stroke_prediction.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# **1. DATA PREPROCESSING**"
      ],
      "metadata": {
        "id": "RLWoHGsMnijP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Configure libraries\n",
        "warnings.filterwarnings('ignore')\n",
        "plt.rcParams['figure.figsize'] = (10, 10)\n",
        "plt.style.use('seaborn')"
      ],
      "metadata": {
        "id": "SRBV6qEZSoSx"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "import os\n",
        "\n",
        "drive.mount('/content/drive')\n",
        "directory=os.getcwd()+'/drive/MyDrive/FDM'\n",
        "dataset = directory+'/stroke_prediction_original.csv'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b884717bUj9S",
        "outputId": "419964b1-b3f4-4d13-8a56-3f491b12e401"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
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        "id": "NdyviQ51NfEq",
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        },
        "outputId": "08fbe1dd-70b0-4b8c-991e-a7313c9d1b8f"
      },
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "      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  \n",
            "Data shape: (5110, 12)\n"
          ]
        }
      ],
      "source": [
        "# Load the dataset\n",
        "\n",
        "data = pd.read_csv(dataset)\n",
        "print(data.head())\n",
        "print(\"Data shape: {}\".format(data.shape))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
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        "id": "8Rg4AcfjOroM",
        "outputId": "d90364b3-768a-4c93-ae56-7e2de2ed482d"
      },
      "execution_count": 96,
      "outputs": [
        {
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          "data": {
            "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  "
            ],
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              "      <td>0.097456</td>\n",
              "      <td>0.054012</td>\n",
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              "      <th>25%</th>\n",
              "      <td>17741.250000</td>\n",
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              "      <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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              "      <th>50%</th>\n",
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              "      <td>28.100000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <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",
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      "cell_type": "code",
      "source": [
        "# Check how many missing values exist in each variable.\n",
        "\n",
        "miss_val = data.isnull().sum()/len(data)*100\n",
        "print(miss_val)\n",
        "print(\"# Missing values in variable bmi\\t\\t: {:.2f}%\".format(miss_val['bmi']))\n",
        "print(\"Data shape: {}\".format(data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wCqkYs6O6is",
        "outputId": "0d229235-02b1-46dd-95f7-6ae9c5e73508"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                   0.000000\n",
            "gender               0.000000\n",
            "age                  0.000000\n",
            "hypertension         0.000000\n",
            "heart_disease        0.000000\n",
            "ever_married         0.000000\n",
            "work_type            0.000000\n",
            "Residence_type       0.000000\n",
            "avg_glucose_level    0.000000\n",
            "bmi                  3.933464\n",
            "smoking_status       0.000000\n",
            "stroke               0.000000\n",
            "dtype: float64\n",
            "# Missing values in variable bmi\t\t: 3.93%\n",
            "Data shape: (5110, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handling Missing Values\n",
        "# Replace missing values in variable 'bmi' with its mean\n",
        "\n",
        "clean_data = data['bmi']=data['bmi'].fillna(data['bmi'].mean())\n",
        "\n",
        "miss_val = data.isnull().sum()/len(data)*100\n",
        "print(miss_val)\n",
        "print(\"# Missing values in variable bmi\\t\\t: {:.2f}%\".format(miss_val['bmi']))\n",
        "print(\"Data shape: {}\".format(data.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BB4rClJsPXfd",
        "outputId": "9b868d43-1219-4cec-9206-598ec6524b41"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "id                   0.0\n",
            "gender               0.0\n",
            "age                  0.0\n",
            "hypertension         0.0\n",
            "heart_disease        0.0\n",
            "ever_married         0.0\n",
            "work_type            0.0\n",
            "Residence_type       0.0\n",
            "avg_glucose_level    0.0\n",
            "bmi                  0.0\n",
            "smoking_status       0.0\n",
            "stroke               0.0\n",
            "dtype: float64\n",
            "# Missing values in variable bmi\t\t: 0.00%\n",
            "Data shape: (5110, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop 'id' column\n",
        "\n",
        "clean_data = data.drop('id', axis=1)"
      ],
      "metadata": {
        "id": "SxGxl5_ONQ03"
      },
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fixing attribute names\n",
        "\n",
        "data.loc[data['work_type'] == 'Private', 'work_type'] = 'Private Sector'\n",
        "data.loc[data['work_type'] == 'children', 'work_type'] = 'Children'\n",
        "data.loc[data['work_type'] == 'Govt_job', 'work_type'] = 'Government Sector'\n",
        "data.loc[data['work_type'] == 'Never_worked', 'work_type'] = 'Never Worked'\n",
        "\n",
        "data.loc[data['smoking_status'] == 'never smoked', 'smoking_status'] = 'Never Smoked'\n",
        "data.loc[data['smoking_status'] == 'formerly smoked', 'smoking_status'] = 'Formerly Smoked'\n",
        "data.loc[data['smoking_status'] == 'smokes', 'smoking_status'] = 'Smokes'\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "yg5HzluQY6-k",
        "outputId": "359ecb73-3b3b-4728-a11d-3bc658416b66"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  \\\n",
              "0  Private Sector          Urban             228.69  36.600000   \n",
              "1   Self-employed          Rural             202.21  28.893237   \n",
              "2  Private Sector          Rural             105.92  32.500000   \n",
              "3  Private Sector          Urban             171.23  34.400000   \n",
              "4   Self-employed          Rural             174.12  24.000000   \n",
              "\n",
              "    smoking_status  stroke  \n",
              "0  Formerly Smoked       1  \n",
              "1     Never Smoked       1  \n",
              "2     Never Smoked       1  \n",
              "3           Smokes       1  \n",
              "4     Never Smoked       1  "
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              "      <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>28.893237</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 Sector</td>\n",
              "      <td>Rural</td>\n",
              "      <td>105.92</td>\n",
              "      <td>32.500000</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 Sector</td>\n",
              "      <td>Urban</td>\n",
              "      <td>171.23</td>\n",
              "      <td>34.400000</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>24.000000</td>\n",
              "      <td>Never Smoked</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f31f88cf-645f-4439-8af0-69f5247599b1')\"\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-f31f88cf-645f-4439-8af0-69f5247599b1 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-f31f88cf-645f-4439-8af0-69f5247599b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the unique values of each categorical attribute\n",
        "\n",
        "print(\"Unique 'gender': {}\".format(clean_data['gender'].unique()))\n",
        "print(\"Unique 'ever_married': {}\".format(clean_data['ever_married'].unique()))\n",
        "# print(\"Unique 'marital_status': {}\".format(clean_data['marital_status'].unique()))\n",
        "print(\"Unique 'work_type': {}\".format(clean_data['work_type'].unique()))\n",
        "print(\"Unique 'Residence_type': {}\".format(clean_data['Residence_type'].unique()))\n",
        "# print(\"Unique 'residence_type': {}\".format(clean_data['residence_type'].unique()))\n",
        "print(\"Unique 'smoking_status': {}\".format(clean_data['smoking_status'].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cerqOg_AQk9c",
        "outputId": "620f2bf4-0134-418b-f5cb-e2649601104a"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unique 'gender': ['Male' 'Female' 'Other']\n",
            "Unique 'ever_married': ['Yes' 'No']\n",
            "Unique 'work_type': ['Private' 'Self-employed' 'Govt_job' 'children' 'Never_worked']\n",
            "Unique 'Residence_type': ['Urban' 'Rural']\n",
            "Unique 'smoking_status': ['formerly smoked' 'never smoked' 'smokes' 'Unknown']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create encoder for each categorical variable\n",
        "\n",
        "label_gender = LabelEncoder()\n",
        "label_married = LabelEncoder()\n",
        "label_work = LabelEncoder()\n",
        "label_residence = LabelEncoder()\n",
        "label_smoking = LabelEncoder()"
      ],
      "metadata": {
        "id": "9eTZAH-HRuEs"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing Categorical values to Numerical values\n",
        "\n",
        "clean_data['gender'] = label_gender.fit_transform(clean_data['gender'])\n",
        "clean_data['ever_married'] = label_married.fit_transform(clean_data['ever_married'])\n",
        "# clean_data['marital_status'] = label_married.fit_transform(clean_data['marital_status'])\n",
        "clean_data['work_type']= label_work.fit_transform(clean_data['work_type'])\n",
        "clean_data['Residence_type']= label_residence.fit_transform(clean_data['Residence_type'])\n",
        "# clean_data['residence_type']= label_residence.fit_transform(clean_data['residence_type'])\n",
        "clean_data['smoking_status']= label_smoking.fit_transform(clean_data['smoking_status'])\n",
        "with pd.option_context('expand_frame_repr', False):\n",
        "    print(clean_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PcVYrYR3SAKE",
        "outputId": "ea45ad1e-293d-4d8b-8f6a-39562534dc05"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   gender   age  hypertension  heart_disease  ever_married  work_type  Residence_type  avg_glucose_level        bmi  smoking_status  stroke\n",
            "0       1  67.0             0              1             1          2               1             228.69  36.600000               1       1\n",
            "1       0  61.0             0              0             1          3               0             202.21  28.893237               2       1\n",
            "2       1  80.0             0              1             1          2               0             105.92  32.500000               2       1\n",
            "3       0  49.0             0              0             1          2               1             171.23  34.400000               3       1\n",
            "4       0  79.0             1              0             1          3               0             174.12  24.000000               2       1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Inter-feature Correlation\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,6))\n",
        "im = ax.matshow(clean_data.corr())\n",
        "ax.set_xticks(np.arange(clean_data.shape[1]))\n",
