337i3iAYiYelSKfR4Psb0AABwIzgBMSYQkIYMcV8bP95ML4gfpdc3ZWWZ6QMAgFhFcAJi0AUXuGsOw0WksTEEAAAHR3ACYtARR9i/in33nbRypbl+4H/Z2e6a4AQAgBvBCYhR7K6HaAp94pSeLrVuba4XAABiEcEJiFHsrodoKSiwN4co1qmTlJhorh8AAGIRwQmIUZ06SUcd5dQLF0rLl5vrB/61fLlUWOjUjOkBAFAWwQmIYTx1QjSwMQQAABUjOAExrPTuegQnRALBCQCAihGcgBjWoYN09NFO/cMP0pIlxtqBTxGcAACoGMEJiHGlD8PlqRPCLTQ4paRIbdua6wUAgFhFcAJiHON6iKSiIvdTzA4dpKQkc/0AABCrCE5AjGvXTure3al//rnsaBVQXatWSfn5Ts2YHgAA5SM4AR7AYbiIFNY3AQBQOQQnwANKB6exYyXLMtML/IXgBABA5RCcAA9o00Y6/ninzs6WfvnFWDvwEYITAACVQ3ACPILDcBEJocEpMVFq395cLwAAxDKCE+AR5e2ux7geaiIYtJ9eFmvf3t6OHAAAlEVwAjyiVSupRw+nXrJE+uknc/3A+9askfbudWrG9AAAODCCE+Ah5W0SAVQX65sAAKg8ghPgIeef764Z10NNlA5OWVlm+gAAwAsIToCHtGghnXSSUy9fLi1aZKwdeBxPnAAAqDyCE+Ax7K6HcAndGCIQkDp2NNcLAACxjuAEeMz559vf5BbjMFxUh2W5nzi1bSulp5vrBwCAWEdwAjymWTPpj3906lWrpAULzPUDb9qwQcrJcWrG9AAAODiCE+BBjOuhpljfBABA1RCcAA867zwpIeRPL7vroaoITgAAVA3BCfCgJk2kXr2c+rffpPnzzfUD7yE4AQBQNQQnwKMY10NNlA5OnTqZ6QMAAK8gOAEede657nG9ceOkYNBcP/AOy5J++cWpW7WSMjPN9QMAgBcQnACPatRIOuUUp167VvrmG3P9wDu2bJF27nRqxvQAAKgYwQnwsCFD3PXYsWb6gLeEHnwrEZwAAKgMghPgYeecIyUmOjXjeqgMNoYAAKDqCE6AhzVoIJ12mlNv2CB99ZW5fuANBCcAAKqO4AR4HLvroapKB6esLDN9AADgJQQnwOPOOUdKSnLq8eOloiJz/SD2hQanZs2kevWMtQIAgGcQnACPq19f6tvXqTdulL780lw/iG3bt0ubNzs1Y3oAAFQOwQnwAcb1UFnsqAcAQPUQnAAfOOssKTnZqRnXw4GwvgkAgOohOAE+cMghUr9+Tr1lizRnjrl+ELvYUQ8AgOohOAE+wWG4qAxG9QAAqB6CE+ATZ54ppaQ49fvvS4WF5vpBbAp94tSwoX0WGAAAqBjBCfCJunWl00936m3bpFmzjLWDGJSTI61b59Q8bQIAoPIIToCPsLseDoYxPQAAqo/gBPjImWdKqalO/f77UkGBuX4QW9gYAgCA6iM4AT5Su7Y0cKBT79ghffaZuX4QWwhOAABUH8EJ8BnG9XAgBCcAAKqP4AT4zKBBUnq6U3/4obR/v7l+EDtCg1O9elLjxsZaAQDAcwhOgM9kZkpnnOHUO3dKn35qrh/EhtxcafVqp+7cWQoEjLUDAIDnEJwAH2JcD6UtWeKuGdMDAKBqCE6ADw0cKGVkOPVHH0n5+cbaQQxgfRMAADVDcAJ8qFYte61Tsd9/l2bMMNcPzCM4AQBQMwQnwKeGDHHXjOvFN4ITAAA1Q3ACfGrAAPvJU7GPPpLy8oy1A8NCg1NmptSihbleAADwIoIT4FPp6dKZZzr17t3StGnm+oE5eXnSihVOzY56AABUHcEJ8DF214MkLV0qBYNOzZgeAABVR3ACfOz006XatZ36k0+kffvM9QMzSq9vysoy0wcAAF5GcAJ8LC1NOussp96zR5oyxVw/MIONIQAAqDmCE+BzjOshO9tdE5wAAKg6ghPgc/36SXXqOPWECdLeveb6QfSFPnFKT5datzbXCwAAXkVwAnwuNVU6+2yn3rtXmjzZWDuIsoICe3OIYp06SYmJ5voBAMCrCE5AHCh9GO7YsWb6QPQtXy4VFjo1Y3oAAFQPwQmIA336SPXqOfWkSfZGEfA/NoYAACA8CE5AHEhJkc45x6n37bPDE/yP4AQAQHgQnIA4we568YngBABAeBCcgDhx2mlS/fpOPXmytHu3uX4QHaHBKSVFatvWXC8AAHgZwQmIE8nJ0rnnOnVenr01OfyrqEhassSpO3SQkpLM9QMAgJcRnIA4wrhefFm1SsrPd2rG9AAAqD6CExBHTjlFatDAqadMkXJyzPWDyGJ9EwAA4UNwAuJIUpJ03nlOvX+/9Mkn5vpBZBGcAAAIH4ITEGdKj+txGK5/EZwAAAgfghMQZ04+WWrUyKmnTZN27TLWDiIoNDglJkrt25vrBQAAryM4AXGm9LheQYH08cfm+kFkBINSdrZTt29vb0cOAACqh+AExCF21/O/NWukvXudmjE9AABqhuAExKE//lFq0sSpp0+Xduww1w/Cr/T6pqwsM30AAOAXBCcgDiUmSuef79SFhdJHHxlrBxHAxhAAAIQXwQmIU4zr+Vvo+iaJ4AQAQE3FRHB65pln1KZNG6WlpemEE07Q/PnzD/ja3r17KxAIlPl1xhlnRLFjwPtOOklq1sypP/1U2r7dXD8Ir9AnToGA1LGjuV4AAPAD48Fp7NixGj58uEaPHq2FCxeqa9eu6t+/v7Zs2VLu6z/44ANt3Lix5NfPP/+sxMREXXDBBVHuHPC2hAQp9I9NUZH0wQfm+kH4WJY7OLVtK6Wnm+sHAAA/SDLdwOOPP66hQ4fqyiuvlCQ9//zzmjRpkl555RXdeeedZV5fv359V/3uu+8qIyPjgMEpPz9f+fn5JXVOTo4kqaCgQAUFBeH6MmJG8dfkx6/Nz0zdt3PPDejJJ52/BsaODerPfy6Kag9eF4t/5tavl3JykkvqTp2CKijgvpYWi/cOlcO98ybum3f5+d5V5WsKWJZlRbCXg9q/f78yMjI0fvx4nX322SXXr7jiCu3atUsfV+JwmS5duqhHjx568cUXy337vffeq/vuu6/M9XfeeUcZGRnV7h3wg2BQGjq0n7Zvtx9HJCRYevXVqapbd7/hzlATixY11L33nlhSn3vuMl1++a8HeQ8AAOLT3r179ac//Um///676tSpc9DXGn3itG3bNhUVFalx48au640bN9bixYsrfP/58+fr559/1ssvv3zA14wYMULDhw8vqXNyctSyZUv169evwt8cLyooKNCMGTPUt29fJScnV/wOiAkm79ullyboySft/x8MBrRnTz9dfHEwqj14WSz+mVuxwj2FfcYZh2ngwDZmmolhsXjvUDncO2/ivnmXn+9d8TRaZRgf1auJl19+WV26dNHxxx9/wNekpqYqNTW1zPXk5GTf3fhQfv/6/MrEfbv4YpUEJ0kaPz5RN9yQGNUe/CCW/swtWeKujzoqSTHSWkyKpXuHquHeeRP3zbv8eO+q8vUY3RyiQYMGSkxM1ObNm13XN2/erCahp3OWIzc3V++++66uvvrqSLYI+N7xx0utWjn17NnSpk3m+kHNlT7DqVMnM30AAOAnRoNTSkqKunfvrpkzZ5ZcCwaDmjlzpnr06HHQ9x03bpzy8/N16aWXRrpNwNcCAfeZTsEgu+t5Wekd9Vq1kjIzzfUDAIBfGN+OfPjw4XrppZf0+uuvKzs7W9dff71yc3NLdtm7/PLLNWLEiDLv9/LLL+vss8/WoYceGu2WAd/hMFz/2LpV2rHDqTn4FgCA8DC+xmnIkCHaunWr7rnnHm3atElHH320pk6dWrJhxJo1a5SQ4M53S5Ys0Zdffqnp06ebaBnwnWOPlQ47TFq1yq7nzJE2bpSaNjXbF6qu9JgewQkAgPAwHpwk6aabbtJNN91U7ttmzZpV5lrHjh1lcBd1wHeKx/UefdSuLUsaP166+WazfaHqCE4AAESG8VE9ALGBcT1/IDgBABAZBCcAkqRu3aR27Zz6yy+l9evN9YPqKR2csrLM9AEAgN8QnABIKru7nmSP68FbQoNTs2ZSvXrGWgEAwFcITgBKDBnirseONdMHqmf7din0WDzG9AAACB+CE4ASRx0ldejg1F9/La1ZY64fVE12trtmTA8AgPAhOAEowbiet7ExBAAAkUNwAuDC7nreRXACACByCE4AXI480j3i9c030urVxtpBFZQe1SM4AQAQPgQnAC7ljeuNG2emF1RN6BOnhg2lBg3M9QIAgN8QnACUccEF7ppxvdiXkyOtW+fUPG0CACC8CE4AyjjiCPtXse++k1auNNcPKsaYHgAAkUVwAlCu0mc68dQptrExBAAAkUVwAlAuxvW8heAEAEBkEZwAlKtTJ/tA3GLffy8tW2auHxxc6eDE4bcAAIQXwQnAAbG7nneEBqd69aQmTYy1AgCALxGcABwQh+F6Q26u+6ytzp3tbeUBAED4EJwAHFD79lK3bk79ww/SkiXm+kH5St8T1jcBABB+BCcAB8VTp9jHxhAAAEQewQnAQbG7XuwjOAEAEHkEJwAH1a6d1L27U//8c9lv1GEWwQkAgMgjOAGoEIfhxrbQ4JSZKbVoYa4XAAD8iuAEoELljetZlple4JaXJ61Y4dTsqAcAQGQQnABUqE0b6fjjnTo7W/rlF2PtIMTSpVIw6NQcfAsAQGQQnABUCrvrxSbWNwEAEB0EJwCVUnpcb+xYxvViAcEJAIDoIDgBqJRWraQePZx66VLpxx/N9QNbdra7JjgBABAZBCcAlca4XuwJfeKUni61bm2uFwAA/IzgBKDSzj/fXbO7nlkFBfaTv2KdOkmJieb6AQDAzwhOACqtRQupZ0+nXr5c+v57c/3Eu+XLpcJCp2ZMDwCAyCE4AagSxvViBxtDAAAQPQQnAFVy3nnuA1YZ1zOH4AQAQPQQnABUSbNm0h//6NSrVkkLFpjrJ56VDk4cfgsAQOQQnABU2ZAh7nrsWDN9xLvQ4JScLLVrZ64XAAD8juAEoMrOPVdKCPnbg3G96CsqkpYsceqOHaWkJHP9AADgdwQnAFXWpInUq5dTr1kjzZ9vrp94tGqVlJ/v1KxvAgAgsghOAKqF3fXMYmMIAACii+AEoFpKj+uNGycFg+b6iTcEJwAAoovgBKBaGjWSTj3VqdeulebNM9dPvCE4AQAQXQQnANXGuJ45ocEpMVFq395cLwAAxAOCE4BqO+cc+5v2YozrRUcwKGVnO3X79lJKirl+AACIBwQnANXWoIF02mlOvWGD9NVX5vqJF2vWSHv3OjUH3wIAEHkEJwA1wmG40cf6JgAAoo/gBKBGzj7bffDq+PH24ayIHIITAADRR3ACUCP160t9+zr1pk3Sl1+a6ycehK5vkghOAABEA8EJQI2xu150hT5xCgSkjh3N9QIAQLwgOAGosbPPlpKTnXr8eKmw0Fg7vmZZ7uDUtq2Unm6uHwAA4gXBCUCN1asn9e/v1Fu2SHPmGGvH1zZskHJynJoxPQAAooPgBCAsGNeLDjaGAADADIITgLA480z3Iazvv8+4XiQQnAAAMIPgBCAs6taVTj/dqbdtkz7/3Fw/fkVwAgDADIITgLApfRgu43rhVzo4depkpg8AAOINwQlA2AweLKWmOvUHH0gFBeb68ZvSO+q1aiVlZprrBwCAeEJwAhA2tWtLAwc69Y4d0mefmevHb7ZutX9PizGmBwBA9BCcAIQVu+tFDuubAAAwh+AEIKwGDXIfyPrBB9L+/eb68ROCEwAA5hCcAIRVZqZ0xhlOvWuX9OmnxtrxFYITAADmEJwAhB3jepFROjhlZZnpAwCAeERwAhB2AwdKGRlO/dFHUn6+sXZ8IzQ4NWsm1atnrBUAAOIOwQlA2NWqZW9NXuz336Xp08314wfbt0ubNzs1Y3oAAEQXwQlARDCuF17Z2e6aMT0AAKKL4AQgIgYMsJ88Ffv4Yykvz1w/XsfGEAAAmEVwAhAR6enSmWc69e7d0rRp5vrxutJPnAhOAABEF8EJQMQMGeKux44104cf8MQJAACzCE4AIqZ/f6l2baf+5BNp3z5z/XhZaHBq2FBq0MBcLwAAxCOCE4CISUuTzjrLqXNzpSlTzPXjVTk50rp1Ts3TJgAAoo/gBCCi2F2v5ljfBACAeQQnABHVr59Ut65TT5hgP3lC5bG+CQAA8whOACIqNVU6+2yn3rtXmjzZWDueRHACAMA8ghOAiGNcr2ZKBycOvwUAIPoITgAirk8fqV49p540Sdqzx1g7nhManOrVk5o0MdYKAABxi+AEIOJSUqRzznHqffvs8ISK5eZKv/3m1J07S4GAuX4AAIhXBCcAUcFhuNWzZIlkWU7N+iYAAMwgOAGIilNPlerXd+rJk6Xdu8314xVsDAEAQGwgOAGIiuRk6dxznTo/396aHAdHcAIAIDYQnABEDbvrVR3BCQCA2EBwAhA1p5wiNWjg1FOmSL//bq4fLwgNTpmZUosW5noBACCeEZwARE1SknTeeU69f7/0ySfm+ol1eXnSihVOzY56AACYQ3ACEFWM61Xe0qVSMOjUHHwLAIA5BCcAUXXyyVKjRk49bZq0a5exdmIa65sAAIgdBCcAUZWUJJ1/vlMXFEgffWSsnZiWne2uCU4AAJhDcAIQdYzrVQ5PnAAAiB0EJwBR17On1KSJU8+YIe3YYa6fWBUanNLTpdatzfUCAEC8IzgBiLrERPe4XmEh43qlFRTYm0MU69TJ/n0DAABmEJwAGFF6XG/sWDN9xKrly+1AWYwxPQAAzCI4ATDipJOkZs2ceuZMads2c/3EGtY3AQAQWwhOAIxISJAuuMCpi4qkDz8010+sITgBABBbCE4AjGF3vQMrHZw4/BYAALMITgCM+cMfpBYtnPqzz6StW831E0tCg1NystSunbleAAAAwQmAQQkJ7qdOwaD0/vvm+okVRUXSkiVO3bGjfXAwAAAwh+AEwCjG9cpatUrKz3dq1jcBAGAewQmAUccfL7Vq5dSzZ0ubNpnrJxawMQQAALGH4ATAqECg7LjeBx+Y6ycWEJwAAIg9BCcAxg0Z4q7j/TBcghMAALGH4ATAuO7dpcMOc+ovvpA2bDDXj2mhwSkxUWrf3lwvAADARnACYFzpcT3Lit/d9YJBKTvbqdu3l1JSzPUDAABsBCcAMYHd9Wxr1kh79zo1B98CABAbCE4AYkK3btLhhzv1l19K69aZ68cU1jcBABCbCE4AYkLpcT1JGj/eTC8mEZwAAIhNBCcAMYNxPff6JongBABArCA4AYgZRx0ldejg1F9/ba/5iSehT5wCAaljR3O9AAAAB8EJQMwob1xv3DgzvZhgWe7g1LatlJ5urh8AAOAgOAGIKaUPw42ncb0NG6ScHKdmTA8AgNhBcAIQU444wr0F9/z50urVxtqJKjaGAAAgdhGcAMSUeB7XIzgBABC7CE4AYk687q5XOjhx+C0AALGD4AQg5nTuLB15pFN/9520YoW5fqKldHDq1MlMHwAAoCyCE4CYFG/jeqV31GvVSqpd21w/AADAjeAEICZdcIG79vu43tat0o4dTs36JgAAYgvBCUBM6tTJPhC32PffS8uWmesn0tgYAgCA2EZwAhCz4ulMJ4ITAACxjeAEIGbF07gewQkAgNhGcAIQs9q3l7p1c+off5QWLzbXTySxFTkAALGN4AQgpsXL7nqhwalZM6lePWOtAACAchCcAMS00sFp7FgzfUTS9u3S5s1OzdMmAABiD8EJQExr21Y69lin/uUX+5efZGe7a9Y3AQAQewhOAGKe38f12BgCAIDYR3ACEPPK213Pssz0Egk8cQIAIPYRnADEvDZtpOOPd+rsbOnnn421E3Y8cQIAIPYRnAB4gp8Pww0NTg0bSg0amOsFAACUj+AEwBPOP99d+2VcLydHWrfOqXnaBABAbCI4AfCEVq2kHj2ceulS+0Bcr2N9EwAA3kBwAuAZpXfX88O4HuubAADwBoITAM8ovbve2LHeH9crHZw4/BYAgNhEcALgGc2bSz17OvWKFdL335vrJxx44gQAgDcQnAB4it/G9UKDU716UpMmxloBAAAHQXAC4CnnnScFAk7t5d31cnOl335z6s6d3V8bAACIHQQnAJ7SrJl08slOvWqV9N135vqpiSVL3KGPMT0AAGIXwQmA5/hlXI/1TQAAeAfBCYDnnHuulBDyt5dXx/UITgAAeAfBCYDnNGki9erl1GvWSPPnm+unughOAAB4B8EJgCcNGeKux44100dNhAanzEypRQtzvQAAgIMjOAHwpNLjeuPGScGguX6qKi/PPoeqWFYWO+oBABDLCE4APKlhQ+nUU5163Tpp3jxz/VTV0qXuoMeYHgAAsY3gBMCzvLy7HuubAADwFoITAM865xwpMdGpvTSul53trglOAADENoITAM9q0EDq08epN2yQ5s41109V8MQJAABvITgB8DSvjuuFBqf0dKl1a3O9AACAihGcAHja2WdLSUlOPX68VFRkrJ1KKSiwN4co1qmTe+QQAADEHoITAE+rX1/q29epN22SvvzSXD+VsXy5VFjo1IzpAQAQ+whOADzPa4fhsr4JAADvITgB8LyzzpKSk536/ffdT3RiTenglJVlpg8AAFB5BCcAnlevntS/v1Nv2SLNmWOsnQrxxAkAAO8hOAHwBS/trhcanJKTpXbtzPUCAAAqh+AEwBfOOktKTXXqWB3XKyqSlixx6o4d3bsCAgCA2ERwAuALdepIp5/u1Nu2SZ9/bq6fA1m1SsrPd2rG9AAA8AaCEwDf8MK4HuubAADwppgITs8884zatGmjtLQ0nXDCCZo/f/5BX79r1y7deOONatq0qVJTU9WhQwdNnjw5St0CiFWDB0tpaU79wQf2YbOxhOAEAIA3GQ9OY8eO1fDhwzV69GgtXLhQXbt2Vf/+/bVly5ZyX79//3717dtXq1ev1vjx47VkyRK99NJLat68eZQ7BxBrateWBg506h07pJkzzfVTHoITAADeZDw4Pf744xo6dKiuvPJKde7cWc8//7wyMjL0yiuvlPv6V155RTt27NBHH32kk046SW3atFGvXr3UtWvXKHcOIBbF+rheaHBKTJTatzfXCwAAqDyjeznt379fCxYs0IgRI0quJSQkqE+fPvr666/LfZ9PPvlEPXr00I033qiPP/5YDRs21J/+9CfdcccdSkxMLPP6/Px85YesxM7JyZEkFRQUqCDWZnjCoPhr8uPX5mfct/Dp109KT0/Svn0BSdKHH1p66qlCpaRE5vNV5d4Fg1J2dpIku7d27SwFAoUxN04YL/hz513cO2/ivnmXn+9dVb4mo8Fp27ZtKioqUuPGjV3XGzdurMWLF5f7PitXrtRnn32mSy65RJMnT9by5ct1ww03qKCgQKNHjy7z+jFjxui+++4rc3369OnKyMgIzxcSg2bMmGG6BVQD9y08unU7Vl99ZY/v7toV0COPfKdjjy1//DdcKnPvNm9O1969/Urq+vU3avLkbyPZFiqBP3fexb3zJu6bd/nx3u3du7fSr/Xc6SHBYFCNGjXSiy++qMTERHXv3l3r16/X//3f/5UbnEaMGKHhw4eX1Dk5OWrZsqX69eunOnXqRLP1qCgoKNCMGTPUt29fJScnm24HlcR9C6+9ewP66iunXr36BN1zT1FEPldV7t2UKQFX3bt3Yw0MXZSFqOLPnXdx77yJ++Zdfr53xdNolWE0ODVo0ECJiYnavHmz6/rmzZvVpEmTct+nadOmSk5Odo3lZWVladOmTdq/f79SSs3jpKamKjX0VMz/SU5O9t2ND+X3r8+vuG/hcdZZUkaGVPxDpE8+SVAwmKBy/ioIm8rcu2XL3HWXLolKTi47Yozo4s+dd3HvvIn75l1+vHdV+XqMbg6RkpKi7t27a2bItlfBYFAzZ85Ujx49yn2fk046ScuXL1cwGCy5tnTpUjVt2rRMaAIQnzIy7K3Ji+XkSNOnm+unGDvqAQDgXcZ31Rs+fLheeuklvf7668rOztb111+v3NxcXXnllZKkyy+/3LV5xPXXX68dO3Zo2LBhWrp0qSZNmqSHH35YN954o6kvAUAMisXd9UKDUyAgdexorhcAAFA1xtc4DRkyRFu3btU999yjTZs26eijj9bUqVNLNoxYs2aNEhKcfNeyZUtNmzZNt912m4466ig1b95cw4YN0x133GHqSwAQgwYMkDIzpT177Prjj6W8PPcBudFkWe7g1LatlJ5uphcAAFB1xoOTJN1000266aabyn3brFmzylzr0aOH5s2bF+GuAHhZerp05pnSO+/Y9e7d0tSp0tlnm+lnwwZ7ZLAYY3oAAHiL8VE9AIiUWBrXY30TAADeRnAC4Fv9+0u1azv1J59I+/aZ6aV0cMrKMtMHAACoHoITAN9KS7O3Ji+WmytNmWKmF544AQDgbQQnAL42ZIi7HjvWTB+lg1OnTmb6AAAA1UNwAuBrfftKdes69cSJ9pOnaCq9o16rVu4RQgAAEPsITgB8LTXVvZPe3r3S5MnR7WHrVmnHDqdmTA8AAO8hOAHwPdO767G+CQAA7yM4AfC9Pn2kQw5x6kmTnINxo4HgBACA9xGcAPheSop0zjlOvW+fvdYpWghOAAB4H8EJQFwwOa7HGU4AAHgfwQlAXDj1VKl+faeePFnavTs6nzs0ODVrJtWrF53PCwAAwofgBCAuJCdL557r1Pn50iefRP7zbt8ubd7s1DxtAgDAmwhOAOJG6cNwozGul53trlnfBACANxGcAMSN3r2lBg2ceupU6fffI/s52RgCAAB/IDgBiBtJSdJ55zn1/v2RH9fjiRMAAP5AcAIQV6K9ux5PnAAA8AeCE4C40quX1KiRU0+bJu3cGbnPFxqcGjZ0jwoCAADvIDgBiCuJidL55zt1QYH08ceR+Vw5OdK6dU7N0yYAALyL4AQg7kRrXI/1TQAA+AfBCUDc6dlTatLEqWfMkHbsCP/nYX0TAAD+QXACEHcSE6ULLnDqwkLpww/D/3lKBycOvwUAwLsITgDiUjTG9XjiBACAfxCcAMSlE0+UmjVz6pkzpW3bwvs5QoNTvXru8UAAAOAtBCcAcSkhwT2uV1QU3nG93Fzpt9+cunNnKRAI38cHAADRRXACELeGDHHXY8eG72MvWSJZllMzpgcAgLcRnADErRNOkFq2dOrPP5e2bAnPx2Z9EwAA/kJwAhC3So/rBYPSBx+E52MTnAAA8BeCE4C4Fqnd9QhOAAD4C8EJQFw7/nipdWunnj1b2rSp5h83NDhlZkotWtT8YwIAAHMITgDiWiDgfuoUDErvv1+zj5mXJ61Y4dRZWeyoBwCA1xGcAMS9cI/rLV1qB7BijOkBAOB9BCcAca97d+mww5z6iy+kDRuq//FY3wQAgP8QnADEvdLjepZVs3G97Gx3TXACAMD7CE4AoPAehssTJwAA/IfgBACSjj5aOvxwp547V1q3rnofKzQ4pae7d+0DAADeRHACAJUd15Ok8eOr/nEKCuzNIYp16iQlJtasNwAAYB7BCQD+Jxy76y1fLhUWOjVjegAA+APBCQD+56ijpI4dnfrrr6U1a6r2MVjfBACAPxGcAOB/yhvXGzeuah+jdHDKyqpZTwAAIDYQnAAgRE3H9XjiBACAPxGcACDEEUe4nxLNny+tXl359w8NTsnJUrt2YWsNAAAYRHACgBDljetV9qlTUZG0ZIlTd+woJSWFrzcAAGAOwQkASqlucFq1SsrPd2rG9AAA8A+CEwCU0rmzdOSRTr1ggbRiRcXvx/omAAD8i+AEAOWozu56BCcAAPyL4AQA5SgdnMaOrfh9CE4AAPgXwQkAytGxo9S1q1MvWiQtXXrw9wkNTomJUvv2EWkNAAAYQHACgAOoyrheMChlZzv14YdLKSmR6QsAAEQfwQkADuCCC9z1wXbXW7NG2rvXqRnTAwDAXwhOAHAA7dtL3bo59Y8/SosXl//a7OyAqyY4AQDgLwQnADiIIUPc9YGeOi1eTHACAMDPCE4AcBCVHdfjiRMAAP5GcAKAg2jbVjr2WKf+5Rf7V2mhG0MEAvaufAAAwD8ITgBQgYp217Ms9xOntm2l9PQoNAYAAKKG4AQAFSjvMFzLcuodO9KUk+MEJ8b0AADwH4ITAFSgdWvphBOcevFi6eefnXrt2tqu1xOcAADwH4ITAFRC6adOoZtElA5OWVlRaAgAAEQVwQkAKuH88931e+8543o8cQIAwP8ITgBQCa1aST16OPXSpdIPP9j/v3Rw6tQpio0BAICoIDgBQCWVdxiuZUnr1jnBqVUrqXZtAQAAnyE4AUAllTeut2WLtHt3Ssk1xvQAAPAnghMAVFLz5lLPnk69YoX03/+6/xolOAEA4E8EJwCogtK76z32GMEJAIB4QHACgCo4/3wp4Jx1qy1bAq63E5wAAPAnghMAVEHTptLJJx/47eyoBwCAPxGcAKAKLEvKzDzw2wcOlNavj14/AAAgOghOAFAFd98tTZp04LfPmyf17i1t3x61lgAAQBQQnACgkn79VXr44Ypft3y59MADke8HAABED8EJACrpuecq/9rXXpNycyPWCgAAiDKCEwBU0qefVv61v/8ufftt5HoBAADRRXACgEras+dgb7Wq+HoAAOAlBCcAqKTGjcu7agempKRgmbc0aRLZfgAAQPQQnACgki66qOy1tLRCvfjidCUnu4NT+/bSMcdEqTEAABBxBCcAqKQrr5Tq1HFfCwSkRo32lXntLbdICfwNCwCAb/DPOgBU0qGHSu++K6WkHPx1Q4ZIN9wQnZ4AAEB0EJwAoAoGDJA+/1w64YSybzv0UPv8prff5mkTAAB+k2S6AQDwmhNPlObNk77/3v5fSXrrLWngQCktzWxvAAAgMviZKABUU7du0jXX2P9/8GBCEwAAfkZwAgAAAIAKEJwAAAAAoAIEJwAAAACoAMEJAAAAACpAcAIAAACAChCcAAAAAKACBCcAAAAAqADBCQAAAAAqQHACAAAAgAoQnAAAAACgAgQnAAAAAKgAwQkAAAAAKkBwAgAAAIAKEJwAAAAAoAIEJwAAAACoAMEJAAAAACpAcAIAAACAChCcAAAAAKACBCcAAAAAqADBCQAAAAAqQHACAAAAgAoQnAAAAACgAgQnAAAAAKgAwQkAAAAAKkBwAgAAAIAKEJwAAAAAoAJJphuINsuyJEk5OTmGO4mMgoIC7d27Vzk5OUpOTjbdDiqJ++Zd3Dvv4t55F/fOm7hv3uXne1ecCYozwsHEXXDavXu3JKlly5aGOwEAAAAQC3bv3q26dese9DUBqzLxykeCwaA2bNig2rVrKxAImG4n7HJyctSyZUutXbtWderUMd0OKon75l3cO+/i3nkX986buG/e5ed7Z1mWdu/erWbNmikh4eCrmOLuiVNCQoJatGhhuo2Iq1Onju/+w44H3Dfv4t55F/fOu7h33sR98y6/3ruKnjQVY3MIAAAAAKgAwQkAAAAAKkBw8pnU1FSNHj1aqamppltBFXDfvIt7513cO+/i3nkT9827uHe2uNscAgAAAACqiidOAAAAAFABghMAAAAAVIDgBAAAAAAVIDgBAAAAQAUITgAAAABQAYITaiwYDJa5tnv3bgOdAAAAAJFBcEKNJSQk6LffftMTTzwhSRo3bpwuv/xy/f7772YbQ8RwikF0lf7hBL//QOwo/vO4c+dO5efnG+4G0Vbe38fx+Hd0vHzNBCfUWGFhoZ577jm9+uqruuKKKzRkyBCdddZZqlu3runWECalv3EPBAKGOok/wWBQCQn2X9VffPGFCgsL+f33mOJvKIqKipSXl2e4G4STZVkKBAKaOHGi/vKXv2jOnDnav3+/6bYQJcFgsOTv47Vr12rlypXav39/3PwdvXHjRi1btkxS/HxfQHBCjSUlJWn06NFq3bq13nzzTV144YX685//LMn+RgHeZllWyTfuL730km699VY99thjWrx4seHO/C/0937UqFG6/PLL9d5775U7HovYVPyN9eTJk3XFFVfo2GOP1d13360JEyaYbg1hEAgE9OGHH+qiiy5Sly5d1K5dO6WkpJhuC1EQ+vfzvffeq0GDBqlPnz468sgj9corr2j79u2GO4ysvLw89e7dW8OHD9eSJUtMtxM1BCfUSPFPUlNSUlSvXj317dtX69at05gxYyRJiYmJhCcPC/1p2ogRI3TXXXfpp59+0ltvvaWLL75Y8+bNM9yhvxX/3o8aNUovvviiXn/9dfXp06fkH2vEvkAgoE8++UQXXHCB2rRpo+HDh2vOnDm6/fbbtWjRItPtoYaWLl2q4cOH64knntCoUaN02GGHqaioSD///LO2bNliuj1EUPHfzw899JCee+45Pfzww1q6dKmaN2+uBx54wPf3Py0tTS+++KIWLFig+++/P25+mMq/vqi24p+kLliwQOvXr9frr7+usWPHqlu3bvr4449d4UmStm3bZrJdVEPxN+jLli1TTk6Opk2bppkzZ+qZZ55Rhw4ddOmllxKeIuy3337TlClT9Morr+jkk09WYmKifv75Zz3wwAP64osvlJOTY7pFHMS2bdv02GOP6eGHH9aDDz6oP/3pT8rOztbAgQN19NFHm24PNZSfn6/69evruOOOU25urp588kmdeuqpGjhwoAYPHhxXP4mPN5ZlKScnRzNnztQ///lPnXHGGZoxY4YWLlyoO+64Q1lZWb6dDggGgwoGg+rVq5fGjx+v6dOn64EHHoiL8ERwQrUUh6YPP/xQAwcO1FNPPaXt27erXr16GjlypI477jh98sknevjhhyVJ99xzj66//noWznrQuHHj1LdvX3377bdq0aKFJOmkk07S3//+dx1zzDG67LLLCE8RlJeXp6VLlyopKUnffPONRowYoUsuuUQvvPCCLr30Us2dO1dS/CzM9Zq0tDTt3btXZ5xxhlatWqXDDz9c55xzjv75z39Kkj799FOtWrXKcJc4mPL+bC1YsECrVq1SIBDQjh079MgjjygrK0uzZ8/Wqaeeqqeeekq5ubmaM2eOgY4RLXl5eVq7dq369++vzz77TBdeeKEeffRRXXfdddq7d6+eeuoprV+/3nSbYbN27Vr9+uuvKiwsLPnB6oknnqj3339f06dP13333ef/8GQB1TR58mQrPT3devnll62tW7e63rZ582brb3/7m9WuXTsrKyvLql+/vjVv3jxDnaImxo8fb/Xr18+qXbu2tWTJEtfbvv32W+uiiy6yMjMzrZ9//tlQh/5RVFRU7vUrrrjCqlWrllWrVi1r2LBh1sSJEy3LsqyuXbtao0aNimaLqIRgMFjyv+vWrbM6d+5svfbaa9bhhx9uXXPNNVZhYaFlWZa1YsUK6+KLL7amTZtmsl1UYPPmzZZl2X8+i4qKrGXLlllNmza1fvzxR8uyLOuTTz6xbr/9duuBBx6w1qxZU/J+PXv2tF599VUTLSMCiv9cl9avXz/r1FNPtTIzM62XX3655PqaNWusnj17WmPHjo1WixG1du1aKzEx0QoEAtb5559v3XDDDdbXX39tbdmyxbIsy1qwYIHVsGFD65JLLvH19wMEJ1RLfn6+dcUVV1h///vfLcuyrD179li//vqrddddd1kvvfSStWHDBmv37t3W9OnTraeeespatmyZ4Y5RGQf6xn3atGlWjx49rOOOO87Kzs52vW3u3LnWqFGjSr4ZRPWE/t6///771nPPPWfdf//91rZt26yioiJr9uzZ1rfffut6n969e1v//ve/o90qDqD4G6t9+/ZZlmWV/JkYOXKkFQgErDPPPNP1+rvuusvq0qWL65ttxJb33nvPateunbVw4cKSa1u2bLGysrKs9evXl9zzgoKCkrcXFRVZI0eOtJo3b26tXLky6j0j/EL/fl67dq21adOmkvqNN96wDjvsMKt///4l1/bs2WMNHDjQOuWUUzz/b2Pxf+M//fST1aNHDysQCFgjRoywTjrpJKt9+/ZW48aNrWHDhlnTp0+3Jk6caB1yyCHWzTffbC1atMhw55ERsCzmO1B1BQUF6tu3rxo1aqSnnnpKo0aN0rJly7Rhwwb9/vvvuuiii0rOdYI3hG57PXv2bOXn56uwsFADBw6UZI8U/d///Z92796tV199VR07dizzMYqKikrWtKF6br/9dr333nvq1KmT9uzZo19//VXvvPOOTj/9dElSbm6ufvvtN91xxx1as2aNFixYoKSkJMNdw/rf+PL06dP18ssva/fu3UpPT9czzzyjpKQk3X777Xr77bf12GOPqaCgQCtWrNCbb76pL774Ql27djXdPg5g8uTJevrpp7Vz5049++yz6tatm5YuXarBgwfrm2++Ub169SQ5f3++/PLLmjNnjqZPn67JkyerW7duZr8AhNXIkSM1efJkrVmzRjfccIP+/Oc/q0WLFrr//vs1btw4HXLIIWrXrp1Wr16t3Nxcfffdd0pOTvb0v415eXlKS0tTQUGBsrOz9Ze//EXBYFCff/65duzYoXHjxmnevHmaPHmyevfurWnTpqmwsFA33XSTHnvsMf/tMmk4uMEjyntEPXHiRKtevXpWZmamde6551rvvPOOZVmWNWbMGOuEE04o+akrvOVvf/ub1axZM6tt27ZWenq61b9//5Kftk6bNs3q37+/1bNnT18/ijflzTfftJo0aVLyk7rp06dbgUDA+vjjjy3Lsv8cfvDBB1avXr2s3r17W/v377csy/L8TzT94qOPPrIyMjKskSNHWq+++qrVvXt3q3nz5ta6deustWvXWqNHj7aysrKs448/3rrwwgutn376yXTLqIRPP/3UOvPMM61jjz3WWrhwobV8+XKrSZMmZUbULcuyZs+ebd1www3W4sWLDXSKcAoGg64nTa+//rrVvHlz64033rAefPBBq1WrVtYll1xiZWdnW/n5+dbMmTOtK664wrrpppusRx99tOQpZOjTSK/ZuHGj1bRpU2vWrFmWZdlfy48//mhlZWVZxxxzjJWTk1NyffPmzdb48eOtW265xerWrZv1yy+/mGw9YnjihApZ//tJ6ty5c/XFF19o69at6tOnjwYMGKANGzZo5cqV6tmzZ8nrhg0bpo0bN+qNN95QWlqa6fZRBS+99JJGjhypKVOmqFGjRsrNzdU555yjhg0b6tVXX1W7du00adIk3Xvvverevbuef/550y37ypgxY7R582Y98cQTGjt2rIYOHap//OMfuu6667R7927VqlVLOTk5+uqrr9S/f38lJiaqsLCQJ04xYNeuXTrzzDM1ePBg/f3vf9f69evVs2dP9enTRy+99FLJ67Zs2aJGjRqV/BQXsav4z9bmzZv13Xff6cUXX9SWLVv0l7/8RW+99ZYGDhyoww47TMFgUAUFBcrLy9MxxxyjI444wrNPF1C+efPm6f3339fRRx+tSy65RJI0ZcoU3X777erSpYvuuOOOcp8ce/lJkyRt2LBB119/vT777DNNmzZNJ554ooqKivTrr7+W/D7MnTtXtWvXdr1fbm6uatWqZaLlyDOb2+AV77//vnXooYdagwcPtq666iorEAhYd955p5WXl1fymh9++MEaMWKEVbduXeuHH34w2C0q4+OPP7b27NnjunbLLbdYQ4YMsSzLeYqxefNmq2XLltbll19e8rqvv/76gOuhUH1XX321demll1qfffaZVbt2bevZZ58teduYMWOskSNHul7PkyZzgsFgyZP4wsJCa8+ePVabNm2s9evXW5s3b7aaN29uXXvttSWvf/vtt638/HzX+yP2vfPOO1bHjh2tFStWWFOnTrXOOuss69BDD7UCgYA1aNAgq0OHDlbHjh2tLl26WEcccQRPmnzg+uuvtz766CPLsuy1Td9//72VlpZmpaamlllTOnnyZOvII4+0Lr/8cmvOnDkm2o24tWvXWpdeeqmVmppqzZ0717Is+++8n376yTrqqKOsrl27ljx5Kp6C8PPfbwQnVGjx4sVW69atrRdeeMGyLHvRY3JysnXnnXeWvGbRokXW5Zdfbh155JG+XRDoJw8//LA1aNAg119uRUVF1v+3d99xNe9/HMBfp6GiYUUlO0lWhGR0s0elkoxcO7m2jJZcO3td7kVmoRKJyLq4ESkrM8mmlKxo0Djn/fvj/M73dsQ1rutU3s/HwyO+43ifc/qO9/fz+bw/ffr0oR49egjLZN0tg4ODqVq1akUGsXPy9O8tW7aM5s+fT0REx48fp2bNmpGKiopc0pSZmUl2dnY0adIkRYXJSP73XXbs7Nu3j3799VfKzc2lrl270qJFi6hGjRr0yy+/CDcRqampZG9vT+Hh4YoIm32hwkU+XFxcaNmyZcK6o0ePUr9+/ahx48ZCVyTZ70VmZub3D5Z9U8+fPyd3d3fh2JUJCgoiXV1d6tOnT5HqsocPHyZdXV2aPXv29wz1P5OdnU05OTlyyx48eEAuLi6kpqZGp0+fJqK/k6fmzZtTjRo1fpjff06c2CfFxcWRlZUVERHduXOnyJNU2c30+fPnKTk5WSExsi8n63d96dIlevXqFRFJbwI1NDQoICBAbtugoCAyMzOjjIyM7x1mqfb27VsaPXo0OTo6EpH0BnvYsGHUqFEjWrlyJb1+/ZouXbpEPXv2pObNmwvfWWl+mldcyW6Or169SpGRkUREFB8fT3p6erRlyxbKycmhMWPGkJaWFvXs2VNuXy8vL2rcuDE9fvz4u8fNvk5UVBS1atWKevbsWWQ855EjR8jGxoYsLS2FJ/BEfFyWdO9/fwEBAbR69Wq5fxsYGNCkSZOKVAo+e/ZsqegBkJSURK1atSIbGxvat2+fkCQREb169YoGDBhAZcqUoejoaCKSJk/x8fHUtm1bunv3rqLC/q44cWJFyE4eR44cobi4OIqJiaHatWtTbGws1a5dm9zc3IQTRFRUFNnY2HDCVIIUPrlHRERQxYoVae3atfTmzRvKzs6mSZMmUe3atcnf35+ys7PpyZMnZGNjU6SFin0bsvnQZN087t27R8OGDaN69epRuXLlqFmzZtShQwcuBKFAsqTp8uXLpKKiQhs2bKBbt27RkiVLyN3dXdguLS2NrKysyMLCgry9vWnz5s3k6upKOjo63BJfwsTExJCRkRGpqqoK313hVojjx4+TlZUVdezYUa7LOisdsrKyqFOnTmRpaSk3N9OmTZuoWrVq5O7uTnfu3CmyX0k+P7948YImTJhAIpGIVFRUqHHjxmRoaEidOnUiLy8vSkpKoosXL9KECRNITU2NLly4QETS91y4G3Jpx4kT+6Do6GgqV64cBQYG0rNnz8jW1pbKli1LAwYMIKK/kysvLy/q0KHDB6sLseLnQ13rBg0aRCYmJuTv7095eXn0+PFj8vb2JjU1NTI0NKR69epR8+bNhZsG7p73df4p6fz555/J0dFRaNF78+YNpaSkUGRkJCUkJAifeUmuzlRSFW5p0tDQIB8fH5JIJGRsbEwikUhoLZRJTk6mcePGUYsWLah58+bk5OTE1fNKoLy8PIqNjaW6detSu3btPlghLSoqiufgKiU+dF1LTk6mPn36UPv27Wnjxo3C8s2bN1ONGjVo2LBhpeah8c2bN8nR0ZFOnTpFbm5u1KtXL/Ly8qJr167RpEmTqGXLlmRgYEANGzakfv36Ufny5UkkEv2Q49k5cWJFPHjwgLy9vYVxF0RE69evJ1NTUxoyZAhdv36dzp8/T9OmTaPy5csLs6ez4q3whSE0NJQOHz4s/HvEiBFUt25d2rBhg/D09ObNmxQcHEyRkZHCUzS+cf/3/Pz8yN/fX25Cze3bt1P9+vXp/v37RPThizgnrN+f7DO/efMmVapUSSicQiSdDNLc3JyMjIzoyJEjcvsVFBRQfn4+5eTk/FBPYksq2UONhw8fUkJCglyXyri4OKpevTp17NhR2O798S+sZCt8jCYkJFBqaqrwMPjx48fk6OhYJHlavXo12dvbl5rz8ubNm8nCwoKIpOe74cOHk4WFBQUHBwvbHDt2jDZt2kTt2rWj2rVrk0gk+iGLoXDixOTcvHmTLC0tqWbNmnKD04mIli5dStbW1qSkpERNmzal5s2bU3x8vGICZV+kcGuHh4cH1a1bl5YsWSI3+/nQoUOpbt265O/vTy9fvizyGiW5C0JxIZFIaPTo0dSkSRMyNjamyZMnU0JCAhERWVlZ0ZAhQxQbIBPIboji4+NJQ0ODNDU1ydjYmKKiooSiKQkJCWRqako2NjZyYwFKy83Uj0B2bgwLC6PatWtT3bp1SV1dnUaMGCF8p7LkqWvXrvzdliLTp0+Xu/H39vammjVrUp06dcjR0ZEuXrxIRNLkycHBgaysrOS67cl+d0rD74Sfnx+Zm5sL1/k7d+4IyVPhcV5ERDk5OZSRkUFPnjxRRKgKx4kTK2LixIlUoUIFsre3L1IM4M2bNxQbG0sPHz6k58+fKyhC9rUWLFhAlStXptjY2A+ud3V1pfr169OKFSsoOzv7O0dX+nzsgnrz5k0KDQ0lExMTsrCwIDs7O/Lx8aGWLVtSUlLSd46SfcyVK1dIWVmZ5s2bR0REbdu2pVq1alFUVJTwlPratWvUoEEDsrW1lSsUwEoOWdf03377ja5fv07bt28nKysrsrW1pZiYGCKSJk+amprUq1cvBUfLvoXz58+TmZkZWVlZ0aNHj+j06dNkYGBAhw8fpmXLlpGDgwPVr1+f4uLiiEiaPDk5OVGDBg1o//79RCQ/JUFJJHsAREQ0Z84c6ty5MxH9fd2SJU+Wlpb0+++/C9v+6D1POHH6wX3soPfw8CBTU1OaPXu2UHGNlVwSiYRevHhBXbp0oS1bthAR0f379ykyMpKcnZ1p8uTJwsnQwcGB+vXrV6IvCMVB4aQpJiaGjhw5IlQiksnIyKCjR49S7969hT7ja9as+d6hsg/Izs4mBwcHmjFjhtzyjyVPTZo0ofbt23/0oQQrfmTnuOnTp8tNw0Ak7ZZkaWlJY8eOJSJpi/v58+eLVFNjJVdERAR16dKFfvrpJ5o5cyatWrVKWHf27Fnq3bs31atXT0ieZMMYSkPvi+TkZHJ2dqajR48SEdHMmTPl5nCUXb9u3bpFw4cPpzZt2tDSpUsVFm9xIiIiUvQkvEwxiAgikQhxcXE4c+YMypQpg9q1a8PGxgYAMGXKFERFRcHBwQHjx49H+fLlhX1Y8SeRSKCkpCS3rGPHjtDS0sKoUaOwdu1avHr1CgYGBjh8+DD69euHDRs2yO3L3/fXKfy5+fj4YM+ePXjz5g1q1aqFevXqISAgoMg+Z8+eRUhICI4dO4ZDhw6hRo0a3zts9p5Hjx4J30N+fj5UVVUBAO3atUNKSgq2bt0KS0tLlClTBpcvX8Yvv/yCXbt2oXr16ooMm32C7PiUned8fX1x8uRJnDhxAioqKsKxu2HDBri7u+PevXuoUqWKgqNm34pYLIaysjIAICIiAuvWrUNMTAx8fX0xdepUYbvY2FgsWbIECQkJ2LBhA9q1a/fB1yiJ7t27h59//hnly5fHvHnzsHv3biQnJyMwMLDIttnZ2Rg4cCAkEgkCAgJQoUIFBURcfHDi9IOSXTjCwsIwbNgwmJmZISMjAzdv3sT48eOxfPlyAIC7uzvOnj0La2treHt7Q0dHR8GRs89ROGnav38/tLW18dNPP2Hbtm3w9/fHpUuXMGnSJHTv3h3t27fHnDlzcOPGDQQGBkJNTa3Ia7Cvs3DhQqxcuRJhYWFo0aIFZs2ahUWLFsHOzg779u0DAOTm5gqf+fnz5zFw4EBs2bIFbdu2VWToP7SPPTAoKCiAiooKgL+Tp8DAQLRq1QpqamrIy8tDmTJlvne47CtER0dDVVUVrVu3xqZNmzBmzBhERUXB0tJSbptRo0bhzz//RLVq1RQYLftWCl/XZMfzoUOHMGfOHKSnp+Po0aOoW7eusP25c+fg4eEBPT09hISElKrr4p07dzBu3DiUK1cODx8+BBGhUaNGUFJSgpKSEnJzcyESiaChoYHU1FSsXbsWhoaGig5b8RTT0MW+tw+Ntbh9+zbp6+sLRSBevnxJISEhVLZsWZoyZYqwnZubG1lbW3PJ8RLi/UIQ9erVo99//52ys7Pp3bt3lJWVVWSiOmtra5owYcL3DrVUu3XrFnXp0oUOHjxIRESHDh0iTU1NGj16NBkaGlLv3r2FbQv3GTcxMeHuesVY4e/K2tqatLW1hbFN3L215OjSpQuZmpoK/+7bty9VqVKFoqOj6c2bN0RENGXKFGrSpMkHi+WwkqfwfdDSpUtp5syZwrUwMjKSOnbsSO3bty9yfbxx40apKADxIYmJidSjRw/S1NSkSpUq0S+//EJdu3albt26kZOTE/Xq1Yu6d+9ON27cUHSoxQYnTj+AwvOQFC5BHRsbS8bGxkVms9+xYwdpaGjQ8ePHhWVPnz79PsGyb8bPz490dXXp9OnTHzzpv3nzhk6ePEndunWjJk2a/PADPr+F9z/nLVu2UFpaGp05c4aqVatG69evJyKiUaNGkUgkorZt28ptHxISQuXLl6dbt259t5jZlyt8rHTv3p3HvZQA7ye1jx49IlNTU2EMW0ZGBg0YMIDU1dWpadOm1K5dO6pQoQJXji2Fpk2bRlWqVKGNGzdSSkqKsDwiIoI6d+5MVlZWRZInotJRPe9Dbt++TTY2NtSlSxeeXuYzlI72RvZRsmblq1evomnTpjh37pywrmzZsrh79y6SkpIASLunAIC1tTX09fWRmpoqbMv9u0uW58+f49ChQ1i6dCnatm2L5ORk/Pnnnxg+fDh8fX0BSLsgbNy4Eaqqqrhw4QJUVFRQUFCg4MhLpoMHD8LT0xP9+/fHpUuXhOVDhw5F1apVERkZiS5dumDw4MEAgLp168Le3h7169eHWCwWtq9VqxbOnz8PY2Pj7/4eWFH0kZ7shY+VQ4cOwcjI6HuGxb6CSCTC8ePHERERgWfPnqF69eoYNGgQ4uLicPXqVejo6CAoKAjbtm3DkCFD4OjoiHPnzsHMzEzRobNvKDIyEsHBwYiMjMSIESNgYGAgHOd2dnaYOHEi1NXVYWNjgydPnsjtW1q66L3PyMgIy5cvh5KSEqZNm4bo6Gi59R87D/6oVBQdAPvvyJKmy5cvo02bNvDx8cGMGTOE9SYmJujRowd+//13lC9fHs2bNwcAVK5cGRUrVkR+fr6iQmf/ko6ODlRVVXHixAlUqFABmzdvRnp6OipUqIBdu3bh7du3WLZsGapUqYKGDRtCSUlJbvwG+3wbNmyAt7c3rK2tkZKSgvbt2+Py5cuoV6+esE1SUhIeP34MdXV15OfnIzY2Fh06dMCECRMA/N3X3sLCQlFv44dG/x/TdPv2bYjFYpQpUwZ16tSRKyDwPj5WSpZnz55h5MiRePz4MUaPHo3OnTtj4sSJ2L59O7Zv347FixcDAPr06aPgSNl/KTU1FdWqVYOJickHCzzY2toiLy8Pp06dQtWqVRUU5fdnbGyM1atXY/LkyfDw8MDKlSuF6xEXiJJXOtNnBkD6dOTWrVto3bo1fH19MW/ePGHdgQMHkJubC1dXVzx//hyzZs3CgQMHcOPGDcyYMQMPHz6EtbW14oJnn00ikRRZpqqqil69euHOnTtwdnaGiYkJFixYgAMHDmDUqFF4/vw5AKBx48ZQUlKCRCLhG8Gv4O/vjzFjxmDDhg3YuXMnDh06hHr16uHOnTvIzc0Vths0aBCePXsGc3NztG3bFomJiRgzZgwA6U07f/aKJRKJsHv3bnTs2BEdOnTAwIED8dtvvwGAcHywkk1XVxcDBgyApqYmjI2N4ePjgxUrVmDq1KlYunQpjh07Jrc9P2UvnZKTk5GcnAxNTU0oKyujoKBAeEBy4sQJPHz4EL1798bKlSuhrKws1yOgtKtXrx6WLFkCQ0ND6OvrKzqcYour6pVi7969w7Bhw/Dnn39i165d6NChAwBg/vz5WLduHf7880+YmJggPDwcwcHB2LNnD4yNjVFQUICdO3eiWbNmCn4H7FMKPw3funUrLl++DLFYjPbt26Nv377IyspCWlqaXFcia2trmJubY9myZYoKu1SIjIyEnZ0dAgMD8fPPPwvL69evD1NTU1y7dg29evXCoEGD0KhRIxw8eBBHjx6FlpYW5s2bBxUVlRJf0rakk7U0paWlwdraGh4eHqhSpQpOnTqF0NBQuLq6Cl1bS1M1rR9JQkICXr16JVSpbNmyJSwtLTF58mQ4OzujTp06OHz4MJo3b45t27Zx1bBS4mPH65UrV+Dg4IC+ffti0aJFwvKMjAz069cPAwYMwNChQ79jpMUPVwf9Z/yYsxRTV1eHm5sb8vLyMHfuXGhqaiI2NhbLly/Hjh07YGJiAgBwdHSEra0tHjx4ALFYjEqVKkFXV1fB0bPPIbsweHh4YNu2bejfvz8KCgowatQonDlzBqtWrYKRkRGys7OF1sRXr17JXTDY17l69SpMTEwQHx+Pfv36QVVVFU5OTnj37h3atm2LevXqYfXq1Xjy5Am2bt0Ke3t72NvbC/tz10jFE4lEOHv2LPbs2YOOHTti8ODBUFFRgbm5OXR0dLBu3ToAgK+vr9DyxMlT8VT4u5H9PTk5Ge7u7khLS8OQIUMwefJkLF68GOvWrUNmZib++usvbN++HXfv3sXVq1eFaQFYyVb4dyE+Ph4FBQWoXLkyateuDSMjI7i4uODPP/+Em5sbvL298fDhQyxduhTPnj2Tewj2o+Kk6Z/xVbuU69ChA5SVlbF8+XL8/PPPePjwIaKiotC6dWuhK4JIJIKKiorcmAxWchw7dgy7d+9GeHg4WrdujdDQUAQGBqJJkybCNsePH8f27duhoqIiFILg1o5/Z9q0aVBWVsbevXvh4eGBO3fuICUlBVFRUahduzYAafcgT09PzJo1S3hQIcNJk+Ll5OQgKCgIO3bsQOPGjYXvRF9fH8OHDwcAbNq0CTk5OfDz8+OkqRiTJUrJyclo3bo1du/ejbdv32LmzJm4dOkSpk6diosXLwqTgB8+fBjTpk2Dq6srnJ2dkZubyw8MSwEiEo7TGTNmCNe95ORkLF26FG5ubsK8TOvWrUPDhg1Rq1Yt6OvrIy4ujq+N7NMUU8yPfQ+Fy69GR0eTjY0NNWvWjI4dO/bBbVjJ8H5J1G3btlGbNm2IiCgsLIy0tLRo3bp1RESUmZlJMTExJJFI6MKFC8K+XHr83yn8Ofr5+VHDhg1JW1tbKOX69u1bIiIKDw+nJk2a0J07dxQWKyuq8Hnv6tWrNGHCBFJTUxPKxcukpqaSj48PNWzYkJ49e8bny2IsKyuLnJ2dqUOHDjR//nwSiUQUGBgorL958yZ17tyZHB0dqU6dOlS+fHm5KTdY6TJnzhzS19cXvuORI0eShoYGzZo1i3Jzc4lIeh6IiYmhO3fu8LWRfTZOnEq5whf6U6dOkb29PXXo0EGYlPP9bVjJsWXLFrpy5Qrt27ePXFxcKCQkhDQ1NYWkiUg66erYsWMpPT1dWFZa56L43mSfY0FBAS1evJhatWpF48ePFybLLCgooB49epCdnR0fY8WE7HvIzs6mvLw8Yfm9e/dozJgxVL9+fdq4caPcPmlpafT8+fPvGif7OidOnCAzMzMSiUTC/EwSiUT4rp8/f04BAQHUp08fEolE1K1bN+EmmpUOEolEmNR13759RCR9gFWhQgXq27cviUQimj17NqWlpRXZl6+N7HNw4vQDKHzTdvLkSbK3t6cuXbrQ3r17FRgV+1KFT+qLFy8mbW1tunXrFl28eJF0dHRIJBLRmjVrhG1ycnKoe/fuNGzYML5x/4+83/LUunVrGj9+PGVkZJCDgwOZmJgIN218UVYs2TFw4MAB6tKlC7Vu3Zo6depEp0+fJiKiBw8e0NixY6l+/fq0efNmRYbKvpDsu01NTSVzc3MyNTUlW1tbio6OFtbLWhLEYjHl5eWRr68vJSQkKCxm9u0cOHCAVq9eLfw7JSWFAgIC6N27dxQdHU3VqlUT1g8ePJg0NTXJw8OD3rx5o6iQWQnGHbZLKSpULFEkEgn/trKywtSpU1FQUIDNmzcjOztbUSGyLyTrt33jxg28ffsWmzdvhrGxMZo3b46AgAAAwIMHD7B//34cP34cvXr1wpMnT+Dv7y/3O8C+ncKl3KdNmwZ7e3tcunQJhoaGSEhIwNWrV6GqqoqCggIeH6NgIpEIkZGRcHR0hLm5ORwdHaGiogInJyds2rQJNWvWxIQJE9C9e3d4enpi+/btig6ZfSbZPDOVKlXC4cOHsWrVKuTl5WHevHk4ffq0MI4XAHJzc6Gqqoq5c+eiQYMGigybfQMXLlyAnZ0d1q1bh1WrVgEADAwM0KtXL6ipqSE4OBgdO3aEm5sbAOnvSKNGjXD69GloamoqMnRWQnE58lKA/l9S9/79+3j58iWaNGkCVVXVj24HAGfPnkX16tW59GoJc/r0aVhZWUFNTQ0BAQHo27evsC4oKAhz5szBq1evULt2bVStWhW7d++GqqoqD3b9Sv9URa3wZyrbrqCgAHPmzEFiYiKCgoKgoqLC1fMU5NmzZ3KD/d++fQsHBwc0adIES5YsEZaPGTMGYWFhiIyMRIsWLXD16lXs2LEDbm5uqFu3riJCZ59Jdk2Lj4/H48ePkZ6eDhcXF5QtWxbHjh3DkiVLoKKiAm9vb7Rr1w7z5s1DhQoV8Msvv/D5sJQ4ffo0OnToAHNzc1StWhU//fQTJk+eDECaJNvZ2aFWrVpYv349RCIRHB0dMX36dLRo0QKA/H0RY5+DE6dSYs+ePRgzZgyUlJSgo6ODBQsWoEuXLihXrpzcdnySKFk+dOO+YsUKTJkyBV5eXpg9e7Zckvzs2TNkZ2dDTU0Nenp6EIlEfOP+lQp/9gEBAbhy5QoAwMzMDIMHD/7o9hKJBCKRiD97BZo5cyZycnIwf/58obRubm4u2rdvj379+mHKlCnIzc0Vyk/LKq3t27cPAJCfn//Bh0+s+AkLC8P48eNRt25dPH36FGKxGPPmzcOAAQMQGRmJdevWISkpCSYmJti/fz8uXLiA5s2bKzps9g15eHggLi4O1apVw6NHj+Ds7IyJEycCABYvXgxvb284Ojrizp07yM/Px5UrV6CiosL3Q+yrcN+REo6I8OTJE8yfPx++vr44fPgwTE1N4enpiZCQEGRlZcltzyeJkoMKlVXdtm0bLl++DABwd3fH/PnzsWjRImzevFluH11dXaG0qmw2dL5x/zqF58jy8vJCfn4+srKy4O7ujilTpnxwe9l3JusayZ+9YjRs2BBDhgxBmTJlkJOTAwBQU1NDxYoVceDAAeHfubm5AIAWLVogLy9P2J+TppLh/Pnz+OWXX7BgwQJER0fjzJkzuH//PtLS0gAANjY28PLywtChQ1GxYkVcv36dk6ZSQPa8X3bMtmvXDtWrV4enpycaNmyI4OBgrFixAoD0/L106VKULVsWVlZWQtIkFov5foh9ne89qIp9G7LBsGKxmHJycmjixImUlZUlrB8yZAgZGxvTxo0bKTMzU1Fhsq9UuJBAeno6iUQisre3p2vXrgnL586dS8rKyuTv76+IEH8IR48epdq1a9PZs2eJiCgkJITKli1bpPIaF98ono4fP05jx46l69evExFRVFQUGRkZ0ciRI+W2GzJkCPXt25fy8vL4uyyGPlZYJSQkhGxsbIhIWm68du3a5OrqKqwvfE0sKCj4b4Nk38WRI0do4cKFct9tbm4uNWzYkGbMmEE5OTk0YsQIsrS0pBUrVgjbFK6iySXH2b/Bj0NLKNlA561bt+LRo0dQV1dHQUGBsH7r1q0YMmQIVqxYgXfv3mHo0KFFuu2x4kvW2uHt7Y23b9+iQYMGOHToEDIzM7F69WqYmprC19cXADBu3DihJYT9O/T/rhuynw8fPkT16tXRunVr7NmzByNHjsTy5csxYsQIZGVl4cKFC7C2tuYnl8VUcnIyAgMDoaKigokTJ6Jdu3bw8PDAokWL0LZtW1hZWSE5ORnh4eGIjY3llqZiSklJCbdv30ZiYiJ69uwpjE+6desWxGIxcnNz0a1bN3Tv3h1r164FAISGhiIhIQG+vr5QUVHhMU2lwJkzZ9C9e3cA0u+3f//+aN68OTp16oQ1a9Zg6dKlyMjIwLx58+Dj44M9e/YgKysLvr6+csc29wRg/wZ31SuhYmNjYW9vjwoVKkBZWRlXr17F4sWL8erVK2GbgIAAGBsbIyAgQC6pYiXDqlWr4O/vj/79+2Pnzp04fvw4bty4gTFjxuDGjRsAAF9fX0yaNAl79uzhqnnfgCwBev78OQCgYsWKqFGjBkJDQzFkyBAsWbIEo0aNAgBER0dj7969SE1NVVi8TJ7sGHj8+DGICIMHD8b69euxe/duLFu2DKmpqRgxYgS2b98OPT09xMfHIz8/H7GxsWjUqJGCo2f/ZMOGDbC3t8e+ffsgFosBAM7Ozrh//z7Kly8PGxsboQAAAMTExODatWt4+/atIsNm35Camhp69OiBzp07o2LFinj+/DkGDBgADw8PXLp0CRkZGYiJiYGenh4WLFiAypUrIyUlha+N7NtSZHMX+zqJiYnk5+dHy5YtE5a5u7tTy5Ytae7cuZSRkSG3fUpKyvcOkX0DQ4cOpcGDB8stu3fvHunq6lL37t3p6tWrwnJZVxbuZvTvbdiwgaZOnUpERLGxsaSlpUUikYh+//13YZucnBzq1q0bjRgxgj/zYkL2PURERFD79u3lurDu2LGDqlWrRmPHjqW7d+/K7cfddkqO8ePHU9myZWnXrl1UUFBAGRkZ5OHhQXXr1qUlS5YQEdHDhw/Jx8eHKlWqRDdu3FBwxOxbO3v2LPXv35+6d+9Ou3btouvXr9PgwYOpd+/eJBKJqH379vT27VsiInr58iVfG9k3x4lTCXP37l366aefSE9PT26yUyJp8mRubk7z58+nly9fKihC9m+JxWKSSCRka2tLtra2wvJ3794REdFvv/1GIpGI7Ozs6NGjR3L7sH9v9uzZpKWlRenp6UREtHPnThKJROTt7U2RkZH0119/UefOnalJkybCTTd/9opT+LPfs2cPqaur08qVK+nmzZty2wUGBpKBgQFNnDhRbqwgK37eH9NUOLkdM2YMaWho0M6dO4lI+jBp3LhxVLVqVapatSqZmZmRkZERXbp06bvGzL6fmJgYcnJyorZt21JUVBQREaWlpZGPjw9duXKFiOR/h3jycfYtceJUwuTn59Ps2bOpVq1a1KVLF7kBkkREU6dOpTp16tCSJUv4Zq6E+NhJPTw8nMqWLUubN2+WWx4YGEiurq6kq6tLQ4cO/R4hlkoSiUTuGJF9D3l5eWRtbU3u7u7CgOItW7ZQvXr1qHLlymRhYUF2dnbCOh50rhjXrl2T++wfP35MTZs2pT/++IOIpOfKnJwcOnDgAD1//pyIpC1P6urq5OnpKTdYnBUfsmPyzp07tGDBArp79y49e/ZMbptRo0aRmpoahYSEEBHRmzdv6Pbt27R+/XqKioqi5OTk7x43+75iY2Opd+/e1L59ewoLC5Nbx4kS+y/xCLlijt6bZ0BFRQU+Pj7Q0NBAcHAwPD094efnB21tbQDAkiVLUKZMGTg5OfGA9RKg8FxB+/fvx/3796Gqqgpra2s4ODjAzc0Nc+fORX5+PoYPH46XL19i586dcHBwgK2tLQYOHAh3d3c0adJEwe+k5Hn/+ChcTrxt27Y4ceIE8vLyoKqqiqFDh6JHjx7Izs6Gurq6UO6d52lSjDVr1iAsLAz79u0Tzn25ubl4/fo1GjZsCIlEgsWLFyMyMhLXr1+HpqYmTp48CRcXF6iqqsLMzIwLQRRTIpEIr169Qrdu3XDv3j0EBwdDRUUFXbp0QcuWLeHk5IR169ZBT08PQ4YMgbKyMuzs7GBkZAQjIyNFh8++kX+afBwALCws4OHhgSVLluC3334TJrcF8I/7MfZv8QS4xZgsaYqJiUFUVBQKCgrQuHFjODo6QiwWY+nSpQgPD4e5uTkWLFgg3ECwksfDwwO7d+9GzZo1Ub58eURERODs2bPQ09PDhg0bsGTJEujp6YGIoKOjg/j4eJw8eRJubm44deoUDAwMFP0WSoxp06bB3t4e7dq1AwBs2rQJu3fvxpo1a1ClShVoaWnh1atXMDY2xqhRozBv3rwPvs6nLuzsv5OVlYW0tDQYGRkhPT0dFStWRH5+Pvr374/ExERkZmaiVatWaN26NUaOHAlLS0vY2NgIc7uw4u358+dYt24dNm/ejOrVq2PixIlYtGgRnjx5AjU1NVhYWGDEiBGYPXs2Hj16hEWLFsHBwUGY7JiVHitXroSRkRFsbW0/uD4uLg7Lli3DjRs3sHbtWlhZWX3nCNmPhq/6xZhIJEJYWBi6deuGY8eO4dChQ3BycsLo0aORm5uLqVOnolevXrh69SrGjx+PzMxMRYfMvkJQUBC2bduGkJAQ/PXXX3B0dAQR4c6dO6hRowZmzZqFS5cu4ddff8Xy5ctx6dIlKCsr4+DBg6hSpQrU1dUV/RZKjMTERLx8+RKtW7cG8HcVtszMTFhbW2P8+PGIjIxEhQoVMHPmTFy8eBGJiYkffC1OmhRDLBZDU1MTRkZGiIuLg42NDSIiIqChoQE/Pz9MnDgR3t7e2LhxIzw9PVGpUiWYmpqiVq1aig6