        "ax.set_yticks(np.arange(clean_data.shape[1]))\n",
        "ax.set_xticklabels(clean_data.columns,rotation=90)\n",
        "ax.set_yticklabels(clean_data.columns)\n",
        "# Create colorbar\n",
        "cbar = ax.figure.colorbar(im, ax=ax)\n",
        "cbar.ax.set_ylabel(\"Correlation\", rotation=-90, va=\"bottom\", fontsize=12)\n",
        "fig.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "HCpu7I93SUGR",
        "outputId": "c0d018be-4c44-4a03-e3ff-a1db0b45fcf0"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram of Features\n",
        "\n",
        "fig = clean_data.hist(figsize=(10,8))\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "43hbxClnSeZT",
        "outputId": "ba77fe30-c2e1-4d5d-f0b2-aa3932ca03ee"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Class (Target) Distribution\n",
        "\n",
        "class_occur = clean_data['stroke'].value_counts()\n",
        "class_names = ['No Stroke','Stroke']\n",
        "fig, ax = plt.subplots()\n",
        "ax.pie(class_occur, labels=class_names, autopct='%1.2f%%',\n",
        "        shadow=True, startangle=0, counterclock=False)\n",
        "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "ax.set_title('Class distribution')\n",
        "plt.show()\n",
        "print(\"# samples associated with no stroke: {}\".format(class_occur[0]))\n",
        "print(\"# samples associated with stroke: {}\".format(class_occur[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "5MMRJw23SqGD",
        "outputId": "4c094cd3-9e58-4fb5-cba3-44e334a95407"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# samples associated with no stroke: 4861\n",
            "# samples associated with stroke: 249\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Handle Imbalanced Classes\n",
        "\n",
        "# create the  object with the desired sampling strategy.\n",
        "smote = SMOTE(sampling_strategy='minority')\n",
        "\n",
        "# fit the object to our training data\n",
        "X, y = smote.fit_resample(clean_data.loc[:,clean_data.columns!='stroke'], clean_data['stroke'])\n",
        "print(\"Shape of X: {}\".format(X.shape))\n",
        "print(\"Shape of y: {}\".format(y.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NnmxT_CYUkKN",
        "outputId": "e45f5eb0-f777-43d3-dddf-7eeb6d69821a"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X: (9722, 10)\n",
            "Shape of y: (9722,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, class_counts = np.unique(y, return_counts=True)\n",
        "class_names = ['No stroke', 'Stroke']\n",
        "fig, ax = plt.subplots()\n",
        "ax.pie(class_counts, labels=class_names, autopct='%1.2f%%',\n",
        "        shadow=True, startangle=90, counterclock=False)\n",
        "ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "ax.set_title('Class distribution')\n",
        "plt.show()\n",
        "print(\"# samples associated with no stroke: {}\".format(class_counts[0]))\n",
        "print(\"# samples associated with stroke: {}\".format(class_counts[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "id": "ibM2buY5U1XD",
        "outputId": "5d74e9e5-ace1-4bec-fdc6-f2062ede0536"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# samples associated with no stroke: 4861\n",
            "# samples associated with stroke: 4861\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the pre-processed dataset\n",
        "\n",
        "clean_data.to_csv('stroke_prediction_preprocessed.csv')"
      ],
      "metadata": {
        "id": "SRXmVXzgaVhc"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data splitting\n",
        "\n",
        "def split_train_test(X,y,test_size=0.3,random_state=None):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=test_size, random_state=random_state, stratify=y)\n",
        "    return X_train, X_test, y_train, y_test\n",
        "\n",
        "X_train, X_test, y_train, y_test = split_train_test(X,y,test_size=0.3,random_state=42)\n",
        "_, train_counts = np.unique(y_train, return_counts=True)\n",
        "_, test_counts = np.unique(y_test, return_counts=True)\n",
        "print(\"[train] # class 0: {} | # class 1: {}\".format(train_counts[0],train_counts[1]))\n",
        "print(\"[test]  # class 0: {} | # class 1: {}\".format(test_counts[0],test_counts[1]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KyD3QIGnV2tM",
        "outputId": "4f78a3a8-7821-46ab-881f-d67d659754f4"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[train] # class 0: 3402 | # class 1: 3403\n",
            "[test]  # class 0: 1459 | # class 1: 1458\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Normalization\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaler = scaler.fit(X_train)\n",
        "\n",
        "X_train_std = scaler.transform(X_train)\n",
        "X_test_std = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "bk8DpdIpXSoc"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. MODELLING**"
      ],
      "metadata": {
        "id": "UyIYIoe_3abo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#function to evaluate a model\n",
        "def evaluate_model(model, X_test, y_test):\n",
        "    from sklearn import metrics\n",
        "\n",
        "    # Predict Test Data \n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate accuracy, precision, recall, f1-score, and kappa score\n",
        "    acc = metrics.accuracy_score(y_test, y_pred)\n",
        "    prec = metrics.precision_score(y_test, y_pred)\n",
        "    rec = metrics.recall_score(y_test, y_pred)\n",
        "    f1 = metrics.f1_score(y_test, y_pred)\n",
        "    kappa = metrics.cohen_kappa_score(y_test, y_pred)\n",
        "\n",
        "    # Calculate area under curve (AUC)\n",
        "    y_pred_proba = model.predict_proba(X_test)[::,1]\n",
        "    fpr, tpr, _ = metrics.roc_curve(y_test, y_pred_proba)\n",
        "    auc = metrics.roc_auc_score(y_test, y_pred_proba)\n",
        "\n",
        "    # Display confussion matrix\n",
        "    cm = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "    return {'acc': acc, 'prec': prec, 'rec': rec, 'f1': f1, 'kappa': kappa, \n",
        "            'fpr': fpr, 'tpr': tpr, 'auc': auc, 'cm': cm}"
      ],
      "metadata": {
        "id": "lgPuBpCNbmvL"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**I. Random Forest**"
      ],
      "metadata": {
        "id": "SIje9HQuZ0fa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Building Random Forest model \n",
        "rf = RandomForestClassifier(random_state=0)\n",
        "rf.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "xIFudJgD3eYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df080816-b499-44ff-cb47-85d32d37444d"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Random Forest Model\n",
        "rf_eval = evaluate_model(rf, X_test, y_test)\n",
        "\n",
        "# Print result\n",
        "print('Accuracy:', rf_eval['acc'])\n",
        "print('Precision:', rf_eval['prec'])\n",
        "print('Recall:', rf_eval['rec'])\n",
        "print('F1 Score:', rf_eval['f1'])\n",
        "print('Area Under Curve:', rf_eval['auc'])\n",
        "print('Confusion Matrix:\\n', rf_eval['cm'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T5MXcM1DakEb",
        "outputId": "591f28f4-2409-4b92-bb01-171e509f863a"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9400068563592733\n",
            "Precision: 0.9190071848465056\n",
            "Recall: 0.9650205761316872\n",
            "F1 Score: 0.9414519906323185\n",
            "Area Under Curve: 0.9871463815248244\n",
            "Confusion Matrix:\n",
            " [[1335  124]\n",
            " [  51 1407]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**II. Decision Tree**"
      ],
      "metadata": {
        "id": "PnfllorrcUVu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "# Building Decision Tree model \n",
        "dt = tree.DecisionTreeClassifier(random_state=0)\n",
        "dt.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k6P6xdNYcTzJ",
        "outputId": "d9f6f43e-cea0-47f2-b035-2feb814fad0a"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Decision Tree Model\n",
        "dt_eval = evaluate_model(dt, X_test, y_test)\n",
        "\n",
        "# Print result\n",
        "print('Accuracy:', dt_eval['acc'])\n",
        "print('Precision:', dt_eval['prec'])\n",
        "print('Recall:', dt_eval['rec'])\n",
        "print('F1 Score:', dt_eval['f1'])\n",
        "print('Area Under Curve:', dt_eval['auc'])\n",
        "print('Confusion Matrix:\\n', dt_eval['cm'])\n",
        "\n",
        "modelAcc = dt_eval['acc']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkFMyFGycjgK",
        "outputId": "cf8e4f22-40b8-42e9-cb34-f9143dbabdcc"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8992115186835791\n",
            "Precision: 0.8828947368421053\n",
            "Recall: 0.9204389574759945\n",
            "F1 Score: 0.9012760241773002\n",
            "Area Under Curve: 0.8992187933370377\n",
            "Confusion Matrix:\n",
            " [[1281  178]\n",
            " [ 116 1342]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**III. Logistic Regression**"
      ],
      "metadata": {
        "id": "tWuN85iOc7pC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "# Building Logistic Regression model \n",
        "lr = LogisticRegression(random_state=0)\n",
        "lr.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z8H326hAc_cC",
        "outputId": "e10c4630-7860-44b3-b1de-3c3efd832234"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Logistic Regression Model\n",
        "lr_eval = evaluate_model(lr, X_test, y_test)\n",
        "\n",
        "# Print result\n",
        "print('Accuracy:', lr_eval['acc'])\n",
        "print('Precision:', lr_eval['prec'])\n",
        "print('Recall:', lr_eval['rec'])\n",
        "print('F1 Score:', lr_eval['f1'])\n",
        "print('Area Under Curve:', lr_eval['auc'])\n",
        "print('Confusion Matrix:\\n', lr_eval['cm'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Da3QqrSbmqRf",
        "outputId": "fff49eba-569e-47cb-8d70-7e98a1c8f687"
      },
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8004799451491258\n",
            "Precision: 0.7840466926070039\n",
            "Recall: 0.8292181069958847\n",
            "F1 Score: 0.8059999999999999\n",
            "Area Under Curve: 0.8850251642752848\n",
            "Confusion Matrix:\n",
            " [[1126  333]\n",
            " [ 249 1209]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**IV. Naive Bayes**"
      ],
      "metadata": {
        "id": "uBYlQ0ho_sXV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "# Building Naive Bayes model \n",
        "nb = GaussianNB()\n",
        "nb.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g5G1758P_uK2",
        "outputId": "0f80b2c0-4ff8-410d-c99f-a643bdd4e63a"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluating the Naive Bayes Model\n",
        "nb_eval = evaluate_model(nb, X_test, y_test)\n",
        "\n",
        "# Print result\n",
        "print('Accuracy:', nb_eval['acc'])\n",
        "print('Precision:', nb_eval['prec'])\n",
        "print('Recall:', nb_eval['rec'])\n",
        "print('F1 Score:', nb_eval['f1'])\n",
        "print('Area Under Curve:', nb_eval['auc'])\n",
        "print('Confusion Matrix:\\n', nb_eval['cm'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tyngbeBr_wVF",
        "outputId": "a133ff3b-5442-4679-f518-35db6e2b719f"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.7836818649297224\n",
            "Precision: 0.7454005934718101\n",
            "Recall: 0.8614540466392319\n",
            "F1 Score: 0.7992363983455297\n",