dfabKlStj5MiRGDNmDK5evYqXL18iLi4O165dw4gRI6CsrIyff/4ZmZmZePjwIebOnSs3gTEruSQSifB3f39/LF68GHp6eh/d3sLCAuPHj4ejoyPatm37PUJkPzrF9RJkn3Lv3j2qUaMGrV27loik/XYPHTpEZcuWpTFjxhCRdDyGr68vdenShVJTUxUZLvtCsn7Ys2bNovHjxxMRUVhYGGlqagoVwd68eSMUgJBJTEyk0aNHk46OjjAQln25oKAgOnLkiPDvDRs20LBhw0hZWZnGjh1L48ePp/r16xfpP8+Kj4yMDDI3NydLS0s6cOBAkfFmGRkZNGPGDNLV1aVbt24pKEr2tdLT02nBggWkpaVF8+fPl1uXmJhI0dHR9MsvvwgTHLPS48KFCzR+/HgKDAz8ov14zCn7r3HiVEz4+/tTTEyM3GD1a9euUd26dSkhIYGI/r7RPnDgACkpKdHBgweJSHqikA1+ZsWbWCwucmKfPn06jRkzhvbs2UOamppCokxEFBAQQL6+vpSdnU1E0hnSg4OD6eeff5YrR84+n0QiodTUVGratCl17dqVIiIihHVisZhOnDhBTk5O1KlTJxKJROTg4KDAaFlhsvPj+fPn6dy5c0QkfbhgbW1NrVq1or179wrH1/79+2nw4MFkaGjIFdaKMVmBo48VM3r27BktXLiQdHR05JInvkEuvU6fPk3q6upUrlw52rhxo6LDYUwOJ07FgEQioWrVqpGJiQmdP39euIBcv36dRCKR8FS8oKCAJBIJZWVlUaNGjYqUI2fFW0REBA0dOpS6du1Ky5cvF5Zv3bqV6tevT+XKlaPVq1cLyzMyMqhHjx7k5eUl9zq5ublFqimyLxcXF0edOnWinj17yiVPREQvXrygxMREmjFjBldfKyZk58WwsDAyMDCg4cOHC3PUyZInCwsL2rdvHxFJk6vly5fT7du3FRYz+2ebNm2i8ePH05MnT4jo08mTtrY2LVq0SG4dV48tnVauXEnlypWjAQMG0IMHDxQdDmMCTpwUTHbSz83NJTMzM2rUqBHFxcUJ81a4uLhQmzZtKC4uTthHLBZTq1athLK7rPhbv349VahQgYYNG0a2trYkEonknp4OHDiQNDU1KTg4mG7fvk3Xr1+n7t27U/PmzXmuoH/pn+bziI2NJWtra+rZsycdOHDgo9tx8lQ8nDhxgsqWLUtbtmwRWtll35UseWrTpg3t2bOHJBIJt0oUc9OmTaPGjRuTj4+P0NX8n5KnxYsXk0gkohUrVnzHKNl/6Z9Khy9cuJD09fVp1qxZwkMSxhSNq+oVA7m5uVBTU0NWVhbMzMxQo0YNLFiwABYWFvjrr7+wbNkypKenY/r06ahSpQr27duHjRs34ty5c6hTp46iw2efsHHjRowbNw7BwcFwdHTE06dPYWNjg4yMDLmKeHZ2drh//z6SkpJgbm4ONTU1/Pnnn1BVVYVYLIaysrKC30nJU7jy3bp163D58mW8efMGffr0QZcuXaClpYW4uDh4eXmhXLlyGDNmDHr27KngqNnHeHt74+nTp9i8ebNwTIjFYigpKUEkEiEzMxPt27dH5cqVsXfvXmhqaio6ZPYJc+fORXh4OLp164aJEycK1UM/NJ1Geno6goOD0b17d9SvX18B0bJvqfD5edOmTTh//jxUVVVhYmKCsWPHAgDmzZuH9evXY+TIkXB1deUKskzhOHFSMNkFIjQ0FH/99RcSExNx8uRJmJmZYdOmTWjWrBlOnjyJrVu3Yvv27TAyMoKSkhK2b9+OZs2aKTp89gkJCQlo3Lgxhg0bho0bNwrLzczM8PTpU0RHRyM/Px8NGjQAADx69AgJCQkwNDSEqakplJSUeK6gb8DLywubNm3C8OHDcevWLTx58gQ//fQTfH19oaOjg7i4OPj4+CA7OxsrVqyApaWlokNmH9CzZ08oKytj//79AOTnuXv48CFq1qyJzMxMvHz5EjVr1lRkqOwTCt80z549G/v27fus5ImnASh9PD09sXnzZtjb2+PBgwdISkpCo0aNcPDgQQCAn58f/P390adPH3h5eaFy5coKjpj90BTX2MVkTp06RRoaGrRx40aKjY2l6Ohoql+/PpmamsoNar579y49fPiQC0GUIA8ePKCpU6dSxYoVadu2bURE1Lt3b6pcuTL17t2bHBwcqEaNGtS7d29avXo13bx5U25/ngH9y73/mW3ZsoXq1KlDFy9eJCLpWDMlJSVq2LAhjR8/nl6/fk1E0uNw3Lhx/JkXU2KxmGbMmEFWVlaUlJQktzwlJYX69+/PRSBKmMLd8n799Vdq1qwZeXl5fbLbHis9zp49S4aGhhQVFUVE0mELR44cIUNDQ+rdu7ewna+vLzk4OPDvBFM4TpyKgWXLllHr1q3lxlG8fv2ajI2NqXnz5hQbGyuMc2ElT0pKCnl6epKWlhY1bNiQWrZsKdz4vXjxgi5dukQDBw6kWrVqUffu3RUcbckn6wsvFotJLBaTv78/zZo1i4iIwsPDqUKFCrRq1SqaNm0aVaxYkSZPnkwvX76Uew1OnhRLdnP05MkTevDgAT19+pSIiOLj40lTU5NGjx4tVBvNy8ujWbNmkZGRET18+FBhMbPPI/tu3759Szk5OXLrZsyYwclTKSc7t8p+hoWFkb6+Pr169UrYJi8vj3bt2kX169en6OhoYbnsd4F/J5giceKkQLKD/9dffyUTExNhuexicvjwYRKJRNSkSRPhaTkrmVJSUmjGjBlUrlw58vPzE5bLkuX8/Hx6/fo137D/S/Hx8SQSiWj37t3CsoyMDEpNTaWUlBRq2rQpLV26lIiIkpOTSV9fn6pXr06LFy8mIr4gFwey7yA8PJxMTU2pYcOGZGBgQJ6enpSRkUHHjh0jfX19ateuHbVt25bs7OyofPny3NpUAsi+28jISHJ2dqYGDRrQr7/+SseOHRO2kSVP06dP54IApUzh5Ojs2bNERHTjxg0yNDSk8PBwuW3v3r1L5cuXLzKPHp+jmaJxR2EFkvXf7tu3L1JSUrBgwQIAgIaGBgCgTJkysLOzg5qaGsqXL6+oMNk3YGBggJEjR2LChAlYsGABNm3aBABQVVUVxjBpa2tDSUkJYrFYwdGWXPr6+nBzc4OLiwv27dsHANDS0oKenh7u3r2LN2/eoEePHgCkA83btWuHGTNmYMqUKQDwwTEV7PsSiUQ4fvw4Bg0ahFGjRuHChQsYPXo0Fi9ejMOHD6NTp07Yv38/XFxcUKdOHbRu3RqxsbE85rMEEIlE2LdvH5ydnWFkZIQRI0YgOjoac+bMQXh4OABgzpw5sLe3x44dO7Bx40Y+H5YS+/fvx+TJk5Geno4JEyagbdu2ePnyJSpWrAgTExMEBQXh7NmzwvY6OjqoVatWkfG9fI5mCqfozO1HIntSEh8fT9u3b6cLFy4I45XmzZtHderUoXnz5hERUWZmJvn4+NDEiRO5m14J8TlPwlJSUsjb25u0tbVp06ZN3yGqH09aWhpNmDCBRCIR7d27V1h+6tQpMjU1pYULF9LNmzfJ1taWhg4dKnxvXLpa8WTfxZgxY+iXX34hIqLHjx+TkZERjRo1SpGhsW/g5s2bZGJiQuvXryciouzsbKpcuTIZGxtTmzZt5I5XPz8/unfvnqJCZd9YeHg46erqUtOmTalSpUp048YNYd3p06epadOm1L17d5o7dy5FRERQ586dyczMjM/LrNjhxOk7CwsLI21tbapbty5VrFiRxo0bR3fv3qXMzExasGABaWlpUa1atahRo0bc/aQEKdzFTtbV8mOJVEpKCvn4+JBIJKL9+/d/l/hKs8ePHxcpmJKamkrjxo2TS57evHlDbm5uZGRkRPr6+tSqVSuhqyR3/1CM98c7yL4HZ2dn2r59O+Xk5JCBgQG5ubkJ63bu3EknTpxQTMDsi8m+t8zMTKFYTkZGBj169Ijq1KlDY8aMoTNnzlD16tXJwsKCduzYoeCI2bdU+Nw6YMAAUlJSIicnpyLjEePi4uiXX36h6tWrU4sWLahnz57C+ZmTJ1accDny74D+X1b18ePHGDt2LOzs7DBw4EChxHidOnUwe/Zs1K1bF3fv3kVERAR0dHRgZWUFIyMjRYfPPqFwedzFixfjypUr+O2331CpUqWP7vP48WMcPHgQI0aM4FLj/0JYWJgwt8fIkSNRtWpVDBgwAACQl5eHadOmYfXq1QgNDUWfPn2QlZUldNlr06YNlJWVudy7AsiOGdm58fXr19DR0RHWT5gwAX/++Seys7Ph4OCAZcuWQVVVFfn5+Rg8eDCMjY0xY8YM/t5KiD179uD06dPw8vICEaFq1aoYMWIE8vLysHbtWmhqasLJyQlxcXFo0aIFAgMDoaWlxd2ySrj3S8dv3LgR2dnZWL58Obp164ZJkybB1NRUOA+IxWK8ffsW2dnZqFKlCkQiEZ+fWfGjyKztR3Lu3DkaN24cOTo60rNnz4TlAQEBZGlpSS4uLnTlyhUFRsj+LQ8PD9LX16c1a9bQ7du3P3s/7or5dXJzc2nMmDGkqalJVapUISsrK6pRowY1atSIevfuTceOHaMTJ07Q9OnTSUlJiQ4fPlzkNfhJ5vcna126f/8+zZ07l9q1a0c1a9YkFxcX2r59OxFJy/hbWlqSoaEhZWdnE5H0u/Lx8aHq1avLlSNnxZOspeHevXtUtWpV2rBhg7BOLBZTmzZtaPr06cIyV1dXWr58uVBNj5VshXthLFmyhLy8vIQWpD179pChoSGNHDlSbgqO98/R3BOAFUfc4vSd+Pn5YeXKlVBRUcGpU6fkWpICAwOxefNmaGtrY+HChTA1NVVgpOxzFX6aduLECQwdOhTbt2+HlZWVgiP7cTx9+hQLFizA/fv30bBhQ7i7uyM8PByHDx/GlStX8O7dOxgZGSEmJgZisRjnz5+Hubm5osP+YcmOmWvXrsHJyQktWrSAlpYWatSogU2bNiE3NxcjRozAnDlzEBYWhlmzZiErKwstW7ZETk4Ozp07hyNHjnAhiBIiKioKd+/exeXLl7Fq1SooKSlBIpEgJycHrq6uyMvLg6OjIxISErB9+3acO3cO+vr6ig6bfUMeHh4ICgrC+PHj0adPH9StWxcAsHfvXkyaNAkdOnRAr169sGnTJly6dAkpKSkAuAgEK8YUnbn9SNasWUN16tSh0aNH04MHD+TW+fv7U/fu3bn8agng6elZZNnmzZupWbNm9O7dO2HZ+0/LuNT4fyMlJYXGjh1LLVq0oDVr1gjLb968SX/99RcNGTKELCwsyMjIiFv3FEj2+3/58mXS1NQkDw8PufLEt27dokGDBpGuri6tXLlSWObp6Umurq40f/78L2rJZYo3YMAAEolE1KxZM2GiaZkDBw5Qp06dqHbt2mRiYsJTbpRC+/fvJ319fYqJiRGWFb4u7t+/n8zNzalJkyZkZWXFY05ZicAtTv8B+n9/3ZycHEgkEmhqagrrFi1ahJ07d8La2hqTJk1CjRo1hHXv9/Nnxc/JkyexaNEiREREyPW7DggIwMyZMxEVFYVatWoBkP4eSCQSBAcHo0uXLqhataqCoi79UlNT4efnh3PnzsHe3h4+Pj7COtnxKPvJfeYV586dO2jcuDGmTp2KuXPnQiwWy40zu3v3LsaNG4fHjx8jPDwc9erVU3TI7AvIjrHs7GyUK1cORIRJkyZh7dq1CA0NhYODg9z2qampICKoqqpCV1dXMUGz/8yqVauwb98+nDhxQvjdeH/c04MHD1BQUIA6depASUmJz8+s2ON5nL4x2ckhMjISAwcORLNmzeDp6YmDBw8CADw9PeHs7IyoqCisWbMGDx48EPblpKn4s7S0RGRkJFRUVLBr1y5hec2aNZGbm4uQkBC8ePECAISb9A0bNmDr1q0KivjHoK+vj+nTp6NVq1aIiIjAokWLhHWyeWBkF22+KCuGRCLB5s2boaWlJdwkKysrQywWQ0VFBUSEunXrwsfHBzdv3sT169fl9udnfMWfSCTCX3/9hREjRuD69esQiURYtWoV+vbti6FDh+LEiRNy2+vp6cHAwICTplLq3bt3SElJQUZGhvDwSklJCfn5+QgNDUVubi5q1aoFIyMjoRsnn59ZcceJ0zcmEokQERGBvn37olGjRpg6dSouXbqEuXPnIigoCADg7e2N/v37Y9euXdi4cSMKCgoUHDX7HGKxGGXKlIFIJEJSUhKGDh0KW1tbAIC1tTXc3Nzg5+eHxYsXY//+/Th58iTs7OyQmZkpTLDK/jt6enpyyZOvry8AyF2ICz/pZN+XkpISxo0bBxcXFwQFBWHhwoUApMmTRCIRtjM3N0elSpWQmpoqtz+PeSgZypQpg/DwcCxatAg3b94EAGzfvh02NjZwcnLCX3/9JWzL32npUPj4Laxhw4Z4/fo19u7di8zMTOH7zsvLw2+//YaAgAC57fn8zEoERfQPLM0SExOpUaNGtG7dOiKSzumjq6tL9evXJwsLCwoJCRG2Xb58OU/wV0IUroR4/PhxIiLavXs31atXj2xtbYV1S5cupXbt2pGamho1a9aMOnfuzHNRfGepqak0aNAgGjlyJPeVL4Zkc2xZWFjQwoULheWy40M2GSaPeSn+3j++ZP+OiYmhypUr04ABAyghIUFYP2jQIBKJRHTy5MnvGif77xQeuxscHEzLly8nLy8vunPnDhERjR07lqpUqUJLliyh2NhYunDhAnXr1o1atGjBY05ZicSJ01f62A3Zw4cPydPTk168eEGPHz8WJvi7ePEi1a5dm5o1a0YbN278ztGyf+PAgQPk6OhI9+7do4kTJ5JIJKJXr15RTk4OhYWFUe3ateWSp6dPn1JSUhLdv39f+D3hC8T39eLFiyKTqrLi42PJExGRu7s7de3alV68eKGg6NiXunnzJj19+pSI/j7ezpw5QxUrVqS+ffvKlZx2dXWlxMREhcTJ/jvTpk0jfX19GjRoELVs2ZLq1q0r3Ou4u7uTubk5iUQiMjMzkysEwQ8UWUnDidNXkN2QPX/+nG7cuEFXr14V1hUUFFB6ejoREbm5uZGLiwu9efOGiKQVhgwNDalXr16UkZHBN3QlRExMDFWrVo0aNGhAFStWpGvXrgnr3r59KyRPvXr1+uD+XE1PcfizL74+lDzNnTuXKlSoIHeMseJLIpFQeno6iUQicnV1/WDypKKiQq6urhQfH6/ASNm3Fh8fTy9fviQiop07d5KhoSFdvnyZiIgOHjxIIpGIdu/eLWyflpZGsbGxdP36deG8zA8UWUnEHUq/kKwizPXr19GjRw/Y2NjAzs4Obm5uAKT99WUDXW/dugV9fX1oaWkBALS0tDBlyhT4+/tDR0eH+3cXc/T/qniWlpawsbFBUlISWrZsCWVlZWEbdXV12NjYYOnSpUhISPjgHE7cb1tx+LMvvmRj0lq2bInIyEhYWFhg/vz5OHbsGBo1aqTo8Ngn0P8LIenq6mLnzp0IDAzE3LlzkZ6eLlzb2rRpAwsLC2zatAlr165FXl6egqNm38Lly5fRvXt3JCUlAQAeP36MNm3aoGnTpggODkb//v3x+++/w8nJCW/evMGDBw9QpUoVWFhYoGHDhlwIgpVofFfxBWRJ05UrV9C6dWtYWVlhy5YtsLW1RUBAANauXQtAWkQgJycHNWrUwK1bt+Dv7w9PT0/s378fzs7OXJa6BJBIJBCJRMKNd9euXREQEIC7d+9i1qxZuHDhgrCtmpoaevbsiTlz5qBSpUofHSjLGJMnS56MjIzw8uVLnD17Fs2bN1d0WOwf0P+rG759+xYSiQRv376Fs7MzQkJC8Pvvv2Pu3Ll4+vSpsL2lpSWCg4MxZcoUlClTRlFhs2/IzMwMOjo6WLVqFQDgxYsX0NDQwMWLFzFq1CgsXLgQo0ePBgCEhIRg+/btyM3NlXsNfqjFSiqex+kLvT8PCQDcv38fJiYmGD9+PJYuXSpse/ToUaxYsQK3b9+Guro6tm3bxjPelwCF55lYvXo1MjIy4O7uDk1NTZw5cwaDBw9GixYt4OnpKdzk7du3D/b29h98DcbYP3v27BkkEgk/VCrmZK1Mhw8fhr+/P16/fg1VVVWsWLECDRo0wP79++Hk5AQXFxe0bNkSycnJ2LZtG65fv47y5csrOnz2DcjmXtuwYQNWrlyJffv24fXr1+jcuTNev36Nbdu2YeDAgQCkybWTkxPq1q2L1atXKzhyxr4NvrP7AoXnIalUqZKwPCQkBPn5+bh9+zZWrlyJwMBAvHv3Dl27dkVERATOnDmDqKgoTppKAPr/PBMAMG3aNCxcuBC6urpIT08HALRt2xZbt27FpUuXMG/ePGzduhV2dnYYPny4XEsTJ02MfT5dXV1OmkoA2XQbvXv3hpmZGdzc3JCXl4dWrVrh9u3bsLOzQ2RkJG7duoU//vgDBw4cwP79+zlpKkVkXdWtra3x7Nkz7N69G+bm5pg6dSoMDAzw6NEjPH36FHFxcXBycsKTJ0+wYsUKADwXGysduMXpCz158gSLFy9GbGwshgwZgszMTCxcuBBjx46FmZkZduzYgcePHyM1NRX169fHpEmTYGdnp+iw2Se8e/cO6urqwr+3bNkCHx8fREREoGXLlgCkJ/3MzExoa2vj3LlzmDp1Kt6+fQttbW0cPnwYqqqqwhNZxhgr6d4/n2VlZcHBwQFdunSBp6cnkpOTYWVlhS5dumD9+vXC9k+fPkWZMmVARKhYsaIC3wH71mQPCJWUlLBw4UL88ccfiI6OhoqKCgICArB06VIoKyvDwMAAenp6OHDgAFRVVYWWKsZKOk6cvkJaWhrmz5+PP//8E3fv3sWRI0fQsWNHAEBBQQFUVFSwZs0aXLp0CVOnToWpqamCI2b/ZMCAAejfvz/s7e2FC/+kSZPw6tUrBAQEICEhAdHR0ULXlIULF6JPnz5IT09HXl4eDAwMoKSkJHz3jDFWksnOg9nZ2ShXrpzQ9fj58+do1aoVjhw5ggoVKsDMzAw2NjZYv349ACAwMBAODg7Q1tZW8Dtg38r+/fvh6ekJHx8ftGvXDrVq1RLWRUdHw9XVFTNnzoSLiwvy8/ORkZGBxMREVKlSBfXq1eNrIyt1uD/RV9DT04Ovry+6desGU1NTxMfHC+tkT2PGjRsHf39/TppKgNq1a6NHjx4AgPz8fABA9erVcfjwYUydOhU///wzjh49CltbW3Tu3BkjRozAixcvUKVKFRgaGnKFIMZYqSISiZCeno5atWohNDQUSkpKICJUrlwZjRs3RnBwMMzNzWFnZ4c1a9YAkI5T27t3Lw4dOqTg6Nm3QkTQ1tZG9erVMXfuXLRr1w5Lly4ViiO1b98eFhYWmDVrFgBAVVUVurq6aN++PerXr8/XRlYq8W/zV6patSq8vb0hkUiwa9cuFBQUwNPTE2XKlBGervDJoniTPUX18/MDAKxduxZEhOHDh6N3797IyMhAREQERowYga5du8LExASnTp3CzZs3i1TO4zFNjLHSRElJCb169cKgQYOgpqYGe3t75Ofno27duli2bBnatGkjVJIFgOXLlyMpKQlt2rRRYNTsWxKJRPjpp5/w008/ISYmBseOHcPy5cuxefNmmJubY/r06XBzc8P9+/cRGBiIwYMHF3kNvjay0oa76v1Lsm578fHx6NSpE2bPnq3okNhnknVHkf20tbXFzZs3MXPmTPTv3x9lypRBVlYWNDU1AUi7YdrZ2UFFRQURERE8lokxVmp8aHxmeno65s+fj9WrVyMsLAyOjo549eoVBgwYgGfPnqFdu3YwNjbGxYsXsWfPHkRFRcHMzEwxb4D9J94fm5SQkIDTp09jyZIlUFVVhba2Nu7duwc7Ozts2rRJgZEy9n1w4vQNpKWlwdvbG8nJyQgJCZGruMeKp8I3CY8fP0b16tUBAIMGDcK5c+fg5eUFZ2dnaGpqIisrC0ePHsWaNWvw8uVLnD9/HqqqqlxynDFWKsjOZdnZ2RCLxXJjlFJTU+Hn54fff/8du3btgpOTE168eIGFCxfi/PnzyM7OhrGxMby9vXni4lLgcwscSSQSbNq0CefOncOmTZtgZmaGixcv8gNFVupx4vSNyCb845K6xV/hhCcoKAg7d+6Eh4cH2rZtCwBwcXHBpUuX4OnpiX79+uHZs2fYunUrnjx5gt9//x0qKio82JUxVqrcvn0bffv2haamJkaOHAk9PT107doVAJCbm4spU6bgjz/+wM6dO+Hs7IyCggIoKSkhPz8fysrKfD4sZdLS0qCnp/fBde+3QsXExMDCwgLKyspcWZaVenym+0Y4YSoZCidNZ86cweHDh3H69Gmoq6tDVVUVrVq1QlBQEFxcXLBkyRIoKytjwIABmDp1KsqWLQuRSASxWMw3CYyxUkMikWDr1q24cuUK1NXVkZGRgZycHFSsWBGtWrXC8OHDMWzYMFSqVAn9+vWDtrY2unXrBiKCmpqaosNn38CePXtgaGiIVq1awcPDAy9evMAff/zxwe9XljTJkiTZuDZ+oMh+BNzixH5IkydPRkREBOzt7ZGeno59+/ahZ8+eGD9+vNDyNHjwYERERCAoKAg9e/YE8PndGBhjrCRJS0vDokWLcPfuXRgZGWHs2LHYsWMHoqOjcfXqVVSsWBF16tTBxYsXkZ6ejqioKFhZWSk6bPYNvH37FoMHD8aePXvQv39/7Nu3DzExMWjSpImiQ2Os2OHEif1wzpw5g969eyM8PFx4UrZr1y7MmzcPxsbGmDZtGlq1agUAmD17Nnx9fXniPsZYqffkyRP4+fnh3LlzGDZsGEaPHg0AiIuLw5MnT+Dv74+UlBRcv34d169f5+k2SgHZw0CxWAwTExM8ePAAW7duxcCBA7kFibEP4COC/XBUVFSgpKQk1wXB2dkZYrEYAwcOhLKystDyNHPmTABF+3QzxlhpY2BggOnTp8PPzw9btmzBq1ev4OPjAwsLCwCAra0tJBIJXr9+jSpVqig4WvZvFe5BERISAjU1NXTu3BmjR49GnTp1YGlpyUWQGHsPHw2sVJM1qL7fsFpQUICUlBQAf096269fP5iYmOD69esIDAwU1gPgpIkx9kPQ19fH9OnTYWFhgYiICCxcuFBYJxKJoKamxklTKSCRSISkacaMGVi0aBFCQkIQGhoKGxsbdO3aFbGxsXJJ06NHjxQVLmPFBidOrNQqfGEoKCgQlltYWKBXr14YOnQo4uPjoaqqCgB48eIFWrRogaFDh2Lnzp24ePGiQuJmjDFF0tPTw/Tp09GyZUscOHBAaHnnblulhywhevDgAZKSkrB06VI0atQIWlpaWLVqFezs7NC9e3ecOnUKb9++Rb9+/bBixQoFR82Y4vEYJ1YqFe5e8Ntvv+HkyZMgItSqVQvLly9HXl4eXFxccOjQIXh7e0NbWxsRERHIz8/HyZMnYW5ujlatWmHt2rUKfieMMaYYPEdh6bZ27Vr8+uuvMDQ0RHBwMExMTIR16enp8PDwQGBgIJo2bYrs7GzcuHFDeNDI2I+KEydWqnl7e2Pjxo1wdXXFs2fPcPToUVStWhWRkZGoUqUKfHx88NdffyEnJwc1a9ZEaGgo1NXV0aZNG/Tt2xeTJk1S9FtgjDGF4TkKS6+cnBy0a9cOly9fxq5du+Do6FhkPFN4eDhev36NQYMGQVlZmQtGsB8eJ06s1EpISICtrS3Wrl2Lbt26AQDu3buH3r17Q0NDA2fPngUAZGRkQF1dHerq6gCk/b03b96MkydPwsjISGHxM8YYY9/Cx4o8vHv3Dubm5gCAwMBA4e8fmnqDiyQxxmOcWCmWkZGB169fo0GDBgCkF4I6deogICAAjx49QlBQEABAS0sL6urqSEpKwqhRo7BhwwYcOHCAkybGGGMlXuGk6fjx49i6dStOnDiBhIQEqKur4/z588jPz4erqysuXboEQFoI5P3n6pw0McaJEyvFGjRoAA0NDezZswcAhKdnhoaG0NDQwJs3bwD8fTGoUqUKnJ2dERMTg2bNmikmaMYYY+wbkiVN06ZNw6BBg7Bw4UIMGzYMLi4uCA0NRdmyZXH58mW8ffsWbm5uiIuLAwCe7J2xD+DEiZUaEolE+DsRQU1NDXZ2dti/fz927twprCtbtizKly8vDHKVPVUrX748OnfujDp16nzfwBljjLH/0I4dO7B161aEhobi6tWr2LlzJ9q0aYMpU6YgPDwcZcuWRXx8PO7du4d169YpOlzGii0e48RKtOPHj+Ps2bPw9fUFULQf982bNzF9+nQ8evQIzZo1g7m5OUJDQ/H8+XPEx8dz1wPGGGOlnpeXFxITE7F3715hWVJSEubMmYPs7Gxs2bIF5cuXR15eHpSVlfnayNhHcIsTK7Fyc3MRGhqK0NBQLFmyBIC0S4Ks5YmI0KBBAyxevBj9+/fH+fPnERwcjCpVquDixYtQVlaGWCxW5FtgjDHG/nNaWlp4+PAhXr58KSwzNjZGhw4dhLmaAKBMmTJ8bWTsH3DixEosNTU1zJw5Ex07dkRYWBgWLVoEQD55AgAjIyNMmjQJ8fHxiIqKQkhICFRVVVFQUMBP1RhjjJUaha99hZmamuLly5fYt28fMjMzheUmJiaoXr06cnNz5bbnayNjH8Zd9ViJl5aWhvnz5+P8+fNwdHSEp6cngL+77T19+hSTJ0+Gra0tBgwYAODDpVYZY4yxkqpwV/W9e/ciKysLYrEYgwYNgpKSEtzd3REUFARvb29YWVmhcuXKGDlyJAoKCnDs2DG+JjL2GThxYqVC4eTJwcEBXl5eAIDU1FQ4OzsjPT0dCQkJPHEfY4yxUqfww0B3d3ds3boVBgYGSElJQe3atfHbb7+hffv28PLywuHDh5GYmIj69etDTU0NZ86cgaqq6kfnemKM/Y0TJ1ZqFE6enJycMHz4cDg7O+Pp06e4fPkyVFVVeQI/xhhjpVZycjKcnJywfv161KxZEwDQq1cvvHr1Cjt27EDTpk2RmJiItLQ0KCsro02bNlBWVkZBQQE/WGTsM3DixEqVtLQ0+Pn54dy5c0hMTISBgQGuXLkijGniCwNjjLHSaMWKFThw4AB0dHSwY8cOqKmpQUlJCWKxGK1atUL58uVx/PjxIvvxA0XGPh+3ybJSRU9PDz4+Pqhfvz7atGnDSRNjjLFS7927d8jLy0NCQgKSkpKgoaEBJSUl5OTkQFlZGcuXL8fly5eRmJiI95+Xc9LE2OfjxImVOnp6eli5ciUOHDjASRNjjLFS5/3qeerq6hgyZAimT5+OpKQkTJ06FYB0wncAyM/Ph7a2NtTU1LgIBGP/At9NslKpQoUKAKQXF06aGGOMlRaFizjcunUL+fn5qF+/PvT09ODq6or8/Hx4eXkhLy8PI0eOhJKSEpYvX45q1aoJ454YY1+HxzgxxhhjjJUw3t7eCAgIQEFBAVRVVTFt2jS4uLigYsWKWL16NXx9fZGfn49ffvkFaWlpCAgIgIaGBlfPY+xf4EfxjDHGGGPFXOGEJyIiAlu2bIG/vz9q1KiBoKAgrF+/HmlpafDy8oKrqytUVVWxYMECKCsrIzQ0FIB0LJS6uroi3wZjJRonTowxxhhjxZwsadqyZQvevXuHqVOnolevXgAAMzMz6OnpYcWKFWjVqhV69+6Nvn37QiKRYNasWdDS0sKcOXM4aWLsX+KueowxxhhjJcDz589hYWGB+/fvY8yYMVizZo1cASRnZ2ekpKQgJiYGAIT5myZMmIA5c+bA19dXkeEzVuJxixNjjDHGWDEnkUhQuXJlhIeHY9KkSTh48CBSUlJQrVo1EBFEIhGaNGmC169fC936KlSoABcXF6iqqsLa2lrRb4GxEo9bnBhjjDHGijE/Pz/k5uZi+vTpKFOmDG7cuAEXFxdIJBKEhYVBV1cXGhoa6Nq1K/T09IQxTTJcEIKxb4NbnBhjjDHGijFVVVX4+vpCU1MTEydORMOGDREUFISff/4ZrVu3Ru3atdG4cWNkZGTg+PHjACC0QgHgpImxb4RbnBhjjDHGiomPtQ798ccfGDduHBYsWAB3d3eUKVMG169fh7u7O+Li4nDmzBk0btwYAHjid8b+I5w4McYYY4wVMwkJCTA1NZVbtmbNGkyYMAELFizApEmToKamhuvXr8PFxQVKSkqIiYlB2bJluWseY/8RPqoYY4wxxhQsNzdX+PuJEyfQqFEj7NixQ26bcePGYdGiRZgxYwY2btyIt2/folGjRggODoaysjIaNGiArKwsTpoY+4/wkcUYY4wxpkBHjx7Fb7/9hnPnzgEAOnbsiClTpmDkyJEICgqS29bOzg4aGhoYP348wsLCAAANGzbE5s2bUb16daSnp3/3+Bn7UXAHWMYYY4wxBdmyZQtmzJiBXr16yZUMX7JkCZSUlDB06FAAgIuLCwBATU0N48aNg5mZGRwdHYXtmzZtihMnTqBMmTLfM3zGfiicODHGGGOMKUBISAjGjRuHLVu2oHv37tDW1pZbv2jRIojFYgwaNAi3b99Gw4YNERgYCCLC/PnzAcgXguCkibH/FheHYIwxxhj7zp49e4a+ffuiT58+GDt2rLA8KysLCQkJEIvFsLS0BAAsXrwYq1evhqamJqpUqYJjx45BVVVVUaEz9sPiFifGGGOMMQVIT09HtWrVhH+vXbsWJ06cQFhYGPT19VGnTh2cOnUKHh4e6NevH1RVVaGnpwclJSUuOc6YAnBxCMYYY4wxBXjz5g0iIyNx4sQJ9OnTB2vXroWuri6OHDmCVatW4cmTJ5g7dy4AoEaNGjAwMICSkhIkEgknTYwpAB91jDHGGGPfma6uLrZu3QonJyecOHECWlpaWLlyJZo2bYpKlSrh1atX0NbWhkQiAQCIRCJhXy43zphicOLEGGOMMaYAnTp1wu3bt5GVlYXatWsXWa+lpQUDAwMFRMYY+xAuDsEYY4wxVow8e/YMw4YNw/Pnz3HmzBkoKysrOiT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            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#@title\n",
        "f,ax1 = plt.subplots(figsize =(10,10))\n",
        "plt.title('Stroke Prediction Dataset',fontsize = 20,color='blue')\n",
        "plt.xticks(rotation= 45)\n",
        "plt.figure(1)\n",
        "\n",
        "sns.pointplot(x=df['algorithms'], y=df['Accuracy'],data=df,color='lime',label=\"Accuracy\")\n",
        "f,ax1 = plt.subplots(figsize =(10,10))\n",
        "plt.xticks(rotation= 45)\n",
        "plt.figure(2)\n",
        "sns.pointplot(x=df['algorithms'], y=df['precision'],data=df,color='red',label=\"precision\")\n",
        "f,ax1 = plt.subplots(figsize =(10,10))\n",
        "plt.xticks(rotation= 45)\n",
        "plt.figure(3)\n",
        "sns.pointplot(x=df['algorithms'], y=df['recall'],data=df,color='black',label=\"recall\")\n",
        "f,ax1 = plt.subplots(figsize =(10,10))\n",
        "plt.xticks(rotation= 45)\n",
        "plt.figure(4)\n",
        "sns.pointplot(x=df['algorithms'], y=df['f1_score'],data=df,color='blue',label=\"f1_score\")\n",
        "plt.xlabel('Algorithms',fontsize = 15,color='blue')\n",
        "# plt.ylabel('Score',fontsize = 15,color='blue')\n",
        "plt.xticks(rotation= 45)\n",
        "\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "id": "-asGGkX8oHV9",
        "outputId": "4d489fba-480a-4c57-91dd-26701e5a7198"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d6d48088-f0e5-4c26-9bd4-b581037d5433 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d6d48088-f0e5-4c26-9bd4-b581037d5433');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   gender  age  hypertension  heart_disease  ever_married  work_type  \\\n",
              "0       1   67             1              1             1          2   \n",
              "\n",
              "   Residence_type  avg_glucose_level   bmi  smoking_status  \n",
              "0               1              228.6  36.6               3  "
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tt = {\"gender\":['Male'],\t\"age\":[67],\t\"hypertension\":[1],\"heart_disease\":[1]\t,\"ever_married\":['Yes'],\t\"work_type\":['Private'],\t\"Residence_type\":['Urban'],\t\"avg_glucose_level\":[228.60],\t\"bmi\":[36.6],\t\"smoking_status\":['smokes']}\n",
        "tt = pd.DataFrame(tt)\n",
        "tt.iloc[:, 0] = labelencodergen.transform(tt.iloc[:, 0])\n",
        "tt.iloc[:, 4] = labelencodermar.transform(tt.iloc[:, 4])\n",
        "tt.iloc[:, 5] = labelencoderwork.transform(tt.iloc[:, 5])\n",
        "tt.iloc[:, 6]= labelencoderres.transform(tt.iloc[:, 6]) \n",
        "tt.iloc[:, 9] = labelencodersmoke.transform(tt.iloc[:, 9])\n",
        "tt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A84UysNL4vBn",
        "outputId": "a3064add-e84a-4d98-f493-9620529f0302"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  0. ,   1. ,   0. ,   0. ,   1. ,   0. ,   1. ,   0. ,   1. ,\n",
              "          0. ,   0. ,   1. ,   0. ,   0. ,   0. ,   1. ,   0. ,   0. ,\n",
              "          0. ,   1. ,  67. , 228.6,  36.6]])"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tt = np.array(ct.transform(tt))\n",
        "tt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTBzwWSwBAOO",
        "outputId": "ba8f9c47-6f68-4445-93a2-b07bca4aad6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "ranclfover.predict(tt)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}