            "Area Under Curve: 0.8559769502195822\n",
            "Confusion Matrix:\n",
            " [[1030  429]\n",
            " [ 202 1256]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##**Model Comparison**"
      ],
      "metadata": {
        "id": "9ORaPLT6sZSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Intitialize figure with two plots\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
        "fig.suptitle('Model Comparison', fontsize=16, fontweight='bold')\n",
        "fig.set_figheight(7)\n",
        "fig.set_figwidth(14)\n",
        "fig.set_facecolor('white')\n",
        "\n",
        "# First plot\n",
        "## set bar size\n",
        "barWidth = 0.2\n",
        "rf_score = [rf_eval['acc'], rf_eval['prec'], rf_eval['rec'], rf_eval['f1']]\n",
        "dt_score = [dt_eval['acc'], dt_eval['prec'], dt_eval['rec'], dt_eval['f1']]\n",
        "lr_score = [lr_eval['acc'], lr_eval['prec'], lr_eval['rec'], lr_eval['f1']]\n",
        "nb_score = [nb_eval['acc'], nb_eval['prec'], nb_eval['rec'], nb_eval['f1']]\n",
        "\n",
        "## Set position of bar on x axis\n",
        "r1 = np.arange(len(lr_score))\n",
        "r2 = [x + barWidth for x in r1]\n",
        "r3 = [x + barWidth for x in r2]\n",
        "r4 = [x + barWidth for x in r3]\n",
        "\n",
        "## Make the plot\n",
        "ax1.bar(r2, rf_score, width=barWidth, edgecolor='white', label='Random Forest')\n",
        "ax1.bar(r1, dt_score, width=barWidth, edgecolor='white', label='Decision Tree')\n",
        "ax1.bar(r3, lr_score, width=barWidth, edgecolor='white', label='Logistic Regression')\n",
        "ax1.bar(r4, nb_score, width=barWidth, edgecolor='white', label='Naive Bayes')\n",
        "\n",
        "## Configure x and y axis\n",
        "ax1.set_xlabel('Metrics', fontweight='bold')\n",
        "labels = ['Accuracy', 'Precision', 'Recall', 'F1']\n",
        "ax1.set_xticks([r + (barWidth * 1.5) for r in range(len(lr_score))], )\n",
        "ax1.set_xticklabels(labels)\n",
        "ax1.set_ylabel('Score', fontweight='bold')\n",
        "ax1.set_ylim(0, 1)\n",
        "\n",
        "## Create legend & title\n",
        "ax1.set_title('Evaluation Metrics', fontsize=14, fontweight='bold')\n",
        "ax1.legend()\n",
        "\n",
        "# Second plot\n",
        "## Comparing ROC Curve\n",
        "ax2.plot([0, 1], [0, 1], color='orange', linestyle='--')\n",
        "ax2.plot(rf_eval['fpr'], rf_eval['tpr'], label='Random Forest, auc = {:0.5f}'.format(rf_eval['auc']))\n",
        "ax2.plot(dt_eval['fpr'], dt_eval['tpr'], label='Decision Tree, auc = {:0.5f}'.format(dt_eval['auc']))\n",
        "ax2.plot(lr_eval['fpr'], lr_eval['tpr'], label='Logistic Regression, auc = {:0.5f}'.format(lr_eval['auc']))\n",
        "ax2.plot(nb_eval['fpr'], nb_eval['tpr'], label='Naive Bayes, auc = {:0.5f}'.format(nb_eval['auc']))\n",
        "\n",
        "## Configure x and y axis\n",
        "ax2.set_xlabel('False Positive Rate', fontweight='bold')\n",
        "ax2.set_ylabel('True Positive Rate', fontweight='bold')\n",
        "\n",
        "## Create legend & title\n",
        "ax2.set_title('ROC Curve', fontsize=14, fontweight='bold')\n",
        "ax2.legend(loc=4)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493
        },
        "id": "zj9YNnSSsbZf",
        "outputId": "79cabca4-4e50-4788-a92a-b477a3fcdb51"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x504 with 2 Axes>"
            ],
            "image/png": 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rcmyVSsXatWudgU9BQQF//PEHKpUKRVEqXM5vv/12yQDJy8uLe++996rqK4QQ4tKK3wetVit//PEH3377Lfv372fp0qVoNBo2b97MsGHDsFqtDBo0iLp163Lo0CEWLlzImjVrmDNnjvPesHr1ap599lk0Gg0PPvggoaGhbN++nWXLlvHbb7+xdOlSl4CqyJXuJ8RNRRGiCiQnJyvR0dFK586dy9xm2rRpSnR0tDJjxgznsj179ijR0dFK//79FUVRlD///FO5//77lRYtWigdO3ZU/vOf/yhms9m5Ljo6WnnhhRcURVGUjz76SImOjlaWLVvmLO/BBx9UoqOjld9//11RFEVJSUlRRo0apcTGxiqtWrVShg8frpw4cUJRFEXp0qWLEh0d7fxZtmyZsmzZMiU6Olr56KOPnGV+//33St++fZUmTZoobdu2VUaPHq2cOnXKuT46Olq57bbblC1btii9e/dWWrZsqYwePVrJzc0t9Tq88MILSnR0tNKvXz+lV69ezuVr165VoqOjlb59+yrR0dFKcnKyoiiKcvz4cWXEiBHOcxg/frySnZ3tvObFf5KTk53lz58/X+nRo4fy0ksvObd98MEHncfbvHmzcu+99ypNmjRR4uPjlXfeecd5rfPy8pTXXntN6dSpk9KkSROld+/eyvLly8v83QohxK2urPugw+FQunbtqkRHRysHDx5U7Ha70q1bNyU6OlpZuXKly7YrVqxQoqOjlZ49eyoOh0OxWCxKp06dlOjoaOXXX3912fazzz5TOnfurPzwww8l6nI5+0VHRytdunRxri+61w4aNMjl9ZgxY5QXXnhBadasWYXu52Xdu4SoTDLGR1Qph8NBenp6iR9FUejVqxcAGzZscG5f1K2rT58+nDt3jhEjRrB//36eeeYZWrduzfz58/nyyy+vuD5jx47l559/pn///jzwwAOsW7eOV199FYBx48bh6+sLQEJCAq1atSqx/++//86YMWM4d+4czz77LN26deOHH37g0UcfxWq1OrczmUxMnTqVAQMGEBoayg8//MDXX39dbt06dOjA4cOHOXbsmPNa+Pr60qBBA+c2FouFRx99lI0bN/Lvf/+bwYMHs2LFCt555x38/PwYO3YsUNhSlZCQgJ+fn3PfmTNn0qdPH+d1L+7YsWM8/vjjHDlyhKeffppmzZoxZ84cpk2bBsDHH3/MggUL6NGjBxMmTMDf358JEybw119/Vei6CyGEKKRSqfDy8gIK39N3797N8ePHiYyM5M4773TZ9s477yQsLIwjR46wZ88edu/ezenTp6lbty7x8fEu2w4dOpT169fTu3fvEse80v3Ks2nTJs6ePcu4ceMueT8v794lRGWSrm6iSqWnp9OpU6cSy7ds2UKjRo2IiIhg+/btZGVl4ePj43yj7N27N1qtls8//xy9Xk9AQADNmjVj1apVbN26lSeeeOKK6vPiiy8CEBERgc1mY+nSpWzduhUofHOeOnUq586dc3YDS0xMdNn/008/BeCtt96iY8eOABw/fpwtW7awefNm57mazWaee+452rVrR6NGjXj00UfZtm1buXXr3Lkzc+fOZc2aNTz22GOsW7eOTp06oVKpnNusX7+e5ORkevfuzeDBgwHYtWsX3333HRMmTKBPnz5MmzaN4ODgEl3Z4uPjefbZZ4HCvufFLV++HLPZzKhRo5zdLby9vbHb7QAcOXIEKLwJt2zZkj59+nDo0KES/c6FEEK4KvoCEMBut/Prr7+yb98+wsLCqFevHqtXrwYgKiqqxL4qlYr69euTkpLC0aNHnd2eS+uCrVaX/V13amrqFe1XHovFwkcffYSHhwdAuffzS9279Hr9FdVBiItJ4COqlJ+fX6nf6BS9yfXs2ZPZs2fz+++/Exsby86dO2nRogWhoaFYrVaWLFnCihUrsFgszn3z8vKuuD47duxgxowZZGZmXtH++/fvB6Bp06bOZY0aNWLLli0cPnzYJchr0qQJgHO8TU5OTrllh4eHU69ePdauXUtsbCynT5+ma9euLt+gHT58GIAff/yRH3/80WX/EydOOG9ApSnecnSxonLr1asHgJubG2+++aZz/V133cXatWsZNGgQtWrVok2bNtxzzz0uLUpCCCFKKu0LwGbNmvHWW2/h5uZ2yf2Lgh21Wo3D4QBwPlZU0Rdol7tfeSIjI13uOeXdz7///nug7HtX0b1HiKslgY+oUjqdjri4uDLXF71Rbtiwgfz8fBwOh7O5ffHixSxdupTGjRszfPhwcnJymDhx4iWPqRRLBFC8+9mBAwdISEjA19eXt99+m4CAAMaPH39ZQZBSSpKBohtJ8ZYZKDx3uLxv07p27crnn3/Ojz/+iJubG3FxcS6BT5GePXsycOBAl2XBwcGYTKYyyy4vKCpq2Snt/KDwG7uoqChWrlzJ1q1b+f7771mxYgXTp0+nR48eFTk1IYS4JRX/AnDZsmX89NNP3Hbbbc4P+0UtPQcOHEBRFJd7iaIoHDhwAIC6des6vwQsaoUvzmKxkJycXGpLfO3atS9rv7Luo8W5u7u7vC7vfl58m9LuXUJUFhnjI25oTZs2JTQ0lA0bNrBu3TpUKpXzjfLgwYMA9O/fn549ezr7RJf14dzb2xuAkydPAoVZ0Q4dOuRcX1Reu3bt6N+/P40aNXIGCheXWRQIXCw6OhqAnTt3Opft2LEDoFK+sbrjjjuwWq188803tG3bFoPB4LK+Tp06QGFXuri4OOLi4vDx8cHHx8elq0BZ9S9L0Q1v7969zv2HDh3K0KFDcTgc7Nq1C5PJxPPPP8+iRYuc46zWrl17xecqhBC3gqIvAOPi4njttdcwGo3MmTPHGdA0aNCAOnXqkJKSwrfffuuy7/fff09qaiqNGjWifv36NGzYkIiICI4ePcovv/zisu2XX35Jnz59mDFjRok6XM5+Xl5enD171hlkJSUlVeg8y7ufV/TeJcTVkhYfUaUKCgpYsmRJieVBQUHcfvvtQOE3QPPmzXOmci5KpxkREQHAd999h9ls5rvvviM4OJj9+/fz/fffExgY6FJms2bNAPj6668JDAzkt99+w8PDwxncFJW3ZcsWPv/8c3744Qfq16/P3r17mTFjBsOGDcNoNJKamsqUKVNKDDIFeOqpp3jssceYOHEiQ4YMISkpie3bt9O4cWNiY2Ov+no1b96cwMBAMjIy6Nq1a4n1cXFxhIaG8ttvv5GQkIC3tzdz586lQYMGLF68GKPRCBTeqObMmUPfvn0rdNxBgwbxxRdfMHfuXLRaLXv27GHjxo0MGTIEtVrN66+/TlJSEk888QTBwcFs3rwZuBAICiGEuDR/f39Gjx7N5MmTeeWVV1iwYAEqlYqEhASGDh3KxIkT2b59O/Xr1+fw4cMsWLAAg8Hg7HqsVquZPHkyw4YNY/To0fz73/8mIiKCnTt3smLFCkJCQujXr1+J417Ofs2bN2fTpk1MnDiRZs2aOeeZq4iy7ueXuncJUVmkxUdUqaysLF5++eUSP3PnznVuU/SNkN1up0+fPs7l999/P3fccQcHDx7k22+/5T//+Q8PP/wwBQUFrFy5ssSxWrduzZNPPgnAhx9+SLt27Wjfvr1zfePGjXnyySex2+3MnTuXgQMHMnHiRHx8fFi4cCE2m43HH38co9HI0qVLneNeiuvYsSOzZs3CYDDw/vvvs3HjRvr378+cOXOueIBocSqVii5dugCUGvjodDrmzJlDp06dWLlyJQsXLqR79+7MnDkTlUqF0WhkyJAhOBwO5s2bR3Z2doWOGxoaytdff02dOnX4+OOP2b59O0888QTjx48HCq/n7bffzvz580lISGDnzp2MHDnyhpyEVgghbmSDBw+mUaNGbNu2jW+++QYovH8tXryYnj178vPPP/Pmm2/y888/c/fdd/Pf//6Xhg0bOvfv0KEDixYtokuXLqxcuZK33nqLjRs3MnDgQBYvXlzmRKQV3e/ll1+madOmrF69mlWrVjkzn1ZEWffzS927hKgsKqWsfkFCCCGEEEIIUU1Ii48QQgghhBCi2rumgc/+/fvp1q1bqRMzbtq0iYEDB3L//feXOtBOCCGEEEIIISrLNQt88vLyeP311+nQoUOp6xMSEpg+fToLFizg999/d2bUEkIIIYQQQojKds0CH51Ox+zZs0vNv56cnIyPjw81a9ZErVYTHx/PH3/8ca2qIoQQQgghhLjFXbN01lqtFq229OLT09Px9/d3vvb39yc5Obnc8mw2O1qtplLrKIQQQlSW9PSKZUksi5+fnszMvEqqzc1JroFcg1v9/EGuwdWef1CQocx1N808PtXlDyAoyHDVN0fhSq5p5ZNrWvmqyzUt74Yiro58uSfXAOQa3OrnD3INruX5V0lWt+DgYDIyMpyv09LSSu0SJ4QQQgghhBCVoUoCn7CwMHJyckhJScFms7Fu3Tpuu+22qqiKEEIIIYQQ4hZwzbq67dq1iylTppCamopWq2XVqlV07dqVsLAwunfvzqRJkxg7diwAffr0ISoq6lpVRQghhBBCCHGLu2aBT5MmTfjqq6/KXN+2bVsWLVp0rQ4vhBBCCCGEEE5V0tVNCCGEEEIIIa4nCXyEEEIIIYQQ1Z4EPkIIIYQQQohq76aZx0cIcWPSaFSVWp7drlRqeUIIIYQQIIGPEOIqaDQqPli6g7QzlTPBcEiAntEDm5Ub/Jw8eYKHHhpETEwDAKxWK3Xq1GPcuAloNFc+6dnQoUNISJhCzZq1rriMIgMH3kVwcAhq9YVG9UWLFlx1ucWdOnWKs2czaNSoSaWWK4QQQlRXEvgIIa5K2pk8TmTkXtdjRkTU5uOPP3O+fuONSaxe/RO9ev3rutajPFOnfoRer79m5ScmbiE/P08CHyGEEKKCJPARQtz0GjVqQkpKMgDTp79HUtJuLBYL/frdw1139eONNyYRGBjEvn17SEs7xauvJhAT04APPniXXbt2EhFRG5vNCsDp02m89dZkrFYrarWaCRNeQaVS8frrrxIaGsbOnTvo3/8eDh06SFLSLvr3v5d77rmvQvVcs2Y1ixbNR6PREBPTkNGjxzF37ixOnEjl5MkTTJ8+izlzPmXHjn9wOOwMGHAf3bv34q+//mT27Jm4u3vg5+fPc8+9wLx5n6HVagkJqUGnTvHX7NoKIYQQ1YUEPkJUM5U95uZGZ7PZ2LDhN/r1uwez2UyNGrV4+unnMJsLuO++ftx1Vz8ALBYL7733Md9+u5SffvoenU7Hzp07mD37S9LTTzNoUH8A5sz5lDvvvJs77ujBunW/MG/eZwwdOpwDB/bz1ltTMZlMDBlyH0uWrMBisfDSS+MrFPjk5uby2Wcz+Pzzb9Dr9YwfP4bExK3nz8HKzJlz2L59G2lpp5gxYzYWi4XHHnuQuLjbWbZsEaNGjaF585b89ttaHA47vXvfia+vrwQ9QgghRAVJ4CNENVIZY25CAvRMHtaxEmtV+Y4fP8aoUcMAOHToIA888BBxcbcDYDJlMWLEY2i1Ws6dy3Tu07x5SwCCgkJIStrN0aOHadSoCWq1mpCQGtSqFQrAvn17GDFiFACtWrXhiy/mABAaGoaPjy9ubjr8/PwJCgomLy+P3NycUus4btwzzjE+vr5+PPPMSMLCIpzd31q2bM3+/XsBaNiwMQA7d25n9+6dznNTFAcZGRl06dKNd999ix49etGtW08CAgIr50IK9u/fz1NPPcUjjzzCgw8+6LJu06ZNvPfee2g0GuLi4hg5cmQV1VIIIURlkMBHiGqmKsbcXG/Fx/i8/PJ4wsNrA7Bt298kJm7l448Lu4F1797ZuU/xxAeKoqAooFZfaB1zOBznn6lQlMLkClarDZVKXWL/i8sqzcVjfNLTk122tdmsuLu7A+Dm5uZ8vPPOuxky5FGXskJDw4iN7cD69b/ywgtjSEh4p7zLIyooLy+P119/nQ4dOpS6PiEhgblz5xISEsKDDz5Iz549qVev3nWupRBCiMoigY8Q4qqEBFTeAP4rKeupp55l7NiniY3tQFbWOYKDQ9BqtWzc+Bt2uwOr1VrqfhERtVm8+BsURSEt7RQnT54AoGHDRiQmbqV7917888/fNGjQ8KrOqUhkZCQpKcfJy1zquNUAACAASURBVMtFr/di27ZEHn54KFu3bnZu06hRE2bM+JAHHngYq9XKzJkfMmbMeL74Yg4DBtzH3XcPIDPzLEePHkatVmO32yulbrcqnU7H7NmzmT17dol1ycnJ+Pj4ULNmTQDi4+P5448/JPARVc5qs3M228xZkxmztfq9B/ik55KVlV/V1ahSt+I1sDms5DlyybNlUzcsmPq+odfkOBL4CCGumN2uMHpgs0ov83LUqhXK7bffwZdfzuWBBx5m/vwvGTVqGJ07x9OxYyemTn2r1P3q1atPnTp1GT78UcLDI6hfPxqAxx8fwVtvvc7Kld+i1brx4ouvYLPZrvq89Ho9I0cWBmkqlZpmzVrQvHkLl8CnadPmtGzZmuHDHwUU+ve/F4CQkBqMHv0UBoMRg8HAoEEPotfrSUiYhK+vHz169L7q+t2KtFotWm3pt8H09HT8/f2dr/39/UlOTr5eVRMVYLM7OJdjJjvPSnaehTzz1f2f2u0KBRY7BRYb+ebCxzIadK8pD083CvIvfGGjKAqmPCtnTAVkmgow5ZX+ZY64vrpkbKVBzrGrKiPZrxmZXmGVVKObxaX/qVLdNhPyzMMYPT0r/egqpax+GjeY9PTsqq5CpQgKMlSbc7lRyDW9QKNR8eKsP6+qq1utQC9mvdhNrmklqy5/p0FBhqquQqWbPn06fn5+LmN8EhMTmTt3LjNmzABgyZIlJCcn89xzz5VZjs1mR6u98rmkROly8izsOJjB8bRsTp3JJe1sHqfP5pFxLh/HTfEJpnLotGoCfT2dP0G+nug93Kq6WjekU0lpZJ0sfL9VFeShslV+sKg63z1aKTZf2+Uyawp7ObjbK2cuvOur9H8+BaUisU2pVCoAFT6e2Yx464krrll5pMVHCCGEuEhwcDAZGRnO12lpaQQHB5e7T2bm1X14qS7B8eVQFIWsXAunM/NJO5uHRYHTGbnkFlgx5VpIzyrgdGaeS8uLCvA1uFM31IcAowdGLx0GvRt6dy0q1ZVntVSrVXjoNHi6a/HUaXHXaVzGAV4v/n56zl70t2TQu2HwdLuq87uRbVp7iMN7TwOg1qhx2B2X2KOQPS8Ph9lcYnnB+YDCw54HReM3ryJAKZVajdrdHc1VzNemA+o0CKZj17ouy6vqvcBqt2KyZJNlycZkNrk8L/6YbckpDHDKoFapMeoMGHUGfNwNGHVGfHQGjEXP3Q346IwYdN5o1VpwWPDe8xz5kc9i96p/1edf3hd0EvgIIYQQFwkLCyMnJ4eUlBRq1KjBunXrmDp1alVX66ZhdzjIzbeRnW8lO9fCoRNZHEjJwmpznF+vkJVjJjPHjMVa9odcb0836of50qi2H1G1jAT5ehJg9MBNW8kfYm8gQUEGPKvBtATpSxaSvXVLmev3eTbklFstwDVQUamoeBfDMoIaD3seNawniMnfA4ChTVuC7h10eSdQTSiKQoHdXCJ4ybKYMJmzXYKbPFv544p0ajeM7kaifGqfD2SMLo8+7kaMOgNebnrUqgr+jyp2DLuG4ZG2HJViJbvJrEo467JJ4COEEOKWtGvXLqZMmUJqaiparZZVq1bRtWtXwsLC6N69O5MmTWLs2LEA9OnTh6ioqCqu8Y3r2KlsDp/IIj2rgCMnTBw+aXIGOWUx6t2o4a8nyNeTED89wX6e1An3w26x4eWhxcvTDU93+ZhyMyke7NjOnOFAQBtOG+uUum3xYKd4oKLRqLFXsMUHbt2gxqE4yLXmkVUU0JTSMlPUamNxlN/VT6/1xOhuJNwQ6myRMeoMJYIbD4175bY6Kgree8bgkbYci29Hshu+X3lll0HeUS7DrTYxpBCikPzvV09NmjThq6++KnN927ZtWbRo0XWs0c1l1+EzLP31EHlmGxlZBc7lKiA0yJsa/p5463V4e2qpFehFw9r+GDzdnBupS/kAdSt297uRXarVpoiz9cbhDd5dCltgjBeCG4PRvcQ+btxY3bxuFDaHjYy8sxw1nSTLnI3JYnI+mizZ558X/jiUsgNEFSqMOm9C9EElWmacj+d/3DRVM1bM6+BreKZ+gdXQAlOLRaCpvCyxZZHAp4JulYkhhRCu5H9fiAscisLBlCwS96ezYccJzBYH3p5aQoO86NoylNAgb8KCvGTQ/U3m4gCn1ECmHM7WG/Jcxr2UFdzcigps5mJBzPkuZkXjaM4/mizZ5FjLT06kVWvx0RmobQh3jp+5MJamqLuZEYPOq+LdzaqA57GP0R/9AJu+PlmtlqO4+VyX40rgcxluhYkhhbhcld0acrnprK8H+d8Xt7LMbDP7kjPZn5zFtv3pZOVaAPB01/D4nQ1p37hGFddQVFRZLTi2M2cA0AYEsM+zIUc9CuerujiQKcutGuAoikKuLQ+T+cKYmazzLTMXLzPbLeWW5an1wKgzUNMrhGCjP+6Kp3PMjI/OiNG9sOuZp9azWiS5sPp1xmpsianZ1yi6wOt2XAl8hBBXTKNR8cnOeZzOOVMp5QV7B/Bk08fKDX5OnjzBQw8NIiamwfk6aBgy5FHatGl3Wcf64YeVeHl5Ex/fpcS6Awf2sX79rwwdOvzyTuC89KSVFJxLxWbORrFbSPMJZlTqt7z2WulzCglxI8rJt7I5KY0l6w5iOT9ex9vTjbjmNWkVHUzD2n7VOsnAza60IKd4gFPcobBOnPaOQqPXk20qzJLWvF34LRfIFLE77GRbcy60xJwPXrIuCmiyLdnYlLInkVWhwtvNi0DPAGfw4hrIXBhPo9PonPtV6+5+ih1UGmzG5pxr92tRDuvrRgIfIcRVOZ1zhpM5p6/rMSMiavPxx58BkJqawgsvjGHSpDepV69+hcvo0+euMtfVrx9D/foxV1y/oEaFZWclb8WSfYrmcffzscyNJG4iW/eeZtaK3dgdCl4eWu7uHEXdWj7UDTWiqey0wOKKlTcGp7Qgp3iAU1y2yQw2MFA4Hqe6tt5Y7FZnd7PyWmhyrLnlpmvWqDQYdQZCDbVcWmNcght3IwY3bzRqmduriC79J7wOTiar5VIcHrWue9ADEvgIIW5yoaFhPPTQYyxfvpjx419i2bLF/PLLT6hUajp3vp3Bgx8kOzubyZNfJjc3F29vbyZNepMFC77C19eXnj3/xauvTsBisWC1WnnuuRfIzc1h+fLFJCS8wy+//Mzf/5uF1Q4ePqEEN7mbjH0/47AVYMlJx5p3huDGffEKbnDJur7xxiS0WjdMpnNMnvw277zzBidOpGKz2Xj88RG0bt2WI0cO8/7776BSqdDr9UycOAmDofpNGipuLIqisO/4OXYfPUvK6Rz2HM9Eq1HTu304t7eohb/Ro6qreEurSBe1i2kDAjC0acuBgLbOOXKKBzjF3czBjqIo5NvyXVtjyhg/k28rKLcsd40OH52REK+gwiDm/NwzxVtojO4GvLT6atHd7Hpyy/wd446HABXqguTCwKcKSOAjhLjpNWjQkG+/XcaJE6n8+usaZs6cC8CTTw6lS5durFixnHbtOnDvvYNYtGg+W7f+5dz377//IigomBdffJXU1BSSk4+j0xV2OcjLy+PTT2fQvOcYTmfZSf3rc/IyDgJgy88iLHYouaf3ce7YnxUKfACMRiMvvPASP/30PQEBgbz44qucO3eOZ58dwZdfLuSDD97l+ecnEh4ewfLlS1i+fDEPPzy0kq+YuJU5HArp5/JJPp3D2WwzFqudP5PSXMaxGb10DOkRTeuY8idtFddH9tYt2DIz0fr5uSwvCm5KS+e8ae0hNu09TfahZKAwuLmZAhyH4iDbknu+hebijGaumc6sDlu5ZXm56fFz96W24cJcM0WPxSfZ9NCWzD4nrp7W9A/Gf+4HxYapxUJsvrFVV5cqO7IQQlSSvLw81Go1e/bsJiUlmaefHn5+eS6nTp1g//69PP74kwDcf/8DQOE4HoDGjZsxe/YnvPvum8THd6V9+44kJm4FIDn5OOHhEWjdPIBcPAPqYDadAMDTPxIArYcPjktM+lZco0aNAdi1awfbt29jx45/ADCbzVitVpKSdjNlSgIAVquVhg0bXcWVEeKCczlmvlq1j6SjmZitruMSNGoV7RuF0KFJDSJrGDDodWWUIq4Xlzlxzgc9daZMK3XbTWsPOVt1ihSN1bnRgh2rw+acX+biOWeyLNnk2XM5m3cOkyWn3O5mapX6fDKAGs5xMhfmoCl89NEZMei80arl425V0eQewCdxACpbNtlN52EJ7FGl9ZG/BFGlZH4UV1d7PdRqVVV0ma1ye/cmER0dg1brRocOtzF+/Esu67/55iuUMuY7CAwM5IsvFpCYuJX//ncpu3fvpHnzlgDnZxAvduNV7KAqmoek2DiHy0hEp9W6OR8feugxunfv5bLew8OD6dNnSTcKUalOZ+YxdeE/ZGQVUMNfT1RNI+HB3gT5eqBWqYiqZcTXW77trmrpSxZybNvf2O0Ol25sWj8/DG3alrrPprWH2P7XhVadItcz4FEUhQK7ueQEms5WmgvL8y7xRZFO44bRzUCUT+0Sc88UTarp427Ey01/Q6drFoDDhvGf+1BbM8hu+BHmGvdUdY0k8BFVR+ZHcVUZGdKCvQMI8mtDavr1S70c7F2yb/n1LCs1NYWFC7/hgw9mAvDJJ9MpKCjA3d2dDz+cxpNPjqJhw0b8/fcWGjZszLffLsPd/cKHgy1bNmOz2ejQ4TYiI6OYNu1tZ+ATHl6blJTjRNUr7Beed+YwAfXvIDf9wFWfa6NGTdi48Te6d+9FZuZZFi9ewPDhI6lXrz5//rmJDh1u45dfVuHr63fZGeuEKC4jK58p32wjM9vM3Z2i6HtbpATWN6D0JQvJXPUTcD7YKaMb28WtO9cyC5tDcZBrzXOOlSmthcZ0/rnFYS23LL3WE6O7kXBDqMvYmYuDm/AagWRk5FTqeYgqotaS0/AjtNk7KAh7pKprA0jgI6qYzI/iqjIypBkrqS4VYbcrPNn0sUov81KOHz/GqFHDsFqtOBx2xo4dT40ahXOJ3HffYEaOfAK1Wk1c3O24u3tw772DSUh4lVGjhqHXezFpUgILFnwNQFhYOJMnv8L8+V+iVqsZOnQ4dnthNyBPT09GjRrNW+/PwGpz4OEXhad/VKUEPl27diMxcQsjRjyG3W7nsceGAfDss+N45503mD//S3Q6dyZNSrjqY4lbl9li57MVSWRmmxl4e136tK9d1VUSxbh0ZzvfwlOrX1+87xwAFAY5q2b+4bJP8S5sRY+X27Jjc9jItuQUpmguGitjPt/17KJJNh1ltJZDYbpmg86bEH2QS/Dic37sTPHnbpqKTWorQfnNT2XNQlFpQOuN1b8zVv/OVV0lJwl8hBBX5XpPOFqzZi1Wr15f5voBA+5lwIB7XZZ5e3vz9tvvuSwrPkfPJ5/MLVFOq1ZtALj99q6s2qd3CdADYy70UXY31iC844hS6+IT3sbl9UsvTXI+12q1TJjwSol9IiOjmDlzTqnlCXE59h3P5NMVu8nKsRBV00Dv2IiqrpIopqwWnoOBbdl1Pti5OMgpel5WoFNgMzsDlouzmhXPbpZjLf8LR61ai4/OQG1DWLHg5cKcM0WJAQw6b+luJlzZ8/D55z5QbGS1+i+K9np+HXtpEvgIIYQQ1Uj6uXx++us46/8pTMRxZ8dIesdGyDfpN4iiVp6iFh6/nr0IundQsSxsh4DiWdiCaNa5pss8MyZLMksP7HZZlmUxYbZbyj22h8YDH3cDNb1CLspqdiHTmY/OgKfWU/5exOVzWPDZ/iBu5/6gIGQAisarqmtUggQ+QgghRDWRW2DlnW+2ccZUgEHvxlP9mhAT4XfpHcV1cXErj65FcxINDUj9eD3mnMIuZRovB/bgbE7WTSbLbGKLJZv5G+xllqlChbebF4GeAecH/xtLzj1zPmWzTiPZ+sQ1otgx7BqG7swvmAO6k93kM1DdeJO3SuAjhBBCVBNr/07hjKmAjk1qMLhbfbw8KjauQlQei93qHCeTZTHB/37BffdBHIqCu6kwo9nOxj4k+TXGeDIQ3bFzhfvp8sjyP0VaxF4ANNkajDoDoYZaziCmeFazohYag5s3GvWN9wFT3EIUBe89Y/BIW47FtyOm5l+B+sYMsiXwEUJUa5IiXNxKks9ndBwQV0eCnkqkKAr5tgKXgMZl3EyxxAD5tgLnfp225dB6T2HmUpOXGrOXmsO19SS1r0XQ5jDUVjfUegeGCBV1Y0MwutcjMmQg9lwNejdPGT8jbgpa0zY8Uv8Pq6E5phaLQKOv6iqV6ZYJfOTDjxC3nps1RbgQVyLfbGPP0bP4eOnwM8icPBXhUBxkW3LPBzQmTJacYlnNimU8s2RjddjKLcvLTY+fuy+1DYUtMfU2HiZwz/ksnfEdiLhnAEadEcv6FCzbTpNjNuNtcOfBpzq4lBPkayDdmn2tTlmISmfzaYWpxSKsPq1Q3HyqujrluiUCH/nwI8S1U9mT0FZ2lribLUW4EFfirz1pLFl3iNwCGwPi6tzyA9OtDtv5JAAl554xWbKdz7OtueWma1ar1BjcvKnpFVIsq9mFx6KxM0adAa1aW2p66uLJCw7vPeiSqa1Og+BrfzGEuEbczqzF6tcJ1DosQT2rujoVcksEPiAffoS4FjQaFSc/ep+CtLRKKc8jJISaz4wpN/g5efIEL7/8AnPnfnXFx0n9YT+BHcJx9/Mssc5eYCMvJQtDvQDS1h/Fo5Ea4yXeKbOSt3Jm3yrc9IUTsDrsFnwi2uJbu0P5O15Df/65iZMnT9C//8Aqq4O4PvLNNr78aR82u4P2jULo0Ta8qqt0TSiKQoHdXGLizCxL9oUWmvPL8mz55ZblpnbDR2cg0hhxfu6Z8wFNsblnfNyNeLnpK9zdrKz01AcC2rJq5h8lAp7KnmxUiOvJ/dRSDDuHYq55f2Eig5vELRP4CCGujYK0NApOnKzqalyW0D7RZa7LP5lN9sGzGOoFEBIXiZ93IKRcukxDreYENboTAIfdxvENH+AVFIOb3r+yqn1Z2rfvWCXHFdffL3+nkG+20T+uDnd1jKzq6lw2h+Ig15qHyZJNqi2Z4+lppQY3JrMJi8NablmeWk98dAbCDKHOgMbHGdBcaK3x0HhUeqtYUUuPX89eHAhoy+G9pyEdsg8lAxLwiOpDl74Kw65hKFoD+REjq7o6l0UCHyFEtXDo0EHee28KKpUKvd6Ll1+ehMHgxT//9zumjCz04T5k7T5No3G3cXBeIqH/ikZxKKSu3IdKq0alURN5X2NS/rcfh9mGe6Ce3ONZaFpb8fa2cTLxG6z5majUbtRocT9unmX3Y1ZrtOgMNbDmnUXr6cve379myJAvyc838/jjI2jdui1btmzmo4+m4e8fSEREbXx9fWnZsjULF35NXl4eo0aNIS3tJAsXfo1GoyUmpiFPPz2GU6dO8frrr6BWq7Hb7bz66uuAqsSyxMStHD58iFGjRrN48QLWrPkZgM6d43nwwUd4441JBAYGsW/fHtLSTvHqqwnExDS4Tr8tURkcDoX5v+xnXWIq3p5udGsdVtVVcmF32M93KzNdGPzvDGiKxs8U/pTX3UyFCoPOmxB9EMaibGbFWmYuTLBpwE1TNQkd0pcsxHbmDNqAAA4EtGX7XxeCHQl4RHXilrkJ444hoHIjq8USbMbmVV2lyyKBjxCiWvjww6k89dSzNG7chG+++YolSxbSsGEjHFYH9Ye1wbQvg4w/k132yUw8SUC7UPxb1CT78FmsORaCO0VQkJZDQJtQco9nAXA06Xc07gZqtvo3ptR/yE1Lwjey7G5sNnM2BeeS0TXpR3bqNnSePnz11YccOJDMs8+O4MsvF/LJJ9N55ZXJ1K1bn5Ejn6Bt21igMIBbsGA5NpuNd95J4NNPP0en0/HKKxPYseMfkpJ20bZtLI888jj79u0lIyODXbu2l1hW5MSJVH78cSWzZ/8fAMOGPUyXLt0AsFgsvPfex3z77VJ++ul7CXxuModPmFiXmApA/85ReLpfn1u62W5xZjO7+LH48xxr+WNitSoNRncjtQ1hzuClpl8gWpt74cSa51trDDrvGzq7WfEuboY2bdm0t7BbffN24RLsiGpFa/oH4z/3gWLD1HwBNr+q6859pSTwEVdMMuWJG8nRo0do3LgJAK1ateHzzz9Dr/fENyoQAEP9AFRq1z84Y4NAUv63D/OZfHybBOMR5EVeiqlE2Zmnj+HpH1m4T2iLUo+ffWI7BedSUBw2bOZsgpvcjdbdm/zMY5iyjjFkyBAsFhtmsxmr1Upa2kmiowsDjfbtO2K3F05QWK9efXQ6HQcO7Cct7RTPPTcKgNzcHE6dOkW7du2ZOPF5srOz6dLlDpo0aYZe71li2fHjRwE4cGAfjRs3RastfLtv2rQ5Bw/uB6B585YABAWFkJS0+4quu6g62w8VBrhDekTTpdW1a+1RFIVv9i7l4LkjmCzZFNjN5W7vofHAx91wPiGA65wzRY8+OgOeWs8S3c2Cggykp98cGc02rT3EgcSjOPK9ofZA1J6eaNL15GSbMRjdJegR1Y426y9Uthyym869aZIZXEwCH3FFJFOeuJHZbFbUajWKolz4YFVKkG2o60/08LaY9mWQvHwPtXrWK7U8lUoNSvnZ5orG+DjsFo5v+Ah3Y63z+2qo3awXX82eUOYHuuIf/tzc3M4/FnZve++9j0ts/8UXC/jrrz/59NOP+de/+tK7950llhUrHaVY3a1Wa+H5ABrNhUkPlUucn7ixJO5P55e/UzDo3ejYtOY1PVaWxcSmk1vQqd0I0gee72pmdLbIXJhYs3D8jE5zY05cWJnWfPI9+7O8AA0eUBj06AvnLvE2SLY2UT0VhA/D6n87dq+yx8ne6CTwEVdMMuUJKMzEdiOUFRVVl127dtCkSTO2bUskJqYhoaFhZG06i0dbf7IPnkVxuH64z9icgiE6AL/mNYDCxAYaL7cS2/nXiCJ9zy4MtZqRk5aE2XSKgPpdS62HWqPDv/4dpCetpFbrIXj4RZBxfAcAmZlnWbx4AcOHj8TfP4Bjx44SFhbOli2badmytUs5ERGRHD16hMzMs/j5+TN37iz69u3P9u3bqFUrlLi42/Hx8WXdutW4ubmVWFbUmhQdHcO8eZ9hsxXOQZKUtJuHHnqMDRt+veJrLarWlr2n+eTbXbhp1TzQPRp3N82ld7oKZwvOARAX1pH+9f51TY91M0hfspDjGd7gBpEFB2nT2Juge++s6moJcU2oLOl4pswjL+p5UKlv6qAHJPARQlwFu12h5jNjKr3MSzl+/BijRg1zvn7qqWcYPXqcM7mBwWBg4sTXcHfX8f5XH3Bgzt94R/qi1bsOfNb5e3Js0S407lpUWjXh/Rtiy7Vw8udDuBk9nNuFx8SyZ2ciyZs+QaXWENL8/nLrZwxtybmjm8hN34+hZjOyc44xaNAgCgosPPZYYb2feOIpXnrpeWrWrEXt2pEurS8AHh4ePPvsWMaNexadzo369WMIDAwiPLw2U6e+iaenHrVazejRz2M2m0ssS0raBUDNmrXo27c/Tz89DIdD4a677qZGjWvbQiCuHbPVzuK1B9BqVLw0pDURIYZrfsyz+WcBCPDwu+bHutGt+eR7jmd4Y9bq0Wvt9J70eFVXSYhrRmXNwiexP27ZO7Dr62GucU9VV+mqSeAjhLgqlT3h6KXUrFmL1avXl7pu+vRZLq9zckyExdZBqeuO1WQmKykdgHqPtQLAM8QbY/0Al33cvHU0Ht/J+TrYOxhNipaaLQeVWSef8DYllkXcdiHFZ4PbHmDWi91curp5eHjw7rsfUrNmLd555w1q1QqjVas2tGp1oaz4+K7Ex7u2LMXENHAmKiju4mWRkVHO5/fccx/33HOfy/qXXprkfH7bbZ257bbOZZ6fuHGs+P0IZ0xmerePuC5BD8CZgkwA/G/hwKdoYtLj3l0wa/V4uinUbxVZ1dUS4tqx5+Hzz324Ze8gP/QxzCEDqrpGlUICHyFEteXlpefU9uOY1mShKAq1etWv6io5KYrCxInj0Ou98PPzp0uXO6q6SuIGZ3c4WJuYip/Bnb63RV16h0pyVgIfsrduwZaZCUY1nm4KD4+T/1dRjTksGLcPwe3cHxSEDCCn4TSqSzYqCXyEENWWVutGi4duu+qxaNdCbGwHYmNvvlSgouokn87BbLHTvlHINR/XU9yt3OJT1NJjy8zkUK2OFGj0GPTuVV0tIa4dxY5h1zDcz6zGHNCd7Cafger6vd9caxL4CCGEEDeB5LQcAKJqXt+0MGcLMvF288JDe2t94C8+P482IIDT3lFgQzK2iWpOjcMjFKtvB0zNvwJ19crSKIGPEOKauNp5nuD6jx8S4kaWlWsBwNf7+gUgiqJwtiCTml6Vl73xZpG9dQsAfj17cSCgLXl/Jcv8PKL6U6nIrZ8ADjNoPC69/U1GAh8hRKXTaFT8uHQXmWfyrrgMvwA9vQc2keBHCArH9+w4VDhvmp/h+gU+2dYcrA4b/h7+1+2YN4L0JQuxnTmDNiCAoHsHsWrmH4C09ojqy/PI+6gcBeTVmVA4nqcaBj0ggY8Q4iqV1rKjVquueELMzDO5l5orVIhbztFT2RxMzaJxlD9hQV7X7bhn8gvH99xKqayLd3EztGnLprWHyDaZpbVHVFseKfPwPvgado8w8iOGo7hV3y86JPARQlyxymjZKc4voHDm87MZuWVuc/LkCe6/vx/z5s2nXr3CLG0//LASgD597ip1n7T1R/GO9MMrwueK6rVu8duYsnNRa3Q47Fa8gmMIjOlxRWUJcSWKWj6jahpRXcfsSrdCRreiBAZFbGfOt6yd7+K2/a9kQFp7RPXkfmop3nvG4HALJKvVd9U66AEJfIQQVynzTF65gcq1EBkZxaefTmfq1I8qtH1IXORVH7NG8/twN9ZAURwc/XUqvrXbo/W4voPMxa3rSltQr1ZR0tzkRwAAIABJREFU4BPgWX0Dn6KsbVq/wnPUBgRgaNPWpYtb83bh0tojqh1d+ioMu4ahaA1ktVqO3evGmfLhWpHARwhx04mJaUhBQQF//72F1q3buqybPv09kpJ2Y7FYGDDgHgiC48uT8G0czKm1h4kc3AydrweWc/kcXbCL+sPbkLJiL+az+SgOhRpdozDUKfsbL4fNjEqlRq3VoTjsnPpnEbaCLBx2CwHR3XHTB3B65zLCOz4FwJkDa3CcNnDwYCSvvPIaKpUKvV7PxImT8PT0ZPLkVzhzJgOLxcLQocNp377jNb124uaUkVUAgI/X9c2wVJ1TWRdPVa3186POlGku66WLm6jONNm7Me54CFRaTC0WYTO2qOoqXRcS+AghbkrDhj1FQsJrfPrpPOcys9lMjRq1ePrp5zCbC7j//n60mdjNud6nYRCmfRkExoaRtTcDn0ZBZO44hdZbR3i/hthyLRz6YhsxI2NLHO/U9sWoNTosOafxqxuPWuuBzZyDPigan/A2WHLPcDLxa2p3fhaH3Y41/xxunr7kpu0hpsWTvP766zz//ETCwyNYvnzJ/7N35/FR19f+x1+zZSaTSSaZkCAgsrXKoiiL2hZtq4afC16ltrTU9qp1aa3W1ir9XY0LVoWqv2pbtYu1anEr1IpKb72l9VLbKigKCgqiEARlEZLMZLLNZLbv748wIWFJJsnMfGeS9/Px8GEms52MMcmZcz7nsHTpH/nMZ2YQDDbwy18+TFNTE6tWvZqV107yz4e7GwFzRlnDwEt8DhxVXTy96xsoK1fUqMVNBrR40TGEh32VSMUsomUzzA4na5T4iEheGjnyKI4+ejz/+79/6/ic0+mksTHIlVdeit1uJxAIdLmPd2IFu/66hSEnH0nje3Uc+R/HULvqY1q2N9DyURCARDRBIpbAard2uW+y1S0Rj7F7zeO0lAzHXT6WcMMOgh+9jsViIR5pP+tUcuQUmnevp3j48VgdLgoKS1i9fj13330nANFolAkTJjJq1GhaW1u4445b+PznT6OqSueG5NB21rZgAUZWZm+wAbRXfNz2QgrtA2fCU+ekp+zMs6iYM/eg22zd1L70WC1uMuDEW8HmBqud5okPmB1N1inxEZG89a1vXc51113DBRfMwW6389Zba1i79k0efPC32O12Zs48tcvtXZUeok1tRIJh4uEYziFuLDYLlZ8fRdnkI1J6TqvNTlHleEL+D9tb3KKtjPzcd0lEQ2z/d/uZo+LhJ7B7zRNYbAUUD29vHygsLOSBBx466GD6Qw/9nnfeWc///M+fefXVf1NdPT8Nr4wMNHsCrfhKXDjs2dugbhgG/pCfoe6KrD1nJnW0tnUaXnBg0rNyRQ1bN+2luUktbjLwWEMfU/rm2bSOvpbwyMvNDscUSnxEpF+Sk9jS9Vi9mRDn85Vz6qlf4IUXlvLlL3+VYLCBysqh2O12Xnnln8TjCRKxeJf7lBw9hE9e2krJ+CEAuI8soXFTHWWTjyDaHKFu1ccMm9n9Hzvhho9xVxxNLNyIw+3DYrHStPsdjEQMALvTg9XhpnHHWo48+VIAxo8fz2uvreSzn53BSy8tp7S0jOLiErZt28qZZ57DpEnHctVVg/MXkXSvLRKnoTnCxNHZbTdribYSSUQHTJtbx3meTsMLDpRMejzFTrW4yYBiidTiXXs+tvBHWGKNZodjGiU+ItJn8bjB2V859qDPW60W/nvJehr8oV49XqC+lUB97ybEff3r/8nzzz8LwPTpJ/PUU4v43ve+zamnfoEZM05hw5/e7HJ778QKNj+8hmOuOgmA0kmVNG8NsPnhNyEBQ08bc8jnSZ7xMYw4zpJhFA8/nliogZ1v/J5Q4CO8I6djd5VS/8HfKT96JsXDjqN5z0as+1qEbrrpJm64oZqnnlpEQYGT2267E7Dw0EO/5IUXlmK1Wrnwwv/s1dcug8OWXe1tmCOGeLL6vPVhPwC+PJ/o1tMQgwN5ip1886rPZik6kcyzRIN4116AvXULraOvJTTmOrNDMo0SHxHpl+R+kQM1+EMZGXM9bNhwbrrpto7LbrebP/95/zmfhx9+vOPjCy/8Jj9e9f/Y3bx3/+1HlHD8bad1XLbYrIycPaHb5zztqzfw9ge1B33e4fYx+gv7f4GUHDm14+PWus2UjvpMx+Vx48bxq1/97qDHuO++wddjLb3z1r7vvRM+VZ7V501OdCt35e9ej56GGCR1bnHzFDuzGaJIZsVb8b79VRxN6wiN+BYtn/qx2RGZSomPiHSRPIJis/V9SaLVmr0Fi7kmEY+yY9VDuEqPxD3kU2aHIwPA+x834HTY+PTI0qw+7/6Jbtl93nRJZYhBklrcZKByf/hTHA2rCA+9gOYJ9+3/JT9IKfERkS4qytxsuO0Ownv29PkxSiZOALqvogxUVpuDo075ntlhyACRMAz2BkIMLy/CbrP2fIc02p/45FfFJ5UhBkkHVnrU4iYDTeuYH4HVRevoa8GSveEouUqJj4gcJLxnD+Fdu/t8f2dlJQyc6bcipmloaiMaSzDUV5j1564PJVvd8uuMTypDDKDrrp7iElV6ZAAxDGwt7xH3TARbIa1j/6/ZEeUMJT4iIiI5aue+c3JH+NI3PTFV/nAAl82F25H9pKsvejvEQLt6ZKAq2vJjCrc/QOMJfyAyRPvhOstu3VxERERStnlH+0S3scO9WX1ewzDwhwOU59FEt85Jz+GGGCStXFFDU6N29cjAU7jt57i33Ue88CiixSeYHU7OUcVHREQkR23Z0YAF+NSIkqw+b2ssRDjelhc7fHpb6YH91R61t8lA4trxGJ7NtxJ3jiA4dRmGU9/fB1LFR0REJAfF4gm27mpkRIUHt8uR1efeP9gg9xOf3lR6QNUeGZicnzyL571rSTjKCU57gUThSLNDykmq+IiIiOSgYHOESCzBkRVFWX/u/Tt8cjvxqX1mMbH6euzl5SlVekDVHhmAjDiF2x/AsHkITl1KvOhosyPKWUp8REREcpjFhL0b/pAfyO3Ep/OenlQqPZ2p2iMDisVGcOrz2FpriJVMMTuanJbRxGfhwoWsW7cOi8VCdXU1kydP7rjuqaeeYtmyZVitVo499lhuuummTIYiImKadCyFBYjHjTREI9Izf7gByN1Wt94sJxUZqOyN68CIEPOeiOEoJeadZnZIOS9jic/q1avZvn07S5YsoaamhurqapYsWQJAc3MzjzzyCH/729+w2+1ceumlvP3225xwgqZPiMjAk46lsK6hQxn2/R8q+RlEDMz7b51sdfPl4FS3viY9By4rFclntpbNeNd+CRJt+E9Zh1EwxOyQ8kLGEp9Vq1ZRVVUFwLhx4wgGgzQ3N+PxeHA4HDgcDlpbW3G73YRCIbze7I7qNIPe9RUZvPq7FFYGn9ZwDACXM/vb1uvDfpy2Aors2d8f1J3+VHo6Jz063yP5zBregXft+VijdTSN/5mSnl7IWOJTV1fHpEmTOi77fD5qa2vxeDw4nU6uvvpqqqqqcDqdzJo1izFjxnT7eGVlbuz27P/wT6d0ves76bZb0hjVwFBRUWx2CJIBPp/H7BByh8Wi12OQCTS1AeAzoTrhDzfgc5WZcr7ocNLR3uYpdvLNqz6b7tBEssYSqcW75nxs4R00f2o+4ZGXmR1SXsnacAPD2F+laG5u5qGHHuKvf/0rHo+Hiy++mE2bNjF+/PjD3j8QaO3zc/e3wpJO6XjX1+9vNr3qk0uvKUBtbZPZIfRbrr2muaC/3+sD6TV1VlTw1G9fI1Df95+FZeVuzv7Ksf16TfUmQ/b4OxIfV1aftzUaIhQLMc47KqvP25OmN98AdKZHBi9LNIh37QXYWzfTOuoHhEZfZ3ZIeSdjiU9lZSV1dXUdl/fu3UtFRQUANTU1jBw5Ep/PB8D06dN59913u018REQGu0B9K/66FrPDkCwJNIUB8JVkt+Kzf4ePL6vPmwp7eXmfkp7Ou3tE8pUlFsQaCxIacQktn759/xkKSVnGFpjOmDGD5cuXA7BhwwYqKyvxeNrbNEaMGEFNTQ3hcPsP9XfffZfRo0dnKhQREZG8429sr/iUZbnVbX/iU5rV5+1Ocl9PX6xcUcO61R8D2t0j+S1ReBSBk/5B84SfKenpo4xVfKZOncqkSZOYO3cuFouF+fPns3TpUoqLi5k5cyaXXXYZF110ETabjSlTpjB9+vRMhSIiIpJ3kmd8sp34dCwvLcydik+yza03+3qSU9ya9iWQx580Urt7JP8YcYo+qCY84lLinmMwCsrNjiivZfSMz7x587pc7tzKNnfuXObOVY+u9I8m5YnIQNUcilLotOHI8mCfZMUnV5aXJqs9vW1zS05xKy5pn+KmpEfyjmHgee96Cnc+ii20ncYTFpsdUd7L2nADSROLBau1/+XNgfKHvvajiMhA1RaJ43Rkf5rp/la33Eh8+lLtSdIUN8lnRVt+TOHOR4l5jqNp0m/MDmdAUOKTZ5wVFfzlj++YPtkpl2g/ioj01cKFC1m3bh0Wi4Xq6momT57ccd1TTz3FsmXLsFqtHHvssdx0001ZjS0cieF2ObL6nNDe6uawOvA4irL+3Afqa7VHwwwk3xVu+znubfcRc4+jYepzGI7cOXOXz5T45CFNdhIR6b/Vq1ezfft2lixZQk1NDdXV1SxZsgRoX7vwyCOP8Le//Q273c6ll17K22+/zQknnJC1+MLROGUl2R1lDeAPBSjPgR0+nff29PZsj4YZSF6reRTP5luJO0cQnPoChlPfx+mSsaluIiIiuWzVqlVUVVUBMG7cOILBIM3NzQA4HA4cDgetra3EYjFCoRBerzdrscXiCSLRBG5ndt+fDMfCtMRac6LNrS97ezonPRpmIHnLN52YZyLBaS+QKDzK7GgGFFV8RERkUKqrq2PSpEkdl30+H7W1tXg8HpxOJ1dffTVVVVU4nU5mzZrFmDFjshab36SJbv5wAwC+QnMTn/4MNAAlPZKnjARYrFA2mcBnVrZ/LGmlxEdERAQwjP3nHpubm3nooYf461//isfj4eKLL2bTpk3dLtouK3Nj7+cEtoqKYgA+CbYnPiOPKOn4XDZ8FN0GwFHlR2T1eZM+fGwR9StX0ba3FoDKU2f0Kg6rzYq3rJDzv5a9lsRMMOO1zyWD8uvf+wqs+T58/gWgmIrK7FWYc1GmvgeU+IiIyKBUWVlJXV1dx+W9e/dSUVEBQE1NDSNHjsTna99lM336dN59991uE59AoO9DZ6D9F31tbRMAWz/2A+CyWzo+lw0f7tkFgDPuzurzJtWvXEVbXXulp3j6iXjOvSDlOFauqCEYCFFc4jQl9nTp/H0wGA3Gr9/WtJ7SN8/FEm8h+NGblE4YOeheg876+z3QXdKkGpqIiAxKM2bMYPny5QBs2LCByspKPB4PACNGjKCmpoZwOAzAu+++y+jRo7MWW/2+pZu+LA83qA+3J1xm7PCpfWYxbXtrsZeVMfbue3s9xU0DDSQf2Vo2U7pmNpZYI02THiI6pMrskAY0VXxERGRQmjp1KpMmTWLu3LlYLBbmz5/P0qVLKS4uZubMmVx22WVcdNFF2Gw2pkyZwvTp07MWm7+xPeHKduLjD5mzw6evE9ySdLZH8pE1vAPv2vOxRutoGv8z2obNMTukAU+Jj4iIDFrz5s3rcrlzK9vcuXOZOzf1qkM6BZsjAJR5CrL6vP5wA3arneICT8afq/aZxR2T22L19QAMn30ennMv6NPjFZc4lfRI/jASlLz9dWzhHTR/aj7hkZeZHdGgoFY3ERGRHNMcjmK1WCjM8jjr+rAfn6sUaxamSTW9+QaxQHuFyV5eTtmZZzHmWxf3+nGSy0pF8orFSssxd9My9gZCo68zO5pBQxUfERGRHNMSiuJ22bO6RLQtHqE52sKRnuEZf67O46rH3n1vvx4r2eamsz2SF+KtWIw4hr2YaNnniJZ9zuyIBhUlPiIiIjmmJRyjyJXdX9GBcHv1pTwLO3ySLW59Oc+TtHJFDVs37aW5qU1tbpIfElFK1l+ENVJHcOpzGA7zFwUPNkp8REREckxrOEZ51ie6JQcb+DL2HMlzPbFAoNfLSZOSCU+yva24xKlqj+Q+I07xhu/grPsbkfIzMGxFZkc0KCnxEbFYsFr7304Sjxs930hEpAfRWJxYPIE7yxWf+o6JbqUZe46OpKesrM/Vns5VnrHjK1XpkdxnGHg2zcP1yZ+Iln6G4PFPgjW7g0uknRIfGfScFRX85Y/vEKjv+/LBsnI3Z3/lWCU/ItJvreEYAO4sDzbwJ1vdMlTxSce5nuQgg+ISJ9+86rNpjlAkM4q23E7hjkeIeY4jeMIfQdUe0yjxEQEC9a3461rMDkNEhNa2fYlPlis+/gye8envnp4kDTKQfGNr3kTh9p8Tc4+jYepzGI7MVVSlZ0p8REREcohZFZ/6cACbxUZJQXHaHzs5zKDszLP6dK4HulZ71N4m+SLuGU/j8YuJeSZgOJWwm02Jj4iISA4xs+JTluYdPukYZgDtSc+61R8DqvZIfnAEVhL1TgOrk0jFmWaHI/togamIiEgOCe1LfFwF2Ut8IvEojZEmyl3pbXNLxzAD2N/idvxJI1XtkZznqPs73jX/Qck7l5sdihxAFR8REZEcEosnAHDYs/feZKBjlHX6Ep90LSlVi5vkE3tgFd513wSLjdDI75gdjhxAiY+IiEgOie2bDmm39X/Mfqr84QaAtFV80jXMQC1ukk9sTevxvv1VMKI0Hv8UUd8pZockB1DiIyIikkOisfaKj92WvYpPfdgPpK/ik45hBqAWN8kftpbNlK79EpZYI03HPkyk4myzQ5JD0BkfERGRHNLR6pbVxCd9rW6dW9z6k/SoxU3yiSOwEmuklubx99I27KtmhyOHoYqPiIhIDkkmPrYsJj7p3OGTrPb0p8UNtLNH8kv4yIuJeqcTL55kdijSDVV8REREckjyjI8jq2d8AlgtVrwFJf16HFV7ZDCxRIMUfngfGHEAJT15QBUfERGRHJKs+NizONWtPhSgzOnFZrX163HSUe3RQAPJC/EQJW/PpaDhVQxHOeEjLzY7IkmBKj4iIiI5pCPxyVKrWzQRozHSlLbBBv2t9miggeS8RJSS9RdR0PAq4aFfIjzim2ZHJClS4iMiIpJDjPZONyxZ6nQLhBswMCh3+bLzhN34+583qsVNcpsRp3jDd3DWLSdSfgZNxz4Mlv5VSiV7lPiIiIgMYv6OiW6l/Xqc5Pme/ti4bhegFjfJUYaBZ9M8XJ/8iWjpZwge/yRYC8yOSnpBiY+IiMgg1pH4FPav4tPf8z0rV9QQDIRU7ZGcZti9xDzHETzhj2ArMjsc6SUlPiIiIoNYcodPeT/O+KRjmpvGV0vOs1ho+fRtBE76G4ajfxVSMYcSHxERkUGsPtT/5aXpqPY0NbbhLStUtUdyjmvHItxb7th/AE+VnrylxEdERGQQ84cDWLBQ5vT263HSUe2ZePzwfsUgkm4Fe57D8973KdzxKNbIHrPDkX5S4iMiIjKI+cMBStOww6evOi8rnfkfE02JQeRQHHV/p+SdyzFsHoJTl5JwHmF2SNJPSnxEREQGqXgiTkNbMG07fPpCZ3skF9kbXsO77ptgsdF4whJiJVPMDknSQImPiIhIDkkk2s8RWMj8Ip9AW7B9h09h/wcb9EXnao/O9kiusLbW4H1rDhhRGicvIuo7xeyQJE3sZgcgIiIi+4UiMQAKXZn/Fe0P+4H+TXTrz2ADVXskFyVco2gbOpto2alEKs42OxxJIyU+IiIiOaQ13J74uJ2Z/xXd34lu6RhjrWqP5IxEG1idYLXTPPEBs6ORDFCrm4iISA4Jte2r+DgzP2ygY3lpHxOf/o6xFskVlkgdZa+dguuj35gdimSQEh8REZEcEmhqw1PowGbN/K/o/ctLfX1+jL5We5Lne0TMZok14l17AfaW97GFd5gdjmSQEh8REZEckUgY1AXDVJYVZuX5Onb4uHq/w6c/Qw1A53skR8RDlLz1NRxNbxMacTEtn77D7Igkg5T4iIiI5IhAUxvxhEFFafYSH6+zBLu19+eJ0tHmpvM9YqpElJL1F1HQ8CrhoV+iecLPwZL5aYpiHiU+IiIiOaK2IQRARakr488VT8QJ9HOHj9rcJJ8Vbn8QZ91yIuVn0HTsw2AxZ4mvZI+muomIiOSIjsTHm/mKT0NbIwkjgc9VmvHn6mzlihrWrf4YUJubmCt01HexxJtpHXMdWAvMDkeyQBUfERGRHFEbTFZ8Mp/4+NMw2KC3Oic9x580Um1uYgpby+Z9H7ho/dQtYCsyNyDJGiU+IiIiOaIuGAZgiDfzrW77E5++t7r1hpIeyQWF2+6nbNXJFOz9i9mhiAmU+IiIiOSItkgcAFc2lpeG/UDfdvj0ZaJbcoqbkh4xi2vHIjybbyZRUEms+DizwxETKPERERHJEbG4AYDdlvnJUv5wAwC+wt4nPr2d6JYcZqApbmKWgj3P43nvByQc5QSnvkCi8CizQxITaLiBiIhIjojFEwDYbdlbXupz9m24QSoT3VauqGHrpr0dE9w0zEDM4Kh7iZJ3LsOwFRGcupS45xizQxKTKPERERHJEbF4Agtgs2ah4hPyU1JQjMPmSPk+tc8spunNN4gFAtjLeq4Ubd20l+am9krP2PGVqvZI9hkG7g9/ChYbjScsIVYyxeyIxERKfERERHJELJ7AbrdiyfASxYSRINAW5KjiEb26X+ekp7s2t2Slp7mpDU+xk29e9dn+hizSNxYLjScswd78LtGyGWZHIyZT4iMiIpIjojEjK21uwbZG4ka8T4MN7GVljL373m5v0znpUXubmMHWsgVLtJ5Y6ckYDq+SHgGU+IiIiOSMeCKRlcEGHed7epH4JCe52cvLU7q9Kj1iFmt4J96152ON1OOfsYaEq3eVTRm4NNVNREQkR0RjiaxUfDp2+PRioltvJ7mJmMESqce79nxs4Y9pHXO9kh7pQomPiIhIjognDBxZTHx8Ll+v7pfqJLfkFDeRbLLEGvGuvQB7ywe0jrqG1jHzzA5JcowSHxERkRwRiyewZaPVLbSv4uNKbZR1bxaWJheV6myPZFU8RMnbc3E0vUVo+EW0fPpOyPCQEMk/SnxERERyRCye3Va3VM/49LbNTYtKJdus0QDW8C7aKmfTPPEXSnrkkDTcQEREJEfE4kZWhhv4wwGKHR4KbAU93rbzUIOe2txEzJJwDafhpJcw7MVgsZkdjuQoVXxERERyRDYqPgkjgT8cyFi1RyRrDAP3ljuxNW9sv1gwBKxOk4OSXKbER0REJAfEEwaGQcYTn6ZIMzEjjq8XE91SrfZosIFkk7vmDoo+vAfPpv9rdiiSJ5T4iIiI5IBYPAGQ8eEGyR0+5X1YXtoTDTaQbCnc9gBFH/6UWOFYGo971OxwJE8o8RERkbwWCASYP38+s2bNoq6ujocffphdu3aZHVavxWLtiY/dmtlfzf6QH0htsEFvprklabCBZJpr5+N4Nt9E3Dmc4LRlGE4l2pIaJT4iIpLXqqur2bx5Mzt37iQej7N582aqq6vNDqvXosnEJ8MVH3+4AUit4qPzPZJrCvYsw7Px+yQcPoJTXyBReJTZIUkeUeIjIiJ57Y033uD+++/H5XIBcN1117Fu3TqTo+q9hGEAYLVmutUt9YoPpL609MlfraK5Sed7JLPiRZ8i7h5HcMpS4p5jzA5H8ozGWYuISF4bMmQIzz77LIlEgvXr1/PKK68wdOhQs8PKWfUp7vDpPMa6J1s37aW5qQ1PsVPneyQzDAMsFuKeiQQ+t1ojq6VPlPiIiEhemzdvHvPmzSMcDnPNNdfgdDq59957zQ4rZ/nDDRQ53Ljs3Y/9TbXNLTnJrbjEyTev+mza4hRJsjW9S/HGa2icvKi9tU1Jj/RRRhOfhQsXsm7dOiwWC9XV1UyePLnjut27d3PdddcRjUaZOHEit99+eyZDERGRAaqqqorly5ezdu1anE4nxx13HEVFRWaHlZMMw8Af9jOsqPuKWG+WlmqSm2SSrWULpWtnY4nUYm9cQ0RneqQfMnbGZ/Xq1Wzfvp0lS5awYMECFixY0OX6u+66i0svvZQ//elP2Gy2vJzAIyIi5pswYQJer5ezzz6b008/nVgsxumnn252WDmpKdpMNBHrts2t9pnFBJb/FUh9qIEmuUkmWMM78a6djTWyl+bxPyUy9EtmhyR5LmMVn1WrVlFVVQXAuHHjCAaDNDc34/F4SCQSrFmzhvvuuw+A+fPnZyoMEREZoBYtWsTjjz+OYRicc845WCztQwECgQDWDI+Ezlf+FM73JFvcys48K6WlpSKZYInU4107G1v4I1rG3UJ45BVmhyQDQMYSn7q6OiZNmtRx2efzUVtbi8fjwe/3U1RUxE9+8hM2bNjA9OnTuf7667t9vLIyN3a7ejrTxefzmB3CgKPXNP30mqbfQHpNzzvvPNxuN7fccgvTpk3Dbm//lWaz2TreeOvJYGvJrg8ll5f6Dnl9b1rcVq6o6TLUQCRtDIOS9f+JveV9WkddQ+uYeWZHJANE1oYbGPvGdCY/3rNnDxdddBEjRozg29/+Ni+//DJf/OIXD3v/QKC1z8+d6S3Y+cjvbyYeN3q+4WHoNT2YXtP002uafv19TSsqitMYTf+UlZUxZ84c7HY7559/fkeVJxQK8bOf/azb3ynQtSW7pqaG6upqlixZ0nF9siV75syZ/PjHP2bXrl0MHz48k19Sxu2v+JQedF1vW9w0yU0yxmKh5VPzie15jpZP3wkW/SyX9MhY4lNZWUldXV3H5b1791JRUQG0/7IaPnw4Rx3VfkDts5/9LJs3b+7xl5SIiMiBJk6cyFVXXcXevXsxDIPm5mbq6up6XGI6GFuyk4lPeeHBFZ/etLhpkptkRCIK0SYAYqUnEys92eSAZKDJWBP0jBkzWL58OQBqnQFOAAAgAElEQVQbNmygsrISj6e9xcJutzNy5Ei2bdvWcf2YMWMyFYqIiAxg1dXVfPLJJ2zevBm3200wGOSee+7p8X51dXWUle0/65JsyQa6tGR//etfHzDjseu7qfhAastKQZPcJAOMBMUbr4KXvoglUm92NDJAZaziM3XqVCZNmsTcuXOxWCzMnz+fpUuXUlxczMyZM6muruaGG27AMAyOPvpoTeAREZE+qampYcWKFcyaNYt7772XPXv28LOf/YyZM2f26nH625Ld37Oo/sYwAC6nI2MthY3RIEWOQo4a1jVh+fCxRcTq63FWVqT03FabFW9ZIed/7YS0x5hL7ZRmGXSvgWHAm9fA7iUw5LMMGVoB9sE9kn7QfQ8cIFNff0bP+Myb1/Uw2vjx4zs+HjVqFH/4wx8y+fQiIjIIDB8+nBdeeAGv18vvfvc7SkpKWLduXY/3S3dLdn/OogJYC9p/JYfbotTWNvXrsQ7FMAz2tNRTWTiky+N3PtvjnjKtx+deuaKGYCBEcYkz7XFWVBRn5GvPJ4PxNXBvuYOiD39JzHMs9i/+hdpAAhhcr0Fng/F7oLP+fv3dJU2a9ykiInnt+uuvp6GhgQsvvJAnn3ySX/3qV5xxxhk93i/XWrKTQydsGRrF3RJtJRKPUH7AKOvejq9Wm5ukU+H2Byn68P8RKxxLw9TnoODwo9ZF+itrU91EREQy4YwzzuhIdE477TRaWloYO3Zsj/fLtZbsSCwOgMOemcSnux0+qZ7t6TzUQAtLpb+soe0UbZ5P3Dmc4LQXMJxDzQ5JBjglPiIikpeCwSD33nsv77//PscccwzXXXcdI0eOZO3atcyePZsXX3yxx8fIpZbsaCwBgMOWmcSnY7BBYd/fUVe1R9IpUTiKxuOfJl44ikThKLPDkUFAiY+IiOSl22+/nRUrVnD00Ufz4osvsnv3bo466iiefvrplCo+uaYj8clQxac+7Ac4qNWtt1Ttkf6yB9cS80wEm4tIxZlmhyODiBIfERHJS6+++iqPPfYYJ5xwArt27aKqqoo333yTH/7wh3zrW98yO7xeSyY+dntmljX6ww3AoVvdRLLF3vA6pWvOJ+I7hcYTntFyUskqJT4iIpKXgsEgxxxzDNA+2a2kpITf//73XdrV8kl03xkfe4Za3fxpqviI9JWt6V28b80Bo43wkZcp6ZGs01Q3ERHJW5ZOfzhZLBZ8Pp+J0fRPJNOtbqEALpuLQnthx+dqn1lMrF7LIiXzbC1bKF07G2usgaZJvyFScbbZIckgpIqPiIjkJcMw+MpXvtKR/DQ2NnLJJZdgs9mwWCwsW7bM5Ah7p6PVLQMVH8Mw8Icb8LlKuySLyVHWxdNP7PExOk90E+kNa3gn3rWzsUb20jT+p7QN+5rZIckgpcRHRETy0okndv1jferUqSZFkh7RaObGWYdiIcLxMOWdJrolqz2pjrLWRDfpK0fg39jCH9Ey7mbCI79tdjgyiCnxERGRvPTEE0+YHUJaZXKcdccoa1d7K2DtM4sJLP8r0Ptqjya6SW+1DZtLwH00sZIpZocig5zO+IiIiOSATJ7x2b+8tBTY3+JWduZZqvZIZsRDFG57AIz2SmbMO1XDDMR0qviIiIjkgEye8UlWfMpd+4c/pNrilqRqj6QsEaVk/SU46/4HLFZCo642OyIRQBUfEREZIOrr60kkEmaH0WfJcdYZqfiEkomPRllLhhkJijdehbPuf4iUn05o5OVmRyTSQYmPiIjktQ0bNnDOOedw6qmnUltby49+9CNWrVpldli9lsmKz/5Wt94nPsnzPSI9Mgw87/8I1+4lRL0nETz+KbBqCqDkDiU+IiKS12655RZOPfVU3G43AKNGjWLhwoUmR9V7mR5uUGAroMjh7vV9db5HUuWuuZPCjx8m5jmW4JRnwFZkdkgiXSjxERGRvLZt2za+853vYLe3H1v98pe/zI4dO0yOqvfaMjjOuj4coNxV1mWHTyo0zU1SZhgAxAvH0DD1OQyH2iol92i4gYiI5LUJEyZw00030dbWxqJFi3j99deZMGGC2WH1WrgtBoDLaUvr44ZiIUKxEGO9o3p9X1V7JGUWC62fuoXQ6B9g2EvMjkbkkFTxERGRvHb77bezY8cOQqEQjz76KIlEgttvv93ssHotlEx8HOlNfPzhBmD/YIPk4tJUqdoj3SnY8wJFm3/cUfFR0iO5TBUfERHJa3v27GHZsmUEAgEKCgrweDxmh9Qn4Uh7q1tBmhOf+pAf2D/YILnDp6fFpZ3b3EQOxVH/v5S8cymG1UnoyItJFI42OySRbqniIyIiee3SSy/lC1/4Ag8//HBenu1JCkfaKz7ODFV8fK6yjmpPKjt81OYm3bE3vI737W+AxUrjCUuU9EheSCnx2bFjB5dffjlTpkxhz5493HXXXWzatCnTsYmIiPTo17/+NaeeeirLli1j9uzZnHvuufz2t781O6xeC7fFKHBYsVrTu92+Ptxe8SkvLOtTtUdtbnIgW9O7eN+aA0YbjZMfJ+o71eyQRFKSUuJz4403UlRU1DENJhaLccstt2Q0MBERkVScdtppLFiwgFdeeYVf/epXhMNhfvazn5kdVq+F2uJpP98D+3f4lLt8AKr2SL9Yw7soXTsba6yBpkm/JlJxttkhiaQspTM+GzZs4KWXXuKcc84B4IorruCss87KaGAiIiKpqKmp4eWXX+bll1/mrbfewmazMXPmTLPD6rW2SCzt53ugPfFxWB14HEXU9uJ+qvbIoSScQ2mrmEWseBJtw7pPoEVyTUqJz6hRo3jggQeIRqP87W9/49VXX2X06NEZDk1ERKRns2bNwm6387nPfY4777yTqqqqvBxwEIrEKfMUpP1x68MBfH3Y4SPSRSIKVgdYbDRP+Dno+0nyUEqJz0033cS1115LS0sLCxYsYMiQIfz85z/PdGwiIiI9uu222zjzzDMpK8vvhYnhthhOX2F6HzPWRku0lVHFI1O+j6a5yYEssUa8a86j7Yg5hEZdraRH8lZKiU9ZWRnLly+npqYGp9PJmDFjKChI/7tSIiIiqbrzzju54YYb2LJlC1u2bDno+ptvvtmEqPomFk8QTxgZ2OHTfr7H5ypN+T463yNdxEOUvP11HI1riXkmtu/rUeIjeSqlxGfOnDm89NJLTJ48OdPxiIiIpOTJJ5/k+uuv58knnzzoOovFkleJT6Z2+HQebNB5lPXhaJqbdJGIUrL+EgoC/6at8jyaJ9yvpEfyWsqJzw033MA555yD2+3u+Pz/+T//J2OBiYiIdCe5VuFQ6xVaWlqyHU6/tO1LfFwFaV5emqz4FJbR9OZ/A92Psla1RzoYCYo3XoWz7n+I+E6j8bhHwKq995LfUvoOXrRoEQD/+te/Oj5nsVh47733MhOViIhIiiZMmMBbb72Fy+UC4JNPPmH27Nm89tprJkeWunC0PfFxFqT3D8tkxcf799UpLy5VtUcAXDsewbV7CVHviQSPfwqsOvMl+S+ln7CPP/54puMQERHplUWLFvH4449jGAbnnHNOx9SyQCCA1ZrSmrqcEY3ta3WzpzfuZMXHsn4TBt1XezTUQDoLj7gIW+gjWsdcB/b8m5IocigpJT4nnXQSr7zyCq+++ioWi4XTTz+d6dOnZzo2ERGRwzrvvPNwu93ccsstTJs2Dbu9/VeazWbjjDPOMDm63jGM9n+n+/iEPxTAbrVjtcSx9lDtUZubAFhD20gUjgark5aj7zA7HJG0SinxefTRR7nnnns63kF77LHHuP3225kzZ05GgxMRETmcsrIy5syZg91u5/zzz8+7Kk82+MMBfM5SYE9Kt1eb2+Dm2vkEnvd+QNOxv6PtiAvMDkck7VJKfJ5++mluvvlmLrzwQgzDYNGiRfzud79T4iMiIqa58soruf/++1m+fDnLly/vcp3FYuHXv/61SZHlhkg8QlO0mRGeYaSa+MjgVbDnBTwbr8FweNvHVosMQCklPn6/n5kzZ3a8m3buuefyy1/+MqOBiYiIdOeDDz7AMAw++OADs0PJScnBBse/vrvHMdYyuDnqV1DyzmUYNjfBKUuJe8abHZJIRqSU+EycOJF58+ZxwQXtZc/nnnuOiRP1boCIiJhnxYoVXf4tXdWHGwAo39xe7elusIEMXvaG1/G+fSFYLDSesJiYd5rZIYlkTEoN0bfeeiu7du3ixhtv5MYbb2Tnzp15tRhOREQGrvfff58nnniCSCTCd7/7Xc4880xef/11s8MynT/s55S3mnEEW3ocY52c6CaDT9HWn4DRRuNxvyfq+7zZ4YhkVEoVn6OPPprly5ezdetWAMaMGYPD4choYCIiIqm45ZZbOPnkk1m2bBlr167l9NNP5yc/+QnPP/+82aGZqj4U4NMfhYGeqz2a6DZ4NU5+AnvDa0SHzDQ7FJGMS6nis379es4991yOPvpohg0bRlVVFevXr890bCIiIj16//33ufzyy1m1ahVnnXUWN954Ix9++KHZYZmu5O+vU9KSwOor63FpKWii22BiDe/C3tC+4NewFyvpkUEjpcTntttuY8yYMQC4XC7Gjx/P7bffntHAREREUuHz+XjuuedYuXIlJ554Itu2baOwsNDssExV+8xijlyzHYCS6SebHI3kEkukHu/a8yldcx62li1mhyOSVSklPh9++CF33NG+xMrhcLBgwQJqamoyGpiIiEgqLrnkEu666y58Ph9VVVX84he/4Pzzzzc7LFM1vfkGABuP81H51e6rPTrfM3hYYk1437oAe8v7hI68lLhbFT4ZXFI643PMMcdw8803c9ZZZwHw4osvdlSAREREzHTxxRfz5S9/GY/HA8D8+fMZOXKkyVGZr7HIyvbP9zyWWOd7Bol4mJK3v46j8S3Cw79By9ELwWIxOyqRrEop8bn11lu5+uqr+ec//wlARUUF999/f0YDExERSUUoFOK3v/0tr776KhaLhdNOO40rrriCgoICs0MzTdxIAFDuKuv2dslqj873DHCJKCXvXEJB4F+0VZ5H04QHwJJS04/IgNLtd31DQwP33HMPEydOZPny5VRVVVFcXMyYMWMYNmxYtmIUERE5rIULF/L73/8et9uNy+XiN7/5Dffee6/ZYZkqkULis3JFDetWfwyo2jPQWaIBbC2bifhOo/G4R8Ca0vveIgNOt4nP/PnzefLJJ2lra+PZZ5/lpZdeYtSoUezZs4d77rknWzGKiIgc1j/+8Q8ef/xxnnjiCZ588kkeeeQRXnzxRbPDMk3tM4uxBIIA+LpJfJItbsefNFLVngHOcFbScOJygsc/BVan2eGImKbblH/16tU89NBDOJ1OXnrpJcrLy3n66af56KOPuPjii7MVo4iIyGHFYjGKioo6Lvt8PqLRqIkRmSs52GDzUS6m99Dqpha3ga1w2/1Eyk8nXnwsRsEQs8MRMV23iU9LSwsTJ04kFovx9ttvU1VVhcPhwOfz0djYmK0YRUREDmvGjBlccsklfP7z7Vvn//Wvf3HKKaeYHJW5wiUuXpni4czC7hMfGbgKtz+IZ/PNRD+ZQsPJL2uQgQg9JD4jR45k8eLFtLW10draykknnQTASy+9xNChQ7MSoIiISHduvfVW7rjjDlasWIHFYuHUU0/lpptuMjssU9Q+s5hYfT2J4gKsFiveghKzQxITuHY+geeDauIFR9B4/CIlPSL7dJv4XHHFFdx4440YhsGYMWOYNWsW77//Prfccgvf+973shWjiIjIIUUiEfx+PwsXLhzUU9ySkm1uW0e5KXN6sVltJkck2Vaw5wU8G68h4SgjOO0FEoWjzQ5JJGd0m/jMnj2bKVOmsHPnTqZMmYLL5WLkyJHccMMNXHLJJVkKUURE5GCvvfYaP/jBD2hsbKSkpISf/vSnnHrqqWaHZTp7eTkrJtv5VA/ne2TgcdT/g5J3LsOwuQlOeZa4Z4LZIYnklB7nGY4aNYpRo0Z1XHa73Up6RETEdPfccw/HHnssM2fOZOXKldx0003861//Mjss0yTb3Ky+MgyMbie6ycCUcA4j4RpB08T7iXmnmx2OSM7RIHcREclLW7ZsYcWKFQwZMoQ5c+Ywbdo0AoEAZWWD8w/+ZJtb/NhjgK097vBJLi6VAcAwwGIh7hmP/3NvgtVhdkQiOUlre0VEJC9FIhE8Hg8ANpsNl8tFJBIxOSpzJKs99vJy6qumAuAr9B329skdPlpcmv+srTWUvnkm1tC2fZ9Q0iNyOKr4iIhI3rrrrruw29t/lYXDYX7xi1/gdrsBuPnmm80MLauS1Z7i6SeyMRwAoNxV2u19tMMn/1nDuyhdMxtbeDsF9S8TPvISs0MSyWlKfEREJG8tXry4y+WlS5cCYLFYBlXiA+1DDSrmzKV+Y/tr4nMduuKjNreBwRKpx7u2PelpGVutpEckBUp8REQkL23atMnsEHJSfSiABQtlTu8hr1ebW/6zxJrwvvVl7C2baD3qu7SO/S+zQxLJCzrjIyIikseS53uS/OEApT3s8FGbW34rfudyHI1rCQ//Bi1H/0QLSkVSpIqPiIhIHut8vieeiNPQFmSsd7S5QUlGtY65nkRBOc0T7geL3sMWSZUSHxERkTzVeZpbxZy51IX8GBiUFx56lLXO9+QxI4El3oJhLyZWehLNpSeZHZFI3tHbBCIiktcCgQDz589n1qxZ1NXV8fDDD7Nr1y6zw8qKztUeaG9zAw65vHTlihrWrf4Y0PmevGMYFL3/X5S+cRaWtr1mRyOSt5T4iIhIXquurmbz5s3s3LmTeDzO5s2bqa6uNjusrElWewDqO0ZZH5z4JIcaHH/SSJ3vyTPurQtxf/wQEAermnVE+kqJj4iI5LU33niD+++/H5fLBcB1113HunXrTI4q8w4cagDgD/mBQ1d8QEMN8lHh9l9StPVu4oWjCU59HsNx+MW0ItI9vW0gIiJ5bciQITz77LMkEgnWr1/PK6+8wtChQ80OK+MObHMD8IcbgK6Jz8oVNWzdtJfmpjY8xTrbk0+cO5/E88GNxAuOoGHaMhLOI8wOSSSvKfEREZG8Nm/ePObNm0c4HOaaa67B6XRy7733mh1WVnRucwOoD/vbd/i4Sjs+1znp0dme/GFp20PxputIOMoITnuBROFos0MSyXtKfEREJK9VVVWxfPly1q5di9PpZPLkyQwZMiSl+y5cuJB169ZhsViorq5m8uTJB93m3nvv5e233+aJJ55Id+h91nmaW2f+cACvswTHAedAPMVOvnnVZ7MZovST4RxK8PinMBylxD0TzA5HZEBQ4iMiInntscce63J5+/btWCwWLrnkkm7vt3r1arZv386SJUuoqamhurqaJUuWdLnNli1beOONN3A4HOkOu18O1eYWT8QJtAUZXTLSrLAkHRrehXgl2AqJDplpdjQiA4oSHxERyWt33333QZ9LJfFZtWoVVVVVAIwbN45gMEhzczMej6fjNnfddRc//OEPefDBB9Maczoc2OYWjDSSMBKHHWwguc/WtAHWnI23+ASCU5/XclKRNFPiIyIiee1///d/Oz4Oh8P8+c9/pqKiosf71dXVMWnSpI7LPp+P2trajsRn6dKlnHTSSYwYMSL9QWdAfSg5ylpTv/KRtbUG79rZEG0gPGyukh6RDMho4pOvvdMiIpI/DkxM/vM//5NZs2bxjW98o1ePYxhGx8cNDQ0sXbqUxx57jD179qR0/7IyN3a7rVfP2fF84RgAhYUFVFQU93j77bb2P4o73/a9ljAAoyqO6PJ56yFum8vyJc60at0JK78EkT0w7ReUHPMdsyMy1aD8HjjAYH8NMvX1ZyzxyefeaRERyR9XXnllx8eGYfDee+917PTpTmVlJXV1dR2X9+7d21Epeu211/D7/XzjG98gEonw0UcfsXDhwm4XowYCrX3+GpL3DYUi1NY2dXvb2mcW07a3Fnt5eZfbbtu7C4CCqLvL5xPxRPv9enjcXFBRUZwXcaaTJVJP6ZtnY2/ZRsvYGyk65vuD7jXobDB+DxxosL8G/f36u0uaMlZHPVzvdGfJ3mkREZG++uCDDzr+2bJlCyNGjGDBggU93m/GjBksX74cgA0bNlBZWdnR5nbWWWfx4osv8sc//pEHH3yQSZMmdZv0ZEvtM4sJLP8r0HWwAUB9uL3VzVeoMz75xBH4F7aW92k96ru0jr3B7HBEBrSMVXwGWu+0iIjkph//+MecfPLJFBQU9Op+U6dOZdKkScydOxeLxcL8+fNZunQpxcXFzJyZm9O0ktPcys48q8tgA2gfZQ3gc5YedD/JXZGhX6LhxOHEvCeCxWJ2OCIDWtaGG5jZOy0H8/k8Pd9IekWvafrpNU2/gfia/uhHP2LZsmVUVvZ+Oee8efO6XB4/fvxBtznyyCNz6hzqgdPckurDAUoKinHY9rePr1xRQ1NjG8UlzmyGKD1JxHDteITwkZeB1U6s9GSzIxIZFDKW+ORS77TNpndQDuT3NxOPGz3f8DD0mh5Mr2n66TVNv/6+prl44Pbss8/myiuv5IwzzsDtdgOpjbMeSBJGgkC4gaOKu3ZRbN20F4Cx43ufFEqGGAmKN16Fa/dirLEGWsf+l9kRiQwaGUt8ZsyYwQMPPMDcuXMP2Tt91llnAbBjxw5uvPHGnOidFhGR/POHP/wBgI0bN3Z8brAlPo2RJuJG/JA7fIpLnHzu9HEmRCUHMQyK3v8vXLsXEy2ZRuioq8yOSGRQyVjik4+90yIikj9uvPFGbr/99i57fAar5A6fzomP2txyj3vrQtwfP0SsaALBqc9i2HOvgioykGX0jE8+9k6LiEh+eP7557n11ls1JAeoD/uBromP2txyS+H2X1K09W7ihaMJTn0ew6FFsyLZlrXhBiIiIulkGAa7d+8+7M6e4cOHZzki8/jDDQCUHzDKWm1uucMSayJecAQNU18g4Rpmdjgig5ISHxERyVuzZs067HXvvfdeFiPJvNpnFhOrr8deXn7Qdf59FZ/yQ5zxkdzQOu4GQkd9W5UeERMp8RERkbx17bXX4nA4er7hAJDc4XPg4lLYf8anTIlPTnHU/4MC/z9p+dR8sFiU9IiYTImPiIjkJYvFwkUXXURhYaHZoWTN4Xb4+NsCeBxFOG29W+IqmWNvWI133YWQiBIe9jXinglmhyQy6FnNDkBERKQvhg0bhtWqX2MJI4E/3EC5S9WEXGFr2oD3ra9AIkzj5N8r6RHJEfqNISIieWnFihU4nRrV3BRpJpaI4XOVdnwuOcpass/auhXv2tlYYw00TXyQSOW5ZockIvso8REREclj/vC+HT6FGmVtNkukntK1s7FF9tB8zF20Df+G2SGJSCc64yMiIpLH6vclPge2ummUdfYZjjIi5WeQKKggdNRVZocjIgdQ4iMiIpLjuh1lvW+iW+dWN8kyIw4WG1isNI+/z+xoROQw1OomIiKS47odZd126IqPZEk8jHftbAq33d9+2WJp/0dEco4SHxERkTxw2FHWqviYJxGj5J1vUeD/J46G18BImB2RiHRDiY+IiEgeqw8HKLK7cdldgCa6ZY2RoHjj1Thr/0LE9wUaj3sULPqzSiSX6f9QERGRHJY833MohmHgDwc00S3bDIOi92/AtfsPREum0Xj802BzmR2ViPRAiY+IiEgO6+58T3O0hWgiSrmrrMvnNdEts5yfLMH98W+IFU0gOPVZDHux2SGJSAo01U1ERCRHdZ7mdqjzPfVhPwC+AxIfyay2oRfQ2riO0KjvYzg0VEIkX6jiIyIikqO6q/YA+MMNgBKfbLGGd+37oICWY35CwjXM3IBEpFeU+IiIiOSww1V7AOpD7RWfA1vdJP0K9v4Z3yuTce5eYnYoItJHSnxERETylD+cHGXdnvhooltmOOr/Qcn6b4HVQdw91uxwRKSPlPiIiIjkqQMTH010Sz97w2q86y4EIHj8H4h5D912KCK5T8MNRERE8lR9OEChvRC3o7Djc5rolj62pg143/oKJMI0Tn6caPkXzQ5JRPpBFR8REZE8ZBgG9eEAPlep2aEMWEU1C7DGGmia+Esilf9hdjgi0k+q+IiIiOShllgrkXiEcpfGKWdK47G/paB+BZGh55kdioikgSo+IiIiOSi5w+dw/KH28z3lGmyQVpZIPfaG19sv2D1KekQGECU+IiIiOainHT71HYMN2lvdNNig/yyxJrxvfYXSNf+Brelds8MRkTRT4iMiIpKjutvh0zHRrXB/q5sGG/RDPEzJugtxNK6hbegFxD0TzY5IRNJMiY+IiEgeSlZ8tLw0DRIxSt65lAL/P2mrOJemiQ+CRX8iiQw0+r9aRETEZAnDAMBisaR8H3/YD+zf4SN9ZCQo3vg9nLX/TcT3BRqPexSsmv0kMhAp8RERETFbe95D6mkP+MMNuGxO3PbCnm8sh2WJNWBvXEO0ZBqNxz8NNpfZIYlIhugtDREREZMZyQ9SzHwMw6A+FMDnKsNisXRMdCsucWYqxAHLcPhomP7X9o/txSZHIyKZpIqPiIiI2fZlPtYUW91CsRDheLijzU0T3XrPteMx7I3rADAKyjEKyk2OSEQyTYmPiIiIyZJnfFJVH24AoLxw//keTXRLnXPXUxS/9wOK370cjLjZ4YhIlijxERERyRGpzjbQYIO+K9j73xRv/B4JeymNkxeBxWZ2SCKSJUp8RERETGYkp7qleMhn//LSso7zPdIzR/3LlKy/BKwuglOf1a4ekUFGiY+IiIjJkp1uqVd89u/w0fme1NiDb+Jd93UAgsf/gZj3RJMjEpFsU+IjIiJist6d8AF/KJn4+ACd70mFYfeScAyhcfJjRMu/aHY4ImICjbMWERExWy8XmNaHAxRYHRQ53JmMakCJF30a/4w3waqR3yKDlSo+IiIiJktWfHrT6uYr9LHqH1t1vqcb1rZP8K45D2trzb5PKOkRGcyU+IiIiJis44zPvsu1zywmViJYXakAACAASURBVF9/yNuGYiFaYyGd7+mBJerHu3Y2Bf6Xce79b7PDEZEcoMRHRETEZAZdpxs0vfkGAMXTDz6A79+3wyc5ylrnew4h1oz3ra9gb95I68grCY36vtkRiUgOUOIjIiJitgMqPgD28nIq5sw96KadJ7rJISTa8K67EEfwTcLDLqTlmLtS7yEUkQFNiY+IiIjJenPGpz60f4ePHKx44/cp8L9MW8W5NE18ECz6U0dE2mmqm4iIiMmMXkx1qw/7gWTi05rJsPJSaOS3IRGmadJDYNWfOSKyn94GERERMdmBww26kzzjU16oik8Hw4B4CwAx7zSaJi8Cm8vkoEQk1yjxERERMdn+VreeUx9/2I/DaufdV/ZolPU+7q13U7a6CmvbJ2aHIiI5TImPiIiIyZKtbqmoDwfwucrYuqkW0Cjrwo9+TdHWhVjiLftLZyIih6DER0RExGxdp1kfVjjWRku0VaOs93HuehrP+/9FvGAoDVOfJ+EaZnZIIpLDlPiIiIiYrKPVrYfbaZT1fgV7/0LxxqtJ2EsJTnuBhHus2SGJSI5T4iMiImKyjuEGPZR8konPYB9lbYkGKN7wHbC6CE59lrhnotkhiUge0JxHERERkxkpjnXrXPGpJZzhqHKX4SijcfLjgIWY90SzwxGRPKHER0REJEf01OpWn6z4FPqAXRmPJ9dYW2tIOIeBzU20/HSzwxGRPKNWNxEREZOlOs66I/FxlWY4otxjDW2j9M1z8K69ABIxs8MRkTykxEdERMRkyVa3nqa6+cMB7BYbG16tHVQ7fKxtn1C65jxsbbuJDD0PrGpYEZHeU+IjIiJishSP+OAPte/w+XAQ7fCxRP14187GFtpGy9gbCB11ldkhiUieUuIjIiJiMiOFRT6ReISmaPPg2uETa8b71lewN2+kdeR3aB17o9kRiUgeU+IjIiJitk4Vn9pnFhOrrz/oJv5wAwDumhGDps2tIPBv7ME1hId9nZZj7u65F1BEpBtqkhURETHZ/uEG0PTmGwAUT+86pjk52CCxqxAYHG1ukYqzCU77C9HSz4BF79WKSP/op4iIiIjJ9g83aK9o2MvLqZgzt8tt/GE/AFaLdWC3uRkJXDsfh0QUgKjvFA0zEJG00E8SEREZtBYuXMi6deuwWCxUV1czefLkjutee+017rvvPqxWK2PGjGHBggVYrZl5vzA53KA7/nADQz8aT6TFwFmSkTDMZxgUfVCN+6NfYWvdSsunbzM7IhEZQFTxERGRQWn16tVs376dJUuWsGDBAhYsWNDl+ltvvZX777+fxYsX09LSwr///e+Mx9TdEZb6kB+v/whgALe5vXsn7o9+RaxoAq2jrjE7GhEZYJT4iIjIoLRq1SqqqqoAGDduHMFgkObm5o7rly5dyhFHtCcaPp+PQCCQsVgSyVa3bgZa+8MBwIJngLa5uT76DbxzK/HC0QSnPo9RUG52SCIywKjVTUREBqW6ujomTZrUcdnn81FbW4vH4wHo+PfevXt59dVX+cEPftDt45WVubHbbX2KpdjjAqCkxIXN1v6eZEVFcZfbNESCHGG1YrNZD7ou7334BLz/f8F1BLaqlygvHniJXW8MuP++vTTYv37Qa5Cpr1+Jj4iICPsHDHRWX1/PlVdeyfz58ykrK+v2/oFAa5+fu7EpDEBTUxhPPAFAbW1Tx/XReJSCD4ZhCztJFCS6XDcQFNbW4LaXYj3979SGKyE8sL6+3qioKB5w/317Y7B//aDXoL9ff3dJk1rdRERkUKqsrKSurq7j8t69e6moqOi43NzczBVXXMG1117LKaeckpWYrIc55BNoaxjQ53tCY67HP2MNlB5rdigiMoAp8RERkUFpxowZLF++HIANGzZQWVnZ0d4GcNddd3HxxRfz+c9/PuOxJM/4HO6IT3KHj81tDJjzPfbgGoo23wZGe4XLKKjo/g4iIv2kVjcRERmUpk6dyqRJk5g7dy4Wi4X58+ezdOlSiouLOeWUU3j++efZvn07f/rTnwA499xz+drXvpaZYLrPe/CH9iU+A2SJp635PbxvXYAlGqSt8j+IeaeZHZKIDAJKfEREZNCaN29el8vjx4/v+Pjdd9/NWhwdp4sOk/n491V8rAMg8bGGtuFdOxtrNEDjpF8r6RGRrMn/n6AiIiL5bl+r2+HO+NQPkMTH2vYJpWvOw9a2m+ajf0Lb8G+YHZKIDCIZrfjkykZsERGRXJY4eKBcF43rHRRE3Fhd+ft70hJrxLt2NrbQNlrG/hehUVebHZKIDDIZ+wmaixuxRf4/e/cdV3X1P3D8dS+XIUOGgAiC5p65N27ULCtzgSma2xTFvQcZztAy3KlpbkuybaWmv6975kJc5QJkyAaBOz6/P8ibCKKJCOr7+Xj46H7W+bzP4Xa5b875nCOEEEVZsf0/obt7N8d+1Z2sSRfKv8Azuikm1mTaNyfNfQhp5aYUdjhCiFdQgfX4PGpF7Psz5oSEhBhfF/SK2EIIIURRdn8NIdPL5wCwqd/AeExn0GFQDCgW2hdzRjfFACo1qNSkVl4AKPCIIX1CCFGQCqzHJzY2Nttib/dXxL7v4RWxW7ZsWVChCCGEEC8MTYkSOHX3MW7HpycCL+jzPQYdxc/2pdjfi7K2VaqsJEgIIQrBc5vVLb8rYtvbW6LRmBRUeK8cBwfrx58k/hNp02dP2vTZkzZ98Rzb93fW8z2Wj3kQqKhRDFhfHIl59HeotPHcKzMC1KaFHZUQ4hVWYInPs14ROz4+7aljMTGRLvWHxcWloNc//S9RadOcpE2fPWnTZy+/berkZPMMoxFPIvJaKgAOZc0KOZL/QFGwujyFYhEb0RavS1LtLZL0CCEKXYH1NxelFbGFEEKIF9GhvdfQpipkmqXxevOShR3OE7P8az6WN5ehs6pCYp0dKBpJmIUQha/AenyK1IrYQgghxAvor7BoABId7uBg0aiQo3kyZtE/YfXXHPQWZUisuxPFrERhhySEEEABP+NTVFbEFkIIIV5UikUm0R6XsDO3LexQnkimY3vS3Idwz+NDDBauhR2OEEIYPbfJDYQQQgjx3+kVA3bmtmjURftXtiojGsXcGdSmpFb5pLDDEUKIHGROSSGEEKKIUgCDouBgYVfYoeTJNO7/KHHgdczDNxZ2KEII8UiS+AghhBBF0KG910hJygAUHCwcCjucR9IknqD4nz6g6DBYuBV2OEII8UiS+AghhBBF0IMTG5Qolvdad4XFJOUitqe7otKnkVRzLdoSrQs7JCGEeCRJfIQQQogiysxKRZRHWJEc6qa+dx3bU51Ra+NJrraEzJLvFHZIQgiRJ0l8hBBCiCLm0N5rJCdloFcMAJQogkPdrK4GYpIRSUqlOWS49S7scIQQ4rGK9hQxQgghxCvo/jA3dak0ABwsit5Qt+Rqi8ks0ZYM156FHYoQQjwR6fERQgghiiCb4uaklr8NgH1RGeqmS0GTcDTrtYmVJD1CiBeK9PgIIYQQRdTd9HhszYpjWhTW8DFkYHvmfUzjD5HQYBc62/qFHZEoAiIjI+jTx4fKlbMWqddqtZQrV4Fx4yZhYmLy1OUOGOBLYOB8SpXK/yK43bq9jbNzSdTqf//ev2TJqnyX+6A7d+4QFxdLtWo1nmm5T+vKlcssXDgPlQrKl6/IuHGTsx2/d+8es2fPJC4uDguLYkydOpMSJRzZt28PW7ZsxNTUFEdHJ6ZODeDXX39i166fjddeunSR33//HwaDgZUrl/LTT9/x44+7gZzvBzs7ewID5z+/ij9GEfgkFUIIIcTDFCA+I4Gyxd0LOxQw6Ch+bgBmcfvIcHoTnU2two5IFCEeHmWyJRKzZwfw+++7eOONtwoxquyCgj7H0tKywMo/deo49+6lFZnE5/PPF+LvP5aqVasTEDCVw4cP0qRJM+Px778PwdW1NIGBCzhz5jSrV69k4sSpfPZZEBs3fo21tTXz589m//69dOrUmU6dOgNw+vRJ9u7NSnI2blxHyZIuKIqS7d4Pvx+KEkl8hBBCiCLIoBgwKIbCf75HMWB9cSTm0d+Tad+CpJrrQG1auDGJXG3fe5Xj/zwf9qw0qOJMjzYV/tM11arV4PbtWwAEBy8iNPQCmZmZdO7clbff7szs2QE4Ojpx6dJFoqLuMGNGIJUrV+Gzzz4hLOwCrq7u6HRaAKKjo5g7dxZarRa1Ws2kSdNRqVR8/PEM3NxKc+7cWd57ryvXrl0lNPQ8773Xna5dezxRnHv2/M62bZswMTGhcuWqjBo1jjVrVhIREU5kZATBwStZvXoFZ8/+icGgp0uXHrRr9wbHjh3hiy+WYW5ugb29A2PGTGTt2lVoNBpKlnTB07NljnvpdDpmzw4gJiaae/fu0b//YJo1a46f32DGjJlAuXIV2LFjGwkJCUyaNI7PPgsiNPQ8JiYmjB8/mXLl/v0ZrF+/huPHj2Yrf+zYSbz2Wjkgq9ctMjKCqlWrA9CsWXNOnDiWLfG5desW9es3AKBWrTp88skcAIoXL05KSjLW1takpCRja5t9mO26dauZMeNjALp188bS0oo1a1Y8UXsXBZL4CCGEEEWQ4Z8Z3Qo18VEUrC5PpVjERrTF65BUewuYWBRePKLI0+l0/O9/++ncuSsZGRm4uLgyYsQYMjLS6dGjM2+/ndVzkJmZyaJFS9i58xt27foJMzMzzp07y86dIYSGXsPH5z0AVq9eQadO79K2bXv++GM3a9euYsCAIVy5cpm5c4NISkrC17cHX3/9PZmZmUydOuGJEp+0tDRWrVrKl19uxtLSkgkTRnPq1Il/6qBl2bLVnDlzmqioOyxd+gWZmZn079+bFi1asWPHNvz8RlOrVh3279+LwaCnY8dO2NnZ5Zr0ACQnJ9GwYWM6duxEePhtpk+fRLNmzXM999ChQ0RHR7Fq1Tr+/PMUe/b8ni3x6dt3AH37Dnhk3RITE7CxsTFu29s7cPdubLZzypevwOHDB2nVqi2nT5/kzp1IAEaNGk+/fr2wtrahUqVKNGjQyHjNxYsXcHYuSYkSjgBYWlrlev+4uLtMmzaB2NhYunTpTvv2HR8Z6/MmiY8QQghRBBmMU1kXXuKj0qdgFvcHOqsqJNYJQdHYPP4iUWh6tKnwn3tnnoWbN2/g5zcYgGvXrtKrVx9atGgFQFJSIkOH9kej0ZCQEG+8platOgA4OZUkNPQC16//RbVqNVCr1ZQs6YKrqxuQ9TzJ0KF+ANStW59161YD4OZWGltbO0xNzbC3d8DJyZm0tDRSU1NyjXHcuJHGZ3zs7Ozx9e1H6dIexuFvderU4/LlMABjT8m5c2e4cOGcsW6KYiA2NpbWrb345JO5tG//Bl5eHYyJQF5sbIpz8eIFvv8+BJVKTVJS4iPPvXDhAjVrZg0nrV27LrVr131s+Xl5eCgaQKdO73Lt2hU+/HAAtWvXxd7eAYPBwOLFQaxe/RWurm7MmDGZAwf2G5O5H37YSceOnfK8l62tLQMHDqVDhzdJSUlh0KC+1K3bAEfHx7fR8yCJjxBCCFEE6YtAj4+isSGh/s+o9OkoZiUKLQ5RtD34TMe0aRNwdy8DZD0PcurUCZYsyRoG1q7dvz0cD058oCgKigJqtcq4z2Aw/PNKZfzirtXqUKnUOa5/uKzcPPyMz+XLYdnO1em0mJubA2Bqamr8b6dO7+Lr2y9bWW5upWnUqAn/93/7mDhxNIGBC/JqHgB+/30XSUlJLF26mqSkJAYO9M2qnerfOut0OmN9FCXzkWU9bqibnZ09iYn/JlaxsTE4OjplO9/U1NQ44UFaWhoHDuwnISEeRVFwcysNQL16DQgLu2hMfE6fPsno0RPyrKelpRVvvfXOP3HYUaVKVW7evF5kEh+ZzloIIYQoggqzx8f8zjdoEk8BoJg6YLDI/8xa4tUwbJg/K1YEk56eTmJiAs7OJdFoNBw4sB+93oBWq831Og+PMly6lJWM3LkTSWRkBABVq1YzDkH788+TVKlS9ZnE6e5ehtu3b5KWlgrA6dOnqFy5WrZzqlWrwcGDWbOXZWRk8OmnWQnOunWrMTHR8O67XWjbtj3Xr/+FWq1Gr9c/8n4JCQmUKuWKWq1m//69xnawsrIyDkM7d+4MADVr1jTW+fLlMBYuzD4rWt++A1iyZFW2f/eTHgCNRkOZMmU5c+ZPAPbv30ujRk2ylXH48AG++GI5AL/99jONGzfD1taO5ORk4uOzeubCwkIpXTprcpXY2BiKFbM0JoWPcurUCYKDFwFZM8dduXIZd3ePPK95nqTHRwghhCiC7ic+9s858TGL/gmb84MwmLsS1+w0qM2e6/3Fi83V1Y1Wrdqyfv0aevXqy6ZN6/HzG0zz5i1p2tSToKC5uV5XoUJFypUrj7e3Ny4ublSsWAmAgQOHMnfux/zww040GlMmT55u7BnJj2LFijF8uD9jx45ApVLz+uu1qVWrNidO/NuTUrNmLerUqceQIf0Ahffe6w5AyZIujBo1DBub4tjY2ODj0xtLS0sCAwOws7NHp9NhZWVNy5atjWW1atWGSZPGEBp6nrfeegdnZ2e+/PIL3nmnCwsXLsDd3d3Y09KgQQPKlHmNYcMGAlm9Of/VyJFj+eSTOSiKgWrVahif1Zk0aQzz5i2ibt36hIR8zeDBH1C8eHECAuZgYmLCmDETmDhxNGZmZpQq5YqXVwcAYmNjsbd3yHaPTz9dwLVrV0lJScHPbzCeni3o1s2HX375kSFD+mEw6PH1/QAnJ+f/HH9BUSmP6hMsYmJikp/6WhMTFR8d/oTIlKef6aSUtTPFb3fgz8sxT11G7UpOdP5zM+kRkU9dhm3tWuyz8CQuNvWpy3BwtOL9IQ3R65/+Ry9tmp20aXbSptm9TG3q5CTPeDxKfn5P7T5xi827rzAh+gfMTNUcKtONhIwkbjc4yjzPGc8wyryZxv0ftqe7AiYk1PsOnV2jx17zLDk52eSrHV8Gr3obvAz1//vvv7h48QJvvvn2U13/MrRBfuS3/nn9npIeHyGEEKKIUch6xqeEhcNjz31WNIknKP6nDygGEutse+5JjxAvi/T0ezRu3LSwwxC5kMRHCCGEKGIUFEB5bs/3mKSEYXu6Kyp9Gkmvr0dbos1zua8QL6P7s8KJokcmNxBCCCGKGL2S9ZD0c5vRTaVBMSlOSrVgMku++3zuKYQQz5n0+AghhBBFzP2pfJ9X4qO3qkBc06NgYvn4k4UQ4gUlPT5CCCFEEWOcyrpYwSU+Km0cxU93xyT1StYOSXqEEC85SXyEEEKIIqbAFy/VpWB7ujvmsb9iHrmlYO4hhBBFjAx1E0IIIYoYQ0EmPoYMbM/0wjTxOOkuPUgrP+3Z30O8MiIjI+jTx4fKlasAYGJigq9vP+rXb/ifyvn55x+wsrKmW7d3chy7cuUS//d/+xgwYMhTxRgc/CmXLl0kLu4u6enpuLq6Uby4LXPmfPJU5RUVmzd/xR9/7AZU9O8/iCZNPLMd37dvD1u2bMTU1BRHRyemTg3AxMSETz6Zy99/X0Oj0TB+/BTKlCnLjRvXWbBgNiqVCnd3D8aOnYRGo2HPnt/YunUjKpWaevUaMGTIcHQ6HfPmfUx4+G30ej3Dh4+iVq3ahdMI/5EkPkIIIUQRY1AMqFVqzE2e8eKhBh3Fzw3ALO4PMhw7klx9Oahk8IfIHw+PMixZsgqA8PDbTJw4moCAOVSoUPGJy8hrzZuKFStTsWLlp45vxIjRQFZy9ddf1/DzG/XUZRUVERHh7N79GytXfklKSgrDhw+kYcMmmJiYGM/57LMgNm78Gmtra+bPn83+/XsxNTUjNTWFFSvWEh5+m8WLg1iw4DOWL/+c3r0/oEmTZqxbt5q9e3fTokUrli8P5quvtlKsmCWDB39A+/YdCQ09j4VFMZYvX8Nff11j7tyP+OKLrwqxNZ6cJD5CCCFEEXKpWFVMMswxWGQ+87KtLk/GPPp7Mu09SXp9HahNn/k9ROEJufojp6PPPdMy6zjXpEuFTk98vptbafr06U9IyHYmTJjKjh3b2b17FyqVmubNW9GzZ2+Sk5OZNWsaqampWFtbExAwhy1bNmBnZ0evXt6MGeNHZmYmWq2WMWMmkpqaQkjIdgIDF7Bnz+9s27YJExMTKleuyqhR41izZiWpqSncvHmD8PDbjBw5liZNmj021tmzA9BoTElKSmDWrHksWDCbiIhwdDodAwcOpV69Bvz99198+ukCVCoVlpaWTJkSgI1N7gtkXrlymUWL5qPRaFCr1Xz88TxSU1OZNm0ia9ZsAGDAAF8CA+ejUqkIDJyJwWDAxaWUsTcGQK/X4+c3OFvZJUu6MH36LOP2qVMnaNy4Kaamptjb2+PiUorr1/+mfPkKxnOKFy9OSkoy1tbWpKQkY2trx+XLYcbptt3cSnPnTiR6vZ7bt29RrVrW/oYNG/Ptt9/Qvv0bfPXVViwtrQCwtbUlKSmRDh3exMurAwD29vYkJiY+0XujKJDERwghhChC7pi6AqBxTX/mZWe49sIk7S+SX/8STIo98/KFAKhSpSo7d+4gIiKcffv2sGzZGgA+/HAArVt78f33ITRs2ITu3X3Ytm0TJ04cM157+PBhnJycmTx5BuHht7l16yZmZlk9n2lpaaxatZQvv9yMpaUlEyaM5tSpEwBER0cRFPQ5R44c4rvvdjxR4gNZycHEiVPZtesnSpRwZPLkGSQkJODvP5T167fy2WefMH78FNzdPQgJ+ZqQkO307Tsg17ISEuIYPXo8lSpVYfXqFfz22y80a9Yi13NXrVqGj08vPD1bsmzZYsLCLlK9eg0ga7jg/R60R4mLu4ud3b9DYe3t7bl7NzZb4jNq1Hj69euFtbUNlSpVokGDRuh0OrZv30yPHj0JD79FREQ4iYkJlCtXgUOHDtCxYyeOHTtCXFwcgDHpuXbtKnfuRFK9ek00Gg0aTVYKsX37Ftq1e+OJ2rookMRHCCGEKGIyzdJwr/sMh6Dp08HEAl3x2iTV3fHsyhVFSpcKnf5T70xBSUtLQ61Wc/HiBW7fvsWIEUP+2Z/KnTsRXL4cxsCBHwLg7d0LyHqOB6B27dosXLiITz6ZQ8uWbWjcuKkxubl16yalS3tgaZk1A2GdOvW4fDkMgNdfz3rGxNnZmZSUlCeO9X4vx/nzZzlz5jRnz/4JQEZGBlqtltDQC8yfHwiAVqulatVqjyzL3r4Ey5cHk5GRTmxsTJ4JweXLYfj7jwVg2DD/J473URQl+7bBYGDx4iBWr/4KV1c3ZsyYzIED+/H0bMm5c2fw8xtE+fIVKVPmNRRFYfhwfxYunMcvv/xI7dp1UR4o8Natm3z00VRmzgw0JjwAO3Zs59KlMBYs+DTf8T8vkvgIIYQQRUDr2BOok+KhhAKontnEBhY3V1Ls9hoS6+7EYOH6TMoUIi9hYaFUqlQZjcaUJk2aMWHC1GzHN2/egPLPBB4Pc3Z2Zt26LZw6dYJvv/2GCxfOUatWHQBUKrJ9IdfptJibmwNke7ZFeTgLyINGY2r8b58+/XMkKxYWFgQHr0SlUj22rMWLg+jVqy+NGzdl8+YN3LuXluM6nU4HgFqtxmDIPc4nGerm6OjEzZs3jNsxMdE4OjoatxMS4lEUBTe30gDUq9eAsLCLeHq2ZPDgYcbzevR4F3t7B9RqNQsWfAbA0aOHuXs3FsjqSZs8eRzTp8/K9pzVjz/u5ODB/zF3blC2ZKiokycahRBCiCKgSsoNrpSoT7pJ1tCSEs8g8TGP3IrNpfGotXFguJfv8oR4nPDw22zdupkePXpRuXJVTp06SXp6Ooqi8NlnQWRkpFO1ajVOnjwOwM6dO/jllx+N1x86dIjjx4/SsGFjRo8eT1hYqPGYu3sZbt++SVpaKgCnT5+icuVH98D8F9Wq1eDAgf0AxMfHsXLlUgAqVKjIkSOHANi9+9dsw/IelpiYgJtbaTIzMzly5CA6nQ5LSyvi4+NQFIW7d2OJiLgNQJUq1Th1KqsNVq9ewfHjR43l3B/q9uC/B5MegLp1G3D48AG0Wi2xsTHExMRQtmw543FbWzuSk5OJj48HspLR0qXduXLlMnPmfATAkSOHqFSpCmq1mjVrVnLo0AEAfv75e+MQvXnzPmbcuEnGWfsg62e8c2cIc+Z8Ykw8XxQvToomhBBCvOSii2d9cUl0uEMJi7r5Ksss+mdsLnyIQWNHQt2dGCzLP4sQhcjh5s0b+PkNRqvVYjDoGTt2Ai4uLgD06NGT4cMHoVaradGiFebmFnTv3pPAwBn4+Q3G0tKKgIBAtmzZCICHhweLFo1h06b1qNVqBgwYgl6vB6BYsWIMH+7P2LEjUKnUvP56bWrVqs2JE0cfGduTatPGi1OnjjN0aH/0ej39+2f1uPj7j2PBgtls2rQeMzNzAgKyhr3NnDmZKVNmYm5uYSyja1dvJk8eh5ubG127evPppwto06Yd9es3ZODAPlSoUNHYazJgwBDmzJnFt99+Q8mSJenXb9B/itfFxYW33+7M8OGDUKlUjBs3CbVazZEjh4iMjOC997oxZswEJk4cjZmZGaVKueLl1QG1Wo2iKAwa1AczM3NmzPgYgHbtOvDxxzNYu3YVtWrVpmlTT27evMGZM6dZvXqF8b4+Pr24cOE8iYmJjBs30rj/00+XYmpa9CdLkcRHCCGEKEKUYlqiPMJwsHj/qcswjfs/ip/rCypzEut8jd6mxjOMUIh/lSrlyu+//98jj3fp0p0uXbpn22dtbc28eYuy7bu/Ro+Tkw3Ll6/JUU7duvUBaNmyDS1btsn1WoBy5So8cmKAh6fMnjo1wPhao9EwadL0HNeULfsay5atzrHf1bU0JibZv0a/+24X3n23i3G7ZcvWAEyZMjPXeBYvXpbr/ifVrZsP3br5ZNvXuHFT4+vmzVvRvHmrHNc9WO/7PDzK5piS2sOjDHv2HMxxrqdnS4YMGf50QRcySXyEiVEH4AAAIABJREFUEEKIIsRgMGClscRCY/H4k3OjT6X42Q9AMZBYZys6u0bPND4hBMbZzcSLRX5iQgghRBFiUAw4FMvH8z0mViS9vg6VNh5tibbPLjAhhJGnZ+7TVIuiTRIfIYQQoohQUFDgqWZ0U6ffxmBqDyZWaB3kS5kQQjxMZnUTQgghipj/OqObOuMOdifewu7ku2DIKKCohBDixSaJjxBCCFHE/JceH5U2DttT72Fy728yHVqA+sWaXlYIIZ4XGeomhBBCFDFPnPjoU7E93R1NygXuuQ8irXzOWamEKEiRkRFMmzaRNWs2PHUZixcvpHt3H1xd3XIcS01N4cKF8zRs2JgNG9ZRp05datR4Pc/yfv75B1avXmEsLz09nU6d3qFz525PHWN+PTjN9Itm8+av+OOP3YCK/v0H0aSJZ7bj+/btYcuWjZiamuLo6MTUqQFotVoCA2eSnJyEVqulX79BNGrUBD+/waSnp2NhkTV5i5/faKpUqZrtHqNH+1OtWl1u3rzBJ5/MAbIWpZ04cRru7h75qoskPkIIIUQRcMv+dTJMrIC0JxvqZsjA9s/3MU08TrpLD1Iqf5K1tL0QLxh//7GPPHbpUhjHjh2hYcPG+Pp+8MRltmnTDj+/UQBkZmbSv38vGjVqSqlSrvkN96k8OM30iyQiIpzdu39j5covSUlJYfjwgTRs2AQTExPjOZ99FsTGjV9jbW3N/Pmz2b9/L4mJCXh4lGHoUD9iY2MYOXIomzfvAGDKlBmUK1fhkfcYOXIw69ZtZefObxgwYAi1a9fll19+ZPPmDUycODVf9ZHERwghhCgC4q1KA1mLlz5Jj49p/GFM4/aT4diR5OrLQSWj10XRce3aVRYtmo9KpcLS0opp0wKwtLRi1qzp3LkTSc2ar7N3726+/fZn/PwGM2bMBHQ6HX5+QYAaMzMzPvpoLosWLSAtLRV3dw/Onz9Lq1ZtadSoCYGBM4mKisTMzJxp0z7Cycn5kbGYmZlRrlwFIiLCcXYuyYIFs4mICEen0zFw4FDq1WvA8eNH+fzzhTg4OOLhUQY7Ozvq1KnH1q0bSUtLw89vNFFRkWzduhETEw2VK1dlxIjR3Llzh48/no5arUav1/+zIKgqx75Tp07w11/X8PMbxfbtW9iz5zcAmjdvSe/eHzB7dgCOjk5cunSR2Nhopk6dReXKVXKtz5Url1m0aD4ajQa1Ws3HH88jNTU1W8/bgAG+BAZmtX9g4EwMBgMuLqWYOjXAmLTo9Xr8/T/MVnbJki5Mnz7LuH3q1AkaN26Kqakp9vb2uLiU4vr1vylf/t/EpXjx4qSkJGNtbU1KSjK2tnYAXL16FYCkpCTjvtw8fA83NzeuX/+bkSP/TYijou7g7Pzon/GTksRHCCGEKCLMdCkklbuOpWmxx56rLdGKxHrfobVtCOqiv2K6KHgxX28l+cTxZ1qmTf0GOHX3efyJD1m8OIhhw/ypXr0Gmzdv4Ouvt1K5clUyMzNYtWodBw/+j+3bt2S75ueff6Bnz540a9aWkyePExd3l/ff9+Wvv67x7rtdOH/+LAC//PIjJUqUICBgNrt3/8qBA/+X5xCyuLi7XLx4gdGjx/P777soUcKRyZNnkJCQgL//UNav38ry5cFMnz6L8uUrMnz4IBo0yFr/6tq1q2zZEoJOp2PBgkBWrPgSMzMzpk+fxNmzfxIaep4GDRrxwQcDuXQpjNjYWM6fP5Nj330REeH88ssPxsVCBw/uS+vWXkBWz9SiRUvYvftHdu366ZGJT0JCHKNHj6dSpSqsXr2C3377hWbNcp/JcdWqZfj49MLTsyXLli0mLOwi1atnLWhsYmLyyMVeH2w7O7t//xBjb2/P3bux2RKfUaPG069fL6ytbahUqZKx7X7++Ue8vTuTnJzMggWfGc9fvXoliYkJlClTFn//sTnu4eDgYLzHlSuXCAycibm5BYsXL88z1ichiY8QQghRhOTZ26MomN/ZTkbJ90Bthtah5fMLTIj/4Pr1v41fsOvWrc+XX67CwsKCmjVrAdCkSbNsw6UAPD1b8tlnCwgNvUzbtu0oU6YsFy6cy1H2pUth1K/fAAAvrw653n/v3t8JCwslMzOTuLi7jBo1Hnt7B86fP8uZM6c5e/ZPADIyMtBqtURFRVKpUlai0bhxU/R6PQAVKlTEzMyMK1cuExV1hzFj/ICsZ4/u3LlDw4aNmTJlPMnJybRu3ZYaNV7H0rJYjn03b14H4MqVS9kWP61ZsxZXr14GoFatOgC4uLhw7NjJR7atvX0Jli8PJiMjndjYGNq1e+OR516+HGYcSjhsmP8jz3tSipJ922AwsHhxEKtXf4WrqxszZkzmwIH9pKamUrKkC4sWBXPlymXmzfuYNWs20L17TypUqIibW2mCguayY8fXudzj35tUrFiZ9eu3EhLyNZ9/vkiGugkhhBAvkxIWDo88Zvn3J1hdC+Re4nFSqgQ9x6jEi8Cpu89T9c4UNJ1Oi1qtRlEU1OqsZEelUqF66Jm0+vUb8s033/D9978QGBhgfEbnYSYmagwGJddj991/xic9PZ0BA3pTqVJlADQaU/r06Z9nsvBgXKampv/8N2t426JFS3Kcv27dFo4dO8KKFUt466136NixU459D5Se7Yu9VqtF9c8w1QcTQeXhDOMBixcH0atXXxo3bsrmzRu4dy8tR1vqdDoA1OpHt9WTDHVzdHTi5s0bxu2YmGgcHR2N2wkJ8SiKgptb1lDdevUaEBZ2kYSEeBo1agxAxYqViI2NQa/X07Jla+O1zZo1Z8+e36lbt362e0RFReHo6MihQwdo2LAxGo2G1q3bEhKy/ZFt8qRkQLAQQghRhDhY5D4W3uLmSqyuBaK38CCt7OjnHJUQ/81rr5U3Dk07ffoUlStXxc2tNJcuhQJw7NgRY6/KfTt2bCMhIYH27Tvi7f0+ly+HoVKpcpxXpUo1Tp3KGtJ38OD/+OqrtY+Mw8LCgg8+GMjnny8CoFq1Ghw4sB+A+Pg4Vq5cCoCDQwlu3LiOXq/n+PGjOcrx8CjL9et/Ex8fB8CaNSuJiYlm9+5f+euvq7Ro0YpBg4Zx6dLFXPfdV6lSZc6fP4dOp0On0xEaesGYlD2pxMQE3NxKk5mZyZEjB9HpdFhaWhEfH4eiKNy9G0tExO0cbbV69Ypsdbs/1O3Bfw8mPQB16zbg8OEDaLVaYmNjiImJoWzZcsbjtrZ2JCcnEx8fD0BYWCilS7vj5uZOaOh5AO7ciaRYMUvUajX+/sNITk4G4PTpk5QrVz7HPaKjoylbthzffx/CoUMHALhw4Tzu7mX+UzvlRnp8hBBCiCIktxndzCO3YnNpPAYzZxLr7sRgkXPaXyEKy82bN/DzG2zcHjZsJKNGjTNObmBjY8OUKTPRaEz56afv+fDDAdSpU4/ixW2zlePm5o6/vz8WFpaYmpoyZcpMEhLiWbEiONvkBV5eHThx4hh+foMxMdEwbVpAnvG1a/cGISHbOXbsCG3aeHHq1HGGDu2PXq+nf/+suAcNGsbUqeMpVcqVMmXK5hiGZ2Fhgb//WMaN88fMzJSKFSvj6OiEu3sZgoLmGL/Yjxo1noyMjBz77icBpUq58s477zFixGAMBoW3334XF5dSj4x95szJTJmS9YzLfV27ejN58jjc3Nzo2tWbTz9dQJs27ahfvyEDB/ahQoWKVKyYlUwNGDCEOXNm8e2331CyZEn69RuUZ1s9zMXFhbff7szw4YNQqVSMGzcJtVqdbXruMWMmMHHiaMzMzChVyhUvrw5kZmYyd+4s/PwGo9frGD9+MiqVinfeeQ9//w8pVqwYjo5O9O8/BAsLi2z3CAgIQK1WM2LEGObN+5jt2zcbp7POL5WSV19aERITk/zU15qYqPjo8CdEpkQ/dRmlrJ0pfrsDf16OeeoyaldyovOfm0mPiHzqMmxr12KfhSdxsalPXYaDoxXvD2mIXv/0P3pp0+ykTbOTNs3uZWpTJyebp772ZZef31O7T9zixq4zANQbUpbaTjWMx8yif6b42V4oJjYk1P8JvU3NfMdaVDk52eSrHV8GL3MbJCUlcurUCVq1aktMTDT+/h8apzi+r7Dqf+zYEdzdPShVypUFC2ZTu3Y92rd/9HC4gvRgG6xcuZQBA4YYnwl6FeT3PZDX76lXpxWFEEKIF8DDQ91M0q6BypzEOl+/1EmPePlZWlqxd+9uNm/egKIYGDFiTGGHZKQoClOmjMPS0gp7ewdat25b2CEBZJsIQeSftKQQQghRhDw8ucG9siPIcOmKwaJwFl4U4lnRaDTMmjW3sMPIVaNGTWjUqElhh5GDp2fu01SLpyOTGwghhBCFzO7wLtT/jEC01BTDJCUMqysfgWIAkKRHCCGeAUl8hBBCiEJmee0CADqNGpP0m9ie6ozl9YWYxh8o5MiEEOLlIYmPEEKIV9acOXPw9vbGx8eHs2fPZjt26NAhunXrhre3N0uXLi3QOBQUDCrQmWuwO/kOJhkRpFScjdZBhrkIIcSzIomPEEKIV9KxY8e4ceMG27ZtY/bs2cyePTvb8cDAQIKDg9myZQsHDx7k6tWrBRbLNZvqZGqsURu0mNz7m9TXxnGv7IgCu58QQryKZHIDIYQQr6TDhw/j5eUFQPny5UlMTCQlJQVra2tu3bqFra0tpUplra/RsmVLDh8+TIUKFQoklth/1uUxL36Ne6UHklZ+eoHcR4hnLTIyAm/vzqxdu4kKFSoC8PPPPwDw5ptv53rNhg3rqFOnLjVqvP5U9/TzG0x6ejoWFhZkZKTTuHEzBgwY8nQVeEFs3vwVf/yxG1DRv/8gmjTxzHZ83749bNmyEVNTUxwdnZg6NYBz584wY8Yk44Kj5ctXYPToCcyeHcClSxeN6yi9/34fmjb1ZOfOHfz443eYmmrw9u5Fq1ZtiY2NYc6cWWi1mRgMWTPxValS9XlX/5mRxEcIIcQrKTY2lurVqxu3HRwciImJwdrampiYGBwcHLIdu3XrVoHGY6ZLwb18JClVgkClKtB7CfEslS37GitWBBMU9PkTne/r+0G+7zllygzKlauAXq+nV6/uvPtuVxwdHfNdblEUERHO7t2/sXLll6SkpDB8+EAaNmySbZHVzz4LYuPGr7G2tmb+/Nns378XB4cS1K5dl8DABTnKHDLEj2bNmhu34+Pj2Lp1I+vXbwXA3/9DmjRpxtatm2jRohWdO3fl3LkzrFq1jEWLggu+0gVEEh8hhBCCrHU88sPe3hKNxuTxJ+biNScViffS6P7eQkzNzPMVx4tOFsl9sdogI8OKWrVe5969e1y9ep4mTZpgY2MBZNVj7ty5nD17loyMDHr27En37t2ZNGkSHTp04PPPP2fp0qW4uroSHh7OiBEj+Prrr/n007ncunULnU7HyJEjadIk+zTTZmYa7O2tcHKyISkpCXNzU9zdnbCwsGDixIlERUWRlpbGiBEj8PDwYPr06WzevBmA5cuXY2VlRdOmTZk1axYqlQorKyvmzZtHsWLFGD9+PDExMWRmZjJixAhatMj9ObuwsDA++ugjNBoNarWaxYsXk5KSwsiRIwkJCQGgS5cufP7556hUKiZNmoRer8fV1ZX58+cbkxa9Xs8HH3yQrexSpUqxYMG/ycr+/edp06YVrq4OgAMeHu4kJkZRuXJl4zkODvaYmRlwcrJBq71HmTKumJiYYG5umuP9ZGFhiq1tsWz7IyP/pmLFCpQunZU81qxZnfDwv3BzK4len46Tkw1qtY6SJR2fy/uzoO4hiY8QQohXkrOzM7Gxscbt6OhonJyccj0WFRWFs7NznuXFx6c9dSytB7/5wGrlmU9dzosuvyu2vwzy0waH9l7jr7DoZxpPuSrONG1T/pHH4+JSSU/X0rfvYAIDZ7JixVqSk9MBuH07FltbRz7/fBUZGen06NGZVq3eID1dS2LiPZo2bcH33/9C1649+O67n2nWrCU//PADVla2LFo0mYSEBPz9hxp7Ie7LzNQxfvxELCwsuHHjOj17+nLvnkJERDi1atWnY8dOhIffZvr0Saxdu5G0tHtcuHAVZ+eS/P77HubODWLGjGmMGzcZd3cPQkK+ZtWqtTRu3Izo6FgWL15BcnIyhw8ffOTP4q+/buHnN4ZKlaqwevUKtmz5mmbNWqDTGYzX6HQG4uJS+eKL5XTp4o2nZ0uWLVvM//53jOrVaxjLWrRoWbayH34P3LgRjoWFlXGflZUNV6/exMHh32nu/fzG8O67nbG2tqFSpUpUrFiTU6dOcOnSZfr3H0hSUhL9+w+iQYPGpKdrWbt2PatWrcbe3p7RoydiZeXAxYthXLlyCzMzM44fP0mVKjV5662uDBrUlx07QkhNTWXZstUF/v9ofj8H8kqaJPERQgjxSmrWrBnBwcH4+Phw4cIFnJ2dsba2BqB06dKkpKRw+/ZtXFxc+OOPPwgKCirkiIUoutzdPahUqQp79vxm3Gdubk5SUiJDh/ZHo9GQkBCf7ZoWLVqzZMlndO3agwMH9jN27CS+//5rjh49xtmzfwKQkZGBVqvF1NQ027X3h7plZmYydep4KlasRJ069bh48QLffx+CSqUmKSkRgPbt32Tv3t/x8uqAlZU1Dg4lCA29wPz5gQBotVqqVq1GmTJlSUtL5eOPp9OiRWu8vNo/sr729iVYvjyYjIx0YmNjaNfujUeee/lyGP7+YwEYNsz/P7Rq7h7unDYYDCxeHMTq1V/h6urGjBmTOXBgP5UrV6Vfv0G0adOOiIhwRowYwrZtO+nQ4U1sbW2pWLEyGzasY+3alYwZM5Fhw/yZNGkMJUqU4LXXyqEoCps3f0WbNl707TuAgwf/x9Kli5kz55N816GwSOIjhBDilVS3bl2qV6+Oj48PKpWKmTNnEhISgo2NDe3atSMgIICxY7O+rLz55pu89tprhRyxEHlr2qZ8nr0zBa1fv4GMGTOCLl26o9FoOH36JKdOnWDJklVoNBratWue7fxy5cpz924MUVF3SE5OxsOjDKampvTp0z/PROJBZmZmNGniydmzfxIbG0NSUhJLl64mKSmJgQN9AfDy6sC0aROwsChGu3YdALCwsCA4eCWqh56nW7lyHefOneWXX37g4MH/MWXKzFzvu3hxEL169aVx46Zs3ryBe/fScpSl0+kAUKvVGAy5D6XV6/X4+3+YbV+ZMu6MH//vBCeOjk7cvHnDuB0TE53teaaEhHgURcHNrTQA9eo1ICzsIp6eLWnbNit5c3MrTYkSJYiJiaZ+/YbGaz09W7Bw4TwA2rTxok2brAlfZs6cgouLK7/88hODBmXF16BBI+O5LypJfIQQQryyxo0bl227SpUqxtcNGjRg27ZtzzskIV5YDg4laN68Jd99F0LXrj1ITEzA2bkkGo2GAwf2o9cb0Gq12a5p0sSTVauW0bx5SwBq1arFL7/8Srt2bxAfH8f27VsYMmR4nvcNDT1Pw4ZNuHs3llKlXFGr1ezfv9d4L3t7e4oXL86vv/7MwoVZEzBUqFCRI0cO0aRJM3bv/hU7O3tsbIpz/fpfdOjwJtWr12DYsIGPvGdiYgJubqXJzMzkyJGDVK9eE0tLK+Lj41AUhbi4u0RE3AagSpVqnDp1nLZt27N69Qpq1apDgwaNADAxMWHJklXZyn54qFfdug3Ytm0TAwYMITExgZiYGONMbQC2tnYkJycTHx+Pvb09YWGh1K5dl99++4XY2Fjef9+Xu3djiYuLw8nJmalTxzNsmD9ubqU5ffokr71WHp1Ox6hRw1i4MJiUlGSuXr1MlSpVKV26NKGh56lSpSoXL4bi7u6R58+iqJPERwghhBBCPBM9e/qyc+cOAOrXb8SmTevx8xtM8+YtadrUk6CgudnOb9myNUOH9mfdui0AdOzYkX37/sfQof3R6/X07z841/vMmTMLCwsLdDodFSpUxMurPVFRd5g0aQyhoed56613cHZ25ssvv6Bfv0G0atWWgwf/h6WlFQD+/uNYsGA2mzatx8zMnICAQEDFypVL+e67ENRqNe+/n9VjNHPmZKZMmYm5uYXx/l27ejN58jjc3Nzo2tWbTz9dQJs27ahfvyEDB/ahQoWKVKyYNfnAgAFDmDNnFt9++w0lS5akX79B/6lNXVxcePvtzgwfPgiVSsW4cZNQq9UcOXKIyMgI3nuvG2PGTGDixNGYmZlRqpQrXl4dyMzMICBgGgcO7Eer1TJu3CRMTU3p2tWbmTOnYGFhQbFixZgyZSYajYbWrb0YOrQfKpWK0aMnoNFo8PXtz7x5s9i793cARo0a/59iL2ok8RFCCCGEEE+lVClXpk4NMG5bWlryww//PufzxRdfGV97e/fKcX3VqtXZv/+ocVuj0TBpUt7rWD3cQ/JgLA9OhNC+fUfj6+PHj9K5c1fjdtmyr7Fs2eocZeQ2VbOra2lMTLJ/ZX733S68+24X43bLlq0B8hgatyzX/U+qWzcfunXzybavceOmxtfNm7eiefNW2Y5rNBoWLPg0R1l169Zn9eqvcuzv2rUHXbv2yLbP0dHxiacpfxGoCzsAIYQQQgghCkJGRgaDB3+AlZUVdevWf6oyqleviUYjfQUvA/kpCiGEEEKIl5K5uTmrVq3LVxmenrmv5SNePNLjI4QQQgghhHjpSeIjhBBCCCGEeOlJ4iOEEEIIIYR46UniI4QQQgghhHjpFWjiM2fOHLy9vfHx8eHs2bPZjh06dIhu3brh7e3N0qVLCzIMIYQQQgghxCuuwBKfY8eOcePGDbZt28bs2bOZPXt2tuOBgYEEBwezZcsWDh48yNWrVwsqFCGEEEIIIcQrrsASn8OHD+Pl5QVA+fLlSUxMJCUlBYBbt25ha2tLqVKlUKvVtGzZksOHDxdUKEIIIYQQQohXXIGt4xMbG0v16tWN2w4ODsTExGBtbU1MTAwODg7Zjt26daugQgHA2bpEvq+3trPA1dHqqctwtLPAomTJfMVh7lgCe3PLfJVhXyJ/198nbfovadPspE2ze9naVAghhHgRPbcFTBVFydf1Tk42+bp+asuR+br+2amb7xIqPoMongVp02dP2vTZkzYVz0t+f089qzJedNIG0gavev1B2qCg6l9gQ92cnZ2JjY01bkdHR+Pk5JTrsaioKJydnQsqFCGEEEIIIcQrrsASn2bNmvHrr78CcOHCBZydnbG2tgagdOnSpKSkcPv2bXQ6HX/88QfNmjUrqFCEEEIIIYQQrziVkt8xaHkICgrixIkTqFQqZs6cSWhoKDY2NrRr147jx48TFBQEQPv27RkwYEBBhSGEEEIIIYR4xRVo4iOEEEIIIYQQRUGBLmAqhBBCCCGEEEWBJD5CCCGEEEKIl54kPk/oxx9/pHr16sTFxRV2KC+F27dvU6dOHXx9fenduzc9evTg999/f+ryZs+e/ci1oEJCQvJV9ovuwbb29fXF29ub6dOno9fr8112o0aNAPD19eXy5cv5Lu9l8XCb+/r6Mnv2bCIjI+nSpQvz588v7BBFIZszZw7e3t74+Phw9uzZbMcOHTpEt27d8Pb2ZunSpYUUYcHKq/5HjhyhR48e+Pj4MHnyZAwGQyFFWbDyaoP7Fi5ciK+v73OO7PnJqw0iIyPp2bMn3bp1Y8aMGYUUYcHKq/6bNm3C29ubnj17Mnv27EKKsOBdvnwZLy8vNm7cmONYgXwWKuKJDBkyROnQoYOyefPmwg7lpXDr1i3lvffeM27Hx8crrVq1Uu7du1eIUb2cHm5rRVGUiRMnKt9++22+y27YsKGiKIrSu3dv5dKlS/ku72WRW5sriqJ88MEHyoIFC5R58+YVQlSiqDh69KgyePBgRVEU5erVq0qPHj2yHe/YsaMSERGh6PV6pWfPnsqVK1cKI8wC87j6t2vXTomMjFQURVFGjBih7Nu377nHWNAe1waKoihXrlxRvL29ld69ez/v8J6Lx7XByJEjld9++01RFEUJCAhQwsPDn3uMBSmv+icnJyutW7dWtFqtoiiK0q9fP+X06dOFEmdBSk1NVXr37q1MmzZN2bBhQ47jBfFZKD0+TyAhIYGzZ88yadIkfvrpJwBCQ0ONWfr9v97mtu/Bv4Rv3LiR4OBgjh49ypAhQ/D19eX8+fOsXbsWb29vunfvzpIlSwBISkpi8ODBvP/++wwZMoTk5GS8vLxITU0F4OTJk/j5+T3vpigwdnZ2ODk5MXPmTKZPn86IESPQ6/VMmTIFX19fevbsyeHDh4G82/n+MV9fXwYMGEBSUhLBwcHGvyQsWLAAHx8funfvzs6dO43XLl++nL59+/LOO+8QERFROI3wHL3++uvcuHGDTZs24ePjw/vvv8/atWuBnO+91NRU7ty5Y+y56NmzJzdv3izkGryYgoODKV++fGGHIQrZ4cOH8fLyAqB8+fIkJiaSkpICwK1bt7C1taVUqVKo1Wpatmxp/Ox7WeRVf8jqpXdxcQHAwcGB+Pj4QomzID2uDQDmzZvH6NGjCyO85yKvNjAYDJw8eZI2bdoAMHPmTFxdXQst1oKQV/1NTU0xNTUlLS0NnU7HvXv3sLW1LcxwC4SZmRlffPFFrmt5FtRnoSQ+T2DXrl20atWK5s2bc/36daKioggMDOSjjz5i69at3L17l/Dw8Fz3Pcrly5dZs2YNNWrUAGDz5s1s376dkJAQUlJSWLNmDZ6enmzevJkmTZpw9OhR2rVrx969ewHYs2cPnTp1ei71fx5u375NQkICer0eW1tbgoOD+eGHH3BycmLDhg0sXbqUOXPmAOTZziEhIfTs2ZMNGzYwcOBAYmJijMeOHz/OlStX2Lp1K+vXr2fJkiXGDxlra2vWr19PixYt+O23355v5Z8zrVbLnj17sLW1ZdeuXWzZsoVNmzbx22+/ERERkeO9d/jwYaKjoxk+fDgbNmyga9eubN68ubCr8UK6v5aZeLXFxsZib29v3HZwcDB+VsXExOA1H4xVAAAP/klEQVTg4JDrsZdFXvWHf/8/iY6O5uDBg7Rs2fK5x1jQHtcGISEhNGzYEDc3t8II77nIqw3i4uKwsrJi7ty59OzZk4ULFxZWmAUmr/qbm5szfPhwvLy8aN26NbVq1eK1114rrFALjEajwcLCItdjBfVZqMl3Ca+AH3/8kWHDhmFiYsIbb7zBzz//zN9//02VKlWArF4EINd9j1K5cmXMzMwAsLCwoHfv3mg0GuLj40lISCA0NBR/f38APvjgAyBr4dfFixfz9ttvc+zYMePxF9Xff/+Nr68viqJgbm7O/Pnz2bZtG6+//joAp0+f5uTJk5w6dQqAjIwMMjMz82zntm3bEhAQwPXr13nzzTez/XX9/PnzNGjQAABLS0sqVKjAjRs3AKhfvz4ALi4uJCQkFGzFC8H9tga4dOkSAwcOxNnZmRs3btCnTx8AUlNTCQ8Pz/W9FxkZSWBgIMHBwSQlJVG9evVCqceL5ME2B2jatCkffvhhIUYkiirlFV9VIrf63717l6FDhzJz5sxsXw5fVg+2QUJCAiEhIXz55ZdERUUVYlTP14NtoCgKUVFR9OnTBzc3NwYPHsy+ffto1apV4QVYwB6sf0pKCitXrmTXrl1YW1vTt29fwsLCjN99xNOTxOcx7ty5w5kzZ5g3bx4qlYr09HRsbGxQq3N2luW270E6nc74+n7SEx4ezrp16/j222+xsrIy9uKYmJjkeKCzSpUqxMbGcvbsWSpWrIi5uXl+q1eoXnvtNTZs2JBt37Zt2zA1NQWyunqHDh2ao2crr3Zu0qQJ33zzDX/88QeTJk1iwoQJxmMqlSrbuVqt1liWiYmJcf/L+CXkwbYeOXKk8S9HrVq1YtasWdnOXbNmTY733ueff46npyc9e/Zk165d7Nu377nE/SLL7f0tBICzszOxsbHG7ejoaJycnHI9FhUVleswkBdZXvWHrC99gwYNYtSoUXh6ehZGiAUurzY4cuQIcXFx9OrVi8zMTG7evMmcOXOYMmVKYYVbIPJqA3t7e1xdXfHw8ACyfrdfuXLlpUp88qr/tWvXcHd3N/Z41K9fn/Pnz79SiU9BfRbKULfH+PHHH+nVqxfff/893333Hbt27SIxMZFy5cpx5swZAKZMmcK1a9coX758jn3W1tbGrrn7PRcPio+Px8HBASsrKy5cuEB4eDharZYaNWpw5MgRALZu3cq3334LQMeOHZk1axZvv/3286h+oapVqxZ79uwBsv76t2jRIoBc2/m+jRs3kpCQwDvvvEPfvn25ePGi8ViNGjU4evQokNW7cfPmTcqUKfO8qlNkjB8/nqCgIKpXr87Ro0e5d+8eiqIQGBhIenp6ru+9+Ph4PDw8UBSFPXv2oNVqC7kWQry4mjVrxq+//grAhQsXcHZ2Ng7vKl26NCkpKdy+fRudTscff/xBs2bNCjPcZy6v+kPWsy19+/alRYsWhRVigcurDe6PLNm+fTtLliyhevXqL13SA3m3gUajwd3dnevXrxuPv2xDvfKqv5ubG9euXSM9PR3IGrFStmzZwgq1UBTUZ6H0+DzGTz/9lG3qWZVKRefOnVEUhXnz5gFQu3Ztypcvz9SpUwkICMi2z9vbm1mzZlGmTBnjXy4eVLVqVaysrPDx8aFevXr4+Pjw0UcfERwczIQJE/D19cXKyoqgoCAA3nzzTdauXUvjxo0LvvKFrGPHjhw5cgQfHx/0er1xMofc2vk+Dw8P/P39sbGxwczMjLlz57JlyxYg6y8mNWrUoFevXuh0OsaOHYulpeVzr1dhc3d3p0OHDmzdupU+ffrQq1cvTExM8PLywsLCgr59++Z479nZ2fHxxx/j5uaGr68v06dP58CBA4VdlRdKVFQU48aNIyYmhnv37nH+/HlmzpxJhQoVCjs08ZzVrVuX6tWr4+Pjg0qlYubMmYSEhGBjY0O7du0ICAhg7NixQNZn/sv2hS+v+nt6erJz505u3LjBN998A0CnTp3w9vYu5Kifrce9B14Fj2uDKVOmMGnSJBRFoVKlSsaJDl4Wj6v/gAED6NOnDyYmJtSpU8c4JP9lcv78eebPn094eDgajYZff/2VNm3aULp06QL7LFQpL+O4npfYjh07CA8PZ+TIkYUdihBCCCGEEC8M6fF5gUyb9v/t3X1M1WUfx/H3kecOjhGkKDMxN6iUEU9hJ/ABMXYUCGs2JrpcNEWdQ0HMTEydMga46fgjBtpMW1sPkmUYDaIUlXgQWOhizVHumIDxJEGBMrn/YJ27Iz7AnXnf4/68tt8fnOtc1/X9XX+cH9/rd/1+13YsFsu43dBOREREROSfojs+IiIiIiIy7unlBiIiIiIiMu4p8RERERERkXFPiY+IiIiIiIx7SnxE7qOqqgo/Pz9mz55Nb2+v9fPly5fj5+dHXl7eXes2NjZSVlZ21/LIyEjWrFnzQOMVEZGH789rxe1HSUnJXesUFRXh5+dHY2Pj3+r7ypUrNn0+88wzJCYm2uxlN1Z+fn7WDa4vX75MUVGRTV+HDh36WzHD8DXwr3GHh4ezY8cOBgYG7lnvr/GIjIXe6iYySg4ODlRUVGA2m+nq6qKhoeG++wC999572NvbExUVdcfy4uJiJkzQ/IOIyHiRnZ1t85vv7Oz80PretGkTK1eupLOzk/Xr17Nx40brJpljVVdXh4ODAzCcoNXV1fHSSy/h7e1NXV0djo6ODyTmiIgIDhw4wNDQEFVVVaxfv56pU6eSnJx81zp/jUdkLPQfl8gohYWFUV5eDsCpU6eYPn067u7uAJSVlREdHU1QUBDJycl0dnaydetWTpw4waeffsrKlSuts4GZmZkEBgZy6dIllixZYt2TyWKxkJSURGBgIGazmVOnTgFQWVlJbGws/v7+LFq0iC+++OK/MwAiInJfTk5OGI1G62FnZ0d7ezuvv/46gYGBhIeHc/jw4RH1bty4wVtvvUVYWBiBgYFs2LCBnp4eAD788EMWLFhAaGgoW7Zs4Y8//rhj3w4ODhiNRqZNm8bcuXP5+eefGRgY4OrVq6xevZqQkBDmzZtHTk4Og4ODAOzbtw+TyURAQACvvvoqV69eBYY32MzKyiIvL4/8/Hyqq6uJjIzkl19+ISgoiCNHjpCbm8vs2bP57bffAHj//ffx8/Pjp59+ora2lvj4eAIDA0lMTMRisdwxZjs7O4xGI66urixcuBAPDw+ampoA+Oijj4iMjCQgIIAVK1bQ0tIyIp6xjI+IEh+RUTKZTJw+fZrBwUHKy8t5/vnnAWhrayMtLY34+Hi++uorrl+/zv79+9m9ezdTp04lNjaWwsJCazvNzc2Ulpbi4+Nj0/7mzZvp7OykpKSEZ599lvT0dHp7e8nOzsbHx4czZ86wevVqvvvuu4d52iIi8jc1Njbi7e3NiRMnWLFiBVlZWXR0dNh8p6Kigk8++YTCwkJOnjyJo6MjFy5c4MKFC7z99tukpKTw2WefUV9fz9GjR+/ZX39/P7W1tcyYMQMnJyfS0tKwWCwUFRWRkZHBwYMH+fjjj2lqaqKgoICMjAzKy8uZPn0658+ft2lrzZo1BAcHExwcTHFxsU1ZTEwMN2/epKKiAhieBPT392fKlCmkpKQQEBDA119/jYeHBzt37rzvOF28eJHOzk58fX0ZHBykqamJtWvXUlpaSnNzM4WFhSPi+U/GR/5/KfERGSWTyUR/fz9VVVWcOXPGOtNUV1dHf38/hw4dIiYmhosXL1JdXY2joyMGgwF7e3ubpQ5LlizB09MTe/t/rzTt6+ujoaGBhQsXMnnyZHbt2kV1dTWurq74+/vzzTffkJ6eTldXl/UOkYiI/O/Ztm0bYWFh1qOpqYnJkydz6dIlXnnlFQoKChgaGuLXX3+1qTdz5kweeeQR0tLSyM/PJzY2FpPJRGVlJUNDQ2RmZrJ06VJaWlqoqqq6Y995eXmEhYURGhpKV1cX2dnZ9Pb2UldXh9ls5vHHHycqKgpvb2/OnTvHlClT8PT0JDMzk9zcXObMmYPZbLZp09HRETs7O+zs7HBxcbEpe/LJJ5k5cybffvstPT091NTUEBcXx48//kh7ezuff/45ZrOZiooKampquNPWkWfPnrWO1csvv0xsbCyvvfYa9vb2GI1G61h0d3fT1tY2Ip6xjI+InvERGSVnZ2eee+458vPzmTBhAqGhocDwsgaAvXv3EhAQAHDP53Zuv3AA3Lp1C8B6URgcHOT333/HaDSya9cuFi1aRGVlJceOHePw4cOUl5c/1HXjIiIyOm+88Qbz5s2z/u3h4cGqVatoaWnhyJEjVFZWsmfPnhFJgI+PDyUlJZSWllJTU8O6devYvHmz9Rpz8OBBJk2aBGB99uZ2q1atIiEhARcXF9zc3ABsXsrzp1u3bmEwGHBzc6O4uJjS0lJqa2t58803OX/+PBkZGaM+38WLF3P06FFMJhMwPLn3Z1K3ceNGoqOj71k/JCSErKwsmpubSUpKwtfXF2dnZyorKykoKGDTpk28+OKLJCYm3jFxGsv4iOiOj8gYREZGUl1dTUREhPWH1d/fHycnJ86ePcvQ0BD79u3j+PHjwPDaZYvFwvXr1+/Z7sSJE5k1axZlZWW0traye/duIiIi6OnpITU1lZaWFtatW0dSUhIdHR10d3f/4+cqIiJj5+bmhpeXl/VwcHCgr68PZ2dnXFxcrMvCrl27ZlOvrKyMvXv3EhUVxfbt25kxYwbNzc2EhIRgMBg4ffo0N2/eZOfOndZnQG83ceJEvLy8rEkPgKurK0FBQXz55ZdYLBZOnjxJS0sL8+fPp76+nvT0dEJCQti6dSvBwcE0NzePaNfOzo7W1lba29tHlMXExNDd3U1eXh4mkwkPDw+eeOIJPD09OXfuHDCclLz77rsYDIYR9Z2cnPDy8sJkMrFs2TLy8vK4fPkyfX19ADz66KP88MMPtLe309PTw8DAgE08YxkfESU+ImOwYMECDAaDdZkbwGOPPUZubi5VVVWYzWba2tpYvHgxMDwT9v3335OSknLftnNycjAajZjNZmprazlw4ADu7u7MnTuX/Px85syZwzvvvMO2bdvw8vL6x85RREQerOTkZDo6OkhISCAhIQFfX19SU1NtvhMUFMSNGzcwm8288MILTJo0ibVr1/L000+zY8cOjh07RlxcHBMmTLC5Bo1GTk4O06ZNIz4+nuzsbDZs2MDSpUt56qmncHd3Z9myZcyfP5++vj62bNkyon50dDTXrl1j+fLlI8p8fHyYNWsWV65cIS4uDhheHrd//35aW1uJjo6mvr6e+Pj4+8aZmpqKi4sL27dvJzw8HJPJxJ49ezh+/DgZGRk0NDTwwQcf2MTzIMZH/n8Yhu5031BERERERGQc0R0fEREREREZ95T4iIiIiIjIuKfER0RERERExj0lPiIiIiIiMu4p8RERERERkXFPiY+IiIiIiIx7SnxERERERGTcU+IjIiIiIiLj3r8AO9KN/bjTT4EAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. OUTPUT**"
      ],
      "metadata": {
        "id": "oAuwQCMiHaM0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving the model\n",
        "import joblib\n",
        "\n",
        "stroke_pred_classification = {\n",
        "    'model': dt,\n",
        "}\n",
        "\n",
        "\n",
        "joblib.dump(stroke_pred_classification, 'stroke_pred_classification.joblib')"
      ],
      "metadata": {
        "id": "p4fK5FxRLJZ3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "551d4ec0-6c0a-419f-9ae9-e9c871cbb935"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['stroke_pred_classification.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    }
  ]
}